diff --git a/arbiter/arbiter-core/src/main/java/org/deeplearning4j/arbiter/optimize/generator/GridSearchCandidateGenerator.java b/arbiter/arbiter-core/src/main/java/org/deeplearning4j/arbiter/optimize/generator/GridSearchCandidateGenerator.java index c923e6c60..c61b62d8b 100644 --- a/arbiter/arbiter-core/src/main/java/org/deeplearning4j/arbiter/optimize/generator/GridSearchCandidateGenerator.java +++ b/arbiter/arbiter-core/src/main/java/org/deeplearning4j/arbiter/optimize/generator/GridSearchCandidateGenerator.java @@ -196,6 +196,11 @@ public class GridSearchCandidateGenerator extends BaseCandidateGenerator { // 0-> [0,0,0], 1-> [1,0,0], 2-> [2,0,0], 3-> [0,1,0] etc //Based on: Nd4j Shape.ind2sub + int countNon1 = 0; + for( int i : numValuesPerParam) + if(i > 1) + countNon1++; + int denom = product; int num = candidateIdx; int[] index = new int[numValuesPerParam.length]; @@ -209,12 +214,11 @@ public class GridSearchCandidateGenerator extends BaseCandidateGenerator { //Now: convert indexes to values in range [0,1] //min value -> 0 //max value -> 1 - double[] out = new double[numValuesPerParam.length]; - for (int i = 0; i < out.length; i++) { - if (numValuesPerParam[i] <= 1) - out[i] = 0.0; - else { - out[i] = index[i] / ((double) (numValuesPerParam[i] - 1)); + double[] out = new double[countNon1]; + int outIdx = 0; + for (int i = 0; i < numValuesPerParam.length; i++) { + if (numValuesPerParam[i] > 1){ + out[outIdx++] = index[i] / ((double) (numValuesPerParam[i] - 1)); } } diff --git a/arbiter/arbiter-deeplearning4j/src/test/java/org/deeplearning4j/arbiter/multilayernetwork/TestMultiLayerSpace.java b/arbiter/arbiter-deeplearning4j/src/test/java/org/deeplearning4j/arbiter/multilayernetwork/TestMultiLayerSpace.java index d4dbe9a3a..58333d930 100644 --- a/arbiter/arbiter-deeplearning4j/src/test/java/org/deeplearning4j/arbiter/multilayernetwork/TestMultiLayerSpace.java +++ b/arbiter/arbiter-deeplearning4j/src/test/java/org/deeplearning4j/arbiter/multilayernetwork/TestMultiLayerSpace.java @@ -21,6 +21,7 @@ import org.deeplearning4j.arbiter.DL4JConfiguration; import org.deeplearning4j.arbiter.MultiLayerSpace; import org.deeplearning4j.arbiter.TestUtils; import org.deeplearning4j.arbiter.conf.updater.AdamSpace; +import org.deeplearning4j.arbiter.conf.updater.NesterovsSpace; import org.deeplearning4j.arbiter.conf.updater.SgdSpace; import org.deeplearning4j.arbiter.layers.*; import org.deeplearning4j.arbiter.optimize.api.Candidate; @@ -80,6 +81,7 @@ import org.nd4j.linalg.lossfunctions.LossFunctions; import org.nd4j.linalg.lossfunctions.LossFunctions.LossFunction; import org.nd4j.linalg.lossfunctions.impl.LossMCXENT; import org.nd4j.linalg.lossfunctions.impl.LossMSE; +import org.nd4j.linalg.primitives.Pair; import java.io.File; import java.lang.reflect.Field; @@ -767,4 +769,52 @@ public class TestMultiLayerSpace extends BaseDL4JTest { assertEquals(expCandidates, count); } } + + + @Test + public void testGridCandidateGenerator(){ + ParameterSpace layerSizeParam = new DiscreteParameterSpace<>(32, 48, 64); + ParameterSpace learningRateParam = new DiscreteParameterSpace<>(0.005, 0.007, 0.01); + + MultiLayerSpace hyperParamaterSpace = new MultiLayerSpace.Builder() + .seed(12345) + .biasInit(1) + .l2(1e-4) + .updater(new NesterovsSpace(learningRateParam)) + .addLayer(new DenseLayerSpace.Builder().nIn(10).nOut(layerSizeParam) + .weightInit(WeightInit.XAVIER) + .activation(Activation.RELU) + .build()) + .addLayer(new DenseLayerSpace.Builder().nIn(layerSizeParam).nOut(layerSizeParam) + .weightInit(WeightInit.XAVIER) + .activation(Activation.RELU) + .build()) + .addLayer(new OutputLayerSpace.Builder() + .lossFunction(LossFunctions.LossFunction.MSE) + .weightInit(WeightInit.XAVIER) + .activation(Activation.SOFTMAX) + .nIn(layerSizeParam).nOut(10).build()) + .build(); + + CandidateGenerator candidateGenerator = new GridSearchCandidateGenerator(hyperParamaterSpace, 30, GridSearchCandidateGenerator.Mode.Sequential, null); +// CandidateGenerator candidateGenerator = new RandomSearchGenerator(hyperParamaterSpace); + + Set> expCandidates = new HashSet<>(); + for(Double d : new double[]{0.005, 0.007, 0.01}){ + for(int i : new int[]{32, 48, 64}){ + expCandidates.add(new Pair<>(d, i)); + } + } + + Set> actCandidates = new HashSet<>(); + while(candidateGenerator.hasMoreCandidates()) { + Candidate conf = candidateGenerator.getCandidate(); + MultiLayerConfiguration mlc = conf.getValue().getMultiLayerConfiguration(); + FeedForwardLayer ffl = ((FeedForwardLayer) mlc.getConf(0).getLayer()); +// System.out.println(ffl.getIUpdater() + ", " + ffl.getNOut()); + actCandidates.add(new Pair<>(ffl.getIUpdater().getLearningRate(0,0), (int)ffl.getNOut())); + } + + assertEquals(expCandidates, actCandidates); + } } diff --git a/arbiter/arbiter-server/src/test/java/org/deeplearning4j/arbiter/server/ArbiterCLIRunnerTest.java b/arbiter/arbiter-server/src/test/java/org/deeplearning4j/arbiter/server/ArbiterCLIRunnerTest.java index 40e8a3e41..3aaff2ed8 100644 --- a/arbiter/arbiter-server/src/test/java/org/deeplearning4j/arbiter/server/ArbiterCLIRunnerTest.java +++ b/arbiter/arbiter-server/src/test/java/org/deeplearning4j/arbiter/server/ArbiterCLIRunnerTest.java @@ -55,6 +55,10 @@ import static org.junit.Assert.assertEquals; @Slf4j public class ArbiterCLIRunnerTest extends BaseDL4JTest { + @Override + public long getTimeoutMilliseconds() { + return 90000; + } @Test public void testCliRunner() throws Exception { @@ -67,7 +71,7 @@ public class ArbiterCLIRunnerTest extends BaseDL4JTest { .l2(new ContinuousParameterSpace(0.0001, 0.01)) .addLayer(new DenseLayerSpace.Builder().nIn(784).nOut(new IntegerParameterSpace(2,10)) .activation(new DiscreteParameterSpace<>(Activation.RELU, Activation.TANH)) - .build(),new IntegerParameterSpace(1,2),true) //1-2 identical layers (except nIn) + .build()) .addLayer(new OutputLayerSpace.Builder().nOut(10).activation(Activation.SOFTMAX) .lossFunction(LossFunctions.LossFunction.MCXENT).build()) .numEpochs(3).build(); diff --git a/deeplearning4j/deeplearning4j-common-tests/pom.xml b/deeplearning4j/deeplearning4j-common-tests/pom.xml index 825c55ca5..23e030df3 100644 --- a/deeplearning4j/deeplearning4j-common-tests/pom.xml +++ b/deeplearning4j/deeplearning4j-common-tests/pom.xml @@ -37,6 +37,10 @@ nd4j-api ${project.version} + + ch.qos.logback + logback-classic + diff --git a/deeplearning4j/deeplearning4j-common-tests/src/main/java/org/deeplearning4j/BaseDL4JTest.java b/deeplearning4j/deeplearning4j-common-tests/src/main/java/org/deeplearning4j/BaseDL4JTest.java index e9b609c45..6ce978ec5 100644 --- a/deeplearning4j/deeplearning4j-common-tests/src/main/java/org/deeplearning4j/BaseDL4JTest.java +++ b/deeplearning4j/deeplearning4j-common-tests/src/main/java/org/deeplearning4j/BaseDL4JTest.java @@ -17,6 +17,7 @@ package org.deeplearning4j; +import ch.qos.logback.classic.LoggerContext; import lombok.extern.slf4j.Slf4j; import org.bytedeco.javacpp.Pointer; import org.junit.After; @@ -31,6 +32,8 @@ import org.nd4j.linalg.api.memory.MemoryWorkspace; import org.nd4j.linalg.api.ops.executioner.OpExecutioner; import org.nd4j.linalg.factory.Nd4j; import org.nd4j.linalg.profiler.ProfilerConfig; +import org.slf4j.ILoggerFactory; +import org.slf4j.LoggerFactory; import java.lang.management.ManagementFactory; import java.util.List; @@ -86,12 +89,12 @@ public abstract class BaseDL4JTest { return getDataType(); } - protected Boolean integrationTest; + protected static Boolean integrationTest; /** * @return True if integration tests maven profile is enabled, false otherwise. */ - public boolean isIntegrationTests(){ + public static boolean isIntegrationTests(){ if(integrationTest == null){ String prop = System.getenv("DL4J_INTEGRATION_TESTS"); integrationTest = Boolean.parseBoolean(prop); @@ -104,7 +107,7 @@ public abstract class BaseDL4JTest { * This can be used to dynamically skip integration tests when the integration test profile is not enabled. * Note that the integration test profile is not enabled by default - "integration-tests" profile */ - public void skipUnlessIntegrationTests(){ + public static void skipUnlessIntegrationTests(){ assumeTrue("Skipping integration test - integration profile is not enabled", isIntegrationTests()); } @@ -139,6 +142,15 @@ public abstract class BaseDL4JTest { //Not really safe to continue testing under this situation... other tests will likely fail with obscure // errors that are hard to track back to this log.error("Open workspace leaked from test! Exiting - {}, isOpen = {} - {}", currWS.getId(), currWS.isScopeActive(), currWS); + System.out.println("Open workspace leaked from test! Exiting - " + currWS.getId() + ", isOpen = " + currWS.isScopeActive() + " - " + currWS); + System.out.flush(); + //Try to flush logs also: + try{ Thread.sleep(1000); } catch (InterruptedException e){ } + ILoggerFactory lf = LoggerFactory.getILoggerFactory(); + if( lf instanceof LoggerContext){ + ((LoggerContext)lf).stop(); + } + try{ Thread.sleep(1000); } catch (InterruptedException e){ } System.exit(1); } diff --git a/deeplearning4j/deeplearning4j-core/pom.xml b/deeplearning4j/deeplearning4j-core/pom.xml index 90c88d4c3..496bb6b1b 100644 --- a/deeplearning4j/deeplearning4j-core/pom.xml +++ b/deeplearning4j/deeplearning4j-core/pom.xml @@ -164,6 +164,20 @@ oshi-core ${oshi.version} + + + + org.reflections + reflections + ${reflections.version} + test + + + com.google.code.findbugs + * + + + diff --git a/deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/AssertTestsExtendBaseClass.java b/deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/AssertTestsExtendBaseClass.java new file mode 100644 index 000000000..20d2967bb --- /dev/null +++ b/deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/AssertTestsExtendBaseClass.java @@ -0,0 +1,72 @@ +/* ****************************************************************************** + * Copyright (c) 2020 Konduit K.K. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ +package org.deeplearning4j; + +import lombok.extern.slf4j.Slf4j; +import org.junit.Test; +import org.reflections.Reflections; +import org.reflections.scanners.MethodAnnotationsScanner; +import org.reflections.util.ClasspathHelper; +import org.reflections.util.ConfigurationBuilder; + +import java.lang.reflect.Method; +import java.util.*; + +import static org.junit.Assert.assertEquals; + +/** + * This class checks that all test classes (i.e., anything with one or more methods annotated with @Test) + * extends BaseDl4JTest - either directly or indirectly. + * Other than a small set of exceptions, all tests must extend this + * + * @author Alex Black + */ +@Slf4j +public class AssertTestsExtendBaseClass extends BaseDL4JTest { + + //Set of classes that are exclusions to the rule (either run manually or have their own logging + timeouts) + private static final Set> exclusions = new HashSet<>(); + + @Test + public void checkTestClasses(){ + + Reflections reflections = new Reflections(new ConfigurationBuilder() + .setUrls(ClasspathHelper.forPackage("org.deeplearning4j")) + .setScanners(new MethodAnnotationsScanner())); + Set methods = reflections.getMethodsAnnotatedWith(Test.class); + Set> s = new HashSet<>(); + for(Method m : methods){ + s.add(m.getDeclaringClass()); + } + + List> l = new ArrayList<>(s); + Collections.sort(l, new Comparator>() { + @Override + public int compare(Class aClass, Class t1) { + return aClass.getName().compareTo(t1.getName()); + } + }); + + int count = 0; + for(Class c : l){ + if(!BaseDL4JTest.class.isAssignableFrom(c) && !exclusions.contains(c)){ + log.error("Test {} does not extend BaseDL4JTest (directly or indirectly). All tests must extend this class for proper memory tracking and timeouts", c); + count++; + } + } + assertEquals("Number of tests not extending BaseDL4JTest", 0, count); + } +} diff --git a/deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/RandomTests.java b/deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/RandomTests.java index 8f727fdf9..b52b7cb49 100644 --- a/deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/RandomTests.java +++ b/deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/RandomTests.java @@ -17,7 +17,7 @@ import org.nd4j.linalg.lossfunctions.LossFunctions; import java.util.concurrent.CountDownLatch; @Ignore -public class RandomTests { +public class RandomTests extends BaseDL4JTest { @Test public void testReproduce() throws Exception { diff --git a/deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/datasets/TestDataSets.java b/deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/datasets/TestDataSets.java index 730038943..bc892905c 100644 --- a/deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/datasets/TestDataSets.java +++ b/deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/datasets/TestDataSets.java @@ -16,11 +16,12 @@ package org.deeplearning4j.datasets; +import org.deeplearning4j.BaseDL4JTest; import org.deeplearning4j.datasets.fetchers.Cifar10Fetcher; import org.deeplearning4j.datasets.fetchers.TinyImageNetFetcher; import org.junit.Test; -public class TestDataSets { +public class TestDataSets extends BaseDL4JTest { @Test public void testTinyImageNetExists() throws Exception { diff --git a/deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/datasets/datavec/RecordReaderDataSetiteratorTest.java b/deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/datasets/datavec/RecordReaderDataSetiteratorTest.java index 6b3047aa5..c20b5855f 100644 --- a/deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/datasets/datavec/RecordReaderDataSetiteratorTest.java +++ b/deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/datasets/datavec/RecordReaderDataSetiteratorTest.java @@ -1006,9 +1006,9 @@ public class RecordReaderDataSetiteratorTest extends BaseDL4JTest { for (RecordMetaData m : meta) { Record r = csv.loadFromMetaData(m); INDArray row = ds.getFeatures().getRow(i); - if(i <= 3) { - System.out.println(m.getLocation() + "\t" + r.getRecord() + "\t" + row); - } +// if(i <= 3) { +// System.out.println(m.getLocation() + "\t" + r.getRecord() + "\t" + row); +// } for (int j = 0; j < 4; j++) { double exp = r.getRecord().get(j).toDouble(); @@ -1017,7 +1017,7 @@ public class RecordReaderDataSetiteratorTest extends BaseDL4JTest { } i++; } - System.out.println(); +// System.out.println(); DataSet fromMeta = rrdsi.loadFromMetaData(meta); assertEquals(ds, fromMeta); diff --git a/deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/datasets/iterator/DummyBlockDataSetIteratorTests.java b/deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/datasets/iterator/DummyBlockDataSetIteratorTests.java index 41cd343a1..2b9700bbe 100644 --- a/deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/datasets/iterator/DummyBlockDataSetIteratorTests.java +++ b/deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/datasets/iterator/DummyBlockDataSetIteratorTests.java @@ -19,6 +19,7 @@ package org.deeplearning4j.datasets.iterator; import lombok.extern.slf4j.Slf4j; import lombok.val; import lombok.var; +import org.deeplearning4j.BaseDL4JTest; import org.deeplearning4j.datasets.iterator.tools.SimpleVariableGenerator; import org.junit.Test; import org.nd4j.linalg.dataset.api.DataSet; @@ -31,7 +32,7 @@ import static org.junit.Assert.assertNotNull; import static org.junit.Assert.assertTrue; @Slf4j -public class DummyBlockDataSetIteratorTests { +public class DummyBlockDataSetIteratorTests extends BaseDL4JTest { @Test public void testBlock_1() throws Exception { diff --git a/deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/datasets/iterator/JointMultiDataSetIteratorTests.java b/deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/datasets/iterator/JointMultiDataSetIteratorTests.java index a2feb91c7..2108c9ec3 100644 --- a/deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/datasets/iterator/JointMultiDataSetIteratorTests.java +++ b/deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/datasets/iterator/JointMultiDataSetIteratorTests.java @@ -18,13 +18,14 @@ package org.deeplearning4j.datasets.iterator; import lombok.extern.slf4j.Slf4j; import lombok.val; +import org.deeplearning4j.BaseDL4JTest; import org.deeplearning4j.datasets.iterator.tools.DataSetGenerator; import org.junit.Test; import static org.junit.Assert.*; @Slf4j -public class JointMultiDataSetIteratorTests { +public class JointMultiDataSetIteratorTests extends BaseDL4JTest { @Test (timeout = 20000L) public void testJMDSI_1() { diff --git a/deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/datasets/iterator/LoaderIteratorTests.java b/deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/datasets/iterator/LoaderIteratorTests.java index 76368e729..aa49c9b50 100644 --- a/deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/datasets/iterator/LoaderIteratorTests.java +++ b/deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/datasets/iterator/LoaderIteratorTests.java @@ -16,6 +16,7 @@ package org.deeplearning4j.datasets.iterator; +import org.deeplearning4j.BaseDL4JTest; import org.deeplearning4j.datasets.iterator.loader.DataSetLoaderIterator; import org.deeplearning4j.datasets.iterator.loader.MultiDataSetLoaderIterator; import org.junit.Test; @@ -37,7 +38,7 @@ import java.util.Random; import static org.junit.Assert.assertEquals; import static org.junit.Assert.assertTrue; -public class LoaderIteratorTests { +public class LoaderIteratorTests extends BaseDL4JTest { @Test public void testDSLoaderIter(){ diff --git a/deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/graph/graphnodes/TestGraphNodes.java b/deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/graph/graphnodes/TestGraphNodes.java index 54d645259..a9816fd7c 100644 --- a/deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/graph/graphnodes/TestGraphNodes.java +++ b/deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/graph/graphnodes/TestGraphNodes.java @@ -17,6 +17,7 @@ package org.deeplearning4j.nn.graph.graphnodes; import lombok.val; +import org.deeplearning4j.BaseDL4JTest; import org.deeplearning4j.nn.api.MaskState; import org.deeplearning4j.nn.conf.ComputationGraphConfiguration; import org.deeplearning4j.nn.conf.NeuralNetConfiguration; @@ -54,7 +55,7 @@ import java.util.Map; import static org.junit.Assert.*; -public class TestGraphNodes { +public class TestGraphNodes extends BaseDL4JTest { @Test public void testMergeNode() { diff --git a/deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/layers/RepeatVectorTest.java b/deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/layers/RepeatVectorTest.java index 5e8537529..f7624ec7a 100644 --- a/deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/layers/RepeatVectorTest.java +++ b/deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/layers/RepeatVectorTest.java @@ -16,6 +16,7 @@ package org.deeplearning4j.nn.layers; +import org.deeplearning4j.BaseDL4JTest; import org.deeplearning4j.nn.api.Layer; import org.deeplearning4j.nn.conf.NeuralNetConfiguration; import org.deeplearning4j.nn.conf.layers.misc.RepeatVector; @@ -32,7 +33,7 @@ import java.util.Arrays; import static org.junit.Assert.assertEquals; import static org.junit.Assert.assertTrue; -public class RepeatVectorTest { +public class RepeatVectorTest extends BaseDL4JTest { private int REPEAT = 4; diff --git a/deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/layers/convolution/Convolution3DTest.java b/deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/layers/convolution/Convolution3DTest.java index d4be87a2a..e9467e83a 100644 --- a/deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/layers/convolution/Convolution3DTest.java +++ b/deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/layers/convolution/Convolution3DTest.java @@ -16,6 +16,7 @@ package org.deeplearning4j.nn.layers.convolution; +import org.deeplearning4j.BaseDL4JTest; import org.deeplearning4j.datasets.iterator.impl.MnistDataSetIterator; import org.deeplearning4j.nn.api.Layer; import org.deeplearning4j.nn.conf.ConvolutionMode; @@ -37,7 +38,7 @@ import static org.junit.Assert.assertTrue; /** * @author Max Pumperla */ -public class Convolution3DTest { +public class Convolution3DTest extends BaseDL4JTest { private int nExamples = 1; private int nChannelsOut = 1; diff --git a/deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/layers/feedforward/embedding/EmbeddingLayerTest.java b/deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/layers/feedforward/embedding/EmbeddingLayerTest.java index 972302d85..96ab25267 100644 --- a/deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/layers/feedforward/embedding/EmbeddingLayerTest.java +++ b/deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/layers/feedforward/embedding/EmbeddingLayerTest.java @@ -47,8 +47,7 @@ import java.util.List; import java.util.Map; import java.util.Random; -import static org.junit.Assert.assertArrayEquals; -import static org.junit.Assert.assertEquals; +import static org.junit.Assert.*; public class EmbeddingLayerTest extends BaseDL4JTest { @@ -725,4 +724,79 @@ public class EmbeddingLayerTest extends BaseDL4JTest { assertEquals(new ActivationIdentity(), l2.getActivationFn()); } + + + @Test + public void testEmbeddingWeightInit(){ + // https://github.com/eclipse/deeplearning4j/issues/8663 + //The embedding layer weight initialization should be independent of the vocabulary size (nIn setting) + + for(WeightInit wi : new WeightInit[]{WeightInit.XAVIER, WeightInit.RELU, WeightInit.XAVIER_UNIFORM, WeightInit.LECUN_NORMAL}) { + + for (boolean seq : new boolean[]{false, true}) { + + Nd4j.getRandom().setSeed(12345); + MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder() + .seed(12345) + .list() + .layer(seq ? + new EmbeddingSequenceLayer.Builder().weightInit(wi).nIn(100).nOut(100).build() : + new EmbeddingLayer.Builder().weightInit(wi).nIn(100).nOut(100).build()) + .build(); + MultiLayerNetwork net = new MultiLayerNetwork(conf); + net.init(); + + Nd4j.getRandom().setSeed(12345); + MultiLayerConfiguration conf2 = new NeuralNetConfiguration.Builder() + .seed(12345) + .list() + .layer(seq ? + new EmbeddingSequenceLayer.Builder().weightInit(wi).nIn(100).nOut(100).build() : + new EmbeddingLayer.Builder().weightInit(wi).nIn(100).nOut(100).build()) + .build(); + MultiLayerNetwork net2 = new MultiLayerNetwork(conf2); + net2.init(); + + Nd4j.getRandom().setSeed(12345); + MultiLayerConfiguration conf3 = new NeuralNetConfiguration.Builder() + .seed(12345) + .list() + .layer(seq ? + new EmbeddingSequenceLayer.Builder().weightInit(wi).nIn(100000).nOut(100).build() : + new EmbeddingLayer.Builder().weightInit(wi).nIn(100000).nOut(100).build()) + .build(); + MultiLayerNetwork net3 = new MultiLayerNetwork(conf3); + net3.init(); + + INDArray p1 = net.params(); + INDArray p2 = net2.params(); + INDArray p3 = net3.params(); + boolean eq = p1.equalsWithEps(p2, 1e-4); + String str = (seq ? "EmbeddingSequenceLayer" : "EmbeddingLayer") + " - " + wi; + assertTrue(str + " p1/p2 params not equal", eq); + + double m1 = p1.meanNumber().doubleValue(); + double s1 = p1.stdNumber().doubleValue(); + + double m3 = p3.meanNumber().doubleValue(); + double s3 = p3.stdNumber().doubleValue(); + + + + assertEquals(str, m1, m3, 0.1); + assertEquals(str, s1, s3, 0.1); + + double re = relErr(s1, s3); + assertTrue(str + " - " + re, re < 0.05); + } + } + + } + + public static double relErr(double d1, double d2){ + if(d1 == 0.0 && d2 == 0.0) + return 0.0; + return Math.abs(d1 - d2) / (Math.abs(d1) + Math.abs(d2)); + } + } diff --git a/deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/layers/ocnn/OCNNOutputLayerTest.java b/deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/layers/ocnn/OCNNOutputLayerTest.java index 3e3c47064..bf158a863 100644 --- a/deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/layers/ocnn/OCNNOutputLayerTest.java +++ b/deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/layers/ocnn/OCNNOutputLayerTest.java @@ -16,6 +16,7 @@ package org.deeplearning4j.nn.layers.ocnn; +import org.deeplearning4j.BaseDL4JTest; import org.deeplearning4j.datasets.iterator.impl.IrisDataSetIterator; import org.deeplearning4j.gradientcheck.GradientCheckUtil; import org.deeplearning4j.nn.conf.MultiLayerConfiguration; @@ -51,7 +52,7 @@ import static org.junit.Assert.assertEquals; import static org.junit.Assert.assertTrue; -public class OCNNOutputLayerTest { +public class OCNNOutputLayerTest extends BaseDL4JTest { private static final boolean PRINT_RESULTS = true; private static final boolean RETURN_ON_FIRST_FAILURE = false; diff --git a/deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/layers/recurrent/TestRecurrentWeightInit.java b/deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/layers/recurrent/TestRecurrentWeightInit.java index 593687283..4c1e5374e 100644 --- a/deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/layers/recurrent/TestRecurrentWeightInit.java +++ b/deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/layers/recurrent/TestRecurrentWeightInit.java @@ -16,6 +16,7 @@ package org.deeplearning4j.nn.layers.recurrent; +import org.deeplearning4j.BaseDL4JTest; import org.deeplearning4j.nn.conf.NeuralNetConfiguration; import org.deeplearning4j.nn.conf.distribution.UniformDistribution; import org.deeplearning4j.nn.conf.layers.GravesLSTM; @@ -27,7 +28,7 @@ import org.nd4j.linalg.api.ndarray.INDArray; import static org.junit.Assert.assertTrue; -public class TestRecurrentWeightInit { +public class TestRecurrentWeightInit extends BaseDL4JTest { @Test public void testRWInit() { diff --git a/deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/layers/samediff/SameDiffCustomLayerTests.java b/deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/layers/samediff/SameDiffCustomLayerTests.java index e672c3b0d..034c3e03b 100644 --- a/deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/layers/samediff/SameDiffCustomLayerTests.java +++ b/deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/layers/samediff/SameDiffCustomLayerTests.java @@ -21,6 +21,7 @@ import org.deeplearning4j.BaseDL4JTest; import org.deeplearning4j.nn.conf.ComputationGraphConfiguration; import org.deeplearning4j.nn.conf.MultiLayerConfiguration; import org.deeplearning4j.nn.conf.NeuralNetConfiguration; +import org.deeplearning4j.nn.conf.graph.GraphVertex; import org.deeplearning4j.nn.conf.inputs.InputType; import org.deeplearning4j.nn.conf.inputs.InvalidInputTypeException; import org.deeplearning4j.nn.conf.layers.OutputLayer; @@ -136,6 +137,11 @@ public class SameDiffCustomLayerTests extends BaseDL4JTest { } private class ValidatingSameDiffVertex extends SameDiffVertex { + @Override + public GraphVertex clone() { + return new ValidatingSameDiffVertex(); + } + @Override public InputType getOutputType(int layerIndex, InputType... vertexInputs) throws InvalidInputTypeException { return vertexInputs[0]; diff --git a/deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/layers/samediff/testlayers/SameDiffDenseVertex.java b/deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/layers/samediff/testlayers/SameDiffDenseVertex.java index 3e3631d5b..481816472 100644 --- a/deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/layers/samediff/testlayers/SameDiffDenseVertex.java +++ b/deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/layers/samediff/testlayers/SameDiffDenseVertex.java @@ -18,6 +18,7 @@ package org.deeplearning4j.nn.layers.samediff.testlayers; import lombok.Data; import lombok.NoArgsConstructor; +import org.deeplearning4j.nn.conf.graph.GraphVertex; import org.deeplearning4j.nn.conf.layers.samediff.SDVertexParams; import org.deeplearning4j.nn.conf.layers.samediff.SameDiffVertex; import org.deeplearning4j.nn.params.DefaultParamInitializer; @@ -74,4 +75,9 @@ public class SameDiffDenseVertex extends SameDiffVertex { public char paramReshapeOrder(String paramName){ return 'f'; //To match DL4J DenseLayer - for easy comparison } + + @Override + public GraphVertex clone() { + return new SameDiffDenseVertex(nIn, nOut, activation, weightInit); + } } diff --git a/deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/layers/samediff/testlayers/SameDiffSimpleLambdaVertex.java b/deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/layers/samediff/testlayers/SameDiffSimpleLambdaVertex.java index 98894c882..d9513cf80 100644 --- a/deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/layers/samediff/testlayers/SameDiffSimpleLambdaVertex.java +++ b/deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/layers/samediff/testlayers/SameDiffSimpleLambdaVertex.java @@ -16,6 +16,7 @@ package org.deeplearning4j.nn.layers.samediff.testlayers; +import org.deeplearning4j.nn.conf.graph.GraphVertex; import org.deeplearning4j.nn.conf.layers.samediff.SameDiffLambdaVertex; import org.nd4j.autodiff.samediff.SDVariable; import org.nd4j.autodiff.samediff.SameDiff; diff --git a/deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/misc/LargeNetTest.java b/deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/misc/LargeNetTest.java index 94b39591b..e728e0beb 100644 --- a/deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/misc/LargeNetTest.java +++ b/deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/misc/LargeNetTest.java @@ -16,6 +16,7 @@ package org.deeplearning4j.nn.misc; +import org.deeplearning4j.BaseDL4JTest; import org.deeplearning4j.nn.conf.ComputationGraphConfiguration; import org.deeplearning4j.nn.conf.MultiLayerConfiguration; import org.deeplearning4j.nn.conf.NeuralNetConfiguration; @@ -35,7 +36,7 @@ import static org.junit.Assert.assertArrayEquals; import static org.junit.Assert.assertEquals; @Ignore //Ignored due to very large memory requirements -public class LargeNetTest { +public class LargeNetTest extends BaseDL4JTest { @Ignore @Test diff --git a/deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/transferlearning/TransferLearningCompGraphTest.java b/deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/transferlearning/TransferLearningCompGraphTest.java index 094ce531d..93341dbe2 100644 --- a/deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/transferlearning/TransferLearningCompGraphTest.java +++ b/deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/transferlearning/TransferLearningCompGraphTest.java @@ -24,6 +24,7 @@ import org.deeplearning4j.nn.conf.NeuralNetConfiguration; import org.deeplearning4j.nn.conf.constraint.UnitNormConstraint; import org.deeplearning4j.nn.conf.distribution.ConstantDistribution; import org.deeplearning4j.nn.conf.distribution.NormalDistribution; +import org.deeplearning4j.nn.conf.graph.AttentionVertex; import org.deeplearning4j.nn.conf.inputs.InputType; import org.deeplearning4j.nn.conf.layers.*; import org.deeplearning4j.nn.conf.layers.misc.FrozenLayer; @@ -35,6 +36,7 @@ import org.deeplearning4j.nn.weights.WeightInitDistribution; import org.deeplearning4j.nn.weights.WeightInitXavier; import org.junit.Test; import org.nd4j.linalg.activations.Activation; +import org.nd4j.linalg.api.buffer.DataType; import org.nd4j.linalg.api.ndarray.INDArray; import org.nd4j.linalg.dataset.DataSet; import org.nd4j.linalg.factory.Nd4j; @@ -44,6 +46,9 @@ import org.nd4j.linalg.learning.config.RmsProp; import org.nd4j.linalg.learning.config.Sgd; import org.nd4j.linalg.lossfunctions.LossFunctions; +import java.util.HashMap; +import java.util.Map; + import static org.junit.Assert.*; /** @@ -565,4 +570,99 @@ public class TransferLearningCompGraphTest extends BaseDL4JTest { assertEquals("Incorrect number of inputs!", 5, newGraph.layerInputSize(afterPoolName)); newGraph.output(input); } + + + + + @Test + public void testTransferLearningSameDiffLayersGraph(){ + + ComputationGraphConfiguration conf = new NeuralNetConfiguration.Builder() + + .graphBuilder() + .addInputs("in") + .layer("l0", new LSTM.Builder().nIn(5).nOut(5).build(), "in") + .layer("l1", new RecurrentAttentionLayer.Builder().nHeads(1).headSize(5).nIn(5).nOut(5).build(), "l0") + .layer("out", new RnnOutputLayer.Builder().nIn(5).nOut(5).activation(Activation.SOFTMAX).build(), "l1") + .setOutputs("out") + .build(); + + ComputationGraph cg = new ComputationGraph(conf); + cg.init(); + + INDArray arr = Nd4j.rand(DataType.FLOAT, 2, 5, 10); + INDArray out = cg.output(arr)[0]; + + + ComputationGraph cg2 = new TransferLearning.GraphBuilder(cg).removeVertexAndConnections("out") + .fineTuneConfiguration(FineTuneConfiguration.builder().updater(new Adam(0.01)).build()) + .removeVertexAndConnections("out") + .addLayer("newOut", new RnnOutputLayer.Builder().nIn(5).nOut(5).activation(Activation.SOFTMAX).build(), "l1") + .setOutputs("newOut") + .build(); + + cg2.output(arr); + + Map m = new HashMap<>(cg.paramTable()); + m.put("newOut_W", m.remove("out_W")); + m.put("newOut_b", m.remove("out_b")); + cg2.setParamTable(m); + + Map p1 = cg.paramTable(); + Map p2 = cg2.paramTable(); + for(String s : p1.keySet()){ + INDArray i1 = p1.get(s); + INDArray i2 = p2.get(s.replaceAll("out", "newOut")); + assertEquals(s, i1, i2); + } + + INDArray out2 = cg2.outputSingle(arr); + assertEquals(out, out2); + } + + @Test + public void testTransferLearningSameDiffLayersGraphVertex(){ + + ComputationGraphConfiguration conf = new NeuralNetConfiguration.Builder() + + .graphBuilder() + .addInputs("in") + .layer("l0", new LSTM.Builder().nIn(5).nOut(5).build(), "in") + .addVertex("l1", new AttentionVertex.Builder().nHeads(1).headSize(5).nInKeys(5).nInQueries(5).nInValues(5).nOut(5).build(), "l0", "l0", "l0") + .layer("out", new RnnOutputLayer.Builder().nIn(5).nOut(5).activation(Activation.SOFTMAX).build(), "l1") + .setOutputs("out") + .build(); + + ComputationGraph cg = new ComputationGraph(conf); + cg.init(); + + INDArray arr = Nd4j.rand(DataType.FLOAT, 2, 5, 10); + INDArray out = cg.output(arr)[0]; + + + ComputationGraph cg2 = new TransferLearning.GraphBuilder(cg).removeVertexAndConnections("out") + .fineTuneConfiguration(FineTuneConfiguration.builder().updater(new Adam(0.01)).build()) + .removeVertexAndConnections("out") + .addLayer("newOut", new RnnOutputLayer.Builder().nIn(5).nOut(5).activation(Activation.SOFTMAX).build(), "l1") + .setOutputs("newOut") + .build(); + + cg2.output(arr); + + Map m = new HashMap<>(cg.paramTable()); + m.put("newOut_W", m.remove("out_W")); + m.put("newOut_b", m.remove("out_b")); + cg2.setParamTable(m); + + Map p1 = cg.paramTable(); + Map p2 = cg2.paramTable(); + for(String s : p1.keySet()){ + INDArray i1 = p1.get(s); + INDArray i2 = p2.get(s.replaceAll("out", "newOut")); + assertEquals(s, i1, i2); + } + + INDArray out2 = cg2.outputSingle(arr); + assertEquals(out, out2); + } } diff --git a/deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/transferlearning/TransferLearningMLNTest.java b/deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/transferlearning/TransferLearningMLNTest.java index 4d15010c9..1ac2bd7bb 100644 --- a/deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/transferlearning/TransferLearningMLNTest.java +++ b/deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/transferlearning/TransferLearningMLNTest.java @@ -41,6 +41,7 @@ import org.deeplearning4j.nn.weights.WeightInitRelu; import org.deeplearning4j.nn.weights.WeightInitXavier; import org.junit.Test; import org.nd4j.linalg.activations.Activation; +import org.nd4j.linalg.api.buffer.DataType; import org.nd4j.linalg.api.ndarray.INDArray; import org.nd4j.linalg.dataset.DataSet; import org.nd4j.linalg.factory.Nd4j; @@ -48,6 +49,8 @@ import org.nd4j.linalg.learning.config.*; import org.nd4j.linalg.lossfunctions.LossFunctions; import org.nd4j.shade.jackson.core.JsonProcessingException; +import java.util.Map; + import static org.junit.Assert.*; /** @@ -689,4 +692,51 @@ public class TransferLearningMLNTest extends BaseDL4JTest { assertEquals("Incorrect number of inputs!", 5, newNet.layerInputSize(2)); newNet.output(input); } + + + @Test + public void testTransferLearningSameDiffLayers(){ + + MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder() + .dataType(DataType.DOUBLE) + .activation(Activation.TANH) + .updater(new Adam(0.01)) + .weightInit(WeightInit.XAVIER) + .list() + .layer(new LSTM.Builder().nOut(8).build()) + .layer( new SelfAttentionLayer.Builder().nOut(4).nHeads(2).projectInput(true).build()) + .layer(new GlobalPoolingLayer.Builder().poolingType(PoolingType.MAX).build()) + .layer(new OutputLayer.Builder().nOut(2).activation(Activation.SOFTMAX) + .lossFunction(LossFunctions.LossFunction.MCXENT).build()) + .setInputType(InputType.recurrent(4)) + .build(); + + MultiLayerNetwork net = new MultiLayerNetwork(conf); + net.init(); + + INDArray in = Nd4j.rand(DataType.FLOAT, 3, 4, 5); + INDArray out = net.output(in); + + MultiLayerNetwork net2 = new TransferLearning.Builder(net) + .fineTuneConfiguration(FineTuneConfiguration.builder().updater(new Adam(0.01)).build()) + .removeLayersFromOutput(1) + .addLayer(new OutputLayer.Builder().nIn(4).nOut(2).activation(Activation.SOFTMAX) + .lossFunction(LossFunctions.LossFunction.MCXENT).build()) + .build(); + + net2.setParam("3_W", net.getParam("3_W")); + net2.setParam("3_b", net.getParam("3_b")); + + Map p1 = net.paramTable(); + Map p2 = net2.paramTable(); + for(String s : p1.keySet()){ + INDArray i1 = p1.get(s); + INDArray i2 = p2.get(s); + assertEquals(s, i1, i2); + } + + INDArray out2 = net2.output(in); + + assertEquals(out, out2); + } } diff --git a/deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/updater/custom/TestCustomUpdater.java b/deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/updater/custom/TestCustomUpdater.java index 128b1705d..c66fb41df 100644 --- a/deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/updater/custom/TestCustomUpdater.java +++ b/deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/updater/custom/TestCustomUpdater.java @@ -16,6 +16,7 @@ package org.deeplearning4j.nn.updater.custom; +import org.deeplearning4j.BaseDL4JTest; import org.deeplearning4j.nn.conf.MultiLayerConfiguration; import org.deeplearning4j.nn.conf.NeuralNetConfiguration; import org.deeplearning4j.nn.conf.layers.BaseLayer; @@ -35,7 +36,7 @@ import static org.junit.Assert.assertTrue; /** * Created by Alex on 09/05/2017. */ -public class TestCustomUpdater { +public class TestCustomUpdater extends BaseDL4JTest { @Test public void testCustomUpdater() { diff --git a/deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/weights/LegacyWeightInitTest.java b/deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/weights/LegacyWeightInitTest.java index a8a680584..1b1273280 100644 --- a/deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/weights/LegacyWeightInitTest.java +++ b/deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/weights/LegacyWeightInitTest.java @@ -16,6 +16,7 @@ package org.deeplearning4j.nn.weights; +import org.deeplearning4j.BaseDL4JTest; import org.deeplearning4j.nn.conf.distribution.*; import org.deeplearning4j.nn.conf.serde.JsonMappers; import org.junit.After; @@ -40,7 +41,7 @@ import static org.junit.Assert.*; * * @author Christian Skarby */ -public class LegacyWeightInitTest { +public class LegacyWeightInitTest extends BaseDL4JTest { private RandomFactory prevFactory; private final static int SEED = 666; diff --git a/deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/weights/WeightInitIdentityTest.java b/deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/weights/WeightInitIdentityTest.java index 06bfc9797..3ee2d64cb 100644 --- a/deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/weights/WeightInitIdentityTest.java +++ b/deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/weights/WeightInitIdentityTest.java @@ -16,6 +16,7 @@ package org.deeplearning4j.nn.weights; +import org.deeplearning4j.BaseDL4JTest; import org.deeplearning4j.nn.conf.ConvolutionMode; import org.deeplearning4j.nn.conf.NeuralNetConfiguration; import org.deeplearning4j.nn.conf.inputs.InputType; @@ -34,7 +35,7 @@ import static org.junit.Assert.assertEquals; * * @author Christian Skarby */ -public class WeightInitIdentityTest { +public class WeightInitIdentityTest extends BaseDL4JTest { /** * Test identity mapping for 1d convolution diff --git a/deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/optimizer/listener/ScoreStatTest.java b/deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/optimizer/listener/ScoreStatTest.java index fdc86c6cc..a7f59bb8c 100644 --- a/deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/optimizer/listener/ScoreStatTest.java +++ b/deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/optimizer/listener/ScoreStatTest.java @@ -1,5 +1,6 @@ package org.deeplearning4j.optimizer.listener; +import org.deeplearning4j.BaseDL4JTest; import org.deeplearning4j.optimize.listeners.CollectScoresIterationListener; import org.junit.Ignore; import org.junit.Test; @@ -7,7 +8,7 @@ import org.junit.Test; import java.util.List; import static org.junit.Assert.*; -public class ScoreStatTest { +public class ScoreStatTest extends BaseDL4JTest { @Test public void testScoreStatSmall() { CollectScoresIterationListener.ScoreStat statTest = new CollectScoresIterationListener.ScoreStat(); diff --git a/deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/regressiontest/MiscRegressionTests.java b/deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/regressiontest/MiscRegressionTests.java index 84bfcd3e8..952084713 100644 --- a/deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/regressiontest/MiscRegressionTests.java +++ b/deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/regressiontest/MiscRegressionTests.java @@ -17,6 +17,7 @@ package org.deeplearning4j.regressiontest; import org.apache.commons.io.FileUtils; +import org.deeplearning4j.BaseDL4JTest; import org.deeplearning4j.nn.conf.ComputationGraphConfiguration; import org.deeplearning4j.nn.conf.MultiLayerConfiguration; import org.deeplearning4j.nn.conf.NeuralNetConfiguration; @@ -36,7 +37,7 @@ import static org.junit.Assert.assertEquals; import static org.junit.Assert.assertNotNull; import static org.junit.Assert.assertTrue; -public class MiscRegressionTests { +public class MiscRegressionTests extends BaseDL4JTest { @Test public void testFrozen() throws Exception { diff --git a/deeplearning4j/deeplearning4j-nlp-parent/deeplearning4j-nlp/src/main/java/org/deeplearning4j/models/fasttext/FastText.java b/deeplearning4j/deeplearning4j-nlp-parent/deeplearning4j-nlp/src/main/java/org/deeplearning4j/models/fasttext/FastText.java index a704e7f10..60459a839 100644 --- a/deeplearning4j/deeplearning4j-nlp-parent/deeplearning4j-nlp/src/main/java/org/deeplearning4j/models/fasttext/FastText.java +++ b/deeplearning4j/deeplearning4j-nlp-parent/deeplearning4j-nlp/src/main/java/org/deeplearning4j/models/fasttext/FastText.java @@ -28,6 +28,7 @@ import java.util.*; @lombok.Builder public class FastText implements WordVectors, Serializable { + private final static String METHOD_NOT_AVAILABLE = "This method is available for text (.vec) models only - binary (.bin) model currently loaded"; // Mandatory @Getter private String inputFile; @Getter private String outputFile; @@ -219,6 +220,7 @@ public class FastText implements WordVectors, Serializable { public void loadBinaryModel(String modelPath) { fastTextImpl.loadModel(modelPath); + modelLoaded = true; } @@ -368,14 +370,12 @@ public class FastText implements WordVectors, Serializable { return words.contains(word); } - protected transient ModelUtils modelUtils; - @Override public Collection wordsNearest(INDArray words, int top) { if (modelVectorsLoaded) { return word2Vec.wordsNearest(words, top); } - return modelUtils.wordsNearest(words, top); + throw new IllegalStateException(METHOD_NOT_AVAILABLE); } @Override @@ -383,7 +383,7 @@ public class FastText implements WordVectors, Serializable { if (modelVectorsLoaded) { return word2Vec.wordsNearestSum(words, top); } - return modelUtils.wordsNearestSum(words, top); + throw new IllegalStateException(METHOD_NOT_AVAILABLE); } @Override @@ -391,7 +391,7 @@ public class FastText implements WordVectors, Serializable { if (modelVectorsLoaded) { return word2Vec.wordsNearestSum(word, n); } - return modelUtils.wordsNearestSum(word, n); + throw new IllegalStateException(METHOD_NOT_AVAILABLE); } @@ -400,7 +400,7 @@ public class FastText implements WordVectors, Serializable { if (modelVectorsLoaded) { return word2Vec.wordsNearestSum(positive, negative, top); } - return modelUtils.wordsNearestSum(positive, negative, top); + throw new IllegalStateException(METHOD_NOT_AVAILABLE); } @Override @@ -408,7 +408,7 @@ public class FastText implements WordVectors, Serializable { if (modelVectorsLoaded) { return word2Vec.accuracy(questions); } - return modelUtils.accuracy(questions); + throw new IllegalStateException(METHOD_NOT_AVAILABLE); } @Override @@ -425,7 +425,7 @@ public class FastText implements WordVectors, Serializable { if (modelVectorsLoaded) { return word2Vec.similarWordsInVocabTo(word, accuracy); } - return modelUtils.similarWordsInVocabTo(word, accuracy); + throw new IllegalStateException(METHOD_NOT_AVAILABLE); } @Override @@ -433,7 +433,7 @@ public class FastText implements WordVectors, Serializable { if (modelVectorsLoaded) { return word2Vec.wordsNearest(positive, negative, top); } - return modelUtils.wordsNearest(positive, negative, top); + throw new IllegalStateException(METHOD_NOT_AVAILABLE); } @@ -442,7 +442,7 @@ public class FastText implements WordVectors, Serializable { if (modelVectorsLoaded) { return word2Vec.wordsNearest(word,n); } - return modelUtils.wordsNearestSum(word, n); + throw new IllegalStateException(METHOD_NOT_AVAILABLE); } @@ -451,7 +451,7 @@ public class FastText implements WordVectors, Serializable { if (modelVectorsLoaded) { return word2Vec.similarity(word, word2); } - return modelUtils.similarity(word, word2); + throw new IllegalStateException(METHOD_NOT_AVAILABLE); } @Override @@ -464,7 +464,6 @@ public class FastText implements WordVectors, Serializable { @Override public void setModelUtils(ModelUtils utils) { - this.modelUtils = utils; } @Override diff --git a/deeplearning4j/deeplearning4j-nlp-parent/deeplearning4j-nlp/src/test/java/org/deeplearning4j/models/fasttext/FastTextTest.java b/deeplearning4j/deeplearning4j-nlp-parent/deeplearning4j-nlp/src/test/java/org/deeplearning4j/models/fasttext/FastTextTest.java index 8d53e8c1a..eba2bce85 100644 --- a/deeplearning4j/deeplearning4j-nlp-parent/deeplearning4j-nlp/src/test/java/org/deeplearning4j/models/fasttext/FastTextTest.java +++ b/deeplearning4j/deeplearning4j-nlp-parent/deeplearning4j-nlp/src/test/java/org/deeplearning4j/models/fasttext/FastTextTest.java @@ -121,6 +121,21 @@ public class FastTextTest extends BaseDL4JTest { assertEquals("__label__soccer", label); } + @Test(expected = IllegalStateException.class) + public void testIllegalState() { + String text = "I like soccer"; + + FastText fastText = new FastText(supModelFile); + assertEquals(48, fastText.vocab().numWords()); + assertEquals("association", fastText.vocab().wordAtIndex(fastText.vocab().numWords() - 1)); + + double[] expected = {-0.006423053797334433, 0.007660661358386278, 0.006068876478821039, -0.004772625397890806, -0.007143457420170307, -0.007735592778772116, -0.005607823841273785, -0.00836215727031231, 0.0011235733982175589, 2.599214785732329E-4, 0.004131870809942484, 0.007203693501651287, 0.0016768622444942594, 0.008694255724549294, -0.0012487826170399785, -0.00393667770549655, -0.006292815785855055, 0.0049359360709786415, -3.356488887220621E-4, -0.009407570585608482, -0.0026168026961386204, -0.00978928804397583, 0.0032913016621023417, -0.0029464277904480696, -0.008649969473481178, 8.056449587456882E-4, 0.0043088337406516075, -0.008980576880276203, 0.008716211654245853, 0.0073893265798687935, -0.007388216909021139, 0.003814412746578455, -0.005518500227481127, 0.004668557550758123, 0.006603693123906851, 0.003820829326286912, 0.007174000144004822, -0.006393063813447952, -0.0019381389720365405, -0.0046371882781386375, -0.006193376146256924, -0.0036685809027403593, 7.58899434003979E-4, -0.003185075242072344, -0.008330358192324638, 3.3206873922608793E-4, -0.005389622412621975, 0.009706716984510422, 0.0037855932023376226, -0.008665262721478939, -0.0032511046156287193, 4.4134497875347733E-4, -0.008377416990697384, -0.009110655635595322, 0.0019723298028111458, 0.007486093323677778, 0.006400121841579676, 0.00902814231812954, 0.00975200068205595, 0.0060582347214221954, -0.0075621469877660275, 1.0270809434587136E-4, -0.00673140911385417, -0.007316927425563335, 0.009916870854794979, -0.0011407854035496712, -4.502215306274593E-4, -0.007612560410052538, 0.008726916275918484, -3.0280642022262327E-5, 0.005529289599508047, -0.007944817654788494, 0.005593308713287115, 0.003423960180953145, 4.1348213562741876E-4, 0.009524818509817123, -0.0025129399728029966, -0.0030074280221015215, -0.007503866218030453, -0.0028124507516622543, -0.006841592025011778, -2.9375351732596755E-4, 0.007195258513092995, -0.007775942329317331, 3.951996040996164E-4, -0.006887971889227629, 0.0032655203249305487, -0.007975360378623009, -4.840183464693837E-6, 0.004651934839785099, 0.0031739831902086735, 0.004644941072911024, -0.007461248897016048, 0.003057275665923953, 0.008903342299163342, 0.006857945583760738, 0.007567950990051031, 0.001506582135334611, 0.0063307867385447025, 0.005645462777465582}; + assertArrayEquals(expected, fastText.getWordVector("association"), 1e-4); + + String label = fastText.predict(text); + fastText.wordsNearest("test",1); + } + @Test public void testPredictProbability() { String text = "I like soccer"; diff --git a/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/ComputationGraphConfiguration.java b/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/ComputationGraphConfiguration.java index 5a5ce5665..6a4bc01aa 100644 --- a/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/ComputationGraphConfiguration.java +++ b/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/ComputationGraphConfiguration.java @@ -427,7 +427,8 @@ public class ComputationGraphConfiguration implements Serializable, Cloneable { if(!disconnected.isEmpty() && !allowNoOutput){ //If allowing no output: by definition we have disconnected vertices throw new IllegalStateException("Invalid configuration: disconnected vertices found - " + disconnected + ". Disconnected vertices are those that do not connect to either another vertex, and are also" - + " not a network output. To disable this error (i.e., allow network configurations with" + + + " not a network output. This vertex can be set as an output using setOutputs(String...). " + + "To disable this error (i.e., allow network configurations with" + " disconnected vertices) use GraphBuilder.allowDisconnected(true)"); } } diff --git a/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/graph/AttentionVertex.java b/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/graph/AttentionVertex.java index 6ca3b35de..eb2a78b11 100644 --- a/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/graph/AttentionVertex.java +++ b/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/graph/AttentionVertex.java @@ -72,6 +72,20 @@ public class AttentionVertex extends SameDiffVertex { this.weightInit = builder.weightInit; } + @Override + public AttentionVertex clone() { + AttentionVertex av = new AttentionVertex(); + av.nInKeys = nInKeys; + av.nInValues = nInValues; + av.nInQueries = nInQueries; + av.nOut = nOut; + av.headSize = headSize; + av.nHeads = nHeads; + av.projectInput = projectInput; + av.weightInit = weightInit; + return av; + } + @Override public InputType getOutputType(int layerIndex, InputType... vertexInputs) throws InvalidInputTypeException { InputType.InputTypeRecurrent queries = (InputType.InputTypeRecurrent) vertexInputs[0]; diff --git a/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/layers/EmbeddingLayer.java b/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/layers/EmbeddingLayer.java index 6cd8630d0..6478b6d59 100644 --- a/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/layers/EmbeddingLayer.java +++ b/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/layers/EmbeddingLayer.java @@ -24,6 +24,7 @@ import org.deeplearning4j.nn.conf.inputs.InputType; import org.deeplearning4j.nn.conf.memory.LayerMemoryReport; import org.deeplearning4j.nn.conf.memory.MemoryReport; import org.deeplearning4j.nn.params.DefaultParamInitializer; +import org.deeplearning4j.nn.params.EmbeddingLayerParamInitializer; import org.deeplearning4j.nn.weights.IWeightInit; import org.deeplearning4j.nn.weights.embeddings.ArrayEmbeddingInitializer; import org.deeplearning4j.nn.weights.embeddings.EmbeddingInitializer; @@ -79,7 +80,7 @@ public class EmbeddingLayer extends FeedForwardLayer { @Override public ParamInitializer initializer() { - return DefaultParamInitializer.getInstance(); + return EmbeddingLayerParamInitializer.getInstance(); } @Override diff --git a/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/layers/EmbeddingSequenceLayer.java b/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/layers/EmbeddingSequenceLayer.java index 93585a1d0..9b7725801 100644 --- a/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/layers/EmbeddingSequenceLayer.java +++ b/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/layers/EmbeddingSequenceLayer.java @@ -24,7 +24,7 @@ import org.deeplearning4j.nn.conf.NeuralNetConfiguration; import org.deeplearning4j.nn.conf.inputs.InputType; import org.deeplearning4j.nn.conf.memory.LayerMemoryReport; import org.deeplearning4j.nn.conf.memory.MemoryReport; -import org.deeplearning4j.nn.params.DefaultParamInitializer; +import org.deeplearning4j.nn.params.EmbeddingLayerParamInitializer; import org.deeplearning4j.nn.weights.IWeightInit; import org.deeplearning4j.nn.weights.embeddings.ArrayEmbeddingInitializer; import org.deeplearning4j.nn.weights.embeddings.EmbeddingInitializer; @@ -92,7 +92,7 @@ public class EmbeddingSequenceLayer extends FeedForwardLayer { @Override public ParamInitializer initializer() { - return DefaultParamInitializer.getInstance(); + return EmbeddingLayerParamInitializer.getInstance(); } @Override diff --git a/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/layers/samediff/SameDiffLambdaVertex.java b/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/layers/samediff/SameDiffLambdaVertex.java index 6890f83e8..d8fc9fd48 100644 --- a/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/layers/samediff/SameDiffLambdaVertex.java +++ b/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/layers/samediff/SameDiffLambdaVertex.java @@ -16,11 +16,13 @@ package org.deeplearning4j.nn.conf.layers.samediff; +import org.deeplearning4j.nn.conf.graph.GraphVertex; import org.nd4j.autodiff.samediff.SDVariable; import org.nd4j.autodiff.samediff.SameDiff; import org.nd4j.base.Preconditions; import org.nd4j.linalg.api.ndarray.INDArray; +import java.lang.reflect.InvocationTargetException; import java.util.*; @@ -75,6 +77,15 @@ public abstract class SameDiffLambdaVertex extends SameDiffVertex { //No op, for lambda vertex } + @Override + public GraphVertex clone() { + try { + return getClass().getConstructor().newInstance(); + } catch (Exception e){ + throw new RuntimeException("Unable to create new instance of class " + getClass().getName() + " from no-arg constructor"); + } + } + protected VertexInputs getInputs(SameDiff sd) { if (inputs == null) { inputs = new VertexInputs(sd); diff --git a/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/layers/samediff/SameDiffVertex.java b/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/layers/samediff/SameDiffVertex.java index bd15b870b..f0066dac7 100644 --- a/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/layers/samediff/SameDiffVertex.java +++ b/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/layers/samediff/SameDiffVertex.java @@ -24,6 +24,7 @@ import org.deeplearning4j.nn.conf.NeuralNetConfiguration; import org.deeplearning4j.nn.conf.graph.GraphVertex; import org.deeplearning4j.nn.conf.inputs.InputType; import org.deeplearning4j.nn.conf.inputs.InvalidInputTypeException; +import org.deeplearning4j.nn.conf.layers.Layer; import org.deeplearning4j.nn.conf.memory.MemoryReport; import org.deeplearning4j.nn.graph.ComputationGraph; import org.deeplearning4j.nn.layers.samediff.SameDiffGraphVertex; @@ -36,6 +37,7 @@ import org.nd4j.linalg.learning.regularization.Regularization; import org.nd4j.linalg.primitives.Pair; import org.nd4j.linalg.util.ArrayUtil; +import java.lang.reflect.Field; import java.util.List; import java.util.Map; @@ -99,11 +101,6 @@ public abstract class SameDiffVertex extends GraphVertex implements TrainingConf return vertexParams; } - @Override - public GraphVertex clone() { - throw new UnsupportedOperationException("Not yet implemented"); - } - @Override public long numParams(boolean backprop) { SDLayerParams params = getVertexParams(); diff --git a/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/graph/ComputationGraph.java b/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/graph/ComputationGraph.java index eb84fefd6..0a34fe95a 100755 --- a/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/graph/ComputationGraph.java +++ b/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/graph/ComputationGraph.java @@ -3394,7 +3394,8 @@ public class ComputationGraph implements Serializable, Model, NeuralNetwork { @Override public void setParamTable(@NonNull Map paramTable) { - Preconditions.checkArgument(paramTable.keySet().equals(paramTable().keySet()), "Cannot set param table: parameter set keys are not equal"); + Map m = paramTable(); + Preconditions.checkArgument(paramTable.keySet().equals(m.keySet()), "Cannot set param table: parameter set keys are not equal"); Map current = paramTable(); //Check shapes before doing partial assigment to avoid leaving net in incorrect state for(String s : current.keySet()){ diff --git a/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/layers/samediff/SameDiffLayer.java b/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/layers/samediff/SameDiffLayer.java index fcf899544..64c2ea25e 100644 --- a/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/layers/samediff/SameDiffLayer.java +++ b/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/layers/samediff/SameDiffLayer.java @@ -237,9 +237,16 @@ public class SameDiffLayer extends AbstractLayer { @Override public void setParams(INDArray params) { - if (params != null) { - throw new UnsupportedOperationException("Not supported"); - } + if(this.params == null && params == null) + return; + if(this.params == null) + throw new IllegalStateException("Cannot set parameters of length " + params.length() + " to a layer with no parameters"); + if(params == null) + throw new IllegalStateException("Cannot set null parameters"); + + Preconditions.checkState(this.params.length() == params.length(), "Cannot assign parameter vector of length %s to a layer with %s parameters", + params.length(), this.params.length()); + this.params.assign(params); } protected void setParams(INDArray params, char order) { diff --git a/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/params/EmbeddingLayerParamInitializer.java b/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/params/EmbeddingLayerParamInitializer.java new file mode 100644 index 000000000..ab50e44f0 --- /dev/null +++ b/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/params/EmbeddingLayerParamInitializer.java @@ -0,0 +1,52 @@ +/* ****************************************************************************** + * Copyright (c) 2020 Konduit K.K. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ +package org.deeplearning4j.nn.params; + +import lombok.val; +import org.deeplearning4j.nn.weights.IWeightInit; +import org.deeplearning4j.nn.weights.WeightInitUtil; +import org.nd4j.linalg.api.ndarray.INDArray; + +/** + * Parameter initializer for EmbeddingLayer and EmbeddingSequenceLayer + * + * @author Alex Black + */ +public class EmbeddingLayerParamInitializer extends DefaultParamInitializer { + + private static final EmbeddingLayerParamInitializer INSTANCE = new EmbeddingLayerParamInitializer(); + + public static EmbeddingLayerParamInitializer getInstance() { + return INSTANCE; + } + + + + protected INDArray createWeightMatrix(long nIn, long nOut, IWeightInit weightInit, + INDArray weightParamView, boolean initializeParameters) { + val shape = new long[] {nIn, nOut}; + + if (initializeParameters) { + INDArray ret = weightInit.init(1, //Fan in - note that fanIn=1 for embedding layer... if we used layer nIn (i.e., vocab size) the init would depend on vocab size (which doesn't make sense) + nOut, //Fan out + shape, IWeightInit.DEFAULT_WEIGHT_INIT_ORDER, weightParamView); + return ret; + } else { + return WeightInitUtil.reshapeWeights(shape, weightParamView); + } + } + +} diff --git a/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/optimize/listeners/CollectScoresListener.java b/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/optimize/listeners/CollectScoresListener.java index 637c367e7..fe3ae20d7 100644 --- a/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/optimize/listeners/CollectScoresListener.java +++ b/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/optimize/listeners/CollectScoresListener.java @@ -19,6 +19,7 @@ package org.deeplearning4j.optimize.listeners; import it.unimi.dsi.fastutil.doubles.DoubleArrayList; import it.unimi.dsi.fastutil.ints.IntArrayList; import lombok.Data; +import lombok.EqualsAndHashCode; import lombok.extern.slf4j.Slf4j; import org.deeplearning4j.nn.api.Model; import org.deeplearning4j.optimize.api.BaseTrainingListener; @@ -32,6 +33,7 @@ import java.io.Serializable; * @author Alex Black */ @Data +@EqualsAndHashCode(callSuper = true) @Slf4j public class CollectScoresListener extends BaseTrainingListener implements Serializable { diff --git a/deeplearning4j/dl4j-integration-tests/readme.md b/deeplearning4j/dl4j-integration-tests/readme.md index 338b40392..e0b9697e1 100644 --- a/deeplearning4j/dl4j-integration-tests/readme.md +++ b/deeplearning4j/dl4j-integration-tests/readme.md @@ -1,16 +1,15 @@ -#DL4J Integration Tests +#DL4J and SameDiff Integration Tests -These tests are designed to check a number of aspects of DL4J: -1. Predictions +These tests are designed to check a number of aspects of DL4J and SameDiff: +1. Predictions (i.e., network output) 2. Training (training curves, parameters, gradient calculation) -3. Evaluation -4. Model serialization -5. Overfitting sanity checks +3. Evaluation (accuracy, etc) +4. Model serialization (saving + loading models) +5. Overfitting sanity checks (make sure we can overfit a single example) 6. Data pipelines -7. Evaluation classes -8. Parallel Wrapper -9. Validating conditions that should always hold (frozen layer params don't change, for example) +7. Parallel Wrapper +8. Validating conditions that should always hold (frozen layer params don't change, for example) They are designed for the following purposes: @@ -19,32 +18,46 @@ They are designed for the following purposes: 3. Detecting significant differences between CPU and CUDA backends 4. Validating implementation via sanity checks on training - i.e., can we overfit a single example? 5. Checking networks and data pipelines on real-world scale data and nets -6. Operating as fully automated pre-release checks (replacing previously used manual checks) +6. Operating as fully automated pre-release checks (replacing manual sanity checks) -## Types of Tests +## Main Classes -The integration tests are set up to be able to run multiple tests on each network configuration. +Explanation of the main classes: +* **IntegrationTestBaselineGenerator**: Run *manually* to generate and save "expected results" for comparing in the future. + Output goes to dl4j-test-resources, for saving/uploading. +* **IntegrationTestRunner**: Actually runs the tests, and compares the output/result to those generated by the baseline generator +* **TestCase**: integration tests extend this +* **testcases/\*.java**: the actual integration test definitions +* **IntegrationTestsDL4J**: entry point for running the DL4J integration tests +* **IntegrationTestsSameDiff**: entry point for running the SameDiff integration tests + +## Types of Test Components + +The integration tests are set up to be able to run multiple types of tests on each network configuration. Networks may be pretrained (from model zoo) or randomly initialized (from specified configuration). Specifically, test cases can be run with any subset of the following components to be tested, by setting TestCase.XYZ boolean options to true or false: -1. testPredictions: Testing output (predictions) on some specified data vs. saved/known good arrays -2. testGradients: Testing gradients on some specified data vs. saved/known good arrays -3. testPretrain: Test layerwise pretraining parameters and training curves -4. testTrainingCurves: Train, and check score vs. iteration -5. testParamsPostTraining: validate params match post training -6. testEvaluation: test the evaluation performance (post training, if 4 or 5 are true) -7. testParallelInference: validate that single net and parallel inference results match -8. testOverfitting: sanity check - try to overfit a single example +1. **testPredictions**: Testing output (predictions) on some specified data vs. saved/known good arrays +2. **testGradients**: Testing gradients on some specified data vs. saved/known good arrays +3. **testPretrain**: Test layerwise pretraining parameters and training curves +4. **testTrainingCurves**: Train, and check score vs. iteration +5. **testParamsPostTraining**: validate params match post training +6. **testEvaluation**: test the evaluation performance (post training, if 4 or 5 are true) +7. **testParallelInference**: validate that single net and parallel inference results match +8. **testOverfitting**: sanity check - try to overfit a single example +See TestCase.java for more details. ## Adding a New Integration Test The process to add a new test is simple: -1. Add a method that creates and returns a TestCase object -2. Add it as a unit test to IntegrationTests class -3. Run IntegrationTestBaselineGenerator (if required) to generate and save the "known good" results. +1. Add a method that creates and returns a TestCase object (example: testcases/MLPTestCases.getMLPMnist()) +2. Add it as a unit test to IntegrationTests class (example: IntegrationTestsDL4J.testMLPMnist()) +3. Run IntegrationTestBaselineGenerator with the new test case, to generate and save the "known good" results. +4. Run the new integration test to make sure it passes, on both CPU and CUDA backends +5. Commit the generated test resources from step 3 to dl4j-test-resources repo Note that IntegrationTestBaselineGenerator assumes you have the dl4j-test-resources cloned parallel to the DL4J mono-repo. \ No newline at end of file diff --git a/deeplearning4j/dl4j-integration-tests/src/test/java/org/deeplearning4j/integration/IntegrationTestBaselineGenerator.java b/deeplearning4j/dl4j-integration-tests/src/test/java/org/deeplearning4j/integration/IntegrationTestBaselineGenerator.java index e2890fc61..a493337c8 100644 --- a/deeplearning4j/dl4j-integration-tests/src/test/java/org/deeplearning4j/integration/IntegrationTestBaselineGenerator.java +++ b/deeplearning4j/dl4j-integration-tests/src/test/java/org/deeplearning4j/integration/IntegrationTestBaselineGenerator.java @@ -1,5 +1,6 @@ /******************************************************************************* * Copyright (c) 2015-2018 Skymind, Inc. + * Copyright (c) 2020 Konduit K.K. * * This program and the accompanying materials are made available under the * terms of the Apache License, Version 2.0 which is available at @@ -16,15 +17,10 @@ package org.deeplearning4j.integration; -import org.nd4j.shade.guava.io.Files; import lombok.extern.slf4j.Slf4j; import org.apache.commons.io.FileUtils; import org.deeplearning4j.datasets.iterator.MultiDataSetWrapperIterator; -import org.deeplearning4j.eval.IEvaluation; -import org.deeplearning4j.integration.testcases.CNN2DTestCases; -import org.deeplearning4j.integration.testcases.MLPTestCases; -import org.deeplearning4j.integration.testcases.RNNTestCases; -import org.deeplearning4j.integration.testcases.UnsupervisedTestCases; +import org.deeplearning4j.integration.testcases.samediff.SameDiffMLPTestCases; import org.deeplearning4j.nn.api.Model; import org.deeplearning4j.nn.conf.ComputationGraphConfiguration; import org.deeplearning4j.nn.conf.MultiLayerConfiguration; @@ -32,20 +28,27 @@ import org.deeplearning4j.nn.graph.ComputationGraph; import org.deeplearning4j.nn.multilayer.MultiLayerNetwork; import org.deeplearning4j.optimize.listeners.CollectScoresListener; import org.deeplearning4j.util.ModelSerializer; +import org.nd4j.autodiff.listeners.records.History; +import org.nd4j.autodiff.samediff.SDVariable; +import org.nd4j.autodiff.samediff.SameDiff; +import org.nd4j.autodiff.samediff.VariableType; import org.nd4j.base.Preconditions; -import org.nd4j.linalg.api.buffer.DataBuffer; -import org.nd4j.linalg.api.buffer.DataType; +import org.nd4j.evaluation.IEvaluation; import org.nd4j.linalg.api.ndarray.INDArray; import org.nd4j.linalg.dataset.api.MultiDataSet; import org.nd4j.linalg.dataset.api.iterator.DataSetIterator; import org.nd4j.linalg.dataset.api.iterator.MultiDataSetIterator; import org.nd4j.linalg.factory.Nd4j; import org.nd4j.linalg.primitives.Pair; +import org.nd4j.shade.guava.io.Files; import java.io.*; +import java.nio.charset.StandardCharsets; import java.util.*; import java.util.stream.Collectors; +import static org.junit.Assert.assertEquals; + /** * Run this manually to generate - or update - the saved files for a specific test. * Places results in dl4j-test-resources: assumes you have the dl4j-test-resources cloned parallel to the DL4J mono-repo. @@ -53,32 +56,31 @@ import java.util.stream.Collectors; @Slf4j public class IntegrationTestBaselineGenerator { - public static final File OUTPUT_DIR = new File("../../dl4j-test-resources/src/main/resources/dl4j-integration-tests").getAbsoluteFile(); + public static final File OUTPUT_DIR_DL4J = new File("../../dl4j-test-resources/src/main/resources/dl4j-integration-tests").getAbsoluteFile(); + public static final File OUTPUT_DIR_SAMEDIFF = new File("../../dl4j-test-resources/src/main/resources/samediff-integration-tests").getAbsoluteFile(); public static void main(String[] args) throws Exception { - if (!OUTPUT_DIR.exists()) { - throw new RuntimeException("output directory (test resources) does not exist!"); + if (!OUTPUT_DIR_DL4J.exists() && !OUTPUT_DIR_SAMEDIFF.exists()) { + throw new RuntimeException("output directories in test resources do not exist!"); } - //All integration tests are run with float precision! - Nd4j.setDataType(DataType.FLOAT); - -// runGeneration( -// MLPTestCases.getMLPMnist(), -// ); + runGeneration( + SameDiffMLPTestCases.getMLPMnist() + ); } private static void runGeneration(TestCase... testCases) throws Exception { for( TestCase tc : testCases ) { + final ModelType modelType = tc.modelType(); //Basic validation: Preconditions.checkState(tc.getTestName() != null, "Test case name is null"); //Run through each test case: - File testBaseDir = new File(OUTPUT_DIR, tc.getTestName()); + File testBaseDir = new File(modelType == ModelType.SAMEDIFF ? OUTPUT_DIR_SAMEDIFF : OUTPUT_DIR_DL4J, tc.getTestName()); if (testBaseDir.exists()) { FileUtils.forceDelete(testBaseDir); } @@ -109,56 +111,62 @@ public class IntegrationTestBaselineGenerator { //First: if test is a random init test: generate the config, and save it MultiLayerNetwork mln = null; ComputationGraph cg = null; - Model m; - boolean isMLN; + SameDiff sd = null; + Model m = null; if (tc.getTestType() == TestCase.TestType.RANDOM_INIT) { Object config = tc.getConfiguration(); - String json; + String json = null; if (config instanceof MultiLayerConfiguration) { MultiLayerConfiguration mlc = (MultiLayerConfiguration) config; - isMLN = true; json = mlc.toJson(); mln = new MultiLayerNetwork(mlc); mln.init(); m = mln; - } else { + } else if (config instanceof ComputationGraphConfiguration){ ComputationGraphConfiguration cgc = (ComputationGraphConfiguration) config; - isMLN = false; json = cgc.toJson(); cg = new ComputationGraph(cgc); cg.init(); m = cg; + } else { + sd = (SameDiff)config; } - File configFile = new File(testBaseDir, "config." + (isMLN ? "mlc.json" : "cgc.json")); - FileUtils.writeStringToFile(configFile, json); - log.info("RANDOM_INIT test - saved configuration: {}", configFile.getAbsolutePath()); File savedModel = new File(testBaseDir, IntegrationTestRunner.RANDOM_INIT_UNTRAINED_MODEL_FILENAME); - ModelSerializer.writeModel(m, savedModel, true); + if(modelType != ModelType.SAMEDIFF) { + File configFile = new File(testBaseDir, "config." + (modelType == ModelType.MLN ? "mlc.json" : "cgc.json")); + FileUtils.writeStringToFile(configFile, json, StandardCharsets.UTF_8); + log.info("RANDOM_INIT test - saved configuration: {}", configFile.getAbsolutePath()); + ModelSerializer.writeModel(m, savedModel, true); + } else { + sd.save(savedModel, true); + } log.info("RANDOM_INIT test - saved randomly initialized model to: {}", savedModel.getAbsolutePath()); } else { //Pretrained model m = tc.getPretrainedModel(); - isMLN = (m instanceof MultiLayerNetwork); - if (isMLN) { + if (m instanceof MultiLayerNetwork) { mln = (MultiLayerNetwork) m; - } else { + } else if(m instanceof ComputationGraph){ cg = (ComputationGraph) m; + } else { + sd = (SameDiff)m; } } //Generate predictions to compare against if (tc.isTestPredictions()) { - List> inputs = tc.getPredictionsTestData(); - Preconditions.checkState(inputs != null && inputs.size() > 0, "Input data is null or length 0 for test: %s", tc.getTestName()); + List> inputs = modelType != ModelType.SAMEDIFF ? tc.getPredictionsTestData() : null; + List> inputsSd = modelType == ModelType.SAMEDIFF ? tc.getPredictionsTestDataSameDiff() : null; +// Preconditions.checkState(inputs != null && inputs.size() > 0, "Input data is null or length 0 for test: %s", tc.getTestName()); File predictionsTestDir = new File(testBaseDir, "predictions"); predictionsTestDir.mkdirs(); int count = 0; - if (isMLN) { + if (modelType == ModelType.MLN) { for (Pair p : inputs) { INDArray f = p.getFirst()[0]; INDArray fm = (p.getSecond() == null ? null : p.getSecond()[0]); @@ -170,7 +178,7 @@ public class IntegrationTestBaselineGenerator { Nd4j.write(out, dos); } } - } else { + } else if(modelType == ModelType.CG) { for (Pair p : inputs) { INDArray[] out = cg.output(false, p.getFirst(), p.getSecond(), null); @@ -182,6 +190,19 @@ public class IntegrationTestBaselineGenerator { } } } + } else { + List outNames = tc.getPredictionsNamesSameDiff(); + for( Map ph : inputsSd ){ + Map out = sd.output(ph, outNames); + + //Save the output... + for(String s : outNames){ + File f = new File(predictionsTestDir, "output_" + (count++) + "_" + s + ".bin"); + try (DataOutputStream dos = new DataOutputStream(new FileOutputStream(f))) { + Nd4j.write(out.get(s), dos); + } + } + } } log.info("Saved predictions for {} inputs to disk in directory: {}", tc.getTestName(), predictionsTestDir); @@ -189,32 +210,46 @@ public class IntegrationTestBaselineGenerator { //Compute and save gradients: if (tc.isTestGradients()) { - MultiDataSet data = tc.getGradientsTestData(); - INDArray gradientFlat; - if (isMLN) { + INDArray gradientFlat = null; + Map grad; + if (modelType == ModelType.MLN) { + MultiDataSet data = tc.getGradientsTestData(); mln.setInput(data.getFeatures(0)); mln.setLabels(data.getLabels(0)); mln.setLayerMaskArrays(data.getFeaturesMaskArray(0), data.getLabelsMaskArray(0)); mln.computeGradientAndScore(); gradientFlat = mln.getFlattenedGradients(); - } else { + grad = m.gradient().gradientForVariable(); + } else if(modelType == ModelType.CG) { + MultiDataSet data = tc.getGradientsTestData(); cg.setInputs(data.getFeatures()); cg.setLabels(data.getLabels()); cg.setLayerMaskArrays(data.getFeaturesMaskArrays(), data.getLabelsMaskArrays()); cg.computeGradientAndScore(); gradientFlat = cg.getFlattenedGradients(); + grad = m.gradient().gradientForVariable(); + } else { + Map ph = tc.getGradientsTestDataSameDiff(); + List allVars = new ArrayList<>(); + for(SDVariable v : sd.variables()){ + if(v.getVariableType() == VariableType.VARIABLE){ + allVars.add(v.name()); + } + } + grad = sd.calculateGradients(ph, allVars); } - File gFlatFile = new File(testBaseDir, IntegrationTestRunner.FLAT_GRADIENTS_FILENAME); - IntegrationTestRunner.write(gradientFlat, gFlatFile); + if(modelType != ModelType.SAMEDIFF) { + File gFlatFile = new File(testBaseDir, IntegrationTestRunner.FLAT_GRADIENTS_FILENAME); + IntegrationTestRunner.write(gradientFlat, gFlatFile); + } //Also save the gradient param table: - Map g = m.gradient().gradientForVariable(); File gradientDir = new File(testBaseDir, "gradients"); gradientDir.mkdir(); - for (String s : g.keySet()) { + for (String s : grad.keySet()) { File f = new File(gradientDir, s + ".bin"); - IntegrationTestRunner.write(g.get(s), f); + IntegrationTestRunner.write(grad.get(s), f); } } @@ -224,7 +259,7 @@ public class IntegrationTestBaselineGenerator { MultiDataSetIterator iter = tc.getUnsupervisedTrainData(); INDArray paramsPostTraining; - if(isMLN){ + if(modelType == ModelType.MLN){ int[] layersToTrain = tc.getUnsupervisedTrainLayersMLN(); Preconditions.checkState(layersToTrain != null, "Layer indices must not be null"); DataSetIterator dsi = new MultiDataSetWrapperIterator(iter); @@ -233,7 +268,7 @@ public class IntegrationTestBaselineGenerator { mln.pretrainLayer(i, dsi); } paramsPostTraining = mln.params(); - } else { + } else if(modelType == ModelType.CG) { String[] layersToTrain = tc.getUnsupervisedTrainLayersCG(); Preconditions.checkState(layersToTrain != null, "Layer names must not be null"); @@ -241,6 +276,8 @@ public class IntegrationTestBaselineGenerator { cg.pretrainLayer(i, iter); } paramsPostTraining = cg.params(); + } else { + throw new UnsupportedOperationException("SameDiff not supported for unsupervised training tests"); } //Save params @@ -251,23 +288,46 @@ public class IntegrationTestBaselineGenerator { //Test training curves: if (tc.isTestTrainingCurves()) { MultiDataSetIterator trainData = tc.getTrainingData(); - CollectScoresListener l = new CollectScoresListener(1); - m.setListeners(l); - if (isMLN) { + CollectScoresListener l = new CollectScoresListener(1); + if(modelType != ModelType.SAMEDIFF) + m.setListeners(l); + + History h = null; + if (modelType == ModelType.MLN) { mln.fit(trainData); - } else { + } else if(modelType == ModelType.CG) { cg.fit(trainData); + } else { + h = sd.fit(trainData, 1); + } + + double[] scores; + if(modelType != ModelType.SAMEDIFF){ + scores = l.getListScore().toDoubleArray(); + } else { + scores = h.lossCurve().getLossValues().toDoubleVector(); } - double[] scores = l.getListScore().toDoubleArray(); File f = new File(testBaseDir, IntegrationTestRunner.TRAINING_CURVE_FILENAME); List s = Arrays.stream(scores).mapToObj(String::valueOf).collect(Collectors.toList()); - FileUtils.writeStringToFile(f, String.join(",", s)); + FileUtils.writeStringToFile(f, String.join(",", s), StandardCharsets.UTF_8); if (tc.isTestParamsPostTraining()) { - File p = new File(testBaseDir, IntegrationTestRunner.PARAMS_POST_TRAIN_FILENAME); - IntegrationTestRunner.write(m.params(), p); + if(modelType == ModelType.SAMEDIFF){ + File p = new File(testBaseDir, IntegrationTestRunner.PARAMS_POST_TRAIN_SAMEDIFF_DIR); + p.mkdirs(); + for(SDVariable v : sd.variables()){ + if(v.getVariableType() == VariableType.VARIABLE){ + INDArray arr = v.getArr(); + File p2 = new File(p, v.name() + ".bin"); + IntegrationTestRunner.write(arr, p2); + } + } + } else { + File p = new File(testBaseDir, IntegrationTestRunner.PARAMS_POST_TRAIN_FILENAME); + IntegrationTestRunner.write(m.params(), p); + } } } @@ -276,11 +336,13 @@ public class IntegrationTestBaselineGenerator { IEvaluation[] evals = tc.getNewEvaluations(); MultiDataSetIterator iter = tc.getEvaluationTestData(); - if (isMLN) { + if (modelType == ModelType.MLN) { DataSetIterator dsi = new MultiDataSetWrapperIterator(iter); mln.doEvaluation(dsi, evals); - } else { + } else if(modelType == ModelType.CG){ cg.doEvaluation(iter, evals); + } else { + evals = tc.doEvaluationSameDiff(sd, iter, evals); } File evalDir = new File(testBaseDir, "evaluation"); @@ -288,7 +350,7 @@ public class IntegrationTestBaselineGenerator { for (int i = 0; i < evals.length; i++) { String json = evals[i].toJson(); File f = new File(evalDir, i + "." + evals[i].getClass().getSimpleName() + ".json"); - FileUtils.writeStringToFile(f, json); + FileUtils.writeStringToFile(f, json, StandardCharsets.UTF_8); } } diff --git a/deeplearning4j/dl4j-integration-tests/src/test/java/org/deeplearning4j/integration/IntegrationTestRunner.java b/deeplearning4j/dl4j-integration-tests/src/test/java/org/deeplearning4j/integration/IntegrationTestRunner.java index b1de34a2c..de1fb07b3 100644 --- a/deeplearning4j/dl4j-integration-tests/src/test/java/org/deeplearning4j/integration/IntegrationTestRunner.java +++ b/deeplearning4j/dl4j-integration-tests/src/test/java/org/deeplearning4j/integration/IntegrationTestRunner.java @@ -1,5 +1,6 @@ /******************************************************************************* * Copyright (c) 2015-2018 Skymind, Inc. + * Copyright (c) 2020 Konduit K.K. * * This program and the accompanying materials are made available under the * terms of the Apache License, Version 2.0 which is available at @@ -17,14 +18,12 @@ package org.deeplearning4j.integration; -import org.nd4j.shade.guava.collect.ImmutableSet; -import org.nd4j.shade.guava.reflect.ClassPath; -import org.deeplearning4j.integration.util.CountingMultiDataSetIterator; import lombok.NonNull; import lombok.extern.slf4j.Slf4j; import org.apache.commons.io.FileUtils; +import org.deeplearning4j.BaseDL4JTest; import org.deeplearning4j.datasets.iterator.MultiDataSetWrapperIterator; -import org.deeplearning4j.eval.*; +import org.deeplearning4j.integration.util.CountingMultiDataSetIterator; import org.deeplearning4j.nn.api.Model; import org.deeplearning4j.nn.conf.BackpropType; import org.deeplearning4j.nn.conf.ComputationGraphConfiguration; @@ -42,9 +41,16 @@ import org.deeplearning4j.parallelism.ParallelInference; import org.deeplearning4j.parallelism.inference.InferenceMode; import org.deeplearning4j.util.ModelSerializer; import org.junit.rules.TemporaryFolder; +import org.nd4j.autodiff.listeners.records.History; +import org.nd4j.autodiff.samediff.SDVariable; +import org.nd4j.autodiff.samediff.SameDiff; +import org.nd4j.autodiff.samediff.VariableType; +import org.nd4j.autodiff.samediff.internal.SameDiffOp; import org.nd4j.base.Preconditions; +import org.nd4j.evaluation.IEvaluation; +import org.nd4j.evaluation.classification.*; +import org.nd4j.evaluation.regression.RegressionEvaluation; import org.nd4j.imports.converters.DifferentialFunctionClassHolder; -import org.nd4j.linalg.api.buffer.DataType; import org.nd4j.linalg.api.ndarray.INDArray; import org.nd4j.linalg.api.ops.Op; import org.nd4j.linalg.api.ops.impl.reduce.longer.MatchCondition; @@ -55,12 +61,15 @@ import org.nd4j.linalg.dataset.api.iterator.MultiDataSetIterator; import org.nd4j.linalg.factory.Nd4j; import org.nd4j.linalg.indexing.BooleanIndexing; import org.nd4j.linalg.indexing.conditions.Conditions; -import org.nd4j.linalg.io.ClassPathResource; import org.nd4j.linalg.ops.transforms.Transforms; import org.nd4j.linalg.primitives.Pair; +import org.nd4j.resources.Resources; +import org.nd4j.shade.guava.collect.ImmutableSet; +import org.nd4j.shade.guava.reflect.ClassPath; import java.io.*; import java.lang.reflect.Modifier; +import java.nio.charset.StandardCharsets; import java.util.*; import java.util.concurrent.atomic.AtomicInteger; @@ -79,6 +88,7 @@ public class IntegrationTestRunner { public static final String FLAT_GRADIENTS_FILENAME = "flattenedGradients.bin"; public static final String TRAINING_CURVE_FILENAME = "trainingCurve.csv"; public static final String PARAMS_POST_TRAIN_FILENAME = "paramsPostTrain.bin"; + public static final String PARAMS_POST_TRAIN_SAMEDIFF_DIR = "paramsPostTrain"; public static final String PARAMS_POST_UNSUPERVISED_FILENAME = "paramsPostUnsupervised.bin"; public static final double MAX_REL_ERROR_SCORES = 1e-4; @@ -148,21 +158,25 @@ public class IntegrationTestRunner { } public static void runTest(TestCase tc, TemporaryFolder testDir) throws Exception { - Preconditions.checkState(Nd4j.dataType() == DataType.FLOAT, "Integration tests must be run with float precision!"); - log.info("Starting test case: {}", tc.getTestName()); + BaseDL4JTest.skipUnlessIntegrationTests(); //Tests will ONLY be run if integration test profile is enabled. + //This could alternatively be done via maven surefire configuration + + final ModelType modelType = tc.modelType(); + log.info("Starting test case: {} - type = {}", tc.getTestName(), modelType); long start = System.currentTimeMillis(); File workingDir = testDir.newFolder(); tc.initialize(workingDir); File testBaseDir = testDir.newFolder(); - new ClassPathResource("dl4j-integration-tests/" + tc.getTestName()).copyDirectory(testBaseDir); +// new ClassPathResource("dl4j-integration-tests/" + tc.getTestName()).copyDirectory(testBaseDir); + Resources.copyDirectory((modelType == ModelType.SAMEDIFF ? "samediff-integration-tests/" : "dl4j-integration-tests/") + tc.getTestName(), testBaseDir); MultiLayerNetwork mln = null; ComputationGraph cg = null; - Model m; - boolean isMLN; + SameDiff sd = null; + Model m = null; if (tc.getTestType() == TestCase.TestType.RANDOM_INIT) { log.info("Checking RANDOM_INIT test case: saved model vs. initialized model"); //Checking randomly initialized model: @@ -173,36 +187,46 @@ public class IntegrationTestRunner { mln = new MultiLayerNetwork(mlc); mln.init(); m = mln; - isMLN = true; MultiLayerNetwork loaded = MultiLayerNetwork.load(savedModel, true); assertEquals("Configs not equal", loaded.getLayerWiseConfigurations(), mln.getLayerWiseConfigurations()); assertEquals("Params not equal", loaded.params(), mln.params()); assertEquals("Param table not equal", loaded.paramTable(), mln.paramTable()); - } else { + } else if(config instanceof ComputationGraphConfiguration ){ ComputationGraphConfiguration cgc = (ComputationGraphConfiguration) config; cg = new ComputationGraph(cgc); cg.init(); m = cg; - isMLN = false; ComputationGraph loaded = ComputationGraph.load(savedModel, true); assertEquals("Configs not equal", loaded.getConfiguration(), cg.getConfiguration()); assertEquals("Params not equal", loaded.params(), cg.params()); assertEquals("Param table not equal", loaded.paramTable(), cg.paramTable()); + } else if(config instanceof SameDiff){ + sd = (SameDiff)config; + SameDiff loaded = SameDiff.load(savedModel, true); + + assertSameDiffEquals(sd, loaded); + } else { + throw new IllegalStateException("Unknown configuration/model type: " + config.getClass()); } } else { m = tc.getPretrainedModel(); - isMLN = (m instanceof MultiLayerNetwork); - if (isMLN) { + if (m instanceof MultiLayerNetwork) { mln = (MultiLayerNetwork) m; - } else { + } else if(m instanceof ComputationGraph) { cg = (ComputationGraph) m; + } else if(m instanceof SameDiff){ + sd = (SameDiff)m; + } else { + throw new IllegalStateException("Unknown model type: " + m.getClass()); } } //Collect information for test coverage - collectCoverageInformation(m); + if(modelType != ModelType.SAMEDIFF) { + collectCoverageInformation(m); + } //Check network output (predictions) @@ -210,15 +234,16 @@ public class IntegrationTestRunner { log.info("Checking predictions: saved output vs. initialized model"); - List> inputs = tc.getPredictionsTestData(); - Preconditions.checkState(inputs != null && inputs.size() > 0, "Input data is null or length 0 for test: %s", tc.getTestName()); + List> inputs = modelType != ModelType.SAMEDIFF ? tc.getPredictionsTestData() : null; + List> inputsSd = modelType == ModelType.SAMEDIFF ? tc.getPredictionsTestDataSameDiff() : null; + Preconditions.checkState(modelType == ModelType.SAMEDIFF || inputs != null && inputs.size() > 0, "Input data is null or length 0 for test: %s", tc.getTestName()); File predictionsTestDir = new File(testBaseDir, "predictions"); predictionsTestDir.mkdirs(); int count = 0; - if (isMLN) { + if (modelType == ModelType.MLN) { for (Pair p : inputs) { INDArray f = p.getFirst()[0]; INDArray fm = (p.getSecond() == null ? null : p.getSecond()[0]); @@ -231,15 +256,15 @@ public class IntegrationTestRunner { outSaved = Nd4j.read(dis); } - INDArray gradExceedsRE = exceedsRelError(outSaved, out, tc.getMaxRelativeErrorOutput(), tc.getMinAbsErrorOutput()); - int countExceeds = gradExceedsRE.sumNumber().intValue(); + INDArray predictionExceedsRE = exceedsRelError(outSaved, out, tc.getMaxRelativeErrorOutput(), tc.getMinAbsErrorOutput()); + int countExceeds = predictionExceedsRE.sumNumber().intValue(); assertEquals("Predictions do not match saved predictions - output", 0, countExceeds); } - } else { + } else if(modelType == ModelType.CG){ for (Pair p : inputs) { INDArray[] out = cg.output(false, p.getFirst(), p.getSecond(), null); - //Save the array(s)... + //Load the previously saved arrays INDArray[] outSaved = new INDArray[out.length]; for (int i = 0; i < out.length; i++) { File outFile = new File(predictionsTestDir, "output_" + (count++) + "_" + i + ".bin"); @@ -249,14 +274,36 @@ public class IntegrationTestRunner { } for( int i=0; i outNames = tc.getPredictionsNamesSameDiff(); + for( Map ph : inputsSd ){ + Map out = sd.output(ph, outNames); + + //Load the previously saved placeholder arrays + Map outSaved = new HashMap<>(); + for(String s : outNames){ + File f = new File(predictionsTestDir, "output_" + (count++) + "_" + s + ".bin"); + try (DataInputStream dis = new DataInputStream(new FileInputStream(f))) { + outSaved.put(s, Nd4j.read(dis)); + } + } + + for(String s : outNames){ + INDArray predictionExceedsRE = exceedsRelError(outSaved.get(s), out.get(s), tc.getMaxRelativeErrorOutput(), tc.getMinAbsErrorOutput()); + int countExceeds = predictionExceedsRE.sumNumber().intValue(); + assertEquals("Predictions do not match saved predictions - output \"" + s + "\"", 0, countExceeds); + } + } } - checkLayerClearance(m); + if(modelType != ModelType.SAMEDIFF) { + checkLayerClearance(m); + } } @@ -264,34 +311,49 @@ public class IntegrationTestRunner { if (tc.isTestGradients()) { log.info("Checking gradients: saved output vs. initialized model"); - MultiDataSet data = tc.getGradientsTestData(); - INDArray gradientFlat; - org.deeplearning4j.nn.api.Layer[] layers; - if (isMLN) { + INDArray gradientFlat = null; + org.deeplearning4j.nn.api.Layer[] layers = null; + Map grad; + if (modelType == ModelType.MLN) { + MultiDataSet data = tc.getGradientsTestData(); mln.setInput(data.getFeatures(0)); mln.setLabels(data.getLabels(0)); mln.setLayerMaskArrays(data.getFeaturesMaskArray(0), data.getLabelsMaskArray(0)); mln.computeGradientAndScore(); gradientFlat = mln.getFlattenedGradients(); layers = mln.getLayers(); - } else { + grad = mln.gradient().gradientForVariable(); + } else if(modelType == ModelType.CG) { + MultiDataSet data = tc.getGradientsTestData(); cg.setInputs(data.getFeatures()); cg.setLabels(data.getLabels()); cg.setLayerMaskArrays(data.getFeaturesMaskArrays(), data.getLabelsMaskArrays()); cg.computeGradientAndScore(); gradientFlat = cg.getFlattenedGradients(); layers = cg.getLayers(); + grad = cg.gradient().gradientForVariable(); + } else { + Map ph = tc.getGradientsTestDataSameDiff(); + List allVars = new ArrayList<>(); + for(SDVariable v : sd.variables()){ + if(v.getVariableType() == VariableType.VARIABLE){ + allVars.add(v.name()); + } + } + grad = sd.calculateGradients(ph, allVars); } - File gFlatFile = new File(testBaseDir, IntegrationTestRunner.FLAT_GRADIENTS_FILENAME); - INDArray gradientFlatSaved = read(gFlatFile); + if(modelType != ModelType.SAMEDIFF) { + File gFlatFile = new File(testBaseDir, IntegrationTestRunner.FLAT_GRADIENTS_FILENAME); + INDArray gradientFlatSaved = read(gFlatFile); - INDArray gradExceedsRE = exceedsRelError(gradientFlatSaved, gradientFlat, tc.getMaxRelativeErrorGradients(), tc.getMinAbsErrorGradients()); - int count = gradExceedsRE.sumNumber().intValue(); - if(count > 0){ - logFailedParams(20, "Gradient", layers, gradExceedsRE, gradientFlatSaved, gradientFlat); + INDArray gradExceedsRE = exceedsRelError(gradientFlatSaved, gradientFlat, tc.getMaxRelativeErrorGradients(), tc.getMinAbsErrorGradients()); + int count = gradExceedsRE.sumNumber().intValue(); + if (count > 0) { + logFailedParams(20, "Gradient", layers, gradExceedsRE, gradientFlatSaved, gradientFlat); + } + assertEquals("Saved flattened gradients: not equal (using relative error)", 0, count); } - assertEquals("Saved flattened gradients: not equal (using relative error)", 0, count); //Load the gradient table: File gradientDir = new File(testBaseDir, "gradients"); @@ -302,12 +364,12 @@ public class IntegrationTestRunner { String key = f.getName(); key = key.substring(0, key.length() - 4); //remove ".bin" INDArray loaded = read(f); - INDArray now = m.gradient().gradientForVariable().get(key); + INDArray now = grad.get(key); - gradExceedsRE = exceedsRelError(gradientFlatSaved, gradientFlat, tc.getMaxRelativeErrorGradients(), tc.getMinAbsErrorGradients()); - count = gradExceedsRE.sumNumber().intValue(); - assertEquals("Saved flattened gradients: not equal (using relative error) for parameter: " + key, 0, count); + INDArray gradExceedsRE = exceedsRelError(loaded, now, tc.getMaxRelativeErrorGradients(), tc.getMinAbsErrorGradients()); + int count = gradExceedsRE.sumNumber().intValue(); + assertEquals("Gradients: not equal (using relative error) for parameter: " + key, 0, count); } } @@ -318,7 +380,7 @@ public class IntegrationTestRunner { INDArray paramsPostTraining; org.deeplearning4j.nn.api.Layer[] layers; - if(isMLN){ + if(modelType == ModelType.MLN){ int[] layersToTrain = tc.getUnsupervisedTrainLayersMLN(); Preconditions.checkState(layersToTrain != null, "Layer indices must not be null"); DataSetIterator dsi = new MultiDataSetWrapperIterator(iter); @@ -328,7 +390,7 @@ public class IntegrationTestRunner { } paramsPostTraining = mln.params(); layers = mln.getLayers(); - } else { + } else if(modelType == ModelType.CG) { String[] layersToTrain = tc.getUnsupervisedTrainLayersCG(); Preconditions.checkState(layersToTrain != null, "Layer names must not be null"); @@ -337,6 +399,8 @@ public class IntegrationTestRunner { } paramsPostTraining = cg.params(); layers = cg.getLayers(); + } else { + throw new UnsupportedOperationException("Unsupported layerwise pretraining not supported for SameDiff models"); } File f = new File(testBaseDir, IntegrationTestRunner.PARAMS_POST_UNSUPERVISED_FILENAME); @@ -360,53 +424,78 @@ public class IntegrationTestRunner { MultiDataSetIterator trainData = tc.getTrainingData(); boolean isTbptt; int tbpttLength; - if(isMLN){ + if(modelType == ModelType.MLN){ isTbptt = mln.getLayerWiseConfigurations().getBackpropType() == BackpropType.TruncatedBPTT; tbpttLength = mln.getLayerWiseConfigurations().getTbpttFwdLength(); - } else { + } else if(modelType == ModelType.CG) { isTbptt = cg.getConfiguration().getBackpropType() == BackpropType.TruncatedBPTT; tbpttLength = cg.getConfiguration().getTbpttFwdLength(); + } else { + isTbptt = false; + tbpttLength = 0; } CountingMultiDataSetIterator countingIter = new CountingMultiDataSetIterator(trainData, isTbptt, tbpttLength); CollectScoresListener l = new CollectScoresListener(1); - m.setListeners(l); + if(modelType != ModelType.SAMEDIFF) { + m.setListeners(l); + } int iterBefore; int epochBefore; int iterAfter; int epochAfter; - Map frozenParamsBefore = getFrozenLayerParamCopies(m); - org.deeplearning4j.nn.api.Layer[] layers; - if (isMLN) { + Map frozenParamsBefore = modelType != ModelType.SAMEDIFF ? getFrozenLayerParamCopies(m) : getConstantCopies(sd); + org.deeplearning4j.nn.api.Layer[] layers = null; + History h = null; + if (modelType == ModelType.MLN) { iterBefore = mln.getIterationCount(); epochBefore = mln.getEpochCount(); mln.fit(countingIter); iterAfter = mln.getIterationCount(); epochAfter = mln.getEpochCount(); layers = mln.getLayers(); - } else { + } else if(modelType == ModelType.CG){ iterBefore = cg.getConfiguration().getIterationCount(); epochBefore = cg.getConfiguration().getEpochCount(); cg.fit(countingIter); iterAfter = cg.getConfiguration().getIterationCount(); epochAfter = cg.getConfiguration().getEpochCount(); layers = cg.getLayers(); + } else { + iterBefore = sd.getTrainingConfig().getIterationCount(); + epochBefore = sd.getTrainingConfig().getEpochCount(); + h = sd.fit(countingIter, 1); + iterAfter = sd.getTrainingConfig().getIterationCount(); + epochAfter = sd.getTrainingConfig().getEpochCount(); } //Check that frozen params (if any) haven't changed during training: - checkFrozenParams(frozenParamsBefore, m); + if(modelType == ModelType.SAMEDIFF) { + checkConstants(frozenParamsBefore, sd); + } else { + checkFrozenParams(frozenParamsBefore, m); + } //Validate the iteration and epoch counts - both for the net, and for the layers int newIters = countingIter.getCurrIter(); assertEquals(iterBefore + newIters, iterAfter); assertEquals(epochBefore + 1, epochAfter); - validateLayerIterCounts(m, epochBefore + 1, iterBefore+newIters); //TODO CURRENTLY FAILING - double[] scores = l.getListScore().toDoubleArray(); + if(modelType != ModelType.SAMEDIFF) { + validateLayerIterCounts(m, epochBefore + 1, iterBefore + newIters); + } + + + double[] scores; + if(modelType == ModelType.SAMEDIFF){ + scores = h.lossCurve().getLossValues().toDoubleVector(); + } else { + scores = l.getListScore().toDoubleArray(); + } File f = new File(testBaseDir, IntegrationTestRunner.TRAINING_CURVE_FILENAME); - String[] s = FileUtils.readFileToString(f).split(","); + String[] s = FileUtils.readFileToString(f, StandardCharsets.UTF_8).split(","); if(tc.isTestTrainingCurves()) { assertEquals("Different number of scores", s.length, scores.length); @@ -426,17 +515,36 @@ public class IntegrationTestRunner { } if (tc.isTestParamsPostTraining()) { - File p = new File(testBaseDir, IntegrationTestRunner.PARAMS_POST_TRAIN_FILENAME); - INDArray paramsExp = read(p); - INDArray z = exceedsRelError(m.params(), paramsExp, tc.getMaxRelativeErrorParamsPostTraining(), tc.getMinAbsErrorParamsPostTraining()); - int count = z.sumNumber().intValue(); - if(count > 0){ - logFailedParams(20, "Parameter", layers, z, paramsExp, m.params()); + if(modelType != ModelType.SAMEDIFF) { + File p = new File(testBaseDir, IntegrationTestRunner.PARAMS_POST_TRAIN_FILENAME); + INDArray paramsExp = read(p); + INDArray z = exceedsRelError(m.params(), paramsExp, tc.getMaxRelativeErrorParamsPostTraining(), tc.getMinAbsErrorParamsPostTraining()); + int count = z.sumNumber().intValue(); + if (count > 0) { + logFailedParams(20, "Parameter", layers, z, paramsExp, m.params()); + } + assertEquals("Number of params exceeded max relative error", 0, count); + } else { + File dir = new File(testBaseDir, IntegrationTestRunner.PARAMS_POST_TRAIN_SAMEDIFF_DIR); + for(SDVariable v : sd.variables()){ + if(v.getVariableType() != VariableType.VARIABLE) + continue; + INDArray paramNow = v.getArr(); + File paramFile = new File(dir, v.name() + ".bin"); + INDArray exp = read(paramFile); + INDArray z = exceedsRelError(paramNow, exp, tc.getMaxRelativeErrorParamsPostTraining(), tc.getMinAbsErrorParamsPostTraining()); + int count = z.sumNumber().intValue(); + if (count > 0) { + logFailedParams(20, "Parameter: " + v.name(), layers, z, exp, paramNow); + } + assertEquals("Number of params exceeded max relative error for parameter: \"" + v.name() + "\"", 0, count); + } } - assertEquals("Number of params exceeded max relative error", 0, count); } - checkLayerClearance(m); + if(modelType != ModelType.SAMEDIFF) { + checkLayerClearance(m); + } } //Check evaluation: @@ -445,17 +553,19 @@ public class IntegrationTestRunner { IEvaluation[] evals = tc.getNewEvaluations(); MultiDataSetIterator iter = tc.getEvaluationTestData(); - if (isMLN) { + if (modelType == ModelType.MLN) { DataSetIterator dsi = new MultiDataSetWrapperIterator(iter); mln.doEvaluation(dsi, evals); - } else { + } else if(modelType == ModelType.CG){ cg.doEvaluation(iter, evals); + } else { + evals = tc.doEvaluationSameDiff(sd, iter, evals); } File evalDir = new File(testBaseDir, "evaluation"); for (int i = 0; i < evals.length; i++) { File f = new File(evalDir, i + "." + evals[i].getClass().getSimpleName() + ".json"); - String json = FileUtils.readFileToString(f); + String json = FileUtils.readFileToString(f, StandardCharsets.UTF_8); IEvaluation e; if (evals[i].getClass() == Evaluation.class) { e = Evaluation.fromJson(json); @@ -479,7 +589,9 @@ public class IntegrationTestRunner { //Evaluation coverage information: evaluationClassesSeen.put(evals[i].getClass(), evaluationClassesSeen.getOrDefault(evals[i].getClass(), 0) + 1); - checkLayerClearance(m); + if(modelType != ModelType.SAMEDIFF) { + checkLayerClearance(m); + } } } @@ -490,15 +602,20 @@ public class IntegrationTestRunner { File f = testDir.newFile(); f.delete(); - ModelSerializer.writeModel(m, f, true); - if (isMLN) { + if (modelType == ModelType.MLN) { + ModelSerializer.writeModel(m, f, true); MultiLayerNetwork restored = MultiLayerNetwork.load(f, true); assertEquals(mln.getLayerWiseConfigurations(), restored.getLayerWiseConfigurations()); assertEquals(mln.params(), restored.params()); - } else { + } else if(modelType == ModelType.CG){ + ModelSerializer.writeModel(m, f, true); ComputationGraph restored = ComputationGraph.load(f, true); assertEquals(cg.getConfiguration(), restored.getConfiguration()); assertEquals(cg.params(), restored.params()); + } else { + sd.save(f, true); + SameDiff restored = SameDiff.load(f, true); + assertSameDiffEquals(sd, restored); } System.gc(); @@ -506,7 +623,7 @@ public class IntegrationTestRunner { //Check parallel inference - if (tc.isTestParallelInference()) { + if (modelType != ModelType.SAMEDIFF && tc.isTestParallelInference()) { List> inputs = tc.getPredictionsTestData(); @@ -515,7 +632,7 @@ public class IntegrationTestRunner { List exp = new ArrayList<>(); for(Pair p : inputs){ INDArray[] out; - if(isMLN){ + if(modelType == ModelType.MLN){ INDArray fm = p.getSecond() == null ? null : p.getSecond()[0]; out = new INDArray[]{mln.output(p.getFirst()[0], false, fm, null)}; } else { @@ -547,37 +664,54 @@ public class IntegrationTestRunner { MultiDataSet toOverfit = tc.getOverfittingData(); for (int i = 0; i < tc.getOverfitNumIterations(); i++) { - if (isMLN) { + if (modelType == ModelType.MLN) { mln.fit(toOverfit); - } else { + } else if(modelType == ModelType.CG){ cg.fit(toOverfit); + } else { + sd.fit(toOverfit); } } //Check: - INDArray[] output; - if (isMLN) { + INDArray[] output = null; + Map outSd = null; + if (modelType == ModelType.MLN) { mln.setLayerMaskArrays(toOverfit.getFeaturesMaskArray(0), null); output = new INDArray[]{mln.output(toOverfit.getFeatures(0))}; - } else { + } else if(modelType == ModelType.CG ){ cg.setLayerMaskArrays(toOverfit.getFeaturesMaskArrays(), null); output = cg.output(toOverfit.getFeatures()); + } else { + List l = sd.getTrainingConfig().getDataSetFeatureMapping(); + Map phMap = new HashMap<>(); + int i=0; + for(String s : l){ + phMap.put(s, toOverfit.getFeatures(i++)); + } + outSd = sd.output(phMap, tc.getPredictionsNamesSameDiff()); } - for (int i = 0; i < output.length; i++) { - INDArray z = exceedsRelError(output[i], toOverfit.getLabels(i), tc.getMaxRelativeErrorOverfit(), tc.getMinAbsErrorOverfit()); + int n = modelType == ModelType.SAMEDIFF ? outSd.size() : output.length; + for (int i = 0; i < n; i++) { + INDArray out = modelType == ModelType.SAMEDIFF ? outSd.get(tc.getPredictionsNamesSameDiff().get(i)) : output[i]; + INDArray label = toOverfit.getLabels(i); + + INDArray z = exceedsRelError(out, label, tc.getMaxRelativeErrorOverfit(), tc.getMinAbsErrorOverfit()); int count = z.sumNumber().intValue(); if (count > 0) { - System.out.println(output[i]); - System.out.println(toOverfit.getLabels(i)); - INDArray re = relativeError(output[i], toOverfit.getLabels(i), tc.getMinAbsErrorOverfit()); + System.out.println(out); + System.out.println(label); + INDArray re = relativeError(out, label, tc.getMinAbsErrorOverfit()); System.out.println("Relative error:"); System.out.println(re); } assertEquals("Number of outputs exceeded max relative error", 0, count); } - checkLayerClearance(m); + if(modelType != ModelType.SAMEDIFF) { + checkLayerClearance(m); + } } long end = System.currentTimeMillis(); @@ -709,6 +843,16 @@ public class IntegrationTestRunner { return out; } + private static Map getConstantCopies(SameDiff sd){ + Map out = new HashMap<>(); + for(SDVariable v : sd.variables()){ + if(v.isConstant()){ + out.put(v.name(), v.getArr()); + } + } + return out; + } + public static void checkFrozenParams(Map copiesBeforeTraining, Model m){ for(Map.Entry e : copiesBeforeTraining.entrySet()){ INDArray actual = m.getParam(e.getKey()); @@ -716,6 +860,13 @@ public class IntegrationTestRunner { } } + public static void checkConstants(Map copiesBefore, SameDiff sd){ + for(Map.Entry e : copiesBefore.entrySet()){ + INDArray actual = sd.getArrForVarName(e.getKey()); + assertEquals(e.getKey(), e.getValue(), actual); + } + } + public static void printCoverageInformation(){ log.info("||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||"); @@ -918,7 +1069,7 @@ public class IntegrationTestRunner { } - public static void logFailedParams(int maxNum, String prefix, org.deeplearning4j.nn.api.Layer[] layers, INDArray exceedsRelError, INDArray exp, INDArray act){ + public static void logFailedParams(int maxNumToPrintOnFailure, String prefix, org.deeplearning4j.nn.api.Layer[] layers, INDArray exceedsRelError, INDArray exp, INDArray act){ long length = exceedsRelError.length(); int logCount = 0; for(int i=0; i= maxNum){ + if(++logCount >= maxNumToPrintOnFailure){ break; } } } } + + public static void assertSameDiffEquals(SameDiff sd1, SameDiff sd2){ + assertEquals(sd1.variableMap().keySet(), sd2.variableMap().keySet()); + assertEquals(sd1.getOps().keySet(), sd2.getOps().keySet()); + assertEquals(sd1.inputs(), sd2.inputs()); + + //Check constant and variable arrays: + for(SDVariable v : sd1.variables()){ + String n = v.name(); + assertEquals(n, v.getVariableType(), sd2.getVariable(n).getVariableType()); + if(v.isConstant() || v.getVariableType() == VariableType.VARIABLE){ + INDArray a1 = v.getArr(); + INDArray a2 = sd2.getVariable(n).getArr(); + assertEquals(n, a1, a2); + } + } + + //Check ops: + for(SameDiffOp o : sd1.getOps().values()){ + SameDiffOp o2 = sd2.getOps().get(o.getName()); + assertEquals(o.getOp().getClass(), o2.getOp().getClass()); + } + } } diff --git a/deeplearning4j/dl4j-integration-tests/src/test/java/org/deeplearning4j/integration/IntegrationTests.java b/deeplearning4j/dl4j-integration-tests/src/test/java/org/deeplearning4j/integration/IntegrationTestsDL4J.java similarity index 80% rename from deeplearning4j/dl4j-integration-tests/src/test/java/org/deeplearning4j/integration/IntegrationTests.java rename to deeplearning4j/dl4j-integration-tests/src/test/java/org/deeplearning4j/integration/IntegrationTestsDL4J.java index 8e2ceef79..11a2189d8 100644 --- a/deeplearning4j/dl4j-integration-tests/src/test/java/org/deeplearning4j/integration/IntegrationTests.java +++ b/deeplearning4j/dl4j-integration-tests/src/test/java/org/deeplearning4j/integration/IntegrationTestsDL4J.java @@ -1,5 +1,6 @@ -/******************************************************************************* +/* ****************************************************************************** * Copyright (c) 2015-2018 Skymind, Inc. + * Copyright (c) 2020 Konduit K.K. * * This program and the accompanying materials are made available under the * terms of the Apache License, Version 2.0 which is available at @@ -17,15 +18,19 @@ package org.deeplearning4j.integration; import org.deeplearning4j.BaseDL4JTest; -import org.deeplearning4j.integration.testcases.*; +import org.deeplearning4j.integration.testcases.dl4j.*; import org.junit.AfterClass; -import org.junit.Ignore; import org.junit.Rule; import org.junit.Test; import org.junit.rules.TemporaryFolder; -@Ignore("AB - 2019/05/27 - Integration tests need to be updated") -public class IntegrationTests extends BaseDL4JTest { +//@Ignore("AB - 2019/05/27 - Integration tests need to be updated") +public class IntegrationTestsDL4J extends BaseDL4JTest { + + @Override + public long getTimeoutMilliseconds() { + return 300_000L; + } @Rule public TemporaryFolder testDir = new TemporaryFolder(); @@ -36,79 +41,72 @@ public class IntegrationTests extends BaseDL4JTest { } // ***** MLPTestCases ***** - @Test(timeout = 20000L) + @Test public void testMLPMnist() throws Exception { IntegrationTestRunner.runTest(MLPTestCases.getMLPMnist(), testDir); } - @Test(timeout = 30000L) + @Test public void testMlpMoon() throws Exception { IntegrationTestRunner.runTest(MLPTestCases.getMLPMoon(), testDir); } // ***** RNNTestCases ***** - @Test(timeout = 30000L) + @Test public void testRnnSeqClassification1() throws Exception { IntegrationTestRunner.runTest(RNNTestCases.getRnnCsvSequenceClassificationTestCase1(), testDir); } - @Test(timeout = 60000L) + @Test public void testRnnSeqClassification2() throws Exception { IntegrationTestRunner.runTest(RNNTestCases.getRnnCsvSequenceClassificationTestCase2(), testDir); } - @Test(timeout = 120000L) + @Test public void testRnnCharacter() throws Exception { IntegrationTestRunner.runTest(RNNTestCases.getRnnCharacterTestCase(), testDir); } // ***** CNN1DTestCases ***** - @Test(timeout = 180000L) + @Test public void testCnn1dCharacter() throws Exception { IntegrationTestRunner.runTest(CNN1DTestCases.getCnn1dTestCaseCharRNN(), testDir); } // ***** CNN2DTestCases ***** - @Test(timeout = 120000L) + @Test public void testLenetMnist() throws Exception { IntegrationTestRunner.runTest(CNN2DTestCases.getLenetMnist(), testDir); } - @Ignore //TODO: https://github.com/deeplearning4j/deeplearning4j/issues/6017 - @Test(timeout = 180000L) + @Test public void testYoloHouseNumbers() throws Exception { IntegrationTestRunner.runTest(CNN2DTestCases.getYoloHouseNumbers(), testDir); } - @Test(timeout = 120000L) + @Test public void testCnn2DLenetTransferDropoutRepeatability() throws Exception { IntegrationTestRunner.runTest(CNN2DTestCases.testLenetTransferDropoutRepeatability(), testDir); } // ***** CNN3DTestCases ***** - @Test(timeout = 180000L) + @Test public void testCnn3dSynthetic() throws Exception { IntegrationTestRunner.runTest(CNN3DTestCases.getCnn3dTestCaseSynthetic(), testDir); } - // ***** UnsupervisedTestCases ***** - @Test(timeout = 120000L) + @Test public void testVAEMnistAnomaly() throws Exception { IntegrationTestRunner.runTest(UnsupervisedTestCases.getVAEMnistAnomaly(), testDir); } // ***** TransferLearningTestCases ***** - @Test(timeout = 360000L) + @Test public void testVgg16Transfer() throws Exception { IntegrationTestRunner.runTest(CNN2DTestCases.getVGG16TransferTinyImagenet(), testDir); } - - - // ***** KerasImportTestCases ***** - //TODO - } diff --git a/deeplearning4j/dl4j-integration-tests/src/test/java/org/deeplearning4j/integration/IntegrationTestsSameDiff.java b/deeplearning4j/dl4j-integration-tests/src/test/java/org/deeplearning4j/integration/IntegrationTestsSameDiff.java new file mode 100644 index 000000000..f16a5e187 --- /dev/null +++ b/deeplearning4j/dl4j-integration-tests/src/test/java/org/deeplearning4j/integration/IntegrationTestsSameDiff.java @@ -0,0 +1,40 @@ +/* ****************************************************************************** + * Copyright (c) 2020 Konduit K.K. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ +package org.deeplearning4j.integration; + +import org.deeplearning4j.BaseDL4JTest; +import org.deeplearning4j.integration.testcases.samediff.SameDiffMLPTestCases; +import org.junit.Rule; +import org.junit.Test; +import org.junit.rules.TemporaryFolder; + +public class IntegrationTestsSameDiff extends BaseDL4JTest { + + @Override + public long getTimeoutMilliseconds() { + return 300_000L; + } + + @Rule + public TemporaryFolder testDir = new TemporaryFolder(); + + + @Test + public void testMLPMnist() throws Exception { + IntegrationTestRunner.runTest(SameDiffMLPTestCases.getMLPMnist(), testDir); + } + +} diff --git a/deeplearning4j/dl4j-integration-tests/src/test/java/org/deeplearning4j/integration/ModelType.java b/deeplearning4j/dl4j-integration-tests/src/test/java/org/deeplearning4j/integration/ModelType.java new file mode 100644 index 000000000..cb47cc229 --- /dev/null +++ b/deeplearning4j/dl4j-integration-tests/src/test/java/org/deeplearning4j/integration/ModelType.java @@ -0,0 +1,20 @@ +/* ****************************************************************************** + * Copyright (c) 2020 Konduit K.K. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ +package org.deeplearning4j.integration; + +public enum ModelType { + MLN, CG, SAMEDIFF +} diff --git a/deeplearning4j/dl4j-integration-tests/src/test/java/org/deeplearning4j/integration/TestCase.java b/deeplearning4j/dl4j-integration-tests/src/test/java/org/deeplearning4j/integration/TestCase.java index 39d72c92a..69e6ab0d9 100644 --- a/deeplearning4j/dl4j-integration-tests/src/test/java/org/deeplearning4j/integration/TestCase.java +++ b/deeplearning4j/dl4j-integration-tests/src/test/java/org/deeplearning4j/integration/TestCase.java @@ -1,5 +1,6 @@ /******************************************************************************* * Copyright (c) 2015-2018 Skymind, Inc. + * Copyright (c) 2020 Konduit K.K. * * This program and the accompanying materials are made available under the * terms of the Apache License, Version 2.0 which is available at @@ -17,8 +18,9 @@ package org.deeplearning4j.integration; import lombok.Data; -import org.deeplearning4j.eval.IEvaluation; import org.deeplearning4j.nn.api.Model; +import org.nd4j.autodiff.samediff.SameDiff; +import org.nd4j.evaluation.IEvaluation; import org.nd4j.linalg.api.ndarray.INDArray; import org.nd4j.linalg.dataset.api.MultiDataSet; import org.nd4j.linalg.dataset.api.iterator.MultiDataSetIterator; @@ -26,6 +28,7 @@ import org.nd4j.linalg.primitives.Pair; import java.io.File; import java.util.List; +import java.util.Map; /** * A single test case for integration tests @@ -37,16 +40,17 @@ public abstract class TestCase { PRETRAINED, RANDOM_INIT } - protected String testName; - protected TestType testType; - protected boolean testPredictions = true; - protected boolean testGradients = true; - protected boolean testUnsupervisedTraining = false; - protected boolean testTrainingCurves = true; - protected boolean testParamsPostTraining = true; - protected boolean testEvaluation = true; - protected boolean testParallelInference = true; - protected boolean testOverfitting = true; + //See: readme.md for more details + protected String testName; //Name of the test, for display purposes + protected TestType testType; //Type of model - from a pretrained model, or a randomly initialized model + protected boolean testPredictions = true; //If true: check the predictions/output. Requires getPredictionsTestData() to be implemented + protected boolean testGradients = true; //If true: check the gradients. Requires getGradientsTestData() to be implemented + protected boolean testUnsupervisedTraining = false; //If true: perform unsupervised training. Only applies to layers like autoencoders, VAEs, etc. Requires getUnsupervisedTrainData() to be implemented + protected boolean testTrainingCurves = true; //If true: perform training, and compare loss vs. iteration. Requires getTrainingData() method + protected boolean testParamsPostTraining = true; //If true: perform training, and compare parameters after training. Requires getTrainingData() method + protected boolean testEvaluation = true; //If true: perform evaluation. Requires getNewEvaluations() and getEvaluationTestData() methods implemented + protected boolean testParallelInference = true; //If true: run the model through ParallelInference. Requires getPredictionsTestData() method. Only applies to DL4J models, NOT SameDiff models + protected boolean testOverfitting = true; //If true: perform overfitting, and ensure the predictions match the training data. Requires both getOverfittingData() and getOverfitNumIterations() protected int[] unsupervisedTrainLayersMLN = null; protected String[] unsupervisedTrainLayersCG = null; @@ -65,6 +69,8 @@ public abstract class TestCase { protected double maxRelativeErrorOverfit = 1e-2; protected double minAbsErrorOverfit = 1e-2; + public abstract ModelType modelType(); + /** * Initialize the test case... many tests don't need this; others may use it to download or create data * @param testWorkingDir Working directory to use for test @@ -88,19 +94,37 @@ public abstract class TestCase { } /** - * Required if testPredictions == true + * Required if testPredictions == true && DL4J model (MultiLayerNetwork or ComputationGraph) */ public List> getPredictionsTestData() throws Exception { throw new RuntimeException("Implementations must override this method if used"); } /** - * Required if testGradients == true + * Required if testPredictions == true && SameDiff model + */ + public List> getPredictionsTestDataSameDiff() throws Exception { + throw new RuntimeException("Implementations must override this method if used"); + } + + public List getPredictionsNamesSameDiff() throws Exception { + throw new RuntimeException("Implementations must override this method if used"); + } + + /** + * Required if testGradients == true && DL4J model */ public MultiDataSet getGradientsTestData() throws Exception { throw new RuntimeException("Implementations must override this method if used"); } + /** + * Required if testGradients == true && SameDiff model + */ + public Map getGradientsTestDataSameDiff() throws Exception { + throw new RuntimeException("Implementations must override this method if used"); + } + /** * Required when testUnsupervisedTraining == true */ @@ -122,6 +146,10 @@ public abstract class TestCase { throw new RuntimeException("Implementations must override this method if used"); } + public IEvaluation[] doEvaluationSameDiff(SameDiff sd, MultiDataSetIterator iter, IEvaluation[] evaluations){ + throw new RuntimeException("Implementations must override this method if used"); + } + /** * Required if testEvaluation == true */ @@ -130,12 +158,19 @@ public abstract class TestCase { } /** - * Required if testOverfitting == true + * Required if testOverfitting == true && DL4J model */ public MultiDataSet getOverfittingData() throws Exception { throw new RuntimeException("Implementations must override this method if used"); } + /** + * Required if testOverfitting == true && SameDiff model + */ + public Map getOverfittingDataSameDiff() throws Exception { + throw new RuntimeException("Implementations must override this method if used"); + } + /** * Required if testOverfitting == true */ diff --git a/deeplearning4j/dl4j-integration-tests/src/test/java/org/deeplearning4j/integration/testcases/TransferLearningTestCases.java b/deeplearning4j/dl4j-integration-tests/src/test/java/org/deeplearning4j/integration/testcases/TransferLearningTestCases.java deleted file mode 100644 index 0ac0b868b..000000000 --- a/deeplearning4j/dl4j-integration-tests/src/test/java/org/deeplearning4j/integration/testcases/TransferLearningTestCases.java +++ /dev/null @@ -1,36 +0,0 @@ -/******************************************************************************* - * Copyright (c) 2015-2018 Skymind, Inc. - * - * This program and the accompanying materials are made available under the - * terms of the Apache License, Version 2.0 which is available at - * https://www.apache.org/licenses/LICENSE-2.0. - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the - * License for the specific language governing permissions and limitations - * under the License. - * - * SPDX-License-Identifier: Apache-2.0 - ******************************************************************************/ - -package org.deeplearning4j.integration.testcases; - -import org.deeplearning4j.integration.TestCase; - -public class TransferLearningTestCases { - - public static TestCase testPartFrozenResNet50(){ - - throw new UnsupportedOperationException("Not yet implemented"); - } - - - public static TestCase testPartFrozenNASNET(){ - - throw new UnsupportedOperationException("Not yet implemented"); - } - - - -} diff --git a/deeplearning4j/dl4j-integration-tests/src/test/java/org/deeplearning4j/integration/testcases/CNN1DTestCases.java b/deeplearning4j/dl4j-integration-tests/src/test/java/org/deeplearning4j/integration/testcases/dl4j/CNN1DTestCases.java similarity index 88% rename from deeplearning4j/dl4j-integration-tests/src/test/java/org/deeplearning4j/integration/testcases/CNN1DTestCases.java rename to deeplearning4j/dl4j-integration-tests/src/test/java/org/deeplearning4j/integration/testcases/dl4j/CNN1DTestCases.java index 91386cdef..379d03356 100644 --- a/deeplearning4j/dl4j-integration-tests/src/test/java/org/deeplearning4j/integration/testcases/CNN1DTestCases.java +++ b/deeplearning4j/dl4j-integration-tests/src/test/java/org/deeplearning4j/integration/testcases/dl4j/CNN1DTestCases.java @@ -1,5 +1,6 @@ -/******************************************************************************* +/* ****************************************************************************** * Copyright (c) 2015-2018 Skymind, Inc. + * Copyright (c) 2020 Konduit K.K. * * This program and the accompanying materials are made available under the * terms of the Apache License, Version 2.0 which is available at @@ -14,22 +15,24 @@ * SPDX-License-Identifier: Apache-2.0 ******************************************************************************/ -package org.deeplearning4j.integration.testcases; +package org.deeplearning4j.integration.testcases.dl4j; import org.deeplearning4j.datasets.iterator.EarlyTerminationDataSetIterator; import org.deeplearning4j.datasets.iterator.impl.MultiDataSetIteratorAdapter; -import org.deeplearning4j.eval.Evaluation; -import org.deeplearning4j.eval.IEvaluation; -import org.deeplearning4j.eval.ROCMultiClass; +import org.deeplearning4j.integration.ModelType; import org.deeplearning4j.integration.TestCase; -import org.deeplearning4j.integration.testcases.misc.CharacterIterator; +import org.deeplearning4j.integration.testcases.dl4j.misc.CharacterIterator; import org.deeplearning4j.nn.conf.ConvolutionMode; import org.deeplearning4j.nn.conf.NeuralNetConfiguration; import org.deeplearning4j.nn.conf.inputs.InputType; import org.deeplearning4j.nn.conf.layers.*; import org.deeplearning4j.nn.conf.layers.convolutional.Cropping1D; import org.deeplearning4j.nn.weights.WeightInit; +import org.nd4j.evaluation.IEvaluation; +import org.nd4j.evaluation.classification.Evaluation; +import org.nd4j.evaluation.classification.ROCMultiClass; import org.nd4j.linalg.activations.Activation; +import org.nd4j.linalg.api.buffer.DataType; import org.nd4j.linalg.api.ndarray.INDArray; import org.nd4j.linalg.dataset.api.MultiDataSet; import org.nd4j.linalg.dataset.api.iterator.DataSetIterator; @@ -64,12 +67,18 @@ public class CNN1DTestCases { int miniBatchSize = 16; int exampleLength = 128; + @Override + public ModelType modelType() { + return ModelType.CG; + } + @Override public Object getConfiguration() throws Exception { CharacterIterator iter = CharacterIterator.getShakespeareIterator(miniBatchSize,exampleLength); int nOut = iter.totalOutcomes(); return new NeuralNetConfiguration.Builder() + .dataType(DataType.FLOAT) .seed(12345) .weightInit(WeightInit.XAVIER) .updater(new Adam(0.01)) diff --git a/deeplearning4j/dl4j-integration-tests/src/test/java/org/deeplearning4j/integration/testcases/CNN2DTestCases.java b/deeplearning4j/dl4j-integration-tests/src/test/java/org/deeplearning4j/integration/testcases/dl4j/CNN2DTestCases.java similarity index 95% rename from deeplearning4j/dl4j-integration-tests/src/test/java/org/deeplearning4j/integration/testcases/CNN2DTestCases.java rename to deeplearning4j/dl4j-integration-tests/src/test/java/org/deeplearning4j/integration/testcases/dl4j/CNN2DTestCases.java index 06a6d6dbc..71336c0a6 100644 --- a/deeplearning4j/dl4j-integration-tests/src/test/java/org/deeplearning4j/integration/testcases/CNN2DTestCases.java +++ b/deeplearning4j/dl4j-integration-tests/src/test/java/org/deeplearning4j/integration/testcases/dl4j/CNN2DTestCases.java @@ -1,5 +1,6 @@ -/******************************************************************************* +/* ****************************************************************************** * Copyright (c) 2015-2018 Skymind, Inc. + * Copyright (c) 2020 Konduit K.K. * * This program and the accompanying materials are made available under the * terms of the Apache License, Version 2.0 which is available at @@ -14,7 +15,7 @@ * SPDX-License-Identifier: Apache-2.0 ******************************************************************************/ -package org.deeplearning4j.integration.testcases; +package org.deeplearning4j.integration.testcases.dl4j; import org.datavec.api.split.FileSplit; import org.datavec.image.loader.NativeImageLoader; @@ -22,16 +23,13 @@ import org.datavec.image.recordreader.objdetect.ObjectDetectionRecordReader; import org.datavec.image.recordreader.objdetect.impl.SvhnLabelProvider; import org.deeplearning4j.datasets.datavec.RecordReaderDataSetIterator; import org.deeplearning4j.datasets.fetchers.SvhnDataFetcher; +import org.deeplearning4j.integration.ModelType; import org.deeplearning4j.integration.TestCase; import org.deeplearning4j.datasets.fetchers.DataSetType; import org.deeplearning4j.datasets.iterator.EarlyTerminationDataSetIterator; import org.deeplearning4j.datasets.iterator.impl.MnistDataSetIterator; import org.deeplearning4j.datasets.iterator.impl.MultiDataSetIteratorAdapter; import org.deeplearning4j.datasets.iterator.impl.TinyImageNetDataSetIterator; -import org.deeplearning4j.eval.Evaluation; -import org.deeplearning4j.eval.EvaluationCalibration; -import org.deeplearning4j.eval.IEvaluation; -import org.deeplearning4j.eval.ROCMultiClass; import org.deeplearning4j.nn.api.Model; import org.deeplearning4j.nn.api.OptimizationAlgorithm; import org.deeplearning4j.nn.conf.*; @@ -47,7 +45,12 @@ import org.deeplearning4j.nn.weights.WeightInit; import org.deeplearning4j.zoo.PretrainedType; import org.deeplearning4j.zoo.model.TinyYOLO; import org.deeplearning4j.zoo.model.VGG16; +import org.nd4j.evaluation.IEvaluation; +import org.nd4j.evaluation.classification.Evaluation; +import org.nd4j.evaluation.classification.EvaluationCalibration; +import org.nd4j.evaluation.classification.ROCMultiClass; import org.nd4j.linalg.activations.Activation; +import org.nd4j.linalg.api.buffer.DataType; import org.nd4j.linalg.api.ndarray.INDArray; import org.nd4j.linalg.dataset.DataSet; import org.nd4j.linalg.dataset.api.MultiDataSet; @@ -82,12 +85,18 @@ public class CNN2DTestCases { testOverfitting = false; } + @Override + public ModelType modelType() { + return ModelType.MLN; + } + public Object getConfiguration() throws Exception { int nChannels = 1; // Number of input channels int outputNum = 10; // The number of possible outcomes int seed = 123; MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder() + .dataType(DataType.FLOAT) .seed(seed) .l2(0.0005) .weightInit(WeightInit.XAVIER) @@ -187,6 +196,11 @@ public class CNN2DTestCases { testOverfitting = false; } + @Override + public ModelType modelType() { + return ModelType.CG; + } + @Override public Model getPretrainedModel() throws Exception { VGG16 vgg16 = VGG16.builder() @@ -269,6 +283,11 @@ public class CNN2DTestCases { testOverfitting = false; } + @Override + public ModelType modelType() { + return ModelType.CG; + } + @Override public Model getPretrainedModel() throws Exception { int nClasses = 10; @@ -372,6 +391,11 @@ public class CNN2DTestCases { testOverfitting = true; } + @Override + public ModelType modelType() { + return ModelType.CG; + } + @Override public Model getPretrainedModel() throws Exception { @@ -381,6 +405,7 @@ public class CNN2DTestCases { lrSchedule.put(3000, 0.001); MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder() + .dataType(DataType.FLOAT) .seed(12345) .l2(0.0005) .weightInit(WeightInit.XAVIER) diff --git a/deeplearning4j/dl4j-integration-tests/src/test/java/org/deeplearning4j/integration/testcases/CNN3DTestCases.java b/deeplearning4j/dl4j-integration-tests/src/test/java/org/deeplearning4j/integration/testcases/dl4j/CNN3DTestCases.java similarity index 87% rename from deeplearning4j/dl4j-integration-tests/src/test/java/org/deeplearning4j/integration/testcases/CNN3DTestCases.java rename to deeplearning4j/dl4j-integration-tests/src/test/java/org/deeplearning4j/integration/testcases/dl4j/CNN3DTestCases.java index 8e2138d52..1192e7893 100644 --- a/deeplearning4j/dl4j-integration-tests/src/test/java/org/deeplearning4j/integration/testcases/CNN3DTestCases.java +++ b/deeplearning4j/dl4j-integration-tests/src/test/java/org/deeplearning4j/integration/testcases/dl4j/CNN3DTestCases.java @@ -1,5 +1,6 @@ -/******************************************************************************* +/* ****************************************************************************** * Copyright (c) 2015-2018 Skymind, Inc. + * Copyright (c) 2020 Konduit K.K. * * This program and the accompanying materials are made available under the * terms of the Apache License, Version 2.0 which is available at @@ -14,35 +15,31 @@ * SPDX-License-Identifier: Apache-2.0 ******************************************************************************/ -package org.deeplearning4j.integration.testcases; +package org.deeplearning4j.integration.testcases.dl4j; -import org.apache.commons.math3.stat.inference.TestUtils; -import org.deeplearning4j.datasets.iterator.EarlyTerminationDataSetIterator; -import org.deeplearning4j.datasets.iterator.impl.MnistDataSetIterator; -import org.deeplearning4j.datasets.iterator.impl.MultiDataSetIteratorAdapter; import org.deeplearning4j.datasets.iterator.impl.SingletonMultiDataSetIterator; -import org.deeplearning4j.eval.Evaluation; -import org.deeplearning4j.eval.IEvaluation; -import org.deeplearning4j.eval.ROCMultiClass; +import org.deeplearning4j.integration.ModelType; import org.deeplearning4j.integration.TestCase; import org.deeplearning4j.nn.conf.ConvolutionMode; import org.deeplearning4j.nn.conf.MultiLayerConfiguration; import org.deeplearning4j.nn.conf.NeuralNetConfiguration; import org.deeplearning4j.nn.conf.inputs.InputType; -import org.deeplearning4j.nn.conf.layers.*; +import org.deeplearning4j.nn.conf.layers.Convolution3D; +import org.deeplearning4j.nn.conf.layers.OutputLayer; +import org.deeplearning4j.nn.conf.layers.PoolingType; +import org.deeplearning4j.nn.conf.layers.Subsampling3DLayer; import org.deeplearning4j.nn.weights.WeightInit; +import org.nd4j.evaluation.IEvaluation; +import org.nd4j.evaluation.classification.Evaluation; import org.nd4j.linalg.activations.Activation; import org.nd4j.linalg.api.ndarray.INDArray; -import org.nd4j.linalg.dataset.DataSet; import org.nd4j.linalg.dataset.api.MultiDataSet; -import org.nd4j.linalg.dataset.api.iterator.DataSetIterator; import org.nd4j.linalg.dataset.api.iterator.MultiDataSetIterator; import org.nd4j.linalg.factory.Nd4j; import org.nd4j.linalg.learning.config.Nesterovs; import org.nd4j.linalg.lossfunctions.LossFunctions; import org.nd4j.linalg.primitives.Pair; -import java.util.ArrayList; import java.util.Collections; import java.util.List; @@ -66,6 +63,11 @@ public class CNN3DTestCases { testOverfitting = false; } + @Override + public ModelType modelType() { + return ModelType.MLN; + } + public Object getConfiguration() throws Exception { int nChannels = 3; // Number of input channels int outputNum = 10; // The number of possible outcomes diff --git a/deeplearning4j/dl4j-integration-tests/src/test/java/org/deeplearning4j/integration/testcases/MLPTestCases.java b/deeplearning4j/dl4j-integration-tests/src/test/java/org/deeplearning4j/integration/testcases/dl4j/MLPTestCases.java similarity index 92% rename from deeplearning4j/dl4j-integration-tests/src/test/java/org/deeplearning4j/integration/testcases/MLPTestCases.java rename to deeplearning4j/dl4j-integration-tests/src/test/java/org/deeplearning4j/integration/testcases/dl4j/MLPTestCases.java index 8a4ed52a5..232219f04 100644 --- a/deeplearning4j/dl4j-integration-tests/src/test/java/org/deeplearning4j/integration/testcases/MLPTestCases.java +++ b/deeplearning4j/dl4j-integration-tests/src/test/java/org/deeplearning4j/integration/testcases/dl4j/MLPTestCases.java @@ -1,5 +1,6 @@ -/******************************************************************************* +/* ****************************************************************************** * Copyright (c) 2015-2018 Skymind, Inc. + * Copyright (c) 2020 Konduit K.K. * * This program and the accompanying materials are made available under the * terms of the Apache License, Version 2.0 which is available at @@ -14,8 +15,9 @@ * SPDX-License-Identifier: Apache-2.0 ******************************************************************************/ -package org.deeplearning4j.integration.testcases; +package org.deeplearning4j.integration.testcases.dl4j; +import org.deeplearning4j.integration.ModelType; import org.deeplearning4j.integration.TestCase; import org.datavec.api.records.reader.RecordReader; import org.datavec.api.records.reader.impl.csv.CSVRecordReader; @@ -24,10 +26,6 @@ import org.deeplearning4j.datasets.datavec.RecordReaderDataSetIterator; import org.deeplearning4j.datasets.iterator.EarlyTerminationDataSetIterator; import org.deeplearning4j.datasets.iterator.impl.MnistDataSetIterator; import org.deeplearning4j.datasets.iterator.impl.MultiDataSetIteratorAdapter; -import org.deeplearning4j.eval.Evaluation; -import org.deeplearning4j.eval.EvaluationCalibration; -import org.deeplearning4j.eval.IEvaluation; -import org.deeplearning4j.eval.ROCMultiClass; import org.deeplearning4j.nn.conf.MultiLayerConfiguration; import org.deeplearning4j.nn.conf.NeuralNetConfiguration; import org.deeplearning4j.nn.conf.inputs.InputType; @@ -35,7 +33,12 @@ import org.deeplearning4j.nn.conf.layers.DenseLayer; import org.deeplearning4j.nn.conf.layers.OutputLayer; import org.deeplearning4j.nn.graph.util.ComputationGraphUtil; import org.deeplearning4j.nn.weights.WeightInit; +import org.nd4j.evaluation.IEvaluation; +import org.nd4j.evaluation.classification.Evaluation; +import org.nd4j.evaluation.classification.EvaluationCalibration; +import org.nd4j.evaluation.classification.ROCMultiClass; import org.nd4j.linalg.activations.Activation; +import org.nd4j.linalg.api.buffer.DataType; import org.nd4j.linalg.api.ndarray.INDArray; import org.nd4j.linalg.dataset.DataSet; import org.nd4j.linalg.dataset.api.MultiDataSet; @@ -76,9 +79,15 @@ public class MLPTestCases { minAbsErrorOverfit = 1e-2; } + @Override + public ModelType modelType() { + return ModelType.MLN; + } + @Override public Object getConfiguration() { return new NeuralNetConfiguration.Builder() + .dataType(DataType.FLOAT) .seed(12345) .updater(new Adam(new MapSchedule.Builder(ScheduleType.ITERATION) .add(0, 5e-2) @@ -168,6 +177,11 @@ public class MLPTestCases { testOverfitting = false; //Not much point here: very simple training data } + @Override + public ModelType modelType() { + return ModelType.MLN; + } + @Override public Object getConfiguration() { int seed = 123; @@ -179,6 +193,7 @@ public class MLPTestCases { //log.info("Build model...."); MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder() + .dataType(DataType.FLOAT) .seed(seed) .updater(new Nesterovs(learningRate, 0.9)) .list() diff --git a/deeplearning4j/dl4j-integration-tests/src/test/java/org/deeplearning4j/integration/testcases/RNNTestCases.java b/deeplearning4j/dl4j-integration-tests/src/test/java/org/deeplearning4j/integration/testcases/dl4j/RNNTestCases.java similarity index 93% rename from deeplearning4j/dl4j-integration-tests/src/test/java/org/deeplearning4j/integration/testcases/RNNTestCases.java rename to deeplearning4j/dl4j-integration-tests/src/test/java/org/deeplearning4j/integration/testcases/dl4j/RNNTestCases.java index a70d8dd2f..f89643380 100644 --- a/deeplearning4j/dl4j-integration-tests/src/test/java/org/deeplearning4j/integration/testcases/RNNTestCases.java +++ b/deeplearning4j/dl4j-integration-tests/src/test/java/org/deeplearning4j/integration/testcases/dl4j/RNNTestCases.java @@ -1,5 +1,6 @@ -/******************************************************************************* +/* ****************************************************************************** * Copyright (c) 2015-2018 Skymind, Inc. + * Copyright (c) 2020 Konduit K.K. * * This program and the accompanying materials are made available under the * terms of the Apache License, Version 2.0 which is available at @@ -14,22 +15,24 @@ * SPDX-License-Identifier: Apache-2.0 ******************************************************************************/ -package org.deeplearning4j.integration.testcases; +package org.deeplearning4j.integration.testcases.dl4j; +import org.deeplearning4j.integration.ModelType; +import org.nd4j.evaluation.IEvaluation; +import org.nd4j.evaluation.classification.Evaluation; +import org.nd4j.evaluation.classification.EvaluationCalibration; +import org.nd4j.evaluation.classification.ROCMultiClass; +import org.nd4j.linalg.api.buffer.DataType; +import org.nd4j.linalg.dataset.api.preprocessor.CompositeMultiDataSetPreProcessor; import org.nd4j.shade.guava.io.Files; import org.deeplearning4j.integration.TestCase; -import org.deeplearning4j.integration.testcases.misc.CharacterIterator; -import org.deeplearning4j.integration.testcases.misc.CompositeMultiDataSetPreProcessor; +import org.deeplearning4j.integration.testcases.dl4j.misc.CharacterIterator; import org.datavec.api.records.reader.SequenceRecordReader; import org.datavec.api.records.reader.impl.csv.CSVSequenceRecordReader; import org.datavec.api.split.NumberedFileInputSplit; import org.deeplearning4j.datasets.datavec.SequenceRecordReaderDataSetIterator; import org.deeplearning4j.datasets.iterator.EarlyTerminationDataSetIterator; import org.deeplearning4j.datasets.iterator.impl.MultiDataSetIteratorAdapter; -import org.deeplearning4j.eval.Evaluation; -import org.deeplearning4j.eval.EvaluationCalibration; -import org.deeplearning4j.eval.IEvaluation; -import org.deeplearning4j.eval.ROCMultiClass; import org.deeplearning4j.nn.conf.BackpropType; import org.deeplearning4j.nn.conf.NeuralNetConfiguration; import org.deeplearning4j.nn.conf.inputs.InputType; @@ -91,6 +94,11 @@ public class RNNTestCases { private int exampleLength = 1000; + @Override + public ModelType modelType() { + return ModelType.MLN; + } + @Override public Object getConfiguration() throws Exception { @@ -101,6 +109,7 @@ public class RNNTestCases { int tbpttLength = 50; //Length for truncated backpropagation through time. i.e., do parameter updates ever 50 characters return new NeuralNetConfiguration.Builder() + .dataType(DataType.FLOAT) .seed(12345) .l2(0.001) .weightInit(WeightInit.XAVIER) @@ -175,9 +184,15 @@ public class RNNTestCases { return normalizer; } + @Override + public ModelType modelType() { + return ModelType.MLN; + } + @Override public Object getConfiguration() throws Exception { return new NeuralNetConfiguration.Builder() + .dataType(DataType.FLOAT) .seed(12345) .updater(new Adam(5e-2)) .l1(1e-3).l2(1e-3) @@ -298,6 +313,7 @@ public class RNNTestCases { @Override public Object getConfiguration() throws Exception { return new NeuralNetConfiguration.Builder() + .dataType(DataType.FLOAT) .seed(12345) .updater(new Adam(5e-2)) .l1(1e-3).l2(1e-3) diff --git a/deeplearning4j/dl4j-integration-tests/src/test/java/org/deeplearning4j/integration/testcases/UnsupervisedTestCases.java b/deeplearning4j/dl4j-integration-tests/src/test/java/org/deeplearning4j/integration/testcases/dl4j/UnsupervisedTestCases.java similarity index 91% rename from deeplearning4j/dl4j-integration-tests/src/test/java/org/deeplearning4j/integration/testcases/UnsupervisedTestCases.java rename to deeplearning4j/dl4j-integration-tests/src/test/java/org/deeplearning4j/integration/testcases/dl4j/UnsupervisedTestCases.java index fe7dd9883..622a6e9cf 100644 --- a/deeplearning4j/dl4j-integration-tests/src/test/java/org/deeplearning4j/integration/testcases/UnsupervisedTestCases.java +++ b/deeplearning4j/dl4j-integration-tests/src/test/java/org/deeplearning4j/integration/testcases/dl4j/UnsupervisedTestCases.java @@ -1,5 +1,6 @@ -/******************************************************************************* +/* ****************************************************************************** * Copyright (c) 2015-2018 Skymind, Inc. + * Copyright (c) 2020 Konduit K.K. * * This program and the accompanying materials are made available under the * terms of the Apache License, Version 2.0 which is available at @@ -14,18 +15,20 @@ * SPDX-License-Identifier: Apache-2.0 ******************************************************************************/ -package org.deeplearning4j.integration.testcases; +package org.deeplearning4j.integration.testcases.dl4j; import org.deeplearning4j.datasets.iterator.EarlyTerminationDataSetIterator; import org.deeplearning4j.datasets.iterator.impl.MnistDataSetIterator; import org.deeplearning4j.datasets.iterator.impl.MultiDataSetIteratorAdapter; +import org.deeplearning4j.integration.ModelType; import org.deeplearning4j.integration.TestCase; import org.deeplearning4j.nn.conf.NeuralNetConfiguration; import org.deeplearning4j.nn.conf.layers.variational.BernoulliReconstructionDistribution; import org.deeplearning4j.nn.conf.layers.variational.VariationalAutoencoder; import org.deeplearning4j.nn.weights.WeightInit; import org.nd4j.linalg.activations.Activation; +import org.nd4j.linalg.api.buffer.DataType; import org.nd4j.linalg.api.ndarray.INDArray; import org.nd4j.linalg.dataset.api.iterator.DataSetIterator; import org.nd4j.linalg.dataset.api.iterator.MultiDataSetIterator; @@ -59,9 +62,15 @@ public class UnsupervisedTestCases { minAbsErrorPretrainParams = 5e-4; } + @Override + public ModelType modelType() { + return ModelType.MLN; + } + @Override public Object getConfiguration() { return new NeuralNetConfiguration.Builder() + .dataType(DataType.FLOAT) .seed(12345) .updater(new Adam(0.05)) .weightInit(WeightInit.XAVIER) diff --git a/deeplearning4j/dl4j-integration-tests/src/test/java/org/deeplearning4j/integration/testcases/misc/CharacterIterator.java b/deeplearning4j/dl4j-integration-tests/src/test/java/org/deeplearning4j/integration/testcases/dl4j/misc/CharacterIterator.java similarity index 99% rename from deeplearning4j/dl4j-integration-tests/src/test/java/org/deeplearning4j/integration/testcases/misc/CharacterIterator.java rename to deeplearning4j/dl4j-integration-tests/src/test/java/org/deeplearning4j/integration/testcases/dl4j/misc/CharacterIterator.java index 3bdcdc6cd..1020f1706 100644 --- a/deeplearning4j/dl4j-integration-tests/src/test/java/org/deeplearning4j/integration/testcases/misc/CharacterIterator.java +++ b/deeplearning4j/dl4j-integration-tests/src/test/java/org/deeplearning4j/integration/testcases/dl4j/misc/CharacterIterator.java @@ -14,7 +14,7 @@ * SPDX-License-Identifier: Apache-2.0 ******************************************************************************/ -package org.deeplearning4j.integration.testcases.misc; +package org.deeplearning4j.integration.testcases.dl4j.misc; import org.apache.commons.io.FileUtils; import org.nd4j.linalg.api.ndarray.INDArray; diff --git a/deeplearning4j/dl4j-integration-tests/src/test/java/org/deeplearning4j/integration/testcases/misc/CompositeMultiDataSetPreProcessor.java b/deeplearning4j/dl4j-integration-tests/src/test/java/org/deeplearning4j/integration/testcases/misc/CompositeMultiDataSetPreProcessor.java deleted file mode 100644 index 2665664fc..000000000 --- a/deeplearning4j/dl4j-integration-tests/src/test/java/org/deeplearning4j/integration/testcases/misc/CompositeMultiDataSetPreProcessor.java +++ /dev/null @@ -1,36 +0,0 @@ -/******************************************************************************* - * Copyright (c) 2015-2018 Skymind, Inc. - * - * This program and the accompanying materials are made available under the - * terms of the Apache License, Version 2.0 which is available at - * https://www.apache.org/licenses/LICENSE-2.0. - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the - * License for the specific language governing permissions and limitations - * under the License. - * - * SPDX-License-Identifier: Apache-2.0 - ******************************************************************************/ - -package org.deeplearning4j.integration.testcases.misc; - -import org.nd4j.linalg.dataset.api.MultiDataSet; -import org.nd4j.linalg.dataset.api.MultiDataSetPreProcessor; - -public class CompositeMultiDataSetPreProcessor implements MultiDataSetPreProcessor { - - private MultiDataSetPreProcessor[] preProcessors; - - public CompositeMultiDataSetPreProcessor(MultiDataSetPreProcessor... preProcessors){ - this.preProcessors = preProcessors; - } - - @Override - public void preProcess(MultiDataSet multiDataSet) { - for(MultiDataSetPreProcessor p : preProcessors){ - p.preProcess(multiDataSet); - } - } -} diff --git a/deeplearning4j/dl4j-integration-tests/src/test/java/org/deeplearning4j/integration/testcases/samediff/SameDiffMLPTestCases.java b/deeplearning4j/dl4j-integration-tests/src/test/java/org/deeplearning4j/integration/testcases/samediff/SameDiffMLPTestCases.java new file mode 100644 index 000000000..a3fdc0c3f --- /dev/null +++ b/deeplearning4j/dl4j-integration-tests/src/test/java/org/deeplearning4j/integration/testcases/samediff/SameDiffMLPTestCases.java @@ -0,0 +1,155 @@ +/* ****************************************************************************** + * Copyright (c) 2020 Konduit K.K. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ +package org.deeplearning4j.integration.testcases.samediff; + +import org.deeplearning4j.datasets.iterator.EarlyTerminationDataSetIterator; +import org.deeplearning4j.datasets.iterator.impl.MnistDataSetIterator; +import org.deeplearning4j.datasets.iterator.impl.MultiDataSetIteratorAdapter; +import org.deeplearning4j.integration.ModelType; +import org.deeplearning4j.integration.TestCase; +import org.nd4j.autodiff.samediff.SDVariable; +import org.nd4j.autodiff.samediff.SameDiff; +import org.nd4j.autodiff.samediff.TrainingConfig; +import org.nd4j.evaluation.IEvaluation; +import org.nd4j.evaluation.classification.Evaluation; +import org.nd4j.linalg.api.buffer.DataType; +import org.nd4j.linalg.api.ndarray.INDArray; +import org.nd4j.linalg.dataset.api.DataSet; +import org.nd4j.linalg.dataset.api.MultiDataSet; +import org.nd4j.linalg.dataset.api.iterator.DataSetIterator; +import org.nd4j.linalg.dataset.api.iterator.MultiDataSetIterator; +import org.nd4j.linalg.factory.Nd4j; +import org.nd4j.linalg.learning.config.Adam; + +import java.util.*; + +public class SameDiffMLPTestCases { + + + public static TestCase getMLPMnist(){ + return new TestCase() { + { + testName = "MLPMnistSD"; + testType = TestType.RANDOM_INIT; + testPredictions = true; + testTrainingCurves = true; + testGradients = true; + testParamsPostTraining = true; + testEvaluation = true; + testOverfitting = true; + maxRelativeErrorOverfit = 2e-2; + minAbsErrorOverfit = 1e-2; + } + + @Override + public ModelType modelType() { + return ModelType.SAMEDIFF; + } + + @Override + public Object getConfiguration() throws Exception { + Nd4j.getRandom().setSeed(12345); + + //Define the network structure: + SameDiff sd = SameDiff.create(); + SDVariable in = sd.placeHolder("in", DataType.FLOAT, -1, 784); + SDVariable label = sd.placeHolder("label", DataType.FLOAT, -1, 10); + + SDVariable w0 = sd.var("w0", Nd4j.rand(DataType.FLOAT, 784, 256)); + SDVariable b0 = sd.var("b0", Nd4j.rand(DataType.FLOAT, 256)); + SDVariable w1 = sd.var("w1", Nd4j.rand(DataType.FLOAT, 256, 10)); + SDVariable b1 = sd.var("b1", Nd4j.rand(DataType.FLOAT, 10)); + + SDVariable a0 = sd.nn.tanh(in.mmul(w0).add(b0)); + SDVariable out = sd.nn.softmax("out", a0.mmul(w1).add(b1)); + SDVariable loss = sd.loss.logLoss("loss", label, out); + + //Also set the training configuration: + sd.setTrainingConfig(TrainingConfig.builder() + .updater(new Adam(0.01)) + .weightDecay(1e-3, true) + .dataSetFeatureMapping("in") //features[0] -> "in" placeholder + .dataSetLabelMapping("label") //labels[0] -> "label" placeholder + .build()); + + return sd; + } + + @Override + public List> getPredictionsTestDataSameDiff() throws Exception { + List> out = new ArrayList<>(); + + DataSetIterator iter = new MnistDataSetIterator(1, true, 12345); + out.add(Collections.singletonMap("in", iter.next().getFeatures())); + + iter = new MnistDataSetIterator(8, true, 12345); + out.add(Collections.singletonMap("in", iter.next().getFeatures())); + + return out; + } + + @Override + public List getPredictionsNamesSameDiff() throws Exception { + return Collections.singletonList("out"); + } + + @Override + public Map getGradientsTestDataSameDiff() throws Exception { + DataSet ds = new MnistDataSetIterator(8, true, 12345).next(); + Map map = new HashMap<>(); + map.put("in", ds.getFeatures()); + map.put("label", ds.getLabels()); + return map; + } + + @Override + public MultiDataSetIterator getTrainingData() throws Exception { + DataSetIterator iter = new MnistDataSetIterator(8, true, 12345); + iter = new EarlyTerminationDataSetIterator(iter, 32); + return new MultiDataSetIteratorAdapter(iter); + } + + @Override + public IEvaluation[] getNewEvaluations() { + return new IEvaluation[]{new Evaluation()}; + } + + @Override + public MultiDataSetIterator getEvaluationTestData() throws Exception { + DataSetIterator iter = new MnistDataSetIterator(8, false, 12345); + iter = new EarlyTerminationDataSetIterator(iter, 32); + return new MultiDataSetIteratorAdapter(iter); + } + + @Override + public IEvaluation[] doEvaluationSameDiff(SameDiff sd, MultiDataSetIterator iter, IEvaluation[] evaluations) { + sd.evaluate(iter, "out", 0, evaluations); + return evaluations; + } + + @Override + public MultiDataSet getOverfittingData() throws Exception { + return new MnistDataSetIterator(1, true, 12345).next().toMultiDataSet(); + } + + @Override + public int getOverfitNumIterations() { + return 100; + } + }; + } + +} diff --git a/libnd4j/CMakeLists.txt b/libnd4j/CMakeLists.txt index cf9d4ff88..712d123be 100755 --- a/libnd4j/CMakeLists.txt +++ b/libnd4j/CMakeLists.txt @@ -1,17 +1,23 @@ cmake_minimum_required(VERSION 3.15) project(libnd4j) set(CMAKE_VERBOSE_MAKEFILE OFF) -option(NATIVE "Optimize for build machine (might not work on others)" OFF) + set(CMAKE_MODULE_PATH "${CMAKE_SOURCE_DIR}/cmake" ${CMAKE_MODULE_PATH}) #ensure we create lib files set(CMAKE_WINDOWS_EXPORT_ALL_SYMBOLS OFF) -option(CHECK_VECTORIZATION "checks for vectorization" OFF) -option(BUILD_TESTS "Build tests" OFF) + +option(SD_NATIVE "Optimize for build machine (might not work on others)" OFF) +option(SD_CHECK_VECTORIZATION "checks for vectorization" OFF) +option(SD_BUILD_TESTS "Build tests" OFF) +option(SD_STATIC_LIB "Build static library" OFF) +option(SD_SHARED_LIB "Build shared library" ON) +option(SD_SANITIZE "Enable Address Sanitizer" ON) + option(FLATBUFFERS_BUILD_FLATC "Enable the build of the flatbuffers compiler" OFF) set(FLATBUFFERS_BUILD_FLATC "OFF" CACHE STRING "Hack to disable flatc build" FORCE) set(CMAKE_CXX_STANDARD 11) -if (CUDA_BLAS) +if (SD_CUDA) enable_language(CUDA) set(CMAKE_CUDA_STANDARD 11) @@ -23,23 +29,23 @@ endif() # MSVC runtime lib can be either "MultiThreaded" or "MultiThreadedDLL", /MT and /MD respectively set(MSVC_RT_LIB "MultiThreadedDLL") -set(X86_BUILD false) +set(SD_X86_BUILD false) -if (NOT IOS_BUILD AND NOT ANDROID_BUILD AND NOT ${ARCH} MATCHES "power*" AND NOT ${ARCH} MATCHES "arm*") - set(X86_BUILD true) +if (NOT SD_IOS_BUILD AND NOT SD_ANDROID_BUILD AND NOT ${SD_ARCH} MATCHES "power*" AND NOT ${SD_ARCH} MATCHES "arm*") + set(SD_X86_BUILD true) endif() # -fsanitize=address # -fsanitize=leak -if (ANDROID_BUILD) +if (SD_ANDROID_BUILD) set(CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE} -O3 -fPIC -Wno-braced-scalar-init -Wno-delete-non-virtual-dtor -Wno-unused-command-line-argument -Wno-dangling-else -D_RELEASE=true") set(CMAKE_CXX_FLAGS_DEBUG "${CMAKE_CXX_FLAGS_DEBUG} -O0 -g -fPIC -Wno-braced-scalar-init -Wno-delete-non-virtual-dtor -Wno-unused-command-line-argument -Wno-dangling-else") elseif (APPLE) set(CMAKE_CXX_FLAGS_RELEASE "-O3 -fPIC -Wno-braced-scalar-init -Wno-delete-non-virtual-dtor -Wno-unused-command-line-argument -Wno-dangling-else -D__APPLE_OS__=true -D_RELEASE=true") set(CMAKE_CXX_FLAGS_DEBUG " -O0 -g -fPIC -Wno-braced-scalar-init -Wno-delete-non-virtual-dtor -Wno-unused-command-line-argument -Wno-dangling-else -D__APPLE_OS__=true") elseif(WIN32) - set(X86_BUILD true) - if (CUDA_BLAS) + set(SD_X86_BUILD true) + if (SD_CUDA) set(CMAKE_CXX_FLAGS_RELEASE "-D_RELEASE=true") set(CMAKE_CXX_FLAGS_DEBUG " /FS /EHsc") else() @@ -50,14 +56,14 @@ else() set(CMAKE_CXX_FLAGS_RELEASE "-O3 -fPIC -fmax-errors=2 -D_RELEASE=true") set(CMAKE_CXX_FLAGS_DEBUG " -g -O0 -fPIC -fmax-errors=2") - if (CPU_BLAS) + if (SD_CPU) set(CMAKE_CXX_FLAGS_DEBUG "${CMAKE_CXX_FLAGS_DEBUG} -fsanitize=address") endif() endif() -if(NATIVE) +if(SD_NATIVE) IF(${CMAKE_SYSTEM_PROCESSOR} MATCHES "ppc64*") - set(X86_BUILD false) + set(SD_X86_BUILD false) set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -mcpu=native") ELSE() set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -march=native") @@ -65,14 +71,13 @@ if(NATIVE) endif() -if(NOT CUDA_BLAS) +if(NOT SD_CUDA) # we need this definition to avoid global memory use within mkldnn add_definitions(-DDNNL_ENABLE_CONCURRENT_EXEC=true) # there's a chance, we have no BLAS provided externally if ("${OPENBLAS_PATH}" STREQUAL "") - #we don't want static OpenBLAS on Apple - set(BLA_STATIC ON) + #we don't want OpenBLAS on Apple if (NOT APPLE) set(BLA_VENDOR "OpenBLAS") endif() @@ -80,23 +85,8 @@ if(NOT CUDA_BLAS) # look around for system blas instead find_package(BLAS REQUIRED) if (BLAS_FOUND) - message("Original library: ${BLAS_LIBRARIES}") - # workaround for for cmake being unable to find static blas library - SET(_TMP_B "") - if (APPLE) - string(REGEX REPLACE "\\.dylib$" ".lib" _TMP_B "${BLAS_LIBRARIES}") - elseif (WIN32) - string(REGEX REPLACE "\\.dll" ".lib" _TMP_B "${BLAS_LIBRARIES}") - else() - string(REGEX REPLACE "\\.so$" ".a" _TMP_B "${BLAS_LIBRARIES}") - endif() - set(BLAS_LIBRARIES "${_TMP_B}") - message("Found external BLAS implementation: ${BLAS_LIBRARIES} ") add_definitions(-D__EXTERNAL_BLAS__=true) - elseif(WIN32) - message("BLAS not found, using downloaded OpenBLAS instead") - add_definitions(-D__EXTERNAL_BLAS__=true) endif() else() # if we have externally provided OPENBLAS_PATH - let's use it @@ -107,7 +97,7 @@ if(NOT CUDA_BLAS) endif() # building cpu_features - if (X86_BUILD) + if (SD_X86_BUILD) add_definitions(-DCPU_FEATURES=true) set(BUILD_PIC "ON" CACHE STRING "Hack to enforce fPIC mode" FORCE) configure_file(./CMakeLists.txt.cpu_features.in cpu_features-download/CMakeLists.txt) @@ -169,7 +159,7 @@ endif() if (${HELPERS_cudnn}) - if (NOT CUDA_BLAS) + if (NOT SD_CUDA) message(FATAL_ERROR "Can't build cuDNN on non-CUDA platform") endif() @@ -231,12 +221,12 @@ include_directories(${CMAKE_CURRENT_BINARY_DIR}/include) if (NOT DEFINED ENV{CLION_IDE}) message("NOT CLION") - include_directories(blas/ include/ include/helpers include/loops include/graph include/execution include/ops include/types include/array include/cnpy include/exceptions) + include_directories(${CMAKE_CURRENT_SOURCE_DIR}/include) add_subdirectory(blas) - if(BUILD_TESTS) + if(SD_BUILD_TESTS) # tests are always compiled with all ops included - set(LIBND4J_ALL_OPS true) - set(LIBND4J_BUILD_MINIFIER true) + set(SD_ALL_OPS true) + set(SD_BUILD_MINIFIER true) add_subdirectory(tests_cpu) endif() endif () @@ -246,7 +236,7 @@ if ($ENV{CLION_IDE}) endif () if (MSVC_DEV) - set(LIBND4J_BUILD_MINIFIER false) + set(SD_BUILD_MINIFIER false) endif () set (CMAKE_INSTALL_PREFIX $ENV{ND4J_HOME}/nd4j-native-parent/nd4j-native/src/main/resources) diff --git a/libnd4j/CMakeLists.txt.mkldnn.in b/libnd4j/CMakeLists.txt.mkldnn.in index e67b3554b..4e4a130e1 100644 --- a/libnd4j/CMakeLists.txt.mkldnn.in +++ b/libnd4j/CMakeLists.txt.mkldnn.in @@ -5,7 +5,7 @@ project(mkldnn-download NONE) include(ExternalProject) ExternalProject_Add(mkldnn GIT_REPOSITORY https://github.com/intel/mkl-dnn.git - GIT_TAG v1.2 + GIT_TAG v1.2.1 SOURCE_DIR "${CMAKE_CURRENT_BINARY_DIR}/mkldnn-src" BINARY_DIR "${CMAKE_CURRENT_BINARY_DIR}/mkldnn-build" CONFIGURE_COMMAND "" diff --git a/libnd4j/CMakeSettings.json b/libnd4j/CMakeSettings.json index 867132ab2..fe7790fa0 100644 --- a/libnd4j/CMakeSettings.json +++ b/libnd4j/CMakeSettings.json @@ -9,7 +9,7 @@ ], "buildRoot": "${env.USERPROFILE}\\CMakeBuilds\\${workspaceHash}\\build\\${name}", "installRoot": "${env.USERPROFILE}\\CMakeBuilds\\${workspaceHash}\\install\\${name}", - "cmakeCommandArgs": " -DCUDA_BLAS=true -DLIBND4J_NAME=nd4jcuda -DMSVC_DEV=true -DCOMPUTE=61 -DBUILD_TESTS=true", + "cmakeCommandArgs": " -DSD_CUDA=true -DLIBND4J_NAME=nd4jcuda -DMSVC_DEV=true -DCOMPUTE=61 -DBUILD_TESTS=true", "buildCommandArgs": "-v", "ctestCommandArgs": "" }, @@ -20,7 +20,7 @@ "buildRoot": "${projectDir}\\out\\build\\${name}", "installRoot": "${projectDir}\\out\\install\\${name}", "cmakeExecutable": "/usr/bin/cmake", - "cmakeCommandArgs": "-DLIBND4J_ALL_OPS=true -DCMAKE_BUILD_TYPE=Debug -DCPU_BLAS=true -DLIBND4J_NAME=nd4jcpu -DBUILD_TESTS=ON -DCMAKE_BUILD_TYPE=Debug -DOPENBLAS_PATH=/usr/lib/openblas-base/ -DEXTENSION=avx2 ", + "cmakeCommandArgs": "-DSD_ALL_OPS=true -DCMAKE_BUILD_TYPE=Debug -DSD_CPU=true -DLIBND4J_NAME=nd4jcpu -DBUILD_TESTS=ON -DCMAKE_BUILD_TYPE=Debug -DOPENBLAS_PATH=/usr/lib/openblas-base/ -DEXTENSION=avx2 ", "buildCommandArgs": "-j 4", "ctestCommandArgs": "", "inheritEnvironments": [ "linux_x64" ], diff --git a/libnd4j/blas/CMakeLists.txt b/libnd4j/blas/CMakeLists.txt index 51a29e522..2dccc680f 100755 --- a/libnd4j/blas/CMakeLists.txt +++ b/libnd4j/blas/CMakeLists.txt @@ -29,19 +29,24 @@ if(APPLE) link_directories(/lib) endif() -if (APPLE_BUILD) - set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -DAPPLE_BUILD=true -mmacosx-version-min=10.10") - set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -DAPPLE_BUILD=true -mmacosx-version-min=10.10") +if (SD_APPLE_BUILD) + set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -DSD_APPLE_BUILD=true -mmacosx-version-min=10.10") + set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -DSD_APPLE_BUILD=true -mmacosx-version-min=10.10") endif() -if (ANDROID_BUILD) - set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -DANDROID_BUILD=true") - set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -DANDROID_BUILD=true") +if (SD_ARM_BUILD) + set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -DSD_ARM_BUILD=true") + set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -DSD_ARM_BUILD=true") endif() -if (IOS_BUILD) - set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -DIOS_BUILD=true") - set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -DIOS_BUILD=true") +if (SD_ANDROID_BUILD) + set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -DSD_ANDROID_BUILD=true") + set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -DSD_ANDROID_BUILD=true") +endif() + +if (SD_IOS_BUILD) + set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -DSD_IOS_BUILD=true") + set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -DSD_IOS_BUILD=true") endif() if(WIN32) @@ -63,33 +68,33 @@ if(WIN32) SET(CMAKE_NINJA_FORCE_RESPONSE_FILE 1 CACHE INTERNAL "") endif() -if ("${LIBND4J_ALL_OPS}") - set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -DLIBND4J_ALL_OPS=true") +if ("${SD_ALL_OPS}") + set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -DSD_ALL_OPS=true") else() - message("_OPS: ${LIBND4J_OPS_LIST}") - foreach(OP "${LIBND4J_OPS_LIST}") + message("_OPS: ${SD_OPS_LIST}") + foreach(OP "${SD_OPS_LIST}") message(STATUS "${OP}") endforeach() - set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${LIBND4J_OPS_LIST}") + set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${SD_OPS_LIST}") endif() -IF(${ARCH} MATCHES "arm*") - set(ARCH_TUNE "-march=${ARCH}") -ELSEIF(${ARCH} MATCHES "power*") - set(ARCH_TUNE "-mcpu=${ARCH} -mtune=${ARCH} -D__POWER") -ELSEIF(${EXTENSION} MATCHES "avx2") +IF(${SD_ARCH} MATCHES "arm*") + set(ARCH_TUNE "-march=${SD_ARCH}") +ELSEIF(${SD_ARCH} MATCHES "power*") + set(ARCH_TUNE "-mcpu=${SD_ARCH} -mtune=${SD_ARCH} -D__POWER") +ELSEIF(${SD_EXTENSION} MATCHES "avx2") message("Building AVX2 binary...") set(ARCH_TUNE "-mmmx -msse -msse2 -msse3 -msse4.1 -msse4.2 -mavx -mavx2 -mfma -mf16c -mprefetchwt1 -DSD_F16C=true -DF_AVX2=true") ELSE() - if ("${ARCH}" STREQUAL "x86-64") + if ("${SD_ARCH}" STREQUAL "x86-64") message("Building x86_64 binary...") set(ARCH_TYPE "generic") set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -DF_X64=true") else() - set(ARCH_TYPE "${ARCH}") + set(ARCH_TYPE "${SD_ARCH}") endif() - IF(${EXTENSION} MATCHES "avx512") + IF(${SD_EXTENSION} MATCHES "avx512") message("Building AVX512 binary...") # we need to set flag here, that we can use hardware f16 conversion + tell that cpu features should be tracked set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -mmmx -msse -msse2 -msse3 -msse4.1 -msse4.2 -mavx -mavx2 -mfma -mf16c -mavx512f -mavx512vl -mavx512bw -mavx512dq -mavx512cd -mbmi -mbmi2 -mprefetchwt1 -mclflushopt -mxsavec -mxsaves -DSD_F16C=true -DF_AVX512=true") @@ -97,11 +102,11 @@ ELSE() if (NOT WIN32) # we don't want this definition for msvc - set(ARCH_TUNE "-march=${ARCH} -mtune=${ARCH_TYPE}") + set(ARCH_TUNE "-march=${SD_ARCH} -mtune=${ARCH_TYPE}") endif() ENDIF() -if ("${CMAKE_CXX_COMPILER_ID}" STREQUAL "AppleClang" AND X86_BUILD) +if ("${CMAKE_CXX_COMPILER_ID}" STREQUAL "AppleClang" AND SD_X86_BUILD) # apple clang but not ios-arm SET( CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${ARCH_TUNE}") elseif ("${CMAKE_CXX_COMPILER_ID}" STREQUAL "Clang") @@ -124,10 +129,10 @@ IF(${CMAKE_SYSTEM_NAME} MATCHES "Linux") include_directories("/usr/include") include_directories("/usr/local/include") ENDIF(${CMAKE_SYSTEM_NAME} MATCHES "Linux") -if(!CUDA_BLAS) - if(!CPU_BLAS) - set(CUDA_BLAS FALSE) - set(CPU_BLAS TRUE) +if(!SD_CUDA) + if(!SD_CPU) + set(SD_CUDA FALSE) + set(SD_CPU TRUE) endif() endif() @@ -136,7 +141,7 @@ if (HAVE_MKLDNN) file(GLOB_RECURSE CUSTOMOPS_MKLDNN_SOURCES false ../include/ops/declarable/platform/mkldnn/*.cpp ../include/ops/declarable/platform/mkldnn/mkldnnUtils.h) endif() -if(CUDA_BLAS) +if(SD_CUDA) message("Build cublas") find_package(CUDA) add_definitions(-D__CUDABLAS__=true) @@ -149,7 +154,7 @@ if(CUDA_BLAS) include_directories(${CUDA_INCLUDE_DIRS}) message("CUDA found!") - if ("${EXPERIMENTAL}" STREQUAL "yes") + if ("${SD_EXPERIMENTAL}" STREQUAL "yes") message("Experimental mode ENABLED") set(CMAKE_CUDA_FLAGS " ${CMAKE_CUDA_FLAGS} -D__ND4J_EXPERIMENTAL__=true") set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -D__ND4J_EXPERIMENTAL__=true") @@ -213,6 +218,7 @@ if(CUDA_BLAS) file(GLOB_RECURSE HELPERS_SOURCES false ../include/helpers/impl/*.cpp ../include/helpers/*.cu ../include/helpers/*.cupp ../include/helpers/*.h) file(GLOB_RECURSE INDEXING_SOURCES false ../include/indexing/*.cpp ../include/indexing/*.h) file(GLOB_RECURSE LOOPS_SOURCES false ../include/loops/impl/*.cpp ../include/loops/*.h) + file(GLOB_RECURSE LEGACY_SOURCES false ../include/legacy/impl/*.cpp ../include/legacy/*.cu ../include/legacy/*.h) file(GLOB_RECURSE LOOPS_SOURCES_CUDA false ../include/loops/*.cu) if (HAVE_CUDNN) @@ -220,43 +226,41 @@ if(CUDA_BLAS) file(GLOB_RECURSE CUSTOMOPS_CUDNN_SOURCES false ../include/ops/declarable/platform/cudnn/*.cu) endif() - add_library(nd4jobj OBJECT cuda/NativeOps.cu cuda/NativeOpExecutioner.cu cuda/BlasVersionHelper.cu Environment.cpp ${LOOPS_SOURCES_CUDA} + add_library(nd4jobj OBJECT ${LOOPS_SOURCES_CUDA} ${LEGACY_SOURCES} ${CUSTOMOPS_HELPERS_SOURCES} ${HELPERS_SOURCES} ${EXEC_SOURCES} - ../include/cnpy/cnpy.cpp ../include/nd4jmemset.h ../include/nd4jmalloc.h - cpu/GraphExecutioner.cpp cuda/NDArray.cu cpu/NDArrayFactory.cpp - Environment.h ${LOOPS_SOURCES} ${ARRAY_SOURCES} ${TYPES_SOURCES} + ${LOOPS_SOURCES} ${ARRAY_SOURCES} ${TYPES_SOURCES} ${MEMORY_SOURCES} ${GRAPH_SOURCES} ${CUSTOMOPS_SOURCES} ${INDEXING_SOURCES} ${EXCEPTIONS_SOURCES} ${OPS_SOURCES} ${PERF_SOURCES} ${CUSTOMOPS_CUDNN_SOURCES} ${CUSTOMOPS_MKLDNN_SOURCES}) - add_library(${LIBND4J_NAME} SHARED $) + add_library(${SD_LIBRARY_NAME} SHARED $) if (WIN32) message("MSVC runtime for library: ${MSVC_RT_LIB}") endif() # static library is built only if we're going to build tests, skip otherwise - if (BUILD_TESTS) - add_library(${LIBND4J_NAME}static STATIC $) - set_property(TARGET ${LIBND4J_NAME}static PROPERTY MSVC_RUNTIME_LIBRARY "${MSVC_RT_LIB}$<$:Debug>") - install(TARGETS ${LIBND4J_NAME}static DESTINATION .) + if (SD_BUILD_TESTS OR SD_STATIC_LIB) + add_library(${SD_LIBRARY_NAME}static STATIC $) + set_property(TARGET ${SD_LIBRARY_NAME}static PROPERTY MSVC_RUNTIME_LIBRARY "${MSVC_RT_LIB}$<$:Debug>") + install(TARGETS ${SD_LIBRARY_NAME}static DESTINATION .) endif() # on windows we want to make sure we use MT or MD, but since we use it in one lib, we must use it everywhere to avoid conflicts set_property(TARGET nd4jobj PROPERTY MSVC_RUNTIME_LIBRARY "${MSVC_RT_LIB}$<$:Debug>") - set_property(TARGET ${LIBND4J_NAME} PROPERTY MSVC_RUNTIME_LIBRARY "${MSVC_RT_LIB}$<$:Debug>") + set_property(TARGET ${SD_LIBRARY_NAME} PROPERTY MSVC_RUNTIME_LIBRARY "${MSVC_RT_LIB}$<$:Debug>") if(WIN32) message("CUDA on Windows: enabling /EHsc") SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} /EHsc /bigobj /std:c++14") endif() - target_link_libraries(${LIBND4J_NAME} ${CUDA_LIBRARIES} ${CUDA_CUBLAS_LIBRARIES} ${CUDA_cusolver_LIBRARY} ${CUDNN} ${MKLDNN}) + target_link_libraries(${SD_LIBRARY_NAME} ${CUDA_LIBRARIES} ${CUDA_CUBLAS_LIBRARIES} ${CUDA_cusolver_LIBRARY} ${CUDNN} ${MKLDNN}) set(CMAKE_LIBRARY_OUTPUT_DIRECTORY ${PROJECT_BINARY_DIR}/cuda) - install(TARGETS ${LIBND4J_NAME} DESTINATION .) + install(TARGETS ${SD_LIBRARY_NAME} DESTINATION .) endif(CUDA_FOUND) -elseif(CPU_BLAS) +elseif(SD_CPU) - if ("${EXPERIMENTAL}" STREQUAL "yes") + if ("${SD_EXPERIMENTAL}" STREQUAL "yes") message("Experimental mode ENABLED") set (CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -D__ND4J_EXPERIMENTAL__=true") set (CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -D__ND4J_EXPERIMENTAL__=true") @@ -274,15 +278,16 @@ elseif(CPU_BLAS) file(GLOB_RECURSE OPS_SOURCES false ../include/ops/impl/*.cpp ../include/ops/declarable/impl/*.cpp ../include/ops/*.h) file(GLOB_RECURSE INDEXING_SOURCES false ../include/indexing/*.cpp ../include/indexing/*.h) file(GLOB_RECURSE HELPERS_SOURCES false ../include/helpers/*.cpp ../include/helpers/*.h) + file(GLOB_RECURSE LEGACY_SOURCES false ../include/legacy/impl/*.cpp ../include/legacy/cpu/*.cpp ../include/legacy/*.h) file(GLOB_RECURSE LOOPS_SOURCES false ../include/loops/*.cpp ../include/loops/*.h) - if (X86_BUILD) + if (SD_X86_BUILD) # we disable platform optimizations for certains files for linux/macos set_source_files_properties(cpu/NativeOps.cpp PROPERTIES COMPILE_FLAGS "-march=x86-64 -mtune=generic") set_source_files_properties(../include/helpers/impl/OpTracker.cpp PROPERTIES COMPILE_FLAGS "-march=x86-64 -mtune=generic") endif() - if(CHECK_VECTORIZATION) + if(SD_CHECK_VECTORIZATION) set(VECT_FILES cpu/NativeOps.cpp ${OPS_SOURCES} ${HELPERS_SOURCES} ${CUSTOMOPS_GENERIC_SOURCES} ${LOOPS_SOURCES}) if("${CMAKE_CXX_COMPILER_ID}" STREQUAL "GNU") @@ -310,33 +315,31 @@ elseif(CPU_BLAS) message("CPU BLAS") add_definitions(-D__CPUBLAS__=true) - add_library(nd4jobj OBJECT cpu/NativeOps.cpp cpu/GraphExecutioner.cpp - cpu/NativeOpExecutioner.cpp cpu/NDArray.cpp cpu/NDArrayFactory.cpp - ../include/cnpy/cnpy.cpp ../include/nd4jmemset.h ../include/nd4jmalloc.h - Environment.cpp Environment.h ${LOOPS_SOURCES} ${HELPERS_SOURCES} ${EXEC_SOURCES} ${ARRAY_SOURCES} ${TYPES_SOURCES} + add_library(nd4jobj OBJECT ${LEGACY_SOURCES} + ${LOOPS_SOURCES} ${HELPERS_SOURCES} ${EXEC_SOURCES} ${ARRAY_SOURCES} ${TYPES_SOURCES} ${MEMORY_SOURCES} ${GRAPH_SOURCES} ${CUSTOMOPS_SOURCES} ${EXCEPTIONS_SOURCES} ${INDEXING_SOURCES} ${CUSTOMOPS_MKLDNN_SOURCES} ${CUSTOMOPS_GENERIC_SOURCES} ${OPS_SOURCES} ${PERF_SOURCES}) if(IOS) - add_library(${LIBND4J_NAME} STATIC $) + add_library(${SD_LIBRARY_NAME} STATIC $) else() # static library is built only if we're going to build tests, skip otherwise - if (BUILD_TESTS) - add_library(${LIBND4J_NAME}static STATIC $) + if (SD_BUILD_TESTS OR SD_STATIC_LIB) + add_library(${SD_LIBRARY_NAME}static STATIC $) endif() - add_library(${LIBND4J_NAME} SHARED $) + add_library(${SD_LIBRARY_NAME} SHARED $) endif() # we're including {MKLDNN} here in case of building from sources. in future that'll replace {MKLDNN_LIBRARIES}. same applies to BLAS if (NOT BLAS_LIBRARIES) set(BLAS_LIBRARIES "") endif() - target_link_libraries(${LIBND4J_NAME} ${MKLDNN} ${MKLDNN_LIBRARIES} ${OPENBLAS_LIBRARIES} ${BLAS_LIBRARIES} ${CPU_FEATURES}) + target_link_libraries(${SD_LIBRARY_NAME} ${MKLDNN} ${MKLDNN_LIBRARIES} ${OPENBLAS_LIBRARIES} ${BLAS_LIBRARIES} ${CPU_FEATURES}) - if ("${LIBND4J_ALL_OPS}" AND "${LIBND4J_BUILD_MINIFIER}") + if ("${SD_ALL_OPS}" AND "${SD_BUILD_MINIFIER}") message(STATUS "Building minifier...") add_executable(minifier ../minifier/minifier.cpp ../minifier/graphopt.cpp) - target_link_libraries(minifier ${LIBND4J_NAME}static ${MKLDNN_LIBRARIES} ${OPENBLAS_LIBRARIES} ${MKLDNN} ${BLAS_LIBRARIES} ${CPU_FEATURES}) + target_link_libraries(minifier ${SD_LIBRARY_NAME}static ${MKLDNN_LIBRARIES} ${OPENBLAS_LIBRARIES} ${MKLDNN} ${BLAS_LIBRARIES} ${CPU_FEATURES}) endif() if ("${CMAKE_CXX_COMPILER_ID}" STREQUAL "GNU" AND "${CMAKE_CXX_COMPILER_VERSION}" VERSION_LESS 4.9) @@ -357,6 +360,6 @@ elseif(CPU_BLAS) SET(CMAKE_EXE_LINKER_FLAGS "${CMAKE_EXE_LINKER_FLAGS} -export-dynamic") endif() - install(TARGETS ${LIBND4J_NAME} DESTINATION .) + install(TARGETS ${SD_LIBRARY_NAME} DESTINATION .) set(CMAKE_LIBRARY_OUTPUT_DIRECTORY ${PROJECT_BINARY_DIR}/cpu) endif() diff --git a/libnd4j/blas/NDArrayFactory.h b/libnd4j/blas/NDArrayFactory.h deleted file mode 100644 index bff199d08..000000000 --- a/libnd4j/blas/NDArrayFactory.h +++ /dev/null @@ -1,191 +0,0 @@ -/******************************************************************************* - * Copyright (c) 2015-2018 Skymind, Inc. - * Copyright (c) 2019-2020 Konduit K.K. - * - * This program and the accompanying materials are made available under the - * terms of the Apache License, Version 2.0 which is available at - * https://www.apache.org/licenses/LICENSE-2.0. - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the - * License for the specific language governing permissions and limitations - * under the License. - * - * SPDX-License-Identifier: Apache-2.0 - ******************************************************************************/ - -// -// Created by raver119 on 2018-09-16. -// @author Oleg Semeniv -// - -#ifndef DEV_TESTS_NDARRAYFACTORY_H -#define DEV_TESTS_NDARRAYFACTORY_H - -#include -#include -#include -//#include -#include -#include - - -namespace nd4j { - class ND4J_EXPORT NDArrayFactory { - private: - template - static void memcpyFromVector(void *ptr, const std::vector &vector); - public: - template - static NDArray* empty_(nd4j::LaunchContext * context = nd4j::LaunchContext ::defaultContext()); - - static NDArray* empty_(nd4j::DataType dataType, nd4j::LaunchContext * context = nd4j::LaunchContext ::defaultContext()); - - template - static NDArray empty(nd4j::LaunchContext * context = nd4j::LaunchContext ::defaultContext()); - - static NDArray empty(nd4j::DataType dataType, nd4j::LaunchContext * context = nd4j::LaunchContext ::defaultContext()); - - template - static NDArray* valueOf(const std::initializer_list& shape, T value, char order = 'c', nd4j::LaunchContext * context = nd4j::LaunchContext ::defaultContext()); - - template - static NDArray* valueOf(const std::vector& shape, T value, char order = 'c', nd4j::LaunchContext * context = nd4j::LaunchContext ::defaultContext()); - - static NDArray* valueOf(const std::vector& shape, const NDArray& value, char order = 'c', nd4j::LaunchContext * context = nd4j::LaunchContext ::defaultContext()); - - template - static NDArray* linspace(T from, T to, Nd4jLong numElements); - - - template - static NDArray* create_(const T value, nd4j::LaunchContext * context = nd4j::LaunchContext ::defaultContext()); - static NDArray* create_(nd4j::DataType dtype, nd4j::LaunchContext * context = nd4j::LaunchContext ::defaultContext()); - - template - static NDArray create(const T value, nd4j::LaunchContext * context = nd4j::LaunchContext ::defaultContext()); - static NDArray create(nd4j::DataType dtype, nd4j::LaunchContext * context = nd4j::LaunchContext ::defaultContext()); - template - static NDArray create(DataType type, const T scalar, nd4j::LaunchContext * context = nd4j::LaunchContext ::defaultContext()); - - - template - static NDArray* vector(Nd4jLong length, T startingValue = (T) 0, nd4j::LaunchContext * context = nd4j::LaunchContext ::defaultContext()); - - template - static NDArray* create_(char order, const std::vector &shape, nd4j::LaunchContext * context = nd4j::LaunchContext ::defaultContext()); - - static NDArray* create_( char order, const std::vector &shape, nd4j::DataType dataType, nd4j::LaunchContext * context = nd4j::LaunchContext ::defaultContext()); - - template - static NDArray* create_(char order, const std::vector &shape, const std::vector &data, nd4j::LaunchContext * context = nd4j::LaunchContext ::defaultContext()); - - template - static NDArray create(char order, const std::vector &shape, const std::vector &data, nd4j::LaunchContext * context = nd4j::LaunchContext ::defaultContext()); - - template - static NDArray create(char order, const std::vector &shape, nd4j::LaunchContext * context = nd4j::LaunchContext ::defaultContext()); - static NDArray create(char order, const std::vector &shape, nd4j::DataType dtype, nd4j::LaunchContext * context = nd4j::LaunchContext ::defaultContext()); - - template - static NDArray create(const std::vector &values, nd4j::LaunchContext * context = nd4j::LaunchContext ::defaultContext()); - -#ifndef __JAVACPP_HACK__ - // this method only available out of javacpp - /** - * This constructor creates vector of T - * - * @param values - */ - - template - static NDArray create(char order, const std::initializer_list& shape, nd4j::LaunchContext * context = nd4j::LaunchContext ::defaultContext()); - - template - static NDArray create(T* buffer, char order, const std::initializer_list& shape, nd4j::LaunchContext * context = nd4j::LaunchContext ::defaultContext()); - - template - static NDArray create(char order, const std::vector &shape, const std::initializer_list& data, nd4j::LaunchContext * context = nd4j::LaunchContext ::defaultContext()); - - /** - * This method creates NDArray from .npy file - * @param fileName - * @return - */ - static NDArray fromNpyFile(const char *fileName); - - /** - * This factory create array from utf8 string - * @return NDArray default dataType UTF8 - */ - static NDArray string(const char *string, nd4j::DataType dtype = nd4j::DataType::UTF8, nd4j::LaunchContext * context = nd4j::LaunchContext ::defaultContext()); - static NDArray* string_(const char *string, nd4j::DataType dtype = nd4j::DataType::UTF8, nd4j::LaunchContext * context = nd4j::LaunchContext ::defaultContext()); - static NDArray* string_(const std::string &string, nd4j::DataType dtype = nd4j::DataType::UTF8, nd4j::LaunchContext * context = nd4j::LaunchContext ::defaultContext()); - static NDArray string(const std::string& string, nd4j::DataType dtype = nd4j::DataType::UTF8, nd4j::LaunchContext* context = nd4j::LaunchContext::defaultContext()); - - /** - * This factory create array from utf16 string - * @return NDArray default dataType UTF16 - */ - static NDArray string(const char16_t* u16string, nd4j::DataType dtype = nd4j::DataType::UTF16, nd4j::LaunchContext* context = nd4j::LaunchContext::defaultContext()); - static NDArray* string_(const char16_t* u16string, nd4j::DataType dtype = nd4j::DataType::UTF16, nd4j::LaunchContext* context = nd4j::LaunchContext::defaultContext()); - static NDArray* string_(const std::u16string& u16string, nd4j::DataType dtype = nd4j::DataType::UTF16, nd4j::LaunchContext* context = nd4j::LaunchContext::defaultContext()); - static NDArray string(const std::u16string& u16string, nd4j::DataType dtype = nd4j::DataType::UTF16, nd4j::LaunchContext* context = nd4j::LaunchContext::defaultContext()); - - /** - * This factory create array from utf32 string - * @return NDArray default dataType UTF32 - */ - static NDArray string(const char32_t* u32string, nd4j::DataType dtype = nd4j::DataType::UTF32, nd4j::LaunchContext* context = nd4j::LaunchContext::defaultContext()); - static NDArray* string_(const char32_t* u32string, nd4j::DataType dtype = nd4j::DataType::UTF32, nd4j::LaunchContext* context = nd4j::LaunchContext::defaultContext()); - static NDArray* string_(const std::u32string& u32string, nd4j::DataType dtype = nd4j::DataType::UTF32, nd4j::LaunchContext* context = nd4j::LaunchContext::defaultContext()); - static NDArray string(const std::u32string& u32string, nd4j::DataType dtype = nd4j::DataType::UTF32, nd4j::LaunchContext* context = nd4j::LaunchContext::defaultContext()); - - /** - * This factory create array from vector of utf8 strings - * @return NDArray default dataType UTF8 - */ - static NDArray string( const std::vector &shape, const std::initializer_list &strings, nd4j::DataType dtype = nd4j::DataType::UTF8, nd4j::LaunchContext * context = nd4j::LaunchContext ::defaultContext()); - static NDArray string( const std::vector &shape, const std::initializer_list &string, nd4j::DataType dtype = nd4j::DataType::UTF8, nd4j::LaunchContext * context = nd4j::LaunchContext ::defaultContext()); - static NDArray string( const std::vector &shape, const std::vector &strings, nd4j::DataType dtype = nd4j::DataType::UTF8, nd4j::LaunchContext * context = nd4j::LaunchContext ::defaultContext()); - static NDArray string( const std::vector &shape, const std::vector &string, nd4j::DataType dtype = nd4j::DataType::UTF8, nd4j::LaunchContext * context = nd4j::LaunchContext ::defaultContext()); - static NDArray* string_( const std::vector &shape, const std::initializer_list &strings, nd4j::DataType dtype = nd4j::DataType::UTF8, nd4j::LaunchContext * context = nd4j::LaunchContext ::defaultContext()); - static NDArray* string_( const std::vector &shape, const std::initializer_list &string, nd4j::DataType dtype = nd4j::DataType::UTF8, nd4j::LaunchContext * context = nd4j::LaunchContext ::defaultContext()); - static NDArray* string_( const std::vector &shape, const std::vector &strings, nd4j::DataType dtype = nd4j::DataType::UTF8, nd4j::LaunchContext * context = nd4j::LaunchContext ::defaultContext()); - static NDArray* string_( const std::vector &shape, const std::vector &string, nd4j::DataType dtype = nd4j::DataType::UTF8, nd4j::LaunchContext * context = nd4j::LaunchContext ::defaultContext()); - - /** - * This factory create array from vector of utf16 strings - * @return NDArray default dataType UTF16 - */ - static NDArray string( const std::vector& shape, const std::initializer_list& strings, nd4j::DataType dtype = nd4j::DataType::UTF16, nd4j::LaunchContext* context = nd4j::LaunchContext::defaultContext()); - static NDArray string( const std::vector& shape, const std::initializer_list& string, nd4j::DataType dtype = nd4j::DataType::UTF16, nd4j::LaunchContext* context = nd4j::LaunchContext::defaultContext()); - static NDArray string( const std::vector& shape, const std::vector& strings, nd4j::DataType dtype = nd4j::DataType::UTF16, nd4j::LaunchContext* context = nd4j::LaunchContext::defaultContext()); - static NDArray string( const std::vector& shape, const std::vector& string, nd4j::DataType dtype = nd4j::DataType::UTF16, nd4j::LaunchContext* context = nd4j::LaunchContext::defaultContext()); - static NDArray* string_( const std::vector& shape, const std::initializer_list& strings, nd4j::DataType dtype = nd4j::DataType::UTF16, nd4j::LaunchContext* context = nd4j::LaunchContext::defaultContext()); - static NDArray* string_( const std::vector& shape, const std::initializer_list& string, nd4j::DataType dtype = nd4j::DataType::UTF16, nd4j::LaunchContext* context = nd4j::LaunchContext::defaultContext()); - static NDArray* string_( const std::vector& shape, const std::vector& strings, nd4j::DataType dtype = nd4j::DataType::UTF16, nd4j::LaunchContext* context = nd4j::LaunchContext::defaultContext()); - static NDArray* string_( const std::vector& shape, const std::vector& string, nd4j::DataType dtype = nd4j::DataType::UTF16, nd4j::LaunchContext* context = nd4j::LaunchContext::defaultContext()); - - /** - * This factory create array from vector of utf32 strings - * @return NDArray default dataType UTF32 - */ - static NDArray string( const std::vector& shape, const std::initializer_list& strings, nd4j::DataType dtype = nd4j::DataType::UTF32, nd4j::LaunchContext* context = nd4j::LaunchContext::defaultContext()); - static NDArray string( const std::vector& shape, const std::initializer_list& string, nd4j::DataType dtype = nd4j::DataType::UTF32, nd4j::LaunchContext* context = nd4j::LaunchContext::defaultContext()); - static NDArray string( const std::vector& shape, const std::vector& strings, nd4j::DataType dtype = nd4j::DataType::UTF32, nd4j::LaunchContext* context = nd4j::LaunchContext::defaultContext()); - static NDArray string( const std::vector& shape, const std::vector& string, nd4j::DataType dtype = nd4j::DataType::UTF32, nd4j::LaunchContext* context = nd4j::LaunchContext::defaultContext()); - static NDArray* string_( const std::vector& shape, const std::initializer_list& strings, nd4j::DataType dtype = nd4j::DataType::UTF32, nd4j::LaunchContext* context = nd4j::LaunchContext::defaultContext()); - static NDArray* string_( const std::vector& shape, const std::initializer_list& string, nd4j::DataType dtype = nd4j::DataType::UTF32, nd4j::LaunchContext* context = nd4j::LaunchContext::defaultContext()); - static NDArray* string_( const std::vector& shape, const std::vector& strings, nd4j::DataType dtype = nd4j::DataType::UTF32, nd4j::LaunchContext* context = nd4j::LaunchContext::defaultContext()); - static NDArray* string_( const std::vector& shape, const std::vector& string, nd4j::DataType dtype = nd4j::DataType::UTF32, nd4j::LaunchContext* context = nd4j::LaunchContext::defaultContext()); - - - static ResultSet createSetOfArrs(const Nd4jLong numOfArrs, const void* buffer, const Nd4jLong* shapeInfo, const Nd4jLong* offsets, nd4j::LaunchContext * context = nd4j::LaunchContext ::defaultContext()); - -#endif - }; -} - -#endif //DEV_TESTS_NDARRAYFACTORY_H diff --git a/libnd4j/blas/cpu/NDArray.macro b/libnd4j/blas/cpu/NDArray.macro deleted file mode 100644 index ae6db6962..000000000 --- a/libnd4j/blas/cpu/NDArray.macro +++ /dev/null @@ -1,148 +0,0 @@ -################################################################################ -# Copyright (c) 2015-2018 Skymind, Inc. -# -# This program and the accompanying materials are made available under the -# terms of the Apache License, Version 2.0 which is available at -# https://www.apache.org/licenses/LICENSE-2.0. -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT -# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the -# License for the specific language governing permissions and limitations -# under the License. -# -# SPDX-License-Identifier: Apache-2.0 -################################################################################ - -#ifndef NDARRAY_MACRO -#define NDARRAY_MACRO - -#include - -//NDArray *other, T *extraParams -BUILD_CALL_1(template void NDArray::template applyPairwiseTransform, float, (NDArray* other, float* extraParams), PAIRWISE_TRANSFORM_OPS) -BUILD_CALL_1(template void NDArray::applyPairwiseTransform, float16, (NDArray* other, float16* extraParams), PAIRWISE_TRANSFORM_OPS) -BUILD_CALL_1(template void NDArray::applyPairwiseTransform, double, (NDArray* other, double* extraParams), PAIRWISE_TRANSFORM_OPS) - -// NDArray *other, NDArray *target, T *extraParams -BUILD_CALL_1(template void nd4j::NDArray::applyPairwiseTransform, float, (NDArray* other, NDArray* target, float* extraParams), PAIRWISE_TRANSFORM_OPS) -BUILD_CALL_1(template void nd4j::NDArray::applyPairwiseTransform, float16, (NDArray* other, NDArray* target, float16* extraParams), PAIRWISE_TRANSFORM_OPS) -BUILD_CALL_1(template void nd4j::NDArray::applyPairwiseTransform, double, (NDArray* other, NDArray* target, double* extraParams), PAIRWISE_TRANSFORM_OPS) - -BUILD_CALL_1(template void nd4j::NDArray::applyScalar, float16, (NDArray& scalar, NDArray* target, float16 *extraParams) const, SCALAR_OPS) -BUILD_CALL_1(template void nd4j::NDArray::applyScalar, float16, (float16 scalar, NDArray* target, float16 *extraParams) const, SCALAR_OPS) - -BUILD_CALL_1(template void nd4j::NDArray::applyScalar, float, (NDArray& scalar, NDArray* target, float *extraParams) const, SCALAR_OPS) -BUILD_CALL_1(template void nd4j::NDArray::applyScalar, float, (float scalar, NDArray* target, float *extraParams) const, SCALAR_OPS) - -BUILD_CALL_1(template void nd4j::NDArray::applyScalar, double, (NDArray& scalar, NDArray* target, double *extraParams) const, SCALAR_OPS) -BUILD_CALL_1(template void nd4j::NDArray::applyScalar, double, (double scalar, NDArray* target, double *extraParams) const, SCALAR_OPS) - - - -BUILD_CALL_1(template float16 nd4j::NDArray::reduceNumber, float16, (float16 *extraParams) const, REDUCE_OPS) -BUILD_CALL_1(template float nd4j::NDArray::reduceNumber, float, (float *extraParams) const, REDUCE_OPS) -BUILD_CALL_1(template double nd4j::NDArray::reduceNumber, double, (double *extraParams) const, REDUCE_OPS) - -BUILD_CALL_1(template Nd4jLong nd4j::NDArray::indexReduceNumber, float16, (float16 *extraParams), INDEX_REDUCE_OPS) -BUILD_CALL_1(template Nd4jLong nd4j::NDArray::indexReduceNumber, float, (float *extraParams), INDEX_REDUCE_OPS) -BUILD_CALL_1(template Nd4jLong nd4j::NDArray::indexReduceNumber, double, (double *extraParams), INDEX_REDUCE_OPS) - -BUILD_CALL_1(template void nd4j::NDArray::applyBroadcast, float16, (std::initializer_list list, const nd4j::NDArray* a, nd4j::NDArray* b, float16* c), BROADCAST_OPS) -BUILD_CALL_1(template void nd4j::NDArray::applyBroadcast, float, (std::initializer_list list, const nd4j::NDArray* a, nd4j::NDArray* b, float* c), BROADCAST_OPS) -BUILD_CALL_1(template void nd4j::NDArray::applyBroadcast, double, (std::initializer_list list, const nd4j::NDArray* a, nd4j::NDArray* b, double* c), BROADCAST_OPS) - -BUILD_CALL_1(template void nd4j::NDArray::applyTrueBroadcast, float16,(const nd4j::NDArray* a, nd4j::NDArray* target, const bool checkTargetShape, float16* c) const, BROADCAST_OPS) -BUILD_CALL_1(template void nd4j::NDArray::applyTrueBroadcast, float, (const nd4j::NDArray* a, nd4j::NDArray* target, const bool checkTargetShape, float* c) const, BROADCAST_OPS) -BUILD_CALL_1(template void nd4j::NDArray::applyTrueBroadcast, double, (const nd4j::NDArray* a, nd4j::NDArray* target, const bool checkTargetShape, double* c) const, BROADCAST_OPS) - -BUILD_CALL_1(template nd4j::NDArray* nd4j::NDArray::applyTrueBroadcast, float16, (const nd4j::NDArray* a, float16* c) const, BROADCAST_OPS) -BUILD_CALL_1(template nd4j::NDArray* nd4j::NDArray::applyTrueBroadcast, float, (const nd4j::NDArray* a, float* c) const, BROADCAST_OPS) -BUILD_CALL_1(template nd4j::NDArray* nd4j::NDArray::applyTrueBroadcast, double, (const nd4j::NDArray* a, double* c) const, BROADCAST_OPS) - -BUILD_CALL_1(template nd4j::NDArray nd4j::NDArray::applyTrueBroadcast, float16, (const nd4j::NDArray& a, float16* c) const, BROADCAST_OPS) -BUILD_CALL_1(template nd4j::NDArray nd4j::NDArray::applyTrueBroadcast, float, (const nd4j::NDArray& a, float* c) const, BROADCAST_OPS) -BUILD_CALL_1(template nd4j::NDArray nd4j::NDArray::applyTrueBroadcast, double, (const nd4j::NDArray& a, double* c) const, BROADCAST_OPS) - -BUILD_CALL_1(template void nd4j::NDArray::applyTransform, float16, (NDArray* target, float16* extraParams), TRANSFORM_OPS) -BUILD_CALL_1(template void nd4j::NDArray::applyTransform, float, (NDArray* target, float* extraParams), TRANSFORM_OPS) -BUILD_CALL_1(template void nd4j::NDArray::applyTransform, double, (NDArray* target, double* extraParams), TRANSFORM_OPS) - -BUILD_CALL_1(template void nd4j::NDArray::applyTransform, float16, (float16* extraParams), TRANSFORM_OPS) -BUILD_CALL_1(template void nd4j::NDArray::applyTransform, float, (float* extraParams), TRANSFORM_OPS) -BUILD_CALL_1(template void nd4j::NDArray::applyTransform, double, (double* extraParams), TRANSFORM_OPS) - -BUILD_CALL_1(template void nd4j::NDArray::applyRandom, float16, (nd4j::random::RandomBuffer *buffer, NDArray* y, NDArray* z, float16* extraParams), RANDOM_OPS) -BUILD_CALL_1(template void nd4j::NDArray::applyRandom, float, (nd4j::random::RandomBuffer *buffer, NDArray* y, NDArray* z, float* extraParams), RANDOM_OPS) -BUILD_CALL_1(template void nd4j::NDArray::applyRandom, double, (nd4j::random::RandomBuffer *buffer, NDArray* y, NDArray* z, double* extraParams), RANDOM_OPS) - -BUILD_CALL_1(template NDArray nd4j::NDArray::transform, float16, (float16* extraParams) const, TRANSFORM_OPS) -BUILD_CALL_1(template NDArray nd4j::NDArray::transform, float, (float* extraParams) const, TRANSFORM_OPS) -BUILD_CALL_1(template NDArray nd4j::NDArray::transform, double, (double* extraParams) const, TRANSFORM_OPS) - -BUILD_CALL_1(template NDArray *nd4j::NDArray::template reduceAlongDimension, float, (const std::vector& dimensions, const bool keepDims, const bool supportOldShapes) const, REDUCE_OPS) -BUILD_CALL_1(template NDArray *nd4j::NDArray::template reduceAlongDimension, float16, (const std::vector& dimensions, const bool keepDims, const bool supportOldShapes) const, REDUCE_OPS) -BUILD_CALL_1(template NDArray *nd4j::NDArray::template reduceAlongDimension, double, (const std::vector& dimensions, const bool keepDims, const bool supportOldShapes) const, REDUCE_OPS) - -BUILD_CALL_1(template NDArray nd4j::NDArray::template reduceAlongDims, float, (const std::vector& dimensions, const bool keepDims, const bool supportOldShapes) const, REDUCE_OPS) -BUILD_CALL_1(template NDArray nd4j::NDArray::template reduceAlongDims, float16, (const std::vector& dimensions, const bool keepDims, const bool supportOldShapes) const, REDUCE_OPS) -BUILD_CALL_1(template NDArray nd4j::NDArray::template reduceAlongDims, double, (const std::vector& dimensions, const bool keepDims, const bool supportOldShapes) const, REDUCE_OPS) - -BUILD_CALL_1(template NDArray *nd4j::NDArray::template reduceAlongDimension, float, (const std::initializer_list& dimensions, const bool keepDims, const bool supportOldShapes) const, REDUCE_OPS) -BUILD_CALL_1(template NDArray *nd4j::NDArray::template reduceAlongDimension, float16, (const std::initializer_list& dimensions, const bool keepDims, const bool supportOldShapes) const, REDUCE_OPS) -BUILD_CALL_1(template NDArray *nd4j::NDArray::template reduceAlongDimension, double, (const std::initializer_list& dimensions, const bool keepDims, const bool supportOldShapes) const, REDUCE_OPS) - -BUILD_CALL_1(template void nd4j::NDArray::template reduceAlongDimension, float, (NDArray* target, const std::vector& dimensions, const bool keepDims, const bool supportOldShapes, float * extras) const, REDUCE_OPS) -BUILD_CALL_1(template void nd4j::NDArray::template reduceAlongDimension, float16, (NDArray* target, const std::vector& dimensions, const bool keepDims, const bool supportOldShapes, float16 * extras) const, REDUCE_OPS) -BUILD_CALL_1(template void nd4j::NDArray::template reduceAlongDimension, double, (NDArray* target, const std::vector& dimension, const bool keepDims, const bool supportOldShapes, double * extras) const, REDUCE_OPS) - -BUILD_CALL_1(template NDArray *nd4j::NDArray::template varianceAlongDimension, float, (const bool biasCorrected, const std::initializer_list& dimensions) const, SUMMARY_STATS_OPS) -BUILD_CALL_1(template NDArray *nd4j::NDArray::template varianceAlongDimension, float16, (const bool biasCorrected, const std::initializer_list& dimensions) const, SUMMARY_STATS_OPS) -BUILD_CALL_1(template NDArray *nd4j::NDArray::template varianceAlongDimension, double, (const bool biasCorrected, const std::initializer_list& dimensions) const, SUMMARY_STATS_OPS) - -BUILD_CALL_1(template void nd4j::NDArray::template varianceAlongDimension, float, (const NDArray *target, const bool biasCorrected, const std::initializer_list& dimensions), SUMMARY_STATS_OPS) -BUILD_CALL_1(template void nd4j::NDArray::template varianceAlongDimension, float16, (const NDArray *target,const bool biasCorrected, const std::initializer_list& dimensions), SUMMARY_STATS_OPS) -BUILD_CALL_1(template void nd4j::NDArray::template varianceAlongDimension, double, (const NDArray *target, const bool biasCorrected, const std::initializer_list& dimensions), SUMMARY_STATS_OPS) - -BUILD_CALL_1(template void nd4j::NDArray::template varianceAlongDimension, float, (const NDArray *target, const bool biasCorrected, const std::vector& dimensions), SUMMARY_STATS_OPS) -BUILD_CALL_1(template void nd4j::NDArray::template varianceAlongDimension, float16, (const NDArray *target,const bool biasCorrected, const std::vector& dimensions), SUMMARY_STATS_OPS) -BUILD_CALL_1(template void nd4j::NDArray::template varianceAlongDimension, double, (const NDArray *target, const bool biasCorrected, const std::vector& dimensions), SUMMARY_STATS_OPS) - -BUILD_CALL_1(template float nd4j::NDArray::template varianceNumber, float, (bool biasCorrected), SUMMARY_STATS_OPS) -BUILD_CALL_1(template float16 nd4j::NDArray::template varianceNumber, float16, (bool biasCorrected), SUMMARY_STATS_OPS) -BUILD_CALL_1(template double nd4j::NDArray::template varianceNumber, double, (bool biasCorrected), SUMMARY_STATS_OPS) - -BUILD_CALL_1(template NDArray *nd4j::NDArray::template applyReduce3, float, (const NDArray* other, const float* extraParams) const, REDUCE3_OPS) -BUILD_CALL_1(template NDArray *nd4j::NDArray::template applyReduce3, float16, (const NDArray* other, const float16* extraParams) const, REDUCE3_OPS) -BUILD_CALL_1(template NDArray *nd4j::NDArray::template applyReduce3, double, (const NDArray* other, const double* extraParams) const, REDUCE3_OPS) - -BUILD_CALL_1(template NDArray *nd4j::NDArray::template applyReduce3, float, (const NDArray* other, const std::vector &dims, const float* extraParams) const, REDUCE3_OPS) -BUILD_CALL_1(template NDArray *nd4j::NDArray::template applyReduce3, float16, (const NDArray* other, const std::vector &dims, const float16* extraParams) const, REDUCE3_OPS) -BUILD_CALL_1(template NDArray *nd4j::NDArray::template applyReduce3, double, (const NDArray* other, const std::vector &dims, const double* extraParams) const, REDUCE3_OPS) - -BUILD_CALL_1(template void nd4j::NDArray::template applyIndexReduce, float, (const NDArray* target, const std::vector & alpha, const float* beta) const, INDEX_REDUCE_OPS) -BUILD_CALL_1(template void nd4j::NDArray::template applyIndexReduce, float16, (const NDArray* target, const std::vector & alpha, const float16* beta) const, INDEX_REDUCE_OPS) -BUILD_CALL_1(template void nd4j::NDArray::template applyIndexReduce, double, (const NDArray* target, const std::vector & alpha, const double* beta) const, INDEX_REDUCE_OPS) - -BUILD_CALL_1(template NDArray *nd4j::NDArray::template applyIndexReduce, float, (const std::vector & alpha, const float* beta) const, INDEX_REDUCE_OPS) -BUILD_CALL_1(template NDArray *nd4j::NDArray::template applyIndexReduce, float16, (const std::vector & alpha, const float16* beta) const, INDEX_REDUCE_OPS) -BUILD_CALL_1(template NDArray *nd4j::NDArray::template applyIndexReduce, double, (const std::vector & alpha, const double* beta) const, INDEX_REDUCE_OPS) - -BUILD_CALL_1(template NDArray *nd4j::NDArray::template applyAllReduce3, float, (const nd4j::NDArray* alpha, const std::vector & beta, float const* gamma) const, REDUCE3_OPS) -BUILD_CALL_1(template NDArray *nd4j::NDArray::template applyAllReduce3, float16, (const nd4j::NDArray* alpha, const std::vector & beta, float16 const* gamma) const, REDUCE3_OPS) -BUILD_CALL_1(template NDArray *nd4j::NDArray::template applyAllReduce3, double, (const nd4j::NDArray* alpha, const std::vector & beta, double const* gamma) const, REDUCE3_OPS) - -template NDArray mmul(const NDArray& left, const NDArray& right); -template NDArray mmul(const NDArray& left, const NDArray& right); -template NDArray mmul(const NDArray& left, const NDArray& right); - -// template NDArray operator-(const float, const NDArray&); -// template NDArray operator-(const float16, const NDArray&); -// template NDArray operator-(const double, const NDArray&); - -// template NDArray operator+(const float, const NDArray&); -// template NDArray operator+(const float16, const NDArray&); -// template NDArray operator+(const double, const NDArray&); - - -#endif \ No newline at end of file diff --git a/libnd4j/buildnativeoperations.sh b/libnd4j/buildnativeoperations.sh index c07756d8c..380238554 100755 --- a/libnd4j/buildnativeoperations.sh +++ b/libnd4j/buildnativeoperations.sh @@ -173,7 +173,7 @@ fi case "$OS" in linux-armhf) export RPI_BIN=$RPI_HOME/tools/arm-bcm2708/arm-rpi-4.9.3-linux-gnueabihf/bin/arm-linux-gnueabihf - export CMAKE_COMMAND="$CMAKE_COMMAND -D CMAKE_TOOLCHAIN_FILE=cmake/rpi.cmake" + export CMAKE_COMMAND="$CMAKE_COMMAND -D CMAKE_TOOLCHAIN_FILE=cmake/rpi.cmake -DSD_ARM_BUILD=true" if [ -z "$ARCH" ]; then ARCH="armv7-r" fi @@ -183,6 +183,7 @@ case "$OS" in if [ -z "$ARCH" ]; then ARCH="armv8-a" fi + export CMAKE_COMMAND="$CMAKE_COMMAND -DSD_ARM_BUILD=true" ;; android-arm) @@ -193,7 +194,7 @@ case "$OS" in export ANDROID_CPP="$ANDROID_NDK/sources/cxx-stl/llvm-libc++/" export ANDROID_CC="$ANDROID_NDK/toolchains/llvm/prebuilt/$KERNEL/bin/clang" export ANDROID_ROOT="$ANDROID_NDK/platforms/android-21/arch-arm/" - export CMAKE_COMMAND="$CMAKE_COMMAND -DCMAKE_TOOLCHAIN_FILE=cmake/android-arm.cmake -DANDROID_BUILD=true" + export CMAKE_COMMAND="$CMAKE_COMMAND -DCMAKE_TOOLCHAIN_FILE=cmake/android-arm.cmake -DSD_ANDROID_BUILD=true" ;; android-arm64) @@ -204,7 +205,7 @@ case "$OS" in export ANDROID_CPP="$ANDROID_NDK/sources/cxx-stl/llvm-libc++/" export ANDROID_CC="$ANDROID_NDK/toolchains/llvm/prebuilt/$KERNEL/bin/clang" export ANDROID_ROOT="$ANDROID_NDK/platforms/android-21/arch-arm64/" - export CMAKE_COMMAND="$CMAKE_COMMAND -DCMAKE_TOOLCHAIN_FILE=cmake/android-arm64.cmake -DANDROID_BUILD=true" + export CMAKE_COMMAND="$CMAKE_COMMAND -DCMAKE_TOOLCHAIN_FILE=cmake/android-arm64.cmake -DSD_ANDROID_BUILD=true" ;; android-x86) @@ -215,7 +216,7 @@ case "$OS" in export ANDROID_CPP="$ANDROID_NDK/sources/cxx-stl/llvm-libc++/" export ANDROID_CC="$ANDROID_NDK/toolchains/llvm/prebuilt/$KERNEL/bin/clang" export ANDROID_ROOT="$ANDROID_NDK/platforms/android-21/arch-x86/" - export CMAKE_COMMAND="$CMAKE_COMMAND -DCMAKE_TOOLCHAIN_FILE=cmake/android-x86.cmake -DANDROID_BUILD=true" + export CMAKE_COMMAND="$CMAKE_COMMAND -DCMAKE_TOOLCHAIN_FILE=cmake/android-x86.cmake -DSD_ANDROID_BUILD=true" ;; android-x86_64) @@ -226,7 +227,7 @@ case "$OS" in export ANDROID_CPP="$ANDROID_NDK/sources/cxx-stl/llvm-libc++/" export ANDROID_CC="$ANDROID_NDK/toolchains/llvm/prebuilt/$KERNEL/bin/clang" export ANDROID_ROOT="$ANDROID_NDK/platforms/android-21/arch-x86_64/" - export CMAKE_COMMAND="$CMAKE_COMMAND -DCMAKE_TOOLCHAIN_FILE=cmake/android-x86_64.cmake -DANDROID_BUILD=true" + export CMAKE_COMMAND="$CMAKE_COMMAND -DCMAKE_TOOLCHAIN_FILE=cmake/android-x86_64.cmake -DSD_ANDROID_BUILD=true" ;; ios-x86_64) @@ -239,7 +240,7 @@ case "$OS" in fi XCODE_PATH="$(xcode-select --print-path)" export IOS_SDK="$XCODE_PATH/Platforms/iPhoneSimulator.platform/Developer/SDKs/iPhoneSimulator$IOS_VERSION.sdk" - export CMAKE_COMMAND="$CMAKE_COMMAND -DCMAKE_TOOLCHAIN_FILE=cmake/ios-x86_64.cmake --debug-trycompile -DIOS_BUILD=true" + export CMAKE_COMMAND="$CMAKE_COMMAND -DCMAKE_TOOLCHAIN_FILE=cmake/ios-x86_64.cmake --debug-trycompile -DSD_IOS_BUILD=true" ;; ios-x86) @@ -252,7 +253,7 @@ case "$OS" in fi XCODE_PATH="$(xcode-select --print-path)" export IOS_SDK="$XCODE_PATH/Platforms/iPhoneSimulator.platform/Developer/SDKs/iPhoneSimulator$IOS_VERSION.sdk" - export CMAKE_COMMAND="$CMAKE_COMMAND -DCMAKE_TOOLCHAIN_FILE=cmake/ios-x86.cmake --debug-trycompile -DIOS_BUILD=true" + export CMAKE_COMMAND="$CMAKE_COMMAND -DCMAKE_TOOLCHAIN_FILE=cmake/ios-x86.cmake --debug-trycompile -DSD_IOS_BUILD=true" ;; ios-arm64) @@ -265,7 +266,7 @@ case "$OS" in fi XCODE_PATH="$(xcode-select --print-path)" export IOS_SDK="$XCODE_PATH/Platforms/iPhoneOS.platform/Developer/SDKs/iPhoneOS$IOS_VERSION.sdk" - export CMAKE_COMMAND="$CMAKE_COMMAND -DCMAKE_TOOLCHAIN_FILE=cmake/ios-arm64.cmake --debug-trycompile -DIOS_BUILD=true" + export CMAKE_COMMAND="$CMAKE_COMMAND -DCMAKE_TOOLCHAIN_FILE=cmake/ios-arm64.cmake --debug-trycompile -DSD_IOS_BUILD=true" ;; ios-arm) @@ -278,7 +279,7 @@ case "$OS" in fi XCODE_PATH="$(xcode-select --print-path)" export IOS_SDK="$XCODE_PATH/Platforms/iPhoneOS.platform/Developer/SDKs/iPhoneOS$IOS_VERSION.sdk" - export CMAKE_COMMAND="$CMAKE_COMMAND -DCMAKE_TOOLCHAIN_FILE=cmake/ios-arm.cmake --debug-trycompile -DIOS_BUILD=true" + export CMAKE_COMMAND="$CMAKE_COMMAND -DCMAKE_TOOLCHAIN_FILE=cmake/ios-arm.cmake --debug-trycompile -DSD_IOS_BUILD=true" ;; ios-armv7) @@ -288,7 +289,7 @@ case "$OS" in LIBTYPE="static" ARCH="armv7" export IOS_SDK="/Applications/Xcode.app/Contents/Developer/Platforms/${iPhoneOS}.platform/Developer/SDKs/${iPhoneOS}${IOS_VERSION}.sdk" - export CMAKE_COMMAND="$CMAKE_COMMAND -DCMAKE_TOOLCHAIN_FILE=cmake/ios-armv7.cmake --debug-trycompile -DIOS_BUILD=true" + export CMAKE_COMMAND="$CMAKE_COMMAND -DCMAKE_TOOLCHAIN_FILE=cmake/ios-armv7.cmake --debug-trycompile -DSD_IOS_BUILD=true" ;; linux*) @@ -298,7 +299,7 @@ case "$OS" in export CC=clang export CXX=clang++ PARALLEL="true" - export CMAKE_COMMAND="$CMAKE_COMMAND -DCMAKE_MACOSX_RPATH=ON -DAPPLE_BUILD=true" + export CMAKE_COMMAND="$CMAKE_COMMAND -DCMAKE_MACOSX_RPATH=ON -DSD_APPLE_BUILD=true" ;; windows*) @@ -375,7 +376,7 @@ fi OPERATIONS_ARG= if [ -z "$OPERATIONS" ]; then - OPERATIONS_ARG="-DLIBND4J_ALL_OPS=true" + OPERATIONS_ARG="-DSD_ALL_OPS=true" else OPERATIONS_ARG=$OPERATIONS fi @@ -385,9 +386,9 @@ if [ -z "$EXPERIMENTAL" ]; then fi if [ "$CHIP" == "cpu" ]; then - BLAS_ARG="-DCPU_BLAS=true -DBLAS=TRUE" + BLAS_ARG="-DSD_CPU=true -DBLAS=TRUE" else - BLAS_ARG="-DCUDA_BLAS=true -DBLAS=TRUE" + BLAS_ARG="-DSD_CUDA=true -DBLAS=TRUE" fi if [ -z "$NAME" ]; then @@ -399,9 +400,9 @@ if [ -z "$NAME" ]; then fi if [ "$LIBTYPE" == "dynamic" ]; then - SHARED_LIBS_ARG="-DBUILD_SHARED_LIBS=OFF" + SHARED_LIBS_ARG="-DSD_SHARED_LIB=OFF" else - SHARED_LIBS_ARG="-DBUILD_SHARED_LIBS=ON" + SHARED_LIBS_ARG="-DSD_SHARED_LIB=ON" fi if [ "$BUILD" == "release" ]; then @@ -428,24 +429,24 @@ if [ "$PACKAGING" == "msi" ]; then fi EXPERIMENTAL_ARG=""; -MINIFIER_ARG="-DLIBND4J_BUILD_MINIFIER=false" -TESTS_ARG="-DBUILD_TESTS=OFF" -NAME_ARG="-DLIBND4J_NAME=$NAME" +MINIFIER_ARG="-DSD_BUILD_MINIFIER=false" +TESTS_ARG="-DSD_BUILD_TESTS=OFF" +NAME_ARG="-DSD_LIBRARY_NAME=$NAME" if [ "$EXPERIMENTAL" == "yes" ]; then - EXPERIMENTAL_ARG="-DEXPERIMENTAL=yes" + EXPERIMENTAL_ARG="-DSD_EXPERIMENTAL=yes" fi if [ "$MINIFIER" == "true" ]; then - MINIFIER_ARG="-DLIBND4J_BUILD_MINIFIER=true" + MINIFIER_ARG="-DSD_BUILD_MINIFIER=true" fi if [ "$TESTS" == "true" ]; then - MINIFIER_ARG="-DLIBND4J_BUILD_MINIFIER=true" - TESTS_ARG="-DBUILD_TESTS=ON" + MINIFIER_ARG="-DSD_BUILD_MINIFIER=true" + TESTS_ARG="-DSD_BUILD_TESTS=ON" fi -ARCH_ARG="-DARCH=$ARCH -DEXTENSION=$CHIP_EXTENSION" +ARCH_ARG="-DSD_ARCH=$ARCH -DSD_EXTENSION=$CHIP_EXTENSION" CUDA_COMPUTE="-DCOMPUTE=$COMPUTE" @@ -536,7 +537,7 @@ echo CHECK_VECTORIZATION = "$CHECK_VECTORIZATION" echo HELPERS = "$HELPERS" mkbuilddir pwd -eval $CMAKE_COMMAND "$BLAS_ARG" "$ARCH_ARG" "$NAME_ARG" -DCHECK_VECTORIZATION="${CHECK_VECTORIZATION}" $HELPERS "$SHARED_LIBS_ARG" "$MINIFIER_ARG" "$OPERATIONS_ARG" "$BUILD_TYPE" "$PACKAGING_ARG" "$EXPERIMENTAL_ARG" "$TESTS_ARG" "$CUDA_COMPUTE" -DOPENBLAS_PATH="$OPENBLAS_PATH" -DDEV=FALSE -DCMAKE_NEED_RESPONSE=YES -DMKL_MULTI_THREADED=TRUE ../.. +eval $CMAKE_COMMAND "$BLAS_ARG" "$ARCH_ARG" "$NAME_ARG" -DSD_CHECK_VECTORIZATION="${CHECK_VECTORIZATION}" $HELPERS "$SHARED_LIBS_ARG" "$MINIFIER_ARG" "$OPERATIONS_ARG" "$BUILD_TYPE" "$PACKAGING_ARG" "$EXPERIMENTAL_ARG" "$TESTS_ARG" "$CUDA_COMPUTE" -DOPENBLAS_PATH="$OPENBLAS_PATH" -DDEV=FALSE -DCMAKE_NEED_RESPONSE=YES -DMKL_MULTI_THREADED=TRUE ../.. if [ "$PARALLEL" == "true" ]; then MAKE_ARGUMENTS="$MAKE_ARGUMENTS -j $MAKEJ" diff --git a/libnd4j/include/array/ArrayOptions.h b/libnd4j/include/array/ArrayOptions.h index 484228fb7..1f0c25705 100644 --- a/libnd4j/include/array/ArrayOptions.h +++ b/libnd4j/include/array/ArrayOptions.h @@ -21,9 +21,9 @@ #ifndef ND4J_ARRAY_OPTIONS_H #define ND4J_ARRAY_OPTIONS_H -#include -#include -#include +#include +#include +#include #include #include #include @@ -87,7 +87,7 @@ #define ARRAY_UNSIGNED 8388608 -namespace nd4j { +namespace sd { class ND4J_EXPORT ArrayOptions { private: @@ -104,7 +104,7 @@ namespace nd4j { static FORCEINLINE _CUDA_HD bool isSparseArray(Nd4jLong *shapeInfo); static FORCEINLINE _CUDA_HD bool isUnsigned(Nd4jLong *shapeInfo); - static FORCEINLINE _CUDA_HD nd4j::DataType dataType(const Nd4jLong *shapeInfo); + static FORCEINLINE _CUDA_HD sd::DataType dataType(const Nd4jLong *shapeInfo); static FORCEINLINE _CUDA_HD SpaceType spaceType(Nd4jLong *shapeInfo); static FORCEINLINE _CUDA_HD SpaceType spaceType(const Nd4jLong *shapeInfo); @@ -119,7 +119,7 @@ namespace nd4j { static FORCEINLINE _CUDA_HD void resetDataType(Nd4jLong *shapeInfo); - static FORCEINLINE _CUDA_HD void setDataType(Nd4jLong *shapeInfo, const nd4j::DataType dataType); + static FORCEINLINE _CUDA_HD void setDataType(Nd4jLong *shapeInfo, const sd::DataType dataType); static FORCEINLINE _CUDA_HD void copyDataType(Nd4jLong* to, const Nd4jLong* from); }; @@ -155,34 +155,34 @@ namespace nd4j { return hasPropertyBitSet(shapeInfo, ARRAY_UNSIGNED); } - FORCEINLINE _CUDA_HD nd4j::DataType ArrayOptions::dataType(const Nd4jLong *shapeInfo) { + FORCEINLINE _CUDA_HD sd::DataType ArrayOptions::dataType(const Nd4jLong *shapeInfo) { /*if (hasPropertyBitSet(shapeInfo, ARRAY_QUANTIZED)) - return nd4j::DataType::QINT8; + return sd::DataType::QINT8; else */if (hasPropertyBitSet(shapeInfo, ARRAY_FLOAT)) - return nd4j::DataType::FLOAT32; + return sd::DataType::FLOAT32; else if (hasPropertyBitSet(shapeInfo, ARRAY_DOUBLE)) - return nd4j::DataType::DOUBLE; + return sd::DataType::DOUBLE; else if (hasPropertyBitSet(shapeInfo, ARRAY_HALF)) - return nd4j::DataType::HALF; + return sd::DataType::HALF; else if (hasPropertyBitSet(shapeInfo, ARRAY_BHALF)) - return nd4j::DataType::BFLOAT16; + return sd::DataType::BFLOAT16; else if (hasPropertyBitSet(shapeInfo, ARRAY_BOOL)) - return nd4j::DataType ::BOOL; + return sd::DataType ::BOOL; else if (hasPropertyBitSet(shapeInfo, ARRAY_UNSIGNED)) { if (hasPropertyBitSet(shapeInfo, ARRAY_CHAR)) - return nd4j::DataType ::UINT8; + return sd::DataType ::UINT8; else if (hasPropertyBitSet(shapeInfo, ARRAY_SHORT)) - return nd4j::DataType ::UINT16; + return sd::DataType ::UINT16; else if (hasPropertyBitSet(shapeInfo, ARRAY_INT)) - return nd4j::DataType ::UINT32; + return sd::DataType ::UINT32; else if (hasPropertyBitSet(shapeInfo, ARRAY_LONG)) - return nd4j::DataType ::UINT64; + return sd::DataType ::UINT64; else if (hasPropertyBitSet(shapeInfo, ARRAY_UTF8)) - return nd4j::DataType ::UTF8; + return sd::DataType ::UTF8; else if (hasPropertyBitSet(shapeInfo, ARRAY_UTF16)) - return nd4j::DataType ::UTF16; + return sd::DataType ::UTF16; else if (hasPropertyBitSet(shapeInfo, ARRAY_UTF32)) - return nd4j::DataType ::UTF32; + return sd::DataType ::UTF32; else { //shape::printShapeInfoLinear("Bad unsigned datatype (not)stored in shape", const_cast(shapeInfo)); #ifndef __CUDA_ARCH__ @@ -191,19 +191,19 @@ namespace nd4j { } } else if (hasPropertyBitSet(shapeInfo, ARRAY_CHAR)) - return nd4j::DataType::INT8; + return sd::DataType::INT8; else if (hasPropertyBitSet(shapeInfo, ARRAY_SHORT)) - return nd4j::DataType::INT16; + return sd::DataType::INT16; else if (hasPropertyBitSet(shapeInfo, ARRAY_INT)) - return nd4j::DataType::INT32; + return sd::DataType::INT32; else if (hasPropertyBitSet(shapeInfo, ARRAY_LONG)) - return nd4j::DataType::INT64; + return sd::DataType::INT64; else if (hasPropertyBitSet(shapeInfo, ARRAY_UTF8)) - return nd4j::DataType::UTF8; + return sd::DataType::UTF8; else if (hasPropertyBitSet(shapeInfo, ARRAY_UTF16)) - return nd4j::DataType::UTF16; + return sd::DataType::UTF16; else if (hasPropertyBitSet(shapeInfo, ARRAY_UTF32)) - return nd4j::DataType::UTF32; + return sd::DataType::UTF32; else { //shape::printShapeInfoLinear("Bad signed datatype (not)stored in shape", const_cast(shapeInfo)); #ifndef __CUDA_ARCH__ @@ -296,63 +296,63 @@ namespace nd4j { unsetPropertyBit(shapeInfo, ARRAY_UNSIGNED); } - FORCEINLINE _CUDA_HD void ArrayOptions::setDataType(Nd4jLong *shapeInfo, const nd4j::DataType dataType) { + FORCEINLINE _CUDA_HD void ArrayOptions::setDataType(Nd4jLong *shapeInfo, const sd::DataType dataType) { resetDataType(shapeInfo); - if (dataType == nd4j::DataType::UINT8 || - dataType == nd4j::DataType::UINT16 || - dataType == nd4j::DataType::UINT32 || - dataType == nd4j::DataType::UINT64) { + if (dataType == sd::DataType::UINT8 || + dataType == sd::DataType::UINT16 || + dataType == sd::DataType::UINT32 || + dataType == sd::DataType::UINT64) { setPropertyBit(shapeInfo, ARRAY_UNSIGNED); } switch (dataType) { - case nd4j::DataType::BOOL: + case sd::DataType::BOOL: setPropertyBit(shapeInfo, ARRAY_BOOL); break; - case nd4j::DataType::HALF: + case sd::DataType::HALF: setPropertyBit(shapeInfo, ARRAY_HALF); break; - case nd4j::DataType::BFLOAT16: + case sd::DataType::BFLOAT16: setPropertyBit(shapeInfo, ARRAY_BHALF); break; - case nd4j::DataType::FLOAT32: + case sd::DataType::FLOAT32: setPropertyBit(shapeInfo, ARRAY_FLOAT); break; - case nd4j::DataType::DOUBLE: + case sd::DataType::DOUBLE: setPropertyBit(shapeInfo, ARRAY_DOUBLE); break; - case nd4j::DataType::INT8: + case sd::DataType::INT8: setPropertyBit(shapeInfo, ARRAY_CHAR); break; - case nd4j::DataType::INT16: + case sd::DataType::INT16: setPropertyBit(shapeInfo, ARRAY_SHORT); break; - case nd4j::DataType::INT32: + case sd::DataType::INT32: setPropertyBit(shapeInfo, ARRAY_INT); break; - case nd4j::DataType::INT64: + case sd::DataType::INT64: setPropertyBit(shapeInfo, ARRAY_LONG); break; - case nd4j::DataType::UINT8: + case sd::DataType::UINT8: setPropertyBit(shapeInfo, ARRAY_CHAR); break; - case nd4j::DataType::UINT16: + case sd::DataType::UINT16: setPropertyBit(shapeInfo, ARRAY_SHORT); break; - case nd4j::DataType::UINT32: + case sd::DataType::UINT32: setPropertyBit(shapeInfo, ARRAY_INT); break; - case nd4j::DataType::UINT64: + case sd::DataType::UINT64: setPropertyBit(shapeInfo, ARRAY_LONG); break; - case nd4j::DataType::UTF8: + case sd::DataType::UTF8: setPropertyBit(shapeInfo, ARRAY_UTF8); break; - case nd4j::DataType::UTF16: + case sd::DataType::UTF16: setPropertyBit(shapeInfo, ARRAY_UTF16); break; - case nd4j::DataType::UTF32: + case sd::DataType::UTF32: setPropertyBit(shapeInfo, ARRAY_UTF32); break; default: diff --git a/libnd4j/include/array/ArrayType.h b/libnd4j/include/array/ArrayType.h index d4d6c9729..83e80bc0f 100644 --- a/libnd4j/include/array/ArrayType.h +++ b/libnd4j/include/array/ArrayType.h @@ -21,7 +21,7 @@ #ifndef ND4J_ARRAY_TYPE_H #define ND4J_ARRAY_TYPE_H -namespace nd4j { +namespace sd { enum ArrayType { DENSE = 1, SPARSE = 2, diff --git a/libnd4j/include/array/ByteOrder.h b/libnd4j/include/array/ByteOrder.h index 5cc490c85..121be9e9d 100644 --- a/libnd4j/include/array/ByteOrder.h +++ b/libnd4j/include/array/ByteOrder.h @@ -21,7 +21,7 @@ #ifndef LIBND4J_BYTEORDER_H #define LIBND4J_BYTEORDER_H -namespace nd4j { +namespace sd { enum ByteOrder { LE = 0, BE = 1, diff --git a/libnd4j/include/array/ByteOrderUtils.h b/libnd4j/include/array/ByteOrderUtils.h index 4250ec9a3..0f335ea65 100644 --- a/libnd4j/include/array/ByteOrderUtils.h +++ b/libnd4j/include/array/ByteOrderUtils.h @@ -23,12 +23,12 @@ #include #include "ByteOrder.h" -#include +#include -namespace nd4j { +namespace sd { class ND4J_EXPORT ByteOrderUtils { public: - static ByteOrder fromFlatByteOrder(nd4j::graph::ByteOrder order); + static ByteOrder fromFlatByteOrder(sd::graph::ByteOrder order); }; } diff --git a/libnd4j/include/array/ConstantDataBuffer.h b/libnd4j/include/array/ConstantDataBuffer.h index fd191b53b..e8bafe114 100644 --- a/libnd4j/include/array/ConstantDataBuffer.h +++ b/libnd4j/include/array/ConstantDataBuffer.h @@ -20,11 +20,11 @@ #ifndef LIBND4J_CONSTANTDATABUFFER_H #define LIBND4J_CONSTANTDATABUFFER_H -#include -#include +#include +#include -namespace nd4j { +namespace sd { class ND4J_EXPORT ConstantDataBuffer { private: Nd4jPointer _primaryBuffer = nullptr; diff --git a/libnd4j/include/array/ConstantDescriptor.h b/libnd4j/include/array/ConstantDescriptor.h index f32c1c8bf..589ba2353 100644 --- a/libnd4j/include/array/ConstantDescriptor.h +++ b/libnd4j/include/array/ConstantDescriptor.h @@ -24,11 +24,11 @@ #include #include #include -#include -#include +#include +#include #include -namespace nd4j { +namespace sd { class ND4J_EXPORT ConstantDescriptor { private: std::vector _integerValues; @@ -59,5 +59,17 @@ namespace nd4j { }; } +#ifndef __JAVACPP_HACK__ + +namespace std { + template<> + class ND4J_EXPORT hash { + public: + size_t operator()(const sd::ConstantDescriptor &k) const; + }; +} + +#endif + #endif //DEV_TESTS_CONSTANTDESCRIPTOR_H diff --git a/libnd4j/include/array/ConstantHolder.h b/libnd4j/include/array/ConstantHolder.h index d0824483e..a404e5808 100644 --- a/libnd4j/include/array/ConstantHolder.h +++ b/libnd4j/include/array/ConstantHolder.h @@ -27,13 +27,13 @@ #include #include -namespace nd4j { +namespace sd { class ConstantHolder { private: int _deviceId = 0; std::mutex _mutex; - std::map _buffers; + std::map _buffers; public: ConstantHolder(const ConstantHolder& other); ConstantHolder() = default; @@ -42,17 +42,17 @@ namespace nd4j { ConstantHolder& operator=(const ConstantHolder& other) = default; ConstantHolder& operator=(ConstantHolder&& other) = default; - bool hasBuffer(nd4j::DataType dataType); + bool hasBuffer(sd::DataType dataType); template bool hasBuffer(); - void addBuffer(ConstantDataBuffer &pointer, nd4j::DataType dataType); + void addBuffer(ConstantDataBuffer &pointer, sd::DataType dataType); template void addBuffer(ConstantDataBuffer &pointer); - ConstantDataBuffer* getConstantDataBuffer(nd4j::DataType dataType); + ConstantDataBuffer* getConstantDataBuffer(sd::DataType dataType); template ConstantDataBuffer* getConstantDataBuffer(); diff --git a/libnd4j/include/array/DataBuffer.h b/libnd4j/include/array/DataBuffer.h index cd27c20b8..59ffe3045 100644 --- a/libnd4j/include/array/DataBuffer.h +++ b/libnd4j/include/array/DataBuffer.h @@ -23,14 +23,14 @@ #define DEV_TESTS_DATABUFFER_H #include -#include -#include -#include +#include +#include +#include #include #include #include -namespace nd4j { +namespace sd { class ND4J_EXPORT DataBuffer { diff --git a/libnd4j/include/array/DataType.h b/libnd4j/include/array/DataType.h index 8ec55342e..cf8baf7d0 100644 --- a/libnd4j/include/array/DataType.h +++ b/libnd4j/include/array/DataType.h @@ -21,7 +21,7 @@ #ifndef ND4J_DATATYPE_H #define ND4J_DATATYPE_H -namespace nd4j { +namespace sd { enum DataType { INHERIT = 0, BOOL = 1, diff --git a/libnd4j/include/array/DataTypeConversions.h b/libnd4j/include/array/DataTypeConversions.h index abc804f5e..44f555533 100644 --- a/libnd4j/include/array/DataTypeConversions.h +++ b/libnd4j/include/array/DataTypeConversions.h @@ -21,17 +21,17 @@ #ifndef LIBND4J_DATATYPECONVERSIONS_H #define LIBND4J_DATATYPECONVERSIONS_H -#include +#include #include -#include +#include #include #include #include #include -#include +#include #include -namespace nd4j { +namespace sd { template class ND4J_EXPORT DataTypeConversions { private: diff --git a/libnd4j/include/array/DataTypeUtils.h b/libnd4j/include/array/DataTypeUtils.h index c307ecd4e..bd89605d1 100644 --- a/libnd4j/include/array/DataTypeUtils.h +++ b/libnd4j/include/array/DataTypeUtils.h @@ -26,20 +26,20 @@ #include #include #include -#include -#include -#include -#include +#include +#include +#include +#include //#include -//#include +//#include #include -namespace nd4j { +namespace sd { class ND4J_EXPORT DataTypeUtils { public: static int asInt(DataType type); static DataType fromInt(int dtype); - static DataType fromFlatDataType(nd4j::graph::DType dtype); + static DataType fromFlatDataType(sd::graph::DType dtype); FORCEINLINE static std::string asString(DataType dataType); template @@ -70,21 +70,21 @@ namespace nd4j { FORCEINLINE static _CUDA_HD size_t sizeOf(DataType type); FORCEINLINE static _CUDA_HD size_t sizeOf(const Nd4jLong* shapeInfo); - FORCEINLINE static _CUDA_HD bool isR(nd4j::DataType dataType); + FORCEINLINE static _CUDA_HD bool isR(sd::DataType dataType); - FORCEINLINE static _CUDA_HD bool isZ(nd4j::DataType dataType); + FORCEINLINE static _CUDA_HD bool isZ(sd::DataType dataType); - FORCEINLINE static _CUDA_HD bool isB(nd4j::DataType dataType); + FORCEINLINE static _CUDA_HD bool isB(sd::DataType dataType); - FORCEINLINE static _CUDA_HD bool isU(nd4j::DataType dataType); + FORCEINLINE static _CUDA_HD bool isU(sd::DataType dataType); - FORCEINLINE static _CUDA_HD bool isS(nd4j::DataType dataType); + FORCEINLINE static _CUDA_HD bool isS(sd::DataType dataType); - FORCEINLINE static nd4j::DataType pickPairwiseResultType(nd4j::DataType typeX, nd4j::DataType typeY); + FORCEINLINE static sd::DataType pickPairwiseResultType(sd::DataType typeX, sd::DataType typeY); - FORCEINLINE static nd4j::DataType pickPairwiseResultType(const Nd4jLong* shapeInfo1, const Nd4jLong* shapeInfo2); + FORCEINLINE static sd::DataType pickPairwiseResultType(const Nd4jLong* shapeInfo1, const Nd4jLong* shapeInfo2); - FORCEINLINE static nd4j::DataType pickFloatingType(nd4j::DataType typeX); + FORCEINLINE static sd::DataType pickFloatingType(sd::DataType typeX); template FORCEINLINE static std::vector convertVector(const std::vector &vector); @@ -106,38 +106,38 @@ namespace nd4j { ///// IMLEMENTATION OF INLINE METHODS ///// ////////////////////////////////////////////////////////////////////////// - FORCEINLINE nd4j::DataType DataTypeUtils::pickFloatingType(nd4j::DataType typeX) { + FORCEINLINE sd::DataType DataTypeUtils::pickFloatingType(sd::DataType typeX) { // if proposed dataType is already floating point - return it if (isR(typeX)) return typeX; return Environment::getInstance()->defaultFloatDataType(); } - FORCEINLINE bool DataTypeUtils::isR(nd4j::DataType dataType) { - return dataType == nd4j::DataType::FLOAT32 || dataType == nd4j::DataType::BFLOAT16 || dataType == nd4j::DataType::HALF || dataType == nd4j::DataType::DOUBLE; + FORCEINLINE bool DataTypeUtils::isR(sd::DataType dataType) { + return dataType == sd::DataType::FLOAT32 || dataType == sd::DataType::BFLOAT16 || dataType == sd::DataType::HALF || dataType == sd::DataType::DOUBLE; } - FORCEINLINE bool DataTypeUtils::isB(nd4j::DataType dataType) { - return dataType == nd4j::DataType::BOOL; + FORCEINLINE bool DataTypeUtils::isB(sd::DataType dataType) { + return dataType == sd::DataType::BOOL; } - FORCEINLINE bool DataTypeUtils::isS(nd4j::DataType dataType) { - return dataType == nd4j::DataType::UTF8 || dataType == nd4j::DataType::UTF16 || dataType == nd4j::DataType::UTF32; + FORCEINLINE bool DataTypeUtils::isS(sd::DataType dataType) { + return dataType == sd::DataType::UTF8 || dataType == sd::DataType::UTF16 || dataType == sd::DataType::UTF32; } - FORCEINLINE bool DataTypeUtils::isZ(nd4j::DataType dataType) { + FORCEINLINE bool DataTypeUtils::isZ(sd::DataType dataType) { return !isR(dataType) && !isB(dataType) && !isS(dataType); } - FORCEINLINE bool DataTypeUtils::isU(nd4j::DataType dataType) { - return dataType == nd4j::DataType::UINT8 || dataType == nd4j::DataType::UINT16 || dataType == nd4j::DataType::UINT32 || dataType == nd4j::DataType::UINT64; + FORCEINLINE bool DataTypeUtils::isU(sd::DataType dataType) { + return dataType == sd::DataType::UINT8 || dataType == sd::DataType::UINT16 || dataType == sd::DataType::UINT32 || dataType == sd::DataType::UINT64; } - FORCEINLINE nd4j::DataType DataTypeUtils::pickPairwiseResultType(nd4j::DataType typeX, nd4j::DataType typeY) { + FORCEINLINE sd::DataType DataTypeUtils::pickPairwiseResultType(sd::DataType typeX, sd::DataType typeY) { // if both dtypes are the same - just return it if (typeX == typeY) return typeX; - auto nd4j_max = [](nd4j::DataType typeX, nd4j::DataType typeY) { + auto nd4j_max = [](sd::DataType typeX, sd::DataType typeY) { return typeX > typeY?typeX:typeY; }; auto rX = isR(typeX); @@ -154,7 +154,7 @@ namespace nd4j { // if both data types are float - return biggest one if (rX && rY) { // if we allow precision boost, then we pick bigger data type - if (nd4j::Environment::getInstance()->precisionBoostAllowed()) { + if (sd::Environment::getInstance()->precisionBoostAllowed()) { return nd4j_max(typeX, typeY); } else { // and we return first operand otherwise @@ -165,7 +165,7 @@ namespace nd4j { // if that's not real type, we apply same rules if (!rX && !rY) { - if (nd4j::Environment::getInstance()->precisionBoostAllowed()) { + if (sd::Environment::getInstance()->precisionBoostAllowed()) { return nd4j_max(typeX, typeY); } else { // and we return first operand otherwise @@ -177,7 +177,7 @@ namespace nd4j { } /////////////////////////////////////////////////////////////////// -FORCEINLINE nd4j::DataType DataTypeUtils::pickPairwiseResultType(const Nd4jLong* shapeInfo1, const Nd4jLong* shapeInfo2) { +FORCEINLINE sd::DataType DataTypeUtils::pickPairwiseResultType(const Nd4jLong* shapeInfo1, const Nd4jLong* shapeInfo2) { return pickPairwiseResultType(ArrayOptions::dataType(shapeInfo1), ArrayOptions::dataType(shapeInfo2)); } @@ -420,31 +420,31 @@ FORCEINLINE _CUDA_HD T DataTypeUtils::eps() { return result; } - FORCEINLINE _CUDA_HD size_t DataTypeUtils::sizeOfElement(nd4j::DataType type) { + FORCEINLINE _CUDA_HD size_t DataTypeUtils::sizeOfElement(sd::DataType type) { switch (type) { - case nd4j::DataType::UINT8: - case nd4j::DataType::INT8: - case nd4j::DataType::FLOAT8: - case nd4j::DataType::QINT8: - case nd4j::DataType::BOOL: return (size_t) 1; + case sd::DataType::UINT8: + case sd::DataType::INT8: + case sd::DataType::FLOAT8: + case sd::DataType::QINT8: + case sd::DataType::BOOL: return (size_t) 1; - case nd4j::DataType::BFLOAT16: - case nd4j::DataType::HALF: - case nd4j::DataType::INT16: - case nd4j::DataType::QINT16: - case nd4j::DataType::UINT16: return (size_t) 2; + case sd::DataType::BFLOAT16: + case sd::DataType::HALF: + case sd::DataType::INT16: + case sd::DataType::QINT16: + case sd::DataType::UINT16: return (size_t) 2; - case nd4j::DataType::UTF8: - case nd4j::DataType::UTF16: - case nd4j::DataType::UTF32: - case nd4j::DataType::INT32: - case nd4j::DataType::UINT32: - case nd4j::DataType::HALF2: - case nd4j::DataType::FLOAT32: return (size_t) 4; + case sd::DataType::UTF8: + case sd::DataType::UTF16: + case sd::DataType::UTF32: + case sd::DataType::INT32: + case sd::DataType::UINT32: + case sd::DataType::HALF2: + case sd::DataType::FLOAT32: return (size_t) 4; - case nd4j::DataType::UINT64: - case nd4j::DataType::INT64: - case nd4j::DataType::DOUBLE: return (size_t) 8; + case sd::DataType::UINT64: + case sd::DataType::INT64: + case sd::DataType::DOUBLE: return (size_t) 8; default: { nd4j_printf("Unknown DataType used: [%i]\n", asInt(type)); @@ -456,41 +456,41 @@ FORCEINLINE _CUDA_HD T DataTypeUtils::eps() { } template - FORCEINLINE _CUDA_HD nd4j::DataType nd4j::DataTypeUtils::fromT() { + FORCEINLINE _CUDA_HD sd::DataType sd::DataTypeUtils::fromT() { if (std::is_same::value) { - return nd4j::DataType::BOOL; + return sd::DataType::BOOL; } else if (std::is_same::value) { - return nd4j::DataType::UTF8; + return sd::DataType::UTF8; } else if (std::is_same::value) { - return nd4j::DataType::UTF16; + return sd::DataType::UTF16; } else if (std::is_same::value) { - return nd4j::DataType::UTF32; + return sd::DataType::UTF32; } else if (std::is_same::value) { - return nd4j::DataType::FLOAT32; + return sd::DataType::FLOAT32; } else if (std::is_same::value) { - return nd4j::DataType::HALF; + return sd::DataType::HALF; } else if (std::is_same::value) { - return nd4j::DataType::BFLOAT16; + return sd::DataType::BFLOAT16; } else if (std::is_same::value) { - return nd4j::DataType::DOUBLE; + return sd::DataType::DOUBLE; } else if (std::is_same::value) { - return nd4j::DataType::INT8; + return sd::DataType::INT8; } else if (std::is_same::value) { - return nd4j::DataType::INT16; + return sd::DataType::INT16; } else if (std::is_same::value) { - return nd4j::DataType::INT32; + return sd::DataType::INT32; } else if (std::is_same::value) { - return nd4j::DataType::INT64; + return sd::DataType::INT64; } else if (std::is_same::value) { - return nd4j::DataType::UINT8; + return sd::DataType::UINT8; } else if (std::is_same::value) { - return nd4j::DataType::UINT16; + return sd::DataType::UINT16; } else if (std::is_same::value) { - return nd4j::DataType::UINT32; + return sd::DataType::UINT32; } else if (std::is_same::value) { - return nd4j::DataType::UINT64; + return sd::DataType::UINT64; } else { - return nd4j::DataType::INHERIT; + return sd::DataType::INHERIT; } } } diff --git a/libnd4j/include/array/ExtraArguments.h b/libnd4j/include/array/ExtraArguments.h index e1f5a69bd..131e8cd92 100644 --- a/libnd4j/include/array/ExtraArguments.h +++ b/libnd4j/include/array/ExtraArguments.h @@ -21,14 +21,14 @@ #ifndef DEV_TESTS_EXTRAARGUMENTS_H #define DEV_TESTS_EXTRAARGUMENTS_H -#include +#include #include #include #include -#include +#include #include -namespace nd4j { +namespace sd { class ND4J_EXPORT ExtraArguments { private: std::vector _fpArgs; @@ -54,7 +54,7 @@ namespace nd4j { template void* argumentsAsT(Nd4jLong offset = 0); - void* argumentsAsT(nd4j::DataType dataType, Nd4jLong offset = 0); + void* argumentsAsT(sd::DataType dataType, Nd4jLong offset = 0); size_t length(); }; diff --git a/libnd4j/include/array/InteropDataBuffer.h b/libnd4j/include/array/InteropDataBuffer.h index 3cbfc2f94..27b17aabb 100644 --- a/libnd4j/include/array/InteropDataBuffer.h +++ b/libnd4j/include/array/InteropDataBuffer.h @@ -18,7 +18,7 @@ // @author raver119@gmail.com // -#include +#include #include #include #include @@ -26,7 +26,7 @@ #ifndef LIBND4J_INTEROPDATABUFFER_H #define LIBND4J_INTEROPDATABUFFER_H -namespace nd4j { +namespace sd { /** * This class is a wrapper for DataBuffer, suitable for sharing DataBuffer between front-end and back-end languages */ @@ -37,7 +37,7 @@ namespace nd4j { public: InteropDataBuffer(InteropDataBuffer &dataBuffer, uint64_t length, uint64_t offset); InteropDataBuffer(std::shared_ptr databuffer); - InteropDataBuffer(size_t elements, nd4j::DataType dtype, bool allocateBoth); + InteropDataBuffer(size_t elements, sd::DataType dtype, bool allocateBoth); ~InteropDataBuffer() = default; #ifndef __JAVACPP_HACK__ diff --git a/libnd4j/blas/NDArray.h b/libnd4j/include/array/NDArray.h similarity index 83% rename from libnd4j/blas/NDArray.h rename to libnd4j/include/array/NDArray.h index 3a68edde1..3fbfcef8e 100644 --- a/libnd4j/blas/NDArray.h +++ b/libnd4j/include/array/NDArray.h @@ -17,11 +17,11 @@ #ifndef NDARRAY_H #define NDARRAY_H -#include +#include #include #include -#include -#include "NativeOpExecutioner.h" +#include +#include "legacy/NativeOpExecutioner.h" #include #include #include @@ -32,13 +32,13 @@ #include #include #include -#include +#include #include #include #include #include -#include -#include +#include +#include #include #include #include @@ -47,7 +47,7 @@ #include -namespace nd4j { +namespace sd { template ::value>::type> ND4J_EXPORT NDArray operator+(const NDArray& arr, const T& scalar); @@ -116,7 +116,7 @@ namespace nd4j { void templatedSet(void *buffer, const Nd4jLong xOffset, const void *value); template - void templatedSet(void *buffer, const Nd4jLong xOfsset, nd4j::DataType dtype, const void *value); + void templatedSet(void *buffer, const Nd4jLong xOfsset, sd::DataType dtype, const void *value); template void templatedAssign(void *xBuffer, const Nd4jLong xOffset, const void *yBuffer, const Nd4jLong yOffset) const; @@ -161,7 +161,7 @@ namespace nd4j { /** * pointer on device launch context (with all data needed there). */ - nd4j::LaunchContext * _context = nd4j::LaunchContext::defaultContext(); + sd::LaunchContext * _context = sd::LaunchContext::defaultContext(); // indicates if array's buffer is within workspace bool _isAttached = false; @@ -174,7 +174,7 @@ namespace nd4j { /** * type of array elements */ - nd4j::DataType _dataType = FLOAT32; + sd::DataType _dataType = FLOAT32; /** * deviceID where this NDArray belongs to @@ -191,72 +191,72 @@ namespace nd4j { * do not allocate memory, memory for array is passed from outside */ #ifndef __JAVACPP_HACK__ - NDArray(std::shared_ptr buffer, const ShapeDescriptor& descriptor, nd4j::LaunchContext* context = nd4j::LaunchContext::defaultContext(), const Nd4jLong offset = 0); + NDArray(std::shared_ptr buffer, const ShapeDescriptor& descriptor, sd::LaunchContext* context = sd::LaunchContext::defaultContext(), const Nd4jLong offset = 0); - NDArray(std::shared_ptr buffer, const char order, const std::vector &shape, nd4j::LaunchContext* context = nd4j::LaunchContext::defaultContext()); + NDArray(std::shared_ptr buffer, const char order, const std::vector &shape, sd::LaunchContext* context = sd::LaunchContext::defaultContext()); /** * This contructors create scalar array containing string utf8 * */ - NDArray(const char* str, nd4j::DataType dtype = nd4j::DataType::UTF8, nd4j::LaunchContext* context = nd4j::LaunchContext::defaultContext()) + NDArray(const char* str, sd::DataType dtype = sd::DataType::UTF8, sd::LaunchContext* context = sd::LaunchContext::defaultContext()) : NDArray(std::string(str), dtype, context) { } - NDArray(const std::string& string, nd4j::DataType dtype = nd4j::DataType::UTF8, nd4j::LaunchContext* context = nd4j::LaunchContext::defaultContext()); + NDArray(const std::string& string, sd::DataType dtype = sd::DataType::UTF8, sd::LaunchContext* context = sd::LaunchContext::defaultContext()); /** * This contructors create scalar array containing string utf16 * */ - NDArray(const char16_t* u16string, nd4j::DataType dtype = nd4j::DataType::UTF16, nd4j::LaunchContext* context = nd4j::LaunchContext::defaultContext()) + NDArray(const char16_t* u16string, sd::DataType dtype = sd::DataType::UTF16, sd::LaunchContext* context = sd::LaunchContext::defaultContext()) : NDArray(std::u16string(u16string), dtype, context) { } - NDArray(const std::u16string& u16string, nd4j::DataType dtype = nd4j::DataType::UTF16, nd4j::LaunchContext* context = nd4j::LaunchContext::defaultContext()); + NDArray(const std::u16string& u16string, sd::DataType dtype = sd::DataType::UTF16, sd::LaunchContext* context = sd::LaunchContext::defaultContext()); /** * This contructors create scalar array containing string utf32 * */ - NDArray(const char32_t* u32string, nd4j::DataType dtype = nd4j::DataType::UTF32, nd4j::LaunchContext* context = nd4j::LaunchContext::defaultContext()) + NDArray(const char32_t* u32string, sd::DataType dtype = sd::DataType::UTF32, sd::LaunchContext* context = sd::LaunchContext::defaultContext()) : NDArray(std::u32string(u32string), dtype, context) { } - NDArray(const std::u32string& u32string, nd4j::DataType dtype = nd4j::DataType::UTF32, nd4j::LaunchContext* context = nd4j::LaunchContext::defaultContext()); + NDArray(const std::u32string& u32string, sd::DataType dtype = sd::DataType::UTF32, sd::LaunchContext* context = sd::LaunchContext::defaultContext()); /** * This contructors create array from vector of utf8 strings * */ - NDArray(const std::vector& shape, const std::vector& strings, nd4j::DataType dtype = nd4j::DataType::UTF8, nd4j::LaunchContext* context = nd4j::LaunchContext::defaultContext()); - NDArray(const std::vector& shape, const std::vector& string, nd4j::DataType dtype = nd4j::DataType::UTF8, nd4j::LaunchContext* context = nd4j::LaunchContext::defaultContext()); + NDArray(const std::vector& shape, const std::vector& strings, sd::DataType dtype = sd::DataType::UTF8, sd::LaunchContext* context = sd::LaunchContext::defaultContext()); + NDArray(const std::vector& shape, const std::vector& string, sd::DataType dtype = sd::DataType::UTF8, sd::LaunchContext* context = sd::LaunchContext::defaultContext()); /** * This contructors create array from vector of utf16 strings * */ - NDArray(const std::vector& shape, const std::vector& strings, nd4j::DataType dtype = nd4j::DataType::UTF16, nd4j::LaunchContext* context = nd4j::LaunchContext::defaultContext()); - NDArray(const std::vector& shape, const std::vector& string, nd4j::DataType dtype = nd4j::DataType::UTF16, nd4j::LaunchContext* context = nd4j::LaunchContext::defaultContext()); + NDArray(const std::vector& shape, const std::vector& strings, sd::DataType dtype = sd::DataType::UTF16, sd::LaunchContext* context = sd::LaunchContext::defaultContext()); + NDArray(const std::vector& shape, const std::vector& string, sd::DataType dtype = sd::DataType::UTF16, sd::LaunchContext* context = sd::LaunchContext::defaultContext()); /** * This contructors create array from vector of utf32 strings * */ - NDArray(const std::vector& shape, const std::vector& strings, nd4j::DataType dtype = nd4j::DataType::UTF32, nd4j::LaunchContext* context = nd4j::LaunchContext::defaultContext()); - NDArray(const std::vector& shape, const std::vector& string, nd4j::DataType dtype = nd4j::DataType::UTF32, nd4j::LaunchContext* context = nd4j::LaunchContext::defaultContext()); + NDArray(const std::vector& shape, const std::vector& strings, sd::DataType dtype = sd::DataType::UTF32, sd::LaunchContext* context = sd::LaunchContext::defaultContext()); + NDArray(const std::vector& shape, const std::vector& string, sd::DataType dtype = sd::DataType::UTF32, sd::LaunchContext* context = sd::LaunchContext::defaultContext()); #endif /** * do not allocate memory, memory for array is passed from outside */ - NDArray(void *buffer, Nd4jLong* shapeInfo, nd4j::LaunchContext* context = nd4j::LaunchContext::defaultContext(), const bool isBuffAlloc = false); + NDArray(void *buffer, Nd4jLong* shapeInfo, sd::LaunchContext* context = sd::LaunchContext::defaultContext(), const bool isBuffAlloc = false); /** * do not allocate memory, memory for array is passed from outside * we suppose the content of both (device and host) buffers is identical */ - NDArray(void *buffer, void *bufferD, Nd4jLong* shapeInfo, nd4j::LaunchContext* context = nd4j::LaunchContext::defaultContext(), const bool isBuffAlloc = false, const bool isBuffDAlloc = false); + NDArray(void *buffer, void *bufferD, Nd4jLong* shapeInfo, sd::LaunchContext* context = sd::LaunchContext::defaultContext(), const bool isBuffAlloc = false, const bool isBuffDAlloc = false); /** * copy constructor @@ -271,34 +271,34 @@ namespace nd4j { /** * constructor, create array stored at given workspace */ - NDArray(nd4j::LaunchContext * context); + NDArray(sd::LaunchContext * context); /** * constructor creates new NDArray using shape information from "shapeInfo", set all elements in new array to zeros, if copyStrides is true then use stride values from "shapeInfo", else calculate strides independently */ - NDArray(Nd4jLong* shapeInfo, const bool copyStrides = false, nd4j::LaunchContext* context = nd4j::LaunchContext::defaultContext()); + NDArray(Nd4jLong* shapeInfo, const bool copyStrides = false, sd::LaunchContext* context = sd::LaunchContext::defaultContext()); /** * constructor creates new NDArray using shape information from "shapeInfo", set all elements in new array to be zeros, if copyStrides is true then use stride values from "shapeInfo", else calculate strides independently * set dtype as array type */ - NDArray(Nd4jLong* shapeInfo, const nd4j::DataType dtype, const bool copyStrides = false, nd4j::LaunchContext* context = nd4j::LaunchContext::defaultContext()); + NDArray(Nd4jLong* shapeInfo, const sd::DataType dtype, const bool copyStrides = false, sd::LaunchContext* context = sd::LaunchContext::defaultContext()); /** * this constructor creates new array using shape information contained in vector argument */ - NDArray(const char order, const std::vector &shape, nd4j::DataType dtype = DOUBLE, nd4j::LaunchContext* context = nd4j::LaunchContext::defaultContext()); + NDArray(const char order, const std::vector &shape, sd::DataType dtype = DOUBLE, sd::LaunchContext* context = sd::LaunchContext::defaultContext()); /** * This constructor creates new array with elements copied from data and using shape information stored in shape, elements from data will be casted to dtype */ - NDArray(const char order, const std::vector &shape, const std::vector& data, nd4j::DataType dtype = DOUBLE, nd4j::LaunchContext* context = nd4j::LaunchContext::defaultContext()); + NDArray(const char order, const std::vector &shape, const std::vector& data, sd::DataType dtype = DOUBLE, sd::LaunchContext* context = sd::LaunchContext::defaultContext()); /** * this constructor creates new array using given buffer (without memory allocation) and shape information stored in shape */ - NDArray(void *buffer, const char order, const std::vector &shape, nd4j::DataType dtype, nd4j::LaunchContext* context = nd4j::LaunchContext::defaultContext(), const bool isBuffAlloc = false); + NDArray(void *buffer, const char order, const std::vector &shape, sd::DataType dtype, sd::LaunchContext* context = sd::LaunchContext::defaultContext(), const bool isBuffAlloc = false); /** * This method returns new array with the same shape & data type @@ -310,19 +310,19 @@ namespace nd4j { * This method returns new uninitialized array with the same shape & data type * @return */ - NDArray ulike(); + NDArray ulike() const; /** * this constructor creates new NDArray with shape matching "other" array, * doesn't copy "other" elements into new array !!! */ - explicit NDArray(const NDArray* other, const bool copyStrides = false, nd4j::LaunchContext* context = nd4j::LaunchContext ::defaultContext()); + explicit NDArray(const NDArray* other, const bool copyStrides = false, sd::LaunchContext* context = sd::LaunchContext ::defaultContext()); /** * this constructor creates scalar(and set its value = 0) or empty array depending on bool argument isScalar */ - NDArray(nd4j::DataType dtype, nd4j::LaunchContext* context = nd4j::LaunchContext::defaultContext(), const bool isScalar = true); + NDArray(sd::DataType dtype, sd::LaunchContext* context = sd::LaunchContext::defaultContext(), const bool isScalar = true); /** * This method blocks until asynchronous operation finishes @@ -392,7 +392,7 @@ namespace nd4j { void operator delete(void* p); - void setContext(nd4j::LaunchContext * context); + void setContext(sd::LaunchContext * context); /** * create a new array by replicating current array by repeats times along given dimension @@ -438,7 +438,7 @@ namespace nd4j { /** * returns _context */ - nd4j::LaunchContext * getContext() const { + sd::LaunchContext * getContext() const { return _context; } @@ -626,17 +626,17 @@ namespace nd4j { * keepDims - if true then put unities in place of reduced dimensions */ - NDArray reduceAlongDimension(nd4j::reduce::FloatOps op, const std::vector& dimensions, const bool keepDims = false, const bool supportOldShapes = false) const; - NDArray reduceAlongDimension(nd4j::reduce::FloatOps op, const std::initializer_list& dimensions, const bool keepDims = false, const bool supportOldShapes = false) const; + NDArray reduceAlongDimension(sd::reduce::FloatOps op, const std::vector& dimensions, const bool keepDims = false, const bool supportOldShapes = false) const; + NDArray reduceAlongDimension(sd::reduce::FloatOps op, const std::initializer_list& dimensions, const bool keepDims = false, const bool supportOldShapes = false) const; - NDArray reduceAlongDimension(nd4j::reduce::SameOps op, const std::vector& dimensions, const bool keepDims = false, const bool supportOldShapes = false) const; - NDArray reduceAlongDimension(nd4j::reduce::SameOps op, const std::initializer_list& dimensions, const bool keepDims = false, const bool supportOldShapes = false) const; + NDArray reduceAlongDimension(sd::reduce::SameOps op, const std::vector& dimensions, const bool keepDims = false, const bool supportOldShapes = false) const; + NDArray reduceAlongDimension(sd::reduce::SameOps op, const std::initializer_list& dimensions, const bool keepDims = false, const bool supportOldShapes = false) const; - NDArray reduceAlongDimension(nd4j::reduce::BoolOps op, const std::vector& dimensions, const bool keepDims = false, const bool supportOldShapes = false) const; - NDArray reduceAlongDimension(nd4j::reduce::BoolOps op, const std::initializer_list& dimensions, const bool keepDims = false, const bool supportOldShapes = false) const; + NDArray reduceAlongDimension(sd::reduce::BoolOps op, const std::vector& dimensions, const bool keepDims = false, const bool supportOldShapes = false) const; + NDArray reduceAlongDimension(sd::reduce::BoolOps op, const std::initializer_list& dimensions, const bool keepDims = false, const bool supportOldShapes = false) const; - NDArray reduceAlongDimension(nd4j::reduce::LongOps op, const std::vector& dimensions, const bool keepDims = false, const bool supportOldShapes = false) const; - NDArray reduceAlongDimension(nd4j::reduce::LongOps op, const std::initializer_list& dimensions, const bool keepDims = false, const bool supportOldShapes = false) const; + NDArray reduceAlongDimension(sd::reduce::LongOps op, const std::vector& dimensions, const bool keepDims = false, const bool supportOldShapes = false) const; + NDArray reduceAlongDimension(sd::reduce::LongOps op, const std::initializer_list& dimensions, const bool keepDims = false, const bool supportOldShapes = false) const; /** * method reduces array by excluding its shapes along dimensions present in given dimensions vector @@ -645,37 +645,37 @@ namespace nd4j { * keepDims - if true then put unities in place of reduced dimensions * extras - extra parameters */ - void reduceAlongDimension(nd4j::reduce::FloatOps op, NDArray& target, const std::vector& dimensions, const bool keepDims = false, const bool supportOldShapes = false, const bool checkTargetShape = true) const; - void reduceAlongDimension(nd4j::reduce::SameOps op, NDArray& target, const std::vector& dimensions, const bool keepDims = false, const bool supportOldShapes = false, const bool checkTargetShape = true) const; - void reduceAlongDimension(nd4j::reduce::BoolOps op, NDArray& target, const std::vector& dimensions, const bool keepDims = false, const bool supportOldShapes = false, const bool checkTargetShape = true) const; - void reduceAlongDimension(nd4j::reduce::LongOps op, NDArray& target, const std::vector& dimensions, const bool keepDims = false, const bool supportOldShapes = false, const bool checkTargetShape = true) const; + void reduceAlongDimension(sd::reduce::FloatOps op, NDArray& target, const std::vector& dimensions, const bool keepDims = false, const bool supportOldShapes = false, const bool checkTargetShape = true) const; + void reduceAlongDimension(sd::reduce::SameOps op, NDArray& target, const std::vector& dimensions, const bool keepDims = false, const bool supportOldShapes = false, const bool checkTargetShape = true) const; + void reduceAlongDimension(sd::reduce::BoolOps op, NDArray& target, const std::vector& dimensions, const bool keepDims = false, const bool supportOldShapes = false, const bool checkTargetShape = true) const; + void reduceAlongDimension(sd::reduce::LongOps op, NDArray& target, const std::vector& dimensions, const bool keepDims = false, const bool supportOldShapes = false, const bool checkTargetShape = true) const; /** * return variance of array elements set * biasCorrected - if true bias correction will be applied */ - NDArray varianceNumber(nd4j::variance::Ops op, bool biasCorrected = true); + NDArray varianceNumber(sd::variance::Ops op, bool biasCorrected = true); /** * apply scalar operation to array * extraParams - extra parameters for operation * returns scalar array */ - NDArray reduceNumber(nd4j::reduce::FloatOps ops, void *extraParams = nullptr) const; - NDArray reduceNumber(nd4j::reduce::SameOps ops, void *extraParams = nullptr) const; - NDArray reduceNumber(nd4j::reduce::BoolOps ops, void *extraParams = nullptr) const; - NDArray reduceNumber(nd4j::reduce::LongOps ops, void *extraParams = nullptr) const; + NDArray reduceNumber(sd::reduce::FloatOps ops, void *extraParams = nullptr) const; + NDArray reduceNumber(sd::reduce::SameOps ops, void *extraParams = nullptr) const; + NDArray reduceNumber(sd::reduce::BoolOps ops, void *extraParams = nullptr) const; + NDArray reduceNumber(sd::reduce::LongOps ops, void *extraParams = nullptr) const; - void reduceNumber(nd4j::reduce::FloatOps ops, NDArray& target, void *extraParams = nullptr) const; - void reduceNumber(nd4j::reduce::SameOps ops, NDArray& target, void *extraParams = nullptr) const; - void reduceNumber(nd4j::reduce::BoolOps ops, NDArray& target, void *extraParams = nullptr) const; - void reduceNumber(nd4j::reduce::LongOps ops, NDArray& target, void *extraParams = nullptr) const; + void reduceNumber(sd::reduce::FloatOps ops, NDArray& target, void *extraParams = nullptr) const; + void reduceNumber(sd::reduce::SameOps ops, NDArray& target, void *extraParams = nullptr) const; + void reduceNumber(sd::reduce::BoolOps ops, NDArray& target, void *extraParams = nullptr) const; + void reduceNumber(sd::reduce::LongOps ops, NDArray& target, void *extraParams = nullptr) const; /** * returns element index which corresponds to some condition imposed by operation * extraParams - extra parameters for operation */ - NDArray indexReduceNumber(nd4j::indexreduce::Ops op, ExtraArguments *extraParams = nullptr); + NDArray indexReduceNumber(sd::indexreduce::Ops op, ExtraArguments *extraParams = nullptr); /** * returns index of max element in a given array (optionally: along given dimension(s)) @@ -687,31 +687,31 @@ namespace nd4j { void makeBothActual() const { syncToDevice(); syncToHost(); } - void applyTransform(nd4j::transform::FloatOps op, NDArray& target, ExtraArguments *extraParams = nullptr); - void applyTransform(nd4j::transform::SameOps op, NDArray& target, ExtraArguments *extraParams = nullptr); - void applyTransform(nd4j::transform::AnyOps op, NDArray& target, ExtraArguments *extraParams = nullptr); - void applyTransform(nd4j::transform::BoolOps op, NDArray& target, ExtraArguments *extraParams = nullptr); - void applyTransform(nd4j::transform::StrictOps op, NDArray& target, ExtraArguments *extraParams = nullptr); + void applyTransform(sd::transform::FloatOps op, NDArray& target, ExtraArguments *extraParams = nullptr); + void applyTransform(sd::transform::SameOps op, NDArray& target, ExtraArguments *extraParams = nullptr); + void applyTransform(sd::transform::AnyOps op, NDArray& target, ExtraArguments *extraParams = nullptr); + void applyTransform(sd::transform::BoolOps op, NDArray& target, ExtraArguments *extraParams = nullptr); + void applyTransform(sd::transform::StrictOps op, NDArray& target, ExtraArguments *extraParams = nullptr); /** * apply OpName transformation to this array and store result in new array to be returned * extraParams - extra parameters for operation */ - NDArray transform(nd4j::transform::FloatOps op, void *extraParams = nullptr) const &; - NDArray transform(nd4j::transform::SameOps op, void *extraParams = nullptr) const &; - NDArray transform(nd4j::transform::BoolOps op, void *extraParams = nullptr) const &; - NDArray transform(nd4j::transform::StrictOps op, void *extraParams = nullptr) const &; - NDArray transform(nd4j::transform::FloatOps op, void *extraParams = nullptr) &&; - NDArray transform(nd4j::transform::SameOps op, void *extraParams = nullptr) &&; - NDArray transform(nd4j::transform::BoolOps op, void *extraParams = nullptr) &&; - NDArray transform(nd4j::transform::StrictOps op, void *extraParams = nullptr) &&; + NDArray transform(sd::transform::FloatOps op, void *extraParams = nullptr) const &; + NDArray transform(sd::transform::SameOps op, void *extraParams = nullptr) const &; + NDArray transform(sd::transform::BoolOps op, void *extraParams = nullptr) const &; + NDArray transform(sd::transform::StrictOps op, void *extraParams = nullptr) const &; + NDArray transform(sd::transform::FloatOps op, void *extraParams = nullptr) &&; + NDArray transform(sd::transform::SameOps op, void *extraParams = nullptr) &&; + NDArray transform(sd::transform::BoolOps op, void *extraParams = nullptr) &&; + NDArray transform(sd::transform::StrictOps op, void *extraParams = nullptr) &&; /** * apply pairwise OpName transformation based on "this" and "other" arras elements, store result in this array * other - second array necessary for pairwise operation * extraParams - extra parameters for operation */ - void applyPairwiseTransform(nd4j::pairwise::Ops op, const NDArray& other, ExtraArguments *extraParams = nullptr); + void applyPairwiseTransform(sd::pairwise::Ops op, const NDArray& other, ExtraArguments *extraParams = nullptr); /** * apply pairwise OpName transformation based on "this" and "other" arras elements, store result in target array @@ -719,11 +719,11 @@ namespace nd4j { * target - where to store result * extraParams - extra parameters for operation */ - void applyPairwiseTransform(nd4j::pairwise::Ops op, const NDArray& other, NDArray& target, ExtraArguments *extraParams = nullptr) const; + void applyPairwiseTransform(sd::pairwise::Ops op, const NDArray& other, NDArray& target, ExtraArguments *extraParams = nullptr) const; - void applyPairwiseTransform(nd4j::pairwise::BoolOps op, const NDArray& other, NDArray& target, ExtraArguments *extraParams = nullptr) const; + void applyPairwiseTransform(sd::pairwise::BoolOps op, const NDArray& other, NDArray& target, ExtraArguments *extraParams = nullptr) const; - void applyPairwiseTransform(nd4j::pairwise::IntOps op, const NDArray& other, NDArray&target, ExtraArguments *extraParams = nullptr) const; + void applyPairwiseTransform(sd::pairwise::IntOps op, const NDArray& other, NDArray&target, ExtraArguments *extraParams = nullptr) const; /** * apply operation which requires broadcasting, broadcast a smaller array (tad) along bigger one (this) @@ -732,23 +732,23 @@ namespace nd4j { * target - where to store result * extraParams - extra parameters for operation */ - void applyBroadcast(nd4j::broadcast::Ops op, const std::initializer_list dimensions, const NDArray& tad, NDArray& target, ExtraArguments* extraArgs = nullptr); + void applyBroadcast(sd::broadcast::Ops op, const std::initializer_list dimensions, const NDArray& tad, NDArray& target, ExtraArguments* extraArgs = nullptr); - void applyBroadcast(nd4j::broadcast::Ops op, const std::vector &dimensions, const NDArray &tad, NDArray &target, ExtraArguments *extraArgs = nullptr); + void applyBroadcast(sd::broadcast::Ops op, const std::vector &dimensions, const NDArray &tad, NDArray &target, ExtraArguments *extraArgs = nullptr); - void applyBroadcast(nd4j::broadcast::BoolOps op, const std::vector &dimensions, const NDArray &tad, NDArray &target, ExtraArguments *extraArgs = nullptr); + void applyBroadcast(sd::broadcast::BoolOps op, const std::vector &dimensions, const NDArray &tad, NDArray &target, ExtraArguments *extraArgs = nullptr); - void applyBroadcast(nd4j::broadcast::IntOps op, const std::vector &dimensions, const NDArray& tad, NDArray &target, ExtraArguments *extraArgs = nullptr); + void applyBroadcast(sd::broadcast::IntOps op, const std::vector &dimensions, const NDArray& tad, NDArray &target, ExtraArguments *extraArgs = nullptr); /** * apply operation which requires broadcasting, broadcast one tensor along another, also this method checks the possibility of broadcasting * other - input array * extraParams - extra parameters for operation */ - NDArray applyTrueBroadcast(nd4j::BroadcastOpsTuple op, const NDArray& other, ExtraArguments *extraArgs = nullptr) const &; - NDArray applyTrueBroadcast(nd4j::BroadcastOpsTuple op, NDArray&& other, ExtraArguments *extraArgs = nullptr) const &; - NDArray applyTrueBroadcast(nd4j::BroadcastOpsTuple op, NDArray&& other, ExtraArguments *extraArgs = nullptr) &&; - NDArray applyTrueBroadcast(nd4j::BroadcastOpsTuple op, const NDArray& other, ExtraArguments *extraArgs = nullptr) &&; + NDArray applyTrueBroadcast(sd::BroadcastOpsTuple op, const NDArray& other, ExtraArguments *extraArgs = nullptr) const &; + NDArray applyTrueBroadcast(sd::BroadcastOpsTuple op, NDArray&& other, ExtraArguments *extraArgs = nullptr) const &; + NDArray applyTrueBroadcast(sd::BroadcastOpsTuple op, NDArray&& other, ExtraArguments *extraArgs = nullptr) &&; + NDArray applyTrueBroadcast(sd::BroadcastOpsTuple op, const NDArray& other, ExtraArguments *extraArgs = nullptr) &&; /** * apply operation which requires broadcasting, broadcast one tensor along another, also this method checks the possibility of broadcasting @@ -757,11 +757,11 @@ namespace nd4j { * checkTargetShape - if true check whether target shape is suitable for broadcasting * extraParams - extra parameters for operation */ - void applyTrueBroadcast(nd4j::BroadcastOpsTuple op, const NDArray& other, NDArray& target, const bool checkTargetShape = true, ExtraArguments *extraArgs = nullptr) const; + void applyTrueBroadcast(sd::BroadcastOpsTuple op, const NDArray& other, NDArray& target, const bool checkTargetShape = true, ExtraArguments *extraArgs = nullptr) const; - void applyTrueBroadcast(nd4j::BroadcastBoolOpsTuple op, const NDArray& other, NDArray& target, const bool checkTargetShape = true, ExtraArguments *extraArgs = nullptr) const; + void applyTrueBroadcast(sd::BroadcastBoolOpsTuple op, const NDArray& other, NDArray& target, const bool checkTargetShape = true, ExtraArguments *extraArgs = nullptr) const; - void applyTrueBroadcast(nd4j::BroadcastIntOpsTuple op, const NDArray& other, NDArray& target, const bool checkTargetShape = true, ExtraArguments *extraArgs = nullptr) const; + void applyTrueBroadcast(sd::BroadcastIntOpsTuple op, const NDArray& other, NDArray& target, const bool checkTargetShape = true, ExtraArguments *extraArgs = nullptr) const; /** @@ -771,13 +771,13 @@ namespace nd4j { * extraParams - extra parameters for operation */ template - void applyScalar(nd4j::scalar::Ops op, const T scalar, NDArray& target, ExtraArguments *extraParams = nullptr); + void applyScalar(sd::scalar::Ops op, const T scalar, NDArray& target, ExtraArguments *extraParams = nullptr); template - void applyScalar(nd4j::scalar::BoolOps op, const T scalar, NDArray& target, ExtraArguments *extraParams = nullptr) const; + void applyScalar(sd::scalar::BoolOps op, const T scalar, NDArray& target, ExtraArguments *extraParams = nullptr) const; template - void applyScalar(nd4j::scalar::IntOps op, const T scalar, NDArray& target, ExtraArguments *extraParams = nullptr) const; + void applyScalar(sd::scalar::IntOps op, const T scalar, NDArray& target, ExtraArguments *extraParams = nullptr) const; /** * apply a scalar operation to an array @@ -785,11 +785,11 @@ namespace nd4j { * target - where to store result * extraParams - extra parameters for operation */ - void applyScalarArr(nd4j::scalar::Ops op, const NDArray& scalar, NDArray& target, ExtraArguments *extraParams = nullptr); + void applyScalarArr(sd::scalar::Ops op, const NDArray& scalar, NDArray& target, ExtraArguments *extraParams = nullptr); - void applyScalarArr(nd4j::scalar::BoolOps op, const NDArray& scalar, NDArray& target, ExtraArguments *extraParams = nullptr) const; + void applyScalarArr(sd::scalar::BoolOps op, const NDArray& scalar, NDArray& target, ExtraArguments *extraParams = nullptr) const; - void applyScalarArr(nd4j::scalar::IntOps op, const NDArray& scalar, NDArray& target, ExtraArguments *extraParams = nullptr) const; + void applyScalarArr(sd::scalar::IntOps op, const NDArray& scalar, NDArray& target, ExtraArguments *extraParams = nullptr) const; #if defined(__CUDABLAS__) //&& defined(BUILD_TESTS) template @@ -840,7 +840,7 @@ namespace nd4j { * dimensions - vector of dimensions to reduce along * extraArgs - extra parameters for operation */ - NDArray applyIndexReduce(nd4j::indexreduce::Ops op, const std::vector& dimensions, const ExtraArguments *extraParams = nullptr) const; + NDArray applyIndexReduce(sd::indexreduce::Ops op, const std::vector& dimensions, const ExtraArguments *extraParams = nullptr) const; /** * reduces dimensions in array relying on index operation OpName @@ -848,14 +848,14 @@ namespace nd4j { * dimensions - vector of dimensions to reduce along * extraArgs - extra parameters for operation */ - void applyIndexReduce(nd4j::indexreduce::Ops op, NDArray& target, const std::vector& dimensions, const ExtraArguments *extraParams = nullptr) const; + void applyIndexReduce(sd::indexreduce::Ops op, NDArray& target, const std::vector& dimensions, const ExtraArguments *extraParams = nullptr) const; /** * apply reduce3 operation OpName to this and other array, return result in new output array * other - input array * extraArgs - extra parameters for operation */ - NDArray applyReduce3(nd4j::reduce3::Ops op, const NDArray& other, const ExtraArguments* extraParams = nullptr) const; + NDArray applyReduce3(sd::reduce3::Ops op, const NDArray& other, const ExtraArguments* extraParams = nullptr) const; /** * apply reduce3 operation OpName to this and other array, return result in new output array @@ -863,7 +863,7 @@ namespace nd4j { * dimensions - vector of dimensions to reduce along (tads not axis) * extraArgs - extra parameters for operation */ - NDArray applyAllReduce3(nd4j::reduce3::Ops op, const NDArray& other, const std::vector& dimensions, const ExtraArguments* extraParams = nullptr) const; + NDArray applyAllReduce3(sd::reduce3::Ops op, const NDArray& other, const std::vector& dimensions, const ExtraArguments* extraParams = nullptr) const; /** * apply reduce3 (exec) operation OpName to this and other array, return result in new output array @@ -871,18 +871,18 @@ namespace nd4j { * dimensions - vector of dimensions to reduce along (same as reduceAlongDimension) * extraArgs - extra parameters for operation */ - NDArray applyReduce3(nd4j::reduce3::Ops op, const NDArray& other, const std::vector& dimensions, const ExtraArguments* extraParams = nullptr) const; + NDArray applyReduce3(sd::reduce3::Ops op, const NDArray& other, const std::vector& dimensions, const ExtraArguments* extraParams = nullptr) const; /** * returns variance along given dimensions * biasCorrected - if true bias correction will be applied * dimensions - vector of dimensions to calculate variance along */ - NDArray varianceAlongDimension(nd4j::variance::Ops op, const bool biasCorrected, const std::vector& dimensions) const; - NDArray varianceAlongDimension(nd4j::variance::Ops op, const bool biasCorrected, const std::initializer_list& dimensions) const; + NDArray varianceAlongDimension(sd::variance::Ops op, const bool biasCorrected, const std::vector& dimensions) const; + NDArray varianceAlongDimension(sd::variance::Ops op, const bool biasCorrected, const std::initializer_list& dimensions) const; - void varianceAlongDimension(nd4j::variance::Ops op, NDArray& target, const bool biasCorrected, const std::vector& dimensions) const; - void varianceAlongDimension(nd4j::variance::Ops op, NDArray& target, const bool biasCorrected, const std::initializer_list& dimensions) const; + void varianceAlongDimension(sd::variance::Ops op, NDArray& target, const bool biasCorrected, const std::vector& dimensions) const; + void varianceAlongDimension(sd::variance::Ops op, NDArray& target, const bool biasCorrected, const std::initializer_list& dimensions) const; #endif @@ -903,14 +903,6 @@ namespace nd4j { */ void transposei(); - /** - * return array pointing on certain range of this array - * index - the number of array to be returned among set of possible arrays - * dimensions - array of dimensions to point on - */ - NDArray tensorAlongDimension(Nd4jLong index, const std::initializer_list& dimensions) const; - NDArray tensorAlongDimension(Nd4jLong index, const std::vector& dimensions) const; - /** * returns the number of arrays pointing on specified dimension(s) * dimensions - array of dimensions to point on @@ -1224,7 +1216,7 @@ namespace nd4j { * set _shapeInfo */ void setShapeInfo(const Nd4jLong *shapeInfo); - void setShapeInfo(const Nd4jLong *shapeInfo, const nd4j::DataType dtype); + void setShapeInfo(const Nd4jLong *shapeInfo, const sd::DataType dtype); void setShapeInfo(const ShapeDescriptor& descriptor); void setShapeInfo(const ConstantDataBuffer& shapeBuffer); @@ -1271,7 +1263,7 @@ namespace nd4j { * set _shapeInfo */ FORCEINLINE void setShapeInfo(Nd4jLong *shapeInfo); - FORCEINLINE void setShapeInfo(Nd4jLong *shapeInfo, const nd4j::DataType dtype); + FORCEINLINE void setShapeInfo(Nd4jLong *shapeInfo, const sd::DataType dtype); /** * returns the value of "dim" dimension @@ -1537,13 +1529,13 @@ void NDArray::setShapeInfo(Nd4jLong *shapeInfo) { _length = shape::length(_shapeInfo); } else { - _dataType = nd4j::DataType::INHERIT; + _dataType = sd::DataType::INHERIT; _length = 0; } } ////////////////////////////////////////////////////////////////////////// -void NDArray::setShapeInfo(Nd4jLong *shapeInfo, const nd4j::DataType dtype) { +void NDArray::setShapeInfo(Nd4jLong *shapeInfo, const sd::DataType dtype) { auto buffer = ConstantShapeHelper::getInstance()->bufferForShapeInfo(shapeInfo); _shapeInfo = buffer.primaryAsT(); _shapeInfoD = buffer.specialAsT(); @@ -1556,7 +1548,7 @@ void NDArray::setShapeInfo(Nd4jLong *shapeInfo, const nd4j::DataType dtype) { _length = shape::length(_shapeInfo); } else { - _dataType = nd4j::DataType::INHERIT; + _dataType = sd::DataType::INHERIT; _length = 0; } } @@ -1702,7 +1694,7 @@ bool NDArray::isSameShape(const std::vector& shape) const{ if (this->rankOf() != (int) shape.size()) return false; for (int e = 0; e < this->rankOf(); e++) { - if (this->shapeOf()[e] != shape.at(e) && shape.at(e) != -1) + if (this->shapeOf()[e] != shape[e] && shape[e] != -1) return false; } return true; @@ -1981,7 +1973,7 @@ Nd4jLong* NDArray::getSpecialShapeInfo() const{ #if defined(__CUDACC__) //&& defined(BUILD_TESTS) // for CUDA we need stil stuff inline -#include "cuda/NDArrayLambda.hpp" +#include #endif } diff --git a/libnd4j/blas/NDArray.hpp b/libnd4j/include/array/NDArray.hXX similarity index 82% rename from libnd4j/blas/NDArray.hpp rename to libnd4j/include/array/NDArray.hXX index f7c6d0684..fa39a00f6 100644 --- a/libnd4j/blas/NDArray.hpp +++ b/libnd4j/include/array/NDArray.hXX @@ -21,14 +21,13 @@ #define __NDARRAY__HPP__ #include -#include -#include -#include -#include +#include +#include +#include +#include #include -#include -namespace nd4j { +namespace sd { template <> ND4J_EXPORT utf8string NDArray::e(const Nd4jLong i) const; @@ -57,7 +56,7 @@ NDArray::NDArray(const NDArray& other) { } //////////////////////////////////////////////////////////////////////// -NDArray::NDArray(const char order, const std::vector &shape, nd4j::DataType dtype, nd4j::LaunchContext * context) { +NDArray::NDArray(const char order, const std::vector &shape, sd::DataType dtype, sd::LaunchContext * context) { if ((int) shape.size() > MAX_RANK) throw std::invalid_argument("Rank of NDArray can't exceed 32"); @@ -76,7 +75,7 @@ NDArray::NDArray(const char order, const std::vector &shape, nd4j::Dat } //////////////////////////////////////////////////////////////////////// -NDArray::NDArray(const char order, const std::vector &shape, const std::vector& data, nd4j::DataType dtype, nd4j::LaunchContext * context) { +NDArray::NDArray(const char order, const std::vector &shape, const std::vector& data, sd::DataType dtype, sd::LaunchContext * context) { if ((int) shape.size() > MAX_RANK) throw std::invalid_argument("Rank of NDArray can't exceed 32"); @@ -109,7 +108,7 @@ NDArray::NDArray(const char order, const std::vector &shape, const std //////////////////////////////////////////////////////////////////////// -NDArray::NDArray(const NDArray *other, const bool copyStrides, nd4j::LaunchContext* context) { +NDArray::NDArray(const NDArray *other, const bool copyStrides, sd::LaunchContext* context) { _context = context; _offset = 0; @@ -125,7 +124,7 @@ NDArray::NDArray(const NDArray *other, const bool copyStrides, nd4j::LaunchConte } //////////////////////////////////////////////////////////////////////// -NDArray::NDArray(void* buffer, const char order, const std::vector &shape, nd4j::DataType dtype, nd4j::LaunchContext * context, const bool isBuffAlloc) { +NDArray::NDArray(void* buffer, const char order, const std::vector &shape, sd::DataType dtype, sd::LaunchContext * context, const bool isBuffAlloc) { if (shape.empty()) throw std::runtime_error("NDArray constructor: input shape is empty !"); @@ -144,7 +143,7 @@ NDArray::NDArray(void* buffer, const char order, const std::vector &sh //////////////////////////////////////////////////////////////////////// // creates new NDArray using shape information from "shapeInfo" array, set all elements in new array to be zeros -NDArray::NDArray(Nd4jLong* shapeInfo, const nd4j::DataType dtype, const bool copyStrides, nd4j::LaunchContext * context) { +NDArray::NDArray(Nd4jLong* shapeInfo, const sd::DataType dtype, const bool copyStrides, sd::LaunchContext * context) { if (shapeInfo == nullptr) throw std::runtime_error("NDArray constructor: can't be initalized without shapeinfo"); @@ -168,7 +167,7 @@ NDArray::NDArray(Nd4jLong* shapeInfo, const nd4j::DataType dtype, const bool cop //////////////////////////////////////////////////////////////////////// // scalar constructor -NDArray::NDArray(nd4j::DataType dtype, nd4j::LaunchContext* context, const bool isScalar) { +NDArray::NDArray(sd::DataType dtype, sd::LaunchContext* context, const bool isScalar) { _context = context; _offset = 0; @@ -203,7 +202,7 @@ NDArray::NDArray(NDArray&& other) noexcept { //////////////////////////////////////////////////////////////////////// //constructor, create empty array at given workspace -NDArray::NDArray(nd4j::LaunchContext * context) { +NDArray::NDArray(sd::LaunchContext * context) { _buffer = std::make_shared(); _shapeInfo = nullptr; _shapeInfoD = nullptr; @@ -214,12 +213,12 @@ NDArray::NDArray(nd4j::LaunchContext * context) { //////////////////////////////////////////////////////////////////////// // creates new NDArray using shape information from "shapeInfo" array, set all elements in new array to be zeros, set dtype as array type -NDArray::NDArray(Nd4jLong* shapeInfo, const bool copyStrides, nd4j::LaunchContext * context): +NDArray::NDArray(Nd4jLong* shapeInfo, const bool copyStrides, sd::LaunchContext * context): NDArray(shapeInfo, ArrayOptions::dataType(shapeInfo), copyStrides, context) { } //////////////////////////////////////////////////////////////////////// -NDArray::NDArray(std::shared_ptr buffer, const ShapeDescriptor& descriptor, nd4j::LaunchContext* context, const Nd4jLong offset) { +NDArray::NDArray(std::shared_ptr buffer, const ShapeDescriptor& descriptor, sd::LaunchContext* context, const Nd4jLong offset) { _context = context; _offset = offset; @@ -233,7 +232,7 @@ NDArray::NDArray(std::shared_ptr buffer, const ShapeDescriptor& desc //////////////////////////////////////////////////////////////////////// // do not allocate memory, memory for array is passed from outside -NDArray::NDArray(void *buffer, Nd4jLong *shapeInfo, nd4j::LaunchContext * context, const bool isBuffAlloc) { +NDArray::NDArray(void *buffer, Nd4jLong *shapeInfo, sd::LaunchContext * context, const bool isBuffAlloc) { if (buffer == nullptr && ArrayOptions::arrayType(shapeInfo) != ArrayType::EMPTY) throw std::runtime_error("NDArray constructor: can't be initalized with nullptr buffer !"); @@ -262,7 +261,7 @@ NDArray::NDArray(void *buffer, Nd4jLong *shapeInfo, nd4j::LaunchContext * contex //////////////////////////////////////////////////////////////////////// // do not allocate memory, memory for array is passed from outside // we suppose the content of both (device and host) buffers is identical -NDArray::NDArray(void *buffer, void* bufferD, Nd4jLong *shapeInfo, nd4j::LaunchContext * context, const bool isBuffAlloc, const bool isBuffDAlloc) { +NDArray::NDArray(void *buffer, void* bufferD, Nd4jLong *shapeInfo, sd::LaunchContext * context, const bool isBuffAlloc, const bool isBuffDAlloc) { if (shapeInfo == nullptr) throw std::runtime_error("NDArray constructor cuda: can't be initalized without shapeinfo"); @@ -280,7 +279,7 @@ NDArray::NDArray(void *buffer, void* bufferD, Nd4jLong *shapeInfo, nd4j::LaunchC } ////////////////////////////////////////////////////////////////////////// -NDArray::NDArray(std::shared_ptr buffer, const char order, const std::vector &shape, nd4j::LaunchContext* context) { +NDArray::NDArray(std::shared_ptr buffer, const char order, const std::vector &shape, sd::LaunchContext* context) { if (shape.empty()) throw std::runtime_error("NDArray constructor: input shape is empty !"); @@ -299,7 +298,7 @@ NDArray::NDArray(std::shared_ptr buffer, const char order, const std } ///////////////////////////////////////////////////////////////////////// // u16 string constructors -NDArray::NDArray(const std::u16string& u16string, nd4j::DataType dtype, nd4j::LaunchContext* context) { +NDArray::NDArray(const std::u16string& u16string, sd::DataType dtype, sd::LaunchContext* context) { if (!DataTypeUtils::isS(dtype)) { throw std::invalid_argument("NDArray::NDArray: invalid DataType, only string dataTypes have to be used"); @@ -351,7 +350,7 @@ NDArray::NDArray(const std::u16string& u16string, nd4j::DataType dtype, nd4j::La ///////////////////////////////////////////////////////////////////////// // u32 string constructors -NDArray::NDArray(const std::u32string& u32string, nd4j::DataType dtype, nd4j::LaunchContext* context) { +NDArray::NDArray(const std::u32string& u32string, sd::DataType dtype, sd::LaunchContext* context) { if (!DataTypeUtils::isS(dtype)) { throw std::invalid_argument("NDArray::NDArray: invalid DataType, only string dataTypes have to be used"); @@ -403,7 +402,7 @@ NDArray::NDArray(const std::u32string& u32string, nd4j::DataType dtype, nd4j::La ///////////////////////////////////////////////////////////////////////// // u8 string constructors ///////////////////////////////////////////////////////////////////////// -NDArray::NDArray(const std::string& str, nd4j::DataType dtype, nd4j::LaunchContext* context) { +NDArray::NDArray(const std::string& str, sd::DataType dtype, sd::LaunchContext* context) { if (!DataTypeUtils::isS(dtype)) { throw std::invalid_argument("NDArray::NDArray: invalid DataType, only string dataTypes have to be used"); @@ -455,7 +454,7 @@ NDArray::NDArray(const std::string& str, nd4j::DataType dtype, nd4j::LaunchConte } ///////////////////////////////////////////////////////////////////////// // constructors for vector of strings -NDArray::NDArray(const std::vector& shape, const std::vector& string, const nd4j::DataType dataType, nd4j::LaunchContext* context) { +NDArray::NDArray(const std::vector& shape, const std::vector& string, const sd::DataType dataType, sd::LaunchContext* context) { if (!DataTypeUtils::isS(dataType)) throw std::invalid_argument("NDArray::NDArray: invalid DataType, only string dataTypes have to be used"); @@ -521,7 +520,7 @@ NDArray::NDArray(const std::vector& shape, const std::vector& shape, const std::vector& string, const nd4j::DataType dataType, nd4j::LaunchContext* context) { +NDArray::NDArray(const std::vector& shape, const std::vector& string, const sd::DataType dataType, sd::LaunchContext* context) { if (!DataTypeUtils::isS(dataType)) throw std::invalid_argument("NDArray::NDArray: invalid DataType, only string dataTypes have to be used"); @@ -589,7 +588,7 @@ NDArray::NDArray(const std::vector& shape, const std::vector& shape, const std::vector& string, nd4j::DataType dtype, nd4j::LaunchContext* context) { +NDArray::NDArray(const std::vector& shape, const std::vector& string, sd::DataType dtype, sd::LaunchContext* context) { if (!DataTypeUtils::isS(dtype)) throw std::invalid_argument("NDArray::NDArray: invalid DataType, only string dataTypes have to be used"); @@ -654,7 +653,7 @@ NDArray::NDArray(const std::vector& shape, const std::vector& shape, const std::vector& string, nd4j::DataType dtype, nd4j::LaunchContext* context) { +NDArray::NDArray(const std::vector& shape, const std::vector& string, sd::DataType dtype, sd::LaunchContext* context) { if (!DataTypeUtils::isS(dtype)) throw std::invalid_argument("NDArray::NDArray: invalid DataType, only string dataTypes have to be used"); @@ -720,7 +719,7 @@ NDArray::NDArray(const std::vector& shape, const std::vector& shape, const std::vector& string, nd4j::DataType dtype, nd4j::LaunchContext* context) { +NDArray::NDArray(const std::vector& shape, const std::vector& string, sd::DataType dtype, sd::LaunchContext* context) { if (!DataTypeUtils::isS(dtype)) throw std::invalid_argument("NDArray::NDArray: invalid DataType, only string dataTypes have to be used"); @@ -786,7 +785,7 @@ NDArray::NDArray(const std::vector& shape, const std::vector& shape, const std::vector& string, nd4j::DataType dtype, nd4j::LaunchContext* context) { +NDArray::NDArray(const std::vector& shape, const std::vector& string, sd::DataType dtype, sd::LaunchContext* context) { if (!DataTypeUtils::isS(dtype)) throw std::invalid_argument("NDArray::NDArray: invalid DataType used"); @@ -980,7 +979,7 @@ std::string NDArray::asString(Nd4jLong limit) { template std::vector NDArray::getBufferAsVector() { std::vector vector(lengthOf()); - for (int e = 0; e < lengthOf(); e++) + for (Nd4jLong e = 0; e < lengthOf(); e++) vector[e] = this->e(e); return vector; } @@ -1197,14 +1196,9 @@ void NDArray::assign(const NDArray& other, bool allowParallelism) { throw std::runtime_error("NDArray::assign: lengths of arrays are mismatched"); } - // memcpy is allowed only for same order c && same ews (being equal to 1) - if (ordering() == other.ordering() && ordering() == 'c' && dataType() == other.dataType() && ews() == 1 && other.ews() == 1) - copyBuffersContinuouslyFrom(other, other.lengthOf() * other.sizeOfT()); - else { - NDArray::prepareSpecialUse({this}, {&other}); - NativeOpExecutioner::execTransformAny(getContext(), transform::Assign, other.getBuffer(), other.getShapeInfo(), other.getSpecialBuffer(), other.getSpecialShapeInfo(), buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), nullptr, nullptr, nullptr, allowParallelism); - NDArray::registerSpecialUse({this}, {&other}); - } + NDArray::prepareSpecialUse({this}, {&other}); + NativeOpExecutioner::execTransformAny(getContext(), transform::Assign, other.getBuffer(), other.getShapeInfo(), other.getSpecialBuffer(), other.getSpecialShapeInfo(), buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), nullptr, nullptr, nullptr, allowParallelism); + NDArray::registerSpecialUse({this}, {&other}); } } @@ -1221,7 +1215,7 @@ void NDArray::assign(const T& value, bool allowParallelism) { auto temp = NDArrayFactory::create(dataType(), value, this->getContext()); NDArray::prepareSpecialUse({this}, {&temp}); - NativeOpExecutioner::execScalar(getContext(), nd4j::scalar::CopyPws, buffer(), getShapeInfo(), specialBuffer(), getSpecialShapeInfo(), buffer(), getShapeInfo(), specialBuffer(), getSpecialShapeInfo(), temp.buffer(), temp.shapeInfo(), temp.specialBuffer(), temp.getSpecialShapeInfo(), nullptr, allowParallelism); + NativeOpExecutioner::execScalar(getContext(), sd::scalar::CopyPws, buffer(), getShapeInfo(), specialBuffer(), getSpecialShapeInfo(), buffer(), getShapeInfo(), specialBuffer(), getSpecialShapeInfo(), temp.buffer(), temp.shapeInfo(), temp.specialBuffer(), temp.getSpecialShapeInfo(), nullptr, allowParallelism); NDArray::registerSpecialUse({this}, {&temp}); } template ND4J_EXPORT void NDArray::assign(const double& value, bool allowParallelism); @@ -1254,7 +1248,7 @@ NDArray* NDArray::detach() { } ////////////////////////////////////////////////////////////////////////// -NDArray NDArray::varianceNumber(nd4j::variance::Ops op, bool biasCorrected) { +NDArray NDArray::varianceNumber(sd::variance::Ops op, bool biasCorrected) { NDArray res(DataTypeUtils::pickFloatingType(dataType()), getContext()); @@ -1273,7 +1267,7 @@ NDArray NDArray::sumNumber() const { NDArray res(dataType(), getContext()); NDArray::prepareSpecialUse({&res}, {this}); - NativeOpExecutioner::execReduceSameScalar(getContext(), nd4j::reduce::SameOps::Sum, getBuffer(), getShapeInfo(), getSpecialBuffer(), getSpecialShapeInfo(), nullptr, res.buffer(), res.shapeInfo(), res.specialBuffer(), res.specialShapeInfo()); + NativeOpExecutioner::execReduceSameScalar(getContext(), sd::reduce::SameOps::Sum, getBuffer(), getShapeInfo(), getSpecialBuffer(), getSpecialShapeInfo(), nullptr, res.buffer(), res.shapeInfo(), res.specialBuffer(), res.specialShapeInfo()); NDArray::registerSpecialUse({&res}, {this}); return res; @@ -1288,7 +1282,7 @@ NDArray NDArray::meanNumber() const { NDArray res(DataTypeUtils::pickFloatingType(dataType()), getContext()); NDArray::prepareSpecialUse({&res}, {this}); - NativeOpExecutioner::execReduceFloatScalar(getContext(), nd4j::reduce::FloatOps::Mean, getBuffer(), getShapeInfo(), getSpecialBuffer(), getSpecialShapeInfo(), nullptr, res.buffer(), res.shapeInfo(), res.specialBuffer(), res.specialShapeInfo()); + NativeOpExecutioner::execReduceFloatScalar(getContext(), sd::reduce::FloatOps::Mean, getBuffer(), getShapeInfo(), getSpecialBuffer(), getSpecialShapeInfo(), nullptr, res.buffer(), res.shapeInfo(), res.specialBuffer(), res.specialShapeInfo()); NDArray::registerSpecialUse({&res}, {this}); return res; } @@ -1297,21 +1291,21 @@ NDArray NDArray::meanNumber() const { bool NDArray::hasNaNs() { if (isS()) throw std::runtime_error("NDArray::hasNaNs: you can't use this method on String array!"); - return this->reduceNumber(nd4j::reduce::IsNan, nullptr).e(0) > 0; + return this->reduceNumber(sd::reduce::IsNan, nullptr).e(0) > 0; } ////////////////////////////////////////////////////////////////////////// bool NDArray::hasInfs() { if (isS()) throw std::runtime_error("NDArray::hasInfs: you can't use this method on String array!"); - return this->reduceNumber(nd4j::reduce::IsInf, nullptr).e(0) > 0; + return this->reduceNumber(sd::reduce::IsInf, nullptr).e(0) > 0; } ////////////////////////////////////////////////////////////////////////// bool NDArray::isFinite() { if (isS()) throw std::runtime_error("NDArray::isFinite: you can't use this method on String array!"); - return this->reduceNumber(nd4j::reduce::IsInfOrNan, nullptr).e(0) == 0; + return this->reduceNumber(sd::reduce::IsInfOrNan, nullptr).e(0) == 0; } ////////////////////////////////////////////////////////////////////////// @@ -1336,11 +1330,11 @@ void NDArray::templatedSet(void *buffer, const Nd4jLong offset, const void *valu BUILD_DOUBLE_TEMPLATE(template ND4J_EXPORT void NDArray::templatedSet, (void *buffer, const Nd4jLong offset, const void *value), LIBND4J_TYPES, LIBND4J_TYPES); ////////////////////////////////////////////////////////////////////////// -void NDArray::setContext(nd4j::LaunchContext *context) { +void NDArray::setContext(sd::LaunchContext *context) { _context = context; if (getContext() == nullptr) - _context = nd4j::LaunchContext ::defaultContext(); // empty context for default cases + _context = sd::LaunchContext ::defaultContext(); // empty context for default cases } ////////////////////////////////////////////////////////////////////////// @@ -1351,7 +1345,7 @@ void* NDArray::bufferWithOffset(Nd4jLong offset) const { ////////////////////////////////////////////////////////////////////////// // eventually method reduces array by excluding its shapes along axes present in dimensions vector -NDArray NDArray::reduceAlongDimension(nd4j::reduce::FloatOps op, const std::vector& dimensions, const bool keepDims, const bool supportOldShapes) const { +NDArray NDArray::reduceAlongDimension(sd::reduce::FloatOps op, const std::vector& dimensions, const bool keepDims, const bool supportOldShapes) const { std::vector copy(dimensions); @@ -1365,7 +1359,7 @@ NDArray NDArray::reduceAlongDimension(nd4j::reduce::FloatOps op, const std::vect } ////////////////////////////////////////////////////////////////////////// -NDArray NDArray::reduceAlongDimension(nd4j::reduce::SameOps op, const std::vector& dimensions, const bool keepDims, const bool supportOldShapes) const { +NDArray NDArray::reduceAlongDimension(sd::reduce::SameOps op, const std::vector& dimensions, const bool keepDims, const bool supportOldShapes) const { std::vector copy(dimensions); @@ -1379,7 +1373,7 @@ NDArray NDArray::reduceAlongDimension(nd4j::reduce::SameOps op, const std::vecto } ////////////////////////////////////////////////////////////////////////// -NDArray NDArray::reduceAlongDimension(nd4j::reduce::BoolOps op, const std::vector& dimensions, const bool keepDims, const bool supportOldShapes) const { +NDArray NDArray::reduceAlongDimension(sd::reduce::BoolOps op, const std::vector& dimensions, const bool keepDims, const bool supportOldShapes) const { std::vector copy(dimensions); @@ -1393,7 +1387,7 @@ NDArray NDArray::reduceAlongDimension(nd4j::reduce::BoolOps op, const std::vecto } ////////////////////////////////////////////////////////////////////////// -NDArray NDArray::reduceAlongDimension(nd4j::reduce::LongOps op, const std::vector& dimensions, const bool keepDims, const bool supportOldShapes) const { +NDArray NDArray::reduceAlongDimension(sd::reduce::LongOps op, const std::vector& dimensions, const bool keepDims, const bool supportOldShapes) const { std::vector copy(dimensions); @@ -1408,27 +1402,27 @@ NDArray NDArray::reduceAlongDimension(nd4j::reduce::LongOps op, const std::vecto ////////////////////////////////////////////////////////////////////////// // method reduces array by excluding its shapes along axes present in dimensions vector -NDArray NDArray::reduceAlongDimension(nd4j::reduce::FloatOps op, const std::initializer_list& dimensions, const bool keepDims, const bool supportOldShapes) const { +NDArray NDArray::reduceAlongDimension(sd::reduce::FloatOps op, const std::initializer_list& dimensions, const bool keepDims, const bool supportOldShapes) const { return reduceAlongDimension(op, std::vector(dimensions), keepDims, supportOldShapes); } ////////////////////////////////////////////////////////////////////////// -NDArray NDArray::reduceAlongDimension(nd4j::reduce::SameOps op, const std::initializer_list& dimensions, const bool keepDims, const bool supportOldShapes) const { +NDArray NDArray::reduceAlongDimension(sd::reduce::SameOps op, const std::initializer_list& dimensions, const bool keepDims, const bool supportOldShapes) const { return reduceAlongDimension(op, std::vector(dimensions), keepDims, supportOldShapes); } ////////////////////////////////////////////////////////////////////////// -NDArray NDArray::reduceAlongDimension(nd4j::reduce::BoolOps op, const std::initializer_list& dimensions, const bool keepDims, const bool supportOldShapes) const { +NDArray NDArray::reduceAlongDimension(sd::reduce::BoolOps op, const std::initializer_list& dimensions, const bool keepDims, const bool supportOldShapes) const { return reduceAlongDimension(op, std::vector(dimensions), keepDims, supportOldShapes); } ////////////////////////////////////////////////////////////////////////// -NDArray NDArray::reduceAlongDimension(nd4j::reduce::LongOps op, const std::initializer_list& dimensions, const bool keepDims, const bool supportOldShapes) const { +NDArray NDArray::reduceAlongDimension(sd::reduce::LongOps op, const std::initializer_list& dimensions, const bool keepDims, const bool supportOldShapes) const { return reduceAlongDimension(op, std::vector(dimensions), keepDims, supportOldShapes); } ////////////////////////////////////////////////////////////////////////// -NDArray NDArray::reduceNumber(nd4j::reduce::FloatOps op, void *extraParams) const { +NDArray NDArray::reduceNumber(sd::reduce::FloatOps op, void *extraParams) const { if (isS()) throw std::runtime_error("NDArray::reduceNumber FloatOps: you can't use this method on String array!"); @@ -1443,7 +1437,7 @@ NDArray NDArray::reduceNumber(nd4j::reduce::FloatOps op, void *extraParams) cons } ////////////////////////////////////////////////////////////////////////// -NDArray NDArray::reduceNumber(nd4j::reduce::SameOps op, void *extraParams) const { +NDArray NDArray::reduceNumber(sd::reduce::SameOps op, void *extraParams) const { if (isS()) throw std::runtime_error("NDArray::reduceNumber SameOps: you can't use this method on String array!"); @@ -1457,7 +1451,7 @@ NDArray NDArray::reduceNumber(nd4j::reduce::SameOps op, void *extraParams) const } ////////////////////////////////////////////////////////////////////////// -NDArray NDArray::reduceNumber(nd4j::reduce::BoolOps op, void *extraParams) const { +NDArray NDArray::reduceNumber(sd::reduce::BoolOps op, void *extraParams) const { if (isS()) throw std::runtime_error("NDArray::reduceNumber BoolOps: you can't use this method on String array!"); @@ -1472,7 +1466,7 @@ NDArray NDArray::reduceNumber(nd4j::reduce::BoolOps op, void *extraParams) const } ////////////////////////////////////////////////////////////////////////// -NDArray NDArray::reduceNumber(nd4j::reduce::LongOps op, void *extraParams) const { +NDArray NDArray::reduceNumber(sd::reduce::LongOps op, void *extraParams) const { if (isS()) throw std::runtime_error("NDArray::reduceNumber LongOps: you can't use this method on String array!"); @@ -1487,7 +1481,7 @@ NDArray NDArray::reduceNumber(nd4j::reduce::LongOps op, void *extraParams) const } ////////////////////////////////////////////////////////////////////////// -void NDArray::reduceNumber(nd4j::reduce::FloatOps op, NDArray& target, void *extraParams) const { +void NDArray::reduceNumber(sd::reduce::FloatOps op, NDArray& target, void *extraParams) const { if (isS()) throw std::runtime_error("NDArray::reduceNumber FloatOps: you can't use this method on String array!"); if(target.lengthOf() != 1 || target.dataType() != DataTypeUtils::pickFloatingType(dataType())) @@ -1499,7 +1493,7 @@ void NDArray::reduceNumber(nd4j::reduce::FloatOps op, NDArray& target, void *ext } ////////////////////////////////////////////////////////////////////////// -void NDArray::reduceNumber(nd4j::reduce::SameOps op, NDArray& target, void *extraParams) const { +void NDArray::reduceNumber(sd::reduce::SameOps op, NDArray& target, void *extraParams) const { if (isS()) throw std::runtime_error("NDArray::reduceNumber SameOps: you can't use this method on String array!"); @@ -1512,7 +1506,7 @@ void NDArray::reduceNumber(nd4j::reduce::SameOps op, NDArray& target, void *extr } ////////////////////////////////////////////////////////////////////////// -void NDArray::reduceNumber(nd4j::reduce::BoolOps op, NDArray& target, void *extraParams) const { +void NDArray::reduceNumber(sd::reduce::BoolOps op, NDArray& target, void *extraParams) const { if (isS()) throw std::runtime_error("NDArray::reduceNumber BoolOps: you can't use this method on String array!"); @@ -1525,7 +1519,7 @@ void NDArray::reduceNumber(nd4j::reduce::BoolOps op, NDArray& target, void *extr } ////////////////////////////////////////////////////////////////////////// -void NDArray::reduceNumber(nd4j::reduce::LongOps op, NDArray& target, void *extraParams) const { +void NDArray::reduceNumber(sd::reduce::LongOps op, NDArray& target, void *extraParams) const { if (isS()) throw std::runtime_error("NDArray::reduceNumber LongOps: you can't use this method on String array!"); @@ -1538,7 +1532,7 @@ void NDArray::reduceNumber(nd4j::reduce::LongOps op, NDArray& target, void *extr } ////////////////////////////////////////////////////////////////////////// -NDArray NDArray::indexReduceNumber(nd4j::indexreduce::Ops op, ExtraArguments *extraParams) { +NDArray NDArray::indexReduceNumber(sd::indexreduce::Ops op, ExtraArguments *extraParams) { if (isS()) throw std::runtime_error("NDArray::indexReduceNumber: you can't use this method on String array!"); @@ -1612,7 +1606,7 @@ void NDArray::printBuffer(const char* msg, Nd4jLong limit, const bool sync) cons } else if (this->isZ()) { for (Nd4jLong e = 0; e < limit; e++) { - if (this->dataType() != nd4j::DataType::INT64 && this->dataType() != nd4j::DataType::UINT64) + if (this->dataType() != sd::DataType::INT64 && this->dataType() != sd::DataType::UINT64) printf("%d", this->e(e)); else printf("%llu", this->e(e)); @@ -1661,19 +1655,19 @@ void NDArray::printLinearBuffer() const { printf("["); - if (this->dataType() == nd4j::DataType::INT32) { + if (this->dataType() == sd::DataType::INT32) { for(Nd4jLong e = 0; e < len; e++) printf("%d, ", this->bufferAsT()[e * ews]); } - else if(this->dataType() == nd4j::DataType::INT64) { + else if(this->dataType() == sd::DataType::INT64) { for(Nd4jLong e = 0; e < len; e++) printf("%lld, ", this->bufferAsT()[e * ews]); } - else if(this->dataType() == nd4j::DataType::FLOAT32) { + else if(this->dataType() == sd::DataType::FLOAT32) { for(Nd4jLong e = 0; e < len; e++) printf("%.3f, ", this->bufferAsT()[e * ews]); } - else if(this->dataType() == nd4j::DataType::DOUBLE) { + else if(this->dataType() == sd::DataType::DOUBLE) { for(Nd4jLong e = 0; e < len; e++) printf("%.3f, ", this->bufferAsT()[e * ews]); } @@ -2128,15 +2122,15 @@ bool NDArray::isIdentityMatrix() { throw std::runtime_error("isIdentityMatrix method: matrix must be square and have rank = 2 !"); const double eps = 1e-5f; - for(int i=0; i(i,i) - 1.f) > eps) + for(Nd4jLong i=0; i(i,i) - 1.f) > eps) return false; - for(int i=0; i(i,j)) > eps) + if(sd::math::nd4j_abs(e(i,j)) > eps) return false; } } @@ -2335,7 +2329,7 @@ NDArray NDArray::asS() const { Nd4jLong dataLength = 0; auto data = bufferAsT() + offsetsLength; - for (int e = 0; e < lengthOf(); e++) { + for (Nd4jLong e = 0; e < lengthOf(); e++) { offsets[e] = dataLength; start = nInputoffsets[e]; stop = nInputoffsets[e + 1]; @@ -2450,16 +2444,16 @@ void NDArray::operator+=(const NDArray& other) { if (isS()) throw std::runtime_error("NDArray::operator+=: you can't use this method on String array!"); if (!Environment::getInstance()->isExperimentalBuild() && this->dataType() != other.dataType() && (this->dataType() != DataType::BOOL || other.dataType() != BOOL)) - throw nd4j::datatype_exception::build("NDArray operator+=: Cannot add different types", this->dataType(), other.dataType()); + throw sd::datatype_exception::build("NDArray operator+=: Cannot add different types", this->dataType(), other.dataType()); if (this->lengthOf() != 1 && other.lengthOf() == 1) { NDArray::prepareSpecialUse({this}, {this, &other}); - NativeOpExecutioner::execScalar(getContext(), nd4j::scalar::Add, buffer(), getShapeInfo(), specialBuffer(), getSpecialShapeInfo(), buffer(), getShapeInfo(), specialBuffer(), getSpecialShapeInfo(), other.getBuffer(), other.getShapeInfo(), other.getSpecialBuffer(), other.getSpecialShapeInfo(), nullptr); + NativeOpExecutioner::execScalar(getContext(), sd::scalar::Add, buffer(), getShapeInfo(), specialBuffer(), getSpecialShapeInfo(), buffer(), getShapeInfo(), specialBuffer(), getSpecialShapeInfo(), other.getBuffer(), other.getShapeInfo(), other.getSpecialBuffer(), other.getSpecialShapeInfo(), nullptr); NDArray::registerSpecialUse({this}, {this, &other}); } else if (other.lengthOf() == lengthOf() && this->rankOf() == other.rankOf()) { NDArray::prepareSpecialUse({this}, {this, &other}); - NativeOpExecutioner::execPairwiseTransform(getContext(), nd4j::pairwise::Add, buffer(), getShapeInfo(), specialBuffer(), getSpecialShapeInfo(), other.getBuffer(), other.getShapeInfo(), other.getSpecialBuffer(), other.getSpecialShapeInfo(), buffer(), getShapeInfo(), specialBuffer(), getSpecialShapeInfo(), nullptr); + NativeOpExecutioner::execPairwiseTransform(getContext(), sd::pairwise::Add, buffer(), getShapeInfo(), specialBuffer(), getSpecialShapeInfo(), other.getBuffer(), other.getShapeInfo(), other.getSpecialBuffer(), other.getSpecialShapeInfo(), buffer(), getShapeInfo(), specialBuffer(), getSpecialShapeInfo(), nullptr); NDArray::registerSpecialUse({this}, {this, &other}); } else{ @@ -2468,11 +2462,11 @@ void NDArray::operator+=(const NDArray& other) { throw std::invalid_argument("NDArray::operator+=: the shapes of this and other arrays are not suitable for broadcast operation !"); if(shape::equalsTypesAndShapesSoft(getShapeInfo(), bShape)) { - this->applyTrueBroadcast(nd4j::BroadcastOpsTuple::Add(), other, *this, false); + this->applyTrueBroadcast(sd::BroadcastOpsTuple::Add(), other, *this, false); } else { NDArray result(bShape, true, getContext()); - this->applyTrueBroadcast(nd4j::BroadcastOpsTuple::Add(), other, result, false); + this->applyTrueBroadcast(sd::BroadcastOpsTuple::Add(), other, result, false); *this = std::move(result); // move assignment operator, zero cost copy } } @@ -2484,16 +2478,16 @@ void NDArray::operator-=(const NDArray& other) { throw std::runtime_error("NDArray::operator-=: you can't use this method on String array!"); if (!Environment::getInstance()->isExperimentalBuild() && this->dataType() != other.dataType() && (this->dataType() != DataType::BOOL || other.dataType() != BOOL)) - throw nd4j::datatype_exception::build("NDArray operator-=: Cannot subtract different types", this->dataType(), other.dataType()); + throw sd::datatype_exception::build("NDArray operator-=: Cannot subtract different types", this->dataType(), other.dataType()); if (lengthOf() != 1 && other.lengthOf() == 1) { NDArray::prepareSpecialUse({this}, {this, &other}); - NativeOpExecutioner::execScalar(getContext(), nd4j::scalar::Subtract, buffer(), getShapeInfo(), specialBuffer(), getSpecialShapeInfo(), buffer(), getShapeInfo(), specialBuffer(), getSpecialShapeInfo(), other.getBuffer(), other.getShapeInfo(), other.getSpecialBuffer(), other.getSpecialShapeInfo(), nullptr); + NativeOpExecutioner::execScalar(getContext(), sd::scalar::Subtract, buffer(), getShapeInfo(), specialBuffer(), getSpecialShapeInfo(), buffer(), getShapeInfo(), specialBuffer(), getSpecialShapeInfo(), other.getBuffer(), other.getShapeInfo(), other.getSpecialBuffer(), other.getSpecialShapeInfo(), nullptr); NDArray::registerSpecialUse({this}, {this, &other}); } else if (other.lengthOf() == lengthOf() && this->rankOf() == other.rankOf()) { NDArray::prepareSpecialUse({this}, {this, &other}); - NativeOpExecutioner::execPairwiseTransform(getContext(), nd4j::pairwise::Subtract, buffer(), getShapeInfo(), specialBuffer(), getSpecialShapeInfo(), other.getBuffer(), other.getShapeInfo(), other.getSpecialBuffer(), other.getSpecialShapeInfo(), buffer(), getShapeInfo(), specialBuffer(), getSpecialShapeInfo(), nullptr); + NativeOpExecutioner::execPairwiseTransform(getContext(), sd::pairwise::Subtract, buffer(), getShapeInfo(), specialBuffer(), getSpecialShapeInfo(), other.getBuffer(), other.getShapeInfo(), other.getSpecialBuffer(), other.getSpecialShapeInfo(), buffer(), getShapeInfo(), specialBuffer(), getSpecialShapeInfo(), nullptr); NDArray::registerSpecialUse({this}, {this, &other}); } else{ @@ -2502,11 +2496,11 @@ void NDArray::operator-=(const NDArray& other) { throw std::invalid_argument("NDArray::operator-=: the shapes of this and other arrays are not suitable for broadcast operation !"); if(shape::equalsTypesAndShapesSoft(getShapeInfo(), bShape)) { - this->applyTrueBroadcast(nd4j::BroadcastOpsTuple::Subtract(), other, *this, false); + this->applyTrueBroadcast(sd::BroadcastOpsTuple::Subtract(), other, *this, false); } else { NDArray result(bShape, true, getContext()); - this->applyTrueBroadcast(nd4j::BroadcastOpsTuple::Subtract(), other, result, false); + this->applyTrueBroadcast(sd::BroadcastOpsTuple::Subtract(), other, result, false); *this = std::move(result); // move assignment operator, zero cost copy } } @@ -2517,16 +2511,16 @@ void NDArray::operator*=(const NDArray& other) { if (isS()) throw std::runtime_error("NDArray::operator*=: you can't use this method on String array!"); if (!Environment::getInstance()->isExperimentalBuild() && this->dataType() != other.dataType() && (this->dataType() != DataType::BOOL || other.dataType() != BOOL)) - throw nd4j::datatype_exception::build("NDArray operator*=: Cannot multiply different types", this->dataType(), other.dataType()); + throw sd::datatype_exception::build("NDArray operator*=: Cannot multiply different types", this->dataType(), other.dataType()); if (lengthOf() != 1 && other.lengthOf() == 1) { NDArray::prepareSpecialUse({this}, {this, &other}); - NativeOpExecutioner::execScalar(getContext(), nd4j::scalar::Multiply, buffer(), getShapeInfo(), specialBuffer(), getSpecialShapeInfo(), buffer(), getShapeInfo(), specialBuffer(), getSpecialShapeInfo(), other.getBuffer(), other.getShapeInfo(), other.getSpecialBuffer(), other.getSpecialShapeInfo(), nullptr); + NativeOpExecutioner::execScalar(getContext(), sd::scalar::Multiply, buffer(), getShapeInfo(), specialBuffer(), getSpecialShapeInfo(), buffer(), getShapeInfo(), specialBuffer(), getSpecialShapeInfo(), other.getBuffer(), other.getShapeInfo(), other.getSpecialBuffer(), other.getSpecialShapeInfo(), nullptr); NDArray::registerSpecialUse({this}, {this, &other}); } else if (other.lengthOf() == lengthOf() && this->rankOf() == other.rankOf()) { NDArray::prepareSpecialUse({this}, {this, &other}); - NativeOpExecutioner::execPairwiseTransform(getContext(), nd4j::pairwise::Multiply, buffer(), getShapeInfo(), specialBuffer(), getSpecialShapeInfo(), other.getBuffer(), other.getShapeInfo(), other.getSpecialBuffer(), other.getSpecialShapeInfo(), buffer(), getShapeInfo(), specialBuffer(), getSpecialShapeInfo(), nullptr); + NativeOpExecutioner::execPairwiseTransform(getContext(), sd::pairwise::Multiply, buffer(), getShapeInfo(), specialBuffer(), getSpecialShapeInfo(), other.getBuffer(), other.getShapeInfo(), other.getSpecialBuffer(), other.getSpecialShapeInfo(), buffer(), getShapeInfo(), specialBuffer(), getSpecialShapeInfo(), nullptr); NDArray::registerSpecialUse({this}, {this, &other}); } else{ @@ -2535,11 +2529,11 @@ void NDArray::operator*=(const NDArray& other) { throw std::invalid_argument("NDArray::operator*=: the shapes of this and other arrays are not suitable for broadcast operation !"); if(shape::equalsTypesAndShapesSoft(_shapeInfo, bShape)) { - this->applyTrueBroadcast(nd4j::BroadcastOpsTuple::Multiply(), other, *this, false); + this->applyTrueBroadcast(sd::BroadcastOpsTuple::Multiply(), other, *this, false); } else { NDArray result(bShape, true, getContext()); - this->applyTrueBroadcast(nd4j::BroadcastOpsTuple::Multiply(), other, result, false); + this->applyTrueBroadcast(sd::BroadcastOpsTuple::Multiply(), other, result, false); *this = std::move(result); // move assignment operator, zero cost copy } } @@ -2553,17 +2547,17 @@ void NDArray::operator/=(const NDArray& other) { throw std::runtime_error("NDArray::operator/=: you can't divide by bool array!"); if (!Environment::getInstance()->isExperimentalBuild() && this->dataType() != other.dataType()) { - throw nd4j::datatype_exception::build("NDArray operator/=: Cannot divide different types", this->dataType(), other.dataType()); + throw sd::datatype_exception::build("NDArray operator/=: Cannot divide different types", this->dataType(), other.dataType()); } if (lengthOf() != 1 && other.lengthOf() == 1) { NDArray::prepareSpecialUse({this}, {this, &other}); - NativeOpExecutioner::execScalar(getContext(), nd4j::scalar::Divide, buffer(), getShapeInfo(), specialBuffer(), getSpecialShapeInfo(), buffer(), getShapeInfo(), specialBuffer(), getSpecialShapeInfo(), other.getBuffer(), other.getShapeInfo(), other.getSpecialBuffer(), other.getSpecialShapeInfo(), nullptr); + NativeOpExecutioner::execScalar(getContext(), sd::scalar::Divide, buffer(), getShapeInfo(), specialBuffer(), getSpecialShapeInfo(), buffer(), getShapeInfo(), specialBuffer(), getSpecialShapeInfo(), other.getBuffer(), other.getShapeInfo(), other.getSpecialBuffer(), other.getSpecialShapeInfo(), nullptr); NDArray::registerSpecialUse({this}, {this, &other}); } else if (other.lengthOf() == lengthOf() && this->rankOf() == other.rankOf()) { NDArray::prepareSpecialUse({this}, {this, &other}); - NativeOpExecutioner::execPairwiseTransform(getContext(), nd4j::pairwise::Divide, buffer(), getShapeInfo(), specialBuffer(), getSpecialShapeInfo(), other.getBuffer(), other.getShapeInfo(), other.getSpecialBuffer(), other.getSpecialShapeInfo(), buffer(), getShapeInfo(), specialBuffer(), getSpecialShapeInfo(), nullptr); + NativeOpExecutioner::execPairwiseTransform(getContext(), sd::pairwise::Divide, buffer(), getShapeInfo(), specialBuffer(), getSpecialShapeInfo(), other.getBuffer(), other.getShapeInfo(), other.getSpecialBuffer(), other.getSpecialShapeInfo(), buffer(), getShapeInfo(), specialBuffer(), getSpecialShapeInfo(), nullptr); NDArray::registerSpecialUse({this}, {this, &other}); } else{ @@ -2572,11 +2566,11 @@ void NDArray::operator/=(const NDArray& other) { throw std::invalid_argument("NDArray::operator/=: the shapes of this and other arrays are not suitable for broadcast operation !"); if(shape::equalsTypesAndShapesSoft(_shapeInfo, bShape)) { - this->applyTrueBroadcast(nd4j::BroadcastOpsTuple::Divide(), other, *this, false); + this->applyTrueBroadcast(sd::BroadcastOpsTuple::Divide(), other, *this, false); } else { NDArray result(bShape, true, getContext()); - this->applyTrueBroadcast(nd4j::BroadcastOpsTuple::Divide(), other, result, false); + this->applyTrueBroadcast(sd::BroadcastOpsTuple::Divide(), other, result, false); *this = std::move(result); // move assignment operator, zero cost copy } } @@ -2592,7 +2586,7 @@ void NDArray::operator+=(const T value) { NDArray::prepareSpecialUse({this}, {&other}); - NativeOpExecutioner::execScalar(getContext(), nd4j::scalar::Add, buffer(), getShapeInfo(), specialBuffer(), getSpecialShapeInfo(), buffer(), getShapeInfo(), specialBuffer(), getSpecialShapeInfo(), other.getBuffer(), other.getShapeInfo(), other.getSpecialBuffer(), other.getSpecialShapeInfo(), nullptr); + NativeOpExecutioner::execScalar(getContext(), sd::scalar::Add, buffer(), getShapeInfo(), specialBuffer(), getSpecialShapeInfo(), buffer(), getShapeInfo(), specialBuffer(), getSpecialShapeInfo(), other.getBuffer(), other.getShapeInfo(), other.getSpecialBuffer(), other.getSpecialShapeInfo(), nullptr); NDArray::registerSpecialUse({this}, {}); } @@ -2614,7 +2608,7 @@ void NDArray::operator-=(const T value) { NDArray::prepareSpecialUse({this}, {&other}); - NativeOpExecutioner::execScalar(getContext(), nd4j::scalar::Subtract, buffer(), getShapeInfo(), specialBuffer(), getSpecialShapeInfo(), buffer(), getShapeInfo(), specialBuffer(), getSpecialShapeInfo(), other.getBuffer(), other.getShapeInfo(), other.getSpecialBuffer(), other.getSpecialShapeInfo(), nullptr); + NativeOpExecutioner::execScalar(getContext(), sd::scalar::Subtract, buffer(), getShapeInfo(), specialBuffer(), getSpecialShapeInfo(), buffer(), getShapeInfo(), specialBuffer(), getSpecialShapeInfo(), other.getBuffer(), other.getShapeInfo(), other.getSpecialBuffer(), other.getSpecialShapeInfo(), nullptr); NDArray::registerSpecialUse({this}, {}); } @@ -2634,7 +2628,7 @@ void NDArray::operator*=(const T scalar) { auto other = NDArrayFactory::create(this->dataType(), scalar, getContext()); NDArray::prepareSpecialUse({this}, {&other}); - NativeOpExecutioner::execScalar(getContext(), nd4j::scalar::Multiply, buffer(), getShapeInfo(), specialBuffer(), getSpecialShapeInfo(), buffer(), getShapeInfo(), specialBuffer(), getSpecialShapeInfo(), other.getBuffer(), other.getShapeInfo(), other.getSpecialBuffer(), other.getSpecialShapeInfo(), nullptr); + NativeOpExecutioner::execScalar(getContext(), sd::scalar::Multiply, buffer(), getShapeInfo(), specialBuffer(), getSpecialShapeInfo(), buffer(), getShapeInfo(), specialBuffer(), getSpecialShapeInfo(), other.getBuffer(), other.getShapeInfo(), other.getSpecialBuffer(), other.getSpecialShapeInfo(), nullptr); NDArray::registerSpecialUse({this}, {}); } @@ -2657,7 +2651,7 @@ void NDArray::operator/=(const T scalar) { auto other = NDArrayFactory::create(this->dataType(), scalar, getContext()); NDArray::prepareSpecialUse({this}, {&other}); - NativeOpExecutioner::execScalar(getContext(), nd4j::scalar::Divide, buffer(), getShapeInfo(), specialBuffer(), getSpecialShapeInfo(), buffer(), getShapeInfo(), specialBuffer(), getSpecialShapeInfo(), other.getBuffer(), other.getShapeInfo(), other.getSpecialBuffer(), other.getSpecialShapeInfo(), nullptr); + NativeOpExecutioner::execScalar(getContext(), sd::scalar::Divide, buffer(), getShapeInfo(), specialBuffer(), getSpecialShapeInfo(), buffer(), getShapeInfo(), specialBuffer(), getSpecialShapeInfo(), other.getBuffer(), other.getShapeInfo(), other.getSpecialBuffer(), other.getSpecialShapeInfo(), nullptr); NDArray::registerSpecialUse({this}, {}); } template ND4J_EXPORT void NDArray::operator/=(const double scalar); @@ -2680,7 +2674,7 @@ NDArray NDArray::operator-() const & { NDArray result(getShapeInfo(), false, getContext()); NDArray::prepareSpecialUse({&result}, {this}); - NativeOpExecutioner::execTransformSame(getContext(), nd4j::transform::Neg, getBuffer(), getShapeInfo(), getSpecialBuffer(), getSpecialShapeInfo(), result.buffer(), result.getShapeInfo(), result.specialBuffer(), result.getSpecialShapeInfo(), nullptr, nullptr, nullptr); + NativeOpExecutioner::execTransformSame(getContext(), sd::transform::Neg, getBuffer(), getShapeInfo(), getSpecialBuffer(), getSpecialShapeInfo(), result.buffer(), result.getShapeInfo(), result.specialBuffer(), result.getSpecialShapeInfo(), nullptr, nullptr, nullptr); NDArray::registerSpecialUse({&result}, {this}); return result; @@ -2692,7 +2686,7 @@ NDArray NDArray::operator-() && { throw std::runtime_error("NDArray::negative-: you can't use this method on String array!"); NDArray::prepareSpecialUse({this}, {this}); - NativeOpExecutioner::execTransformSame(getContext(), nd4j::transform::Neg, getBuffer(), getShapeInfo(), getSpecialBuffer(), getSpecialShapeInfo(), buffer(), getShapeInfo(), specialBuffer(), getSpecialShapeInfo(), nullptr, nullptr, nullptr); + NativeOpExecutioner::execTransformSame(getContext(), sd::transform::Neg, getBuffer(), getShapeInfo(), getSpecialBuffer(), getSpecialShapeInfo(), buffer(), getShapeInfo(), specialBuffer(), getSpecialShapeInfo(), nullptr, nullptr, nullptr); NDArray::registerSpecialUse({this}, {this}); return std::move(*this); @@ -2795,7 +2789,7 @@ NDArray NDArray::quantize(const NDArray& array) { } ////////////////////////////////////////////////////////////////////////// -void NDArray::applyTrueBroadcast(nd4j::BroadcastOpsTuple op, const NDArray& other, NDArray& target, const bool checkTargetShape, ExtraArguments *extraArgs) const { +void NDArray::applyTrueBroadcast(sd::BroadcastOpsTuple op, const NDArray& other, NDArray& target, const bool checkTargetShape, ExtraArguments *extraArgs) const { if (isS()) throw std::runtime_error("NDArray::applyTrueBroadcast: you can't use this method on String array!"); @@ -2806,15 +2800,15 @@ void NDArray::applyTrueBroadcast(nd4j::BroadcastOpsTuple op, const NDArray& othe if (isEmpty() || other.isEmpty()) return; - if (lengthOf() == 1) { - target.assign(this); - target.applyPairwiseTransform(op.p, other, extraArgs); - return; - } - if (other.lengthOf() == 1) { - const_cast(this)->applyScalarArr(op.s, other, target, extraArgs); - return; - } + // if (lengthOf() == 1) { + // target.assign(this); + // target.applyPairwiseTransform(op.p, other, extraArgs); + // return; + // } + // if (other.lengthOf() == 1) { + // const_cast(this)->applyScalarArr(op.s, other, target, extraArgs); + // return; + // } if(checkTargetShape) { Nd4jLong* newShapeInfo = nullptr; @@ -2824,36 +2818,46 @@ void NDArray::applyTrueBroadcast(nd4j::BroadcastOpsTuple op, const NDArray& othe throw std::runtime_error("NDArray::applyTrueBroadcast method: the shape or type of target array is wrong !"); } - if(target.isSameShape(this) || target.isSameShape(other)) { - const_cast(this)->applyBroadcast(op.b, ShapeUtils::getDimsWithSameShape(*this, other), other, target, extraArgs); - return; + Nd4jLong* xShapeInfoH = getShapeInfo(); + Nd4jLong* yShapeInfoH = other.getShapeInfo(); + Nd4jLong* xShapeInfoD = getSpecialShapeInfo(); + Nd4jLong* yShapeInfoD = other.getSpecialShapeInfo(); + + if(!isSameShape(target)) { + auto xPack = ConstantShapeHelper::getInstance()->createShapeInfoWithUnitiesForBroadcast(target.getShapeInfo(), getShapeInfo(), getContext()->getWorkspace()); + xShapeInfoH = reinterpret_cast(xPack.primary()); + xShapeInfoD = reinterpret_cast(xPack.special()); + } + if(!other.isSameShape(target)) { + auto yPack = ConstantShapeHelper::getInstance()->createShapeInfoWithUnitiesForBroadcast(target.getShapeInfo(), other.getShapeInfo(), other.getContext()->getWorkspace()); + yShapeInfoH = reinterpret_cast(yPack.primary()); + yShapeInfoD = reinterpret_cast(yPack.special()); } - #ifdef __ND4J_EXPERIMENTAL__ - BUILD_PAIRWISE_SELECTOR(dataType(), other.dataType(), target.dataType(), helpers::TrueBroadcastHelper, ::exec(op.b, *this, other, target), LIBND4J_TYPES, LIBND4J_TYPES); - #else - BUILD_SINGLE_SELECTOR_THRICE(dataType(), helpers::TrueBroadcastHelper, ::exec(op.b, *this, other, target), LIBND4J_TYPES); - #endif + NDArray::prepareSpecialUse({&target}, {this, &other}); + NativeOpExecutioner::execBroadcast(getContext(), op.b, getBuffer(), xShapeInfoH, getSpecialBuffer(), xShapeInfoD, other.getBuffer(), yShapeInfoH, other.getSpecialBuffer(), yShapeInfoD, target.buffer(), target.shapeInfo(), target.specialBuffer(), target.specialShapeInfo()); + registerSpecialUse({&target}, {this, &other}); } ////////////////////////////////////////////////////////////////////////// -void NDArray::applyTrueBroadcast(nd4j::BroadcastBoolOpsTuple op, const NDArray& other, NDArray& target, const bool checkTargetShape, ExtraArguments *extraArgs) const { +void NDArray::applyTrueBroadcast(sd::BroadcastBoolOpsTuple op, const NDArray& other, NDArray& target, const bool checkTargetShape, ExtraArguments *extraArgs) const { + if (isS()) throw std::runtime_error("NDArray::applyTrueBroadcast bool: you can't use this method on String array!"); if (isEmpty() || other.isEmpty()) return; - if (lengthOf() == 1) { - NDArray temp(target._shapeInfo, dataType(), false, getContext()); - temp.assign(this); - temp.applyPairwiseTransform(op.p, other, target, extraArgs); - return; - } - if (other.lengthOf() == 1) { - this->applyScalarArr(op.s, other, target, extraArgs); - return; - } + // if (lengthOf() == 1) { + // NDArray temp(target._shapeInfo, dataType(), false, getContext()); + // temp.assign(this); + // temp.applyPairwiseTransform(op.p, other, target, extraArgs); + // return; + // } + // if (other.lengthOf() == 1) { + // this->applyScalarArr(op.s, other, target, extraArgs); + // return; + // } if(checkTargetShape) { Nd4jLong* newShapeInfo = nullptr; @@ -2865,16 +2869,29 @@ void NDArray::applyTrueBroadcast(nd4j::BroadcastBoolOpsTuple op, const NDArray& throw std::invalid_argument("NDArray::applyTrueBroadcast bool method: this and other arrays must have the same type !"); } - if(target.isSameShape(this) || target.isSameShape(other)) { - const_cast(this)->applyBroadcast(op.b, ShapeUtils::getDimsWithSameShape(*this, other), other, target, extraArgs); - return; + Nd4jLong* xShapeInfoH = getShapeInfo(); + Nd4jLong* yShapeInfoH = other.getShapeInfo(); + Nd4jLong* xShapeInfoD = getSpecialShapeInfo(); + Nd4jLong* yShapeInfoD = other.getSpecialShapeInfo(); + + if(!isSameShape(target)) { + auto xPack = ConstantShapeHelper::getInstance()->createShapeInfoWithUnitiesForBroadcast(target.getShapeInfo(), getShapeInfo(), getContext()->getWorkspace()); + xShapeInfoH = reinterpret_cast(xPack.primary()); + xShapeInfoD = reinterpret_cast(xPack.special()); + } + if(!other.isSameShape(target)) { + auto yPack = ConstantShapeHelper::getInstance()->createShapeInfoWithUnitiesForBroadcast(target.getShapeInfo(), other.getShapeInfo(), other.getContext()->getWorkspace()); + yShapeInfoH = reinterpret_cast(yPack.primary()); + yShapeInfoD = reinterpret_cast(yPack.special()); } - BUILD_DOUBLE_SELECTOR(dataType(), target.dataType(), helpers::TrueBroadcastBoolHelper, ::exec(op.b, *this, other, target), LIBND4J_TYPES, BOOL_TYPES); + NDArray::prepareSpecialUse({&target}, {this, &other}); + NativeOpExecutioner::execBroadcastBool(getContext(), op.b, getBuffer(), xShapeInfoH, getSpecialBuffer(), xShapeInfoD, other.getBuffer(), yShapeInfoH, other.getSpecialBuffer(), yShapeInfoD, target.buffer(), target.shapeInfo(), target.specialBuffer(), target.specialShapeInfo(), nullptr); + registerSpecialUse({&target}, {this, &other}); } ////////////////////////////////////////////////////////////////////////// -void NDArray::applyTrueBroadcast(nd4j::BroadcastIntOpsTuple op, const NDArray& other, NDArray& target, const bool checkTargetShape, ExtraArguments *extraArgs) const { +void NDArray::applyTrueBroadcast(sd::BroadcastIntOpsTuple op, const NDArray& other, NDArray& target, const bool checkTargetShape, ExtraArguments *extraArgs) const { if (isS()) throw std::runtime_error("NDArray::applyTrueBroadcast bool: you can't use this method on String array!"); @@ -2882,16 +2899,16 @@ void NDArray::applyTrueBroadcast(nd4j::BroadcastIntOpsTuple op, const NDArray& o if (isEmpty() || other.isEmpty()) return; - if (lengthOf() == 1) { - NDArray temp(target._shapeInfo, dataType(), false, getContext()); - temp.assign(this); - temp.applyPairwiseTransform(op.p, other, target, extraArgs); - return; - } - if (other.lengthOf() == 1) { - this->applyScalarArr(op.s, other, target, extraArgs); - return; - } + // if (lengthOf() == 1) { + // NDArray temp(target._shapeInfo, dataType(), false, getContext()); + // temp.assign(this); + // temp.applyPairwiseTransform(op.p, other, target, extraArgs); + // return; + // } + // if (other.lengthOf() == 1) { + // this->applyScalarArr(op.s, other, target, extraArgs); + // return; + // } if(checkTargetShape) { Nd4jLong* newShapeInfo = nullptr; @@ -2903,16 +2920,29 @@ void NDArray::applyTrueBroadcast(nd4j::BroadcastIntOpsTuple op, const NDArray& o throw std::invalid_argument("NDArray::applyTrueBroadcast int method: this and other arrays must have the same type !"); } - if(target.isSameShape(this) || target.isSameShape(other)) { - const_cast(this)->applyBroadcast(op.b, ShapeUtils::getDimsWithSameShape(*this, other), other, target, extraArgs); - return; + Nd4jLong* xShapeInfoH = getShapeInfo(); + Nd4jLong* yShapeInfoH = other.getShapeInfo(); + Nd4jLong* xShapeInfoD = getSpecialShapeInfo(); + Nd4jLong* yShapeInfoD = other.getSpecialShapeInfo(); + + if(!isSameShape(target)) { + auto xPack = ConstantShapeHelper::getInstance()->createShapeInfoWithUnitiesForBroadcast(target.getShapeInfo(), getShapeInfo(), getContext()->getWorkspace()); + xShapeInfoH = reinterpret_cast(xPack.primary()); + xShapeInfoD = reinterpret_cast(xPack.special()); + } + if(!other.isSameShape(target)) { + auto yPack = ConstantShapeHelper::getInstance()->createShapeInfoWithUnitiesForBroadcast(target.getShapeInfo(), other.getShapeInfo(), other.getContext()->getWorkspace()); + yShapeInfoH = reinterpret_cast(yPack.primary()); + yShapeInfoD = reinterpret_cast(yPack.special()); } - BUILD_SINGLE_SELECTOR(dataType(), helpers::TrueBroadcastIntHelper, ::exec(op.b, *this, other, target), INTEGER_TYPES); + NDArray::prepareSpecialUse({&target}, {this, &other}); + NativeOpExecutioner::execBroadcastInt(getContext(), op.b, getBuffer(), xShapeInfoH, getSpecialBuffer(), xShapeInfoD, other.getBuffer(), yShapeInfoH, other.getSpecialBuffer(), yShapeInfoD, target.buffer(), target.shapeInfo(), target.specialBuffer(), target.specialShapeInfo()); + registerSpecialUse({&target}, {this, &other}); } ////////////////////////////////////////////////////////////////////////// -NDArray NDArray::applyTrueBroadcast(nd4j::BroadcastOpsTuple op, const NDArray& other, ExtraArguments *extraArgs) const & { +NDArray NDArray::applyTrueBroadcast(sd::BroadcastOpsTuple op, const NDArray& other, ExtraArguments *extraArgs) const & { if (isEmpty() || other.isEmpty()) { if (isEmpty()) return NDArray(*this); @@ -2931,7 +2961,7 @@ NDArray NDArray::applyTrueBroadcast(nd4j::BroadcastOpsTuple op, const NDArray& o } ////////////////////////////////////////////////////////////////////////// -NDArray NDArray::applyTrueBroadcast(nd4j::BroadcastOpsTuple op, NDArray&& other, ExtraArguments *extraArgs) const & { +NDArray NDArray::applyTrueBroadcast(sd::BroadcastOpsTuple op, NDArray&& other, ExtraArguments *extraArgs) const & { if (isEmpty() || other.isEmpty()) { if (isEmpty()) return NDArray(*this); @@ -2955,7 +2985,7 @@ NDArray NDArray::applyTrueBroadcast(nd4j::BroadcastOpsTuple op, NDArray&& other, } ////////////////////////////////////////////////////////////////////////// -NDArray NDArray::applyTrueBroadcast(nd4j::BroadcastOpsTuple op, const NDArray& other, ExtraArguments *extraArgs) && { +NDArray NDArray::applyTrueBroadcast(sd::BroadcastOpsTuple op, const NDArray& other, ExtraArguments *extraArgs) && { if (isEmpty() || other.isEmpty()) { if (isEmpty()) return NDArray(*this); @@ -2979,7 +3009,7 @@ NDArray NDArray::applyTrueBroadcast(nd4j::BroadcastOpsTuple op, const NDArray& o } ////////////////////////////////////////////////////////////////////////// -NDArray NDArray::applyTrueBroadcast(nd4j::BroadcastOpsTuple op, NDArray&& other, ExtraArguments *extraArgs) && { +NDArray NDArray::applyTrueBroadcast(sd::BroadcastOpsTuple op, NDArray&& other, ExtraArguments *extraArgs) && { if (isEmpty() || other.isEmpty()) { if (isEmpty()) return NDArray(*this); @@ -3012,7 +3042,11 @@ NDArray NDArray::applyTrueBroadcast(nd4j::BroadcastOpsTuple op, NDArray&& other, } ////////////////////////////////////////////////////////////////////////// -void NDArray::applyBroadcast(nd4j::broadcast::Ops op, const std::vector& dimensions, const NDArray& other, NDArray& target, ExtraArguments* extraArgs) { +void NDArray::applyBroadcast(sd::broadcast::Ops op, const std::vector& dimensions, const NDArray& other, NDArray& target, ExtraArguments* extraArgs) { + + if (dimensions.size() == 0) + return; + if (isS()) throw std::runtime_error("NDArray::applyBroadcast: you can't use this method on String array!"); if(((op == broadcast::Divide || op == broadcast::FloorDiv || op == broadcast::FloorMod) && other.isB()) || (op == broadcast::ReverseDivide && this->isB())) @@ -3023,53 +3057,50 @@ void NDArray::applyBroadcast(nd4j::broadcast::Ops op, const std::vector& di return; } - if (dimensions.size() == 0) - return; - - if (other.lengthOf() == lengthOf() && this->rankOf() == other.rankOf()) { - NDArray::prepareSpecialUse({&target}, {this, &other}); - NativeOpExecutioner::execPairwiseTransform(getContext(), fromBroadcastToPairwise(op), buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), other.getBuffer(), other.getShapeInfo(), other.getSpecialBuffer(), other.getSpecialShapeInfo(), target.buffer(), target.shapeInfo(), target.specialBuffer(), target.specialShapeInfo(), nullptr); - NDArray::registerSpecialUse({&target}, {this, &other}); - return; - } - - NDArray *min(nullptr), *max(nullptr); - if((lengthOf() > other.lengthOf()) || (lengthOf() == other.lengthOf() && rankOf() >= other.rankOf())) { - max = this; - min = const_cast(&other); - } - else { - max = const_cast(&other); - min = this; - } + // if (other.lengthOf() == lengthOf() && this->rankOf() == other.rankOf()) { + // NDArray::prepareSpecialUse({&target}, {this, &other}); + // NativeOpExecutioner::execPairwiseTransform(getContext(), fromBroadcastToPairwise(op), buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), other.getBuffer(), other.getShapeInfo(), other.getSpecialBuffer(), other.getSpecialShapeInfo(), target.buffer(), target.shapeInfo(), target.specialBuffer(), target.specialShapeInfo(), nullptr); + // NDArray::registerSpecialUse({&target}, {this, &other}); + // return; + // } if(target.dataType() != DataTypeUtils::pickPairwiseResultType(shapeInfo(), other.getShapeInfo())) throw std::invalid_argument("NDArray::applyBroadcast method: wrong type of target array !"); - if(!target.isSameShape(max)) - throw std::invalid_argument("NDArray::applyBroadcast method: max and target arrays must have the same shape !"); + if(!target.isSameShape(this) && !target.isSameShape(other)) + throw std::invalid_argument("NDArray::applyBroadcast method: one of of two input arrays (this or other) should has the same shape as target array!"); std::vector copy(dimensions); if (dimensions.size() > 1) std::sort(copy.begin(), copy.end()); - Nd4jLong tadLength = shape::tadLength(max->shapeInfo(), copy.data(), (int) copy.size()); - if (tadLength != min->lengthOf()) - throw std::runtime_error("NDArray::applyBroadcast method: tad length mismatch !"); + Nd4jLong* xShapeInfoH = getShapeInfo(); + Nd4jLong* yShapeInfoH = other.getShapeInfo(); + Nd4jLong* xShapeInfoD = getSpecialShapeInfo(); + Nd4jLong* yShapeInfoD = other.getSpecialShapeInfo(); - auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(max->shapeInfo(), copy); - auto packZ = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(target.shapeInfo(), copy); + if(!isSameShape(target)) { + auto xPack = ConstantShapeHelper::getInstance()->createShapeInfoWithUnitiesForBroadcast(target.getShapeInfo(), getShapeInfo(), getContext()->getWorkspace(), copy); + xShapeInfoH = reinterpret_cast(xPack.primary()); + xShapeInfoD = reinterpret_cast(xPack.special()); + } + if(!other.isSameShape(target)) { + auto yPack = ConstantShapeHelper::getInstance()->createShapeInfoWithUnitiesForBroadcast(target.getShapeInfo(), other.getShapeInfo(), other.getContext()->getWorkspace(), copy); + yShapeInfoH = reinterpret_cast(yPack.primary()); + yShapeInfoD = reinterpret_cast(yPack.special()); + } NDArray::prepareSpecialUse({&target}, {this, &other}); - if(max == this) - NativeOpExecutioner::execBroadcast( getContext(), op, buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), other.getBuffer(), other.getShapeInfo(), other.getSpecialBuffer(), other.getSpecialShapeInfo(), target.buffer(), target.shapeInfo(), target.specialBuffer(), target.specialShapeInfo(), copy.data(), (int)copy.size(), packX.platformShapeInfo(), packX.platformOffsets(), packZ.platformShapeInfo(), packZ.platformOffsets()); - else - NativeOpExecutioner::execInverseBroadcast(getContext(), op, buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), other.getBuffer(), other.getShapeInfo(), other.getSpecialBuffer(), other.getSpecialShapeInfo(), target.buffer(), target.shapeInfo(), target.specialBuffer(), target.specialShapeInfo(), copy.data(), (int)copy.size(), packX.platformShapeInfo(), packX.platformOffsets(), packZ.platformShapeInfo(), packZ.platformOffsets()); + NativeOpExecutioner::execBroadcast(getContext(), op, buffer(), xShapeInfoH, specialBuffer(), xShapeInfoD, other.getBuffer(), yShapeInfoH, other.getSpecialBuffer(), yShapeInfoD, target.buffer(), target.shapeInfo(), target.specialBuffer(), target.specialShapeInfo()); registerSpecialUse({&target}, {this, &other}); } ////////////////////////////////////////////////////////////////////////// -void NDArray::applyBroadcast(nd4j::broadcast::BoolOps op, const std::vector& dimensions, const NDArray& other, NDArray& target, ExtraArguments* extraArgs) { +void NDArray::applyBroadcast(sd::broadcast::BoolOps op, const std::vector& dimensions, const NDArray& other, NDArray& target, ExtraArguments* extraArgs) { + + if (dimensions.size() == 0) + return; + if (isS()) throw std::runtime_error("NDArray::applyBroadcast BoolOps: you can't use this method on String array!"); if(isEmpty() || other.isEmpty()) { @@ -3078,30 +3109,17 @@ void NDArray::applyBroadcast(nd4j::broadcast::BoolOps op, const std::vector return; } - if (dimensions.size() == 0) - return; - - if (other.lengthOf() == lengthOf() && this->rankOf() == other.rankOf()) { - NDArray::prepareSpecialUse({&target}, {this, &other}); - NativeOpExecutioner::execPairwiseBoolTransform(getContext(), fromBroadcastToPairwiseBool(op), buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), other.getBuffer(), other.getShapeInfo(), other.getSpecialBuffer(), other.getSpecialShapeInfo(), target.buffer(), target.shapeInfo(), target.specialBuffer(), target.specialShapeInfo(), nullptr); - NDArray::registerSpecialUse({&target}, {this, &other}); - return; - } - - NDArray *min(nullptr), *max(nullptr); - if((lengthOf() > other.lengthOf()) || (lengthOf() == other.lengthOf() && rankOf() >= other.rankOf())) { - max = this; - min = const_cast(&other); - } - else { - max = const_cast(&other); - min = this; - } + // if (other.lengthOf() == lengthOf() && this->rankOf() == other.rankOf()) { + // NDArray::prepareSpecialUse({&target}, {this, &other}); + // NativeOpExecutioner::execPairwiseBoolTransform(getContext(), fromBroadcastToPairwiseBool(op), buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), other.getBuffer(), other.getShapeInfo(), other.getSpecialBuffer(), other.getSpecialShapeInfo(), target.buffer(), target.shapeInfo(), target.specialBuffer(), target.specialShapeInfo(), nullptr); + // NDArray::registerSpecialUse({&target}, {this, &other}); + // return; + // } if(target.dataType() != DataType::BOOL) throw std::invalid_argument("NDArray::applyBroadcast bool method: type of target array must be BOOL!"); - if(!target.isSameShape(max)) - throw std::invalid_argument("NDArray::applyBroadcast bool method: max and target arrays must have the same shape !"); + if(!target.isSameShape(this) && !target.isSameShape(other)) + throw std::invalid_argument("NDArray::applyBroadcast bool method: one of of two input arrays (this or other) should has the same shape as target array!"); if(_dataType != other._dataType) throw std::invalid_argument("NDArray::applyBroadcast bool method: this and other arrays must have the same type !"); @@ -3110,25 +3128,34 @@ void NDArray::applyBroadcast(nd4j::broadcast::BoolOps op, const std::vector if (dimensions.size() > 1) std::sort(copy.begin(), copy.end()); - Nd4jLong tadLength = shape::tadLength(max->shapeInfo(), copy.data(), (int) copy.size()); - if (tadLength != min->lengthOf()) - throw std::runtime_error("Tad length mismatch"); + Nd4jLong* xShapeInfoH = getShapeInfo(); + Nd4jLong* yShapeInfoH = other.getShapeInfo(); + Nd4jLong* xShapeInfoD = getSpecialShapeInfo(); + Nd4jLong* yShapeInfoD = other.getSpecialShapeInfo(); - auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(max->shapeInfo(), copy); - auto packZ = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(target.shapeInfo(), copy); + if(!isSameShape(target)) { + auto xPack = ConstantShapeHelper::getInstance()->createShapeInfoWithUnitiesForBroadcast(target.getShapeInfo(), getShapeInfo(), getContext()->getWorkspace(), copy); + xShapeInfoH = reinterpret_cast(xPack.primary()); + xShapeInfoD = reinterpret_cast(xPack.special()); + } + if(!other.isSameShape(target)) { + auto yPack = ConstantShapeHelper::getInstance()->createShapeInfoWithUnitiesForBroadcast(target.getShapeInfo(), other.getShapeInfo(), other.getContext()->getWorkspace(), copy); + yShapeInfoH = reinterpret_cast(yPack.primary()); + yShapeInfoD = reinterpret_cast(yPack.special()); + } - // TODO: eventually we want separate tads here NDArray::prepareSpecialUse({&target}, {this, &other}); - if(max == this) - NativeOpExecutioner::execBroadcastBool( getContext(), op, buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), other.getBuffer(), other.getShapeInfo(), other.getSpecialBuffer(), other.getSpecialShapeInfo(), target.buffer(), target.shapeInfo(), target.specialBuffer(), target.specialShapeInfo(), nullptr, copy.data(), (int)copy.size(), packX.platformShapeInfo(), packX.platformOffsets(), packZ.platformShapeInfo(), packZ.platformOffsets()); - else - NativeOpExecutioner::execInverseBroadcastBool(getContext(), op, buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), other.getBuffer(), other.getShapeInfo(), other.getSpecialBuffer(), other.getSpecialShapeInfo(), target.buffer(), target.shapeInfo(), target.specialBuffer(), target.specialShapeInfo(), nullptr, copy.data(), (int)copy.size(), packX.platformShapeInfo(), packX.platformOffsets(), packZ.platformShapeInfo(), packZ.platformOffsets()); + NativeOpExecutioner::execBroadcastBool(getContext(), op, buffer(), xShapeInfoH, specialBuffer(), xShapeInfoD, other.getBuffer(), yShapeInfoH, other.getSpecialBuffer(), yShapeInfoD, target.buffer(), target.shapeInfo(), target.specialBuffer(), target.specialShapeInfo(), nullptr); registerSpecialUse({&target}, {this, &other}); } ////////////////////////////////////////////////////////////////////////// -void NDArray::applyBroadcast(nd4j::broadcast::IntOps op, const std::vector& dimensions, const NDArray& other, NDArray& target, ExtraArguments* extraArgs) { +void NDArray::applyBroadcast(sd::broadcast::IntOps op, const std::vector& dimensions, const NDArray& other, NDArray& target, ExtraArguments* extraArgs) { + + if (dimensions.empty()) + return; + if (!isZ()) throw std::runtime_error("NDArray::applyBroadcast IntOps: you can't use this method on non-Integer array!"); if(isEmpty() || other.isEmpty()) { @@ -3137,30 +3164,17 @@ void NDArray::applyBroadcast(nd4j::broadcast::IntOps op, const std::vector& return; } - if (dimensions.empty()) - return; - - if (other.lengthOf() == lengthOf() && this->rankOf() == other.rankOf()) { - NDArray::prepareSpecialUse({&target}, {this, &other}); - NativeOpExecutioner::execPairwiseIntTransform(getContext(), fromBroadcastToPairwiseInt(op), buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), other.getBuffer(), other.getShapeInfo(), other.getSpecialBuffer(), other.getSpecialShapeInfo(), target.buffer(), target.shapeInfo(), target.specialBuffer(), target.specialShapeInfo(), nullptr); - NDArray::registerSpecialUse({&target}, {this, &other}); - return; - } - - NDArray *min(nullptr), *max(nullptr); - if((lengthOf() > other.lengthOf()) || (lengthOf() == other.lengthOf() && rankOf() >= other.rankOf())) { - max = this; - min = const_cast(&other); - } - else { - max = const_cast(&other); - min = this; - } + // if (other.lengthOf() == lengthOf() && this->rankOf() == other.rankOf()) { + // NDArray::prepareSpecialUse({&target}, {this, &other}); + // NativeOpExecutioner::execPairwiseIntTransform(getContext(), fromBroadcastToPairwiseInt(op), buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), other.getBuffer(), other.getShapeInfo(), other.getSpecialBuffer(), other.getSpecialShapeInfo(), target.buffer(), target.shapeInfo(), target.specialBuffer(), target.specialShapeInfo(), nullptr); + // NDArray::registerSpecialUse({&target}, {this, &other}); + // return; + // } if(target.dataType() != dataType()) throw std::invalid_argument("NDArray::applyBroadcast int method: type of target array must be the same as input!"); - if(!target.isSameShape(max)) - throw std::invalid_argument("NDArray::applyBroadcast int method: max and target arrays must have the same shape !"); + if(!target.isSameShape(this) && !target.isSameShape(other)) + throw std::invalid_argument("NDArray::applyBroadcast int method: one of of two input arrays (this or other) should has the same shape as target array!"); if(_dataType != other._dataType) throw std::invalid_argument("NDArray::applyBroadcast int method: this and other arrays must have the same type !"); @@ -3169,32 +3183,37 @@ void NDArray::applyBroadcast(nd4j::broadcast::IntOps op, const std::vector& if (dimensions.size() > 1) std::sort(copy.begin(), copy.end()); - Nd4jLong tadLength = shape::tadLength(max->shapeInfo(), copy.data(), (int) copy.size()); - if (tadLength != min->lengthOf()) - throw std::runtime_error("Tad length mismatch"); + Nd4jLong* xShapeInfoH = getShapeInfo(); + Nd4jLong* yShapeInfoH = other.getShapeInfo(); + Nd4jLong* xShapeInfoD = getSpecialShapeInfo(); + Nd4jLong* yShapeInfoD = other.getSpecialShapeInfo(); - auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(max->shapeInfo(), copy); - auto packZ = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(target.shapeInfo(), copy); + if(!isSameShape(target)) { + auto xPack = ConstantShapeHelper::getInstance()->createShapeInfoWithUnitiesForBroadcast(target.getShapeInfo(), getShapeInfo(), getContext()->getWorkspace(), copy); + xShapeInfoH = reinterpret_cast(xPack.primary()); + xShapeInfoD = reinterpret_cast(xPack.special()); + } + if(!other.isSameShape(target)) { + auto yPack = ConstantShapeHelper::getInstance()->createShapeInfoWithUnitiesForBroadcast(target.getShapeInfo(), other.getShapeInfo(), other.getContext()->getWorkspace(), copy); + yShapeInfoH = reinterpret_cast(yPack.primary()); + yShapeInfoD = reinterpret_cast(yPack.special()); + } - // TODO: eventually we want separate tads here NDArray::prepareSpecialUse({&target}, {this, &other}); - if(max == this) - NativeOpExecutioner::execBroadcastInt( getContext(), op, buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), other.getBuffer(), other.getShapeInfo(), other.getSpecialBuffer(), other.getSpecialShapeInfo(), target.buffer(), target.shapeInfo(), target.specialBuffer(), target.specialShapeInfo(), copy.data(), (int)copy.size(), packX.platformShapeInfo(), packX.platformOffsets(), packZ.platformShapeInfo(), packZ.platformOffsets()); - else - NativeOpExecutioner::execInverseBroadcastInt(getContext(), op, buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), other.getBuffer(), other.getShapeInfo(), other.getSpecialBuffer(), other.getSpecialShapeInfo(), target.buffer(), target.shapeInfo(), target.specialBuffer(), target.specialShapeInfo(), copy.data(), (int)copy.size(), packX.platformShapeInfo(), packX.platformOffsets(), packZ.platformShapeInfo(), packZ.platformOffsets()); + NativeOpExecutioner::execBroadcastInt(getContext(), op, buffer(), xShapeInfoH, specialBuffer(), xShapeInfoD, other.getBuffer(), yShapeInfoH, other.getSpecialBuffer(), yShapeInfoD, target.buffer(), target.shapeInfo(), target.specialBuffer(), target.specialShapeInfo()); registerSpecialUse({&target}, {this, &other}); } ////////////////////////////////////////////////////////////////////////// -void NDArray::applyBroadcast(nd4j::broadcast::Ops op, const std::initializer_list dimensions, const NDArray& tadArray, NDArray& target, ExtraArguments* extraArgs) { +void NDArray::applyBroadcast(sd::broadcast::Ops op, const std::initializer_list dimensions, const NDArray& tadArray, NDArray& target, ExtraArguments* extraArgs) { std::vector vec(dimensions); applyBroadcast(op, vec, tadArray, target, extraArgs); } //////////////////////////////////////////////////////////////////////// void* NDArray::operator new(size_t i) { - if (nd4j::memory::MemoryRegistrator::getInstance()->hasWorkspaceAttached()) { - nd4j::memory::Workspace* ws = nd4j::memory::MemoryRegistrator::getInstance()->getWorkspace(); + if (sd::memory::MemoryRegistrator::getInstance()->hasWorkspaceAttached()) { + sd::memory::Workspace* ws = sd::memory::MemoryRegistrator::getInstance()->getWorkspace(); return ws->allocateBytes((Nd4jLong) i); } else { @@ -3206,7 +3225,7 @@ void* NDArray::operator new(size_t i) { //////////////////////////////////////////////////////////////////////// void NDArray::operator delete(void* p) { - if (!nd4j::memory::MemoryRegistrator::getInstance()->hasWorkspaceAttached()) + if (!sd::memory::MemoryRegistrator::getInstance()->hasWorkspaceAttached()) free(p); } @@ -3265,7 +3284,7 @@ bool NDArray::reshapei(const char order, const std::vector& cshape, co if (i != j) shapeLength *= shape_[j]; - Nd4jLong realShape = nd4j::math::nd4j_abs(lengthOf() / shapeLength); + Nd4jLong realShape = sd::math::nd4j_abs(lengthOf() / shapeLength); auto thisNewShape = new Nd4jLong[shape.size()]; for (int j = 0; j < (int) shape.size(); j++) @@ -3290,7 +3309,7 @@ bool NDArray::reshapei(const char order, const std::vector& cshape, co if(platformBuffer() == nullptr || arrLength != this->lengthOf()) { this->printShapeInfo("Mismatched shape"); - nd4j::Logger::printv("Shape requested: ", shape); + sd::Logger::printv("Shape requested: ", shape); nd4j_debug("Requested length in reshape: %i; Existing length: %i;\n", arrLength, this->lengthOf()); throw std::runtime_error("NDArray::reshapei: bad input shape!"); } @@ -3332,13 +3351,13 @@ void NDArray::nullify() { //////////////////////////////////////////////////////////////////////// template -void NDArray::templatedSet(void *buffer, const Nd4jLong xOfsset, nd4j::DataType dtype, const void *value) { +void NDArray::templatedSet(void *buffer, const Nd4jLong xOfsset, sd::DataType dtype, const void *value) { BUILD_SINGLE_PARTIAL_SELECTOR(dtype, templatedSet< , T>(buffer, xOfsset, value), LIBND4J_TYPES); } -BUILD_SINGLE_TEMPLATE(template ND4J_EXPORT void NDArray::templatedSet, (void *buffer, const Nd4jLong xOfsset, nd4j::DataType dtype, const void *value), LIBND4J_TYPES); +BUILD_SINGLE_TEMPLATE(template ND4J_EXPORT void NDArray::templatedSet, (void *buffer, const Nd4jLong xOfsset, sd::DataType dtype, const void *value), LIBND4J_TYPES); //////////////////////////////////////////////////////////////////////// -void NDArray::applyPairwiseTransform(nd4j::pairwise::Ops op, const NDArray& other, NDArray& target, ExtraArguments *extraParams) const{ +void NDArray::applyPairwiseTransform(sd::pairwise::Ops op, const NDArray& other, NDArray& target, ExtraArguments *extraParams) const{ if (isS()) throw std::runtime_error("NDArray::applyPairwiseTransform: you can't use this method on String array!"); if (other.lengthOf() != target.lengthOf()) @@ -3355,7 +3374,7 @@ void NDArray::applyPairwiseTransform(nd4j::pairwise::Ops op, const NDArray& othe } //////////////////////////////////////////////////////////////////////// -void NDArray::applyPairwiseTransform(nd4j::pairwise::BoolOps op, const NDArray& other, NDArray& target, ExtraArguments *extraParams) const{ +void NDArray::applyPairwiseTransform(sd::pairwise::BoolOps op, const NDArray& other, NDArray& target, ExtraArguments *extraParams) const{ if (isS()) throw std::runtime_error("NDArray::applyPairwiseTransform BoolOps: you can't use this method on String array!"); if (other.lengthOf() != target.lengthOf()) @@ -3371,7 +3390,7 @@ void NDArray::applyPairwiseTransform(nd4j::pairwise::BoolOps op, const NDArray& } //////////////////////////////////////////////////////////////////////// -void NDArray::applyPairwiseTransform(nd4j::pairwise::IntOps op, const NDArray& other, NDArray& target, ExtraArguments *extraParams) const{ +void NDArray::applyPairwiseTransform(sd::pairwise::IntOps op, const NDArray& other, NDArray& target, ExtraArguments *extraParams) const{ if (isS()) throw std::runtime_error("NDArray::applyPairwiseTransform IntOps: you can't use this method on String array!"); if (other.lengthOf() != target.lengthOf()) @@ -3387,7 +3406,7 @@ void NDArray::applyPairwiseTransform(nd4j::pairwise::IntOps op, const NDArray& o } ////////////////////////////////////////////////////////////////////////// -void NDArray::applyPairwiseTransform(nd4j::pairwise::Ops op, const NDArray& other, ExtraArguments *extraParams) { +void NDArray::applyPairwiseTransform(sd::pairwise::Ops op, const NDArray& other, ExtraArguments *extraParams) { applyPairwiseTransform(op, other, *this, extraParams); } @@ -3401,7 +3420,7 @@ void NDArray::templatedDoubleAssign(void *xBuffer, const Nd4jLong xOffset, const BUILD_DOUBLE_TEMPLATE(template ND4J_EXPORT void NDArray::templatedDoubleAssign, (void *xBuffer, const Nd4jLong xOffset, const void *yBuffer, const Nd4jLong yOffset) const, LIBND4J_TYPES, LIBND4J_TYPES); //////////////////////////////////////////////////////////////////////// -void NDArray::varianceAlongDimension(nd4j::variance::Ops op, NDArray& target, const bool biasCorrected, const std::vector& dimensions) const { +void NDArray::varianceAlongDimension(sd::variance::Ops op, NDArray& target, const bool biasCorrected, const std::vector& dimensions) const { if (isS()) throw std::runtime_error("NDArray::varianceAlongDimension: you can't use this method on String array!"); @@ -3415,8 +3434,8 @@ void NDArray::varianceAlongDimension(nd4j::variance::Ops op, NDArray& target, co NativeOpExecutioner::execSummaryStatsScalar(getContext(), op, getBuffer(), getShapeInfo(), getSpecialBuffer(), getSpecialShapeInfo(), nullptr, target.getBuffer(), target.getShapeInfo(), target.getSpecialBuffer(), target.getSpecialShapeInfo(), biasCorrected); else { std::vector copy(dimensions); - auto pDims = nd4j::Environment::getInstance()->isCPU() ? copy.data() : nullptr; - auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(this->getShapeInfo(), dimensions); + auto pDims = sd::Environment::getInstance()->isCPU() ? copy.data() : nullptr; + auto packX = sd::ConstantTadHelper::getInstance()->tadForDimensions(this->getShapeInfo(), dimensions); NativeOpExecutioner::execSummaryStats(getContext(), op, getBuffer(), getShapeInfo(), getSpecialBuffer(), getSpecialShapeInfo(), nullptr, target.buffer(), target.shapeInfo(), target.getSpecialBuffer(), target.specialShapeInfo(), pDims, dimensions.size(), packX.platformShapeInfo(), packX.platformOffsets(), biasCorrected); synchronize("NDArray::varianceAlongDimension"); } @@ -3425,7 +3444,7 @@ void NDArray::varianceAlongDimension(nd4j::variance::Ops op, NDArray& target, co } //////////////////////////////////////////////////////////////////////// -NDArray NDArray::varianceAlongDimension(nd4j::variance::Ops op, const bool biasCorrected, const std::vector& dimensions) const { +NDArray NDArray::varianceAlongDimension(sd::variance::Ops op, const bool biasCorrected, const std::vector& dimensions) const { if (isS()) throw std::runtime_error("NDArray::varianceAlongDimension: you can't use this method on String array!"); @@ -3442,12 +3461,12 @@ NDArray NDArray::varianceAlongDimension(nd4j::variance::Ops op, const bool biasC } //////////////////////////////////////////////////////////////////////// -NDArray NDArray::varianceAlongDimension(nd4j::variance::Ops op, const bool biasCorrected, const std::initializer_list& dimensions) const { +NDArray NDArray::varianceAlongDimension(sd::variance::Ops op, const bool biasCorrected, const std::initializer_list& dimensions) const { return varianceAlongDimension(op, biasCorrected, std::vector(dimensions)); } //////////////////////////////////////////////////////////////////////// -void NDArray::varianceAlongDimension(nd4j::variance::Ops op, NDArray &target, const bool biasCorrected, const std::initializer_list& dimensions) const { +void NDArray::varianceAlongDimension(sd::variance::Ops op, NDArray &target, const bool biasCorrected, const std::initializer_list& dimensions) const { varianceAlongDimension(op, target, biasCorrected, std::vector(dimensions)); } @@ -3524,7 +3543,7 @@ bool NDArray::equalsTo(const NDArray *other, double eps) const { // string is special case, we'll compare them one by one, considering both arrays are guaranteed to have the same length if (dataType() == DataType::UTF8) { - for (int e = 0; e < this->lengthOf(); e++) { + for (Nd4jLong e = 0; e < this->lengthOf(); e++) { auto s1 = this->e(e); auto s2 = other->e(e); @@ -3533,7 +3552,7 @@ bool NDArray::equalsTo(const NDArray *other, double eps) const { } } else if (dataType() == DataType::UTF16) { - for (int e = 0; e < this->lengthOf(); e++) { + for (Nd4jLong e = 0; e < this->lengthOf(); e++) { auto s1 = this->e(e); auto s2 = other->e(e); @@ -3542,7 +3561,7 @@ bool NDArray::equalsTo(const NDArray *other, double eps) const { } } else { - for (int e = 0; e < this->lengthOf(); e++) { + for (Nd4jLong e = 0; e < this->lengthOf(); e++) { auto s1 = this->e(e); auto s2 = other->e(e); @@ -3554,7 +3573,7 @@ bool NDArray::equalsTo(const NDArray *other, double eps) const { return true; } else { // regular numeric types - NDArray tmp(nd4j::DataType::FLOAT32, getContext()); // scalar = 0 + NDArray tmp(sd::DataType::FLOAT32, getContext()); // scalar = 0 ExtraArguments extras({0.0, 0.0, eps}); @@ -3795,7 +3814,7 @@ NDArray NDArray::e(const Nd4jLong i) const { ////////////////////////////////////////////////////////////////////////// // perform array transformation -void NDArray::applyTransform(nd4j::transform::FloatOps op, NDArray& target, ExtraArguments *extraParams) { +void NDArray::applyTransform(sd::transform::FloatOps op, NDArray& target, ExtraArguments *extraParams) { if (isS()) throw std::runtime_error("NDArray::applyTransform FloatOps: you can't use this method on String array!"); @@ -3809,7 +3828,7 @@ void NDArray::applyTransform(nd4j::transform::FloatOps op, NDArray& target, Extr } //////////////////////////////////////////////////////////////////////// -void NDArray::applyTransform(nd4j::transform::AnyOps op, NDArray& target, ExtraArguments *extraParams) { +void NDArray::applyTransform(sd::transform::AnyOps op, NDArray& target, ExtraArguments *extraParams) { if (isS()) throw std::runtime_error("NDArray::applyTransform AnyOps: you can't use this method on String array!"); @@ -3820,7 +3839,7 @@ void NDArray::applyTransform(nd4j::transform::AnyOps op, NDArray& target, ExtraA } //////////////////////////////////////////////////////////////////////// -void NDArray::applyTransform(nd4j::transform::SameOps op, NDArray& target, ExtraArguments *extraParams) { +void NDArray::applyTransform(sd::transform::SameOps op, NDArray& target, ExtraArguments *extraParams) { if (isS()) throw std::runtime_error("NDArray::applyTransform SameOps: you can't use this method on String array!"); @@ -3834,7 +3853,7 @@ void NDArray::applyTransform(nd4j::transform::SameOps op, NDArray& target, Extra } //////////////////////////////////////////////////////////////////////// -void NDArray::applyTransform(nd4j::transform::StrictOps op, NDArray& target, ExtraArguments *extraParams) { +void NDArray::applyTransform(sd::transform::StrictOps op, NDArray& target, ExtraArguments *extraParams) { if (isS()) throw std::runtime_error("NDArray::applyTransform StrictOps: you can't use this method on String array!"); @@ -3847,7 +3866,7 @@ void NDArray::applyTransform(nd4j::transform::StrictOps op, NDArray& target, Ext } //////////////////////////////////////////////////////////////////////// -void NDArray::applyTransform(nd4j::transform::BoolOps op, NDArray& target, ExtraArguments *extraParams) { +void NDArray::applyTransform(sd::transform::BoolOps op, NDArray& target, ExtraArguments *extraParams) { if (isS()) throw std::runtime_error("NDArray::applyTransform BoolOps: you can't use this method on String array!"); @@ -3860,7 +3879,7 @@ void NDArray::applyTransform(nd4j::transform::BoolOps op, NDArray& target, Extra } //////////////////////////////////////////////////////////////////////// -NDArray NDArray::transform(nd4j::transform::FloatOps op, void *extraParams) const & { +NDArray NDArray::transform(sd::transform::FloatOps op, void *extraParams) const & { if (isS()) throw std::runtime_error("NDArray::transform FloatOps: you can't use this method on String array!"); @@ -3874,7 +3893,7 @@ NDArray NDArray::transform(nd4j::transform::FloatOps op, void *extraParams) cons } //////////////////////////////////////////////////////////////////////// -NDArray NDArray::transform(nd4j::transform::FloatOps op, void *extraParams) && { +NDArray NDArray::transform(sd::transform::FloatOps op, void *extraParams) && { if (isS()) throw std::runtime_error("NDArray::transform SameOps: you can't use this method on String array!"); @@ -3886,7 +3905,7 @@ NDArray NDArray::transform(nd4j::transform::FloatOps op, void *extraParams) && { } //////////////////////////////////////////////////////////////////////// -NDArray NDArray::transform(nd4j::transform::SameOps op, void *extraParams) const & { +NDArray NDArray::transform(sd::transform::SameOps op, void *extraParams) const & { if (isS()) throw std::runtime_error("NDArray::transform SameOps: you can't use this method on String array!"); @@ -3900,7 +3919,7 @@ NDArray NDArray::transform(nd4j::transform::SameOps op, void *extraParams) const } //////////////////////////////////////////////////////////////////////// -NDArray NDArray::transform(nd4j::transform::SameOps op, void *extraParams) && { +NDArray NDArray::transform(sd::transform::SameOps op, void *extraParams) && { if (isS()) throw std::runtime_error("NDArray::transform SameOps: you can't use this method on String array!"); @@ -3912,7 +3931,7 @@ NDArray NDArray::transform(nd4j::transform::SameOps op, void *extraParams) && { } //////////////////////////////////////////////////////////////////////// -NDArray NDArray::transform(nd4j::transform::StrictOps op, void *extraParams) const & { +NDArray NDArray::transform(sd::transform::StrictOps op, void *extraParams) const & { if (!this->isR()) throw std::runtime_error("Source array must have one of FLOAT types"); @@ -3926,7 +3945,7 @@ NDArray NDArray::transform(nd4j::transform::StrictOps op, void *extraParams) con } //////////////////////////////////////////////////////////////////////// -NDArray NDArray::transform(nd4j::transform::StrictOps op, void *extraParams) && { +NDArray NDArray::transform(sd::transform::StrictOps op, void *extraParams) && { if (!this->isR()) throw std::runtime_error("Source array must have one of FLOAT types"); @@ -3938,11 +3957,11 @@ NDArray NDArray::transform(nd4j::transform::StrictOps op, void *extraParams) && } //////////////////////////////////////////////////////////////////////// -NDArray NDArray::transform(nd4j::transform::BoolOps op, void *extraParams) const & { +NDArray NDArray::transform(sd::transform::BoolOps op, void *extraParams) const & { if (isS()) throw std::runtime_error("NDArray::transform BoolOps: you can't use this method on String array!"); - NDArray result(ordering(), getShapeAsVector(), nd4j::DataType::BOOL, getContext()); + NDArray result(ordering(), getShapeAsVector(), sd::DataType::BOOL, getContext()); NDArray::prepareSpecialUse({&result}, {this}); NativeOpExecutioner::execTransformBool(getContext(), op, getBuffer(), getShapeInfo(), getSpecialBuffer(), getSpecialShapeInfo(), result.buffer(), result.shapeInfo(), result.specialBuffer(), result.specialShapeInfo(), extraParams, nullptr, nullptr); @@ -3952,7 +3971,7 @@ NDArray NDArray::transform(nd4j::transform::BoolOps op, void *extraParams) const } //////////////////////////////////////////////////////////////////////// -NDArray NDArray::transform(nd4j::transform::BoolOps op, void *extraParams) && { +NDArray NDArray::transform(sd::transform::BoolOps op, void *extraParams) && { if (isS()) throw std::runtime_error("NDArray::transform BoolOps: you can't use this method on String array!"); @@ -3964,7 +3983,7 @@ NDArray NDArray::transform(nd4j::transform::BoolOps op, void *extraParams) && { } ////////////////////////////////////////////////////////////////////////// -void NDArray::applyScalarArr(nd4j::scalar::Ops op, const NDArray& scalar, NDArray& target, ExtraArguments *extraParams) { +void NDArray::applyScalarArr(sd::scalar::Ops op, const NDArray& scalar, NDArray& target, ExtraArguments *extraParams) { if (isS()) throw std::runtime_error("NDArray::applyScalarArr: you can't use this method on String array!"); if (scalar.lengthOf() != 1) @@ -3979,7 +3998,7 @@ void NDArray::applyScalarArr(nd4j::scalar::Ops op, const NDArray& scalar, NDArra } ////////////////////////////////////////////////////////////////////////// -void NDArray::applyScalarArr(nd4j::scalar::BoolOps op, const NDArray& scalar, NDArray &target, ExtraArguments *extraParams) const { +void NDArray::applyScalarArr(sd::scalar::BoolOps op, const NDArray& scalar, NDArray &target, ExtraArguments *extraParams) const { if (isS()) throw std::runtime_error("NDArray::applyScalarArr BoolOps: you can't use this method on String array!"); if (!target.isB()) @@ -3995,7 +4014,7 @@ void NDArray::applyScalarArr(nd4j::scalar::BoolOps op, const NDArray& scalar, ND } ////////////////////////////////////////////////////////////////////////// -void NDArray::applyScalarArr(nd4j::scalar::IntOps op, const NDArray& scalar, NDArray &target, ExtraArguments *extraParams) const { +void NDArray::applyScalarArr(sd::scalar::IntOps op, const NDArray& scalar, NDArray &target, ExtraArguments *extraParams) const { if (isS()) throw std::runtime_error("NDArray::applyScalarArr IntOps: you can't use this method on String array!"); @@ -4013,69 +4032,69 @@ void NDArray::applyScalarArr(nd4j::scalar::IntOps op, const NDArray& scalar, NDA //////////////////////////////////////////////////////////////////////// template -void NDArray::applyScalar(nd4j::scalar::IntOps op, const T scalar, NDArray& target, ExtraArguments *extraParams) const { +void NDArray::applyScalar(sd::scalar::IntOps op, const T scalar, NDArray& target, ExtraArguments *extraParams) const { NDArray scalarArr = NDArrayFactory::create(this->dataType(), scalar, getContext()); applyScalarArr(op, scalarArr, target, extraParams); } -template <> ND4J_EXPORT void NDArray::applyScalar(nd4j::scalar::IntOps op, const NDArray& scalar, NDArray &target, ExtraArguments *extraParams) const { throw std::runtime_error("NDArray::applyScalar method: do not use me!");} -template ND4J_EXPORT void NDArray::applyScalar(nd4j::scalar::IntOps op, const double scalar, NDArray &target, ExtraArguments *extraParams) const; -template ND4J_EXPORT void NDArray::applyScalar(nd4j::scalar::IntOps op, const float scalar, NDArray &target, ExtraArguments *extraParams) const; -template ND4J_EXPORT void NDArray::applyScalar(nd4j::scalar::IntOps op, const float16 scalar, NDArray &target, ExtraArguments *extraParams) const; -template ND4J_EXPORT void NDArray::applyScalar(nd4j::scalar::IntOps op, const bfloat16 scalar, NDArray &target, ExtraArguments *extraParams) const; -template ND4J_EXPORT void NDArray::applyScalar(nd4j::scalar::IntOps op, const Nd4jLong scalar, NDArray &target, ExtraArguments *extraParams) const; -template ND4J_EXPORT void NDArray::applyScalar(nd4j::scalar::IntOps op, const int scalar, NDArray &target, ExtraArguments *extraParams) const; -template ND4J_EXPORT void NDArray::applyScalar(nd4j::scalar::IntOps op, const int16_t scalar, NDArray &target, ExtraArguments *extraParams) const; -template ND4J_EXPORT void NDArray::applyScalar(nd4j::scalar::IntOps op, const int8_t scalar, NDArray &target, ExtraArguments *extraParams) const; -template ND4J_EXPORT void NDArray::applyScalar(nd4j::scalar::IntOps op, const uint8_t scalar, NDArray &target, ExtraArguments *extraParams) const; -template ND4J_EXPORT void NDArray::applyScalar(nd4j::scalar::IntOps op, const bool scalar, NDArray &target, ExtraArguments *extraParams) const; +template <> ND4J_EXPORT void NDArray::applyScalar(sd::scalar::IntOps op, const NDArray& scalar, NDArray &target, ExtraArguments *extraParams) const { throw std::runtime_error("NDArray::applyScalar method: do not use me!");} +template ND4J_EXPORT void NDArray::applyScalar(sd::scalar::IntOps op, const double scalar, NDArray &target, ExtraArguments *extraParams) const; +template ND4J_EXPORT void NDArray::applyScalar(sd::scalar::IntOps op, const float scalar, NDArray &target, ExtraArguments *extraParams) const; +template ND4J_EXPORT void NDArray::applyScalar(sd::scalar::IntOps op, const float16 scalar, NDArray &target, ExtraArguments *extraParams) const; +template ND4J_EXPORT void NDArray::applyScalar(sd::scalar::IntOps op, const bfloat16 scalar, NDArray &target, ExtraArguments *extraParams) const; +template ND4J_EXPORT void NDArray::applyScalar(sd::scalar::IntOps op, const Nd4jLong scalar, NDArray &target, ExtraArguments *extraParams) const; +template ND4J_EXPORT void NDArray::applyScalar(sd::scalar::IntOps op, const int scalar, NDArray &target, ExtraArguments *extraParams) const; +template ND4J_EXPORT void NDArray::applyScalar(sd::scalar::IntOps op, const int16_t scalar, NDArray &target, ExtraArguments *extraParams) const; +template ND4J_EXPORT void NDArray::applyScalar(sd::scalar::IntOps op, const int8_t scalar, NDArray &target, ExtraArguments *extraParams) const; +template ND4J_EXPORT void NDArray::applyScalar(sd::scalar::IntOps op, const uint8_t scalar, NDArray &target, ExtraArguments *extraParams) const; +template ND4J_EXPORT void NDArray::applyScalar(sd::scalar::IntOps op, const bool scalar, NDArray &target, ExtraArguments *extraParams) const; //////////////////////////////////////////////////////////////////////// template -void NDArray::applyScalar(nd4j::scalar::Ops op, const T scalar, NDArray& target, ExtraArguments *extraParams) { +void NDArray::applyScalar(sd::scalar::Ops op, const T scalar, NDArray& target, ExtraArguments *extraParams) { auto scalarArr = NDArrayFactory::create(dataType(), scalar, this->getContext()); applyScalarArr(op, scalarArr, target, extraParams); } -template <> ND4J_EXPORT void NDArray::applyScalar(nd4j::scalar::Ops op, const NDArray& scalar, NDArray &target, ExtraArguments *extraParams) { throw std::runtime_error("NDArray::applyScalar method: do not use me!");} -template ND4J_EXPORT void NDArray::applyScalar(nd4j::scalar::Ops op, const double scalar, NDArray &target, ExtraArguments *extraParams); -template ND4J_EXPORT void NDArray::applyScalar(nd4j::scalar::Ops op, const float scalar, NDArray &target, ExtraArguments *extraParams); -template ND4J_EXPORT void NDArray::applyScalar(nd4j::scalar::Ops op, const float16 scalar, NDArray &target, ExtraArguments *extraParams); -template ND4J_EXPORT void NDArray::applyScalar(nd4j::scalar::Ops op, const bfloat16 scalar, NDArray &target, ExtraArguments *extraParams); -template ND4J_EXPORT void NDArray::applyScalar(nd4j::scalar::Ops op, const Nd4jLong scalar, NDArray &target, ExtraArguments *extraParams); -template ND4J_EXPORT void NDArray::applyScalar(nd4j::scalar::Ops op, const int scalar, NDArray &target, ExtraArguments *extraParams); -template ND4J_EXPORT void NDArray::applyScalar(nd4j::scalar::Ops op, const int16_t scalar, NDArray &target, ExtraArguments *extraParams); -template ND4J_EXPORT void NDArray::applyScalar(nd4j::scalar::Ops op, const int8_t scalar, NDArray &target, ExtraArguments *extraParams); -template ND4J_EXPORT void NDArray::applyScalar(nd4j::scalar::Ops op, const uint8_t scalar, NDArray &target, ExtraArguments *extraParams); -template ND4J_EXPORT void NDArray::applyScalar(nd4j::scalar::Ops op, const bool scalar, NDArray &target, ExtraArguments *extraParams); +template <> ND4J_EXPORT void NDArray::applyScalar(sd::scalar::Ops op, const NDArray& scalar, NDArray &target, ExtraArguments *extraParams) { throw std::runtime_error("NDArray::applyScalar method: do not use me!");} +template ND4J_EXPORT void NDArray::applyScalar(sd::scalar::Ops op, const double scalar, NDArray &target, ExtraArguments *extraParams); +template ND4J_EXPORT void NDArray::applyScalar(sd::scalar::Ops op, const float scalar, NDArray &target, ExtraArguments *extraParams); +template ND4J_EXPORT void NDArray::applyScalar(sd::scalar::Ops op, const float16 scalar, NDArray &target, ExtraArguments *extraParams); +template ND4J_EXPORT void NDArray::applyScalar(sd::scalar::Ops op, const bfloat16 scalar, NDArray &target, ExtraArguments *extraParams); +template ND4J_EXPORT void NDArray::applyScalar(sd::scalar::Ops op, const Nd4jLong scalar, NDArray &target, ExtraArguments *extraParams); +template ND4J_EXPORT void NDArray::applyScalar(sd::scalar::Ops op, const int scalar, NDArray &target, ExtraArguments *extraParams); +template ND4J_EXPORT void NDArray::applyScalar(sd::scalar::Ops op, const int16_t scalar, NDArray &target, ExtraArguments *extraParams); +template ND4J_EXPORT void NDArray::applyScalar(sd::scalar::Ops op, const int8_t scalar, NDArray &target, ExtraArguments *extraParams); +template ND4J_EXPORT void NDArray::applyScalar(sd::scalar::Ops op, const uint8_t scalar, NDArray &target, ExtraArguments *extraParams); +template ND4J_EXPORT void NDArray::applyScalar(sd::scalar::Ops op, const bool scalar, NDArray &target, ExtraArguments *extraParams); //////////////////////////////////////////////////////////////////////// template -void NDArray::applyScalar(nd4j::scalar::BoolOps op, const T scalar, NDArray &target, ExtraArguments *extraParams) const { +void NDArray::applyScalar(sd::scalar::BoolOps op, const T scalar, NDArray &target, ExtraArguments *extraParams) const { NDArray scalarArr = NDArrayFactory::create(scalar, getContext()); applyScalarArr(op, scalarArr, target, extraParams); } -template <> ND4J_EXPORT void NDArray::applyScalar(nd4j::scalar::BoolOps op, const NDArray& scalar, NDArray &target, ExtraArguments *extraParams) const { throw std::runtime_error("NDArray::applyScalar method: do not use me!");} -template ND4J_EXPORT void NDArray::applyScalar(nd4j::scalar::BoolOps op, const double scalar, NDArray &target, ExtraArguments *extraParams) const; -template ND4J_EXPORT void NDArray::applyScalar(nd4j::scalar::BoolOps op, const float scalar, NDArray &target, ExtraArguments *extraParams) const; -template ND4J_EXPORT void NDArray::applyScalar(nd4j::scalar::BoolOps op, const float16 scalar, NDArray &target, ExtraArguments *extraParams) const; -template ND4J_EXPORT void NDArray::applyScalar(nd4j::scalar::BoolOps op, const bfloat16 scalar, NDArray &target, ExtraArguments *extraParams) const; -template ND4J_EXPORT void NDArray::applyScalar(nd4j::scalar::BoolOps op, const Nd4jLong scalar, NDArray &target, ExtraArguments *extraParams) const; -template ND4J_EXPORT void NDArray::applyScalar(nd4j::scalar::BoolOps op, const int scalar, NDArray &target, ExtraArguments *extraParams) const; -template ND4J_EXPORT void NDArray::applyScalar(nd4j::scalar::BoolOps op, const int16_t scalar, NDArray &target, ExtraArguments *extraParams) const; -template ND4J_EXPORT void NDArray::applyScalar(nd4j::scalar::BoolOps op, const int8_t scalar, NDArray &target, ExtraArguments *extraParams) const; -template ND4J_EXPORT void NDArray::applyScalar(nd4j::scalar::BoolOps op, const uint8_t scalar, NDArray &target, ExtraArguments *extraParams) const; -template ND4J_EXPORT void NDArray::applyScalar(nd4j::scalar::BoolOps op, const bool scalar, NDArray &target, ExtraArguments *extraParams) const; +template <> ND4J_EXPORT void NDArray::applyScalar(sd::scalar::BoolOps op, const NDArray& scalar, NDArray &target, ExtraArguments *extraParams) const { throw std::runtime_error("NDArray::applyScalar method: do not use me!");} +template ND4J_EXPORT void NDArray::applyScalar(sd::scalar::BoolOps op, const double scalar, NDArray &target, ExtraArguments *extraParams) const; +template ND4J_EXPORT void NDArray::applyScalar(sd::scalar::BoolOps op, const float scalar, NDArray &target, ExtraArguments *extraParams) const; +template ND4J_EXPORT void NDArray::applyScalar(sd::scalar::BoolOps op, const float16 scalar, NDArray &target, ExtraArguments *extraParams) const; +template ND4J_EXPORT void NDArray::applyScalar(sd::scalar::BoolOps op, const bfloat16 scalar, NDArray &target, ExtraArguments *extraParams) const; +template ND4J_EXPORT void NDArray::applyScalar(sd::scalar::BoolOps op, const Nd4jLong scalar, NDArray &target, ExtraArguments *extraParams) const; +template ND4J_EXPORT void NDArray::applyScalar(sd::scalar::BoolOps op, const int scalar, NDArray &target, ExtraArguments *extraParams) const; +template ND4J_EXPORT void NDArray::applyScalar(sd::scalar::BoolOps op, const int16_t scalar, NDArray &target, ExtraArguments *extraParams) const; +template ND4J_EXPORT void NDArray::applyScalar(sd::scalar::BoolOps op, const int8_t scalar, NDArray &target, ExtraArguments *extraParams) const; +template ND4J_EXPORT void NDArray::applyScalar(sd::scalar::BoolOps op, const uint8_t scalar, NDArray &target, ExtraArguments *extraParams) const; +template ND4J_EXPORT void NDArray::applyScalar(sd::scalar::BoolOps op, const bool scalar, NDArray &target, ExtraArguments *extraParams) const; //////////////////////////////////////////////////////////////////////// -void NDArray::applyIndexReduce(nd4j::indexreduce::Ops op, NDArray& target, const std::vector& dimensions, const ExtraArguments *extraParams) const { +void NDArray::applyIndexReduce(sd::indexreduce::Ops op, NDArray& target, const std::vector& dimensions, const ExtraArguments *extraParams) const { if (isS()) throw std::runtime_error("NDArray::applyIndexReduce: you can't use this method on String array!"); - if (target.dataType() != nd4j::DataType::INT64 && target.dataType() != nd4j::DataType::INT32) + if (target.dataType() != sd::DataType::INT64 && target.dataType() != sd::DataType::INT32) throw std::runtime_error("NDArray::applyIndexReduce operations return INT32/INT64"); void* params = extraParams != nullptr ? const_cast(extraParams)->argumentsAsT(this->dataType()) : nullptr; @@ -4088,8 +4107,8 @@ void NDArray::applyIndexReduce(nd4j::indexreduce::Ops op, NDArray& target, const else { std::vector copy = dimensions; shape::checkDimensions(rankOf(), copy); - auto pDims = nd4j::Environment::getInstance()->isCPU() ? copy.data() : nullptr; - auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(getShapeInfo(), copy); + auto pDims = sd::Environment::getInstance()->isCPU() ? copy.data() : nullptr; + auto packX = sd::ConstantTadHelper::getInstance()->tadForDimensions(getShapeInfo(), copy); NativeOpExecutioner::execIndexReduce(getContext(), op, getBuffer(), getShapeInfo(), getSpecialBuffer(), getSpecialShapeInfo(), params, target.buffer(), target.shapeInfo(), target.specialBuffer(), target.specialShapeInfo(), pDims, copy.size(), packX.platformShapeInfo(), packX.platformOffsets()); synchronize("NDArray::applyIndexReduce"); } @@ -4099,7 +4118,7 @@ void NDArray::applyIndexReduce(nd4j::indexreduce::Ops op, NDArray& target, const //////////////////////////////////////////////////////////////////////// // reduce dimensions in this array relying on index operations -NDArray NDArray::applyIndexReduce(nd4j::indexreduce::Ops op, const std::vector& dimensions, const ExtraArguments* extraParams ) const { +NDArray NDArray::applyIndexReduce(sd::indexreduce::Ops op, const std::vector& dimensions, const ExtraArguments* extraParams ) const { std::vector copy = dimensions; auto newShape = ShapeUtils::evalReduceShapeInfo('c', copy, *this, DataType::INT64, false, false, getContext()->getWorkspace()); @@ -4112,7 +4131,7 @@ NDArray NDArray::applyIndexReduce(nd4j::indexreduce::Ops op, const std::vector& dimensions, const ExtraArguments* extraParams) const { +NDArray NDArray::applyReduce3(sd::reduce3::Ops op, const NDArray& other, const std::vector& dimensions, const ExtraArguments* extraParams) const { if (isS()) throw std::runtime_error("NDArray::applyReduce3: you can't use this method on String array!"); @@ -4162,10 +4181,10 @@ NDArray NDArray::applyReduce3(nd4j::reduce3::Ops op, const NDArray& other, const } else { - auto pDims = nd4j::Environment::getInstance()->isCPU() ? copy.data() : nullptr; + auto pDims = sd::Environment::getInstance()->isCPU() ? copy.data() : nullptr; - auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(getShapeInfo(), copy); - auto packY = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(other.getShapeInfo(), copy); + auto packX = sd::ConstantTadHelper::getInstance()->tadForDimensions(getShapeInfo(), copy); + auto packY = sd::ConstantTadHelper::getInstance()->tadForDimensions(other.getShapeInfo(), copy); if(!shape::equalsSoft(packX.primaryShapeInfo(), packY.primaryShapeInfo()) || (packX.numberOfTads() != packY.numberOfTads() && packX.numberOfTads() != 1 && packY.numberOfTads() != 1)) throw std::runtime_error("NDArray::applyReduce3 cuda method: arrays tads are inconsistent !"); @@ -4180,7 +4199,7 @@ NDArray NDArray::applyReduce3(nd4j::reduce3::Ops op, const NDArray& other, const //////////////////////////////////////////////////////////////////////// // apply reduce3 (execAll) operations to this and other array, return result in new output array -NDArray NDArray::applyAllReduce3(nd4j::reduce3::Ops op, const NDArray& other, const std::vector& dimensions, const ExtraArguments* extraParams) const { +NDArray NDArray::applyAllReduce3(sd::reduce3::Ops op, const NDArray& other, const std::vector& dimensions, const ExtraArguments* extraParams) const { if (isS()) throw std::runtime_error("NDArray::applyAllReduce3: you can't use this method on String array!"); if(dataType() != other.dataType()) @@ -4207,7 +4226,7 @@ NDArray NDArray::applyAllReduce3(nd4j::reduce3::Ops op, const NDArray& other, co // create dynamic array of extra parameters if array extraParams is empty (==nullptr) void* params = extraParams != nullptr ? const_cast(extraParams)->argumentsAsT(dataType()) : nullptr; - auto pDims = nd4j::Environment::getInstance()->isCPU() ? copy.data() : nullptr; + auto pDims = sd::Environment::getInstance()->isCPU() ? copy.data() : nullptr; NDArray::prepareSpecialUse({&result}, {this, &other}); NativeOpExecutioner::execReduce3All(getContext(), op, getBuffer(), getShapeInfo(), getSpecialBuffer(), getSpecialShapeInfo(), params, other.getBuffer(), other.getShapeInfo(), other.getSpecialBuffer(), other.getSpecialShapeInfo(), result.buffer(), result.shapeInfo(), result.specialBuffer(), result.specialShapeInfo(), pDims, copy.size(), packX.platformShapeInfo(), packX.platformOffsets(), packY.platformShapeInfo(), packY.platformOffsets()); @@ -4218,7 +4237,7 @@ NDArray NDArray::applyAllReduce3(nd4j::reduce3::Ops op, const NDArray& other, co ////////////////////////////////////////////////////////////////////////// // method reduces array by excluding its shapes along axes present in dimensions vector -void NDArray::reduceAlongDimension(nd4j::reduce::FloatOps op, NDArray& target, const std::vector& dimensions, const bool keepDims, const bool supportOldShapes, const bool checkTargetShape) const { +void NDArray::reduceAlongDimension(sd::reduce::FloatOps op, NDArray& target, const std::vector& dimensions, const bool keepDims, const bool supportOldShapes, const bool checkTargetShape) const { if (isS()) throw std::runtime_error("NDArray::reduceAlongDimension FloatOps: you can't use this method on String array!"); @@ -4239,7 +4258,7 @@ void NDArray::reduceAlongDimension(nd4j::reduce::FloatOps op, NDArray& target, c NativeOpExecutioner::execReduceFloatScalar(getContext(), op, getBuffer(), getShapeInfo(), getSpecialBuffer(), getSpecialShapeInfo(),nullptr, target.getBuffer(), target.getShapeInfo(), target.getSpecialBuffer(), target.getSpecialShapeInfo()); } else { - auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(getShapeInfo(), copy); + auto packX = sd::ConstantTadHelper::getInstance()->tadForDimensions(getShapeInfo(), copy); NativeOpExecutioner::execReduceFloat(getContext(), op, getBuffer(), getShapeInfo(), getSpecialBuffer(), getSpecialShapeInfo(), nullptr, target.getBuffer(), target.getShapeInfo(), target.getSpecialBuffer(), target.getSpecialShapeInfo(), copy.data(), copy.size(), packX.platformShapeInfo(), packX.platformOffsets()); } synchronize("NDArray::reduceAlongDimension FloatOps"); @@ -4249,7 +4268,7 @@ void NDArray::reduceAlongDimension(nd4j::reduce::FloatOps op, NDArray& target, c ////////////////////////////////////////////////////////////////////////// // method reduces array by excluding its shapes along axes present in dimensions vector -void NDArray::reduceAlongDimension(nd4j::reduce::SameOps op, NDArray& target, const std::vector& dimensions, const bool keepDims, const bool supportOldShapes, const bool checkTargetShape) const { +void NDArray::reduceAlongDimension(sd::reduce::SameOps op, NDArray& target, const std::vector& dimensions, const bool keepDims, const bool supportOldShapes, const bool checkTargetShape) const { if (isS()) throw std::runtime_error("NDArray::reduceAlongDimension SameOps: you can't use this method on String array!"); @@ -4270,8 +4289,8 @@ void NDArray::reduceAlongDimension(nd4j::reduce::SameOps op, NDArray& target, co NativeOpExecutioner::execReduceSameScalar(getContext(), op, getBuffer(), getShapeInfo(), getSpecialBuffer(), getSpecialShapeInfo(), nullptr, target.getBuffer(), target.getShapeInfo(), target.getSpecialBuffer(), target.getSpecialShapeInfo()); } else { //if (!isEmpty()) { - auto pDims = nd4j::Environment::getInstance()->isCPU() ? copy.data() : nullptr; - auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(this->getShapeInfo(), copy); + auto pDims = sd::Environment::getInstance()->isCPU() ? copy.data() : nullptr; + auto packX = sd::ConstantTadHelper::getInstance()->tadForDimensions(this->getShapeInfo(), copy); NativeOpExecutioner::execReduceSame(getContext(), op, getBuffer(), getShapeInfo(), getSpecialBuffer(), getSpecialShapeInfo(), nullptr, target.getBuffer(), target.getShapeInfo(), target.getSpecialBuffer(), target.getSpecialShapeInfo(), pDims, copy.size(), packX.platformShapeInfo(), packX.platformOffsets()); } synchronize("NDArray::reduceAlongDimension SameOps"); @@ -4281,7 +4300,7 @@ void NDArray::reduceAlongDimension(nd4j::reduce::SameOps op, NDArray& target, co ////////////////////////////////////////////////////////////////////////// // method reduces array by excluding its shapes along axes present in dimensions vector -void NDArray::reduceAlongDimension(nd4j::reduce::LongOps op, NDArray& target, const std::vector& dimensions, const bool keepDims, const bool supportOldShapes, const bool checkTargetShape) const { +void NDArray::reduceAlongDimension(sd::reduce::LongOps op, NDArray& target, const std::vector& dimensions, const bool keepDims, const bool supportOldShapes, const bool checkTargetShape) const { if (isS()) throw std::runtime_error("NDArray::reduceAlongDimension LongOps: you can't use this method on String array!"); @@ -4302,8 +4321,8 @@ void NDArray::reduceAlongDimension(nd4j::reduce::LongOps op, NDArray& target, co NativeOpExecutioner::execReduceLongScalar(getContext(), op, getBuffer(), getShapeInfo(), getSpecialBuffer(), getSpecialShapeInfo(), nullptr, target.getBuffer(), target.getShapeInfo(), target.getSpecialBuffer(), target.getSpecialShapeInfo()); } else { - auto pDims = nd4j::Environment::getInstance()->isCPU() ? copy.data() : nullptr; - auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(this->getShapeInfo(), copy); + auto pDims = sd::Environment::getInstance()->isCPU() ? copy.data() : nullptr; + auto packX = sd::ConstantTadHelper::getInstance()->tadForDimensions(this->getShapeInfo(), copy); NativeOpExecutioner::execReduceLong(getContext(), op, getBuffer(), getShapeInfo(), getSpecialBuffer(), getSpecialShapeInfo(), nullptr, target.getBuffer(), target.getShapeInfo(), target.getSpecialBuffer(), target.getSpecialShapeInfo(), pDims, copy.size(), packX.platformShapeInfo(), packX.platformOffsets()); } synchronize("NDArray::reduceAlongDimension LongOps"); @@ -4313,7 +4332,7 @@ void NDArray::reduceAlongDimension(nd4j::reduce::LongOps op, NDArray& target, co ////////////////////////////////////////////////////////////////////////// // method reduces array by excluding its shapes along axes present in dimensions vector -void NDArray::reduceAlongDimension(nd4j::reduce::BoolOps op, NDArray& target, const std::vector& dimensions, const bool keepDims, const bool supportOldShapes, const bool checkTargetShape) const { +void NDArray::reduceAlongDimension(sd::reduce::BoolOps op, NDArray& target, const std::vector& dimensions, const bool keepDims, const bool supportOldShapes, const bool checkTargetShape) const { if (isS()) throw std::runtime_error("NDArray::reduceAlongDimension BoolOps cuda: you can't use this method on String array!"); @@ -4334,8 +4353,8 @@ void NDArray::reduceAlongDimension(nd4j::reduce::BoolOps op, NDArray& target, co NativeOpExecutioner::execReduceBoolScalar(getContext(), op, getBuffer(), getShapeInfo(), getSpecialBuffer(), getSpecialShapeInfo(), nullptr, target.getBuffer(), target.getShapeInfo(), target.getSpecialBuffer(), target.getSpecialShapeInfo()); } else { - auto pDims = nd4j::Environment::getInstance()->isCPU() ? copy.data() : nullptr; - auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(this->getShapeInfo(), copy); + auto pDims = sd::Environment::getInstance()->isCPU() ? copy.data() : nullptr; + auto packX = sd::ConstantTadHelper::getInstance()->tadForDimensions(this->getShapeInfo(), copy); NativeOpExecutioner::execReduceBool(getContext(), op, getBuffer(), getShapeInfo(), getSpecialBuffer(), getSpecialShapeInfo(), nullptr, target.getBuffer(), target.getShapeInfo(), target.getSpecialBuffer(), target.getSpecialShapeInfo(), pDims, copy.size(), packX.platformShapeInfo(), packX.platformOffsets()); } synchronize("NDArray::reduceAlongDimension LongOps"); @@ -4506,10 +4525,10 @@ void NDArray::addRowVector(const NDArray& row, NDArray& target) const { int dimension = 1; - auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(this->getShapeInfo(), dimension); + auto packX = sd::ConstantTadHelper::getInstance()->tadForDimensions(this->getShapeInfo(), dimension); NDArray::prepareSpecialUse({&target}, {this, &row}); - NativeOpExecutioner::execBroadcast(getContext(), nd4j::broadcast::Ops::Add, getBuffer(), getShapeInfo(), getSpecialBuffer(), getSpecialShapeInfo(), row.getBuffer(), row.getShapeInfo(), row.getSpecialBuffer(), row.getSpecialShapeInfo(), target.getBuffer(), target.getShapeInfo(), target.getSpecialBuffer(), target.getSpecialShapeInfo(), nullptr, 1, packX.platformShapeInfo(), packX.platformOffsets(), nullptr, nullptr); + NativeOpExecutioner::execBroadcast(getContext(), sd::broadcast::Ops::Add, getBuffer(), getShapeInfo(), getSpecialBuffer(), getSpecialShapeInfo(), row.getBuffer(), row.getShapeInfo(), row.getSpecialBuffer(), row.getSpecialShapeInfo(), target.getBuffer(), target.getShapeInfo(), target.getSpecialBuffer(), target.getSpecialShapeInfo(), nullptr, 1, packX.platformShapeInfo(), packX.platformOffsets(), nullptr, nullptr); NDArray::registerSpecialUse({&target}, {this, &row}); } @@ -4525,10 +4544,10 @@ void NDArray::subRowVector(const NDArray& row, NDArray& target) const { int dimension = 1; - auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(this->getShapeInfo(), dimension); + auto packX = sd::ConstantTadHelper::getInstance()->tadForDimensions(this->getShapeInfo(), dimension); NDArray::prepareSpecialUse({&target}, {this, &row}); - NativeOpExecutioner::execBroadcast(getContext(), nd4j::broadcast::Ops::Subtract, getBuffer(), getShapeInfo(), getSpecialBuffer(), getSpecialShapeInfo(), row.getBuffer(), row.getShapeInfo(), row.getSpecialBuffer(), row.getSpecialShapeInfo(), target.getBuffer(), target.getShapeInfo(), target.getSpecialBuffer(), target.getSpecialShapeInfo(), &dimension, 1, packX.platformShapeInfo(), packX.platformOffsets(), nullptr, nullptr); + NativeOpExecutioner::execBroadcast(getContext(), sd::broadcast::Ops::Subtract, getBuffer(), getShapeInfo(), getSpecialBuffer(), getSpecialShapeInfo(), row.getBuffer(), row.getShapeInfo(), row.getSpecialBuffer(), row.getSpecialShapeInfo(), target.getBuffer(), target.getShapeInfo(), target.getSpecialBuffer(), target.getSpecialShapeInfo(), &dimension, 1, packX.platformShapeInfo(), packX.platformOffsets(), nullptr, nullptr); NDArray::registerSpecialUse({&target}, {this, &row}); } @@ -4545,10 +4564,10 @@ void NDArray::mulRowVector(const NDArray &row, NDArray &target) const { int dimension = 1; - auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(this->getShapeInfo(), dimension); + auto packX = sd::ConstantTadHelper::getInstance()->tadForDimensions(this->getShapeInfo(), dimension); NDArray::prepareSpecialUse({&target}, {this, &row}); - NativeOpExecutioner::execBroadcast(getContext(), nd4j::broadcast::Ops::Multiply, getBuffer(), getShapeInfo(), getSpecialBuffer(), getSpecialShapeInfo(), row.getBuffer(), row.getShapeInfo(), row.getSpecialBuffer(), row.getSpecialShapeInfo(), target.getBuffer(), target.getShapeInfo(), target.getSpecialBuffer(), target.getSpecialShapeInfo(), nullptr, 1, packX.platformShapeInfo(), packX.platformOffsets(), nullptr, nullptr); + NativeOpExecutioner::execBroadcast(getContext(), sd::broadcast::Ops::Multiply, getBuffer(), getShapeInfo(), getSpecialBuffer(), getSpecialShapeInfo(), row.getBuffer(), row.getShapeInfo(), row.getSpecialBuffer(), row.getSpecialShapeInfo(), target.getBuffer(), target.getShapeInfo(), target.getSpecialBuffer(), target.getSpecialShapeInfo(), nullptr, 1, packX.platformShapeInfo(), packX.platformOffsets(), nullptr, nullptr); NDArray::registerSpecialUse({&target}, {this, &row}); } @@ -4566,10 +4585,10 @@ void NDArray::divRowVector(const NDArray &row, NDArray &target) const { int dimension = 1; - auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(this->getShapeInfo(), dimension); + auto packX = sd::ConstantTadHelper::getInstance()->tadForDimensions(this->getShapeInfo(), dimension); NDArray::prepareSpecialUse({&target}, {this, &row}); - NativeOpExecutioner::execBroadcast(getContext(), nd4j::broadcast::Divide, getBuffer(), getShapeInfo(), getSpecialBuffer(), getSpecialShapeInfo(), row.getBuffer(), row.getShapeInfo(), row.getSpecialBuffer(), row.getSpecialShapeInfo(), target.getBuffer(), target.getShapeInfo(), target.getSpecialBuffer(), target.getSpecialShapeInfo(), nullptr, 1, packX.platformShapeInfo(), packX.platformOffsets(), nullptr, nullptr); + NativeOpExecutioner::execBroadcast(getContext(), sd::broadcast::Divide, getBuffer(), getShapeInfo(), getSpecialBuffer(), getSpecialShapeInfo(), row.getBuffer(), row.getShapeInfo(), row.getSpecialBuffer(), row.getSpecialShapeInfo(), target.getBuffer(), target.getShapeInfo(), target.getSpecialBuffer(), target.getSpecialShapeInfo(), nullptr, 1, packX.platformShapeInfo(), packX.platformOffsets(), nullptr, nullptr); NDArray::registerSpecialUse({&target}, {this, &row}); } @@ -4584,10 +4603,10 @@ void NDArray::addiRowVector(const NDArray& row) { int dimension = 1; - auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(this->getShapeInfo(), dimension); + auto packX = sd::ConstantTadHelper::getInstance()->tadForDimensions(this->getShapeInfo(), dimension); NDArray::prepareSpecialUse({this}, {&row}); - NativeOpExecutioner::execBroadcast(getContext(), nd4j::broadcast::Ops::Add, getBuffer(), getShapeInfo(), getSpecialBuffer(), getSpecialShapeInfo(), row.getBuffer(), row.getShapeInfo(), row.getSpecialBuffer(), row.getSpecialShapeInfo(), this->buffer(), this->shapeInfo(), this->specialBuffer(), this->specialShapeInfo(), nullptr, 1, packX.platformShapeInfo(), packX.platformOffsets(), nullptr, nullptr); + NativeOpExecutioner::execBroadcast(getContext(), sd::broadcast::Ops::Add, getBuffer(), getShapeInfo(), getSpecialBuffer(), getSpecialShapeInfo(), row.getBuffer(), row.getShapeInfo(), row.getSpecialBuffer(), row.getSpecialShapeInfo(), this->buffer(), this->shapeInfo(), this->specialBuffer(), this->specialShapeInfo(), nullptr, 1, packX.platformShapeInfo(), packX.platformOffsets(), nullptr, nullptr); NDArray::registerSpecialUse({this}, {&row}); } @@ -4602,10 +4621,10 @@ void NDArray::addColumnVector(const NDArray &column, NDArray &target) const { int dimension = 0; - auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(this->getShapeInfo(), dimension); + auto packX = sd::ConstantTadHelper::getInstance()->tadForDimensions(this->getShapeInfo(), dimension); NDArray::prepareSpecialUse({&target}, {this, &column}); - NativeOpExecutioner::execBroadcast(getContext(), nd4j::broadcast::Ops::Add, getBuffer(), getShapeInfo(), getSpecialBuffer(), getSpecialShapeInfo(), column.getBuffer(), column.getShapeInfo(), column.getSpecialBuffer(), column.getSpecialShapeInfo(), target.getBuffer(), target.getShapeInfo(), target.getSpecialBuffer(), target.getSpecialShapeInfo(), nullptr, 1, packX.platformShapeInfo(), packX.platformOffsets(), nullptr, nullptr); + NativeOpExecutioner::execBroadcast(getContext(), sd::broadcast::Ops::Add, getBuffer(), getShapeInfo(), getSpecialBuffer(), getSpecialShapeInfo(), column.getBuffer(), column.getShapeInfo(), column.getSpecialBuffer(), column.getSpecialShapeInfo(), target.getBuffer(), target.getShapeInfo(), target.getSpecialBuffer(), target.getSpecialShapeInfo(), nullptr, 1, packX.platformShapeInfo(), packX.platformOffsets(), nullptr, nullptr); NDArray::registerSpecialUse({&target}, {this, &column}); } @@ -4619,10 +4638,10 @@ void NDArray::addiColumnVector(const NDArray &column) { int dimension = 0; - auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(this->getShapeInfo(), dimension); + auto packX = sd::ConstantTadHelper::getInstance()->tadForDimensions(this->getShapeInfo(), dimension); NDArray::prepareSpecialUse({this}, {&column}); - NativeOpExecutioner::execBroadcast(getContext(), nd4j::broadcast::Ops::Add, getBuffer(), getShapeInfo(), getSpecialBuffer(), getSpecialShapeInfo(), column.getBuffer(), column.getShapeInfo(), column.getSpecialBuffer(), column.getSpecialShapeInfo(), this->buffer(), this->shapeInfo(), this->specialBuffer(), this->specialShapeInfo(), nullptr, 1, packX.platformShapeInfo(), packX.platformOffsets(), nullptr, nullptr); + NativeOpExecutioner::execBroadcast(getContext(), sd::broadcast::Ops::Add, getBuffer(), getShapeInfo(), getSpecialBuffer(), getSpecialShapeInfo(), column.getBuffer(), column.getShapeInfo(), column.getSpecialBuffer(), column.getSpecialShapeInfo(), this->buffer(), this->shapeInfo(), this->specialBuffer(), this->specialShapeInfo(), nullptr, 1, packX.platformShapeInfo(), packX.platformOffsets(), nullptr, nullptr); NDArray::registerSpecialUse({this}, {&column}); } @@ -4636,10 +4655,10 @@ void NDArray::muliColumnVector(const NDArray& column) { int dimension = 0; - auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(this->getShapeInfo(), dimension); + auto packX = sd::ConstantTadHelper::getInstance()->tadForDimensions(this->getShapeInfo(), dimension); NDArray::prepareSpecialUse({this}, {&column}); - NativeOpExecutioner::execBroadcast(getContext(), nd4j::broadcast::Ops::Multiply, getBuffer(), getShapeInfo(), getSpecialBuffer(), getSpecialShapeInfo(), column.getBuffer(), column.getShapeInfo(), column.getSpecialBuffer(), column.getSpecialShapeInfo(), this->buffer(), this->shapeInfo(), this->specialBuffer(), this->specialShapeInfo(), nullptr, 1, packX.platformShapeInfo(), packX.platformOffsets(), nullptr, nullptr); + NativeOpExecutioner::execBroadcast(getContext(), sd::broadcast::Ops::Multiply, getBuffer(), getShapeInfo(), getSpecialBuffer(), getSpecialShapeInfo(), column.getBuffer(), column.getShapeInfo(), column.getSpecialBuffer(), column.getSpecialShapeInfo(), this->buffer(), this->shapeInfo(), this->specialBuffer(), this->specialShapeInfo(), nullptr, 1, packX.platformShapeInfo(), packX.platformOffsets(), nullptr, nullptr); NDArray::registerSpecialUse({this}, {&column}); } @@ -4725,7 +4744,7 @@ NDArray NDArray::like() { } //////////////////////////////////////////////////////////////////////// -NDArray NDArray::ulike() { +NDArray NDArray::ulike() const{ return NDArray(this, false, getContext()); } @@ -4801,7 +4820,7 @@ ResultSet NDArray::allTensorsAlongDimension(const std::vector &dimensions) auto pack = ConstantTadHelper::getInstance()->tadForDimensions(_shapeInfo, const_cast(dimensions.data()), dimensions.size()); auto numTads = pack.numberOfTads(); - for (int idx = 0; idx < numTads; idx++ ) { + for (Nd4jLong idx = 0; idx < numTads; idx++ ) { auto array = new NDArray(_buffer, ShapeDescriptor(pack.primaryShapeInfo()), getContext(), pack.primaryOffsets()[idx] + getBufferOffset()); array->_isView = true; result.push_back(array); @@ -4810,30 +4829,6 @@ ResultSet NDArray::allTensorsAlongDimension(const std::vector &dimensions) return result; } -////////////////////////////////////////////////////////////////////////// -NDArray NDArray::tensorAlongDimension(Nd4jLong index, const std::vector& dimensions) const { - std::vector copy(dimensions); - shape::checkDimensions(rankOf(), copy); - - Nd4jLong tadLength = shape::tadLength(this->getShapeInfo(), copy.data(), copy.size()); - Nd4jLong numTads = this->lengthOf() / tadLength; - - if (index >= numTads) - throw std::runtime_error("Can't get index higher than total number of TADs"); - - auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(this->getShapeInfo(), copy); - - NDArray array(_buffer, ShapeDescriptor(packX.primaryShapeInfo()), getContext(), packX.primaryOffsets()[index] + getBufferOffset()); - array._isView = true; - - return array; -} - -////////////////////////////////////////////////////////////////////////// -NDArray NDArray::tensorAlongDimension(Nd4jLong index, const std::initializer_list& dimensions) const { - return tensorAlongDimension(index, std::vector(dimensions)); -} - //////////////////////////////////////////////////////////////////////// // operator returns sub-array with buffer pointing at this->_buffer + certain offset NDArray NDArray::operator()(const std::vector& idx, const bool keepUnitiesInShape, const bool isStrided) const { @@ -4841,63 +4836,73 @@ NDArray NDArray::operator()(const std::vector& idx, const bool keepUni if(isEmpty()) throw std::invalid_argument("NDArray::operator(sub-arrays): array is empty !"); - const int rank = rankOf(); - Nd4jLong *newShapeInfo = ShapeBuilders::copyShapeInfo(getShapeInfo(), true, getContext()->getWorkspace()); + // Nd4jLong *outShapeInfo = nullptr; + // ALLOCATE(outShapeInfo, workspace, shape::shapeInfoLength(inShapeInfo), Nd4jLong); - auto shapeOf = shape::shapeOf(newShapeInfo); - auto stridesOf = shape::stride(newShapeInfo); + int numOfUntiesInSubArrShape = 0; - Nd4jLong offset = 0; - int n(isStrided ? 3 : 2), first, last, stride; - - for (int d = rank - 1; d >= 0; --d) { - - if (idx[n * d] != idx[n * d + 1]) { - - first = idx[n * d] >= 0 ? idx[n * d] : idx[n * d] + sizeAt(d) + 1; - last = idx[n * d + 1] >= 0 ? idx[n * d + 1] : idx[n * d + 1] + sizeAt(d) + 1; - stride = isStrided ? idx[n * d + 2] : 1; - - shapeOf[d] = (last - first + stride - 1) / stride; // ceil (last - first) / stride; - offset += first * stridesOf[d]; - - if(shapeOf[d] != 1) - stridesOf[d] *= stride; - } - } - - Nd4jLong *newShapeInfo2 = newShapeInfo; + Nd4jLong* subArrShapeInfo = nullptr; if(!keepUnitiesInShape) { - std::vector dimsWithUnities; + int n(isStrided ? 3 : 2), first, last; - for (uint d = 0; d < rank; ++d) - if(idx[n*d] != idx[n*d+1] && shapeOf[d] == 1) - dimsWithUnities.push_back(d); + // calculate the number of unities in shape + for (uint d = 0; d < rankOf(); ++d) { - if(!dimsWithUnities.empty()) - newShapeInfo2 = ShapeBuilders::copyShapeInfoWithoutUnites(newShapeInfo, dimsWithUnities.size(), dimsWithUnities.data(), getContext()->getWorkspace()); + if (idx[n * d] != idx[n * d + 1]) { + + first = idx[n * d] >= 0 ? idx[n * d] : idx[n * d] + sizeAt(d) + 1; + last = idx[n * d + 1] >= 0 ? idx[n * d + 1] : idx[n * d + 1] + sizeAt(d) + 1; + if(last - first == 1) + ++numOfUntiesInSubArrShape; + } + } } - // check if there is possibility to set ews = 1 - shape::checkStridesEwsAndOrder(newShapeInfo2); + ALLOCATE(subArrShapeInfo, getContext()->getWorkspace(), shape::shapeInfoLength(rankOf() - numOfUntiesInSubArrShape), Nd4jLong); - NDArray result(_buffer, ShapeDescriptor(newShapeInfo2), getContext(), offset + getBufferOffset()); + Nd4jLong offset; + + shape::calcSubArrShapeInfoAndOffset(idx.data(), getShapeInfo(), subArrShapeInfo, offset, keepUnitiesInShape, isStrided, numOfUntiesInSubArrShape); + + NDArray result(_buffer, ShapeDescriptor(subArrShapeInfo), getContext(), offset + getBufferOffset()); result._isView = true; - RELEASE(newShapeInfo, getContext()->getWorkspace()); - if(newShapeInfo != newShapeInfo2) - RELEASE(newShapeInfo2, getContext()->getWorkspace()); + RELEASE(subArrShapeInfo, getContext()->getWorkspace()); return result; } //////////////////////////////////////////////////////////////////////// NDArray NDArray::operator()(const Nd4jLong subArrIdx, const std::vector& dimsToExclude, bool keepUnitiesInShape) const { + std::vector idxRanges(2 * rankOf()); - ShapeUtils::evalIdxRangesForSubArr(subArrIdx, _shapeInfo, dimsToExclude, idxRanges.data()); + const auto rank = rankOf(); + const auto subArrRank = static_cast(dimsToExclude.size()); + + if(subArrRank > rank) + throw std::invalid_argument("NDArray::operator(const Nd4jLong subArrIdx, const std::vector& dimsToExclude, bool keepUnitiesInShape): static method: dimsToExclude is empty or has size > rank of array !"); + + memset(idxRanges.data(), 0, 2 * rank * sizeof(Nd4jLong)); + + // subArrRank == 0 means whole array, idxRanges should contain zeros only + + if(subArrRank != 0) { + + std::vector shapeOfSubArr(subArrRank), indexes(subArrRank); + for(int i = 0; i < subArrRank; ++i) + shapeOfSubArr[i] = sizeAt(dimsToExclude[i]); + + shape::index2coords(subArrIdx, subArrRank, shapeOfSubArr.data(), indexes.data()); + + for(int i = 0; i < subArrRank; ++i) { + int currIdx = 2 * dimsToExclude[i]; + idxRanges[currIdx] = indexes[i]; + idxRanges[currIdx + 1] = indexes[i] + 1; + } + } return (*this)(idxRanges, keepUnitiesInShape); } @@ -4916,7 +4921,7 @@ void NDArray::getSubArrShapeAndOffsets(const std::vector& dimsToExclude, Nd ALLOCATE(subArrShapeInfo, getContext()->getWorkspace(), shape::shapeInfoLength(subArrRank), Nd4jLong); ALLOCATE(subArrOffsets, getContext()->getWorkspace(), numOfSubArrs, Nd4jLong); - shape::calcSubArrShapeAndOffsets(_shapeInfo, numOfSubArrs, dimsToExclude.size(), dimsToExclude.data(), subArrShapeInfo, subArrOffsets, keepUnitiesInShape); + shape::calcSubArrsShapeInfoAndOffsets(_shapeInfo, numOfSubArrs, dimsToExclude.size(), dimsToExclude.data(), subArrShapeInfo, subArrOffsets, keepUnitiesInShape); } ////////////////////////////////////////////////////////////////////////// @@ -4940,13 +4945,13 @@ void NDArray::setShapeInfo(const Nd4jLong *shapeInfo) { _dataType = ArrayOptions::dataType(_shapeInfo); } else { - _dataType = nd4j::DataType::INHERIT; + _dataType = sd::DataType::INHERIT; _shapeInfoD = _shapeInfo = nullptr; } } //////////////////////////////////////////////////////////////////////// -void NDArray::setShapeInfo(const Nd4jLong *shapeInfo, const nd4j::DataType dtype) { +void NDArray::setShapeInfo(const Nd4jLong *shapeInfo, const sd::DataType dtype) { if (shapeInfo != nullptr) { @@ -4967,7 +4972,7 @@ void NDArray::setShapeInfo(const Nd4jLong *shapeInfo, const nd4j::DataType dtype _dataType = dtype; } else { - _dataType = nd4j::DataType::INHERIT; + _dataType = sd::DataType::INHERIT; _shapeInfoD = _shapeInfo = nullptr; } } @@ -5022,7 +5027,7 @@ NDArray operator+(NDArray&& arr, const T& scalar) { auto tmp = NDArrayFactory::create(arr.dataType(), scalar, arr.getContext()); NDArray::prepareSpecialUse({&arr}, {&arr, &tmp}); - NativeOpExecutioner::execScalar(arr.getContext(), nd4j::scalar::Add, arr.getBuffer(), arr.getShapeInfo(), arr.getSpecialBuffer(), arr.getSpecialShapeInfo(), arr.buffer(), arr.getShapeInfo(), arr.specialBuffer(), arr.getSpecialShapeInfo(), tmp.buffer(), tmp.shapeInfo(), tmp.specialBuffer(), tmp.specialShapeInfo(), nullptr); + NativeOpExecutioner::execScalar(arr.getContext(), sd::scalar::Add, arr.getBuffer(), arr.getShapeInfo(), arr.getSpecialBuffer(), arr.getSpecialShapeInfo(), arr.buffer(), arr.getShapeInfo(), arr.specialBuffer(), arr.getSpecialShapeInfo(), tmp.buffer(), tmp.shapeInfo(), tmp.specialBuffer(), tmp.specialShapeInfo(), nullptr); NDArray::registerSpecialUse({&arr}, {&arr, &tmp}); return std::move(arr); @@ -5044,7 +5049,7 @@ NDArray operator+(const NDArray& arr, const T& scalar) { NDArray result(arr.getShapeInfo(), DataTypeUtils::pickPairwiseResultType(arr.dataType(), DataTypeUtils::fromT()), false, arr.getContext()); NDArray::prepareSpecialUse({&result}, {&arr, &tmp}); - NativeOpExecutioner::execScalar(arr.getContext(), nd4j::scalar::Add, arr.getBuffer(), arr.getShapeInfo(), arr.getSpecialBuffer(), arr.getSpecialShapeInfo(), result.buffer(), result.getShapeInfo(), result.specialBuffer(), result.getSpecialShapeInfo(), tmp.buffer(), tmp.shapeInfo(), tmp.specialBuffer(), tmp.specialShapeInfo(), nullptr); + NativeOpExecutioner::execScalar(arr.getContext(), sd::scalar::Add, arr.getBuffer(), arr.getShapeInfo(), arr.getSpecialBuffer(), arr.getSpecialShapeInfo(), result.buffer(), result.getShapeInfo(), result.specialBuffer(), result.getSpecialShapeInfo(), tmp.buffer(), tmp.shapeInfo(), tmp.specialBuffer(), tmp.specialShapeInfo(), nullptr); NDArray::registerSpecialUse({&result}, {&arr, &tmp}); return result; @@ -5092,7 +5097,7 @@ NDArray operator-(NDArray&& arr, const T& scalar) { auto tmp = NDArrayFactory::create(arr.dataType(), scalar, arr.getContext()); NDArray::prepareSpecialUse({&arr}, {&arr, &tmp}); - NativeOpExecutioner::execScalar(arr.getContext(), nd4j::scalar::Subtract, arr.getBuffer(), arr.getShapeInfo(), arr.getSpecialBuffer(), arr.getSpecialShapeInfo(), arr.buffer(), arr.getShapeInfo(), arr.specialBuffer(), arr.getSpecialShapeInfo(), tmp.buffer(), tmp.shapeInfo(), tmp.specialBuffer(), tmp.specialShapeInfo(), nullptr); + NativeOpExecutioner::execScalar(arr.getContext(), sd::scalar::Subtract, arr.getBuffer(), arr.getShapeInfo(), arr.getSpecialBuffer(), arr.getSpecialShapeInfo(), arr.buffer(), arr.getShapeInfo(), arr.specialBuffer(), arr.getSpecialShapeInfo(), tmp.buffer(), tmp.shapeInfo(), tmp.specialBuffer(), tmp.specialShapeInfo(), nullptr); NDArray::registerSpecialUse({&arr}, {&arr, &tmp}); return std::move(arr); @@ -5111,7 +5116,7 @@ NDArray operator-(const NDArray& arr, const T& scalar) { NDArray result(arr.getShapeInfo(), DataTypeUtils::pickPairwiseResultType(arr.dataType(), DataTypeUtils::fromT()), false, arr.getContext()); NDArray::prepareSpecialUse({&result}, {&arr, &tmp}); - NativeOpExecutioner::execScalar(arr.getContext(), nd4j::scalar::Subtract, arr.getBuffer(), arr.getShapeInfo(), arr.getSpecialBuffer(), arr.getSpecialShapeInfo(), result.buffer(), result.getShapeInfo(), result.specialBuffer(), result.getSpecialShapeInfo(), tmp.buffer(), tmp.shapeInfo(), tmp.specialBuffer(), tmp.specialShapeInfo(), nullptr); + NativeOpExecutioner::execScalar(arr.getContext(), sd::scalar::Subtract, arr.getBuffer(), arr.getShapeInfo(), arr.getSpecialBuffer(), arr.getSpecialShapeInfo(), result.buffer(), result.getShapeInfo(), result.specialBuffer(), result.getSpecialShapeInfo(), tmp.buffer(), tmp.shapeInfo(), tmp.specialBuffer(), tmp.specialShapeInfo(), nullptr); NDArray::registerSpecialUse({&result}, {&arr, &tmp}); return result; @@ -5135,7 +5140,7 @@ NDArray operator-(const T& scalar, NDArray&& arr) { auto tmp = NDArrayFactory::create(arr.dataType(), scalar, arr.getContext()); NDArray::prepareSpecialUse({&arr}, {&arr, &tmp}); - NativeOpExecutioner::execScalar(arr.getContext(), nd4j::scalar::ReverseSubtract, arr.getBuffer(), arr.getShapeInfo(), arr.getSpecialBuffer(), arr.getSpecialShapeInfo(), arr.getBuffer(), arr.getShapeInfo(), arr.specialBuffer(), arr.specialShapeInfo(), tmp.buffer(), tmp.shapeInfo(), tmp.specialBuffer(), tmp.specialShapeInfo(), nullptr); + NativeOpExecutioner::execScalar(arr.getContext(), sd::scalar::ReverseSubtract, arr.getBuffer(), arr.getShapeInfo(), arr.getSpecialBuffer(), arr.getSpecialShapeInfo(), arr.getBuffer(), arr.getShapeInfo(), arr.specialBuffer(), arr.specialShapeInfo(), tmp.buffer(), tmp.shapeInfo(), tmp.specialBuffer(), tmp.specialShapeInfo(), nullptr); NDArray::registerSpecialUse({&arr}, {&arr, &tmp}); return std::move(arr); @@ -5158,7 +5163,7 @@ NDArray operator-(const T& scalar, const NDArray& arr) { NDArray result(arr.getShapeInfo(), DataTypeUtils::pickPairwiseResultType(arr.dataType(), DataTypeUtils::fromT()), false, arr.getContext()); NDArray::prepareSpecialUse({&result}, {&arr, &tmp}); - NativeOpExecutioner::execScalar(arr.getContext(), nd4j::scalar::ReverseSubtract, arr.getBuffer(), arr.getShapeInfo(), arr.getSpecialBuffer(), arr.getSpecialShapeInfo(), result.getBuffer(), result.getShapeInfo(), result.specialBuffer(), result.specialShapeInfo(), tmp.buffer(), tmp.shapeInfo(), tmp.specialBuffer(), tmp.specialShapeInfo(), nullptr); + NativeOpExecutioner::execScalar(arr.getContext(), sd::scalar::ReverseSubtract, arr.getBuffer(), arr.getShapeInfo(), arr.getSpecialBuffer(), arr.getSpecialShapeInfo(), result.getBuffer(), result.getShapeInfo(), result.specialBuffer(), result.specialShapeInfo(), tmp.buffer(), tmp.shapeInfo(), tmp.specialBuffer(), tmp.specialShapeInfo(), nullptr); NDArray::registerSpecialUse({&result}, {&arr, &tmp}); return result; @@ -5183,7 +5188,7 @@ NDArray operator*(NDArray&& arr, const T& scalar) { auto tmp = NDArrayFactory::create(arr.dataType(), scalar, arr.getContext()); NDArray::prepareSpecialUse({&arr}, {&arr, &tmp}); - NativeOpExecutioner::execScalar(arr.getContext(), nd4j::scalar::Multiply, arr.getBuffer(), arr.getShapeInfo(), arr.getSpecialBuffer(), arr.getSpecialShapeInfo(), arr.buffer(), arr.getShapeInfo(), arr.specialBuffer(), arr.getSpecialShapeInfo(), tmp.buffer(), tmp.shapeInfo(), tmp.specialBuffer(), tmp.specialShapeInfo(), nullptr); + NativeOpExecutioner::execScalar(arr.getContext(), sd::scalar::Multiply, arr.getBuffer(), arr.getShapeInfo(), arr.getSpecialBuffer(), arr.getSpecialShapeInfo(), arr.buffer(), arr.getShapeInfo(), arr.specialBuffer(), arr.getSpecialShapeInfo(), tmp.buffer(), tmp.shapeInfo(), tmp.specialBuffer(), tmp.specialShapeInfo(), nullptr); NDArray::registerSpecialUse({&arr}, {&arr, &tmp}); return std::move(arr); @@ -5206,7 +5211,7 @@ NDArray operator*(const NDArray& arr, const T& scalar) { NDArray result(arr.getShapeInfo(), DataTypeUtils::pickPairwiseResultType(arr.dataType(), DataTypeUtils::fromT()), false, arr.getContext()); NDArray::prepareSpecialUse({&result}, {&arr, &tmp}); - NativeOpExecutioner::execScalar(arr.getContext(), nd4j::scalar::Multiply, arr.getBuffer(), arr.getShapeInfo(), arr.getSpecialBuffer(), arr.getSpecialShapeInfo(), result.buffer(), result.getShapeInfo(), result.specialBuffer(), result.getSpecialShapeInfo(), tmp.buffer(), tmp.shapeInfo(), tmp.specialBuffer(), tmp.specialShapeInfo(), nullptr); + NativeOpExecutioner::execScalar(arr.getContext(), sd::scalar::Multiply, arr.getBuffer(), arr.getShapeInfo(), arr.getSpecialBuffer(), arr.getSpecialShapeInfo(), result.buffer(), result.getShapeInfo(), result.specialBuffer(), result.getSpecialShapeInfo(), tmp.buffer(), tmp.shapeInfo(), tmp.specialBuffer(), tmp.specialShapeInfo(), nullptr); NDArray::registerSpecialUse({&result}, {&arr, &tmp}); return result; @@ -5259,7 +5264,7 @@ NDArray operator/(NDArray&& arr, const T& scalar) { auto tmp = NDArrayFactory::create(arr.dataType(), scalar, arr.getContext()); NDArray::prepareSpecialUse({&arr}, {&arr, &tmp}); - NativeOpExecutioner::execScalar(arr.getContext(), nd4j::scalar::Divide, arr.getBuffer(), arr.getShapeInfo(), arr.getSpecialBuffer(), arr.getSpecialShapeInfo(), arr.buffer(), arr.getShapeInfo(), arr.specialBuffer(), arr.getSpecialShapeInfo(), tmp.buffer(), tmp.shapeInfo(), tmp.specialBuffer(), tmp.specialShapeInfo(), nullptr); + NativeOpExecutioner::execScalar(arr.getContext(), sd::scalar::Divide, arr.getBuffer(), arr.getShapeInfo(), arr.getSpecialBuffer(), arr.getSpecialShapeInfo(), arr.buffer(), arr.getShapeInfo(), arr.specialBuffer(), arr.getSpecialShapeInfo(), tmp.buffer(), tmp.shapeInfo(), tmp.specialBuffer(), tmp.specialShapeInfo(), nullptr); NDArray::registerSpecialUse({&arr}, {&arr, &tmp}); return std::move(arr); @@ -5281,7 +5286,7 @@ NDArray operator/(const NDArray& arr, const T& scalar) { NDArray result(arr.getShapeInfo(), DataTypeUtils::pickPairwiseResultType(arr.dataType(), DataTypeUtils::fromT()), false, arr.getContext()); NDArray::prepareSpecialUse({&result}, {&arr, &tmp}); - NativeOpExecutioner::execScalar(arr.getContext(), nd4j::scalar::Divide, arr.getBuffer(), arr.getShapeInfo(), arr.getSpecialBuffer(), arr.getSpecialShapeInfo(), result.buffer(), result.getShapeInfo(), result.specialBuffer(), result.getSpecialShapeInfo(), tmp.buffer(), tmp.shapeInfo(), tmp.specialBuffer(), tmp.specialShapeInfo(), nullptr); + NativeOpExecutioner::execScalar(arr.getContext(), sd::scalar::Divide, arr.getBuffer(), arr.getShapeInfo(), arr.getSpecialBuffer(), arr.getSpecialShapeInfo(), result.buffer(), result.getShapeInfo(), result.specialBuffer(), result.getSpecialShapeInfo(), tmp.buffer(), tmp.shapeInfo(), tmp.specialBuffer(), tmp.specialShapeInfo(), nullptr); NDArray::registerSpecialUse({&result}, {&arr, &tmp}); return result; @@ -5306,7 +5311,7 @@ NDArray operator/(const T& scalar, NDArray&& arr) { auto tmp = NDArrayFactory::create(arr.dataType(), scalar, arr.getContext()); NDArray::prepareSpecialUse({&arr}, {&arr, &tmp}); - NativeOpExecutioner::execScalar(arr.getContext(), nd4j::scalar::ReverseDivide, arr.getBuffer(), arr.getShapeInfo(), arr.getSpecialBuffer(), arr.getSpecialShapeInfo(), arr.getBuffer(), arr.getShapeInfo(), arr.specialBuffer(), arr.specialShapeInfo(), tmp.buffer(), tmp.shapeInfo(), tmp.specialBuffer(), tmp.specialShapeInfo(), nullptr); + NativeOpExecutioner::execScalar(arr.getContext(), sd::scalar::ReverseDivide, arr.getBuffer(), arr.getShapeInfo(), arr.getSpecialBuffer(), arr.getSpecialShapeInfo(), arr.getBuffer(), arr.getShapeInfo(), arr.specialBuffer(), arr.specialShapeInfo(), tmp.buffer(), tmp.shapeInfo(), tmp.specialBuffer(), tmp.specialShapeInfo(), nullptr); NDArray::registerSpecialUse({&arr}, {&arr, &tmp}); return std::move(arr); @@ -5330,7 +5335,7 @@ NDArray operator/(const T& scalar, const NDArray& arr) { NDArray result(arr.getShapeInfo(), DataTypeUtils::pickPairwiseResultType(arr.dataType(), DataTypeUtils::fromT()), false, arr.getContext()); NDArray::prepareSpecialUse({&result}, {&arr, &tmp}); - NativeOpExecutioner::execScalar(arr.getContext(), nd4j::scalar::ReverseDivide, arr.getBuffer(), arr.getShapeInfo(), arr.getSpecialBuffer(), arr.getSpecialShapeInfo(), result.getBuffer(), result.getShapeInfo(), result.specialBuffer(), result.specialShapeInfo(), tmp.buffer(), tmp.shapeInfo(), tmp.specialBuffer(), tmp.specialShapeInfo(), nullptr); + NativeOpExecutioner::execScalar(arr.getContext(), sd::scalar::ReverseDivide, arr.getBuffer(), arr.getShapeInfo(), arr.getSpecialBuffer(), arr.getSpecialShapeInfo(), result.getBuffer(), result.getShapeInfo(), result.specialBuffer(), result.specialShapeInfo(), tmp.buffer(), tmp.shapeInfo(), tmp.specialBuffer(), tmp.specialShapeInfo(), nullptr); NDArray::registerSpecialUse({&result}, {&arr, &tmp}); return result; @@ -5347,7 +5352,7 @@ NDArray operator+(T1&& arr1, T2&& arr2) { if (arr1.isS() || arr2.isS()) throw std::runtime_error("operator+(T&& arr1, T&& arr2): you can't use this method on String arrays!"); if (!Environment::getInstance()->isExperimentalBuild() && arr1.dataType() != arr2.dataType() && (arr1.dataType() != DataType::BOOL || arr2.dataType() != BOOL)) - throw nd4j::datatype_exception::build("operator+(T&& arr1, T&& arr2): Cannot multiply different types", arr1.dataType(), arr2.dataType()); + throw sd::datatype_exception::build("operator+(T&& arr1, T&& arr2): Cannot multiply different types", arr1.dataType(), arr2.dataType()); PointersManager pointersManager(arr1.getContext(), "operator+(T&& arr1, T&& arr2)"); @@ -5365,7 +5370,7 @@ NDArray operator+(T1&& arr1, T2&& arr2) { result = new NDArray(arr1.getShapeInfo(), DataTypeUtils::pickPairwiseResultType(arr1.getShapeInfo(), arr2.getShapeInfo()), false, arr1.getContext()); NDArray::prepareSpecialUse({result}, {&arr1, &arr2}); - NativeOpExecutioner::execPairwiseTransform(arr1.getContext(), nd4j::pairwise::Add, arr1.getBuffer(), arr1.getShapeInfo(), arr1.getSpecialBuffer(), arr1.getSpecialShapeInfo(), arr2.getBuffer(), arr2.getShapeInfo(), arr2.getSpecialBuffer(), arr2.getSpecialShapeInfo(), result->buffer(), result->getShapeInfo(), result->specialBuffer(), result->getSpecialShapeInfo(), nullptr); + NativeOpExecutioner::execPairwiseTransform(arr1.getContext(), sd::pairwise::Add, arr1.getBuffer(), arr1.getShapeInfo(), arr1.getSpecialBuffer(), arr1.getSpecialShapeInfo(), arr2.getBuffer(), arr2.getShapeInfo(), arr2.getSpecialBuffer(), arr2.getSpecialShapeInfo(), result->buffer(), result->getShapeInfo(), result->specialBuffer(), result->getSpecialShapeInfo(), nullptr); NDArray::registerSpecialUse({result}, {&arr1, &arr2}); if(!isArr1Rvalue && !isArr2Rvalue) { @@ -5377,7 +5382,7 @@ NDArray operator+(T1&& arr1, T2&& arr2) { return std::move(*result); } - return std::forward(arr1).applyTrueBroadcast(nd4j::BroadcastOpsTuple::Add(), std::forward(arr2)); + return std::forward(arr1).applyTrueBroadcast(sd::BroadcastOpsTuple::Add(), std::forward(arr2)); } template ND4J_EXPORT NDArray operator+(NDArray& arr1, NDArray& arr2); template ND4J_EXPORT NDArray operator+(NDArray& arr1, NDArray&& arr2); @@ -5397,7 +5402,7 @@ NDArray operator-(T1&& arr1, T2&& arr2) { if (arr1.isS() || arr2.isS()) throw std::runtime_error("operator-(T&& arr1, T&& arr2): you can't use this method on String arrays!"); if (!Environment::getInstance()->isExperimentalBuild() && arr1.dataType() != arr2.dataType() && (arr1.dataType() != DataType::BOOL || arr2.dataType() != BOOL)) - throw nd4j::datatype_exception::build("operator-(T&& arr1, T&& arr2): Cannot multiply different types", arr1.dataType(), arr2.dataType()); + throw sd::datatype_exception::build("operator-(T&& arr1, T&& arr2): Cannot multiply different types", arr1.dataType(), arr2.dataType()); PointersManager pointersManager(arr1.getContext(), "operator-(T&& arr1, T&& arr2)"); @@ -5415,7 +5420,7 @@ NDArray operator-(T1&& arr1, T2&& arr2) { result = new NDArray(arr1.getShapeInfo(), DataTypeUtils::pickPairwiseResultType(arr1.getShapeInfo(), arr2.getShapeInfo()), false, arr1.getContext()); NDArray::prepareSpecialUse({result}, {&arr1, &arr2}); - NativeOpExecutioner::execPairwiseTransform(arr1.getContext(), nd4j::pairwise::Subtract, arr1.getBuffer(), arr1.getShapeInfo(), arr1.getSpecialBuffer(), arr1.getSpecialShapeInfo(), arr2.getBuffer(), arr2.getShapeInfo(), arr2.getSpecialBuffer(), arr2.getSpecialShapeInfo(), result->buffer(), result->getShapeInfo(), result->specialBuffer(), result->getSpecialShapeInfo(), nullptr); + NativeOpExecutioner::execPairwiseTransform(arr1.getContext(), sd::pairwise::Subtract, arr1.getBuffer(), arr1.getShapeInfo(), arr1.getSpecialBuffer(), arr1.getSpecialShapeInfo(), arr2.getBuffer(), arr2.getShapeInfo(), arr2.getSpecialBuffer(), arr2.getSpecialShapeInfo(), result->buffer(), result->getShapeInfo(), result->specialBuffer(), result->getSpecialShapeInfo(), nullptr); NDArray::registerSpecialUse({result}, {&arr1, &arr2}); if(!isArr1Rvalue && !isArr2Rvalue) { @@ -5427,7 +5432,7 @@ NDArray operator-(T1&& arr1, T2&& arr2) { return std::move(*result); } - return std::forward(arr1).applyTrueBroadcast(nd4j::BroadcastOpsTuple::Subtract(), std::forward(arr2)); + return std::forward(arr1).applyTrueBroadcast(sd::BroadcastOpsTuple::Subtract(), std::forward(arr2)); } template ND4J_EXPORT NDArray operator-(NDArray& arr1, NDArray& arr2); template ND4J_EXPORT NDArray operator-(NDArray& arr1, NDArray&& arr2); @@ -5447,7 +5452,7 @@ NDArray operator*(T1&& arr1, T2&& arr2) { if (arr1.isS() || arr2.isS()) throw std::runtime_error("operator*(T&& arr1, T&& arr2): you can't use this method on String arrays!"); if (!Environment::getInstance()->isExperimentalBuild() && arr1.dataType() != arr2.dataType() && (arr1.dataType() != DataType::BOOL || arr2.dataType() != BOOL)) - throw nd4j::datatype_exception::build("operator*(T&& arr1, T&& arr2): Cannot multiply different types", arr1.dataType(), arr2.dataType()); + throw sd::datatype_exception::build("operator*(T&& arr1, T&& arr2): Cannot multiply different types", arr1.dataType(), arr2.dataType()); PointersManager pointersManager(arr1.getContext(), "operator*(T&& arr1, T&& arr2)"); @@ -5465,7 +5470,7 @@ NDArray operator*(T1&& arr1, T2&& arr2) { result = new NDArray(arr1.getShapeInfo(), DataTypeUtils::pickPairwiseResultType(arr1.getShapeInfo(), arr2.getShapeInfo()), false, arr1.getContext()); NDArray::prepareSpecialUse({result}, {&arr1, &arr2}); - NativeOpExecutioner::execPairwiseTransform(arr1.getContext(), nd4j::pairwise::Multiply, arr1.getBuffer(), arr1.getShapeInfo(), arr1.getSpecialBuffer(), arr1.getSpecialShapeInfo(), arr2.getBuffer(), arr2.getShapeInfo(), arr2.getSpecialBuffer(), arr2.getSpecialShapeInfo(), result->buffer(), result->getShapeInfo(), result->specialBuffer(), result->getSpecialShapeInfo(), nullptr); + NativeOpExecutioner::execPairwiseTransform(arr1.getContext(), sd::pairwise::Multiply, arr1.getBuffer(), arr1.getShapeInfo(), arr1.getSpecialBuffer(), arr1.getSpecialShapeInfo(), arr2.getBuffer(), arr2.getShapeInfo(), arr2.getSpecialBuffer(), arr2.getSpecialShapeInfo(), result->buffer(), result->getShapeInfo(), result->specialBuffer(), result->getSpecialShapeInfo(), nullptr); NDArray::registerSpecialUse({result}, {&arr1, &arr2}); if(!isArr1Rvalue && !isArr2Rvalue) { @@ -5477,7 +5482,7 @@ NDArray operator*(T1&& arr1, T2&& arr2) { return std::move(*result); } - return std::forward(arr1).applyTrueBroadcast(nd4j::BroadcastOpsTuple::Multiply(), std::forward(arr2)); + return std::forward(arr1).applyTrueBroadcast(sd::BroadcastOpsTuple::Multiply(), std::forward(arr2)); } template ND4J_EXPORT NDArray operator*(NDArray& arr1, NDArray& arr2); template ND4J_EXPORT NDArray operator*(NDArray& arr1, NDArray&& arr2); @@ -5497,7 +5502,7 @@ NDArray operator/(T1&& arr1, T2&& arr2) { if (arr1.isS() || arr2.isS()) throw std::runtime_error("operator/(T&& arr1, T&& arr2): you can't use this method on String arrays!"); if (!Environment::getInstance()->isExperimentalBuild() && arr1.dataType() != arr2.dataType() && (arr1.dataType() != DataType::BOOL || arr2.dataType() != BOOL)) - throw nd4j::datatype_exception::build("operator/(T&& arr1, T&& arr2): Cannot multiply different types", arr1.dataType(), arr2.dataType()); + throw sd::datatype_exception::build("operator/(T&& arr1, T&& arr2): Cannot multiply different types", arr1.dataType(), arr2.dataType()); PointersManager pointersManager(arr1.getContext(), "operator/(T&& arr1, T&& arr2)"); @@ -5515,7 +5520,7 @@ NDArray operator/(T1&& arr1, T2&& arr2) { result = new NDArray(arr1.getShapeInfo(), DataTypeUtils::pickPairwiseResultType(arr1.getShapeInfo(), arr2.getShapeInfo()), false, arr1.getContext()); NDArray::prepareSpecialUse({result}, {&arr1, &arr2}); - NativeOpExecutioner::execPairwiseTransform(arr1.getContext(), nd4j::pairwise::Divide, arr1.getBuffer(), arr1.getShapeInfo(), arr1.getSpecialBuffer(), arr1.getSpecialShapeInfo(), arr2.getBuffer(), arr2.getShapeInfo(), arr2.getSpecialBuffer(), arr2.getSpecialShapeInfo(), result->buffer(), result->getShapeInfo(), result->specialBuffer(), result->getSpecialShapeInfo(), nullptr); + NativeOpExecutioner::execPairwiseTransform(arr1.getContext(), sd::pairwise::Divide, arr1.getBuffer(), arr1.getShapeInfo(), arr1.getSpecialBuffer(), arr1.getSpecialShapeInfo(), arr2.getBuffer(), arr2.getShapeInfo(), arr2.getSpecialBuffer(), arr2.getSpecialShapeInfo(), result->buffer(), result->getShapeInfo(), result->specialBuffer(), result->getSpecialShapeInfo(), nullptr); NDArray::registerSpecialUse({result}, {&arr1, &arr2}); if(!isArr1Rvalue && !isArr2Rvalue) { @@ -5527,7 +5532,7 @@ NDArray operator/(T1&& arr1, T2&& arr2) { return std::move(*result); } - return std::forward(arr1).applyTrueBroadcast(nd4j::BroadcastOpsTuple::Divide(), std::forward(arr2)); + return std::forward(arr1).applyTrueBroadcast(sd::BroadcastOpsTuple::Divide(), std::forward(arr2)); } template ND4J_EXPORT NDArray operator/(NDArray& arr1, NDArray& arr2); template ND4J_EXPORT NDArray operator/(NDArray& arr1, NDArray&& arr2); @@ -5580,7 +5585,7 @@ template ND4J_EXPORT NDArray operator/(NDArray&& arr1, N // for(int i=0; i(i,j)*e(i,k); -// if(nd4j::math::nd4j_abs(dot) > eps ) +// if(sd::math::nd4j_abs(dot) > eps ) // return false; // dot = 0.f; @@ -5589,7 +5594,7 @@ template ND4J_EXPORT NDArray operator/(NDArray&& arr1, N // for(int j=0; j(i,j)*e(i,j); -// if(dot != 0.f && nd4j::math::nd4j_abs(nd4j::math::nd4j_sqrt(dot) - 1.f) > eps) +// if(dot != 0.f && sd::math::nd4j_abs(sd::math::nd4j_sqrt(dot) - 1.f) > eps) // return false; // dot = 0.f; @@ -5601,7 +5606,7 @@ template ND4J_EXPORT NDArray operator/(NDArray&& arr1, N // for(int j=0; j(i,j)*e(k,j); -// if(nd4j::math::nd4j_abs(dot) > eps ) +// if(sd::math::nd4j_abs(dot) > eps ) // return false; // dot = 0.; @@ -5611,7 +5616,7 @@ template ND4J_EXPORT NDArray operator/(NDArray&& arr1, N // for(int j=0; j(i,j)*e(i,j); -// if(dot!= 0. && nd4j::math::nd4j_abs(nd4j::math::nd4j_sqrt(dot) - 1.) > eps) +// if(dot!= 0. && sd::math::nd4j_abs(sd::math::nd4j_sqrt(dot) - 1.) > eps) // return false; // dot = 0.; // } diff --git a/libnd4j/include/array/NDArrayFactory.h b/libnd4j/include/array/NDArrayFactory.h new file mode 100644 index 000000000..f25c68fb4 --- /dev/null +++ b/libnd4j/include/array/NDArrayFactory.h @@ -0,0 +1,191 @@ +/******************************************************************************* + * Copyright (c) 2015-2018 Skymind, Inc. + * Copyright (c) 2019-2020 Konduit K.K. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ + +// +// Created by raver119 on 2018-09-16. +// @author Oleg Semeniv +// + +#ifndef DEV_TESTS_NDARRAYFACTORY_H +#define DEV_TESTS_NDARRAYFACTORY_H + +#include +#include +#include +//#include +#include +#include + + +namespace sd { + class ND4J_EXPORT NDArrayFactory { + private: + template + static void memcpyFromVector(void *ptr, const std::vector &vector); + public: + template + static NDArray* empty_(sd::LaunchContext * context = sd::LaunchContext ::defaultContext()); + + static NDArray* empty_(sd::DataType dataType, sd::LaunchContext * context = sd::LaunchContext ::defaultContext()); + + template + static NDArray empty(sd::LaunchContext * context = sd::LaunchContext ::defaultContext()); + + static NDArray empty(sd::DataType dataType, sd::LaunchContext * context = sd::LaunchContext ::defaultContext()); + + template + static NDArray* valueOf(const std::initializer_list& shape, T value, char order = 'c', sd::LaunchContext * context = sd::LaunchContext ::defaultContext()); + + template + static NDArray* valueOf(const std::vector& shape, T value, char order = 'c', sd::LaunchContext * context = sd::LaunchContext ::defaultContext()); + + static NDArray* valueOf(const std::vector& shape, const NDArray& value, char order = 'c', sd::LaunchContext * context = sd::LaunchContext ::defaultContext()); + + template + static NDArray* linspace(T from, T to, Nd4jLong numElements); + + + template + static NDArray* create_(const T value, sd::LaunchContext * context = sd::LaunchContext ::defaultContext()); + static NDArray* create_(sd::DataType dtype, sd::LaunchContext * context = sd::LaunchContext ::defaultContext()); + + template + static NDArray create(const T value, sd::LaunchContext * context = sd::LaunchContext ::defaultContext()); + static NDArray create(sd::DataType dtype, sd::LaunchContext * context = sd::LaunchContext ::defaultContext()); + template + static NDArray create(DataType type, const T scalar, sd::LaunchContext * context = sd::LaunchContext ::defaultContext()); + + + template + static NDArray* vector(Nd4jLong length, T startingValue = (T) 0, sd::LaunchContext * context = sd::LaunchContext ::defaultContext()); + + template + static NDArray* create_(char order, const std::vector &shape, sd::LaunchContext * context = sd::LaunchContext ::defaultContext()); + + static NDArray* create_( char order, const std::vector &shape, sd::DataType dataType, sd::LaunchContext * context = sd::LaunchContext ::defaultContext()); + + template + static NDArray* create_(char order, const std::vector &shape, const std::vector &data, sd::LaunchContext * context = sd::LaunchContext ::defaultContext()); + + template + static NDArray create(char order, const std::vector &shape, const std::vector &data, sd::LaunchContext * context = sd::LaunchContext ::defaultContext()); + + template + static NDArray create(char order, const std::vector &shape, sd::LaunchContext * context = sd::LaunchContext ::defaultContext()); + static NDArray create(char order, const std::vector &shape, sd::DataType dtype, sd::LaunchContext * context = sd::LaunchContext ::defaultContext()); + + template + static NDArray create(const std::vector &values, sd::LaunchContext * context = sd::LaunchContext ::defaultContext()); + +#ifndef __JAVACPP_HACK__ + // this method only available out of javacpp + /** + * This constructor creates vector of T + * + * @param values + */ + + template + static NDArray create(char order, const std::initializer_list& shape, sd::LaunchContext * context = sd::LaunchContext ::defaultContext()); + + template + static NDArray create(T* buffer, char order, const std::initializer_list& shape, sd::LaunchContext * context = sd::LaunchContext ::defaultContext()); + + template + static NDArray create(char order, const std::vector &shape, const std::initializer_list& data, sd::LaunchContext * context = sd::LaunchContext ::defaultContext()); + + /** + * This method creates NDArray from .npy file + * @param fileName + * @return + */ + static NDArray fromNpyFile(const char *fileName); + + /** + * This factory create array from utf8 string + * @return NDArray default dataType UTF8 + */ + static NDArray string(const char *string, sd::DataType dtype = sd::DataType::UTF8, sd::LaunchContext * context = sd::LaunchContext ::defaultContext()); + static NDArray* string_(const char *string, sd::DataType dtype = sd::DataType::UTF8, sd::LaunchContext * context = sd::LaunchContext ::defaultContext()); + static NDArray* string_(const std::string &string, sd::DataType dtype = sd::DataType::UTF8, sd::LaunchContext * context = sd::LaunchContext ::defaultContext()); + static NDArray string(const std::string& string, sd::DataType dtype = sd::DataType::UTF8, sd::LaunchContext* context = sd::LaunchContext::defaultContext()); + + /** + * This factory create array from utf16 string + * @return NDArray default dataType UTF16 + */ + static NDArray string(const char16_t* u16string, sd::DataType dtype = sd::DataType::UTF16, sd::LaunchContext* context = sd::LaunchContext::defaultContext()); + static NDArray* string_(const char16_t* u16string, sd::DataType dtype = sd::DataType::UTF16, sd::LaunchContext* context = sd::LaunchContext::defaultContext()); + static NDArray* string_(const std::u16string& u16string, sd::DataType dtype = sd::DataType::UTF16, sd::LaunchContext* context = sd::LaunchContext::defaultContext()); + static NDArray string(const std::u16string& u16string, sd::DataType dtype = sd::DataType::UTF16, sd::LaunchContext* context = sd::LaunchContext::defaultContext()); + + /** + * This factory create array from utf32 string + * @return NDArray default dataType UTF32 + */ + static NDArray string(const char32_t* u32string, sd::DataType dtype = sd::DataType::UTF32, sd::LaunchContext* context = sd::LaunchContext::defaultContext()); + static NDArray* string_(const char32_t* u32string, sd::DataType dtype = sd::DataType::UTF32, sd::LaunchContext* context = sd::LaunchContext::defaultContext()); + static NDArray* string_(const std::u32string& u32string, sd::DataType dtype = sd::DataType::UTF32, sd::LaunchContext* context = sd::LaunchContext::defaultContext()); + static NDArray string(const std::u32string& u32string, sd::DataType dtype = sd::DataType::UTF32, sd::LaunchContext* context = sd::LaunchContext::defaultContext()); + + /** + * This factory create array from vector of utf8 strings + * @return NDArray default dataType UTF8 + */ + static NDArray string( const std::vector &shape, const std::initializer_list &strings, sd::DataType dtype = sd::DataType::UTF8, sd::LaunchContext * context = sd::LaunchContext ::defaultContext()); + static NDArray string( const std::vector &shape, const std::initializer_list &string, sd::DataType dtype = sd::DataType::UTF8, sd::LaunchContext * context = sd::LaunchContext ::defaultContext()); + static NDArray string( const std::vector &shape, const std::vector &strings, sd::DataType dtype = sd::DataType::UTF8, sd::LaunchContext * context = sd::LaunchContext ::defaultContext()); + static NDArray string( const std::vector &shape, const std::vector &string, sd::DataType dtype = sd::DataType::UTF8, sd::LaunchContext * context = sd::LaunchContext ::defaultContext()); + static NDArray* string_( const std::vector &shape, const std::initializer_list &strings, sd::DataType dtype = sd::DataType::UTF8, sd::LaunchContext * context = sd::LaunchContext ::defaultContext()); + static NDArray* string_( const std::vector &shape, const std::initializer_list &string, sd::DataType dtype = sd::DataType::UTF8, sd::LaunchContext * context = sd::LaunchContext ::defaultContext()); + static NDArray* string_( const std::vector &shape, const std::vector &strings, sd::DataType dtype = sd::DataType::UTF8, sd::LaunchContext * context = sd::LaunchContext ::defaultContext()); + static NDArray* string_( const std::vector &shape, const std::vector &string, sd::DataType dtype = sd::DataType::UTF8, sd::LaunchContext * context = sd::LaunchContext ::defaultContext()); + + /** + * This factory create array from vector of utf16 strings + * @return NDArray default dataType UTF16 + */ + static NDArray string( const std::vector& shape, const std::initializer_list& strings, sd::DataType dtype = sd::DataType::UTF16, sd::LaunchContext* context = sd::LaunchContext::defaultContext()); + static NDArray string( const std::vector& shape, const std::initializer_list& string, sd::DataType dtype = sd::DataType::UTF16, sd::LaunchContext* context = sd::LaunchContext::defaultContext()); + static NDArray string( const std::vector& shape, const std::vector& strings, sd::DataType dtype = sd::DataType::UTF16, sd::LaunchContext* context = sd::LaunchContext::defaultContext()); + static NDArray string( const std::vector& shape, const std::vector& string, sd::DataType dtype = sd::DataType::UTF16, sd::LaunchContext* context = sd::LaunchContext::defaultContext()); + static NDArray* string_( const std::vector& shape, const std::initializer_list& strings, sd::DataType dtype = sd::DataType::UTF16, sd::LaunchContext* context = sd::LaunchContext::defaultContext()); + static NDArray* string_( const std::vector& shape, const std::initializer_list& string, sd::DataType dtype = sd::DataType::UTF16, sd::LaunchContext* context = sd::LaunchContext::defaultContext()); + static NDArray* string_( const std::vector& shape, const std::vector& strings, sd::DataType dtype = sd::DataType::UTF16, sd::LaunchContext* context = sd::LaunchContext::defaultContext()); + static NDArray* string_( const std::vector& shape, const std::vector& string, sd::DataType dtype = sd::DataType::UTF16, sd::LaunchContext* context = sd::LaunchContext::defaultContext()); + + /** + * This factory create array from vector of utf32 strings + * @return NDArray default dataType UTF32 + */ + static NDArray string( const std::vector& shape, const std::initializer_list& strings, sd::DataType dtype = sd::DataType::UTF32, sd::LaunchContext* context = sd::LaunchContext::defaultContext()); + static NDArray string( const std::vector& shape, const std::initializer_list& string, sd::DataType dtype = sd::DataType::UTF32, sd::LaunchContext* context = sd::LaunchContext::defaultContext()); + static NDArray string( const std::vector& shape, const std::vector& strings, sd::DataType dtype = sd::DataType::UTF32, sd::LaunchContext* context = sd::LaunchContext::defaultContext()); + static NDArray string( const std::vector& shape, const std::vector& string, sd::DataType dtype = sd::DataType::UTF32, sd::LaunchContext* context = sd::LaunchContext::defaultContext()); + static NDArray* string_( const std::vector& shape, const std::initializer_list& strings, sd::DataType dtype = sd::DataType::UTF32, sd::LaunchContext* context = sd::LaunchContext::defaultContext()); + static NDArray* string_( const std::vector& shape, const std::initializer_list& string, sd::DataType dtype = sd::DataType::UTF32, sd::LaunchContext* context = sd::LaunchContext::defaultContext()); + static NDArray* string_( const std::vector& shape, const std::vector& strings, sd::DataType dtype = sd::DataType::UTF32, sd::LaunchContext* context = sd::LaunchContext::defaultContext()); + static NDArray* string_( const std::vector& shape, const std::vector& string, sd::DataType dtype = sd::DataType::UTF32, sd::LaunchContext* context = sd::LaunchContext::defaultContext()); + + + static ResultSet createSetOfArrs(const Nd4jLong numOfArrs, const void* buffer, const Nd4jLong* shapeInfo, const Nd4jLong* offsets, sd::LaunchContext * context = sd::LaunchContext ::defaultContext()); + +#endif + }; +} + +#endif //DEV_TESTS_NDARRAYFACTORY_H diff --git a/libnd4j/blas/cuda/NDArrayLambda.hpp b/libnd4j/include/array/NDArrayLambda.hXX similarity index 91% rename from libnd4j/blas/cuda/NDArrayLambda.hpp rename to libnd4j/include/array/NDArrayLambda.hXX index 15028dfaa..50d9bc8d6 100644 --- a/libnd4j/blas/cuda/NDArrayLambda.hpp +++ b/libnd4j/include/array/NDArrayLambda.hXX @@ -17,17 +17,17 @@ #ifndef CUDA_LAMBDA_HELPER #define CUDA_LAMBDA_HELPER -#include -#include +#include +#include #include #include #include -static Nd4jLong __device__ __noinline__ __getIndexOffset(Nd4jLong index, Nd4jLong *shapeInfo) { +static Nd4jLong __device__ __noinline__ getIndexOffset(Nd4jLong index, Nd4jLong *shapeInfo) { return shape::getIndexOffset(index, shapeInfo); } -static Nd4jLong __device__ __noinline__ __length(Nd4jLong *shapeInfo) { +static Nd4jLong __device__ __noinline__ length(Nd4jLong *shapeInfo) { return shape::length(shapeInfo); } @@ -94,7 +94,7 @@ static _CUDA_G void lambdaKernel(void* vx, Nd4jLong *xShapeInfo, void *vz, Nd4jL auto xOrder = shape::order(xShapeInfo); auto zOrder = shape::order(zShapeInfo); - auto zLength = __length(zShapeInfo); + auto zLength = length(zShapeInfo); auto tid = threadIdx.x + blockIdx.x * blockDim.x; @@ -103,8 +103,8 @@ static _CUDA_G void lambdaKernel(void* vx, Nd4jLong *xShapeInfo, void *vz, Nd4jL z[e * zEws] = lambda(x[e * xEws]); } else { for (uint e = tid; e < zLength; e += blockDim.x * gridDim.x) { - auto xOffset = __getIndexOffset(e, xShapeInfo); - auto zOffset = __getIndexOffset(e, zShapeInfo); + auto xOffset = getIndexOffset(e, xShapeInfo); + auto zOffset = getIndexOffset(e, zShapeInfo); z[zOffset] = lambda(x[xOffset]); } @@ -123,7 +123,7 @@ static _CUDA_G void lambdaIndexedKernel(void* vx, Nd4jLong *xShapeInfo, void *vz auto xOrder = shape::order(xShapeInfo); auto zOrder = shape::order(zShapeInfo); - auto zLength = __length(zShapeInfo); + auto zLength = length(zShapeInfo); auto tid = threadIdx.x + blockIdx.x * blockDim.x; @@ -132,8 +132,8 @@ static _CUDA_G void lambdaIndexedKernel(void* vx, Nd4jLong *xShapeInfo, void *vz z[e * zEws] = lambda(e, x[e * xEws]); } else { for (uint e = tid; e < zLength; e += blockDim.x * gridDim.x) { - auto xOffset = __getIndexOffset(e, xShapeInfo); - auto zOffset = __getIndexOffset(e, zShapeInfo); + auto xOffset = getIndexOffset(e, xShapeInfo); + auto zOffset = getIndexOffset(e, zShapeInfo); z[zOffset] = lambda(e, x[xOffset]); } @@ -155,7 +155,7 @@ static _CUDA_G void lambdaIndexedPairwiseKernel(void* vx, Nd4jLong *xShapeInfo, auto yOrder = shape::order(yShapeInfo); auto zOrder = shape::order(zShapeInfo); - auto zLength = __length(zShapeInfo); + auto zLength = length(zShapeInfo); auto tid = threadIdx.x + blockIdx.x * blockDim.x; @@ -164,9 +164,9 @@ static _CUDA_G void lambdaIndexedPairwiseKernel(void* vx, Nd4jLong *xShapeInfo, z[e * zEws] = lambda(e, x[e * xEws], y[e * yEws]); } else { for (uint e = tid; e < zLength; e += blockDim.x * gridDim.x) { - auto xOffset = __getIndexOffset(e, xShapeInfo); - auto yOffset = __getIndexOffset(e, yShapeInfo); - auto zOffset = __getIndexOffset(e, zShapeInfo); + auto xOffset = getIndexOffset(e, xShapeInfo); + auto yOffset = getIndexOffset(e, yShapeInfo); + auto zOffset = getIndexOffset(e, zShapeInfo); z[zOffset] = lambda(e, x[xOffset], y[yOffset]); } @@ -188,7 +188,7 @@ static _CUDA_G void lambdaPairwiseKernel(void* vx, Nd4jLong *xShapeInfo, void* v auto yOrder = shape::order(yShapeInfo); auto zOrder = shape::order(zShapeInfo); - auto zLength = __length(zShapeInfo); + auto zLength = length(zShapeInfo); auto tid = threadIdx.x + blockIdx.x * blockDim.x; @@ -197,9 +197,9 @@ static _CUDA_G void lambdaPairwiseKernel(void* vx, Nd4jLong *xShapeInfo, void* v z[e * zEws] = lambda(x[e * xEws], y[e * yEws]); } else { for (uint e = tid; e < zLength; e += blockDim.x * gridDim.x) { - auto xOffset = __getIndexOffset(e, xShapeInfo); - auto yOffset = __getIndexOffset(e, yShapeInfo); - auto zOffset = __getIndexOffset(e, zShapeInfo); + auto xOffset = getIndexOffset(e, xShapeInfo); + auto yOffset = getIndexOffset(e, yShapeInfo); + auto zOffset = getIndexOffset(e, zShapeInfo); z[zOffset] = lambda(x[xOffset], y[yOffset]); } @@ -224,7 +224,7 @@ static _CUDA_G void lambdaTriplewiseKernel(void* vw, Nd4jLong *wShapeInfo, void* auto yOrder = shape::order(yShapeInfo); auto zOrder = shape::order(zShapeInfo); - auto zLength = __length(zShapeInfo); + auto zLength = length(zShapeInfo); auto tid = threadIdx.x + blockIdx.x * blockDim.x; @@ -233,10 +233,10 @@ static _CUDA_G void lambdaTriplewiseKernel(void* vw, Nd4jLong *wShapeInfo, void* z[e * zEws] = lambda(w[e * wEws], x[e * xEws], y[e * yEws]); } else { for (uint e = tid; e < zLength; e += blockDim.x * gridDim.x) { - auto wOffset = __getIndexOffset(e, wShapeInfo); - auto xOffset = __getIndexOffset(e, xShapeInfo); - auto yOffset = __getIndexOffset(e, yShapeInfo); - auto zOffset = __getIndexOffset(e, zShapeInfo); + auto wOffset = getIndexOffset(e, wShapeInfo); + auto xOffset = getIndexOffset(e, xShapeInfo); + auto yOffset = getIndexOffset(e, yShapeInfo); + auto zOffset = getIndexOffset(e, zShapeInfo); z[zOffset] = lambda(w[wOffset], x[xOffset], y[yOffset]); } diff --git a/libnd4j/include/array/NDArrayList.h b/libnd4j/include/array/NDArrayList.h index 843b69a91..e446213f2 100644 --- a/libnd4j/include/array/NDArrayList.h +++ b/libnd4j/include/array/NDArrayList.h @@ -26,25 +26,25 @@ #include #include #include -#include +#include #include -#include +#include -namespace nd4j { +namespace sd { class ND4J_EXPORT NDArrayList { private: // workspace where chunks belong to - //nd4j::memory::Workspace* _workspace = nullptr; - nd4j::LaunchContext * _context = nd4j::LaunchContext ::defaultContext(); + //sd::memory::Workspace* _workspace = nullptr; + sd::LaunchContext * _context = sd::LaunchContext ::defaultContext(); // numeric and symbolic ids of this list std::pair _id; std::string _name; - nd4j::DataType _dtype; + sd::DataType _dtype; // stored chunks - std::map _chunks; + MAP_IMPL _chunks; // just a counter, for stored elements std::atomic _elements; @@ -65,7 +65,7 @@ namespace nd4j { NDArrayList(int height, bool expandable = false); ~NDArrayList(); - nd4j::DataType dataType(); + sd::DataType dataType(); NDArray* read(int idx); NDArray* readRaw(int idx); @@ -82,8 +82,8 @@ namespace nd4j { std::pair& id(); std::string& name(); - //nd4j::memory::Workspace* workspace(); - nd4j::LaunchContext * context(); + //sd::memory::Workspace* workspace(); + sd::LaunchContext * context(); NDArrayList* clone(); bool equals(NDArrayList& other); diff --git a/libnd4j/include/array/ResultSet.h b/libnd4j/include/array/ResultSet.h index ddcfdcdf9..6c80e7b18 100644 --- a/libnd4j/include/array/ResultSet.h +++ b/libnd4j/include/array/ResultSet.h @@ -15,11 +15,11 @@ ******************************************************************************/ // -// This class is suited for execution results representation. -// +// This class is suited for execution results representation. +// // PLESE NOTE: It will delete all stored NDArrays upon destructor call // -// Created by raver119 on 07.09.17. +// @author raver119@gmail.com // #ifndef LIBND4J_RESULTSET_H @@ -27,22 +27,27 @@ #include #include -#include -#include +#include +#include -namespace nd4j { +namespace sd { class NDArray; // forward declaration of template class NDArray - + class ND4J_EXPORT ResultSet { private: - std::vector _content; + std::vector _content; Nd4jStatus _status = ND4J_STATUS_OK; bool _removable = true; + void delContent(); + public: - // default constructor - ResultSet(const nd4j::graph::FlatResult* result = nullptr); + explicit ResultSet(); + +#ifndef __JAVACPP_HACK__ + ResultSet(const sd::graph::FlatResult* result); +#endif ResultSet(const ResultSet& other) noexcept; @@ -53,13 +58,13 @@ namespace nd4j { // move assignment operator ResultSet& operator=(ResultSet&& other) noexcept; - + ~ResultSet(); int size(); - nd4j::NDArray* at(const unsigned long idx) const; - nd4j::NDArray* operator[](const unsigned long idx) const; - void push_back(nd4j::NDArray* array); + sd::NDArray* at(const unsigned long idx) const; + sd::NDArray* operator[](const unsigned long idx) const; + void push_back(sd::NDArray* array); Nd4jStatus status(); void setStatus(Nd4jStatus status); diff --git a/libnd4j/include/array/ShapeDescriptor.h b/libnd4j/include/array/ShapeDescriptor.h index ddfd45a38..6e2299ba0 100644 --- a/libnd4j/include/array/ShapeDescriptor.h +++ b/libnd4j/include/array/ShapeDescriptor.h @@ -23,12 +23,12 @@ #include #include -#include -#include -#include +#include +#include +#include #include -namespace nd4j { +namespace sd { class ND4J_EXPORT ShapeDescriptor { @@ -44,7 +44,7 @@ class ND4J_EXPORT ShapeDescriptor { public: ShapeDescriptor(const ShapeDescriptor &other); ShapeDescriptor(const Nd4jLong *shapeInfo, bool inheritDtype = true); - explicit ShapeDescriptor(const Nd4jLong *shapeInfo, const nd4j::DataType dtypeOverride); + explicit ShapeDescriptor(const Nd4jLong *shapeInfo, const sd::DataType dtypeOverride); explicit ShapeDescriptor(const Nd4jLong *shapeInfo, const Nd4jLong *dtypeOverride); explicit ShapeDescriptor(const Nd4jLong *shapeInfo, const Nd4jLong *dtypeOverride, const Nd4jLong *orderOverride); explicit ShapeDescriptor(const DataType type, const Nd4jLong length); @@ -85,9 +85,19 @@ class ND4J_EXPORT ShapeDescriptor { static ShapeDescriptor scalarDescriptor(const DataType type); static ShapeDescriptor vectorDescriptor(const Nd4jLong length, const DataType type); }; - - } +#ifndef __JAVACPP_HACK__ + +namespace std { + template<> + class ND4J_EXPORT hash { + public: + size_t operator()(const sd::ShapeDescriptor &k) const; + }; +} + +#endif + #endif //DEV_TESTS_SHAPEDESCRIPTOR_H diff --git a/libnd4j/include/array/ShapeList.h b/libnd4j/include/array/ShapeList.h index 7994e3a6f..2d0fde4ad 100644 --- a/libnd4j/include/array/ShapeList.h +++ b/libnd4j/include/array/ShapeList.h @@ -22,10 +22,10 @@ #define LIBND4J_SHAPELIST_H #include -#include -#include +#include +#include -namespace nd4j { +namespace sd { class ND4J_EXPORT ShapeList { protected: std::vector _shapes; diff --git a/libnd4j/include/array/SpaceType.h b/libnd4j/include/array/SpaceType.h index 3d24af12a..b6c6dfbbc 100644 --- a/libnd4j/include/array/SpaceType.h +++ b/libnd4j/include/array/SpaceType.h @@ -21,7 +21,7 @@ #ifndef ND4J_SPACE_TYPE_H #define ND4J_SPACE_TYPE_H -namespace nd4j { +namespace sd { enum SpaceType { CONTINUOUS = 1, COMPLEX = 2, diff --git a/libnd4j/include/array/SparseType.h b/libnd4j/include/array/SparseType.h index 8c11a9242..3b77a1626 100644 --- a/libnd4j/include/array/SparseType.h +++ b/libnd4j/include/array/SparseType.h @@ -21,7 +21,7 @@ #ifndef LIBND4J_SPARSETYPE_H #define LIBND4J_SPARSETYPE_H -namespace nd4j { +namespace sd { enum SparseType { CSR = 1, CSC = 2, diff --git a/libnd4j/include/array/TadDescriptor.h b/libnd4j/include/array/TadDescriptor.h index 3943a4689..01ea1caa1 100644 --- a/libnd4j/include/array/TadDescriptor.h +++ b/libnd4j/include/array/TadDescriptor.h @@ -22,9 +22,9 @@ #define DEV_TESTS_TADDESCRIPTOR_H #include "ShapeDescriptor.h" -#include +#include -namespace nd4j { +namespace sd { class ND4J_EXPORT TadDescriptor { private: ShapeDescriptor _originalShape; @@ -53,9 +53,22 @@ namespace nd4j { std::vector& axis(); ShapeDescriptor& originalShape(); + ShapeDescriptor const& originalShapeConst() const; bool areUnitiesinShape() const; }; } +#ifndef __JAVACPP_HACK__ + +namespace std { + template<> + class ND4J_EXPORT hash { + public: + size_t operator()(const sd::TadDescriptor &k) const; + }; +} + +#endif + #endif //DEV_TESTS_TADDESCRIPTOR_H diff --git a/libnd4j/include/array/TadPack.h b/libnd4j/include/array/TadPack.h index 2d195277d..09b084548 100644 --- a/libnd4j/include/array/TadPack.h +++ b/libnd4j/include/array/TadPack.h @@ -23,7 +23,7 @@ #include "ConstantDataBuffer.h" -namespace nd4j { +namespace sd { class ND4J_EXPORT TadPack { private: ConstantDataBuffer _tadShape; diff --git a/libnd4j/include/array/cpu/DataBuffer.cpp b/libnd4j/include/array/cpu/DataBuffer.cpp index ccd782adc..2575e2ba4 100644 --- a/libnd4j/include/array/cpu/DataBuffer.cpp +++ b/libnd4j/include/array/cpu/DataBuffer.cpp @@ -19,10 +19,10 @@ // @author Yurii Shyrma (iuriish@yahoo.com) // -#include "../DataBuffer.h" -#include +#include +#include -namespace nd4j { +namespace sd { void DataBuffer::expand(const uint64_t size) { if (size > _lenInBytes) { // allocate new buffer diff --git a/libnd4j/blas/cpu/NDArray.cpp b/libnd4j/include/array/cpu/NDArray.cpp similarity index 95% rename from libnd4j/blas/cpu/NDArray.cpp rename to libnd4j/include/array/cpu/NDArray.cpp index 58d4b3c34..1d97ba61c 100644 --- a/libnd4j/blas/cpu/NDArray.cpp +++ b/libnd4j/include/array/cpu/NDArray.cpp @@ -17,15 +17,15 @@ #ifndef NDARRAY_CPP #define NDARRAY_CPP -#include "../NDArray.h" -#include "../NDArrayFactory.h" -#include "NativeOpExecutioner.h" -#include +#include +#include +#include +#include #include #include -#include +#include #include -#include +#include #include #include #include @@ -38,16 +38,16 @@ #include #include #include -#include +#include #include #include #include #include -#include +#include -namespace nd4j { +namespace sd { //////////////////////////////////////////////////////////////////////// @@ -95,22 +95,29 @@ void NDArray::fillAsTriangular(const float val, int lower, int upper, NDArray& t const bool areSameOffsets = shape::haveSameShapeAndStrides(getShapeInfo(), target.getShapeInfo()); - auto func = PRAGMA_THREADS_FOR { - Nd4jLong coords[MAX_RANK]; + + int coords[MAX_RANK], temp; + for (auto i = start; i < stop; i++) { - shape::index2coords(i, target.getShapeInfo(), coords); + + shape::index2coordsCPU(start, i, target.getShapeInfo(), coords); const auto zOffset = shape::getOffset(target.getShapeInfo(), coords); // if( (row + upper < col) || (row + lower > col) ) if ((coords[zRank - 2] + upper < coords[zRank - 1]) || (coords[zRank - 2] + lower > coords[zRank - 1])) z[zOffset] = value; else if (this != &target) { // when this and target are different arrays - if (xRank != zRank) + if (xRank != zRank) { + temp = coords[0]; coords[0] = coords[1]; + } const auto xOffset = areSameOffsets ? zOffset : shape::getOffset(getShapeInfo(), coords); z[zOffset] = x[xOffset]; + + if (xRank != zRank) // restore first coordinate + coords[0] = temp; } } }; @@ -308,7 +315,7 @@ void NDArray::tile(const std::vector& reps, NDArray& target) const { // fill newBuff, loop through all elements of newBuff // looping through _buffer goes automatically by means of getSubArrayIndex applying const int ews = target.ews(); - const int targetLen = target.lengthOf(); + const auto targetLen = target.lengthOf(); if(target.ordering() == 'c' && ews == 1) { // ews == 1 always here //#pragma omp parallel for simd if(targetLen > Environment::getInstance()->elementwiseThreshold()) schedule(guided) for(Nd4jLong i=0; i 1) { for (uint j = 0; j < repSize; ++j) { coords[axis] -= repeats[j]; @@ -394,6 +405,8 @@ static void repeat_(const NDArray& input, NDArray& output, const std::vector + +//NDArray *other, T *extraParams +BUILD_CALL_1(template void NDArray::template applyPairwiseTransform, float, (NDArray* other, float* extraParams), PAIRWISE_TRANSFORM_OPS) +BUILD_CALL_1(template void NDArray::applyPairwiseTransform, float16, (NDArray* other, float16* extraParams), PAIRWISE_TRANSFORM_OPS) +BUILD_CALL_1(template void NDArray::applyPairwiseTransform, double, (NDArray* other, double* extraParams), PAIRWISE_TRANSFORM_OPS) + +// NDArray *other, NDArray *target, T *extraParams +BUILD_CALL_1(template void sd::NDArray::applyPairwiseTransform, float, (NDArray* other, NDArray* target, float* extraParams), PAIRWISE_TRANSFORM_OPS) +BUILD_CALL_1(template void sd::NDArray::applyPairwiseTransform, float16, (NDArray* other, NDArray* target, float16* extraParams), PAIRWISE_TRANSFORM_OPS) +BUILD_CALL_1(template void sd::NDArray::applyPairwiseTransform, double, (NDArray* other, NDArray* target, double* extraParams), PAIRWISE_TRANSFORM_OPS) + +BUILD_CALL_1(template void sd::NDArray::applyScalar, float16, (NDArray& scalar, NDArray* target, float16 *extraParams) const, SCALAR_OPS) +BUILD_CALL_1(template void sd::NDArray::applyScalar, float16, (float16 scalar, NDArray* target, float16 *extraParams) const, SCALAR_OPS) + +BUILD_CALL_1(template void sd::NDArray::applyScalar, float, (NDArray& scalar, NDArray* target, float *extraParams) const, SCALAR_OPS) +BUILD_CALL_1(template void sd::NDArray::applyScalar, float, (float scalar, NDArray* target, float *extraParams) const, SCALAR_OPS) + +BUILD_CALL_1(template void sd::NDArray::applyScalar, double, (NDArray& scalar, NDArray* target, double *extraParams) const, SCALAR_OPS) +BUILD_CALL_1(template void sd::NDArray::applyScalar, double, (double scalar, NDArray* target, double *extraParams) const, SCALAR_OPS) + + + +BUILD_CALL_1(template float16 sd::NDArray::reduceNumber, float16, (float16 *extraParams) const, REDUCE_OPS) +BUILD_CALL_1(template float sd::NDArray::reduceNumber, float, (float *extraParams) const, REDUCE_OPS) +BUILD_CALL_1(template double sd::NDArray::reduceNumber, double, (double *extraParams) const, REDUCE_OPS) + +BUILD_CALL_1(template Nd4jLong sd::NDArray::indexReduceNumber, float16, (float16 *extraParams), INDEX_REDUCE_OPS) +BUILD_CALL_1(template Nd4jLong sd::NDArray::indexReduceNumber, float, (float *extraParams), INDEX_REDUCE_OPS) +BUILD_CALL_1(template Nd4jLong sd::NDArray::indexReduceNumber, double, (double *extraParams), INDEX_REDUCE_OPS) + +BUILD_CALL_1(template void sd::NDArray::applyBroadcast, float16, (std::initializer_list list, const sd::NDArray* a, sd::NDArray* b, float16* c), BROADCAST_OPS) +BUILD_CALL_1(template void sd::NDArray::applyBroadcast, float, (std::initializer_list list, const sd::NDArray* a, sd::NDArray* b, float* c), BROADCAST_OPS) +BUILD_CALL_1(template void sd::NDArray::applyBroadcast, double, (std::initializer_list list, const sd::NDArray* a, sd::NDArray* b, double* c), BROADCAST_OPS) + +BUILD_CALL_1(template void sd::NDArray::applyTrueBroadcast, float16,(const sd::NDArray* a, sd::NDArray* target, const bool checkTargetShape, float16* c) const, BROADCAST_OPS) +BUILD_CALL_1(template void sd::NDArray::applyTrueBroadcast, float, (const sd::NDArray* a, sd::NDArray* target, const bool checkTargetShape, float* c) const, BROADCAST_OPS) +BUILD_CALL_1(template void sd::NDArray::applyTrueBroadcast, double, (const sd::NDArray* a, sd::NDArray* target, const bool checkTargetShape, double* c) const, BROADCAST_OPS) + +BUILD_CALL_1(template sd::NDArray* sd::NDArray::applyTrueBroadcast, float16, (const sd::NDArray* a, float16* c) const, BROADCAST_OPS) +BUILD_CALL_1(template sd::NDArray* sd::NDArray::applyTrueBroadcast, float, (const sd::NDArray* a, float* c) const, BROADCAST_OPS) +BUILD_CALL_1(template sd::NDArray* sd::NDArray::applyTrueBroadcast, double, (const sd::NDArray* a, double* c) const, BROADCAST_OPS) + +BUILD_CALL_1(template sd::NDArray sd::NDArray::applyTrueBroadcast, float16, (const sd::NDArray& a, float16* c) const, BROADCAST_OPS) +BUILD_CALL_1(template sd::NDArray sd::NDArray::applyTrueBroadcast, float, (const sd::NDArray& a, float* c) const, BROADCAST_OPS) +BUILD_CALL_1(template sd::NDArray sd::NDArray::applyTrueBroadcast, double, (const sd::NDArray& a, double* c) const, BROADCAST_OPS) + +BUILD_CALL_1(template void sd::NDArray::applyTransform, float16, (NDArray* target, float16* extraParams), TRANSFORM_OPS) +BUILD_CALL_1(template void sd::NDArray::applyTransform, float, (NDArray* target, float* extraParams), TRANSFORM_OPS) +BUILD_CALL_1(template void sd::NDArray::applyTransform, double, (NDArray* target, double* extraParams), TRANSFORM_OPS) + +BUILD_CALL_1(template void sd::NDArray::applyTransform, float16, (float16* extraParams), TRANSFORM_OPS) +BUILD_CALL_1(template void sd::NDArray::applyTransform, float, (float* extraParams), TRANSFORM_OPS) +BUILD_CALL_1(template void sd::NDArray::applyTransform, double, (double* extraParams), TRANSFORM_OPS) + +BUILD_CALL_1(template void sd::NDArray::applyRandom, float16, (sd::random::RandomBuffer *buffer, NDArray* y, NDArray* z, float16* extraParams), RANDOM_OPS) +BUILD_CALL_1(template void sd::NDArray::applyRandom, float, (sd::random::RandomBuffer *buffer, NDArray* y, NDArray* z, float* extraParams), RANDOM_OPS) +BUILD_CALL_1(template void sd::NDArray::applyRandom, double, (sd::random::RandomBuffer *buffer, NDArray* y, NDArray* z, double* extraParams), RANDOM_OPS) + +BUILD_CALL_1(template NDArray sd::NDArray::transform, float16, (float16* extraParams) const, TRANSFORM_OPS) +BUILD_CALL_1(template NDArray sd::NDArray::transform, float, (float* extraParams) const, TRANSFORM_OPS) +BUILD_CALL_1(template NDArray sd::NDArray::transform, double, (double* extraParams) const, TRANSFORM_OPS) + +BUILD_CALL_1(template NDArray *sd::NDArray::template reduceAlongDimension, float, (const std::vector& dimensions, const bool keepDims, const bool supportOldShapes) const, REDUCE_OPS) +BUILD_CALL_1(template NDArray *sd::NDArray::template reduceAlongDimension, float16, (const std::vector& dimensions, const bool keepDims, const bool supportOldShapes) const, REDUCE_OPS) +BUILD_CALL_1(template NDArray *sd::NDArray::template reduceAlongDimension, double, (const std::vector& dimensions, const bool keepDims, const bool supportOldShapes) const, REDUCE_OPS) + +BUILD_CALL_1(template NDArray sd::NDArray::template reduceAlongDims, float, (const std::vector& dimensions, const bool keepDims, const bool supportOldShapes) const, REDUCE_OPS) +BUILD_CALL_1(template NDArray sd::NDArray::template reduceAlongDims, float16, (const std::vector& dimensions, const bool keepDims, const bool supportOldShapes) const, REDUCE_OPS) +BUILD_CALL_1(template NDArray sd::NDArray::template reduceAlongDims, double, (const std::vector& dimensions, const bool keepDims, const bool supportOldShapes) const, REDUCE_OPS) + +BUILD_CALL_1(template NDArray *sd::NDArray::template reduceAlongDimension, float, (const std::initializer_list& dimensions, const bool keepDims, const bool supportOldShapes) const, REDUCE_OPS) +BUILD_CALL_1(template NDArray *sd::NDArray::template reduceAlongDimension, float16, (const std::initializer_list& dimensions, const bool keepDims, const bool supportOldShapes) const, REDUCE_OPS) +BUILD_CALL_1(template NDArray *sd::NDArray::template reduceAlongDimension, double, (const std::initializer_list& dimensions, const bool keepDims, const bool supportOldShapes) const, REDUCE_OPS) + +BUILD_CALL_1(template void sd::NDArray::template reduceAlongDimension, float, (NDArray* target, const std::vector& dimensions, const bool keepDims, const bool supportOldShapes, float * extras) const, REDUCE_OPS) +BUILD_CALL_1(template void sd::NDArray::template reduceAlongDimension, float16, (NDArray* target, const std::vector& dimensions, const bool keepDims, const bool supportOldShapes, float16 * extras) const, REDUCE_OPS) +BUILD_CALL_1(template void sd::NDArray::template reduceAlongDimension, double, (NDArray* target, const std::vector& dimension, const bool keepDims, const bool supportOldShapes, double * extras) const, REDUCE_OPS) + +BUILD_CALL_1(template NDArray *sd::NDArray::template varianceAlongDimension, float, (const bool biasCorrected, const std::initializer_list& dimensions) const, SUMMARY_STATS_OPS) +BUILD_CALL_1(template NDArray *sd::NDArray::template varianceAlongDimension, float16, (const bool biasCorrected, const std::initializer_list& dimensions) const, SUMMARY_STATS_OPS) +BUILD_CALL_1(template NDArray *sd::NDArray::template varianceAlongDimension, double, (const bool biasCorrected, const std::initializer_list& dimensions) const, SUMMARY_STATS_OPS) + +BUILD_CALL_1(template void sd::NDArray::template varianceAlongDimension, float, (const NDArray *target, const bool biasCorrected, const std::initializer_list& dimensions), SUMMARY_STATS_OPS) +BUILD_CALL_1(template void sd::NDArray::template varianceAlongDimension, float16, (const NDArray *target,const bool biasCorrected, const std::initializer_list& dimensions), SUMMARY_STATS_OPS) +BUILD_CALL_1(template void sd::NDArray::template varianceAlongDimension, double, (const NDArray *target, const bool biasCorrected, const std::initializer_list& dimensions), SUMMARY_STATS_OPS) + +BUILD_CALL_1(template void sd::NDArray::template varianceAlongDimension, float, (const NDArray *target, const bool biasCorrected, const std::vector& dimensions), SUMMARY_STATS_OPS) +BUILD_CALL_1(template void sd::NDArray::template varianceAlongDimension, float16, (const NDArray *target,const bool biasCorrected, const std::vector& dimensions), SUMMARY_STATS_OPS) +BUILD_CALL_1(template void sd::NDArray::template varianceAlongDimension, double, (const NDArray *target, const bool biasCorrected, const std::vector& dimensions), SUMMARY_STATS_OPS) + +BUILD_CALL_1(template float sd::NDArray::template varianceNumber, float, (bool biasCorrected), SUMMARY_STATS_OPS) +BUILD_CALL_1(template float16 sd::NDArray::template varianceNumber, float16, (bool biasCorrected), SUMMARY_STATS_OPS) +BUILD_CALL_1(template double sd::NDArray::template varianceNumber, double, (bool biasCorrected), SUMMARY_STATS_OPS) + +BUILD_CALL_1(template NDArray *sd::NDArray::template applyReduce3, float, (const NDArray* other, const float* extraParams) const, REDUCE3_OPS) +BUILD_CALL_1(template NDArray *sd::NDArray::template applyReduce3, float16, (const NDArray* other, const float16* extraParams) const, REDUCE3_OPS) +BUILD_CALL_1(template NDArray *sd::NDArray::template applyReduce3, double, (const NDArray* other, const double* extraParams) const, REDUCE3_OPS) + +BUILD_CALL_1(template NDArray *sd::NDArray::template applyReduce3, float, (const NDArray* other, const std::vector &dims, const float* extraParams) const, REDUCE3_OPS) +BUILD_CALL_1(template NDArray *sd::NDArray::template applyReduce3, float16, (const NDArray* other, const std::vector &dims, const float16* extraParams) const, REDUCE3_OPS) +BUILD_CALL_1(template NDArray *sd::NDArray::template applyReduce3, double, (const NDArray* other, const std::vector &dims, const double* extraParams) const, REDUCE3_OPS) + +BUILD_CALL_1(template void sd::NDArray::template applyIndexReduce, float, (const NDArray* target, const std::vector & alpha, const float* beta) const, INDEX_REDUCE_OPS) +BUILD_CALL_1(template void sd::NDArray::template applyIndexReduce, float16, (const NDArray* target, const std::vector & alpha, const float16* beta) const, INDEX_REDUCE_OPS) +BUILD_CALL_1(template void sd::NDArray::template applyIndexReduce, double, (const NDArray* target, const std::vector & alpha, const double* beta) const, INDEX_REDUCE_OPS) + +BUILD_CALL_1(template NDArray *sd::NDArray::template applyIndexReduce, float, (const std::vector & alpha, const float* beta) const, INDEX_REDUCE_OPS) +BUILD_CALL_1(template NDArray *sd::NDArray::template applyIndexReduce, float16, (const std::vector & alpha, const float16* beta) const, INDEX_REDUCE_OPS) +BUILD_CALL_1(template NDArray *sd::NDArray::template applyIndexReduce, double, (const std::vector & alpha, const double* beta) const, INDEX_REDUCE_OPS) + +BUILD_CALL_1(template NDArray *sd::NDArray::template applyAllReduce3, float, (const sd::NDArray* alpha, const std::vector & beta, float const* gamma) const, REDUCE3_OPS) +BUILD_CALL_1(template NDArray *sd::NDArray::template applyAllReduce3, float16, (const sd::NDArray* alpha, const std::vector & beta, float16 const* gamma) const, REDUCE3_OPS) +BUILD_CALL_1(template NDArray *sd::NDArray::template applyAllReduce3, double, (const sd::NDArray* alpha, const std::vector & beta, double const* gamma) const, REDUCE3_OPS) + +template NDArray mmul(const NDArray& left, const NDArray& right); +template NDArray mmul(const NDArray& left, const NDArray& right); +template NDArray mmul(const NDArray& left, const NDArray& right); + +// template NDArray operator-(const float, const NDArray&); +// template NDArray operator-(const float16, const NDArray&); +// template NDArray operator-(const double, const NDArray&); + +// template NDArray operator+(const float, const NDArray&); +// template NDArray operator+(const float16, const NDArray&); +// template NDArray operator+(const double, const NDArray&); + + +#endif \ No newline at end of file diff --git a/libnd4j/blas/cpu/NDArrayLambda.hpp b/libnd4j/include/array/cpu/NDArrayLambda.hpp similarity index 100% rename from libnd4j/blas/cpu/NDArrayLambda.hpp rename to libnd4j/include/array/cpu/NDArrayLambda.hpp diff --git a/libnd4j/include/array/cuda/DataBuffer.cu b/libnd4j/include/array/cuda/DataBuffer.cu index 28e0c432f..922b6967b 100644 --- a/libnd4j/include/array/cuda/DataBuffer.cu +++ b/libnd4j/include/array/cuda/DataBuffer.cu @@ -20,14 +20,14 @@ // #include "../DataBuffer.h" -#include -#include +#include +#include #include #include #include #include -namespace nd4j { +namespace sd { void DataBuffer::expand(const uint64_t size) { if (size > _lenInBytes) { // allocate new buffer @@ -67,19 +67,19 @@ namespace nd4j { void DataBuffer::allocateSpecial() { if (_specialBuffer == nullptr && getLenInBytes() > 0) { - auto deviceId = nd4j::AffinityManager::currentDeviceId(); + auto deviceId = sd::AffinityManager::currentDeviceId(); if (_workspace == nullptr) - if (!nd4j::memory::MemoryCounter::getInstance()->validate(getLenInBytes())) - throw nd4j::allocation_exception::build("Requested amount exceeds device limits", nd4j::memory::MemoryCounter::getInstance()->deviceLimit(deviceId), getLenInBytes()); + if (!sd::memory::MemoryCounter::getInstance()->validate(getLenInBytes())) + throw sd::allocation_exception::build("Requested amount exceeds device limits", sd::memory::MemoryCounter::getInstance()->deviceLimit(deviceId), getLenInBytes()); ALLOCATE_SPECIAL(_specialBuffer, _workspace, getLenInBytes(), int8_t); _isOwnerSpecial = true; if (_workspace == nullptr) { - nd4j::memory::MemoryCounter::getInstance()->countIn(deviceId, getLenInBytes()); - nd4j::memory::MemoryCounter::getInstance()->countIn(nd4j::memory::MemoryType::DEVICE, getLenInBytes()); + sd::memory::MemoryCounter::getInstance()->countIn(deviceId, getLenInBytes()); + sd::memory::MemoryCounter::getInstance()->countIn(sd::memory::MemoryType::DEVICE, getLenInBytes()); } } } @@ -135,8 +135,8 @@ void DataBuffer::deleteSpecial() { // count out towards DataBuffer device, only if we're not in workspace if (_workspace == nullptr) { - nd4j::memory::MemoryCounter::getInstance()->countOut(_deviceId, getLenInBytes()); - nd4j::memory::MemoryCounter::getInstance()->countOut(nd4j::memory::MemoryType::DEVICE, getLenInBytes()); + sd::memory::MemoryCounter::getInstance()->countOut(_deviceId, getLenInBytes()); + sd::memory::MemoryCounter::getInstance()->countOut(sd::memory::MemoryType::DEVICE, getLenInBytes()); } } } diff --git a/libnd4j/blas/cuda/NDArray.cu b/libnd4j/include/array/cuda/NDArray.cu similarity index 94% rename from libnd4j/blas/cuda/NDArray.cu rename to libnd4j/include/array/cuda/NDArray.cu index 81c8070b3..9ddb8be59 100644 --- a/libnd4j/blas/cuda/NDArray.cu +++ b/libnd4j/include/array/cuda/NDArray.cu @@ -17,14 +17,14 @@ #ifndef NDARRAY_CPP #define NDARRAY_CPP -#include "../NDArray.h" -#include "../NDArrayFactory.h" -#include "NativeOpExecutioner.h" +#include +#include +#include #include #include -#include +#include #include -#include +#include #include #include #include @@ -37,17 +37,17 @@ #include #include #include -#include +#include #include #include #include -#include +#include #include -#include -#include "../NDArray.hpp" -#include +#include +#include +#include -namespace nd4j { +namespace sd { void* NDArray::platformBuffer() { return specialBuffer(); } void* NDArray::getPlatformBuffer() const { return getSpecialBuffer(); } @@ -85,12 +85,12 @@ __global__ static void fillAsTriangularCuda(const void* vx, const Nd4jLong* xSha const auto x = reinterpret_cast(vx); auto z = reinterpret_cast(vz); - __shared__ int zRank, xRank, areSameOffsets; // xRank == zRank always, except when xRank = 1, in this case zRank = 2 - __shared__ Nd4jLong zLen, totalThreads, *sharedMem; // xLen == zLen, except when xRank = 1, in this case zLen = 2*xLen + __shared__ int zRank, xRank, areSameOffsets, *sharedMem; // xRank == zRank always, except when xRank = 1, in this case zRank = 2 + __shared__ Nd4jLong zLen, totalThreads; // xLen == zLen, except when xRank = 1, in this case zLen = 2*xLen if (threadIdx.x == 0) { extern __shared__ unsigned char shmem[]; - sharedMem = reinterpret_cast(shmem); + sharedMem = reinterpret_cast(shmem); areSameOffsets = shape::haveSameShapeAndStrides(xShapeInfo, zShapeInfo); xRank = shape::rank(xShapeInfo); zRank = shape::rank(zShapeInfo); @@ -137,7 +137,7 @@ void NDArray::fillAsTriangular(const float val, int lower, int upper, NDArray& t const int threadsPerBlock = MAX_NUM_THREADS / 4; const int blocksPerGrid = (target.lengthOf() + threadsPerBlock - 1) / threadsPerBlock; - const int sharedMem = threadsPerBlock * sizeof(decltype(*target.getShapeInfo())) * target.rankOf() + 128; + const int sharedMem = threadsPerBlock * sizeof(int) * target.rankOf() + 128; PointersManager manager(getContext(), "NDArray::fillAsTriangular"); @@ -155,12 +155,12 @@ __global__ static void identityMatrixCuda(void* vx, const Nd4jLong* xShapeInfo, auto x = reinterpret_cast(vx); - __shared__ int rank; - __shared__ Nd4jLong len, totalThreads, *sharedMem; // xLen == zLen, except when xRank = 1, in this case zLen = 2*xLen + __shared__ int rank, *sharedMem; + __shared__ Nd4jLong len, totalThreads; // xLen == zLen, except when xRank = 1, in this case zLen = 2*xLen if (threadIdx.x == 0) { extern __shared__ unsigned char shmem[]; - sharedMem = reinterpret_cast(shmem); + sharedMem = reinterpret_cast(shmem); rank = shape::rank(xShapeInfo); len = shape::length(xShapeInfo); totalThreads = gridDim.x * blockDim.x; @@ -201,7 +201,7 @@ void NDArray::setIdentity() { const int threadsPerBlock = MAX_NUM_THREADS / 4; const int blocksPerGrid = (lengthOf() + threadsPerBlock - 1) / threadsPerBlock; - const int sharedMem = threadsPerBlock * sizeof(decltype(getShapeInfo())) * rankOf() + 128; + const int sharedMem = threadsPerBlock * sizeof(int) * rankOf() + 128; PointersManager manager(getContext(), "NDArray::setIdentity"); @@ -398,13 +398,13 @@ __global__ static void repeatCuda(const void* vx, const Nd4jLong* xShapeInfo, const X* x = reinterpret_cast(vx); Z* z = reinterpret_cast(vz); - __shared__ int rank; - __shared__ Nd4jLong zLen, totalThreads, *sharedMem; // xLen = zLen + __shared__ int rank, *sharedMem; + __shared__ Nd4jLong zLen, totalThreads; // xLen = zLen if (threadIdx.x == 0) { extern __shared__ unsigned char shmem[]; - sharedMem = reinterpret_cast(shmem); + sharedMem = reinterpret_cast(shmem); rank = shape::rank(zShapeInfo); // xRank = zRank zLen = shape::length(zShapeInfo); // xLen <= zLen @@ -460,7 +460,7 @@ NDArray NDArray::repeat(const int axis, const std::vector& repeats) const { const int threadsPerBlock = MAX_NUM_THREADS / 2; const int blocksPerGrid = (output.lengthOf() + threadsPerBlock - 1) / threadsPerBlock; - const int sharedMem = output.rankOf() * sizeof(Nd4jLong) * threadsPerBlock + 128; + const int sharedMem = output.rankOf() * sizeof(int) * threadsPerBlock + 128; PointersManager manager(getContext(), "NDArray::repeat(const int axis, const std::vector& repeats)"); @@ -484,7 +484,7 @@ void NDArray::repeat(const int axis, const std::vector& repeats, NDArray& t const int threadsPerBlock = MAX_NUM_THREADS / 2; const int blocksPerGrid = (target.lengthOf() + threadsPerBlock - 1) / threadsPerBlock; - const int sharedMem = target.rankOf() * sizeof(Nd4jLong) * threadsPerBlock + 128; + const int sharedMem = target.rankOf() * sizeof(int) * threadsPerBlock + 128; PointersManager manager(getContext(), "NDArray::repeat(const int axis, const std::vector& repeats)"); @@ -569,6 +569,6 @@ template void NDArray::printCurrentBuffer(const bool host, const char* m #endif -} // end namespace nd4j +} // end namespace sd #endif diff --git a/libnd4j/include/array/impl/ByteOrderUtils.cpp b/libnd4j/include/array/impl/ByteOrderUtils.cpp index e23734cab..0220ccac8 100644 --- a/libnd4j/include/array/impl/ByteOrderUtils.cpp +++ b/libnd4j/include/array/impl/ByteOrderUtils.cpp @@ -21,8 +21,8 @@ #include -namespace nd4j { - ByteOrder ByteOrderUtils::fromFlatByteOrder(nd4j::graph::ByteOrder order) { +namespace sd { + ByteOrder ByteOrderUtils::fromFlatByteOrder(sd::graph::ByteOrder order) { return (ByteOrder) order; } } \ No newline at end of file diff --git a/libnd4j/include/array/impl/ConstantDataBuffer.cpp b/libnd4j/include/array/impl/ConstantDataBuffer.cpp index 90a631392..20c842266 100644 --- a/libnd4j/include/array/impl/ConstantDataBuffer.cpp +++ b/libnd4j/include/array/impl/ConstantDataBuffer.cpp @@ -20,7 +20,7 @@ #include "../ConstantDataBuffer.h" -namespace nd4j { +namespace sd { ConstantDataBuffer::ConstantDataBuffer(Nd4jPointer primary, Nd4jPointer special, Nd4jLong numEelements, Nd4jLong sizeOf) { _primaryBuffer = primary; _specialBuffer = special; diff --git a/libnd4j/include/array/impl/ConstantDescriptor.cpp b/libnd4j/include/array/impl/ConstantDescriptor.cpp index b64523096..ebb27090d 100644 --- a/libnd4j/include/array/impl/ConstantDescriptor.cpp +++ b/libnd4j/include/array/impl/ConstantDescriptor.cpp @@ -19,10 +19,10 @@ // #include -#include +#include #include -namespace nd4j { +namespace sd { ConstantDescriptor::ConstantDescriptor(double* values, int length) { for (int e = 0; e < length; e++) _floatValues.emplace_back(values[e]); @@ -75,3 +75,25 @@ namespace nd4j { return isInteger() ? _integerValues.size() : isFloat() ? _floatValues.size() : 0L; } } + +namespace std { + size_t hash::operator()(const sd::ConstantDescriptor &k) const { + using std::hash; + // Compute individual hash values for first, + // second and third and combine them using XOR + // and bit shifting: + size_t hashVal = 0; + size_t i = 0; + if (k.isInteger()) { + for (auto v: k.integerValues()) { + hashVal ^= std::hash()(v) + 0x9e3779b9 + (hashVal << 6) + (hashVal >> 2); + } + } + else { + for (auto v: k.floatValues()) { + hashVal ^= std::hash()(v) + 0x9e3779b9 + (hashVal << 6) + (hashVal >> 2); + } + } + return hashVal; + } +} diff --git a/libnd4j/include/array/impl/ConstantHolder.cpp b/libnd4j/include/array/impl/ConstantHolder.cpp index b3adf8516..08637862c 100644 --- a/libnd4j/include/array/impl/ConstantHolder.cpp +++ b/libnd4j/include/array/impl/ConstantHolder.cpp @@ -18,17 +18,17 @@ // Created by raver on 5/17/2019. // -#include +#include #include -#include +#include -namespace nd4j { +namespace sd { ConstantHolder::ConstantHolder(const ConstantHolder& other) { _buffers = other._buffers; _deviceId = other._deviceId; } - bool ConstantHolder::hasBuffer(nd4j::DataType dataType) { + bool ConstantHolder::hasBuffer(sd::DataType dataType) { return _buffers.count(dataType) > 0; } @@ -42,7 +42,7 @@ namespace nd4j { } BUILD_SINGLE_TEMPLATE(template ND4J_EXPORT bool ConstantHolder::hasBuffer, (void), LIBND4J_TYPES); - void ConstantHolder::addBuffer(ConstantDataBuffer &pointer, nd4j::DataType dataType) { + void ConstantHolder::addBuffer(ConstantDataBuffer &pointer, sd::DataType dataType) { _buffers[dataType] = pointer; } @@ -52,7 +52,7 @@ namespace nd4j { } BUILD_SINGLE_TEMPLATE(template ND4J_EXPORT void ConstantHolder::addBuffer, (ConstantDataBuffer& cb), LIBND4J_TYPES); - ConstantDataBuffer* ConstantHolder::getConstantDataBuffer(nd4j::DataType dataType) { + ConstantDataBuffer* ConstantHolder::getConstantDataBuffer(sd::DataType dataType) { if (!hasBuffer(dataType)) throw std::runtime_error("Requested dataType is absent in storage"); diff --git a/libnd4j/include/array/impl/DataBuffer.cpp b/libnd4j/include/array/impl/DataBuffer.cpp index 36758c684..262460e8c 100644 --- a/libnd4j/include/array/impl/DataBuffer.cpp +++ b/libnd4j/include/array/impl/DataBuffer.cpp @@ -26,7 +26,7 @@ #include #include -namespace nd4j { +namespace sd { ///// IMLEMENTATION OF COMMON METHODS ///// @@ -41,7 +41,7 @@ namespace nd4j { _workspace = nullptr; _isOwnerPrimary = false; _isOwnerSpecial = false; - _deviceId = nd4j::AffinityManager::currentDeviceId(); + _deviceId = sd::AffinityManager::currentDeviceId(); setCountersToZero(); } @@ -83,7 +83,7 @@ namespace nd4j { _workspace = workspace; _isOwnerPrimary = isOwnerPrimary; _isOwnerSpecial = isOwnerSpecial; - _deviceId = nd4j::AffinityManager::currentDeviceId(); + _deviceId = sd::AffinityManager::currentDeviceId(); setCountersToZero(); @@ -115,7 +115,7 @@ namespace nd4j { _dataType = dataType; _workspace = workspace; - _deviceId = nd4j::AffinityManager::currentDeviceId(); + _deviceId = sd::AffinityManager::currentDeviceId(); setCountersToZero(); @@ -134,7 +134,7 @@ namespace nd4j { _primaryBuffer = nullptr; _specialBuffer = nullptr; - _deviceId = nd4j::AffinityManager::currentDeviceId(); + _deviceId = sd::AffinityManager::currentDeviceId(); setCountersToZero(); @@ -234,17 +234,17 @@ namespace nd4j { void DataBuffer::allocatePrimary() { if (_primaryBuffer == nullptr && getLenInBytes() > 0) { - auto deviceId = nd4j::AffinityManager::currentDeviceId(); + auto deviceId = sd::AffinityManager::currentDeviceId(); // check if this allocation won't bring us above limit if (_workspace == nullptr) { if (Environment::getInstance()->isCPU()) { // on cpu backend we validate against device 0 for now - if (!nd4j::memory::MemoryCounter::getInstance()->validate(getLenInBytes())) - throw nd4j::allocation_exception::build("Requested amount exceeds HOST device limits", nd4j::memory::MemoryCounter::getInstance()->deviceLimit(deviceId), getLenInBytes()); + if (!sd::memory::MemoryCounter::getInstance()->validate(getLenInBytes())) + throw sd::allocation_exception::build("Requested amount exceeds HOST device limits", sd::memory::MemoryCounter::getInstance()->deviceLimit(deviceId), getLenInBytes()); } else { // in heterogenous mode we valdate against device group - if (!nd4j::memory::MemoryCounter::getInstance()->validateGroup(nd4j::memory::MemoryType::HOST, getLenInBytes())) - throw nd4j::allocation_exception::build("Requested amount exceeds HOST group limits", nd4j::memory::MemoryCounter::getInstance()->groupLimit(nd4j::memory::MemoryType::HOST), getLenInBytes()); + if (!sd::memory::MemoryCounter::getInstance()->validateGroup(sd::memory::MemoryType::HOST, getLenInBytes())) + throw sd::allocation_exception::build("Requested amount exceeds HOST group limits", sd::memory::MemoryCounter::getInstance()->groupLimit(sd::memory::MemoryType::HOST), getLenInBytes()); } } @@ -254,9 +254,9 @@ namespace nd4j { // count in towards current deviceId if we're not in workspace mode if (_workspace == nullptr) { if (Environment::getInstance()->isCPU()) // we don't want this counter to be added to CUDA device - nd4j::memory::MemoryCounter::getInstance()->countIn(deviceId, getLenInBytes()); + sd::memory::MemoryCounter::getInstance()->countIn(deviceId, getLenInBytes()); - nd4j::memory::MemoryCounter::getInstance()->countIn(nd4j::memory::MemoryType::HOST, getLenInBytes()); + sd::memory::MemoryCounter::getInstance()->countIn(sd::memory::MemoryType::HOST, getLenInBytes()); } } } @@ -280,9 +280,9 @@ namespace nd4j { // count out towards DataBuffer device, only if we're not in workspace if (_workspace == nullptr) { if (Environment::getInstance()->isCPU()) - nd4j::memory::MemoryCounter::getInstance()->countOut(_deviceId, getLenInBytes()); + sd::memory::MemoryCounter::getInstance()->countOut(_deviceId, getLenInBytes()); - nd4j::memory::MemoryCounter::getInstance()->countOut(nd4j::memory::MemoryType::HOST, getLenInBytes()); + sd::memory::MemoryCounter::getInstance()->countOut(sd::memory::MemoryType::HOST, getLenInBytes()); } } } diff --git a/libnd4j/include/array/impl/DataTypeUtils.cpp b/libnd4j/include/array/impl/DataTypeUtils.cpp index cdf688b25..481fa4149 100644 --- a/libnd4j/include/array/impl/DataTypeUtils.cpp +++ b/libnd4j/include/array/impl/DataTypeUtils.cpp @@ -22,12 +22,12 @@ #include #include -namespace nd4j { +namespace sd { DataType DataTypeUtils::fromInt(int val) { return (DataType) val; } - DataType DataTypeUtils::fromFlatDataType(nd4j::graph::DType dtype) { + DataType DataTypeUtils::fromFlatDataType(sd::graph::DType dtype) { return (DataType) dtype; } diff --git a/libnd4j/include/array/impl/ExtraArguments.cpp b/libnd4j/include/array/impl/ExtraArguments.cpp index f9174ea0f..084f327cc 100644 --- a/libnd4j/include/array/impl/ExtraArguments.cpp +++ b/libnd4j/include/array/impl/ExtraArguments.cpp @@ -29,7 +29,7 @@ #include #endif -namespace nd4j { +namespace sd { ExtraArguments::ExtraArguments(std::initializer_list arguments) { _fpArgs = arguments; } @@ -122,7 +122,7 @@ namespace nd4j { BUILD_SINGLE_TEMPLATE(template ND4J_EXPORT void *ExtraArguments::argumentsAsT, (Nd4jLong offset), LIBND4J_TYPES); - void* ExtraArguments::argumentsAsT(nd4j::DataType dataType, Nd4jLong offset) { + void* ExtraArguments::argumentsAsT(sd::DataType dataType, Nd4jLong offset) { if (_fpArgs.empty() && _intArgs.empty()) return nullptr; diff --git a/libnd4j/include/array/impl/InteropDataBuffer.cpp b/libnd4j/include/array/impl/InteropDataBuffer.cpp index cffc1462b..d0a381612 100644 --- a/libnd4j/include/array/impl/InteropDataBuffer.cpp +++ b/libnd4j/include/array/impl/InteropDataBuffer.cpp @@ -23,7 +23,7 @@ #include #include -namespace nd4j { +namespace sd { InteropDataBuffer::InteropDataBuffer(InteropDataBuffer &dataBuffer, uint64_t length, uint64_t offset) { _dataBuffer = dataBuffer.getDataBuffer(); @@ -39,7 +39,7 @@ namespace nd4j { _dataBuffer = databuffer; } - InteropDataBuffer::InteropDataBuffer(size_t elements, nd4j::DataType dtype, bool allocateBoth) { + InteropDataBuffer::InteropDataBuffer(size_t elements, sd::DataType dtype, bool allocateBoth) { if (elements == 0) { _dataBuffer = std::make_shared(); _dataBuffer->setDataType(dtype); @@ -95,7 +95,7 @@ namespace nd4j { } void InteropDataBuffer::prepareSpecialUse(const std::vector& writeList, const std::vector& readList, bool synchronizeWritables) { - auto currentDeviceId = nd4j::AffinityManager::currentDeviceId(); + auto currentDeviceId = sd::AffinityManager::currentDeviceId(); for (const auto &v:readList) { if (v == nullptr) continue; diff --git a/libnd4j/blas/cpu/NDArrayFactory.cpp b/libnd4j/include/array/impl/NDArrayFactory.cpp similarity index 74% rename from libnd4j/blas/cpu/NDArrayFactory.cpp rename to libnd4j/include/array/impl/NDArrayFactory.cpp index 736452b48..870fdc198 100644 --- a/libnd4j/blas/cpu/NDArrayFactory.cpp +++ b/libnd4j/include/array/impl/NDArrayFactory.cpp @@ -20,30 +20,30 @@ // @author Oleg Semeniv // -#include +#include #include -#include -#include -#include -#include +#include +#include +#include +#include #include -#include -#include +#include +#include -namespace nd4j { +namespace sd { //////////////////////////////////////////////////////////////////////// template <> - ND4J_EXPORT NDArray NDArrayFactory::create(const char order, const std::vector &shape, const std::vector &data, nd4j::LaunchContext * context) { + ND4J_EXPORT NDArray NDArrayFactory::create(const char order, const std::vector &shape, const std::vector &data, sd::LaunchContext * context) { if ((int) shape.size() > MAX_RANK) throw std::invalid_argument("NDArrayFactory::create: rank of NDArray can't exceed 32 !"); - ShapeDescriptor descriptor(nd4j::DataType::BOOL, order, shape); + ShapeDescriptor descriptor(sd::DataType::BOOL, order, shape); if (descriptor.arrLength() != data.size()) { nd4j_printf("NDArrayFactory::create: data size [%i] doesn't match shape length [%lld]\n", data.size(), descriptor.arrLength()); @@ -54,7 +54,7 @@ namespace nd4j { ALLOCATE(hostBuffer, context->getWorkspace(), data.size(), bool); std::copy(data.begin(), data.end(), hostBuffer); - std::shared_ptr buffer = std::make_shared(hostBuffer, data.size() * sizeof(bool), nd4j::DataType::BOOL, true, context->getWorkspace()); + std::shared_ptr buffer = std::make_shared(hostBuffer, data.size() * sizeof(bool), sd::DataType::BOOL, true, context->getWorkspace()); NDArray result(buffer, descriptor, context); @@ -63,7 +63,7 @@ namespace nd4j { //////////////////////////////////////////////////////////////////////// template - NDArray NDArrayFactory::create(const char order, const std::vector &shape, const std::vector &data, nd4j::LaunchContext * context) { + NDArray NDArrayFactory::create(const char order, const std::vector &shape, const std::vector &data, sd::LaunchContext * context) { if ((int) shape.size() > MAX_RANK) throw std::invalid_argument("NDArrayFactory::create: rank of NDArray can't exceed 32 !"); @@ -81,25 +81,25 @@ namespace nd4j { return result; } - template ND4J_EXPORT NDArray NDArrayFactory::create(const char order, const std::vector &shape, const std::vector& data, nd4j::LaunchContext * context); - template ND4J_EXPORT NDArray NDArrayFactory::create(const char order, const std::vector &shape, const std::vector& data, nd4j::LaunchContext * context); - template ND4J_EXPORT NDArray NDArrayFactory::create(const char order, const std::vector &shape, const std::vector& data, nd4j::LaunchContext * context); - template ND4J_EXPORT NDArray NDArrayFactory::create(const char order, const std::vector &shape, const std::vector& data, nd4j::LaunchContext * context); - template ND4J_EXPORT NDArray NDArrayFactory::create(const char order, const std::vector &shape, const std::vector& data, nd4j::LaunchContext * context); - template ND4J_EXPORT NDArray NDArrayFactory::create(const char order, const std::vector &shape, const std::vector& data, nd4j::LaunchContext * context); - template ND4J_EXPORT NDArray NDArrayFactory::create(const char order, const std::vector &shape, const std::vector& data, nd4j::LaunchContext * context); - template ND4J_EXPORT NDArray NDArrayFactory::create(const char order, const std::vector &shape, const std::vector& data, nd4j::LaunchContext * context); - template ND4J_EXPORT NDArray NDArrayFactory::create(const char order, const std::vector &shape, const std::vector& data, nd4j::LaunchContext * context); - template ND4J_EXPORT NDArray NDArrayFactory::create(const char order, const std::vector &shape, const std::vector& data, nd4j::LaunchContext * context); - template ND4J_EXPORT NDArray NDArrayFactory::create(const char order, const std::vector &shape, const std::vector& data, nd4j::LaunchContext * context); - template ND4J_EXPORT NDArray NDArrayFactory::create(const char order, const std::vector &shape, const std::vector& data, nd4j::LaunchContext * context); + template ND4J_EXPORT NDArray NDArrayFactory::create(const char order, const std::vector &shape, const std::vector& data, sd::LaunchContext * context); + template ND4J_EXPORT NDArray NDArrayFactory::create(const char order, const std::vector &shape, const std::vector& data, sd::LaunchContext * context); + template ND4J_EXPORT NDArray NDArrayFactory::create(const char order, const std::vector &shape, const std::vector& data, sd::LaunchContext * context); + template ND4J_EXPORT NDArray NDArrayFactory::create(const char order, const std::vector &shape, const std::vector& data, sd::LaunchContext * context); + template ND4J_EXPORT NDArray NDArrayFactory::create(const char order, const std::vector &shape, const std::vector& data, sd::LaunchContext * context); + template ND4J_EXPORT NDArray NDArrayFactory::create(const char order, const std::vector &shape, const std::vector& data, sd::LaunchContext * context); + template ND4J_EXPORT NDArray NDArrayFactory::create(const char order, const std::vector &shape, const std::vector& data, sd::LaunchContext * context); + template ND4J_EXPORT NDArray NDArrayFactory::create(const char order, const std::vector &shape, const std::vector& data, sd::LaunchContext * context); + template ND4J_EXPORT NDArray NDArrayFactory::create(const char order, const std::vector &shape, const std::vector& data, sd::LaunchContext * context); + template ND4J_EXPORT NDArray NDArrayFactory::create(const char order, const std::vector &shape, const std::vector& data, sd::LaunchContext * context); + template ND4J_EXPORT NDArray NDArrayFactory::create(const char order, const std::vector &shape, const std::vector& data, sd::LaunchContext * context); + template ND4J_EXPORT NDArray NDArrayFactory::create(const char order, const std::vector &shape, const std::vector& data, sd::LaunchContext * context); //////////////////////////////////////////////////////////////////////// template -NDArray* NDArrayFactory::create_(const char order, const std::vector &shape, nd4j::LaunchContext * context) { +NDArray* NDArrayFactory::create_(const char order, const std::vector &shape, sd::LaunchContext * context) { return create_(order, shape, DataTypeUtils::fromT(), context); } -BUILD_SINGLE_TEMPLATE(template ND4J_EXPORT NDArray* NDArrayFactory::create_, (const char order, const std::vector &shape, nd4j::LaunchContext * context), LIBND4J_TYPES); +BUILD_SINGLE_TEMPLATE(template ND4J_EXPORT NDArray* NDArrayFactory::create_, (const char order, const std::vector &shape, sd::LaunchContext * context), LIBND4J_TYPES); //////////////////////////////////////////////////////////////////////// template @@ -128,44 +128,44 @@ template ND4J_EXPORT void NDArrayFactory::memcpyFromVector(void *ptr, const std: #ifndef __JAVACPP_HACK__ //////////////////////////////////////////////////////////////////////// template - NDArray* NDArrayFactory::valueOf(const std::initializer_list& shape, const T value, const char order, nd4j::LaunchContext * context) { + NDArray* NDArrayFactory::valueOf(const std::initializer_list& shape, const T value, const char order, sd::LaunchContext * context) { return valueOf(std::vector(shape), value, order); } - template ND4J_EXPORT NDArray* NDArrayFactory::valueOf(const std::initializer_list& shape, const double value, const char order, nd4j::LaunchContext * context); - template ND4J_EXPORT NDArray* NDArrayFactory::valueOf(const std::initializer_list& shape, const float value, const char order, nd4j::LaunchContext * context); - template ND4J_EXPORT NDArray* NDArrayFactory::valueOf(const std::initializer_list& shape, const float16 value, const char order, nd4j::LaunchContext * context); - template ND4J_EXPORT NDArray* NDArrayFactory::valueOf(const std::initializer_list& shape, const bfloat16 value, const char order, nd4j::LaunchContext * context); - template ND4J_EXPORT NDArray* NDArrayFactory::valueOf(const std::initializer_list& shape, const Nd4jLong value, const char order, nd4j::LaunchContext * context); - template ND4J_EXPORT NDArray* NDArrayFactory::valueOf(const std::initializer_list& shape, const int value, const char order, nd4j::LaunchContext * context); - template ND4J_EXPORT NDArray* NDArrayFactory::valueOf(const std::initializer_list& shape, const uint8_t value, const char order, nd4j::LaunchContext * context); - template ND4J_EXPORT NDArray* NDArrayFactory::valueOf(const std::initializer_list& shape, const int8_t value, const char order, nd4j::LaunchContext * context); - template ND4J_EXPORT NDArray* NDArrayFactory::valueOf(const std::initializer_list& shape, const int16_t value, const char order, nd4j::LaunchContext * context); - template ND4J_EXPORT NDArray* NDArrayFactory::valueOf(const std::initializer_list& shape, const bool value, const char order, nd4j::LaunchContext * context); + template ND4J_EXPORT NDArray* NDArrayFactory::valueOf(const std::initializer_list& shape, const double value, const char order, sd::LaunchContext * context); + template ND4J_EXPORT NDArray* NDArrayFactory::valueOf(const std::initializer_list& shape, const float value, const char order, sd::LaunchContext * context); + template ND4J_EXPORT NDArray* NDArrayFactory::valueOf(const std::initializer_list& shape, const float16 value, const char order, sd::LaunchContext * context); + template ND4J_EXPORT NDArray* NDArrayFactory::valueOf(const std::initializer_list& shape, const bfloat16 value, const char order, sd::LaunchContext * context); + template ND4J_EXPORT NDArray* NDArrayFactory::valueOf(const std::initializer_list& shape, const Nd4jLong value, const char order, sd::LaunchContext * context); + template ND4J_EXPORT NDArray* NDArrayFactory::valueOf(const std::initializer_list& shape, const int value, const char order, sd::LaunchContext * context); + template ND4J_EXPORT NDArray* NDArrayFactory::valueOf(const std::initializer_list& shape, const uint8_t value, const char order, sd::LaunchContext * context); + template ND4J_EXPORT NDArray* NDArrayFactory::valueOf(const std::initializer_list& shape, const int8_t value, const char order, sd::LaunchContext * context); + template ND4J_EXPORT NDArray* NDArrayFactory::valueOf(const std::initializer_list& shape, const int16_t value, const char order, sd::LaunchContext * context); + template ND4J_EXPORT NDArray* NDArrayFactory::valueOf(const std::initializer_list& shape, const bool value, const char order, sd::LaunchContext * context); //////////////////////////////////////////////////////////////////////// template - NDArray NDArrayFactory::create(const char order, const std::vector &shape, const std::initializer_list& data, nd4j::LaunchContext * context) { + NDArray NDArrayFactory::create(const char order, const std::vector &shape, const std::initializer_list& data, sd::LaunchContext * context) { std::vector vec(data); return create(order, shape, vec, context); } - template ND4J_EXPORT NDArray NDArrayFactory::create(const char order, const std::vector &shape, const std::initializer_list& data, nd4j::LaunchContext * context); - template ND4J_EXPORT NDArray NDArrayFactory::create(const char order, const std::vector &shape, const std::initializer_list& data, nd4j::LaunchContext * context); - template ND4J_EXPORT NDArray NDArrayFactory::create(const char order, const std::vector &shape, const std::initializer_list& data, nd4j::LaunchContext * context); - template ND4J_EXPORT NDArray NDArrayFactory::create(const char order, const std::vector &shape, const std::initializer_list& data, nd4j::LaunchContext * context); - template ND4J_EXPORT NDArray NDArrayFactory::create(const char order, const std::vector &shape, const std::initializer_list& data, nd4j::LaunchContext * context); - template ND4J_EXPORT NDArray NDArrayFactory::create(const char order, const std::vector &shape, const std::initializer_list& data, nd4j::LaunchContext * context); - template ND4J_EXPORT NDArray NDArrayFactory::create(const char order, const std::vector &shape, const std::initializer_list& data, nd4j::LaunchContext * context); - template ND4J_EXPORT NDArray NDArrayFactory::create(const char order, const std::vector &shape, const std::initializer_list& data, nd4j::LaunchContext * context); - template ND4J_EXPORT NDArray NDArrayFactory::create(const char order, const std::vector &shape, const std::initializer_list& data, nd4j::LaunchContext * context); - template ND4J_EXPORT NDArray NDArrayFactory::create(const char order, const std::vector &shape, const std::initializer_list& data, nd4j::LaunchContext * context); - template ND4J_EXPORT NDArray NDArrayFactory::create(const char order, const std::vector &shape, const std::initializer_list& data, nd4j::LaunchContext * context); - template ND4J_EXPORT NDArray NDArrayFactory::create(const char order, const std::vector &shape, const std::initializer_list& data, nd4j::LaunchContext * context); + template ND4J_EXPORT NDArray NDArrayFactory::create(const char order, const std::vector &shape, const std::initializer_list& data, sd::LaunchContext * context); + template ND4J_EXPORT NDArray NDArrayFactory::create(const char order, const std::vector &shape, const std::initializer_list& data, sd::LaunchContext * context); + template ND4J_EXPORT NDArray NDArrayFactory::create(const char order, const std::vector &shape, const std::initializer_list& data, sd::LaunchContext * context); + template ND4J_EXPORT NDArray NDArrayFactory::create(const char order, const std::vector &shape, const std::initializer_list& data, sd::LaunchContext * context); + template ND4J_EXPORT NDArray NDArrayFactory::create(const char order, const std::vector &shape, const std::initializer_list& data, sd::LaunchContext * context); + template ND4J_EXPORT NDArray NDArrayFactory::create(const char order, const std::vector &shape, const std::initializer_list& data, sd::LaunchContext * context); + template ND4J_EXPORT NDArray NDArrayFactory::create(const char order, const std::vector &shape, const std::initializer_list& data, sd::LaunchContext * context); + template ND4J_EXPORT NDArray NDArrayFactory::create(const char order, const std::vector &shape, const std::initializer_list& data, sd::LaunchContext * context); + template ND4J_EXPORT NDArray NDArrayFactory::create(const char order, const std::vector &shape, const std::initializer_list& data, sd::LaunchContext * context); + template ND4J_EXPORT NDArray NDArrayFactory::create(const char order, const std::vector &shape, const std::initializer_list& data, sd::LaunchContext * context); + template ND4J_EXPORT NDArray NDArrayFactory::create(const char order, const std::vector &shape, const std::initializer_list& data, sd::LaunchContext * context); + template ND4J_EXPORT NDArray NDArrayFactory::create(const char order, const std::vector &shape, const std::initializer_list& data, sd::LaunchContext * context); #endif //////////////////////////////////////////////////////////////////////// template - NDArray* NDArrayFactory::create_(const T scalar, nd4j::LaunchContext * context) { + NDArray* NDArrayFactory::create_(const T scalar, sd::LaunchContext * context) { std::shared_ptr buffer = std::make_shared(1 * sizeof(T), DataTypeUtils::fromT(), context->getWorkspace(), true); @@ -178,22 +178,22 @@ template ND4J_EXPORT void NDArrayFactory::memcpyFromVector(void *ptr, const std: return res; } - template ND4J_EXPORT NDArray* NDArrayFactory::create_(const double scalar, nd4j::LaunchContext * context); - template ND4J_EXPORT NDArray* NDArrayFactory::create_(const float scalar, nd4j::LaunchContext * context); - template ND4J_EXPORT NDArray* NDArrayFactory::create_(const float16 scalar, nd4j::LaunchContext * context); - template ND4J_EXPORT NDArray* NDArrayFactory::create_(const bfloat16 scalar, nd4j::LaunchContext * context); - template ND4J_EXPORT NDArray* NDArrayFactory::create_(const Nd4jLong scalar, nd4j::LaunchContext * context); - template ND4J_EXPORT NDArray* NDArrayFactory::create_(const int scalar, nd4j::LaunchContext * context); - template ND4J_EXPORT NDArray* NDArrayFactory::create_(const bool scalar, nd4j::LaunchContext * context); - template ND4J_EXPORT NDArray* NDArrayFactory::create_(const int8_t scalar, nd4j::LaunchContext * context); - template ND4J_EXPORT NDArray* NDArrayFactory::create_(const uint8_t scalar, nd4j::LaunchContext * context); - template ND4J_EXPORT NDArray* NDArrayFactory::create_(const uint16_t scalar, nd4j::LaunchContext * context); - template ND4J_EXPORT NDArray* NDArrayFactory::create_(const uint32_t scalar, nd4j::LaunchContext * context); - template ND4J_EXPORT NDArray* NDArrayFactory::create_(const uint64_t scalar, nd4j::LaunchContext * context); - template ND4J_EXPORT NDArray* NDArrayFactory::create_(const int16_t scalar, nd4j::LaunchContext * context); + template ND4J_EXPORT NDArray* NDArrayFactory::create_(const double scalar, sd::LaunchContext * context); + template ND4J_EXPORT NDArray* NDArrayFactory::create_(const float scalar, sd::LaunchContext * context); + template ND4J_EXPORT NDArray* NDArrayFactory::create_(const float16 scalar, sd::LaunchContext * context); + template ND4J_EXPORT NDArray* NDArrayFactory::create_(const bfloat16 scalar, sd::LaunchContext * context); + template ND4J_EXPORT NDArray* NDArrayFactory::create_(const Nd4jLong scalar, sd::LaunchContext * context); + template ND4J_EXPORT NDArray* NDArrayFactory::create_(const int scalar, sd::LaunchContext * context); + template ND4J_EXPORT NDArray* NDArrayFactory::create_(const bool scalar, sd::LaunchContext * context); + template ND4J_EXPORT NDArray* NDArrayFactory::create_(const int8_t scalar, sd::LaunchContext * context); + template ND4J_EXPORT NDArray* NDArrayFactory::create_(const uint8_t scalar, sd::LaunchContext * context); + template ND4J_EXPORT NDArray* NDArrayFactory::create_(const uint16_t scalar, sd::LaunchContext * context); + template ND4J_EXPORT NDArray* NDArrayFactory::create_(const uint32_t scalar, sd::LaunchContext * context); + template ND4J_EXPORT NDArray* NDArrayFactory::create_(const uint64_t scalar, sd::LaunchContext * context); + template ND4J_EXPORT NDArray* NDArrayFactory::create_(const int16_t scalar, sd::LaunchContext * context); template - NDArray NDArrayFactory::create(nd4j::DataType type, const T scalar, nd4j::LaunchContext * context) { + NDArray NDArrayFactory::create(sd::DataType type, const T scalar, sd::LaunchContext * context) { if (type == DataTypeUtils::fromT()) return NDArrayFactory::create(scalar, context); @@ -204,23 +204,23 @@ template ND4J_EXPORT void NDArrayFactory::memcpyFromVector(void *ptr, const std: return res; } -// BUILD_DOUBLE_TEMPLATE(template ND4J_EXPORT NDArray NDArrayFactory::create, (DataType type, const T scalar, nd4j::LaunchContext * context), LIBND4J_TYPES); - template ND4J_EXPORT NDArray NDArrayFactory::create(DataType type, const double scalar, nd4j::LaunchContext * context); - template ND4J_EXPORT NDArray NDArrayFactory::create(DataType type, const float scalar, nd4j::LaunchContext * context); - template ND4J_EXPORT NDArray NDArrayFactory::create(DataType type, const float16 scalar, nd4j::LaunchContext * context); - template ND4J_EXPORT NDArray NDArrayFactory::create(DataType type, const bfloat16 scalar, nd4j::LaunchContext * context); - template ND4J_EXPORT NDArray NDArrayFactory::create(DataType type, const Nd4jLong scalar, nd4j::LaunchContext * context); - template ND4J_EXPORT NDArray NDArrayFactory::create(DataType type, const int scalar, nd4j::LaunchContext * context); - template ND4J_EXPORT NDArray NDArrayFactory::create(DataType type, const int8_t scalar, nd4j::LaunchContext * context); - template ND4J_EXPORT NDArray NDArrayFactory::create(DataType type, const uint8_t scalar, nd4j::LaunchContext * context); - template ND4J_EXPORT NDArray NDArrayFactory::create(DataType type, const uint16_t scalar, nd4j::LaunchContext* workspace); - template ND4J_EXPORT NDArray NDArrayFactory::create(DataType type, const uint32_t scalar, nd4j::LaunchContext* workspace); - template ND4J_EXPORT NDArray NDArrayFactory::create(DataType type, const uint64_t scalar, nd4j::LaunchContext* workspace); - template ND4J_EXPORT NDArray NDArrayFactory::create(DataType type, const int16_t scalar, nd4j::LaunchContext * context); - template ND4J_EXPORT NDArray NDArrayFactory::create(DataType type, const bool scalar, nd4j::LaunchContext * context); +// BUILD_DOUBLE_TEMPLATE(template ND4J_EXPORT NDArray NDArrayFactory::create, (DataType type, const T scalar, sd::LaunchContext * context), LIBND4J_TYPES); + template ND4J_EXPORT NDArray NDArrayFactory::create(DataType type, const double scalar, sd::LaunchContext * context); + template ND4J_EXPORT NDArray NDArrayFactory::create(DataType type, const float scalar, sd::LaunchContext * context); + template ND4J_EXPORT NDArray NDArrayFactory::create(DataType type, const float16 scalar, sd::LaunchContext * context); + template ND4J_EXPORT NDArray NDArrayFactory::create(DataType type, const bfloat16 scalar, sd::LaunchContext * context); + template ND4J_EXPORT NDArray NDArrayFactory::create(DataType type, const Nd4jLong scalar, sd::LaunchContext * context); + template ND4J_EXPORT NDArray NDArrayFactory::create(DataType type, const int scalar, sd::LaunchContext * context); + template ND4J_EXPORT NDArray NDArrayFactory::create(DataType type, const int8_t scalar, sd::LaunchContext * context); + template ND4J_EXPORT NDArray NDArrayFactory::create(DataType type, const uint8_t scalar, sd::LaunchContext * context); + template ND4J_EXPORT NDArray NDArrayFactory::create(DataType type, const uint16_t scalar, sd::LaunchContext* workspace); + template ND4J_EXPORT NDArray NDArrayFactory::create(DataType type, const uint32_t scalar, sd::LaunchContext* workspace); + template ND4J_EXPORT NDArray NDArrayFactory::create(DataType type, const uint64_t scalar, sd::LaunchContext* workspace); + template ND4J_EXPORT NDArray NDArrayFactory::create(DataType type, const int16_t scalar, sd::LaunchContext * context); + template ND4J_EXPORT NDArray NDArrayFactory::create(DataType type, const bool scalar, sd::LaunchContext * context); template - NDArray NDArrayFactory::create(const T scalar, nd4j::LaunchContext * context) { + NDArray NDArrayFactory::create(const T scalar, sd::LaunchContext * context) { std::shared_ptr buffer = std::make_shared(1 * sizeof(T), DataTypeUtils::fromT(), context->getWorkspace(), true); @@ -233,73 +233,73 @@ template ND4J_EXPORT void NDArrayFactory::memcpyFromVector(void *ptr, const std: return res; } - template ND4J_EXPORT NDArray NDArrayFactory::create(const double scalar, nd4j::LaunchContext * context); - template ND4J_EXPORT NDArray NDArrayFactory::create(const float scalar, nd4j::LaunchContext * context); - template ND4J_EXPORT NDArray NDArrayFactory::create(const float16 scalar, nd4j::LaunchContext * context); - template ND4J_EXPORT NDArray NDArrayFactory::create(const bfloat16 scalar, nd4j::LaunchContext * context); - template ND4J_EXPORT NDArray NDArrayFactory::create(const Nd4jLong scalar, nd4j::LaunchContext * context); - template ND4J_EXPORT NDArray NDArrayFactory::create(const int scalar, nd4j::LaunchContext * context); - template ND4J_EXPORT NDArray NDArrayFactory::create(const int8_t scalar, nd4j::LaunchContext * context); - template ND4J_EXPORT NDArray NDArrayFactory::create(const uint8_t scalar, nd4j::LaunchContext * context); - template ND4J_EXPORT NDArray NDArrayFactory::create(const int16_t scalar, nd4j::LaunchContext * context); - template ND4J_EXPORT NDArray NDArrayFactory::create(const uint16_t scalar, nd4j::LaunchContext* workspace); - template ND4J_EXPORT NDArray NDArrayFactory::create(const uint32_t scalar, nd4j::LaunchContext* workspace); - template ND4J_EXPORT NDArray NDArrayFactory::create(const uint64_t scalar, nd4j::LaunchContext* workspace); - template ND4J_EXPORT NDArray NDArrayFactory::create(const bool scalar, nd4j::LaunchContext * context); + template ND4J_EXPORT NDArray NDArrayFactory::create(const double scalar, sd::LaunchContext * context); + template ND4J_EXPORT NDArray NDArrayFactory::create(const float scalar, sd::LaunchContext * context); + template ND4J_EXPORT NDArray NDArrayFactory::create(const float16 scalar, sd::LaunchContext * context); + template ND4J_EXPORT NDArray NDArrayFactory::create(const bfloat16 scalar, sd::LaunchContext * context); + template ND4J_EXPORT NDArray NDArrayFactory::create(const Nd4jLong scalar, sd::LaunchContext * context); + template ND4J_EXPORT NDArray NDArrayFactory::create(const int scalar, sd::LaunchContext * context); + template ND4J_EXPORT NDArray NDArrayFactory::create(const int8_t scalar, sd::LaunchContext * context); + template ND4J_EXPORT NDArray NDArrayFactory::create(const uint8_t scalar, sd::LaunchContext * context); + template ND4J_EXPORT NDArray NDArrayFactory::create(const int16_t scalar, sd::LaunchContext * context); + template ND4J_EXPORT NDArray NDArrayFactory::create(const uint16_t scalar, sd::LaunchContext* workspace); + template ND4J_EXPORT NDArray NDArrayFactory::create(const uint32_t scalar, sd::LaunchContext* workspace); + template ND4J_EXPORT NDArray NDArrayFactory::create(const uint64_t scalar, sd::LaunchContext* workspace); + template ND4J_EXPORT NDArray NDArrayFactory::create(const bool scalar, sd::LaunchContext * context); //////////////////////////////////////////////////////////////////////// template -NDArray* NDArrayFactory::create_(const char order, const std::vector &shape, const std::vector &data, nd4j::LaunchContext * context) { +NDArray* NDArrayFactory::create_(const char order, const std::vector &shape, const std::vector &data, sd::LaunchContext * context) { return new NDArray(NDArrayFactory::create(order, shape, data, context)); } -template ND4J_EXPORT NDArray* NDArrayFactory::create_(const char order, const std::vector &shape, const std::vector &data, nd4j::LaunchContext * context); -template ND4J_EXPORT NDArray* NDArrayFactory::create_(const char order, const std::vector &shape, const std::vector &data, nd4j::LaunchContext * context); -template ND4J_EXPORT NDArray* NDArrayFactory::create_(const char order, const std::vector &shape, const std::vector &data, nd4j::LaunchContext * context); -template ND4J_EXPORT NDArray* NDArrayFactory::create_(const char order, const std::vector &shape, const std::vector &data, nd4j::LaunchContext * context); -template ND4J_EXPORT NDArray* NDArrayFactory::create_(const char order, const std::vector &shape, const std::vector &data, nd4j::LaunchContext * context); -template ND4J_EXPORT NDArray* NDArrayFactory::create_(const char order, const std::vector &shape, const std::vector &data, nd4j::LaunchContext * context); -template ND4J_EXPORT NDArray* NDArrayFactory::create_(const char order, const std::vector &shape, const std::vector &data, nd4j::LaunchContext * context); -template ND4J_EXPORT NDArray* NDArrayFactory::create_(const char order, const std::vector &shape, const std::vector &data, nd4j::LaunchContext * context); -template ND4J_EXPORT NDArray* NDArrayFactory::create_(const char order, const std::vector &shape, const std::vector &data, nd4j::LaunchContext * context); -template ND4J_EXPORT NDArray* NDArrayFactory::create_(const char order, const std::vector &shape, const std::vector &data, nd4j::LaunchContext * context); -template ND4J_EXPORT NDArray* NDArrayFactory::create_(const char order, const std::vector &shape, const std::vector &data, nd4j::LaunchContext * context); -template ND4J_EXPORT NDArray* NDArrayFactory::create_(const char order, const std::vector &shape, const std::vector &data, nd4j::LaunchContext * context); -template ND4J_EXPORT NDArray* NDArrayFactory::create_(const char order, const std::vector &shape, const std::vector &data, nd4j::LaunchContext * context); +template ND4J_EXPORT NDArray* NDArrayFactory::create_(const char order, const std::vector &shape, const std::vector &data, sd::LaunchContext * context); +template ND4J_EXPORT NDArray* NDArrayFactory::create_(const char order, const std::vector &shape, const std::vector &data, sd::LaunchContext * context); +template ND4J_EXPORT NDArray* NDArrayFactory::create_(const char order, const std::vector &shape, const std::vector &data, sd::LaunchContext * context); +template ND4J_EXPORT NDArray* NDArrayFactory::create_(const char order, const std::vector &shape, const std::vector &data, sd::LaunchContext * context); +template ND4J_EXPORT NDArray* NDArrayFactory::create_(const char order, const std::vector &shape, const std::vector &data, sd::LaunchContext * context); +template ND4J_EXPORT NDArray* NDArrayFactory::create_(const char order, const std::vector &shape, const std::vector &data, sd::LaunchContext * context); +template ND4J_EXPORT NDArray* NDArrayFactory::create_(const char order, const std::vector &shape, const std::vector &data, sd::LaunchContext * context); +template ND4J_EXPORT NDArray* NDArrayFactory::create_(const char order, const std::vector &shape, const std::vector &data, sd::LaunchContext * context); +template ND4J_EXPORT NDArray* NDArrayFactory::create_(const char order, const std::vector &shape, const std::vector &data, sd::LaunchContext * context); +template ND4J_EXPORT NDArray* NDArrayFactory::create_(const char order, const std::vector &shape, const std::vector &data, sd::LaunchContext * context); +template ND4J_EXPORT NDArray* NDArrayFactory::create_(const char order, const std::vector &shape, const std::vector &data, sd::LaunchContext * context); +template ND4J_EXPORT NDArray* NDArrayFactory::create_(const char order, const std::vector &shape, const std::vector &data, sd::LaunchContext * context); +template ND4J_EXPORT NDArray* NDArrayFactory::create_(const char order, const std::vector &shape, const std::vector &data, sd::LaunchContext * context); //////////////////////////////////////////////////////////////////////// template <> - ND4J_EXPORT NDArray* NDArrayFactory::valueOf(const std::vector& shape, NDArray* value, const char order, nd4j::LaunchContext * context) { + ND4J_EXPORT NDArray* NDArrayFactory::valueOf(const std::vector& shape, NDArray* value, const char order, sd::LaunchContext * context) { auto result = create_(order, shape, value->dataType(), context); result->assign(*value); return result; } template <> - ND4J_EXPORT NDArray* NDArrayFactory::valueOf(const std::vector& shape, NDArray& value, const char order, nd4j::LaunchContext * context) { + ND4J_EXPORT NDArray* NDArrayFactory::valueOf(const std::vector& shape, NDArray& value, const char order, sd::LaunchContext * context) { auto result = create_(order, shape, value.dataType(), context); result->assign(value); return result; } template - NDArray* NDArrayFactory::valueOf(const std::vector& shape, const T value, const char order, nd4j::LaunchContext * context) { + NDArray* NDArrayFactory::valueOf(const std::vector& shape, const T value, const char order, sd::LaunchContext * context) { auto result = create_(order, shape, DataTypeUtils::fromT()); result->assign(value); return result; } - template ND4J_EXPORT NDArray* NDArrayFactory::valueOf(const std::vector& shape, const double value, const char order, nd4j::LaunchContext * context); - template ND4J_EXPORT NDArray* NDArrayFactory::valueOf(const std::vector& shape, const float value, const char order, nd4j::LaunchContext * context); - template ND4J_EXPORT NDArray* NDArrayFactory::valueOf(const std::vector& shape, const float16 value, const char order, nd4j::LaunchContext * context); - template ND4J_EXPORT NDArray* NDArrayFactory::valueOf(const std::vector& shape, const bfloat16 value, const char order, nd4j::LaunchContext * context); - template ND4J_EXPORT NDArray* NDArrayFactory::valueOf(const std::vector& shape, const Nd4jLong value, const char order, nd4j::LaunchContext * context); - template ND4J_EXPORT NDArray* NDArrayFactory::valueOf(const std::vector& shape, const int value, const char order, nd4j::LaunchContext * context); - template ND4J_EXPORT NDArray* NDArrayFactory::valueOf(const std::vector& shape, const int16_t value, const char order, nd4j::LaunchContext * context); - template ND4J_EXPORT NDArray* NDArrayFactory::valueOf(const std::vector& shape, const int8_t value, const char order, nd4j::LaunchContext * context); - template ND4J_EXPORT NDArray* NDArrayFactory::valueOf(const std::vector& shape, const uint8_t value, const char order, nd4j::LaunchContext * context); - template ND4J_EXPORT NDArray* NDArrayFactory::valueOf(const std::vector& shape, const bool value, const char order, nd4j::LaunchContext * context); + template ND4J_EXPORT NDArray* NDArrayFactory::valueOf(const std::vector& shape, const double value, const char order, sd::LaunchContext * context); + template ND4J_EXPORT NDArray* NDArrayFactory::valueOf(const std::vector& shape, const float value, const char order, sd::LaunchContext * context); + template ND4J_EXPORT NDArray* NDArrayFactory::valueOf(const std::vector& shape, const float16 value, const char order, sd::LaunchContext * context); + template ND4J_EXPORT NDArray* NDArrayFactory::valueOf(const std::vector& shape, const bfloat16 value, const char order, sd::LaunchContext * context); + template ND4J_EXPORT NDArray* NDArrayFactory::valueOf(const std::vector& shape, const Nd4jLong value, const char order, sd::LaunchContext * context); + template ND4J_EXPORT NDArray* NDArrayFactory::valueOf(const std::vector& shape, const int value, const char order, sd::LaunchContext * context); + template ND4J_EXPORT NDArray* NDArrayFactory::valueOf(const std::vector& shape, const int16_t value, const char order, sd::LaunchContext * context); + template ND4J_EXPORT NDArray* NDArrayFactory::valueOf(const std::vector& shape, const int8_t value, const char order, sd::LaunchContext * context); + template ND4J_EXPORT NDArray* NDArrayFactory::valueOf(const std::vector& shape, const uint8_t value, const char order, sd::LaunchContext * context); + template ND4J_EXPORT NDArray* NDArrayFactory::valueOf(const std::vector& shape, const bool value, const char order, sd::LaunchContext * context); //////////////////////////////////////////////////////////////////////// @@ -331,7 +331,7 @@ template ND4J_EXPORT NDArray* NDArrayFactory::create_(const char order, const st //////////////////////////////////////////////////////////////////////// template - NDArray* NDArrayFactory::vector(Nd4jLong length, const T value, nd4j::LaunchContext * context) { + NDArray* NDArrayFactory::vector(Nd4jLong length, const T value, sd::LaunchContext * context) { std::shared_ptr buffer = std::make_shared(length * sizeof(T), DataTypeUtils::fromT(), context->getWorkspace(), true); @@ -344,37 +344,37 @@ template ND4J_EXPORT NDArray* NDArrayFactory::create_(const char order, const st return res; } - template ND4J_EXPORT NDArray* NDArrayFactory::vector(Nd4jLong length, const double startingValue, nd4j::LaunchContext * context); - template ND4J_EXPORT NDArray* NDArrayFactory::vector(Nd4jLong length, const float startingValue, nd4j::LaunchContext * context); - template ND4J_EXPORT NDArray* NDArrayFactory::vector(Nd4jLong length, const float16 startingValue, nd4j::LaunchContext * context); - template ND4J_EXPORT NDArray* NDArrayFactory::vector(Nd4jLong length, const bfloat16 startingValue, nd4j::LaunchContext * context); - template ND4J_EXPORT NDArray* NDArrayFactory::vector(Nd4jLong length, const Nd4jLong startingValue, nd4j::LaunchContext * context); - template ND4J_EXPORT NDArray* NDArrayFactory::vector(Nd4jLong length, const int startingValue, nd4j::LaunchContext * context); - template ND4J_EXPORT NDArray* NDArrayFactory::vector(Nd4jLong length, const uint8_t startingValue, nd4j::LaunchContext * context); - template ND4J_EXPORT NDArray* NDArrayFactory::vector(Nd4jLong length, const uint16_t startingValue, nd4j::LaunchContext *workspace); - template ND4J_EXPORT NDArray* NDArrayFactory::vector(Nd4jLong length, const uint32_t startingValue, nd4j::LaunchContext *workspace); - template ND4J_EXPORT NDArray* NDArrayFactory::vector(Nd4jLong length, const uint64_t startingValue, nd4j::LaunchContext *workspace); - template ND4J_EXPORT NDArray* NDArrayFactory::vector(Nd4jLong length, const int8_t startingValue, nd4j::LaunchContext * context); - template ND4J_EXPORT NDArray* NDArrayFactory::vector(Nd4jLong length, const int16_t startingValue, nd4j::LaunchContext * context); - template ND4J_EXPORT NDArray* NDArrayFactory::vector(Nd4jLong length, const bool startingValue, nd4j::LaunchContext * context); + template ND4J_EXPORT NDArray* NDArrayFactory::vector(Nd4jLong length, const double startingValue, sd::LaunchContext * context); + template ND4J_EXPORT NDArray* NDArrayFactory::vector(Nd4jLong length, const float startingValue, sd::LaunchContext * context); + template ND4J_EXPORT NDArray* NDArrayFactory::vector(Nd4jLong length, const float16 startingValue, sd::LaunchContext * context); + template ND4J_EXPORT NDArray* NDArrayFactory::vector(Nd4jLong length, const bfloat16 startingValue, sd::LaunchContext * context); + template ND4J_EXPORT NDArray* NDArrayFactory::vector(Nd4jLong length, const Nd4jLong startingValue, sd::LaunchContext * context); + template ND4J_EXPORT NDArray* NDArrayFactory::vector(Nd4jLong length, const int startingValue, sd::LaunchContext * context); + template ND4J_EXPORT NDArray* NDArrayFactory::vector(Nd4jLong length, const uint8_t startingValue, sd::LaunchContext * context); + template ND4J_EXPORT NDArray* NDArrayFactory::vector(Nd4jLong length, const uint16_t startingValue, sd::LaunchContext *workspace); + template ND4J_EXPORT NDArray* NDArrayFactory::vector(Nd4jLong length, const uint32_t startingValue, sd::LaunchContext *workspace); + template ND4J_EXPORT NDArray* NDArrayFactory::vector(Nd4jLong length, const uint64_t startingValue, sd::LaunchContext *workspace); + template ND4J_EXPORT NDArray* NDArrayFactory::vector(Nd4jLong length, const int8_t startingValue, sd::LaunchContext * context); + template ND4J_EXPORT NDArray* NDArrayFactory::vector(Nd4jLong length, const int16_t startingValue, sd::LaunchContext * context); + template ND4J_EXPORT NDArray* NDArrayFactory::vector(Nd4jLong length, const bool startingValue, sd::LaunchContext * context); //////////////////////////////////////////////////////////////////////// template - NDArray NDArrayFactory::create(const char order, const std::initializer_list& shape, nd4j::LaunchContext * context) { + NDArray NDArrayFactory::create(const char order, const std::initializer_list& shape, sd::LaunchContext * context) { std::vector vec(shape); return create(order, vec, context); } - BUILD_SINGLE_TEMPLATE(template ND4J_EXPORT NDArray NDArrayFactory::create, (const char, const std::initializer_list&, nd4j::LaunchContext * context), LIBND4J_TYPES); + BUILD_SINGLE_TEMPLATE(template ND4J_EXPORT NDArray NDArrayFactory::create, (const char, const std::initializer_list&, sd::LaunchContext * context), LIBND4J_TYPES); //////////////////////////////////////////////////////////////////////// template - NDArray NDArrayFactory::create(const char order, const std::vector &shape, nd4j::LaunchContext * context) { + NDArray NDArrayFactory::create(const char order, const std::vector &shape, sd::LaunchContext * context) { return create(order, shape, DataTypeUtils::fromT(), context); } - BUILD_SINGLE_TEMPLATE(template ND4J_EXPORT NDArray NDArrayFactory::create, (const char order, const std::vector &shape, nd4j::LaunchContext * context), LIBND4J_TYPES); + BUILD_SINGLE_TEMPLATE(template ND4J_EXPORT NDArray NDArrayFactory::create, (const char order, const std::vector &shape, sd::LaunchContext * context), LIBND4J_TYPES); //////////////////////////////////////////////////////////////////////// -NDArray NDArrayFactory::create(const char order, const std::vector &shape, nd4j::DataType dtype, nd4j::LaunchContext* context) { +NDArray NDArrayFactory::create(const char order, const std::vector &shape, sd::DataType dtype, sd::LaunchContext* context) { if ((int) shape.size() > MAX_RANK) throw std::invalid_argument("NDArrayFactory::create: rank of NDArray can't exceed 32"); @@ -392,7 +392,7 @@ NDArray NDArrayFactory::create(const char order, const std::vector &sh //////////////////////////////////////////////////////////////////////// -NDArray NDArrayFactory::create(nd4j::DataType dtype, nd4j::LaunchContext * context) { +NDArray NDArrayFactory::create(sd::DataType dtype, sd::LaunchContext * context) { std::shared_ptr buffer = std::make_shared(DataTypeUtils::sizeOfElement(dtype), dtype, context->getWorkspace(), true); @@ -403,7 +403,7 @@ NDArray NDArrayFactory::create(nd4j::DataType dtype, nd4j::LaunchContext * conte return res; } -NDArray* NDArrayFactory::create_(nd4j::DataType dtype, nd4j::LaunchContext * context) { +NDArray* NDArrayFactory::create_(sd::DataType dtype, sd::LaunchContext * context) { auto result = new NDArray(); *result = NDArrayFactory::create(dtype, context); return result; @@ -411,7 +411,7 @@ NDArray* NDArrayFactory::create_(nd4j::DataType dtype, nd4j::LaunchContext * con //////////////////////////////////////////////////////////////////////// template -NDArray NDArrayFactory::create(const std::vector &values, nd4j::LaunchContext * context) { +NDArray NDArrayFactory::create(const std::vector &values, sd::LaunchContext * context) { std::shared_ptr buffer = std::make_shared(values.size() * sizeof(T), DataTypeUtils::fromT(), context->getWorkspace(), true); @@ -424,21 +424,21 @@ NDArray NDArrayFactory::create(const std::vector &values, nd4j::LaunchContext return res; } -template ND4J_EXPORT NDArray NDArrayFactory::create(const std::vector &values, nd4j::LaunchContext * context); -template ND4J_EXPORT NDArray NDArrayFactory::create(const std::vector &values, nd4j::LaunchContext * context); -template ND4J_EXPORT NDArray NDArrayFactory::create(const std::vector &values, nd4j::LaunchContext * context); -template ND4J_EXPORT NDArray NDArrayFactory::create(const std::vector &values, nd4j::LaunchContext * context); -template ND4J_EXPORT NDArray NDArrayFactory::create(const std::vector &values, nd4j::LaunchContext * context); -template ND4J_EXPORT NDArray NDArrayFactory::create(const std::vector &values, nd4j::LaunchContext * context); -template ND4J_EXPORT NDArray NDArrayFactory::create(const std::vector &values, nd4j::LaunchContext * context); -template ND4J_EXPORT NDArray NDArrayFactory::create(const std::vector &values, nd4j::LaunchContext * context); -template ND4J_EXPORT NDArray NDArrayFactory::create(const std::vector &values, nd4j::LaunchContext * context); -template ND4J_EXPORT NDArray NDArrayFactory::create(const std::vector &values, nd4j::LaunchContext * context); -template ND4J_EXPORT NDArray NDArrayFactory::create(const std::vector &values, nd4j::LaunchContext * context); +template ND4J_EXPORT NDArray NDArrayFactory::create(const std::vector &values, sd::LaunchContext * context); +template ND4J_EXPORT NDArray NDArrayFactory::create(const std::vector &values, sd::LaunchContext * context); +template ND4J_EXPORT NDArray NDArrayFactory::create(const std::vector &values, sd::LaunchContext * context); +template ND4J_EXPORT NDArray NDArrayFactory::create(const std::vector &values, sd::LaunchContext * context); +template ND4J_EXPORT NDArray NDArrayFactory::create(const std::vector &values, sd::LaunchContext * context); +template ND4J_EXPORT NDArray NDArrayFactory::create(const std::vector &values, sd::LaunchContext * context); +template ND4J_EXPORT NDArray NDArrayFactory::create(const std::vector &values, sd::LaunchContext * context); +template ND4J_EXPORT NDArray NDArrayFactory::create(const std::vector &values, sd::LaunchContext * context); +template ND4J_EXPORT NDArray NDArrayFactory::create(const std::vector &values, sd::LaunchContext * context); +template ND4J_EXPORT NDArray NDArrayFactory::create(const std::vector &values, sd::LaunchContext * context); +template ND4J_EXPORT NDArray NDArrayFactory::create(const std::vector &values, sd::LaunchContext * context); //////////////////////////////////////////////////////////////////////// template - NDArray* NDArrayFactory::empty_(nd4j::LaunchContext * context) { + NDArray* NDArrayFactory::empty_(sd::LaunchContext * context) { auto shapeInfo = ShapeBuilders::createScalarShapeInfo(DataTypeUtils::fromT(), context->getWorkspace()); ArrayOptions::setPropertyBit(shapeInfo, ARRAY_EMPTY); auto result = new NDArray(nullptr, shapeInfo, context, false); @@ -447,11 +447,11 @@ template ND4J_EXPORT NDArray NDArrayFactory::create(const std::vector &val return result; } - BUILD_SINGLE_TEMPLATE(template ND4J_EXPORT NDArray* NDArrayFactory::empty_, (nd4j::LaunchContext * context), LIBND4J_TYPES); + BUILD_SINGLE_TEMPLATE(template ND4J_EXPORT NDArray* NDArrayFactory::empty_, (sd::LaunchContext * context), LIBND4J_TYPES); - NDArray* NDArrayFactory::empty_(nd4j::DataType dataType, nd4j::LaunchContext * context) { + NDArray* NDArrayFactory::empty_(sd::DataType dataType, sd::LaunchContext * context) { if (context == nullptr) - context = nd4j::LaunchContext ::defaultContext(); + context = sd::LaunchContext ::defaultContext(); auto shapeInfo = ShapeBuilders::createScalarShapeInfo(dataType, context->getWorkspace()); ArrayOptions::setPropertyBit(shapeInfo, ARRAY_EMPTY); @@ -464,13 +464,13 @@ template ND4J_EXPORT NDArray NDArrayFactory::create(const std::vector &val //////////////////////////////////////////////////////////////////////// template - NDArray NDArrayFactory::empty(nd4j::LaunchContext * context) { + NDArray NDArrayFactory::empty(sd::LaunchContext * context) { return empty(DataTypeUtils::fromT(), context); } - BUILD_SINGLE_TEMPLATE(template ND4J_EXPORT NDArray NDArrayFactory::empty, (nd4j::LaunchContext * context), LIBND4J_TYPES); + BUILD_SINGLE_TEMPLATE(template ND4J_EXPORT NDArray NDArrayFactory::empty, (sd::LaunchContext * context), LIBND4J_TYPES); //////////////////////////////////////////////////////////////////////// - NDArray NDArrayFactory::empty(nd4j::DataType dataType, nd4j::LaunchContext * context) { + NDArray NDArrayFactory::empty(sd::DataType dataType, sd::LaunchContext * context) { auto shapeInfo = ShapeBuilders::createScalarShapeInfo(dataType, context->getWorkspace()); ArrayOptions::setPropertyBit(shapeInfo, ARRAY_EMPTY); NDArray result(nullptr, shapeInfo, context, false); @@ -481,21 +481,21 @@ template ND4J_EXPORT NDArray NDArrayFactory::create(const std::vector &val } //////////////////////////////////////////////////////////////////////// - NDArray* NDArrayFactory::valueOf(const std::vector& shape, const NDArray& value, const char order, nd4j::LaunchContext * context) { + NDArray* NDArrayFactory::valueOf(const std::vector& shape, const NDArray& value, const char order, sd::LaunchContext * context) { auto res = NDArrayFactory::create_(order, shape, value.dataType(), context); res->assign(const_cast(value)); return res; } //////////////////////////////////////////////////////////////////////// - NDArray* NDArrayFactory::create_( const char order, const std::vector &shape, nd4j::DataType dataType, nd4j::LaunchContext * context) { + NDArray* NDArrayFactory::create_( const char order, const std::vector &shape, sd::DataType dataType, sd::LaunchContext * context) { return new NDArray(order, shape, dataType, context); } //////////////////////////////////////////////////////////////////////// template -NDArray NDArrayFactory::create(T* buffer, const char order, const std::initializer_list& shape, nd4j::LaunchContext * context) { +NDArray NDArrayFactory::create(T* buffer, const char order, const std::initializer_list& shape, sd::LaunchContext * context) { if ((int) shape.size() > MAX_RANK) throw std::invalid_argument("NDArrayFactory::create: Rank of NDArray can't exceed 32"); @@ -510,89 +510,89 @@ NDArray NDArrayFactory::create(T* buffer, const char order, const std::initializ return result; } -template ND4J_EXPORT NDArray NDArrayFactory::create(double* buffer, const char order, const std::initializer_list& shape, nd4j::LaunchContext * context); -template ND4J_EXPORT NDArray NDArrayFactory::create(float* buffer, const char order, const std::initializer_list& shape, nd4j::LaunchContext * context); -template ND4J_EXPORT NDArray NDArrayFactory::create(float16* buffer, const char order, const std::initializer_list& shape, nd4j::LaunchContext * context); -template ND4J_EXPORT NDArray NDArrayFactory::create(bfloat16* buffer, const char order, const std::initializer_list& shape, nd4j::LaunchContext * context); -template ND4J_EXPORT NDArray NDArrayFactory::create(Nd4jLong * buffer, const char order, const std::initializer_list& shape, nd4j::LaunchContext * context); -template ND4J_EXPORT NDArray NDArrayFactory::create(int* buffer, const char order, const std::initializer_list& shape, nd4j::LaunchContext * context); -template ND4J_EXPORT NDArray NDArrayFactory::create(bool* buffer, const char order, const std::initializer_list& shape, nd4j::LaunchContext * context); -template ND4J_EXPORT NDArray NDArrayFactory::create(uint8_t * buffer, const char order, const std::initializer_list& shape, nd4j::LaunchContext * context); -template ND4J_EXPORT NDArray NDArrayFactory::create(int8_t* buffer, const char order, const std::initializer_list& shape, nd4j::LaunchContext * context); -template ND4J_EXPORT NDArray NDArrayFactory::create(int16_t* buffer, const char order, const std::initializer_list& shape, nd4j::LaunchContext * context); +template ND4J_EXPORT NDArray NDArrayFactory::create(double* buffer, const char order, const std::initializer_list& shape, sd::LaunchContext * context); +template ND4J_EXPORT NDArray NDArrayFactory::create(float* buffer, const char order, const std::initializer_list& shape, sd::LaunchContext * context); +template ND4J_EXPORT NDArray NDArrayFactory::create(float16* buffer, const char order, const std::initializer_list& shape, sd::LaunchContext * context); +template ND4J_EXPORT NDArray NDArrayFactory::create(bfloat16* buffer, const char order, const std::initializer_list& shape, sd::LaunchContext * context); +template ND4J_EXPORT NDArray NDArrayFactory::create(Nd4jLong * buffer, const char order, const std::initializer_list& shape, sd::LaunchContext * context); +template ND4J_EXPORT NDArray NDArrayFactory::create(int* buffer, const char order, const std::initializer_list& shape, sd::LaunchContext * context); +template ND4J_EXPORT NDArray NDArrayFactory::create(bool* buffer, const char order, const std::initializer_list& shape, sd::LaunchContext * context); +template ND4J_EXPORT NDArray NDArrayFactory::create(uint8_t * buffer, const char order, const std::initializer_list& shape, sd::LaunchContext * context); +template ND4J_EXPORT NDArray NDArrayFactory::create(int8_t* buffer, const char order, const std::initializer_list& shape, sd::LaunchContext * context); +template ND4J_EXPORT NDArray NDArrayFactory::create(int16_t* buffer, const char order, const std::initializer_list& shape, sd::LaunchContext * context); ///////////////////////////////////////////////////////////////////////////////////// - NDArray NDArrayFactory::string(const char16_t* u16string, nd4j::DataType dtype, nd4j::LaunchContext* context) { + NDArray NDArrayFactory::string(const char16_t* u16string, sd::DataType dtype, sd::LaunchContext* context) { return NDArray(u16string, dtype, context); } ///////////////////////////////////////////////////////////////////////// - NDArray* NDArrayFactory::string_(const char16_t* u16string, nd4j::DataType dtype, nd4j::LaunchContext* context) { + NDArray* NDArrayFactory::string_(const char16_t* u16string, sd::DataType dtype, sd::LaunchContext* context) { return string_(std::u16string(u16string), dtype, context); } ///////////////////////////////////////////////////////////////////////// - NDArray* NDArrayFactory::string_(const std::u16string& u16string, nd4j::DataType dtype, nd4j::LaunchContext* context) { + NDArray* NDArrayFactory::string_(const std::u16string& u16string, sd::DataType dtype, sd::LaunchContext* context) { auto res = new NDArray(); *res = NDArray(u16string, dtype, context); return res; } ///////////////////////////////////////////////////////////////////////// - NDArray NDArrayFactory::string(const std::u16string& u16string, nd4j::DataType dtype, nd4j::LaunchContext* context) { + NDArray NDArrayFactory::string(const std::u16string& u16string, sd::DataType dtype, sd::LaunchContext* context) { return NDArray(u16string, dtype, context); } ///////////////////////////////////////////////////////////////////////// - NDArray NDArrayFactory::string(const char32_t* u32string, nd4j::DataType dtype, nd4j::LaunchContext* context) { + NDArray NDArrayFactory::string(const char32_t* u32string, sd::DataType dtype, sd::LaunchContext* context) { return NDArray(u32string, dtype, context); } ///////////////////////////////////////////////////////////////////////// - NDArray* NDArrayFactory::string_(const char32_t* u32string, nd4j::DataType dtype, nd4j::LaunchContext* context) { + NDArray* NDArrayFactory::string_(const char32_t* u32string, sd::DataType dtype, sd::LaunchContext* context) { return string_(std::u32string(u32string), dtype, context); } ///////////////////////////////////////////////////////////////////////// - NDArray* NDArrayFactory::string_(const std::u32string& u32string, nd4j::DataType dtype, nd4j::LaunchContext* context) { + NDArray* NDArrayFactory::string_(const std::u32string& u32string, sd::DataType dtype, sd::LaunchContext* context) { auto res = new NDArray(); *res = NDArray(u32string, dtype, context); return res; } ///////////////////////////////////////////////////////////////////////// - NDArray NDArrayFactory::string(const std::u32string& u32string, nd4j::DataType dtype, nd4j::LaunchContext* context) { + NDArray NDArrayFactory::string(const std::u32string& u32string, sd::DataType dtype, sd::LaunchContext* context) { return NDArray(u32string, dtype, context); } ///////////////////////////////////////////////////////////////////////// - NDArray NDArrayFactory::string(const char* str, nd4j::DataType dtype, nd4j::LaunchContext* context) { + NDArray NDArrayFactory::string(const char* str, sd::DataType dtype, sd::LaunchContext* context) { return NDArray(str, dtype, context); } ///////////////////////////////////////////////////////////////////////// - NDArray* NDArrayFactory::string_(const char* str, nd4j::DataType dtype, nd4j::LaunchContext* context) { + NDArray* NDArrayFactory::string_(const char* str, sd::DataType dtype, sd::LaunchContext* context) { return string_(std::string(str), dtype, context); } ///////////////////////////////////////////////////////////////////////// - NDArray* NDArrayFactory::string_(const std::string& str, nd4j::DataType dtype, nd4j::LaunchContext* context) { + NDArray* NDArrayFactory::string_(const std::string& str, sd::DataType dtype, sd::LaunchContext* context) { auto res = new NDArray(); *res = NDArray(str, dtype, context); return res; } ///////////////////////////////////////////////////////////////////////// - NDArray NDArrayFactory::string(const std::string& str, nd4j::DataType dtype, nd4j::LaunchContext* context) { + NDArray NDArrayFactory::string(const std::string& str, sd::DataType dtype, sd::LaunchContext* context) { return NDArray(str, dtype, context); } ///////////////////////////////////////////////////////////////////////// - NDArray NDArrayFactory::string(const std::vector &shape, const std::initializer_list &strings, nd4j::DataType dataType, nd4j::LaunchContext * context) { + NDArray NDArrayFactory::string(const std::vector &shape, const std::initializer_list &strings, sd::DataType dataType, sd::LaunchContext * context) { return NDArray(shape, std::vector(strings), dataType, context); } ///////////////////////////////////////////////////////////////////////// - NDArray NDArrayFactory::string( const std::vector &shape, const std::vector &strings, nd4j::DataType dataType, nd4j::LaunchContext * context) { + NDArray NDArrayFactory::string( const std::vector &shape, const std::vector &strings, sd::DataType dataType, sd::LaunchContext * context) { return NDArray( shape, strings, dataType, context); } ///////////////////////////////////////////////////////////////////////// - NDArray NDArrayFactory::string( const std::vector &shape, const std::initializer_list &string, nd4j::DataType dataType, nd4j::LaunchContext * context) { + NDArray NDArrayFactory::string( const std::vector &shape, const std::initializer_list &string, sd::DataType dataType, sd::LaunchContext * context) { return NDArray( shape, std::vector(string), dataType, context); } ///////////////////////////////////////////////////////////////////////// - NDArray* NDArrayFactory::string_( const std::vector &shape, const std::initializer_list &strings, nd4j::DataType dataType, nd4j::LaunchContext * context) { + NDArray* NDArrayFactory::string_( const std::vector &shape, const std::initializer_list &strings, sd::DataType dataType, sd::LaunchContext * context) { return NDArrayFactory::string_( shape, std::vector(strings), dataType, context); } ///////////////////////////////////////////////////////////////////////// - NDArray* NDArrayFactory::string_( const std::vector &shape, const std::vector &strings, nd4j::DataType dataType, nd4j::LaunchContext * context) { + NDArray* NDArrayFactory::string_( const std::vector &shape, const std::vector &strings, sd::DataType dataType, sd::LaunchContext * context) { std::vector vec(strings.size()); int cnt = 0; for (auto s:strings) @@ -601,37 +601,37 @@ template ND4J_EXPORT NDArray NDArrayFactory::create(int16_t* buffer, const char return NDArrayFactory::string_( shape, vec, dataType, context); } ///////////////////////////////////////////////////////////////////////// - NDArray* NDArrayFactory::string_( const std::vector &shape, const std::initializer_list &string, nd4j::DataType dataType, nd4j::LaunchContext * context) { + NDArray* NDArrayFactory::string_( const std::vector &shape, const std::initializer_list &string, sd::DataType dataType, sd::LaunchContext * context) { return NDArrayFactory::string_( shape, std::vector(string), dataType, context); } ///////////////////////////////////////////////////////////////////////// - NDArray NDArrayFactory::string( const std::vector &shape, const std::vector &string, nd4j::DataType dataType, nd4j::LaunchContext * context) { + NDArray NDArrayFactory::string( const std::vector &shape, const std::vector &string, sd::DataType dataType, sd::LaunchContext * context) { return NDArray(shape, string, dataType, context); } ///////////////////////////////////////////////////////////////////////// - NDArray* NDArrayFactory::string_(const std::vector &shape, const std::vector &string, nd4j::DataType dataType, nd4j::LaunchContext * context) { + NDArray* NDArrayFactory::string_(const std::vector &shape, const std::vector &string, sd::DataType dataType, sd::LaunchContext * context) { auto res = new NDArray(); *res = NDArray( shape, string, dataType, context); return res; } ///////////////////////////////////////////////////////////////////////// - NDArray NDArrayFactory::string(const std::vector& shape, const std::initializer_list& strings, nd4j::DataType dataType, nd4j::LaunchContext* context) { + NDArray NDArrayFactory::string(const std::vector& shape, const std::initializer_list& strings, sd::DataType dataType, sd::LaunchContext* context) { return NDArray( shape, std::vector(strings), dataType, context); } ///////////////////////////////////////////////////////////////////////// - NDArray NDArrayFactory::string( const std::vector& shape, const std::vector& strings, nd4j::DataType dataType, nd4j::LaunchContext* context) { + NDArray NDArrayFactory::string( const std::vector& shape, const std::vector& strings, sd::DataType dataType, sd::LaunchContext* context) { return NDArray( shape, strings, dataType, context); } ///////////////////////////////////////////////////////////////////////// - NDArray NDArrayFactory::string( const std::vector& shape, const std::initializer_list& string, nd4j::DataType dataType, nd4j::LaunchContext* context) { + NDArray NDArrayFactory::string( const std::vector& shape, const std::initializer_list& string, sd::DataType dataType, sd::LaunchContext* context) { return NDArray( shape, std::vector(string), dataType, context); } ///////////////////////////////////////////////////////////////////////// - NDArray* NDArrayFactory::string_( const std::vector& shape, const std::initializer_list& strings, nd4j::DataType dataType, nd4j::LaunchContext* context) { + NDArray* NDArrayFactory::string_( const std::vector& shape, const std::initializer_list& strings, sd::DataType dataType, sd::LaunchContext* context) { return NDArrayFactory::string_( shape, std::vector(strings), dataType, context); } ///////////////////////////////////////////////////////////////////////// - NDArray* NDArrayFactory::string_( const std::vector& shape, const std::vector& strings, nd4j::DataType dataType, nd4j::LaunchContext* context) { + NDArray* NDArrayFactory::string_( const std::vector& shape, const std::vector& strings, sd::DataType dataType, sd::LaunchContext* context) { std::vector vec(strings.size()); int cnt = 0; for (auto s : strings) @@ -640,37 +640,37 @@ template ND4J_EXPORT NDArray NDArrayFactory::create(int16_t* buffer, const char return NDArrayFactory::string_( shape, vec, dataType, context); } ///////////////////////////////////////////////////////////////////////// - NDArray* NDArrayFactory::string_( const std::vector& shape, const std::initializer_list& string, nd4j::DataType dataType, nd4j::LaunchContext* context) { + NDArray* NDArrayFactory::string_( const std::vector& shape, const std::initializer_list& string, sd::DataType dataType, sd::LaunchContext* context) { return NDArrayFactory::string_( shape, std::vector(string), dataType, context); } ///////////////////////////////////////////////////////////////////////// - NDArray* NDArrayFactory::string_( const std::vector& shape, const std::vector& string, nd4j::DataType dataType, nd4j::LaunchContext* context) { + NDArray* NDArrayFactory::string_( const std::vector& shape, const std::vector& string, sd::DataType dataType, sd::LaunchContext* context) { auto res = new NDArray(); *res = NDArray( shape, string, dataType, context); return res; } ///////////////////////////////////////////////////////////////////////// - NDArray NDArrayFactory::string( const std::vector& shape, const std::vector& string, nd4j::DataType dtype, nd4j::LaunchContext* context) { + NDArray NDArrayFactory::string( const std::vector& shape, const std::vector& string, sd::DataType dtype, sd::LaunchContext* context) { return NDArray( shape, string, dtype, context); } ///////////////////////////////////////////////////////////////////////// - NDArray NDArrayFactory::string( const std::vector& shape, const std::initializer_list& strings, nd4j::DataType dataType, nd4j::LaunchContext* context) { + NDArray NDArrayFactory::string( const std::vector& shape, const std::initializer_list& strings, sd::DataType dataType, sd::LaunchContext* context) { return NDArray( shape, std::vector(strings), dataType, context); } ///////////////////////////////////////////////////////////////////////// - NDArray NDArrayFactory::string( const std::vector& shape, const std::vector& strings, nd4j::DataType dataType, nd4j::LaunchContext* context) { + NDArray NDArrayFactory::string( const std::vector& shape, const std::vector& strings, sd::DataType dataType, sd::LaunchContext* context) { return NDArray( shape, strings, dataType, context); } ///////////////////////////////////////////////////////////////////////// - NDArray NDArrayFactory::string( const std::vector& shape, const std::initializer_list& string, nd4j::DataType dataType, nd4j::LaunchContext* context) { + NDArray NDArrayFactory::string( const std::vector& shape, const std::initializer_list& string, sd::DataType dataType, sd::LaunchContext* context) { return NDArray(shape, std::vector(string), dataType, context); } ///////////////////////////////////////////////////////////////////////// - NDArray* NDArrayFactory::string_( const std::vector& shape, const std::initializer_list& strings, nd4j::DataType dataType, nd4j::LaunchContext* context) { + NDArray* NDArrayFactory::string_( const std::vector& shape, const std::initializer_list& strings, sd::DataType dataType, sd::LaunchContext* context) { return NDArrayFactory::string_( shape, std::vector(strings), dataType, context); } ///////////////////////////////////////////////////////////////////////// - NDArray* NDArrayFactory::string_( const std::vector& shape, const std::vector& strings, nd4j::DataType dataType, nd4j::LaunchContext* context) { + NDArray* NDArrayFactory::string_( const std::vector& shape, const std::vector& strings, sd::DataType dataType, sd::LaunchContext* context) { std::vector vec(strings.size()); int cnt = 0; for (auto s : strings) @@ -678,23 +678,23 @@ template ND4J_EXPORT NDArray NDArrayFactory::create(int16_t* buffer, const char return NDArrayFactory::string_( shape, vec, dataType, context); } ///////////////////////////////////////////////////////////////////////// - NDArray* NDArrayFactory::string_( const std::vector& shape, const std::initializer_list& string, nd4j::DataType dataType, nd4j::LaunchContext* context) { + NDArray* NDArrayFactory::string_( const std::vector& shape, const std::initializer_list& string, sd::DataType dataType, sd::LaunchContext* context) { return NDArrayFactory::string_( shape, std::vector(string), dataType, context); } ///////////////////////////////////////////////////////////////////////// - NDArray* NDArrayFactory::string_( const std::vector& shape, const std::vector& string, nd4j::DataType dataType, nd4j::LaunchContext* context) { + NDArray* NDArrayFactory::string_( const std::vector& shape, const std::vector& string, sd::DataType dataType, sd::LaunchContext* context) { auto res = new NDArray(); *res = NDArray( shape, string, dataType, context); return res; } ///////////////////////////////////////////////////////////////////////// - NDArray NDArrayFactory::string(const std::vector& shape, const std::vector& string, nd4j::DataType dtype, nd4j::LaunchContext* context) { + NDArray NDArrayFactory::string(const std::vector& shape, const std::vector& string, sd::DataType dtype, sd::LaunchContext* context) { return NDArray( shape, string, dtype, context); } NDArray NDArrayFactory::fromNpyFile(const char *fileName) { - auto size = nd4j::graph::getFileSize(fileName); + auto size = sd::graph::getFileSize(fileName); if (size < 0) throw std::runtime_error("File doesn't exit"); @@ -705,7 +705,7 @@ template ND4J_EXPORT NDArray NDArrayFactory::create(int16_t* buffer, const char auto length = shape::length(shape); int8_t *buffer = nullptr; - nd4j::memory::Workspace *workspace = nullptr; + sd::memory::Workspace *workspace = nullptr; auto byteLen = length * DataTypeUtils::sizeOfElement(ArrayOptions::dataType(shape)); ALLOCATE(buffer, workspace, byteLen, int8_t); diff --git a/libnd4j/include/array/impl/NDArrayList.cpp b/libnd4j/include/array/impl/NDArrayList.cpp index 81ac9ac2d..deb7828d8 100644 --- a/libnd4j/include/array/impl/NDArrayList.cpp +++ b/libnd4j/include/array/impl/NDArrayList.cpp @@ -24,7 +24,7 @@ #include #include -namespace nd4j { +namespace sd { NDArrayList::NDArrayList(int height, bool expandable) { _expandable = expandable; _elements.store(0); @@ -47,7 +47,7 @@ namespace nd4j { return new NDArray(readRaw(idx)->dup()); } - nd4j::DataType NDArrayList::dataType() { + sd::DataType NDArrayList::dataType() { return _dtype; } @@ -144,7 +144,7 @@ namespace nd4j { NDArray* NDArrayList::stack() { // FIXME: this is bad for perf, but ok as poc - nd4j::ops::stack op; + sd::ops::stack op; std::vector inputs; std::vector targs; std::vector iargs({0}); @@ -160,9 +160,7 @@ namespace nd4j { auto result = op.evaluate(inputs); - auto array = new NDArray(result->at(0)->dup()); - - delete result; + auto array = new NDArray(result.at(0)->dup()); return array; } @@ -175,7 +173,7 @@ namespace nd4j { return _name; } - nd4j::LaunchContext * NDArrayList::context() { + sd::LaunchContext * NDArrayList::context() { return _context; } diff --git a/libnd4j/include/array/impl/ResultSet.cpp b/libnd4j/include/array/impl/ResultSet.cpp index 33c04f851..d9d824d46 100644 --- a/libnd4j/include/array/impl/ResultSet.cpp +++ b/libnd4j/include/array/impl/ResultSet.cpp @@ -21,39 +21,40 @@ #include #include -namespace nd4j { - ResultSet::ResultSet(const nd4j::graph::FlatResult* result) { - if (result != nullptr) { - for (int e = 0; e < result->variables()->size(); e++) { - auto var = result->variables()->Get(e); +namespace sd { + ResultSet::ResultSet() { + // + } - NDArray* array; + ResultSet::ResultSet(const sd::graph::FlatResult* result) { + for (int e = 0; e < result->variables()->size(); e++) { + auto var = result->variables()->Get(e); - if (var->ndarray() != nullptr) { - array = nd4j::graph::FlatUtils::fromFlatArray(var->ndarray()); - } else if (var->shape() != nullptr) { - std::vector shapeInfo; - for (int i = 0; i < var->shape()->size(); i++) { - shapeInfo.emplace_back(var->shape()->Get(i)); - } + NDArray* array; - // we just create empty array here - int s0 = shapeInfo.at(0); - - std::vector shape; - for (int i = 0; i < s0; i++) { - shape.emplace_back(shapeInfo.at(i + 1)); - } - - array = new NDArray((char) shapeInfo.at(shapeInfo.size() - 1), shape, DataTypeUtils::fromFlatDataType(var->dtype())); - } else { - nd4j_printf("Either shape or NDArray should be defined in FlatResult variable\n",""); - throw std::runtime_error("Empty variable"); + if (var->ndarray() != nullptr) { + array = sd::graph::FlatUtils::fromFlatArray(var->ndarray()); + } else if (var->shape() != nullptr) { + std::vector shapeInfo; + for (int i = 0; i < var->shape()->size(); i++) { + shapeInfo.emplace_back(var->shape()->Get(i)); } + // we just create empty array here + int s0 = shapeInfo.at(0); - _content.push_back(array); + std::vector shape; + for (int i = 0; i < s0; i++) { + shape.emplace_back(shapeInfo.at(i + 1)); + } + + array = new NDArray((char) shapeInfo.at(shapeInfo.size() - 1), shape, DataTypeUtils::fromFlatDataType(var->dtype())); + } else { + nd4j_printf("Either shape or NDArray should be defined in FlatResult variable\n",""); + throw std::runtime_error("Empty variable"); } + + _content.push_back(array); } } @@ -76,15 +77,16 @@ namespace nd4j { } //////////////////////////////////////////////////////////////////////// - // move assignment operator +// move assignment operator ResultSet& ResultSet::operator=(ResultSet&& other) noexcept { - if (this == &other) + if (this == &other) return *this; - this->~ResultSet(); + delContent(); _content = std::move(other._content); + _status = other._status; _removable = other._removable; other._removable = false; @@ -97,10 +99,10 @@ namespace nd4j { if (this == &other) return *this; - this->~ResultSet(); + delContent(); - for (const auto v:other._content) - _content.emplace_back(v); + for (const auto v : other._content) + _content.push_back(v); _status = other._status; _removable = false; @@ -108,11 +110,15 @@ namespace nd4j { return *this; } + void ResultSet::delContent() { + if (_removable) + for (auto v : _content) + delete v; + } ResultSet::~ResultSet() { - if (_removable) - for (auto v: _content) - delete v; + + delContent(); } void ResultSet::setNonRemovable() { @@ -123,15 +129,15 @@ namespace nd4j { return (int) _content.size(); } - nd4j::NDArray* ResultSet::at(const unsigned long idx) const { + sd::NDArray* ResultSet::at(const unsigned long idx) const { return _content.at(idx); } - nd4j::NDArray* ResultSet::operator[](const unsigned long idx) const { + sd::NDArray* ResultSet::operator[](const unsigned long idx) const { return _content[idx]; } - void ResultSet::push_back(nd4j::NDArray *array) { + void ResultSet::push_back(sd::NDArray *array) { _content.emplace_back(array); } diff --git a/libnd4j/include/array/impl/ShapeDescriptor.cpp b/libnd4j/include/array/impl/ShapeDescriptor.cpp index 356177163..3ef096312 100644 --- a/libnd4j/include/array/impl/ShapeDescriptor.cpp +++ b/libnd4j/include/array/impl/ShapeDescriptor.cpp @@ -18,131 +18,93 @@ // @author raver119@gmail.com // -#include "../ShapeDescriptor.h" -#include -#include +#include +#include +#include -using namespace nd4j; +namespace sd { ////////////////////////////////////////////////////////////////////////// // equal to operator -bool ShapeDescriptor::operator==(const ShapeDescriptor& other) const { + bool ShapeDescriptor::operator==(const ShapeDescriptor &other) const { - if(_empty != other._empty) - return false; - if(_rank != other._rank) - return false; - if(_order != other._order) - return false; - if(_dataType != other._dataType) - return false; - if(_ews != other._ews) - return false; + if (_empty != other._empty) + return false; + if (_rank != other._rank) + return false; + if (_order != other._order) + return false; + if (_dataType != other._dataType) + return false; + if (_ews != other._ews) + return false; - if(_shape != other._shape) - return false; + if (_shape != other._shape) + return false; - if(_strides != other._strides) - return false; + if (_strides != other._strides) + return false; - return true; -} + return true; + } ////////////////////////////////////////////////////////////////////////// // less than operator -bool ShapeDescriptor::operator<(const ShapeDescriptor& other) const { - return std::tie(_empty, _rank, _dataType, _ews, _order, _shape, _strides) < std::tie(other._empty, other._rank, other._dataType, other._ews, other._order, other._shape, other._strides); -} + bool ShapeDescriptor::operator<(const ShapeDescriptor &other) const { + return std::tie(_empty, _rank, _dataType, _ews, _order, _shape, _strides) < + std::tie(other._empty, other._rank, other._dataType, other._ews, other._order, other._shape, + other._strides); + } -Nd4jLong* ShapeDescriptor::toShapeInfo() const { - if (_empty) { - if (_rank == 0) - return ShapeBuilders::emptyShapeInfo(_dataType); - else { - return ShapeBuilders::emptyShapeInfo(_dataType, _order, _shape); + Nd4jLong *ShapeDescriptor::toShapeInfo() const { + if (_empty) { + if (_rank == 0) + return ShapeBuilders::emptyShapeInfo(_dataType); + else { + return ShapeBuilders::emptyShapeInfo(_dataType, _order, _shape); + } + } + + + switch (_rank) { + case 0: { + auto shapeInfo = ShapeBuilders::createScalarShapeInfo(_dataType); + shapeInfo[2] = _ews; + return shapeInfo; + } + case 1: { + auto shapeInfo = ShapeBuilders::createVectorShapeInfo(_dataType, _shape[0]); + shapeInfo[2 + _rank * 2] = _ews; + shapeInfo[2] = _strides[0]; + shapeInfo[2 + _rank * 2 + 1] = _order; + return shapeInfo; + } + default: { + auto shapeInfo = ShapeBuilders::createShapeInfo(_dataType, _order, _shape); + + for (int e = 0; e < _rank; e++) + shapeInfo[e + 1 + _rank] = _strides[e]; + + shapeInfo[2 + _rank * 2] = _ews; + + return shapeInfo; + } } } + ShapeDescriptor::ShapeDescriptor(const DataType type, const char order, const Nd4jLong *shape, const int rank) + : _dataType(type), _order(order), _rank(rank), _ews(1) { + _shape.resize(rank); + _strides.resize(rank); - switch (_rank) { - case 0: { - auto shapeInfo = ShapeBuilders::createScalarShapeInfo(_dataType); - shapeInfo[2] = _ews; - return shapeInfo; - } - case 1: { - auto shapeInfo = ShapeBuilders::createVectorShapeInfo(_dataType, _shape[0]); - shapeInfo[2 + _rank * 2] = _ews; - shapeInfo[2] = _strides[0]; - shapeInfo[2 + _rank * 2 + 1] = _order; - return shapeInfo; - } - default: { - auto shapeInfo = ShapeBuilders::createShapeInfo(_dataType, _order, _shape); + for (int e = 0; e < rank; e++) + _shape[e] = shape[e]; - for (int e = 0; e < _rank; e++) - shapeInfo[e + 1 + _rank] = _strides[e]; + if (order == 'c') + shape::calcStrides(_shape.data(), _shape.size(), _strides.data()); + else + shape::calcStridesFortran(_shape.data(), _shape.size(), _strides.data()); - shapeInfo[2 + _rank * 2] = _ews; - - return shapeInfo; - } - } -} - -ShapeDescriptor::ShapeDescriptor(const DataType type, const char order, const Nd4jLong *shape, const int rank) : _dataType(type), _order(order), _rank(rank), _ews(1){ - _shape.resize(rank); - _strides.resize(rank); - - for (int e = 0; e < rank; e++) - _shape[e] = shape[e]; - - if (order == 'c') - shape::calcStrides(_shape.data(), _shape.size(), _strides.data()); - else - shape::calcStridesFortran(_shape.data(), _shape.size(), _strides.data()); - - - for (auto v:_shape) { - if (v == 0) { - _empty = true; - break; - } - } -} - -ShapeDescriptor::ShapeDescriptor(const DataType type, const char order, const Nd4jLong *shape, const Nd4jLong *strides, const int rank, Nd4jLong ews, const bool empty) { - _shape.resize(rank); - _strides.resize(rank); - - _dataType = type; - _order = order; - _rank = rank; - _empty = empty; - _ews = ews; - - for (int e = 0; e < rank; e++) - _shape[e] = shape[e]; - - for (int e = 0; e < rank; e++) - _strides[e] = strides[e]; - - - for (auto v:_shape) { - if (v == 0) { - _empty = true; - break; - } - } -} - -////////////////////////////////////////////////////////////////////////// -ShapeDescriptor::ShapeDescriptor(const DataType type, const char order, const std::vector &shape): _dataType(type), _order(order), _shape(shape) { - _rank = shape.size(); - _ews = 1; - - if (_rank > 0) { - _strides.resize(_rank); for (auto v:_shape) { if (v == 0) { @@ -150,188 +112,269 @@ ShapeDescriptor::ShapeDescriptor(const DataType type, const char order, const st break; } } + } - // no point calculating strides for empty arrays - if (!_empty) { - if (order == 'c') - shape::calcStrides(_shape.data(), shape.size(), _strides.data()); - else - shape::calcStridesFortran(_shape.data(), shape.size(), _strides.data()); - } else { - // all strides set to 0 - memset(_strides.data(), 0, sizeof(Nd4jLong) * shape.size()); + ShapeDescriptor::ShapeDescriptor(const DataType type, const char order, const Nd4jLong *shape, + const Nd4jLong *strides, const int rank, Nd4jLong ews, const bool empty) { + _shape.resize(rank); + _strides.resize(rank); + + _dataType = type; + _order = order; + _rank = rank; + _empty = empty; + _ews = ews; + + for (int e = 0; e < rank; e++) + _shape[e] = shape[e]; + + for (int e = 0; e < rank; e++) + _strides[e] = strides[e]; + + + for (auto v:_shape) { + if (v == 0) { + _empty = true; + break; + } } } -} ////////////////////////////////////////////////////////////////////////// -ShapeDescriptor::ShapeDescriptor(const DataType type, const char order, const std::initializer_list &shape): _dataType(type), _order(order), _shape(shape) { - _rank = shape.size(); - _ews = 1; + ShapeDescriptor::ShapeDescriptor(const DataType type, const char order, const std::vector &shape) + : _dataType(type), _order(order), _shape(shape) { + _rank = shape.size(); + _ews = 1; - _strides.resize(shape.size()); - if (order == 'c') - shape::calcStrides(_shape.data(), shape.size(), _strides.data()); - else - shape::calcStridesFortran(_shape.data(), shape.size(), _strides.data()); + if (_rank > 0) { + _strides.resize(_rank); - for (auto v:_shape) { - if (v == 0) { - _empty = true; - break; + for (auto v:_shape) { + if (v == 0) { + _empty = true; + break; + } + } + + // no point calculating strides for empty arrays + if (!_empty) { + if (order == 'c') + shape::calcStrides(_shape.data(), shape.size(), _strides.data()); + else + shape::calcStridesFortran(_shape.data(), shape.size(), _strides.data()); + } else { + // all strides set to 0 + memset(_strides.data(), 0, sizeof(Nd4jLong) * shape.size()); + } } } -} ////////////////////////////////////////////////////////////////////////// -ShapeDescriptor::ShapeDescriptor(const DataType type, const char order, const std::vector &shape, const std::vector &strides, const Nd4jLong ews): ShapeDescriptor(type, order, shape, strides) { - _ews = ews; -} + ShapeDescriptor::ShapeDescriptor(const DataType type, const char order, + const std::initializer_list &shape) : _dataType(type), _order(order), + _shape(shape) { + _rank = shape.size(); + _ews = 1; -ShapeDescriptor::ShapeDescriptor(const DataType type, const Nd4jLong length) : _dataType(type), _ews(1), _order('c'), _rank(1), _empty(false) { - _shape = {length}; - _strides = {1}; -} - -ShapeDescriptor::ShapeDescriptor(const Nd4jLong *shapeInfo, bool inheritDtype) { - _order = shape::order(shapeInfo); - _ews = shape::elementWiseStride(shapeInfo); - _rank = shape::rank(shapeInfo); - - if (inheritDtype) - _dataType = ArrayOptions::dataType(shapeInfo); - - _empty = shape::isEmpty(shapeInfo); - - for (int e = 0; e < _rank; e++) { - _shape.emplace_back(shapeInfo[e + 1]); - if (shapeInfo[e + 1] == 0) - _empty = true; - } - - for (int e = 0; e < _rank; e++) - _strides.emplace_back(shapeInfo[e + 1 + _rank]); -} - -ShapeDescriptor::ShapeDescriptor(const Nd4jLong *shapeInfo, const nd4j::DataType dtypeOverride) : ShapeDescriptor::ShapeDescriptor(shapeInfo, false) { - _dataType = dtypeOverride; -} - -ShapeDescriptor::ShapeDescriptor(const Nd4jLong *shapeInfo, const Nd4jLong *dtypeOverride) : ShapeDescriptor::ShapeDescriptor(shapeInfo, ArrayOptions::dataType(dtypeOverride)) { - // -} - -ShapeDescriptor::ShapeDescriptor(const Nd4jLong *shapeInfo, const Nd4jLong *dtypeOverride, const Nd4jLong *orderOverride) : ShapeDescriptor::ShapeDescriptor(shapeInfo, ArrayOptions::dataType(dtypeOverride)) { - _order = shape::order(orderOverride); -} - -int ShapeDescriptor::rank() const { - return _rank; -} - -Nd4jLong ShapeDescriptor::ews() const { - return _ews; -} - -Nd4jLong ShapeDescriptor::arrLength() const { - - Nd4jLong len = 1; - for(const auto& dim : const_cast(this)->shape()) - len *= dim; - return len; -} - -char ShapeDescriptor::order() const { - return _order; -} - -DataType ShapeDescriptor::dataType() const { - return _dataType; -} - -bool ShapeDescriptor::isEmpty() const { - return _empty; -} -std::vector& ShapeDescriptor::shape() { - return _shape; -} - -std::vector& ShapeDescriptor::strides() { - return _strides; -} - -ShapeDescriptor::ShapeDescriptor(const ShapeDescriptor &other) { - _rank = other._rank; - _ews = other._ews; - _empty = other._empty; - _dataType = other._dataType; - _order = other._order; - _shape = other._shape; - _strides = other._strides; -} - -////////////////////////////////////////////////////////////////////////// -ShapeDescriptor::ShapeDescriptor(const DataType type, const char order, const std::vector &shape, const std::vector &strides): _dataType(type), _order(order), _shape(shape) { - - if (strides.empty() && !shape.empty()) { _strides.resize(shape.size()); if (order == 'c') shape::calcStrides(_shape.data(), shape.size(), _strides.data()); else shape::calcStridesFortran(_shape.data(), shape.size(), _strides.data()); - } - else { - _strides = strides; - } - - for (auto v:_shape) { - if (v == 0) { - _empty = true; - break; + for (auto v:_shape) { + if (v == 0) { + _empty = true; + break; + } } } -} -ShapeDescriptor ShapeDescriptor::emptyDescriptor(const DataType type) { - ShapeDescriptor descriptor; - descriptor._dataType = type; - descriptor._empty = true; - descriptor._rank = 0; - descriptor._order = 'c'; - descriptor._ews = 1; - - return descriptor; -} - -ShapeDescriptor ShapeDescriptor::scalarDescriptor(const DataType type) { - ShapeDescriptor descriptor; - descriptor._dataType = type; - descriptor._empty = false; - descriptor._rank = 0; - descriptor._order = 'c'; - descriptor._ews = 1; - - return descriptor; -} - -ShapeDescriptor ShapeDescriptor::vectorDescriptor(const Nd4jLong length, const DataType type) { - ShapeDescriptor descriptor; - descriptor._dataType = type; - descriptor._shape.emplace_back(length); - - if (length > 0) - descriptor._strides.emplace_back(1); - else { - descriptor._strides.emplace_back(0); - descriptor._empty = true; +////////////////////////////////////////////////////////////////////////// + ShapeDescriptor::ShapeDescriptor(const DataType type, const char order, const std::vector &shape, + const std::vector &strides, const Nd4jLong ews) : ShapeDescriptor(type, + order, + shape, + strides) { + _ews = ews; } - descriptor._order = 'c'; - descriptor._ews = 1; - descriptor._rank = 1; + ShapeDescriptor::ShapeDescriptor(const DataType type, const Nd4jLong length) : _dataType(type), _ews(1), + _order('c'), _rank(1), + _empty(false) { + _shape = {length}; + _strides = {1}; + } - return descriptor; + ShapeDescriptor::ShapeDescriptor(const Nd4jLong *shapeInfo, bool inheritDtype) { + _order = shape::order(shapeInfo); + _ews = shape::elementWiseStride(shapeInfo); + _rank = shape::rank(shapeInfo); + + if (inheritDtype) + _dataType = ArrayOptions::dataType(shapeInfo); + + _empty = shape::isEmpty(shapeInfo); + + for (int e = 0; e < _rank; e++) { + _shape.emplace_back(shapeInfo[e + 1]); + if (shapeInfo[e + 1] == 0) + _empty = true; + } + + for (int e = 0; e < _rank; e++) + _strides.emplace_back(shapeInfo[e + 1 + _rank]); + } + + ShapeDescriptor::ShapeDescriptor(const Nd4jLong *shapeInfo, const sd::DataType dtypeOverride) + : ShapeDescriptor::ShapeDescriptor(shapeInfo, false) { + _dataType = dtypeOverride; + } + + ShapeDescriptor::ShapeDescriptor(const Nd4jLong *shapeInfo, const Nd4jLong *dtypeOverride) + : ShapeDescriptor::ShapeDescriptor(shapeInfo, ArrayOptions::dataType(dtypeOverride)) { + // + } + + ShapeDescriptor::ShapeDescriptor(const Nd4jLong *shapeInfo, const Nd4jLong *dtypeOverride, + const Nd4jLong *orderOverride) : ShapeDescriptor::ShapeDescriptor(shapeInfo, + ArrayOptions::dataType( + dtypeOverride)) { + _order = shape::order(orderOverride); + } + + int ShapeDescriptor::rank() const { + return _rank; + } + + Nd4jLong ShapeDescriptor::ews() const { + return _ews; + } + + Nd4jLong ShapeDescriptor::arrLength() const { + + Nd4jLong len = 1; + for (const auto &dim : const_cast(this)->shape()) + len *= dim; + return len; + } + + char ShapeDescriptor::order() const { + return _order; + } + + DataType ShapeDescriptor::dataType() const { + return _dataType; + } + + bool ShapeDescriptor::isEmpty() const { + return _empty; + } + + std::vector &ShapeDescriptor::shape() { + return _shape; + } + + std::vector &ShapeDescriptor::strides() { + return _strides; + } + + ShapeDescriptor::ShapeDescriptor(const ShapeDescriptor &other) { + _rank = other._rank; + _ews = other._ews; + _empty = other._empty; + _dataType = other._dataType; + _order = other._order; + _shape = other._shape; + _strides = other._strides; + } + +////////////////////////////////////////////////////////////////////////// + ShapeDescriptor::ShapeDescriptor(const DataType type, const char order, const std::vector &shape, + const std::vector &strides) : _dataType(type), _order(order), + _shape(shape) { + + if (strides.empty() && !shape.empty()) { + _strides.resize(shape.size()); + if (order == 'c') + shape::calcStrides(_shape.data(), shape.size(), _strides.data()); + else + shape::calcStridesFortran(_shape.data(), shape.size(), _strides.data()); + } else { + _strides = strides; + } + + + for (auto v:_shape) { + if (v == 0) { + _empty = true; + break; + } + } + } + + ShapeDescriptor ShapeDescriptor::emptyDescriptor(const DataType type) { + ShapeDescriptor descriptor; + descriptor._dataType = type; + descriptor._empty = true; + descriptor._rank = 0; + descriptor._order = 'c'; + descriptor._ews = 1; + + return descriptor; + } + + ShapeDescriptor ShapeDescriptor::scalarDescriptor(const DataType type) { + ShapeDescriptor descriptor; + descriptor._dataType = type; + descriptor._empty = false; + descriptor._rank = 0; + descriptor._order = 'c'; + descriptor._ews = 1; + + return descriptor; + } + + ShapeDescriptor ShapeDescriptor::vectorDescriptor(const Nd4jLong length, const DataType type) { + ShapeDescriptor descriptor; + descriptor._dataType = type; + descriptor._shape.emplace_back(length); + + if (length > 0) + descriptor._strides.emplace_back(1); + else { + descriptor._strides.emplace_back(0); + descriptor._empty = true; + } + + descriptor._order = 'c'; + descriptor._ews = 1; + descriptor._rank = 1; + + return descriptor; + } +} + +namespace std { + size_t hash::operator()(const sd::ShapeDescriptor &k) const { + auto res = std::hash()(k.arrLength()); + res ^= std::hash()(k.order()) + 0x9e3779b9 + (res << 6) + (res >> 2); + res ^= k.dataType() + 0x9e3779b9 + (res << 6) + (res >> 2); + res ^= std::hash()(k.rank()) + 0x9e3779b9 + (res << 6) + (res >> 2); + res ^= std::hash()(k.ews()) + 0x9e3779b9 + (res << 6) + (res >> 2); + auto shapes = const_cast(k).shape(); + auto strides = const_cast(k).strides(); + for (auto s: shapes) { + res ^= std::hash()(s) + 0x9e3779b9 + (res << 6) + (res >> 2); + } + + for (auto s: strides) { + res ^= std::hash()(s) + 0x9e3779b9 + (res << 6) + (res >> 2); + } + + return res; + } } + diff --git a/libnd4j/include/array/impl/ShapeList.cpp b/libnd4j/include/array/impl/ShapeList.cpp index 1a1e02adb..1a883cc7e 100644 --- a/libnd4j/include/array/impl/ShapeList.cpp +++ b/libnd4j/include/array/impl/ShapeList.cpp @@ -18,10 +18,10 @@ // @author raver119@gmail.com // -#include +#include #include -namespace nd4j { +namespace sd { //ShapeList::ShapeList(bool autoRemovable) { // _autoremovable = autoRemovable; // } diff --git a/libnd4j/include/array/impl/TadDescriptor.cpp b/libnd4j/include/array/impl/TadDescriptor.cpp index b6c8ba69d..e2ec7480e 100644 --- a/libnd4j/include/array/impl/TadDescriptor.cpp +++ b/libnd4j/include/array/impl/TadDescriptor.cpp @@ -22,7 +22,7 @@ #include #include "../TadDescriptor.h" -namespace nd4j { +namespace sd { TadDescriptor::TadDescriptor(const TadDescriptor &other) { _originalShape = other._originalShape; _axis = other._axis; @@ -65,11 +65,30 @@ namespace nd4j { return _axis; } - ShapeDescriptor& TadDescriptor::originalShape() { + ShapeDescriptor& TadDescriptor::originalShape(){ + return _originalShape; + } + + ShapeDescriptor const& TadDescriptor::originalShapeConst() const{ return _originalShape; } bool TadDescriptor::areUnitiesinShape() const { return _unitiesInShape; } +} + +namespace std { + size_t hash::operator()(const sd::TadDescriptor &k) const { + // Compute individual hash values for first, + // second and third and combine them using XOR + // and bit shifting: + auto res = std::hash()((int)k.areUnitiesinShape()); + res ^= std::hash()(k.originalShapeConst()) + 0x9e3779b9 + (res << 6) + (res >> 2); + auto axes = const_cast(k).axis(); + for (auto a: axes) { + res ^= std::hash()(a) + 0x9e3779b9 + (res << 6) + (res >> 2); + } + return res; + } } \ No newline at end of file diff --git a/libnd4j/include/array/impl/TadPack.cpp b/libnd4j/include/array/impl/TadPack.cpp index 6bfc76eb1..1bd5b8f70 100644 --- a/libnd4j/include/array/impl/TadPack.cpp +++ b/libnd4j/include/array/impl/TadPack.cpp @@ -19,10 +19,10 @@ // #include "../TadPack.h" -#include +#include #include -namespace nd4j { +namespace sd { TadPack::TadPack(ConstantDataBuffer &shapes, ConstantDataBuffer &offets, Nd4jLong numTads) { _tadShape = shapes; _tadOffsets = offets; @@ -49,11 +49,11 @@ namespace nd4j { } Nd4jLong* TadPack::platformShapeInfo() const { - return nd4j::Environment::getInstance()->isCPU() ? primaryShapeInfo() : specialShapeInfo(); + return sd::Environment::getInstance()->isCPU() ? primaryShapeInfo() : specialShapeInfo(); } Nd4jLong* TadPack::platformOffsets() const { - return nd4j::Environment::getInstance()->isCPU() ? primaryOffsets() : specialOffsets(); + return sd::Environment::getInstance()->isCPU() ? primaryOffsets() : specialOffsets(); } int TadPack::shapeInfoLength() const { diff --git a/libnd4j/include/cblas.h b/libnd4j/include/cblas.h index 0d484a2e0..18970a9b0 100755 --- a/libnd4j/include/cblas.h +++ b/libnd4j/include/cblas.h @@ -48,7 +48,7 @@ #endif #ifndef CBLAS_H -#include +#include #ifdef __cplusplus extern "C" { diff --git a/libnd4j/include/cblas_enum_conversion.h b/libnd4j/include/cblas_enum_conversion.h index b2a854f41..6ff6fe557 100755 --- a/libnd4j/include/cblas_enum_conversion.h +++ b/libnd4j/include/cblas_enum_conversion.h @@ -30,7 +30,7 @@ enum CBLAS_UPLO {CblasUpper=121, CblasLower=122}; enum CBLAS_DIAG {CblasNonUnit=131, CblasUnit=132}; enum CBLAS_SIDE {CblasLeft=141, CblasRight=142}; */ -#include +#include #ifdef __cplusplus extern "C" { diff --git a/libnd4j/include/cnpy/cnpy.h b/libnd4j/include/cnpy/cnpy.h index 06ff3336d..ea847c3e7 100644 --- a/libnd4j/include/cnpy/cnpy.h +++ b/libnd4j/include/cnpy/cnpy.h @@ -38,7 +38,7 @@ #include #include #include -#include +#include #include #include #include @@ -49,8 +49,8 @@ #include #include #include -#include -#include +#include +#include #include @@ -69,7 +69,7 @@ namespace cnpy { } }; - struct ND4J_EXPORT npz_t : public std::map { + struct ND4J_EXPORT npz_t : public std::unordered_map { void destruct() { npz_t::iterator it = this->begin(); for(; it != this->end(); ++it) (*it).second.destruct(); @@ -238,7 +238,7 @@ namespace cnpy { ND4J_EXPORT npz_t npzLoad(std::string fname); - ND4J_EXPORT nd4j::DataType dataTypeFromHeader(char *data); + ND4J_EXPORT sd::DataType dataTypeFromHeader(char *data); /** * Parse the numpy header from * the given file diff --git a/libnd4j/include/exceptions/allocation_exception.h b/libnd4j/include/exceptions/allocation_exception.h index 458650037..1e9b6653b 100644 --- a/libnd4j/include/exceptions/allocation_exception.h +++ b/libnd4j/include/exceptions/allocation_exception.h @@ -23,8 +23,8 @@ #include #include -#include -#include +#include +#include #if defined(_MSC_VER) @@ -33,7 +33,7 @@ #endif -namespace nd4j { +namespace sd { class ND4J_EXPORT allocation_exception : public std::runtime_error { public: allocation_exception(std::string message); diff --git a/libnd4j/include/exceptions/cuda_exception.h b/libnd4j/include/exceptions/cuda_exception.h index 5150033e8..2dc98eec3 100644 --- a/libnd4j/include/exceptions/cuda_exception.h +++ b/libnd4j/include/exceptions/cuda_exception.h @@ -23,7 +23,7 @@ #include #include -#include +#include #if defined(_MSC_VER) @@ -32,7 +32,7 @@ #endif -namespace nd4j { +namespace sd { class ND4J_EXPORT cuda_exception : public std::runtime_error { public: cuda_exception(std::string message); diff --git a/libnd4j/include/exceptions/datatype_exception.h b/libnd4j/include/exceptions/datatype_exception.h index 171a2b13b..74829d54c 100644 --- a/libnd4j/include/exceptions/datatype_exception.h +++ b/libnd4j/include/exceptions/datatype_exception.h @@ -24,7 +24,7 @@ #include #include #include -#include +#include #if defined(_MSC_VER) @@ -33,16 +33,16 @@ #endif -namespace nd4j { +namespace sd { class ND4J_EXPORT datatype_exception : public std::runtime_error { public: datatype_exception(std::string message); ~datatype_exception() = default; - static datatype_exception build(std::string message, nd4j::DataType actual); - static datatype_exception build(std::string message, nd4j::DataType expected, nd4j::DataType actual); - static datatype_exception build(std::string message, nd4j::DataType expected, nd4j::DataType actualX, nd4j::DataType actualY); + static datatype_exception build(std::string message, sd::DataType actual); + static datatype_exception build(std::string message, sd::DataType expected, sd::DataType actual); + static datatype_exception build(std::string message, sd::DataType expected, sd::DataType actualX, sd::DataType actualY); }; } diff --git a/libnd4j/include/exceptions/graph_exception.h b/libnd4j/include/exceptions/graph_exception.h index 440fa5aa4..7c9345a4d 100644 --- a/libnd4j/include/exceptions/graph_exception.h +++ b/libnd4j/include/exceptions/graph_exception.h @@ -23,8 +23,8 @@ #include #include -#include -#include +#include +#include #if defined(_MSC_VER) @@ -33,7 +33,7 @@ #endif -namespace nd4j { +namespace sd { class ND4J_EXPORT graph_exception : public std::runtime_error { protected: Nd4jLong _graphId; diff --git a/libnd4j/include/exceptions/graph_execution_exception.h b/libnd4j/include/exceptions/graph_execution_exception.h index 92b02e2ee..37f8e636e 100644 --- a/libnd4j/include/exceptions/graph_execution_exception.h +++ b/libnd4j/include/exceptions/graph_execution_exception.h @@ -21,11 +21,11 @@ #ifndef DEV_TESTS_GRAPH_EXECUTION_EXCEPTION_H #define DEV_TESTS_GRAPH_EXECUTION_EXCEPTION_H -#include -#include +#include +#include #include #include -#include +#include #if defined(_MSC_VER) @@ -34,7 +34,7 @@ #endif -namespace nd4j { +namespace sd { class ND4J_EXPORT graph_execution_exception: public graph_exception { public: explicit graph_execution_exception(Nd4jLong graphId); diff --git a/libnd4j/include/exceptions/graph_exists_exception.h b/libnd4j/include/exceptions/graph_exists_exception.h index 985770ad3..63554c31b 100644 --- a/libnd4j/include/exceptions/graph_exists_exception.h +++ b/libnd4j/include/exceptions/graph_exists_exception.h @@ -21,11 +21,11 @@ #ifndef DEV_TESTS_GRAPH_EXISTS_EXCEPTION_H #define DEV_TESTS_GRAPH_EXISTS_EXCEPTION_H -#include -#include +#include +#include #include #include -#include +#include #if defined(_MSC_VER) @@ -34,7 +34,7 @@ #endif -namespace nd4j { +namespace sd { class ND4J_EXPORT graph_exists_exception: public graph_exception { public: explicit graph_exists_exception(Nd4jLong graphId); diff --git a/libnd4j/include/exceptions/impl/allocation_exception.cpp b/libnd4j/include/exceptions/impl/allocation_exception.cpp index 85c3e72aa..46f2ef5c8 100644 --- a/libnd4j/include/exceptions/impl/allocation_exception.cpp +++ b/libnd4j/include/exceptions/impl/allocation_exception.cpp @@ -19,9 +19,9 @@ // #include -#include +#include -namespace nd4j { +namespace sd { allocation_exception::allocation_exception(std::string message) : std::runtime_error(message){ // } diff --git a/libnd4j/include/exceptions/impl/cuda_exception.cpp b/libnd4j/include/exceptions/impl/cuda_exception.cpp index d1bb4d53c..91de6c251 100644 --- a/libnd4j/include/exceptions/impl/cuda_exception.cpp +++ b/libnd4j/include/exceptions/impl/cuda_exception.cpp @@ -19,9 +19,9 @@ // #include -#include +#include -namespace nd4j { +namespace sd { cuda_exception::cuda_exception(std::string message) : std::runtime_error(message){ // } diff --git a/libnd4j/include/exceptions/impl/datatype_exception.cpp b/libnd4j/include/exceptions/impl/datatype_exception.cpp index 0dfe5ab38..9aab37951 100644 --- a/libnd4j/include/exceptions/impl/datatype_exception.cpp +++ b/libnd4j/include/exceptions/impl/datatype_exception.cpp @@ -21,19 +21,19 @@ #include #include -namespace nd4j { +namespace sd { datatype_exception::datatype_exception(std::string message) : std::runtime_error(message){ // } - datatype_exception datatype_exception::build(std::string message, nd4j::DataType expected, nd4j::DataType actual) { + datatype_exception datatype_exception::build(std::string message, sd::DataType expected, sd::DataType actual) { auto exp = DataTypeUtils::asString(expected); auto act = DataTypeUtils::asString(actual); message += "; Expected: [" + exp + "]; Actual: [" + act + "]"; return datatype_exception(message); } - datatype_exception datatype_exception::build(std::string message, nd4j::DataType expected, nd4j::DataType actualX, nd4j::DataType actualY) { + datatype_exception datatype_exception::build(std::string message, sd::DataType expected, sd::DataType actualX, sd::DataType actualY) { auto exp = DataTypeUtils::asString(expected); auto actX = DataTypeUtils::asString(actualX); auto actY = DataTypeUtils::asString(actualY); @@ -41,7 +41,7 @@ namespace nd4j { return datatype_exception(message); } - datatype_exception datatype_exception::build(std::string message, nd4j::DataType actual) { + datatype_exception datatype_exception::build(std::string message, sd::DataType actual) { auto act = DataTypeUtils::asString(actual); message += "; Actual: [" + act + "]"; return datatype_exception(message); diff --git a/libnd4j/include/exceptions/impl/graph_exception.cpp b/libnd4j/include/exceptions/impl/graph_exception.cpp index 48be2cd75..fa2210a1d 100644 --- a/libnd4j/include/exceptions/impl/graph_exception.cpp +++ b/libnd4j/include/exceptions/impl/graph_exception.cpp @@ -21,7 +21,7 @@ #include #include -namespace nd4j { +namespace sd { graph_exception::graph_exception(std::string message, Nd4jLong graphId) : std::runtime_error(message) { this->_message = message; this->_graphId = graphId; diff --git a/libnd4j/include/exceptions/impl/graph_execution_exception.cpp b/libnd4j/include/exceptions/impl/graph_execution_exception.cpp index 6f38695d8..086796517 100644 --- a/libnd4j/include/exceptions/impl/graph_execution_exception.cpp +++ b/libnd4j/include/exceptions/impl/graph_execution_exception.cpp @@ -21,7 +21,7 @@ #include #include -namespace nd4j { +namespace sd { graph_execution_exception::graph_execution_exception(Nd4jLong graphId) : graph_exception(StringUtils::buildGraphErrorMessage("Caught exception during graph execution", graphId), graphId) { _graphId = graphId; } diff --git a/libnd4j/include/exceptions/impl/graph_exists_exception.cpp b/libnd4j/include/exceptions/impl/graph_exists_exception.cpp index 1b3b1a84e..535a74a6a 100644 --- a/libnd4j/include/exceptions/impl/graph_exists_exception.cpp +++ b/libnd4j/include/exceptions/impl/graph_exists_exception.cpp @@ -21,7 +21,7 @@ #include #include -namespace nd4j { +namespace sd { graph_exists_exception::graph_exists_exception(Nd4jLong graphId) : graph_exception(StringUtils::buildGraphErrorMessage("Graph with given ID already exists", graphId), graphId) { _graphId = graphId; } diff --git a/libnd4j/include/exceptions/impl/no_results_exception.cpp b/libnd4j/include/exceptions/impl/no_results_exception.cpp index 7bc18cac7..ce3122ffb 100644 --- a/libnd4j/include/exceptions/impl/no_results_exception.cpp +++ b/libnd4j/include/exceptions/impl/no_results_exception.cpp @@ -21,7 +21,7 @@ #include #include -namespace nd4j { +namespace sd { no_results_exception::no_results_exception(Nd4jLong graphId) : graph_exception(StringUtils::buildGraphErrorMessage("Got no results after graph execution", graphId), graphId) { _graphId = graphId; } diff --git a/libnd4j/include/exceptions/impl/unknown_graph_exception.cpp b/libnd4j/include/exceptions/impl/unknown_graph_exception.cpp index f30e1a1bd..ad73f3d33 100644 --- a/libnd4j/include/exceptions/impl/unknown_graph_exception.cpp +++ b/libnd4j/include/exceptions/impl/unknown_graph_exception.cpp @@ -21,7 +21,7 @@ #include #include -namespace nd4j { +namespace sd { unknown_graph_exception::unknown_graph_exception(Nd4jLong graphId) : graph_exception(StringUtils::buildGraphErrorMessage("Unknown graph", graphId), graphId) { _graphId = graphId; } diff --git a/libnd4j/include/exceptions/no_results_exception.h b/libnd4j/include/exceptions/no_results_exception.h index 0fa1bb167..b2687854b 100644 --- a/libnd4j/include/exceptions/no_results_exception.h +++ b/libnd4j/include/exceptions/no_results_exception.h @@ -21,11 +21,11 @@ #ifndef DEV_TESTS_NO_RESULTS_EXCEPTION_H #define DEV_TESTS_NO_RESULTS_EXCEPTION_H -#include -#include +#include +#include #include #include -#include +#include #if defined(_MSC_VER) @@ -34,7 +34,7 @@ #endif -namespace nd4j { +namespace sd { class ND4J_EXPORT no_results_exception: public graph_exception { public: explicit no_results_exception(Nd4jLong graphId); diff --git a/libnd4j/include/exceptions/unknown_graph_exception.h b/libnd4j/include/exceptions/unknown_graph_exception.h index 83efc9dcf..917aeb757 100644 --- a/libnd4j/include/exceptions/unknown_graph_exception.h +++ b/libnd4j/include/exceptions/unknown_graph_exception.h @@ -21,11 +21,11 @@ #ifndef DEV_TESTS_UNKNOWN_GRAPH_EXCEPTION_H #define DEV_TESTS_UNKNOWN_GRAPH_EXCEPTION_H -#include -#include +#include +#include #include #include -#include +#include #if defined(_MSC_VER) @@ -34,7 +34,7 @@ #endif -namespace nd4j { +namespace sd { class ND4J_EXPORT unknown_graph_exception: public graph_exception { public: explicit unknown_graph_exception(Nd4jLong graphId); diff --git a/libnd4j/include/execution/AffinityManager.h b/libnd4j/include/execution/AffinityManager.h index 463d6942e..757f637ce 100644 --- a/libnd4j/include/execution/AffinityManager.h +++ b/libnd4j/include/execution/AffinityManager.h @@ -21,12 +21,12 @@ #ifndef LIBND4J_AFFINITYMANAGER_H #define LIBND4J_AFFINITYMANAGER_H -#include -#include +#include +#include #include #include -namespace nd4j { +namespace sd { class ND4J_EXPORT AffinityManager { private: static std::atomic _lastDevice; diff --git a/libnd4j/include/execution/CallableInterface.h b/libnd4j/include/execution/CallableInterface.h index 7e5502af1..aad83b379 100644 --- a/libnd4j/include/execution/CallableInterface.h +++ b/libnd4j/include/execution/CallableInterface.h @@ -21,7 +21,7 @@ #ifndef SAMEDIFF_CALLABLEINTERFACE_H #define SAMEDIFF_CALLABLEINTERFACE_H -#include +#include #include #include #include diff --git a/libnd4j/include/execution/CallableWithArguments.h b/libnd4j/include/execution/CallableWithArguments.h index ebf1f0019..28ef8433e 100644 --- a/libnd4j/include/execution/CallableWithArguments.h +++ b/libnd4j/include/execution/CallableWithArguments.h @@ -25,7 +25,7 @@ #include #include #include -#include +#include namespace samediff { class CallableWithArguments { diff --git a/libnd4j/include/execution/ContextBuffers.h b/libnd4j/include/execution/ContextBuffers.h index 67c428d27..c14671e42 100644 --- a/libnd4j/include/execution/ContextBuffers.h +++ b/libnd4j/include/execution/ContextBuffers.h @@ -21,11 +21,11 @@ #ifndef LIBND4J_CONTEXTBUFFERS_H #define LIBND4J_CONTEXTBUFFERS_H -#include -#include +#include +#include #include -namespace nd4j { +namespace sd { class ND4J_EXPORT ContextBuffers { private: void* _reductionPointer = nullptr; diff --git a/libnd4j/include/execution/ErrorReference.h b/libnd4j/include/execution/ErrorReference.h index 2b68d5855..b71090248 100644 --- a/libnd4j/include/execution/ErrorReference.h +++ b/libnd4j/include/execution/ErrorReference.h @@ -22,7 +22,7 @@ #define DEV_TESTS_ERRORREFERENCE_H #include -#include +#include namespace sd { class ND4J_EXPORT ErrorReference { diff --git a/libnd4j/include/execution/Executor.h b/libnd4j/include/execution/Executor.h index 26d5365ad..a9eaa6ad3 100644 --- a/libnd4j/include/execution/Executor.h +++ b/libnd4j/include/execution/Executor.h @@ -21,7 +21,7 @@ #ifndef SD_EXECUTOR_H #define SD_EXECUTOR_H -namespace nd4j { +namespace sd { class Executor { public: static void execute() { diff --git a/libnd4j/include/execution/LaunchContext.h b/libnd4j/include/execution/LaunchContext.h index 689d79369..e2efa1418 100644 --- a/libnd4j/include/execution/LaunchContext.h +++ b/libnd4j/include/execution/LaunchContext.h @@ -35,9 +35,9 @@ #include "config.h" #endif -#include +#include #include -#include +#include #include #include #include @@ -46,7 +46,7 @@ -namespace nd4j { +namespace sd { class ND4J_EXPORT LaunchContext { @@ -68,7 +68,7 @@ class ND4J_EXPORT LaunchContext { bool _isAllocated = false; #endif // CUDA - nd4j::memory::Workspace* _workspace = nullptr; + sd::memory::Workspace* _workspace = nullptr; int _deviceID = 0; public: @@ -100,8 +100,8 @@ class ND4J_EXPORT LaunchContext { LaunchContext(Nd4jPointer cudaStream, Nd4jPointer reductionPointer = nullptr, Nd4jPointer scalarPointer = nullptr, Nd4jPointer allocationPointer = nullptr); LaunchContext(); ~LaunchContext(); - nd4j::memory::Workspace* getWorkspace() const { return _workspace; } - void setWorkspace(nd4j::memory::Workspace* theWorkspace) { + sd::memory::Workspace* getWorkspace() const { return _workspace; } + void setWorkspace(sd::memory::Workspace* theWorkspace) { _workspace = theWorkspace; } diff --git a/libnd4j/include/execution/Threads.h b/libnd4j/include/execution/Threads.h index 3a1fd8951..2ea8295a8 100644 --- a/libnd4j/include/execution/Threads.h +++ b/libnd4j/include/execution/Threads.h @@ -21,10 +21,10 @@ #define SAMEDIFF_THREADS_H #include -#include -#include -#include -#include +#include +#include +#include +#include namespace samediff { class ND4J_EXPORT ThreadsHelper { @@ -107,7 +107,7 @@ namespace samediff { * @param increment * @return */ - static int parallel_for(FUNC_1D function, int64_t start, int64_t stop, int64_t increment = 1, uint32_t numThreads = nd4j::Environment::getInstance()->maxMasterThreads()); + static int parallel_for(FUNC_1D function, int64_t start, int64_t stop, int64_t increment = 1, uint32_t numThreads = sd::Environment::getInstance()->maxMasterThreads()); /** * This function executes 1 dimensional loop for a given number of threads @@ -119,7 +119,7 @@ namespace samediff { * @param numThreads * @return */ - static int parallel_tad(FUNC_1D function, int64_t start, int64_t stop, int64_t increment = 1, uint32_t numThreads = nd4j::Environment::getInstance()->maxMasterThreads()); + static int parallel_tad(FUNC_1D function, int64_t start, int64_t stop, int64_t increment = 1, uint32_t numThreads = sd::Environment::getInstance()->maxMasterThreads()); /** * This method will execute function splitting 2 nested loops space with multiple threads @@ -134,7 +134,7 @@ namespace samediff { * @param inc_y * @return */ - static int parallel_for(FUNC_2D function, int64_t start_x, int64_t stop_x, int64_t inc_x, int64_t start_y, int64_t stop_y, int64_t inc_y, uint64_t numThreads = nd4j::Environment::getInstance()->maxMasterThreads(), bool debug = false); + static int parallel_for(FUNC_2D function, int64_t start_x, int64_t stop_x, int64_t inc_x, int64_t start_y, int64_t stop_y, int64_t inc_y, uint64_t numThreads = sd::Environment::getInstance()->maxMasterThreads(), bool debug = false); /** * This method will execute function splitting 3 nested loops space with multiple threads @@ -152,7 +152,7 @@ namespace samediff { * @param inc_z * @return */ - static int parallel_for(FUNC_3D function, int64_t start_x, int64_t stop_x, int64_t inc_x, int64_t start_y, int64_t stop_y, int64_t inc_y, int64_t start_z, int64_t stop_z, int64_t inc_z, uint64_t numThreads = nd4j::Environment::getInstance()->maxMasterThreads()); + static int parallel_for(FUNC_3D function, int64_t start_x, int64_t stop_x, int64_t inc_x, int64_t start_y, int64_t stop_y, int64_t inc_y, int64_t start_z, int64_t stop_z, int64_t inc_z, uint64_t numThreads = sd::Environment::getInstance()->maxMasterThreads()); /** * @@ -160,18 +160,18 @@ namespace samediff { * @param numThreads * @return */ - static int parallel_do(FUNC_DO function, uint64_t numThreads = nd4j::Environment::getInstance()->maxMasterThreads()); + static int parallel_do(FUNC_DO function, uint64_t numThreads = sd::Environment::getInstance()->maxMasterThreads()); - static int64_t parallel_long(FUNC_RL function, FUNC_AL aggregator, int64_t start, int64_t stop, int64_t increment = 1, uint64_t numThreads = nd4j::Environment::getInstance()->maxMasterThreads()); + static int64_t parallel_long(FUNC_RL function, FUNC_AL aggregator, int64_t start, int64_t stop, int64_t increment = 1, uint64_t numThreads = sd::Environment::getInstance()->maxMasterThreads()); - static double parallel_double(FUNC_RD function, FUNC_AD aggregator, int64_t start, int64_t stop, int64_t increment = 1, uint64_t numThreads = nd4j::Environment::getInstance()->maxMasterThreads()); + static double parallel_double(FUNC_RD function, FUNC_AD aggregator, int64_t start, int64_t stop, int64_t increment = 1, uint64_t numThreads = sd::Environment::getInstance()->maxMasterThreads()); /** * This method will execute function in parallel preserving the parts to be aligned increment size * PLEASE NOTE: this function can use smaller number of threads than requested. * */ - static int parallel_aligned_increment(FUNC_1D function, int64_t start, int64_t stop, int64_t increment, size_t type_size = sizeof(float), uint32_t req_numThreads = nd4j::Environment::getInstance()->maxMasterThreads()); + static int parallel_aligned_increment(FUNC_1D function, int64_t start, int64_t stop, int64_t increment, size_t type_size = sizeof(float), uint32_t req_numThreads = sd::Environment::getInstance()->maxMasterThreads()); }; } diff --git a/libnd4j/include/execution/cpu/AffinityManager.cpp b/libnd4j/include/execution/cpu/AffinityManager.cpp index 7927982a6..32df63d0d 100644 --- a/libnd4j/include/execution/cpu/AffinityManager.cpp +++ b/libnd4j/include/execution/cpu/AffinityManager.cpp @@ -20,7 +20,7 @@ #include -namespace nd4j { +namespace sd { int AffinityManager::currentDeviceId() { return 0; } diff --git a/libnd4j/include/execution/cpu/ContextBuffers.cpp b/libnd4j/include/execution/cpu/ContextBuffers.cpp index 0038990c2..3b1c566a8 100644 --- a/libnd4j/include/execution/cpu/ContextBuffers.cpp +++ b/libnd4j/include/execution/cpu/ContextBuffers.cpp @@ -20,7 +20,7 @@ #include #include -namespace nd4j { +namespace sd { ContextBuffers::ContextBuffers() { _deviceId = AffinityManager::currentDeviceId(); } diff --git a/libnd4j/include/execution/cpu/LaunchContext.cpp b/libnd4j/include/execution/cpu/LaunchContext.cpp index 1c34f25d9..6217e0707 100644 --- a/libnd4j/include/execution/cpu/LaunchContext.cpp +++ b/libnd4j/include/execution/cpu/LaunchContext.cpp @@ -19,21 +19,21 @@ // #include -#include +#include #include #include -#if defined(IOS_BUILD) || defined(APPLE_BUILD) || defined(ANDROID_BUILD) -nd4j::ContextBuffers contextBuffers = nd4j::ContextBuffers(); +#if defined(SD_IOS_BUILD) || defined(SD_APPLE_BUILD) || defined(SD_ANDROID_BUILD) +sd::ContextBuffers contextBuffers = sd::ContextBuffers(); #else -thread_local nd4j::ContextBuffers contextBuffers = nd4j::ContextBuffers(); +thread_local sd::ContextBuffers contextBuffers = sd::ContextBuffers(); #endif #ifdef HAVE_MKLDNN #include #endif -namespace nd4j { +namespace sd { LaunchContext::~LaunchContext() { #ifdef HAVE_MKLDNN diff --git a/libnd4j/include/execution/cuda/AffinityManager.cu b/libnd4j/include/execution/cuda/AffinityManager.cu index 2743fb710..cdfe7c107 100644 --- a/libnd4j/include/execution/cuda/AffinityManager.cu +++ b/libnd4j/include/execution/cuda/AffinityManager.cu @@ -18,14 +18,14 @@ // @author raver119@gmail.com // -#include +#include #include #include -#include +#include thread_local int globalThreadToDevice = -1; -namespace nd4j { +namespace sd { std::mutex AffinityManager::_currentMutex; std::mutex AffinityManager::_numberMutex; int AffinityManager::_numberOfDevices = -1; diff --git a/libnd4j/include/execution/cuda/ContextBuffers.cu b/libnd4j/include/execution/cuda/ContextBuffers.cu index e018cf807..9411a27d5 100644 --- a/libnd4j/include/execution/cuda/ContextBuffers.cu +++ b/libnd4j/include/execution/cuda/ContextBuffers.cu @@ -20,15 +20,15 @@ #include #include -#include -#include +#include +#include #include #include #include #include -namespace nd4j { +namespace sd { ContextBuffers::ContextBuffers() { //nd4j_printf("Creating ContextBuffers for device [%i]\n", AffinityManager::currentDeviceId()); _deviceId = AffinityManager::currentDeviceId(); diff --git a/libnd4j/include/execution/cuda/LaunchContext.cu b/libnd4j/include/execution/cuda/LaunchContext.cu index 3145ca8d3..28193c3b0 100644 --- a/libnd4j/include/execution/cuda/LaunchContext.cu +++ b/libnd4j/include/execution/cuda/LaunchContext.cu @@ -19,15 +19,15 @@ // #include -#include +#include #include #include #include #include -thread_local nd4j::ContextBuffers contextBuffers = nd4j::ContextBuffers(); +thread_local sd::ContextBuffers contextBuffers = sd::ContextBuffers(); -namespace nd4j { +namespace sd { std::vector> LaunchContext::_contexts = std::vector>(); std::mutex LaunchContext::_mutex; diff --git a/libnd4j/include/execution/impl/BlockingQueue.cpp b/libnd4j/include/execution/impl/BlockingQueue.cpp index ff483fd28..21c3b4c6a 100644 --- a/libnd4j/include/execution/impl/BlockingQueue.cpp +++ b/libnd4j/include/execution/impl/BlockingQueue.cpp @@ -19,7 +19,7 @@ // #include -#include +#include #include namespace samediff { diff --git a/libnd4j/include/execution/impl/ThreadPool.cpp b/libnd4j/include/execution/impl/ThreadPool.cpp index 5d9e2d5eb..b02c4c4d5 100644 --- a/libnd4j/include/execution/impl/ThreadPool.cpp +++ b/libnd4j/include/execution/impl/ThreadPool.cpp @@ -78,7 +78,7 @@ namespace samediff { ThreadPool::ThreadPool() { // TODO: number of threads must reflect number of cores for UMA system. In case of NUMA it should be per-device pool // FIXME: on mobile phones this feature must NOT be used - _available = nd4j::Environment::getInstance()->maxThreads(); + _available = sd::Environment::getInstance()->maxThreads(); _queues.resize(_available.load()); _threads.resize(_available.load()); diff --git a/libnd4j/include/execution/impl/Threads.cpp b/libnd4j/include/execution/impl/Threads.cpp index 94710731e..2d0ae1144 100644 --- a/libnd4j/include/execution/impl/Threads.cpp +++ b/libnd4j/include/execution/impl/Threads.cpp @@ -22,18 +22,18 @@ #include #include #include -#include -#include +#include +#include namespace samediff { int ThreadsHelper::numberOfThreads(int maxThreads, uint64_t numberOfElements) { // let's see how many threads we actually need first - auto optimalThreads = nd4j::math::nd4j_max(1, numberOfElements / 1024); + auto optimalThreads = sd::math::nd4j_max(1, numberOfElements / 1024); // now return the smallest value - return nd4j::math::nd4j_min(optimalThreads, maxThreads); + return sd::math::nd4j_min(optimalThreads, maxThreads); } Span3::Span3(int64_t startX, int64_t stopX, int64_t incX, int64_t startY, int64_t stopY, int64_t incY, int64_t startZ, int64_t stopZ, int64_t incZ) { @@ -264,7 +264,7 @@ namespace samediff { int ThreadsHelper::numberOfThreads2d(int maxThreads, uint64_t iters_x, uint64_t iters_y) { // in some cases there's nothing to think about, part 1 if (iters_x < maxThreads && iters_y < maxThreads) - return nd4j::math::nd4j_max(iters_x, iters_y); + return sd::math::nd4j_max(iters_x, iters_y); auto remX = iters_x % maxThreads; auto remY = iters_y % maxThreads; diff --git a/libnd4j/include/execution/impl/Ticket.cpp b/libnd4j/include/execution/impl/Ticket.cpp index 5bf911fd0..98cb05376 100644 --- a/libnd4j/include/execution/impl/Ticket.cpp +++ b/libnd4j/include/execution/impl/Ticket.cpp @@ -31,7 +31,7 @@ namespace samediff { Ticket::Ticket() { _acquired = true; - _interfaces.resize(nd4j::Environment::getInstance()->maxThreads()); + _interfaces.resize(sd::Environment::getInstance()->maxThreads()); } bool Ticket::acquired() { diff --git a/libnd4j/include/graph/ArgumentsList.h b/libnd4j/include/graph/ArgumentsList.h index eb1800444..75bdf857a 100644 --- a/libnd4j/include/graph/ArgumentsList.h +++ b/libnd4j/include/graph/ArgumentsList.h @@ -21,13 +21,13 @@ #ifndef LIBND4J_INPUTLIST_H #define LIBND4J_INPUTLIST_H -#include -#include -#include +#include +#include +#include #include #include -namespace nd4j { +namespace sd { namespace graph { class ND4J_EXPORT ArgumentsList { protected: diff --git a/libnd4j/include/graph/Context.h b/libnd4j/include/graph/Context.h index d1e8a4dad..96d7e8b12 100644 --- a/libnd4j/include/graph/Context.h +++ b/libnd4j/include/graph/Context.h @@ -23,7 +23,7 @@ #define LIBND4J_CONTEXT_H #include -#include +#include #include #include #include @@ -39,26 +39,26 @@ #include #endif -namespace nd4j { +namespace sd { namespace graph { /** * This class defines input desired for any given node/operation within graph */ - class ND4J_EXPORT Context : public nd4j::graph::ContextPrototype { + class ND4J_EXPORT Context : public sd::graph::ContextPrototype { protected: - nd4j::memory::Workspace* _workspace = nullptr; - nd4j::graph::VariableSpace* _variableSpace = nullptr; + sd::memory::Workspace* _workspace = nullptr; + sd::graph::VariableSpace* _variableSpace = nullptr; std::pair _executionTime; - nd4j::random::RandomBuffer* _rng = nullptr; + sd::random::RandomBuffer* _rng = nullptr; - nd4j::DataType _dataType = nd4j::DataType::FLOAT32; + sd::DataType _dataType = sd::DataType::FLOAT32; // branch for divergent_op int _branch = 0; // temporary context for standalone ops execution LaunchContext* _context = nullptr; - std::vector _dataTypes; + std::vector _dataTypes; // fields for fast execution (out-of-graph ops use) std::vector _fastpath_in; @@ -69,6 +69,9 @@ namespace nd4j { // in some cases we might be able to skip shape function for validation purposes bool _shapeFunctionOverride = false; + + // special flag used during conversion from Graph exec to FastPath exec + bool _forbidFastPath = false; public: Context(ContextPrototype* prototype, VariableSpace* variableSpace); @@ -84,30 +87,30 @@ namespace nd4j { Nd4jLong getOuterTime(); Nd4jLong getInnerTime(); - nd4j::DataType dataType() override; + sd::DataType dataType() override; - nd4j::DataType dataType(int index) override; - void setDataType(int index, nd4j::DataType type) override; + sd::DataType dataType(int index) override; + void setDataType(int index, sd::DataType type) override; // these methods are related to Workspace abstraction bool hasWorkspaceProvided(); - void attachWorkspace(nd4j::memory::Workspace* workspace); + void attachWorkspace(sd::memory::Workspace* workspace); void forgetWorkspace(); // these methods return full-time workspace - nd4j::memory::Workspace* getWorkspace(); - nd4j::memory::Workspace* workspace(); - nd4j::memory::Workspace* fWorkspace(); + sd::memory::Workspace* getWorkspace(); + sd::memory::Workspace* workspace(); + sd::memory::Workspace* fWorkspace(); // this method returns workspace for temporary allocations - nd4j::memory::Workspace* tWorkspace(); + sd::memory::Workspace* tWorkspace(); // this method returns workspace for object allocations - nd4j::memory::Workspace* oWorkspace(); + sd::memory::Workspace* oWorkspace(); void setVariableSpace(VariableSpace* variableSpace); - nd4j::random::RandomBuffer* getRNG(); - void setRNG(nd4j::random::RandomBuffer* rng); + sd::random::RandomBuffer* getRNG(); + void setRNG(sd::random::RandomBuffer* rng); void setTargetEngine(samediff::Engine engine); @@ -176,11 +179,17 @@ namespace nd4j { // methods used in java interop /** - * This method checks, if Context uses fastpath variable access + * This method checks if Context uses fastpath variable access * @return */ bool isFastPath(); + /** + * Method allows to forbid FastPath execution + * @param reallyForbid + */ + void forbidFastPath(bool reallyForbid); + #ifndef __JAVACPP_HACK__ std::vector& fastpath_in(); std::vector& fastpath_out(); @@ -197,12 +206,12 @@ namespace nd4j { void setTArguments(double *arguments, int numberOfArguments); void setIArguments(Nd4jLong *arguments, int numberOfArguments); void setBArguments(bool *arguments, int numberOfArguments); - void setDArguments(nd4j::DataType *arguments, int numberOfArguments); + void setDArguments(sd::DataType *arguments, int numberOfArguments); void setTArguments(const std::vector &tArgs); void setIArguments(const std::vector &tArgs); void setBArguments(const std::vector &tArgs); - void setDArguments(const std::vector &dArgs); + void setDArguments(const std::vector &dArgs); /** * This method purges fastpath in/out contents and releases all the handles. diff --git a/libnd4j/include/graph/ContextPrototype.h b/libnd4j/include/graph/ContextPrototype.h index fac664598..57d773dbb 100644 --- a/libnd4j/include/graph/ContextPrototype.h +++ b/libnd4j/include/graph/ContextPrototype.h @@ -23,10 +23,10 @@ #define ND4J_CONTEXT_PROTOTYPE_H #include -#include +#include #include -#include -#include +#include +#include #include #include #include @@ -35,7 +35,7 @@ #include #endif -namespace nd4j { +namespace sd { namespace graph { class ND4J_EXPORT ContextPrototype { @@ -47,10 +47,10 @@ namespace nd4j { std::vector _iArgs; std::vector _bArgs; std::vector _axis; - std::vector _dArgs; + std::vector _dArgs; // TODO: remove this field - nd4j::DataType _dataType = nd4j::DataType::FLOAT32; + sd::DataType _dataType = sd::DataType::FLOAT32; bool _isInplace; // opNum for legacy XYZ ops @@ -58,17 +58,17 @@ namespace nd4j { uint64_t _rootSeed; RandomGenerator _randomGenerator; - std::vector _dataTypes; + std::vector _dataTypes; - nd4j::ops::OpDescriptor* _opDescriptor; - bool _useMKLDNN = nd4j::Environment::getInstance()->isUseMKLDNN(); + sd::ops::OpDescriptor* _opDescriptor; + bool _useMKLDNN = sd::Environment::getInstance()->isUseMKLDNN(); // target engine for execution samediff::Engine _engine = DEFAULT_ENGINE; samediff::ExecutionMode _execMode = samediff::ExecutionMode::MODE_UNDEFINED; public: - explicit ContextPrototype(nd4j::ops::OpDescriptor* opDescriptor = nullptr, int nodeId = 1, bool inPlace = false); + explicit ContextPrototype(sd::ops::OpDescriptor* opDescriptor = nullptr, int nodeId = 1, bool inPlace = false); ~ContextPrototype() = default; int getNodeId(); @@ -77,11 +77,11 @@ namespace nd4j { // this method returns true, if inputs are defined bool hasVariablesFilled(); - void setOpDescriptor(nd4j::ops::OpDescriptor* opDescriptor); + void setOpDescriptor(sd::ops::OpDescriptor* opDescriptor); - virtual nd4j::DataType dataType(); - virtual nd4j::DataType dataType(int index); - virtual void setDataType(int index, nd4j::DataType type); + virtual sd::DataType dataType(); + virtual sd::DataType dataType(int index); + virtual void setDataType(int index, sd::DataType type); bool isInplace(); void markInplace(bool reallyInplace); @@ -96,7 +96,7 @@ namespace nd4j { std::vector* getTArguments(); std::vector* getIArguments(); std::vector* getBArguments(); - std::vector* getDArguments(); + std::vector* getDArguments(); std::vector* getAxis(); samediff::Engine engine(); diff --git a/libnd4j/include/graph/ExecutionResult.h b/libnd4j/include/graph/ExecutionResult.h index b1a1b1737..b1f16032c 100644 --- a/libnd4j/include/graph/ExecutionResult.h +++ b/libnd4j/include/graph/ExecutionResult.h @@ -24,17 +24,18 @@ #include #include #include +#include #include #include #include -namespace nd4j { +namespace sd { namespace graph { class ExecutionResult { private: std::vector _variables; - std::map _stringIdMap; - std::map, Variable *> _pairIdMap; + MAP_IMPL _stringIdMap; + MAP_IMPL, Variable *> _pairIdMap; // this flag is used to optionally release variables bool _releasable = false; diff --git a/libnd4j/include/graph/ExecutorConfiguration.h b/libnd4j/include/graph/ExecutorConfiguration.h index be96c057d..40f299f02 100644 --- a/libnd4j/include/graph/ExecutorConfiguration.h +++ b/libnd4j/include/graph/ExecutorConfiguration.h @@ -22,22 +22,22 @@ #define LIBND4J_EXECUTORCONFIGURATION_H #include -#include -#include +#include +#include -namespace nd4j { +namespace sd { namespace graph { class ND4J_EXPORT ExecutorConfiguration { public: - nd4j::graph::ProfilingMode _profilingMode; - nd4j::graph::ExecutionMode _executionMode; - nd4j::graph::OutputMode _outputMode; + sd::graph::ProfilingMode _profilingMode; + sd::graph::ExecutionMode _executionMode; + sd::graph::OutputMode _outputMode; bool _timestats; Nd4jLong _footprintForward = 0L; Nd4jLong _footprintBackward = 0L; Direction _direction = Direction_FORWARD_ONLY; - explicit ExecutorConfiguration(const nd4j::graph::FlatConfiguration *conf = nullptr); + explicit ExecutorConfiguration(const sd::graph::FlatConfiguration *conf = nullptr); ~ExecutorConfiguration() = default; ExecutorConfiguration* clone(); diff --git a/libnd4j/include/graph/FlatUtils.h b/libnd4j/include/graph/FlatUtils.h index 939db1fb7..1b2a02dca 100644 --- a/libnd4j/include/graph/FlatUtils.h +++ b/libnd4j/include/graph/FlatUtils.h @@ -22,12 +22,12 @@ #define LIBND4J_FLATUTILS_H #include -#include +#include #include #include -#include +#include -namespace nd4j { +namespace sd { namespace graph { class ND4J_EXPORT FlatUtils { public: @@ -35,7 +35,7 @@ namespace nd4j { static std::pair fromLongPair(LongPair* pair); - static NDArray* fromFlatArray(const nd4j::graph::FlatArray* flatArray); + static NDArray* fromFlatArray(const sd::graph::FlatArray* flatArray); static flatbuffers::Offset toFlatArray(flatbuffers::FlatBufferBuilder &builder, NDArray &array); }; diff --git a/libnd4j/include/graph/FlowPath.h b/libnd4j/include/graph/FlowPath.h index fae19fd0b..597520249 100644 --- a/libnd4j/include/graph/FlowPath.h +++ b/libnd4j/include/graph/FlowPath.h @@ -21,19 +21,21 @@ #ifndef LIBND4J_FLOWPATH_H #define LIBND4J_FLOWPATH_H +#include +#include #include -#include +#include #include #include #include -#include +#include -namespace nd4j { +namespace sd { namespace graph { class ND4J_EXPORT FlowPath { private: - std::map _states; - std::map _frames; + MAP_IMPL _states; + MAP_IMPL _frames; void ensureNode(int nodeId); void ensureFrame(int nodeId); diff --git a/libnd4j/include/graph/FrameState.h b/libnd4j/include/graph/FrameState.h index ea3011a6d..1c0edbc0b 100644 --- a/libnd4j/include/graph/FrameState.h +++ b/libnd4j/include/graph/FrameState.h @@ -22,10 +22,10 @@ #define LIBND4J_FRAMESTATE_H #include -#include -#include +#include +#include -namespace nd4j { +namespace sd { namespace graph { class ND4J_EXPORT FrameState { private: diff --git a/libnd4j/include/graph/Graph.h b/libnd4j/include/graph/Graph.h index 00efb3c52..a160872fd 100644 --- a/libnd4j/include/graph/Graph.h +++ b/libnd4j/include/graph/Graph.h @@ -24,6 +24,7 @@ #include #include #include +#include //#include #include #include @@ -36,7 +37,7 @@ #include #include -namespace nd4j { +namespace sd { namespace graph { class ND4J_EXPORT Graph { @@ -50,10 +51,10 @@ namespace nd4j { // vector holds ID's of top nodes only std::vector *_nodes; - std::map *_mapped; + MAP_IMPL *_mapped; - std::map *> *_onion; - std::map _unmapped; + MAP_IMPL *> *_onion; + MAP_IMPL _unmapped; std::vector _unmappedMap; // macOS? std::mutex _mutexPreprocessing; @@ -63,15 +64,15 @@ namespace nd4j { std::vector _autos; - std::map _mappedScopes; + MAP_IMPL _mappedScopes; std::vector _scopes; //////////////////////////////////////// - Nd4jStatus validateNode(nd4j::graph::Node *node); + Nd4jStatus validateNode(sd::graph::Node *node); void expandOnion(int newLayer); - void injectNode(nd4j::graph::Node *node); + void injectNode(sd::graph::Node *node); void pushToOutputOnce(int id); @@ -104,39 +105,39 @@ namespace nd4j { int numberOfPlaceholders(); - std::vector* getPlaceholders(); + std::vector* getPlaceholders(); /** * This method returns pointer to thread_local VariableSpace * @return */ - nd4j::graph::VariableSpace *getVariableSpace(); + sd::graph::VariableSpace *getVariableSpace(); /** * This method adds given node to the graph * * @param node */ - void addNode(nd4j::graph::Node *node); + void addNode(sd::graph::Node *node); /** * This method returns layered representation of the graph * * @return */ - std::map *> *getOnion(); + MAP_IMPL *> *getOnion(); /** * This method returns map of all nodes of the graph * @return */ - std::map *getMapped(); + MAP_IMPL* getMapped(); /** * This method returns outputs of this graph * @return */ - std::vector *fetchOutputs(); + std::vector *fetchOutputs(); /** * This method returns pointer to ExecutorConfiguration @@ -155,7 +156,7 @@ namespace nd4j { * This method returns all nodes at once (order is NOT guaranteed) * @return */ - std::vector *getAllNodes(); + std::vector *getAllNodes(); /** * This method prints out Graph op-by-op, and respective inputs @@ -165,7 +166,7 @@ namespace nd4j { /** * This method collect all ops from the graph into ops vector */ - std::vector getOperations(); + std::vector getOperations(); /** * This method returns Scope ptr specified with id @@ -233,7 +234,7 @@ namespace nd4j { return &_output; } - FORCEINLINE std::map* scopes() { + FORCEINLINE MAP_IMPL* scopes() { return &_mappedScopes; } diff --git a/libnd4j/blas/GraphExecutioner.h b/libnd4j/include/graph/GraphExecutioner.h similarity index 89% rename from libnd4j/blas/GraphExecutioner.h rename to libnd4j/include/graph/GraphExecutioner.h index 6ea459b2f..148b27951 100644 --- a/libnd4j/blas/GraphExecutioner.h +++ b/libnd4j/include/graph/GraphExecutioner.h @@ -31,13 +31,13 @@ #include #include #include -#include +#include #define TF_INPUT "Placeholder" #define TF_CONST "Const" #define TF_VAR "VariableV2" -namespace nd4j { +namespace sd { namespace graph { class ND4J_EXPORT GraphExecutioner { @@ -45,7 +45,7 @@ namespace nd4j { public: - //static Nd4jStatus executeFlatNode(nd4j::graph::Graph *graph, nd4j::graph::Node *node, nd4j::graph::VariableSpace *variableSpace); + //static Nd4jStatus executeFlatNode(sd::graph::Graph *graph, sd::graph::Node *node, sd::graph::VariableSpace *variableSpace); static Nd4jStatus executeFlatNode(Graph *graph, Node *node, VariableSpace *variableSpace); @@ -62,7 +62,7 @@ namespace nd4j { * @param pointer Pointer to FlatBuffer * @return pointer to FlatBuffer with result */ - static nd4j::graph::ResultWrapper* executeFlatBuffer(Nd4jPointer pointer); + static sd::graph::ResultWrapper* executeFlatBuffer(Nd4jPointer pointer); static flatbuffers::Offset execute(Graph *graph, flatbuffers::FlatBufferBuilder &builder, const FlatInferenceRequest* request); diff --git a/libnd4j/include/graph/GraphHolder.h b/libnd4j/include/graph/GraphHolder.h index f740ad4ca..07e091f42 100644 --- a/libnd4j/include/graph/GraphHolder.h +++ b/libnd4j/include/graph/GraphHolder.h @@ -19,20 +19,21 @@ // #include -#include +#include #include +#include #include #include #include -namespace nd4j { +namespace sd { namespace graph { class ND4J_EXPORT GraphHolder { private: static GraphHolder *_INSTANCE; - std::map _graphF; + MAP_IMPL _graphF; - std::map _locks; + MAP_IMPL _locks; GraphHolder() = default; ~GraphHolder() = default; diff --git a/libnd4j/include/graph/GraphState.h b/libnd4j/include/graph/GraphState.h index 52c6f9e16..89343997f 100644 --- a/libnd4j/include/graph/GraphState.h +++ b/libnd4j/include/graph/GraphState.h @@ -21,20 +21,21 @@ #ifndef LIBND4J_GRAPHSTATE_H #define LIBND4J_GRAPHSTATE_H -#include -#include -#include +#include +#include +#include #include #include +#include #include -#include +#include #include #include #include #include #include -namespace nd4j { +namespace sd { namespace graph { class ND4J_EXPORT GraphState { @@ -43,7 +44,7 @@ namespace graph { Nd4jLong _id = 0; // map of scopes. Scope id is used as key, since it's referred in calls later anyway - std::map _scopes; + MAP_IMPL _scopes; // this variable space holds temp references VariableSpace _variableSpace; @@ -93,7 +94,7 @@ namespace graph { * @param op * @return */ - Nd4jStatus attachOpToScope(int scopeId, int nodeId, nd4j::ops::DeclarableOp *op, ArgumentsList inputs); + Nd4jStatus attachOpToScope(int scopeId, int nodeId, sd::ops::DeclarableOp *op, ArgumentsList inputs); /** * This method returns pointer to the scope with given id diff --git a/libnd4j/include/graph/GraphUtils.h b/libnd4j/include/graph/GraphUtils.h index 6938bbea8..3aaf820ae 100644 --- a/libnd4j/include/graph/GraphUtils.h +++ b/libnd4j/include/graph/GraphUtils.h @@ -25,12 +25,12 @@ #include #include -namespace nd4j { +namespace sd { namespace graph { class ND4J_EXPORT GraphUtils { public: - typedef std::vector OpList; + typedef std::vector OpList; public: static bool filterOperations(OpList& ops); diff --git a/libnd4j/include/graph/InferenceRequest.h b/libnd4j/include/graph/InferenceRequest.h index 9449d7bc1..b445fa0e1 100644 --- a/libnd4j/include/graph/InferenceRequest.h +++ b/libnd4j/include/graph/InferenceRequest.h @@ -20,13 +20,13 @@ #ifndef DEV_TESTS_INFERENCEREQUEST_H #define DEV_TESTS_INFERENCEREQUEST_H -#include -#include -#include +#include +#include +#include #include #include "ExecutorConfiguration.h" -namespace nd4j { +namespace sd { namespace graph { class ND4J_EXPORT InferenceRequest { private: diff --git a/libnd4j/include/graph/Intervals.h b/libnd4j/include/graph/Intervals.h index 74cc2cf3c..3a7964076 100644 --- a/libnd4j/include/graph/Intervals.h +++ b/libnd4j/include/graph/Intervals.h @@ -21,12 +21,12 @@ #ifndef LIBND4J_INTERVALS_H #define LIBND4J_INTERVALS_H -#include +#include #include #include -#include +#include -namespace nd4j { +namespace sd { class ND4J_EXPORT Intervals { diff --git a/libnd4j/include/graph/Node.h b/libnd4j/include/graph/Node.h index f07bfac18..5fde65f3c 100644 --- a/libnd4j/include/graph/Node.h +++ b/libnd4j/include/graph/Node.h @@ -22,15 +22,15 @@ #define LIBND4J_GNODE_H #include -#include +#include #include -#include +#include #include "Context.h" #include #include -namespace nd4j { +namespace sd { namespace graph { @@ -39,7 +39,7 @@ namespace nd4j { class ND4J_EXPORT Node { protected: // TODO: this field must be removed - nd4j::DataType _dataType; + sd::DataType _dataType; OpType _opType; ContextPrototype* _protoContext = nullptr; @@ -80,8 +80,8 @@ namespace nd4j { OpClass _opClass; // these fields are used to store embedded CustomOps and Graph in case of Graph-in-Graph scenario - nd4j::graph::Graph * _graph= nullptr; - nd4j::ops::DeclarableOp *_customOp = nullptr; + sd::graph::Graph * _graph= nullptr; + sd::ops::DeclarableOp *_customOp = nullptr; // each node can be active or inactive, if used with divergents, like IF statements bool _active = true; @@ -96,14 +96,14 @@ namespace nd4j { Nd4jLong _frameId = -1; public: - explicit Node(nd4j::ops::DeclarableOp *customOp, int id = 0, std::initializer_list input = {}, std::initializer_list output = {}, std::initializer_list dimensions = {}, float scalar = 0.0f, std::initializer_list tArgs = {}, std::initializer_list iArgs = {}); + explicit Node(sd::ops::DeclarableOp *customOp, int id = 0, std::initializer_list input = {}, std::initializer_list output = {}, std::initializer_list dimensions = {}, float scalar = 0.0f, std::initializer_list tArgs = {}, std::initializer_list iArgs = {}); explicit Node(OpType opType = OpType_TRANSFORM_SAME, int opNum = 0, int id = 0, std::initializer_list input = {}, std::initializer_list output = {}, std::initializer_list dimensions = {}, float scalar = 0.0f, std::initializer_list tArgs = {}, std::initializer_list iArgs = {}); - explicit Node(const nd4j::graph::FlatNode *node); + explicit Node(const sd::graph::FlatNode *node); ~Node(); bool equals(Node *other); - nd4j::DataType dataType(); + sd::DataType dataType(); ContextPrototype *protoContext(); OpType opType(); Nd4jLong opNum(); @@ -168,12 +168,12 @@ namespace nd4j { ContextPrototype* getContextPrototype(); bool hasBlockAttached(); - void setCustomOp(nd4j::ops::DeclarableOp *customOp = nullptr); - nd4j::ops::DeclarableOp* getCustomOp(); + void setCustomOp(sd::ops::DeclarableOp *customOp = nullptr); + sd::ops::DeclarableOp* getCustomOp(); bool hasCustomOp(); - void setGraph(nd4j::graph::Graph* graph = nullptr); - nd4j::graph::Graph* getGraph(); + void setGraph(sd::graph::Graph* graph = nullptr); + sd::graph::Graph* getGraph(); bool hasGraphEmbedded(); bool isInplace(); @@ -234,7 +234,7 @@ namespace nd4j { } - static nd4j::ops::DeclarableOp* buildOpByType(OpType opType, int numInputs, int numIArgs, int numTArgs, int opNum, NDArray *scalar); + static sd::ops::DeclarableOp* buildOpByType(OpType opType, int numInputs, int numIArgs, int numTArgs, int opNum, NDArray *scalar); static void deleteOpByType(OpType opType, void *op); }; } diff --git a/libnd4j/include/graph/NodeState.h b/libnd4j/include/graph/NodeState.h index ece1e317e..5e0a7a6d2 100644 --- a/libnd4j/include/graph/NodeState.h +++ b/libnd4j/include/graph/NodeState.h @@ -21,10 +21,10 @@ #ifndef LIBND4J_NODESTATE_H #define LIBND4J_NODESTATE_H -#include -#include +#include +#include -namespace nd4j { +namespace sd { namespace graph { class ND4J_EXPORT NodeState { private: diff --git a/libnd4j/include/graph/RandomGenerator.h b/libnd4j/include/graph/RandomGenerator.h index de475b8f8..ef06c345d 100644 --- a/libnd4j/include/graph/RandomGenerator.h +++ b/libnd4j/include/graph/RandomGenerator.h @@ -22,9 +22,9 @@ #define LIBND4J_GRAPH_RNG_H #include -#include -#include -#include +#include +#include +#include #include #include #include @@ -35,7 +35,7 @@ #include #endif -namespace nd4j { +namespace sd { namespace graph { #ifdef __CUDACC__ class ND4J_EXPORT CudaManagedRandomGenerator { diff --git a/libnd4j/include/graph/RandomGenerator.hpp b/libnd4j/include/graph/RandomGenerator.hpp index 9efeaefdc..fbbc8bad1 100644 --- a/libnd4j/include/graph/RandomGenerator.hpp +++ b/libnd4j/include/graph/RandomGenerator.hpp @@ -19,14 +19,14 @@ // // relies on xoroshiro64** and xoroshiro128 implementations -#include -#include +#include +#include #include #include #include #include -namespace nd4j { +namespace sd { namespace graph { diff --git a/libnd4j/include/graph/ResultWrapper.h b/libnd4j/include/graph/ResultWrapper.h index a19a6bd5b..fe5193097 100644 --- a/libnd4j/include/graph/ResultWrapper.h +++ b/libnd4j/include/graph/ResultWrapper.h @@ -21,11 +21,11 @@ #ifndef LIBND4J_RESULTWRAPPER_H #define LIBND4J_RESULTWRAPPER_H -#include -#include -#include +#include +#include +#include -namespace nd4j { +namespace sd { namespace graph { class ND4J_EXPORT ResultWrapper { private: diff --git a/libnd4j/include/graph/Scope.h b/libnd4j/include/graph/Scope.h index 5cbbf8bc0..42b99c18e 100644 --- a/libnd4j/include/graph/Scope.h +++ b/libnd4j/include/graph/Scope.h @@ -25,7 +25,7 @@ #include #include -namespace nd4j { +namespace sd { namespace graph { /** diff --git a/libnd4j/include/graph/SessionLocalStorage.h b/libnd4j/include/graph/SessionLocalStorage.h index dd8051fc2..3cb77ec3a 100644 --- a/libnd4j/include/graph/SessionLocalStorage.h +++ b/libnd4j/include/graph/SessionLocalStorage.h @@ -22,18 +22,20 @@ #define LIBND4J_SESSIONLOCALSTORAGE_H #include +#include +#include #include "VariableSpace.h" #include "Context.h" #include "Stash.h" #include -namespace nd4j{ +namespace sd{ namespace graph { class ND4J_EXPORT SessionLocalStorage { protected: std::atomic _sessionCounter; - std::map _threadSession; - std::map _threadVariableSpace; + MAP_IMPL _threadSession; + MAP_IMPL _threadVariableSpace; VariableSpace* _variableSpace; Stash* _stash; diff --git a/libnd4j/include/graph/Stash.h b/libnd4j/include/graph/Stash.h index 83a7ec066..ba431d057 100644 --- a/libnd4j/include/graph/Stash.h +++ b/libnd4j/include/graph/Stash.h @@ -22,44 +22,70 @@ #define LIBND4J_STASH_H //#include -#include -#include +#include +#include +#include #include #include -#include +#include +#include -namespace nd4j { +namespace sd { namespace graph { class ND4J_EXPORT KeyPair { int _node; std::string _name; public: - KeyPair(int node = 0, const char * name = nullptr); + KeyPair(int node = 0, const char *name = nullptr); - bool operator<(const KeyPair& other) const; + bool operator<(const KeyPair &other) const; + + bool operator==(const KeyPair &other) const { + return _node == other._node; + } + + int key() const { return _node; } + std::string name() const { return _name; } }; + } +} +#ifndef __JAVACPP_HACK__ + +namespace std { + template <> + class ND4J_EXPORT hash { + public: + size_t operator()(const sd::graph::KeyPair& k) const; + }; +}; + +#endif + +namespace sd { + namespace graph { class ND4J_EXPORT Stash { protected: - std::map _stash; - std::vector _handles; + std::map _stash; + std::vector _handles; public: Stash(); ~Stash(); - //void storeArray(nd4j::graph::Block& block, const char *name, nd4j::NDArray *array); - void storeArray(int nodeId, const char *name, nd4j::NDArray *array); + //void storeArray(sd::graph::Block& block, const char *name, sd::NDArray *array); + void storeArray(int nodeId, const char *name, sd::NDArray *array); - //bool checkStash(nd4j::graph::Block& block, const char *name); + //bool checkStash(sd::graph::Block& block, const char *name); bool checkStash(int nodeId, const char *name); - //nd4j::NDArray* extractArray(nd4j::graph::Block& block, const char *name); - nd4j::NDArray* extractArray(int nodeId, const char *name); + //sd::NDArray* extractArray(sd::graph::Block& block, const char *name); + sd::NDArray* extractArray(int nodeId, const char *name); void clear(); }; } + } diff --git a/libnd4j/include/Status.h b/libnd4j/include/graph/Status.h similarity index 91% rename from libnd4j/include/Status.h rename to libnd4j/include/graph/Status.h index 5baa7cca2..42794488d 100644 --- a/libnd4j/include/Status.h +++ b/libnd4j/include/graph/Status.h @@ -21,12 +21,12 @@ #ifndef ND4J_STATUS_H #define ND4J_STATUS_H -#include -#include -#include +#include +#include +#include #include -namespace nd4j { +namespace sd { class ND4J_EXPORT Status { public: static FORCEINLINE Nd4jStatus OK() { diff --git a/libnd4j/include/graph/TimeHolder.h b/libnd4j/include/graph/TimeHolder.h index d3509e8ba..191a75bac 100644 --- a/libnd4j/include/graph/TimeHolder.h +++ b/libnd4j/include/graph/TimeHolder.h @@ -22,10 +22,10 @@ #define LIBND4J_TIMEHOLDER_H #include -#include -#include +#include +#include -namespace nd4j { +namespace sd { namespace graph { class ND4J_EXPORT TimeHolder { private: diff --git a/libnd4j/include/graph/Variable.h b/libnd4j/include/graph/Variable.h index 60f977e97..b3ac74533 100644 --- a/libnd4j/include/graph/Variable.h +++ b/libnd4j/include/graph/Variable.h @@ -22,20 +22,45 @@ #define LIBND4J_VARIABLE_H #include -#include +#include #include #include #include #include #include -namespace nd4j { +#ifndef __JAVACPP_HACK__ + +namespace std { + + template <> + class ND4J_EXPORT hash> { + public: + size_t operator()(const std::pair& k) const; + }; + + template <> + class ND4J_EXPORT hash { + public: + size_t operator()(const bfloat16& k) const; + }; + + template <> + class ND4J_EXPORT hash { + public: + size_t operator()(const float16& k) const; + }; +}; + +#endif + +namespace sd { namespace graph { class ND4J_EXPORT Variable { protected: int _id = 0; int _index = 0; - nd4j::NDArray *_ndarray = nullptr; + sd::NDArray *_ndarray = nullptr; std::string _name; std::vector _shape; @@ -50,15 +75,19 @@ namespace nd4j { //InputType _variableType = InputType_UNDEFINED; //DataType _dataType = INHERIT; - nd4j::NDArrayList *_list = nullptr; + sd::NDArrayList *_list = nullptr; VariableType _variableType = VariableType::NDARRAY; public: Variable(bool placeHolder); - Variable(nd4j::NDArray *arrayw, const char *name, int id, int idx = 0); - Variable(nd4j::NDArray *array = nullptr, const char *name = nullptr); - Variable(const nd4j::graph::FlatVariable *flatVariable); + Variable(sd::NDArray *arrayw, const char *name, int id, int idx = 0); + Variable(sd::NDArray *array = nullptr, const char *name = nullptr); + +#ifndef __JAVACPP_HACK__ + Variable(const sd::graph::FlatVariable *flatVariable); +#endif + ~Variable(); Variable* clone(); @@ -67,12 +96,12 @@ namespace nd4j { ND4J_EXPORT Variable* asT(); bool hasNDArray(); - nd4j::NDArray* getNDArray(); - void setNDArray(nd4j::NDArray *array); + sd::NDArray* getNDArray(); + void setNDArray(sd::NDArray *array); bool hasNDArrayList(); - nd4j::NDArrayList* getNDArrayList(); - void setNDArrayList(nd4j::NDArrayList* list); + sd::NDArrayList* getNDArrayList(); + void setNDArrayList(sd::NDArrayList* list); bool isExternal(); bool isReadOnly(); diff --git a/libnd4j/include/graph/VariableProxy.h b/libnd4j/include/graph/VariableProxy.h index c2a6e9c62..1569b477d 100644 --- a/libnd4j/include/graph/VariableProxy.h +++ b/libnd4j/include/graph/VariableProxy.h @@ -20,7 +20,7 @@ #include -namespace nd4j { +namespace sd { namespace graph { class ND4J_EXPORT VariableProxy: public VariableSpace { protected: @@ -35,7 +35,7 @@ namespace nd4j { virtual int numberOfPlaceholders(); virtual std::vector* getPlaceholders(); - virtual nd4j::memory::Workspace *workspace(); + virtual sd::memory::Workspace *workspace(); virtual bool hasExternalVariable(int it); virtual bool hasExternalVariable(std::pair& pair); @@ -46,18 +46,18 @@ namespace nd4j { virtual bool hasVariable(std::pair& pair); virtual bool hasVariable(std::string *symbol); - virtual nd4j::graph::Variable *getVariable(int id); - virtual nd4j::graph::Variable *getVariable(int id, int idx); - virtual nd4j::graph::Variable *getVariable(std::pair& pair); - virtual nd4j::graph::Variable *getVariable(std::string *symbol); + virtual sd::graph::Variable *getVariable(int id); + virtual sd::graph::Variable *getVariable(int id, int idx); + virtual sd::graph::Variable *getVariable(std::pair& pair); + virtual sd::graph::Variable *getVariable(std::string *symbol); virtual std::vector getVariables(); - virtual void putVariable(std::pair& pair, NDArray *array); + virtual Variable* putVariable(std::pair& pair, NDArray *array); virtual void putVariable(std::pair& pair, Variable *variable); virtual void putVariable(int id, Variable *variable); virtual void putVariable(int id, NDArray *array); - virtual void putVariable(int id, int idx, NDArray *array); + virtual Variable* putVariable(int id, int idx, NDArray *array); virtual void putVariable(int id, int idx, NDArray &array); virtual void putVariable(int id, int idx, Variable *array); @@ -68,7 +68,7 @@ namespace nd4j { virtual void putOutputVariable(Variable *variable); - virtual void trackList(nd4j::NDArrayList *list); + virtual void trackList(sd::NDArrayList *list); // memory-related statistics virtual Nd4jLong externalMemory(); @@ -79,9 +79,9 @@ namespace nd4j { virtual int internalEntries(); virtual int totalEntries(); - virtual nd4j::graph::VariableSpace *clone(); + virtual sd::graph::VariableSpace *clone(); - virtual nd4j::graph::Stash* getStash(); + virtual sd::graph::Stash* getStash(); virtual void setFlowPath(FlowPath* timers); virtual FlowPath* flowPath(); }; diff --git a/libnd4j/include/graph/VariableSpace.h b/libnd4j/include/graph/VariableSpace.h index 81abaf6e8..ea3c6370d 100644 --- a/libnd4j/include/graph/VariableSpace.h +++ b/libnd4j/include/graph/VariableSpace.h @@ -28,7 +28,7 @@ #include #include #include -#include +#include #include #include #include @@ -36,24 +36,24 @@ #include -namespace nd4j { +namespace sd { namespace graph { class ND4J_EXPORT VariableSpace { protected: - nd4j::memory::Workspace *_workspace; + sd::memory::Workspace *_workspace; // stash is NOT cloned - nd4j::graph::Stash _stash; + sd::graph::Stash _stash; - std::map, Variable*> _paired; - std::map _symbolic; - std::map _variables; + MAP_IMPL, Variable*> _paired; + MAP_IMPL _symbolic; + MAP_IMPL _variables; std::vector _external; std::vector _internal; - std::vector _lists; + std::vector _lists; - std::vector _placeholders; + std::vector _placeholders; void silentPutVariable(std::pair& pair, Variable *variable); @@ -61,9 +61,9 @@ namespace nd4j { std::mutex _varmap; - std::map _temporary; + MAP_IMPL _temporary; - std::vector *_handles; + std::vector *_handles; FlowPath* _flow = nullptr; @@ -75,7 +75,7 @@ namespace nd4j { virtual int numberOfPlaceholders(); virtual std::vector* getPlaceholders(); - virtual void setWorkspace(nd4j::memory::Workspace *workspace); + virtual void setWorkspace(sd::memory::Workspace *workspace); virtual LaunchContext* launchContext(); @@ -88,25 +88,25 @@ namespace nd4j { virtual bool hasVariable(std::pair& pair); virtual bool hasVariable(std::string *symbol); - virtual nd4j::graph::Variable* getVariable(int id); - virtual nd4j::graph::Variable* getVariable(int id, int idx); - virtual nd4j::graph::Variable* getVariable(std::pair& pair); - virtual nd4j::graph::Variable* getVariable(std::string *symbol); + virtual sd::graph::Variable* getVariable(int id); + virtual sd::graph::Variable* getVariable(int id, int idx); + virtual sd::graph::Variable* getVariable(std::pair& pair); + virtual sd::graph::Variable* getVariable(std::string *symbol); virtual std::vector getVariables(); - virtual void putVariable(std::pair& pair, NDArray *array); + virtual Variable* putVariable(std::pair& pair, NDArray *array); virtual void putVariable(std::pair& pair, Variable *variable); virtual void putVariable(int id, Variable *variable); virtual void putVariable(int id, NDArray *array); - virtual void putVariable(int id, int idx, NDArray *array); + virtual Variable* putVariable(int id, int idx, NDArray *array); virtual void putVariable(int id, int idx, NDArray &array); virtual void putVariable(int id, int idx, Variable *array); virtual void dropVariable(std::pair &pair); virtual void dropVariable(int id, int idx); - virtual void trackList(nd4j::NDArrayList *list); + virtual void trackList(sd::NDArrayList *list); virtual void putOutputVariable(Variable *variable); @@ -121,17 +121,17 @@ namespace nd4j { virtual int internalEntries(); virtual int totalEntries(); - virtual nd4j::graph::VariableSpace* clone(); + virtual sd::graph::VariableSpace* clone(); std::vector *handles(); - nd4j::graph::VariableSpace* asT(); + sd::graph::VariableSpace* asT(); void injectVariable(std::pair &pair, Variable* variable); - virtual nd4j::graph::Stash* getStash(); + virtual sd::graph::Stash* getStash(); - virtual std::vector * getExternalVariables(); + virtual std::vector * getExternalVariables(); virtual void setFlowPath(FlowPath* timers); virtual FlowPath* flowPath(); diff --git a/libnd4j/include/graph/VariableType.h b/libnd4j/include/graph/VariableType.h index 5c3ca6057..28883f9b1 100644 --- a/libnd4j/include/graph/VariableType.h +++ b/libnd4j/include/graph/VariableType.h @@ -21,7 +21,7 @@ #ifndef ND4J_VARIABLE_TYPE_H #define ND4J_VARIABLE_TYPE_H -namespace nd4j { +namespace sd { namespace graph { enum VariableType { NDARRAY = 0, diff --git a/libnd4j/include/graph/VariablesSet.h b/libnd4j/include/graph/VariablesSet.h index 070ac4442..682b7fce4 100644 --- a/libnd4j/include/graph/VariablesSet.h +++ b/libnd4j/include/graph/VariablesSet.h @@ -23,16 +23,16 @@ #include #include -#include -#include +#include +#include #include -namespace nd4j { +namespace sd { namespace graph { class ND4J_EXPORT VariablesSet { protected: - std::vector _holder; + std::vector _holder; Nd4jStatus _status; public: VariablesSet(Nd4jStatus status = ND4J_STATUS_OK); diff --git a/libnd4j/include/graph/exceptions/impl/unresolved_input_exception.cpp b/libnd4j/include/graph/exceptions/impl/unresolved_input_exception.cpp index ac8e511c7..fe6e45875 100644 --- a/libnd4j/include/graph/exceptions/impl/unresolved_input_exception.cpp +++ b/libnd4j/include/graph/exceptions/impl/unresolved_input_exception.cpp @@ -18,10 +18,10 @@ // @author raver119@gmail.com // -#include -#include +#include +#include -namespace nd4j { +namespace sd { namespace graph { unresolved_input_exception::unresolved_input_exception(std::string message) : std::runtime_error(message) { // diff --git a/libnd4j/include/graph/exceptions/impl/unresolved_output_exception.cpp b/libnd4j/include/graph/exceptions/impl/unresolved_output_exception.cpp index 4ccc10c71..df8b5eb00 100644 --- a/libnd4j/include/graph/exceptions/impl/unresolved_output_exception.cpp +++ b/libnd4j/include/graph/exceptions/impl/unresolved_output_exception.cpp @@ -18,11 +18,11 @@ // @author raver119@gmail.com // -#include -#include +#include +#include #include -namespace nd4j { +namespace sd { namespace graph { unresolved_output_exception::unresolved_output_exception(std::string message) : std::runtime_error(message) { // diff --git a/libnd4j/include/graph/exceptions/unresolved_input_exception.h b/libnd4j/include/graph/exceptions/unresolved_input_exception.h index e779ecff2..5e38977a9 100644 --- a/libnd4j/include/graph/exceptions/unresolved_input_exception.h +++ b/libnd4j/include/graph/exceptions/unresolved_input_exception.h @@ -25,7 +25,7 @@ #include #include -namespace nd4j { +namespace sd { namespace graph { class unresolved_input_exception : public std::runtime_error { public: diff --git a/libnd4j/include/graph/exceptions/unresolved_output_exception.h b/libnd4j/include/graph/exceptions/unresolved_output_exception.h index 6040d3278..05d39c514 100644 --- a/libnd4j/include/graph/exceptions/unresolved_output_exception.h +++ b/libnd4j/include/graph/exceptions/unresolved_output_exception.h @@ -25,7 +25,7 @@ #include #include -namespace nd4j { +namespace sd { namespace graph { class unresolved_output_exception : public std::runtime_error { public: diff --git a/libnd4j/include/graph/execution/LogicConditional.h b/libnd4j/include/graph/execution/LogicConditional.h index 0a39ef05f..ffaf6f098 100644 --- a/libnd4j/include/graph/execution/LogicConditional.h +++ b/libnd4j/include/graph/execution/LogicConditional.h @@ -21,11 +21,11 @@ #ifndef LIBND4J_LOGICCONDITIONAL_H #define LIBND4J_LOGICCONDITIONAL_H -#include -#include -#include +#include +#include +#include -namespace nd4j { +namespace sd { namespace graph { /** * This class is responsible for execution logic of Conditional logical abstraction diff --git a/libnd4j/include/graph/execution/LogicEnter.h b/libnd4j/include/graph/execution/LogicEnter.h index a4f64c83d..d770ff10a 100644 --- a/libnd4j/include/graph/execution/LogicEnter.h +++ b/libnd4j/include/graph/execution/LogicEnter.h @@ -21,10 +21,10 @@ #ifndef LIBND4J_LOGICENTER_H #define LIBND4J_LOGICENTER_H -#include +#include #include -namespace nd4j { +namespace sd { namespace graph { class LogicEnter { public: diff --git a/libnd4j/include/graph/execution/LogicExecutor.h b/libnd4j/include/graph/execution/LogicExecutor.h index 7b8e5e89e..541b3fc84 100644 --- a/libnd4j/include/graph/execution/LogicExecutor.h +++ b/libnd4j/include/graph/execution/LogicExecutor.h @@ -21,11 +21,11 @@ #ifndef LIBND4J_LOGICEXECUTOR_H #define LIBND4J_LOGICEXECUTOR_H -#include +#include #include #include -namespace nd4j { +namespace sd { namespace graph { /** * This class acts as switch for picking logic execution based on opNum, unique for each logical op diff --git a/libnd4j/include/graph/execution/LogicExit.h b/libnd4j/include/graph/execution/LogicExit.h index 617338e14..d182e26fb 100644 --- a/libnd4j/include/graph/execution/LogicExit.h +++ b/libnd4j/include/graph/execution/LogicExit.h @@ -21,10 +21,10 @@ #ifndef LIBND4J_LOGICEXIT_H #define LIBND4J_LOGICEXIT_H -#include +#include #include -namespace nd4j { +namespace sd { namespace graph { class LogicExit { public: diff --git a/libnd4j/include/graph/execution/LogicExpose.h b/libnd4j/include/graph/execution/LogicExpose.h index 3b86270db..046f3e64e 100644 --- a/libnd4j/include/graph/execution/LogicExpose.h +++ b/libnd4j/include/graph/execution/LogicExpose.h @@ -21,11 +21,11 @@ #ifndef LIBND4J_LOGICEXPOSE_H #define LIBND4J_LOGICEXPOSE_H -#include +#include #include #include -namespace nd4j { +namespace sd { namespace graph { class LogicExpose { public: diff --git a/libnd4j/include/graph/execution/LogicLoopCond.h b/libnd4j/include/graph/execution/LogicLoopCond.h index 8e8d89c55..36693232b 100644 --- a/libnd4j/include/graph/execution/LogicLoopCond.h +++ b/libnd4j/include/graph/execution/LogicLoopCond.h @@ -21,10 +21,10 @@ #ifndef LIBND4J_LOGICLOOPCOND_H #define LIBND4J_LOGICLOOPCOND_H -#include +#include #include -namespace nd4j { +namespace sd { namespace graph { class LogicLoopCond { public: diff --git a/libnd4j/include/graph/execution/LogicMerge.h b/libnd4j/include/graph/execution/LogicMerge.h index eebb7fea8..fe20c9d66 100644 --- a/libnd4j/include/graph/execution/LogicMerge.h +++ b/libnd4j/include/graph/execution/LogicMerge.h @@ -21,10 +21,10 @@ #ifndef LIBND4J_LOGICMERGE_H #define LIBND4J_LOGICMERGE_H -#include +#include #include -namespace nd4j { +namespace sd { namespace graph { class LogicMerge { public: diff --git a/libnd4j/include/graph/execution/LogicNextIteration.h b/libnd4j/include/graph/execution/LogicNextIteration.h index b4cfff800..5b9600909 100644 --- a/libnd4j/include/graph/execution/LogicNextIteration.h +++ b/libnd4j/include/graph/execution/LogicNextIteration.h @@ -21,10 +21,10 @@ #ifndef LIBND4J_LOGICNEXTITERATION_H #define LIBND4J_LOGICNEXTITERATION_H -#include +#include #include -namespace nd4j { +namespace sd { namespace graph { class LogicNextIeration { public: diff --git a/libnd4j/include/graph/execution/LogicReturn.h b/libnd4j/include/graph/execution/LogicReturn.h index 072b6c2a2..2cc6107c5 100644 --- a/libnd4j/include/graph/execution/LogicReturn.h +++ b/libnd4j/include/graph/execution/LogicReturn.h @@ -22,11 +22,11 @@ #define LIBND4J_LOGICRETURN_H -#include +#include #include #include -namespace nd4j { +namespace sd { namespace graph { /** * This class is responsible for execution logic of Return logical abstraction diff --git a/libnd4j/include/graph/execution/LogicScope.h b/libnd4j/include/graph/execution/LogicScope.h index ab2924a92..a7a8d6b7a 100644 --- a/libnd4j/include/graph/execution/LogicScope.h +++ b/libnd4j/include/graph/execution/LogicScope.h @@ -21,11 +21,11 @@ #ifndef LIBND4J_LOGICSCOPE_H #define LIBND4J_LOGICSCOPE_H -#include +#include #include #include -namespace nd4j { +namespace sd { namespace graph { /** * This class is responsible for execution logic of Scope logical abstraction diff --git a/libnd4j/include/graph/execution/LogicSwitch.h b/libnd4j/include/graph/execution/LogicSwitch.h index e61c6b9cc..d91959d91 100644 --- a/libnd4j/include/graph/execution/LogicSwitch.h +++ b/libnd4j/include/graph/execution/LogicSwitch.h @@ -21,11 +21,11 @@ #ifndef LIBND4J_LOGICSWITCH_H #define LIBND4J_LOGICSWITCH_H -#include +#include #include #include -namespace nd4j { +namespace sd { namespace graph { /** * This class is responsible for execution logic of Switch logical abstraction diff --git a/libnd4j/include/graph/execution/LogicWhile.h b/libnd4j/include/graph/execution/LogicWhile.h index b0bbcef05..6e4b2ea3a 100644 --- a/libnd4j/include/graph/execution/LogicWhile.h +++ b/libnd4j/include/graph/execution/LogicWhile.h @@ -21,11 +21,11 @@ #ifndef LIBND4J_LOGICWHILE_H #define LIBND4J_LOGICWHILE_H -#include +#include #include #include -namespace nd4j { +namespace sd { namespace graph { /** * This class is responsible for execution logic of While logical abstraction diff --git a/libnd4j/include/graph/execution/impl/LogicConditional.cpp b/libnd4j/include/graph/execution/impl/LogicConditional.cpp index fb1f0fa1e..25627df45 100644 --- a/libnd4j/include/graph/execution/impl/LogicConditional.cpp +++ b/libnd4j/include/graph/execution/impl/LogicConditional.cpp @@ -19,12 +19,12 @@ // #include -#include +#include #include -#include +#include -namespace nd4j { +namespace sd { namespace graph { Nd4jStatus LogicConditional::processNode(Graph *graph, Node *node) { auto __variableSpace = graph->getVariableSpace(); @@ -130,7 +130,7 @@ namespace nd4j { } } - return nd4j::Status::OK(); + return sd::Status::OK(); } } } \ No newline at end of file diff --git a/libnd4j/include/graph/execution/impl/LogicEnter.cpp b/libnd4j/include/graph/execution/impl/LogicEnter.cpp index aa69e97ac..f10ff792f 100644 --- a/libnd4j/include/graph/execution/impl/LogicEnter.cpp +++ b/libnd4j/include/graph/execution/impl/LogicEnter.cpp @@ -19,10 +19,10 @@ // #include -#include +#include -namespace nd4j { +namespace sd { namespace graph { Nd4jStatus LogicEnter::processNode(Graph *graph, Node *node) { // this op replicates input variable into the frame. basically happens once for single loop. @@ -68,7 +68,7 @@ namespace nd4j { } } - return nd4j::Status::OK(); + return sd::Status::OK(); } } } \ No newline at end of file diff --git a/libnd4j/include/graph/execution/impl/LogicExecutor.cpp b/libnd4j/include/graph/execution/impl/LogicExecutor.cpp index 5826e6745..fd7ce3e85 100644 --- a/libnd4j/include/graph/execution/impl/LogicExecutor.cpp +++ b/libnd4j/include/graph/execution/impl/LogicExecutor.cpp @@ -32,31 +32,31 @@ #include -namespace nd4j { +namespace sd { namespace graph { Nd4jStatus LogicExecutor::processNode(Graph *graph, Node *node) { switch (node->opNum()) { - case nd4j::logic::While: + case sd::logic::While: return LogicWhile::processNode(graph, node); - case nd4j::logic::Scope: + case sd::logic::Scope: return LogicScope::processNode(graph, node); - case nd4j::logic::Conditional: + case sd::logic::Conditional: return LogicConditional::processNode(graph, node); - case nd4j::logic::Switch: + case sd::logic::Switch: return LogicSwitch::processNode(graph, node); - case nd4j::logic::Return: + case sd::logic::Return: return LogicReturn::processNode(graph, node); - case nd4j::logic::Expose: + case sd::logic::Expose: return LogicExpose::processNode(graph, node); - case nd4j::logic::Merge: + case sd::logic::Merge: return LogicMerge::processNode(graph, node); - case nd4j::logic::LoopCond: + case sd::logic::LoopCond: return LogicLoopCond::processNode(graph, node); - case nd4j::logic::NextIteration: + case sd::logic::NextIteration: return LogicNextIeration::processNode(graph, node); - case nd4j::logic::Exit: + case sd::logic::Exit: return LogicExit::processNode(graph, node); - case nd4j::logic::Enter: + case sd::logic::Enter: return LogicEnter::processNode(graph, node); } diff --git a/libnd4j/include/graph/execution/impl/LogicExit.cpp b/libnd4j/include/graph/execution/impl/LogicExit.cpp index b5e5c0e60..9a0e21793 100644 --- a/libnd4j/include/graph/execution/impl/LogicExit.cpp +++ b/libnd4j/include/graph/execution/impl/LogicExit.cpp @@ -21,7 +21,7 @@ #include -namespace nd4j { +namespace sd { namespace graph { Nd4jStatus LogicExit::processNode(Graph *graph, Node *node) { // this op is basically no-op diff --git a/libnd4j/include/graph/execution/impl/LogicExpose.cpp b/libnd4j/include/graph/execution/impl/LogicExpose.cpp index b2a454fcf..b19e1df55 100644 --- a/libnd4j/include/graph/execution/impl/LogicExpose.cpp +++ b/libnd4j/include/graph/execution/impl/LogicExpose.cpp @@ -20,7 +20,7 @@ #include -namespace nd4j { +namespace sd { namespace graph { Nd4jStatus LogicExpose::processNode(Graph *graph, Node *node) { // do we really want this? diff --git a/libnd4j/include/graph/execution/impl/LogicLoopCond.cpp b/libnd4j/include/graph/execution/impl/LogicLoopCond.cpp index 3b035f212..292452719 100644 --- a/libnd4j/include/graph/execution/impl/LogicLoopCond.cpp +++ b/libnd4j/include/graph/execution/impl/LogicLoopCond.cpp @@ -21,7 +21,7 @@ #include -namespace nd4j { +namespace sd { namespace graph { Nd4jStatus LogicLoopCond::processNode(Graph *graph, Node *node) { auto __variableSpace = graph->getVariableSpace(); diff --git a/libnd4j/include/graph/execution/impl/LogicMerge.cpp b/libnd4j/include/graph/execution/impl/LogicMerge.cpp index aa0d49a20..9d032a93f 100644 --- a/libnd4j/include/graph/execution/impl/LogicMerge.cpp +++ b/libnd4j/include/graph/execution/impl/LogicMerge.cpp @@ -19,9 +19,9 @@ // #include -#include +#include -namespace nd4j { +namespace sd { namespace graph { Nd4jStatus LogicMerge::processNode(Graph *graph, Node *node) { // at merge node only one of inputs exist if that's just switch and other node isn't LogicNextItration diff --git a/libnd4j/include/graph/execution/impl/LogicNextIteration.cpp b/libnd4j/include/graph/execution/impl/LogicNextIteration.cpp index 439cc6bc1..fb7eaa513 100644 --- a/libnd4j/include/graph/execution/impl/LogicNextIteration.cpp +++ b/libnd4j/include/graph/execution/impl/LogicNextIteration.cpp @@ -21,7 +21,7 @@ #include -namespace nd4j { +namespace sd { namespace graph { Nd4jStatus LogicNextIeration::processNode(Graph *graph, Node *node) { auto __variableSpace = graph->getVariableSpace(); diff --git a/libnd4j/include/graph/execution/impl/LogicReturn.cpp b/libnd4j/include/graph/execution/impl/LogicReturn.cpp index b5f39c16e..c9dbafd6d 100644 --- a/libnd4j/include/graph/execution/impl/LogicReturn.cpp +++ b/libnd4j/include/graph/execution/impl/LogicReturn.cpp @@ -20,9 +20,9 @@ #include "graph/execution/LogicReturn.h" #include -#include +#include -namespace nd4j { +namespace sd { namespace graph { Nd4jStatus LogicReturn::processNode(Graph *graph, Node *node) { auto __variableSpace = graph->getVariableSpace(); @@ -49,7 +49,7 @@ namespace nd4j { nd4j_debug("In after: [%f]; Out after: [%f]\n", varIn->getNDArray()->meanNumber().e(0), varOut->getNDArray()->meanNumber().e(0)); } - return nd4j::Status::OK(); + return sd::Status::OK(); } } } diff --git a/libnd4j/include/graph/execution/impl/LogicScope.cpp b/libnd4j/include/graph/execution/impl/LogicScope.cpp index bfd58e1c7..1773aa6ea 100644 --- a/libnd4j/include/graph/execution/impl/LogicScope.cpp +++ b/libnd4j/include/graph/execution/impl/LogicScope.cpp @@ -19,15 +19,15 @@ // #include -#include +#include -namespace nd4j { +namespace sd { namespace graph { Nd4jStatus LogicScope::processNode(Graph *graph, Node *node) { // this op is basically no-op // we just know it exists - return nd4j::Status::OK(); + return sd::Status::OK(); } } } \ No newline at end of file diff --git a/libnd4j/include/graph/execution/impl/LogicSwitch.cpp b/libnd4j/include/graph/execution/impl/LogicSwitch.cpp index c19d339dc..1089046a3 100644 --- a/libnd4j/include/graph/execution/impl/LogicSwitch.cpp +++ b/libnd4j/include/graph/execution/impl/LogicSwitch.cpp @@ -18,12 +18,12 @@ // Created by raver119 on 21.10.17. // -#include +#include #include -#include -#include +#include +#include -namespace nd4j { +namespace sd { namespace graph { Nd4jStatus LogicSwitch::processNode(Graph* graph, Node* node) { auto __variableSpace = graph->getVariableSpace(); @@ -102,7 +102,7 @@ namespace nd4j { } } - return nd4j::Status::OK(); + return sd::Status::OK(); }; } } diff --git a/libnd4j/include/graph/execution/impl/LogicWhile.cpp b/libnd4j/include/graph/execution/impl/LogicWhile.cpp index 147c35248..1dfd3aaf2 100644 --- a/libnd4j/include/graph/execution/impl/LogicWhile.cpp +++ b/libnd4j/include/graph/execution/impl/LogicWhile.cpp @@ -20,12 +20,12 @@ #include #include -#include +#include #include -#include +#include -namespace nd4j { +namespace sd { namespace graph { Nd4jStatus LogicWhile::processNode(Graph *graph, Node *node) { auto __variableSpace = graph->getVariableSpace(); @@ -138,7 +138,7 @@ namespace nd4j { return ND4J_STATUS_KERNEL_FAILURE; } - return nd4j::Status::OK(); + return sd::Status::OK(); } } } diff --git a/libnd4j/include/graph/generated/array_generated.h b/libnd4j/include/graph/generated/array_generated.h index e3b3bbe60..5c4c0d7af 100644 --- a/libnd4j/include/graph/generated/array_generated.h +++ b/libnd4j/include/graph/generated/array_generated.h @@ -6,7 +6,7 @@ #include "flatbuffers/flatbuffers.h" -namespace nd4j { +namespace sd { namespace graph { struct FlatArray; @@ -236,7 +236,7 @@ inline flatbuffers::Offset CreateFlatArrayDirect( const std::vector *buffer = nullptr, DType dtype = DType_INHERIT, ByteOrder byteOrder = ByteOrder_LE) { - return nd4j::graph::CreateFlatArray( + return sd::graph::CreateFlatArray( _fbb, shape ? _fbb.CreateVector(*shape) : 0, buffer ? _fbb.CreateVector(*buffer) : 0, @@ -244,37 +244,37 @@ inline flatbuffers::Offset CreateFlatArrayDirect( byteOrder); } -inline const nd4j::graph::FlatArray *GetFlatArray(const void *buf) { - return flatbuffers::GetRoot(buf); +inline const sd::graph::FlatArray *GetFlatArray(const void *buf) { + return flatbuffers::GetRoot(buf); } -inline const nd4j::graph::FlatArray *GetSizePrefixedFlatArray(const void *buf) { - return flatbuffers::GetSizePrefixedRoot(buf); +inline const sd::graph::FlatArray *GetSizePrefixedFlatArray(const void *buf) { + return flatbuffers::GetSizePrefixedRoot(buf); } inline bool VerifyFlatArrayBuffer( flatbuffers::Verifier &verifier) { - return verifier.VerifyBuffer(nullptr); + return verifier.VerifyBuffer(nullptr); } inline bool VerifySizePrefixedFlatArrayBuffer( flatbuffers::Verifier &verifier) { - return verifier.VerifySizePrefixedBuffer(nullptr); + return verifier.VerifySizePrefixedBuffer(nullptr); } inline void FinishFlatArrayBuffer( flatbuffers::FlatBufferBuilder &fbb, - flatbuffers::Offset root) { + flatbuffers::Offset root) { fbb.Finish(root); } inline void FinishSizePrefixedFlatArrayBuffer( flatbuffers::FlatBufferBuilder &fbb, - flatbuffers::Offset root) { + flatbuffers::Offset root) { fbb.FinishSizePrefixed(root); } } // namespace graph -} // namespace nd4j +} // namespace sd #endif // FLATBUFFERS_GENERATED_ARRAY_ND4J_GRAPH_H_ diff --git a/libnd4j/include/graph/generated/config_generated.h b/libnd4j/include/graph/generated/config_generated.h index 686d3dc2f..2c12027a2 100644 --- a/libnd4j/include/graph/generated/config_generated.h +++ b/libnd4j/include/graph/generated/config_generated.h @@ -6,7 +6,7 @@ #include "flatbuffers/flatbuffers.h" -namespace nd4j { +namespace sd { namespace graph { struct FlatConfiguration; @@ -258,37 +258,37 @@ inline flatbuffers::Offset CreateFlatConfiguration( return builder_.Finish(); } -inline const nd4j::graph::FlatConfiguration *GetFlatConfiguration(const void *buf) { - return flatbuffers::GetRoot(buf); +inline const sd::graph::FlatConfiguration *GetFlatConfiguration(const void *buf) { + return flatbuffers::GetRoot(buf); } -inline const nd4j::graph::FlatConfiguration *GetSizePrefixedFlatConfiguration(const void *buf) { - return flatbuffers::GetSizePrefixedRoot(buf); +inline const sd::graph::FlatConfiguration *GetSizePrefixedFlatConfiguration(const void *buf) { + return flatbuffers::GetSizePrefixedRoot(buf); } inline bool VerifyFlatConfigurationBuffer( flatbuffers::Verifier &verifier) { - return verifier.VerifyBuffer(nullptr); + return verifier.VerifyBuffer(nullptr); } inline bool VerifySizePrefixedFlatConfigurationBuffer( flatbuffers::Verifier &verifier) { - return verifier.VerifySizePrefixedBuffer(nullptr); + return verifier.VerifySizePrefixedBuffer(nullptr); } inline void FinishFlatConfigurationBuffer( flatbuffers::FlatBufferBuilder &fbb, - flatbuffers::Offset root) { + flatbuffers::Offset root) { fbb.Finish(root); } inline void FinishSizePrefixedFlatConfigurationBuffer( flatbuffers::FlatBufferBuilder &fbb, - flatbuffers::Offset root) { + flatbuffers::Offset root) { fbb.FinishSizePrefixed(root); } } // namespace graph -} // namespace nd4j +} // namespace sd #endif // FLATBUFFERS_GENERATED_CONFIG_ND4J_GRAPH_H_ diff --git a/libnd4j/include/graph/generated/graph.grpc.fb.cc b/libnd4j/include/graph/generated/graph.grpc.fb.cc index 52d585abb..5146f49f2 100644 --- a/libnd4j/include/graph/generated/graph.grpc.fb.cc +++ b/libnd4j/include/graph/generated/graph.grpc.fb.cc @@ -13,7 +13,7 @@ #include #include #include -namespace nd4j { +namespace sd { namespace graph { static const char* GraphInferenceServer_method_names[] = { @@ -138,6 +138,6 @@ GraphInferenceServer::Service::~Service() { } -} // namespace nd4j +} // namespace sd } // namespace graph diff --git a/libnd4j/include/graph/generated/graph.grpc.fb.h b/libnd4j/include/graph/generated/graph.grpc.fb.h index 28a1e9275..0167f48d5 100644 --- a/libnd4j/include/graph/generated/graph.grpc.fb.h +++ b/libnd4j/include/graph/generated/graph.grpc.fb.h @@ -24,7 +24,7 @@ class ServerCompletionQueue; class ServerContext; } // namespace grpc -namespace nd4j { +namespace sd { namespace graph { class GraphInferenceServer final { @@ -366,7 +366,7 @@ class GraphInferenceServer final { }; } // namespace graph -} // namespace nd4j +} // namespace sd #endif // GRPC_graph__INCLUDED diff --git a/libnd4j/include/graph/generated/graph_generated.h b/libnd4j/include/graph/generated/graph_generated.h index 518e5d2cf..1285e4607 100644 --- a/libnd4j/include/graph/generated/graph_generated.h +++ b/libnd4j/include/graph/generated/graph_generated.h @@ -15,7 +15,7 @@ #include "utils_generated.h" #include "variable_generated.h" -namespace nd4j { +namespace sd { namespace graph { struct UpdaterState; @@ -96,7 +96,7 @@ inline flatbuffers::Offset CreateUpdaterStateDirect( const char *paramName = nullptr, const std::vector> *updaterStateKeys = nullptr, const std::vector> *updaterStateValues = nullptr) { - return nd4j::graph::CreateUpdaterState( + return sd::graph::CreateUpdaterState( _fbb, paramName ? _fbb.CreateString(paramName) : 0, updaterStateKeys ? _fbb.CreateVector>(*updaterStateKeys) : 0, @@ -248,7 +248,7 @@ inline flatbuffers::Offset CreateFlatGraphDirect( const std::vector> *lossVariables = nullptr, const char *trainingConfig = nullptr, const std::vector> *updaterState = nullptr) { - return nd4j::graph::CreateFlatGraph( + return sd::graph::CreateFlatGraph( _fbb, id, variables ? _fbb.CreateVector>(*variables) : 0, @@ -341,37 +341,37 @@ inline flatbuffers::Offset CreateFlatResponse( return builder_.Finish(); } -inline const nd4j::graph::FlatGraph *GetFlatGraph(const void *buf) { - return flatbuffers::GetRoot(buf); +inline const sd::graph::FlatGraph *GetFlatGraph(const void *buf) { + return flatbuffers::GetRoot(buf); } -inline const nd4j::graph::FlatGraph *GetSizePrefixedFlatGraph(const void *buf) { - return flatbuffers::GetSizePrefixedRoot(buf); +inline const sd::graph::FlatGraph *GetSizePrefixedFlatGraph(const void *buf) { + return flatbuffers::GetSizePrefixedRoot(buf); } inline bool VerifyFlatGraphBuffer( flatbuffers::Verifier &verifier) { - return verifier.VerifyBuffer(nullptr); + return verifier.VerifyBuffer(nullptr); } inline bool VerifySizePrefixedFlatGraphBuffer( flatbuffers::Verifier &verifier) { - return verifier.VerifySizePrefixedBuffer(nullptr); + return verifier.VerifySizePrefixedBuffer(nullptr); } inline void FinishFlatGraphBuffer( flatbuffers::FlatBufferBuilder &fbb, - flatbuffers::Offset root) { + flatbuffers::Offset root) { fbb.Finish(root); } inline void FinishSizePrefixedFlatGraphBuffer( flatbuffers::FlatBufferBuilder &fbb, - flatbuffers::Offset root) { + flatbuffers::Offset root) { fbb.FinishSizePrefixed(root); } } // namespace graph -} // namespace nd4j +} // namespace sd #endif // FLATBUFFERS_GENERATED_GRAPH_ND4J_GRAPH_H_ diff --git a/libnd4j/include/graph/generated/nd4j/graph/ByteOrder.cs b/libnd4j/include/graph/generated/nd4j/graph/ByteOrder.cs index 463de04af..5b20d143f 100644 --- a/libnd4j/include/graph/generated/nd4j/graph/ByteOrder.cs +++ b/libnd4j/include/graph/generated/nd4j/graph/ByteOrder.cs @@ -2,7 +2,7 @@ // automatically generated by the FlatBuffers compiler, do not modify // -namespace nd4j.graph +namespace sd.graph { public enum ByteOrder : sbyte diff --git a/libnd4j/include/graph/generated/nd4j/graph/DType.cs b/libnd4j/include/graph/generated/nd4j/graph/DType.cs index 9062dc881..f6fd2778c 100644 --- a/libnd4j/include/graph/generated/nd4j/graph/DType.cs +++ b/libnd4j/include/graph/generated/nd4j/graph/DType.cs @@ -2,7 +2,7 @@ // automatically generated by the FlatBuffers compiler, do not modify // -namespace nd4j.graph +namespace sd.graph { public enum DType : sbyte diff --git a/libnd4j/include/graph/generated/nd4j/graph/Direction.cs b/libnd4j/include/graph/generated/nd4j/graph/Direction.cs index 9929b12e6..d93c1e947 100644 --- a/libnd4j/include/graph/generated/nd4j/graph/Direction.cs +++ b/libnd4j/include/graph/generated/nd4j/graph/Direction.cs @@ -2,7 +2,7 @@ // automatically generated by the FlatBuffers compiler, do not modify // -namespace nd4j.graph +namespace sd.graph { public enum Direction : sbyte diff --git a/libnd4j/include/graph/generated/nd4j/graph/ExecutionMode.cs b/libnd4j/include/graph/generated/nd4j/graph/ExecutionMode.cs index 826037783..77f54df5b 100644 --- a/libnd4j/include/graph/generated/nd4j/graph/ExecutionMode.cs +++ b/libnd4j/include/graph/generated/nd4j/graph/ExecutionMode.cs @@ -2,7 +2,7 @@ // automatically generated by the FlatBuffers compiler, do not modify // -namespace nd4j.graph +namespace sd.graph { public enum ExecutionMode : sbyte diff --git a/libnd4j/include/graph/generated/nd4j/graph/FlatArray.cs b/libnd4j/include/graph/generated/nd4j/graph/FlatArray.cs index 60d836aeb..6a4aad3f2 100644 --- a/libnd4j/include/graph/generated/nd4j/graph/FlatArray.cs +++ b/libnd4j/include/graph/generated/nd4j/graph/FlatArray.cs @@ -2,7 +2,7 @@ // automatically generated by the FlatBuffers compiler, do not modify // -namespace nd4j.graph +namespace sd.graph { using global::System; diff --git a/libnd4j/include/graph/generated/nd4j/graph/FlatArrayList.cs b/libnd4j/include/graph/generated/nd4j/graph/FlatArrayList.cs index 0720ed410..dd2e17d32 100644 --- a/libnd4j/include/graph/generated/nd4j/graph/FlatArrayList.cs +++ b/libnd4j/include/graph/generated/nd4j/graph/FlatArrayList.cs @@ -2,7 +2,7 @@ // automatically generated by the FlatBuffers compiler, do not modify // -namespace nd4j.graph +namespace sd.graph { using global::System; diff --git a/libnd4j/include/graph/generated/nd4j/graph/FlatConfiguration.cs b/libnd4j/include/graph/generated/nd4j/graph/FlatConfiguration.cs index ca50e9973..5c2edbf9b 100644 --- a/libnd4j/include/graph/generated/nd4j/graph/FlatConfiguration.cs +++ b/libnd4j/include/graph/generated/nd4j/graph/FlatConfiguration.cs @@ -2,7 +2,7 @@ // automatically generated by the FlatBuffers compiler, do not modify // -namespace nd4j.graph +namespace sd.graph { using global::System; diff --git a/libnd4j/include/graph/generated/nd4j/graph/FlatDropRequest.cs b/libnd4j/include/graph/generated/nd4j/graph/FlatDropRequest.cs index 3aba26796..6d2f5f68b 100644 --- a/libnd4j/include/graph/generated/nd4j/graph/FlatDropRequest.cs +++ b/libnd4j/include/graph/generated/nd4j/graph/FlatDropRequest.cs @@ -2,7 +2,7 @@ // automatically generated by the FlatBuffers compiler, do not modify // -namespace nd4j.graph +namespace sd.graph { using global::System; diff --git a/libnd4j/include/graph/generated/nd4j/graph/FlatGraph.cs b/libnd4j/include/graph/generated/nd4j/graph/FlatGraph.cs index 67eef75b8..15978e301 100644 --- a/libnd4j/include/graph/generated/nd4j/graph/FlatGraph.cs +++ b/libnd4j/include/graph/generated/nd4j/graph/FlatGraph.cs @@ -2,7 +2,7 @@ // automatically generated by the FlatBuffers compiler, do not modify // -namespace nd4j.graph +namespace sd.graph { using global::System; diff --git a/libnd4j/include/graph/generated/nd4j/graph/FlatInferenceRequest.cs b/libnd4j/include/graph/generated/nd4j/graph/FlatInferenceRequest.cs index c4a5e7283..88e36dad5 100644 --- a/libnd4j/include/graph/generated/nd4j/graph/FlatInferenceRequest.cs +++ b/libnd4j/include/graph/generated/nd4j/graph/FlatInferenceRequest.cs @@ -2,7 +2,7 @@ // automatically generated by the FlatBuffers compiler, do not modify // -namespace nd4j.graph +namespace sd.graph { using global::System; diff --git a/libnd4j/include/graph/generated/nd4j/graph/FlatNode.cs b/libnd4j/include/graph/generated/nd4j/graph/FlatNode.cs index c94e0fcc4..3f951c0e9 100644 --- a/libnd4j/include/graph/generated/nd4j/graph/FlatNode.cs +++ b/libnd4j/include/graph/generated/nd4j/graph/FlatNode.cs @@ -2,7 +2,7 @@ // automatically generated by the FlatBuffers compiler, do not modify // -namespace nd4j.graph +namespace sd.graph { using global::System; diff --git a/libnd4j/include/graph/generated/nd4j/graph/FlatProperties.cs b/libnd4j/include/graph/generated/nd4j/graph/FlatProperties.cs index ae116fcb0..471dd9f3d 100644 --- a/libnd4j/include/graph/generated/nd4j/graph/FlatProperties.cs +++ b/libnd4j/include/graph/generated/nd4j/graph/FlatProperties.cs @@ -2,7 +2,7 @@ // automatically generated by the FlatBuffers compiler, do not modify // -namespace nd4j.graph +namespace sd.graph { using global::System; diff --git a/libnd4j/include/graph/generated/nd4j/graph/FlatResponse.cs b/libnd4j/include/graph/generated/nd4j/graph/FlatResponse.cs index a69698370..2acd7728d 100644 --- a/libnd4j/include/graph/generated/nd4j/graph/FlatResponse.cs +++ b/libnd4j/include/graph/generated/nd4j/graph/FlatResponse.cs @@ -2,7 +2,7 @@ // automatically generated by the FlatBuffers compiler, do not modify // -namespace nd4j.graph +namespace sd.graph { using global::System; diff --git a/libnd4j/include/graph/generated/nd4j/graph/FlatResult.cs b/libnd4j/include/graph/generated/nd4j/graph/FlatResult.cs index c35c6cb1d..20147a93e 100644 --- a/libnd4j/include/graph/generated/nd4j/graph/FlatResult.cs +++ b/libnd4j/include/graph/generated/nd4j/graph/FlatResult.cs @@ -2,7 +2,7 @@ // automatically generated by the FlatBuffers compiler, do not modify // -namespace nd4j.graph +namespace sd.graph { using global::System; diff --git a/libnd4j/include/graph/generated/nd4j/graph/FlatTiming.cs b/libnd4j/include/graph/generated/nd4j/graph/FlatTiming.cs index 739c0741e..16aa8895c 100644 --- a/libnd4j/include/graph/generated/nd4j/graph/FlatTiming.cs +++ b/libnd4j/include/graph/generated/nd4j/graph/FlatTiming.cs @@ -2,7 +2,7 @@ // automatically generated by the FlatBuffers compiler, do not modify // -namespace nd4j.graph +namespace sd.graph { using global::System; diff --git a/libnd4j/include/graph/generated/nd4j/graph/FlatVariable.cs b/libnd4j/include/graph/generated/nd4j/graph/FlatVariable.cs index 325094654..9331965be 100644 --- a/libnd4j/include/graph/generated/nd4j/graph/FlatVariable.cs +++ b/libnd4j/include/graph/generated/nd4j/graph/FlatVariable.cs @@ -2,7 +2,7 @@ // automatically generated by the FlatBuffers compiler, do not modify // -namespace nd4j.graph +namespace sd.graph { using global::System; diff --git a/libnd4j/include/graph/generated/nd4j/graph/FrameIteration.cs b/libnd4j/include/graph/generated/nd4j/graph/FrameIteration.cs index b3ded5375..6b73ff261 100644 --- a/libnd4j/include/graph/generated/nd4j/graph/FrameIteration.cs +++ b/libnd4j/include/graph/generated/nd4j/graph/FrameIteration.cs @@ -2,7 +2,7 @@ // automatically generated by the FlatBuffers compiler, do not modify // -namespace nd4j.graph +namespace sd.graph { using global::System; diff --git a/libnd4j/include/graph/generated/nd4j/graph/InputType.cs b/libnd4j/include/graph/generated/nd4j/graph/InputType.cs index 997dd62b2..0172b846d 100644 --- a/libnd4j/include/graph/generated/nd4j/graph/InputType.cs +++ b/libnd4j/include/graph/generated/nd4j/graph/InputType.cs @@ -2,7 +2,7 @@ // automatically generated by the FlatBuffers compiler, do not modify // -namespace nd4j.graph +namespace sd.graph { public enum InputType : sbyte diff --git a/libnd4j/include/graph/generated/nd4j/graph/IntPair.cs b/libnd4j/include/graph/generated/nd4j/graph/IntPair.cs index 665842db7..38dad82ed 100644 --- a/libnd4j/include/graph/generated/nd4j/graph/IntPair.cs +++ b/libnd4j/include/graph/generated/nd4j/graph/IntPair.cs @@ -2,7 +2,7 @@ // automatically generated by the FlatBuffers compiler, do not modify // -namespace nd4j.graph +namespace sd.graph { using global::System; diff --git a/libnd4j/include/graph/generated/nd4j/graph/IntTriple.cs b/libnd4j/include/graph/generated/nd4j/graph/IntTriple.cs index 2d3d2a03e..e1c5ac1a4 100644 --- a/libnd4j/include/graph/generated/nd4j/graph/IntTriple.cs +++ b/libnd4j/include/graph/generated/nd4j/graph/IntTriple.cs @@ -2,7 +2,7 @@ // automatically generated by the FlatBuffers compiler, do not modify // -namespace nd4j.graph +namespace sd.graph { using global::System; diff --git a/libnd4j/include/graph/generated/nd4j/graph/LongPair.cs b/libnd4j/include/graph/generated/nd4j/graph/LongPair.cs index 248c83ea4..b28c9bd0b 100644 --- a/libnd4j/include/graph/generated/nd4j/graph/LongPair.cs +++ b/libnd4j/include/graph/generated/nd4j/graph/LongPair.cs @@ -2,7 +2,7 @@ // automatically generated by the FlatBuffers compiler, do not modify // -namespace nd4j.graph +namespace sd.graph { using global::System; diff --git a/libnd4j/include/graph/generated/nd4j/graph/LongTriple.cs b/libnd4j/include/graph/generated/nd4j/graph/LongTriple.cs index fbb5fb7cd..dc49143c3 100644 --- a/libnd4j/include/graph/generated/nd4j/graph/LongTriple.cs +++ b/libnd4j/include/graph/generated/nd4j/graph/LongTriple.cs @@ -2,7 +2,7 @@ // automatically generated by the FlatBuffers compiler, do not modify // -namespace nd4j.graph +namespace sd.graph { using global::System; diff --git a/libnd4j/include/graph/generated/nd4j/graph/OpClass.cs b/libnd4j/include/graph/generated/nd4j/graph/OpClass.cs index 4ff4c2f3b..45ac68a3a 100644 --- a/libnd4j/include/graph/generated/nd4j/graph/OpClass.cs +++ b/libnd4j/include/graph/generated/nd4j/graph/OpClass.cs @@ -2,7 +2,7 @@ // automatically generated by the FlatBuffers compiler, do not modify // -namespace nd4j.graph +namespace sd.graph { public enum OpClass : sbyte diff --git a/libnd4j/include/graph/generated/nd4j/graph/OpType.cs b/libnd4j/include/graph/generated/nd4j/graph/OpType.cs index f9a5c7c18..2b9d27bce 100644 --- a/libnd4j/include/graph/generated/nd4j/graph/OpType.cs +++ b/libnd4j/include/graph/generated/nd4j/graph/OpType.cs @@ -2,7 +2,7 @@ // automatically generated by the FlatBuffers compiler, do not modify // -namespace nd4j.graph +namespace sd.graph { public enum OpType : sbyte diff --git a/libnd4j/include/graph/generated/nd4j/graph/OutputMode.cs b/libnd4j/include/graph/generated/nd4j/graph/OutputMode.cs index 8e88b51d9..4cd916dbe 100644 --- a/libnd4j/include/graph/generated/nd4j/graph/OutputMode.cs +++ b/libnd4j/include/graph/generated/nd4j/graph/OutputMode.cs @@ -2,7 +2,7 @@ // automatically generated by the FlatBuffers compiler, do not modify // -namespace nd4j.graph +namespace sd.graph { public enum OutputMode : sbyte diff --git a/libnd4j/include/graph/generated/nd4j/graph/ProfilingMode.cs b/libnd4j/include/graph/generated/nd4j/graph/ProfilingMode.cs index 837deeffd..1d9b90ca3 100644 --- a/libnd4j/include/graph/generated/nd4j/graph/ProfilingMode.cs +++ b/libnd4j/include/graph/generated/nd4j/graph/ProfilingMode.cs @@ -2,7 +2,7 @@ // automatically generated by the FlatBuffers compiler, do not modify // -namespace nd4j.graph +namespace sd.graph { public enum ProfilingMode : sbyte diff --git a/libnd4j/include/graph/generated/nd4j/graph/UIAddName.cs b/libnd4j/include/graph/generated/nd4j/graph/UIAddName.cs index bf15cee55..cc2f6fb85 100644 --- a/libnd4j/include/graph/generated/nd4j/graph/UIAddName.cs +++ b/libnd4j/include/graph/generated/nd4j/graph/UIAddName.cs @@ -2,7 +2,7 @@ // automatically generated by the FlatBuffers compiler, do not modify // -namespace nd4j.graph +namespace sd.graph { using global::System; diff --git a/libnd4j/include/graph/generated/nd4j/graph/UIEvent.cs b/libnd4j/include/graph/generated/nd4j/graph/UIEvent.cs index 1843a48bd..a8775d67d 100644 --- a/libnd4j/include/graph/generated/nd4j/graph/UIEvent.cs +++ b/libnd4j/include/graph/generated/nd4j/graph/UIEvent.cs @@ -2,7 +2,7 @@ // automatically generated by the FlatBuffers compiler, do not modify // -namespace nd4j.graph +namespace sd.graph { using global::System; diff --git a/libnd4j/include/graph/generated/nd4j/graph/UIEventSubtype.cs b/libnd4j/include/graph/generated/nd4j/graph/UIEventSubtype.cs index 99bed5876..d4da48a25 100644 --- a/libnd4j/include/graph/generated/nd4j/graph/UIEventSubtype.cs +++ b/libnd4j/include/graph/generated/nd4j/graph/UIEventSubtype.cs @@ -2,7 +2,7 @@ // automatically generated by the FlatBuffers compiler, do not modify // -namespace nd4j.graph +namespace sd.graph { public enum UIEventSubtype : sbyte diff --git a/libnd4j/include/graph/generated/nd4j/graph/UIEventType.cs b/libnd4j/include/graph/generated/nd4j/graph/UIEventType.cs index 6d167e0ac..32427dac2 100644 --- a/libnd4j/include/graph/generated/nd4j/graph/UIEventType.cs +++ b/libnd4j/include/graph/generated/nd4j/graph/UIEventType.cs @@ -2,7 +2,7 @@ // automatically generated by the FlatBuffers compiler, do not modify // -namespace nd4j.graph +namespace sd.graph { public enum UIEventType : sbyte diff --git a/libnd4j/include/graph/generated/nd4j/graph/UIGraphStructure.cs b/libnd4j/include/graph/generated/nd4j/graph/UIGraphStructure.cs index 97a491bb8..ad8e16ab9 100644 --- a/libnd4j/include/graph/generated/nd4j/graph/UIGraphStructure.cs +++ b/libnd4j/include/graph/generated/nd4j/graph/UIGraphStructure.cs @@ -2,7 +2,7 @@ // automatically generated by the FlatBuffers compiler, do not modify // -namespace nd4j.graph +namespace sd.graph { using global::System; diff --git a/libnd4j/include/graph/generated/nd4j/graph/UIHardwareState.cs b/libnd4j/include/graph/generated/nd4j/graph/UIHardwareState.cs index 68ea3ecf8..a3444a2ea 100644 --- a/libnd4j/include/graph/generated/nd4j/graph/UIHardwareState.cs +++ b/libnd4j/include/graph/generated/nd4j/graph/UIHardwareState.cs @@ -2,7 +2,7 @@ // automatically generated by the FlatBuffers compiler, do not modify // -namespace nd4j.graph +namespace sd.graph { using global::System; diff --git a/libnd4j/include/graph/generated/nd4j/graph/UIHistogram.cs b/libnd4j/include/graph/generated/nd4j/graph/UIHistogram.cs index 81390e3c9..1801bef68 100644 --- a/libnd4j/include/graph/generated/nd4j/graph/UIHistogram.cs +++ b/libnd4j/include/graph/generated/nd4j/graph/UIHistogram.cs @@ -2,7 +2,7 @@ // automatically generated by the FlatBuffers compiler, do not modify // -namespace nd4j.graph +namespace sd.graph { using global::System; diff --git a/libnd4j/include/graph/generated/nd4j/graph/UIHistogramType.cs b/libnd4j/include/graph/generated/nd4j/graph/UIHistogramType.cs index 18a46f293..3b8c3218b 100644 --- a/libnd4j/include/graph/generated/nd4j/graph/UIHistogramType.cs +++ b/libnd4j/include/graph/generated/nd4j/graph/UIHistogramType.cs @@ -2,7 +2,7 @@ // automatically generated by the FlatBuffers compiler, do not modify // -namespace nd4j.graph +namespace sd.graph { public enum UIHistogramType : sbyte diff --git a/libnd4j/include/graph/generated/nd4j/graph/UIInfoType.cs b/libnd4j/include/graph/generated/nd4j/graph/UIInfoType.cs index a122bdda3..676d60530 100644 --- a/libnd4j/include/graph/generated/nd4j/graph/UIInfoType.cs +++ b/libnd4j/include/graph/generated/nd4j/graph/UIInfoType.cs @@ -2,7 +2,7 @@ // automatically generated by the FlatBuffers compiler, do not modify // -namespace nd4j.graph +namespace sd.graph { public enum UIInfoType : sbyte diff --git a/libnd4j/include/graph/generated/nd4j/graph/UIOp.cs b/libnd4j/include/graph/generated/nd4j/graph/UIOp.cs index a60715762..f8b3b4fbd 100644 --- a/libnd4j/include/graph/generated/nd4j/graph/UIOp.cs +++ b/libnd4j/include/graph/generated/nd4j/graph/UIOp.cs @@ -2,7 +2,7 @@ // automatically generated by the FlatBuffers compiler, do not modify // -namespace nd4j.graph +namespace sd.graph { using global::System; diff --git a/libnd4j/include/graph/generated/nd4j/graph/UIStaticInfoRecord.cs b/libnd4j/include/graph/generated/nd4j/graph/UIStaticInfoRecord.cs index f2a28fb5e..410a3c37b 100644 --- a/libnd4j/include/graph/generated/nd4j/graph/UIStaticInfoRecord.cs +++ b/libnd4j/include/graph/generated/nd4j/graph/UIStaticInfoRecord.cs @@ -2,7 +2,7 @@ // automatically generated by the FlatBuffers compiler, do not modify // -namespace nd4j.graph +namespace sd.graph { using global::System; diff --git a/libnd4j/include/graph/generated/nd4j/graph/UISummaryStatistics.cs b/libnd4j/include/graph/generated/nd4j/graph/UISummaryStatistics.cs index 7bc069e5c..0f63d2d7c 100644 --- a/libnd4j/include/graph/generated/nd4j/graph/UISummaryStatistics.cs +++ b/libnd4j/include/graph/generated/nd4j/graph/UISummaryStatistics.cs @@ -2,7 +2,7 @@ // automatically generated by the FlatBuffers compiler, do not modify // -namespace nd4j.graph +namespace sd.graph { using global::System; diff --git a/libnd4j/include/graph/generated/nd4j/graph/UISystemInfo.cs b/libnd4j/include/graph/generated/nd4j/graph/UISystemInfo.cs index bd81fde6c..6adbcf98e 100644 --- a/libnd4j/include/graph/generated/nd4j/graph/UISystemInfo.cs +++ b/libnd4j/include/graph/generated/nd4j/graph/UISystemInfo.cs @@ -2,7 +2,7 @@ // automatically generated by the FlatBuffers compiler, do not modify // -namespace nd4j.graph +namespace sd.graph { using global::System; diff --git a/libnd4j/include/graph/generated/nd4j/graph/UIVariable.cs b/libnd4j/include/graph/generated/nd4j/graph/UIVariable.cs index 4b646b3bb..1f8d14971 100644 --- a/libnd4j/include/graph/generated/nd4j/graph/UIVariable.cs +++ b/libnd4j/include/graph/generated/nd4j/graph/UIVariable.cs @@ -2,7 +2,7 @@ // automatically generated by the FlatBuffers compiler, do not modify // -namespace nd4j.graph +namespace sd.graph { using global::System; diff --git a/libnd4j/include/graph/generated/nd4j/graph/UpdaterState.cs b/libnd4j/include/graph/generated/nd4j/graph/UpdaterState.cs index 02f8c419b..8cb6e07f0 100644 --- a/libnd4j/include/graph/generated/nd4j/graph/UpdaterState.cs +++ b/libnd4j/include/graph/generated/nd4j/graph/UpdaterState.cs @@ -2,7 +2,7 @@ // automatically generated by the FlatBuffers compiler, do not modify // -namespace nd4j.graph +namespace sd.graph { using global::System; diff --git a/libnd4j/include/graph/generated/nd4j/graph/VarType.cs b/libnd4j/include/graph/generated/nd4j/graph/VarType.cs index 462dfa7e7..4649bfef6 100644 --- a/libnd4j/include/graph/generated/nd4j/graph/VarType.cs +++ b/libnd4j/include/graph/generated/nd4j/graph/VarType.cs @@ -2,7 +2,7 @@ // automatically generated by the FlatBuffers compiler, do not modify // -namespace nd4j.graph +namespace sd.graph { public enum VarType : sbyte diff --git a/libnd4j/include/graph/generated/node_generated.h b/libnd4j/include/graph/generated/node_generated.h index 92f4ab126..a39f2490c 100644 --- a/libnd4j/include/graph/generated/node_generated.h +++ b/libnd4j/include/graph/generated/node_generated.h @@ -10,7 +10,7 @@ #include "properties_generated.h" #include "utils_generated.h" -namespace nd4j { +namespace sd { namespace graph { struct FlatNode; @@ -324,7 +324,7 @@ inline flatbuffers::Offset CreateFlatNodeDirect( const std::vector> *varControlDeps = nullptr, const std::vector> *controlDepFor = nullptr, const std::vector *extraTypes = nullptr) { - return nd4j::graph::CreateFlatNode( + return sd::graph::CreateFlatNode( _fbb, id, name ? _fbb.CreateString(name) : 0, @@ -351,37 +351,37 @@ inline flatbuffers::Offset CreateFlatNodeDirect( extraTypes ? _fbb.CreateVector(*extraTypes) : 0); } -inline const nd4j::graph::FlatNode *GetFlatNode(const void *buf) { - return flatbuffers::GetRoot(buf); +inline const sd::graph::FlatNode *GetFlatNode(const void *buf) { + return flatbuffers::GetRoot(buf); } -inline const nd4j::graph::FlatNode *GetSizePrefixedFlatNode(const void *buf) { - return flatbuffers::GetSizePrefixedRoot(buf); +inline const sd::graph::FlatNode *GetSizePrefixedFlatNode(const void *buf) { + return flatbuffers::GetSizePrefixedRoot(buf); } inline bool VerifyFlatNodeBuffer( flatbuffers::Verifier &verifier) { - return verifier.VerifyBuffer(nullptr); + return verifier.VerifyBuffer(nullptr); } inline bool VerifySizePrefixedFlatNodeBuffer( flatbuffers::Verifier &verifier) { - return verifier.VerifySizePrefixedBuffer(nullptr); + return verifier.VerifySizePrefixedBuffer(nullptr); } inline void FinishFlatNodeBuffer( flatbuffers::FlatBufferBuilder &fbb, - flatbuffers::Offset root) { + flatbuffers::Offset root) { fbb.Finish(root); } inline void FinishSizePrefixedFlatNodeBuffer( flatbuffers::FlatBufferBuilder &fbb, - flatbuffers::Offset root) { + flatbuffers::Offset root) { fbb.FinishSizePrefixed(root); } } // namespace graph -} // namespace nd4j +} // namespace sd #endif // FLATBUFFERS_GENERATED_NODE_ND4J_GRAPH_H_ diff --git a/libnd4j/include/graph/generated/properties_generated.h b/libnd4j/include/graph/generated/properties_generated.h index 4f88b6249..34138fe86 100644 --- a/libnd4j/include/graph/generated/properties_generated.h +++ b/libnd4j/include/graph/generated/properties_generated.h @@ -8,7 +8,7 @@ #include "array_generated.h" -namespace nd4j { +namespace sd { namespace graph { struct FlatProperties; @@ -143,7 +143,7 @@ inline flatbuffers::Offset CreateFlatPropertiesDirect( const std::vector *b = nullptr, const std::vector> *s = nullptr, const std::vector *shape = nullptr) { - return nd4j::graph::CreateFlatProperties( + return sd::graph::CreateFlatProperties( _fbb, name ? _fbb.CreateString(name) : 0, i ? _fbb.CreateVector(*i) : 0, @@ -155,37 +155,37 @@ inline flatbuffers::Offset CreateFlatPropertiesDirect( shape ? _fbb.CreateVector(*shape) : 0); } -inline const nd4j::graph::FlatProperties *GetFlatProperties(const void *buf) { - return flatbuffers::GetRoot(buf); +inline const sd::graph::FlatProperties *GetFlatProperties(const void *buf) { + return flatbuffers::GetRoot(buf); } -inline const nd4j::graph::FlatProperties *GetSizePrefixedFlatProperties(const void *buf) { - return flatbuffers::GetSizePrefixedRoot(buf); +inline const sd::graph::FlatProperties *GetSizePrefixedFlatProperties(const void *buf) { + return flatbuffers::GetSizePrefixedRoot(buf); } inline bool VerifyFlatPropertiesBuffer( flatbuffers::Verifier &verifier) { - return verifier.VerifyBuffer(nullptr); + return verifier.VerifyBuffer(nullptr); } inline bool VerifySizePrefixedFlatPropertiesBuffer( flatbuffers::Verifier &verifier) { - return verifier.VerifySizePrefixedBuffer(nullptr); + return verifier.VerifySizePrefixedBuffer(nullptr); } inline void FinishFlatPropertiesBuffer( flatbuffers::FlatBufferBuilder &fbb, - flatbuffers::Offset root) { + flatbuffers::Offset root) { fbb.Finish(root); } inline void FinishSizePrefixedFlatPropertiesBuffer( flatbuffers::FlatBufferBuilder &fbb, - flatbuffers::Offset root) { + flatbuffers::Offset root) { fbb.FinishSizePrefixed(root); } } // namespace graph -} // namespace nd4j +} // namespace sd #endif // FLATBUFFERS_GENERATED_PROPERTIES_ND4J_GRAPH_H_ diff --git a/libnd4j/include/graph/generated/request_generated.h b/libnd4j/include/graph/generated/request_generated.h index 2084e2066..00c782311 100644 --- a/libnd4j/include/graph/generated/request_generated.h +++ b/libnd4j/include/graph/generated/request_generated.h @@ -11,7 +11,7 @@ #include "utils_generated.h" #include "variable_generated.h" -namespace nd4j { +namespace sd { namespace graph { struct FlatInferenceRequest; @@ -84,44 +84,44 @@ inline flatbuffers::Offset CreateFlatInferenceRequestDirec int64_t id = 0, const std::vector> *variables = nullptr, flatbuffers::Offset configuration = 0) { - return nd4j::graph::CreateFlatInferenceRequest( + return sd::graph::CreateFlatInferenceRequest( _fbb, id, variables ? _fbb.CreateVector>(*variables) : 0, configuration); } -inline const nd4j::graph::FlatInferenceRequest *GetFlatInferenceRequest(const void *buf) { - return flatbuffers::GetRoot(buf); +inline const sd::graph::FlatInferenceRequest *GetFlatInferenceRequest(const void *buf) { + return flatbuffers::GetRoot(buf); } -inline const nd4j::graph::FlatInferenceRequest *GetSizePrefixedFlatInferenceRequest(const void *buf) { - return flatbuffers::GetSizePrefixedRoot(buf); +inline const sd::graph::FlatInferenceRequest *GetSizePrefixedFlatInferenceRequest(const void *buf) { + return flatbuffers::GetSizePrefixedRoot(buf); } inline bool VerifyFlatInferenceRequestBuffer( flatbuffers::Verifier &verifier) { - return verifier.VerifyBuffer(nullptr); + return verifier.VerifyBuffer(nullptr); } inline bool VerifySizePrefixedFlatInferenceRequestBuffer( flatbuffers::Verifier &verifier) { - return verifier.VerifySizePrefixedBuffer(nullptr); + return verifier.VerifySizePrefixedBuffer(nullptr); } inline void FinishFlatInferenceRequestBuffer( flatbuffers::FlatBufferBuilder &fbb, - flatbuffers::Offset root) { + flatbuffers::Offset root) { fbb.Finish(root); } inline void FinishSizePrefixedFlatInferenceRequestBuffer( flatbuffers::FlatBufferBuilder &fbb, - flatbuffers::Offset root) { + flatbuffers::Offset root) { fbb.FinishSizePrefixed(root); } } // namespace graph -} // namespace nd4j +} // namespace sd #endif // FLATBUFFERS_GENERATED_REQUEST_ND4J_GRAPH_H_ diff --git a/libnd4j/include/graph/generated/result_generated.h b/libnd4j/include/graph/generated/result_generated.h index aa41959eb..04c458a9f 100644 --- a/libnd4j/include/graph/generated/result_generated.h +++ b/libnd4j/include/graph/generated/result_generated.h @@ -12,7 +12,7 @@ #include "utils_generated.h" #include "variable_generated.h" -namespace nd4j { +namespace sd { namespace graph { struct FlatTiming; @@ -86,7 +86,7 @@ inline flatbuffers::Offset CreateFlatTimingDirect( int32_t id = 0, const char *name = nullptr, flatbuffers::Offset timing = 0) { - return nd4j::graph::CreateFlatTiming( + return sd::graph::CreateFlatTiming( _fbb, id, name ? _fbb.CreateString(name) : 0, @@ -184,7 +184,7 @@ inline flatbuffers::Offset CreateFlatResultDirect( const std::vector> *timing = nullptr, int64_t footprintForward = 0, int64_t footprintBackward = 0) { - return nd4j::graph::CreateFlatResult( + return sd::graph::CreateFlatResult( _fbb, id, variables ? _fbb.CreateVector>(*variables) : 0, @@ -193,37 +193,37 @@ inline flatbuffers::Offset CreateFlatResultDirect( footprintBackward); } -inline const nd4j::graph::FlatResult *GetFlatResult(const void *buf) { - return flatbuffers::GetRoot(buf); +inline const sd::graph::FlatResult *GetFlatResult(const void *buf) { + return flatbuffers::GetRoot(buf); } -inline const nd4j::graph::FlatResult *GetSizePrefixedFlatResult(const void *buf) { - return flatbuffers::GetSizePrefixedRoot(buf); +inline const sd::graph::FlatResult *GetSizePrefixedFlatResult(const void *buf) { + return flatbuffers::GetSizePrefixedRoot(buf); } inline bool VerifyFlatResultBuffer( flatbuffers::Verifier &verifier) { - return verifier.VerifyBuffer(nullptr); + return verifier.VerifyBuffer(nullptr); } inline bool VerifySizePrefixedFlatResultBuffer( flatbuffers::Verifier &verifier) { - return verifier.VerifySizePrefixedBuffer(nullptr); + return verifier.VerifySizePrefixedBuffer(nullptr); } inline void FinishFlatResultBuffer( flatbuffers::FlatBufferBuilder &fbb, - flatbuffers::Offset root) { + flatbuffers::Offset root) { fbb.Finish(root); } inline void FinishSizePrefixedFlatResultBuffer( flatbuffers::FlatBufferBuilder &fbb, - flatbuffers::Offset root) { + flatbuffers::Offset root) { fbb.FinishSizePrefixed(root); } } // namespace graph -} // namespace nd4j +} // namespace sd #endif // FLATBUFFERS_GENERATED_RESULT_ND4J_GRAPH_H_ diff --git a/libnd4j/include/graph/generated/uigraphevents_generated.h b/libnd4j/include/graph/generated/uigraphevents_generated.h index ca62c1a60..b3430a5c7 100644 --- a/libnd4j/include/graph/generated/uigraphevents_generated.h +++ b/libnd4j/include/graph/generated/uigraphevents_generated.h @@ -8,7 +8,7 @@ #include "array_generated.h" -namespace nd4j { +namespace sd { namespace graph { struct UIEvent; @@ -336,7 +336,7 @@ inline flatbuffers::Offset CreateFrameIterationDirect( flatbuffers::FlatBufferBuilder &_fbb, const char *frame = nullptr, uint16_t iteration = 0) { - return nd4j::graph::CreateFrameIteration( + return sd::graph::CreateFrameIteration( _fbb, frame ? _fbb.CreateString(frame) : 0, iteration); @@ -397,7 +397,7 @@ inline flatbuffers::Offset CreateUIAddNameDirect( flatbuffers::FlatBufferBuilder &_fbb, int32_t nameIdx = 0, const char *name = nullptr) { - return nd4j::graph::CreateUIAddName( + return sd::graph::CreateUIAddName( _fbb, nameIdx, name ? _fbb.CreateString(name) : 0); @@ -448,7 +448,7 @@ inline flatbuffers::Offset CreateFlatArrayList( inline flatbuffers::Offset CreateFlatArrayListDirect( flatbuffers::FlatBufferBuilder &_fbb, const std::vector> *list = nullptr) { - return nd4j::graph::CreateFlatArrayList( + return sd::graph::CreateFlatArrayList( _fbb, list ? _fbb.CreateVector>(*list) : 0); } @@ -544,7 +544,7 @@ inline flatbuffers::Offset CreateUIHistogramDirect( flatbuffers::Offset binranges = 0, flatbuffers::Offset y = 0, const std::vector> *binlabels = nullptr) { - return nd4j::graph::CreateUIHistogram( + return sd::graph::CreateUIHistogram( _fbb, type, numbins, @@ -740,13 +740,13 @@ inline flatbuffers::Offset CreateUIHardwareStateDirect( flatbuffers::FlatBufferBuilder &_fbb, const std::vector *gpuMemory = nullptr, int64_t hostMemory = 0) { - return nd4j::graph::CreateUIHardwareState( + return sd::graph::CreateUIHardwareState( _fbb, gpuMemory ? _fbb.CreateVector(*gpuMemory) : 0, hostMemory); } } // namespace graph -} // namespace nd4j +} // namespace sd #endif // FLATBUFFERS_GENERATED_UIGRAPHEVENTS_ND4J_GRAPH_H_ diff --git a/libnd4j/include/graph/generated/uigraphstatic_generated.h b/libnd4j/include/graph/generated/uigraphstatic_generated.h index 8536a18ba..b6545f53a 100644 --- a/libnd4j/include/graph/generated/uigraphstatic_generated.h +++ b/libnd4j/include/graph/generated/uigraphstatic_generated.h @@ -10,7 +10,7 @@ #include "utils_generated.h" #include "variable_generated.h" -namespace nd4j { +namespace sd { namespace graph { struct UIStaticInfoRecord; @@ -232,7 +232,7 @@ inline flatbuffers::Offset CreateUIGraphStructureDirect( const std::vector> *outputs = nullptr, const std::vector> *variables = nullptr, const std::vector> *ops = nullptr) { - return nd4j::graph::CreateUIGraphStructure( + return sd::graph::CreateUIGraphStructure( _fbb, inputs ? _fbb.CreateVector>(*inputs) : 0, inputsPair ? _fbb.CreateVector>(*inputsPair) : 0, @@ -431,7 +431,7 @@ inline flatbuffers::Offset CreateUIVariableDirect( const char *gradientVariable = nullptr, const char *uiLabelExtra = nullptr, flatbuffers::Offset constantValue = 0) { - return nd4j::graph::CreateUIVariable( + return sd::graph::CreateUIVariable( _fbb, id, name ? _fbb.CreateString(name) : 0, @@ -555,7 +555,7 @@ inline flatbuffers::Offset CreateUIOpDirect( const std::vector> *outputs = nullptr, const std::vector> *controlDeps = nullptr, const char *uiLabelExtra = nullptr) { - return nd4j::graph::CreateUIOp( + return sd::graph::CreateUIOp( _fbb, name ? _fbb.CreateString(name) : 0, opName ? _fbb.CreateString(opName) : 0, @@ -566,6 +566,6 @@ inline flatbuffers::Offset CreateUIOpDirect( } } // namespace graph -} // namespace nd4j +} // namespace sd #endif // FLATBUFFERS_GENERATED_UIGRAPHSTATIC_ND4J_GRAPH_H_ diff --git a/libnd4j/include/graph/generated/utils_generated.h b/libnd4j/include/graph/generated/utils_generated.h index 9b885ce80..8e7896bb4 100644 --- a/libnd4j/include/graph/generated/utils_generated.h +++ b/libnd4j/include/graph/generated/utils_generated.h @@ -6,7 +6,7 @@ #include "flatbuffers/flatbuffers.h" -namespace nd4j { +namespace sd { namespace graph { struct LongPair; @@ -512,6 +512,6 @@ inline flatbuffers::Offset CreateIntTriple( } } // namespace graph -} // namespace nd4j +} // namespace sd #endif // FLATBUFFERS_GENERATED_UTILS_ND4J_GRAPH_H_ diff --git a/libnd4j/include/graph/generated/variable_generated.h b/libnd4j/include/graph/generated/variable_generated.h index 465490722..a0e43a5af 100644 --- a/libnd4j/include/graph/generated/variable_generated.h +++ b/libnd4j/include/graph/generated/variable_generated.h @@ -9,7 +9,7 @@ #include "array_generated.h" #include "utils_generated.h" -namespace nd4j { +namespace sd { namespace graph { struct FlatVariable; @@ -201,7 +201,7 @@ inline flatbuffers::Offset CreateFlatVariableDirect( const std::vector> *controlDeps = nullptr, const std::vector> *controlDepForOp = nullptr, const std::vector> *controlDepsForVar = nullptr) { - return nd4j::graph::CreateFlatVariable( + return sd::graph::CreateFlatVariable( _fbb, id, name ? _fbb.CreateString(name) : 0, @@ -215,37 +215,37 @@ inline flatbuffers::Offset CreateFlatVariableDirect( controlDepsForVar ? _fbb.CreateVector>(*controlDepsForVar) : 0); } -inline const nd4j::graph::FlatVariable *GetFlatVariable(const void *buf) { - return flatbuffers::GetRoot(buf); +inline const sd::graph::FlatVariable *GetFlatVariable(const void *buf) { + return flatbuffers::GetRoot(buf); } -inline const nd4j::graph::FlatVariable *GetSizePrefixedFlatVariable(const void *buf) { - return flatbuffers::GetSizePrefixedRoot(buf); +inline const sd::graph::FlatVariable *GetSizePrefixedFlatVariable(const void *buf) { + return flatbuffers::GetSizePrefixedRoot(buf); } inline bool VerifyFlatVariableBuffer( flatbuffers::Verifier &verifier) { - return verifier.VerifyBuffer(nullptr); + return verifier.VerifyBuffer(nullptr); } inline bool VerifySizePrefixedFlatVariableBuffer( flatbuffers::Verifier &verifier) { - return verifier.VerifySizePrefixedBuffer(nullptr); + return verifier.VerifySizePrefixedBuffer(nullptr); } inline void FinishFlatVariableBuffer( flatbuffers::FlatBufferBuilder &fbb, - flatbuffers::Offset root) { + flatbuffers::Offset root) { fbb.Finish(root); } inline void FinishSizePrefixedFlatVariableBuffer( flatbuffers::FlatBufferBuilder &fbb, - flatbuffers::Offset root) { + flatbuffers::Offset root) { fbb.FinishSizePrefixed(root); } } // namespace graph -} // namespace nd4j +} // namespace sd #endif // FLATBUFFERS_GENERATED_VARIABLE_ND4J_GRAPH_H_ diff --git a/libnd4j/include/graph/impl/ArgumentsList.cpp b/libnd4j/include/graph/impl/ArgumentsList.cpp index 217c8b8e2..71ba8c479 100644 --- a/libnd4j/include/graph/impl/ArgumentsList.cpp +++ b/libnd4j/include/graph/impl/ArgumentsList.cpp @@ -20,7 +20,7 @@ #include -namespace nd4j { +namespace sd { namespace graph { ArgumentsList::ArgumentsList(std::initializer_list arguments) { _arguments = arguments; diff --git a/libnd4j/include/graph/impl/Context.cpp b/libnd4j/include/graph/impl/Context.cpp index 5add8280d..954329f42 100644 --- a/libnd4j/include/graph/impl/Context.cpp +++ b/libnd4j/include/graph/impl/Context.cpp @@ -18,13 +18,13 @@ // @author raver119@gmail.com // -#include +#include #include #include #include -namespace nd4j { +namespace sd { namespace graph { Context::Context(ContextPrototype* prototype, VariableSpace* variableSpace) { _variableSpace = variableSpace; @@ -61,16 +61,16 @@ namespace nd4j { if (variableSpace != nullptr && variableSpace->launchContext()->getWorkspace() != nullptr) this->_workspace = variableSpace->launchContext()->getWorkspace(); } - nd4j::DataType Context::dataType(int index) { + sd::DataType Context::dataType(int index) { return _dataType; } - nd4j::DataType Context::dataType() { + sd::DataType Context::dataType() { return dataType(0); } - void Context::setDataType(int index, nd4j::DataType type) { + void Context::setDataType(int index, sd::DataType type) { if (this->_dataTypes.size() > (size_t)index) _dataTypes[index] = type; _dataType = type; @@ -115,7 +115,7 @@ namespace nd4j { return this->_workspace != nullptr; } - void Context::attachWorkspace(nd4j::memory::Workspace* workspace) { + void Context::attachWorkspace(sd::memory::Workspace* workspace) { this->_workspace = workspace; } @@ -136,26 +136,38 @@ namespace nd4j { } bool Context::isFastPath() { - return !(_fastpath_in.empty() && _fastpath_out.empty()); + auto ie = _fastpath_in.empty(); + auto io = _fastpath_out.empty(); + // two options here. + // either both IN/OUT are filled + auto b1 = (!ie && !io) || (!ie && _isInplace); + + // or at least something is filled, and FastPath is NOT forbidden + auto b2 = (!ie || !io) && !_forbidFastPath; + return b1 || b2; + } + + void Context::forbidFastPath(bool reallyForbid) { + _forbidFastPath = reallyForbid; } VariableSpace *Context::getVariableSpace() { return _variableSpace; } - nd4j::memory::Workspace* Context::getWorkspace() { + sd::memory::Workspace* Context::getWorkspace() { return _workspace; } - nd4j::memory::Workspace* Context::workspace() { + sd::memory::Workspace* Context::workspace() { return _workspace; } - nd4j::random::RandomBuffer* Context::getRNG() { + sd::random::RandomBuffer* Context::getRNG() { return _rng; } - void Context::setRNG(nd4j::random::RandomBuffer* rng) { + void Context::setRNG(sd::random::RandomBuffer* rng) { _rng = rng; } @@ -197,19 +209,19 @@ namespace nd4j { _variableSpace->flowPath()->markBranch(this->nodeId(), branch); } - Nd4jLong nd4j::graph::Context::getOuterTime(){ + Nd4jLong sd::graph::Context::getOuterTime(){ return this->_executionTime.first; } - Nd4jLong nd4j::graph::Context::getInnerTime(){ + Nd4jLong sd::graph::Context::getInnerTime(){ return this->_executionTime.second; } - void nd4j::graph::Context::setOuterTime(Nd4jLong time){ + void sd::graph::Context::setOuterTime(Nd4jLong time){ this->_executionTime.first = time; } - void nd4j::graph::Context::setInnerTime(Nd4jLong time){ + void sd::graph::Context::setInnerTime(Nd4jLong time){ this->_executionTime.second = time; } @@ -261,12 +273,12 @@ namespace nd4j { } Variable* Context::variable(std::pair& p) { - if (!_variableSpace->hasVariable(p)) { + try { + return _variableSpace->getVariable(p); + } catch (std::exception &e) { nd4j_printf("Node %i; Non-existent variable requested: [%i:%i]\n", this->_nodeId, p.first, p.second); throw std::runtime_error("Bad variable"); } - - return _variableSpace->getVariable(p); } void Context::pushNDArrayToVariableSpace(int nodeId, int index, NDArray *array, bool removable) { @@ -358,15 +370,15 @@ namespace nd4j { return getVariable(idx)->getNDArray(); } - nd4j::memory::Workspace *Context::fWorkspace() { + sd::memory::Workspace *Context::fWorkspace() { return workspace(); } - nd4j::memory::Workspace *Context::tWorkspace() { + sd::memory::Workspace *Context::tWorkspace() { return nullptr; } - nd4j::memory::Workspace *Context::oWorkspace() { + sd::memory::Workspace *Context::oWorkspace() { return nullptr; } @@ -439,7 +451,7 @@ namespace nd4j { NDArray *array; if (dataBuffer != nullptr) - array = new NDArray(dataBuffer->dataBuffer(), reinterpret_cast(shapeInfo), nd4j::LaunchContext::defaultContext(), dataBuffer->offset() / DataTypeUtils::sizeOf(ArrayOptions::dataType(reinterpret_cast(shapeInfo)))); + array = new NDArray(dataBuffer->dataBuffer(), reinterpret_cast(shapeInfo), sd::LaunchContext::defaultContext(), dataBuffer->offset() / DataTypeUtils::sizeOf(ArrayOptions::dataType(reinterpret_cast(shapeInfo)))); else array = new NDArray(nullptr, nullptr, reinterpret_cast(shapeInfo)); @@ -458,7 +470,7 @@ namespace nd4j { NDArray *array; if (dataBuffer != nullptr) - array = new NDArray(dataBuffer->dataBuffer(), reinterpret_cast(shapeInfo), nd4j::LaunchContext::defaultContext(), dataBuffer->offset() / DataTypeUtils::sizeOf(ArrayOptions::dataType(reinterpret_cast(shapeInfo)))); + array = new NDArray(dataBuffer->dataBuffer(), reinterpret_cast(shapeInfo), sd::LaunchContext::defaultContext(), dataBuffer->offset() / DataTypeUtils::sizeOf(ArrayOptions::dataType(reinterpret_cast(shapeInfo)))); else array = new NDArray(nullptr, nullptr, reinterpret_cast(shapeInfo)); @@ -552,13 +564,13 @@ namespace nd4j { return _execMode == samediff::ExecutionMode::MODE_INFERENCE; } - void Context::setDArguments(nd4j::DataType *arguments, int numberOfArguments) { + void Context::setDArguments(sd::DataType *arguments, int numberOfArguments) { _dArgs.clear(); for (int e = 0; e < numberOfArguments; e++) _dArgs.emplace_back(arguments[e]); } - void Context::setDArguments(const std::vector &dArgs) { + void Context::setDArguments(const std::vector &dArgs) { _dArgs.clear(); for (auto d:dArgs) _dArgs.emplace_back(d); diff --git a/libnd4j/include/graph/impl/ContextPrototype.cpp b/libnd4j/include/graph/impl/ContextPrototype.cpp index e8432aea0..417c46b3a 100644 --- a/libnd4j/include/graph/impl/ContextPrototype.cpp +++ b/libnd4j/include/graph/impl/ContextPrototype.cpp @@ -18,14 +18,14 @@ // @author raver119@gmail.com // -#include -#include +#include +#include #include #include -namespace nd4j { +namespace sd { namespace graph { - ContextPrototype::ContextPrototype(nd4j::ops::OpDescriptor* opDescriptor, int nodeId, bool inPlace) { + ContextPrototype::ContextPrototype(sd::ops::OpDescriptor* opDescriptor, int nodeId, bool inPlace) { _nodeId = nodeId; _isInplace = inPlace; _opDescriptor = opDescriptor; @@ -106,15 +106,15 @@ namespace nd4j { return getNodeId(); } - nd4j::DataType ContextPrototype::dataType() { + sd::DataType ContextPrototype::dataType() { return dataType(0); } - nd4j::DataType ContextPrototype::dataType(int index) { + sd::DataType ContextPrototype::dataType(int index) { return _dataType; } - void ContextPrototype::setDataType(int index, nd4j::DataType type) { + void ContextPrototype::setDataType(int index, sd::DataType type) { // if (_outputs->size() == 0) _dataType = type; } @@ -154,7 +154,7 @@ namespace nd4j { return clone; } - void ContextPrototype::setOpDescriptor(nd4j::ops::OpDescriptor* opDescriptor) { + void ContextPrototype::setOpDescriptor(sd::ops::OpDescriptor* opDescriptor) { _opDescriptor = opDescriptor; } @@ -174,7 +174,7 @@ namespace nd4j { return clone; } - std::vector *ContextPrototype::getDArguments() { + std::vector *ContextPrototype::getDArguments() { return &_dArgs; } diff --git a/libnd4j/include/graph/impl/ExecutionResult.cpp b/libnd4j/include/graph/impl/ExecutionResult.cpp index e887efb55..fd2bed054 100644 --- a/libnd4j/include/graph/impl/ExecutionResult.cpp +++ b/libnd4j/include/graph/impl/ExecutionResult.cpp @@ -18,12 +18,12 @@ // @author raver119@gmail.com // -#include -#include -#include +#include +#include +#include #include -namespace nd4j { +namespace sd { namespace graph { ExecutionResult::ExecutionResult(const FlatResult* flatResult) { if (flatResult->variables() != nullptr) { diff --git a/libnd4j/include/graph/impl/ExecutorConfiguration.cpp b/libnd4j/include/graph/impl/ExecutorConfiguration.cpp index 0d702a62d..f296ef3cd 100644 --- a/libnd4j/include/graph/impl/ExecutorConfiguration.cpp +++ b/libnd4j/include/graph/impl/ExecutorConfiguration.cpp @@ -20,9 +20,9 @@ #include -namespace nd4j { +namespace sd { namespace graph { - ExecutorConfiguration::ExecutorConfiguration(const nd4j::graph::FlatConfiguration *conf) { + ExecutorConfiguration::ExecutorConfiguration(const sd::graph::FlatConfiguration *conf) { if (conf != nullptr) { _profilingMode = conf->profilingMode(); _executionMode = conf->executionMode(); diff --git a/libnd4j/include/graph/impl/FlatUtils.cpp b/libnd4j/include/graph/impl/FlatUtils.cpp index f582220da..e6984bb97 100644 --- a/libnd4j/include/graph/impl/FlatUtils.cpp +++ b/libnd4j/include/graph/impl/FlatUtils.cpp @@ -23,10 +23,10 @@ #include #include #include -#include +#include -namespace nd4j { +namespace sd { namespace graph { std::pair FlatUtils::fromIntPair(IntPair *pair) { return std::pair(pair->first(), pair->second()); @@ -36,7 +36,7 @@ namespace nd4j { return std::pair(pair->first(), pair->second()); } - NDArray* FlatUtils::fromFlatArray(const nd4j::graph::FlatArray *flatArray) { + NDArray* FlatUtils::fromFlatArray(const sd::graph::FlatArray *flatArray) { auto rank = static_cast(flatArray->shape()->Get(0)); auto newShape = new Nd4jLong[shape::shapeInfoLength(rank)]; memcpy(newShape, flatArray->shape()->data(), shape::shapeInfoByteLength(rank)); @@ -52,7 +52,7 @@ namespace nd4j { // TODO fix UTF16 and UTF32 if (dtype == UTF8) { bool isBe = BitwiseUtils::isBE(); - bool canKeep = (isBe && flatArray->byteOrder() == nd4j::graph::ByteOrder_BE) || (!isBe && flatArray->byteOrder() == nd4j::graph::ByteOrder_LE); + bool canKeep = (isBe && flatArray->byteOrder() == sd::graph::ByteOrder_BE) || (!isBe && flatArray->byteOrder() == sd::graph::ByteOrder_LE); std::vector substrings(length); std::vector shapeVector(rank); @@ -96,7 +96,7 @@ namespace nd4j { BUILD_SINGLE_SELECTOR(dtype, DataTypeConversions, ::convertType(newBuffer, (void *)flatArray->buffer()->data(), dtype, ByteOrderUtils::fromFlatByteOrder(flatArray->byteOrder()), length), LIBND4J_TYPES); - auto array = new NDArray(newBuffer, newShape, nd4j::LaunchContext::defaultContext(), true); + auto array = new NDArray(newBuffer, newShape, sd::LaunchContext::defaultContext(), true); delete[] newShape; return array; @@ -108,9 +108,9 @@ namespace nd4j { auto fBuffer = builder.CreateVector(byteVector); auto fShape = builder.CreateVector(array.getShapeInfoAsFlatVector()); - auto bo = static_cast(BitwiseUtils::asByteOrder()); + auto bo = static_cast(BitwiseUtils::asByteOrder()); - return CreateFlatArray(builder, fShape, fBuffer, static_cast(array.dataType()), bo); + return CreateFlatArray(builder, fShape, fBuffer, static_cast(array.dataType()), bo); } } } \ No newline at end of file diff --git a/libnd4j/include/graph/impl/FlowPath.cpp b/libnd4j/include/graph/impl/FlowPath.cpp index e80167a28..79fe67b30 100644 --- a/libnd4j/include/graph/impl/FlowPath.cpp +++ b/libnd4j/include/graph/impl/FlowPath.cpp @@ -20,7 +20,7 @@ #include -namespace nd4j { +namespace sd { namespace graph { void FlowPath::ensureNode(int nodeId) { diff --git a/libnd4j/include/graph/impl/FrameState.cpp b/libnd4j/include/graph/impl/FrameState.cpp index db79c3f08..d312a4f39 100644 --- a/libnd4j/include/graph/impl/FrameState.cpp +++ b/libnd4j/include/graph/impl/FrameState.cpp @@ -21,7 +21,7 @@ #include -namespace nd4j { +namespace sd { namespace graph { FrameState::FrameState(Nd4jLong id) { this->_id = id; diff --git a/libnd4j/include/graph/impl/Graph.cpp b/libnd4j/include/graph/impl/Graph.cpp index 4b337dd0d..15db128a8 100644 --- a/libnd4j/include/graph/impl/Graph.cpp +++ b/libnd4j/include/graph/impl/Graph.cpp @@ -22,16 +22,16 @@ #include #include #include -#include +#include #include #include #include #include #include -#include -#include +#include +#include -namespace nd4j { +namespace sd { namespace graph { std::vector* Graph::getAllNodes() { return &_handles; @@ -51,7 +51,7 @@ namespace nd4j { Nd4jLong lastStep = 0L; std::vector shapes; - std::map, Nd4jLong*> shapesMap; + MAP_IMPL, Nd4jLong*> shapesMap; int cntFD = 0; @@ -155,7 +155,7 @@ namespace nd4j { Nd4jLong *newShape = nullptr; // if that's scalar output - we don't care about previous node - if (node->getDimensions()->size() == 0 || (node->getDimensions()->size() == 1 && node->getDimensions()->at(0) == nd4j::DataTypeUtils::max())) { + if (node->getDimensions()->size() == 0 || (node->getDimensions()->size() == 1 && node->getDimensions()->at(0) == sd::DataTypeUtils::max())) { newShape = new Nd4jLong[8]; newShape[0] = 2; @@ -249,11 +249,11 @@ namespace nd4j { return res; } - std::map * Graph::getMapped() { + MAP_IMPL * Graph::getMapped() { return _mapped; } - std::map *>* Graph::getOnion() { + MAP_IMPL *>* Graph::getOnion() { return _onion; } @@ -518,7 +518,7 @@ namespace nd4j { return ND4J_STATUS_OK; } - typename std::map::iterator fit; + typename MAP_IMPL::iterator fit; int cnts = 0; for ( fit = _unmapped.begin(); fit != _unmapped.end(); fit++ ) { int tK = fit->first; @@ -535,7 +535,7 @@ namespace nd4j { std::vector queue; // first pass for unmapped nodes, we try to build tale here - typename std::map::iterator it; + typename MAP_IMPL::iterator it; int cntf = 0; nd4j_debug("-----------\n",""); for ( it = _unmapped.begin(); it != _unmapped.end(); it++ ) { @@ -702,7 +702,7 @@ namespace nd4j { prepareOutputs(); - return nd4j::Status::OK(); + return sd::Status::OK(); } void Graph::tagInplaceNodes() { @@ -866,8 +866,8 @@ namespace nd4j { } Graph::Graph(const FlatGraph *flatGraph, VariableSpace *variableSpace) { - this->_onion = new std::map *>(); - this->_mapped = new std::map (); + this->_onion = new MAP_IMPL *>(); + this->_mapped = new MAP_IMPL (); this->_nodes = new std::vector(); this->_variableSpace = variableSpace == nullptr ? new VariableSpace() : variableSpace; bool trusted = flatGraph != nullptr; @@ -883,7 +883,7 @@ namespace nd4j { // if memory reqs were set - initialize workspace if (_configuration->_footprintForward > 0) { - nd4j::memory::Workspace *workspace = this->_variableSpace->launchContext()->getWorkspace(); + sd::memory::Workspace *workspace = this->_variableSpace->launchContext()->getWorkspace(); workspace->expandBy(_configuration->_footprintForward); } @@ -1175,10 +1175,10 @@ namespace nd4j { return ND4J_STATUS_OK; } - std::vector Graph::getOperations() { + std::vector Graph::getOperations() { buildGraph(); // nd4j_printf("\nRetrieving ops from the Graph and collect them...\n", ""); - std::vector res; + std::vector res; int opCnt = 0; for (int l = 0; l < _onion->size(); l++) { @@ -1187,7 +1187,7 @@ namespace nd4j { for (int n = 0; n < layerSize; n++) { Node* node = _onion->at(l)->at(n); if (node->name() == nullptr) continue; - nd4j::ops::OpDescriptor* pOpDescriptor = nullptr; + sd::ops::OpDescriptor* pOpDescriptor = nullptr; std::string opNameStr; //node->name(); int numInputs = 0; int numOutputs = 0; @@ -1221,7 +1221,7 @@ namespace nd4j { if (pOpDescriptor) res.emplace_back(*pOpDescriptor); else - res.emplace_back(nd4j::ops::OpDescriptor(numInputs, numOutputs, opNameStr, inplace)); + res.emplace_back(sd::ops::OpDescriptor(numInputs, numOutputs, opNameStr, inplace)); } } @@ -1236,7 +1236,7 @@ namespace nd4j { //printOutNode(node); if (node->name() == nullptr) continue; std::string opNameStr; //node->name(); - nd4j::ops::OpDescriptor* pOpDescriptor = nullptr; + sd::ops::OpDescriptor* pOpDescriptor = nullptr; int numInputs = 0; int numOutputs = 0; @@ -1264,7 +1264,7 @@ namespace nd4j { if (pOpDescriptor != nullptr) res.emplace_back(*pOpDescriptor); else - res.emplace_back(nd4j::ops::OpDescriptor(numInputs, numOutputs, opNameStr, inplace)); + res.emplace_back(sd::ops::OpDescriptor(numInputs, numOutputs, opNameStr, inplace)); } } diff --git a/libnd4j/blas/cpu/GraphExecutioner.cpp b/libnd4j/include/graph/impl/GraphExecutioner.cpp similarity index 96% rename from libnd4j/blas/cpu/GraphExecutioner.cpp rename to libnd4j/include/graph/impl/GraphExecutioner.cpp index 98b3204cd..c673d2b31 100644 --- a/libnd4j/blas/cpu/GraphExecutioner.cpp +++ b/libnd4j/include/graph/impl/GraphExecutioner.cpp @@ -24,12 +24,12 @@ //#include -#include -#include +#include +#include #include -#include -#include -#include +#include +#include +#include #include #include #include @@ -46,9 +46,9 @@ #include #include #include -#include +#include #include -#include +#include #include #include #include @@ -56,7 +56,7 @@ #include #include -namespace nd4j{ +namespace sd{ namespace graph { /** @@ -88,7 +88,7 @@ namespace graph { Context context(node->getContextPrototype(), variableSpace); - if (nd4j::Environment::getInstance()->isDebugAndVerbose()) { + if (sd::Environment::getInstance()->isDebugAndVerbose()) { //nd4j_debug("Input variables: %i\n", node->input()->size()); printf(" Inputs: {"); for (int e = 0; e < node->input()->size(); e++) { @@ -218,7 +218,7 @@ Nd4jStatus GraphExecutioner::execute(Graph *graph, VariableSpace* variableSpace) Nd4jLong tb0 = Environment::getInstance()->isProfiling() ? GraphProfile::currentTime() : 0L; graph->buildGraph(); - auto footprintForward = nd4j::memory::MemoryRegistrator::getInstance()->getGraphMemoryFootprint(graph->hashCode()); + auto footprintForward = sd::memory::MemoryRegistrator::getInstance()->getGraphMemoryFootprint(graph->hashCode()); if (footprintForward > 0) { if (__variableSpace->launchContext()->getWorkspace() != nullptr) { // this method will work only if current workspace size is smaller then proposed value @@ -273,7 +273,7 @@ Nd4jStatus GraphExecutioner::execute(Graph *graph, VariableSpace* variableSpace) nd4j_debug("Step: %lld; Node: %i <%s>\n", exec_counter, node->id(), node->name()->c_str()); // on first non-Exit node after loop we can rewind (if planned) - if (!(node->opType() == OpType_LOGIC && node->opNum() == nd4j::logic::Exit)) { + if (!(node->opType() == OpType_LOGIC && node->opNum() == sd::logic::Exit)) { // VALIDATED // if we're out of frame - let's remove it from queue @@ -289,7 +289,7 @@ Nd4jStatus GraphExecutioner::execute(Graph *graph, VariableSpace* variableSpace) // TODO: move inactivity check right here bool shouldSkip = false; - if (node->opType() == OpType_LOGIC && node->opNum() == nd4j::logic::Merge) { + if (node->opType() == OpType_LOGIC && node->opNum() == sd::logic::Merge) { // Merge node has own checkout logic auto inputId0 = node->input()->at(0); @@ -344,7 +344,7 @@ Nd4jStatus GraphExecutioner::execute(Graph *graph, VariableSpace* variableSpace) flowPath->markNodeActive(node->id(), true); - if (node->opType() == OpType_LOGIC && node->opNum() == nd4j::logic::Enter) { + if (node->opType() == OpType_LOGIC && node->opNum() == sd::logic::Enter) { // Enter operation // VALIDATED @@ -363,7 +363,7 @@ Nd4jStatus GraphExecutioner::execute(Graph *graph, VariableSpace* variableSpace) if (status != Status::OK()) return status; - } else if (node->opType() == OpType_LOGIC && node->opNum() == nd4j::logic::NextIteration) { + } else if (node->opType() == OpType_LOGIC && node->opNum() == sd::logic::NextIteration) { /** * NextIteration is special case: after successful execution of this op - we're changing execution position */ @@ -391,7 +391,7 @@ Nd4jStatus GraphExecutioner::execute(Graph *graph, VariableSpace* variableSpace) } - } else if (node->opType() == OpType_LOGIC && node->opNum() == nd4j::logic::Exit) { + } else if (node->opType() == OpType_LOGIC && node->opNum() == sd::logic::Exit) { // Exit node is another special case: it can rewind executioner to specific point in graph // VALIDATED @@ -458,7 +458,7 @@ Nd4jStatus GraphExecutioner::execute(Graph *graph, VariableSpace* variableSpace) // now we skip all branches except of this active one } - if (nd4j::Environment::getInstance()->isDebugAndVerbose()) { + if (sd::Environment::getInstance()->isDebugAndVerbose()) { if (__variableSpace->getVariable(node->id())->hasNDArray()) { auto array = __variableSpace->getVariable(node->id())->getNDArray(); @@ -491,7 +491,7 @@ Nd4jStatus GraphExecutioner::execute(Graph *graph, VariableSpace* variableSpace) if (__variableSpace->launchContext()->getWorkspace() != nullptr) { auto m = __variableSpace->launchContext()->getWorkspace()->getAllocatedSize(); auto h = graph->hashCode(); - nd4j::memory::MemoryRegistrator::getInstance()->setGraphMemoryFootprintIfGreater(h, m); + sd::memory::MemoryRegistrator::getInstance()->setGraphMemoryFootprintIfGreater(h, m); } if (tempFlow) { @@ -511,7 +511,7 @@ Nd4jStatus GraphExecutioner::execute(Graph *graph, VariableSpace* variableSpace) * 5) Returns pointer to FlatBuffer results buffer * */ - nd4j::graph::ResultWrapper* GraphExecutioner::executeFlatBuffer(Nd4jPointer pointer) { + sd::graph::ResultWrapper* GraphExecutioner::executeFlatBuffer(Nd4jPointer pointer) { uint8_t *buffer = reinterpret_cast(pointer); // nd4j_debug("Trying to restore graph\n", 0); @@ -585,7 +585,7 @@ Nd4jStatus GraphExecutioner::execute(Graph *graph, VariableSpace* variableSpace) auto fName = builder.CreateString(*(var->getName())); auto id = CreateIntPair(builder, var->id(), var->index()); - auto fv = CreateFlatVariable(builder, id, fName, static_cast(array->dataType()), 0, fArray); + auto fv = CreateFlatVariable(builder, id, fName, static_cast(array->dataType()), 0, fArray); variables_vector.push_back(fv); arrays++; @@ -742,7 +742,7 @@ Graph* GraphExecutioner::importFromTensorFlow(const char *fileName) { nd4j_verbose("Node id: [%i]; name: [%s]; opName: [%s]\n", n + 1, node.name().c_str(), node.op().c_str()); - nd4j::ops::DeclarableOp *op = nd4j::ops::OpRegistrator::getInstance()->getOperationFloat(node.op().c_str()); + sd::ops::DeclarableOp *op = sd::ops::OpRegistrator::getInstance()->getOperationFloat(node.op().c_str()); if (op == nullptr) { nd4j_verbose("Op wasn't found: %s\n", node.op().c_str()); @@ -837,9 +837,9 @@ uint8_t* readFlatBuffers(const char * filename) { int cnt = 0; int b = 0; while (cnt < fileLen) { - b += fread(data + cnt, 1, 1, in); + b = fread(data + cnt, 1, 1, in); - cnt++; + cnt += b; } fclose(in); @@ -863,7 +863,7 @@ flatbuffers::Offset GraphExecutioner::execute(Graph *graph, flatbuff graph->printOut(); auto status = GraphExecutioner::execute(graph); - if (status != nd4j::Status::OK()) + if (status != sd::Status::OK()) throw graph_execution_exception(request->id()); auto outputs = graph->fetchOutputs(); diff --git a/libnd4j/include/graph/impl/GraphHolder.cpp b/libnd4j/include/graph/impl/GraphHolder.cpp index e14f5d6ee..c480508f5 100644 --- a/libnd4j/include/graph/impl/GraphHolder.cpp +++ b/libnd4j/include/graph/impl/GraphHolder.cpp @@ -19,11 +19,11 @@ // #include -#include +#include #include #include -namespace nd4j { +namespace sd { namespace graph { GraphHolder* GraphHolder::getInstance() { if (_INSTANCE == 0) @@ -38,7 +38,7 @@ namespace nd4j { _graphF[graphId] = graph; - nd4j::SimpleReadWriteLock lock; + sd::SimpleReadWriteLock lock; _locks[graphId] = lock; } diff --git a/libnd4j/include/graph/impl/GraphState.cpp b/libnd4j/include/graph/impl/GraphState.cpp index 52691d801..a8b25603a 100644 --- a/libnd4j/include/graph/impl/GraphState.cpp +++ b/libnd4j/include/graph/impl/GraphState.cpp @@ -22,7 +22,7 @@ #include -namespace nd4j { +namespace sd { namespace graph { GraphState::GraphState(Nd4jLong id) { _id = id; diff --git a/libnd4j/include/graph/impl/GraphUtils.cpp b/libnd4j/include/graph/impl/GraphUtils.cpp index 4af7df1a2..15f674ce1 100644 --- a/libnd4j/include/graph/impl/GraphUtils.cpp +++ b/libnd4j/include/graph/impl/GraphUtils.cpp @@ -35,7 +35,7 @@ //#include //#include #endif -namespace nd4j { +namespace sd { namespace graph { bool GraphUtils::filterOperations(GraphUtils::OpList& ops) { @@ -69,7 +69,7 @@ std::string GraphUtils::makeCommandLine(GraphUtils::OpList& ops) { std::string res; if (!ops.empty()) { - res += std::string(" -g \"-DLIBND4J_OPS_LIST='"); + res += std::string(" -g \"-DSD_OPS_LIST='"); //res += *(ops[0].getOpName()); for (int i = 0; i < ops.size(); i++) { res += std::string("-DOP_"); diff --git a/libnd4j/include/graph/impl/InferenceRequest.cpp b/libnd4j/include/graph/impl/InferenceRequest.cpp index c60d0a08c..29fde1eb1 100644 --- a/libnd4j/include/graph/impl/InferenceRequest.cpp +++ b/libnd4j/include/graph/impl/InferenceRequest.cpp @@ -21,7 +21,7 @@ #include -namespace nd4j { +namespace sd { namespace graph { InferenceRequest::InferenceRequest(Nd4jLong graphId, ExecutorConfiguration *configuration) { this->_id = graphId; diff --git a/libnd4j/include/graph/impl/Intervals.cpp b/libnd4j/include/graph/impl/Intervals.cpp index d5c879eaa..1a89c797f 100644 --- a/libnd4j/include/graph/impl/Intervals.cpp +++ b/libnd4j/include/graph/impl/Intervals.cpp @@ -19,7 +19,7 @@ // #include -namespace nd4j { +namespace sd { // default constructor Intervals::Intervals(): _content({{}}) {} diff --git a/libnd4j/include/graph/impl/Node.cpp b/libnd4j/include/graph/impl/Node.cpp index 4c79ccb3e..e3ea75ef9 100644 --- a/libnd4j/include/graph/impl/Node.cpp +++ b/libnd4j/include/graph/impl/Node.cpp @@ -40,52 +40,52 @@ #include #include #include -#include +#include -namespace nd4j { +namespace sd { namespace graph { - void nd4j::graph::Node::setOuterTime(Nd4jLong time){ + void sd::graph::Node::setOuterTime(Nd4jLong time){ // if (hasBlockAttached()) // _block->setOuterTime(time); } - void nd4j::graph::Node::setInnerTime(Nd4jLong time){ + void sd::graph::Node::setInnerTime(Nd4jLong time){ // if (hasBlockAttached()) // _block->setInnerTime(time); } - void nd4j::graph::Node::setGraph(nd4j::graph::Graph* graph) { + void sd::graph::Node::setGraph(sd::graph::Graph* graph) { _graph = graph; } - nd4j::graph::Graph* nd4j::graph::Node::getGraph() { + sd::graph::Graph* sd::graph::Node::getGraph() { return _graph; } - bool nd4j::graph::Node::hasGraphEmbedded() { + bool sd::graph::Node::hasGraphEmbedded() { return _graph != nullptr; } - void nd4j::graph::Node::markInplace(bool reallyInplace) { + void sd::graph::Node::markInplace(bool reallyInplace) { _isInplace = reallyInplace; if (_protoContext != nullptr) { _protoContext->markInplace(reallyInplace); } } - OpClass nd4j::graph::Node::getOpClass() { + OpClass sd::graph::Node::getOpClass() { return _opClass; } - bool nd4j::graph::Node::hasBlockAttached() { + bool sd::graph::Node::hasBlockAttached() { return _protoContext != nullptr; } - bool nd4j::graph::Node::isInplace() { + bool sd::graph::Node::isInplace() { return _isInplace; } - bool nd4j::graph::Node::isDivergencePoint() { + bool sd::graph::Node::isDivergencePoint() { if (hasCustomOp()) { return _customOp->getOpDescriptor()->isDivergent(); } else if (opType() == OpType_LOGIC && opNum() == 30) @@ -94,11 +94,11 @@ namespace nd4j { return false; } - void nd4j::graph::Node::setActive(bool reallyActive) { + void sd::graph::Node::setActive(bool reallyActive) { _active = reallyActive; } - bool nd4j::graph::Node::isActive() { + bool sd::graph::Node::isActive() { return _active; } @@ -110,7 +110,7 @@ namespace nd4j { _frameId = frameId; } - ContextPrototype * nd4j::graph::Node::getContextPrototype() { + ContextPrototype * sd::graph::Node::getContextPrototype() { if (_protoContext == nullptr) _protoContext = new ContextPrototype(this->getCustomOp() != nullptr ? this->getCustomOp()->getOpDescriptor() : nullptr, this->id()); if (_protoContext->inputs()->empty()) { @@ -121,22 +121,22 @@ namespace nd4j { return _protoContext; } - void nd4j::graph::Node::setContextPrototype(ContextPrototype *block) { + void sd::graph::Node::setContextPrototype(ContextPrototype *block) { if (_protoContext != nullptr) throw std::runtime_error("Block already exists"); _protoContext = block; } - void nd4j::graph::Node::setId(int id) { + void sd::graph::Node::setId(int id) { _id = id; } - nd4j::ops::DeclarableOp* nd4j::graph::Node::getCustomOp() { + sd::ops::DeclarableOp* sd::graph::Node::getCustomOp() { return _customOp; } - void nd4j::graph::Node::setCustomOp(nd4j::ops::DeclarableOp *customOp) { + void sd::graph::Node::setCustomOp(sd::ops::DeclarableOp *customOp) { _customOp = customOp; // divergent ops (Switch etc) are always inplace, they don't allocate anything @@ -144,40 +144,40 @@ namespace nd4j { _isInplace = true; } - bool nd4j::graph::Node::hasCustomOp() { + bool sd::graph::Node::hasCustomOp() { return _customOp != nullptr; } - std::string * nd4j::graph::Node::name() { + std::string * sd::graph::Node::name() { return this->getName(); } - std::string * nd4j::graph::Node::getName() { + std::string * sd::graph::Node::getName() { return &_name; } - void nd4j::graph::Node::setName(const std::string& name) { + void sd::graph::Node::setName(const std::string& name) { _name = name.c_str(); } - void nd4j::graph::Node::setName(std::string *name) { + void sd::graph::Node::setName(std::string *name) { _name = *name; } - double nd4j::graph::Node::scalar() { + double sd::graph::Node::scalar() { return _scalar.e(0); }; - void nd4j::graph::Node::pickInput(std::pair& pair) { + void sd::graph::Node::pickInput(std::pair& pair) { _input.push_back(pair); } - void nd4j::graph::Node::pickInput(int inputId, int outputId) { + void sd::graph::Node::pickInput(int inputId, int outputId) { std::pair p(inputId,outputId); pickInput(p); } - void nd4j::graph::Node::pickInput(int inputId) { + void sd::graph::Node::pickInput(int inputId) { pickInput(inputId, 0); if (inputId < 0) @@ -186,25 +186,25 @@ namespace nd4j { _hasInternalInputs = true; } - void nd4j::graph::Node::pickExternalOutput(int outputId) { + void sd::graph::Node::pickExternalOutput(int outputId) { std::pair pair(outputId, 0); _output.push_back(pair); _hasExternalOutputs = true; } - void nd4j::graph::Node::pickOutputOnce(int outputId) { + void sd::graph::Node::pickOutputOnce(int outputId) { std::pair pair(outputId, 0); if (std::find(_output.begin(), _output.end(), pair) == _output.end()) pickOutput(outputId); } - void nd4j::graph::Node::pickOutput(int nodeId, int outputId) { + void sd::graph::Node::pickOutput(int nodeId, int outputId) { std::pair pair(nodeId, outputId); _output.emplace_back(pair); } - void nd4j::graph::Node::pickOutput(int outputId) { + void sd::graph::Node::pickOutput(int outputId) { std::pair pair(outputId, 0); _output.emplace_back(pair); @@ -214,47 +214,47 @@ namespace nd4j { _hasInternalOutputs = true; } - int * nd4j::graph::Node::getDimensionsPtr() { + int * sd::graph::Node::getDimensionsPtr() { return _dim; } - std::vector * nd4j::graph::Node::getDimensions() { + std::vector * sd::graph::Node::getDimensions() { return &_dimensions; } - int nd4j::graph::Node::getLayer() { + int sd::graph::Node::getLayer() { return _layer; } - void nd4j::graph::Node::setLayer(int layer) { + void sd::graph::Node::setLayer(int layer) { _layer = layer; } - bool nd4j::graph::Node::hasExternalOutputs() { + bool sd::graph::Node::hasExternalOutputs() { return _hasExternalOutputs; } - bool nd4j::graph::Node::hasExternalInputs() { + bool sd::graph::Node::hasExternalInputs() { return _hasExternalInputs; } - bool nd4j::graph::Node::hasInternalOutputs() { + bool sd::graph::Node::hasInternalOutputs() { return _hasInternalOutputs; } - bool nd4j::graph::Node::hasInternalInputs() { + bool sd::graph::Node::hasInternalInputs() { return _hasInternalInputs; } - bool nd4j::graph::Node::isMultiInput() { + bool sd::graph::Node::isMultiInput() { return _input.size() > 1; } - bool nd4j::graph::Node::isMultiOutput() { + bool sd::graph::Node::isMultiOutput() { return _output.size() > 1; } - double * nd4j::graph::Node::extraParams() { + double * sd::graph::Node::extraParams() { return _extraParams; } @@ -266,23 +266,23 @@ namespace nd4j { _referencedBy.emplace_back(nodeId); } - nd4j::graph::OpType nd4j::graph::Node::opType() { + sd::graph::OpType sd::graph::Node::opType() { return _opType; } - int nd4j::graph::Node::id() { + int sd::graph::Node::id() { return _id; } - Nd4jLong nd4j::graph::Node::opNum() { + Nd4jLong sd::graph::Node::opNum() { return _opNum; } - std::vector> *nd4j::graph::Node::input() { + std::vector> *sd::graph::Node::input() { return &_input; } - std::vector> *nd4j::graph::Node::output() { + std::vector> *sd::graph::Node::output() { return &_output; } @@ -313,12 +313,12 @@ namespace nd4j { } BUILD_SINGLE_TEMPLATE(template ND4J_EXPORT Node* Node::asT, (), LIBND4J_TYPES); - nd4j::graph::Node::Node(nd4j::ops::DeclarableOp *customOp, int id, std::initializer_list input, std::initializer_list output, std::initializer_list dimensions, float scalar, std::initializer_list tArgs, std::initializer_list iArgs) { + sd::graph::Node::Node(sd::ops::DeclarableOp *customOp, int id, std::initializer_list input, std::initializer_list output, std::initializer_list dimensions, float scalar, std::initializer_list tArgs, std::initializer_list iArgs) { this->_opType = OpType_CUSTOM; this->_id = id; this->_opNum = customOp->getOpHash(); this->_extraParams = nullptr; - this->_dataType = nd4j::DataType::FLOAT32; // float as default + this->_dataType = sd::DataType::FLOAT32; // float as default this->_dim = nullptr; this->_customOp = customOp; @@ -358,16 +358,16 @@ namespace nd4j { this->setContextPrototype(block); } - void nd4j::graph::Node::setOpType(OpType opType) { + void sd::graph::Node::setOpType(OpType opType) { this->_opType = opType; } - nd4j::graph::Node::Node(OpType opType, int opNum, int id, std::initializer_list input, std::initializer_list output, std::initializer_list dimensions, float scalar, std::initializer_list tArgs, std::initializer_list iArgs) { + sd::graph::Node::Node(OpType opType, int opNum, int id, std::initializer_list input, std::initializer_list output, std::initializer_list dimensions, float scalar, std::initializer_list tArgs, std::initializer_list iArgs) { this->_opType = opType; this->_id = id; this->_opNum = opNum; this->_extraParams = nullptr; - this->_dataType = nd4j::DataType::FLOAT32; // float as default + this->_dataType = sd::DataType::FLOAT32; // float as default this->_dim = nullptr; _hasExternalInputs = false; @@ -455,14 +455,14 @@ namespace nd4j { } }; - nd4j::graph::Node::Node(const nd4j::graph::FlatNode *node) { + sd::graph::Node::Node(const sd::graph::FlatNode *node) { _hasExternalInputs = false; _hasExternalOutputs = false; _hasInternalInputs = false; _hasInternalOutputs = false; _extraParams = nullptr; _dim = nullptr; - _dataType = nd4j::DataType::FLOAT32; // float as default + _dataType = sd::DataType::FLOAT32; // float as default if (node->scope_id() != 0) this->_scope_id = node->scope_id(); @@ -470,7 +470,7 @@ namespace nd4j { this->_scope_name = node->scope_name()->str(); if (node->scalar() != nullptr) { - auto scalar = nd4j::graph::FlatUtils::fromFlatArray(node->scalar()); + auto scalar = sd::graph::FlatUtils::fromFlatArray(node->scalar()); _scalar = *scalar; delete scalar; } @@ -589,7 +589,7 @@ namespace nd4j { if (node->extraTypes() != nullptr && node->extraTypes()->size() > 0) { for (int e = 0; e < (int) node->extraTypes()->size(); e++) { - block->getDArguments()->emplace_back((nd4j::DataType) node->extraTypes()->Get(e)); + block->getDArguments()->emplace_back((sd::DataType) node->extraTypes()->Get(e)); } } @@ -626,7 +626,7 @@ namespace nd4j { if (node->extraTypes() != nullptr && node->extraTypes()->size() > 0) { for (int e = 0; e < (int) node->extraTypes()->size(); e++) { - block->getDArguments()->emplace_back((nd4j::DataType) node->extraTypes()->Get(e)); + block->getDArguments()->emplace_back((sd::DataType) node->extraTypes()->Get(e)); } } @@ -636,7 +636,7 @@ namespace nd4j { block->setOpDescriptor(this->getCustomOp()->getOpDescriptor()); } } else if (this->_opType == OpType_CUSTOM) { - auto op = nd4j::ops::OpRegistrator::getInstance()->getOperation(this->opNum()); + auto op = sd::ops::OpRegistrator::getInstance()->getOperation(this->opNum()); if (op == nullptr) { nd4j_verbose("Can't find operation: %lld\n", this->opNum()); throw std::runtime_error("Can't find requested operation"); @@ -666,7 +666,7 @@ namespace nd4j { if (node->extraTypes() != nullptr && node->extraTypes()->size() > 0) { for (int e = 0; e < (int) node->extraTypes()->size(); e++) { - block->getDArguments()->emplace_back((nd4j::DataType) node->extraTypes()->Get(e)); + block->getDArguments()->emplace_back((sd::DataType) node->extraTypes()->Get(e)); } } @@ -682,7 +682,7 @@ namespace nd4j { } } - nd4j::DataType Node::dataType() { + sd::DataType Node::dataType() { return _dataType; } @@ -690,7 +690,7 @@ namespace nd4j { return _protoContext; } - nd4j::graph::Node::~Node() { + sd::graph::Node::~Node() { if (_extraParams != nullptr) delete[] _extraParams; @@ -705,132 +705,132 @@ namespace nd4j { } } - int nd4j::graph::Node::getRewindNode() { + int sd::graph::Node::getRewindNode() { return _rewindNode; } - void nd4j::graph::Node::setRewindNode(int nodeId) { + void sd::graph::Node::setRewindNode(int nodeId) { _rewindNode = nodeId; } - std::pair& nd4j::graph::Node::getRewindLayer() { + std::pair& sd::graph::Node::getRewindLayer() { return _rewindLayer; }; - void nd4j::graph::Node::setRewindLayer(int layerId, int stepId) { + void sd::graph::Node::setRewindLayer(int layerId, int stepId) { _rewindLayer.first = layerId; _rewindLayer.second = stepId; } - bool nd4j::graph::Node::equals(Node *other) { + bool sd::graph::Node::equals(Node *other) { if (_opType == other->_opType && _dataType == other->_dataType && _opNum == other->_opNum) return true; return false; } - void nd4j::graph::Node::deleteOpByType(OpType opType, void *op) { + void sd::graph::Node::deleteOpByType(OpType opType, void *op) { switch (opType) { case OpType_PAIRWISE: - delete reinterpret_cast(op); + delete reinterpret_cast(op); break; case OpType_PAIRWISE_BOOL: - delete reinterpret_cast(op); + delete reinterpret_cast(op); break; case OpType_TRANSFORM_STRICT: - delete reinterpret_cast(op); + delete reinterpret_cast(op); break; case OpType_TRANSFORM_SAME: - delete reinterpret_cast(op); + delete reinterpret_cast(op); break; case OpType_TRANSFORM_FLOAT: - delete reinterpret_cast(op); + delete reinterpret_cast(op); break; case OpType_TRANSFORM_BOOL: - delete reinterpret_cast(op); + delete reinterpret_cast(op); break; case OpType_SCALAR: - delete reinterpret_cast(op); + delete reinterpret_cast(op); break; case OpType_SCALAR_BOOL: - delete reinterpret_cast(op); + delete reinterpret_cast(op); break; case OpType_REDUCE_3: - delete reinterpret_cast(op); + delete reinterpret_cast(op); break; case OpType_REDUCE_SAME: - delete reinterpret_cast(op); + delete reinterpret_cast(op); break; case OpType_REDUCE_FLOAT: - delete reinterpret_cast(op); + delete reinterpret_cast(op); break; case OpType_REDUCE_LONG: - delete reinterpret_cast(op); + delete reinterpret_cast(op); break; case OpType_REDUCE_BOOL: - delete reinterpret_cast(op); + delete reinterpret_cast(op); break; case OpType_INDEX_REDUCE: - delete reinterpret_cast(op); + delete reinterpret_cast(op); break; case OpType_SUMMARYSTATS: - delete reinterpret_cast(op); + delete reinterpret_cast(op); break; case OpType_RANDOM: - delete reinterpret_cast(op); + delete reinterpret_cast(op); break; case OpType_BROADCAST: - delete reinterpret_cast(op); + delete reinterpret_cast(op); break; case OpType_BROADCAST_BOOL: - delete reinterpret_cast(op); + delete reinterpret_cast(op); break; case OpType_CUSTOM: - delete reinterpret_cast(op); + delete reinterpret_cast(op); break; default: throw std::runtime_error("Bad opType passed in"); } } - nd4j::ops::DeclarableOp* nd4j::graph::Node::buildOpByType(OpType opType, int numInputs, int numIArgs, int numTArgs, int opNum, NDArray *scalar) { + sd::ops::DeclarableOp* sd::graph::Node::buildOpByType(OpType opType, int numInputs, int numIArgs, int numTArgs, int opNum, NDArray *scalar) { switch (opType) { case OpType_PAIRWISE: - return new nd4j::ops::LegacyPairwiseTransformOp(opNum); + return new sd::ops::LegacyPairwiseTransformOp(opNum); case OpType_PAIRWISE_BOOL: - return new nd4j::ops::LegacyPairwiseTransformBoolOp(opNum); + return new sd::ops::LegacyPairwiseTransformBoolOp(opNum); case OpType_TRANSFORM_STRICT: - return new nd4j::ops::LegacyTransformStrictOp(opNum); + return new sd::ops::LegacyTransformStrictOp(opNum); case OpType_TRANSFORM_SAME: - return new nd4j::ops::LegacyTransformSameOp(opNum); + return new sd::ops::LegacyTransformSameOp(opNum); case OpType_TRANSFORM_FLOAT: - return new nd4j::ops::LegacyTransformFloatOp(opNum); + return new sd::ops::LegacyTransformFloatOp(opNum); case OpType_TRANSFORM_BOOL: - return new nd4j::ops::LegacyTransformBoolOp(opNum); + return new sd::ops::LegacyTransformBoolOp(opNum); case OpType_SCALAR: - return scalar == nullptr ? new nd4j::ops::LegacyScalarOp(opNum) : new nd4j::ops::LegacyScalarOp(opNum, *scalar); + return scalar == nullptr ? new sd::ops::LegacyScalarOp(opNum) : new sd::ops::LegacyScalarOp(opNum, *scalar); case OpType_SCALAR_BOOL: - return scalar == nullptr ? new nd4j::ops::LegacyScalarBoolOp(opNum) : new nd4j::ops::LegacyScalarBoolOp(opNum, *scalar); + return scalar == nullptr ? new sd::ops::LegacyScalarBoolOp(opNum) : new sd::ops::LegacyScalarBoolOp(opNum, *scalar); case OpType_REDUCE_3: - return new nd4j::ops::LegacyReduce3Op(opNum); + return new sd::ops::LegacyReduce3Op(opNum); case OpType_REDUCE_SAME: - return new nd4j::ops::LegacyReduceSameOp(opNum); + return new sd::ops::LegacyReduceSameOp(opNum); case OpType_REDUCE_FLOAT: - return new nd4j::ops::LegacyReduceFloatOp(opNum); + return new sd::ops::LegacyReduceFloatOp(opNum); case OpType_REDUCE_LONG: - return new nd4j::ops::LegacyReduceLongOp(opNum); + return new sd::ops::LegacyReduceLongOp(opNum); case OpType_REDUCE_BOOL: - return new nd4j::ops::LegacyReduceBoolOp(opNum); + return new sd::ops::LegacyReduceBoolOp(opNum); case OpType_INDEX_REDUCE: - return new nd4j::ops::LegacyIndexReduceOp(opNum); + return new sd::ops::LegacyIndexReduceOp(opNum); case OpType_SUMMARYSTATS: - return new nd4j::ops::LegacyStatsOp(opNum); + return new sd::ops::LegacyStatsOp(opNum); case OpType_RANDOM: - return new nd4j::ops::LegacyRandomOp(opNum); + return new sd::ops::LegacyRandomOp(opNum); case OpType_BROADCAST: - return new nd4j::ops::LegacyBroadcastOp(opNum); + return new sd::ops::LegacyBroadcastOp(opNum); case OpType_BROADCAST_BOOL: - return new nd4j::ops::LegacyBroadcastBoolOp(opNum); + return new sd::ops::LegacyBroadcastBoolOp(opNum); default: throw std::runtime_error("Bad opType passed in"); } @@ -846,7 +846,7 @@ namespace nd4j { Node* Node::clone() { - if (this->_customOp && this->_opType == nd4j::graph::OpType_CUSTOM) { + if (this->_customOp && this->_opType == sd::graph::OpType_CUSTOM) { auto clone = new Node(this->_customOp, _id); clone->pullValues(this); return clone; @@ -860,7 +860,7 @@ namespace nd4j { if (!_isDeductable) clone->_customOp = _customOp; else { - auto c = dynamic_cast(_customOp); + auto c = dynamic_cast(_customOp); clone->_customOp = c->clone(); } diff --git a/libnd4j/include/graph/impl/NodeState.cpp b/libnd4j/include/graph/impl/NodeState.cpp index 1e463622b..d09de9c57 100644 --- a/libnd4j/include/graph/impl/NodeState.cpp +++ b/libnd4j/include/graph/impl/NodeState.cpp @@ -20,7 +20,7 @@ #include -namespace nd4j { +namespace sd { namespace graph { NodeState::NodeState(int id) { _id = id; diff --git a/libnd4j/include/graph/impl/ResultWrapper.cpp b/libnd4j/include/graph/impl/ResultWrapper.cpp index 00fad0acb..277644acf 100644 --- a/libnd4j/include/graph/impl/ResultWrapper.cpp +++ b/libnd4j/include/graph/impl/ResultWrapper.cpp @@ -22,7 +22,7 @@ #include -namespace nd4j { +namespace sd { namespace graph { ResultWrapper::ResultWrapper(Nd4jLong size, Nd4jPointer ptr) { if (size <= 0) diff --git a/libnd4j/include/graph/impl/Scope.cpp b/libnd4j/include/graph/impl/Scope.cpp index 60e22a83d..84a8f2f0d 100644 --- a/libnd4j/include/graph/impl/Scope.cpp +++ b/libnd4j/include/graph/impl/Scope.cpp @@ -18,9 +18,9 @@ // Created by raver119 on 14.10.2017. // -#include "Scope.h" +#include -namespace nd4j { +namespace sd { namespace graph { Scope::Scope(int id, const char *name) { _id = id; diff --git a/libnd4j/include/graph/impl/SessionLocalStorage.cpp b/libnd4j/include/graph/impl/SessionLocalStorage.cpp index 59ad21c90..9c512b0b6 100644 --- a/libnd4j/include/graph/impl/SessionLocalStorage.cpp +++ b/libnd4j/include/graph/impl/SessionLocalStorage.cpp @@ -18,11 +18,11 @@ // @author raver119@gmail.com // -#include -#include +#include +#include #include -namespace nd4j { +namespace sd { namespace graph { SessionLocalStorage::SessionLocalStorage(VariableSpace* variableSpace, Stash* stash) { // we start from 1, since key 0 holds original VariableSpace @@ -104,7 +104,7 @@ namespace nd4j { return ntid; } - Nd4jLong nd4j::graph::SessionLocalStorage::startSession() { + Nd4jLong sd::graph::SessionLocalStorage::startSession() { auto tid = getThreadId(); nd4j_debug("Adding ThreadId: %i;\n", (int) tid); diff --git a/libnd4j/include/graph/impl/Stash.cpp b/libnd4j/include/graph/impl/Stash.cpp index 5a48062f7..618e01c87 100644 --- a/libnd4j/include/graph/impl/Stash.cpp +++ b/libnd4j/include/graph/impl/Stash.cpp @@ -20,14 +20,24 @@ #include -namespace nd4j { + +namespace std { + size_t hash::operator()(const sd::graph::KeyPair& k) const { + using std::hash; + auto res = std::hash()(k.name()); + res ^= std::hash()(k.key()) + 0x9e3779b9 + (res << 6) + (res >> 2); + return res; + } +} + +namespace sd { namespace graph { - nd4j::graph::KeyPair::KeyPair(int node, const char * name) { + sd::graph::KeyPair::KeyPair(int node, const char * name) { _node = node; _name = std::string(name); } - bool nd4j::graph::KeyPair::operator<(const KeyPair& other) const { + bool sd::graph::KeyPair::operator<(const KeyPair& other) const { if (_node < other._node) return true; else if (_node > other._node) @@ -36,42 +46,42 @@ namespace nd4j { return _name < other._name; } - nd4j::graph::Stash::Stash() { + sd::graph::Stash::Stash() { // } - nd4j::graph::Stash::~Stash() { + sd::graph::Stash::~Stash() { if (_handles.size() > 0) this->clear(); } /* -bool nd4j::graph::Stash::checkStash(nd4j::graph::Block& block, const char *name) { +bool sd::graph::Stash::checkStash(sd::graph::Block& block, const char *name) { return checkStash(block.getNodeId(), name); } */ - bool nd4j::graph::Stash::checkStash(int nodeId, const char *name) { + bool sd::graph::Stash::checkStash(int nodeId, const char *name) { KeyPair kp(nodeId, name); return _stash.count(kp) > 0; } /* -nd4j::NDArray* nd4j::graph::Stash::extractArray(nd4j::graph::Block& block, const char *name) { +sd::NDArray* sd::graph::Stash::extractArray(sd::graph::Block& block, const char *name) { return extractArray(block.getNodeId(), name); } */ - nd4j::NDArray* nd4j::graph::Stash::extractArray(int nodeId, const char *name) { + sd::NDArray* sd::graph::Stash::extractArray(int nodeId, const char *name) { KeyPair kp(nodeId, name); return _stash[kp]; } /* -void nd4j::graph::Stash::storeArray(nd4j::graph::Block& block, const char *name, nd4j::NDArray *array) { +void sd::graph::Stash::storeArray(sd::graph::Block& block, const char *name, sd::NDArray *array) { storeArray(block.getNodeId(), name, array); } */ - void nd4j::graph::Stash::storeArray(int nodeId, const char *name, nd4j::NDArray *array) { + void sd::graph::Stash::storeArray(int nodeId, const char *name, sd::NDArray *array) { KeyPair kp(nodeId, name); _stash[kp] = array; @@ -79,7 +89,7 @@ void nd4j::graph::Stash::storeArray(nd4j::graph::Block& block, const char *name, _handles.push_back(array); } - void nd4j::graph::Stash::clear() { + void sd::graph::Stash::clear() { for (auto v: _handles) delete v; diff --git a/libnd4j/include/graph/impl/TimeHolder.cpp b/libnd4j/include/graph/impl/TimeHolder.cpp index b22740943..a292a8997 100644 --- a/libnd4j/include/graph/impl/TimeHolder.cpp +++ b/libnd4j/include/graph/impl/TimeHolder.cpp @@ -20,7 +20,7 @@ #include -namespace nd4j { +namespace sd { namespace graph { void TimeHolder::setOuterTime(int nodeId, Nd4jLong time) { diff --git a/libnd4j/include/graph/impl/Variable.cpp b/libnd4j/include/graph/impl/Variable.cpp index c2c5ff61f..e87c51897 100644 --- a/libnd4j/include/graph/impl/Variable.cpp +++ b/libnd4j/include/graph/impl/Variable.cpp @@ -26,7 +26,7 @@ #include #include -namespace nd4j { +namespace sd { namespace graph { template @@ -52,7 +52,7 @@ namespace nd4j { } BUILD_SINGLE_TEMPLATE(template ND4J_EXPORT Variable* Variable::asT, (), LIBND4J_TYPES); - nd4j::graph::Variable* nd4j::graph::Variable::clone() { + sd::graph::Variable* sd::graph::Variable::clone() { auto result = new Variable(this->isPlaceholder()); result->_external = this->_external; result->_id = this->_id; @@ -72,47 +72,47 @@ namespace nd4j { return result; } - void nd4j::graph::Variable::setIndex(int index) { + void sd::graph::Variable::setIndex(int index) { _index = index; } - bool nd4j::graph::Variable::hasNDArray() { + bool sd::graph::Variable::hasNDArray() { return _ndarray != nullptr; } - void nd4j::graph::Variable::setVariableType(VariableType variableType) { + void sd::graph::Variable::setVariableType(VariableType variableType) { _variableType = variableType; } - bool nd4j::graph::Variable::hasNDArrayList() { + bool sd::graph::Variable::hasNDArrayList() { return _list != nullptr; } - bool nd4j::graph::Variable::isPlaceholder() { + bool sd::graph::Variable::isPlaceholder() { return _placeholder; } - std::string * nd4j::graph::Variable::getName() { + std::string * sd::graph::Variable::getName() { return &_name; } - void nd4j::graph::Variable::setName(std::string *name) { + void sd::graph::Variable::setName(std::string *name) { _name = *name; } - int nd4j::graph::Variable::id() { + int sd::graph::Variable::id() { return _id; } - int nd4j::graph::Variable::index() { + int sd::graph::Variable::index() { return _index; } - void nd4j::graph::Variable::setId(int id) { + void sd::graph::Variable::setId(int id) { _id = id; } - bool nd4j::graph::Variable::isEmpty() { + bool sd::graph::Variable::isEmpty() { if (_variableType == VariableType::NDARRAY) return _ndarray == nullptr || !_ndarray->nonNull(); else if (_variableType == VariableType::ARRAY_LIST) @@ -121,29 +121,29 @@ namespace nd4j { return false; } - bool nd4j::graph::Variable::isExternal() { + bool sd::graph::Variable::isExternal() { return _external; } - bool nd4j::graph::Variable::isReadOnly() { + bool sd::graph::Variable::isReadOnly() { return _readOnly; } - void nd4j::graph::Variable::markExternal(bool reallyExternal) { + void sd::graph::Variable::markExternal(bool reallyExternal) { this->_external = reallyExternal; } - void nd4j::graph::Variable::markRemovable(bool reallyRemovable) { + void sd::graph::Variable::markRemovable(bool reallyRemovable) { if (!reallyRemovable) nd4j_debug("",""); this->_removable = reallyRemovable; } - void nd4j::graph::Variable::markReadOnly(bool reallyReadOnly) { + void sd::graph::Variable::markReadOnly(bool reallyReadOnly) { this->_readOnly = reallyReadOnly; } - nd4j::NDArray * nd4j::graph::Variable::getNDArray() { + sd::NDArray * sd::graph::Variable::getNDArray() { if (_variableType != VariableType::NDARRAY) { nd4j_printf("Variable[%i:%i/<%s>] is has [%s] type, but NDArray was requested\n", this->_id, this->_index, this->_name.c_str(), EnumUtils::_VariableTypeToString(_variableType)); } @@ -162,7 +162,7 @@ namespace nd4j { return this->_ndarray; } - nd4j::NDArrayList * nd4j::graph::Variable::getNDArrayList() { + sd::NDArrayList * sd::graph::Variable::getNDArrayList() { if (_variableType != VariableType::ARRAY_LIST) { nd4j_debug("Variable[%i:%i/<%s>] is has [%s] type, but NDArrayList was requested\n", this->_id, this->_index, this->_name.c_str(), EnumUtils::_VariableTypeToString(_variableType)); } @@ -175,24 +175,24 @@ namespace nd4j { } - void nd4j::graph::Variable::setNDArrayList(nd4j::NDArrayList * list) { + void sd::graph::Variable::setNDArrayList(sd::NDArrayList * list) { this->_variableType = VariableType::ARRAY_LIST; this->_list = list; } - void nd4j::graph::Variable::setNDArray(nd4j::NDArray * array) { + void sd::graph::Variable::setNDArray(sd::NDArray * array) { this->_variableType = VariableType::NDARRAY; this->_ndarray = array; } - VariableType nd4j::graph::Variable::variableType() { + VariableType sd::graph::Variable::variableType() { return _variableType; } - nd4j::graph::Variable::Variable(const nd4j::graph::FlatVariable *flatVariable) { + sd::graph::Variable::Variable(const sd::graph::FlatVariable *flatVariable) { auto vid = flatVariable->id(); this->_id = vid->first(); this->_index = vid->second(); @@ -211,7 +211,7 @@ namespace nd4j { // ????? if (flatVariable->ndarray() != nullptr) { auto ar = flatVariable->ndarray(); - _ndarray = nd4j::graph::FlatUtils::fromFlatArray(ar); + _ndarray = sd::graph::FlatUtils::fromFlatArray(ar); } _variableType = VariableType::NDARRAY; @@ -223,9 +223,9 @@ namespace nd4j { auto ar = flatVariable->ndarray(); if (ar->dtype() == DType_UTF8) { - _ndarray = nd4j::graph::FlatUtils::fromFlatArray(ar); + _ndarray = sd::graph::FlatUtils::fromFlatArray(ar); } else { - _ndarray = nd4j::graph::FlatUtils::fromFlatArray(ar); + _ndarray = sd::graph::FlatUtils::fromFlatArray(ar); } _variableType = VariableType::NDARRAY; @@ -236,7 +236,7 @@ namespace nd4j { // ????? if (flatVariable->ndarray() != nullptr) { auto ar = flatVariable->ndarray(); - _ndarray = nd4j::graph::FlatUtils::fromFlatArray(ar); + _ndarray = sd::graph::FlatUtils::fromFlatArray(ar); // _ndarray->triggerAllocationFlag(true); } @@ -249,7 +249,7 @@ namespace nd4j { if (flatVariable->ndarray() != nullptr) { auto ar = flatVariable->ndarray(); - _ndarray = nd4j::graph::FlatUtils::fromFlatArray(ar); + _ndarray = sd::graph::FlatUtils::fromFlatArray(ar); // _ndarray->triggerAllocationFlag(true); _variableType = VariableType::NDARRAY; @@ -270,16 +270,16 @@ namespace nd4j { } } - std::vector& nd4j::graph::Variable::shape() { + std::vector& sd::graph::Variable::shape() { return _shape; } - nd4j::graph::Variable::Variable(bool placeholder) { + sd::graph::Variable::Variable(bool placeholder) { _placeholder = placeholder; } - nd4j::graph::Variable::Variable(NDArray *array, const char *name ) { + sd::graph::Variable::Variable(NDArray *array, const char *name ) { _ndarray = array; _external = false; @@ -293,13 +293,13 @@ namespace nd4j { } - nd4j::graph::Variable::Variable(NDArray *array, const char *name, int id, int idx) : Variable(array, name) { + sd::graph::Variable::Variable(NDArray *array, const char *name, int id, int idx) : Variable(array, name) { _id = id; _index = idx; } - nd4j::graph::Variable::~Variable() { + sd::graph::Variable::~Variable() { //nd4j_printf("Removing variable [%i:%i]\n", _id, _index); if (_variableType == VariableType::NDARRAY) { nd4j_debug("Removing variable <%i:%i>\n", _id, _index); @@ -323,7 +323,7 @@ namespace nd4j { auto fBuffer = builder.CreateVector(array->asByteVector()); // packing array - auto fArray = CreateFlatArray(builder, fShape, fBuffer, (nd4j::graph::DType) array->dataType()); + auto fArray = CreateFlatArray(builder, fShape, fBuffer, (sd::graph::DType) array->dataType()); // packing id/index of this var auto fVid = CreateIntPair(builder, this->_id, this->_index); @@ -334,10 +334,27 @@ namespace nd4j { stringId = builder.CreateString(this->_name); // returning array - return CreateFlatVariable(builder, fVid, stringId, static_cast(array->dataType()), 0, fArray); + return CreateFlatVariable(builder, fVid, stringId, static_cast(array->dataType()), 0, fArray); } else { throw std::runtime_error("Variable::asFlatVariable isn't possible for NDArrayList"); } } } +} + +namespace std { + + size_t hash>::operator()(const std::pair& k) const { + auto v = std::hash()(k.first); + v ^= std::hash()(k.second) + 0x9e3779b9 + (v << 6) + (v >> 2); + return v; + } + + size_t hash::operator()(const bfloat16& k) const { + return std::hash()((float)k); + } + + size_t hash::operator()(const float16& k) const { + return std::hash()((float)k); + } } \ No newline at end of file diff --git a/libnd4j/include/graph/impl/VariableProxy.cpp b/libnd4j/include/graph/impl/VariableProxy.cpp index e8abf1310..2736e2a9e 100644 --- a/libnd4j/include/graph/impl/VariableProxy.cpp +++ b/libnd4j/include/graph/impl/VariableProxy.cpp @@ -18,10 +18,10 @@ // @author raver119@gmail.com // -#include +#include #include -namespace nd4j { +namespace sd { namespace graph { VariableProxy::VariableProxy(VariableSpace* ref) { @@ -110,7 +110,7 @@ namespace nd4j { } - nd4j::graph::Variable *VariableProxy::getVariable(int id) { + sd::graph::Variable *VariableProxy::getVariable(int id) { if (_current->hasVariable(id)) return _current->getVariable(id); @@ -122,7 +122,7 @@ namespace nd4j { } - nd4j::graph::Variable *VariableProxy::getVariable(int id, int idx) { + sd::graph::Variable *VariableProxy::getVariable(int id, int idx) { if (_current->hasVariable(id, idx)) return _current->getVariable(id, idx); @@ -134,7 +134,7 @@ namespace nd4j { } - nd4j::graph::Variable *VariableProxy::getVariable(std::pair& pair) { + sd::graph::Variable *VariableProxy::getVariable(std::pair& pair) { if (_current->hasVariable(pair)) return _current->getVariable(pair); @@ -146,7 +146,7 @@ namespace nd4j { } - nd4j::graph::Variable *VariableProxy::getVariable(std::string *symbol) { + sd::graph::Variable *VariableProxy::getVariable(std::string *symbol) { if (_current->hasVariable(symbol)) return _current->getVariable(symbol); @@ -172,8 +172,8 @@ namespace nd4j { } - void VariableProxy::putVariable(std::pair& pair, NDArray *array) { - _current->putVariable(pair, array); + Variable* VariableProxy::putVariable(std::pair& pair, NDArray *array) { + return _current->putVariable(pair, array); } @@ -191,12 +191,12 @@ namespace nd4j { _current->putVariable(id, array); } - void nd4j::graph::VariableProxy::putVariable(int id, int idx, NDArray &array) { + void sd::graph::VariableProxy::putVariable(int id, int idx, NDArray &array) { _current->putVariable(id, idx, array); } - void VariableProxy::putVariable(int id, int idx, NDArray *array) { - _current->putVariable(id, idx, array); + Variable* VariableProxy::putVariable(int id, int idx, NDArray *array) { + return _current->putVariable(id, idx, array); } @@ -205,12 +205,12 @@ namespace nd4j { } - void VariableProxy::trackList(nd4j::NDArrayList* list) { + void VariableProxy::trackList(sd::NDArrayList* list) { _current->trackList(list); } - nd4j::graph::Stash* VariableProxy::getStash() { + sd::graph::Stash* VariableProxy::getStash() { return _current->getStash(); } @@ -260,7 +260,7 @@ namespace nd4j { } - nd4j::graph::VariableSpace* VariableProxy::clone() { + sd::graph::VariableSpace* VariableProxy::clone() { auto clone = new VariableProxy(_backed); delete clone->_current; @@ -279,7 +279,7 @@ namespace nd4j { } - nd4j::memory::Workspace * nd4j::graph::VariableProxy::workspace() { + sd::memory::Workspace * sd::graph::VariableProxy::workspace() { return _workspace; } } diff --git a/libnd4j/include/graph/impl/VariableSpace.cpp b/libnd4j/include/graph/impl/VariableSpace.cpp index 735f0260a..0e8634d07 100644 --- a/libnd4j/include/graph/impl/VariableSpace.cpp +++ b/libnd4j/include/graph/impl/VariableSpace.cpp @@ -19,19 +19,19 @@ // #include -#include +#include -namespace nd4j { +namespace sd { namespace graph { - std::vector * nd4j::graph::VariableSpace::getExternalVariables() { + std::vector * sd::graph::VariableSpace::getExternalVariables() { return &_external; } - nd4j::graph::Stash* nd4j::graph::VariableSpace::getStash() { + sd::graph::Stash* sd::graph::VariableSpace::getStash() { return &_stash; } - nd4j::graph::VariableSpace* nd4j::graph::VariableSpace::clone() { + sd::graph::VariableSpace* sd::graph::VariableSpace::clone() { auto result = new VariableSpace(); for (auto const& x : _paired) { @@ -45,12 +45,12 @@ namespace nd4j { return result; } - void VariableSpace::setWorkspace(nd4j::memory::Workspace *workspace) { + void VariableSpace::setWorkspace(sd::memory::Workspace *workspace) { //_workspace = *workspace; } - nd4j::graph::VariableSpace* nd4j::graph::VariableSpace::asT() { + sd::graph::VariableSpace* sd::graph::VariableSpace::asT() { auto result = new VariableSpace(); for (auto const& x : _paired) { @@ -65,7 +65,7 @@ namespace nd4j { } - void nd4j::graph::VariableSpace::injectVariable(std::pair &pair, Variable* variable) { + void sd::graph::VariableSpace::injectVariable(std::pair &pair, Variable* variable) { if (pair.second == 0) { if (pair.first < 0) this->_variables[pair.first] = variable; @@ -81,23 +81,23 @@ namespace nd4j { this->_handles->push_back(variable); } - std::vector * nd4j::graph::VariableSpace::getPlaceholders() { + std::vector * sd::graph::VariableSpace::getPlaceholders() { return &_placeholders; } - int nd4j::graph::VariableSpace ::numberOfPlaceholders() { + int sd::graph::VariableSpace ::numberOfPlaceholders() { return _placeholders.size(); } - bool nd4j::graph::VariableSpace::hasVariable(std::string *symbol) { + bool sd::graph::VariableSpace::hasVariable(std::string *symbol) { return _symbolic.count(*symbol) == 1; } - nd4j::graph::Variable * nd4j::graph::VariableSpace::getVariable(std::string *symbol) { + sd::graph::Variable * sd::graph::VariableSpace::getVariable(std::string *symbol) { return _symbolic.at(*symbol); } - bool nd4j::graph::VariableSpace::hasVariable(int id, int index) { + bool sd::graph::VariableSpace::hasVariable(int id, int index) { std::pair pair(id, index); return hasVariable(pair); } @@ -126,57 +126,47 @@ namespace nd4j { return var->isExternal(); } - nd4j::graph::Variable * nd4j::graph::VariableSpace::getVariable(int id, int index) { + sd::graph::Variable * sd::graph::VariableSpace::getVariable(int id, int index) { std::pair pair(id, index); return getVariable(pair); } - nd4j::graph::Variable * nd4j::graph::VariableSpace::getVariable(std::pair& pair) { -// if (pair.first == 0) -// throw "0 requested"; - - //nd4j_debug("Requested variable: [%i:%i]\n", pair.first, pair.second); - + sd::graph::Variable * sd::graph::VariableSpace::getVariable(std::pair& pair) { if (pair.first < 0) return getVariable(pair.first); - else if (_paired.count(pair) > 0) + else return _paired.at(pair); - else { - if (hasVariable(pair.first) && pair.second == 0) - return getVariable(pair.first); - } nd4j_printf("Unknown variable requested: [%i,%i]\n", pair.first, pair.second); - - return nullptr; + throw std::runtime_error("Unknown variable requested"); } - bool nd4j::graph::VariableSpace::hasVariable(int id) { + bool sd::graph::VariableSpace::hasVariable(int id) { return _variables.count(id) == 1 || _temporary.count(id) == 1; } - bool nd4j::graph::VariableSpace::hasVariable(std::pair& id) { + bool sd::graph::VariableSpace::hasVariable(std::pair& id) { return _paired.count(id) > 0; } - void nd4j::graph::VariableSpace::putOutputVariable(Variable *variable) { + void sd::graph::VariableSpace::putOutputVariable(Variable *variable) { //putVariable(_auto_counter--, variable); putVariable(variable->id(), variable); } - int nd4j::graph::VariableSpace::externalEntries() { + int sd::graph::VariableSpace::externalEntries() { return _external.size(); } - int nd4j::graph::VariableSpace::internalEntries() { + int sd::graph::VariableSpace::internalEntries() { return _internal.size(); } - int nd4j::graph::VariableSpace::totalEntries() { + int sd::graph::VariableSpace::totalEntries() { return externalEntries() + internalEntries(); } - Nd4jLong nd4j::graph::VariableSpace::externalMemory() { + Nd4jLong sd::graph::VariableSpace::externalMemory() { Nd4jLong size = 0; for (auto n: _external) { size += n->getNDArray()->memoryFootprint(); @@ -197,7 +187,7 @@ namespace nd4j { return result; } - Nd4jLong nd4j::graph::VariableSpace::internalMemory() { + Nd4jLong sd::graph::VariableSpace::internalMemory() { Nd4jLong size = 0; for (auto n: _internal) { size += n->getNDArray()->memoryFootprint(); @@ -206,35 +196,36 @@ namespace nd4j { return size; } - Nd4jLong nd4j::graph::VariableSpace::totalMemory() { + Nd4jLong sd::graph::VariableSpace::totalMemory() { return externalMemory() + internalMemory(); } - void nd4j::graph::VariableSpace::putVariable(std::pair& pair, NDArray *array) { + Variable* sd::graph::VariableSpace::putVariable(std::pair& pair, NDArray *array) { auto variable = new Variable(array, nullptr, pair.first, pair.second); this->putVariable(pair, variable); + return variable; } - void nd4j::graph::VariableSpace::putVariable(int node, int idx, NDArray *array) { + Variable* sd::graph::VariableSpace::putVariable(int node, int idx, NDArray *array) { std::pair pair(node, idx); - this->putVariable(pair, array); + return this->putVariable(pair, array); } - void nd4j::graph::VariableSpace::putVariable(int node, int idx, Variable *variable) { + void sd::graph::VariableSpace::putVariable(int node, int idx, Variable *variable) { std::pair pair(node, idx); this->putVariable(pair, variable); } - void nd4j::graph::VariableSpace::silentPutVariable(std::pair& pair, Variable *variable) { + void sd::graph::VariableSpace::silentPutVariable(std::pair& pair, Variable *variable) { _varmap.lock(); - //std::pair, nd4j::graph::Variable *> p(pair, variable); + //std::pair, sd::graph::Variable *> p(pair, variable); _paired[pair] = variable; _varmap.unlock(); } - void nd4j::graph::VariableSpace::putVariable(std::pair& pair, Variable *variable) { + void sd::graph::VariableSpace::putVariable(std::pair& pair, Variable *variable) { silentPutVariable(pair, variable); if (variable->isPlaceholder()) @@ -256,11 +247,11 @@ namespace nd4j { } } - void VariableSpace::trackList(nd4j::NDArrayList* list) { + void VariableSpace::trackList(sd::NDArrayList* list) { _lists.emplace_back(list); } - void nd4j::graph::VariableSpace::putVariable(int id, Variable *variable) { + void sd::graph::VariableSpace::putVariable(int id, Variable *variable) { // we don't want to add variables more then once if (_variables.count(id) > 0 || _temporary.count(id) > 0) { auto local = id < 0 ? _variables.at(id) : _temporary.at(id); @@ -286,7 +277,7 @@ namespace nd4j { variable->setId(id); if (variable->getName() != nullptr && variable->getName()->length() != 0) { - //std::pair pair(*(variable->getName()), variable); + //std::pair pair(*(variable->getName()), variable); _symbolic[*(variable->getName())] = variable; } @@ -314,8 +305,8 @@ namespace nd4j { } } - void nd4j::graph::VariableSpace::putVariable(int id, int idx, NDArray &array) { - auto *var = new nd4j::graph::Variable(&array, "", id, idx); + void sd::graph::VariableSpace::putVariable(int id, int idx, NDArray &array) { + auto *var = new sd::graph::Variable(&array, "", id, idx); var->markRemovable(false); var->markReadOnly(true); @@ -329,39 +320,31 @@ namespace nd4j { delete var; } - void nd4j::graph::VariableSpace::putVariable(int id, NDArray *array) { - auto *var = new nd4j::graph::Variable(array); + void sd::graph::VariableSpace::putVariable(int id, NDArray *array) { + auto *var = new sd::graph::Variable(array); this->putVariable(id, var); } - nd4j::graph::Variable * nd4j::graph::VariableSpace::getVariable(int id) { -// _varmap.lock(); - + sd::graph::Variable * sd::graph::VariableSpace::getVariable(int id) { if (id < 0) { - auto v = _variables.at(id); - // _varmap.unlock(); - - return v; + return _variables.at(id); } else { - auto v = _temporary.at(id); - // _varmap.unlock(); - - return v; + return _temporary.at(id); } } - LaunchContext* nd4j::graph::VariableSpace::launchContext() { + LaunchContext* sd::graph::VariableSpace::launchContext() { return LaunchContext::defaultContext(); } - std::vector* nd4j::graph::VariableSpace::handles() { + std::vector* sd::graph::VariableSpace::handles() { return _handles; } /* * FIXME: this thing have nice chances to become backend-specific! */ - nd4j::graph::VariableSpace::~VariableSpace() { + sd::graph::VariableSpace::~VariableSpace() { // loop through variables and release them for (auto p: *_handles) { delete p; diff --git a/libnd4j/include/graph/impl/VariablesSet.cpp b/libnd4j/include/graph/impl/VariablesSet.cpp index 9b854da30..80f8e3728 100644 --- a/libnd4j/include/graph/impl/VariablesSet.cpp +++ b/libnd4j/include/graph/impl/VariablesSet.cpp @@ -20,7 +20,7 @@ #include -namespace nd4j { +namespace sd { namespace graph { Nd4jStatus VariablesSet::status() { return _status; diff --git a/libnd4j/include/graph/profiling/GraphProfile.h b/libnd4j/include/graph/profiling/GraphProfile.h index 28daf3ccf..f0ada4f90 100644 --- a/libnd4j/include/graph/profiling/GraphProfile.h +++ b/libnd4j/include/graph/profiling/GraphProfile.h @@ -22,14 +22,14 @@ #define ND4J_GRAPH_PROFILE_H #include "NodeProfile.h" -#include -#include +#include +#include #include #include #include #include -namespace nd4j { +namespace sd { namespace graph { class ND4J_EXPORT GraphProfile { private: diff --git a/libnd4j/include/graph/profiling/GraphProfilingHelper.h b/libnd4j/include/graph/profiling/GraphProfilingHelper.h index af4107e42..d32d99374 100644 --- a/libnd4j/include/graph/profiling/GraphProfilingHelper.h +++ b/libnd4j/include/graph/profiling/GraphProfilingHelper.h @@ -25,7 +25,7 @@ #include #include "GraphProfile.h" -namespace nd4j { +namespace sd { namespace graph { class GraphProfilingHelper { public: diff --git a/libnd4j/include/graph/profiling/NodeProfile.h b/libnd4j/include/graph/profiling/NodeProfile.h index 62df0c34a..871eb5748 100644 --- a/libnd4j/include/graph/profiling/NodeProfile.h +++ b/libnd4j/include/graph/profiling/NodeProfile.h @@ -21,12 +21,12 @@ #ifndef LIBND4J_NODE_PROFILE_H #define LIBND4J_NODE_PROFILE_H -#include -#include +#include +#include #include #include -namespace nd4j { +namespace sd { namespace graph { class ND4J_EXPORT NodeProfile { private: diff --git a/libnd4j/include/graph/profiling/impl/GraphProfile.cpp b/libnd4j/include/graph/profiling/impl/GraphProfile.cpp index ea8e7bc49..14ce54a0f 100644 --- a/libnd4j/include/graph/profiling/impl/GraphProfile.cpp +++ b/libnd4j/include/graph/profiling/impl/GraphProfile.cpp @@ -21,10 +21,10 @@ #include #include #include -#include +#include #include -namespace nd4j { +namespace sd { namespace graph { GraphProfile::GraphProfile() { updateLast(); @@ -199,8 +199,8 @@ namespace nd4j { return a->getExecutionTime() > b->getExecutionTime(); }); - nd4j_printf("\nTop 30 reports by EXEC:\n", ""); - auto limit = nd4j::math::nd4j_min(30, sorted.size()); + nd4j_printf("\nTop 50 reports by EXEC:\n", ""); + auto limit = sd::math::nd4j_min(50, sorted.size()); for (int e = 0; e < limit; e++) { sorted[e]->printOut(); } diff --git a/libnd4j/include/graph/profiling/impl/GraphProfilingHelper.cpp b/libnd4j/include/graph/profiling/impl/GraphProfilingHelper.cpp index cbea09616..03c2411e2 100644 --- a/libnd4j/include/graph/profiling/impl/GraphProfilingHelper.cpp +++ b/libnd4j/include/graph/profiling/impl/GraphProfilingHelper.cpp @@ -19,9 +19,9 @@ // #include -#include +#include -namespace nd4j { +namespace sd { namespace graph { GraphProfile *GraphProfilingHelper::profile(Graph *graph, int iterations) { diff --git a/libnd4j/include/graph/profiling/impl/NodeProfile.cpp b/libnd4j/include/graph/profiling/impl/NodeProfile.cpp index a6a990eb8..bd48fbd28 100644 --- a/libnd4j/include/graph/profiling/impl/NodeProfile.cpp +++ b/libnd4j/include/graph/profiling/impl/NodeProfile.cpp @@ -22,7 +22,7 @@ #include #include -namespace nd4j { +namespace sd { namespace graph { NodeProfile::NodeProfile(int id, const char *name) { _id = id; diff --git a/libnd4j/include/graph/scheme/array.fbs b/libnd4j/include/graph/scheme/array.fbs index 2ffce58bd..bb8118aad 100644 --- a/libnd4j/include/graph/scheme/array.fbs +++ b/libnd4j/include/graph/scheme/array.fbs @@ -14,7 +14,7 @@ * SPDX-License-Identifier: Apache-2.0 ******************************************************************************/ -namespace nd4j.graph; +namespace sd.graph; // byte order for arrays/buffers enum ByteOrder:byte { diff --git a/libnd4j/include/graph/scheme/config.fbs b/libnd4j/include/graph/scheme/config.fbs index 6bd8e688c..0aac9c05d 100644 --- a/libnd4j/include/graph/scheme/config.fbs +++ b/libnd4j/include/graph/scheme/config.fbs @@ -14,7 +14,7 @@ * SPDX-License-Identifier: Apache-2.0 ******************************************************************************/ -namespace nd4j.graph; +namespace sd.graph; enum ProfilingMode:byte { NONE, // no checks for Z values diff --git a/libnd4j/include/graph/scheme/graph.fbs b/libnd4j/include/graph/scheme/graph.fbs index 0d5383dff..040af1642 100644 --- a/libnd4j/include/graph/scheme/graph.fbs +++ b/libnd4j/include/graph/scheme/graph.fbs @@ -22,7 +22,7 @@ include "result.fbs"; include "request.fbs"; include "array.fbs"; -namespace nd4j.graph; +namespace sd.graph; table UpdaterState { paramName:string; //Name of the parameter the updater state is for diff --git a/libnd4j/include/graph/scheme/node.fbs b/libnd4j/include/graph/scheme/node.fbs index 8e63186f5..e3ad32b76 100644 --- a/libnd4j/include/graph/scheme/node.fbs +++ b/libnd4j/include/graph/scheme/node.fbs @@ -18,7 +18,7 @@ include "array.fbs"; include "utils.fbs"; include "properties.fbs"; -namespace nd4j.graph; +namespace sd.graph; // this structure describes single operation within graph diff --git a/libnd4j/include/graph/scheme/properties.fbs b/libnd4j/include/graph/scheme/properties.fbs index 326c9450a..57ce5f7f3 100644 --- a/libnd4j/include/graph/scheme/properties.fbs +++ b/libnd4j/include/graph/scheme/properties.fbs @@ -16,7 +16,7 @@ include "array.fbs"; -namespace nd4j.graph; +namespace sd.graph; table FlatProperties { diff --git a/libnd4j/include/graph/scheme/request.fbs b/libnd4j/include/graph/scheme/request.fbs index 1a4b1ec0a..a55b5ce82 100644 --- a/libnd4j/include/graph/scheme/request.fbs +++ b/libnd4j/include/graph/scheme/request.fbs @@ -17,7 +17,7 @@ include "variable.fbs"; include "config.fbs"; -namespace nd4j.graph; +namespace sd.graph; table FlatInferenceRequest { id:long; // id of the graph to be executed diff --git a/libnd4j/include/graph/scheme/result.fbs b/libnd4j/include/graph/scheme/result.fbs index 3f01c649b..f479a209a 100644 --- a/libnd4j/include/graph/scheme/result.fbs +++ b/libnd4j/include/graph/scheme/result.fbs @@ -18,7 +18,7 @@ include "node.fbs"; include "utils.fbs"; include "variable.fbs"; -namespace nd4j.graph; +namespace sd.graph; table FlatTiming { id:int; // ID of the node for this time report diff --git a/libnd4j/include/graph/scheme/uigraphevents.fbs b/libnd4j/include/graph/scheme/uigraphevents.fbs index 9e24c6535..eb9fa13d6 100644 --- a/libnd4j/include/graph/scheme/uigraphevents.fbs +++ b/libnd4j/include/graph/scheme/uigraphevents.fbs @@ -16,7 +16,7 @@ include "array.fbs"; //For FlatArray -namespace nd4j.graph; +namespace sd.graph; /* An "Event" is any value that may occur multiple times (score vs. iteration, or accuracy for example) diff --git a/libnd4j/include/graph/scheme/uigraphstatic.fbs b/libnd4j/include/graph/scheme/uigraphstatic.fbs index 814c28fa5..b0b19ce17 100644 --- a/libnd4j/include/graph/scheme/uigraphstatic.fbs +++ b/libnd4j/include/graph/scheme/uigraphstatic.fbs @@ -18,7 +18,7 @@ include "utils.fbs"; //For: IntPair include "variable.fbs"; //For: VarType include "array.fbs"; //For: DataType -namespace nd4j.graph; +namespace sd.graph; enum UIInfoType:byte { GRAPH_STRUCTURE, diff --git a/libnd4j/include/graph/scheme/utils.fbs b/libnd4j/include/graph/scheme/utils.fbs index 67ce2e461..f2186869b 100644 --- a/libnd4j/include/graph/scheme/utils.fbs +++ b/libnd4j/include/graph/scheme/utils.fbs @@ -14,7 +14,7 @@ * SPDX-License-Identifier: Apache-2.0 ******************************************************************************/ -namespace nd4j.graph; +namespace sd.graph; table LongPair { first:long; // first diff --git a/libnd4j/include/graph/scheme/variable.fbs b/libnd4j/include/graph/scheme/variable.fbs index 1e8010d43..da5c3fb11 100644 --- a/libnd4j/include/graph/scheme/variable.fbs +++ b/libnd4j/include/graph/scheme/variable.fbs @@ -17,7 +17,7 @@ include "array.fbs"; include "utils.fbs"; -namespace nd4j.graph; +namespace sd.graph; // Variable type for variables enum VarType:byte { diff --git a/libnd4j/include/helpers/ArrayUtils.h b/libnd4j/include/helpers/ArrayUtils.h index 54e1a4f19..2ecebeb4a 100644 --- a/libnd4j/include/helpers/ArrayUtils.h +++ b/libnd4j/include/helpers/ArrayUtils.h @@ -24,9 +24,9 @@ #include #include #include -#include +#include -namespace nd4j { +namespace sd { namespace ArrayUtils { void toIntPtr(std::initializer_list list, int* target); void toIntPtr(std::vector& list, int* target); diff --git a/libnd4j/include/helpers/AttentionHelper.h b/libnd4j/include/helpers/AttentionHelper.h index 186f959fd..02d9da995 100644 --- a/libnd4j/include/helpers/AttentionHelper.h +++ b/libnd4j/include/helpers/AttentionHelper.h @@ -21,14 +21,14 @@ #ifndef LIBND4J_ATTENTIONHELPER_H #define LIBND4J_ATTENTIONHELPER_H -#include "NDArray.h" +#include "array/NDArray.h" -namespace nd4j { +namespace sd { class ND4J_EXPORT AttentionHelper { public: - static nd4j::NDArray multiHeadProject(const nd4j::NDArray* input, const nd4j::NDArray* projectionMatrix, nd4j::LaunchContext * context = nd4j::LaunchContext ::defaultContext()); - static void multiHeadProjectBp(const nd4j::NDArray* input, const nd4j::NDArray* projectionMatrix, const nd4j::NDArray* eps, nd4j::NDArray* dLdInput, nd4j::NDArray* dLdProjectionMatrix, nd4j::LaunchContext * context = nd4j::LaunchContext ::defaultContext()); + static sd::NDArray multiHeadProject(const sd::NDArray* input, const sd::NDArray* projectionMatrix, sd::LaunchContext * context = sd::LaunchContext ::defaultContext()); + static void multiHeadProjectBp(const sd::NDArray* input, const sd::NDArray* projectionMatrix, const sd::NDArray* eps, sd::NDArray* dLdInput, sd::NDArray* dLdProjectionMatrix, sd::LaunchContext * context = sd::LaunchContext ::defaultContext()); }; } diff --git a/libnd4j/include/helpers/BenchmarkHelper.h b/libnd4j/include/helpers/BenchmarkHelper.h index 8dc946a2a..f76f787d8 100644 --- a/libnd4j/include/helpers/BenchmarkHelper.h +++ b/libnd4j/include/helpers/BenchmarkHelper.h @@ -32,17 +32,17 @@ #include #include #include -#include -#include -#include -#include -#include -#include -#include -#include +#include +#include +#include +#include +#include +#include +#include +#include #include -namespace nd4j { +namespace sd { class ND4J_EXPORT BenchmarkHelper { private: @@ -54,7 +54,7 @@ namespace nd4j { void benchmarkScalarOperation(scalar::Ops op, std::string testName, double value, NDArray &x, NDArray &z); - void benchmarkDeclarableOp(nd4j::ops::DeclarableOp &op, std::string testName, Context &context); + void benchmarkDeclarableOp(sd::ops::DeclarableOp &op, std::string testName, Context &context); void benchmarkGEMM(char orderA, std::initializer_list shapeA, char orderB, std::initializer_list shapeB, char orderC, std::initializer_list shapeC); diff --git a/libnd4j/include/helpers/BitwiseUtils.h b/libnd4j/include/helpers/BitwiseUtils.h index 6defc4c49..6b7e5c231 100644 --- a/libnd4j/include/helpers/BitwiseUtils.h +++ b/libnd4j/include/helpers/BitwiseUtils.h @@ -23,11 +23,11 @@ #include #include -#include -#include +#include +#include #include -namespace nd4j { +namespace sd { class ND4J_EXPORT BitwiseUtils { public: @@ -55,7 +55,7 @@ namespace nd4j { * This method returns enum * @return */ - static nd4j::ByteOrder asByteOrder(); + static sd::ByteOrder asByteOrder(); /** * This method swaps bytes: LE vs BE diff --git a/libnd4j/include/helpers/BlasHelper.h b/libnd4j/include/helpers/BlasHelper.h index 3b6179b68..b2fe7b60c 100644 --- a/libnd4j/include/helpers/BlasHelper.h +++ b/libnd4j/include/helpers/BlasHelper.h @@ -21,7 +21,7 @@ #ifndef LIBND4J_BLAS_HELPER_H #define LIBND4J_BLAS_HELPER_H -#include +#include #include #include #include @@ -34,7 +34,7 @@ #define CUSOLVERAPI #endif -namespace nd4j { +namespace sd { typedef enum{ CUBLAS_STATUS_SUCCESS =0, CUBLAS_STATUS_NOT_INITIALIZED =1, @@ -415,8 +415,8 @@ namespace nd4j { template bool hasGEMM(); - bool hasGEMM(const nd4j::DataType dtype); - bool hasGEMV(const nd4j::DataType dtype); + bool hasGEMM(const sd::DataType dtype); + bool hasGEMV(const sd::DataType dtype); template bool hasBatchedGEMM(); diff --git a/libnd4j/include/helpers/ConstantHelper.h b/libnd4j/include/helpers/ConstantHelper.h index 6aad7c387..3e5681fb6 100644 --- a/libnd4j/include/helpers/ConstantHelper.h +++ b/libnd4j/include/helpers/ConstantHelper.h @@ -21,9 +21,9 @@ #ifndef DEV_TESTS_CONSTANTHELPER_H #define DEV_TESTS_CONSTANTHELPER_H -#include -#include -#include +#include +#include +#include #include #include #include @@ -32,13 +32,13 @@ #include #include -namespace nd4j { +namespace sd { class ND4J_EXPORT ConstantHelper { private: static ConstantHelper* _INSTANCE; ConstantHelper(); - std::vector> _cache; + std::vector> _cache; // tracking of per-device constant memory buffers (CUDA only atm) std::vector _devicePointers; @@ -55,7 +55,7 @@ namespace nd4j { static int getNumberOfDevices(); void* replicatePointer(void *src, size_t numBytes, memory::Workspace *workspace = nullptr); - ConstantDataBuffer* constantBuffer(const ConstantDescriptor &descriptor, nd4j::DataType dataType); + ConstantDataBuffer* constantBuffer(const ConstantDescriptor &descriptor, sd::DataType dataType); Nd4jLong getCachedAmount(int deviceId); }; diff --git a/libnd4j/include/helpers/ConstantShapeHelper.h b/libnd4j/include/helpers/ConstantShapeHelper.h index d5ea9abe9..4454776a4 100644 --- a/libnd4j/include/helpers/ConstantShapeHelper.h +++ b/libnd4j/include/helpers/ConstantShapeHelper.h @@ -21,24 +21,24 @@ #ifndef DEV_TESTS_CONSTANTSHAPEHELPER_H #define DEV_TESTS_CONSTANTSHAPEHELPER_H -#include -#include +#include +#include #include #include #include -#include +#include #include #include -#include +#include -namespace nd4j { +namespace sd { class ND4J_EXPORT ConstantShapeHelper { private: static ConstantShapeHelper *_INSTANCE; std::mutex _mutex; - std::vector> _cache; + std::vector> _cache; ConstantShapeHelper(); @@ -48,21 +48,22 @@ namespace nd4j { static ConstantShapeHelper* getInstance(); - ConstantDataBuffer bufferForShapeInfo(nd4j::DataType dataType, char order, const std::vector &shape); + ConstantDataBuffer bufferForShapeInfo(sd::DataType dataType, char order, const std::vector &shape); ConstantDataBuffer bufferForShapeInfo(const ShapeDescriptor &descriptor); ConstantDataBuffer bufferForShapeInfo(const Nd4jLong *shapeInfo); - ConstantDataBuffer bufferForShapeInfo(const nd4j::DataType dataType, const char order, const int rank, const Nd4jLong* shape); + ConstantDataBuffer bufferForShapeInfo(const sd::DataType dataType, const char order, const int rank, const Nd4jLong* shape); + ConstantDataBuffer createShapeInfoWithUnitiesForBroadcast(const Nd4jLong* maxShapeInfo, const Nd4jLong* minShapeInfo, sd::memory::Workspace* workspace = nullptr, const std::vector dimensions = {}); - Nd4jLong* emptyShapeInfo(const nd4j::DataType dataType); - Nd4jLong* scalarShapeInfo(const nd4j::DataType dataType); - Nd4jLong* vectorShapeInfo(const Nd4jLong length, const nd4j::DataType dataType); + Nd4jLong* emptyShapeInfo(const sd::DataType dataType); + Nd4jLong* scalarShapeInfo(const sd::DataType dataType); + Nd4jLong* vectorShapeInfo(const Nd4jLong length, const sd::DataType dataType); Nd4jLong* createShapeInfo(const ShapeDescriptor &descriptor); - Nd4jLong* createShapeInfo(const nd4j::DataType dataType, const char order, const std::vector &shape); - Nd4jLong* createShapeInfo(const nd4j::DataType dataType, const char order, const int rank, const Nd4jLong* shape); - Nd4jLong* createShapeInfo(const nd4j::DataType dataType, const Nd4jLong* shapeInfo); + Nd4jLong* createShapeInfo(const sd::DataType dataType, const char order, const std::vector &shape); + Nd4jLong* createShapeInfo(const sd::DataType dataType, const char order, const int rank, const Nd4jLong* shape); + Nd4jLong* createShapeInfo(const sd::DataType dataType, const Nd4jLong* shapeInfo); - Nd4jLong* createFromExisting(Nd4jLong *shapeInfo, nd4j::memory::Workspace *workspace); + Nd4jLong* createFromExisting(Nd4jLong *shapeInfo, sd::memory::Workspace *workspace); Nd4jLong* createFromExisting(Nd4jLong *shapeInfo, bool destroyOriginal = true); bool checkBufferExistenceForShapeInfo(ShapeDescriptor &descriptor); diff --git a/libnd4j/include/helpers/ConstantTadHelper.h b/libnd4j/include/helpers/ConstantTadHelper.h index 79ee7dcd4..80efaa86f 100644 --- a/libnd4j/include/helpers/ConstantTadHelper.h +++ b/libnd4j/include/helpers/ConstantTadHelper.h @@ -22,9 +22,9 @@ #ifndef DEV_TESTS_CONSTANTTADHELPER_H #define DEV_TESTS_CONSTANTTADHELPER_H -#include -#include -#include +#include +#include +#include #include #include #include @@ -32,13 +32,13 @@ #include #include -namespace nd4j { +namespace sd { class ND4J_EXPORT ConstantTadHelper { private: static ConstantTadHelper *_INSTANCE; std::mutex _mutex; - std::vector> _cache; + std::vector> _cache; ConstantTadHelper(); public: diff --git a/libnd4j/include/helpers/CudaLaunchHelper.h b/libnd4j/include/helpers/CudaLaunchHelper.h index 9fec14764..6bf44317f 100644 --- a/libnd4j/include/helpers/CudaLaunchHelper.h +++ b/libnd4j/include/helpers/CudaLaunchHelper.h @@ -22,12 +22,12 @@ #define LIBND4J_CUDALAUNCHHELPER_H -#include -#include -#include +#include +#include +#include #include -namespace nd4j { +namespace sd { class ND4J_EXPORT CudaLaunchHelper { public: static Triple getFlatLaunchParams(Nd4jLong length, int SM, int CORES, int SHARED_MEMORY); diff --git a/libnd4j/include/helpers/DebugHelper.h b/libnd4j/include/helpers/DebugHelper.h index 3c3fe1d58..b0387dd8c 100644 --- a/libnd4j/include/helpers/DebugHelper.h +++ b/libnd4j/include/helpers/DebugHelper.h @@ -21,10 +21,10 @@ #ifndef LIBND4J_DEBUGHELPER_H #define LIBND4J_DEBUGHELPER_H -#include -#include -#include -#include +#include +#include +#include +#include #include @@ -35,8 +35,8 @@ #include #endif -#include -namespace nd4j { +#include +namespace sd { class NDArray; class ND4J_EXPORT DebugHelper { public: diff --git a/libnd4j/include/helpers/DebugInfo.h b/libnd4j/include/helpers/DebugInfo.h index 345ce0b61..c2efb00fe 100644 --- a/libnd4j/include/helpers/DebugInfo.h +++ b/libnd4j/include/helpers/DebugInfo.h @@ -21,13 +21,13 @@ #ifndef LIBND4J__DEBUG_INFO_HELPER__H #define LIBND4J__DEBUG_INFO_HELPER__H -#include -#include -#include -#include +#include +#include +#include +#include #include -#include -#include +#include +#include #ifdef __CUDACC__ @@ -37,7 +37,7 @@ #endif -namespace nd4j { +namespace sd { struct ND4J_EXPORT DebugInfo { double _minValue; double _maxValue; @@ -51,10 +51,10 @@ namespace nd4j { }; FORCEINLINE bool operator==(DebugInfo const& first, DebugInfo const& second) { - return nd4j::math::nd4j_abs(first._minValue - second._minValue) < 0.000001 && - nd4j::math::nd4j_abs(first._maxValue - second._maxValue) < 0.000001 && - nd4j::math::nd4j_abs(first._meanValue - second._meanValue) < 0.000001 && - nd4j::math::nd4j_abs(first._stdDevValue - second._stdDevValue) < 0.000001 && + return sd::math::nd4j_abs(first._minValue - second._minValue) < 0.000001 && + sd::math::nd4j_abs(first._maxValue - second._maxValue) < 0.000001 && + sd::math::nd4j_abs(first._meanValue - second._meanValue) < 0.000001 && + sd::math::nd4j_abs(first._stdDevValue - second._stdDevValue) < 0.000001 && first._zeroCount == second._zeroCount && first._positiveCount == second._positiveCount && first._negativeCount == second._negativeCount && diff --git a/libnd4j/include/helpers/EnumUtils.h b/libnd4j/include/helpers/EnumUtils.h index 28b80b50e..6138117c7 100644 --- a/libnd4j/include/helpers/EnumUtils.h +++ b/libnd4j/include/helpers/EnumUtils.h @@ -24,11 +24,11 @@ #include #include -namespace nd4j { +namespace sd { class EnumUtils { public: - static const char * _VariableTypeToString(nd4j::graph::VariableType variableType); - static const char * _OpTypeToString(nd4j::graph::OpType opType); + static const char * _VariableTypeToString(sd::graph::VariableType variableType); + static const char * _OpTypeToString(sd::graph::OpType opType); static const char * _LogicOpToString(int opNum); }; } diff --git a/libnd4j/include/helpers/GradCheck.h b/libnd4j/include/helpers/GradCheck.h index 32f66109a..0d184a5a1 100644 --- a/libnd4j/include/helpers/GradCheck.h +++ b/libnd4j/include/helpers/GradCheck.h @@ -22,10 +22,10 @@ #define LIBND4J_GRADCHECK_H -#include +#include #include -namespace nd4j { +namespace sd { class ND4J_EXPORT GradCheck { diff --git a/libnd4j/include/helpers/LoopKind.h b/libnd4j/include/helpers/LoopKind.h index d97f3b225..95e9238ad 100644 --- a/libnd4j/include/helpers/LoopKind.h +++ b/libnd4j/include/helpers/LoopKind.h @@ -23,15 +23,15 @@ // #include -#include -// #include -// #include +#include +// #include +// #include // #include // #include // #include // #include -namespace nd4j { +namespace sd { class ND4J_EXPORT LoopKind { @@ -107,12 +107,22 @@ LoopKind::Kind LoopKind::deduceKindOfLoopBroadcast(const Nd4jLong* xShapeInfo, c bool bNotCommonVectorCase = (countUnityDimsInY != yRank - 1) && (countUnityDimsInX != xRank - 1); - if (3 == xRank && bNDLoopsRanks && bNotCommonVectorCase) - return nd4j::LoopKind::BROADCAST_3D; - if (4 == xRank && bNDLoopsRanks && bNotCommonVectorCase) - return nd4j::LoopKind::BROADCAST_4D; - if (5 == xRank && bNDLoopsRanks && bNotCommonVectorCase) - return nd4j::LoopKind::BROADCAST_5D; + + if (bNDLoopsRanks && bNotCommonVectorCase) { + // case x[3,4,5] * y[1,4,5] = z[3,4,5] or reverse x[1,4,5] + y[3,4,5] = z[3,4,5] + if (sd::LoopKind::EWS1 == deduceKindOfLoopXYZ(xShapeInfo, yShapeInfo, zShapeInfo) + && (1 == shape::sizeAt(yShapeInfo, 0) || 1 == shape::sizeAt(xShapeInfo, 0))) { + return EWS1; + } + + if (3 == xRank) + return sd::LoopKind::BROADCAST_3D; + if (4 == xRank) + return sd::LoopKind::BROADCAST_4D; + if (5 == xRank) + return sd::LoopKind::BROADCAST_5D; + + } if (xRank == yRank && xRank == zRank && xOrder == 'c' && yOrder == 'c' && zOrder == 'c' && xEws == 1 && yEws == 1 && zEws == 1 && xRank >= 2) { @@ -126,12 +136,12 @@ LoopKind::Kind LoopKind::deduceKindOfLoopBroadcast(const Nd4jLong* xShapeInfo, c auto detect = xShapeInfo[xRank] == 1 ? -1 : (yShapeInfo[xRank] == 1) ? 1 : 0; if (detect == 1) - return nd4j::LoopKind::BROADCAST_SCALAR_Y; + return sd::LoopKind::BROADCAST_SCALAR_Y; else if (detect == -1) - return nd4j::LoopKind::BROADCAST_SCALAR_X; + return sd::LoopKind::BROADCAST_SCALAR_X; } - return nd4j::LoopKind::COMMON; + return sd::LoopKind::COMMON; } ////////////////////////////////////////////////////////////////////////////// diff --git a/libnd4j/include/helpers/Loops.h b/libnd4j/include/helpers/Loops.h index fb1582056..508b84f20 100644 --- a/libnd4j/include/helpers/Loops.h +++ b/libnd4j/include/helpers/Loops.h @@ -14,26 +14,26 @@ * SPDX-License-Identifier: Apache-2.0 ******************************************************************************/ -// -// @author Yurii Shyrma (iuriish@yahoo.com), created on 14.03.2019 -// + // + // @author Yurii Shyrma (iuriish@yahoo.com), created on 14.03.2019 + // #ifndef LIBND4J_LOOPS_H #define LIBND4J_LOOPS_H #include -#include -#include -#include -#include -#include -#include -#include +#include +#include +#include +#include +#include +#include +#include #include -#include +#include #include -namespace nd4j { +namespace sd { template class ND4J_EXPORT ReductionLoops { @@ -45,7 +45,7 @@ namespace nd4j { }; template - class ReductionFloatLoops : public ReductionLoops { + class ReductionFloatLoops : public ReductionLoops { public: static void wrapper(const int opNum, X* x, Nd4jLong* xShapeInfo, Z* z, Nd4jLong* zShapeInfo, Nd4jLong* tadShapeInfo, Nd4jLong* tadOffsets, Z* extraParams, int64_t start, int64_t stop); @@ -54,7 +54,7 @@ namespace nd4j { }; template - class ND4J_EXPORT ReductionBoolLoops : public ReductionLoops { + class ND4J_EXPORT ReductionBoolLoops : public ReductionLoops { public: static void wrapper(const int opNum, X* x, Nd4jLong* xShapeInfo, Z* z, Nd4jLong* zShapeInfo, Nd4jLong* tadShapeInfo, Nd4jLong* tadOffsets, X* extraParams, int64_t start, int64_t stop); @@ -63,7 +63,7 @@ namespace nd4j { }; template - class ND4J_EXPORT ReductionLongLoops : public ReductionLoops { + class ND4J_EXPORT ReductionLongLoops : public ReductionLoops { public: static void wrapper(const int opNum, X* x, Nd4jLong* xShapeInfo, Z* z, Nd4jLong* zShapeInfo, Nd4jLong* tadShapeInfo, Nd4jLong* tadOffsets, X* extraParams, int64_t start, int64_t stop); @@ -72,7 +72,7 @@ namespace nd4j { }; template - class ND4J_EXPORT ReductionSameLoops : public ReductionLoops { + class ND4J_EXPORT ReductionSameLoops : public ReductionLoops { public: static void wrapper(const int opNum, X* x, Nd4jLong* xShapeInfo, X* z, Nd4jLong* zShapeInfo, Nd4jLong* tadShapeInfo, Nd4jLong* tadOffsets, X* extraParams, int64_t start, int64_t stop); @@ -125,158 +125,158 @@ namespace nd4j { -/* -////////////////////////////////////////////////////////////////////////////// -template -void Loops::loopXYZ(const X* x, const Nd4jLong* xShapeInfo, - const Y* y, const Nd4jLong* yShapeInfo, - Z* z, const Nd4jLong* zShapeInfo, - Z* extraParams, - std::function op) { + /* + ////////////////////////////////////////////////////////////////////////////// + template + void Loops::loopXYZ(const X* x, const Nd4jLong* xShapeInfo, + const Y* y, const Nd4jLong* yShapeInfo, + Z* z, const Nd4jLong* zShapeInfo, + Z* extraParams, + std::function op) { - const LoopKind::Kind kindOfLoop = LoopKind::deduceKindOfLoopXYZ(xShapeInfo, yShapeInfo, zShapeInfo); + const LoopKind::Kind kindOfLoop = LoopKind::deduceKindOfLoopXYZ(xShapeInfo, yShapeInfo, zShapeInfo); - const Nd4jLong* xShape = shape::shapeOf(xShapeInfo); - const Nd4jLong* xStride = shape::stride(xShapeInfo); - const Nd4jLong* yStride = shape::stride(yShapeInfo); - const Nd4jLong* zStride = shape::stride(zShapeInfo); + const Nd4jLong* xShape = shape::shapeOf(xShapeInfo); + const Nd4jLong* xStride = shape::stride(xShapeInfo); + const Nd4jLong* yStride = shape::stride(yShapeInfo); + const Nd4jLong* zStride = shape::stride(zShapeInfo); - const Nd4jLong len = shape::length(xShapeInfo); + const Nd4jLong len = shape::length(xShapeInfo); - OmpLaunchHelper threadsInfo(len); + OmpLaunchHelper threadsInfo(len); - switch (kindOfLoop) { + switch (kindOfLoop) { - case LoopKind::EWS1: { - PRAGMA_OMP_PARALLEL_THREADS(threadsInfo._numThreads) - { - const auto threadNum = omp_get_thread_num(); - const auto threadOffset = threadsInfo.getThreadOffset(threadNum); - const auto lenPerThread = static_cast(threadsInfo.getItersPerThread(threadNum)); + case LoopKind::EWS1: { + PRAGMA_OMP_PARALLEL_THREADS(threadsInfo._numThreads) + { + const auto threadNum = omp_get_thread_num(); + const auto threadOffset = threadsInfo.getThreadOffset(threadNum); + const auto lenPerThread = static_cast(threadsInfo.getItersPerThread(threadNum)); - const auto xi = x + threadOffset; - const auto yi = y + threadOffset; - auto zi = z + threadOffset; + const auto xi = x + threadOffset; + const auto yi = y + threadOffset; + auto zi = z + threadOffset; - PRAGMA_OMP_SIMD - for (uint i = 0; i < lenPerThread; i++) - zi[i] = op(xi[i], yi[i], extraParams); + PRAGMA_OMP_SIMD + for (uint i = 0; i < lenPerThread; i++) + zi[i] = op(xi[i], yi[i], extraParams); + } } - } - break; + break; - case LoopKind::EWSNONZERO: { - const uint xEws = shape::elementWiseStride(xShapeInfo); - const uint yEws = shape::elementWiseStride(yShapeInfo); - const uint zEws = shape::elementWiseStride(zShapeInfo); + case LoopKind::EWSNONZERO: { + const uint xEws = shape::elementWiseStride(xShapeInfo); + const uint yEws = shape::elementWiseStride(yShapeInfo); + const uint zEws = shape::elementWiseStride(zShapeInfo); - PRAGMA_OMP_PARALLEL_THREADS(threadsInfo._numThreads) - { - const auto threadNum = omp_get_thread_num(); - const auto threadOffset = threadsInfo.getThreadOffset(threadNum); - const auto lenPerThread = static_cast(threadsInfo.getItersPerThread(threadNum)); - const auto xi = x + threadOffset * xEws; - const auto yi = y + threadOffset * yEws; - auto zi = z + threadOffset * zEws; + PRAGMA_OMP_PARALLEL_THREADS(threadsInfo._numThreads) + { + const auto threadNum = omp_get_thread_num(); + const auto threadOffset = threadsInfo.getThreadOffset(threadNum); + const auto lenPerThread = static_cast(threadsInfo.getItersPerThread(threadNum)); + const auto xi = x + threadOffset * xEws; + const auto yi = y + threadOffset * yEws; + auto zi = z + threadOffset * zEws; - PRAGMA_OMP_SIMD - for (uint i = 0; i < lenPerThread; i++) - zi[i*zEws] = op(xi[i*xEws], yi[i*yEws], extraParams); + PRAGMA_OMP_SIMD + for (uint i = 0; i < lenPerThread; i++) + zi[i*zEws] = op(xi[i*xEws], yi[i*yEws], extraParams); + } } - } - break; + break; - case LoopKind::RANK1: { - PRAGMA_OMP_PARALLEL_FOR - for (uint i0 = 0; i0 < len; ++i0) - z[i0 * zStride[0]] = op(x[i0 * xStride[0]], y[i0 * yStride[0]], extraParams); - } - break; + case LoopKind::RANK1: { + PRAGMA_OMP_PARALLEL_FOR + for (uint i0 = 0; i0 < len; ++i0) + z[i0 * zStride[0]] = op(x[i0 * xStride[0]], y[i0 * yStride[0]], extraParams); + } + break; - case LoopKind::RANK2: { - PRAGMA_OMP_PARALLEL_FOR_SIMD - for (uint i0 = 0; i0 < xShape[0]; ++i0) - for (uint i1 = 0; i1 < xShape[1]; ++i1) - z[i0 * zStride[0] + i1 * zStride[1]] = op(x[i0 * xStride[0] + i1 * xStride[1]], y[i0 * yStride[0] + i1 * yStride[1]], extraParams); - } - break; + case LoopKind::RANK2: { + PRAGMA_OMP_PARALLEL_FOR_SIMD + for (uint i0 = 0; i0 < xShape[0]; ++i0) + for (uint i1 = 0; i1 < xShape[1]; ++i1) + z[i0 * zStride[0] + i1 * zStride[1]] = op(x[i0 * xStride[0] + i1 * xStride[1]], y[i0 * yStride[0] + i1 * yStride[1]], extraParams); + } + break; - case LoopKind::RANK3: { - PRAGMA_OMP_PARALLEL_FOR_SIMD_COLLAPSE(2) - for (uint i0 = 0; i0 < xShape[0]; ++i0) - for (uint i1 = 0; i1 < xShape[1]; ++i1) - for (uint i2 = 0; i2 < xShape[2]; ++i2) - z[i0*zStride[0]+i1*zStride[1]+i2*zStride[2]] = op(x[i0*xStride[0]+i1*xStride[1]+i2*xStride[2]], y[i0*yStride[0]+i1*yStride[1]+i2*yStride[2]], extraParams); - } - break; + case LoopKind::RANK3: { + PRAGMA_OMP_PARALLEL_FOR_SIMD_COLLAPSE(2) + for (uint i0 = 0; i0 < xShape[0]; ++i0) + for (uint i1 = 0; i1 < xShape[1]; ++i1) + for (uint i2 = 0; i2 < xShape[2]; ++i2) + z[i0*zStride[0]+i1*zStride[1]+i2*zStride[2]] = op(x[i0*xStride[0]+i1*xStride[1]+i2*xStride[2]], y[i0*yStride[0]+i1*yStride[1]+i2*yStride[2]], extraParams); + } + break; - case LoopKind::RANK4: { - PRAGMA_OMP_PARALLEL_FOR_SIMD_COLLAPSE(3) - for (uint i0 = 0; i0 < xShape[0]; ++i0) - for (uint i1 = 0; i1 < xShape[1]; ++i1) - for (uint i2 = 0; i2 < xShape[2]; ++i2) - for (uint i3 = 0; i3 < xShape[3]; ++i3) - z[i0*zStride[0]+i1*zStride[1]+i2*zStride[2]+i3*zStride[3]] = op(x[i0*xStride[0]+i1*xStride[1]+i2*xStride[2]+i3*xStride[3]], y[i0*yStride[0]+i1*yStride[1]+i2*yStride[2]+i3*yStride[3]], extraParams); - } - break; + case LoopKind::RANK4: { + PRAGMA_OMP_PARALLEL_FOR_SIMD_COLLAPSE(3) + for (uint i0 = 0; i0 < xShape[0]; ++i0) + for (uint i1 = 0; i1 < xShape[1]; ++i1) + for (uint i2 = 0; i2 < xShape[2]; ++i2) + for (uint i3 = 0; i3 < xShape[3]; ++i3) + z[i0*zStride[0]+i1*zStride[1]+i2*zStride[2]+i3*zStride[3]] = op(x[i0*xStride[0]+i1*xStride[1]+i2*xStride[2]+i3*xStride[3]], y[i0*yStride[0]+i1*yStride[1]+i2*yStride[2]+i3*yStride[3]], extraParams); + } + break; - case LoopKind::RANK5: { - PRAGMA_OMP_PARALLEL_FOR_SIMD_COLLAPSE(4) - for (uint i0 = 0; i0 < xShape[0]; ++i0) - for (uint i1 = 0; i1 < xShape[1]; ++i1) - for (uint i2 = 0; i2 < xShape[2]; ++i2) - for (uint i3 = 0; i3 < xShape[3]; ++i3) - for (uint i4 = 0; i4 < xShape[4]; ++i4) - z[i0*zStride[0]+i1*zStride[1]+i2*zStride[2]+i3*zStride[3]+i4*zStride[4]] = op(x[i0*xStride[0]+i1*xStride[1]+i2*xStride[2]+i3*xStride[3]+i4*xStride[4]], y[i0*yStride[0]+i1*yStride[1]+i2*yStride[2]+i3*yStride[3]+i4*yStride[4]], extraParams); - } - break; + case LoopKind::RANK5: { + PRAGMA_OMP_PARALLEL_FOR_SIMD_COLLAPSE(4) + for (uint i0 = 0; i0 < xShape[0]; ++i0) + for (uint i1 = 0; i1 < xShape[1]; ++i1) + for (uint i2 = 0; i2 < xShape[2]; ++i2) + for (uint i3 = 0; i3 < xShape[3]; ++i3) + for (uint i4 = 0; i4 < xShape[4]; ++i4) + z[i0*zStride[0]+i1*zStride[1]+i2*zStride[2]+i3*zStride[3]+i4*zStride[4]] = op(x[i0*xStride[0]+i1*xStride[1]+i2*xStride[2]+i3*xStride[3]+i4*xStride[4]], y[i0*yStride[0]+i1*yStride[1]+i2*yStride[2]+i3*yStride[3]+i4*yStride[4]], extraParams); + } + break; - default: { - uint xShapeInfoCast[MAX_RANK]; - uint yShapeInfoCast[MAX_RANK]; - uint zShapeInfoCast[MAX_RANK]; + default: { + uint xShapeInfoCast[MAX_RANK]; + uint yShapeInfoCast[MAX_RANK]; + uint zShapeInfoCast[MAX_RANK]; - bool canCastX = DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast); - bool canCastY = DataTypeUtils::castShapeInfo(yShapeInfo, yShapeInfoCast); - bool canCastZ = DataTypeUtils::castShapeInfo(zShapeInfo, zShapeInfoCast); + bool canCastX = DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast); + bool canCastY = DataTypeUtils::castShapeInfo(yShapeInfo, yShapeInfoCast); + bool canCastZ = DataTypeUtils::castShapeInfo(zShapeInfo, zShapeInfoCast); - PRAGMA_OMP_PARALLEL_THREADS(threadsInfo._numThreads) - { - auto threadNum = omp_get_thread_num(); - auto threadOffset = threadsInfo.getThreadOffset(threadNum); - auto lenPerThread = static_cast(threadsInfo.getItersPerThread(threadNum)); - PRAGMA_OMP_SIMD - for (uint i = 0; i < lenPerThread; i++) { - auto xOffset = shape::indexOffset(i + threadOffset, xShapeInfo, xShapeInfoCast, canCastX); - auto yOffset = shape::indexOffset(i + threadOffset, yShapeInfo, yShapeInfoCast, canCastY); - auto zOffset = shape::indexOffset(i + threadOffset, zShapeInfo, zShapeInfoCast, canCastZ); - z[zOffset] = op(x[xOffset], y[yOffset], extraParams); + PRAGMA_OMP_PARALLEL_THREADS(threadsInfo._numThreads) + { + auto threadNum = omp_get_thread_num(); + auto threadOffset = threadsInfo.getThreadOffset(threadNum); + auto lenPerThread = static_cast(threadsInfo.getItersPerThread(threadNum)); + PRAGMA_OMP_SIMD + for (uint i = 0; i < lenPerThread; i++) { + auto xOffset = shape::indexOffset(i + threadOffset, xShapeInfo, xShapeInfoCast, canCastX); + auto yOffset = shape::indexOffset(i + threadOffset, yShapeInfo, yShapeInfoCast, canCastY); + auto zOffset = shape::indexOffset(i + threadOffset, zShapeInfo, zShapeInfoCast, canCastZ); + z[zOffset] = op(x[xOffset], y[yOffset], extraParams); + } } } } } -} -*/ + */ -////////////////////////////////////////////////////////////////////////////// + ////////////////////////////////////////////////////////////////////////////// template template - void nd4j::ReductionLoops::loopReduce(X* x, Nd4jLong* xShapeInfo, - Z* z, Nd4jLong* zShapeInfo, - Nd4jLong* tadShapeInfo, Nd4jLong* tadOffsets, - E* extraParams, int64_t start, int64_t stop) { + void sd::ReductionLoops::loopReduce(X* x, Nd4jLong* xShapeInfo, + Z* z, Nd4jLong* zShapeInfo, + Nd4jLong* tadShapeInfo, Nd4jLong* tadOffsets, + E* extraParams, int64_t start, int64_t stop) { const LoopKind::Kind kindOfLoop = LoopKind::deduceKindOfLoopTadXZ(xShapeInfo, zShapeInfo, tadShapeInfo); - const Nd4jLong zLen = shape::length(zShapeInfo); + const Nd4jLong zLen = shape::length(zShapeInfo); const Nd4jLong tadLen = shape::length(tadShapeInfo); const uint tadEws = shape::elementWiseStride(tadShapeInfo); - const uint zEws = shape::elementWiseStride(zShapeInfo); + const uint zEws = shape::elementWiseStride(zShapeInfo); - const Nd4jLong* tadShape = shape::shapeOf(tadShapeInfo); + const Nd4jLong* tadShape = shape::shapeOf(tadShapeInfo); const Nd4jLong* tadStride = shape::stride(tadShapeInfo); int numThreads = OmpLaunchHelper::tadThreads(tadLen, zLen); @@ -298,192 +298,192 @@ void Loops::loopXYZ(const X* x, const Nd4jLong* xShapeInfo, // printf("%u - %lld\n", i, zOffset); // } // } - case LoopKind::SMALLARR2DX: { - const auto uTadLen = static_cast(tadLen); - const auto uZLenMinusOne = static_cast(zLen - 1); - const auto xLen = static_cast(zLen * uTadLen); - const auto sv = static_cast(OpType::startingValue(x)); + case LoopKind::SMALLARR2DX: { + const auto uTadLen = static_cast(tadLen); + const auto uZLenMinusOne = static_cast(zLen - 1); + const auto xLen = static_cast(zLen * uTadLen); + const auto sv = static_cast(OpType::startingValue(x)); - for (uint i = 0; i <= uZLenMinusOne; i++) - z[i] = OpType::startingValue(x); + for (uint i = 0; i <= uZLenMinusOne; i++) + z[i] = OpType::startingValue(x); - uint zOffset = 0; - for (uint i = 0; i < xLen; ++i) { - z[zOffset] = OpType::update(z[zOffset], OpType::op(x[i], extraParams), extraParams); - zOffset = zOffset == uZLenMinusOne ? 0 : zOffset + 1; + uint zOffset = 0; + for (uint i = 0; i < xLen; ++i) { + z[zOffset] = OpType::update(z[zOffset], OpType::op(x[i], extraParams), extraParams); + zOffset = zOffset == uZLenMinusOne ? 0 : zOffset + 1; + } + + for (uint i = 0; i <= uZLenMinusOne; i++) + z[i] = OpType::postProcess(z[i], tadLen, extraParams); + } + break; + + //*********************************************// + case LoopKind::EWS1: { + for (auto i = start; i < stop; i++) { + auto tad = x + tadOffsets[i]; + auto s = OpType::startingValue(tad); + + for (Nd4jLong j = 0; j < tadLen; j++) + s = OpType::update(s, OpType::op(tad[j], extraParams), extraParams); + + z[i] = OpType::postProcess(s, tadLen, extraParams); + }; + } + break; + + //*********************************************// + case LoopKind::EWSNONZERO: { + for (auto i = start; i < stop; i++) { + auto tad = x + tadOffsets[i]; + auto s = OpType::startingValue(tad); + + for (Nd4jLong j = 0; j < tadLen; j++) + s = OpType::update(s, OpType::op(tad[j * tadEws], extraParams), extraParams); + + z[i * zEws] = OpType::postProcess(s, tadLen, extraParams); + }; + } + break; + + //*********************************************// + case LoopKind::RANK1: { + for (auto i = start; i < stop; i++) { + auto tad = x + tadOffsets[i]; + auto s = OpType::startingValue(tad); + + for (Nd4jLong i0 = 0; i0 < tadLen; ++i0) + s = OpType::update(s, OpType::op(tad[i0 * tadStride[0]], extraParams), extraParams); + + z[i] = OpType::postProcess(s, tadLen, extraParams); + }; + } + break; + + //*********************************************// + case LoopKind::RANK2: { + for (auto i = start; i < stop; i++) { + auto tad = x + tadOffsets[i]; + auto s = OpType::startingValue(tad); + + for (Nd4jLong i0 = 0; i0 < tadShape[0]; ++i0) + for (Nd4jLong i1 = 0; i1 < tadShape[1]; ++i1) + s = OpType::update(s, OpType::op(tad[i0 * tadStride[0] + i1 * tadStride[1]], extraParams), extraParams); + + z[i] = OpType::postProcess(s, tadLen, extraParams); + }; + } + break; + + //*********************************************// + case LoopKind::RANK3: { + for (auto i = start; i < stop; i++) { + auto tad = x + tadOffsets[i]; + auto s = OpType::startingValue(tad); + + for (Nd4jLong i0 = 0; i0 < tadShape[0]; ++i0) + for (Nd4jLong i1 = 0; i1 < tadShape[1]; ++i1) + for (Nd4jLong i2 = 0; i2 < tadShape[2]; ++i2) + s = OpType::update(s, OpType::op(tad[i0 * tadStride[0] + i1 * tadStride[1] + i2 * tadStride[2]], extraParams), extraParams); + + z[i] = OpType::postProcess(s, tadLen, extraParams); + }; + } + break; + + //*********************************************// + case LoopKind::RANK4: { + for (auto i = start; i < stop; i++) { + auto tad = x + tadOffsets[i]; + auto s = OpType::startingValue(tad); + + for (Nd4jLong i0 = 0; i0 < tadShape[0]; ++i0) + for (Nd4jLong i1 = 0; i1 < tadShape[1]; ++i1) + for (Nd4jLong i2 = 0; i2 < tadShape[2]; ++i2) + for (Nd4jLong i3 = 0; i3 < tadShape[3]; ++i3) + s = OpType::update(s, OpType::op(tad[i0 * tadStride[0] + i1 * tadStride[1] + i2 * tadStride[2] + i3 * tadStride[3]], extraParams), extraParams); + + z[i] = OpType::postProcess(s, tadLen, extraParams); + }; + } + break; + + //*********************************************// + case LoopKind::RANK5: { + for (auto i = start; i < stop; i++) { + auto tad = x + tadOffsets[i]; + auto s = OpType::startingValue(tad); + + for (Nd4jLong i0 = 0; i0 < tadShape[0]; ++i0) + for (Nd4jLong i1 = 0; i1 < tadShape[1]; ++i1) + for (Nd4jLong i2 = 0; i2 < tadShape[2]; ++i2) + for (Nd4jLong i3 = 0; i3 < tadShape[3]; ++i3) + for (Nd4jLong i4 = 0; i4 < tadShape[4]; ++i4) + s = OpType::update(s, OpType::op(tad[i0 * tadStride[0] + i1 * tadStride[1] + i2 * tadStride[2] + i3 * tadStride[3] + i4 * tadStride[4]], extraParams), extraParams); + + z[i] = OpType::postProcess(s, tadLen, extraParams); + }; + } + break; + + //*********************************************// + case LoopKind::X_EWSNONZERO: { + uint castZShapeInfo[MAX_RANK]; + const bool canCastZ = sd::DataTypeUtils::castShapeInfo(zShapeInfo, castZShapeInfo); + + for (auto i = start; i < stop; i++) { + auto tad = x + tadOffsets[i]; + auto s = OpType::startingValue(tad); + + for (Nd4jLong j = 0; j < tadLen; j++) + s = OpType::update(s, OpType::op(tad[j * tadEws], extraParams), extraParams); + + auto zOffset = shape::indexOffset(i, zShapeInfo, castZShapeInfo, canCastZ); + z[zOffset] = OpType::postProcess(s, tadLen, extraParams); + }; + } + break; + + //*********************************************// + case LoopKind::Z_EWSNONZERO: { + uint castTadShapeInfo[MAX_RANK]; + const bool canCastTad = sd::DataTypeUtils::castShapeInfo(tadShapeInfo, castTadShapeInfo); + + for (auto i = start; i < stop; i++) { + auto tad = x + tadOffsets[i]; + auto s = OpType::startingValue(tad); + + for (Nd4jLong j = 0; j < tadLen; j++) { + auto tadOffset = shape::indexOffset(j, tadShapeInfo, castTadShapeInfo, canCastTad); + s = OpType::update(s, OpType::op(tad[tadOffset], extraParams), extraParams); } - for (uint i = 0; i <= uZLenMinusOne; i++) - z[i] = OpType::postProcess(z[i], tadLen, extraParams); - } - break; + z[i * zEws] = OpType::postProcess(s, tadLen, extraParams); + }; + } + break; - //*********************************************// - case LoopKind::EWS1: { - for (auto i = start; i < stop; i++) { - auto tad = x + tadOffsets[i]; - auto s = OpType::startingValue(tad); + //*********************************************// + default: { + auto innertadOffsets = new Nd4jLong[tadLen]; + shape::calcOffsets(tadShapeInfo, innertadOffsets); - for (uint j = 0; j < tadLen; j++) - s = OpType::update(s, OpType::op(tad[j], extraParams), extraParams); + uint castZShapeInfo[MAX_RANK]; + const bool canCastZ = sd::DataTypeUtils::castShapeInfo(zShapeInfo, castZShapeInfo); - z[i] = OpType::postProcess(s, tadLen, extraParams); - }; - } - break; + for (auto i = start; i < stop; i++) { + auto tad = x + tadOffsets[i]; + auto s = OpType::startingValue(tad); - //*********************************************// - case LoopKind::EWSNONZERO: { - for (auto i = start; i < stop; i++) { - auto tad = x + tadOffsets[i]; - auto s = OpType::startingValue(tad); + for (Nd4jLong j = 0; j < tadLen; j++) + s = OpType::update(s, OpType::op(tad[innertadOffsets[j]], extraParams), extraParams); - for (uint j = 0; j < tadLen; j++) - s = OpType::update(s, OpType::op(tad[j * tadEws], extraParams), extraParams); + auto zOffset = shape::indexOffset(i, zShapeInfo, castZShapeInfo, canCastZ); + z[zOffset] = OpType::postProcess(s, tadLen, extraParams); + }; - z[i * zEws] = OpType::postProcess(s, tadLen, extraParams); - }; - } - break; - - //*********************************************// - case LoopKind::RANK1: { - for (auto i = start; i < stop; i++) { - auto tad = x + tadOffsets[i]; - auto s = OpType::startingValue(tad); - - for (uint i0 = 0; i0 < tadLen; ++i0) - s = OpType::update(s, OpType::op(tad[i0 * tadStride[0]], extraParams), extraParams); - - z[i] = OpType::postProcess(s, tadLen, extraParams); - }; - } - break; - - //*********************************************// - case LoopKind::RANK2: { - for (auto i = start; i < stop; i++) { - auto tad = x + tadOffsets[i]; - auto s = OpType::startingValue(tad); - - for (uint i0 = 0; i0 < tadShape[0]; ++i0) - for (uint i1 = 0; i1 < tadShape[1]; ++i1) - s = OpType::update(s, OpType::op(tad[i0 * tadStride[0] + i1 * tadStride[1]], extraParams), extraParams); - - z[i] = OpType::postProcess(s, tadLen, extraParams); - }; - } - break; - - //*********************************************// - case LoopKind::RANK3: { - for (auto i = start; i < stop; i++) { - auto tad = x + tadOffsets[i]; - auto s = OpType::startingValue(tad); - - for (uint i0 = 0; i0 < tadShape[0]; ++i0) - for (uint i1 = 0; i1 < tadShape[1]; ++i1) - for (uint i2 = 0; i2 < tadShape[2]; ++i2) - s = OpType::update(s, OpType::op(tad[i0 * tadStride[0] + i1 * tadStride[1] + i2 * tadStride[2]], extraParams), extraParams); - - z[i] = OpType::postProcess(s, tadLen, extraParams); - }; - } - break; - - //*********************************************// - case LoopKind::RANK4: { - for (auto i = start; i < stop; i++) { - auto tad = x + tadOffsets[i]; - auto s = OpType::startingValue(tad); - - for (uint i0 = 0; i0 < tadShape[0]; ++i0) - for (uint i1 = 0; i1 < tadShape[1]; ++i1) - for (uint i2 = 0; i2 < tadShape[2]; ++i2) - for (uint i3 = 0; i3 < tadShape[3]; ++i3) - s = OpType::update(s, OpType::op(tad[i0 * tadStride[0] + i1 * tadStride[1] + i2 * tadStride[2] + i3 * tadStride[3]], extraParams), extraParams); - - z[i] = OpType::postProcess(s, tadLen, extraParams); - }; - } - break; - - //*********************************************// - case LoopKind::RANK5: { - for (auto i = start; i < stop; i++) { - auto tad = x + tadOffsets[i]; - auto s = OpType::startingValue(tad); - - for (uint i0 = 0; i0 < tadShape[0]; ++i0) - for (uint i1 = 0; i1 < tadShape[1]; ++i1) - for (uint i2 = 0; i2 < tadShape[2]; ++i2) - for (uint i3 = 0; i3 < tadShape[3]; ++i3) - for (uint i4 = 0; i4 < tadShape[4]; ++i4) - s = OpType::update(s, OpType::op(tad[i0 * tadStride[0] + i1 * tadStride[1] + i2 * tadStride[2] + i3 * tadStride[3] + i4 * tadStride[4]], extraParams), extraParams); - - z[i] = OpType::postProcess(s, tadLen, extraParams); - }; - } - break; - - //*********************************************// - case LoopKind::X_EWSNONZERO: { - uint castZShapeInfo[MAX_RANK]; - const bool canCastZ = nd4j::DataTypeUtils::castShapeInfo(zShapeInfo, castZShapeInfo); - - for (auto i = start; i < stop; i++) { - auto tad = x + tadOffsets[i]; - auto s = OpType::startingValue(tad); - - for (uint j = 0; j < tadLen; j++) - s = OpType::update(s, OpType::op(tad[j * tadEws], extraParams), extraParams); - - auto zOffset = shape::indexOffset(i, zShapeInfo, castZShapeInfo, canCastZ); - z[zOffset] = OpType::postProcess(s, tadLen, extraParams); - }; - } - break; - - //*********************************************// - case LoopKind::Z_EWSNONZERO: { - uint castTadShapeInfo[MAX_RANK]; - const bool canCastTad = nd4j::DataTypeUtils::castShapeInfo(tadShapeInfo, castTadShapeInfo); - - for (auto i = start; i < stop; i++) { - auto tad = x + tadOffsets[i]; - auto s = OpType::startingValue(tad); - - for (uint j = 0; j < tadLen; j++) { - auto tadOffset = shape::indexOffset(j, tadShapeInfo, castTadShapeInfo, canCastTad); - s = OpType::update(s, OpType::op(tad[tadOffset], extraParams), extraParams); - } - - z[i * zEws] = OpType::postProcess(s, tadLen, extraParams); - }; - } - break; - - //*********************************************// - default: { - auto innertadOffsets = new Nd4jLong[tadLen]; - shape::calcOffsets(tadShapeInfo, innertadOffsets); - - uint castZShapeInfo[MAX_RANK]; - const bool canCastZ = nd4j::DataTypeUtils::castShapeInfo(zShapeInfo, castZShapeInfo); - - for (auto i = start; i < stop; i++) { - auto tad = x + tadOffsets[i]; - auto s = OpType::startingValue(tad); - - for (uint j = 0; j < tadLen; j++) - s = OpType::update(s, OpType::op(tad[innertadOffsets[j]], extraParams), extraParams); - - auto zOffset = shape::indexOffset(i, zShapeInfo, castZShapeInfo, canCastZ); - z[zOffset] = OpType::postProcess(s, tadLen, extraParams); - }; - - delete[] innertadOffsets; - } + delete[] innertadOffsets; + } } } @@ -492,13 +492,13 @@ void Loops::loopXYZ(const X* x, const Nd4jLong* xShapeInfo, ////////////////////////////////////////////////////////////////////////////// template template - void nd4j::TransformLoops::loopTransform(X* x, Nd4jLong* xShapeInfo, - Z* z, Nd4jLong* zShapeInfo, - E* extraParams, uint64_t threadId, uint64_t numThreads) { + void sd::TransformLoops::loopTransform(X* x, Nd4jLong* xShapeInfo, + Z* z, Nd4jLong* zShapeInfo, + E* extraParams, uint64_t threadId, uint64_t numThreads) { const LoopKind::Kind kindOfLoop = LoopKind::deduceKindOfLoopXZ(xShapeInfo, zShapeInfo); - const Nd4jLong* xShape = shape::shapeOf(const_cast(xShapeInfo)); + const Nd4jLong* xShape = shape::shapeOf(const_cast(xShapeInfo)); const Nd4jLong* xStride = shape::stride(const_cast(xShapeInfo)); const Nd4jLong* zStride = shape::stride(const_cast(zShapeInfo)); @@ -510,182 +510,183 @@ void Loops::loopXYZ(const X* x, const Nd4jLong* xShapeInfo, switch (kindOfLoop) { //*********************************************// - case LoopKind::EWS1: { - auto span = samediff::Span::build(threadId, numThreads, 0, len, 1); - int64_t start = span.startX(), stop = span.stopX(); + case LoopKind::EWS1: { + auto span = samediff::Span::build(threadId, numThreads, 0, len, 1); + int64_t start = span.startX(), stop = span.stopX(); - for (auto i = start; i < stop; i++) - z[i] = OpType::op(x[i], extraParams); + for (auto i = start; i < stop; i++) + z[i] = OpType::op(x[i], extraParams); + } + break; + + //*********************************************// + case LoopKind::EWSNONZERO: { + const uint xEws = shape::elementWiseStride(xShapeInfo); + const uint zEws = shape::elementWiseStride(zShapeInfo); + + auto span = samediff::Span::build(threadId, numThreads, 0, len, 1); + int64_t start = span.startX(), stop = span.stopX(); + + for (auto i = start; i < stop; i++) + z[i * zEws] = OpType::op(x[i * xEws], extraParams); + } + break; + + //*********************************************// + case LoopKind::Z_EWSNONZERO: { + const uint zEws = shape::elementWiseStride(zShapeInfo); + uint castXShapeInfo[MAX_RANK]; + const bool canCastX = sd::DataTypeUtils::castShapeInfo(xShapeInfo, castXShapeInfo); + + auto span = samediff::Span::build(threadId, numThreads, 0, len, 1); + int64_t start = span.startX(), stop = span.stopX(); + + if (zEws > 1) { + for (auto i = start; i < stop; i++) { + const auto xOffset = shape::indexOffset(i, xShapeInfo, castXShapeInfo, canCastX); + z[i * zEws] = OpType::op(x[xOffset], extraParams); } - break; - - //*********************************************// - case LoopKind::EWSNONZERO: { - const uint xEws = shape::elementWiseStride(xShapeInfo); - const uint zEws = shape::elementWiseStride(zShapeInfo); - - auto span = samediff::Span::build(threadId, numThreads, 0, len, 1); - int64_t start = span.startX(), stop = span.stopX(); - - for (auto i = start; i < stop; i++) - z[i*zEws] = OpType::op(x[i*xEws], extraParams); + } + else { + for (auto i = start; i < stop; i++) { + const auto xOffset = shape::indexOffset(i, xShapeInfo, castXShapeInfo, canCastX); + z[i] = OpType::op(x[xOffset], extraParams); } - break; + } + } + break; - //*********************************************// - case LoopKind::Z_EWSNONZERO: { - const uint zEws = shape::elementWiseStride(zShapeInfo); - uint castXShapeInfo[MAX_RANK]; - const bool canCastX = nd4j::DataTypeUtils::castShapeInfo(xShapeInfo, castXShapeInfo); + //*********************************************// + case LoopKind::RANK1: { + auto span = samediff::Span::build(threadId, numThreads, 0, len, 1); - auto span = samediff::Span::build(threadId, numThreads, 0, len, 1); - int64_t start = span.startX(), stop = span.stopX(); + for (auto i0 = span.startX(); i0 < span.stopX(); i0++) + z[i0 * zStride[0]] = OpType::op(x[i0 * xStride[0]], extraParams); + } + break; + + //*********************************************// + case LoopKind::RANK2: { + auto uXShape0 = static_cast(xShape[0]); + auto uXShape1 = static_cast(xShape[1]); + + auto loop = samediff::ThreadsHelper::pickLoop2d(numThreads, uXShape0, uXShape1); + auto span = samediff::Span2::build(loop, threadId, numThreads, 0, uXShape0, 1, 0, uXShape1, 1); + + for (auto i0 = span.startX(); i0 < span.stopX(); i0++) { + auto z0 = i0 * zStride[0]; + auto x0 = i0 * xStride[0]; + + for (auto i1 = span.startY(); i1 < span.stopY(); ++i1) + z[z0 + i1 * zStride[1]] = OpType::op(x[x0 + i1 * xStride[1]], extraParams); + } + } + break; + + //*********************************************// + case LoopKind::RANK3: { + auto uXShape0 = xShape[0]; + auto uXShape1 = xShape[1]; + auto uXShape2 = xShape[2]; + + auto loop = samediff::ThreadsHelper::pickLoop2d(numThreads, uXShape0, uXShape1); + auto span = samediff::Span2::build(loop, threadId, numThreads, 0, uXShape0, 1, 0, uXShape1, 1); + + + for (auto i0 = span.startX(); i0 < span.stopX(); i0++) + for (auto i1 = span.startY(); i1 < span.stopY(); i1++) { + auto z0 = i0 * zStride[0] + i1 * zStride[1]; + auto x0 = i0 * xStride[0] + i1 * xStride[1]; + + for (Nd4jLong i2 = 0; i2 < uXShape2; ++i2) + z[z0 + i2 * zStride[2]] = OpType::op(x[x0 + i2 * xStride[2]], extraParams); + } + } + break; + + //*********************************************// + case LoopKind::RANK4: { + auto uXShape0 = xShape[0]; + auto uXShape1 = xShape[1]; + auto uXShape2 = xShape[2]; + auto uXShape3 = xShape[3]; + + auto loop = samediff::ThreadsHelper::pickLoop3d(numThreads, uXShape0, uXShape1, uXShape2); + auto span = samediff::Span3::build(loop, threadId, numThreads, 0, uXShape0, 1, 0, uXShape1, 1, 0, uXShape2, 1); + + for (auto i0 = span.startX(); i0 < span.stopX(); i0++) + for (auto i1 = span.startY(); i1 < span.stopY(); i1++) + for (auto i2 = span.startZ(); i2 < span.stopZ(); i2++) { + auto x0 = i0 * xStride[0] + i1 * xStride[1] + i2 * xStride[2]; + auto z0 = i0 * zStride[0] + i1 * zStride[1] + i2 * zStride[2]; + + for (Nd4jLong i3 = 0; i3 < uXShape3; ++i3) + z[z0 + i3 * zStride[3]] = OpType::op(x[x0 + i3 * xStride[3]], extraParams); + } + } + break; + + //*********************************************// + case LoopKind::RANK5: { + auto uXShape0 = xShape[0]; + auto uXShape1 = xShape[1]; + auto uXShape2 = xShape[2]; + auto uXShape3 = xShape[3]; + auto uXShape4 = xShape[4]; + + auto loop = samediff::ThreadsHelper::pickLoop3d(numThreads, uXShape0, uXShape1, uXShape2); + auto span = samediff::Span3::build(loop, threadId, numThreads, 0, uXShape0, 1, 0, uXShape1, 1, 0, uXShape2, 1); + + + for (auto i0 = span.startX(); i0 < span.stopX(); i0++) + for (auto i1 = span.startY(); i1 < span.stopY(); i1++) + for (auto i2 = span.startZ(); i2 < span.stopZ(); i2++) { + auto z0 = i0 * zStride[0] + i1 * zStride[1] + i2 * zStride[2]; + auto x0 = i0 * xStride[0] + i1 * xStride[1] + i2 * xStride[2]; + + for (Nd4jLong i3 = 0; i3 < uXShape3; ++i3) { + + auto z1 = z0 + i3 * zStride[3]; + auto x1 = x0 + i3 * xStride[3]; + + for (Nd4jLong i4 = 0; i4 < uXShape4; ++i4) + z[z1 + i4 * zStride[4]] = OpType::op(x[x1 + i4 * xStride[4]], extraParams); - if (zEws > 1) { - for (auto i = start; i < stop; i++) { - const auto xOffset = shape::indexOffset(i, xShapeInfo, castXShapeInfo, canCastX); - z[i * zEws] = OpType::op(x[xOffset], extraParams); - } - } else { - for (auto i = start; i < stop; i++) { - const auto xOffset = shape::indexOffset(i, xShapeInfo, castXShapeInfo, canCastX); - z[i] = OpType::op(x[xOffset], extraParams); } } - } - break; - //*********************************************// - case LoopKind::RANK1: { - auto span = samediff::Span::build(threadId, numThreads, 0, len, 1); + } + break; - for (auto i0 = span.startX(); i0 < span.stopX(); i0++) - z[i0 * zStride[0]] = OpType::op(x[i0 * xStride[0]], extraParams); - } - break; + //*********************************************// + default: { + uint xShapeInfoCast[MAX_RANK]; + uint zShapeInfoCast[MAX_RANK]; - //*********************************************// - case LoopKind::RANK2: { - auto uXShape0 = static_cast(xShape[0]); - auto uXShape1 = static_cast(xShape[1]); + bool canCastX = DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast); + bool canCastZ = DataTypeUtils::castShapeInfo(zShapeInfo, zShapeInfoCast); - auto loop = samediff::ThreadsHelper::pickLoop2d(numThreads, uXShape0, uXShape1); - auto span = samediff::Span2::build(loop, threadId, numThreads, 0, uXShape0, 1, 0, uXShape1, 1); + auto span = samediff::Span::build(threadId, numThreads, 0, len, 1); - for (auto i0 = span.startX(); i0 < span.stopX(); i0++) { - auto z0 = i0 * zStride[0]; - auto x0 = i0 * xStride[0]; - - for (uint i1 = span.startY(); i1 < span.stopY(); ++i1) - z[z0 + i1 * zStride[1]] = OpType::op(x[x0 + i1 * xStride[1]], extraParams); - } - } - break; - - //*********************************************// - case LoopKind::RANK3: { - auto uXShape0 = static_cast(xShape[0]); - auto uXShape1 = static_cast(xShape[1]); - auto uXShape2 = static_cast(xShape[2]); - - auto loop = samediff::ThreadsHelper::pickLoop2d(numThreads, uXShape0, uXShape1); - auto span = samediff::Span2::build(loop, threadId, numThreads, 0, uXShape0, 1, 0, uXShape1, 1); - - - for (auto i0 = span.startX(); i0 < span.stopX(); i0++) - for (auto i1 = span.startY(); i1 < span.stopY(); i1++) { - auto z0 = i0 * zStride[0] + i1 * zStride[1]; - auto x0 = i0 * xStride[0] + i1 * xStride[1]; - - for (uint i2 = 0; i2 < uXShape2; ++i2) - z[z0 + i2 * zStride[2]] = OpType::op(x[x0 + i2 * xStride[2]], extraParams); - } - } - break; - - //*********************************************// - case LoopKind::RANK4: { - auto uXShape0 = static_cast(xShape[0]); - auto uXShape1 = static_cast(xShape[1]); - auto uXShape2 = static_cast(xShape[2]); - auto uXShape3 = static_cast(xShape[3]); - - auto loop = samediff::ThreadsHelper::pickLoop3d(numThreads, uXShape0, uXShape1, uXShape2); - auto span = samediff::Span3::build(loop, threadId, numThreads, 0, uXShape0, 1, 0, uXShape1, 1, 0, uXShape2, 1); - - for (auto i0 = span.startX(); i0 < span.stopX(); i0++) - for (auto i1 = span.startY(); i1 < span.stopY(); i1++) - for (auto i2 = span.startZ(); i2 < span.stopZ(); i2++) { - auto x0 = i0 * xStride[0] + i1 * xStride[1] + i2 * xStride[2]; - auto z0 = i0 * zStride[0] + i1 * zStride[1] + i2 * zStride[2]; - - for (uint i3 = 0; i3 < uXShape3; ++i3) - z[z0 + i3 * zStride[3]] = OpType::op(x[x0 + i3 * xStride[3]], extraParams); - } - } - break; - - //*********************************************// - case LoopKind::RANK5: { - auto uXShape0 = static_cast(xShape[0]); - auto uXShape1 = static_cast(xShape[1]); - auto uXShape2 = static_cast(xShape[2]); - auto uXShape3 = static_cast(xShape[3]); - auto uXShape4 = static_cast(xShape[4]); - - auto loop = samediff::ThreadsHelper::pickLoop3d(numThreads, uXShape0, uXShape1, uXShape2); - auto span = samediff::Span3::build(loop, threadId, numThreads, 0, uXShape0, 1, 0, uXShape1, 1, 0, uXShape2, 1); - - - for (auto i0 = span.startX(); i0 < span.stopX(); i0++) - for (auto i1 = span.startY(); i1 < span.stopY(); i1++) - for (auto i2 = span.startZ(); i2 < span.stopZ(); i2++) { - auto z0 = i0 * zStride[0] + i1 * zStride[1] + i2 * zStride[2]; - auto x0 = i0 * xStride[0] + i1 * xStride[1] + i2 * xStride[2]; - - for (uint i3 = 0; i3 < uXShape3; ++i3) { - - auto z1 = z0 + i3 * zStride[3]; - auto x1 = x0 + i3 * xStride[3]; - - for (uint i4 = 0; i4 < uXShape4; ++i4) - z[z1 + i4 * zStride[4]] = OpType::op(x[x1 + i4 * xStride[4]], extraParams); - - } - } - - } - break; - - //*********************************************// - default: { - uint xShapeInfoCast[MAX_RANK]; - uint zShapeInfoCast[MAX_RANK]; - - bool canCastX = DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast); - bool canCastZ = DataTypeUtils::castShapeInfo(zShapeInfo, zShapeInfoCast); - - auto span = samediff::Span::build(threadId, numThreads, 0, len, 1); - - for (auto i = span.startX(); i < span.stopX(); i++) { - auto xOffset = shape::indexOffset(i, xShapeInfo, xShapeInfoCast, canCastX); - auto zOffset = shape::indexOffset(i, zShapeInfo, zShapeInfoCast, canCastZ); - z[zOffset] = OpType::op(x[xOffset], extraParams); - } - } + for (auto i = span.startX(); i < span.stopX(); i++) { + auto xOffset = shape::indexOffset(i, xShapeInfo, xShapeInfoCast, canCastX); + auto zOffset = shape::indexOffset(i, zShapeInfo, zShapeInfoCast, canCastZ); + z[zOffset] = OpType::op(x[xOffset], extraParams); + } + } } } -////////////////////////////////////////////////////////////////////////////// + ////////////////////////////////////////////////////////////////////////////// template template - void nd4j::Reduction3Loops::loopReduce3(X* x, Nd4jLong* xShapeInfo, - X* y, Nd4jLong* yShapeInfo, - Z* z, Nd4jLong* zShapeInfo, - int* dims, int dimsLen, - Z* extraParameters, int64_t start, int64_t stop) { + void sd::Reduction3Loops::loopReduce3(X* x, Nd4jLong* xShapeInfo, + X* y, Nd4jLong* yShapeInfo, + Z* z, Nd4jLong* zShapeInfo, + int* dims, int dimsLen, + Z* extraParameters, int64_t start, int64_t stop) { // both tads have same shape, however strides and ews may differ @@ -694,29 +695,29 @@ void Loops::loopXYZ(const X* x, const Nd4jLong* xShapeInfo, const Nd4jLong xLen = shape::length(xShapeInfo); const Nd4jLong yLen = shape::length(yShapeInfo); - Nd4jLong *xTadShapeInfo = nullptr, *yTadShapeInfo = nullptr, *xTadOffsets = nullptr, *yTadOffsets = nullptr; + Nd4jLong* xTadShapeInfo = nullptr, * yTadShapeInfo = nullptr, * xTadOffsets = nullptr, * yTadOffsets = nullptr; TadPack tadPackX, tadPackY; std::vector zeroOffsets; - if(xLen == yLen) { - tadPackX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(xShapeInfo, dims, dimsLen); - tadPackY = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(yShapeInfo, dims, dimsLen); + if (xLen == yLen) { + tadPackX = sd::ConstantTadHelper::getInstance()->tadForDimensions(xShapeInfo, dims, dimsLen); + tadPackY = sd::ConstantTadHelper::getInstance()->tadForDimensions(yShapeInfo, dims, dimsLen); xTadShapeInfo = tadPackX.primaryShapeInfo(); yTadShapeInfo = tadPackY.primaryShapeInfo(); - xTadOffsets = tadPackX.primaryOffsets(); - yTadOffsets = tadPackY.primaryOffsets(); + xTadOffsets = tadPackX.primaryOffsets(); + yTadOffsets = tadPackY.primaryOffsets(); } - else if(yLen > xLen) { - tadPackY = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(yShapeInfo, dims, dimsLen); + else if (yLen > xLen) { + tadPackY = sd::ConstantTadHelper::getInstance()->tadForDimensions(yShapeInfo, dims, dimsLen); xTadShapeInfo = xShapeInfo; yTadShapeInfo = tadPackY.primaryShapeInfo(); - yTadOffsets = tadPackY.primaryOffsets(); + yTadOffsets = tadPackY.primaryOffsets(); } else { - tadPackX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(xShapeInfo, dims, dimsLen); + tadPackX = sd::ConstantTadHelper::getInstance()->tadForDimensions(xShapeInfo, dims, dimsLen); yTadShapeInfo = yShapeInfo; xTadShapeInfo = tadPackX.primaryShapeInfo(); - xTadOffsets = tadPackX.primaryOffsets(); + xTadOffsets = tadPackX.primaryOffsets(); } @@ -724,162 +725,196 @@ void Loops::loopXYZ(const X* x, const Nd4jLong* xShapeInfo, const auto xTadEws = shape::elementWiseStride(xTadShapeInfo); const auto yTadEws = shape::elementWiseStride(yTadShapeInfo); - const auto zEws = shape::elementWiseStride(zShapeInfo); + const auto zEws = shape::elementWiseStride(zShapeInfo); - const auto zLen = shape::length(zShapeInfo); + const auto zLen = shape::length(zShapeInfo); const auto tadLen = shape::length(xTadShapeInfo); - const auto tadShape = shape::shapeOf(xTadShapeInfo); - const auto xTadStride = shape::stride(xTadShapeInfo); - const auto yTadStride = shape::stride(xTadShapeInfo); + const auto tadShape = shape::shapeOf(xTadShapeInfo); + const auto xTadStride = shape::stride(xTadShapeInfo); + const auto yTadStride = shape::stride(xTadShapeInfo); int numThreads = OmpLaunchHelper::tadThreads(tadLen, zLen); switch (kindOfLoop) { - //*********************************************// - case LoopKind::EWS1: { - Z extraParams[3]; - for (auto i = start; i < stop; i++) { - extraParams[0] = param0; - extraParams[1] = param1; - extraParams[2] = param2; + //*********************************************// + case LoopKind::EWS1: { + Z extraParams[3]; + for (auto i = start; i < stop; i++) { + extraParams[0] = param0; + extraParams[1] = param1; + extraParams[2] = param2; - const auto xTad = xTadOffsets ? x + xTadOffsets[i] : x; - const auto yTad = yTadOffsets ? y + yTadOffsets[i] : y; - auto s = OpType::startingValue(xTad); + const auto xTad = xTadOffsets ? x + xTadOffsets[i] : x; + const auto yTad = yTadOffsets ? y + yTadOffsets[i] : y; + auto s = OpType::startingValue(xTad); - for (uint j = 0; j < tadLen; ++j) - s = OpType::update(s, OpType::op(xTad[j], yTad[j], extraParams), extraParams); + for (Nd4jLong j = 0; j < tadLen; ++j) + s = OpType::update(s, OpType::op(xTad[j], yTad[j], extraParams), extraParams); - z[i] = OpType::postProcess(s, tadLen, extraParams); - }; - } - break; + z[i] = OpType::postProcess(s, tadLen, extraParams); + }; + } + break; - //*********************************************// - case LoopKind::EWSNONZERO: { - Z extraParams[3]; - for (auto i = start; i < stop; i++) { - extraParams[0] = param0; - extraParams[1] = param1; - extraParams[2] = param2; + //*********************************************// + case LoopKind::EWSNONZERO: { + Z extraParams[3]; + for (auto i = start; i < stop; i++) { + extraParams[0] = param0; + extraParams[1] = param1; + extraParams[2] = param2; - const auto xTad = xTadOffsets ? x + xTadOffsets[i] : x; - const auto yTad = yTadOffsets ? y + yTadOffsets[i] : y; - auto s = OpType::startingValue(xTad); + const auto xTad = xTadOffsets ? x + xTadOffsets[i] : x; + const auto yTad = yTadOffsets ? y + yTadOffsets[i] : y; + auto s = OpType::startingValue(xTad); - for (uint j = 0; j < tadLen; ++j) - s = OpType::update(s, OpType::op(xTad[j * xTadEws], yTad[j * yTadEws], extraParams), extraParams); + for (Nd4jLong j = 0; j < tadLen; ++j) + s = OpType::update(s, OpType::op(xTad[j * xTadEws], yTad[j * yTadEws], extraParams), extraParams); - z[i * zEws] = OpType::postProcess(s, tadLen, extraParams); - }; - } - break; + z[i * zEws] = OpType::postProcess(s, tadLen, extraParams); + }; + } + break; - //*********************************************// - case LoopKind::RANK1: { - Z extraParams[3]; - for (auto i = start; i < stop; i++) { - extraParams[0] = param0; - extraParams[1] = param1; - extraParams[2] = param2; + //*********************************************// + case LoopKind::RANK1: { + Z extraParams[3]; + for (auto i = start; i < stop; i++) { + extraParams[0] = param0; + extraParams[1] = param1; + extraParams[2] = param2; - const auto xTad = xTadOffsets ? x + xTadOffsets[i] : x; - const auto yTad = yTadOffsets ? y + yTadOffsets[i] : y; - auto s = OpType::startingValue(xTad); + const auto xTad = xTadOffsets ? x + xTadOffsets[i] : x; + const auto yTad = yTadOffsets ? y + yTadOffsets[i] : y; + auto s = OpType::startingValue(xTad); - for (uint i0 = 0; i0 < tadLen; ++i0) { - const auto xTadOffset = i0 * xTadStride[0]; - const auto yTadOffset = i0 * yTadStride[0]; + for (Nd4jLong i0 = 0; i0 < tadLen; ++i0) { + const auto xTadOffset = i0 * xTadStride[0]; + const auto yTadOffset = i0 * yTadStride[0]; + s = OpType::update(s, OpType::op(xTad[xTadOffset], yTad[yTadOffset], extraParams), extraParams); + } + + z[i * zEws] = OpType::postProcess(s, tadLen, extraParams); + }; + } + break; + + //*********************************************// + case LoopKind::RANK2: { + Z extraParams[3]; + for (auto i = start; i < stop; i++) { + extraParams[0] = param0; + extraParams[1] = param1; + extraParams[2] = param2; + + const auto xTad = xTadOffsets ? x + xTadOffsets[i] : x; + const auto yTad = yTadOffsets ? y + yTadOffsets[i] : y; + auto s = OpType::startingValue(xTad); + + for (Nd4jLong i0 = 0; i0 < tadShape[0]; ++i0) { + for (Nd4jLong i1 = 0; i1 < tadShape[1]; ++i1) { + const auto xTadOffset = i0 * xTadStride[0] + i1 * xTadStride[1]; + const auto yTadOffset = i0 * yTadStride[0] + i1 * yTadStride[1]; s = OpType::update(s, OpType::op(xTad[xTadOffset], yTad[yTadOffset], extraParams), extraParams); } + } + z[i * zEws] = OpType::postProcess(s, tadLen, extraParams); + }; + } + break; - z[i * zEws] = OpType::postProcess(s, tadLen, extraParams); - }; - } - break; + //*********************************************// + case LoopKind::RANK3: { + Z extraParams[3]; + for (auto i = start; i < stop; i++) { + extraParams[0] = param0; + extraParams[1] = param1; + extraParams[2] = param2; - //*********************************************// - case LoopKind::RANK2: { - Z extraParams[3]; - for (auto i = start; i < stop; i++) { - extraParams[0] = param0; - extraParams[1] = param1; - extraParams[2] = param2; + const auto xTad = xTadOffsets ? x + xTadOffsets[i] : x; + const auto yTad = yTadOffsets ? y + yTadOffsets[i] : y; + auto s = OpType::startingValue(xTad); - const auto xTad = xTadOffsets ? x + xTadOffsets[i] : x; - const auto yTad = yTadOffsets ? y + yTadOffsets[i] : y; - auto s = OpType::startingValue(xTad); - - for (uint i0 = 0; i0 < tadShape[0]; ++i0) { - for (uint i1 = 0; i1 < tadShape[1]; ++i1) { - const auto xTadOffset = i0 * xTadStride[0] + i1 * xTadStride[1]; - const auto yTadOffset = i0 * yTadStride[0] + i1 * yTadStride[1]; + for (Nd4jLong i0 = 0; i0 < tadShape[0]; ++i0) { + for (Nd4jLong i1 = 0; i1 < tadShape[1]; ++i1) { + for (Nd4jLong i2 = 0; i2 < tadShape[2]; ++i2) { + const auto xTadOffset = i0 * xTadStride[0] + i1 * xTadStride[1] + i2 * xTadStride[2]; + const auto yTadOffset = i0 * yTadStride[0] + i1 * yTadStride[1] + i2 * yTadStride[2]; s = OpType::update(s, OpType::op(xTad[xTadOffset], yTad[yTadOffset], extraParams), extraParams); } } - z[i * zEws] = OpType::postProcess(s, tadLen, extraParams); - }; - } - break; + } + z[i * zEws] = OpType::postProcess(s, tadLen, extraParams); + }; + } + break; - //*********************************************// - case LoopKind::RANK3: { - Z extraParams[3]; - for (auto i = start; i < stop; i++) { - extraParams[0] = param0; - extraParams[1] = param1; - extraParams[2] = param2; + //*********************************************// + case LoopKind::RANK4: { + Z extraParams[3]; + for (auto i = start; i < stop; i++) { + extraParams[0] = param0; + extraParams[1] = param1; + extraParams[2] = param2; - const auto xTad = xTadOffsets ? x + xTadOffsets[i] : x; - const auto yTad = yTadOffsets ? y + yTadOffsets[i] : y; - auto s = OpType::startingValue(xTad); + const auto xTad = xTadOffsets ? x + xTadOffsets[i] : x; + const auto yTad = yTadOffsets ? y + yTadOffsets[i] : y; + auto s = OpType::startingValue(xTad); - for (uint i0 = 0; i0 < tadShape[0]; ++i0) { - for (uint i1 = 0; i1 < tadShape[1]; ++i1) { - for (uint i2 = 0; i2 < tadShape[2]; ++i2) { - const auto xTadOffset = i0 * xTadStride[0] + i1 * xTadStride[1] + i2 * xTadStride[2]; - const auto yTadOffset = i0 * yTadStride[0] + i1 * yTadStride[1] + i2 * yTadStride[2]; + for (Nd4jLong i0 = 0; i0 < tadShape[0]; ++i0) { + for (Nd4jLong i1 = 0; i1 < tadShape[1]; ++i1) { + for (Nd4jLong i2 = 0; i2 < tadShape[2]; ++i2) { + for (Nd4jLong i3 = 0; i3 < tadShape[3]; ++i3) { + const auto xTadOffset = i0 * xTadStride[0] + i1 * xTadStride[1] + i2 * xTadStride[2] + i3 * xTadStride[3]; + const auto yTadOffset = i0 * yTadStride[0] + i1 * yTadStride[1] + i2 * yTadStride[2] + i3 * yTadStride[3]; s = OpType::update(s, OpType::op(xTad[xTadOffset], yTad[yTadOffset], extraParams), extraParams); } } } - z[i * zEws] = OpType::postProcess(s, tadLen, extraParams); - }; - } - break; + } + z[i * zEws] = OpType::postProcess(s, tadLen, extraParams); + }; + } + break; - //*********************************************// - case LoopKind::RANK4: { - Z extraParams[3]; - for (auto i = start; i < stop; i++) { - extraParams[0] = param0; - extraParams[1] = param1; - extraParams[2] = param2; + //*********************************************// + case LoopKind::RANK5: { + Z extraParams[3]; + for (auto i = start; i < stop; i++) { + extraParams[0] = param0; + extraParams[1] = param1; + extraParams[2] = param2; - const auto xTad = xTadOffsets ? x + xTadOffsets[i] : x; - const auto yTad = yTadOffsets ? y + yTadOffsets[i] : y; - auto s = OpType::startingValue(xTad); + const auto xTad = xTadOffsets ? x + xTadOffsets[i] : x; + const auto yTad = yTadOffsets ? y + yTadOffsets[i] : y; + auto s = OpType::startingValue(xTad); - for (uint i0 = 0; i0 < tadShape[0]; ++i0) { - for (uint i1 = 0; i1 < tadShape[1]; ++i1) { - for (uint i2 = 0; i2 < tadShape[2]; ++i2) { - for (uint i3 = 0; i3 < tadShape[3]; ++i3) { - const auto xTadOffset = i0 * xTadStride[0] + i1 * xTadStride[1] + i2 * xTadStride[2] + i3 * xTadStride[3]; - const auto yTadOffset = i0 * yTadStride[0] + i1 * yTadStride[1] + i2 * yTadStride[2] + i3 * yTadStride[3]; + for (Nd4jLong i0 = 0; i0 < tadShape[0]; ++i0) { + for (Nd4jLong i1 = 0; i1 < tadShape[1]; ++i1) { + for (Nd4jLong i2 = 0; i2 < tadShape[2]; ++i2) { + for (Nd4jLong i3 = 0; i3 < tadShape[3]; ++i3) { + for (Nd4jLong i4 = 0; i4 < tadShape[4]; ++i4) { + const auto xTadOffset = i0 * xTadStride[0] + i1 * xTadStride[1] + i2 * xTadStride[2] + i3 * xTadStride[3] + i4 * xTadStride[4]; + const auto yTadOffset = i0 * yTadStride[0] + i1 * yTadStride[1] + i2 * yTadStride[2] + i3 * yTadStride[3] + i4 * yTadStride[4]; s = OpType::update(s, OpType::op(xTad[xTadOffset], yTad[yTadOffset], extraParams), extraParams); } } } } - z[i * zEws] = OpType::postProcess(s, tadLen, extraParams); - }; - } - break; + } + z[i * zEws] = OpType::postProcess(s, tadLen, extraParams); + }; + } + break; - //*********************************************// - case LoopKind::RANK5: { + //*********************************************// + default: { + uint castXTadShapeInfo[MAX_RANK]; + const bool canCastXTad = sd::DataTypeUtils::castShapeInfo(xTadShapeInfo, castXTadShapeInfo); + + if (shape::haveSameShapeAndStrides(xTadShapeInfo, yTadShapeInfo)) { Z extraParams[3]; for (auto i = start; i < stop; i++) { extraParams[0] = param0; @@ -890,83 +925,49 @@ void Loops::loopXYZ(const X* x, const Nd4jLong* xShapeInfo, const auto yTad = yTadOffsets ? y + yTadOffsets[i] : y; auto s = OpType::startingValue(xTad); - for (uint i0 = 0; i0 < tadShape[0]; ++i0) { - for (uint i1 = 0; i1 < tadShape[1]; ++i1) { - for (uint i2 = 0; i2 < tadShape[2]; ++i2) { - for (uint i3 = 0; i3 < tadShape[3]; ++i3) { - for (uint i4 = 0; i4 < tadShape[4]; ++i4) { - const auto xTadOffset = i0 * xTadStride[0] + i1 * xTadStride[1] + i2 * xTadStride[2] + i3 * xTadStride[3] + i4 * xTadStride[4]; - const auto yTadOffset = i0 * yTadStride[0] + i1 * yTadStride[1] + i2 * yTadStride[2] + i3 * yTadStride[3] + i4 * yTadStride[4]; - s = OpType::update(s, OpType::op(xTad[xTadOffset], yTad[yTadOffset], extraParams), extraParams); - } - } - } - } + for (Nd4jLong j = 0; j < tadLen; ++j) { + const auto tadOffset = shape::indexOffset(j, xTadShapeInfo, castXTadShapeInfo, canCastXTad); + s = OpType::update(s, OpType::op(xTad[tadOffset], yTad[tadOffset], extraParams), extraParams); + } + + z[i * zEws] = OpType::postProcess(s, tadLen, extraParams); + }; + } + else { + uint castYTadShapeInfo[MAX_RANK]; + const bool canCastYTad = sd::DataTypeUtils::castShapeInfo(yTadShapeInfo, castYTadShapeInfo); + + Z extraParams[3]; + for (auto i = start; i < stop; i++) { + extraParams[0] = param0; + extraParams[1] = param1; + extraParams[2] = param2; + + const auto xTad = xTadOffsets ? x + xTadOffsets[i] : x; + const auto yTad = yTadOffsets ? y + yTadOffsets[i] : y; + auto s = OpType::startingValue(xTad); + + for (Nd4jLong j = 0; j < tadLen; ++j) { + const auto xTadOffset = shape::indexOffset(j, xTadShapeInfo, castXTadShapeInfo, canCastXTad); + const auto yTadOffset = shape::indexOffset(j, yTadShapeInfo, castYTadShapeInfo, canCastYTad); + s = OpType::update(s, OpType::op(xTad[xTadOffset], yTad[yTadOffset], extraParams), extraParams); } z[i * zEws] = OpType::postProcess(s, tadLen, extraParams); }; } - break; - - //*********************************************// - default: { - uint castXTadShapeInfo[MAX_RANK]; - const bool canCastXTad = nd4j::DataTypeUtils::castShapeInfo(xTadShapeInfo, castXTadShapeInfo); - - if(shape::haveSameShapeAndStrides(xTadShapeInfo, yTadShapeInfo)) { - Z extraParams[3]; - for (auto i = start; i < stop; i++) { - extraParams[0] = param0; - extraParams[1] = param1; - extraParams[2] = param2; - - const auto xTad = xTadOffsets ? x + xTadOffsets[i] : x; - const auto yTad = yTadOffsets ? y + yTadOffsets[i] : y; - auto s = OpType::startingValue(xTad); - - for (uint j = 0; j < tadLen; ++j) { - const auto tadOffset = shape::indexOffset(j, xTadShapeInfo, castXTadShapeInfo, canCastXTad); - s = OpType::update(s, OpType::op(xTad[tadOffset], yTad[tadOffset], extraParams), extraParams); - } - - z[i * zEws] = OpType::postProcess(s, tadLen, extraParams); - }; - } - else { - uint castYTadShapeInfo[MAX_RANK]; - const bool canCastYTad = nd4j::DataTypeUtils::castShapeInfo(yTadShapeInfo, castYTadShapeInfo); - - Z extraParams[3]; - for (auto i = start; i < stop; i++) { - extraParams[0] = param0; - extraParams[1] = param1; - extraParams[2] = param2; - - const auto xTad = xTadOffsets ? x + xTadOffsets[i] : x; - const auto yTad = yTadOffsets ? y + yTadOffsets[i] : y; - auto s = OpType::startingValue(xTad); - - for (uint j = 0; j < tadLen; ++j) { - const auto xTadOffset = shape::indexOffset(j, xTadShapeInfo, castXTadShapeInfo, canCastXTad); - const auto yTadOffset = shape::indexOffset(j, yTadShapeInfo, castYTadShapeInfo, canCastYTad); - s = OpType::update(s, OpType::op(xTad[xTadOffset], yTad[yTadOffset], extraParams), extraParams); - } - z[i * zEws] = OpType::postProcess(s, tadLen, extraParams); - }; - } - } + } } } -////////////////////////////////////////////////////////////////////////////// + ////////////////////////////////////////////////////////////////////////////// template template - void nd4j::Reduction3Loops::loopReduce3All(X* x, Nd4jLong* xShapeInfo, - X* y, Nd4jLong* yShapeInfo, - Z* z, Nd4jLong* zShapeInfo, - Nd4jLong* xTadShapeInfo, Nd4jLong* xTadOffsets, - Nd4jLong* yTadShapeInfo, Nd4jLong* yTadOffsets, - Z* extraParameters, int64_t start, int64_t stop) { + void sd::Reduction3Loops::loopReduce3All(X* x, Nd4jLong* xShapeInfo, + X* y, Nd4jLong* yShapeInfo, + Z* z, Nd4jLong* zShapeInfo, + Nd4jLong* xTadShapeInfo, Nd4jLong* xTadOffsets, + Nd4jLong* yTadShapeInfo, Nd4jLong* yTadOffsets, + Z* extraParameters, int64_t start, int64_t stop) { // both tads have same shape, however strides and ews may differ @@ -976,186 +977,223 @@ void Loops::loopXYZ(const X* x, const Nd4jLong* xShapeInfo, const auto xTadEws = shape::elementWiseStride(xTadShapeInfo); const auto yTadEws = shape::elementWiseStride(yTadShapeInfo); - const auto zEws = shape::elementWiseStride(zShapeInfo); + const auto zEws = shape::elementWiseStride(zShapeInfo); - const auto zLen = shape::length(zShapeInfo); + const auto zLen = shape::length(zShapeInfo); const auto tadLen = shape::length(xTadShapeInfo); const auto numXTads = shape::length(xShapeInfo) / tadLen; const auto numYTads = shape::length(yShapeInfo) / tadLen; - const auto tadShape = shape::shapeOf(xTadShapeInfo); - const auto xTadStride = shape::stride(xTadShapeInfo); - const auto yTadStride = shape::stride(yTadShapeInfo); + const auto tadShape = shape::shapeOf(xTadShapeInfo); + const auto xTadStride = shape::stride(xTadShapeInfo); + const auto yTadStride = shape::stride(yTadShapeInfo); const auto startVal = OpType::startingValue(x); - int numThreads = OmpLaunchHelper::tadThreads(tadLen, numXTads*numYTads); + int numThreads = OmpLaunchHelper::tadThreads(tadLen, numXTads * numYTads); switch (kindOfLoop) { - //*********************************************// - case LoopKind::EWS1: { - Z extraParams[3]; - for (auto ix = 0; ix < numXTads; ix++) { - for (auto iy = 0; iy < numYTads; iy++) { - extraParams[0] = param0; - extraParams[1] = param1; - extraParams[2] = param2; + //*********************************************// + case LoopKind::EWS1: { + Z extraParams[3]; + for (Nd4jLong ix = 0; ix < numXTads; ix++) { + for (Nd4jLong iy = 0; iy < numYTads; iy++) { + extraParams[0] = param0; + extraParams[1] = param1; + extraParams[2] = param2; - const auto xTad = x + xTadOffsets[ix]; - const auto yTad = y + yTadOffsets[iy]; - const auto zInd = ix * numYTads + iy; - auto s = startVal; + const auto xTad = x + xTadOffsets[ix]; + const auto yTad = y + yTadOffsets[iy]; + const auto zInd = ix * numYTads + iy; + auto s = startVal; - for (uint j = 0; j < tadLen; ++j) - s = OpType::update(s, OpType::op(xTad[j], yTad[j], extraParams), extraParams); + for (Nd4jLong j = 0; j < tadLen; ++j) + s = OpType::update(s, OpType::op(xTad[j], yTad[j], extraParams), extraParams); - z[zInd] = OpType::postProcess(s, tadLen, extraParams); + z[zInd] = OpType::postProcess(s, tadLen, extraParams); + } + }; + } + break; + + //*********************************************// + case LoopKind::EWSNONZERO: { + Z extraParams[3]; + for (Nd4jLong ix = 0; ix < numXTads; ix++) { + for (Nd4jLong iy = 0; iy < numYTads; iy++) { + extraParams[0] = param0; + extraParams[1] = param1; + extraParams[2] = param2; + + const auto xTad = x + xTadOffsets[ix]; + const auto yTad = y + yTadOffsets[iy]; + const auto zInd = ix * numYTads + iy; + auto s = startVal; + + for (Nd4jLong j = 0; j < tadLen; ++j) + s = OpType::update(s, OpType::op(xTad[j * xTadEws], yTad[j * yTadEws], extraParams), extraParams); + + z[zInd * zEws] = OpType::postProcess(s, tadLen, extraParams); + } + }; + } + break; + + //*********************************************// + case LoopKind::RANK1: { + Z extraParams[3]; + for (Nd4jLong ix = 0; ix < numXTads; ix++) { + for (Nd4jLong iy = 0; iy < numYTads; iy++) { + extraParams[0] = param0; + extraParams[1] = param1; + extraParams[2] = param2; + + const auto xTad = x + xTadOffsets[ix]; + const auto yTad = y + yTadOffsets[iy]; + const auto zInd = ix * numYTads + iy; + auto s = startVal; + + for (Nd4jLong i0 = 0; i0 < tadLen; ++i0) { + const auto xTadOffset = i0 * xTadStride[0]; + const auto yTadOffset = i0 * yTadStride[0]; + s = OpType::update(s, OpType::op(xTad[xTadOffset], yTad[yTadOffset], extraParams), extraParams); } - }; - } - break; + z[zInd * zEws] = OpType::postProcess(s, tadLen, extraParams); + } + }; + } + break; - //*********************************************// - case LoopKind::EWSNONZERO: { - Z extraParams[3]; - for (auto ix = 0; ix < numXTads; ix++) { - for (auto iy = 0; iy < numYTads; iy++) { - extraParams[0] = param0; - extraParams[1] = param1; - extraParams[2] = param2; + //*********************************************// + case LoopKind::RANK2: { + Z extraParams[3]; + for (Nd4jLong ix = 0; ix < numXTads; ix++) { + for (Nd4jLong iy = 0; iy < numYTads; iy++) { + extraParams[0] = param0; + extraParams[1] = param1; + extraParams[2] = param2; - const auto xTad = x + xTadOffsets[ix]; - const auto yTad = y + yTadOffsets[iy]; - const auto zInd = ix * numYTads + iy; - auto s = startVal; + const auto xTad = x + xTadOffsets[ix]; + const auto yTad = y + yTadOffsets[iy]; + const auto zInd = ix * numYTads + iy; + auto s = startVal; - for (uint j = 0; j < tadLen; ++j) - s = OpType::update(s, OpType::op(xTad[j * xTadEws], yTad[j * yTadEws], extraParams), extraParams); - - z[zInd * zEws] = OpType::postProcess(s, tadLen, extraParams); - } - }; - } - break; - - //*********************************************// - case LoopKind::RANK1: { - Z extraParams[3]; - for (auto ix = 0; ix < numXTads; ix++) { - for (auto iy = 0; iy < numYTads; iy++) { - extraParams[0] = param0; - extraParams[1] = param1; - extraParams[2] = param2; - - const auto xTad = x + xTadOffsets[ix]; - const auto yTad = y + yTadOffsets[iy]; - const auto zInd = ix * numYTads + iy; - auto s = startVal; - - for (uint i0 = 0; i0 < tadLen; ++i0) { - const auto xTadOffset = i0 * xTadStride[0]; - const auto yTadOffset = i0 * yTadStride[0]; + for (Nd4jLong i0 = 0; i0 < tadShape[0]; ++i0) { + for (Nd4jLong i1 = 0; i1 < tadShape[1]; ++i1) { + const auto xTadOffset = i0 * xTadStride[0] + i1 * xTadStride[1]; + const auto yTadOffset = i0 * yTadStride[0] + i1 * yTadStride[1]; s = OpType::update(s, OpType::op(xTad[xTadOffset], yTad[yTadOffset], extraParams), extraParams); } - z[zInd * zEws] = OpType::postProcess(s, tadLen, extraParams); } - }; - } - break; + z[zInd * zEws] = OpType::postProcess(s, tadLen, extraParams); + } + }; + } + break; - //*********************************************// - case LoopKind::RANK2: { - Z extraParams[3]; - for (auto ix = 0; ix < numXTads; ix++) { - for (auto iy = 0; iy < numYTads; iy++) { - extraParams[0] = param0; - extraParams[1] = param1; - extraParams[2] = param2; + //*********************************************// + case LoopKind::RANK3: { + Z extraParams[3]; + for (Nd4jLong ix = 0; ix < numXTads; ix++) { + for (Nd4jLong iy = 0; iy < numYTads; iy++) { + extraParams[0] = param0; + extraParams[1] = param1; + extraParams[2] = param2; - const auto xTad = x + xTadOffsets[ix]; - const auto yTad = y + yTadOffsets[iy]; - const auto zInd = ix * numYTads + iy; - auto s = startVal; + const auto xTad = x + xTadOffsets[ix]; + const auto yTad = y + yTadOffsets[iy]; + const auto zInd = ix * numYTads + iy; + auto s = startVal; - for (uint i0 = 0; i0 < tadShape[0]; ++i0) { - for (uint i1 = 0; i1 < tadShape[1]; ++i1) { - const auto xTadOffset = i0 * xTadStride[0] + i1 * xTadStride[1]; - const auto yTadOffset = i0 * yTadStride[0] + i1 * yTadStride[1]; + for (Nd4jLong i0 = 0; i0 < tadShape[0]; ++i0) { + for (Nd4jLong i1 = 0; i1 < tadShape[1]; ++i1) { + for (Nd4jLong i2 = 0; i2 < tadShape[2]; ++i2) { + const auto xTadOffset = i0 * xTadStride[0] + i1 * xTadStride[1] + i2 * xTadStride[2]; + const auto yTadOffset = i0 * yTadStride[0] + i1 * yTadStride[1] + i2 * yTadStride[2]; s = OpType::update(s, OpType::op(xTad[xTadOffset], yTad[yTadOffset], extraParams), extraParams); } } - z[zInd * zEws] = OpType::postProcess(s, tadLen, extraParams); } - }; - } - break; + z[zInd * zEws] = OpType::postProcess(s, tadLen, extraParams); + } + }; + } + break; - //*********************************************// - case LoopKind::RANK3: { - Z extraParams[3]; - for (auto ix = 0; ix < numXTads; ix++) { - for (auto iy = 0; iy < numYTads; iy++) { - extraParams[0] = param0; - extraParams[1] = param1; - extraParams[2] = param2; + //*********************************************// + case LoopKind::RANK4: { + Z extraParams[3]; + for (Nd4jLong ix = 0; ix < numXTads; ix++) { + for (Nd4jLong iy = 0; iy < numYTads; iy++) { + extraParams[0] = param0; + extraParams[1] = param1; + extraParams[2] = param2; - const auto xTad = x + xTadOffsets[ix]; - const auto yTad = y + yTadOffsets[iy]; - const auto zInd = ix * numYTads + iy; - auto s = startVal; + const auto xTad = x + xTadOffsets[ix]; + const auto yTad = y + yTadOffsets[iy]; + const auto zInd = ix * numYTads + iy; + auto s = startVal; - for (uint i0 = 0; i0 < tadShape[0]; ++i0) { - for (uint i1 = 0; i1 < tadShape[1]; ++i1) { - for (uint i2 = 0; i2 < tadShape[2]; ++i2) { - const auto xTadOffset = i0 * xTadStride[0] + i1 * xTadStride[1] + i2 * xTadStride[2]; - const auto yTadOffset = i0 * yTadStride[0] + i1 * yTadStride[1] + i2 * yTadStride[2]; + for (Nd4jLong i0 = 0; i0 < tadShape[0]; ++i0) { + for (Nd4jLong i1 = 0; i1 < tadShape[1]; ++i1) { + for (Nd4jLong i2 = 0; i2 < tadShape[2]; ++i2) { + for (Nd4jLong i3 = 0; i3 < tadShape[3]; ++i3) { + const auto xTadOffset = i0 * xTadStride[0] + i1 * xTadStride[1] + i2 * xTadStride[2] + i3 * xTadStride[3]; + const auto yTadOffset = i0 * yTadStride[0] + i1 * yTadStride[1] + i2 * yTadStride[2] + i3 * yTadStride[3]; s = OpType::update(s, OpType::op(xTad[xTadOffset], yTad[yTadOffset], extraParams), extraParams); } } } - z[zInd * zEws] = OpType::postProcess(s, tadLen, extraParams); } - }; - } - break; + z[zInd * zEws] = OpType::postProcess(s, tadLen, extraParams); + } + }; + } + break; - //*********************************************// - case LoopKind::RANK4: { - Z extraParams[3]; - for (auto ix = 0; ix < numXTads; ix++) { - for (auto iy = 0; iy < numYTads; iy++) { - extraParams[0] = param0; - extraParams[1] = param1; - extraParams[2] = param2; + //*********************************************// + case LoopKind::RANK5: { + Z extraParams[3]; + for (Nd4jLong ix = 0; ix < numXTads; ix++) { + for (Nd4jLong iy = 0; iy < numYTads; iy++) { + extraParams[0] = param0; + extraParams[1] = param1; + extraParams[2] = param2; - const auto xTad = x + xTadOffsets[ix]; - const auto yTad = y + yTadOffsets[iy]; - const auto zInd = ix * numYTads + iy; - auto s = startVal; + const auto xTad = x + xTadOffsets[ix]; + const auto yTad = y + yTadOffsets[iy]; + const auto zInd = ix * numYTads + iy; + auto s = startVal; - for (uint i0 = 0; i0 < tadShape[0]; ++i0) { - for (uint i1 = 0; i1 < tadShape[1]; ++i1) { - for (uint i2 = 0; i2 < tadShape[2]; ++i2) { - for (uint i3 = 0; i3 < tadShape[3]; ++i3) { - const auto xTadOffset = i0 * xTadStride[0] + i1 * xTadStride[1] + i2 * xTadStride[2] + i3 * xTadStride[3]; - const auto yTadOffset = i0 * yTadStride[0] + i1 * yTadStride[1] + i2 * yTadStride[2] + i3 * yTadStride[3]; + for (Nd4jLong i0 = 0; i0 < tadShape[0]; ++i0) { + for (Nd4jLong i1 = 0; i1 < tadShape[1]; ++i1) { + for (Nd4jLong i2 = 0; i2 < tadShape[2]; ++i2) { + for (Nd4jLong i3 = 0; i3 < tadShape[3]; ++i3) { + for (Nd4jLong i4 = 0; i4 < tadShape[4]; ++i4) { + const auto xTadOffset = i0 * xTadStride[0] + i1 * xTadStride[1] + i2 * xTadStride[2] + i3 * xTadStride[3] + i4 * xTadStride[4]; + const auto yTadOffset = i0 * yTadStride[0] + i1 * yTadStride[1] + i2 * yTadStride[2] + i3 * yTadStride[3] + i4 * yTadStride[4]; s = OpType::update(s, OpType::op(xTad[xTadOffset], yTad[yTadOffset], extraParams), extraParams); } } } } - z[zInd * zEws] = OpType::postProcess(s, tadLen, extraParams); } - }; - } - break; + z[zInd * zEws] = OpType::postProcess(start, tadLen, extraParams); + } + }; + } + break; - //*********************************************// - case LoopKind::RANK5: { + //*********************************************// + default: { + uint castXTadShapeInfo[MAX_RANK]; + const bool canCastXTad = sd::DataTypeUtils::castShapeInfo(xTadShapeInfo, castXTadShapeInfo); + + if (shape::haveSameShapeAndStrides(xTadShapeInfo, yTadShapeInfo)) { Z extraParams[3]; - for (auto ix = 0; ix < numXTads; ix++) { - for (auto iy = 0; iy < numYTads; iy++) { + for (Nd4jLong ix = 0; ix < numXTads; ix++) { + for (Nd4jLong iy = 0; iy < numYTads; iy++) { extraParams[0] = param0; extraParams[1] = param1; extraParams[2] = param2; @@ -1165,79 +1203,42 @@ void Loops::loopXYZ(const X* x, const Nd4jLong* xShapeInfo, const auto zInd = ix * numYTads + iy; auto s = startVal; - for (uint i0 = 0; i0 < tadShape[0]; ++i0) { - for (uint i1 = 0; i1 < tadShape[1]; ++i1) { - for (uint i2 = 0; i2 < tadShape[2]; ++i2) { - for (uint i3 = 0; i3 < tadShape[3]; ++i3) { - for (uint i4 = 0; i4 < tadShape[4]; ++i4) { - const auto xTadOffset = i0 * xTadStride[0] + i1 * xTadStride[1] + i2 * xTadStride[2] + i3 * xTadStride[3] + i4 * xTadStride[4]; - const auto yTadOffset = i0 * yTadStride[0] + i1 * yTadStride[1] + i2 * yTadStride[2] + i3 * yTadStride[3] + i4 * yTadStride[4]; - s = OpType::update(s, OpType::op(xTad[xTadOffset], yTad[yTadOffset], extraParams), extraParams); - } - } - } - } + for (Nd4jLong j = 0; j < tadLen; ++j) { + const auto tadOffset = shape::indexOffset(j, xTadShapeInfo, castXTadShapeInfo, canCastXTad); + s = OpType::update(s, OpType::op(xTad[tadOffset], yTad[tadOffset], extraParams), extraParams); } - z[zInd * zEws] = OpType::postProcess(start, tadLen, extraParams); + z[zInd * zEws] = OpType::postProcess(s, tadLen, extraParams); } }; } - break; + else { + uint castYTadShapeInfo[MAX_RANK]; + const bool canCastYTad = sd::DataTypeUtils::castShapeInfo(yTadShapeInfo, castYTadShapeInfo); - //*********************************************// - default: { - uint castXTadShapeInfo[MAX_RANK]; - const bool canCastXTad = nd4j::DataTypeUtils::castShapeInfo(xTadShapeInfo, castXTadShapeInfo); + Z extraParams[3]; + for (Nd4jLong ix = 0; ix < numXTads; ix++) { + for (Nd4jLong iy = 0; iy < numYTads; iy++) { + extraParams[0] = param0; + extraParams[1] = param1; + extraParams[2] = param2; - if(shape::haveSameShapeAndStrides(xTadShapeInfo, yTadShapeInfo)) { - Z extraParams[3]; - for (auto ix = 0; ix < numXTads; ix++) { - for (auto iy = 0; iy < numYTads; iy++) { - extraParams[0] = param0; - extraParams[1] = param1; - extraParams[2] = param2; + const auto xTad = x + xTadOffsets[ix]; + const auto yTad = y + yTadOffsets[iy]; + const auto zInd = ix * numYTads + iy; + auto s = startVal; - const auto xTad = x + xTadOffsets[ix]; - const auto yTad = y + yTadOffsets[iy]; - const auto zInd = ix * numYTads + iy; - auto s = startVal; - - for (uint j = 0; j < tadLen; ++j) { - const auto tadOffset = shape::indexOffset(j, xTadShapeInfo, castXTadShapeInfo, canCastXTad); - s = OpType::update(s, OpType::op(xTad[tadOffset], yTad[tadOffset], extraParams), extraParams); - } - z[zInd * zEws] = OpType::postProcess(s, tadLen, extraParams); + for (Nd4jLong j = 0; j < tadLen; ++j) { + const auto xTadOffset = shape::indexOffset(j, xTadShapeInfo, castXTadShapeInfo, canCastXTad); + const auto yTadOffset = shape::indexOffset(j, yTadShapeInfo, castYTadShapeInfo, canCastYTad); + s = OpType::update(s, OpType::op(xTad[xTadOffset], yTad[yTadOffset], extraParams), extraParams); } - }; - } - else { - uint castYTadShapeInfo[MAX_RANK]; - const bool canCastYTad = nd4j::DataTypeUtils::castShapeInfo(yTadShapeInfo, castYTadShapeInfo); - Z extraParams[3]; - for (auto ix = 0; ix < numXTads; ix++) { - for (auto iy = 0; iy < numYTads; iy++) { - extraParams[0] = param0; - extraParams[1] = param1; - extraParams[2] = param2; - - const auto xTad = x + xTadOffsets[ix]; - const auto yTad = y + yTadOffsets[iy]; - const auto zInd = ix * numYTads + iy; - auto s = startVal; - - for (uint j = 0; j < tadLen; ++j) { - const auto xTadOffset = shape::indexOffset(j, xTadShapeInfo, castXTadShapeInfo, canCastXTad); - const auto yTadOffset = shape::indexOffset(j, yTadShapeInfo, castYTadShapeInfo, canCastYTad); - s = OpType::update(s, OpType::op(xTad[xTadOffset], yTad[yTadOffset], extraParams), extraParams); - } - - z[zInd * zEws] = OpType::postProcess(s, tadLen, extraParams); - } - }; - } + z[zInd * zEws] = OpType::postProcess(s, tadLen, extraParams); + } + }; } } + } } diff --git a/libnd4j/include/helpers/Loops.hpp b/libnd4j/include/helpers/Loops.hpp index 95b844340..852ef4808 100644 --- a/libnd4j/include/helpers/Loops.hpp +++ b/libnd4j/include/helpers/Loops.hpp @@ -22,12 +22,12 @@ //#define LIBND4J_LOOPS_CPP #include -#include -#include -#include +#include +#include +#include -namespace nd4j { +namespace sd { } diff --git a/libnd4j/include/helpers/LoopsCoordsHelper.h b/libnd4j/include/helpers/LoopsCoordsHelper.h index 35f9d2063..cd578b62a 100644 --- a/libnd4j/include/helpers/LoopsCoordsHelper.h +++ b/libnd4j/include/helpers/LoopsCoordsHelper.h @@ -23,9 +23,9 @@ #include #include #include -#include -#include -namespace nd4j { +#include +#include +namespace sd { #if defined(__GNUC__) #define likely(x) __builtin_expect( (x), 1) @@ -78,16 +78,16 @@ namespace nd4j { ZipCoordsState() {} }; -#define COORDS(x,index) ((x).::nd4j::CoordsState<(index)>::coord) -#define STRIDE(x,index) ((x).::nd4j::CoordsState<(index)>::stride) -#define LAST_NUM(x,index) ((x).::nd4j::CoordsState<(index)>::last_num) -#define OF_ADJUST(x,index) ((x).::nd4j::CoordsState<(index)>::adjust) -#define ZIP_LAST_NUM(x,index) ((x).::nd4j::ZipCoordsState<(index)>::last_num) -#define ZIP_COORDS(x,index) ((x).::nd4j::ZipCoordsState<(index)>::coord) -#define ZIP_STRIDE1(x,index) ((x).::nd4j::ZipCoordsState<(index)>::stride1) -#define ZIP_STRIDE2(x,index) ((x).::nd4j::ZipCoordsState<(index)>::stride2) -#define ZIP_OF_ADJUST1(x,index) ((x).::nd4j::ZipCoordsState<(index)>::adjust1) -#define ZIP_OF_ADJUST2(x,index) ((x).::nd4j::ZipCoordsState<(index)>::adjust2) +#define COORDS(x,index) ((x).::sd::CoordsState<(index)>::coord) +#define STRIDE(x,index) ((x).::sd::CoordsState<(index)>::stride) +#define LAST_NUM(x,index) ((x).::sd::CoordsState<(index)>::last_num) +#define OF_ADJUST(x,index) ((x).::sd::CoordsState<(index)>::adjust) +#define ZIP_LAST_NUM(x,index) ((x).::sd::ZipCoordsState<(index)>::last_num) +#define ZIP_COORDS(x,index) ((x).::sd::ZipCoordsState<(index)>::coord) +#define ZIP_STRIDE1(x,index) ((x).::sd::ZipCoordsState<(index)>::stride1) +#define ZIP_STRIDE2(x,index) ((x).::sd::ZipCoordsState<(index)>::stride2) +#define ZIP_OF_ADJUST1(x,index) ((x).::sd::ZipCoordsState<(index)>::adjust1) +#define ZIP_OF_ADJUST2(x,index) ((x).::sd::ZipCoordsState<(index)>::adjust2) FORCEINLINE void index2coords_C(Nd4jLong index, const Nd4jLong rank, const Nd4jLong* bases, Nd4jLong* coords) { diff --git a/libnd4j/include/helpers/MKLDNNStream.h b/libnd4j/include/helpers/MKLDNNStream.h index f88ec0e62..f575c48d9 100644 --- a/libnd4j/include/helpers/MKLDNNStream.h +++ b/libnd4j/include/helpers/MKLDNNStream.h @@ -27,7 +27,7 @@ #if defined(HAVE_MKLDNN) -namespace nd4j { +namespace sd { class MKLDNNStream { protected: std::string _opName; @@ -47,7 +47,7 @@ namespace nd4j { static bool isSupported(const std::vector &arrays) { // FIXME: strict float support doesn't work anymore for (auto v:arrays) { - if (v != nullptr && v->dataType() != nd4j::DataType::FLOAT32) { + if (v != nullptr && v->dataType() != sd::DataType::FLOAT32) { return false; } } diff --git a/libnd4j/include/helpers/MmulHelper.h b/libnd4j/include/helpers/MmulHelper.h index 76244d050..6e38be5c1 100644 --- a/libnd4j/include/helpers/MmulHelper.h +++ b/libnd4j/include/helpers/MmulHelper.h @@ -22,45 +22,45 @@ #ifndef LIBND4J_MMULHELPER_H #define LIBND4J_MMULHELPER_H -#include "NDArray.h" +#include "array/NDArray.h" -namespace nd4j { +namespace sd { class ND4J_EXPORT MmulHelper { private: // multiptication N-dimensions tensor on other N-dimensions one - static nd4j::NDArray* mmulNxN(const nd4j::NDArray* A, const nd4j::NDArray* B, nd4j::NDArray* C, const double alpha = 1.0, const double beta = 0.0, const char outOrder = 'f'); + static sd::NDArray* mmulNxN(const sd::NDArray* A, const sd::NDArray* B, sd::NDArray* C, const double alpha = 1.0, const double beta = 0.0, const char outOrder = 'f'); // dot product of vectors (X * Y) = Z[0] - static nd4j::NDArray* dot(const nd4j::NDArray* X, const nd4j::NDArray* Y, nd4j::NDArray* Z, const double alpha = 1.0, const double beta = 0.0); + static sd::NDArray* dot(const sd::NDArray* X, const sd::NDArray* Y, sd::NDArray* Z, const double alpha = 1.0, const double beta = 0.0); // multiptication Matrix to Matrix - static nd4j::NDArray* mmulMxM(const nd4j::NDArray* A, const nd4j::NDArray* B, nd4j::NDArray* C, double alpha = 1.0, double beta = 0.0, const char outOrder = 'f'); + static sd::NDArray* mmulMxM(const sd::NDArray* A, const sd::NDArray* B, sd::NDArray* C, double alpha = 1.0, double beta = 0.0, const char outOrder = 'f'); // multiptication Matrix to vector - static nd4j::NDArray* mmulMxV(const nd4j::NDArray* A, const nd4j::NDArray* B, nd4j::NDArray* C, double alpha = 1.0, double beta = 0.0, const char outOrder = 'f'); + static sd::NDArray* mmulMxV(const sd::NDArray* A, const sd::NDArray* B, sd::NDArray* C, double alpha = 1.0, double beta = 0.0, const char outOrder = 'f'); public: - static nd4j::NDArray* mmul(const nd4j::NDArray* A, const nd4j::NDArray* B, nd4j::NDArray* C = nullptr, const double alpha = 1.0, const double beta = 0.0, const char outOrder = 'f'); + static sd::NDArray* mmul(const sd::NDArray* A, const sd::NDArray* B, sd::NDArray* C = nullptr, const double alpha = 1.0, const double beta = 0.0, const char outOrder = 'f'); - static nd4j::NDArray* tensorDot(const nd4j::NDArray* A, const nd4j::NDArray* B, const std::initializer_list& axesA, const std::initializer_list& axesB = {}); + static sd::NDArray* tensorDot(const sd::NDArray* A, const sd::NDArray* B, const std::initializer_list& axesA, const std::initializer_list& axesB = {}); - static nd4j::NDArray* tensorDot(const nd4j::NDArray* A, const nd4j::NDArray* B, const std::vector& axesA, const std::vector& axesB); + static sd::NDArray* tensorDot(const sd::NDArray* A, const sd::NDArray* B, const std::vector& axesA, const std::vector& axesB); - static void tensorDot(const nd4j::NDArray* a, const nd4j::NDArray* b, nd4j::NDArray* c, const std::vector& axes_a, const std::vector& axes_b, const std::vector& permutForC = {}); + static void tensorDot(const sd::NDArray* a, const sd::NDArray* b, sd::NDArray* c, const std::vector& axes_a, const std::vector& axes_b, const std::vector& permutForC = {}); #ifndef __JAVACPP_HACK__ /** * modif - (can be empty) vector containing a subsequence of permutation/reshaping arrays (in any order), user must take care of correctness of such arrays by himself */ - static void tensorDot(const nd4j::NDArray* a, const nd4j::NDArray* b, nd4j::NDArray* c, const std::vector>& modifA, const std::vector>& modifB, const std::vector>& modifC); - static nd4j::NDArray* tensorDot(const nd4j::NDArray* a, const nd4j::NDArray* b, const std::vector>& modifA, const std::vector>& modifB); + static void tensorDot(const sd::NDArray* a, const sd::NDArray* b, sd::NDArray* c, const std::vector>& modifA, const std::vector>& modifB, const std::vector>& modifC); + static sd::NDArray* tensorDot(const sd::NDArray* a, const sd::NDArray* b, const std::vector>& modifA, const std::vector>& modifB); #endif - static void matmul(const nd4j::NDArray* x, const nd4j::NDArray* y, nd4j::NDArray* z, const bool transX, const bool transY); + static void matmul(const sd::NDArray* x, const sd::NDArray* y, sd::NDArray* z, const bool transX, const bool transY); }; } diff --git a/libnd4j/include/helpers/OmpLaunchHelper.h b/libnd4j/include/helpers/OmpLaunchHelper.h index dac93cbe2..3e0e50391 100644 --- a/libnd4j/include/helpers/OmpLaunchHelper.h +++ b/libnd4j/include/helpers/OmpLaunchHelper.h @@ -23,10 +23,10 @@ #define LIBND4J_OMPLAUNCHHELPER_H #include -#include -#include +#include +#include -namespace nd4j { +namespace sd { class ND4J_EXPORT OmpLaunchHelper { diff --git a/libnd4j/include/helpers/OpArgsHolder.h b/libnd4j/include/helpers/OpArgsHolder.h index 5d792105c..a9432f134 100644 --- a/libnd4j/include/helpers/OpArgsHolder.h +++ b/libnd4j/include/helpers/OpArgsHolder.h @@ -22,10 +22,10 @@ #define LIBND4J_OPARGSHOLDER_H -#include -#include +#include +#include -namespace nd4j { +namespace sd { class ND4J_EXPORT OpArgsHolder { diff --git a/libnd4j/include/helpers/OpBenchmark.h b/libnd4j/include/helpers/OpBenchmark.h index 0a4b32824..328b20dce 100644 --- a/libnd4j/include/helpers/OpBenchmark.h +++ b/libnd4j/include/helpers/OpBenchmark.h @@ -21,13 +21,13 @@ #ifndef DEV_TESTS_OPEXECUTIONER_H #define DEV_TESTS_OPEXECUTIONER_H -#include -#include +#include +#include #include -#include +#include #include -namespace nd4j { +namespace sd { class ND4J_EXPORT OpBenchmark { protected: int _opNum = 0; diff --git a/libnd4j/include/helpers/OpTracker.h b/libnd4j/include/helpers/OpTracker.h index dd03f154f..122f4f32b 100644 --- a/libnd4j/include/helpers/OpTracker.h +++ b/libnd4j/include/helpers/OpTracker.h @@ -24,12 +24,12 @@ #include #include #include -#include +#include #include #include -#include +#include -namespace nd4j { +namespace sd { class ND4J_EXPORT OpTracker { private: static OpTracker* _INSTANCE; @@ -37,7 +37,7 @@ namespace nd4j { std::string _export; int _operations = 0; - std::map> _map; + std::map> _map; OpTracker() = default; ~OpTracker() = default; @@ -50,8 +50,8 @@ namespace nd4j { int totalGroups(); int totalOperations(); - void storeOperation(nd4j::graph::OpType opType, const nd4j::ops::OpDescriptor& descriptor); - void storeOperation(nd4j::graph::OpType opType, const char* opName, const Nd4jLong opNum); + void storeOperation(sd::graph::OpType opType, const sd::ops::OpDescriptor& descriptor); + void storeOperation(sd::graph::OpType opType, const char* opName, const Nd4jLong opNum); const char* exportOperations(); }; diff --git a/libnd4j/include/helpers/PointersManager.h b/libnd4j/include/helpers/PointersManager.h index 50fdbccf9..4f7af9409 100644 --- a/libnd4j/include/helpers/PointersManager.h +++ b/libnd4j/include/helpers/PointersManager.h @@ -26,21 +26,21 @@ #include #include -#include +#include -namespace nd4j { +namespace sd { class ND4J_EXPORT PointersManager { private: - nd4j::LaunchContext *_context; + sd::LaunchContext *_context; std::vector _pOnGlobMem; std::string _funcName; public: - PointersManager(const nd4j::LaunchContext* context, const std::string& funcName = ""); + PointersManager(const sd::LaunchContext* context, const std::string& funcName = ""); ~PointersManager(); diff --git a/libnd4j/include/helpers/RandomLauncher.h b/libnd4j/include/helpers/RandomLauncher.h index 2e477e079..49e961062 100644 --- a/libnd4j/include/helpers/RandomLauncher.h +++ b/libnd4j/include/helpers/RandomLauncher.h @@ -18,30 +18,30 @@ // @author raver119@gmail.com // -#include +#include #include #include #include -namespace nd4j { +namespace sd { class ND4J_EXPORT RandomLauncher { public: - static void applyDropOut(nd4j::LaunchContext *context, nd4j::graph::RandomGenerator& rng, NDArray *array, double retainProb, NDArray* z = nullptr); - static void applyInvertedDropOut(nd4j::LaunchContext *context, nd4j::graph::RandomGenerator& rng, NDArray *array, double retainProb, NDArray* z = nullptr); - static void applyAlphaDropOut(nd4j::LaunchContext *context, nd4j::graph::RandomGenerator& rng, NDArray *array, double retainProb, double alpha, double beta, double alphaPrime, NDArray* z = nullptr); + static void applyDropOut(sd::LaunchContext *context, sd::graph::RandomGenerator& rng, NDArray *array, double retainProb, NDArray* z = nullptr); + static void applyInvertedDropOut(sd::LaunchContext *context, sd::graph::RandomGenerator& rng, NDArray *array, double retainProb, NDArray* z = nullptr); + static void applyAlphaDropOut(sd::LaunchContext *context, sd::graph::RandomGenerator& rng, NDArray *array, double retainProb, double alpha, double beta, double alphaPrime, NDArray* z = nullptr); - static void fillUniform(nd4j::LaunchContext *context, nd4j::graph::RandomGenerator& rng, NDArray* array, double from, double to); + static void fillUniform(sd::LaunchContext *context, sd::graph::RandomGenerator& rng, NDArray* array, double from, double to); - static void fillGaussian(nd4j::LaunchContext *context, nd4j::graph::RandomGenerator& rng, NDArray* array, double mean, double stdev); + static void fillGaussian(sd::LaunchContext *context, sd::graph::RandomGenerator& rng, NDArray* array, double mean, double stdev); - static void fillExponential(nd4j::LaunchContext *context, nd4j::graph::RandomGenerator& rng, NDArray* array, double lambda); + static void fillExponential(sd::LaunchContext *context, sd::graph::RandomGenerator& rng, NDArray* array, double lambda); - static void fillLogNormal(nd4j::LaunchContext *context, nd4j::graph::RandomGenerator& rng, NDArray* array, double mean, double stdev); + static void fillLogNormal(sd::LaunchContext *context, sd::graph::RandomGenerator& rng, NDArray* array, double mean, double stdev); - static void fillTruncatedNormal(nd4j::LaunchContext *context, nd4j::graph::RandomGenerator& rng, NDArray* array, double mean, double stdev); + static void fillTruncatedNormal(sd::LaunchContext *context, sd::graph::RandomGenerator& rng, NDArray* array, double mean, double stdev); - static void fillBinomial(nd4j::LaunchContext *context, nd4j::graph::RandomGenerator& rng, NDArray* array, int trials, double prob); + static void fillBinomial(sd::LaunchContext *context, sd::graph::RandomGenerator& rng, NDArray* array, int trials, double prob); - static void fillBernoulli(nd4j::LaunchContext *context, nd4j::graph::RandomGenerator& rng, NDArray* array, double prob); + static void fillBernoulli(sd::LaunchContext *context, sd::graph::RandomGenerator& rng, NDArray* array, double prob); }; } \ No newline at end of file diff --git a/libnd4j/include/helpers/ShapeBuilders.h b/libnd4j/include/helpers/ShapeBuilders.h index 2d71c7ab2..e2c29a280 100644 --- a/libnd4j/include/helpers/ShapeBuilders.h +++ b/libnd4j/include/helpers/ShapeBuilders.h @@ -23,25 +23,25 @@ #include #include -#include +#include #include #include #include -namespace nd4j { +namespace sd { class ND4J_EXPORT ShapeBuilders { public: - static Nd4jLong* createScalarShapeInfo(nd4j::DataType dataType, nd4j::memory::Workspace* workspace = nullptr); + static Nd4jLong* createScalarShapeInfo(sd::DataType dataType, sd::memory::Workspace* workspace = nullptr); - static Nd4jLong* createVectorShapeInfo(const nd4j::DataType dataType, const Nd4jLong length, nd4j::memory::Workspace* workspace = nullptr); + static Nd4jLong* createVectorShapeInfo(const sd::DataType dataType, const Nd4jLong length, sd::memory::Workspace* workspace = nullptr); /** * create shapeInfo for given order basing on shape stored in shapeOnly vector * memory allocation for shapeInfo is on given workspace */ - static Nd4jLong* createShapeInfo(const nd4j::DataType dataType, const char order, int rank, const Nd4jLong* shapeOnly, memory::Workspace* workspace = nullptr); - static Nd4jLong* createShapeInfo(const nd4j::DataType dataType, const char order, const std::vector& shapeOnly, memory::Workspace* workspace = nullptr); - static Nd4jLong* createShapeInfo(const nd4j::DataType dataType, const char order, const std::initializer_list& shapeOnly, memory::Workspace* workspace = nullptr); + static Nd4jLong* createShapeInfo(const sd::DataType dataType, const char order, int rank, const Nd4jLong* shapeOnly, memory::Workspace* workspace = nullptr); + static Nd4jLong* createShapeInfo(const sd::DataType dataType, const char order, const std::vector& shapeOnly, memory::Workspace* workspace = nullptr); + static Nd4jLong* createShapeInfo(const sd::DataType dataType, const char order, const std::initializer_list& shapeOnly, memory::Workspace* workspace = nullptr); /** * allocates memory for new shapeInfo and copy all information from inShapeInfo to new shapeInfo @@ -58,9 +58,9 @@ namespace nd4j { */ static Nd4jLong* copyShapeInfoWithoutUnites(const Nd4jLong* inShapeInfo, const int dimsSize, const int* dimsToExclude, memory::Workspace* workspace = nullptr); - static Nd4jLong* emptyShapeInfo(const nd4j::DataType dataType, memory::Workspace* workspace = nullptr); + static Nd4jLong* emptyShapeInfo(const sd::DataType dataType, memory::Workspace* workspace = nullptr); - static Nd4jLong* emptyShapeInfo(const nd4j::DataType dataType, const char order, const std::vector &shape, memory::Workspace* workspace = nullptr); + static Nd4jLong* emptyShapeInfo(const sd::DataType dataType, const char order, const std::vector &shape, memory::Workspace* workspace = nullptr); }; } diff --git a/libnd4j/include/helpers/ShapeUtils.h b/libnd4j/include/helpers/ShapeUtils.h index 39ea3edaa..8d2a119c3 100644 --- a/libnd4j/include/helpers/ShapeUtils.h +++ b/libnd4j/include/helpers/ShapeUtils.h @@ -22,9 +22,9 @@ #define LIBND4J_SHAPEUTILS_H #include -#include +#include -namespace nd4j { +namespace sd { class ND4J_EXPORT ShapeUtils { @@ -35,28 +35,28 @@ namespace nd4j { static std::vector evalShapeForTensorDot(const NDArray* a, const NDArray* b, const std::vector& axesA, const std::vector& axesB, std::vector& permutAt, std::vector& permutBt, std::vector& shapeAt, std::vector& shapeBt); // evaluate resulting shape after reduce operation - static Nd4jLong* evalReduceShapeInfo(const char order, std::vector& dimensions, const NDArray& arr, const nd4j::DataType dataType, const bool keepDims = false, const bool supportOldShapes = false, nd4j::memory::Workspace* workspace = nullptr); - static Nd4jLong* evalReduceShapeInfo(const char order, std::vector& dimensions, const Nd4jLong* shapeInfo, const nd4j::DataType dataType, const bool keepDims = false, const bool supportOldShapes = false, nd4j::memory::Workspace* workspace = nullptr); - static Nd4jLong* evalReduceShapeInfo(const char order, std::vector& dimensions, const NDArray& arr, const bool keepDims = false, const bool supportOldShapes = false, nd4j::memory::Workspace* workspace = nullptr); - static Nd4jLong* evalReduceShapeInfo(const char order, std::vector& dimensions, const Nd4jLong* shapeInfo, const bool keepDims = false, const bool supportOldShapes = false, nd4j::memory::Workspace* workspace = nullptr); + static Nd4jLong* evalReduceShapeInfo(const char order, std::vector& dimensions, const NDArray& arr, const sd::DataType dataType, const bool keepDims = false, const bool supportOldShapes = false, sd::memory::Workspace* workspace = nullptr); + static Nd4jLong* evalReduceShapeInfo(const char order, std::vector& dimensions, const Nd4jLong* shapeInfo, const sd::DataType dataType, const bool keepDims = false, const bool supportOldShapes = false, sd::memory::Workspace* workspace = nullptr); + static Nd4jLong* evalReduceShapeInfo(const char order, std::vector& dimensions, const NDArray& arr, const bool keepDims = false, const bool supportOldShapes = false, sd::memory::Workspace* workspace = nullptr); + static Nd4jLong* evalReduceShapeInfo(const char order, std::vector& dimensions, const Nd4jLong* shapeInfo, const bool keepDims = false, const bool supportOldShapes = false, sd::memory::Workspace* workspace = nullptr); /** * evaluate output shape for reduce operation when input shape is empty * behavior is analogous to tf */ - static Nd4jLong* evalReduceShapeInfoEmpty(const char order, std::vector& dimensions, const Nd4jLong *shapeInfo, const nd4j::DataType dataType, const bool keepDims, nd4j::memory::Workspace* workspace); + static Nd4jLong* evalReduceShapeInfoEmpty(const char order, std::vector& dimensions, const Nd4jLong *shapeInfo, const sd::DataType dataType, const bool keepDims, sd::memory::Workspace* workspace); // evaluate shape for array which is result of repeat operation applied to arr static std::vector evalRepeatShape(int axis, const std::vector& repeats, const NDArray& arr); // evaluate shapeInfo of permuted array // if setContigStrides = true, then set contiguous strides in output shapeInfo in accordance with arr order - static Nd4jLong* evalPermShapeInfo(const int* dimensions, const int rank, const NDArray& arr, nd4j::memory::Workspace* workspace, const bool setContigStrides = false); - static Nd4jLong* evalPermShapeInfo(const Nd4jLong* dimensions, const int rank, const NDArray& arr, nd4j::memory::Workspace* workspace); + static Nd4jLong* evalPermShapeInfo(const int* dimensions, const int rank, const NDArray& arr, sd::memory::Workspace* workspace, const bool setContigStrides = false); + static Nd4jLong* evalPermShapeInfo(const Nd4jLong* dimensions, const int rank, const NDArray& arr, sd::memory::Workspace* workspace); // evaluate shapeInfo of transposed array // if setContigStrides = true, then set contiguous strides in output shapeInfo in accordance with arr order - static Nd4jLong* evalTranspShapeInfo(const NDArray& arr, nd4j::memory::Workspace* workspace, const bool setContigStrides = false); + static Nd4jLong* evalTranspShapeInfo(const NDArray& arr, sd::memory::Workspace* workspace, const bool setContigStrides = false); static bool copyVectorPart(std::vector& target, std::vector& source, int rank, int offset); @@ -72,8 +72,8 @@ namespace nd4j { // check the possibility of broadcast operation, if true then return shapeInfo of resulting array // if evalMinMax == false then array with larger rank has to be passed as first argument - static bool evalBroadcastShapeInfo(const NDArray& max, const NDArray& min, const bool evalMinMax, Nd4jLong*& resultShapeInfo, nd4j::memory::Workspace* workspace); - static bool evalBroadcastShapeInfo(Nd4jLong *max, Nd4jLong *min, const bool evalMinMax, Nd4jLong*& resultShapeInfo, nd4j::memory::Workspace* workspace); + static bool evalBroadcastShapeInfo(const NDArray& max, const NDArray& min, const bool evalMinMax, Nd4jLong*& resultShapeInfo, sd::memory::Workspace* workspace); + static bool evalBroadcastShapeInfo(Nd4jLong *max, Nd4jLong *min, const bool evalMinMax, Nd4jLong*& resultShapeInfo, sd::memory::Workspace* workspace); // evaluate sorted vector of max axes to create tads along in case of simple broadcast operation // if simple broadcast is not possible then empty vector is returned @@ -88,7 +88,7 @@ namespace nd4j { static std::vector getDimsWithSameShape(const NDArray& max, const NDArray& min); // evaluate shapeInfo for resulting array of tile operation - static Nd4jLong* evalTileShapeInfo(const NDArray& arr, const std::vector& reps, nd4j::memory::Workspace* workspace); + static Nd4jLong* evalTileShapeInfo(const NDArray& arr, const std::vector& reps, sd::memory::Workspace* workspace); // returns shape part of shapeInfo as std::vector static std::vector pullShapeFromShapeInfo(Nd4jLong *shapeInfo); @@ -104,13 +104,13 @@ namespace nd4j { static std::vector shapeAsVector(const Nd4jLong* shapeInfo); // evaluate shapeInfo for diagonal array which is made using input arr elements as diagonal - static Nd4jLong* evalDiagShapeInfo(const Nd4jLong* shapeInfo, nd4j::memory::Workspace* workspace); + static Nd4jLong* evalDiagShapeInfo(const Nd4jLong* shapeInfo, sd::memory::Workspace* workspace); static std::vector evalBroadcastBackwardAxis(const Nd4jLong *operand, const Nd4jLong *result); // utility to calculate matrix product shape with give source shapes and additional params // returns ShapeList pointer with result shape - static Nd4jLong* matrixProductShape(Nd4jLong* theFirstShape, Nd4jLong* theSecondShape, bool shouldTranspondFirst, bool shouldTranspondSecond, nd4j::DataType dtype, nd4j::memory::Workspace* workspace); + static Nd4jLong* matrixProductShape(Nd4jLong* theFirstShape, Nd4jLong* theSecondShape, bool shouldTranspondFirst, bool shouldTranspondSecond, sd::DataType dtype, sd::memory::Workspace* workspace); /** * This method evaluates permutation vector necessary for reducing of shapeFrom to shapeTo @@ -138,16 +138,6 @@ namespace nd4j { */ static Nd4jLong getNumOfSubArrs(const Nd4jLong* shapeInfo, const std::vector& dimsToExclude); - /** - * evaluate indexes ranges that define sub-array of array having shape=shapeInfo - * subArrIdx - index of current sub-array - * shapeInfo - shapeInfo of array for which to evaluate sub-arrays - * dimsToExclude - MUST BE SORTED, dimensions to evaluate sub-arrays along, i.e. when shape is [2,3,4,5] and dimsToExclude={0,2}, then there will be 8 sub-arrays with shape [3,5], - * if dimsToExclude is empty then idxRanges containing all zeros (means whole array) will be returned. - * idxRanges - where to put result, the length of idxRanges must be equal to 2*shapeInfo[0] - */ - static void evalIdxRangesForSubArr(const Nd4jLong subArrIdx, const Nd4jLong* shapeInfo, const std::vector& dimsToExclude, Nd4jLong* idxRanges); - /** * return shape without unities, for example if shape is [1,2,1,3] then [2,3] will be returned * if unities are not present in given shapeInfo then exactly identical shape will be returned, for example [2,3] -> [2,3] @@ -202,6 +192,11 @@ namespace nd4j { static bool isSubArrayCase(const NDArray& arr1, const NDArray& arr2, std::vector& sameDims); */ + + /* + * comparing of shapes, not strides + */ + static bool areShapesEqual(const Nd4jLong* shapeInfo, const std::vector& shapeOnly); }; diff --git a/libnd4j/include/helpers/SimpleReadWriteLock.h b/libnd4j/include/helpers/SimpleReadWriteLock.h index b7637f355..5d1fce711 100644 --- a/libnd4j/include/helpers/SimpleReadWriteLock.h +++ b/libnd4j/include/helpers/SimpleReadWriteLock.h @@ -23,7 +23,7 @@ #include #include -#include +#include /** * This class provides PRIMITIVE read-write lock, and should NOT be used outside of GraphServer due to its inefficiency. @@ -31,7 +31,7 @@ * * Basic idea: write lock won't be obtained before all read requests served */ -namespace nd4j { +namespace sd { class ND4J_EXPORT SimpleReadWriteLock { private: std::atomic _read_locks; diff --git a/libnd4j/include/helpers/StringUtils.h b/libnd4j/include/helpers/StringUtils.h index 7a0e2a960..ef9586637 100644 --- a/libnd4j/include/helpers/StringUtils.h +++ b/libnd4j/include/helpers/StringUtils.h @@ -23,15 +23,15 @@ #ifndef LIBND4J_STRINGUTILS_H #define LIBND4J_STRINGUTILS_H -#include -#include +#include +#include #include #include #include -#include -#include +#include +#include -namespace nd4j { +namespace sd { class ND4J_EXPORT StringUtils { public: template diff --git a/libnd4j/include/helpers/TAD.h b/libnd4j/include/helpers/TAD.h index fb52e639c..6df5a05a2 100644 --- a/libnd4j/include/helpers/TAD.h +++ b/libnd4j/include/helpers/TAD.h @@ -23,7 +23,7 @@ #include -#include +#include namespace shape { @@ -443,7 +443,7 @@ namespace shape { INLINEDEF void TAD::createTadOnlyShapeInfo() { this->tadOnlyShapeInfo = this->shapeInfoOnlyShapeAndStride(); - nd4j::ArrayOptions::setDataType(this->tadOnlyShapeInfo, nd4j::ArrayOptions::dataType(this->originalShapeInfo)); + sd::ArrayOptions::setDataType(this->tadOnlyShapeInfo, sd::ArrayOptions::dataType(this->originalShapeInfo)); // possible optimization goes here if (shape::order(this->originalShapeInfo) == 'c' diff --git a/libnd4j/include/helpers/benchmark/BasicSuit.h b/libnd4j/include/helpers/benchmark/BasicSuit.h index 4c06d4e66..1e4c156fb 100644 --- a/libnd4j/include/helpers/benchmark/BasicSuit.h +++ b/libnd4j/include/helpers/benchmark/BasicSuit.h @@ -21,7 +21,7 @@ #ifndef DEV_TESTS_BASICSUIT_H #define DEV_TESTS_BASICSUIT_H -namespace nd4j { +namespace sd { class BasicSuit { protected: diff --git a/libnd4j/include/helpers/benchmark/BoolParameters.h b/libnd4j/include/helpers/benchmark/BoolParameters.h index d6a6cbadb..bac8a0c5c 100644 --- a/libnd4j/include/helpers/benchmark/BoolParameters.h +++ b/libnd4j/include/helpers/benchmark/BoolParameters.h @@ -27,7 +27,7 @@ #include "Parameters.h" #include "ParametersSpace.h" -namespace nd4j { +namespace sd { class BoolParameters : public ParametersSpace { protected: diff --git a/libnd4j/include/helpers/benchmark/BroadcastBenchmark.h b/libnd4j/include/helpers/benchmark/BroadcastBenchmark.h index ac2a643e9..3a043be59 100644 --- a/libnd4j/include/helpers/benchmark/BroadcastBenchmark.h +++ b/libnd4j/include/helpers/benchmark/BroadcastBenchmark.h @@ -23,7 +23,7 @@ #ifndef DEV_TESTS_BROADCASTBENCHMARK_H #define DEV_TESTS_BROADCASTBENCHMARK_H -namespace nd4j { +namespace sd { class ND4J_EXPORT BroadcastBenchmark : public OpBenchmark { public: BroadcastBenchmark() : OpBenchmark() { diff --git a/libnd4j/include/helpers/benchmark/DeclarableBenchmark.h b/libnd4j/include/helpers/benchmark/DeclarableBenchmark.h index 0aa8c35a6..f9347eb05 100644 --- a/libnd4j/include/helpers/benchmark/DeclarableBenchmark.h +++ b/libnd4j/include/helpers/benchmark/DeclarableBenchmark.h @@ -22,25 +22,25 @@ #ifndef DEV_TESTS_DECLARABLEBENCHMARK_H #define DEV_TESTS_DECLARABLEBENCHMARK_H -#include -#include -#include -#include -#include -#include +#include +#include +#include +#include +#include +#include -namespace nd4j { +namespace sd { class ND4J_EXPORT DeclarableBenchmark : public OpBenchmark { protected: - nd4j::ops::DeclarableOp *_op = nullptr; - nd4j::graph::Context *_context = nullptr; + sd::ops::DeclarableOp *_op = nullptr; + sd::graph::Context *_context = nullptr; public: - DeclarableBenchmark(nd4j::ops::DeclarableOp &op, std::string name = 0) : OpBenchmark() { + DeclarableBenchmark(sd::ops::DeclarableOp &op, std::string name = 0) : OpBenchmark() { _op = &op; //ops::OpRegistrator::getInstance()->getOperation(op.getOpHash()); _testName = name; } - void setContext(nd4j::graph::Context *ctx) { + void setContext(sd::graph::Context *ctx) { _context = ctx; } diff --git a/libnd4j/include/helpers/benchmark/IntParameters.h b/libnd4j/include/helpers/benchmark/IntParameters.h index 1615fcaa7..10a1763e4 100644 --- a/libnd4j/include/helpers/benchmark/IntParameters.h +++ b/libnd4j/include/helpers/benchmark/IntParameters.h @@ -27,7 +27,7 @@ #include "Parameters.h" #include "ParametersSpace.h" -namespace nd4j { +namespace sd { class IntParameters : public ParametersSpace { protected: int _start; diff --git a/libnd4j/include/helpers/benchmark/IntPowerParameters.h b/libnd4j/include/helpers/benchmark/IntPowerParameters.h index 81dabfb86..82c58bb23 100644 --- a/libnd4j/include/helpers/benchmark/IntPowerParameters.h +++ b/libnd4j/include/helpers/benchmark/IntPowerParameters.h @@ -27,7 +27,7 @@ #include "Parameters.h" #include "ParametersSpace.h" -namespace nd4j { +namespace sd { class IntPowerParameters : public ParametersSpace { protected: int _base; @@ -47,7 +47,7 @@ namespace nd4j { std::vector evaluate() override { std::vector result; for (int e = _start; e <= _stop; e += _step) { - result.emplace_back(nd4j::math::nd4j_pow(_base, e)); + result.emplace_back(sd::math::nd4j_pow(_base, e)); } return result; } diff --git a/libnd4j/include/helpers/benchmark/MatrixBenchmark.h b/libnd4j/include/helpers/benchmark/MatrixBenchmark.h index 7c1330648..eb8fd2619 100644 --- a/libnd4j/include/helpers/benchmark/MatrixBenchmark.h +++ b/libnd4j/include/helpers/benchmark/MatrixBenchmark.h @@ -18,13 +18,13 @@ // @author raver119@gmail.com // -#include "../OpBenchmark.h" -#include +#include +#include #ifndef DEV_TESTS_MATRIXBENCHMARK_H #define DEV_TESTS_MATRIXBENCHMARK_H -namespace nd4j { +namespace sd { class ND4J_EXPORT MatrixBenchmark : public OpBenchmark { private: float _alpha = 1.0f; diff --git a/libnd4j/include/helpers/benchmark/PairwiseBenchmark.h b/libnd4j/include/helpers/benchmark/PairwiseBenchmark.h index 68229b583..ca92e96b3 100644 --- a/libnd4j/include/helpers/benchmark/PairwiseBenchmark.h +++ b/libnd4j/include/helpers/benchmark/PairwiseBenchmark.h @@ -23,9 +23,9 @@ #ifndef DEV_TESTS_PAIRWISEBENCHMARK_H #define DEV_TESTS_PAIRWISEBENCHMARK_H -using namespace nd4j::graph; +using namespace sd::graph; -namespace nd4j { +namespace sd { class ND4J_EXPORT PairwiseBenchmark : public OpBenchmark { public: PairwiseBenchmark() : OpBenchmark() { diff --git a/libnd4j/include/helpers/benchmark/Parameters.h b/libnd4j/include/helpers/benchmark/Parameters.h index 934837e8e..eee443574 100644 --- a/libnd4j/include/helpers/benchmark/Parameters.h +++ b/libnd4j/include/helpers/benchmark/Parameters.h @@ -25,7 +25,7 @@ #include #include -namespace nd4j { +namespace sd { class Parameters { private: std::map _intParams; diff --git a/libnd4j/include/helpers/benchmark/ParametersBatch.h b/libnd4j/include/helpers/benchmark/ParametersBatch.h index 4a7119937..68c4dfb9f 100644 --- a/libnd4j/include/helpers/benchmark/ParametersBatch.h +++ b/libnd4j/include/helpers/benchmark/ParametersBatch.h @@ -21,11 +21,11 @@ #ifndef DEV_TESTS_PARAMETERSBATCH_H #define DEV_TESTS_PARAMETERSBATCH_H -#include "ParametersSpace.h" +#include #include -#include +#include -namespace nd4j { +namespace sd { class ParametersBatch { protected: std::vector _spaces; @@ -46,7 +46,7 @@ namespace nd4j { int totalIterations = 1; // hehe - Nd4jLong xCoords[MAX_RANK]; + int xCoords[MAX_RANK]; Nd4jLong xShape[MAX_RANK]; int xRank = _spaces.size(); @@ -61,10 +61,9 @@ namespace nd4j { //nd4j_printf("Total Iterations: %i\n", totalIterations); - - for (int i = 0; i < totalIterations; i++) { - shape::index2coords(i, xRank, xShape, xCoords); + if (xRank > 0) + shape::index2coords(i, xRank, xShape, xCoords); Parameters params; for (int j = 0; j < xRank; j++) { diff --git a/libnd4j/include/helpers/benchmark/ParametersSpace.h b/libnd4j/include/helpers/benchmark/ParametersSpace.h index d245e2319..a7c59f9a6 100644 --- a/libnd4j/include/helpers/benchmark/ParametersSpace.h +++ b/libnd4j/include/helpers/benchmark/ParametersSpace.h @@ -23,7 +23,7 @@ #include -namespace nd4j { +namespace sd { class ParametersSpace { protected: std::string _name; diff --git a/libnd4j/include/helpers/benchmark/PredefinedParameters.h b/libnd4j/include/helpers/benchmark/PredefinedParameters.h index 5f683857d..f2a7fc347 100644 --- a/libnd4j/include/helpers/benchmark/PredefinedParameters.h +++ b/libnd4j/include/helpers/benchmark/PredefinedParameters.h @@ -23,7 +23,7 @@ #include "ParametersSpace.h" -namespace nd4j { +namespace sd { class PredefinedParameters : public ParametersSpace{ std::vector _params; public: diff --git a/libnd4j/include/helpers/benchmark/ReductionBenchmark.h b/libnd4j/include/helpers/benchmark/ReductionBenchmark.h index 460f2e272..a1dc0126f 100644 --- a/libnd4j/include/helpers/benchmark/ReductionBenchmark.h +++ b/libnd4j/include/helpers/benchmark/ReductionBenchmark.h @@ -25,9 +25,9 @@ #ifndef DEV_TESTS_REDUCEBENCHMARK_H #define DEV_TESTS_REDUCEBENCHMARK_H -using namespace nd4j::graph; +using namespace sd::graph; -namespace nd4j { +namespace sd { class ND4J_EXPORT ReductionBenchmark : public OpBenchmark { protected: int _opType; //0=Float, 1=Same diff --git a/libnd4j/include/helpers/benchmark/ScalarBenchmark.h b/libnd4j/include/helpers/benchmark/ScalarBenchmark.h index d24c31b84..3b0cdecf5 100644 --- a/libnd4j/include/helpers/benchmark/ScalarBenchmark.h +++ b/libnd4j/include/helpers/benchmark/ScalarBenchmark.h @@ -22,9 +22,9 @@ #ifndef DEV_TESTS_SCALARBENCHMARK_H #define DEV_TESTS_SCALARBENCHMARK_H -using namespace nd4j::graph; +using namespace sd::graph; -namespace nd4j { +namespace sd { class ND4J_EXPORT ScalarBenchmark : public OpBenchmark { public: ScalarBenchmark() : OpBenchmark() { diff --git a/libnd4j/include/helpers/benchmark/TransformBenchmark.h b/libnd4j/include/helpers/benchmark/TransformBenchmark.h index 02f19a7ed..024857633 100644 --- a/libnd4j/include/helpers/benchmark/TransformBenchmark.h +++ b/libnd4j/include/helpers/benchmark/TransformBenchmark.h @@ -22,9 +22,9 @@ #ifndef DEV_TESTS_TRANSFORMBENCHMARK_H #define DEV_TESTS_TRANSFORMBENCHMARK_H -using namespace nd4j::graph; +using namespace sd::graph; -namespace nd4j { +namespace sd { class ND4J_EXPORT TransformBenchmark : public OpBenchmark { protected: diff --git a/libnd4j/include/helpers/biDiagonalUp.h b/libnd4j/include/helpers/biDiagonalUp.h index 279b2a63b..aaf64d41d 100644 --- a/libnd4j/include/helpers/biDiagonalUp.h +++ b/libnd4j/include/helpers/biDiagonalUp.h @@ -21,10 +21,10 @@ #ifndef LIBND4J_BIDIAGONALUP_H #define LIBND4J_BIDIAGONALUP_H -#include -#include "NDArray.h" +#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { diff --git a/libnd4j/include/helpers/cpu/ConstantHelper.cpp b/libnd4j/include/helpers/cpu/ConstantHelper.cpp index 2ba2cc4e0..10b8a52c3 100644 --- a/libnd4j/include/helpers/cpu/ConstantHelper.cpp +++ b/libnd4j/include/helpers/cpu/ConstantHelper.cpp @@ -21,20 +21,21 @@ #ifndef __CUDABLAS__ -#include +#include #include #include #include -#include +#include #include -namespace nd4j { +namespace sd { + ConstantHelper::ConstantHelper() { int numDevices = getNumberOfDevices(); _cache.resize(numDevices); _counters.resize(numDevices); for (int e = 0; e < numDevices; e++) { - std::map map; + MAP_IMPL map; _cache[e] = map; _counters[e] = 0L; @@ -43,7 +44,7 @@ namespace nd4j { ConstantHelper* ConstantHelper::getInstance() { if (!_INSTANCE) - _INSTANCE = new nd4j::ConstantHelper(); + _INSTANCE = new sd::ConstantHelper(); return _INSTANCE; } @@ -69,7 +70,7 @@ namespace nd4j { return AffinityManager::numberOfDevices(); } - ConstantDataBuffer* ConstantHelper::constantBuffer(const ConstantDescriptor &descriptor, nd4j::DataType dataType) { + ConstantDataBuffer* ConstantHelper::constantBuffer(const ConstantDescriptor &descriptor, sd::DataType dataType) { const auto deviceId = getCurrentDevice(); // we're locking away cache modification @@ -99,9 +100,9 @@ namespace nd4j { // create buffer with this dtype if (descriptor.isFloat()) { - BUILD_DOUBLE_SELECTOR(nd4j::DataType::DOUBLE, dataType, nd4j::TypeCast::convertGeneric, (nullptr, const_cast(descriptor.floatValues().data()), descriptor.length(), cbuff), (nd4j::DataType::DOUBLE, double), LIBND4J_TYPES); + BUILD_DOUBLE_SELECTOR(sd::DataType::DOUBLE, dataType, sd::TypeCast::convertGeneric, (nullptr, const_cast(descriptor.floatValues().data()), descriptor.length(), cbuff), (sd::DataType::DOUBLE, double), LIBND4J_TYPES); } else if (descriptor.isInteger()) { - BUILD_DOUBLE_SELECTOR(nd4j::DataType::INT64, dataType, nd4j::TypeCast::convertGeneric, (nullptr, const_cast(descriptor.integerValues().data()), descriptor.length(), cbuff), (nd4j::DataType::INT64, Nd4jLong), LIBND4J_TYPES); + BUILD_DOUBLE_SELECTOR(sd::DataType::INT64, dataType, sd::TypeCast::convertGeneric, (nullptr, const_cast(descriptor.integerValues().data()), descriptor.length(), cbuff), (sd::DataType::INT64, Nd4jLong), LIBND4J_TYPES); } ConstantDataBuffer dataBuffer(cbuff, nullptr, descriptor.length(), DataTypeUtils::sizeOf(dataType)); @@ -122,7 +123,7 @@ namespace nd4j { return _counters[deviceId]; } - nd4j::ConstantHelper* nd4j::ConstantHelper::_INSTANCE = 0; + sd::ConstantHelper* sd::ConstantHelper::_INSTANCE = 0; } #endif \ No newline at end of file diff --git a/libnd4j/include/helpers/cpu/ConstantShapeHelper.cpp b/libnd4j/include/helpers/cpu/ConstantShapeHelper.cpp index bcedd727e..a69614906 100644 --- a/libnd4j/include/helpers/cpu/ConstantShapeHelper.cpp +++ b/libnd4j/include/helpers/cpu/ConstantShapeHelper.cpp @@ -20,16 +20,16 @@ #ifndef __CUDABLAS__ -#include "../ConstantShapeHelper.h" -#include -#include -#include +#include +#include +#include +#include -namespace nd4j { +namespace sd { ConstantShapeHelper::ConstantShapeHelper() { _cache.resize(32); for (int e = 0; e < 32; e++) { - std::map cache; + MAP_IMPL cache; _cache[e] = cache; } } @@ -41,12 +41,12 @@ namespace nd4j { return _INSTANCE; } - ConstantDataBuffer ConstantShapeHelper::bufferForShapeInfo(nd4j::DataType dataType, char order, const std::vector &shape) { + ConstantDataBuffer ConstantShapeHelper::bufferForShapeInfo(sd::DataType dataType, char order, const std::vector &shape) { ShapeDescriptor descriptor(dataType, order, shape); return bufferForShapeInfo(descriptor); } - ConstantDataBuffer ConstantShapeHelper::bufferForShapeInfo(const nd4j::DataType dataType, const char order, const int rank, const Nd4jLong* shape) { + ConstantDataBuffer ConstantShapeHelper::bufferForShapeInfo(const sd::DataType dataType, const char order, const int rank, const Nd4jLong* shape) { ShapeDescriptor descriptor(dataType, order, shape, rank); return bufferForShapeInfo(descriptor); } @@ -94,31 +94,31 @@ namespace nd4j { return result; } - Nd4jLong* ConstantShapeHelper::createShapeInfo(const nd4j::DataType dataType, const char order, const int rank, const Nd4jLong* shape) { + Nd4jLong* ConstantShapeHelper::createShapeInfo(const sd::DataType dataType, const char order, const int rank, const Nd4jLong* shape) { ShapeDescriptor descriptor(dataType, order, shape, rank); return bufferForShapeInfo(descriptor).primaryAsT(); } - Nd4jLong* ConstantShapeHelper::createShapeInfo(const nd4j::DataType dataType, const Nd4jLong* shapeInfo) { + Nd4jLong* ConstantShapeHelper::createShapeInfo(const sd::DataType dataType, const Nd4jLong* shapeInfo) { return ConstantShapeHelper::createShapeInfo(dataType, shape::order(shapeInfo), shape::rank(shapeInfo), shape::shapeOf(const_cast(shapeInfo))); } - Nd4jLong* ConstantShapeHelper::emptyShapeInfo(const nd4j::DataType dataType) { + Nd4jLong* ConstantShapeHelper::emptyShapeInfo(const sd::DataType dataType) { auto descriptor = ShapeDescriptor::emptyDescriptor(dataType); return bufferForShapeInfo(descriptor).primaryAsT(); } - Nd4jLong* ConstantShapeHelper::scalarShapeInfo(const nd4j::DataType dataType) { + Nd4jLong* ConstantShapeHelper::scalarShapeInfo(const sd::DataType dataType) { auto descriptor = ShapeDescriptor::scalarDescriptor(dataType); return bufferForShapeInfo(descriptor).primaryAsT(); } - Nd4jLong* ConstantShapeHelper::vectorShapeInfo(const Nd4jLong length, const nd4j::DataType dataType) { + Nd4jLong* ConstantShapeHelper::vectorShapeInfo(const Nd4jLong length, const sd::DataType dataType) { auto descriptor = ShapeDescriptor::vectorDescriptor(length, dataType); return bufferForShapeInfo(descriptor).primaryAsT(); } - Nd4jLong* ConstantShapeHelper::createShapeInfo(const nd4j::DataType dataType, const char order, const std::vector &shape) { + Nd4jLong* ConstantShapeHelper::createShapeInfo(const sd::DataType dataType, const char order, const std::vector &shape) { ShapeDescriptor descriptor(dataType, order, shape); return bufferForShapeInfo(descriptor).primaryAsT(); } @@ -137,7 +137,7 @@ namespace nd4j { return result; } - Nd4jLong* ConstantShapeHelper::createFromExisting(Nd4jLong *shapeInfo, nd4j::memory::Workspace *workspace) { + Nd4jLong* ConstantShapeHelper::createFromExisting(Nd4jLong *shapeInfo, sd::memory::Workspace *workspace) { ShapeDescriptor descriptor(shapeInfo); auto result = createShapeInfo(descriptor); @@ -146,7 +146,62 @@ namespace nd4j { return result; } - nd4j::ConstantShapeHelper* nd4j::ConstantShapeHelper::_INSTANCE = 0; + +//////////////////////////////////////////////////////////////////////// +ConstantDataBuffer ConstantShapeHelper::createShapeInfoWithUnitiesForBroadcast(const Nd4jLong* maxShapeInfo, const Nd4jLong* minShapeInfo, sd::memory::Workspace* workspace, const std::vector dimensions) { + + Nd4jLong* newShapeInfo = nullptr; + ALLOCATE(newShapeInfo, workspace, shape::shapeInfoLength(shape::rank(maxShapeInfo)), Nd4jLong); + + newShapeInfo[0] = shape::rank(maxShapeInfo); + + sd::ArrayOptions::copyDataType(newShapeInfo, minShapeInfo); // type + newShapeInfo[2 * newShapeInfo[0] + 2] = shape::elementWiseStride(minShapeInfo); // ews + newShapeInfo[2 * newShapeInfo[0] + 3] = shape::order(minShapeInfo); // order + + if(!dimensions.empty()) { + + for(uint k = 0, j = 0, i = 0; i < shape::rank(maxShapeInfo); ++i) { + + if(j < dimensions.size() && dimensions[j] == i) { + shape::shapeOf(newShapeInfo)[i] = shape::shapeOf(minShapeInfo)[k]; + shape::stride(newShapeInfo)[i] = shape::stride(minShapeInfo)[k++]; + ++j; + } + else{ + shape::shapeOf(newShapeInfo)[i] = 1; + shape::stride(newShapeInfo)[i] = 0; + if(shape::sizeAt(minShapeInfo, k) == 1 && dimensions.size() != shape::rank(minShapeInfo)) + ++k; + } + } + } + else{ + + for(int j = shape::rank(minShapeInfo) - 1, i = shape::rank(maxShapeInfo) - 1; i >=0 ; --i) { + + if(j >= 0) { + shape::shapeOf(newShapeInfo)[i] = shape::shapeOf(minShapeInfo)[j]; + shape::stride(newShapeInfo)[i] = shape::shapeOf(minShapeInfo)[j] == 1 ? 0 : shape::stride(minShapeInfo)[j]; + --j; + } + else { + shape::shapeOf(newShapeInfo)[i] = 1; + shape::stride(newShapeInfo)[i] = 0; + } + } + } + + ShapeDescriptor descriptor(newShapeInfo); + + RELEASE(newShapeInfo, workspace); + + return bufferForShapeInfo(descriptor); +} + + +sd::ConstantShapeHelper* sd::ConstantShapeHelper::_INSTANCE = 0; + } #endif \ No newline at end of file diff --git a/libnd4j/include/helpers/cpu/ConstantTadHelper.cpp b/libnd4j/include/helpers/cpu/ConstantTadHelper.cpp index d48cfca61..ea32db7e6 100644 --- a/libnd4j/include/helpers/cpu/ConstantTadHelper.cpp +++ b/libnd4j/include/helpers/cpu/ConstantTadHelper.cpp @@ -19,15 +19,16 @@ // #include "../ConstantTadHelper.h" -#include -#include +#include +#include #ifndef __CUDABLAS__ -namespace nd4j { + +namespace sd { ConstantTadHelper::ConstantTadHelper() { - std::map pack; + MAP_IMPL pack; _cache.emplace_back(pack); } @@ -72,7 +73,7 @@ namespace nd4j { auto oPtr = new Nd4jLong[numOfSubArrs]; if (numOfSubArrs > 0) - shape::calcSubArrShapeAndOffsets(shapeInfo, numOfSubArrs, dimsToExclude.size(), dimsToExclude.data(), sPtr, oPtr, descriptor.areUnitiesinShape()); + shape::calcSubArrsShapeInfoAndOffsets(shapeInfo, numOfSubArrs, dimsToExclude.size(), dimsToExclude.data(), sPtr, oPtr, descriptor.areUnitiesinShape()); ConstantDataBuffer shapesBuffer(sPtr, nullptr, shape::shapeInfoLength(subArrRank)*sizeof(Nd4jLong), DataType::INT64); @@ -112,7 +113,7 @@ namespace nd4j { } } - nd4j::ConstantTadHelper* nd4j::ConstantTadHelper::_INSTANCE = 0; + sd::ConstantTadHelper* sd::ConstantTadHelper::_INSTANCE = 0; } #endif \ No newline at end of file diff --git a/libnd4j/include/helpers/cpu/MmulHelper.cpp b/libnd4j/include/helpers/cpu/MmulHelper.cpp index f0e8846e3..62d8153ef 100644 --- a/libnd4j/include/helpers/cpu/MmulHelper.cpp +++ b/libnd4j/include/helpers/cpu/MmulHelper.cpp @@ -19,14 +19,14 @@ // @author Yurii Shyrma (iuriish@yahoo.com) // #include "../MmulHelper.h" -#include +#include #include #include #include #include -namespace nd4j { +namespace sd { ////////////////////////////////////////////////////////////////////////////// // MXK x KxN = MxN -> actual sequence of axes doesn't matter @@ -63,7 +63,7 @@ static void usualGemm(const NDArray* vA, const NDArray* vB, NDArray* vC, for (auto i = start; i < stop; ++i) { // evaluate C coordinates - shape::index2coords(i, cShapeInfo, cCoords.data()); + shape::index2coordsCPU(start, i, cShapeInfo, cCoords.data()); // evaluate A coordinates aCoords[aMaxis] = cCoords[cMaxis]; @@ -78,7 +78,7 @@ static void usualGemm(const NDArray* vA, const NDArray* vB, NDArray* vC, T3 val = A[aOffset] * B[bOffset]; // first iteration - for (uint j = 1; j < K; ++j) { // rest iterations + for (int j = 1; j < K; ++j) { // rest iterations aOffset += shape::stride(aShapeInfo)[aKaxis]; bOffset += shape::stride(bShapeInfo)[bKaxis]; val = val + A[aOffset] * B[bOffset]; @@ -131,7 +131,7 @@ static void usualGemv(const NDArray* vA, const NDArray* vX, NDArray* vY, const T3 val = A[aOffset] * X[xOffset]; // first iteration - for (uint j = 1; j < N; ++j) { // rest iterations + for (int j = 1; j < N; ++j) { // rest iterations aOffset += aNstride; xOffset += incx; val = val + A[aOffset] * X[xOffset]; @@ -163,7 +163,7 @@ static void usualDot(const Nd4jLong length, const double alpha, const void* vX, T3 sum = 0; PRAGMA_OMP_PARALLEL_FOR_ARGS(OMP_IF(length > Environment::getInstance()->elementwiseThreshold()) schedule(guided) reduction(OMP_SUMT:sum)) - for(int i = 0; i < length; ++i) + for(Nd4jLong i = 0; i < length; ++i) sum += X[i * incx] * Y[i * incy]; if(betaPersent) @@ -282,7 +282,7 @@ NDArray* MmulHelper::mmulMxM(const NDArray* A, const NDArray* B, NDArray* C, con //////////////////////////////////////////////////////////////////////////// // MXN x N = M -NDArray* MmulHelper::mmulMxV(const NDArray* A, const NDArray* X, nd4j::NDArray* Y, const double alpha, const double beta, const char outOrder) { +NDArray* MmulHelper::mmulMxV(const NDArray* A, const NDArray* X, sd::NDArray* Y, const double alpha, const double beta, const char outOrder) { if (X->dataType() != A->dataType()) throw datatype_exception::build("mmulMxV expects all data types to be the same", A->dataType(), X->dataType()); @@ -362,7 +362,7 @@ NDArray* MmulHelper::mmulMxV(const NDArray* A, const NDArray* X, nd4j::NDArray* //////////////////////////////////////////////////////////////////////////// // (X * Y) = Z[0] -NDArray* MmulHelper::dot(const NDArray* X, const NDArray* Y, nd4j::NDArray* Z, const double alpha, const double beta) { +NDArray* MmulHelper::dot(const NDArray* X, const NDArray* Y, sd::NDArray* Z, const double alpha, const double beta) { if (X->dataType() != Y->dataType()) throw datatype_exception::build("Dot expects all data types to be the same", X->dataType(), Y->dataType()); @@ -433,12 +433,12 @@ static void batchedGemm(const NDArray* vA, const NDArray* vB, NDArray* vC, auto func = PRAGMA_THREADS_FOR { - std::vector aCoords(aRank), bCoords(bRank), cCoords(cRank); + std::vector aCoords(aRank), bCoords(bRank), cCoords(cRank); for (auto i = start; i < stop; ++i) { // evaluate C coordinates - shape::index2coords(i, cShapeInfo, cCoords.data()); + shape::index2coordsCPU(start, i, cShapeInfo, cCoords.data()); // calculate index of current batch Nd4jLong batchInd; @@ -462,7 +462,7 @@ static void batchedGemm(const NDArray* vA, const NDArray* vB, NDArray* vC, T3 val = A[aOffset] * B[bOffset]; // first iteration - for (uint j = 1; j < K; ++j) { // rest iterations + for (int j = 1; j < K; ++j) { // rest iterations aOffset += shape::stride(aShapeInfo)[aKaxis]; bOffset += shape::stride(bShapeInfo)[bKaxis]; val = val + A[aOffset] * B[bOffset]; diff --git a/libnd4j/include/helpers/cpu/PointersManager.cpp b/libnd4j/include/helpers/cpu/PointersManager.cpp index 8116d3703..61eb7b2ec 100644 --- a/libnd4j/include/helpers/cpu/PointersManager.cpp +++ b/libnd4j/include/helpers/cpu/PointersManager.cpp @@ -20,16 +20,16 @@ #ifndef __CUDABLAS__ -#include +#include #include -#include +#include #include -namespace nd4j { +namespace sd { ////////////////////////////////////////////////////////////////////////// -PointersManager::PointersManager(const nd4j::LaunchContext *context, const std::string& funcName) { - _context = const_cast(context); +PointersManager::PointersManager(const sd::LaunchContext *context, const std::string& funcName) { + _context = const_cast(context); _funcName = funcName; } diff --git a/libnd4j/include/helpers/cpu/biDiagonalUp.cpp b/libnd4j/include/helpers/cpu/biDiagonalUp.cpp index 79c77b944..4623a93ad 100644 --- a/libnd4j/include/helpers/cpu/biDiagonalUp.cpp +++ b/libnd4j/include/helpers/cpu/biDiagonalUp.cpp @@ -19,19 +19,19 @@ // -#include -#include -#include +#include +#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { ////////////////////////////////////////////////////////////////////////// -BiDiagonalUp::BiDiagonalUp(const NDArray& matrix): _HHmatrix(nd4j::NDArrayFactory::create(matrix.ordering(), {matrix.sizeAt(0), matrix.sizeAt(1)}, matrix.dataType(), matrix.getContext())), - _HHbidiag(nd4j::NDArrayFactory::create(matrix.ordering(), {matrix.sizeAt(1), matrix.sizeAt(1)}, matrix.dataType(), matrix.getContext())) { +BiDiagonalUp::BiDiagonalUp(const NDArray& matrix): _HHmatrix(sd::NDArrayFactory::create(matrix.ordering(), {matrix.sizeAt(0), matrix.sizeAt(1)}, matrix.dataType(), matrix.getContext())), + _HHbidiag(sd::NDArrayFactory::create(matrix.ordering(), {matrix.sizeAt(1), matrix.sizeAt(1)}, matrix.dataType(), matrix.getContext())) { // input validation if(matrix.rankOf() != 2 || matrix.isScalar()) @@ -58,7 +58,7 @@ BiDiagonalUp::BiDiagonalUp(const NDArray& matrix): _HHmatrix(nd4j::NDArrayFactor T _x, _y; - for(int i = 0; i < cols-1; ++i ) { + for(Nd4jLong i = 0; i < cols-1; ++i ) { // evaluate Householder matrix nullifying columns column = new NDArray(_HHmatrix({i,rows, i,i+1}, true)); diff --git a/libnd4j/include/helpers/cpu/cublasHelper.cpp b/libnd4j/include/helpers/cpu/cublasHelper.cpp index 3dba2d31e..f6f718702 100644 --- a/libnd4j/include/helpers/cpu/cublasHelper.cpp +++ b/libnd4j/include/helpers/cpu/cublasHelper.cpp @@ -20,7 +20,7 @@ #include "../cublasHelper.h" -namespace nd4j { +namespace sd { static void* handle_() { return nullptr; } @@ -39,7 +39,7 @@ namespace nd4j { CublasHelper* CublasHelper::getInstance() { if (!_INSTANCE) - _INSTANCE = new nd4j::CublasHelper(); + _INSTANCE = new sd::CublasHelper(); return _INSTANCE; } @@ -57,5 +57,5 @@ namespace nd4j { } - nd4j::CublasHelper* nd4j::CublasHelper::_INSTANCE = 0; + sd::CublasHelper* sd::CublasHelper::_INSTANCE = 0; } \ No newline at end of file diff --git a/libnd4j/include/helpers/cpu/hhColPivQR.cpp b/libnd4j/include/helpers/cpu/hhColPivQR.cpp index f6f4e3c6c..e118b0bf1 100644 --- a/libnd4j/include/helpers/cpu/hhColPivQR.cpp +++ b/libnd4j/include/helpers/cpu/hhColPivQR.cpp @@ -18,11 +18,11 @@ // Created by Yurii Shyrma on 11.01.2018 // -#include -#include -#include +#include +#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { diff --git a/libnd4j/include/helpers/cpu/hhSequence.cpp b/libnd4j/include/helpers/cpu/hhSequence.cpp index 60cbf6ec5..8a2a35329 100644 --- a/libnd4j/include/helpers/cpu/hhSequence.cpp +++ b/libnd4j/include/helpers/cpu/hhSequence.cpp @@ -18,11 +18,11 @@ // Created by Yurii Shyrma on 02.01.2018 // -#include -#include -#include +#include +#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { @@ -30,7 +30,7 @@ namespace helpers { ////////////////////////////////////////////////////////////////////////// HHsequence::HHsequence(const NDArray& vectors, const NDArray& coeffs, const char type): _vectors(vectors), _coeffs(coeffs) { - _diagSize = nd4j::math::nd4j_min(_vectors.sizeAt(0), _vectors.sizeAt(1)); + _diagSize = sd::math::nd4j_min(_vectors.sizeAt(0), _vectors.sizeAt(1)); _shift = 0; _type = type; } @@ -117,7 +117,7 @@ void HHsequence::_applyTo(NDArray& dest) { BUILD_SINGLE_SELECTOR(xType, _mulLeft, (matrix), FLOAT_TYPES); } - BUILD_SINGLE_TEMPLATE(template void HHsequence::_applyTo, (nd4j::NDArray &dest), FLOAT_TYPES); + BUILD_SINGLE_TEMPLATE(template void HHsequence::_applyTo, (sd::NDArray &dest), FLOAT_TYPES); BUILD_SINGLE_TEMPLATE(template void HHsequence::_mulLeft, (NDArray& matrix), FLOAT_TYPES); } } diff --git a/libnd4j/include/helpers/cpu/householder.cpp b/libnd4j/include/helpers/cpu/householder.cpp index 024695583..69d4ca3db 100644 --- a/libnd4j/include/helpers/cpu/householder.cpp +++ b/libnd4j/include/helpers/cpu/householder.cpp @@ -18,10 +18,10 @@ // Created by Yurii Shyrma on 18.12.2017 // -#include -#include +#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { diff --git a/libnd4j/include/helpers/cpu/jacobiSVD.cpp b/libnd4j/include/helpers/cpu/jacobiSVD.cpp index 4ba2bfe0a..372a2a409 100644 --- a/libnd4j/include/helpers/cpu/jacobiSVD.cpp +++ b/libnd4j/include/helpers/cpu/jacobiSVD.cpp @@ -18,12 +18,12 @@ // Created by Yurii Shyrma on 11.01.2018 // -#include -#include -#include +#include +#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { diff --git a/libnd4j/include/helpers/cpu/loops/IndexReductionLoops.hpp b/libnd4j/include/helpers/cpu/loops/IndexReductionLoops.hpp index b661d02e7..fe6019b5a 100644 --- a/libnd4j/include/helpers/cpu/loops/IndexReductionLoops.hpp +++ b/libnd4j/include/helpers/cpu/loops/IndexReductionLoops.hpp @@ -26,14 +26,14 @@ using namespace simdOps; ////////////////////////////////////////////////////////////////////////////// template template -void nd4j::IndexReductionLoops::loopIndexReduce(X* x, Nd4jLong* xShapeInfo, +void sd::IndexReductionLoops::loopIndexReduce(X* x, Nd4jLong* xShapeInfo, Z* z, Nd4jLong* zShapeInfo, Nd4jLong* tadShapeInfo, Nd4jLong* tadOffsets, X* extraParams) { - nd4j::LoopKind::Kind kindOfLoop = nd4j::LoopKind::deduceKindOfLoopTadXZ(xShapeInfo, zShapeInfo, tadShapeInfo); - if(kindOfLoop == nd4j::LoopKind::SMALLARR2DX) - kindOfLoop = nd4j::LoopKind::EWSNONZERO; + sd::LoopKind::Kind kindOfLoop = sd::LoopKind::deduceKindOfLoopTadXZ(xShapeInfo, zShapeInfo, tadShapeInfo); + if(kindOfLoop == sd::LoopKind::SMALLARR2DX) + kindOfLoop = sd::LoopKind::EWSNONZERO; const Nd4jLong zLen = shape::length(zShapeInfo); const Nd4jLong tadLen = shape::length(tadShapeInfo); @@ -46,14 +46,14 @@ void nd4j::IndexReductionLoops::loopIndexReduce(X* x, Nd4jLong* xShapeInfo, switch (kindOfLoop) { //*********************************************// - case nd4j::LoopKind::EWS1: { + case sd::LoopKind::EWS1: { auto func = PRAGMA_THREADS_FOR { for (auto i = start; i < stop; i++) { auto tad = const_cast(x) + tadOffsets[i]; auto indexValue = OpType::startingIndexValue(tad); - for (uint j = 0; j < tadLen; j++) { + for (Nd4jLong j = 0; j < tadLen; j++) { functions::indexreduce::IndexValue comp(tad[j], j); indexValue = OpType::update(indexValue, comp, extraParams); } @@ -67,14 +67,14 @@ void nd4j::IndexReductionLoops::loopIndexReduce(X* x, Nd4jLong* xShapeInfo, break; //*********************************************// - case nd4j::LoopKind::EWSNONZERO: { + case sd::LoopKind::EWSNONZERO: { auto func = PRAGMA_THREADS_FOR { for (auto i = start; i < stop; i++) { auto tad = const_cast(x) + tadOffsets[i]; auto indexValue = OpType::startingIndexValue(tad); - for (uint j = 0; j < tadLen; j++) { + for (Nd4jLong j = 0; j < tadLen; j++) { functions::indexreduce::IndexValue comp(tad[j * tadEws], j); indexValue = OpType::update(indexValue, comp, extraParams); } @@ -88,14 +88,14 @@ void nd4j::IndexReductionLoops::loopIndexReduce(X* x, Nd4jLong* xShapeInfo, break; //*********************************************// - case nd4j::LoopKind::RANK1: { + case sd::LoopKind::RANK1: { auto func = PRAGMA_THREADS_FOR { for (auto i = start; i < stop; i++) { auto tad = const_cast(x) + tadOffsets[i]; auto indexValue = OpType::startingIndexValue(tad); - for (uint i0 = 0; i0 < tadLen; ++i0) { + for (Nd4jLong i0 = 0; i0 < tadLen; ++i0) { functions::indexreduce::IndexValue comp(tad[i0 * tadStride[0]], i0); indexValue = OpType::update(indexValue, comp, extraParams); } @@ -109,7 +109,7 @@ void nd4j::IndexReductionLoops::loopIndexReduce(X* x, Nd4jLong* xShapeInfo, break; //*********************************************// - case nd4j::LoopKind::RANK2: { + case sd::LoopKind::RANK2: { Nd4jLong newStride[2]; shape::updateStrides(2, tadShape, newStride, 'c'); @@ -118,8 +118,8 @@ void nd4j::IndexReductionLoops::loopIndexReduce(X* x, Nd4jLong* xShapeInfo, auto tad = const_cast(x) + tadOffsets[i]; auto indexValue = OpType::startingIndexValue(tad); - for (uint i0 = 0; i0 < tadShape[0]; ++i0) { - for (uint i1 = 0; i1 < tadShape[1]; ++i1) { + for (Nd4jLong i0 = 0; i0 < tadShape[0]; ++i0) { + for (Nd4jLong i1 = 0; i1 < tadShape[1]; ++i1) { const auto tadOffset = i0 * tadStride[0] + i1 * tadStride[1]; const auto tadIndex = i0 * newStride[0] + i1; functions::indexreduce::IndexValue comp(tad[tadOffset], tadIndex); @@ -136,7 +136,7 @@ void nd4j::IndexReductionLoops::loopIndexReduce(X* x, Nd4jLong* xShapeInfo, break; //*********************************************// - case nd4j::LoopKind::RANK3: { + case sd::LoopKind::RANK3: { Nd4jLong newStride[3]; shape::updateStrides(3, tadShape, newStride, 'c'); @@ -145,9 +145,9 @@ void nd4j::IndexReductionLoops::loopIndexReduce(X* x, Nd4jLong* xShapeInfo, auto tad = const_cast(x) + tadOffsets[i]; auto indexValue = OpType::startingIndexValue(tad); - for (uint i0 = 0; i0 < tadShape[0]; ++i0) { - for (uint i1 = 0; i1 < tadShape[1]; ++i1) { - for (uint i2 = 0; i2 < tadShape[2]; ++i2) { + for (Nd4jLong i0 = 0; i0 < tadShape[0]; ++i0) { + for (Nd4jLong i1 = 0; i1 < tadShape[1]; ++i1) { + for (Nd4jLong i2 = 0; i2 < tadShape[2]; ++i2) { const auto tadOffset = i0 * tadStride[0] + i1 * tadStride[1] + i2 * tadStride[2]; const auto tadIndex = i0 * newStride[0] + i1 * newStride[1] + i2; functions::indexreduce::IndexValue comp(tad[tadOffset], tadIndex); @@ -165,7 +165,7 @@ void nd4j::IndexReductionLoops::loopIndexReduce(X* x, Nd4jLong* xShapeInfo, break; //*********************************************// - case nd4j::LoopKind::RANK4: { + case sd::LoopKind::RANK4: { Nd4jLong newStride[4]; shape::updateStrides(4, tadShape, newStride, 'c'); @@ -174,10 +174,10 @@ void nd4j::IndexReductionLoops::loopIndexReduce(X* x, Nd4jLong* xShapeInfo, auto tad = const_cast(x) + tadOffsets[i]; auto indexValue = OpType::startingIndexValue(tad); - for (uint i0 = 0; i0 < tadShape[0]; ++i0) { - for (uint i1 = 0; i1 < tadShape[1]; ++i1) { - for (uint i2 = 0; i2 < tadShape[2]; ++i2) { - for (uint i3 = 0; i3 < tadShape[3]; ++i3) { + for (Nd4jLong i0 = 0; i0 < tadShape[0]; ++i0) { + for (Nd4jLong i1 = 0; i1 < tadShape[1]; ++i1) { + for (Nd4jLong i2 = 0; i2 < tadShape[2]; ++i2) { + for (Nd4jLong i3 = 0; i3 < tadShape[3]; ++i3) { const auto tadOffset = i0 * tadStride[0] + i1 * tadStride[1] + i2 * tadStride[2] + i3 * tadStride[3]; const auto tadIndex = i0 * newStride[0] + i1 * newStride[1] + i2 * newStride[2] + i3; functions::indexreduce::IndexValue comp(tad[tadOffset], tadIndex); @@ -196,7 +196,7 @@ void nd4j::IndexReductionLoops::loopIndexReduce(X* x, Nd4jLong* xShapeInfo, break; //*********************************************// - case nd4j::LoopKind::RANK5: { + case sd::LoopKind::RANK5: { Nd4jLong newStride[5]; shape::updateStrides(5, tadShape, newStride, 'c'); @@ -205,11 +205,11 @@ void nd4j::IndexReductionLoops::loopIndexReduce(X* x, Nd4jLong* xShapeInfo, auto tad = const_cast(x) + tadOffsets[i]; auto indexValue = OpType::startingIndexValue(tad); - for (uint i0 = 0; i0 < tadShape[0]; ++i0) { - for (uint i1 = 0; i1 < tadShape[1]; ++i1) { - for (uint i2 = 0; i2 < tadShape[2]; ++i2) { - for (uint i3 = 0; i3 < tadShape[3]; ++i3) { - for (uint i4 = 0; i4 < tadShape[4]; ++i4) { + for (Nd4jLong i0 = 0; i0 < tadShape[0]; ++i0) { + for (Nd4jLong i1 = 0; i1 < tadShape[1]; ++i1) { + for (Nd4jLong i2 = 0; i2 < tadShape[2]; ++i2) { + for (Nd4jLong i3 = 0; i3 < tadShape[3]; ++i3) { + for (Nd4jLong i4 = 0; i4 < tadShape[4]; ++i4) { const auto tadOffset = i0 * tadStride[0] + i1 * tadStride[1] + i2 * tadStride[2] + i3 * tadStride[3] + i4 * tadStride[4]; const auto tadIndex = i0 * newStride[0] + i1 * newStride[1] + i2 * newStride[2] + i3 * newStride[3] + i4; functions::indexreduce::IndexValue comp(tad[tadOffset], tadIndex); @@ -229,16 +229,16 @@ void nd4j::IndexReductionLoops::loopIndexReduce(X* x, Nd4jLong* xShapeInfo, break; //*********************************************// - case nd4j::LoopKind::X_EWSNONZERO: { + case sd::LoopKind::X_EWSNONZERO: { uint castZShapeInfo[MAX_RANK]; - const bool canCastZ = nd4j::DataTypeUtils::castShapeInfo(zShapeInfo, castZShapeInfo); + const bool canCastZ = sd::DataTypeUtils::castShapeInfo(zShapeInfo, castZShapeInfo); auto func = PRAGMA_THREADS_FOR { for (auto i = start; i < stop; i++) { auto tad = const_cast(x) + tadOffsets[i]; auto indexValue = OpType::startingIndexValue(tad); - for (uint j = 0; j < tadLen; j++) { + for (Nd4jLong j = 0; j < tadLen; j++) { functions::indexreduce::IndexValue comp(tad[j * tadEws], j); indexValue = OpType::update(indexValue, comp, extraParams); } @@ -253,16 +253,16 @@ void nd4j::IndexReductionLoops::loopIndexReduce(X* x, Nd4jLong* xShapeInfo, break; //*********************************************// - case nd4j::LoopKind::Z_EWSNONZERO: { + case sd::LoopKind::Z_EWSNONZERO: { uint castTadShapeInfo[MAX_RANK]; - const bool canCastTad = nd4j::DataTypeUtils::castShapeInfo(tadShapeInfo, castTadShapeInfo); + const bool canCastTad = sd::DataTypeUtils::castShapeInfo(tadShapeInfo, castTadShapeInfo); auto func = PRAGMA_THREADS_FOR { for (auto i = start; i < stop; i++) { auto tad = const_cast(x) + tadOffsets[i]; auto indexValue = OpType::startingIndexValue(tad); - for (uint j = 0; j < tadLen; j++) { + for (Nd4jLong j = 0; j < tadLen; j++) { auto tadOffset = shape::indexOffset(j, tadShapeInfo, castTadShapeInfo, canCastTad); functions::indexreduce::IndexValue comp(tad[tadOffset], j); indexValue = OpType::update(indexValue, comp, extraParams); @@ -280,15 +280,15 @@ void nd4j::IndexReductionLoops::loopIndexReduce(X* x, Nd4jLong* xShapeInfo, default: { uint castTadShapeInfo[MAX_RANK]; uint castZShapeInfo[MAX_RANK]; - const bool canCastTad = nd4j::DataTypeUtils::castShapeInfo(tadShapeInfo, castTadShapeInfo); - const bool canCastZ = nd4j::DataTypeUtils::castShapeInfo(zShapeInfo, castZShapeInfo); + const bool canCastTad = sd::DataTypeUtils::castShapeInfo(tadShapeInfo, castTadShapeInfo); + const bool canCastZ = sd::DataTypeUtils::castShapeInfo(zShapeInfo, castZShapeInfo); auto func = PRAGMA_THREADS_FOR { for (auto i = start; i < stop; i++) { auto tad = const_cast(x) + tadOffsets[i]; auto indexValue = OpType::startingIndexValue(tad); - for (uint j = 0; j < tadLen; j++) { + for (Nd4jLong j = 0; j < tadLen; j++) { auto tadOffset = shape::indexOffset(j, tadShapeInfo, castTadShapeInfo, canCastTad); functions::indexreduce::IndexValue comp(tad[tadOffset], j); indexValue = OpType::update(indexValue, comp, extraParams); @@ -305,7 +305,7 @@ void nd4j::IndexReductionLoops::loopIndexReduce(X* x, Nd4jLong* xShapeInfo, } template -void nd4j::IndexReductionLoops::wrapIndexReduce(const int opNum, void* vx, Nd4jLong* xShapeInfo, void* vz, Nd4jLong* zShapeInfo, Nd4jLong* tadShapeInfo, Nd4jLong* tadOffsets, void* vextraParams) { +void sd::IndexReductionLoops::wrapIndexReduce(const int opNum, void* vx, Nd4jLong* xShapeInfo, void* vz, Nd4jLong* zShapeInfo, Nd4jLong* tadShapeInfo, Nd4jLong* tadOffsets, void* vextraParams) { auto x = reinterpret_cast(vx); auto z = reinterpret_cast(vz); auto extraParams = reinterpret_cast(vextraParams); diff --git a/libnd4j/include/helpers/cpu/loops/IndexReductionLoops_int32_0.cpp b/libnd4j/include/helpers/cpu/loops/IndexReductionLoops_int32_0.cpp index a694e42ca..68ae29fc9 100644 --- a/libnd4j/include/helpers/cpu/loops/IndexReductionLoops_int32_0.cpp +++ b/libnd4j/include/helpers/cpu/loops/IndexReductionLoops_int32_0.cpp @@ -21,4 +21,4 @@ #include "./IndexReductionLoops.hpp" -BUILD_DOUBLE_TEMPLATE(template void nd4j::IndexReductionLoops, ::wrapIndexReduce(const int opNum, void* vx, Nd4jLong* xShapeInfo, void* z, Nd4jLong* zShapeInfo, Nd4jLong* tadShapeInfo, Nd4jLong* tadOffsets, void* vextraParams), LIBND4J_TYPES_0, (nd4j::DataType::INT32, int32_t)); \ No newline at end of file +BUILD_DOUBLE_TEMPLATE(template void sd::IndexReductionLoops, ::wrapIndexReduce(const int opNum, void* vx, Nd4jLong* xShapeInfo, void* z, Nd4jLong* zShapeInfo, Nd4jLong* tadShapeInfo, Nd4jLong* tadOffsets, void* vextraParams), LIBND4J_TYPES_0, (sd::DataType::INT32, int32_t)); \ No newline at end of file diff --git a/libnd4j/include/helpers/cpu/loops/IndexReductionLoops_int32_1.cpp b/libnd4j/include/helpers/cpu/loops/IndexReductionLoops_int32_1.cpp index 236428c4a..fe68715ca 100644 --- a/libnd4j/include/helpers/cpu/loops/IndexReductionLoops_int32_1.cpp +++ b/libnd4j/include/helpers/cpu/loops/IndexReductionLoops_int32_1.cpp @@ -21,4 +21,4 @@ #include "./IndexReductionLoops.hpp" -BUILD_DOUBLE_TEMPLATE(template void nd4j::IndexReductionLoops, ::wrapIndexReduce(const int opNum, void* vx, Nd4jLong* xShapeInfo, void* z, Nd4jLong* zShapeInfo, Nd4jLong* tadShapeInfo, Nd4jLong* tadOffsets, void* vextraParams), LIBND4J_TYPES_1, (nd4j::DataType::INT32, int32_t)); \ No newline at end of file +BUILD_DOUBLE_TEMPLATE(template void sd::IndexReductionLoops, ::wrapIndexReduce(const int opNum, void* vx, Nd4jLong* xShapeInfo, void* z, Nd4jLong* zShapeInfo, Nd4jLong* tadShapeInfo, Nd4jLong* tadOffsets, void* vextraParams), LIBND4J_TYPES_1, (sd::DataType::INT32, int32_t)); \ No newline at end of file diff --git a/libnd4j/include/helpers/cpu/loops/IndexReductionLoops_int32_2.cpp b/libnd4j/include/helpers/cpu/loops/IndexReductionLoops_int32_2.cpp index 173744c97..8627003fd 100644 --- a/libnd4j/include/helpers/cpu/loops/IndexReductionLoops_int32_2.cpp +++ b/libnd4j/include/helpers/cpu/loops/IndexReductionLoops_int32_2.cpp @@ -21,4 +21,4 @@ #include "./IndexReductionLoops.hpp" -BUILD_DOUBLE_TEMPLATE(template void nd4j::IndexReductionLoops, ::wrapIndexReduce(const int opNum, void* vx, Nd4jLong* xShapeInfo, void* z, Nd4jLong* zShapeInfo, Nd4jLong* tadShapeInfo, Nd4jLong* tadOffsets, void* vextraParams), LIBND4J_TYPES_2, (nd4j::DataType::INT32, int32_t)); \ No newline at end of file +BUILD_DOUBLE_TEMPLATE(template void sd::IndexReductionLoops, ::wrapIndexReduce(const int opNum, void* vx, Nd4jLong* xShapeInfo, void* z, Nd4jLong* zShapeInfo, Nd4jLong* tadShapeInfo, Nd4jLong* tadOffsets, void* vextraParams), LIBND4J_TYPES_2, (sd::DataType::INT32, int32_t)); \ No newline at end of file diff --git a/libnd4j/include/helpers/cpu/loops/IndexReductionLoops_int32_3.cpp b/libnd4j/include/helpers/cpu/loops/IndexReductionLoops_int32_3.cpp index fbb2fde50..8b2f4e1a7 100644 --- a/libnd4j/include/helpers/cpu/loops/IndexReductionLoops_int32_3.cpp +++ b/libnd4j/include/helpers/cpu/loops/IndexReductionLoops_int32_3.cpp @@ -21,4 +21,4 @@ #include "./IndexReductionLoops.hpp" -BUILD_DOUBLE_TEMPLATE(template void nd4j::IndexReductionLoops, ::wrapIndexReduce(const int opNum, void* vx, Nd4jLong* xShapeInfo, void* z, Nd4jLong* zShapeInfo, Nd4jLong* tadShapeInfo, Nd4jLong* tadOffsets, void* vextraParams), LIBND4J_TYPES_3, (nd4j::DataType::INT32, int32_t)); \ No newline at end of file +BUILD_DOUBLE_TEMPLATE(template void sd::IndexReductionLoops, ::wrapIndexReduce(const int opNum, void* vx, Nd4jLong* xShapeInfo, void* z, Nd4jLong* zShapeInfo, Nd4jLong* tadShapeInfo, Nd4jLong* tadOffsets, void* vextraParams), LIBND4J_TYPES_3, (sd::DataType::INT32, int32_t)); \ No newline at end of file diff --git a/libnd4j/include/helpers/cpu/loops/IndexReductionLoops_int32_4.cpp b/libnd4j/include/helpers/cpu/loops/IndexReductionLoops_int32_4.cpp index 1fd8196e7..e87921565 100644 --- a/libnd4j/include/helpers/cpu/loops/IndexReductionLoops_int32_4.cpp +++ b/libnd4j/include/helpers/cpu/loops/IndexReductionLoops_int32_4.cpp @@ -21,4 +21,4 @@ #include "./IndexReductionLoops.hpp" -BUILD_DOUBLE_TEMPLATE(template void nd4j::IndexReductionLoops, ::wrapIndexReduce(const int opNum, void* vx, Nd4jLong* xShapeInfo, void* z, Nd4jLong* zShapeInfo, Nd4jLong* tadShapeInfo, Nd4jLong* tadOffsets, void* vextraParams), LIBND4J_TYPES_4, (nd4j::DataType::INT32, int32_t)); \ No newline at end of file +BUILD_DOUBLE_TEMPLATE(template void sd::IndexReductionLoops, ::wrapIndexReduce(const int opNum, void* vx, Nd4jLong* xShapeInfo, void* z, Nd4jLong* zShapeInfo, Nd4jLong* tadShapeInfo, Nd4jLong* tadOffsets, void* vextraParams), LIBND4J_TYPES_4, (sd::DataType::INT32, int32_t)); \ No newline at end of file diff --git a/libnd4j/include/helpers/cpu/loops/IndexReductionLoops_int32_5.cpp b/libnd4j/include/helpers/cpu/loops/IndexReductionLoops_int32_5.cpp index 1378c661c..062b006fd 100644 --- a/libnd4j/include/helpers/cpu/loops/IndexReductionLoops_int32_5.cpp +++ b/libnd4j/include/helpers/cpu/loops/IndexReductionLoops_int32_5.cpp @@ -21,4 +21,4 @@ #include "./IndexReductionLoops.hpp" -BUILD_DOUBLE_TEMPLATE(template void nd4j::IndexReductionLoops, ::wrapIndexReduce(const int opNum, void* vx, Nd4jLong* xShapeInfo, void* z, Nd4jLong* zShapeInfo, Nd4jLong* tadShapeInfo, Nd4jLong* tadOffsets, void* vextraParams), LIBND4J_TYPES_5, (nd4j::DataType::INT32, int32_t)); \ No newline at end of file +BUILD_DOUBLE_TEMPLATE(template void sd::IndexReductionLoops, ::wrapIndexReduce(const int opNum, void* vx, Nd4jLong* xShapeInfo, void* z, Nd4jLong* zShapeInfo, Nd4jLong* tadShapeInfo, Nd4jLong* tadOffsets, void* vextraParams), LIBND4J_TYPES_5, (sd::DataType::INT32, int32_t)); \ No newline at end of file diff --git a/libnd4j/include/helpers/cpu/loops/IndexReductionLoops_int32_6.cpp b/libnd4j/include/helpers/cpu/loops/IndexReductionLoops_int32_6.cpp index 5b338bb09..4182de6fd 100644 --- a/libnd4j/include/helpers/cpu/loops/IndexReductionLoops_int32_6.cpp +++ b/libnd4j/include/helpers/cpu/loops/IndexReductionLoops_int32_6.cpp @@ -21,4 +21,4 @@ #include "./IndexReductionLoops.hpp" -BUILD_DOUBLE_TEMPLATE(template void nd4j::IndexReductionLoops, ::wrapIndexReduce(const int opNum, void* vx, Nd4jLong* xShapeInfo, void* z, Nd4jLong* zShapeInfo, Nd4jLong* tadShapeInfo, Nd4jLong* tadOffsets, void* vextraParams), LIBND4J_TYPES_6, (nd4j::DataType::INT32, int32_t)); \ No newline at end of file +BUILD_DOUBLE_TEMPLATE(template void sd::IndexReductionLoops, ::wrapIndexReduce(const int opNum, void* vx, Nd4jLong* xShapeInfo, void* z, Nd4jLong* zShapeInfo, Nd4jLong* tadShapeInfo, Nd4jLong* tadOffsets, void* vextraParams), LIBND4J_TYPES_6, (sd::DataType::INT32, int32_t)); \ No newline at end of file diff --git a/libnd4j/include/helpers/cpu/loops/IndexReductionLoops_int32_7.cpp b/libnd4j/include/helpers/cpu/loops/IndexReductionLoops_int32_7.cpp index 7dfe7d939..53a4ed23f 100644 --- a/libnd4j/include/helpers/cpu/loops/IndexReductionLoops_int32_7.cpp +++ b/libnd4j/include/helpers/cpu/loops/IndexReductionLoops_int32_7.cpp @@ -21,4 +21,4 @@ #include "./IndexReductionLoops.hpp" -BUILD_DOUBLE_TEMPLATE(template void nd4j::IndexReductionLoops, ::wrapIndexReduce(const int opNum, void* vx, Nd4jLong* xShapeInfo, void* z, Nd4jLong* zShapeInfo, Nd4jLong* tadShapeInfo, Nd4jLong* tadOffsets, void* vextraParams), LIBND4J_TYPES_7, (nd4j::DataType::INT32, int32_t)); \ No newline at end of file +BUILD_DOUBLE_TEMPLATE(template void sd::IndexReductionLoops, ::wrapIndexReduce(const int opNum, void* vx, Nd4jLong* xShapeInfo, void* z, Nd4jLong* zShapeInfo, Nd4jLong* tadShapeInfo, Nd4jLong* tadOffsets, void* vextraParams), LIBND4J_TYPES_7, (sd::DataType::INT32, int32_t)); \ No newline at end of file diff --git a/libnd4j/include/helpers/cpu/loops/IndexReductionLoops_int32_8.cpp b/libnd4j/include/helpers/cpu/loops/IndexReductionLoops_int32_8.cpp index 14e91685d..2cf4b6ae7 100644 --- a/libnd4j/include/helpers/cpu/loops/IndexReductionLoops_int32_8.cpp +++ b/libnd4j/include/helpers/cpu/loops/IndexReductionLoops_int32_8.cpp @@ -21,4 +21,4 @@ #include "./IndexReductionLoops.hpp" -BUILD_DOUBLE_TEMPLATE(template void nd4j::IndexReductionLoops, ::wrapIndexReduce(const int opNum, void* vx, Nd4jLong* xShapeInfo, void* z, Nd4jLong* zShapeInfo, Nd4jLong* tadShapeInfo, Nd4jLong* tadOffsets, void* vextraParams), LIBND4J_TYPES_8, (nd4j::DataType::INT32, int32_t)); \ No newline at end of file +BUILD_DOUBLE_TEMPLATE(template void sd::IndexReductionLoops, ::wrapIndexReduce(const int opNum, void* vx, Nd4jLong* xShapeInfo, void* z, Nd4jLong* zShapeInfo, Nd4jLong* tadShapeInfo, Nd4jLong* tadOffsets, void* vextraParams), LIBND4J_TYPES_8, (sd::DataType::INT32, int32_t)); \ No newline at end of file diff --git a/libnd4j/include/helpers/cpu/loops/IndexReductionLoops_int32_9.cpp b/libnd4j/include/helpers/cpu/loops/IndexReductionLoops_int32_9.cpp index 677802476..b6b1da4a0 100644 --- a/libnd4j/include/helpers/cpu/loops/IndexReductionLoops_int32_9.cpp +++ b/libnd4j/include/helpers/cpu/loops/IndexReductionLoops_int32_9.cpp @@ -21,4 +21,4 @@ #include "./IndexReductionLoops.hpp" -BUILD_DOUBLE_TEMPLATE(template void nd4j::IndexReductionLoops, ::wrapIndexReduce(const int opNum, void* vx, Nd4jLong* xShapeInfo, void* z, Nd4jLong* zShapeInfo, Nd4jLong* tadShapeInfo, Nd4jLong* tadOffsets, void* vextraParams), LIBND4J_TYPES_9, (nd4j::DataType::INT32, int32_t)); \ No newline at end of file +BUILD_DOUBLE_TEMPLATE(template void sd::IndexReductionLoops, ::wrapIndexReduce(const int opNum, void* vx, Nd4jLong* xShapeInfo, void* z, Nd4jLong* zShapeInfo, Nd4jLong* tadShapeInfo, Nd4jLong* tadOffsets, void* vextraParams), LIBND4J_TYPES_9, (sd::DataType::INT32, int32_t)); \ No newline at end of file diff --git a/libnd4j/include/helpers/cpu/loops/IndexReductionLoops_int64_0.cpp b/libnd4j/include/helpers/cpu/loops/IndexReductionLoops_int64_0.cpp index cb295f479..de4cf1872 100644 --- a/libnd4j/include/helpers/cpu/loops/IndexReductionLoops_int64_0.cpp +++ b/libnd4j/include/helpers/cpu/loops/IndexReductionLoops_int64_0.cpp @@ -21,4 +21,4 @@ #include "./IndexReductionLoops.hpp" -BUILD_DOUBLE_TEMPLATE(template void nd4j::IndexReductionLoops, ::wrapIndexReduce(const int opNum, void* vx, Nd4jLong* xShapeInfo, void* z, Nd4jLong* zShapeInfo, Nd4jLong* tadShapeInfo, Nd4jLong* tadOffsets, void* vextraParams), LIBND4J_TYPES_0, (nd4j::DataType::INT64, Nd4jLong)); \ No newline at end of file +BUILD_DOUBLE_TEMPLATE(template void sd::IndexReductionLoops, ::wrapIndexReduce(const int opNum, void* vx, Nd4jLong* xShapeInfo, void* z, Nd4jLong* zShapeInfo, Nd4jLong* tadShapeInfo, Nd4jLong* tadOffsets, void* vextraParams), LIBND4J_TYPES_0, (sd::DataType::INT64, Nd4jLong)); \ No newline at end of file diff --git a/libnd4j/include/helpers/cpu/loops/IndexReductionLoops_int64_1.cpp b/libnd4j/include/helpers/cpu/loops/IndexReductionLoops_int64_1.cpp index 8b5914d84..71a19bab2 100644 --- a/libnd4j/include/helpers/cpu/loops/IndexReductionLoops_int64_1.cpp +++ b/libnd4j/include/helpers/cpu/loops/IndexReductionLoops_int64_1.cpp @@ -21,4 +21,4 @@ #include "./IndexReductionLoops.hpp" -BUILD_DOUBLE_TEMPLATE(template void nd4j::IndexReductionLoops, ::wrapIndexReduce(const int opNum, void* vx, Nd4jLong* xShapeInfo, void* z, Nd4jLong* zShapeInfo, Nd4jLong* tadShapeInfo, Nd4jLong* tadOffsets, void* vextraParams), LIBND4J_TYPES_1, (nd4j::DataType::INT64, Nd4jLong)); \ No newline at end of file +BUILD_DOUBLE_TEMPLATE(template void sd::IndexReductionLoops, ::wrapIndexReduce(const int opNum, void* vx, Nd4jLong* xShapeInfo, void* z, Nd4jLong* zShapeInfo, Nd4jLong* tadShapeInfo, Nd4jLong* tadOffsets, void* vextraParams), LIBND4J_TYPES_1, (sd::DataType::INT64, Nd4jLong)); \ No newline at end of file diff --git a/libnd4j/include/helpers/cpu/loops/IndexReductionLoops_int64_2.cpp b/libnd4j/include/helpers/cpu/loops/IndexReductionLoops_int64_2.cpp index 97d8f5906..22d430e9e 100644 --- a/libnd4j/include/helpers/cpu/loops/IndexReductionLoops_int64_2.cpp +++ b/libnd4j/include/helpers/cpu/loops/IndexReductionLoops_int64_2.cpp @@ -21,4 +21,4 @@ #include "./IndexReductionLoops.hpp" -BUILD_DOUBLE_TEMPLATE(template void nd4j::IndexReductionLoops, ::wrapIndexReduce(const int opNum, void* vx, Nd4jLong* xShapeInfo, void* z, Nd4jLong* zShapeInfo, Nd4jLong* tadShapeInfo, Nd4jLong* tadOffsets, void* vextraParams), LIBND4J_TYPES_2, (nd4j::DataType::INT64, Nd4jLong)); \ No newline at end of file +BUILD_DOUBLE_TEMPLATE(template void sd::IndexReductionLoops, ::wrapIndexReduce(const int opNum, void* vx, Nd4jLong* xShapeInfo, void* z, Nd4jLong* zShapeInfo, Nd4jLong* tadShapeInfo, Nd4jLong* tadOffsets, void* vextraParams), LIBND4J_TYPES_2, (sd::DataType::INT64, Nd4jLong)); \ No newline at end of file diff --git a/libnd4j/include/helpers/cpu/loops/IndexReductionLoops_int64_3.cpp b/libnd4j/include/helpers/cpu/loops/IndexReductionLoops_int64_3.cpp index 6463c8584..c2434f63a 100644 --- a/libnd4j/include/helpers/cpu/loops/IndexReductionLoops_int64_3.cpp +++ b/libnd4j/include/helpers/cpu/loops/IndexReductionLoops_int64_3.cpp @@ -21,4 +21,4 @@ #include "./IndexReductionLoops.hpp" -BUILD_DOUBLE_TEMPLATE(template void nd4j::IndexReductionLoops, ::wrapIndexReduce(const int opNum, void* vx, Nd4jLong* xShapeInfo, void* z, Nd4jLong* zShapeInfo, Nd4jLong* tadShapeInfo, Nd4jLong* tadOffsets, void* vextraParams), LIBND4J_TYPES_3, (nd4j::DataType::INT64, Nd4jLong)); \ No newline at end of file +BUILD_DOUBLE_TEMPLATE(template void sd::IndexReductionLoops, ::wrapIndexReduce(const int opNum, void* vx, Nd4jLong* xShapeInfo, void* z, Nd4jLong* zShapeInfo, Nd4jLong* tadShapeInfo, Nd4jLong* tadOffsets, void* vextraParams), LIBND4J_TYPES_3, (sd::DataType::INT64, Nd4jLong)); \ No newline at end of file diff --git a/libnd4j/include/helpers/cpu/loops/IndexReductionLoops_int64_4.cpp b/libnd4j/include/helpers/cpu/loops/IndexReductionLoops_int64_4.cpp index 365e2a70f..be628bb63 100644 --- a/libnd4j/include/helpers/cpu/loops/IndexReductionLoops_int64_4.cpp +++ b/libnd4j/include/helpers/cpu/loops/IndexReductionLoops_int64_4.cpp @@ -21,4 +21,4 @@ #include "./IndexReductionLoops.hpp" -BUILD_DOUBLE_TEMPLATE(template void nd4j::IndexReductionLoops, ::wrapIndexReduce(const int opNum, void* vx, Nd4jLong* xShapeInfo, void* z, Nd4jLong* zShapeInfo, Nd4jLong* tadShapeInfo, Nd4jLong* tadOffsets, void* vextraParams), LIBND4J_TYPES_4, (nd4j::DataType::INT64, Nd4jLong)); \ No newline at end of file +BUILD_DOUBLE_TEMPLATE(template void sd::IndexReductionLoops, ::wrapIndexReduce(const int opNum, void* vx, Nd4jLong* xShapeInfo, void* z, Nd4jLong* zShapeInfo, Nd4jLong* tadShapeInfo, Nd4jLong* tadOffsets, void* vextraParams), LIBND4J_TYPES_4, (sd::DataType::INT64, Nd4jLong)); \ No newline at end of file diff --git a/libnd4j/include/helpers/cpu/loops/IndexReductionLoops_int64_5.cpp b/libnd4j/include/helpers/cpu/loops/IndexReductionLoops_int64_5.cpp index 77e6bf5be..a5e8a596f 100644 --- a/libnd4j/include/helpers/cpu/loops/IndexReductionLoops_int64_5.cpp +++ b/libnd4j/include/helpers/cpu/loops/IndexReductionLoops_int64_5.cpp @@ -21,4 +21,4 @@ #include "./IndexReductionLoops.hpp" -BUILD_DOUBLE_TEMPLATE(template void nd4j::IndexReductionLoops, ::wrapIndexReduce(const int opNum, void* vx, Nd4jLong* xShapeInfo, void* z, Nd4jLong* zShapeInfo, Nd4jLong* tadShapeInfo, Nd4jLong* tadOffsets, void* vextraParams), LIBND4J_TYPES_5, (nd4j::DataType::INT64, Nd4jLong)); \ No newline at end of file +BUILD_DOUBLE_TEMPLATE(template void sd::IndexReductionLoops, ::wrapIndexReduce(const int opNum, void* vx, Nd4jLong* xShapeInfo, void* z, Nd4jLong* zShapeInfo, Nd4jLong* tadShapeInfo, Nd4jLong* tadOffsets, void* vextraParams), LIBND4J_TYPES_5, (sd::DataType::INT64, Nd4jLong)); \ No newline at end of file diff --git a/libnd4j/include/helpers/cpu/loops/IndexReductionLoops_int64_6.cpp b/libnd4j/include/helpers/cpu/loops/IndexReductionLoops_int64_6.cpp index f355d655e..3e96a0574 100644 --- a/libnd4j/include/helpers/cpu/loops/IndexReductionLoops_int64_6.cpp +++ b/libnd4j/include/helpers/cpu/loops/IndexReductionLoops_int64_6.cpp @@ -21,4 +21,4 @@ #include "./IndexReductionLoops.hpp" -BUILD_DOUBLE_TEMPLATE(template void nd4j::IndexReductionLoops, ::wrapIndexReduce(const int opNum, void* vx, Nd4jLong* xShapeInfo, void* z, Nd4jLong* zShapeInfo, Nd4jLong* tadShapeInfo, Nd4jLong* tadOffsets, void* vextraParams), LIBND4J_TYPES_6, (nd4j::DataType::INT64, Nd4jLong)); \ No newline at end of file +BUILD_DOUBLE_TEMPLATE(template void sd::IndexReductionLoops, ::wrapIndexReduce(const int opNum, void* vx, Nd4jLong* xShapeInfo, void* z, Nd4jLong* zShapeInfo, Nd4jLong* tadShapeInfo, Nd4jLong* tadOffsets, void* vextraParams), LIBND4J_TYPES_6, (sd::DataType::INT64, Nd4jLong)); \ No newline at end of file diff --git a/libnd4j/include/helpers/cpu/loops/IndexReductionLoops_int64_7.cpp b/libnd4j/include/helpers/cpu/loops/IndexReductionLoops_int64_7.cpp index 542587b18..a6c02301f 100644 --- a/libnd4j/include/helpers/cpu/loops/IndexReductionLoops_int64_7.cpp +++ b/libnd4j/include/helpers/cpu/loops/IndexReductionLoops_int64_7.cpp @@ -21,4 +21,4 @@ #include "./IndexReductionLoops.hpp" -BUILD_DOUBLE_TEMPLATE(template void nd4j::IndexReductionLoops, ::wrapIndexReduce(const int opNum, void* vx, Nd4jLong* xShapeInfo, void* z, Nd4jLong* zShapeInfo, Nd4jLong* tadShapeInfo, Nd4jLong* tadOffsets, void* vextraParams), LIBND4J_TYPES_7, (nd4j::DataType::INT64, Nd4jLong)); \ No newline at end of file +BUILD_DOUBLE_TEMPLATE(template void sd::IndexReductionLoops, ::wrapIndexReduce(const int opNum, void* vx, Nd4jLong* xShapeInfo, void* z, Nd4jLong* zShapeInfo, Nd4jLong* tadShapeInfo, Nd4jLong* tadOffsets, void* vextraParams), LIBND4J_TYPES_7, (sd::DataType::INT64, Nd4jLong)); \ No newline at end of file diff --git a/libnd4j/include/helpers/cpu/loops/IndexReductionLoops_int64_8.cpp b/libnd4j/include/helpers/cpu/loops/IndexReductionLoops_int64_8.cpp index bccc40219..e461c9bcd 100644 --- a/libnd4j/include/helpers/cpu/loops/IndexReductionLoops_int64_8.cpp +++ b/libnd4j/include/helpers/cpu/loops/IndexReductionLoops_int64_8.cpp @@ -21,4 +21,4 @@ #include "./IndexReductionLoops.hpp" -BUILD_DOUBLE_TEMPLATE(template void nd4j::IndexReductionLoops, ::wrapIndexReduce(const int opNum, void* vx, Nd4jLong* xShapeInfo, void* z, Nd4jLong* zShapeInfo, Nd4jLong* tadShapeInfo, Nd4jLong* tadOffsets, void* vextraParams), LIBND4J_TYPES_8, (nd4j::DataType::INT64, Nd4jLong)); \ No newline at end of file +BUILD_DOUBLE_TEMPLATE(template void sd::IndexReductionLoops, ::wrapIndexReduce(const int opNum, void* vx, Nd4jLong* xShapeInfo, void* z, Nd4jLong* zShapeInfo, Nd4jLong* tadShapeInfo, Nd4jLong* tadOffsets, void* vextraParams), LIBND4J_TYPES_8, (sd::DataType::INT64, Nd4jLong)); \ No newline at end of file diff --git a/libnd4j/include/helpers/cpu/loops/IndexReductionLoops_int64_9.cpp b/libnd4j/include/helpers/cpu/loops/IndexReductionLoops_int64_9.cpp index 85475046c..33e5ba403 100644 --- a/libnd4j/include/helpers/cpu/loops/IndexReductionLoops_int64_9.cpp +++ b/libnd4j/include/helpers/cpu/loops/IndexReductionLoops_int64_9.cpp @@ -21,4 +21,4 @@ #include "./IndexReductionLoops.hpp" -BUILD_DOUBLE_TEMPLATE(template void nd4j::IndexReductionLoops, ::wrapIndexReduce(const int opNum, void* vx, Nd4jLong* xShapeInfo, void* z, Nd4jLong* zShapeInfo, Nd4jLong* tadShapeInfo, Nd4jLong* tadOffsets, void* vextraParams), LIBND4J_TYPES_9, (nd4j::DataType::INT64, Nd4jLong)); \ No newline at end of file +BUILD_DOUBLE_TEMPLATE(template void sd::IndexReductionLoops, ::wrapIndexReduce(const int opNum, void* vx, Nd4jLong* xShapeInfo, void* z, Nd4jLong* zShapeInfo, Nd4jLong* tadShapeInfo, Nd4jLong* tadOffsets, void* vextraParams), LIBND4J_TYPES_9, (sd::DataType::INT64, Nd4jLong)); \ No newline at end of file diff --git a/libnd4j/include/helpers/cpu/loops/Reduction3Loops_0.cpp b/libnd4j/include/helpers/cpu/loops/Reduction3Loops_0.cpp index b8405553e..f721c5994 100644 --- a/libnd4j/include/helpers/cpu/loops/Reduction3Loops_0.cpp +++ b/libnd4j/include/helpers/cpu/loops/Reduction3Loops_0.cpp @@ -18,13 +18,13 @@ // @author raver119@gmail.com // -#include -#include +#include +#include #include using namespace simdOps; -namespace nd4j { +namespace sd { template template diff --git a/libnd4j/include/helpers/cpu/loops/Reduction3Loops_1.cpp b/libnd4j/include/helpers/cpu/loops/Reduction3Loops_1.cpp index 44ccea08c..19a248896 100644 --- a/libnd4j/include/helpers/cpu/loops/Reduction3Loops_1.cpp +++ b/libnd4j/include/helpers/cpu/loops/Reduction3Loops_1.cpp @@ -18,13 +18,13 @@ // @author raver119@gmail.com // -#include -#include +#include +#include #include using namespace simdOps; -namespace nd4j { +namespace sd { template template diff --git a/libnd4j/include/helpers/cpu/loops/Reduction3Loops_2.cpp b/libnd4j/include/helpers/cpu/loops/Reduction3Loops_2.cpp index ec261a7ea..e90050e4e 100644 --- a/libnd4j/include/helpers/cpu/loops/Reduction3Loops_2.cpp +++ b/libnd4j/include/helpers/cpu/loops/Reduction3Loops_2.cpp @@ -18,13 +18,13 @@ // @author raver119@gmail.com // -#include -#include +#include +#include #include using namespace simdOps; -namespace nd4j { +namespace sd { template template diff --git a/libnd4j/include/helpers/cpu/loops/Reduction3Loops_3.cpp b/libnd4j/include/helpers/cpu/loops/Reduction3Loops_3.cpp index 3b1efadc9..d109d1013 100644 --- a/libnd4j/include/helpers/cpu/loops/Reduction3Loops_3.cpp +++ b/libnd4j/include/helpers/cpu/loops/Reduction3Loops_3.cpp @@ -18,13 +18,13 @@ // @author raver119@gmail.com // -#include -#include +#include +#include #include using namespace simdOps; -namespace nd4j { +namespace sd { template template diff --git a/libnd4j/include/helpers/cpu/loops/ReductionLoops.hpp b/libnd4j/include/helpers/cpu/loops/ReductionLoops.hpp index 0709e5f3c..943da43ab 100644 --- a/libnd4j/include/helpers/cpu/loops/ReductionLoops.hpp +++ b/libnd4j/include/helpers/cpu/loops/ReductionLoops.hpp @@ -19,4 +19,4 @@ // #include -#include +#include diff --git a/libnd4j/include/helpers/cpu/loops/ReductionLoops_bool.cpp b/libnd4j/include/helpers/cpu/loops/ReductionLoops_bool.cpp index 151bc6a82..31ec60d93 100644 --- a/libnd4j/include/helpers/cpu/loops/ReductionLoops_bool.cpp +++ b/libnd4j/include/helpers/cpu/loops/ReductionLoops_bool.cpp @@ -22,7 +22,7 @@ using namespace simdOps; -namespace nd4j { +namespace sd { template template diff --git a/libnd4j/include/helpers/cpu/loops/ReductionLoops_float_0.cpp b/libnd4j/include/helpers/cpu/loops/ReductionLoops_float_0.cpp index af8b0b451..f4243d1c9 100644 --- a/libnd4j/include/helpers/cpu/loops/ReductionLoops_float_0.cpp +++ b/libnd4j/include/helpers/cpu/loops/ReductionLoops_float_0.cpp @@ -19,12 +19,12 @@ // #include "ReductionLoops.hpp" -#include +#include #include using namespace simdOps; -namespace nd4j { +namespace sd { template template diff --git a/libnd4j/include/helpers/cpu/loops/ReductionLoops_float_1.cpp b/libnd4j/include/helpers/cpu/loops/ReductionLoops_float_1.cpp index 137ffc011..1c5b46d40 100644 --- a/libnd4j/include/helpers/cpu/loops/ReductionLoops_float_1.cpp +++ b/libnd4j/include/helpers/cpu/loops/ReductionLoops_float_1.cpp @@ -19,12 +19,12 @@ // #include "ReductionLoops.hpp" -#include +#include #include using namespace simdOps; -namespace nd4j { +namespace sd { template template diff --git a/libnd4j/include/helpers/cpu/loops/ReductionLoops_float_2.cpp b/libnd4j/include/helpers/cpu/loops/ReductionLoops_float_2.cpp index 79b11b419..08ca08cdb 100644 --- a/libnd4j/include/helpers/cpu/loops/ReductionLoops_float_2.cpp +++ b/libnd4j/include/helpers/cpu/loops/ReductionLoops_float_2.cpp @@ -19,12 +19,12 @@ // #include "ReductionLoops.hpp" -#include +#include #include using namespace simdOps; -namespace nd4j { +namespace sd { template template diff --git a/libnd4j/include/helpers/cpu/loops/ReductionLoops_float_3.cpp b/libnd4j/include/helpers/cpu/loops/ReductionLoops_float_3.cpp index ddedd6c18..7735c2125 100644 --- a/libnd4j/include/helpers/cpu/loops/ReductionLoops_float_3.cpp +++ b/libnd4j/include/helpers/cpu/loops/ReductionLoops_float_3.cpp @@ -19,12 +19,12 @@ // #include "ReductionLoops.hpp" -#include +#include #include using namespace simdOps; -namespace nd4j { +namespace sd { template template diff --git a/libnd4j/include/helpers/cpu/loops/ReductionLoops_long.cpp b/libnd4j/include/helpers/cpu/loops/ReductionLoops_long.cpp index 2e7708497..e4f4ab2e0 100644 --- a/libnd4j/include/helpers/cpu/loops/ReductionLoops_long.cpp +++ b/libnd4j/include/helpers/cpu/loops/ReductionLoops_long.cpp @@ -24,12 +24,12 @@ using namespace simdOps; #include "ReductionLoops.hpp" -#include +#include #include using namespace simdOps; -namespace nd4j { +namespace sd { template template diff --git a/libnd4j/include/helpers/cpu/loops/ReductionLoops_same.cpp b/libnd4j/include/helpers/cpu/loops/ReductionLoops_same.cpp index 08a67ec59..6188a90f5 100644 --- a/libnd4j/include/helpers/cpu/loops/ReductionLoops_same.cpp +++ b/libnd4j/include/helpers/cpu/loops/ReductionLoops_same.cpp @@ -22,7 +22,7 @@ using namespace simdOps; -namespace nd4j { +namespace sd { template template diff --git a/libnd4j/include/helpers/cpu/svd.cpp b/libnd4j/include/helpers/cpu/svd.cpp index 4bf2be639..4e257b267 100644 --- a/libnd4j/include/helpers/cpu/svd.cpp +++ b/libnd4j/include/helpers/cpu/svd.cpp @@ -18,14 +18,14 @@ // @author Yurii Shyrma (iuriish@yahoo.com), created on 03.01.2018 // -#include -#include -#include +#include +#include +#include #include -#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { @@ -374,7 +374,7 @@ T SVD::secularEq(const T diff, const NDArray& col0, const NDArray& diag, cons auto len = permut.lengthOf(); T res = 1.; T item; - for(int i=0; i(i); item = col0.e(j) / ((diagShifted.e(j) - diff) * (diag.e(j) + shift + diff)); res += item * col0.e(j); @@ -383,7 +383,6 @@ T SVD::secularEq(const T diff, const NDArray& col0, const NDArray& diag, cons return res; } - ////////////////////////////////////////////////////////////////////////// template void SVD::calcSingVals(const NDArray& col0, const NDArray& diag, const NDArray& permut, NDArray& singVals, NDArray& shifts, NDArray& mus) { @@ -394,7 +393,7 @@ void SVD::calcSingVals(const NDArray& col0, const NDArray& diag, const NDArra while(curLen > 1 && col0.e(curLen-1) == (T)0.f) --curLen; - for (int k = 0; k < len; ++k) { + for (Nd4jLong k = 0; k < len; ++k) { if (col0.e(k) == (T)0.f || curLen==1) { diff --git a/libnd4j/include/helpers/cublasHelper.h b/libnd4j/include/helpers/cublasHelper.h index f07cc178c..0300f3698 100644 --- a/libnd4j/include/helpers/cublasHelper.h +++ b/libnd4j/include/helpers/cublasHelper.h @@ -21,12 +21,12 @@ #ifndef DEV_TESTS_CUBLASHELPER_H #define DEV_TESTS_CUBLASHELPER_H -#include -#include +#include +#include #include #include -namespace nd4j { +namespace sd { class ND4J_EXPORT CublasHelper { private: static CublasHelper *_INSTANCE; diff --git a/libnd4j/include/helpers/cuda/ConstantHelper.cu b/libnd4j/include/helpers/cuda/ConstantHelper.cu index 47e276f4a..62d932489 100644 --- a/libnd4j/include/helpers/cuda/ConstantHelper.cu +++ b/libnd4j/include/helpers/cuda/ConstantHelper.cu @@ -20,12 +20,12 @@ // #include -#include -#include -#include +#include +#include +#include #include -#include -#include +#include +#include #include #include #include @@ -34,7 +34,7 @@ __constant__ char deviceConstantMemory[CONSTANT_LIMIT]; -namespace nd4j { +namespace sd { static void* getConstantSpace() { Nd4jPointer dConstAddr; auto dZ = cudaGetSymbolAddress(reinterpret_cast(&dConstAddr), deviceConstantMemory); @@ -70,7 +70,7 @@ namespace nd4j { throw cuda_exception::build("cudaSetDevice failed", res); auto constant = getConstantSpace(); - std::map devCache; + MAP_IMPL devCache; _devicePointers[e] = constant; _deviceOffsets[e] = 0; @@ -86,7 +86,7 @@ namespace nd4j { ConstantHelper* ConstantHelper::getInstance() { if (!_INSTANCE) - _INSTANCE = new nd4j::ConstantHelper(); + _INSTANCE = new sd::ConstantHelper(); return _INSTANCE; } @@ -133,7 +133,7 @@ namespace nd4j { } } - ConstantDataBuffer* ConstantHelper::constantBuffer(const ConstantDescriptor &descriptor, nd4j::DataType dataType) { + ConstantDataBuffer* ConstantHelper::constantBuffer(const ConstantDescriptor &descriptor, sd::DataType dataType) { const auto deviceId = getCurrentDevice(); // all cache modifications are synchronous @@ -161,9 +161,9 @@ namespace nd4j { // create buffer with this dtype if (descriptor.isFloat()) { - BUILD_DOUBLE_SELECTOR(nd4j::DataType::DOUBLE, dataType, nd4j::SpecialTypeConverter::convertGeneric, (nullptr, const_cast(descriptor.floatValues().data()), descriptor.length(), cbuff), (nd4j::DataType::DOUBLE, double), LIBND4J_TYPES); + BUILD_DOUBLE_SELECTOR(sd::DataType::DOUBLE, dataType, sd::SpecialTypeConverter::convertGeneric, (nullptr, const_cast(descriptor.floatValues().data()), descriptor.length(), cbuff), (sd::DataType::DOUBLE, double), LIBND4J_TYPES); } else if (descriptor.isInteger()) { - BUILD_DOUBLE_SELECTOR(nd4j::DataType::INT64, dataType, nd4j::SpecialTypeConverter::convertGeneric, (nullptr, const_cast(descriptor.integerValues().data()), descriptor.length(), cbuff), (nd4j::DataType::INT64, Nd4jLong), LIBND4J_TYPES); + BUILD_DOUBLE_SELECTOR(sd::DataType::INT64, dataType, sd::SpecialTypeConverter::convertGeneric, (nullptr, const_cast(descriptor.integerValues().data()), descriptor.length(), cbuff), (sd::DataType::INT64, Nd4jLong), LIBND4J_TYPES); } auto dbuff = replicatePointer(cbuff, descriptor.length() * DataTypeUtils::sizeOf(dataType)); @@ -185,5 +185,5 @@ namespace nd4j { return _counters[deviceId]; } - nd4j::ConstantHelper* nd4j::ConstantHelper::_INSTANCE = 0; + sd::ConstantHelper* sd::ConstantHelper::_INSTANCE = 0; } \ No newline at end of file diff --git a/libnd4j/include/helpers/cuda/ConstantShapeHelper.cu b/libnd4j/include/helpers/cuda/ConstantShapeHelper.cu index 4f7a4a485..ebce6aac5 100644 --- a/libnd4j/include/helpers/cuda/ConstantShapeHelper.cu +++ b/libnd4j/include/helpers/cuda/ConstantShapeHelper.cu @@ -20,19 +20,19 @@ #include "../ConstantShapeHelper.h" #include -#include -#include -#include -#include +#include +#include +#include +#include -namespace nd4j { +namespace sd { ConstantShapeHelper::ConstantShapeHelper() { auto numDevices = AffinityManager::numberOfDevices(); _cache.resize(numDevices); for (int e = 0; e < numDevices; e++) { - std::map cache; + MAP_IMPL cache; _cache[e] = cache; } } @@ -44,12 +44,12 @@ namespace nd4j { return _INSTANCE; } - ConstantDataBuffer ConstantShapeHelper::bufferForShapeInfo(nd4j::DataType dataType, char order, const std::vector &shape) { + ConstantDataBuffer ConstantShapeHelper::bufferForShapeInfo(sd::DataType dataType, char order, const std::vector &shape) { ShapeDescriptor descriptor(dataType, order, shape); return bufferForShapeInfo(descriptor); } - ConstantDataBuffer ConstantShapeHelper::bufferForShapeInfo(const nd4j::DataType dataType, const char order, const int rank, const Nd4jLong* shape) { + ConstantDataBuffer ConstantShapeHelper::bufferForShapeInfo(const sd::DataType dataType, const char order, const int rank, const Nd4jLong* shape) { ShapeDescriptor descriptor(dataType, order, shape, rank); return bufferForShapeInfo(descriptor); } @@ -83,31 +83,31 @@ namespace nd4j { return _cache[deviceId].count(descriptor) != 0; } - Nd4jLong* ConstantShapeHelper::createShapeInfo(const nd4j::DataType dataType, const char order, const int rank, const Nd4jLong* shape) { + Nd4jLong* ConstantShapeHelper::createShapeInfo(const sd::DataType dataType, const char order, const int rank, const Nd4jLong* shape) { ShapeDescriptor descriptor(dataType, order, shape, rank); return bufferForShapeInfo(descriptor).primaryAsT(); } - Nd4jLong* ConstantShapeHelper::createShapeInfo(const nd4j::DataType dataType, const Nd4jLong* shapeInfo) { + Nd4jLong* ConstantShapeHelper::createShapeInfo(const sd::DataType dataType, const Nd4jLong* shapeInfo) { return ConstantShapeHelper::createShapeInfo(dataType, shape::order(shapeInfo), shape::rank(shapeInfo), shape::shapeOf(const_cast(shapeInfo))); } - Nd4jLong* ConstantShapeHelper::emptyShapeInfo(const nd4j::DataType dataType) { + Nd4jLong* ConstantShapeHelper::emptyShapeInfo(const sd::DataType dataType) { auto descriptor = ShapeDescriptor::emptyDescriptor(dataType); return bufferForShapeInfo(descriptor).primaryAsT(); } - Nd4jLong* ConstantShapeHelper::scalarShapeInfo(const nd4j::DataType dataType) { + Nd4jLong* ConstantShapeHelper::scalarShapeInfo(const sd::DataType dataType) { auto descriptor = ShapeDescriptor::scalarDescriptor(dataType); return bufferForShapeInfo(descriptor).primaryAsT(); } - Nd4jLong* ConstantShapeHelper::vectorShapeInfo(const Nd4jLong length, const nd4j::DataType dataType) { + Nd4jLong* ConstantShapeHelper::vectorShapeInfo(const Nd4jLong length, const sd::DataType dataType) { auto descriptor = ShapeDescriptor::vectorDescriptor(length, dataType); return bufferForShapeInfo(descriptor).primaryAsT(); } - Nd4jLong* ConstantShapeHelper::createShapeInfo(const nd4j::DataType dataType, const char order, const std::vector &shape) { + Nd4jLong* ConstantShapeHelper::createShapeInfo(const sd::DataType dataType, const char order, const std::vector &shape) { ShapeDescriptor descriptor(dataType, order, shape); return bufferForShapeInfo(descriptor).primaryAsT(); } @@ -126,7 +126,7 @@ namespace nd4j { return result; } - Nd4jLong* ConstantShapeHelper::createFromExisting(Nd4jLong *shapeInfo, nd4j::memory::Workspace *workspace) { + Nd4jLong* ConstantShapeHelper::createFromExisting(Nd4jLong *shapeInfo, sd::memory::Workspace *workspace) { ShapeDescriptor descriptor(shapeInfo); auto result = createShapeInfo(descriptor); @@ -135,5 +135,59 @@ namespace nd4j { return result; } - nd4j::ConstantShapeHelper* nd4j::ConstantShapeHelper::_INSTANCE = 0; +//////////////////////////////////////////////////////////////////////// +ConstantDataBuffer ConstantShapeHelper::createShapeInfoWithUnitiesForBroadcast(const Nd4jLong* maxShapeInfo, const Nd4jLong* minShapeInfo, sd::memory::Workspace* workspace, const std::vector dimensions) { + + Nd4jLong* newShapeInfo = nullptr; + ALLOCATE(newShapeInfo, workspace, shape::shapeInfoLength(shape::rank(maxShapeInfo)), Nd4jLong); + + newShapeInfo[0] = shape::rank(maxShapeInfo); + + sd::ArrayOptions::copyDataType(newShapeInfo, minShapeInfo); // type + newShapeInfo[2 * newShapeInfo[0] + 2] = shape::elementWiseStride(minShapeInfo); // ews + newShapeInfo[2 * newShapeInfo[0] + 3] = shape::order(minShapeInfo); // order + + if(!dimensions.empty()) { + + for(uint k = 0, j = 0, i = 0; i < shape::rank(maxShapeInfo); ++i) { + + if(j < dimensions.size() && dimensions[j] == i) { + shape::shapeOf(newShapeInfo)[i] = shape::shapeOf(minShapeInfo)[k]; + shape::stride(newShapeInfo)[i] = shape::stride(minShapeInfo)[k++]; + ++j; + } + else{ + shape::shapeOf(newShapeInfo)[i] = 1; + shape::stride(newShapeInfo)[i] = 0; + if(shape::sizeAt(minShapeInfo, k) == 1 && dimensions.size() != shape::rank(minShapeInfo)) + ++k; + } + } + } + else{ + + for(int j = shape::rank(minShapeInfo) - 1, i = shape::rank(maxShapeInfo) - 1; i >=0 ; --i) { + + if(j >= 0) { + shape::shapeOf(newShapeInfo)[i] = shape::shapeOf(minShapeInfo)[j]; + shape::stride(newShapeInfo)[i] = shape::shapeOf(minShapeInfo)[j] == 1 ? 0 : shape::stride(minShapeInfo)[j]; + --j; + } + else { + shape::shapeOf(newShapeInfo)[i] = 1; + shape::stride(newShapeInfo)[i] = 0; + } + } + } + + ShapeDescriptor descriptor(newShapeInfo); + + RELEASE(newShapeInfo, workspace); + + return bufferForShapeInfo(descriptor); +} + + +sd::ConstantShapeHelper* sd::ConstantShapeHelper::_INSTANCE = 0; + } \ No newline at end of file diff --git a/libnd4j/include/helpers/cuda/ConstantTadHelper.cu b/libnd4j/include/helpers/cuda/ConstantTadHelper.cu index 747e295e2..8463bab9c 100644 --- a/libnd4j/include/helpers/cuda/ConstantTadHelper.cu +++ b/libnd4j/include/helpers/cuda/ConstantTadHelper.cu @@ -19,19 +19,19 @@ // #include "../ConstantTadHelper.h" -#include -#include -#include +#include +#include +#include #include #include -#include +#include -namespace nd4j { +namespace sd { ConstantTadHelper::ConstantTadHelper() { auto numDevices = AffinityManager::numberOfDevices(); for (int e = 0; e < numDevices; e++) { - std::map pack; + MAP_IMPL pack; _cache.emplace_back(pack); } } @@ -77,7 +77,7 @@ namespace nd4j { auto oPtr = new Nd4jLong[numOfSubArrs]; if (numOfSubArrs > 0) - shape::calcSubArrShapeAndOffsets(shapeInfo, numOfSubArrs, dimsToExclude.size(), dimsToExclude.data(), sPtr, oPtr, descriptor.areUnitiesinShape()); + shape::calcSubArrsShapeInfoAndOffsets(shapeInfo, numOfSubArrs, dimsToExclude.size(), dimsToExclude.data(), sPtr, oPtr, descriptor.areUnitiesinShape()); Nd4jPointer soPtr; auto res = cudaMalloc(reinterpret_cast(&soPtr), numOfSubArrs * sizeof(Nd4jLong)); @@ -108,5 +108,5 @@ namespace nd4j { } } - nd4j::ConstantTadHelper* nd4j::ConstantTadHelper::_INSTANCE = 0; + sd::ConstantTadHelper* sd::ConstantTadHelper::_INSTANCE = 0; } \ No newline at end of file diff --git a/libnd4j/include/helpers/cuda/PointersManager.cu b/libnd4j/include/helpers/cuda/PointersManager.cu index a5e099980..dc5fe15f5 100644 --- a/libnd4j/include/helpers/cuda/PointersManager.cu +++ b/libnd4j/include/helpers/cuda/PointersManager.cu @@ -19,17 +19,17 @@ // @author raver119@gmail.com // -#include +#include #include -#include -#include +#include +#include #include -namespace nd4j { +namespace sd { ////////////////////////////////////////////////////////////////////////// -PointersManager::PointersManager(const nd4j::LaunchContext* context, const std::string& funcName) { - _context = const_cast(context); +PointersManager::PointersManager(const sd::LaunchContext* context, const std::string& funcName) { + _context = const_cast(context); _funcName = funcName; } @@ -42,7 +42,7 @@ void* PointersManager::replicatePointer(const void* src, const size_t numberOfBy if (cudaResult != 0) throw cuda_exception::build(_funcName + ": cannot allocate global memory on device!", cudaResult); } else { - dst = _context->getWorkspace()->allocateBytes(nd4j::memory::MemoryType::DEVICE, numberOfBytes); + dst = _context->getWorkspace()->allocateBytes(sd::memory::MemoryType::DEVICE, numberOfBytes); } if (_context != nullptr) @@ -84,8 +84,8 @@ static __global__ void printDevContentOnDev_(const void* pDev, const Nd4jLong le //////////////////////////////////////////////////////////////////////// template void PointersManager::printDevContentOnDevFromHost(const void* pDev, const Nd4jLong len, const int tid) { - printDevContentOnDev_<<<512, 512, 1024, *nd4j::LaunchContext ::defaultContext()->getCudaStream()>>>(pDev, len, tid); - auto res = cudaStreamSynchronize(*nd4j::LaunchContext ::defaultContext()->getCudaStream()); + printDevContentOnDev_<<<512, 512, 1024, *sd::LaunchContext ::defaultContext()->getCudaStream()>>>(pDev, len, tid); + auto res = cudaStreamSynchronize(*sd::LaunchContext ::defaultContext()->getCudaStream()); if (res != 0) throw std::runtime_error("PointersManager::printDevContentOnDevFromHost: cudaStreamSynchronize failed!"); } diff --git a/libnd4j/include/helpers/cuda_off/MmulHelper.cu b/libnd4j/include/helpers/cuda_off/MmulHelper.cu index bf366dc29..5e9304e88 100644 --- a/libnd4j/include/helpers/cuda_off/MmulHelper.cu +++ b/libnd4j/include/helpers/cuda_off/MmulHelper.cu @@ -22,12 +22,12 @@ #include #include #include "../MmulHelper.h" -#include -#include -#include +#include +#include +#include #include -namespace nd4j { +namespace sd { ////////////////////////////////////////////////////////////////////////////// // MXK x KxN = MxN -> actual sequence of axes doesn't matter @@ -40,15 +40,15 @@ static __global__ void usualCudaGemm(const void* vA, const Nd4jLong* aShapeInfo, const T2* B = reinterpret_cast(vB); T3* C = reinterpret_cast< T3*>(vC); - __shared__ int K; + __shared__ int K, *coords; __shared__ bool betaPresent; - __shared__ Nd4jLong cLen, totalThreads, *coords; + __shared__ Nd4jLong cLen, totalThreads; __shared__ T3 alphaZ, betaZ; if (threadIdx.x == 0) { extern __shared__ unsigned char shmem[]; - coords = reinterpret_cast(shmem); + coords = reinterpret_cast(shmem); cLen = shape::length(cShapeInfo); K = shape::shapeOf(const_cast(aShapeInfo))[aKaxis]; @@ -263,7 +263,7 @@ NDArray* MmulHelper::mmulMxM(const NDArray* A, const NDArray* B, NDArray* C, dou const int threadsPerBlock = MAX_NUM_THREADS / 2; const int blocksPerGrid = (C->lengthOf() + threadsPerBlock - 1) / threadsPerBlock; - const int sharedMem = threadsPerBlock * sizeof(Nd4jLong) * 6 + 128; // 6 = aRank + bRank + cRank + const int sharedMem = threadsPerBlock * sizeof(int) * 6 + 128; // 6 = aRank + bRank + cRank NDArray::prepareSpecialUse({C}, {A, B}); // BUILD_TRIPLE_SELECTOR(aType, bType, cType, usualGemm, (blocksPerGrid, threadsPerBlock, sharedMem, stream, A->getSpecialBuffer(), A->getSpecialShapeInfo(), B->getSpecialBuffer(), B->getSpecialShapeInfo(), C->getSpecialBuffer(), C->getSpecialShapeInfo(), 0, 1, 0, 1, 0, 1, alpha, beta), NUMERIC_TYPES, NUMERIC_TYPES, FLOAT_TYPES); @@ -357,7 +357,7 @@ NDArray* MmulHelper::mmulMxM(const NDArray* A, const NDArray* B, NDArray* C, dou //////////////////////////////////////////////////////////////////////////// // MXN x N = M -NDArray* MmulHelper::mmulMxV(const NDArray* A, const NDArray* X, nd4j::NDArray* Y, const double alpha, const double beta, const char outOrder) { +NDArray* MmulHelper::mmulMxV(const NDArray* A, const NDArray* X, sd::NDArray* Y, const double alpha, const double beta, const char outOrder) { int xLenDim, yLenDim(0); @@ -463,7 +463,7 @@ NDArray* MmulHelper::mmulMxV(const NDArray* A, const NDArray* X, nd4j::NDArray* //////////////////////////////////////////////////////////////////////////// // (X * Y) = Z[0] -NDArray* MmulHelper::dot(const NDArray* X, const NDArray* Y, nd4j::NDArray* Z, const double alpha, const double beta) { +NDArray* MmulHelper::dot(const NDArray* X, const NDArray* Y, sd::NDArray* Z, const double alpha, const double beta) { int xLenDim(0), yLenDim(0); @@ -529,14 +529,14 @@ static __global__ void batchedCudaGemm(const void* vA, const Nd4jLong* aShapeInf T3* C = reinterpret_cast< T3*>(vC); __shared__ bool betaPresent; - __shared__ int aRank, bRank, cRank, K; - __shared__ Nd4jLong cLen, totalThreads, *coords; + __shared__ int aRank, bRank, cRank, K, *coords; + __shared__ Nd4jLong cLen, totalThreads; __shared__ T3 alphaZ, betaZ; if (threadIdx.x == 0) { extern __shared__ unsigned char shmem[]; - coords = reinterpret_cast(shmem); + coords = reinterpret_cast(shmem); cLen = shape::length(cShapeInfo); K = shape::shapeOf(const_cast(aShapeInfo))[aKaxis]; @@ -649,7 +649,7 @@ NDArray* MmulHelper::mmulNxN(const NDArray* A, const NDArray* B, NDArray* C, con const int threadsPerBlock = MAX_NUM_THREADS / 8; const int blocksPerGrid = (C->lengthOf() + threadsPerBlock - 1) / threadsPerBlock; - const int sharedMem = threadsPerBlock * sizeof(Nd4jLong) * (aRank + bRank + cRank) + 128; + const int sharedMem = threadsPerBlock * sizeof(int) * (aRank + bRank + cRank) + 128; PointersManager manager(A->getContext(), "MmulHelper::mmulNxN"); @@ -940,16 +940,16 @@ NDArray* MmulHelper::mmulNxNold2(const NDArray* A, const NDArray* B, NDArray* C, std::vector aSubArrs(bS), bSubArrs(bS), cSubArrs(bS); if(aRank > 2) - shape::calcSubArrShapeAndOffsets(pA->getShapeInfo(), bS, dimsToExclude.size(), dimsToExclude.data(), subArrShapeInfo.data(), subArrOffsets.data()); + shape::calcSubArrsShapeInfoAndOffsets(pA->getShapeInfo(), bS, dimsToExclude.size(), dimsToExclude.data(), subArrShapeInfo.data(), subArrOffsets.data()); for (int i = 0; i < bS; ++i) aSubArrs[i] = aRank == 2 ? pA->getSpecialBuffer() : pA->getSpecialBuffer() + subArrOffsets[i] * pA->sizeOfT(); if(bRank > 2) - shape::calcSubArrShapeAndOffsets(pB->getShapeInfo(), bS, dimsToExclude.size(), dimsToExclude.data(), subArrShapeInfo.data(), subArrOffsets.data()); + shape::calcSubArrsShapeInfoAndOffsets(pB->getShapeInfo(), bS, dimsToExclude.size(), dimsToExclude.data(), subArrShapeInfo.data(), subArrOffsets.data()); for (int i = 0; i < bS; ++i) bSubArrs[i] = bRank == 2 ? pB->getSpecialBuffer() : pB->getSpecialBuffer() + subArrOffsets[i] * pB->sizeOfT(); - shape::calcSubArrShapeAndOffsets(pC->getShapeInfo(), bS, dimsToExclude.size(), dimsToExclude.data(), subArrShapeInfo.data(), subArrOffsets.data()); + shape::calcSubArrsShapeInfoAndOffsets(pC->getShapeInfo(), bS, dimsToExclude.size(), dimsToExclude.data(), subArrShapeInfo.data(), subArrOffsets.data()); for (int i = 0; i < bS; ++i) cSubArrs[i] = pC->getSpecialBuffer() + subArrOffsets[i] * pC->sizeOfT(); diff --git a/libnd4j/include/helpers/cuda_off/cublasHelper.cu b/libnd4j/include/helpers/cuda_off/cublasHelper.cu index 7204862eb..7ab2d7d63 100644 --- a/libnd4j/include/helpers/cuda_off/cublasHelper.cu +++ b/libnd4j/include/helpers/cuda_off/cublasHelper.cu @@ -33,7 +33,7 @@ #endif -namespace nd4j { +namespace sd { std::mutex CublasHelper::_mutex; static void* handle_() { @@ -105,7 +105,7 @@ namespace nd4j { CublasHelper* CublasHelper::getInstance() { _mutex.lock(); if (!_INSTANCE) - _INSTANCE = new nd4j::CublasHelper(); + _INSTANCE = new sd::CublasHelper(); _mutex.unlock(); return _INSTANCE; @@ -140,5 +140,5 @@ namespace nd4j { } - nd4j::CublasHelper* nd4j::CublasHelper::_INSTANCE = 0; + sd::CublasHelper* sd::CublasHelper::_INSTANCE = 0; } \ No newline at end of file diff --git a/libnd4j/include/helpers/helper_generator.h b/libnd4j/include/helpers/helper_generator.h index 0e741e931..ecf87ae81 100644 --- a/libnd4j/include/helpers/helper_generator.h +++ b/libnd4j/include/helpers/helper_generator.h @@ -21,10 +21,10 @@ #ifndef LIBND4J_HELPER_GENERATOR_H #define LIBND4J_HELPER_GENERATOR_H -#include -#include +#include +#include #include -#include +#include #ifdef _MSC_VER // include for uint64_t on MSVC @@ -48,7 +48,7 @@ #endif -namespace nd4j { +namespace sd { namespace random { #ifdef __CUDACC__ @@ -111,7 +111,7 @@ namespace nd4j { this->synchronizer = 0; this->devBuffer = devBuffer; - cudaMalloc(&devHolder, sizeof(nd4j::random::RandomBuffer)); + cudaMalloc(&devHolder, sizeof(sd::random::RandomBuffer)); } __host__ @@ -125,8 +125,8 @@ namespace nd4j { } __host__ - void propagateToDevice(nd4j::random::RandomBuffer *buffer, cudaStream_t stream) { - cudaMemcpyAsync(devHolder, buffer, sizeof(nd4j::random::RandomBuffer), cudaMemcpyHostToDevice, stream); + void propagateToDevice(sd::random::RandomBuffer *buffer, cudaStream_t stream) { + cudaMemcpyAsync(devHolder, buffer, sizeof(sd::random::RandomBuffer), cudaMemcpyHostToDevice, stream); } __host__ __device__ @@ -231,7 +231,7 @@ namespace nd4j { uint64_t _CUDA_HD next64(uint64_t shiftedSeed) { const auto s0 = static_cast(shiftedSeed); - auto s1 = static_cast(shiftedSeed) % nd4j::DataTypeUtils::max() + 11; + auto s1 = static_cast(shiftedSeed) % sd::DataTypeUtils::max() + 11; uint64_t r0, r1; s1 ^= s0; @@ -246,7 +246,7 @@ namespace nd4j { } uint64_t static _CUDA_HD inline safeShift(uint64_t x, uint64_t y) { - if (y != 0 && x > nd4j::DataTypeUtils::max() / y) { + if (y != 0 && x > sd::DataTypeUtils::max() / y) { return x / y + 11; } else return (x * y) + 11; } @@ -349,7 +349,7 @@ namespace nd4j { */ int _CUDA_D nextInt() { auto u = nextUInt64(); - return u <= nd4j::DataTypeUtils::max() ? static_cast(u) : static_cast(u % nd4j::DataTypeUtils::max()); + return u <= sd::DataTypeUtils::max() ? static_cast(u) : static_cast(u % sd::DataTypeUtils::max()); }; uint64_t _CUDA_D nextUInt64() { @@ -395,7 +395,7 @@ namespace nd4j { template _CUDA_D T nextT() { auto u = static_cast(nextUInt64()); - auto m = static_cast(nd4j::DataTypeUtils::max()); + auto m = static_cast(sd::DataTypeUtils::max()); return static_cast(u / m); } @@ -432,7 +432,7 @@ namespace nd4j { */ inline int _CUDA_D relativeInt(Nd4jLong index) { auto u = relativeUInt64(index); - return u <= nd4j::DataTypeUtils::max() ? static_cast(u) : static_cast(u % nd4j::DataTypeUtils::max()); + return u <= sd::DataTypeUtils::max() ? static_cast(u) : static_cast(u % sd::DataTypeUtils::max()); } /** @@ -476,7 +476,7 @@ namespace nd4j { * FIXME: once we add support for additional datatypes this code must be tweaked */ auto u = static_cast(relativeUInt64(index)); - auto m = static_cast (nd4j::DataTypeUtils::max()); + auto m = static_cast (sd::DataTypeUtils::max()); return static_cast(u / m); } @@ -516,11 +516,11 @@ namespace nd4j { Nd4jLong limit; Nd4jLong seed; uint64_t *buffer; - nd4j::random::RandomBuffer *realBuffer; + sd::random::RandomBuffer *realBuffer; public: - _CUDA_HD IGenerator(nd4j::random::RandomBuffer *buffer) { + _CUDA_HD IGenerator(sd::random::RandomBuffer *buffer) { this->limit = buffer->getSize(); this->buffer = reinterpret_cast(buffer->getBuffer()); this->realBuffer = buffer; @@ -600,7 +600,7 @@ namespace nd4j { } public: - _CUDA_HD Xoroshiro128(nd4j::random::RandomBuffer *buffer) : IGenerator(buffer) { + _CUDA_HD Xoroshiro128(sd::random::RandomBuffer *buffer) : IGenerator(buffer) { // } diff --git a/libnd4j/include/helpers/helper_hash.h b/libnd4j/include/helpers/helper_hash.h index 81817ce35..1b032238f 100644 --- a/libnd4j/include/helpers/helper_hash.h +++ b/libnd4j/include/helpers/helper_hash.h @@ -23,11 +23,11 @@ #define LIBND4J_HELPER_HASH_H #include -#include -#include +#include +#include #include -namespace nd4j { +namespace sd { namespace ops { class ND4J_EXPORT HashHelper { private: diff --git a/libnd4j/include/helpers/helper_ptrmap.h b/libnd4j/include/helpers/helper_ptrmap.h index 1eebee0ca..4f2ec128c 100644 --- a/libnd4j/include/helpers/helper_ptrmap.h +++ b/libnd4j/include/helpers/helper_ptrmap.h @@ -27,7 +27,7 @@ #define ptr_def inline #endif -namespace nd4j { +namespace sd { /** * This class is a simple wrapper to represent batch arguments as single surface of parameters. diff --git a/libnd4j/include/helpers/helper_random.h b/libnd4j/include/helpers/helper_random.h index f685eff40..6f2523e05 100644 --- a/libnd4j/include/helpers/helper_random.h +++ b/libnd4j/include/helpers/helper_random.h @@ -34,25 +34,25 @@ #endif -namespace nd4j { +namespace sd { namespace random { template class RandomHelper { private: - nd4j::random::IGenerator *generator; - nd4j::random::RandomBuffer *buffer; + sd::random::IGenerator *generator; + sd::random::RandomBuffer *buffer; public: - _CUDA_HD RandomHelper(nd4j::random::IGenerator *generator) { + _CUDA_HD RandomHelper(sd::random::IGenerator *generator) { this->generator = generator; this->buffer = generator->getBuffer(); } - _CUDA_HD RandomHelper(nd4j::random::RandomBuffer *buffer) { + _CUDA_HD RandomHelper(sd::random::RandomBuffer *buffer) { this->buffer = buffer; } @@ -117,7 +117,7 @@ namespace nd4j { * @return */ inline _CUDA_D T nextT() { - return (T) nextUInt() / (T) nd4j::DataTypeUtils::max(); + return (T) nextUInt() / (T) sd::DataTypeUtils::max(); } /** @@ -150,7 +150,7 @@ namespace nd4j { * relative methods are made as workaround for lock-free concurrent execution */ inline _CUDA_D int relativeInt(Nd4jLong index) { - return (int) (relativeUInt(index) % (nd4j::DataTypeUtils::max() + 1)); + return (int) (relativeUInt(index) % (sd::DataTypeUtils::max() + 1)); } /** @@ -190,8 +190,8 @@ namespace nd4j { inline _CUDA_D T relativeT(Nd4jLong index) { if (sizeof(T) < 4) { // FIXME: this is fast hack for short types, like fp16. This should be improved. - return (T)((float) relativeUInt(index) / (float) nd4j::DataTypeUtils::max()); - } else return (T) relativeUInt(index) / (T) nd4j::DataTypeUtils::max(); + return (T)((float) relativeUInt(index) / (float) sd::DataTypeUtils::max()); + } else return (T) relativeUInt(index) / (T) sd::DataTypeUtils::max(); } /** diff --git a/libnd4j/include/helpers/hhColPivQR.h b/libnd4j/include/helpers/hhColPivQR.h index 8c1ae4988..28dd42f64 100644 --- a/libnd4j/include/helpers/hhColPivQR.h +++ b/libnd4j/include/helpers/hhColPivQR.h @@ -21,10 +21,10 @@ #ifndef LIBND4J_HHCOLPICQR_H #define LIBND4J_HHCOLPICQR_H -#include -#include "NDArray.h" +#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { diff --git a/libnd4j/include/helpers/hhSequence.h b/libnd4j/include/helpers/hhSequence.h index b3104f1ae..31855a86c 100644 --- a/libnd4j/include/helpers/hhSequence.h +++ b/libnd4j/include/helpers/hhSequence.h @@ -21,9 +21,9 @@ #ifndef LIBND4J_HHSEQUENCE_H #define LIBND4J_HHSEQUENCE_H -#include "NDArray.h" +#include "array/NDArray.h" -namespace nd4j { +namespace sd { namespace ops { namespace helpers { diff --git a/libnd4j/include/helpers/householder.h b/libnd4j/include/helpers/householder.h index c8924865b..e71769901 100644 --- a/libnd4j/include/helpers/householder.h +++ b/libnd4j/include/helpers/householder.h @@ -22,9 +22,9 @@ #define LIBND4J_HOUSEHOLDER_H -#include "NDArray.h" +#include "array/NDArray.h" -namespace nd4j { +namespace sd { namespace ops { namespace helpers { diff --git a/libnd4j/include/helpers/impl/ArrayUtils.cpp b/libnd4j/include/helpers/impl/ArrayUtils.cpp index bd7107945..004cb1546 100644 --- a/libnd4j/include/helpers/impl/ArrayUtils.cpp +++ b/libnd4j/include/helpers/impl/ArrayUtils.cpp @@ -20,7 +20,7 @@ #include -namespace nd4j { +namespace sd { namespace ArrayUtils { void toIntPtr(std::initializer_list list, int* target) { std::vector vec(list); diff --git a/libnd4j/include/helpers/impl/AttentionHelper.cpp b/libnd4j/include/helpers/impl/AttentionHelper.cpp index 731c9e56a..bd5d006f2 100644 --- a/libnd4j/include/helpers/impl/AttentionHelper.cpp +++ b/libnd4j/include/helpers/impl/AttentionHelper.cpp @@ -26,9 +26,9 @@ #include "../AttentionHelper.h" #include -namespace nd4j { +namespace sd { - nd4j::NDArray AttentionHelper::multiHeadProject(const nd4j::NDArray *input, const nd4j::NDArray *projectionMatrix, nd4j::LaunchContext * context) { + sd::NDArray AttentionHelper::multiHeadProject(const sd::NDArray *input, const sd::NDArray *projectionMatrix, sd::LaunchContext * context) { auto miniBatchSize = input->sizeAt(0); auto seqLength = input->sizeAt(2); auto numHeads = projectionMatrix->sizeAt(0); @@ -39,7 +39,7 @@ namespace nd4j { auto projectionPrep = projectionMatrix->reshape('c', {numHeads * projectionMatrix->sizeAt(1), projectionMatrix->sizeAt(2)}); //[nHeads, hS, nIn] -> [nHeads*hS, nIn] NDArray projected('c', {numHeads * projectionMatrix->sizeAt(1), (miniBatchSize * seqLength)}, input->dataType(), context); //[nHeads*hS, batch*timeSteps] - nd4j::ops::matmul mmul; + sd::ops::matmul mmul; mmul.execute({&projectionPrep, &inputPrep}, {&projected}); projected.reshapei({numHeads, projectedSize, miniBatchSize, seqLength}); @@ -48,9 +48,9 @@ namespace nd4j { return projected; } - void AttentionHelper::multiHeadProjectBp(const nd4j::NDArray *input, const nd4j::NDArray *projectionMatrix, - const nd4j::NDArray *eps, nd4j::NDArray *dLdInput, - nd4j::NDArray *dLdProjectionMatrix, nd4j::LaunchContext * context) { + void AttentionHelper::multiHeadProjectBp(const sd::NDArray *input, const sd::NDArray *projectionMatrix, + const sd::NDArray *eps, sd::NDArray *dLdInput, + sd::NDArray *dLdProjectionMatrix, sd::LaunchContext * context) { auto miniBatchSize = input->sizeAt(0); auto seqLength = input->sizeAt(2); auto numHeads = projectionMatrix->sizeAt(0); @@ -63,7 +63,7 @@ namespace nd4j { auto inputPrep = inputPerm.reshape('c', {input->sizeAt(1), (miniBatchSize * seqLength)}); auto projectionPrep = projectionMatrix->reshape('c', {numHeads * projectionMatrix->sizeAt(1), projectionMatrix->sizeAt(2)}); - nd4j::ops::matmul_bp mmulBp; + sd::ops::matmul_bp mmulBp; NDArray dLdProjectionPrep(projectionPrep.shapeInfo(), false, context); NDArray dLdInputPrep(inputPrep.shapeInfo(), false, context); mmulBp.execute({&projectionPrep, &inputPrep, &epsReshaped}, std::vector{&dLdProjectionPrep, &dLdInputPrep}, {}, {}, {}); diff --git a/libnd4j/include/helpers/impl/BenchmarkHelper.cpp b/libnd4j/include/helpers/impl/BenchmarkHelper.cpp index cbe0c0729..9e85cc5b7 100644 --- a/libnd4j/include/helpers/impl/BenchmarkHelper.cpp +++ b/libnd4j/include/helpers/impl/BenchmarkHelper.cpp @@ -20,11 +20,11 @@ #include "../BenchmarkHelper.h" -#include +#include #include #include -namespace nd4j { +namespace sd { BenchmarkHelper::BenchmarkHelper(unsigned int warmUpIterations, unsigned int runIterations) { _wIterations = warmUpIterations; _rIterations = runIterations; @@ -59,9 +59,9 @@ namespace nd4j { auto n = NDArrayFactory::create(timings, LaunchContext::defaultContext()); - auto stdev = n.varianceNumber(nd4j::variance::SummaryStatsStandardDeviation, false).e(0); - auto min = n.reduceNumber(nd4j::reduce::Min).e(0); - auto max = n.reduceNumber(nd4j::reduce::Max).e(0); + auto stdev = n.varianceNumber(sd::variance::SummaryStatsStandardDeviation, false).e(0); + auto min = n.reduceNumber(sd::reduce::Min).e(0); + auto max = n.reduceNumber(sd::reduce::Max).e(0); // opNum, DataType, Shape, average time, median time auto t = benchmark.dataType(); @@ -77,7 +77,7 @@ namespace nd4j { // printing out stuff snprintf(const_cast(temp.data()), temp.length(), "%s\t%i\t%i\t%i\t%s\t%s\t%s\t%s\t%s\t%s\t%lld\t%lld\t%lld\t%lld\t%.2f\n", benchmark.testName().c_str(), benchmark.opNum(), _wIterations, _rIterations, t.c_str(), inpl.c_str(), s.c_str(), strides.c_str(), a.c_str(), o.c_str(), - nd4j::math::nd4j_floor(sumT), median, min, max, stdev); + sd::math::nd4j_floor(sumT), median, min, max, stdev); auto pos = temp.find('\n'); return temp.substr(0, pos + 1); @@ -109,9 +109,9 @@ namespace nd4j { Nd4jLong median = timings[_rIterations / 2]; NDArray n = NDArrayFactory::create(timings, nullptr); - double stdev = n.varianceNumber(nd4j::variance::SummaryStatsStandardDeviation, false).e(0); - Nd4jLong min = n.reduceNumber(nd4j::reduce::Min).e(0); - Nd4jLong max = n.reduceNumber(nd4j::reduce::Max).e(0); + double stdev = n.varianceNumber(sd::variance::SummaryStatsStandardDeviation, false).e(0); + Nd4jLong min = n.reduceNumber(sd::reduce::Min).e(0); + Nd4jLong max = n.reduceNumber(sd::reduce::Max).e(0); // opNum, DataType, Shape, average time, median time auto t = DataTypeUtils::asString(x.dataType()); @@ -129,7 +129,7 @@ namespace nd4j { // printing out stuff nd4j_printf("%s\t%i\t%i\t%i\t%s\t%s\t%s\t%s\t%s\tn/a\t%lld\t%lld\t%lld\t%lld\t%.2f\n", testName.c_str(), op, _wIterations, _rIterations, t.c_str(), inpl.c_str(), s.c_str(), stride.c_str(), o.c_str(), - nd4j::math::nd4j_floor(sumT), median, min, max, stdev); + sd::math::nd4j_floor(sumT), median, min, max, stdev); } std::string BenchmarkHelper::runOperationSuit(std::initializer_list benchmarks, const char *msg) { diff --git a/libnd4j/include/helpers/impl/BitwiseUtils.cpp b/libnd4j/include/helpers/impl/BitwiseUtils.cpp index 61e066d57..e3f4ce92a 100644 --- a/libnd4j/include/helpers/impl/BitwiseUtils.cpp +++ b/libnd4j/include/helpers/impl/BitwiseUtils.cpp @@ -20,10 +20,10 @@ #include #include -#include +#include #include -namespace nd4j { +namespace sd { bool BitwiseUtils::isBE() { short int word = 0x0001; @@ -76,7 +76,7 @@ namespace nd4j { return bits; } - nd4j::ByteOrder BitwiseUtils::asByteOrder() { + sd::ByteOrder BitwiseUtils::asByteOrder() { return isBE() ? ByteOrder::BE : ByteOrder::LE; } } diff --git a/libnd4j/include/helpers/impl/BlasHelper.cpp b/libnd4j/include/helpers/impl/BlasHelper.cpp index bf52fe2c6..378c8a6f1 100644 --- a/libnd4j/include/helpers/impl/BlasHelper.cpp +++ b/libnd4j/include/helpers/impl/BlasHelper.cpp @@ -19,7 +19,7 @@ // #include -namespace nd4j { +namespace sd { BlasHelper* BlasHelper::getInstance() { if (_instance == 0) _instance = new BlasHelper(); @@ -74,6 +74,9 @@ namespace nd4j { template <> bool BlasHelper::hasGEMV() { + if (sd::Environment::getInstance()->blasFallback()) + return false; + #if defined(__EXTERNAL_BLAS__) || defined(HAVE_OPENBLAS) return true; #else @@ -83,6 +86,9 @@ namespace nd4j { template <> bool BlasHelper::hasGEMV() { + if (sd::Environment::getInstance()->blasFallback()) + return false; + #if defined(__EXTERNAL_BLAS__) || defined(HAVE_OPENBLAS) return true; #else @@ -130,8 +136,11 @@ namespace nd4j { return false; } - bool BlasHelper::hasGEMV(const nd4j::DataType dtype) { + bool BlasHelper::hasGEMV(const sd::DataType dtype) { if(dtype == DataType::FLOAT32) { + if (sd::Environment::getInstance()->blasFallback()) + return false; + #if defined(__EXTERNAL_BLAS__) || defined(HAVE_OPENBLAS) return true; #else @@ -139,6 +148,9 @@ namespace nd4j { #endif } if(dtype == DataType::DOUBLE) { + if (sd::Environment::getInstance()->blasFallback()) + return false; + #if defined(__EXTERNAL_BLAS__) || defined(HAVE_OPENBLAS) return true; #else @@ -150,6 +162,9 @@ namespace nd4j { template <> bool BlasHelper::hasGEMM() { + if (sd::Environment::getInstance()->blasFallback()) + return false; + #if defined(__EXTERNAL_BLAS__) || defined(HAVE_OPENBLAS) return true; #else @@ -159,6 +174,9 @@ namespace nd4j { template <> bool BlasHelper::hasGEMM() { + if (sd::Environment::getInstance()->blasFallback()) + return false; + #if defined(__EXTERNAL_BLAS__) || defined(HAVE_OPENBLAS) return true; #else @@ -206,8 +224,11 @@ namespace nd4j { return false; } - bool BlasHelper:: hasGEMM(const nd4j::DataType dtype) { + bool BlasHelper:: hasGEMM(const sd::DataType dtype) { if(dtype == DataType::FLOAT32) { + if (sd::Environment::getInstance()->blasFallback()) + return false; + #if defined(__EXTERNAL_BLAS__) || defined(HAVE_OPENBLAS) return true; #else @@ -215,6 +236,9 @@ namespace nd4j { #endif } if(dtype == DataType::DOUBLE) { + if (sd::Environment::getInstance()->blasFallback()) + return false; + #if defined(__EXTERNAL_BLAS__) || defined(HAVE_OPENBLAS) return true; #else @@ -227,11 +251,17 @@ namespace nd4j { template <> bool BlasHelper::hasBatchedGEMM() { + if (sd::Environment::getInstance()->blasFallback()) + return false; + return _hasSgemmBatch; } template <> bool BlasHelper::hasBatchedGEMM() { + if (sd::Environment::getInstance()->blasFallback()) + return false; + return _hasDgemmBatch; } diff --git a/libnd4j/include/helpers/impl/CudaLaunchHelper.cpp b/libnd4j/include/helpers/impl/CudaLaunchHelper.cpp index 947895d8c..d0bcce11e 100644 --- a/libnd4j/include/helpers/impl/CudaLaunchHelper.cpp +++ b/libnd4j/include/helpers/impl/CudaLaunchHelper.cpp @@ -19,9 +19,9 @@ // #include -#include +#include -namespace nd4j { +namespace sd { Triple CudaLaunchHelper::getFlatLaunchParams(Nd4jLong length, int SM, int CORES, int SHARED_MEMORY) { // TODO: to be implemented Triple triple(1, 2, 3); @@ -31,11 +31,11 @@ namespace nd4j { int CudaLaunchHelper::getReductionBlocks(Nd4jLong xLength, int blockSize) { int div = xLength / blockSize; - int can = nd4j::math::nd4j_max(div, 1); + int can = sd::math::nd4j_max(div, 1); if (xLength % blockSize != 0 && xLength > blockSize) can++; // not more then 512 blocks - return nd4j::math::nd4j_min(can, 512); + return sd::math::nd4j_min(can, 512); } } diff --git a/libnd4j/include/helpers/impl/DebugHelper.cpp b/libnd4j/include/helpers/impl/DebugHelper.cpp index 704c463e6..d24068a65 100644 --- a/libnd4j/include/helpers/impl/DebugHelper.cpp +++ b/libnd4j/include/helpers/impl/DebugHelper.cpp @@ -19,13 +19,13 @@ // #include -#include -#include +#include +#include #include #include #include -namespace nd4j { +namespace sd { DebugInfo DebugHelper::debugStatistics(NDArray const* input) { DebugInfo info; DebugHelper::retrieveDebugStatistics(&info, input); @@ -50,11 +50,11 @@ namespace nd4j { info->_maxValue = info->_minValue; info->_meanValue = info->_minValue; info->_stdDevValue = info->_minValue; - info->_zeroCount = nd4j::math::nd4j_abs(input->e(0)) > 0.00001? 0: 1; + info->_zeroCount = sd::math::nd4j_abs(input->e(0)) > 0.00001? 0: 1; info->_positiveCount = input->e(0) > 0?1:0; info->_negativeCount = input->e(0) < 0?1:0; - info->_infCount = nd4j::math::nd4j_isinf(input->e(0)); - info->_nanCount = nd4j::math::nd4j_isnan(input->e(0)); + info->_infCount = sd::math::nd4j_isinf(input->e(0)); + info->_nanCount = sd::math::nd4j_isnan(input->e(0)); } else if (input->lengthOf() > 0) { // TO DO: here processing for all elements with array @@ -62,11 +62,11 @@ namespace nd4j { auto _maxValue = input->e(0); auto _meanValue = input->e(0); auto _stdDevValue = 0.; //info->_minValue; - auto _zeroCount = nd4j::math::nd4j_abs(input->e(0)) > 0.00001? 0L : 1L; + auto _zeroCount = sd::math::nd4j_abs(input->e(0)) > 0.00001? 0L : 1L; auto _positiveCount = input->e(0) > 0? 1L : 0L; auto _negativeCount = input->e(0) < 0? 1L : 0L; - auto _infCount = nd4j::math::nd4j_isinf(input->e(0)) ? 1L : 0L; - auto _nanCount = nd4j::math::nd4j_isnan(input->e(0)) ? 1L : 0L; + auto _infCount = sd::math::nd4j_isinf(input->e(0)) ? 1L : 0L; + auto _nanCount = sd::math::nd4j_isnan(input->e(0)) ? 1L : 0L; PRAGMA_OMP_PARALLEL_FOR_ARGS(schedule(guided) reduction(+:_nanCount,_infCount,_meanValue,_zeroCount,_positiveCount,_negativeCount) reduction(min:_minValue) reduction(max:_maxValue)) for (Nd4jLong e = 1; e < input->lengthOf(); e++) { @@ -74,18 +74,18 @@ PRAGMA_OMP_PARALLEL_FOR_ARGS(schedule(guided) reduction(+:_nanCount,_infCount,_m auto n = e + 1.; // auto delta = current - _meanValue; // auto delta2 = delta * delta; - _minValue = nd4j::math::nd4j_min(current, _minValue); - _maxValue = nd4j::math::nd4j_max(current, _maxValue); + _minValue = sd::math::nd4j_min(current, _minValue); + _maxValue = sd::math::nd4j_max(current, _maxValue); _meanValue += current; //_meanValue += delta / n; // this is a perfect formula but not working with omp in this notation //_stdDevValue += delta2 * e / n; - _zeroCount += nd4j::math::nd4j_abs(current) > 0.00001 ? 0 : 1; + _zeroCount += sd::math::nd4j_abs(current) > 0.00001 ? 0 : 1; _positiveCount += current > 0 ? 1 : 0; _negativeCount += current < 0 ? 1 : 0; - _infCount += nd4j::math::nd4j_isinf(current); - _nanCount += nd4j::math::nd4j_isnan(current); + _infCount += sd::math::nd4j_isinf(current); + _nanCount += sd::math::nd4j_isnan(current); } *info = {_minValue, _maxValue, _meanValue / input->lengthOf(), _stdDevValue, _zeroCount, _positiveCount, _negativeCount, _infCount, _nanCount}; _stdDevValue = 0; //math::nd4j_sqrt(info->_stdDevValue / (input->lengthOf() - 1)); diff --git a/libnd4j/include/helpers/impl/EnumUtils.cpp b/libnd4j/include/helpers/impl/EnumUtils.cpp index 15fff8e43..a18592d0b 100644 --- a/libnd4j/include/helpers/impl/EnumUtils.cpp +++ b/libnd4j/include/helpers/impl/EnumUtils.cpp @@ -21,10 +21,10 @@ #include #include -using namespace nd4j::graph; +using namespace sd::graph; -namespace nd4j { - const char * EnumUtils::_VariableTypeToString(nd4j::graph::VariableType variableType) { +namespace sd { + const char * EnumUtils::_VariableTypeToString(sd::graph::VariableType variableType) { switch (variableType) { case NDARRAY: return "NDARRAY"; case ARRAY_LIST: return "ARRAY_LIST"; @@ -33,7 +33,7 @@ namespace nd4j { } } - const char * EnumUtils::_OpTypeToString(nd4j::graph::OpType opType) { + const char * EnumUtils::_OpTypeToString(sd::graph::OpType opType) { switch(opType) { case OpType_REDUCE_SAME: return "REDUCE_SAME"; case OpType_REDUCE_BOOL: return "REDUCE_BOOL"; diff --git a/libnd4j/include/helpers/impl/GradCheck.cpp b/libnd4j/include/helpers/impl/GradCheck.cpp index 8b24e5f16..2643a7b6d 100644 --- a/libnd4j/include/helpers/impl/GradCheck.cpp +++ b/libnd4j/include/helpers/impl/GradCheck.cpp @@ -18,11 +18,11 @@ // @author Yurii Shyrma (iuriish@yahoo.com), created on 16.07.2018 // -#include -#include +#include +#include -namespace nd4j { +namespace sd { ////////////////////////////////////////////////////////////////////////// void GradCheck::fillGradArrays(const LossFunc loss, const std::vector& gradArrs) { @@ -60,9 +60,9 @@ bool GradCheck::checkGrad(ops::DeclarableOp& opFF, ops::DeclarableOp& opBP, cons fillGradArrays(loss, std::vector(&inArrsBP[numInArrsFF], &inArrsBP[numInArrsFF + numInGradArrsBP])); // back prop pass - ResultSet* outArrsBP = opBP.execute(argsHolderBP); // number of output arrays in back prop = numInArrsFF; + ResultSet outArrsBP = opBP.execute(argsHolderBP); // number of output arrays in back prop = numInArrsFF; - NDArray tmpScalar(nd4j::DataType::DOUBLE, inArrsFF[0]->getContext()); // scalar = 0 + NDArray tmpScalar(sd::DataType::DOUBLE, inArrsFF[0]->getContext()); // scalar = 0 for(int i = 0; i < numInArrsFF; ++i) { // loop through input array @@ -78,18 +78,17 @@ bool GradCheck::checkGrad(ops::DeclarableOp& opFF, ops::DeclarableOp& opBP, cons // add epsilon, feed forward inArrsFF[i]->p(j, orig + EPSILON); - ResultSet* outArrsFF = opFF.execute(argsHolderFF); - int numOutArrs = outArrsFF->size(); + ResultSet outArrsFF = opFF.execute(argsHolderFF); + int numOutArrs = outArrsFF.size(); double scorePlus = 0.; for(int k = 0; k < numOutArrs; ++k) { // loop through output arrays if(loss == SUM) - outArrsFF->at(k)->reduceNumber(reduce::Sum, tmpScalar); + outArrsFF.at(k)->reduceNumber(reduce::Sum, tmpScalar); else - outArrsFF->at(k)->reduceNumber(reduce::Mean, tmpScalar); + outArrsFF.at(k)->reduceNumber(reduce::Mean, tmpScalar); scorePlus += tmpScalar.e(0); } - delete outArrsFF; // subtract epsilon, feed forward inArrsFF[i]->p(j, orig - EPSILON); @@ -98,12 +97,11 @@ bool GradCheck::checkGrad(ops::DeclarableOp& opFF, ops::DeclarableOp& opBP, cons for(int k = 0; k < numOutArrs; ++k) { // loop through output arrays if(loss == SUM) - outArrsFF->at(k)->reduceNumber(reduce::Sum, tmpScalar); + outArrsFF.at(k)->reduceNumber(reduce::Sum, tmpScalar); else - outArrsFF->at(k)->reduceNumber(reduce::Mean, tmpScalar); + outArrsFF.at(k)->reduceNumber(reduce::Mean, tmpScalar); scoreMinus += tmpScalar.e(0); } - delete outArrsFF; // restore initial element value inArrsFF[i]->p(j, orig); @@ -116,7 +114,7 @@ bool GradCheck::checkGrad(ops::DeclarableOp& opFF, ops::DeclarableOp& opBP, cons } // get analytical gradient - const double analyticGrad = outArrsBP->at(i)->e(j); + const double analyticGrad = outArrsBP.at(i)->e(j); if(std::isnan(analyticGrad) || std::isinf(analyticGrad)) { printf("GradCheck::checkGrad: got wrong value for analytical gradient for input array # %i and its element at position %lld ! \n", i, j); throw std::runtime_error(""); @@ -138,13 +136,11 @@ bool GradCheck::checkGrad(ops::DeclarableOp& opFF, ops::DeclarableOp& opBP, cons continue; printf("numericalGrad = %f, analyticGrad = %f \n", numericalGrad, analyticGrad); printf("GradCheck::checkGrad: got RELERROR = %f > MAXRELERROR(%f) for input array # %i and its element at position %lld ! \n", relError, MAXRELERR, i, j); - delete outArrsBP; return false; } } } - delete outArrsBP; return true; } diff --git a/libnd4j/include/helpers/impl/MmulHelper.cpp b/libnd4j/include/helpers/impl/MmulHelper.cpp index abc353132..bc525622a 100644 --- a/libnd4j/include/helpers/impl/MmulHelper.cpp +++ b/libnd4j/include/helpers/impl/MmulHelper.cpp @@ -24,19 +24,19 @@ #include "../MmulHelper.h" #include #include -#include +#include -namespace nd4j { +namespace sd { ////////////////////////////////////////////////////////////////////////// -nd4j::NDArray* nd4j::MmulHelper::tensorDot(const nd4j::NDArray* A, const nd4j::NDArray* B, const std::initializer_list& axesA, const std::initializer_list& axesB) { +sd::NDArray* sd::MmulHelper::tensorDot(const sd::NDArray* A, const sd::NDArray* B, const std::initializer_list& axesA, const std::initializer_list& axesB) { std::vector aA(axesA); std::vector aB(axesB); return tensorDot(A, B, aA, aB); } ////////////////////////////////////////////////////////////////////////// -nd4j::NDArray* nd4j::MmulHelper::tensorDot(const nd4j::NDArray* a, const nd4j::NDArray* b, const std::vector& axes_0, const std::vector& axes_1) { +sd::NDArray* sd::MmulHelper::tensorDot(const sd::NDArray* a, const sd::NDArray* b, const std::vector& axes_0, const std::vector& axes_1) { std::vector permutAt, permutBt; std::vector shapeAt, shapeBt; @@ -68,7 +68,7 @@ nd4j::NDArray* nd4j::MmulHelper::tensorDot(const nd4j::NDArray* a, const nd4j::N } ////////////////////////////////////////////////////////////////////////// -void nd4j::MmulHelper::tensorDot(const nd4j::NDArray* a, const nd4j::NDArray* b, nd4j::NDArray* c, const std::vector& axes_a, const std::vector& axes_b, const std::vector& permutForC) { +void sd::MmulHelper::tensorDot(const sd::NDArray* a, const sd::NDArray* b, sd::NDArray* c, const std::vector& axes_a, const std::vector& axes_b, const std::vector& permutForC) { std::vector permutAt, permutBt; std::vector shapeAt, shapeBt; @@ -112,7 +112,7 @@ void nd4j::MmulHelper::tensorDot(const nd4j::NDArray* a, const nd4j::NDArray* b, #ifndef __JAVACPP_HACK__ ////////////////////////////////////////////////////////////////////////// -void nd4j::MmulHelper::tensorDot(const NDArray* a, const NDArray* b, NDArray* c, const std::vector>& modifA, const std::vector>& modifB, const std::vector>& modifC) { +void sd::MmulHelper::tensorDot(const NDArray* a, const NDArray* b, NDArray* c, const std::vector>& modifA, const std::vector>& modifB, const std::vector>& modifC) { NDArray *aPR(const_cast(a)), *bPR(const_cast(b)); std::string whatToDoWithA, whatToDoWithB, whatToDoWithC; // "" - nothing; "p" - permutation; "r" - reshaping; "pr" - permutation+reshaping; "rp" - reshaping/permutation, and so on; if another string is produced - throw exception @@ -163,7 +163,7 @@ void nd4j::MmulHelper::tensorDot(const NDArray* a, const NDArray* b, NDArray* c, } ////////////////////////////////////////////////////////////////////////// -NDArray* nd4j::MmulHelper::tensorDot(const nd4j::NDArray* a, const nd4j::NDArray* b, const std::vector>& modifA, const std::vector>& modifB) { +NDArray* sd::MmulHelper::tensorDot(const sd::NDArray* a, const sd::NDArray* b, const std::vector>& modifA, const std::vector>& modifB) { NDArray *aPR(const_cast(a)), *bPR(const_cast(b)); std::string whatToDoWithA, whatToDoWithB; // "" - nothing; "p" - permutation only; "r" - reshaping only; "pr" - permutation+reshaping; "rp" - reshaping/permutation; another string - throw exception @@ -198,7 +198,7 @@ NDArray* nd4j::MmulHelper::tensorDot(const nd4j::NDArray* a, const nd4j::NDArray ////////////////////////////////////////////////////////////////////////// -nd4j::NDArray* MmulHelper::mmul(const nd4j::NDArray* A, const nd4j::NDArray* B, nd4j::NDArray* C , const double alpha, const double beta, const char outOrder) { +sd::NDArray* MmulHelper::mmul(const sd::NDArray* A, const sd::NDArray* B, sd::NDArray* C , const double alpha, const double beta, const char outOrder) { int lenDim; const int aRank = A->rankOf(); @@ -239,7 +239,7 @@ nd4j::NDArray* MmulHelper::mmul(const nd4j::NDArray* A, const nd4j::NDArray* B, ////////////////////////////////////////////////////////////////////////// - void MmulHelper::matmul(const nd4j::NDArray* x, const nd4j::NDArray* y, nd4j::NDArray* z, const bool transX, const bool transY) { + void MmulHelper::matmul(const sd::NDArray* x, const sd::NDArray* y, sd::NDArray* z, const bool transX, const bool transY) { int xRank = x->rankOf(); int yRank = y->rankOf(); diff --git a/libnd4j/include/helpers/impl/OmpLaunchHelper.cpp b/libnd4j/include/helpers/impl/OmpLaunchHelper.cpp index 80e456e29..0e409a952 100644 --- a/libnd4j/include/helpers/impl/OmpLaunchHelper.cpp +++ b/libnd4j/include/helpers/impl/OmpLaunchHelper.cpp @@ -19,14 +19,14 @@ // @author Yurii Shyrma (iuriish@yahoo.com) // -#include -#include -#include +#include +#include +#include #ifdef _OPENMP #include #endif -namespace nd4j { +namespace sd { //////////////////////////////////////////////////////////////////////////////// @@ -43,11 +43,11 @@ OmpLaunchHelper::OmpLaunchHelper(const Nd4jLong N, float desiredNumThreads) { else if(desiredNumThreads < 1) desiredNumThreads = 1; else - desiredNumThreads = nd4j::math::nd4j_min(omp_get_max_threads(), desiredNumThreads); + desiredNumThreads = sd::math::nd4j_min(omp_get_max_threads(), desiredNumThreads); #else - desiredNumThreads = nd4j::Environment::getInstance()->maxThreads(); + desiredNumThreads = sd::Environment::getInstance()->maxThreads(); #endif - _numThreads = nd4j::math::nd4j_min(N / maxItersPerThread, desiredNumThreads); + _numThreads = sd::math::nd4j_min(N / maxItersPerThread, desiredNumThreads); } _itersPerThread = N / _numThreads; @@ -75,7 +75,7 @@ Nd4jLong OmpLaunchHelper::betterSpan(Nd4jLong N) { #ifdef _OPENMP return betterThreads(N, omp_get_max_threads()); #else - return betterThreads(N, nd4j::Environment::getInstance()->maxThreads());; + return betterThreads(N, sd::Environment::getInstance()->maxThreads());; #endif } @@ -84,7 +84,7 @@ Nd4jLong OmpLaunchHelper::betterSpan(Nd4jLong N) { if (N < t) return 1; else { - return static_cast(nd4j::math::nd4j_min(N / t, maxThreads)); + return static_cast(sd::math::nd4j_min(N / t, maxThreads)); } } @@ -92,7 +92,7 @@ Nd4jLong OmpLaunchHelper::betterSpan(Nd4jLong N) { #ifdef _OPENMP auto maxThreads = omp_get_max_threads(); #else - auto maxThreads = nd4j::Environment::getInstance()->maxThreads(); + auto maxThreads = sd::Environment::getInstance()->maxThreads(); #endif // if there's only 1 thread allowed - nothing to do here @@ -106,6 +106,6 @@ Nd4jLong OmpLaunchHelper::betterSpan(Nd4jLong N) { return 1; // by default we're spawning as many threads we can, but not more than number of TADs - return nd4j::math::nd4j_min(numTads, maxThreads); + return sd::math::nd4j_min(numTads, maxThreads); } } diff --git a/libnd4j/include/helpers/impl/OpArgsHolder.cpp b/libnd4j/include/helpers/impl/OpArgsHolder.cpp index 816253bc6..7b82a85d9 100644 --- a/libnd4j/include/helpers/impl/OpArgsHolder.cpp +++ b/libnd4j/include/helpers/impl/OpArgsHolder.cpp @@ -18,10 +18,10 @@ // @author Yurii Shyrma (iuriish@yahoo.com), created on 15.07.2018 // -#include +#include -namespace nd4j { +namespace sd { //////////////////////////////////////////////////////////////////////// // default constructor diff --git a/libnd4j/include/helpers/impl/OpBenchmark.cpp b/libnd4j/include/helpers/impl/OpBenchmark.cpp index 304ca9723..6cb0dc08a 100644 --- a/libnd4j/include/helpers/impl/OpBenchmark.cpp +++ b/libnd4j/include/helpers/impl/OpBenchmark.cpp @@ -20,7 +20,7 @@ #include "../OpBenchmark.h" -namespace nd4j { +namespace sd { OpBenchmark::OpBenchmark(std::string name, NDArray *x, NDArray *y, NDArray *z) { _testName = name; _x = x; diff --git a/libnd4j/include/helpers/impl/OpTracker.cpp b/libnd4j/include/helpers/impl/OpTracker.cpp index 1fc4f330d..bb82ab0d1 100644 --- a/libnd4j/include/helpers/impl/OpTracker.cpp +++ b/libnd4j/include/helpers/impl/OpTracker.cpp @@ -21,13 +21,13 @@ #include #include #include -#include +#include -using namespace nd4j::ops; -using namespace nd4j::graph; +using namespace sd::ops; +using namespace sd::graph; -namespace nd4j { +namespace sd { OpTracker* OpTracker::getInstance() { if (_INSTANCE == 0) @@ -36,7 +36,7 @@ namespace nd4j { return _INSTANCE; } - void OpTracker::storeOperation(nd4j::graph::OpType opType, const OpDescriptor& descriptor) { + void OpTracker::storeOperation(sd::graph::OpType opType, const OpDescriptor& descriptor) { // check out CPU features if (!::isMinimalRequirementsMet()) { @@ -75,7 +75,7 @@ namespace nd4j { _map[opType].emplace_back(descriptor); } - void OpTracker::storeOperation(nd4j::graph::OpType opType, const char* opName, const Nd4jLong opNum) { + void OpTracker::storeOperation(sd::graph::OpType opType, const char* opName, const Nd4jLong opNum) { OpDescriptor descriptor(0, opName, false); descriptor.setOpNum((int) opNum); descriptor.setHash(-1); @@ -119,5 +119,5 @@ namespace nd4j { return _export.c_str(); } - nd4j::OpTracker* nd4j::OpTracker::_INSTANCE = 0; + sd::OpTracker* sd::OpTracker::_INSTANCE = 0; } diff --git a/libnd4j/include/helpers/impl/Parameters.cpp b/libnd4j/include/helpers/impl/Parameters.cpp index d2678832f..356ad5a5a 100644 --- a/libnd4j/include/helpers/impl/Parameters.cpp +++ b/libnd4j/include/helpers/impl/Parameters.cpp @@ -21,7 +21,7 @@ #include "../benchmark/Parameters.h" #include -namespace nd4j { +namespace sd { Parameters* Parameters::addIntParam(std::string string, int param) { _intParams[string] = param; return this; diff --git a/libnd4j/include/helpers/impl/RandomLauncher.cpp b/libnd4j/include/helpers/impl/RandomLauncher.cpp index 099040dc5..8114c2ec4 100644 --- a/libnd4j/include/helpers/impl/RandomLauncher.cpp +++ b/libnd4j/include/helpers/impl/RandomLauncher.cpp @@ -19,16 +19,16 @@ // #include -#include +#include #include #include //#include #include -namespace nd4j { +namespace sd { // FIXME: implement this - void RandomLauncher::applyDropOut(nd4j::LaunchContext *context, nd4j::graph::RandomGenerator& rng, NDArray *array, double retainProb, NDArray* z) { + void RandomLauncher::applyDropOut(sd::LaunchContext *context, sd::graph::RandomGenerator& rng, NDArray *array, double retainProb, NDArray* z) { if (z == nullptr) z = array; @@ -39,7 +39,7 @@ namespace nd4j { pm.synchronize(); } - void RandomLauncher::applyInvertedDropOut(nd4j::LaunchContext *context, nd4j::graph::RandomGenerator& rng, NDArray *array, double retainProb, NDArray* z) { + void RandomLauncher::applyInvertedDropOut(sd::LaunchContext *context, sd::graph::RandomGenerator& rng, NDArray *array, double retainProb, NDArray* z) { if (z == nullptr) z = array; @@ -50,7 +50,7 @@ namespace nd4j { pm.synchronize(); } - void RandomLauncher::applyAlphaDropOut(nd4j::LaunchContext *context, nd4j::graph::RandomGenerator& rng, NDArray *array, double retainProb, double alpha, double beta, double alphaPrime, NDArray* z) { + void RandomLauncher::applyAlphaDropOut(sd::LaunchContext *context, sd::graph::RandomGenerator& rng, NDArray *array, double retainProb, double alpha, double beta, double alphaPrime, NDArray* z) { if (z == nullptr) z = array; @@ -61,7 +61,7 @@ namespace nd4j { pm.synchronize(); } - void RandomLauncher::fillBernoulli(nd4j::LaunchContext *context, nd4j::graph::RandomGenerator& rng, NDArray* array, double prob) { + void RandomLauncher::fillBernoulli(sd::LaunchContext *context, sd::graph::RandomGenerator& rng, NDArray* array, double prob) { ExtraArguments arguments({prob}); PointersManager pm(context, "fillBernoulli"); @@ -69,7 +69,7 @@ namespace nd4j { pm.synchronize(); } - void RandomLauncher::fillUniform(nd4j::LaunchContext *context, nd4j::graph::RandomGenerator& rng, NDArray* array, double from, double to) { + void RandomLauncher::fillUniform(sd::LaunchContext *context, sd::graph::RandomGenerator& rng, NDArray* array, double from, double to) { ExtraArguments arguments({from, to}); PointersManager pm(context, "fillUniform"); @@ -77,7 +77,7 @@ namespace nd4j { pm.synchronize(); } - void RandomLauncher::fillGaussian(nd4j::LaunchContext *context, nd4j::graph::RandomGenerator& rng, NDArray* array, double mean, double stdev) { + void RandomLauncher::fillGaussian(sd::LaunchContext *context, sd::graph::RandomGenerator& rng, NDArray* array, double mean, double stdev) { ExtraArguments arguments({mean, stdev}); PointersManager pm(context, "fillGaussian"); @@ -85,7 +85,7 @@ namespace nd4j { pm.synchronize(); } - void RandomLauncher::fillExponential(nd4j::LaunchContext *context, nd4j::graph::RandomGenerator& rng, NDArray* array, double lambda) { + void RandomLauncher::fillExponential(sd::LaunchContext *context, sd::graph::RandomGenerator& rng, NDArray* array, double lambda) { ExtraArguments arguments({lambda}); PointersManager pm(context, "fillExponential"); @@ -93,7 +93,7 @@ namespace nd4j { pm.synchronize(); } - void RandomLauncher::fillLogNormal(nd4j::LaunchContext *context, nd4j::graph::RandomGenerator& rng, NDArray* array, double mean, double stdev) { + void RandomLauncher::fillLogNormal(sd::LaunchContext *context, sd::graph::RandomGenerator& rng, NDArray* array, double mean, double stdev) { ExtraArguments arguments({mean, stdev}); PointersManager pm(context, "fillLogNormal"); @@ -101,7 +101,7 @@ namespace nd4j { pm.synchronize(); } - void RandomLauncher::fillTruncatedNormal(nd4j::LaunchContext *context, nd4j::graph::RandomGenerator& rng, NDArray* array, double mean, double stdev) { + void RandomLauncher::fillTruncatedNormal(sd::LaunchContext *context, sd::graph::RandomGenerator& rng, NDArray* array, double mean, double stdev) { ExtraArguments arguments({mean, stdev}); PointersManager pm(context, "fillTruncatedNormal"); @@ -109,7 +109,7 @@ namespace nd4j { pm.synchronize(); } - void RandomLauncher::fillBinomial(nd4j::LaunchContext *context, nd4j::graph::RandomGenerator& rng, NDArray* array, int trials, double prob) { + void RandomLauncher::fillBinomial(sd::LaunchContext *context, sd::graph::RandomGenerator& rng, NDArray* array, int trials, double prob) { ExtraArguments arguments({(double) trials, prob}); PointersManager pm(context, "fillBinomial"); diff --git a/libnd4j/include/helpers/impl/ShapeBuilders.cpp b/libnd4j/include/helpers/impl/ShapeBuilders.cpp index d8443e180..7c0c7fed6 100644 --- a/libnd4j/include/helpers/impl/ShapeBuilders.cpp +++ b/libnd4j/include/helpers/impl/ShapeBuilders.cpp @@ -20,10 +20,10 @@ #include -namespace nd4j { +namespace sd { - Nd4jLong* ShapeBuilders::createScalarShapeInfo(const nd4j::DataType dataType, nd4j::memory::Workspace* workspace) { + Nd4jLong* ShapeBuilders::createScalarShapeInfo(const sd::DataType dataType, sd::memory::Workspace* workspace) { Nd4jLong *newShape; ALLOCATE(newShape, workspace, shape::shapeInfoLength(0), Nd4jLong); newShape[0] = 0; @@ -31,12 +31,12 @@ namespace nd4j { newShape[2] = 1; newShape[3] = 99; - nd4j::ArrayOptions::setDataType(newShape, dataType); + sd::ArrayOptions::setDataType(newShape, dataType); return newShape; } - Nd4jLong* ShapeBuilders::createVectorShapeInfo(const nd4j::DataType dataType, const Nd4jLong length, nd4j::memory::Workspace* workspace) { + Nd4jLong* ShapeBuilders::createVectorShapeInfo(const sd::DataType dataType, const Nd4jLong length, sd::memory::Workspace* workspace) { Nd4jLong *newShape; ALLOCATE(newShape, workspace, shape::shapeInfoLength(1), Nd4jLong); @@ -47,13 +47,13 @@ namespace nd4j { newShape[4] = 1; newShape[5] = 99; - nd4j::ArrayOptions::setDataType(newShape, dataType); + sd::ArrayOptions::setDataType(newShape, dataType); return newShape; } //////////////////////////////////////////////////////////////////////////////// - Nd4jLong* ShapeBuilders::createShapeInfo(const nd4j::DataType dataType, const char order, int rank, const Nd4jLong* shapeOnly, memory::Workspace* workspace) { + Nd4jLong* ShapeBuilders::createShapeInfo(const sd::DataType dataType, const char order, int rank, const Nd4jLong* shapeOnly, memory::Workspace* workspace) { Nd4jLong* shapeInfo = nullptr; if(rank == 0) { // scalar case @@ -79,19 +79,19 @@ namespace nd4j { ArrayOptions::setPropertyBit(shapeInfo, ARRAY_EMPTY); } - nd4j::ArrayOptions::setDataType(shapeInfo, dataType); + sd::ArrayOptions::setDataType(shapeInfo, dataType); } return shapeInfo; } - Nd4jLong* ShapeBuilders::emptyShapeInfo(const nd4j::DataType dataType, memory::Workspace* workspace) { + Nd4jLong* ShapeBuilders::emptyShapeInfo(const sd::DataType dataType, memory::Workspace* workspace) { auto shapeInfo = createScalarShapeInfo(dataType, workspace); ArrayOptions::setPropertyBit(shapeInfo, ARRAY_EMPTY); return shapeInfo; } - Nd4jLong* ShapeBuilders::emptyShapeInfo(const nd4j::DataType dataType, const char order, const std::vector &shape, memory::Workspace* workspace) { + Nd4jLong* ShapeBuilders::emptyShapeInfo(const sd::DataType dataType, const char order, const std::vector &shape, memory::Workspace* workspace) { auto shapeInfo = createShapeInfo(dataType, order, shape, workspace); memset(shape::stride(shapeInfo), 0, shape.size() * sizeof(Nd4jLong)); ArrayOptions::setPropertyBit(shapeInfo, ARRAY_EMPTY); @@ -99,13 +99,13 @@ namespace nd4j { } //////////////////////////////////////////////////////////////////////////////// - Nd4jLong* ShapeBuilders::createShapeInfo(const nd4j::DataType dataType, const char order, const std::vector& shapeOnly, memory::Workspace* workspace) { + Nd4jLong* ShapeBuilders::createShapeInfo(const sd::DataType dataType, const char order, const std::vector& shapeOnly, memory::Workspace* workspace) { return ShapeBuilders::createShapeInfo(dataType, order, shapeOnly.size(), shapeOnly.data(), workspace); } //////////////////////////////////////////////////////////////////////////////// - Nd4jLong* ShapeBuilders::createShapeInfo(const nd4j::DataType dataType, const char order, const std::initializer_list& shapeOnly, memory::Workspace* workspace) { + Nd4jLong* ShapeBuilders::createShapeInfo(const sd::DataType dataType, const char order, const std::initializer_list& shapeOnly, memory::Workspace* workspace) { return ShapeBuilders::createShapeInfo(dataType, order, std::vector(shapeOnly), workspace); } diff --git a/libnd4j/include/helpers/impl/ShapeUtils.cpp b/libnd4j/include/helpers/impl/ShapeUtils.cpp index 10babeae1..aa8e917cc 100644 --- a/libnd4j/include/helpers/impl/ShapeUtils.cpp +++ b/libnd4j/include/helpers/impl/ShapeUtils.cpp @@ -27,7 +27,7 @@ #include -namespace nd4j { +namespace sd { ////////////////////////////////////////////////////////////////////////// // evaluate shape for array resulting from tensorDot operation, also evaluate shapes and dimensions permutations for transposition of two input arrays @@ -124,7 +124,7 @@ std::vector ShapeUtils::evalShapeForTensorDot(const NDArray* a, cons ////////////////////////////////////////////////////////////////////////// // evaluate output shape for reduce operation when input shape is empty -Nd4jLong* ShapeUtils::evalReduceShapeInfoEmpty(const char order, std::vector& dimsToExclude, const Nd4jLong *shapeInfo, const nd4j::DataType dataType, const bool keepDims, nd4j::memory::Workspace* workspace) { +Nd4jLong* ShapeUtils::evalReduceShapeInfoEmpty(const char order, std::vector& dimsToExclude, const Nd4jLong *shapeInfo, const sd::DataType dataType, const bool keepDims, sd::memory::Workspace* workspace) { if (dimsToExclude.size() == 0) { // return copy of input shape Nd4jLong* outShapeInfo = ShapeBuilders::copyShapeInfoAndType(shapeInfo, dataType, true, workspace); @@ -171,22 +171,22 @@ Nd4jLong* ShapeUtils::evalReduceShapeInfoEmpty(const char order, std::vectorbufferForShapeInfo(descriptor).primaryAsT(); } -Nd4jLong* ShapeUtils::evalReduceShapeInfo(const char order, std::vector& dimsToExclude, const NDArray& arr, const bool keepDims, const bool supportOldShapes, nd4j::memory::Workspace* workspace) { +Nd4jLong* ShapeUtils::evalReduceShapeInfo(const char order, std::vector& dimsToExclude, const NDArray& arr, const bool keepDims, const bool supportOldShapes, sd::memory::Workspace* workspace) { return evalReduceShapeInfo(order, dimsToExclude, arr, arr.dataType(), keepDims, supportOldShapes, workspace); } -Nd4jLong* ShapeUtils::evalReduceShapeInfo(const char order, std::vector& dimsToExclude, const Nd4jLong* shapeInfo, const bool keepDims, const bool supportOldShapes, nd4j::memory::Workspace* workspace) { +Nd4jLong* ShapeUtils::evalReduceShapeInfo(const char order, std::vector& dimsToExclude, const Nd4jLong* shapeInfo, const bool keepDims, const bool supportOldShapes, sd::memory::Workspace* workspace) { return evalReduceShapeInfo(order, dimsToExclude, shapeInfo, ArrayOptions::dataType(shapeInfo), keepDims, supportOldShapes, workspace); } ////////////////////////////////////////////////////////////////////////// -Nd4jLong* ShapeUtils::evalReduceShapeInfo(const char order, std::vector& dimsToExclude, const NDArray& arr, const nd4j::DataType dataType, const bool keepDims, const bool supportOldShapes, nd4j::memory::Workspace* workspace) { +Nd4jLong* ShapeUtils::evalReduceShapeInfo(const char order, std::vector& dimsToExclude, const NDArray& arr, const sd::DataType dataType, const bool keepDims, const bool supportOldShapes, sd::memory::Workspace* workspace) { return evalReduceShapeInfo(order, dimsToExclude, arr.getShapeInfo(), dataType, keepDims, supportOldShapes, workspace); } ////////////////////////////////////////////////////////////////////////// // evaluate shape resulting from reduce operation -Nd4jLong* ShapeUtils::evalReduceShapeInfo(const char order, std::vector& dimsToExclude, const Nd4jLong *shapeInfo, const nd4j::DataType dataType, const bool keepDims, const bool supportOldShapes, nd4j::memory::Workspace* workspace) { +Nd4jLong* ShapeUtils::evalReduceShapeInfo(const char order, std::vector& dimsToExclude, const Nd4jLong *shapeInfo, const sd::DataType dataType, const bool keepDims, const bool supportOldShapes, sd::memory::Workspace* workspace) { if(ArrayOptions::arrayType(shapeInfo) == ArrayType::EMPTY) return ShapeUtils::evalReduceShapeInfoEmpty(order, dimsToExclude, shapeInfo, dataType, keepDims, workspace); @@ -306,7 +306,6 @@ std::vector ShapeUtils::evalRepeatShape(int axis, const std::vector ShapeUtils::evalRepeatShape(int axis, const std::vector dims(dimensions, dimensions + rank); return evalPermShapeInfo(dims.data(), rank, arr, workspace); @@ -355,7 +354,7 @@ Nd4jLong* ShapeUtils::evalPermShapeInfo(const int* dimensions, const int rank, c ////////////////////////////////////////////////////////////////////////// // evaluate shapeInfo of transposed array - Nd4jLong* ShapeUtils::evalTranspShapeInfo(const NDArray& arr, nd4j::memory::Workspace* workspace, const bool setContigStrides) { + Nd4jLong* ShapeUtils::evalTranspShapeInfo(const NDArray& arr, sd::memory::Workspace* workspace, const bool setContigStrides) { int rank = arr.rankOf(); std::vector dimensions(rank); @@ -444,11 +443,11 @@ bool ShapeUtils::areShapesBroadcastable(const std::vector& shape1, con ////////////////////////////////////////////////////////////////////////// // check the possibility of broadcast operation, if true then return shapeInfo of resulting array // if evalMinMax == false the array with larger rank has to be passed as first argument -bool ShapeUtils::evalBroadcastShapeInfo(const NDArray &max, const NDArray &min, const bool evalMinMax, Nd4jLong*& resultShapeInfo, nd4j::memory::Workspace* workspace) { +bool ShapeUtils::evalBroadcastShapeInfo(const NDArray &max, const NDArray &min, const bool evalMinMax, Nd4jLong*& resultShapeInfo, sd::memory::Workspace* workspace) { return evalBroadcastShapeInfo(max.getShapeInfo(), min.getShapeInfo(), evalMinMax, resultShapeInfo, workspace); } -bool ShapeUtils::evalBroadcastShapeInfo(Nd4jLong *max, Nd4jLong *min, const bool evalMinMax, Nd4jLong*& resultShapeInfo, nd4j::memory::Workspace* workspace) { +bool ShapeUtils::evalBroadcastShapeInfo(Nd4jLong *max, Nd4jLong *min, const bool evalMinMax, Nd4jLong*& resultShapeInfo, sd::memory::Workspace* workspace) { // check whether broadcast operation is possible for input arrays if(!areShapesBroadcastable(max, min)) @@ -561,7 +560,7 @@ std::vector ShapeUtils::getDimsWithSameShape(const NDArray& arr1, const NDA ////////////////////////////////////////////////////////////////////////// // evaluate shapeInfo for resulting array from tile operation -Nd4jLong* ShapeUtils::evalTileShapeInfo(const NDArray& arr, const std::vector& reps, nd4j::memory::Workspace* workspace) { +Nd4jLong* ShapeUtils::evalTileShapeInfo(const NDArray& arr, const std::vector& reps, sd::memory::Workspace* workspace) { // check whether reps contains at least one zero (then throw exception) or whether all elements in reps are unities (then simply reshape or do nothing) int repsSize = reps.size(); Nd4jLong product = 1; @@ -725,7 +724,7 @@ std::vector ShapeUtils::shapeAsVector(const Nd4jLong* shapeInfo) { ////////////////////////////////////////////////////////////////////////// // evaluate shapeInfo for diagonal array which is made using input arr elements as diagonal -Nd4jLong* ShapeUtils::evalDiagShapeInfo(const Nd4jLong* shapeInfoConst, nd4j::memory::Workspace* workspace){ +Nd4jLong* ShapeUtils::evalDiagShapeInfo(const Nd4jLong* shapeInfoConst, sd::memory::Workspace* workspace){ auto shapeInfo = const_cast(shapeInfoConst); const auto rank = shape::rank(shapeInfo); @@ -766,7 +765,7 @@ std::vector ShapeUtils::evalBroadcastBackwardAxis(const Nd4jLong *operandSh } //////////////////////////////////////////////////////////////////////////////// -Nd4jLong* ShapeUtils::matrixProductShape(Nd4jLong* theFirstShape, Nd4jLong* theSecondShape, bool shouldTranspondFirst, bool shouldTranspondSecond, nd4j::DataType dtype, nd4j::memory::Workspace* workspace) { +Nd4jLong* ShapeUtils::matrixProductShape(Nd4jLong* theFirstShape, Nd4jLong* theSecondShape, bool shouldTranspondFirst, bool shouldTranspondSecond, sd::DataType dtype, sd::memory::Workspace* workspace) { auto inA = theFirstShape; auto inB = theSecondShape; @@ -823,7 +822,7 @@ Nd4jLong* ShapeUtils::matrixProductShape(Nd4jLong* theFirstShape, Nd4jLong* theS (shape::isScalar(tmpA) && shape::isVector(tmpB))) { // element-wise shape[0] = 1; - shape[1] = (int) nd4j::math::nd4j_max(shape::length(tmpA), shape::length(tmpB)); + shape[1] = (int) sd::math::nd4j_max(shape::length(tmpA), shape::length(tmpB)); } else if (shape::isRowVector(tmpA) && shape::isRowVector(tmpB)) { // dot case shape[0] = 1; @@ -974,35 +973,6 @@ Nd4jLong ShapeUtils::getNumOfSubArrs(const Nd4jLong* shapeInfo, const std::vecto return numOfSubArrs; } -//////////////////////////////////////////////////////////////////////////////// -void ShapeUtils::evalIdxRangesForSubArr(const Nd4jLong subArrIdx, const Nd4jLong* shapeInfo, const std::vector& dimsToExclude, Nd4jLong* idxRanges) { - - const auto rank = shape::rank(shapeInfo); - const auto subArrRank = static_cast(dimsToExclude.size()); - - if(subArrRank > rank) - throw std::invalid_argument("ShapeUtils::evalIdxRangesForSubArr static method: dimsToExclude is empty or has size > rank of array !"); - - if(subArrRank == 0) { // means whole array - memset(idxRanges, 0, 2 * rank * sizeof(Nd4jLong)); - return; - } - - std::vector shapeOfSubArr(subArrRank), indexes(subArrRank); - for(int i = 0; i < subArrRank; ++i) - shapeOfSubArr[i] = shapeInfo[dimsToExclude[i] + 1]; - - shape::index2coords(subArrIdx, subArrRank, shapeOfSubArr.data(), indexes.data()); - - memset(idxRanges, 0, 2 * rank * sizeof(Nd4jLong)); - - for(int i = 0; i < subArrRank; ++i) { - int currIdx = 2 * dimsToExclude[i]; - idxRanges[currIdx] = indexes[i]; - idxRanges[currIdx + 1] = indexes[i] + 1; - } -} - //////////////////////////////////////////////////////////////////////////////// std::vector ShapeUtils::evalDimsWithoutUnities(const Nd4jLong* shapeInfo) { @@ -1070,7 +1040,7 @@ void ShapeUtils::copyCertainStridesFromShapeInfo(const Nd4jLong* inShapeInfo, co } else { - auto dimEx = nd4j::ShapeUtils::evalDimsToExclude(nRank, dimsSize, dims); + auto dimEx = sd::ShapeUtils::evalDimsToExclude(nRank, dimsSize, dims); for (int i = 0, it = 0; i < nRank; ++i) { auto nCount = std::count(dimEx.cbegin(), dimEx.cend(), i); @@ -1080,6 +1050,19 @@ void ShapeUtils::copyCertainStridesFromShapeInfo(const Nd4jLong* inShapeInfo, co } } } + +bool ShapeUtils::areShapesEqual(const Nd4jLong* shapeInfo, const std::vector& shapeOnly) { + + if(shape::rank(shapeInfo) != shapeOnly.size()) + return false; + + for(uint i = 0; i < shape::rank(shapeInfo); ++i) + if(shape::shapeOf(shapeInfo)[i] != shapeOnly[i]) + return false; + + return true; +} + //////////////////////////////////////////////////////////////////////////////// /* bool ShapeUtils::isSubArrayCase(const NDArray& arr1, const NDArray& arr2, std::vector& sameDims) { diff --git a/libnd4j/include/helpers/impl/SimpleReadWriteLock.cpp b/libnd4j/include/helpers/impl/SimpleReadWriteLock.cpp index 22be1d6ca..52682b925 100644 --- a/libnd4j/include/helpers/impl/SimpleReadWriteLock.cpp +++ b/libnd4j/include/helpers/impl/SimpleReadWriteLock.cpp @@ -21,7 +21,7 @@ #include -namespace nd4j { +namespace sd { SimpleReadWriteLock::SimpleReadWriteLock(const SimpleReadWriteLock& other) { _read_locks.store(other._read_locks.load()); _write_locks.store(other._write_locks.load()); diff --git a/libnd4j/include/helpers/impl/StringUtils.cpp b/libnd4j/include/helpers/impl/StringUtils.cpp index 045dcea73..5ac2fd8cc 100644 --- a/libnd4j/include/helpers/impl/StringUtils.cpp +++ b/libnd4j/include/helpers/impl/StringUtils.cpp @@ -23,7 +23,7 @@ #include #include -namespace nd4j { +namespace sd { static FORCEINLINE bool match(const uint8_t *haystack, const uint8_t *needle, uint64_t length) { for (int e = 0; e < length; e++) if (haystack[e] != needle[e]) @@ -49,7 +49,7 @@ namespace nd4j { uint64_t StringUtils::byteLength(const NDArray &array) { if (!array.isS()) - throw nd4j::datatype_exception::build("StringUtils::byteLength expects one of String types;", array.dataType()); + throw sd::datatype_exception::build("StringUtils::byteLength expects one of String types;", array.dataType()); auto buffer = array.bufferAsT(); return buffer[array.lengthOf()]; diff --git a/libnd4j/include/helpers/impl/TAD.cpp b/libnd4j/include/helpers/impl/TAD.cpp index 1c768f2a7..5d31827da 100644 --- a/libnd4j/include/helpers/impl/TAD.cpp +++ b/libnd4j/include/helpers/impl/TAD.cpp @@ -19,8 +19,8 @@ // -#include "TAD.h" -#include +#include +#include namespace shape { diff --git a/libnd4j/include/helpers/impl/helper_hash.cpp b/libnd4j/include/helpers/impl/helper_hash.cpp index 54eb1731a..b12acb273 100644 --- a/libnd4j/include/helpers/impl/helper_hash.cpp +++ b/libnd4j/include/helpers/impl/helper_hash.cpp @@ -21,7 +21,7 @@ #include #include -namespace nd4j { +namespace sd { namespace ops { HashHelper* HashHelper::getInstance() { @@ -65,7 +65,7 @@ namespace nd4j { return h; } - nd4j::ops::HashHelper* nd4j::ops::HashHelper::_INSTANCE = 0; + sd::ops::HashHelper* sd::ops::HashHelper::_INSTANCE = 0; } } diff --git a/libnd4j/include/helpers/impl/logger.cpp b/libnd4j/include/helpers/impl/logger.cpp index 8c0f09a92..59d8f98bc 100644 --- a/libnd4j/include/helpers/impl/logger.cpp +++ b/libnd4j/include/helpers/impl/logger.cpp @@ -20,7 +20,7 @@ #include -namespace nd4j { +namespace sd { #ifdef __CUDACC__ diff --git a/libnd4j/include/helpers/impl/unicode.cpp b/libnd4j/include/helpers/impl/unicode.cpp index 2e49faf3e..6ebbe7c1b 100644 --- a/libnd4j/include/helpers/impl/unicode.cpp +++ b/libnd4j/include/helpers/impl/unicode.cpp @@ -18,9 +18,9 @@ // @author Oleg Semeniv // -#include +#include -namespace nd4j { +namespace sd { namespace unicode { constexpr uint32_t ONEBYTEBOUND = 0x00000080; diff --git a/libnd4j/include/helpers/jacobiSVD.h b/libnd4j/include/helpers/jacobiSVD.h index 6b22f472a..f6f161bbb 100644 --- a/libnd4j/include/helpers/jacobiSVD.h +++ b/libnd4j/include/helpers/jacobiSVD.h @@ -21,10 +21,10 @@ #ifndef LIBND4J_JACOBISVD_H #define LIBND4J_JACOBISVD_H -#include -#include "NDArray.h" +#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { diff --git a/libnd4j/include/helpers/logger.h b/libnd4j/include/helpers/logger.h index 193935e0d..c13785ff7 100644 --- a/libnd4j/include/helpers/logger.h +++ b/libnd4j/include/helpers/logger.h @@ -23,32 +23,32 @@ #include #include -#include +#include #include #include -#include -#include -#include +#include +#include +#include #ifndef __CUDA_ARCH__ -#define nd4j_debug(FORMAT, ...) if (nd4j::Environment::getInstance()->isDebug() && nd4j::Environment::getInstance()->isVerbose()) nd4j::Logger::info(FORMAT, __VA_ARGS__); -#define nd4j_logger(FORMAT, ...) if (nd4j::Environment::getInstance()->isDebug() && nd4j::Environment::getInstance()->isVerbose()) nd4j::Logger::info(FORMAT, __VA_ARGS__); -#define nd4j_verbose(FORMAT, ...) if (nd4j::Environment::getInstance()->isVerbose()) nd4j::Logger::info(FORMAT, __VA_ARGS__); -#define nd4j_printf(FORMAT, ...) nd4j::Logger::info(FORMAT, __VA_ARGS__); -#define nd4j_printv(FORMAT, VECTOR) nd4j::Logger::printv(FORMAT, VECTOR); +#define nd4j_debug(FORMAT, ...) if (sd::Environment::getInstance()->isDebug() && sd::Environment::getInstance()->isVerbose()) sd::Logger::info(FORMAT, __VA_ARGS__); +#define nd4j_logger(FORMAT, ...) if (sd::Environment::getInstance()->isDebug() && sd::Environment::getInstance()->isVerbose()) sd::Logger::info(FORMAT, __VA_ARGS__); +#define nd4j_verbose(FORMAT, ...) if (sd::Environment::getInstance()->isVerbose()) sd::Logger::info(FORMAT, __VA_ARGS__); +#define nd4j_printf(FORMAT, ...) sd::Logger::info(FORMAT, __VA_ARGS__); +#define nd4j_printv(FORMAT, VECTOR) sd::Logger::printv(FORMAT, VECTOR); #else #define nd4j_debug(FORMAT, A, ...) #define nd4j_logger(FORMAT, A, ...) #define nd4j_verbose(FORMAT, ...) -#define nd4j_printf(FORMAT, ...) nd4j::Logger::info(FORMAT, __VA_ARGS__); +#define nd4j_printf(FORMAT, ...) sd::Logger::info(FORMAT, __VA_ARGS__); #define nd4j_printv(FORMAT, VECTOR) #endif -namespace nd4j { +namespace sd { class ND4J_EXPORT Logger { public: diff --git a/libnd4j/include/helpers/shape.h b/libnd4j/include/helpers/shape.h index d4e95c65f..a6b22ba6d 100644 --- a/libnd4j/include/helpers/shape.h +++ b/libnd4j/include/helpers/shape.h @@ -26,13 +26,13 @@ #include #include -#include "../dll.h" -#include "../nd4jmalloc.h" -#include "../templatemath.h" +#include "system/dll.h" +#include "system/nd4jmalloc.h" +#include "math/templatemath.h" #include "../helpers/logger.h" -#include "../pointercast.h" +#include "system/pointercast.h" #include "../cnpy/cnpy.h" -#include +#include #define MAX_DIMENSION 0x7fffffff #define MAX_NUM_THREADS 1024 @@ -52,7 +52,7 @@ #define INLINEDEF inline #endif -#include "../pairwise_util.h" +#include "system/pairwise_util.h" #include #include @@ -117,7 +117,8 @@ namespace shape { ND4J_EXPORT _CUDA_HD bool haveSameShapeAndStrides(const Nd4jLong *shapeInfo1, const Nd4jLong *shapeInfo2); ND4J_EXPORT _CUDA_HD bool haveSameShapeAndStrides(const Nd4jLong *shapeInfo1, const Nd4jLong *shapeInfo2, const Nd4jLong *shapeInfo3); - ND4J_EXPORT _CUDA_HD int sizeAt(const Nd4jLong *shape, const int dim); + ND4J_EXPORT _CUDA_HD int sizeAt(const Nd4jLong *shapeInfo, const int dim); + ND4J_EXPORT _CUDA_HD Nd4jLong strideAt(const Nd4jLong *shapeInfo, const int dim); template ND4J_EXPORT _CUDA_HD void fill(T* buffer, T value, Nd4jLong length); @@ -141,17 +142,17 @@ namespace shape { * Get the shape info buffer * for the given rank and shape. */ - ND4J_EXPORT _CUDA_HD Nd4jLong *shapeBuffer(int rank, nd4j::DataType dtype, Nd4jLong *shape); + ND4J_EXPORT _CUDA_HD Nd4jLong *shapeBuffer(int rank, sd::DataType dtype, Nd4jLong *shape); - ND4J_EXPORT _CUDA_HD Nd4jLong *shapeBuffer(int rank, nd4j::DataType dtype, Nd4jLong *shape, Nd4jLong *buffer); + ND4J_EXPORT _CUDA_HD Nd4jLong *shapeBuffer(int rank, sd::DataType dtype, Nd4jLong *shape, Nd4jLong *buffer); /** * Get the shape info buffer * for the given rank and shape. */ - ND4J_EXPORT _CUDA_HD Nd4jLong *shapeBufferFortran(int rank, nd4j::DataType dtype, Nd4jLong *shape); + ND4J_EXPORT _CUDA_HD Nd4jLong *shapeBufferFortran(int rank, sd::DataType dtype, Nd4jLong *shape); - ND4J_EXPORT _CUDA_HD Nd4jLong *shapeBufferFortran(int rank, nd4j::DataType dtype, Nd4jLong *shape, Nd4jLong *output); + ND4J_EXPORT _CUDA_HD Nd4jLong *shapeBufferFortran(int rank, sd::DataType dtype, Nd4jLong *shape, Nd4jLong *output); #ifdef __CUDACC__ @@ -469,9 +470,6 @@ namespace shape { ND4J_EXPORT _CUDA_HD int rank(const int *shapeInfo); ND4J_EXPORT _CUDA_HD int rank(const unsigned int *shapeInfo); - // returns pointer on elementWiseStride - ND4J_EXPORT _CUDA_HD Nd4jLong* ews(Nd4jLong* shapeInfo); - /** * returns pointer on elementWiseStride */ @@ -917,12 +915,14 @@ namespace shape { ND4J_EXPORT _CUDA_HD void index2coords(Nd4jLong index, const Nd4jLong *shapeInfo, uint *coords); ND4J_EXPORT _CUDA_HD void index2coords(Nd4jLong index, const int rank, const Nd4jLong *shape, Nd4jLong *coords); ND4J_EXPORT _CUDA_HD void index2coords(Nd4jLong index, const int rank, const Nd4jLong *shape, int *coords); + + ND4J_EXPORT _CUDA_HD void index2coordsCPU(const Nd4jLong& startIndex, const Nd4jLong& index, const Nd4jLong *shapeInfo, Nd4jLong *coords); + ND4J_EXPORT _CUDA_HD void index2coordsCPU(const Nd4jLong& startIndex, const Nd4jLong& index, const Nd4jLong *shapeInfo, int *coords); + /** * take into account only dimensions stored in tadDims, tadDims must be sorted in increasing order! */ - ND4J_EXPORT _CUDA_HD void index2coords(Nd4jLong index, const Nd4jLong *shapeInfo, Nd4jLong *coords, const int dimsSize, const int* tadDims); - - + ND4J_EXPORT _CUDA_HD void index2coords(Nd4jLong index, const Nd4jLong *shapeInfo, int *coords, const int dimsSize, const int* tadDims); /** * Convert coordinates to the corresponding linear index (sequence number in other words) @@ -931,11 +931,11 @@ namespace shape { ND4J_EXPORT _CUDA_HD Nd4jLong coords2index(const Nd4jLong *shapeInfo, const Nd4jLong *coords); ND4J_EXPORT _CUDA_HD Nd4jLong coords2index(const Nd4jLong *shapeInfo, const int *coords); ND4J_EXPORT _CUDA_HD Nd4jLong coords2index(const Nd4jLong *shapeInfo, const uint *coords); - ND4J_EXPORT _CUDA_HD Nd4jLong coords2index(const int rank, const Nd4jLong *shape, const Nd4jLong *coords); + ND4J_EXPORT _CUDA_HD Nd4jLong coords2index(const int rank, const Nd4jLong *shape, const int *coords); /** * take into account only dimensions stored in tadDims, tadDims must be sorted in increasing order! */ - ND4J_EXPORT _CUDA_HD Nd4jLong coords2index(const Nd4jLong *shapeInfo, const Nd4jLong *coords, const int dimsSize, const int* tadDims); + ND4J_EXPORT _CUDA_HD Nd4jLong coords2index(const Nd4jLong *shapeInfo, const int *coords, const int dimsSize, const int* tadDims); /** * increment n-dimensional array by one iteration by changing coord appropriately @@ -990,17 +990,17 @@ namespace shape { // function calculates the coordinates of min array (and saves them into minIdxs) given coordinates of max array (already stored in maxIdxs) // dimsToExclude - should be sorted in increasing order // dimsLen - length of dimsToExclude, if not set (= -1), then it is calculated as maxRank - minRank - ND4J_EXPORT _CUDA_HD void maxIndToMinInd(Nd4jLong* maxIdxs, Nd4jLong* minIdxs, const Nd4jLong* maxShapeInfo, const Nd4jLong* minShapeInfo, const int* dimsToExclude = nullptr, const int dimsLen = -1); + ND4J_EXPORT _CUDA_HD void maxIndToMinInd(int* maxIdxs, int* minIdxs, const Nd4jLong* maxShapeInfo, const Nd4jLong* minShapeInfo, const int* dimsToExclude = nullptr, const int dimsLen = -1); // calculate indexes of max-array, these output indexes correspond to one minIdx index of min-array which is sub-array of max-array // dimsToExclude - should be sorted in increasing order - ND4J_EXPORT _CUDA_HD int outerArrayIndexes(Nd4jLong* maxIdxs, const Nd4jLong minIdx, const Nd4jLong* maxShapeInfo, const Nd4jLong* minShapeInfo, const int* dimsToExclude = nullptr); + ND4J_EXPORT _CUDA_HD int outerArrayIndexes(int* maxIdxs, const Nd4jLong minIdx, const Nd4jLong* maxShapeInfo, const Nd4jLong* minShapeInfo, const int* dimsToExclude = nullptr); // calculate offsets of max-array, these offsets correspond to one minIdx index of min-array which is sub-array of max-array // maxOffsets - will contain calculated offsets of max-array, buffer for maxOffsets should be allocated beforehand // dimsToExclude - should be sorted in increasing order // memBuff - auxiliary memory buffer (size = 2 * max_rank) for coordinates and increments storing, should be allocated beforehand - ND4J_EXPORT _CUDA_HD int outerArrayOffsets(Nd4jLong* maxOffsets, const Nd4jLong minIdx, const Nd4jLong* maxShapeInfo, const Nd4jLong* minShapeInfo, Nd4jLong* memBuff, const int* dimsToExclude = nullptr); + ND4J_EXPORT _CUDA_HD int outerArrayOffsets(Nd4jLong* maxOffsets, const Nd4jLong minIdx, const Nd4jLong* maxShapeInfo, const Nd4jLong* minShapeInfo, int* memBuff, const int* dimsToExclude = nullptr); // calculates offsets for entities (elements or sub-arrays), shape in context of sub-array means dimensions excluded from outer array // rank is equal to size of shape @@ -1008,7 +1008,7 @@ namespace shape { ND4J_EXPORT void calcOffsets(const Nd4jLong* shapeInfo, Nd4jLong* offsets, const char order = 'c'); // ND4J_EXPORT void calcOffsets(const Nd4jLong *xShapeInfo, Nd4jLong*& xOffsets, const Nd4jLong *yShapeInfo, Nd4jLong*& yOffsets, const char order = 'c'); // ND4J_EXPORT void calcOffsets(const Nd4jLong *xShapeInfo, Nd4jLong*& xOffsets, const Nd4jLong *yShapeInfo, Nd4jLong*& yOffsets, const Nd4jLong* zShapeInfo, Nd4jLong*& zOffsets, const char order = 'c'); - ND4J_EXPORT _CUDA_HD void shapeOldScalar(nd4j::DataType dtype, Nd4jLong* const buffer, const char order); + ND4J_EXPORT _CUDA_HD void shapeOldScalar(sd::DataType dtype, Nd4jLong* const buffer, const char order); // deduce order and element-wise stride // if array is scalar or unit length vector then ews = 1 and order is preserved @@ -1029,7 +1029,23 @@ namespace shape { * subArrOffsets - output argument, contains successive sub-arrays offsets from original this-buffer * keepUnitiesInShape - if false then eliminate unities from sub-array shapeInfo, for example {1,a,1,b} -> {a,b} */ - ND4J_EXPORT _CUDA_HD void calcSubArrShapeAndOffsets(const Nd4jLong* wholeShapeInfo, const Nd4jLong numOfSubArrs, const int dimsSize, const int* dimsToExclude, Nd4jLong* subArrShapeInfo, Nd4jLong* subArrOffsets, bool keepUnitiesInShape = false); + ND4J_EXPORT _CUDA_HD void calcSubArrsShapeInfoAndOffsets(const Nd4jLong* wholeShapeInfo, const Nd4jLong numOfSubArrs, const int dimsSize, const int* dimsToExclude, Nd4jLong* subArrShapeInfo, Nd4jLong* subArrOffsets, bool keepUnitiesInShape = false); + + /** + * processes only one sub-array, evaluates shapeInfo of sub-array and its buffer offset from original array + * arguments: + * idx - input argument, intervals of indexes which define the sub-array to point on, + * when isStrided = false then idx has form {dim0Start,dim0End, dim1Start,dim1End, ....} and length (2 * maxRank) + * when isStrided = true then idx has form {dim0Start,dim0End,dim0Stride, dim1Start,dim1End,dim1Stride, ....} and length (3 * maxRank) + * when (dimStart == dimEnd) then whole range will be used for current dimension + * maxShapeInfo - input argument, shapeInfo of original array + * minShapeInfo - output argument, shapeInfo of sub-array to be deduced + * minOffset - output argument, offset of sub-array buffer offsets from original buffer + * keepUnitiesInShape - input argument, if false then eliminate unities from sub-array shapeInfo, for example {1,a,1,b} -> {a,b} + * isStrided - input argument, if true then idx has length (3 * this->rankOf()) and contains additional stride numbers which correspond to stride between dimStart and dimEnd, + * numOfUntiesInMinShape - input argument, number of occurrences in idx when (dimEnd - dimStart) = 1 + */ + ND4J_EXPORT void calcSubArrShapeInfoAndOffset(const Nd4jLong* idx, const Nd4jLong* maxShapeInfo, Nd4jLong* minShapeInfo, Nd4jLong& minOffset, const bool keepUnitiesInShape = false, const bool isStrided = false, const int numOfUntiesInMinShape = 0); /** * for example inShapeInfo is {3, 2,1,4, 4,4,1, 16384,1,99} @@ -1046,6 +1062,12 @@ namespace shape { */ INLINEDEF _CUDA_HD void excludeUnitiesFromShapeInfo(const Nd4jLong* inShapeInfo, const int dimsSize, const int* dimsToExclude, Nd4jLong* outShapeInfo); + /** + * get stride over contiguous axis (contiguous axis must have stride = 1) + * for example when inShapeInfo is {4, 2,5,4,3, 60,1,5,20, 16384,0,99} then output is 5 (that is smallest stride in inShapeInfo except those equal to 1) + */ + // INLINEDEF _CUDA_HD Nd4jLong strideOverContigAxis(const int axis, const Nd4jLong* inShapeInfo); + @@ -1207,7 +1229,7 @@ __device__ INLINEDEF Nd4jLong *cuMalloc(Nd4jLong *buffer, long size) { retShapeLength = 2; } - auto ret = shape::shapeBuffer(retShapeLength, nd4j::ArrayOptions::dataType(originalShapeBuffer), retShape); + auto ret = shape::shapeBuffer(retShapeLength, sd::ArrayOptions::dataType(originalShapeBuffer), retShape); delete[] retShape; return ret; @@ -1681,7 +1703,7 @@ __device__ INLINEDEF Nd4jLong *cuMalloc(Nd4jLong *buffer, long size) { * Get the shape info buffer * for the given rank and shape. */ - INLINEDEF _CUDA_HD Nd4jLong *shapeBuffer(int rank, nd4j::DataType dtype, Nd4jLong *shape) { + INLINEDEF _CUDA_HD Nd4jLong *shapeBuffer(int rank, sd::DataType dtype, Nd4jLong *shape) { Nd4jLong *stride = shape::calcStrides(shape, rank); traceNew(11); @@ -1697,7 +1719,7 @@ __device__ INLINEDEF Nd4jLong *cuMalloc(Nd4jLong *buffer, long size) { auto shapeInfoBuffer = shape::toShapeBuffer(shapeInfo); delete[] stride; delete shapeInfo; - nd4j::ArrayOptions::setDataType(shapeInfoBuffer, dtype); + sd::ArrayOptions::setDataType(shapeInfoBuffer, dtype); return shapeInfoBuffer; } @@ -1706,7 +1728,7 @@ __device__ INLINEDEF Nd4jLong *cuMalloc(Nd4jLong *buffer, long size) { * * This method is used only for SoftMax */ - INLINEDEF _CUDA_HD Nd4jLong *shapeBuffer(int rank, nd4j::DataType dtype, Nd4jLong *shape, Nd4jLong *buffer) { + INLINEDEF _CUDA_HD Nd4jLong *shapeBuffer(int rank, sd::DataType dtype, Nd4jLong *shape, Nd4jLong *buffer) { Nd4jLong stride[MAX_RANK]; shape::calcStrides(shape,rank, stride); @@ -1721,7 +1743,7 @@ __device__ INLINEDEF Nd4jLong *cuMalloc(Nd4jLong *buffer, long size) { shapeInfo.order = 'c'; shapeInfo.elementWiseStride = elementWiseStride; shape::toShapeBuffer(&shapeInfo, buffer); - nd4j::ArrayOptions::setDataType(buffer, dtype); + sd::ArrayOptions::setDataType(buffer, dtype); return buffer; } @@ -1729,7 +1751,7 @@ __device__ INLINEDEF Nd4jLong *cuMalloc(Nd4jLong *buffer, long size) { * Get the shape info buffer * for the given rank and shape. */ - INLINEDEF _CUDA_HD Nd4jLong *shapeBufferFortran(int rank, nd4j::DataType dtype, Nd4jLong *shape) { + INLINEDEF _CUDA_HD Nd4jLong *shapeBufferFortran(int rank, sd::DataType dtype, Nd4jLong *shape) { auto stride = shape::calcStridesFortran(shape,rank); traceNew(12); @@ -1746,11 +1768,11 @@ __device__ INLINEDEF Nd4jLong *cuMalloc(Nd4jLong *buffer, long size) { auto shapeInfoBuffer = shape::toShapeBuffer(shapeInfo); delete[] stride; delete shapeInfo; - nd4j::ArrayOptions::setDataType(shapeInfoBuffer, dtype); + sd::ArrayOptions::setDataType(shapeInfoBuffer, dtype); return shapeInfoBuffer; } - INLINEDEF _CUDA_HD Nd4jLong *shapeBufferFortran(int rank, nd4j::DataType dtype, Nd4jLong *shape, Nd4jLong *output) { + INLINEDEF _CUDA_HD Nd4jLong *shapeBufferFortran(int rank, sd::DataType dtype, Nd4jLong *shape, Nd4jLong *output) { Nd4jLong stride[MAX_RANK]; shape::calcStridesFortran(shape,rank, stride); @@ -1765,7 +1787,7 @@ __device__ INLINEDEF Nd4jLong *cuMalloc(Nd4jLong *buffer, long size) { shapeInfo.order = 'f'; shapeInfo.elementWiseStride = elementWiseStride; shape::toShapeBuffer(&shapeInfo, output); - nd4j::ArrayOptions::setDataType(output, dtype); + sd::ArrayOptions::setDataType(output, dtype); return output; } @@ -1812,7 +1834,7 @@ INLINEDEF _CUDA_HD Nd4jLong coords2index(const Nd4jLong *shapeInfo, const uint * } ////////////////////////////////////////////////////////////////////// -INLINEDEF _CUDA_HD Nd4jLong coords2index(const int rank, const Nd4jLong *shape, const Nd4jLong *indices) { +INLINEDEF _CUDA_HD Nd4jLong coords2index(const int rank, const Nd4jLong *shape, const int *indices) { Nd4jLong index, shift = 1;; @@ -1825,7 +1847,7 @@ INLINEDEF _CUDA_HD Nd4jLong coords2index(const int rank, const Nd4jLong *shape, return index; } -INLINEDEF _CUDA_HD Nd4jLong coords2index(const Nd4jLong *shapeInfo, const Nd4jLong *coords, const int dimsSize, const int* tadDims) { +INLINEDEF _CUDA_HD Nd4jLong coords2index(const Nd4jLong *shapeInfo, const int *coords, const int dimsSize, const int* tadDims) { Nd4jLong index, shift = 1;; @@ -2947,10 +2969,8 @@ INLINEDEF _CUDA_HD bool haveSameShapeAndStrides(const Nd4jLong *shapeInfo1, cons if (shapeInfo1[0] == 0) return true; - int range = 2 * shapeInfo1[0]; - - for (int e = 1; e <= range; e++) - if (shapeInfo1[e] != shapeInfo2[e]) + for (uint e = 0; e < static_cast(shape::rank(shapeInfo1)); ++e) + if (shape::shapeOf(shapeInfo1)[e] != shape::shapeOf(shapeInfo2)[e] || shape::stride(shapeInfo1)[e] != shape::stride(shapeInfo2)[e]) return false; return true; @@ -2961,13 +2981,22 @@ INLINEDEF _CUDA_HD bool haveSameShapeAndStrides(const Nd4jLong *shapeInfo1, cons return shape::haveSameShapeAndStrides(shapeInfo1, shapeInfo2) && shape::haveSameShapeAndStrides(shapeInfo1, shapeInfo3); } - INLINEDEF _CUDA_HD int sizeAt(const Nd4jLong *shape, const int dim) { - if (0 == rank(shape)) + INLINEDEF _CUDA_HD int sizeAt(const Nd4jLong *shapeInfo, const int dim) { + if (0 == rank(shapeInfo)) return 1; if (dim >= 0) - return shape[1+dim]; + return shapeInfo[1+dim]; else - return shape[1+(rank(shape) + dim)]; + return shapeInfo[1+(rank(shapeInfo) + dim)]; + } + + INLINEDEF _CUDA_HD Nd4jLong strideAt(const Nd4jLong *shapeInfo, const int dim) { + if (0 == rank(shapeInfo)) + return 1; + if (dim >= 0) + return shapeInfo[1 + rank(shapeInfo) + dim]; + else + return shapeInfo[1 + 2*rank(shapeInfo) + dim]; } /** @@ -3006,7 +3035,7 @@ INLINEDEF _CUDA_HD bool haveSameShapeAndStrides(const Nd4jLong *shapeInfo1, cons */ template INLINEDEF _CUDA_HD T* range(int from, int to, int increment) { - int diff = nd4j::math::nd4j_abs(from - to); + int diff = sd::math::nd4j_abs(from - to); int retLength = diff / increment; T *ret; @@ -3186,7 +3215,7 @@ INLINEDEF _CUDA_HD bool haveSameShapeAndStrides(const Nd4jLong *shapeInfo1, cons } else if(rank == dimensionLength) return shape::prodLong(shape,rank); - int absSelta = nd4j::math::nd4j_abs(rank - dimensionLength); + int absSelta = sd::math::nd4j_abs(rank - dimensionLength); traceNew(27); auto ret2 = shape::removeIndex(shape, dimension, rank, dimensionLength); auto ret = prodLong(ret2, absSelta); @@ -3337,7 +3366,7 @@ INLINEDEF _CUDA_HD Nd4jLong getOffset(const Nd4jLong *shapeInfo, const uint *coo * */ INLINEDEF _CUDA_HD int tadsPerBlock(int blockSize, int tads) { - return nd4j::math::nd4j_ceil(tads / (double) blockSize); + return sd::math::nd4j_ceil(tads / (double) blockSize); } /** @@ -3748,7 +3777,7 @@ INLINEDEF _CUDA_HD Nd4jLong getOffset(const Nd4jLong *shapeInfo, const uint *coo INLINEDEF _CUDA_HD Nd4jLong *shapeBufferOfNpy(int rank, unsigned int* shape,bool fortranOrder) { if(fortranOrder) { - Nd4jLong *shapeBufferRet = shape::shapeBufferFortran(rank, nd4j::FLOAT32,(Nd4jLong *) shape); + Nd4jLong *shapeBufferRet = shape::shapeBufferFortran(rank, sd::FLOAT32,(Nd4jLong *) shape); return shapeBufferRet; } else { @@ -3757,7 +3786,7 @@ INLINEDEF _CUDA_HD Nd4jLong getOffset(const Nd4jLong *shapeInfo, const uint *coo newShape[i] = shape[i]; } - Nd4jLong *shapeBufferRet = shape::shapeBuffer(rank, nd4j::FLOAT32, newShape); + Nd4jLong *shapeBufferRet = shape::shapeBuffer(rank, sd::FLOAT32, newShape); delete[] newShape; return shapeBufferRet; @@ -3948,7 +3977,7 @@ INLINEDEF _CUDA_HD bool areStridesDefault(const Nd4jLong* shapeInfo) { // target[shape::shapeInfoLength(newRank) - 3] = 0; // target[shape::shapeInfoLength(newRank) - 2] = 0; // target[shape::shapeInfoLength(newRank) - 1] = isFOrder ? 102 : 99; -// nd4j::ArrayOptions::setDataType(target, nd4j::ArrayOptions::dataType(oldShape)); +// sd::ArrayOptions::setDataType(target, sd::ArrayOptions::dataType(oldShape)); // delete[] olddims; // delete[] oldstrides; @@ -4098,7 +4127,7 @@ INLINEDEF _CUDA_HD bool reshapeC(const Nd4jLong* oldShapeInfo, Nd4jLong* newShap *shape::ews(newShapeInfo) = oldEws; // ews } - newShapeInfo[2 * newRank + 1] = shape::type(oldShapeInfo); // type + sd::ArrayOptions::copyDataType(newShapeInfo, oldShapeInfo); // type return true; } @@ -4247,7 +4276,7 @@ INLINEDEF _CUDA_HD bool reshapeC(const Nd4jLong* oldShapeInfo, Nd4jLong* newShap // max array is outer for min array, min array is sub-array of max array // function calculates the coordinates of min array (and saves them into minIdxs) given coordinates of max array (already stored in maxIdxs) -INLINEDEF _CUDA_HD void maxIndToMinInd(Nd4jLong* maxIdxs, Nd4jLong* minIdxs, const Nd4jLong* maxShapeInfo, const Nd4jLong* minShapeInfo, const int* dimsToExclude, int dimsLen) { +INLINEDEF _CUDA_HD void maxIndToMinInd(int* maxIdxs, int* minIdxs, const Nd4jLong* maxShapeInfo, const Nd4jLong* minShapeInfo, const int* dimsToExclude, int dimsLen) { const auto maxRank = shape::rank(maxShapeInfo); const auto minRank = shape::rank(minShapeInfo); @@ -4333,10 +4362,10 @@ INLINEDEF _CUDA_HD void maxIndToMinInd(Nd4jLong* maxIdxs, Nd4jLong* minIdxs, con ////////////////////////////////////////////////////////////////////// INLINEDEF _CUDA_HD Nd4jLong subArrayIndex(const Nd4jLong maxIdx, const Nd4jLong* maxShapeInfo, const Nd4jLong* minShapeInfo, const int* dimsToExclude, const int dimsLen) { - Nd4jLong maxIdxs[MAX_RANK]; + int maxIdxs[MAX_RANK]; shape::index2coords(const_cast(maxIdx), maxShapeInfo, maxIdxs); - Nd4jLong minIdxs[MAX_RANK]; + int minIdxs[MAX_RANK]; maxIndToMinInd(maxIdxs, minIdxs, maxShapeInfo, minShapeInfo, dimsToExclude, dimsLen); return shape::coords2index(minShapeInfo, minIdxs); @@ -4345,17 +4374,17 @@ INLINEDEF _CUDA_HD void maxIndToMinInd(Nd4jLong* maxIdxs, Nd4jLong* minIdxs, con ////////////////////////////////////////////////////////////////////// INLINEDEF _CUDA_HD Nd4jLong subArrayOffset(const Nd4jLong maxIdx, const Nd4jLong* maxShapeInfo, const Nd4jLong* minShapeInfo, const int* dimsToExclude, const int dimsLen) { - Nd4jLong maxIdxs[MAX_RANK]; + int maxIdxs[MAX_RANK]; shape::index2coords(const_cast(maxIdx), maxShapeInfo, maxIdxs); - Nd4jLong minIdxs[MAX_RANK]; + int minIdxs[MAX_RANK]; maxIndToMinInd(maxIdxs, minIdxs, maxShapeInfo, minShapeInfo, dimsToExclude, dimsLen); return getOffset(minShapeInfo, minIdxs); } ////////////////////////////////////////////////////////////////////// - INLINEDEF _CUDA_HD int outerArrayOffsets(Nd4jLong* maxOffsets, const Nd4jLong minIdx, const Nd4jLong* maxShapeInfo, const Nd4jLong* minShapeInfo, Nd4jLong* memBuff, const int* dimsToExclude) { + INLINEDEF _CUDA_HD int outerArrayOffsets(Nd4jLong* maxOffsets, const Nd4jLong minIdx, const Nd4jLong* maxShapeInfo, const Nd4jLong* minShapeInfo, int* memBuff, const int* dimsToExclude) { const auto rankMin = shape::rank(minShapeInfo); const auto rankMax = shape::rank(maxShapeInfo); @@ -4365,8 +4394,8 @@ INLINEDEF _CUDA_HD void maxIndToMinInd(Nd4jLong* maxIdxs, Nd4jLong* minIdxs, con const auto diff = rankMax - rankMin; // the size of dimsToExclude is equal to diff - Nd4jLong* indices = memBuff; - Nd4jLong* increment = memBuff + rankMax; + int* indices = memBuff; + int* increment = memBuff + rankMax; int N, minI, maxI; @@ -4428,7 +4457,7 @@ INLINEDEF _CUDA_HD void maxIndToMinInd(Nd4jLong* maxIdxs, Nd4jLong* minIdxs, con } ////////////////////////////////////////////////////////////////////// - INLINEDEF _CUDA_HD int outerArrayIndexes(Nd4jLong* maxIdxs, const Nd4jLong minIdx, const Nd4jLong* maxShapeInfo, const Nd4jLong* minShapeInfo, const int* dimsToExclude) { + INLINEDEF _CUDA_HD int outerArrayIndexes(int* maxIdxs, const Nd4jLong minIdx, const Nd4jLong* maxShapeInfo, const Nd4jLong* minShapeInfo, const int* dimsToExclude) { const auto rankMin = shape::rank(minShapeInfo); const auto rankMax = shape::rank(maxShapeInfo); @@ -4440,9 +4469,7 @@ INLINEDEF _CUDA_HD void maxIndToMinInd(Nd4jLong* maxIdxs, Nd4jLong* minIdxs, con const auto diff = rankMax - rankMin; // the size of dimsToExclude is equal to diff - Nd4jLong buffer[MAX_RANK]; - Nd4jLong* indices = buffer; - Nd4jLong* increment = buffer + MAX_RANK/2; + int indices[MAX_RANK], increment[MAX_RANK]; int N, minI, maxI; @@ -4503,7 +4530,7 @@ INLINEDEF _CUDA_HD void maxIndToMinInd(Nd4jLong* maxIdxs, Nd4jLong* minIdxs, con return N; } - INLINEDEF _CUDA_HD void shapeOldScalar(nd4j::DataType dataType, Nd4jLong* const buffer, const char order) { + INLINEDEF _CUDA_HD void shapeOldScalar(sd::DataType dataType, Nd4jLong* const buffer, const char order) { buffer[0] = 2; buffer[1] = 1; @@ -4513,7 +4540,7 @@ INLINEDEF _CUDA_HD void maxIndToMinInd(Nd4jLong* maxIdxs, Nd4jLong* minIdxs, con buffer[6] = 1; buffer[7] = (int)order; - nd4j::ArrayOptions::setDataType(buffer, dataType); + sd::ArrayOptions::setDataType(buffer, dataType); } template @@ -4683,21 +4710,6 @@ INLINEDEF void _CUDA_HD checkStridesEwsAndOrder(Nd4jLong* shapeInfo, const char if(contiguous) { - // for example we have shapeInfo = {3, 5,1,1, 4,4,1, ...} then we should change it to shapeInfo = {3, 5,1,1, 4,4,4, ...ews=4} - if(numOfNonUnities < rank) { // unities are present in shape - - int indNonUnit = rank - 1; - - while(shape::shapeOf(shapeInfo)[indNonUnit--] == 1) - - for(int j = indNonUnit + 2; j < rank; ++j) - shape::stride(shapeInfo)[j] = stridesNoUnities[numOfNonUnities - 1]; - - for(int j = indNonUnit; j >= 0; --j) - if(shape::shapeOf(shapeInfo)[j] == 1) - shape::stride(shapeInfo)[j] = shape::shapeOf(shapeInfo)[j + 1] * shape::stride(shapeInfo)[j + 1]; - } - *shape::ews(shapeInfo) = stridesNoUnities[numOfNonUnities - 1]; shapeInfo[rank * 2 + 3] = 99; return; @@ -4715,21 +4727,6 @@ INLINEDEF void _CUDA_HD checkStridesEwsAndOrder(Nd4jLong* shapeInfo, const char if(contiguous) { - // for example we have shapeInfo = {3, 1,1,5, 1,4,4, ...} then we should change it to shapeInfo = {3, 1,1,5, 4,4,4, ...ews=4} - if(numOfNonUnities < rank) { // unities are present in shape - - int indNonUnit = 0; - - while(shape::shapeOf(shapeInfo)[indNonUnit++] == 1) - - for(int j = 0; j < indNonUnit - 1; ++j) - shape::stride(shapeInfo)[j] = stridesNoUnities[0]; - - for(int j = indNonUnit; j < rank; ++j) - if(shape::shapeOf(shapeInfo)[j] == 1) - shape::stride(shapeInfo)[j] = shape::shapeOf(shapeInfo)[j - 1] * shape::stride(shapeInfo)[j - 1]; - } - *shape::ews(shapeInfo) = stridesNoUnities[0]; shapeInfo[rank * 2 + 3] = 102; return; @@ -4740,7 +4737,7 @@ INLINEDEF void _CUDA_HD checkStridesEwsAndOrder(Nd4jLong* shapeInfo, const char } ////////////////////////////////////////////////////////////////////// -INLINEDEF _CUDA_HD void calcSubArrShapeAndOffsets(const Nd4jLong* wholeShapeInfo, const Nd4jLong numOfSubArrs, const int dimsSize, const int* dimsToExclude, Nd4jLong* subArrShapeInfo, Nd4jLong* subArrOffsets, bool keepUnitiesInShape) { +INLINEDEF _CUDA_HD void calcSubArrsShapeInfoAndOffsets(const Nd4jLong* wholeShapeInfo, const Nd4jLong numOfSubArrs, const int dimsSize, const int* dimsToExclude, Nd4jLong* subArrShapeInfo, Nd4jLong* subArrOffsets, bool keepUnitiesInShape) { const int rank = shape::rank(wholeShapeInfo); @@ -4753,7 +4750,7 @@ INLINEDEF _CUDA_HD void calcSubArrShapeAndOffsets(const Nd4jLong* wholeShapeInfo const int subArrRank = keepUnitiesInShape ? rank : rank - dimsSize; subArrShapeInfo[0] = subArrRank; // rank - subArrShapeInfo[2 * subArrRank + 1] = shape::type(wholeShapeInfo); // type + sd::ArrayOptions::copyDataType(subArrShapeInfo, wholeShapeInfo); // type subArrShapeInfo[2 * subArrRank + 3] = shape::order(wholeShapeInfo); // order Nd4jLong* shape = new Nd4jLong[dimsSize]; @@ -4788,6 +4785,54 @@ INLINEDEF _CUDA_HD void calcSubArrShapeAndOffsets(const Nd4jLong* wholeShapeInfo delete []shape; } +////////////////////////////////////////////////////////////////////// +INLINEDEF void calcSubArrShapeInfoAndOffset(const Nd4jLong* idx, const Nd4jLong* maxShapeInfo, Nd4jLong* minShapeInfo, Nd4jLong& minOffset, const bool keepUnitiesInShape, const bool isStrided, const int numOfUntiesInMinShape) { + + const uint maxRank = shape::rank(maxShapeInfo); + minOffset = 0; + uint first, last, stride, n(isStrided ? 3 : 2); + + minShapeInfo[0] = keepUnitiesInShape ? maxRank : maxRank - numOfUntiesInMinShape; + + for (uint step = 0, j = 0, i = 0; i < maxRank; ++i, step += n) { + + if (idx[step] == idx[step + 1]) { // means whole dimension + shape::shapeOf(minShapeInfo)[j] = shape::shapeOf(maxShapeInfo)[i]; + shape::stride(minShapeInfo)[j++] = shape::stride(maxShapeInfo)[i]; + } + else { + + first = idx[step] >= 0 ? idx[step] : idx[step] + shape::sizeAt(maxShapeInfo, i) + 1; + last = idx[step + 1] >= 0 ? idx[step + 1] : idx[step + 1] + shape::sizeAt(maxShapeInfo, i) + 1; + + if(last < first) + throw("shape::calcSubArrShapeInfoAndOffset: negative range in input indexes is found!"); + + if(isStrided) { + stride = idx[step + 2]; + last /*resulting sub-array axis*/ = (last - first + stride - 1) / stride; // ceil (last - first) / stride; + } + else { + stride = 1; + last /*resulting sub-array axis*/ = last - first; + } + + minOffset += first * shape::stride(maxShapeInfo)[i]; + + if(!keepUnitiesInShape && last == 1) + continue; + + shape::shapeOf(minShapeInfo)[j] = last; + shape::stride(minShapeInfo)[j++] = last == 1 ? shape::stride(maxShapeInfo)[i] : shape::stride(maxShapeInfo)[i] * stride; + } + } + + minShapeInfo[2 * shape::rank(minShapeInfo) + 3] = shape::order(maxShapeInfo); // order + sd::ArrayOptions::copyDataType(minShapeInfo, maxShapeInfo); // type + + shape::checkStridesEwsAndOrder(minShapeInfo); +} + ////////////////////////////////////////////////////////////////////// INLINEDEF void _CUDA_HD index2coords(Nd4jLong index, const Nd4jLong *shapeInfo, Nd4jLong *coords) { @@ -4839,7 +4884,7 @@ INLINEDEF void _CUDA_HD index2coords(Nd4jLong index, const int rank, const Nd4jL } ////////////////////////////////////////////////////////////////////// -INLINEDEF void _CUDA_HD index2coords(Nd4jLong index, const Nd4jLong *shapeInfo, Nd4jLong *coords, const int dimsSize, const int* tadDims) { +INLINEDEF _CUDA_HD void index2coords(Nd4jLong index, const Nd4jLong *shapeInfo, int *coords, const int dimsSize, const int* tadDims) { for(uint i = dimsSize - 1; i > 0; --i) { coords[tadDims[i]] = index % shapeInfo[1 + tadDims[i]]; @@ -4848,6 +4893,34 @@ INLINEDEF void _CUDA_HD index2coords(Nd4jLong index, const Nd4jLong *shapeInfo, coords[tadDims[0]] = index; // last iteration } +////////////////////////////////////////////////////////////////////// +INLINEDEF _CUDA_HD void index2coordsCPU(const Nd4jLong& startIndex, const Nd4jLong& index, const Nd4jLong *shapeInfo, Nd4jLong *coords) { + + if(startIndex == index) { + shape::index2coords(index, shapeInfo, coords); + } + else { + int axis = shapeInfo[0] - 1; + while(coords[axis] == shape::sizeAt(shapeInfo, axis) - 1) + coords[axis--] = 0; + ++coords[axis]; + } +} + +////////////////////////////////////////////////////////////////////// +INLINEDEF _CUDA_HD void index2coordsCPU(const Nd4jLong& startIndex, const Nd4jLong& index, const Nd4jLong *shapeInfo, int *coords) { + + if(startIndex == index) { + shape::index2coords(index, shapeInfo, coords); + } + else { + int axis = shapeInfo[0] - 1; + while(coords[axis] == shape::sizeAt(shapeInfo, axis) - 1) + coords[axis--] = 0; + ++coords[axis]; + } +} + ////////////////////////////////////////////////////////////////////// // INLINEDEF _CUDA_HD void calcOffsets(const Nd4jLong *xShapeInfo, Nd4jLong*& xOffsets, const Nd4jLong *yShapeInfo, Nd4jLong*& yOffsets, const Nd4jLong* zShapeInfo, Nd4jLong*& zOffsets, const char order) { @@ -5077,12 +5150,33 @@ INLINEDEF _CUDA_HD void excludeUnitiesFromShapeInfo(const Nd4jLong* inShapeInfo, shape::stride(outShapeInfo)[k++] = shape::stride(inShapeInfo)[i]; } - outShapeInfo[2 * outShapeInfo[0] + 1] = shape::type(inShapeInfo); // type - *shape::ews(outShapeInfo) = shape::elementWiseStride(inShapeInfo); // ews - outShapeInfo[2 * outShapeInfo[0] + 3] = shape::order(inShapeInfo); // order + sd::ArrayOptions::copyDataType(outShapeInfo, inShapeInfo); // type + *shape::ews(outShapeInfo) = shape::elementWiseStride(inShapeInfo); // ews + outShapeInfo[2 * outShapeInfo[0] + 3] = shape::order(inShapeInfo); // order } +////////////////////////////////////////////////////////////////////// +// INLINEDEF _CUDA_HD Nd4jLong strideOverContigAxis(const int axis, const Nd4jLong* inShapeInfo) { + +// Nd4jLong result = 9223372036854775807LL; + +// for(uint i = 0; i < shape::rank(inShapeInfo); ++i) { + +// const auto currentStride = shape::stride(inShapeInfo)[i]; + +// if(i == axis || shape::shapeOf(inShapeInfo)[i] == 1) +// continue; + +// if(result > currentStride) +// result = currentStride; +// } + +// return result == 9223372036854775807LL ? 1 : result; +// } + + + } #endif /* SHAPE_H_ */ diff --git a/libnd4j/include/helpers/svd.h b/libnd4j/include/helpers/svd.h index f02732ce7..58007bf37 100644 --- a/libnd4j/include/helpers/svd.h +++ b/libnd4j/include/helpers/svd.h @@ -21,10 +21,10 @@ #ifndef LIBND4J_SVD_H #define LIBND4J_SVD_H -#include -#include "NDArray.h" +#include +#include "array/NDArray.h" -namespace nd4j { +namespace sd { namespace ops { namespace helpers { diff --git a/libnd4j/include/helpers/unicode.h b/libnd4j/include/helpers/unicode.h index 239b71201..6db4841db 100644 --- a/libnd4j/include/helpers/unicode.h +++ b/libnd4j/include/helpers/unicode.h @@ -21,9 +21,9 @@ #ifndef LIBND4J_UNICODE_H #define LIBND4J_UNICODE_H -#include +#include -namespace nd4j { +namespace sd { namespace unicode { /** diff --git a/libnd4j/include/indexing/IndicesList.h b/libnd4j/include/indexing/IndicesList.h index 2094be9a5..a652615d5 100644 --- a/libnd4j/include/indexing/IndicesList.h +++ b/libnd4j/include/indexing/IndicesList.h @@ -24,7 +24,7 @@ #include #include "NDIndex.h" -namespace nd4j { +namespace sd { class ND4J_EXPORT IndicesList { protected: std::vector _indices; diff --git a/libnd4j/include/indexing/NDIndex.h b/libnd4j/include/indexing/NDIndex.h index baa67104d..799da4e6c 100644 --- a/libnd4j/include/indexing/NDIndex.h +++ b/libnd4j/include/indexing/NDIndex.h @@ -21,11 +21,11 @@ #ifndef LIBND4J_NDINDEX_H #define LIBND4J_NDINDEX_H -#include +#include #include -#include +#include -namespace nd4j { +namespace sd { class ND4J_EXPORT NDIndex { protected: std::vector _indices; diff --git a/libnd4j/include/indexing/impl/IndicesList.cpp b/libnd4j/include/indexing/impl/IndicesList.cpp index d9d02c3ca..5acbf57d5 100644 --- a/libnd4j/include/indexing/impl/IndicesList.cpp +++ b/libnd4j/include/indexing/impl/IndicesList.cpp @@ -20,23 +20,23 @@ #include -using namespace nd4j; +using namespace sd; -nd4j::IndicesList::IndicesList(std::initializer_list list) { +sd::IndicesList::IndicesList(std::initializer_list list) { for (auto v: list) _indices.emplace_back(v); } -nd4j::IndicesList::~IndicesList() { +sd::IndicesList::~IndicesList() { for(auto v: _indices) delete v; } -int nd4j::IndicesList::size() { +int sd::IndicesList::size() { return (int) _indices.size(); } -bool nd4j::IndicesList::isScalar() { +bool sd::IndicesList::isScalar() { if (_indices.size() == 1) { return _indices.at(0)->isPoint(); } @@ -44,10 +44,10 @@ bool nd4j::IndicesList::isScalar() { return false; } -nd4j::NDIndex* nd4j::IndicesList::at(int idx) { +sd::NDIndex* sd::IndicesList::at(int idx) { return _indices.at(idx); } -void nd4j::IndicesList::push_back(NDIndex* idx) { +void sd::IndicesList::push_back(NDIndex* idx) { _indices.emplace_back(idx); } \ No newline at end of file diff --git a/libnd4j/include/indexing/impl/NDIndex.cpp b/libnd4j/include/indexing/impl/NDIndex.cpp index 1bf4388bb..43aaf0914 100644 --- a/libnd4j/include/indexing/impl/NDIndex.cpp +++ b/libnd4j/include/indexing/impl/NDIndex.cpp @@ -20,7 +20,7 @@ #include -namespace nd4j { +namespace sd { bool NDIndex::isInterval() { return false; @@ -30,11 +30,11 @@ namespace nd4j { return _stride; } - nd4j::NDIndexAll::NDIndexAll() : nd4j::NDIndex() { + sd::NDIndexAll::NDIndexAll() : sd::NDIndex() { _indices.push_back(-1); } - nd4j::NDIndexPoint::NDIndexPoint(Nd4jLong point) : nd4j::NDIndex() { + sd::NDIndexPoint::NDIndexPoint(Nd4jLong point) : sd::NDIndex() { this->_indices.push_back(point); } @@ -52,34 +52,34 @@ namespace nd4j { - nd4j::NDIndexInterval::NDIndexInterval(Nd4jLong start, Nd4jLong end, Nd4jLong stride) : nd4j::NDIndex() { + sd::NDIndexInterval::NDIndexInterval(Nd4jLong start, Nd4jLong end, Nd4jLong stride) : sd::NDIndex() { this->_stride = stride; for (int e = start; e < end; e+= stride) this->_indices.push_back(e); } - bool nd4j::NDIndex::isAll() { + bool sd::NDIndex::isAll() { return _indices.size() == 1 && _indices.at(0) == -1; } - bool nd4j::NDIndex::isPoint() { + bool sd::NDIndex::isPoint() { return _indices.size() == 1 && _indices.at(0) >= 0; } - std::vector &nd4j::NDIndex::getIndices() { + std::vector &sd::NDIndex::getIndices() { return _indices; } - nd4j::NDIndex *nd4j::NDIndex::all() { + sd::NDIndex *sd::NDIndex::all() { return new NDIndexAll(); } - nd4j::NDIndex *nd4j::NDIndex::point(Nd4jLong pt) { + sd::NDIndex *sd::NDIndex::point(Nd4jLong pt) { return new NDIndexPoint(pt); } - nd4j::NDIndex *nd4j::NDIndex::interval(Nd4jLong start, Nd4jLong end, Nd4jLong stride) { + sd::NDIndex *sd::NDIndex::interval(Nd4jLong start, Nd4jLong end, Nd4jLong stride) { return new NDIndexInterval(start, end, stride); } } \ No newline at end of file diff --git a/libnd4j/blas/NativeOpExecutioner.h b/libnd4j/include/legacy/NativeOpExecutioner.h similarity index 83% rename from libnd4j/blas/NativeOpExecutioner.h rename to libnd4j/include/legacy/NativeOpExecutioner.h index b4a5fdea4..4d55a3357 100644 --- a/libnd4j/blas/NativeOpExecutioner.h +++ b/libnd4j/include/legacy/NativeOpExecutioner.h @@ -23,7 +23,7 @@ #include -#include +#include #include #include #include @@ -45,8 +45,8 @@ public: * @param result * @param resultShapeInfo */ - static void execIndexReduceScalar(nd4j::LaunchContext *lc, - int opNum, + static void execIndexReduceScalar(sd::LaunchContext *lc, + int opNum, void *hX, Nd4jLong *hXShapeInfo, void *dX, Nd4jLong *dXShapeInfo, void *extraParams, @@ -66,7 +66,7 @@ public: * @param dimension * @param dimensionLength */ - static void execReduce3Scalar(nd4j::LaunchContext *lc, + static void execReduce3Scalar(sd::LaunchContext *lc, int opNum, void *hX, Nd4jLong *hXShapeInfo, void *dX, Nd4jLong *dXShapeInfo, @@ -75,7 +75,7 @@ public: void *dY, Nd4jLong *dYShapeInfo, void *hZ, Nd4jLong *hZShapeInfo, void *dZ, Nd4jLong *dZShapeInfo); - + /** * @@ -88,7 +88,7 @@ public: * @param result * @param resultShapeInfo */ - static void execReduce3(nd4j::LaunchContext *lc, + static void execReduce3(sd::LaunchContext *lc, int opNum, void *hX, Nd4jLong *hXShapeInfo, void *dX, Nd4jLong *dXShapeInfo, @@ -111,7 +111,7 @@ public: * @param dimension * @param dimensionLength */ - static void execReduce3(nd4j::LaunchContext *lc, + static void execReduce3(sd::LaunchContext *lc, int opNum, void *hX, Nd4jLong *hXShapeInfo, void *dX, Nd4jLong *dXShapeInfo, @@ -124,7 +124,7 @@ public: Nd4jLong *xTadOnlyShapeInfo, Nd4jLong *xTadOffsets, Nd4jLong *yTadOnlyShapeInfo, Nd4jLong *yTadOffsets); - static void execReduce3All(nd4j::LaunchContext *lc, + static void execReduce3All(sd::LaunchContext *lc, int opNum, void *hX, Nd4jLong *hXShapeInfo, void *dX, Nd4jLong *dXShapeInfo, @@ -148,7 +148,7 @@ public: * @param dimension * @param dimensionLength */ - static void execIndexReduce(nd4j::LaunchContext *lc, + static void execIndexReduce(sd::LaunchContext *lc, int opNum, void *hX, Nd4jLong *hXShapeInfo, void *dX, Nd4jLong *dXShapeInfo, @@ -169,7 +169,7 @@ public: * @param extraParams * @param n */ - static void execScalar(nd4j::LaunchContext *lc, + static void execScalar(sd::LaunchContext *lc, int opNum, void *hX, Nd4jLong *hXShapeInfo, void *dX, Nd4jLong *dXShapeInfo, @@ -179,7 +179,7 @@ public: void *dScalar, Nd4jLong *dSscalarShapeInfo, void *extraParams, bool allowParallelism = true); -static void execScalarBool(nd4j::LaunchContext *lc, +static void execScalarBool(sd::LaunchContext *lc, int opNum, void *hX, Nd4jLong *hXShapeInfo, void *dX, Nd4jLong *dXShapeInfo, @@ -189,7 +189,7 @@ static void execScalarBool(nd4j::LaunchContext *lc, void *dScalar, Nd4jLong *dSscalarShapeInfo, void *extraParams, bool allowParallelism = true); -static void execScalarInt(nd4j::LaunchContext *lc, +static void execScalarInt(sd::LaunchContext *lc, int opNum, void *hX, Nd4jLong *hXShapeInfo, void *dX, Nd4jLong *dXShapeInfo, @@ -199,7 +199,7 @@ static void execScalarInt(nd4j::LaunchContext *lc, void *dScalar, Nd4jLong *dSscalarShapeInfo, void *extraParams, bool allowParallelism = true); - static void execScalar(nd4j::LaunchContext *lc, + static void execScalar(sd::LaunchContext *lc, int opNum, void *hX, Nd4jLong *hXShapeInfo, void *dX, Nd4jLong *dXShapeInfo, @@ -212,7 +212,7 @@ static void execScalarInt(nd4j::LaunchContext *lc, Nd4jLong *tadShapeInfo, Nd4jLong *tadOffsets, Nd4jLong *tadShapeInfoZ, Nd4jLong *tadOffsetsZ); - static void execScalarBool(nd4j::LaunchContext *lc, + static void execScalarBool(sd::LaunchContext *lc, int opNum, void *hX, Nd4jLong *hXShapeInfo, void *dX, Nd4jLong *dXShapeInfo, @@ -225,7 +225,7 @@ static void execScalarInt(nd4j::LaunchContext *lc, Nd4jLong *tadShapeInfo, Nd4jLong *tadOffsets, Nd4jLong *tadShapeInfoZ, Nd4jLong *tadOffsetsZ); - static void execScalarInt(nd4j::LaunchContext *lc, + static void execScalarInt(sd::LaunchContext *lc, int opNum, void *hX, Nd4jLong *hXShapeInfo, void *dX, Nd4jLong *dXShapeInfo, @@ -251,7 +251,7 @@ static void execScalarInt(nd4j::LaunchContext *lc, * @param dimension * @param dimensionLength */ - static void execBroadcast(nd4j::LaunchContext *lc, + static void execBroadcast(sd::LaunchContext *lc, int opNum, void *hX, Nd4jLong *hXShapeInfo, void *dX, Nd4jLong *dXShapeInfo, @@ -263,7 +263,16 @@ static void execScalarInt(nd4j::LaunchContext *lc, Nd4jLong *tadOnlyShapeInfo, Nd4jLong *tadOffsets, Nd4jLong *tadOnlyShapeInfoZ,Nd4jLong *tadOffsetsZ); - static void execInverseBroadcast(nd4j::LaunchContext *lc, + static void execBroadcast(sd::LaunchContext* lc, + const int opNum, + const void *hX, const Nd4jLong *hXShapeInfo, + const void *dX, const Nd4jLong *dXShapeInfo, + const void *hY, const Nd4jLong *hYShapeInfo, + const void *dY, const Nd4jLong *dYShapeInfo, + void *hZ, const Nd4jLong *hZShapeInfo, + void *dZ, const Nd4jLong *dZShapeInfo); + + static void execInverseBroadcast(sd::LaunchContext *lc, int opNum, void *x, Nd4jLong *xShapeInfo, void *dX, Nd4jLong *dXShapeInfo, @@ -276,7 +285,7 @@ static void execScalarInt(nd4j::LaunchContext *lc, Nd4jLong *tadOnlyShapeInfoZ, Nd4jLong *tadOffsetsZ); - static void execBroadcastBool(nd4j::LaunchContext *lc, + static void execBroadcastBool(sd::LaunchContext *lc, int opNum, void *hX, Nd4jLong *hXShapeInfo, void *dX, Nd4jLong *dXShapeInfo, @@ -289,7 +298,16 @@ static void execScalarInt(nd4j::LaunchContext *lc, Nd4jLong *tadOnlyShapeInfo, Nd4jLong *tadOffsets, Nd4jLong *tadOnlyShapeInfoZ,Nd4jLong *tadOffsetsZ); - static void execInverseBroadcastBool(nd4j::LaunchContext *lc, + static void execBroadcastBool(sd::LaunchContext* lc, const int opNum, + const void *hX, const Nd4jLong *hXShapeInfo, + const void *dX, const Nd4jLong *dXShapeInfo, + const void *hY, const Nd4jLong *hYShapeInfo, + const void *dY, const Nd4jLong *dYShapeInfo, + void *hZ, const Nd4jLong *hZShapeInfo, + void *dZ, const Nd4jLong *dZShapeInfo, + void *extraParams); + + static void execInverseBroadcastBool(sd::LaunchContext *lc, int opNum, void *x, Nd4jLong *xShapeInfo, void *dX, Nd4jLong *dXShapeInfo, @@ -302,7 +320,7 @@ static void execScalarInt(nd4j::LaunchContext *lc, Nd4jLong *tadOnlyShapeInfo, Nd4jLong *tadOffsets, Nd4jLong *tadOnlyShapeInfoZ, Nd4jLong *tadOffsetsZ); - static void execBroadcastInt(nd4j::LaunchContext *lc, + static void execBroadcastInt(sd::LaunchContext *lc, int opNum, void *hX, Nd4jLong *hXShapeInfo, void *dX, Nd4jLong *dXShapeInfo, @@ -314,7 +332,15 @@ static void execScalarInt(nd4j::LaunchContext *lc, Nd4jLong *tadOnlyShapeInfo, Nd4jLong *tadOffsets, Nd4jLong *tadOnlyShapeInfoZ,Nd4jLong *tadOffsetsZ); - static void execInverseBroadcastInt(nd4j::LaunchContext *lc, + static void execBroadcastInt(sd::LaunchContext* lc, const int opNum, + const void *hX, const Nd4jLong *hXShapeInfo, + const void *dX, const Nd4jLong *dXShapeInfo, + const void *hY, const Nd4jLong *hYShapeInfo, + const void *dY, const Nd4jLong *dYShapeInfo, + void *hZ, const Nd4jLong *hZShapeInfo, + void *dZ, const Nd4jLong *dZShapeInfo); + + static void execInverseBroadcastInt(sd::LaunchContext *lc, int opNum, void *x, Nd4jLong *xShapeInfo, void *dX, Nd4jLong *dXShapeInfo, @@ -325,7 +351,7 @@ static void execScalarInt(nd4j::LaunchContext *lc, int *dimension, int dimensionLength, Nd4jLong *tadOnlyShapeInfo, Nd4jLong *tadOffsets, Nd4jLong *tadOnlyShapeInfoZ, Nd4jLong *tadOffsetsZ); - + /** * * @param opNum @@ -338,7 +364,7 @@ static void execScalarInt(nd4j::LaunchContext *lc, * @param extraParams * @param n */ - static void execPairwiseTransform(nd4j::LaunchContext *lc, + static void execPairwiseTransform(sd::LaunchContext *lc, int opNum, void *hX, Nd4jLong *hXShapeInfo, void *dX, Nd4jLong *dXShapeInfo, @@ -348,7 +374,7 @@ static void execScalarInt(nd4j::LaunchContext *lc, void *dZ, Nd4jLong *dZShapeInfo, void *extraParams); - static void execPairwiseBoolTransform(nd4j::LaunchContext *lc, + static void execPairwiseBoolTransform(sd::LaunchContext *lc, int opNum, void *hX, Nd4jLong *hXShapeInfo, void *dX, Nd4jLong *dXShapeInfo, @@ -358,7 +384,7 @@ static void execScalarInt(nd4j::LaunchContext *lc, void *dZ, Nd4jLong *dZShapeInfo, void *extraParams); - static void execPairwiseIntTransform(nd4j::LaunchContext *lc, + static void execPairwiseIntTransform(sd::LaunchContext *lc, int opNum, void *hX, Nd4jLong *hXShapeInfo, void *dX, Nd4jLong *dXShapeInfo, @@ -378,7 +404,7 @@ static void execScalarInt(nd4j::LaunchContext *lc, * @param extraParams * @param n */ - static void execTransformFloat(nd4j::LaunchContext *lc, + static void execTransformFloat(sd::LaunchContext *lc, int opNum, void *hX, Nd4jLong *hXShapeInfo, void *dX, Nd4jLong *dXShapeInfo, @@ -387,7 +413,7 @@ static void execScalarInt(nd4j::LaunchContext *lc, void *extraParams, Nd4jLong *tadShapeInfo, Nd4jLong *tadOffsets); -static void execTransformAny(nd4j::LaunchContext *lc, +static void execTransformAny(sd::LaunchContext *lc, int opNum, void *hX, Nd4jLong *hXShapeInfo, void *dX, Nd4jLong *dXShapeInfo, @@ -396,7 +422,7 @@ static void execTransformAny(nd4j::LaunchContext *lc, void *extraParams, Nd4jLong *tadShapeInfo, Nd4jLong *tadOffsets, bool allowParallelism = true); -static void execTransformStrict(nd4j::LaunchContext *lc, +static void execTransformStrict(sd::LaunchContext *lc, int opNum, void *hX, Nd4jLong *hXShapeInfo, void *dX, Nd4jLong *dXShapeInfo, @@ -405,7 +431,7 @@ static void execTransformStrict(nd4j::LaunchContext *lc, void *extraParams, Nd4jLong *tadShapeInfo, Nd4jLong *tadOffsets); -static void execTransformSame(nd4j::LaunchContext *lc, +static void execTransformSame(sd::LaunchContext *lc, int opNum, void *hX, Nd4jLong *hXShapeInfo, void *dX, Nd4jLong *dXShapeInfo, @@ -414,14 +440,14 @@ static void execTransformSame(nd4j::LaunchContext *lc, void *extraParams, Nd4jLong *tadShapeInfo, Nd4jLong *tadOffsets); -static void execTransformBool(nd4j::LaunchContext *lc, +static void execTransformBool(sd::LaunchContext *lc, int opNum, void *hX, Nd4jLong *hXShapeInfo, void *dX, Nd4jLong *dXShapeInfo, void *hZ, Nd4jLong *hZShapeInfo, void *dZ, Nd4jLong *dZShapeInfo, void *extraParams, - Nd4jLong *tadShapeInfo, Nd4jLong *tadOffsets); + Nd4jLong *tadShapeInfo, Nd4jLong *tadOffsets); /** * * @param opNum @@ -431,7 +457,7 @@ static void execTransformBool(nd4j::LaunchContext *lc, * @param result * @param resultShapeInfo */ - static void execReduceFloat(nd4j::LaunchContext *lc, + static void execReduceFloat(sd::LaunchContext *lc, int opNum, void *hX, Nd4jLong *hXShapeInfo, void *dX, Nd4jLong *dXShapeInfo, @@ -441,7 +467,7 @@ static void execTransformBool(nd4j::LaunchContext *lc, int *dimension, int dimensionLength, Nd4jLong *tadShapeInfo, Nd4jLong *tadOffsets); - static void execReduceSame(nd4j::LaunchContext *lc, + static void execReduceSame(sd::LaunchContext *lc, int opNum, void *hX, Nd4jLong *hXShapeInfo, void *dX, Nd4jLong *dXShapeInfo, @@ -451,7 +477,7 @@ static void execTransformBool(nd4j::LaunchContext *lc, int *dimension, int dimensionLength, Nd4jLong *tadShapeInfo, Nd4jLong *tadOffsets); - static void execReduceBool(nd4j::LaunchContext *lc, + static void execReduceBool(sd::LaunchContext *lc, int opNum, void *hX, Nd4jLong *hXShapeInfo, void *dX, Nd4jLong *dXShapeInfo, @@ -461,7 +487,7 @@ static void execTransformBool(nd4j::LaunchContext *lc, int *dimension, int dimensionLength, Nd4jLong *tadShapeInfo, Nd4jLong *tadOffsets); - static void execReduceLong(nd4j::LaunchContext *lc, + static void execReduceLong(sd::LaunchContext *lc, int opNum, void *hX, Nd4jLong *hXShapeInfo, void *dX, Nd4jLong *dXShapeInfo, @@ -479,7 +505,7 @@ static void execTransformBool(nd4j::LaunchContext *lc, * @param extraParams * @return */ - static void execReduceFloatScalar(nd4j::LaunchContext *lc, + static void execReduceFloatScalar(sd::LaunchContext *lc, int opNum, void *hX, Nd4jLong *hXShapeInfo, void *dX, Nd4jLong *dXShapeInfo, @@ -487,7 +513,7 @@ static void execTransformBool(nd4j::LaunchContext *lc, void *hZ, Nd4jLong *hZShapeInfo, void *dZ, Nd4jLong *dZShapeInfo); - static void execReduceBoolScalar(nd4j::LaunchContext *lc, + static void execReduceBoolScalar(sd::LaunchContext *lc, int opNum, void *hX, Nd4jLong *hXShapeInfo, void *dX, Nd4jLong *dXShapeInfo, @@ -495,7 +521,7 @@ static void execTransformBool(nd4j::LaunchContext *lc, void *hZ, Nd4jLong *hZShapeInfo, void *dZ, Nd4jLong *dZShapeInfo); - static void execReduceSameScalar(nd4j::LaunchContext *lc, + static void execReduceSameScalar(sd::LaunchContext *lc, int opNum, void *hX, Nd4jLong *hXShapeInfo, void *dX, Nd4jLong *dXShapeInfo, @@ -503,15 +529,15 @@ static void execTransformBool(nd4j::LaunchContext *lc, void *hZ, Nd4jLong *hZShapeInfo, void *dZ, Nd4jLong *dZShapeInfo); - static void execReduceLongScalar(nd4j::LaunchContext *lc, + static void execReduceLongScalar(sd::LaunchContext *lc, int opNum, void *hX, Nd4jLong *hXShapeInfo, void *dX, Nd4jLong *dXShapeInfo, void *extraParams, void *hZ, Nd4jLong *hZShapeInfo, - void *dZ, Nd4jLong *dZShapeInfo); + void *dZ, Nd4jLong *dZShapeInfo); - static void execReduce3TAD(nd4j::LaunchContext *lc, + static void execReduce3TAD(sd::LaunchContext *lc, int opNum, void *hX, Nd4jLong *hXShapeInfo, void *dX, Nd4jLong *dXShapeInfo, @@ -520,7 +546,7 @@ static void execTransformBool(nd4j::LaunchContext *lc, void *dY, Nd4jLong *dYShapeInfo, void *hZ, Nd4jLong *hZShapeInfo, void *dZ, Nd4jLong *dZShapeInfo, - int *dimension, int dimensionLength, + int *dimension, int dimensionLength, Nd4jLong *tadShapeInfo, Nd4jLong *tadOffsets, Nd4jLong *yTadShapeInfo, Nd4jLong *yTadOffsets); @@ -535,7 +561,7 @@ static void execTransformBool(nd4j::LaunchContext *lc, * @param dimension * @param dimensionLength */ - static void execSummaryStats(nd4j::LaunchContext *lc, + static void execSummaryStats(sd::LaunchContext *lc, int opNum, void *hX, Nd4jLong *hXShapeInfo, void *dX, Nd4jLong *dXShapeInfo, @@ -544,7 +570,7 @@ static void execTransformBool(nd4j::LaunchContext *lc, void *dZ, Nd4jLong *dZShapeInfo, int *dimension, int dimensionLength, Nd4jLong *tadShapeInfo, Nd4jLong *tadOffsets, - bool biasCorrected); + bool biasCorrected); /** * @@ -555,7 +581,7 @@ static void execTransformBool(nd4j::LaunchContext *lc, * @param result * @param resultShapeInfo */ - static void execSummaryStats(nd4j::LaunchContext *lc, + static void execSummaryStats(sd::LaunchContext *lc, int opNum, void *hX, Nd4jLong *hXShapeInfo, void *dX, Nd4jLong *dXShapeInfo, @@ -573,24 +599,24 @@ static void execTransformBool(nd4j::LaunchContext *lc, * @param result * @param resultShapeInfo */ - static void execSummaryStatsScalar(nd4j::LaunchContext *lc, + static void execSummaryStatsScalar(sd::LaunchContext *lc, int opNum, void *hX, Nd4jLong *hXShapeInfo, void *dX, Nd4jLong *dXShapeInfo, void *extraParams, void *hZ, Nd4jLong *hZShapeInfo, void *dZ, Nd4jLong *dZShapeInfo, - bool biasCorrected); + bool biasCorrected); - static void execRandom(nd4j::LaunchContext *lc, + static void execRandom(sd::LaunchContext *lc, int opNum, Nd4jPointer state, void *hZ, Nd4jLong *hZShapeBuffer, void *dZ, Nd4jLong *dZShapeBuffer, void *extraArguments); - static void execRandom(nd4j::LaunchContext *lc, + static void execRandom(sd::LaunchContext *lc, int opNum, Nd4jPointer state, void *hX, Nd4jLong *hXShapeBuffer, @@ -599,7 +625,7 @@ static void execTransformBool(nd4j::LaunchContext *lc, void *dZ, Nd4jLong *dZShapeBuffer, void *extraArguments); - static void execRandom(nd4j::LaunchContext *lc, + static void execRandom(sd::LaunchContext *lc, int opNum, Nd4jPointer state, void *hX, Nd4jLong *hXShapeBuffer, @@ -613,7 +639,7 @@ static void execTransformBool(nd4j::LaunchContext *lc, template - static FORCEINLINE void execAggregate(nd4j::LaunchContext *lc, + static FORCEINLINE void execAggregate(sd::LaunchContext *lc, int opNum, void **varguments, int numArguments, @@ -627,35 +653,35 @@ static void execTransformBool(nd4j::LaunchContext *lc, int numRealArguments) { } - + inline static void execSort(void *x, Nd4jLong *xShapeInfo, bool descending) { - auto xType = nd4j::ArrayOptions::dataType(xShapeInfo); + auto xType = sd::ArrayOptions::dataType(xShapeInfo); - BUILD_SINGLE_SELECTOR(xType, nd4j::SpecialMethods, ::sortGeneric(x, xShapeInfo, descending), LIBND4J_TYPES); + BUILD_SINGLE_SELECTOR(xType, sd::SpecialMethods, ::sortGeneric(x, xShapeInfo, descending), LIBND4J_TYPES); } static void execSort(void *x, Nd4jLong *xShapeInfo, int *dimension, int dimensionLength, Nd4jLong *tadShapeInfo, Nd4jLong *tadOffsets, bool descending) { - auto xType = nd4j::ArrayOptions::dataType(xShapeInfo); + auto xType = sd::ArrayOptions::dataType(xShapeInfo); - BUILD_SINGLE_SELECTOR(xType, nd4j::SpecialMethods, ::sortTadGeneric(x, xShapeInfo, dimension, dimensionLength, tadShapeInfo, tadOffsets, descending), LIBND4J_TYPES); + BUILD_SINGLE_SELECTOR(xType, sd::SpecialMethods, ::sortTadGeneric(x, xShapeInfo, dimension, dimensionLength, tadShapeInfo, tadOffsets, descending), LIBND4J_TYPES); } inline static void execSortCooIndices(Nd4jLong *indices, void *values, Nd4jLong length, int rank) { - nd4j::sparse::SparseUtils::sortCooIndicesGeneric(indices, reinterpret_cast(values), length, rank); + sd::sparse::SparseUtils::sortCooIndicesGeneric(indices, reinterpret_cast(values), length, rank); } inline static Nd4jLong encodeBitmap(void *dx, Nd4jLong *xShapeInfo, Nd4jLong N, int *dz, float threshold) { - auto xType = nd4j::ArrayOptions::dataType(xShapeInfo); + auto xType = sd::ArrayOptions::dataType(xShapeInfo); - BUILD_SINGLE_SELECTOR(xType, return nd4j::SpecialMethods, ::encodeBitmapGeneric(dx, xShapeInfo, N, dz, threshold), FLOAT_TYPES); + BUILD_SINGLE_SELECTOR(xType, return sd::SpecialMethods, ::encodeBitmapGeneric(dx, xShapeInfo, N, dz, threshold), FLOAT_TYPES); } inline static void decodeBitmap(void *dx, Nd4jLong N, void *dz, Nd4jLong *zShapeInfo) { - auto zType = nd4j::ArrayOptions::dataType(zShapeInfo); + auto zType = sd::ArrayOptions::dataType(zShapeInfo); - BUILD_SINGLE_SELECTOR(zType, nd4j::SpecialMethods, ::decodeBitmapGeneric(dx, N, dz, zShapeInfo), FLOAT_TYPES); + BUILD_SINGLE_SELECTOR(zType, sd::SpecialMethods, ::decodeBitmapGeneric(dx, N, dz, zShapeInfo), FLOAT_TYPES); } }; diff --git a/libnd4j/blas/NativeOps.h b/libnd4j/include/legacy/NativeOps.h similarity index 97% rename from libnd4j/blas/NativeOps.h rename to libnd4j/include/legacy/NativeOps.h index f4d1d261b..ea8352362 100755 --- a/libnd4j/blas/NativeOps.h +++ b/libnd4j/include/legacy/NativeOps.h @@ -42,9 +42,9 @@ #endif */ -#include +#include #include -#include +#include //DO NOT REMOVE: THIS IS AN EDITOR SEMANTICS THING FOR CLION //IT DEFINES THE EXPORT MACRO FOR THE EDITOR AND THEN @@ -54,7 +54,7 @@ #else #define ND4J_EXPORT #endif -#include +#include /* int tad_threshold = 1; @@ -75,10 +75,10 @@ bool verbose = false; #include #include #include -#include +#include #include -typedef nd4j::InteropDataBuffer OpaqueDataBuffer; +typedef sd::InteropDataBuffer OpaqueDataBuffer; extern "C" { @@ -782,7 +782,7 @@ ND4J_EXPORT void enableVerboseMode(bool reallyEnable); */ ND4J_EXPORT void setGridLimit(int gridSize); -typedef nd4j::TadPack OpaqueTadPack; +typedef sd::TadPack OpaqueTadPack; /** * @@ -961,7 +961,7 @@ ND4J_EXPORT void execAggregate(Nd4jPointer *extraPointers, int numIntArrays, void *realArguments, int numRealArguments, - nd4j::DataType dtype); + sd::DataType dtype); ND4J_EXPORT void batchExecutor(Nd4jPointer *extraPointers, @@ -974,7 +974,7 @@ ND4J_EXPORT void batchExecutor(Nd4jPointer *extraPointers, int maxIdx, int maxReals, void *ptrToArguments, - nd4j::DataType dtype); + sd::DataType dtype); ND4J_EXPORT void execAggregateBatch(Nd4jPointer *extraPointers, int numAggregates, @@ -986,7 +986,7 @@ ND4J_EXPORT void execAggregateBatch(Nd4jPointer *extraPointers, int maxIdx, int maxReals, void *ptrToArguments, - nd4j::DataType dtype); + sd::DataType dtype); /** * Random operations @@ -1127,7 +1127,7 @@ extern "C" { static Nd4jPointer numpyHeaderForNd4j(Nd4jPointer data,Nd4jPointer shapeBuffer,Nd4jLong wordSize,Nd4jLong *headerSize) { auto shapeBufferCast = reinterpret_cast(shapeBuffer); - auto type = nd4j::ArrayOptions::dataType(shapeBufferCast); + auto type = sd::ArrayOptions::dataType(shapeBufferCast); BUILD_SINGLE_SELECTOR(type, return _numpyHeaderForNd4j, (data, shapeBuffer, wordSize, headerSize), LIBND4J_TYPES); } @@ -1192,7 +1192,7 @@ extern "C" { static Nd4jPointer numpyFromNd4j(Nd4jPointer data,Nd4jPointer shapeBuffer,Nd4jLong wordSize) { auto shapeBufferCast = reinterpret_cast(shapeBuffer); - auto type = nd4j::ArrayOptions::dataType(shapeBufferCast); + auto type = sd::ArrayOptions::dataType(shapeBufferCast); BUILD_SINGLE_SELECTOR(type, return _numpyFromNd4j, (data, shapeBuffer, wordSize), LIBND4J_TYPES); } @@ -1499,7 +1499,7 @@ ND4J_EXPORT Nd4jLong* mmapFile(Nd4jPointer *extraPointers, const char *fileName, ND4J_EXPORT void munmapFile(Nd4jPointer *extraPointers, Nd4jLong* ptrMap, Nd4jLong length); -typedef nd4j::graph::ResultWrapper OpaqueResultWrapper; +typedef sd::graph::ResultWrapper OpaqueResultWrapper; // flatbuffers execution ND4J_EXPORT OpaqueResultWrapper* executeFlatGraph(Nd4jPointer *extraPointers, Nd4jPointer flatBufferPointer); @@ -1515,7 +1515,7 @@ ND4J_EXPORT const char* getAllOperations(); ND4J_EXPORT int execCustomOp(Nd4jPointer* extraPointers, Nd4jLong hash, Nd4jPointer* inputBuffers, Nd4jPointer* inputShapes, int numInputs, Nd4jPointer* outputBuffers, Nd4jPointer* outputShapes, int numOutputs, double* tArgs, int numTArgs, Nd4jLong *iArgs, int numIArgs, bool* bArgs, int numBArgs, bool isInplace); ND4J_EXPORT int execCustomOp2(Nd4jPointer* extraPointers, Nd4jLong hash, Nd4jPointer opContext); -typedef nd4j::ShapeList OpaqueShapeList; +typedef sd::ShapeList OpaqueShapeList; ND4J_EXPORT OpaqueShapeList* calculateOutputShapes(Nd4jPointer* extraPointers, Nd4jLong hash, Nd4jPointer* inputShapes, int numInputShapes, double* tArgs, int numTArgs, Nd4jLong *iArgs, int numIArgs); ND4J_EXPORT OpaqueShapeList* calculateOutputShapes2(Nd4jPointer* extraPointers, Nd4jLong hash, Nd4jPointer* inputBuffers, Nd4jPointer* inputShapes, int numInputShapes, double* tArgs, int numTArgs, Nd4jLong *iArgs, int numIArgs, bool *bArgs, int numBArgs, int *dArgs, int numDArgs); @@ -1527,8 +1527,8 @@ ND4J_EXPORT void deleteShapeList(Nd4jPointer shapeList); ND4J_EXPORT int registerGraph(Nd4jPointer *extraPointers, Nd4jLong graphId, Nd4jPointer flatBufferPointer); -typedef nd4j::graph::VariablesSet OpaqueVariablesSet; -typedef nd4j::graph::Variable OpaqueVariable; +typedef sd::graph::VariablesSet OpaqueVariablesSet; +typedef sd::graph::Variable OpaqueVariable; ND4J_EXPORT OpaqueVariablesSet *executeStoredGraph(Nd4jPointer *extraPointers, Nd4jLong graphId, Nd4jPointer *inputBuffers, Nd4jPointer *inputShapes, int* inputIndices, int numInputs); @@ -1578,13 +1578,13 @@ ND4J_EXPORT void scatterUpdate(Nd4jPointer *extraPointers, int opCode, int numOf ND4J_EXPORT void inspectArray(Nd4jPointer *extraPointers, Nd4jPointer buffer, Nd4jLong *shapeInfo, Nd4jPointer specialBuffer, Nd4jLong *specialShapeInfo, Nd4jPointer debugInfo); -typedef nd4j::ConstantDataBuffer OpaqueConstantDataBuffer; +typedef sd::ConstantDataBuffer OpaqueConstantDataBuffer; -ND4J_EXPORT OpaqueConstantDataBuffer* shapeBuffer(int rank, Nd4jLong *shape, Nd4jLong *strides, nd4j::DataType dtype, char order, Nd4jLong ews, bool empty); +ND4J_EXPORT OpaqueConstantDataBuffer* shapeBuffer(int rank, Nd4jLong *shape, Nd4jLong *strides, sd::DataType dtype, char order, Nd4jLong ews, bool empty); -ND4J_EXPORT OpaqueConstantDataBuffer* constantBufferLong(nd4j::DataType dtype, Nd4jLong *data, int length); -ND4J_EXPORT OpaqueConstantDataBuffer* constantBufferDouble(nd4j::DataType dtype, double *data, int length); -ND4J_EXPORT OpaqueConstantDataBuffer* constantBuffer(nd4j::DataType dtype, nd4j::ConstantDescriptor *descriptor); +ND4J_EXPORT OpaqueConstantDataBuffer* constantBufferLong(sd::DataType dtype, Nd4jLong *data, int length); +ND4J_EXPORT OpaqueConstantDataBuffer* constantBufferDouble(sd::DataType dtype, double *data, int length); +ND4J_EXPORT OpaqueConstantDataBuffer* constantBuffer(sd::DataType dtype, sd::ConstantDescriptor *descriptor); ND4J_EXPORT Nd4jPointer getConstantDataBufferPrimary(OpaqueConstantDataBuffer* dbf); ND4J_EXPORT Nd4jPointer getConstantDataBufferSpecial(OpaqueConstantDataBuffer* dbf); @@ -1593,8 +1593,8 @@ ND4J_EXPORT Nd4jLong getConstantDataBufferSizeOf(OpaqueConstantDataBuffer* dbf); ND4J_EXPORT void deleteShapeBuffer(OpaqueConstantDataBuffer* ptr); -typedef nd4j::graph::Context OpaqueContext; -typedef nd4j::graph::RandomGenerator OpaqueRandomGenerator; +typedef sd::graph::Context OpaqueContext; +typedef sd::graph::RandomGenerator OpaqueRandomGenerator; ND4J_EXPORT OpaqueContext* createGraphContext(int nodeId); ND4J_EXPORT OpaqueRandomGenerator* getGraphContextRandomGenerator(OpaqueContext* ptr); @@ -1625,7 +1625,7 @@ ND4J_EXPORT void deleteRandomGenerator(OpaqueRandomGenerator* ptr); ND4J_EXPORT const char* runLightBenchmarkSuit(bool printOut); ND4J_EXPORT const char* runFullBenchmarkSuit(bool printOut); -typedef nd4j::LaunchContext OpaqueLaunchContext; +typedef sd::LaunchContext OpaqueLaunchContext; ND4J_EXPORT OpaqueLaunchContext* defaultLaunchContext(); ND4J_EXPORT Nd4jPointer lcScalarPointer(OpaqueLaunchContext* lc); diff --git a/libnd4j/blas/cpu/NativeOpExecutioner.cpp b/libnd4j/include/legacy/cpu/NativeOpExecutioner.cpp similarity index 69% rename from libnd4j/blas/cpu/NativeOpExecutioner.cpp rename to libnd4j/include/legacy/cpu/NativeOpExecutioner.cpp index 1fedb0241..b3f15e345 100644 --- a/libnd4j/blas/cpu/NativeOpExecutioner.cpp +++ b/libnd4j/include/legacy/cpu/NativeOpExecutioner.cpp @@ -16,19 +16,19 @@ #include -#include -#include "NativeOpExecutioner.h" +#include +#include "legacy/NativeOpExecutioner.h" #include -#include +#include -#include -#include -#include +#include +#include +#include -#include -#include -#include +#include +#include +#include #include #include @@ -50,7 +50,7 @@ #include #include #include -#include +#include #include #include #include @@ -76,7 +76,7 @@ * @param hZ * @param hZShapeInfo */ -void NativeOpExecutioner::execIndexReduceScalar(nd4j::LaunchContext *lc, int opNum, +void NativeOpExecutioner::execIndexReduceScalar(sd::LaunchContext *lc, int opNum, void *hX, Nd4jLong *hXShapeInfo, void *dX, Nd4jLong *dXShapeInfo, void *extraParams, @@ -85,8 +85,8 @@ void NativeOpExecutioner::execIndexReduceScalar(nd4j::LaunchContext *lc, int op - auto xType = nd4j::ArrayOptions::dataType(hXShapeInfo); - auto zType = nd4j::ArrayOptions::dataType(hZShapeInfo); + auto xType = sd::ArrayOptions::dataType(hXShapeInfo); + auto zType = sd::ArrayOptions::dataType(hZShapeInfo); auto hz = reinterpret_cast(hZ); BUILD_DOUBLE_SELECTOR(xType, zType, hz[0] = functions::indexreduce::IndexReduce, ::execScalar(opNum,hX,hXShapeInfo,extraParams), LIBND4J_TYPES, INDEXING_TYPES); @@ -105,7 +105,7 @@ void NativeOpExecutioner::execIndexReduceScalar(nd4j::LaunchContext *lc, int op * @param dimensionLength */ -void NativeOpExecutioner::execIndexReduce(nd4j::LaunchContext *lc, +void NativeOpExecutioner::execIndexReduce(sd::LaunchContext *lc, int opNum, void *hX, Nd4jLong *hXShapeInfo, void *dX, Nd4jLong *dXShapeInfo, @@ -116,8 +116,8 @@ void NativeOpExecutioner::execIndexReduce(nd4j::LaunchContext *lc, Nd4jLong *tadShapeInfo, Nd4jLong *tadOffsets) { - auto xType = nd4j::ArrayOptions::dataType(hXShapeInfo); - auto zType = nd4j::ArrayOptions::dataType(hZShapeInfo); + auto xType = sd::ArrayOptions::dataType(hXShapeInfo); + auto zType = sd::ArrayOptions::dataType(hZShapeInfo); Nd4jLong* hz = reinterpret_cast(hZ); BUILD_DOUBLE_SELECTOR(xType, zType, functions::indexreduce::IndexReduce, ::exec(opNum, hX, hXShapeInfo, extraParams, hz, hZShapeInfo, dimension, dimensionLength, tadShapeInfo, tadOffsets), LIBND4J_TYPES, INDEXING_TYPES); @@ -138,7 +138,7 @@ void NativeOpExecutioner::execIndexReduce(nd4j::LaunchContext *lc, * @param dimensionLength */ -void NativeOpExecutioner::execBroadcast(nd4j::LaunchContext *lc, +void NativeOpExecutioner::execBroadcast(sd::LaunchContext *lc, int opNum, void *hX, Nd4jLong *hXShapeInfo, void *dX, Nd4jLong *dXShapeInfo, @@ -152,9 +152,9 @@ void NativeOpExecutioner::execBroadcast(nd4j::LaunchContext *lc, - auto xType = nd4j::ArrayOptions::dataType(hXShapeInfo); - auto yType = nd4j::ArrayOptions::dataType(hYShapeInfo); - auto zType = nd4j::ArrayOptions::dataType(hZShapeInfo); + auto xType = sd::ArrayOptions::dataType(hXShapeInfo); + auto yType = sd::ArrayOptions::dataType(hYShapeInfo); + auto zType = sd::ArrayOptions::dataType(hZShapeInfo); if (shape::isEmpty(hXShapeInfo) || shape::isEmpty(hYShapeInfo)) return; @@ -163,7 +163,7 @@ void NativeOpExecutioner::execBroadcast(nd4j::LaunchContext *lc, BUILD_PAIRWISE_SELECTOR(xType, yType, zType, functions::broadcast::Broadcast, ::exec(opNum, hX, hXShapeInfo, hY, hYShapeInfo, hZ, hZShapeInfo, dimension, dimensionLength, tadOnlyShapeInfo, tadOffsets, tadOnlyShapeInfoZ, tadOffsetsZ), LIBND4J_TYPES, LIBND4J_TYPES); #else - auto loopKind = nd4j::LoopKind::deduceKindOfLoopBroadcast(hXShapeInfo, hYShapeInfo, hZShapeInfo); + auto loopKind = sd::LoopKind::deduceKindOfLoopBroadcast(hXShapeInfo, hYShapeInfo, hZShapeInfo); auto func = PRAGMA_THREADS_FOR { BUILD_SINGLE_SELECTOR_THRICE(xType, functions::broadcast::Broadcast, ::exec(opNum, hX, hXShapeInfo, hY, hYShapeInfo, hZ, hZShapeInfo, dimension, dimensionLength, tadOnlyShapeInfo, tadOffsets, tadOnlyShapeInfoZ, tadOffsetsZ, loopKind, start, stop), LIBND4J_TYPES); @@ -172,23 +172,23 @@ void NativeOpExecutioner::execBroadcast(nd4j::LaunchContext *lc, Nd4jLong numTads = 0; switch (loopKind) { - case nd4j::LoopKind::BROADCAST_SCALAR_X: { + case sd::LoopKind::BROADCAST_SCALAR_X: { numTads = shape::length(hXShapeInfo); } break; - case nd4j::LoopKind::BROADCAST_SCALAR_Y: { + case sd::LoopKind::BROADCAST_SCALAR_Y: { numTads = shape::length(hYShapeInfo); } break; - case nd4j::LoopKind::BROADCAST_3D: { + case sd::LoopKind::BROADCAST_3D: { numTads = shape::sizeAt(hZShapeInfo, 0); } break; - case nd4j::LoopKind::BROADCAST_4D: { + case sd::LoopKind::BROADCAST_4D: { numTads = shape::sizeAt(hZShapeInfo, 0) * shape::sizeAt(hZShapeInfo, 1); } break; - case nd4j::LoopKind::BROADCAST_5D: { + case sd::LoopKind::BROADCAST_5D: { numTads = shape::sizeAt(hZShapeInfo, 0) * shape::sizeAt(hZShapeInfo, 1); } break; @@ -204,7 +204,31 @@ void NativeOpExecutioner::execBroadcast(nd4j::LaunchContext *lc, #endif } -void NativeOpExecutioner::execInverseBroadcast(nd4j::LaunchContext *lc, +//////////////////////////////////////////////////////////////////////// +void NativeOpExecutioner::execBroadcast(sd::LaunchContext* lc, const int opNum, + const void *hX, const Nd4jLong *hXShapeInfo, + const void *dX, const Nd4jLong *dXShapeInfo, + const void *hY, const Nd4jLong *hYShapeInfo, + const void *dY, const Nd4jLong *dYShapeInfo, + void *hZ, const Nd4jLong *hZShapeInfo, + void *dZ, const Nd4jLong *dZShapeInfo) { + + if (shape::isEmpty(hXShapeInfo) || shape::isEmpty(hYShapeInfo)) + return; + + + auto xType = sd::ArrayOptions::dataType(hXShapeInfo); + auto yType = sd::ArrayOptions::dataType(hYShapeInfo); + auto zType = sd::ArrayOptions::dataType(hZShapeInfo); + + #ifdef __ND4J_EXPERIMENTAL__ + BUILD_PAIRWISE_SELECTOR(xType, yType, zType, functions::broadcast::Broadcast, ::exec(opNum, hX, hXShapeInfo, hY, hYShapeInfo, hZ, hZShapeInfo), LIBND4J_TYPES, LIBND4J_TYPES); + #else + BUILD_SINGLE_SELECTOR_THRICE(xType, functions::broadcast::Broadcast, ::exec(opNum, hX, hXShapeInfo, hY, hYShapeInfo, hZ, hZShapeInfo), LIBND4J_TYPES); + #endif +} + +void NativeOpExecutioner::execInverseBroadcast(sd::LaunchContext *lc, int opNum, void *hX, Nd4jLong *hXShapeInfo, void *dX, Nd4jLong *dXShapeInfo, @@ -215,16 +239,16 @@ void NativeOpExecutioner::execInverseBroadcast(nd4j::LaunchContext *lc, int *dimension, int dimensionLength, Nd4jLong *tadOnlyShapeInfo, Nd4jLong *tadOffsets, Nd4jLong *tadOnlyShapeInfoZ,Nd4jLong *tadOffsetsZ) { - auto xType = nd4j::ArrayOptions::dataType(hXShapeInfo); - auto yType = nd4j::ArrayOptions::dataType(hYShapeInfo); - auto zType = nd4j::ArrayOptions::dataType(hZShapeInfo); + auto xType = sd::ArrayOptions::dataType(hXShapeInfo); + auto yType = sd::ArrayOptions::dataType(hYShapeInfo); + auto zType = sd::ArrayOptions::dataType(hZShapeInfo); if (shape::isEmpty(hXShapeInfo) || shape::isEmpty(hYShapeInfo)) return; - if (!nd4j::Environment::getInstance()->isExperimentalBuild()) - if ((yType != xType && yType != nd4j::DataType::BOOL) || xType != zType) - throw nd4j::datatype_exception::build("NativeOps::execBroadcast both operands must have same data type", xType, yType); + if (!sd::Environment::getInstance()->isExperimentalBuild()) + if ((yType != xType && yType != sd::DataType::BOOL) || xType != zType) + throw sd::datatype_exception::build("NativeOps::execBroadcast both operands must have same data type", xType, yType); #ifdef __ND4J_EXPERIMENTAL__ BUILD_PAIRWISE_SELECTOR(xType, yType, zType, functions::broadcast::Broadcast, ::execInverse(opNum, hX, hXShapeInfo, hY, hYShapeInfo, hZ, hZShapeInfo, dimension, dimensionLength, tadOnlyShapeInfo, tadOffsets, tadOnlyShapeInfoZ, tadOffsetsZ), LIBND4J_TYPES, LIBND4J_TYPES); @@ -244,7 +268,7 @@ void NativeOpExecutioner::execInverseBroadcast(nd4j::LaunchContext *lc, //////////////////////////////////////////////////////////////////////// -void NativeOpExecutioner::execBroadcastBool(nd4j::LaunchContext *lc, +void NativeOpExecutioner::execBroadcastBool(sd::LaunchContext *lc, int opNum, void *hX, Nd4jLong *hXShapeInfo, void *dX, Nd4jLong *dXShapeInfo, @@ -258,13 +282,12 @@ void NativeOpExecutioner::execBroadcastBool(nd4j::LaunchContext *lc, Nd4jLong *tadOnlyShapeInfoZ,Nd4jLong *tadOffsetsZ) { - auto xType = nd4j::ArrayOptions::dataType(hXShapeInfo); - auto yType = nd4j::ArrayOptions::dataType(hYShapeInfo); - auto zType = nd4j::ArrayOptions::dataType(hZShapeInfo); - if (shape::isEmpty(hXShapeInfo) || shape::isEmpty(hYShapeInfo)) return; + auto xType = sd::ArrayOptions::dataType(hXShapeInfo); + auto zType = sd::ArrayOptions::dataType(hZShapeInfo); + auto func = PRAGMA_THREADS_FOR { BUILD_DOUBLE_SELECTOR(xType, zType, functions::broadcast::BroadcastBool, ::exec(opNum, hX, hXShapeInfo, hY, hYShapeInfo, hZ, hZShapeInfo, extraParams, dimension, dimensionLength, tadOnlyShapeInfo, tadOffsets, tadOnlyShapeInfoZ, tadOffsetsZ, start, stop), LIBND4J_TYPES, BOOL_TYPES); }; @@ -276,7 +299,27 @@ void NativeOpExecutioner::execBroadcastBool(nd4j::LaunchContext *lc, samediff::Threads::parallel_tad(func, 0, numTads); } -void NativeOpExecutioner::execInverseBroadcastBool(nd4j::LaunchContext *lc, +//////////////////////////////////////////////////////////////////////// +void NativeOpExecutioner::execBroadcastBool(sd::LaunchContext* lc, const int opNum, + const void *hX, const Nd4jLong *hXShapeInfo, + const void *dX, const Nd4jLong *dXShapeInfo, + const void *hY, const Nd4jLong *hYShapeInfo, + const void *dY, const Nd4jLong *dYShapeInfo, + void *hZ, const Nd4jLong *hZShapeInfo, + void *dZ, const Nd4jLong *dZShapeInfo, + void *extraParams) { + + if (shape::isEmpty(hXShapeInfo) || shape::isEmpty(hYShapeInfo)) + return; + + auto xType = sd::ArrayOptions::dataType(hXShapeInfo); + auto zType = sd::ArrayOptions::dataType(hZShapeInfo); + + BUILD_DOUBLE_SELECTOR(xType, zType, functions::broadcast::BroadcastBool, ::exec(opNum, hX, hXShapeInfo, hY, hYShapeInfo, hZ, hZShapeInfo, extraParams), LIBND4J_TYPES, BOOL_TYPES); +} + + +void NativeOpExecutioner::execInverseBroadcastBool(sd::LaunchContext *lc, int opNum, void *hX, Nd4jLong *hXShapeInfo, void *dX, Nd4jLong *dXShapeInfo, @@ -289,16 +332,16 @@ void NativeOpExecutioner::execInverseBroadcastBool(nd4j::LaunchContext *lc, Nd4jLong *tadOnlyShapeInfo, Nd4jLong *tadOffsets, Nd4jLong *tadOnlyShapeInfoZ,Nd4jLong *tadOffsetsZ) { - auto xType = nd4j::ArrayOptions::dataType(hXShapeInfo); - auto yType = nd4j::ArrayOptions::dataType(hYShapeInfo); - auto zType = nd4j::ArrayOptions::dataType(hZShapeInfo); + auto xType = sd::ArrayOptions::dataType(hXShapeInfo); + auto yType = sd::ArrayOptions::dataType(hYShapeInfo); + auto zType = sd::ArrayOptions::dataType(hZShapeInfo); if (shape::isEmpty(hXShapeInfo) || shape::isEmpty(hYShapeInfo)) return; - if (!nd4j::Environment::getInstance()->isExperimentalBuild()) - if (yType != xType || nd4j::DataType::BOOL != zType) - throw nd4j::datatype_exception::build("NativeOps::execInverseBroadcastBool both operands must have same data type", xType, yType); + if (!sd::Environment::getInstance()->isExperimentalBuild()) + if (yType != xType || sd::DataType::BOOL != zType) + throw sd::datatype_exception::build("NativeOps::execInverseBroadcastBool both operands must have same data type", xType, yType); auto func = PRAGMA_THREADS_FOR { BUILD_DOUBLE_SELECTOR(xType, zType, functions::broadcast::BroadcastBool, ::execInverse(opNum, hX, hXShapeInfo, hY, hYShapeInfo, hZ, hZShapeInfo, extraParams, dimension, dimensionLength, tadOnlyShapeInfo, tadOffsets, tadOnlyShapeInfoZ, tadOffsetsZ, start, stop), LIBND4J_TYPES, BOOL_TYPES); @@ -314,7 +357,7 @@ void NativeOpExecutioner::execInverseBroadcastBool(nd4j::LaunchContext *lc, //////////////////////////////////////////////////////////////////////// -void NativeOpExecutioner::execBroadcastInt(nd4j::LaunchContext *lc, +void NativeOpExecutioner::execBroadcastInt(sd::LaunchContext *lc, int opNum, void *hX, Nd4jLong *hXShapeInfo, void *dX, Nd4jLong *dXShapeInfo, @@ -327,18 +370,18 @@ void NativeOpExecutioner::execBroadcastInt(nd4j::LaunchContext *lc, Nd4jLong *tadOnlyShapeInfoZ,Nd4jLong *tadOffsetsZ) { - auto xType = nd4j::ArrayOptions::dataType(hXShapeInfo); - auto yType = nd4j::ArrayOptions::dataType(hYShapeInfo); - auto zType = nd4j::ArrayOptions::dataType(hZShapeInfo); + auto xType = sd::ArrayOptions::dataType(hXShapeInfo); + auto yType = sd::ArrayOptions::dataType(hYShapeInfo); + auto zType = sd::ArrayOptions::dataType(hZShapeInfo); if (shape::isEmpty(hXShapeInfo) || shape::isEmpty(hYShapeInfo)) return; if (xType != yType || xType != zType) - throw nd4j::datatype_exception::build("NativeOpExecutioner::execBroadcastInt", zType, xType, yType); + throw sd::datatype_exception::build("NativeOpExecutioner::execBroadcastInt", zType, xType, yType); - if (!nd4j::DataTypeUtils::isZ(zType)) - throw nd4j::datatype_exception::build("NativeOpExecutioner::execBroadcastInt requires integer data type", zType); + if (!sd::DataTypeUtils::isZ(zType)) + throw sd::datatype_exception::build("NativeOpExecutioner::execBroadcastInt requires integer data type", zType); auto func = PRAGMA_THREADS_FOR { BUILD_SINGLE_SELECTOR(xType, functions::broadcast::BroadcastInt, ::exec(opNum, hX, hXShapeInfo, hY, hYShapeInfo, hZ, hZShapeInfo, dimension, dimensionLength, tadOnlyShapeInfo, tadOffsets, tadOnlyShapeInfoZ, tadOffsetsZ, start, stop), INTEGER_TYPES); @@ -351,7 +394,34 @@ void NativeOpExecutioner::execBroadcastInt(nd4j::LaunchContext *lc, samediff::Threads::parallel_tad(func, 0, numTads); } -void NativeOpExecutioner::execInverseBroadcastInt(nd4j::LaunchContext *lc, +//////////////////////////////////////////////////////////////////////// +void NativeOpExecutioner::execBroadcastInt(sd::LaunchContext *lc, const int opNum, + const void *hX, const Nd4jLong *hXShapeInfo, + const void *dX, const Nd4jLong *dXShapeInfo, + const void *hY, const Nd4jLong *hYShapeInfo, + const void *dY, const Nd4jLong *dYShapeInfo, + void *hZ, const Nd4jLong *hZShapeInfo, + void *dZ, const Nd4jLong *dZShapeInfo) { + + + auto xType = sd::ArrayOptions::dataType(hXShapeInfo); + auto yType = sd::ArrayOptions::dataType(hYShapeInfo); + auto zType = sd::ArrayOptions::dataType(hZShapeInfo); + + if (shape::isEmpty(hXShapeInfo) || shape::isEmpty(hYShapeInfo)) + return; + + if (xType != yType || xType != zType) + throw sd::datatype_exception::build("NativeOpExecutioner::execBroadcastInt", zType, xType, yType); + + if (!sd::DataTypeUtils::isZ(zType)) + throw sd::datatype_exception::build("NativeOpExecutioner::execBroadcastInt requires integer data type", zType); + + BUILD_SINGLE_SELECTOR(xType, functions::broadcast::BroadcastInt, ::exec(opNum, hX, hXShapeInfo, hY, hYShapeInfo, hZ, hZShapeInfo), INTEGER_TYPES); + +} + +void NativeOpExecutioner::execInverseBroadcastInt(sd::LaunchContext *lc, int opNum, void *hX, Nd4jLong *hXShapeInfo, void *dX, Nd4jLong *dXShapeInfo, @@ -363,18 +433,18 @@ void NativeOpExecutioner::execInverseBroadcastInt(nd4j::LaunchContext *lc, Nd4jLong *tadOnlyShapeInfo, Nd4jLong *tadOffsets, Nd4jLong *tadOnlyShapeInfoZ,Nd4jLong *tadOffsetsZ) { - auto xType = nd4j::ArrayOptions::dataType(hXShapeInfo); - auto yType = nd4j::ArrayOptions::dataType(hYShapeInfo); - auto zType = nd4j::ArrayOptions::dataType(hZShapeInfo); + auto xType = sd::ArrayOptions::dataType(hXShapeInfo); + auto yType = sd::ArrayOptions::dataType(hYShapeInfo); + auto zType = sd::ArrayOptions::dataType(hZShapeInfo); if (shape::isEmpty(hXShapeInfo) || shape::isEmpty(hYShapeInfo)) return; if (xType != yType || xType != zType) - throw nd4j::datatype_exception::build("NativeOpExecutioner::execInverseBroadcastInt", zType, xType, yType); + throw sd::datatype_exception::build("NativeOpExecutioner::execInverseBroadcastInt", zType, xType, yType); - if (!nd4j::DataTypeUtils::isZ(zType)) - throw nd4j::datatype_exception::build("NativeOpExecutioner::execInverseBroadcastInt requires integer data type", zType); + if (!sd::DataTypeUtils::isZ(zType)) + throw sd::datatype_exception::build("NativeOpExecutioner::execInverseBroadcastInt requires integer data type", zType); auto func = PRAGMA_THREADS_FOR { BUILD_SINGLE_SELECTOR(xType, functions::broadcast::BroadcastInt,::execInverse(opNum, hX, hXShapeInfo, hY, hYShapeInfo, hZ, hZShapeInfo, dimension, dimensionLength, tadOnlyShapeInfo, tadOffsets, tadOnlyShapeInfoZ, tadOffsetsZ, start, stop), INTEGER_TYPES); @@ -400,7 +470,7 @@ void NativeOpExecutioner::execInverseBroadcastInt(nd4j::LaunchContext *lc, * @param extraParams * @param n */ -void NativeOpExecutioner::execPairwiseTransform(nd4j::LaunchContext *lc, +void NativeOpExecutioner::execPairwiseTransform(sd::LaunchContext *lc, int opNum, void *hX, Nd4jLong *hXShapeInfo, void *dX, Nd4jLong *dXShapeInfo, @@ -410,9 +480,9 @@ void NativeOpExecutioner::execPairwiseTransform(nd4j::LaunchContext *lc, void *dZ, Nd4jLong *dZShapeInfo, void *extraParams) { - auto xType = nd4j::ArrayOptions::dataType(hXShapeInfo); - auto yType = nd4j::ArrayOptions::dataType(hYShapeInfo); - auto zType = nd4j::ArrayOptions::dataType(hZShapeInfo); + auto xType = sd::ArrayOptions::dataType(hXShapeInfo); + auto yType = sd::ArrayOptions::dataType(hYShapeInfo); + auto zType = sd::ArrayOptions::dataType(hZShapeInfo); if (shape::isEmpty(hXShapeInfo) || shape::isEmpty(hYShapeInfo)) return; @@ -427,13 +497,13 @@ void NativeOpExecutioner::execPairwiseTransform(nd4j::LaunchContext *lc, }; auto zLen = shape::length(hZShapeInfo); - samediff::Threads::parallel_for(func, 0, zLen, 1, nd4j::math::nd4j_max(1, nd4j::math::nd4j_min(zLen / 1024, nd4j::Environment::getInstance()->maxMasterThreads()))); + samediff::Threads::parallel_for(func, 0, zLen, 1, sd::math::nd4j_max(1, sd::math::nd4j_min(zLen / 1024, sd::Environment::getInstance()->maxMasterThreads()))); #endif } //////////////////////////////////////////////////////////////////////// -void NativeOpExecutioner::execPairwiseBoolTransform(nd4j::LaunchContext *lc, +void NativeOpExecutioner::execPairwiseBoolTransform(sd::LaunchContext *lc, int opNum, void *hX, Nd4jLong *hXShapeInfo, void *dX, Nd4jLong *dXShapeInfo, @@ -444,30 +514,30 @@ void NativeOpExecutioner::execPairwiseBoolTransform(nd4j::LaunchContext *lc, void *extraParams) { - auto xType = nd4j::ArrayOptions::dataType(hXShapeInfo); - auto yType = nd4j::ArrayOptions::dataType(hYShapeInfo); - auto zType = nd4j::ArrayOptions::dataType(hZShapeInfo); + auto xType = sd::ArrayOptions::dataType(hXShapeInfo); + auto yType = sd::ArrayOptions::dataType(hYShapeInfo); + auto zType = sd::ArrayOptions::dataType(hZShapeInfo); if (shape::isEmpty(hXShapeInfo) || shape::isEmpty(hYShapeInfo)) return; if (xType != yType) - throw nd4j::datatype_exception::build("NativeOpExecutioner::execPairwiseBoolTransform", xType, yType); + throw sd::datatype_exception::build("NativeOpExecutioner::execPairwiseBoolTransform", xType, yType); - if (zType != nd4j::DataType::BOOL) - throw nd4j::datatype_exception::build("NativeOpExecutioner::execPairwiseBoolTransform", nd4j::DataType::BOOL, zType); + if (zType != sd::DataType::BOOL) + throw sd::datatype_exception::build("NativeOpExecutioner::execPairwiseBoolTransform", sd::DataType::BOOL, zType); auto func = PRAGMA_THREADS_FOR { BUILD_DOUBLE_SELECTOR(xType, zType, functions::pairwise_transforms::PairWiseBoolTransform, ::exec(opNum, hX, hXShapeInfo, hY, hYShapeInfo, hZ, hZShapeInfo, extraParams, start, stop), LIBND4J_TYPES, BOOL_TYPES); }; auto zLen = shape::length(hZShapeInfo); - samediff::Threads::parallel_for(func, 0, zLen, 1, nd4j::math::nd4j_max(1, nd4j::math::nd4j_min(zLen / 1024, nd4j::Environment::getInstance()->maxMasterThreads()))); + samediff::Threads::parallel_for(func, 0, zLen, 1, sd::math::nd4j_max(1, sd::math::nd4j_min(zLen / 1024, sd::Environment::getInstance()->maxMasterThreads()))); } //////////////////////////////////////////////////////////////////////// -void NativeOpExecutioner::execPairwiseIntTransform(nd4j::LaunchContext *lc, +void NativeOpExecutioner::execPairwiseIntTransform(sd::LaunchContext *lc, int opNum, void *hX, Nd4jLong *hXShapeInfo, void *dX, Nd4jLong *dXShapeInfo, @@ -477,25 +547,25 @@ void NativeOpExecutioner::execPairwiseIntTransform(nd4j::LaunchContext *lc, void *dZ, Nd4jLong *dZShapeInfo, void *extraParams) { - auto xType = nd4j::ArrayOptions::dataType(hXShapeInfo); - auto yType = nd4j::ArrayOptions::dataType(hYShapeInfo); - auto zType = nd4j::ArrayOptions::dataType(hZShapeInfo); + auto xType = sd::ArrayOptions::dataType(hXShapeInfo); + auto yType = sd::ArrayOptions::dataType(hYShapeInfo); + auto zType = sd::ArrayOptions::dataType(hZShapeInfo); if (shape::isEmpty(hXShapeInfo) || shape::isEmpty(hYShapeInfo)) return; if (xType != yType || xType != zType) - throw nd4j::datatype_exception::build("NativeOpExecutioner::execPairwiseIntTransform", zType, xType, yType); + throw sd::datatype_exception::build("NativeOpExecutioner::execPairwiseIntTransform", zType, xType, yType); - if (!nd4j::DataTypeUtils::isZ(zType)) - throw nd4j::datatype_exception::build("NativeOpExecutioner::execSPairwiseInt requires integer data type", zType); + if (!sd::DataTypeUtils::isZ(zType)) + throw sd::datatype_exception::build("NativeOpExecutioner::execSPairwiseInt requires integer data type", zType); auto func = PRAGMA_THREADS_FOR { BUILD_SINGLE_SELECTOR(xType, functions::pairwise_transforms::PairWiseIntTransform, ::exec(opNum, hX, hXShapeInfo, hY, hYShapeInfo, hZ, hZShapeInfo, extraParams, start, stop), INTEGER_TYPES); }; auto zLen = shape::length(hZShapeInfo); - samediff::Threads::parallel_for(func, 0, zLen, 1, nd4j::math::nd4j_max(1, nd4j::math::nd4j_min(zLen / 1024, nd4j::Environment::getInstance()->maxMasterThreads()))); + samediff::Threads::parallel_for(func, 0, zLen, 1, sd::math::nd4j_max(1, sd::math::nd4j_min(zLen / 1024, sd::Environment::getInstance()->maxMasterThreads()))); } @@ -509,7 +579,7 @@ void NativeOpExecutioner::execPairwiseIntTransform(nd4j::LaunchContext *lc, * @param hZ * @param hZShapeInfo */ -void NativeOpExecutioner::execReduceFloat(nd4j::LaunchContext *lc, +void NativeOpExecutioner::execReduceFloat(sd::LaunchContext *lc, int opNum, void *hX, Nd4jLong *hXShapeInfo, void *dX, Nd4jLong *dXShapeInfo, @@ -521,8 +591,8 @@ void NativeOpExecutioner::execReduceFloat(nd4j::LaunchContext *lc, - auto xType = nd4j::ArrayOptions::dataType(hXShapeInfo); - auto zType = nd4j::ArrayOptions::dataType(hZShapeInfo); + auto xType = sd::ArrayOptions::dataType(hXShapeInfo); + auto zType = sd::ArrayOptions::dataType(hZShapeInfo); // nothing to do here if result is empty if (shape::isEmpty(hZShapeInfo)) @@ -532,13 +602,13 @@ void NativeOpExecutioner::execReduceFloat(nd4j::LaunchContext *lc, BUILD_DOUBLE_SELECTOR(xType, zType, functions::reduce::ReduceFloatFunction, ::exec(opNum, hX, hXShapeInfo, extraParams, hZ, hZShapeInfo, dimension, dimensionLength, tadShapeInfo, tadOffsets, start, stop), LIBND4J_TYPES, FLOAT_TYPES); }; - const nd4j::LoopKind::Kind kindOfLoop = nd4j::LoopKind::deduceKindOfLoopTadXZ(hXShapeInfo, hZShapeInfo, tadShapeInfo); + const sd::LoopKind::Kind kindOfLoop = sd::LoopKind::deduceKindOfLoopTadXZ(hXShapeInfo, hZShapeInfo, tadShapeInfo); - samediff::Threads::parallel_tad(func, 0, shape::length(hZShapeInfo), 1, kindOfLoop == nd4j::LoopKind::Kind::SMALLARR2DX ? 1 : nd4j::Environment::getInstance()->maxMasterThreads()); + samediff::Threads::parallel_tad(func, 0, shape::length(hZShapeInfo), 1, kindOfLoop == sd::LoopKind::Kind::SMALLARR2DX ? 1 : sd::Environment::getInstance()->maxMasterThreads()); } //////////////////////////////////////////////////////////////////////// -void NativeOpExecutioner::execReduceSame(nd4j::LaunchContext *lc, +void NativeOpExecutioner::execReduceSame(sd::LaunchContext *lc, int opNum, void *hX, Nd4jLong *hXShapeInfo, void *dX, Nd4jLong *dXShapeInfo, @@ -549,8 +619,8 @@ void NativeOpExecutioner::execReduceSame(nd4j::LaunchContext *lc, Nd4jLong *tadShapeInfo, Nd4jLong *tadOffsets) { - auto xType = nd4j::ArrayOptions::dataType(hXShapeInfo); - auto zType = nd4j::ArrayOptions::dataType(hZShapeInfo); + auto xType = sd::ArrayOptions::dataType(hXShapeInfo); + auto zType = sd::ArrayOptions::dataType(hZShapeInfo); // nothing to do here if result is empty if (shape::isEmpty(hZShapeInfo)) @@ -560,13 +630,13 @@ void NativeOpExecutioner::execReduceSame(nd4j::LaunchContext *lc, BUILD_SINGLE_SELECTOR(xType, functions::reduce::ReduceSameFunction, ::exec(opNum, hX, hXShapeInfo, extraParams, hZ, hZShapeInfo, dimension, dimensionLength, tadShapeInfo, tadOffsets, start, stop), LIBND4J_TYPES); }; - const nd4j::LoopKind::Kind kindOfLoop = nd4j::LoopKind::deduceKindOfLoopTadXZ(hXShapeInfo, hZShapeInfo, tadShapeInfo); + const sd::LoopKind::Kind kindOfLoop = sd::LoopKind::deduceKindOfLoopTadXZ(hXShapeInfo, hZShapeInfo, tadShapeInfo); - samediff::Threads::parallel_tad(func, 0, shape::length(hZShapeInfo), 1, kindOfLoop == nd4j::LoopKind::Kind::SMALLARR2DX ? 1 : nd4j::Environment::getInstance()->maxMasterThreads()); + samediff::Threads::parallel_tad(func, 0, shape::length(hZShapeInfo), 1, kindOfLoop == sd::LoopKind::Kind::SMALLARR2DX ? 1 : sd::Environment::getInstance()->maxMasterThreads()); } //////////////////////////////////////////////////////////////////////// -void NativeOpExecutioner::execReduceBool(nd4j::LaunchContext *lc, +void NativeOpExecutioner::execReduceBool(sd::LaunchContext *lc, int opNum, void *hX, Nd4jLong *hXShapeInfo, void *dX, Nd4jLong *dXShapeInfo, @@ -577,8 +647,8 @@ void NativeOpExecutioner::execReduceBool(nd4j::LaunchContext *lc, Nd4jLong *tadShapeInfo, Nd4jLong *tadOffsets) { - auto xType = nd4j::ArrayOptions::dataType(hXShapeInfo); - auto zType = nd4j::ArrayOptions::dataType(hZShapeInfo); + auto xType = sd::ArrayOptions::dataType(hXShapeInfo); + auto zType = sd::ArrayOptions::dataType(hZShapeInfo); // nothing to do here if result is empty if (shape::isEmpty(hZShapeInfo)) @@ -588,13 +658,13 @@ void NativeOpExecutioner::execReduceBool(nd4j::LaunchContext *lc, BUILD_DOUBLE_SELECTOR(xType, zType, functions::reduce::ReduceBoolFunction, ::exec(opNum, hX, hXShapeInfo, extraParams, hZ, hZShapeInfo, dimension, dimensionLength, tadShapeInfo, tadOffsets, start, stop), LIBND4J_TYPES, BOOL_TYPES); }; - const nd4j::LoopKind::Kind kindOfLoop = nd4j::LoopKind::deduceKindOfLoopTadXZ(hXShapeInfo, hZShapeInfo, tadShapeInfo); + const sd::LoopKind::Kind kindOfLoop = sd::LoopKind::deduceKindOfLoopTadXZ(hXShapeInfo, hZShapeInfo, tadShapeInfo); - samediff::Threads::parallel_tad(func, 0, shape::length(hZShapeInfo), 1, kindOfLoop == nd4j::LoopKind::Kind::SMALLARR2DX ? 1 : nd4j::Environment::getInstance()->maxMasterThreads()); + samediff::Threads::parallel_tad(func, 0, shape::length(hZShapeInfo), 1, kindOfLoop == sd::LoopKind::Kind::SMALLARR2DX ? 1 : sd::Environment::getInstance()->maxMasterThreads()); } //////////////////////////////////////////////////////////////////////// -void NativeOpExecutioner::execReduceLong(nd4j::LaunchContext *lc, +void NativeOpExecutioner::execReduceLong(sd::LaunchContext *lc, int opNum, void *hX, Nd4jLong *hXShapeInfo, void *dX, Nd4jLong *dXShapeInfo, @@ -605,8 +675,8 @@ void NativeOpExecutioner::execReduceLong(nd4j::LaunchContext *lc, Nd4jLong *tadShapeInfo, Nd4jLong *tadOffsets) { - auto xType = nd4j::ArrayOptions::dataType(hXShapeInfo); - auto zType = nd4j::ArrayOptions::dataType(hZShapeInfo); + auto xType = sd::ArrayOptions::dataType(hXShapeInfo); + auto zType = sd::ArrayOptions::dataType(hZShapeInfo); // nothing to do here if result is empty if (shape::isEmpty(hZShapeInfo)) @@ -616,9 +686,9 @@ void NativeOpExecutioner::execReduceLong(nd4j::LaunchContext *lc, BUILD_DOUBLE_SELECTOR(xType, zType, functions::reduce::ReduceLongFunction, ::exec(opNum, hX, hXShapeInfo, extraParams, hZ, hZShapeInfo, dimension, dimensionLength, tadShapeInfo, tadOffsets, start, stop), LIBND4J_TYPES, LONG_TYPES); }; - const nd4j::LoopKind::Kind kindOfLoop = nd4j::LoopKind::deduceKindOfLoopTadXZ(hXShapeInfo, hZShapeInfo, tadShapeInfo); + const sd::LoopKind::Kind kindOfLoop = sd::LoopKind::deduceKindOfLoopTadXZ(hXShapeInfo, hZShapeInfo, tadShapeInfo); - samediff::Threads::parallel_tad(func, 0, shape::length(hZShapeInfo), 1, kindOfLoop == nd4j::LoopKind::Kind::SMALLARR2DX ? 1 : nd4j::Environment::getInstance()->maxMasterThreads()); + samediff::Threads::parallel_tad(func, 0, shape::length(hZShapeInfo), 1, kindOfLoop == sd::LoopKind::Kind::SMALLARR2DX ? 1 : sd::Environment::getInstance()->maxMasterThreads()); } //////////////////////////////////////////////////////////////////////// @@ -630,7 +700,7 @@ void NativeOpExecutioner::execReduceLong(nd4j::LaunchContext *lc, * @param extraParams * @return */ -void NativeOpExecutioner::execReduceFloatScalar(nd4j::LaunchContext *lc, +void NativeOpExecutioner::execReduceFloatScalar(sd::LaunchContext *lc, int opNum, void *hX, Nd4jLong *hXShapeInfo, void *dX, Nd4jLong *dXShapeInfo, @@ -639,14 +709,14 @@ void NativeOpExecutioner::execReduceFloatScalar(nd4j::LaunchContext *lc, void *dZ, Nd4jLong *dZShapeInfo) { - auto xType = nd4j::ArrayOptions::dataType(hXShapeInfo); - auto zType = nd4j::ArrayOptions::dataType(hZShapeInfo); + auto xType = sd::ArrayOptions::dataType(hXShapeInfo); + auto zType = sd::ArrayOptions::dataType(hZShapeInfo); BUILD_DOUBLE_SELECTOR(xType, zType, functions::reduce::ReduceFloatFunction, ::execScalar(opNum, hX, hXShapeInfo, extraParams, hZ, hZShapeInfo), LIBND4J_TYPES, FLOAT_TYPES); } //////////////////////////////////////////////////////////////////////// -void NativeOpExecutioner::execReduceSameScalar(nd4j::LaunchContext *lc, +void NativeOpExecutioner::execReduceSameScalar(sd::LaunchContext *lc, int opNum, void *hX, Nd4jLong *hXShapeInfo, void *dX, Nd4jLong *dXShapeInfo, @@ -655,13 +725,13 @@ void NativeOpExecutioner::execReduceSameScalar(nd4j::LaunchContext *lc, void *dZ, Nd4jLong *dZShapeInfo) { - auto xType = nd4j::ArrayOptions::dataType(hXShapeInfo); + auto xType = sd::ArrayOptions::dataType(hXShapeInfo); BUILD_SINGLE_SELECTOR(xType, functions::reduce::ReduceSameFunction, ::execScalar(opNum, hX, hXShapeInfo, extraParams, hZ, hZShapeInfo), LIBND4J_TYPES); } //////////////////////////////////////////////////////////////////////// -void NativeOpExecutioner::execReduceBoolScalar(nd4j::LaunchContext *lc, +void NativeOpExecutioner::execReduceBoolScalar(sd::LaunchContext *lc, int opNum, void *hX, Nd4jLong *hXShapeInfo, void *dX, Nd4jLong *dXShapeInfo, @@ -671,14 +741,14 @@ void NativeOpExecutioner::execReduceBoolScalar(nd4j::LaunchContext *lc, - auto xType = nd4j::ArrayOptions::dataType(hXShapeInfo); - auto zType = nd4j::ArrayOptions::dataType(hZShapeInfo); + auto xType = sd::ArrayOptions::dataType(hXShapeInfo); + auto zType = sd::ArrayOptions::dataType(hZShapeInfo); BUILD_DOUBLE_SELECTOR(xType, zType, functions::reduce::ReduceBoolFunction, ::execScalar(opNum, hX, hXShapeInfo, extraParams, hZ, hZShapeInfo), LIBND4J_TYPES, BOOL_TYPES); } //////////////////////////////////////////////////////////////////////// -void NativeOpExecutioner::execReduceLongScalar(nd4j::LaunchContext *lc, +void NativeOpExecutioner::execReduceLongScalar(sd::LaunchContext *lc, int opNum, void *hX, Nd4jLong *hXShapeInfo, void *dX, Nd4jLong *dXShapeInfo, @@ -687,8 +757,8 @@ void NativeOpExecutioner::execReduceLongScalar(nd4j::LaunchContext *lc, void *dZ, Nd4jLong *dZShapeInfo) { - auto xType = nd4j::ArrayOptions::dataType(hXShapeInfo); - auto zType = nd4j::ArrayOptions::dataType(hZShapeInfo); + auto xType = sd::ArrayOptions::dataType(hXShapeInfo); + auto zType = sd::ArrayOptions::dataType(hZShapeInfo); BUILD_DOUBLE_SELECTOR(xType, zType, functions::reduce::ReduceLongFunction, ::execScalar(opNum, hX, hXShapeInfo, extraParams, hZ, hZShapeInfo), LIBND4J_TYPES, LONG_TYPES); } @@ -708,7 +778,7 @@ void NativeOpExecutioner::execReduceLongScalar(nd4j::LaunchContext *lc, * @param dimension * @param dimensionLength */ -void NativeOpExecutioner::execReduce3Scalar(nd4j::LaunchContext *lc, +void NativeOpExecutioner::execReduce3Scalar(sd::LaunchContext *lc, int opNum, void *hX, Nd4jLong *hXShapeInfo, void *dX, Nd4jLong *dXShapeInfo, @@ -717,8 +787,8 @@ void NativeOpExecutioner::execReduce3Scalar(nd4j::LaunchContext *lc, void *dY, Nd4jLong *dYShapeInfo, void *hZ, Nd4jLong *hZShapeInfo, void *dZ, Nd4jLong *dZShapeInfo) { - auto xType = nd4j::ArrayOptions::dataType(hXShapeInfo); - auto zType = nd4j::ArrayOptions::dataType(hZShapeInfo); + auto xType = sd::ArrayOptions::dataType(hXShapeInfo); + auto zType = sd::ArrayOptions::dataType(hZShapeInfo); BUILD_DOUBLE_SELECTOR(xType, zType, functions::reduce3::Reduce3, ::execScalar(opNum, hX, hXShapeInfo, extraParamsVals, hY, hYShapeInfo, hZ, hZShapeInfo), LIBND4J_TYPES, FLOAT_TYPES); @@ -736,7 +806,7 @@ void NativeOpExecutioner::execReduce3Scalar(nd4j::LaunchContext *lc, * @param hZ * @param hZShapeInfo */ -void NativeOpExecutioner::execReduce3(nd4j::LaunchContext *lc, +void NativeOpExecutioner::execReduce3(sd::LaunchContext *lc, int opNum, void *hX, Nd4jLong *hXShapeInfo, void *dX, Nd4jLong *dXShapeInfo, @@ -747,15 +817,15 @@ void NativeOpExecutioner::execReduce3(nd4j::LaunchContext *lc, void *dZ, Nd4jLong *dZShapeInfo) { - auto xType = nd4j::ArrayOptions::dataType(hXShapeInfo); - auto zType = nd4j::ArrayOptions::dataType(hZShapeInfo); + auto xType = sd::ArrayOptions::dataType(hXShapeInfo); + auto zType = sd::ArrayOptions::dataType(hZShapeInfo); //BUILD_DOUBLE_SELECTOR(xType, zType, functions::reduce3::Reduce3, ::exec(opNum, hX, hXShapeInfo, extraParamsVals, hY, hYShapeInfo, hZ, hZShapeInfo, nullptr, 0), LIBND4J_TYPES, FLOAT_TYPES); NativeOpExecutioner::execReduce3Scalar(lc, opNum, hX, hXShapeInfo, dX, dXShapeInfo, extraParamsVals, hY, hYShapeInfo, dY, dYShapeInfo, hZ, hZShapeInfo, dZ, dZShapeInfo); } //////////////////////////////////////////////////////////////////////// -void NativeOpExecutioner::execReduce3(nd4j::LaunchContext *lc, +void NativeOpExecutioner::execReduce3(sd::LaunchContext *lc, int opNum, void *hX, Nd4jLong *hXShapeInfo, void *dX, Nd4jLong *dXShapeInfo, @@ -769,22 +839,22 @@ void NativeOpExecutioner::execReduce3(nd4j::LaunchContext *lc, Nd4jLong *yTadOnlyShapeInfo, Nd4jLong *yTadOffsets) { - auto xType = nd4j::ArrayOptions::dataType(hXShapeInfo); - auto zType = nd4j::ArrayOptions::dataType(hZShapeInfo); + auto xType = sd::ArrayOptions::dataType(hXShapeInfo); + auto zType = sd::ArrayOptions::dataType(hZShapeInfo); const auto xLen = shape::length(hXShapeInfo); const auto yLen = shape::length(hYShapeInfo); - nd4j::TadPack tadPack; + sd::TadPack tadPack; if(xLen == yLen) { - tadPack = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(hXShapeInfo, dimension, dimensionLength); + tadPack = sd::ConstantTadHelper::getInstance()->tadForDimensions(hXShapeInfo, dimension, dimensionLength); } else if(yLen > xLen) { - tadPack = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(hYShapeInfo, dimension, dimensionLength); + tadPack = sd::ConstantTadHelper::getInstance()->tadForDimensions(hYShapeInfo, dimension, dimensionLength); } else { - tadPack = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(hXShapeInfo, dimension, dimensionLength); + tadPack = sd::ConstantTadHelper::getInstance()->tadForDimensions(hXShapeInfo, dimension, dimensionLength); } auto func = PRAGMA_THREADS_FOR { @@ -796,7 +866,7 @@ void NativeOpExecutioner::execReduce3(nd4j::LaunchContext *lc, //////////////////////////////////////////////////////////////////////// -void NativeOpExecutioner::execReduce3All(nd4j::LaunchContext *lc, +void NativeOpExecutioner::execReduce3All(sd::LaunchContext *lc, int opNum, void *hX, Nd4jLong *hXShapeInfo, void *dX, Nd4jLong *dXShapeInfo, @@ -810,10 +880,10 @@ void NativeOpExecutioner::execReduce3All(nd4j::LaunchContext *lc, Nd4jLong *yTadShapeInfo, Nd4jLong *yOffsets) { - auto xType = nd4j::ArrayOptions::dataType(hXShapeInfo); - auto zType = nd4j::ArrayOptions::dataType(hZShapeInfo); + auto xType = sd::ArrayOptions::dataType(hXShapeInfo); + auto zType = sd::ArrayOptions::dataType(hZShapeInfo); - auto tadPack = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(hXShapeInfo, dimension, dimensionLength); + auto tadPack = sd::ConstantTadHelper::getInstance()->tadForDimensions(hXShapeInfo, dimension, dimensionLength); // TODO: make it 2d auto func = PRAGMA_THREADS_FOR { @@ -824,7 +894,7 @@ void NativeOpExecutioner::execReduce3All(nd4j::LaunchContext *lc, } //////////////////////////////////////////////////////////////////////// -void NativeOpExecutioner::execReduce3TAD(nd4j::LaunchContext *lc, +void NativeOpExecutioner::execReduce3TAD(sd::LaunchContext *lc, int opNum, void *hX, Nd4jLong *hXShapeInfo, void *dX, Nd4jLong *dXShapeInfo, @@ -839,22 +909,22 @@ void NativeOpExecutioner::execReduce3TAD(nd4j::LaunchContext *lc, - auto xType = nd4j::ArrayOptions::dataType(hXShapeInfo); - auto zType = nd4j::ArrayOptions::dataType(hZShapeInfo); + auto xType = sd::ArrayOptions::dataType(hXShapeInfo); + auto zType = sd::ArrayOptions::dataType(hZShapeInfo); const auto xLen = shape::length(hXShapeInfo); const auto yLen = shape::length(hYShapeInfo); - nd4j::TadPack tadPack; + sd::TadPack tadPack; if(xLen == yLen) { - tadPack = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(hXShapeInfo, dimension, dimensionLength); + tadPack = sd::ConstantTadHelper::getInstance()->tadForDimensions(hXShapeInfo, dimension, dimensionLength); } else if(yLen > xLen) { - tadPack = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(hYShapeInfo, dimension, dimensionLength); + tadPack = sd::ConstantTadHelper::getInstance()->tadForDimensions(hYShapeInfo, dimension, dimensionLength); } else { - tadPack = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(hXShapeInfo, dimension, dimensionLength); + tadPack = sd::ConstantTadHelper::getInstance()->tadForDimensions(hXShapeInfo, dimension, dimensionLength); } auto func = PRAGMA_THREADS_FOR { @@ -877,7 +947,7 @@ void NativeOpExecutioner::execReduce3TAD(nd4j::LaunchContext *lc, * @param extraParams * @param n */ -void NativeOpExecutioner::execScalar(nd4j::LaunchContext *lc, +void NativeOpExecutioner::execScalar(sd::LaunchContext *lc, int opNum, void *hX, Nd4jLong *hXShapeInfo, void *dX, Nd4jLong *dXShapeInfo, @@ -887,9 +957,9 @@ void NativeOpExecutioner::execScalar(nd4j::LaunchContext *lc, void *dScalar, Nd4jLong *dScalarShapeInfo, void *extraParams, bool allowParallelism) { - auto xType = nd4j::ArrayOptions::dataType(hXShapeInfo); - auto yType = nd4j::ArrayOptions::dataType(hScalarShapeInfo); - auto zType = nd4j::ArrayOptions::dataType(hZShapeInfo); + auto xType = sd::ArrayOptions::dataType(hXShapeInfo); + auto yType = sd::ArrayOptions::dataType(hScalarShapeInfo); + auto zType = sd::ArrayOptions::dataType(hZShapeInfo); if (shape::isEmpty(hXShapeInfo) || shape::isEmpty(hScalarShapeInfo)) return; @@ -898,20 +968,20 @@ void NativeOpExecutioner::execScalar(nd4j::LaunchContext *lc, BUILD_PAIRWISE_SELECTOR(xType, yType, zType, functions::scalar::ScalarTransform, ::transform(opNum, hX, hXShapeInfo, hZ, hZShapeInfo, hScalar, extraParams), LIBND4J_TYPES, LIBND4J_TYPES); #else if (xType != yType || xType != zType) - throw nd4j::datatype_exception::build("NativeOpExecutioner::execScalar", zType, xType, yType); + throw sd::datatype_exception::build("NativeOpExecutioner::execScalar", zType, xType, yType); auto func = PRAGMA_THREADS_FOR { BUILD_SINGLE_SELECTOR_THRICE(xType, functions::scalar::ScalarTransform,::transform(opNum, hX, hXShapeInfo, hZ, hZShapeInfo, hScalar, extraParams, start, stop), LIBND4J_TYPES); }; auto zLen = shape::length(hZShapeInfo); - samediff::Threads::parallel_for(func, 0, zLen, 1, !allowParallelism ? 1 : nd4j::math::nd4j_max(1, nd4j::math::nd4j_min(zLen / 1024, nd4j::Environment::getInstance()->maxMasterThreads()))); + samediff::Threads::parallel_for(func, 0, zLen, 1, !allowParallelism ? 1 : sd::math::nd4j_max(1, sd::math::nd4j_min(zLen / 1024, sd::Environment::getInstance()->maxMasterThreads()))); #endif } //////////////////////////////////////////////////////////////////////// -void NativeOpExecutioner::execScalar(nd4j::LaunchContext *lc, +void NativeOpExecutioner::execScalar(sd::LaunchContext *lc, int opNum, void *hX, Nd4jLong *hXShapeInfo, void *dX, Nd4jLong *dXShapeInfo, @@ -924,9 +994,9 @@ void NativeOpExecutioner::execScalar(nd4j::LaunchContext *lc, Nd4jLong *tadShapeInfo, Nd4jLong *tadOffsets, Nd4jLong *tadShapeInfoZ, Nd4jLong *tadOffsetsZ) { - auto xType = nd4j::ArrayOptions::dataType(hXShapeInfo); - auto yType = nd4j::ArrayOptions::dataType(hScalarShapeInfo); - auto zType = nd4j::ArrayOptions::dataType(hZShapeInfo); + auto xType = sd::ArrayOptions::dataType(hXShapeInfo); + auto yType = sd::ArrayOptions::dataType(hScalarShapeInfo); + auto zType = sd::ArrayOptions::dataType(hZShapeInfo); if (shape::isEmpty(hXShapeInfo) || shape::isEmpty(hScalarShapeInfo)) return; @@ -935,20 +1005,20 @@ void NativeOpExecutioner::execScalar(nd4j::LaunchContext *lc, BUILD_PAIRWISE_SELECTOR(xType, yType, zType, functions::scalar::ScalarTransform, ::transform(opNum, hX, hXShapeInfo, extraParams, hZ, hZShapeInfo, hScalars, dimension, dimensionLength, tadShapeInfo, tadOffsets, tadShapeInfoZ, tadOffsetsZ), LIBND4J_TYPES, LIBND4J_TYPES); #else if (xType != yType || xType != zType) - throw nd4j::datatype_exception::build("NativeOpExecutioner::execScalar", zType, xType, yType); + throw sd::datatype_exception::build("NativeOpExecutioner::execScalar", zType, xType, yType); auto func = PRAGMA_THREADS_FOR { BUILD_SINGLE_SELECTOR_THRICE(xType, functions::scalar::ScalarTransform, ::transform(opNum, hX, hXShapeInfo, extraParams, hZ, hZShapeInfo, hScalars, dimension, dimensionLength, tadShapeInfo, tadOffsets, tadShapeInfoZ, tadOffsetsZ, start, stop), LIBND4J_TYPES); }; auto yLen = shape::length(hScalarShapeInfo); - samediff::Threads::parallel_tad(func, 0, yLen, 1, nd4j::math::nd4j_min(yLen, nd4j::Environment::getInstance()->maxMasterThreads())); + samediff::Threads::parallel_tad(func, 0, yLen, 1, sd::math::nd4j_min(yLen, sd::Environment::getInstance()->maxMasterThreads())); #endif } //////////////////////////////////////////////////////////////////////// -void NativeOpExecutioner::execScalarBool(nd4j::LaunchContext *lc, +void NativeOpExecutioner::execScalarBool(sd::LaunchContext *lc, int opNum, void *hX, Nd4jLong *hXShapeInfo, void *dX, Nd4jLong *dXShapeInfo, @@ -958,30 +1028,30 @@ void NativeOpExecutioner::execScalarBool(nd4j::LaunchContext *lc, void *dScalar, Nd4jLong *dSscalarShapeInfo, void *extraParams, bool allowParallelism) { - auto xType = nd4j::ArrayOptions::dataType(hXShapeInfo); - auto yType = nd4j::ArrayOptions::dataType(hSscalarShapeInfo); - auto zType = nd4j::ArrayOptions::dataType(hZShapeInfo); + auto xType = sd::ArrayOptions::dataType(hXShapeInfo); + auto yType = sd::ArrayOptions::dataType(hSscalarShapeInfo); + auto zType = sd::ArrayOptions::dataType(hZShapeInfo); if (shape::isEmpty(hXShapeInfo) || shape::isEmpty(hSscalarShapeInfo)) return; if (xType != yType) - throw nd4j::datatype_exception::build("NativeOpExecutioner::execScalarBool", xType, yType); + throw sd::datatype_exception::build("NativeOpExecutioner::execScalarBool", xType, yType); - if (zType != nd4j::DataType::BOOL) - throw nd4j::datatype_exception::build("NativeOpExecutioner::execScalarBool", nd4j::DataType::BOOL, zType); + if (zType != sd::DataType::BOOL) + throw sd::datatype_exception::build("NativeOpExecutioner::execScalarBool", sd::DataType::BOOL, zType); auto func = PRAGMA_THREADS_FOR { BUILD_DOUBLE_SELECTOR(xType, zType, functions::scalar::ScalarBoolTransform, ::transform(opNum, hX, hXShapeInfo, hZ, hZShapeInfo, hScalar, extraParams, start, stop), LIBND4J_TYPES, BOOL_TYPES); }; auto zLen = shape::length(hZShapeInfo); - samediff::Threads::parallel_for(func, 0, zLen, 1, !allowParallelism ? 1 : nd4j::math::nd4j_max(1, nd4j::math::nd4j_min(zLen / 1024, nd4j::Environment::getInstance()->maxMasterThreads()))); + samediff::Threads::parallel_for(func, 0, zLen, 1, !allowParallelism ? 1 : sd::math::nd4j_max(1, sd::math::nd4j_min(zLen / 1024, sd::Environment::getInstance()->maxMasterThreads()))); } //////////////////////////////////////////////////////////////////////// -void NativeOpExecutioner::execScalarBool(nd4j::LaunchContext *lc, +void NativeOpExecutioner::execScalarBool(sd::LaunchContext *lc, int opNum, void *hX, Nd4jLong *hXShapeInfo, void *dX, Nd4jLong *dXShapeInfo, @@ -994,29 +1064,29 @@ void NativeOpExecutioner::execScalarBool(nd4j::LaunchContext *lc, Nd4jLong *tadShapeInfo, Nd4jLong *tadOffsets, Nd4jLong *tadShapeInfoZ, Nd4jLong *tadOffsetsZ) { - auto xType = nd4j::ArrayOptions::dataType(hXShapeInfo); - auto yType = nd4j::ArrayOptions::dataType(hScalarShapeInfo); - auto zType = nd4j::ArrayOptions::dataType(hZShapeInfo); + auto xType = sd::ArrayOptions::dataType(hXShapeInfo); + auto yType = sd::ArrayOptions::dataType(hScalarShapeInfo); + auto zType = sd::ArrayOptions::dataType(hZShapeInfo); if (shape::isEmpty(hXShapeInfo) || shape::isEmpty(hScalarShapeInfo)) return; if (xType != yType) - throw nd4j::datatype_exception::build("NativeOpExecutioner::execScalarBool", xType, yType); + throw sd::datatype_exception::build("NativeOpExecutioner::execScalarBool", xType, yType); - if (zType != nd4j::DataType::BOOL) - throw nd4j::datatype_exception::build("NativeOpExecutioner::execScalarBool", nd4j::DataType::BOOL, zType); + if (zType != sd::DataType::BOOL) + throw sd::datatype_exception::build("NativeOpExecutioner::execScalarBool", sd::DataType::BOOL, zType); auto func = PRAGMA_THREADS_FOR { BUILD_DOUBLE_SELECTOR(xType, zType, functions::scalar::ScalarBoolTransform, ::transform(opNum, hX, hXShapeInfo, extraParams, hZ, hZShapeInfo, hScalars, dimension, dimensionLength, tadShapeInfo, tadOffsets, tadShapeInfoZ, tadOffsetsZ, start, stop), LIBND4J_TYPES, BOOL_TYPES); }; auto yLen = shape::length(hScalarShapeInfo); - samediff::Threads::parallel_tad(func, 0, yLen, 1, nd4j::math::nd4j_min(yLen, nd4j::Environment::getInstance()->maxMasterThreads())); + samediff::Threads::parallel_tad(func, 0, yLen, 1, sd::math::nd4j_min(yLen, sd::Environment::getInstance()->maxMasterThreads())); } //////////////////////////////////////////////////////////////////////// -void NativeOpExecutioner::execScalarInt(nd4j::LaunchContext *lc, +void NativeOpExecutioner::execScalarInt(sd::LaunchContext *lc, int opNum, void *hX, Nd4jLong *hXShapeInfo, void *dX, Nd4jLong *dXShapeInfo, @@ -1026,30 +1096,30 @@ void NativeOpExecutioner::execScalarInt(nd4j::LaunchContext *lc, void *dScalar, Nd4jLong *dSscalarShapeInfo, void *extraParams, bool allowParallelism) { - auto xType = nd4j::ArrayOptions::dataType(hXShapeInfo); - auto yType = nd4j::ArrayOptions::dataType(hSscalarShapeInfo); - auto zType = nd4j::ArrayOptions::dataType(hZShapeInfo); + auto xType = sd::ArrayOptions::dataType(hXShapeInfo); + auto yType = sd::ArrayOptions::dataType(hSscalarShapeInfo); + auto zType = sd::ArrayOptions::dataType(hZShapeInfo); if (shape::isEmpty(hXShapeInfo) || shape::isEmpty(hSscalarShapeInfo)) return; if (xType != yType || xType != zType) - throw nd4j::datatype_exception::build("NativeOpExecutioner::execScalarInt", xType, yType); + throw sd::datatype_exception::build("NativeOpExecutioner::execScalarInt", xType, yType); - if (!nd4j::DataTypeUtils::isZ(zType)) - throw nd4j::datatype_exception::build("NativeOpExecutioner::execScalarInt", nd4j::DataType::INT32, zType); + if (!sd::DataTypeUtils::isZ(zType)) + throw sd::datatype_exception::build("NativeOpExecutioner::execScalarInt", sd::DataType::INT32, zType); auto func = PRAGMA_THREADS_FOR { BUILD_SINGLE_SELECTOR(xType, functions::scalar::ScalarIntTransform, ::transform(opNum, hX, hXShapeInfo, hZ, hZShapeInfo, hScalar, extraParams, start, stop), INTEGER_TYPES); }; auto zLen = shape::length(hZShapeInfo); - samediff::Threads::parallel_for(func, 0, zLen, 1, !allowParallelism ? 1 : nd4j::math::nd4j_max(1, nd4j::math::nd4j_min(zLen / 1024, nd4j::Environment::getInstance()->maxMasterThreads()))); + samediff::Threads::parallel_for(func, 0, zLen, 1, !allowParallelism ? 1 : sd::math::nd4j_max(1, sd::math::nd4j_min(zLen / 1024, sd::Environment::getInstance()->maxMasterThreads()))); } //////////////////////////////////////////////////////////////////////// -void NativeOpExecutioner::execScalarInt(nd4j::LaunchContext *lc, +void NativeOpExecutioner::execScalarInt(sd::LaunchContext *lc, int opNum, void *hX, Nd4jLong *hXShapeInfo, void *dX, Nd4jLong *dXShapeInfo, @@ -1062,25 +1132,25 @@ void NativeOpExecutioner::execScalarInt(nd4j::LaunchContext *lc, Nd4jLong *tadShapeInfo, Nd4jLong *tadOffsets, Nd4jLong *tadShapeInfoZ, Nd4jLong *tadOffsetsZ) { - auto xType = nd4j::ArrayOptions::dataType(hXShapeInfo); - auto yType = nd4j::ArrayOptions::dataType(hScalarShapeInfo); - auto zType = nd4j::ArrayOptions::dataType(hZShapeInfo); + auto xType = sd::ArrayOptions::dataType(hXShapeInfo); + auto yType = sd::ArrayOptions::dataType(hScalarShapeInfo); + auto zType = sd::ArrayOptions::dataType(hZShapeInfo); if (shape::isEmpty(hXShapeInfo) || shape::isEmpty(hScalarShapeInfo)) return; if (xType != yType || xType != zType) - throw nd4j::datatype_exception::build("NativeOpExecutioner::execScalarInt", xType, yType); + throw sd::datatype_exception::build("NativeOpExecutioner::execScalarInt", xType, yType); - if (!nd4j::DataTypeUtils::isZ(zType)) - throw nd4j::datatype_exception::build("NativeOpExecutioner::execScalarInt requires integer data type", zType); + if (!sd::DataTypeUtils::isZ(zType)) + throw sd::datatype_exception::build("NativeOpExecutioner::execScalarInt requires integer data type", zType); auto func = PRAGMA_THREADS_FOR { BUILD_SINGLE_SELECTOR(xType, functions::scalar::ScalarIntTransform, ::transform(opNum, hX, hXShapeInfo, extraParams, hZ, hZShapeInfo, hScalars, dimension, dimensionLength, tadShapeInfo, tadOffsets, tadShapeInfoZ, tadOffsetsZ, start, stop), INTEGER_TYPES); }; auto yLen = shape::length(hScalarShapeInfo); - samediff::Threads::parallel_tad(func, 0, yLen, 1, nd4j::math::nd4j_min(yLen, nd4j::Environment::getInstance()->maxMasterThreads())); + samediff::Threads::parallel_tad(func, 0, yLen, 1, sd::math::nd4j_min(yLen, sd::Environment::getInstance()->maxMasterThreads())); } //////////////////////////////////////////////////////////////////////// @@ -1093,7 +1163,7 @@ void NativeOpExecutioner::execScalarInt(nd4j::LaunchContext *lc, * @param hZ * @param hZShapeInfo */ -void NativeOpExecutioner::execSummaryStats(nd4j::LaunchContext *lc, +void NativeOpExecutioner::execSummaryStats(sd::LaunchContext *lc, int opNum, void *hX, Nd4jLong *hXShapeInfo, void *dX, Nd4jLong *dXShapeInfo, @@ -1103,8 +1173,8 @@ void NativeOpExecutioner::execSummaryStats(nd4j::LaunchContext *lc, bool biasCorrected) { - auto xType = nd4j::ArrayOptions::dataType(hXShapeInfo); - auto zType = nd4j::ArrayOptions::dataType(hZShapeInfo); + auto xType = sd::ArrayOptions::dataType(hXShapeInfo); + auto zType = sd::ArrayOptions::dataType(hZShapeInfo); BUILD_DOUBLE_SELECTOR(xType, zType, functions::summarystats::SummaryStatsReduce, ::exec(opNum, biasCorrected, hX, hXShapeInfo, extraParams, hZ, hZShapeInfo, nullptr, 1), LIBND4J_TYPES, FLOAT_TYPES); } @@ -1119,7 +1189,7 @@ void NativeOpExecutioner::execSummaryStats(nd4j::LaunchContext *lc, * @param hZ * @param hZShapeInfo */ -void NativeOpExecutioner::execSummaryStatsScalar(nd4j::LaunchContext *lc, +void NativeOpExecutioner::execSummaryStatsScalar(sd::LaunchContext *lc, int opNum, void *hX, Nd4jLong *hXShapeInfo, void *dX, Nd4jLong *dXShapeInfo, @@ -1129,8 +1199,8 @@ void NativeOpExecutioner::execSummaryStatsScalar(nd4j::LaunchContext *lc, bool biasCorrected) { - auto xType = nd4j::ArrayOptions::dataType(hXShapeInfo); - auto zType = nd4j::ArrayOptions::dataType(hZShapeInfo); + auto xType = sd::ArrayOptions::dataType(hXShapeInfo); + auto zType = sd::ArrayOptions::dataType(hZShapeInfo); BUILD_DOUBLE_SELECTOR(xType, zType, functions::summarystats::SummaryStatsReduce, ::execScalar(opNum, biasCorrected, hX, hXShapeInfo, extraParams, hZ, hZShapeInfo), LIBND4J_TYPES, FLOAT_TYPES); } @@ -1147,7 +1217,7 @@ void NativeOpExecutioner::execSummaryStatsScalar(nd4j::LaunchContext *lc, * @param dimension * @param dimensionLength */ -void NativeOpExecutioner::execSummaryStats(nd4j::LaunchContext *lc, +void NativeOpExecutioner::execSummaryStats(sd::LaunchContext *lc, int opNum, void *hX, Nd4jLong *hXShapeInfo, void *dX, Nd4jLong *dXShapeInfo, @@ -1157,8 +1227,8 @@ void NativeOpExecutioner::execSummaryStats(nd4j::LaunchContext *lc, int *dimension, int dimensionLength, Nd4jLong *tadShapeInfo, Nd4jLong *tadOffsets, bool biasCorrected) { - auto xType = nd4j::ArrayOptions::dataType(hXShapeInfo); - auto zType = nd4j::ArrayOptions::dataType(hZShapeInfo); + auto xType = sd::ArrayOptions::dataType(hXShapeInfo); + auto zType = sd::ArrayOptions::dataType(hZShapeInfo); BUILD_DOUBLE_SELECTOR(xType, zType, functions::summarystats::SummaryStatsReduce, ::exec(opNum, biasCorrected, hX, hXShapeInfo, extraParams, hZ, hZShapeInfo, dimension, dimensionLength), LIBND4J_TYPES, FLOAT_TYPES); } @@ -1175,7 +1245,7 @@ void NativeOpExecutioner::execSummaryStats(nd4j::LaunchContext *lc, * @param extraParams * @param n */ -void NativeOpExecutioner::execTransformFloat(nd4j::LaunchContext *lc, +void NativeOpExecutioner::execTransformFloat(sd::LaunchContext *lc, int opNum, void *hX, Nd4jLong *hXShapeInfo, void *dX, Nd4jLong *dXShapeInfo, @@ -1183,8 +1253,8 @@ void NativeOpExecutioner::execTransformFloat(nd4j::LaunchContext *lc, void *dZ, Nd4jLong *dZShapeInfo, void *extraParams, Nd4jLong *tadShapeInfo, Nd4jLong *tadOffsets) { - auto xType = nd4j::ArrayOptions::dataType(hXShapeInfo); - auto zType = nd4j::ArrayOptions::dataType(hZShapeInfo); + auto xType = sd::ArrayOptions::dataType(hXShapeInfo); + auto zType = sd::ArrayOptions::dataType(hZShapeInfo); if (shape::isEmpty(hXShapeInfo)) return; @@ -1193,11 +1263,11 @@ void NativeOpExecutioner::execTransformFloat(nd4j::LaunchContext *lc, BUILD_DOUBLE_SELECTOR(xType, zType, functions::transform::TransformFloat, ::exec(opNum, hX, hXShapeInfo, hZ, hZShapeInfo, extraParams, thread_id, numThreads), LIBND4J_TYPES, FLOAT_TYPES); }; - samediff::Threads::parallel_do(func, nd4j::math::nd4j_max(1, nd4j::math::nd4j_min(shape::length(hZShapeInfo) / 1024, nd4j::Environment::getInstance()->maxMasterThreads()))); + samediff::Threads::parallel_do(func, sd::math::nd4j_max(1, sd::math::nd4j_min(shape::length(hZShapeInfo) / 1024, sd::Environment::getInstance()->maxMasterThreads()))); } //////////////////////////////////////////////////////////////////////// -void NativeOpExecutioner::execTransformBool(nd4j::LaunchContext *lc, +void NativeOpExecutioner::execTransformBool(sd::LaunchContext *lc, int opNum, void *hX, Nd4jLong *hXShapeInfo, void *dX, Nd4jLong *dXShapeInfo, @@ -1205,8 +1275,8 @@ void NativeOpExecutioner::execTransformBool(nd4j::LaunchContext *lc, void *dZ, Nd4jLong *dZShapeInfo, void *extraParams, Nd4jLong *tadShapeInfo, Nd4jLong *tadOffsets) { - auto xType = nd4j::ArrayOptions::dataType(hXShapeInfo); - auto zType = nd4j::ArrayOptions::dataType(hZShapeInfo); + auto xType = sd::ArrayOptions::dataType(hXShapeInfo); + auto zType = sd::ArrayOptions::dataType(hZShapeInfo); if (shape::isEmpty(hXShapeInfo)) return; @@ -1215,11 +1285,11 @@ void NativeOpExecutioner::execTransformBool(nd4j::LaunchContext *lc, BUILD_DOUBLE_SELECTOR(xType, zType, functions::transform::TransformBool, ::exec(opNum, hX, hXShapeInfo, hZ, hZShapeInfo, extraParams, thread_id, numThreads), LIBND4J_TYPES, BOOL_TYPES); }; - samediff::Threads::parallel_do(func, nd4j::math::nd4j_max(1, nd4j::math::nd4j_min(shape::length(hZShapeInfo) / 1024, nd4j::Environment::getInstance()->maxMasterThreads()))); + samediff::Threads::parallel_do(func, sd::math::nd4j_max(1, sd::math::nd4j_min(shape::length(hZShapeInfo) / 1024, sd::Environment::getInstance()->maxMasterThreads()))); } //////////////////////////////////////////////////////////////////////// -void NativeOpExecutioner::execTransformAny(nd4j::LaunchContext *lc, +void NativeOpExecutioner::execTransformAny(sd::LaunchContext *lc, int opNum, void *hX, Nd4jLong *hXShapeInfo, void *dX, Nd4jLong *dXShapeInfo, @@ -1227,21 +1297,28 @@ void NativeOpExecutioner::execTransformAny(nd4j::LaunchContext *lc, void *dZ, Nd4jLong *dZShapeInfo, void *extraParams, Nd4jLong *tadShapeInfo, Nd4jLong *tadOffsets, bool allowParallelism) { - auto xType = nd4j::ArrayOptions::dataType(hXShapeInfo); - auto zType = nd4j::ArrayOptions::dataType(hZShapeInfo); + auto xType = sd::ArrayOptions::dataType(hXShapeInfo); + auto zType = sd::ArrayOptions::dataType(hZShapeInfo); if (shape::isEmpty(hXShapeInfo)) return; - auto func = PRAGMA_THREADS_DO { - BUILD_DOUBLE_SELECTOR(xType, zType, functions::transform::TransformAny, ::exec(opNum, hX, hXShapeInfo, hZ, hZShapeInfo, extraParams, thread_id, numThreads), LIBND4J_TYPES, LIBND4J_TYPES); - }; + if (opNum == sd::transform::Assign && shape::order(hXShapeInfo) == shape::order(hZShapeInfo) && shape::order(hXShapeInfo) == 'c' && xType == zType && shape::elementWiseStride(hXShapeInfo) == 1 && shape::elementWiseStride(hZShapeInfo) == 1) { - samediff::Threads::parallel_do(func, nd4j::math::nd4j_max(1, nd4j::math::nd4j_min(shape::length(hZShapeInfo) / 1024, nd4j::Environment::getInstance()->maxMasterThreads()))); + memcpy(hZ, hX, shape::length(hXShapeInfo) * sd::DataTypeUtils::sizeOfElement(xType)); + } + else { + auto func = PRAGMA_THREADS_DO { + + BUILD_DOUBLE_SELECTOR(xType, zType, functions::transform::TransformAny, ::exec(opNum, hX, hXShapeInfo, hZ, hZShapeInfo, extraParams, thread_id, numThreads), LIBND4J_TYPES, LIBND4J_TYPES); + }; + + samediff::Threads::parallel_do(func, sd::math::nd4j_max(1, sd::math::nd4j_min(shape::length(hZShapeInfo) / 1024, sd::Environment::getInstance()->maxMasterThreads()))); + } } //////////////////////////////////////////////////////////////////////// -void NativeOpExecutioner::execTransformSame(nd4j::LaunchContext *lc, +void NativeOpExecutioner::execTransformSame(sd::LaunchContext *lc, int opNum, void *hX, Nd4jLong *hXShapeInfo, void *dX, Nd4jLong *dXShapeInfo, @@ -1249,8 +1326,8 @@ void NativeOpExecutioner::execTransformSame(nd4j::LaunchContext *lc, void *dZ, Nd4jLong *dZShapeInfo, void *extraParams, Nd4jLong *tadShapeInfo, Nd4jLong *tadOffsets) { - auto xType = nd4j::ArrayOptions::dataType(hXShapeInfo); - auto zType = nd4j::ArrayOptions::dataType(hZShapeInfo); + auto xType = sd::ArrayOptions::dataType(hXShapeInfo); + auto zType = sd::ArrayOptions::dataType(hZShapeInfo); if (shape::isEmpty(hXShapeInfo)) return; @@ -1259,11 +1336,11 @@ void NativeOpExecutioner::execTransformSame(nd4j::LaunchContext *lc, BUILD_SINGLE_SELECTOR(xType, functions::transform::TransformSame, ::exec(opNum, hX, hXShapeInfo, hZ, hZShapeInfo, extraParams, thread_id, numThreads), LIBND4J_TYPES); }; - samediff::Threads::parallel_do(func, nd4j::math::nd4j_max(1, nd4j::math::nd4j_min(shape::length(hZShapeInfo) / 1024, nd4j::Environment::getInstance()->maxMasterThreads()))); + samediff::Threads::parallel_do(func, sd::math::nd4j_max(1, sd::math::nd4j_min(shape::length(hZShapeInfo) / 1024, sd::Environment::getInstance()->maxMasterThreads()))); } //////////////////////////////////////////////////////////////////////// -void NativeOpExecutioner::execTransformStrict(nd4j::LaunchContext *lc, +void NativeOpExecutioner::execTransformStrict(sd::LaunchContext *lc, int opNum, void *hX, Nd4jLong *hXShapeInfo, void *dX, Nd4jLong *dXShapeInfo, @@ -1271,8 +1348,8 @@ void NativeOpExecutioner::execTransformStrict(nd4j::LaunchContext *lc, void *dZ, Nd4jLong *dZShapeInfo, void *extraParams, Nd4jLong *tadShapeInfo, Nd4jLong *tadOffsets) { - auto xType = nd4j::ArrayOptions::dataType(hXShapeInfo); - auto zType = nd4j::ArrayOptions::dataType(hZShapeInfo); + auto xType = sd::ArrayOptions::dataType(hXShapeInfo); + auto zType = sd::ArrayOptions::dataType(hZShapeInfo); if (shape::isEmpty(hXShapeInfo)) return; @@ -1281,11 +1358,11 @@ void NativeOpExecutioner::execTransformStrict(nd4j::LaunchContext *lc, BUILD_SINGLE_SELECTOR(xType, functions::transform::TransformStrict, ::exec(opNum, hX, hXShapeInfo, hZ, hZShapeInfo, extraParams, thread_id, numThreads), FLOAT_TYPES); }; - samediff::Threads::parallel_do(func, nd4j::math::nd4j_max(1, nd4j::math::nd4j_min(shape::length(hZShapeInfo) / 1024, nd4j::Environment::getInstance()->maxMasterThreads()))); + samediff::Threads::parallel_do(func, sd::math::nd4j_max(1, sd::math::nd4j_min(shape::length(hZShapeInfo) / 1024, sd::Environment::getInstance()->maxMasterThreads()))); } //////////////////////////////////////////////////////////////////////// -void NativeOpExecutioner::execRandom(nd4j::LaunchContext *lc, +void NativeOpExecutioner::execRandom(sd::LaunchContext *lc, int opNum, Nd4jPointer state, void *hZ, Nd4jLong *hZShapeInfo, @@ -1293,16 +1370,16 @@ void NativeOpExecutioner::execRandom(nd4j::LaunchContext *lc, void *extraArguments) { - auto zType = nd4j::ArrayOptions::dataType(hZShapeInfo); + auto zType = sd::ArrayOptions::dataType(hZShapeInfo); BUILD_SINGLE_SELECTOR(zType, functions::random::RandomFunction, ::execTransform(opNum, state, hZ, hZShapeInfo, extraArguments), FLOAT_TYPES); - auto rng = reinterpret_cast(state); + auto rng = reinterpret_cast(state); rng->rewindH(shape::length(hZShapeInfo)); } //////////////////////////////////////////////////////////////////////// -void NativeOpExecutioner::execRandom(nd4j::LaunchContext *lc, +void NativeOpExecutioner::execRandom(sd::LaunchContext *lc, int opNum, Nd4jPointer state, void *hX, Nd4jLong *hXShapeInfo, @@ -1312,16 +1389,16 @@ void NativeOpExecutioner::execRandom(nd4j::LaunchContext *lc, void *extraArguments) { - auto zType = nd4j::ArrayOptions::dataType(hZShapeInfo); + auto zType = sd::ArrayOptions::dataType(hZShapeInfo); BUILD_SINGLE_SELECTOR(zType, functions::random::RandomFunction, ::execTransform(opNum, state, hX, hXShapeInfo, hZ, hZShapeInfo, extraArguments), FLOAT_TYPES); - auto rng = reinterpret_cast(state); + auto rng = reinterpret_cast(state); rng->rewindH(shape::length(hZShapeInfo)); } //////////////////////////////////////////////////////////////////////// -void NativeOpExecutioner::execRandom(nd4j::LaunchContext *lc, +void NativeOpExecutioner::execRandom(sd::LaunchContext *lc, int opNum, Nd4jPointer state, void *hX, Nd4jLong *hXShapeInfo, @@ -1333,11 +1410,11 @@ void NativeOpExecutioner::execRandom(nd4j::LaunchContext *lc, void *extraArguments) { - auto xType = nd4j::ArrayOptions::dataType(hZShapeInfo); + auto xType = sd::ArrayOptions::dataType(hZShapeInfo); BUILD_SINGLE_SELECTOR(xType, functions::random::RandomFunction, ::execTransform(opNum, state, hX, hXShapeInfo, hY, hYShapeInfo, hZ, hZShapeInfo, extraArguments), FLOAT_TYPES); - auto rng = reinterpret_cast(state); + auto rng = reinterpret_cast(state); rng->rewindH(shape::length(hZShapeInfo)); } diff --git a/libnd4j/blas/cpu/NativeOps.cpp b/libnd4j/include/legacy/cpu/NativeOps.cpp similarity index 76% rename from libnd4j/blas/cpu/NativeOps.cpp rename to libnd4j/include/legacy/cpu/NativeOps.cpp index e82f2224e..cf04acbe7 100644 --- a/libnd4j/blas/cpu/NativeOps.cpp +++ b/libnd4j/include/legacy/cpu/NativeOps.cpp @@ -20,18 +20,18 @@ #define __STDC_CONSTANT_MACROS -#include "../NativeOps.h" -#include "NativeOpExecutioner.h" -#include "../NDArray.h" -#include "../GraphExecutioner.h" +#include +#include "legacy/NativeOpExecutioner.h" +#include +#include #include -#include +#include #include #include #include #include -#include -#include +#include +#include #include #include #include @@ -65,8 +65,8 @@ bool experimentalSupport = false; #endif #include -#include "../Environment.h" -#include +#include +#include #include #include #include @@ -81,16 +81,16 @@ bool experimentalSupport = false; #include #endif -using namespace nd4j; +using namespace sd; void setElementThreshold(int num) { if (num > 0) - nd4j::Environment::getInstance()->setElementwiseThreshold(num); + sd::Environment::getInstance()->setElementwiseThreshold(num); } void setTADThreshold(int num) { if (num > 0) - nd4j::Environment::getInstance()->setTadThreshold(num); + sd::Environment::getInstance()->setTadThreshold(num); } /** @@ -108,8 +108,8 @@ void execIndexReduceScalar(Nd4jPointer *extraPointers, try { NativeOpExecutioner::execIndexReduceScalar(nullptr, opNum, dbX->primary(), hXShapeInfo, dbX->special(), dXShapeInfo, extraParams, dbZ->primary(), hZShapeInfo, dbZ->special(), dZShapeInfo); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); } } @@ -133,7 +133,7 @@ void execIndexReduce(Nd4jPointer *extraPointers,int opNum, auto dimension = reinterpret_cast(dbDimension->primary()); int dimensionLength = static_cast(shape::length(hDimensionShape)); - auto tadPack = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(hXShapeInfo, dimension, + auto tadPack = sd::ConstantTadHelper::getInstance()->tadForDimensions(hXShapeInfo, dimension, dimensionLength); auto hTADShapeInfo = tadPack.primaryShapeInfo(); @@ -156,8 +156,8 @@ void execIndexReduce(Nd4jPointer *extraPointers,int opNum, hTADShapeInfo, hTADOffsets); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); } } @@ -184,9 +184,9 @@ void execBroadcast(Nd4jPointer *extraPointers, auto dimension = reinterpret_cast(dbDimension->primary()); int dimensionLength = static_cast(shape::length(hDimensionShape)); - auto tadPackX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(hXShapeInfo, dimension, + auto tadPackX = sd::ConstantTadHelper::getInstance()->tadForDimensions(hXShapeInfo, dimension, dimensionLength); - auto tadPackZ = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(hZShapeInfo, dimension, + auto tadPackZ = sd::ConstantTadHelper::getInstance()->tadForDimensions(hZShapeInfo, dimension, dimensionLength); auto hTADShapeInfo = tadPackX.primaryShapeInfo(); @@ -209,8 +209,8 @@ void execBroadcast(Nd4jPointer *extraPointers, dimension, dimensionLength, hTADShapeInfo, hTADOffsets, hTADShapeInfoZ, hTADOffsetsZ); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); } } @@ -225,9 +225,9 @@ void execBroadcastBool(Nd4jPointer *extraPointers, auto dimension = reinterpret_cast(dbDimension->primary()); int dimensionLength = static_cast(shape::length(hDimensionShape)); - auto tadPackX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(hXShapeInfo, dimension, + auto tadPackX = sd::ConstantTadHelper::getInstance()->tadForDimensions(hXShapeInfo, dimension, dimensionLength); - auto tadPackZ = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(hZShapeInfo, dimension, + auto tadPackZ = sd::ConstantTadHelper::getInstance()->tadForDimensions(hZShapeInfo, dimension, dimensionLength); auto hTADShapeInfo = tadPackX.primaryShapeInfo(); @@ -252,8 +252,8 @@ void execBroadcastBool(Nd4jPointer *extraPointers, dimensionLength, hTADShapeInfo, hTADOffsets, hTADShapeInfoZ, hTADOffsetsZ); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); } } @@ -293,8 +293,8 @@ void execPairwiseTransform( dZShapeInfo, extraParams); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); } } @@ -323,8 +323,8 @@ void execPairwiseTransformBool( dZShapeInfo, extraParams); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); } } @@ -357,8 +357,8 @@ void execReduceFloat( dbZ->special(), dZShapeInfo); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); } } @@ -382,8 +382,8 @@ void execReduceSame( dbZ->special(), dZShapeInfo); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); } } @@ -406,8 +406,8 @@ void execReduceBool( dbZ->special(), dZShapeInfo); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); } } @@ -430,8 +430,8 @@ void execReduceLong( dbZ->special(), dZShapeInfo); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); } } @@ -454,7 +454,7 @@ void execReduceFloat2(Nd4jPointer *extraPointers, auto dimension = reinterpret_cast(dbDimension->primary()); int dimensionLength = static_cast(shape::length(hDimensionShape)); - auto tadPackX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(hXShapeInfo, dimension, + auto tadPackX = sd::ConstantTadHelper::getInstance()->tadForDimensions(hXShapeInfo, dimension, dimensionLength); auto hTADShapeInfo = tadPackX.primaryShapeInfo(); @@ -475,8 +475,8 @@ void execReduceFloat2(Nd4jPointer *extraPointers, hTADShapeInfo, hTADOffsets); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); } } @@ -490,7 +490,7 @@ void execReduceBool2(Nd4jPointer *extraPointers, auto dimension = reinterpret_cast(dbDimension->primary()); int dimensionLength = static_cast(shape::length(hDimensionShape)); - auto tadPack = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(hXShapeInfo, dimension, + auto tadPack = sd::ConstantTadHelper::getInstance()->tadForDimensions(hXShapeInfo, dimension, dimensionLength); auto hTADShapeInfo = tadPack.primaryShapeInfo(); @@ -511,8 +511,8 @@ void execReduceBool2(Nd4jPointer *extraPointers, hTADShapeInfo, hTADOffsets); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); } } @@ -526,7 +526,7 @@ void execReduceSame2(Nd4jPointer *extraPointers, auto dimension = reinterpret_cast(dbDimension->primary()); int dimensionLength = static_cast(shape::length(hDimensionShape)); - auto tadPack = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(hXShapeInfo, dimension, + auto tadPack = sd::ConstantTadHelper::getInstance()->tadForDimensions(hXShapeInfo, dimension, dimensionLength); auto hTADShapeInfo = tadPack.primaryShapeInfo(); @@ -547,8 +547,8 @@ void execReduceSame2(Nd4jPointer *extraPointers, hTADShapeInfo, hTADOffsets); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); } } @@ -562,7 +562,7 @@ void execReduceLong2(Nd4jPointer *extraPointers, auto dimension = reinterpret_cast(dbDimension->primary()); int dimensionLength = static_cast(shape::length(hDimensionShape)); - auto tadPack = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(hXShapeInfo, dimension, + auto tadPack = sd::ConstantTadHelper::getInstance()->tadForDimensions(hXShapeInfo, dimension, dimensionLength); auto hTADShapeInfo = tadPack.primaryShapeInfo(); @@ -583,8 +583,8 @@ void execReduceLong2(Nd4jPointer *extraPointers, hTADShapeInfo, hTADOffsets); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); } } @@ -609,8 +609,8 @@ void execReduce3(Nd4jPointer *extraPointers, NativeOpExecutioner::execReduce3(nullptr, opNum, dbX->primary(), hXShapeInfo, dbX->special(), dXShapeInfo, extraParams, dbY->primary(), hYShapeInfo, dbY->special(), dYShapeInfo, dbZ->primary(), hZShapeInfo, dbZ->special(), dZShapeInfo); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); } } @@ -632,8 +632,8 @@ void execReduce3Scalar(Nd4jPointer *extraPointers,int opNum, NativeOpExecutioner::execReduce3Scalar(nullptr, opNum, dbX->primary(), hXShapeInfo, dbX->special(), dXShapeInfo, extraParams, dbY->primary(), hYShapeInfo, dbY->special(), dYShapeInfo, dbZ->primary(), hZShapeInfo, dbZ->special(), dZShapeInfo); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); } } /** @@ -669,7 +669,7 @@ void execReduce3Tad(Nd4jPointer *extraPointers, yTadOnlyShapeInfo, yTadOffsets); } else { // going tad-way - auto tadPack = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(hXShapeInfo, dimension, + auto tadPack = sd::ConstantTadHelper::getInstance()->tadForDimensions(hXShapeInfo, dimension, dimensionLength); auto hTADShapeInfo = tadPack.primaryShapeInfo(); @@ -681,8 +681,8 @@ void execReduce3Tad(Nd4jPointer *extraPointers, hTADOffsets, nullptr, nullptr); } } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); } } @@ -725,8 +725,8 @@ void execScalar( dScalarShapeInfo, extraParams); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); } } @@ -754,8 +754,8 @@ void execScalarBool( dScalarShapeInfo, extraParams); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); } } @@ -786,8 +786,8 @@ void execSummaryStatsScalar(Nd4jPointer *extraPointers, dZShapeInfo, biasCorrected); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); } } /** @@ -819,8 +819,8 @@ void execSummaryStats(Nd4jPointer *extraPointers, dZShapeInfo, biasCorrected); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); } } /** @@ -864,8 +864,8 @@ void execSummaryStatsTad(Nd4jPointer *extraPointers, tadOffsets, biasCorrected); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); } } @@ -900,8 +900,8 @@ void execTransformFloat( nullptr, nullptr); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); } } @@ -926,8 +926,8 @@ void execTransformSame( nullptr, nullptr); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); } } @@ -952,8 +952,8 @@ void execTransformBool( nullptr, nullptr); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); } } @@ -978,8 +978,8 @@ void execTransformAny( nullptr, nullptr); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); } } @@ -1004,8 +1004,8 @@ void execTransformStrict( nullptr, nullptr); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); } } @@ -1030,8 +1030,8 @@ void execReduce3All(Nd4jPointer *extraPointers, hYShapeInfo, dbY->special(), dYShapeInfo, dbZ->primary(), hZShapeInfo, dbZ->special(), dZShapeInfo, dimension, dimensionLength, xTadShapeInfo, xOffsets, yTadShapeInfo, yOffsets); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); } } @@ -1050,12 +1050,12 @@ void specialConcat( Nd4jPointer *tadPointers, Nd4jPointer *offsetPointers) { try { - auto zType = nd4j::ArrayOptions::dataType(hZShapeInfo); + auto zType = sd::ArrayOptions::dataType(hZShapeInfo); - BUILD_SINGLE_SELECTOR(zType, nd4j::SpecialMethods,::concatCpuGeneric(dimension, numArrays, data, inputShapeInfo, hZ, hZShapeInfo), LIBND4J_TYPES); + BUILD_SINGLE_SELECTOR(zType, sd::SpecialMethods,::concatCpuGeneric(dimension, numArrays, data, inputShapeInfo, hZ, hZShapeInfo), LIBND4J_TYPES); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); } } @@ -1068,7 +1068,7 @@ void initializeDevicesAndFunctions() { } void initializeFunctions(Nd4jPointer *functions) { - nd4j::BlasHelper::getInstance()->initializeFunctions(functions); + sd::BlasHelper::getInstance()->initializeFunctions(functions); } /** @@ -1216,45 +1216,45 @@ int getAvailableDevices() { } void enableDebugMode(bool reallyEnable) { - nd4j::Environment::getInstance()->setDebug(reallyEnable); + sd::Environment::getInstance()->setDebug(reallyEnable); } void enableVerboseMode(bool reallyEnable) { - nd4j::Environment::getInstance()->setVerbose(reallyEnable); + sd::Environment::getInstance()->setVerbose(reallyEnable); } void setGridLimit(int gridSize) { // no-op } -nd4j::TadPack* tadOnlyShapeInfo(Nd4jLong *hXShapeInfo, int *dimension, int dimensionLength) { +sd::TadPack* tadOnlyShapeInfo(Nd4jLong *hXShapeInfo, int *dimension, int dimensionLength) { auto pack = new TadPack(); try { - *pack = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(hXShapeInfo, dimension, dimensionLength); + *pack = sd::ConstantTadHelper::getInstance()->tadForDimensions(hXShapeInfo, dimension, dimensionLength); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); } return pack; } -Nd4jLong* getPrimaryShapeInfo(nd4j::TadPack* pack) { +Nd4jLong* getPrimaryShapeInfo(sd::TadPack* pack) { return pack->primaryShapeInfo(); } -Nd4jLong* getPrimaryOffsets(nd4j::TadPack* pack) { +Nd4jLong* getPrimaryOffsets(sd::TadPack* pack) { return pack->primaryOffsets(); } -Nd4jLong* getSpecialShapeInfo(nd4j::TadPack* pack) { +Nd4jLong* getSpecialShapeInfo(sd::TadPack* pack) { return pack->specialShapeInfo(); } -Nd4jLong* getSpecialOffsets(nd4j::TadPack* pack) { +Nd4jLong* getSpecialOffsets(sd::TadPack* pack) { return pack->specialOffsets(); } -Nd4jLong getNumberOfTads(nd4j::TadPack* pack) { +Nd4jLong getNumberOfTads(sd::TadPack* pack) { return pack->numberOfTads(); } -int getShapeInfoLength(nd4j::TadPack* pack) { +int getShapeInfoLength(sd::TadPack* pack) { return pack->shapeInfoLength(); } @@ -1287,8 +1287,8 @@ void pullRowsGeneric(void *vx, const auto tadLength = shape::length(tadShapeInfo); int elementsPerThread = n / TAD_THRESHOLD; - int _threads = nd4j::math::nd4j_max(1, elementsPerThread); - _threads = nd4j::math::nd4j_min(_threads, nd4j::Environment::getInstance()->maxThreads()); + int _threads = sd::math::nd4j_max(1, elementsPerThread); + _threads = sd::math::nd4j_min(_threads, sd::Environment::getInstance()->maxThreads()); auto func = PRAGMA_THREADS_FOR { for (auto idx = start; idx < stop; idx++) { @@ -1300,16 +1300,16 @@ void pullRowsGeneric(void *vx, if (xEWS == 1 && zEWS == 1) { PRAGMA_OMP_SIMD - for (int i = 0; i < tadLength; i++) { + for (Nd4jLong i = 0; i < tadLength; i++) { rZ[i] = rX[i]; } } else if (xEWS >= 1 && zEWS >= 1) { PRAGMA_OMP_SIMD - for (int i = 0; i < tadLength; i++) { + for (Nd4jLong i = 0; i < tadLength; i++) { rZ[i * zEWS] = rX[i * xEWS]; } } else { - for (int i = 0; i < tadLength; i++) { + for (Nd4jLong i = 0; i < tadLength; i++) { auto xOffset = xTadOffsetForBlock + shape::getIndexOffset(i, tadShapeInfo); auto zOffset = zTadOffsetForBlock + shape::getIndexOffset(i, zTadShapeInfo); hZ[zOffset] = hX[xOffset]; @@ -1331,12 +1331,12 @@ void pullRows(Nd4jPointer *extraPointers, Nd4jLong *zTadShapeInfo, Nd4jLong *zTadOffsets) { try { - auto xType = nd4j::ArrayOptions::dataType(hXShapeInfo); + auto xType = sd::ArrayOptions::dataType(hXShapeInfo); BUILD_SINGLE_SELECTOR(xType, pullRowsGeneric, (dbX->primary(), hXShapeInfo, dbZ->primary(), hZShapeInfo, n, indexes, tadShapeInfo, tadOffsets, zTadShapeInfo, zTadOffsets), LIBND4J_TYPES); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); } } @@ -1387,12 +1387,12 @@ void tear(Nd4jPointer *extraPointers, Nd4jLong *tadShapeInfo, Nd4jLong *tadOffsets) { try { - auto xType = nd4j::ArrayOptions::dataType(hXShapeInfo); + auto xType = sd::ArrayOptions::dataType(hXShapeInfo); BUILD_SINGLE_SELECTOR(xType, tearGeneric, (dbX->primary(), hXShapeInfo, targets, hZShapeInfo, tadShapeInfo, tadOffsets), LIBND4J_TYPES); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); } } @@ -1406,12 +1406,12 @@ void average(Nd4jPointer *extras, Nd4jLong length, bool propagate) { try { - auto xType = nd4j::ArrayOptions::dataType(hXShapeInfo); + auto xType = sd::ArrayOptions::dataType(hXShapeInfo); - BUILD_SINGLE_SELECTOR(xType, nd4j::SpecialMethods, ::averageGeneric(hX, z, hZShapeInfo, n, length, propagate), LIBND4J_TYPES); + BUILD_SINGLE_SELECTOR(xType, sd::SpecialMethods, ::averageGeneric(hX, z, hZShapeInfo, n, length, propagate), LIBND4J_TYPES); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); } } @@ -1423,12 +1423,12 @@ void accumulate(Nd4jPointer *extras, int n, Nd4jLong length) { try { - auto xType = nd4j::ArrayOptions::dataType(hXShapeInfo); + auto xType = sd::ArrayOptions::dataType(hXShapeInfo); - BUILD_SINGLE_SELECTOR(xType, nd4j::SpecialMethods, ::accumulateGeneric(hX, hz, hZShapeInfo, n, length), LIBND4J_TYPES); + BUILD_SINGLE_SELECTOR(xType, sd::SpecialMethods, ::accumulateGeneric(hX, hz, hZShapeInfo, n, length), LIBND4J_TYPES); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); } } @@ -1502,7 +1502,7 @@ void shuffleGeneric(void **hX, Nd4jLong **hXShapeInfo, void **dz, Nd4jLong **hZS if (swapIdx < 0) continue; - nd4j::math::nd4j_swap(hX[r * ews], hX[swapIdx * ews]); + sd::math::nd4j_swap(hX[r * ews], hX[swapIdx * ews]); } } else { for (Nd4jLong r = 0; r < numTads; r++) { @@ -1517,12 +1517,12 @@ void shuffleGeneric(void **hX, Nd4jLong **hXShapeInfo, void **dz, Nd4jLong **hZS if (tadEWS == 1) { for (Nd4jLong i = 0; i < tadLength; i++) { - nd4j::math::nd4j_swap(rX[i], rY[i]); + sd::math::nd4j_swap(rX[i], rY[i]); } } else { for (Nd4jLong i = 0; i < tadLength; i++) { auto offset = shape::getIndexOffset(i, tadOnlyShapeInfo[f]); - nd4j::math::nd4j_swap(hX[offset + oldOffset], hX[offset + newOffset]); + sd::math::nd4j_swap(hX[offset + oldOffset], hX[offset + newOffset]); } } } @@ -1548,19 +1548,19 @@ void shuffle(Nd4jPointer *extras, auto tadOnlyShapeInfo = reinterpret_cast(tadShapeInfo); auto tadOffset = reinterpret_cast(tadOffsets); - auto xType = nd4j::ArrayOptions::dataType(xShape[0]); + auto xType = sd::ArrayOptions::dataType(xShape[0]); BUILD_SINGLE_SELECTOR(xType, shuffleGeneric, (hX, xShape, hz, zShape, N, shuffleMap, tadOnlyShapeInfo, tadOffset), LIBND4J_TYPES); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); } } bool isExperimentalEnabled() { - return nd4j::Environment::getInstance()->isExperimentalBuild(); + return sd::Environment::getInstance()->isExperimentalBuild(); } @@ -1607,8 +1607,8 @@ void execScalarTad(Nd4jPointer *extraPointers, tadShapeInfoZ, tadOffsetsZ); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); } } @@ -1647,8 +1647,8 @@ void execScalarBoolTad(Nd4jPointer *extraPointers, tadShapeInfoZ, tadOffsetsZ); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); } } @@ -1666,8 +1666,8 @@ const char * getDeviceName(int deviceId) { sprintf(name, "x86-compatible CPU"); } } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); } @@ -1686,7 +1686,7 @@ void execAggregate(Nd4jPointer *extraPointers,int opNum, int numIntArrays, void *realArguments, int numRealArguments, - nd4j::DataType dtype) { + sd::DataType dtype) { } @@ -1700,7 +1700,7 @@ void batchExecutor(Nd4jPointer *extraPointers, int maxIdx, int maxReals, void *ptrToArguments, - nd4j::DataType dtype) { + sd::DataType dtype) { } @@ -1714,7 +1714,7 @@ void execAggregateBatch(Nd4jPointer *extraPointers, int maxIdx, int maxReals, void *ptrToArguments, - nd4j::DataType dtype) { + sd::DataType dtype) { } @@ -1727,8 +1727,8 @@ void execRandom(Nd4jPointer *extraPointers, try { NativeOpExecutioner::execRandom(nullptr, opNum, state, dbZ->primary(), hZShapeInfo, dbZ->special(), dZShapeInfo, extraArguments); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); } } @@ -1742,8 +1742,8 @@ void execRandom3(Nd4jPointer *extraPointers, try { NativeOpExecutioner::execRandom(nullptr, opNum, state, dbX->primary(), hXShapeInfo, dbX->special(), dXShapeInfo, dbY->primary(), hYShapeInfo, dbY->special(), dYShapeInfo, dbZ->primary(), hZShapeInfo, dbZ->special(), dZShapeInfo, extraArguments); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); } } @@ -1756,8 +1756,8 @@ void execRandom2(Nd4jPointer *extraPointers, try { NativeOpExecutioner::execRandom(nullptr, opNum, state, dbX->primary(), hXShapeInfo, dbX->special(), dXShapeInfo, dbZ->primary(), hZShapeInfo, dbZ->special(), dZShapeInfo, extraArguments); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); } } @@ -1767,28 +1767,28 @@ Nd4jPointer initRandom(Nd4jPointer *extraPointers, long seed, long bufferSize, N return (Nd4jPointer) generator; } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); return nullptr; } } void refreshBuffer(Nd4jPointer *extraPointers, long seed, Nd4jPointer ptrRandom) { - auto generator = reinterpret_cast (ptrRandom); + auto generator = reinterpret_cast (ptrRandom); generator->setStates(seed); } void reSeedBuffer(Nd4jPointer *extraPointers, long seed, Nd4jPointer ptrRandom) { - auto generator = reinterpret_cast (ptrRandom); + auto generator = reinterpret_cast (ptrRandom); generator->setStates(seed); } void destroyRandom(Nd4jPointer ptrBuffer) { - auto buffer = reinterpret_cast(ptrBuffer); + auto buffer = reinterpret_cast(ptrBuffer); delete buffer; } @@ -1825,8 +1825,8 @@ void sort(Nd4jPointer *extraPointers, try { NativeOpExecutioner::execSort(hX, hXShapeInfo, descending); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); } } @@ -1841,8 +1841,8 @@ void sortTad(Nd4jPointer *extraPointers, try { NativeOpExecutioner::execSort(hX, hXShapeInfo, dimension, dimensionLength, tadShapeInfo, tadOffsets, descending); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); } } @@ -1854,8 +1854,8 @@ void sortCooIndices(Nd4jPointer *extraPointers, try { NativeOpExecutioner::execSortCooIndices(indices, values, length, rank); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); } } @@ -1889,8 +1889,8 @@ try { return hZ; } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); return nullptr; } } @@ -1906,25 +1906,25 @@ void munmapFile(Nd4jPointer *extraPointers, Nd4jLong *ptrMap, Nd4jLong length) { delete[] ptrMap; } -nd4j::graph::ResultWrapper* executeFlatGraph(Nd4jPointer *extraPointers, Nd4jPointer flatBufferPointer) { +sd::graph::ResultWrapper* executeFlatGraph(Nd4jPointer *extraPointers, Nd4jPointer flatBufferPointer) { try { - return nd4j::graph::GraphExecutioner::executeFlatBuffer(flatBufferPointer); + return sd::graph::GraphExecutioner::executeFlatBuffer(flatBufferPointer); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); return nullptr; } } -Nd4jLong getResultWrapperSize(nd4j::graph::ResultWrapper* ptr) { +Nd4jLong getResultWrapperSize(sd::graph::ResultWrapper* ptr) { return ptr->size(); } -Nd4jPointer getResultWrapperPointer(nd4j::graph::ResultWrapper* ptr) { +Nd4jPointer getResultWrapperPointer(sd::graph::ResultWrapper* ptr) { return ptr->pointer(); } const char* getAllCustomOps() { - return nd4j::ops::OpRegistrator::getInstance()->getAllCustomOperations(); + return sd::ops::OpRegistrator::getInstance()->getAllCustomOperations(); } template @@ -1936,7 +1936,7 @@ FORCEINLINE int estimateThresholdGeneric(Nd4jPointer *extraPointers, Nd4jPointer int64_t cnt = 0; PRAGMA_OMP_SIMD for (auto e = start; e < stop; e++) { - auto v = nd4j::math::nd4j_abs(buffer[e]); + auto v = sd::math::nd4j_abs(buffer[e]); if (v >= threshold) cnt++; } @@ -1953,31 +1953,31 @@ int estimateThreshold(Nd4jPointer *extraPointers, Nd4jPointer hX, Nd4jLong *hXSh auto xType = ArrayOptions::dataType(hXShapeInfo); BUILD_SINGLE_SELECTOR(xType, return estimateThresholdGeneric, (extraPointers, hX, N, threshold), FLOAT_TYPES); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); return 0; } } -Nd4jLong getShapeListSize(nd4j::ShapeList* list) { +Nd4jLong getShapeListSize(sd::ShapeList* list) { return list->size(); } -Nd4jLong* getShape(nd4j::ShapeList* list, Nd4jLong i) { +Nd4jLong* getShape(sd::ShapeList* list, Nd4jLong i) { return list->at(i); } void deleteShapeList(Nd4jPointer shapeList) { - auto list = reinterpret_cast(shapeList); + auto list = reinterpret_cast(shapeList); //list->destroy(); delete list; } -nd4j::ShapeList* _calculateOutputShapes(Nd4jPointer* extraPointers, nd4j::ops::DeclarableOp* op, Nd4jPointer* inputBuffers, Nd4jPointer* inputShapes, int numInputShapes, double* tArgs, int numTArgs, Nd4jLong *iArgs, int numIArgs, bool *bArgs, int numBArgs, int *dArgs, int numDArgs) { - nd4j::graph::VariableSpace varSpace; +sd::ShapeList* _calculateOutputShapes(Nd4jPointer* extraPointers, sd::ops::DeclarableOp* op, Nd4jPointer* inputBuffers, Nd4jPointer* inputShapes, int numInputShapes, double* tArgs, int numTArgs, Nd4jLong *iArgs, int numIArgs, bool *bArgs, int numBArgs, int *dArgs, int numDArgs) { + sd::graph::VariableSpace varSpace; Context block(2, &varSpace); - nd4j::ShapeList inShapes; + sd::ShapeList inShapes; for (int e = 0; e < numIArgs; e++) block.getIArguments()->push_back(iArgs[e]); @@ -1989,15 +1989,15 @@ nd4j::ShapeList* _calculateOutputShapes(Nd4jPointer* extraPointers, nd4j::ops::D block.getBArguments()->push_back(bArgs[e]); for (int e = 0; e < numDArgs; e++) - block.getDArguments()->push_back((nd4j::DataType) dArgs[e]); + block.getDArguments()->push_back((sd::DataType) dArgs[e]); for (int e = 0; e < numInputShapes; e++) { auto shape_ = reinterpret_cast(inputShapes[e]); // we shouldn't copy buffer if that's empty array - void *buffer_ = nd4j::ArrayOptions::arrayType(shape_) == ArrayType::EMPTY ? nullptr : inputBuffers[e]; + void *buffer_ = sd::ArrayOptions::arrayType(shape_) == ArrayType::EMPTY ? nullptr : inputBuffers[e]; - auto array = new nd4j::NDArray(buffer_, shape_, varSpace.launchContext(), false); + auto array = new sd::NDArray(buffer_, shape_, varSpace.launchContext(), false); // block should contain references to proper variable varSpace.putVariable(1, e, array); @@ -2018,21 +2018,21 @@ nd4j::ShapeList* _calculateOutputShapes(Nd4jPointer* extraPointers, nd4j::ops::D return shapeList; } -nd4j::ShapeList* calculateOutputShapes2(Nd4jPointer* extraPointers, Nd4jLong hash, Nd4jPointer* inputBuffers, Nd4jPointer* inputShapes, int numInputShapes, double* tArgs, int numTArgs, Nd4jLong *iArgs, int numIArgs, bool *bArgs, int numBArgs, int *dArgs, int numDArgs) { +sd::ShapeList* calculateOutputShapes2(Nd4jPointer* extraPointers, Nd4jLong hash, Nd4jPointer* inputBuffers, Nd4jPointer* inputShapes, int numInputShapes, double* tArgs, int numTArgs, Nd4jLong *iArgs, int numIArgs, bool *bArgs, int numBArgs, int *dArgs, int numDArgs) { try { - auto op = nd4j::ops::OpRegistrator::getInstance()->getOperation(hash); + auto op = sd::ops::OpRegistrator::getInstance()->getOperation(hash); return _calculateOutputShapes(extraPointers, op, inputBuffers, inputShapes, numInputShapes, tArgs, numTArgs, iArgs, numIArgs, bArgs, numBArgs, dArgs, numDArgs); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); return nullptr; } } -nd4j::ShapeList* _calculateOutputShapes(Nd4jPointer* extraPointers, nd4j::ops::DeclarableOp *op, Nd4jPointer* inputShapes, int numInputShapes, double *tArgs, int numTArgs, Nd4jLong *iArgs, int numIArgs) { +sd::ShapeList* _calculateOutputShapes(Nd4jPointer* extraPointers, sd::ops::DeclarableOp *op, Nd4jPointer* inputShapes, int numInputShapes, double *tArgs, int numTArgs, Nd4jLong *iArgs, int numIArgs) { Context block(1); - nd4j::ShapeList inShapes; + sd::ShapeList inShapes; for (int e = 0; e < numIArgs; e++) block.getIArguments()->push_back(iArgs[e]); @@ -2049,39 +2049,39 @@ nd4j::ShapeList* _calculateOutputShapes(Nd4jPointer* extraPointers, nd4j::ops::D return shapeList; } -nd4j::ShapeList* calculateOutputShapes(Nd4jPointer* extraPointers, Nd4jLong hash, Nd4jPointer* inputShapes, int numInputShapes, double* tArgs, int numTArgs, Nd4jLong *iArgs, int numIArgs) { +sd::ShapeList* calculateOutputShapes(Nd4jPointer* extraPointers, Nd4jLong hash, Nd4jPointer* inputShapes, int numInputShapes, double* tArgs, int numTArgs, Nd4jLong *iArgs, int numIArgs) { try { - auto op = nd4j::ops::OpRegistrator::getInstance()->getOperation(hash); + auto op = sd::ops::OpRegistrator::getInstance()->getOperation(hash); return _calculateOutputShapes(extraPointers, op, inputShapes, numInputShapes, tArgs, numTArgs, iArgs, numIArgs); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); return nullptr; } } int execCustomOp2(Nd4jPointer* extraPointers, Nd4jLong hash, Nd4jPointer opContext) { try { - auto op = nd4j::ops::OpRegistrator::getInstance()->getOperation(hash); + auto op = sd::ops::OpRegistrator::getInstance()->getOperation(hash); auto context = reinterpret_cast(opContext); return op->execute(context); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); return 20; } } -Nd4jStatus realExec(nd4j::ops::DeclarableOp* op, Nd4jPointer* extraPointers, Nd4jLong hash, Nd4jPointer* inputBuffers, Nd4jPointer* inputShapes, int numInputs, Nd4jPointer* outputBuffers, Nd4jPointer* outputShapes, int numOutputs, double* tArgs, int numTArgs, Nd4jLong *iArgs, int numIArgs, bool* bArgs, int numBArgs, bool isInplace) { +Nd4jStatus realExec(sd::ops::DeclarableOp* op, Nd4jPointer* extraPointers, Nd4jLong hash, Nd4jPointer* inputBuffers, Nd4jPointer* inputShapes, int numInputs, Nd4jPointer* outputBuffers, Nd4jPointer* outputShapes, int numOutputs, double* tArgs, int numTArgs, Nd4jLong *iArgs, int numIArgs, bool* bArgs, int numBArgs, bool isInplace) { if (op == nullptr) nd4j_printf("Can't find requested operation: [%lld]\n", hash); // we're using the same fake nodeId everywhere here - std::vector inputs(numInputs); - std::vector outputs(numOutputs); + std::vector inputs(numInputs); + std::vector outputs(numOutputs); std::vector ttArgs(numTArgs); std::vector iiArgs(numIArgs); std::vector biArgs(numBArgs); @@ -2089,9 +2089,9 @@ Nd4jStatus realExec(nd4j::ops::DeclarableOp* op, Nd4jPointer* extraPointers, Nd4 // filling block now with inputs for (int e = 0; e < numInputs; e++) { auto shape = reinterpret_cast(inputShapes[e]); - void *buffer = nd4j::ArrayOptions::arrayType(shape) == ArrayType::EMPTY ? nullptr : inputBuffers[e]; + void *buffer = sd::ArrayOptions::arrayType(shape) == ArrayType::EMPTY ? nullptr : inputBuffers[e]; - inputs[e] = new nd4j::NDArray(buffer, shape); + inputs[e] = new sd::NDArray(buffer, shape); } // if not inplace - transferring output arrays @@ -2100,12 +2100,12 @@ Nd4jStatus realExec(nd4j::ops::DeclarableOp* op, Nd4jPointer* extraPointers, Nd4 for (int e = 0; e < numOutputs; e++) { // we want to keep original output shape intact auto shape = shape::copyShape(reinterpret_cast(outputShapes[e])); - void *buffer = nd4j::ArrayOptions::arrayType(shape) == ArrayType::EMPTY ? nullptr : outputBuffers[e]; + void *buffer = sd::ArrayOptions::arrayType(shape) == ArrayType::EMPTY ? nullptr : outputBuffers[e]; // FIXME: revisit this. bool canNullify = true; for (int i = 0; i < numInputs; i++) { - void *ibuffer = nd4j::ArrayOptions::arrayType(shape) == ArrayType::EMPTY ? nullptr : inputBuffers[i]; + void *ibuffer = sd::ArrayOptions::arrayType(shape) == ArrayType::EMPTY ? nullptr : inputBuffers[i]; if (ibuffer == buffer) { canNullify = false; break; @@ -2115,7 +2115,7 @@ Nd4jStatus realExec(nd4j::ops::DeclarableOp* op, Nd4jPointer* extraPointers, Nd4 if (canNullify) memset((uint8_t *) buffer, '\0', shape::length(shape) * DataTypeUtils::sizeOfElement(ArrayOptions::dataType(shape))); - auto array = new nd4j::NDArray(buffer, shape); + auto array = new sd::NDArray(buffer, shape); outputs[e] = array; // and we want to release shape copy once we're done @@ -2133,7 +2133,7 @@ Nd4jStatus realExec(nd4j::ops::DeclarableOp* op, Nd4jPointer* extraPointers, Nd4 biArgs[e] = bArgs[e]; // hypothetically at this point we have everything filled - auto hZ = op->execute(inputs, outputs, ttArgs, iiArgs, biArgs, std::vector(), isInplace); + auto hZ = op->execute(inputs, outputs, ttArgs, iiArgs, biArgs, std::vector(), isInplace); //auto hZ = op->execute(inputs, ttArgs, iiArgs, isInplace); @@ -2161,40 +2161,40 @@ Nd4jStatus realExec(nd4j::ops::DeclarableOp* op, Nd4jPointer* extraPointers, Nd4 int execCustomOp(Nd4jPointer* extraPointers, Nd4jLong hash, Nd4jPointer* inputBuffers, Nd4jPointer* inputShapes, int numInputs, Nd4jPointer* outputBuffers, Nd4jPointer* outputShapes, int numOutputs, double* tArgs, int numTArgs, Nd4jLong *iArgs, int numIArgs, bool* bArgs, int numBArgs, bool isInplace) { try { - auto op = nd4j::ops::OpRegistrator::getInstance()->getOperation(hash); + auto op = sd::ops::OpRegistrator::getInstance()->getOperation(hash); return realExec(op, extraPointers, hash, inputBuffers, inputShapes, numInputs, outputBuffers, outputShapes, numOutputs, tArgs, numTArgs, iArgs, numIArgs, bArgs, numBArgs, isInplace); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); return 1; } } int registerGraph(Nd4jPointer *extraPointers, Nd4jLong graphId, Nd4jPointer flatBufferPointer) { try { - auto graph = nd4j::graph::GraphExecutioner::importFromFlatPointer(flatBufferPointer); + auto graph = sd::graph::GraphExecutioner::importFromFlatPointer(flatBufferPointer); - nd4j::graph::GraphHolder::getInstance()->registerGraph(graphId, graph); + sd::graph::GraphHolder::getInstance()->registerGraph(graphId, graph); return ND4J_STATUS_OK; } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); return 1; } } static VariablesSet* executeStoredGraphT(Nd4jPointer *extraPointers, Nd4jLong graphId, Nd4jPointer *inputBuffers, Nd4jPointer *inputShapes, int* inputIndices, int numInputs) { - auto graph = nd4j::graph::GraphHolder::getInstance()->cloneGraph(graphId); + auto graph = sd::graph::GraphHolder::getInstance()->cloneGraph(graphId); auto varSpace = graph->getVariableSpace(); - std::vector handles; + std::vector handles; for (int e = 0; e < numInputs; e++) { auto idx = inputIndices[e]; // we'll delete this array later, together with cloned VariableSpace - auto array = new nd4j::NDArray(inputBuffers[e], reinterpret_cast(inputShapes[e])); + auto array = new sd::NDArray(inputBuffers[e], reinterpret_cast(inputShapes[e])); handles.emplace_back(array); if (varSpace->hasVariable(idx)) { @@ -2207,8 +2207,8 @@ static VariablesSet* executeStoredGraphT(Nd4jPointer *extraPointers, Nd4jLong gr varSpace->putVariable(idx, array); } - auto hZ = nd4j::graph::GraphExecutioner::execute(graph, varSpace); - auto varSet = new nd4j::graph::VariablesSet(hZ); + auto hZ = sd::graph::GraphExecutioner::execute(graph, varSpace); + auto varSet = new sd::graph::VariablesSet(hZ); if (hZ == ND4J_STATUS_OK) { // pull back results, and provide them @@ -2230,47 +2230,47 @@ static VariablesSet* executeStoredGraphT(Nd4jPointer *extraPointers, Nd4jLong gr return varSet; } -nd4j::graph::VariablesSet* executeStoredGraph(Nd4jPointer *extraPointers, Nd4jLong graphId, Nd4jPointer *inputBuffers, Nd4jPointer *inputShapes, int* inputIndices, int numInputs) { +sd::graph::VariablesSet* executeStoredGraph(Nd4jPointer *extraPointers, Nd4jLong graphId, Nd4jPointer *inputBuffers, Nd4jPointer *inputShapes, int* inputIndices, int numInputs) { return nullptr; } -Nd4jLong getVariablesSetSize(nd4j::graph::VariablesSet* set) { +Nd4jLong getVariablesSetSize(sd::graph::VariablesSet* set) { return set->size(); } -Nd4jStatus getVariablesSetStatus(nd4j::graph::VariablesSet* set) { +Nd4jStatus getVariablesSetStatus(sd::graph::VariablesSet* set) { return set->status(); } -nd4j::graph::Variable* getVariable(nd4j::graph::VariablesSet* set, Nd4jLong i) { +sd::graph::Variable* getVariable(sd::graph::VariablesSet* set, Nd4jLong i) { return set->at(i); } -int getVariableId(nd4j::graph::Variable* variable) { +int getVariableId(sd::graph::Variable* variable) { return variable->id(); } -int getVariableIndex(nd4j::graph::Variable* variable) { +int getVariableIndex(sd::graph::Variable* variable) { return variable->index(); } -const char* getVariableName(nd4j::graph::Variable* variable) { +const char* getVariableName(sd::graph::Variable* variable) { return variable->getName()->c_str(); } -Nd4jLong* getVariableShape(nd4j::graph::Variable* variable) { +Nd4jLong* getVariableShape(sd::graph::Variable* variable) { return variable->getNDArray()->shapeInfo(); } -void* getVariableBuffer(nd4j::graph::Variable* variable) { +void* getVariableBuffer(sd::graph::Variable* variable) { return variable->getNDArray()->buffer(); } int unregisterGraph(Nd4jPointer *extraPointers, Nd4jLong graphId) { - nd4j::graph::GraphHolder::getInstance()->dropGraphAny(graphId); + sd::graph::GraphHolder::getInstance()->dropGraphAny(graphId); - return nd4j::Status::OK(); + return sd::Status::OK(); } void deletePointerArray(Nd4jPointer pointer) { @@ -2293,25 +2293,25 @@ void deleteLongArray(Nd4jPointer pointer) { delete[] ptr; } -void deleteVariablesSet(nd4j::graph::VariablesSet* pointer) { +void deleteVariablesSet(sd::graph::VariablesSet* pointer) { delete pointer; } const char* getAllOperations() { - return nd4j::OpTracker::getInstance()->exportOperations(); + return sd::OpTracker::getInstance()->exportOperations(); } Nd4jPointer getGraphState(Nd4jLong id) { - return (Nd4jPointer) new nd4j::graph::GraphState(id); + return (Nd4jPointer) new sd::graph::GraphState(id); } void deleteGraphState(Nd4jPointer state) { - auto stateP = reinterpret_cast(state); + auto stateP = reinterpret_cast(state); delete stateP; } -Nd4jStatus execCustomOpWithScope_(Nd4jPointer *extraPointers, nd4j::graph::GraphState *state, Nd4jLong opHash, Nd4jLong *scopes, int numScopes, Nd4jPointer *inputBuffers, Nd4jPointer *inputShapes, int numInputs, Nd4jPointer *outputBuffers, Nd4jPointer *outputShapes, int numOutputs) { +Nd4jStatus execCustomOpWithScope_(Nd4jPointer *extraPointers, sd::graph::GraphState *state, Nd4jLong opHash, Nd4jLong *scopes, int numScopes, Nd4jPointer *inputBuffers, Nd4jPointer *inputShapes, int numInputs, Nd4jPointer *outputBuffers, Nd4jPointer *outputShapes, int numOutputs) { /** * That's basically exec, with VariableSpace provided in GraphState: * depending on operation (i.e. while of if), different logic executors could be used @@ -2329,7 +2329,7 @@ Nd4jStatus execCustomOpWithScope_(Nd4jPointer *extraPointers, nd4j::graph::Graph auto buffer = inputBuffers[e]; auto shapeInfo = reinterpret_cast(inputShapes[e]); - auto array = new nd4j::NDArray(buffer, shapeInfo, varSpace->launchContext()); + auto array = new sd::NDArray(buffer, shapeInfo, varSpace->launchContext()); // now we just put array to VarSpace varSpace->putVariable(0, e, array); @@ -2378,17 +2378,17 @@ Nd4jStatus execCustomOpWithScope_(Nd4jPointer *extraPointers, nd4j::graph::Graph Nd4jStatus execCustomOpWithScope(Nd4jPointer *extraPointers, Nd4jPointer state, Nd4jLong opHash, Nd4jLong *scopes, int numScopes, Nd4jPointer *inputBuffers, Nd4jPointer *inputShapes, int numInputs, Nd4jPointer *outputBuffers, Nd4jPointer *outputShapes, int numOutputs) { try { - return execCustomOpWithScope_(extraPointers, reinterpret_cast(state), opHash, scopes, numScopes, inputBuffers, inputShapes, numInputs, outputBuffers, outputShapes, numOutputs); + return execCustomOpWithScope_(extraPointers, reinterpret_cast(state), opHash, scopes, numScopes, inputBuffers, inputShapes, numInputs, outputBuffers, outputShapes, numOutputs); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); return 1; } } void deleteResultWrapper(Nd4jPointer ptr) { // just 0 room for compiler s@!t - auto p = reinterpret_cast(ptr); + auto p = reinterpret_cast(ptr); delete p; } @@ -2402,113 +2402,113 @@ void convertTypes(Nd4jPointer *extras, int srcType, Nd4jPointer hX, Nd4jLong N, if (srcType == ND4J_FLOAT8) { if (dstType == ND4J_FLOAT8) { - // convertGeneric(hx, N, hz); + // convertGeneric(hx, N, hz); } else if (dstType == ND4J_INT8) { - //nd4j::TypeCast::convertGeneric(nullptr, hx, N, hz); + //sd::TypeCast::convertGeneric(nullptr, hx, N, hz); } else if (dstType == ND4J_UINT8) { - //nd4j::TypeCast::convertGeneric(nullptr, hx, N, hz); + //sd::TypeCast::convertGeneric(nullptr, hx, N, hz); } else if (dstType == ND4J_FLOAT16) { - //nd4j::TypeCast::convertGeneric(nullptr, hx, N, hz); + //sd::TypeCast::convertGeneric(nullptr, hx, N, hz); } else if (dstType == ND4J_INT16) { - //nd4j::TypeCast::convertGeneric(nullptr, hx, N, hz); + //sd::TypeCast::convertGeneric(nullptr, hx, N, hz); } else if (dstType == ND4J_UINT16) { - //nd4j::TypeCast::convertGeneric(nullptr, hx, N, hz); + //sd::TypeCast::convertGeneric(nullptr, hx, N, hz); } else if (dstType == ND4J_FLOAT24) { } else if (dstType == ND4J_FLOAT32) { - //nd4j::TypeCast::convertGeneric(nullptr, hx, N, hz); + //sd::TypeCast::convertGeneric(nullptr, hx, N, hz); } else if (dstType == ND4J_DOUBLE) { - //nd4j::TypeCast::convertGeneric(nullptr, hx, N, hz); + //sd::TypeCast::convertGeneric(nullptr, hx, N, hz); } else { //nd4j_printf("Unsupported types conversion: [%i] -> [%i]\n", srcType, dstType); } } else if (srcType == ND4J_INT8) { if (dstType == ND4J_FLOAT8) { - //nd4j::TypeCast::convertGeneric(nullptr, hx, N, hz); + //sd::TypeCast::convertGeneric(nullptr, hx, N, hz); } else if (dstType == ND4J_INT8) { - //convertGeneric(hx, N, hz); + //convertGeneric(hx, N, hz); } else if (dstType == ND4J_UINT8) { - nd4j::TypeCast::convertGeneric(nullptr, hx, N, hz); + sd::TypeCast::convertGeneric(nullptr, hx, N, hz); } else if (dstType == ND4J_FLOAT16) { - nd4j::TypeCast::convertGeneric(nullptr, hx, N, hz); + sd::TypeCast::convertGeneric(nullptr, hx, N, hz); } else if (dstType == ND4J_INT16) { - nd4j::TypeCast::convertGeneric(nullptr, hx, N, hz); + sd::TypeCast::convertGeneric(nullptr, hx, N, hz); } else if (dstType == ND4J_UINT16) { - //nd4j::TypeCast::convertGeneric(nullptr, hx, N, hz); + //sd::TypeCast::convertGeneric(nullptr, hx, N, hz); } else if (dstType == ND4J_FLOAT24) { // TODO: eventually we might want to add it } else if (dstType == ND4J_FLOAT32) { - nd4j::TypeCast::convertGeneric(nullptr, hx, N, hz); + sd::TypeCast::convertGeneric(nullptr, hx, N, hz); } else if (dstType == ND4J_DOUBLE) { - nd4j::TypeCast::convertGeneric(nullptr, hx, N, hz); + sd::TypeCast::convertGeneric(nullptr, hx, N, hz); } else { nd4j_printf("Unsupported types conversion: [%i] -> [%i]\n", srcType, dstType); } } else if (srcType == ND4J_UINT8) { if (dstType == ND4J_FLOAT8) { - // nd4j::TypeCast::convertGeneric(nullptr, hx, N, hz); + // sd::TypeCast::convertGeneric(nullptr, hx, N, hz); } else if (dstType == ND4J_INT8) { - nd4j::TypeCast::convertGeneric(nullptr, hx, N, hz); + sd::TypeCast::convertGeneric(nullptr, hx, N, hz); } else if (dstType == ND4J_UINT8) { - nd4j::TypeCast::convertGeneric(nullptr, hx, N, hz); + sd::TypeCast::convertGeneric(nullptr, hx, N, hz); } else if (dstType == ND4J_FLOAT16) { - nd4j::TypeCast::convertGeneric(nullptr, hx, N, hz); + sd::TypeCast::convertGeneric(nullptr, hx, N, hz); } else if (dstType == ND4J_INT16) { - nd4j::TypeCast::convertGeneric(nullptr, hx, N, hz); + sd::TypeCast::convertGeneric(nullptr, hx, N, hz); } else if (dstType == ND4J_UINT16) { - // nd4j::TypeCast::convertGeneric(nullptr, hx, N, hz); + // sd::TypeCast::convertGeneric(nullptr, hx, N, hz); } else if (dstType == ND4J_FLOAT24) { // TODO: still might want to add } else if (dstType == ND4J_FLOAT32) { - nd4j::TypeCast::convertGeneric(nullptr, hx, N, hz); + sd::TypeCast::convertGeneric(nullptr, hx, N, hz); } else if (dstType == ND4J_DOUBLE) { - nd4j::TypeCast::convertGeneric(nullptr, hx, N, hz); + sd::TypeCast::convertGeneric(nullptr, hx, N, hz); } else { nd4j_printf("Unsupported types conversion: [%i] -> [%i]\n", srcType, dstType); } } else if (srcType == ND4J_FLOAT16) { if (dstType == ND4J_FLOAT8) { - // nd4j::TypeCast::convertGeneric(nullptr, hx, N, hz); + // sd::TypeCast::convertGeneric(nullptr, hx, N, hz); } else if (dstType == ND4J_INT8) { - nd4j::TypeCast::convertGeneric(nullptr, hx, N, hz); + sd::TypeCast::convertGeneric(nullptr, hx, N, hz); } else if (dstType == ND4J_UINT8) { - nd4j::TypeCast::convertGeneric(nullptr, hx, N, hz); + sd::TypeCast::convertGeneric(nullptr, hx, N, hz); } else if (dstType == ND4J_FLOAT16) { - nd4j::TypeCast::convertGeneric(nullptr, hx, N, hz); + sd::TypeCast::convertGeneric(nullptr, hx, N, hz); } else if (dstType == ND4J_INT16) { - nd4j::TypeCast::convertGeneric(nullptr, hx, N, hz); + sd::TypeCast::convertGeneric(nullptr, hx, N, hz); } else if (dstType == ND4J_UINT16) { -// nd4j::TypeCast::convertGeneric(nullptr, hx, N, hz); +// sd::TypeCast::convertGeneric(nullptr, hx, N, hz); } else if (dstType == ND4J_FLOAT24) { // TODO: .... ^^^ } else if (dstType == ND4J_FLOAT32) { - nd4j::TypeCast::convertGeneric(nullptr, hx, N, hz); + sd::TypeCast::convertGeneric(nullptr, hx, N, hz); } else if (dstType == ND4J_DOUBLE) { - nd4j::TypeCast::convertGeneric(nullptr, hx, N, hz); + sd::TypeCast::convertGeneric(nullptr, hx, N, hz); } else if (dstType == ND4J_THRESHOLD) { - nd4j::TypeCast::convertToThreshold(nullptr, hx, N, hz); + sd::TypeCast::convertToThreshold(nullptr, hx, N, hz); } else { nd4j_printf("Unsupported types conversion: [%i] -> [%i]\n", srcType, dstType); } } else if (srcType == ND4J_INT16) { if (dstType == ND4J_FLOAT8) { - // nd4j::TypeCast::convertGeneric(nullptr, hx, N, hz); + // sd::TypeCast::convertGeneric(nullptr, hx, N, hz); } else if (dstType == ND4J_INT8) { - nd4j::TypeCast::convertGeneric(nullptr, hx, N, hz); + sd::TypeCast::convertGeneric(nullptr, hx, N, hz); } else if (dstType == ND4J_UINT8) { - nd4j::TypeCast::convertGeneric(nullptr, hx, N, hz); + sd::TypeCast::convertGeneric(nullptr, hx, N, hz); } else if (dstType == ND4J_FLOAT16) { - nd4j::TypeCast::convertGeneric(nullptr, hx, N, hz); + sd::TypeCast::convertGeneric(nullptr, hx, N, hz); } else if (dstType == ND4J_INT16) { - //nd4j::TypeCast::convertGeneric(nullptr, hx, N, hz); + //sd::TypeCast::convertGeneric(nullptr, hx, N, hz); } else if (dstType == ND4J_UINT16) { -// nd4j::TypeCast::convertGeneric(nullptr, hx, N, hz); +// sd::TypeCast::convertGeneric(nullptr, hx, N, hz); } else if (dstType == ND4J_FLOAT24) { // TODO... } else if (dstType == ND4J_FLOAT32) { - nd4j::TypeCast::convertGeneric(nullptr, hx, N, hz); + sd::TypeCast::convertGeneric(nullptr, hx, N, hz); } else if (dstType == ND4J_DOUBLE) { - nd4j::TypeCast::convertGeneric(nullptr, hx, N, hz); + sd::TypeCast::convertGeneric(nullptr, hx, N, hz); } else { printf("Unsupported types conversion: [%i] -> [%i]\n", srcType, dstType); } @@ -2516,57 +2516,57 @@ void convertTypes(Nd4jPointer *extras, int srcType, Nd4jPointer hX, Nd4jLong N, } else if (srcType == ND4J_FLOAT32) { if (dstType == ND4J_FLOAT8) { - // nd4j::TypeCast::convertGeneric(nullptr, hx, N, hz); + // sd::TypeCast::convertGeneric(nullptr, hx, N, hz); } else if (dstType == ND4J_INT8) { - nd4j::TypeCast::convertGeneric(nullptr, hx, N, hz); + sd::TypeCast::convertGeneric(nullptr, hx, N, hz); } else if (dstType == ND4J_UINT8) { - nd4j::TypeCast::convertGeneric(nullptr, hx, N, hz); + sd::TypeCast::convertGeneric(nullptr, hx, N, hz); } else if (dstType == ND4J_FLOAT16) { - nd4j::TypeCast::convertGeneric(nullptr, hx, N, hz); + sd::TypeCast::convertGeneric(nullptr, hx, N, hz); } else if (dstType == ND4J_INT16) { - nd4j::TypeCast::convertGeneric(nullptr, hx, N, hz); + sd::TypeCast::convertGeneric(nullptr, hx, N, hz); } else if (dstType == ND4J_UINT16) { -// nd4j::TypeCast::convertGeneric(nullptr, hx, N, hz); +// sd::TypeCast::convertGeneric(nullptr, hx, N, hz); } else if (dstType == ND4J_FLOAT24) { } else if (dstType == ND4J_DOUBLE) { - nd4j::TypeCast::convertGeneric(nullptr, hx, N, hz); + sd::TypeCast::convertGeneric(nullptr, hx, N, hz); } else if (dstType == ND4J_THRESHOLD) { - nd4j::TypeCast::convertToThreshold(nullptr, hx, N, hz); + sd::TypeCast::convertToThreshold(nullptr, hx, N, hz); } else { nd4j_printf("Unsupported types conversion: [%i] -> [%i]\n", srcType, dstType); } } else if (srcType == ND4J_DOUBLE) { if (dstType == ND4J_FLOAT8) { - // nd4j::TypeCast::convertGeneric(nullptr, hx, N, hz); + // sd::TypeCast::convertGeneric(nullptr, hx, N, hz); } else if (dstType == ND4J_INT8) { - nd4j::TypeCast::convertGeneric(nullptr, hx, N, hz); + sd::TypeCast::convertGeneric(nullptr, hx, N, hz); } else if (dstType == ND4J_UINT8) { - nd4j::TypeCast::convertGeneric(nullptr, hx, N, hz); + sd::TypeCast::convertGeneric(nullptr, hx, N, hz); } else if (dstType == ND4J_FLOAT16) { - nd4j::TypeCast::convertGeneric(nullptr, hx, N, hz); + sd::TypeCast::convertGeneric(nullptr, hx, N, hz); } else if (dstType == ND4J_INT16) { - nd4j::TypeCast::convertGeneric(nullptr, hx, N, hz); + sd::TypeCast::convertGeneric(nullptr, hx, N, hz); } else if (dstType == ND4J_UINT16) { -// nd4j::TypeCast::convertGeneric(nullptr, hx, N, hz); +// sd::TypeCast::convertGeneric(nullptr, hx, N, hz); } else if (dstType == ND4J_FLOAT24) { } else if (dstType == ND4J_FLOAT32) { - nd4j::TypeCast::convertGeneric(nullptr, hx, N, hz); + sd::TypeCast::convertGeneric(nullptr, hx, N, hz); } else if (dstType == ND4J_DOUBLE) { // } else if (dstType == ND4J_THRESHOLD) { - nd4j::TypeCast::convertToThreshold(nullptr, hx, N, hz); + sd::TypeCast::convertToThreshold(nullptr, hx, N, hz); } else { nd4j_printf("Unsupported types conversion: [%i] -> [%i]\n", srcType, dstType); } } else if (srcType == ND4J_THRESHOLD) { if (dstType == ND4J_FLOAT16) { - nd4j::TypeCast::convertFromThreshold(nullptr, hx, N, hz); + sd::TypeCast::convertFromThreshold(nullptr, hx, N, hz); } else if (dstType == ND4J_FLOAT32) { - nd4j::TypeCast::convertFromThreshold(nullptr, hx, N, hz); + sd::TypeCast::convertFromThreshold(nullptr, hx, N, hz); } else if (dstType == ND4J_DOUBLE) { - nd4j::TypeCast::convertFromThreshold(nullptr, hx, N, hz); + sd::TypeCast::convertFromThreshold(nullptr, hx, N, hz); } else { nd4j_printf("Unsupported types conversion: [%i] -> [%i]\n", srcType, dstType); } @@ -2577,27 +2577,27 @@ void convertTypes(Nd4jPointer *extras, int srcType, Nd4jPointer hX, Nd4jLong N, /* void fillUtf8String(Nd4jPointer *extraPointers, const char **strings, int numStrings, Nd4jPointer buffer) { - auto hZ = reinterpret_cast(buffer); + auto hZ = reinterpret_cast(buffer); for (int e = 0; e < numStrings; e++) { - hZ[e] = reinterpret_cast(createUtf8String(extraPointers, strings[e])); + hZ[e] = reinterpret_cast(createUtf8String(extraPointers, strings[e])); } } */ Nd4jPointer createUtf8String(Nd4jPointer *extraPointers, const char *string, int length) { - auto u = new nd4j::utf8string(string, length); + auto u = new sd::utf8string(string, length); return reinterpret_cast(u); } Nd4jLong getUtf8StringLength(Nd4jPointer *extraPointers, Nd4jPointer ptr) { - return reinterpret_cast(ptr)->_length; + return reinterpret_cast(ptr)->_length; } char* getUtf8StringBuffer(Nd4jPointer *extraPointers, Nd4jPointer ptr) { - return reinterpret_cast(ptr)->_buffer; + return reinterpret_cast(ptr)->_buffer; } void deleteUtf8String(Nd4jPointer *extraPointers, Nd4jPointer ptr) { - delete(reinterpret_cast(ptr)); + delete(reinterpret_cast(ptr)); } template @@ -2668,20 +2668,20 @@ void scatterUpdate(Nd4jPointer *extraPointers, int opCode, int numOfSubArrs, try { BUILD_SINGLE_SELECTOR(iType, _scatterUpdate, (extraPointers, opCode, numOfSubArrs, hX, hXShapeInfo, hXOffsets, dX, dXShapeInfo, dXOffsets, hY, hYShapeInfo, hYOffsets, dY, dYShapeInfo, dYOffsets, hIindexes, hIndicesShapeInfo, dIindexes, dIndicesShapeInfo), INDEXING_TYPES); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); } } void inspectArray(Nd4jPointer *extraPointers, Nd4jPointer buffer, Nd4jLong *shapeInfo, Nd4jPointer specialBuffer, Nd4jLong *specialShapeInfo, Nd4jPointer debugInfo) { try { - auto p = reinterpret_cast(debugInfo); + auto p = reinterpret_cast(debugInfo); NDArray array(buffer, shapeInfo); - nd4j::DebugHelper::retrieveDebugStatistics(p, &array); + sd::DebugHelper::retrieveDebugStatistics(p, &array); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); } } @@ -2692,85 +2692,85 @@ void tryPointer(Nd4jPointer extra, Nd4jPointer p, int len) { for (int i = 0; i < len; i++) cnt += buf[cnt]; } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); } } -nd4j::ConstantDataBuffer* shapeBuffer(int rank, Nd4jLong *shape, Nd4jLong *strides, nd4j::DataType dtype, char order, Nd4jLong ews, bool empty) { +sd::ConstantDataBuffer* shapeBuffer(int rank, Nd4jLong *shape, Nd4jLong *strides, sd::DataType dtype, char order, Nd4jLong ews, bool empty) { try { auto buffer = new ConstantDataBuffer(); - *buffer = nd4j::ConstantShapeHelper::getInstance()->bufferForShapeInfo( + *buffer = sd::ConstantShapeHelper::getInstance()->bufferForShapeInfo( ShapeDescriptor(dtype, order, shape, strides, rank, ews, empty)); return buffer; } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); return nullptr; } } -void deleteShapeBuffer(nd4j::ConstantDataBuffer* ptr) { +void deleteShapeBuffer(sd::ConstantDataBuffer* ptr) { delete ptr; } -void deleteTadPack(nd4j::TadPack* ptr) { +void deleteTadPack(sd::TadPack* ptr) { delete ptr; } -nd4j::ConstantDataBuffer* constantBufferLong(nd4j::DataType dtype, Nd4jLong *data, int length) { +sd::ConstantDataBuffer* constantBufferLong(sd::DataType dtype, Nd4jLong *data, int length) { return nullptr; } -nd4j::ConstantDataBuffer* constantBufferDouble(nd4j::DataType dtype, double *data, int length) { +sd::ConstantDataBuffer* constantBufferDouble(sd::DataType dtype, double *data, int length) { return nullptr; } -nd4j::ConstantDataBuffer* constantBuffer(nd4j::DataType dtype, nd4j::ConstantDescriptor *descriptor) { +sd::ConstantDataBuffer* constantBuffer(sd::DataType dtype, sd::ConstantDescriptor *descriptor) { try { - return nd4j::ConstantHelper::getInstance()->constantBuffer(*descriptor, dtype); + return sd::ConstantHelper::getInstance()->constantBuffer(*descriptor, dtype); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); return nullptr; } } -Nd4jPointer getConstantDataBufferPrimary(nd4j::ConstantDataBuffer* dbf) { +Nd4jPointer getConstantDataBufferPrimary(sd::ConstantDataBuffer* dbf) { return dbf->primary(); } -Nd4jPointer getConstantDataBufferSpecial(nd4j::ConstantDataBuffer* dbf) { +Nd4jPointer getConstantDataBufferSpecial(sd::ConstantDataBuffer* dbf) { return dbf->special(); } -Nd4jLong getConstantDataBufferLength(nd4j::ConstantDataBuffer* dbf) { +Nd4jLong getConstantDataBufferLength(sd::ConstantDataBuffer* dbf) { return dbf->length(); } -Nd4jLong getConstantDataBufferSizeOf(nd4j::ConstantDataBuffer* dbf) { +Nd4jLong getConstantDataBufferSizeOf(sd::ConstantDataBuffer* dbf) { return dbf->sizeOf(); } -nd4j::graph::Context* createGraphContext(int nodeId) { +sd::graph::Context* createGraphContext(int nodeId) { try { - return new nd4j::graph::Context(nodeId); + return new sd::graph::Context(nodeId); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); return nullptr; } } -nd4j::graph::RandomGenerator* getGraphContextRandomGenerator(nd4j::graph::Context* ptr) { +sd::graph::RandomGenerator* getGraphContextRandomGenerator(sd::graph::Context* ptr) { return &ptr->randomGenerator(); } -void markGraphContextInplace(nd4j::graph::Context* ptr, bool reallyInplace) { +void markGraphContextInplace(sd::graph::Context* ptr, bool reallyInplace) { ptr->markInplace(reallyInplace); } -void setGraphContextCudaContext(nd4j::graph::Context* ptr, void *stream, void *reductionPointer, void *allocationPointer) { +void setGraphContextCudaContext(sd::graph::Context* ptr, void *stream, void *reductionPointer, void *allocationPointer) { } -void setGraphContextInputArray(nd4j::graph::Context* ptr, int index, void *buffer, void *shapeInfo, void *specialBuffer, void *specialShapeInfo) { +void setGraphContextInputArray(sd::graph::Context* ptr, int index, void *buffer, void *shapeInfo, void *specialBuffer, void *specialShapeInfo) { ptr->setInputArray(index, buffer, shapeInfo, specialBuffer, specialShapeInfo); } -void setGraphContextOutputArray(nd4j::graph::Context* ptr, int index, void *buffer, void *shapeInfo, void *specialBuffer, void *specialShapeInfo) { +void setGraphContextOutputArray(sd::graph::Context* ptr, int index, void *buffer, void *shapeInfo, void *specialBuffer, void *specialShapeInfo) { ptr->setOutputArray(index, buffer, shapeInfo, specialBuffer, specialShapeInfo); } @@ -2782,25 +2782,25 @@ void setGraphContextOutputBuffer(OpaqueContext* ptr, int index, OpaqueDataBuffer ptr->setOutputArray(index, buffer, shapeInfo, specialShapeInfo); } -void setGraphContextTArguments(nd4j::graph::Context* ptr, double *arguments, int numberOfArguments) { +void setGraphContextTArguments(sd::graph::Context* ptr, double *arguments, int numberOfArguments) { ptr->setTArguments(arguments, numberOfArguments); } -void setGraphContextIArguments(nd4j::graph::Context* ptr, Nd4jLong *arguments, int numberOfArguments) { +void setGraphContextIArguments(sd::graph::Context* ptr, Nd4jLong *arguments, int numberOfArguments) { ptr->setIArguments(arguments, numberOfArguments); } -void setGraphContextBArguments(nd4j::graph::Context* ptr, bool *arguments, int numberOfArguments) { +void setGraphContextBArguments(sd::graph::Context* ptr, bool *arguments, int numberOfArguments) { ptr->setBArguments(arguments, numberOfArguments); } void setGraphContextDArguments(OpaqueContext* ptr, int *arguments, int numberOfArguments) { - std::vector dtypes(numberOfArguments); + std::vector dtypes(numberOfArguments); for (int e = 0; e < numberOfArguments; e++) - dtypes[e] = (nd4j::DataType) arguments[e]; + dtypes[e] = (sd::DataType) arguments[e]; ptr->setDArguments(dtypes); } -void deleteGraphContext(nd4j::graph::Context* ptr) { +void deleteGraphContext(sd::graph::Context* ptr) { delete ptr; } @@ -2819,31 +2819,31 @@ void ctxPurge(OpaqueContext* ptr) { ptr->clearFastPath(); } -nd4j::graph::RandomGenerator* createRandomGenerator(Nd4jLong rootSeed, Nd4jLong nodeSeed) { - return new nd4j::graph::RandomGenerator(rootSeed, nodeSeed); +sd::graph::RandomGenerator* createRandomGenerator(Nd4jLong rootSeed, Nd4jLong nodeSeed) { + return new sd::graph::RandomGenerator(rootSeed, nodeSeed); } -Nd4jLong getRandomGeneratorRootState(nd4j::graph::RandomGenerator* ptr) { +Nd4jLong getRandomGeneratorRootState(sd::graph::RandomGenerator* ptr) { return ptr->rootState(); } -Nd4jLong getRandomGeneratorNodeState(nd4j::graph::RandomGenerator* ptr) { +Nd4jLong getRandomGeneratorNodeState(sd::graph::RandomGenerator* ptr) { return ptr->nodeState(); } -void setRandomGeneratorStates(nd4j::graph::RandomGenerator* ptr, Nd4jLong rootSeed, Nd4jLong nodeSeed) { +void setRandomGeneratorStates(sd::graph::RandomGenerator* ptr, Nd4jLong rootSeed, Nd4jLong nodeSeed) { ptr->setStates(rootSeed, nodeSeed); } -int getRandomGeneratorRelativeInt(nd4j::graph::RandomGenerator* ptr, Nd4jLong index) { +int getRandomGeneratorRelativeInt(sd::graph::RandomGenerator* ptr, Nd4jLong index) { return ptr->relativeInt(index); } -Nd4jLong getRandomGeneratorRelativeLong(nd4j::graph::RandomGenerator* ptr, Nd4jLong index) { +Nd4jLong getRandomGeneratorRelativeLong(sd::graph::RandomGenerator* ptr, Nd4jLong index) { return ptr->relativeLong(index); } -void deleteRandomGenerator(nd4j::graph::RandomGenerator* ptr) { +void deleteRandomGenerator(sd::graph::RandomGenerator* ptr) { delete ptr; } @@ -2870,19 +2870,19 @@ Nd4jPointer shapeBufferForNumpy(Nd4jPointer npyArray) { Nd4jLong *shapeBuffer; if (shape.size() == 1 && shape[0] == 0) { // scalar case - shapeBuffer = nd4j::ShapeBuilders::createScalarShapeInfo(dtype); + shapeBuffer = sd::ShapeBuilders::createScalarShapeInfo(dtype); } else if (_empty) { if (shapeSize > 0) - shapeBuffer = nd4j::ShapeBuilders::emptyShapeInfo(dtype, arr.fortranOrder ? 'f' : 'c', shape); + shapeBuffer = sd::ShapeBuilders::emptyShapeInfo(dtype, arr.fortranOrder ? 'f' : 'c', shape); else - shapeBuffer = nd4j::ShapeBuilders::emptyShapeInfo(dtype); + shapeBuffer = sd::ShapeBuilders::emptyShapeInfo(dtype); } else { - shapeBuffer = nd4j::ShapeBuilders::createShapeInfo(dtype, arr.fortranOrder ? 'f' : 'c', shape); + shapeBuffer = sd::ShapeBuilders::createShapeInfo(dtype, arr.fortranOrder ? 'f' : 'c', shape); } - return reinterpret_cast(nd4j::ConstantShapeHelper::getInstance()->createFromExisting(shapeBuffer, true)); + return reinterpret_cast(sd::ConstantShapeHelper::getInstance()->createFromExisting(shapeBuffer, true)); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); return nullptr; } } @@ -2897,10 +2897,10 @@ void sortByKey(Nd4jPointer *extraPointers, auto xType = ArrayOptions::dataType(xShapeInfo); auto yType = ArrayOptions::dataType(yShapeInfo); - BUILD_DOUBLE_SELECTOR(xType, yType, nd4j::DoubleMethods, ::sortByKey(x, xShapeInfo, y, yShapeInfo, descending), LIBND4J_TYPES, LIBND4J_TYPES); + BUILD_DOUBLE_SELECTOR(xType, yType, sd::DoubleMethods, ::sortByKey(x, xShapeInfo, y, yShapeInfo, descending), LIBND4J_TYPES, LIBND4J_TYPES); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); } } @@ -2914,10 +2914,10 @@ void sortByValue(Nd4jPointer *extraPointers, auto xType = ArrayOptions::dataType(xShapeInfo); auto yType = ArrayOptions::dataType(yShapeInfo); - BUILD_DOUBLE_SELECTOR(xType, yType, nd4j::DoubleMethods, ::sortByValue(x, xShapeInfo, y, yShapeInfo, descending), LIBND4J_TYPES, LIBND4J_TYPES); + BUILD_DOUBLE_SELECTOR(xType, yType, sd::DoubleMethods, ::sortByValue(x, xShapeInfo, y, yShapeInfo, descending), LIBND4J_TYPES, LIBND4J_TYPES); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); } } @@ -2933,10 +2933,10 @@ void sortTadByKey(Nd4jPointer *extraPointers, auto xType = ArrayOptions::dataType(xShapeInfo); auto yType = ArrayOptions::dataType(yShapeInfo); - BUILD_DOUBLE_SELECTOR(xType, yType, nd4j::DoubleMethods, ::sortTadByKey(x, xShapeInfo, y, yShapeInfo, dimension, dimensionLength, descending), LIBND4J_TYPES, LIBND4J_TYPES); + BUILD_DOUBLE_SELECTOR(xType, yType, sd::DoubleMethods, ::sortTadByKey(x, xShapeInfo, y, yShapeInfo, dimension, dimensionLength, descending), LIBND4J_TYPES, LIBND4J_TYPES); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); } } @@ -2952,16 +2952,16 @@ void sortTadByValue(Nd4jPointer *extraPointers, auto xType = ArrayOptions::dataType(xShapeInfo); auto yType = ArrayOptions::dataType(yShapeInfo); - BUILD_DOUBLE_SELECTOR(xType, yType, nd4j::DoubleMethods, ::sortTadByValue(x, xShapeInfo, y, yShapeInfo, dimension, dimensionLength, descending), LIBND4J_TYPES, LIBND4J_TYPES); + BUILD_DOUBLE_SELECTOR(xType, yType, sd::DoubleMethods, ::sortTadByValue(x, xShapeInfo, y, yShapeInfo, dimension, dimensionLength, descending), LIBND4J_TYPES, LIBND4J_TYPES); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); } } const char* runLightBenchmarkSuit(bool printOut) { try { - nd4j::LightBenchmarkSuit suit; + sd::LightBenchmarkSuit suit; auto result = suit.runSuit(); if (printOut) @@ -2973,19 +2973,19 @@ const char* runLightBenchmarkSuit(bool printOut) { return chars; } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); return nullptr; } } Nd4jLong getCachedMemory(int deviceId) { - return nd4j::ConstantHelper::getInstance()->getCachedAmount(deviceId); + return sd::ConstantHelper::getInstance()->getCachedAmount(deviceId); } const char* runFullBenchmarkSuit(bool printOut) { try { - nd4j::FullBenchmarkSuit suit; + sd::FullBenchmarkSuit suit; auto result = suit.runSuit(); if (printOut) @@ -2997,13 +2997,13 @@ const char* runFullBenchmarkSuit(bool printOut) { return chars; } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); return nullptr; } } -nd4j::LaunchContext* defaultLaunchContext() { +sd::LaunchContext* defaultLaunchContext() { return LaunchContext::defaultContext(); } @@ -3036,11 +3036,11 @@ Nd4jPointer lcSolverHandle(OpaqueLaunchContext* lc) { } int lastErrorCode() { - return nd4j::LaunchContext::defaultContext()->errorReference()->errorCode(); + return sd::LaunchContext::defaultContext()->errorReference()->errorCode(); } const char* lastErrorMessage() { - return nd4j::LaunchContext::defaultContext()->errorReference()->errorMessage(); + return sd::LaunchContext::defaultContext()->errorReference()->errorMessage(); } void ctxShapeFunctionOverride(OpaqueContext* ptr, bool reallyOverride) { @@ -3118,10 +3118,10 @@ bool isOptimalRequirementsMet() { OpaqueDataBuffer* allocateDataBuffer(Nd4jLong elements, int dataType, bool allocateBoth) { try { auto dtype = DataTypeUtils::fromInt(dataType); - return new nd4j::InteropDataBuffer(elements * DataTypeUtils::sizeOf(dtype) , dtype, allocateBoth); + return new sd::InteropDataBuffer(elements * DataTypeUtils::sizeOf(dtype) , dtype, allocateBoth); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); return nullptr; } } @@ -3158,8 +3158,8 @@ void dbExpandBuffer(OpaqueDataBuffer *dataBuffer, Nd4jLong elements) { try { dataBuffer->dataBuffer()->expand(elements * DataTypeUtils::sizeOf(dataBuffer->dataBuffer()->getDataType())); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); } } diff --git a/libnd4j/blas/cuda/BlasVersionHelper.cu b/libnd4j/include/legacy/cuda/BlasVersionHelper.cu similarity index 94% rename from libnd4j/blas/cuda/BlasVersionHelper.cu rename to libnd4j/include/legacy/cuda/BlasVersionHelper.cu index 1f80a0cc0..04b0e78f1 100644 --- a/libnd4j/blas/cuda/BlasVersionHelper.cu +++ b/libnd4j/include/legacy/cuda/BlasVersionHelper.cu @@ -18,9 +18,9 @@ // @author raver119@gmail.com // -#include "../BlasVersionHelper.h" +#include -namespace nd4j { +namespace sd { BlasVersionHelper::BlasVersionHelper() { _blasMajorVersion = __CUDACC_VER_MAJOR__; _blasMinorVersion = __CUDACC_VER_MINOR__; diff --git a/libnd4j/blas/cuda/NativeOpExecutioner.cu b/libnd4j/include/legacy/cuda/NativeOpExecutioner.cu similarity index 78% rename from libnd4j/blas/cuda/NativeOpExecutioner.cu rename to libnd4j/include/legacy/cuda/NativeOpExecutioner.cu index 1e0685dc4..00a9ea03f 100644 --- a/libnd4j/blas/cuda/NativeOpExecutioner.cu +++ b/libnd4j/include/legacy/cuda/NativeOpExecutioner.cu @@ -14,16 +14,16 @@ * SPDX-License-Identifier: Apache-2.0 ******************************************************************************/ -#include "../NativeOpExecutioner.h" +#include #include -#include +#include #include -#include +#include #include #include #include #include -#include +#include #include #include @@ -55,12 +55,12 @@ #include #include -using namespace nd4j; +using namespace sd; /** * This is utility kernel, that updates given special buffer with proper values in device memory */ -extern "C" __global__ void prepareShapeBuffer(int *dimension, int *maxDimension, Nd4jLong *specialPointer, int rows, nd4j::DataType dataType) { +extern "C" __global__ void prepareShapeBuffer(int *dimension, int *maxDimension, Nd4jLong *specialPointer, int rows, sd::DataType dataType) { Nd4jLong tid = blockIdx.x * blockDim.x + threadIdx.x; if (tid > 0) return; @@ -85,7 +85,7 @@ extern "C" __global__ void prepareShapeBuffer(int *dimension, int *maxDimension, //////////////////////////////////////////////////////////////////////// -void NativeOpExecutioner::execPairwiseTransform(nd4j::LaunchContext *lc, +void NativeOpExecutioner::execPairwiseTransform(sd::LaunchContext *lc, int opNum, void *hX, Nd4jLong *hXShapeInfo, void *dX, Nd4jLong *dXShapeInfo, @@ -97,9 +97,9 @@ void NativeOpExecutioner::execPairwiseTransform(nd4j::LaunchContext *lc, auto stream = lc->getCudaStream(); - auto xType = nd4j::ArrayOptions::dataType(hXShapeInfo); - auto yType = nd4j::ArrayOptions::dataType(hYShapeInfo); - auto zType = nd4j::ArrayOptions::dataType(hZShapeInfo); + auto xType = sd::ArrayOptions::dataType(hXShapeInfo); + auto yType = sd::ArrayOptions::dataType(hYShapeInfo); + auto zType = sd::ArrayOptions::dataType(hZShapeInfo); if (shape::isEmpty(hXShapeInfo) || shape::isEmpty(hYShapeInfo)) return; @@ -126,7 +126,7 @@ void NativeOpExecutioner::execPairwiseTransform(nd4j::LaunchContext *lc, } //////////////////////////////////////////////////////////////////////// -void NativeOpExecutioner::execPairwiseBoolTransform( nd4j::LaunchContext *lc, +void NativeOpExecutioner::execPairwiseBoolTransform( sd::LaunchContext *lc, int opNum, void *hX, Nd4jLong *hXShapeInfo, void *dX, Nd4jLong *dXShapeInfo, @@ -138,18 +138,18 @@ void NativeOpExecutioner::execPairwiseBoolTransform( nd4j::LaunchContext *lc, auto stream = lc->getCudaStream(); - auto xType = nd4j::ArrayOptions::dataType(hXShapeInfo); - auto yType = nd4j::ArrayOptions::dataType(hYShapeInfo); - auto zType = nd4j::ArrayOptions::dataType(hZShapeInfo); + auto xType = sd::ArrayOptions::dataType(hXShapeInfo); + auto yType = sd::ArrayOptions::dataType(hYShapeInfo); + auto zType = sd::ArrayOptions::dataType(hZShapeInfo); if (shape::isEmpty(hXShapeInfo) || shape::isEmpty(hYShapeInfo)) return; if (!DataTypeUtils::isB(zType)) - throw nd4j::datatype_exception::build("NativeOpExecutioner::execPairwiseBoolTransform wrong Z operand data type", nd4j::DataType::BOOL, zType); + throw sd::datatype_exception::build("NativeOpExecutioner::execPairwiseBoolTransform wrong Z operand data type", sd::DataType::BOOL, zType); if (yType != xType) - throw nd4j::datatype_exception::build("NativeOpExecutioner::execPairwiseBoolTransform both operands must have same data type", xType, yType); + throw sd::datatype_exception::build("NativeOpExecutioner::execPairwiseBoolTransform both operands must have same data type", xType, yType); dim3 launchDims(256, 1024, 16384); @@ -162,7 +162,7 @@ void NativeOpExecutioner::execPairwiseBoolTransform( nd4j::LaunchContext *lc, } //////////////////////////////////////////////////////////////////////// -void NativeOpExecutioner::execPairwiseIntTransform( nd4j::LaunchContext *lc, +void NativeOpExecutioner::execPairwiseIntTransform( sd::LaunchContext *lc, int opNum, void *hX, Nd4jLong *hXShapeInfo, void *dX, Nd4jLong *dXShapeInfo, @@ -174,18 +174,18 @@ void NativeOpExecutioner::execPairwiseIntTransform( nd4j::LaunchContext *lc, auto stream = lc->getCudaStream(); - auto xType = nd4j::ArrayOptions::dataType(hXShapeInfo); - auto yType = nd4j::ArrayOptions::dataType(hYShapeInfo); - auto zType = nd4j::ArrayOptions::dataType(hZShapeInfo); + auto xType = sd::ArrayOptions::dataType(hXShapeInfo); + auto yType = sd::ArrayOptions::dataType(hYShapeInfo); + auto zType = sd::ArrayOptions::dataType(hZShapeInfo); if (shape::isEmpty(hXShapeInfo) || shape::isEmpty(hYShapeInfo)) return; if (!DataTypeUtils::isZ(zType)) - throw nd4j::datatype_exception::build("NativeOpExecutioner::execPairwiseIntTransform wrong Z operand data type", nd4j::DataType::BOOL, zType); + throw sd::datatype_exception::build("NativeOpExecutioner::execPairwiseIntTransform wrong Z operand data type", sd::DataType::BOOL, zType); if (yType != xType || zType != xType) - throw nd4j::datatype_exception::build("NativeOpExecutioner::execPairwiseIntTransform both operands must have same data type", xType, yType); + throw sd::datatype_exception::build("NativeOpExecutioner::execPairwiseIntTransform both operands must have same data type", xType, yType); dim3 launchDims(256, 1024, 16384); @@ -198,7 +198,7 @@ void NativeOpExecutioner::execPairwiseIntTransform( nd4j::LaunchContext *lc, } //////////////////////////////////////////////////////////////////////// -void NativeOpExecutioner::execSummaryStatsScalar(nd4j::LaunchContext *lc, +void NativeOpExecutioner::execSummaryStatsScalar(sd::LaunchContext *lc, int opNum, void *hX, Nd4jLong *hXShapeInfo, void *dX, Nd4jLong *dXShapeInfo, @@ -212,8 +212,8 @@ void NativeOpExecutioner::execSummaryStatsScalar(nd4j::LaunchContext *lc, dim3 launchDims = dim3(256, 256, 32768); - auto xType = nd4j::ArrayOptions::dataType(hXShapeInfo); - auto zType = nd4j::ArrayOptions::dataType(hZShapeInfo); + auto xType = sd::ArrayOptions::dataType(hXShapeInfo); + auto zType = sd::ArrayOptions::dataType(hZShapeInfo); BUILD_DOUBLE_SELECTOR(xType, zType, functions::summarystats::SummaryStatsReduce, ::execSummaryStatsReduceScalar(launchDims, stream, opNum, dX, dXShapeInfo, hXShapeInfo, extraParams, dZ, dZShapeInfo, hZShapeInfo, nullptr, nullptr, biasCorrected, reductionPointer), LIBND4J_TYPES, FLOAT_TYPES); @@ -224,7 +224,7 @@ void NativeOpExecutioner::execSummaryStatsScalar(nd4j::LaunchContext *lc, } //////////////////////////////////////////////////////////////////////// -void NativeOpExecutioner::execBroadcastBool(nd4j::LaunchContext *lc, +void NativeOpExecutioner::execBroadcastBool(sd::LaunchContext *lc, int opNum, void *hX, Nd4jLong *hXShapeInfo, void *dX, Nd4jLong *dXShapeInfo, @@ -239,9 +239,9 @@ void NativeOpExecutioner::execBroadcastBool(nd4j::LaunchContext *lc, auto stream = lc->getCudaStream(); - auto xType = nd4j::ArrayOptions::dataType(hXShapeInfo); - auto yType = nd4j::ArrayOptions::dataType(hYShapeInfo); - auto zType = nd4j::ArrayOptions::dataType(hZShapeInfo); + auto xType = sd::ArrayOptions::dataType(hXShapeInfo); + auto yType = sd::ArrayOptions::dataType(hYShapeInfo); + auto zType = sd::ArrayOptions::dataType(hZShapeInfo); if (shape::isEmpty(hXShapeInfo) || shape::isEmpty(hYShapeInfo)) return; @@ -252,7 +252,7 @@ void NativeOpExecutioner::execBroadcastBool(nd4j::LaunchContext *lc, if (yType != xType) throw std::runtime_error("NativeOpExecutioner::execBroadcastBool requires both X & Y operands to have same type"); - if (nd4j::Environment::getInstance()->isDebugAndVerbose()) + if (sd::Environment::getInstance()->isDebugAndVerbose()) printf("F3B opNum:[%i]\n", opNum); dim3 launchDims(256, 256, 1024); @@ -265,7 +265,40 @@ void NativeOpExecutioner::execBroadcastBool(nd4j::LaunchContext *lc, throw cuda_exception::build("execBroadcastBool failed", res); } -void NativeOpExecutioner::execInverseBroadcastBool(nd4j::LaunchContext *lc, +//////////////////////////////////////////////////////////////////////// +void NativeOpExecutioner::execBroadcastBool(sd::LaunchContext* lc, const int opNum, + const void *hX, const Nd4jLong *hXShapeInfo, + const void *dX, const Nd4jLong *dXShapeInfo, + const void *hY, const Nd4jLong *hYShapeInfo, + const void *dY, const Nd4jLong *dYShapeInfo, + void *hZ, const Nd4jLong *hZShapeInfo, + void *dZ, const Nd4jLong *dZShapeInfo, + void *extraParams) { + + if (shape::isEmpty(hXShapeInfo) || shape::isEmpty(hYShapeInfo)) + return; + + auto stream = lc->getCudaStream(); + + auto xType = sd::ArrayOptions::dataType(hXShapeInfo); + auto zType = sd::ArrayOptions::dataType(hZShapeInfo); + + dim3 launchDims; + + launchDims.y = MAX_NUM_THREADS / 4; // threadsPerBlock + launchDims.x = (shape::length(hZShapeInfo) + launchDims.y - 1) / launchDims.y; // blocksPerGrid + launchDims.z = 1024; // shared memory + + BUILD_DOUBLE_SELECTOR(xType, zType, functions::broadcast::BroadcastBool, ::execBroadcast(launchDims, stream, opNum, dX, dXShapeInfo, dY, dYShapeInfo, dZ, dZShapeInfo, extraParams), LIBND4J_TYPES, BOOL_TYPES); + + // TODO: remove after the release + auto res = cudaStreamSynchronize(*stream); + if (res != 0) + throw cuda_exception::build("execBroadcastBool failed", res); +} + + +void NativeOpExecutioner::execInverseBroadcastBool(sd::LaunchContext *lc, int opNum, void *hX, Nd4jLong *hXShapeInfo, void *dX, Nd4jLong *dXShapeInfo, @@ -279,9 +312,9 @@ void NativeOpExecutioner::execInverseBroadcastBool(nd4j::LaunchContext *lc, Nd4jLong *tadOnlyShapeInfoZ,Nd4jLong *tadOffsetsZ) { auto stream = lc->getCudaStream(); - auto xType = nd4j::ArrayOptions::dataType(hXShapeInfo); - auto yType = nd4j::ArrayOptions::dataType(hYShapeInfo); - auto zType = nd4j::ArrayOptions::dataType(hZShapeInfo); + auto xType = sd::ArrayOptions::dataType(hXShapeInfo); + auto yType = sd::ArrayOptions::dataType(hYShapeInfo); + auto zType = sd::ArrayOptions::dataType(hZShapeInfo); if (shape::isEmpty(hXShapeInfo) || shape::isEmpty(hYShapeInfo)) return; @@ -302,9 +335,8 @@ void NativeOpExecutioner::execInverseBroadcastBool(nd4j::LaunchContext *lc, throw cuda_exception::build("execInverseBroadcastBool failed", res); } - //////////////////////////////////////////////////////////////////////// -void NativeOpExecutioner::execBroadcastInt(nd4j::LaunchContext *lc, +void NativeOpExecutioner::execBroadcastInt(sd::LaunchContext *lc, int opNum, void *hX, Nd4jLong *hXShapeInfo, void *dX, Nd4jLong *dXShapeInfo, @@ -318,9 +350,9 @@ void NativeOpExecutioner::execBroadcastInt(nd4j::LaunchContext *lc, auto stream = lc->getCudaStream(); - auto xType = nd4j::ArrayOptions::dataType(hXShapeInfo); - auto yType = nd4j::ArrayOptions::dataType(hYShapeInfo); - auto zType = nd4j::ArrayOptions::dataType(hZShapeInfo); + auto xType = sd::ArrayOptions::dataType(hXShapeInfo); + auto yType = sd::ArrayOptions::dataType(hYShapeInfo); + auto zType = sd::ArrayOptions::dataType(hZShapeInfo); if (shape::isEmpty(hXShapeInfo) || shape::isEmpty(hYShapeInfo)) return; @@ -341,7 +373,45 @@ void NativeOpExecutioner::execBroadcastInt(nd4j::LaunchContext *lc, throw cuda_exception::build("execBroadcastBool failed", res); } -void NativeOpExecutioner::execInverseBroadcastInt(nd4j::LaunchContext *lc, +//////////////////////////////////////////////////////////////////////// +void NativeOpExecutioner::execBroadcastInt(sd::LaunchContext* lc, const int opNum, + const void *hX, const Nd4jLong *hXShapeInfo, + const void *dX, const Nd4jLong *dXShapeInfo, + const void *hY, const Nd4jLong *hYShapeInfo, + const void *dY, const Nd4jLong *dYShapeInfo, + void *hZ, const Nd4jLong *hZShapeInfo, + void *dZ, const Nd4jLong *dZShapeInfo) { + + auto stream = lc->getCudaStream(); + + auto xType = sd::ArrayOptions::dataType(hXShapeInfo); + auto yType = sd::ArrayOptions::dataType(hYShapeInfo); + auto zType = sd::ArrayOptions::dataType(hZShapeInfo); + + if (shape::isEmpty(hXShapeInfo) || shape::isEmpty(hYShapeInfo)) + return; + + if (!DataTypeUtils::isZ(zType)) + throw std::runtime_error("NativeOpExecutioner::execBroadcastInt requires Z operand to have INT type"); + + if (yType != xType || zType != xType) + throw std::runtime_error("NativeOpExecutioner::execBroadcastInt requires both X & Y operands to have same type"); + + dim3 launchDims; + + launchDims.y = MAX_NUM_THREADS / 4; // threadsPerBlock + launchDims.x = (shape::length(hZShapeInfo) + launchDims.y - 1) / launchDims.y; // blocksPerGrid + launchDims.z = 1024; // shared memory + + BUILD_SINGLE_SELECTOR(xType, functions::broadcast::BroadcastInt, ::execBroadcast(launchDims, stream, opNum, dX, dXShapeInfo, dY, dYShapeInfo, dZ, dZShapeInfo), INTEGER_TYPES) + + // TODO: remove after the release + auto res = cudaStreamSynchronize(*stream); + if (res != 0) + throw cuda_exception::build("execBroadcastBool failed", res); +} + +void NativeOpExecutioner::execInverseBroadcastInt(sd::LaunchContext *lc, int opNum, void *hX, Nd4jLong *hXShapeInfo, void *dX, Nd4jLong *dXShapeInfo, @@ -354,9 +424,9 @@ void NativeOpExecutioner::execInverseBroadcastInt(nd4j::LaunchContext *lc, Nd4jLong *tadOnlyShapeInfoZ,Nd4jLong *tadOffsetsZ) { auto stream = lc->getCudaStream(); - auto xType = nd4j::ArrayOptions::dataType(hXShapeInfo); - auto yType = nd4j::ArrayOptions::dataType(hYShapeInfo); - auto zType = nd4j::ArrayOptions::dataType(hZShapeInfo); + auto xType = sd::ArrayOptions::dataType(hXShapeInfo); + auto yType = sd::ArrayOptions::dataType(hYShapeInfo); + auto zType = sd::ArrayOptions::dataType(hZShapeInfo); if (shape::isEmpty(hXShapeInfo) || shape::isEmpty(hYShapeInfo)) return; @@ -367,7 +437,7 @@ void NativeOpExecutioner::execInverseBroadcastInt(nd4j::LaunchContext *lc, if (yType != xType || zType != xType) throw std::runtime_error("NativeOpExecutioner::execBroadcastInt requires both X & Y operands to have same type"); - if (nd4j::Environment::getInstance()->isDebugAndVerbose()) + if (sd::Environment::getInstance()->isDebugAndVerbose()) printf("F3BI opNum:[%i]\n", opNum); dim3 launchDims(256, 256, 1024); @@ -393,7 +463,7 @@ void NativeOpExecutioner::execInverseBroadcastInt(nd4j::LaunchContext *lc, * @param dimension * @param dimensionLength */ -void NativeOpExecutioner::execBroadcast(nd4j::LaunchContext *lc, +void NativeOpExecutioner::execBroadcast(sd::LaunchContext *lc, int opNum, void *hX, Nd4jLong *hXShapeInfo, void *dX, Nd4jLong *dXShapeInfo, @@ -407,9 +477,9 @@ void NativeOpExecutioner::execBroadcast(nd4j::LaunchContext *lc, auto stream = lc->getCudaStream(); - auto xType = nd4j::ArrayOptions::dataType(hXShapeInfo); - auto yType = nd4j::ArrayOptions::dataType(hYShapeInfo); - auto zType = nd4j::ArrayOptions::dataType(hZShapeInfo); + auto xType = sd::ArrayOptions::dataType(hXShapeInfo); + auto yType = sd::ArrayOptions::dataType(hYShapeInfo); + auto zType = sd::ArrayOptions::dataType(hZShapeInfo); if (shape::isEmpty(hXShapeInfo) || shape::isEmpty(hYShapeInfo)) return; @@ -428,7 +498,43 @@ void NativeOpExecutioner::execBroadcast(nd4j::LaunchContext *lc, throw cuda_exception::build("execBroadcast failed", res); } -void NativeOpExecutioner::execInverseBroadcast(nd4j::LaunchContext *lc, +//////////////////////////////////////////////////////////////////////// +void NativeOpExecutioner::execBroadcast(sd::LaunchContext *lc, const int opNum, + const void *hX, const Nd4jLong *hXShapeInfo, + const void *dX, const Nd4jLong *dXShapeInfo, + const void *hY, const Nd4jLong *hYShapeInfo, + const void *dY, const Nd4jLong *dYShapeInfo, + void *hZ, const Nd4jLong *hZShapeInfo, + void *dZ, const Nd4jLong *dZShapeInfo) { + + auto stream = lc->getCudaStream(); + + auto xType = sd::ArrayOptions::dataType(hXShapeInfo); + auto yType = sd::ArrayOptions::dataType(hYShapeInfo); + auto zType = sd::ArrayOptions::dataType(hZShapeInfo); + + if (shape::isEmpty(hXShapeInfo) || shape::isEmpty(hYShapeInfo)) + return; + + dim3 launchDims; + + launchDims.y = MAX_NUM_THREADS / 4; // threadsPerBlock + launchDims.x = (shape::length(hZShapeInfo) + launchDims.y - 1) / launchDims.y; // blocksPerGrid + launchDims.z = 1024; // shared memory + +#ifdef __ND4J_EXPERIMENTAL__ + BUILD_PAIRWISE_SELECTOR(xType, yType, zType, functions::broadcast::Broadcast, ::execBroadcast(launchDims, stream, opNum, dX, dXShapeInfo, dY, dYShapeInfo, dZ, dZShapeInfo), LIBND4J_TYPES, LIBND4J_TYPES); +#else + BUILD_SINGLE_SELECTOR_THRICE(xType, functions::broadcast::Broadcast, ::execBroadcast(launchDims, stream, opNum, dX, dXShapeInfo, dY, dYShapeInfo, dZ, dZShapeInfo), LIBND4J_TYPES); +#endif + + // TODO: remove after the release + auto res = cudaStreamSynchronize(*stream); + if (res != 0) + throw cuda_exception::build("execBroadcast failed", res); +} + +void NativeOpExecutioner::execInverseBroadcast(sd::LaunchContext *lc, int opNum, void *hX, Nd4jLong *hXShapeInfo, void *dX, Nd4jLong *dXShapeInfo, @@ -442,9 +548,9 @@ void NativeOpExecutioner::execInverseBroadcast(nd4j::LaunchContext *lc, auto stream = lc->getCudaStream(); - auto xType = nd4j::ArrayOptions::dataType(hXShapeInfo); - auto yType = nd4j::ArrayOptions::dataType(hYShapeInfo); - auto zType = nd4j::ArrayOptions::dataType(hZShapeInfo); + auto xType = sd::ArrayOptions::dataType(hXShapeInfo); + auto yType = sd::ArrayOptions::dataType(hYShapeInfo); + auto zType = sd::ArrayOptions::dataType(hZShapeInfo); if (shape::isEmpty(hXShapeInfo) || shape::isEmpty(hYShapeInfo)) return; @@ -464,7 +570,7 @@ void NativeOpExecutioner::execInverseBroadcast(nd4j::LaunchContext *lc, } //////////////////////////////////////////////////////////////////////// -void NativeOpExecutioner::execReduceSame(nd4j::LaunchContext *lc, +void NativeOpExecutioner::execReduceSame(sd::LaunchContext *lc, int opNum, void *hX, Nd4jLong *hXShapeInfo, void *dX, Nd4jLong *dXShapeInfo, @@ -477,11 +583,11 @@ void NativeOpExecutioner::execReduceSame(nd4j::LaunchContext *lc, auto stream = lc->getCudaStream(); auto reductionPointer = lc->getReductionPointer(); - if (nd4j::Environment::getInstance()->isDebugAndVerbose()) + if (sd::Environment::getInstance()->isDebugAndVerbose()) printf("SF7 opNum:[%i]\n", opNum); - auto xType = nd4j::ArrayOptions::dataType(hXShapeInfo); - auto zType = nd4j::ArrayOptions::dataType(hZShapeInfo); + auto xType = sd::ArrayOptions::dataType(hXShapeInfo); + auto zType = sd::ArrayOptions::dataType(hZShapeInfo); auto xRank = shape::rank(hXShapeInfo); if (zType != xType) @@ -499,7 +605,7 @@ void NativeOpExecutioner::execReduceSame(nd4j::LaunchContext *lc, } //////////////////////////////////////////////////////////////////////// -void NativeOpExecutioner::execReduceLong(nd4j::LaunchContext *lc, +void NativeOpExecutioner::execReduceLong(sd::LaunchContext *lc, int opNum, void *hX, Nd4jLong *hXShapeInfo, void *dX, Nd4jLong *dXShapeInfo, @@ -512,14 +618,14 @@ void NativeOpExecutioner::execReduceLong(nd4j::LaunchContext *lc, auto stream = lc->getCudaStream(); auto reductionPointer = lc->getReductionPointer(); - if (nd4j::Environment::getInstance()->isDebugAndVerbose()) + if (sd::Environment::getInstance()->isDebugAndVerbose()) printf("LF7 opNum:[%i]\n", opNum); - auto xType = nd4j::ArrayOptions::dataType(hXShapeInfo); - auto zType = nd4j::ArrayOptions::dataType(hZShapeInfo); + auto xType = sd::ArrayOptions::dataType(hXShapeInfo); + auto zType = sd::ArrayOptions::dataType(hZShapeInfo); - if (zType != nd4j::DataType::INT64) - throw datatype_exception::build("NativeOpExecutioner::execReduceLong wrong Z data type", nd4j::DataType::INT64, zType); + if (zType != sd::DataType::INT64) + throw datatype_exception::build("NativeOpExecutioner::execReduceLong wrong Z data type", sd::DataType::INT64, zType); auto xRank = shape::rank(hXShapeInfo); auto numBlocks = shape::length(hZShapeInfo); @@ -535,7 +641,7 @@ void NativeOpExecutioner::execReduceLong(nd4j::LaunchContext *lc, } //////////////////////////////////////////////////////////////////////// -void NativeOpExecutioner::execReduceBool(nd4j::LaunchContext *lc, +void NativeOpExecutioner::execReduceBool(sd::LaunchContext *lc, int opNum, void *hX, Nd4jLong *hXShapeInfo, void *dX, Nd4jLong *dXShapeInfo, @@ -548,13 +654,13 @@ void NativeOpExecutioner::execReduceBool(nd4j::LaunchContext *lc, auto stream = lc->getCudaStream(); auto reductionPointer = lc->getReductionPointer(); - if (nd4j::Environment::getInstance()->isDebugAndVerbose()) + if (sd::Environment::getInstance()->isDebugAndVerbose()) printf("BF7 opNum:[%i]\n", opNum); - auto xType = nd4j::ArrayOptions::dataType(hXShapeInfo); - auto zType = nd4j::ArrayOptions::dataType(hZShapeInfo); + auto xType = sd::ArrayOptions::dataType(hXShapeInfo); + auto zType = sd::ArrayOptions::dataType(hZShapeInfo); - if (zType != nd4j::DataType::BOOL) + if (zType != sd::DataType::BOOL) throw std::runtime_error("NativeOpExecutioner::execReduceBool requires Z operand to have BOOL type"); auto xRank = shape::rank(hXShapeInfo); @@ -581,7 +687,7 @@ void NativeOpExecutioner::execReduceBool(nd4j::LaunchContext *lc, * @param dimension * @param dimensionLength */ -void NativeOpExecutioner::execIndexReduce(nd4j::LaunchContext *lc, +void NativeOpExecutioner::execIndexReduce(sd::LaunchContext *lc, int opNum, void *hX, Nd4jLong *hXShapeInfo, void *dX, Nd4jLong *dXShapeInfo, @@ -595,15 +701,15 @@ void NativeOpExecutioner::execIndexReduce(nd4j::LaunchContext *lc, auto reductionPointer = lc->getReductionPointer(); auto allocationPointer = lc->getAllocationPointer(); - if (nd4j::Environment::getInstance()->isDebugAndVerbose()) + if (sd::Environment::getInstance()->isDebugAndVerbose()) printf("F2 opNum:[%i]\n", opNum); - auto xType = nd4j::ArrayOptions::dataType(hXShapeInfo); - auto zType = nd4j::ArrayOptions::dataType(hZShapeInfo); + auto xType = sd::ArrayOptions::dataType(hXShapeInfo); + auto zType = sd::ArrayOptions::dataType(hZShapeInfo); auto numBlocks = shape::length(hZShapeInfo); dim3 launchDims(numBlocks == 0 ? 1 : numBlocks, 256, 32768); - if (zType != nd4j::DataType::INT64 && zType != nd4j::DataType::INT32) + if (zType != sd::DataType::INT64 && zType != sd::DataType::INT32) throw datatype_exception::build("NativeOpExecutioner::execIndexReduce requires Z operand to have INT32/INT64 type", zType); auto dz = reinterpret_cast(dZ); @@ -626,7 +732,7 @@ void NativeOpExecutioner::execIndexReduce(nd4j::LaunchContext *lc, * @param dZ * @param dZShapeInfo */ -void NativeOpExecutioner::execReduceFloat(nd4j::LaunchContext *lc, +void NativeOpExecutioner::execReduceFloat(sd::LaunchContext *lc, int opNum, void *hX, Nd4jLong *hXShapeInfo, void *dX, Nd4jLong *dXShapeInfo, @@ -639,11 +745,11 @@ void NativeOpExecutioner::execReduceFloat(nd4j::LaunchContext *lc, auto stream = lc->getCudaStream(); auto reductionPointer = lc->getReductionPointer(); - if (nd4j::Environment::getInstance()->isDebugAndVerbose()) + if (sd::Environment::getInstance()->isDebugAndVerbose()) printf("F8 opNum:[%i]\n", opNum); - auto xType = nd4j::ArrayOptions::dataType(hXShapeInfo); - auto zType = nd4j::ArrayOptions::dataType(hZShapeInfo); + auto xType = sd::ArrayOptions::dataType(hXShapeInfo); + auto zType = sd::ArrayOptions::dataType(hZShapeInfo); auto xRank = shape::rank(hXShapeInfo); auto numBlocks = shape::length(hZShapeInfo); @@ -666,7 +772,7 @@ void NativeOpExecutioner::execReduceFloat(nd4j::LaunchContext *lc, * @param extraParams */ //////////////////////////////////////////////////////////////////////// -void NativeOpExecutioner::execIndexReduceScalar(nd4j::LaunchContext *lc, +void NativeOpExecutioner::execIndexReduceScalar(sd::LaunchContext *lc, int opNum, void *hX, Nd4jLong *hXShapeInfo, void *dX, Nd4jLong *dXShapeInfo, @@ -674,7 +780,7 @@ void NativeOpExecutioner::execIndexReduceScalar(nd4j::LaunchContext *lc, void *hZ, Nd4jLong *hZShapeInfo, void *dZ, Nd4jLong *dZShapeInfo){ - if (nd4j::Environment::getInstance()->isDebug()) + if (sd::Environment::getInstance()->isDebug()) printf("F1 opNum:[%i]\n", opNum); auto stream = lc->getCudaStream(); @@ -686,17 +792,17 @@ void NativeOpExecutioner::execIndexReduceScalar(nd4j::LaunchContext *lc, auto numBlocks = CudaLaunchHelper::getReductionBlocks(xLength, blockWidth); dim3 launchDims(numBlocks == 0 ? 1 : numBlocks, blockWidth, 32768); - if (nd4j::Environment::getInstance()->isDebugAndVerbose() && launchDims.x == 1) + if (sd::Environment::getInstance()->isDebugAndVerbose() && launchDims.x == 1) printf("AF1 opNum:[%i]\n", opNum); - auto xType = nd4j::ArrayOptions::dataType(hXShapeInfo); - auto zType = nd4j::ArrayOptions::dataType(hZShapeInfo); + auto xType = sd::ArrayOptions::dataType(hXShapeInfo); + auto zType = sd::ArrayOptions::dataType(hZShapeInfo); // FIXME: we want Z to be one of integer types //if (!DataTypeUtils::isZ(zType)) - // throw nd4j::datatype_exception("NativeOpExecutioner::execIndexReduceScalar requires Z operand to have one of integer types") - if (zType != nd4j::DataType::INT64 && zType != nd4j::DataType::INT32) - throw nd4j::datatype_exception::build("NativeOpExecutioner::execIndexReduceScalar requires Z operand to have INT32/INT64 data type", zType); + // throw sd::datatype_exception("NativeOpExecutioner::execIndexReduceScalar requires Z operand to have one of integer types") + if (zType != sd::DataType::INT64 && zType != sd::DataType::INT32) + throw sd::datatype_exception::build("NativeOpExecutioner::execIndexReduceScalar requires Z operand to have INT32/INT64 data type", zType); auto dz = reinterpret_cast(dZ); @@ -717,7 +823,7 @@ void NativeOpExecutioner::execIndexReduceScalar(nd4j::LaunchContext *lc, //////////////////////////////////////////////////////////////////////// -void NativeOpExecutioner::execReduceFloatScalar(nd4j::LaunchContext *lc, +void NativeOpExecutioner::execReduceFloatScalar(sd::LaunchContext *lc, int opNum, void *hX, Nd4jLong *hXShapeInfo, void *dX, Nd4jLong *dXShapeInfo, @@ -728,8 +834,8 @@ void NativeOpExecutioner::execReduceFloatScalar(nd4j::LaunchContext *lc, auto stream = lc->getCudaStream(); auto reductionPointer = lc->getReductionPointer(); - auto xType = nd4j::ArrayOptions::dataType(hXShapeInfo); - auto zType = nd4j::ArrayOptions::dataType(hZShapeInfo); + auto xType = sd::ArrayOptions::dataType(hXShapeInfo); + auto zType = sd::ArrayOptions::dataType(hZShapeInfo); auto xLength = shape::length(hXShapeInfo); auto blockWidth = 256; @@ -746,7 +852,7 @@ void NativeOpExecutioner::execReduceFloatScalar(nd4j::LaunchContext *lc, //////////////////////////////////////////////////////////////////////// -void NativeOpExecutioner::execReduceBoolScalar(nd4j::LaunchContext *lc, +void NativeOpExecutioner::execReduceBoolScalar(sd::LaunchContext *lc, int opNum, void *hX, Nd4jLong *hXShapeInfo, void *dX, Nd4jLong *dXShapeInfo, @@ -757,10 +863,10 @@ void NativeOpExecutioner::execReduceBoolScalar(nd4j::LaunchContext *lc, auto stream = lc->getCudaStream(); auto reductionPointer = lc->getReductionPointer(); - auto xType = nd4j::ArrayOptions::dataType(hXShapeInfo); - auto zType = nd4j::ArrayOptions::dataType(hZShapeInfo); + auto xType = sd::ArrayOptions::dataType(hXShapeInfo); + auto zType = sd::ArrayOptions::dataType(hZShapeInfo); - if (zType != nd4j::DataType::BOOL) + if (zType != sd::DataType::BOOL) throw std::runtime_error("NativeOpExecutioner::execReduceBoolScalar requires Z operand to have BOOL type"); auto xLength = shape::length(hXShapeInfo); @@ -777,7 +883,7 @@ void NativeOpExecutioner::execReduceBoolScalar(nd4j::LaunchContext *lc, } //////////////////////////////////////////////////////////////////////// -void NativeOpExecutioner::execReduceSameScalar(nd4j::LaunchContext *lc, +void NativeOpExecutioner::execReduceSameScalar(sd::LaunchContext *lc, int opNum, void *hX, Nd4jLong *hXShapeInfo, void *dX, Nd4jLong *dXShapeInfo, @@ -788,8 +894,8 @@ void NativeOpExecutioner::execReduceSameScalar(nd4j::LaunchContext *lc, auto stream = lc->getCudaStream(); auto reductionPointer = lc->getReductionPointer(); - auto xType = nd4j::ArrayOptions::dataType(hXShapeInfo); - auto zType = nd4j::ArrayOptions::dataType(hZShapeInfo); + auto xType = sd::ArrayOptions::dataType(hXShapeInfo); + auto zType = sd::ArrayOptions::dataType(hZShapeInfo); if (zType != xType) throw datatype_exception::build("NativeOpExecutioner::execReduceSameScalar requires both X & Z operands to have same type", xType, zType); @@ -808,7 +914,7 @@ void NativeOpExecutioner::execReduceSameScalar(nd4j::LaunchContext *lc, } //////////////////////////////////////////////////////////////////////// -void NativeOpExecutioner::execReduceLongScalar(nd4j::LaunchContext *lc, +void NativeOpExecutioner::execReduceLongScalar(sd::LaunchContext *lc, int opNum, void *hX, Nd4jLong *hXShapeInfo, void *dX, Nd4jLong *dXShapeInfo, @@ -819,11 +925,11 @@ void NativeOpExecutioner::execReduceLongScalar(nd4j::LaunchContext *lc, auto stream = lc->getCudaStream(); auto reductionPointer = lc->getReductionPointer(); - auto xType = nd4j::ArrayOptions::dataType(hXShapeInfo); - auto zType = nd4j::ArrayOptions::dataType(hZShapeInfo); + auto xType = sd::ArrayOptions::dataType(hXShapeInfo); + auto zType = sd::ArrayOptions::dataType(hZShapeInfo); - if (zType != nd4j::DataType::INT64) - throw datatype_exception::build("NativeOpExecutioner::execReduceLongScalar wrong Z data type", nd4j::DataType::INT64, zType); + if (zType != sd::DataType::INT64) + throw datatype_exception::build("NativeOpExecutioner::execReduceLongScalar wrong Z data type", sd::DataType::INT64, zType); auto xLength = shape::length(hXShapeInfo); auto blockWidth = 256; @@ -839,7 +945,7 @@ void NativeOpExecutioner::execReduceLongScalar(nd4j::LaunchContext *lc, } //////////////////////////////////////////////////////////////////////// -void NativeOpExecutioner::execTransformSame(nd4j::LaunchContext *lc, +void NativeOpExecutioner::execTransformSame(sd::LaunchContext *lc, int opNum, void *hX, Nd4jLong *hXShapeInfo, void *dX, Nd4jLong *dXShapeInfo, @@ -873,7 +979,7 @@ void NativeOpExecutioner::execTransformSame(nd4j::LaunchContext *lc, } //////////////////////////////////////////////////////////////////////// -void NativeOpExecutioner::execTransformBool(nd4j::LaunchContext *lc, +void NativeOpExecutioner::execTransformBool(sd::LaunchContext *lc, int opNum, void *hX, Nd4jLong *hXShapeInfo, void *dX, Nd4jLong *dXShapeInfo, @@ -907,7 +1013,7 @@ void NativeOpExecutioner::execTransformBool(nd4j::LaunchContext *lc, } //////////////////////////////////////////////////////////////////////// -void NativeOpExecutioner::execTransformAny(nd4j::LaunchContext *lc, +void NativeOpExecutioner::execTransformAny(sd::LaunchContext *lc, int opNum, void *hX, Nd4jLong *hXShapeInfo, void *dX, Nd4jLong *dXShapeInfo, @@ -926,9 +1032,14 @@ void NativeOpExecutioner::execTransformAny(nd4j::LaunchContext *lc, if (shape::isEmpty(hXShapeInfo)) return; - dim3 launchDims(512, 512, 2048); + if (opNum == sd::transform::Assign && shape::order(hXShapeInfo) == shape::order(hZShapeInfo) && shape::order(hXShapeInfo) == 'c' && xType == zType && shape::elementWiseStride(hXShapeInfo) == 1 && shape::elementWiseStride(hZShapeInfo) == 1) { + cudaMemcpyAsync(dZ, dX, shape::length(hXShapeInfo) * sd::DataTypeUtils::sizeOfElement(xType), cudaMemcpyDeviceToDevice, *stream); + } + else { - BUILD_DOUBLE_SELECTOR(xType, zType, functions::transform::TransformAny, ::executeTransformShaped(launchDims, stream, opNum, dX, dXShapeInfo, xRank, extraParams, dZ, dZShapeInfo, zRank, nullptr, nullptr, nullptr, nullptr), LIBND4J_TYPES, LIBND4J_TYPES); + dim3 launchDims(512, 512, 2048); + BUILD_DOUBLE_SELECTOR(xType, zType, functions::transform::TransformAny, ::executeTransformShaped(launchDims, stream, opNum, dX, dXShapeInfo, xRank, extraParams, dZ, dZShapeInfo, zRank, nullptr, nullptr, nullptr, nullptr), LIBND4J_TYPES, LIBND4J_TYPES); + } // TODO: remove after the release auto res = cudaStreamSynchronize(*stream); @@ -937,7 +1048,7 @@ void NativeOpExecutioner::execTransformAny(nd4j::LaunchContext *lc, } //////////////////////////////////////////////////////////////////////// -void NativeOpExecutioner::execTransformStrict(nd4j::LaunchContext *lc, +void NativeOpExecutioner::execTransformStrict(sd::LaunchContext *lc, int opNum, void *hX, Nd4jLong *hXShapeInfo, void *dX, Nd4jLong *dXShapeInfo, @@ -971,7 +1082,7 @@ void NativeOpExecutioner::execTransformStrict(nd4j::LaunchContext *lc, } //////////////////////////////////////////////////////////////////////// -void NativeOpExecutioner::execTransformFloat(nd4j::LaunchContext *lc, +void NativeOpExecutioner::execTransformFloat(sd::LaunchContext *lc, int opNum, void *hX, Nd4jLong *hXShapeInfo, void *dX, Nd4jLong *dXShapeInfo, @@ -1005,7 +1116,7 @@ void NativeOpExecutioner::execTransformFloat(nd4j::LaunchContext *lc, //////////////////////////////////////////////////////////////////////// -void NativeOpExecutioner::execSummaryStats(nd4j::LaunchContext *lc, +void NativeOpExecutioner::execSummaryStats(sd::LaunchContext *lc, int opNum, void *hX, Nd4jLong *hXShapeInfo, void *dX, Nd4jLong *dXShapeInfo, @@ -1019,11 +1130,11 @@ void NativeOpExecutioner::execSummaryStats(nd4j::LaunchContext *lc, dim3 launchDims = dim3(256, 256, 32768); - auto xType = nd4j::ArrayOptions::dataType(hXShapeInfo); - auto zType = nd4j::ArrayOptions::dataType(hZShapeInfo); + auto xType = sd::ArrayOptions::dataType(hXShapeInfo); + auto zType = sd::ArrayOptions::dataType(hZShapeInfo); if (!DataTypeUtils::isR(zType)) - throw nd4j::datatype_exception::build("NativeOpExecutioner::execSummaryStats requires Z operand to have floating point data type", zType); + throw sd::datatype_exception::build("NativeOpExecutioner::execSummaryStats requires Z operand to have floating point data type", zType); BUILD_DOUBLE_SELECTOR(xType, zType, functions::summarystats::SummaryStatsReduce, ::execSummaryStatsReduce(launchDims, stream, opNum, dX, dXShapeInfo, hXShapeInfo, extraParams, dZ, dZShapeInfo, hZShapeInfo, nullptr, nullptr, biasCorrected, reductionPointer), LIBND4J_TYPES, FLOAT_TYPES); @@ -1034,7 +1145,7 @@ void NativeOpExecutioner::execSummaryStats(nd4j::LaunchContext *lc, } //////////////////////////////////////////////////////////////////////// -void NativeOpExecutioner::execSummaryStats(nd4j::LaunchContext *lc, +void NativeOpExecutioner::execSummaryStats(sd::LaunchContext *lc, int opNum, void *hX, Nd4jLong *hXShapeInfo, void *dX, Nd4jLong *dXShapeInfo, @@ -1049,11 +1160,11 @@ void NativeOpExecutioner::execSummaryStats(nd4j::LaunchContext *lc, dim3 launchDims = dim3(256, 256, 32768); - auto xType = nd4j::ArrayOptions::dataType(hXShapeInfo); - auto zType = nd4j::ArrayOptions::dataType(hZShapeInfo); + auto xType = sd::ArrayOptions::dataType(hXShapeInfo); + auto zType = sd::ArrayOptions::dataType(hZShapeInfo); if (!DataTypeUtils::isR(zType)) - throw nd4j::datatype_exception::build("NativeOpExecutioner::execSummaryStats requires Z operand to have floating point data type", zType); + throw sd::datatype_exception::build("NativeOpExecutioner::execSummaryStats requires Z operand to have floating point data type", zType); BUILD_DOUBLE_SELECTOR(xType, zType, functions::summarystats::SummaryStatsReduce, ::execSummaryStatsReduce(launchDims, stream, opNum, dX, dXShapeInfo, hXShapeInfo, extraParams, dZ, dZShapeInfo, hZShapeInfo, dimension, dimensionLength, tadShapeInfo, tadOffsets, biasCorrected, reductionPointer), LIBND4J_TYPES, FLOAT_TYPES); @@ -1065,7 +1176,7 @@ void NativeOpExecutioner::execSummaryStats(nd4j::LaunchContext *lc, //////////////////////////////////////////////////////////////////////// -void NativeOpExecutioner::execReduce3(nd4j::LaunchContext *lc, +void NativeOpExecutioner::execReduce3(sd::LaunchContext *lc, int opNum, void *hX, Nd4jLong *hXShapeInfo, void *dX, Nd4jLong *dXShapeInfo, @@ -1079,19 +1190,19 @@ void NativeOpExecutioner::execReduce3(nd4j::LaunchContext *lc, auto reductionPointer = lc->getReductionPointer(); auto allocationPointer = lc->getAllocationPointer(); - auto xType = nd4j::ArrayOptions::dataType(hXShapeInfo); - auto yType = nd4j::ArrayOptions::dataType(hYShapeInfo); - auto zType = nd4j::ArrayOptions::dataType(hZShapeInfo); + auto xType = sd::ArrayOptions::dataType(hXShapeInfo); + auto yType = sd::ArrayOptions::dataType(hYShapeInfo); + auto zType = sd::ArrayOptions::dataType(hZShapeInfo); auto blockWidth = 256; auto numBlocks = CudaLaunchHelper::getReductionBlocks(shape::length(hXShapeInfo), blockWidth); dim3 launchDims(numBlocks == 0 ? 1 : numBlocks, blockWidth, 32768); if (xType != yType) - throw nd4j::datatype_exception::build("NativeOpExecutioner::execReduce3 requires Y operand to have X type", xType, yType); + throw sd::datatype_exception::build("NativeOpExecutioner::execReduce3 requires Y operand to have X type", xType, yType); if (!DataTypeUtils::isR(zType)) - throw nd4j::datatype_exception::build("NativeOpExecutioner::execReduce3 requires Z operand to have floating point data type", zType); + throw sd::datatype_exception::build("NativeOpExecutioner::execReduce3 requires Z operand to have floating point data type", zType); BUILD_DOUBLE_SELECTOR(xType, zType, functions::reduce3::Reduce3, ::execScalar(launchDims, stream, opNum, dX, dXShapeInfo, dY, dYShapeInfo, extraParams, dZ, dZShapeInfo, allocationPointer, reductionPointer, nullptr), LIBND4J_TYPES, FLOAT_TYPES); @@ -1102,7 +1213,7 @@ void NativeOpExecutioner::execReduce3(nd4j::LaunchContext *lc, } //////////////////////////////////////////////////////////////////////// -void NativeOpExecutioner::execReduce3(nd4j::LaunchContext *lc, +void NativeOpExecutioner::execReduce3(sd::LaunchContext *lc, int opNum, void *hX, Nd4jLong *hXShapeInfo, void *dX, Nd4jLong *dXShapeInfo, @@ -1123,15 +1234,15 @@ void NativeOpExecutioner::execReduce3(nd4j::LaunchContext *lc, auto stream = lc->getCudaStream(); auto allocationPointer = lc->getAllocationPointer(); - auto xType = nd4j::ArrayOptions::dataType(hXShapeInfo); - auto yType = nd4j::ArrayOptions::dataType(hYShapeInfo); - auto zType = nd4j::ArrayOptions::dataType(hZShapeInfo); + auto xType = sd::ArrayOptions::dataType(hXShapeInfo); + auto yType = sd::ArrayOptions::dataType(hYShapeInfo); + auto zType = sd::ArrayOptions::dataType(hZShapeInfo); if (xType != yType) - throw nd4j::datatype_exception::build("NativeOpExecutioner::execReduce3 requires Y operand to have X type", xType, yType); + throw sd::datatype_exception::build("NativeOpExecutioner::execReduce3 requires Y operand to have X type", xType, yType); if (!DataTypeUtils::isR(zType)) - throw nd4j::datatype_exception::build("NativeOpExecutioner::execReduce3 requires Z operand to have floating point data type", zType); + throw sd::datatype_exception::build("NativeOpExecutioner::execReduce3 requires Z operand to have floating point data type", zType); auto numBlocks = shape::length(hZShapeInfo); @@ -1155,7 +1266,7 @@ void NativeOpExecutioner::execReduce3(nd4j::LaunchContext *lc, } //////////////////////////////////////////////////////////////////////// -void NativeOpExecutioner::execReduce3Scalar(nd4j::LaunchContext *lc, +void NativeOpExecutioner::execReduce3Scalar(sd::LaunchContext *lc, int opNum, void *hX, Nd4jLong *hXShapeInfo, void *dX, Nd4jLong *dXShapeInfo, @@ -1170,9 +1281,9 @@ void NativeOpExecutioner::execReduce3Scalar(nd4j::LaunchContext *lc, auto allocationPointer = lc->getAllocationPointer(); auto reductionPointer = lc->getReductionPointer(); - auto xType = nd4j::ArrayOptions::dataType(hXShapeInfo); - auto yType = nd4j::ArrayOptions::dataType(hYShapeInfo); - auto zType = nd4j::ArrayOptions::dataType(hZShapeInfo); + auto xType = sd::ArrayOptions::dataType(hXShapeInfo); + auto yType = sd::ArrayOptions::dataType(hYShapeInfo); + auto zType = sd::ArrayOptions::dataType(hZShapeInfo); auto xLength = shape::length(hXShapeInfo); auto blockWidth = 256; @@ -1180,10 +1291,10 @@ void NativeOpExecutioner::execReduce3Scalar(nd4j::LaunchContext *lc, dim3 launchDims(numBlocks == 0 ? 1 : numBlocks, blockWidth, 32768); if (xType != yType) - throw nd4j::datatype_exception::build("NativeOpExecutioner::execReduce3Scalar requires Y operand to have X type", xType, yType); + throw sd::datatype_exception::build("NativeOpExecutioner::execReduce3Scalar requires Y operand to have X type", xType, yType); if (!DataTypeUtils::isR(zType)) - throw nd4j::datatype_exception::build("NativeOpExecutioner::execReduce3Scalar requires Z operand to have floating point data type", zType); + throw sd::datatype_exception::build("NativeOpExecutioner::execReduce3Scalar requires Z operand to have floating point data type", zType); BUILD_DOUBLE_SELECTOR(xType, zType, functions::reduce3::Reduce3, ::execScalar(launchDims, stream, opNum, dX, dXShapeInfo, dY, dYShapeInfo, extraParams, dZ, dZShapeInfo, allocationPointer, reductionPointer, nullptr), LIBND4J_TYPES, FLOAT_TYPES); @@ -1195,7 +1306,7 @@ void NativeOpExecutioner::execReduce3Scalar(nd4j::LaunchContext *lc, //////////////////////////////////////////////////////////////////////// -void NativeOpExecutioner::execScalarBool(nd4j::LaunchContext *lc, +void NativeOpExecutioner::execScalarBool(sd::LaunchContext *lc, int opNum, void *hX, Nd4jLong *hXShapeInfo, void *dX, Nd4jLong *dXShapeInfo, @@ -1209,9 +1320,9 @@ void NativeOpExecutioner::execScalarBool(nd4j::LaunchContext *lc, dim3 launchDims = dim3(256, 512, 8192); - auto xType = nd4j::ArrayOptions::dataType(hXShapeInfo); - auto yType = nd4j::ArrayOptions::dataType(hScalarShapeInfo); - auto zType = nd4j::ArrayOptions::dataType(hZShapeInfo); + auto xType = sd::ArrayOptions::dataType(hXShapeInfo); + auto yType = sd::ArrayOptions::dataType(hScalarShapeInfo); + auto zType = sd::ArrayOptions::dataType(hZShapeInfo); if (shape::isEmpty(hXShapeInfo) || shape::isEmpty(hScalarShapeInfo)) return; @@ -1231,7 +1342,7 @@ void NativeOpExecutioner::execScalarBool(nd4j::LaunchContext *lc, } //////////////////////////////////////////////////////////////////////// -void NativeOpExecutioner::execScalarBool(nd4j::LaunchContext *lc, +void NativeOpExecutioner::execScalarBool(sd::LaunchContext *lc, int opNum, void *hX, Nd4jLong *hXShapeInfo, void *dX, Nd4jLong *dXShapeInfo, @@ -1248,9 +1359,9 @@ void NativeOpExecutioner::execScalarBool(nd4j::LaunchContext *lc, dim3 launchDims(256, 512, 8192); - auto xType = nd4j::ArrayOptions::dataType(hXShapeInfo); - auto yType = nd4j::ArrayOptions::dataType(hScalarShapeInfo); - auto zType = nd4j::ArrayOptions::dataType(hZShapeInfo); + auto xType = sd::ArrayOptions::dataType(hXShapeInfo); + auto yType = sd::ArrayOptions::dataType(hScalarShapeInfo); + auto zType = sd::ArrayOptions::dataType(hZShapeInfo); if (shape::isEmpty(hXShapeInfo) || shape::isEmpty(hScalarShapeInfo)) return; @@ -1270,7 +1381,7 @@ void NativeOpExecutioner::execScalarBool(nd4j::LaunchContext *lc, } //////////////////////////////////////////////////////////////////////// -void NativeOpExecutioner::execScalarInt(nd4j::LaunchContext *lc, +void NativeOpExecutioner::execScalarInt(sd::LaunchContext *lc, int opNum, void *hX, Nd4jLong *hXShapeInfo, void *dX, Nd4jLong *dXShapeInfo, @@ -1284,9 +1395,9 @@ void NativeOpExecutioner::execScalarInt(nd4j::LaunchContext *lc, dim3 launchDims = dim3(256, 512, 8192); - auto xType = nd4j::ArrayOptions::dataType(hXShapeInfo); - auto yType = nd4j::ArrayOptions::dataType(hScalarShapeInfo); - auto zType = nd4j::ArrayOptions::dataType(hZShapeInfo); + auto xType = sd::ArrayOptions::dataType(hXShapeInfo); + auto yType = sd::ArrayOptions::dataType(hScalarShapeInfo); + auto zType = sd::ArrayOptions::dataType(hZShapeInfo); if (shape::isEmpty(hXShapeInfo) || shape::isEmpty(hScalarShapeInfo)) return; @@ -1306,7 +1417,7 @@ void NativeOpExecutioner::execScalarInt(nd4j::LaunchContext *lc, } //////////////////////////////////////////////////////////////////////// -void NativeOpExecutioner::execScalarInt(nd4j::LaunchContext *lc, +void NativeOpExecutioner::execScalarInt(sd::LaunchContext *lc, int opNum, void *hX, Nd4jLong *hXShapeInfo, void *dX, Nd4jLong *dXShapeInfo, @@ -1323,9 +1434,9 @@ void NativeOpExecutioner::execScalarInt(nd4j::LaunchContext *lc, dim3 launchDims(256, 512, 8192); - auto xType = nd4j::ArrayOptions::dataType(hXShapeInfo); - auto yType = nd4j::ArrayOptions::dataType(hScalarShapeInfo); - auto zType = nd4j::ArrayOptions::dataType(hZShapeInfo); + auto xType = sd::ArrayOptions::dataType(hXShapeInfo); + auto yType = sd::ArrayOptions::dataType(hScalarShapeInfo); + auto zType = sd::ArrayOptions::dataType(hZShapeInfo); if (shape::isEmpty(hXShapeInfo) || shape::isEmpty(hScalarShapeInfo)) return; @@ -1345,7 +1456,7 @@ void NativeOpExecutioner::execScalarInt(nd4j::LaunchContext *lc, } //////////////////////////////////////////////////////////////////////// -void NativeOpExecutioner::execScalar(nd4j::LaunchContext *lc, +void NativeOpExecutioner::execScalar(sd::LaunchContext *lc, int opNum, void *hX, Nd4jLong *hXShapeInfo, void *dX, Nd4jLong *dXShapeInfo, @@ -1359,9 +1470,9 @@ void NativeOpExecutioner::execScalar(nd4j::LaunchContext *lc, dim3 launchDims(256, 512, 8192); - auto xType = nd4j::ArrayOptions::dataType(hXShapeInfo); - auto yType = nd4j::ArrayOptions::dataType(hScalarShapeInfo); - auto zType = nd4j::ArrayOptions::dataType(hZShapeInfo); + auto xType = sd::ArrayOptions::dataType(hXShapeInfo); + auto yType = sd::ArrayOptions::dataType(hScalarShapeInfo); + auto zType = sd::ArrayOptions::dataType(hZShapeInfo); if (shape::isEmpty(hXShapeInfo) || shape::isEmpty(hScalarShapeInfo)) return; @@ -1380,7 +1491,7 @@ void NativeOpExecutioner::execScalar(nd4j::LaunchContext *lc, } //////////////////////////////////////////////////////////////////////// -void NativeOpExecutioner::execScalar(nd4j::LaunchContext *lc, +void NativeOpExecutioner::execScalar(sd::LaunchContext *lc, int opNum, void *hX, Nd4jLong *hXShapeInfo, void *dX, Nd4jLong *dXShapeInfo, @@ -1395,9 +1506,9 @@ void NativeOpExecutioner::execScalar(nd4j::LaunchContext *lc, auto stream = lc->getCudaStream(); - auto xType = nd4j::ArrayOptions::dataType(hXShapeInfo); - auto yType = nd4j::ArrayOptions::dataType(hScalarShapeInfo); - auto zType = nd4j::ArrayOptions::dataType(hZShapeInfo); + auto xType = sd::ArrayOptions::dataType(hXShapeInfo); + auto yType = sd::ArrayOptions::dataType(hScalarShapeInfo); + auto zType = sd::ArrayOptions::dataType(hZShapeInfo); if (shape::isEmpty(hXShapeInfo) || shape::isEmpty(hScalarShapeInfo)) return; @@ -1417,7 +1528,7 @@ void NativeOpExecutioner::execScalar(nd4j::LaunchContext *lc, } //////////////////////////////////////////////////////////////////////// -void NativeOpExecutioner::execRandom(nd4j::LaunchContext *lc, +void NativeOpExecutioner::execRandom(sd::LaunchContext *lc, int opNum, Nd4jPointer stateHost, void *hZ, Nd4jLong *hZShapeInfo, @@ -1425,7 +1536,7 @@ void NativeOpExecutioner::execRandom(nd4j::LaunchContext *lc, void *extraArguments) { auto stream = lc->getCudaStream(); - auto sizeOf = sizeof(nd4j::graph::RandomGenerator); + auto sizeOf = sizeof(sd::graph::RandomGenerator); Nd4jPointer stateDevice; cudaError_t res = cudaMalloc(reinterpret_cast(&stateDevice), sizeOf); @@ -1433,9 +1544,9 @@ void NativeOpExecutioner::execRandom(nd4j::LaunchContext *lc, checkCudaErrors(cudaMemcpyAsync(stateDevice, stateHost, sizeOf, cudaMemcpyHostToDevice, *stream)); dim3 launchDims = dim3(512, 512, 32768); - auto zType = nd4j::ArrayOptions::dataType(hZShapeInfo); + auto zType = sd::ArrayOptions::dataType(hZShapeInfo); - auto rng = reinterpret_cast(stateHost); + auto rng = reinterpret_cast(stateHost); // functions::random::RandomFunction::executeCudaSingle(launchDims, extraPointers, opNum, stateHost, dZ, dZShapeInfo, extraArguments), BUILD_SINGLE_SELECTOR(zType, functions::random::RandomFunction, ::executeCudaSingle(launchDims, stream, opNum, stateDevice, dZ, dZShapeInfo, extraArguments), FLOAT_TYPES); @@ -1450,7 +1561,7 @@ void NativeOpExecutioner::execRandom(nd4j::LaunchContext *lc, } //////////////////////////////////////////////////////////////////////// -void NativeOpExecutioner::execRandom(nd4j::LaunchContext *lc, +void NativeOpExecutioner::execRandom(sd::LaunchContext *lc, int opNum, Nd4jPointer stateHost, void *hX, Nd4jLong *hXShapeInfo, @@ -1461,17 +1572,17 @@ void NativeOpExecutioner::execRandom(nd4j::LaunchContext *lc, auto stream = lc->getCudaStream(); - auto sizeOf = sizeof(nd4j::graph::RandomGenerator); + auto sizeOf = sizeof(sd::graph::RandomGenerator); Nd4jPointer stateDevice; cudaError_t res = cudaMalloc(reinterpret_cast(&stateDevice), sizeOf); checkCudaErrors(cudaStreamSynchronize(*stream)); checkCudaErrors(cudaMemcpyAsync(stateDevice, stateHost, sizeOf, cudaMemcpyHostToDevice, *stream)); - auto rng = reinterpret_cast(stateHost); + auto rng = reinterpret_cast(stateHost); dim3 launchDims = dim3(512, 512, 32768); - auto xType = nd4j::ArrayOptions::dataType(hZShapeInfo); + auto xType = sd::ArrayOptions::dataType(hZShapeInfo); // functions::random::RandomFunction::executeCudaDouble(launchDims, extraPointers, opNum, stateHost, dX, dXShapeInfo, dZ, dZShapeInfo, extraArguments); BUILD_SINGLE_SELECTOR(xType, functions::random::RandomFunction, ::executeCudaDouble(launchDims, stream, opNum, stateDevice, dX, dXShapeInfo, dZ, dZShapeInfo, extraArguments), FLOAT_TYPES); @@ -1485,7 +1596,7 @@ void NativeOpExecutioner::execRandom(nd4j::LaunchContext *lc, } //////////////////////////////////////////////////////////////////////// -void NativeOpExecutioner::execRandom(nd4j::LaunchContext *lc, +void NativeOpExecutioner::execRandom(sd::LaunchContext *lc, int opNum, Nd4jPointer stateHost, void *hX, Nd4jLong *hXShapeInfo, @@ -1497,17 +1608,17 @@ void NativeOpExecutioner::execRandom(nd4j::LaunchContext *lc, void *extraArguments) { auto stream = lc->getCudaStream(); - auto sizeOf = sizeof(nd4j::graph::RandomGenerator); + auto sizeOf = sizeof(sd::graph::RandomGenerator); Nd4jPointer stateDevice; cudaError_t res = cudaMalloc(reinterpret_cast(&stateDevice), sizeOf); checkCudaErrors(cudaStreamSynchronize(*stream)); checkCudaErrors(cudaMemcpyAsync(stateDevice, stateHost, sizeOf, cudaMemcpyHostToDevice, *stream)); - auto rng = reinterpret_cast(stateHost); + auto rng = reinterpret_cast(stateHost); dim3 launchDims = dim3(512, 512, 32768); - auto xType = nd4j::ArrayOptions::dataType(hZShapeInfo); + auto xType = sd::ArrayOptions::dataType(hZShapeInfo); // functions::random::RandomFunction::executeCudaTriple(launchDims, extraPointers, opNum, stateHost, dX, dXShapeInfo, dY, dYShapeInfo, dZ, dZShapeInfo, extraArguments); BUILD_SINGLE_SELECTOR(xType, functions::random::RandomFunction, ::executeCudaTriple(launchDims, stream, opNum, stateDevice, dX, dXShapeInfo, dY, dYShapeInfo, dZ, dZShapeInfo, extraArguments), FLOAT_TYPES); @@ -1521,7 +1632,7 @@ void NativeOpExecutioner::execRandom(nd4j::LaunchContext *lc, } //////////////////////////////////////////////////////////////////////// -void NativeOpExecutioner::execReduce3All(nd4j::LaunchContext *lc, +void NativeOpExecutioner::execReduce3All(sd::LaunchContext *lc, int opNum, void *hX, Nd4jLong *hXShapeInfo, void *dX, Nd4jLong *dXShapeInfo, @@ -1538,20 +1649,20 @@ void NativeOpExecutioner::execReduce3All(nd4j::LaunchContext *lc, auto allocationPointer = lc->getAllocationPointer(); auto reductionPointer = lc->getReductionPointer(); - if (nd4j::Environment::getInstance()->isDebugAndVerbose()) + if (sd::Environment::getInstance()->isDebugAndVerbose()) printf("D119 opNum:[%i]\n", opNum); dim3 launchDims(shape::length(hZShapeInfo), 256, 32768); - if (nd4j::Environment::getInstance()->isVerbose() && launchDims.x == 1) + if (sd::Environment::getInstance()->isVerbose() && launchDims.x == 1) printf("AD119 opNum:[%i]\n", opNum); - auto xType = nd4j::ArrayOptions::dataType(hXShapeInfo); - auto yType = nd4j::ArrayOptions::dataType(hYShapeInfo); - auto zType = nd4j::ArrayOptions::dataType(hZShapeInfo); + auto xType = sd::ArrayOptions::dataType(hXShapeInfo); + auto yType = sd::ArrayOptions::dataType(hYShapeInfo); + auto zType = sd::ArrayOptions::dataType(hZShapeInfo); if (yType != xType) - throw nd4j::datatype_exception::build("NativeOpExecutioner::execReduce3All both operands must have same data type", xType, yType); + throw sd::datatype_exception::build("NativeOpExecutioner::execReduce3All both operands must have same data type", xType, yType); BUILD_DOUBLE_SELECTOR(xType, zType, functions::reduce3::Reduce3, ::execAll(launchDims, stream, opNum, dX, dXShapeInfo, dY, dYShapeInfo, extraParamsVals, dZ, dZShapeInfo, dimension, dimensionLength, 1, allocationPointer, xTadShapeInfo, xOffsets, yTadShapeInfo, yOffsets), LIBND4J_TYPES, FLOAT_TYPES); @@ -1563,7 +1674,7 @@ void NativeOpExecutioner::execReduce3All(nd4j::LaunchContext *lc, //////////////////////////////////////////////////////////////////////// -void NativeOpExecutioner::execReduce3TAD(nd4j::LaunchContext *lc, +void NativeOpExecutioner::execReduce3TAD(sd::LaunchContext *lc, int opNum, void *hX, Nd4jLong *hXShapeInfo, void *dX, Nd4jLong *dXShapeInfo, @@ -1584,15 +1695,15 @@ void NativeOpExecutioner::execReduce3TAD(nd4j::LaunchContext *lc, auto stream = lc->getCudaStream(); auto allocationPointer = lc->getAllocationPointer(); - auto xType = nd4j::ArrayOptions::dataType(hXShapeInfo); - auto yType = nd4j::ArrayOptions::dataType(hYShapeInfo); - auto zType = nd4j::ArrayOptions::dataType(hZShapeInfo); + auto xType = sd::ArrayOptions::dataType(hXShapeInfo); + auto yType = sd::ArrayOptions::dataType(hYShapeInfo); + auto zType = sd::ArrayOptions::dataType(hZShapeInfo); if (xType != yType) - throw nd4j::datatype_exception::build("NativeOpExecutioner::execReduce3TAD requires Y operand to have X type", xType, yType); + throw sd::datatype_exception::build("NativeOpExecutioner::execReduce3TAD requires Y operand to have X type", xType, yType); if (!DataTypeUtils::isR(zType)) - throw nd4j::datatype_exception::build("NativeOpExecutioner::execReduce3TAD requires Z operand to have floating point data type", zType); + throw sd::datatype_exception::build("NativeOpExecutioner::execReduce3TAD requires Z operand to have floating point data type", zType); auto numBlocks = shape::length(hZShapeInfo); dim3 launchDims(numBlocks == 0 ? 1 : numBlocks, 256, 32768); diff --git a/libnd4j/blas/cuda/NativeOps.cu b/libnd4j/include/legacy/cuda/NativeOps.cu similarity index 75% rename from libnd4j/blas/cuda/NativeOps.cu rename to libnd4j/include/legacy/cuda/NativeOps.cu index 07ce876ea..1a4de3de5 100755 --- a/libnd4j/blas/cuda/NativeOps.cu +++ b/libnd4j/include/legacy/cuda/NativeOps.cu @@ -15,11 +15,11 @@ ******************************************************************************/ -#include "NativeOpExecutioner.h" -#include "../NativeOps.h" +#include +#include #include -#include +#include #include @@ -29,25 +29,25 @@ #include #include #include -#include +#include #include #include #include -#include +#include #include #include #include -#include +#include //#include #include -#include +#include #include -using namespace nd4j; +using namespace sd; #include #include @@ -122,18 +122,18 @@ int getDeviceSharedThreshold(int deviceId) { -nd4j::buffer::Buffer * createScalarBuffer(cudaStream_t stream) { +sd::buffer::Buffer * createScalarBuffer(cudaStream_t stream) { Nd4jLong *scalarShapeInfo = shape::createScalarShapeInfo(); - nd4j::buffer::Buffer *buff = nd4j::buffer::createBuffer(scalarShapeInfo,shape::shapeInfoLength(2), stream); - nd4j::buffer::copyDataToGpu(&buff, stream); + sd::buffer::Buffer *buff = sd::buffer::createBuffer(scalarShapeInfo,shape::shapeInfoLength(2), stream); + sd::buffer::copyDataToGpu(&buff, stream); return buff; } class ScalarShapeInformation { private: - nd4j::buffer::Buffer *scalarDimension; - nd4j::buffer::Buffer *scalarShapeInfo; + sd::buffer::Buffer *scalarDimension; + sd::buffer::Buffer *scalarShapeInfo; // std::thread::id threadId; public: @@ -143,14 +143,14 @@ public: CHECK_ALLOC(scalarDimensionBuff, "Failed to allocate ShapeInfoBuffer", sizeof(Nd4jLong)); scalarDimensionBuff[0] = MAX_DIMENSION; - scalarDimension = nd4j::buffer::createBuffer(scalarDimensionBuff,1, stream); + scalarDimension = sd::buffer::createBuffer(scalarDimensionBuff,1, stream); scalarShapeInfo = createScalarBuffer(stream); // threadId = std::this_thread::get_id(); } ~ScalarShapeInformation() { - nd4j::buffer::freeBuffer(&scalarShapeInfo); - nd4j::buffer::freeBuffer(&scalarDimension); + sd::buffer::freeBuffer(&scalarShapeInfo); + sd::buffer::freeBuffer(&scalarDimension); } @@ -178,7 +178,7 @@ public: template class ScalarInfo { - nd4j::buffer::Buffer *scalarData; + sd::buffer::Buffer *scalarData; ScalarShapeInformation *shapeInfo; T finalResult; cudaStream_t streamRef; @@ -189,13 +189,13 @@ public: CHECK_ALLOC(scalarResult, "Failed to allocate new scalar buffer", sizeof(T)); shapeInfo = new ScalarShapeInformation(stream); - scalarData = nd4j::buffer::createBuffer(scalarResult,1, stream); + scalarData = sd::buffer::createBuffer(scalarResult,1, stream); streamRef = stream; - nd4j::buffer::copyDataToGpu(&scalarData, stream); + sd::buffer::copyDataToGpu(&scalarData, stream); } T getFinalResultFromDevice() { - nd4j::buffer::copyDataFromGpu(&scalarData, streamRef); + sd::buffer::copyDataFromGpu(&scalarData, streamRef); return scalarData->data[0]; } @@ -222,7 +222,7 @@ public: } ~ScalarInfo() { - nd4j::buffer::freeBuffer(&scalarData); + sd::buffer::freeBuffer(&scalarData); delete shapeInfo; } }; @@ -243,8 +243,8 @@ void execPairwiseTransform( Nd4jPointer *extraPointers, InteropDataBuffer::registerSpecialUse({dbZ}, {dbX, dbY}); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); } } @@ -267,8 +267,8 @@ void execPairwiseTransformBool(Nd4jPointer *extraPointers, InteropDataBuffer::registerSpecialUse({dbZ}, {dbX, dbY}); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); } } @@ -291,8 +291,8 @@ void execSummaryStatsScalar(Nd4jPointer *extraPointers, InteropDataBuffer::registerSpecialUse({dbZ}, {dbX}); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); } } @@ -328,8 +328,8 @@ void execBroadcastBool(Nd4jPointer *extraPointers, InteropDataBuffer::registerSpecialUse({dbZ}, {dbX, dbY}); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); } } @@ -367,9 +367,9 @@ void execBroadcast( auto tadOnlyShapeInfoZ = reinterpret_cast(extraPointers[12]); auto tadOffsetsZ = reinterpret_cast(extraPointers[13]); - auto xType = nd4j::ArrayOptions::dataType(hXShapeInfo); - auto yType = nd4j::ArrayOptions::dataType(hYShapeInfo); - auto zType = nd4j::ArrayOptions::dataType(hZShapeInfo); + auto xType = sd::ArrayOptions::dataType(hXShapeInfo); + auto yType = sd::ArrayOptions::dataType(hYShapeInfo); + auto zType = sd::ArrayOptions::dataType(hZShapeInfo); LaunchContext lc(extraPointers[1], extraPointers[4], extraPointers[5], extraPointers[3]); NativeOpExecutioner::execBroadcast(&lc, opNum, @@ -381,8 +381,8 @@ void execBroadcast( InteropDataBuffer::registerSpecialUse({dbZ}, {dbX, dbY}); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); } } @@ -413,8 +413,8 @@ void execReduceFloat(Nd4jPointer *extraPointers, InteropDataBuffer::registerSpecialUse({dbZ}, {dbX}); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); } } @@ -435,8 +435,8 @@ void execReduceSame(Nd4jPointer *extraPointers, InteropDataBuffer::registerSpecialUse({dbZ}, {dbX}); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); } } @@ -454,7 +454,7 @@ void execReduceSame2(Nd4jPointer *extraPointers, auto dimension = reinterpret_cast(dbDimension->primary()); int dimensionLength = static_cast(shape::length(hDimensionShape)); - auto tadPack = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(hXShapeInfo, + auto tadPack = sd::ConstantTadHelper::getInstance()->tadForDimensions(hXShapeInfo, dimension, shape::length(hDimensionShape)); @@ -468,8 +468,8 @@ void execReduceSame2(Nd4jPointer *extraPointers, InteropDataBuffer::registerSpecialUse({dbZ}, {dbX}); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); } } @@ -487,7 +487,7 @@ void execReduceLong2(Nd4jPointer *extraPointers, auto dimension = reinterpret_cast(dbDimension->primary()); int dimensionLength = static_cast(shape::length(hDimensionShape)); - auto tadPack = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(hXShapeInfo, + auto tadPack = sd::ConstantTadHelper::getInstance()->tadForDimensions(hXShapeInfo, dimension, shape::length(hDimensionShape)); @@ -501,8 +501,8 @@ void execReduceLong2(Nd4jPointer *extraPointers, InteropDataBuffer::registerSpecialUse({dbZ}, {dbX}); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); } } @@ -521,11 +521,11 @@ void execReduceLong(Nd4jPointer *extraPointers, auto reductionPointer = reinterpret_cast(extraPointers[4]); - auto xType = nd4j::ArrayOptions::dataType(hXShapeInfo); - auto zType = nd4j::ArrayOptions::dataType(hZShapeInfo); + auto xType = sd::ArrayOptions::dataType(hXShapeInfo); + auto zType = sd::ArrayOptions::dataType(hZShapeInfo); - if (zType != nd4j::DataType::INT64) - throw datatype_exception::build("execReduceLong wrong Z data type", nd4j::DataType::INT64, zType); + if (zType != sd::DataType::INT64) + throw datatype_exception::build("execReduceLong wrong Z data type", sd::DataType::INT64, zType); auto xLength = shape::length(hXShapeInfo); auto blockWidth = 256; @@ -539,12 +539,12 @@ void execReduceLong(Nd4jPointer *extraPointers, dbZ->special(), ConstantShapeHelper::getInstance()->bufferForShapeInfo(hZShapeInfo).specialAsT(), hXShapeInfo, nullptr, 0, reductionPointer, dTADShapeInfo), LIBND4J_TYPES, LONG_TYPES); - nd4j::DebugHelper::checkErrorCode(stream, "execReduceLong(...) failed"); + sd::DebugHelper::checkErrorCode(stream, "execReduceLong(...) failed"); InteropDataBuffer::registerSpecialUse({dbZ}, {dbX}); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); } } @@ -562,7 +562,7 @@ void execReduceBool2(Nd4jPointer *extraPointers, auto dimension = reinterpret_cast(dbDimension->primary()); int dimensionLength = static_cast(shape::length(hDimensionShape)); - auto tadPack = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(hXShapeInfo, + auto tadPack = sd::ConstantTadHelper::getInstance()->tadForDimensions(hXShapeInfo, dimension, shape::length(hDimensionShape)); @@ -576,8 +576,8 @@ void execReduceBool2(Nd4jPointer *extraPointers, InteropDataBuffer::registerSpecialUse({dbZ}, {dbX}); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); } } @@ -596,10 +596,10 @@ void execReduceBool(Nd4jPointer *extraPointers, auto reductionPointer = reinterpret_cast(extraPointers[4]); - auto xType = nd4j::ArrayOptions::dataType(hXShapeInfo); - auto zType = nd4j::ArrayOptions::dataType(hZShapeInfo); + auto xType = sd::ArrayOptions::dataType(hXShapeInfo); + auto zType = sd::ArrayOptions::dataType(hZShapeInfo); - if (zType != nd4j::DataType::BOOL) + if (zType != sd::DataType::BOOL) throw std::runtime_error("execReduceBool requires Z operand to have BOOL type"); auto xLength = shape::length(hXShapeInfo); @@ -614,12 +614,12 @@ void execReduceBool(Nd4jPointer *extraPointers, dbZ->special(), ConstantShapeHelper::getInstance()->bufferForShapeInfo(hZShapeInfo).specialAsT(), hZShapeInfo, nullptr, 0, reductionPointer, dTADShapeInfo), LIBND4J_TYPES, BOOL_TYPES); - nd4j::DebugHelper::checkErrorCode(stream, "execReduceBool(...) failed"); + sd::DebugHelper::checkErrorCode(stream, "execReduceBool(...) failed"); InteropDataBuffer::registerSpecialUse({dbZ}, {dbX}); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); } } @@ -648,7 +648,7 @@ void execIndexReduce(Nd4jPointer *extraPointers, auto dimension = reinterpret_cast(dbDimension->primary()); int dimensionLength = static_cast(shape::length(hDimensionShape)); - auto tadPack = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(hXShapeInfo, + auto tadPack = sd::ConstantTadHelper::getInstance()->tadForDimensions(hXShapeInfo, dimension, shape::length(hDimensionShape)); @@ -662,8 +662,8 @@ void execIndexReduce(Nd4jPointer *extraPointers, InteropDataBuffer::registerSpecialUse({dbZ}, {dbX}); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); } } @@ -690,7 +690,7 @@ void execReduceFloat2(Nd4jPointer *extraPointers, auto dimension = reinterpret_cast(dbDimension->primary()); int dimensionLength = static_cast(shape::length(hDimensionShape)); - auto tadPack = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(hXShapeInfo, + auto tadPack = sd::ConstantTadHelper::getInstance()->tadForDimensions(hXShapeInfo, dimension, shape::length(hDimensionShape)); @@ -704,8 +704,8 @@ void execReduceFloat2(Nd4jPointer *extraPointers, InteropDataBuffer::registerSpecialUse({dbZ}, {dbX}); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); } } @@ -734,8 +734,8 @@ void execIndexReduceScalar( InteropDataBuffer::registerSpecialUse({dbZ}, {dbX}); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); } } @@ -759,8 +759,8 @@ void execTransformSame(Nd4jPointer *extraPointers,int opNum, InteropDataBuffer::registerSpecialUse({dbZ}, {dbX}); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); } } @@ -784,8 +784,8 @@ void execTransformBool(Nd4jPointer *extraPointers,int opNum, InteropDataBuffer::registerSpecialUse({dbZ}, {dbX}); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); } } @@ -810,8 +810,8 @@ void execTransformAny(Nd4jPointer *extraPointers,int opNum, InteropDataBuffer::registerSpecialUse({dbZ}, {dbX}); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); } } @@ -835,8 +835,8 @@ void execTransformStrict(Nd4jPointer *extraPointers,int opNum, InteropDataBuffer::registerSpecialUse({dbZ}, {dbX}); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); } } @@ -860,8 +860,8 @@ void execTransformFloat(Nd4jPointer *extraPointers,int opNum, InteropDataBuffer::registerSpecialUse({dbZ}, {dbX}); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); } } @@ -939,7 +939,7 @@ void enableP2P(bool enable) { cudaDeviceDisablePeerAccess(dY); } } else { - if (nd4j::Environment::getInstance()->isVerbose()) printf("Peer access [%i] -> [%i] isn't possible\n", dX, dY); + if (sd::Environment::getInstance()->isVerbose()) printf("Peer access [%i] -> [%i] isn't possible\n", dX, dY); } } } @@ -977,13 +977,13 @@ void initializeDevicesAndFunctions() { if (supportedP2P && devCnt > 1) enableP2P(allowedP2P); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); } } void initializeFunctions(Nd4jPointer *functions) { - nd4j::BlasHelper::getInstance()->initializeDeviceFunctions(functions); + sd::BlasHelper::getInstance()->initializeDeviceFunctions(functions); /* cublasSgemv = (CublasSgemv)functions[0]; cublasDgemv = (CublasDgemv)functions[1]; @@ -1010,8 +1010,8 @@ Nd4jPointer mallocHost(Nd4jLong memorySize, int flags) { // cudaHostAllocMapped |cudaHostAllocPortable auto res = cudaHostAlloc(reinterpret_cast(&pointer), memorySize + 8, cudaHostAllocDefault); if (res != 0) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(res); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage("cudaHostAlloc failed"); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(res); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage("cudaHostAlloc failed"); } return reinterpret_cast(pointer); @@ -1029,8 +1029,8 @@ Nd4jPointer mallocDevice(Nd4jLong memorySize, int deviceId, int flags) { Nd4jPointer pointer; auto res = cudaMalloc(reinterpret_cast(&pointer), memorySize + 8); if (res != 0) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(res); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage("cudaMalloc failed"); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(res); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage("cudaMalloc failed"); } return reinterpret_cast(pointer); @@ -1044,8 +1044,8 @@ Nd4jPointer mallocDevice(Nd4jLong memorySize, int deviceId, int flags) { int freeHost(Nd4jPointer pointer) { auto res = cudaFreeHost(reinterpret_cast(pointer)); if (res != 0) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(res); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage("cudaFreeHost failed"); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(res); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage("cudaFreeHost failed"); } return 1L; @@ -1062,8 +1062,8 @@ int freeDevice(Nd4jPointer pointer, int deviceId) { // we're intentionally skipping if (res != 0 && res != 1) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(res); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage("cudaFree failed"); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(res); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage("cudaFree failed"); } return res == 0 ? 1L : 0L; @@ -1079,8 +1079,8 @@ Nd4jPointer createStream() { auto stream = new cudaStream_t(); auto dZ = cudaStreamCreate(stream); if (dZ != 0) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(dZ); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage("cudaStreamCreate failed"); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(dZ); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage("cudaStreamCreate failed"); } return stream; @@ -1093,8 +1093,8 @@ Nd4jPointer createEvent() { auto dZ = cudaEventCreateWithFlags(reinterpret_cast(&nativeEvent), cudaEventDisableTiming); if (dZ != 0) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(dZ); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage("cudaEventCreateWithFlags failed"); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(dZ); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage("cudaEventCreateWithFlags failed"); } return nativeEvent; @@ -1106,8 +1106,8 @@ int registerEvent(Nd4jPointer event, Nd4jPointer stream) { auto dZ = cudaEventRecord(*pEvent, *pStream); if (dZ != 0) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(dZ); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage("cudaEventRecord failed"); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(dZ); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage("cudaEventRecord failed"); } return 1; @@ -1189,8 +1189,8 @@ int memcpySync(Nd4jPointer dst, Nd4jPointer src, Nd4jLong size, int flags, Nd4jP } break; default: { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage("UNDEFNED MEMCPY"); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage("UNDEFNED MEMCPY"); return 0; } } @@ -1200,8 +1200,8 @@ int memcpySync(Nd4jPointer dst, Nd4jPointer src, Nd4jLong size, int flags, Nd4jP printf("Failed on [%p] -> [%p], size: [%i], direction: [%i], dZ: [%i]\n", src, dst, size, flags, static_cast(dZ)); fflush(stdout); fflush(stderr); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(dZ); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage("cudaMemcpy failed"); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(dZ); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage("cudaMemcpy failed"); return 0; } @@ -1213,7 +1213,7 @@ int memcpyAsync(Nd4jPointer dst, Nd4jPointer src, Nd4jLong size, int flags, Nd4j cudaMemcpyKind kind; - //nd4j::DebugHelper::checkErrorCode(pStream, "Preliminary sync failed"); + //sd::DebugHelper::checkErrorCode(pStream, "Preliminary sync failed"); switch (flags) { case 0: { @@ -1233,8 +1233,8 @@ int memcpyAsync(Nd4jPointer dst, Nd4jPointer src, Nd4jLong size, int flags, Nd4j } break; default: { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage("UNDEFNED MEMCPY"); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage("UNDEFNED MEMCPY"); return 0; } } @@ -1245,8 +1245,8 @@ int memcpyAsync(Nd4jPointer dst, Nd4jPointer src, Nd4jLong size, int flags, Nd4j printf("Failed on [%p] -> [%p], size: [%i], direction: [%i], dZ: [%i]\n", src, dst, size, flags, static_cast(dZ)); fflush(stdout); fflush(stderr); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(dZ); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage("cudaMemcpyAsync failed"); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(dZ); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage("cudaMemcpyAsync failed"); return 0; } @@ -1256,8 +1256,8 @@ int memcpyAsync(Nd4jPointer dst, Nd4jPointer src, Nd4jLong size, int flags, Nd4j int memsetSync(Nd4jPointer dst, int value, Nd4jLong size, int flags, Nd4jPointer reserved) { auto dZ = cudaMemset(reinterpret_cast(dst), value, static_cast(size)); if (dZ != 0) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(dZ); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage("cudaMemset failed"); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(dZ); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage("cudaMemset failed"); } return 1; @@ -1268,8 +1268,8 @@ int memsetAsync(Nd4jPointer dst, int value, Nd4jLong size, int flags, Nd4jPointe auto dZ = cudaMemsetAsync(reinterpret_cast(dst), value, static_cast(size), *pStream); if (dZ != 0) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(dZ); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage("cudaMemsetAsync failed"); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(dZ); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage("cudaMemsetAsync failed"); } return 1; @@ -1279,8 +1279,8 @@ int destroyEvent(Nd4jPointer event) { auto pEvent = reinterpret_cast(&event); auto dZ = cudaEventDestroy(*pEvent); if (dZ != 0) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(dZ); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage("cudaEventDestroy failed"); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(dZ); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage("cudaEventDestroy failed"); } return 1; @@ -1291,8 +1291,8 @@ int streamSynchronize(Nd4jPointer stream) { auto dZ = cudaStreamSynchronize(*pStream); if (dZ != 0) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(dZ); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage("cudaStreamSynchronize failed"); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(dZ); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage("cudaStreamSynchronize failed"); } return 1L; @@ -1303,8 +1303,8 @@ int eventSynchronize(Nd4jPointer event) { auto dZ = cudaEventSynchronize(*pEvent); if (dZ != 0) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(dZ); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage("cudaEventSynchronize failed"); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(dZ); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage("cudaEventSynchronize failed"); } return 1L; @@ -1317,7 +1317,7 @@ int getAvailableDevices() { } void enableDebugMode(bool reallyEnable) { - nd4j::Environment::getInstance()->setDebug(reallyEnable); + sd::Environment::getInstance()->setDebug(reallyEnable); } void setGridLimit(int gridSize) { @@ -1345,7 +1345,7 @@ void setOmpNumThreads(int threads) { } void enableVerboseMode(bool reallyEnable) { - nd4j::Environment::getInstance()->setVerbose(reallyEnable); + sd::Environment::getInstance()->setVerbose(reallyEnable); } int getDeviceMajor(int device) { @@ -1370,12 +1370,12 @@ void specialConcat( void *dZ, Nd4jLong *dZShapeInfo, Nd4jPointer *tadPointers, Nd4jPointer *offsetPointers) { try { - BUILD_SINGLE_SELECTOR(ArrayOptions::dataType(dZShapeInfo), nd4j::SpecialMethods, + BUILD_SINGLE_SELECTOR(ArrayOptions::dataType(dZShapeInfo), sd::SpecialMethods, ::concatCpuGeneric(dimension, numArrays, data, inputShapeInfo, dZ, dZShapeInfo), LIBND4J_TYPES); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); } } @@ -1383,34 +1383,34 @@ void specialConcat( /** * This method saves */ -nd4j::TadPack* tadOnlyShapeInfo(Nd4jLong *dXShapeInfo, int *dimension, int dimensionLength) { +sd::TadPack* tadOnlyShapeInfo(Nd4jLong *dXShapeInfo, int *dimension, int dimensionLength) { try { auto pack = new TadPack(); - *pack = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(dXShapeInfo, dimension, dimensionLength); + *pack = sd::ConstantTadHelper::getInstance()->tadForDimensions(dXShapeInfo, dimension, dimensionLength); return pack; } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); return nullptr; } } -Nd4jLong* getPrimaryShapeInfo(nd4j::TadPack* pack) { +Nd4jLong* getPrimaryShapeInfo(sd::TadPack* pack) { return pack->primaryShapeInfo(); } -Nd4jLong* getPrimaryOffsets(nd4j::TadPack* pack) { +Nd4jLong* getPrimaryOffsets(sd::TadPack* pack) { return pack->primaryOffsets(); } -Nd4jLong* getSpecialShapeInfo(nd4j::TadPack* pack) { +Nd4jLong* getSpecialShapeInfo(sd::TadPack* pack) { return pack->specialShapeInfo(); } -Nd4jLong* getSpecialOffsets(nd4j::TadPack* pack) { +Nd4jLong* getSpecialOffsets(sd::TadPack* pack) { return pack->specialOffsets(); } -Nd4jLong getNumberOfTads(nd4j::TadPack* pack) { +Nd4jLong getNumberOfTads(sd::TadPack* pack) { return pack->numberOfTads(); } -int getShapeInfoLength(nd4j::TadPack* pack) { +int getShapeInfoLength(sd::TadPack* pack) { return pack->shapeInfoLength(); } @@ -1440,8 +1440,8 @@ int memcpyConstantAsync(Nd4jLong dst, Nd4jPointer src, Nd4jLong size, int flags, } auto dZ = cudaMemcpyToSymbolAsync(deviceConstantMemory, const_cast(src), size, dst, kind, *pStream); if (dZ != 0) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(dZ); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage("cudaMemcpyToSymbolAsync failed"); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(dZ); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage("cudaMemcpyToSymbolAsync failed"); } return 1; @@ -1452,8 +1452,8 @@ Nd4jPointer getConstantSpace() { cudaError_t dZ = cudaGetSymbolAddress(reinterpret_cast(&dConstAddr), deviceConstantMemory); if (dZ != 0) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(dZ); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage("cudaGetSymbolAddress failed"); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(dZ); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage("cudaGetSymbolAddress failed"); } return dConstAddr; @@ -1473,7 +1473,7 @@ void pullRows(Nd4jPointer *extraPointers, cudaStream_t *stream = reinterpret_cast(extraPointers[1]); dim3 launchDims(64, 256, 1024); - auto xType = nd4j::ArrayOptions::dataType(xShapeInfo); + auto xType = sd::ArrayOptions::dataType(xShapeInfo); BUILD_SINGLE_SELECTOR(xType, pullRowsKernelGeneric, (launchDims, stream, dbX->special(), dbZ->special(), n, indexes, tadShapeInfo, tadOffsets, zTadShapeInfo, zTadOffsets), LIBND4J_TYPES); @@ -1482,8 +1482,8 @@ void pullRows(Nd4jPointer *extraPointers, InteropDataBuffer::registerSpecialUse({dbZ}, {dbX}); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); } } @@ -1502,24 +1502,24 @@ void average(Nd4jPointer *extras, auto dX = reinterpret_cast(dx); - if (nd4j::Environment::getInstance()->isDebugAndVerbose()) + if (sd::Environment::getInstance()->isDebugAndVerbose()) printf("averageFloat called\n"); - auto xType = nd4j::ArrayOptions::dataType(xShapeInfo); + auto xType = sd::ArrayOptions::dataType(xShapeInfo); // launching on gpu if (mode == 0) { dim3 launchDims(256, 256, 4096); BUILD_SINGLE_SELECTOR(xType, averagingKernelGeneric, (launchDims, stream, dX, dz, n, length, propagate), LIBND4J_TYPES); - nd4j::DebugHelper::checkErrorCode(stream, "AverageFloat(...) failed"); + sd::DebugHelper::checkErrorCode(stream, "AverageFloat(...) failed"); } else { // launching on host memory - BUILD_SINGLE_SELECTOR(xType, nd4j::SpecialMethods, ::averageGeneric(x, z, zShapeInfo, n, length, propagate), + BUILD_SINGLE_SELECTOR(xType, sd::SpecialMethods, ::averageGeneric(x, z, zShapeInfo, n, length, propagate), LIBND4J_TYPES); } } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); } } @@ -1536,24 +1536,24 @@ void accumulate(Nd4jPointer *extras, auto dX = reinterpret_cast(dx); - if (nd4j::Environment::getInstance()->isDebugAndVerbose()) + if (sd::Environment::getInstance()->isDebugAndVerbose()) printf("accumulateFloat called\n"); - auto xType = nd4j::ArrayOptions::dataType(xShapeInfo); + auto xType = sd::ArrayOptions::dataType(xShapeInfo); // launching on gpu if (mode == 0) { dim3 launchDims(n, 256, 16384); BUILD_SINGLE_SELECTOR(xType, accumulateKernelGeneric, (launchDims, stream, dX, dz, n, length), LIBND4J_TYPES); - nd4j::DebugHelper::checkErrorCode(stream, "AccumulateFloat(...) failed"); + sd::DebugHelper::checkErrorCode(stream, "AccumulateFloat(...) failed"); } else { // launching on host memory - BUILD_SINGLE_SELECTOR(xType, nd4j::SpecialMethods, ::accumulateGeneric(x, z, zShapeInfo, n, length), + BUILD_SINGLE_SELECTOR(xType, sd::SpecialMethods, ::accumulateGeneric(x, z, zShapeInfo, n, length), LIBND4J_TYPES); } } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); } } @@ -1577,30 +1577,30 @@ void shuffle(Nd4jPointer *extras, auto tadOnlyShapeInfo = reinterpret_cast(tadShapeInfo); auto tadOffset = reinterpret_cast(tadOffsets); - auto xType = nd4j::ArrayOptions::dataType(xShape[0]); + auto xType = sd::ArrayOptions::dataType(xShape[0]); dim3 launchDims(256, 512, 8192); BUILD_SINGLE_SELECTOR(xType, shuffleKernelGeneric, (launchDims, stream, dX, dxShape, dZ, N, shuffleMap, tadOnlyShapeInfo, tadOffset), LIBND4J_TYPES); - nd4j::DebugHelper::checkErrorCode(stream, "shuffle(...) failed"); + sd::DebugHelper::checkErrorCode(stream, "shuffle(...) failed"); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); } } bool isExperimentalEnabled() { - return nd4j::Environment::getInstance()->isExperimentalBuild(); + return sd::Environment::getInstance()->isExperimentalBuild(); } void setOmpMinThreads(int threads) { - minThreads = nd4j::math::nd4j_max(32, threads); - minThreads = nd4j::math::nd4j_min(maxThreads, minThreads); + minThreads = sd::math::nd4j_max(32, threads); + minThreads = sd::math::nd4j_min(maxThreads, minThreads); } int getDevice() { - return nd4j::AffinityManager::currentDeviceId(); + return sd::AffinityManager::currentDeviceId(); } void setElementThreshold(int num) { @@ -1630,8 +1630,8 @@ void execSummaryStats(Nd4jPointer *extraPointers, InteropDataBuffer::registerSpecialUse({dbZ}, {dbX}); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); } } @@ -1662,8 +1662,8 @@ void execSummaryStatsTad(Nd4jPointer *extraPointers, InteropDataBuffer::registerSpecialUse({dbZ}, {dbX, dbDimension}); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); } } @@ -1686,8 +1686,8 @@ void execReduce3(Nd4jPointer *extraPointers, InteropDataBuffer::registerSpecialUse({dbZ}, {dbX, dbY}); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); } } @@ -1708,7 +1708,7 @@ void execReduce3Tad(Nd4jPointer *extraPointers, auto dimension = reinterpret_cast(dbDimension->primary()); int dimensionLength = static_cast(shape::length(hDimensionShape)); - auto tadPack = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(hXShapeInfo, + auto tadPack = sd::ConstantTadHelper::getInstance()->tadForDimensions(hXShapeInfo, dimension, shape::length(hDimensionShape)); auto tadLength = shape::length(tadPack.primaryShapeInfo()); @@ -1737,8 +1737,8 @@ void execReduce3Tad(Nd4jPointer *extraPointers, InteropDataBuffer::registerSpecialUse({dbZ}, {dbX, dbY}); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); } } @@ -1760,8 +1760,8 @@ void execReduce3Scalar(Nd4jPointer *extraPointers,int opNum, InteropDataBuffer::registerSpecialUse({dbZ}, {dbX, dbY}); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); } } @@ -1784,8 +1784,8 @@ void execScalarBool(Nd4jPointer *extraPointers, InteropDataBuffer::registerSpecialUse({dbZ}, {dbX, dbScalar}); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); } } @@ -1817,8 +1817,8 @@ void execScalarBoolTad(Nd4jPointer *extraPointers, InteropDataBuffer::registerSpecialUse({dbZ}, {dbX, dbScalars}); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); } } @@ -1841,8 +1841,8 @@ void execScalar(Nd4jPointer *extraPointers, InteropDataBuffer::registerSpecialUse({dbZ}, {dbX, dbScalar}); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); } } @@ -1865,12 +1865,12 @@ void execScalarTad(Nd4jPointer *extraPointers, cudaStream_t *stream = reinterpret_cast(extraPointers[1]); - auto xType = nd4j::ArrayOptions::dataType(hXShapeInfo); - auto yType = nd4j::ArrayOptions::dataType(hScalarShapeInfo); - auto zType = nd4j::ArrayOptions::dataType(hZShapeInfo); + auto xType = sd::ArrayOptions::dataType(hXShapeInfo); + auto yType = sd::ArrayOptions::dataType(hScalarShapeInfo); + auto zType = sd::ArrayOptions::dataType(hZShapeInfo); - if (yType != xType && yType != nd4j::DataType::BOOL && !isExperimentalEnabled()) - throw nd4j::datatype_exception::build("execScalar both operands must have same data type", xType, yType); + if (yType != xType && yType != sd::DataType::BOOL && !isExperimentalEnabled()) + throw sd::datatype_exception::build("execScalar both operands must have same data type", xType, yType); dim3 launchDims(256, 256, 16384); @@ -1884,8 +1884,8 @@ void execScalarTad(Nd4jPointer *extraPointers, InteropDataBuffer::registerSpecialUse({dbZ}, {dbX, dbScalars}); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); } } @@ -1901,7 +1901,7 @@ void execAggregate(Nd4jPointer *extraPointers, int numIntArrays, void *realArguments, int numRealArguments, - nd4j::DataType dtype) { + sd::DataType dtype) { } @@ -1915,7 +1915,7 @@ void batchExecutor(Nd4jPointer *extraPointers, int maxIdx, int maxReals, void *ptrToArguments, - nd4j::DataType dtype) { + sd::DataType dtype) { } void execAggregateBatch(Nd4jPointer *extraPointers, @@ -1923,7 +1923,7 @@ void execAggregateBatch(Nd4jPointer *extraPointers, int maxArgs, int maxShapes, int maxIntArrays, int maxIntArraySize, int maxIdx, int maxReals, - void *ptrToArguments, nd4j::DataType dtype) { + void *ptrToArguments, sd::DataType dtype) { } @@ -1943,8 +1943,8 @@ void execRandom(Nd4jPointer *extraPointers, InteropDataBuffer::registerSpecialUse({dbZ}, {}); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); } } @@ -1964,8 +1964,8 @@ void execRandom2(Nd4jPointer *extraPointers, int opNum, Nd4jPointer stateHost, InteropDataBuffer::registerSpecialUse({dbZ}, {dbX}); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); } } @@ -1987,8 +1987,8 @@ void execRandom3(Nd4jPointer *extraPointers, int opNum, Nd4jPointer stateHost, InteropDataBuffer::registerSpecialUse({dbZ}, {dbX, dbY}); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); } } @@ -2002,18 +2002,18 @@ Nd4jPointer initRandom(Nd4jPointer *extraPointers, long seed, long bufferSize, N // cudaStreamSynchronize(*stream); auto ptrDev = reinterpret_cast(ptrToBuffer); - auto buffer = new nd4j::random::RandomBuffer(seed, bufferSize, reinterpret_cast(ptrHost), reinterpret_cast(ptrDev)); + auto buffer = new sd::random::RandomBuffer(seed, bufferSize, reinterpret_cast(ptrHost), reinterpret_cast(ptrDev)); buffer->propagateToDevice(buffer, *stream); - nd4j::DebugHelper::checkErrorCode(stream, "initRandom(...) failed A"); + sd::DebugHelper::checkErrorCode(stream, "initRandom(...) failed A"); // we generate sequence in the host memory - nd4j::random::Xoroshiro128 generator(buffer); + sd::random::Xoroshiro128 generator(buffer); generator.refreshBuffer(); // and copy it to gpu cudaMemcpyAsync(ptrDev, ptrHost, bufferSize * 8, cudaMemcpyHostToDevice, *stream); - nd4j::DebugHelper::checkErrorCode(stream, "initRandom(...) failed B"); + sd::DebugHelper::checkErrorCode(stream, "initRandom(...) failed B"); return buffer; } @@ -2021,7 +2021,7 @@ Nd4jPointer initRandom(Nd4jPointer *extraPointers, long seed, long bufferSize, N void destroyRandom(Nd4jPointer ptrBuffer) { - nd4j::random::RandomBuffer *buffer = reinterpret_cast (ptrBuffer); + sd::random::RandomBuffer *buffer = reinterpret_cast (ptrBuffer); // FIXME: it's bad thing, but we can't know in advance, which stream(s) where using this generator in practice cudaDeviceSynchronize(); @@ -2031,7 +2031,7 @@ void destroyRandom(Nd4jPointer ptrBuffer) { void refreshBuffer(Nd4jPointer *extraPointers, long seed, Nd4jPointer ptrRandom) { - nd4j::random::RandomBuffer *buffer = reinterpret_cast (ptrRandom); + sd::random::RandomBuffer *buffer = reinterpret_cast (ptrRandom); unsigned long long *ptrHost = reinterpret_cast(extraPointers[0]); cudaStream_t *stream = reinterpret_cast(extraPointers[1]); @@ -2045,7 +2045,7 @@ void refreshBuffer(Nd4jPointer *extraPointers, long seed, Nd4jPointer ptrRandom) buffer->propagateToDevice(buffer, *stream); // refresh buffer on host size - nd4j::random::Xoroshiro128 generator(buffer); + sd::random::Xoroshiro128 generator(buffer); generator.refreshBuffer(); // copy back to gpu @@ -2054,7 +2054,7 @@ void refreshBuffer(Nd4jPointer *extraPointers, long seed, Nd4jPointer ptrRandom) void reSeedBuffer(Nd4jPointer *extraPointers, long seed, Nd4jPointer ptrRandom) { - nd4j::random::RandomBuffer *buffer = reinterpret_cast (ptrRandom); + sd::random::RandomBuffer *buffer = reinterpret_cast (ptrRandom); cudaStream_t *stream = reinterpret_cast(extraPointers[1]); cudaStreamSynchronize(*stream); @@ -2101,17 +2101,17 @@ void tear(Nd4jPointer *extras, cudaStream_t *stream = reinterpret_cast(extras[1]); dim3 launchDims(512, 512, 512); - auto xType = nd4j::ArrayOptions::dataType(xShapeInfo); + auto xType = sd::ArrayOptions::dataType(xShapeInfo); BUILD_SINGLE_SELECTOR(xType, tearKernelGeneric, (launchDims, stream, dbX->special(), dXShapeInfo, targets, zShapeInfo, tadShapeInfo, tadOffsets), LIBND4J_TYPES); - nd4j::DebugHelper::checkErrorCode(stream, "tearFloat(...) failed"); + sd::DebugHelper::checkErrorCode(stream, "tearFloat(...) failed"); InteropDataBuffer::registerSpecialUse({}, {dbX}); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); } } @@ -2122,15 +2122,15 @@ void prescanArrayRecursive(Nd4jPointer *extras, int *dZ, int *dX, int numElement auto g_scanBlockSums = reinterpret_cast(extras[2]); int blockSize = 512; // max size of the thread blocks - int numBlocks = nd4j::math::nd4j_max(1, static_cast(ceil(static_cast(numElements) / (2.f * blockSize)))); + int numBlocks = sd::math::nd4j_max(1, static_cast(ceil(static_cast(numElements) / (2.f * blockSize)))); int numThreads; if (numBlocks > 1) numThreads = blockSize; - else if (nd4j::isPowerOfTwo(numElements)) + else if (sd::isPowerOfTwo(numElements)) numThreads = numElements / 2; else - numThreads = nd4j::floorPow2(numElements); + numThreads = sd::floorPow2(numElements); int numEltsPerBlock = numThreads * 2; @@ -2138,7 +2138,7 @@ void prescanArrayRecursive(Nd4jPointer *extras, int *dZ, int *dX, int numElement // compute the smallest power of 2 able to compute its scan. int numEltsLastBlock = numElements - (numBlocks-1) * numEltsPerBlock; - int numThreadsLastBlock = nd4j::math::nd4j_max(1, numEltsLastBlock / 2); + int numThreadsLastBlock = sd::math::nd4j_max(1, numEltsLastBlock / 2); int np2LastBlock = 0; int sharedMemLastBlock = 0; @@ -2171,9 +2171,9 @@ void prescanArrayRecursive(Nd4jPointer *extras, int *dZ, int *dX, int numElement // execute the scan if (numBlocks > 1) { - nd4j::prescanLauncher(grid, threads, sharedMemSize, stream, dZ, dX, g_scanBlockSums[level], numThreads * 2, 0, 0); + sd::prescanLauncher(grid, threads, sharedMemSize, stream, dZ, dX, g_scanBlockSums[level], numThreads * 2, 0, 0); if (np2LastBlock) { - nd4j::prescanLauncher(gridOnes, threadsOnes, sharedMemLastBlock, stream, dZ, dX, g_scanBlockSums[level], numEltsLastBlock, numBlocks - 1, numElements - numEltsLastBlock); + sd::prescanLauncher(gridOnes, threadsOnes, sharedMemLastBlock, stream, dZ, dX, g_scanBlockSums[level], numEltsLastBlock, numBlocks - 1, numElements - numEltsLastBlock); } // After scanning all the sub-blocks, we are mostly done. But now we @@ -2183,18 +2183,18 @@ void prescanArrayRecursive(Nd4jPointer *extras, int *dZ, int *dX, int numElement // recursive (CPU) call prescanArrayRecursive(extras, g_scanBlockSums[level], g_scanBlockSums[level], numBlocks, level+1); - nd4j::uniformAdd<<>>(dZ, g_scanBlockSums[level], numElements - numEltsLastBlock, 0, 0); + sd::uniformAdd<<>>(dZ, g_scanBlockSums[level], numElements - numEltsLastBlock, 0, 0); if (np2LastBlock) { - nd4j::uniformAdd<<<1, numThreadsLastBlock, 1024, *stream>>>(dZ, g_scanBlockSums[level], numEltsLastBlock, numBlocks - 1, numElements - numEltsLastBlock); + sd::uniformAdd<<<1, numThreadsLastBlock, 1024, *stream>>>(dZ, g_scanBlockSums[level], numEltsLastBlock, numBlocks - 1, numElements - numEltsLastBlock); } } else if (isPowerOfTwo(numElements)) { - nd4j::prescanLauncher(grid, threads, sharedMemSize, stream, dZ, dX, 0, numThreads * 2, 0, 0); + sd::prescanLauncher(grid, threads, sharedMemSize, stream, dZ, dX, 0, numThreads * 2, 0, 0); } else { - nd4j::prescanLauncher(grid, threads, sharedMemSize, stream, dZ, dX, 0, numElements, 0, 0); + sd::prescanLauncher(grid, threads, sharedMemSize, stream, dZ, dX, 0, numElements, 0, 0); } - nd4j::DebugHelper::checkErrorCode(stream, "prescanArray(...) failed"); + sd::DebugHelper::checkErrorCode(stream, "prescanArray(...) failed"); } @@ -2206,13 +2206,13 @@ void encodeThresholdP1(Nd4jPointer *extras, void *dx, Nd4jLong *hXShapeInfo, Nd4 int numBlocks = N / blockSize + (N % blockSize ? 1 : 0); dim3 launchDims(numBlocks, blockSize, 1024); - auto xType = nd4j::ArrayOptions::dataType(hXShapeInfo); + auto xType = sd::ArrayOptions::dataType(hXShapeInfo); BUILD_SINGLE_SELECTOR(xType, encoderKernelP1Generic, (launchDims, stream, dx, N, dz, threshold), LIBND4J_TYPES); - nd4j::DebugHelper::checkErrorCode(stream, "encodeThresholdP1Float(...) failed"); + sd::DebugHelper::checkErrorCode(stream, "encodeThresholdP1Float(...) failed"); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); } } @@ -2223,10 +2223,10 @@ void encodeThresholdP2Int(Nd4jPointer *extraPointers, int *dx, Nd4jLong N, int * cudaStream_t *stream = reinterpret_cast(extraPointers[1]); //encoderKernelP2Float<<>>(dx, N, dz); prescanArrayRecursive(extraPointers, dz, dx + 1, (int) N, 0); - nd4j::DebugHelper::checkErrorCode(stream, "encodeThresholdP2Int(...) failed"); + sd::DebugHelper::checkErrorCode(stream, "encodeThresholdP2Int(...) failed"); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); } } @@ -2238,13 +2238,13 @@ void encodeThresholdP3(Nd4jPointer *extraPointers, void *dx, Nd4jLong *hXShapeIn int numBlocks = N / blockSize + (N % blockSize ? 1 : 0); dim3 launchDims(numBlocks, blockSize, 4096); - auto xType = nd4j::ArrayOptions::dataType(hXShapeInfo); + auto xType = sd::ArrayOptions::dataType(hXShapeInfo); BUILD_SINGLE_SELECTOR(xType, encoderKernelP3Generic, (launchDims, stream, dx, offsets, N, dz), LIBND4J_TYPES); - nd4j::DebugHelper::checkErrorCode(stream, "encodeThresholdP3Float(...) failed"); + sd::DebugHelper::checkErrorCode(stream, "encodeThresholdP3Float(...) failed"); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); } } @@ -2257,13 +2257,13 @@ void decodeThreshold(Nd4jPointer *extraPointers, void *dx, Nd4jLong N, void *dz, int numBlocks = N / blockSize + (N % blockSize ? 1 : 0); dim3 launchDims(numBlocks, blockSize, 1024); - auto zType = nd4j::ArrayOptions::dataType(zShapeInfo); + auto zType = sd::ArrayOptions::dataType(zShapeInfo); BUILD_SINGLE_SELECTOR(zType, decoderKernelGeneric, (launchDims, stream, dx, N, dz), LIBND4J_TYPES); - nd4j::DebugHelper::checkErrorCode(stream, "decodeThresholdFloat(...) failed"); + sd::DebugHelper::checkErrorCode(stream, "decodeThresholdFloat(...) failed"); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); } } @@ -2295,8 +2295,8 @@ void execReduce3All(Nd4jPointer *extraPointers, InteropDataBuffer::registerSpecialUse({dbZ}, {dbX, dbY}); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); } } @@ -2310,12 +2310,12 @@ void sort(Nd4jPointer *extraPointers, auto xLength = shape::length(xShapeInfo); auto xEWS = shape::elementWiseStride(xShapeInfo); - auto xType = nd4j::ArrayOptions::dataType(xShapeInfo); + auto xType = sd::ArrayOptions::dataType(xShapeInfo); // check if xLength is a power of 2, and use bitonic sort, if that's the case if ((xLength != 0) && ((xLength & (xLength - 1)) == 0) && (xLength <= 1024 * 1024 * 10)) { - int numThreads = nd4j::math::nd4j_min(512, xLength); + int numThreads = sd::math::nd4j_min(512, xLength); int numBlocks = xLength / numThreads; if (xLength % numThreads > 0 || numBlocks == 0) numBlocks++; @@ -2330,12 +2330,12 @@ void sort(Nd4jPointer *extraPointers, } } } else { - int numThreads = nd4j::math::nd4j_min(512, xLength); + int numThreads = sd::math::nd4j_min(512, xLength); int numBlocks = xLength / numThreads; if (xLength % numThreads > 0 || numBlocks == 0) numBlocks++; - numBlocks = nd4j::math::nd4j_min(512, numBlocks); + numBlocks = sd::math::nd4j_min(512, numBlocks); dim3 launchDims(numBlocks, numThreads, 32768); int max = 2, dg = 0; @@ -2359,10 +2359,10 @@ void sort(Nd4jPointer *extraPointers, } } - nd4j::DebugHelper::checkErrorCode(stream, "sort(...) failed"); + sd::DebugHelper::checkErrorCode(stream, "sort(...) failed"); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); } } @@ -2379,8 +2379,8 @@ void sortByKey(Nd4jPointer *extraPointers, auto xLength = shape::length(xShapeInfo); auto yLength = shape::length(yShapeInfo); auto xEWS = shape::elementWiseStride(xShapeInfo); - auto xType = nd4j::ArrayOptions::dataType(xShapeInfo); - auto yType = nd4j::ArrayOptions::dataType(yShapeInfo); + auto xType = sd::ArrayOptions::dataType(xShapeInfo); + auto yType = sd::ArrayOptions::dataType(yShapeInfo); if (shape::isEmpty(xShapeInfo) || shape::isEmpty(yShapeInfo)) return; @@ -2391,7 +2391,7 @@ void sortByKey(Nd4jPointer *extraPointers, // check if xLength is a power of 2, and use bitonic sort, if that's the case if ((xLength != 0) && ((xLength & (xLength - 1)) == 0) && (xLength <= 1024 * 1024 * 10)) { - int numThreads = nd4j::math::nd4j_min(512, xLength); + int numThreads = sd::math::nd4j_min(512, xLength); int numBlocks = xLength / numThreads; if (xLength % numThreads > 0 || numBlocks == 0) numBlocks++; @@ -2406,12 +2406,12 @@ void sortByKey(Nd4jPointer *extraPointers, } } } else { - int numThreads = nd4j::math::nd4j_min(512, xLength); + int numThreads = sd::math::nd4j_min(512, xLength); int numBlocks = xLength / numThreads; if (xLength % numThreads > 0 || numBlocks == 0) numBlocks++; - numBlocks = nd4j::math::nd4j_min(512, numBlocks); + numBlocks = sd::math::nd4j_min(512, numBlocks); dim3 launchDims(numBlocks, numThreads, 32768); int max = 2, dg = 0; @@ -2436,8 +2436,8 @@ void sortByKey(Nd4jPointer *extraPointers, } } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); } } @@ -2453,8 +2453,8 @@ void sortByValue(Nd4jPointer *extraPointers, auto xLength = shape::length(xShapeInfo); auto yLength = shape::length(yShapeInfo); auto xEWS = shape::elementWiseStride(xShapeInfo); - auto xType = nd4j::ArrayOptions::dataType(yShapeInfo); - auto yType = nd4j::ArrayOptions::dataType(xShapeInfo); + auto xType = sd::ArrayOptions::dataType(yShapeInfo); + auto yType = sd::ArrayOptions::dataType(xShapeInfo); if (shape::isEmpty(xShapeInfo) || shape::isEmpty(yShapeInfo)) return; @@ -2465,7 +2465,7 @@ void sortByValue(Nd4jPointer *extraPointers, // check if xLength is a power of 2, and use bitonic sort, if that's the case if ((xLength != 0) && ((xLength & (xLength - 1)) == 0) && (xLength <= 1024 * 1024 * 10)) { - int numThreads = nd4j::math::nd4j_min(512, xLength); + int numThreads = sd::math::nd4j_min(512, xLength); int numBlocks = xLength / numThreads; if (xLength % numThreads > 0 || numBlocks == 0) numBlocks++; @@ -2480,12 +2480,12 @@ void sortByValue(Nd4jPointer *extraPointers, } } } else { - int numThreads = nd4j::math::nd4j_min(512, xLength); + int numThreads = sd::math::nd4j_min(512, xLength); int numBlocks = xLength / numThreads; if (xLength % numThreads > 0 || numBlocks == 0) numBlocks++; - numBlocks = nd4j::math::nd4j_min(512, numBlocks); + numBlocks = sd::math::nd4j_min(512, numBlocks); dim3 launchDims(numBlocks, numThreads, 32768); int max = 2, dg = 0; @@ -2509,8 +2509,8 @@ void sortByValue(Nd4jPointer *extraPointers, } } } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); } } @@ -2528,18 +2528,18 @@ void sortTadByKey(Nd4jPointer *extraPointers, auto stream = reinterpret_cast(extraPointers[1]); auto context = extraPointers[0] == 0 ? LaunchContext::defaultContext() : reinterpret_cast(extraPointers[0]); - auto tadPack = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(xShapeInfo, dimension, dimensionLength); + auto tadPack = sd::ConstantTadHelper::getInstance()->tadForDimensions(xShapeInfo, dimension, dimensionLength); dim3 launchDims((int) tadPack.numberOfTads(), 256, 2048); - auto xType = nd4j::ArrayOptions::dataType(xShapeInfo); - auto yType = nd4j::ArrayOptions::dataType(yShapeInfo); + auto xType = sd::ArrayOptions::dataType(xShapeInfo); + auto yType = sd::ArrayOptions::dataType(yShapeInfo); BUILD_DOUBLE_SELECTOR(xType, yType, oesTadGenericKey, (launchDims, stream, dX, dXShapeInfo, dy, dyShapeInfo, nullptr, dimensionLength, tadPack.platformShapeInfo(), tadPack.platformOffsets(), descending), LIBND4J_TYPES, LIBND4J_TYPES); - nd4j::DebugHelper::checkErrorCode(stream, "sortTadKey(...) failed"); + sd::DebugHelper::checkErrorCode(stream, "sortTadKey(...) failed"); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); } } @@ -2555,19 +2555,19 @@ void sortTadByValue(Nd4jPointer *extraPointers, auto stream = reinterpret_cast(extraPointers[1]); auto context = extraPointers[0] == 0 ? LaunchContext::defaultContext() : reinterpret_cast(extraPointers[0]); - auto tadPack = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(xShapeInfo, dimension, dimensionLength); + auto tadPack = sd::ConstantTadHelper::getInstance()->tadForDimensions(xShapeInfo, dimension, dimensionLength); dim3 launchDims((int) tadPack.numberOfTads(), 256, 2048); - auto xType = nd4j::ArrayOptions::dataType(yShapeInfo); - auto yType = nd4j::ArrayOptions::dataType(xShapeInfo); + auto xType = sd::ArrayOptions::dataType(yShapeInfo); + auto yType = sd::ArrayOptions::dataType(xShapeInfo); BUILD_DOUBLE_SELECTOR(xType, yType, oesTadGenericKey, (launchDims, stream, dy, dyShapeInfo, dX, dXShapeInfo, nullptr, dimensionLength, tadPack.platformShapeInfo(), tadPack.platformOffsets(), descending), LIBND4J_TYPES, LIBND4J_TYPES); - nd4j::DebugHelper::checkErrorCode(stream, "sortTadValue(...) failed"); + sd::DebugHelper::checkErrorCode(stream, "sortTadValue(...) failed"); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); } } @@ -2585,17 +2585,17 @@ void sortTad(Nd4jPointer *extraPointers, auto stream = reinterpret_cast(extraPointers[1]); auto context = extraPointers[0] == 0 ? LaunchContext::defaultContext() : reinterpret_cast(extraPointers[0]); - auto tadPack = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(xShapeInfo, dimension, dimensionLength); + auto tadPack = sd::ConstantTadHelper::getInstance()->tadForDimensions(xShapeInfo, dimension, dimensionLength); dim3 launchDims((int) tadPack.numberOfTads(), 512, 33768); - auto xType = nd4j::ArrayOptions::dataType(xShapeInfo); + auto xType = sd::ArrayOptions::dataType(xShapeInfo); BUILD_SINGLE_SELECTOR(xType, oesTadGeneric, (launchDims, stream, dX, dXShapeInfo, nullptr, dimensionLength, tadShapeInfo, tadOffsets, descending), LIBND4J_TYPES); - nd4j::DebugHelper::checkErrorCode(stream, "sortTad(...) failed"); + sd::DebugHelper::checkErrorCode(stream, "sortTad(...) failed"); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); } } @@ -2616,20 +2616,20 @@ Nd4jLong encodeBitmap(Nd4jPointer *extraPointers, int *reductionPointer = reinterpret_cast(extraPointers[3]); dim3 launchDims(512, 512, 32768); - auto xType = nd4j::ArrayOptions::dataType(hXShapeInfo); + auto xType = sd::ArrayOptions::dataType(hXShapeInfo); BUILD_SINGLE_SELECTOR(xType, cudaEncodeBitmapGeneric, (launchDims, stream, dx, N, dz, resultPointer, reductionPointer, threshold), LIBND4J_TYPES); - nd4j::DebugHelper::checkErrorCode(stream, "encodeBitmapFloat(...) failed"); + sd::DebugHelper::checkErrorCode(stream, "encodeBitmapFloat(...) failed"); Nd4jLong dZ = (Nd4jLong) resultPointer[0]; resultPointer[0] = 0; return dZ; } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); return 0; } } @@ -2642,13 +2642,13 @@ void decodeBitmap(Nd4jPointer *extraPointers, try { cudaStream_t *stream = reinterpret_cast(extraPointers[1]); dim3 launchDims(512, 512, 16384); - auto xType = nd4j::ArrayOptions::dataType(zShapeInfo); + auto xType = sd::ArrayOptions::dataType(zShapeInfo); BUILD_SINGLE_SELECTOR(xType, cudaDecodeBitmapGeneric, (launchDims, stream, dx, N, dz), LIBND4J_TYPES); - nd4j::DebugHelper::checkErrorCode(stream, "decodeBitmapFloat(...) failed"); + sd::DebugHelper::checkErrorCode(stream, "decodeBitmapFloat(...) failed"); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); } } @@ -2661,33 +2661,33 @@ void munmapFile(Nd4jPointer *extraPointers, Nd4jLong* ptrMap, Nd4jLong length) { } -nd4j::graph::ResultWrapper* executeFlatGraph(Nd4jPointer *extraPointers, Nd4jPointer flatBufferPointer) { +sd::graph::ResultWrapper* executeFlatGraph(Nd4jPointer *extraPointers, Nd4jPointer flatBufferPointer) { try { - return nd4j::graph::GraphExecutioner::executeFlatBuffer(flatBufferPointer); + return sd::graph::GraphExecutioner::executeFlatBuffer(flatBufferPointer); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); return nullptr; } } -Nd4jLong getResultWrapperSize(nd4j::graph::ResultWrapper* ptr) { +Nd4jLong getResultWrapperSize(sd::graph::ResultWrapper* ptr) { return ptr->size(); } -Nd4jPointer getResultWrapperPointer(nd4j::graph::ResultWrapper* ptr) { +Nd4jPointer getResultWrapperPointer(sd::graph::ResultWrapper* ptr) { return ptr->pointer(); } const char* getAllCustomOps() { - return nd4j::ops::OpRegistrator::getInstance()->getAllCustomOperations(); + return sd::ops::OpRegistrator::getInstance()->getAllCustomOperations(); } -nd4j::ShapeList* _calculateOutputShapes(Nd4jPointer* extraPointers, nd4j::ops::DeclarableOp* op, Nd4jPointer* inputBuffers, Nd4jPointer* inputShapes, int numInputShapes, double* tArgs, int numTArgs, Nd4jLong *iArgs, int numIArgs, bool *bArgs, int numBArgs, int *dArgs, int numDArgs) { - nd4j::graph::VariableSpace varSpace; +sd::ShapeList* _calculateOutputShapes(Nd4jPointer* extraPointers, sd::ops::DeclarableOp* op, Nd4jPointer* inputBuffers, Nd4jPointer* inputShapes, int numInputShapes, double* tArgs, int numTArgs, Nd4jLong *iArgs, int numIArgs, bool *bArgs, int numBArgs, int *dArgs, int numDArgs) { + sd::graph::VariableSpace varSpace; Context block(2, &varSpace); - nd4j::ShapeList inShapes; + sd::ShapeList inShapes; for (int e = 0; e < numIArgs; e++) block.getIArguments()->push_back(iArgs[e]); @@ -2699,16 +2699,16 @@ nd4j::ShapeList* _calculateOutputShapes(Nd4jPointer* extraPointers, nd4j::ops::D block.getBArguments()->push_back(bArgs[e]); for (int e = 0; e < numDArgs; e++) - block.getDArguments()->push_back((nd4j::DataType) dArgs[e]); + block.getDArguments()->push_back((sd::DataType) dArgs[e]); for (int e = 0; e < numInputShapes; e++) { auto shape_ = reinterpret_cast(inputShapes[e]); // we shouldn't copy buffer if that's empty array - void *buffer_ = nd4j::ArrayOptions::arrayType(shape_) == ArrayType::EMPTY ? nullptr : inputBuffers[e]; - void *bufferD_ = nd4j::ArrayOptions::arrayType(shape_) == ArrayType::EMPTY ? nullptr : inputBuffers[e + numInputShapes]; + void *buffer_ = sd::ArrayOptions::arrayType(shape_) == ArrayType::EMPTY ? nullptr : inputBuffers[e]; + void *bufferD_ = sd::ArrayOptions::arrayType(shape_) == ArrayType::EMPTY ? nullptr : inputBuffers[e + numInputShapes]; - auto array = new nd4j::NDArray(buffer_, bufferD_, shape_); + auto array = new sd::NDArray(buffer_, bufferD_, shape_); // block should contain references to proper variable varSpace.putVariable(1, e, array); @@ -2725,22 +2725,22 @@ nd4j::ShapeList* _calculateOutputShapes(Nd4jPointer* extraPointers, nd4j::ops::D return shapeList; } -nd4j::ShapeList* calculateOutputShapes2(Nd4jPointer* extraPointers, Nd4jLong hash, Nd4jPointer* inputBuffers, Nd4jPointer* inputShapes, int numInputShapes, double* tArgs, int numTArgs, Nd4jLong *iArgs, int numIArgs, bool *bArgs, int numBArgs, int *dArgs, int numDArgs) { +sd::ShapeList* calculateOutputShapes2(Nd4jPointer* extraPointers, Nd4jLong hash, Nd4jPointer* inputBuffers, Nd4jPointer* inputShapes, int numInputShapes, double* tArgs, int numTArgs, Nd4jLong *iArgs, int numIArgs, bool *bArgs, int numBArgs, int *dArgs, int numDArgs) { try { - auto op = nd4j::ops::OpRegistrator::getInstance()->getOperation(hash); + auto op = sd::ops::OpRegistrator::getInstance()->getOperation(hash); return _calculateOutputShapes(extraPointers, op, inputBuffers, inputShapes, numInputShapes, tArgs, numTArgs, iArgs, numIArgs, bArgs, numBArgs, dArgs, numDArgs); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); return nullptr; } } -nd4j::ShapeList* _calculateOutputShapes(Nd4jPointer* extraPointers, nd4j::ops::DeclarableOp* op, Nd4jPointer* inputShapes, int numInputShapes, double* tArgs, int numTArgs, Nd4jLong *iArgs, int numIArgs) { +sd::ShapeList* _calculateOutputShapes(Nd4jPointer* extraPointers, sd::ops::DeclarableOp* op, Nd4jPointer* inputShapes, int numInputShapes, double* tArgs, int numTArgs, Nd4jLong *iArgs, int numIArgs) { Context block(1); - nd4j::ShapeList inShapes; + sd::ShapeList inShapes; for (int e = 0; e < numIArgs; e++) block.getIArguments()->push_back(iArgs[e]); @@ -2756,34 +2756,34 @@ nd4j::ShapeList* _calculateOutputShapes(Nd4jPointer* extraPointers, nd4j::ops::D return shapeList; } -nd4j::ShapeList* calculateOutputShapes(Nd4jPointer* extraPointers, Nd4jLong hash, Nd4jPointer* inputShapes, int numInputShapes, double* tArgs, int numTArgs, Nd4jLong *iArgs, int numIArgs) { +sd::ShapeList* calculateOutputShapes(Nd4jPointer* extraPointers, Nd4jLong hash, Nd4jPointer* inputShapes, int numInputShapes, double* tArgs, int numTArgs, Nd4jLong *iArgs, int numIArgs) { try { - auto op = nd4j::ops::OpRegistrator::getInstance()->getOperation(hash); + auto op = sd::ops::OpRegistrator::getInstance()->getOperation(hash); return _calculateOutputShapes(extraPointers, op, inputShapes, numInputShapes, tArgs, numTArgs, iArgs, numIArgs); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); return nullptr; } } -Nd4jLong getShapeListSize(nd4j::ShapeList* list) { +Nd4jLong getShapeListSize(sd::ShapeList* list) { return list->size(); } -Nd4jLong* getShape(nd4j::ShapeList* list, Nd4jLong i) { +Nd4jLong* getShape(sd::ShapeList* list, Nd4jLong i) { return list->at(i); } -static FORCEINLINE Nd4jStatus realExec(nd4j::ops::DeclarableOp* op, Nd4jPointer* extraPointers, Nd4jLong hash, Nd4jPointer* inputBuffers, Nd4jPointer* inputShapes, int numInputs, Nd4jPointer* outputBuffers, Nd4jPointer* outputShapes, int numOutputs, double* tArgs, int numTArgs, Nd4jLong *iArgs, int numIArgs, bool* bArgs, int numBArgs, bool isInplace) { +static FORCEINLINE Nd4jStatus realExec(sd::ops::DeclarableOp* op, Nd4jPointer* extraPointers, Nd4jLong hash, Nd4jPointer* inputBuffers, Nd4jPointer* inputShapes, int numInputs, Nd4jPointer* outputBuffers, Nd4jPointer* outputShapes, int numOutputs, double* tArgs, int numTArgs, Nd4jLong *iArgs, int numIArgs, bool* bArgs, int numBArgs, bool isInplace) { if (op == nullptr) nd4j_printf("Can't find requested operation: [%lld]\n", hash); // we're using the same fake nodeId everywhere here - std::vector inputs(numInputs); - std::vector outputs(numOutputs); + std::vector inputs(numInputs); + std::vector outputs(numOutputs); std::vector ttArgs(numTArgs); std::vector bbArgs(numBArgs); std::vector iiArgs(numIArgs); @@ -2791,10 +2791,10 @@ static FORCEINLINE Nd4jStatus realExec(nd4j::ops::DeclarableOp* op, Nd4jPointer* // filling block now with inputs for (int e = 0; e < numInputs; e++) { auto shape = reinterpret_cast(inputShapes[e]); - void *buffer = nd4j::ArrayOptions::arrayType(shape) == ArrayType::EMPTY ? nullptr : inputBuffers[e]; - void *bufferD = nd4j::ArrayOptions::arrayType(shape) == ArrayType::EMPTY ? nullptr : inputBuffers[e + numInputs]; + void *buffer = sd::ArrayOptions::arrayType(shape) == ArrayType::EMPTY ? nullptr : inputBuffers[e]; + void *bufferD = sd::ArrayOptions::arrayType(shape) == ArrayType::EMPTY ? nullptr : inputBuffers[e + numInputs]; - inputs[e] = new nd4j::NDArray(buffer, bufferD, shape); + inputs[e] = new sd::NDArray(buffer, bufferD, shape); } // if not inplace - transferring output arrays @@ -2803,13 +2803,13 @@ static FORCEINLINE Nd4jStatus realExec(nd4j::ops::DeclarableOp* op, Nd4jPointer* for (int e = 0; e < numOutputs; e++) { // we want to keep original output shape intact auto shape = shape::copyShape(reinterpret_cast(outputShapes[e])); - void *buffer = nd4j::ArrayOptions::arrayType(shape) == ArrayType::EMPTY ? nullptr : outputBuffers[e]; - void *bufferD = nd4j::ArrayOptions::arrayType(shape) == ArrayType::EMPTY ? nullptr : outputBuffers[e + numOutputs]; + void *buffer = sd::ArrayOptions::arrayType(shape) == ArrayType::EMPTY ? nullptr : outputBuffers[e]; + void *bufferD = sd::ArrayOptions::arrayType(shape) == ArrayType::EMPTY ? nullptr : outputBuffers[e + numOutputs]; // FIXME: revisit this. bool canNullify = true; for (int i = 0; i < numInputs; i++) { - void *ibuffer = nd4j::ArrayOptions::arrayType(shape) == ArrayType::EMPTY ? nullptr : inputBuffers[i]; + void *ibuffer = sd::ArrayOptions::arrayType(shape) == ArrayType::EMPTY ? nullptr : inputBuffers[i]; if (ibuffer == buffer) { canNullify = false; break; @@ -2819,7 +2819,7 @@ static FORCEINLINE Nd4jStatus realExec(nd4j::ops::DeclarableOp* op, Nd4jPointer* if (canNullify && buffer != nullptr) memset((uint8_t *) buffer, '\0', shape::length(shape) * DataTypeUtils::sizeOfElement(ArrayOptions::dataType(shape))); - auto array = new nd4j::NDArray(buffer, bufferD, shape); + auto array = new sd::NDArray(buffer, bufferD, shape); outputs[e] = array; } @@ -2834,7 +2834,7 @@ static FORCEINLINE Nd4jStatus realExec(nd4j::ops::DeclarableOp* op, Nd4jPointer* // hypothetically at this point we have everything filled - auto dZ = op->execute(inputs, outputs, ttArgs, iiArgs, bbArgs, std::vector(), isInplace); + auto dZ = op->execute(inputs, outputs, ttArgs, iiArgs, bbArgs, std::vector(), isInplace); //auto dZ = op->execute(inputs, ttArgs, iiArgs, isInplace); @@ -2861,27 +2861,27 @@ static FORCEINLINE Nd4jStatus realExec(nd4j::ops::DeclarableOp* op, Nd4jPointer* int execCustomOp(Nd4jPointer* extraPointers, Nd4jLong hash, Nd4jPointer* inputBuffers, Nd4jPointer* inputShapes, int numInputs, Nd4jPointer* outputBuffers, Nd4jPointer* outputShapes, int numOutputs, double* tArgs, int numTArgs, Nd4jLong *iArgs, int numIArgs, bool* bArgs, int numBArgs, bool isInplace) { try { - auto op = nd4j::ops::OpRegistrator::getInstance()->getOperation(hash); + auto op = sd::ops::OpRegistrator::getInstance()->getOperation(hash); return realExec(op, extraPointers, hash, inputBuffers, inputShapes, numInputs, outputBuffers, outputShapes, numOutputs, tArgs, numTArgs, iArgs, numIArgs, bArgs, numBArgs, isInplace); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); return 1; } } int execCustomOp2(Nd4jPointer* extraPointers, Nd4jLong hash, Nd4jPointer opContext) { try { - auto op = nd4j::ops::OpRegistrator::getInstance()->getOperation(hash); + auto op = sd::ops::OpRegistrator::getInstance()->getOperation(hash); auto context = reinterpret_cast(opContext); auto result = op->execute(context); auto res = cudaStreamSynchronize(*context->launchContext()->getCudaStream()); if (res != 0) - throw nd4j::cuda_exception::build("customOp execution failed", res); + throw sd::cuda_exception::build("customOp execution failed", res); for (auto v:context->fastpath_in()) { if (!v->isEmpty()) @@ -2895,38 +2895,38 @@ int execCustomOp2(Nd4jPointer* extraPointers, Nd4jLong hash, Nd4jPointer opConte return result; } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); return 1; } } int registerGraph(Nd4jPointer *extraPointers, Nd4jLong graphId, Nd4jPointer flatBufferPointer) { try { - auto graph = nd4j::graph::GraphExecutioner::importFromFlatPointer(flatBufferPointer); + auto graph = sd::graph::GraphExecutioner::importFromFlatPointer(flatBufferPointer); - nd4j::graph::GraphHolder::getInstance()->registerGraph(graphId, graph); + sd::graph::GraphHolder::getInstance()->registerGraph(graphId, graph); return ND4J_STATUS_OK; } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); return 1; } } static VariablesSet* executeStoredGraphT(Nd4jPointer *extraPointers, Nd4jLong graphId, Nd4jPointer *inputBuffers, Nd4jPointer *inputShapes, int* inputIndices, int numInputs) { - auto graph = nd4j::graph::GraphHolder::getInstance()->pullGraph(graphId); + auto graph = sd::graph::GraphHolder::getInstance()->pullGraph(graphId); auto varSpace = graph->getVariableSpace()->clone(); - std::vector handles; + std::vector handles; for (int e = 0; e < numInputs; e++) { auto idx = inputIndices[e]; // we'll delete this array later, together with cloned VariableSpace - auto array = new nd4j::NDArray(inputBuffers[e], reinterpret_cast(inputShapes[e])); + auto array = new sd::NDArray(inputBuffers[e], reinterpret_cast(inputShapes[e])); handles.emplace_back(array); if (varSpace->hasVariable(idx)) { @@ -2939,8 +2939,8 @@ static VariablesSet* executeStoredGraphT(Nd4jPointer *extraPointers, Nd4jLong gr varSpace->putVariable(idx, array); } - auto dZ = nd4j::graph::GraphExecutioner::execute(graph, varSpace); - auto varSet = new nd4j::graph::VariablesSet(dZ); + auto dZ = sd::graph::GraphExecutioner::execute(graph, varSpace); + auto varSet = new sd::graph::VariablesSet(dZ); if (dZ == ND4J_STATUS_OK) { // pull back results, and provide them @@ -2966,52 +2966,52 @@ VariablesSet* executeStoredGraph(Nd4jPointer *extraPointers, Nd4jLong graphId, N try { return executeStoredGraphT(extraPointers, graphId, inputBuffers, inputShapes, inputIndices, numInputs); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); return nullptr; } } -Nd4jLong getVariablesSetSize(nd4j::graph::VariablesSet* set) { +Nd4jLong getVariablesSetSize(sd::graph::VariablesSet* set) { return set->size(); } -Nd4jStatus getVariablesSetStatus(nd4j::graph::VariablesSet* set) { +Nd4jStatus getVariablesSetStatus(sd::graph::VariablesSet* set) { return set->status(); } -nd4j::graph::Variable* getVariable(nd4j::graph::VariablesSet* set, Nd4jLong i) { +sd::graph::Variable* getVariable(sd::graph::VariablesSet* set, Nd4jLong i) { return set->at(i); } -int getVariableId(nd4j::graph::Variable* variable) { +int getVariableId(sd::graph::Variable* variable) { return variable->id(); } -int getVariableIndex(nd4j::graph::Variable* variable) { +int getVariableIndex(sd::graph::Variable* variable) { return variable->index(); } -const char* getVariableName(nd4j::graph::Variable* variable) { +const char* getVariableName(sd::graph::Variable* variable) { return variable->getName()->c_str(); } -Nd4jLong* getVariableShape(nd4j::graph::Variable* variable) { +Nd4jLong* getVariableShape(sd::graph::Variable* variable) { return variable->getNDArray()->shapeInfo(); } -void* getVariableBuffer(nd4j::graph::Variable* variable) { +void* getVariableBuffer(sd::graph::Variable* variable) { return variable->getNDArray()->buffer(); } int unregisterGraph(Nd4jPointer *extraPointers, Nd4jLong graphId) { try { - nd4j::graph::GraphHolder::getInstance()->dropGraphAny(graphId); + sd::graph::GraphHolder::getInstance()->dropGraphAny(graphId); return ND4J_STATUS_OK; } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); return 1; } } @@ -3036,33 +3036,33 @@ void deleteLongArray(Nd4jPointer pointer) { delete[] ptr; } -void deleteVariablesSet(nd4j::graph::VariablesSet* pointer) { +void deleteVariablesSet(sd::graph::VariablesSet* pointer) { delete pointer; } void deleteShapeList(Nd4jPointer shapeList) { - nd4j::ShapeList* list = reinterpret_cast(shapeList); + sd::ShapeList* list = reinterpret_cast(shapeList); //list->destroy(); delete list; } const char* getAllOperations() { - return nd4j::OpTracker::getInstance()->exportOperations(); + return sd::OpTracker::getInstance()->exportOperations(); } Nd4jPointer getGraphState(Nd4jLong id) { - return (Nd4jPointer) new nd4j::graph::GraphState(id); + return (Nd4jPointer) new sd::graph::GraphState(id); } void deleteGraphState(Nd4jPointer state) { - auto stateP = reinterpret_cast(state); + auto stateP = reinterpret_cast(state); delete stateP; } -Nd4jStatus execCustomOpWithScope(Nd4jPointer *extraPointers, nd4j::graph::GraphState *state, Nd4jLong opHash, Nd4jLong *scopes, int numScopes, Nd4jPointer *inputBuffers, Nd4jPointer *inputShapes, int numInputs, Nd4jPointer *outputBuffers, Nd4jPointer *outputShapes, int numOutputs) { +Nd4jStatus execCustomOpWithScope(Nd4jPointer *extraPointers, sd::graph::GraphState *state, Nd4jLong opHash, Nd4jLong *scopes, int numScopes, Nd4jPointer *inputBuffers, Nd4jPointer *inputShapes, int numInputs, Nd4jPointer *outputBuffers, Nd4jPointer *outputShapes, int numOutputs) { /** * That's basically exec, with VariableSpace provided in GraphState: * depending on operation (i.e. while of if), different logic executors could be used @@ -3080,7 +3080,7 @@ Nd4jStatus execCustomOpWithScope(Nd4jPointer *extraPointers, nd4j::graph::GraphS auto buffer = inputBuffers[e]; auto shapeInfo = reinterpret_cast(inputShapes[e]); - auto array = new nd4j::NDArray(buffer, shapeInfo, varSpace->launchContext()); + auto array = new sd::NDArray(buffer, shapeInfo, varSpace->launchContext()); // now we just put array to VarSpace varSpace->putVariable(0, e, array); @@ -3129,19 +3129,19 @@ Nd4jStatus execCustomOpWithScope(Nd4jPointer *extraPointers, nd4j::graph::GraphS Nd4jStatus execCustomOpWithScope(Nd4jPointer *extraPointers, Nd4jPointer state, Nd4jLong opHash, Nd4jLong *scopes, int numScopes, Nd4jPointer *inputBuffers, Nd4jPointer *inputShapes, int numInputs, Nd4jPointer *outputBuffers, Nd4jPointer *outputShapes, int numOutputs) { try { - return execCustomOpWithScope(extraPointers, reinterpret_cast(state), opHash, scopes, + return execCustomOpWithScope(extraPointers, reinterpret_cast(state), opHash, scopes, numScopes, inputBuffers, inputShapes, numInputs, outputBuffers, outputShapes, numOutputs); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); return 1; } } void deleteResultWrapper(Nd4jPointer ptr) { // just 0 room for compiler s@!t - auto p = reinterpret_cast(ptr); + auto p = reinterpret_cast(ptr); delete p; } @@ -3160,113 +3160,113 @@ void convertTypes(Nd4jPointer *extras, int srcType, Nd4jPointer dX, Nd4jLong N, if (srcType == ND4J_FLOAT8) { if (dstType == ND4J_FLOAT8) { - // convertKernel(extras, dx, N, dz); + // convertKernel(extras, dx, N, dz); } else if (dstType == ND4J_INT8) { - //nd4j::TypeCast::convertGenericCuda(extras, dx, N, dz); + //sd::TypeCast::convertGenericCuda(extras, dx, N, dz); } else if (dstType == ND4J_UINT8) { - //nd4j::TypeCast::convertGenericCuda(extras, dx, N, dz); + //sd::TypeCast::convertGenericCuda(extras, dx, N, dz); } else if (dstType == ND4J_FLOAT16) { - //nd4j::TypeCast::convertGenericCuda(extras, dx, N, dz); + //sd::TypeCast::convertGenericCuda(extras, dx, N, dz); } else if (dstType == ND4J_INT16) { - //nd4j::TypeCast::convertGenericCuda(extras, dx, N, dz); + //sd::TypeCast::convertGenericCuda(extras, dx, N, dz); } else if (dstType == ND4J_UINT16) { - //nd4j::TypeCast::convertGenericCuda(extras, dx, N, dz); + //sd::TypeCast::convertGenericCuda(extras, dx, N, dz); } else if (dstType == ND4J_FLOAT24) { } else if (dstType == ND4J_FLOAT32) { - //nd4j::TypeCast::convertGenericCuda(extras, dx, N, dz); + //sd::TypeCast::convertGenericCuda(extras, dx, N, dz); } else if (dstType == ND4J_DOUBLE) { - //nd4j::TypeCast::convertGenericCuda(extras, dx, N, dz); + //sd::TypeCast::convertGenericCuda(extras, dx, N, dz); } else { nd4j_printf("Unsupported types conversion: [%i] -> [%i]\n", srcType, dstType); } } else if (srcType == ND4J_INT8) { if (dstType == ND4J_FLOAT8) { - //nd4j::TypeCast::convertGenericCuda(extras, dx, N, dz); + //sd::TypeCast::convertGenericCuda(extras, dx, N, dz); } else if (dstType == ND4J_INT8) { - //convertKernel(extras, dx, N, dz); + //convertKernel(extras, dx, N, dz); } else if (dstType == ND4J_UINT8) { - nd4j::TypeCast::convertGenericCuda(extras, dx, N, dz); + sd::TypeCast::convertGenericCuda(extras, dx, N, dz); } else if (dstType == ND4J_FLOAT16) { - nd4j::TypeCast::convertGenericCuda(extras, dx, N, dz); + sd::TypeCast::convertGenericCuda(extras, dx, N, dz); } else if (dstType == ND4J_INT16) { - nd4j::TypeCast::convertGenericCuda(extras, dx, N, dz); + sd::TypeCast::convertGenericCuda(extras, dx, N, dz); } else if (dstType == ND4J_UINT16) { - nd4j::TypeCast::convertGenericCuda(extras, dx, N, dz); + sd::TypeCast::convertGenericCuda(extras, dx, N, dz); } else if (dstType == ND4J_FLOAT24) { // TODO: eventually we might want to add it } else if (dstType == ND4J_FLOAT32) { - nd4j::TypeCast::convertGenericCuda(extras, dx, N, dz); + sd::TypeCast::convertGenericCuda(extras, dx, N, dz); } else if (dstType == ND4J_DOUBLE) { - nd4j::TypeCast::convertGenericCuda(extras, dx, N, dz); + sd::TypeCast::convertGenericCuda(extras, dx, N, dz); } else { nd4j_printf("Unsupported types conversion: [%i] -> [%i]\n", srcType, dstType); } } else if (srcType == ND4J_UINT8) { if (dstType == ND4J_FLOAT8) { - //nd4j::TypeCast::convertGenericCuda(extras, dx, N, dz); + //sd::TypeCast::convertGenericCuda(extras, dx, N, dz); } else if (dstType == ND4J_INT8) { - nd4j::TypeCast::convertGenericCuda(extras, dx, N, dz); + sd::TypeCast::convertGenericCuda(extras, dx, N, dz); } else if (dstType == ND4J_UINT8) { - nd4j::TypeCast::convertGenericCuda(extras, dx, N, dz); + sd::TypeCast::convertGenericCuda(extras, dx, N, dz); } else if (dstType == ND4J_FLOAT16) { - nd4j::TypeCast::convertGenericCuda(extras, dx, N, dz); + sd::TypeCast::convertGenericCuda(extras, dx, N, dz); } else if (dstType == ND4J_INT16) { - nd4j::TypeCast::convertGenericCuda(extras, dx, N, dz); + sd::TypeCast::convertGenericCuda(extras, dx, N, dz); } else if (dstType == ND4J_UINT16) { - nd4j::TypeCast::convertGenericCuda(extras, dx, N, dz); + sd::TypeCast::convertGenericCuda(extras, dx, N, dz); } else if (dstType == ND4J_FLOAT24) { // TODO: still might want to add } else if (dstType == ND4J_FLOAT32) { - nd4j::TypeCast::convertGenericCuda(extras, dx, N, dz); + sd::TypeCast::convertGenericCuda(extras, dx, N, dz); } else if (dstType == ND4J_DOUBLE) { - nd4j::TypeCast::convertGenericCuda(extras, dx, N, dz); + sd::TypeCast::convertGenericCuda(extras, dx, N, dz); } else { nd4j_printf("Unsupported types conversion: [%i] -> [%i]\n", srcType, dstType); } } else if (srcType == ND4J_FLOAT16) { if (dstType == ND4J_FLOAT8) { - //nd4j::TypeCast::convertGenericCuda(extras, dx, N, dz); + //sd::TypeCast::convertGenericCuda(extras, dx, N, dz); } else if (dstType == ND4J_INT8) { - nd4j::TypeCast::convertGenericCuda(extras, dx, N, dz); + sd::TypeCast::convertGenericCuda(extras, dx, N, dz); } else if (dstType == ND4J_UINT8) { - nd4j::TypeCast::convertGenericCuda(extras, dx, N, dz); + sd::TypeCast::convertGenericCuda(extras, dx, N, dz); } else if (dstType == ND4J_FLOAT16) { - nd4j::TypeCast::convertGenericCuda(extras, dx, N, dz); + sd::TypeCast::convertGenericCuda(extras, dx, N, dz); } else if (dstType == ND4J_INT16) { - nd4j::TypeCast::convertGenericCuda(extras, dx, N, dz); + sd::TypeCast::convertGenericCuda(extras, dx, N, dz); } else if (dstType == ND4J_UINT16) { - nd4j::TypeCast::convertGenericCuda(extras, dx, N, dz); + sd::TypeCast::convertGenericCuda(extras, dx, N, dz); } else if (dstType == ND4J_FLOAT24) { // TODO: .... ^^^ } else if (dstType == ND4J_FLOAT32) { - nd4j::TypeCast::convertGenericCuda(extras, dx, N, dz); + sd::TypeCast::convertGenericCuda(extras, dx, N, dz); } else if (dstType == ND4J_DOUBLE) { - nd4j::TypeCast::convertGenericCuda(extras, dx, N, dz); + sd::TypeCast::convertGenericCuda(extras, dx, N, dz); } else if (dstType == ND4J_THRESHOLD) { - //nd4j::convertToThreshold(nullptr, dx, N, dz); + //sd::convertToThreshold(nullptr, dx, N, dz); } else { nd4j_printf("Unsupported types conversion: [%i] -> [%i]\n", srcType, dstType); } } else if (srcType == ND4J_INT16) { if (dstType == ND4J_FLOAT8) { - //nd4j::TypeCast::convertGenericCuda(extras, dx, N, dz); + //sd::TypeCast::convertGenericCuda(extras, dx, N, dz); } else if (dstType == ND4J_INT8) { - nd4j::TypeCast::convertGenericCuda(extras, dx, N, dz); + sd::TypeCast::convertGenericCuda(extras, dx, N, dz); } else if (dstType == ND4J_UINT8) { - nd4j::TypeCast::convertGenericCuda(extras, dx, N, dz); + sd::TypeCast::convertGenericCuda(extras, dx, N, dz); } else if (dstType == ND4J_FLOAT16) { - nd4j::TypeCast::convertGenericCuda(extras, dx, N, dz); + sd::TypeCast::convertGenericCuda(extras, dx, N, dz); } else if (dstType == ND4J_INT16) { - nd4j::TypeCast::convertGenericCuda(extras, dx, N, dz); + sd::TypeCast::convertGenericCuda(extras, dx, N, dz); } else if (dstType == ND4J_UINT16) { - nd4j::TypeCast::convertGenericCuda(extras, dx, N, dz); + sd::TypeCast::convertGenericCuda(extras, dx, N, dz); } else if (dstType == ND4J_FLOAT24) { // TODO... } else if (dstType == ND4J_FLOAT32) { - nd4j::TypeCast::convertGenericCuda(extras, dx, N, dz); + sd::TypeCast::convertGenericCuda(extras, dx, N, dz); } else if (dstType == ND4J_DOUBLE) { - nd4j::TypeCast::convertGenericCuda(extras, dx, N, dz); + sd::TypeCast::convertGenericCuda(extras, dx, N, dz); } else { printf("Unsupported types conversion: [%i] -> [%i]\n", srcType, dstType); } @@ -3274,57 +3274,57 @@ void convertTypes(Nd4jPointer *extras, int srcType, Nd4jPointer dX, Nd4jLong N, } else if (srcType == ND4J_FLOAT32) { if (dstType == ND4J_FLOAT8) { - //nd4j::TypeCast::convertGenericCuda(extras, dx, N, dz); + //sd::TypeCast::convertGenericCuda(extras, dx, N, dz); } else if (dstType == ND4J_INT8) { - nd4j::TypeCast::convertGenericCuda(extras, dx, N, dz); + sd::TypeCast::convertGenericCuda(extras, dx, N, dz); } else if (dstType == ND4J_UINT8) { - nd4j::TypeCast::convertGenericCuda(extras, dx, N, dz); + sd::TypeCast::convertGenericCuda(extras, dx, N, dz); } else if (dstType == ND4J_FLOAT16) { - nd4j::TypeCast::convertGenericCuda(extras, dx, N, dz); + sd::TypeCast::convertGenericCuda(extras, dx, N, dz); } else if (dstType == ND4J_INT16) { - nd4j::TypeCast::convertGenericCuda(extras, dx, N, dz); + sd::TypeCast::convertGenericCuda(extras, dx, N, dz); } else if (dstType == ND4J_UINT16) { - nd4j::TypeCast::convertGenericCuda(extras, dx, N, dz); + sd::TypeCast::convertGenericCuda(extras, dx, N, dz); } else if (dstType == ND4J_FLOAT24) { } else if (dstType == ND4J_DOUBLE) { - nd4j::TypeCast::convertGenericCuda(extras, dx, N, dz); + sd::TypeCast::convertGenericCuda(extras, dx, N, dz); } else if (dstType == ND4J_THRESHOLD) { - //nd4j::convertToThreshold(nullptr, dx, N, dz); + //sd::convertToThreshold(nullptr, dx, N, dz); } else { nd4j_printf("Unsupported types conversion: [%i] -> [%i]\n", srcType, dstType); } } else if (srcType == ND4J_DOUBLE) { if (dstType == ND4J_FLOAT8) { - //nd4j::TypeCast::convertGenericCuda(extras, dx, N, dz); + //sd::TypeCast::convertGenericCuda(extras, dx, N, dz); } else if (dstType == ND4J_INT8) { - nd4j::TypeCast::convertGenericCuda(extras, dx, N, dz); + sd::TypeCast::convertGenericCuda(extras, dx, N, dz); } else if (dstType == ND4J_UINT8) { - nd4j::TypeCast::convertGenericCuda(extras, dx, N, dz); + sd::TypeCast::convertGenericCuda(extras, dx, N, dz); } else if (dstType == ND4J_FLOAT16) { - nd4j::TypeCast::convertGenericCuda(extras, dx, N, dz); + sd::TypeCast::convertGenericCuda(extras, dx, N, dz); } else if (dstType == ND4J_INT16) { - nd4j::TypeCast::convertGenericCuda(extras, dx, N, dz); + sd::TypeCast::convertGenericCuda(extras, dx, N, dz); } else if (dstType == ND4J_UINT16) { - nd4j::TypeCast::convertGenericCuda(extras, dx, N, dz); + sd::TypeCast::convertGenericCuda(extras, dx, N, dz); } else if (dstType == ND4J_FLOAT24) { } else if (dstType == ND4J_FLOAT32) { - nd4j::TypeCast::convertGenericCuda(extras, dx, N, dz); + sd::TypeCast::convertGenericCuda(extras, dx, N, dz); } else if (dstType == ND4J_DOUBLE) { // } else if (dstType == ND4J_THRESHOLD) { - //nd4j::convertToThreshold(nullptr, dx, N, dz); + //sd::convertToThreshold(nullptr, dx, N, dz); } else { nd4j_printf("Unsupported types conversion: [%i] -> [%i]\n", srcType, dstType); } } else if (srcType == ND4J_THRESHOLD) { if (dstType == ND4J_FLOAT16) { - //nd4j::convertFromThreshold(nullptr, dx, N, dz); + //sd::convertFromThreshold(nullptr, dx, N, dz); } else if (dstType == ND4J_FLOAT32) { - //nd4j::convertFromThreshold(nullptr, dx, N, dz); + //sd::convertFromThreshold(nullptr, dx, N, dz); } else if (dstType == ND4J_DOUBLE) { - //nd4j::convertFromThreshold(nullptr, dx, N, dz); + //sd::convertFromThreshold(nullptr, dx, N, dz); } else { nd4j_printf("Unsupported types conversion: [%i] -> [%i]\n", srcType, dstType); } @@ -3332,25 +3332,25 @@ void convertTypes(Nd4jPointer *extras, int srcType, Nd4jPointer dX, Nd4jLong N, nd4j_printf("Unsupported types conversion: [%i] -> [%i]\n", srcType, dstType); } } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); } } Nd4jPointer createUtf8String(Nd4jPointer *extraPointers, const char *string, int length) { - auto u = new nd4j::utf8string(string, length); + auto u = new sd::utf8string(string, length); return reinterpret_cast(u); } Nd4jLong getUtf8StringLength(Nd4jPointer *extraPointers, Nd4jPointer ptr) { - return reinterpret_cast(ptr)->_length; + return reinterpret_cast(ptr)->_length; } char* getUtf8StringBuffer(Nd4jPointer *extraPointers, Nd4jPointer ptr) { - return reinterpret_cast(ptr)->_buffer; + return reinterpret_cast(ptr)->_buffer; } void deleteUtf8String(Nd4jPointer *extraPointers, Nd4jPointer ptr) { - delete(reinterpret_cast(ptr)); + delete(reinterpret_cast(ptr)); } /////////////////////////////////////////////////////////////////// @@ -3442,22 +3442,22 @@ void scatterUpdate(Nd4jPointer *extraPointers, int opCode, int numOfSubArrs, (stream, opCode, numOfSubArrs, dX, dXShapeInfo, dXOffsets, dY, dYShapeInfo, dYOffsets, dIindexes), LIBND4J_TYPES, INDEXING_TYPES); - nd4j::DebugHelper::checkErrorCode(stream, "scatterUpdate(...) failed"); + sd::DebugHelper::checkErrorCode(stream, "scatterUpdate(...) failed"); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); } } void inspectArray(Nd4jPointer *extraPointers, Nd4jPointer buffer, Nd4jLong *shapeInfo, Nd4jPointer specialBuffer, Nd4jLong *specialShapeInfo, Nd4jPointer debugInfo) { try { LaunchContext lc(extraPointers[1], extraPointers[4], extraPointers[5], extraPointers[3]); - auto p = reinterpret_cast(debugInfo); + auto p = reinterpret_cast(debugInfo); NDArray array(buffer, specialBuffer, shapeInfo, &lc); - nd4j::DebugHelper::retrieveDebugStatistics(p, &array); + sd::DebugHelper::retrieveDebugStatistics(p, &array); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); } } @@ -3483,36 +3483,36 @@ void tryPointer(Nd4jPointer extra, Nd4jPointer p, int len) { auto e = cudaStreamSynchronize(stream); if (e != 0) - throw nd4j::cuda_exception::build("tryPointer failed", e); + throw sd::cuda_exception::build("tryPointer failed", e); cudaStreamDestroy(stream); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); } } int dataTypeFromNpyHeader(void *header) { return (int) cnpy::dataTypeFromHeader(reinterpret_cast(header)); } -nd4j::ConstantDataBuffer* shapeBuffer(int rank, Nd4jLong *shape, Nd4jLong *strides, nd4j::DataType dtype, char order, Nd4jLong ews, bool empty) { +sd::ConstantDataBuffer* shapeBuffer(int rank, Nd4jLong *shape, Nd4jLong *strides, sd::DataType dtype, char order, Nd4jLong ews, bool empty) { try { auto buffer = new ConstantDataBuffer(); - *buffer = nd4j::ConstantShapeHelper::getInstance()->bufferForShapeInfo( + *buffer = sd::ConstantShapeHelper::getInstance()->bufferForShapeInfo( ShapeDescriptor(dtype, order, shape, strides, rank, ews, empty)); return buffer; } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); return nullptr; } } -void deleteShapeBuffer(nd4j::ConstantDataBuffer* ptr) { +void deleteShapeBuffer(sd::ConstantDataBuffer* ptr) { delete ptr; } -void deleteTadPack(nd4j::TadPack* ptr) { +void deleteTadPack(sd::TadPack* ptr) { delete ptr; } @@ -3521,61 +3521,61 @@ bool isBlasVersionMatches(int major, int minor, int build) { if (!result) { nd4j_printf("CUDA/cuBLAS version mismatch. Expected: %i.%i.%i but got %i.%i.%i instead\n", Environment::getInstance()->_blasMajorVersion, Environment::getInstance()->_blasMinorVersion, Environment::getInstance()->_blasPatchVersion, major, minor, build); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(152); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage("CUDA/cuBLAS version mismatch"); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(152); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage("CUDA/cuBLAS version mismatch"); } return result; } -nd4j::ConstantDataBuffer* constantBufferLong(nd4j::DataType dtype, Nd4jLong *data, int length) { - return nd4j::ConstantHelper::getInstance()->constantBuffer(ConstantDescriptor(data, length), dtype); +sd::ConstantDataBuffer* constantBufferLong(sd::DataType dtype, Nd4jLong *data, int length) { + return sd::ConstantHelper::getInstance()->constantBuffer(ConstantDescriptor(data, length), dtype); } -nd4j::ConstantDataBuffer* constantBufferDouble(nd4j::DataType dtype, double *data, int length) { - return nd4j::ConstantHelper::getInstance()->constantBuffer(ConstantDescriptor(data, length), dtype); +sd::ConstantDataBuffer* constantBufferDouble(sd::DataType dtype, double *data, int length) { + return sd::ConstantHelper::getInstance()->constantBuffer(ConstantDescriptor(data, length), dtype); } -nd4j::ConstantDataBuffer* constantBuffer(nd4j::DataType dtype, nd4j::ConstantDescriptor *descriptor) { - return nd4j::ConstantHelper::getInstance()->constantBuffer(*descriptor, dtype); +sd::ConstantDataBuffer* constantBuffer(sd::DataType dtype, sd::ConstantDescriptor *descriptor) { + return sd::ConstantHelper::getInstance()->constantBuffer(*descriptor, dtype); } -Nd4jPointer getConstantDataBufferPrimary(nd4j::ConstantDataBuffer* dbf) { +Nd4jPointer getConstantDataBufferPrimary(sd::ConstantDataBuffer* dbf) { return dbf->primary(); } -Nd4jPointer getConstantDataBufferSpecial(nd4j::ConstantDataBuffer* dbf) { +Nd4jPointer getConstantDataBufferSpecial(sd::ConstantDataBuffer* dbf) { return dbf->special(); } -Nd4jLong getConstantDataBufferLength(nd4j::ConstantDataBuffer* dbf) { +Nd4jLong getConstantDataBufferLength(sd::ConstantDataBuffer* dbf) { return dbf->length(); } -Nd4jLong getConstantDataBufferSizeOf(nd4j::ConstantDataBuffer* dbf) { +Nd4jLong getConstantDataBufferSizeOf(sd::ConstantDataBuffer* dbf) { return dbf->sizeOf(); } -nd4j::graph::Context* createGraphContext(int nodeId) { - return new nd4j::graph::Context(nodeId); +sd::graph::Context* createGraphContext(int nodeId) { + return new sd::graph::Context(nodeId); } -nd4j::graph::RandomGenerator* getGraphContextRandomGenerator(nd4j::graph::Context* ptr) { +sd::graph::RandomGenerator* getGraphContextRandomGenerator(sd::graph::Context* ptr) { return &ptr->randomGenerator(); } -void markGraphContextInplace(nd4j::graph::Context* ptr, bool reallyInplace) { +void markGraphContextInplace(sd::graph::Context* ptr, bool reallyInplace) { ptr->markInplace(reallyInplace); } -void setGraphContextCudaContext(nd4j::graph::Context* ptr, void *stream, void *reductionPointer, void *allocationPointer) { +void setGraphContextCudaContext(sd::graph::Context* ptr, void *stream, void *reductionPointer, void *allocationPointer) { ptr->setCudaContext(stream, reductionPointer, allocationPointer); } -void setGraphContextInputArray(nd4j::graph::Context* ptr, int index, void *buffer, void *shapeInfo, void *specialBuffer, void *specialShapeInfo) { +void setGraphContextInputArray(sd::graph::Context* ptr, int index, void *buffer, void *shapeInfo, void *specialBuffer, void *specialShapeInfo) { ptr->setInputArray(index, buffer, shapeInfo, specialBuffer, specialShapeInfo); } -void setGraphContextOutputArray(nd4j::graph::Context* ptr, int index, void *buffer, void *shapeInfo, void *specialBuffer, void *specialShapeInfo) { +void setGraphContextOutputArray(sd::graph::Context* ptr, int index, void *buffer, void *shapeInfo, void *specialBuffer, void *specialShapeInfo) { ptr->setOutputArray(index, buffer, shapeInfo, specialBuffer, specialShapeInfo); } @@ -3587,62 +3587,62 @@ void setGraphContextOutputBuffer(OpaqueContext* ptr, int index, OpaqueDataBuffer ptr->setOutputArray(index, buffer, shapeInfo, specialShapeInfo); } -void setGraphContextTArguments(nd4j::graph::Context* ptr, double *arguments, int numberOfArguments) { +void setGraphContextTArguments(sd::graph::Context* ptr, double *arguments, int numberOfArguments) { ptr->setTArguments(arguments, numberOfArguments); } -void setGraphContextIArguments(nd4j::graph::Context* ptr, Nd4jLong *arguments, int numberOfArguments) { +void setGraphContextIArguments(sd::graph::Context* ptr, Nd4jLong *arguments, int numberOfArguments) { ptr->setIArguments(arguments, numberOfArguments); } -void setGraphContextBArguments(nd4j::graph::Context* ptr, bool *arguments, int numberOfArguments) { +void setGraphContextBArguments(sd::graph::Context* ptr, bool *arguments, int numberOfArguments) { ptr->setBArguments(arguments, numberOfArguments); } void setGraphContextDArguments(OpaqueContext* ptr, int *arguments, int numberOfArguments) { - std::vector dtypes(numberOfArguments); + std::vector dtypes(numberOfArguments); for (int e = 0; e < numberOfArguments; e++) - dtypes[e] = (nd4j::DataType) arguments[e]; + dtypes[e] = (sd::DataType) arguments[e]; ptr->setDArguments(dtypes); } -void deleteGraphContext(nd4j::graph::Context* ptr) { +void deleteGraphContext(sd::graph::Context* ptr) { delete ptr; } -nd4j::graph::RandomGenerator* createRandomGenerator(Nd4jLong rootSeed, Nd4jLong nodeSeed) { +sd::graph::RandomGenerator* createRandomGenerator(Nd4jLong rootSeed, Nd4jLong nodeSeed) { try { - return new nd4j::graph::RandomGenerator(rootSeed, nodeSeed); + return new sd::graph::RandomGenerator(rootSeed, nodeSeed); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); return nullptr; } } -Nd4jLong getRandomGeneratorRootState(nd4j::graph::RandomGenerator* ptr) { +Nd4jLong getRandomGeneratorRootState(sd::graph::RandomGenerator* ptr) { return ptr->rootState(); } -Nd4jLong getRandomGeneratorNodeState(nd4j::graph::RandomGenerator* ptr) { +Nd4jLong getRandomGeneratorNodeState(sd::graph::RandomGenerator* ptr) { return ptr->nodeState(); } -void setRandomGeneratorStates(nd4j::graph::RandomGenerator* ptr, Nd4jLong rootSeed, Nd4jLong nodeSeed) { +void setRandomGeneratorStates(sd::graph::RandomGenerator* ptr, Nd4jLong rootSeed, Nd4jLong nodeSeed) { ptr->setStates(rootSeed, nodeSeed); } -int getRandomGeneratorRelativeInt(nd4j::graph::RandomGenerator* ptr, Nd4jLong index) { +int getRandomGeneratorRelativeInt(sd::graph::RandomGenerator* ptr, Nd4jLong index) { return ptr->relativeInt(index); } -Nd4jLong getRandomGeneratorRelativeLong(nd4j::graph::RandomGenerator* ptr, Nd4jLong index) { +Nd4jLong getRandomGeneratorRelativeLong(sd::graph::RandomGenerator* ptr, Nd4jLong index) { return ptr->relativeLong(index); } -void deleteRandomGenerator(nd4j::graph::RandomGenerator* ptr) { +void deleteRandomGenerator(sd::graph::RandomGenerator* ptr) { delete ptr; } @@ -3665,27 +3665,27 @@ Nd4jPointer shapeBufferForNumpy(Nd4jPointer npyArray) { Nd4jLong *shapeBuffer; if (shape.size() == 1 && shape[0] == 0) { // scalar case - shapeBuffer = nd4j::ShapeBuilders::createScalarShapeInfo(dtype); + shapeBuffer = sd::ShapeBuilders::createScalarShapeInfo(dtype); } else if (_empty) { if (shapeSize > 0) - shapeBuffer = nd4j::ShapeBuilders::emptyShapeInfo(dtype, arr.fortranOrder ? 'f' : 'c', shape); + shapeBuffer = sd::ShapeBuilders::emptyShapeInfo(dtype, arr.fortranOrder ? 'f' : 'c', shape); else - shapeBuffer = nd4j::ShapeBuilders::emptyShapeInfo(dtype); + shapeBuffer = sd::ShapeBuilders::emptyShapeInfo(dtype); } else { - shapeBuffer = nd4j::ShapeBuilders::createShapeInfo(dtype, arr.fortranOrder ? 'f' : 'c', shape); + shapeBuffer = sd::ShapeBuilders::createShapeInfo(dtype, arr.fortranOrder ? 'f' : 'c', shape); } - return reinterpret_cast(nd4j::ConstantShapeHelper::getInstance()->createFromExisting(shapeBuffer, + return reinterpret_cast(sd::ConstantShapeHelper::getInstance()->createFromExisting(shapeBuffer, true)); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); return nullptr; } } const char* runLightBenchmarkSuit(bool printOut) { try { - nd4j::LightBenchmarkSuit suit; + sd::LightBenchmarkSuit suit; auto result = suit.runSuit(); if (printOut) @@ -3697,15 +3697,15 @@ const char* runLightBenchmarkSuit(bool printOut) { return chars; } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); return nullptr; } } const char* runFullBenchmarkSuit(bool printOut) { try { - nd4j::FullBenchmarkSuit suit; + sd::FullBenchmarkSuit suit; auto result = suit.runSuit(); if (printOut) @@ -3717,17 +3717,17 @@ const char* runFullBenchmarkSuit(bool printOut) { return chars; } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); return nullptr; } } Nd4jLong getCachedMemory(int deviceId) { - return nd4j::ConstantHelper::getInstance()->getCachedAmount(deviceId); + return sd::ConstantHelper::getInstance()->getCachedAmount(deviceId); } -nd4j::LaunchContext* defaultLaunchContext() { +sd::LaunchContext* defaultLaunchContext() { return LaunchContext::defaultContext(); } @@ -3760,11 +3760,11 @@ Nd4jPointer lcSolverHandle(OpaqueLaunchContext* lc) { } int lastErrorCode() { - return nd4j::LaunchContext::defaultContext()->errorReference()->errorCode(); + return sd::LaunchContext::defaultContext()->errorReference()->errorCode(); } const char* lastErrorMessage() { - return nd4j::LaunchContext::defaultContext()->errorReference()->errorMessage(); + return sd::LaunchContext::defaultContext()->errorReference()->errorMessage(); } void ctxShapeFunctionOverride(OpaqueContext* ptr, bool reallyOverride) { @@ -3805,10 +3805,10 @@ void ctxSetExecutionMode(OpaqueContext* ptr, int execMode) { OpaqueDataBuffer* allocateDataBuffer(Nd4jLong elements, int dataType, bool allocateBoth) { try { auto dtype = DataTypeUtils::fromInt(dataType); - return new nd4j::InteropDataBuffer(elements * DataTypeUtils::sizeOf(dtype), dtype, allocateBoth); + return new sd::InteropDataBuffer(elements * DataTypeUtils::sizeOf(dtype), dtype, allocateBoth); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); return nullptr; } } @@ -3845,8 +3845,8 @@ void dbExpandBuffer(OpaqueDataBuffer *dataBuffer, Nd4jLong elements) { try { dataBuffer->dataBuffer()->expand(elements * DataTypeUtils::sizeOf(dataBuffer->dataBuffer()->getDataType())); } catch (std::exception &e) { - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); - nd4j::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); + sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1); + sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what()); } } diff --git a/libnd4j/blas/Environment.cpp b/libnd4j/include/legacy/impl/Environment.cpp similarity index 88% rename from libnd4j/blas/Environment.cpp rename to libnd4j/include/legacy/impl/Environment.cpp index 3b9502534..fae3a28dc 100644 --- a/libnd4j/blas/Environment.cpp +++ b/libnd4j/include/legacy/impl/Environment.cpp @@ -22,7 +22,7 @@ #include #include #include -#include "Environment.h" +#include "system/Environment.h" #include #include #include @@ -38,12 +38,12 @@ #include #include -#include "BlasVersionHelper.h" +#include #endif -namespace nd4j { +namespace sd { - nd4j::Environment::Environment() { + sd::Environment::Environment() { _tadThreshold.store(1); _elementThreshold.store(1024); _verbose.store(false); @@ -51,7 +51,7 @@ namespace nd4j { _profile.store(false); _precBoost.store(false); _leaks.store(false); - _dataType.store(nd4j::DataType::FLOAT32); + _dataType.store(sd::DataType::FLOAT32); _maxThreads = std::thread::hardware_concurrency(); _maxMasterThreads = _maxThreads.load(); @@ -162,6 +162,11 @@ namespace nd4j { // still do nothing } } + + const char* blas_fallback = std::getenv("SD_BLAS_FALLBACK"); + if (blas_fallback != nullptr) { + _blasFallback = true; + } #endif #ifdef __CUDABLAS__ @@ -189,7 +194,11 @@ namespace nd4j { #endif } - nd4j::Environment::~Environment() { + bool sd::Environment::blasFallback() { + return _blasFallback; + } + + sd::Environment::~Environment() { // } @@ -220,7 +229,7 @@ namespace nd4j { return _experimental; } - nd4j::DataType Environment::defaultFloatDataType() { + sd::DataType Environment::defaultFloatDataType() { return _dataType.load(); } @@ -228,8 +237,8 @@ namespace nd4j { return _capabilities; } - void Environment::setDefaultFloatDataType(nd4j::DataType dtype) { - if (dtype != nd4j::DataType::FLOAT32 && dtype != nd4j::DataType::DOUBLE && dtype != nd4j::DataType::FLOAT8 && dtype != nd4j::DataType::HALF) + void Environment::setDefaultFloatDataType(sd::DataType dtype) { + if (dtype != sd::DataType::FLOAT32 && dtype != sd::DataType::DOUBLE && dtype != sd::DataType::FLOAT8 && dtype != sd::DataType::HALF) throw std::runtime_error("Default Float data type must be one of [FLOAT8, FLOAT16, FLOAT32, DOUBLE]"); _dataType.store(dtype); @@ -344,27 +353,27 @@ namespace nd4j { } void Environment::setGroupLimit(int group, Nd4jLong numBytes) { - nd4j::memory::MemoryCounter::getInstance()->setGroupLimit((nd4j::memory::MemoryType) group, numBytes); + sd::memory::MemoryCounter::getInstance()->setGroupLimit((sd::memory::MemoryType) group, numBytes); } void Environment::setDeviceLimit(int deviceId, Nd4jLong numBytes) { - nd4j::memory::MemoryCounter::getInstance()->setDeviceLimit(deviceId, numBytes); + sd::memory::MemoryCounter::getInstance()->setDeviceLimit(deviceId, numBytes); } Nd4jLong Environment::getGroupLimit(int group) { - return nd4j::memory::MemoryCounter::getInstance()->groupLimit((nd4j::memory::MemoryType) group); + return sd::memory::MemoryCounter::getInstance()->groupLimit((sd::memory::MemoryType) group); } Nd4jLong Environment::getDeviceLimit(int deviceId) { - return nd4j::memory::MemoryCounter::getInstance()->deviceLimit(deviceId); + return sd::memory::MemoryCounter::getInstance()->deviceLimit(deviceId); } Nd4jLong Environment::getGroupCounter(int group) { - return nd4j::memory::MemoryCounter::getInstance()->allocatedGroup((nd4j::memory::MemoryType) group); + return sd::memory::MemoryCounter::getInstance()->allocatedGroup((sd::memory::MemoryType) group); } Nd4jLong Environment::getDeviceCounter(int deviceId) { - return nd4j::memory::MemoryCounter::getInstance()->allocatedDevice(deviceId); + return sd::memory::MemoryCounter::getInstance()->allocatedDevice(deviceId); } uint64_t Environment::maxPrimaryMemory() { @@ -375,6 +384,6 @@ namespace nd4j { return _maxTotalSpecialMemory.load(); } - nd4j::Environment *nd4j::Environment::_instance = 0; + sd::Environment *sd::Environment::_instance = 0; } diff --git a/libnd4j/include/cnpy/cnpy.cpp b/libnd4j/include/legacy/impl/cnpy.cpp similarity index 96% rename from libnd4j/include/cnpy/cnpy.cpp rename to libnd4j/include/legacy/impl/cnpy.cpp index a09b38bfd..ee4fa36b0 100644 --- a/libnd4j/include/cnpy/cnpy.cpp +++ b/libnd4j/include/legacy/impl/cnpy.cpp @@ -26,9 +26,9 @@ //Released under MIT License //license available in LICENSE file, or at http://www.opensource.org/licenses/mit-license.php -#include +#include #include -#include"cnpy.h" +#include #include @@ -104,7 +104,7 @@ char cnpy::mapType() { else return '?'; } -nd4j::DataType cnpy::dataTypeFromHeader(char *data) { +sd::DataType cnpy::dataTypeFromHeader(char *data) { // indices for type & data size const int st = 10; @@ -120,31 +120,31 @@ nd4j::DataType cnpy::dataTypeFromHeader(char *data) { switch (t) { case 'b': - return nd4j::DataType::BOOL; + return sd::DataType::BOOL; case 'i': switch (s) { - case '1': return nd4j::DataType::INT8; - case '2': return nd4j::DataType::INT16; - case '4': return nd4j::DataType::INT32; - case '8': return nd4j::DataType::INT64; + case '1': return sd::DataType::INT8; + case '2': return sd::DataType::INT16; + case '4': return sd::DataType::INT32; + case '8': return sd::DataType::INT64; default: throw std::runtime_error("Only data sizes of [1, 2, 4, 8] are supported for Integer data types import"); } case 'f': switch (s) { - case '1': return nd4j::DataType::FLOAT8; - case '2': return nd4j::DataType::HALF; - case '4': return nd4j::DataType::FLOAT32; - case '8': return nd4j::DataType::DOUBLE; + case '1': return sd::DataType::FLOAT8; + case '2': return sd::DataType::HALF; + case '4': return sd::DataType::FLOAT32; + case '8': return sd::DataType::DOUBLE; default: throw std::runtime_error("Only data sizes of [1, 2, 4, 8] are supported for Float data types import"); } case 'u': switch (s) { - case '1': return nd4j::DataType::UINT8; - case '2': return nd4j::DataType::UINT16; - case '4': return nd4j::DataType::UINT32; - case '8': return nd4j::DataType::UINT64; + case '1': return sd::DataType::UINT8; + case '2': return sd::DataType::UINT16; + case '4': return sd::DataType::UINT32; + case '8': return sd::DataType::UINT64; default: throw std::runtime_error("Only data sizes of [1, 2, 4, 8] are supported for Unsigned data types import"); } diff --git a/libnd4j/include/loops/BroadcastPairwiseConverter.h b/libnd4j/include/loops/BroadcastPairwiseConverter.h index f1fda4a9a..acb7e8d64 100644 --- a/libnd4j/include/loops/BroadcastPairwiseConverter.h +++ b/libnd4j/include/loops/BroadcastPairwiseConverter.h @@ -21,10 +21,10 @@ #ifndef DEV_TESTS_BROADCASTPAIRWISECONVERTER_H #define DEV_TESTS_BROADCASTPAIRWISECONVERTER_H -#include +#include #include -namespace nd4j { +namespace sd { ////////////////////////////////////////////////////////////////////////// inline pairwise::Ops fromBroadcastToPairwise(broadcast::Ops op) { diff --git a/libnd4j/include/loops/BroadcastScalarConverter.h b/libnd4j/include/loops/BroadcastScalarConverter.h index ee745901a..12006c293 100644 --- a/libnd4j/include/loops/BroadcastScalarConverter.h +++ b/libnd4j/include/loops/BroadcastScalarConverter.h @@ -20,10 +20,10 @@ #ifndef DEV_TESTS_BROADCASTSCALARCONVERTER_H #define DEV_TESTS_BROADCASTSCALARCONVERTER_H -#include +#include #include -namespace nd4j { +namespace sd { inline bool isConvertibleToScalar(broadcast::Ops op) { int opNum = (int) op; diff --git a/libnd4j/include/loops/TrueBroadcastHelper.h b/libnd4j/include/loops/TrueBroadcastHelper.h deleted file mode 100644 index 4101aa08e..000000000 --- a/libnd4j/include/loops/TrueBroadcastHelper.h +++ /dev/null @@ -1,84 +0,0 @@ -/******************************************************************************* - * Copyright (c) 2015-2018 Skymind, Inc. - * - * This program and the accompanying materials are made available under the - * terms of the Apache License, Version 2.0 which is available at - * https://www.apache.org/licenses/LICENSE-2.0. - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the - * License for the specific language governing permissions and limitations - * under the License. - * - * SPDX-License-Identifier: Apache-2.0 - ******************************************************************************/ - -// -// @author Yurii Shyrma (iuriish@yahoo.com) -// - -#ifndef LIBND4J_TRUEBROADCASTHELPER_H -#define LIBND4J_TRUEBROADCASTHELPER_H - -#include - -namespace nd4j { -namespace helpers { - -//////////////////////////////////////////////////////////////////////// -template -class TrueBroadcastHelper { - - #ifdef __CUDACC__ - template - static __host__ void execLauncher(dim3 launchDims, cudaStream_t *stream, const void *vx, const Nd4jLong *xShapeInfo, const void *vy, const Nd4jLong *yShapeInfo, void *vz, const Nd4jLong *zShapeInfo); - #else - template - static void exec(const NDArray& xArr, const NDArray& yArr, NDArray& zArr); - #endif - - public: - static void exec(const nd4j::broadcast::Ops opNum, const NDArray& xArr, const NDArray& yArr, NDArray& zArr); -}; - -template -class TrueBroadcastBoolHelper { - - #ifdef __CUDACC__ - template - static __host__ void execLauncher(dim3 launchDims, cudaStream_t *stream, const void *vx, const Nd4jLong *xShapeInfo, const void *vy, const Nd4jLong *yShapeInfo, void *vz, const Nd4jLong *zShapeInfo); - #else - template - static void exec(const NDArray& xArr, const NDArray& yArr, NDArray& zArr); - #endif - - public: - - static void exec(const nd4j::broadcast::BoolOps opNum, const NDArray& xArr, const NDArray& yArr, NDArray& zArr); -}; - -//////////////////////////////////////////////////////////////////////// -template -class TrueBroadcastIntHelper { - - #ifdef __CUDACC__ - template - static __host__ void execLauncher(dim3 launchDims, cudaStream_t *stream, const void *vx, const Nd4jLong *xShapeInfo, const void *vy, const Nd4jLong *yShapeInfo, void *vz, const Nd4jLong *zShapeInfo); - #else - template - static void exec(const NDArray& xArr, const NDArray& yArr, NDArray& zArr); - #endif - - public: - - static void exec(const nd4j::broadcast::IntOps opNum, const NDArray& xArr, const NDArray& yArr, NDArray& zArr); -}; - - -} -} - - - -#endif //LIBND4J_BIDIAGONALUP_H diff --git a/libnd4j/include/loops/broadcasting.h b/libnd4j/include/loops/broadcasting.h index ebf702004..20c95588c 100755 --- a/libnd4j/include/loops/broadcasting.h +++ b/libnd4j/include/loops/broadcasting.h @@ -23,12 +23,12 @@ #ifndef BROADCASTING_H_ #define BROADCASTING_H_ -#include +#include #include -#include -#include +#include +#include #include -#include +#include #include #ifdef __CUDACC__ @@ -69,11 +69,21 @@ namespace functions { int *dimension, int dimensionLength, Nd4jLong *tadOnlyShapeInfo, Nd4jLong *tadOffsets, Nd4jLong *tadOnlyShapeInfoZ, Nd4jLong *tadOffsetsZ); + template + static __device__ void transformCuda(const void *x, const Nd4jLong *xShapeInfo, + const void *y, const Nd4jLong *yShapeInfo, + void *z, const Nd4jLong *zShapeInfo); + template static __host__ void intermediateBroadcast(dim3 launchDims, cudaStream_t *stream, void *x, Nd4jLong *xShapeInfo, void *y, Nd4jLong *yShapeInfo, void *result, Nd4jLong *resultShapeInfo, int *dimension, int dimensionLength, Nd4jLong *tadOnlyShapeInfo, Nd4jLong *tadOffsets, Nd4jLong *tadOnlyShapeInfoZ, Nd4jLong *tadOffsetsZ); + template + static __host__ void intermediateBroadcast(dim3 launchDims, cudaStream_t *stream, const void *x, const Nd4jLong *xShapeInfo, const void *y, const Nd4jLong *yShapeInfo, void *z, const Nd4jLong *zShapeInfo); + static __host__ void execBroadcast(dim3 launchDims, cudaStream_t *stream, int opNum, void *x, Nd4jLong *xShapeInfo, void *y, Nd4jLong *yShapeInfo, void *result, Nd4jLong *resultShapeInfo, int *dimension, int dimensionLength, Nd4jLong *tadOnlyShapeInfo, Nd4jLong *tadOffsets, Nd4jLong *tadOnlyShapeInfoZ, Nd4jLong *tadOffsetsZ); + static __host__ void execBroadcast(dim3 launchDims, cudaStream_t *stream, const int opNum, const void *x, const Nd4jLong *xShapeInfo, const void *y, const Nd4jLong *yShapeInfo, void *z, const Nd4jLong *zShapeInfo); + template static __device__ void transformInverseCuda( @@ -123,7 +133,7 @@ namespace functions { Nd4jLong *tadOffset, Nd4jLong *tadShapeInfoZ, Nd4jLong *tadOffsetZ, - nd4j::LoopKind::Kind loopKind, + sd::LoopKind::Kind loopKind, uint64_t start, uint64_t stop); @@ -151,7 +161,7 @@ namespace functions { Nd4jLong *tadOffset, Nd4jLong *tadShapeInfoZ, Nd4jLong *tadOffsetZ, - nd4j::LoopKind::Kind loopKind, + sd::LoopKind::Kind loopKind, uint64_t start, uint64_t stop); @@ -170,6 +180,17 @@ namespace functions { Nd4jLong *tadOffsetZ, uint64_t start, uint64_t stop); + + static void exec(const int opNum, + const void *x, const Nd4jLong *xShapeInfo, + const void *y, const Nd4jLong *yShapeInfo, + void *z, const Nd4jLong *zShapeInfo); + + template + static void exec(const void *x, const Nd4jLong *xShapeInfo, + const void *y, const Nd4jLong *yShapeInfo, + void *z, const Nd4jLong *zShapeInfo); + #endif }; } diff --git a/libnd4j/include/loops/broadcasting_bool.h b/libnd4j/include/loops/broadcasting_bool.h index 7ba5fa9eb..9bab82c81 100644 --- a/libnd4j/include/loops/broadcasting_bool.h +++ b/libnd4j/include/loops/broadcasting_bool.h @@ -23,12 +23,12 @@ #ifndef BROADCASTING_BOOL_H_ #define BROADCASTING_BOOL_H_ -#include +#include #include -#include -#include +#include +#include #include -#include +#include #include #ifdef __CUDACC__ @@ -69,11 +69,30 @@ namespace functions { int *dimension, int dimensionLength, Nd4jLong *tadOnlyShapeInfo, Nd4jLong *tadOffsets, Nd4jLong *tadOnlyShapeInfoZ, Nd4jLong *tadOffsetsZ); + template + static __device__ void transformCuda(const void *x, const Nd4jLong *xShapeInfo, + const void *y, const Nd4jLong *yShapeInfo, + void *z, const Nd4jLong *zShapeInfo, + void *extraParams); + template static __host__ void intermediateBroadcast(dim3 launchDims, cudaStream_t *stream, void *x, Nd4jLong *xShapeInfo, void *y, Nd4jLong *yShapeInfo, void *result, Nd4jLong *resultShapeInfo, void *extraParams, int *dimension, int dimensionLength, Nd4jLong *tadOnlyShapeInfo, Nd4jLong *tadOffsets, Nd4jLong *tadOnlyShapeInfoZ, Nd4jLong *tadOffsetsZ); + template + static __host__ void intermediateBroadcast(dim3 launchDims, cudaStream_t *stream, + const void *x, const Nd4jLong *xShapeInfo, + const void *y, const Nd4jLong *yShapeInfo, + void *z, const Nd4jLong *zShapeInfo, + void *extraParams); + static __host__ void execBroadcast(dim3 launchDims, cudaStream_t *stream, int opNum, void *x, Nd4jLong *xShapeInfo, void *y, Nd4jLong *yShapeInfo, void *result, Nd4jLong *resultShapeInfo, void *extraParams, int *dimension, int dimensionLength, Nd4jLong *tadOnlyShapeInfo, Nd4jLong *tadOffsets, Nd4jLong *tadOnlyShapeInfoZ, Nd4jLong *tadOffsetsZ); + static __host__ void execBroadcast(dim3 launchDims, cudaStream_t *stream, const int opNum, + const void *x, const Nd4jLong *xShapeInfo, + const void *y, const Nd4jLong *yShapeInfo, + void *z, const Nd4jLong *zShapeInfo, + void *extraParams); + template static __device__ void transformInverseCuda( void *x, @@ -110,6 +129,12 @@ namespace functions { uint64_t start, uint64_t stop); + static void exec(const int opNum, + const void *x, const Nd4jLong *xShapeInfo, + const void *y, const Nd4jLong *yShapeInfo, + void *z, const Nd4jLong *zShapeInfo, + void *extraParams); + static void execInverse(int opNum, void *x, Nd4jLong *xShapeInfo, @@ -155,6 +180,12 @@ namespace functions { uint64_t start, uint64_t stop); + template + static void exec(const void *x, const Nd4jLong *xShapeInfo, + const void *y, const Nd4jLong *yShapeInfo, + void *z, const Nd4jLong *zShapeInfo, + void *extraParams); + template static void execInverse(void *x, Nd4jLong *xShapeInfo, diff --git a/libnd4j/include/loops/broadcasting_int.h b/libnd4j/include/loops/broadcasting_int.h index 92e4ca7dd..81149ad8a 100644 --- a/libnd4j/include/loops/broadcasting_int.h +++ b/libnd4j/include/loops/broadcasting_int.h @@ -23,12 +23,12 @@ #ifndef BROADCASTING_INT_H_ #define BROADCASTING_INT_H_ -#include +#include #include -#include -#include +#include +#include #include -#include +#include #include #ifdef __CUDACC__ @@ -68,11 +68,27 @@ namespace functions { int *dimension, int dimensionLength, Nd4jLong *tadOnlyShapeInfo, Nd4jLong *tadOffsets, Nd4jLong *tadOnlyShapeInfoZ, Nd4jLong *tadOffsetsZ); + template + static __device__ void transformCuda(const void *x, const Nd4jLong *xShapeInfo, + const void *y, const Nd4jLong *yShapeInfo, + void *z, const Nd4jLong *zShapeInfo); + template static __host__ void intermediateBroadcast(dim3 launchDims, cudaStream_t *stream, void *x, Nd4jLong *xShapeInfo, void *y, Nd4jLong *yShapeInfo, void *result, Nd4jLong *resultShapeInfo, int *dimension, int dimensionLength, Nd4jLong *tadOnlyShapeInfo, Nd4jLong *tadOffsets, Nd4jLong *tadOnlyShapeInfoZ, Nd4jLong *tadOffsetsZ); + template + static __host__ void intermediateBroadcast(dim3 launchDims, cudaStream_t *stream, + const void *x, const Nd4jLong *xShapeInfo, + const void *y, const Nd4jLong *yShapeInfo, + void *z, const Nd4jLong *zShapeInfo); + static __host__ void execBroadcast(dim3 launchDims, cudaStream_t *stream, int opNum, void *x, Nd4jLong *xShapeInfo, void *y, Nd4jLong *yShapeInfo, void *result, Nd4jLong *resultShapeInfo, int *dimension, int dimensionLength, Nd4jLong *tadOnlyShapeInfo, Nd4jLong *tadOffsets, Nd4jLong *tadOnlyShapeInfoZ, Nd4jLong *tadOffsetsZ); + static __host__ void execBroadcast(dim3 launchDims, cudaStream_t *stream, const int opNum, + const void *x, const Nd4jLong *xShapeInfo, + const void *y, const Nd4jLong *yShapeInfo, + void *z, const Nd4jLong *zShapeInfo); + template static __device__ void transformInverseCuda( void *x, @@ -107,6 +123,11 @@ namespace functions { uint64_t start, uint64_t stop); + static void exec(const int opNum, + const void *x, const Nd4jLong *xShapeInfo, + const void *y, const Nd4jLong *yShapeInfo, + void *z, const Nd4jLong *zShapeInfo); + static void execInverse(int opNum, void *x, Nd4jLong *xShapeInfo, @@ -150,6 +171,11 @@ namespace functions { uint64_t start, uint64_t stop); + template + static void exec(const void *x, const Nd4jLong *xShapeInfo, + const void *y, const Nd4jLong *yShapeInfo, + void *z, const Nd4jLong *zShapeInfo); + template static void execInverse(void *x, Nd4jLong *xShapeInfo, diff --git a/libnd4j/include/loops/cpu/TrueBroadcastHelper.hpp b/libnd4j/include/loops/cpu/TrueBroadcastHelper.hpp deleted file mode 100644 index f047d1136..000000000 --- a/libnd4j/include/loops/cpu/TrueBroadcastHelper.hpp +++ /dev/null @@ -1,291 +0,0 @@ -/******************************************************************************* - * Copyright (c) 2015-2018 Skymind, Inc. - * - * This program and the accompanying materials are made available under the - * terms of the Apache License, Version 2.0 which is available at - * https://www.apache.org/licenses/LICENSE-2.0. - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the - * License for the specific language governing permissions and limitations - * under the License. - * - * SPDX-License-Identifier: Apache-2.0 - ******************************************************************************/ - - // - // @author Yurii Shyrma (iuriish@yahoo.com) - // - -#include -#include -#include - -using namespace simdOps; - -namespace nd4j { - namespace helpers { - - //////////////////////////////////////////////////////////////////////// - template - template - void TrueBroadcastHelper::exec(const NDArray& xArr, const NDArray& yArr, NDArray& zArr) { - - - const X* x = reinterpret_cast(xArr.getBuffer()); - const Y* y = reinterpret_cast(yArr.getBuffer()); - Z* z = reinterpret_cast(zArr.getBuffer()); - - const auto xShapeInfo = xArr.getShapeInfo(); - const auto yShapeInfo = yArr.getShapeInfo(); - const auto zShapeInfo = zArr.getShapeInfo(); - - const int xRank = xArr.rankOf(); - const int yRank = yArr.rankOf(); - const int zRank = zArr.rankOf(); - - bool bSpecialCase = (1 == xArr.ews() && 'c' == xArr.ordering() && - 1 == yArr.ews() && 'c' == yArr.ordering() && - 1 == zArr.ews() && 'c' == zArr.ordering()); - - if (bSpecialCase && yArr.isColumnVector() && 1 == xArr.sizeAt(-1) ) { - auto yLen = (uint32_t)yArr.lengthOf(); - auto func = PRAGMA_THREADS_FOR{ - for (uint32_t i = start; i < stop; i++) { - auto rZ = z + (i * yLen); - auto v = x[i]; - for (uint32_t j = 0; j < yLen; j++) { - rZ[j] = OpType::op(v, y[j]); - } - } - }; - samediff::Threads::parallel_tad(func, 0, xArr.lengthOf()); - return; - } - - - auto yShapeInt = yArr.getShapeAsVectorInt(); - auto xShapeInt = xArr.getShapeAsVectorInt(); - auto nCountY = std::count_if(yShapeInt.cbegin(), yShapeInt.cend(), [](int i) { return i == 1; }); - auto nCountX = std::count_if(xShapeInt.cbegin(), xShapeInt.cend(), [](int i) { return i == 1; }); - - bool bSpecialCase2 = (xRank == zRank && yRank == zRank && 1 == xArr.sizeAt(-1) && 1 == yArr.sizeAt(-2) && 1 == nCountY && 1 == nCountX); - - if (bSpecialCase && bSpecialCase2) { - - int zDim1 = zArr.sizeAt(-2); - int zDim2 = zArr.sizeAt(-1); - - int nLen = zArr.lengthOf() / yArr.sizeAt(-1); - - auto func = PRAGMA_THREADS_FOR{ - for (uint32_t total = start; total < stop; total++) { - - uint32_t i = total / zDim1; - uint32_t j = total % zDim1; - - uint32_t index = (i * zDim1) + j; - auto rZ = z + (index * zDim2); - auto rY = y + (i * zDim2); - auto rX = x[index]; - - for (uint32_t n = 0; n < zDim2; n++) { - rZ[n] = OpType::op(rX, rY[n]); - } - } - }; - samediff::Threads::parallel_tad(func, 0, nLen, 1); - return; - } - - - const Nd4jLong zLen = zArr.lengthOf(); - auto func = PRAGMA_THREADS_FOR{ - std::vector xCoords(xArr.rankOf()), yCoords(yArr.rankOf()), zCoords(zArr.rankOf()); - - for (auto i = start; i < stop; ++i) { - - shape::index2coords(i, zShapeInfo, zCoords.data()); - - for (int ix = xRank - 1, iy = yRank - 1, iz = zRank - 1; iz >= 0; --iz) { - - if (ix >= 0) { - if (xShapeInfo[ix + 1] == zShapeInfo[iz + 1]) { - xCoords[ix--] = zCoords[iz]; - } - else { - xCoords[ix--] = 0; - } - } - - if (iy >= 0) { - if (yShapeInfo[iy + 1] == zShapeInfo[iz + 1]) { - yCoords[iy--] = zCoords[iz]; - } - else { - yCoords[iy--] = 0; - } - } - } - - const auto xOffset = shape::getOffset(xShapeInfo, xCoords.data()); - const auto yOffset = shape::getOffset(yShapeInfo, yCoords.data()); - const auto zOffset = shape::getOffset(zShapeInfo, zCoords.data()); - - z[zOffset] = OpType::op(x[xOffset], y[yOffset]); - } - }; - - samediff::Threads::parallel_for(func, 0, zLen); - } - - template - void TrueBroadcastHelper::exec(const nd4j::broadcast::Ops opNum, const NDArray& xArr, const NDArray& yArr, NDArray& zArr) { - DISPATCH_BY_OPNUM_TTT(exec, PARAMS(xArr, yArr, zArr), BROADCAST_OPS); - } - - //////////////////////////////////////////////////////////////////////// - template - template - void TrueBroadcastBoolHelper::exec(const NDArray& xArr, const NDArray& yArr, NDArray& zArr) { - - const X* x = reinterpret_cast(xArr.getBuffer()); - const X* y = reinterpret_cast(yArr.getBuffer()); - Z* z = reinterpret_cast(zArr.getBuffer()); - - const auto xShapeInfo = xArr.getShapeInfo(); - const auto yShapeInfo = yArr.getShapeInfo(); - const auto zShapeInfo = zArr.getShapeInfo(); - - const int xRank = xArr.rankOf(); - const int yRank = yArr.rankOf(); - const int zRank = zArr.rankOf(); - - const Nd4jLong zLen = zArr.lengthOf(); - - auto func = PRAGMA_THREADS_FOR{ - std::vector xCoords(xArr.rankOf()), yCoords(yArr.rankOf()), zCoords(zArr.rankOf()); - - for (auto i = start; i < stop; ++i) { - - shape::index2coords(i, zShapeInfo, zCoords.data()); - - for (int ix = xRank - 1, iy = yRank - 1, iz = zRank - 1; iz >= 0; --iz) { - - if (ix >= 0) { - if (xShapeInfo[ix + 1] == zShapeInfo[iz + 1]) { - xCoords[ix--] = zCoords[iz]; - } - else { - xCoords[ix--] = 0; - } - } - - if (iy >= 0) { - if (yShapeInfo[iy + 1] == zShapeInfo[iz + 1]) { - yCoords[iy--] = zCoords[iz]; - } - else { - yCoords[iy--] = 0; - } - } - } - - const auto xOffset = shape::getOffset(xShapeInfo, xCoords.data()); - const auto yOffset = shape::getOffset(yShapeInfo, yCoords.data()); - const auto zOffset = shape::getOffset(zShapeInfo, zCoords.data()); - - z[zOffset] = OpType::op(x[xOffset], y[yOffset], nullptr); - } - }; - - samediff::Threads::parallel_for(func, 0, zLen); - } - - template - void TrueBroadcastBoolHelper::exec(const nd4j::broadcast::BoolOps opNum, const NDArray& xArr, const NDArray& yArr, NDArray& zArr) { - DISPATCH_BY_OPNUM_TT(exec, PARAMS(xArr, yArr, zArr), BROADCAST_BOOL_OPS); - } - - //////////////////////////////////////////////////////////////////////// - template - template - void TrueBroadcastIntHelper::exec(const NDArray& xArr, const NDArray& yArr, NDArray& zArr) { - - const X* x = reinterpret_cast(xArr.getBuffer()); - const X* y = reinterpret_cast(yArr.getBuffer()); - X* z = reinterpret_cast(zArr.getBuffer()); - - const auto xShapeInfo = xArr.getShapeInfo(); - const auto yShapeInfo = yArr.getShapeInfo(); - const auto zShapeInfo = zArr.getShapeInfo(); - - const int xRank = xArr.rankOf(); - const int yRank = yArr.rankOf(); - const int zRank = zArr.rankOf(); - - const Nd4jLong zLen = zArr.lengthOf(); - - auto func = PRAGMA_THREADS_FOR{ - std::vector xCoords(xArr.rankOf()), yCoords(yArr.rankOf()), zCoords(zArr.rankOf()); - - for (auto i = start; i < stop; ++i) { - - shape::index2coords(i, zShapeInfo, zCoords.data()); - - for (int ix = xRank - 1, iy = yRank - 1, iz = zRank - 1; iz >= 0; --iz) { - - if (ix >= 0) { - if (xShapeInfo[ix + 1] == zShapeInfo[iz + 1]) { - xCoords[ix--] = zCoords[iz]; - } - else { - xCoords[ix--] = 0; - } - } - - if (iy >= 0) { - if (yShapeInfo[iy + 1] == zShapeInfo[iz + 1]) { - yCoords[iy--] = zCoords[iz]; - } - else { - yCoords[iy--] = 0; - } - } - } - - const auto xOffset = shape::getOffset(xShapeInfo, xCoords.data()); - const auto yOffset = shape::getOffset(yShapeInfo, yCoords.data()); - const auto zOffset = shape::getOffset(zShapeInfo, zCoords.data()); - - z[zOffset] = OpType::op(x[xOffset], y[yOffset]); - } - }; - - samediff::Threads::parallel_for(func, 0, zLen); - } - - template - void TrueBroadcastIntHelper::exec(const nd4j::broadcast::IntOps opNum, const NDArray& xArr, const NDArray& yArr, NDArray& zArr) { - DISPATCH_BY_OPNUM_T(exec, PARAMS(xArr, yArr, zArr), BROADCAST_INT_OPS); - } - - /* - BUILD_PAIRWISE_TEMPLATE(template class ND4J_EXPORT TrueBroadcastHelper, , PAIRWISE_TYPES_0); - BUILD_PAIRWISE_TEMPLATE(template class ND4J_EXPORT TrueBroadcastHelper, , PAIRWISE_TYPES_1); - BUILD_PAIRWISE_TEMPLATE(template class ND4J_EXPORT TrueBroadcastHelper, , PAIRWISE_TYPES_2); - BUILD_PAIRWISE_TEMPLATE(template class ND4J_EXPORT TrueBroadcastHelper, , PAIRWISE_TYPES_3); - BUILD_PAIRWISE_TEMPLATE(template class ND4J_EXPORT TrueBroadcastHelper, , PAIRWISE_TYPES_4); - BUILD_PAIRWISE_TEMPLATE(template class ND4J_EXPORT TrueBroadcastHelper, , PAIRWISE_TYPES_5); - BUILD_PAIRWISE_TEMPLATE(template class ND4J_EXPORT TrueBroadcastHelper, , PAIRWISE_TYPES_6); - BUILD_PAIRWISE_TEMPLATE(template class ND4J_EXPORT TrueBroadcastHelper, , PAIRWISE_TYPES_7); - BUILD_PAIRWISE_TEMPLATE(template class ND4J_EXPORT TrueBroadcastHelper, , PAIRWISE_TYPES_8); - BUILD_PAIRWISE_TEMPLATE(template class ND4J_EXPORT TrueBroadcastHelper, , PAIRWISE_TYPES_9); - - BUILD_DOUBLE_TEMPLATE(template class ND4J_EXPORT TrueBroadcastBoolHelper, , LIBND4J_TYPES, BOOL_TYPES); - - BUILD_SINGLE_TEMPLATE(template class ND4J_EXPORT TrueBroadcastIntHelper, , INTEGER_TYPES); - */ - } -} diff --git a/libnd4j/include/loops/cpu/broadcasting.hpp b/libnd4j/include/loops/cpu/broadcasting.hpp index 62058bd20..8de52cca7 100644 --- a/libnd4j/include/loops/cpu/broadcasting.hpp +++ b/libnd4j/include/loops/cpu/broadcasting.hpp @@ -18,11 +18,11 @@ // @author raver119@gmail.com // -#include +#include #include #include #include -#include +#include #include #include #include @@ -30,7 +30,7 @@ using namespace simdOps; namespace functions { - namespace broadcast { +namespace broadcast { template void Broadcast::execInverse(const int opNum, @@ -76,7 +76,7 @@ namespace functions { Nd4jLong *xTadOffset, Nd4jLong *zTadShapeInfo, Nd4jLong *zTadOffset, - nd4j::LoopKind::Kind loopKind, + sd::LoopKind::Kind loopKind, uint64_t start, uint64_t stop) { DISPATCH_BY_OPNUM_TTT(exec, PARAMS(x, @@ -93,6 +93,7 @@ namespace functions { zTadOffset, loopKind, start, stop), BROADCAST_OPS); } + template template void Broadcast::exec(void *vx, @@ -107,7 +108,7 @@ namespace functions { Nd4jLong *xTadOffset, Nd4jLong *zTadShapeInfo, Nd4jLong *zTadOffset, - nd4j::LoopKind::Kind loopKind, + sd::LoopKind::Kind loopKind, uint64_t start, uint64_t stop) { @@ -123,7 +124,7 @@ namespace functions { auto tadOffsets = xTadOffset; if (xTadShapeInfo == nullptr || tadOffsets == nullptr) { - auto tadPack = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(xShapeInfo, dimension, dimensionLength); + auto tadPack = sd::ConstantTadHelper::getInstance()->tadForDimensions(xShapeInfo, dimension, dimensionLength); xTadShapeShapeInfo = tadPack.primaryShapeInfo(); tadOffsets = tadPack.primaryOffsets(); @@ -146,15 +147,15 @@ namespace functions { auto zEws = shape::elementWiseStride(zTadShapeInfo); - const nd4j::LoopKind::Kind kindOfLoop = - (loopKind == nd4j::LoopKind::BROADCAST_SCALAR_X || - loopKind == nd4j::LoopKind::BROADCAST_SCALAR_Y || - loopKind == nd4j::LoopKind::BROADCAST_3D || - loopKind == nd4j::LoopKind::BROADCAST_4D || - loopKind == nd4j::LoopKind::BROADCAST_5D) - ? loopKind : nd4j::LoopKind::deduceKindOfLoopXYZ(xTadShapeShapeInfo, yShapeInfo, zTadShapeInfo); + const sd::LoopKind::Kind kindOfLoop = + (loopKind == sd::LoopKind::BROADCAST_SCALAR_X || + loopKind == sd::LoopKind::BROADCAST_SCALAR_Y || + loopKind == sd::LoopKind::BROADCAST_3D || + loopKind == sd::LoopKind::BROADCAST_4D || + loopKind == sd::LoopKind::BROADCAST_5D) + ? loopKind : sd::LoopKind::deduceKindOfLoopXYZ(xTadShapeShapeInfo, yShapeInfo, zTadShapeInfo); - if (kindOfLoop == nd4j::LoopKind::EWS1) { + if (kindOfLoop == sd::LoopKind::EWS1) { for (auto i = start; i < stop; i++) { auto oX = x + tadOffsets[i]; auto oZ = z + zTadOffset[i]; @@ -164,7 +165,7 @@ namespace functions { oZ[f] = OpType::op(oX[f], y[f]); } } - else if(kindOfLoop == nd4j::LoopKind::EWSNONZERO){ + else if(kindOfLoop == sd::LoopKind::EWSNONZERO){ for (auto i = start; i < stop; i++) { auto oX = x + tadOffsets[i]; auto oZ = z + zTadOffset[i]; @@ -173,7 +174,7 @@ namespace functions { for (unsigned int f = 0; f < tadLength; f++) oZ[f * zEws] = OpType::op(oX[f * xEws], y[f * yEws]); } - } else if(kindOfLoop == nd4j::LoopKind::BROADCAST_SCALAR_X){ + } else if(kindOfLoop == sd::LoopKind::BROADCAST_SCALAR_X){ // this loop effectively turns broadcast into series of scalar ops auto loopLength = yShapeInfo[shape::rank(yShapeInfo)]; @@ -184,10 +185,10 @@ namespace functions { const auto oX = x[i]; PRAGMA_OMP_SIMD - for (unsigned int f = 0; f < loopLength; f++) + for (Nd4jLong f = 0; f < loopLength; f++) oZ[f] = OpType::op(oX, oY[f]); } - } else if(kindOfLoop == nd4j::LoopKind::BROADCAST_SCALAR_Y){ + } else if(kindOfLoop == sd::LoopKind::BROADCAST_SCALAR_Y){ // this loop effectively turns broadcast into series of scalar ops auto loopLength = xShapeInfo[shape::rank(xShapeInfo)]; @@ -198,11 +199,11 @@ namespace functions { const auto oY = y[i]; PRAGMA_OMP_SIMD - for (unsigned int f = 0; f < loopLength; f++) + for (Nd4jLong f = 0; f < loopLength; f++) oZ[f] = OpType::op(oX[f], oY); } } - else if (kindOfLoop == nd4j::LoopKind::BROADCAST_3D) { + else if (kindOfLoop == sd::LoopKind::BROADCAST_3D) { int xRank = shape::rank(xShapeInfo); int yRank = shape::rank(yShapeInfo); @@ -211,16 +212,16 @@ namespace functions { auto zStrides = shape::stride(zShapeInfo); Nd4jLong yStrides[3] = { 0,0,0 }; - nd4j::ShapeUtils::copyCertainStridesFromShapeInfo(yShapeInfo, xRank, dimensionLength, dimension, yStrides); + sd::ShapeUtils::copyCertainStridesFromShapeInfo(yShapeInfo, xRank, dimensionLength, dimension, yStrides); - uint32_t nSize1 = shape::sizeAt(zShapeInfo, 1); - uint32_t nSize2 = shape::sizeAt(zShapeInfo, 2); + uint64_t nSize1 = shape::sizeAt(zShapeInfo, 1); + uint64_t nSize2 = shape::sizeAt(zShapeInfo, 2); - for (uint32_t index0 = start; index0 < stop; index0++) { + for (auto index0 = start; index0 < stop; index0++) { PRAGMA_OMP_SIMD - for (uint32_t index1 = 0; index1 < nSize1; index1++) { - for (uint32_t index2 = 0; index2 < nSize2; index2++) { + for (uint64_t index1 = 0; index1 < nSize1; index1++) { + for (uint64_t index2 = 0; index2 < nSize2; index2++) { auto rX = x + (xStrides[0] * index0 + xStrides[1] * index1 + xStrides[2] * index2); auto rY = y + (yStrides[0] * index0 + yStrides[1] * index1 + yStrides[2] * index2); auto rZ = z + (zStrides[0] * index0 + zStrides[1] * index1 + zStrides[2] * index2); @@ -231,7 +232,7 @@ namespace functions { } } - else if (kindOfLoop == nd4j::LoopKind::BROADCAST_4D) { + else if (kindOfLoop == sd::LoopKind::BROADCAST_4D) { int xRank = shape::rank(xShapeInfo); int yRank = shape::rank(yShapeInfo); @@ -240,20 +241,20 @@ namespace functions { auto zStrides = shape::stride(zShapeInfo); Nd4jLong yStrides[4] = { 0,0,0,0 }; - nd4j::ShapeUtils::copyCertainStridesFromShapeInfo(yShapeInfo, xRank, dimensionLength, dimension, yStrides); + sd::ShapeUtils::copyCertainStridesFromShapeInfo(yShapeInfo, xRank, dimensionLength, dimension, yStrides); - uint32_t nSize1 = shape::sizeAt(zShapeInfo, 1); - uint32_t nSize2 = shape::sizeAt(zShapeInfo, 2); - uint32_t nSize3 = shape::sizeAt(zShapeInfo, 3); + uint64_t nSize1 = shape::sizeAt(zShapeInfo, 1); + uint64_t nSize2 = shape::sizeAt(zShapeInfo, 2); + uint64_t nSize3 = shape::sizeAt(zShapeInfo, 3); - for (uint32_t i = start; i < stop; i++) { + for (auto i = start; i < stop; i++) { - uint32_t index0 = i / nSize1; - uint32_t index1 = i % nSize1; + uint64_t index0 = i / nSize1; + uint64_t index1 = i % nSize1; PRAGMA_OMP_SIMD - for (uint32_t index2 = 0; index2 < nSize2; index2++) { - for (uint32_t index3 = 0; index3 < nSize3; index3++) { + for (uint64_t index2 = 0; index2 < nSize2; index2++) { + for (uint64_t index3 = 0; index3 < nSize3; index3++) { auto rX = x + (xStrides[0] * index0 + xStrides[1] * index1 + xStrides[2] * index2 + xStrides[3] * index3); auto rY = y + (yStrides[0] * index0 + yStrides[1] * index1 + yStrides[2] * index2 + yStrides[3] * index3); auto rZ = z + (zStrides[0] * index0 + zStrides[1] * index1 + zStrides[2] * index2 + zStrides[3] * index3); @@ -263,7 +264,7 @@ namespace functions { } } - else if (kindOfLoop == nd4j::LoopKind::BROADCAST_5D) { + else if (kindOfLoop == sd::LoopKind::BROADCAST_5D) { int xRank = shape::rank(xShapeInfo); int yRank = shape::rank(yShapeInfo); @@ -272,14 +273,14 @@ namespace functions { auto zStrides = shape::stride(zShapeInfo); Nd4jLong yStrides[5] = { 0,0,0,0,0 }; - nd4j::ShapeUtils::copyCertainStridesFromShapeInfo(yShapeInfo, xRank, dimensionLength, dimension, yStrides); + sd::ShapeUtils::copyCertainStridesFromShapeInfo(yShapeInfo, xRank, dimensionLength, dimension, yStrides); uint32_t nSize1 = shape::sizeAt(zShapeInfo, 1); uint32_t nSize2 = shape::sizeAt(zShapeInfo, 2); uint32_t nSize3 = shape::sizeAt(zShapeInfo, 3); uint32_t nSize4 = shape::sizeAt(zShapeInfo, 4); - for (uint32_t i = start; i < stop; i++) { + for (auto i = start; i < stop; i++) { uint32_t index0 = i / nSize1; uint32_t index1 = i % nSize1; @@ -301,7 +302,7 @@ namespace functions { } else if(shape::haveSameShapeAndStrides(xTadShapeShapeInfo, yShapeInfo) && shape::haveSameShapeAndStrides(xTadShapeShapeInfo, zTadShapeInfo)) { uint tadShapeShapeInfoCast[MAX_RANK]; - bool canCastX = nd4j::DataTypeUtils::castShapeInfo(xTadShapeShapeInfo, tadShapeShapeInfoCast); + bool canCastX = sd::DataTypeUtils::castShapeInfo(xTadShapeShapeInfo, tadShapeShapeInfoCast); for (auto i = start; i < stop; i++) { auto oX = x + tadOffsets[i]; @@ -317,8 +318,8 @@ namespace functions { else if(shape::haveSameShapeAndStrides(xTadShapeShapeInfo, yShapeInfo)) { uint tadShapeShapeInfoCast[MAX_RANK]; uint tadShapeInfoZCast[MAX_RANK]; - bool canCastX = nd4j::DataTypeUtils::castShapeInfo(xTadShapeShapeInfo, tadShapeShapeInfoCast); - bool canCastZ = nd4j::DataTypeUtils::castShapeInfo(zTadShapeInfo, tadShapeInfoZCast); + bool canCastX = sd::DataTypeUtils::castShapeInfo(xTadShapeShapeInfo, tadShapeShapeInfoCast); + bool canCastZ = sd::DataTypeUtils::castShapeInfo(zTadShapeInfo, tadShapeInfoZCast); for (auto i = start; i < stop; i++) { @@ -326,7 +327,7 @@ namespace functions { auto oX = x + tadOffsets[i]; PRAGMA_OMP_SIMD - for (int f = 0; f < tadLength; f++) { + for (unsigned int f = 0; f < tadLength; f++) { auto offset = shape::indexOffset(f, xTadShapeShapeInfo, tadShapeShapeInfoCast, canCastX); auto zOffset = shape::indexOffset(f, zTadShapeInfo, tadShapeInfoZCast, canCastZ); oZ[zOffset] = OpType::op(oX[offset], y[offset]); @@ -336,15 +337,15 @@ namespace functions { else if(shape::haveSameShapeAndStrides(xTadShapeShapeInfo, zTadShapeInfo)) { uint tadShapeShapeInfoCast[MAX_RANK]; uint yShapeInfoCast[MAX_RANK]; - bool canCastX = nd4j::DataTypeUtils::castShapeInfo(xTadShapeShapeInfo, tadShapeShapeInfoCast); - bool canCastY = nd4j::DataTypeUtils::castShapeInfo(yShapeInfo, yShapeInfoCast); + bool canCastX = sd::DataTypeUtils::castShapeInfo(xTadShapeShapeInfo, tadShapeShapeInfoCast); + bool canCastY = sd::DataTypeUtils::castShapeInfo(yShapeInfo, yShapeInfoCast); for (auto i = start; i < stop; i++) { auto oZ = z + zTadOffset[i]; auto oX = x + tadOffsets[i]; PRAGMA_OMP_SIMD - for (int f = 0; f < tadLength; f++) { + for (unsigned int f = 0; f < tadLength; f++) { auto offset = shape::indexOffset(f, xTadShapeShapeInfo, tadShapeShapeInfoCast, canCastX); auto yOffset = shape::indexOffset(f, yShapeInfo, yShapeInfoCast, canCastY); oZ[offset] = OpType::op(oX[offset], y[yOffset]); @@ -354,15 +355,15 @@ namespace functions { else if(shape::haveSameShapeAndStrides(yShapeInfo, zTadShapeInfo)) { uint tadShapeShapeInfoCast[MAX_RANK]; uint yShapeInfoCast[MAX_RANK]; - bool canCastX = nd4j::DataTypeUtils::castShapeInfo(xTadShapeShapeInfo, tadShapeShapeInfoCast); - bool canCastY = nd4j::DataTypeUtils::castShapeInfo(yShapeInfo, yShapeInfoCast); + bool canCastX = sd::DataTypeUtils::castShapeInfo(xTadShapeShapeInfo, tadShapeShapeInfoCast); + bool canCastY = sd::DataTypeUtils::castShapeInfo(yShapeInfo, yShapeInfoCast); for (auto i = start; i < stop; i++) { auto oZ = z + zTadOffset[i]; auto oX = x + tadOffsets[i]; PRAGMA_OMP_SIMD - for (int f = 0; f < tadLength; f++) { + for (unsigned int f = 0; f < tadLength; f++) { auto xOffset = shape::indexOffset(f, xTadShapeShapeInfo, tadShapeShapeInfoCast, canCastX); auto offset = shape::indexOffset(f, yShapeInfo, yShapeInfoCast, canCastY); oZ[offset] = OpType::op(oX[xOffset], y[offset]); @@ -373,16 +374,16 @@ namespace functions { uint tadShapeShapeInfoCast[MAX_RANK]; uint tadShapeInfoZCast[MAX_RANK]; uint yShapeInfoCast[MAX_RANK]; - bool canCastX = nd4j::DataTypeUtils::castShapeInfo(xTadShapeShapeInfo, tadShapeShapeInfoCast); - bool canCastY = nd4j::DataTypeUtils::castShapeInfo(yShapeInfo, yShapeInfoCast); - bool canCastZ = nd4j::DataTypeUtils::castShapeInfo(zTadShapeInfo, tadShapeInfoZCast); + bool canCastX = sd::DataTypeUtils::castShapeInfo(xTadShapeShapeInfo, tadShapeShapeInfoCast); + bool canCastY = sd::DataTypeUtils::castShapeInfo(yShapeInfo, yShapeInfoCast); + bool canCastZ = sd::DataTypeUtils::castShapeInfo(zTadShapeInfo, tadShapeInfoZCast); for (auto i = start; i < stop; i++) { auto oZ = z + zTadOffset[i]; auto oX = x + tadOffsets[i]; PRAGMA_OMP_SIMD - for (int f = 0; f < tadLength; f++) { + for (unsigned int f = 0; f < tadLength; f++) { auto xOffset = shape::indexOffset(f, xTadShapeShapeInfo, tadShapeShapeInfoCast, canCastX); auto yOffset = shape::indexOffset(f, yShapeInfo, yShapeInfoCast, canCastY); auto zOffset = shape::indexOffset(f, zTadShapeInfo, tadShapeInfoZCast, canCastZ); @@ -423,7 +424,7 @@ namespace functions { auto tadOffsets = yTadOffset; if (yTadShapeInfo == nullptr || tadOffsets == nullptr) { - auto tadPack = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(yShapeInfo, dimension, dimensionLength); + auto tadPack = sd::ConstantTadHelper::getInstance()->tadForDimensions(yShapeInfo, dimension, dimensionLength); yTadShapeShapeInfo = tadPack.primaryShapeInfo(); tadOffsets = tadPack.primaryOffsets(); @@ -442,16 +443,16 @@ namespace functions { auto lenX = shape::length(xShapeInfo); int tadsPerThread = tads / TAD_THRESHOLD; - int threads = nd4j::math::nd4j_max(1, tadsPerThread); - threads = nd4j::math::nd4j_min(threads, nd4j::Environment::getInstance()->maxThreads()); + int threads = sd::math::nd4j_max(1, tadsPerThread); + threads = sd::math::nd4j_min(threads, sd::Environment::getInstance()->maxThreads()); auto yEws = shape::elementWiseStride(yTadShapeShapeInfo); auto xEws = shape::elementWiseStride(xShapeInfo); auto zEws = shape::elementWiseStride(zTadShapeInfo); - const nd4j::LoopKind::Kind kindOfLoop = nd4j::LoopKind::deduceKindOfLoopXYZ(yTadShapeShapeInfo, xShapeInfo, zTadShapeInfo); + const sd::LoopKind::Kind kindOfLoop = sd::LoopKind::deduceKindOfLoopXYZ(yTadShapeShapeInfo, xShapeInfo, zTadShapeInfo); - if(kindOfLoop == nd4j::LoopKind::EWS1) { + if(kindOfLoop == sd::LoopKind::EWS1) { for (auto i = start; i < stop; i++) { auto oY = y + tadOffsets[i]; auto oZ = z + zTadOffset[i]; @@ -461,7 +462,7 @@ namespace functions { oZ[f] = OpType::op(x[f], oY[f]); } } - else if(kindOfLoop == nd4j::LoopKind::EWSNONZERO) { + else if(kindOfLoop == sd::LoopKind::EWSNONZERO) { for (auto i = start; i < stop; i++) { auto oY = y + tadOffsets[i]; auto oZ = z + zTadOffset[i]; @@ -473,7 +474,7 @@ namespace functions { } else if(shape::haveSameShapeAndStrides(yTadShapeShapeInfo, xShapeInfo) && shape::haveSameShapeAndStrides(yTadShapeShapeInfo, zTadShapeInfo)) { uint tadShapeShapeInfoCast[MAX_RANK]; - bool canCastY = nd4j::DataTypeUtils::castShapeInfo(yTadShapeShapeInfo, tadShapeShapeInfoCast); + bool canCastY = sd::DataTypeUtils::castShapeInfo(yTadShapeShapeInfo, tadShapeShapeInfoCast); for (auto i = start; i < stop; i++) { auto oY = x + tadOffsets[i]; @@ -489,15 +490,15 @@ namespace functions { else if(shape::haveSameShapeAndStrides(yTadShapeShapeInfo, xShapeInfo)) { uint tadShapeShapeInfoCast[MAX_RANK]; uint tadShapeInfoZCast[MAX_RANK]; - bool canCastY = nd4j::DataTypeUtils::castShapeInfo(yTadShapeShapeInfo, tadShapeShapeInfoCast); - bool canCastZ = nd4j::DataTypeUtils::castShapeInfo(zTadShapeInfo, tadShapeInfoZCast); + bool canCastY = sd::DataTypeUtils::castShapeInfo(yTadShapeShapeInfo, tadShapeShapeInfoCast); + bool canCastZ = sd::DataTypeUtils::castShapeInfo(zTadShapeInfo, tadShapeInfoZCast); for (auto i = start; i < stop; i++) { auto oZ = z + zTadOffset[i]; auto oY = y + tadOffsets[i]; PRAGMA_OMP_SIMD - for (int f = 0; f < tadLength; f++) { + for (unsigned int f = 0; f < tadLength; f++) { auto offset = shape::indexOffset(f, yTadShapeShapeInfo, tadShapeShapeInfoCast, canCastY); auto zOffset = shape::indexOffset(f, zTadShapeInfo, tadShapeInfoZCast, canCastZ); oZ[zOffset] = OpType::op(x[offset], oY[offset]); @@ -507,15 +508,15 @@ namespace functions { else if(shape::haveSameShapeAndStrides(yTadShapeShapeInfo, zTadShapeInfo)) { uint tadShapeShapeInfoCast[MAX_RANK]; uint xShapeInfoCast[MAX_RANK]; - bool canCastX = nd4j::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast); - bool canCastY = nd4j::DataTypeUtils::castShapeInfo(yTadShapeShapeInfo, tadShapeShapeInfoCast); + bool canCastX = sd::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast); + bool canCastY = sd::DataTypeUtils::castShapeInfo(yTadShapeShapeInfo, tadShapeShapeInfoCast); for (auto i = start; i < stop; i++) { auto oZ = z + zTadOffset[i]; auto oY = y + tadOffsets[i]; PRAGMA_OMP_SIMD - for (int f = 0; f < tadLength; f++) { + for (unsigned int f = 0; f < tadLength; f++) { auto offset = shape::indexOffset(f, yTadShapeShapeInfo, tadShapeShapeInfoCast, canCastY); auto xOffset = shape::indexOffset(f, yShapeInfo, xShapeInfoCast, canCastX); oZ[offset] = OpType::op(x[xOffset], oY[offset]); @@ -525,15 +526,15 @@ namespace functions { else if(shape::haveSameShapeAndStrides(xShapeInfo, zTadShapeInfo)) { uint tadShapeShapeInfoCast[MAX_RANK]; uint xShapeInfoCast[MAX_RANK]; - bool canCastX = nd4j::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast); - bool canCastY = nd4j::DataTypeUtils::castShapeInfo(yTadShapeShapeInfo, tadShapeShapeInfoCast); + bool canCastX = sd::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast); + bool canCastY = sd::DataTypeUtils::castShapeInfo(yTadShapeShapeInfo, tadShapeShapeInfoCast); for (auto i = start; i < stop; i++) { auto oZ = z + zTadOffset[i]; auto oY = y + tadOffsets[i]; PRAGMA_OMP_SIMD - for (int f = 0; f < tadLength; f++) { + for (unsigned int f = 0; f < tadLength; f++) { auto yOffset = shape::indexOffset(f, yTadShapeShapeInfo, tadShapeShapeInfoCast, canCastY); auto offset = shape::indexOffset(f, xShapeInfo, xShapeInfoCast, canCastX); oZ[offset] = OpType::op(x[offset], oY[yOffset]); @@ -544,16 +545,16 @@ namespace functions { uint tadShapeShapeInfoCast[MAX_RANK]; uint tadShapeInfoZCast[MAX_RANK]; uint xShapeInfoCast[MAX_RANK]; - bool canCastX = nd4j::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast); - bool canCastY = nd4j::DataTypeUtils::castShapeInfo(yTadShapeShapeInfo, tadShapeShapeInfoCast); - bool canCastZ = nd4j::DataTypeUtils::castShapeInfo(zTadShapeInfo, tadShapeInfoZCast); + bool canCastX = sd::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast); + bool canCastY = sd::DataTypeUtils::castShapeInfo(yTadShapeShapeInfo, tadShapeShapeInfoCast); + bool canCastZ = sd::DataTypeUtils::castShapeInfo(zTadShapeInfo, tadShapeInfoZCast); for (auto i = start; i < stop; i++) { auto oZ = z + zTadOffset[i]; auto oY = y + tadOffsets[i]; PRAGMA_OMP_SIMD - for (int f = 0; f < tadLength; f++) { + for (unsigned int f = 0; f < tadLength; f++) { auto xOffset = shape::indexOffset(f, xShapeInfo, xShapeInfoCast, canCastX); auto yOffset = shape::indexOffset(f, yTadShapeShapeInfo, tadShapeShapeInfoCast, canCastY); auto zOffset = shape::indexOffset(f, zTadShapeInfo, tadShapeInfoZCast, canCastZ); @@ -562,5 +563,313 @@ namespace functions { }; } } + + +//////////////////////////////////////////////////////////////////////// +template + void Broadcast::exec(const int opNum, const void *x, const Nd4jLong *xShapeInfo, const void *y, const Nd4jLong *yShapeInfo, void *z, const Nd4jLong *zShapeInfo) { + + DISPATCH_BY_OPNUM_TTT(exec, PARAMS(x, xShapeInfo, y, yShapeInfo, z, zShapeInfo), BROADCAST_OPS); +} + +//////////////////////////////////////////////////////////////////////// +template +static void execRank1(const X *x, const Nd4jLong *xShapeInfo, const Y *y, const Nd4jLong *yShapeInfo, Z* z, const Nd4jLong *zShapeInfo) { + + uint zAxis0 = shape::sizeAt(zShapeInfo, 0); + Nd4jLong xStrd0 = shape::strideAt(xShapeInfo, 0); + Nd4jLong yStrd0 = shape::strideAt(yShapeInfo, 0); + Nd4jLong zStrd0 = shape::strideAt(zShapeInfo, 0); + + auto func = PRAGMA_THREADS_FOR{ + + if(zStrd0 == 1 && xStrd0 == 1 && yStrd0 == 0) { + for (auto i0 = start; i0 < stop; ++i0) + z[i0] = OpType::op(x[i0], *y); + } + else if(zStrd0 == 1 && xStrd0 == 0 && yStrd0 == 1) { + for (auto i0 = start; i0 < stop; ++i0) + z[i0] = OpType::op(*x, y[i0]); + } + else if(zStrd0 == 1 && xStrd0 == 1 && yStrd0 == 1) { + for (auto i0 = start; i0 < stop; ++i0) + z[i0] = OpType::op(x[i0], y[i0]); + } + else { + for (auto i0 = start; i0 < stop; ++i0) + z[i0 * zStrd0] = OpType::op(x[i0 * xStrd0], y[i0 * yStrd0]); + } + }; + samediff::Threads::parallel_tad(func, 0, zAxis0); +} + +//////////////////////////////////////////////////////////////////////// +template +static void execRank2(const X *x, const Nd4jLong *xShapeInfo, const Y *y, const Nd4jLong *yShapeInfo, Z* z, const Nd4jLong *zShapeInfo) { + + uint zAxis0 = shape::sizeAt(zShapeInfo, shape::order(zShapeInfo) == 'c' ? 0 : 1); + Nd4jLong xStrd0 = shape::strideAt(xShapeInfo, shape::order(zShapeInfo) == 'c' ? 0 : 1); + Nd4jLong yStrd0 = shape::strideAt(yShapeInfo, shape::order(zShapeInfo) == 'c' ? 0 : 1); + Nd4jLong zStrd0 = shape::strideAt(zShapeInfo, shape::order(zShapeInfo) == 'c' ? 0 : 1); + + uint zAxis1 = shape::sizeAt(zShapeInfo, shape::order(zShapeInfo) == 'c' ? 1 : 0); + Nd4jLong xStrd1 = shape::strideAt(xShapeInfo, shape::order(zShapeInfo) == 'c' ? 1 : 0); + Nd4jLong yStrd1 = shape::strideAt(yShapeInfo, shape::order(zShapeInfo) == 'c' ? 1 : 0); + Nd4jLong zStrd1 = shape::strideAt(zShapeInfo, shape::order(zShapeInfo) == 'c' ? 1 : 0); + + auto func = PRAGMA_THREADS_FOR{ + + for (auto i0 = start; i0 < stop; ++i0) { + + auto x0 = x + i0 * xStrd0; + auto y0 = y + i0 * yStrd0; + auto z0 = z + i0 * zStrd0; + + if(zStrd1 == 1 && xStrd1 == 1 && yStrd1 == 0) + for (uint i1 = 0; i1 < zAxis1; ++i1) + z0[i1] = OpType::op(x0[i1], *y0); + else if(zStrd1 == 1 && xStrd1 == 0 && yStrd1 == 1) + for (uint i1 = 0; i1 < zAxis1; ++i1) + z0[i1] = OpType::op(*x0, y0[i1]); + else if(zStrd1 == 1 && xStrd1 == 1 && yStrd1 == 1) + for (uint i1 = 0; i1 < zAxis1; ++i1) + z0[i1] = OpType::op(x0[i1], y0[i1]); + else + for (uint i1 = 0; i1 < zAxis1; ++i1) + z0[i1 * zStrd1] = OpType::op(x0[i1 * xStrd1], y0[i1 * yStrd1]); + } + }; + + samediff::Threads::parallel_tad(func, 0, zAxis0); +} + +//////////////////////////////////////////////////////////////////////// +template +static void execRank3(const X *x, const Nd4jLong *xShapeInfo, const Y *y, const Nd4jLong *yShapeInfo, Z* z, const Nd4jLong *zShapeInfo) { + + uint zAxis0 = shape::sizeAt(zShapeInfo, shape::order(zShapeInfo) == 'c' ? 0 : 2); + Nd4jLong xStrd0 = shape::strideAt(xShapeInfo, shape::order(zShapeInfo) == 'c' ? 0 : 2); + Nd4jLong yStrd0 = shape::strideAt(yShapeInfo, shape::order(zShapeInfo) == 'c' ? 0 : 2); + Nd4jLong zStrd0 = shape::strideAt(zShapeInfo, shape::order(zShapeInfo) == 'c' ? 0 : 2); + + uint zAxis1 = shape::sizeAt(zShapeInfo, 1); + Nd4jLong xStrd1 = shape::strideAt(xShapeInfo, 1); + Nd4jLong yStrd1 = shape::strideAt(yShapeInfo, 1); + Nd4jLong zStrd1 = shape::strideAt(zShapeInfo, 1); + + uint zAxis2 = shape::sizeAt(zShapeInfo, shape::order(zShapeInfo) == 'c' ? 2 : 0); + Nd4jLong xStrd2 = shape::strideAt(xShapeInfo, shape::order(zShapeInfo) == 'c' ? 2 : 0); + Nd4jLong yStrd2 = shape::strideAt(yShapeInfo, shape::order(zShapeInfo) == 'c' ? 2 : 0); + Nd4jLong zStrd2 = shape::strideAt(zShapeInfo, shape::order(zShapeInfo) == 'c' ? 2 : 0); + + auto func = PRAGMA_THREADS_FOR_2D { + + for (auto i0 = start_x; i0 < stop_x; ++i0) { + for (auto i1 = start_y; i1 < stop_y; ++i1) { + + auto x1 = x + i0 * xStrd0 + i1 * xStrd1; + auto y1 = y + i0 * yStrd0 + i1 * yStrd1; + auto z1 = z + i0 * zStrd0 + i1 * zStrd1; + + if(zStrd2 == 1 && xStrd2 == 1 && yStrd2 == 0) + for (uint i2 = 0; i2 < zAxis2; ++i2) + z1[i2] = OpType::op(x1[i2], *y1); + else if(zStrd2 == 1 && xStrd2 == 0 && yStrd2 == 1) + for (uint i2 = 0; i2 < zAxis2; ++i2) + z1[i2] = OpType::op(*x1, y1[i2]); + else if(zStrd2 == 1 && xStrd2 == 1 && yStrd2 == 1) + for (uint i2 = 0; i2 < zAxis2; ++i2) + z1[i2] = OpType::op(x1[i2], y1[i2]); + else + for (uint i2 = 0; i2 < zAxis2; ++i2) + z1[i2 * zStrd2] = OpType::op(x1[i2 * xStrd2], y1[i2 * yStrd2]); + } + } + }; + + samediff::Threads::parallel_for(func, 0,zAxis0,1, 0,zAxis1,1); +} + +//////////////////////////////////////////////////////////////////////// +template +static void execRank4(const X *x, const Nd4jLong *xShapeInfo, const Y *y, const Nd4jLong *yShapeInfo, Z* z, const Nd4jLong *zShapeInfo) { + + uint zAxis0 = shape::sizeAt(zShapeInfo, shape::order(zShapeInfo) == 'c' ? 0 : 3); + Nd4jLong xStrd0 = shape::strideAt(xShapeInfo, shape::order(zShapeInfo) == 'c' ? 0 : 3); + Nd4jLong yStrd0 = shape::strideAt(yShapeInfo, shape::order(zShapeInfo) == 'c' ? 0 : 3); + Nd4jLong zStrd0 = shape::strideAt(zShapeInfo, shape::order(zShapeInfo) == 'c' ? 0 : 3); + + uint zAxis1 = shape::sizeAt(zShapeInfo, shape::order(zShapeInfo) == 'c' ? 1 : 2); + Nd4jLong xStrd1 = shape::strideAt(xShapeInfo, shape::order(zShapeInfo) == 'c' ? 1 : 2); + Nd4jLong yStrd1 = shape::strideAt(yShapeInfo, shape::order(zShapeInfo) == 'c' ? 1 : 2); + Nd4jLong zStrd1 = shape::strideAt(zShapeInfo, shape::order(zShapeInfo) == 'c' ? 1 : 2); + + uint zAxis2 = shape::sizeAt(zShapeInfo, shape::order(zShapeInfo) == 'c' ? 2 : 1); + Nd4jLong xStrd2 = shape::strideAt(xShapeInfo, shape::order(zShapeInfo) == 'c' ? 2 : 1); + Nd4jLong yStrd2 = shape::strideAt(yShapeInfo, shape::order(zShapeInfo) == 'c' ? 2 : 1); + Nd4jLong zStrd2 = shape::strideAt(zShapeInfo, shape::order(zShapeInfo) == 'c' ? 2 : 1); + + uint zAxis3 = shape::sizeAt(zShapeInfo, shape::order(zShapeInfo) == 'c' ? 3 : 0); + Nd4jLong xStrd3 = shape::strideAt(xShapeInfo, shape::order(zShapeInfo) == 'c' ? 3 : 0); + Nd4jLong yStrd3 = shape::strideAt(yShapeInfo, shape::order(zShapeInfo) == 'c' ? 3 : 0); + Nd4jLong zStrd3 = shape::strideAt(zShapeInfo, shape::order(zShapeInfo) == 'c' ? 3 : 0); + + auto func = PRAGMA_THREADS_FOR_3D { + + for (auto i0 = start_x; i0 < stop_x; ++i0) { + for (auto i1 = start_y; i1 < stop_y; ++i1) { + for (auto i2 = start_z; i2 < stop_z; ++i2) { + + auto x2 = x + i0 * xStrd0 + i1 * xStrd1 + i2 * xStrd2; + auto y2 = y + i0 * yStrd0 + i1 * yStrd1 + i2 * yStrd2; + auto z2 = z + i0 * zStrd0 + i1 * zStrd1 + i2 * zStrd2; + + if(zStrd3 == 1 && xStrd3 == 1 && yStrd3 == 0) + for (uint i3 = 0; i3 < zAxis3; ++i3) + z2[i3] = OpType::op(x2[i3], *y2); + else if(zStrd3 == 1 && xStrd3 == 0 && yStrd3 == 1) + for (uint i3 = 0; i3 < zAxis3; ++i3) + z2[i3] = OpType::op(*x2, y2[i3]); + else if(zStrd3 == 1 && xStrd3 == 1 && yStrd3 == 1) + for (uint i3 = 0; i3 < zAxis3; ++i3) + z2[i3] = OpType::op(x2[i3], y2[i3]); + else + for (uint i3 = 0; i3 < zAxis3; ++i3) + z2[i3 * zStrd3] = OpType::op(x2[i3 * xStrd3], y2[i3 * yStrd3]); + } + } + } + }; + + samediff::Threads::parallel_for(func, 0,zAxis0,1, 0,zAxis1,1, 0,zAxis2,1); +} + +//////////////////////////////////////////////////////////////////////// +template +static void execRank5(const X *x, const Nd4jLong *xShapeInfo, const Y *y, const Nd4jLong *yShapeInfo, Z* z, const Nd4jLong *zShapeInfo) { + + uint zAxis0 = shape::sizeAt(zShapeInfo, shape::order(zShapeInfo) == 'c' ? 0 : 4); + Nd4jLong xStrd0 = shape::strideAt(xShapeInfo, shape::order(zShapeInfo) == 'c' ? 0 : 4); + Nd4jLong yStrd0 = shape::strideAt(yShapeInfo, shape::order(zShapeInfo) == 'c' ? 0 : 4); + Nd4jLong zStrd0 = shape::strideAt(zShapeInfo, shape::order(zShapeInfo) == 'c' ? 0 : 4); + + uint zAxis1 = shape::sizeAt(zShapeInfo, shape::order(zShapeInfo) == 'c' ? 1 : 3); + Nd4jLong xStrd1 = shape::strideAt(xShapeInfo, shape::order(zShapeInfo) == 'c' ? 1 : 3); + Nd4jLong yStrd1 = shape::strideAt(yShapeInfo, shape::order(zShapeInfo) == 'c' ? 1 : 3); + Nd4jLong zStrd1 = shape::strideAt(zShapeInfo, shape::order(zShapeInfo) == 'c' ? 1 : 3); + + uint zAxis2 = shape::sizeAt(zShapeInfo, 2); + Nd4jLong xStrd2 = shape::strideAt(xShapeInfo, 2); + Nd4jLong yStrd2 = shape::strideAt(yShapeInfo, 2); + Nd4jLong zStrd2 = shape::strideAt(zShapeInfo, 2); + + uint zAxis3 = shape::sizeAt(zShapeInfo, shape::order(zShapeInfo) == 'c' ? 3 : 1); + Nd4jLong xStrd3 = shape::strideAt(xShapeInfo, shape::order(zShapeInfo) == 'c' ? 3 : 1); + Nd4jLong yStrd3 = shape::strideAt(yShapeInfo, shape::order(zShapeInfo) == 'c' ? 3 : 1); + Nd4jLong zStrd3 = shape::strideAt(zShapeInfo, shape::order(zShapeInfo) == 'c' ? 3 : 1); + + uint zAxis4 = shape::sizeAt(zShapeInfo, shape::order(zShapeInfo) == 'c' ? 4 : 0); + Nd4jLong xStrd4 = shape::strideAt(xShapeInfo, shape::order(zShapeInfo) == 'c' ? 4 : 0); + Nd4jLong yStrd4 = shape::strideAt(yShapeInfo, shape::order(zShapeInfo) == 'c' ? 4 : 0); + Nd4jLong zStrd4 = shape::strideAt(zShapeInfo, shape::order(zShapeInfo) == 'c' ? 4 : 0); + + auto func = PRAGMA_THREADS_FOR_3D { + + for (auto i0 = start_x; i0 < stop_x; ++i0) { + for (auto i1 = start_y; i1 < stop_y; ++i1) { + for (auto i2 = start_z; i2 < stop_z; ++i2) { + for (uint i3 = 0; i3 < zAxis3; ++i3) { + + auto x3 = x + i0 * xStrd0 + i1 * xStrd1 + i2 * xStrd2 + i3 * xStrd3; + auto y3 = y + i0 * yStrd0 + i1 * yStrd1 + i2 * yStrd2 + i3 * yStrd3; + auto z3 = z + i0 * zStrd0 + i1 * zStrd1 + i2 * zStrd2 + i3 * zStrd3; + + if(zStrd4 == 1 && xStrd4 == 1 && yStrd4 == 0) + for (uint i4 = 0; i4 < zAxis4; ++i4) + z3[i4] = OpType::op(x3[i4], *y3); + else if(zStrd4 == 1 && xStrd4 == 0 && yStrd4 == 1) + for (uint i4 = 0; i4 < zAxis4; ++i4) + z3[i4] = OpType::op(*x3, y3[i4]); + else if(zStrd4 == 1 && xStrd4 == 1 && yStrd4 == 1) + for (uint i4 = 0; i4 < zAxis4; ++i4) + z3[i4] = OpType::op(x3[i4], y3[i4]); + else + for (uint i4 = 0; i4 < zAxis4; ++i4) + z3[i4 * zStrd4] = OpType::op(x3[i4 * xStrd4], y3[i4 * yStrd4]); + } + } + } + } + }; + + samediff::Threads::parallel_for(func, 0,zAxis0,1, 0,zAxis1,1, 0,zAxis2,1); +} + +//////////////////////////////////////////////////////////////////////// +template +static void execDefault(const X *x, const Nd4jLong *xShapeInfo, const Y *y, const Nd4jLong *yShapeInfo, Z* z, const Nd4jLong *zShapeInfo) { + + const bool xzSameOffsets = shape::haveSameShapeAndStrides(xShapeInfo, zShapeInfo); + const bool yzSameOffsets = shape::haveSameShapeAndStrides(yShapeInfo, zShapeInfo); + + const int rank = shape::rank(zShapeInfo); // xRank = yRank = zRank + + auto func = PRAGMA_THREADS_FOR{ + + int xCoords[MAX_RANK], yCoords[MAX_RANK], zCoords[MAX_RANK]; + + for (auto i = start; i < stop; ++i) { + + shape::index2coordsCPU(start, i, zShapeInfo, zCoords); + + for (uint j = 0; j < rank; ++j) { + xCoords[j] = shape::sizeAt(xShapeInfo, j) == 1 ? 0 : zCoords[j]; + yCoords[j] = shape::sizeAt(yShapeInfo, j) == 1 ? 0 : zCoords[j]; + } + + const auto zOffset = shape::getOffset(zShapeInfo, zCoords); + const auto xOffset = xzSameOffsets ? zOffset : shape::getOffset(xShapeInfo, xCoords); + const auto yOffset = yzSameOffsets ? zOffset : shape::getOffset(yShapeInfo, yCoords); + + z[zOffset] = OpType::op(x[xOffset], y[yOffset]); + } + }; + + samediff::Threads::parallel_for(func, 0, shape::length(zShapeInfo)); +} + +//////////////////////////////////////////////////////////////////////// +template +template +void Broadcast::exec(const void *vx, const Nd4jLong *xShapeInfo, const void *vy, const Nd4jLong *yShapeInfo, void *vz, const Nd4jLong *zShapeInfo) { + + const X* x = reinterpret_cast(vx); + const Y* y = reinterpret_cast(vy); + Z* z = reinterpret_cast(vz); + + const int rank = shape::rank(zShapeInfo); // xRank = yRank = zRank + + switch (rank) { + + case 1: + execRank1(x, xShapeInfo, y, yShapeInfo, z, zShapeInfo); + break; + case 2: + execRank2(x, xShapeInfo, y, yShapeInfo, z, zShapeInfo); + break; + case 3: + execRank3(x, xShapeInfo, y, yShapeInfo, z, zShapeInfo); + break; + case 4: + execRank4(x, xShapeInfo, y, yShapeInfo, z, zShapeInfo); + break; + case 5: + execRank5(x, xShapeInfo, y, yShapeInfo, z, zShapeInfo); + break; + default: + execDefault(x, xShapeInfo, y, yShapeInfo, z, zShapeInfo); } +} + +} } \ No newline at end of file diff --git a/libnd4j/include/loops/cpu/broadcasting_bool.cpp b/libnd4j/include/loops/cpu/broadcasting_bool.cpp deleted file mode 100644 index 8d62b9506..000000000 --- a/libnd4j/include/loops/cpu/broadcasting_bool.cpp +++ /dev/null @@ -1,449 +0,0 @@ -/******************************************************************************* - * Copyright (c) 2015-2018 Skymind, Inc. - * - * This program and the accompanying materials are made available under the - * terms of the Apache License, Version 2.0 which is available at - * https://www.apache.org/licenses/LICENSE-2.0. - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the - * License for the specific language governing permissions and limitations - * under the License. - * - * SPDX-License-Identifier: Apache-2.0 - ******************************************************************************/ - -// -// @author raver119@gmail.com -// - -#include -#include -#include -#include -#include -#include -#include - -using namespace simdOps; - -namespace functions { - namespace broadcast { - - template - void BroadcastBool::exec(const int opNum, - void *x, - Nd4jLong *xShapeInfo, - void *y, - Nd4jLong *yShapeInfo, - void *z, - Nd4jLong *zShapeInfo, - void *extraParams, - int *dimension, - int dimensionLength, - Nd4jLong *xTadShapeInfo, - Nd4jLong *xTadOffset, - Nd4jLong *zTadShapeInfo, - Nd4jLong *zTadOffset, - uint64_t start, - uint64_t stop) { - DISPATCH_BY_OPNUM_TT(exec, PARAMS(x, - xShapeInfo, - y, - yShapeInfo, - z, - zShapeInfo, - extraParams, - dimension, - dimensionLength, - xTadShapeInfo, - xTadOffset, - zTadShapeInfo, - zTadOffset, start, stop), BROADCAST_BOOL_OPS); - } - - template - void BroadcastBool::execInverse(const int opNum, - void *x, - Nd4jLong *xShapeInfo, - void *y, - Nd4jLong *yShapeInfo, - void *z, - Nd4jLong *zShapeInfo, - void *extraParams, - int *dimension, - int dimensionLength, - Nd4jLong *xTadShapeInfo, - Nd4jLong *xTadOffset, - Nd4jLong *zTadShapeInfo, - Nd4jLong *zTadOffset, - uint64_t start, - uint64_t stop) { - DISPATCH_BY_OPNUM_TT(execInverse, PARAMS(x, - xShapeInfo, - y, - yShapeInfo, - z, - zShapeInfo, - extraParams, - dimension, - dimensionLength, - xTadShapeInfo, - xTadOffset, - zTadShapeInfo, - zTadOffset, start, stop), BROADCAST_BOOL_OPS); - } - - template - template - void BroadcastBool::exec(void *vx, - Nd4jLong *xShapeInfo, - void *vy, - Nd4jLong *yShapeInfo, - void *vz, - Nd4jLong *zShapeInfo, - void *vextraParams, - int *dimension, - int dimensionLength, - Nd4jLong *xTadShapeInfo, - Nd4jLong *xTadOffset, - Nd4jLong *zTadShapeInfo, - Nd4jLong *zTadOffset, - uint64_t start, - uint64_t stop) { - - auto x = reinterpret_cast(vx); - auto y = reinterpret_cast(vy); - auto z = reinterpret_cast(vz); - auto extraParams = reinterpret_cast(vextraParams); - - //decompose in to several sub tads after - //moving all dimensions (in sorted order) - //to the back. - //permuted version of the x shape info for setting up the tad problem - auto xTadShapeShapeInfo = xTadShapeInfo; - auto tadOffsets = xTadOffset; - - if (xTadShapeInfo == nullptr || tadOffsets == nullptr) { - auto tadPack = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(xShapeInfo, dimension, dimensionLength); - - xTadShapeShapeInfo = tadPack.primaryShapeInfo(); - tadOffsets = tadPack.primaryOffsets(); - } - - //int *resultStride = shape::stride(xTadShapeShapeInfo); - unsigned int tadLength = shape::length(xTadShapeShapeInfo);//shape::length(xTadShapeShapeInfo); - unsigned int tads = shape::length(xShapeInfo) / tadLength; - - if (zTadShapeInfo == nullptr) { - zTadShapeInfo = xTadShapeShapeInfo; - zTadOffset = tadOffsets; - } - - auto lenZ = shape::length(zTadShapeInfo); - auto lenY = shape::length(yShapeInfo); - - int tadsPerThread = tads / TAD_THRESHOLD; - int threads = nd4j::math::nd4j_max(1, tadsPerThread); - threads = nd4j::math::nd4j_min(threads, nd4j::Environment::getInstance()->maxThreads()); - - auto xEws = shape::elementWiseStride(xTadShapeShapeInfo); - auto yEws = shape::elementWiseStride(yShapeInfo); - auto zEws = shape::elementWiseStride(zTadShapeInfo); - - const nd4j::LoopKind::Kind kindOfLoop = nd4j::LoopKind::deduceKindOfLoopXYZ(xTadShapeShapeInfo, yShapeInfo, zTadShapeInfo); - - if (kindOfLoop == nd4j::LoopKind::EWS1) { - for (auto i = start; i < stop; i++) { - auto oX = x + tadOffsets[i]; - auto oZ = z + zTadOffset[i]; - - PRAGMA_OMP_SIMD - for (unsigned int f = 0; f < tadLength; f++) - oZ[f] = OpType::op(oX[f], y[f], extraParams); - } - } - else if(kindOfLoop == nd4j::LoopKind::EWSNONZERO) { - for (auto i = start; i < stop; i ++) { - auto oX = x + tadOffsets[i]; - auto oZ = z + zTadOffset[i]; - - PRAGMA_OMP_SIMD - for (unsigned int f = 0; f < tadLength; f++) - oZ[f * zEws] = OpType::op(oX[f * xEws], y[f * yEws], extraParams); - }; - } - else if(shape::haveSameShapeAndStrides(xTadShapeShapeInfo, yShapeInfo) && shape::haveSameShapeAndStrides(xTadShapeShapeInfo, zTadShapeInfo)) { - uint tadShapeShapeInfoCast[MAX_RANK]; - bool canCastX = nd4j::DataTypeUtils::castShapeInfo(xTadShapeShapeInfo, tadShapeShapeInfoCast); - - for (auto i = start; i < stop; i ++) { - auto oZ = z + zTadOffset[i]; - auto oX = x + tadOffsets[i]; - - PRAGMA_OMP_SIMD - for (int f = 0; f < tadLength; f++) { - auto offset = shape::indexOffset(f, xTadShapeShapeInfo, tadShapeShapeInfoCast, canCastX); - oZ[offset] = OpType::op(oX[offset], y[offset], extraParams); - } - }; - } - else if(shape::haveSameShapeAndStrides(xTadShapeShapeInfo, yShapeInfo)) { - uint tadShapeShapeInfoCast[MAX_RANK]; - uint tadShapeInfoZCast[MAX_RANK]; - bool canCastX = nd4j::DataTypeUtils::castShapeInfo(xTadShapeShapeInfo, tadShapeShapeInfoCast); - bool canCastZ = nd4j::DataTypeUtils::castShapeInfo(zTadShapeInfo, tadShapeInfoZCast); - - for (auto i = start; i < stop; i ++) { - auto oZ = z + zTadOffset[i]; - auto oX = x + tadOffsets[i]; - - PRAGMA_OMP_SIMD - for (int f = 0; f < tadLength; f++) { - auto offset = shape::indexOffset(f, xTadShapeShapeInfo, tadShapeShapeInfoCast, canCastX); - auto zOffset = shape::indexOffset(f, zTadShapeInfo, tadShapeInfoZCast, canCastZ); - oZ[zOffset] = OpType::op(oX[offset], y[offset], extraParams); - } - }; - } - else if(shape::haveSameShapeAndStrides(xTadShapeShapeInfo, zTadShapeInfo)) { - uint tadShapeShapeInfoCast[MAX_RANK]; - uint yShapeInfoCast[MAX_RANK]; - bool canCastX = nd4j::DataTypeUtils::castShapeInfo(xTadShapeShapeInfo, tadShapeShapeInfoCast); - bool canCastY = nd4j::DataTypeUtils::castShapeInfo(yShapeInfo, yShapeInfoCast); - - for (auto i = start; i < stop; i ++) { - auto oZ = z + zTadOffset[i]; - auto oX = x + tadOffsets[i]; - - PRAGMA_OMP_SIMD - for (int f = 0; f < tadLength; f++) { - auto offset = shape::indexOffset(f, xTadShapeShapeInfo, tadShapeShapeInfoCast, canCastX); - auto yOffset = shape::indexOffset(f, yShapeInfo, yShapeInfoCast, canCastY); - oZ[offset] = OpType::op(oX[offset], y[yOffset], extraParams); - } - }; - - } - else if(shape::haveSameShapeAndStrides(yShapeInfo, zTadShapeInfo)) { - uint tadShapeShapeInfoCast[MAX_RANK]; - uint yShapeInfoCast[MAX_RANK]; - bool canCastX = nd4j::DataTypeUtils::castShapeInfo(xTadShapeShapeInfo, tadShapeShapeInfoCast); - bool canCastY = nd4j::DataTypeUtils::castShapeInfo(yShapeInfo, yShapeInfoCast); - - for (auto i = start; i < stop; i ++) { - auto oZ = z + zTadOffset[i]; - auto oX = x + tadOffsets[i]; - - PRAGMA_OMP_SIMD - for (int f = 0; f < tadLength; f++) { - auto xOffset = shape::indexOffset(f, xTadShapeShapeInfo, tadShapeShapeInfoCast, canCastX); - auto offset = shape::indexOffset(f, yShapeInfo, yShapeInfoCast, canCastY); - oZ[offset] = OpType::op(oX[xOffset], y[offset], extraParams); - } - }; - } - else { - uint tadShapeShapeInfoCast[MAX_RANK]; - uint tadShapeInfoZCast[MAX_RANK]; - uint yShapeInfoCast[MAX_RANK]; - bool canCastX = nd4j::DataTypeUtils::castShapeInfo(xTadShapeShapeInfo, tadShapeShapeInfoCast); - bool canCastY = nd4j::DataTypeUtils::castShapeInfo(yShapeInfo, yShapeInfoCast); - bool canCastZ = nd4j::DataTypeUtils::castShapeInfo(zTadShapeInfo, tadShapeInfoZCast); - - for (auto i = start; i < stop; i ++) { - auto oZ = z + zTadOffset[i]; - auto oX = x + tadOffsets[i]; - - PRAGMA_OMP_SIMD - for (int f = 0; f < tadLength; f++) { - auto xOffset = shape::indexOffset(f, xTadShapeShapeInfo, tadShapeShapeInfoCast, canCastX); - auto yOffset = shape::indexOffset(f, yShapeInfo, yShapeInfoCast, canCastY); - auto zOffset = shape::indexOffset(f, zTadShapeInfo, tadShapeInfoZCast, canCastZ); - oZ[zOffset] = OpType::op(oX[xOffset], y[yOffset], extraParams); - } - }; - } - } - - - template - template - void BroadcastBool::execInverse(void *vx, - Nd4jLong *xShapeInfo, - void *vy, - Nd4jLong *yShapeInfo, - void *vz, - Nd4jLong *zShapeInfo, - void *vextraParams, - int *dimension, - int dimensionLength, - Nd4jLong *yTadShapeInfo, - Nd4jLong *yTadOffset, - Nd4jLong *zTadShapeInfo, - Nd4jLong *zTadOffset, - uint64_t start, - uint64_t stop) { - - auto x = reinterpret_cast(vx); - auto y = reinterpret_cast(vy); - auto z = reinterpret_cast(vz); - auto extraParams = reinterpret_cast(vextraParams); - - //decompose in to several sub tads after - //moving all dimensions (in sorted order) - //to the back. - //permuted version of the x shape info for setting up the tad problem - auto yTadShapeShapeInfo = yTadShapeInfo; - auto tadOffsets = yTadOffset; - - if (yTadShapeInfo == nullptr || tadOffsets == nullptr) { - auto tadPack = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(yShapeInfo, dimension, dimensionLength); - - yTadShapeShapeInfo = tadPack.primaryShapeInfo(); - tadOffsets = tadPack.primaryOffsets(); - } - - //int *resultStride = shape::stride(yTadShapeShapeInfo); - unsigned int tadLength = shape::length(yTadShapeShapeInfo); - unsigned int tads = shape::length(yShapeInfo) / tadLength; - - if (zTadShapeInfo == nullptr) { - zTadShapeInfo = yTadShapeShapeInfo; - zTadOffset = tadOffsets; - } - - auto lenZ = shape::length(zTadShapeInfo); - auto lenX = shape::length(xShapeInfo); - - int tadsPerThread = tads / TAD_THRESHOLD; - int threads = nd4j::math::nd4j_max(1, tadsPerThread); - threads = nd4j::math::nd4j_min(threads, nd4j::Environment::getInstance()->maxThreads()); - - auto yEws = shape::elementWiseStride(yTadShapeShapeInfo); - auto xEws = shape::elementWiseStride(xShapeInfo); - auto zEws = shape::elementWiseStride(zTadShapeInfo); - - const nd4j::LoopKind::Kind kindOfLoop = nd4j::LoopKind::deduceKindOfLoopXYZ(yTadShapeShapeInfo, xShapeInfo, zTadShapeInfo); - - if (kindOfLoop == nd4j::LoopKind::EWS1) { - for (auto i = start; i < stop; i ++) { - auto oY = y + tadOffsets[i]; - auto oZ = z + zTadOffset[i]; - - PRAGMA_OMP_SIMD - for (unsigned int f = 0; f < tadLength; f++) - oZ[f] = OpType::op(x[f], oY[f], extraParams); - } - } - else if(kindOfLoop == nd4j::LoopKind::EWSNONZERO) { - for (auto i = start; i < stop; i ++) { - auto oY = y + tadOffsets[i]; - auto oZ = z + zTadOffset[i]; - - PRAGMA_OMP_SIMD - for (uint f = 0; f < tadLength; f++) - oZ[f * zEws] = OpType::op(x[f * xEws], oY[f * yEws], extraParams); - } - } - else if(shape::haveSameShapeAndStrides(yTadShapeShapeInfo, xShapeInfo) && shape::haveSameShapeAndStrides(yTadShapeShapeInfo, zTadShapeInfo)) { - - uint tadShapeShapeInfoCast[MAX_RANK]; - bool canCastY = nd4j::DataTypeUtils::castShapeInfo(yTadShapeShapeInfo, tadShapeShapeInfoCast); - - for (auto i = start; i < stop; i ++) { - auto oY = y + tadOffsets[i]; - auto oZ = z + zTadOffset[i]; - - PRAGMA_OMP_SIMD - for (int f = 0; f < tadLength; f++) { - auto offset = shape::indexOffset(f, yTadShapeShapeInfo, tadShapeShapeInfoCast, canCastY); - oZ[offset] = OpType::op(x[offset], oY[offset], extraParams); - } - } - } - else if(shape::haveSameShapeAndStrides(yTadShapeShapeInfo, xShapeInfo)) { - - uint tadShapeShapeInfoCast[MAX_RANK]; - uint tadShapeInfoZCast[MAX_RANK]; - bool canCastY = nd4j::DataTypeUtils::castShapeInfo(yTadShapeShapeInfo, tadShapeShapeInfoCast); - bool canCastZ = nd4j::DataTypeUtils::castShapeInfo(zTadShapeInfo, tadShapeInfoZCast); - - for (auto i = start; i < stop; i ++) { - auto oZ = z + zTadOffset[i]; - auto oY = y + tadOffsets[i]; - - PRAGMA_OMP_SIMD - for (int f = 0; f < tadLength; f++) { - auto offset = shape::indexOffset(f, yTadShapeShapeInfo, tadShapeShapeInfoCast, canCastY); - auto zOffset = shape::indexOffset(f, zTadShapeInfo, tadShapeInfoZCast, canCastZ); - oZ[zOffset] = OpType::op(x[offset], oY[offset], extraParams); - } - } - } - else if(shape::haveSameShapeAndStrides(yTadShapeShapeInfo, zTadShapeInfo)) { - - uint tadShapeShapeInfoCast[MAX_RANK]; - uint xShapeInfoCast[MAX_RANK]; - bool canCastX = nd4j::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast); - bool canCastY = nd4j::DataTypeUtils::castShapeInfo(yTadShapeShapeInfo, tadShapeShapeInfoCast); - - for (auto i = start; i < stop; i ++) { - auto oZ = z + zTadOffset[i]; - auto oY = y + tadOffsets[i]; - - PRAGMA_OMP_SIMD - for (int f = 0; f < tadLength; f++) { - auto offset = shape::indexOffset(f, yTadShapeShapeInfo, tadShapeShapeInfoCast, canCastY); - auto xOffset = shape::indexOffset(f, xShapeInfo, xShapeInfoCast, canCastX); - oZ[offset] = OpType::op(x[xOffset], oY[offset], extraParams); - } - } - } - else if(shape::haveSameShapeAndStrides(xShapeInfo, zTadShapeInfo)) { - - uint tadShapeShapeInfoCast[MAX_RANK]; - uint xShapeInfoCast[MAX_RANK]; - bool canCastX = nd4j::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast); - bool canCastY = nd4j::DataTypeUtils::castShapeInfo(yTadShapeShapeInfo, tadShapeShapeInfoCast); - - for (auto i = start; i < stop; i ++) { - auto oZ = z + zTadOffset[i]; - auto oY = y + tadOffsets[i]; - - PRAGMA_OMP_SIMD - for (int f = 0; f < tadLength; f++) { - auto yOffset = shape::indexOffset(f, yTadShapeShapeInfo, tadShapeShapeInfoCast, canCastY); - auto offset = shape::indexOffset(f, xShapeInfo, xShapeInfoCast, canCastX); - oZ[offset] = OpType::op(x[offset], oY[yOffset], extraParams); - } - } - } - else { - - uint xShapeInfoCast[MAX_RANK]; - uint tadShapeShapeInfoCast[MAX_RANK]; - uint tadShapeInfoZCast[MAX_RANK]; - bool canCastX = nd4j::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast); - bool canCastY = nd4j::DataTypeUtils::castShapeInfo(yTadShapeShapeInfo, tadShapeShapeInfoCast); - bool canCastZ = nd4j::DataTypeUtils::castShapeInfo(zTadShapeInfo, tadShapeInfoZCast); - - for (auto i = start; i < stop; i ++) { - auto oZ = z + zTadOffset[i]; - auto oY = y + tadOffsets[i]; - - PRAGMA_OMP_SIMD - for (int f = 0; f < tadLength; f++) { - auto xOffset = shape::indexOffset(f, xShapeInfo, xShapeInfoCast, canCastX); - auto yOffset = shape::indexOffset(f, yTadShapeShapeInfo, tadShapeShapeInfoCast, canCastY); - auto zOffset = shape::indexOffset(f, zTadShapeInfo, tadShapeInfoZCast, canCastZ); - oZ[zOffset] = OpType::op(x[xOffset], oY[yOffset], extraParams); - } - } - } - } - - BUILD_DOUBLE_TEMPLATE(template class ND4J_EXPORT BroadcastBool, , LIBND4J_TYPES, BOOL_TYPES); - } -} \ No newline at end of file diff --git a/libnd4j/include/loops/cpu/broadcasting_bool.hpp b/libnd4j/include/loops/cpu/broadcasting_bool.hpp new file mode 100644 index 000000000..21b40cb55 --- /dev/null +++ b/libnd4j/include/loops/cpu/broadcasting_bool.hpp @@ -0,0 +1,763 @@ +/******************************************************************************* + * Copyright (c) 2015-2018 Skymind, Inc. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ + +// +// @author raver119@gmail.com +// + +#include +#include +#include +#include +#include +#include +#include + +using namespace simdOps; + +namespace functions { +namespace broadcast { + + template + void BroadcastBool::exec(const int opNum, + void *x, + Nd4jLong *xShapeInfo, + void *y, + Nd4jLong *yShapeInfo, + void *z, + Nd4jLong *zShapeInfo, + void *extraParams, + int *dimension, + int dimensionLength, + Nd4jLong *xTadShapeInfo, + Nd4jLong *xTadOffset, + Nd4jLong *zTadShapeInfo, + Nd4jLong *zTadOffset, + uint64_t start, + uint64_t stop) { + DISPATCH_BY_OPNUM_TT(exec, PARAMS(x, + xShapeInfo, + y, + yShapeInfo, + z, + zShapeInfo, + extraParams, + dimension, + dimensionLength, + xTadShapeInfo, + xTadOffset, + zTadShapeInfo, + zTadOffset, start, stop), BROADCAST_BOOL_OPS); + } + + template + void BroadcastBool::exec(const int opNum, + const void *x, const Nd4jLong *xShapeInfo, + const void *y, const Nd4jLong *yShapeInfo, + void *z, const Nd4jLong *zShapeInfo, + void* extraParams) { + + DISPATCH_BY_OPNUM_TT(exec, PARAMS(x, xShapeInfo, y, yShapeInfo, z, zShapeInfo, extraParams), BROADCAST_BOOL_OPS); + } + + template + void BroadcastBool::execInverse(const int opNum, + void *x, + Nd4jLong *xShapeInfo, + void *y, + Nd4jLong *yShapeInfo, + void *z, + Nd4jLong *zShapeInfo, + void *extraParams, + int *dimension, + int dimensionLength, + Nd4jLong *xTadShapeInfo, + Nd4jLong *xTadOffset, + Nd4jLong *zTadShapeInfo, + Nd4jLong *zTadOffset, + uint64_t start, + uint64_t stop) { + DISPATCH_BY_OPNUM_TT(execInverse, PARAMS(x, + xShapeInfo, + y, + yShapeInfo, + z, + zShapeInfo, + extraParams, + dimension, + dimensionLength, + xTadShapeInfo, + xTadOffset, + zTadShapeInfo, + zTadOffset, start, stop), BROADCAST_BOOL_OPS); + } + + template + template + void BroadcastBool::exec(void *vx, + Nd4jLong *xShapeInfo, + void *vy, + Nd4jLong *yShapeInfo, + void *vz, + Nd4jLong *zShapeInfo, + void *vextraParams, + int *dimension, + int dimensionLength, + Nd4jLong *xTadShapeInfo, + Nd4jLong *xTadOffset, + Nd4jLong *zTadShapeInfo, + Nd4jLong *zTadOffset, + uint64_t start, + uint64_t stop) { + + auto x = reinterpret_cast(vx); + auto y = reinterpret_cast(vy); + auto z = reinterpret_cast(vz); + auto extraParams = reinterpret_cast(vextraParams); + + //decompose in to several sub tads after + //moving all dimensions (in sorted order) + //to the back. + //permuted version of the x shape info for setting up the tad problem + auto xTadShapeShapeInfo = xTadShapeInfo; + auto tadOffsets = xTadOffset; + + if (xTadShapeInfo == nullptr || tadOffsets == nullptr) { + auto tadPack = sd::ConstantTadHelper::getInstance()->tadForDimensions(xShapeInfo, dimension, dimensionLength); + + xTadShapeShapeInfo = tadPack.primaryShapeInfo(); + tadOffsets = tadPack.primaryOffsets(); + } + + //int *resultStride = shape::stride(xTadShapeShapeInfo); + unsigned int tadLength = shape::length(xTadShapeShapeInfo);//shape::length(xTadShapeShapeInfo); + unsigned int tads = shape::length(xShapeInfo) / tadLength; + + if (zTadShapeInfo == nullptr) { + zTadShapeInfo = xTadShapeShapeInfo; + zTadOffset = tadOffsets; + } + + auto lenZ = shape::length(zTadShapeInfo); + auto lenY = shape::length(yShapeInfo); + + int tadsPerThread = tads / TAD_THRESHOLD; + int threads = sd::math::nd4j_max(1, tadsPerThread); + threads = sd::math::nd4j_min(threads, sd::Environment::getInstance()->maxThreads()); + + auto xEws = shape::elementWiseStride(xTadShapeShapeInfo); + auto yEws = shape::elementWiseStride(yShapeInfo); + auto zEws = shape::elementWiseStride(zTadShapeInfo); + + const sd::LoopKind::Kind kindOfLoop = sd::LoopKind::deduceKindOfLoopXYZ(xTadShapeShapeInfo, yShapeInfo, zTadShapeInfo); + + if (kindOfLoop == sd::LoopKind::EWS1) { + for (auto i = start; i < stop; i++) { + auto oX = x + tadOffsets[i]; + auto oZ = z + zTadOffset[i]; + + PRAGMA_OMP_SIMD + for (unsigned int f = 0; f < tadLength; f++) + oZ[f] = OpType::op(oX[f], y[f], extraParams); + } + } + else if(kindOfLoop == sd::LoopKind::EWSNONZERO) { + for (auto i = start; i < stop; i ++) { + auto oX = x + tadOffsets[i]; + auto oZ = z + zTadOffset[i]; + + PRAGMA_OMP_SIMD + for (unsigned int f = 0; f < tadLength; f++) + oZ[f * zEws] = OpType::op(oX[f * xEws], y[f * yEws], extraParams); + }; + } + else if(shape::haveSameShapeAndStrides(xTadShapeShapeInfo, yShapeInfo) && shape::haveSameShapeAndStrides(xTadShapeShapeInfo, zTadShapeInfo)) { + uint tadShapeShapeInfoCast[MAX_RANK]; + bool canCastX = sd::DataTypeUtils::castShapeInfo(xTadShapeShapeInfo, tadShapeShapeInfoCast); + + for (auto i = start; i < stop; i ++) { + auto oZ = z + zTadOffset[i]; + auto oX = x + tadOffsets[i]; + + PRAGMA_OMP_SIMD + for (unsigned int f = 0; f < tadLength; f++) { + auto offset = shape::indexOffset(f, xTadShapeShapeInfo, tadShapeShapeInfoCast, canCastX); + oZ[offset] = OpType::op(oX[offset], y[offset], extraParams); + } + }; + } + else if(shape::haveSameShapeAndStrides(xTadShapeShapeInfo, yShapeInfo)) { + uint tadShapeShapeInfoCast[MAX_RANK]; + uint tadShapeInfoZCast[MAX_RANK]; + bool canCastX = sd::DataTypeUtils::castShapeInfo(xTadShapeShapeInfo, tadShapeShapeInfoCast); + bool canCastZ = sd::DataTypeUtils::castShapeInfo(zTadShapeInfo, tadShapeInfoZCast); + + for (auto i = start; i < stop; i ++) { + auto oZ = z + zTadOffset[i]; + auto oX = x + tadOffsets[i]; + + PRAGMA_OMP_SIMD + for (unsigned int f = 0; f < tadLength; f++) { + auto offset = shape::indexOffset(f, xTadShapeShapeInfo, tadShapeShapeInfoCast, canCastX); + auto zOffset = shape::indexOffset(f, zTadShapeInfo, tadShapeInfoZCast, canCastZ); + oZ[zOffset] = OpType::op(oX[offset], y[offset], extraParams); + } + }; + } + else if(shape::haveSameShapeAndStrides(xTadShapeShapeInfo, zTadShapeInfo)) { + uint tadShapeShapeInfoCast[MAX_RANK]; + uint yShapeInfoCast[MAX_RANK]; + bool canCastX = sd::DataTypeUtils::castShapeInfo(xTadShapeShapeInfo, tadShapeShapeInfoCast); + bool canCastY = sd::DataTypeUtils::castShapeInfo(yShapeInfo, yShapeInfoCast); + + for (auto i = start; i < stop; i ++) { + auto oZ = z + zTadOffset[i]; + auto oX = x + tadOffsets[i]; + + PRAGMA_OMP_SIMD + for (unsigned int f = 0; f < tadLength; f++) { + auto offset = shape::indexOffset(f, xTadShapeShapeInfo, tadShapeShapeInfoCast, canCastX); + auto yOffset = shape::indexOffset(f, yShapeInfo, yShapeInfoCast, canCastY); + oZ[offset] = OpType::op(oX[offset], y[yOffset], extraParams); + } + }; + + } + else if(shape::haveSameShapeAndStrides(yShapeInfo, zTadShapeInfo)) { + uint tadShapeShapeInfoCast[MAX_RANK]; + uint yShapeInfoCast[MAX_RANK]; + bool canCastX = sd::DataTypeUtils::castShapeInfo(xTadShapeShapeInfo, tadShapeShapeInfoCast); + bool canCastY = sd::DataTypeUtils::castShapeInfo(yShapeInfo, yShapeInfoCast); + + for (auto i = start; i < stop; i ++) { + auto oZ = z + zTadOffset[i]; + auto oX = x + tadOffsets[i]; + + PRAGMA_OMP_SIMD + for (unsigned int f = 0; f < tadLength; f++) { + auto xOffset = shape::indexOffset(f, xTadShapeShapeInfo, tadShapeShapeInfoCast, canCastX); + auto offset = shape::indexOffset(f, yShapeInfo, yShapeInfoCast, canCastY); + oZ[offset] = OpType::op(oX[xOffset], y[offset], extraParams); + } + }; + } + else { + uint tadShapeShapeInfoCast[MAX_RANK]; + uint tadShapeInfoZCast[MAX_RANK]; + uint yShapeInfoCast[MAX_RANK]; + bool canCastX = sd::DataTypeUtils::castShapeInfo(xTadShapeShapeInfo, tadShapeShapeInfoCast); + bool canCastY = sd::DataTypeUtils::castShapeInfo(yShapeInfo, yShapeInfoCast); + bool canCastZ = sd::DataTypeUtils::castShapeInfo(zTadShapeInfo, tadShapeInfoZCast); + + for (auto i = start; i < stop; i ++) { + auto oZ = z + zTadOffset[i]; + auto oX = x + tadOffsets[i]; + + PRAGMA_OMP_SIMD + for (unsigned int f = 0; f < tadLength; f++) { + auto xOffset = shape::indexOffset(f, xTadShapeShapeInfo, tadShapeShapeInfoCast, canCastX); + auto yOffset = shape::indexOffset(f, yShapeInfo, yShapeInfoCast, canCastY); + auto zOffset = shape::indexOffset(f, zTadShapeInfo, tadShapeInfoZCast, canCastZ); + oZ[zOffset] = OpType::op(oX[xOffset], y[yOffset], extraParams); + } + }; + } + } + + template + template + void BroadcastBool::execInverse(void *vx, + Nd4jLong *xShapeInfo, + void *vy, + Nd4jLong *yShapeInfo, + void *vz, + Nd4jLong *zShapeInfo, + void *vextraParams, + int *dimension, + int dimensionLength, + Nd4jLong *yTadShapeInfo, + Nd4jLong *yTadOffset, + Nd4jLong *zTadShapeInfo, + Nd4jLong *zTadOffset, + uint64_t start, + uint64_t stop) { + + auto x = reinterpret_cast(vx); + auto y = reinterpret_cast(vy); + auto z = reinterpret_cast(vz); + auto extraParams = reinterpret_cast(vextraParams); + + //decompose in to several sub tads after + //moving all dimensions (in sorted order) + //to the back. + //permuted version of the x shape info for setting up the tad problem + auto yTadShapeShapeInfo = yTadShapeInfo; + auto tadOffsets = yTadOffset; + + if (yTadShapeInfo == nullptr || tadOffsets == nullptr) { + auto tadPack = sd::ConstantTadHelper::getInstance()->tadForDimensions(yShapeInfo, dimension, dimensionLength); + + yTadShapeShapeInfo = tadPack.primaryShapeInfo(); + tadOffsets = tadPack.primaryOffsets(); + } + + //int *resultStride = shape::stride(yTadShapeShapeInfo); + unsigned int tadLength = shape::length(yTadShapeShapeInfo); + unsigned int tads = shape::length(yShapeInfo) / tadLength; + + if (zTadShapeInfo == nullptr) { + zTadShapeInfo = yTadShapeShapeInfo; + zTadOffset = tadOffsets; + } + + auto lenZ = shape::length(zTadShapeInfo); + auto lenX = shape::length(xShapeInfo); + + int tadsPerThread = tads / TAD_THRESHOLD; + int threads = sd::math::nd4j_max(1, tadsPerThread); + threads = sd::math::nd4j_min(threads, sd::Environment::getInstance()->maxThreads()); + + auto yEws = shape::elementWiseStride(yTadShapeShapeInfo); + auto xEws = shape::elementWiseStride(xShapeInfo); + auto zEws = shape::elementWiseStride(zTadShapeInfo); + + const sd::LoopKind::Kind kindOfLoop = sd::LoopKind::deduceKindOfLoopXYZ(yTadShapeShapeInfo, xShapeInfo, zTadShapeInfo); + + if (kindOfLoop == sd::LoopKind::EWS1) { + for (auto i = start; i < stop; i ++) { + auto oY = y + tadOffsets[i]; + auto oZ = z + zTadOffset[i]; + + PRAGMA_OMP_SIMD + for (unsigned int f = 0; f < tadLength; f++) + oZ[f] = OpType::op(x[f], oY[f], extraParams); + } + } + else if(kindOfLoop == sd::LoopKind::EWSNONZERO) { + for (auto i = start; i < stop; i ++) { + auto oY = y + tadOffsets[i]; + auto oZ = z + zTadOffset[i]; + + PRAGMA_OMP_SIMD + for (uint f = 0; f < tadLength; f++) + oZ[f * zEws] = OpType::op(x[f * xEws], oY[f * yEws], extraParams); + } + } + else if(shape::haveSameShapeAndStrides(yTadShapeShapeInfo, xShapeInfo) && shape::haveSameShapeAndStrides(yTadShapeShapeInfo, zTadShapeInfo)) { + + uint tadShapeShapeInfoCast[MAX_RANK]; + bool canCastY = sd::DataTypeUtils::castShapeInfo(yTadShapeShapeInfo, tadShapeShapeInfoCast); + + for (auto i = start; i < stop; i ++) { + auto oY = y + tadOffsets[i]; + auto oZ = z + zTadOffset[i]; + + PRAGMA_OMP_SIMD + for (unsigned int f = 0; f < tadLength; f++) { + auto offset = shape::indexOffset(f, yTadShapeShapeInfo, tadShapeShapeInfoCast, canCastY); + oZ[offset] = OpType::op(x[offset], oY[offset], extraParams); + } + } + } + else if(shape::haveSameShapeAndStrides(yTadShapeShapeInfo, xShapeInfo)) { + + uint tadShapeShapeInfoCast[MAX_RANK]; + uint tadShapeInfoZCast[MAX_RANK]; + bool canCastY = sd::DataTypeUtils::castShapeInfo(yTadShapeShapeInfo, tadShapeShapeInfoCast); + bool canCastZ = sd::DataTypeUtils::castShapeInfo(zTadShapeInfo, tadShapeInfoZCast); + + for (auto i = start; i < stop; i ++) { + auto oZ = z + zTadOffset[i]; + auto oY = y + tadOffsets[i]; + + PRAGMA_OMP_SIMD + for (unsigned int f = 0; f < tadLength; f++) { + auto offset = shape::indexOffset(f, yTadShapeShapeInfo, tadShapeShapeInfoCast, canCastY); + auto zOffset = shape::indexOffset(f, zTadShapeInfo, tadShapeInfoZCast, canCastZ); + oZ[zOffset] = OpType::op(x[offset], oY[offset], extraParams); + } + } + } + else if(shape::haveSameShapeAndStrides(yTadShapeShapeInfo, zTadShapeInfo)) { + + uint tadShapeShapeInfoCast[MAX_RANK]; + uint xShapeInfoCast[MAX_RANK]; + bool canCastX = sd::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast); + bool canCastY = sd::DataTypeUtils::castShapeInfo(yTadShapeShapeInfo, tadShapeShapeInfoCast); + + for (auto i = start; i < stop; i ++) { + auto oZ = z + zTadOffset[i]; + auto oY = y + tadOffsets[i]; + + PRAGMA_OMP_SIMD + for (unsigned int f = 0; f < tadLength; f++) { + auto offset = shape::indexOffset(f, yTadShapeShapeInfo, tadShapeShapeInfoCast, canCastY); + auto xOffset = shape::indexOffset(f, xShapeInfo, xShapeInfoCast, canCastX); + oZ[offset] = OpType::op(x[xOffset], oY[offset], extraParams); + } + } + } + else if(shape::haveSameShapeAndStrides(xShapeInfo, zTadShapeInfo)) { + + uint tadShapeShapeInfoCast[MAX_RANK]; + uint xShapeInfoCast[MAX_RANK]; + bool canCastX = sd::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast); + bool canCastY = sd::DataTypeUtils::castShapeInfo(yTadShapeShapeInfo, tadShapeShapeInfoCast); + + for (auto i = start; i < stop; i ++) { + auto oZ = z + zTadOffset[i]; + auto oY = y + tadOffsets[i]; + + PRAGMA_OMP_SIMD + for (unsigned int f = 0; f < tadLength; f++) { + auto yOffset = shape::indexOffset(f, yTadShapeShapeInfo, tadShapeShapeInfoCast, canCastY); + auto offset = shape::indexOffset(f, xShapeInfo, xShapeInfoCast, canCastX); + oZ[offset] = OpType::op(x[offset], oY[yOffset], extraParams); + } + } + } + else { + + uint xShapeInfoCast[MAX_RANK]; + uint tadShapeShapeInfoCast[MAX_RANK]; + uint tadShapeInfoZCast[MAX_RANK]; + bool canCastX = sd::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast); + bool canCastY = sd::DataTypeUtils::castShapeInfo(yTadShapeShapeInfo, tadShapeShapeInfoCast); + bool canCastZ = sd::DataTypeUtils::castShapeInfo(zTadShapeInfo, tadShapeInfoZCast); + + for (auto i = start; i < stop; i ++) { + auto oZ = z + zTadOffset[i]; + auto oY = y + tadOffsets[i]; + + PRAGMA_OMP_SIMD + for (unsigned int f = 0; f < tadLength; f++) { + auto xOffset = shape::indexOffset(f, xShapeInfo, xShapeInfoCast, canCastX); + auto yOffset = shape::indexOffset(f, yTadShapeShapeInfo, tadShapeShapeInfoCast, canCastY); + auto zOffset = shape::indexOffset(f, zTadShapeInfo, tadShapeInfoZCast, canCastZ); + oZ[zOffset] = OpType::op(x[xOffset], oY[yOffset], extraParams); + } + } + } + } + +//////////////////////////////////////////////////////////////////////// +template +static void execRank1(const X *x, const Nd4jLong *xShapeInfo, const X *y, const Nd4jLong *yShapeInfo, Z* z, const Nd4jLong *zShapeInfo, X* extraParams) { + + uint zAxis0 = shape::sizeAt(zShapeInfo, 0); + Nd4jLong xStrd0 = shape::strideAt(xShapeInfo, 0); + Nd4jLong yStrd0 = shape::strideAt(yShapeInfo, 0); + Nd4jLong zStrd0 = shape::strideAt(zShapeInfo, 0); + + auto func = PRAGMA_THREADS_FOR{ + + if(zStrd0 == 1 && xStrd0 == 1 && yStrd0 == 0) { + for (auto i0 = start; i0 < stop; ++i0) + z[i0] = OpType::op(x[i0], *y, extraParams); + } + else if(zStrd0 == 1 && xStrd0 == 0 && yStrd0 == 1) { + for (auto i0 = start; i0 < stop; ++i0) + z[i0] = OpType::op(*x, y[i0], extraParams); + } + else if(zStrd0 == 1 && xStrd0 == 1 && yStrd0 == 1) { + for (auto i0 = start; i0 < stop; ++i0) + z[i0] = OpType::op(x[i0], y[i0], extraParams); + } + else { + for (auto i0 = start; i0 < stop; ++i0) + z[i0 * zStrd0] = OpType::op(x[i0 * xStrd0], y[i0 * yStrd0], extraParams); + } + }; + samediff::Threads::parallel_tad(func, 0, zAxis0); +} + +//////////////////////////////////////////////////////////////////////// +template +static void execRank2(const X *x, const Nd4jLong *xShapeInfo, const X *y, const Nd4jLong *yShapeInfo, Z* z, const Nd4jLong *zShapeInfo, X* extraParams) { + + uint zAxis0 = shape::sizeAt(zShapeInfo, shape::order(zShapeInfo) == 'c' ? 0 : 1); + Nd4jLong xStrd0 = shape::strideAt(xShapeInfo, shape::order(zShapeInfo) == 'c' ? 0 : 1); + Nd4jLong yStrd0 = shape::strideAt(yShapeInfo, shape::order(zShapeInfo) == 'c' ? 0 : 1); + Nd4jLong zStrd0 = shape::strideAt(zShapeInfo, shape::order(zShapeInfo) == 'c' ? 0 : 1); + + uint zAxis1 = shape::sizeAt(zShapeInfo, shape::order(zShapeInfo) == 'c' ? 1 : 0); + Nd4jLong xStrd1 = shape::strideAt(xShapeInfo, shape::order(zShapeInfo) == 'c' ? 1 : 0); + Nd4jLong yStrd1 = shape::strideAt(yShapeInfo, shape::order(zShapeInfo) == 'c' ? 1 : 0); + Nd4jLong zStrd1 = shape::strideAt(zShapeInfo, shape::order(zShapeInfo) == 'c' ? 1 : 0); + + auto func = PRAGMA_THREADS_FOR{ + + for (auto i0 = start; i0 < stop; ++i0) { + + auto x0 = x + i0 * xStrd0; + auto y0 = y + i0 * yStrd0; + auto z0 = z + i0 * zStrd0; + + if(zStrd1 == 1 && xStrd1 == 1 && yStrd1 == 0) + for (uint i1 = 0; i1 < zAxis1; ++i1) + z0[i1] = OpType::op(x0[i1], *y0, extraParams); + else if(zStrd1 == 1 && xStrd1 == 0 && yStrd1 == 1) + for (uint i1 = 0; i1 < zAxis1; ++i1) + z0[i1] = OpType::op(*x0, y0[i1], extraParams); + else if(zStrd1 == 1 && xStrd1 == 1 && yStrd1 == 1) + for (uint i1 = 0; i1 < zAxis1; ++i1) + z0[i1] = OpType::op(x0[i1], y0[i1], extraParams); + else + for (uint i1 = 0; i1 < zAxis1; ++i1) + z0[i1 * zStrd1] = OpType::op(x0[i1 * xStrd1], y0[i1 * yStrd1], extraParams); + } + }; + + samediff::Threads::parallel_tad(func, 0, zAxis0); +} + +//////////////////////////////////////////////////////////////////////// +template +static void execRank3(const X *x, const Nd4jLong *xShapeInfo, const X *y, const Nd4jLong *yShapeInfo, Z* z, const Nd4jLong *zShapeInfo, X* extraParams) { + + uint zAxis0 = shape::sizeAt(zShapeInfo, shape::order(zShapeInfo) == 'c' ? 0 : 2); + Nd4jLong xStrd0 = shape::strideAt(xShapeInfo, shape::order(zShapeInfo) == 'c' ? 0 : 2); + Nd4jLong yStrd0 = shape::strideAt(yShapeInfo, shape::order(zShapeInfo) == 'c' ? 0 : 2); + Nd4jLong zStrd0 = shape::strideAt(zShapeInfo, shape::order(zShapeInfo) == 'c' ? 0 : 2); + + uint zAxis1 = shape::sizeAt(zShapeInfo, 1); + Nd4jLong xStrd1 = shape::strideAt(xShapeInfo, 1); + Nd4jLong yStrd1 = shape::strideAt(yShapeInfo, 1); + Nd4jLong zStrd1 = shape::strideAt(zShapeInfo, 1); + + uint zAxis2 = shape::sizeAt(zShapeInfo, shape::order(zShapeInfo) == 'c' ? 2 : 0); + Nd4jLong xStrd2 = shape::strideAt(xShapeInfo, shape::order(zShapeInfo) == 'c' ? 2 : 0); + Nd4jLong yStrd2 = shape::strideAt(yShapeInfo, shape::order(zShapeInfo) == 'c' ? 2 : 0); + Nd4jLong zStrd2 = shape::strideAt(zShapeInfo, shape::order(zShapeInfo) == 'c' ? 2 : 0); + + auto func = PRAGMA_THREADS_FOR_2D { + + for (auto i0 = start_x; i0 < stop_x; ++i0) { + for (auto i1 = start_y; i1 < stop_y; ++i1) { + + auto x1 = x + i0 * xStrd0 + i1 * xStrd1; + auto y1 = y + i0 * yStrd0 + i1 * yStrd1; + auto z1 = z + i0 * zStrd0 + i1 * zStrd1; + + if(zStrd2 == 1 && xStrd2 == 1 && yStrd2 == 0) + for (uint i2 = 0; i2 < zAxis2; ++i2) + z1[i2] = OpType::op(x1[i2], *y1, extraParams); + else if(zStrd2 == 1 && xStrd2 == 0 && yStrd2 == 1) + for (uint i2 = 0; i2 < zAxis2; ++i2) + z1[i2] = OpType::op(*x1, y1[i2], extraParams); + else if(zStrd2 == 1 && xStrd2 == 1 && yStrd2 == 1) + for (uint i2 = 0; i2 < zAxis2; ++i2) + z1[i2] = OpType::op(x1[i2], y1[i2], extraParams); + else + for (uint i2 = 0; i2 < zAxis2; ++i2) + z1[i2 * zStrd2] = OpType::op(x1[i2 * xStrd2], y1[i2 * yStrd2], extraParams); + } + } + }; + + samediff::Threads::parallel_for(func, 0,zAxis0,1, 0,zAxis1,1); +} + +//////////////////////////////////////////////////////////////////////// +template +static void execRank4(const X *x, const Nd4jLong *xShapeInfo, const X *y, const Nd4jLong *yShapeInfo, Z* z, const Nd4jLong *zShapeInfo, X* extraParams) { + + uint zAxis0 = shape::sizeAt(zShapeInfo, shape::order(zShapeInfo) == 'c' ? 0 : 3); + Nd4jLong xStrd0 = shape::strideAt(xShapeInfo, shape::order(zShapeInfo) == 'c' ? 0 : 3); + Nd4jLong yStrd0 = shape::strideAt(yShapeInfo, shape::order(zShapeInfo) == 'c' ? 0 : 3); + Nd4jLong zStrd0 = shape::strideAt(zShapeInfo, shape::order(zShapeInfo) == 'c' ? 0 : 3); + + uint zAxis1 = shape::sizeAt(zShapeInfo, shape::order(zShapeInfo) == 'c' ? 1 : 2); + Nd4jLong xStrd1 = shape::strideAt(xShapeInfo, shape::order(zShapeInfo) == 'c' ? 1 : 2); + Nd4jLong yStrd1 = shape::strideAt(yShapeInfo, shape::order(zShapeInfo) == 'c' ? 1 : 2); + Nd4jLong zStrd1 = shape::strideAt(zShapeInfo, shape::order(zShapeInfo) == 'c' ? 1 : 2); + + uint zAxis2 = shape::sizeAt(zShapeInfo, shape::order(zShapeInfo) == 'c' ? 2 : 1); + Nd4jLong xStrd2 = shape::strideAt(xShapeInfo, shape::order(zShapeInfo) == 'c' ? 2 : 1); + Nd4jLong yStrd2 = shape::strideAt(yShapeInfo, shape::order(zShapeInfo) == 'c' ? 2 : 1); + Nd4jLong zStrd2 = shape::strideAt(zShapeInfo, shape::order(zShapeInfo) == 'c' ? 2 : 1); + + uint zAxis3 = shape::sizeAt(zShapeInfo, shape::order(zShapeInfo) == 'c' ? 3 : 0); + Nd4jLong xStrd3 = shape::strideAt(xShapeInfo, shape::order(zShapeInfo) == 'c' ? 3 : 0); + Nd4jLong yStrd3 = shape::strideAt(yShapeInfo, shape::order(zShapeInfo) == 'c' ? 3 : 0); + Nd4jLong zStrd3 = shape::strideAt(zShapeInfo, shape::order(zShapeInfo) == 'c' ? 3 : 0); + + auto func = PRAGMA_THREADS_FOR_3D { + + for (auto i0 = start_x; i0 < stop_x; ++i0) { + for (auto i1 = start_y; i1 < stop_y; ++i1) { + for (auto i2 = start_z; i2 < stop_z; ++i2) { + + auto x2 = x + i0 * xStrd0 + i1 * xStrd1 + i2 * xStrd2; + auto y2 = y + i0 * yStrd0 + i1 * yStrd1 + i2 * yStrd2; + auto z2 = z + i0 * zStrd0 + i1 * zStrd1 + i2 * zStrd2; + + if(zStrd3 == 1 && xStrd3 == 1 && yStrd3 == 0) + for (uint i3 = 0; i3 < zAxis3; ++i3) + z2[i3] = OpType::op(x2[i3], *y2, extraParams); + else if(zStrd3 == 1 && xStrd3 == 0 && yStrd3 == 1) + for (uint i3 = 0; i3 < zAxis3; ++i3) + z2[i3] = OpType::op(*x2, y2[i3], extraParams); + else if(zStrd3 == 1 && xStrd3 == 1 && yStrd3 == 1) + for (uint i3 = 0; i3 < zAxis3; ++i3) + z2[i3] = OpType::op(x2[i3], y2[i3], extraParams); + else + for (uint i3 = 0; i3 < zAxis3; ++i3) + z2[i3 * zStrd3] = OpType::op(x2[i3 * xStrd3], y2[i3 * yStrd3], extraParams); + } + } + } + }; + + samediff::Threads::parallel_for(func, 0,zAxis0,1, 0,zAxis1,1, 0,zAxis2,1); +} + +//////////////////////////////////////////////////////////////////////// +template +static void execRank5(const X *x, const Nd4jLong *xShapeInfo, const X *y, const Nd4jLong *yShapeInfo, Z* z, const Nd4jLong *zShapeInfo, X* extraParams) { + + uint zAxis0 = shape::sizeAt(zShapeInfo, shape::order(zShapeInfo) == 'c' ? 0 : 4); + Nd4jLong xStrd0 = shape::strideAt(xShapeInfo, shape::order(zShapeInfo) == 'c' ? 0 : 4); + Nd4jLong yStrd0 = shape::strideAt(yShapeInfo, shape::order(zShapeInfo) == 'c' ? 0 : 4); + Nd4jLong zStrd0 = shape::strideAt(zShapeInfo, shape::order(zShapeInfo) == 'c' ? 0 : 4); + + uint zAxis1 = shape::sizeAt(zShapeInfo, shape::order(zShapeInfo) == 'c' ? 1 : 3); + Nd4jLong xStrd1 = shape::strideAt(xShapeInfo, shape::order(zShapeInfo) == 'c' ? 1 : 3); + Nd4jLong yStrd1 = shape::strideAt(yShapeInfo, shape::order(zShapeInfo) == 'c' ? 1 : 3); + Nd4jLong zStrd1 = shape::strideAt(zShapeInfo, shape::order(zShapeInfo) == 'c' ? 1 : 3); + + uint zAxis2 = shape::sizeAt(zShapeInfo, 2); + Nd4jLong xStrd2 = shape::strideAt(xShapeInfo, 2); + Nd4jLong yStrd2 = shape::strideAt(yShapeInfo, 2); + Nd4jLong zStrd2 = shape::strideAt(zShapeInfo, 2); + + uint zAxis3 = shape::sizeAt(zShapeInfo, shape::order(zShapeInfo) == 'c' ? 3 : 1); + Nd4jLong xStrd3 = shape::strideAt(xShapeInfo, shape::order(zShapeInfo) == 'c' ? 3 : 1); + Nd4jLong yStrd3 = shape::strideAt(yShapeInfo, shape::order(zShapeInfo) == 'c' ? 3 : 1); + Nd4jLong zStrd3 = shape::strideAt(zShapeInfo, shape::order(zShapeInfo) == 'c' ? 3 : 1); + + uint zAxis4 = shape::sizeAt(zShapeInfo, shape::order(zShapeInfo) == 'c' ? 4 : 0); + Nd4jLong xStrd4 = shape::strideAt(xShapeInfo, shape::order(zShapeInfo) == 'c' ? 4 : 0); + Nd4jLong yStrd4 = shape::strideAt(yShapeInfo, shape::order(zShapeInfo) == 'c' ? 4 : 0); + Nd4jLong zStrd4 = shape::strideAt(zShapeInfo, shape::order(zShapeInfo) == 'c' ? 4 : 0); + + auto func = PRAGMA_THREADS_FOR_3D { + + for (auto i0 = start_x; i0 < stop_x; ++i0) { + for (auto i1 = start_y; i1 < stop_y; ++i1) { + for (auto i2 = start_z; i2 < stop_z; ++i2) { + for (uint i3 = 0; i3 < zAxis3; ++i3) { + + auto x3 = x + i0 * xStrd0 + i1 * xStrd1 + i2 * xStrd2 + i3 * xStrd3; + auto y3 = y + i0 * yStrd0 + i1 * yStrd1 + i2 * yStrd2 + i3 * yStrd3; + auto z3 = z + i0 * zStrd0 + i1 * zStrd1 + i2 * zStrd2 + i3 * zStrd3; + + if(zStrd4 == 1 && xStrd4 == 1 && yStrd4 == 0) + for (uint i4 = 0; i4 < zAxis4; ++i4) + z3[i4] = OpType::op(x3[i4], *y3, extraParams); + else if(zStrd4 == 1 && xStrd4 == 0 && yStrd4 == 1) + for (uint i4 = 0; i4 < zAxis4; ++i4) + z3[i4] = OpType::op(*x3, y3[i4], extraParams); + else if(zStrd4 == 1 && xStrd4 == 1 && yStrd4 == 1) + for (uint i4 = 0; i4 < zAxis4; ++i4) + z3[i4] = OpType::op(x3[i4], y3[i4], extraParams); + else + for (uint i4 = 0; i4 < zAxis4; ++i4) + z3[i4 * zStrd4] = OpType::op(x3[i4 * xStrd4], y3[i4 * yStrd4], extraParams); + } + } + } + } + }; + + samediff::Threads::parallel_for(func, 0,zAxis0,1, 0,zAxis1,1, 0,zAxis2,1); +} + +//////////////////////////////////////////////////////////////////////// +template +static void execDefault(const X *x, const Nd4jLong *xShapeInfo, const X *y, const Nd4jLong *yShapeInfo, Z* z, const Nd4jLong *zShapeInfo, X* extraParams) { + + const bool xzSameOffsets = shape::haveSameShapeAndStrides(xShapeInfo, zShapeInfo); + const bool yzSameOffsets = shape::haveSameShapeAndStrides(yShapeInfo, zShapeInfo); + + const int rank = shape::rank(zShapeInfo); // xRank = yRank = zRank + + auto func = PRAGMA_THREADS_FOR{ + + int xCoords[MAX_RANK], yCoords[MAX_RANK], zCoords[MAX_RANK]; + + for (auto i = start; i < stop; ++i) { + + shape::index2coordsCPU(start, i, zShapeInfo, zCoords); + + for (uint j = 0; j < rank; ++j) { + xCoords[j] = shape::sizeAt(xShapeInfo, j) == 1 ? 0 : zCoords[j]; + yCoords[j] = shape::sizeAt(yShapeInfo, j) == 1 ? 0 : zCoords[j]; + } + + const auto zOffset = shape::getOffset(zShapeInfo, zCoords); + const auto xOffset = xzSameOffsets ? zOffset : shape::getOffset(xShapeInfo, xCoords); + const auto yOffset = yzSameOffsets ? zOffset : shape::getOffset(yShapeInfo, yCoords); + + z[zOffset] = OpType::op(x[xOffset], y[yOffset], extraParams); + } + }; + + samediff::Threads::parallel_for(func, 0, shape::length(zShapeInfo)); +} +//////////////////////////////////////////////////////////////////////// +template +template +void BroadcastBool::exec(const void *vx, const Nd4jLong *xShapeInfo, + const void *vy, const Nd4jLong *yShapeInfo, + void *vz, const Nd4jLong *zShapeInfo, + void *vextraParams) { + + const X* x = reinterpret_cast(vx); + const X* y = reinterpret_cast(vy); + Z* z = reinterpret_cast(vz); + + X* extraParams = reinterpret_cast(vextraParams); + + const int rank = shape::rank(zShapeInfo); // xRank = yRank = zRank + + switch (rank) { + + case 1: + execRank1(x, xShapeInfo, y, yShapeInfo, z, zShapeInfo, extraParams); + break; + case 2: + execRank2(x, xShapeInfo, y, yShapeInfo, z, zShapeInfo, extraParams); + break; + case 3: + execRank3(x, xShapeInfo, y, yShapeInfo, z, zShapeInfo, extraParams); + break; + case 4: + execRank4(x, xShapeInfo, y, yShapeInfo, z, zShapeInfo, extraParams); + break; + case 5: + execRank5(x, xShapeInfo, y, yShapeInfo, z, zShapeInfo, extraParams); + break; + default: + execDefault(x, xShapeInfo, y, yShapeInfo, z, zShapeInfo, extraParams); + } +} + + //BUILD_DOUBLE_TEMPLATE(template class ND4J_EXPORT BroadcastBool, , LIBND4J_TYPES, BOOL_TYPES); + + +} +} \ No newline at end of file diff --git a/libnd4j/include/loops/cpu/broadcasting_int.cpp b/libnd4j/include/loops/cpu/broadcasting_int.cpp deleted file mode 100644 index 9dcce7545..000000000 --- a/libnd4j/include/loops/cpu/broadcasting_int.cpp +++ /dev/null @@ -1,435 +0,0 @@ -/******************************************************************************* - * Copyright (c) 2015-2018 Skymind, Inc. - * - * This program and the accompanying materials are made available under the - * terms of the Apache License, Version 2.0 which is available at - * https://www.apache.org/licenses/LICENSE-2.0. - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the - * License for the specific language governing permissions and limitations - * under the License. - * - * SPDX-License-Identifier: Apache-2.0 - ******************************************************************************/ - -// -// @author raver119@gmail.com -// - -#include -#include -#include -#include -#include -#include -#include - -using namespace simdOps; - -namespace functions { - namespace broadcast { - - template - void BroadcastInt::exec(const int opNum, - void *x, - Nd4jLong *xShapeInfo, - void *y, - Nd4jLong *yShapeInfo, - void *z, - Nd4jLong *zShapeInfo, - int *dimension, - int dimensionLength, - Nd4jLong *xTadShapeInfo, - Nd4jLong *xTadOffset, - Nd4jLong *zTadShapeInfo, - Nd4jLong *zTadOffset, - uint64_t start, - uint64_t stop) { - DISPATCH_BY_OPNUM_T(exec, PARAMS(x, - xShapeInfo, - y, - yShapeInfo, - z, - zShapeInfo, - dimension, - dimensionLength, - xTadShapeInfo, - xTadOffset, - zTadShapeInfo, - zTadOffset, start, stop), BROADCAST_INT_OPS); - } - - template - void BroadcastInt::execInverse(const int opNum, - void *x, - Nd4jLong *xShapeInfo, - void *y, - Nd4jLong *yShapeInfo, - void *z, - Nd4jLong *zShapeInfo, - int *dimension, - int dimensionLength, - Nd4jLong *xTadShapeInfo, - Nd4jLong *xTadOffset, - Nd4jLong *zTadShapeInfo, - Nd4jLong *zTadOffset, - uint64_t start, - uint64_t stop) { - DISPATCH_BY_OPNUM_T(execInverse, PARAMS(x, - xShapeInfo, - y, - yShapeInfo, - z, - zShapeInfo, - dimension, - dimensionLength, - xTadShapeInfo, - xTadOffset, - zTadShapeInfo, - zTadOffset, start, stop), BROADCAST_INT_OPS); - } - - template - template - void BroadcastInt::exec(void *vx, - Nd4jLong *xShapeInfo, - void *vy, - Nd4jLong *yShapeInfo, - void *vz, - Nd4jLong *zShapeInfo, - int *dimension, - int dimensionLength, - Nd4jLong *xTadShapeInfo, - Nd4jLong *xTadOffset, - Nd4jLong *zTadShapeInfo, - Nd4jLong *zTadOffset, - uint64_t start, - uint64_t stop) { - - auto x = reinterpret_cast(vx); - auto y = reinterpret_cast(vy); - auto z = reinterpret_cast(vz); - - //decompose in to several sub tads after - //moving all dimensions (in sorted order) - //to the back. - //permuted version of the x shape info for setting up the tad problem - auto xTadShapeShapeInfo = xTadShapeInfo; - auto tadOffsets = xTadOffset; - - if (xTadShapeInfo == nullptr || tadOffsets == nullptr) { - auto tadPack = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(xShapeInfo, dimension, dimensionLength); - - xTadShapeShapeInfo = tadPack.primaryShapeInfo(); - tadOffsets = tadPack.primaryOffsets(); - } - - //int *resultStride = shape::stride(xTadShapeShapeInfo); - unsigned int tadLength = shape::length(xTadShapeShapeInfo);//shape::length(xTadShapeShapeInfo); - unsigned int tads = shape::length(xShapeInfo) / tadLength; - - if (zTadShapeInfo == nullptr) { - zTadShapeInfo = xTadShapeShapeInfo; - zTadOffset = tadOffsets; - } - - auto lenZ = shape::length(zTadShapeInfo); - auto lenY = shape::length(yShapeInfo); - - int tadsPerThread = tads / TAD_THRESHOLD; - int threads = nd4j::math::nd4j_max(1, tadsPerThread); - threads = nd4j::math::nd4j_min(threads, nd4j::Environment::getInstance()->maxThreads()); - - auto xEws = shape::elementWiseStride(xTadShapeShapeInfo); - auto yEws = shape::elementWiseStride(yShapeInfo); - auto zEws = shape::elementWiseStride(zTadShapeInfo); - - const nd4j::LoopKind::Kind kindOfLoop = nd4j::LoopKind::deduceKindOfLoopXYZ(xTadShapeShapeInfo, yShapeInfo, zTadShapeInfo); - - if (kindOfLoop == nd4j::LoopKind::EWS1) { - for (auto i = start; i < stop; i ++) { - auto oX = x + tadOffsets[i]; - auto oZ = z + zTadOffset[i]; - - PRAGMA_OMP_SIMD - for (unsigned int f = 0; f < tadLength; f++) - oZ[f] = OpType::op(oX[f], y[f]); - }; - } - else if(kindOfLoop == nd4j::LoopKind::EWSNONZERO) { - for (auto i = start; i < stop; i ++) { - auto oX = x + tadOffsets[i]; - auto oZ = z + zTadOffset[i]; - - PRAGMA_OMP_SIMD - for (unsigned int f = 0; f < tadLength; f++) - oZ[f * zEws] = OpType::op(oX[f * xEws], y[f * yEws]); - }; - } - else if(shape::haveSameShapeAndStrides(xTadShapeShapeInfo, yShapeInfo) && shape::haveSameShapeAndStrides(xTadShapeShapeInfo, zTadShapeInfo)) { - uint tadShapeShapeInfoCast[MAX_RANK]; - bool canCastX = nd4j::DataTypeUtils::castShapeInfo(xTadShapeShapeInfo, tadShapeShapeInfoCast); - - for (auto i = start; i < stop; i ++) { - auto oZ = z + zTadOffset[i]; - auto oX = x + tadOffsets[i]; - - PRAGMA_OMP_SIMD - for (int f = 0; f < tadLength; f++) { - auto offset = shape::indexOffset(f, xTadShapeShapeInfo, tadShapeShapeInfoCast, canCastX); - oZ[offset] = OpType::op(oX[offset], y[offset]); - } - }; - } - else if(shape::haveSameShapeAndStrides(xTadShapeShapeInfo, yShapeInfo)) { - uint tadShapeShapeInfoCast[MAX_RANK]; - uint tadShapeInfoZCast[MAX_RANK]; - bool canCastX = nd4j::DataTypeUtils::castShapeInfo(xTadShapeShapeInfo, tadShapeShapeInfoCast); - bool canCastZ = nd4j::DataTypeUtils::castShapeInfo(zTadShapeInfo, tadShapeInfoZCast); - - for (auto i = start; i < stop; i ++) { - auto oZ = z + zTadOffset[i]; - auto oX = x + tadOffsets[i]; - - PRAGMA_OMP_SIMD - for (int f = 0; f < tadLength; f++) { - auto offset = shape::indexOffset(f, xTadShapeShapeInfo, tadShapeShapeInfoCast, canCastX); - auto zOffset = shape::indexOffset(f, zTadShapeInfo, tadShapeInfoZCast, canCastZ); - oZ[zOffset] = OpType::op(oX[offset], y[offset]); - } - }; - } - else if(shape::haveSameShapeAndStrides(xTadShapeShapeInfo, zTadShapeInfo)) { - uint tadShapeShapeInfoCast[MAX_RANK]; - uint yShapeInfoCast[MAX_RANK]; - bool canCastX = nd4j::DataTypeUtils::castShapeInfo(xTadShapeShapeInfo, tadShapeShapeInfoCast); - bool canCastY = nd4j::DataTypeUtils::castShapeInfo(yShapeInfo, yShapeInfoCast); - - for (auto i = start; i < stop; i ++) { - auto oZ = z + zTadOffset[i]; - auto oX = x + tadOffsets[i]; - - PRAGMA_OMP_SIMD - for (int f = 0; f < tadLength; f++) { - auto offset = shape::indexOffset(f, xTadShapeShapeInfo, tadShapeShapeInfoCast, canCastX); - auto yOffset = shape::indexOffset(f, yShapeInfo, yShapeInfoCast, canCastY); - oZ[offset] = OpType::op(oX[offset], y[yOffset]); - } - }; - } - else if(shape::haveSameShapeAndStrides(yShapeInfo, zTadShapeInfo)) { - uint tadShapeShapeInfoCast[MAX_RANK]; - uint yShapeInfoCast[MAX_RANK]; - bool canCastX = nd4j::DataTypeUtils::castShapeInfo(xTadShapeShapeInfo, tadShapeShapeInfoCast); - bool canCastY = nd4j::DataTypeUtils::castShapeInfo(yShapeInfo, yShapeInfoCast); - - for (auto i = start; i < stop; i ++) { - auto oZ = z + zTadOffset[i]; - auto oX = x + tadOffsets[i]; - - PRAGMA_OMP_SIMD - for (int f = 0; f < tadLength; f++) { - auto xOffset = shape::indexOffset(f, xTadShapeShapeInfo, tadShapeShapeInfoCast, canCastX); - auto offset = shape::indexOffset(f, yShapeInfo, yShapeInfoCast, canCastY); - oZ[offset] = OpType::op(oX[xOffset], y[offset]); - } - }; - } - else { - uint tadShapeShapeInfoCast[MAX_RANK]; - uint tadShapeInfoZCast[MAX_RANK]; - uint yShapeInfoCast[MAX_RANK]; - bool canCastX = nd4j::DataTypeUtils::castShapeInfo(xTadShapeShapeInfo, tadShapeShapeInfoCast); - bool canCastY = nd4j::DataTypeUtils::castShapeInfo(yShapeInfo, yShapeInfoCast); - bool canCastZ = nd4j::DataTypeUtils::castShapeInfo(zTadShapeInfo, tadShapeInfoZCast); - - for (auto i = start; i < stop; i ++) { - auto oZ = z + zTadOffset[i]; - auto oX = x + tadOffsets[i]; - - PRAGMA_OMP_SIMD - for (int f = 0; f < tadLength; f++) { - auto xOffset = shape::indexOffset(f, xTadShapeShapeInfo, tadShapeShapeInfoCast, canCastX); - auto yOffset = shape::indexOffset(f, yShapeInfo, yShapeInfoCast, canCastY); - auto zOffset = shape::indexOffset(f, zTadShapeInfo, tadShapeInfoZCast, canCastZ); - oZ[zOffset] = OpType::op(oX[xOffset], y[yOffset]); - } - }; - } - } - - - template - template - void BroadcastInt::execInverse(void *vx, - Nd4jLong *xShapeInfo, - void *vy, - Nd4jLong *yShapeInfo, - void *vz, - Nd4jLong *zShapeInfo, - int *dimension, - int dimensionLength, - Nd4jLong *yTadShapeInfo, - Nd4jLong *yTadOffset, - Nd4jLong *zTadShapeInfo, - Nd4jLong *zTadOffset, - uint64_t start, - uint64_t stop) { - - auto x = reinterpret_cast(vx); - auto y = reinterpret_cast(vy); - auto z = reinterpret_cast(vz); - - //decompose in to several sub tads after - //moving all dimensions (in sorted order) - //to the back. - //permuted version of the x shape info for setting up the tad problem - auto yTadShapeShapeInfo = yTadShapeInfo; - auto tadOffsets = yTadOffset; - - if (yTadShapeInfo == nullptr || tadOffsets == nullptr) { - auto tadPack = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(yShapeInfo, dimension, dimensionLength); - - yTadShapeShapeInfo = tadPack.primaryShapeInfo(); - tadOffsets = tadPack.primaryOffsets(); - } - - //int *resultStride = shape::stride(yTadShapeShapeInfo); - unsigned int tadLength = shape::length(yTadShapeShapeInfo); - unsigned int tads = shape::length(yShapeInfo) / tadLength; - - if (zTadShapeInfo == nullptr) { - zTadShapeInfo = yTadShapeShapeInfo; - zTadOffset = tadOffsets; - } - - auto lenZ = shape::length(zTadShapeInfo); - auto lenX = shape::length(xShapeInfo); - - int tadsPerThread = tads / TAD_THRESHOLD; - int threads = nd4j::math::nd4j_max(1, tadsPerThread); - threads = nd4j::math::nd4j_min(threads, nd4j::Environment::getInstance()->maxThreads()); - - auto yEws = shape::elementWiseStride(yTadShapeShapeInfo); - auto xEws = shape::elementWiseStride(xShapeInfo); - auto zEws = shape::elementWiseStride(zTadShapeInfo); - - const nd4j::LoopKind::Kind kindOfLoop = nd4j::LoopKind::deduceKindOfLoopXYZ(yTadShapeShapeInfo, xShapeInfo, zTadShapeInfo); - - if (kindOfLoop == nd4j::LoopKind::EWS1) { - for (auto i = start; i < stop; i ++) { - auto oY = y + tadOffsets[i]; - auto oZ = z + zTadOffset[i]; - - PRAGMA_OMP_SIMD - for (unsigned int f = 0; f < tadLength; f++) - oZ[f] = OpType::op(x[f], oY[f]); - }; - } - else if(kindOfLoop == nd4j::LoopKind::EWSNONZERO) { - for (auto i = start; i < stop; i ++) { - auto oY = y + tadOffsets[i]; - auto oZ = z + zTadOffset[i]; - - PRAGMA_OMP_SIMD - for (uint f = 0; f < tadLength; f++) - oZ[f * zEws] = OpType::op(x[f * xEws], oY[f * yEws]); - }; - } - else if(shape::haveSameShapeAndStrides(yTadShapeShapeInfo, xShapeInfo) && shape::haveSameShapeAndStrides(yTadShapeShapeInfo, zTadShapeInfo)) { - uint tadShapeShapeInfoCast[MAX_RANK]; - bool canCastY = nd4j::DataTypeUtils::castShapeInfo(yTadShapeShapeInfo, tadShapeShapeInfoCast); - - for (auto i = start; i < stop; i ++) { - auto oY = y + tadOffsets[i]; - auto oZ = z + zTadOffset[i]; - - PRAGMA_OMP_SIMD - for (int f = 0; f < tadLength; f++) { - auto offset = shape::indexOffset(f, yTadShapeShapeInfo, tadShapeShapeInfoCast, canCastY); - oZ[offset] = OpType::op(x[offset], oY[offset]); - } - }; - } - else if(shape::haveSameShapeAndStrides(yTadShapeShapeInfo, xShapeInfo)) { - - uint tadShapeShapeInfoCast[MAX_RANK]; - uint tadShapeInfoZCast[MAX_RANK]; - bool canCastY = nd4j::DataTypeUtils::castShapeInfo(yTadShapeShapeInfo, tadShapeShapeInfoCast); - bool canCastZ = nd4j::DataTypeUtils::castShapeInfo(zTadShapeInfo, tadShapeInfoZCast); - - for (auto i = start; i < stop; i ++) { - auto oZ = z + zTadOffset[i]; - auto oY = y + tadOffsets[i]; - - for (int f = 0; f < tadLength; f++) { - auto offset = shape::indexOffset(f, yTadShapeShapeInfo, tadShapeShapeInfoCast, canCastY); - auto zOffset = shape::indexOffset(f, zTadShapeInfo, tadShapeInfoZCast, canCastZ); - oZ[zOffset] = OpType::op(x[offset], oY[offset]); - } - }; - } - else if(shape::haveSameShapeAndStrides(yTadShapeShapeInfo, zTadShapeInfo)) { - uint tadShapeShapeInfoCast[MAX_RANK]; - uint xShapeInfoCast[MAX_RANK]; - bool canCastX = nd4j::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast); - bool canCastY = nd4j::DataTypeUtils::castShapeInfo(yTadShapeShapeInfo, tadShapeShapeInfoCast); - - for (auto i = start; i < stop; i ++) { - auto oZ = z + zTadOffset[i]; - auto oY = y + tadOffsets[i]; - - PRAGMA_OMP_SIMD - for (int f = 0; f < tadLength; f++) { - auto offset = shape::indexOffset(f, yTadShapeShapeInfo, tadShapeShapeInfoCast, canCastY); - auto xOffset = shape::indexOffset(f, xShapeInfo, xShapeInfoCast, canCastX); - oZ[offset] = OpType::op(x[xOffset], oY[offset]); - } - }; - } - else if(shape::haveSameShapeAndStrides(xShapeInfo, zTadShapeInfo)) { - uint tadShapeShapeInfoCast[MAX_RANK]; - uint xShapeInfoCast[MAX_RANK]; - bool canCastX = nd4j::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast); - bool canCastY = nd4j::DataTypeUtils::castShapeInfo(yTadShapeShapeInfo, tadShapeShapeInfoCast); - - for (auto i = start; i < stop; i ++) { - auto oZ = z + zTadOffset[i]; - auto oY = y + tadOffsets[i]; - - PRAGMA_OMP_SIMD - for (int f = 0; f < tadLength; f++) { - auto yOffset = shape::indexOffset(f, yTadShapeShapeInfo, tadShapeShapeInfoCast, canCastY); - auto offset = shape::indexOffset(f, xShapeInfo, xShapeInfoCast, canCastX); - oZ[offset] = OpType::op(x[offset], oY[yOffset]); - } - }; - } - else { - uint xShapeInfoCast[MAX_RANK]; - uint tadShapeShapeInfoCast[MAX_RANK]; - uint tadShapeInfoZCast[MAX_RANK]; - bool canCastX = nd4j::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast); - bool canCastY = nd4j::DataTypeUtils::castShapeInfo(yTadShapeShapeInfo, tadShapeShapeInfoCast); - bool canCastZ = nd4j::DataTypeUtils::castShapeInfo(zTadShapeInfo, tadShapeInfoZCast); - - for (auto i = start; i < stop; i ++) { - auto oZ = z + zTadOffset[i]; - auto oY = y + tadOffsets[i]; - - PRAGMA_OMP_SIMD - for (int f = 0; f < tadLength; f++) { - auto xOffset = shape::indexOffset(f, xShapeInfo, xShapeInfoCast, canCastX); - auto yOffset = shape::indexOffset(f, yTadShapeShapeInfo, tadShapeShapeInfoCast, canCastY); - auto zOffset = shape::indexOffset(f, zTadShapeInfo, tadShapeInfoZCast, canCastZ); - oZ[zOffset] = OpType::op(x[xOffset], oY[yOffset]); - } - }; - } - } - - BUILD_SINGLE_TEMPLATE(template class ND4J_EXPORT BroadcastInt, , INTEGER_TYPES); - } -} \ No newline at end of file diff --git a/libnd4j/include/loops/cpu/broadcasting_int.hpp b/libnd4j/include/loops/cpu/broadcasting_int.hpp new file mode 100644 index 000000000..456994b16 --- /dev/null +++ b/libnd4j/include/loops/cpu/broadcasting_int.hpp @@ -0,0 +1,745 @@ +/******************************************************************************* + * Copyright (c) 2015-2018 Skymind, Inc. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ + +// +// @author raver119@gmail.com +// + +#include +#include +#include +#include +#include +#include +#include + +using namespace simdOps; + +namespace functions { + namespace broadcast { + + template + void BroadcastInt::exec(const int opNum, + void *x, + Nd4jLong *xShapeInfo, + void *y, + Nd4jLong *yShapeInfo, + void *z, + Nd4jLong *zShapeInfo, + int *dimension, + int dimensionLength, + Nd4jLong *xTadShapeInfo, + Nd4jLong *xTadOffset, + Nd4jLong *zTadShapeInfo, + Nd4jLong *zTadOffset, + uint64_t start, + uint64_t stop) { + DISPATCH_BY_OPNUM_T(exec, PARAMS(x, + xShapeInfo, + y, + yShapeInfo, + z, + zShapeInfo, + dimension, + dimensionLength, + xTadShapeInfo, + xTadOffset, + zTadShapeInfo, + zTadOffset, start, stop), BROADCAST_INT_OPS); + } + + template + void BroadcastInt::exec(const int opNum, + const void *x, const Nd4jLong *xShapeInfo, + const void *y, const Nd4jLong *yShapeInfo, + void *z, const Nd4jLong *zShapeInfo) { + + DISPATCH_BY_OPNUM_T(exec, PARAMS(x, xShapeInfo, y, yShapeInfo, z, zShapeInfo), BROADCAST_INT_OPS); + } + + template + void BroadcastInt::execInverse(const int opNum, + void *x, + Nd4jLong *xShapeInfo, + void *y, + Nd4jLong *yShapeInfo, + void *z, + Nd4jLong *zShapeInfo, + int *dimension, + int dimensionLength, + Nd4jLong *xTadShapeInfo, + Nd4jLong *xTadOffset, + Nd4jLong *zTadShapeInfo, + Nd4jLong *zTadOffset, + uint64_t start, + uint64_t stop) { + DISPATCH_BY_OPNUM_T(execInverse, PARAMS(x, + xShapeInfo, + y, + yShapeInfo, + z, + zShapeInfo, + dimension, + dimensionLength, + xTadShapeInfo, + xTadOffset, + zTadShapeInfo, + zTadOffset, start, stop), BROADCAST_INT_OPS); + } + + template + template + void BroadcastInt::exec(void *vx, + Nd4jLong *xShapeInfo, + void *vy, + Nd4jLong *yShapeInfo, + void *vz, + Nd4jLong *zShapeInfo, + int *dimension, + int dimensionLength, + Nd4jLong *xTadShapeInfo, + Nd4jLong *xTadOffset, + Nd4jLong *zTadShapeInfo, + Nd4jLong *zTadOffset, + uint64_t start, + uint64_t stop) { + + auto x = reinterpret_cast(vx); + auto y = reinterpret_cast(vy); + auto z = reinterpret_cast(vz); + + //decompose in to several sub tads after + //moving all dimensions (in sorted order) + //to the back. + //permuted version of the x shape info for setting up the tad problem + auto xTadShapeShapeInfo = xTadShapeInfo; + auto tadOffsets = xTadOffset; + + if (xTadShapeInfo == nullptr || tadOffsets == nullptr) { + auto tadPack = sd::ConstantTadHelper::getInstance()->tadForDimensions(xShapeInfo, dimension, dimensionLength); + + xTadShapeShapeInfo = tadPack.primaryShapeInfo(); + tadOffsets = tadPack.primaryOffsets(); + } + + //int *resultStride = shape::stride(xTadShapeShapeInfo); + unsigned int tadLength = shape::length(xTadShapeShapeInfo);//shape::length(xTadShapeShapeInfo); + unsigned int tads = shape::length(xShapeInfo) / tadLength; + + if (zTadShapeInfo == nullptr) { + zTadShapeInfo = xTadShapeShapeInfo; + zTadOffset = tadOffsets; + } + + auto lenZ = shape::length(zTadShapeInfo); + auto lenY = shape::length(yShapeInfo); + + int tadsPerThread = tads / TAD_THRESHOLD; + int threads = sd::math::nd4j_max(1, tadsPerThread); + threads = sd::math::nd4j_min(threads, sd::Environment::getInstance()->maxThreads()); + + auto xEws = shape::elementWiseStride(xTadShapeShapeInfo); + auto yEws = shape::elementWiseStride(yShapeInfo); + auto zEws = shape::elementWiseStride(zTadShapeInfo); + + const sd::LoopKind::Kind kindOfLoop = sd::LoopKind::deduceKindOfLoopXYZ(xTadShapeShapeInfo, yShapeInfo, zTadShapeInfo); + + if (kindOfLoop == sd::LoopKind::EWS1) { + for (auto i = start; i < stop; i ++) { + auto oX = x + tadOffsets[i]; + auto oZ = z + zTadOffset[i]; + + PRAGMA_OMP_SIMD + for (unsigned int f = 0; f < tadLength; f++) + oZ[f] = OpType::op(oX[f], y[f]); + }; + } + else if(kindOfLoop == sd::LoopKind::EWSNONZERO) { + for (auto i = start; i < stop; i ++) { + auto oX = x + tadOffsets[i]; + auto oZ = z + zTadOffset[i]; + + PRAGMA_OMP_SIMD + for (unsigned int f = 0; f < tadLength; f++) + oZ[f * zEws] = OpType::op(oX[f * xEws], y[f * yEws]); + }; + } + else if(shape::haveSameShapeAndStrides(xTadShapeShapeInfo, yShapeInfo) && shape::haveSameShapeAndStrides(xTadShapeShapeInfo, zTadShapeInfo)) { + uint tadShapeShapeInfoCast[MAX_RANK]; + bool canCastX = sd::DataTypeUtils::castShapeInfo(xTadShapeShapeInfo, tadShapeShapeInfoCast); + + for (auto i = start; i < stop; i ++) { + auto oZ = z + zTadOffset[i]; + auto oX = x + tadOffsets[i]; + + PRAGMA_OMP_SIMD + for (unsigned int f = 0; f < tadLength; f++) { + auto offset = shape::indexOffset(f, xTadShapeShapeInfo, tadShapeShapeInfoCast, canCastX); + oZ[offset] = OpType::op(oX[offset], y[offset]); + } + }; + } + else if(shape::haveSameShapeAndStrides(xTadShapeShapeInfo, yShapeInfo)) { + uint tadShapeShapeInfoCast[MAX_RANK]; + uint tadShapeInfoZCast[MAX_RANK]; + bool canCastX = sd::DataTypeUtils::castShapeInfo(xTadShapeShapeInfo, tadShapeShapeInfoCast); + bool canCastZ = sd::DataTypeUtils::castShapeInfo(zTadShapeInfo, tadShapeInfoZCast); + + for (auto i = start; i < stop; i ++) { + auto oZ = z + zTadOffset[i]; + auto oX = x + tadOffsets[i]; + + PRAGMA_OMP_SIMD + for (unsigned int f = 0; f < tadLength; f++) { + auto offset = shape::indexOffset(f, xTadShapeShapeInfo, tadShapeShapeInfoCast, canCastX); + auto zOffset = shape::indexOffset(f, zTadShapeInfo, tadShapeInfoZCast, canCastZ); + oZ[zOffset] = OpType::op(oX[offset], y[offset]); + } + }; + } + else if(shape::haveSameShapeAndStrides(xTadShapeShapeInfo, zTadShapeInfo)) { + uint tadShapeShapeInfoCast[MAX_RANK]; + uint yShapeInfoCast[MAX_RANK]; + bool canCastX = sd::DataTypeUtils::castShapeInfo(xTadShapeShapeInfo, tadShapeShapeInfoCast); + bool canCastY = sd::DataTypeUtils::castShapeInfo(yShapeInfo, yShapeInfoCast); + + for (auto i = start; i < stop; i ++) { + auto oZ = z + zTadOffset[i]; + auto oX = x + tadOffsets[i]; + + PRAGMA_OMP_SIMD + for (unsigned int f = 0; f < tadLength; f++) { + auto offset = shape::indexOffset(f, xTadShapeShapeInfo, tadShapeShapeInfoCast, canCastX); + auto yOffset = shape::indexOffset(f, yShapeInfo, yShapeInfoCast, canCastY); + oZ[offset] = OpType::op(oX[offset], y[yOffset]); + } + }; + } + else if(shape::haveSameShapeAndStrides(yShapeInfo, zTadShapeInfo)) { + uint tadShapeShapeInfoCast[MAX_RANK]; + uint yShapeInfoCast[MAX_RANK]; + bool canCastX = sd::DataTypeUtils::castShapeInfo(xTadShapeShapeInfo, tadShapeShapeInfoCast); + bool canCastY = sd::DataTypeUtils::castShapeInfo(yShapeInfo, yShapeInfoCast); + + for (auto i = start; i < stop; i ++) { + auto oZ = z + zTadOffset[i]; + auto oX = x + tadOffsets[i]; + + PRAGMA_OMP_SIMD + for (unsigned int f = 0; f < tadLength; f++) { + auto xOffset = shape::indexOffset(f, xTadShapeShapeInfo, tadShapeShapeInfoCast, canCastX); + auto offset = shape::indexOffset(f, yShapeInfo, yShapeInfoCast, canCastY); + oZ[offset] = OpType::op(oX[xOffset], y[offset]); + } + }; + } + else { + uint tadShapeShapeInfoCast[MAX_RANK]; + uint tadShapeInfoZCast[MAX_RANK]; + uint yShapeInfoCast[MAX_RANK]; + bool canCastX = sd::DataTypeUtils::castShapeInfo(xTadShapeShapeInfo, tadShapeShapeInfoCast); + bool canCastY = sd::DataTypeUtils::castShapeInfo(yShapeInfo, yShapeInfoCast); + bool canCastZ = sd::DataTypeUtils::castShapeInfo(zTadShapeInfo, tadShapeInfoZCast); + + for (auto i = start; i < stop; i ++) { + auto oZ = z + zTadOffset[i]; + auto oX = x + tadOffsets[i]; + + PRAGMA_OMP_SIMD + for (unsigned int f = 0; f < tadLength; f++) { + auto xOffset = shape::indexOffset(f, xTadShapeShapeInfo, tadShapeShapeInfoCast, canCastX); + auto yOffset = shape::indexOffset(f, yShapeInfo, yShapeInfoCast, canCastY); + auto zOffset = shape::indexOffset(f, zTadShapeInfo, tadShapeInfoZCast, canCastZ); + oZ[zOffset] = OpType::op(oX[xOffset], y[yOffset]); + } + }; + } + } + + + template + template + void BroadcastInt::execInverse(void *vx, + Nd4jLong *xShapeInfo, + void *vy, + Nd4jLong *yShapeInfo, + void *vz, + Nd4jLong *zShapeInfo, + int *dimension, + int dimensionLength, + Nd4jLong *yTadShapeInfo, + Nd4jLong *yTadOffset, + Nd4jLong *zTadShapeInfo, + Nd4jLong *zTadOffset, + uint64_t start, + uint64_t stop) { + + auto x = reinterpret_cast(vx); + auto y = reinterpret_cast(vy); + auto z = reinterpret_cast(vz); + + //decompose in to several sub tads after + //moving all dimensions (in sorted order) + //to the back. + //permuted version of the x shape info for setting up the tad problem + auto yTadShapeShapeInfo = yTadShapeInfo; + auto tadOffsets = yTadOffset; + + if (yTadShapeInfo == nullptr || tadOffsets == nullptr) { + auto tadPack = sd::ConstantTadHelper::getInstance()->tadForDimensions(yShapeInfo, dimension, dimensionLength); + + yTadShapeShapeInfo = tadPack.primaryShapeInfo(); + tadOffsets = tadPack.primaryOffsets(); + } + + //int *resultStride = shape::stride(yTadShapeShapeInfo); + unsigned int tadLength = shape::length(yTadShapeShapeInfo); + unsigned int tads = shape::length(yShapeInfo) / tadLength; + + if (zTadShapeInfo == nullptr) { + zTadShapeInfo = yTadShapeShapeInfo; + zTadOffset = tadOffsets; + } + + auto lenZ = shape::length(zTadShapeInfo); + auto lenX = shape::length(xShapeInfo); + + int tadsPerThread = tads / TAD_THRESHOLD; + int threads = sd::math::nd4j_max(1, tadsPerThread); + threads = sd::math::nd4j_min(threads, sd::Environment::getInstance()->maxThreads()); + + auto yEws = shape::elementWiseStride(yTadShapeShapeInfo); + auto xEws = shape::elementWiseStride(xShapeInfo); + auto zEws = shape::elementWiseStride(zTadShapeInfo); + + const sd::LoopKind::Kind kindOfLoop = sd::LoopKind::deduceKindOfLoopXYZ(yTadShapeShapeInfo, xShapeInfo, zTadShapeInfo); + + if (kindOfLoop == sd::LoopKind::EWS1) { + for (auto i = start; i < stop; i ++) { + auto oY = y + tadOffsets[i]; + auto oZ = z + zTadOffset[i]; + + PRAGMA_OMP_SIMD + for (unsigned int f = 0; f < tadLength; f++) + oZ[f] = OpType::op(x[f], oY[f]); + }; + } + else if(kindOfLoop == sd::LoopKind::EWSNONZERO) { + for (auto i = start; i < stop; i ++) { + auto oY = y + tadOffsets[i]; + auto oZ = z + zTadOffset[i]; + + PRAGMA_OMP_SIMD + for (uint f = 0; f < tadLength; f++) + oZ[f * zEws] = OpType::op(x[f * xEws], oY[f * yEws]); + }; + } + else if(shape::haveSameShapeAndStrides(yTadShapeShapeInfo, xShapeInfo) && shape::haveSameShapeAndStrides(yTadShapeShapeInfo, zTadShapeInfo)) { + uint tadShapeShapeInfoCast[MAX_RANK]; + bool canCastY = sd::DataTypeUtils::castShapeInfo(yTadShapeShapeInfo, tadShapeShapeInfoCast); + + for (auto i = start; i < stop; i ++) { + auto oY = y + tadOffsets[i]; + auto oZ = z + zTadOffset[i]; + + PRAGMA_OMP_SIMD + for (uint f = 0; f < tadLength; f++) { + auto offset = shape::indexOffset(f, yTadShapeShapeInfo, tadShapeShapeInfoCast, canCastY); + oZ[offset] = OpType::op(x[offset], oY[offset]); + } + }; + } + else if(shape::haveSameShapeAndStrides(yTadShapeShapeInfo, xShapeInfo)) { + + uint tadShapeShapeInfoCast[MAX_RANK]; + uint tadShapeInfoZCast[MAX_RANK]; + bool canCastY = sd::DataTypeUtils::castShapeInfo(yTadShapeShapeInfo, tadShapeShapeInfoCast); + bool canCastZ = sd::DataTypeUtils::castShapeInfo(zTadShapeInfo, tadShapeInfoZCast); + + for (auto i = start; i < stop; i ++) { + auto oZ = z + zTadOffset[i]; + auto oY = y + tadOffsets[i]; + + for (uint f = 0; f < tadLength; f++) { + auto offset = shape::indexOffset(f, yTadShapeShapeInfo, tadShapeShapeInfoCast, canCastY); + auto zOffset = shape::indexOffset(f, zTadShapeInfo, tadShapeInfoZCast, canCastZ); + oZ[zOffset] = OpType::op(x[offset], oY[offset]); + } + }; + } + else if(shape::haveSameShapeAndStrides(yTadShapeShapeInfo, zTadShapeInfo)) { + uint tadShapeShapeInfoCast[MAX_RANK]; + uint xShapeInfoCast[MAX_RANK]; + bool canCastX = sd::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast); + bool canCastY = sd::DataTypeUtils::castShapeInfo(yTadShapeShapeInfo, tadShapeShapeInfoCast); + + for (auto i = start; i < stop; i ++) { + auto oZ = z + zTadOffset[i]; + auto oY = y + tadOffsets[i]; + + PRAGMA_OMP_SIMD + for (uint f = 0; f < tadLength; f++) { + auto offset = shape::indexOffset(f, yTadShapeShapeInfo, tadShapeShapeInfoCast, canCastY); + auto xOffset = shape::indexOffset(f, xShapeInfo, xShapeInfoCast, canCastX); + oZ[offset] = OpType::op(x[xOffset], oY[offset]); + } + }; + } + else if(shape::haveSameShapeAndStrides(xShapeInfo, zTadShapeInfo)) { + uint tadShapeShapeInfoCast[MAX_RANK]; + uint xShapeInfoCast[MAX_RANK]; + bool canCastX = sd::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast); + bool canCastY = sd::DataTypeUtils::castShapeInfo(yTadShapeShapeInfo, tadShapeShapeInfoCast); + + for (auto i = start; i < stop; i ++) { + auto oZ = z + zTadOffset[i]; + auto oY = y + tadOffsets[i]; + + PRAGMA_OMP_SIMD + for (uint f = 0; f < tadLength; f++) { + auto yOffset = shape::indexOffset(f, yTadShapeShapeInfo, tadShapeShapeInfoCast, canCastY); + auto offset = shape::indexOffset(f, xShapeInfo, xShapeInfoCast, canCastX); + oZ[offset] = OpType::op(x[offset], oY[yOffset]); + } + }; + } + else { + uint xShapeInfoCast[MAX_RANK]; + uint tadShapeShapeInfoCast[MAX_RANK]; + uint tadShapeInfoZCast[MAX_RANK]; + bool canCastX = sd::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast); + bool canCastY = sd::DataTypeUtils::castShapeInfo(yTadShapeShapeInfo, tadShapeShapeInfoCast); + bool canCastZ = sd::DataTypeUtils::castShapeInfo(zTadShapeInfo, tadShapeInfoZCast); + + for (auto i = start; i < stop; i ++) { + auto oZ = z + zTadOffset[i]; + auto oY = y + tadOffsets[i]; + + PRAGMA_OMP_SIMD + for (uint f = 0; f < tadLength; f++) { + auto xOffset = shape::indexOffset(f, xShapeInfo, xShapeInfoCast, canCastX); + auto yOffset = shape::indexOffset(f, yTadShapeShapeInfo, tadShapeShapeInfoCast, canCastY); + auto zOffset = shape::indexOffset(f, zTadShapeInfo, tadShapeInfoZCast, canCastZ); + oZ[zOffset] = OpType::op(x[xOffset], oY[yOffset]); + } + }; + } + } + +//////////////////////////////////////////////////////////////////////// +template +static void execRank1(const X *x, const Nd4jLong *xShapeInfo, const X *y, const Nd4jLong *yShapeInfo, X* z, const Nd4jLong *zShapeInfo) { + + uint zAxis0 = shape::sizeAt(zShapeInfo, 0); + Nd4jLong xStrd0 = shape::strideAt(xShapeInfo, 0); + Nd4jLong yStrd0 = shape::strideAt(yShapeInfo, 0); + Nd4jLong zStrd0 = shape::strideAt(zShapeInfo, 0); + + auto func = PRAGMA_THREADS_FOR{ + + if(zStrd0 == 1 && xStrd0 == 1 && yStrd0 == 0) { + for (auto i0 = start; i0 < stop; ++i0) + z[i0] = OpType::op(x[i0], *y); + } + else if(zStrd0 == 1 && xStrd0 == 0 && yStrd0 == 1) { + for (auto i0 = start; i0 < stop; ++i0) + z[i0] = OpType::op(*x, y[i0]); + } + else if(zStrd0 == 1 && xStrd0 == 1 && yStrd0 == 1) { + for (auto i0 = start; i0 < stop; ++i0) + z[i0] = OpType::op(x[i0], y[i0]); + } + else { + for (auto i0 = start; i0 < stop; ++i0) + z[i0 * zStrd0] = OpType::op(x[i0 * xStrd0], y[i0 * yStrd0]); + } + }; + samediff::Threads::parallel_tad(func, 0, zAxis0); +} + +//////////////////////////////////////////////////////////////////////// +template +static void execRank2(const X *x, const Nd4jLong *xShapeInfo, const X *y, const Nd4jLong *yShapeInfo, X* z, const Nd4jLong *zShapeInfo) { + + uint zAxis0 = shape::sizeAt(zShapeInfo, shape::order(zShapeInfo) == 'c' ? 0 : 1); + Nd4jLong xStrd0 = shape::strideAt(xShapeInfo, shape::order(zShapeInfo) == 'c' ? 0 : 1); + Nd4jLong yStrd0 = shape::strideAt(yShapeInfo, shape::order(zShapeInfo) == 'c' ? 0 : 1); + Nd4jLong zStrd0 = shape::strideAt(zShapeInfo, shape::order(zShapeInfo) == 'c' ? 0 : 1); + + uint zAxis1 = shape::sizeAt(zShapeInfo, shape::order(zShapeInfo) == 'c' ? 1 : 0); + Nd4jLong xStrd1 = shape::strideAt(xShapeInfo, shape::order(zShapeInfo) == 'c' ? 1 : 0); + Nd4jLong yStrd1 = shape::strideAt(yShapeInfo, shape::order(zShapeInfo) == 'c' ? 1 : 0); + Nd4jLong zStrd1 = shape::strideAt(zShapeInfo, shape::order(zShapeInfo) == 'c' ? 1 : 0); + + auto func = PRAGMA_THREADS_FOR{ + + for (auto i0 = start; i0 < stop; ++i0) { + + auto x0 = x + i0 * xStrd0; + auto y0 = y + i0 * yStrd0; + auto z0 = z + i0 * zStrd0; + + if(zStrd1 == 1 && xStrd1 == 1 && yStrd1 == 0) + for (uint i1 = 0; i1 < zAxis1; ++i1) + z0[i1] = OpType::op(x0[i1], *y0); + else if(zStrd1 == 1 && xStrd1 == 0 && yStrd1 == 1) + for (uint i1 = 0; i1 < zAxis1; ++i1) + z0[i1] = OpType::op(*x0, y0[i1]); + else if(zStrd1 == 1 && xStrd1 == 1 && yStrd1 == 1) + for (uint i1 = 0; i1 < zAxis1; ++i1) + z0[i1] = OpType::op(x0[i1], y0[i1]); + else + for (uint i1 = 0; i1 < zAxis1; ++i1) + z0[i1 * zStrd1] = OpType::op(x0[i1 * xStrd1], y0[i1 * yStrd1]); + } + }; + + samediff::Threads::parallel_tad(func, 0, zAxis0); +} + +//////////////////////////////////////////////////////////////////////// +template +static void execRank3(const X *x, const Nd4jLong *xShapeInfo, const X *y, const Nd4jLong *yShapeInfo, X* z, const Nd4jLong *zShapeInfo) { + + uint zAxis0 = shape::sizeAt(zShapeInfo, shape::order(zShapeInfo) == 'c' ? 0 : 2); + Nd4jLong xStrd0 = shape::strideAt(xShapeInfo, shape::order(zShapeInfo) == 'c' ? 0 : 2); + Nd4jLong yStrd0 = shape::strideAt(yShapeInfo, shape::order(zShapeInfo) == 'c' ? 0 : 2); + Nd4jLong zStrd0 = shape::strideAt(zShapeInfo, shape::order(zShapeInfo) == 'c' ? 0 : 2); + + uint zAxis1 = shape::sizeAt(zShapeInfo, 1); + Nd4jLong xStrd1 = shape::strideAt(xShapeInfo, 1); + Nd4jLong yStrd1 = shape::strideAt(yShapeInfo, 1); + Nd4jLong zStrd1 = shape::strideAt(zShapeInfo, 1); + + uint zAxis2 = shape::sizeAt(zShapeInfo, shape::order(zShapeInfo) == 'c' ? 2 : 0); + Nd4jLong xStrd2 = shape::strideAt(xShapeInfo, shape::order(zShapeInfo) == 'c' ? 2 : 0); + Nd4jLong yStrd2 = shape::strideAt(yShapeInfo, shape::order(zShapeInfo) == 'c' ? 2 : 0); + Nd4jLong zStrd2 = shape::strideAt(zShapeInfo, shape::order(zShapeInfo) == 'c' ? 2 : 0); + + auto func = PRAGMA_THREADS_FOR_2D { + + for (auto i0 = start_x; i0 < stop_x; ++i0) { + for (auto i1 = start_y; i1 < stop_y; ++i1) { + + auto x1 = x + i0 * xStrd0 + i1 * xStrd1; + auto y1 = y + i0 * yStrd0 + i1 * yStrd1; + auto z1 = z + i0 * zStrd0 + i1 * zStrd1; + + if(zStrd2 == 1 && xStrd2 == 1 && yStrd2 == 0) + for (uint i2 = 0; i2 < zAxis2; ++i2) + z1[i2] = OpType::op(x1[i2], *y1); + else if(zStrd2 == 1 && xStrd2 == 0 && yStrd2 == 1) + for (uint i2 = 0; i2 < zAxis2; ++i2) + z1[i2] = OpType::op(*x1, y1[i2]); + else if(zStrd2 == 1 && xStrd2 == 1 && yStrd2 == 1) + for (uint i2 = 0; i2 < zAxis2; ++i2) + z1[i2] = OpType::op(x1[i2], y1[i2]); + else + for (uint i2 = 0; i2 < zAxis2; ++i2) + z1[i2 * zStrd2] = OpType::op(x1[i2 * xStrd2], y1[i2 * yStrd2]); + } + } + }; + + samediff::Threads::parallel_for(func, 0,zAxis0,1, 0,zAxis1,1); +} + +//////////////////////////////////////////////////////////////////////// +template +static void execRank4(const X *x, const Nd4jLong *xShapeInfo, const X *y, const Nd4jLong *yShapeInfo, X* z, const Nd4jLong *zShapeInfo) { + + uint zAxis0 = shape::sizeAt(zShapeInfo, shape::order(zShapeInfo) == 'c' ? 0 : 3); + Nd4jLong xStrd0 = shape::strideAt(xShapeInfo, shape::order(zShapeInfo) == 'c' ? 0 : 3); + Nd4jLong yStrd0 = shape::strideAt(yShapeInfo, shape::order(zShapeInfo) == 'c' ? 0 : 3); + Nd4jLong zStrd0 = shape::strideAt(zShapeInfo, shape::order(zShapeInfo) == 'c' ? 0 : 3); + + uint zAxis1 = shape::sizeAt(zShapeInfo, shape::order(zShapeInfo) == 'c' ? 1 : 2); + Nd4jLong xStrd1 = shape::strideAt(xShapeInfo, shape::order(zShapeInfo) == 'c' ? 1 : 2); + Nd4jLong yStrd1 = shape::strideAt(yShapeInfo, shape::order(zShapeInfo) == 'c' ? 1 : 2); + Nd4jLong zStrd1 = shape::strideAt(zShapeInfo, shape::order(zShapeInfo) == 'c' ? 1 : 2); + + uint zAxis2 = shape::sizeAt(zShapeInfo, shape::order(zShapeInfo) == 'c' ? 2 : 1); + Nd4jLong xStrd2 = shape::strideAt(xShapeInfo, shape::order(zShapeInfo) == 'c' ? 2 : 1); + Nd4jLong yStrd2 = shape::strideAt(yShapeInfo, shape::order(zShapeInfo) == 'c' ? 2 : 1); + Nd4jLong zStrd2 = shape::strideAt(zShapeInfo, shape::order(zShapeInfo) == 'c' ? 2 : 1); + + uint zAxis3 = shape::sizeAt(zShapeInfo, shape::order(zShapeInfo) == 'c' ? 3 : 0); + Nd4jLong xStrd3 = shape::strideAt(xShapeInfo, shape::order(zShapeInfo) == 'c' ? 3 : 0); + Nd4jLong yStrd3 = shape::strideAt(yShapeInfo, shape::order(zShapeInfo) == 'c' ? 3 : 0); + Nd4jLong zStrd3 = shape::strideAt(zShapeInfo, shape::order(zShapeInfo) == 'c' ? 3 : 0); + + auto func = PRAGMA_THREADS_FOR_3D { + + for (auto i0 = start_x; i0 < stop_x; ++i0) { + for (auto i1 = start_y; i1 < stop_y; ++i1) { + for (auto i2 = start_z; i2 < stop_z; ++i2) { + + auto x2 = x + i0 * xStrd0 + i1 * xStrd1 + i2 * xStrd2; + auto y2 = y + i0 * yStrd0 + i1 * yStrd1 + i2 * yStrd2; + auto z2 = z + i0 * zStrd0 + i1 * zStrd1 + i2 * zStrd2; + + if(zStrd3 == 1 && xStrd3 == 1 && yStrd3 == 0) + for (uint i3 = 0; i3 < zAxis3; ++i3) + z2[i3] = OpType::op(x2[i3], *y2); + else if(zStrd3 == 1 && xStrd3 == 0 && yStrd3 == 1) + for (uint i3 = 0; i3 < zAxis3; ++i3) + z2[i3] = OpType::op(*x2, y2[i3]); + else if(zStrd3 == 1 && xStrd3 == 1 && yStrd3 == 1) + for (uint i3 = 0; i3 < zAxis3; ++i3) + z2[i3] = OpType::op(x2[i3], y2[i3]); + else + for (uint i3 = 0; i3 < zAxis3; ++i3) + z2[i3 * zStrd3] = OpType::op(x2[i3 * xStrd3], y2[i3 * yStrd3]); + } + } + } + }; + + samediff::Threads::parallel_for(func, 0,zAxis0,1, 0,zAxis1,1, 0,zAxis2,1); +} + +//////////////////////////////////////////////////////////////////////// +template +static void execRank5(const X *x, const Nd4jLong *xShapeInfo, const X *y, const Nd4jLong *yShapeInfo, X* z, const Nd4jLong *zShapeInfo) { + + uint zAxis0 = shape::sizeAt(zShapeInfo, shape::order(zShapeInfo) == 'c' ? 0 : 4); + Nd4jLong xStrd0 = shape::strideAt(xShapeInfo, shape::order(zShapeInfo) == 'c' ? 0 : 4); + Nd4jLong yStrd0 = shape::strideAt(yShapeInfo, shape::order(zShapeInfo) == 'c' ? 0 : 4); + Nd4jLong zStrd0 = shape::strideAt(zShapeInfo, shape::order(zShapeInfo) == 'c' ? 0 : 4); + + uint zAxis1 = shape::sizeAt(zShapeInfo, shape::order(zShapeInfo) == 'c' ? 1 : 3); + Nd4jLong xStrd1 = shape::strideAt(xShapeInfo, shape::order(zShapeInfo) == 'c' ? 1 : 3); + Nd4jLong yStrd1 = shape::strideAt(yShapeInfo, shape::order(zShapeInfo) == 'c' ? 1 : 3); + Nd4jLong zStrd1 = shape::strideAt(zShapeInfo, shape::order(zShapeInfo) == 'c' ? 1 : 3); + + uint zAxis2 = shape::sizeAt(zShapeInfo, 2); + Nd4jLong xStrd2 = shape::strideAt(xShapeInfo, 2); + Nd4jLong yStrd2 = shape::strideAt(yShapeInfo, 2); + Nd4jLong zStrd2 = shape::strideAt(zShapeInfo, 2); + + uint zAxis3 = shape::sizeAt(zShapeInfo, shape::order(zShapeInfo) == 'c' ? 3 : 1); + Nd4jLong xStrd3 = shape::strideAt(xShapeInfo, shape::order(zShapeInfo) == 'c' ? 3 : 1); + Nd4jLong yStrd3 = shape::strideAt(yShapeInfo, shape::order(zShapeInfo) == 'c' ? 3 : 1); + Nd4jLong zStrd3 = shape::strideAt(zShapeInfo, shape::order(zShapeInfo) == 'c' ? 3 : 1); + + uint zAxis4 = shape::sizeAt(zShapeInfo, shape::order(zShapeInfo) == 'c' ? 4 : 0); + Nd4jLong xStrd4 = shape::strideAt(xShapeInfo, shape::order(zShapeInfo) == 'c' ? 4 : 0); + Nd4jLong yStrd4 = shape::strideAt(yShapeInfo, shape::order(zShapeInfo) == 'c' ? 4 : 0); + Nd4jLong zStrd4 = shape::strideAt(zShapeInfo, shape::order(zShapeInfo) == 'c' ? 4 : 0); + + auto func = PRAGMA_THREADS_FOR_3D { + + for (auto i0 = start_x; i0 < stop_x; ++i0) { + for (auto i1 = start_y; i1 < stop_y; ++i1) { + for (auto i2 = start_z; i2 < stop_z; ++i2) { + for (uint i3 = 0; i3 < zAxis3; ++i3) { + + auto x3 = x + i0 * xStrd0 + i1 * xStrd1 + i2 * xStrd2 + i3 * xStrd3; + auto y3 = y + i0 * yStrd0 + i1 * yStrd1 + i2 * yStrd2 + i3 * yStrd3; + auto z3 = z + i0 * zStrd0 + i1 * zStrd1 + i2 * zStrd2 + i3 * zStrd3; + + if(zStrd4 == 1 && xStrd4 == 1 && yStrd4 == 0) + for (uint i4 = 0; i4 < zAxis4; ++i4) + z3[i4] = OpType::op(x3[i4], *y3); + else if(zStrd4 == 1 && xStrd4 == 0 && yStrd4 == 1) + for (uint i4 = 0; i4 < zAxis4; ++i4) + z3[i4] = OpType::op(*x3, y3[i4]); + else if(zStrd4 == 1 && xStrd4 == 1 && yStrd4 == 1) + for (uint i4 = 0; i4 < zAxis4; ++i4) + z3[i4] = OpType::op(x3[i4], y3[i4]); + else + for (uint i4 = 0; i4 < zAxis4; ++i4) + z3[i4 * zStrd4] = OpType::op(x3[i4 * xStrd4], y3[i4 * yStrd4]); + } + } + } + } + }; + + samediff::Threads::parallel_for(func, 0,zAxis0,1, 0,zAxis1,1, 0,zAxis2,1); +} + +//////////////////////////////////////////////////////////////////////// +template +static void execDefault(const X *x, const Nd4jLong *xShapeInfo, const X *y, const Nd4jLong *yShapeInfo, X* z, const Nd4jLong *zShapeInfo) { + + const bool xzSameOffsets = shape::haveSameShapeAndStrides(xShapeInfo, zShapeInfo); + const bool yzSameOffsets = shape::haveSameShapeAndStrides(yShapeInfo, zShapeInfo); + + const int rank = shape::rank(zShapeInfo); // xRank = yRank = zRank + + auto func = PRAGMA_THREADS_FOR{ + + int xCoords[MAX_RANK], yCoords[MAX_RANK], zCoords[MAX_RANK]; + + for (auto i = start; i < stop; ++i) { + + shape::index2coordsCPU(start, i, zShapeInfo, zCoords); + + for (uint j = 0; j < rank; ++j) { + xCoords[j] = shape::sizeAt(xShapeInfo, j) == 1 ? 0 : zCoords[j]; + yCoords[j] = shape::sizeAt(yShapeInfo, j) == 1 ? 0 : zCoords[j]; + } + + const auto zOffset = shape::getOffset(zShapeInfo, zCoords); + const auto xOffset = xzSameOffsets ? zOffset : shape::getOffset(xShapeInfo, xCoords); + const auto yOffset = yzSameOffsets ? zOffset : shape::getOffset(yShapeInfo, yCoords); + + z[zOffset] = OpType::op(x[xOffset], y[yOffset]); + } + }; + + samediff::Threads::parallel_for(func, 0, shape::length(zShapeInfo)); +} + +//////////////////////////////////////////////////////////////////////// +template +template +void BroadcastInt::exec(const void *vx, const Nd4jLong *xShapeInfo, + const void *vy, const Nd4jLong *yShapeInfo, + void *vz, const Nd4jLong *zShapeInfo) { + + const X* x = reinterpret_cast(vx); + const X* y = reinterpret_cast(vy); + X* z = reinterpret_cast(vz); + + const int rank = shape::rank(zShapeInfo); // xRank = yRank = zRank + + switch (rank) { + + case 1: + execRank1(x, xShapeInfo, y, yShapeInfo, z, zShapeInfo); + break; + case 2: + execRank2(x, xShapeInfo, y, yShapeInfo, z, zShapeInfo); + break; + case 3: + execRank3(x, xShapeInfo, y, yShapeInfo, z, zShapeInfo); + break; + case 4: + execRank4(x, xShapeInfo, y, yShapeInfo, z, zShapeInfo); + break; + case 5: + execRank5(x, xShapeInfo, y, yShapeInfo, z, zShapeInfo); + break; + default: + execDefault(x, xShapeInfo, y, yShapeInfo, z, zShapeInfo); + } +} + +//BUILD_SINGLE_TEMPLATE(template class ND4J_EXPORT BroadcastInt, , INTEGER_TYPES); +} +} \ No newline at end of file diff --git a/libnd4j/include/loops/cpu/compilation_units/TrueBroadcastHelper_4.cpp b/libnd4j/include/loops/cpu/compilation_units/TrueBroadcastHelper_4.cpp deleted file mode 100644 index 3fb868278..000000000 --- a/libnd4j/include/loops/cpu/compilation_units/TrueBroadcastHelper_4.cpp +++ /dev/null @@ -1,27 +0,0 @@ -/******************************************************************************* - * Copyright (c) 2019-2020 Konduit K.K. - * - * This program and the accompanying materials are made available under the - * terms of the Apache License, Version 2.0 which is available at - * https://www.apache.org/licenses/LICENSE-2.0. - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the - * License for the specific language governing permissions and limitations - * under the License. - * - * SPDX-License-Identifier: Apache-2.0 - ******************************************************************************/ - -// -// @author raver119@gmail.com -// - -#include "../TrueBroadcastHelper.hpp" - -namespace nd4j { - namespace helpers { - BUILD_PAIRWISE_TEMPLATE(template class ND4J_EXPORT TrueBroadcastHelper, , PAIRWISE_TYPES_3); - } -} diff --git a/libnd4j/include/loops/cpu/compilation_units/TrueBroadcastHelper_5.cpp b/libnd4j/include/loops/cpu/compilation_units/TrueBroadcastHelper_5.cpp deleted file mode 100644 index 9b281516a..000000000 --- a/libnd4j/include/loops/cpu/compilation_units/TrueBroadcastHelper_5.cpp +++ /dev/null @@ -1,27 +0,0 @@ -/******************************************************************************* - * Copyright (c) 2019-2020 Konduit K.K. - * - * This program and the accompanying materials are made available under the - * terms of the Apache License, Version 2.0 which is available at - * https://www.apache.org/licenses/LICENSE-2.0. - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the - * License for the specific language governing permissions and limitations - * under the License. - * - * SPDX-License-Identifier: Apache-2.0 - ******************************************************************************/ - -// -// @author raver119@gmail.com -// - -#include "../TrueBroadcastHelper.hpp" - -namespace nd4j { - namespace helpers { - BUILD_PAIRWISE_TEMPLATE(template class ND4J_EXPORT TrueBroadcastHelper, , PAIRWISE_TYPES_4); - } -} diff --git a/libnd4j/include/loops/cpu/compilation_units/TrueBroadcastHelper_6.cpp b/libnd4j/include/loops/cpu/compilation_units/TrueBroadcastHelper_6.cpp deleted file mode 100644 index dbdd65907..000000000 --- a/libnd4j/include/loops/cpu/compilation_units/TrueBroadcastHelper_6.cpp +++ /dev/null @@ -1,27 +0,0 @@ -/******************************************************************************* - * Copyright (c) 2019-2020 Konduit K.K. - * - * This program and the accompanying materials are made available under the - * terms of the Apache License, Version 2.0 which is available at - * https://www.apache.org/licenses/LICENSE-2.0. - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the - * License for the specific language governing permissions and limitations - * under the License. - * - * SPDX-License-Identifier: Apache-2.0 - ******************************************************************************/ - -// -// @author raver119@gmail.com -// - -#include "../TrueBroadcastHelper.hpp" - -namespace nd4j { - namespace helpers { - BUILD_PAIRWISE_TEMPLATE(template class ND4J_EXPORT TrueBroadcastHelper, , PAIRWISE_TYPES_5); - } -} diff --git a/libnd4j/include/loops/cpu/compilation_units/TrueBroadcastHelper_7.cpp b/libnd4j/include/loops/cpu/compilation_units/TrueBroadcastHelper_7.cpp deleted file mode 100644 index 5b3beaec6..000000000 --- a/libnd4j/include/loops/cpu/compilation_units/TrueBroadcastHelper_7.cpp +++ /dev/null @@ -1,27 +0,0 @@ -/******************************************************************************* - * Copyright (c) 2019-2020 Konduit K.K. - * - * This program and the accompanying materials are made available under the - * terms of the Apache License, Version 2.0 which is available at - * https://www.apache.org/licenses/LICENSE-2.0. - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the - * License for the specific language governing permissions and limitations - * under the License. - * - * SPDX-License-Identifier: Apache-2.0 - ******************************************************************************/ - -// -// @author raver119@gmail.com -// - -#include "../TrueBroadcastHelper.hpp" - -namespace nd4j { - namespace helpers { - BUILD_PAIRWISE_TEMPLATE(template class ND4J_EXPORT TrueBroadcastHelper, , PAIRWISE_TYPES_6); - } -} diff --git a/libnd4j/include/loops/cpu/compilation_units/TrueBroadcastHelper_8.cpp b/libnd4j/include/loops/cpu/compilation_units/TrueBroadcastHelper_8.cpp deleted file mode 100644 index 51e81d32a..000000000 --- a/libnd4j/include/loops/cpu/compilation_units/TrueBroadcastHelper_8.cpp +++ /dev/null @@ -1,27 +0,0 @@ -/******************************************************************************* - * Copyright (c) 2019-2020 Konduit K.K. - * - * This program and the accompanying materials are made available under the - * terms of the Apache License, Version 2.0 which is available at - * https://www.apache.org/licenses/LICENSE-2.0. - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the - * License for the specific language governing permissions and limitations - * under the License. - * - * SPDX-License-Identifier: Apache-2.0 - ******************************************************************************/ - -// -// @author raver119@gmail.com -// - -#include "../TrueBroadcastHelper.hpp" - -namespace nd4j { - namespace helpers { - BUILD_PAIRWISE_TEMPLATE(template class ND4J_EXPORT TrueBroadcastHelper, , PAIRWISE_TYPES_7); - } -} diff --git a/libnd4j/include/loops/cpu/compilation_units/TrueBroadcastHelper_9.cpp b/libnd4j/include/loops/cpu/compilation_units/TrueBroadcastHelper_9.cpp deleted file mode 100644 index 77f5e5720..000000000 --- a/libnd4j/include/loops/cpu/compilation_units/TrueBroadcastHelper_9.cpp +++ /dev/null @@ -1,27 +0,0 @@ -/******************************************************************************* - * Copyright (c) 2019-2020 Konduit K.K. - * - * This program and the accompanying materials are made available under the - * terms of the Apache License, Version 2.0 which is available at - * https://www.apache.org/licenses/LICENSE-2.0. - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the - * License for the specific language governing permissions and limitations - * under the License. - * - * SPDX-License-Identifier: Apache-2.0 - ******************************************************************************/ - -// -// @author raver119@gmail.com -// - -#include "../TrueBroadcastHelper.hpp" - -namespace nd4j { - namespace helpers { - BUILD_PAIRWISE_TEMPLATE(template class ND4J_EXPORT TrueBroadcastHelper, , PAIRWISE_TYPES_8); - } -} diff --git a/libnd4j/include/loops/cpu/compilation_units/TrueBroadcastHelper_bool.cpp b/libnd4j/include/loops/cpu/compilation_units/TrueBroadcastHelper_bool.cpp deleted file mode 100644 index c2e116666..000000000 --- a/libnd4j/include/loops/cpu/compilation_units/TrueBroadcastHelper_bool.cpp +++ /dev/null @@ -1,27 +0,0 @@ -/******************************************************************************* - * Copyright (c) 2019-2020 Konduit K.K. - * - * This program and the accompanying materials are made available under the - * terms of the Apache License, Version 2.0 which is available at - * https://www.apache.org/licenses/LICENSE-2.0. - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the - * License for the specific language governing permissions and limitations - * under the License. - * - * SPDX-License-Identifier: Apache-2.0 - ******************************************************************************/ - -// -// @author raver119@gmail.com -// - -#include "../TrueBroadcastHelper.hpp" - -namespace nd4j { - namespace helpers { - BUILD_DOUBLE_TEMPLATE(template class ND4J_EXPORT TrueBroadcastBoolHelper, , LIBND4J_TYPES, BOOL_TYPES); - } -} diff --git a/libnd4j/include/loops/cpu/compilation_units/broadcast_bool_p0.cpp b/libnd4j/include/loops/cpu/compilation_units/broadcast_bool_p0.cpp new file mode 100644 index 000000000..08ebd92f7 --- /dev/null +++ b/libnd4j/include/loops/cpu/compilation_units/broadcast_bool_p0.cpp @@ -0,0 +1,27 @@ +/******************************************************************************* + * Copyright (c) 2015-2018 Skymind, Inc. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ + +// +// Created by raver119 on 23/09/18. +// + +#include "../broadcasting_bool.hpp" + +namespace functions { + namespace broadcast { + BUILD_DOUBLE_TEMPLATE(template class ND4J_EXPORT BroadcastBool, , LIBND4J_TYPES_0, BOOL_TYPES); + } +} \ No newline at end of file diff --git a/libnd4j/include/loops/cpu/compilation_units/broadcast_bool_p1.cpp b/libnd4j/include/loops/cpu/compilation_units/broadcast_bool_p1.cpp new file mode 100644 index 000000000..16e4c817a --- /dev/null +++ b/libnd4j/include/loops/cpu/compilation_units/broadcast_bool_p1.cpp @@ -0,0 +1,27 @@ +/******************************************************************************* + * Copyright (c) 2015-2018 Skymind, Inc. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ + +// +// Created by raver119 on 23/09/18. +// + +#include "../broadcasting_bool.hpp" + +namespace functions { + namespace broadcast { + BUILD_DOUBLE_TEMPLATE(template class ND4J_EXPORT BroadcastBool, , LIBND4J_TYPES_1, BOOL_TYPES); + } +} \ No newline at end of file diff --git a/libnd4j/include/loops/cpu/compilation_units/broadcast_bool_p2.cpp b/libnd4j/include/loops/cpu/compilation_units/broadcast_bool_p2.cpp new file mode 100644 index 000000000..10b32ca41 --- /dev/null +++ b/libnd4j/include/loops/cpu/compilation_units/broadcast_bool_p2.cpp @@ -0,0 +1,27 @@ +/******************************************************************************* + * Copyright (c) 2015-2018 Skymind, Inc. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ + +// +// Created by raver119 on 23/09/18. +// + +#include "../broadcasting_bool.hpp" + +namespace functions { + namespace broadcast { + BUILD_DOUBLE_TEMPLATE(template class ND4J_EXPORT BroadcastBool, , LIBND4J_TYPES_2, BOOL_TYPES); + } +} \ No newline at end of file diff --git a/libnd4j/include/loops/cpu/compilation_units/broadcast_bool_p3.cpp b/libnd4j/include/loops/cpu/compilation_units/broadcast_bool_p3.cpp new file mode 100644 index 000000000..547ddd371 --- /dev/null +++ b/libnd4j/include/loops/cpu/compilation_units/broadcast_bool_p3.cpp @@ -0,0 +1,27 @@ +/******************************************************************************* + * Copyright (c) 2015-2018 Skymind, Inc. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ + +// +// Created by raver119 on 23/09/18. +// + +#include "../broadcasting_bool.hpp" + +namespace functions { + namespace broadcast { + BUILD_DOUBLE_TEMPLATE(template class ND4J_EXPORT BroadcastBool, , LIBND4J_TYPES_3, BOOL_TYPES); + } +} \ No newline at end of file diff --git a/libnd4j/include/loops/cpu/compilation_units/broadcast_bool_p4.cpp b/libnd4j/include/loops/cpu/compilation_units/broadcast_bool_p4.cpp new file mode 100644 index 000000000..3c7dee0a0 --- /dev/null +++ b/libnd4j/include/loops/cpu/compilation_units/broadcast_bool_p4.cpp @@ -0,0 +1,27 @@ +/******************************************************************************* + * Copyright (c) 2015-2018 Skymind, Inc. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ + +// +// Created by raver119 on 23/09/18. +// + +#include "../broadcasting_bool.hpp" + +namespace functions { + namespace broadcast { + BUILD_DOUBLE_TEMPLATE(template class ND4J_EXPORT BroadcastBool, , LIBND4J_TYPES_4, BOOL_TYPES); + } +} \ No newline at end of file diff --git a/libnd4j/include/loops/cpu/compilation_units/broadcast_bool_p5.cpp b/libnd4j/include/loops/cpu/compilation_units/broadcast_bool_p5.cpp new file mode 100644 index 000000000..b71925dab --- /dev/null +++ b/libnd4j/include/loops/cpu/compilation_units/broadcast_bool_p5.cpp @@ -0,0 +1,27 @@ +/******************************************************************************* + * Copyright (c) 2015-2018 Skymind, Inc. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ + +// +// Created by raver119 on 23/09/18. +// + +#include "../broadcasting_bool.hpp" + +namespace functions { + namespace broadcast { + BUILD_DOUBLE_TEMPLATE(template class ND4J_EXPORT BroadcastBool, , LIBND4J_TYPES_5, BOOL_TYPES); + } +} \ No newline at end of file diff --git a/libnd4j/include/loops/cpu/compilation_units/broadcast_bool_p6.cpp b/libnd4j/include/loops/cpu/compilation_units/broadcast_bool_p6.cpp new file mode 100644 index 000000000..23eedd289 --- /dev/null +++ b/libnd4j/include/loops/cpu/compilation_units/broadcast_bool_p6.cpp @@ -0,0 +1,27 @@ +/******************************************************************************* + * Copyright (c) 2015-2018 Skymind, Inc. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ + +// +// Created by raver119 on 23/09/18. +// + +#include "../broadcasting_bool.hpp" + +namespace functions { + namespace broadcast { + BUILD_DOUBLE_TEMPLATE(template class ND4J_EXPORT BroadcastBool, , LIBND4J_TYPES_6, BOOL_TYPES); + } +} \ No newline at end of file diff --git a/libnd4j/include/loops/cpu/compilation_units/broadcast_bool_p7.cpp b/libnd4j/include/loops/cpu/compilation_units/broadcast_bool_p7.cpp new file mode 100644 index 000000000..c18e7641e --- /dev/null +++ b/libnd4j/include/loops/cpu/compilation_units/broadcast_bool_p7.cpp @@ -0,0 +1,27 @@ +/******************************************************************************* + * Copyright (c) 2015-2018 Skymind, Inc. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ + +// +// Created by raver119 on 23/09/18. +// + +#include "../broadcasting_bool.hpp" + +namespace functions { + namespace broadcast { + BUILD_DOUBLE_TEMPLATE(template class ND4J_EXPORT BroadcastBool, , LIBND4J_TYPES_7, BOOL_TYPES); + } +} \ No newline at end of file diff --git a/libnd4j/include/loops/cpu/compilation_units/broadcast_bool_p8.cpp b/libnd4j/include/loops/cpu/compilation_units/broadcast_bool_p8.cpp new file mode 100644 index 000000000..efee34519 --- /dev/null +++ b/libnd4j/include/loops/cpu/compilation_units/broadcast_bool_p8.cpp @@ -0,0 +1,27 @@ +/******************************************************************************* + * Copyright (c) 2015-2018 Skymind, Inc. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ + +// +// Created by raver119 on 23/09/18. +// + +#include "../broadcasting_bool.hpp" + +namespace functions { + namespace broadcast { + BUILD_DOUBLE_TEMPLATE(template class ND4J_EXPORT BroadcastBool, , LIBND4J_TYPES_8, BOOL_TYPES); + } +} \ No newline at end of file diff --git a/libnd4j/include/loops/cpu/compilation_units/broadcast_bool_p9.cpp b/libnd4j/include/loops/cpu/compilation_units/broadcast_bool_p9.cpp new file mode 100644 index 000000000..2ab193285 --- /dev/null +++ b/libnd4j/include/loops/cpu/compilation_units/broadcast_bool_p9.cpp @@ -0,0 +1,27 @@ +/******************************************************************************* + * Copyright (c) 2015-2018 Skymind, Inc. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ + +// +// Created by raver119 on 23/09/18. +// + +#include "../broadcasting_bool.hpp" + +namespace functions { + namespace broadcast { + BUILD_DOUBLE_TEMPLATE(template class ND4J_EXPORT BroadcastBool, , LIBND4J_TYPES_9, BOOL_TYPES); + } +} \ No newline at end of file diff --git a/libnd4j/include/loops/cpu/compilation_units/broadcast_int_p0.cpp b/libnd4j/include/loops/cpu/compilation_units/broadcast_int_p0.cpp new file mode 100644 index 000000000..d3f5ada43 --- /dev/null +++ b/libnd4j/include/loops/cpu/compilation_units/broadcast_int_p0.cpp @@ -0,0 +1,27 @@ +/******************************************************************************* + * Copyright (c) 2015-2018 Skymind, Inc. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ + +// +// Created by raver119 on 23/09/18. +// + +#include "../broadcasting_int.hpp" + +namespace functions { + namespace broadcast { + BUILD_SINGLE_TEMPLATE(template class ND4J_EXPORT BroadcastInt, , INTEGER_TYPES_0); + } +} \ No newline at end of file diff --git a/libnd4j/include/loops/cpu/compilation_units/broadcast_int_p1.cpp b/libnd4j/include/loops/cpu/compilation_units/broadcast_int_p1.cpp new file mode 100644 index 000000000..82969bdb0 --- /dev/null +++ b/libnd4j/include/loops/cpu/compilation_units/broadcast_int_p1.cpp @@ -0,0 +1,27 @@ +/******************************************************************************* + * Copyright (c) 2015-2018 Skymind, Inc. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ + +// +// Created by raver119 on 23/09/18. +// + +#include "../broadcasting_int.hpp" + +namespace functions { + namespace broadcast { + BUILD_SINGLE_TEMPLATE(template class ND4J_EXPORT BroadcastInt, , INTEGER_TYPES_1); + } +} \ No newline at end of file diff --git a/libnd4j/include/loops/cpu/compilation_units/broadcast_int_p2.cpp b/libnd4j/include/loops/cpu/compilation_units/broadcast_int_p2.cpp new file mode 100644 index 000000000..53d928111 --- /dev/null +++ b/libnd4j/include/loops/cpu/compilation_units/broadcast_int_p2.cpp @@ -0,0 +1,27 @@ +/******************************************************************************* + * Copyright (c) 2015-2018 Skymind, Inc. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ + +// +// Created by raver119 on 23/09/18. +// + +#include "../broadcasting_int.hpp" + +namespace functions { + namespace broadcast { + BUILD_SINGLE_TEMPLATE(template class ND4J_EXPORT BroadcastInt, , INTEGER_TYPES_2); + } +} \ No newline at end of file diff --git a/libnd4j/include/loops/cpu/compilation_units/broadcast_int_p3.cpp b/libnd4j/include/loops/cpu/compilation_units/broadcast_int_p3.cpp new file mode 100644 index 000000000..eba7b78d1 --- /dev/null +++ b/libnd4j/include/loops/cpu/compilation_units/broadcast_int_p3.cpp @@ -0,0 +1,27 @@ +/******************************************************************************* + * Copyright (c) 2015-2018 Skymind, Inc. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ + +// +// Created by raver119 on 23/09/18. +// + +#include "../broadcasting_int.hpp" + +namespace functions { + namespace broadcast { + BUILD_SINGLE_TEMPLATE(template class ND4J_EXPORT BroadcastInt, , INTEGER_TYPES_3); + } +} \ No newline at end of file diff --git a/libnd4j/include/loops/cpu/compilation_units/broadcast_int_p4.cpp b/libnd4j/include/loops/cpu/compilation_units/broadcast_int_p4.cpp new file mode 100644 index 000000000..47b7350f2 --- /dev/null +++ b/libnd4j/include/loops/cpu/compilation_units/broadcast_int_p4.cpp @@ -0,0 +1,27 @@ +/******************************************************************************* + * Copyright (c) 2015-2018 Skymind, Inc. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ + +// +// Created by raver119 on 23/09/18. +// + +#include "../broadcasting_int.hpp" + +namespace functions { + namespace broadcast { + BUILD_SINGLE_TEMPLATE(template class ND4J_EXPORT BroadcastInt, , INTEGER_TYPES_4); + } +} \ No newline at end of file diff --git a/libnd4j/include/loops/cpu/compilation_units/broadcast_int_p5.cpp b/libnd4j/include/loops/cpu/compilation_units/broadcast_int_p5.cpp new file mode 100644 index 000000000..3afad08f6 --- /dev/null +++ b/libnd4j/include/loops/cpu/compilation_units/broadcast_int_p5.cpp @@ -0,0 +1,27 @@ +/******************************************************************************* + * Copyright (c) 2015-2018 Skymind, Inc. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ + +// +// Created by raver119 on 23/09/18. +// + +#include "../broadcasting_int.hpp" + +namespace functions { + namespace broadcast { + BUILD_SINGLE_TEMPLATE(template class ND4J_EXPORT BroadcastInt, , INTEGER_TYPES_5); + } +} \ No newline at end of file diff --git a/libnd4j/include/loops/cpu/compilation_units/broadcast_int_p6.cpp b/libnd4j/include/loops/cpu/compilation_units/broadcast_int_p6.cpp new file mode 100644 index 000000000..286c2680f --- /dev/null +++ b/libnd4j/include/loops/cpu/compilation_units/broadcast_int_p6.cpp @@ -0,0 +1,27 @@ +/******************************************************************************* + * Copyright (c) 2015-2018 Skymind, Inc. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ + +// +// Created by raver119 on 23/09/18. +// + +#include "../broadcasting_int.hpp" + +namespace functions { + namespace broadcast { + BUILD_SINGLE_TEMPLATE(template class ND4J_EXPORT BroadcastInt, , INTEGER_TYPES_6); + } +} \ No newline at end of file diff --git a/libnd4j/include/loops/cpu/compilation_units/broadcast_int_p7.cpp b/libnd4j/include/loops/cpu/compilation_units/broadcast_int_p7.cpp new file mode 100644 index 000000000..242441561 --- /dev/null +++ b/libnd4j/include/loops/cpu/compilation_units/broadcast_int_p7.cpp @@ -0,0 +1,27 @@ +/******************************************************************************* + * Copyright (c) 2015-2018 Skymind, Inc. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ + +// +// Created by raver119 on 23/09/18. +// + +#include "../broadcasting_int.hpp" + +namespace functions { + namespace broadcast { + BUILD_SINGLE_TEMPLATE(template class ND4J_EXPORT BroadcastInt, , INTEGER_TYPES_7); + } +} \ No newline at end of file diff --git a/libnd4j/include/loops/cpu/compilation_units/broadcast_p10.cpp b/libnd4j/include/loops/cpu/compilation_units/broadcast_p10.cpp new file mode 100644 index 000000000..983305007 --- /dev/null +++ b/libnd4j/include/loops/cpu/compilation_units/broadcast_p10.cpp @@ -0,0 +1,27 @@ +/******************************************************************************* + * Copyright (c) 2015-2018 Skymind, Inc. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ + +// +// Created by raver119 on 23/09/18. +// + +#include "../broadcasting.hpp" + +namespace functions { + namespace broadcast { + BUILD_PAIRWISE_TEMPLATE(template class ND4J_EXPORT Broadcast, , PAIRWISE_TYPES_10); + } +} \ No newline at end of file diff --git a/libnd4j/include/loops/cpu/compilation_units/broadcast_p11.cpp b/libnd4j/include/loops/cpu/compilation_units/broadcast_p11.cpp new file mode 100644 index 000000000..206b14763 --- /dev/null +++ b/libnd4j/include/loops/cpu/compilation_units/broadcast_p11.cpp @@ -0,0 +1,27 @@ +/******************************************************************************* + * Copyright (c) 2015-2018 Skymind, Inc. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ + +// +// Created by raver119 on 23/09/18. +// + +#include "../broadcasting.hpp" + +namespace functions { + namespace broadcast { + BUILD_PAIRWISE_TEMPLATE(template class ND4J_EXPORT Broadcast, , PAIRWISE_TYPES_11); + } +} \ No newline at end of file diff --git a/libnd4j/include/loops/cpu/compilation_units/broadcast_p12.cpp b/libnd4j/include/loops/cpu/compilation_units/broadcast_p12.cpp new file mode 100644 index 000000000..825c07adf --- /dev/null +++ b/libnd4j/include/loops/cpu/compilation_units/broadcast_p12.cpp @@ -0,0 +1,27 @@ +/******************************************************************************* + * Copyright (c) 2015-2018 Skymind, Inc. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ + +// +// Created by raver119 on 23/09/18. +// + +#include "../broadcasting.hpp" + +namespace functions { + namespace broadcast { + BUILD_PAIRWISE_TEMPLATE(template class ND4J_EXPORT Broadcast, , PAIRWISE_TYPES_12); + } +} \ No newline at end of file diff --git a/libnd4j/include/loops/cpu/compilation_units/indexreduce_int32_0.cpp b/libnd4j/include/loops/cpu/compilation_units/indexreduce_int32_0.cpp index c3f71b5c4..89b85485a 100644 --- a/libnd4j/include/loops/cpu/compilation_units/indexreduce_int32_0.cpp +++ b/libnd4j/include/loops/cpu/compilation_units/indexreduce_int32_0.cpp @@ -23,6 +23,6 @@ namespace functions { namespace indexreduce { - BUILD_DOUBLE_TEMPLATE(template class ND4J_EXPORT IndexReduce, , LIBND4J_TYPES_0, (nd4j::DataType::INT32, int32_t)); + BUILD_DOUBLE_TEMPLATE(template class ND4J_EXPORT IndexReduce, , LIBND4J_TYPES_0, (sd::DataType::INT32, int32_t)); } } \ No newline at end of file diff --git a/libnd4j/include/loops/cpu/compilation_units/indexreduce_int32_1.cpp b/libnd4j/include/loops/cpu/compilation_units/indexreduce_int32_1.cpp index 2eeb1e37d..ada7844cb 100644 --- a/libnd4j/include/loops/cpu/compilation_units/indexreduce_int32_1.cpp +++ b/libnd4j/include/loops/cpu/compilation_units/indexreduce_int32_1.cpp @@ -23,6 +23,6 @@ namespace functions { namespace indexreduce { - BUILD_DOUBLE_TEMPLATE(template class ND4J_EXPORT IndexReduce, , LIBND4J_TYPES_1, (nd4j::DataType::INT32, int32_t)); + BUILD_DOUBLE_TEMPLATE(template class ND4J_EXPORT IndexReduce, , LIBND4J_TYPES_1, (sd::DataType::INT32, int32_t)); } } \ No newline at end of file diff --git a/libnd4j/include/loops/cpu/compilation_units/indexreduce_int32_2.cpp b/libnd4j/include/loops/cpu/compilation_units/indexreduce_int32_2.cpp index da0c55f51..47dce2d5a 100644 --- a/libnd4j/include/loops/cpu/compilation_units/indexreduce_int32_2.cpp +++ b/libnd4j/include/loops/cpu/compilation_units/indexreduce_int32_2.cpp @@ -23,6 +23,6 @@ namespace functions { namespace indexreduce { - BUILD_DOUBLE_TEMPLATE(template class ND4J_EXPORT IndexReduce, , LIBND4J_TYPES_2, (nd4j::DataType::INT32, int32_t)); + BUILD_DOUBLE_TEMPLATE(template class ND4J_EXPORT IndexReduce, , LIBND4J_TYPES_2, (sd::DataType::INT32, int32_t)); } } \ No newline at end of file diff --git a/libnd4j/include/loops/cpu/compilation_units/indexreduce_int32_3.cpp b/libnd4j/include/loops/cpu/compilation_units/indexreduce_int32_3.cpp index 3c255aa4f..c3d33e7f1 100644 --- a/libnd4j/include/loops/cpu/compilation_units/indexreduce_int32_3.cpp +++ b/libnd4j/include/loops/cpu/compilation_units/indexreduce_int32_3.cpp @@ -23,6 +23,6 @@ namespace functions { namespace indexreduce { - BUILD_DOUBLE_TEMPLATE(template class ND4J_EXPORT IndexReduce, , LIBND4J_TYPES_3, (nd4j::DataType::INT32, int32_t)); + BUILD_DOUBLE_TEMPLATE(template class ND4J_EXPORT IndexReduce, , LIBND4J_TYPES_3, (sd::DataType::INT32, int32_t)); } } \ No newline at end of file diff --git a/libnd4j/include/loops/cpu/compilation_units/indexreduce_int32_4.cpp b/libnd4j/include/loops/cpu/compilation_units/indexreduce_int32_4.cpp index e04ce70e2..37a81e441 100644 --- a/libnd4j/include/loops/cpu/compilation_units/indexreduce_int32_4.cpp +++ b/libnd4j/include/loops/cpu/compilation_units/indexreduce_int32_4.cpp @@ -23,6 +23,6 @@ namespace functions { namespace indexreduce { - BUILD_DOUBLE_TEMPLATE(template class ND4J_EXPORT IndexReduce, , LIBND4J_TYPES_4, (nd4j::DataType::INT32, int32_t)); + BUILD_DOUBLE_TEMPLATE(template class ND4J_EXPORT IndexReduce, , LIBND4J_TYPES_4, (sd::DataType::INT32, int32_t)); } } \ No newline at end of file diff --git a/libnd4j/include/loops/cpu/compilation_units/indexreduce_int32_5.cpp b/libnd4j/include/loops/cpu/compilation_units/indexreduce_int32_5.cpp index 17dd63e46..1d6555ddf 100644 --- a/libnd4j/include/loops/cpu/compilation_units/indexreduce_int32_5.cpp +++ b/libnd4j/include/loops/cpu/compilation_units/indexreduce_int32_5.cpp @@ -23,6 +23,6 @@ namespace functions { namespace indexreduce { - BUILD_DOUBLE_TEMPLATE(template class ND4J_EXPORT IndexReduce, , LIBND4J_TYPES_5, (nd4j::DataType::INT32, int32_t)); + BUILD_DOUBLE_TEMPLATE(template class ND4J_EXPORT IndexReduce, , LIBND4J_TYPES_5, (sd::DataType::INT32, int32_t)); } } \ No newline at end of file diff --git a/libnd4j/include/loops/cpu/compilation_units/indexreduce_int32_6.cpp b/libnd4j/include/loops/cpu/compilation_units/indexreduce_int32_6.cpp index c84fb089a..0bb8aef4d 100644 --- a/libnd4j/include/loops/cpu/compilation_units/indexreduce_int32_6.cpp +++ b/libnd4j/include/loops/cpu/compilation_units/indexreduce_int32_6.cpp @@ -23,6 +23,6 @@ namespace functions { namespace indexreduce { - BUILD_DOUBLE_TEMPLATE(template class ND4J_EXPORT IndexReduce, , LIBND4J_TYPES_6, (nd4j::DataType::INT32, int32_t)); + BUILD_DOUBLE_TEMPLATE(template class ND4J_EXPORT IndexReduce, , LIBND4J_TYPES_6, (sd::DataType::INT32, int32_t)); } } \ No newline at end of file diff --git a/libnd4j/include/loops/cpu/compilation_units/indexreduce_int32_7.cpp b/libnd4j/include/loops/cpu/compilation_units/indexreduce_int32_7.cpp index 155c4e3f4..a7d3c733f 100644 --- a/libnd4j/include/loops/cpu/compilation_units/indexreduce_int32_7.cpp +++ b/libnd4j/include/loops/cpu/compilation_units/indexreduce_int32_7.cpp @@ -23,6 +23,6 @@ namespace functions { namespace indexreduce { - BUILD_DOUBLE_TEMPLATE(template class ND4J_EXPORT IndexReduce, , LIBND4J_TYPES_7, (nd4j::DataType::INT32, int32_t)); + BUILD_DOUBLE_TEMPLATE(template class ND4J_EXPORT IndexReduce, , LIBND4J_TYPES_7, (sd::DataType::INT32, int32_t)); } } \ No newline at end of file diff --git a/libnd4j/include/loops/cpu/compilation_units/indexreduce_int32_8.cpp b/libnd4j/include/loops/cpu/compilation_units/indexreduce_int32_8.cpp index cdcfd8c51..8c5de9653 100644 --- a/libnd4j/include/loops/cpu/compilation_units/indexreduce_int32_8.cpp +++ b/libnd4j/include/loops/cpu/compilation_units/indexreduce_int32_8.cpp @@ -23,6 +23,6 @@ namespace functions { namespace indexreduce { - BUILD_DOUBLE_TEMPLATE(template class ND4J_EXPORT IndexReduce, , LIBND4J_TYPES_8, (nd4j::DataType::INT32, int32_t)); + BUILD_DOUBLE_TEMPLATE(template class ND4J_EXPORT IndexReduce, , LIBND4J_TYPES_8, (sd::DataType::INT32, int32_t)); } } \ No newline at end of file diff --git a/libnd4j/include/loops/cpu/compilation_units/indexreduce_int32_9.cpp b/libnd4j/include/loops/cpu/compilation_units/indexreduce_int32_9.cpp index 683245ef4..f61d604e2 100644 --- a/libnd4j/include/loops/cpu/compilation_units/indexreduce_int32_9.cpp +++ b/libnd4j/include/loops/cpu/compilation_units/indexreduce_int32_9.cpp @@ -23,6 +23,6 @@ namespace functions { namespace indexreduce { - BUILD_DOUBLE_TEMPLATE(template class ND4J_EXPORT IndexReduce, , LIBND4J_TYPES_9, (nd4j::DataType::INT32, int32_t)); + BUILD_DOUBLE_TEMPLATE(template class ND4J_EXPORT IndexReduce, , LIBND4J_TYPES_9, (sd::DataType::INT32, int32_t)); } } \ No newline at end of file diff --git a/libnd4j/include/loops/cpu/compilation_units/indexreduce_int64_0.cpp b/libnd4j/include/loops/cpu/compilation_units/indexreduce_int64_0.cpp index 6818dea5f..d399f5e0e 100644 --- a/libnd4j/include/loops/cpu/compilation_units/indexreduce_int64_0.cpp +++ b/libnd4j/include/loops/cpu/compilation_units/indexreduce_int64_0.cpp @@ -23,6 +23,6 @@ namespace functions { namespace indexreduce { - BUILD_DOUBLE_TEMPLATE(template class ND4J_EXPORT IndexReduce, , LIBND4J_TYPES_0, (nd4j::DataType::INT64, Nd4jLong)); + BUILD_DOUBLE_TEMPLATE(template class ND4J_EXPORT IndexReduce, , LIBND4J_TYPES_0, (sd::DataType::INT64, Nd4jLong)); } } \ No newline at end of file diff --git a/libnd4j/include/loops/cpu/compilation_units/indexreduce_int64_1.cpp b/libnd4j/include/loops/cpu/compilation_units/indexreduce_int64_1.cpp index c15541da5..c4df4d2e4 100644 --- a/libnd4j/include/loops/cpu/compilation_units/indexreduce_int64_1.cpp +++ b/libnd4j/include/loops/cpu/compilation_units/indexreduce_int64_1.cpp @@ -23,6 +23,6 @@ namespace functions { namespace indexreduce { - BUILD_DOUBLE_TEMPLATE(template class ND4J_EXPORT IndexReduce, , LIBND4J_TYPES_1, (nd4j::DataType::INT64, Nd4jLong)); + BUILD_DOUBLE_TEMPLATE(template class ND4J_EXPORT IndexReduce, , LIBND4J_TYPES_1, (sd::DataType::INT64, Nd4jLong)); } } \ No newline at end of file diff --git a/libnd4j/include/loops/cpu/compilation_units/indexreduce_int64_2.cpp b/libnd4j/include/loops/cpu/compilation_units/indexreduce_int64_2.cpp index a95682991..538e369eb 100644 --- a/libnd4j/include/loops/cpu/compilation_units/indexreduce_int64_2.cpp +++ b/libnd4j/include/loops/cpu/compilation_units/indexreduce_int64_2.cpp @@ -23,6 +23,6 @@ namespace functions { namespace indexreduce { - BUILD_DOUBLE_TEMPLATE(template class ND4J_EXPORT IndexReduce, , LIBND4J_TYPES_2, (nd4j::DataType::INT64, Nd4jLong)); + BUILD_DOUBLE_TEMPLATE(template class ND4J_EXPORT IndexReduce, , LIBND4J_TYPES_2, (sd::DataType::INT64, Nd4jLong)); } } \ No newline at end of file diff --git a/libnd4j/include/loops/cpu/compilation_units/indexreduce_int64_3.cpp b/libnd4j/include/loops/cpu/compilation_units/indexreduce_int64_3.cpp index 22597879c..b0d082bce 100644 --- a/libnd4j/include/loops/cpu/compilation_units/indexreduce_int64_3.cpp +++ b/libnd4j/include/loops/cpu/compilation_units/indexreduce_int64_3.cpp @@ -23,6 +23,6 @@ namespace functions { namespace indexreduce { - BUILD_DOUBLE_TEMPLATE(template class ND4J_EXPORT IndexReduce, , LIBND4J_TYPES_3, (nd4j::DataType::INT64, Nd4jLong)); + BUILD_DOUBLE_TEMPLATE(template class ND4J_EXPORT IndexReduce, , LIBND4J_TYPES_3, (sd::DataType::INT64, Nd4jLong)); } } \ No newline at end of file diff --git a/libnd4j/include/loops/cpu/compilation_units/indexreduce_int64_4.cpp b/libnd4j/include/loops/cpu/compilation_units/indexreduce_int64_4.cpp index a5b2afb12..98e13bb63 100644 --- a/libnd4j/include/loops/cpu/compilation_units/indexreduce_int64_4.cpp +++ b/libnd4j/include/loops/cpu/compilation_units/indexreduce_int64_4.cpp @@ -23,6 +23,6 @@ namespace functions { namespace indexreduce { - BUILD_DOUBLE_TEMPLATE(template class ND4J_EXPORT IndexReduce, , LIBND4J_TYPES_4, (nd4j::DataType::INT64, Nd4jLong)); + BUILD_DOUBLE_TEMPLATE(template class ND4J_EXPORT IndexReduce, , LIBND4J_TYPES_4, (sd::DataType::INT64, Nd4jLong)); } } \ No newline at end of file diff --git a/libnd4j/include/loops/cpu/compilation_units/indexreduce_int64_5.cpp b/libnd4j/include/loops/cpu/compilation_units/indexreduce_int64_5.cpp index 08797092a..4b7f599d9 100644 --- a/libnd4j/include/loops/cpu/compilation_units/indexreduce_int64_5.cpp +++ b/libnd4j/include/loops/cpu/compilation_units/indexreduce_int64_5.cpp @@ -23,6 +23,6 @@ namespace functions { namespace indexreduce { - BUILD_DOUBLE_TEMPLATE(template class ND4J_EXPORT IndexReduce, , LIBND4J_TYPES_5, (nd4j::DataType::INT64, Nd4jLong)); + BUILD_DOUBLE_TEMPLATE(template class ND4J_EXPORT IndexReduce, , LIBND4J_TYPES_5, (sd::DataType::INT64, Nd4jLong)); } } \ No newline at end of file diff --git a/libnd4j/include/loops/cpu/compilation_units/indexreduce_int64_6.cpp b/libnd4j/include/loops/cpu/compilation_units/indexreduce_int64_6.cpp index b7ca6d81e..8d7de9822 100644 --- a/libnd4j/include/loops/cpu/compilation_units/indexreduce_int64_6.cpp +++ b/libnd4j/include/loops/cpu/compilation_units/indexreduce_int64_6.cpp @@ -23,6 +23,6 @@ namespace functions { namespace indexreduce { - BUILD_DOUBLE_TEMPLATE(template class ND4J_EXPORT IndexReduce, , LIBND4J_TYPES_6, (nd4j::DataType::INT64, Nd4jLong)); + BUILD_DOUBLE_TEMPLATE(template class ND4J_EXPORT IndexReduce, , LIBND4J_TYPES_6, (sd::DataType::INT64, Nd4jLong)); } } \ No newline at end of file diff --git a/libnd4j/include/loops/cpu/compilation_units/indexreduce_int64_7.cpp b/libnd4j/include/loops/cpu/compilation_units/indexreduce_int64_7.cpp index 2eb10091a..8f9befddb 100644 --- a/libnd4j/include/loops/cpu/compilation_units/indexreduce_int64_7.cpp +++ b/libnd4j/include/loops/cpu/compilation_units/indexreduce_int64_7.cpp @@ -23,6 +23,6 @@ namespace functions { namespace indexreduce { - BUILD_DOUBLE_TEMPLATE(template class ND4J_EXPORT IndexReduce, , LIBND4J_TYPES_7, (nd4j::DataType::INT64, Nd4jLong)); + BUILD_DOUBLE_TEMPLATE(template class ND4J_EXPORT IndexReduce, , LIBND4J_TYPES_7, (sd::DataType::INT64, Nd4jLong)); } } \ No newline at end of file diff --git a/libnd4j/include/loops/cpu/compilation_units/indexreduce_int64_8.cpp b/libnd4j/include/loops/cpu/compilation_units/indexreduce_int64_8.cpp index f2a04cc8f..b38112631 100644 --- a/libnd4j/include/loops/cpu/compilation_units/indexreduce_int64_8.cpp +++ b/libnd4j/include/loops/cpu/compilation_units/indexreduce_int64_8.cpp @@ -23,6 +23,6 @@ namespace functions { namespace indexreduce { - BUILD_DOUBLE_TEMPLATE(template class ND4J_EXPORT IndexReduce, , LIBND4J_TYPES_8, (nd4j::DataType::INT64, Nd4jLong)); + BUILD_DOUBLE_TEMPLATE(template class ND4J_EXPORT IndexReduce, , LIBND4J_TYPES_8, (sd::DataType::INT64, Nd4jLong)); } } \ No newline at end of file diff --git a/libnd4j/include/loops/cpu/compilation_units/indexreduce_int64_9.cpp b/libnd4j/include/loops/cpu/compilation_units/indexreduce_int64_9.cpp index 062db6187..baacdc432 100644 --- a/libnd4j/include/loops/cpu/compilation_units/indexreduce_int64_9.cpp +++ b/libnd4j/include/loops/cpu/compilation_units/indexreduce_int64_9.cpp @@ -23,6 +23,6 @@ namespace functions { namespace indexreduce { - BUILD_DOUBLE_TEMPLATE(template class ND4J_EXPORT IndexReduce, , LIBND4J_TYPES_9, (nd4j::DataType::INT64, Nd4jLong)); + BUILD_DOUBLE_TEMPLATE(template class ND4J_EXPORT IndexReduce, , LIBND4J_TYPES_9, (sd::DataType::INT64, Nd4jLong)); } } \ No newline at end of file diff --git a/libnd4j/include/loops/cpu/compilation_units/pairwise_p10.cpp b/libnd4j/include/loops/cpu/compilation_units/pairwise_p10.cpp new file mode 100644 index 000000000..4a8aaf94a --- /dev/null +++ b/libnd4j/include/loops/cpu/compilation_units/pairwise_p10.cpp @@ -0,0 +1,27 @@ +/******************************************************************************* + * Copyright (c) 2015-2018 Skymind, Inc. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ + +// +// Created by raver119 on 23/09/18. +// + +#include "loops/cpu/pairwise.hpp" + +namespace functions { + namespace pairwise_transforms { + BUILD_PAIRWISE_TEMPLATE(template class ND4J_EXPORT PairWiseTransform, , PAIRWISE_TYPES_10); + } +} \ No newline at end of file diff --git a/libnd4j/include/loops/cpu/compilation_units/pairwise_p11.cpp b/libnd4j/include/loops/cpu/compilation_units/pairwise_p11.cpp new file mode 100644 index 000000000..1f4eb1389 --- /dev/null +++ b/libnd4j/include/loops/cpu/compilation_units/pairwise_p11.cpp @@ -0,0 +1,27 @@ +/******************************************************************************* + * Copyright (c) 2015-2018 Skymind, Inc. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ + +// +// Created by raver119 on 23/09/18. +// + +#include "loops/cpu/pairwise.hpp" + +namespace functions { + namespace pairwise_transforms { + BUILD_PAIRWISE_TEMPLATE(template class ND4J_EXPORT PairWiseTransform, , PAIRWISE_TYPES_11); + } +} \ No newline at end of file diff --git a/libnd4j/include/loops/cpu/compilation_units/pairwise_p12.cpp b/libnd4j/include/loops/cpu/compilation_units/pairwise_p12.cpp new file mode 100644 index 000000000..3c0984db9 --- /dev/null +++ b/libnd4j/include/loops/cpu/compilation_units/pairwise_p12.cpp @@ -0,0 +1,27 @@ +/******************************************************************************* + * Copyright (c) 2015-2018 Skymind, Inc. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ + +// +// Created by raver119 on 23/09/18. +// + +#include "loops/cpu/pairwise.hpp" + +namespace functions { + namespace pairwise_transforms { + BUILD_PAIRWISE_TEMPLATE(template class ND4J_EXPORT PairWiseTransform, , PAIRWISE_TYPES_12); + } +} \ No newline at end of file diff --git a/deeplearning4j/dl4j-integration-tests/src/test/java/org/deeplearning4j/integration/testcases/KerasImportTestCases.java b/libnd4j/include/loops/cpu/compilation_units/scalar_p10.cpp similarity index 78% rename from deeplearning4j/dl4j-integration-tests/src/test/java/org/deeplearning4j/integration/testcases/KerasImportTestCases.java rename to libnd4j/include/loops/cpu/compilation_units/scalar_p10.cpp index ed071563f..7175a8603 100644 --- a/deeplearning4j/dl4j-integration-tests/src/test/java/org/deeplearning4j/integration/testcases/KerasImportTestCases.java +++ b/libnd4j/include/loops/cpu/compilation_units/scalar_p10.cpp @@ -14,12 +14,14 @@ * SPDX-License-Identifier: Apache-2.0 ******************************************************************************/ -package org.deeplearning4j.integration.testcases; +// +// Created by raver on 9/28/2018. +// -/** - * Integration tests starting from Keras model - */ -public class KerasImportTestCases { +#include "../scalar.hpp" - -} +namespace functions { + namespace scalar { + BUILD_PAIRWISE_TEMPLATE(template class ND4J_EXPORT ScalarTransform, , PAIRWISE_TYPES_10); + } +} \ No newline at end of file diff --git a/libnd4j/include/loops/cpu/compilation_units/scalar_p11.cpp b/libnd4j/include/loops/cpu/compilation_units/scalar_p11.cpp new file mode 100644 index 000000000..a6b7bafac --- /dev/null +++ b/libnd4j/include/loops/cpu/compilation_units/scalar_p11.cpp @@ -0,0 +1,27 @@ +/******************************************************************************* + * Copyright (c) 2015-2018 Skymind, Inc. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ + +// +// Created by raver on 9/28/2018. +// + +#include "../scalar.hpp" + +namespace functions { + namespace scalar { + BUILD_PAIRWISE_TEMPLATE(template class ND4J_EXPORT ScalarTransform, , PAIRWISE_TYPES_11); + } +} \ No newline at end of file diff --git a/libnd4j/include/loops/cpu/compilation_units/scalar_p12.cpp b/libnd4j/include/loops/cpu/compilation_units/scalar_p12.cpp new file mode 100644 index 000000000..69cbeb7ff --- /dev/null +++ b/libnd4j/include/loops/cpu/compilation_units/scalar_p12.cpp @@ -0,0 +1,27 @@ +/******************************************************************************* + * Copyright (c) 2015-2018 Skymind, Inc. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ + +// +// Created by raver on 9/28/2018. +// + +#include "../scalar.hpp" + +namespace functions { + namespace scalar { + BUILD_PAIRWISE_TEMPLATE(template class ND4J_EXPORT ScalarTransform, , PAIRWISE_TYPES_12); + } +} \ No newline at end of file diff --git a/libnd4j/include/loops/cpu/indexreduce.hpp b/libnd4j/include/loops/cpu/indexreduce.hpp index 8d3af7eb4..d4abd8c82 100644 --- a/libnd4j/include/loops/cpu/indexreduce.hpp +++ b/libnd4j/include/loops/cpu/indexreduce.hpp @@ -18,13 +18,13 @@ // Created by raver on 4/9/2018. // -#include "../indexreduce.h" -#include -#include +#include +#include +#include #include #include #include -#include "../legacy_ops.h" +#include using namespace simdOps; @@ -60,11 +60,11 @@ Nd4jLong IndexReduce::execScalar(void *vx, Nd4jLong *xShapeInfo, void *vex auto startingIndex = OpType::startingIndexValue(x); auto len = shape::length(xShapeInfo); auto xEws = shape::elementWiseStride(xShapeInfo); - nd4j::OmpLaunchHelper info(len); + sd::OmpLaunchHelper info(len); uint xShapeInfoCast[MAX_RANK]; - bool canCastX = nd4j::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast); - int maxThreads = nd4j::math::nd4j_min(64, nd4j::Environment::getInstance()->maxThreads()); + bool canCastX = sd::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast); + int maxThreads = sd::math::nd4j_min(64, sd::Environment::getInstance()->maxThreads()); IndexValue intermediatery[64]; for (int e = 0; e < maxThreads; e++) intermediatery[e].index = -1; @@ -119,12 +119,12 @@ void IndexReduce::exec(void *vx, Nd4jLong *xShapeInfo, const Nd4jLong zLen = shape::length(zShapeInfo); - if(nd4j::ArrayOptions::arrayType(xShapeInfo) == nd4j::ArrayType::EMPTY) { - if(nd4j::ArrayOptions::arrayType(zShapeInfo) == nd4j::ArrayType::EMPTY) + if(sd::ArrayOptions::arrayType(xShapeInfo) == sd::ArrayType::EMPTY) { + if(sd::ArrayOptions::arrayType(zShapeInfo) == sd::ArrayType::EMPTY) return; const auto indexValue = OpType::startingIndexValue(x); - for (uint i = 0; i < zLen; i++) + for (Nd4jLong i = 0; i < zLen; i++) z[i] = (Z) indexValue.index; return; @@ -142,13 +142,13 @@ void IndexReduce::exec(void *vx, Nd4jLong *xShapeInfo, if (dimensionLength < 1) return; - auto tadPack = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(xShapeInfo, dimension, dimensionLength); + auto tadPack = sd::ConstantTadHelper::getInstance()->tadForDimensions(xShapeInfo, dimension, dimensionLength); tadOnlyShapeInfo = tadPack.primaryShapeInfo(); tadOffsets = tadPack.primaryOffsets(); } - nd4j::IndexReductionLoops::template loopIndexReduce(x, xShapeInfo, z, zShapeInfo, tadOnlyShapeInfo, tadOffsets, extraParams); + sd::IndexReductionLoops::template loopIndexReduce(x, xShapeInfo, z, zShapeInfo, tadOnlyShapeInfo, tadOffsets, extraParams); } } diff --git a/libnd4j/include/loops/cpu/pairwise.hpp b/libnd4j/include/loops/cpu/pairwise.hpp index 1fc85e5d8..27c97efa9 100644 --- a/libnd4j/include/loops/cpu/pairwise.hpp +++ b/libnd4j/include/loops/cpu/pairwise.hpp @@ -21,11 +21,11 @@ #include #include #include -#include -#include +#include +#include #include -#include -#include +#include +#include #include using namespace simdOps; @@ -135,7 +135,7 @@ namespace functions { if (shape::isScalar(yShapeInfo)) { uint xShapeInfoCast[MAX_RANK]; - const bool canCastX = nd4j::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast); + const bool canCastX = sd::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast); if(shape::haveSameShapeAndStrides(xShapeInfo, zShapeInfo)) { PRAGMA_OMP_SIMD @@ -146,7 +146,7 @@ namespace functions { } else { uint zShapeInfoCast[MAX_RANK]; - const bool canCastZ = nd4j::DataTypeUtils::castShapeInfo(zShapeInfo, zShapeInfoCast); + const bool canCastZ = sd::DataTypeUtils::castShapeInfo(zShapeInfo, zShapeInfoCast); PRAGMA_OMP_SIMD for(auto i = start; i < stop; i++) { @@ -160,20 +160,20 @@ namespace functions { - const nd4j::LoopKind::Kind kindOfLoop = nd4j::LoopKind::deduceKindOfLoopXYZ(xShapeInfo, yShapeInfo, zShapeInfo); + const sd::LoopKind::Kind kindOfLoop = sd::LoopKind::deduceKindOfLoopXYZ(xShapeInfo, yShapeInfo, zShapeInfo); const bool sameShapesXY = shape::shapeEquals(xShapeInfo, yShapeInfo); - if ((kindOfLoop == nd4j::LoopKind::EWS1 || kindOfLoop == nd4j::LoopKind::EWSNONZERO) && sameShapesXY) { + if ((kindOfLoop == sd::LoopKind::EWS1 || kindOfLoop == sd::LoopKind::EWSNONZERO) && sameShapesXY) { exec(x, xEws, y, yEws, z, zEws, extraParams, n, start, stop); } - else if ((kindOfLoop == nd4j::LoopKind::EWS1 || kindOfLoop == nd4j::LoopKind::EWSNONZERO) && !sameShapesXY) { //not same shape + else if ((kindOfLoop == sd::LoopKind::EWS1 || kindOfLoop == sd::LoopKind::EWSNONZERO) && !sameShapesXY) { //not same shape exec(x, xEws, y, yEws, z, zEws, extraParams, shape::length(yShapeInfo), start, stop); } else { if(shape::haveSameShapeAndStrides(xShapeInfo, yShapeInfo) && shape::haveSameShapeAndStrides(xShapeInfo, zShapeInfo)) { uint xShapeInfoCast[MAX_RANK]; - bool canCastX = nd4j::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast); + bool canCastX = sd::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast); PRAGMA_OMP_SIMD for (auto i = start; i < stop; i++) { @@ -184,8 +184,8 @@ namespace functions { else if(shape::haveSameShapeAndStrides(xShapeInfo, yShapeInfo)) { uint xShapeInfoCast[MAX_RANK]; uint zShapeInfoCast[MAX_RANK]; - bool canCastX = nd4j::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast); - bool canCastZ = nd4j::DataTypeUtils::castShapeInfo(zShapeInfo, zShapeInfoCast); + bool canCastX = sd::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast); + bool canCastZ = sd::DataTypeUtils::castShapeInfo(zShapeInfo, zShapeInfoCast); PRAGMA_OMP_SIMD for (auto i = start; i < stop; i++) { @@ -197,8 +197,8 @@ namespace functions { else if(shape::haveSameShapeAndStrides(xShapeInfo, zShapeInfo)) { uint xShapeInfoCast[MAX_RANK]; uint yShapeInfoCast[MAX_RANK]; - bool canCastX = nd4j::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast); - bool canCastY = nd4j::DataTypeUtils::castShapeInfo(yShapeInfo, yShapeInfoCast); + bool canCastX = sd::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast); + bool canCastY = sd::DataTypeUtils::castShapeInfo(yShapeInfo, yShapeInfoCast); PRAGMA_OMP_SIMD for (auto i = start; i < stop; i++) { @@ -210,8 +210,8 @@ namespace functions { else if(shape::haveSameShapeAndStrides(yShapeInfo, zShapeInfo)) { uint xShapeInfoCast[MAX_RANK]; uint yShapeInfoCast[MAX_RANK]; - bool canCastX = nd4j::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast); - bool canCastY = nd4j::DataTypeUtils::castShapeInfo(yShapeInfo, yShapeInfoCast); + bool canCastX = sd::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast); + bool canCastY = sd::DataTypeUtils::castShapeInfo(yShapeInfo, yShapeInfoCast); PRAGMA_OMP_SIMD for (auto i = start; i < stop; i++) { @@ -224,9 +224,9 @@ namespace functions { uint xShapeInfoCast[MAX_RANK]; uint yShapeInfoCast[MAX_RANK]; uint zShapeInfoCast[MAX_RANK]; - bool canCastX = nd4j::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast); - bool canCastY = nd4j::DataTypeUtils::castShapeInfo(yShapeInfo, yShapeInfoCast); - bool canCastZ = nd4j::DataTypeUtils::castShapeInfo(zShapeInfo, zShapeInfoCast); + bool canCastX = sd::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast); + bool canCastY = sd::DataTypeUtils::castShapeInfo(yShapeInfo, yShapeInfoCast); + bool canCastZ = sd::DataTypeUtils::castShapeInfo(zShapeInfo, zShapeInfoCast); PRAGMA_OMP_SIMD for (auto i = start; i < stop; i++) { diff --git a/libnd4j/include/loops/cpu/pairwise_bool.cpp b/libnd4j/include/loops/cpu/pairwise_bool.cpp index 2259c37b0..d77413e8c 100644 --- a/libnd4j/include/loops/cpu/pairwise_bool.cpp +++ b/libnd4j/include/loops/cpu/pairwise_bool.cpp @@ -20,8 +20,8 @@ #include #include -#include -#include +#include +#include #include using namespace simdOps; @@ -129,7 +129,7 @@ namespace functions { if (shape::isScalar(yShapeInfo)) { uint xShapeInfoCast[MAX_RANK]; - const bool canCastX = nd4j::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast); + const bool canCastX = sd::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast); if(shape::haveSameShapeAndStrides(xShapeInfo, zShapeInfo)) { @@ -141,7 +141,7 @@ namespace functions { } else { uint zShapeInfoCast[MAX_RANK]; - const bool canCastZ = nd4j::DataTypeUtils::castShapeInfo(zShapeInfo, zShapeInfoCast); + const bool canCastZ = sd::DataTypeUtils::castShapeInfo(zShapeInfo, zShapeInfoCast); PRAGMA_OMP_SIMD for(auto i = start; i < stop; i++) { @@ -153,19 +153,19 @@ namespace functions { return; } - const nd4j::LoopKind::Kind kindOfLoop = nd4j::LoopKind::deduceKindOfLoopXYZ(xShapeInfo, yShapeInfo, zShapeInfo); + const sd::LoopKind::Kind kindOfLoop = sd::LoopKind::deduceKindOfLoopXYZ(xShapeInfo, yShapeInfo, zShapeInfo); const bool sameShapesXY = shape::shapeEquals(xShapeInfo, yShapeInfo); - if ((kindOfLoop == nd4j::LoopKind::EWS1 || kindOfLoop == nd4j::LoopKind::EWSNONZERO) && sameShapesXY) { + if ((kindOfLoop == sd::LoopKind::EWS1 || kindOfLoop == sd::LoopKind::EWSNONZERO) && sameShapesXY) { exec(x, xEws, y, yEws, z, zEws, extraParams, n, start, stop); } - else if ((kindOfLoop == nd4j::LoopKind::EWS1 || kindOfLoop == nd4j::LoopKind::EWSNONZERO) && !sameShapesXY) { //not same shape + else if ((kindOfLoop == sd::LoopKind::EWS1 || kindOfLoop == sd::LoopKind::EWSNONZERO) && !sameShapesXY) { //not same shape exec(x, xEws, y, yEws, z, zEws, extraParams, shape::length(yShapeInfo), start, stop); } else { if(shape::haveSameShapeAndStrides(xShapeInfo, yShapeInfo) && shape::haveSameShapeAndStrides(xShapeInfo, zShapeInfo)) { uint xShapeInfoCast[MAX_RANK]; - const bool canCastX = nd4j::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast); + const bool canCastX = sd::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast); PRAGMA_OMP_SIMD for (auto i = start; i < stop; i++) { @@ -176,8 +176,8 @@ namespace functions { else if(shape::haveSameShapeAndStrides(xShapeInfo, yShapeInfo)) { uint xShapeInfoCast[MAX_RANK]; uint zShapeInfoCast[MAX_RANK]; - const bool canCastX = nd4j::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast); - const bool canCastZ = nd4j::DataTypeUtils::castShapeInfo(zShapeInfo, zShapeInfoCast); + const bool canCastX = sd::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast); + const bool canCastZ = sd::DataTypeUtils::castShapeInfo(zShapeInfo, zShapeInfoCast); PRAGMA_OMP_SIMD for (auto i = start; i < stop; i++) { @@ -189,8 +189,8 @@ namespace functions { else if(shape::haveSameShapeAndStrides(xShapeInfo, zShapeInfo)) { uint xShapeInfoCast[MAX_RANK]; uint yShapeInfoCast[MAX_RANK]; - const bool canCastX = nd4j::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast); - const bool canCastY = nd4j::DataTypeUtils::castShapeInfo(yShapeInfo, yShapeInfoCast); + const bool canCastX = sd::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast); + const bool canCastY = sd::DataTypeUtils::castShapeInfo(yShapeInfo, yShapeInfoCast); PRAGMA_OMP_SIMD for (auto i = start; i < stop; i++) { @@ -202,8 +202,8 @@ namespace functions { else if(shape::haveSameShapeAndStrides(yShapeInfo, zShapeInfo)) { uint xShapeInfoCast[MAX_RANK]; uint yShapeInfoCast[MAX_RANK]; - const bool canCastX = nd4j::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast); - const bool canCastY = nd4j::DataTypeUtils::castShapeInfo(yShapeInfo, yShapeInfoCast); + const bool canCastX = sd::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast); + const bool canCastY = sd::DataTypeUtils::castShapeInfo(yShapeInfo, yShapeInfoCast); PRAGMA_OMP_SIMD for (auto i = start; i < stop; i++) { @@ -216,9 +216,9 @@ namespace functions { uint xShapeInfoCast[MAX_RANK]; uint yShapeInfoCast[MAX_RANK]; uint zShapeInfoCast[MAX_RANK]; - const bool canCastX = nd4j::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast); - const bool canCastY = nd4j::DataTypeUtils::castShapeInfo(yShapeInfo, yShapeInfoCast); - const bool canCastZ = nd4j::DataTypeUtils::castShapeInfo(zShapeInfo, zShapeInfoCast); + const bool canCastX = sd::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast); + const bool canCastY = sd::DataTypeUtils::castShapeInfo(yShapeInfo, yShapeInfoCast); + const bool canCastZ = sd::DataTypeUtils::castShapeInfo(zShapeInfo, zShapeInfoCast); PRAGMA_OMP_SIMD for (auto i = start; i < stop; i++) { diff --git a/libnd4j/include/loops/cpu/pairwise_int.cpp b/libnd4j/include/loops/cpu/pairwise_int.cpp index 673951d6a..9af092a0f 100644 --- a/libnd4j/include/loops/cpu/pairwise_int.cpp +++ b/libnd4j/include/loops/cpu/pairwise_int.cpp @@ -20,8 +20,8 @@ #include #include -#include -#include +#include +#include #include using namespace simdOps; @@ -129,7 +129,7 @@ namespace functions { if (shape::isScalar(yShapeInfo)) { uint xShapeInfoCast[MAX_RANK]; - const bool canCastX = nd4j::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast); + const bool canCastX = sd::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast); if(shape::haveSameShapeAndStrides(xShapeInfo, zShapeInfo)) { PRAGMA_OMP_SIMD @@ -140,7 +140,7 @@ namespace functions { } else { uint zShapeInfoCast[MAX_RANK]; - const bool canCastZ = nd4j::DataTypeUtils::castShapeInfo(zShapeInfo, zShapeInfoCast); + const bool canCastZ = sd::DataTypeUtils::castShapeInfo(zShapeInfo, zShapeInfoCast); PRAGMA_OMP_SIMD for(auto i = start; i < stop; i++) { @@ -152,20 +152,20 @@ namespace functions { return; } - const nd4j::LoopKind::Kind kindOfLoop = nd4j::LoopKind::deduceKindOfLoopXYZ(xShapeInfo, yShapeInfo, zShapeInfo); + const sd::LoopKind::Kind kindOfLoop = sd::LoopKind::deduceKindOfLoopXYZ(xShapeInfo, yShapeInfo, zShapeInfo); const bool sameShapesXY = shape::shapeEquals(xShapeInfo, yShapeInfo); - if ((kindOfLoop == nd4j::LoopKind::EWS1 || kindOfLoop == nd4j::LoopKind::EWSNONZERO) && sameShapesXY) { + if ((kindOfLoop == sd::LoopKind::EWS1 || kindOfLoop == sd::LoopKind::EWSNONZERO) && sameShapesXY) { exec(x, xEws, y, yEws, z, zEws, extraParams, n, start, stop); } - else if ((kindOfLoop == nd4j::LoopKind::EWS1 || kindOfLoop == nd4j::LoopKind::EWSNONZERO) && !sameShapesXY) { //not same shape + else if ((kindOfLoop == sd::LoopKind::EWS1 || kindOfLoop == sd::LoopKind::EWSNONZERO) && !sameShapesXY) { //not same shape exec(x, xEws, y, yEws, z, zEws, extraParams, shape::length(yShapeInfo), start, stop); } else { if(shape::haveSameShapeAndStrides(xShapeInfo, yShapeInfo) && shape::haveSameShapeAndStrides(xShapeInfo, zShapeInfo)) { uint xShapeInfoCast[MAX_RANK]; - const bool canCastX = nd4j::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast); + const bool canCastX = sd::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast); PRAGMA_OMP_SIMD for (auto i = start; i < stop; i++) { @@ -176,8 +176,8 @@ namespace functions { else if(shape::haveSameShapeAndStrides(xShapeInfo, yShapeInfo)) { uint xShapeInfoCast[MAX_RANK]; uint zShapeInfoCast[MAX_RANK]; - const bool canCastX = nd4j::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast); - const bool canCastZ = nd4j::DataTypeUtils::castShapeInfo(zShapeInfo, zShapeInfoCast); + const bool canCastX = sd::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast); + const bool canCastZ = sd::DataTypeUtils::castShapeInfo(zShapeInfo, zShapeInfoCast); PRAGMA_OMP_SIMD for (auto i = start; i < stop; i++) { @@ -189,8 +189,8 @@ namespace functions { else if(shape::haveSameShapeAndStrides(xShapeInfo, zShapeInfo)) { uint xShapeInfoCast[MAX_RANK]; uint yShapeInfoCast[MAX_RANK]; - const bool canCastX = nd4j::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast); - const bool canCastY = nd4j::DataTypeUtils::castShapeInfo(yShapeInfo, yShapeInfoCast); + const bool canCastX = sd::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast); + const bool canCastY = sd::DataTypeUtils::castShapeInfo(yShapeInfo, yShapeInfoCast); PRAGMA_OMP_SIMD for (auto i = start; i < stop; i++) { @@ -202,8 +202,8 @@ namespace functions { else if(shape::haveSameShapeAndStrides(yShapeInfo, zShapeInfo)) { uint xShapeInfoCast[MAX_RANK]; uint yShapeInfoCast[MAX_RANK]; - const bool canCastX = nd4j::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast); - const bool canCastY = nd4j::DataTypeUtils::castShapeInfo(yShapeInfo, yShapeInfoCast); + const bool canCastX = sd::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast); + const bool canCastY = sd::DataTypeUtils::castShapeInfo(yShapeInfo, yShapeInfoCast); PRAGMA_OMP_SIMD for (auto i = start; i < stop; i++) { @@ -216,9 +216,9 @@ namespace functions { uint xShapeInfoCast[MAX_RANK]; uint yShapeInfoCast[MAX_RANK]; uint zShapeInfoCast[MAX_RANK]; - const bool canCastX = nd4j::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast); - const bool canCastY = nd4j::DataTypeUtils::castShapeInfo(yShapeInfo, yShapeInfoCast); - const bool canCastZ = nd4j::DataTypeUtils::castShapeInfo(zShapeInfo, zShapeInfoCast); + const bool canCastX = sd::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast); + const bool canCastY = sd::DataTypeUtils::castShapeInfo(yShapeInfo, yShapeInfoCast); + const bool canCastZ = sd::DataTypeUtils::castShapeInfo(zShapeInfo, zShapeInfoCast); PRAGMA_OMP_SIMD for (auto i = start; i < stop; i++) { diff --git a/libnd4j/include/loops/cpu/random.hpp b/libnd4j/include/loops/cpu/random.hpp index ab9793694..034179f07 100644 --- a/libnd4j/include/loops/cpu/random.hpp +++ b/libnd4j/include/loops/cpu/random.hpp @@ -20,9 +20,9 @@ // #include -#include +#include #include -#include +#include using namespace randomOps; @@ -53,7 +53,7 @@ namespace functions { auto length = shape::length(zShapeInfo); - nd4j::graph::RandomGenerator* rng = reinterpret_cast(state); + sd::graph::RandomGenerator* rng = reinterpret_cast(state); if(shape::haveSameShapeAndStrides(xShapeInfo, yShapeInfo) && shape::haveSameShapeAndStrides(xShapeInfo, zShapeInfo)) { @@ -71,7 +71,7 @@ namespace functions { } else{ uint xShapeInfoCast[MAX_RANK]; - const bool canCastX = nd4j::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast); + const bool canCastX = sd::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast); auto func = PRAGMA_THREADS_FOR { PRAGMA_OMP_SIMD @@ -88,12 +88,12 @@ namespace functions { uint xShapeInfoCast[MAX_RANK]; uint zShapeInfoCast[MAX_RANK]; - const bool canCastX = nd4j::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast); - const bool canCastZ = nd4j::DataTypeUtils::castShapeInfo(zShapeInfo, zShapeInfoCast); + const bool canCastX = sd::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast); + const bool canCastZ = sd::DataTypeUtils::castShapeInfo(zShapeInfo, zShapeInfoCast); auto func = PRAGMA_THREADS_FOR { PRAGMA_OMP_SIMD - for (uint64_t i = start; i < stop; i++) { + for (auto i = start; i < stop; i++) { auto offset = shape::indexOffset(i, xShapeInfo, xShapeInfoCast, canCastX); auto zOffset = shape::indexOffset(i, zShapeInfo, zShapeInfoCast, canCastZ); z[zOffset] = OpClass::op(x[offset], y[offset], i, length, rng, extraArguments); @@ -106,12 +106,12 @@ namespace functions { uint xShapeInfoCast[MAX_RANK]; uint yShapeInfoCast[MAX_RANK]; - const bool canCastX = nd4j::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast); - const bool canCastY = nd4j::DataTypeUtils::castShapeInfo(yShapeInfo, yShapeInfoCast); + const bool canCastX = sd::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast); + const bool canCastY = sd::DataTypeUtils::castShapeInfo(yShapeInfo, yShapeInfoCast); auto func = PRAGMA_THREADS_FOR { PRAGMA_OMP_SIMD - for (uint64_t i = start; i < stop; i++) { + for (auto i = start; i < stop; i++) { auto offset = shape::indexOffset(i, xShapeInfo, xShapeInfoCast, canCastX); auto yOffset = shape::indexOffset(i, yShapeInfo, yShapeInfoCast, canCastY); z[offset] = OpClass::op(x[offset], y[yOffset], i, length, rng, extraArguments); @@ -124,12 +124,12 @@ namespace functions { uint xShapeInfoCast[MAX_RANK]; uint yShapeInfoCast[MAX_RANK]; - const bool canCastX = nd4j::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast); - const bool canCastY = nd4j::DataTypeUtils::castShapeInfo(yShapeInfo, yShapeInfoCast); + const bool canCastX = sd::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast); + const bool canCastY = sd::DataTypeUtils::castShapeInfo(yShapeInfo, yShapeInfoCast); auto func = PRAGMA_THREADS_FOR { PRAGMA_OMP_SIMD - for (uint64_t i = start; i < stop; i++) { + for (auto i = start; i < stop; i++) { auto xOffset = shape::indexOffset(i, xShapeInfo, xShapeInfoCast, canCastX); auto offset = shape::indexOffset(i, yShapeInfo, yShapeInfoCast, canCastY); z[offset] = OpClass::op(x[xOffset], y[offset], i, length, rng, extraArguments); @@ -143,13 +143,13 @@ namespace functions { uint xShapeInfoCast[MAX_RANK]; uint yShapeInfoCast[MAX_RANK]; uint zShapeInfoCast[MAX_RANK]; - const bool canCastX = nd4j::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast); - const bool canCastY = nd4j::DataTypeUtils::castShapeInfo(yShapeInfo, yShapeInfoCast); - const bool canCastZ = nd4j::DataTypeUtils::castShapeInfo(zShapeInfo, zShapeInfoCast); + const bool canCastX = sd::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast); + const bool canCastY = sd::DataTypeUtils::castShapeInfo(yShapeInfo, yShapeInfoCast); + const bool canCastZ = sd::DataTypeUtils::castShapeInfo(zShapeInfo, zShapeInfoCast); auto func = PRAGMA_THREADS_FOR { PRAGMA_OMP_SIMD - for (uint64_t i = start; i < stop; i++) { + for (auto i = start; i < stop; i++) { auto xOffset = shape::indexOffset(i, xShapeInfo, xShapeInfoCast, canCastX); auto yOffset = shape::indexOffset(i, yShapeInfo, yShapeInfoCast, canCastY); auto zOffset = shape::indexOffset(i, zShapeInfo, zShapeInfoCast, canCastZ); @@ -178,9 +178,9 @@ namespace functions { auto length = shape::length(zShapeInfo); uint xShapeInfoCast[MAX_RANK]; - const bool canCastX = nd4j::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast); + const bool canCastX = sd::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast); - nd4j::graph::RandomGenerator* rng = reinterpret_cast(state); + sd::graph::RandomGenerator* rng = reinterpret_cast(state); if(shape::haveSameShapeAndStrides(xShapeInfo, zShapeInfo)) { @@ -197,7 +197,7 @@ namespace functions { else{ auto func = PRAGMA_THREADS_FOR { PRAGMA_OMP_SIMD - for (uint64_t i = start; i < stop; i++) { + for (auto i = start; i < stop; i++) { auto offset = shape::indexOffset(i, xShapeInfo, xShapeInfoCast, canCastX); z[offset] = OpClass::op(x[offset], i, length, rng, extraArguments); } @@ -209,11 +209,11 @@ namespace functions { else { uint zShapeInfoCast[MAX_RANK]; - const bool canCastZ = nd4j::DataTypeUtils::castShapeInfo(zShapeInfo, zShapeInfoCast); + const bool canCastZ = sd::DataTypeUtils::castShapeInfo(zShapeInfo, zShapeInfoCast); auto func = PRAGMA_THREADS_FOR { PRAGMA_OMP_SIMD - for (uint64_t i = start; i < stop; i++) { + for (auto i = start; i < stop; i++) { auto xOffset = shape::indexOffset(i, xShapeInfo, xShapeInfoCast, canCastX); auto zOffset = shape::indexOffset(i, zShapeInfo, zShapeInfoCast, canCastZ); z[zOffset] = OpClass::op(x[xOffset], i, length, rng, extraArguments); @@ -234,7 +234,7 @@ namespace functions { auto length = shape::length(zShapeInfo); - nd4j::graph::RandomGenerator* rng = reinterpret_cast(state); + sd::graph::RandomGenerator* rng = reinterpret_cast(state); if(shape::elementWiseStride(zShapeInfo) == 1){ @@ -248,14 +248,14 @@ namespace functions { samediff::Threads::parallel_for(func, 0, length, 1); } else{ - nd4j::OmpLaunchHelper info(length); + sd::OmpLaunchHelper info(length); uint zShapeInfoCast[MAX_RANK]; - const bool canCastZ = nd4j::DataTypeUtils::castShapeInfo(zShapeInfo, zShapeInfoCast); + const bool canCastZ = sd::DataTypeUtils::castShapeInfo(zShapeInfo, zShapeInfoCast); auto func = PRAGMA_THREADS_FOR { PRAGMA_OMP_SIMD - for (uint64_t i = start; i < stop; i++) { + for (auto i = start; i < stop; i++) { auto offset = shape::indexOffset(i, zShapeInfo, zShapeInfoCast, canCastZ); z[offset] = OpClass::op(i, length, rng, extraArguments); } diff --git a/libnd4j/include/loops/cpu/reduce/reduce_bool.cpp b/libnd4j/include/loops/cpu/reduce/reduce_bool.cpp index 1ee820853..afb441a45 100644 --- a/libnd4j/include/loops/cpu/reduce/reduce_bool.cpp +++ b/libnd4j/include/loops/cpu/reduce/reduce_bool.cpp @@ -20,10 +20,10 @@ // #include -#include +#include #include #include -#include +#include #include #include @@ -50,12 +50,12 @@ namespace functions { return; } - if(nd4j::ArrayOptions::arrayType(xShapeInfo) == nd4j::ArrayType::EMPTY) { - if(nd4j::ArrayOptions::arrayType(zShapeInfo) == nd4j::ArrayType::EMPTY) + if(sd::ArrayOptions::arrayType(xShapeInfo) == sd::ArrayType::EMPTY) { + if(sd::ArrayOptions::arrayType(zShapeInfo) == sd::ArrayType::EMPTY) return; const auto startingVal = OpType::startingValue(x); - for (uint i = 0; i < length; i++) + for (Nd4jLong i = 0; i < length; i++) z[i] = startingVal; return; } @@ -66,9 +66,9 @@ namespace functions { else { auto startingValue = OpType::startingValue(x); uint xShapeInfoCast[MAX_RANK]; - const bool canCastX = nd4j::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast); + const bool canCastX = sd::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast); - for (auto i = 0; i < length; i++) + for (Nd4jLong i = 0; i < length; i++) startingValue = OpType::update(startingValue, OpType::op(x[shape::indexOffset(i, xShapeInfo, xShapeInfoCast, canCastX)], extraParams), extraParams); z[0] = OpType::postProcess(startingValue, length, extraParams); @@ -92,9 +92,9 @@ namespace functions { else { auto startingValue = OpType::startingValue(x); uint xShapeInfoCast[MAX_RANK]; - bool canCastX = nd4j::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast); + bool canCastX = sd::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast); - for (auto i = 0; i < length; i++) + for (Nd4jLong i = 0; i < length; i++) startingValue = OpType::update(startingValue, OpType::op(x[shape::indexOffset(i, xShapeInfo, xShapeInfoCast, canCastX)], extraParams), extraParams); return OpType::postProcess(startingValue, length, extraParams); @@ -151,12 +151,12 @@ namespace functions { auto resultLength = shape::length(zShapeInfo); - if(nd4j::ArrayOptions::arrayType(xShapeInfo) == nd4j::ArrayType::EMPTY) { - if(nd4j::ArrayOptions::arrayType(zShapeInfo) == nd4j::ArrayType::EMPTY) + if(sd::ArrayOptions::arrayType(xShapeInfo) == sd::ArrayType::EMPTY) { + if(sd::ArrayOptions::arrayType(zShapeInfo) == sd::ArrayType::EMPTY) return; const auto startingVal = OpType::startingValue(x); - for (uint i = 0; i < resultLength; i++) + for (Nd4jLong i = 0; i < resultLength; i++) z[i] = startingVal; return; } @@ -178,15 +178,15 @@ namespace functions { if (dimensionLength < 1) return; - auto tadPack = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(xShapeInfo, dimension, dimensionLength); + auto tadPack = sd::ConstantTadHelper::getInstance()->tadForDimensions(xShapeInfo, dimension, dimensionLength); tadOnlyShapeInfo = tadPack.primaryShapeInfo(); tadOffsets = tadPack.primaryOffsets(); } #ifdef INLINE_LOOPS - nd4j::ReductionLoops::template loopReduce(x, xShapeInfo, z, zShapeInfo, tadOnlyShapeInfo, tadOffsets, extraParams, start, stop); + sd::ReductionLoops::template loopReduce(x, xShapeInfo, z, zShapeInfo, tadOnlyShapeInfo, tadOffsets, extraParams, start, stop); #else - nd4j::ReductionBoolLoops::template innerloopReduce(x, xShapeInfo, z, zShapeInfo, tadOnlyShapeInfo, tadOffsets, extraParams, start, stop); + sd::ReductionBoolLoops::template innerloopReduce(x, xShapeInfo, z, zShapeInfo, tadOnlyShapeInfo, tadOffsets, extraParams, start, stop); #endif } @@ -208,7 +208,7 @@ namespace functions { Z _CUDA_H ReduceBoolFunction::execScalar(void *vx, Nd4jLong xEws, Nd4jLong length, void *vextraParams) { auto x = reinterpret_cast(vx); auto extraParams = reinterpret_cast(vextraParams); - int maxThreads = nd4j::math::nd4j_min(64, nd4j::Environment::getInstance()->maxThreads()); + int maxThreads = sd::math::nd4j_min(64, sd::Environment::getInstance()->maxThreads()); Z intermediate[64]; PRAGMA_OMP_SIMD diff --git a/libnd4j/include/loops/cpu/reduce/reduce_float.hpp b/libnd4j/include/loops/cpu/reduce/reduce_float.hpp index d0a80a3f5..40c24f4fa 100644 --- a/libnd4j/include/loops/cpu/reduce/reduce_float.hpp +++ b/libnd4j/include/loops/cpu/reduce/reduce_float.hpp @@ -20,10 +20,10 @@ // #include -#include +#include #include #include -#include +#include #include #include @@ -47,19 +47,19 @@ namespace functions { if (shape::isEmpty(xShapeInfo)) { if (std::is_same>::value) { - z[0] = nd4j::DataTypeUtils::nanOrZero(); + z[0] = sd::DataTypeUtils::nanOrZero(); } else { z[0] = OpType::startingValue(x); } return; } - if(nd4j::ArrayOptions::arrayType(xShapeInfo) == nd4j::ArrayType::EMPTY) { - if(nd4j::ArrayOptions::arrayType(zShapeInfo) == nd4j::ArrayType::EMPTY) + if(sd::ArrayOptions::arrayType(xShapeInfo) == sd::ArrayType::EMPTY) { + if(sd::ArrayOptions::arrayType(zShapeInfo) == sd::ArrayType::EMPTY) return; const auto startingVal = OpType::startingValue(x); - for (uint i = 0; i < length; i++) + for (Nd4jLong i = 0; i < length; i++) z[i] = startingVal; return; @@ -71,8 +71,8 @@ namespace functions { else { auto startingValue = OpType::startingValue(x); uint xShapeInfoCast[MAX_RANK]; - const bool canCastX = nd4j::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast); - int maxThreads = nd4j::math::nd4j_min(64, nd4j::Environment::getInstance()->maxThreads()); + const bool canCastX = sd::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast); + int maxThreads = sd::math::nd4j_min(64, sd::Environment::getInstance()->maxThreads()); Z intermediate[64]; PRAGMA_OMP_SIMD @@ -111,9 +111,9 @@ namespace functions { else { auto startingValue = OpType::startingValue(x); uint xShapeInfoCast[MAX_RANK]; - bool canCastX = nd4j::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast); + bool canCastX = sd::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast); - for (auto i = 0; i < length; i++) + for (Nd4jLong i = 0; i < length; i++) startingValue = OpType::update(startingValue, OpType::op(x[shape::indexOffset(i, xShapeInfo, xShapeInfoCast, canCastX)], extraParams), extraParams); return OpType::postProcess(startingValue, length, extraParams); @@ -179,12 +179,12 @@ namespace functions { auto resultLength = shape::length(zShapeInfo); - if(nd4j::ArrayOptions::arrayType(xShapeInfo) == nd4j::ArrayType::EMPTY) { - if(nd4j::ArrayOptions::arrayType(zShapeInfo) == nd4j::ArrayType::EMPTY) + if(sd::ArrayOptions::arrayType(xShapeInfo) == sd::ArrayType::EMPTY) { + if(sd::ArrayOptions::arrayType(zShapeInfo) == sd::ArrayType::EMPTY) return; - const auto startingVal = std::is_same>::value ? nd4j::DataTypeUtils::nanOrZero() : static_cast(OpType::startingValue(x)); + const auto startingVal = std::is_same>::value ? sd::DataTypeUtils::nanOrZero() : static_cast(OpType::startingValue(x)); - for (uint i = 0; i < resultLength; i++) + for (Nd4jLong i = 0; i < resultLength; i++) z[i] = startingVal; return; } @@ -211,15 +211,15 @@ namespace functions { if (dimensionLength < 0) return; - auto tadPack = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(xShapeInfo, dimension, dimensionLength); + auto tadPack = sd::ConstantTadHelper::getInstance()->tadForDimensions(xShapeInfo, dimension, dimensionLength); tadOnlyShapeInfo = tadPack.primaryShapeInfo(); tadOffsets = tadPack.primaryOffsets(); } #ifdef INLINE_LOOPS - nd4j::ReductionLoops::template loopReduce(x, xShapeInfo, z, zShapeInfo, tadOnlyShapeInfo, tadOffsets, extraParams, start, stop); + sd::ReductionLoops::template loopReduce(x, xShapeInfo, z, zShapeInfo, tadOnlyShapeInfo, tadOffsets, extraParams, start, stop); #else - nd4j::ReductionFloatLoops::template innerloopReduce(x, xShapeInfo, z, zShapeInfo, tadOnlyShapeInfo, tadOffsets, extraParams, start, stop); + sd::ReductionFloatLoops::template innerloopReduce(x, xShapeInfo, z, zShapeInfo, tadOnlyShapeInfo, tadOffsets, extraParams, start, stop); #endif } @@ -242,7 +242,7 @@ namespace functions { auto x = reinterpret_cast(vx); auto extraParams = reinterpret_cast(vextraParams); - int maxThreads = nd4j::math::nd4j_min(64, nd4j::Environment::getInstance()->maxThreads()); + int maxThreads = sd::math::nd4j_min(64, sd::Environment::getInstance()->maxThreads()); Z intermediate[64]; PRAGMA_OMP_SIMD diff --git a/libnd4j/include/loops/cpu/reduce/reduce_long.cpp b/libnd4j/include/loops/cpu/reduce/reduce_long.cpp index e53c9ac8e..98b462ebd 100644 --- a/libnd4j/include/loops/cpu/reduce/reduce_long.cpp +++ b/libnd4j/include/loops/cpu/reduce/reduce_long.cpp @@ -20,10 +20,10 @@ // #include -#include +#include #include #include -#include +#include #include #include @@ -50,12 +50,12 @@ namespace functions { return; } - if(nd4j::ArrayOptions::arrayType(xShapeInfo) == nd4j::ArrayType::EMPTY) { - if(nd4j::ArrayOptions::arrayType(zShapeInfo) == nd4j::ArrayType::EMPTY) + if(sd::ArrayOptions::arrayType(xShapeInfo) == sd::ArrayType::EMPTY) { + if(sd::ArrayOptions::arrayType(zShapeInfo) == sd::ArrayType::EMPTY) return; const auto startingVal = OpType::startingValue(x); - for (uint i = 0; i < length; i++) + for (Nd4jLong i = 0; i < length; i++) z[i] = startingVal; return; } @@ -66,8 +66,8 @@ namespace functions { else { auto startingValue = OpType::startingValue(x); uint xShapeInfoCast[MAX_RANK]; - const bool canCastX = nd4j::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast); - int maxThreads = nd4j::math::nd4j_min(64, nd4j::Environment::getInstance()->maxThreads()); + const bool canCastX = sd::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast); + int maxThreads = sd::math::nd4j_min(64, sd::Environment::getInstance()->maxThreads()); Z intermediate[64]; PRAGMA_OMP_SIMD @@ -108,9 +108,9 @@ namespace functions { else { auto startingValue = OpType::startingValue(x); uint xShapeInfoCast[MAX_RANK]; - bool canCastX = nd4j::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast); + bool canCastX = sd::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast); - for (auto i = 0; i < length; i++) + for (Nd4jLong i = 0; i < length; i++) startingValue = OpType::update(startingValue, OpType::op(x[shape::indexOffset(i, xShapeInfo, xShapeInfoCast, canCastX)], extraParams), extraParams); return OpType::postProcess(startingValue, length, extraParams); @@ -168,12 +168,12 @@ namespace functions { auto resultLength = shape::length(zShapeInfo); - if(nd4j::ArrayOptions::arrayType(xShapeInfo) == nd4j::ArrayType::EMPTY) { - if(nd4j::ArrayOptions::arrayType(zShapeInfo) == nd4j::ArrayType::EMPTY) + if(sd::ArrayOptions::arrayType(xShapeInfo) == sd::ArrayType::EMPTY) { + if(sd::ArrayOptions::arrayType(zShapeInfo) == sd::ArrayType::EMPTY) return; const auto startingVal = OpType::startingValue(x); - for (uint i = 0; i < resultLength; i++) + for (Nd4jLong i = 0; i < resultLength; i++) z[i] = startingVal; return; } @@ -200,15 +200,15 @@ namespace functions { if (dimensionLength < 1) return; - auto tadPack = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(xShapeInfo, dimension, dimensionLength); + auto tadPack = sd::ConstantTadHelper::getInstance()->tadForDimensions(xShapeInfo, dimension, dimensionLength); tadOnlyShapeInfo = tadPack.primaryShapeInfo(); tadOffsets = tadPack.primaryOffsets(); } #ifdef INLINE_LOOPS - nd4j::ReductionLoops::template loopReduce(x, xShapeInfo, z, zShapeInfo, tadOnlyShapeInfo, tadOffsets, extraParams, start, stop); + sd::ReductionLoops::template loopReduce(x, xShapeInfo, z, zShapeInfo, tadOnlyShapeInfo, tadOffsets, extraParams, start, stop); #else - nd4j::ReductionLongLoops::template innerloopReduce(x, xShapeInfo, z, zShapeInfo, tadOnlyShapeInfo, tadOffsets, extraParams, start, stop); + sd::ReductionLongLoops::template innerloopReduce(x, xShapeInfo, z, zShapeInfo, tadOnlyShapeInfo, tadOffsets, extraParams, start, stop); #endif } @@ -231,7 +231,7 @@ namespace functions { auto x = reinterpret_cast(vx); auto extraParams = reinterpret_cast(vextraParams); - int maxThreads = nd4j::math::nd4j_min(64, nd4j::Environment::getInstance()->maxThreads()); + int maxThreads = sd::math::nd4j_min(64, sd::Environment::getInstance()->maxThreads()); Z intermediate[64]; PRAGMA_OMP_SIMD diff --git a/libnd4j/include/loops/cpu/reduce/reduce_same.cpp b/libnd4j/include/loops/cpu/reduce/reduce_same.cpp index 929d9c4ff..f357b7e64 100644 --- a/libnd4j/include/loops/cpu/reduce/reduce_same.cpp +++ b/libnd4j/include/loops/cpu/reduce/reduce_same.cpp @@ -20,10 +20,10 @@ // #include -#include +#include #include #include -#include +#include #include #include #include @@ -52,12 +52,12 @@ namespace functions { return; } - if(nd4j::ArrayOptions::arrayType(xShapeInfo) == nd4j::ArrayType::EMPTY) { - if(nd4j::ArrayOptions::arrayType(zShapeInfo) == nd4j::ArrayType::EMPTY) + if(sd::ArrayOptions::arrayType(xShapeInfo) == sd::ArrayType::EMPTY) { + if(sd::ArrayOptions::arrayType(zShapeInfo) == sd::ArrayType::EMPTY) return; const auto startingVal = OpType::startingValue(x); - for (uint i = 0; i < length; i++) + for (Nd4jLong i = 0; i < length; i++) z[i] = startingVal; return; } @@ -68,8 +68,8 @@ namespace functions { else { auto startingValue = OpType::startingValue(x); uint xShapeInfoCast[MAX_RANK]; - const bool canCastX = nd4j::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast); - int maxThreads = nd4j::math::nd4j_min(64, nd4j::Environment::getInstance()->maxThreads()); + const bool canCastX = sd::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast); + int maxThreads = sd::math::nd4j_min(64, sd::Environment::getInstance()->maxThreads()); X intermediate[64]; PRAGMA_OMP_SIMD @@ -109,9 +109,9 @@ namespace functions { } else { auto startingValue = OpType::startingValue(x); uint xShapeInfoCast[MAX_RANK]; - bool canCastX = nd4j::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast); + bool canCastX = sd::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast); - for (auto i = 0; i < length; i++) + for (Nd4jLong i = 0; i < length; i++) startingValue = OpType::update(startingValue, OpType::op(x[shape::indexOffset(i, xShapeInfo, xShapeInfoCast, canCastX)], extraParams), extraParams); return OpType::postProcess(startingValue, length, extraParams); @@ -177,12 +177,12 @@ namespace functions { auto zLength = shape::length(zShapeInfo); - if(nd4j::ArrayOptions::arrayType(xShapeInfo) == nd4j::ArrayType::EMPTY) { - if(nd4j::ArrayOptions::arrayType(zShapeInfo) == nd4j::ArrayType::EMPTY) + if(sd::ArrayOptions::arrayType(xShapeInfo) == sd::ArrayType::EMPTY) { + if(sd::ArrayOptions::arrayType(zShapeInfo) == sd::ArrayType::EMPTY) return; const auto startingVal = OpType::startingValue(x); - for (uint i = 0; i < zLength; i++) + for (Nd4jLong i = 0; i < zLength; i++) z[i] = startingVal; return; } @@ -209,15 +209,15 @@ namespace functions { if (dimensionLength < 1) return; - auto tadPack = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(xShapeInfo, dimension, dimensionLength); + auto tadPack = sd::ConstantTadHelper::getInstance()->tadForDimensions(xShapeInfo, dimension, dimensionLength); tadOnlyShapeInfo = tadPack.primaryShapeInfo(); tadOffsets = tadPack.primaryOffsets(); } #ifdef INLINE_LOOPS - nd4j::ReductionLoops::template loopReduce(x, xShapeInfo, z, zShapeInfo, tadOnlyShapeInfo, tadOffsets, extraParams, start, stop); + sd::ReductionLoops::template loopReduce(x, xShapeInfo, z, zShapeInfo, tadOnlyShapeInfo, tadOffsets, extraParams, start, stop); #else - nd4j::ReductionSameLoops::template innerloopReduce(x, xShapeInfo, z, zShapeInfo, tadOnlyShapeInfo, tadOffsets, extraParams, start, stop); + sd::ReductionSameLoops::template innerloopReduce(x, xShapeInfo, z, zShapeInfo, tadOnlyShapeInfo, tadOffsets, extraParams, start, stop); #endif } @@ -240,7 +240,7 @@ namespace functions { auto x = reinterpret_cast(vx); auto extraParams = reinterpret_cast(vextraParams); - int maxThreads = nd4j::math::nd4j_min(64, nd4j::Environment::getInstance()->maxThreads()); + int maxThreads = sd::math::nd4j_min(64, sd::Environment::getInstance()->maxThreads()); X intermediate[64]; PRAGMA_OMP_SIMD diff --git a/libnd4j/include/loops/cpu/reduce3.hpp b/libnd4j/include/loops/cpu/reduce3.hpp index c24a3d474..961c6b1c8 100644 --- a/libnd4j/include/loops/cpu/reduce3.hpp +++ b/libnd4j/include/loops/cpu/reduce3.hpp @@ -19,11 +19,11 @@ #include -#include +#include #include #include #include -#include +#include #include using namespace simdOps; @@ -48,12 +48,12 @@ void Reduce3::execScalar(void *vx, Nd4jLong *xShapeInfo, auto xEws = shape::elementWiseStride(xShapeInfo); auto yEws = shape::elementWiseStride(yShapeInfo); - if(nd4j::ArrayOptions::arrayType(xShapeInfo) == nd4j::ArrayType::EMPTY || nd4j::ArrayOptions::arrayType(yShapeInfo) == nd4j::ArrayType::EMPTY) { - if(nd4j::ArrayOptions::arrayType(zShapeInfo) == nd4j::ArrayType::EMPTY) + if(sd::ArrayOptions::arrayType(xShapeInfo) == sd::ArrayType::EMPTY || sd::ArrayOptions::arrayType(yShapeInfo) == sd::ArrayType::EMPTY) { + if(sd::ArrayOptions::arrayType(zShapeInfo) == sd::ArrayType::EMPTY) return; const auto startingVal = OpType::startingValue(x); - for (uint i = 0; i < length; i++) + for (Nd4jLong i = 0; i < length; i++) z[i] = startingVal; return; @@ -62,10 +62,10 @@ void Reduce3::execScalar(void *vx, Nd4jLong *xShapeInfo, Z extraParamsVals[3] = {(Z) 0.0f, (Z) 0.0f, (Z) 0.0f}; uint xShapeInfoCast[MAX_RANK]; - const bool canCastX = nd4j::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast); + const bool canCastX = sd::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast); Z startingVal = OpType::startingValue(x); - int maxThreads = nd4j::math::nd4j_min(64, nd4j::Environment::getInstance()->maxThreads()); + int maxThreads = sd::math::nd4j_min(64, sd::Environment::getInstance()->maxThreads()); Z intermediate[64]; Z extraParamsLocal[3 * 64]; @@ -84,9 +84,9 @@ void Reduce3::execScalar(void *vx, Nd4jLong *xShapeInfo, } } - nd4j::LoopKind::Kind kindOfLoop = nd4j::LoopKind::deduceKindOfLoopXZ(xShapeInfo, yShapeInfo); + sd::LoopKind::Kind kindOfLoop = sd::LoopKind::deduceKindOfLoopXZ(xShapeInfo, yShapeInfo); - if (kindOfLoop == nd4j::LoopKind::EWS1) { + if (kindOfLoop == sd::LoopKind::EWS1) { auto func = PRAGMA_THREADS_FOR { for (auto i = start; i < stop; i++) { intermediate[thread_id] = OpType::update(intermediate[thread_id], OpType::op(x[i], y[i], extraParamsLocal + 3 * thread_id), extraParamsLocal + 3 * thread_id); @@ -107,7 +107,7 @@ void Reduce3::execScalar(void *vx, Nd4jLong *xShapeInfo, maxThreads = samediff::Threads::parallel_for(func, 0, length, 1, maxThreads); } else { uint yShapeInfoCast[MAX_RANK]; - const bool canCastY = nd4j::DataTypeUtils::castShapeInfo(yShapeInfo, yShapeInfoCast); + const bool canCastY = sd::DataTypeUtils::castShapeInfo(yShapeInfo, yShapeInfoCast); auto func = PRAGMA_THREADS_FOR { for (auto i = start; i < stop; i++) { @@ -162,9 +162,9 @@ void Reduce3::exec(void *vx, Nd4jLong *xShapeInfo, return; } #ifdef INLINE_LOOPS - nd4j::Reduction3Loops::template loopReduce3(x, xShapeInfo, y, yShapeInfo, z, zShapeInfo, dimension, dimensionLength, extraParams, start, stop); + sd::Reduction3Loops::template loopReduce3(x, xShapeInfo, y, yShapeInfo, z, zShapeInfo, dimension, dimensionLength, extraParams, start, stop); #else - nd4j::Reduction3Loops::template innerloopReduce3(x, xShapeInfo, y, yShapeInfo, z, zShapeInfo, dimension, dimensionLength, extraParams, start, stop); + sd::Reduction3Loops::template innerloopReduce3(x, xShapeInfo, y, yShapeInfo, z, zShapeInfo, dimension, dimensionLength, extraParams, start, stop); #endif } @@ -183,9 +183,9 @@ void Reduce3::exec(void *vx, Nd4jLong *xShapeInfo, auto z = reinterpret_cast(vz); auto extraParams = reinterpret_cast(vextraParams); #ifdef INLINE_LOOPS - nd4j::Reduction3Loops::template loopReduce3(x, xShapeInfo, y, yShapeInfo, z, zShapeInfo, dimension, dimensionLength, extraParams, start, stop); + sd::Reduction3Loops::template loopReduce3(x, xShapeInfo, y, yShapeInfo, z, zShapeInfo, dimension, dimensionLength, extraParams, start, stop); #else - nd4j::Reduction3Loops::template innerloopReduce3(x, xShapeInfo, y, yShapeInfo, z, zShapeInfo, dimension, dimensionLength, extraParams, start, stop); + sd::Reduction3Loops::template innerloopReduce3(x, xShapeInfo, y, yShapeInfo, z, zShapeInfo, dimension, dimensionLength, extraParams, start, stop); #endif } @@ -207,9 +207,9 @@ void Reduce3:: execAll(void *vx, Nd4jLong *xShapeInfo, auto extraParams = reinterpret_cast(vextraParams); #ifdef INLINE_LOOPS - nd4j::Reduction3Loops::template loopReduce3All(x, xShapeInfo, y, yShapeInfo, z, zShapeInfo, xTadShapeInfo, xOffsets, yTadShapeInfo, yOffsets, extraParams, start, stop); + sd::Reduction3Loops::template loopReduce3All(x, xShapeInfo, y, yShapeInfo, z, zShapeInfo, xTadShapeInfo, xOffsets, yTadShapeInfo, yOffsets, extraParams, start, stop); #else - nd4j::Reduction3Loops::template innerloopReduce3All(x, xShapeInfo, y, yShapeInfo, z, zShapeInfo, xTadShapeInfo, xOffsets, yTadShapeInfo, yOffsets, extraParams, start, stop); + sd::Reduction3Loops::template innerloopReduce3All(x, xShapeInfo, y, yShapeInfo, z, zShapeInfo, xTadShapeInfo, xOffsets, yTadShapeInfo, yOffsets, extraParams, start, stop); #endif } diff --git a/libnd4j/include/loops/cpu/scalar.hpp b/libnd4j/include/loops/cpu/scalar.hpp index 071913e22..d93db7c8f 100644 --- a/libnd4j/include/loops/cpu/scalar.hpp +++ b/libnd4j/include/loops/cpu/scalar.hpp @@ -19,9 +19,9 @@ // #include "../scalar.h" -#include +#include #include -#include +#include #include #include "../legacy_ops.h" @@ -58,22 +58,22 @@ void ScalarTransform::transform(void *vx, Nd4jLong *xShapeInfo, const int tadLength = shape::tadLength(xShapeInfo, dimension, dimensionLength); const int numTads = shape::length(xShapeInfo) / tadLength; - nd4j::LoopKind::Kind kindOfLoop = nd4j::LoopKind::deduceKindOfLoopXZ(xTadShapeInfo, zTadShapeInfo); + sd::LoopKind::Kind kindOfLoop = sd::LoopKind::deduceKindOfLoopXZ(xTadShapeInfo, zTadShapeInfo); - if (kindOfLoop != nd4j::LoopKind::EWS1 && kindOfLoop != nd4j::LoopKind::EWSNONZERO) { + if (kindOfLoop != sd::LoopKind::EWS1 && kindOfLoop != sd::LoopKind::EWSNONZERO) { printf("ScalarTransform::transform: super-bad loop visited. Shouldn't ever happen\n"); return; } - int num_threads = nd4j::math::nd4j_min(numTads, nd4j::Environment::getInstance()->maxThreads()); + int num_threads = sd::math::nd4j_min(numTads, sd::Environment::getInstance()->maxThreads()); - if (kindOfLoop == nd4j::LoopKind::EWS1) { + if (kindOfLoop == sd::LoopKind::EWS1) { for (auto r = start; r < stop; r++) { auto oZ = z + zTadOffsets[r]; auto oX = x + xTadOffsets[r]; PRAGMA_OMP_SIMD - for (unsigned int f = 0; f < tadLength; f++) + for (int f = 0; f < tadLength; f++) oZ[f] = OpType::op(oX[f], scalars[r], extraParams); }; } @@ -83,7 +83,7 @@ void ScalarTransform::transform(void *vx, Nd4jLong *xShapeInfo, auto oX = x + xTadOffsets[r]; PRAGMA_OMP_SIMD - for (unsigned int f = 0; f < tadLength; f++) + for (int f = 0; f < tadLength; f++) oZ[f * zTadEws] = OpType::op(oX[f * xTadEws], scalars[r], extraParams); }; } @@ -147,15 +147,15 @@ void ScalarTransform::transform(void *vx, Nd4jLong *xShapeInfo, const auto xEws = shape::elementWiseStride(xShapeInfo); const auto zEws = shape::elementWiseStride(zShapeInfo); - nd4j::LoopKind::Kind kindOfLoop = nd4j::LoopKind::deduceKindOfLoopXZ(xShapeInfo, zShapeInfo); + sd::LoopKind::Kind kindOfLoop = sd::LoopKind::deduceKindOfLoopXZ(xShapeInfo, zShapeInfo); - if (kindOfLoop == nd4j::LoopKind::EWS1 || kindOfLoop == nd4j::LoopKind::EWSNONZERO) { + if (kindOfLoop == sd::LoopKind::EWS1 || kindOfLoop == sd::LoopKind::EWSNONZERO) { transform(x, xEws, z, zEws, vscalar, extraParams, len, start, stop); } else { uint xShapeInfoCast[MAX_RANK]; - const bool canCastX = nd4j::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast); + const bool canCastX = sd::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast); if(shape::haveSameShapeAndStrides(xShapeInfo, zShapeInfo)) { PRAGMA_OMP_SIMD @@ -166,7 +166,7 @@ void ScalarTransform::transform(void *vx, Nd4jLong *xShapeInfo, } else { uint zShapeInfoCast[MAX_RANK]; - const bool canCastZ = nd4j::DataTypeUtils::castShapeInfo(zShapeInfo, zShapeInfoCast); + const bool canCastZ = sd::DataTypeUtils::castShapeInfo(zShapeInfo, zShapeInfoCast); PRAGMA_OMP_SIMD for (auto i = start; i < stop; i++) { diff --git a/libnd4j/include/loops/cpu/scalar_bool.cpp b/libnd4j/include/loops/cpu/scalar_bool.cpp index d6dce445b..c6f437ba8 100644 --- a/libnd4j/include/loops/cpu/scalar_bool.cpp +++ b/libnd4j/include/loops/cpu/scalar_bool.cpp @@ -19,9 +19,9 @@ // #include "../scalar_bool.h" -#include +#include #include -#include +#include #include #include "../legacy_ops.h" @@ -59,22 +59,22 @@ namespace functions { const int tadLength = shape::tadLength(xShapeInfo, dimension, dimensionLength); const int numTads = shape::length(xShapeInfo) / tadLength; - nd4j::LoopKind::Kind kindOfLoop = nd4j::LoopKind::deduceKindOfLoopXZ(xTadShapeInfo, zTadShapeInfo); + sd::LoopKind::Kind kindOfLoop = sd::LoopKind::deduceKindOfLoopXZ(xTadShapeInfo, zTadShapeInfo); - if (kindOfLoop != nd4j::LoopKind::EWS1 && kindOfLoop != nd4j::LoopKind::EWSNONZERO) { + if (kindOfLoop != sd::LoopKind::EWS1 && kindOfLoop != sd::LoopKind::EWSNONZERO) { printf("ScalarBoolTransform::transform: super-bad loop visited. Shouldn't ever happen\n"); return; } - int num_threads = nd4j::math::nd4j_min(numTads, nd4j::Environment::getInstance()->maxThreads()); + int num_threads = sd::math::nd4j_min(numTads, sd::Environment::getInstance()->maxThreads()); - if (kindOfLoop == nd4j::LoopKind::EWS1) { + if (kindOfLoop == sd::LoopKind::EWS1) { for (auto r = start; r < stop; r++) { auto oZ = z + zTadOffsets[r]; auto oX = x + xTadOffsets[r]; PRAGMA_OMP_SIMD - for (unsigned int f = 0; f < tadLength; f++) + for (int f = 0; f < tadLength; f++) oZ[f] = OpType::op(oX[f], scalars[r], extraParams); }; } @@ -84,7 +84,7 @@ namespace functions { auto oX = x + xTadOffsets[r]; PRAGMA_OMP_SIMD - for (unsigned int f = 0; f < tadLength; f++) + for (int f = 0; f < tadLength; f++) oZ[f * zTadEws] = OpType::op(oX[f * xTadEws], scalars[r], extraParams); }; } @@ -152,15 +152,15 @@ namespace functions { auto zEws = shape::elementWiseStride(zShapeInfo); auto len = shape::length(xShapeInfo); - nd4j::LoopKind::Kind kindOfLoop = nd4j::LoopKind::deduceKindOfLoopXZ(xShapeInfo, zShapeInfo); + sd::LoopKind::Kind kindOfLoop = sd::LoopKind::deduceKindOfLoopXZ(xShapeInfo, zShapeInfo); - if (kindOfLoop == nd4j::LoopKind::EWS1 || kindOfLoop == nd4j::LoopKind::EWSNONZERO) { + if (kindOfLoop == sd::LoopKind::EWS1 || kindOfLoop == sd::LoopKind::EWSNONZERO) { transform(x, xEws, z, zEws, vscalar, extraParams, len, start, stop); return; } uint xShapeInfoCast[MAX_RANK]; - const bool canCastX = nd4j::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast); + const bool canCastX = sd::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast); if(shape::haveSameShapeAndStrides(xShapeInfo, zShapeInfo)) { PRAGMA_OMP_SIMD @@ -171,7 +171,7 @@ namespace functions { } else { uint zShapeInfoCast[MAX_RANK]; - const bool canCastZ = nd4j::DataTypeUtils::castShapeInfo(zShapeInfo, zShapeInfoCast); + const bool canCastZ = sd::DataTypeUtils::castShapeInfo(zShapeInfo, zShapeInfoCast); PRAGMA_OMP_SIMD for (auto i = start; i < stop; i++) { diff --git a/libnd4j/include/loops/cpu/scalar_int.cpp b/libnd4j/include/loops/cpu/scalar_int.cpp index 5f2308418..ed85e28ef 100644 --- a/libnd4j/include/loops/cpu/scalar_int.cpp +++ b/libnd4j/include/loops/cpu/scalar_int.cpp @@ -19,9 +19,9 @@ // #include "../scalar_int.h" -#include +#include #include -#include +#include #include #include "../legacy_ops.h" @@ -59,22 +59,22 @@ namespace functions { const int tadLength = shape::tadLength(xShapeInfo, dimension, dimensionLength); const int numTads = shape::length(xShapeInfo) / tadLength; - nd4j::LoopKind::Kind kindOfLoop = nd4j::LoopKind::deduceKindOfLoopXZ(xTadShapeInfo, zTadShapeInfo); + sd::LoopKind::Kind kindOfLoop = sd::LoopKind::deduceKindOfLoopXZ(xTadShapeInfo, zTadShapeInfo); - if (kindOfLoop != nd4j::LoopKind::EWS1 && kindOfLoop != nd4j::LoopKind::EWSNONZERO) { + if (kindOfLoop != sd::LoopKind::EWS1 && kindOfLoop != sd::LoopKind::EWSNONZERO) { printf("ScalarIntTransform::transform: super-bad loop visited. Shouldn't ever happen\n"); return; } - int num_threads = nd4j::math::nd4j_min(numTads, nd4j::Environment::getInstance()->maxThreads()); + int num_threads = sd::math::nd4j_min(numTads, sd::Environment::getInstance()->maxThreads()); - if (kindOfLoop == nd4j::LoopKind::EWS1) { + if (kindOfLoop == sd::LoopKind::EWS1) { for (auto r = start; r < stop; r++) { auto oZ = z + zTadOffsets[r]; auto oX = x + xTadOffsets[r]; PRAGMA_OMP_SIMD - for (unsigned int f = 0; f < tadLength; f++) + for (int f = 0; f < tadLength; f++) oZ[f] = OpType::op(oX[f], scalars[r], extraParams); }; } @@ -84,7 +84,7 @@ namespace functions { auto oX = x + xTadOffsets[r]; PRAGMA_OMP_SIMD - for (unsigned int f = 0; f < tadLength; f++) + for (int f = 0; f < tadLength; f++) oZ[f * zTadEws] = OpType::op(oX[f * xTadEws], scalars[r], extraParams); }; } @@ -154,15 +154,15 @@ namespace functions { auto zEws = shape::elementWiseStride(zShapeInfo); auto len = shape::length(xShapeInfo); - nd4j::LoopKind::Kind kindOfLoop = nd4j::LoopKind::deduceKindOfLoopXZ(xShapeInfo, zShapeInfo); + sd::LoopKind::Kind kindOfLoop = sd::LoopKind::deduceKindOfLoopXZ(xShapeInfo, zShapeInfo); - if (kindOfLoop == nd4j::LoopKind::EWS1 || kindOfLoop == nd4j::LoopKind::EWSNONZERO) { + if (kindOfLoop == sd::LoopKind::EWS1 || kindOfLoop == sd::LoopKind::EWSNONZERO) { transform(x, xEws, z, zEws, vscalar, extraParams, len, start, stop); return; } uint xShapeInfoCast[MAX_RANK]; - const bool canCastX = nd4j::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast); + const bool canCastX = sd::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast); if(shape::haveSameShapeAndStrides(xShapeInfo, zShapeInfo)) { PRAGMA_OMP_SIMD @@ -173,7 +173,7 @@ namespace functions { } else { uint zShapeInfoCast[MAX_RANK]; - const bool canCastZ = nd4j::DataTypeUtils::castShapeInfo(zShapeInfo, zShapeInfoCast); + const bool canCastZ = sd::DataTypeUtils::castShapeInfo(zShapeInfo, zShapeInfoCast); PRAGMA_OMP_SIMD for (auto i = start; i < stop; i++) { @@ -201,15 +201,14 @@ namespace functions { auto scalar = reinterpret_cast(vscalar)[0]; auto extraParams = reinterpret_cast(vextraParams); - if (xEws == 1 && zEws == 1) { - PRAGMA_OMP_SIMD - for (auto i = start; i < stop; i++) - z[i] = OpType::op(x[i], scalar, extraParams); - } - else { - PRAGMA_OMP_SIMD - for (auto i = start; i < stop; i++) - z[i * zEws] = OpType::op(x[i * xEws], scalar, extraParams); + if (scalar < (sizeof(X) * 8)) { + if (xEws == 1 && zEws == 1) { + for (auto i = start; i < stop; i++) + z[i] = OpType::op(x[i], scalar, extraParams); + } else { + for (auto i = start; i < stop; i++) + z[i * zEws] = OpType::op(x[i * xEws], scalar, extraParams); + } } } diff --git a/libnd4j/include/loops/cpu/summarystatsreduce.cpp b/libnd4j/include/loops/cpu/summarystatsreduce.cpp index 2e36b8085..f6b44b75c 100644 --- a/libnd4j/include/loops/cpu/summarystatsreduce.cpp +++ b/libnd4j/include/loops/cpu/summarystatsreduce.cpp @@ -19,7 +19,7 @@ // #include -#include +#include #include #include #include @@ -89,9 +89,9 @@ namespace functions { auto length = shape::length(xShapeInfo); uint xShapeInfoCast[MAX_RANK]; - const bool canCast = nd4j::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast); + const bool canCast = sd::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast); - for (uint64_t i = 0; i < length; i++) { + for (Nd4jLong i = 0; i < length; i++) { auto xOffset = shape::indexOffset(i, xShapeInfo, xShapeInfoCast, canCast); SummaryStatsData curr; @@ -116,15 +116,15 @@ namespace functions { auto x = reinterpret_cast(vx); auto z = reinterpret_cast(vz); auto extraParams = reinterpret_cast(vextraParams); - int resultLength = shape::length(zShapeInfo); + auto resultLength = shape::length(zShapeInfo); - if(nd4j::ArrayOptions::arrayType(xShapeInfo) == nd4j::ArrayType::EMPTY) { - if(nd4j::ArrayOptions::arrayType(zShapeInfo) == nd4j::ArrayType::EMPTY) + if(sd::ArrayOptions::arrayType(xShapeInfo) == sd::ArrayType::EMPTY) { + if(sd::ArrayOptions::arrayType(zShapeInfo) == sd::ArrayType::EMPTY) return; SummaryStatsData comp; comp.initWithValue(x[0]); - for (uint i = 0; i < resultLength; i++) + for (Nd4jLong i = 0; i < resultLength; i++) z[i] = OpType::getValue(biasCorrected, comp); return; } @@ -138,7 +138,7 @@ namespace functions { if (dimensionLength < 1) return; - auto tadPack = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(xShapeInfo, dimension, dimensionLength); + auto tadPack = sd::ConstantTadHelper::getInstance()->tadForDimensions(xShapeInfo, dimension, dimensionLength); //pre squeezed: this is for keeping the pointer to the original //shape information for tad offset @@ -155,7 +155,7 @@ namespace functions { auto tadOrder = shape::order(tadPack.primaryShapeInfo()); uint tadShapeShapeInfoCast[MAX_RANK]; - const bool canCast = tadEWS == 1 && tadOrder == 'c' ? false : nd4j::DataTypeUtils::castShapeInfo(tadShapeShapeInfo, tadShapeShapeInfoCast); + const bool canCast = tadEWS == 1 && tadOrder == 'c' ? false : sd::DataTypeUtils::castShapeInfo(tadShapeShapeInfo, tadShapeShapeInfoCast); auto func = PRAGMA_THREADS_FOR { for (auto r = start; r < stop; r++) { @@ -166,14 +166,14 @@ namespace functions { comp.initWithValue(tx[0]); if (tadEWS == 1 && tadOrder == 'c') { - for (int i = 1; i < tadLength; i++) { + for (Nd4jLong i = 1; i < tadLength; i++) { SummaryStatsData indexVal2; indexVal2.initWithValue(tx[i]); comp = update(comp, OpType::op(indexVal2, extraParams), extraParams); } } else { - for (int i = 1; i < tadLength; i++) { + for (Nd4jLong i = 1; i < tadLength; i++) { auto xOffset = shape::indexOffset(i, tadShapeShapeInfo, tadShapeShapeInfoCast, canCast); SummaryStatsData indexVal2; diff --git a/libnd4j/include/loops/cpu/transform/transform_any.cpp b/libnd4j/include/loops/cpu/transform/transform_any.cpp index 5b3c4a0f8..3fc9af1b3 100644 --- a/libnd4j/include/loops/cpu/transform/transform_any.cpp +++ b/libnd4j/include/loops/cpu/transform/transform_any.cpp @@ -18,8 +18,8 @@ // @author raver119@gmail.com // -#include -#include +#include +#include #include #include #include @@ -52,7 +52,7 @@ void _CUDA_H TransformAny::exec(void *vx, Nd4jLong *xShapeInfo, auto z = reinterpret_cast(vz); auto extraParams = reinterpret_cast(vextraParams); - nd4j::TransformLoops::template loopTransform(x, xShapeInfo, z, zShapeInfo, extraParams, threadId, numThreads); + sd::TransformLoops::template loopTransform(x, xShapeInfo, z, zShapeInfo, extraParams, threadId, numThreads); } diff --git a/libnd4j/include/loops/cpu/transform/transform_bool.cpp b/libnd4j/include/loops/cpu/transform/transform_bool.cpp index fdfde93f5..7302ef970 100644 --- a/libnd4j/include/loops/cpu/transform/transform_bool.cpp +++ b/libnd4j/include/loops/cpu/transform/transform_bool.cpp @@ -18,8 +18,8 @@ // @author raver119@gmail.com // -#include -#include +#include +#include #include #include #include @@ -54,7 +54,7 @@ namespace functions { auto z = reinterpret_cast(vz); auto extraParams = reinterpret_cast(vextraParams); - nd4j::TransformLoops::template loopTransform(x, xShapeInfo, z, zShapeInfo, extraParams, threadId, numThreads); + sd::TransformLoops::template loopTransform(x, xShapeInfo, z, zShapeInfo, extraParams, threadId, numThreads); } BUILD_DOUBLE_TEMPLATE(template class ND4J_EXPORT TransformBool, , LIBND4J_TYPES, BOOL_TYPES); diff --git a/libnd4j/include/loops/cpu/transform/transform_float.cpp b/libnd4j/include/loops/cpu/transform/transform_float.cpp index 8e164a90f..833b263f1 100644 --- a/libnd4j/include/loops/cpu/transform/transform_float.cpp +++ b/libnd4j/include/loops/cpu/transform/transform_float.cpp @@ -18,8 +18,8 @@ // @author raver119@gmail.com // -#include -#include +#include +#include #include #include #include @@ -53,7 +53,7 @@ namespace functions { auto z = reinterpret_cast(vz); auto extraParams = reinterpret_cast(vextraParams); - nd4j::TransformLoops::template loopTransform(x, xShapeInfo, z, zShapeInfo, extraParams, threadId, numThreads); + sd::TransformLoops::template loopTransform(x, xShapeInfo, z, zShapeInfo, extraParams, threadId, numThreads); } BUILD_DOUBLE_TEMPLATE(template class ND4J_EXPORT TransformFloat, , LIBND4J_TYPES, FLOAT_TYPES); diff --git a/libnd4j/include/loops/cpu/transform/transform_same.cpp b/libnd4j/include/loops/cpu/transform/transform_same.cpp index 67f7762f0..bc9d2e525 100644 --- a/libnd4j/include/loops/cpu/transform/transform_same.cpp +++ b/libnd4j/include/loops/cpu/transform/transform_same.cpp @@ -18,8 +18,8 @@ // @author raver119@gmail.com // -#include -#include +#include +#include #include #include #include @@ -52,7 +52,7 @@ namespace functions { auto extraParams = reinterpret_cast(vextraParams); - nd4j::TransformLoops::template loopTransform(x, xShapeInfo, z, zShapeInfo, extraParams, threadId, numThreads); + sd::TransformLoops::template loopTransform(x, xShapeInfo, z, zShapeInfo, extraParams, threadId, numThreads); } BUILD_SINGLE_TEMPLATE(template class ND4J_EXPORT TransformSame, , LIBND4J_TYPES); diff --git a/libnd4j/include/loops/cpu/transform/transform_strict.cpp b/libnd4j/include/loops/cpu/transform/transform_strict.cpp index 29964e3e0..2ef3b808e 100644 --- a/libnd4j/include/loops/cpu/transform/transform_strict.cpp +++ b/libnd4j/include/loops/cpu/transform/transform_strict.cpp @@ -18,8 +18,8 @@ // @author raver119@gmail.com // -#include -#include +#include +#include #include #include #include @@ -53,7 +53,7 @@ namespace functions { auto z = reinterpret_cast(vz); auto extraParams = reinterpret_cast(vextraParams); - nd4j::TransformLoops::template loopTransform(x, xShapeInfo, z, zShapeInfo, extraParams, threadId, numThreads); + sd::TransformLoops::template loopTransform(x, xShapeInfo, z, zShapeInfo, extraParams, threadId, numThreads); } BUILD_SINGLE_TEMPLATE(template class ND4J_EXPORT TransformStrict, , FLOAT_TYPES); diff --git a/libnd4j/include/loops/cuda/TrueBroadcastHelper.cu b/libnd4j/include/loops/cuda/TrueBroadcastHelper.cu deleted file mode 100644 index 12c3eb0c5..000000000 --- a/libnd4j/include/loops/cuda/TrueBroadcastHelper.cu +++ /dev/null @@ -1,312 +0,0 @@ -/******************************************************************************* - * Copyright (c) 2015-2018 Skymind, Inc. - * - * This program and the accompanying materials are made available under the - * terms of the Apache License, Version 2.0 which is available at - * https://www.apache.org/licenses/LICENSE-2.0. - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the - * License for the specific language governing permissions and limitations - * under the License. - * - * SPDX-License-Identifier: Apache-2.0 - ******************************************************************************/ - -// -// @author Yurii Shyrma (iuriish@yahoo.com) -// - -// #include -#include -#include -#include -#include -#include -#include -// #include -// #include - -using namespace simdOps; - -namespace nd4j { -namespace helpers { - -//////////////////////////////////////////////////////////////////////// -template -__global__ static void trueBroadcastCuda(const void* vx, const Nd4jLong* xShapeInfo, const void* vy, const Nd4jLong* yShapeInfo, void* vz, const Nd4jLong* zShapeInfo) { - - const auto x = reinterpret_cast(vx); - const auto y = reinterpret_cast(vy); - auto z = reinterpret_cast(vz); - - __shared__ int xRank, yRank, zRank; - __shared__ Nd4jLong zLen, totalThreads, *sharedMem; // xLen == zLen, except when xRank = 1, in this case zLen = 2*xLen - - if (threadIdx.x == 0) { - extern __shared__ unsigned char shmem[]; - sharedMem = reinterpret_cast(shmem); - - xRank = shape::rank(xShapeInfo); - yRank = shape::rank(yShapeInfo); - zRank = shape::rank(zShapeInfo); - - zLen = shape::length(zShapeInfo); - totalThreads = gridDim.x * blockDim.x; - } - __syncthreads(); - - auto xCoords = sharedMem + threadIdx.x * (xRank + yRank + zRank); - auto yCoords = xCoords + xRank; - auto zCoords = yCoords + yRank; - - const auto tid = blockIdx.x * blockDim.x + threadIdx.x; - - for (Nd4jLong i = tid; i < zLen; i += totalThreads) { - - shape::index2coords(i, zShapeInfo, zCoords); - - for(int ix = xRank - 1, iy = yRank - 1, iz = zRank - 1; iz >= 0; --iz) { - - if(ix >= 0) - if(xShapeInfo[ix + 1] == zShapeInfo[iz + 1]) - xCoords[ix--] = zCoords[iz]; - else - xCoords[ix--] = 0; - - if(iy >= 0) - if(yShapeInfo[iy + 1] == zShapeInfo[iz + 1]) - yCoords[iy--] = zCoords[iz]; - else - yCoords[iy--] = 0; - } - - const auto xOffset = shape::getOffset(xShapeInfo, xCoords); - const auto zOffset = shape::getOffset(zShapeInfo, zCoords); - const auto yOffset = shape::getOffset(yShapeInfo, yCoords); - - z[zOffset] = OpType::op(x[xOffset], y[yOffset], nullptr); - } -} - -//////////////////////////////////////////////////////////////////////// -template -template -void TrueBroadcastHelper::execLauncher(dim3 launchDims, cudaStream_t *stream, const void *vx, const Nd4jLong *xShapeInfo, const void *vy, const Nd4jLong *yShapeInfo, void *vz, const Nd4jLong *zShapeInfo) { - - trueBroadcastCuda<<>>(vx, xShapeInfo, vy, yShapeInfo, vz, zShapeInfo); -} - -////////////////////////////////////////////////////////////////////////// -template -void TrueBroadcastHelper::exec(const nd4j::broadcast::Ops opNum, const NDArray& xArr, const NDArray& yArr, NDArray& zArr) { - - dim3 launchDims; - - launchDims.y = MAX_NUM_THREADS / 8; // threadsPerBlock - launchDims.x = (zArr.lengthOf() + launchDims.y - 1) / launchDims.y; // blocksPerGrid - launchDims.z = sizeof(Nd4jLong) * launchDims.y * (xArr.rankOf() + yArr.rankOf() + zArr.rankOf()) + 128; // sharedMe - - PointersManager manager(xArr.getContext(), "TrueBroadcastHelper::exec"); - - NDArray::prepareSpecialUse({&zArr}, {&xArr, &yArr}); - - DISPATCH_BY_OPNUM_TTT(execLauncher, PARAMS(launchDims, xArr.getContext()->getCudaStream(), xArr.getSpecialBuffer(), xArr.getSpecialShapeInfo(), yArr.getSpecialBuffer(), yArr.getSpecialShapeInfo(), zArr.specialBuffer(), zArr.specialShapeInfo()), OPS_A(BROADCAST_OPS)); - - NDArray::registerSpecialUse({&zArr}, {&xArr, &yArr}); - - manager.synchronize(); -} - -//////////////////////////////////////////////////////////////////////// -template -__global__ static void trueBroadcastBoolCuda(const void* vx, const Nd4jLong* xShapeInfo, const void* vy, const Nd4jLong* yShapeInfo, void* vz, const Nd4jLong* zShapeInfo) { - - const auto x = reinterpret_cast(vx); - const auto y = reinterpret_cast(vy); - auto z = reinterpret_cast(vz); - - __shared__ int xRank, yRank, zRank; - __shared__ Nd4jLong zLen, totalThreads, *sharedMem; // xLen == zLen, except when xRank = 1, in this case zLen = 2*xLen - - if (threadIdx.x == 0) { - extern __shared__ unsigned char shmem[]; - sharedMem = reinterpret_cast(shmem); - - xRank = shape::rank(xShapeInfo); - yRank = shape::rank(yShapeInfo); - zRank = shape::rank(zShapeInfo); - - zLen = shape::length(zShapeInfo); - totalThreads = gridDim.x * blockDim.x; - } - __syncthreads(); - - auto xCoords = sharedMem + threadIdx.x * (xRank + yRank + zRank); - auto yCoords = xCoords + xRank; - auto zCoords = yCoords + yRank; - - const auto tid = blockIdx.x * blockDim.x + threadIdx.x; - - for (Nd4jLong i = tid; i < zLen; i += totalThreads) { - - shape::index2coords(i, zShapeInfo, zCoords); - - for(int ix = xRank - 1, iy = yRank - 1, iz = zRank - 1; iz >= 0; --iz) { - - if(ix >= 0) - if(xShapeInfo[ix + 1] == zShapeInfo[iz + 1]) - xCoords[ix--] = zCoords[iz]; - else - xCoords[ix--] = 0; - - if(iy >= 0) - if(yShapeInfo[iy + 1] == zShapeInfo[iz + 1]) - yCoords[iy--] = zCoords[iz]; - else - yCoords[iy--] = 0; - } - - const auto xOffset = shape::getOffset(xShapeInfo, xCoords); - const auto zOffset = shape::getOffset(zShapeInfo, zCoords); - const auto yOffset = shape::getOffset(yShapeInfo, yCoords); - - z[zOffset] = OpType::op(x[xOffset], y[yOffset], nullptr); - } -} - -//////////////////////////////////////////////////////////////////////// -template -template -void TrueBroadcastBoolHelper::execLauncher(dim3 launchDims, cudaStream_t *stream, const void *vx, const Nd4jLong *xShapeInfo, const void *vy, const Nd4jLong *yShapeInfo, void *vz, const Nd4jLong *zShapeInfo) { - - trueBroadcastBoolCuda<<>>(vx, xShapeInfo, vy, yShapeInfo, vz, zShapeInfo); -} - -////////////////////////////////////////////////////////////////////////// -template -void TrueBroadcastBoolHelper::exec(const nd4j::broadcast::BoolOps opNum, const NDArray& xArr, const NDArray& yArr, NDArray& zArr) { - - dim3 launchDims; - - launchDims.y = MAX_NUM_THREADS / 8; // threadsPerBlock - launchDims.x = (zArr.lengthOf() + launchDims.y - 1) / launchDims.y; // blocksPerGrid - launchDims.z = sizeof(Nd4jLong) * launchDims.y * (xArr.rankOf() + yArr.rankOf() + zArr.rankOf()) + 128; // sharedMe - - PointersManager manager(xArr.getContext(), "TrueBroadcastBoolHelper::exec"); - - NDArray::prepareSpecialUse({&zArr}, {&xArr, &yArr}); - - DISPATCH_BY_OPNUM_TT(execLauncher, PARAMS(launchDims, xArr.getContext()->getCudaStream(), xArr.getSpecialBuffer(), xArr.getSpecialShapeInfo(), yArr.getSpecialBuffer(), yArr.getSpecialShapeInfo(), zArr.specialBuffer(), zArr.specialShapeInfo()), OPS_A(BROADCAST_BOOL_OPS)); - - NDArray::registerSpecialUse({&zArr}, {&xArr, &yArr}); - - manager.synchronize(); -} - -//////////////////////////////////////////////////////////////////////// -template -__global__ static void trueBroadcastIntCuda(const void* vx, const Nd4jLong* xShapeInfo, const void* vy, const Nd4jLong* yShapeInfo, void* vz, const Nd4jLong* zShapeInfo) { - - const auto x = reinterpret_cast(vx); - const auto y = reinterpret_cast(vy); - auto z = reinterpret_cast(vz); - - __shared__ int xRank, yRank, zRank; - __shared__ Nd4jLong zLen, totalThreads, *sharedMem; // xLen == zLen, except when xRank = 1, in this case zLen = 2*xLen - - if (threadIdx.x == 0) { - extern __shared__ unsigned char shmem[]; - sharedMem = reinterpret_cast(shmem); - - xRank = shape::rank(xShapeInfo); - yRank = shape::rank(yShapeInfo); - zRank = shape::rank(zShapeInfo); - - zLen = shape::length(zShapeInfo); - totalThreads = gridDim.x * blockDim.x; - } - __syncthreads(); - - auto xCoords = sharedMem + threadIdx.x * (xRank + yRank + zRank); - auto yCoords = xCoords + xRank; - auto zCoords = yCoords + yRank; - - const auto tid = blockIdx.x * blockDim.x + threadIdx.x; - - for (Nd4jLong i = tid; i < zLen; i += totalThreads) { - - shape::index2coords(i, zShapeInfo, zCoords); - - for(int ix = xRank - 1, iy = yRank - 1, iz = zRank - 1; iz >= 0; --iz) { - - if(ix >= 0) - if(xShapeInfo[ix + 1] == zShapeInfo[iz + 1]) - xCoords[ix--] = zCoords[iz]; - else - xCoords[ix--] = 0; - - if(iy >= 0) - if(yShapeInfo[iy + 1] == zShapeInfo[iz + 1]) - yCoords[iy--] = zCoords[iz]; - else - yCoords[iy--] = 0; - } - - const auto xOffset = shape::getOffset(xShapeInfo, xCoords); - const auto zOffset = shape::getOffset(zShapeInfo, zCoords); - const auto yOffset = shape::getOffset(yShapeInfo, yCoords); - - z[zOffset] = OpType::op(x[xOffset], y[yOffset]); - } -} - -//////////////////////////////////////////////////////////////////////// -template -template -void TrueBroadcastIntHelper::execLauncher(dim3 launchDims, cudaStream_t *stream, const void *vx, const Nd4jLong *xShapeInfo, const void *vy, const Nd4jLong *yShapeInfo, void *vz, const Nd4jLong *zShapeInfo) { - - trueBroadcastIntCuda<<>>(vx, xShapeInfo, vy, yShapeInfo, vz, zShapeInfo); -} - -////////////////////////////////////////////////////////////////////////// -template -void TrueBroadcastIntHelper::exec(const nd4j::broadcast::IntOps opNum, const NDArray& xArr, const NDArray& yArr, NDArray& zArr) { - - dim3 launchDims; - - launchDims.y = MAX_NUM_THREADS / 8; // threadsPerBlock - launchDims.x = (zArr.lengthOf() + launchDims.y - 1) / launchDims.y; // blocksPerGrid - launchDims.z = sizeof(Nd4jLong) * launchDims.y * (xArr.rankOf() + yArr.rankOf() + zArr.rankOf()) + 128; // sharedMe - - PointersManager manager(xArr.getContext(), "TrueBroadcastIntHelper::exec"); - - NDArray::prepareSpecialUse({&zArr}, {&xArr, &yArr}); - - DISPATCH_BY_OPNUM_T(execLauncher, PARAMS(launchDims, xArr.getContext()->getCudaStream(), xArr.getSpecialBuffer(), xArr.getSpecialShapeInfo(), yArr.getSpecialBuffer(), yArr.getSpecialShapeInfo(), zArr.specialBuffer(), zArr.specialShapeInfo()), OPS_A(BROADCAST_INT_OPS)); - - NDArray::registerSpecialUse({&zArr}, {&xArr, &yArr}); - - manager.synchronize(); -} - - - -BUILD_PAIRWISE_TEMPLATE(template class ND4J_EXPORT TrueBroadcastHelper, , PAIRWISE_TYPES_0); -BUILD_PAIRWISE_TEMPLATE(template class ND4J_EXPORT TrueBroadcastHelper, , PAIRWISE_TYPES_1); -BUILD_PAIRWISE_TEMPLATE(template class ND4J_EXPORT TrueBroadcastHelper, , PAIRWISE_TYPES_2); -BUILD_PAIRWISE_TEMPLATE(template class ND4J_EXPORT TrueBroadcastHelper, , PAIRWISE_TYPES_3); -BUILD_PAIRWISE_TEMPLATE(template class ND4J_EXPORT TrueBroadcastHelper, , PAIRWISE_TYPES_4); -BUILD_PAIRWISE_TEMPLATE(template class ND4J_EXPORT TrueBroadcastHelper, , PAIRWISE_TYPES_5); -BUILD_PAIRWISE_TEMPLATE(template class ND4J_EXPORT TrueBroadcastHelper, , PAIRWISE_TYPES_6); -BUILD_PAIRWISE_TEMPLATE(template class ND4J_EXPORT TrueBroadcastHelper, , PAIRWISE_TYPES_7); -BUILD_PAIRWISE_TEMPLATE(template class ND4J_EXPORT TrueBroadcastHelper, , PAIRWISE_TYPES_8); -BUILD_PAIRWISE_TEMPLATE(template class ND4J_EXPORT TrueBroadcastHelper, , PAIRWISE_TYPES_9); - -BUILD_DOUBLE_TEMPLATE(template class ND4J_EXPORT TrueBroadcastBoolHelper, , LIBND4J_TYPES, BOOL_TYPES); - -BUILD_SINGLE_TEMPLATE(template class ND4J_EXPORT TrueBroadcastIntHelper, , INTEGER_TYPES); - -} -} \ No newline at end of file diff --git a/libnd4j/include/loops/cuda/broadcasting.chpp b/libnd4j/include/loops/cuda/broadcasting.chpp index 086e216e6..848522a35 100644 --- a/libnd4j/include/loops/cuda/broadcasting.chpp +++ b/libnd4j/include/loops/cuda/broadcasting.chpp @@ -18,17 +18,17 @@ // @author raver119@gmail.com // -#include +#include #include #include #include -#include +#include #include #include #include #include -#include -#include +#include +#include using namespace simdOps; @@ -46,6 +46,15 @@ static __global__ void broadcastSimple( functions::broadcast::Broadcast::template transformCuda(x,xShapeInfo,y,yShapeInfo,z,zShapeInfo,dimension,dimensionLength,tadOnlyShapeInfo,tadOffsets,tadOnlyShapeInfoZ,tadOffsetsZ); } +template +static __global__ void broadcastSimple(const void *x, const Nd4jLong *xShapeInfo, + const void *y, const Nd4jLong *yShapeInfo, + void *z, const Nd4jLong *zShapeInfo ) { + + functions::broadcast::Broadcast::template transformCuda(x, xShapeInfo, y, yShapeInfo, z, zShapeInfo); +} + + template static __global__ void broadcastInverseSimple( void *x, @@ -64,11 +73,11 @@ static __global__ void broadcastInverseSimple( namespace functions { namespace broadcast { - static Nd4jLong __device__ __noinline__ _getIndexOffset(Nd4jLong index, Nd4jLong *shapeInfo) { + static Nd4jLong __device__ __noinline__ getIndexOffset(Nd4jLong index, Nd4jLong *shapeInfo) { return shape::getIndexOffset(index, shapeInfo); } - static Nd4jLong __device__ __noinline__ _length(Nd4jLong *shapeInfo) { + static Nd4jLong __device__ __noinline__ length(Nd4jLong *shapeInfo) { return shape::length(shapeInfo); } @@ -78,6 +87,12 @@ namespace functions { broadcastSimple<<>>(x, xShapeInfo, y, yShapeInfo, z, zShapeInfo, dimension, dimensionLength, tadOnlyShapeInfo, tadOffsets, tadOnlyShapeInfoZ, tadOffsetsZ); } + template + template + __host__ void Broadcast::intermediateBroadcast(dim3 launchDims, cudaStream_t *stream, const void *x, const Nd4jLong *xShapeInfo, const void *y, const Nd4jLong *yShapeInfo, void *z, const Nd4jLong *zShapeInfo) { + broadcastSimple<<>>(x, xShapeInfo, y, yShapeInfo, z, zShapeInfo); + } + template __host__ void Broadcast::execBroadcast(dim3 launchDims, cudaStream_t *stream, int opNum, void *x, Nd4jLong *xShapeInfo, void *y, Nd4jLong *yShapeInfo, void *z, Nd4jLong *zShapeInfo, int *dimension, int dimensionLength, Nd4jLong *tadOnlyShapeInfo, Nd4jLong *tadOffsets, Nd4jLong *tadOnlyShapeInfoZ, Nd4jLong *tadOffsetsZ) { DISPATCH_BY_OPNUM_TTT(intermediateBroadcast, PARAMS(launchDims, stream, x, xShapeInfo, y, yShapeInfo, z, zShapeInfo, dimension, dimensionLength, tadOnlyShapeInfo, tadOffsets, tadOnlyShapeInfoZ, tadOffsetsZ), OPS_A(BROADCAST_OPS)) @@ -85,6 +100,13 @@ namespace functions { DEBUG_KERNEL(stream, opNum); } + template + __host__ void Broadcast::execBroadcast(dim3 launchDims, cudaStream_t *stream, const int opNum, const void *x, const Nd4jLong *xShapeInfo, const void *y, const Nd4jLong *yShapeInfo, void *z, const Nd4jLong *zShapeInfo) { + DISPATCH_BY_OPNUM_TTT(intermediateBroadcast, PARAMS(launchDims, stream, x, xShapeInfo, y, yShapeInfo, z, zShapeInfo), OPS_A(BROADCAST_OPS)) + + DEBUG_KERNEL(stream, opNum); + } + template template __host__ void Broadcast::intermediateInverseBroadcast(dim3 launchDims, cudaStream_t *stream, void *x, Nd4jLong *xShapeInfo, void *y, Nd4jLong *yShapeInfo, void *z, Nd4jLong *zShapeInfo, int *dimension, int dimensionLength, Nd4jLong *tadOnlyShapeInfo, Nd4jLong *tadOffsets, Nd4jLong *tadOnlyShapeInfoZ, Nd4jLong *tadOffsetsZ) { @@ -128,9 +150,9 @@ namespace functions { if (threadIdx.x == 0) { - tadLength = _length(tadOnlyShapeInfo); + tadLength = length(tadOnlyShapeInfo); tadEWS = shape::elementWiseStride(tadOnlyShapeInfo); - numTads = _length(yShapeInfo) / tadLength; + numTads = length(yShapeInfo) / tadLength; xEWS = shape::elementWiseStride(xShapeInfo); zEWS = shape::elementWiseStride(tadOnlyShapeInfoZ); } @@ -154,9 +176,9 @@ namespace functions { else { // it is expected that x and z tads and y array all have the same length for (Nd4jLong i = threadIdx.x; i < tadLength; i+= blockDim.x) { - auto xOffset = _getIndexOffset(i, xShapeInfo); - auto yOffset = _getIndexOffset(i, tadOnlyShapeInfo); - auto zOffset = _getIndexOffset(i, tadOnlyShapeInfoZ); + auto xOffset = getIndexOffset(i, xShapeInfo); + auto yOffset = getIndexOffset(i, tadOnlyShapeInfo); + auto zOffset = getIndexOffset(i, tadOnlyShapeInfoZ); rZ[zOffset] = OpType::op(x[xOffset], rY[yOffset]); } } @@ -193,9 +215,9 @@ namespace functions { __shared__ Nd4jLong zEWS; if (threadIdx.x == 0) { - tadLength = _length(tadOnlyShapeInfo); + tadLength = length(tadOnlyShapeInfo); tadEWS = shape::elementWiseStride(tadOnlyShapeInfo); - numTads = _length(xShapeInfo) / tadLength; + numTads = length(xShapeInfo) / tadLength; yEWS = shape::elementWiseStride(yShapeInfo); zEWS = shape::elementWiseStride(tadOnlyShapeInfoZ); } @@ -219,15 +241,63 @@ namespace functions { // it is expected that x and z tads and y array all have the same length for (Nd4jLong i = threadIdx.x; i < tadLength; i+= blockDim.x) { - auto xOffset = _getIndexOffset(i, tadOnlyShapeInfo); - auto yOffset = _getIndexOffset(i, yShapeInfo); - auto zOffset = _getIndexOffset(i, tadOnlyShapeInfoZ); + auto xOffset = getIndexOffset(i, tadOnlyShapeInfo); + auto yOffset = getIndexOffset(i, yShapeInfo); + auto zOffset = getIndexOffset(i, tadOnlyShapeInfoZ); rZ[zOffset] = OpType::op(rX[xOffset], y[yOffset]); } } } } +//////////////////////////////////////////////////////////////////////// +template +template +__device__ void Broadcast::transformCuda( + const void *vx, const Nd4jLong *xShapeInfo, + const void *vy, const Nd4jLong *yShapeInfo, + void *vz, const Nd4jLong *zShapeInfo) { + + const X* x = reinterpret_cast(vx); + const Y* y = reinterpret_cast(vy); + Z* z = reinterpret_cast(vz); + + __shared__ Nd4jLong zLen; + __shared__ int rank; + __shared__ bool xzSameOffsets, yzSameOffsets; + + if (threadIdx.x == 0) { + + zLen = shape::length(zShapeInfo); + rank = shape::rank(zShapeInfo); + + xzSameOffsets = shape::haveSameShapeAndStrides(xShapeInfo, zShapeInfo); + yzSameOffsets = shape::haveSameShapeAndStrides(yShapeInfo, zShapeInfo); + } + __syncthreads(); + + + const auto tid = blockIdx.x * blockDim.x + threadIdx.x; + + int xCoords[MAX_RANK], yCoords[MAX_RANK], zCoords[MAX_RANK]; + + for (int i = tid; i < zLen; i += blockDim.x * gridDim.x) { + + shape::index2coords(i, zShapeInfo, zCoords); + + for (uint j = 0; j < rank; ++j) { + xCoords[j] = shape::sizeAt(xShapeInfo, j) == 1 ? 0 : zCoords[j]; + yCoords[j] = shape::sizeAt(yShapeInfo, j) == 1 ? 0 : zCoords[j]; + } + + const auto zOffset = shape::getOffset(zShapeInfo, zCoords); + const auto xOffset = xzSameOffsets ? zOffset : shape::getOffset(xShapeInfo, xCoords); + const auto yOffset = yzSameOffsets ? zOffset : shape::getOffset(yShapeInfo, yCoords); + + z[zOffset] = OpType::op(x[xOffset], y[yOffset]); + } +} + /* BUILD_PAIRWISE_TEMPLATE(template class ND4J_EXPORT Broadcast, , PAIRWISE_TYPES_0); BUILD_PAIRWISE_TEMPLATE(template class ND4J_EXPORT Broadcast, , PAIRWISE_TYPES_1); diff --git a/libnd4j/include/loops/cuda/broadcasting.cu b/libnd4j/include/loops/cuda/broadcasting.cu index 8846e5473..55c882c3f 100644 --- a/libnd4j/include/loops/cuda/broadcasting.cu +++ b/libnd4j/include/loops/cuda/broadcasting.cu @@ -18,17 +18,17 @@ // @author raver119@gmail.com // -#include +#include #include #include #include -#include +#include #include #include #include #include -#include -#include +#include +#include namespace functions { namespace broadcast { diff --git a/libnd4j/include/loops/cuda/broadcasting_bool.cu b/libnd4j/include/loops/cuda/broadcasting_bool.cu index d5a45ceec..1c7bc358e 100644 --- a/libnd4j/include/loops/cuda/broadcasting_bool.cu +++ b/libnd4j/include/loops/cuda/broadcasting_bool.cu @@ -18,16 +18,16 @@ // @author raver119@gmail.com // -#include +#include #include #include #include -#include +#include #include #include #include #include -#include +#include using namespace simdOps; @@ -47,6 +47,15 @@ static __global__ void broadcastBoolSimple( functions::broadcast::BroadcastBool::template transformCuda(x,xShapeInfo,y,yShapeInfo,z,zShapeInfo, extraParams, dimension,dimensionLength,tadOnlyShapeInfo,tadOffsets,tadOnlyShapeInfoZ,tadOffsetsZ); } +////////////////////////////////////////////////////////////////////////// +template +static __global__ void broadcastBoolSimple(const void *x, const Nd4jLong *xShapeInfo, + const void *y, const Nd4jLong *yShapeInfo, + void *z, const Nd4jLong *zShapeInfo, + void *extraParams) { + + functions::broadcast::BroadcastBool::template transformCuda(x, xShapeInfo, y, yShapeInfo, z, zShapeInfo, extraParams); +} ////////////////////////////////////////////////////////////////////////// template static __global__ void broadcastBoolInverseSimple( @@ -64,29 +73,55 @@ static __global__ void broadcastBoolInverseSimple( } namespace functions { - namespace broadcast { -////////////////////////////////////////////////////////////////////////// - template - template - __host__ void BroadcastBool::intermediateBroadcast(dim3 launchDims, cudaStream_t *stream, void *x, Nd4jLong *xShapeInfo, void *y, Nd4jLong *yShapeInfo, void *z, Nd4jLong *zShapeInfo, void *extraParams, int *dimension, int dimensionLength, Nd4jLong *tadOnlyShapeInfo, Nd4jLong *tadOffsets, Nd4jLong *tadOnlyShapeInfoZ, Nd4jLong *tadOffsetsZ) { - broadcastBoolSimple<<>>(x, xShapeInfo, y, yShapeInfo, z, zShapeInfo, extraParams, dimension, dimensionLength, tadOnlyShapeInfo, tadOffsets, tadOnlyShapeInfoZ, tadOffsetsZ); - nd4j::DebugHelper::checkErrorCode(stream, "intermediateBroadcastBool(...) failed"); - } +namespace broadcast { ////////////////////////////////////////////////////////////////////////// - template - __host__ void BroadcastBool::execBroadcast(dim3 launchDims, cudaStream_t *stream, int opNum, void *x, Nd4jLong *xShapeInfo, void *y, Nd4jLong *yShapeInfo, void *z, Nd4jLong *zShapeInfo, void *extraParams, int *dimension, int dimensionLength, Nd4jLong *tadOnlyShapeInfo, Nd4jLong *tadOffsets, Nd4jLong *tadOnlyShapeInfoZ, Nd4jLong *tadOffsetsZ) { - DISPATCH_BY_OPNUM_TT(intermediateBroadcast, PARAMS(launchDims, stream, x, xShapeInfo, y, yShapeInfo, z, zShapeInfo, extraParams, dimension, dimensionLength, tadOnlyShapeInfo, tadOffsets, tadOnlyShapeInfoZ, tadOffsetsZ), OPS_A(BROADCAST_BOOL_OPS)) +template +template +__host__ void BroadcastBool::intermediateBroadcast(dim3 launchDims, cudaStream_t *stream, void *x, Nd4jLong *xShapeInfo, void *y, Nd4jLong *yShapeInfo, void *z, Nd4jLong *zShapeInfo, void *extraParams, int *dimension, int dimensionLength, Nd4jLong *tadOnlyShapeInfo, Nd4jLong *tadOffsets, Nd4jLong *tadOnlyShapeInfoZ, Nd4jLong *tadOffsetsZ) { + broadcastBoolSimple<<>>(x, xShapeInfo, y, yShapeInfo, z, zShapeInfo, extraParams, dimension, dimensionLength, tadOnlyShapeInfo, tadOffsets, tadOnlyShapeInfoZ, tadOffsetsZ); + sd::DebugHelper::checkErrorCode(stream, "intermediateBroadcastBool(...) failed"); +} - DEBUG_KERNEL(stream, opNum); - } +////////////////////////////////////////////////////////////////////////// +template +template +__host__ void BroadcastBool::intermediateBroadcast(dim3 launchDims, cudaStream_t *stream, + const void *x, const Nd4jLong *xShapeInfo, + const void *y, const Nd4jLong *yShapeInfo, + void *z, const Nd4jLong *zShapeInfo, + void *extraParams) { + + broadcastBoolSimple<<>>(x, xShapeInfo, y, yShapeInfo, z, zShapeInfo, extraParams); + sd::DebugHelper::checkErrorCode(stream, "intermediateBroadcastBool(...) failed"); +} + +////////////////////////////////////////////////////////////////////////// +template +__host__ void BroadcastBool::execBroadcast(dim3 launchDims, cudaStream_t *stream, int opNum, void *x, Nd4jLong *xShapeInfo, void *y, Nd4jLong *yShapeInfo, void *z, Nd4jLong *zShapeInfo, void *extraParams, int *dimension, int dimensionLength, Nd4jLong *tadOnlyShapeInfo, Nd4jLong *tadOffsets, Nd4jLong *tadOnlyShapeInfoZ, Nd4jLong *tadOffsetsZ) { + + DISPATCH_BY_OPNUM_TT(intermediateBroadcast, PARAMS(launchDims, stream, x, xShapeInfo, y, yShapeInfo, z, zShapeInfo, extraParams, dimension, dimensionLength, tadOnlyShapeInfo, tadOffsets, tadOnlyShapeInfoZ, tadOffsetsZ), OPS_A(BROADCAST_BOOL_OPS)) + DEBUG_KERNEL(stream, opNum); +} + +////////////////////////////////////////////////////////////////////////// +template +__host__ void BroadcastBool::execBroadcast(dim3 launchDims, cudaStream_t *stream, const int opNum, + const void *x, const Nd4jLong *xShapeInfo, + const void *y, const Nd4jLong *yShapeInfo, + void *z, const Nd4jLong *zShapeInfo, + void *extraParams) { + + DISPATCH_BY_OPNUM_TT(intermediateBroadcast, PARAMS(launchDims, stream, x, xShapeInfo, y, yShapeInfo, z, zShapeInfo, extraParams), OPS_A(BROADCAST_BOOL_OPS)) + DEBUG_KERNEL(stream, opNum); +} ////////////////////////////////////////////////////////////////////////// template template __host__ void BroadcastBool::intermediateInverseBroadcast(dim3 launchDims, cudaStream_t *stream, void *x, Nd4jLong *xShapeInfo, void *y, Nd4jLong *yShapeInfo, void *z, Nd4jLong *zShapeInfo, void *extraParams, int *dimension, int dimensionLength, Nd4jLong *tadOnlyShapeInfo, Nd4jLong *tadOffsets, Nd4jLong *tadOnlyShapeInfoZ, Nd4jLong *tadOffsetsZ) { broadcastBoolInverseSimple<<>>(x, xShapeInfo, y, yShapeInfo, z, zShapeInfo, extraParams, dimension, dimensionLength, tadOnlyShapeInfo, tadOffsets, tadOnlyShapeInfoZ, tadOffsetsZ); - nd4j::DebugHelper::checkErrorCode(stream, "intermediateBroadcastBool(...) failed"); + sd::DebugHelper::checkErrorCode(stream, "intermediateBroadcastBool(...) failed"); } ////////////////////////////////////////////////////////////////////////// @@ -229,7 +264,57 @@ namespace functions { } } +////////////////////////////////////////////////////////////////////////// +template +template +__device__ void BroadcastBool::transformCuda(const void *vx, const Nd4jLong *xShapeInfo, + const void *vy, const Nd4jLong *yShapeInfo, + void *vz, const Nd4jLong *zShapeInfo, + void *vextraParams) { - BUILD_DOUBLE_TEMPLATE(template class ND4J_EXPORT BroadcastBool, , LIBND4J_TYPES, BOOL_TYPES); + const X* x = reinterpret_cast(vx); + const X* y = reinterpret_cast(vy); + Z* z = reinterpret_cast(vz); + + auto extraParams = reinterpret_cast(vextraParams); + + __shared__ Nd4jLong zLen; + __shared__ int rank; + __shared__ bool xzSameOffsets, yzSameOffsets; + + if (threadIdx.x == 0) { + + zLen = shape::length(zShapeInfo); + rank = shape::rank(zShapeInfo); + + xzSameOffsets = shape::haveSameShapeAndStrides(xShapeInfo, zShapeInfo); + yzSameOffsets = shape::haveSameShapeAndStrides(yShapeInfo, zShapeInfo); } + __syncthreads(); + + + const auto tid = blockIdx.x * blockDim.x + threadIdx.x; + + int xCoords[MAX_RANK], yCoords[MAX_RANK], zCoords[MAX_RANK]; + + for (int i = tid; i < zLen; i += blockDim.x * gridDim.x) { + + shape::index2coords(i, zShapeInfo, zCoords); + + for (uint j = 0; j < rank; ++j) { + xCoords[j] = shape::sizeAt(xShapeInfo, j) == 1 ? 0 : zCoords[j]; + yCoords[j] = shape::sizeAt(yShapeInfo, j) == 1 ? 0 : zCoords[j]; + } + + const auto zOffset = shape::getOffset(zShapeInfo, zCoords); + const auto xOffset = xzSameOffsets ? zOffset : shape::getOffset(xShapeInfo, xCoords); + const auto yOffset = yzSameOffsets ? zOffset : shape::getOffset(yShapeInfo, yCoords); + + z[zOffset] = OpType::op(x[xOffset], y[yOffset], extraParams); + } +} + + +BUILD_DOUBLE_TEMPLATE(template class ND4J_EXPORT BroadcastBool, , LIBND4J_TYPES, BOOL_TYPES); +} } \ No newline at end of file diff --git a/libnd4j/include/loops/cuda/broadcasting_int.cu b/libnd4j/include/loops/cuda/broadcasting_int.cu index f183c009e..998ac9ae8 100644 --- a/libnd4j/include/loops/cuda/broadcasting_int.cu +++ b/libnd4j/include/loops/cuda/broadcasting_int.cu @@ -18,16 +18,16 @@ // @author raver119@gmail.com // -#include +#include #include #include #include -#include +#include #include #include #include #include -#include +#include using namespace simdOps; @@ -46,6 +46,15 @@ static __global__ void broadcastIntSimple( functions::broadcast::BroadcastInt::template transformCuda(x,xShapeInfo,y,yShapeInfo,z,zShapeInfo,dimension,dimensionLength,tadOnlyShapeInfo,tadOffsets,tadOnlyShapeInfoZ,tadOffsetsZ); } +////////////////////////////////////////////////////////////////////////// +template +static __global__ void broadcastIntSimple(const void *x, const Nd4jLong *xShapeInfo, + const void *y, const Nd4jLong *yShapeInfo, + void *z, const Nd4jLong *zShapeInfo) { + + functions::broadcast::BroadcastInt::template transformCuda(x, xShapeInfo, y, yShapeInfo, z, zShapeInfo); +} + ////////////////////////////////////////////////////////////////////////// template static __global__ void broadcastBoolInverseSimple( @@ -62,19 +71,40 @@ static __global__ void broadcastBoolInverseSimple( } namespace functions { - namespace broadcast { +namespace broadcast { ////////////////////////////////////////////////////////////////////////// - template - template - __host__ void BroadcastInt::intermediateBroadcast(dim3 launchDims, cudaStream_t *stream, void *x, Nd4jLong *xShapeInfo, void *y, Nd4jLong *yShapeInfo, void *z, Nd4jLong *zShapeInfo, int *dimension, int dimensionLength, Nd4jLong *tadOnlyShapeInfo, Nd4jLong *tadOffsets, Nd4jLong *tadOnlyShapeInfoZ, Nd4jLong *tadOffsetsZ) { - broadcastIntSimple<<>>(x, xShapeInfo, y, yShapeInfo, z, zShapeInfo, dimension, dimensionLength, tadOnlyShapeInfo, tadOffsets, tadOnlyShapeInfoZ, tadOffsetsZ); - } +template +template +__host__ void BroadcastInt::intermediateBroadcast(dim3 launchDims, cudaStream_t *stream, void *x, Nd4jLong *xShapeInfo, void *y, Nd4jLong *yShapeInfo, void *z, Nd4jLong *zShapeInfo, int *dimension, int dimensionLength, Nd4jLong *tadOnlyShapeInfo, Nd4jLong *tadOffsets, Nd4jLong *tadOnlyShapeInfoZ, Nd4jLong *tadOffsetsZ) { + broadcastIntSimple<<>>(x, xShapeInfo, y, yShapeInfo, z, zShapeInfo, dimension, dimensionLength, tadOnlyShapeInfo, tadOffsets, tadOnlyShapeInfoZ, tadOffsetsZ); +} ////////////////////////////////////////////////////////////////////////// - template - __host__ void BroadcastInt::execBroadcast(dim3 launchDims, cudaStream_t *stream, int opNum, void *x, Nd4jLong *xShapeInfo, void *y, Nd4jLong *yShapeInfo, void *z, Nd4jLong *zShapeInfo, int *dimension, int dimensionLength, Nd4jLong *tadOnlyShapeInfo, Nd4jLong *tadOffsets, Nd4jLong *tadOnlyShapeInfoZ, Nd4jLong *tadOffsetsZ) { - DISPATCH_BY_OPNUM_T(intermediateBroadcast, PARAMS(launchDims, stream, x, xShapeInfo, y, yShapeInfo, z, zShapeInfo, dimension, dimensionLength, tadOnlyShapeInfo, tadOffsets, tadOnlyShapeInfoZ, tadOffsetsZ), OPS_A(BROADCAST_INT_OPS)) - } +template +template +__host__ void BroadcastInt::intermediateBroadcast(dim3 launchDims, cudaStream_t *stream, + const void *x, const Nd4jLong *xShapeInfo, + const void *y, const Nd4jLong *yShapeInfo, + void *z, const Nd4jLong *zShapeInfo) { + + broadcastIntSimple<<>>(x, xShapeInfo, y, yShapeInfo, z, zShapeInfo); +} + +////////////////////////////////////////////////////////////////////////// +template +__host__ void BroadcastInt::execBroadcast(dim3 launchDims, cudaStream_t *stream, int opNum, void *x, Nd4jLong *xShapeInfo, void *y, Nd4jLong *yShapeInfo, void *z, Nd4jLong *zShapeInfo, int *dimension, int dimensionLength, Nd4jLong *tadOnlyShapeInfo, Nd4jLong *tadOffsets, Nd4jLong *tadOnlyShapeInfoZ, Nd4jLong *tadOffsetsZ) { + DISPATCH_BY_OPNUM_T(intermediateBroadcast, PARAMS(launchDims, stream, x, xShapeInfo, y, yShapeInfo, z, zShapeInfo, dimension, dimensionLength, tadOnlyShapeInfo, tadOffsets, tadOnlyShapeInfoZ, tadOffsetsZ), OPS_A(BROADCAST_INT_OPS)) +} + +////////////////////////////////////////////////////////////////////////// +template +__host__ void BroadcastInt::execBroadcast(dim3 launchDims, cudaStream_t *stream, const int opNum, + const void *x, const Nd4jLong *xShapeInfo, + const void *y, const Nd4jLong *yShapeInfo, + void *z, const Nd4jLong *zShapeInfo) { + + DISPATCH_BY_OPNUM_T(intermediateBroadcast, PARAMS(launchDims, stream, x, xShapeInfo, y, yShapeInfo, z, zShapeInfo), OPS_A(BROADCAST_INT_OPS)) +} ////////////////////////////////////////////////////////////////////////// template @@ -217,6 +247,54 @@ namespace functions { } } - BUILD_SINGLE_TEMPLATE(template class ND4J_EXPORT BroadcastInt, , INTEGER_TYPES); +////////////////////////////////////////////////////////////////////////// +template +template +__device__ void BroadcastInt::transformCuda(const void *vx, const Nd4jLong *xShapeInfo, + const void *vy, const Nd4jLong *yShapeInfo, + void *vz, const Nd4jLong *zShapeInfo) { + + const X* x = reinterpret_cast(vx); + const X* y = reinterpret_cast(vy); + X* z = reinterpret_cast(vz); + + __shared__ Nd4jLong zLen; + __shared__ int rank; + __shared__ bool xzSameOffsets, yzSameOffsets; + + if (threadIdx.x == 0) { + + zLen = shape::length(zShapeInfo); + rank = shape::rank(zShapeInfo); + + xzSameOffsets = shape::haveSameShapeAndStrides(xShapeInfo, zShapeInfo); + yzSameOffsets = shape::haveSameShapeAndStrides(yShapeInfo, zShapeInfo); } + __syncthreads(); + + + const auto tid = blockIdx.x * blockDim.x + threadIdx.x; + + int xCoords[MAX_RANK], yCoords[MAX_RANK], zCoords[MAX_RANK]; + + for (int i = tid; i < zLen; i += blockDim.x * gridDim.x) { + + shape::index2coords(i, zShapeInfo, zCoords); + + for (uint j = 0; j < rank; ++j) { + xCoords[j] = shape::sizeAt(xShapeInfo, j) == 1 ? 0 : zCoords[j]; + yCoords[j] = shape::sizeAt(yShapeInfo, j) == 1 ? 0 : zCoords[j]; + } + + const auto zOffset = shape::getOffset(zShapeInfo, zCoords); + const auto xOffset = xzSameOffsets ? zOffset : shape::getOffset(xShapeInfo, xCoords); + const auto yOffset = yzSameOffsets ? zOffset : shape::getOffset(yShapeInfo, yCoords); + + z[zOffset] = OpType::op(x[xOffset], y[yOffset]); + } +} + + +BUILD_SINGLE_TEMPLATE(template class ND4J_EXPORT BroadcastInt, , INTEGER_TYPES); +} } \ No newline at end of file diff --git a/libnd4j/include/loops/cpu/compilation_units/TrueBroadcastHelper_int.cpp b/libnd4j/include/loops/cuda/compilation_units/broadcasting/broadcasting_10.cu similarity index 77% rename from libnd4j/include/loops/cpu/compilation_units/TrueBroadcastHelper_int.cpp rename to libnd4j/include/loops/cuda/compilation_units/broadcasting/broadcasting_10.cu index 04b0fc3e4..4d19a893c 100644 --- a/libnd4j/include/loops/cpu/compilation_units/TrueBroadcastHelper_int.cpp +++ b/libnd4j/include/loops/cuda/compilation_units/broadcasting/broadcasting_10.cu @@ -1,5 +1,5 @@ /******************************************************************************* - * Copyright (c) 2019-2020 Konduit K.K. + * Copyright (c) 2015-2018 Skymind, Inc. * * This program and the accompanying materials are made available under the * terms of the Apache License, Version 2.0 which is available at @@ -18,10 +18,10 @@ // @author raver119@gmail.com // -#include "../TrueBroadcastHelper.hpp" +#include "../../broadcasting.chpp" -namespace nd4j { - namespace helpers { - BUILD_SINGLE_TEMPLATE(template class ND4J_EXPORT TrueBroadcastIntHelper, , INTEGER_TYPES); +namespace functions { + namespace broadcast { + BUILD_PAIRWISE_TEMPLATE(template class ND4J_EXPORT Broadcast, , PAIRWISE_TYPES_10); } -} +} \ No newline at end of file diff --git a/libnd4j/include/loops/cpu/compilation_units/TrueBroadcastHelper_2.cpp b/libnd4j/include/loops/cuda/compilation_units/broadcasting/broadcasting_11.cu similarity index 77% rename from libnd4j/include/loops/cpu/compilation_units/TrueBroadcastHelper_2.cpp rename to libnd4j/include/loops/cuda/compilation_units/broadcasting/broadcasting_11.cu index 4d020fdfa..8b643965b 100644 --- a/libnd4j/include/loops/cpu/compilation_units/TrueBroadcastHelper_2.cpp +++ b/libnd4j/include/loops/cuda/compilation_units/broadcasting/broadcasting_11.cu @@ -1,5 +1,5 @@ /******************************************************************************* - * Copyright (c) 2019-2020 Konduit K.K. + * Copyright (c) 2015-2018 Skymind, Inc. * * This program and the accompanying materials are made available under the * terms of the Apache License, Version 2.0 which is available at @@ -18,10 +18,10 @@ // @author raver119@gmail.com // -#include "../TrueBroadcastHelper.hpp" +#include "../../broadcasting.chpp" -namespace nd4j { - namespace helpers { - BUILD_PAIRWISE_TEMPLATE(template class ND4J_EXPORT TrueBroadcastHelper, , PAIRWISE_TYPES_1); +namespace functions { + namespace broadcast { + BUILD_PAIRWISE_TEMPLATE(template class ND4J_EXPORT Broadcast, , PAIRWISE_TYPES_11); } -} +} \ No newline at end of file diff --git a/libnd4j/include/loops/cpu/compilation_units/TrueBroadcastHelper_1.cpp b/libnd4j/include/loops/cuda/compilation_units/broadcasting/broadcasting_12.cu similarity index 77% rename from libnd4j/include/loops/cpu/compilation_units/TrueBroadcastHelper_1.cpp rename to libnd4j/include/loops/cuda/compilation_units/broadcasting/broadcasting_12.cu index 6c9eb4022..935297a53 100644 --- a/libnd4j/include/loops/cpu/compilation_units/TrueBroadcastHelper_1.cpp +++ b/libnd4j/include/loops/cuda/compilation_units/broadcasting/broadcasting_12.cu @@ -1,5 +1,5 @@ /******************************************************************************* - * Copyright (c) 2019-2020 Konduit K.K. + * Copyright (c) 2015-2018 Skymind, Inc. * * This program and the accompanying materials are made available under the * terms of the Apache License, Version 2.0 which is available at @@ -18,10 +18,10 @@ // @author raver119@gmail.com // -#include "../TrueBroadcastHelper.hpp" +#include "../../broadcasting.chpp" -namespace nd4j { - namespace helpers { - BUILD_PAIRWISE_TEMPLATE(template class ND4J_EXPORT TrueBroadcastHelper, , PAIRWISE_TYPES_0); +namespace functions { + namespace broadcast { + BUILD_PAIRWISE_TEMPLATE(template class ND4J_EXPORT Broadcast, , PAIRWISE_TYPES_12); } -} +} \ No newline at end of file diff --git a/libnd4j/include/loops/cpu/compilation_units/TrueBroadcastHelper_0.cpp b/libnd4j/include/loops/cuda/compilation_units/pairwise/pairwise_10.cu similarity index 76% rename from libnd4j/include/loops/cpu/compilation_units/TrueBroadcastHelper_0.cpp rename to libnd4j/include/loops/cuda/compilation_units/pairwise/pairwise_10.cu index 4685e8f5b..fac835b18 100644 --- a/libnd4j/include/loops/cpu/compilation_units/TrueBroadcastHelper_0.cpp +++ b/libnd4j/include/loops/cuda/compilation_units/pairwise/pairwise_10.cu @@ -1,5 +1,5 @@ /******************************************************************************* - * Copyright (c) 2019-2020 Konduit K.K. + * Copyright (c) 2015-2018 Skymind, Inc. * * This program and the accompanying materials are made available under the * terms of the Apache License, Version 2.0 which is available at @@ -18,10 +18,10 @@ // @author raver119@gmail.com // -#include "../TrueBroadcastHelper.hpp" +#include "../../pairwise.chpp" -namespace nd4j { - namespace helpers { - BUILD_PAIRWISE_TEMPLATE(template class ND4J_EXPORT TrueBroadcastHelper, , PAIRWISE_TYPES_9); +namespace functions { + namespace pairwise_transforms { + BUILD_PAIRWISE_TEMPLATE(template class ND4J_EXPORT PairWiseTransform, , PAIRWISE_TYPES_10); } -} +} \ No newline at end of file diff --git a/libnd4j/include/loops/cuda/compilation_units/pairwise/pairwise_11.cu b/libnd4j/include/loops/cuda/compilation_units/pairwise/pairwise_11.cu new file mode 100644 index 000000000..f01ef7eb3 --- /dev/null +++ b/libnd4j/include/loops/cuda/compilation_units/pairwise/pairwise_11.cu @@ -0,0 +1,27 @@ +/******************************************************************************* + * Copyright (c) 2015-2018 Skymind, Inc. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ + +// +// @author raver119@gmail.com +// + +#include "../../pairwise.chpp" + +namespace functions { + namespace pairwise_transforms { + BUILD_PAIRWISE_TEMPLATE(template class ND4J_EXPORT PairWiseTransform, , PAIRWISE_TYPES_11); + } +} \ No newline at end of file diff --git a/libnd4j/include/loops/cuda/compilation_units/pairwise/pairwise_12.cu b/libnd4j/include/loops/cuda/compilation_units/pairwise/pairwise_12.cu new file mode 100644 index 000000000..8cf8c367f --- /dev/null +++ b/libnd4j/include/loops/cuda/compilation_units/pairwise/pairwise_12.cu @@ -0,0 +1,27 @@ +/******************************************************************************* + * Copyright (c) 2015-2018 Skymind, Inc. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ + +// +// @author raver119@gmail.com +// + +#include "../../pairwise.chpp" + +namespace functions { + namespace pairwise_transforms { + BUILD_PAIRWISE_TEMPLATE(template class ND4J_EXPORT PairWiseTransform, , PAIRWISE_TYPES_12); + } +} \ No newline at end of file diff --git a/libnd4j/include/loops/cuda/compilation_units/scalar/scalar_10.cu b/libnd4j/include/loops/cuda/compilation_units/scalar/scalar_10.cu new file mode 100644 index 000000000..e06cad235 --- /dev/null +++ b/libnd4j/include/loops/cuda/compilation_units/scalar/scalar_10.cu @@ -0,0 +1,27 @@ +/******************************************************************************* + * Copyright (c) 2015-2018 Skymind, Inc. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ + +// +// @author raver119@gmail.com +// + +#include "../../scalar.chpp" + +namespace functions { + namespace scalar { + BUILD_PAIRWISE_TEMPLATE(template class ND4J_EXPORT ScalarTransform, , PAIRWISE_TYPES_10); + } +} \ No newline at end of file diff --git a/libnd4j/include/loops/cuda/compilation_units/scalar/scalar_11.cu b/libnd4j/include/loops/cuda/compilation_units/scalar/scalar_11.cu new file mode 100644 index 000000000..3c5549339 --- /dev/null +++ b/libnd4j/include/loops/cuda/compilation_units/scalar/scalar_11.cu @@ -0,0 +1,27 @@ +/******************************************************************************* + * Copyright (c) 2015-2018 Skymind, Inc. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ + +// +// @author raver119@gmail.com +// + +#include "../../scalar.chpp" + +namespace functions { + namespace scalar { + BUILD_PAIRWISE_TEMPLATE(template class ND4J_EXPORT ScalarTransform, , PAIRWISE_TYPES_11); + } +} \ No newline at end of file diff --git a/libnd4j/include/loops/cuda/compilation_units/scalar/scalar_12.cu b/libnd4j/include/loops/cuda/compilation_units/scalar/scalar_12.cu new file mode 100644 index 000000000..7f7f74156 --- /dev/null +++ b/libnd4j/include/loops/cuda/compilation_units/scalar/scalar_12.cu @@ -0,0 +1,27 @@ +/******************************************************************************* + * Copyright (c) 2015-2018 Skymind, Inc. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ + +// +// @author raver119@gmail.com +// + +#include "../../scalar.chpp" + +namespace functions { + namespace scalar { + BUILD_PAIRWISE_TEMPLATE(template class ND4J_EXPORT ScalarTransform, , PAIRWISE_TYPES_12); + } +} \ No newline at end of file diff --git a/libnd4j/include/loops/cuda/indexreduce.cu b/libnd4j/include/loops/cuda/indexreduce.cu index aeb2d9d36..6383458c9 100644 --- a/libnd4j/include/loops/cuda/indexreduce.cu +++ b/libnd4j/include/loops/cuda/indexreduce.cu @@ -18,9 +18,9 @@ // Created by raver on 4/9/2018. // -#include +#include #include "../indexreduce.h" -#include +#include #include #include @@ -230,9 +230,9 @@ namespace functions { } __syncthreads(); - if(nd4j::ArrayOptions::arrayType(xShapeInfo) == nd4j::ArrayType::EMPTY) { + if(sd::ArrayOptions::arrayType(xShapeInfo) == sd::ArrayType::EMPTY) { - if(nd4j::ArrayOptions::arrayType(zShapeInfo) == nd4j::ArrayType::EMPTY) + if(sd::ArrayOptions::arrayType(zShapeInfo) == sd::ArrayType::EMPTY) return; for (uint i = blockIdx.x * blockDim.x + threadIdx.x; i < zLen; i += gridDim.x * blockDim.x) @@ -268,7 +268,7 @@ namespace functions { } __syncthreads(); - aggregatePartials(&sPartials, threadIdx.x, nd4j::math::nd4j_min(blockDim.x, tadLength),extraParams); + aggregatePartials(&sPartials, threadIdx.x, sd::math::nd4j_min(blockDim.x, tadLength),extraParams); __syncthreads(); if (threadIdx.x == 0) { @@ -289,7 +289,7 @@ namespace functions { } __syncthreads(); - aggregatePartials(&sPartials, threadIdx.x, nd4j::math::nd4j_min(blockDim.x, tadLength),extraParams); + aggregatePartials(&sPartials, threadIdx.x, sd::math::nd4j_min(blockDim.x, tadLength),extraParams); __syncthreads(); if (threadIdx.x == 0) { @@ -320,7 +320,7 @@ namespace functions { sPartials[threadIdx.x] = reduction; __syncthreads(); - aggregatePartials(&sPartials, threadIdx.x, nd4j::math::nd4j_min(blockDim.x, (int) n),extraParams); + aggregatePartials(&sPartials, threadIdx.x, sd::math::nd4j_min(blockDim.x, (int) n),extraParams); __syncthreads(); if (gridDim.x > 1) { @@ -352,7 +352,7 @@ namespace functions { } __syncthreads(); - aggregatePartials(&sPartials, threadIdx.x, nd4j::math::nd4j_min(gridDim.x, blockDim.x),extraParams); + aggregatePartials(&sPartials, threadIdx.x, sd::math::nd4j_min(gridDim.x, blockDim.x),extraParams); __syncthreads(); if (tid == 0) { diff --git a/libnd4j/include/loops/cuda/inplace_loops/reduce_same_inplace.h b/libnd4j/include/loops/cuda/inplace_loops/reduce_same_inplace.h index 5df583e61..1989cadc5 100644 --- a/libnd4j/include/loops/cuda/inplace_loops/reduce_same_inplace.h +++ b/libnd4j/include/loops/cuda/inplace_loops/reduce_same_inplace.h @@ -24,8 +24,8 @@ #include #include -#include -#include +#include +#include using namespace simdOps; @@ -118,7 +118,7 @@ namespace functions { sPartials[threadIdx.x] = OpType::update(sPartials[threadIdx.x], OpType::op(x[shape::getIndexOffset(i, xShapeInfo)], extraParams), extraParams); __syncthreads(); - aggregatePartials(sPartials, threadIdx.x, nd4j::math::nd4j_min(blockDim.x, len), extraParams); + aggregatePartials(sPartials, threadIdx.x, sd::math::nd4j_min(blockDim.x, len), extraParams); __syncthreads(); @@ -150,7 +150,7 @@ namespace functions { sPartials[threadIdx.x] = OpType::update(sPartials[threadIdx.x], reductionBuffer[i], extraParams); __syncthreads(); - aggregatePartials(sPartials, threadIdx.x, nd4j::math::nd4j_min(gridDim.x, blockDim.x), extraParams); + aggregatePartials(sPartials, threadIdx.x, sd::math::nd4j_min(gridDim.x, blockDim.x), extraParams); __syncthreads(); if (threadIdx.x == 0) { diff --git a/libnd4j/include/loops/cuda/inplace_loops/scalar_inplace.h b/libnd4j/include/loops/cuda/inplace_loops/scalar_inplace.h index 9e061003d..df1a87ba8 100644 --- a/libnd4j/include/loops/cuda/inplace_loops/scalar_inplace.h +++ b/libnd4j/include/loops/cuda/inplace_loops/scalar_inplace.h @@ -24,8 +24,8 @@ #include #include -#include -#include +#include +#include using namespace simdOps; diff --git a/libnd4j/include/loops/cuda/inplace_loops/transform_strict_inplace.h b/libnd4j/include/loops/cuda/inplace_loops/transform_strict_inplace.h index b10b23d09..c4b94fca5 100644 --- a/libnd4j/include/loops/cuda/inplace_loops/transform_strict_inplace.h +++ b/libnd4j/include/loops/cuda/inplace_loops/transform_strict_inplace.h @@ -23,8 +23,8 @@ #include #include -#include -#include +#include +#include using namespace simdOps; diff --git a/libnd4j/include/loops/cuda/legacy/grid_shaped.legacy b/libnd4j/include/loops/cuda/legacy/grid_shaped.legacy index 6a43107f7..086647728 100644 --- a/libnd4j/include/loops/cuda/legacy/grid_shaped.legacy +++ b/libnd4j/include/loops/cuda/legacy/grid_shaped.legacy @@ -17,8 +17,8 @@ -#include -#include +#include +#include #include #include #include diff --git a/libnd4j/include/loops/cuda/legacy/grid_strided.legacy b/libnd4j/include/loops/cuda/legacy/grid_strided.legacy index 74c5322a3..f5f88f5a1 100644 --- a/libnd4j/include/loops/cuda/legacy/grid_strided.legacy +++ b/libnd4j/include/loops/cuda/legacy/grid_strided.legacy @@ -17,8 +17,8 @@ -#include -#include +#include +#include #include #include #include diff --git a/libnd4j/include/loops/cuda/legacy/reduce.legacy b/libnd4j/include/loops/cuda/legacy/reduce.legacy index 1ae7985de..1f9b1a1c5 100644 --- a/libnd4j/include/loops/cuda/legacy/reduce.legacy +++ b/libnd4j/include/loops/cuda/legacy/reduce.legacy @@ -18,7 +18,7 @@ // @author raver119@gmail.com // -#include +#include #include #include #include @@ -172,7 +172,7 @@ namespace functions { DISPATCH_SIMPLE(reduceScalarSimple, float, PARAMS(x, xShapeInfo, extraParams, z, zShapeInfo, nullptr, 1, reductionBuffer, tadOnlyShapeInfo), OPS_A(REDUCE_OPS)) - nd4j::DebugHelper::checkErrorCode(stream, "execReduceScalarFloat(...) failed"); + sd::DebugHelper::checkErrorCode(stream, "execReduceScalarFloat(...) failed"); } template <> @@ -180,7 +180,7 @@ namespace functions { DISPATCH_SIMPLE(reduceScalarSimple, float16, PARAMS(x, xShapeInfo, extraParams, z, zShapeInfo, nullptr, 1, reductionBuffer, tadOnlyShapeInfo), OPS_A(REDUCE_OPS)) - nd4j::DebugHelper::checkErrorCode(stream, "execReduceScalarHalf(...) failed"); + sd::DebugHelper::checkErrorCode(stream, "execReduceScalarHalf(...) failed"); } template <> @@ -188,7 +188,7 @@ namespace functions { DISPATCH_SIMPLE(reduceScalarSimple, double, PARAMS(x, xShapeInfo, extraParams, z, zShapeInfo, nullptr, 1, reductionBuffer, tadOnlyShapeInfo), OPS_A(REDUCE_OPS)) - nd4j::DebugHelper::checkErrorCode(stream, "execReduceScalarDouble(...) failed"); + sd::DebugHelper::checkErrorCode(stream, "execReduceScalarDouble(...) failed"); } template <> @@ -304,7 +304,7 @@ namespace functions { __syncthreads(); // aggregate. do NOT reduce for elements > tadLength - aggregatePartials(sPartials, threadIdx.x, nd4j::math::nd4j_min(blockDim.x, tadLength), extraParams); + aggregatePartials(sPartials, threadIdx.x, sd::math::nd4j_min(blockDim.x, tadLength), extraParams); __syncthreads(); @@ -364,7 +364,7 @@ namespace functions { } __syncthreads(); - aggregatePartials(sPartials, threadIdx.x, nd4j::math::nd4j_min(blockDim.x, n), extraParams); + aggregatePartials(sPartials, threadIdx.x, sd::math::nd4j_min(blockDim.x, n), extraParams); __syncthreads(); @@ -398,7 +398,7 @@ namespace functions { - aggregatePartials(sPartials, threadIdx.x, nd4j::math::nd4j_min(gridDim.x, blockDim.x), extraParams); + aggregatePartials(sPartials, threadIdx.x, sd::math::nd4j_min(gridDim.x, blockDim.x), extraParams); __syncthreads(); if (threadIdx.x == 0) { @@ -468,7 +468,7 @@ namespace functions { __syncthreads(); // aggregate. do NOT reduce for elements > tadLength - aggregatePartials(sPartials, threadIdx.x, nd4j::math::nd4j_min(blockDim.x, tadLength), extraParams); + aggregatePartials(sPartials, threadIdx.x, sd::math::nd4j_min(blockDim.x, tadLength), extraParams); __syncthreads(); if (threadIdx.x == 0) @@ -533,7 +533,7 @@ namespace functions { __syncthreads(); // aggregate. do NOT reduce for elements > tadLength - aggregatePartials(sPartials, threadIdx.x, nd4j::math::nd4j_min(blockDim.x, tadLength), extraParams); + aggregatePartials(sPartials, threadIdx.x, sd::math::nd4j_min(blockDim.x, tadLength), extraParams); __syncthreads(); diff --git a/libnd4j/include/loops/cuda/legacy/scalar_temp.legacy b/libnd4j/include/loops/cuda/legacy/scalar_temp.legacy index 7bc30271f..f9ea86883 100644 --- a/libnd4j/include/loops/cuda/legacy/scalar_temp.legacy +++ b/libnd4j/include/loops/cuda/legacy/scalar_temp.legacy @@ -15,7 +15,7 @@ ******************************************************************************/ -#include +#include diff --git a/libnd4j/include/loops/cuda/legacy/transform.legacy b/libnd4j/include/loops/cuda/legacy/transform.legacy index 6a8344916..e7f76751a 100644 --- a/libnd4j/include/loops/cuda/legacy/transform.legacy +++ b/libnd4j/include/loops/cuda/legacy/transform.legacy @@ -18,9 +18,9 @@ // @author raver119@gmail.com // -#include +#include #include -#include +#include #include #include @@ -173,7 +173,7 @@ namespace functions { DISPATCH_SIMPLE(transformShaped, float16, PARAMS(x, xShape, xRank, extraParams, z, zShape, zRank, allocationPointer, reductionPointer, tadShapeInfo, tadOffsets), OPS_A(TRANSFORM_OPS)) - if (nd4j::Environment::getInstance()->isDebug()) + if (sd::Environment::getInstance()->isDebug()) checkCudaErrors(cudaStreamSynchronize(*stream)); } diff --git a/libnd4j/include/loops/cuda/pairwise_bool.cu b/libnd4j/include/loops/cuda/pairwise_bool.cu index 05adbbce4..f697de814 100644 --- a/libnd4j/include/loops/cuda/pairwise_bool.cu +++ b/libnd4j/include/loops/cuda/pairwise_bool.cu @@ -104,8 +104,8 @@ void _CUDA_H PairWiseBoolTransform::intermediateShaped(dim3& launchDims, cu //////////////////////////////////////////////////////////////////////////////// template void PairWiseBoolTransform::executeCudaShaped(dim3& launchDims, cudaStream_t *stream, int opNum, void *vx, Nd4jLong *xShapeInfo, void *vy, Nd4jLong *yShapeInfo, void *vz, Nd4jLong *zShapeInfo, void *vextraParams) { - auto xType = nd4j::DataTypeUtils::fromT(); - auto yType = nd4j::DataTypeUtils::fromT(); + auto xType = sd::DataTypeUtils::fromT(); + auto yType = sd::DataTypeUtils::fromT(); DISPATCH_BY_OPNUM_TT(intermediateShaped, PARAMS(launchDims, stream, vx, xShapeInfo, vy, yShapeInfo, vz, zShapeInfo, vextraParams), PAIRWISE_BOOL_OPS); } diff --git a/libnd4j/include/loops/cuda/pairwise_int.cu b/libnd4j/include/loops/cuda/pairwise_int.cu index 85dce56f2..44447605e 100644 --- a/libnd4j/include/loops/cuda/pairwise_int.cu +++ b/libnd4j/include/loops/cuda/pairwise_int.cu @@ -104,7 +104,7 @@ void _CUDA_H PairWiseIntTransform::intermediateShaped(dim3& launchDims, cudaS //////////////////////////////////////////////////////////////////////////////// template void PairWiseIntTransform::executeCudaShaped(dim3& launchDims, cudaStream_t *stream, int opNum, void *vx, Nd4jLong *xShapeInfo, void *vy, Nd4jLong *yShapeInfo, void *vz, Nd4jLong *zShapeInfo, void *vextraParams) { - auto xType = nd4j::DataTypeUtils::fromT(); + auto xType = sd::DataTypeUtils::fromT(); DISPATCH_BY_OPNUM_T(intermediateShaped, PARAMS(launchDims, stream, vx, xShapeInfo, vy, yShapeInfo, vz, zShapeInfo, vextraParams), PAIRWISE_INT_OPS); } diff --git a/libnd4j/include/loops/cuda/random.cu b/libnd4j/include/loops/cuda/random.cu index 47ced2769..c7550b926 100644 --- a/libnd4j/include/loops/cuda/random.cu +++ b/libnd4j/include/loops/cuda/random.cu @@ -18,13 +18,13 @@ // @author raver119@gmail.com // -#include +#include #include -#include +#include #include #include #include -#include +#include using namespace randomOps; @@ -130,10 +130,10 @@ namespace functions { __shared__ char yOrder; __shared__ char zOrder; - __shared__ nd4j::graph::RandomGenerator *buffer; + __shared__ sd::graph::RandomGenerator *buffer; __shared__ unsigned char *cB; __shared__ unsigned char *dB; - nd4j::graph::RandomGenerator *devBuffer; + sd::graph::RandomGenerator *devBuffer; if (threadIdx.x == 0) { length = shape::length(zShapeBuffer); xEWS = shape::elementWiseStride(xShapeBuffer); @@ -144,15 +144,15 @@ namespace functions { zOrder = shape::order(zShapeBuffer); extern __shared__ unsigned char shmem[]; - buffer = (nd4j::graph::RandomGenerator *) shmem; + buffer = (sd::graph::RandomGenerator *) shmem; cB = shmem; - devBuffer = reinterpret_cast (state); + devBuffer = reinterpret_cast (state); dB = reinterpret_cast (state); } __syncthreads(); // using this loop instead of memcpy - for (int e = threadIdx.x; e < sizeof(nd4j::graph::RandomGenerator); e+= blockDim.x) + for (int e = threadIdx.x; e < sizeof(sd::graph::RandomGenerator); e+= blockDim.x) cB[e] = dB[e]; __syncthreads(); @@ -192,16 +192,16 @@ namespace functions { __shared__ char xOrder; __shared__ char zOrder; - __shared__ nd4j::graph::RandomGenerator *buffer; + __shared__ sd::graph::RandomGenerator *buffer; __shared__ unsigned char *cB; __shared__ unsigned char *dB; - __shared__ nd4j::graph::RandomGenerator *devBuffer; + __shared__ sd::graph::RandomGenerator *devBuffer; if (threadIdx.x == 0) { extern __shared__ unsigned char shmem[]; - buffer = (nd4j::graph::RandomGenerator *) shmem; + buffer = (sd::graph::RandomGenerator *) shmem; cB = shmem; - devBuffer = reinterpret_cast (state); + devBuffer = reinterpret_cast (state); dB = reinterpret_cast (state); length = shape::length(zShapeBuffer); @@ -213,7 +213,7 @@ namespace functions { __syncthreads(); // using this loop instead of memcpy - for (int e = threadIdx.x; e < sizeof(nd4j::graph::RandomGenerator); e+= blockDim.x) + for (int e = threadIdx.x; e < sizeof(sd::graph::RandomGenerator); e+= blockDim.x) cB[e] = dB[e]; __syncthreads(); @@ -245,16 +245,16 @@ namespace functions { __shared__ Nd4jLong length; __shared__ Nd4jLong ews; - __shared__ nd4j::graph::RandomGenerator *buffer; + __shared__ sd::graph::RandomGenerator *buffer; __shared__ unsigned char *cB; __shared__ unsigned char *dB; - __shared__ nd4j::graph::RandomGenerator *devBuffer; + __shared__ sd::graph::RandomGenerator *devBuffer; if (threadIdx.x == 0) { extern __shared__ unsigned char shmem[]; - buffer = (nd4j::graph::RandomGenerator *) shmem; + buffer = (sd::graph::RandomGenerator *) shmem; cB = shmem; - devBuffer = reinterpret_cast (state); + devBuffer = reinterpret_cast (state); dB = reinterpret_cast (state); length = shape::length(zShapeBuffer); ews = shape::elementWiseStride(zShapeBuffer); @@ -262,7 +262,7 @@ namespace functions { __syncthreads(); // using this loop instead of memcpy - for (int e = threadIdx.x; e < sizeof(nd4j::graph::RandomGenerator); e+= blockDim.x) + for (int e = threadIdx.x; e < sizeof(sd::graph::RandomGenerator); e+= blockDim.x) cB[e] = dB[e]; __syncthreads(); diff --git a/libnd4j/include/loops/cuda/reduce/reduce_bool.cu b/libnd4j/include/loops/cuda/reduce/reduce_bool.cu index 52ca3decc..3aa2626a2 100644 --- a/libnd4j/include/loops/cuda/reduce/reduce_bool.cu +++ b/libnd4j/include/loops/cuda/reduce/reduce_bool.cu @@ -19,7 +19,7 @@ // @author Yurii Shyrma (iuriish@yahoo.com) // -#include +#include #include #include #include @@ -135,7 +135,7 @@ __device__ void ReduceBoolFunction::transformCudaXD( void *vx, Nd4jLong *xS __syncthreads(); // aggregate. do NOT reduce for elements > tadLength - aggregatePartials(sPartials, threadIdx.x, nd4j::math::nd4j_min(blockDim.x, tadLength), extraParams); + aggregatePartials(sPartials, threadIdx.x, sd::math::nd4j_min(blockDim.x, tadLength), extraParams); __syncthreads(); @@ -183,7 +183,7 @@ __device__ void ReduceBoolFunction::execScalarCuda(void *vx, Nd4jLong *xSha sPartials[threadIdx.x] = OpType::update(sPartials[threadIdx.x], OpType::op(x[shape::getIndexOffset(i, xShapeInfo)], extraParams), extraParams); __syncthreads(); - aggregatePartials(sPartials, threadIdx.x, nd4j::math::nd4j_min(blockDim.x, len), extraParams); + aggregatePartials(sPartials, threadIdx.x, sd::math::nd4j_min(blockDim.x, len), extraParams); __syncthreads(); if (gridDim.x > 1) { @@ -214,7 +214,7 @@ __device__ void ReduceBoolFunction::execScalarCuda(void *vx, Nd4jLong *xSha sPartials[threadIdx.x] = OpType::update(sPartials[threadIdx.x], reductionBuffer[i], extraParams); __syncthreads(); - aggregatePartials(sPartials, threadIdx.x, nd4j::math::nd4j_min(gridDim.x, blockDim.x), extraParams); + aggregatePartials(sPartials, threadIdx.x, sd::math::nd4j_min(gridDim.x, blockDim.x), extraParams); __syncthreads(); if (threadIdx.x == 0) { @@ -246,19 +246,19 @@ __host__ void ReduceBoolFunction::intermediateXD(dim3 launchDims, cudaStrea const auto startingVal = static_cast(OpType::startingValue(reinterpret_cast(x))); - auto res = cudaMemcpyAsync(nd4j::LaunchContext::defaultContext()->getScalarPointer(), &startingVal, sizeof(Z), cudaMemcpyHostToDevice, *stream); + auto res = cudaMemcpyAsync(sd::LaunchContext::defaultContext()->getScalarPointer(), &startingVal, sizeof(Z), cudaMemcpyHostToDevice, *stream); if (res != 0) - throw nd4j::cuda_exception::build("ReduceBoolFunction::intermediateXD: failed to copy temporary scalar", res); + throw sd::cuda_exception::build("ReduceBoolFunction::intermediateXD: failed to copy temporary scalar", res); - auto ptr = nd4j::LaunchContext::defaultContext()->getScalarPointer(); + auto ptr = sd::LaunchContext::defaultContext()->getScalarPointer(); // scalar assign functions::scalar::ScalarTransform::executeCudaShaped(launchDims, stream, 14, z, zShapeInfo, hZShapeInfo, z, zShapeInfo, hZShapeInfo, ptr, nullptr); - nd4j::DebugHelper::checkErrorCode(stream, "reduceBoolDim empty(...) failed"); + sd::DebugHelper::checkErrorCode(stream, "reduceBoolDim empty(...) failed"); } else { simpleReduce<<>>(x, xShapeInfo, extraParams, z, zShapeInfo, dimension, dimensionLength, reductionPointer, tadShapeInfo, tadOffsets); - nd4j::DebugHelper::checkErrorCode(stream, "reduceBoolDim(...) failed"); + sd::DebugHelper::checkErrorCode(stream, "reduceBoolDim(...) failed"); } } @@ -276,14 +276,14 @@ __host__ void ReduceBoolFunction::intermediateScalar(dim3 launchDims, cudaS auto res = cudaMemcpyAsync(z, &startingVal, sizeof(Z), cudaMemcpyHostToDevice, *stream); if (res != 0) - throw nd4j::cuda_exception::build("ReduceBoolFunction::intermediateScalar: failed to copy resulting scalar", res); + throw sd::cuda_exception::build("ReduceBoolFunction::intermediateScalar: failed to copy resulting scalar", res); - nd4j::DebugHelper::checkErrorCode(stream, "reduceBoolScalar empty(...) failed"); + sd::DebugHelper::checkErrorCode(stream, "reduceBoolScalar empty(...) failed"); } else { simpleScalar<<>>(x, xShapeInfo, extraParams, z, zShapeInfo, dimension, dimensionLength, reductionBuffer, tadOnlyShapeInfo); - nd4j::DebugHelper::checkErrorCode(stream, "reduceBoolScalar(...) failed"); + sd::DebugHelper::checkErrorCode(stream, "reduceBoolScalar(...) failed"); } } @@ -292,7 +292,7 @@ template _CUDA_H void ReduceBoolFunction::execReduceScalar(dim3 launchDims, cudaStream_t *stream, int opNum, void *x, Nd4jLong *xShapeInfo, Nd4jLong *hXShapeInfo, void *extraParams, void *z, Nd4jLong *zShapeInfo, Nd4jLong *hZShapeInfo, int *dimension, int dimensionLength, void *reductionBuffer, Nd4jLong *tadOnlyShapeInfo) { DISPATCH_BY_OPNUM_TT(intermediateScalar, PARAMS(launchDims, stream, x, xShapeInfo, hXShapeInfo, extraParams, z, zShapeInfo, hZShapeInfo, dimension, dimensionLength, reductionBuffer, tadOnlyShapeInfo), OPS_A(REDUCE_BOOL_OPS)); - nd4j::DebugHelper::checkErrorCode(stream, "execReduceScalarFloat(...) failed"); + sd::DebugHelper::checkErrorCode(stream, "execReduceScalarFloat(...) failed"); } //////////////////////////////////////////////////////////////////////// diff --git a/libnd4j/include/loops/cuda/reduce/reduce_float.chpp b/libnd4j/include/loops/cuda/reduce/reduce_float.chpp index 110cc0f68..e1b95ae55 100644 --- a/libnd4j/include/loops/cuda/reduce/reduce_float.chpp +++ b/libnd4j/include/loops/cuda/reduce/reduce_float.chpp @@ -20,13 +20,13 @@ #include #include -#include +#include #include #include #include #include #include -#include +#include #include #include @@ -135,7 +135,7 @@ __device__ void ReduceFloatFunction::transformCudaXD( void *vx, Nd4jLong *x __syncthreads(); // aggregate. do NOT reduce for elements > tadLength - aggregatePartials(sPartials, threadIdx.x, nd4j::math::nd4j_min(blockDim.x, tadLength), extraParams); + aggregatePartials(sPartials, threadIdx.x, sd::math::nd4j_min(blockDim.x, tadLength), extraParams); __syncthreads(); if (threadIdx.x == 0) @@ -182,7 +182,7 @@ __device__ void ReduceFloatFunction::execScalarCuda(void *vx, Nd4jLong *xSh sPartials[threadIdx.x] = OpType::update(sPartials[threadIdx.x], OpType::op(x[shape::getIndexOffset(i, xShapeInfo)], extraParams), extraParams); __syncthreads(); - aggregatePartials(sPartials, threadIdx.x, nd4j::math::nd4j_min(blockDim.x, len), extraParams); + aggregatePartials(sPartials, threadIdx.x, sd::math::nd4j_min(blockDim.x, len), extraParams); __syncthreads(); if (gridDim.x > 1) { @@ -213,7 +213,7 @@ __device__ void ReduceFloatFunction::execScalarCuda(void *vx, Nd4jLong *xSh sPartials[threadIdx.x] = OpType::update(sPartials[threadIdx.x], reductionBuffer[i], extraParams); __syncthreads(); - aggregatePartials(sPartials, threadIdx.x, nd4j::math::nd4j_min(gridDim.x, blockDim.x), extraParams); + aggregatePartials(sPartials, threadIdx.x, sd::math::nd4j_min(gridDim.x, blockDim.x), extraParams); __syncthreads(); if (threadIdx.x == 0) { @@ -241,12 +241,12 @@ __host__ void ReduceFloatFunction::intermediateXD(dim3 launchDims, cudaStre if(shape::isEmpty(hZShapeInfo)) return; - const auto startingVal = std::is_same>::value ? nd4j::DataTypeUtils::nanOrZero() : static_cast(OpType::startingValue(reinterpret_cast(x))); - auto res = cudaMemcpyAsync(nd4j::LaunchContext::defaultContext()->getScalarPointer(), &startingVal, sizeof(Z), cudaMemcpyHostToDevice, *stream); + const auto startingVal = std::is_same>::value ? sd::DataTypeUtils::nanOrZero() : static_cast(OpType::startingValue(reinterpret_cast(x))); + auto res = cudaMemcpyAsync(sd::LaunchContext::defaultContext()->getScalarPointer(), &startingVal, sizeof(Z), cudaMemcpyHostToDevice, *stream); if (res != 0) - throw nd4j::cuda_exception::build("ReduceFloatFunction::intermediateXD: failed to copy temporary scalar", res); + throw sd::cuda_exception::build("ReduceFloatFunction::intermediateXD: failed to copy temporary scalar", res); - auto ptr = nd4j::LaunchContext::defaultContext()->getScalarPointer(); + auto ptr = sd::LaunchContext::defaultContext()->getScalarPointer(); // scalar assign functions::scalar::ScalarTransform::executeCudaShaped(launchDims, stream, 14, z, zShape, hZShapeInfo, z, zShape, hZShapeInfo, ptr, nullptr); @@ -266,11 +266,11 @@ __host__ void ReduceFloatFunction::intermediateScalar(dim3 launchDims, cuda if (shape::isEmpty(hZShapeInfo)) return; - const auto startingVal = std::is_same>::value ? nd4j::DataTypeUtils::nanOrZero() : static_cast(OpType::startingValue(reinterpret_cast(x))); + const auto startingVal = std::is_same>::value ? sd::DataTypeUtils::nanOrZero() : static_cast(OpType::startingValue(reinterpret_cast(x))); auto res = cudaMemcpyAsync(z, &startingVal, sizeof(Z), cudaMemcpyHostToDevice, *stream); if (res != 0) - throw nd4j::cuda_exception::build("ReduceFloatFunction::intermediateScalar: failed to copy resulting scalar", res); + throw sd::cuda_exception::build("ReduceFloatFunction::intermediateScalar: failed to copy resulting scalar", res); } else { simpleScalar << < launchDims.x, launchDims.y, launchDims.z, *stream>>>(x, xShapeInfo, extraParams, z, zShapeInfo, dimension, dimensionLength, reductionBuffer, tadOnlyShapeInfo); @@ -282,7 +282,7 @@ template _CUDA_H void ReduceFloatFunction::execReduceScalar(dim3 launchDims, cudaStream_t *stream, int opNum, void *x, Nd4jLong *xShapeInfo, Nd4jLong *hXShapeInfo, void *extraParams, void *z, Nd4jLong *zShapeInfo, Nd4jLong *hZShapeInfo, int *dimension, int dimensionLength, void *reductionBuffer, Nd4jLong *tadOnlyShapeInfo) { DISPATCH_BY_OPNUM_TT(intermediateScalar, PARAMS(launchDims, stream, x, xShapeInfo, hXShapeInfo, extraParams, z, zShapeInfo, hZShapeInfo, dimension, dimensionLength, reductionBuffer, tadOnlyShapeInfo), OPS_A(REDUCE_FLOAT_OPS)); - nd4j::DebugHelper::checkErrorCode(stream, "execReduceScalarFloat(...) failed"); + sd::DebugHelper::checkErrorCode(stream, "execReduceScalarFloat(...) failed"); } //////////////////////////////////////////////////////////////////////// diff --git a/libnd4j/include/loops/cuda/reduce/reduce_long.cu b/libnd4j/include/loops/cuda/reduce/reduce_long.cu index 79ab25280..e55ecd11c 100644 --- a/libnd4j/include/loops/cuda/reduce/reduce_long.cu +++ b/libnd4j/include/loops/cuda/reduce/reduce_long.cu @@ -19,7 +19,7 @@ // @author Yurii Shyrma (iuriish@yahoo.com) // -#include +#include #include #include #include @@ -156,7 +156,7 @@ __device__ void ReduceLongFunction::transformCudaXD( void *vx, Nd4jLong *xS __syncthreads(); // aggregate. do NOT reduce for elements > tadLength - aggregatePartials(sPartials, threadIdx.x, nd4j::math::nd4j_min(blockDim.x, tadLength), extraParams); + aggregatePartials(sPartials, threadIdx.x, sd::math::nd4j_min(blockDim.x, tadLength), extraParams); __syncthreads(); if (threadIdx.x == 0) @@ -203,7 +203,7 @@ __device__ void ReduceLongFunction::execScalarCuda(void *vx, Nd4jLong *xSha sPartials[threadIdx.x] = OpType::update(sPartials[threadIdx.x], OpType::op(x[shape::getIndexOffset(i, xShapeInfo)], extraParams), extraParams); __syncthreads(); - aggregatePartials(sPartials, threadIdx.x, nd4j::math::nd4j_min(blockDim.x, len), extraParams); + aggregatePartials(sPartials, threadIdx.x, sd::math::nd4j_min(blockDim.x, len), extraParams); __syncthreads(); if (gridDim.x > 1) { @@ -233,7 +233,7 @@ __device__ void ReduceLongFunction::execScalarCuda(void *vx, Nd4jLong *xSha sPartials[threadIdx.x] = OpType::update(sPartials[threadIdx.x], reductionBuffer[i], extraParams); __syncthreads(); - aggregatePartials(sPartials, threadIdx.x, nd4j::math::nd4j_min(gridDim.x, blockDim.x), extraParams); + aggregatePartials(sPartials, threadIdx.x, sd::math::nd4j_min(gridDim.x, blockDim.x), extraParams); __syncthreads(); if (threadIdx.x == 0) { @@ -263,11 +263,11 @@ __host__ void ReduceLongFunction::intermediateXD(dim3 launchDims, cudaStrea const auto startingVal = static_cast(OpType::startingValue(reinterpret_cast(x))); - auto res = cudaMemcpyAsync(nd4j::LaunchContext::defaultContext()->getScalarPointer(), &startingVal, sizeof(Z), cudaMemcpyHostToDevice, *stream); + auto res = cudaMemcpyAsync(sd::LaunchContext::defaultContext()->getScalarPointer(), &startingVal, sizeof(Z), cudaMemcpyHostToDevice, *stream); if (res != 0) - throw nd4j::cuda_exception::build("ReduceLongFunction::intermediateXD: failed to copy temporary scalar", res); + throw sd::cuda_exception::build("ReduceLongFunction::intermediateXD: failed to copy temporary scalar", res); - auto ptr = nd4j::LaunchContext::defaultContext()->getScalarPointer(); + auto ptr = sd::LaunchContext::defaultContext()->getScalarPointer(); // scalar assign functions::scalar::ScalarTransform::executeCudaShaped(launchDims, stream, 14, z, zShapeInfo, hXShapeInfo, z, zShapeInfo, hZShapeInfo, ptr, nullptr); @@ -291,7 +291,7 @@ __host__ void ReduceLongFunction::intermediateScalar(dim3 launchDims, cudaS auto res = cudaMemcpyAsync(z, &startingVal, sizeof(Z), cudaMemcpyHostToDevice, *stream); if (res != 0) - throw nd4j::cuda_exception::build("ReduceLongFunction::intermediateScalar: failed to copy resulting scalar", res); + throw sd::cuda_exception::build("ReduceLongFunction::intermediateScalar: failed to copy resulting scalar", res); } else { simpleScalar<<>>(x, xShapeInfo, extraParams, z, zShapeInfo, dimension, dimensionLength, reductionBuffer, tadOnlyShapeInfo); @@ -303,7 +303,7 @@ template _CUDA_H void ReduceLongFunction::execReduceScalar(dim3 launchDims, cudaStream_t *stream, int opNum, void *x, Nd4jLong *xShapeInfo, Nd4jLong* hXShapeInfo, void *extraParams, void *z, Nd4jLong *zShapeInfo, Nd4jLong* hZShapeInfo, int *dimension, int dimensionLength, void *reductionBuffer, Nd4jLong *tadOnlyShapeInfo) { DISPATCH_BY_OPNUM_TT(intermediateScalar, PARAMS(launchDims, stream, x, xShapeInfo, hXShapeInfo, extraParams, z, zShapeInfo, hZShapeInfo, dimension, dimensionLength, reductionBuffer, tadOnlyShapeInfo), OPS_A(REDUCE_LONG_OPS)); - nd4j::DebugHelper::checkErrorCode(stream, "execReduceScalarFloat(...) failed"); + sd::DebugHelper::checkErrorCode(stream, "execReduceScalarFloat(...) failed"); } //////////////////////////////////////////////////////////////////////// diff --git a/libnd4j/include/loops/cuda/reduce/reduce_same.cu b/libnd4j/include/loops/cuda/reduce/reduce_same.cu index bcf5bab7f..c3c74c806 100644 --- a/libnd4j/include/loops/cuda/reduce/reduce_same.cu +++ b/libnd4j/include/loops/cuda/reduce/reduce_same.cu @@ -19,7 +19,7 @@ // @author Yurii Shyrma (iuriish@yahoo.com) // -#include +#include #include #include #include @@ -145,7 +145,7 @@ __device__ void ReduceSameFunction::transformCudaXD( void *vx, Nd4jLong *xSha __syncthreads(); // aggregate. do NOT reduce for elements > tadLength - aggregatePartials(sPartials, threadIdx.x, nd4j::math::nd4j_min(blockDim.x, tadLength), extraParams); + aggregatePartials(sPartials, threadIdx.x, sd::math::nd4j_min(blockDim.x, tadLength), extraParams); __syncthreads(); if (threadIdx.x == 0) @@ -200,7 +200,7 @@ __device__ void ReduceSameFunction::execScalarCuda(void *vx, Nd4jLong *xShape sPartials[threadIdx.x] = OpType::update(sPartials[threadIdx.x], OpType::op(x[shape::getIndexOffset(i, xShapeInfo)], extraParams), extraParams); __syncthreads(); - aggregatePartials(sPartials, threadIdx.x, nd4j::math::nd4j_min(blockDim.x, len), extraParams); + aggregatePartials(sPartials, threadIdx.x, sd::math::nd4j_min(blockDim.x, len), extraParams); __syncthreads(); if (gridDim.x > 1) { @@ -230,7 +230,7 @@ __device__ void ReduceSameFunction::execScalarCuda(void *vx, Nd4jLong *xShape sPartials[threadIdx.x] = OpType::update(sPartials[threadIdx.x], reductionBuffer[i], extraParams); __syncthreads(); - aggregatePartials(sPartials, threadIdx.x, nd4j::math::nd4j_min(gridDim.x, blockDim.x), extraParams); + aggregatePartials(sPartials, threadIdx.x, sd::math::nd4j_min(gridDim.x, blockDim.x), extraParams); __syncthreads(); if (threadIdx.x == 0) { @@ -260,11 +260,11 @@ __host__ void ReduceSameFunction::intermediateXD(dim3 launchDims, cudaStream_ const auto startingVal = static_cast(OpType::startingValue(reinterpret_cast(x))); - auto res = cudaMemcpyAsync(nd4j::LaunchContext::defaultContext()->getScalarPointer(), &startingVal, sizeof(X), cudaMemcpyHostToDevice, *stream); + auto res = cudaMemcpyAsync(sd::LaunchContext::defaultContext()->getScalarPointer(), &startingVal, sizeof(X), cudaMemcpyHostToDevice, *stream); if (res != 0) - throw nd4j::cuda_exception::build("ReduceSameFunction::intermediateXD: failed to copy temporary scalar", res); + throw sd::cuda_exception::build("ReduceSameFunction::intermediateXD: failed to copy temporary scalar", res); - auto ptr = nd4j::LaunchContext::defaultContext()->getScalarPointer(); + auto ptr = sd::LaunchContext::defaultContext()->getScalarPointer(); // scalar assign functions::scalar::ScalarTransform::executeCudaShaped(launchDims, stream, 14, z, zShapeInfo, hXShapeInfo, z, zShapeInfo, hZShapeInfo, ptr, nullptr); @@ -288,7 +288,7 @@ __host__ void ReduceSameFunction::intermediateScalar(dim3 launchDims, cudaStr auto res = cudaMemcpyAsync(z, &startingVal, sizeof(X), cudaMemcpyHostToDevice, *stream); if (res != 0) - throw nd4j::cuda_exception::build("ReduceSameFunction::intermediateScalar: failed to copy resulting scalar", res); + throw sd::cuda_exception::build("ReduceSameFunction::intermediateScalar: failed to copy resulting scalar", res); } else { simpleScalar<<>>(x, xShapeInfo, extraParams, z, zShapeInfo, dimension, dimensionLength, reductionBuffer, tadOnlyShapeInfo); @@ -300,7 +300,7 @@ template _CUDA_H void ReduceSameFunction::execReduceScalar(dim3 launchDims, cudaStream_t *stream, int opNum, void *x, Nd4jLong *xShapeInfo, Nd4jLong* hXShapeInfo, void *extraParams, void *z, Nd4jLong *zShapeInfo, Nd4jLong* hZShapeInfo, int *dimension, int dimensionLength, void *reductionBuffer, Nd4jLong *tadOnlyShapeInfo) { DISPATCH_BY_OPNUM_T(intermediateScalar, PARAMS(launchDims, stream, x, xShapeInfo, hXShapeInfo, extraParams, z, zShapeInfo, hZShapeInfo, dimension, dimensionLength, reductionBuffer, tadOnlyShapeInfo), REDUCE_SAME_OPS); - nd4j::DebugHelper::checkErrorCode(stream, "execReduceScalarSame(...) failed"); + sd::DebugHelper::checkErrorCode(stream, "execReduceScalarSame(...) failed"); } //////////////////////////////////////////////////////////////////////// diff --git a/libnd4j/include/loops/cuda/reduce3.chpp b/libnd4j/include/loops/cuda/reduce3.chpp index ac1d1adc3..2fa16e9ac 100644 --- a/libnd4j/include/loops/cuda/reduce3.chpp +++ b/libnd4j/include/loops/cuda/reduce3.chpp @@ -18,11 +18,11 @@ // @author Yurii Shyrma (iuriish@yahoo.com), created on 19.11.2018 -#include +#include #include #include #include -#include +#include using namespace simdOps; @@ -168,7 +168,7 @@ __device__ void Reduce3::execScalarCuda( void *vx, Nd4jLong *xShapeInfo, } __syncthreads(); - aggregatePartials(reinterpret_cast(sPartials), threadIdx.x, nd4j::math::nd4j_min(blockDim.x, length), extraZ); + aggregatePartials(reinterpret_cast(sPartials), threadIdx.x, sd::math::nd4j_min(blockDim.x, length), extraZ); __syncthreads(); if (gridDim.x > 1) { @@ -214,7 +214,7 @@ __device__ void Reduce3::execScalarCuda( void *vx, Nd4jLong *xShapeInfo, sPartials[threadIdx.x] = OpType::update(sPartials[threadIdx.x], static_cast(reductionBuffer)[i], extraZ); __syncthreads(); - aggregatePartials(reinterpret_cast(sPartials), threadIdx.x, nd4j::math::nd4j_min(gridDim.x, blockDim.x), extraZ); + aggregatePartials(reinterpret_cast(sPartials), threadIdx.x, sd::math::nd4j_min(gridDim.x, blockDim.x), extraZ); __syncthreads(); if (threadIdx.x == 0) @@ -324,7 +324,7 @@ __device__ void Reduce3::transformAll( void *vx, Nd4jLong *xShapeInfo, __syncthreads(); } - aggregatePartials(reinterpret_cast(sPartials), threadIdx.x, nd4j::math::nd4j_min(blockDim.x, xTadLength), extraZ); + aggregatePartials(reinterpret_cast(sPartials), threadIdx.x, sd::math::nd4j_min(blockDim.x, xTadLength), extraZ); __syncthreads(); if (threadIdx.x == 0) { @@ -410,7 +410,7 @@ __device__ void Reduce3::transform(void *vx, Nd4jLong *xShapeInfo, } __syncthreads(); - aggregatePartials(reinterpret_cast(sPartials), threadIdx.x, nd4j::math::nd4j_min(blockDim.x, tadLen), extraZ); + aggregatePartials(reinterpret_cast(sPartials), threadIdx.x, sd::math::nd4j_min(blockDim.x, tadLen), extraZ); __syncthreads(); if (threadIdx.x == 0) @@ -440,7 +440,7 @@ __device__ void Reduce3::transform(void *vx, Nd4jLong *xShapeInfo, } __syncthreads(); - aggregatePartials(reinterpret_cast(sPartials), threadIdx.x, nd4j::math::nd4j_min(blockDim.x, tadLen), extraZ); + aggregatePartials(reinterpret_cast(sPartials), threadIdx.x, sd::math::nd4j_min(blockDim.x, tadLen), extraZ); __syncthreads(); if (threadIdx.x == 0) @@ -515,7 +515,7 @@ __host__ void Reduce3::exec(dim3 launchDims, cudaStream_t *stream, Nd4jLong *yTadOnlyShapeInfo, Nd4jLong *yTadOffsets) { execGeneric<<>>(opNum, vx, xShapeInfo, vy, yShapeInfo, extraParams, vz, zShapeInfo, dimension, dimensionLength, postProcessOrNot, allocationPointer, tadOnlyShapeInfo, tadOffsets, yTadOnlyShapeInfo, yTadOffsets); - nd4j::DebugHelper::checkErrorCode(stream, "reduce3exec(...) failed"); + sd::DebugHelper::checkErrorCode(stream, "reduce3exec(...) failed"); } //////////////////////////////////////////////////////////////////////// @@ -533,7 +533,7 @@ __host__ void Reduce3::exec(dim3 launchDims, cudaStream_t *stream, Nd4jLong *yTadOnlyShapeInfo, Nd4jLong *yTadOffsets) { execAllGeneric<<>>(opNum, vx, xShapeInfo, vy, yShapeInfo, extraParams, vz, zShapeInfo, dimension, dimensionLength, postProcessOrNot, allocationPointer, tadOnlyShapeInfo, tadOffsets, yTadOnlyShapeInfo, yTadOffsets); - nd4j::DebugHelper::checkErrorCode(stream, "execAllGeneric(...) failed"); + sd::DebugHelper::checkErrorCode(stream, "execAllGeneric(...) failed"); } //////////////////////////////////////////////////////////////////////// @@ -549,7 +549,7 @@ __host__ void Reduce3::execScalar(dim3 launchDims, cudaStream_t *stream, Nd4jLong *tadOnlyShapeInfo) { execScalarGeneric<<>>(opNum, vx, xShapeInfo, vy, yShapeInfo, extraParams, vz, zShapeInfo, allocationPointer, reductionBuffer, tadOnlyShapeInfo); - nd4j::DebugHelper::checkErrorCode(stream, "execScalarGeneric(...) failed"); + sd::DebugHelper::checkErrorCode(stream, "execScalarGeneric(...) failed"); } diff --git a/libnd4j/include/loops/cuda/reduce3.cu b/libnd4j/include/loops/cuda/reduce3.cu index 4f0e0457c..c1d63e8dd 100644 --- a/libnd4j/include/loops/cuda/reduce3.cu +++ b/libnd4j/include/loops/cuda/reduce3.cu @@ -19,11 +19,11 @@ // -#include +#include #include #include #include -#include +#include namespace functions { namespace reduce3 { diff --git a/libnd4j/include/loops/cuda/scalar.chpp b/libnd4j/include/loops/cuda/scalar.chpp index 7277138f9..ec1b42334 100644 --- a/libnd4j/include/loops/cuda/scalar.chpp +++ b/libnd4j/include/loops/cuda/scalar.chpp @@ -24,7 +24,7 @@ #include "loops/scalar.h" #include #include -#include +#include #include #include @@ -137,7 +137,7 @@ void _CUDA_H ScalarTransform::intermediateShaped(dim3& launchDims, cudaSt auto length = shape::length(hxShapeInfo); scalarSimpleShaped<<>>(vx, vscalar, xShapeInfo, vextraParams, vz, zShapeInfo, allocPointer); - nd4j::DebugHelper::checkErrorCode(stream, "scalarSimpleShapedA(...) failed"); + sd::DebugHelper::checkErrorCode(stream, "scalarSimpleShapedA(...) failed"); } //////////////////////////////////////////////////////////////////////////////// @@ -145,14 +145,14 @@ template template void _CUDA_H ScalarTransform::intermediateAlongDimension(dim3& launchDims, cudaStream_t *stream, void *x, Nd4jLong *xShapeInfo, void *z, Nd4jLong *zShapeInfo, void *scalars, void *extraParams, int *dimension, int dimensionLength, Nd4jLong *tadShapeInfo, Nd4jLong *tadOffsets, Nd4jLong *tadShapeInfoZ, Nd4jLong *tadOffsetsZ) { scalarAlongDimension<<>>(x, xShapeInfo, extraParams, z, zShapeInfo, scalars, dimension, dimensionLength, tadShapeInfo, tadOffsets, tadShapeInfoZ, tadOffsetsZ); - nd4j::DebugHelper::checkErrorCode(stream, "scalarAlongDimA(...) failed"); + sd::DebugHelper::checkErrorCode(stream, "scalarAlongDimA(...) failed"); } //////////////////////////////////////////////////////////////////////////////// template void ScalarTransform::executeCudaShaped(dim3& launchDims, cudaStream_t *stream, int opNum, void *vx, Nd4jLong *xShapeInfo, Nd4jLong *hxShapeInfo, void *vz, Nd4jLong *zShapeInfo, Nd4jLong *hzShapeInfo, void* vscalar, void *vextraParams) { - if (nd4j::Environment::getInstance()->isDebugAndVerbose()) + if (sd::Environment::getInstance()->isDebugAndVerbose()) printf("H14 opNum:[%i]\n", opNum); DISPATCH_BY_OPNUM_TTT(intermediateShaped, PARAMS(launchDims, stream, vx, xShapeInfo, hxShapeInfo, vz, zShapeInfo, hzShapeInfo, vscalar, vextraParams, nullptr), SCALAR_OPS); diff --git a/libnd4j/include/loops/cuda/scalar.cu b/libnd4j/include/loops/cuda/scalar.cu index 67cbc7a98..26c3e5cb8 100644 --- a/libnd4j/include/loops/cuda/scalar.cu +++ b/libnd4j/include/loops/cuda/scalar.cu @@ -21,7 +21,7 @@ #include "loops/scalar.h" #include #include -#include +#include #include #include diff --git a/libnd4j/include/loops/cuda/scalar_bool.cu b/libnd4j/include/loops/cuda/scalar_bool.cu index bb498c3a9..1c8929ef3 100644 --- a/libnd4j/include/loops/cuda/scalar_bool.cu +++ b/libnd4j/include/loops/cuda/scalar_bool.cu @@ -20,7 +20,7 @@ // #include "../scalar_bool.h" -#include +#include #include #include "../legacy_ops.h" @@ -193,7 +193,7 @@ _CUDA_H void ScalarBoolTransform::intermediateAlongDimension(dim3& launchD Nd4jLong *tadShapeInfoZ, Nd4jLong *tadOffsetsZ) { scalarAlongDimension<<>>(x, xShapeInfo, extraParams, z, zShapeInfo, scalars, dimension, dimensionLength, tadShapeInfo, tadOffsets, tadShapeInfoZ, tadOffsetsZ); - nd4j::DebugHelper::checkErrorCode(stream, "scalarAlongDim(...) failed"); + sd::DebugHelper::checkErrorCode(stream, "scalarAlongDim(...) failed"); } //////////////////////////////////////////////////////////////////////// @@ -206,7 +206,7 @@ void _CUDA_H ScalarBoolTransform::intermediateShaped(dim3& launchDims, cuda void *vextraParams, int *allocPointer){ scalarSimpleShaped<<>>(vx, vscalar, xShapeInfo, vextraParams, vz, zShapeInfo, allocPointer); - nd4j::DebugHelper::checkErrorCode(stream, "scalarSimpleShaped(...) failed"); + sd::DebugHelper::checkErrorCode(stream, "scalarSimpleShaped(...) failed"); } //////////////////////////////////////////////////////////////////////// @@ -218,7 +218,7 @@ void ScalarBoolTransform::executeCudaShaped(dim3& launchDims, cudaStream_t void* vscalar, void *vextraParams) { - if (nd4j::Environment::getInstance()->isDebugAndVerbose()) + if (sd::Environment::getInstance()->isDebugAndVerbose()) printf("H14 opNum:[%i]\n", opNum); DISPATCH_BY_OPNUM_TT(intermediateShaped, PARAMS(launchDims, stream, vx, xShapeInfo, vz, zShapeInfo, vscalar, vextraParams, nullptr), SCALAR_BOOL_OPS); diff --git a/libnd4j/include/loops/cuda/scalar_int.cu b/libnd4j/include/loops/cuda/scalar_int.cu index f25beca82..bb761c76c 100644 --- a/libnd4j/include/loops/cuda/scalar_int.cu +++ b/libnd4j/include/loops/cuda/scalar_int.cu @@ -20,7 +20,7 @@ // #include "../scalar_int.h" -#include +#include #include #include "../legacy_ops.h" @@ -216,7 +216,7 @@ void ScalarIntTransform::executeCudaShaped(dim3& launchDims, cudaStream_t *st void* vscalar, void *vextraParams) { - if (nd4j::Environment::getInstance()->isDebugAndVerbose()) + if (sd::Environment::getInstance()->isDebugAndVerbose()) printf("H14 opNum:[%i]\n", opNum); DISPATCH_BY_OPNUM_T(intermediateShaped, PARAMS(launchDims, stream, vx, xShapeInfo, vz, zShapeInfo, vscalar, vextraParams, nullptr), SCALAR_INT_OPS); diff --git a/libnd4j/include/loops/cuda/specials/accumulateKernel.cu b/libnd4j/include/loops/cuda/specials/accumulateKernel.cu index c649d6834..6d6dd42a4 100644 --- a/libnd4j/include/loops/cuda/specials/accumulateKernel.cu +++ b/libnd4j/include/loops/cuda/specials/accumulateKernel.cu @@ -21,7 +21,7 @@ #include -namespace nd4j { +namespace sd { /////////////////////////////////////////////////////////////////////// /** @@ -83,7 +83,7 @@ namespace nd4j { accumulateKernelGeneric(dim3 &launchDims, cudaStream_t *stream, void **vx, void *vz, int n, const Nd4jLong length) { execAccumulateKernel<<< launchDims.x, launchDims.y, launchDims.z, *stream>>> (vx, vz, n, length); - nd4j::DebugHelper::checkErrorCode(stream, "accumulate(...) failed"); + sd::DebugHelper::checkErrorCode(stream, "accumulate(...) failed"); } BUILD_SINGLE_TEMPLATE(template void ND4J_EXPORT accumulateKernelGeneric, (dim3 & launchDims, cudaStream_t * stream, void * *vx, void * vz, int n, const Nd4jLong length), LIBND4J_TYPES); diff --git a/libnd4j/include/loops/cuda/specials/averagingKernel.cu b/libnd4j/include/loops/cuda/specials/averagingKernel.cu index 0b6872c75..798b273cf 100644 --- a/libnd4j/include/loops/cuda/specials/averagingKernel.cu +++ b/libnd4j/include/loops/cuda/specials/averagingKernel.cu @@ -21,7 +21,7 @@ #include -namespace nd4j { +namespace sd { /////////////////////////////////////////////////////////////////////// template @@ -97,7 +97,7 @@ namespace nd4j { bool propagate) { execAveragingKernel<<< launchDims.x, launchDims.y, launchDims.z, *stream>>>(vdx, vdz, n, length, propagate); - nd4j::DebugHelper::checkErrorCode(stream, "averaging(...) failed"); + sd::DebugHelper::checkErrorCode(stream, "averaging(...) failed"); } BUILD_SINGLE_TEMPLATE(template void ND4J_EXPORT averagingKernelGeneric, (dim3 & launchDims, cudaStream_t * stream, void * *vdx, void * vdz, int n, Nd4jLong length, bool propagate), LIBND4J_TYPES); diff --git a/libnd4j/include/loops/cuda/specials/concatKernel.cu b/libnd4j/include/loops/cuda/specials/concatKernel.cu index b6ba2f00e..a4a849e49 100644 --- a/libnd4j/include/loops/cuda/specials/concatKernel.cu +++ b/libnd4j/include/loops/cuda/specials/concatKernel.cu @@ -21,7 +21,7 @@ #include -namespace nd4j { +namespace sd { /////////////////////////////////////////////////////////////////////// template __device__ void concatKernel(int numArrays, @@ -137,7 +137,7 @@ namespace nd4j { T *dataTAD = currentData + inputOffset; T *resultTAD = result + resultOffset; - Nd4jLong sub[MAX_RANK]; + int sub[MAX_RANK]; shape::index2coords(arrOffset, zTadShape, sub); @@ -166,7 +166,7 @@ namespace nd4j { auto dataTAD = currentData + inputOffset; auto resultTAD = result + resultOffset; - Nd4jLong sub[MAX_RANK]; + int sub[MAX_RANK]; shape::index2coords(arrOffset, zTadShape, sub); Nd4jLong baseOffset = shape::getOffset(zTadShape, sub); @@ -199,7 +199,7 @@ namespace nd4j { resultTAD[baseIdx + k * tadEWS] = dataTAD[k]; } } else { - Nd4jLong yIdx[MAX_RANK]; + int yIdx[MAX_RANK]; auto yRank = shape::rank(currentTad); for (int i = threadIdx.x; i < yLength; i+= blockDim.x) { @@ -214,8 +214,8 @@ namespace nd4j { //if (threadIdx.x == 0 && blockIdx.x == 0) // printf("Branch C; yLength: %i;\n", yLength); - Nd4jLong zIdx[MAX_RANK]; - Nd4jLong yIdx[MAX_RANK]; + int zIdx[MAX_RANK]; + int yIdx[MAX_RANK]; auto yRank = shape::rank(currentTad); auto tadRank = shape::rank(zTadShape); @@ -263,7 +263,7 @@ namespace nd4j { execConcatKernel<<>>(numArrays, data, inputShapeInfos, vz, zShapeInfo, tadPointers, offsetPointers, zTadShape, zOffsets); - nd4j::DebugHelper::checkErrorCode(stream, "concatGenericLegacy(...) failed"); + sd::DebugHelper::checkErrorCode(stream, "concatGenericLegacy(...) failed"); } BUILD_SINGLE_TEMPLATE(template void ND4J_EXPORT concatKernelGeneric, (dim3 & launchDims, cudaStream_t * stream, int numArrays, Nd4jPointer * data, Nd4jPointer * inputShapeInfos, void * vz, Nd4jLong *zShapeInfo, Nd4jPointer * tadPointers, Nd4jPointer * offsetPointers, Nd4jLong * zTadShape, Nd4jLong * zOffsets), LIBND4J_TYPES); diff --git a/libnd4j/include/loops/cuda/specials/concatKernelHStack.cu b/libnd4j/include/loops/cuda/specials/concatKernelHStack.cu index 229e65f34..8ef9dfd24 100644 --- a/libnd4j/include/loops/cuda/specials/concatKernelHStack.cu +++ b/libnd4j/include/loops/cuda/specials/concatKernelHStack.cu @@ -21,7 +21,7 @@ #include -namespace nd4j { +namespace sd { /////////////////////////////////////////////////////////////////////// template @@ -88,7 +88,7 @@ namespace nd4j { void *vz, Nd4jLong *zShapeInfo) { execConcatKernelHStack<<>>(numArrays, data, inputShapeInfos, vz, zShapeInfo); - nd4j::DebugHelper::checkErrorCode(stream, "concatHStack(...) failed"); + sd::DebugHelper::checkErrorCode(stream, "concatHStack(...) failed"); } BUILD_SINGLE_TEMPLATE(template void ND4J_EXPORT concatKernelHStackGeneric, (dim3 & launchDims, cudaStream_t * stream, int numArrays, Nd4jPointer * data, Nd4jPointer * inputShapeInfos, void * vz, Nd4jLong * zShapeInfo), LIBND4J_TYPES); diff --git a/libnd4j/include/loops/cuda/specials/concatKernelScalar.cu b/libnd4j/include/loops/cuda/specials/concatKernelScalar.cu index f08c3fae0..6614480f2 100644 --- a/libnd4j/include/loops/cuda/specials/concatKernelScalar.cu +++ b/libnd4j/include/loops/cuda/specials/concatKernelScalar.cu @@ -21,7 +21,7 @@ #include -namespace nd4j { +namespace sd { /////////////////////////////////////////////////////////////////////// template @@ -48,7 +48,7 @@ namespace nd4j { concatKernelScalarGeneric(dim3 &launchDims, cudaStream_t *stream, int numArrays, Nd4jPointer *data, void *vz) { execConcatKernelScalar<<>>(numArrays, data, vz); - nd4j::DebugHelper::checkErrorCode(stream, "concatScalar(...) failed"); + sd::DebugHelper::checkErrorCode(stream, "concatScalar(...) failed"); } BUILD_SINGLE_TEMPLATE(template void ND4J_EXPORT concatKernelScalarGeneric, (dim3 & launchDims, cudaStream_t * stream, int numArrays, Nd4jPointer * data, void * vz), LIBND4J_TYPES); diff --git a/libnd4j/include/loops/cuda/specials/concatKernelVStack.cu b/libnd4j/include/loops/cuda/specials/concatKernelVStack.cu index 27011e9d8..f95bad413 100644 --- a/libnd4j/include/loops/cuda/specials/concatKernelVStack.cu +++ b/libnd4j/include/loops/cuda/specials/concatKernelVStack.cu @@ -21,7 +21,7 @@ #include -namespace nd4j { +namespace sd { /////////////////////////////////////////////////////////////////////// template @@ -78,7 +78,7 @@ namespace nd4j { void *vz, Nd4jLong *zShapeInfo) { execConcatKernelVStack<<>>(numArrays, data, inputShapeInfos, vz, zShapeInfo); - nd4j::DebugHelper::checkErrorCode(stream, "concatVStack(...) failed"); + sd::DebugHelper::checkErrorCode(stream, "concatVStack(...) failed"); } BUILD_SINGLE_TEMPLATE(template void ND4J_EXPORT concatKernelVStackGeneric, (dim3 & launchDims, cudaStream_t * stream, int numArrays, Nd4jPointer * data, Nd4jPointer * inputShapeInfos, void * vz, Nd4jLong *zShapeInfo), LIBND4J_TYPES); diff --git a/libnd4j/include/loops/cuda/specials/convertHalfs.cu b/libnd4j/include/loops/cuda/specials/convertHalfs.cu index b1cd645ab..dec1705a4 100644 --- a/libnd4j/include/loops/cuda/specials/convertHalfs.cu +++ b/libnd4j/include/loops/cuda/specials/convertHalfs.cu @@ -21,7 +21,7 @@ #include -namespace nd4j { +namespace sd { /////////////////////////////////////////////////////////////////////// template @@ -39,7 +39,7 @@ namespace nd4j { __host__ void convertHalfsToGeneric(dim3 &launchDims, cudaStream_t *stream, half *dx, Nd4jLong n, void *dz) { execConvertHalfs<<>>(dx, n, dz); - nd4j::DebugHelper::checkErrorCode(stream, "convertHalfsToGeneric(...) failed"); + sd::DebugHelper::checkErrorCode(stream, "convertHalfsToGeneric(...) failed"); } BUILD_SINGLE_TEMPLATE(template void ND4J_EXPORT convertHalfsToGeneric, (dim3 & launchDims, cudaStream_t * stream, half * dx, Nd4jLong n, void * dz), LIBND4J_TYPES); diff --git a/libnd4j/include/loops/cuda/specials/convertToHalf.cu b/libnd4j/include/loops/cuda/specials/convertToHalf.cu index c3bfa3dc3..d86982d03 100644 --- a/libnd4j/include/loops/cuda/specials/convertToHalf.cu +++ b/libnd4j/include/loops/cuda/specials/convertToHalf.cu @@ -21,7 +21,7 @@ #include -namespace nd4j { +namespace sd { //////////////////////////////////////////////////////////////////////// template @@ -37,7 +37,7 @@ namespace nd4j { template __host__ void convertToHalfGeneric(dim3 &launchDims, cudaStream_t *stream, void *dx, Nd4jLong n, half *dz) { execConvertToHalf<<>>(dx, n, dz); - nd4j::DebugHelper::checkErrorCode(stream, "convertToHalfs(...) failed"); + sd::DebugHelper::checkErrorCode(stream, "convertToHalfs(...) failed"); } BUILD_SINGLE_TEMPLATE(template void ND4J_EXPORT convertToHalfGeneric, (dim3 & launchDims, cudaStream_t * stream, void * dx, Nd4jLong n, half * dz), LIBND4J_TYPES); diff --git a/libnd4j/include/loops/cuda/specials/fillDimensionalIsMax.cu b/libnd4j/include/loops/cuda/specials/fillDimensionalIsMax.cu index e39ff6bec..813de162d 100644 --- a/libnd4j/include/loops/cuda/specials/fillDimensionalIsMax.cu +++ b/libnd4j/include/loops/cuda/specials/fillDimensionalIsMax.cu @@ -21,7 +21,7 @@ #include -namespace nd4j { +namespace sd { //////////////////////////////////////////////////////////////////////// @@ -88,7 +88,7 @@ namespace nd4j { Nd4jLong *tadOffsets) { execfillDimensionalIsMax<<>>(dX, dZ, zShapeInfo, tadOnlyShapeInfo, dimension, dimensionLength, tadOffsets); - nd4j::DebugHelper::checkErrorCode(stream, "fillDimensionalIsMax(...) failed"); + sd::DebugHelper::checkErrorCode(stream, "fillDimensionalIsMax(...) failed"); } BUILD_SINGLE_TEMPLATE(template void ND4J_EXPORT fillDimensionalIsMaxGeneric, (dim3& launchDims, cudaStream_t *stream, void *dX, void *dZ, Nd4jLong *zShapeInfo, Nd4jLong *tadOnlyShapeInfo, int *dimension, int dimensionLength, Nd4jLong *tadOffsets), LIBND4J_TYPES); } \ No newline at end of file diff --git a/libnd4j/include/loops/cuda/specials/fillIsMax.cu b/libnd4j/include/loops/cuda/specials/fillIsMax.cu index c9ed51d28..1a994a13c 100644 --- a/libnd4j/include/loops/cuda/specials/fillIsMax.cu +++ b/libnd4j/include/loops/cuda/specials/fillIsMax.cu @@ -21,7 +21,7 @@ #include -namespace nd4j { +namespace sd { //////////////////////////////////////////////////////////////////////// template @@ -37,7 +37,7 @@ namespace nd4j { template __host__ void fillIsMaxGeneric(dim3 &launchDims, cudaStream_t *stream, void *dx, Nd4jLong *xShapeInfo, Nd4jLong length, long idx) { execFillIsMax<<>>(dx, xShapeInfo, length, idx); - nd4j::DebugHelper::checkErrorCode(stream, "fillIsMax(...) failed"); + sd::DebugHelper::checkErrorCode(stream, "fillIsMax(...) failed"); } diff --git a/libnd4j/include/loops/cuda/specials/flatten.cu b/libnd4j/include/loops/cuda/specials/flatten.cu index faec2ec90..b0bbf58e1 100644 --- a/libnd4j/include/loops/cuda/specials/flatten.cu +++ b/libnd4j/include/loops/cuda/specials/flatten.cu @@ -22,7 +22,7 @@ #include #include -namespace nd4j { +namespace sd { //////////////////////////////////////////////////////////////////////// template @@ -62,7 +62,7 @@ __host__ void flattenKernelGeneric(dim3& launchDims, cudaStream_t *stream, void *vy, Nd4jLong *yShapeInfo) { flattenKernel<<>>(extraPointers, dOffset, order, vz, zShapeInfo, vy, yShapeInfo); - nd4j::DebugHelper::checkErrorCode(stream, "flattenGeneric(...) failed"); + sd::DebugHelper::checkErrorCode(stream, "flattenGeneric(...) failed"); } BUILD_SINGLE_TEMPLATE(template void ND4J_EXPORT flattenKernelGeneric, (dim3& launchDims, cudaStream_t *stream, Nd4jPointer *extraPointers, int dOffset, char order, void *vz, Nd4jLong *zShapeInfo, void *vy, Nd4jLong *yShapeInfo), LIBND4J_TYPES); diff --git a/libnd4j/include/loops/cuda/specials/pullRowsKernel.cu b/libnd4j/include/loops/cuda/specials/pullRowsKernel.cu index 9730565e6..7ef6a46db 100644 --- a/libnd4j/include/loops/cuda/specials/pullRowsKernel.cu +++ b/libnd4j/include/loops/cuda/specials/pullRowsKernel.cu @@ -21,7 +21,7 @@ #include -namespace nd4j { +namespace sd { /////////////////////////////////////////////////////////////////////// template @@ -84,7 +84,7 @@ namespace nd4j { Nd4jLong *zTadShapeInfo, Nd4jLong *zTadOffsets) { execPullRowsKernel<<>>(vx, vz, len, indexes, tadShapeInfo, tadOffsets, zTadShapeInfo, zTadOffsets); - nd4j::DebugHelper::checkErrorCode(stream, "pullRows(...) failed"); + sd::DebugHelper::checkErrorCode(stream, "pullRows(...) failed"); } BUILD_SINGLE_TEMPLATE(template void ND4J_EXPORT pullRowsKernelGeneric, (dim3 & launchDims, cudaStream_t * stream, void * vx, void * vz, Nd4jLong len, Nd4jLong * indexes, Nd4jLong * tadShapeInfo, Nd4jLong * tadOffsets, Nd4jLong *zTadShapeInfo, Nd4jLong * zTadOffsets), LIBND4J_TYPES); diff --git a/libnd4j/include/loops/cuda/specials/setDiagonalKernel.cu b/libnd4j/include/loops/cuda/specials/setDiagonalKernel.cu index 809a318ee..bb063180c 100644 --- a/libnd4j/include/loops/cuda/specials/setDiagonalKernel.cu +++ b/libnd4j/include/loops/cuda/specials/setDiagonalKernel.cu @@ -19,8 +19,8 @@ // #include -#include -namespace nd4j { +#include +namespace sd { //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// // set up given value to upper diagonal given diff --git a/libnd4j/include/loops/cuda/specials/shuffleKernel.cu b/libnd4j/include/loops/cuda/specials/shuffleKernel.cu index c842cad4a..db63c2af7 100644 --- a/libnd4j/include/loops/cuda/specials/shuffleKernel.cu +++ b/libnd4j/include/loops/cuda/specials/shuffleKernel.cu @@ -21,7 +21,7 @@ #include -namespace nd4j { +namespace sd { //////////////////////////////////////////////////////////////////////// template @@ -118,7 +118,7 @@ namespace nd4j { Nd4jLong **tadOnlyShapeInfo, Nd4jLong **tadOffsets) { execShuffleKernel<<>>(vdX, xShapeInfo, vdZ, N, shuffleMap, tadOnlyShapeInfo, tadOffsets); - nd4j::DebugHelper::checkErrorCode(stream, "shuffleGeneric(...) failed"); + sd::DebugHelper::checkErrorCode(stream, "shuffleGeneric(...) failed"); } BUILD_SINGLE_TEMPLATE(template void ND4J_EXPORT shuffleKernelGeneric, (dim3 & launchDims, cudaStream_t * stream, void * *vdX, Nd4jLong * *xShapeInfo, void **vdZ, int N, int * shuffleMap, Nd4jLong * *tadOnlyShapeInfo, Nd4jLong * *tadOffsets), LIBND4J_TYPES); diff --git a/libnd4j/include/loops/cuda/specials/swapUnsafeKernel.cu b/libnd4j/include/loops/cuda/specials/swapUnsafeKernel.cu index 1b7820f64..796ea85c0 100644 --- a/libnd4j/include/loops/cuda/specials/swapUnsafeKernel.cu +++ b/libnd4j/include/loops/cuda/specials/swapUnsafeKernel.cu @@ -20,7 +20,7 @@ #include -namespace nd4j { +namespace sd { //////////////////////////////////////////////////////////////////////////////////////////////////////////////////// // kernel to swap two NDArrays vals as linear sequences @@ -47,7 +47,7 @@ namespace nd4j { auto xOffset = shape::getIndexOffset(i * xEws, theFirstShape); auto yOffset = shape::getIndexOffset(i * yEws, theSecondShape); - nd4j::math::nd4j_swap(output[xOffset], input[yOffset]); + sd::math::nd4j_swap(output[xOffset], input[yOffset]); } } diff --git a/libnd4j/include/loops/cuda/specials/tearKernel.cu b/libnd4j/include/loops/cuda/specials/tearKernel.cu index e12aa485f..a6285b5a5 100644 --- a/libnd4j/include/loops/cuda/specials/tearKernel.cu +++ b/libnd4j/include/loops/cuda/specials/tearKernel.cu @@ -21,7 +21,7 @@ #include -namespace nd4j { +namespace sd { //////////////////////////////////////////////////////////////////////// template @@ -88,7 +88,7 @@ namespace nd4j { Nd4jLong *tadShapeInfo, Nd4jLong *tadOffsets) { execTearKernel<<>>(vx, xShapeInfo, targets, zShapeInfo, tadShapeInfo, tadOffsets); - nd4j::DebugHelper::checkErrorCode(stream, "tear(...) failed"); + sd::DebugHelper::checkErrorCode(stream, "tear(...) failed"); } BUILD_SINGLE_TEMPLATE(template void ND4J_EXPORT tearKernelGeneric, (dim3 & launchDims, cudaStream_t * stream, void * vx, Nd4jLong * xShapeInfo, Nd4jPointer *targets, Nd4jLong * zShapeInfo, Nd4jLong * tadShapeInfo, Nd4jLong * tadOffsets), LIBND4J_TYPES); diff --git a/libnd4j/include/loops/cuda/specials/tileKernel.cu b/libnd4j/include/loops/cuda/specials/tileKernel.cu index 257942484..d6076d6cb 100644 --- a/libnd4j/include/loops/cuda/specials/tileKernel.cu +++ b/libnd4j/include/loops/cuda/specials/tileKernel.cu @@ -20,18 +20,15 @@ #include -namespace nd4j { - static Nd4jLong __device__ __noinline__ _getIndexOffset(Nd4jLong index, Nd4jLong *shapeInfo) { +namespace sd { + static Nd4jLong __device__ __noinline__ getIndexOffset_(Nd4jLong index, Nd4jLong *shapeInfo) { return shape::getIndexOffset(index, shapeInfo); } - static Nd4jLong __device__ __noinline__ _subArrayOffset(Nd4jLong index, Nd4jLong *shapeInfoA, Nd4jLong *shapeInfoB) { + static Nd4jLong __device__ __noinline__ subArrayOffset(Nd4jLong index, Nd4jLong *shapeInfoA, Nd4jLong *shapeInfoB) { return shape::subArrayOffset(index, shapeInfoA, shapeInfoB); } - static Nd4jLong __device__ __noinline__ _length(Nd4jLong *shapeInfo) { - return shape::length(shapeInfo); - } //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// // tileKernel: @@ -48,13 +45,13 @@ namespace nd4j { int totalThreads = gridDim.x * blockDim.x; if (shape::order(outputShape) == 'c') { // ews == 1 always here for (int i = tid; i < resultLength; i += totalThreads) { - auto yOffset = _subArrayOffset(i, outputShape, inputShape); + auto yOffset = subArrayOffset(i, outputShape, inputShape); *(reinterpret_cast(outputBuffer) + i) = *(reinterpret_cast(inputBuffer) + yOffset); } } else { for (int i = tid; i < resultLength; i += totalThreads) { - auto xOffset = _getIndexOffset(i, outputShape); - auto yOffset = _subArrayOffset(i, outputShape, inputShape); + auto xOffset = getIndexOffset_(i, outputShape); + auto yOffset = subArrayOffset(i, outputShape, inputShape); *(reinterpret_cast(outputBuffer) + xOffset) = *(reinterpret_cast(inputBuffer) + yOffset); } } @@ -83,20 +80,20 @@ namespace nd4j { if (ordering == 'c' && ews == 1) { // ews == 1 always here for (int i = tid; i < resultLength; i += totalThreads) { - auto yOffset = _subArrayOffset(i, outputShape, inputShape); + auto yOffset = subArrayOffset(i, outputShape, inputShape); *(reinterpret_cast(outputBuffer) + i) = static_cast(*(reinterpret_cast(inputBuffer) + yOffset)); } } else if (ordering == 'c' && ews > 1) { for (int i = tid; i < resultLength; i += totalThreads) { - auto yOffset = _subArrayOffset(i, outputShape, inputShape); + auto yOffset = subArrayOffset(i, outputShape, inputShape); *(reinterpret_cast(outputBuffer) + i * ews) = static_cast(*(reinterpret_cast(inputBuffer) + yOffset)); } } else { for (int i = tid; i < resultLength; i += totalThreads) { - auto xOffset = _getIndexOffset(i, outputShape); - auto yOffset = _subArrayOffset(i, outputShape, inputShape); + auto xOffset = getIndexOffset_(i, outputShape); + auto yOffset = subArrayOffset(i, outputShape, inputShape); *(reinterpret_cast(outputBuffer) + xOffset) = static_cast(*(reinterpret_cast(inputBuffer) + yOffset)); } } diff --git a/libnd4j/include/loops/cuda/summarystatsreduce.cu b/libnd4j/include/loops/cuda/summarystatsreduce.cu index e505929e6..c858d8098 100644 --- a/libnd4j/include/loops/cuda/summarystatsreduce.cu +++ b/libnd4j/include/loops/cuda/summarystatsreduce.cu @@ -19,19 +19,19 @@ // -#include +#include #include #include -#include +#include #include #include #include -#include -#include +#include +#include #include #include #include -#include +#include using namespace simdOps; @@ -211,7 +211,7 @@ void _CUDA_G summaryStatsReduceT(int op, void *dx, Nd4jLong *xShapeInfo, int xRa sPartials[threadIdx.x] = update(sPartials[threadIdx.x], OpType::op(indexVal2, extraParams), extraParams); } __syncthreads(); - aggregatePartials(&sPartials, threadIdx.x, nd4j::math::nd4j_min(blockDim.x, tadLength), extraParams); + aggregatePartials(&sPartials, threadIdx.x, sd::math::nd4j_min(blockDim.x, tadLength), extraParams); __syncthreads(); if (threadIdx.x == 0) { @@ -237,7 +237,7 @@ void _CUDA_G summaryStatsReduceT(int op, void *dx, Nd4jLong *xShapeInfo, int xRa } __syncthreads(); - aggregatePartials(&sPartials, threadIdx.x, nd4j::math::nd4j_min(blockDim.x, tadLength), extraParams); + aggregatePartials(&sPartials, threadIdx.x, sd::math::nd4j_min(blockDim.x, tadLength), extraParams); __syncthreads(); if (threadIdx.x == 0) { @@ -344,7 +344,7 @@ void _CUDA_G summaryStatsReduceT(int op, void *dx, Nd4jLong *xShapeInfo, int xRa auto z = reinterpret_cast(vz); auto reductionPointerA = reinterpret_cast(reductionBuffer); - if (nd4j::Environment::getInstance()->isDebugAndVerbose()) + if (sd::Environment::getInstance()->isDebugAndVerbose()) printf("D16 opNum:[%i]\n", opNum); summaryStatsReduceT<<>>( @@ -359,7 +359,7 @@ void _CUDA_G summaryStatsReduceT(int op, void *dx, Nd4jLong *xShapeInfo, int xRa 1,biasCorrected, nullptr, reductionPointerA, tadShapeInfo, tadOffsets); // this is blocking method since method should return scalar - nd4j::DebugHelper::checkErrorCode(stream, "execSSReduceScalar(...) failed"); + sd::DebugHelper::checkErrorCode(stream, "execSSReduceScalar(...) failed"); } template @@ -369,7 +369,7 @@ void _CUDA_G summaryStatsReduceT(int op, void *dx, Nd4jLong *xShapeInfo, int xRa auto z = static_cast(vz); auto extraParams = static_cast(vextraParams); - if (nd4j::Environment::getInstance()->isDebugAndVerbose()) + if (sd::Environment::getInstance()->isDebugAndVerbose()) printf("F17 opNum:[%i]\n", opNum); auto reductionPointerA = reinterpret_cast(reductionBuffer); @@ -396,7 +396,7 @@ void _CUDA_G summaryStatsReduceT(int op, void *dx, Nd4jLong *xShapeInfo, int xRa auto z = static_cast(vz); auto extraParams = static_cast(vextraParams); - if (nd4j::Environment::getInstance()->isDebugAndVerbose()) + if (sd::Environment::getInstance()->isDebugAndVerbose()) printf("D18 opNum:[%i]\n", opNum); summaryStatsReduceT<<>>( diff --git a/libnd4j/include/loops/cuda/transform/transform_any.cu b/libnd4j/include/loops/cuda/transform/transform_any.cu index 37a0ac804..d13b94599 100644 --- a/libnd4j/include/loops/cuda/transform/transform_any.cu +++ b/libnd4j/include/loops/cuda/transform/transform_any.cu @@ -18,10 +18,10 @@ // @author raver119@gmail.com // -#include +#include #include #include -#include +#include #include #include @@ -111,7 +111,7 @@ namespace functions { template _CUDA_H void TransformAny::intermediateShaped(dim3 launchDims, cudaStream_t *stream, void *x, Nd4jLong *xShape, int xRank, void *extraParams, void *z, Nd4jLong *zShape, int zRank, int *allocationPointer, void *reductionPointer, Nd4jLong *tadShapeInfo, Nd4jLong *tadOffsets) { transformAnySimple<<>>(x, xShape, xRank, extraParams, z, zShape, zRank, allocationPointer, reductionPointer, tadShapeInfo, tadOffsets); - nd4j::DebugHelper::checkErrorCode(stream, "transformAny(...) failed"); + sd::DebugHelper::checkErrorCode(stream, "transformAny(...) failed"); } BUILD_DOUBLE_TEMPLATE(template class ND4J_EXPORT TransformAny, , LIBND4J_TYPES, LIBND4J_TYPES); diff --git a/libnd4j/include/loops/cuda/transform/transform_bool.cu b/libnd4j/include/loops/cuda/transform/transform_bool.cu index d64328494..fec14a745 100644 --- a/libnd4j/include/loops/cuda/transform/transform_bool.cu +++ b/libnd4j/include/loops/cuda/transform/transform_bool.cu @@ -18,10 +18,10 @@ // @author raver119@gmail.com // -#include +#include #include #include -#include +#include #include #include @@ -117,7 +117,7 @@ namespace functions { template _CUDA_H void TransformBool::intermediateShaped(dim3 launchDims, cudaStream_t *stream, void *x, Nd4jLong *xShape, int xRank, void *extraParams, void *z, Nd4jLong *zShape, int zRank, int *allocationPointer, void *reductionPointer, Nd4jLong *tadShapeInfo, Nd4jLong *tadOffsets) { transformBoolSimple<<>>(x, xShape, xRank, extraParams, z, zShape, zRank, allocationPointer, reductionPointer, tadShapeInfo, tadOffsets); - nd4j::DebugHelper::checkErrorCode(stream, "transformBool(...) failed"); + sd::DebugHelper::checkErrorCode(stream, "transformBool(...) failed"); } BUILD_DOUBLE_TEMPLATE(template class ND4J_EXPORT TransformBool, , LIBND4J_TYPES, BOOL_TYPES); diff --git a/libnd4j/include/loops/cuda/transform/transform_float.cu b/libnd4j/include/loops/cuda/transform/transform_float.cu index 2e82efdb3..f631fd4d7 100644 --- a/libnd4j/include/loops/cuda/transform/transform_float.cu +++ b/libnd4j/include/loops/cuda/transform/transform_float.cu @@ -18,10 +18,10 @@ // @author raver119@gmail.com // -#include +#include #include #include -#include +#include #include #include @@ -139,7 +139,7 @@ namespace functions { template _CUDA_H void TransformFloat::intermediateShaped(dim3 launchDims, cudaStream_t *stream, void *x, Nd4jLong *xShape, int xRank, void *extraParams, void *z, Nd4jLong *zShape, int zRank, int *allocationPointer, void *reductionPointer, Nd4jLong *tadShapeInfo, Nd4jLong *tadOffsets) { transformFloatSimple<<>>(x, xShape, xRank, extraParams, z, zShape, zRank, allocationPointer, reductionPointer, tadShapeInfo, tadOffsets); - nd4j::DebugHelper::checkErrorCode(stream, "transformFloat(...) failed"); + sd::DebugHelper::checkErrorCode(stream, "transformFloat(...) failed"); } BUILD_DOUBLE_TEMPLATE(template class ND4J_EXPORT TransformFloat, , LIBND4J_TYPES, FLOAT_TYPES); diff --git a/libnd4j/include/loops/cuda/transform/transform_same.cu b/libnd4j/include/loops/cuda/transform/transform_same.cu index 0a66590a5..368a9b602 100644 --- a/libnd4j/include/loops/cuda/transform/transform_same.cu +++ b/libnd4j/include/loops/cuda/transform/transform_same.cu @@ -18,10 +18,10 @@ // @author raver119@gmail.com // -#include +#include #include #include -#include +#include #include #include @@ -115,7 +115,7 @@ namespace functions { template _CUDA_H void TransformSame::intermediateShaped(dim3 launchDims, cudaStream_t *stream, void *x, Nd4jLong *xShape, int xRank, void *extraParams, void *z, Nd4jLong *zShape, int zRank, int *allocationPointer, void *reductionPointer, Nd4jLong *tadShapeInfo, Nd4jLong *tadOffsets) { transformSameSimple<<>>(x, xShape, xRank, extraParams, z, zShape, zRank, allocationPointer, reductionPointer, tadShapeInfo, tadOffsets); - nd4j::DebugHelper::checkErrorCode(stream, "transformSame(...) failed"); + sd::DebugHelper::checkErrorCode(stream, "transformSame(...) failed"); } BUILD_SINGLE_TEMPLATE(template class ND4J_EXPORT TransformSame, , LIBND4J_TYPES); diff --git a/libnd4j/include/loops/cuda/transform/transform_strict.cu b/libnd4j/include/loops/cuda/transform/transform_strict.cu index 35ab0b1dc..155e5aa23 100644 --- a/libnd4j/include/loops/cuda/transform/transform_strict.cu +++ b/libnd4j/include/loops/cuda/transform/transform_strict.cu @@ -18,10 +18,10 @@ // @author raver119@gmail.com // -#include +#include #include #include -#include +#include #include #include @@ -116,7 +116,7 @@ namespace functions { template _CUDA_H void TransformStrict::intermediateShaped(dim3 launchDims, cudaStream_t *stream, void *x, Nd4jLong *xShape, int xRank, void *extraParams, void *z, Nd4jLong *zShape, int zRank, int *allocationPointer, void *reductionPointer, Nd4jLong *tadShapeInfo, Nd4jLong *tadOffsets) { transformStrictSimple<<>>(x, xShape, xRank, extraParams, z, zShape, zRank, allocationPointer, reductionPointer, tadShapeInfo, tadOffsets); - nd4j::DebugHelper::checkErrorCode(stream, "transformStrict(...) failed"); + sd::DebugHelper::checkErrorCode(stream, "transformStrict(...) failed"); } BUILD_SINGLE_TEMPLATE(template class ND4J_EXPORT TransformStrict, , FLOAT_TYPES); diff --git a/libnd4j/include/loops/cuda/type_conversions.cu b/libnd4j/include/loops/cuda/type_conversions.cu index 5afb23f79..8c38561f4 100644 --- a/libnd4j/include/loops/cuda/type_conversions.cu +++ b/libnd4j/include/loops/cuda/type_conversions.cu @@ -22,13 +22,13 @@ #include #include -namespace nd4j { +namespace sd { template void TypeCast::convertGenericCuda(Nd4jPointer *extras, void *dx, Nd4jLong N, void *dz) { auto stream = reinterpret_cast(&extras[1]); - nd4j::convertKernel<<<256, 1024, 1024, *stream>>>(dx, N, dz); - nd4j::DebugHelper::checkErrorCode(stream, "convertGeneric(...) failed"); + sd::convertKernel<<<256, 1024, 1024, *stream>>>(dx, N, dz); + sd::DebugHelper::checkErrorCode(stream, "convertGeneric(...) failed"); }; @@ -228,7 +228,7 @@ template __host__ void encoderKernelP1Generic(dim3 &launchDims, cudaStream_t *stream, void *dx, Nd4jLong N, void *dz, float threshold) { execEncoderKernelP1<<>>(dx, N, dz, threshold); - nd4j::DebugHelper::checkErrorCode(stream, "encoderP1(...) failed"); + sd::DebugHelper::checkErrorCode(stream, "encoderP1(...) failed"); } BUILD_SINGLE_TEMPLATE(template void ND4J_EXPORT encoderKernelP1Generic, (dim3 &launchDims, cudaStream_t *stream, void *dx, Nd4jLong N, void *dz, float threshold), LIBND4J_TYPES); @@ -243,7 +243,7 @@ __global__ static void execEncoderKernelP3(void *dx, int *offsets, Nd4jLong N, v template __host__ void encoderKernelP3Generic(dim3 &launchDims, cudaStream_t *stream, void *dx, int *offsets, Nd4jLong N, void *dz) { execEncoderKernelP3<<>>(dx, offsets, N, dz); - nd4j::DebugHelper::checkErrorCode(stream, "encoderP3(...) failed"); + sd::DebugHelper::checkErrorCode(stream, "encoderP3(...) failed"); } BUILD_SINGLE_TEMPLATE(template void ND4J_EXPORT encoderKernelP3Generic, (dim3 &launchDims, cudaStream_t *stream, void *dx, int *offsets, Nd4jLong N, void *dz), LIBND4J_TYPES); @@ -259,7 +259,7 @@ template __host__ void decoderKernelGeneric(dim3 &launchDims, cudaStream_t *stream, void *dx, Nd4jLong N, void *dz) { execDecoderKernel<<>>(dx, N, dz); - nd4j::DebugHelper::checkErrorCode(stream, "execDecoder(...) failed"); + sd::DebugHelper::checkErrorCode(stream, "execDecoder(...) failed"); } BUILD_SINGLE_TEMPLATE(template void ND4J_EXPORT decoderKernelGeneric, (dim3 &launchDims, cudaStream_t *stream, void *dx, Nd4jLong N, void *dz), LIBND4J_TYPES); @@ -276,7 +276,7 @@ template __host__ void cudaEncodeBitmapGeneric(dim3 &launchDims, cudaStream_t *stream, void *vdx, Nd4jLong N, int *dz, int *scalar, int *reductionBuffer, float threshold) { execCudaEncodeBitmapKernel<<>>(vdx, N, dz, scalar, reductionBuffer, threshold); - nd4j::DebugHelper::checkErrorCode(stream, "encodeBitmap(...) failed"); + sd::DebugHelper::checkErrorCode(stream, "encodeBitmap(...) failed"); } BUILD_SINGLE_TEMPLATE(template void ND4J_EXPORT cudaEncodeBitmapGeneric, (dim3 &launchDims, cudaStream_t *stream, void *vdx, Nd4jLong N, int *dz, int *scalar, int *reductionBuffer, float threshold), LIBND4J_TYPES); @@ -293,7 +293,7 @@ template __host__ void cudaDecodeBitmapGeneric(dim3 &launchDims, cudaStream_t *stream, void *dx, Nd4jLong N, void *vdz) { execCudaDecodeBitmapKernel<<>>(dx, N, vdz); - nd4j::DebugHelper::checkErrorCode(stream, "cudeDecodeBitmap(...) failed"); + sd::DebugHelper::checkErrorCode(stream, "cudeDecodeBitmap(...) failed"); } BUILD_SINGLE_TEMPLATE(template void ND4J_EXPORT cudaDecodeBitmapGeneric, (dim3 &launchDims, cudaStream_t *stream, void *dx, Nd4jLong N, void *vdz), LIBND4J_TYPES); @@ -301,7 +301,7 @@ BUILD_SINGLE_TEMPLATE(template void ND4J_EXPORT cudaDecodeBitmapGeneric, (dim3 & template __host__ void prescanLauncher(dim3 &blocks, dim3 &threads, int shmem, cudaStream_t *stream, int *g_odata, const int *g_idata, int *g_blockSums, int n, int blockIndex, int baseIndex) { prescan<<>>(g_odata, g_idata, g_blockSums, n, blockIndex, baseIndex); - nd4j::DebugHelper::checkErrorCode(stream, "prescan(...) failed"); + sd::DebugHelper::checkErrorCode(stream, "prescan(...) failed"); }; template @@ -309,7 +309,7 @@ BUILD_SINGLE_TEMPLATE(template void ND4J_EXPORT cudaDecodeBitmapGeneric, (dim3 & auto x = reinterpret_cast(dx); auto z = reinterpret_cast(dz); - nd4j::convertKernelGeneric(x, N, z); + sd::convertKernelGeneric(x, N, z); } diff --git a/libnd4j/include/loops/impl/type_conversions.cpp b/libnd4j/include/loops/impl/type_conversions.cpp index 36c95e731..16914bd86 100644 --- a/libnd4j/include/loops/impl/type_conversions.cpp +++ b/libnd4j/include/loops/impl/type_conversions.cpp @@ -19,12 +19,12 @@ // #include -#include +#include #include -#include +#include #include -namespace nd4j { +namespace sd { template _CUDA_H void TypeCast::convertFromQuantized(Nd4jPointer *extras, void *dx, Nd4jLong N, void *dz) { @@ -32,15 +32,15 @@ namespace nd4j { auto z = reinterpret_cast(dz); auto fx = reinterpret_cast(dx); - auto amin = nd4j::math::nd4j_abs(fx[0]); - auto amax = nd4j::math::nd4j_abs(fx[1]); + auto amin = sd::math::nd4j_abs(fx[0]); + auto amax = sd::math::nd4j_abs(fx[1]); auto x = reinterpret_cast(dx) + 8; for (Nd4jLong e = 0; e < N; e++) { - z[e] = static_cast(static_cast(x[e]) / static_cast(DataTypeUtils::max()) * nd4j::math::nd4j_max(amin, amax)); + z[e] = static_cast(static_cast(x[e]) / static_cast(DataTypeUtils::max()) * sd::math::nd4j_max(amin, amax)); } } @@ -76,13 +76,13 @@ namespace nd4j { fz[0] = min; fz[1] = max; - auto amax = nd4j::math::nd4j_abs(max); - auto amin = nd4j::math::nd4j_abs(min); + auto amax = sd::math::nd4j_abs(max); + auto amin = sd::math::nd4j_abs(min); // now we actually apply quantization auto func = PRAGMA_THREADS_FOR { for (auto e = start; e < stop; e++) { - rz[e] = static_cast(nd4j::math::nd4j_round( 1.0f * static_cast(x[e]) / nd4j::math::nd4j_max(amax, amin) * max_byte)); + rz[e] = static_cast(sd::math::nd4j_round( 1.0f * static_cast(x[e]) / sd::math::nd4j_max(amax, amin) * max_byte)); } }; @@ -179,7 +179,7 @@ PRAGMA_OMP_ATOMIC_ARGS(write) auto func = PRAGMA_THREADS_FOR { for (auto e = start; e < stop; e++) { int el = x[e]; - int ael = nd4j::math::nd4j_abs(el) - 1; + int ael = sd::math::nd4j_abs(el) - 1; z[ael] += el > 0 ? static_cast(threshold) : static_cast(-threshold); } }; diff --git a/libnd4j/include/loops/indexreduce.h b/libnd4j/include/loops/indexreduce.h index ad4472dec..677d83db9 100755 --- a/libnd4j/include/loops/indexreduce.h +++ b/libnd4j/include/loops/indexreduce.h @@ -26,10 +26,10 @@ #ifdef _OPENMP #include #endif -#include +#include #include -#include -#include +#include +#include #include #ifdef __CUDACC__ @@ -41,7 +41,7 @@ #include -#include "../pairwise_util.h" +#include "system/pairwise_util.h" #include "legacy_ops.h" diff --git a/libnd4j/include/loops/legacy_ops.h b/libnd4j/include/loops/legacy_ops.h index ea32b154c..95f83be1a 100644 --- a/libnd4j/include/loops/legacy_ops.h +++ b/libnd4j/include/loops/legacy_ops.h @@ -186,7 +186,7 @@ (1, SummaryStatsStandardDeviation) #define SCALAR_INT_OPS \ - (0, ShiftLeft), \ + (0, ShiftLeft) ,\ (1, ShiftRight), \ (2, CyclicShiftLeft), \ (3, CyclicShiftRight), \ diff --git a/libnd4j/include/loops/pairwise_bool.h b/libnd4j/include/loops/pairwise_bool.h index f7a65c3f5..fee96df84 100644 --- a/libnd4j/include/loops/pairwise_bool.h +++ b/libnd4j/include/loops/pairwise_bool.h @@ -26,13 +26,13 @@ #ifdef _OPENMP #include #endif -#include +#include #include -#include -#include +#include +#include #include #include -#include +#include #include #ifdef __CUDACC__ diff --git a/libnd4j/include/loops/pairwise_int.h b/libnd4j/include/loops/pairwise_int.h index aa6437d17..4144963c7 100644 --- a/libnd4j/include/loops/pairwise_int.h +++ b/libnd4j/include/loops/pairwise_int.h @@ -26,13 +26,13 @@ #ifdef _OPENMP #include #endif -#include +#include #include -#include -#include +#include +#include #include #include -#include +#include #include #ifdef __CUDACC__ diff --git a/libnd4j/include/loops/pairwise_transform.h b/libnd4j/include/loops/pairwise_transform.h index 0109b309f..8576481f5 100755 --- a/libnd4j/include/loops/pairwise_transform.h +++ b/libnd4j/include/loops/pairwise_transform.h @@ -27,10 +27,10 @@ #include #endif -#include +#include #include #include -#include +#include #include #include "legacy_ops.h" #include diff --git a/libnd4j/include/loops/reduce3.h b/libnd4j/include/loops/reduce3.h index 178bac7c2..597e450b1 100755 --- a/libnd4j/include/loops/reduce3.h +++ b/libnd4j/include/loops/reduce3.h @@ -26,17 +26,17 @@ #define EXTRA_PARAMS_LENGTH 10 -#include +#include #ifdef _OPENMP #include #endif -#include -#include +#include +#include #include #include #include -#include -#include +#include +#include #include #ifdef __CUDACC__ diff --git a/libnd4j/include/loops/reduce_bool.h b/libnd4j/include/loops/reduce_bool.h index 540a6041d..815557d41 100644 --- a/libnd4j/include/loops/reduce_bool.h +++ b/libnd4j/include/loops/reduce_bool.h @@ -17,18 +17,18 @@ #ifndef REDUCE_BOOL_H #define REDUCE_BOOL_H -#include +#include //#include #include #include #ifdef _OPENMP #include #endif -#include -#include -#include +#include +#include +#include #include -#include +#include #pragma once #ifdef __CUDACC__ diff --git a/libnd4j/include/loops/reduce_float.h b/libnd4j/include/loops/reduce_float.h index ff2c0e668..6ff3f88ab 100644 --- a/libnd4j/include/loops/reduce_float.h +++ b/libnd4j/include/loops/reduce_float.h @@ -17,18 +17,18 @@ #ifndef REDUCE_FLOAT_H #define REDUCE_FLOAT_H -#include +#include //#include #include #include #ifdef _OPENMP #include #endif -#include -#include -#include +#include +#include +#include #include -#include +#include #pragma once #ifdef __CUDACC__ diff --git a/libnd4j/include/loops/reduce_long.h b/libnd4j/include/loops/reduce_long.h index a5d2a9498..4c83e1057 100644 --- a/libnd4j/include/loops/reduce_long.h +++ b/libnd4j/include/loops/reduce_long.h @@ -17,18 +17,18 @@ #ifndef REDUCE_LONG_H #define REDUCE_LONG_H -#include +#include //#include #include #include #ifdef _OPENMP #include #endif -#include -#include -#include +#include +#include +#include #include -#include +#include #pragma once #ifdef __CUDACC__ diff --git a/libnd4j/include/loops/reduce_same.h b/libnd4j/include/loops/reduce_same.h index e828ecf46..641551b6f 100644 --- a/libnd4j/include/loops/reduce_same.h +++ b/libnd4j/include/loops/reduce_same.h @@ -17,18 +17,18 @@ #ifndef REDUCE_SAME_H #define REDUCE_SAME_H -#include +#include //#include #include #include #ifdef _OPENMP #include #endif -#include -#include -#include +#include +#include +#include #include -#include +#include #pragma once #ifdef __CUDACC__ diff --git a/libnd4j/include/loops/scalar.h b/libnd4j/include/loops/scalar.h index 0f32dedf3..dc3a5b16c 100755 --- a/libnd4j/include/loops/scalar.h +++ b/libnd4j/include/loops/scalar.h @@ -23,16 +23,16 @@ #ifndef SCALAR_H_ #define SCALAR_H_ -#include -#include +#include +#include #include #ifdef __JNI__ #include #endif -#include +#include #include -#include +#include #include "helpers/logger.h" #ifdef __CUDACC__ diff --git a/libnd4j/include/loops/scalar_bool.h b/libnd4j/include/loops/scalar_bool.h index a5931ddfb..0b26531b2 100644 --- a/libnd4j/include/loops/scalar_bool.h +++ b/libnd4j/include/loops/scalar_bool.h @@ -23,16 +23,16 @@ #ifndef SCALAR_BOOL_H_ #define SCALAR_BOOL_H_ -#include +#include #ifdef __JNI__ #include #endif -#include +#include #include -#include +#include #include "helpers/logger.h" -#include +#include #include #ifdef __CUDACC__ diff --git a/libnd4j/include/loops/scalar_int.h b/libnd4j/include/loops/scalar_int.h index 509d7574f..dde7af4c7 100644 --- a/libnd4j/include/loops/scalar_int.h +++ b/libnd4j/include/loops/scalar_int.h @@ -23,16 +23,16 @@ #ifndef SCALAR_INT_H_ #define SCALAR_INT_H_ -#include +#include #ifdef __JNI__ #include #endif -#include +#include #include -#include +#include #include "helpers/logger.h" -#include +#include #include #ifdef __CUDACC__ diff --git a/libnd4j/include/loops/special_kernels.h b/libnd4j/include/loops/special_kernels.h index df7c0c617..52cdb7fdd 100644 --- a/libnd4j/include/loops/special_kernels.h +++ b/libnd4j/include/loops/special_kernels.h @@ -23,9 +23,9 @@ #include -#include +#include #include -#include +#include #include #include #include @@ -33,7 +33,7 @@ #include #include -namespace nd4j { +namespace sd { template _CUDA_H void fillIsMaxGeneric(dim3 &launchDims, cudaStream_t *stream, void *dx, Nd4jLong *xShapeInfo, Nd4jLong length, long idx); diff --git a/libnd4j/include/loops/summarystatsreduce.h b/libnd4j/include/loops/summarystatsreduce.h index afaee9c47..0a429cd2b 100755 --- a/libnd4j/include/loops/summarystatsreduce.h +++ b/libnd4j/include/loops/summarystatsreduce.h @@ -23,8 +23,8 @@ #ifndef SUMMARYSTATSREDUCE_H_ #define SUMMARYSTATSREDUCE_H_ -#include -#include +#include +#include #include #ifdef __CUDACC__ @@ -41,7 +41,7 @@ #endif #include -#include +#include #include "legacy_ops.h" @@ -123,7 +123,7 @@ namespace functions { return M2 / n; } - _CUDA_HD double skewness() { return M2 > 0.0 ? nd4j::math::nd4j_sqrt(n) * M3 / nd4j::math::nd4j_pow(M2, 1.5) : 0.0; } + _CUDA_HD double skewness() { return M2 > 0.0 ? sd::math::nd4j_sqrt(n) * M3 / sd::math::nd4j_pow(M2, 1.5) : 0.0; } _CUDA_HD double kurtosis() { return M2 > 0.0 ? n * M4 / (M2 * M2) : 0; } @@ -247,8 +247,8 @@ namespace functions { //Basic number of samples (n), min, and max vz.n = n; - vz.min = nd4j::math::nd4j_min(x.min, y.min); - vz.max = nd4j::math::nd4j_max(x.max, y.max); + vz.min = sd::math::nd4j_min(x.min, y.min); + vz.max = sd::math::nd4j_max(x.max, y.max); double meanD = x.mean + delta * y.n / n; vz.mean = meanD; double M2D = x.M2 + y.M2; diff --git a/libnd4j/include/loops/transform_any.h b/libnd4j/include/loops/transform_any.h index d97e3e90e..22d56a4d3 100644 --- a/libnd4j/include/loops/transform_any.h +++ b/libnd4j/include/loops/transform_any.h @@ -25,14 +25,14 @@ #ifndef TRANSFORM_ANY_H_ #define TRANSFORM_ANY_H_ #include -#include +#include #include #ifdef _OPENMP #include #endif -#include -#include +#include +#include //#include //#include diff --git a/libnd4j/include/loops/transform_bool.h b/libnd4j/include/loops/transform_bool.h index 4c87ae58c..56a7f8f7e 100644 --- a/libnd4j/include/loops/transform_bool.h +++ b/libnd4j/include/loops/transform_bool.h @@ -25,14 +25,14 @@ #ifndef TRANSFORM_BOOL_H_ #define TRANSFORM_BOOL_H_ #include -#include +#include #include #ifdef _OPENMP #include #endif -#include -#include +#include +#include //#include //#include diff --git a/libnd4j/include/loops/transform_float.h b/libnd4j/include/loops/transform_float.h index ae28e069f..1d9b6fb71 100644 --- a/libnd4j/include/loops/transform_float.h +++ b/libnd4j/include/loops/transform_float.h @@ -25,14 +25,14 @@ #ifndef TRANSFORM_FLOAT_H_ #define TRANSFORM_FLOAT_H_ #include -#include +#include #include #ifdef _OPENMP #include #endif -#include -#include +#include +#include //#include //#include diff --git a/libnd4j/include/loops/transform_same.h b/libnd4j/include/loops/transform_same.h index ae5b498e6..cb36ba872 100644 --- a/libnd4j/include/loops/transform_same.h +++ b/libnd4j/include/loops/transform_same.h @@ -25,14 +25,14 @@ #ifndef TRANSFORM_SAME_H_ #define TRANSFORM_SAME_H_ #include -#include +#include #include #ifdef _OPENMP #include #endif -#include -#include +#include +#include //#include //#include diff --git a/libnd4j/include/loops/transform_strict.h b/libnd4j/include/loops/transform_strict.h index 96917ebc1..b7ba63e46 100644 --- a/libnd4j/include/loops/transform_strict.h +++ b/libnd4j/include/loops/transform_strict.h @@ -25,14 +25,14 @@ #ifndef TRANSFORM_STRICT_H_ #define TRANSFORM_STRICT_H_ #include -#include +#include #include #ifdef _OPENMP #include #endif -#include -#include +#include +#include //#include //#include diff --git a/libnd4j/include/loops/type_conversions.h b/libnd4j/include/loops/type_conversions.h index d6029d7af..ff5ac5400 100644 --- a/libnd4j/include/loops/type_conversions.h +++ b/libnd4j/include/loops/type_conversions.h @@ -36,20 +36,20 @@ #define ND4J_FLOAT24 119 // not supported after all. might want to add support later. #include -#include +#include #include #include #include #include #include #include -#include +#include #define NUM_BANKS 32 #define LOG_NUM_BANKS 4 -namespace nd4j { +namespace sd { typedef union { float f_; @@ -121,7 +121,7 @@ namespace nd4j { //basically, for phase One we want do calculation: how many eligible values we have, and which blocks will be holding data Nd4jLong tid = blockIdx.x * blockDim.x + threadIdx.x; - int pass = tid < N && nd4j::math::nd4j_abs(x[tid]) >= static_cast(threshold) ? 1 : 0; + int pass = tid < N && sd::math::nd4j_abs(x[tid]) >= static_cast(threshold) ? 1 : 0; int bp=__syncthreads_count(pass); if (threadIdx.x == 0) { @@ -168,7 +168,7 @@ namespace nd4j { if (tid < N) { T value = x[tid]; - int pred = nd4j::math::nd4j_abs(value) >= static_cast(threshold) ? 1 : 0; + int pred = sd::math::nd4j_abs(value) >= static_cast(threshold) ? 1 : 0; int w_i = threadIdx.x/warpSize; //warp index int w_l = tid % warpSize;//thread index within a warp unsigned int t_m = INT_MAX >> (warpSize-w_l-1); //thread mask (ERROR IN THE PAPER minus one is required) @@ -231,7 +231,7 @@ namespace nd4j { for (int e = tid; e < limit; e += blockDim.x * gridDim.x) { int el = x[e+4]; - int ael = nd4j::math::nd4j_abs(el) - 1; + int ael = sd::math::nd4j_abs(el) - 1; // TODO: investigate, if += would work better here, as in "decoded accumulation" z[ael] += el > 0 ? threshold : -threshold; @@ -267,7 +267,7 @@ namespace nd4j { for (Nd4jLong i = tid; i < loopLimit; i += blockDim.x * gridDim.x) { // all threads in block reading stuff T val = i < N ? dx[i] : off; - T abs = nd4j::math::nd4j_abs(val); + T abs = sd::math::nd4j_abs(val); int byteId = i / 16 + 4; int bitId = i % 16; diff --git a/libnd4j/include/platformmath.h b/libnd4j/include/math/platformmath.h similarity index 82% rename from libnd4j/include/platformmath.h rename to libnd4j/include/math/platformmath.h index b58e8f7f6..e4990cc87 100644 --- a/libnd4j/include/platformmath.h +++ b/libnd4j/include/math/platformmath.h @@ -23,7 +23,7 @@ #include #include -#include +#include #include #ifdef __CUDACC__ @@ -80,10 +80,11 @@ union PAIR { #else #define math_def #include + #endif -namespace nd4j { +namespace sd { namespace math { template math_def FORCEINLINE T p_exp(T value); @@ -145,6 +146,12 @@ namespace nd4j { template math_def FORCEINLINE T p_rint(T value); + template + math_def FORCEINLINE T p_rotl(T value, T shift); + + template + math_def FORCEINLINE T p_rotr(T value, T shift); + template math_def FORCEINLINE T p_remainder(T val1, T val2); @@ -751,6 +758,116 @@ namespace nd4j { math_def FORCEINLINE double p_atanh(double value) { return atanh(value); } + +///////// + template + math_def FORCEINLINE T _rotate_left(T value, T shift); + + template + math_def FORCEINLINE T _rotate_right(T value, T shift); + + template <> + math_def FORCEINLINE int8_t _rotate_left(int8_t value, int8_t shift) { + return value << shift | value >> (8 - shift); + } + + template <> + math_def FORCEINLINE int8_t _rotate_right(int8_t value, int8_t shift) { + return value >> shift | value << (8 - shift); + } + + template <> + math_def FORCEINLINE uint8_t _rotate_left(uint8_t value, uint8_t shift) { + return value << shift | value >> (8 - shift); + } + + template <> + math_def FORCEINLINE uint8_t _rotate_right(uint8_t value, uint8_t shift) { + return value >> shift | value << (8 - shift); + } + + template <> + math_def FORCEINLINE int16_t _rotate_left(int16_t value, int16_t shift) { + return value << shift | value >> (16 - shift); + } + + template <> + math_def FORCEINLINE int16_t _rotate_right(int16_t value, int16_t shift) { + return value >> shift | value << (16 - shift); + } + + template <> + math_def FORCEINLINE uint16_t _rotate_left(uint16_t value, uint16_t shift) { + return value << shift | value >> (16 - shift); + } + + template <> + math_def FORCEINLINE uint16_t _rotate_right(uint16_t value, uint16_t shift) { + return value >> shift | value << (16 - shift); + } + + template <> + math_def FORCEINLINE int _rotate_left(int value, int shift) { + return value << shift | value >> (32 - shift); + } + + template <> + math_def FORCEINLINE int _rotate_right(int value, int shift) { + return value >> shift | value << (32 - shift); + } + + template <> + math_def FORCEINLINE uint32_t _rotate_left(uint32_t value, uint32_t shift) { + return value << shift | value >> (32 - shift); + } + + template <> + math_def FORCEINLINE uint32_t _rotate_right(uint32_t value, uint32_t shift) { + return value >> shift | value << (32 - shift); + } + + template <> + math_def FORCEINLINE Nd4jLong _rotate_left(Nd4jLong value, Nd4jLong shift) { + return value << shift | value >> (64 - shift); + } + + template <> + math_def FORCEINLINE Nd4jLong _rotate_right(Nd4jLong value, Nd4jLong shift) { + return value >> shift | value << (64 - shift); + } + + template <> + math_def FORCEINLINE uint64_t _rotate_left(uint64_t value, uint64_t shift) { +#ifdef SD_ARM_BUILD + // TODO: eventually remove this once gcc fixes the bug + Nd4jLong val = _rotate_left(*reinterpret_cast(&value), *reinterpret_cast(&shift)); + return *reinterpret_cast(&val); +#else + return value << shift | value >> (64 - shift); +#endif + } + + template <> + math_def FORCEINLINE uint64_t _rotate_right(uint64_t value, uint64_t shift) { +#ifdef SD_ARM_BUILD + // TODO: eventually remove this once gcc fixes the bug + Nd4jLong val = _rotate_right(*reinterpret_cast(&value), *reinterpret_cast(&shift)); + return *reinterpret_cast(&val); +#else + return value >> shift | value << (64 - shift); +#endif + } + + + template + math_def FORCEINLINE T p_rotl(T value, T shift) { + return _rotate_left(value, shift); + } + + template + math_def FORCEINLINE T p_rotr(T value, T shift) { + return _rotate_right(value, shift); + } } } diff --git a/libnd4j/include/templatemath.h b/libnd4j/include/math/templatemath.h similarity index 96% rename from libnd4j/include/templatemath.h rename to libnd4j/include/math/templatemath.h index 48021d734..c220231d8 100644 --- a/libnd4j/include/templatemath.h +++ b/libnd4j/include/math/templatemath.h @@ -25,10 +25,10 @@ #ifndef TEMPLATEMATH_H_ #define TEMPLATEMATH_H_ -#include -#include -#include -#include +#include +#include +#include +#include #define BFLOAT16_MAX_VALUE 32737. #define HALF_MAX_VALUE 65504. @@ -44,7 +44,7 @@ #define M_PI 3.14159265358979323846 #endif -namespace nd4j { +namespace sd { #ifdef __CUDACC__ #endif @@ -77,6 +77,12 @@ namespace nd4j { template math_def inline Z nd4j_softplus(T val); + template + math_def inline T nd4j_rotl(T val, T shift); + + template + math_def inline T nd4j_rotr(T val, T shift); + //#ifndef __CUDACC__ template math_def inline Z nd4j_dot(X *x, Y *y, int length); @@ -194,7 +200,7 @@ namespace nd4j { template math_def inline Z nd4j_softsign(T val) { - return val / ((T) 1.0f + nd4j::math::nd4j_abs(val)); + return val / ((T) 1.0f + sd::math::nd4j_abs(val)); } template @@ -578,7 +584,7 @@ namespace nd4j { template math_def inline bool nd4j_eq(T d1, T d2, double eps) { - if (nd4j::math::nd4j_isinf(d1) && nd4j::math::nd4j_isinf(d2)) { + if (sd::math::nd4j_isinf(d1) && sd::math::nd4j_isinf(d2)) { if (d1 > 0 && d2 > 0) return true; else if (d1 < 0 && d2 < 0) @@ -587,7 +593,7 @@ namespace nd4j { return false; } - auto diff = static_cast(nd4j::math::nd4j_abs(d1 - d2)); + auto diff = static_cast(sd::math::nd4j_abs(d1 - d2)); // works well except in the range of very large numbers @@ -596,7 +602,7 @@ namespace nd4j { // Knuth approach // works well except in the range of very small numbers - if (diff <= nd4j::math::nd4j_max(nd4j::math::nd4j_abs(static_cast(d1)), nd4j::math::nd4j_abs(static_cast(d2))) * eps) + if (diff <= sd::math::nd4j_max(sd::math::nd4j_abs(static_cast(d1)), sd::math::nd4j_abs(static_cast(d2))) * eps) return true; return false; @@ -783,7 +789,7 @@ namespace nd4j { X t = static_cast(2.0f); X e = static_cast(M_E); - auto p = nd4j::math::nd4j_pow(e, val * t); + auto p = sd::math::nd4j_pow(e, val * t); return (p - o)/ (p + o); } @@ -793,7 +799,7 @@ namespace nd4j { X t = static_cast(-2.0f); X e = static_cast(M_E); - auto p = nd4j::math::nd4j_pow(e, val * t); + auto p = sd::math::nd4j_pow(e, val * t); return (o - p) / (o + p); } @@ -801,7 +807,7 @@ namespace nd4j { math_def inline float neu_tanh(float val, float sign) { float e(M_E); float av = sign * val; - auto p = nd4j::math::nd4j_pow(e, -av * 2.f); + auto p = sd::math::nd4j_pow(e, -av * 2.f); return (1 - p) / (1 + p); } @@ -817,6 +823,16 @@ namespace nd4j { return val <= 0 ? neg_tanh(val) : pos_tanh(val); } + template + math_def inline T nd4j_rotl(T val, T shift) { + return p_rotl(val, shift); + } + + template + math_def inline T nd4j_rotr(T val, T shift) { + return p_rotr(val, shift); + } + template math_def inline Z nd4j_erf(X val) { return p_erf(static_cast(val)); @@ -933,7 +949,7 @@ namespace nd4j { // return DataTypeUtils::infOrMax(); } - return nd4j::math::nd4j_exp(nd4j::math::nd4j_lgamma(a)); + return sd::math::nd4j_exp(sd::math::nd4j_lgamma(a)); } template @@ -1689,29 +1705,29 @@ inline __device__ bfloat16 nd4j_atomicDiv(bfloat16* address, bfloat16 #endif #pragma omp declare reduction(maxTF : float,double,float16,bfloat16 : \ - omp_out = nd4j::math::nd4j_max(omp_in, omp_out) )\ + omp_out = sd::math::nd4j_max(omp_in, omp_out) )\ initializer (omp_priv=-MAX_FLOAT) #pragma omp declare reduction(minTF : float,double,float16,bfloat16 : \ - omp_out = nd4j::math::nd4j_min(omp_in, omp_out) )\ + omp_out = sd::math::nd4j_min(omp_in, omp_out) )\ initializer (omp_priv=MAX_FLOAT) #pragma omp declare reduction(maxT : float,double,float16,bfloat16,int,Nd4jLong,Nd4jULong,int8_t,uint8_t,bool,int16_t,uint16_t,uint32_t : \ - omp_out = nd4j::math::nd4j_max(omp_in, omp_out) )\ + omp_out = sd::math::nd4j_max(omp_in, omp_out) )\ initializer (omp_priv=0) #pragma omp declare reduction(minT : float,double,float16,bfloat16,int,Nd4jLong,Nd4jULong,int8_t,uint8_t,bool,int16_t,uint16_t,uint32_t : \ - omp_out = nd4j::math::nd4j_min(omp_in, omp_out) )\ + omp_out = sd::math::nd4j_min(omp_in, omp_out) )\ initializer (omp_priv=0) #pragma omp declare reduction(amaxT : float,double,float16,bfloat16,int,Nd4jLong,Nd4jULong,int8_t,uint8_t,bool,int16_t,uint16_t,uint32_t : \ - omp_out = nd4j::math::nd4j_max(nd4j::math::nd4j_abs(omp_in), nd4j::math::nd4j_abs(omp_out)) ) + omp_out = sd::math::nd4j_max(sd::math::nd4j_abs(omp_in), sd::math::nd4j_abs(omp_out)) ) #pragma omp declare reduction(aminT : float,double,float16,bfloat16,int,Nd4jLong,Nd4jULong,int8_t,uint8_t,bool,int16_t,uint16_t,uint32_t : \ - omp_out = nd4j::math::nd4j_min(nd4j::math::nd4j_abs(omp_in), nd4j::math::nd4j_abs(omp_out)) ) + omp_out = sd::math::nd4j_min(sd::math::nd4j_abs(omp_in), sd::math::nd4j_abs(omp_out)) ) #pragma omp declare reduction(asumT : float,double,float16,bfloat16,int,Nd4jLong,Nd4jULong,int8_t,uint8_t,bool,int16_t,uint16_t,uint32_t : \ - omp_out = nd4j::math::nd4j_abs(omp_in) + nd4j::math::nd4j_abs(omp_out))\ + omp_out = sd::math::nd4j_abs(omp_in) + sd::math::nd4j_abs(omp_out))\ initializer (omp_priv=0) #pragma omp declare reduction(sumT : float,double,float16,bfloat16,int,Nd4jLong,Nd4jULong,int8_t,uint8_t,bool,int16_t,uint16_t,uint32_t : \ diff --git a/libnd4j/include/memory/AllocationEntry.h b/libnd4j/include/memory/AllocationEntry.h index 3e690466b..815a5c992 100644 --- a/libnd4j/include/memory/AllocationEntry.h +++ b/libnd4j/include/memory/AllocationEntry.h @@ -21,11 +21,11 @@ #ifndef DEV_TESTS_ALLOCATIONENTRY_H #define DEV_TESTS_ALLOCATIONENTRY_H -#include +#include #include #include -namespace nd4j { +namespace sd { namespace memory { class AllocationEntry { private: diff --git a/libnd4j/include/memory/ExternalWorkspace.h b/libnd4j/include/memory/ExternalWorkspace.h index c1f3b4864..772afc608 100644 --- a/libnd4j/include/memory/ExternalWorkspace.h +++ b/libnd4j/include/memory/ExternalWorkspace.h @@ -21,10 +21,10 @@ #ifndef LIBND4J_EXTERNALWORKSPACE_H #define LIBND4J_EXTERNALWORKSPACE_H -#include -#include +#include +#include -namespace nd4j { +namespace sd { namespace memory { class ND4J_EXPORT ExternalWorkspace { private: diff --git a/libnd4j/include/memory/MemoryCounter.h b/libnd4j/include/memory/MemoryCounter.h index bf8ff60dc..91aaeecff 100644 --- a/libnd4j/include/memory/MemoryCounter.h +++ b/libnd4j/include/memory/MemoryCounter.h @@ -21,13 +21,13 @@ #ifndef SD_MEMORYCOUNTER_H #define SD_MEMORYCOUNTER_H -#include -#include +#include +#include #include #include #include -namespace nd4j { +namespace sd { namespace memory { /** * This class provides simple per-device counter @@ -44,13 +44,13 @@ namespace nd4j { // TODO: change this wrt heterogenous stuff on next iteration // per-group counters - std::map _groupCounters; + std::map _groupCounters; // per-device limits std::map _deviceLimits; // per-group limits - std::map _groupLimits; + std::map _groupLimits; MemoryCounter(); ~MemoryCounter() = default; @@ -79,7 +79,7 @@ namespace nd4j { * @param numBytes * @return TRUE if allocated ammount will keep us below limit, FALSE otherwise */ - bool validateGroup(nd4j::memory::MemoryType group, Nd4jLong numBytes); + bool validateGroup(sd::memory::MemoryType group, Nd4jLong numBytes); /** * This method adds specified number of bytes to specified counter @@ -87,7 +87,7 @@ namespace nd4j { * @param numBytes */ void countIn(int deviceId, Nd4jLong numBytes); - void countIn(nd4j::memory::MemoryType group, Nd4jLong numBytes); + void countIn(sd::memory::MemoryType group, Nd4jLong numBytes); /** * This method subtracts specified number of bytes from specified counter @@ -95,7 +95,7 @@ namespace nd4j { * @param numBytes */ void countOut(int deviceId, Nd4jLong numBytes); - void countOut(nd4j::memory::MemoryType group, Nd4jLong numBytes); + void countOut(sd::memory::MemoryType group, Nd4jLong numBytes); /** * This method returns amount of memory allocated on specified device @@ -109,7 +109,7 @@ namespace nd4j { * @param group * @return */ - Nd4jLong allocatedGroup(nd4j::memory::MemoryType group); + Nd4jLong allocatedGroup(sd::memory::MemoryType group); /** * This method allows to set per-device memory limits @@ -130,14 +130,14 @@ namespace nd4j { * @param group * @param numBytes */ - void setGroupLimit(nd4j::memory::MemoryType group, Nd4jLong numBytes); + void setGroupLimit(sd::memory::MemoryType group, Nd4jLong numBytes); /** * This method returns current group limit in bytes * @param group * @return */ - Nd4jLong groupLimit(nd4j::memory::MemoryType group); + Nd4jLong groupLimit(sd::memory::MemoryType group); }; } } diff --git a/libnd4j/include/memory/MemoryRegistrator.h b/libnd4j/include/memory/MemoryRegistrator.h index 53e97d35e..ad1b0333a 100644 --- a/libnd4j/include/memory/MemoryRegistrator.h +++ b/libnd4j/include/memory/MemoryRegistrator.h @@ -22,17 +22,19 @@ #define LIBND4J_MEMORYREGISTRATOR_H #include "Workspace.h" +#include +#include #include #include -#include +#include -namespace nd4j { +namespace sd { namespace memory { class ND4J_EXPORT MemoryRegistrator { protected: static MemoryRegistrator* _INSTANCE; Workspace* _workspace; - std::map _footprint; + MAP_IMPL _footprint; std::mutex _lock; MemoryRegistrator(); diff --git a/libnd4j/include/memory/MemoryReport.h b/libnd4j/include/memory/MemoryReport.h index 636178d45..647886ab5 100644 --- a/libnd4j/include/memory/MemoryReport.h +++ b/libnd4j/include/memory/MemoryReport.h @@ -21,10 +21,10 @@ #ifndef LIBND4J_MEMORYREPORT_H #define LIBND4J_MEMORYREPORT_H -#include -#include +#include +#include -namespace nd4j { +namespace sd { namespace memory { class ND4J_EXPORT MemoryReport { private: diff --git a/libnd4j/include/memory/MemoryTracker.h b/libnd4j/include/memory/MemoryTracker.h index 097d2903d..38bb926ca 100644 --- a/libnd4j/include/memory/MemoryTracker.h +++ b/libnd4j/include/memory/MemoryTracker.h @@ -23,12 +23,12 @@ #include #include -#include +#include #include #include "AllocationEntry.h" -#include +#include -namespace nd4j { +namespace sd { namespace memory { /** * This class is used for tracking memory allocation wrt their allocation points in code diff --git a/libnd4j/include/memory/MemoryType.h b/libnd4j/include/memory/MemoryType.h index 9a2e9b0e3..113d8d16d 100644 --- a/libnd4j/include/memory/MemoryType.h +++ b/libnd4j/include/memory/MemoryType.h @@ -5,7 +5,7 @@ #ifndef DEV_TESTS_MEMORYTYPE_H #define DEV_TESTS_MEMORYTYPE_H -namespace nd4j { +namespace sd { namespace memory { enum MemoryType { HOST = 0, diff --git a/libnd4j/include/memory/MemoryUtils.h b/libnd4j/include/memory/MemoryUtils.h index 5fe27898c..027008238 100644 --- a/libnd4j/include/memory/MemoryUtils.h +++ b/libnd4j/include/memory/MemoryUtils.h @@ -22,9 +22,9 @@ #define LIBND4J_MEMORYUTILS_H #include "MemoryReport.h" -#include +#include -namespace nd4j { +namespace sd { namespace memory { class ND4J_EXPORT MemoryUtils { public: diff --git a/libnd4j/include/memory/Workspace.h b/libnd4j/include/memory/Workspace.h index 269ed9ca7..c97f6a178 100644 --- a/libnd4j/include/memory/Workspace.h +++ b/libnd4j/include/memory/Workspace.h @@ -27,13 +27,13 @@ #include #include #include -#include -#include +#include +#include #include #include #include -namespace nd4j { +namespace sd { namespace memory { class ND4J_EXPORT Workspace { diff --git a/libnd4j/include/memory/cpu/Workspace.cpp b/libnd4j/include/memory/cpu/Workspace.cpp index e297e28b0..ae60f1eea 100644 --- a/libnd4j/include/memory/cpu/Workspace.cpp +++ b/libnd4j/include/memory/cpu/Workspace.cpp @@ -21,17 +21,17 @@ // -#include +#include #include #include #include #include "../Workspace.h" #include -#include +#include #include -namespace nd4j { +namespace sd { namespace memory { Workspace::Workspace(ExternalWorkspace *external) { if (external->sizeHost() > 0) { @@ -182,7 +182,7 @@ namespace nd4j { return _spillsSize.load(); } - void* Workspace::allocateBytes(nd4j::memory::MemoryType type, Nd4jLong numBytes) { + void* Workspace::allocateBytes(sd::memory::MemoryType type, Nd4jLong numBytes) { if (type == DEVICE) throw std::runtime_error("CPU backend doesn't have device memory"); @@ -211,7 +211,7 @@ namespace nd4j { Workspace* Workspace::clone() { // for clone we take whatever is higher: current allocated size, or allocated size of current loop - return new Workspace(nd4j::math::nd4j_max(this->getCurrentSize(), this->_cycleAllocations.load())); + return new Workspace(sd::math::nd4j_max(this->getCurrentSize(), this->_cycleAllocations.load())); } } } diff --git a/libnd4j/include/memory/cuda/Workspace.cu b/libnd4j/include/memory/cuda/Workspace.cu index aeb6b4752..9d2286156 100644 --- a/libnd4j/include/memory/cuda/Workspace.cu +++ b/libnd4j/include/memory/cuda/Workspace.cu @@ -20,20 +20,20 @@ // @author raver119@gmail.com // -#include +#include #include #include #include #include "../Workspace.h" #include -#include +#include #include -#include +#include #include #include -namespace nd4j { +namespace sd { namespace memory { Workspace::Workspace(ExternalWorkspace *external) { if (external->sizeHost() > 0) { @@ -162,7 +162,7 @@ namespace nd4j { void* Workspace::allocateBytes(Nd4jLong numBytes) { - return allocateBytes(nd4j::memory::MemoryType::HOST, numBytes); + return allocateBytes(sd::memory::MemoryType::HOST, numBytes); } Nd4jLong Workspace::getAllocatedSize() { @@ -183,7 +183,7 @@ namespace nd4j { return _spillsSize.load(); } - void* Workspace::allocateBytes(nd4j::memory::MemoryType type, Nd4jLong numBytes) { + void* Workspace::allocateBytes(sd::memory::MemoryType type, Nd4jLong numBytes) { switch (type) { case HOST: { if (numBytes < 1) @@ -270,7 +270,7 @@ namespace nd4j { Workspace* Workspace::clone() { // for clone we take whatever is higher: current allocated size, or allocated size of current loop - return new Workspace(nd4j::math::nd4j_max(this->getCurrentSize(), this->_cycleAllocations.load())); + return new Workspace(sd::math::nd4j_max(this->getCurrentSize(), this->_cycleAllocations.load())); } Nd4jLong Workspace::getAllocatedSecondarySize() { diff --git a/libnd4j/include/memory/impl/AllocationEntry.cpp b/libnd4j/include/memory/impl/AllocationEntry.cpp index c0df16a3f..6b4d85bb1 100644 --- a/libnd4j/include/memory/impl/AllocationEntry.cpp +++ b/libnd4j/include/memory/impl/AllocationEntry.cpp @@ -20,7 +20,7 @@ #include -namespace nd4j { +namespace sd { namespace memory { AllocationEntry::AllocationEntry(MemoryType type, Nd4jLong ptr, Nd4jLong numBytes, std::string &stack) { _pointer = ptr; diff --git a/libnd4j/include/memory/impl/ExternalWorkspace.cpp b/libnd4j/include/memory/impl/ExternalWorkspace.cpp index fffba2216..c4feb181d 100644 --- a/libnd4j/include/memory/impl/ExternalWorkspace.cpp +++ b/libnd4j/include/memory/impl/ExternalWorkspace.cpp @@ -20,7 +20,7 @@ #include -namespace nd4j { +namespace sd { namespace memory { ExternalWorkspace::ExternalWorkspace(Nd4jPointer ptrH, Nd4jLong sizeH, Nd4jPointer ptrD, Nd4jLong sizeD) { _ptrH = ptrH; diff --git a/libnd4j/include/memory/impl/MemoryCounter.cpp b/libnd4j/include/memory/impl/MemoryCounter.cpp index 0dc845e37..96be34681 100644 --- a/libnd4j/include/memory/impl/MemoryCounter.cpp +++ b/libnd4j/include/memory/impl/MemoryCounter.cpp @@ -20,14 +20,14 @@ #include "../MemoryCounter.h" #include -#include +#include #include -namespace nd4j { +namespace sd { namespace memory { MemoryCounter::MemoryCounter() { - auto numDevices = nd4j::AffinityManager::numberOfDevices(); + auto numDevices = sd::AffinityManager::numberOfDevices(); // setting default 0s for (int e = 0; e < numDevices; e++) { @@ -36,12 +36,12 @@ namespace nd4j { } // setting initial values for limits - _groupLimits[nd4j::memory::MemoryType::HOST] = nd4j::Environment::getInstance()->maxPrimaryMemory(); - _groupLimits[nd4j::memory::MemoryType::DEVICE] = nd4j::Environment::getInstance()->maxSpecialMemory(); + _groupLimits[sd::memory::MemoryType::HOST] = sd::Environment::getInstance()->maxPrimaryMemory(); + _groupLimits[sd::memory::MemoryType::DEVICE] = sd::Environment::getInstance()->maxSpecialMemory(); // setting initial counter values - _groupCounters[nd4j::memory::MemoryType::HOST] = 0; - _groupCounters[nd4j::memory::MemoryType::DEVICE] = 0; + _groupCounters[sd::memory::MemoryType::HOST] = 0; + _groupCounters[sd::memory::MemoryType::DEVICE] = 0; } MemoryCounter* MemoryCounter::getInstance() { @@ -56,7 +56,7 @@ namespace nd4j { _deviceCounters[deviceId] += numBytes; } - void MemoryCounter::countIn(nd4j::memory::MemoryType group, Nd4jLong numBytes) { + void MemoryCounter::countIn(sd::memory::MemoryType group, Nd4jLong numBytes) { std::lock_guard lock(_locker); _groupCounters[group] += numBytes; } @@ -66,13 +66,13 @@ namespace nd4j { _deviceCounters[deviceId] -= numBytes; } - void MemoryCounter::countOut(nd4j::memory::MemoryType group, Nd4jLong numBytes) { + void MemoryCounter::countOut(sd::memory::MemoryType group, Nd4jLong numBytes) { std::lock_guard lock(_locker); _groupCounters[group] -= numBytes; } bool MemoryCounter::validate(Nd4jLong numBytes) { - auto deviceId = nd4j::AffinityManager::currentDeviceId(); + auto deviceId = sd::AffinityManager::currentDeviceId(); return validateDevice(deviceId, numBytes); } @@ -87,7 +87,7 @@ namespace nd4j { return numBytes + dAlloc <= dLimit; } - bool MemoryCounter::validateGroup(nd4j::memory::MemoryType group, Nd4jLong numBytes) { + bool MemoryCounter::validateGroup(sd::memory::MemoryType group, Nd4jLong numBytes) { std::lock_guard lock(_locker); auto gLimit = _groupLimits[group]; if (gLimit <= 0) @@ -103,7 +103,7 @@ namespace nd4j { return _deviceCounters[deviceId]; } - Nd4jLong MemoryCounter::allocatedGroup(nd4j::memory::MemoryType group) { + Nd4jLong MemoryCounter::allocatedGroup(sd::memory::MemoryType group) { std::lock_guard lock(_locker); return _groupCounters[group]; } @@ -113,7 +113,7 @@ namespace nd4j { _deviceLimits[deviceId] = numBytes; } - void MemoryCounter::setGroupLimit(nd4j::memory::MemoryType group, Nd4jLong numBytes) { + void MemoryCounter::setGroupLimit(sd::memory::MemoryType group, Nd4jLong numBytes) { std::lock_guard lock(_locker); _groupLimits[group] = numBytes; } @@ -123,7 +123,7 @@ namespace nd4j { return _deviceLimits[deviceId]; } - Nd4jLong MemoryCounter::groupLimit(nd4j::memory::MemoryType group) { + Nd4jLong MemoryCounter::groupLimit(sd::memory::MemoryType group) { std::lock_guard lock(_locker); return _groupLimits[group]; } diff --git a/libnd4j/include/memory/impl/MemoryRegistrator.cpp b/libnd4j/include/memory/impl/MemoryRegistrator.cpp index 0eba40af9..31b4b0eae 100644 --- a/libnd4j/include/memory/impl/MemoryRegistrator.cpp +++ b/libnd4j/include/memory/impl/MemoryRegistrator.cpp @@ -20,7 +20,7 @@ #include -namespace nd4j { +namespace sd { namespace memory { MemoryRegistrator::MemoryRegistrator() { diff --git a/libnd4j/include/memory/impl/MemoryReport.cpp b/libnd4j/include/memory/impl/MemoryReport.cpp index 533f7ac4f..0c623b0ce 100644 --- a/libnd4j/include/memory/impl/MemoryReport.cpp +++ b/libnd4j/include/memory/impl/MemoryReport.cpp @@ -20,42 +20,42 @@ #include "memory/MemoryReport.h" -bool nd4j::memory::MemoryReport::operator<(const nd4j::memory::MemoryReport &other) const { +bool sd::memory::MemoryReport::operator<(const sd::memory::MemoryReport &other) const { return this->_rss < other._rss; } -bool nd4j::memory::MemoryReport::operator>(const nd4j::memory::MemoryReport &other) const { +bool sd::memory::MemoryReport::operator>(const sd::memory::MemoryReport &other) const { return this->_rss > other._rss; } -bool nd4j::memory::MemoryReport::operator==(const nd4j::memory::MemoryReport &other) const { +bool sd::memory::MemoryReport::operator==(const sd::memory::MemoryReport &other) const { return this->_rss == other._rss; } -bool nd4j::memory::MemoryReport::operator!=(const nd4j::memory::MemoryReport &other) const { +bool sd::memory::MemoryReport::operator!=(const sd::memory::MemoryReport &other) const { return this->_rss != other._rss; } -bool nd4j::memory::MemoryReport::operator<=(const nd4j::memory::MemoryReport &other) const { +bool sd::memory::MemoryReport::operator<=(const sd::memory::MemoryReport &other) const { return this->_rss <= other._rss; } -bool nd4j::memory::MemoryReport::operator>=(const nd4j::memory::MemoryReport &other) const { +bool sd::memory::MemoryReport::operator>=(const sd::memory::MemoryReport &other) const { return this->_rss >= other._rss; } -Nd4jLong nd4j::memory::MemoryReport::getVM() const { +Nd4jLong sd::memory::MemoryReport::getVM() const { return _vm; } -void nd4j::memory::MemoryReport::setVM(Nd4jLong _vm) { +void sd::memory::MemoryReport::setVM(Nd4jLong _vm) { MemoryReport::_vm = _vm; } -Nd4jLong nd4j::memory::MemoryReport::getRSS() const { +Nd4jLong sd::memory::MemoryReport::getRSS() const { return _rss; } -void nd4j::memory::MemoryReport::setRSS(Nd4jLong _rss) { +void sd::memory::MemoryReport::setRSS(Nd4jLong _rss) { MemoryReport::_rss = _rss; } diff --git a/libnd4j/include/memory/impl/MemoryTracker.cpp b/libnd4j/include/memory/impl/MemoryTracker.cpp index 53a64f14d..be3019b08 100644 --- a/libnd4j/include/memory/impl/MemoryTracker.cpp +++ b/libnd4j/include/memory/impl/MemoryTracker.cpp @@ -25,7 +25,7 @@ #include -#if defined(__GNUC__) && !defined(__MINGW64__) && !defined(ANDROID_BUILD) && !defined(IOS_BUILD) && !defined(APPLE_BUILD) +#if defined(__GNUC__) && !defined(__MINGW64__) && !defined(SD_ANDROID_BUILD) && !defined(SD_IOS_BUILD) && !defined(SD_APPLE_BUILD) #include #include @@ -33,7 +33,7 @@ #endif -namespace nd4j { +namespace sd { namespace memory { MemoryTracker::MemoryTracker() { @@ -47,7 +47,7 @@ namespace nd4j { return _INSTANCE; } -#if defined(__GNUC__) && !defined(__MINGW64__) && !defined(ANDROID_BUILD) && !defined(IOS_BUILD) && !defined(APPLE_BUILD) +#if defined(__GNUC__) && !defined(__MINGW64__) && !defined(SD_ANDROID_BUILD) && !defined(SD_IOS_BUILD) && !defined(SD_APPLE_BUILD) std::string demangle(char *message) { char *mangled_name = 0, *offset_begin = 0, *offset_end = 0; @@ -95,7 +95,7 @@ namespace nd4j { #endif void MemoryTracker::countIn(MemoryType type, Nd4jPointer ptr, Nd4jLong numBytes) { -#if defined(__GNUC__) && !defined(__MINGW64__) && !defined(ANDROID_BUILD) && !defined(IOS_BUILD) && !defined(APPLE_BUILD) +#if defined(__GNUC__) && !defined(__MINGW64__) && !defined(SD_ANDROID_BUILD) && !defined(SD_IOS_BUILD) && !defined(SD_APPLE_BUILD) if (Environment::getInstance()->isDetectingLeaks()) { auto lptr = reinterpret_cast(ptr); @@ -129,7 +129,7 @@ namespace nd4j { } void MemoryTracker::countOut(Nd4jPointer ptr) { -#if defined(__GNUC__) && !defined(__MINGW64__) && !defined(ANDROID_BUILD) && !defined(IOS_BUILD) && !defined(APPLE_BUILD) +#if defined(__GNUC__) && !defined(__MINGW64__) && !defined(SD_ANDROID_BUILD) && !defined(SD_IOS_BUILD) && !defined(SD_APPLE_BUILD) if (Environment::getInstance()->isDetectingLeaks()) { auto lptr = reinterpret_cast(ptr); diff --git a/libnd4j/include/memory/impl/MemoryUtils.cpp b/libnd4j/include/memory/impl/MemoryUtils.cpp index ac611fd64..8500a044e 100644 --- a/libnd4j/include/memory/impl/MemoryUtils.cpp +++ b/libnd4j/include/memory/impl/MemoryUtils.cpp @@ -35,7 +35,7 @@ #endif -bool nd4j::memory::MemoryUtils::retrieveMemoryStatistics(nd4j::memory::MemoryReport &report) { +bool sd::memory::MemoryUtils::retrieveMemoryStatistics(sd::memory::MemoryReport &report) { #if defined(__APPLE__) nd4j_debug("APPLE route\n", ""); /* diff --git a/libnd4j/include/ops/BroadcastBoolOpsTuple.h b/libnd4j/include/ops/BroadcastBoolOpsTuple.h index 7b0f96505..188186b4c 100644 --- a/libnd4j/include/ops/BroadcastBoolOpsTuple.h +++ b/libnd4j/include/ops/BroadcastBoolOpsTuple.h @@ -21,28 +21,28 @@ #ifndef DEV_TESTS_BROADCASTBOOLOPSTUPLE_H #define DEV_TESTS_BROADCASTBOOLOPSTUPLE_H -#include -#include +#include +#include -namespace nd4j { +namespace sd { class ND4J_EXPORT BroadcastBoolOpsTuple { private: public: - nd4j::scalar::BoolOps s; - nd4j::pairwise::BoolOps p; - nd4j::broadcast::BoolOps b; + sd::scalar::BoolOps s; + sd::pairwise::BoolOps p; + sd::broadcast::BoolOps b; BroadcastBoolOpsTuple() = default; ~BroadcastBoolOpsTuple() = default; - BroadcastBoolOpsTuple(nd4j::scalar::BoolOps scalar, nd4j::pairwise::BoolOps pairwise, nd4j::broadcast::BoolOps broadcast) { + BroadcastBoolOpsTuple(sd::scalar::BoolOps scalar, sd::pairwise::BoolOps pairwise, sd::broadcast::BoolOps broadcast) { s = scalar; p = pairwise; b = broadcast; } - static BroadcastBoolOpsTuple custom(nd4j::scalar::BoolOps scalar, nd4j::pairwise::BoolOps pairwise, nd4j::broadcast::BoolOps broadcast); + static BroadcastBoolOpsTuple custom(sd::scalar::BoolOps scalar, sd::pairwise::BoolOps pairwise, sd::broadcast::BoolOps broadcast); }; } diff --git a/libnd4j/include/ops/BroadcastIntOpsTuple.h b/libnd4j/include/ops/BroadcastIntOpsTuple.h index c96244b1a..258719004 100644 --- a/libnd4j/include/ops/BroadcastIntOpsTuple.h +++ b/libnd4j/include/ops/BroadcastIntOpsTuple.h @@ -21,28 +21,28 @@ #ifndef DEV_TESTS_BROADCASTINTOPSTUPLE_H #define DEV_TESTS_BROADCASTINTOPSTUPLE_H -#include -#include +#include +#include -namespace nd4j { +namespace sd { class ND4J_EXPORT BroadcastIntOpsTuple { private: public: - nd4j::scalar::IntOps s; - nd4j::pairwise::IntOps p; - nd4j::broadcast::IntOps b; + sd::scalar::IntOps s; + sd::pairwise::IntOps p; + sd::broadcast::IntOps b; BroadcastIntOpsTuple() = default; ~BroadcastIntOpsTuple() = default; - BroadcastIntOpsTuple(nd4j::scalar::IntOps scalar, nd4j::pairwise::IntOps pairwise, nd4j::broadcast::IntOps broadcast) { + BroadcastIntOpsTuple(sd::scalar::IntOps scalar, sd::pairwise::IntOps pairwise, sd::broadcast::IntOps broadcast) { s = scalar; p = pairwise; b = broadcast; } - static BroadcastIntOpsTuple custom(nd4j::scalar::IntOps scalar, nd4j::pairwise::IntOps pairwise, nd4j::broadcast::IntOps broadcast); + static BroadcastIntOpsTuple custom(sd::scalar::IntOps scalar, sd::pairwise::IntOps pairwise, sd::broadcast::IntOps broadcast); }; } diff --git a/libnd4j/include/ops/BroadcastOpsTuple.h b/libnd4j/include/ops/BroadcastOpsTuple.h index 1bcd2df8b..34e2c6039 100644 --- a/libnd4j/include/ops/BroadcastOpsTuple.h +++ b/libnd4j/include/ops/BroadcastOpsTuple.h @@ -21,28 +21,28 @@ #ifndef DEV_TESTS_BROADCASTOPSTUPLE_H #define DEV_TESTS_BROADCASTOPSTUPLE_H -#include -#include +#include +#include -namespace nd4j { +namespace sd { class ND4J_EXPORT BroadcastOpsTuple { private: public: - nd4j::scalar::Ops s; - nd4j::pairwise::Ops p; - nd4j::broadcast::Ops b; + sd::scalar::Ops s; + sd::pairwise::Ops p; + sd::broadcast::Ops b; BroadcastOpsTuple() = default; ~BroadcastOpsTuple() = default; - BroadcastOpsTuple(nd4j::scalar::Ops scalar, nd4j::pairwise::Ops pairwise, nd4j::broadcast::Ops broadcast) { + BroadcastOpsTuple(sd::scalar::Ops scalar, sd::pairwise::Ops pairwise, sd::broadcast::Ops broadcast) { s = scalar; p = pairwise; b = broadcast; } - static BroadcastOpsTuple custom(nd4j::scalar::Ops scalar, nd4j::pairwise::Ops pairwise, nd4j::broadcast::Ops broadcast); + static BroadcastOpsTuple custom(sd::scalar::Ops scalar, sd::pairwise::Ops pairwise, sd::broadcast::Ops broadcast); static BroadcastOpsTuple Add(); static BroadcastOpsTuple Assign(); diff --git a/libnd4j/include/ops/InputType.h b/libnd4j/include/ops/InputType.h index 44bd92b65..4deff4900 100644 --- a/libnd4j/include/ops/InputType.h +++ b/libnd4j/include/ops/InputType.h @@ -21,7 +21,7 @@ #ifndef ND4J_INPUTTYPE_H #define ND4J_INPUTTYPE_H -namespace nd4j { +namespace sd { namespace ops { enum InputType { InputType_BOOLEAN = 0, diff --git a/libnd4j/include/ops/declarable/BooleanOp.h b/libnd4j/include/ops/declarable/BooleanOp.h index c13555407..b04ca8eca 100644 --- a/libnd4j/include/ops/declarable/BooleanOp.h +++ b/libnd4j/include/ops/declarable/BooleanOp.h @@ -25,7 +25,7 @@ #include "OpDescriptor.h" #include "DeclarableOp.h" -namespace nd4j { +namespace sd { namespace ops { class ND4J_EXPORT BooleanOp : public DeclarableOp { protected: @@ -36,12 +36,12 @@ namespace nd4j { public: BooleanOp(const char *name, int numInputs, bool scalar); - bool verify(const std::vector& args); - bool verify(nd4j::graph::Context& block); + bool verify(const std::vector& args); + bool verify(sd::graph::Context& block); Nd4jStatus execute(Context* block) override; - ShapeList *calculateOutputShape(ShapeList *inputShape, nd4j::graph::Context& block) override; + ShapeList *calculateOutputShape(ShapeList *inputShape, sd::graph::Context& block) override; }; } } diff --git a/libnd4j/include/ops/declarable/BroadcastableOp.h b/libnd4j/include/ops/declarable/BroadcastableOp.h index 39435195b..9bc756128 100644 --- a/libnd4j/include/ops/declarable/BroadcastableOp.h +++ b/libnd4j/include/ops/declarable/BroadcastableOp.h @@ -26,7 +26,7 @@ #include "DeclarableOp.h" #include "DeclarableCustomOp.h" -namespace nd4j { +namespace sd { namespace ops { class ND4J_EXPORT BroadcastableOp : public DeclarableCustomOp{ protected: @@ -34,7 +34,7 @@ namespace nd4j { public: BroadcastableOp(const char *name, int numTArgs, int numIArgs); - ShapeList *calculateOutputShape(ShapeList *inputShape, nd4j::graph::Context& block) override; + ShapeList *calculateOutputShape(ShapeList *inputShape, sd::graph::Context& block) override; }; } } diff --git a/libnd4j/include/ops/declarable/CustomOperations.h b/libnd4j/include/ops/declarable/CustomOperations.h index 0b0e42809..1a1624c08 100644 --- a/libnd4j/include/ops/declarable/CustomOperations.h +++ b/libnd4j/include/ops/declarable/CustomOperations.h @@ -45,19 +45,19 @@ #include #include #include -#include +#include #include #include -#include +#include #include #include -#include +#include #include -#include -#include +#include +#include -namespace nd4j { +namespace sd { struct ND4J_EXPORT _loader { _loader(); }; diff --git a/libnd4j/include/ops/declarable/DeclarableCustomOp.h b/libnd4j/include/ops/declarable/DeclarableCustomOp.h index 49d3735d4..4aa133a4b 100644 --- a/libnd4j/include/ops/declarable/DeclarableCustomOp.h +++ b/libnd4j/include/ops/declarable/DeclarableCustomOp.h @@ -23,9 +23,9 @@ #include -namespace nd4j { +namespace sd { namespace ops { - class ND4J_EXPORT DeclarableCustomOp : public nd4j::ops::DeclarableOp { + class ND4J_EXPORT DeclarableCustomOp : public sd::ops::DeclarableOp { protected: /** * This method executes this Op @@ -34,7 +34,7 @@ namespace nd4j { public: DeclarableCustomOp(int numInputs, int numOutputs, const char *opName, bool allowsInplace, int tArgs, int iArgs); - ShapeList* calculateOutputShape(ShapeList* inputShapes, nd4j::graph::Context& block) override = 0; + ShapeList* calculateOutputShape(ShapeList* inputShapes, sd::graph::Context& block) override = 0; }; } } diff --git a/libnd4j/include/ops/declarable/DeclarableListOp.h b/libnd4j/include/ops/declarable/DeclarableListOp.h index 2d6115027..cc77ee17b 100644 --- a/libnd4j/include/ops/declarable/DeclarableListOp.h +++ b/libnd4j/include/ops/declarable/DeclarableListOp.h @@ -26,15 +26,15 @@ #include #include -using namespace nd4j::graph; +using namespace sd::graph; -namespace nd4j { +namespace sd { namespace ops { - class ND4J_EXPORT DeclarableListOp : public nd4j::ops::DeclarableOp { + class ND4J_EXPORT DeclarableListOp : public sd::ops::DeclarableOp { protected: Nd4jStatus validateAndExecute(Context& block) override = 0; - nd4j::NDArray* getZ(Context& block, int inputId) ; + sd::NDArray* getZ(Context& block, int inputId) ; void setupResult(NDArray* array, Context& block); void setupResultList(NDArrayList* arrayList, Context& block); @@ -45,10 +45,10 @@ namespace nd4j { Nd4jStatus execute(Context* block) override; - ResultSet* execute(NDArrayList* list, std::initializer_list inputs, std::initializer_list tArgs, std::initializer_list iArgs); - ResultSet* execute(NDArrayList* list, std::vector& inputs, std::vector& tArgs, std::vector& iArgs); + ResultSet execute(NDArrayList* list, std::initializer_list inputs, std::initializer_list tArgs, std::initializer_list iArgs); + ResultSet execute(NDArrayList* list, std::vector& inputs, std::vector& tArgs, std::vector& iArgs); - ShapeList* calculateOutputShape(ShapeList* inputShape, nd4j::graph::Context& block) override; + ShapeList* calculateOutputShape(ShapeList* inputShape, sd::graph::Context& block) override; }; } } diff --git a/libnd4j/include/ops/declarable/DeclarableOp.h b/libnd4j/include/ops/declarable/DeclarableOp.h index 78f5fcaa4..5c01eecc8 100644 --- a/libnd4j/include/ops/declarable/DeclarableOp.h +++ b/libnd4j/include/ops/declarable/DeclarableOp.h @@ -23,15 +23,15 @@ #include #include -#include -#include +#include +#include #include #include "OpDescriptor.h" #include #include #include #include -#include +#include #include //#include @@ -39,9 +39,9 @@ #include #include -using namespace nd4j::graph; +using namespace sd::graph; -namespace nd4j { +namespace sd { namespace ops { Nd4jStatus ND4J_EXPORT conditionHelper(const char *file, int line, int condition, int argNumber, const char *format, ...); @@ -68,7 +68,7 @@ namespace nd4j { private: std::mutex _registrator; bool _registered = false; - + std::string _name; protected: OpDescriptor *_descriptor; NDArray *_scalar = nullptr; @@ -105,7 +105,7 @@ namespace nd4j { */ void storeResult(Context &block, int outputNumber, NDArray& array); void storeResult(Context &block, int outputNumber, NDArray* array); - nd4j::NDArray* getZ(Context& block, int inputId = 0); + sd::NDArray* getZ(Context& block, int inputId = 0); /** * This method pre-allocates NDArrays for Op output, in case they are not available at op execution time @@ -132,12 +132,12 @@ namespace nd4j { // this method returns OpDescriptor, describing this Op instance OpDescriptor *getOpDescriptor(); - Nd4jStatus validateDataTypes(Context& block); + virtual Nd4jStatus validateDataTypes(Context& block); /** * This method should be available in each implemented Op, and should return Op output shape(s), for a given input shape(s) */ - virtual ShapeList* calculateOutputShape(ShapeList* inputShape, nd4j::graph::Context& block) = 0; + virtual ShapeList* calculateOutputShape(ShapeList* inputShape, sd::graph::Context& block) = 0; /** * Returns opName @@ -174,19 +174,19 @@ namespace nd4j { template ::value>> Nd4jStatus execute(const std::vector &inputs, const std::vector &outputs, std::initializer_list tArgs); - Nd4jStatus execute(const std::vector &inputs, const std::vector &outputs, const std::vector &tArgs, const std::vector &iArgs, const std::vector &bArgs = std::vector(), const std::vector &dArgs = std::vector(), bool isInplace = false); + Nd4jStatus execute(const std::vector &inputs, const std::vector &outputs, const std::vector &tArgs, const std::vector &iArgs, const std::vector &bArgs = std::vector(), const std::vector &dArgs = std::vector(), bool isInplace = false); - - nd4j::ResultSet* evaluate(const std::vector &inputs); + sd::ResultSet evaluate(const std::vector &inputs); template ::value>> - nd4j::ResultSet* evaluate(const std::vector &inputs, std::initializer_list args); + sd::ResultSet evaluate(const std::vector &inputs, std::initializer_list args); - nd4j::ResultSet* evaluate(const std::vector &inputs, const std::vector &tArgs, const std::vector &iArgs, const std::vector &bArgs = std::vector(), const std::vector &dArgs = std::vector(), bool isInplace = false); + sd::ResultSet evaluate(const std::vector &inputs, const std::vector &tArgs, const std::vector &iArgs, const std::vector &bArgs = std::vector(), const std::vector &dArgs = std::vector(), bool isInplace = false); - Nd4jStatus execute(nd4j::graph::RandomGenerator& rng, const std::vector& inputs, const std::vector& outputs, const std::vector& tArgs, const std::vector& iArgs, const std::vector& bArgs, const std::vector &dArgs = std::vector(), bool isInplace = false, nd4j::DataType type = nd4j::DataType::FLOAT32); + Nd4jStatus execute(sd::graph::RandomGenerator& rng, const std::vector& inputs, const std::vector& outputs, const std::vector& tArgs, const std::vector& iArgs, const std::vector& bArgs, const std::vector &dArgs = std::vector(), bool isInplace = false, sd::DataType type = sd::DataType::FLOAT32); + + sd::ResultSet execute(const sd::OpArgsHolder& holder, bool isInplace = false); - nd4j::ResultSet* execute(const nd4j::OpArgsHolder& holder, bool isInplace = false); // There methods provide various validation options Nd4jStatus validateNonEmptyInput(Context& block); diff --git a/libnd4j/include/ops/declarable/DeclarableReductionOp.h b/libnd4j/include/ops/declarable/DeclarableReductionOp.h index 5306f60eb..11f4ec410 100644 --- a/libnd4j/include/ops/declarable/DeclarableReductionOp.h +++ b/libnd4j/include/ops/declarable/DeclarableReductionOp.h @@ -23,9 +23,9 @@ #include -namespace nd4j { +namespace sd { namespace ops { - class ND4J_EXPORT DeclarableReductionOp : public nd4j::ops::DeclarableOp { + class ND4J_EXPORT DeclarableReductionOp : public sd::ops::DeclarableOp { protected: /** * This method executes this Op @@ -34,7 +34,7 @@ namespace nd4j { public: DeclarableReductionOp(int numInputs, int numOutputs, const char *opName, bool allowsInplace, int tArgs, int iArgs); - ShapeList* calculateOutputShape(ShapeList* inputShape, nd4j::graph::Context& block) override; + ShapeList* calculateOutputShape(ShapeList* inputShape, sd::graph::Context& block) override; }; } } diff --git a/libnd4j/include/ops/declarable/LegacyBroadcastBoolOp.h b/libnd4j/include/ops/declarable/LegacyBroadcastBoolOp.h index d72ed612d..67787ca4b 100644 --- a/libnd4j/include/ops/declarable/LegacyBroadcastBoolOp.h +++ b/libnd4j/include/ops/declarable/LegacyBroadcastBoolOp.h @@ -23,7 +23,7 @@ #include -namespace nd4j { +namespace sd { namespace ops { /** * This class provides wrapper for broadcast operations. @@ -35,7 +35,7 @@ namespace nd4j { LegacyBroadcastBoolOp(); LegacyBroadcastBoolOp(int opNum); - ShapeList* calculateOutputShape(ShapeList* inputShape, nd4j::graph::Context& block) override; + ShapeList* calculateOutputShape(ShapeList* inputShape, sd::graph::Context& block) override; LegacyOp* clone() override; }; } diff --git a/libnd4j/include/ops/declarable/LegacyBroadcastOp.h b/libnd4j/include/ops/declarable/LegacyBroadcastOp.h index 7502b6ce7..755277397 100644 --- a/libnd4j/include/ops/declarable/LegacyBroadcastOp.h +++ b/libnd4j/include/ops/declarable/LegacyBroadcastOp.h @@ -23,7 +23,7 @@ #include -namespace nd4j { +namespace sd { namespace ops { /** * This class provides wrapper for broadcast operations. @@ -35,7 +35,7 @@ namespace nd4j { LegacyBroadcastOp(); LegacyBroadcastOp(int opNum); - ShapeList* calculateOutputShape(ShapeList* inputShape, nd4j::graph::Context& block) override; + ShapeList* calculateOutputShape(ShapeList* inputShape, sd::graph::Context& block) override; LegacyOp* clone() override; }; } diff --git a/libnd4j/include/ops/declarable/LegacyIndexReduceOp.h b/libnd4j/include/ops/declarable/LegacyIndexReduceOp.h index b023cdc0d..fae0c5e8f 100644 --- a/libnd4j/include/ops/declarable/LegacyIndexReduceOp.h +++ b/libnd4j/include/ops/declarable/LegacyIndexReduceOp.h @@ -23,7 +23,7 @@ #include -namespace nd4j { +namespace sd { namespace ops { /** * This class provides wrapper for IndexAccumulation operations. i.e. IndexMax or IndexAbsoluteMin etc @@ -37,7 +37,7 @@ namespace nd4j { LegacyIndexReduceOp(); LegacyIndexReduceOp(int opNum); - ShapeList* calculateOutputShape(ShapeList* inputShape, nd4j::graph::Context& block) override; + ShapeList* calculateOutputShape(ShapeList* inputShape, sd::graph::Context& block) override; LegacyOp* clone() override; }; } diff --git a/libnd4j/include/ops/declarable/LegacyOp.h b/libnd4j/include/ops/declarable/LegacyOp.h index a7c7ad055..0dfd91a42 100644 --- a/libnd4j/include/ops/declarable/LegacyOp.h +++ b/libnd4j/include/ops/declarable/LegacyOp.h @@ -22,9 +22,9 @@ #define LIBND4J_LEGACYOP_H #include -#include +#include -namespace nd4j { +namespace sd { namespace ops { /** @@ -48,7 +48,7 @@ namespace nd4j { ~LegacyOp() = default; // All Op classes provide own specific implementation for this method - ShapeList* calculateOutputShape(ShapeList* inputShape, nd4j::graph::Context& block) override = 0; + ShapeList* calculateOutputShape(ShapeList* inputShape, sd::graph::Context& block) override = 0; virtual LegacyOp* clone() = 0; }; } diff --git a/libnd4j/include/ops/declarable/LegacyPairwiseTransformBoolOp.h b/libnd4j/include/ops/declarable/LegacyPairwiseTransformBoolOp.h index 5a2eb431f..16a482811 100644 --- a/libnd4j/include/ops/declarable/LegacyPairwiseTransformBoolOp.h +++ b/libnd4j/include/ops/declarable/LegacyPairwiseTransformBoolOp.h @@ -23,7 +23,7 @@ #include -namespace nd4j { +namespace sd { namespace ops { /** * This class provides wrapper for Pairwise transform operations @@ -35,7 +35,7 @@ namespace nd4j { LegacyPairwiseTransformBoolOp(); LegacyPairwiseTransformBoolOp(int opNum); - ShapeList* calculateOutputShape(ShapeList* inputShape, nd4j::graph::Context& block) override; + ShapeList* calculateOutputShape(ShapeList* inputShape, sd::graph::Context& block) override; LegacyOp* clone() override; }; } diff --git a/libnd4j/include/ops/declarable/LegacyPairwiseTransformOp.h b/libnd4j/include/ops/declarable/LegacyPairwiseTransformOp.h index 27a3a6f8d..81bbdc715 100644 --- a/libnd4j/include/ops/declarable/LegacyPairwiseTransformOp.h +++ b/libnd4j/include/ops/declarable/LegacyPairwiseTransformOp.h @@ -23,7 +23,7 @@ #include -namespace nd4j { +namespace sd { namespace ops { /** * This class provides wrapper for Pairwise transform operations @@ -35,7 +35,7 @@ namespace nd4j { LegacyPairwiseTransformOp(); LegacyPairwiseTransformOp(int opNum); - ShapeList* calculateOutputShape(ShapeList* inputShape, nd4j::graph::Context& block) override; + ShapeList* calculateOutputShape(ShapeList* inputShape, sd::graph::Context& block) override; LegacyOp* clone() override; }; } diff --git a/libnd4j/include/ops/declarable/LegacyRandomOp.h b/libnd4j/include/ops/declarable/LegacyRandomOp.h index 072825ef0..c0bab879d 100644 --- a/libnd4j/include/ops/declarable/LegacyRandomOp.h +++ b/libnd4j/include/ops/declarable/LegacyRandomOp.h @@ -25,7 +25,7 @@ #include #include -namespace nd4j { +namespace sd { namespace ops { /** * This class provides wrapper for Random operations (i.e. linspace or Uniform) @@ -41,11 +41,13 @@ namespace nd4j { template Nd4jStatus validateAndExecute_(Context &block); - nd4j::ResultSet* execute(nd4j::graph::RandomGenerator& rng, std::initializer_list inputs, std::initializer_list tArgs, std::initializer_list iArgs, bool isInplace = false); - nd4j::ResultSet* execute(nd4j::graph::RandomGenerator& rng, std::vector& inputs, std::vector& tArgs, std::vector& iArgs, bool isInplace = false); + sd::ResultSet execute(sd::graph::RandomGenerator& rng, std::initializer_list inputs, std::initializer_list tArgs, std::initializer_list iArgs, bool isInplace = false); + sd::ResultSet execute(sd::graph::RandomGenerator& rng, std::vector& inputs, std::vector& tArgs, std::vector& iArgs, bool isInplace = false); + Nd4jStatus execute(Context* block) override; - ShapeList* calculateOutputShape(ShapeList* inputShape, nd4j::graph::Context& block) override; + Nd4jStatus validateDataTypes(Context& block) override; + ShapeList* calculateOutputShape(ShapeList* inputShape, sd::graph::Context& block) override; LegacyOp* clone() override; }; } diff --git a/libnd4j/include/ops/declarable/LegacyReduce3Op.h b/libnd4j/include/ops/declarable/LegacyReduce3Op.h index 9882f4cae..b0a06bd94 100644 --- a/libnd4j/include/ops/declarable/LegacyReduce3Op.h +++ b/libnd4j/include/ops/declarable/LegacyReduce3Op.h @@ -23,7 +23,7 @@ #include -namespace nd4j { +namespace sd { namespace ops { /** * This class provides wrapper for Reduce3 operations (i.e. dot, cosineDistance etc) @@ -35,7 +35,7 @@ namespace nd4j { LegacyReduce3Op(); LegacyReduce3Op(int opNum); - ShapeList* calculateOutputShape(ShapeList* inputShape, nd4j::graph::Context& block) override; + ShapeList* calculateOutputShape(ShapeList* inputShape, sd::graph::Context& block) override; LegacyOp* clone() override; }; } diff --git a/libnd4j/include/ops/declarable/LegacyReduceBoolOp.h b/libnd4j/include/ops/declarable/LegacyReduceBoolOp.h index e685cd38c..11cd52146 100644 --- a/libnd4j/include/ops/declarable/LegacyReduceBoolOp.h +++ b/libnd4j/include/ops/declarable/LegacyReduceBoolOp.h @@ -23,7 +23,7 @@ #include -namespace nd4j { +namespace sd { namespace ops { class ND4J_EXPORT LegacyReduceBoolOp : public LegacyOp { protected: @@ -32,7 +32,7 @@ namespace nd4j { LegacyReduceBoolOp(); LegacyReduceBoolOp(int opNum); - ShapeList* calculateOutputShape(ShapeList* inputShape, nd4j::graph::Context& block) override; + ShapeList* calculateOutputShape(ShapeList* inputShape, sd::graph::Context& block) override; LegacyOp* clone() override; }; } diff --git a/libnd4j/include/ops/declarable/LegacyReduceFloatOp.h b/libnd4j/include/ops/declarable/LegacyReduceFloatOp.h index f85b98384..ed36a04fe 100644 --- a/libnd4j/include/ops/declarable/LegacyReduceFloatOp.h +++ b/libnd4j/include/ops/declarable/LegacyReduceFloatOp.h @@ -23,7 +23,7 @@ #include -namespace nd4j { +namespace sd { namespace ops { class ND4J_EXPORT LegacyReduceFloatOp : public LegacyOp { protected: @@ -32,7 +32,7 @@ namespace nd4j { LegacyReduceFloatOp(); LegacyReduceFloatOp(int opNum); - ShapeList* calculateOutputShape(ShapeList* inputShape, nd4j::graph::Context& block) override; + ShapeList* calculateOutputShape(ShapeList* inputShape, sd::graph::Context& block) override; LegacyOp* clone() override; }; } diff --git a/libnd4j/include/ops/declarable/LegacyReduceLongOp.h b/libnd4j/include/ops/declarable/LegacyReduceLongOp.h index 171739379..4f23a9717 100644 --- a/libnd4j/include/ops/declarable/LegacyReduceLongOp.h +++ b/libnd4j/include/ops/declarable/LegacyReduceLongOp.h @@ -23,7 +23,7 @@ #include -namespace nd4j { +namespace sd { namespace ops { class ND4J_EXPORT LegacyReduceLongOp : public LegacyOp { protected: @@ -32,7 +32,7 @@ namespace nd4j { LegacyReduceLongOp(); LegacyReduceLongOp(int opNum); - ShapeList* calculateOutputShape(ShapeList* inputShape, nd4j::graph::Context& block) override; + ShapeList* calculateOutputShape(ShapeList* inputShape, sd::graph::Context& block) override; LegacyOp* clone() override; }; } diff --git a/libnd4j/include/ops/declarable/LegacyReduceOp.h b/libnd4j/include/ops/declarable/LegacyReduceOp.h index 4796afdb1..3e289fe25 100644 --- a/libnd4j/include/ops/declarable/LegacyReduceOp.h +++ b/libnd4j/include/ops/declarable/LegacyReduceOp.h @@ -23,7 +23,7 @@ //#include /* -namespace nd4j { +namespace sd { namespace ops { class ND4J_EXPORT LegacyReduceOp : public LegacyOp { protected: @@ -32,7 +32,7 @@ namespace nd4j { LegacyReduceOp(); LegacyReduceOp(int opNum); - ShapeList* calculateOutputShape(ShapeList* inputShape, nd4j::graph::Context& block); + ShapeList* calculateOutputShape(ShapeList* inputShape, sd::graph::Context& block); virtual LegacyOp* clone(); }; } diff --git a/libnd4j/include/ops/declarable/LegacyReduceSameOp.h b/libnd4j/include/ops/declarable/LegacyReduceSameOp.h index daee7c16c..86cc06a0e 100644 --- a/libnd4j/include/ops/declarable/LegacyReduceSameOp.h +++ b/libnd4j/include/ops/declarable/LegacyReduceSameOp.h @@ -23,7 +23,7 @@ #include -namespace nd4j { +namespace sd { namespace ops { class ND4J_EXPORT LegacyReduceSameOp: public LegacyOp { protected: @@ -32,7 +32,7 @@ namespace nd4j { LegacyReduceSameOp(); LegacyReduceSameOp(int opNum); - ShapeList* calculateOutputShape(ShapeList* inputShape, nd4j::graph::Context& block) override; + ShapeList* calculateOutputShape(ShapeList* inputShape, sd::graph::Context& block) override; LegacyOp* clone() override; }; } diff --git a/libnd4j/include/ops/declarable/LegacyScalarBoolOp.h b/libnd4j/include/ops/declarable/LegacyScalarBoolOp.h index 915caa980..0d52eee9d 100644 --- a/libnd4j/include/ops/declarable/LegacyScalarBoolOp.h +++ b/libnd4j/include/ops/declarable/LegacyScalarBoolOp.h @@ -23,7 +23,7 @@ #include -namespace nd4j { +namespace sd { namespace ops { /** * This class provides wrapper for scalar transform operations, i.e. a + b = c, where either a or b is scalar primitive and other operand is NDArray @@ -37,7 +37,7 @@ namespace nd4j { LegacyScalarBoolOp(int opNum); LegacyScalarBoolOp(int opNum, NDArray &scalar); - ShapeList* calculateOutputShape(ShapeList* inputShape, nd4j::graph::Context& block) override; + ShapeList* calculateOutputShape(ShapeList* inputShape, sd::graph::Context& block) override; LegacyOp* clone() override; }; } diff --git a/libnd4j/include/ops/declarable/LegacyScalarOp.h b/libnd4j/include/ops/declarable/LegacyScalarOp.h index 3cc000c85..9f2a1a23a 100644 --- a/libnd4j/include/ops/declarable/LegacyScalarOp.h +++ b/libnd4j/include/ops/declarable/LegacyScalarOp.h @@ -23,7 +23,7 @@ #include -namespace nd4j { +namespace sd { namespace ops { /** * This class provides wrapper for scalar transform operations, i.e. a + b = c, where either a or b is scalar primitive and other operand is NDArray @@ -37,7 +37,7 @@ namespace nd4j { LegacyScalarOp(int opNum); LegacyScalarOp(int opNum, NDArray &scalar); - ShapeList* calculateOutputShape(ShapeList* inputShape, nd4j::graph::Context& block) override; + ShapeList* calculateOutputShape(ShapeList* inputShape, sd::graph::Context& block) override; LegacyOp* clone() override; }; } diff --git a/libnd4j/include/ops/declarable/LegacyStatsOp.h b/libnd4j/include/ops/declarable/LegacyStatsOp.h index 81ffd4d1b..74520b9dd 100644 --- a/libnd4j/include/ops/declarable/LegacyStatsOp.h +++ b/libnd4j/include/ops/declarable/LegacyStatsOp.h @@ -23,7 +23,7 @@ #include -namespace nd4j { +namespace sd { namespace ops { /** * This class provides wrapper for SummaryStats operations: Variance and Standard Deviation @@ -35,7 +35,7 @@ namespace nd4j { LegacyStatsOp(); LegacyStatsOp(int opNum); - ShapeList* calculateOutputShape(ShapeList* inputShape, nd4j::graph::Context &block) override; + ShapeList* calculateOutputShape(ShapeList* inputShape, sd::graph::Context &block) override; LegacyOp* clone() override; }; } diff --git a/libnd4j/include/ops/declarable/LegacyTransformAnyOp.h b/libnd4j/include/ops/declarable/LegacyTransformAnyOp.h index 34c30ba09..f98ccd4c8 100644 --- a/libnd4j/include/ops/declarable/LegacyTransformAnyOp.h +++ b/libnd4j/include/ops/declarable/LegacyTransformAnyOp.h @@ -24,7 +24,7 @@ #include -namespace nd4j { +namespace sd { namespace ops { /** * This class provides wrapper for Transform operations (i.e. Pow or OneMinus) @@ -36,7 +36,7 @@ namespace nd4j { LegacyTransformAnyOp(); LegacyTransformAnyOp(int opNum); - ShapeList* calculateOutputShape(ShapeList* inputShape, nd4j::graph::Context &block) override; + ShapeList* calculateOutputShape(ShapeList* inputShape, sd::graph::Context &block) override; LegacyOp* clone() override; }; } diff --git a/libnd4j/include/ops/declarable/LegacyTransformBoolOp.h b/libnd4j/include/ops/declarable/LegacyTransformBoolOp.h index 23e761979..d64dd4b01 100644 --- a/libnd4j/include/ops/declarable/LegacyTransformBoolOp.h +++ b/libnd4j/include/ops/declarable/LegacyTransformBoolOp.h @@ -25,7 +25,7 @@ #include -namespace nd4j { +namespace sd { namespace ops { /** * This class provides wrapper for Transform operations (i.e. Pow or OneMinus) @@ -37,7 +37,7 @@ namespace nd4j { LegacyTransformBoolOp(); LegacyTransformBoolOp(int opNum); - ShapeList* calculateOutputShape(ShapeList* inputShape, nd4j::graph::Context &block) override; + ShapeList* calculateOutputShape(ShapeList* inputShape, sd::graph::Context &block) override; LegacyOp* clone() override; }; } diff --git a/libnd4j/include/ops/declarable/LegacyTransformFloatOp.h b/libnd4j/include/ops/declarable/LegacyTransformFloatOp.h index 3327ad9f3..37bd0edce 100644 --- a/libnd4j/include/ops/declarable/LegacyTransformFloatOp.h +++ b/libnd4j/include/ops/declarable/LegacyTransformFloatOp.h @@ -24,7 +24,7 @@ #include -namespace nd4j { +namespace sd { namespace ops { /** * This class provides wrapper for Transform operations (i.e. Pow or OneMinus) @@ -36,7 +36,7 @@ namespace nd4j { LegacyTransformFloatOp(); LegacyTransformFloatOp(int opNum); - ShapeList* calculateOutputShape(ShapeList* inputShape, nd4j::graph::Context &block) override; + ShapeList* calculateOutputShape(ShapeList* inputShape, sd::graph::Context &block) override; LegacyOp* clone() override; }; } diff --git a/libnd4j/include/ops/declarable/LegacyTransformOp.h b/libnd4j/include/ops/declarable/LegacyTransformOp.h index f5524768e..7eb265bcb 100644 --- a/libnd4j/include/ops/declarable/LegacyTransformOp.h +++ b/libnd4j/include/ops/declarable/LegacyTransformOp.h @@ -24,7 +24,7 @@ //#include #ifdef ONLY_SAME_TRANSFORM -namespace nd4j { +namespace sd { namespace ops { /** * This class provides wrapper for Transform operations (i.e. Pow or OneMinus) @@ -36,7 +36,7 @@ namespace nd4j { LegacyTransformOp(); LegacyTransformOp(int opNum); - ShapeList* calculateOutputShape(ShapeList* inputShape, nd4j::graph::Context &block); + ShapeList* calculateOutputShape(ShapeList* inputShape, sd::graph::Context &block); virtual LegacyOp* clone(); }; } diff --git a/libnd4j/include/ops/declarable/LegacyTransformSameOp.h b/libnd4j/include/ops/declarable/LegacyTransformSameOp.h index 7b847562b..4d9312daf 100644 --- a/libnd4j/include/ops/declarable/LegacyTransformSameOp.h +++ b/libnd4j/include/ops/declarable/LegacyTransformSameOp.h @@ -25,7 +25,7 @@ #include -namespace nd4j { +namespace sd { namespace ops { /** * This class provides wrapper for Transform operations (i.e. Pow or OneMinus) @@ -37,7 +37,7 @@ namespace nd4j { LegacyTransformSameOp(); LegacyTransformSameOp(int opNum); - ShapeList* calculateOutputShape(ShapeList* inputShape, nd4j::graph::Context &block) override; + ShapeList* calculateOutputShape(ShapeList* inputShape, sd::graph::Context &block) override; LegacyOp* clone() override; }; } diff --git a/libnd4j/include/ops/declarable/LegacyTransformStrictOp.h b/libnd4j/include/ops/declarable/LegacyTransformStrictOp.h index 4d1722b01..ee48c02b7 100644 --- a/libnd4j/include/ops/declarable/LegacyTransformStrictOp.h +++ b/libnd4j/include/ops/declarable/LegacyTransformStrictOp.h @@ -25,7 +25,7 @@ #include -namespace nd4j { +namespace sd { namespace ops { /** * This class provides wrapper for Transform operations (i.e. Pow or OneMinus) @@ -37,7 +37,7 @@ namespace nd4j { LegacyTransformStrictOp(); LegacyTransformStrictOp(int opNum); - ShapeList* calculateOutputShape(ShapeList* inputShape, nd4j::graph::Context &block) override; + ShapeList* calculateOutputShape(ShapeList* inputShape, sd::graph::Context &block) override; LegacyOp* clone() override; }; } diff --git a/libnd4j/include/ops/declarable/LogicOp.h b/libnd4j/include/ops/declarable/LogicOp.h index 70fa3a6ff..d3ad59af2 100644 --- a/libnd4j/include/ops/declarable/LogicOp.h +++ b/libnd4j/include/ops/declarable/LogicOp.h @@ -23,7 +23,7 @@ #include "DeclarableOp.h" -namespace nd4j { +namespace sd { namespace ops { /** @@ -34,11 +34,11 @@ namespace nd4j { */ class ND4J_EXPORT LogicOp : public DeclarableOp { protected: - Nd4jStatus validateAndExecute(nd4j::graph::Context& block) override; + Nd4jStatus validateAndExecute(sd::graph::Context& block) override; public: LogicOp(const char *name); - ShapeList* calculateOutputShape(ShapeList* inputShape, nd4j::graph::Context &block) override; + ShapeList* calculateOutputShape(ShapeList* inputShape, sd::graph::Context &block) override; }; } } diff --git a/libnd4j/include/ops/declarable/OpDescriptor.h b/libnd4j/include/ops/declarable/OpDescriptor.h index 302559ad8..3feff5916 100644 --- a/libnd4j/include/ops/declarable/OpDescriptor.h +++ b/libnd4j/include/ops/declarable/OpDescriptor.h @@ -23,14 +23,13 @@ #include #include -#include #include #include #include #include #include -namespace nd4j { +namespace sd { namespace ops { /** @@ -53,7 +52,7 @@ namespace nd4j { int _numOutputs = 1; // enum for ops. deprecated. will be removed - nd4j::graph::OpClass _opClass; + sd::graph::OpClass _opClass; // special flag for divergent ops - ops that CAN and WILL modify graph behavior. Literally: IF, CASE. bool _divergent = false; @@ -80,18 +79,18 @@ namespace nd4j { bool _sameMode = false; - std::vector _allowedIns; - std::vector _allowedOuts; + std::vector _allowedIns; + std::vector _allowedOuts; // optional per-input configuration - std::map> _outputTypes; - std::map> _inputTypes; + MAP_IMPL> _outputTypes; + MAP_IMPL> _inputTypes; // field for ops that allow data type override at runtime bool _dtypeOverride = false; - bool checkDataTypesMatch(nd4j::DataType needle, std::vector &haystack) const; + bool checkDataTypesMatch(sd::DataType needle, std::vector &haystack) const; public: // default constructor OpDescriptor(int numInputs, int numOutputs, std::string opName, bool allowsInplace); @@ -163,23 +162,23 @@ namespace nd4j { OpDescriptor* setInputType(InputType type); - OpDescriptor* setAllowedInputTypes(const std::initializer_list &dtype); - OpDescriptor* setAllowedOutputTypes(const std::initializer_list &dtype); - OpDescriptor* setAllowedInputTypes(int index, const std::vector &dtype); - OpDescriptor* setAllowedOutputTypes(int index, const std::vector &dtype); - OpDescriptor* setAllowedInputTypes(int index, nd4j::DataType dtype); - OpDescriptor* setAllowedOutputTypes(int index, nd4j::DataType dtype); - OpDescriptor* setAllowedInputTypes(nd4j::DataType dtype); - OpDescriptor* setAllowedOutputTypes(nd4j::DataType dtype); + OpDescriptor* setAllowedInputTypes(const std::initializer_list &dtype); + OpDescriptor* setAllowedOutputTypes(const std::initializer_list &dtype); + OpDescriptor* setAllowedInputTypes(int index, const std::vector &dtype); + OpDescriptor* setAllowedOutputTypes(int index, const std::vector &dtype); + OpDescriptor* setAllowedInputTypes(int index, sd::DataType dtype); + OpDescriptor* setAllowedOutputTypes(int index, sd::DataType dtype); + OpDescriptor* setAllowedInputTypes(sd::DataType dtype); + OpDescriptor* setAllowedOutputTypes(sd::DataType dtype); OpDescriptor* allowOverride(bool reallyAllow); OpDescriptor* setSameMode(bool reallySame); - OpDescriptor* setInputType(int idx, nd4j::DataType dtype); - OpDescriptor* setOutputType(int idx, nd4j::DataType dtype); + OpDescriptor* setInputType(int idx, sd::DataType dtype); + OpDescriptor* setOutputType(int idx, sd::DataType dtype); - std::vector getOutputTypesForOutput(int index); + std::vector getOutputTypesForOutput(int index); - bool checkInputMatch(int index, nd4j::DataType dataType); - bool checkOutputMatch(int index, nd4j::DataType dataType); + bool checkInputMatch(int index, sd::DataType dataType); + bool checkOutputMatch(int index, sd::DataType dataType); bool isSameMode(); bool isInherit(int index); diff --git a/libnd4j/include/ops/declarable/OpRegistrator.h b/libnd4j/include/ops/declarable/OpRegistrator.h index 789b361f3..3a9fb3df6 100644 --- a/libnd4j/include/ops/declarable/OpRegistrator.h +++ b/libnd4j/include/ops/declarable/OpRegistrator.h @@ -21,7 +21,7 @@ #ifndef LIBND4J_OPREGISTRATOR_H #define LIBND4J_OPREGISTRATOR_H -#include +#include #include #include #include @@ -33,7 +33,27 @@ #include #include -namespace nd4j { +#ifndef __JAVACPP_HACK__ + +namespace std { + + template <> + class hash> { + public: + size_t operator()(const std::pair& k) const; + }; + + template <> + class hash> { + public: + size_t operator()(const std::pair& k) const; + }; +}; + +#endif + + +namespace sd { namespace ops { /** * This class provides runtime ops lookup, based on opName or opHash. @@ -59,17 +79,17 @@ namespace nd4j { #endif }; - std::map _msvc; + MAP_IMPL _msvc; // pointers to our operations - std::map _declarablesLD; - std::map _declarablesD; - std::vector _uniqueD; + MAP_IMPL _declarablesLD; + MAP_IMPL _declarablesD; + std::vector _uniqueD; // pointers to platform-specific helpers - std::map, nd4j::ops::platforms::PlatformHelper*> _helpersLH; - std::map, nd4j::ops::platforms::PlatformHelper*> _helpersH; - std::vector _uniqueH; + MAP_IMPL, sd::ops::platforms::PlatformHelper*> _helpersLH; + MAP_IMPL, sd::ops::platforms::PlatformHelper*> _helpersH; + std::vector _uniqueH; std::mutex _locker; std::string _opsList; @@ -94,18 +114,18 @@ namespace nd4j { * * @param op */ - bool registerOperation(const char* name, nd4j::ops::DeclarableOp* op); - bool registerOperation(nd4j::ops::DeclarableOp *op); + bool registerOperation(const char* name, sd::ops::DeclarableOp* op); + bool registerOperation(sd::ops::DeclarableOp *op); - void registerHelper(nd4j::ops::platforms::PlatformHelper* op); + void registerHelper(sd::ops::platforms::PlatformHelper* op); bool hasHelper(Nd4jLong hash, samediff::Engine engine); - nd4j::ops::DeclarableOp* getOperation(const char *name); - nd4j::ops::DeclarableOp* getOperation(Nd4jLong hash); - nd4j::ops::DeclarableOp* getOperation(std::string &name); + sd::ops::DeclarableOp* getOperation(const char *name); + sd::ops::DeclarableOp* getOperation(Nd4jLong hash); + sd::ops::DeclarableOp* getOperation(std::string &name); - nd4j::ops::platforms::PlatformHelper* getPlatformHelper(Nd4jLong hash, samediff::Engine engine); + sd::ops::platforms::PlatformHelper* getPlatformHelper(Nd4jLong hash, samediff::Engine engine); std::vector getAllHashes(); diff --git a/libnd4j/include/ops/declarable/OpTuple.h b/libnd4j/include/ops/declarable/OpTuple.h index fc0fd594a..7458ef3d0 100644 --- a/libnd4j/include/ops/declarable/OpTuple.h +++ b/libnd4j/include/ops/declarable/OpTuple.h @@ -23,24 +23,24 @@ #include #include -#include +#include -namespace nd4j { +namespace sd { namespace ops { class ND4J_EXPORT OpTuple { public: std::string _opName; - std::vector _inputs; - std::vector _outputs; + std::vector _inputs; + std::vector _outputs; std::vector _tArgs; std::vector _iArgs; OpTuple(const char *opName); - OpTuple(const char *opName, std::initializer_list&& inputs, std::initializer_list&& tArgs, std::initializer_list&& iArgs); + OpTuple(const char *opName, std::initializer_list&& inputs, std::initializer_list&& tArgs, std::initializer_list&& iArgs); ~OpTuple(); - OpTuple* addInput(nd4j::NDArray *array); - OpTuple* addOutput(nd4j::NDArray *array); + OpTuple* addInput(sd::NDArray *array); + OpTuple* addOutput(sd::NDArray *array); OpTuple* setTArgs(std::initializer_list tArgs); OpTuple* setIArgs(std::initializer_list iArgs); }; diff --git a/libnd4j/include/ops/declarable/PlatformHelper.h b/libnd4j/include/ops/declarable/PlatformHelper.h index afa0107fc..b34a936ee 100644 --- a/libnd4j/include/ops/declarable/PlatformHelper.h +++ b/libnd4j/include/ops/declarable/PlatformHelper.h @@ -21,14 +21,14 @@ #ifndef SD_PLATFORMHELPER_H #define SD_PLATFORMHELPER_H -#include +#include #include #include #include -#include -#include +#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace platforms { /** @@ -77,7 +77,7 @@ namespace nd4j { * @param inputId * @return */ - nd4j::NDArray *getZ(graph::Context &ctx, int inputId); + sd::NDArray *getZ(graph::Context &ctx, int inputId); }; } } diff --git a/libnd4j/include/ops/declarable/generic/CustomOperations.cpp b/libnd4j/include/ops/declarable/generic/CustomOperations.cpp index b2b0d165e..c13430ce3 100644 --- a/libnd4j/include/ops/declarable/generic/CustomOperations.cpp +++ b/libnd4j/include/ops/declarable/generic/CustomOperations.cpp @@ -24,7 +24,7 @@ #include #include -namespace nd4j { +namespace sd { _loader::_loader() { // @@ -47,5 +47,5 @@ namespace nd4j { //#endif }; - static nd4j::_loader loader; + static sd::_loader loader; } \ No newline at end of file diff --git a/libnd4j/include/ops/declarable/generic/activations/crelu.cpp b/libnd4j/include/ops/declarable/generic/activations/crelu.cpp index a0ba6aa11..539b21145 100644 --- a/libnd4j/include/ops/declarable/generic/activations/crelu.cpp +++ b/libnd4j/include/ops/declarable/generic/activations/crelu.cpp @@ -18,13 +18,13 @@ // @author raver119@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_crelu) #include #include #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(crelu, 1, 1, false, 0, 0) { auto x = INPUT_VARIABLE(0); @@ -32,7 +32,7 @@ namespace nd4j { REQUIRE_TRUE(x->isR(), 0, "CRELU: input must be real type"); auto tmp = x->dup(); - tmp.applyTransform(nd4j::transform::Neg, tmp); + tmp.applyTransform(sd::transform::Neg, tmp); auto z = OUTPUT_VARIABLE(0); @@ -41,7 +41,7 @@ namespace nd4j { // TODO: make this configurable? double threshold = 0.0; - z->applyScalar(nd4j::scalar::RELU, threshold, *z); + z->applyScalar(sd::scalar::RELU, threshold, *z); STORE_RESULT(z); @@ -72,30 +72,28 @@ namespace nd4j { auto epsilon = OUTPUT_VARIABLE(0); // at first step we build fwd activation - nd4j::ops::crelu op; + sd::ops::crelu op; auto tmpResult = op.evaluate({input}); - if (tmpResult->status() != ND4J_STATUS_OK) - return tmpResult->status(); + if (tmpResult.status() != ND4J_STATUS_OK) + return tmpResult.status(); - auto actv = tmpResult->at(0); + auto actv = tmpResult.at(0); // now we do RELU backward pass //actv->applyPairwiseTransform(pairwise::RELUDerivativeE, *epsilon, nullptr); helpers::reluDerivative(block.launchContext(), actv, epsilonNext); // now we split updated array into 2 chunks along last dimension - nd4j::ops::concat_bp opc; + sd::ops::concat_bp opc; auto dec = opc.evaluate({input, input, actv}, {-1}); - if (dec->status() != ND4J_STATUS_OK) - return dec->status(); + if (dec.status() != ND4J_STATUS_OK) + return dec.status(); // and now we subtract two parts of epsilons and pass result out - auto pos = dec->at(0); - auto neg = dec->at(1); + auto pos = dec.at(0); + auto neg = dec.at(1); - pos->applyPairwiseTransform(nd4j::pairwise::Subtract, *neg, *epsilon); + pos->applyPairwiseTransform(sd::pairwise::Subtract, *neg, *epsilon); - delete tmpResult; - delete dec; return ND4J_STATUS_OK; } diff --git a/libnd4j/include/ops/declarable/generic/activations/cube.cpp b/libnd4j/include/ops/declarable/generic/activations/cube.cpp index 75a33ab79..d71906d29 100644 --- a/libnd4j/include/ops/declarable/generic/activations/cube.cpp +++ b/libnd4j/include/ops/declarable/generic/activations/cube.cpp @@ -19,19 +19,19 @@ // -#include +#include #if NOT_EXCLUDED(OP_cube) #include #include -namespace nd4j { +namespace sd { namespace ops { CONFIGURABLE_OP_IMPL(cube, 1, 1, true, 0, 0) { auto input = INPUT_VARIABLE(0); auto output = OUTPUT_VARIABLE(0); - input->applyTransform(nd4j::transform::Cube, *output); + input->applyTransform(sd::transform::Cube, *output); STORE_RESULT(output); return Status::OK(); diff --git a/libnd4j/include/ops/declarable/generic/activations/elu.cpp b/libnd4j/include/ops/declarable/generic/activations/elu.cpp index 85becd858..f89f0d2c7 100644 --- a/libnd4j/include/ops/declarable/generic/activations/elu.cpp +++ b/libnd4j/include/ops/declarable/generic/activations/elu.cpp @@ -18,12 +18,12 @@ // @author raver119@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_elu) #include #include -namespace nd4j { +namespace sd { namespace ops { CONFIGURABLE_OP_IMPL(elu, 1, 1, true, -2, 0) { @@ -32,7 +32,7 @@ namespace nd4j { const auto alpha = block.numT() > 0 ? T_ARG(0) : 1.f; - input->applyScalar(nd4j::scalar::ELU, alpha, *output); + input->applyScalar(sd::scalar::ELU, alpha, *output); return Status::OK(); } diff --git a/libnd4j/include/ops/declarable/generic/activations/hardsigmoid.cpp b/libnd4j/include/ops/declarable/generic/activations/hardsigmoid.cpp index d8b937a0a..ba498fea9 100644 --- a/libnd4j/include/ops/declarable/generic/activations/hardsigmoid.cpp +++ b/libnd4j/include/ops/declarable/generic/activations/hardsigmoid.cpp @@ -18,19 +18,19 @@ // @author raver119@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_hardsigmoid) #include #include -namespace nd4j { +namespace sd { namespace ops { CONFIGURABLE_OP_IMPL(hardsigmoid, 1, 1, true, 0, 0) { auto input = INPUT_VARIABLE(0); auto output = OUTPUT_VARIABLE(0); - input->applyTransform(nd4j::transform::HardSigmoid, *output); + input->applyTransform(sd::transform::HardSigmoid, *output); STORE_RESULT(output); return Status::OK(); diff --git a/libnd4j/include/ops/declarable/generic/activations/hardtanh.cpp b/libnd4j/include/ops/declarable/generic/activations/hardtanh.cpp index a4d9fe4e6..0a245e6a0 100644 --- a/libnd4j/include/ops/declarable/generic/activations/hardtanh.cpp +++ b/libnd4j/include/ops/declarable/generic/activations/hardtanh.cpp @@ -18,19 +18,19 @@ // @author raver119@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_hardtanh) #include #include -namespace nd4j { +namespace sd { namespace ops { CONFIGURABLE_OP_IMPL(hardtanh, 1, 1, true, 0, 0) { auto input = INPUT_VARIABLE(0); auto output = OUTPUT_VARIABLE(0); - input->applyTransform(nd4j::transform::HardTanh, *output); + input->applyTransform(sd::transform::HardTanh, *output); STORE_RESULT(output); return Status::OK(); diff --git a/libnd4j/include/ops/declarable/generic/activations/identity.cpp b/libnd4j/include/ops/declarable/generic/activations/identity.cpp index e424772fc..38e4a3ae8 100644 --- a/libnd4j/include/ops/declarable/generic/activations/identity.cpp +++ b/libnd4j/include/ops/declarable/generic/activations/identity.cpp @@ -18,19 +18,22 @@ // @author raver119@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_identity) #include -namespace nd4j { +namespace sd { namespace ops { OP_IMPL(identity, 1, 1, true) { - auto first = INPUT_VARIABLE(0); auto z = OUTPUT_VARIABLE(0); - if (!block.isInplace()) - first->applyTransform(nd4j::transform::Identity, *z); + if (!block.isInplace()) { + auto first = INPUT_VARIABLE(0); + + // we hope for memcpy here + z->assign(first); + } return Status::OK(); } diff --git a/libnd4j/include/ops/declarable/generic/activations/identity_n.cpp b/libnd4j/include/ops/declarable/generic/activations/identity_n.cpp index b96ab9a3f..4b7088660 100644 --- a/libnd4j/include/ops/declarable/generic/activations/identity_n.cpp +++ b/libnd4j/include/ops/declarable/generic/activations/identity_n.cpp @@ -18,12 +18,12 @@ // @author sgazeos@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_identity_n) #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(identity_n, 1, 1, true, 0, 0) { @@ -52,8 +52,8 @@ namespace nd4j { DECLARE_TYPES(identity_n) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) - ->setAllowedOutputTypes(nd4j::DataType::ANY); + ->setAllowedInputTypes(sd::DataType::ANY) + ->setAllowedOutputTypes(sd::DataType::ANY); } } diff --git a/libnd4j/include/ops/declarable/generic/activations/lrelu.cpp b/libnd4j/include/ops/declarable/generic/activations/lrelu.cpp index 80404135f..2f4c2dc04 100644 --- a/libnd4j/include/ops/declarable/generic/activations/lrelu.cpp +++ b/libnd4j/include/ops/declarable/generic/activations/lrelu.cpp @@ -18,12 +18,12 @@ // @author raver119@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_lrelu) #include #include -namespace nd4j { +namespace sd { namespace ops { CONFIGURABLE_OP_IMPL(lrelu, 1, 1, true, -2, 0) { auto input = INPUT_VARIABLE(0); @@ -31,7 +31,7 @@ namespace nd4j { float alpha = block.numT() > 0 ? T_ARG(0) : 0.01f; - input->applyScalar(nd4j::scalar::LeakyRELU, alpha, *output); + input->applyScalar(sd::scalar::LeakyRELU, alpha, *output); STORE_RESULT(output); return Status::OK(); diff --git a/libnd4j/include/ops/declarable/generic/activations/prelu.cpp b/libnd4j/include/ops/declarable/generic/activations/prelu.cpp index 2bf3578b1..b7d260a4c 100644 --- a/libnd4j/include/ops/declarable/generic/activations/prelu.cpp +++ b/libnd4j/include/ops/declarable/generic/activations/prelu.cpp @@ -19,14 +19,14 @@ // -#include +#include #if NOT_EXCLUDED(OP_prelu) #include #include #include -namespace nd4j { +namespace sd { namespace ops { diff --git a/libnd4j/include/ops/declarable/generic/activations/rationaltanh.cpp b/libnd4j/include/ops/declarable/generic/activations/rationaltanh.cpp index 5bae4d2dc..3386d1578 100644 --- a/libnd4j/include/ops/declarable/generic/activations/rationaltanh.cpp +++ b/libnd4j/include/ops/declarable/generic/activations/rationaltanh.cpp @@ -18,19 +18,19 @@ // @author raver119@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_rationaltanh) #include #include -namespace nd4j { +namespace sd { namespace ops { CONFIGURABLE_OP_IMPL(rationaltanh, 1, 1, true, 0, 0) { auto input = INPUT_VARIABLE(0); auto output = OUTPUT_VARIABLE(0); - input->applyTransform(nd4j::transform::RationalTanh, *output); + input->applyTransform(sd::transform::RationalTanh, *output); STORE_RESULT(output); return Status::OK(); diff --git a/libnd4j/include/ops/declarable/generic/activations/rectifiedtanh.cpp b/libnd4j/include/ops/declarable/generic/activations/rectifiedtanh.cpp index 40738c343..641ee0d0e 100644 --- a/libnd4j/include/ops/declarable/generic/activations/rectifiedtanh.cpp +++ b/libnd4j/include/ops/declarable/generic/activations/rectifiedtanh.cpp @@ -18,19 +18,19 @@ // @author raver119@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_rectifiedtanh) #include #include -namespace nd4j { +namespace sd { namespace ops { CONFIGURABLE_OP_IMPL(rectifiedtanh, 1, 1, true, 0, 0) { auto input = INPUT_VARIABLE(0); auto output = OUTPUT_VARIABLE(0); - input->applyTransform(nd4j::transform::RectifiedTanh, *output); + input->applyTransform(sd::transform::RectifiedTanh, *output); STORE_RESULT(output); return Status::OK(); diff --git a/libnd4j/include/ops/declarable/generic/activations/relu.cpp b/libnd4j/include/ops/declarable/generic/activations/relu.cpp index 2c8b978ff..3b42c2e5a 100644 --- a/libnd4j/include/ops/declarable/generic/activations/relu.cpp +++ b/libnd4j/include/ops/declarable/generic/activations/relu.cpp @@ -18,13 +18,13 @@ // @author raver119@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_relu) #include #include -namespace nd4j { +namespace sd { namespace ops { CONFIGURABLE_OP_IMPL(relu, 1, 1, true, 1, 0) { auto first = INPUT_VARIABLE(0); @@ -32,7 +32,7 @@ namespace nd4j { auto scalar = block.numT() > 0 ? block.getTArguments()->at(0) : 0.0; - first->applyScalar(nd4j::scalar::RELU, scalar, *z); + first->applyScalar(sd::scalar::RELU, scalar, *z); STORE_RESULT(*z); diff --git a/libnd4j/include/ops/declarable/generic/activations/relu6.cpp b/libnd4j/include/ops/declarable/generic/activations/relu6.cpp index cf12d1592..129c09480 100644 --- a/libnd4j/include/ops/declarable/generic/activations/relu6.cpp +++ b/libnd4j/include/ops/declarable/generic/activations/relu6.cpp @@ -18,13 +18,13 @@ // @author Yurii Shyrma, created on 16.02.2018 // -#include +#include #if NOT_EXCLUDED(OP_relu6) #include #include -namespace nd4j { +namespace sd { namespace ops { @@ -33,7 +33,7 @@ CONFIGURABLE_OP_IMPL(relu6, 1, 1, true, 1, 0) { auto input = INPUT_VARIABLE(0); auto output = OUTPUT_VARIABLE(0); - input->applyScalar(nd4j::scalar::RELU6, T_ARG(0), *output); + input->applyScalar(sd::scalar::RELU6, T_ARG(0), *output); return Status::OK(); } diff --git a/libnd4j/include/ops/declarable/generic/activations/selu.cpp b/libnd4j/include/ops/declarable/generic/activations/selu.cpp index ca16f6832..7fc6aa11a 100644 --- a/libnd4j/include/ops/declarable/generic/activations/selu.cpp +++ b/libnd4j/include/ops/declarable/generic/activations/selu.cpp @@ -18,19 +18,19 @@ // @author raver119@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_selu) #include #include -namespace nd4j { +namespace sd { namespace ops { CONFIGURABLE_OP_IMPL(selu, 1, 1, true, 0, 0) { auto first = INPUT_VARIABLE(0); auto z = OUTPUT_VARIABLE(0); - first->applyTransform(nd4j::transform::SELU, *z); + first->applyTransform(sd::transform::SELU, *z); STORE_RESULT(*z); diff --git a/libnd4j/include/ops/declarable/generic/activations/sigmoid.cpp b/libnd4j/include/ops/declarable/generic/activations/sigmoid.cpp index fb8e507a7..047d973e6 100644 --- a/libnd4j/include/ops/declarable/generic/activations/sigmoid.cpp +++ b/libnd4j/include/ops/declarable/generic/activations/sigmoid.cpp @@ -18,18 +18,18 @@ // @author raver119@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_sigmoid) #include #include -namespace nd4j { +namespace sd { namespace ops { CONFIGURABLE_OP_IMPL(sigmoid, 1, 1, true, 0, 0) { auto first = INPUT_VARIABLE(0); auto z = OUTPUT_VARIABLE(0); - first->applyTransform(nd4j::transform::Sigmoid, *z); + first->applyTransform(sd::transform::Sigmoid, *z); STORE_RESULT(*z); diff --git a/libnd4j/include/ops/declarable/generic/activations/softplus.cpp b/libnd4j/include/ops/declarable/generic/activations/softplus.cpp index bd538ab71..5cd17e752 100644 --- a/libnd4j/include/ops/declarable/generic/activations/softplus.cpp +++ b/libnd4j/include/ops/declarable/generic/activations/softplus.cpp @@ -18,19 +18,19 @@ // @author raver119@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_softplus) #include #include -namespace nd4j { +namespace sd { namespace ops { CONFIGURABLE_OP_IMPL(softplus, 1, 1, true, 0, 0) { auto first = INPUT_VARIABLE(0); auto z = OUTPUT_VARIABLE(0); - first->applyTransform(nd4j::transform::SoftPlus, *z); + first->applyTransform(sd::transform::SoftPlus, *z); STORE_RESULT(*z); diff --git a/libnd4j/include/ops/declarable/generic/activations/softsign.cpp b/libnd4j/include/ops/declarable/generic/activations/softsign.cpp index 99e52ab68..c7fb15fdd 100644 --- a/libnd4j/include/ops/declarable/generic/activations/softsign.cpp +++ b/libnd4j/include/ops/declarable/generic/activations/softsign.cpp @@ -18,19 +18,19 @@ // @author raver119@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_softsign) #include #include -namespace nd4j { +namespace sd { namespace ops { CONFIGURABLE_OP_IMPL(softsign, 1, 1, true, 0, 0) { auto first = INPUT_VARIABLE(0); auto z = OUTPUT_VARIABLE(0); - first->applyTransform(nd4j::transform::SoftSign, *z); + first->applyTransform(sd::transform::SoftSign, *z); STORE_RESULT(*z); diff --git a/libnd4j/include/ops/declarable/generic/activations/tanh.cpp b/libnd4j/include/ops/declarable/generic/activations/tanh.cpp index 5677da728..a42552f75 100644 --- a/libnd4j/include/ops/declarable/generic/activations/tanh.cpp +++ b/libnd4j/include/ops/declarable/generic/activations/tanh.cpp @@ -18,19 +18,19 @@ // @author raver119@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_tanh) #include #include -namespace nd4j { +namespace sd { namespace ops { CONFIGURABLE_OP_IMPL(tanh, 1, 1, true, 0, 0) { auto first = INPUT_VARIABLE(0); auto z = OUTPUT_VARIABLE(0); - first->applyTransform(nd4j::transform::Tanh, *z); + first->applyTransform(sd::transform::Tanh, *z); STORE_RESULT(*z); diff --git a/libnd4j/include/ops/declarable/generic/activations/thresholdedrelu.cpp b/libnd4j/include/ops/declarable/generic/activations/thresholdedrelu.cpp index b45327dea..a0cba155a 100644 --- a/libnd4j/include/ops/declarable/generic/activations/thresholdedrelu.cpp +++ b/libnd4j/include/ops/declarable/generic/activations/thresholdedrelu.cpp @@ -19,13 +19,13 @@ // -#include +#include #if NOT_EXCLUDED(OP_thresholdedrelu) #include #include #include -namespace nd4j { +namespace sd { namespace ops { diff --git a/libnd4j/include/ops/declarable/generic/bitwise/bits_hamming_distance.cpp b/libnd4j/include/ops/declarable/generic/bitwise/bits_hamming_distance.cpp index f2a39b270..10f7095e0 100644 --- a/libnd4j/include/ops/declarable/generic/bitwise/bits_hamming_distance.cpp +++ b/libnd4j/include/ops/declarable/generic/bitwise/bits_hamming_distance.cpp @@ -18,14 +18,14 @@ // @author raver119@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_bits_hamming_distance) #include #include #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(bits_hamming_distance, 2, 1, true, 0, 0) { auto x = INPUT_VARIABLE(0); @@ -41,7 +41,7 @@ namespace nd4j { } DECLARE_SHAPE_FN(bits_hamming_distance) { - return SHAPELIST(ConstantShapeHelper::getInstance()->scalarShapeInfo(nd4j::DataType::INT64)); + return SHAPELIST(ConstantShapeHelper::getInstance()->scalarShapeInfo(sd::DataType::INT64)); } DECLARE_TYPES(bits_hamming_distance) { diff --git a/libnd4j/include/ops/declarable/generic/bitwise/bitwise_and.cpp b/libnd4j/include/ops/declarable/generic/bitwise/bitwise_and.cpp index 6eb3728ed..1e951c1d9 100644 --- a/libnd4j/include/ops/declarable/generic/bitwise/bitwise_and.cpp +++ b/libnd4j/include/ops/declarable/generic/bitwise/bitwise_and.cpp @@ -18,14 +18,14 @@ // @author raver119@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_bitwise_and) #include #include #include -namespace nd4j { +namespace sd { namespace ops { BROADCASTABLE_OP_IMPL(bitwise_and, 0, 0) { auto x = INPUT_VARIABLE(0); diff --git a/libnd4j/include/ops/declarable/generic/bitwise/bitwise_or.cpp b/libnd4j/include/ops/declarable/generic/bitwise/bitwise_or.cpp index 4683e3f3e..cd20a8434 100644 --- a/libnd4j/include/ops/declarable/generic/bitwise/bitwise_or.cpp +++ b/libnd4j/include/ops/declarable/generic/bitwise/bitwise_or.cpp @@ -18,14 +18,14 @@ // @author raver119@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_bitwise_or) #include #include #include -namespace nd4j { +namespace sd { namespace ops { BROADCASTABLE_OP_IMPL(bitwise_or, 0, 0) { auto x = INPUT_VARIABLE(0); diff --git a/libnd4j/include/ops/declarable/generic/bitwise/bitwise_xor.cpp b/libnd4j/include/ops/declarable/generic/bitwise/bitwise_xor.cpp index 1d79a84f3..0af9fe759 100644 --- a/libnd4j/include/ops/declarable/generic/bitwise/bitwise_xor.cpp +++ b/libnd4j/include/ops/declarable/generic/bitwise/bitwise_xor.cpp @@ -18,14 +18,14 @@ // @author raver119@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_bitwise_xor) #include #include #include -namespace nd4j { +namespace sd { namespace ops { BROADCASTABLE_OP_IMPL(bitwise_xor, 0, 0) { auto x = INPUT_VARIABLE(0); diff --git a/libnd4j/include/ops/declarable/generic/bitwise/cyclic_rshift.cpp b/libnd4j/include/ops/declarable/generic/bitwise/cyclic_rshift.cpp index 7a2c61c95..cc0c4827b 100644 --- a/libnd4j/include/ops/declarable/generic/bitwise/cyclic_rshift.cpp +++ b/libnd4j/include/ops/declarable/generic/bitwise/cyclic_rshift.cpp @@ -18,14 +18,14 @@ // @author raver119@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_cyclic_rshift_bits) #include #include #include -namespace nd4j { +namespace sd { namespace ops { BROADCASTABLE_OP_IMPL(cyclic_rshift_bits, 0, 0) { auto x = INPUT_VARIABLE(0); diff --git a/libnd4j/include/ops/declarable/generic/bitwise/cyclic_shift.cpp b/libnd4j/include/ops/declarable/generic/bitwise/cyclic_shift.cpp index 0a1c3d5c8..f2b36a6d8 100644 --- a/libnd4j/include/ops/declarable/generic/bitwise/cyclic_shift.cpp +++ b/libnd4j/include/ops/declarable/generic/bitwise/cyclic_shift.cpp @@ -18,14 +18,14 @@ // @author raver119@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_cyclic_shift_bits) #include #include #include -namespace nd4j { +namespace sd { namespace ops { BROADCASTABLE_OP_IMPL(cyclic_shift_bits, 0, 0) { auto x = INPUT_VARIABLE(0); diff --git a/libnd4j/include/ops/declarable/generic/bitwise/rshift.cpp b/libnd4j/include/ops/declarable/generic/bitwise/rshift.cpp index 0543cc72d..8b44d2a6f 100644 --- a/libnd4j/include/ops/declarable/generic/bitwise/rshift.cpp +++ b/libnd4j/include/ops/declarable/generic/bitwise/rshift.cpp @@ -18,14 +18,14 @@ // @author raver119@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_rshift_bits) #include #include #include -namespace nd4j { +namespace sd { namespace ops { BROADCASTABLE_OP_IMPL(rshift_bits, 0, 0) { auto x = INPUT_VARIABLE(0); diff --git a/libnd4j/include/ops/declarable/generic/bitwise/shift.cpp b/libnd4j/include/ops/declarable/generic/bitwise/shift.cpp index 4f0fec82d..7d0647e1b 100644 --- a/libnd4j/include/ops/declarable/generic/bitwise/shift.cpp +++ b/libnd4j/include/ops/declarable/generic/bitwise/shift.cpp @@ -18,14 +18,14 @@ // @author raver119@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_shift_bits) #include #include #include -namespace nd4j { +namespace sd { namespace ops { BROADCASTABLE_OP_IMPL(shift_bits, 0, 0) { auto x = INPUT_VARIABLE(0); diff --git a/libnd4j/include/ops/declarable/generic/bitwise/toggle_bits.cpp b/libnd4j/include/ops/declarable/generic/bitwise/toggle_bits.cpp index 4aaae3c0d..0ba6fbcc7 100644 --- a/libnd4j/include/ops/declarable/generic/bitwise/toggle_bits.cpp +++ b/libnd4j/include/ops/declarable/generic/bitwise/toggle_bits.cpp @@ -18,14 +18,14 @@ // Created by raver119 on 23.11.17. // -#include +#include #if NOT_EXCLUDED(OP_toggle_bits) #include #include #include -namespace nd4j { +namespace sd { namespace ops { OP_IMPL(toggle_bits, -1, -1, true) { diff --git a/libnd4j/include/ops/declarable/generic/blas/axpy.cpp b/libnd4j/include/ops/declarable/generic/blas/axpy.cpp index 65d20589f..7c1150599 100644 --- a/libnd4j/include/ops/declarable/generic/blas/axpy.cpp +++ b/libnd4j/include/ops/declarable/generic/blas/axpy.cpp @@ -18,12 +18,12 @@ // @author raver119@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_axpy) #include -namespace nd4j { +namespace sd { namespace ops { CONFIGURABLE_OP_IMPL(axpy, 2, 1, false, -2, 0) { auto x = INPUT_VARIABLE(0); diff --git a/libnd4j/include/ops/declarable/generic/blas/batched_gemm.cpp b/libnd4j/include/ops/declarable/generic/blas/batched_gemm.cpp index 67a839e7b..194af35b8 100644 --- a/libnd4j/include/ops/declarable/generic/blas/batched_gemm.cpp +++ b/libnd4j/include/ops/declarable/generic/blas/batched_gemm.cpp @@ -18,13 +18,13 @@ // @author raver119@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_batched_gemm) #include #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(batched_gemm, -1, -1, false, 0, 9) { @@ -85,7 +85,7 @@ CUSTOM_OP_IMPL(batched_gemm, -1, -1, false, 0, 9) { REQUIRE_TRUE(vA.size() == vB.size() && vA.size() == vC.size() && vA.size() == batchSize, 0, "BatchedGemm: mismatched numbers of A, B, C for unknown reason"); - nd4j::ops::helpers::bgemm(vA, vB, vC, alpha, beta, transA, transB, M, N, K, ldA, ldB, ldC); + sd::ops::helpers::bgemm(vA, vB, vC, alpha, beta, transA, transB, M, N, K, ldA, ldB, ldC); return Status::OK(); }; diff --git a/libnd4j/include/ops/declarable/generic/blas/matmul.cpp b/libnd4j/include/ops/declarable/generic/blas/matmul.cpp index a673b1988..6209e7bbf 100644 --- a/libnd4j/include/ops/declarable/generic/blas/matmul.cpp +++ b/libnd4j/include/ops/declarable/generic/blas/matmul.cpp @@ -20,13 +20,13 @@ // @author Yurii Shyrma (iuriish@yahoo.com), fully rewritten // -#include +#include #if NOT_EXCLUDED(OP_matmul) #include -#include +#include -namespace nd4j { +namespace sd { namespace ops { ////////////////////////////////////////////////////////////////////// @@ -163,7 +163,7 @@ F F T [a,b] [b,c] [c,a] [c,a] */ - nd4j::ops::matmul op; + sd::ops::matmul op; op.execute({eps, y}, {dldx}, {}, {transZ, !transY, transX}, {}); op.execute({x, eps}, {dldy}, {}, {!transX, transZ, transY}, {}); diff --git a/libnd4j/include/ops/declarable/generic/blas/svd.cpp b/libnd4j/include/ops/declarable/generic/blas/svd.cpp index 8db2c2ff3..ca5fd52c2 100644 --- a/libnd4j/include/ops/declarable/generic/blas/svd.cpp +++ b/libnd4j/include/ops/declarable/generic/blas/svd.cpp @@ -18,13 +18,13 @@ // @author Yurii Shyrma (iuriish@yahoo.com), created on 20.01.2018 // -#include +#include #if NOT_EXCLUDED(OP_svd) #include #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(svd, 1, 1, false, 0, 3) { diff --git a/libnd4j/include/ops/declarable/generic/blas/tensormmul.cpp b/libnd4j/include/ops/declarable/generic/blas/tensormmul.cpp index 3db3b6097..889bd4957 100644 --- a/libnd4j/include/ops/declarable/generic/blas/tensormmul.cpp +++ b/libnd4j/include/ops/declarable/generic/blas/tensormmul.cpp @@ -18,16 +18,16 @@ // @author raver119@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_tensormmul) #include #include #include -#include +#include -namespace nd4j { +namespace sd { namespace ops { //////////////////////////////////////////////////////////////////////// @@ -78,7 +78,7 @@ DECLARE_SHAPE_FN(tensormmul) { // evaluate shapes std::vector permutAt, permutBt; std::vector shapeAt, shapeBt; - auto outShape = nd4j::ShapeUtils::evalShapeForTensorDot(aShapeInfo, bShapeInfo, axes_0, axes_1, permutAt, permutBt, shapeAt, shapeBt); + auto outShape = sd::ShapeUtils::evalShapeForTensorDot(aShapeInfo, bShapeInfo, axes_0, axes_1, permutAt, permutBt, shapeAt, shapeBt); return SHAPELIST(ConstantShapeHelper::getInstance()->createShapeInfo(ShapeDescriptor(ArrayOptions::dataType(aShapeInfo), 'c', outShape))); } diff --git a/libnd4j/include/ops/declarable/generic/boolean/boolean_not.cpp b/libnd4j/include/ops/declarable/generic/boolean/boolean_not.cpp index 83cbc9004..56047f16c 100644 --- a/libnd4j/include/ops/declarable/generic/boolean/boolean_not.cpp +++ b/libnd4j/include/ops/declarable/generic/boolean/boolean_not.cpp @@ -18,12 +18,12 @@ // Created by raver on 6/6/2018. // -#include +#include #if NOT_EXCLUDED(OP_boolean_not) #include -namespace nd4j { +namespace sd { namespace ops { OP_IMPL(boolean_not, 1, 1,true) { auto x = INPUT_VARIABLE(0); diff --git a/libnd4j/include/ops/declarable/generic/boolean/choose.cpp b/libnd4j/include/ops/declarable/generic/boolean/choose.cpp index c0e6ee49f..9689c9cd5 100644 --- a/libnd4j/include/ops/declarable/generic/boolean/choose.cpp +++ b/libnd4j/include/ops/declarable/generic/boolean/choose.cpp @@ -18,14 +18,14 @@ // @author Adam Gibson // -#include +#include #if NOT_EXCLUDED(OP_choose) #include #include #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(choose, -1, 2, false, -2, -1) { @@ -88,7 +88,7 @@ namespace nd4j { auto newShape = ConstantShapeHelper::getInstance()->vectorShapeInfo(numResults.e(0), ArrayOptions::dataType(inputShape->at(0))); - auto shapeScalar = ConstantShapeHelper::getInstance()->scalarShapeInfo(nd4j::DataType::INT64); + auto shapeScalar = ConstantShapeHelper::getInstance()->scalarShapeInfo(sd::DataType::INT64); return SHAPELIST(newShape, shapeScalar); } diff --git a/libnd4j/include/ops/declarable/generic/boolean/eq_scalar.cpp b/libnd4j/include/ops/declarable/generic/boolean/eq_scalar.cpp index 08db9aa1f..c0623ebb7 100644 --- a/libnd4j/include/ops/declarable/generic/boolean/eq_scalar.cpp +++ b/libnd4j/include/ops/declarable/generic/boolean/eq_scalar.cpp @@ -18,12 +18,12 @@ // Created by raver119 on 13.10.2017. // -#include +#include #if NOT_EXCLUDED(OP_eq_scalar) #include -namespace nd4j { +namespace sd { namespace ops { BOOLEAN_OP_IMPL(eq_scalar, 2, true) { auto x = INPUT_VARIABLE(0); diff --git a/libnd4j/include/ops/declarable/generic/boolean/gt_scalar.cpp b/libnd4j/include/ops/declarable/generic/boolean/gt_scalar.cpp index ca7ffb63f..d40c501d4 100644 --- a/libnd4j/include/ops/declarable/generic/boolean/gt_scalar.cpp +++ b/libnd4j/include/ops/declarable/generic/boolean/gt_scalar.cpp @@ -18,12 +18,12 @@ // Created by raver119 on 13.10.2017. // -#include +#include #if NOT_EXCLUDED(OP_gt_scalar) #include -namespace nd4j { +namespace sd { namespace ops { BOOLEAN_OP_IMPL(gt_scalar, 2, true) { auto x = INPUT_VARIABLE(0); diff --git a/libnd4j/include/ops/declarable/generic/boolean/gte_scalar.cpp b/libnd4j/include/ops/declarable/generic/boolean/gte_scalar.cpp index 7c28159d5..d555f5d24 100644 --- a/libnd4j/include/ops/declarable/generic/boolean/gte_scalar.cpp +++ b/libnd4j/include/ops/declarable/generic/boolean/gte_scalar.cpp @@ -18,12 +18,12 @@ // Created by raver119 on 13.10.2017. // -#include +#include #if NOT_EXCLUDED(OP_gte_scalar) #include -namespace nd4j { +namespace sd { namespace ops { BOOLEAN_OP_IMPL(gte_scalar, 2, true) { auto x = INPUT_VARIABLE(0); diff --git a/libnd4j/include/ops/declarable/generic/boolean/is_non_decreasing.cpp b/libnd4j/include/ops/declarable/generic/boolean/is_non_decreasing.cpp index 37fa1350a..4dd8ca605 100644 --- a/libnd4j/include/ops/declarable/generic/boolean/is_non_decreasing.cpp +++ b/libnd4j/include/ops/declarable/generic/boolean/is_non_decreasing.cpp @@ -18,13 +18,13 @@ // @author @cpuheater // -#include +#include #if NOT_EXCLUDED(OP_is_non_decreasing) #include #include -namespace nd4j { +namespace sd { namespace ops { BOOLEAN_OP_IMPL(is_non_decreasing, 1, true) { auto input = INPUT_VARIABLE(0); @@ -35,7 +35,7 @@ namespace nd4j { bool isNonDecreasing = true; - nd4j::ops::helpers::compare_elem(block.launchContext(), input, false, isNonDecreasing); + sd::ops::helpers::compare_elem(block.launchContext(), input, false, isNonDecreasing); if (isNonDecreasing) return ND4J_STATUS_TRUE; diff --git a/libnd4j/include/ops/declarable/generic/boolean/is_numeric_tensor.cpp b/libnd4j/include/ops/declarable/generic/boolean/is_numeric_tensor.cpp index cdaa49241..184b7b0a6 100644 --- a/libnd4j/include/ops/declarable/generic/boolean/is_numeric_tensor.cpp +++ b/libnd4j/include/ops/declarable/generic/boolean/is_numeric_tensor.cpp @@ -18,13 +18,13 @@ // @author @cpuheater // -#include +#include #if NOT_EXCLUDED(OP_is_numeric_tensor) #include #include -namespace nd4j { +namespace sd { namespace ops { BOOLEAN_OP_IMPL(is_numeric_tensor, 1, true) { diff --git a/libnd4j/include/ops/declarable/generic/boolean/is_strictly_increasing.cpp b/libnd4j/include/ops/declarable/generic/boolean/is_strictly_increasing.cpp index a41852394..0c434cf57 100644 --- a/libnd4j/include/ops/declarable/generic/boolean/is_strictly_increasing.cpp +++ b/libnd4j/include/ops/declarable/generic/boolean/is_strictly_increasing.cpp @@ -18,13 +18,13 @@ // @author @cpuheater // -#include +#include #if NOT_EXCLUDED(OP_is_strictly_increasing) #include #include -namespace nd4j { +namespace sd { namespace ops { BOOLEAN_OP_IMPL(is_strictly_increasing, 1, true) { auto input = INPUT_VARIABLE(0); @@ -35,7 +35,7 @@ namespace nd4j { bool isStrictlyIncreasing = true; - nd4j::ops::helpers::compare_elem(block.launchContext(), input, true, isStrictlyIncreasing); + sd::ops::helpers::compare_elem(block.launchContext(), input, true, isStrictlyIncreasing); if (isStrictlyIncreasing) return ND4J_STATUS_TRUE; diff --git a/libnd4j/include/ops/declarable/generic/boolean/lt_scalar.cpp b/libnd4j/include/ops/declarable/generic/boolean/lt_scalar.cpp index 722d3cbd1..1c4f7ab27 100644 --- a/libnd4j/include/ops/declarable/generic/boolean/lt_scalar.cpp +++ b/libnd4j/include/ops/declarable/generic/boolean/lt_scalar.cpp @@ -18,12 +18,12 @@ // Created by raver119 on 13.10.2017. // -#include +#include #if NOT_EXCLUDED(OP_lt_scalar) #include -namespace nd4j { +namespace sd { namespace ops { BOOLEAN_OP_IMPL(lt_scalar, 2, true) { auto x = INPUT_VARIABLE(0); diff --git a/libnd4j/include/ops/declarable/generic/boolean/lte_scalar.cpp b/libnd4j/include/ops/declarable/generic/boolean/lte_scalar.cpp index c950445dd..07a72cfed 100644 --- a/libnd4j/include/ops/declarable/generic/boolean/lte_scalar.cpp +++ b/libnd4j/include/ops/declarable/generic/boolean/lte_scalar.cpp @@ -18,12 +18,12 @@ // Created by raver119 on 13.10.2017. // -#include +#include #if NOT_EXCLUDED(OP_lte_scalar) #include -namespace nd4j { +namespace sd { namespace ops { BOOLEAN_OP_IMPL(lte_scalar, 2, true) { auto x = INPUT_VARIABLE(0); diff --git a/libnd4j/include/ops/declarable/generic/boolean/neq_scalar.cpp b/libnd4j/include/ops/declarable/generic/boolean/neq_scalar.cpp index a90f90516..1c05b9fc1 100644 --- a/libnd4j/include/ops/declarable/generic/boolean/neq_scalar.cpp +++ b/libnd4j/include/ops/declarable/generic/boolean/neq_scalar.cpp @@ -18,12 +18,12 @@ // Created by raver119 on 13.10.2017. // -#include +#include #if NOT_EXCLUDED(OP_neq_scalar) #include -namespace nd4j { +namespace sd { namespace ops { BOOLEAN_OP_IMPL(neq_scalar, 2, true) { auto x = INPUT_VARIABLE(0); diff --git a/libnd4j/include/ops/declarable/generic/boolean/select.cpp b/libnd4j/include/ops/declarable/generic/boolean/select.cpp index 92cb5e421..e8e257258 100644 --- a/libnd4j/include/ops/declarable/generic/boolean/select.cpp +++ b/libnd4j/include/ops/declarable/generic/boolean/select.cpp @@ -18,13 +18,13 @@ // @author raver119@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_select) #include #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(select, 3, 1, false, 0, 0) { auto cond = INPUT_VARIABLE(0); diff --git a/libnd4j/include/ops/declarable/generic/boolean/where.cpp b/libnd4j/include/ops/declarable/generic/boolean/where.cpp index 6aa646cb6..c72c10d6b 100644 --- a/libnd4j/include/ops/declarable/generic/boolean/where.cpp +++ b/libnd4j/include/ops/declarable/generic/boolean/where.cpp @@ -18,14 +18,14 @@ // @author raver119@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_where) #include #include #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(Where, 1, 1, false, 0, 0) { auto condition = INPUT_VARIABLE(0); @@ -112,12 +112,12 @@ namespace nd4j { newShape[5] = 0; newShape[6] = 1; newShape[7] = 99; - ShapeUtils::updateStridesAndType(newShape, nd4j::DataType::INT64, 'c'); + ShapeUtils::updateStridesAndType(newShape, sd::DataType::INT64, 'c'); newShape = CONSTANT(newShape); } else { - newShape = ConstantShapeHelper::getInstance()->emptyShapeInfo(nd4j::DataType::INT64); + newShape = ConstantShapeHelper::getInstance()->emptyShapeInfo(sd::DataType::INT64); } return SHAPELIST(newShape); diff --git a/libnd4j/include/ops/declarable/generic/boolean/where_np.cpp b/libnd4j/include/ops/declarable/generic/boolean/where_np.cpp index aa6169bae..65cb52cdd 100644 --- a/libnd4j/include/ops/declarable/generic/boolean/where_np.cpp +++ b/libnd4j/include/ops/declarable/generic/boolean/where_np.cpp @@ -18,7 +18,7 @@ // @author Adam Gibson // -#include +#include #include #if NOT_EXCLUDED(OP_where_np) @@ -26,7 +26,7 @@ #include #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(where_np, -1, 1, false, 0, 0) { auto condition = INPUT_VARIABLE(0); @@ -102,10 +102,12 @@ namespace nd4j { REQUIRE_TRUE(block.width() == 1, 0, "Where op takes either 1 or 3 operands, But got %d operands instead", block.width()); // if (output->isEmpty()) Nd4jLong width = condition->rankOf(); - nd4j::ops::Where op; - std::unique_ptr res(op.evaluate({condition})); - REQUIRE_OK(res->status()); - NDArray* whereTrue = res->at(0); + + sd::ops::Where op; + auto res(op.evaluate({condition})); + REQUIRE_OK(res.status()); + NDArray* whereTrue = res.at(0); + if (whereTrue->isEmpty()) return ND4J_STATUS_OK; for (Nd4jLong outNext = 0; outNext < width; ++outNext) { @@ -137,11 +139,11 @@ namespace nd4j { // output shape - a tuple of rank(inShape) 1D tensors with numOfTrue len if (numOfTrue) { for (Nd4jLong e = 0; e < condition->rankOf(); ++e) { - shapes->push_back(ConstantShapeHelper::getInstance()->vectorShapeInfo(numOfTrue, nd4j::DataType::INT64)); + shapes->push_back(ConstantShapeHelper::getInstance()->vectorShapeInfo(numOfTrue, sd::DataType::INT64)); } } else { - shapes->push_back(ConstantShapeHelper::getInstance()->emptyShapeInfo(nd4j::DataType::INT64)); + shapes->push_back(ConstantShapeHelper::getInstance()->emptyShapeInfo(sd::DataType::INT64)); } } return shapes; @@ -149,9 +151,9 @@ namespace nd4j { DECLARE_TYPES(where_np) { getOpDescriptor() - ->setAllowedInputTypes(0, nd4j::DataType::BOOL) - ->setAllowedInputTypes(1, nd4j::DataType::ANY) - ->setAllowedInputTypes(2, nd4j::DataType::ANY) + ->setAllowedInputTypes(0, sd::DataType::BOOL) + ->setAllowedInputTypes(1, sd::DataType::ANY) + ->setAllowedInputTypes(2, sd::DataType::ANY) ->setAllowedOutputTypes( {ALL_FLOATS, ALL_INTS}); } } diff --git a/libnd4j/include/ops/declarable/generic/broadcastable/add.cpp b/libnd4j/include/ops/declarable/generic/broadcastable/add.cpp index 415a2c37a..936addea5 100644 --- a/libnd4j/include/ops/declarable/generic/broadcastable/add.cpp +++ b/libnd4j/include/ops/declarable/generic/broadcastable/add.cpp @@ -18,13 +18,13 @@ // @author raver119@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_add) #include #include -namespace nd4j { +namespace sd { namespace ops { BROADCASTABLE_OP_IMPL(add, 0, 0) { auto x = INPUT_VARIABLE(0); @@ -33,7 +33,7 @@ namespace nd4j { BROADCAST_CHECK_EMPTY(x,y,z); - auto tZ = BroadcastHelper::broadcastApply(nd4j::BroadcastOpsTuple::Add(), x, y, z); + auto tZ = BroadcastHelper::broadcastApply(sd::BroadcastOpsTuple::Add(), x, y, z); if (tZ == nullptr) return ND4J_STATUS_KERNEL_FAILURE; else if (tZ != z) @@ -71,7 +71,7 @@ namespace nd4j { gradX->assign(epsNext); } else if (y->isScalar()) { // scalar case - auto tmp = epsNext->reduceNumber(nd4j::reduce::Sum); + auto tmp = epsNext->reduceNumber(sd::reduce::Sum); gradY->assign(tmp); gradX->assign(epsNext); } else { @@ -80,13 +80,13 @@ namespace nd4j { auto axisY = ShapeUtils::evalBroadcastBackwardAxis(y->shapeInfo(), epsNext->shapeInfo()); if (axisX.size() > 0) { - auto sum = epsNext->reduceAlongDimension(nd4j::reduce::Sum, axisX); + auto sum = epsNext->reduceAlongDimension(sd::reduce::Sum, axisX); gradX->assign(sum); } else gradX->assign(epsNext); if (axisY.size() > 0) { - auto sum = epsNext->reduceAlongDimension(nd4j::reduce::Sum, axisY); + auto sum = epsNext->reduceAlongDimension(sd::reduce::Sum, axisY); gradY->assign(sum); } else gradY->assign(epsNext); diff --git a/libnd4j/include/ops/declarable/generic/broadcastable/assign.cpp b/libnd4j/include/ops/declarable/generic/broadcastable/assign.cpp index 24b673a8c..aeaa5d128 100644 --- a/libnd4j/include/ops/declarable/generic/broadcastable/assign.cpp +++ b/libnd4j/include/ops/declarable/generic/broadcastable/assign.cpp @@ -18,13 +18,13 @@ // Created by raver119 on 24.11.17. // -#include +#include #if NOT_EXCLUDED(OP_assign) #include #include -namespace nd4j { +namespace sd { namespace ops { BROADCASTABLE_OP_IMPL(assign, 0, 0) { auto x = INPUT_VARIABLE(0); @@ -33,7 +33,7 @@ namespace nd4j { BROADCAST_CHECK_EMPTY(x,y,z); - auto tZ = BroadcastHelper::broadcastApply(nd4j::BroadcastOpsTuple::Assign(), x, y, z); + auto tZ = BroadcastHelper::broadcastApply(sd::BroadcastOpsTuple::Assign(), x, y, z); if (tZ == nullptr) return ND4J_STATUS_KERNEL_FAILURE; else if (tZ != z) { @@ -71,14 +71,14 @@ namespace nd4j { if (x->isSameShape(y)) { gradY->assign(epsNext); } else if (y->isScalar()) { - auto sum = epsNext->reduceNumber(nd4j::reduce::Sum); + auto sum = epsNext->reduceNumber(sd::reduce::Sum); gradY->assign(sum); } else { // broadcastable auto axisY = ShapeUtils::evalBroadcastBackwardAxis(y->shapeInfo(), epsNext->shapeInfo()); if (axisY.size() > 0) { - auto sum = epsNext->reduceAlongDimension(nd4j::reduce::Sum, axisY); + auto sum = epsNext->reduceAlongDimension(sd::reduce::Sum, axisY); gradY->assign(sum); } else gradY->assign(epsNext); diff --git a/libnd4j/include/ops/declarable/generic/broadcastable/atan2.cpp b/libnd4j/include/ops/declarable/generic/broadcastable/atan2.cpp index 32a7d7d65..ed60f5925 100644 --- a/libnd4j/include/ops/declarable/generic/broadcastable/atan2.cpp +++ b/libnd4j/include/ops/declarable/generic/broadcastable/atan2.cpp @@ -19,12 +19,12 @@ // @author Yurii Shyrma (iuriish@yahoo.com) // -#include +#include #if NOT_EXCLUDED(OP_tf_atan2) #include -namespace nd4j { +namespace sd { namespace ops { BROADCASTABLE_OP_IMPL(tf_atan2, 0, 0) { @@ -36,7 +36,7 @@ BROADCASTABLE_OP_IMPL(tf_atan2, 0, 0) { BROADCAST_CHECK_EMPTY(x,y,z); // auto tZ = BroadcastHelper::template broadcastApply>(y, x, z); - x->applyTrueBroadcast(nd4j::BroadcastOpsTuple::custom(scalar::Atan2, pairwise::Atan2, broadcast::Atan2), *y, *z, true); + x->applyTrueBroadcast(sd::BroadcastOpsTuple::custom(scalar::Atan2, pairwise::Atan2, broadcast::Atan2), *y, *z, true); // if (tZ == nullptr) // return ND4J_STATUS_KERNEL_FAILURE; diff --git a/libnd4j/include/ops/declarable/generic/broadcastable/boolean_and.cpp b/libnd4j/include/ops/declarable/generic/broadcastable/boolean_and.cpp index f10bf97e7..32593ecf6 100644 --- a/libnd4j/include/ops/declarable/generic/broadcastable/boolean_and.cpp +++ b/libnd4j/include/ops/declarable/generic/broadcastable/boolean_and.cpp @@ -18,12 +18,12 @@ // Created by raver on 6/6/2018. // -#include +#include #if NOT_EXCLUDED(OP_boolean_or) #include -namespace nd4j { +namespace sd { namespace ops { BROADCASTABLE_OP_IMPL(boolean_and, 0, 0) { auto x = INPUT_VARIABLE(0); diff --git a/libnd4j/include/ops/declarable/generic/broadcastable/boolean_or.cpp b/libnd4j/include/ops/declarable/generic/broadcastable/boolean_or.cpp index bf0f66410..1dbb69f30 100644 --- a/libnd4j/include/ops/declarable/generic/broadcastable/boolean_or.cpp +++ b/libnd4j/include/ops/declarable/generic/broadcastable/boolean_or.cpp @@ -18,12 +18,12 @@ // Created by raver on 6/6/2018. // -#include +#include #if NOT_EXCLUDED(OP_boolean_or) #include -namespace nd4j { +namespace sd { namespace ops { BROADCASTABLE_OP_IMPL(boolean_or, 0, 0) { auto x = INPUT_VARIABLE(0); diff --git a/libnd4j/include/ops/declarable/generic/broadcastable/boolean_xor.cpp b/libnd4j/include/ops/declarable/generic/broadcastable/boolean_xor.cpp index 6e83f31ea..8f242fbda 100644 --- a/libnd4j/include/ops/declarable/generic/broadcastable/boolean_xor.cpp +++ b/libnd4j/include/ops/declarable/generic/broadcastable/boolean_xor.cpp @@ -18,12 +18,12 @@ // Created by raver on 6/6/2018. // -#include +#include #if NOT_EXCLUDED(OP_boolean_xor) #include -namespace nd4j { +namespace sd { namespace ops { BROADCASTABLE_OP_IMPL(boolean_xor, 0, 0) { auto x = INPUT_VARIABLE(0); diff --git a/libnd4j/include/ops/declarable/generic/broadcastable/divide.cpp b/libnd4j/include/ops/declarable/generic/broadcastable/divide.cpp index 1811781f1..cd907de36 100644 --- a/libnd4j/include/ops/declarable/generic/broadcastable/divide.cpp +++ b/libnd4j/include/ops/declarable/generic/broadcastable/divide.cpp @@ -18,13 +18,13 @@ // @author raver119@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_divide) #include #include -namespace nd4j { +namespace sd { namespace ops { BROADCASTABLE_OP_IMPL(divide, 0, 0) { auto x = INPUT_VARIABLE(0); diff --git a/libnd4j/include/ops/declarable/generic/broadcastable/divide_no_nan.cpp b/libnd4j/include/ops/declarable/generic/broadcastable/divide_no_nan.cpp index 3cf808b8e..9ef300e1c 100644 --- a/libnd4j/include/ops/declarable/generic/broadcastable/divide_no_nan.cpp +++ b/libnd4j/include/ops/declarable/generic/broadcastable/divide_no_nan.cpp @@ -18,13 +18,13 @@ // @author George A. Shulinok // -#include +#include #if NOT_EXCLUDED(OP_divide_no_nan) #include #include -namespace nd4j { +namespace sd { namespace ops { BROADCASTABLE_OP_IMPL(divide_no_nan, 0, 0) { auto x = INPUT_VARIABLE(0); diff --git a/libnd4j/include/ops/declarable/generic/broadcastable/equals.cpp b/libnd4j/include/ops/declarable/generic/broadcastable/equals.cpp index 440cdf0d3..c82fe6748 100644 --- a/libnd4j/include/ops/declarable/generic/broadcastable/equals.cpp +++ b/libnd4j/include/ops/declarable/generic/broadcastable/equals.cpp @@ -21,7 +21,7 @@ #include #include -namespace nd4j { +namespace sd { namespace ops { BROADCASTABLE_OP_IMPL(equals, 0, 0) { auto x = INPUT_VARIABLE(0); diff --git a/libnd4j/include/ops/declarable/generic/broadcastable/floordiv.cpp b/libnd4j/include/ops/declarable/generic/broadcastable/floordiv.cpp index 10d898522..d0a59bcc1 100644 --- a/libnd4j/include/ops/declarable/generic/broadcastable/floordiv.cpp +++ b/libnd4j/include/ops/declarable/generic/broadcastable/floordiv.cpp @@ -18,13 +18,13 @@ // @author raver119@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_floordiv) #include #include -namespace nd4j { +namespace sd { namespace ops { BROADCASTABLE_OP_IMPL(floordiv, 0, 0) { auto x = INPUT_VARIABLE(0); diff --git a/libnd4j/include/ops/declarable/generic/broadcastable/floormod.cpp b/libnd4j/include/ops/declarable/generic/broadcastable/floormod.cpp index 062d3cfab..52377739b 100644 --- a/libnd4j/include/ops/declarable/generic/broadcastable/floormod.cpp +++ b/libnd4j/include/ops/declarable/generic/broadcastable/floormod.cpp @@ -18,13 +18,13 @@ // @author raver119@gmail.com // modified by sgazeos@gmail.com with backprop implementation. // -#include +#include #if NOT_EXCLUDED(OP_floormod) #include #include -namespace nd4j { +namespace sd { namespace ops { BROADCASTABLE_OP_IMPL(floormod, 0, 0) { auto x = INPUT_VARIABLE(0); @@ -65,11 +65,12 @@ namespace nd4j { auto gradX = OUTPUT_VARIABLE(0); auto gradY = OUTPUT_VARIABLE(1); gradX->assign(epsNext); - nd4j::ops::floormod op; - std::unique_ptr tmpResult(op.evaluate({x, y})); + + sd::ops::floormod op; + auto tmpResult(op.evaluate({x, y})); if (gradY->rankOf() == gradX->rankOf()) - epsNext->applyPairwiseTransform(pairwise::Multiply, *tmpResult->at(0), *gradY); + epsNext->applyPairwiseTransform(pairwise::Multiply, *tmpResult.at(0), *gradY); else // epsNext is greater than gradY { std::vector dims(epsNext->rankOf() * 2); @@ -77,7 +78,7 @@ namespace nd4j { for (Nd4jLong d = 0; d < gap; d++) { dims[d * 2 + 1] = 1; } - auto tempIn((*tmpResult->at(0))(dims)); + auto tempIn((*tmpResult.at(0))(dims)); (*epsNext)(dims).applyPairwiseTransform(pairwise::Multiply, tempIn, *gradY); } return Status::OK(); diff --git a/libnd4j/include/ops/declarable/generic/broadcastable/greater.cpp b/libnd4j/include/ops/declarable/generic/broadcastable/greater.cpp index 9f381f403..961259946 100644 --- a/libnd4j/include/ops/declarable/generic/broadcastable/greater.cpp +++ b/libnd4j/include/ops/declarable/generic/broadcastable/greater.cpp @@ -21,7 +21,7 @@ #include #include -namespace nd4j { +namespace sd { namespace ops { BROADCASTABLE_OP_IMPL(greater, 0, 0) { auto x = INPUT_VARIABLE(0); diff --git a/libnd4j/include/ops/declarable/generic/broadcastable/greater_equal.cpp b/libnd4j/include/ops/declarable/generic/broadcastable/greater_equal.cpp index caa1f05a0..1adbad420 100644 --- a/libnd4j/include/ops/declarable/generic/broadcastable/greater_equal.cpp +++ b/libnd4j/include/ops/declarable/generic/broadcastable/greater_equal.cpp @@ -20,7 +20,7 @@ #include -namespace nd4j { +namespace sd { namespace ops { BROADCASTABLE_OP_IMPL(greater_equal, 0, 0) { auto x = INPUT_VARIABLE(0); diff --git a/libnd4j/include/ops/declarable/generic/broadcastable/igamma.cpp b/libnd4j/include/ops/declarable/generic/broadcastable/igamma.cpp index 6bd1c88ed..9fa07424c 100644 --- a/libnd4j/include/ops/declarable/generic/broadcastable/igamma.cpp +++ b/libnd4j/include/ops/declarable/generic/broadcastable/igamma.cpp @@ -18,13 +18,13 @@ // @author sgazeos@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_igamma) #include #include -namespace nd4j { +namespace sd { namespace ops { BROADCASTABLE_OP_IMPL(igamma, 0, 0) { auto x = INPUT_VARIABLE(0); diff --git a/libnd4j/include/ops/declarable/generic/broadcastable/igammac.cpp b/libnd4j/include/ops/declarable/generic/broadcastable/igammac.cpp index 89494dc4b..deeacd4ef 100644 --- a/libnd4j/include/ops/declarable/generic/broadcastable/igammac.cpp +++ b/libnd4j/include/ops/declarable/generic/broadcastable/igammac.cpp @@ -18,13 +18,13 @@ // @author sgazeos@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_igammac) #include #include -namespace nd4j { +namespace sd { namespace ops { BROADCASTABLE_OP_IMPL(igammac, 0, 0) { auto x = INPUT_VARIABLE(0); diff --git a/libnd4j/include/ops/declarable/generic/broadcastable/less.cpp b/libnd4j/include/ops/declarable/generic/broadcastable/less.cpp index c1715bec2..ba5c72fa4 100644 --- a/libnd4j/include/ops/declarable/generic/broadcastable/less.cpp +++ b/libnd4j/include/ops/declarable/generic/broadcastable/less.cpp @@ -20,7 +20,7 @@ #include -namespace nd4j { +namespace sd { namespace ops { BROADCASTABLE_OP_IMPL(less, 0, 0) { auto x = INPUT_VARIABLE(0); diff --git a/libnd4j/include/ops/declarable/generic/broadcastable/less_equal.cpp b/libnd4j/include/ops/declarable/generic/broadcastable/less_equal.cpp index 3041938c6..b602f1374 100644 --- a/libnd4j/include/ops/declarable/generic/broadcastable/less_equal.cpp +++ b/libnd4j/include/ops/declarable/generic/broadcastable/less_equal.cpp @@ -20,7 +20,7 @@ #include -namespace nd4j { +namespace sd { namespace ops { BROADCASTABLE_OP_IMPL(less_equal, 0, 0) { auto x = INPUT_VARIABLE(0); diff --git a/libnd4j/include/ops/declarable/generic/broadcastable/maximum.cpp b/libnd4j/include/ops/declarable/generic/broadcastable/maximum.cpp index a18626b48..dfb6b3d66 100644 --- a/libnd4j/include/ops/declarable/generic/broadcastable/maximum.cpp +++ b/libnd4j/include/ops/declarable/generic/broadcastable/maximum.cpp @@ -18,13 +18,13 @@ // Created by raver119 on 12.10.2017. // -#include +#include #if NOT_EXCLUDED(OP_maximum) #include #include #include -namespace nd4j { +namespace sd { namespace ops { BROADCASTABLE_OP_IMPL(maximum, 0, 0) { auto x = INPUT_VARIABLE(0); diff --git a/libnd4j/include/ops/declarable/generic/broadcastable/meshgrid.cpp b/libnd4j/include/ops/declarable/generic/broadcastable/meshgrid.cpp index 04444e53a..b07f50202 100644 --- a/libnd4j/include/ops/declarable/generic/broadcastable/meshgrid.cpp +++ b/libnd4j/include/ops/declarable/generic/broadcastable/meshgrid.cpp @@ -18,14 +18,14 @@ // @author Yurii Shyrma (iuriish@yahoo.com), created on 05.02.2018 // -#include +#include #if NOT_EXCLUDED(OP_meshgrid) #include #include #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(meshgrid, -1, -1, false, 0, 0) { diff --git a/libnd4j/include/ops/declarable/generic/broadcastable/minimum.cpp b/libnd4j/include/ops/declarable/generic/broadcastable/minimum.cpp index d2fb43558..ef8645d1d 100644 --- a/libnd4j/include/ops/declarable/generic/broadcastable/minimum.cpp +++ b/libnd4j/include/ops/declarable/generic/broadcastable/minimum.cpp @@ -18,14 +18,14 @@ // Created by raver119 on 12.10.2017. // -#include +#include #if NOT_EXCLUDED(OP_minimum) #include #include #include -namespace nd4j { +namespace sd { namespace ops { BROADCASTABLE_OP_IMPL(minimum, 0, 0) { auto x = INPUT_VARIABLE(0); diff --git a/libnd4j/include/ops/declarable/generic/broadcastable/mod.cpp b/libnd4j/include/ops/declarable/generic/broadcastable/mod.cpp index 5ae075c99..95c710d17 100644 --- a/libnd4j/include/ops/declarable/generic/broadcastable/mod.cpp +++ b/libnd4j/include/ops/declarable/generic/broadcastable/mod.cpp @@ -18,13 +18,13 @@ // @author raver119@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_mod) #include #include -namespace nd4j { +namespace sd { namespace ops { BROADCASTABLE_OP_IMPL(mod, 0, 0) { auto x = INPUT_VARIABLE(0); diff --git a/libnd4j/include/ops/declarable/generic/broadcastable/multiply.cpp b/libnd4j/include/ops/declarable/generic/broadcastable/multiply.cpp index d50ffacaa..3ddbe57ca 100644 --- a/libnd4j/include/ops/declarable/generic/broadcastable/multiply.cpp +++ b/libnd4j/include/ops/declarable/generic/broadcastable/multiply.cpp @@ -19,12 +19,12 @@ // @author Yurii Shyrma (iuriish@yahoo.com) // -#include +#include #if NOT_EXCLUDED(OP_multiply) #include -namespace nd4j { +namespace sd { namespace ops { BROADCASTABLE_OP_IMPL(multiply, 0, 0) { @@ -38,7 +38,7 @@ namespace ops { const bool areShapesBroadcastable = ShapeUtils::evalBroadcastShapeInfo(x->getShapeInfo(), y->getShapeInfo(), true, zShapeInfo, block.getWorkspace()); REQUIRE_TRUE(areShapesBroadcastable, 0, "MULTIPLY OP: the shapes of x %s and y %s are not suitable for broadcast !", ShapeUtils::shapeAsString(x).c_str(), ShapeUtils::shapeAsString(y).c_str()); - auto tZ = BroadcastHelper::broadcastApply(nd4j::BroadcastOpsTuple::Multiply(), x, y, z); + auto tZ = BroadcastHelper::broadcastApply(sd::BroadcastOpsTuple::Multiply(), x, y, z); if (tZ == nullptr) return ND4J_STATUS_KERNEL_FAILURE; else if (tZ != z) diff --git a/libnd4j/include/ops/declarable/generic/broadcastable/not_equals.cpp b/libnd4j/include/ops/declarable/generic/broadcastable/not_equals.cpp index 1dede042d..fddd653e9 100644 --- a/libnd4j/include/ops/declarable/generic/broadcastable/not_equals.cpp +++ b/libnd4j/include/ops/declarable/generic/broadcastable/not_equals.cpp @@ -20,7 +20,7 @@ #include -namespace nd4j { +namespace sd { namespace ops { BROADCASTABLE_OP_IMPL(not_equals, 0, 0) { auto x = INPUT_VARIABLE(0); diff --git a/libnd4j/include/ops/declarable/generic/broadcastable/percentile.cpp b/libnd4j/include/ops/declarable/generic/broadcastable/percentile.cpp index 3c50edbc7..e2bf723b3 100644 --- a/libnd4j/include/ops/declarable/generic/broadcastable/percentile.cpp +++ b/libnd4j/include/ops/declarable/generic/broadcastable/percentile.cpp @@ -18,14 +18,14 @@ // @author Yurii Shyrma (iuriish@yahoo.com), created on 17.05.2018 // -#include +#include #if NOT_EXCLUDED(OP_percentile) #include -#include +#include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(percentile, 1, 1, false, 1, -2) { diff --git a/libnd4j/include/ops/declarable/generic/broadcastable/pow.cpp b/libnd4j/include/ops/declarable/generic/broadcastable/pow.cpp index 1fe3e359b..5a1ac02c5 100644 --- a/libnd4j/include/ops/declarable/generic/broadcastable/pow.cpp +++ b/libnd4j/include/ops/declarable/generic/broadcastable/pow.cpp @@ -19,13 +19,13 @@ // @author Oleh Semeniv (oleg.semeniv@gmail.com) // -#include +#include #if NOT_EXCLUDED(OP_Pow) #include #include -namespace nd4j { +namespace sd { namespace ops { BROADCASTABLE_OP_IMPL(Pow, 0, 0) { auto x = INPUT_VARIABLE(0); @@ -75,7 +75,7 @@ namespace ops { // dL/dy = x^y * log(x) * dL/dz auto temp = x->applyTrueBroadcast(BroadcastOpsTuple::Pow(), *y); // a = x^y x->applyTransform(transform::Log, *dLdx); // b = log(x) - dLdx->applyScalar(nd4j::scalar::ReplaceNans, 0, *dLdx); + dLdx->applyScalar(sd::scalar::ReplaceNans, 0, *dLdx); temp *= *dLdx; // c = b*a temp *= *dLdz; // dL/dy = c * dL/dz if (dLdy->isSameShape(*dLdz)) { diff --git a/libnd4j/include/ops/declarable/generic/broadcastable/realdiv.cpp b/libnd4j/include/ops/declarable/generic/broadcastable/realdiv.cpp index 3e7445cf0..3691ffb55 100644 --- a/libnd4j/include/ops/declarable/generic/broadcastable/realdiv.cpp +++ b/libnd4j/include/ops/declarable/generic/broadcastable/realdiv.cpp @@ -18,13 +18,13 @@ // @author raver119@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_realdiv) #include #include -namespace nd4j { +namespace sd { namespace ops { BROADCASTABLE_OP_IMPL(realdiv, 0, 0) { auto x = INPUT_VARIABLE(0); diff --git a/libnd4j/include/ops/declarable/generic/broadcastable/reverse_divide.cpp b/libnd4j/include/ops/declarable/generic/broadcastable/reverse_divide.cpp index 04c4c926e..0b6ea7d2a 100644 --- a/libnd4j/include/ops/declarable/generic/broadcastable/reverse_divide.cpp +++ b/libnd4j/include/ops/declarable/generic/broadcastable/reverse_divide.cpp @@ -18,13 +18,13 @@ // @author raver119@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_reversedivide) #include #include -namespace nd4j { +namespace sd { namespace ops { BROADCASTABLE_OP_IMPL(reversedivide, 0, 0) { auto x = INPUT_VARIABLE(0); diff --git a/libnd4j/include/ops/declarable/generic/broadcastable/reverse_mod.cpp b/libnd4j/include/ops/declarable/generic/broadcastable/reverse_mod.cpp index 9dea93699..bb25fada6 100644 --- a/libnd4j/include/ops/declarable/generic/broadcastable/reverse_mod.cpp +++ b/libnd4j/include/ops/declarable/generic/broadcastable/reverse_mod.cpp @@ -18,13 +18,13 @@ // @author raver119@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_reversemod) #include #include -namespace nd4j { +namespace sd { namespace ops { BROADCASTABLE_OP_IMPL(reversemod, 0, 0) { auto x = INPUT_VARIABLE(0); diff --git a/libnd4j/include/ops/declarable/generic/broadcastable/reverse_subtract.cpp b/libnd4j/include/ops/declarable/generic/broadcastable/reverse_subtract.cpp index dbb14c78b..5d33c7cea 100644 --- a/libnd4j/include/ops/declarable/generic/broadcastable/reverse_subtract.cpp +++ b/libnd4j/include/ops/declarable/generic/broadcastable/reverse_subtract.cpp @@ -18,13 +18,13 @@ // @author raver119@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_reversesubtract) #include #include -namespace nd4j { +namespace sd { namespace ops { BROADCASTABLE_OP_IMPL(reversesubtract, 0, 0) { auto x = INPUT_VARIABLE(0); diff --git a/libnd4j/include/ops/declarable/generic/broadcastable/squared_subtract.cpp b/libnd4j/include/ops/declarable/generic/broadcastable/squared_subtract.cpp index ae9c93d4d..6f5482512 100644 --- a/libnd4j/include/ops/declarable/generic/broadcastable/squared_subtract.cpp +++ b/libnd4j/include/ops/declarable/generic/broadcastable/squared_subtract.cpp @@ -18,13 +18,13 @@ // Created by raver119 on 23.11.17. // -#include +#include #if NOT_EXCLUDED(OP_squaredsubtract) #include #include -namespace nd4j { +namespace sd { namespace ops { BROADCASTABLE_OP_IMPL(squaredsubtract, 0, 0) { auto x = INPUT_VARIABLE(0); diff --git a/libnd4j/include/ops/declarable/generic/broadcastable/subtract.cpp b/libnd4j/include/ops/declarable/generic/broadcastable/subtract.cpp index 40bbb8559..fac1c5dfa 100644 --- a/libnd4j/include/ops/declarable/generic/broadcastable/subtract.cpp +++ b/libnd4j/include/ops/declarable/generic/broadcastable/subtract.cpp @@ -18,13 +18,13 @@ // @author raver119@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_subtract) #include #include -namespace nd4j { +namespace sd { namespace ops { BROADCASTABLE_OP_IMPL(subtract, 0, 0) { auto x = INPUT_VARIABLE(0); diff --git a/libnd4j/include/ops/declarable/generic/broadcastable/template.tpl b/libnd4j/include/ops/declarable/generic/broadcastable/template.tpl index 65b90dccf..cc311b9f4 100644 --- a/libnd4j/include/ops/declarable/generic/broadcastable/template.tpl +++ b/libnd4j/include/ops/declarable/generic/broadcastable/template.tpl @@ -20,7 +20,7 @@ #include -namespace nd4j { +namespace sd { namespace ops { } diff --git a/libnd4j/include/ops/declarable/generic/broadcastable/truncatediv.cpp b/libnd4j/include/ops/declarable/generic/broadcastable/truncatediv.cpp index 33aae6d78..60900a5d9 100644 --- a/libnd4j/include/ops/declarable/generic/broadcastable/truncatediv.cpp +++ b/libnd4j/include/ops/declarable/generic/broadcastable/truncatediv.cpp @@ -21,7 +21,7 @@ #include #include -namespace nd4j { +namespace sd { namespace ops { BROADCASTABLE_OP_IMPL(truncatediv, 0, 0) { auto x = INPUT_VARIABLE(0); diff --git a/libnd4j/include/ops/declarable/generic/compat/compat_sparse_to_dense.cpp b/libnd4j/include/ops/declarable/generic/compat/compat_sparse_to_dense.cpp index 4a84dbdac..2d6cf5f12 100644 --- a/libnd4j/include/ops/declarable/generic/compat/compat_sparse_to_dense.cpp +++ b/libnd4j/include/ops/declarable/generic/compat/compat_sparse_to_dense.cpp @@ -18,13 +18,13 @@ // @author raver119@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_split_string) #include #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(compat_sparse_to_dense, 4, 1, false, 0, 0) { auto indices = INPUT_VARIABLE(0); @@ -37,7 +37,7 @@ namespace nd4j { if (block.width() > 3) def = INPUT_VARIABLE(3); - nd4j::ops::helpers::compat_sparse_to_dense(*values, *indices, def, *output); + sd::ops::helpers::compat_sparse_to_dense(*values, *indices, def, *output); return Status::OK(); }; @@ -63,9 +63,9 @@ namespace nd4j { getOpDescriptor() ->setAllowedInputTypes(0, {ALL_INTS}) // indices ->setAllowedInputTypes(1, {ALL_INTS}) // shape - ->setAllowedInputTypes(2,nd4j::DataType::ANY) // sparse values - ->setAllowedInputTypes(3,nd4j::DataType::ANY) // default value - ->setAllowedOutputTypes(nd4j::DataType::ANY); + ->setAllowedInputTypes(2,sd::DataType::ANY) // sparse values + ->setAllowedInputTypes(3,sd::DataType::ANY) // default value + ->setAllowedOutputTypes(sd::DataType::ANY); } } } diff --git a/libnd4j/include/ops/declarable/generic/compat/compat_string_split.cpp b/libnd4j/include/ops/declarable/generic/compat/compat_string_split.cpp index ac88a4a60..a59e3f02c 100644 --- a/libnd4j/include/ops/declarable/generic/compat/compat_string_split.cpp +++ b/libnd4j/include/ops/declarable/generic/compat/compat_string_split.cpp @@ -18,13 +18,13 @@ // @author raver119@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_split_string) #include #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(compat_string_split, 2, 2, false, 0, 0) { auto input = INPUT_VARIABLE(0); @@ -39,8 +39,7 @@ namespace nd4j { delim->syncToHost(); // output rank N+1 wrt input rank - std::vector ocoords(input->rankOf() + 1); - std::vector icoords(input->rankOf()); + std::vector icoords(input->rankOf()); // getting buffer lengths // FIXME: it'll be bigger, since it'll include delimiters, @@ -54,7 +53,7 @@ namespace nd4j { auto s = input->e(e); // getting base index - shape::index2coords(e, input->shapeInfo(), icoords.data()); + shape::index2coordsCPU(0, e, input->shapeInfo(), icoords.data()); // getting number of substrings auto cnt = StringUtils::countSubarrays(s.c_str(), s.length(), d.c_str(), d.length()) + 1; @@ -122,8 +121,8 @@ namespace nd4j { // values tensor is going to be vector always // indices tensor is going to be vector with length equal to values.length * output rank - auto valuesShape = ConstantShapeHelper::getInstance()->vectorShapeInfo(cnt, nd4j::DataType::UTF8); - auto indicesShape = ConstantShapeHelper::getInstance()->vectorShapeInfo(cnt * (input->rankOf() + 1), nd4j::DataType::INT64); + auto valuesShape = ConstantShapeHelper::getInstance()->vectorShapeInfo(cnt, sd::DataType::UTF8); + auto indicesShape = ConstantShapeHelper::getInstance()->vectorShapeInfo(cnt * (input->rankOf() + 1), sd::DataType::INT64); return SHAPELIST(indicesShape, valuesShape); } diff --git a/libnd4j/include/ops/declarable/generic/datatypes/bitcast.cpp b/libnd4j/include/ops/declarable/generic/datatypes/bitcast.cpp index 8591d3449..fe42d7057 100644 --- a/libnd4j/include/ops/declarable/generic/datatypes/bitcast.cpp +++ b/libnd4j/include/ops/declarable/generic/datatypes/bitcast.cpp @@ -18,13 +18,13 @@ // @author George A. Shulinok // -#include +#include #if NOT_EXCLUDED(OP_bitcast) #include #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(bitcast, 1, 1, false, 0, 1) { auto input = INPUT_VARIABLE(0); @@ -94,8 +94,8 @@ namespace nd4j { DECLARE_TYPES(bitcast) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) - ->setAllowedOutputTypes(nd4j::DataType::ANY); + ->setAllowedInputTypes(sd::DataType::ANY) + ->setAllowedOutputTypes(sd::DataType::ANY); } } } diff --git a/libnd4j/include/ops/declarable/generic/datatypes/cast.cpp b/libnd4j/include/ops/declarable/generic/datatypes/cast.cpp index ad7b7fee2..cf8729d2f 100644 --- a/libnd4j/include/ops/declarable/generic/datatypes/cast.cpp +++ b/libnd4j/include/ops/declarable/generic/datatypes/cast.cpp @@ -18,13 +18,13 @@ // @author raver119@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_cast) #include #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(cast, 1, 1, false, 0, 1) { auto input = INPUT_VARIABLE(0); @@ -54,8 +54,8 @@ namespace nd4j { DECLARE_TYPES(cast) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) - ->setAllowedOutputTypes(nd4j::DataType::ANY); + ->setAllowedInputTypes(sd::DataType::ANY) + ->setAllowedOutputTypes(sd::DataType::ANY); } } } diff --git a/libnd4j/include/ops/declarable/generic/datatypes/to_double.cpp b/libnd4j/include/ops/declarable/generic/datatypes/to_double.cpp index 9d3e5aaed..4eae77a5a 100644 --- a/libnd4j/include/ops/declarable/generic/datatypes/to_double.cpp +++ b/libnd4j/include/ops/declarable/generic/datatypes/to_double.cpp @@ -18,12 +18,12 @@ // @author raver119@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_to_double) #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(to_double, 1, 1, true, 0, 0) { auto input = INPUT_VARIABLE(0); @@ -39,8 +39,8 @@ namespace nd4j { DECLARE_TYPES(to_double) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) - ->setAllowedOutputTypes(nd4j::DataType::DOUBLE); + ->setAllowedInputTypes(sd::DataType::ANY) + ->setAllowedOutputTypes(sd::DataType::DOUBLE); } DECLARE_SHAPE_FN(to_double) { diff --git a/libnd4j/include/ops/declarable/generic/datatypes/to_float16.cpp b/libnd4j/include/ops/declarable/generic/datatypes/to_float16.cpp index d6818bec4..aa8ceb045 100644 --- a/libnd4j/include/ops/declarable/generic/datatypes/to_float16.cpp +++ b/libnd4j/include/ops/declarable/generic/datatypes/to_float16.cpp @@ -18,12 +18,12 @@ // @author raver119@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_to_float16) #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(to_float16, 1, 1, true, 0, 0) { auto input = INPUT_VARIABLE(0); @@ -39,8 +39,8 @@ namespace nd4j { DECLARE_TYPES(to_float16) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) - ->setAllowedOutputTypes(nd4j::DataType::HALF); + ->setAllowedInputTypes(sd::DataType::ANY) + ->setAllowedOutputTypes(sd::DataType::HALF); } DECLARE_SHAPE_FN(to_float16) { diff --git a/libnd4j/include/ops/declarable/generic/datatypes/to_float32.cpp b/libnd4j/include/ops/declarable/generic/datatypes/to_float32.cpp index 4ca46bb82..23a924f9c 100644 --- a/libnd4j/include/ops/declarable/generic/datatypes/to_float32.cpp +++ b/libnd4j/include/ops/declarable/generic/datatypes/to_float32.cpp @@ -18,12 +18,12 @@ // @author raver119@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_to_float32) #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(to_float32, 1, 1, true, 0, 0) { auto input = INPUT_VARIABLE(0); @@ -39,8 +39,8 @@ namespace nd4j { DECLARE_TYPES(to_float32) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) - ->setAllowedOutputTypes(nd4j::DataType::FLOAT32); + ->setAllowedInputTypes(sd::DataType::ANY) + ->setAllowedOutputTypes(sd::DataType::FLOAT32); } DECLARE_SHAPE_FN(to_float32) { diff --git a/libnd4j/include/ops/declarable/generic/datatypes/to_int32.cpp b/libnd4j/include/ops/declarable/generic/datatypes/to_int32.cpp index 897868be5..c28fa6049 100644 --- a/libnd4j/include/ops/declarable/generic/datatypes/to_int32.cpp +++ b/libnd4j/include/ops/declarable/generic/datatypes/to_int32.cpp @@ -18,12 +18,12 @@ // @author raver119@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_to_int32) #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(to_int32, 1, 1, true, 0, 0) { auto input = INPUT_VARIABLE(0); @@ -39,8 +39,8 @@ namespace nd4j { DECLARE_TYPES(to_int32) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) - ->setAllowedOutputTypes(nd4j::DataType::INT32); + ->setAllowedInputTypes(sd::DataType::ANY) + ->setAllowedOutputTypes(sd::DataType::INT32); } DECLARE_SHAPE_FN(to_int32) { auto outShape = ShapeBuilders::copyShapeInfoAndType(inputShape->at(0), DataType::INT32, true, block.workspace()); diff --git a/libnd4j/include/ops/declarable/generic/datatypes/to_int64.cpp b/libnd4j/include/ops/declarable/generic/datatypes/to_int64.cpp index 6fa728254..cb994ccfe 100644 --- a/libnd4j/include/ops/declarable/generic/datatypes/to_int64.cpp +++ b/libnd4j/include/ops/declarable/generic/datatypes/to_int64.cpp @@ -18,12 +18,12 @@ // @author raver119@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_to_int64) #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(to_int64, 1, 1, true, 0, 0) { auto input = INPUT_VARIABLE(0); @@ -39,8 +39,8 @@ namespace nd4j { DECLARE_TYPES(to_int64) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) - ->setAllowedOutputTypes(nd4j::DataType::INT64); + ->setAllowedInputTypes(sd::DataType::ANY) + ->setAllowedOutputTypes(sd::DataType::INT64); } DECLARE_SHAPE_FN(to_int64) { auto outShape = ShapeBuilders::copyShapeInfoAndType(inputShape->at(0), DataType::INT64, true, block.workspace()); diff --git a/libnd4j/include/ops/declarable/generic/datatypes/to_uint32.cpp b/libnd4j/include/ops/declarable/generic/datatypes/to_uint32.cpp index 6805855f1..f62d9cd9b 100644 --- a/libnd4j/include/ops/declarable/generic/datatypes/to_uint32.cpp +++ b/libnd4j/include/ops/declarable/generic/datatypes/to_uint32.cpp @@ -18,12 +18,12 @@ // @author raver119@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_to_uint32) #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(to_uint32, 1, 1, true, 0, 0) { auto input = INPUT_VARIABLE(0); @@ -39,8 +39,8 @@ namespace nd4j { DECLARE_TYPES(to_uint32) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) - ->setAllowedOutputTypes(nd4j::DataType::INT32); + ->setAllowedInputTypes(sd::DataType::ANY) + ->setAllowedOutputTypes(sd::DataType::INT32); } DECLARE_SHAPE_FN(to_uint32) { auto outShape = ShapeBuilders::copyShapeInfoAndType(inputShape->at(0), DataType::UINT32, true, block.workspace()); diff --git a/libnd4j/include/ops/declarable/generic/datatypes/to_uint64.cpp b/libnd4j/include/ops/declarable/generic/datatypes/to_uint64.cpp index fe61821a5..dc337ea1b 100644 --- a/libnd4j/include/ops/declarable/generic/datatypes/to_uint64.cpp +++ b/libnd4j/include/ops/declarable/generic/datatypes/to_uint64.cpp @@ -18,12 +18,12 @@ // @author raver119@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_to_uint64) #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(to_uint64, 1, 1, true, 0, 0) { auto input = INPUT_VARIABLE(0); @@ -39,8 +39,8 @@ namespace nd4j { DECLARE_TYPES(to_uint64) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) - ->setAllowedOutputTypes(nd4j::DataType::INT8); + ->setAllowedInputTypes(sd::DataType::ANY) + ->setAllowedOutputTypes(sd::DataType::INT8); } DECLARE_SHAPE_FN(to_uint64) { auto outShape = ShapeBuilders::copyShapeInfoAndType(inputShape->at(0), DataType::UINT64, true, block.workspace()); diff --git a/libnd4j/include/ops/declarable/generic/flow/flow_control_ops.cpp b/libnd4j/include/ops/declarable/generic/flow/flow_control_ops.cpp index 5296e8844..108660c7b 100644 --- a/libnd4j/include/ops/declarable/generic/flow/flow_control_ops.cpp +++ b/libnd4j/include/ops/declarable/generic/flow/flow_control_ops.cpp @@ -20,9 +20,9 @@ #include -#include +#include -namespace nd4j { +namespace sd { namespace ops { /** * This operation is, basically IF statement diff --git a/libnd4j/include/ops/declarable/generic/grad/broadcast_gradient_args.cpp b/libnd4j/include/ops/declarable/generic/grad/broadcast_gradient_args.cpp index 34d979160..e4dbcd6d5 100644 --- a/libnd4j/include/ops/declarable/generic/grad/broadcast_gradient_args.cpp +++ b/libnd4j/include/ops/declarable/generic/grad/broadcast_gradient_args.cpp @@ -18,12 +18,12 @@ // @author raver119@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_broadcastgradientargs) #include -namespace nd4j { +namespace sd { namespace ops { /** * PLEASE NOTE: This op is disabled atm, and reserved for future releases. @@ -38,7 +38,7 @@ namespace nd4j { DECLARE_TYPES(broadcastgradientargs) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY); + ->setAllowedInputTypes(sd::DataType::ANY); } } } diff --git a/libnd4j/include/ops/declarable/generic/helpers/BroadcastHelper.h b/libnd4j/include/ops/declarable/generic/helpers/BroadcastHelper.h index e497be416..7df331c4d 100644 --- a/libnd4j/include/ops/declarable/generic/helpers/BroadcastHelper.h +++ b/libnd4j/include/ops/declarable/generic/helpers/BroadcastHelper.h @@ -21,17 +21,17 @@ #ifndef LIBND4J_BROADCAST_HELPER_H #define LIBND4J_BROADCAST_HELPER_H -#include +#include #include #include #include -#include +#include -namespace nd4j { +namespace sd { namespace ops { class BroadcastHelper { public: - static FORCEINLINE NDArray* broadcastApply(nd4j::BroadcastOpsTuple op, NDArray* x, NDArray* y, NDArray* z, ExtraArguments *extraArgs = nullptr) { + static FORCEINLINE NDArray* broadcastApply(sd::BroadcastOpsTuple op, NDArray* x, NDArray* y, NDArray* z, ExtraArguments *extraArgs = nullptr) { if(x->isEmpty() || y->isEmpty()) { if(!z->isEmpty()) @@ -98,7 +98,7 @@ namespace nd4j { return z; } - static FORCEINLINE NDArray* broadcastApply(nd4j::BroadcastBoolOpsTuple op, NDArray* x, NDArray* y, NDArray* z, ExtraArguments *extraArgs = nullptr) { + static FORCEINLINE NDArray* broadcastApply(sd::BroadcastBoolOpsTuple op, NDArray* x, NDArray* y, NDArray* z, ExtraArguments *extraArgs = nullptr) { if(x->isEmpty() || y->isEmpty()) { if(!z->isEmpty()) diff --git a/libnd4j/include/ops/declarable/generic/helpers/ScatterHelper.h b/libnd4j/include/ops/declarable/generic/helpers/ScatterHelper.h index d802ee8fd..4d464a745 100644 --- a/libnd4j/include/ops/declarable/generic/helpers/ScatterHelper.h +++ b/libnd4j/include/ops/declarable/generic/helpers/ScatterHelper.h @@ -22,14 +22,14 @@ #ifndef LIBND4J_SCATTERHELPER_H #define LIBND4J_SCATTERHELPER_H -#include -#include -#include +#include +#include +#include #include #include -namespace nd4j { +namespace sd { namespace ops { diff --git a/libnd4j/include/ops/declarable/generic/images/hsvToRgb.cpp b/libnd4j/include/ops/declarable/generic/images/hsvToRgb.cpp index f1f8522d7..d5211e498 100644 --- a/libnd4j/include/ops/declarable/generic/images/hsvToRgb.cpp +++ b/libnd4j/include/ops/declarable/generic/images/hsvToRgb.cpp @@ -23,7 +23,7 @@ #include #include -namespace nd4j { +namespace sd { namespace ops { CONFIGURABLE_OP_IMPL(hsv_to_rgb, 1, 1, true, 0, 0) { diff --git a/libnd4j/include/ops/declarable/generic/images/rgbToGrs.cpp b/libnd4j/include/ops/declarable/generic/images/rgbToGrs.cpp index aa2bec9da..f7378d333 100644 --- a/libnd4j/include/ops/declarable/generic/images/rgbToGrs.cpp +++ b/libnd4j/include/ops/declarable/generic/images/rgbToGrs.cpp @@ -23,7 +23,7 @@ #include #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(rgb_to_grs, 1, 1, false, 0, 0) { diff --git a/libnd4j/include/ops/declarable/generic/images/rgbToHsv.cpp b/libnd4j/include/ops/declarable/generic/images/rgbToHsv.cpp index 2ba45bea9..ac5a27c66 100644 --- a/libnd4j/include/ops/declarable/generic/images/rgbToHsv.cpp +++ b/libnd4j/include/ops/declarable/generic/images/rgbToHsv.cpp @@ -25,7 +25,7 @@ #include #include -namespace nd4j { +namespace sd { namespace ops { CONFIGURABLE_OP_IMPL(rgb_to_hsv, 1, 1, true, 0, 0) { diff --git a/libnd4j/include/ops/declarable/generic/images/rgbToYiq.cpp b/libnd4j/include/ops/declarable/generic/images/rgbToYiq.cpp index 6d202ee4a..40c936e4f 100644 --- a/libnd4j/include/ops/declarable/generic/images/rgbToYiq.cpp +++ b/libnd4j/include/ops/declarable/generic/images/rgbToYiq.cpp @@ -1,60 +1,60 @@ -/******************************************************************************* - * Copyright (c) 2019 Konduit K.K. - * - * This program and the accompanying materials are made available under the - * terms of the Apache License, Version 2.0 which is available at - * https://www.apache.org/licenses/LICENSE-2.0. - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the - * License for the specific language governing permissions and limitations - * under the License. - * - * SPDX-License-Identifier: Apache-2.0 - ******************************************************************************/ - - // - // @author AbdelRauf (rauf@konduit.ai) - // - -#include -#include -#include -#include - -namespace nd4j { - namespace ops { - - - - CONFIGURABLE_OP_IMPL(rgb_to_yiq, 1, 1, true, 0, 0) { - - auto input = INPUT_VARIABLE(0); - auto output = OUTPUT_VARIABLE(0); - - if (input->isEmpty()) - return Status::OK(); - - const int rank = input->rankOf(); - const int arg_size = block.getIArguments()->size(); - const int dimC = arg_size > 0 ? (INT_ARG(0) >= 0 ? INT_ARG(0) : INT_ARG(0) + rank) : rank - 1; - - REQUIRE_TRUE(rank >= 1, 0, "RGBtoYIQ: Fails to meet the rank requirement: %i >= 1 ", rank); - if (arg_size > 0) { - REQUIRE_TRUE(dimC >= 0 && dimC < rank, 0, "Index of the Channel dimension out of range: %i not in [%i,%i) ", INT_ARG(0), -rank, rank); - } - REQUIRE_TRUE(input->sizeAt(dimC) == 3, 0, "RGBtoYIQ: operation expects 3 channels (R, G, B), but got %i instead", input->sizeAt(dimC)); - - helpers::transformRgbYiq(block.launchContext(), input, output, dimC); - - return Status::OK(); - } - - - DECLARE_TYPES(rgb_to_yiq) { - getOpDescriptor()->setAllowedInputTypes({ ALL_FLOATS }) - ->setSameMode(true); - } - } -} +/******************************************************************************* + * Copyright (c) 2019 Konduit K.K. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ + + // + // @author AbdelRauf (rauf@konduit.ai) + // + +#include +#include +#include +#include + +namespace sd { + namespace ops { + + + + CONFIGURABLE_OP_IMPL(rgb_to_yiq, 1, 1, true, 0, 0) { + + auto input = INPUT_VARIABLE(0); + auto output = OUTPUT_VARIABLE(0); + + if (input->isEmpty()) + return Status::OK(); + + const int rank = input->rankOf(); + const int arg_size = block.getIArguments()->size(); + const int dimC = arg_size > 0 ? (INT_ARG(0) >= 0 ? INT_ARG(0) : INT_ARG(0) + rank) : rank - 1; + + REQUIRE_TRUE(rank >= 1, 0, "RGBtoYIQ: Fails to meet the rank requirement: %i >= 1 ", rank); + if (arg_size > 0) { + REQUIRE_TRUE(dimC >= 0 && dimC < rank, 0, "Index of the Channel dimension out of range: %i not in [%i,%i) ", INT_ARG(0), -rank, rank); + } + REQUIRE_TRUE(input->sizeAt(dimC) == 3, 0, "RGBtoYIQ: operation expects 3 channels (R, G, B), but got %i instead", input->sizeAt(dimC)); + + helpers::transformRgbYiq(block.launchContext(), input, output, dimC); + + return Status::OK(); + } + + + DECLARE_TYPES(rgb_to_yiq) { + getOpDescriptor()->setAllowedInputTypes({ ALL_FLOATS }) + ->setSameMode(true); + } + } +} diff --git a/libnd4j/include/ops/declarable/generic/images/rgbToYuv.cpp b/libnd4j/include/ops/declarable/generic/images/rgbToYuv.cpp index 58dd8a432..b52b5a8a6 100644 --- a/libnd4j/include/ops/declarable/generic/images/rgbToYuv.cpp +++ b/libnd4j/include/ops/declarable/generic/images/rgbToYuv.cpp @@ -25,7 +25,7 @@ #include #include -namespace nd4j { +namespace sd { namespace ops { CONFIGURABLE_OP_IMPL(rgb_to_yuv, 1, 1, true, 0, 0) { diff --git a/libnd4j/include/ops/declarable/generic/images/yiqToRgb.cpp b/libnd4j/include/ops/declarable/generic/images/yiqToRgb.cpp index 287aa150a..e339fb74b 100644 --- a/libnd4j/include/ops/declarable/generic/images/yiqToRgb.cpp +++ b/libnd4j/include/ops/declarable/generic/images/yiqToRgb.cpp @@ -1,61 +1,61 @@ -/******************************************************************************* - * Copyright (c) 2019 Konduit K.K. - * - * This program and the accompanying materials are made available under the - * terms of the Apache License, Version 2.0 which is available at - * https://www.apache.org/licenses/LICENSE-2.0. - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the - * License for the specific language governing permissions and limitations - * under the License. - * - * SPDX-License-Identifier: Apache-2.0 - ******************************************************************************/ - -// -// @author AbdelRauf (rauf@konduit.ai) -// - -#include -#include -#include -#include - -namespace nd4j { - namespace ops { - - - - CONFIGURABLE_OP_IMPL(yiq_to_rgb, 1, 1, true, 0, 0) { - - auto input = INPUT_VARIABLE(0); - auto output = OUTPUT_VARIABLE(0); - - if (input->isEmpty()) - return Status::OK(); - - const int rank = input->rankOf(); - const int arg_size = block.getIArguments()->size(); - const int dimC = arg_size > 0 ? (INT_ARG(0) >= 0 ? INT_ARG(0) : INT_ARG(0) + rank) : rank - 1; - - REQUIRE_TRUE(rank >= 1, 0, "YIQtoRGB: Fails to meet the rank requirement: %i >= 1 ", rank); - if (arg_size > 0) { - REQUIRE_TRUE(dimC >= 0 && dimC < rank, 0, "Index of the Channel dimension out of range: %i not in [%i,%i) ", INT_ARG(0), -rank, rank); - } - REQUIRE_TRUE(input->sizeAt(dimC) == 3, 0, "YIQtoRGB: operation expects 3 channels (Y, I, Q), but got %i instead", input->sizeAt(dimC)); - - helpers::transformYiqRgb(block.launchContext(), input, output, dimC); - - return Status::OK(); - } - - - DECLARE_TYPES(yiq_to_rgb) { - getOpDescriptor()->setAllowedInputTypes({ ALL_FLOATS }) - ->setSameMode(true); - } - - } -} +/******************************************************************************* + * Copyright (c) 2019 Konduit K.K. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ + +// +// @author AbdelRauf (rauf@konduit.ai) +// + +#include +#include +#include +#include + +namespace sd { + namespace ops { + + + + CONFIGURABLE_OP_IMPL(yiq_to_rgb, 1, 1, true, 0, 0) { + + auto input = INPUT_VARIABLE(0); + auto output = OUTPUT_VARIABLE(0); + + if (input->isEmpty()) + return Status::OK(); + + const int rank = input->rankOf(); + const int arg_size = block.getIArguments()->size(); + const int dimC = arg_size > 0 ? (INT_ARG(0) >= 0 ? INT_ARG(0) : INT_ARG(0) + rank) : rank - 1; + + REQUIRE_TRUE(rank >= 1, 0, "YIQtoRGB: Fails to meet the rank requirement: %i >= 1 ", rank); + if (arg_size > 0) { + REQUIRE_TRUE(dimC >= 0 && dimC < rank, 0, "Index of the Channel dimension out of range: %i not in [%i,%i) ", INT_ARG(0), -rank, rank); + } + REQUIRE_TRUE(input->sizeAt(dimC) == 3, 0, "YIQtoRGB: operation expects 3 channels (Y, I, Q), but got %i instead", input->sizeAt(dimC)); + + helpers::transformYiqRgb(block.launchContext(), input, output, dimC); + + return Status::OK(); + } + + + DECLARE_TYPES(yiq_to_rgb) { + getOpDescriptor()->setAllowedInputTypes({ ALL_FLOATS }) + ->setSameMode(true); + } + + } +} diff --git a/libnd4j/include/ops/declarable/generic/images/yuvToRgb.cpp b/libnd4j/include/ops/declarable/generic/images/yuvToRgb.cpp index 90ca217ce..48d4e379a 100644 --- a/libnd4j/include/ops/declarable/generic/images/yuvToRgb.cpp +++ b/libnd4j/include/ops/declarable/generic/images/yuvToRgb.cpp @@ -23,7 +23,7 @@ #include #include -namespace nd4j { +namespace sd { namespace ops { CONFIGURABLE_OP_IMPL(yuv_to_rgb, 1, 1, true, 0, 0) { diff --git a/libnd4j/include/ops/declarable/generic/kernels/knn_mindistance.cpp b/libnd4j/include/ops/declarable/generic/kernels/knn_mindistance.cpp index a7e825a9c..8ef699aa2 100644 --- a/libnd4j/include/ops/declarable/generic/kernels/knn_mindistance.cpp +++ b/libnd4j/include/ops/declarable/generic/kernels/knn_mindistance.cpp @@ -18,13 +18,13 @@ // @author raver119@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_knn_mindistance) #include #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(knn_mindistance, 3, 1, false, 0, 0) { auto input = INPUT_VARIABLE(0); diff --git a/libnd4j/include/ops/declarable/generic/list/clone_list.cpp b/libnd4j/include/ops/declarable/generic/list/clone_list.cpp index 46160d041..d100153ec 100644 --- a/libnd4j/include/ops/declarable/generic/list/clone_list.cpp +++ b/libnd4j/include/ops/declarable/generic/list/clone_list.cpp @@ -18,12 +18,12 @@ // @author raver119@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_clone_list) #include -namespace nd4j { +namespace sd { namespace ops { LIST_OP_IMPL(clone_list, 1, 1, 0, 0) { auto list = INPUT_LIST(0); diff --git a/libnd4j/include/ops/declarable/generic/list/create_list.cpp b/libnd4j/include/ops/declarable/generic/list/create_list.cpp index 40ef17e73..606558e7e 100644 --- a/libnd4j/include/ops/declarable/generic/list/create_list.cpp +++ b/libnd4j/include/ops/declarable/generic/list/create_list.cpp @@ -18,12 +18,12 @@ // Created by raver119 on 06.11.2017. // -#include +#include #if NOT_EXCLUDED(OP_create_list) #include -namespace nd4j { +namespace sd { namespace ops { LIST_OP_IMPL(create_list, 1, 2, 0, -2) { int height = 0; diff --git a/libnd4j/include/ops/declarable/generic/list/gather_list.cpp b/libnd4j/include/ops/declarable/generic/list/gather_list.cpp index 4c3b0a389..943313ad0 100644 --- a/libnd4j/include/ops/declarable/generic/list/gather_list.cpp +++ b/libnd4j/include/ops/declarable/generic/list/gather_list.cpp @@ -18,12 +18,12 @@ // @author raver119@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_gather_list) #include -namespace nd4j { +namespace sd { namespace ops { LIST_OP_IMPL(gather_list, 2, 1, 0, -2) { auto list = INPUT_LIST(0); diff --git a/libnd4j/include/ops/declarable/generic/list/pick_list.cpp b/libnd4j/include/ops/declarable/generic/list/pick_list.cpp index 8b7cc17db..1254456bd 100644 --- a/libnd4j/include/ops/declarable/generic/list/pick_list.cpp +++ b/libnd4j/include/ops/declarable/generic/list/pick_list.cpp @@ -18,12 +18,12 @@ // Created by raver119 on 06.11.2017. // -#include +#include #if NOT_EXCLUDED(OP_pick_list) #include -namespace nd4j { +namespace sd { namespace ops { LIST_OP_IMPL(pick_list, 1, 1, 0, -2) { auto list = INPUT_LIST(0); diff --git a/libnd4j/include/ops/declarable/generic/list/read_list.cpp b/libnd4j/include/ops/declarable/generic/list/read_list.cpp index 92d08d977..a1320b9b3 100644 --- a/libnd4j/include/ops/declarable/generic/list/read_list.cpp +++ b/libnd4j/include/ops/declarable/generic/list/read_list.cpp @@ -18,12 +18,12 @@ // Created by raver119 on 06.11.2017. // -#include +#include #if NOT_EXCLUDED(OP_read_list) #include -namespace nd4j { +namespace sd { namespace ops { LIST_OP_IMPL(read_list, 1, 1, 0, 0) { auto list = INPUT_LIST(0); diff --git a/libnd4j/include/ops/declarable/generic/list/scatter_list.cpp b/libnd4j/include/ops/declarable/generic/list/scatter_list.cpp index 2d854ae0b..38a4da7bd 100644 --- a/libnd4j/include/ops/declarable/generic/list/scatter_list.cpp +++ b/libnd4j/include/ops/declarable/generic/list/scatter_list.cpp @@ -18,13 +18,13 @@ // Created by raver119 on 06.11.2017. // -#include +#include #if NOT_EXCLUDED(OP_scatter_list) #include #include -namespace nd4j { +namespace sd { namespace ops { LIST_OP_IMPL(scatter_list, 1, 1, 0, -2) { NDArrayList *list = nullptr; diff --git a/libnd4j/include/ops/declarable/generic/list/size_list.cpp b/libnd4j/include/ops/declarable/generic/list/size_list.cpp index fa0aa1e0d..9c4d7ff70 100644 --- a/libnd4j/include/ops/declarable/generic/list/size_list.cpp +++ b/libnd4j/include/ops/declarable/generic/list/size_list.cpp @@ -18,12 +18,12 @@ // Created by raver119 on 06.11.2017. // -#include +#include #if NOT_EXCLUDED(OP_size_list) #include -namespace nd4j { +namespace sd { namespace ops { LIST_OP_IMPL(size_list, 1, 1, 0, 0) { auto list = INPUT_LIST(0); diff --git a/libnd4j/include/ops/declarable/generic/list/split_list.cpp b/libnd4j/include/ops/declarable/generic/list/split_list.cpp index 5a403dd06..c49047961 100644 --- a/libnd4j/include/ops/declarable/generic/list/split_list.cpp +++ b/libnd4j/include/ops/declarable/generic/list/split_list.cpp @@ -18,13 +18,13 @@ // Created by raver119 on 06.11.2017. // -#include +#include #if NOT_EXCLUDED(OP_split_list) #include #include -namespace nd4j { +namespace sd { namespace ops { LIST_OP_IMPL(split_list, 2, 1, 0, -2) { NDArrayList *list = nullptr; diff --git a/libnd4j/include/ops/declarable/generic/list/stack_list.cpp b/libnd4j/include/ops/declarable/generic/list/stack_list.cpp index a97b30179..a0f0f4220 100644 --- a/libnd4j/include/ops/declarable/generic/list/stack_list.cpp +++ b/libnd4j/include/ops/declarable/generic/list/stack_list.cpp @@ -19,12 +19,12 @@ // @author raver119@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_stack_list) #include -namespace nd4j { +namespace sd { namespace ops { LIST_OP_IMPL(stack_list, 1, 1, 0, 0) { auto list = INPUT_LIST(0); diff --git a/libnd4j/include/ops/declarable/generic/list/unstack_list.cpp b/libnd4j/include/ops/declarable/generic/list/unstack_list.cpp index b5e5f207e..5f4522949 100644 --- a/libnd4j/include/ops/declarable/generic/list/unstack_list.cpp +++ b/libnd4j/include/ops/declarable/generic/list/unstack_list.cpp @@ -18,12 +18,12 @@ // @author raver119@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_unstack_list) #include -namespace nd4j { +namespace sd { namespace ops { LIST_OP_IMPL(unstack_list, 1, 1, 0, 0) { auto outputList = INPUT_LIST(0); diff --git a/libnd4j/include/ops/declarable/generic/list/write_list.cpp b/libnd4j/include/ops/declarable/generic/list/write_list.cpp index c9b32234e..c61bcb68b 100644 --- a/libnd4j/include/ops/declarable/generic/list/write_list.cpp +++ b/libnd4j/include/ops/declarable/generic/list/write_list.cpp @@ -18,12 +18,12 @@ // @author raver119@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_write_list) #include -namespace nd4j { +namespace sd { namespace ops { LIST_OP_IMPL(write_list, 2, 1, 0, -2) { auto list = INPUT_LIST(0); diff --git a/libnd4j/include/ops/declarable/generic/loss/absoluteDifference.cpp b/libnd4j/include/ops/declarable/generic/loss/absoluteDifference.cpp index ba488df65..812588710 100644 --- a/libnd4j/include/ops/declarable/generic/loss/absoluteDifference.cpp +++ b/libnd4j/include/ops/declarable/generic/loss/absoluteDifference.cpp @@ -18,11 +18,11 @@ // Created by Yurii Shyrma on 20.11.2017. // -#include +#include #if NOT_EXCLUDED(OP_absolute_difference_loss) #include -namespace nd4j { +namespace sd { namespace ops { @@ -51,7 +51,7 @@ CUSTOM_OP_IMPL(absolute_difference_loss, 3, 1, false, 0, 1) { if(!weights->isScalar() && !weights->isSameShape(predictions)) weightsBroad = new NDArray(weights->tileToShape(predictions->getShapeInfo())); - NDArray E = (*predictions - *labels).transform(nd4j::transform::Abs); + NDArray E = (*predictions - *labels).transform(sd::transform::Abs); E *= *weightsBroad; switch (reductionMode) { @@ -101,7 +101,7 @@ CUSTOM_OP_IMPL(absolute_difference_loss, 3, 1, false, 0, 1) { } DECLARE_TYPES(absolute_difference_loss) { - getOpDescriptor()->setAllowedInputTypes(nd4j::DataType::ANY)->setAllowedOutputTypes({ALL_FLOATS}); + getOpDescriptor()->setAllowedInputTypes(sd::DataType::ANY)->setAllowedOutputTypes({ALL_FLOATS}); } DECLARE_SHAPE_FN(absolute_difference_loss) { @@ -169,10 +169,10 @@ CUSTOM_OP_IMPL(absolute_difference_loss_grad, 3, 3, false, 0, 1) { NDArray E = *predictions - *labels; // dE_i/dp_i = sign(p_i - y_i) - E.applyTransform(nd4j::transform::Sign, *dLdp); // dE/dp + E.applyTransform(sd::transform::Sign, *dLdp); // dE/dp // dE_i/dy_i = -sign(p_i - y_i) - E.applyTransform(nd4j::transform::Abs, E); + E.applyTransform(sd::transform::Abs, E); switch (reductionMode) { @@ -261,7 +261,7 @@ CUSTOM_OP_IMPL(absolute_difference_loss_grad, 3, 3, false, 0, 1) { DECLARE_TYPES(absolute_difference_loss_grad) { - getOpDescriptor()->setAllowedInputTypes(nd4j::DataType::ANY)->setAllowedOutputTypes({ALL_FLOATS}); + getOpDescriptor()->setAllowedInputTypes(sd::DataType::ANY)->setAllowedOutputTypes({ALL_FLOATS}); } DECLARE_SHAPE_FN(absolute_difference_loss_grad) { diff --git a/libnd4j/include/ops/declarable/generic/loss/cosineDistance.cpp b/libnd4j/include/ops/declarable/generic/loss/cosineDistance.cpp index 7fe75c03a..10995c90b 100644 --- a/libnd4j/include/ops/declarable/generic/loss/cosineDistance.cpp +++ b/libnd4j/include/ops/declarable/generic/loss/cosineDistance.cpp @@ -18,13 +18,13 @@ // @author Yurii Shyrma (iuriish@yahoo.com), created on 22.11.2017 // -#include +#include #if NOT_EXCLUDED(OP_cosine_distance_loss) #include #include -namespace nd4j { +namespace sd { namespace ops { @@ -118,7 +118,7 @@ CUSTOM_OP_IMPL(cosine_distance_loss, 3, 1, false, 0, 2) { ////////////////////////////////////////////////////////////////////////// DECLARE_TYPES(cosine_distance_loss) { - getOpDescriptor()->setAllowedInputTypes(nd4j::DataType::ANY)->setAllowedOutputTypes({ALL_FLOATS}); + getOpDescriptor()->setAllowedInputTypes(sd::DataType::ANY)->setAllowedOutputTypes({ALL_FLOATS}); } ////////////////////////////////////////////////////////////////////////// @@ -304,7 +304,7 @@ CUSTOM_OP_IMPL(cosine_distance_loss_grad, 3, 3, false, 0, 2) { ////////////////////////////////////////////////////////////////////////// DECLARE_TYPES(cosine_distance_loss_grad) { - getOpDescriptor()->setAllowedInputTypes(nd4j::DataType::ANY)->setAllowedOutputTypes({ALL_FLOATS}); + getOpDescriptor()->setAllowedInputTypes(sd::DataType::ANY)->setAllowedOutputTypes({ALL_FLOATS}); } ////////////////////////////////////////////////////////////////////////// diff --git a/libnd4j/include/ops/declarable/generic/loss/hingeLoss.cpp b/libnd4j/include/ops/declarable/generic/loss/hingeLoss.cpp index 8670bf9e1..244083a03 100644 --- a/libnd4j/include/ops/declarable/generic/loss/hingeLoss.cpp +++ b/libnd4j/include/ops/declarable/generic/loss/hingeLoss.cpp @@ -18,12 +18,12 @@ // @author Yurii Shyrma (iuriish@yahoo.com), created on 23.11.2017 // -#include +#include #if NOT_EXCLUDED(OP_hinge_loss) #include -namespace nd4j { +namespace sd { namespace ops { ////////////////////////////////////////////////////////////////////////// @@ -106,7 +106,7 @@ namespace nd4j { ////////////////////////////////////////////////////////////////////////// DECLARE_TYPES(hinge_loss) { - getOpDescriptor()->setAllowedInputTypes(nd4j::DataType::ANY)->setAllowedOutputTypes({ALL_FLOATS}); + getOpDescriptor()->setAllowedInputTypes(sd::DataType::ANY)->setAllowedOutputTypes({ALL_FLOATS}); } ////////////////////////////////////////////////////////////////////////// @@ -176,7 +176,7 @@ namespace nd4j { // turn E into gradient mask NDArray gradientMask(E.getShapeInfo(), block.getWorkspace()); - E.applyTransform(nd4j::transform::Sign, gradientMask); + E.applyTransform(sd::transform::Sign, gradientMask); dLdp->assign(-z * gradientMask); dLdl->assign(-2.f * (*logits) * gradientMask); @@ -271,7 +271,7 @@ namespace nd4j { DECLARE_TYPES(hinge_loss_grad) { - getOpDescriptor()->setAllowedInputTypes(nd4j::DataType::ANY)->setAllowedOutputTypes({ALL_FLOATS}); + getOpDescriptor()->setAllowedInputTypes(sd::DataType::ANY)->setAllowedOutputTypes({ALL_FLOATS}); } DECLARE_SHAPE_FN(hinge_loss_grad) { diff --git a/libnd4j/include/ops/declarable/generic/loss/huberLoss.cpp b/libnd4j/include/ops/declarable/generic/loss/huberLoss.cpp index e844b4126..0c05de0ba 100644 --- a/libnd4j/include/ops/declarable/generic/loss/huberLoss.cpp +++ b/libnd4j/include/ops/declarable/generic/loss/huberLoss.cpp @@ -18,12 +18,12 @@ // @author Yurii Shyrma (iuriish@yahoo.com), created on 23.11.2017 // -#include +#include #if NOT_EXCLUDED(OP_huber_loss) #include -namespace nd4j { +namespace sd { namespace ops { @@ -111,7 +111,7 @@ CUSTOM_OP_IMPL(huber_loss, 3, 1, false, 1, 1) { ////////////////////////////////////////////////////////////////////////// DECLARE_TYPES(huber_loss) { - getOpDescriptor()->setAllowedInputTypes(nd4j::DataType::ANY)->setAllowedOutputTypes({ALL_FLOATS}); + getOpDescriptor()->setAllowedInputTypes(sd::DataType::ANY)->setAllowedOutputTypes({ALL_FLOATS}); } ////////////////////////////////////////////////////////////////////////// @@ -286,7 +286,7 @@ DECLARE_SHAPE_FN(huber_loss) { DECLARE_TYPES(huber_loss_grad) { - getOpDescriptor()->setAllowedInputTypes(nd4j::DataType::ANY)->setAllowedOutputTypes({ALL_FLOATS}); + getOpDescriptor()->setAllowedInputTypes(sd::DataType::ANY)->setAllowedOutputTypes({ALL_FLOATS}); } DECLARE_SHAPE_FN(huber_loss_grad) { diff --git a/libnd4j/include/ops/declarable/generic/loss/l2_loss.cpp b/libnd4j/include/ops/declarable/generic/loss/l2_loss.cpp index c42789015..3afeea2ba 100644 --- a/libnd4j/include/ops/declarable/generic/loss/l2_loss.cpp +++ b/libnd4j/include/ops/declarable/generic/loss/l2_loss.cpp @@ -18,12 +18,12 @@ // Created by GS 31.01.2018 // -#include +#include #if NOT_EXCLUDED(OP_l2_loss) #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(l2_loss, 1, 1, false, 0, 0) { auto input = INPUT_VARIABLE(0); @@ -43,7 +43,7 @@ namespace nd4j { DECLARE_TYPES(l2_loss) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_FLOATS}); } } diff --git a/libnd4j/include/ops/declarable/generic/loss/logLoss.cpp b/libnd4j/include/ops/declarable/generic/loss/logLoss.cpp index f83947c69..dc889d5c9 100644 --- a/libnd4j/include/ops/declarable/generic/loss/logLoss.cpp +++ b/libnd4j/include/ops/declarable/generic/loss/logLoss.cpp @@ -18,12 +18,12 @@ // @author Yurii Shyrma (iuriish@yahoo.com), created on 23.11.2017 // -#include +#include #if NOT_EXCLUDED(OP_log_loss) #include -namespace nd4j { +namespace sd { namespace ops { @@ -108,7 +108,7 @@ CUSTOM_OP_IMPL(log_loss, 3, 1, false, 1, 1) { ////////////////////////////////////////////////////////////////////////// DECLARE_TYPES(log_loss) { - getOpDescriptor()->setAllowedInputTypes(nd4j::DataType::ANY)->setAllowedOutputTypes({ALL_FLOATS}); + getOpDescriptor()->setAllowedInputTypes(sd::DataType::ANY)->setAllowedOutputTypes({ALL_FLOATS}); } ////////////////////////////////////////////////////////////////////////// @@ -277,7 +277,7 @@ CUSTOM_OP_IMPL(log_loss_grad, 3, 3, false, 1, 1) { ////////////////////////////////////////////////////////////////////////// DECLARE_TYPES(log_loss_grad) { - getOpDescriptor()->setAllowedInputTypes(nd4j::DataType::ANY)->setAllowedOutputTypes({ALL_FLOATS}); + getOpDescriptor()->setAllowedInputTypes(sd::DataType::ANY)->setAllowedOutputTypes({ALL_FLOATS}); } ////////////////////////////////////////////////////////////////////////// diff --git a/libnd4j/include/ops/declarable/generic/loss/log_poisson_loss.cpp b/libnd4j/include/ops/declarable/generic/loss/log_poisson_loss.cpp index 0d85c6e23..9a00b4eb4 100644 --- a/libnd4j/include/ops/declarable/generic/loss/log_poisson_loss.cpp +++ b/libnd4j/include/ops/declarable/generic/loss/log_poisson_loss.cpp @@ -18,12 +18,12 @@ // @author raver119@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_log_poisson_loss) #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(log_poisson_loss, 3, 1, true, 0, 1) { auto log_predictions = INPUT_VARIABLE(0); @@ -111,7 +111,7 @@ namespace ops { ////////////////////////////////////////////////////////////////////////// DECLARE_TYPES(log_poisson_loss) { - getOpDescriptor()->setAllowedInputTypes(nd4j::DataType::ANY)->setAllowedOutputTypes({ALL_FLOATS}); + getOpDescriptor()->setAllowedInputTypes(sd::DataType::ANY)->setAllowedOutputTypes({ALL_FLOATS}); } ////////////////////////////////////////////////////////////////////////// @@ -279,7 +279,7 @@ namespace ops { DECLARE_TYPES(log_poisson_loss_grad) { - getOpDescriptor()->setAllowedInputTypes(nd4j::DataType::ANY)->setAllowedOutputTypes({ALL_FLOATS}); + getOpDescriptor()->setAllowedInputTypes(sd::DataType::ANY)->setAllowedOutputTypes({ALL_FLOATS}); } DECLARE_SHAPE_FN(log_poisson_loss_grad) { diff --git a/libnd4j/include/ops/declarable/generic/loss/meanPairWsSqErr.cpp b/libnd4j/include/ops/declarable/generic/loss/meanPairWsSqErr.cpp index ef511921f..312a32674 100644 --- a/libnd4j/include/ops/declarable/generic/loss/meanPairWsSqErr.cpp +++ b/libnd4j/include/ops/declarable/generic/loss/meanPairWsSqErr.cpp @@ -22,14 +22,14 @@ // @author Paul Dubs // -#include +#include #if NOT_EXCLUDED(OP_mean_pairwssqerr_loss) #include #include #include -namespace nd4j { +namespace sd { namespace ops { @@ -181,7 +181,7 @@ namespace nd4j { ////////////////////////////////////////////////////////////////////////// DECLARE_TYPES(mean_pairwssqerr_loss) { - getOpDescriptor()->setAllowedInputTypes(nd4j::DataType::ANY)->setAllowedOutputTypes({ALL_FLOATS}); + getOpDescriptor()->setAllowedInputTypes(sd::DataType::ANY)->setAllowedOutputTypes({ALL_FLOATS}); } ////////////////////////////////////////////////////////////////////////// @@ -349,7 +349,7 @@ namespace nd4j { } DECLARE_TYPES(mean_pairwssqerr_loss_grad) { - getOpDescriptor()->setAllowedInputTypes(nd4j::DataType::ANY)->setAllowedOutputTypes({ALL_FLOATS}); + getOpDescriptor()->setAllowedInputTypes(sd::DataType::ANY)->setAllowedOutputTypes({ALL_FLOATS}); } DECLARE_SHAPE_FN(mean_pairwssqerr_loss_grad) { diff --git a/libnd4j/include/ops/declarable/generic/loss/meanSqErr.cpp b/libnd4j/include/ops/declarable/generic/loss/meanSqErr.cpp index f446d0bf0..c5925fe90 100644 --- a/libnd4j/include/ops/declarable/generic/loss/meanSqErr.cpp +++ b/libnd4j/include/ops/declarable/generic/loss/meanSqErr.cpp @@ -18,12 +18,12 @@ // @author Yurii Shyrma (iuriish@yahoo.com), created on 25.11.2017 // -#include +#include #if NOT_EXCLUDED(OP_mean_sqerr_loss) #include -namespace nd4j { +namespace sd { namespace ops { @@ -107,7 +107,7 @@ CUSTOM_OP_IMPL(mean_sqerr_loss, 3, 1, false, 0, 1) { DECLARE_TYPES(mean_sqerr_loss) { - getOpDescriptor()->setAllowedInputTypes(nd4j::DataType::ANY)->setAllowedOutputTypes({ALL_FLOATS}); + getOpDescriptor()->setAllowedInputTypes(sd::DataType::ANY)->setAllowedOutputTypes({ALL_FLOATS}); } @@ -268,7 +268,7 @@ CUSTOM_OP_IMPL(mean_sqerr_loss_grad, 3, 3, false, 0, 1) { DECLARE_TYPES(mean_sqerr_loss_grad) { - getOpDescriptor()->setAllowedInputTypes(nd4j::DataType::ANY)->setAllowedOutputTypes({ALL_FLOATS}); + getOpDescriptor()->setAllowedInputTypes(sd::DataType::ANY)->setAllowedOutputTypes({ALL_FLOATS}); } DECLARE_SHAPE_FN(mean_sqerr_loss_grad) { diff --git a/libnd4j/include/ops/declarable/generic/loss/sigmCrossEntropy.cpp b/libnd4j/include/ops/declarable/generic/loss/sigmCrossEntropy.cpp index 5b0075466..4d3c5749c 100644 --- a/libnd4j/include/ops/declarable/generic/loss/sigmCrossEntropy.cpp +++ b/libnd4j/include/ops/declarable/generic/loss/sigmCrossEntropy.cpp @@ -18,13 +18,13 @@ // @author Yurii Shyrma (iuriish@yahoo.com), created on 25.11.2017 // -#include +#include #if NOT_EXCLUDED(OP_sigm_cross_entropy_loss) #include #include -namespace nd4j { +namespace sd { namespace ops { @@ -118,7 +118,7 @@ CUSTOM_OP_IMPL(sigm_cross_entropy_loss, 3, 1, false, 1, 1) { ////////////////////////////////////////////////////////////////////////// DECLARE_TYPES(sigm_cross_entropy_loss) { - getOpDescriptor()->setAllowedInputTypes(nd4j::DataType::ANY)->setAllowedOutputTypes({ALL_FLOATS}); + getOpDescriptor()->setAllowedInputTypes(sd::DataType::ANY)->setAllowedOutputTypes({ALL_FLOATS}); } ////////////////////////////////////////////////////////////////////////// @@ -292,7 +292,7 @@ CUSTOM_OP_IMPL(sigm_cross_entropy_loss_grad, 3, 3, false, 1, 1) { ////////////////////////////////////////////////////////////////////////// DECLARE_TYPES(sigm_cross_entropy_loss_grad) { - getOpDescriptor()->setAllowedInputTypes(nd4j::DataType::ANY)->setAllowedOutputTypes({ALL_FLOATS}); + getOpDescriptor()->setAllowedInputTypes(sd::DataType::ANY)->setAllowedOutputTypes({ALL_FLOATS}); } ////////////////////////////////////////////////////////////////////////// diff --git a/libnd4j/include/ops/declarable/generic/loss/softmaxCrossEntropy.cpp b/libnd4j/include/ops/declarable/generic/loss/softmaxCrossEntropy.cpp index a1a197fae..3ea9ce2bd 100644 --- a/libnd4j/include/ops/declarable/generic/loss/softmaxCrossEntropy.cpp +++ b/libnd4j/include/ops/declarable/generic/loss/softmaxCrossEntropy.cpp @@ -18,12 +18,12 @@ // @author Yurii Shyrma (iuriish@yahoo.com), created on 25.11.2017. // -#include +#include #if NOT_EXCLUDED(OP_softmax_cross_entropy_loss) #include -namespace nd4j { +namespace sd { namespace ops { @@ -253,8 +253,8 @@ CUSTOM_OP_IMPL(softmax_cross_entropy_loss_grad, 3, 3, false, 1, 1) { *dLdl *= *weights; } else { - dLdp->applyBroadcast(nd4j::broadcast::Multiply, dimensions, *weightsBroad, *dLdp); - dLdl->applyBroadcast(nd4j::broadcast::Multiply, dimensions, *weightsBroad, *dLdl); + dLdp->applyBroadcast(sd::broadcast::Multiply, dimensions, *weightsBroad, *dLdp); + dLdl->applyBroadcast(sd::broadcast::Multiply, dimensions, *weightsBroad, *dLdl); if(weights != weightsBroad) { std::vector axesToReduceAlong = ShapeUtils::evalBroadcastBackwardAxis(weights->getShapeInfo(), weightsBroad->getShapeInfo()); @@ -289,8 +289,8 @@ CUSTOM_OP_IMPL(softmax_cross_entropy_loss_grad, 3, 3, false, 1, 1) { else { NDArray temp = *weightsBroad / sum; - dLdp->applyBroadcast(nd4j::broadcast::Multiply, dimensions, temp, *dLdp); - dLdl->applyBroadcast(nd4j::broadcast::Multiply, dimensions, temp, *dLdl); + dLdp->applyBroadcast(sd::broadcast::Multiply, dimensions, temp, *dLdp); + dLdl->applyBroadcast(sd::broadcast::Multiply, dimensions, temp, *dLdl); if(weights != weightsBroad) { std::vector axesToReduceAlong = ShapeUtils::evalBroadcastBackwardAxis(weights->getShapeInfo(), weightsBroad->getShapeInfo()); @@ -326,8 +326,8 @@ CUSTOM_OP_IMPL(softmax_cross_entropy_loss_grad, 3, 3, false, 1, 1) { } else { NDArray temp = *weightsBroad / numOfNonZeroWeights; - dLdp->applyBroadcast(nd4j::broadcast::Multiply, dimensions, temp, *dLdp); - dLdl->applyBroadcast(nd4j::broadcast::Multiply, dimensions, temp, *dLdl); + dLdp->applyBroadcast(sd::broadcast::Multiply, dimensions, temp, *dLdp); + dLdl->applyBroadcast(sd::broadcast::Multiply, dimensions, temp, *dLdl); if(weights != weightsBroad) { std::vector axesToReduceAlong = ShapeUtils::evalBroadcastBackwardAxis(weights->getShapeInfo(), weightsBroad->getShapeInfo()); diff --git a/libnd4j/include/ops/declarable/generic/loss/softmaxCrossEntropyWithLogits.cpp b/libnd4j/include/ops/declarable/generic/loss/softmaxCrossEntropyWithLogits.cpp index 5e88ec0e6..6dab14365 100644 --- a/libnd4j/include/ops/declarable/generic/loss/softmaxCrossEntropyWithLogits.cpp +++ b/libnd4j/include/ops/declarable/generic/loss/softmaxCrossEntropyWithLogits.cpp @@ -18,12 +18,12 @@ // @author Yurii Shyrma (iuriish@yahoo.com), created on 18.06.2018 // -#include +#include #if NOT_EXCLUDED(OP_softmax_cross_entropy_loss_with_logits) #include -namespace nd4j { +namespace sd { namespace ops { @@ -53,7 +53,7 @@ CUSTOM_OP_IMPL(softmax_cross_entropy_loss_with_logits, 2, 1, false, 0, 0) { ////////////////////////////////////////////////////////////////////////// DECLARE_TYPES(softmax_cross_entropy_loss_with_logits) { - getOpDescriptor()->setAllowedInputTypes(nd4j::DataType::ANY)->setAllowedOutputTypes({ALL_FLOATS}); + getOpDescriptor()->setAllowedInputTypes(sd::DataType::ANY)->setAllowedOutputTypes({ALL_FLOATS}); } ////////////////////////////////////////////////////////////////////////// @@ -113,7 +113,7 @@ CUSTOM_OP_IMPL(softmax_cross_entropy_loss_with_logits_grad, 2, 2, false, 0, 0) { ////////////////////////////////////////////////////////////////////////// DECLARE_TYPES(softmax_cross_entropy_loss_with_logits_grad) { - getOpDescriptor()->setAllowedInputTypes(nd4j::DataType::ANY)->setAllowedOutputTypes({ALL_FLOATS}); + getOpDescriptor()->setAllowedInputTypes(sd::DataType::ANY)->setAllowedOutputTypes({ALL_FLOATS}); } ////////////////////////////////////////////////////////////////////////// diff --git a/libnd4j/include/ops/declarable/generic/loss/sparseSoftmaxCrossEntropyWithLogits.cpp b/libnd4j/include/ops/declarable/generic/loss/sparseSoftmaxCrossEntropyWithLogits.cpp index e7c8da123..c641bf12f 100644 --- a/libnd4j/include/ops/declarable/generic/loss/sparseSoftmaxCrossEntropyWithLogits.cpp +++ b/libnd4j/include/ops/declarable/generic/loss/sparseSoftmaxCrossEntropyWithLogits.cpp @@ -18,13 +18,13 @@ // @author Yurii Shyrma (iuriish@yahoo.com), created on 29.08.2018 // -#include +#include #if NOT_EXCLUDED(OP_sparse_softmax_cross_entropy_loss_with_logits) #include #include -namespace nd4j { +namespace sd { namespace ops { diff --git a/libnd4j/include/ops/declarable/generic/nlp/cbow.cpp b/libnd4j/include/ops/declarable/generic/nlp/cbow.cpp index 21906f4eb..9b5ed1918 100644 --- a/libnd4j/include/ops/declarable/generic/nlp/cbow.cpp +++ b/libnd4j/include/ops/declarable/generic/nlp/cbow.cpp @@ -18,13 +18,13 @@ // @author raver119@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_cbow) #include #include -namespace nd4j { +namespace sd { namespace ops { CONFIGURABLE_OP_IMPL(cbow, 15, 15, true, 0, 0) { auto target = INPUT_VARIABLE(0); @@ -62,7 +62,7 @@ namespace nd4j { REQUIRE_TRUE(syn0->dataType() == expTable->dataType(), 0, "CBOW: expTable must have the same data type as syn0 table"); - nd4j::ops::helpers::cbow(*syn0, *syn1, *syn1neg, *expTable, *negTable, *target, *ngStarter, nsRounds, *context, *lockedWords, *indices, *codes, *alpha, *randomValue, *numLabels, *inferenceVector, trainWords, numWorkers); + sd::ops::helpers::cbow(*syn0, *syn1, *syn1neg, *expTable, *negTable, *target, *ngStarter, nsRounds, *context, *lockedWords, *indices, *codes, *alpha, *randomValue, *numLabels, *inferenceVector, trainWords, numWorkers); return Status::OK(); @@ -70,22 +70,22 @@ namespace nd4j { DECLARE_TYPES(cbow) { getOpDescriptor() - ->setAllowedInputTypes(0, nd4j::DataType::INT32) - ->setAllowedInputTypes(1, nd4j::DataType::INT32) - ->setAllowedInputTypes(2, nd4j::DataType::INT32) - ->setAllowedInputTypes(3, nd4j::DataType::INT32) - ->setAllowedInputTypes(4, nd4j::DataType::INT8) + ->setAllowedInputTypes(0, sd::DataType::INT32) + ->setAllowedInputTypes(1, sd::DataType::INT32) + ->setAllowedInputTypes(2, sd::DataType::INT32) + ->setAllowedInputTypes(3, sd::DataType::INT32) + ->setAllowedInputTypes(4, sd::DataType::INT8) ->setAllowedInputTypes(5, {ALL_FLOATS}) ->setAllowedInputTypes(6, {ALL_FLOATS}) ->setAllowedInputTypes(7, {ALL_FLOATS}) ->setAllowedInputTypes(8, {ALL_FLOATS}) ->setAllowedInputTypes(9, {ALL_FLOATS}) ->setAllowedInputTypes(10, {ALL_FLOATS}) - ->setAllowedInputTypes(11, nd4j::DataType::INT64) - ->setAllowedInputTypes(12, nd4j::DataType::INT32) - ->setAllowedInputTypes(13, nd4j::DataType::INT32) + ->setAllowedInputTypes(11, sd::DataType::INT64) + ->setAllowedInputTypes(12, sd::DataType::INT32) + ->setAllowedInputTypes(13, sd::DataType::INT32) ->setAllowedInputTypes(14, {ALL_FLOATS}) - ->setAllowedOutputTypes(nd4j::DataType::ANY); + ->setAllowedOutputTypes(sd::DataType::ANY); } } } diff --git a/libnd4j/include/ops/declarable/generic/nlp/skipgram.cpp b/libnd4j/include/ops/declarable/generic/nlp/skipgram.cpp index a97e1a79e..921662fa6 100644 --- a/libnd4j/include/ops/declarable/generic/nlp/skipgram.cpp +++ b/libnd4j/include/ops/declarable/generic/nlp/skipgram.cpp @@ -18,13 +18,13 @@ // @author raver119@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_skipgram) #include #include -namespace nd4j { +namespace sd { namespace ops { CONFIGURABLE_OP_IMPL(skipgram, 12, 12, true, 0, 0) { auto target = INPUT_VARIABLE(0); @@ -60,26 +60,26 @@ namespace nd4j { REQUIRE_TRUE(syn0->dataType() == expTable->dataType(), 0, "SkipGram: expTable must have the same data type as syn0 table"); - nd4j::ops::helpers::skipgram(*syn0, *syn1, *syn1neg, *expTable, *negTable, *target, *ngStarter, nsRounds, *indices, *codes, *alpha, *randomValue, *inferenceVector, isPreciseMode, numWorkers); + sd::ops::helpers::skipgram(*syn0, *syn1, *syn1neg, *expTable, *negTable, *target, *ngStarter, nsRounds, *indices, *codes, *alpha, *randomValue, *inferenceVector, isPreciseMode, numWorkers); return Status::OK(); } DECLARE_TYPES(skipgram) { getOpDescriptor() - ->setAllowedInputTypes(0, nd4j::DataType::INT32) - ->setAllowedInputTypes(1, nd4j::DataType::INT32) - ->setAllowedInputTypes(2, nd4j::DataType::INT32) - ->setAllowedInputTypes(3, nd4j::DataType::INT8) + ->setAllowedInputTypes(0, sd::DataType::INT32) + ->setAllowedInputTypes(1, sd::DataType::INT32) + ->setAllowedInputTypes(2, sd::DataType::INT32) + ->setAllowedInputTypes(3, sd::DataType::INT8) ->setAllowedInputTypes(4, {ALL_FLOATS}) ->setAllowedInputTypes(5, {ALL_FLOATS}) ->setAllowedInputTypes(6, {ALL_FLOATS}) ->setAllowedInputTypes(7, {ALL_FLOATS}) ->setAllowedInputTypes(8, {ALL_FLOATS}) ->setAllowedInputTypes(9, {ALL_FLOATS}) - ->setAllowedInputTypes(10, nd4j::DataType::INT64) + ->setAllowedInputTypes(10, sd::DataType::INT64) ->setAllowedInputTypes(11, {ALL_FLOATS}) - ->setAllowedOutputTypes(nd4j::DataType::ANY); + ->setAllowedOutputTypes(sd::DataType::ANY); } /* diff --git a/libnd4j/include/ops/declarable/generic/nn/apply_sgd.cpp b/libnd4j/include/ops/declarable/generic/nn/apply_sgd.cpp index cc558c905..389d07c7b 100644 --- a/libnd4j/include/ops/declarable/generic/nn/apply_sgd.cpp +++ b/libnd4j/include/ops/declarable/generic/nn/apply_sgd.cpp @@ -18,13 +18,13 @@ // @author raver119@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_apply_sgd) #include #include -namespace nd4j { +namespace sd { namespace ops { CONFIGURABLE_OP_IMPL(apply_sgd, 2, 1, true, -2, 0) { auto parameters = INPUT_VARIABLE(0); diff --git a/libnd4j/include/ops/declarable/generic/nn/batchnorm.cpp b/libnd4j/include/ops/declarable/generic/nn/batchnorm.cpp index 3cf088ae9..e69b370ca 100644 --- a/libnd4j/include/ops/declarable/generic/nn/batchnorm.cpp +++ b/libnd4j/include/ops/declarable/generic/nn/batchnorm.cpp @@ -20,13 +20,13 @@ // @author Yurii Shyrma (iuriish@yahoo.com) // -#include +#include #if NOT_EXCLUDED(OP_batchnorm) #include #include -namespace nd4j { +namespace sd { namespace ops { @@ -61,7 +61,7 @@ CUSTOM_OP_IMPL(batchnorm, 3, 1, false, 1, 2) { else axes.push_back(inRank-1); // default dimension to reduce along is last dimension - const int numOfAxes = axes.size(); + const uint numOfAxes = axes.size(); REQUIRE_TRUE(numOfAxes <= inRank, 0, "BATCHNORM op: too big number of input axes to normalize over, expected number should be less or equal to rank of input array, but got %i and %i correspondingly !", numOfAxes, inRank); // evaluate expected shape for mean, variance and gamma. These 3 arrays should have identical shapes @@ -83,14 +83,14 @@ CUSTOM_OP_IMPL(batchnorm, 3, 1, false, 1, 2) { REQUIRE_TRUE(beta->isSameShape(expShape), 0, "BATCHNORM op: wrong shape of beta array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expShape).c_str(), ShapeUtils::shapeAsString(beta).c_str()); // types of all input arrays should be the same - for(int i = 1; i < block.width(); ++i) + for(unsigned long i = 1; i < block.width(); ++i) REQUIRE_TRUE(INPUT_VARIABLE(0)->dataType() == INPUT_VARIABLE(i)->dataType(), 0, "BATCHNORM op: types of all input arrays should be the same !"); nd4j_debug("MKL-DNN is not used for batchnorm!\n", 0); // formula: output = gamma * ((input - mean) / sqrt(variance + epsilon)) + beta // auto v = input->varianceAlongDimension(variance::SummaryStatsVariance, false, ShapeUtils::evalDimsToExclude(input->rankOf(), axes)); - // auto m = input->reduceAlongDimension(nd4j::reduce::Mean, ShapeUtils::evalDimsToExclude(input->rankOf(), axes)); + // auto m = input->reduceAlongDimension(sd::reduce::Mean, ShapeUtils::evalDimsToExclude(input->rankOf(), axes)); helpers::batchnorm(input, mean, variance, gamma, beta, output, axes, epsilon); @@ -101,11 +101,11 @@ CUSTOM_OP_IMPL(batchnorm, 3, 1, false, 1, 2) { // stdInv *= *gamma; // // empty array with same shape as input - // input->applyBroadcast(nd4j::broadcast::Subtract, axes, m, output); - // output->applyBroadcast(nd4j::broadcast::Multiply, axes, &stdInv); + // input->applyBroadcast(sd::broadcast::Subtract, axes, m, output); + // output->applyBroadcast(sd::broadcast::Multiply, axes, &stdInv); // if(applyOffset) - // output->applyBroadcast(nd4j::broadcast::Add, axes, beta); + // output->applyBroadcast(sd::broadcast::Add, axes, beta); // delete v; // delete m; @@ -167,7 +167,7 @@ CUSTOM_OP_IMPL(batchnorm_bp, 4, 3, false, 1, 2) { else axes.push_back(inRank-1); // default dimension to reduce along is last dimension - const int numOfAxes = axes.size(); + const uint numOfAxes = axes.size(); REQUIRE_TRUE(numOfAxes <= inRank, 0, "BATCHNORM_BP op: too big number of input axes to normalize over, expected number should be less or equal to rank of input array, but got %i and %i correspondingly !", numOfAxes, inRank); // evaluate expected shape for mean, variance and gamma. These 3 arrays should have identical shapes @@ -191,7 +191,7 @@ CUSTOM_OP_IMPL(batchnorm_bp, 4, 3, false, 1, 2) { REQUIRE_TRUE(input->isSameShape(dLdO), 0, "BATCHNORM_BP op: wrong shape of output gradients array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(input).c_str(), ShapeUtils::shapeAsString(dLdO).c_str()); // types of all input arrays should be the same (except dLdO) - for(int i = 1; i < block.width() - 2; ++i) + for(unsigned long i = 1; i < block.width() - 2; ++i) REQUIRE_TRUE(INPUT_VARIABLE(0)->dataType() == INPUT_VARIABLE(i)->dataType(), 0, "BATCHNORM_BP op: types of arrays (input, mean, variance, gamma, beta) should be the same !"); // ***** calculations ***** // @@ -218,7 +218,7 @@ CUSTOM_OP_IMPL(batchnorm_bp, 4, 3, false, 1, 2) { // dLdB = g_sum // variance = input->varianceAlongDimension(variance::SummaryStatsVariance, false, ShapeUtils::evalDimsToExclude(input->rankOf(), axes)); - // mean = input->reduceAlongDimension(nd4j::reduce::Mean, ShapeUtils::evalDimsToExclude(input->rankOf(), axes)); + // mean = input->reduceAlongDimension(sd::reduce::Mean, ShapeUtils::evalDimsToExclude(input->rankOf(), axes)); const auto excludedAxes = ShapeUtils::evalDimsToExclude(inRank, axes); const bool keepUnitiesInShape = inRank == mean->rankOf(); @@ -228,7 +228,7 @@ CUSTOM_OP_IMPL(batchnorm_bp, 4, 3, false, 1, 2) { // input - mean NDArray xMinusMean(input); // empty array with same shape as input - input->applyBroadcast(nd4j::broadcast::Subtract, axes, *mean, xMinusMean); + input->applyBroadcast(sd::broadcast::Subtract, axes, *mean, xMinusMean); // stdInv NDArray stdInv = *variance + epsilon; @@ -236,11 +236,11 @@ CUSTOM_OP_IMPL(batchnorm_bp, 4, 3, false, 1, 2) { stdInv.applyTransform(transform::Sqrt, stdInv); // 1 / (variance + epsilon)^0.5 // dvdm (use dLdM as storage for dvdm) - xMinusMean.reduceAlongDimension(nd4j::reduce::Sum, *dLdM, excludedAxes, keepUnitiesInShape); + xMinusMean.reduceAlongDimension(sd::reduce::Sum, *dLdM, excludedAxes, keepUnitiesInShape); *dLdM *= -Ninv; // g_sum - auto gSum = dLdO->reduceAlongDimension(nd4j::reduce::Sum, excludedAxes, keepUnitiesInShape); + auto gSum = dLdO->reduceAlongDimension(sd::reduce::Sum, excludedAxes, keepUnitiesInShape); // dLdB if(applyOffset) @@ -248,11 +248,11 @@ CUSTOM_OP_IMPL(batchnorm_bp, 4, 3, false, 1, 2) { // stdInv * (g - g_sum/N) (use dLdI as storage for this expression) gSum *= Ninv; - dLdO->applyBroadcast(nd4j::broadcast::Subtract, axes, gSum, *dLdI); - dLdI->applyBroadcast(nd4j::broadcast::Multiply, axes, stdInv, *dLdI); + dLdO->applyBroadcast(sd::broadcast::Subtract, axes, gSum, *dLdI); + dLdI->applyBroadcast(sd::broadcast::Multiply, axes, stdInv, *dLdI); // dLdV <- [g*(x - m)]_sum - (xMinusMean * *dLdO).reduceAlongDimension(nd4j::reduce::Sum, *dLdV, excludedAxes, keepUnitiesInShape); + (xMinusMean * *dLdO).reduceAlongDimension(sd::reduce::Sum, *dLdV, excludedAxes, keepUnitiesInShape); // dLdG *dLdV *= stdInv; @@ -264,13 +264,13 @@ CUSTOM_OP_IMPL(batchnorm_bp, 4, 3, false, 1, 2) { *dLdV *= -Ninv; // -0.5f * (2 / N); // dfdv * (dvdm + (x - m)) (use xMinusMean as storage for this expression) - xMinusMean.applyBroadcast(nd4j::broadcast::Add, axes, *dLdM, xMinusMean); - xMinusMean.applyBroadcast(nd4j::broadcast::Multiply, axes, *dLdV, xMinusMean); + xMinusMean.applyBroadcast(sd::broadcast::Add, axes, *dLdM, xMinusMean); + xMinusMean.applyBroadcast(sd::broadcast::Multiply, axes, *dLdV, xMinusMean); // dLdI *dLdI += xMinusMean; if(applyScale) - dLdI->applyBroadcast(nd4j::broadcast::Multiply, axes, *gamma, *dLdI); + dLdI->applyBroadcast(sd::broadcast::Multiply, axes, *gamma, *dLdI); *dLdM = 0; // put zeros so far *dLdV = 0; // put zeros so far @@ -280,11 +280,11 @@ CUSTOM_OP_IMPL(batchnorm_bp, 4, 3, false, 1, 2) { // std.applyTransform(transform::Reciprocal); // 1 / (variance + epsilon) // std.applyTransform(transform::Sqrt); // 1 / (variance + epsilon)^0.5 // NDArray xMu(input); - // input->applyBroadcast(nd4j::broadcast::Subtract, axes, mean, &xMu); + // input->applyBroadcast(sd::broadcast::Subtract, axes, mean, &xMu); // NDArray xHat(input); - // xMu.applyBroadcast(nd4j::broadcast::Multiply, axes, &std, &xHat); + // xMu.applyBroadcast(sd::broadcast::Multiply, axes, &std, &xHat); // NDArray dxhat(input); - // dLdO->applyBroadcast(nd4j::broadcast::Multiply, axes, gamma, &dxhat); + // dLdO->applyBroadcast(sd::broadcast::Multiply, axes, gamma, &dxhat); // NDArray temp = dxhat*xMu; // temp.reduceAlongDimension(reduce::Sum, dLdV, excludedAxes, keepUnitiesInShape); // *dLdV *= -0.5f * std*std*std; @@ -295,10 +295,10 @@ CUSTOM_OP_IMPL(batchnorm_bp, 4, 3, false, 1, 2) { // NDArray dLdmu = *dxmu1 + *dxmu2; // dLdmu *= (1.f /N); // *dLdV *= (2.f/N); - // dxhat.applyBroadcast(nd4j::broadcast::Multiply, axes, &std); - // xMu.applyBroadcast(nd4j::broadcast::Multiply, axes, dLdV); + // dxhat.applyBroadcast(sd::broadcast::Multiply, axes, &std); + // xMu.applyBroadcast(sd::broadcast::Multiply, axes, dLdV); // dxhat += xMu; - // dxhat.applyBroadcast(nd4j::broadcast::Add, axes, &dLdmu, dLdI); + // dxhat.applyBroadcast(sd::broadcast::Add, axes, &dLdmu, dLdI); // delete dxmu1; // delete dxmu2; // xHat *= *dLdO; @@ -309,12 +309,12 @@ CUSTOM_OP_IMPL(batchnorm_bp, 4, 3, false, 1, 2) { DECLARE_TYPES(batchnorm_bp) { getOpDescriptor() - ->setAllowedInputTypes(0, nd4j::DataType::ANY) - ->setAllowedInputTypes(1, nd4j::DataType::ANY) - ->setAllowedInputTypes(2, nd4j::DataType::ANY) + ->setAllowedInputTypes(0, sd::DataType::ANY) + ->setAllowedInputTypes(1, sd::DataType::ANY) + ->setAllowedInputTypes(2, sd::DataType::ANY) ->setAllowedInputTypes(3, {ALL_FLOATS}) - ->setAllowedInputTypes(4, nd4j::DataType::ANY) - ->setAllowedInputTypes(5, nd4j::DataType::ANY) + ->setAllowedInputTypes(4, sd::DataType::ANY) + ->setAllowedInputTypes(5, sd::DataType::ANY) ->setAllowedOutputTypes({ALL_FLOATS}); } diff --git a/libnd4j/include/ops/declarable/generic/nn/convo/col2im.cpp b/libnd4j/include/ops/declarable/generic/nn/convo/col2im.cpp index d09ccdb68..b68c4c211 100644 --- a/libnd4j/include/ops/declarable/generic/nn/convo/col2im.cpp +++ b/libnd4j/include/ops/declarable/generic/nn/convo/col2im.cpp @@ -18,13 +18,13 @@ // Created by raver119 on 17.10.2017. // -#include +#include #if NOT_EXCLUDED(OP_col2im) #include #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(col2im, 1, 1, false, 0, 9) { auto x = INPUT_VARIABLE(0); diff --git a/libnd4j/include/ops/declarable/generic/nn/convo/conv1d.cpp b/libnd4j/include/ops/declarable/generic/nn/convo/conv1d.cpp index c5e26c73e..bb32b1780 100644 --- a/libnd4j/include/ops/declarable/generic/nn/convo/conv1d.cpp +++ b/libnd4j/include/ops/declarable/generic/nn/convo/conv1d.cpp @@ -19,14 +19,14 @@ // @author Yurii Shyrma -#include +#include #if NOT_EXCLUDED(OP_conv1d) #include #include #include -namespace nd4j { +namespace sd { namespace ops { @@ -63,8 +63,8 @@ CUSTOM_OP_IMPL(conv1d, 2, 1, false, 0, 5) { int iC = input->sizeAt(indIOioC); // input channels int oC = weights->sizeAt(indWoC); // output channels - std::string expectedWeightsShape = ShapeUtils::shapeAsString({kW, iC, oC}); - REQUIRE_TRUE(expectedWeightsShape == ShapeUtils::shapeAsString(weights), 0, "CUSTOM CONV1D OP: wrong shape of weights array, expected is %s, but got %s instead !", expectedWeightsShape.c_str(), ShapeUtils::shapeAsString(weights).c_str()); + std::vector expectedWeightsShape = {kW, iC, oC}; + REQUIRE_TRUE(weights->isSameShape(expectedWeightsShape), 0, "CUSTOM CONV1D OP: wrong shape of weights array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedWeightsShape).c_str(), ShapeUtils::shapeAsString(weights).c_str()); if (bias) REQUIRE_TRUE(bias->rankOf() <= 2 && oC == bias->lengthOf(), 0, "CUSTOM CONV1D OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got %i, %i instead !", oC, bias->rankOf(), bias->lengthOf()); @@ -82,7 +82,7 @@ CUSTOM_OP_IMPL(conv1d, 2, 1, false, 0, 5) { auto outputReshaped = output ->reshape(output->ordering(), reshapeForOutput, false); auto weightsReshaped = weights->reshape(weights->ordering(), {1, weights->sizeAt(0), weights->sizeAt(1), weights->sizeAt(2)}); // [kW, iC, oC] -> [1, kW, iC, oC] - nd4j::ops::conv2d conv2d; + sd::ops::conv2d conv2d; const Nd4jStatus status = conv2d.execute({&inputReshaped, &weightsReshaped, bias}, {&outputReshaped}, {}, {1,kW, 1,sW, 0,pW, 1,dW, paddingMode, !isNCW}, {}); if (status != ND4J_STATUS_OK) return status; @@ -123,8 +123,8 @@ DECLARE_SHAPE_FN(conv1d) { int iC = inputShapeInfo[indIOioC+1]; // input channels int oC = weightsShapeInfo[indWoC+1]; // output channels - std::string expectedWeightsShape = ShapeUtils::shapeAsString({kW, iC, oC}); - REQUIRE_TRUE(expectedWeightsShape == ShapeUtils::shapeAsString(weightsShapeInfo), 0, "CUSTOM CONV1D OP: wrong shape of weights array, expected is %s, but got %s instead !", expectedWeightsShape.c_str(), ShapeUtils::shapeAsString(weightsShapeInfo).c_str()); + std::vector expectedWeightsShape = {kW, iC, oC}; + REQUIRE_TRUE(ShapeUtils::areShapesEqual(weightsShapeInfo, expectedWeightsShape), 0, "CUSTOM CONV1D OP: wrong shape of weights array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedWeightsShape).c_str(), ShapeUtils::shapeAsString(weightsShapeInfo).c_str()); if (biasShapeInfo) REQUIRE_TRUE(biasShapeInfo[0] <= 2 && oC == shape::length(biasShapeInfo), 0, "CUSTOM CONV1D OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got %i, %i instead !", oC, biasShapeInfo[0], shape::length(biasShapeInfo)); @@ -198,10 +198,10 @@ CUSTOM_OP_IMPL(conv1d_bp, 3, 2, false, 0, 5) { int trueoH, trueoW; // true output height, width ConvolutionUtils::calcOutSizePool2D(trueoH,trueoW, 1,kW, 1,sW, 0,pW, 1,dW, 1,iW, paddingMode); - std::string expectedGradOShape = ShapeUtils::shapeAsString(ShapeUtils::composeShapeUsingDimsAndIdx({bS,oC,trueoW, 0,indIOioC,indIiW})); - std::string expectedWeightsShape = ShapeUtils::shapeAsString({kW, iC, oC}); - REQUIRE_TRUE(expectedGradOShape == ShapeUtils::shapeAsString(gradO), 0, "CUSTOM CONV1D_BP OP: wrong shape of output gradients (next epsilon) array, expected is %s, but got %s instead !", expectedGradOShape.c_str(), ShapeUtils::shapeAsString(gradO).c_str()); - REQUIRE_TRUE(expectedWeightsShape == ShapeUtils::shapeAsString(weights), 0, "CUSTOM CONV1D_BP OP: wrong shape of weights array, expected is %s, but got %s instead !", expectedWeightsShape.c_str(), ShapeUtils::shapeAsString(weights).c_str()); + std::vector expectedGradOShape = ShapeUtils::composeShapeUsingDimsAndIdx({bS,oC,trueoW, 0,indIOioC,indIiW}); + std::vector expectedWeightsShape = {kW, iC, oC}; + REQUIRE_TRUE(gradO->isSameShape(expectedGradOShape), 0, "CUSTOM CONV1D_BP OP: wrong shape of output gradients (next epsilon) array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedGradOShape).c_str(), ShapeUtils::shapeAsString(gradO).c_str()); + REQUIRE_TRUE(weights->isSameShape(expectedWeightsShape), 0, "CUSTOM CONV1D_BP OP: wrong shape of weights array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedWeightsShape).c_str(), ShapeUtils::shapeAsString(weights).c_str()); if(bias) REQUIRE_TRUE(bias->rankOf() <= 2 && oC == bias->lengthOf(), 0, "CUSTOM CONV1D_BP OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got %i, %i instead !", oC, bias->rankOf(), bias->lengthOf()); @@ -221,7 +221,7 @@ CUSTOM_OP_IMPL(conv1d_bp, 3, 2, false, 0, 5) { auto weightsReshaped = weights->reshape(weights->ordering(),{1, weights->sizeAt(0), weights->sizeAt(1), weights->sizeAt(2)}); // [kW, iC, oC] -> [1, kW, iC, oC] auto gradWReshaped = gradW ->reshape(gradW->ordering(), {1, weights->sizeAt(0), weights->sizeAt(1), weights->sizeAt(2)}, false);// [kW, iC, oC] -> [1, kW, iC, oC] - nd4j::ops::conv2d_bp conv2dBP; + sd::ops::conv2d_bp conv2dBP; auto status = conv2dBP.execute({&inputReshaped, &weightsReshaped, bias, &gradOReshaped}, {&gradIReshaped, &gradWReshaped, gradB}, {}, {1,kW, 1,sW, 0,pW, 1,dW, paddingMode, !isNCW}, {}); if (status != ND4J_STATUS_OK) return status; @@ -267,10 +267,10 @@ DECLARE_SHAPE_FN(conv1d_bp) { int trueoH, trueoW; // true output height, width ConvolutionUtils::calcOutSizePool2D(trueoH,trueoW, 1,kW, 1,sW, 0,pW, 1,dW, 1,iW, paddingMode); - std::string expectedGradOShape = ShapeUtils::shapeAsString(ShapeUtils::composeShapeUsingDimsAndIdx({bS,oC,trueoW, 0,indIOioC,indIiW})); - std::string expectedWeightsShape = ShapeUtils::shapeAsString({kW, iC, oC}); - REQUIRE_TRUE(expectedGradOShape == ShapeUtils::shapeAsString(gradOShapeInfo), 0, "CUSTOM CONV1D_BP OP: wrong shape of output gradients (next epsilon) array, expected is %s, but got %s instead !", expectedGradOShape.c_str(), ShapeUtils::shapeAsString(gradOShapeInfo).c_str()); - REQUIRE_TRUE(expectedWeightsShape == ShapeUtils::shapeAsString(weightsShapeInfo), 0, "CUSTOM CONV1D_BP OP: wrong shape of weights array, expected is %s, but got %s instead !", expectedWeightsShape.c_str(), ShapeUtils::shapeAsString(weightsShapeInfo).c_str()); + std::vector expectedGradOShape = ShapeUtils::composeShapeUsingDimsAndIdx({bS,oC,trueoW, 0,indIOioC,indIiW}); + std::vector expectedWeightsShape = {kW, iC, oC}; + REQUIRE_TRUE(ShapeUtils::areShapesEqual(gradOShapeInfo, expectedGradOShape), 0, "CUSTOM CONV1D_BP OP: wrong shape of output gradients (next epsilon) array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedGradOShape).c_str(), ShapeUtils::shapeAsString(gradOShapeInfo).c_str()); + REQUIRE_TRUE(ShapeUtils::areShapesEqual(weightsShapeInfo, expectedWeightsShape), 0, "CUSTOM CONV1D_BP OP: wrong shape of weights array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedWeightsShape).c_str(), ShapeUtils::shapeAsString(weightsShapeInfo).c_str()); if(biasShapeInfo) REQUIRE_TRUE(biasShapeInfo[0] <= 2 && oC == shape::length(biasShapeInfo), 0, "CUSTOM CONV1D_BP OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got %i, %i instead !", oC, biasShapeInfo[0], shape::length(biasShapeInfo)); diff --git a/libnd4j/include/ops/declarable/generic/nn/convo/conv2d.cpp b/libnd4j/include/ops/declarable/generic/nn/convo/conv2d.cpp index 26d03358a..29b6777ec 100644 --- a/libnd4j/include/ops/declarable/generic/nn/convo/conv2d.cpp +++ b/libnd4j/include/ops/declarable/generic/nn/convo/conv2d.cpp @@ -21,16 +21,16 @@ #ifndef LIBND4J_CONVO_OPS_H #define LIBND4J_CONVO_OPS_H -#include +#include #if NOT_EXCLUDED(OP_conv2d) -#include +#include #include #include #include -#include +#include -namespace nd4j { +namespace sd { namespace ops { @@ -58,8 +58,8 @@ CUSTOM_OP_IMPL(conv2d, 2, 1, false, 0, 9) { int indIOioC, indIiH, indWoC, indWiC, indWkH, indOoH; // corresponding indexes ConvolutionUtils::getSizesAndIndexesConv2d(isNCHW, *input, *output, bS, iC, iH, iW, oC, oH, oW, indIOioC, indIiH, indWiC, indWoC, indWkH, indOoH); - std::string expectedWeightsShape = ShapeUtils::shapeAsString({kH, kW, iC, oC}); - REQUIRE_TRUE(expectedWeightsShape == ShapeUtils::shapeAsString(weights), 0, "CUSTOM CONV2D OP: wrong shape of weights array, expected is %s, but got %s instead !", expectedWeightsShape.c_str(), ShapeUtils::shapeAsString(weights).c_str()); + std::vector expectedWeightsShape = {kH, kW, iC, oC}; + REQUIRE_TRUE(weights->isSameShape(expectedWeightsShape), 0, "CUSTOM CONV2D OP: wrong shape of weights array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedWeightsShape).c_str(), ShapeUtils::shapeAsString(weights).c_str()); if (bias) REQUIRE_TRUE(bias->rankOf() <= 2 && oC == bias->lengthOf(), 0, "CUSTOM CONV2D OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got %i, %i instead !", oC, bias->rankOf(), bias->lengthOf()); @@ -109,8 +109,8 @@ DECLARE_SHAPE_FN(conv2d) { const int iC = inputShapeInfo[indIOioC+1]; // input channels const int oC = weightsShapeInfo[indWoC+1]; // output channels - std::string expectedWeightsShape = ShapeUtils::shapeAsString({kH, kW, iC, oC}); - REQUIRE_TRUE(expectedWeightsShape == ShapeUtils::shapeAsString(weightsShapeInfo), 0, "CUSTOM CONV2D OP: wrong shape of weights array, expected is %s, but got %s instead !", expectedWeightsShape.c_str(), ShapeUtils::shapeAsString(weightsShapeInfo).c_str()); + std::vector expectedWeightsShape = {kH, kW, iC, oC}; + REQUIRE_TRUE(ShapeUtils::areShapesEqual(weightsShapeInfo, expectedWeightsShape), 0, "CUSTOM CONV2D OP: wrong shape of weights array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedWeightsShape).c_str(), ShapeUtils::shapeAsString(weightsShapeInfo).c_str()); if (biasShapeInfo) REQUIRE_TRUE(biasShapeInfo[0] <= 2 && oC == shape::length(biasShapeInfo), 0, "CUSTOM CONV2D OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got %i, %i instead !", oC, biasShapeInfo[0], shape::length(biasShapeInfo)); @@ -140,7 +140,7 @@ DECLARE_SHAPE_FN(conv2d) { DECLARE_TYPES(conv2d) { getOpDescriptor() - ->setAllowedInputTypes(0, nd4j::DataType::ANY) + ->setAllowedInputTypes(0, sd::DataType::ANY) ->setAllowedInputTypes(1, {ALL_FLOATS}) ->setAllowedInputTypes(2, {ALL_FLOATS}) ->setAllowedOutputTypes({ALL_FLOATS}); @@ -148,7 +148,7 @@ DECLARE_SHAPE_FN(conv2d) { DECLARE_TYPES(conv2d_bp) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_FLOATS}); } @@ -187,10 +187,10 @@ CUSTOM_OP_IMPL(conv2d_bp, 3, 2, false, 0, 9) { int trueoH, trueoW; // true output height, width ConvolutionUtils::calcOutSizePool2D(trueoH, trueoW, kH, kW, sH, sW, pH, pW, dH, dW, iH, iW, isSameMode); - std::string expectedGradOShape = ShapeUtils::shapeAsString(ShapeUtils::composeShapeUsingDimsAndIdx({bS,oC,trueoH,trueoW, 0,indIOioC,indOoH,indOoH+1})); - std::string expectedWeightsShape = ShapeUtils::shapeAsString({kH, kW, iC, oC}); - REQUIRE_TRUE(expectedGradOShape == ShapeUtils::shapeAsString(gradO), 0, "CUSTOM CONV2D_BP OP: wrong shape of output gradients (next epsilon) array, expected is %s, but got %s instead !", expectedGradOShape.c_str(), ShapeUtils::shapeAsString(gradO).c_str()); - REQUIRE_TRUE(expectedWeightsShape == ShapeUtils::shapeAsString(weights), 0, "CUSTOM CONV2D_BP OP: wrong shape of weights array, expected is %s, but got %s instead !", expectedWeightsShape.c_str(), ShapeUtils::shapeAsString(weights).c_str()); + std::vectorexpectedGradOShape = ShapeUtils::composeShapeUsingDimsAndIdx({bS,oC,trueoH,trueoW, 0,indIOioC,indOoH,indOoH+1}); + std::vectorexpectedWeightsShape = {kH, kW, iC, oC}; + REQUIRE_TRUE(gradO->isSameShape(expectedGradOShape), 0, "CUSTOM CONV2D_BP OP: wrong shape of output gradients (next epsilon) array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedGradOShape).c_str(), ShapeUtils::shapeAsString(gradO).c_str()); + REQUIRE_TRUE(weights->isSameShape(expectedWeightsShape), 0, "CUSTOM CONV2D_BP OP: wrong shape of weights array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedWeightsShape).c_str(), ShapeUtils::shapeAsString(weights).c_str()); if(bias) REQUIRE_TRUE(bias->rankOf() <= 2 && oC == bias->lengthOf(), 0, "CUSTOM CONV2D_BP OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got %i, %i instead !", oC, bias->rankOf(), bias->lengthOf()); @@ -242,10 +242,10 @@ DECLARE_SHAPE_FN(conv2d_bp) { int trueoH, trueoW; // true output height, width ConvolutionUtils::calcOutSizePool2D(trueoH, trueoW, kH, kW, sH, sW, pH, pW, dH, dW, iH, iW, isSameMode); - std::string expectedGradOShape = ShapeUtils::shapeAsString(ShapeUtils::composeShapeUsingDimsAndIdx({bS,oC,trueoH,trueoW, 0,indIOioC,indOoH,indOoH+1})); - std::string expectedWeightsShape = ShapeUtils::shapeAsString({kH, kW, iC, oC}); - REQUIRE_TRUE(expectedGradOShape == ShapeUtils::shapeAsString(gradOShapeInfo), 0, "CUSTOM CONV2D_BP OP: wrong shape of output gradients (next epsilon) array, expected is %s, but got %s instead !", expectedGradOShape.c_str(), ShapeUtils::shapeAsString(gradOShapeInfo).c_str()); - REQUIRE_TRUE(expectedWeightsShape == ShapeUtils::shapeAsString(weightsShapeInfo), 0, "CUSTOM CONV2D_BP OP: wrong shape of weights array, expected is %s, but got %s instead !", expectedWeightsShape.c_str(), ShapeUtils::shapeAsString(weightsShapeInfo).c_str()); + std::vector expectedGradOShape = ShapeUtils::composeShapeUsingDimsAndIdx({bS,oC,trueoH,trueoW, 0,indIOioC,indOoH,indOoH+1}); + std::vector expectedWeightsShape = {kH, kW, iC, oC}; + REQUIRE_TRUE(ShapeUtils::areShapesEqual(gradOShapeInfo, expectedGradOShape), 0, "CUSTOM CONV2D_BP OP: wrong shape of output gradients (next epsilon) array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedGradOShape).c_str(), ShapeUtils::shapeAsString(gradOShapeInfo).c_str()); + REQUIRE_TRUE(ShapeUtils::areShapesEqual(weightsShapeInfo, expectedWeightsShape), 0, "CUSTOM CONV2D_BP OP: wrong shape of weights array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedWeightsShape).c_str(), ShapeUtils::shapeAsString(weightsShapeInfo).c_str()); if(biasShapeInfo) REQUIRE_TRUE(biasShapeInfo[0] <= 2 && oC == shape::length(biasShapeInfo), 0, "CUSTOM CONV2D_BP OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got %i, %i instead !", oC, biasShapeInfo[0], shape::length(biasShapeInfo)); @@ -300,10 +300,10 @@ CUSTOM_OP_IMPL(conv2d_input_bp, 3, 1, false, 0, 9) { int trueoH, trueoW; // true output height, width ConvolutionUtils::calcOutSizePool2D(trueoH, trueoW, kH, kW, sH, sW, pH, pW, dH, dW, iH, iW, isSameMode); - std::string expectedGradOShape = ShapeUtils::shapeAsString(ShapeUtils::composeShapeUsingDimsAndIdx({bS,oC,trueoH,trueoW, 0,indIOioC,indOoH,indOoH+1})); - std::string expectedWeightsShape = ShapeUtils::shapeAsString({kH, kW, iC, oC}); - REQUIRE_TRUE(expectedGradOShape == ShapeUtils::shapeAsString(gradO), 0, "CUSTOM CONV2D_INPUT_BP OP: wrong shape of output gradients (next epsilon) array, expected is %s, but got %s instead !", expectedGradOShape.c_str(), ShapeUtils::shapeAsString(gradO).c_str()); - REQUIRE_TRUE(expectedWeightsShape == ShapeUtils::shapeAsString(weights), 0, "CUSTOM CONV2D_INPUT_BP OP: wrong shape of weights array, expected is %s, but got %s instead !", expectedWeightsShape.c_str(), ShapeUtils::shapeAsString(weights).c_str()); + std::vector expectedGradOShape = ShapeUtils::composeShapeUsingDimsAndIdx({bS,oC,trueoH,trueoW, 0,indIOioC,indOoH,indOoH+1}); + std::vector expectedWeightsShape = {kH, kW, iC, oC}; + REQUIRE_TRUE(gradO->isSameShape(expectedGradOShape), 0, "CUSTOM CONV2D_INPUT_BP OP: wrong shape of output gradients (next epsilon) array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedGradOShape).c_str(), ShapeUtils::shapeAsString(gradO).c_str()); + REQUIRE_TRUE(weights->isSameShape(expectedWeightsShape), 0, "CUSTOM CONV2D_INPUT_BP OP: wrong shape of weights array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedWeightsShape).c_str(), ShapeUtils::shapeAsString(weights).c_str()); ConvolutionUtils::conv2dBP(block, &input, weights, nullptr, gradO, gradI, nullptr, nullptr, kH,kW,sH,sW,pH,pW,dH,dW,isSameMode,isNCHW); @@ -312,7 +312,7 @@ CUSTOM_OP_IMPL(conv2d_input_bp, 3, 1, false, 0, 9) { DECLARE_TYPES(conv2d_input_bp) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_FLOATS}); } @@ -360,10 +360,10 @@ DECLARE_SHAPE_FN(conv2d_input_bp) { int trueoH, trueoW; // true output height, width ConvolutionUtils::calcOutSizePool2D(trueoH, trueoW, kH, kW, sH, sW, pH, pW, dH, dW, iH, iW, isSameMode); - std::string expectedGradOShape = ShapeUtils::shapeAsString(ShapeUtils::composeShapeUsingDimsAndIdx({bS,oC,trueoH,trueoW, 0,indIOioC,indOoH,indOoH+1})); - std::string expectedWeightsShape = ShapeUtils::shapeAsString({kH, kW, iC, oC}); - REQUIRE_TRUE(expectedGradOShape == ShapeUtils::shapeAsString(gradOShapeInfo), 0, "CUSTOM CONV2D_INPUT_BP OP: wrong shape of output gradients (next epsilon) array, expected is %s, but got %s instead !", expectedGradOShape.c_str(), ShapeUtils::shapeAsString(gradOShapeInfo).c_str()); - REQUIRE_TRUE(expectedWeightsShape == ShapeUtils::shapeAsString(weightsShapeInfo), 0, "CUSTOM CONV2D_INPUT_BP OP: wrong shape of weights array, expected is %s, but got %s instead !", expectedWeightsShape.c_str(), ShapeUtils::shapeAsString(weightsShapeInfo).c_str()); + std::vector expectedGradOShape = ShapeUtils::composeShapeUsingDimsAndIdx({bS,oC,trueoH,trueoW, 0,indIOioC,indOoH,indOoH+1}); + std::vector expectedWeightsShape = {kH, kW, iC, oC}; + REQUIRE_TRUE(ShapeUtils::areShapesEqual(gradOShapeInfo, expectedGradOShape), 0, "CUSTOM CONV2D_INPUT_BP OP: wrong shape of output gradients (next epsilon) array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedGradOShape).c_str(), ShapeUtils::shapeAsString(gradOShapeInfo).c_str()); + REQUIRE_TRUE(ShapeUtils::areShapesEqual(weightsShapeInfo, expectedWeightsShape), 0, "CUSTOM CONV2D_INPUT_BP OP: wrong shape of weights array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedWeightsShape).c_str(), ShapeUtils::shapeAsString(weightsShapeInfo).c_str()); Nd4jLong* gradIshapeInfo(nullptr); ALLOCATE(gradIshapeInfo, block.getWorkspace(), shape::shapeInfoLength(rank), Nd4jLong); diff --git a/libnd4j/include/ops/declarable/generic/nn/convo/conv3d.cpp b/libnd4j/include/ops/declarable/generic/nn/convo/conv3d.cpp index 7ce42756d..669af1940 100644 --- a/libnd4j/include/ops/declarable/generic/nn/convo/conv3d.cpp +++ b/libnd4j/include/ops/declarable/generic/nn/convo/conv3d.cpp @@ -19,15 +19,15 @@ // @author Yurii Shyrma, created on 05.02.2018 // -#include +#include #if NOT_EXCLUDED(OP_conv3dnew) #include #include #include -#include +#include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(conv3dnew, 2, 1, false, 0, 13) { @@ -59,8 +59,8 @@ CUSTOM_OP_IMPL(conv3dnew, 2, 1, false, 0, 13) { ConvolutionUtils::getSizesAndIndexesConv3d(isNCDHW, *input, *output, bS, iC, iD, iH, iW, oC, oD, oH, oW, indIOioC, indIOioD, indWiC, indWoC, indWkD); REQUIRE_TRUE(paddingMode < 2, 0, "CUSTOM CONV3D OP: causal padding mode (paddingMode = 2) is not allowed for this operation !"); - std::string expectedWeightsShape = ShapeUtils::shapeAsString({kD, kH, kW, iC, oC}); - REQUIRE_TRUE(expectedWeightsShape == ShapeUtils::shapeAsString(weights), 0, "CUSTOM CONV3D OP: wrong shape of weights array, expected is %s, but got %s instead !", expectedWeightsShape.c_str(), ShapeUtils::shapeAsString(weights).c_str()); + std::vector expectedWeightsShape = {kD, kH, kW, iC, oC}; + REQUIRE_TRUE(weights->isSameShape(expectedWeightsShape), 0, "CUSTOM CONV3D OP: wrong shape of weights array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedWeightsShape).c_str(), ShapeUtils::shapeAsString(weights).c_str()); if (bias) REQUIRE_TRUE(bias->rankOf() <= 2 && oC == bias->lengthOf(), 0, "CUSTOM CONV3D OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got %i, %i instead !", oC, bias->rankOf(), bias->lengthOf()); @@ -92,7 +92,7 @@ CUSTOM_OP_IMPL(conv3dnew, 2, 1, false, 0, 13) { DECLARE_TYPES(conv3dnew) { getOpDescriptor() - ->setAllowedInputTypes(0, nd4j::DataType::ANY) + ->setAllowedInputTypes(0, sd::DataType::ANY) ->setAllowedInputTypes(1, {ALL_FLOATS}) ->setAllowedInputTypes(2, {ALL_FLOATS}) ->setAllowedOutputTypes({ALL_FLOATS}); @@ -139,8 +139,8 @@ DECLARE_SHAPE_FN(conv3dnew) { int iC = inputShapeInfo[indIOioC+1]; // input channels int oC = weightsShapeInfo[indWoC+1]; // output channels - std::string expectedWeightsShape = ShapeUtils::shapeAsString({kD, kH, kW, iC, oC}); - REQUIRE_TRUE(expectedWeightsShape == ShapeUtils::shapeAsString(weightsShapeInfo), 0, "CUSTOM CONV3D OP: wrong shape of weights array, expected is %s, but got %s instead !", expectedWeightsShape.c_str(), ShapeUtils::shapeAsString(weightsShapeInfo).c_str()); + std::vector expectedWeightsShape = {kD, kH, kW, iC, oC}; + REQUIRE_TRUE(ShapeUtils::areShapesEqual(weightsShapeInfo, expectedWeightsShape), 0, "CUSTOM CONV3D OP: wrong shape of weights array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedWeightsShape).c_str(), ShapeUtils::shapeAsString(weightsShapeInfo).c_str()); if (biasShapeInfo) REQUIRE_TRUE(biasShapeInfo[0] <= 2 && oC == shape::length(biasShapeInfo), 0, "CUSTOM CONV3D OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got %i, %i instead !", oC, biasShapeInfo[0], shape::length(biasShapeInfo)); @@ -209,10 +209,10 @@ CUSTOM_OP_IMPL(conv3dnew_bp, 3, 2, false, 0, 13) { ConvolutionUtils::calcOutSizePool3D(trueoD, trueoH, trueoW, kD, kH, kW, sD, sH, sW, pD, pH, pW, dD, dH, dW, iD, iH, iW, paddingMode); REQUIRE_TRUE(paddingMode < 2, 0, "CUSTOM CONV3D_BP OP: causal padding mode (paddingMode = 2) is not allowed for this operation !"); - std::string expectedGradOShape = ShapeUtils::shapeAsString(ShapeUtils::composeShapeUsingDimsAndIdx({bS,oC,trueoD,trueoH,trueoW, 0,indIOioC,indIOioD,indIOioD+1,indIOioD+2})); - std::string expectedWeightsShape = ShapeUtils::shapeAsString({kD, kH, kW, iC, oC}); - REQUIRE_TRUE(expectedGradOShape == ShapeUtils::shapeAsString(gradO), 0, "CUSTOM CONV3D_BP OP: wrong shape of output gradients (next epsilon) array, expected is %s, but got %s instead !", expectedGradOShape.c_str(), ShapeUtils::shapeAsString(gradO).c_str()); - REQUIRE_TRUE(expectedWeightsShape == ShapeUtils::shapeAsString(weights), 0, "CUSTOM CONV3D_BP OP: wrong shape of weights array, expected is %s, but got %s instead !", expectedWeightsShape.c_str(), ShapeUtils::shapeAsString(weights).c_str()); + std::vector expectedGradOShape = ShapeUtils::composeShapeUsingDimsAndIdx({bS,oC,trueoD,trueoH,trueoW, 0,indIOioC,indIOioD,indIOioD+1,indIOioD+2}); + std::vector expectedWeightsShape = {kD, kH, kW, iC, oC}; + REQUIRE_TRUE(gradO->isSameShape(expectedGradOShape), 0, "CUSTOM CONV3D_BP OP: wrong shape of output gradients (next epsilon) array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedGradOShape).c_str(), ShapeUtils::shapeAsString(gradO).c_str()); + REQUIRE_TRUE(weights->isSameShape(expectedWeightsShape), 0, "CUSTOM CONV3D_BP OP: wrong shape of weights array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedWeightsShape).c_str(), ShapeUtils::shapeAsString(weights).c_str()); if(bias) REQUIRE_TRUE(bias->rankOf() <= 2 && oC == bias->lengthOf(), 0, "CUSTOM CONV3D_BP OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got %i, %i instead !", oC, bias->rankOf(), bias->lengthOf()); @@ -259,7 +259,7 @@ CUSTOM_OP_IMPL(conv3dnew_bp, 3, 2, false, 0, 13) { DECLARE_TYPES(conv3dnew_bp) { getOpDescriptor() - ->setAllowedInputTypes(0, nd4j::DataType::ANY) + ->setAllowedInputTypes(0, sd::DataType::ANY) ->setAllowedInputTypes(1, {ALL_FLOATS}) ->setAllowedInputTypes(2, {ALL_FLOATS}) ->setAllowedInputTypes(3, {ALL_FLOATS}) @@ -313,10 +313,10 @@ DECLARE_SHAPE_FN(conv3dnew_bp) { int trueoD, trueoH, trueoW; // true output depth/height/width ConvolutionUtils::calcOutSizePool3D(trueoD, trueoH, trueoW, kD, kH, kW, sD, sH, sW, pD, pH, pW, dD, dH, dW, iD, iH, iW, paddingMode); - std::string expectedGradOShape = ShapeUtils::shapeAsString(ShapeUtils::composeShapeUsingDimsAndIdx({bS,oC,trueoD,trueoH,trueoW, 0,indIOioC,indIiD,indIiD+1,indIiD+2})); - std::string expectedWeightsShape = ShapeUtils::shapeAsString({kD, kH, kW, iC, oC}); - REQUIRE_TRUE(expectedGradOShape == ShapeUtils::shapeAsString(gradOShapeInfo), 0, "CUSTOM CONV3D_BP OP: wrong shape of output gradients (next epsilon) array, expected is %s, but got %s instead !", expectedGradOShape.c_str(), ShapeUtils::shapeAsString(gradOShapeInfo).c_str()); - REQUIRE_TRUE(expectedWeightsShape == ShapeUtils::shapeAsString(weightsShapeInfo), 0, "CUSTOM CONV3D_BP OP: wrong shape of weights array, expected is %s, but got %s instead !", expectedWeightsShape.c_str(), ShapeUtils::shapeAsString(weightsShapeInfo).c_str()); + std::vector expectedGradOShape = ShapeUtils::composeShapeUsingDimsAndIdx({bS,oC,trueoD,trueoH,trueoW, 0,indIOioC,indIiD,indIiD+1,indIiD+2}); + std::vector expectedWeightsShape = {kD, kH, kW, iC, oC}; + REQUIRE_TRUE(ShapeUtils::areShapesEqual(gradOShapeInfo, expectedGradOShape), 0, "CUSTOM CONV3D_BP OP: wrong shape of output gradients (next epsilon) array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedGradOShape).c_str(), ShapeUtils::shapeAsString(gradOShapeInfo).c_str()); + REQUIRE_TRUE(ShapeUtils::areShapesEqual(weightsShapeInfo, expectedWeightsShape), 0, "CUSTOM CONV3D_BP OP: wrong shape of weights array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedWeightsShape).c_str(), ShapeUtils::shapeAsString(weightsShapeInfo).c_str()); if(biasShapeInfo) REQUIRE_TRUE(biasShapeInfo[0] <= 2 && oC == shape::length(biasShapeInfo), 0, "CUSTOM CONV3D_BP OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got %i, %i instead !", oC, biasShapeInfo[0], shape::length(biasShapeInfo)); diff --git a/libnd4j/include/ops/declarable/generic/nn/convo/deconv2d.cpp b/libnd4j/include/ops/declarable/generic/nn/convo/deconv2d.cpp index e3632f36a..9f94d1459 100644 --- a/libnd4j/include/ops/declarable/generic/nn/convo/deconv2d.cpp +++ b/libnd4j/include/ops/declarable/generic/nn/convo/deconv2d.cpp @@ -19,17 +19,17 @@ // @author Yurii Shyrma // -#include +#include #if NOT_EXCLUDED(OP_deconv2d) #include -#include -#include +#include +#include #include #include #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(deconv2d, 2, 1, false, 0, 9) { @@ -74,7 +74,7 @@ CUSTOM_OP_IMPL(deconv2d, 2, 1, false, 0, 9) { //----- calculation of output -----// // NHWC: [kH, kW, oC, iC] x [bS, iH, iW, iC] = [kH, kW, oC, bS, iH, iW] // NCHW: [kH, kW, oC, iC] x [bS, iC, iH, iW] = [kH, kW, oC, bS, iH, iW] - nd4j::MmulHelper::tensorDot(weights, input, &columns, {indWiC}, {indIOioC}, {2, 3, 1, 0, 4, 5}); + sd::MmulHelper::tensorDot(weights, input, &columns, {indWiC}, {indIOioC}, {2, 3, 1, 0, 4, 5}); LaunchContext* ctx = block.launchContext(); helpers::col2im(*ctx, columns, *output, sH, sW, pH, pW, oH, oW, dH, dW); // [bS, oC, kH, kW, iH, iW] is de-convoluted to [bS, oC, oH, oW] @@ -90,7 +90,7 @@ CUSTOM_OP_IMPL(deconv2d, 2, 1, false, 0, 9) { } DECLARE_TYPES(deconv2d) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_FLOATS}); } @@ -155,7 +155,7 @@ DECLARE_SHAPE_FN(deconv2d) { DECLARE_TYPES(deconv2d_bp) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_FLOATS}); } @@ -208,7 +208,7 @@ CUSTOM_OP_IMPL(deconv2d_bp, 3, 2, false, 0, 9) { // ----- calculation of gradI -> pass it through conv2d_ff ----- // - nd4j::ops::conv2d conv2d; + sd::ops::conv2d conv2d; const Nd4jStatus status = conv2d.execute({gradO, weights}, {gradI}, {}, {kH,kW, sH,sW, pH,pW, dH,dW, isSameMode, !isNCHW}, {}); if (status != ND4J_STATUS_OK) return status; diff --git a/libnd4j/include/ops/declarable/generic/nn/convo/deconv2d_tf.cpp b/libnd4j/include/ops/declarable/generic/nn/convo/deconv2d_tf.cpp index d5a61d397..70fc46e0c 100644 --- a/libnd4j/include/ops/declarable/generic/nn/convo/deconv2d_tf.cpp +++ b/libnd4j/include/ops/declarable/generic/nn/convo/deconv2d_tf.cpp @@ -19,13 +19,13 @@ // @author Yurii Shyrma // -#include +#include #if NOT_EXCLUDED(OP_deconv2d) #include #include -namespace nd4j { +namespace sd { namespace ops { ////////////////////////////////////////////////////////////////////////// @@ -77,7 +77,7 @@ CUSTOM_OP_IMPL(deconv2d_tf, 3, 1, false, 0, 9) { DECLARE_TYPES(deconv2d_tf) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_FLOATS}); } diff --git a/libnd4j/include/ops/declarable/generic/nn/convo/deconv3d.cpp b/libnd4j/include/ops/declarable/generic/nn/convo/deconv3d.cpp index 78d275c69..d4899fbab 100644 --- a/libnd4j/include/ops/declarable/generic/nn/convo/deconv3d.cpp +++ b/libnd4j/include/ops/declarable/generic/nn/convo/deconv3d.cpp @@ -18,15 +18,15 @@ // @author Yurii Shyrma (iuriish@yahoo.com), created on 05.09.2018 // -#include +#include #if NOT_EXCLUDED(OP_deconv3d) #include #include #include -#include +#include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(deconv3d, 2, 1, false, 0, 13) { @@ -75,7 +75,7 @@ CUSTOM_OP_IMPL(deconv3d, 2, 1, false, 0, 13) { //----- calculation of output -----// // NDHWC: [kD, kH, kW, oC, iC] x [bS, iD, iH, iW, iC] = [kD, kH, kW, oC, bS, iD, iH, iW] // NCDHW: [kD, kH, kW, oC, iC] x [bS, iC, iD, iH, iW] = [kD, kH, kW, oC, bS, iD, iH, iW] - nd4j::MmulHelper::tensorDot(weights, input, &columns, {indWiC}, {indIOioC}, {2, 3, 4, 1, 0, 5, 6, 7}); // [bS, oC, kD, kH, kW, iD, iH, iW] -> [kD, kH, kW, oC, bS, iD, iH, iW] + sd::MmulHelper::tensorDot(weights, input, &columns, {indWiC}, {indIOioC}, {2, 3, 4, 1, 0, 5, 6, 7}); // [bS, oC, kD, kH, kW, iD, iH, iW] -> [kD, kH, kW, oC, bS, iD, iH, iW] ConvolutionUtils::col2vol(block, columns, *output, sD, sH, sW, pD, pH, pW, dD, dH, dW); // [bS, oC, kD, kH, kW, iD, iH, iW] is de-convoluted to [bS, oC, oD, oH, oW] //----- add biases if required -----// @@ -92,7 +92,7 @@ CUSTOM_OP_IMPL(deconv3d, 2, 1, false, 0, 13) { DECLARE_TYPES(deconv3d) { getOpDescriptor() - ->setAllowedInputTypes(0, nd4j::DataType::ANY) + ->setAllowedInputTypes(0, sd::DataType::ANY) ->setAllowedInputTypes(1, {ALL_FLOATS}) ->setAllowedInputTypes(2, {ALL_FLOATS}) ->setAllowedOutputTypes({ALL_FLOATS}); @@ -220,7 +220,7 @@ CUSTOM_OP_IMPL(deconv3d_bp, 3, 2, false, 0, 13) { ConvolutionUtils::calcPadding3D(pD, pH, pW, iD, iH, iW, oD, oH, oW, kD, kH, kW, sD, sH, sW, dD, dH, dW); // ----- calculation of gradI -> pass it through conv3d_ff ----- // - nd4j::ops::conv3dnew conv3d; + sd::ops::conv3dnew conv3d; const Nd4jStatus status = conv3d.execute({gradO, weights}, {gradI}, {}, {kD,kH,kW, sD,sH,sW, pD,pH,pW, dD,dH,dW, isSameMode, !isNCDHW}, {}); if (status != ND4J_STATUS_OK) return status; @@ -257,7 +257,7 @@ CUSTOM_OP_IMPL(deconv3d_bp, 3, 2, false, 0, 13) { DECLARE_TYPES(deconv3d_bp) { getOpDescriptor() - ->setAllowedInputTypes(0, nd4j::DataType::ANY) + ->setAllowedInputTypes(0, sd::DataType::ANY) ->setAllowedInputTypes(1, {ALL_FLOATS}) ->setAllowedInputTypes(2, {ALL_FLOATS}) ->setAllowedInputTypes(3, {ALL_FLOATS}) diff --git a/libnd4j/include/ops/declarable/generic/nn/convo/depthwiseConv2d.cpp b/libnd4j/include/ops/declarable/generic/nn/convo/depthwiseConv2d.cpp index e18836688..c04bcf6dd 100644 --- a/libnd4j/include/ops/declarable/generic/nn/convo/depthwiseConv2d.cpp +++ b/libnd4j/include/ops/declarable/generic/nn/convo/depthwiseConv2d.cpp @@ -18,15 +18,15 @@ // @author Yurii Shyrma (iuriish@yahoo.com) // -#include +#include #if NOT_EXCLUDED(OP_depthwise_conv2d) -#include +#include #include -#include +#include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(depthwise_conv2d, 2, 1, false, 0, 9) { @@ -69,7 +69,7 @@ CUSTOM_OP_IMPL(depthwise_conv2d, 2, 1, false, 0, 9) { DECLARE_TYPES(depthwise_conv2d) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_FLOATS}); } DECLARE_SHAPE_FN(depthwise_conv2d) { @@ -140,7 +140,7 @@ DECLARE_SHAPE_FN(depthwise_conv2d) { DECLARE_TYPES(depthwise_conv2d_bp) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_FLOATS}); } diff --git a/libnd4j/include/ops/declarable/generic/nn/convo/dilation2d.cpp b/libnd4j/include/ops/declarable/generic/nn/convo/dilation2d.cpp index 6a85d2c7d..ea1193400 100644 --- a/libnd4j/include/ops/declarable/generic/nn/convo/dilation2d.cpp +++ b/libnd4j/include/ops/declarable/generic/nn/convo/dilation2d.cpp @@ -18,13 +18,13 @@ // @author raver119@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_dilation2d) #include #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(dilation2d, 2, 1, false, 0, 1) { auto input = INPUT_VARIABLE(0); @@ -83,7 +83,7 @@ namespace ops { DECLARE_TYPES(dilation2d) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_FLOATS}); } diff --git a/libnd4j/include/ops/declarable/generic/nn/convo/im2col.cpp b/libnd4j/include/ops/declarable/generic/nn/convo/im2col.cpp index e9084fb61..179dd3005 100644 --- a/libnd4j/include/ops/declarable/generic/nn/convo/im2col.cpp +++ b/libnd4j/include/ops/declarable/generic/nn/convo/im2col.cpp @@ -18,7 +18,7 @@ // Created by raver119 on 17.10.2017. // -#include +#include #if NOT_EXCLUDED(OP_im2col) #include @@ -26,7 +26,7 @@ #include #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(im2col, 1, 1, false, 0, 9) { auto x = INPUT_VARIABLE(0); @@ -51,7 +51,7 @@ namespace nd4j { // FIXME: zeropad value is void LaunchContext* ctx = block.launchContext(); - nd4j::ops::helpers::im2col(*ctx, *x, *z, kernelHeight, kernelWidth, strideY, strideX, padHeight, padWidth, dY, dX, NDArrayFactory::create(zeroPadVal, block.launchContext())); + sd::ops::helpers::im2col(*ctx, *x, *z, kernelHeight, kernelWidth, strideY, strideX, padHeight, padWidth, dY, dX, NDArrayFactory::create(zeroPadVal, block.launchContext())); STORE_RESULT(*z); diff --git a/libnd4j/include/ops/declarable/generic/nn/convo/ismax.cpp b/libnd4j/include/ops/declarable/generic/nn/convo/ismax.cpp index 13de73e81..d786504ad 100644 --- a/libnd4j/include/ops/declarable/generic/nn/convo/ismax.cpp +++ b/libnd4j/include/ops/declarable/generic/nn/convo/ismax.cpp @@ -19,13 +19,13 @@ // @author Yurii Shyrma (iuriish@yahoo.com) // -#include +#include #if NOT_EXCLUDED(OP_ismax) #include #include -namespace nd4j { +namespace sd { namespace ops { CONFIGURABLE_OP_IMPL(ismax, 1, 1, true, 0, -2) { diff --git a/libnd4j/include/ops/declarable/generic/nn/convo/pointwiseConv2d.cpp b/libnd4j/include/ops/declarable/generic/nn/convo/pointwiseConv2d.cpp index 69435ecb2..02d81493a 100644 --- a/libnd4j/include/ops/declarable/generic/nn/convo/pointwiseConv2d.cpp +++ b/libnd4j/include/ops/declarable/generic/nn/convo/pointwiseConv2d.cpp @@ -21,7 +21,7 @@ #include #include -namespace nd4j { +namespace sd { namespace ops { @@ -31,13 +31,13 @@ CUSTOM_OP_IMPL(pointwise_conv2d, 2, 1, false, 0, 0) { auto input = INPUT_VARIABLE(0); // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW) auto weights = INPUT_VARIABLE(1); // [1, 1, iC, oC] always auto bias = block.width() > 2 ? INPUT_VARIABLE(2) : nullptr; // [oC] - + auto output = OUTPUT_VARIABLE(0); // [bS, iH, iW, oC] (NHWC) or [bS, oC, iH, iW] (NCHW) REQUIRE_TRUE(input->rankOf() == 4, 0, "CUSTOM POINTWISECONV2D OP: rank of input array must be equal to 4, but got %i instead !", input->rankOf()); REQUIRE_TRUE(weights->rankOf() == 4, 0, "CUSTOM POINTWISECONV2D OP: rank of weights array must be equal to 4, but got %i instead !", weights->rankOf()); if(bias) - REQUIRE_TRUE(bias->rankOf() <= 2, 0, "CUSTOM POINTWISECONV2D OP: rank of biases array must be equal <= 2, but got %i instead !", bias->rankOf()); + REQUIRE_TRUE(bias->rankOf() <= 2, 0, "CUSTOM POINTWISECONV2D OP: rank of biases array must be equal <= 2, but got %i instead !", bias->rankOf()); int kH = 1; // filter(kernel) height int kW = 1; // filter(kernel) width @@ -46,18 +46,18 @@ CUSTOM_OP_IMPL(pointwise_conv2d, 2, 1, false, 0, 0) { int pH = 0; // paddings height int pW = 0; // paddings width int dH = 1; // dilations height - int dW = 1; // dilations width + int dW = 1; // dilations width int isNCHW = block.getIArguments()->size() > 0 ? !INT_ARG(0) : 1; // INT_ARG(0): 0-NCHW, 1-NHWC - + int bS, iC, iH, iW, oC, oH, oW; // batch size, input channels, input height/width, output channels, output height/width; int indIOioC, indIiH, indWoC, indWiC, indWkH, indOoH; // corresponding indexes ConvolutionUtils::getSizesAndIndexesConv2d(isNCHW, *input, *output, bS, iC, iH, iW, oC, oH, oW, indIOioC, indIiH, indWiC, indWoC, indWkH, indOoH); - std::string expectedWeightsShape = ShapeUtils::shapeAsString({1, 1, iC, oC}); - REQUIRE_TRUE(expectedWeightsShape == ShapeUtils::shapeAsString(weights), 0, "CUSTOM POINTWISECONV2D OP: wrong shape of weights array, expected is %s, but got %s instead !", expectedWeightsShape.c_str(), ShapeUtils::shapeAsString(weights).c_str()); - if (bias) - REQUIRE_TRUE(bias->rankOf() <= 2 && oC == bias->lengthOf(), 0, "CUSTOM POINTWISECONV2D OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got %i, %i instead !", oC, bias->rankOf(), bias->lengthOf()); - + std::vector expectedWeightsShape = {1, 1, iC, oC}; + REQUIRE_TRUE(weights->isSameShape(expectedWeightsShape), 0, "CUSTOM POINTWISECONV2D OP: wrong shape of weights array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedWeightsShape).c_str(), ShapeUtils::shapeAsString(weights).c_str()); + if (bias) + REQUIRE_TRUE(bias->rankOf() <= 2 && oC == bias->lengthOf(), 0, "CUSTOM POINTWISECONV2D OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got %i, %i instead !", oC, bias->rankOf(), bias->lengthOf()); + ConvolutionUtils::conv2d(block, input, weights, bias, output, kH,kW, sH,sW, pH,pW, dH,dW, 1/*isSameMode*/, isNCHW); return Status::OK(); @@ -65,13 +65,13 @@ CUSTOM_OP_IMPL(pointwise_conv2d, 2, 1, false, 0, 0) { DECLARE_TYPES(pointwise_conv2d) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_FLOATS}); } DECLARE_SHAPE_FN(pointwise_conv2d) { - + Nd4jLong* inputShapeInfo = inputShape->at(0); // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW) Nd4jLong* weightsShapeInfo = inputShape->at(1); // [1, 1, iC, oC] always Nd4jLong* biasShapeInfo = block.width() > 2 ? inputShape->at(2) : nullptr; // [oC] @@ -89,18 +89,18 @@ DECLARE_SHAPE_FN(pointwise_conv2d) { indIOioC = 1; const int bS = inputShapeInfo[1]; // batch size - const int iC = inputShapeInfo[indIOioC+1]; // input channels + const int iC = inputShapeInfo[indIOioC+1]; // input channels const int oC = weightsShapeInfo[indWoC+1]; // output channels - std::string expectedWeightsShape = ShapeUtils::shapeAsString({1, 1, iC, oC}); - REQUIRE_TRUE(expectedWeightsShape == ShapeUtils::shapeAsString(weightsShapeInfo), 0, "POINTWISECONV2D OP: wrong shape of weights array, expected is %s, but got %s instead !", expectedWeightsShape.c_str(), ShapeUtils::shapeAsString(weightsShapeInfo).c_str()); - if (biasShapeInfo) - REQUIRE_TRUE(biasShapeInfo[0] <= 2 && oC == shape::length(biasShapeInfo), 0, "POINTWISECONV2D OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got %i, %i instead !", oC, biasShapeInfo[0], shape::length(biasShapeInfo)); + std::vector expectedWeightsShape = {1, 1, iC, oC}; + REQUIRE_TRUE(ShapeUtils::areShapesEqual(weightsShapeInfo, expectedWeightsShape), 0, "POINTWISECONV2D OP: wrong shape of weights array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedWeightsShape).c_str(), ShapeUtils::shapeAsString(weightsShapeInfo).c_str()); + if (biasShapeInfo) + REQUIRE_TRUE(biasShapeInfo[0] <= 2 && oC == shape::length(biasShapeInfo), 0, "POINTWISECONV2D OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got %i, %i instead !", oC, biasShapeInfo[0], shape::length(biasShapeInfo)); auto outputShapeInfo = ShapeBuilders::copyShapeInfoAndType(inputShapeInfo, weightsShapeInfo, true, block.getWorkspace()); // do not forget to put oC instead of iC in outputShapeInfo - outputShapeInfo[indIOioC + 1] = oC; + outputShapeInfo[indIOioC + 1] = oC; shape::updateStrides(outputShapeInfo, shape::order(inputShapeInfo)); diff --git a/libnd4j/include/ops/declarable/generic/nn/convo/sconv2d.cpp b/libnd4j/include/ops/declarable/generic/nn/convo/sconv2d.cpp index 392cd3128..928643493 100644 --- a/libnd4j/include/ops/declarable/generic/nn/convo/sconv2d.cpp +++ b/libnd4j/include/ops/declarable/generic/nn/convo/sconv2d.cpp @@ -19,14 +19,14 @@ // @author Yurii Shyrma, changed on 20.03.2018 // -#include +#include #if NOT_EXCLUDED(OP_sconv2d) #include #include #include -namespace nd4j { +namespace sd { namespace ops { @@ -73,11 +73,11 @@ CUSTOM_OP_IMPL(sconv2d, 2, 1, false, 0, 9) { ConvolutionUtils::getSizesAndIndexesConv2d(isNCHW, *input, *output, bS, iC, iH, iW, oC, oH, oW, indIOioC, indIiH, indWiC, indWmC, indWkH, indOoH); mC = weightsDepth->sizeAt(indWmC); // channels multiplier - std::string expectedWeightsDShape = ShapeUtils::shapeAsString({kH, kW, iC, mC}); - REQUIRE_TRUE(expectedWeightsDShape == ShapeUtils::shapeAsString(weightsDepth), 0, " SCONV2D OP: wrong shape of weightsDepth array, expected is %s, but got %s instead !", expectedWeightsDShape.c_str(), ShapeUtils::shapeAsString(weightsDepth).c_str()); + std::vector expectedWeightsDShape = {kH, kW, iC, mC}; + REQUIRE_TRUE(weightsDepth->isSameShape(expectedWeightsDShape), 0, " SCONV2D OP: wrong shape of weightsDepth array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedWeightsDShape).c_str(), ShapeUtils::shapeAsString(weightsDepth).c_str()); if(weightsPoint) { - std::string expectedWeightsPShape = ShapeUtils::shapeAsString({1, 1, iC*mC, oC}); - REQUIRE_TRUE(expectedWeightsPShape == ShapeUtils::shapeAsString(weightsPoint), 0, " SCONV2D OP: wrong shape of weightsPoint array, expected is %s, but got %s instead !", expectedWeightsPShape.c_str(), ShapeUtils::shapeAsString(weightsPoint).c_str()); + std::vector expectedWeightsPShape = {1, 1, iC*mC, oC}; + REQUIRE_TRUE(weightsPoint->isSameShape(expectedWeightsPShape), 0, " SCONV2D OP: wrong shape of weightsPoint array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedWeightsPShape).c_str(), ShapeUtils::shapeAsString(weightsPoint).c_str()); } if (bias) REQUIRE_TRUE(oC == bias->lengthOf(), 0, " SCONV2D OP: length of bias array must be equal to outChannels, but got %i instead", bias->lengthOf()); @@ -95,7 +95,7 @@ CUSTOM_OP_IMPL(sconv2d, 2, 1, false, 0, 9) { DECLARE_TYPES(sconv2d) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_FLOATS}); } @@ -151,11 +151,11 @@ DECLARE_SHAPE_FN(sconv2d) { const int mC = weightsDShapeInfo[indWmC+1]; // channel multiplier const int oC = weightsPShapeInfo ? weightsPShapeInfo[indWmC+1] : iC*mC; // output channels (oC or iC*mC) - std::string expectedWeightsDShape = ShapeUtils::shapeAsString({kH, kW, iC, mC}); - REQUIRE_TRUE(expectedWeightsDShape == ShapeUtils::shapeAsString(weightsDShapeInfo), 0, "SCONV2D OP: wrong shape of depth weights array, expected is %s, but got %s instead !", expectedWeightsDShape.c_str(), ShapeUtils::shapeAsString(weightsDShapeInfo).c_str()); + std::vector expectedWeightsDShape = {kH, kW, iC, mC}; + REQUIRE_TRUE(ShapeUtils::areShapesEqual(weightsDShapeInfo, expectedWeightsDShape), 0, "SCONV2D OP: wrong shape of depth weights array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedWeightsDShape).c_str(), ShapeUtils::shapeAsString(weightsDShapeInfo).c_str()); if(weightsPShapeInfo) { - std::string expectedWeightsPShape = ShapeUtils::shapeAsString({1, 1, iC*mC, oC}); - REQUIRE_TRUE(expectedWeightsPShape == ShapeUtils::shapeAsString(weightsPShapeInfo), 0, "SCONV2D OP: wrong shape of point array, expected is %s, but got %s instead !", expectedWeightsPShape.c_str(), ShapeUtils::shapeAsString(weightsPShapeInfo).c_str()); + std::vector expectedWeightsPShape = {1, 1, iC*mC, oC}; + REQUIRE_TRUE(ShapeUtils::areShapesEqual(weightsPShapeInfo, expectedWeightsPShape), 0, "SCONV2D OP: wrong shape of point array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedWeightsPShape).c_str(), ShapeUtils::shapeAsString(weightsPShapeInfo).c_str()); } if (biasShapeInfo) REQUIRE_TRUE(biasShapeInfo[0] <= 2 && oC == shape::length(biasShapeInfo), 0, "SCONV2D OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got %i, %i instead !", oC, biasShapeInfo[0], shape::length(biasShapeInfo)); @@ -186,7 +186,7 @@ DECLARE_SHAPE_FN(sconv2d) { DECLARE_TYPES(sconv2d_bp) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_FLOATS}); } @@ -250,13 +250,13 @@ CUSTOM_OP_IMPL(sconv2d_bp, 3, 2, false, 0, 9) { ConvolutionUtils::getSizesAndIndexesConv2d(isNCHW, *input, *gradO, bS, iC, iH, iW, oC, oH, oW, indIOioC, indIiH, indWiC, indWmC, indWkH, indOoH); mC = weightsDepth->sizeAt(indWmC); // channels multiplier - std::string expectedWeightsDShape = ShapeUtils::shapeAsString({kH, kW, iC, mC}); - REQUIRE_TRUE(expectedWeightsDShape == ShapeUtils::shapeAsString(weightsDepth), 0, " SCONV2D_BP OP: wrong shape of weightsDepth array, expected is %s, but got %s instead !", expectedWeightsDShape.c_str(), ShapeUtils::shapeAsString(weightsDepth).c_str()); - REQUIRE_TRUE(expectedWeightsDShape == ShapeUtils::shapeAsString(gradWD), 0, " SCONV2D_BP OP: wrong shape of gradWD array, expected is %s, but got %s instead !", expectedWeightsDShape.c_str(), ShapeUtils::shapeAsString(gradWD).c_str()); + std::vector expectedWeightsDShape = {kH, kW, iC, mC}; + REQUIRE_TRUE(weightsDepth->isSameShape(expectedWeightsDShape), 0, " SCONV2D_BP OP: wrong shape of weightsDepth array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedWeightsDShape).c_str(), ShapeUtils::shapeAsString(weightsDepth).c_str()); + REQUIRE_TRUE(gradWD->isSameShape(expectedWeightsDShape), 0, " SCONV2D_BP OP: wrong shape of gradWD array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedWeightsDShape).c_str(), ShapeUtils::shapeAsString(gradWD).c_str()); if(weightsPoint) { - std::string expectedWeightsPShape = ShapeUtils::shapeAsString({1, 1, iC*mC, oC}); - REQUIRE_TRUE(expectedWeightsPShape == ShapeUtils::shapeAsString(weightsPoint), 0, " SCONV2D_BP OP: wrong shape of weightsPoint array, expected is %s, but got %s instead !", expectedWeightsPShape.c_str(), ShapeUtils::shapeAsString(weightsPoint).c_str()); - REQUIRE_TRUE(expectedWeightsPShape == ShapeUtils::shapeAsString(gradWP), 0, " SCONV2D_BP OP: wrong shape of gradWP array, expected is %s, but got %s instead !", expectedWeightsPShape.c_str(), ShapeUtils::shapeAsString(gradWP).c_str()); + std::vector expectedWeightsPShape = {1, 1, iC*mC, oC}; + REQUIRE_TRUE(weightsPoint->isSameShape(expectedWeightsPShape), 0, " SCONV2D_BP OP: wrong shape of weightsPoint array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedWeightsPShape).c_str(), ShapeUtils::shapeAsString(weightsPoint).c_str()); + REQUIRE_TRUE(gradWP->isSameShape(expectedWeightsPShape), 0, " SCONV2D_BP OP: wrong shape of gradWP array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedWeightsPShape).c_str(), ShapeUtils::shapeAsString(gradWP).c_str()); } if (bias) { REQUIRE_TRUE(oC == bias->lengthOf(), 0, " SCONV2D_BP OP: length of bias array must be equal to outChannels, but got %i instead", bias->lengthOf()); @@ -265,7 +265,7 @@ CUSTOM_OP_IMPL(sconv2d_bp, 3, 2, false, 0, 9) { // if (iC == 1) { // nd4j_debug(" SCONV2D_BP OP: for input_channels=1 this op is equivalent to standard conv2d_bp \n",""); - // nd4j::ops::conv2d_bp op; + // sd::ops::conv2d_bp op; // return op.execute(&block); // } @@ -354,13 +354,13 @@ DECLARE_SHAPE_FN(sconv2d_bp) { int trueoH, trueoW; // true output height, width ConvolutionUtils::calcOutSizePool2D(trueoH, trueoW, kH, kW, sH, sW, pH, pW, dH, dW, iH, iW, isSameMode); - std::string expectedGradOShapeInfo = ShapeUtils::shapeAsString(ShapeUtils::composeShapeUsingDimsAndIdx({bS,oC,trueoH,trueoW, 0,indIOioC,indIiH,indIiH+1})); - REQUIRE_TRUE(expectedGradOShapeInfo == ShapeUtils::shapeAsString(gradOShapeInfo), 0, "SCONV2D_BP OP: wrong shape of output gradients (next epsilon) array, expected is %s, but got %s instead !", expectedGradOShapeInfo.c_str(), ShapeUtils::shapeAsString(gradOShapeInfo).c_str()); - std::string expectedWeightsDShape = ShapeUtils::shapeAsString({kH, kW, iC, mC}); - REQUIRE_TRUE(expectedWeightsDShape == ShapeUtils::shapeAsString(weightsDShapeInfo), 0, "SCONV2D_BP OP: wrong shape of depth weights array, expected is %s, but got %s instead !", expectedWeightsDShape.c_str(), ShapeUtils::shapeAsString(weightsDShapeInfo).c_str()); + std::vector expectedGradOShapeInfo = ShapeUtils::composeShapeUsingDimsAndIdx({bS,oC,trueoH,trueoW, 0,indIOioC,indIiH,indIiH+1}); + REQUIRE_TRUE(ShapeUtils::areShapesEqual(gradOShapeInfo, expectedGradOShapeInfo), 0, "SCONV2D_BP OP: wrong shape of output gradients (next epsilon) array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedGradOShapeInfo).c_str(), ShapeUtils::shapeAsString(gradOShapeInfo).c_str()); + std::vector expectedWeightsDShape = {kH, kW, iC, mC}; + REQUIRE_TRUE(ShapeUtils::areShapesEqual(weightsDShapeInfo, expectedWeightsDShape), 0, "SCONV2D_BP OP: wrong shape of depth weights array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedWeightsDShape).c_str(), ShapeUtils::shapeAsString(weightsDShapeInfo).c_str()); if(weightsPShapeInfo) { - std::string expectedWeightsPShape = ShapeUtils::shapeAsString({1, 1, iC*mC, oC}); - REQUIRE_TRUE(expectedWeightsPShape == ShapeUtils::shapeAsString(weightsPShapeInfo), 0, "SCONV2D_BP OP: wrong shape of point array, expected is %s, but got %s instead !", expectedWeightsPShape.c_str(), ShapeUtils::shapeAsString(weightsPShapeInfo).c_str()); + std::vector expectedWeightsPShape = {1, 1, iC*mC, oC}; + REQUIRE_TRUE(ShapeUtils::areShapesEqual(weightsPShapeInfo, expectedWeightsPShape), 0, "SCONV2D_BP OP: wrong shape of point array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedWeightsPShape).c_str(), ShapeUtils::shapeAsString(weightsPShapeInfo).c_str()); } if (biasShapeInfo) REQUIRE_TRUE((biasShapeInfo[0] == 1 || biasShapeInfo[0] == 2) && oC == shape::length(biasShapeInfo), 0, "SCONV2D_BP OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got %i, %i instead !", oC, biasShapeInfo[0], shape::length(biasShapeInfo)); diff --git a/libnd4j/include/ops/declarable/generic/nn/convo/upsampling2d.cpp b/libnd4j/include/ops/declarable/generic/nn/convo/upsampling2d.cpp index 2978feff1..4f04eb921 100644 --- a/libnd4j/include/ops/declarable/generic/nn/convo/upsampling2d.cpp +++ b/libnd4j/include/ops/declarable/generic/nn/convo/upsampling2d.cpp @@ -19,13 +19,13 @@ // @author Yurii Shyrma (iuriish@yahoo.com), changed on 03.05.2018 // -#include +#include #if NOT_EXCLUDED(OP_upsampling2d) #include #include -namespace nd4j { +namespace sd { namespace ops { @@ -49,7 +49,7 @@ DECLARE_SYN(upsampling, upsampling2d); DECLARE_TYPES(upsampling2d) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_FLOATS}); } @@ -87,7 +87,7 @@ DECLARE_SHAPE_FN(upsampling2d) { DECLARE_TYPES(upsampling2d_bp) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_FLOATS}); } diff --git a/libnd4j/include/ops/declarable/generic/nn/convo/upsampling3d.cpp b/libnd4j/include/ops/declarable/generic/nn/convo/upsampling3d.cpp index 9cdbbffc2..f88f3705f 100644 --- a/libnd4j/include/ops/declarable/generic/nn/convo/upsampling3d.cpp +++ b/libnd4j/include/ops/declarable/generic/nn/convo/upsampling3d.cpp @@ -18,13 +18,13 @@ // @author Yurii Shyrma (iuriish@yahoo.com), created on 04.05.2018 // -#include +#include #if NOT_EXCLUDED(OP_upsampling3d) #include #include -namespace nd4j { +namespace sd { namespace ops { @@ -48,7 +48,7 @@ CUSTOM_OP_IMPL(upsampling3d, 1, 1, false, 0, 3) { DECLARE_TYPES(upsampling3d) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_FLOATS}); } @@ -89,7 +89,7 @@ DECLARE_SHAPE_FN(upsampling3d) { DECLARE_TYPES(upsampling3d_bp) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_FLOATS}); } diff --git a/libnd4j/include/ops/declarable/generic/nn/dot_product_attention.cpp b/libnd4j/include/ops/declarable/generic/nn/dot_product_attention.cpp index 726083deb..bd0cf329a 100644 --- a/libnd4j/include/ops/declarable/generic/nn/dot_product_attention.cpp +++ b/libnd4j/include/ops/declarable/generic/nn/dot_product_attention.cpp @@ -18,14 +18,14 @@ // @author Paul Dubs // -#include +#include #if NOT_EXCLUDED(OP_dot_product_attention) #include #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(dot_product_attention, 3, -1, false, 0, 2) { @@ -67,7 +67,7 @@ namespace ops { "dot_product_attention: Keys and Values must have the same timestep length. " "But got keys = %i, values = %i", keys->sizeAt(-1), values->sizeAt(-1)); - nd4j::ops::matmul mmul; + sd::ops::matmul mmul; mmul.execute({keys, queries}, {weights}, {}, {1}, {}); if(normalization) { *weights /= sqrt((double)keys->sizeAt(-2)); @@ -90,7 +90,7 @@ namespace ops { *weights += (reshapedMask - 1) * 1e9; } - nd4j::ops::softmax softmax; + sd::ops::softmax softmax; softmax.execute({weights}, std::vector{weights}, {}, {-2}, {}, {}, true); mmul.execute({values, weights}, {output}, {}, {}, {}); @@ -113,8 +113,8 @@ namespace ops { auto keys_shape = inputShape->at(1); auto values_shape = inputShape->at(2); - auto weights_shape = ConstantShapeHelper::getInstance()->createShapeInfo(nd4j::ArrayOptions::dataType(values_shape), 'c', ShapeUtils::evalShapeForMatmul(keys_shape, query_shape, true, false)); - auto output_shape = ConstantShapeHelper::getInstance()->createShapeInfo(nd4j::ArrayOptions::dataType(values_shape), 'c', ShapeUtils::evalShapeForMatmul(values_shape, weights_shape, false, false)); + auto weights_shape = ConstantShapeHelper::getInstance()->createShapeInfo(sd::ArrayOptions::dataType(values_shape), 'c', ShapeUtils::evalShapeForMatmul(keys_shape, query_shape, true, false)); + auto output_shape = ConstantShapeHelper::getInstance()->createShapeInfo(sd::ArrayOptions::dataType(values_shape), 'c', ShapeUtils::evalShapeForMatmul(values_shape, weights_shape, false, false)); if(INT_ARG(1)){ return SHAPELIST(output_shape, weights_shape); @@ -166,7 +166,7 @@ namespace ops { auto weightShape = ShapeUtils::evalShapeForMatmul(keys->getShapeInfo(), queries->getShapeInfo(), true, false); - nd4j::ops::matmul mmul; + sd::ops::matmul mmul; NDArray preSoftmax('c', weightShape, values->dataType(), block.launchContext()); mmul.execute({keys, queries}, {&preSoftmax},{}, {1}, {}); @@ -184,15 +184,15 @@ namespace ops { } NDArray weights('c', weightShape, values->dataType(), block.launchContext()); - nd4j::ops::softmax softmax; + sd::ops::softmax softmax; softmax.execute({&preSoftmax}, {&weights},{}, {-2}, {}); - nd4j::ops::matmul_bp mmul_bp; + sd::ops::matmul_bp mmul_bp; NDArray dLdw(weights.getShapeInfo(), block.workspace()); mmul_bp.execute({values, &weights, eps}, std::vector{dLdv, &dLdw}, {}, {}, {}); NDArray dLds(preSoftmax.shapeInfo(), block.workspace()); - nd4j::ops::softmax_bp softmax_bp; + sd::ops::softmax_bp softmax_bp; softmax_bp.execute({&preSoftmax, &dLdw}, {&dLds}, {}, {-2}, {}); if(normalization) diff --git a/libnd4j/include/ops/declarable/generic/nn/fusedBatchNorm.cpp b/libnd4j/include/ops/declarable/generic/nn/fusedBatchNorm.cpp index 0754000a3..6e911e405 100644 --- a/libnd4j/include/ops/declarable/generic/nn/fusedBatchNorm.cpp +++ b/libnd4j/include/ops/declarable/generic/nn/fusedBatchNorm.cpp @@ -18,17 +18,17 @@ // Created by raver119 on 29/10/17. // -#include +#include #if NOT_EXCLUDED(OP_fused_batch_norm) #include -namespace nd4j { +namespace sd { namespace ops { DECLARE_TYPES(fused_batch_norm) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_FLOATS}); } diff --git a/libnd4j/include/ops/declarable/generic/nn/logSoftmax.cpp b/libnd4j/include/ops/declarable/generic/nn/logSoftmax.cpp index f5cc78e2b..64aadce37 100644 --- a/libnd4j/include/ops/declarable/generic/nn/logSoftmax.cpp +++ b/libnd4j/include/ops/declarable/generic/nn/logSoftmax.cpp @@ -18,13 +18,13 @@ // @author Yurii Shyrma (iuriish@yahoo.com), created on 01.02.2018 // -#include +#include #if NOT_EXCLUDED(OP_log_softmax) #include #include -namespace nd4j { +namespace sd { namespace ops { diff --git a/libnd4j/include/ops/declarable/generic/nn/lrn.cpp b/libnd4j/include/ops/declarable/generic/nn/lrn.cpp index 17a2d7175..e9546d1db 100644 --- a/libnd4j/include/ops/declarable/generic/nn/lrn.cpp +++ b/libnd4j/include/ops/declarable/generic/nn/lrn.cpp @@ -20,18 +20,18 @@ // @author Yurii Shyrma (iuriish@yahoo.com) -> back prop author // -#include +#include #if NOT_EXCLUDED(OP_lrn) #include #include -namespace nd4j { +namespace sd { namespace ops { DECLARE_TYPES(lrn) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_FLOATS}); } @@ -51,7 +51,7 @@ namespace nd4j { DECLARE_TYPES(lrn_bp) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_FLOATS}); } diff --git a/libnd4j/include/ops/declarable/generic/nn/multi_head_dot_product_attention.cpp b/libnd4j/include/ops/declarable/generic/nn/multi_head_dot_product_attention.cpp index cff8545b2..f9b7284f1 100644 --- a/libnd4j/include/ops/declarable/generic/nn/multi_head_dot_product_attention.cpp +++ b/libnd4j/include/ops/declarable/generic/nn/multi_head_dot_product_attention.cpp @@ -18,13 +18,13 @@ // @author Paul Dubs // -#include +#include #if NOT_EXCLUDED(OP_multi_head_dot_product_attention) #include #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(multi_head_dot_product_attention, 7, -1, false, 0, 2) { @@ -100,14 +100,14 @@ namespace ops { // Apply Attention // attnResults = [minibatch, numHeads, projectedSize, seqLenth NDArray attnResults('c', {projectedQueries.sizeAt(0), projectedValues.sizeAt(1), projectedValues.sizeAt(2), projectedQueries.sizeAt(3)}, projectedValues.dataType(), block.launchContext()); - nd4j::ops::dot_product_attention attention; + sd::ops::dot_product_attention attention; attention.execute({&projectedQueries, &projectedKeys, &projectedValues, mask}, {&attnResults, weights ? OUTPUT_VARIABLE(1) : nullptr}, {}, {normalization, weights}, {}); // Project attention results attnResults.permutei({0, 3, 1, 2}); attnResults.reshapei(attnResults.ordering(), {miniBatchSize * queryCount, numHeads * projectedValuesSize}); - nd4j::ops::matmul mmul; + sd::ops::matmul mmul; NDArray projRes('c', {attnResults.sizeAt(0), Wo->sizeAt(1)}, values->dataType(), block.launchContext()); mmul.execute({&attnResults, Wo},{&projRes}, {}, {}, {}); projRes.reshapei(projRes.ordering(), {miniBatchSize, queryCount, outSize}); @@ -138,8 +138,8 @@ namespace ops { auto numHeads = shape::sizeAt(WkShape, 0); auto timeSteps = shape::sizeAt(keysShape, 2); - auto weightsShape = ConstantShapeHelper::getInstance()->createShapeInfo(nd4j::ArrayOptions::dataType(valuesShape), 'c', {batchSize, numHeads, timeSteps, queryCount}); - auto outputShape = ConstantShapeHelper::getInstance()->createShapeInfo(nd4j::ArrayOptions::dataType(valuesShape), 'c', {batchSize, outSize, queryCount}); + auto weightsShape = ConstantShapeHelper::getInstance()->createShapeInfo(sd::ArrayOptions::dataType(valuesShape), 'c', {batchSize, numHeads, timeSteps, queryCount}); + auto outputShape = ConstantShapeHelper::getInstance()->createShapeInfo(sd::ArrayOptions::dataType(valuesShape), 'c', {batchSize, outSize, queryCount}); if(INT_ARG(1)){ return SHAPELIST(outputShape, weightsShape); @@ -227,7 +227,7 @@ namespace ops { // Apply Attention NDArray attnResults('c', {projectedQueries.sizeAt(0), projectedValues.sizeAt(1), projectedValues.sizeAt(2), projectedQueries.sizeAt(3)}, projectedValues.dataType(), block.launchContext()); - nd4j::ops::dot_product_attention attention; + sd::ops::dot_product_attention attention; attention.execute({&projectedQueries, &projectedKeys, &projectedValues, mask}, {&attnResults}, {}, {normalization, 0}, {}); // Project attention results @@ -237,7 +237,7 @@ namespace ops { // dLdWo auto epsPerm = eps->permute({0, 2, 1}); auto epsPostReshape = epsPerm.reshape(eps->ordering(), {miniBatchSize * queryCount, outSize}); - nd4j::ops::matmul_bp matmulBp; + sd::ops::matmul_bp matmulBp; NDArray dLdPreWo(attnResults.shapeInfo(), false, block.launchContext()); matmulBp.execute({&attnResults, Wo, &epsPostReshape}, std::vector{&dLdPreWo, dLdWo}, {}, {}, {}); @@ -245,7 +245,7 @@ namespace ops { dLdPreWo.reshapei({miniBatchSize, queryCount, numHeads, projectedValues.sizeAt(2)}); dLdPreWo.permutei({0, 2, 3, 1}); - nd4j::ops::dot_product_attention_bp attentionBp; + sd::ops::dot_product_attention_bp attentionBp; NDArray dLdProjectedQueries(projectedQueries.shapeInfo(), false, block.launchContext()); NDArray dLdProjectedKeys(projectedKeys.shapeInfo(), false, block.launchContext()); NDArray dLdProjectedValues(projectedValues.shapeInfo(), false, block.launchContext()); diff --git a/libnd4j/include/ops/declarable/generic/nn/pooling/avgpool2d.cpp b/libnd4j/include/ops/declarable/generic/nn/pooling/avgpool2d.cpp index 873ac545a..406f330ab 100644 --- a/libnd4j/include/ops/declarable/generic/nn/pooling/avgpool2d.cpp +++ b/libnd4j/include/ops/declarable/generic/nn/pooling/avgpool2d.cpp @@ -19,13 +19,13 @@ // @author Yurii Shyrma (iuriish@yahoo.com), changed on 14.05.2018 // -#include +#include #if NOT_EXCLUDED(OP_avgpool2d) #include #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(avgpool2d, 1, 1, false, 0, 10) { @@ -83,7 +83,7 @@ DECLARE_SYN(avgpool, avgpool2d); DECLARE_TYPES(avgpool2d) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_FLOATS}); } @@ -138,7 +138,7 @@ DECLARE_SHAPE_FN(avgpool2d) { DECLARE_TYPES(avgpool2d_bp) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_FLOATS}); } @@ -168,11 +168,10 @@ CUSTOM_OP_IMPL(avgpool2d_bp, 2, 1, false, 0, 10) { int indIOioC, indIiH, indWoC, indWiC, indWkH, indOoH; // corresponding indexes ConvolutionUtils::getSizesAndIndexesConv2d(isNCHW, *input, *gradO, bS, iC, iH, iW, oC, oH, oW, indIOioC, indIiH, indWiC, indWoC, indWkH, indOoH); - std::string expectedGradOShape = ShapeUtils::shapeAsString(ShapeUtils::composeShapeUsingDimsAndIdx({bS,iC,oH,oW, 0,indIOioC,indIiH,indIiH+1})); - std::string expectedGradIShape = ShapeUtils::shapeAsString(ShapeUtils::composeShapeUsingDimsAndIdx({bS,iC,iH,iW, 0,indIOioC,indIiH,indIiH+1})); - REQUIRE_TRUE(expectedGradOShape == ShapeUtils::shapeAsString(gradO), 0, "AVGPOOL2D_BP op: wrong shape of output's gradients array (next epsilon), expected is %s, but got %s instead !", expectedGradOShape.c_str(), ShapeUtils::shapeAsString(gradO).c_str()); - REQUIRE_TRUE(expectedGradIShape == ShapeUtils::shapeAsString(gradI), 0, "AVGPOOL2D_BP op: wrong shape of input's gradients array (epsilon), expected is %s, but got %s instead !", expectedGradIShape.c_str(), ShapeUtils::shapeAsString(gradI).c_str()); - + std::vector expectedGradOShape = ShapeUtils::composeShapeUsingDimsAndIdx({bS,iC,oH,oW, 0,indIOioC,indIiH,indIiH+1}); + std::vector expectedGradIShape = ShapeUtils::composeShapeUsingDimsAndIdx({bS,iC,iH,iW, 0,indIOioC,indIiH,indIiH+1}); + REQUIRE_TRUE(gradO->isSameShape(expectedGradOShape), 0, "AVGPOOL2D_BP op: wrong shape of output's gradients array (next epsilon), expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedGradOShape).c_str(), ShapeUtils::shapeAsString(gradO).c_str()); + REQUIRE_TRUE(gradI->isSameShape(expectedGradIShape), 0, "AVGPOOL2D_BP op: wrong shape of input's gradients array (epsilon), expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedGradIShape).c_str(), ShapeUtils::shapeAsString(gradI).c_str()); if(!isNCHW) { input = new NDArray(input->permute({0, 3, 1, 2})); // [bS, iH, iW, iC] -> [bS, iC, iH, iW] diff --git a/libnd4j/include/ops/declarable/generic/nn/pooling/avgpool3d.cpp b/libnd4j/include/ops/declarable/generic/nn/pooling/avgpool3d.cpp index b72a1f6f7..76a7377a0 100644 --- a/libnd4j/include/ops/declarable/generic/nn/pooling/avgpool3d.cpp +++ b/libnd4j/include/ops/declarable/generic/nn/pooling/avgpool3d.cpp @@ -18,13 +18,13 @@ // @author Yurii Shyrma (iuriish@yahoo.com), created on 01.03.2018 // -#include +#include #if NOT_EXCLUDED(OP_avgpool3dnew) #include #include -namespace nd4j { +namespace sd { namespace ops { @@ -57,8 +57,8 @@ CUSTOM_OP_IMPL(avgpool3dnew, 1, 1, false, 0, 14) { int indIOioC, indIOioD, indWoC, indWiC, indWkD; // corresponding indexes ConvolutionUtils::getSizesAndIndexesConv3d(isNCDHW, *input, *output, bS, iC, iD, iH, iW, oC, oD, oH, oW, indIOioC, indIOioD, indWiC, indWoC, indWkD); - std::string expectedOutputShape = ShapeUtils::shapeAsString(ShapeUtils::composeShapeUsingDimsAndIdx({bS,iC,oD,oH,oW, 0,indIOioC,indIOioD,indIOioD+1,indIOioD+2})); - REQUIRE_TRUE(expectedOutputShape == ShapeUtils::shapeAsString(output), 0, "AVGPOOL3DNEW OP: wrong shape of output array, expected is %s, but got %s instead !", expectedOutputShape.c_str(), ShapeUtils::shapeAsString(output).c_str()); + std::vector expectedOutputShape = ShapeUtils::composeShapeUsingDimsAndIdx({bS,iC,oD,oH,oW, 0,indIOioC,indIOioD,indIOioD+1,indIOioD+2}); + REQUIRE_TRUE(output->isSameShape(expectedOutputShape), 0, "AVGPOOL3DNEW OP: wrong shape of output array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedOutputShape).c_str(), ShapeUtils::shapeAsString(output).c_str()); if(!isNCDHW) { input = new NDArray(input->permute({0, 4, 1, 2, 3})); // [bS, iD, iH, iW, iC] -> [bS, iC, iD, iH, iW] @@ -81,7 +81,7 @@ CUSTOM_OP_IMPL(avgpool3dnew, 1, 1, false, 0, 14) { DECLARE_TYPES(avgpool3dnew) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_FLOATS}); } @@ -140,7 +140,7 @@ DECLARE_SHAPE_FN(avgpool3dnew) { DECLARE_TYPES(avgpool3dnew_bp) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_FLOATS}); } @@ -174,10 +174,10 @@ CUSTOM_OP_IMPL(avgpool3dnew_bp, 2, 1, false, 0, 14) { int indIOioC, indIOioD, indWoC, indWiC, indWkD; // corresponding indexes ConvolutionUtils::getSizesAndIndexesConv3d(isNCDHW, *input, *gradO, bS, iC, iD, iH, iW, oC, oD, oH, oW, indIOioC, indIOioD, indWiC, indWoC, indWkD); - std::string expectedGradOShape = ShapeUtils::shapeAsString(ShapeUtils::composeShapeUsingDimsAndIdx({bS,iC,oD,oH,oW, 0,indIOioC,indIOioD,indIOioD+1,indIOioD+2})); - std::string expectedGradIShape = ShapeUtils::shapeAsString(ShapeUtils::composeShapeUsingDimsAndIdx({bS,iC,iD,iH,iW, 0,indIOioC,indIOioD,indIOioD+1,indIOioD+2})); - REQUIRE_TRUE(expectedGradOShape == ShapeUtils::shapeAsString(gradO), 0, "AVGPOOL3DNEW_BP op: wrong shape of output's gradients array (next epsilon), expected is %s, but got %s instead !", expectedGradOShape.c_str(), ShapeUtils::shapeAsString(gradO).c_str()); - REQUIRE_TRUE(expectedGradIShape == ShapeUtils::shapeAsString(gradI), 0, "AVGPOOL3DNEW_BP op: wrong shape of input's gradients array (epsilon), expected is %s, but got %s instead !", expectedGradIShape.c_str(), ShapeUtils::shapeAsString(gradI).c_str()); + std::vector expectedGradOShape = ShapeUtils::composeShapeUsingDimsAndIdx({bS,iC,oD,oH,oW, 0,indIOioC,indIOioD,indIOioD+1,indIOioD+2}); + std::vector expectedGradIShape = ShapeUtils::composeShapeUsingDimsAndIdx({bS,iC,iD,iH,iW, 0,indIOioC,indIOioD,indIOioD+1,indIOioD+2}); + REQUIRE_TRUE(gradO->isSameShape(expectedGradOShape), 0, "AVGPOOL3DNEW_BP op: wrong shape of output's gradients array (next epsilon), expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedGradOShape).c_str(), ShapeUtils::shapeAsString(gradO).c_str()); + REQUIRE_TRUE(gradI->isSameShape(expectedGradIShape), 0, "AVGPOOL3DNEW_BP op: wrong shape of input's gradients array (epsilon), expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedGradIShape).c_str(), ShapeUtils::shapeAsString(gradI).c_str()); if(!isNCDHW) { input = new NDArray(input->permute({0, 4, 1, 2, 3})); // [bS, iD, iH, iW, iC] -> [bS, iC, iD, iH, iW] diff --git a/libnd4j/include/ops/declarable/generic/nn/pooling/maxpool2d.cpp b/libnd4j/include/ops/declarable/generic/nn/pooling/maxpool2d.cpp index 13ba252e7..9e7115a73 100644 --- a/libnd4j/include/ops/declarable/generic/nn/pooling/maxpool2d.cpp +++ b/libnd4j/include/ops/declarable/generic/nn/pooling/maxpool2d.cpp @@ -19,13 +19,13 @@ // @author Yurii Shyrma (iuriish@yahoo.com), changed on 09.05.2018 // -#include +#include #if NOT_EXCLUDED(OP_maxpool2d) #include #include -namespace nd4j { +namespace sd { namespace ops { @@ -87,7 +87,7 @@ DECLARE_SYN(maxpool, maxpool2d); DECLARE_TYPES(maxpool2d) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setSameMode(true); } @@ -141,7 +141,7 @@ DECLARE_SHAPE_FN(maxpool2d) { DECLARE_TYPES(maxpool2d_bp) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_FLOATS}); } @@ -170,10 +170,10 @@ CUSTOM_OP_IMPL(maxpool2d_bp, 2, 1, false, 0, 10) { int indIOioC, indIiH, indWoC, indWiC, indWkH, indOoH; // corresponding indexes ConvolutionUtils::getSizesAndIndexesConv2d(isNCHW, *input, *gradO, bS, iC, iH, iW, oC, oH, oW, indIOioC, indIiH, indWiC, indWoC, indWkH, indOoH); - std::string expectedGradOShape = ShapeUtils::shapeAsString(ShapeUtils::composeShapeUsingDimsAndIdx({bS,iC,oH,oW, 0,indIOioC,indIiH,indIiH+1})); - std::string expectedGradIShape = ShapeUtils::shapeAsString(ShapeUtils::composeShapeUsingDimsAndIdx({bS,iC,iH,iW, 0,indIOioC,indIiH,indIiH+1})); - REQUIRE_TRUE(expectedGradOShape == ShapeUtils::shapeAsString(gradO), 0, "MAXPOOL2D_BP op: wrong shape of output's gradients array (next epsilon), expected is %s, but got %s instead !", expectedGradOShape.c_str(), ShapeUtils::shapeAsString(gradO).c_str()); - REQUIRE_TRUE(expectedGradIShape == ShapeUtils::shapeAsString(gradI), 0, "MAXPOOL2D_BP op: wrong shape of input's gradients array (epsilon), expected is %s, but got %s instead !", expectedGradIShape.c_str(), ShapeUtils::shapeAsString(gradI).c_str()); + std::vector expectedGradOShape = ShapeUtils::composeShapeUsingDimsAndIdx({bS,iC,oH,oW, 0,indIOioC,indIiH,indIiH+1}); + std::vector expectedGradIShape = ShapeUtils::composeShapeUsingDimsAndIdx({bS,iC,iH,iW, 0,indIOioC,indIiH,indIiH+1}); + REQUIRE_TRUE(gradO->isSameShape(expectedGradOShape), 0, "MAXPOOL2D_BP op: wrong shape of output's gradients array (next epsilon), expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedGradOShape).c_str(), ShapeUtils::shapeAsString(gradO).c_str()); + REQUIRE_TRUE(gradI->isSameShape(expectedGradIShape), 0, "MAXPOOL2D_BP op: wrong shape of input's gradients array (epsilon), expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedGradIShape).c_str(), ShapeUtils::shapeAsString(gradI).c_str()); if(!isNCHW) { input = new NDArray(input->permute({0, 3, 1, 2})); // [bS, iH, iW, iC] -> [bS, iC, iH, iW] diff --git a/libnd4j/include/ops/declarable/generic/nn/pooling/maxpool3d.cpp b/libnd4j/include/ops/declarable/generic/nn/pooling/maxpool3d.cpp index be905e22f..2804d5a7f 100644 --- a/libnd4j/include/ops/declarable/generic/nn/pooling/maxpool3d.cpp +++ b/libnd4j/include/ops/declarable/generic/nn/pooling/maxpool3d.cpp @@ -18,13 +18,13 @@ // @author Yurii Shyrma (iuriish@yahoo.com), created on 19.02.2018 // -#include +#include #if NOT_EXCLUDED(OP_maxpool3dnew) #include #include -namespace nd4j { +namespace sd { namespace ops { @@ -57,8 +57,8 @@ CUSTOM_OP_IMPL(maxpool3dnew, 1, 1, false, 0, 14) { int indIOioC, indIOioD, indWoC, indWiC, indWkD; // corresponding indexes ConvolutionUtils::getSizesAndIndexesConv3d(isNCDHW, *input, *output, bS, iC, iD, iH, iW, oC, oD, oH, oW, indIOioC, indIOioD, indWiC, indWoC, indWkD); - std::string expectedOutputShape = ShapeUtils::shapeAsString(ShapeUtils::composeShapeUsingDimsAndIdx({bS,iC,oD,oH,oW, 0,indIOioC,indIOioD,indIOioD+1,indIOioD+2})); - REQUIRE_TRUE(expectedOutputShape == ShapeUtils::shapeAsString(output), 0, "MAXPOOL3D op: wrong shape of output array, expected is %s, but got %s instead !", expectedOutputShape.c_str(), ShapeUtils::shapeAsString(output).c_str()); + std::vector expectedOutputShape = ShapeUtils::composeShapeUsingDimsAndIdx({bS,iC,oD,oH,oW, 0,indIOioC,indIOioD,indIOioD+1,indIOioD+2}); + REQUIRE_TRUE(output->isSameShape(expectedOutputShape), 0, "MAXPOOL3D op: wrong shape of output array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedOutputShape).c_str(), ShapeUtils::shapeAsString(output).c_str()); // REQUIRE_TRUE(iD >= kD && iH >= kH && iW >= kW, 0, "MAXPOOL3D OP: the input depth/height/width must be greater or equal to kernel(filter) depth/height/width, but got [%i, %i, %i] and [%i, %i, %i] correspondingly !", iD,iH,iW, kD,kH,kW); // REQUIRE_TRUE(kD/2 >= pD && kH/2 >= pH && kW/2 >= pW, 0, "MAXPOOL3D OP: pad depth/height/width must not be greater than half of kernel depth/height/width, but got [%i, %i, %i] and [%i, %i, %i] correspondingly !", pD,pH,pW, kD,kH,kW); @@ -82,7 +82,7 @@ CUSTOM_OP_IMPL(maxpool3dnew, 1, 1, false, 0, 14) { DECLARE_TYPES(maxpool3dnew) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setSameMode(true); } @@ -142,7 +142,7 @@ DECLARE_SHAPE_FN(maxpool3dnew) { DECLARE_TYPES(maxpool3dnew_bp) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_FLOATS}); } @@ -176,10 +176,10 @@ CUSTOM_OP_IMPL(maxpool3dnew_bp, 2, 1, false, 0, 14) { int indIOioC, indIOioD, indWoC, indWiC, indWkD; // corresponding indexes ConvolutionUtils::getSizesAndIndexesConv3d(isNCDHW, *input, *gradO, bS, iC, iD, iH, iW, oC, oD, oH, oW, indIOioC, indIOioD, indWiC, indWoC, indWkD); - std::string expectedGradOShape = ShapeUtils::shapeAsString(ShapeUtils::composeShapeUsingDimsAndIdx({bS,iC,oD,oH,oW, 0,indIOioC,indIOioD,indIOioD+1,indIOioD+2})); - std::string expectedGradIShape = ShapeUtils::shapeAsString(ShapeUtils::composeShapeUsingDimsAndIdx({bS,iC,iD,iH,iW, 0,indIOioC,indIOioD,indIOioD+1,indIOioD+2})); - REQUIRE_TRUE(expectedGradOShape == ShapeUtils::shapeAsString(gradO), 0, "MAXPOOL3DNEW_BP op: wrong shape of output's gradients array (next epsilon), expected is %s, but got %s instead !", expectedGradOShape.c_str(), ShapeUtils::shapeAsString(gradO).c_str()); - REQUIRE_TRUE(expectedGradIShape == ShapeUtils::shapeAsString(gradI), 0, "MAXPOOL3DNEW_BP op: wrong shape of input's gradients array (epsilon), expected is %s, but got %s instead !", expectedGradIShape.c_str(), ShapeUtils::shapeAsString(gradI).c_str()); + std::vector expectedGradOShape = ShapeUtils::composeShapeUsingDimsAndIdx({bS,iC,oD,oH,oW, 0,indIOioC,indIOioD,indIOioD+1,indIOioD+2}); + std::vector expectedGradIShape = ShapeUtils::composeShapeUsingDimsAndIdx({bS,iC,iD,iH,iW, 0,indIOioC,indIOioD,indIOioD+1,indIOioD+2}); + REQUIRE_TRUE(gradO->isSameShape(expectedGradOShape), 0, "MAXPOOL3DNEW_BP op: wrong shape of output's gradients array (next epsilon), expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedGradOShape).c_str(), ShapeUtils::shapeAsString(gradO).c_str()); + REQUIRE_TRUE(gradI->isSameShape(expectedGradIShape), 0, "MAXPOOL3DNEW_BP op: wrong shape of input's gradients array (epsilon), expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedGradIShape).c_str(), ShapeUtils::shapeAsString(gradI).c_str()); if(!isNCDHW) { input = new NDArray(input->permute({0, 4, 1, 2, 3})); // [bS, iD, iH, iW, iC] -> [bS, iC, iD, iH, iW] diff --git a/libnd4j/include/ops/declarable/generic/nn/pooling/maxpool_with_argmax.cpp b/libnd4j/include/ops/declarable/generic/nn/pooling/maxpool_with_argmax.cpp index 5fe7455fc..fabfd9bad 100644 --- a/libnd4j/include/ops/declarable/generic/nn/pooling/maxpool_with_argmax.cpp +++ b/libnd4j/include/ops/declarable/generic/nn/pooling/maxpool_with_argmax.cpp @@ -18,14 +18,14 @@ // Created by GS at 2/20/18 // -#include +#include #if NOT_EXCLUDED(OP_max_pool_with_argmax) #include #include #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(max_pool_with_argmax, 1, 2, false, 0, 9) { @@ -44,7 +44,7 @@ namespace nd4j { DECLARE_TYPES(max_pool_with_argmax) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes(0, DataType::INHERIT) ->setAllowedOutputTypes(1, {ALL_INTS}); diff --git a/libnd4j/include/ops/declarable/generic/nn/pooling/pnormpool2d.cpp b/libnd4j/include/ops/declarable/generic/nn/pooling/pnormpool2d.cpp index 5c7dc28cd..746f74da2 100644 --- a/libnd4j/include/ops/declarable/generic/nn/pooling/pnormpool2d.cpp +++ b/libnd4j/include/ops/declarable/generic/nn/pooling/pnormpool2d.cpp @@ -19,13 +19,13 @@ // @author Yurii Shyrma (iuriish@yahoo.com), changed on 14.05.2018 // -#include +#include #if NOT_EXCLUDED(OP_pnormpool2d) #include #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(pnormpool2d, 1, 1, false, 0, 10) { @@ -83,7 +83,7 @@ namespace nd4j { DECLARE_TYPES(pnormpool2d) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_FLOATS}); } @@ -136,7 +136,7 @@ namespace nd4j { DECLARE_TYPES(pnormpool2d_bp) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_FLOATS}); } @@ -169,10 +169,10 @@ CUSTOM_OP_IMPL(pnormpool2d_bp, 2, 1, false, 1, 10) { int indIOioC, indIiH, indWoC, indWiC, indWkH, indOoH; // corresponding indexes ConvolutionUtils::getSizesAndIndexesConv2d(isNCHW, *input, *gradO, bS, iC, iH, iW, oC, oH, oW, indIOioC, indIiH, indWiC, indWoC, indWkH, indOoH); - std::string expectedGradOShape = ShapeUtils::shapeAsString(ShapeUtils::composeShapeUsingDimsAndIdx({bS,iC,oH,oW, 0,indIOioC,indIiH,indIiH+1})); - std::string expectedGradIShape = ShapeUtils::shapeAsString(ShapeUtils::composeShapeUsingDimsAndIdx({bS,iC,iH,iW, 0,indIOioC,indIiH,indIiH+1})); - REQUIRE_TRUE(expectedGradOShape == ShapeUtils::shapeAsString(gradO), 0, "PNORMPOOL2D_BP op: wrong shape of output's gradients array (next epsilon), expected is %s, but got %s instead !", expectedGradOShape.c_str(), ShapeUtils::shapeAsString(gradO).c_str()); - REQUIRE_TRUE(expectedGradIShape == ShapeUtils::shapeAsString(gradI), 0, "PNORMPOOL2D_BP op: wrong shape of input's gradients array (epsilon), expected is %s, but got %s instead !", expectedGradIShape.c_str(), ShapeUtils::shapeAsString(gradI).c_str()); + std::vector expectedGradOShape = ShapeUtils::composeShapeUsingDimsAndIdx({bS,iC,oH,oW, 0,indIOioC,indIiH,indIiH+1}); + std::vector expectedGradIShape = ShapeUtils::composeShapeUsingDimsAndIdx({bS,iC,iH,iW, 0,indIOioC,indIiH,indIiH+1}); + REQUIRE_TRUE(gradO->isSameShape(expectedGradOShape), 0, "PNORMPOOL2D_BP op: wrong shape of output's gradients array (next epsilon), expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedGradOShape).c_str(), ShapeUtils::shapeAsString(gradO).c_str()); + REQUIRE_TRUE(gradI->isSameShape(expectedGradIShape), 0, "PNORMPOOL2D_BP op: wrong shape of input's gradients array (epsilon), expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedGradIShape).c_str(), ShapeUtils::shapeAsString(gradI).c_str()); if(!isNCHW) { input = new NDArray(input->permute({0, 3, 1, 2})); // [bS, iH, iW, iC] -> [bS, iC, iH, iW] diff --git a/libnd4j/include/ops/declarable/generic/nn/relu_layer.cpp b/libnd4j/include/ops/declarable/generic/nn/relu_layer.cpp index 22c7a9137..94a4a0ca4 100644 --- a/libnd4j/include/ops/declarable/generic/nn/relu_layer.cpp +++ b/libnd4j/include/ops/declarable/generic/nn/relu_layer.cpp @@ -20,7 +20,7 @@ #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(relu_layer, 3, 1, false, 0, 0) { auto x = INPUT_VARIABLE(0); @@ -35,13 +35,13 @@ namespace nd4j { auto output = OUTPUT_VARIABLE(0); - nd4j::ops::xw_plus_b op; + sd::ops::xw_plus_b op; auto status = op.execute({x, w, b}, {output}); REQUIRE_TRUE(Status::OK() == status, 0, "relu_layer: xw_plus_b op failed on input data."); auto scalar = block.numT() > 0 ? block.getTArguments()->at(0) : 0.0; - output->applyScalar(nd4j::scalar::RELU, scalar, *output); + output->applyScalar(sd::scalar::RELU, scalar, *output); return Status::OK(); } @@ -56,7 +56,7 @@ namespace nd4j { DECLARE_TYPES(relu_layer) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) // ->setAllowedInputTypes(1, {ALL_FLOATS}) ->setAllowedOutputTypes({ALL_FLOATS}); } diff --git a/libnd4j/include/ops/declarable/generic/nn/softmax.cpp b/libnd4j/include/ops/declarable/generic/nn/softmax.cpp index 06bd6d379..d5c58bb7a 100644 --- a/libnd4j/include/ops/declarable/generic/nn/softmax.cpp +++ b/libnd4j/include/ops/declarable/generic/nn/softmax.cpp @@ -19,13 +19,13 @@ // @author Yurii Shyrma (iuriish@yahoo.com) // -#include +#include #if NOT_EXCLUDED(OP_softmax) #include #include -namespace nd4j { +namespace sd { namespace ops { DECLARE_TYPES(softmax) { diff --git a/libnd4j/include/ops/declarable/generic/parity_ops.cpp b/libnd4j/include/ops/declarable/generic/parity_ops.cpp index d64759faf..3595512a2 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops.cpp @@ -24,12 +24,12 @@ #define LIBND4J_PARITY_OPS_H #include -#include +#include #include -#include +#include #include #include -#include +#include #include #include #include @@ -39,7 +39,7 @@ #include #include -namespace nd4j { +namespace sd { namespace ops { } diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/adjust_contrast.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/adjust_contrast.cpp index 65f01cf6c..796dbb80b 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/adjust_contrast.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/adjust_contrast.cpp @@ -18,13 +18,13 @@ // @author George A. Shulinok // -#include +#include #if NOT_EXCLUDED(OP_adjust_contrast) #include -#include +#include -namespace nd4j { +namespace sd { namespace ops { //////////////////////////////////////////////////////////////////// @@ -68,7 +68,7 @@ CONFIGURABLE_OP_IMPL(adjust_contrast, 1, 1, true, 0, 0) { } DECLARE_TYPES(adjust_contrast) { - getOpDescriptor()->setAllowedInputTypes(nd4j::DataType::ANY) + getOpDescriptor()->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_FLOATS}) ->setSameMode(true); } @@ -119,7 +119,7 @@ CONFIGURABLE_OP_IMPL(adjust_contrast_v2, 1, 1, true, 0, 0) { } DECLARE_TYPES(adjust_contrast_v2) { - getOpDescriptor()->setAllowedInputTypes(nd4j::DataType::ANY) + getOpDescriptor()->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_FLOATS}) ->setSameMode(true); } diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/adjust_hue.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/adjust_hue.cpp index 003ff6e75..436fae28d 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/adjust_hue.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/adjust_hue.cpp @@ -19,13 +19,13 @@ // @author Yurii Shyrma (iuriish@yahoo.com) // -#include +#include #if NOT_EXCLUDED(OP_adjust_hue) #include #include -namespace nd4j { +namespace sd { namespace ops { @@ -69,7 +69,7 @@ CONFIGURABLE_OP_IMPL(adjust_hue, 1, 1, true, 0, 0) { } DECLARE_TYPES(adjust_hue) { - getOpDescriptor()->setAllowedInputTypes(nd4j::DataType::ANY) + getOpDescriptor()->setAllowedInputTypes(sd::DataType::ANY) ->setSameMode(true); } diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/adjust_saturation.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/adjust_saturation.cpp index 0a8eaf0c3..5be1699f4 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/adjust_saturation.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/adjust_saturation.cpp @@ -18,14 +18,14 @@ // @author raver119@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_adjust_saturation) #include #include -#include +#include -namespace nd4j { +namespace sd { namespace ops { CONFIGURABLE_OP_IMPL(adjust_saturation, 1, 1, true, 0, 0) { @@ -66,7 +66,7 @@ CONFIGURABLE_OP_IMPL(adjust_saturation, 1, 1, true, 0, 0) { } DECLARE_TYPES(adjust_saturation) { - getOpDescriptor()->setAllowedInputTypes(nd4j::DataType::ANY) + getOpDescriptor()->setAllowedInputTypes(sd::DataType::ANY) ->setSameMode(true); } diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/argmax.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/argmax.cpp index 10e036b61..928a0f7d0 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/argmax.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/argmax.cpp @@ -18,18 +18,18 @@ // Created by raver119 on 01.11.2017. // Modified by GS 4/5/2018 -#include +#include #if NOT_EXCLUDED(OP_argmax) #include #include #include -namespace nd4j { +namespace sd { namespace ops { DECLARE_TYPES(argmax) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_INTS}); } @@ -78,8 +78,8 @@ namespace nd4j { } // special case - output is scalar - if (dims.size() == 0 || (dims.size() == 1 && dims.at(0) == nd4j::DataTypeUtils::max())) { - return SHAPELIST(ConstantShapeHelper::getInstance()->scalarShapeInfo(nd4j::DataType::INT64)); + if (dims.size() == 0 || (dims.size() == 1 && dims.at(0) == sd::DataTypeUtils::max())) { + return SHAPELIST(ConstantShapeHelper::getInstance()->scalarShapeInfo(sd::DataType::INT64)); } return SHAPELIST(ShapeUtils::evalReduceShapeInfo('c', dims, inputShape->at(0), DataType::INT64, false, false, block.getWorkspace())); diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/argmin.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/argmin.cpp index 554b7b95b..f4fb25daa 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/argmin.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/argmin.cpp @@ -18,18 +18,18 @@ // Created by raver119 on 01.11.2017. // Modified by GS 4/5/2018. -#include +#include #if NOT_EXCLUDED(OP_argmin) #include #include -namespace nd4j { +namespace sd { namespace ops { DECLARE_TYPES(argmin) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_INTS}); } @@ -77,7 +77,7 @@ namespace nd4j { } // special case - output is scalar - if (dims.size() == 0 || (dims.size() == 1 && dims.at(0) == nd4j::DataTypeUtils::max())) { + if (dims.size() == 0 || (dims.size() == 1 && dims.at(0) == sd::DataTypeUtils::max())) { return SHAPELIST(ConstantShapeHelper::getInstance()->scalarShapeInfo(DataType::INT64)); } diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/assert.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/assert.cpp index cd8d50df2..362d51c83 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/assert.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/assert.cpp @@ -18,13 +18,13 @@ // @author raver119@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_Assert) #include #include -namespace nd4j { +namespace sd { namespace ops { OP_IMPL(Assert, 1, 1, false) { auto x = INPUT_VARIABLE(0); diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/batch_to_space.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/batch_to_space.cpp index fe2575f1f..e915df7f0 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/batch_to_space.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/batch_to_space.cpp @@ -33,13 +33,13 @@ limitations under the License. // @author Yurii Shyrma (iuriish@yahoo.com) // -#include +#include #if NOT_EXCLUDED(OP_batch_to_space) #include #include -namespace nd4j { +namespace sd { namespace ops { @@ -83,7 +83,7 @@ CUSTOM_OP_IMPL(batch_to_space, 2, 1, false, 0, 1) { //////////////////////////////////////////////////////////////////////////////// DECLARE_TYPES(batch_to_space) { - getOpDescriptor()->setAllowedInputTypes(0, nd4j::DataType::ANY) + getOpDescriptor()->setAllowedInputTypes(0, sd::DataType::ANY) ->setAllowedInputTypes(1, {ALL_INTS}) ->setSameMode(true); } diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/batch_to_space_nd.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/batch_to_space_nd.cpp index 147944acc..312fff7ec 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/batch_to_space_nd.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/batch_to_space_nd.cpp @@ -33,13 +33,13 @@ limitations under the License. // @author Yurii Shyrma (iuriish@yahoo.com) // -#include +#include #if NOT_EXCLUDED(OP_batch_to_space_nd) #include #include -namespace nd4j { +namespace sd { namespace ops { @@ -58,7 +58,7 @@ CUSTOM_OP_IMPL(batch_to_space_nd, 3, 1, false, 0, 0) { const uint numOfSpatialDims = blockShape->sizeAt(0); - const auto product = blockShape->reduceNumber(nd4j::reduce::Prod).e(0); + const auto product = blockShape->reduceNumber(sd::reduce::Prod).e(0); REQUIRE_TRUE(input->sizeAt(0) % product == 0, 0, "BatchToSpaceND: first dimension of input array must be divisible by product of blockShape array elements (= %lld), but got first dimension equal to %i", product, input->sizeAt(0)); if(crop->sizeAt(0) != numOfSpatialDims || crop->sizeAt(1) != 2) { @@ -82,7 +82,7 @@ CUSTOM_OP_IMPL(batch_to_space_nd, 3, 1, false, 0, 0) { //////////////////////////////////////////////////////////////////////////////// DECLARE_TYPES(batch_to_space_nd) { - getOpDescriptor()->setAllowedInputTypes(0, nd4j::DataType::ANY) + getOpDescriptor()->setAllowedInputTypes(0, sd::DataType::ANY) ->setAllowedInputTypes(1, {ALL_INTS}) ->setAllowedInputTypes(2, {ALL_INTS}) ->setSameMode(true); @@ -97,7 +97,7 @@ DECLARE_SHAPE_FN(batch_to_space_nd) { REQUIRE_TRUE(blockShapeInfo[0] == 1, 0, "BatchToSpaceND: rank of blockShape array must be equal to one, but got %i instead !", blockShapeInfo[0]); - const auto product = INPUT_VARIABLE(1)->reduceNumber(nd4j::reduce::Prod).e(0); + const auto product = INPUT_VARIABLE(1)->reduceNumber(sd::reduce::Prod).e(0); REQUIRE_TRUE(inputShapeInfo[1] % product == 0, 0, "BatchToSpaceND: first dimension of input array must be divisible by product of blockShape array elements (= %lld), but got first dimension equal to %i", product, inputShapeInfo[1]); const auto numOfSpatialDims = blockShapeInfo[1]; diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/betaInc.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/betaInc.cpp index 1b09bbf77..1850f10a1 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/betaInc.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/betaInc.cpp @@ -18,13 +18,13 @@ // @author Yurii Shyrma (iuriish@yahoo.com), created on 12.12.2017 // -#include +#include #if NOT_EXCLUDED(OP_betainc) #include #include -namespace nd4j { +namespace sd { namespace ops { DECLARE_TYPES(betainc) { diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/bias_add.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/bias_add.cpp index 0c88a9c53..eec864c5e 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/bias_add.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/bias_add.cpp @@ -19,13 +19,13 @@ // @author Yurii Shyrma (iuriish@yahoo.com) // -#include +#include #if NOT_EXCLUDED(OP_biasadd) #include #include -namespace nd4j { +namespace sd { namespace ops { //////////////////////////////////////////////////////////////////// @@ -46,7 +46,7 @@ CUSTOM_OP_IMPL(biasadd, 2, 1, true, 0, 0) { REQUIRE_TRUE(output->isSameShape(input), 0, "BIASADD CUSTOM_OP: wrong shape of output array, expected is %s but got %s instead !", ShapeUtils::shapeAsString(input).c_str(), ShapeUtils::shapeAsString(output).c_str()); helpers::addBias(block, *input, *bias, *output, isNCHW); - // input->applyBroadcast(nd4j::broadcast::Add, {channelDim}, bias, output); + // input->applyBroadcast(sd::broadcast::Add, {channelDim}, bias, output); return Status::OK(); } @@ -63,7 +63,7 @@ DECLARE_SHAPE_FN(biasadd) { DECLARE_TYPES(biasadd) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_FLOATS}); } @@ -82,7 +82,7 @@ CUSTOM_OP_IMPL(biasadd_bp, 3, 2, false, 0, 0) { gradI->assign(gradO); - gradO->reduceAlongDimension(nd4j::reduce::Sum, *gradB, ShapeUtils::evalDimsToExclude(gradO->rankOf(), {channelDim})); + gradO->reduceAlongDimension(sd::reduce::Sum, *gradB, ShapeUtils::evalDimsToExclude(gradO->rankOf(), {channelDim})); return ND4J_STATUS_OK; } @@ -104,7 +104,7 @@ DECLARE_SHAPE_FN(biasadd_bp) { DECLARE_TYPES(biasadd_bp) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_FLOATS}); } diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/bincount.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/bincount.cpp index 0eff84cf0..3b9fc3916 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/bincount.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/bincount.cpp @@ -18,19 +18,19 @@ // @author raver119@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_bincount) //#include #include #include -namespace nd4j { +namespace sd { namespace ops { DECLARE_TYPES(bincount) { getOpDescriptor() - ->setAllowedInputTypes(0, nd4j::DataType::INT32) - ->setAllowedInputTypes(1, nd4j::DataType::ANY) + ->setAllowedInputTypes(0, sd::DataType::INT32) + ->setAllowedInputTypes(1, sd::DataType::ANY) ->setAllowedOutputTypes({ALL_INTS, ALL_FLOATS}); } @@ -45,9 +45,9 @@ namespace nd4j { maxLength = values->e(maxIndex) + 1; if (block.numI() > 0) { - minLength = nd4j::math::nd4j_max(INT_ARG(0), 0); + minLength = sd::math::nd4j_max(INT_ARG(0), 0); if (block.numI() == 2) - maxLength = nd4j::math::nd4j_min(maxLength, INT_ARG(1)); + maxLength = sd::math::nd4j_min(maxLength, INT_ARG(1)); } if (block.width() == 2) { // the second argument is weights @@ -69,8 +69,8 @@ namespace nd4j { REQUIRE_TRUE(values->isSameShape(weights), 0, "bincount: the input and weights shapes should be equals"); } - minLength = nd4j::math::nd4j_max(minLength, 0); - maxLength = nd4j::math::nd4j_min(maxLength, values->e(maxIndex) + 1); + minLength = sd::math::nd4j_max(minLength, 0); + maxLength = sd::math::nd4j_min(maxLength, values->e(maxIndex) + 1); auto result = OUTPUT_VARIABLE(0); result->assign(0.0f); @@ -83,32 +83,32 @@ namespace nd4j { DECLARE_SHAPE_FN(bincount) { auto shapeList = SHAPELIST(); auto in = INPUT_VARIABLE(0); - nd4j::DataType dtype = DataType::INT32; + sd::DataType dtype = DataType::INT32; if (block.width() > 1) dtype = ArrayOptions::dataType(inputShape->at(1)); else if (block.numI() > 2) - dtype = (nd4j::DataType)INT_ARG(2); + dtype = (sd::DataType)INT_ARG(2); int maxIndex = in->argMax(); int maxLength = in->e(maxIndex) + 1; int outLength = maxLength; if (block.numI() > 0) - outLength = nd4j::math::nd4j_max(maxLength, INT_ARG(0)); + outLength = sd::math::nd4j_max(maxLength, INT_ARG(0)); if (block.numI() > 1) - outLength = nd4j::math::nd4j_min(outLength, INT_ARG(1)); + outLength = sd::math::nd4j_min(outLength, INT_ARG(1)); if (block.width() == 3) { // the second argument is min and the third is max auto min= INPUT_VARIABLE(1)->e(0); auto max = INPUT_VARIABLE(2)->e(0); - outLength = nd4j::math::nd4j_max(maxLength, min); - outLength = nd4j::math::nd4j_min(outLength, max); + outLength = sd::math::nd4j_max(maxLength, min); + outLength = sd::math::nd4j_min(outLength, max); } else if (block.width() > 3) { auto min= INPUT_VARIABLE(2); auto max = INPUT_VARIABLE(3); - outLength = nd4j::math::nd4j_max(maxLength, min->e(0)); - outLength = nd4j::math::nd4j_min(outLength, max->e(0)); + outLength = sd::math::nd4j_max(maxLength, min->e(0)); + outLength = sd::math::nd4j_min(outLength, max->e(0)); } auto newshape = ConstantShapeHelper::getInstance()->vectorShapeInfo(outLength, dtype); diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/broadcast_dynamic_shape.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/broadcast_dynamic_shape.cpp index fa95997be..2f90adb78 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/broadcast_dynamic_shape.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/broadcast_dynamic_shape.cpp @@ -18,12 +18,12 @@ // @author Yurii Shyrma (iuriish@yahoo.com) // -#include +#include #if NOT_EXCLUDED(OP_broadcast_dynamic_shape) #include -namespace nd4j { +namespace sd { namespace ops { ////////////////////////////////////////////////////////////////////////// @@ -46,8 +46,8 @@ CUSTOM_OP_IMPL(broadcast_dynamic_shape, 2, 1, false, 0, 0) { // fill rank and data type xShapeInfo[0] = x->lengthOf(); yShapeInfo[0] = y->lengthOf(); - ArrayOptions::setDataType(xShapeInfo.data(), nd4j::DataType::INT64); // fill with some data type, it doesn't matter what type exactly to choose - ArrayOptions::setDataType(yShapeInfo.data(), nd4j::DataType::INT64); + ArrayOptions::setDataType(xShapeInfo.data(), sd::DataType::INT64); // fill with some data type, it doesn't matter what type exactly to choose + ArrayOptions::setDataType(yShapeInfo.data(), sd::DataType::INT64); for (Nd4jLong i = 0; i < x->lengthOf(); ++i) xShapeInfo[i + 1] = x->e(i); diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/check_numerics.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/check_numerics.cpp index fee3f751c..561c6bb5b 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/check_numerics.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/check_numerics.cpp @@ -18,12 +18,12 @@ // @author raver119@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_check_numerics) #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(check_numerics, 2, 1, true, 0, 0) { @@ -47,7 +47,7 @@ namespace nd4j { DECLARE_TYPES(check_numerics) { getOpDescriptor() ->setAllowedInputTypes(0, {ALL_FLOATS}) - ->setAllowedInputTypes(1, nd4j::DataType::UTF8) + ->setAllowedInputTypes(1, sd::DataType::UTF8) ->setAllowedOutputTypes({ALL_FLOATS}); } } diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/cholesky.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/cholesky.cpp index d433c3e8d..dfc3830ca 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/cholesky.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/cholesky.cpp @@ -18,12 +18,12 @@ // Created by GS at 11/12/2018 // -#include +#include #if NOT_EXCLUDED(OP_cholesky) #include #include -namespace nd4j { +namespace sd { namespace ops { OP_IMPL(cholesky, 1, 1, true) { NDArray* input = INPUT_VARIABLE(0); @@ -32,11 +32,11 @@ namespace nd4j { REQUIRE_TRUE(input->rankOf() >=2, 0, "cholesky: The rank of input array should not less than 2, but %i is given", input->rankOf()); REQUIRE_TRUE(input->sizeAt(-1) == input->sizeAt(-2), 0, "cholesky: The last two dimmensions should be equal, but %i and %i are given", input->sizeAt(-1), input->sizeAt(-2)); REQUIRE_TRUE(helpers::checkCholeskyInput(block.launchContext(), input), 0, "cholesky: The input tensor should be positive-defined and symmetric."); - return helpers::cholesky(block.launchContext(), input, output); + return helpers::cholesky(block.launchContext(), input, output, block.isInplace()); } DECLARE_TYPES(cholesky) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_FLOATS}); } diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/compare_and_bitpack.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/compare_and_bitpack.cpp index 1a30e0c91..43a7c7c75 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/compare_and_bitpack.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/compare_and_bitpack.cpp @@ -21,9 +21,9 @@ #include #include #include -#include +#include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(compare_and_bitpack, 2, 1, false, 0, 0) { auto x = INPUT_VARIABLE(0); diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/confusion_matrix.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/confusion_matrix.cpp index 9139f63b2..cc8a64fa6 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/confusion_matrix.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/confusion_matrix.cpp @@ -18,17 +18,17 @@ // @author @cpuheater // -#include +#include #if NOT_EXCLUDED(OP_confusion_matrix) #include #include -#include +#include #include #include #include -namespace nd4j { +namespace sd { namespace ops { DECLARE_TYPES(confusion_matrix) { getOpDescriptor() @@ -65,9 +65,9 @@ namespace nd4j { auto labels = INPUT_VARIABLE(0); auto predictions = INPUT_VARIABLE(1); auto dtype = block.dataType(); - dtype = nd4j::DataType::INT64; // dtype - should be a param with int argument + dtype = sd::DataType::INT64; // dtype - should be a param with int argument if (block.numI() > 1) - dtype = (nd4j::DataType)INT_ARG(1); + dtype = (sd::DataType)INT_ARG(1); int numClasses = 0; diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/crop_and_resize.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/crop_and_resize.cpp index cdce8a95a..b8ce12d64 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/crop_and_resize.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/crop_and_resize.cpp @@ -18,14 +18,14 @@ // @author sgazeos@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_crop_and_resize) //#include #include #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(crop_and_resize, 4, 1, false, 0, 0) { diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/cross.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/cross.cpp index 57d7f3a87..0d701cf71 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/cross.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/cross.cpp @@ -18,13 +18,13 @@ // @author raver119@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_cross) #include #include -namespace nd4j { +namespace sd { namespace ops { DECLARE_TYPES(cross) { getOpDescriptor() diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/depth_to_space.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/depth_to_space.cpp index 3e2ff1596..63c351c34 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/depth_to_space.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/depth_to_space.cpp @@ -18,14 +18,14 @@ // @author raver119@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_depth_to_space) #include #include #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(depth_to_space, 1, 1, false, 0, 2) { int block_size = INT_ARG(0); @@ -53,7 +53,7 @@ namespace ops { DECLARE_TYPES(depth_to_space) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setSameMode(true); } diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/diag.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/diag.cpp index 7067b78e8..d67ca057b 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/diag.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/diag.cpp @@ -18,13 +18,13 @@ // @author Yurii Shyrma (iuriish@yahoo.com), created on 06.12.2017 // -#include +#include #if NOT_EXCLUDED(OP_diag) #include #include -namespace nd4j { +namespace sd { namespace ops { ////////////////////////////////////////////////////////////////////////// @@ -48,7 +48,7 @@ DECLARE_SYN(MatrixDiag, diag); DECLARE_TYPES(diag) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setSameMode(true); } diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/diagPart.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/diagPart.cpp index 4a93606f4..925c4b6c1 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/diagPart.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/diagPart.cpp @@ -18,13 +18,13 @@ // @author Yurii Shyrma (iuriish@yahoo.com), created on 06.12.2017. // -#include +#include #if NOT_EXCLUDED(OP_diag_part) #include #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(diag_part, 1, 1, false, 0, 0) { @@ -46,7 +46,7 @@ namespace ops { DECLARE_TYPES(diag_part) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setSameMode(true); } diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/digamma.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/digamma.cpp index 8a2894be7..17afcc10b 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/digamma.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/digamma.cpp @@ -19,13 +19,13 @@ // @author Yurii Shyrma (iuriish@yahoo.com) // -#include +#include #if NOT_EXCLUDED(OP_digamma) #include #include -namespace nd4j { +namespace sd { namespace ops { CONFIGURABLE_OP_IMPL(digamma, 1, 1, false, 0, 0) { diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/draw_bounding_boxes.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/draw_bounding_boxes.cpp index 8578f287e..d143bdcf8 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/draw_bounding_boxes.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/draw_bounding_boxes.cpp @@ -18,12 +18,12 @@ // @author George A. Shulinok // -#include +#include #if NOT_EXCLUDED(OP_draw_bounding_boxes) #include #include -namespace nd4j { +namespace sd { namespace ops { OP_IMPL(draw_bounding_boxes, 3, 1, true) { diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/dropout.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/dropout.cpp index f5b807c53..79c3c5cde 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/dropout.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/dropout.cpp @@ -18,13 +18,13 @@ // Created by GS // -#include +#include #if NOT_EXCLUDED(OP_dropout) #include #include -namespace nd4j { +namespace sd { namespace ops { diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/dynamic_parititon.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/dynamic_parititon.cpp index 49c9ed5e8..6a055c02c 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/dynamic_parititon.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/dynamic_parititon.cpp @@ -18,14 +18,14 @@ // @author GS // -#include +#include #if NOT_EXCLUDED(OP_dynamic_partition) #include #include #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(dynamic_partition, 2, 1, false, 0, 1) { auto input = INPUT_VARIABLE(0); @@ -86,13 +86,13 @@ namespace ops { DECLARE_TYPES(dynamic_partition) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_FLOATS, ALL_INTS}); } DECLARE_TYPES(dynamic_partition_bp) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setSameMode(true); } @@ -113,23 +113,21 @@ namespace ops { originalIndices.linspace(0); ops::dynamic_partition op; auto res = op.evaluate({&originalIndices, indices}, {numPartition}); - REQUIRE_TRUE(res->status() == ND4J_STATUS_OK, 0, "dynamic_partition_bp: Error with dynamic partitioning."); + REQUIRE_TRUE(res.status() == ND4J_STATUS_OK, 0, "dynamic_partition_bp: Error with dynamic partitioning."); ops::dynamic_stitch stichOp; std::vector partitions(numPartition * 2); - for (size_t i = 0; i < res->size(); i++) { - partitions[i] = res->at(i); + for (size_t i = 0; i < res.size(); i++) { + partitions[i] = res.at(i); partitions[i + numPartition] = gradOutList[i]; } auto result = stichOp.evaluate(partitions, {numPartition}); - REQUIRE_TRUE(result->status() == ND4J_STATUS_OK, 0, "dynamic_partition_bp: Error with dynamic partitioning."); - result->at(0)->reshapei(outputList[0]->getShapeAsVector()); + REQUIRE_TRUE(result.status() == ND4J_STATUS_OK, 0, "dynamic_partition_bp: Error with dynamic partitioning."); + result.at(0)->reshapei(outputList[0]->getShapeAsVector()); outputList[1]->assign(indices); - outputList[0]->assign(result->at(0)); + outputList[0]->assign(result.at(0)); // helpers::dynamicPartitionFunctorBP(block.launchContext(), input, indices, gradOutList, outputList); - delete res; - delete result; return ND4J_STATUS_OK; } diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/dynamic_stitch.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/dynamic_stitch.cpp index e6913dc34..ecf0e5324 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/dynamic_stitch.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/dynamic_stitch.cpp @@ -18,13 +18,13 @@ // @author GS // -#include +#include #if NOT_EXCLUDED(OP_dynamic_stitch) #include #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(dynamic_stitch, 2, 1, false, 0, 0) { int numOfData = block.width(); @@ -54,7 +54,7 @@ namespace ops { DECLARE_TYPES(dynamic_stitch) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_INTS, ALL_FLOATS}); } diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/embedding_lookup.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/embedding_lookup.cpp index 9df3d52b8..0888854ee 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/embedding_lookup.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/embedding_lookup.cpp @@ -18,7 +18,7 @@ // Created by GS // -#include +#include #if NOT_EXCLUDED(OP_embedding_lookup) #include @@ -27,7 +27,7 @@ #include -namespace nd4j { +namespace sd { namespace ops { @@ -64,20 +64,20 @@ CUSTOM_OP_IMPL(embedding_lookup, 2, 1, false, 0, 1) { int lastIndDim = indeces->lengthOf(); int partition_mode = INT_ARG(0); // partition_mode == 0 - i.e. 'mod' , 1 - 'div' - nd4j::ops::gather op; + sd::ops::gather op; - std::unique_ptr result(op.evaluate({input, indeces}, {0})); - REQUIRE_TRUE(result->status() == Status::OK(), 0, "embedding_lookup: cannot retrieve results from gather op."); - REQUIRE_TRUE(result->at(0)->isSameShape(output), 0, "embedding_lookup: wrong shape of return from gather op."); - output->assign(result->at(0)); + auto result(op.evaluate({input, indeces}, {0})); + REQUIRE_TRUE(result.status() == Status::OK(), 0, "embedding_lookup: cannot retrieve results from gather op."); + REQUIRE_TRUE(result.at(0)->isSameShape(output), 0, "embedding_lookup: wrong shape of return from gather op."); + output->assign(result.at(0)); } return Status::OK(); } DECLARE_TYPES(embedding_lookup) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) - ->setAllowedOutputTypes(nd4j::DataType::ANY); + ->setAllowedInputTypes(sd::DataType::ANY) + ->setAllowedOutputTypes(sd::DataType::ANY); } DECLARE_SHAPE_FN(embedding_lookup) { diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/expose.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/expose.cpp index de67a283d..fd3315157 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/expose.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/expose.cpp @@ -20,7 +20,7 @@ #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(expose, -1, -1, true, 0, 0) { @@ -49,7 +49,7 @@ namespace nd4j { DECLARE_TYPES(expose) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setSameMode(true); } diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/extract_image_patches.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/extract_image_patches.cpp index 6ebc0a72d..1bcb8ef36 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/extract_image_patches.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/extract_image_patches.cpp @@ -21,7 +21,7 @@ #include #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(extract_image_patches, 1, 1, false, 0, 7) { auto input = INPUT_VARIABLE(0); @@ -47,7 +47,7 @@ namespace nd4j { DECLARE_TYPES(extract_image_patches) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setSameMode(true); } diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/fake_quant_with_min_max_vars.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/fake_quant_with_min_max_vars.cpp index ea16b2274..291e8b7c1 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/fake_quant_with_min_max_vars.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/fake_quant_with_min_max_vars.cpp @@ -18,12 +18,12 @@ // @author George Shulinok (sgazeos@gmail.com), created on 13.11.2018 // -#include +#include #if NOT_EXCLUDED(OP_fake_quant_with_min_max_vars) #include #include -namespace nd4j { +namespace sd { namespace ops { CONFIGURABLE_OP_IMPL(fake_quant_with_min_max_vars, 1, 1, true, 0, 0) { diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/fake_quant_with_min_max_vars_per_channel.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/fake_quant_with_min_max_vars_per_channel.cpp index 8f379911b..4af483e22 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/fake_quant_with_min_max_vars_per_channel.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/fake_quant_with_min_max_vars_per_channel.cpp @@ -18,12 +18,12 @@ // @author George Shulinok , created on 08.10.2019 // -#include +#include #if NOT_EXCLUDED(OP_fake_quant_with_min_max_vars_per_channel) #include #include -namespace nd4j { +namespace sd { namespace ops { CONFIGURABLE_OP_IMPL(fake_quant_with_min_max_vars_per_channel, 3, 1, true, 0, 0) { diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/fill.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/fill.cpp index 9fc508860..18b9ce2b8 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/fill.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/fill.cpp @@ -18,12 +18,12 @@ // @author raver119@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_fill) #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(fill, 1, 1, false, -2, 0) { @@ -75,16 +75,16 @@ namespace nd4j { newShape[e+1] = shapeArray->e(e); } - nd4j::DataType dataType; + sd::DataType dataType; if (block.width() > 1) { dataType = INPUT_VARIABLE(1)->dataType(); } else if (block.numT() > 0) { dataType = Environment::getInstance()->defaultFloatDataType(); } else if (block.numI() > 0) { - dataType = nd4j::DataType::INT32; + dataType = sd::DataType::INT32; } else if (block.numB() > 0) { - dataType = nd4j::DataType::BOOL; + dataType = sd::DataType::BOOL; } else throw std::runtime_error("Fill: missing value to fill output array with"); diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/fill_as.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/fill_as.cpp index b981804a4..f2d74572d 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/fill_as.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/fill_as.cpp @@ -18,12 +18,12 @@ // Created by raver119 on 01.11.2017. // -#include +#include #if NOT_EXCLUDED(OP_fill_as) #include -namespace nd4j { +namespace sd { namespace ops { CONFIGURABLE_OP_IMPL(fill_as, 1, 1, true, 0, 0) { auto output = OUTPUT_VARIABLE(0); @@ -47,7 +47,7 @@ namespace nd4j { DECLARE_TYPES(fill_as) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setSameMode(true); } } diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/image_resize.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/image_resize.cpp index cf26b69b3..3ceba93d8 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/image_resize.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/image_resize.cpp @@ -18,13 +18,13 @@ // @author sgazeos@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_image_resize) #include #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(image_resize, 2, 1, false, 0, 0) { diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/in_top_k.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/in_top_k.cpp index f0c7e7027..a243842d2 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/in_top_k.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/in_top_k.cpp @@ -18,14 +18,14 @@ // @author raver119@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_in_top_k) //#include #include #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(in_top_k, 2, 1, true, 0, 1) { auto predictions = INPUT_VARIABLE(0); @@ -46,7 +46,7 @@ namespace nd4j { auto in = inputShape->at(1); int shapeRank = shape::rank(in); - auto aShape = ConstantShapeHelper::getInstance()->createShapeInfo(nd4j::DataType::BOOL, shape::order(in), shape::rank(in), shape::shapeOf(in)); + auto aShape = ConstantShapeHelper::getInstance()->createShapeInfo(sd::DataType::BOOL, shape::order(in), shape::rank(in), shape::shapeOf(in)); shapeList->push_back(aShape); return shapeList; } diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/lgamma.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/lgamma.cpp index 615190c2f..c39f8b55d 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/lgamma.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/lgamma.cpp @@ -19,13 +19,13 @@ // @author George A. Shulinok // -#include +#include #if NOT_EXCLUDED(OP_lgamma) #include #include -namespace nd4j { +namespace sd { namespace ops { OP_IMPL(lgamma, 1, 1, true) { diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/lin_space.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/lin_space.cpp index 8d30185b1..54fd8fb0e 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/lin_space.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/lin_space.cpp @@ -18,12 +18,12 @@ // @author sgazeos@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_lin_space) #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(lin_space, 3, 1, false, 0, 0) { diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/listdiff.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/listdiff.cpp index 4f20e19fa..49c7a2957 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/listdiff.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/listdiff.cpp @@ -18,14 +18,14 @@ // @author raver119@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_listdiff) #include #include // this op will probably never become GPU-compatible -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(listdiff, 2, 2, false, 0, 0) { auto values = INPUT_VARIABLE(0); diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/lstsq.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/lstsq.cpp new file mode 100644 index 000000000..df55db586 --- /dev/null +++ b/libnd4j/include/ops/declarable/generic/parity_ops/lstsq.cpp @@ -0,0 +1,134 @@ +/******************************************************************************* + * Copyright (c) 2020 Konduit, K.K. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ + +// +// Created by GS at 01/28/2020 +// + +#include +#if NOT_EXCLUDED(OP_lstsq) + +#include +#include + +namespace sd { + namespace ops { + + CUSTOM_OP_IMPL(lstsq, 2, 1, false, 0, 0) { + auto a = INPUT_VARIABLE(0); + auto b = INPUT_VARIABLE(1); + auto z = OUTPUT_VARIABLE(0); + bool fastFlag = true; + double l2_factor = 0.; + if (block.numB() > 0) { + fastFlag = B_ARG(0); + } + if (block.numT() > 0) { + l2_factor = T_ARG(0); + } + REQUIRE_TRUE(a->rankOf() >=2, 0, "lstsq: The rank of input left tensor should not be less than 2, but %i is given", a->rankOf()); + REQUIRE_TRUE(b->rankOf() >=2, 0, "lstsq: The rank of input right tensor should not be less than 2, but %i is given", b->rankOf()); + +// REQUIRE_TRUE(a->sizeAt(-1) == a->sizeAt(-2), 0, "lstsq: The last two dimmensions should be equal, but %i and %i are given", a->sizeAt(-1), a->sizeAt(-2)); + REQUIRE_TRUE(a->sizeAt(-2) == b->sizeAt(-2), 0, "lstsq: The last dimmension of left part should be equal to prelast of right part, but %i and %i are given", a->sizeAt(-1), b->sizeAt(-2)); + //REQUIRE_TRUE(l2_factor == 0., 0, "lstsq: Implementation of operation is not finished for factor difference from 0."); + if (a->isEmpty() || b->isEmpty() || z->isEmpty()) + return Status::OK(); + + auto res = helpers::leastSquaresSolveFunctor(block.launchContext(), a, b, l2_factor, fastFlag, z); + + return res; + } + + CUSTOM_OP_IMPL(solve_ls, 2, 1, false, 0, 0) { + auto a = INPUT_VARIABLE(0); + auto b = INPUT_VARIABLE(1); + auto z = OUTPUT_VARIABLE(0); + bool fastFlag = true; + double l2_factor = 0.; + if (block.numB() > 0) { + fastFlag = B_ARG(0); + } + if (block.numT() > 0) { + l2_factor = T_ARG(0); + } + REQUIRE_TRUE(a->rankOf() >=2, 0, "lstsq: The rank of input left tensor should not be less than 2, but %i is given", a->rankOf()); + REQUIRE_TRUE(b->rankOf() >=2, 0, "lstsq: The rank of input right tensor should not be less than 2, but %i is given", b->rankOf()); + +// REQUIRE_TRUE(a->sizeAt(-1) == a->sizeAt(-2), 0, "lstsq: The last two dimmensions should be equal, but %i and %i are given", a->sizeAt(-1), a->sizeAt(-2)); + REQUIRE_TRUE(a->sizeAt(-2) == b->sizeAt(-2), 0, "lstsq: The last dimmension of left part should be equal to prelast of right part, but %i and %i are given", a->sizeAt(-1), b->sizeAt(-2)); + //REQUIRE_TRUE(l2_factor == 0., 0, "lstsq: Implementation of operation is not finished for factor difference from 0."); + auto res = Status::OK(); + if (a->isEmpty() || b->isEmpty() || z->isEmpty()) + return res; + + res = helpers::leastSquaresSolveFunctor(block.launchContext(), a, b, l2_factor, fastFlag, z); + + return res; + } + + DECLARE_SYN(MatrixSolveLs, lstsq); + + DECLARE_SHAPE_FN(lstsq) { + auto in0 = inputShape->at(0); + auto in1 = inputShape->at(1); + auto shapeOf = ShapeUtils::shapeAsVector(in1); + auto rank = shapeOf.size(); + shapeOf[rank - 2] = shape::sizeAt(in0, -1); + + if (shape::isEmpty(in0) || shape::isEmpty(in1)) { + shapeOf[rank - 1] = 0; // set output shape to empty + } + auto resShape = ConstantShapeHelper::getInstance()->createShapeInfo(ArrayOptions::dataType(in0), shape::order(in1), shapeOf);//ShapeBuilders::copyShapeInfoAndType(in1, in0, true, block.workspace()); + if (shapeOf[rank - 1] == 0) { + ArrayOptions::setPropertyBit(resShape, ARRAY_EMPTY); + } + return SHAPELIST(resShape); + } + + DECLARE_TYPES(lstsq) { + getOpDescriptor() + ->setAllowedInputTypes({ALL_FLOATS}) + ->setAllowedOutputTypes({ALL_FLOATS}) + ->setSameMode(false); + } + DECLARE_SHAPE_FN(solve_ls) { + auto in0 = inputShape->at(0); + auto in1 = inputShape->at(1); + auto shapeOf = ShapeUtils::shapeAsVector(in1); + auto rank = shapeOf.size(); + shapeOf[rank - 2] = shape::sizeAt(in0, -1); + + if (shape::isEmpty(in0) || shape::isEmpty(in1)) { + shapeOf[rank - 1] = 0; // set output shape to empty + } + auto resShape = ConstantShapeHelper::getInstance()->createShapeInfo(ArrayOptions::dataType(in0), shape::order(in1), shapeOf);//ShapeBuilders::copyShapeInfoAndType(in1, in0, true, block.workspace()); + if (shapeOf[rank - 1] == 0) { + ArrayOptions::setPropertyBit(resShape, ARRAY_EMPTY); + } + return SHAPELIST(resShape); + } + + DECLARE_TYPES(solve_ls) { + getOpDescriptor() + ->setAllowedInputTypes({ALL_FLOATS}) + ->setAllowedOutputTypes({ALL_FLOATS}) + ->setSameMode(false); + } + } +} + +#endif \ No newline at end of file diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/lup.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/lup.cpp index e0e960159..e0b1eb8d7 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/lup.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/lup.cpp @@ -18,12 +18,12 @@ // Created by GS at 12/10/2019 // -#include +#include #if NOT_EXCLUDED(OP_matrix_inverse) #include #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(lu, 1, 2, false, 0, 0) { auto input = INPUT_VARIABLE(0); @@ -32,7 +32,7 @@ namespace nd4j { auto p = OUTPUT_VARIABLE(1); if (block.getIArguments()->size()) { DataType dtype = (DataType)INT_ARG(0); - REQUIRE_TRUE(dtype == nd4j::DataType::INT32 || dtype == nd4j::DataType::INT64, 0, "lu: Permutation data type should be 32bit or 64bit int only, but '%s' given.", DataTypeUtils::asString(dtype).c_str()); } + REQUIRE_TRUE(dtype == sd::DataType::INT32 || dtype == sd::DataType::INT64, 0, "lu: Permutation data type should be 32bit or 64bit int only, but '%s' given.", DataTypeUtils::asString(dtype).c_str()); } REQUIRE_TRUE(input->rankOf() >=2, 0, "lu: The rank of input array should not less than 2, but %i is given", input->rankOf()); REQUIRE_TRUE(input->sizeAt(-1) == input->sizeAt(-2), 0, "lu: The last two dimmensions should be equal, but %i and %i are given", input->sizeAt(-1), input->sizeAt(-2)); @@ -45,10 +45,10 @@ namespace nd4j { auto in = inputShape->at(0); auto shapeVector = ShapeUtils::shapeAsVector(in); auto luShape = ShapeBuilders::copyShapeInfoAndType(in, in, true, block.workspace()); - auto dtype = nd4j::DataType::INT32; + auto dtype = sd::DataType::INT32; if (block.getIArguments()->size()) { dtype = (DataType)INT_ARG(0); - REQUIRE_TRUE(dtype == nd4j::DataType::INT32 || dtype == nd4j::DataType::INT64, 0, "lu: Permutation data type should be 32bit or 64bit int only, but '%s' given.", DataTypeUtils::asString(dtype).c_str()); + REQUIRE_TRUE(dtype == sd::DataType::INT32 || dtype == sd::DataType::INT64, 0, "lu: Permutation data type should be 32bit or 64bit int only, but '%s' given.", DataTypeUtils::asString(dtype).c_str()); } auto luP = ShapeBuilders::createShapeInfo(dtype, shape::order(in), shapeVector.size() - 1, shapeVector.data(), block.workspace()); @@ -59,7 +59,7 @@ namespace nd4j { getOpDescriptor() ->setAllowedInputTypes({ALL_FLOATS}) ->setAllowedOutputTypes(0, {ALL_FLOATS}) - ->setAllowedOutputTypes(1, {nd4j::DataType::INT32, nd4j::DataType::INT64}) + ->setAllowedOutputTypes(1, {sd::DataType::INT32, sd::DataType::INT64}) ->setSameMode(false); } } diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/matrixDiagPart.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/matrixDiagPart.cpp index 8cef439de..9d4a00be3 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/matrixDiagPart.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/matrixDiagPart.cpp @@ -22,7 +22,7 @@ #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(matrix_diag_part, 1, 1, false, 0, 0) { auto input = INPUT_VARIABLE(0); @@ -43,7 +43,7 @@ namespace nd4j { REQUIRE_TRUE(inRank >= 2, 0, "CUSTOM_OP matrix_diag_part: input array must have rank >= 2, but %i given!", inRank); int outRank = inRank - 1; - int lastDimension = nd4j::math::nd4j_min(shape::sizeAt(in, -1), shape::sizeAt(in, -2)); + int lastDimension = sd::math::nd4j_min(shape::sizeAt(in, -1), shape::sizeAt(in, -2)); if(outRank == 1) { //output shape is a vector with size min(sizeAt(0), sizeAt(1)) outShapeInfo = ConstantShapeHelper::getInstance()->vectorShapeInfo(lastDimension, ArrayOptions::dataType(in)); @@ -63,7 +63,7 @@ namespace nd4j { DECLARE_TYPES(matrix_diag_part) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setSameMode(true); } } diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/matrixSetDiag.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/matrixSetDiag.cpp index 3a52057a5..222074c81 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/matrixSetDiag.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/matrixSetDiag.cpp @@ -18,13 +18,13 @@ // @author Yurii Shyrma (iuriish@yahoo.com) // -#include +#include #if NOT_EXCLUDED(OP_matrix_set_diag) #include #include -namespace nd4j { +namespace sd { namespace ops { CONFIGURABLE_OP_IMPL(matrix_set_diag, 2, 1, false, 0, 0) { @@ -38,7 +38,7 @@ CONFIGURABLE_OP_IMPL(matrix_set_diag, 2, 1, false, 0, 0) { for(int i = 0; i < diagonal->rankOf() - 1; ++i) REQUIRE_TRUE(diagonal->sizeAt(i) == input->sizeAt(i), 0, "MATRIX_SET_DIAG op: the shapes of diagonal and input arrays must be equal till last diagonal dimension but one, however got diagonal=%s and input=%s instead !", ShapeUtils::shapeAsString(diagonal).c_str(), ShapeUtils::shapeAsString(input).c_str()); - REQUIRE_TRUE(diagonal->sizeAt(-1) == (int)nd4j::math::nd4j_min(input->sizeAt(-1), input->sizeAt(-2)), 0, "MATRIX_SET_DIAG op: the value of last dimension of diagonal array must be equal to min(input_last_shape=%i, input_last_but_one_shape=%i), but got %i instead !", input->sizeAt(-1), input->sizeAt(-2), diagonal->sizeAt(-1)); + REQUIRE_TRUE(diagonal->sizeAt(-1) == (int)sd::math::nd4j_min(input->sizeAt(-1), input->sizeAt(-2)), 0, "MATRIX_SET_DIAG op: the value of last dimension of diagonal array must be equal to min(input_last_shape=%i, input_last_but_one_shape=%i), but got %i instead !", input->sizeAt(-1), input->sizeAt(-2), diagonal->sizeAt(-1)); helpers::matrixSetDiag(block.launchContext(), *input, *diagonal, *output, false); @@ -49,7 +49,7 @@ DECLARE_SYN(MatrixSetDiag, matrix_set_diag); DECLARE_TYPES(matrix_set_diag) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setSameMode(true); } diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/matrix_band_part.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/matrix_band_part.cpp index 8e4be83b9..08e059c37 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/matrix_band_part.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/matrix_band_part.cpp @@ -18,12 +18,12 @@ // @author GS , created on 8/22/2018 // -#include +#include #if NOT_EXCLUDED(OP_matrix_band_part) #include #include -namespace nd4j { +namespace sd { namespace ops { CONFIGURABLE_OP_IMPL(matrix_band_part, 1, 1, true, 0, 2) { diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/matrix_determinant.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/matrix_determinant.cpp index 1beff9dab..6c1fd40fe 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/matrix_determinant.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/matrix_determinant.cpp @@ -18,12 +18,12 @@ // Created by GS at 2/26/2018 // -#include +#include #include #include #if NOT_EXCLUDED(OP_matrix_determinant) -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(matrix_determinant, 1, 1, false, 0, 0) { auto input = INPUT_VARIABLE(0); @@ -55,7 +55,7 @@ namespace nd4j { DECLARE_TYPES(matrix_determinant) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_FLOATS}); } } @@ -64,11 +64,11 @@ namespace nd4j { #endif #if NOT_EXCLUDED(OP_log_matrix_determinant) -namespace nd4j { +namespace sd { namespace ops { DECLARE_TYPES(log_matrix_determinant) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_FLOATS}); } @@ -104,11 +104,11 @@ namespace nd4j { #endif #if NOT_EXCLUDED(OP_logdet) -namespace nd4j { +namespace sd { namespace ops { DECLARE_TYPES(logdet) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_FLOATS}); } diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/matrix_diag.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/matrix_diag.cpp index c3e73da84..6e95d127d 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/matrix_diag.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/matrix_diag.cpp @@ -22,7 +22,7 @@ #include #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(matrix_diag, 1, 1, false, 0, 0) { @@ -60,7 +60,7 @@ DECLARE_SHAPE_FN(matrix_diag) { DECLARE_TYPES(matrix_diag) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setSameMode(true); } } diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/matrix_inverse.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/matrix_inverse.cpp index 0b6ef5acb..6a595a92b 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/matrix_inverse.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/matrix_inverse.cpp @@ -18,12 +18,12 @@ // Created by GS at 2/27/2018 // -#include +#include #if NOT_EXCLUDED(OP_matrix_inverse) #include #include -namespace nd4j { +namespace sd { namespace ops { OP_IMPL(matrix_inverse, 1, 1, true) { auto input = INPUT_VARIABLE(0); @@ -37,7 +37,7 @@ namespace nd4j { DECLARE_TYPES(matrix_inverse) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setSameMode(true); } } diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/moments.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/moments.cpp index 5e76fefec..c8fdf2e48 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/moments.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/moments.cpp @@ -18,13 +18,13 @@ // Created by sgazeos@gmail.com on 26.01.2018. // -#include +#include #if NOT_EXCLUDED(OP_moments) #include #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(moments, 1, 2, false, 0, -2) { auto input = INPUT_VARIABLE(0); @@ -82,7 +82,7 @@ namespace nd4j { DECLARE_TYPES(moments) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_FLOATS}); } } diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/non_max_suppression.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/non_max_suppression.cpp index e0812c403..c32ee1ba9 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/non_max_suppression.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/non_max_suppression.cpp @@ -22,7 +22,7 @@ #include -namespace nd4j { +namespace sd { namespace ops { #if NOT_EXCLUDED(OP_image_non_max_suppression) CUSTOM_OP_IMPL(non_max_suppression, 2, 1, false, 0, 0) { @@ -112,14 +112,14 @@ namespace nd4j { } DECLARE_TYPES(non_max_suppression) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_INDICES}); } #endif #if NOT_EXCLUDED(OP_image_non_max_suppression_v3) DECLARE_TYPES(non_max_suppression_v3) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_INDICES}); } diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/non_max_suppression_overlaps.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/non_max_suppression_overlaps.cpp index 4f405d8c8..a8477c63a 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/non_max_suppression_overlaps.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/non_max_suppression_overlaps.cpp @@ -23,7 +23,7 @@ #if NOT_EXCLUDED(OP_image_non_max_suppression_overlaps) -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(non_max_suppression_overlaps, 2, 1, false, 0, 0) { auto boxes = INPUT_VARIABLE(0); diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/norm.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/norm.cpp index e74a28184..64c2e5ccb 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/norm.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/norm.cpp @@ -18,13 +18,13 @@ // @author raver119@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_norm) #include #include -namespace nd4j { +namespace sd { namespace ops { REDUCTION_OP_IMPL(norm, 1, 1, false, 1, -2) { auto input = INPUT_VARIABLE(0); @@ -97,7 +97,7 @@ namespace nd4j { DECLARE_TYPES(norm) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_FLOATS}); } } diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/normalize_moments.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/normalize_moments.cpp index 15f295995..d3ccff82a 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/normalize_moments.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/normalize_moments.cpp @@ -18,12 +18,12 @@ // Created by george@skymind.io on 26.01.2018. // -#include +#include #if NOT_EXCLUDED(OP_normalize_moments) #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(normalize_moments, 3, 2, false, 1, 0) { auto counts = INPUT_VARIABLE(0); @@ -75,7 +75,7 @@ namespace nd4j { DECLARE_TYPES(normalize_moments) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_FLOATS}); } } diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/nth_element.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/nth_element.cpp index 57d0191b2..b1b68c23d 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/nth_element.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/nth_element.cpp @@ -21,7 +21,7 @@ #include #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(nth_element, 2, 1, false, 0, 0) { auto input = INPUT_VARIABLE(0); @@ -70,8 +70,8 @@ namespace nd4j { } DECLARE_TYPES(nth_element) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) - ->setAllowedOutputTypes(nd4j::DataType::ANY); + ->setAllowedInputTypes(sd::DataType::ANY) + ->setAllowedOutputTypes(sd::DataType::ANY); } } diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/onehot.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/onehot.cpp index 49d91275f..d64499ecf 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/onehot.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/onehot.cpp @@ -18,14 +18,14 @@ // Created by raver119 on 01/11/17. // -#include +#include #if NOT_EXCLUDED(OP_onehot) #include #include #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(onehot, 1, 1, false, -2, -2) { auto input = INPUT_VARIABLE(0); @@ -74,7 +74,7 @@ namespace nd4j { DECLARE_SHAPE_FN(onehot) { auto inShape = inputShape->at(0); - nd4j::DataType dtype = block.numD() > 0 ? D_ARG(0) : nd4j::DataType::FLOAT32; + sd::DataType dtype = block.numD() > 0 ? D_ARG(0) : sd::DataType::FLOAT32; int depth = -1; Nd4jLong axis = -1; @@ -108,7 +108,7 @@ namespace nd4j { DECLARE_TYPES(onehot) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_FLOATS, ALL_INTS}); } } diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/ones_as.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/ones_as.cpp index 702aa6711..dccebf8c9 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/ones_as.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/ones_as.cpp @@ -18,12 +18,12 @@ // Created by raver119 on 01.11.2017. // -#include +#include #if NOT_EXCLUDED(OP_ones_as) #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(ones_as, 1, 1, false, 0, 0) { auto output = OUTPUT_VARIABLE(0); @@ -36,7 +36,7 @@ namespace nd4j { DECLARE_SHAPE_FN(ones_as) { auto in = inputShape->at(0); auto dtype = block.numD() ? D_ARG(0) : ArrayOptions::dataType(in); - auto shape = nd4j::ConstantShapeHelper::getInstance()->createShapeInfo(dtype, in); + auto shape = sd::ConstantShapeHelper::getInstance()->createShapeInfo(dtype, in); nd4j_printf("numD: %i; dtype: %s\n", block.numD(), DataTypeUtils::asString(dtype).c_str()); @@ -45,8 +45,8 @@ namespace nd4j { DECLARE_TYPES(ones_as) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) - ->setAllowedOutputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) + ->setAllowedOutputTypes(sd::DataType::ANY) ->setSameMode(false); } } diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/parallelStack.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/parallelStack.cpp index 26ff1b596..b6a2ba1e1 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/parallelStack.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/parallelStack.cpp @@ -18,13 +18,13 @@ // @author Yurii Shyrma (iuriish@yahoo.com), created on 01.11.2017 // -#include +#include #if NOT_EXCLUDED(OP_parallel_stack) #include #include -namespace nd4j { +namespace sd { namespace ops { @@ -41,14 +41,14 @@ CUSTOM_OP_IMPL(parallel_stack, -1, 1, false, 0, 0) { inArrs[i] = INPUT_VARIABLE(i); const int dim = 0; - helpers::stack(block.launchContext(), inArrs, output, dim); + helpers::stack(block.launchContext(), inArrs, *output, dim); return Status::OK(); } DECLARE_TYPES(parallel_stack) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_FLOATS}); } diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/polygamma.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/polygamma.cpp index 1cfd86a26..35ffdcbc6 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/polygamma.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/polygamma.cpp @@ -18,13 +18,13 @@ // @author Yurii Shyrma (iuriish@yahoo.com) // -#include +#include #if NOT_EXCLUDED(OP_polygamma) #include #include -namespace nd4j { +namespace sd { namespace ops { CONFIGURABLE_OP_IMPL(polygamma, 2, 1, false, 0, 0) { @@ -37,8 +37,8 @@ CONFIGURABLE_OP_IMPL(polygamma, 2, 1, false, 0, 0) { Nd4jLong arrLen = n->lengthOf(); // FIXME: this shit should be single op call, not a loop! - auto nNegative = n->reduceNumber(nd4j::reduce::IsNegative, nullptr); - auto xPositive = x->reduceNumber(nd4j::reduce::IsPositive, nullptr); + auto nNegative = n->reduceNumber(sd::reduce::IsNegative, nullptr); + auto xPositive = x->reduceNumber(sd::reduce::IsPositive, nullptr); bool nPositiveFlag = !nNegative.e(0); // require all n >= 0 bool xPositiveFlag = xPositive.e(0); // require all x > 0 REQUIRE_TRUE(nPositiveFlag, 0, "POLYGAMMA op: all elements of n array must be >= 0 !"); diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/qr.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/qr.cpp index 32247a1cd..2cf9156ce 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/qr.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/qr.cpp @@ -18,12 +18,12 @@ // Created by GS at 12/20/2019 // -#include +#include #include #include #if NOT_EXCLUDED(OP_qr) -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(qr, 1, 2, false, 0, 0) { auto input = INPUT_VARIABLE(0); @@ -35,8 +35,9 @@ namespace nd4j { REQUIRE_TRUE(input->rankOf() >=2, 0, "qr: The rank of input array should not be less than 2, but %i is given", input->rankOf()); REQUIRE_TRUE((fullMatricies && outputQ->sizeAt(-1) == input->sizeAt(-2)) || (!fullMatricies && outputQ->isSameShape(input)), 0, "qr: The last dimmensions should be equal to result Q, but %i and %i are given", outputQ->sizeAt(-1), input->sizeAt(-2)); REQUIRE_TRUE((fullMatricies && outputR->sizeAt(-1) == input->sizeAt(-1)) || (!fullMatricies && outputR->sizeAt(-1) == outputR->sizeAt(-2)), 0, "qr: The last dimmensions should be equal to result R, but %i and %i are given", outputR->sizeAt(-1), input->sizeAt(-1)); + if (!input->isEmpty() && !outputQ->isEmpty() && !outputR->isEmpty()) + helpers::qr(block.launchContext(), input, outputQ, outputR, fullMatricies); - helpers::qr(block.launchContext(), input, outputQ, outputR, fullMatricies); return Status::OK(); } diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/range.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/range.cpp index 7faf82b08..a39e07912 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/range.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/range.cpp @@ -19,13 +19,13 @@ // @author Yurii Shyrma (iuriish@yahoo.com) // -#include +#include #if NOT_EXCLUDED(OP_range) #include #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(range, -2, 1, false, -2, -2) { @@ -130,7 +130,7 @@ DECLARE_SHAPE_FN(range) { const int numIArgs = block.getIArguments()->size(); Nd4jLong steps = 0; - nd4j::DataType dataType = block.numD() ? D_ARG(0) : nd4j::DataType::INHERIT; + sd::DataType dataType = block.numD() ? D_ARG(0) : sd::DataType::INHERIT; if (numInArrs > 0) { auto isR = INPUT_VARIABLE(0)->isR(); @@ -213,16 +213,16 @@ DECLARE_SHAPE_FN(range) { if (limit == start){ //Return [0] to match TF - return SHAPELIST(ConstantShapeHelper::getInstance()->vectorShapeInfo(0, nd4j::DataType::INT32)); + return SHAPELIST(ConstantShapeHelper::getInstance()->vectorShapeInfo(0, sd::DataType::INT32)); } REQUIRE_TRUE(delta != 0, 0, "CUSTOM RANGE OP: delta should not be equal to zero !"); if (!block.numD()) { if (limit > DataTypeUtils::max()) - dataType = nd4j::DataType::INT64; + dataType = sd::DataType::INT64; else - dataType = nd4j::DataType::INT32; + dataType = sd::DataType::INT32; } steps = (limit - start) / delta; @@ -257,7 +257,7 @@ DECLARE_SHAPE_FN(range) { if (!block.numD()) { if (Environment::getInstance()->precisionBoostAllowed()) - dataType = nd4j::DataType::DOUBLE; + dataType = sd::DataType::DOUBLE; else dataType = Environment::getInstance()->defaultFloatDataType(); } @@ -276,7 +276,7 @@ DECLARE_SHAPE_FN(range) { DECLARE_TYPES(range) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_FLOATS, ALL_INTS}); } } diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/rank.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/rank.cpp index 6035b267d..8a617dc59 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/rank.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/rank.cpp @@ -18,12 +18,12 @@ // Created by raver119 on 01.11.2017. // -#include +#include #if NOT_EXCLUDED(OP_rank) #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(rank, 1, 1, false, 0, 0) { auto input = INPUT_VARIABLE(0); @@ -37,13 +37,13 @@ namespace nd4j { return Status::OK(); } DECLARE_SHAPE_FN(rank) { - return SHAPELIST(ConstantShapeHelper::getInstance()->scalarShapeInfo(nd4j::DataType::INT32)); + return SHAPELIST(ConstantShapeHelper::getInstance()->scalarShapeInfo(sd::DataType::INT32)); } DECLARE_TYPES(rank) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_INTS, ALL_FLOATS}) ->allowOverride(true); } diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/reduceMean.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/reduceMean.cpp index f83994606..90560bbb6 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/reduceMean.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/reduceMean.cpp @@ -22,7 +22,7 @@ #include #include -namespace nd4j { +namespace sd { namespace ops { ////////////////////////////////////////////////////////////////////////// @@ -79,7 +79,7 @@ DECLARE_SHAPE_FN(reduce_mean) { DECLARE_TYPES(reduce_mean) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_FLOATS}); } @@ -150,7 +150,7 @@ DECLARE_SHAPE_FN(reduce_mean_bp) { DECLARE_TYPES(reduce_mean_bp) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_FLOATS}); } diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/reduceStDev.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/reduceStDev.cpp index 6a3e7c050..1682b9d72 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/reduceStDev.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/reduceStDev.cpp @@ -22,7 +22,7 @@ #include #include -namespace nd4j { +namespace sd { namespace ops { ////////////////////////////////////////////////////////////////////////// @@ -90,7 +90,7 @@ DECLARE_SHAPE_FN(reduce_stdev) { DECLARE_TYPES(reduce_stdev) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_FLOATS}); } @@ -169,7 +169,7 @@ DECLARE_SHAPE_FN(reduce_stdev_bp) { DECLARE_TYPES(reduce_stdev_bp) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_FLOATS}); } diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/reduceVariance.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/reduceVariance.cpp index 16bfdc8a9..cd7441304 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/reduceVariance.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/reduceVariance.cpp @@ -22,7 +22,7 @@ #include #include -namespace nd4j { +namespace sd { namespace ops { ////////////////////////////////////////////////////////////////////////// @@ -88,7 +88,7 @@ DECLARE_SHAPE_FN(reduce_variance) { DECLARE_TYPES(reduce_variance) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_FLOATS}); } @@ -166,7 +166,7 @@ DECLARE_SHAPE_FN(reduce_variance_bp) { DECLARE_TYPES(reduce_variance_bp) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_FLOATS}); } diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/reduce_dot.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/reduce_dot.cpp index 9569f524e..75cb40ca2 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/reduce_dot.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/reduce_dot.cpp @@ -22,7 +22,7 @@ #include #include -namespace nd4j { +namespace sd { namespace ops { #if NOT_EXCLUDED(OP_reduce_dot_bp) @@ -114,7 +114,7 @@ DECLARE_SHAPE_FN(reduce_dot_bp) { DECLARE_TYPES(reduce_dot_bp) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_FLOATS}); } diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/reduce_logsumexp.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/reduce_logsumexp.cpp index a02b4db9b..805db1883 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/reduce_logsumexp.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/reduce_logsumexp.cpp @@ -20,7 +20,7 @@ #include #include -namespace nd4j { +namespace sd { namespace ops { #if NOT_EXCLUDED(OP_reduce_logsumexp) diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/reduce_max.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/reduce_max.cpp index 870017e8d..3d2dbe57e 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/reduce_max.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/reduce_max.cpp @@ -23,7 +23,7 @@ #include #include -namespace nd4j { +namespace sd { namespace ops { #if NOT_EXCLUDED(OP_reduce_max) @@ -84,7 +84,7 @@ DECLARE_SHAPE_FN(reduce_max) { DECLARE_TYPES(reduce_max) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setSameMode(true); } @@ -116,12 +116,12 @@ CUSTOM_OP_IMPL(reduce_max_bp, 2, 1, false, 0, 0) { if(gradO->lengthOf() == 1) { - auto indOfMaxElem = input->indexReduceNumber(nd4j::indexreduce::IndexMax); + auto indOfMaxElem = input->indexReduceNumber(sd::indexreduce::IndexMax); gradI->p(indOfMaxElem.t(0), gradO->e(0)); } else { - auto indicesArr = input->applyIndexReduce(nd4j::indexreduce::IndexMax, dimensions); + auto indicesArr = input->applyIndexReduce(sd::indexreduce::IndexMax, dimensions); helpers::scatterSimple(block.launchContext(), 6, *gradI, *gradO, indicesArr, ShapeUtils::evalDimsToExclude(gradI->rankOf(), dimensions)); // 6 corresponds to copy operation } @@ -151,7 +151,7 @@ DECLARE_SHAPE_FN(reduce_max_bp) { DECLARE_TYPES(reduce_max_bp) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_FLOATS}); } diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/reduce_min.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/reduce_min.cpp index e8b073de8..254cfe021 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/reduce_min.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/reduce_min.cpp @@ -23,7 +23,7 @@ #include #include -namespace nd4j { +namespace sd { namespace ops { #if NOT_EXCLUDED(OP_reduce_min) @@ -84,7 +84,7 @@ DECLARE_SHAPE_FN(reduce_min) { DECLARE_TYPES(reduce_min) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setSameMode(true); } @@ -119,12 +119,12 @@ CUSTOM_OP_IMPL(reduce_min_bp, 2, 1, false, 0, 0) { if(gradO->lengthOf() == 1) { - auto indOfMaxElem = input->indexReduceNumber(nd4j::indexreduce::IndexMin); + auto indOfMaxElem = input->indexReduceNumber(sd::indexreduce::IndexMin); gradI->p(indOfMaxElem.e(0), gradO->e(0)); } else { - auto indicesArr = input->applyIndexReduce(nd4j::indexreduce::IndexMin, dimensions); + auto indicesArr = input->applyIndexReduce(sd::indexreduce::IndexMin, dimensions); helpers::scatterSimple(block.launchContext(), 6, *gradI, *gradO, indicesArr, ShapeUtils::evalDimsToExclude(gradI->rankOf(), dimensions)); // 6 corresponds to copy operation } @@ -153,7 +153,7 @@ DECLARE_SHAPE_FN(reduce_min_bp) { DECLARE_TYPES(reduce_min_bp) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_FLOATS}); } diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/reduce_norm1.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/reduce_norm1.cpp index 172f3df8e..31261fe5c 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/reduce_norm1.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/reduce_norm1.cpp @@ -22,7 +22,7 @@ #include #include -namespace nd4j { +namespace sd { namespace ops { #if NOT_EXCLUDED(OP_reduce_norm1) @@ -82,7 +82,7 @@ DECLARE_SHAPE_FN(reduce_norm1) { DECLARE_TYPES(reduce_norm1) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_FLOATS}); } #endif @@ -100,7 +100,7 @@ CUSTOM_OP_IMPL(reduce_norm1_bp, 2, 1, false, 0, 0) { auto gradO = INPUT_VARIABLE(1); auto gradI = OUTPUT_VARIABLE(0); - input->applyTransform(nd4j::transform::Sign, *gradI); + input->applyTransform(sd::transform::Sign, *gradI); if (gradO->lengthOf() == 1) { *gradI *= *gradO; @@ -158,7 +158,7 @@ DECLARE_SHAPE_FN(reduce_norm1_bp) { DECLARE_TYPES(reduce_norm1_bp) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_FLOATS}); } diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/reduce_norm2.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/reduce_norm2.cpp index e54518359..c9ea8e374 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/reduce_norm2.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/reduce_norm2.cpp @@ -22,7 +22,7 @@ #include #include -namespace nd4j { +namespace sd { namespace ops { #if NOT_EXCLUDED(OP_reduce_norm2) @@ -82,7 +82,7 @@ DECLARE_SHAPE_FN(reduce_norm2) { DECLARE_TYPES(reduce_norm2) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_FLOATS}); } #endif @@ -156,7 +156,7 @@ DECLARE_SHAPE_FN(reduce_norm2_bp) { DECLARE_TYPES(reduce_norm2_bp) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_FLOATS}); } diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/reduce_norm_max.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/reduce_norm_max.cpp index c71310947..b1a018900 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/reduce_norm_max.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/reduce_norm_max.cpp @@ -23,7 +23,7 @@ #include #include -namespace nd4j { +namespace sd { namespace ops { #if NOT_EXCLUDED(OP_reduce_norm_max) @@ -84,7 +84,7 @@ DECLARE_SHAPE_FN(reduce_norm_max) { DECLARE_TYPES(reduce_norm_max) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_FLOATS}); } #endif @@ -116,16 +116,16 @@ CUSTOM_OP_IMPL(reduce_norm_max_bp, 2, 1, false, 0, 0) { if(gradO->lengthOf() == 1) { - auto indOfAbsMaxElem = input->indexReduceNumber(nd4j::indexreduce::IndexAbsoluteMax); + auto indOfAbsMaxElem = input->indexReduceNumber(sd::indexreduce::IndexAbsoluteMax); const Nd4jLong ind = indOfAbsMaxElem.t(0); const int sign = input->e(ind) >= 0 ? 1 : -1; gradI->p(ind, sign * gradO->e(0)); } else { - auto indicesArr = input->applyIndexReduce(nd4j::indexreduce::IndexAbsoluteMax, dimensions); + auto indicesArr = input->applyIndexReduce(sd::indexreduce::IndexAbsoluteMax, dimensions); helpers::scatterSimple(block.launchContext(), 6, *gradI, *gradO, indicesArr, ShapeUtils::evalDimsToExclude(gradI->rankOf(), dimensions)); // 6 corresponds to copy operation - *gradI *= input->transform(nd4j::transform::Sign); + *gradI *= input->transform(sd::transform::Sign); } return Status::OK(); @@ -152,7 +152,7 @@ DECLARE_SHAPE_FN(reduce_norm_max_bp) { DECLARE_TYPES(reduce_norm_max_bp) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_FLOATS}); } diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/reduce_prod.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/reduce_prod.cpp index 965b6dcaa..e873220ef 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/reduce_prod.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/reduce_prod.cpp @@ -22,7 +22,7 @@ #include #include -namespace nd4j { +namespace sd { namespace ops { #if NOT_EXCLUDED(OP_reduce_prod) @@ -82,7 +82,7 @@ DECLARE_SHAPE_FN(reduce_prod) { DECLARE_TYPES(reduce_prod) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_FLOATS}); } @@ -97,7 +97,7 @@ CUSTOM_OP_IMPL(reduce_prod_bp, 2, 1, false, 0, 0) { auto gradI = OUTPUT_VARIABLE(0); if (gradO->lengthOf() == 1) { - gradI->assign(input->reduceNumber(nd4j::reduce::Prod)); + gradI->assign(input->reduceNumber(sd::reduce::Prod)); *gradI /= *input; *gradI *= gradO->e(0); } @@ -124,7 +124,7 @@ CUSTOM_OP_IMPL(reduce_prod_bp, 2, 1, false, 0, 0) { // *** calculations *** // auto products = input->reduceAlongDimension(reduce::Prod, dimensions, true); - gradI->applyTrueBroadcast(nd4j::BroadcastOpsTuple::Assign(), products, *gradI); + gradI->applyTrueBroadcast(sd::BroadcastOpsTuple::Assign(), products, *gradI); *gradI /= *input; if(!keepDims) { @@ -158,7 +158,7 @@ DECLARE_SHAPE_FN(reduce_prod_bp) { DECLARE_TYPES(reduce_prod_bp) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_FLOATS}); } diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/reduce_sqnorm.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/reduce_sqnorm.cpp index e42050ff6..0c53a261b 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/reduce_sqnorm.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/reduce_sqnorm.cpp @@ -21,7 +21,7 @@ #include #include -namespace nd4j { +namespace sd { namespace ops { #if NOT_EXCLUDED(OP_reduce_sqnorm) @@ -82,7 +82,7 @@ DECLARE_SHAPE_FN(reduce_sqnorm) { DECLARE_TYPES(reduce_sqnorm) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_FLOATS}); } @@ -155,7 +155,7 @@ DECLARE_SHAPE_FN(reduce_sqnorm_bp) { DECLARE_TYPES(reduce_sqnorm_bp) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_FLOATS}); } diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/reduce_sum.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/reduce_sum.cpp index 522164593..0f4a5f467 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/reduce_sum.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/reduce_sum.cpp @@ -22,7 +22,7 @@ #include #include -namespace nd4j { +namespace sd { namespace ops { #if NOT_EXCLUDED(OP_reduce_sum) @@ -82,7 +82,7 @@ DECLARE_SHAPE_FN(reduce_sum) { DECLARE_TYPES(reduce_sum) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setSameMode(true); } #endif @@ -123,9 +123,9 @@ CUSTOM_OP_IMPL(reduce_sum_bp, 2, 1, false, 0, 0) { if(!keepDims) { auto gradOShapeKeepDims = ShapeUtils::evalReduceShapeInfo(gradO->ordering(), dimensions, *input, true, false, block.getWorkspace()); auto r = gradO->reshape(gradO->ordering(), ShapeUtils::pullShapeFromShapeInfo(gradOShapeKeepDims)); // for example could be something like [a,b] -> [1,a,1,b] - gradI->applyTrueBroadcast(nd4j::BroadcastOpsTuple::Assign(), r, *gradI); + gradI->applyTrueBroadcast(sd::BroadcastOpsTuple::Assign(), r, *gradI); } else - gradI->applyTrueBroadcast(nd4j::BroadcastOpsTuple::Assign(), *gradO, *gradI); + gradI->applyTrueBroadcast(sd::BroadcastOpsTuple::Assign(), *gradO, *gradI); } return Status::OK(); @@ -152,7 +152,7 @@ DECLARE_SHAPE_FN(reduce_sum_bp) { DECLARE_TYPES(reduce_sum_bp) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_FLOATS}); } diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/resize_area.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/resize_area.cpp index dc304e4a9..4ae03cc25 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/resize_area.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/resize_area.cpp @@ -18,13 +18,13 @@ // @author sgazeos@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_resize_area) #include #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(resize_area, 1, 1, false, 0, -2) { diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/resize_bicubic.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/resize_bicubic.cpp index 63da432c7..a867a2147 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/resize_bicubic.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/resize_bicubic.cpp @@ -18,13 +18,13 @@ // @author sgazeos@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_resize_bicubic) #include #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(resize_bicubic, 2, 1, false, 0, 0) { diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/resize_linear.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/resize_linear.cpp index fa7054c29..6d72bf889 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/resize_linear.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/resize_linear.cpp @@ -19,13 +19,13 @@ // @author sgazeos@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_resize_bilinear) //#include #include #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(resize_bilinear, 1, 1, false, 0, -2) { @@ -120,7 +120,7 @@ namespace nd4j { } DECLARE_TYPES(resize_bilinear) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_FLOATS}); } diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/resize_neighbor.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/resize_neighbor.cpp index 9d6ac8a81..3454fb897 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/resize_neighbor.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/resize_neighbor.cpp @@ -19,14 +19,14 @@ // @author sgazeos@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_resize_nearest_neighbor) //#include #include #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(resize_nearest_neighbor, 1, 1, false, 0, -2) { diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/rint.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/rint.cpp index fbff41c47..1e1058397 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/rint.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/rint.cpp @@ -18,12 +18,12 @@ // @author raver119@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_rint) #include -namespace nd4j { +namespace sd { namespace ops { OP_IMPL(rint, 1, 1, true) { auto x = INPUT_VARIABLE(0); @@ -37,7 +37,7 @@ namespace nd4j { DECLARE_TYPES(rint) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_FLOATS}); } } diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/roll.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/roll.cpp index f93fc198e..75f102fa0 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/roll.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/roll.cpp @@ -18,14 +18,14 @@ // @author raver119@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_roll) #include #include #include -namespace nd4j { +namespace sd { namespace ops { CONFIGURABLE_OP_IMPL(roll, 1, 1, true, 0, 0) { @@ -98,10 +98,10 @@ namespace ops { DECLARE_TYPES(roll) { getOpDescriptor() - ->setAllowedInputTypes(0,nd4j::DataType::ANY) - ->setAllowedInputTypes(1,nd4j::DataType::INT32) // TODO: all ints in future - ->setAllowedInputTypes(2,nd4j::DataType::INT32) - ->setAllowedOutputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(0,sd::DataType::ANY) + ->setAllowedInputTypes(1,sd::DataType::INT32) // TODO: all ints in future + ->setAllowedInputTypes(2,sd::DataType::INT32) + ->setAllowedOutputTypes(sd::DataType::ANY) ->setSameMode(true); } } diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/scatter_add.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/scatter_add.cpp index db4eeeff6..e624afeb1 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/scatter_add.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/scatter_add.cpp @@ -19,13 +19,13 @@ // @author Yurii Shyrma (iuriish@yahoo.com) // -#include +#include #if NOT_EXCLUDED(OP_scatter_add) #include #include -namespace nd4j { +namespace sd { namespace ops { OP_IMPL(scatter_add, 3, 1, true) { diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/scatter_div.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/scatter_div.cpp index 40ddbd424..fd0b2a730 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/scatter_div.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/scatter_div.cpp @@ -19,13 +19,13 @@ // @author Yurii Shyrma (iuriish@yahoo.com) // -#include +#include #if NOT_EXCLUDED(OP_scatter_div) #include #include -namespace nd4j { +namespace sd { namespace ops { OP_IMPL(scatter_div, 3, 1, true) { diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/scatter_max.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/scatter_max.cpp index 4ec55f088..b3342c5a5 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/scatter_max.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/scatter_max.cpp @@ -18,13 +18,13 @@ // @author Yurii Shyrma (iuriish@yahoo.com), created on 1.08.2018 // -#include +#include #if NOT_EXCLUDED(OP_scatter_max) #include #include -namespace nd4j { +namespace sd { namespace ops { OP_IMPL(scatter_max, 3, 1, true) { diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/scatter_min.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/scatter_min.cpp index ea8dbf081..d37adb692 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/scatter_min.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/scatter_min.cpp @@ -18,13 +18,13 @@ // @author Yurii Shyrma (iuriish@yahoo.com), created on 1.08.2018 // -#include +#include #if NOT_EXCLUDED(OP_scatter_min) #include #include -namespace nd4j { +namespace sd { namespace ops { OP_IMPL(scatter_min, 3, 1, true) { diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/scatter_mul.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/scatter_mul.cpp index 4685cef5d..9bf5be748 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/scatter_mul.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/scatter_mul.cpp @@ -19,13 +19,13 @@ // @author Yurii Shyrma (iuriish@yahoo.com) // -#include +#include #if NOT_EXCLUDED(OP_scatter_mul) #include #include -namespace nd4j { +namespace sd { namespace ops { OP_IMPL(scatter_mul, 3, 1, true) { auto input = INPUT_VARIABLE(0); diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/scatter_nd.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/scatter_nd.cpp index 4c3884e07..7c2194c6c 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/scatter_nd.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/scatter_nd.cpp @@ -18,13 +18,13 @@ // @author Yurii Shyrma (iuriish@yahoo.com), created on 21.08.2018 // -#include +#include #if NOT_EXCLUDED(OP_scatter_nd) #include #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(scatter_nd, 3, 1, false, 0, 0) { diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/scatter_nd_add.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/scatter_nd_add.cpp index 43c2c66ed..8fb4288ee 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/scatter_nd_add.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/scatter_nd_add.cpp @@ -18,13 +18,13 @@ // @author Yurii Shyrma (iuriish@yahoo.com), created on 22.08.2018 // -#include +#include #if NOT_EXCLUDED(OP_scatter_nd_add) #include #include -namespace nd4j { +namespace sd { namespace ops { OP_IMPL(scatter_nd_add, 3, 1, true) { diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/scatter_nd_sub.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/scatter_nd_sub.cpp index eb4768f86..6cfa5d046 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/scatter_nd_sub.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/scatter_nd_sub.cpp @@ -18,13 +18,13 @@ // @author Yurii Shyrma (iuriish@yahoo.com), created on 24.08.2018 // -#include +#include #if NOT_EXCLUDED(OP_scatter_nd_sub) #include #include -namespace nd4j { +namespace sd { namespace ops { OP_IMPL(scatter_nd_sub, 3, 1, true) { diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/scatter_nd_update.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/scatter_nd_update.cpp index e6bfb8703..b6122c724 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/scatter_nd_update.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/scatter_nd_update.cpp @@ -18,13 +18,13 @@ // @author Yurii Shyrma (iuriish@yahoo.com), created on 24.08.2018 // -#include +#include #if NOT_EXCLUDED(OP_scatter_nd_update) #include #include -namespace nd4j { +namespace sd { namespace ops { OP_IMPL(scatter_nd_update, 3, 1, true) { diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/scatter_sub.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/scatter_sub.cpp index 1971b346f..c955ac042 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/scatter_sub.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/scatter_sub.cpp @@ -19,13 +19,13 @@ // @author Yurii Shyrma (iuriish@yahoo.com) // -#include +#include #if NOT_EXCLUDED(OP_scatter_sub) #include #include -namespace nd4j { +namespace sd { namespace ops { OP_IMPL(scatter_sub, 3, 1, true) { auto input = INPUT_VARIABLE(0); diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/scatter_upd.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/scatter_upd.cpp index 081d1cd76..ef54b9813 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/scatter_upd.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/scatter_upd.cpp @@ -18,13 +18,13 @@ // Created by raver119 on 24.11.17. // -#include +#include #if NOT_EXCLUDED(OP_scatter_upd) #include #include -namespace nd4j { +namespace sd { namespace ops { OP_IMPL(scatter_upd, 3, 1, true) { auto input = INPUT_VARIABLE(0); diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/segment_max.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/segment_max.cpp index f78f2034f..fe469e5ec 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/segment_max.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/segment_max.cpp @@ -21,7 +21,7 @@ #include #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(segment_max, 2, 1, false, 0, 0) { auto input = INPUT_VARIABLE(0); @@ -94,7 +94,7 @@ namespace nd4j { } DECLARE_TYPES(segment_max_bp) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes(0, {ALL_FLOATS}) ->setAllowedOutputTypes(1, {ALL_INTS}) ->setSameMode(true); diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/segment_mean.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/segment_mean.cpp index 7f9c0664a..ef35f4839 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/segment_mean.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/segment_mean.cpp @@ -21,7 +21,7 @@ #include #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(segment_mean, 2, 1, false, 0, 0) { auto input = INPUT_VARIABLE(0); @@ -93,7 +93,7 @@ namespace nd4j { } DECLARE_TYPES(segment_mean_bp) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes(0, {ALL_FLOATS}) ->setAllowedOutputTypes(1, {ALL_INTS}) ->setSameMode(false); diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/segment_min.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/segment_min.cpp index a8c9f1b01..9c4a11255 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/segment_min.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/segment_min.cpp @@ -21,7 +21,7 @@ #include #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(segment_min, 2, 1, false, 0, 0) { auto input = INPUT_VARIABLE(0); @@ -91,7 +91,7 @@ namespace nd4j { } DECLARE_TYPES(segment_min_bp) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes(0, {ALL_FLOATS}) ->setAllowedOutputTypes(1, {ALL_INTS}) ->setSameMode(true); diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/segment_prod.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/segment_prod.cpp index a2c24de56..576fae508 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/segment_prod.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/segment_prod.cpp @@ -21,7 +21,7 @@ #include #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(segment_prod, 2, 1, false, 0, 0) { auto input = INPUT_VARIABLE(0); @@ -97,7 +97,7 @@ namespace nd4j { DECLARE_TYPES(segment_prod_bp) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes(0, {ALL_FLOATS}) ->setAllowedOutputTypes(1, {ALL_INTS}) ->setSameMode(false); diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/segment_sum.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/segment_sum.cpp index 46efaf2a8..203797e34 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/segment_sum.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/segment_sum.cpp @@ -21,7 +21,7 @@ #include #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(segment_sum, 2, 1, false, 0, 0) { auto input = INPUT_VARIABLE(0); @@ -81,12 +81,12 @@ namespace nd4j { DECLARE_TYPES(segment_sum) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setSameMode(true); } DECLARE_TYPES(segment_sum_bp) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes(0, {ALL_FLOATS}) ->setAllowedOutputTypes(1, {ALL_INTS}) ->setSameMode(false); diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/sequence_mask.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/sequence_mask.cpp index 477b298a3..310180685 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/sequence_mask.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/sequence_mask.cpp @@ -21,7 +21,7 @@ #include #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(sequence_mask, 1, 1, false, 0, 0) { auto input = INPUT_VARIABLE(0); @@ -99,7 +99,7 @@ namespace nd4j { DECLARE_TYPES(sequence_mask) { getOpDescriptor() ->setAllowedInputTypes({ALL_INTS}) - ->setAllowedOutputTypes(nd4j::DataType::ANY); + ->setAllowedOutputTypes(sd::DataType::ANY); } } } diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/size.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/size.cpp index ed8927f98..d31e782c6 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/size.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/size.cpp @@ -18,12 +18,12 @@ // Created by raver119 on 01.11.2017. // -#include +#include #if NOT_EXCLUDED(OP_size) #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(size, 1, 1, false, 0, 0) { auto input = INPUT_VARIABLE(0); @@ -37,12 +37,12 @@ namespace nd4j { return Status::OK(); } DECLARE_SHAPE_FN(size) { - return SHAPELIST(ConstantShapeHelper::getInstance()->scalarShapeInfo(nd4j::DataType::INT64)); + return SHAPELIST(ConstantShapeHelper::getInstance()->scalarShapeInfo(sd::DataType::INT64)); } DECLARE_TYPES(size) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_INTS, ALL_FLOATS}) ->allowOverride(true); } diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/slice.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/slice.cpp index d2a390eb9..dc4671ef7 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/slice.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/slice.cpp @@ -18,13 +18,13 @@ // Created by raver119 on 02.11.2017. // -#include +#include //#if NOT_EXCLUDED(OP_slice) #include #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(slice, 1, 1, false, 0, -2) { auto input = INPUT_VARIABLE(0); @@ -70,7 +70,7 @@ namespace nd4j { empty = true; //Don't break to perform input validation on other dims } - + indices[2*e] = start; indices[2*e+1] = start + size; } @@ -80,8 +80,29 @@ namespace nd4j { return Status::OK(); } - auto sub = (*input)(indices, true); - output->assign(sub); + Nd4jLong* subArrShapeInfo = nullptr; + ALLOCATE(subArrShapeInfo, block.getWorkspace(), shape::shapeInfoLength(input->rankOf()), Nd4jLong); + + Nd4jLong offset; + + shape::calcSubArrShapeInfoAndOffset(indices.data(), input->getShapeInfo(), subArrShapeInfo, offset, true); + + auto subArrShapeInfoPack = ConstantShapeHelper::getInstance()->bufferForShapeInfo(subArrShapeInfo); + + NDArray::prepareSpecialUse({output}, {input}); + + NativeOpExecutioner::execTransformAny(block.launchContext(), sd::transform::Assign, + input->bufferWithOffset(offset), reinterpret_cast(subArrShapeInfoPack.primary()), + input->specialBufferWithOffset(offset), reinterpret_cast(subArrShapeInfoPack.special()), + output->buffer(), output->shapeInfo(), output->specialBuffer(), output->specialShapeInfo(), + nullptr, nullptr, nullptr, true); + + NDArray::registerSpecialUse({output}, {input}); + + RELEASE(subArrShapeInfo, block.getWorkspace()); + + // auto sub = (*input)(indices, true); + // output->assign(sub); STORE_RESULT(output); @@ -90,7 +111,7 @@ namespace nd4j { DECLARE_TYPES(slice) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setSameMode(true); } @@ -116,7 +137,7 @@ namespace nd4j { REQUIRE_TRUE(begin.size() == x_rank, 0, "Begin array should have length of [%i] but got [%i] instead", x_rank, begin.size()); REQUIRE_TRUE(sz.size() == x_rank, 0, "Size array should have length of [%i] but got [%i] instead", x_rank, sz.size()); - + std::vector shape; auto empty = false; for (int e = 0; e < x_rank; e++) { @@ -145,7 +166,7 @@ namespace nd4j { DECLARE_TYPES(slice_bp) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_FLOATS}); } @@ -186,12 +207,12 @@ namespace nd4j { size = input->sizeAt(e) - start; } REQUIRE_TRUE(size > 0, 0, "Slice: interval for dimension %i is less then 1", e); - + indices[2*e] = start; indices[2*e + 1] = start + size; } auto sub = (*output)(indices, true); - sub.assign(epsNext); + sub.assign(epsNext); return Status::OK(); } diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/solve.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/solve.cpp index 5790ae960..154001684 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/solve.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/solve.cpp @@ -18,12 +18,12 @@ // Created by GS at 01/22/2020 // -#include +#include #if NOT_EXCLUDED(OP_solve) #include #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(solve, 2, 1, false, 0, 0) { auto a = INPUT_VARIABLE(0); @@ -35,12 +35,15 @@ namespace nd4j { if (block.numB() > 0) { useAdjoint = B_ARG(0); } - + REQUIRE_TRUE(shape::shapeEquals(a->rankOf() - 2, a->shapeInfo(), b->rankOf() - 2, b->shapeInfo()), 0, "solve: Input shapes should be alike."); REQUIRE_TRUE(a->rankOf() >=2, 0, "solve: The rank of input left tensor should not be less than 2, but %i is given", a->rankOf()); REQUIRE_TRUE(b->rankOf() >=2, 0, "solve: The rank of input right tensor should not be less than 2, but %i is given", b->rankOf()); REQUIRE_TRUE(a->sizeAt(-1) == a->sizeAt(-2), 0, "solve: The last two dimmensions should be equal, but %i and %i are given", a->sizeAt(-1), a->sizeAt(-2)); REQUIRE_TRUE(a->sizeAt(-1) == b->sizeAt(-2), 0, "solve: The last dimmension of left part should be equal to prelast of right part, but %i and %i are given", a->sizeAt(-1), b->sizeAt(-2)); + if (a->isEmpty() || b->isEmpty() || z->isEmpty()) + return Status::OK(); + auto input = a; if (useAdjoint) { auto adjointA = a->ulike(); diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/space_to_batch.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/space_to_batch.cpp index 2f297c893..12b981ac2 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/space_to_batch.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/space_to_batch.cpp @@ -17,13 +17,13 @@ limitations under the License. // @author raver119@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_space_to_batch) #include #include -namespace nd4j { +namespace sd { namespace ops { @@ -60,7 +60,7 @@ CUSTOM_OP_IMPL(space_to_batch, 2, 1, false, 0, 1) { //////////////////////////////////////////////////////////////////////////////// DECLARE_TYPES(space_to_batch) { - getOpDescriptor()->setAllowedInputTypes(0, nd4j::DataType::ANY) + getOpDescriptor()->setAllowedInputTypes(0, sd::DataType::ANY) ->setAllowedInputTypes(1, {ALL_INTS}) ->setSameMode(true); } diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/space_to_batch_nd.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/space_to_batch_nd.cpp index d3939e303..a782f5b02 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/space_to_batch_nd.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/space_to_batch_nd.cpp @@ -16,13 +16,13 @@ limitations under the License. // @author Yurii Shyrma (iuriish@yahoo.com) // -#include +#include #if NOT_EXCLUDED(OP_space_to_batch_nd) #include #include -namespace nd4j { +namespace sd { namespace ops { @@ -64,7 +64,7 @@ CUSTOM_OP_IMPL(space_to_batch_nd, 3, 1, false, 0, 0) { //////////////////////////////////////////////////////////////////////////////// DECLARE_TYPES(space_to_batch_nd) { - getOpDescriptor()->setAllowedInputTypes(0, nd4j::DataType::ANY) + getOpDescriptor()->setAllowedInputTypes(0, sd::DataType::ANY) ->setAllowedInputTypes(1, {ALL_INTS}) ->setAllowedInputTypes(2, {ALL_INTS}) ->setSameMode(true); @@ -88,7 +88,7 @@ DECLARE_SHAPE_FN(space_to_batch_nd) { std::vector outShape(inputShapeInfo + 1, inputShapeInfo + 1 + inputShapeInfo[0]); - outShape[0] *= INPUT_VARIABLE(1)->reduceNumber(nd4j::reduce::Prod).e(0); + outShape[0] *= INPUT_VARIABLE(1)->reduceNumber(sd::reduce::Prod).e(0); for (uint i = 0; i < numOfSpatialDims; ++i) outShape[i + 1] = (outShape[i + 1] + INPUT_VARIABLE(2)->e(i,0) + INPUT_VARIABLE(2)->e(i,1)) / INPUT_VARIABLE(1)->e(i); diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/space_to_depth.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/space_to_depth.cpp index f6ad8c163..3daf62ccd 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/space_to_depth.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/space_to_depth.cpp @@ -18,19 +18,19 @@ // @author raver119@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_space_to_depth) #include #include #include -namespace nd4j { +namespace sd { namespace ops { DECLARE_TYPES(space_to_depth) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setSameMode(true); } diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/split.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/split.cpp index 2176ae711..60a80378e 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/split.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/split.cpp @@ -18,13 +18,14 @@ // @author raver119@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_split) #include +#include #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(split, 1, -1, false, 0, 1) { NDArray *input = nullptr; @@ -48,7 +49,7 @@ namespace ops { input = a; } } - + //Edge case: splitting empty array (mainly for TF import compatibility) -> return N empty arrays if(input->isEmpty()){ for( int i=0; i< num_splits; i++ ){ @@ -65,30 +66,13 @@ namespace ops { REQUIRE_TRUE(input->sizeAt(axis) % num_splits == 0, 0, "Split: num_splits has wrong value, remainder of division should be 0, but it's %i", input->sizeAt(axis) % num_splits); - int pos = 0; - int split = input->sizeAt(axis) / num_splits; - std::vector indices(2 * input->rankOf()); - + std::vector outArrs(num_splits); for (int e = 0; e < num_splits; e++) { - - auto out = OUTPUT_VARIABLE(e); - - for (int d = 0; d < input->rankOf(); d++) { - if (d == axis) { - indices[2*d] = pos; - indices[2*d + 1] = pos + split; - } - else - indices[2*d] = indices[2*d + 1] = 0; - } - - auto sub = (*input)(indices, true); - - out->assign(sub); - - pos += split; + outArrs[e] = OUTPUT_VARIABLE(e); } + helpers::split(block.launchContext(), *input, outArrs, axis); + return Status::OK(); } @@ -101,7 +85,7 @@ namespace ops { DECLARE_SHAPE_FN(split) { int num_splits = INT_ARG(0); Nd4jLong *input = nullptr; - nd4j::DataType dataType; + sd::DataType dataType; // axis is 0 by default int axis = 0; @@ -128,17 +112,17 @@ namespace ops { inputVar = 0; } } - + auto shapes = SHAPELIST(); - + //Edge case: splitting empty array (mainly for TF import compatibility) -> return N empty arrays - if(INPUT_VARIABLE(inputVar)->isEmpty()){ - for (int e = 0; e < num_splits; e++) { - auto empty = ConstantShapeHelper::getInstance()->emptyShapeInfo(dataType); - shapes->push_back(empty); - } - return shapes; - } + // if(INPUT_VARIABLE(inputVar)->isEmpty()){ + // for (int e = 0; e < num_splits; e++) { + // auto empty = ConstantShapeHelper::getInstance()->emptyShapeInfo(dataType); + // shapes->push_back(empty); + // } + // return shapes; + // } if (block.numI() == 2) axis = INT_ARG(1); @@ -151,9 +135,9 @@ namespace ops { for (int e = 0; e < shape::rank(input); e++) if (e == axis) shape[e] = shape::sizeAt(input, e) / num_splits; - else + else shape[e] = shape::sizeAt(input, e); - + for (int e = 0; e < num_splits; e++) { auto newShape = ConstantShapeHelper::getInstance()->createShapeInfo(dataType, shape::order(input), shape); shapes->push_back(newShape); diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/split_v.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/split_v.cpp index 0e36c15ea..0bda3a6be 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/split_v.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/split_v.cpp @@ -18,12 +18,12 @@ // @author raver119@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_split_v) #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(split_v, 2, -1, false, 0, -2) { auto input = INPUT_VARIABLE(0); diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/square.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/square.cpp index 34da37897..adc3a2cb5 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/square.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/square.cpp @@ -18,12 +18,12 @@ // Created by raver119 on 01/11/17. // -#include +#include #if NOT_EXCLUDED(OP_square) #include -namespace nd4j { +namespace sd { namespace ops { OP_IMPL(square, 1, 1, true) { auto input = INPUT_VARIABLE(0); @@ -37,7 +37,7 @@ namespace nd4j { DECLARE_TYPES(square) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setSameMode(true); } } diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/stack.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/stack.cpp index e52f83129..a78442b03 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/stack.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/stack.cpp @@ -18,13 +18,13 @@ // @author Yurii Shyrma (iuriish@yahoo.com), created on 01.11.2017. // -#include +#include #if NOT_EXCLUDED(OP_stack) #include #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(stack, -1, 1, false, 0, 0) { @@ -50,7 +50,7 @@ CUSTOM_OP_IMPL(stack, -1, 1, false, 0, 0) { for(int i = 0; i < block.width(); ++i) inArrs[i] = INPUT_VARIABLE(i); - helpers::stack(block.launchContext(), inArrs, output, dim); + helpers::stack(block.launchContext(), inArrs, *output, dim); return Status::OK(); } diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/stop_gradient.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/stop_gradient.cpp index 81f81c326..621df4462 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/stop_gradient.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/stop_gradient.cpp @@ -18,18 +18,21 @@ // @author raver119@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_stop_gradient) #include -namespace nd4j { +namespace sd { namespace ops { OP_IMPL(stop_gradient, 1, 1, true) { - auto x = INPUT_VARIABLE(0); auto out = OUTPUT_VARIABLE(0); - // just for lulz - x->applyTransform(transform::Identity, *out); + + if (!block.isInplace()) { + auto x = INPUT_VARIABLE(0); + // we hope for memcpy here + out->assign(x); + } return Status::OK(); } @@ -37,7 +40,7 @@ namespace nd4j { DECLARE_TYPES(stop_gradient) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setSameMode(true); } } diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/strided_slice.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/strided_slice.cpp index 4b622e821..747331ef0 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/strided_slice.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/strided_slice.cpp @@ -13,7 +13,7 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#include +#include #if NOT_EXCLUDED(OP_strided_slice) #include @@ -21,7 +21,7 @@ limitations under the License. #include #include -namespace nd4j { +namespace sd { namespace ops { constexpr int kShrinkAxis = -1, kNewAxis = -2; @@ -70,7 +70,7 @@ namespace nd4j { for (int e = 0; e < sparse_spec.dims; e++) { if ((1 << e) & sparse_spec.ellipsis_mask) { - int next_index = nd4j::math::nd4j_min(this->dims - (sparse_spec.dims - e) + 1 + sparse_spec.num_add_axis_after_ellipsis, this->dims); + int next_index = sd::math::nd4j_min(this->dims - (sparse_spec.dims - e) + 1 + sparse_spec.num_add_axis_after_ellipsis, this->dims); for (; full_index < next_index; full_index++) { // new_axis' aren't real axis so you have to skip @@ -411,8 +411,24 @@ namespace nd4j { // } // else { if (indices.size()) { - auto sub = (*x)(indices, true, true); - z->assign(sub); + Nd4jLong* subArrShapeInfo = nullptr; + ALLOCATE(subArrShapeInfo, block.getWorkspace(), shape::shapeInfoLength(x->rankOf()), Nd4jLong); + Nd4jLong offset; + + shape::calcSubArrShapeInfoAndOffset(indices.data(), x->getShapeInfo(), subArrShapeInfo, offset, true, true); + auto subArrShapeInfoPack = ConstantShapeHelper::getInstance()->bufferForShapeInfo(subArrShapeInfo); + + NDArray::prepareSpecialUse({z}, {x}); + + NativeOpExecutioner::execTransformAny(block.launchContext(), sd::transform::Assign, + x->bufferWithOffset(offset), reinterpret_cast(subArrShapeInfoPack.primary()), + x->specialBufferWithOffset(offset), reinterpret_cast(subArrShapeInfoPack.special()), + z->buffer(), z->shapeInfo(), z->specialBuffer(), z->specialShapeInfo(), + nullptr, nullptr, nullptr, true); + + NDArray::registerSpecialUse({z}, {x}); + + RELEASE(subArrShapeInfo, block.getWorkspace()); } else if (!z->isEmpty()){ z->assign(x->e(0)); @@ -646,13 +662,13 @@ namespace nd4j { DECLARE_TYPES(strided_slice) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setSameMode(true); } DECLARE_TYPES(strided_slice_bp) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_FLOATS}); } } diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/sufficient_statistics.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/sufficient_statistics.cpp index ed7698d15..9a9fb730b 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/sufficient_statistics.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/sufficient_statistics.cpp @@ -18,12 +18,12 @@ // Created by george@skymind.io on 2/21/2018. // Modified by sgazeos@gmail.com on 4/4/2018 -#include +#include #if NOT_EXCLUDED(OP_sufficient_statistics) #include #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(sufficient_statistics, 2, 3, false, 0, 0) { auto input = INPUT_VARIABLE(0); diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/tear.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/tear.cpp index c76435622..61850ab0e 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/tear.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/tear.cpp @@ -18,14 +18,14 @@ // Created by raver119 on 12.10.2017. // -#include +#include #if NOT_EXCLUDED(OP_tear) #include -#include +#include #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(tear, 1, -1, false, 0, -1) { auto input = INPUT_VARIABLE(0); @@ -57,7 +57,7 @@ namespace nd4j { if (dims.size() > 1) std::sort(dims.begin(), dims.end()); - auto tadPack = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(inShape, dims); + auto tadPack = sd::ConstantTadHelper::getInstance()->tadForDimensions(inShape, dims); auto numTads = tadPack.numberOfTads(); auto result = SHAPELIST(); @@ -71,7 +71,7 @@ namespace nd4j { DECLARE_TYPES(tear) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setSameMode(true); } } diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/top_k.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/top_k.cpp index bd16cdd79..799572794 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/top_k.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/top_k.cpp @@ -18,14 +18,14 @@ // @author sgazeos@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_top_k) //#include #include #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(top_k, 1, 2, false, 0, -1) { auto x = INPUT_VARIABLE(0); @@ -76,7 +76,7 @@ namespace nd4j { aShape[shapeRank] = k; shape::updateStrides(aShape, shape::order(in)); - shapeList->push_back(ConstantShapeHelper::getInstance()->createShapeInfo(ShapeDescriptor(aShape, (e == 0?ArrayOptions::dataType(in):nd4j::DataType::INT64)))); + shapeList->push_back(ConstantShapeHelper::getInstance()->createShapeInfo(ShapeDescriptor(aShape, (e == 0?ArrayOptions::dataType(in):sd::DataType::INT64)))); RELEASE(aShape, block.getWorkspace()); } @@ -85,8 +85,8 @@ namespace nd4j { DECLARE_TYPES(top_k) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) - ->setAllowedOutputTypes(0, nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) + ->setAllowedOutputTypes(0, sd::DataType::ANY) ->setAllowedOutputTypes(1, {ALL_INDICES}); } } diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/triangular_solve.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/triangular_solve.cpp index 181f47d3d..c9d23753c 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/triangular_solve.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/triangular_solve.cpp @@ -18,12 +18,12 @@ // Created by GS at 01/14/2020 // -#include +#include #if NOT_EXCLUDED(OP_triangual_solve) #include #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(triangular_solve, 2, 1, false, 0, 0) { auto a = INPUT_VARIABLE(0); diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/unique.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/unique.cpp index 715720e07..64b915c53 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/unique.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/unique.cpp @@ -18,13 +18,13 @@ // @author raver119@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_unique) #include #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(unique, 1, 2, false, 0, 0) { auto x = INPUT_VARIABLE(0); @@ -53,7 +53,7 @@ namespace nd4j { valuesShape = ConstantShapeHelper::getInstance()->vectorShapeInfo(uniqueCount, ArrayOptions::dataType(in)); } // second output is always LONG - indicesShape = ConstantShapeHelper::getInstance()->vectorShapeInfo(shape::length(in), nd4j::DataType::INT64); + indicesShape = ConstantShapeHelper::getInstance()->vectorShapeInfo(shape::length(in), sd::DataType::INT64); //COPY_SHAPE_EX(in, indicesShape, block.getWorkspace()); @@ -80,17 +80,17 @@ namespace nd4j { auto valuesShape = ConstantShapeHelper::getInstance()->vectorShapeInfo(uniqueCount, source->dataType()); // second output is always LONG - auto indicesShape = ConstantShapeHelper::getInstance()->vectorShapeInfo(source->lengthOf(), nd4j::DataType::INT64); + auto indicesShape = ConstantShapeHelper::getInstance()->vectorShapeInfo(source->lengthOf(), sd::DataType::INT64); // third one as well - auto countsShape = ConstantShapeHelper::getInstance()->vectorShapeInfo(uniqueCount, nd4j::DataType::INT64); + auto countsShape = ConstantShapeHelper::getInstance()->vectorShapeInfo(uniqueCount, sd::DataType::INT64); return SHAPELIST(valuesShape, indicesShape, countsShape); } DECLARE_TYPES(unique) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes(0, {ALL_INTS, ALL_FLOATS}) ->setAllowedOutputTypes(1, {ALL_INTS}); } diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/unsorted_segment_max.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/unsorted_segment_max.cpp index d8bf36aed..14bee3853 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/unsorted_segment_max.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/unsorted_segment_max.cpp @@ -21,7 +21,7 @@ #include #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(unsorted_segment_max, 2, 1, false, 0, 0) { auto input = INPUT_VARIABLE(0); diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/unsorted_segment_mean.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/unsorted_segment_mean.cpp index 7e34cb296..38032c0d7 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/unsorted_segment_mean.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/unsorted_segment_mean.cpp @@ -21,7 +21,7 @@ #include #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(unsorted_segment_mean, 2, 1, false, 0, 0) { auto input = INPUT_VARIABLE(0); diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/unsorted_segment_min.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/unsorted_segment_min.cpp index e011350ed..5d6c58b16 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/unsorted_segment_min.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/unsorted_segment_min.cpp @@ -21,7 +21,7 @@ #include #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(unsorted_segment_min, 2, 1, false, 0, 0) { auto input = INPUT_VARIABLE(0); diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/unsorted_segment_prod.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/unsorted_segment_prod.cpp index 695207c4b..1bb1d5bf5 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/unsorted_segment_prod.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/unsorted_segment_prod.cpp @@ -21,7 +21,7 @@ #include #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(unsorted_segment_prod, 2, 1, false, 0, 0) { auto input = INPUT_VARIABLE(0); diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/unsorted_segment_sqrt_n.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/unsorted_segment_sqrt_n.cpp index 1286002b8..e29a86a42 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/unsorted_segment_sqrt_n.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/unsorted_segment_sqrt_n.cpp @@ -21,7 +21,7 @@ #include #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(unsorted_segment_sqrt_n, 2, 1, false, 0, 0) { auto input = INPUT_VARIABLE(0); diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/unsorted_segment_sum.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/unsorted_segment_sum.cpp index a761718d1..89c6cb76f 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/unsorted_segment_sum.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/unsorted_segment_sum.cpp @@ -21,7 +21,7 @@ #include #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(unsorted_segment_sum, 2, 1, false, 0, 0) { auto input = INPUT_VARIABLE(0); @@ -85,7 +85,7 @@ namespace nd4j { getOpDescriptor() ->setAllowedOutputTypes(0, {ALL_FLOATS}) ->setAllowedOutputTypes(1, {ALL_INTS}) - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setSameMode(false); } diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/unstack.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/unstack.cpp index a44510104..beebcad86 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/unstack.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/unstack.cpp @@ -16,125 +16,114 @@ // // @author raver119@gmail.com +// @author Yurii Shyrma (iuriish@yahoo.com) // -#include +#include #if NOT_EXCLUDED(OP_unstack) #include -#include +#include -namespace nd4j { - namespace ops { - CUSTOM_OP_IMPL(unstack, 1, -1, false, 0, 1) { - auto input = INPUT_VARIABLE(0); +namespace sd { +namespace ops { - auto dim = INT_ARG(0); - if (dim < 0) - dim += input->rankOf(); +CUSTOM_OP_IMPL(unstack, 1, -1, false, 0, 1) { + + auto input = INPUT_VARIABLE(0); + + auto dim = INT_ARG(0); + if (dim < 0) + dim += input->rankOf(); - REQUIRE_TRUE(dim < input->rankOf(), 0, "Unstack dimension should be lower then rank of input %i, but got dimension=%i !", input->rankOf(), dim); - REQUIRE_TRUE(dim >= 0, 0, "Unstack dimension should be non-negative value, but got %i !", dim); + REQUIRE_TRUE(dim < input->rankOf(), 0, "Unstack dimension should be lower then rank of input %i, but got dimension=%i !", input->rankOf(), dim); + REQUIRE_TRUE(dim >= 0, 0, "Unstack dimension should be non-negative value, but got %i !", dim); - if(input->isEmpty()) - return Status::OK(); + if(input->isEmpty()) + return Status::OK(); - std::vector dims; - for (int e = 0; e < input->rankOf(); e++) - if (e != dim) - dims.emplace_back(e); - if (dims.size() == 0 && input->rankOf() == 1) { // split vector into lenthOf scalars - for (Nd4jLong e = 0; e < input->lengthOf(); e++) { - auto outE = OUTPUT_VARIABLE(e); - outE->assign(input->e(e)); - } - } + std::vector outArrs(input->sizeAt(dim)); + for(uint i = 0; i < outArrs.size(); ++i) + outArrs[i] = OUTPUT_VARIABLE(i); - auto tads = input->allTensorsAlongDimension(dims); - //nd4j_printf("Tad size: %d\n",tads.size()); - for (int e = 0; e < tads.size(); e++) { - //nd4j_printf("Calling assign at index %d\n",e); - auto outE = OUTPUT_VARIABLE(e); - auto tadAtE = tads.at(e); + helpers::unstack(block.launchContext(), *input, outArrs, dim); - outE->assign(tadAtE); + return Status::OK(); +} - this->storeResult(block, e, *outE); - } +DECLARE_SYN(unpack, unstack); - return Status::OK(); - } +DECLARE_SHAPE_FN(unstack) { + auto inShapeInfo = inputShape->at(0); - DECLARE_SYN(unpack, unstack); + auto dim = INT_ARG(0); + if (dim < 0) + dim += shape::rank(inShapeInfo); - DECLARE_SHAPE_FN(unstack) { - auto inShape = inputShape->at(0); + REQUIRE_TRUE(dim < inShapeInfo[0], 0, "UNSTACK op: dimension should be lower then rank of input %i, but got dimension=%i !", inShapeInfo[0], dim); + REQUIRE_TRUE(dim >= 0, 0, "UNSTACK op: dimension should be non-negative value, but got %i !", dim); - auto dim = INT_ARG(0); - if (dim < 0) - dim += shape::rank(inShape); + if(ArrayOptions::arrayType(inShapeInfo) == ArrayType::EMPTY) { - REQUIRE_TRUE(dim < inShape[0], 0, "UNSTACK op: dimension should be lower then rank of input %i, but got dimension=%i !", inShape[0], dim); - REQUIRE_TRUE(dim >= 0, 0, "UNSTACK op: dimension should be non-negative value, but got %i !", dim); + if(shape::shapeOf(inShapeInfo)[dim] == 0) + return SHAPELIST(); - if(ArrayOptions::arrayType(inShape) == ArrayType::EMPTY) { - if(shape::shapeOf(inShape)[dim] == 0) - return SHAPELIST(); - const Nd4jLong numTads = shape::shapeOf(inShape)[dim]; - std::vector outShape; - for(uint i = 0; i < shape::rank(inShape); ++i) - if(i != dim) - outShape.push_back(shape::shapeOf(inShape)[i]); + const Nd4jLong numTads = shape::shapeOf(inShapeInfo)[dim]; + std::vector outShape; + for(uint i = 0; i < shape::rank(inShapeInfo); ++i) + if(i != dim) + outShape.push_back(shape::shapeOf(inShapeInfo)[i]); - auto result = SHAPELIST(); - for(uint i = 0; i < numTads; ++i) - result->push_back(ConstantShapeHelper::getInstance()->createShapeInfo(ArrayOptions::dataType(inShape), shape::order(inShape), outShape)); - return result; - } + auto result = SHAPELIST(); + for(uint i = 0; i < numTads; ++i) + result->push_back(ConstantShapeHelper::getInstance()->createShapeInfo(ArrayOptions::dataType(inShapeInfo), shape::order(inShapeInfo), outShape)); - std::vector dims; - for (int e = 0; e < shape::rank(inShape); e++) - if (e != dim) - dims.emplace_back(e); - if (dims.size() == 0 && shape::rank(inShape) == 1) { // split vector into lenthOf scalars - // - auto result = SHAPELIST(); - for (Nd4jLong e = 0; e < shape::length(inShape); e++) - result->push_back(ConstantShapeHelper::getInstance()->scalarShapeInfo(ArrayOptions::dataType(inShape))); - return result; - } - - auto tadPack = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(inShape, dims); - auto numTads = tadPack.numberOfTads(); - - std::vector shape(shape::rank(tadPack.primaryShapeInfo())); - for (int e = 0; e < shape::rank(tadPack.primaryShapeInfo()); e++) - shape[e] = shape::shapeOf(tadPack.primaryShapeInfo())[e]; - - // remove leading and trailing 1 - if (inShape[0] == 2 && shape.size() == 2) { - if (shape[0] == 1) { - shape.erase(shape.begin()); - } else if (shape[1] == 1) { - shape.erase(shape.end()); - } - } - - auto result = SHAPELIST(); - for (int e = 0; e < numTads; e++) { - auto newShape = ConstantShapeHelper::getInstance()->createShapeInfo(ArrayOptions::dataType(inShape), shape::order(inShape), shape); - result->push_back(newShape); - } - return result; - } - - DECLARE_TYPES(unstack) { - getOpDescriptor() - ->setAllowedInputTypes({ALL_FLOATS, ALL_INTS}) - ->setSameMode(true); - } + return result; } + + std::vector dims = ShapeUtils::evalDimsToExclude(inShapeInfo[0], {dim}); + + if (dims.size() == 0 && shape::rank(inShapeInfo) == 1) { // split vector into lenthOf scalars + + auto result = SHAPELIST(); + for (Nd4jLong e = 0; e < shape::length(inShapeInfo); e++) + result->push_back(ConstantShapeHelper::getInstance()->scalarShapeInfo(ArrayOptions::dataType(inShapeInfo))); + + return result; + } + + std::vector subArrShape(shape::rank(inShapeInfo) - 1); + + for(uint j = 0, i = 0; i < shape::rank(inShapeInfo); i++) + if(i != dim) + subArrShape[j++] = shape::shapeOf(inShapeInfo)[i]; + + // remove leading and trailing 1 + if (inShapeInfo[0] == 2 && subArrShape.size() == 2) { + + if (subArrShape[0] == 1) + subArrShape.erase(subArrShape.begin()); + else if (subArrShape[1] == 1) + subArrShape.erase(subArrShape.end()); + } + + auto result = SHAPELIST(); + for (int e = 0; e < shape::shapeOf(inShapeInfo)[dim]; e++) { + auto newShape = ConstantShapeHelper::getInstance()->createShapeInfo(ArrayOptions::dataType(inShapeInfo), shape::order(inShapeInfo), subArrShape); + result->push_back(newShape); + } + return result; +} + +DECLARE_TYPES(unstack) { + getOpDescriptor() + ->setAllowedInputTypes({ALL_FLOATS, ALL_INTS}) + ->setSameMode(true); +} + + +} } #endif \ No newline at end of file diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/weighted_cross_entropy_with_logits.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/weighted_cross_entropy_with_logits.cpp index ff6537b1a..71d337c7c 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/weighted_cross_entropy_with_logits.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/weighted_cross_entropy_with_logits.cpp @@ -18,13 +18,13 @@ // @author @shugeo // -#include +#include #if NOT_EXCLUDED(OP_weighted_cross_entropy_with_logits) #include #include -namespace nd4j { +namespace sd { namespace ops { OP_IMPL(weighted_cross_entropy_with_logits, 3, 1, true) { @@ -43,7 +43,7 @@ namespace ops { DECLARE_TYPES(weighted_cross_entropy_with_logits) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_FLOATS}); } } diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/xw_plus_b.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/xw_plus_b.cpp index ce68df1a0..ad7a430f4 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/xw_plus_b.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/xw_plus_b.cpp @@ -19,14 +19,14 @@ // // -#include +#include #if NOT_EXCLUDED(OP_xw_plus_b) #include #include -#include +#include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(xw_plus_b, 3, 1, false, 0, 0) { auto x = INPUT_VARIABLE(0); @@ -55,7 +55,7 @@ namespace nd4j { DECLARE_TYPES(xw_plus_b) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_FLOATS}); } } diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/zero_fraction.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/zero_fraction.cpp index 2b623d23e..f70e92cf5 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/zero_fraction.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/zero_fraction.cpp @@ -18,12 +18,12 @@ // Created by GS 31.01.2018 // -#include +#include #if NOT_EXCLUDED(OP_zero_fraction) #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(zero_fraction, 1, 1, false, 0, 0) { auto input = INPUT_VARIABLE(0); @@ -48,12 +48,12 @@ namespace nd4j { return Status::OK(); } DECLARE_SHAPE_FN(zero_fraction) { - return SHAPELIST(ConstantShapeHelper::getInstance()->scalarShapeInfo(nd4j::DataType::DOUBLE)); + return SHAPELIST(ConstantShapeHelper::getInstance()->scalarShapeInfo(sd::DataType::DOUBLE)); } DECLARE_TYPES(zero_fraction) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_FLOATS}); } } diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/zeros_as.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/zeros_as.cpp index 56b4264d0..6d475af53 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/zeros_as.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/zeros_as.cpp @@ -18,12 +18,12 @@ // Created by raver119 on 12.10.2017. // -#include +#include #if NOT_EXCLUDED(OP_zeros_as) #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(zeros_as, 1, 1, false, 0, 0) { auto out = OUTPUT_VARIABLE(0); @@ -39,15 +39,15 @@ namespace nd4j { DECLARE_SHAPE_FN(zeros_as) { auto in = inputShape->at(0); auto dtype = block.numD() ? D_ARG(0) : ArrayOptions::dataType(in); - auto shape = nd4j::ConstantShapeHelper::getInstance()->createShapeInfo(dtype, in); + auto shape = sd::ConstantShapeHelper::getInstance()->createShapeInfo(dtype, in); return SHAPELIST(shape); } DECLARE_TYPES(zeros_as) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) - ->setAllowedOutputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) + ->setAllowedOutputTypes(sd::DataType::ANY) ->setSameMode(false); } } diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/zeta.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/zeta.cpp index eb10e53af..6aba1fc5f 100644 --- a/libnd4j/include/ops/declarable/generic/parity_ops/zeta.cpp +++ b/libnd4j/include/ops/declarable/generic/parity_ops/zeta.cpp @@ -18,13 +18,13 @@ // @author Yurii Shyrma (iuriish@yahoo.com), created on 12.12.2017 // -#include +#include #if NOT_EXCLUDED(OP_zeta) #include #include -namespace nd4j { +namespace sd { namespace ops { CONFIGURABLE_OP_IMPL(zeta, 2, 1, false, 0, 0) { auto x = INPUT_VARIABLE(0); diff --git a/libnd4j/include/ops/declarable/generic/random/bernoulli.cpp b/libnd4j/include/ops/declarable/generic/random/bernoulli.cpp index 338760921..1441448c9 100644 --- a/libnd4j/include/ops/declarable/generic/random/bernoulli.cpp +++ b/libnd4j/include/ops/declarable/generic/random/bernoulli.cpp @@ -18,13 +18,13 @@ // @author raver119@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_random_bernoulli) #include #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(random_bernoulli, 1, 1, true, 1, 0) { auto rng = block.getRng(); @@ -60,7 +60,7 @@ namespace nd4j { DECLARE_TYPES(random_bernoulli) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_FLOATS}); } } diff --git a/libnd4j/include/ops/declarable/generic/random/exponential.cpp b/libnd4j/include/ops/declarable/generic/random/exponential.cpp index bc942fc9b..8605ffafe 100644 --- a/libnd4j/include/ops/declarable/generic/random/exponential.cpp +++ b/libnd4j/include/ops/declarable/generic/random/exponential.cpp @@ -18,13 +18,13 @@ // @author raver119@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_random_exponential) #include #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(random_exponential, 1, 1, true, 1, 0) { // uniform distribution @@ -69,7 +69,7 @@ namespace nd4j { DECLARE_TYPES(random_exponential) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_FLOATS}); } } diff --git a/libnd4j/include/ops/declarable/generic/random/gamma.cpp b/libnd4j/include/ops/declarable/generic/random/gamma.cpp index 672eba422..d508e1929 100644 --- a/libnd4j/include/ops/declarable/generic/random/gamma.cpp +++ b/libnd4j/include/ops/declarable/generic/random/gamma.cpp @@ -18,13 +18,13 @@ // @author George A. Shulinok // -#include +#include #if NOT_EXCLUDED(OP_random_gamma) #include #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(random_gamma, 2, 1, false, 0, 0) { // gamma distribution diff --git a/libnd4j/include/ops/declarable/generic/random/get_seed.cpp b/libnd4j/include/ops/declarable/generic/random/get_seed.cpp index 2161a2378..7042ae6dd 100644 --- a/libnd4j/include/ops/declarable/generic/random/get_seed.cpp +++ b/libnd4j/include/ops/declarable/generic/random/get_seed.cpp @@ -18,12 +18,12 @@ // @author raver119@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_get_seed) #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(get_seed, -2, 1, false, 0, 0) { // REQUIRE_TRUE(block.getRNG() != nullptr, 0, "RNG should be defined in Graph"); @@ -42,7 +42,7 @@ namespace nd4j { DECLARE_TYPES(get_seed) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes(DataType::INT64); } } diff --git a/libnd4j/include/ops/declarable/generic/random/multinomial.cpp b/libnd4j/include/ops/declarable/generic/random/multinomial.cpp index bbdee17f4..c86417ef0 100644 --- a/libnd4j/include/ops/declarable/generic/random/multinomial.cpp +++ b/libnd4j/include/ops/declarable/generic/random/multinomial.cpp @@ -19,14 +19,14 @@ // @author Oleh Semeniv (oleg.semeniv@gmail.com) // -#include +#include #if NOT_EXCLUDED(OP_random_multinomial) #include #include #include -namespace nd4j { +namespace sd { namespace ops { /////////////////////// /** @@ -98,14 +98,14 @@ namespace nd4j { auto dimA = (0 == dimC) ? 1 : 0; nShape[dimA] = numOfSamples; - DataType nType = (argSize > 1) ? ( INT_ARG(1) >= 0 ? static_cast(INT_ARG(1)) : nd4j::DataType::INT64) : nd4j::DataType::INT64; + DataType nType = (argSize > 1) ? ( INT_ARG(1) >= 0 ? static_cast(INT_ARG(1)) : sd::DataType::INT64) : sd::DataType::INT64; return SHAPELIST(ConstantShapeHelper::getInstance()->createShapeInfo(nType, input->ordering(), nShape)); } DECLARE_TYPES(random_multinomial) { getOpDescriptor() ->setAllowedInputTypes(0, { ALL_FLOATS, ALL_INTS }) - ->setAllowedInputTypes(1, { nd4j::DataType::INT32 }) + ->setAllowedInputTypes(1, { sd::DataType::INT32 }) ->setAllowedOutputTypes(0, { ALL_INDICES }); } } diff --git a/libnd4j/include/ops/declarable/generic/random/normal.cpp b/libnd4j/include/ops/declarable/generic/random/normal.cpp index 781d495f0..8bfbd8db6 100644 --- a/libnd4j/include/ops/declarable/generic/random/normal.cpp +++ b/libnd4j/include/ops/declarable/generic/random/normal.cpp @@ -18,13 +18,13 @@ // @author raver119@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_random_normal) #include #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(random_normal, 1, 1, true, 2, 0) { // normal distribution @@ -56,7 +56,7 @@ namespace nd4j { DECLARE_TYPES(random_normal) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_FLOATS}); } } diff --git a/libnd4j/include/ops/declarable/generic/random/poisson.cpp b/libnd4j/include/ops/declarable/generic/random/poisson.cpp index 935bed095..74f3a8570 100644 --- a/libnd4j/include/ops/declarable/generic/random/poisson.cpp +++ b/libnd4j/include/ops/declarable/generic/random/poisson.cpp @@ -18,13 +18,13 @@ // @author George A. Shulinok // -#include +#include #if NOT_EXCLUDED(OP_random_poisson) #include #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(random_poisson, 2, 1, false, 0, 0) { // gamma distribution diff --git a/libnd4j/include/ops/declarable/generic/random/random_crop.cpp b/libnd4j/include/ops/declarable/generic/random/random_crop.cpp index 701a57c83..2ac2495d3 100644 --- a/libnd4j/include/ops/declarable/generic/random/random_crop.cpp +++ b/libnd4j/include/ops/declarable/generic/random/random_crop.cpp @@ -22,7 +22,7 @@ #include #include -namespace nd4j { +namespace sd { namespace ops { @@ -65,7 +65,7 @@ DECLARE_SHAPE_FN(random_crop) { DECLARE_TYPES(random_crop) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_FLOATS}); } } diff --git a/libnd4j/include/ops/declarable/generic/random/random_shuffle.cpp b/libnd4j/include/ops/declarable/generic/random/random_shuffle.cpp index e9eb73dbf..012d2e55a 100644 --- a/libnd4j/include/ops/declarable/generic/random/random_shuffle.cpp +++ b/libnd4j/include/ops/declarable/generic/random/random_shuffle.cpp @@ -18,13 +18,13 @@ // @author Yurii Shyrma (iuriish@yahoo.com), created on 26.01.2018 // -#include +#include #if NOT_EXCLUDED(OP_random_shuffle) #include #include -namespace nd4j { +namespace sd { namespace ops { OP_IMPL(random_shuffle, 1, 1, true) { @@ -33,8 +33,8 @@ OP_IMPL(random_shuffle, 1, 1, true) { const bool isInplace = block.isInplace(); auto output = isInplace ? nullptr : OUTPUT_VARIABLE(0); -// nd4j::random::RandomBuffer* rng = block.getRNG(); - nd4j::graph::RandomGenerator rng = block.randomGenerator(); +// sd::random::RandomBuffer* rng = block.getRNG(); + sd::graph::RandomGenerator rng = block.randomGenerator(); // REQUIRE_TRUE(rng != nullptr, 0, "RANDOM_SHUFFLE op: RNG should be defined in Graph !"); helpers::randomShuffle(block.launchContext(), *input, *output, rng, isInplace); @@ -45,7 +45,7 @@ OP_IMPL(random_shuffle, 1, 1, true) { DECLARE_TYPES(random_shuffle) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setSameMode(true); } } diff --git a/libnd4j/include/ops/declarable/generic/random/set_seed.cpp b/libnd4j/include/ops/declarable/generic/random/set_seed.cpp index fa9dcf992..f4c240d50 100644 --- a/libnd4j/include/ops/declarable/generic/random/set_seed.cpp +++ b/libnd4j/include/ops/declarable/generic/random/set_seed.cpp @@ -18,13 +18,13 @@ // @author raver119@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_set_seed) #include -#include +#include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(set_seed, -2, 1, false, 0, -2) { // REQUIRE_TRUE(block.getRNG() != nullptr, 0, "RNG should be defined in Graph"); diff --git a/libnd4j/include/ops/declarable/generic/random/uniform.cpp b/libnd4j/include/ops/declarable/generic/random/uniform.cpp index fd65f842a..6dec96739 100644 --- a/libnd4j/include/ops/declarable/generic/random/uniform.cpp +++ b/libnd4j/include/ops/declarable/generic/random/uniform.cpp @@ -19,14 +19,14 @@ // Created by raver119 on 29/10/17. // -#include +#include #if NOT_EXCLUDED(OP_randomuniform) #include #include #include -namespace nd4j { +namespace sd { namespace ops { /////////////////////// /** diff --git a/libnd4j/include/ops/declarable/generic/recurrent/dynamicBidirectionalRNN.cpp b/libnd4j/include/ops/declarable/generic/recurrent/dynamicBidirectionalRNN.cpp index 7f536a9ea..33fd5e8ea 100644 --- a/libnd4j/include/ops/declarable/generic/recurrent/dynamicBidirectionalRNN.cpp +++ b/libnd4j/include/ops/declarable/generic/recurrent/dynamicBidirectionalRNN.cpp @@ -20,7 +20,7 @@ #include -namespace nd4j { +namespace sd { namespace ops { @@ -54,15 +54,15 @@ CUSTOM_OP_IMPL(dynamic_bidirectional_rnn, 7, 4, false, 0, 0) { maxTimeStep = INPUT_VARIABLE(9); break; } - + auto hFW = OUTPUT_VARIABLE(0); // cell outputs for forward RNN [time x bS x numUnitsFW] or [bS x time x numUnitsFW], shape depends on timeMajor parameter auto hBW = OUTPUT_VARIABLE(1); // cell outputs for backward RNN [time x bS x numUnitsBW] or [bS x time x numUnitsBW], shape depends on timeMajor parameter auto hFWFinal = OUTPUT_VARIABLE(2); // final cell out for forward RNN [bS x numUnitsFW] auto hBWFinal = OUTPUT_VARIABLE(3); // final cell out for backward RNN [bS x numUnitsBF] REQUIRE_TRUE(x->rankOf() == 3, 0, "DYNAMIC_BIDIRECTIONAL_RNN custom operation: input array must have rank = 3, but got %i instead !", x->rankOf()); - REQUIRE_TRUE(WxFW->rankOf() == 2, 0, "DYNAMIC_BIDIRECTIONAL_RNN custom operation: input-to-hidden weights array (for forward RNN) must have rank = 2, but got %i instead !", WxFW->rankOf()); - REQUIRE_TRUE(WxBW->rankOf() == 2, 0, "DYNAMIC_BIDIRECTIONAL_RNN custom operation: input-to-hidden weights array (for backward RNN) must have rank = 2, but got %i instead !", WxBW->rankOf()); + REQUIRE_TRUE(WxFW->rankOf() == 2, 0, "DYNAMIC_BIDIRECTIONAL_RNN custom operation: input-to-hidden weights array (for forward RNN) must have rank = 2, but got %i instead !", WxFW->rankOf()); + REQUIRE_TRUE(WxBW->rankOf() == 2, 0, "DYNAMIC_BIDIRECTIONAL_RNN custom operation: input-to-hidden weights array (for backward RNN) must have rank = 2, but got %i instead !", WxBW->rankOf()); const int inRank = x->rankOf(); const int time = timeMajor ? x->sizeAt(0) : x->sizeAt(1); @@ -70,50 +70,55 @@ CUSTOM_OP_IMPL(dynamic_bidirectional_rnn, 7, 4, false, 0, 0) { const int numUnitsFW = WxFW->sizeAt(1); const int numUnitsBW = WxBW->sizeAt(1); - REQUIRE_TRUE(ShapeUtils::shapeAsString(WhFW) == ShapeUtils::shapeAsString({numUnitsFW, numUnitsFW}), 0, "DYNAMIC_BIDIRECTIONAL_RNN custom operation: wrong shape of hidden-to-hidden weights array (for forward RNN), expected is %s but got %s instead !", ShapeUtils::shapeAsString({numUnitsFW, numUnitsFW}).c_str(), ShapeUtils::shapeAsString(WhFW).c_str()); - REQUIRE_TRUE(ShapeUtils::shapeAsString(WhBW) == ShapeUtils::shapeAsString({numUnitsBW, numUnitsBW}), 0, "DYNAMIC_BIDIRECTIONAL_RNN custom operation: wrong shape of hidden-to-hidden weights array (for backward RNN), expected is %s but got %s instead !", ShapeUtils::shapeAsString({numUnitsBW, numUnitsBW}).c_str(), ShapeUtils::shapeAsString(WhBW).c_str()); - REQUIRE_TRUE(ShapeUtils::shapeAsString(bFW) == ShapeUtils::shapeAsString({2*numUnitsFW}), 0, "DYNAMIC_BIDIRECTIONAL_RNN custom operation: wrong shape of biases array (for forward RNN), expected is %s, but got %s instead !", ShapeUtils::shapeAsString({2*numUnitsFW}).c_str(), ShapeUtils::shapeAsString(bFW).c_str()); - REQUIRE_TRUE(ShapeUtils::shapeAsString(bBW) == ShapeUtils::shapeAsString({2*numUnitsBW}), 0, "DYNAMIC_BIDIRECTIONAL_RNN custom operation: wrong shape of biases array (for backward RNN), expected is %s, but got %s instead !", ShapeUtils::shapeAsString({2*numUnitsBW}).c_str(), ShapeUtils::shapeAsString(bBW).c_str()); - if(h0FW) - REQUIRE_TRUE(ShapeUtils::shapeAsString(h0FW) == ShapeUtils::shapeAsString({bS, numUnitsFW}), 0, "DYNAMIC_BIDIRECTIONAL_RNN custom operation: wrong shape of initial cell output array (for forward RNN), expected is %s but got %s instead !", ShapeUtils::shapeAsString({bS, numUnitsFW}).c_str(), ShapeUtils::shapeAsString(h0FW).c_str()); - if(h0BW) - REQUIRE_TRUE(ShapeUtils::shapeAsString(h0BW) == ShapeUtils::shapeAsString({bS, numUnitsBW}), 0, "DYNAMIC_BIDIRECTIONAL_RNN custom operation: wrong shape of initial cell output array (for backward RNN), expected is %s but got %s instead !", ShapeUtils::shapeAsString({bS, numUnitsBW}).c_str(), ShapeUtils::shapeAsString(h0BW).c_str()); - if(maxTimeStep) - REQUIRE_TRUE(ShapeUtils::shapeAsString(maxTimeStep) == ShapeUtils::shapeAsString({bS}), 0, "DYNAMIC_BIDIRECTIONAL_RNN custom operation: wrong shape of maxTimeStep array, expected is [%i], but got %s instead !", bS, ShapeUtils::shapeAsString(maxTimeStep).c_str()); + std::vector expectedWhFWshape = {numUnitsFW, numUnitsFW}; + std::vector expectedWhBWshape = {numUnitsBW, numUnitsBW}; + std::vector expectedbFWshape = {2*numUnitsFW}; + std::vector expectedbBWshape = {2*numUnitsBW}; + REQUIRE_TRUE(WhFW->isSameShape(expectedWhFWshape), 0, "DYNAMIC_BIDIRECTIONAL_RNN custom operation: wrong shape of hidden-to-hidden weights array (for forward RNN), expected is %s but got %s instead !", ShapeUtils::shapeAsString(expectedWhFWshape).c_str(), ShapeUtils::shapeAsString(WhFW).c_str()); + REQUIRE_TRUE(WhBW->isSameShape(expectedWhBWshape) , 0, "DYNAMIC_BIDIRECTIONAL_RNN custom operation: wrong shape of hidden-to-hidden weights array (for backward RNN), expected is %s but got %s instead !", ShapeUtils::shapeAsString(expectedWhBWshape).c_str(), ShapeUtils::shapeAsString(WhBW).c_str()); + REQUIRE_TRUE(bFW->isSameShape(expectedbFWshape), 0, "DYNAMIC_BIDIRECTIONAL_RNN custom operation: wrong shape of biases array (for forward RNN), expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedbFWshape).c_str(), ShapeUtils::shapeAsString(bFW).c_str()); + REQUIRE_TRUE(bBW->isSameShape(expectedbBWshape), 0, "DYNAMIC_BIDIRECTIONAL_RNN custom operation: wrong shape of biases array (for backward RNN), expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedbBWshape).c_str(), ShapeUtils::shapeAsString(bBW).c_str()); + if(h0FW) { + std::vector expectedh0FWshape = {bS, numUnitsFW}; + REQUIRE_TRUE(h0FW->isSameShape(expectedh0FWshape), 0, "DYNAMIC_BIDIRECTIONAL_RNN custom operation: wrong shape of initial cell output array (for forward RNN), expected is %s but got %s instead !", ShapeUtils::shapeAsString(expectedh0FWshape).c_str(), ShapeUtils::shapeAsString(h0FW).c_str()); + } + if(h0BW) { + std::vector expectedh0BWshape = {bS, numUnitsBW}; + REQUIRE_TRUE(h0BW->isSameShape(expectedh0BWshape), 0, "DYNAMIC_BIDIRECTIONAL_RNN custom operation: wrong shape of initial cell output array (for backward RNN), expected is %s but got %s instead !", ShapeUtils::shapeAsString(expectedh0BWshape).c_str(), ShapeUtils::shapeAsString(h0BW).c_str()); + } + if(maxTimeStep) { + std::vector expectedmaxTimeStepshape = {bS}; + REQUIRE_TRUE(maxTimeStep->isSameShape(expectedmaxTimeStepshape), 0, "DYNAMIC_BIDIRECTIONAL_RNN custom operation: wrong shape of maxTimeStep array, expected is [%i], but got %s instead !", bS, ShapeUtils::shapeAsString(maxTimeStep).c_str()); + } // forward steps - nd4j::ops::dynamic_rnn dynamicRnn; + sd::ops::dynamic_rnn dynamicRnn; auto resultsFW = dynamicRnn.evaluate({x, WxFW, WhFW, bFW, h0FW, maxTimeStep}, {timeMajor}); - hFW->assign(resultsFW->at(0)); // [time x bS x numUnitsFW] or [bS x time x numUnitsFW] - hFWFinal->assign(resultsFW->at(1)); + hFW->assign(resultsFW.at(0)); // [time x bS x numUnitsFW] or [bS x time x numUnitsFW] + hFWFinal->assign(resultsFW.at(1)); auto seqLen = maxTimeStep; if(seqLen == nullptr) { // FIXME: which datatype should be used here? - seqLen = new NDArray(x->ordering(), {bS}, nd4j::DataType::INT64, block.launchContext()); + seqLen = new NDArray(x->ordering(), {bS}, sd::DataType::INT64, block.launchContext()); seqLen->assign(time); // set each element of seqLen to be equal to time } // reverse x - nd4j::ops::reverse_sequence reverse; + sd::ops::reverse_sequence reverse; auto resultsIn = timeMajor ? reverse.evaluate({x, seqLen}, {0, 1}) : reverse.evaluate({x, seqLen}, {1, 0}); - REQUIRE_TRUE (resultsIn->status() == ND4J_STATUS_OK, 0, "dynamic_bidirectional_rnn: there is a problem with reverse on the sequence."); - auto revInput = resultsIn->at(0); + REQUIRE_TRUE (resultsIn.status() == ND4J_STATUS_OK, 0, "dynamic_bidirectional_rnn: there is a problem with reverse on the sequence."); + auto revInput = resultsIn.at(0); - // backward steps + // backward steps auto resultsBW = dynamicRnn.evaluate({revInput, WxBW, WhBW, bBW, h0BW, maxTimeStep}, {timeMajor}); - auto hBWtemp = resultsBW->at(0); // [time x bS x numUnitsBW] or [ bS x time xnumUnitsBW] - hBWFinal->assign(resultsBW->at(1)); + auto hBWtemp = resultsBW.at(0); // [time x bS x numUnitsBW] or [ bS x time xnumUnitsBW] + hBWFinal->assign(resultsBW.at(1)); - // reverse hBWtemp + // reverse hBWtemp auto resultsOut = timeMajor ? reverse.evaluate({hBWtemp, seqLen}, {0, 1}) : reverse.evaluate({hBWtemp, seqLen}, {1, 0}); - hBW->assign(resultsOut->at(0)); - - delete resultsOut; - delete resultsBW; - delete resultsIn; - delete resultsFW; + hBW->assign(resultsOut.at(0)); if(seqLen != maxTimeStep) delete seqLen; @@ -123,12 +128,12 @@ CUSTOM_OP_IMPL(dynamic_bidirectional_rnn, 7, 4, false, 0, 0) { DECLARE_TYPES(dynamic_bidirectional_rnn) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_FLOATS}); } -DECLARE_SHAPE_FN(dynamic_bidirectional_rnn) { +DECLARE_SHAPE_FN(dynamic_bidirectional_rnn) { auto x = INPUT_VARIABLE(0); // input [time x bS x inSize] or [bS x time x inSize], shape depends on timeMajor parameter auto WxFW = INPUT_VARIABLE(1); // input-to-hidden weights for forward RNN, [inSize x numUnitsFW] @@ -143,7 +148,7 @@ DECLARE_SHAPE_FN(dynamic_bidirectional_rnn) { NDArray* maxTimeStep = nullptr; // vector [bS] containing integer values within [0,time), each element of this vector set max time step per each input in batch, this means there are no calculations for time >= maxTimeStep const int timeMajor = block.getIArguments()->size() > 0 ? INT_ARG(0) : 0; // if true then [time, bS, ...], else [bS, time, ...] - + switch(block.width()) { case 8: maxTimeStep = INPUT_VARIABLE(7); @@ -160,8 +165,8 @@ DECLARE_SHAPE_FN(dynamic_bidirectional_rnn) { } REQUIRE_TRUE(x->rankOf() == 3, 0, "DYNAMIC_BIDIRECTIONAL_RNN custom operation: input array must have rank = 3, but got %i instead !", x->rankOf()); - REQUIRE_TRUE(WxFW->rankOf() == 2, 0, "DYNAMIC_BIDIRECTIONAL_RNN custom operation: input-to-hidden weights array (for forward RNN) must have rank = 2, but got %i instead !", WxFW->rankOf()); - REQUIRE_TRUE(WxBW->rankOf() == 2, 0, "DYNAMIC_BIDIRECTIONAL_RNN custom operation: input-to-hidden weights array (for backward RNN) must have rank = 2, but got %i instead !", WxBW->rankOf()); + REQUIRE_TRUE(WxFW->rankOf() == 2, 0, "DYNAMIC_BIDIRECTIONAL_RNN custom operation: input-to-hidden weights array (for forward RNN) must have rank = 2, but got %i instead !", WxFW->rankOf()); + REQUIRE_TRUE(WxBW->rankOf() == 2, 0, "DYNAMIC_BIDIRECTIONAL_RNN custom operation: input-to-hidden weights array (for backward RNN) must have rank = 2, but got %i instead !", WxBW->rankOf()); const int inRank = x->rankOf(); const int time = timeMajor ? x->sizeAt(0) : x->sizeAt(1); @@ -169,16 +174,28 @@ DECLARE_SHAPE_FN(dynamic_bidirectional_rnn) { const int numUnitsFW = WxFW->sizeAt(1); const int numUnitsBW = WxBW->sizeAt(1); - REQUIRE_TRUE(ShapeUtils::shapeAsString(WhFW) == ShapeUtils::shapeAsString({numUnitsFW, numUnitsFW}), 0, "DYNAMIC_BIDIRECTIONAL_RNN custom operation: wrong shape of hidden-to-hidden weights array (for forward RNN), expected is %s but got %s instead !", ShapeUtils::shapeAsString({numUnitsFW, numUnitsFW}).c_str(), ShapeUtils::shapeAsString(WhFW).c_str()); - REQUIRE_TRUE(ShapeUtils::shapeAsString(WhBW) == ShapeUtils::shapeAsString({numUnitsBW, numUnitsBW}), 0, "DYNAMIC_BIDIRECTIONAL_RNN custom operation: wrong shape of hidden-to-hidden weights array (for backward RNN), expected is %s but got %s instead !", ShapeUtils::shapeAsString({numUnitsBW, numUnitsBW}).c_str(), ShapeUtils::shapeAsString(WhBW).c_str()); - REQUIRE_TRUE(ShapeUtils::shapeAsString(bFW) == ShapeUtils::shapeAsString({2*numUnitsFW}), 0, "DYNAMIC_BIDIRECTIONAL_RNN custom operation: wrong shape of biases array (for forward RNN), expected is %s, but got %s instead !", ShapeUtils::shapeAsString({2*numUnitsFW}).c_str(), ShapeUtils::shapeAsString(bFW).c_str()); - REQUIRE_TRUE(ShapeUtils::shapeAsString(bBW) == ShapeUtils::shapeAsString({2*numUnitsBW}), 0, "DYNAMIC_BIDIRECTIONAL_RNN custom operation: wrong shape of biases array (for backward RNN), expected is %s, but got %s instead !", ShapeUtils::shapeAsString({2*numUnitsBW}).c_str(), ShapeUtils::shapeAsString(bBW).c_str()); - if(h0FW) - REQUIRE_TRUE(ShapeUtils::shapeAsString(h0FW) == ShapeUtils::shapeAsString({bS, numUnitsFW}), 0, "DYNAMIC_BIDIRECTIONAL_RNN custom operation: wrong shape of initial cell output array (for forward RNN), expected is %s but got %s instead !", ShapeUtils::shapeAsString({bS, numUnitsFW}).c_str(), ShapeUtils::shapeAsString(h0FW).c_str()); - if(h0BW) - REQUIRE_TRUE(ShapeUtils::shapeAsString(h0BW) == ShapeUtils::shapeAsString({bS, numUnitsBW}), 0, "DYNAMIC_BIDIRECTIONAL_RNN custom operation: wrong shape of initial cell output array (for backward RNN), expected is %s but got %s instead !", ShapeUtils::shapeAsString({bS, numUnitsBW}).c_str(), ShapeUtils::shapeAsString(h0BW).c_str()); - if(maxTimeStep) - REQUIRE_TRUE(ShapeUtils::shapeAsString(maxTimeStep) == ShapeUtils::shapeAsString({bS}), 0, "DYNAMIC_BIDIRECTIONAL_RNN custom operation: wrong shape of maxTimeStep array, expected is [%i], but got %s instead !", bS, ShapeUtils::shapeAsString(maxTimeStep).c_str()); + + std::vector expectedWhFWshape = {numUnitsFW, numUnitsFW}; + std::vector expectedWhBWshape = {numUnitsBW, numUnitsBW}; + std::vector expectedbFWshape = {2*numUnitsFW}; + std::vector expectedbBWshape = {2*numUnitsBW}; + + REQUIRE_TRUE(WhFW->isSameShape(expectedWhFWshape), 0, "DYNAMIC_BIDIRECTIONAL_RNN custom operation: wrong shape of hidden-to-hidden weights array (for forward RNN), expected is %s but got %s instead !", ShapeUtils::shapeAsString(expectedWhFWshape).c_str(), ShapeUtils::shapeAsString(WhFW).c_str()); + REQUIRE_TRUE(WhBW->isSameShape(expectedWhBWshape), 0, "DYNAMIC_BIDIRECTIONAL_RNN custom operation: wrong shape of hidden-to-hidden weights array (for backward RNN), expected is %s but got %s instead !", ShapeUtils::shapeAsString(expectedWhBWshape).c_str(), ShapeUtils::shapeAsString(WhBW).c_str()); + REQUIRE_TRUE(bFW->isSameShape(expectedbFWshape), 0, "DYNAMIC_BIDIRECTIONAL_RNN custom operation: wrong shape of biases array (for forward RNN), expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedbFWshape).c_str(), ShapeUtils::shapeAsString(bFW).c_str()); + REQUIRE_TRUE(bBW->isSameShape(expectedbBWshape), 0, "DYNAMIC_BIDIRECTIONAL_RNN custom operation: wrong shape of biases array (for backward RNN), expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedbBWshape).c_str(), ShapeUtils::shapeAsString(bBW).c_str()); + if(h0FW) { + std::vector expectedh0FWshape = {bS, numUnitsFW}; + REQUIRE_TRUE(h0FW->isSameShape(expectedh0FWshape), 0, "DYNAMIC_BIDIRECTIONAL_RNN custom operation: wrong shape of initial cell output array (for forward RNN), expected is %s but got %s instead !", ShapeUtils::shapeAsString(expectedh0FWshape).c_str(), ShapeUtils::shapeAsString(h0FW).c_str()); + } + if(h0BW) { + std::vector expectedh0BWshape = {bS, numUnitsBW}; + REQUIRE_TRUE(h0BW->isSameShape(expectedh0BWshape), 0, "DYNAMIC_BIDIRECTIONAL_RNN custom operation: wrong shape of initial cell output array (for backward RNN), expected is %s but got %s instead !", ShapeUtils::shapeAsString(expectedh0BWshape).c_str(), ShapeUtils::shapeAsString(h0BW).c_str()); + } + if(maxTimeStep) { + std::vector expectedmaxTimeStepshape = {bS}; + REQUIRE_TRUE(maxTimeStep->isSameShape(expectedmaxTimeStepshape), 0, "DYNAMIC_BIDIRECTIONAL_RNN custom operation: wrong shape of maxTimeStep array, expected is [%i], but got %s instead !", bS, ShapeUtils::shapeAsString(maxTimeStep).c_str()); + } // evaluate output shapeInfos Nd4jLong *hFWShapeInfo(nullptr), *hBWShapeInfo(nullptr), *hFWFinalPrevShapeInfo(nullptr), *hBWFinalPrevShapeInfo(nullptr); @@ -187,13 +204,13 @@ DECLARE_SHAPE_FN(dynamic_bidirectional_rnn) { ALLOCATE(hFWFinalPrevShapeInfo, block.getWorkspace(), shape::shapeInfoLength(inRank-1), Nd4jLong); ALLOCATE(hBWFinalPrevShapeInfo, block.getWorkspace(), shape::shapeInfoLength(inRank-1), Nd4jLong); - hFWShapeInfo[0] = hBWShapeInfo[0] = inRank; + hFWShapeInfo[0] = hBWShapeInfo[0] = inRank; hFWShapeInfo[1] = hBWShapeInfo[1] = timeMajor ? time : bS; hFWShapeInfo[2] = hBWShapeInfo[2] = timeMajor ? bS : time; hFWShapeInfo[3] = numUnitsFW; hBWShapeInfo[3] = numUnitsBW; hFWFinalPrevShapeInfo[0] = hBWFinalPrevShapeInfo[0] = inRank-1; - hFWFinalPrevShapeInfo[1] = hBWFinalPrevShapeInfo[1] = bS; + hFWFinalPrevShapeInfo[1] = hBWFinalPrevShapeInfo[1] = bS; hFWFinalPrevShapeInfo[2] = numUnitsFW; hBWFinalPrevShapeInfo[2] = numUnitsBW; @@ -201,15 +218,9 @@ DECLARE_SHAPE_FN(dynamic_bidirectional_rnn) { ShapeUtils::updateStridesAndType(hBWShapeInfo, x->getShapeInfo(), x->ordering()); ShapeUtils::updateStridesAndType(hFWFinalPrevShapeInfo, x->getShapeInfo(), x->ordering()); ShapeUtils::updateStridesAndType(hBWFinalPrevShapeInfo, x->getShapeInfo(), x->ordering()); - + return SHAPELIST(CONSTANT(hFWShapeInfo), CONSTANT(hBWShapeInfo), CONSTANT(hFWFinalPrevShapeInfo), CONSTANT(hBWFinalPrevShapeInfo)); -} - - - - - - +} } diff --git a/libnd4j/include/ops/declarable/generic/recurrent/dynamicRNN.cpp b/libnd4j/include/ops/declarable/generic/recurrent/dynamicRNN.cpp index 9fe30b345..41696638d 100644 --- a/libnd4j/include/ops/declarable/generic/recurrent/dynamicRNN.cpp +++ b/libnd4j/include/ops/declarable/generic/recurrent/dynamicRNN.cpp @@ -21,7 +21,7 @@ #include #include -namespace nd4j { +namespace sd { namespace ops { @@ -60,12 +60,18 @@ CUSTOM_OP_IMPL(dynamic_rnn, 4, 2, false, 0, 0) { const int bS = timeMajor ? x->sizeAt(1) : x->sizeAt(0); const int numUnits = Wx->sizeAt(1); - REQUIRE_TRUE(ShapeUtils::shapeAsString(Wh) == ShapeUtils::shapeAsString({numUnits, numUnits}), 0, "DYNAMIC_RNN custom operation: wrong shape of hidden-to-hidden weights array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString({numUnits, numUnits}).c_str(), ShapeUtils::shapeAsString(Wh).c_str()); - REQUIRE_TRUE(ShapeUtils::shapeAsString(b) == ShapeUtils::shapeAsString({2*numUnits}), 0, "DYNAMIC_RNN custom operation: wrong shape of biases array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString({2*numUnits}).c_str(), ShapeUtils::shapeAsString(b).c_str()); - if(h0) - REQUIRE_TRUE(ShapeUtils::shapeAsString(h0) == ShapeUtils::shapeAsString({bS, numUnits}), 0, "DYNAMIC_RNN custom operation: wrong shape of initial cell output array, expected is %s but got %s instead !", ShapeUtils::shapeAsString({bS, numUnits}).c_str(), ShapeUtils::shapeAsString(h0).c_str()); - if(maxTimeStep) - REQUIRE_TRUE(ShapeUtils::shapeAsString(maxTimeStep) == ShapeUtils::shapeAsString({bS}), 0, "DYNAMIC_RNN custom operation: wrong shape of maxTimeStep array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString({bS}).c_str(), ShapeUtils::shapeAsString(maxTimeStep).c_str()); + std::vector expectedWhShape = {numUnits, numUnits}; + std::vector expectedBShape = {2*numUnits}; + REQUIRE_TRUE(Wh->isSameShape(expectedWhShape), 0, "DYNAMIC_RNN custom operation: wrong shape of hidden-to-hidden weights array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedWhShape).c_str(), ShapeUtils::shapeAsString(Wh).c_str()); + REQUIRE_TRUE(b->isSameShape(expectedBShape), 0, "DYNAMIC_RNN custom operation: wrong shape of biases array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedBShape).c_str(), ShapeUtils::shapeAsString(b).c_str()); + if(h0) { + std::vector expectedh0Shape = {bS, numUnits}; + REQUIRE_TRUE(h0->isSameShape(expectedh0Shape), 0, "DYNAMIC_RNN custom operation: wrong shape of initial cell output array, expected is %s but got %s instead !", ShapeUtils::shapeAsString(expectedh0Shape).c_str(), ShapeUtils::shapeAsString(h0).c_str()); + } + if(maxTimeStep) { + std::vector expectedmaxTimeStepShape = {bS}; + REQUIRE_TRUE(maxTimeStep->isSameShape(expectedmaxTimeStepShape), 0, "DYNAMIC_RNN custom operation: wrong shape of maxTimeStep array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedmaxTimeStepShape).c_str(), ShapeUtils::shapeAsString(maxTimeStep).c_str()); + } if(timeMajor == false) { x = new NDArray(x->permute({1, 0, 2})); // [bS x time x inSize] -> [time x bS x inSize] @@ -85,7 +91,7 @@ CUSTOM_OP_IMPL(dynamic_rnn, 4, 2, false, 0, 0) { DECLARE_TYPES(dynamic_rnn) { getOpDescriptor() - ->setAllowedInputTypes(0, nd4j::DataType::ANY) + ->setAllowedInputTypes(0, sd::DataType::ANY) ->setAllowedInputTypes(1, {ALL_FLOATS}) ->setAllowedInputTypes(2, {ALL_FLOATS}) ->setAllowedInputTypes(3, {ALL_FLOATS}) @@ -127,12 +133,19 @@ DECLARE_SHAPE_FN(dynamic_rnn) { const int bS = timeMajor ? xShapeInfo[2] : xShapeInfo[1]; const int numUnits = WxShapeInfo[2]; - REQUIRE_TRUE(ShapeUtils::shapeAsString(WhShapeInfo) == ShapeUtils::shapeAsString({numUnits, numUnits}), 0, "DYNAMIC_RNN custom operation: wrong shape of hidden-to-hidden weights array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString({numUnits, numUnits}).c_str(), ShapeUtils::shapeAsString(WhShapeInfo).c_str()); - REQUIRE_TRUE(ShapeUtils::shapeAsString(bShapeInfo) == ShapeUtils::shapeAsString({2*numUnits}), 0, "DYNAMIC_RNN custom operation: wrong shape of biases array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString({2*numUnits}).c_str(), ShapeUtils::shapeAsString(bShapeInfo).c_str()); - if(h0ShapeInfo) - REQUIRE_TRUE(ShapeUtils::shapeAsString(h0ShapeInfo) == ShapeUtils::shapeAsString({bS, numUnits}), 0, "DYNAMIC_RNN custom operation: wrong shape of initial cell output array, expected is %s but got %s instead !", ShapeUtils::shapeAsString({bS, numUnits}).c_str(), ShapeUtils::shapeAsString(h0ShapeInfo).c_str()); - if(maxTimeStepShapeInfo) - REQUIRE_TRUE(ShapeUtils::shapeAsString(maxTimeStepShapeInfo) == ShapeUtils::shapeAsString({bS}), 0, "DYNAMIC_RNN custom operation: wrong shape of maxTimeStep array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString({bS}).c_str(), ShapeUtils::shapeAsString(maxTimeStepShapeInfo).c_str()); + + std::vector expectedWhShape = {numUnits, numUnits}; + std::vector expectedBShape = {2*numUnits}; + REQUIRE_TRUE(ShapeUtils::areShapesEqual(WhShapeInfo, expectedWhShape), 0, "DYNAMIC_RNN custom operation: wrong shape of hidden-to-hidden weights array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedWhShape).c_str(), ShapeUtils::shapeAsString(WhShapeInfo).c_str()); + REQUIRE_TRUE(ShapeUtils::areShapesEqual(bShapeInfo, expectedBShape), 0, "DYNAMIC_RNN custom operation: wrong shape of biases array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedBShape).c_str(), ShapeUtils::shapeAsString(bShapeInfo).c_str()); + if(h0ShapeInfo) { + std::vector expectedh0Shape = {bS, numUnits}; + REQUIRE_TRUE(ShapeUtils::areShapesEqual(h0ShapeInfo, expectedh0Shape), 0, "DYNAMIC_RNN custom operation: wrong shape of initial cell output array, expected is %s but got %s instead !", ShapeUtils::shapeAsString(expectedh0Shape).c_str(), ShapeUtils::shapeAsString(h0ShapeInfo).c_str()); + } + if(maxTimeStepShapeInfo) { + std::vector expectedmaxTimeStepShape = {bS}; + REQUIRE_TRUE(ShapeUtils::areShapesEqual(maxTimeStepShapeInfo, expectedmaxTimeStepShape), 0, "DYNAMIC_RNN custom operation: wrong shape of maxTimeStep array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedmaxTimeStepShape).c_str(), ShapeUtils::shapeAsString(maxTimeStepShapeInfo).c_str()); + } // evaluate output shapeInfos Nd4jLong *hShapeInfo(nullptr), *hPrevShapeInfo(nullptr); diff --git a/libnd4j/include/ops/declarable/generic/recurrent/gru.cpp b/libnd4j/include/ops/declarable/generic/recurrent/gru.cpp index 7a9ca3d16..c6cd2e8f1 100644 --- a/libnd4j/include/ops/declarable/generic/recurrent/gru.cpp +++ b/libnd4j/include/ops/declarable/generic/recurrent/gru.cpp @@ -18,13 +18,13 @@ // created by Yurii Shyrma on 15.02.2018 // -#include +#include #if NOT_EXCLUDED(OP_gru) #include #include -namespace nd4j { +namespace sd { namespace ops { @@ -32,47 +32,43 @@ namespace ops { CUSTOM_OP_IMPL(gru, 5, 1, false, 0, 0) { auto x = INPUT_VARIABLE(0); // input [time x bS x iS] auto h0 = INPUT_VARIABLE(1); // initial cell output (at time step = 0) [bS x nU] - + auto Wx = INPUT_VARIABLE(2); // input-to-hidden weights, [iS x 3*nU] auto Wh = INPUT_VARIABLE(3); // hidden-to-hidden weights, [nU x 3*nU] auto b = INPUT_VARIABLE(4); // biases, [3*nU] - + auto h = OUTPUT_VARIABLE(0); // cell outputs [time x bS x nU], that is per each time step - const int rank = x->rankOf(); // = 3 + const int rank = x->rankOf(); // = 3 const int time = x->sizeAt(0); const int bS = x->sizeAt(1); const int iS = x->sizeAt(2); const int nU = h0->sizeAt(1); - const std::string h0Shape = ShapeUtils::shapeAsString(h0); - const std::string h0CorrectShape = ShapeUtils::shapeAsString({bS, nU}); - const std::string wxShape = ShapeUtils::shapeAsString(Wx); - const std::string wxCorrectShape = ShapeUtils::shapeAsString({iS, 3*nU}); - const std::string whShape = ShapeUtils::shapeAsString(Wh); - const std::string whCorrectShape = ShapeUtils::shapeAsString({nU, 3*nU}); - const std::string bShape = ShapeUtils::shapeAsString(b); - const std::string bCorrectShape = ShapeUtils::shapeAsString({3*nU}); - - REQUIRE_TRUE(h0Shape == h0CorrectShape, 0, "GRU operation: wrong shape of previous cell output array, expected is %s, but got %s instead !", h0CorrectShape.c_str(), h0Shape.c_str()); - REQUIRE_TRUE(wxShape == wxCorrectShape, 0, "GRU operation: wrong shape of input-to-hidden weights array, expected is %s, but got %s instead !", wxCorrectShape.c_str(), wxShape.c_str()); - REQUIRE_TRUE(whShape == whCorrectShape, 0, "GRU operation: wrong shape of hidden-to-hidden weights array, expected is %s, but got %s instead !", whCorrectShape.c_str(), whShape.c_str()); - REQUIRE_TRUE(bShape == bCorrectShape, 0, "GRU operation: wrong shape of biases array, expected is %s, but got %s instead !", bCorrectShape.c_str(), bShape.c_str()); + const std::vector h0CorrectShape = {bS, nU}; + const std::vector wxCorrectShape = {iS, 3*nU}; + const std::vector whCorrectShape = {nU, 3*nU}; + const std::vector bCorrectShape = {3*nU}; + + REQUIRE_TRUE(h0->isSameShape(h0CorrectShape), 0, "GRU operation: wrong shape of previous cell output array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(h0CorrectShape).c_str(), ShapeUtils::shapeAsString(h0).c_str()); + REQUIRE_TRUE(Wx->isSameShape(wxCorrectShape), 0, "GRU operation: wrong shape of input-to-hidden weights array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(wxCorrectShape).c_str(), ShapeUtils::shapeAsString(Wx).c_str()); + REQUIRE_TRUE(Wh->isSameShape(whCorrectShape), 0, "GRU operation: wrong shape of hidden-to-hidden weights array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(whCorrectShape).c_str(), ShapeUtils::shapeAsString(Wh).c_str()); + REQUIRE_TRUE(b->isSameShape(bCorrectShape), 0, "GRU operation: wrong shape of biases array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(bCorrectShape).c_str(), ShapeUtils::shapeAsString(b).c_str()); helpers::gruTimeLoop(block.launchContext(), x, h0, Wx, Wh, b, h); - + return Status::OK(); } DECLARE_TYPES(gru) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_FLOATS}); } -DECLARE_SHAPE_FN(gru) { +DECLARE_SHAPE_FN(gru) { const auto xShapeInfo = inputShape->at(0); // input [time x bS x inSize] const auto h0ShapeInfo = inputShape->at(1); // initial cell output [bS x numUnits], that is at time step t=0 const auto WxShapeInfo = inputShape->at(2); // input-to-hidden weights, [inSize x 3*numUnits] @@ -85,34 +81,30 @@ DECLARE_SHAPE_FN(gru) { const auto inSize = xShapeInfo[3]; const auto numUnits = h0ShapeInfo[2]; - const std::string h0Shape = ShapeUtils::shapeAsString(h0ShapeInfo); - const std::string h0CorrectShape = ShapeUtils::shapeAsString({bS, numUnits}); - const std::string wxShape = ShapeUtils::shapeAsString(WxShapeInfo); - const std::string wxCorrectShape = ShapeUtils::shapeAsString({inSize, 3*numUnits}); - const std::string whShape = ShapeUtils::shapeAsString(WhShapeInfo); - const std::string whCorrectShape = ShapeUtils::shapeAsString({numUnits, 3*numUnits}); - const std::string bShape = ShapeUtils::shapeAsString(bShapeInfo); - const std::string bCorrectShape = ShapeUtils::shapeAsString({3*numUnits}); - - REQUIRE_TRUE(h0Shape == h0CorrectShape, 0, "GRU operation: wrong shape of previous cell output array, expected is %s, but got %s instead !", h0CorrectShape.c_str(), h0Shape.c_str()); - REQUIRE_TRUE(wxShape == wxCorrectShape, 0, "GRU operation: wrong shape of input-to-hidden weights array, expected is %s, but got %s instead !", wxCorrectShape.c_str(), wxShape.c_str()); - REQUIRE_TRUE(whShape == whCorrectShape, 0, "GRU operation: wrong shape of hidden-to-hidden weights array, expected is %s, but got %s instead !", whCorrectShape.c_str(), whShape.c_str()); - REQUIRE_TRUE(bShape == bCorrectShape, 0, "GRU operation: wrong shape of biases array, expected is %s, but got %s instead !", bCorrectShape.c_str(), bShape.c_str()); + const std::vector h0CorrectShape = {bS, numUnits}; + const std::vector wxCorrectShape = {inSize, 3*numUnits}; + const std::vector whCorrectShape = {numUnits, 3*numUnits}; + const std::vector bCorrectShape = {3*numUnits}; + + REQUIRE_TRUE(ShapeUtils::areShapesEqual(h0ShapeInfo, h0CorrectShape), 0, "GRU operation: wrong shape of previous cell output array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(h0CorrectShape).c_str(), ShapeUtils::shapeAsString(h0ShapeInfo).c_str()); + REQUIRE_TRUE(ShapeUtils::areShapesEqual(WxShapeInfo, wxCorrectShape), 0, "GRU operation: wrong shape of input-to-hidden weights array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(wxCorrectShape).c_str(), ShapeUtils::shapeAsString(WxShapeInfo).c_str()); + REQUIRE_TRUE(ShapeUtils::areShapesEqual(WhShapeInfo, whCorrectShape), 0, "GRU operation: wrong shape of hidden-to-hidden weights array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(whCorrectShape).c_str(), ShapeUtils::shapeAsString(WhShapeInfo).c_str()); + REQUIRE_TRUE(ShapeUtils::areShapesEqual(bShapeInfo, bCorrectShape), 0, "GRU operation: wrong shape of biases array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(bCorrectShape).c_str(), ShapeUtils::shapeAsString(bShapeInfo).c_str()); // evaluate output shapeInfo Nd4jLong *hShapeInfo(nullptr); ALLOCATE(hShapeInfo, block.getWorkspace(), shape::shapeInfoLength(rank), Nd4jLong); - + hShapeInfo[0] = rank; hShapeInfo[1] = time; hShapeInfo[2] = bS; hShapeInfo[3] = numUnits; ShapeUtils::updateStridesAndType(hShapeInfo, xShapeInfo, shape::order(h0ShapeInfo)); - + return SHAPELIST(hShapeInfo); -} +} } diff --git a/libnd4j/include/ops/declarable/generic/recurrent/gruCell.cpp b/libnd4j/include/ops/declarable/generic/recurrent/gruCell.cpp index ddd18dc84..204a1ca63 100644 --- a/libnd4j/include/ops/declarable/generic/recurrent/gruCell.cpp +++ b/libnd4j/include/ops/declarable/generic/recurrent/gruCell.cpp @@ -19,13 +19,13 @@ // @author Alex Black // -#include +#include #if NOT_EXCLUDED(OP_gruCell) #include #include -namespace nd4j { +namespace sd { namespace ops { @@ -70,7 +70,7 @@ CUSTOM_OP_IMPL(gruCell, 6, 4, false, 0, 0) { DECLARE_TYPES(gruCell) { getOpDescriptor() - ->setAllowedInputTypes(0, nd4j::DataType::ANY) + ->setAllowedInputTypes(0, sd::DataType::ANY) ->setAllowedInputTypes(1, {ALL_FLOATS}) ->setAllowedInputTypes(2, {ALL_FLOATS}) ->setAllowedInputTypes(3, {ALL_FLOATS}) @@ -145,30 +145,21 @@ CUSTOM_OP_IMPL(gruCell_bp, 10, 6, false, 0, 0) { REQUIRE_TRUE(x->rankOf() == 2, 0, "GRU_CELL_BP: rank of input array x must be 2, but got %i instead", x->rankOf()); - const std::string hiShape = ShapeUtils::shapeAsString(hi); - const std::string hiCorrectShape = ShapeUtils::shapeAsString({bS, nU}); - const std::string wShape = ShapeUtils::shapeAsString(W); - const std::string wCorrectShape = ShapeUtils::shapeAsString({iS+nU, 2*nU}); - const std::string wcShape = ShapeUtils::shapeAsString(Wc); - const std::string wcCorrectShape = ShapeUtils::shapeAsString({iS+nU, nU}); - const std::string bShape = ShapeUtils::shapeAsString(b); - const std::string bCorrectShape = ShapeUtils::shapeAsString({2*nU}); - const std::string bcShape = ShapeUtils::shapeAsString(bc); - const std::string bcCorrectShape = ShapeUtils::shapeAsString({nU}); - const std::string dLdrShape = ShapeUtils::shapeAsString(dLdr); - const std::string dLduShape = ShapeUtils::shapeAsString(dLdu); - const std::string dLdcShape = ShapeUtils::shapeAsString(dLdc); - const std::string dLdhShape = ShapeUtils::shapeAsString(dLdh); + const std::vector hiCorrectShape = {bS, nU}; + const std::vector wCorrectShape = {iS+nU, 2*nU}; + const std::vector wcCorrectShape = {iS+nU, nU}; + const std::vector bCorrectShape = {2*nU}; + const std::vector bcCorrectShape = {nU}; - REQUIRE_TRUE(hiShape == hiCorrectShape, 0, "GRU_CELL_BP op: wrong shape of previous cell output array, expected is %s, but got %s instead !", hiCorrectShape.c_str(), hiShape.c_str()); - REQUIRE_TRUE(wShape == wCorrectShape, 0, "GRU_CELL_BP op: wrong shape of weights array, expected is %s, but got %s instead !", wCorrectShape.c_str(), wShape.c_str()); - REQUIRE_TRUE(wcShape == wcCorrectShape, 0, "GRU_CELL_BP op: wrong shape of c weights array, expected is %s, but got %s instead !", wcCorrectShape.c_str(), wcShape.c_str()); - REQUIRE_TRUE(bShape == bCorrectShape, 0, "GRU_CELL_BP op: wrong shape of biases array, expected is %s, but got %s instead !", bCorrectShape.c_str(), bShape.c_str()); - REQUIRE_TRUE(bcShape == bcCorrectShape, 0, "GRU_CELL_BP op: wrong shape of c biases array, expected is %s, but got %s instead !", bcCorrectShape.c_str(), bcShape.c_str()); - REQUIRE_TRUE(dLdrShape == hiCorrectShape, 0, "GRU_CELL_BP op: wrong shape of dLdr array (gradient wrt reset gate), expected is %s, but got %s instead !", hiCorrectShape.c_str(), dLdrShape.c_str()); - REQUIRE_TRUE(dLduShape == hiCorrectShape, 0, "GRU_CELL_BP op: wrong shape of dLdu array (gradient wrt update gate), expected is %s, but got %s instead !", hiCorrectShape.c_str(), dLduShape.c_str()); - REQUIRE_TRUE(dLdcShape == hiCorrectShape, 0, "GRU_CELL_BP op: wrong shape of dLdc array (gradient wrt cell state), expected is %s, but got %s instead !", hiCorrectShape.c_str(), dLdcShape.c_str()); - REQUIRE_TRUE(dLdhShape == hiCorrectShape, 0, "GRU_CELL_BP op: wrong shape of dLdh array (gradient wrt current cell output), expected is %s, but got %s instead !", hiCorrectShape.c_str(), dLdhShape.c_str()); + REQUIRE_TRUE(hi->isSameShape(hiCorrectShape), 0, "GRU_CELL_BP op: wrong shape of previous cell output array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(hiCorrectShape).c_str(), ShapeUtils::shapeAsString(hi).c_str()); + REQUIRE_TRUE(W->isSameShape(wCorrectShape), 0, "GRU_CELL_BP op: wrong shape of weights array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(wCorrectShape).c_str(), ShapeUtils::shapeAsString(W).c_str()); + REQUIRE_TRUE(Wc->isSameShape(wcCorrectShape), 0, "GRU_CELL_BP op: wrong shape of c weights array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(wcCorrectShape).c_str(), ShapeUtils::shapeAsString(Wc).c_str()); + REQUIRE_TRUE(b->isSameShape(bCorrectShape), 0, "GRU_CELL_BP op: wrong shape of biases array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(bCorrectShape).c_str(), ShapeUtils::shapeAsString(b).c_str()); + REQUIRE_TRUE(bc->isSameShape(bcCorrectShape), 0, "GRU_CELL_BP op: wrong shape of c biases array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(bcCorrectShape).c_str(), ShapeUtils::shapeAsString(bc).c_str()); + REQUIRE_TRUE(dLdr->isSameShape(hiCorrectShape), 0, "GRU_CELL_BP op: wrong shape of dLdr array (gradient wrt reset gate), expected is %s, but got %s instead !", ShapeUtils::shapeAsString(hiCorrectShape).c_str(), ShapeUtils::shapeAsString(dLdr).c_str()); + REQUIRE_TRUE(dLdu->isSameShape(hiCorrectShape), 0, "GRU_CELL_BP op: wrong shape of dLdu array (gradient wrt update gate), expected is %s, but got %s instead !", ShapeUtils::shapeAsString(hiCorrectShape).c_str(), ShapeUtils::shapeAsString(dLdu).c_str()); + REQUIRE_TRUE(dLdc->isSameShape(hiCorrectShape), 0, "GRU_CELL_BP op: wrong shape of dLdc array (gradient wrt cell state), expected is %s, but got %s instead !", ShapeUtils::shapeAsString(hiCorrectShape).c_str(), ShapeUtils::shapeAsString(dLdc).c_str()); + REQUIRE_TRUE(dLdh->isSameShape(hiCorrectShape), 0, "GRU_CELL_BP op: wrong shape of dLdh array (gradient wrt current cell output), expected is %s, but got %s instead !", ShapeUtils::shapeAsString(hiCorrectShape).c_str(), ShapeUtils::shapeAsString(dLdh).c_str()); helpers::gruCellBP(block.launchContext(), x, hi, W, Wc, b, bc, dLdr, dLdu, dLdc, dLdh, dLdx, dLdhi, dLdW, dLdWc, dLdb, dLdbc); @@ -177,7 +168,7 @@ CUSTOM_OP_IMPL(gruCell_bp, 10, 6, false, 0, 0) { DECLARE_TYPES(gruCell_bp) { getOpDescriptor() - ->setAllowedInputTypes(0, nd4j::DataType::ANY) + ->setAllowedInputTypes(0, sd::DataType::ANY) ->setAllowedInputTypes(1, {ALL_FLOATS}) ->setAllowedInputTypes(2, {ALL_FLOATS}) ->setAllowedInputTypes(3, {ALL_FLOATS}) @@ -210,30 +201,21 @@ DECLARE_SHAPE_FN(gruCell_bp) { REQUIRE_TRUE(xShapeInfo[0] == 2, 0, "GRU_CELL_BP: rank of input array x must be 2, but got %i instead", xShapeInfo[0]); - const std::string hiShape = ShapeUtils::shapeAsString(hiShapeInfo); - const std::string hiCorrectShape = ShapeUtils::shapeAsString({bS, nU}); - const std::string wShape = ShapeUtils::shapeAsString(wShapeInfo); - const std::string wCorrectShape = ShapeUtils::shapeAsString({iS+nU, 2*nU}); - const std::string wcShape = ShapeUtils::shapeAsString(wcShapeInfo); - const std::string wcCorrectShape = ShapeUtils::shapeAsString({iS+nU, nU}); - const std::string bShape = ShapeUtils::shapeAsString(bShapeInfo); - const std::string bCorrectShape = ShapeUtils::shapeAsString({2*nU}); - const std::string bcShape = ShapeUtils::shapeAsString(bcShapeInfo); - const std::string bcCorrectShape = ShapeUtils::shapeAsString({nU}); - const std::string dLdrShape = ShapeUtils::shapeAsString(dLdrShapeInfo); - const std::string dLduShape = ShapeUtils::shapeAsString(dLduShapeInfo); - const std::string dLdcShape = ShapeUtils::shapeAsString(dLdcShapeInfo); - const std::string dLdhShape = ShapeUtils::shapeAsString(dLdhShapeInfo); + const std::vector hiCorrectShape = {bS, nU}; + const std::vector wCorrectShape = {iS+nU, 2*nU}; + const std::vector wcCorrectShape = {iS+nU, nU}; + const std::vector bCorrectShape = {2*nU}; + const std::vector bcCorrectShape = {nU}; - REQUIRE_TRUE(hiShape == hiCorrectShape, 0, "GRU_CELL_BP op: wrong shape of previous cell output array, expected is %s, but got %s instead !", hiCorrectShape.c_str(), hiShape.c_str()); - REQUIRE_TRUE(wShape == wCorrectShape, 0, "GRU_CELL_BP op: wrong shape of weights array, expected is %s, but got %s instead !", wCorrectShape.c_str(), wShape.c_str()); - REQUIRE_TRUE(wcShape == wcCorrectShape, 0, "GRU_CELL_BP op: wrong shape of c weights array, expected is %s, but got %s instead !", wcCorrectShape.c_str(), wcShape.c_str()); - REQUIRE_TRUE(bShape == bCorrectShape, 0, "GRU_CELL_BP op: wrong shape of biases array, expected is %s, but got %s instead !", bCorrectShape.c_str(), bShape.c_str()); - REQUIRE_TRUE(bcShape == bcCorrectShape, 0, "GRU_CELL_BP op: wrong shape of c biases array, expected is %s, but got %s instead !", bcCorrectShape.c_str(), bcShape.c_str()); - REQUIRE_TRUE(dLdrShape == hiCorrectShape, 0, "GRU_CELL_BP op: wrong shape of dLdr array (gradient wrt reset gate), expected is %s, but got %s instead !", hiCorrectShape.c_str(), dLdrShape.c_str()); - REQUIRE_TRUE(dLduShape == hiCorrectShape, 0, "GRU_CELL_BP op: wrong shape of dLdu array (gradient wrt update gate), expected is %s, but got %s instead !", hiCorrectShape.c_str(), dLduShape.c_str()); - REQUIRE_TRUE(dLdcShape == hiCorrectShape, 0, "GRU_CELL_BP op: wrong shape of dLdc array (gradient wrt cell state), expected is %s, but got %s instead !", hiCorrectShape.c_str(), dLdcShape.c_str()); - REQUIRE_TRUE(dLdhShape == hiCorrectShape, 0, "GRU_CELL_BP op: wrong shape of dLdh array (gradient wrt current cell output), expected is %s, but got %s instead !", hiCorrectShape.c_str(), dLdhShape.c_str()); + REQUIRE_TRUE(ShapeUtils::areShapesEqual(hiShapeInfo, hiCorrectShape), 0, "GRU_CELL_BP op: wrong shape of previous cell output array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(hiCorrectShape).c_str(), ShapeUtils::shapeAsString(hiShapeInfo).c_str()); + REQUIRE_TRUE(ShapeUtils::areShapesEqual(wShapeInfo, wCorrectShape), 0, "GRU_CELL_BP op: wrong shape of weights array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(wCorrectShape).c_str(), ShapeUtils::shapeAsString(wShapeInfo).c_str()); + REQUIRE_TRUE(ShapeUtils::areShapesEqual(wcShapeInfo, wcCorrectShape), 0, "GRU_CELL_BP op: wrong shape of c weights array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(wcCorrectShape).c_str(), ShapeUtils::shapeAsString(wcShapeInfo).c_str()); + REQUIRE_TRUE(ShapeUtils::areShapesEqual(bShapeInfo, bCorrectShape), 0, "GRU_CELL_BP op: wrong shape of biases array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(bCorrectShape).c_str(), ShapeUtils::shapeAsString(bShapeInfo).c_str()); + REQUIRE_TRUE(ShapeUtils::areShapesEqual(bcShapeInfo, bcCorrectShape), 0, "GRU_CELL_BP op: wrong shape of c biases array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(bcCorrectShape).c_str(), ShapeUtils::shapeAsString(bcShapeInfo).c_str()); + REQUIRE_TRUE(ShapeUtils::areShapesEqual(dLdrShapeInfo, hiCorrectShape), 0, "GRU_CELL_BP op: wrong shape of dLdr array (gradient wrt reset gate), expected is %s, but got %s instead !", ShapeUtils::shapeAsString(hiCorrectShape).c_str(), ShapeUtils::shapeAsString(dLdrShapeInfo).c_str()); + REQUIRE_TRUE(ShapeUtils::areShapesEqual(dLduShapeInfo, hiCorrectShape), 0, "GRU_CELL_BP op: wrong shape of dLdu array (gradient wrt update gate), expected is %s, but got %s instead !", ShapeUtils::shapeAsString(hiCorrectShape).c_str(), ShapeUtils::shapeAsString(dLduShapeInfo).c_str()); + REQUIRE_TRUE(ShapeUtils::areShapesEqual(dLdcShapeInfo, hiCorrectShape), 0, "GRU_CELL_BP op: wrong shape of dLdc array (gradient wrt cell state), expected is %s, but got %s instead !", ShapeUtils::shapeAsString(hiCorrectShape).c_str(), ShapeUtils::shapeAsString(dLdcShapeInfo).c_str()); + REQUIRE_TRUE(ShapeUtils::areShapesEqual(dLdhShapeInfo, hiCorrectShape), 0, "GRU_CELL_BP op: wrong shape of dLdh array (gradient wrt current cell output), expected is %s, but got %s instead !", ShapeUtils::shapeAsString(hiCorrectShape).c_str(), ShapeUtils::shapeAsString(dLdhShapeInfo).c_str()); Nd4jLong *dLdxShapeInfo = nullptr; COPY_SHAPE(xShapeInfo, dLdxShapeInfo); diff --git a/libnd4j/include/ops/declarable/generic/recurrent/lstm.cpp b/libnd4j/include/ops/declarable/generic/recurrent/lstm.cpp index 463d8b507..915be3129 100644 --- a/libnd4j/include/ops/declarable/generic/recurrent/lstm.cpp +++ b/libnd4j/include/ops/declarable/generic/recurrent/lstm.cpp @@ -18,13 +18,13 @@ // @author Yurii Shyrma, created on 15.02.2018 // -#include +#include #if NOT_EXCLUDED(OP_lstm) #include #include -namespace nd4j { +namespace sd { namespace ops { @@ -39,10 +39,10 @@ CUSTOM_OP_IMPL(lstm, 8, 2, false, 3, 2) { auto Wc = INPUT_VARIABLE(5); // diagonal weights for peephole connections [3*numUnits] auto Wp = INPUT_VARIABLE(6); // projection weights [numUnits x numProj] auto b = INPUT_VARIABLE(7); // biases, [4*numUnits] - + auto h = OUTPUT_VARIABLE(0); // cell outputs [time x bS x numProj], that is per each time step auto c = OUTPUT_VARIABLE(1); // cell states [time x bS x numUnits] that is per each time step - + const int peephole = INT_ARG(0); // if 1, provide peephole connections const int projection = INT_ARG(1); // if 1, then projection is performed, if false then numProj==numUnits is mandatory!!!! @@ -59,28 +59,21 @@ CUSTOM_OP_IMPL(lstm, 8, 2, false, 3, 2) { const int numUnits = c0->sizeAt(1); // input shapes validation - const std::string h0Shape = ShapeUtils::shapeAsString(h0); - const std::string correctH0Shape = ShapeUtils::shapeAsString({bS, numProj}); - const std::string c0Shape = ShapeUtils::shapeAsString(c0); - const std::string correctC0Shape = ShapeUtils::shapeAsString({bS, numUnits}); - const std::string WxShape = ShapeUtils::shapeAsString(Wx); - const std::string correctWxShape = ShapeUtils::shapeAsString({inSize, 4*numUnits}); - const std::string WhShape = ShapeUtils::shapeAsString(Wh); - const std::string correctWhShape = ShapeUtils::shapeAsString({numProj, 4*numUnits}); - const std::string WcShape = ShapeUtils::shapeAsString(Wc); - const std::string correctWcShape = ShapeUtils::shapeAsString({3*numUnits}); - const std::string WpShape = ShapeUtils::shapeAsString(Wp); - const std::string correctWpShape = ShapeUtils::shapeAsString({numUnits, numProj}); - const std::string bShape = ShapeUtils::shapeAsString(b); - const std::string correctBShape = ShapeUtils::shapeAsString({4*numUnits}); + const std::vector correctH0Shape = {bS, numProj}; + const std::vector correctC0Shape = {bS, numUnits}; + const std::vector correctWxShape = {inSize, 4*numUnits}; + const std::vector correctWhShape = {numProj, 4*numUnits}; + const std::vector correctWcShape = {3*numUnits}; + const std::vector correctWpShape = {numUnits, numProj}; + const std::vector correctBShape = {4*numUnits}; - REQUIRE_TRUE(correctH0Shape == h0Shape, 0, "LSTM operation: wrong shape of initial cell output, expected is %s, but got %s instead !", correctH0Shape.c_str(), h0Shape.c_str()); - REQUIRE_TRUE(correctC0Shape == c0Shape, 0, "LSTM operation: wrong shape of initial cell state, expected is %s, but got %s instead !", correctC0Shape.c_str(), c0Shape.c_str()); - REQUIRE_TRUE(correctWxShape == WxShape, 0, "LSTM operation: wrong shape of input-to-hidden weights, expected is %s, but got %s instead !", correctWxShape.c_str(), WxShape.c_str()); - REQUIRE_TRUE(correctWhShape == WhShape, 0, "LSTM operation: wrong shape of hidden-to-hidden weights, expected is %s, but got %s instead !", correctWhShape.c_str(), WhShape.c_str()); - REQUIRE_TRUE(correctWcShape == WcShape, 0, "LSTM operation: wrong shape of diagonal weights for peephole connections, expected is %s, but got %s instead !", correctWcShape.c_str(), WcShape.c_str()); - REQUIRE_TRUE(correctWpShape == WpShape, 0, "LSTM operation: wrong shape of projection weights, expected is %s, but got %s instead !", correctWpShape.c_str(), WpShape.c_str()); - REQUIRE_TRUE(correctBShape == bShape, 0, "LSTM operation: wrong shape of biases, expected is %s, but got %s instead !", correctBShape.c_str(), bShape.c_str()); + REQUIRE_TRUE(h0->isSameShape(correctH0Shape), 0, "LSTM operation: wrong shape of initial cell output, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(correctH0Shape).c_str(), ShapeUtils::shapeAsString(h0).c_str()); + REQUIRE_TRUE(c0->isSameShape(correctC0Shape), 0, "LSTM operation: wrong shape of initial cell state, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(correctC0Shape).c_str(), ShapeUtils::shapeAsString(c0).c_str()); + REQUIRE_TRUE(Wx->isSameShape(correctWxShape), 0, "LSTM operation: wrong shape of input-to-hidden weights, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(correctWxShape).c_str(), ShapeUtils::shapeAsString(Wx).c_str()); + REQUIRE_TRUE(Wh->isSameShape(correctWhShape), 0, "LSTM operation: wrong shape of hidden-to-hidden weights, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(correctWhShape).c_str(), ShapeUtils::shapeAsString(Wh).c_str()); + REQUIRE_TRUE(Wc->isSameShape(correctWcShape), 0, "LSTM operation: wrong shape of diagonal weights for peephole connections, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(correctWcShape).c_str(), ShapeUtils::shapeAsString(Wc).c_str()); + REQUIRE_TRUE(Wp->isSameShape(correctWpShape), 0, "LSTM operation: wrong shape of projection weights, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(correctWpShape).c_str(), ShapeUtils::shapeAsString(Wp).c_str()); + REQUIRE_TRUE(b->isSameShape(correctBShape), 0, "LSTM operation: wrong shape of biases, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(correctBShape).c_str(), ShapeUtils::shapeAsString(b).c_str()); REQUIRE_TRUE(!(!projection && numUnits != numProj), 0, "LSTM operation: projection option is switched of, and in this case output dimensionality for the projection matrices (numProj) must be equal to number of units in lstmCell !"); helpers::lstmTimeLoop(block.launchContext(), x, h0, c0, Wx, Wh, Wc, Wp, b, h, c, {(double)peephole, (double)projection, clippingCellValue, clippingProjValue, forgetBias}); @@ -90,12 +83,12 @@ CUSTOM_OP_IMPL(lstm, 8, 2, false, 3, 2) { DECLARE_TYPES(lstm) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_FLOATS}); } -DECLARE_SHAPE_FN(lstm) { +DECLARE_SHAPE_FN(lstm) { auto xShapeInfo = inputShape->at(0); // input [time x bS x inSize] auto h0ShapeInfo = inputShape->at(1); // initial cell output (at time step = 0) [bS x numProj], in case of projection=false -> numProj == numUnits !!! @@ -113,37 +106,30 @@ DECLARE_SHAPE_FN(lstm) { const int inSize = xShapeInfo[3]; const int numProj = h0ShapeInfo[2]; const int numUnits = c0ShapeInfo[2]; - + // input shapes validation - const std::string h0Shape = ShapeUtils::shapeAsString(h0ShapeInfo); - const std::string correctH0Shape = ShapeUtils::shapeAsString({bS, numProj}); - const std::string c0Shape = ShapeUtils::shapeAsString(c0ShapeInfo); - const std::string correctC0Shape = ShapeUtils::shapeAsString({bS, numUnits}); - const std::string WxShape = ShapeUtils::shapeAsString(WxShapeInfo); - const std::string correctWxShape = ShapeUtils::shapeAsString({inSize, 4*numUnits}); - const std::string WhShape = ShapeUtils::shapeAsString(WhShapeInfo); - const std::string correctWhShape = ShapeUtils::shapeAsString({numProj, 4*numUnits}); - const std::string WcShape = ShapeUtils::shapeAsString(WcShapeInfo); - const std::string correctWcShape = ShapeUtils::shapeAsString({3*numUnits}); - const std::string WpShape = ShapeUtils::shapeAsString(WpShapeInfo); - const std::string correctWpShape = ShapeUtils::shapeAsString({numUnits, numProj}); - const std::string bShape = ShapeUtils::shapeAsString(bShapeInfo); - const std::string correctBShape = ShapeUtils::shapeAsString({4*numUnits}); + const std::vector correctH0Shape = {bS, numProj}; + const std::vector correctC0Shape = {bS, numUnits}; + const std::vector correctWxShape = {inSize, 4*numUnits}; + const std::vector correctWhShape = {numProj, 4*numUnits}; + const std::vector correctWcShape = {3*numUnits}; + const std::vector correctWpShape = {numUnits, numProj}; + const std::vector correctBShape = {4*numUnits}; + + REQUIRE_TRUE(ShapeUtils::areShapesEqual(h0ShapeInfo, correctH0Shape), 0, "LSTM operation: wrong shape of initial cell output, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(correctH0Shape).c_str(), ShapeUtils::shapeAsString(h0ShapeInfo).c_str()); + REQUIRE_TRUE(ShapeUtils::areShapesEqual(c0ShapeInfo, correctC0Shape), 0, "LSTM operation: wrong shape of initial cell state, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(correctC0Shape).c_str(), ShapeUtils::shapeAsString(c0ShapeInfo).c_str()); + REQUIRE_TRUE(ShapeUtils::areShapesEqual(WxShapeInfo, correctWxShape), 0, "LSTM operation: wrong shape of input-to-hidden weights, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(correctWxShape).c_str(), ShapeUtils::shapeAsString(WxShapeInfo).c_str()); + REQUIRE_TRUE(ShapeUtils::areShapesEqual(WhShapeInfo, correctWhShape), 0, "LSTM operation: wrong shape of hidden-to-hidden weights, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(correctWhShape).c_str(), ShapeUtils::shapeAsString(WhShapeInfo).c_str()); + REQUIRE_TRUE(ShapeUtils::areShapesEqual(WcShapeInfo, correctWcShape), 0, "LSTM operation: wrong shape of diagonal weights for peephole connections, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(correctWcShape).c_str(), ShapeUtils::shapeAsString(WcShapeInfo).c_str()); + REQUIRE_TRUE(ShapeUtils::areShapesEqual(WpShapeInfo, correctWpShape), 0, "LSTM operation: wrong shape of projection weights, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(correctWpShape).c_str(), ShapeUtils::shapeAsString(WpShapeInfo).c_str()); + REQUIRE_TRUE(ShapeUtils::areShapesEqual(bShapeInfo, correctBShape), 0, "LSTM operation: wrong shape of biases, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(correctBShape).c_str(), ShapeUtils::shapeAsString(bShapeInfo).c_str()); - REQUIRE_TRUE(correctH0Shape == h0Shape, 0, "LSTM operation: wrong shape of initial cell output, expected is %s, but got %s instead !", correctH0Shape.c_str(), h0Shape.c_str()); - REQUIRE_TRUE(correctC0Shape == c0Shape, 0, "LSTM operation: wrong shape of initial cell state, expected is %s, but got %s instead !", correctC0Shape.c_str(), c0Shape.c_str()); - REQUIRE_TRUE(correctWxShape == WxShape, 0, "LSTM operation: wrong shape of input-to-hidden weights, expected is %s, but got %s instead !", correctWxShape.c_str(), WxShape.c_str()); - REQUIRE_TRUE(correctWhShape == WhShape, 0, "LSTM operation: wrong shape of hidden-to-hidden weights, expected is %s, but got %s instead !", correctWhShape.c_str(), WhShape.c_str()); - REQUIRE_TRUE(correctWcShape == WcShape, 0, "LSTM operation: wrong shape of diagonal weights for peephole connections, expected is %s, but got %s instead !", correctWcShape.c_str(), WcShape.c_str()); - REQUIRE_TRUE(correctWpShape == WpShape, 0, "LSTM operation: wrong shape of projection weights, expected is %s, but got %s instead !", correctWpShape.c_str(), WpShape.c_str()); - REQUIRE_TRUE(correctBShape == bShape, 0, "LSTM operation: wrong shape of biases, expected is %s, but got %s instead !", correctBShape.c_str(), bShape.c_str()); - // evaluate output shapeInfos Nd4jLong *hShapeInfo(nullptr), *cShapeInfo(nullptr); ALLOCATE(hShapeInfo, block.getWorkspace(), shape::shapeInfoLength(rank), Nd4jLong); // [time x bS x numProj] ALLOCATE(cShapeInfo, block.getWorkspace(), shape::shapeInfoLength(rank), Nd4jLong); // [time x bS x numUnits] - + hShapeInfo[0] = cShapeInfo[0] = rank; hShapeInfo[1] = cShapeInfo[1] = time; hShapeInfo[2] = cShapeInfo[2] = bS; @@ -152,9 +138,9 @@ DECLARE_SHAPE_FN(lstm) { ShapeUtils::updateStridesAndType(hShapeInfo, xShapeInfo, shape::order(h0ShapeInfo)); ShapeUtils::updateStridesAndType(cShapeInfo, xShapeInfo, shape::order(c0ShapeInfo)); - + return SHAPELIST(CONSTANT(hShapeInfo), CONSTANT(cShapeInfo)); -} +} diff --git a/libnd4j/include/ops/declarable/generic/recurrent/lstmBlock.cpp b/libnd4j/include/ops/declarable/generic/recurrent/lstmBlock.cpp index 9517caa47..3225f3f74 100644 --- a/libnd4j/include/ops/declarable/generic/recurrent/lstmBlock.cpp +++ b/libnd4j/include/ops/declarable/generic/recurrent/lstmBlock.cpp @@ -18,13 +18,13 @@ // @author Alex Black // -#include +#include #if NOT_EXCLUDED(OP_lstmBlock) #include #include -namespace nd4j { +namespace sd { namespace ops { @@ -68,7 +68,7 @@ CUSTOM_OP_IMPL(lstmBlock, 9, 7, false, 2, 2) { DECLARE_TYPES(lstmBlock) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_FLOATS}); } diff --git a/libnd4j/include/ops/declarable/generic/recurrent/lstmBlockCell.cpp b/libnd4j/include/ops/declarable/generic/recurrent/lstmBlockCell.cpp index 446b523c1..333854ba3 100644 --- a/libnd4j/include/ops/declarable/generic/recurrent/lstmBlockCell.cpp +++ b/libnd4j/include/ops/declarable/generic/recurrent/lstmBlockCell.cpp @@ -18,13 +18,13 @@ // @author Alex Black // -#include +#include #if NOT_EXCLUDED(OP_lstmBlockCell) #include #include -namespace nd4j { +namespace sd { namespace ops { @@ -79,7 +79,7 @@ CUSTOM_OP_IMPL(lstmBlockCell, 8, 7, false, 2, 1) { DECLARE_TYPES(lstmBlockCell) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_FLOATS}); } diff --git a/libnd4j/include/ops/declarable/generic/recurrent/lstmCell.cpp b/libnd4j/include/ops/declarable/generic/recurrent/lstmCell.cpp index 9d7e70728..20a9e6710 100644 --- a/libnd4j/include/ops/declarable/generic/recurrent/lstmCell.cpp +++ b/libnd4j/include/ops/declarable/generic/recurrent/lstmCell.cpp @@ -18,13 +18,13 @@ // @author Yurii Shyrma, created on 30.11.2017 // -#include +#include #if NOT_EXCLUDED(OP_lstmCell) #include #include -namespace nd4j { +namespace sd { namespace ops { @@ -39,10 +39,10 @@ CUSTOM_OP_IMPL(lstmCell, 8, 2, false, 3, 2) { auto Wc = INPUT_VARIABLE(5); // diagonal weights for peephole connections [3*numUnits] auto Wp = INPUT_VARIABLE(6); // projection weights [numUnits x numProj] auto b = INPUT_VARIABLE(7); // biases, [4*numUnits] - + auto ht = OUTPUT_VARIABLE(0); // current cell output [bS x numProj], that is at current time step t auto ct = OUTPUT_VARIABLE(1); // current cell state [bS x numUnits], that is at current time step t - + const int peephole = INT_ARG(0); // if 1, provide peephole connections const int projection = INT_ARG(1); // if 1, then projection is performed, if false then numProj==numUnits is mandatory!!!! @@ -51,97 +51,83 @@ CUSTOM_OP_IMPL(lstmCell, 8, 2, false, 3, 2) { const double clippingProjValue = T_ARG(1); // clipping value for projected ht, if it is not equal to zero, then projected cell output is clipped const double forgetBias = T_ARG(2); - const int rank = xt->rankOf(); + const int rank = xt->rankOf(); const int bS = xt->sizeAt(0); const int inSize = xt->sizeAt(1); const int numProj = ht_1->sizeAt(1); - const int numUnits = ct_1->sizeAt(1); - - // input shapes validation - const std::string ht_1Shape = ShapeUtils::shapeAsString(ht_1); - const std::string correctHt_1Shape = ShapeUtils::shapeAsString({bS, numProj}); - const std::string ct_1Shape = ShapeUtils::shapeAsString(ct_1); - const std::string correctCt_1Shape = ShapeUtils::shapeAsString({bS, numUnits}); - const std::string WxShape = ShapeUtils::shapeAsString(Wx); - const std::string correctWxShape = ShapeUtils::shapeAsString({inSize, 4*numUnits}); - const std::string WhShape = ShapeUtils::shapeAsString(Wh); - const std::string correctWhShape = ShapeUtils::shapeAsString({numProj, 4*numUnits}); - const std::string WcShape = ShapeUtils::shapeAsString(Wc); - const std::string correctWcShape = ShapeUtils::shapeAsString({3*numUnits}); - const std::string WpShape = ShapeUtils::shapeAsString(Wp); - const std::string correctWpShape = ShapeUtils::shapeAsString({numUnits, numProj}); - const std::string bShape = ShapeUtils::shapeAsString(b); - const std::string correctBShape = ShapeUtils::shapeAsString({4*numUnits}); + const int numUnits = ct_1->sizeAt(1); - REQUIRE_TRUE(correctHt_1Shape == ht_1Shape, 0, "LSTMCELL operation: wrong shape of initial cell output, expected is %s, but got %s instead !", correctHt_1Shape.c_str(), ht_1Shape.c_str()); - REQUIRE_TRUE(correctCt_1Shape == ct_1Shape, 0, "LSTMCELL operation: wrong shape of initial cell state, expected is %s, but got %s instead !", correctCt_1Shape.c_str(), ct_1Shape.c_str()); - REQUIRE_TRUE(correctWxShape == WxShape, 0, "LSTMCELL operation: wrong shape of input-to-hidden weights, expected is %s, but got %s instead !", correctWxShape.c_str(), WxShape.c_str()); - REQUIRE_TRUE(correctWhShape == WhShape, 0, "LSTMCELL operation: wrong shape of hidden-to-hidden weights, expected is %s, but got %s instead !", correctWhShape.c_str(), WhShape.c_str()); - REQUIRE_TRUE(correctWcShape == WcShape, 0, "LSTMCELL operation: wrong shape of diagonal weights for peephole connections, expected is %s, but got %s instead !", correctWcShape.c_str(), WcShape.c_str()); - REQUIRE_TRUE(correctWpShape == WpShape, 0, "LSTMCELL operation: wrong shape of projection weights, expected is %s, but got %s instead !", correctWpShape.c_str(), WpShape.c_str()); - REQUIRE_TRUE(correctBShape == bShape, 0, "LSTMCELL operation: wrong shape of biases, expected is %s, but got %s instead !", correctBShape.c_str(), bShape.c_str()); + // input shapes validation + const std::vector correctHt_1Shape = {bS, numProj}; + const std::vector correctCt_1Shape = {bS, numUnits}; + const std::vector correctWxShape = {inSize, 4*numUnits}; + const std::vector correctWhShape = {numProj, 4*numUnits}; + const std::vector correctWcShape = {3*numUnits}; + const std::vector correctWpShape = {numUnits, numProj}; + const std::vector correctBShape = {4*numUnits}; + + REQUIRE_TRUE(ht_1->isSameShape(correctHt_1Shape), 0, "LSTMCELL operation: wrong shape of initial cell output, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(correctHt_1Shape).c_str(), ShapeUtils::shapeAsString(ht_1).c_str()); + REQUIRE_TRUE(ct_1->isSameShape(correctCt_1Shape), 0, "LSTMCELL operation: wrong shape of initial cell state, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(correctCt_1Shape).c_str(), ShapeUtils::shapeAsString(ct_1).c_str()); + REQUIRE_TRUE(Wx->isSameShape(correctWxShape), 0, "LSTMCELL operation: wrong shape of input-to-hidden weights, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(correctWxShape).c_str(), ShapeUtils::shapeAsString(Wx).c_str()); + REQUIRE_TRUE(Wh->isSameShape(correctWhShape), 0, "LSTMCELL operation: wrong shape of hidden-to-hidden weights, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(correctWhShape).c_str(), ShapeUtils::shapeAsString(Wh).c_str()); + REQUIRE_TRUE(Wc->isSameShape(correctWcShape), 0, "LSTMCELL operation: wrong shape of diagonal weights for peephole connections, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(correctWcShape).c_str(), ShapeUtils::shapeAsString(Wc).c_str()); + REQUIRE_TRUE(Wp->isSameShape(correctWpShape), 0, "LSTMCELL operation: wrong shape of projection weights, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(correctWpShape).c_str(), ShapeUtils::shapeAsString(Wp).c_str()); + REQUIRE_TRUE(b->isSameShape(correctBShape), 0, "LSTMCELL operation: wrong shape of biases, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(correctBShape).c_str(), ShapeUtils::shapeAsString(b).c_str()); REQUIRE_TRUE(!(!projection && numUnits != numProj), 0, "LSTMCELL operation: projection option is switched of, and in this case output dimensionality for the projection matrices (numProj) must be equal to number of units in lstmCell !"); - - // calculations + + // calculations helpers::lstmCell(block.launchContext(), xt,ht_1,ct_1, Wx,Wh,Wc,Wp, b, ht,ct, {(double)peephole, (double)projection, clippingCellValue, clippingProjValue, forgetBias}); - + return Status::OK(); } DECLARE_TYPES(lstmCell) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_FLOATS}); } -DECLARE_SHAPE_FN(lstmCell) { +DECLARE_SHAPE_FN(lstmCell) { auto xtShapeInfo = inputShape->at(0); // input [bS x inSize] - auto ht_1ShapeInfo = inputShape->at(1); // previous cell output [bS x numProj], that is at previous time step t-1, in case of projection=false -> numProj=numUnits!!! - auto ct_1ShapeInfo = inputShape->at(2); // previous cell state [bS x numUnits], that is at previous time step t-1 + auto ht_1ShapeInfo = inputShape->at(1); // previous cell output [bS x numProj], that is at previous time step t-1, in case of projection=false -> numProj=numUnits!!! + auto ct_1ShapeInfo = inputShape->at(2); // previous cell state [bS x numUnits], that is at previous time step t-1 + + auto WxShapeInfo = inputShape->at(3); // input-to-hidden weights, [inSize x 4*numUnits] + auto WhShapeInfo = inputShape->at(4); // hidden-to-hidden weights, [numProj x 4*numUnits] + auto WcShapeInfo = inputShape->at(5); // diagonal weights for peephole connections [3*numUnits] + auto WpShapeInfo = inputShape->at(6); // projection weights [numUnits x numProj] + auto bShapeInfo = inputShape->at(7); // biases, [4*numUnits] - auto WxShapeInfo = inputShape->at(3); // input-to-hidden weights, [inSize x 4*numUnits] - auto WhShapeInfo = inputShape->at(4); // hidden-to-hidden weights, [numProj x 4*numUnits] - auto WcShapeInfo = inputShape->at(5); // diagonal weights for peephole connections [3*numUnits] - auto WpShapeInfo = inputShape->at(6); // projection weights [numUnits x numProj] - auto bShapeInfo = inputShape->at(7); // biases, [4*numUnits] - const int rank = shape::rank(xtShapeInfo); const auto bS = xtShapeInfo[1]; const auto inSize = xtShapeInfo[2]; const auto numProj = ht_1ShapeInfo[2]; const auto numUnits = ct_1ShapeInfo[2]; - - // input shapes validation - const std::string ht_1Shape = ShapeUtils::shapeAsString(ht_1ShapeInfo); - const std::string correctHt_1Shape = ShapeUtils::shapeAsString({bS, numProj}); - const std::string ct_1Shape = ShapeUtils::shapeAsString(ct_1ShapeInfo); - const std::string correctCt_1Shape = ShapeUtils::shapeAsString({bS, numUnits}); - const std::string WxShape = ShapeUtils::shapeAsString(WxShapeInfo); - const std::string correctWxShape = ShapeUtils::shapeAsString({inSize, 4*numUnits}); - const std::string WhShape = ShapeUtils::shapeAsString(WhShapeInfo); - const std::string correctWhShape = ShapeUtils::shapeAsString({numProj, 4*numUnits}); - const std::string WcShape = ShapeUtils::shapeAsString(WcShapeInfo ); - const std::string correctWcShape = ShapeUtils::shapeAsString({3*numUnits}); - const std::string WpShape = ShapeUtils::shapeAsString(WpShapeInfo); - const std::string correctWpShape = ShapeUtils::shapeAsString({numUnits, numProj}); - const std::string bShape = ShapeUtils::shapeAsString(bShapeInfo); - const std::string correctBShape = ShapeUtils::shapeAsString({4*numUnits}); - REQUIRE_TRUE(correctHt_1Shape == ht_1Shape, 0, "LSTMCELL operation: wrong shape of initial cell output, expected is %s, but got %s instead !", correctHt_1Shape.c_str(), ht_1Shape.c_str()); - REQUIRE_TRUE(correctCt_1Shape == ct_1Shape, 0, "LSTMCELL operation: wrong shape of initial cell state, expected is %s, but got %s instead !", correctCt_1Shape.c_str(), ct_1Shape.c_str()); - REQUIRE_TRUE(correctWxShape == WxShape, 0, "LSTMCELL operation: wrong shape of input-to-hidden weights, expected is %s, but got %s instead !", correctWxShape.c_str(), WxShape.c_str()); - REQUIRE_TRUE(correctWhShape == WhShape, 0, "LSTMCELL operation: wrong shape of hidden-to-hidden weights, expected is %s, but got %s instead !", correctWhShape.c_str(), WhShape.c_str()); - REQUIRE_TRUE(correctWcShape == WcShape, 0, "LSTMCELL operation: wrong shape of diagonal weights for peephole connections, expected is %s, but got %s instead !", correctWcShape.c_str(), WcShape.c_str()); - REQUIRE_TRUE(correctWpShape == WpShape, 0, "LSTMCELL operation: wrong shape of projection weights, expected is %s, but got %s instead !", correctWpShape.c_str(), WpShape.c_str()); - REQUIRE_TRUE(correctBShape == bShape, 0, "LSTMCELL operation: wrong shape of biases, expected is %s, but got %s instead !", correctBShape.c_str(), bShape.c_str()); - + // input shapes validation + const std::vector correctHt_1Shape = {bS, numProj}; + const std::vector correctCt_1Shape = {bS, numUnits}; + const std::vector correctWxShape = {inSize, 4*numUnits}; + const std::vector correctWhShape = {numProj, 4*numUnits}; + const std::vector correctWcShape = {3*numUnits}; + const std::vector correctWpShape = {numUnits, numProj}; + const std::vector correctBShape = {4*numUnits}; + + REQUIRE_TRUE(ShapeUtils::areShapesEqual(ht_1ShapeInfo, correctHt_1Shape), 0, "LSTMCELL operation: wrong shape of initial cell output, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(correctHt_1Shape).c_str(), ShapeUtils::shapeAsString(ht_1ShapeInfo).c_str()); + REQUIRE_TRUE(ShapeUtils::areShapesEqual(ct_1ShapeInfo, correctCt_1Shape), 0, "LSTMCELL operation: wrong shape of initial cell state, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(correctCt_1Shape).c_str(), ShapeUtils::shapeAsString(ct_1ShapeInfo).c_str()); + REQUIRE_TRUE(ShapeUtils::areShapesEqual(WxShapeInfo, correctWxShape), 0, "LSTMCELL operation: wrong shape of input-to-hidden weights, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(correctWxShape).c_str(), ShapeUtils::shapeAsString(WxShapeInfo).c_str()); + REQUIRE_TRUE(ShapeUtils::areShapesEqual(WhShapeInfo, correctWhShape), 0, "LSTMCELL operation: wrong shape of hidden-to-hidden weights, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(correctWhShape).c_str(), ShapeUtils::shapeAsString(WhShapeInfo).c_str()); + REQUIRE_TRUE(ShapeUtils::areShapesEqual(WcShapeInfo, correctWcShape), 0, "LSTMCELL operation: wrong shape of diagonal weights for peephole connections, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(correctWcShape).c_str(), ShapeUtils::shapeAsString(WcShapeInfo).c_str()); + REQUIRE_TRUE(ShapeUtils::areShapesEqual(WpShapeInfo, correctWpShape), 0, "LSTMCELL operation: wrong shape of projection weights, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(correctWpShape).c_str(), ShapeUtils::shapeAsString(WpShapeInfo).c_str()); + REQUIRE_TRUE(ShapeUtils::areShapesEqual(bShapeInfo, correctBShape), 0, "LSTMCELL operation: wrong shape of biases, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(correctBShape).c_str(), ShapeUtils::shapeAsString(bShapeInfo).c_str()); + // evaluate output shapeInfos Nd4jLong *hShapeInfo(nullptr), *cShapeInfo(nullptr); ALLOCATE(hShapeInfo, block.getWorkspace(), shape::shapeInfoLength(rank), Nd4jLong); // [bS x numProj] ALLOCATE(cShapeInfo, block.getWorkspace(), shape::shapeInfoLength(rank), Nd4jLong); // [bS x numUnits] - + hShapeInfo[0] = cShapeInfo[0] = rank; hShapeInfo[1] = cShapeInfo[1] = bS; hShapeInfo[2] = numProj; @@ -154,7 +140,7 @@ DECLARE_SHAPE_FN(lstmCell) { RELEASE(hShapeInfo, block.workspace()); RELEASE(cShapeInfo, block.workspace()); return result; -} +} } } diff --git a/libnd4j/include/ops/declarable/generic/recurrent/lstmLayer.cpp b/libnd4j/include/ops/declarable/generic/recurrent/lstmLayer.cpp index 3b794b945..3a02b8a70 100644 --- a/libnd4j/include/ops/declarable/generic/recurrent/lstmLayer.cpp +++ b/libnd4j/include/ops/declarable/generic/recurrent/lstmLayer.cpp @@ -18,13 +18,13 @@ // @author Yurii Shyrma (iuriish@yahoo.com) // -#include +#include #if NOT_EXCLUDED(OP_lstmLayer) #include #include -namespace nd4j { +namespace sd { namespace ops { @@ -304,7 +304,7 @@ CUSTOM_OP_IMPL(lstmLayer, 3, 1, false, 1, 5) { DECLARE_TYPES(lstmLayer) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_FLOATS}); } @@ -332,7 +332,7 @@ DECLARE_SHAPE_FN(lstmLayer) { if(x->isR()) type = x->dataType(); else - type = nd4j::DataType::FLOAT32; + type = sd::DataType::FLOAT32; std::vector shapes; diff --git a/libnd4j/include/ops/declarable/generic/recurrent/sru.cpp b/libnd4j/include/ops/declarable/generic/recurrent/sru.cpp index 6ca57d297..9b78a5c56 100644 --- a/libnd4j/include/ops/declarable/generic/recurrent/sru.cpp +++ b/libnd4j/include/ops/declarable/generic/recurrent/sru.cpp @@ -20,15 +20,15 @@ //@author Yurii Shyrma // -#include +#include #if NOT_EXCLUDED(OP_sru) #include #include -#include +#include #include -namespace nd4j { +namespace sd { namespace ops { ////////////////////////////////////////////////////////////////////////// @@ -54,20 +54,15 @@ CUSTOM_OP_IMPL(sru, 5, 2, false, 0, 0) { if(mask) REQUIRE_TRUE(mask->rankOf() == rank-1, 0, "SRU operation: wrong rank of mask array, expected is %i, but got %i instead !", rank-1, mask->rankOf()); - const std::string wShape = ShapeUtils::shapeAsString(w); - const std::string wCorrectShape = ShapeUtils::shapeAsString({3*inSize, inSize}); - const std::string bShape = ShapeUtils::shapeAsString(b); - const std::string bCorrectShape = ShapeUtils::shapeAsString({2*inSize}); - const std::string c0Shape = ShapeUtils::shapeAsString(c0); - const std::string c0CorrectShape = ShapeUtils::shapeAsString({bS, inSize}); + const std::vector wCorrectShape = {3*inSize, inSize}; + const std::vector bCorrectShape = {2*inSize}; + const std::vector c0CorrectShape = {bS, inSize}; - REQUIRE_TRUE(wShape == wCorrectShape, 0, "SRU operation: wrong shape of weights array, expected is %s, but got %s instead !", wCorrectShape.c_str(), wShape.c_str()); - REQUIRE_TRUE(bShape == bCorrectShape, 0, "SRU operation: wrong shape of biases array, expected is %s, but got %s instead !", bCorrectShape.c_str(), bShape.c_str()); - REQUIRE_TRUE(c0Shape == c0CorrectShape, 0, "SRU operation: wrong shape of initial state array, expected is %s, but got %s instead !", c0CorrectShape.c_str(), c0Shape.c_str()); - if(mask) { - const std::string maskShape = ShapeUtils::shapeAsString(mask); - REQUIRE_TRUE(maskShape == c0CorrectShape, 0, "SRU operation: wrong shape of mask array, expected is %s, but got %s instead !", c0CorrectShape.c_str(), maskShape.c_str()); - } + REQUIRE_TRUE(w->isSameShape(wCorrectShape), 0, "SRU operation: wrong shape of weights array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(wCorrectShape).c_str(), ShapeUtils::shapeAsString(w).c_str()); + REQUIRE_TRUE(b->isSameShape(bCorrectShape), 0, "SRU operation: wrong shape of biases array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(bCorrectShape).c_str(), ShapeUtils::shapeAsString(b).c_str()); + REQUIRE_TRUE(c0->isSameShape(c0CorrectShape), 0, "SRU operation: wrong shape of initial state array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(c0CorrectShape).c_str(), ShapeUtils::shapeAsString(c0).c_str()); + if(mask) + REQUIRE_TRUE(mask->isSameShape(c0CorrectShape), 0, "SRU operation: wrong shape of mask array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(c0CorrectShape).c_str(), ShapeUtils::shapeAsString(mask).c_str()); // xm = x * mask auto xm = x; @@ -87,7 +82,7 @@ CUSTOM_OP_IMPL(sru, 5, 2, false, 0, 0) { DECLARE_TYPES(sru) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_FLOATS}); } @@ -111,20 +106,15 @@ DECLARE_SHAPE_FN(sru) { if(maskShapeInfo) REQUIRE_TRUE(maskShapeInfo[0] == rank-1, 0, "SRU operation: wrong rank of mask array, expected is %i, but got %i instead !", rank-1, maskShapeInfo[0]); - const std::string wShape = ShapeUtils::shapeAsString(wShapeInfo); - const std::string wCorrectShape = ShapeUtils::shapeAsString({3*inSize, inSize}); - const std::string bShape = ShapeUtils::shapeAsString(bShapeInfo); - const std::string bCorrectShape = ShapeUtils::shapeAsString({2*inSize}); - const std::string c0Shape = ShapeUtils::shapeAsString(c0ShapeInfo); - const std::string c0CorrectShape = ShapeUtils::shapeAsString({bS, inSize}); + const std::vector wCorrectShape = {3*inSize, inSize}; + const std::vector bCorrectShape = {2*inSize}; + const std::vector c0CorrectShape = {bS, inSize}; - REQUIRE_TRUE(wShape == wCorrectShape, 0, "SRU operation: wrong shape of weights array, expected is %s, but got %s instead !", wCorrectShape.c_str(), wShape.c_str()); - REQUIRE_TRUE(bShape == bCorrectShape, 0, "SRU operation: wrong shape of biases array, expected is %s, but got %s instead !", bCorrectShape.c_str(), bShape.c_str()); - REQUIRE_TRUE(c0Shape == c0CorrectShape, 0, "SRU operation: wrong shape of initial state array, expected is %s, but got %s instead !", c0CorrectShape.c_str(), c0Shape.c_str()); - if(maskShapeInfo) { - const std::string maskShape = ShapeUtils::shapeAsString(maskShapeInfo); - REQUIRE_TRUE(maskShape == c0CorrectShape, 0, "SRU operation: wrong shape of mask array, expected is %s, but got %s instead !", c0CorrectShape.c_str(), maskShape.c_str()); - } + REQUIRE_TRUE(ShapeUtils::areShapesEqual(wShapeInfo, wCorrectShape), 0, "SRU operation: wrong shape of weights array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(wCorrectShape).c_str(), ShapeUtils::shapeAsString(wShapeInfo).c_str()); + REQUIRE_TRUE(ShapeUtils::areShapesEqual(bShapeInfo, bCorrectShape), 0, "SRU operation: wrong shape of biases array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(bCorrectShape).c_str(), ShapeUtils::shapeAsString(bShapeInfo).c_str()); + REQUIRE_TRUE(ShapeUtils::areShapesEqual(c0ShapeInfo, c0CorrectShape), 0, "SRU operation: wrong shape of initial state array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(c0CorrectShape).c_str(), ShapeUtils::shapeAsString(c0ShapeInfo).c_str()); + if(maskShapeInfo) + REQUIRE_TRUE(ShapeUtils::areShapesEqual(maskShapeInfo, c0CorrectShape), 0, "SRU operation: wrong shape of mask array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(c0CorrectShape).c_str(), ShapeUtils::shapeAsString(maskShapeInfo).c_str()); Nd4jLong* newShapeInfo1 = nullptr; ALLOCATE(newShapeInfo1, block.getWorkspace(), shape::shapeInfoLength(rank), Nd4jLong); // [bS x inSize x time] @@ -304,7 +294,7 @@ CUSTOM_OP_IMPL(sru_bp, 8, 4, true, 0, 0) { DECLARE_TYPES(sru_bp) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_FLOATS}); } @@ -350,20 +340,15 @@ CUSTOM_OP_IMPL(sru_bi, 5, 2, true, 0, 0) { if(mask) REQUIRE_TRUE(mask->rankOf() == rank-1, 0, "SRU_BI operation: wrong rank of mask array, expected is %i, but got %i instead !", rank-1, mask->rankOf()); - const std::string wShape = ShapeUtils::shapeAsString(w); - const std::string wCorrectShape = ShapeUtils::shapeAsString({2*inSize, 6*inSize}); - const std::string bShape = ShapeUtils::shapeAsString(b); - const std::string bCorrectShape = ShapeUtils::shapeAsString({4*inSize}); - const std::string c0Shape = ShapeUtils::shapeAsString(c0); - const std::string c0CorrectShape = ShapeUtils::shapeAsString({bS, 2*inSize}); + const std::vector wCorrectShape = {2*inSize, 6*inSize}; + const std::vector bCorrectShape = {4*inSize}; + const std::vector c0CorrectShape = {bS, 2*inSize}; - REQUIRE_TRUE(wShape == wCorrectShape, 0, "SRU_BI operation: wrong shape of weights array, expected is %s, but got %s instead !", wCorrectShape.c_str(), wShape.c_str()); - REQUIRE_TRUE(bShape == bCorrectShape, 0, "SRU_BI operation: wrong shape of biases array, expected is %s, but got %s instead !", bCorrectShape.c_str(), bShape.c_str()); - REQUIRE_TRUE(c0Shape == c0CorrectShape, 0, "SRU_BI operation: wrong shape of initial state array, expected is %s, but got %s instead !", c0CorrectShape.c_str(), c0Shape.c_str()); - if(mask) { - const std::string maskShape = ShapeUtils::shapeAsString(mask); - REQUIRE_TRUE(maskShape == c0CorrectShape, 0, "SRU_BI operation: wrong shape of mask array, expected is %s, but got %s instead !", c0CorrectShape.c_str(), maskShape.c_str()); - } + REQUIRE_TRUE(w->isSameShape(wCorrectShape), 0, "SRU_BI operation: wrong shape of weights array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(wCorrectShape).c_str(), ShapeUtils::shapeAsString(w).c_str()); + REQUIRE_TRUE(b->isSameShape(bCorrectShape), 0, "SRU_BI operation: wrong shape of biases array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(bCorrectShape).c_str(), ShapeUtils::shapeAsString(b).c_str()); + REQUIRE_TRUE(c0->isSameShape(c0CorrectShape), 0, "SRU_BI operation: wrong shape of initial state array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(c0CorrectShape).c_str(), ShapeUtils::shapeAsString(c0).c_str()); + if(mask) + REQUIRE_TRUE(mask->isSameShape(c0CorrectShape), 0, "SRU_BI operation: wrong shape of mask array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(c0CorrectShape).c_str(), ShapeUtils::shapeAsString(mask).c_str()); helpers::sruBI(block.launchContext(), x, w, b, c0, mask, ht, ct); @@ -372,7 +357,7 @@ CUSTOM_OP_IMPL(sru_bi, 5, 2, true, 0, 0) { DECLARE_TYPES(sru_bi) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_FLOATS}); } @@ -397,20 +382,16 @@ DECLARE_SHAPE_FN(sru_bi) { if(maskShapeInfo) REQUIRE_TRUE(maskShapeInfo[0] == rank-1, 0, "SRU_BI operation: wrong rank of mask array, expected is %i, but got %i instead !", rank-1, maskShapeInfo[0]); - const std::string wShape = ShapeUtils::shapeAsString(wShapeInfo); - const std::string wCorrectShape = ShapeUtils::shapeAsString({2*inSize, 6*inSize}); - const std::string bShape = ShapeUtils::shapeAsString(bShapeInfo); - const std::string bCorrectShape = ShapeUtils::shapeAsString({4*inSize}); - const std::string c0Shape = ShapeUtils::shapeAsString(c0ShapeInfo); - const std::string c0CorrectShape = ShapeUtils::shapeAsString({bS, 2*inSize}); + const std::vector wCorrectShape = {2*inSize, 6*inSize}; + const std::vector bCorrectShape = {4*inSize}; + const std::vector c0CorrectShape = {bS, 2*inSize}; + + REQUIRE_TRUE(ShapeUtils::areShapesEqual(wShapeInfo, wCorrectShape), 0, "SRU_BI operation: wrong shape of weights array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(wCorrectShape).c_str(), ShapeUtils::shapeAsString(wShapeInfo).c_str()); + REQUIRE_TRUE(ShapeUtils::areShapesEqual(bShapeInfo, bCorrectShape), 0, "SRU_BI operation: wrong shape of biases array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(bCorrectShape).c_str(), ShapeUtils::shapeAsString(bShapeInfo).c_str()); + REQUIRE_TRUE(ShapeUtils::areShapesEqual(c0ShapeInfo, c0CorrectShape), 0, "SRU_BI operation: wrong shape of initial state array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(c0CorrectShape).c_str(), ShapeUtils::shapeAsString(c0ShapeInfo).c_str()); + if(maskShapeInfo) + REQUIRE_TRUE(ShapeUtils::areShapesEqual(maskShapeInfo, c0CorrectShape), 0, "SRU_BI operation: wrong shape of mask array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(c0CorrectShape).c_str(), ShapeUtils::shapeAsString(maskShapeInfo).c_str()); - REQUIRE_TRUE(wShape == wCorrectShape, 0, "SRU_BI operation: wrong shape of weights array, expected is %s, but got %s instead !", wCorrectShape.c_str(), wShape.c_str()); - REQUIRE_TRUE(bShape == bCorrectShape, 0, "SRU_BI operation: wrong shape of biases array, expected is %s, but got %s instead !", bCorrectShape.c_str(), bShape.c_str()); - REQUIRE_TRUE(c0Shape == c0CorrectShape, 0, "SRU_BI operation: wrong shape of initial state array, expected is %s, but got %s instead !", c0CorrectShape.c_str(), c0Shape.c_str()); - if(maskShapeInfo) { - const std::string maskShape = ShapeUtils::shapeAsString(maskShapeInfo); - REQUIRE_TRUE(maskShape == c0CorrectShape, 0, "SRU_BI operation: wrong shape of mask array, expected is %s, but got %s instead !", c0CorrectShape.c_str(), maskShape.c_str()); - } char order = shape::order(xShapeInfo); @@ -422,7 +403,7 @@ DECLARE_SHAPE_FN(sru_bi) { DECLARE_TYPES(sru_bi_bp) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_FLOATS}); } @@ -453,23 +434,17 @@ CUSTOM_OP_IMPL(sru_bi_bp, 8, 4, true, 0, 0) { if(mask) REQUIRE_TRUE(mask->rankOf() == rank-1, 0, "SRU_BI_BP operation: wrong rank of mask array, expected is %i, but got %i instead !", rank-1, mask->rankOf()); - const std::string wShape = ShapeUtils::shapeAsString(w); - const std::string wCorrectShape = ShapeUtils::shapeAsString({2*inSize, 6*inSize}); - const std::string bShape = ShapeUtils::shapeAsString(b); - const std::string bCorrectShape = ShapeUtils::shapeAsString({4*inSize}); - const std::string c0Shape = ShapeUtils::shapeAsString(c0); - const std::string c0CorrectShape = ShapeUtils::shapeAsString({bS, 2*inSize}); - const std::string ctShape = ShapeUtils::shapeAsString(ct); - const std::string ctCorrectShape = ShapeUtils::shapeAsString({time, bS, 2*inSize}); + const std::vector wCorrectShape = {2*inSize, 6*inSize}; + const std::vector bCorrectShape = {4*inSize}; + const std::vector c0CorrectShape = {bS, 2*inSize}; + const std::vector ctCorrectShape = {time, bS, 2*inSize}; - REQUIRE_TRUE(wShape == wCorrectShape, 0, "SRU_BI operation: wrong shape of weights array, expected is %s, but got %s instead !", wCorrectShape.c_str(), wShape.c_str()); - REQUIRE_TRUE(bShape == bCorrectShape, 0, "SRU_BI operation: wrong shape of biases array, expected is %s, but got %s instead !", bCorrectShape.c_str(), bShape.c_str()); - REQUIRE_TRUE(c0Shape == c0CorrectShape, 0, "SRU_BI operation: wrong shape of initial state array, expected is %s, but got %s instead !", c0CorrectShape.c_str(), c0Shape.c_str()); - REQUIRE_TRUE(ctShape == ctCorrectShape, 0, "SRU_BI operation: wrong shape of state array, expected is %s, but got %s instead !", ctCorrectShape.c_str(), ctShape.c_str()); - if(mask) { - const std::string maskShape = ShapeUtils::shapeAsString(mask); - REQUIRE_TRUE(maskShape == c0CorrectShape, 0, "SRU_BI operation: wrong shape of mask array, expected is %s, but got %s instead !", c0CorrectShape.c_str(), maskShape.c_str()); - } + REQUIRE_TRUE(w->isSameShape(wCorrectShape), 0, "SRU_BI operation: wrong shape of weights array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(wCorrectShape).c_str(), ShapeUtils::shapeAsString(w).c_str()); + REQUIRE_TRUE(b->isSameShape(bCorrectShape), 0, "SRU_BI operation: wrong shape of biases array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(bCorrectShape).c_str(), ShapeUtils::shapeAsString(b).c_str()); + REQUIRE_TRUE(c0->isSameShape(c0CorrectShape), 0, "SRU_BI operation: wrong shape of initial state array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(c0CorrectShape).c_str(), ShapeUtils::shapeAsString(c0).c_str()); + REQUIRE_TRUE(ct->isSameShape(ctCorrectShape), 0, "SRU_BI operation: wrong shape of state array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(ctCorrectShape).c_str(), ShapeUtils::shapeAsString(ct).c_str()); + if(mask) + REQUIRE_TRUE(mask->isSameShape(c0CorrectShape), 0, "SRU_BI operation: wrong shape of mask array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(c0CorrectShape).c_str(), ShapeUtils::shapeAsString(mask).c_str()); auto gradI = OUTPUT_VARIABLE(0); // [time x bS x 2*inSize] auto gradW = OUTPUT_VARIABLE(1); // [time x 2*inSize x 6*inSize] @@ -507,29 +482,21 @@ DECLARE_SHAPE_FN(sru_bi_bp) { if(maskShapeInfo) REQUIRE_TRUE(maskShapeInfo[0] == rank-1, 0, "SRU_BI_BP operation: wrong rank of mask array, expected is %i, but got %i instead !", rank-1, maskShapeInfo[0]); - const std::string wShape = ShapeUtils::shapeAsString(wShapeInfo); - const std::string wCorrectShape = ShapeUtils::shapeAsString({2*inSize, 6*inSize}); - const std::string bShape = ShapeUtils::shapeAsString(bShapeInfo); - const std::string bCorrectShape = ShapeUtils::shapeAsString({4*inSize}); - const std::string c0Shape = ShapeUtils::shapeAsString(c0ShapeInfo); - const std::string c0CorrectShape = ShapeUtils::shapeAsString({bS, 2*inSize}); - const std::string ctShape = ShapeUtils::shapeAsString(ctShapeInfo); - const std::string ctCorrectShape = ShapeUtils::shapeAsString({time, bS, 2*inSize}); - const std::string inGradC0Shape = ShapeUtils::shapeAsString(inGradC0ShapeInfo); - const std::string inGradC0CorrectShape = ShapeUtils::shapeAsString({bS, 2*inSize}); - const std::string inGradHtShape = ShapeUtils::shapeAsString(inGradHtShapeInfo); - const std::string inGradHtCorrectShape = ShapeUtils::shapeAsString({time, bS, 2*inSize}); + const std::vector wCorrectShape = {2*inSize, 6*inSize}; + const std::vector bCorrectShape = {4*inSize}; + const std::vector c0CorrectShape = {bS, 2*inSize}; + const std::vector ctCorrectShape = {time, bS, 2*inSize}; + const std::vector inGradC0CorrectShape = {bS, 2*inSize}; + const std::vector inGradHtCorrectShape = {time, bS, 2*inSize}; - REQUIRE_TRUE(wShape == wCorrectShape, 0, "SRU_BI operation: wrong shape of weights array, expected is %s, but got %s instead !", wCorrectShape.c_str(), wShape.c_str()); - REQUIRE_TRUE(bShape == bCorrectShape, 0, "SRU_BI operation: wrong shape of biases array, expected is %s, but got %s instead !", bCorrectShape.c_str(), bShape.c_str()); - REQUIRE_TRUE(c0Shape == c0CorrectShape, 0, "SRU_BI operation: wrong shape of initial state array, expected is %s, but got %s instead !", c0CorrectShape.c_str(), c0Shape.c_str()); - REQUIRE_TRUE(ctShape == ctCorrectShape, 0, "SRU_BI operation: wrong shape of state array, expected is %s, but got %s instead !", ctCorrectShape.c_str(), ctShape.c_str()); - REQUIRE_TRUE(inGradC0Shape == inGradC0CorrectShape, 0, "SRU_BI operation: wrong shape of gradient c0 array, expected is %s, but got %s instead !", inGradC0CorrectShape.c_str(), inGradC0Shape.c_str()); - REQUIRE_TRUE(inGradHtShape == inGradHtCorrectShape, 0, "SRU_BI operation: wrong shape of gradient ht array, expected is %s, but got %s instead !", inGradHtCorrectShape.c_str(), inGradHtShape.c_str()); - if(maskShapeInfo) { - const std::string maskShape = ShapeUtils::shapeAsString(maskShapeInfo); - REQUIRE_TRUE(maskShape == c0CorrectShape, 0, "SRU_BI operation: wrong shape of mask array, expected is %s, but got %s instead !", c0CorrectShape.c_str(), maskShape.c_str()); - } + REQUIRE_TRUE(ShapeUtils::areShapesEqual(wShapeInfo, wCorrectShape), 0, "SRU_BI operation: wrong shape of weights array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(wCorrectShape).c_str(), ShapeUtils::shapeAsString(wShapeInfo).c_str()); + REQUIRE_TRUE(ShapeUtils::areShapesEqual(bShapeInfo, bCorrectShape), 0, "SRU_BI operation: wrong shape of biases array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(bCorrectShape).c_str(), ShapeUtils::shapeAsString(bShapeInfo).c_str()); + REQUIRE_TRUE(ShapeUtils::areShapesEqual(c0ShapeInfo, c0CorrectShape), 0, "SRU_BI operation: wrong shape of initial state array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(c0CorrectShape).c_str(), ShapeUtils::shapeAsString(c0ShapeInfo).c_str()); + REQUIRE_TRUE(ShapeUtils::areShapesEqual(ctShapeInfo, ctCorrectShape), 0, "SRU_BI operation: wrong shape of state array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(ctCorrectShape).c_str(), ShapeUtils::shapeAsString(ctShapeInfo).c_str()); + REQUIRE_TRUE(ShapeUtils::areShapesEqual(inGradC0ShapeInfo, inGradC0CorrectShape), 0, "SRU_BI operation: wrong shape of gradient c0 array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(inGradC0CorrectShape).c_str(), ShapeUtils::shapeAsString(inGradC0ShapeInfo).c_str()); + REQUIRE_TRUE(ShapeUtils::areShapesEqual(inGradHtShapeInfo, inGradHtCorrectShape), 0, "SRU_BI operation: wrong shape of gradient ht array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(inGradHtCorrectShape).c_str(), ShapeUtils::shapeAsString(inGradHtShapeInfo).c_str()); + if(maskShapeInfo) + REQUIRE_TRUE(ShapeUtils::areShapesEqual(maskShapeInfo, c0CorrectShape), 0, "SRU_BI operation: wrong shape of mask array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(c0CorrectShape).c_str(), ShapeUtils::shapeAsString(maskShapeInfo).c_str()); const char order = shape::order(xShapeInfo); @@ -679,7 +646,7 @@ DECLARE_SHAPE_FN(sru_bi_bp) { // DECLARE_TYPES(sru_logic) { // getOpDescriptor() -// ->setAllowedInputTypes(nd4j::DataType::ANY) +// ->setAllowedInputTypes(sd::DataType::ANY) // ->setAllowedOutputTypes({ALL_FLOATS}); // } @@ -781,7 +748,7 @@ DECLARE_SHAPE_FN(sru_bi_bp) { // DECLARE_TYPES(sru_old) { // getOpDescriptor() -// ->setAllowedInputTypes(nd4j::DataType::ANY) +// ->setAllowedInputTypes(sd::DataType::ANY) // ->setAllowedOutputTypes({ALL_FLOATS}); // } @@ -952,7 +919,7 @@ DECLARE_SHAPE_FN(sru_bi_bp) { // DECLARE_TYPES(sru_bp_logic) { // getOpDescriptor() -// ->setAllowedInputTypes(nd4j::DataType::ANY) +// ->setAllowedInputTypes(sd::DataType::ANY) // ->setAllowedOutputTypes({ALL_FLOATS}); // } diff --git a/libnd4j/include/ops/declarable/generic/recurrent/sruCell.cpp b/libnd4j/include/ops/declarable/generic/recurrent/sruCell.cpp index 23b2ec172..ee446037c 100644 --- a/libnd4j/include/ops/declarable/generic/recurrent/sruCell.cpp +++ b/libnd4j/include/ops/declarable/generic/recurrent/sruCell.cpp @@ -18,20 +18,20 @@ // @author Yurii Shyrma, created on 05.12.2017 // -#include +#include #if NOT_EXCLUDED(OP_sruCell) #include #include -namespace nd4j { +namespace sd { namespace ops { ////////////////////////////////////////////////////////////////////////// CUSTOM_OP_IMPL(sruCell, 4, 2, false, 0, 0) { - auto xt = INPUT_VARIABLE(0); // input [bS x inSize], bS - batch size, inSize - number of features + auto xt = INPUT_VARIABLE(0); // input [bS x inSize], bS - batch size, inSize - number of features auto ct_1 = INPUT_VARIABLE(1); // previous cell state ct [bS x inSize], that is at previous time step t-1 auto w = INPUT_VARIABLE(2); // weights [inSize x 3*inSize] auto b = INPUT_VARIABLE(3); // biases [2*inSize] @@ -40,71 +40,65 @@ CUSTOM_OP_IMPL(sruCell, 4, 2, false, 0, 0) { auto ct = OUTPUT_VARIABLE(1); // current cell state [bS x inSize], that is at current time step t const int rank = xt->rankOf(); - const int bS = xt->sizeAt(0); + const int bS = xt->sizeAt(0); const int inSize = xt->sizeAt(1); // inSize - number of features // input shapes validation - const std::string ct_1Shape = ShapeUtils::shapeAsString(ct_1); - const std::string correctCt_1Shape = ShapeUtils::shapeAsString({bS, inSize}); - const std::string WShape = ShapeUtils::shapeAsString(w); - const std::string correctWShape = ShapeUtils::shapeAsString({inSize, 3*inSize}); - const std::string bShape = ShapeUtils::shapeAsString(b); - const std::string correctBShape = ShapeUtils::shapeAsString({2*inSize}); + const std::vector correctCt_1Shape = {bS, inSize}; + const std::vector correctWShape = {inSize, 3*inSize}; + const std::vector correctBShape = {2*inSize}; - REQUIRE_TRUE(correctCt_1Shape == ct_1Shape, 0, "SRUCELL operation: wrong shape of previous cell state, expected is %s, but got %s instead !", correctCt_1Shape.c_str(), ct_1Shape.c_str()); - REQUIRE_TRUE(correctWShape == WShape, 0, "SRUCELL operation: wrong shape of weights, expected is %s, but got %s instead !", correctWShape.c_str(), WShape.c_str()); - REQUIRE_TRUE(correctBShape == bShape, 0, "SRUCELL operation: wrong shape of biases, expected is %s, but got %s instead !", correctBShape.c_str(), bShape.c_str()); + REQUIRE_TRUE(ct_1->isSameShape(correctCt_1Shape), 0, "SRUCELL operation: wrong shape of previous cell state, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(correctCt_1Shape).c_str(), ShapeUtils::shapeAsString(ct_1).c_str()); + REQUIRE_TRUE(w->isSameShape(correctWShape), 0, "SRUCELL operation: wrong shape of weights, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(correctWShape).c_str(), ShapeUtils::shapeAsString(w).c_str()); + REQUIRE_TRUE(b->isSameShape(correctBShape), 0, "SRUCELL operation: wrong shape of biases, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(correctBShape).c_str(), ShapeUtils::shapeAsString(b).c_str()); // fixme: shitty initializer lists helpers::sruCell(block.launchContext(), xt, ct_1, w, b, ht, ct); - + return Status::OK(); } DECLARE_TYPES(sruCell) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_FLOATS}); } DECLARE_SHAPE_FN(sruCell) { auto xtShapeInfo = inputShape->at(0); // input [bS x inSize], bS - batch size, inSize - number of features - auto ct_1ShapeInfo = inputShape->at(1); // previous cell state ct [bS x inSize], that is at previous time step t-1 + auto ct_1ShapeInfo = inputShape->at(1); // previous cell state ct [bS x inSize], that is at previous time step t-1 auto wShapeInfo = inputShape->at(2); // weights [inSize x 3*inSize] auto bShapeInfo = inputShape->at(3); // biases [2*inSize] const int rank = xtShapeInfo[0]; - const int bS = xtShapeInfo[1]; + const int bS = xtShapeInfo[1]; const int inSize = xtShapeInfo[2]; // inSize - number of features // input shapes validation - const std::string ct_1Shape = ShapeUtils::shapeAsString(ct_1ShapeInfo); - const std::string correctCt_1Shape = ShapeUtils::shapeAsString({bS, inSize}); - const std::string WShape = ShapeUtils::shapeAsString(wShapeInfo); - const std::string correctWShape = ShapeUtils::shapeAsString({inSize, 3*inSize}); - const std::string bShape = ShapeUtils::shapeAsString(bShapeInfo); - const std::string correctBShape = ShapeUtils::shapeAsString({2*inSize}); + const std::vector correctCt_1Shape = {bS, inSize}; + const std::vector correctWShape = {inSize, 3*inSize}; + const std::vector correctBShape = {2*inSize}; + + REQUIRE_TRUE(ShapeUtils::areShapesEqual(ct_1ShapeInfo, correctCt_1Shape) , 0, "SRUCELL operation: wrong shape of previous cell state, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(correctCt_1Shape).c_str(), ShapeUtils::shapeAsString(ct_1ShapeInfo).c_str()); + REQUIRE_TRUE(ShapeUtils::areShapesEqual(wShapeInfo ,correctWShape), 0, "SRUCELL operation: wrong shape of weights, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(correctWShape).c_str(), ShapeUtils::shapeAsString(wShapeInfo).c_str()); + REQUIRE_TRUE(ShapeUtils::areShapesEqual(bShapeInfo ,correctBShape), 0, "SRUCELL operation: wrong shape of biases, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(correctBShape).c_str(), ShapeUtils::shapeAsString(bShapeInfo).c_str()); - REQUIRE_TRUE(correctCt_1Shape == ct_1Shape, 0, "SRUCELL operation: wrong shape of previous cell state, expected is %s, but got %s instead !", correctCt_1Shape.c_str(), ct_1Shape.c_str()); - REQUIRE_TRUE(correctWShape == WShape, 0, "SRUCELL operation: wrong shape of weights, expected is %s, but got %s instead !", correctWShape.c_str(), WShape.c_str()); - REQUIRE_TRUE(correctBShape == bShape, 0, "SRUCELL operation: wrong shape of biases, expected is %s, but got %s instead !", correctBShape.c_str(), bShape.c_str()); - // evaluate output shapeInfos Nd4jLong *hShapeInfo(nullptr), *cShapeInfo(nullptr); ALLOCATE(hShapeInfo, block.getWorkspace(), shape::shapeInfoLength(rank), Nd4jLong); // [bS x numProj] ALLOCATE(cShapeInfo, block.getWorkspace(), shape::shapeInfoLength(rank), Nd4jLong); // [bS x numUnits] - + hShapeInfo[0] = cShapeInfo[0] = rank; hShapeInfo[1] = cShapeInfo[1] = bS; hShapeInfo[2] = cShapeInfo[2] = inSize; - + ShapeUtils::updateStridesAndType(hShapeInfo, ct_1ShapeInfo, shape::order(ct_1ShapeInfo)); ShapeUtils::updateStridesAndType(cShapeInfo, ct_1ShapeInfo, shape::order(ct_1ShapeInfo)); - + return SHAPELIST(ConstantShapeHelper::getInstance()->createFromExisting(hShapeInfo, block.workspace()), ConstantShapeHelper::getInstance()->createFromExisting(cShapeInfo, block.workspace())); -} +} diff --git a/libnd4j/include/ops/declarable/generic/recurrent/staticBidirectionalRNN.cpp b/libnd4j/include/ops/declarable/generic/recurrent/staticBidirectionalRNN.cpp index 453480ed6..bc27c08f6 100644 --- a/libnd4j/include/ops/declarable/generic/recurrent/staticBidirectionalRNN.cpp +++ b/libnd4j/include/ops/declarable/generic/recurrent/staticBidirectionalRNN.cpp @@ -23,7 +23,7 @@ #include #include -namespace nd4j { +namespace sd { namespace ops { @@ -55,14 +55,14 @@ CUSTOM_OP_IMPL(static_bidirectional_rnn, 7, 3, false, 0, 0) { maxTimeStep = INPUT_VARIABLE(9); break; } - + auto h = OUTPUT_VARIABLE(0); // cell outputs [time x bS x (numUnitsFW + numUnitsBW)], that is per each time step auto hFWFinal = OUTPUT_VARIABLE(1); // final cell out for forward RNN [bS x numUnitsFW] auto hBWFinal = OUTPUT_VARIABLE(2); // final cell out for backward RNN [bS x numUnitsBF] REQUIRE_TRUE(x->rankOf() == 3, 0, "STATIC_BIDIRECTIONAL_RNN custom operation: input array must have rank = 3, but got %i instead !", x->rankOf()); - REQUIRE_TRUE(WxFW->rankOf() == 2, 0, "STATIC_BIDIRECTIONAL_RNN custom operation: input-to-hidden weights array (for forward RNN) must have rank = 2, but got %i instead !", WxFW->rankOf()); - REQUIRE_TRUE(WxBW->rankOf() == 2, 0, "STATIC_BIDIRECTIONAL_RNN custom operation: input-to-hidden weights array (for backward RNN) must have rank = 2, but got %i instead !", WxBW->rankOf()); + REQUIRE_TRUE(WxFW->rankOf() == 2, 0, "STATIC_BIDIRECTIONAL_RNN custom operation: input-to-hidden weights array (for forward RNN) must have rank = 2, but got %i instead !", WxFW->rankOf()); + REQUIRE_TRUE(WxBW->rankOf() == 2, 0, "STATIC_BIDIRECTIONAL_RNN custom operation: input-to-hidden weights array (for backward RNN) must have rank = 2, but got %i instead !", WxBW->rankOf()); const Nd4jLong inRank = x->rankOf(); const Nd4jLong time = x->sizeAt(0); @@ -70,39 +70,48 @@ CUSTOM_OP_IMPL(static_bidirectional_rnn, 7, 3, false, 0, 0) { const Nd4jLong numUnitsFW = WxFW->sizeAt(1); const Nd4jLong numUnitsBW = WxBW->sizeAt(1); - REQUIRE_TRUE(ShapeUtils::shapeAsString(WhFW) == ShapeUtils::shapeAsString({numUnitsFW, numUnitsFW}), 0, "STATIC_BIDIRECTIONAL_RNN custom operation: wrong shape of hidden-to-hidden weights array (for forward RNN), expected is %s but got %s instead !", ShapeUtils::shapeAsString({numUnitsFW, numUnitsFW}).c_str(), ShapeUtils::shapeAsString(WhFW).c_str()); - REQUIRE_TRUE(ShapeUtils::shapeAsString(WhBW) == ShapeUtils::shapeAsString({numUnitsBW, numUnitsBW}), 0, "STATIC_BIDIRECTIONAL_RNN custom operation: wrong shape of hidden-to-hidden weights array (for backward RNN), expected is %s but got %s instead !", ShapeUtils::shapeAsString({numUnitsBW, numUnitsBW}).c_str(), ShapeUtils::shapeAsString(WhBW).c_str()); - REQUIRE_TRUE(ShapeUtils::shapeAsString(bFW) == ShapeUtils::shapeAsString({2*numUnitsFW}), 0, "STATIC_BIDIRECTIONAL_RNN custom operation: wrong shape of biases array (for forward RNN), expected is %s, but got %s instead !", ShapeUtils::shapeAsString({2*numUnitsFW}).c_str(), ShapeUtils::shapeAsString(bFW).c_str()); - REQUIRE_TRUE(ShapeUtils::shapeAsString(bBW) == ShapeUtils::shapeAsString({2*numUnitsBW}), 0, "STATIC_BIDIRECTIONAL_RNN custom operation: wrong shape of biases array (for backward RNN), expected is %s, but got %s instead !", ShapeUtils::shapeAsString({2*numUnitsBW}).c_str(), ShapeUtils::shapeAsString(bBW).c_str()); - if(h0FW) - REQUIRE_TRUE(ShapeUtils::shapeAsString(h0FW) == ShapeUtils::shapeAsString({bS, numUnitsFW}), 0, "STATIC_BIDIRECTIONAL_RNN custom operation: wrong shape of initial cell output array (for forward RNN), expected is %s but got %s instead !", ShapeUtils::shapeAsString({bS, numUnitsFW}).c_str(), ShapeUtils::shapeAsString(h0FW).c_str()); - if(h0BW) - REQUIRE_TRUE(ShapeUtils::shapeAsString(h0BW) == ShapeUtils::shapeAsString({bS, numUnitsBW}), 0, "STATIC_BIDIRECTIONAL_RNN custom operation: wrong shape of initial cell output array (for backward RNN), expected is %s but got %s instead !", ShapeUtils::shapeAsString({bS, numUnitsBW}).c_str(), ShapeUtils::shapeAsString(h0BW).c_str()); - if(maxTimeStep) - REQUIRE_TRUE(ShapeUtils::shapeAsString(maxTimeStep) == ShapeUtils::shapeAsString({bS}), 0, "STATIC_BIDIRECTIONAL_RNN custom operation: wrong shape of maxTimeStep array, expected is [%i], but got %s instead !", bS, ShapeUtils::shapeAsString(maxTimeStep).c_str()); + const std::vector expectedWhFWshape = {numUnitsFW, numUnitsFW}; + const std::vector expectedWhBWshape = {numUnitsBW, numUnitsBW}; + const std::vector expectedbFWshape = {2 * numUnitsFW}; + const std::vector expectedbBWshape = {2 * numUnitsBW}; - // forward steps + REQUIRE_TRUE(WhFW->isSameShape(expectedWhFWshape), 0, "STATIC_BIDIRECTIONAL_RNN custom operation: wrong shape of hidden-to-hidden weights array (for forward RNN), expected is %s but got %s instead !", ShapeUtils::shapeAsString(expectedWhFWshape).c_str(), ShapeUtils::shapeAsString(WhFW).c_str()); + REQUIRE_TRUE(WhBW->isSameShape(expectedWhBWshape), 0, "STATIC_BIDIRECTIONAL_RNN custom operation: wrong shape of hidden-to-hidden weights array (for backward RNN), expected is %s but got %s instead !", ShapeUtils::shapeAsString(expectedWhBWshape).c_str(), ShapeUtils::shapeAsString(WhBW).c_str()); + REQUIRE_TRUE(bFW->isSameShape(expectedbFWshape), 0, "STATIC_BIDIRECTIONAL_RNN custom operation: wrong shape of biases array (for forward RNN), expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedbFWshape).c_str(), ShapeUtils::shapeAsString(bFW).c_str()); + REQUIRE_TRUE(bBW->isSameShape(expectedbBWshape), 0, "STATIC_BIDIRECTIONAL_RNN custom operation: wrong shape of biases array (for backward RNN), expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedbBWshape).c_str(), ShapeUtils::shapeAsString(bBW).c_str()); + if(h0FW) { + const std::vector expectedh0FWshape = {bS, numUnitsFW}; + REQUIRE_TRUE(h0FW->isSameShape(expectedh0FWshape), 0, "STATIC_BIDIRECTIONAL_RNN custom operation: wrong shape of initial cell output array (for forward RNN), expected is %s but got %s instead !", ShapeUtils::shapeAsString(expectedh0FWshape).c_str(), ShapeUtils::shapeAsString(h0FW).c_str()); + } + if(h0BW) { + const std::vector expectedh0BWshape = {bS, numUnitsBW}; + REQUIRE_TRUE(h0BW->isSameShape(expectedh0BWshape), 0, "STATIC_BIDIRECTIONAL_RNN custom operation: wrong shape of initial cell output array (for backward RNN), expected is %s but got %s instead !", ShapeUtils::shapeAsString(expectedh0BWshape).c_str(), ShapeUtils::shapeAsString(h0BW).c_str()); + } + if(maxTimeStep) + REQUIRE_TRUE(maxTimeStep->isSameShape({bS}), 0, "STATIC_BIDIRECTIONAL_RNN custom operation: wrong shape of maxTimeStep array, expected is [%i], but got %s instead !", bS, ShapeUtils::shapeAsString(maxTimeStep).c_str()); + + // forward steps auto hFW = new NDArray(x->ordering(), {time, bS, numUnitsFW}, x->dataType(), block.launchContext()); helpers::rnnTimeLoop(block.launchContext(), x, WxFW, WhFW, bFW, h0FW, maxTimeStep, hFW, hFWFinal); - auto seqLen = maxTimeStep; + auto seqLen = maxTimeStep; if(seqLen == nullptr) { // seqLen = new NDArray(x->ordering(), {x->sizeAt(1)}, x->dataType(), block.launchContext()); // [bS] - seqLen = new NDArray(x->ordering(), {x->sizeAt(1)}, nd4j::DataType::INT64, block.launchContext()); // [bS] + seqLen = new NDArray(x->ordering(), {x->sizeAt(1)}, sd::DataType::INT64, block.launchContext()); // [bS] *seqLen = x->sizeAt(0); // set each element of seqLen to be equal to time - } - - // reverse x + } + + // reverse x auto revOut = new NDArray(x, false, block.launchContext()); helpers::reverseSequence(block.launchContext(), x, seqLen, revOut, 0, 1); - // backward steps + // backward steps auto hBW = new NDArray(x->ordering(), {time, bS, numUnitsBW}, x->dataType(), block.launchContext()); - + helpers::rnnTimeLoop(block.launchContext(), revOut, WxBW, WhBW, bBW, h0BW, maxTimeStep, hBW, hBWFinal); - // reverse hBW - auto hBWcopy = new NDArray(*hBW); + // reverse hBW + auto hBWcopy = new NDArray(*hBW); helpers::reverseSequence(block.launchContext(), hBWcopy, seqLen, hBW, 0, 1); // concatenate hFW and hBW along last third dimension @@ -117,18 +126,18 @@ CUSTOM_OP_IMPL(static_bidirectional_rnn, 7, 3, false, 0, 0) { if(seqLen != maxTimeStep) delete seqLen; - + return Status::OK(); } DECLARE_TYPES(static_bidirectional_rnn) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_FLOATS}); } -DECLARE_SHAPE_FN(static_bidirectional_rnn) { +DECLARE_SHAPE_FN(static_bidirectional_rnn) { auto xShapeInfo = inputShape->at(0); // input [time x bS x inSize] auto WxFWShapeInfo = inputShape->at(1); // input-to-hidden weights for forward RNN, [inSize x numUnitsFW] @@ -167,16 +176,25 @@ DECLARE_SHAPE_FN(static_bidirectional_rnn) { const int numUnitsFW = WxFWShapeInfo[2]; const int numUnitsBW = WxBWShapeInfo[2]; - REQUIRE_TRUE(ShapeUtils::shapeAsString(WhFWShapeInfo) == ShapeUtils::shapeAsString({numUnitsFW, numUnitsFW}), 0, "STATIC_BIDIRECTIONAL_RNN custom operation: wrong shape of hidden-to-hidden weights array (for forward RNN), expected is %s but got %s instead !", ShapeUtils::shapeAsString({numUnitsFW, numUnitsFW}).c_str(), ShapeUtils::shapeAsString(WhFWShapeInfo).c_str()); - REQUIRE_TRUE(ShapeUtils::shapeAsString(WhBWShapeInfo) == ShapeUtils::shapeAsString({numUnitsBW, numUnitsBW}), 0, "STATIC_BIDIRECTIONAL_RNN custom operation: wrong shape of hidden-to-hidden weights array (for backward RNN), expected is %s but got %s instead !", ShapeUtils::shapeAsString({numUnitsBW, numUnitsBW}).c_str(), ShapeUtils::shapeAsString(WhBWShapeInfo).c_str()); - REQUIRE_TRUE(ShapeUtils::shapeAsString(bFWShapeInfo) == ShapeUtils::shapeAsString({2*numUnitsFW}), 0, "STATIC_BIDIRECTIONAL_RNN custom operation: wrong shape of biases array (for forward RNN), expected is %s, but got %s instead !", ShapeUtils::shapeAsString({2*numUnitsFW}).c_str(), ShapeUtils::shapeAsString(bFWShapeInfo).c_str()); - REQUIRE_TRUE(ShapeUtils::shapeAsString(bBWShapeInfo) == ShapeUtils::shapeAsString({2*numUnitsBW}), 0, "STATIC_BIDIRECTIONAL_RNN custom operation: wrong shape of biases array (for backward RNN), expected is %s, but got %s instead !", ShapeUtils::shapeAsString({2*numUnitsBW}).c_str(), ShapeUtils::shapeAsString(bBWShapeInfo).c_str()); - if(h0FWShapeInfo) - REQUIRE_TRUE(ShapeUtils::shapeAsString(h0FWShapeInfo) == ShapeUtils::shapeAsString({bS, numUnitsFW}), 0, "STATIC_BIDIRECTIONAL_RNN custom operation: wrong shape of initial cell output array (for forward RNN), expected is %s but got %s instead !", ShapeUtils::shapeAsString({bS, numUnitsFW}).c_str(), ShapeUtils::shapeAsString(h0FWShapeInfo).c_str()); - if(h0BWShapeInfo) - REQUIRE_TRUE(ShapeUtils::shapeAsString(h0BWShapeInfo) == ShapeUtils::shapeAsString({bS, numUnitsBW}), 0, "STATIC_BIDIRECTIONAL_RNN custom operation: wrong shape of initial cell output array (for backward RNN), expected is %s but got %s instead !", ShapeUtils::shapeAsString({bS, numUnitsBW}).c_str(), ShapeUtils::shapeAsString(h0BWShapeInfo).c_str()); + const std::vector expectedWhFWshape = {numUnitsFW, numUnitsFW}; + const std::vector expectedWhBWshape = {numUnitsBW, numUnitsBW}; + const std::vector expectedbFWshape = {2 * numUnitsFW}; + const std::vector expectedbBWshape = {2 * numUnitsBW}; + + REQUIRE_TRUE(ShapeUtils::areShapesEqual(WhFWShapeInfo, expectedWhFWshape), 0, "STATIC_BIDIRECTIONAL_RNN custom operation: wrong shape of hidden-to-hidden weights array (for forward RNN), expected is %s but got %s instead !", ShapeUtils::shapeAsString(expectedWhFWshape).c_str(), ShapeUtils::shapeAsString(WhFWShapeInfo).c_str()); + REQUIRE_TRUE(ShapeUtils::areShapesEqual(WhBWShapeInfo, expectedWhBWshape), 0, "STATIC_BIDIRECTIONAL_RNN custom operation: wrong shape of hidden-to-hidden weights array (for backward RNN), expected is %s but got %s instead !", ShapeUtils::shapeAsString(expectedWhBWshape).c_str(), ShapeUtils::shapeAsString(WhBWShapeInfo).c_str()); + REQUIRE_TRUE(ShapeUtils::areShapesEqual(bFWShapeInfo, expectedbFWshape), 0, "STATIC_BIDIRECTIONAL_RNN custom operation: wrong shape of biases array (for forward RNN), expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedbFWshape).c_str(), ShapeUtils::shapeAsString(bFWShapeInfo).c_str()); + REQUIRE_TRUE(ShapeUtils::areShapesEqual(bBWShapeInfo, expectedbBWshape), 0, "STATIC_BIDIRECTIONAL_RNN custom operation: wrong shape of biases array (for backward RNN), expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedbBWshape).c_str(), ShapeUtils::shapeAsString(bBWShapeInfo).c_str()); + if(h0FWShapeInfo) { + const std::vector expectedh0FWshape = {bS, numUnitsFW}; + REQUIRE_TRUE(ShapeUtils::areShapesEqual(h0FWShapeInfo, expectedh0FWshape), 0, "STATIC_BIDIRECTIONAL_RNN custom operation: wrong shape of initial cell output array (for forward RNN), expected is %s but got %s instead !", ShapeUtils::shapeAsString(expectedh0FWshape).c_str(), ShapeUtils::shapeAsString(h0FWShapeInfo).c_str()); + } + if(h0BWShapeInfo) { + const std::vector expectedh0BWshape = {bS, numUnitsBW}; + REQUIRE_TRUE(ShapeUtils::areShapesEqual(h0BWShapeInfo, expectedh0BWshape), 0, "STATIC_BIDIRECTIONAL_RNN custom operation: wrong shape of initial cell output array (for backward RNN), expected is %s but got %s instead !", ShapeUtils::shapeAsString(expectedh0BWshape).c_str(), ShapeUtils::shapeAsString(h0BWShapeInfo).c_str()); + } if(maxTimeStepShapeInfo) - REQUIRE_TRUE(ShapeUtils::shapeAsString(maxTimeStepShapeInfo) == ShapeUtils::shapeAsString({bS}), 0, "STATIC_BIDIRECTIONAL_RNN custom operation: wrong shape of maxTimeStep array, expected is [%i], but got %s instead !", bS, ShapeUtils::shapeAsString(maxTimeStepShapeInfo).c_str()); + REQUIRE_TRUE(ShapeUtils::areShapesEqual(maxTimeStepShapeInfo, {bS}), 0, "STATIC_BIDIRECTIONAL_RNN custom operation: wrong shape of maxTimeStep array, expected is [%i], but got %s instead !", bS, ShapeUtils::shapeAsString(maxTimeStepShapeInfo).c_str()); // evaluate output shapeInfos Nd4jLong *hShapeInfo(nullptr), *hFWFinalPrevShapeInfo(nullptr), *hBWFinalPrevShapeInfo(nullptr); @@ -195,9 +213,9 @@ DECLARE_SHAPE_FN(static_bidirectional_rnn) { ShapeUtils::updateStridesAndType(hShapeInfo, xShapeInfo, shape::order(xShapeInfo)); ShapeUtils::updateStridesAndType(hFWFinalPrevShapeInfo, xShapeInfo, shape::order(xShapeInfo)); ShapeUtils::updateStridesAndType(hBWFinalPrevShapeInfo, xShapeInfo, shape::order(xShapeInfo)); - + return SHAPELIST(CONSTANT(hShapeInfo), CONSTANT(hFWFinalPrevShapeInfo), CONSTANT(hBWFinalPrevShapeInfo)); -} +} diff --git a/libnd4j/include/ops/declarable/generic/recurrent/staticRNN.cpp b/libnd4j/include/ops/declarable/generic/recurrent/staticRNN.cpp index 7eea9e7f2..4100f6745 100644 --- a/libnd4j/include/ops/declarable/generic/recurrent/staticRNN.cpp +++ b/libnd4j/include/ops/declarable/generic/recurrent/staticRNN.cpp @@ -21,7 +21,7 @@ #include #include -namespace nd4j { +namespace sd { namespace ops { @@ -58,12 +58,17 @@ CUSTOM_OP_IMPL(static_rnn, 4, 2, false, 0, 0) { const int inSize = x->sizeAt(2); const int numUnits = Wx->sizeAt(1); - REQUIRE_TRUE(ShapeUtils::shapeAsString(Wh) == ShapeUtils::shapeAsString({numUnits, numUnits}), 0, "STATIC_RNN custom operation: wrong shape of hidden-to-hidden weights array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString({numUnits, numUnits}).c_str(), ShapeUtils::shapeAsString(Wh).c_str()); - REQUIRE_TRUE(ShapeUtils::shapeAsString(b) == ShapeUtils::shapeAsString({2*numUnits}), 0, "STATIC_RNN custom operation: wrong shape of biases array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString({2*numUnits}).c_str(), ShapeUtils::shapeAsString(b).c_str()); - if(h0) - REQUIRE_TRUE(ShapeUtils::shapeAsString(h0) == ShapeUtils::shapeAsString({bS, numUnits}), 0, "STATIC_RNN custom operation: wrong shape of initial cell output array, expected is %s but got %s instead !", ShapeUtils::shapeAsString({bS, numUnits}).c_str(), ShapeUtils::shapeAsString(h0).c_str()); + const std::vector expectedWhShape = {numUnits, numUnits}; + const std::vector expectedbShape = {2 * numUnits}; + + REQUIRE_TRUE(Wh->isSameShape(expectedWhShape), 0, "STATIC_RNN custom operation: wrong shape of hidden-to-hidden weights array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedWhShape).c_str(), ShapeUtils::shapeAsString(Wh).c_str()); + REQUIRE_TRUE(b->isSameShape(expectedbShape), 0, "STATIC_RNN custom operation: wrong shape of biases array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedbShape).c_str(), ShapeUtils::shapeAsString(b).c_str()); + if(h0) { + const std::vector expectedh0Shape = {bS, numUnits}; + REQUIRE_TRUE(h0->isSameShape(expectedh0Shape), 0, "STATIC_RNN custom operation: wrong shape of initial cell output array, expected is %s but got %s instead !", ShapeUtils::shapeAsString(expectedh0Shape).c_str(), ShapeUtils::shapeAsString(h0).c_str()); + } if(maxTimeStep) - REQUIRE_TRUE(ShapeUtils::shapeAsString(maxTimeStep) == ShapeUtils::shapeAsString({bS}), 0, "STATIC_RNN custom operation: wrong shape of maxTimeStep array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString({bS}).c_str(), ShapeUtils::shapeAsString(maxTimeStep).c_str()); + REQUIRE_TRUE(maxTimeStep->isSameShape({bS}), 0, "STATIC_RNN custom operation: wrong shape of maxTimeStep array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString({bS}).c_str(), ShapeUtils::shapeAsString(maxTimeStep).c_str()); helpers::rnnTimeLoop(block.launchContext(), x, Wx, Wh, b, h0, maxTimeStep, h, hFinal); @@ -73,7 +78,7 @@ CUSTOM_OP_IMPL(static_rnn, 4, 2, false, 0, 0) { DECLARE_TYPES(static_rnn) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_FLOATS}); } @@ -107,12 +112,17 @@ DECLARE_SHAPE_FN(static_rnn) { const int bS = xShapeInfo[2]; const int numUnits = WxShapeInfo[2]; - REQUIRE_TRUE(ShapeUtils::shapeAsString(WhShapeInfo) == ShapeUtils::shapeAsString({numUnits, numUnits}), 0, "STATIC_RNN custom operation: wrong shape of hidden-to-hidden weights array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString({numUnits, numUnits}).c_str(), ShapeUtils::shapeAsString(WhShapeInfo).c_str()); - REQUIRE_TRUE(ShapeUtils::shapeAsString(bShapeInfo) == ShapeUtils::shapeAsString({2*numUnits}), 0, "STATIC_RNN custom operation: wrong shape of biases array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString({2*numUnits}).c_str(), ShapeUtils::shapeAsString(bShapeInfo).c_str()); - if(h0ShapeInfo) - REQUIRE_TRUE(ShapeUtils::shapeAsString(h0ShapeInfo) == ShapeUtils::shapeAsString({bS, numUnits}), 0, "STATIC_RNN custom operation: wrong shape of initial cell output array, expected is %s but got %s instead !", ShapeUtils::shapeAsString({bS, numUnits}).c_str(), ShapeUtils::shapeAsString(h0ShapeInfo).c_str()); + const std::vector expectedWhShape = {numUnits, numUnits}; + const std::vector expectedbShape = {2 * numUnits}; + + REQUIRE_TRUE(ShapeUtils::areShapesEqual(WhShapeInfo, expectedWhShape), 0, "STATIC_RNN custom operation: wrong shape of hidden-to-hidden weights array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedWhShape).c_str(), ShapeUtils::shapeAsString(WhShapeInfo).c_str()); + REQUIRE_TRUE(ShapeUtils::areShapesEqual(bShapeInfo, expectedbShape), 0, "STATIC_RNN custom operation: wrong shape of biases array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedbShape).c_str(), ShapeUtils::shapeAsString(bShapeInfo).c_str()); + if(h0ShapeInfo){ + const std::vector expectedh0Shape = {bS, numUnits}; + REQUIRE_TRUE(ShapeUtils::areShapesEqual(h0ShapeInfo, expectedh0Shape), 0, "STATIC_RNN custom operation: wrong shape of initial cell output array, expected is %s but got %s instead !", ShapeUtils::shapeAsString(expectedh0Shape).c_str(), ShapeUtils::shapeAsString(h0ShapeInfo).c_str()); + } if(maxTimeStepShapeInfo) - REQUIRE_TRUE(ShapeUtils::shapeAsString(maxTimeStepShapeInfo) == ShapeUtils::shapeAsString({bS}), 0, "STATIC_RNN custom operation: wrong shape of maxTimeStep array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString({bS}).c_str(), ShapeUtils::shapeAsString(maxTimeStepShapeInfo).c_str()); + REQUIRE_TRUE(ShapeUtils::areShapesEqual(maxTimeStepShapeInfo, {bS}), 0, "STATIC_RNN custom operation: wrong shape of maxTimeStep array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString({bS}).c_str(), ShapeUtils::shapeAsString(maxTimeStepShapeInfo).c_str()); // evaluate output shapeInfos Nd4jLong *hShapeInfo(nullptr), *hPrevShapeInfo(nullptr); diff --git a/libnd4j/include/ops/declarable/generic/shape/broadcast_to.cpp b/libnd4j/include/ops/declarable/generic/shape/broadcast_to.cpp index ee8f0a864..49961bfe2 100644 --- a/libnd4j/include/ops/declarable/generic/shape/broadcast_to.cpp +++ b/libnd4j/include/ops/declarable/generic/shape/broadcast_to.cpp @@ -18,12 +18,12 @@ // @author Yurii Shyrma (iuriish@yahoo.com), created on 03.09.2018 // -#include +#include #if NOT_EXCLUDED(OP_broadcast_to) #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(broadcast_to, 2, 1, false, 0, 0) { diff --git a/libnd4j/include/ops/declarable/generic/shape/create.cpp b/libnd4j/include/ops/declarable/generic/shape/create.cpp index c87f63a56..c79b55497 100644 --- a/libnd4j/include/ops/declarable/generic/shape/create.cpp +++ b/libnd4j/include/ops/declarable/generic/shape/create.cpp @@ -18,12 +18,12 @@ // @author raver119@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_shapes_of) #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(create, 1, 1, false, 0, 1) { @@ -44,13 +44,13 @@ namespace nd4j { auto shape = shapeInput->getBufferAsVector(); - return SHAPELIST(nd4j::ConstantShapeHelper::getInstance()->createShapeInfo(dtype, order, shape)); + return SHAPELIST(sd::ConstantShapeHelper::getInstance()->createShapeInfo(dtype, order, shape)); } DECLARE_TYPES(create) { getOpDescriptor() ->setAllowedInputTypes({ALL_INTS}) - ->setAllowedOutputTypes(nd4j::DataType::ANY); + ->setAllowedOutputTypes(sd::DataType::ANY); } } } diff --git a/libnd4j/include/ops/declarable/generic/shape/evaluate_reduction_shape.cpp b/libnd4j/include/ops/declarable/generic/shape/evaluate_reduction_shape.cpp index 85e51d136..6a0ad187c 100644 --- a/libnd4j/include/ops/declarable/generic/shape/evaluate_reduction_shape.cpp +++ b/libnd4j/include/ops/declarable/generic/shape/evaluate_reduction_shape.cpp @@ -18,12 +18,12 @@ // @author raver119@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_evaluate_reduction_shape) #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(evaluate_reduction_shape, 2, 1, false, 0, 0) { auto inputShape = INPUT_VARIABLE(0); @@ -34,7 +34,7 @@ namespace nd4j { auto shape = inputShape->asVectorT(); - auto tempShapeInfo = ConstantShapeHelper::getInstance()->createShapeInfo(nd4j::DataType::INT64, 'c', shape); + auto tempShapeInfo = ConstantShapeHelper::getInstance()->createShapeInfo(sd::DataType::INT64, 'c', shape); auto tempReductionShapeInfo = ShapeUtils::evalReduceShapeInfo('c', axis, tempShapeInfo, keepDims, oldFormat, block.workspace()); REQUIRE_TRUE(output->lengthOf() == shape::rank(tempReductionShapeInfo), 0, "evaluate_reduction_shape: output length should be %i, but got %i instead", shape::rank(tempReductionShapeInfo), output->lengthOf()); @@ -49,7 +49,7 @@ namespace nd4j { getOpDescriptor() ->setAllowedInputTypes(0, {ALL_INTS}) ->setAllowedInputTypes(1, {ALL_INTS}) - ->setAllowedOutputTypes(0, nd4j::DataType::INT64); + ->setAllowedOutputTypes(0, sd::DataType::INT64); } DECLARE_SHAPE_FN(evaluate_reduction_shape) { @@ -64,16 +64,16 @@ namespace nd4j { if (keepDims) { if (oldFormat) { // for oldFormat we can't go below rank 2 - length = nd4j::math::nd4j_max(2, length); + length = sd::math::nd4j_max(2, length); } } else { length -= axis.size(); if (oldFormat) { - length = nd4j::math::nd4j_max(2, length); + length = sd::math::nd4j_max(2, length); } } - return SHAPELIST(ConstantShapeHelper::getInstance()->vectorShapeInfo(length, nd4j::DataType::INT64)); + return SHAPELIST(ConstantShapeHelper::getInstance()->vectorShapeInfo(length, sd::DataType::INT64)); } } } diff --git a/libnd4j/include/ops/declarable/generic/shape/expand_dims.cpp b/libnd4j/include/ops/declarable/generic/shape/expand_dims.cpp index efa723c20..86900c264 100644 --- a/libnd4j/include/ops/declarable/generic/shape/expand_dims.cpp +++ b/libnd4j/include/ops/declarable/generic/shape/expand_dims.cpp @@ -18,12 +18,12 @@ // Created by raver119 on 02.11.2017. // -#include +#include #if NOT_EXCLUDED(OP_expand_dims) #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(expand_dims, 1, 1, false, 0, -2) { auto input = INPUT_VARIABLE(0); @@ -41,9 +41,10 @@ namespace nd4j { REQUIRE_TRUE(axis >= 0 && axis <= input->rankOf()+1, 0, "ExpandDims: axis should be in range of 0...%i in this case, but got %i instead", input->rankOf() + 1, axis); - std::vector shape; + std::vector shape(input->rankOf()); + for(int e = 0; e < input->rankOf(); e++) - shape.emplace_back(input->sizeAt(e)); + shape[input->sizeAt(e)]; shape.insert(shape.begin() + axis, 1); @@ -58,7 +59,7 @@ namespace nd4j { DECLARE_TYPES(expand_dims) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setSameMode(true); } diff --git a/libnd4j/include/ops/declarable/generic/shape/order.cpp b/libnd4j/include/ops/declarable/generic/shape/order.cpp index dfb1789bc..5b978f48f 100644 --- a/libnd4j/include/ops/declarable/generic/shape/order.cpp +++ b/libnd4j/include/ops/declarable/generic/shape/order.cpp @@ -18,12 +18,12 @@ // Created by raver119 on 12.02.18. // -#include +#include #if NOT_EXCLUDED(OP_order) #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(order, 1, 1, false, 0, 1) { auto input = INPUT_VARIABLE(0); @@ -36,7 +36,7 @@ namespace nd4j { DECLARE_TYPES(order) { getOpDescriptor() - ->setAllowedInputTypes(0, nd4j::DataType::ANY) + ->setAllowedInputTypes(0, sd::DataType::ANY) ->setAllowedOutputTypes({ALL_INTS}); } diff --git a/libnd4j/include/ops/declarable/generic/shape/permute.cpp b/libnd4j/include/ops/declarable/generic/shape/permute.cpp index 63c20e888..f612aec92 100644 --- a/libnd4j/include/ops/declarable/generic/shape/permute.cpp +++ b/libnd4j/include/ops/declarable/generic/shape/permute.cpp @@ -19,13 +19,13 @@ // @author Yurii Shyrma (iuriish@yahoo.com) // -#include +#include #if NOT_EXCLUDED(OP_permute) #include #include -namespace nd4j { +namespace sd { namespace ops { ////////////////////////////////////////////////////////////////////////// @@ -55,7 +55,7 @@ CUSTOM_OP_IMPL(permute, 1, 1, true, 0, -2) { ////////////////////////////////////////////////////////////////////////// DECLARE_TYPES(permute) { getOpDescriptor() - ->setAllowedInputTypes(0, nd4j::DataType::ANY) + ->setAllowedInputTypes(0, sd::DataType::ANY) ->setAllowedInputTypes(1, {ALL_INTS}) ->setSameMode(true); } diff --git a/libnd4j/include/ops/declarable/generic/shape/reshape.cpp b/libnd4j/include/ops/declarable/generic/shape/reshape.cpp index 4a06455eb..ace58a0b8 100644 --- a/libnd4j/include/ops/declarable/generic/shape/reshape.cpp +++ b/libnd4j/include/ops/declarable/generic/shape/reshape.cpp @@ -18,12 +18,12 @@ // Created by raver119 on 29/10/17. // -#include +#include #if NOT_EXCLUDED(OP_reshape) #include -namespace nd4j { +namespace sd { namespace ops { ////////////////////////////////////////////////////////////////////////// @@ -122,12 +122,17 @@ CUSTOM_OP_IMPL(reshape, 1, 1, false, 0, -2) { nd4j_printv("Reshape: new shape", shapeNew); } - if (s->isEmpty()) { + if (s->isScalar()) { // just a scalar z->assign(x); } else { - auto xr = x->reshape(order, shapeNew); - z->assign(xr); + // in some cases we might go away with simple memcpy call instead of assign call + if (x->ordering() == 'c' && z->ordering() == x->ordering() && shape::reshapeC(x->shapeInfo(), z->shapeInfo())) { + z->dataBuffer()->copyBufferFrom(*x->dataBuffer().get(), z->lengthOf() * DataTypeUtils::sizeOfElement(z->dataType()), 0, x->bufferOffset()); + } else { + auto xr = x->reshape(order, shapeNew); + z->assign(xr); + } } return Status::OK(); @@ -140,7 +145,7 @@ CUSTOM_OP_IMPL(reshape, 1, 1, false, 0, -2) { DECLARE_TYPES(reshape) { getOpDescriptor() - ->setAllowedInputTypes(0, nd4j::DataType::ANY) + ->setAllowedInputTypes(0, sd::DataType::ANY) ->setAllowedInputTypes(1, {ALL_INTS}) ->setSameMode(true); } diff --git a/libnd4j/include/ops/declarable/generic/shape/reshape_as.cpp b/libnd4j/include/ops/declarable/generic/shape/reshape_as.cpp index 3035f104b..90e2ff398 100644 --- a/libnd4j/include/ops/declarable/generic/shape/reshape_as.cpp +++ b/libnd4j/include/ops/declarable/generic/shape/reshape_as.cpp @@ -18,12 +18,12 @@ // Created by raver119 on 29/10/17. // -#include +#include #if NOT_EXCLUDED(OP_reshapeas) #include -namespace nd4j { +namespace sd { namespace ops { @@ -52,7 +52,7 @@ namespace nd4j { DECLARE_TYPES(reshapeas) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setSameMode(true); } } diff --git a/libnd4j/include/ops/declarable/generic/shape/shape.cpp b/libnd4j/include/ops/declarable/generic/shape/shape.cpp index 676b347cb..e2db3db3e 100644 --- a/libnd4j/include/ops/declarable/generic/shape/shape.cpp +++ b/libnd4j/include/ops/declarable/generic/shape/shape.cpp @@ -18,12 +18,12 @@ // @author raver119@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_shape) #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(shape_of, 1, 1, false, 0, 0) { auto x = INPUT_VARIABLE(0); @@ -51,7 +51,7 @@ namespace nd4j { DECLARE_TYPES(shape_of) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_INTS}); } } diff --git a/libnd4j/include/ops/declarable/generic/shape/shapes.cpp b/libnd4j/include/ops/declarable/generic/shape/shapes.cpp index 8f772ef35..6481d1db3 100644 --- a/libnd4j/include/ops/declarable/generic/shape/shapes.cpp +++ b/libnd4j/include/ops/declarable/generic/shape/shapes.cpp @@ -18,12 +18,12 @@ // @author raver119@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_shapes_of) #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(shapes_of, -1, -1, false, 0, 0) { for (int e = 0; e < block.width(); e++) { @@ -43,7 +43,7 @@ namespace nd4j { for (int e = 0; e < inputShape->size(); e++) { auto inShape = inputShape->at(e); - shapeList->push_back(ConstantShapeHelper::getInstance()->vectorShapeInfo(shape::rank(inShape), nd4j::DataType::INT64)); + shapeList->push_back(ConstantShapeHelper::getInstance()->vectorShapeInfo(shape::rank(inShape), sd::DataType::INT64)); } return shapeList; @@ -51,7 +51,7 @@ namespace nd4j { DECLARE_TYPES(shapes_of) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_INTS}); } } diff --git a/libnd4j/include/ops/declarable/generic/shape/size_at.cpp b/libnd4j/include/ops/declarable/generic/shape/size_at.cpp index 0f1e9c669..2c27b018a 100644 --- a/libnd4j/include/ops/declarable/generic/shape/size_at.cpp +++ b/libnd4j/include/ops/declarable/generic/shape/size_at.cpp @@ -18,12 +18,12 @@ // Created by raver119 on 12.02.18. // -#include +#include #if NOT_EXCLUDED(OP_size_at) #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(size_at, 1, 1, false, 0, 1) { auto input = INPUT_VARIABLE(0); @@ -42,12 +42,12 @@ namespace nd4j { } DECLARE_SHAPE_FN(size_at) { - return SHAPELIST(ConstantShapeHelper::getInstance()->scalarShapeInfo(nd4j::DataType::INT64)); + return SHAPELIST(ConstantShapeHelper::getInstance()->scalarShapeInfo(sd::DataType::INT64)); } DECLARE_TYPES(size_at) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes(DataType::INT64) ->allowOverride(true); } diff --git a/libnd4j/include/ops/declarable/generic/shape/squeeze.cpp b/libnd4j/include/ops/declarable/generic/shape/squeeze.cpp index 22e229643..812947422 100644 --- a/libnd4j/include/ops/declarable/generic/shape/squeeze.cpp +++ b/libnd4j/include/ops/declarable/generic/shape/squeeze.cpp @@ -18,12 +18,12 @@ // @author raver119@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_squeeze) #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(squeeze, 1, 1, false, 0, -2) { auto input = INPUT_VARIABLE(0); @@ -86,7 +86,7 @@ namespace nd4j { DECLARE_TYPES(squeeze) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setSameMode(true); } diff --git a/libnd4j/include/ops/declarable/generic/shape/tile_to_shape.cpp b/libnd4j/include/ops/declarable/generic/shape/tile_to_shape.cpp index d71fbddd5..687d79f25 100644 --- a/libnd4j/include/ops/declarable/generic/shape/tile_to_shape.cpp +++ b/libnd4j/include/ops/declarable/generic/shape/tile_to_shape.cpp @@ -18,12 +18,12 @@ // @author raver119@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_tile_to_shape) #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(tile_to_shape, 1, 1, false, 0, -1) { @@ -55,13 +55,13 @@ namespace ops { DECLARE_TYPES(tile_to_shape) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setSameMode(true); } DECLARE_TYPES(tile_to_shape_bp) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_FLOATS}); } diff --git a/libnd4j/include/ops/declarable/generic/shape/transpose.cpp b/libnd4j/include/ops/declarable/generic/shape/transpose.cpp index 4ec586370..0b12f415f 100644 --- a/libnd4j/include/ops/declarable/generic/shape/transpose.cpp +++ b/libnd4j/include/ops/declarable/generic/shape/transpose.cpp @@ -19,13 +19,13 @@ // @author Yurii Shyrma (iuriish@yahoo.com) // -#include +#include #if NOT_EXCLUDED(OP_transpose) #include #include -namespace nd4j { +namespace sd { namespace ops { ////////////////////////////////////////////////////////////////////////// @@ -54,7 +54,7 @@ CUSTOM_OP_IMPL(transpose, 1, 1, false, 0, 0) { DECLARE_TYPES(transpose) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setSameMode(true); } diff --git a/libnd4j/include/ops/declarable/generic/strings/split_string.cpp b/libnd4j/include/ops/declarable/generic/strings/split_string.cpp index 4af4e3aac..e42591b5c 100644 --- a/libnd4j/include/ops/declarable/generic/strings/split_string.cpp +++ b/libnd4j/include/ops/declarable/generic/strings/split_string.cpp @@ -18,12 +18,12 @@ // @author raver119@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_split_string) #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(split_string, 2, 1, true, 0, 0) { auto input = INPUT_VARIABLE(0); diff --git a/libnd4j/include/ops/declarable/generic/tests/noop.cpp b/libnd4j/include/ops/declarable/generic/tests/noop.cpp index 0c8e51804..37980c7e6 100644 --- a/libnd4j/include/ops/declarable/generic/tests/noop.cpp +++ b/libnd4j/include/ops/declarable/generic/tests/noop.cpp @@ -18,12 +18,12 @@ // @author raver119@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_noop) #include -namespace nd4j { +namespace sd { namespace ops { OP_IMPL(noop, -2, -2, true) { // Fastest op ever. @@ -32,7 +32,7 @@ namespace nd4j { DECLARE_TYPES(noop) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setSameMode(true); } } diff --git a/libnd4j/include/ops/declarable/generic/tests/test_output_reshape.cpp b/libnd4j/include/ops/declarable/generic/tests/test_output_reshape.cpp index eab2cc320..7ded29e20 100644 --- a/libnd4j/include/ops/declarable/generic/tests/test_output_reshape.cpp +++ b/libnd4j/include/ops/declarable/generic/tests/test_output_reshape.cpp @@ -18,12 +18,12 @@ // @author raver119@gmail.com // -#include +#include #if NOT_EXCLUDED(test_output_reshape) #include -namespace nd4j { +namespace sd { namespace ops { OP_IMPL(test_output_reshape, 1, 1, true) { auto input = INPUT_VARIABLE(0); @@ -39,7 +39,7 @@ namespace nd4j { DECLARE_TYPES(test_output_reshape) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setSameMode(true); } } diff --git a/libnd4j/include/ops/declarable/generic/tests/test_scalar.cpp b/libnd4j/include/ops/declarable/generic/tests/test_scalar.cpp index 0009e6696..437222052 100644 --- a/libnd4j/include/ops/declarable/generic/tests/test_scalar.cpp +++ b/libnd4j/include/ops/declarable/generic/tests/test_scalar.cpp @@ -18,12 +18,12 @@ // Created by raver119 on 24.02.18. // -#include +#include #if NOT_EXCLUDED(OP_test_scalar) #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(test_scalar, 1, 1, false, 0, 0) { auto input = INPUT_VARIABLE(0); @@ -57,7 +57,7 @@ namespace nd4j { DECLARE_TYPES(test_scalar) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setSameMode(true); } } diff --git a/libnd4j/include/ops/declarable/generic/tests/testcustom.cpp b/libnd4j/include/ops/declarable/generic/tests/testcustom.cpp index 73b3c1721..89480e5bc 100644 --- a/libnd4j/include/ops/declarable/generic/tests/testcustom.cpp +++ b/libnd4j/include/ops/declarable/generic/tests/testcustom.cpp @@ -18,12 +18,12 @@ // @author raver119@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_testcustom) #include -namespace nd4j { +namespace sd { namespace ops { ////////////////////////////////////////////////////////////////////////// CUSTOM_OP_IMPL(testcustom, 1, 1, false, 0, -1) { @@ -46,7 +46,7 @@ namespace nd4j { DECLARE_TYPES(testcustom) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setSameMode(true); } } diff --git a/libnd4j/include/ops/declarable/generic/tests/testop2i2o.cpp b/libnd4j/include/ops/declarable/generic/tests/testop2i2o.cpp index 7c257b903..f4d4d3159 100644 --- a/libnd4j/include/ops/declarable/generic/tests/testop2i2o.cpp +++ b/libnd4j/include/ops/declarable/generic/tests/testop2i2o.cpp @@ -18,12 +18,12 @@ // @author raver119@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_testop2i2o) #include -namespace nd4j { +namespace sd { namespace ops { ////////////////////////////////////////////////////////////////////////// // test op, non-divergent @@ -46,7 +46,7 @@ namespace nd4j { DECLARE_TYPES(testop2i2o) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setSameMode(true); } } diff --git a/libnd4j/include/ops/declarable/generic/tests/testreduction.cpp b/libnd4j/include/ops/declarable/generic/tests/testreduction.cpp index 9b59ee3e4..a0749ed7f 100644 --- a/libnd4j/include/ops/declarable/generic/tests/testreduction.cpp +++ b/libnd4j/include/ops/declarable/generic/tests/testreduction.cpp @@ -18,12 +18,12 @@ // @author raver119@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_testreduction) #include -namespace nd4j { +namespace sd { namespace ops { REDUCTION_OP_IMPL(testreduction, 1, 1, false, 0, -1) { auto z = OUTPUT_VARIABLE(0); @@ -34,7 +34,7 @@ namespace nd4j { DECLARE_TYPES(testreduction) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setSameMode(true); } } diff --git a/libnd4j/include/ops/declarable/generic/thrid_party/firas_sparse.cpp b/libnd4j/include/ops/declarable/generic/thrid_party/firas_sparse.cpp index 21164f520..d385c2fa9 100644 --- a/libnd4j/include/ops/declarable/generic/thrid_party/firas_sparse.cpp +++ b/libnd4j/include/ops/declarable/generic/thrid_party/firas_sparse.cpp @@ -20,22 +20,22 @@ // @author raver119@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_firas_sparse) #ifndef LIBND4J_THIRD_PARTY_H #define LIBND4J_THIRD_PARTY_H -#include +#include #include -#include +#include #include -#include +#include #include #include #include -namespace nd4j { +namespace sd { namespace ops { @@ -99,7 +99,7 @@ namespace nd4j { DECLARE_TYPES(firas_sparse) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_FLOATS}); } } diff --git a/libnd4j/include/ops/declarable/generic/transforms/clip_by_averaged_norm.cpp b/libnd4j/include/ops/declarable/generic/transforms/clip_by_averaged_norm.cpp index d8633e125..958a90410 100644 --- a/libnd4j/include/ops/declarable/generic/transforms/clip_by_averaged_norm.cpp +++ b/libnd4j/include/ops/declarable/generic/transforms/clip_by_averaged_norm.cpp @@ -18,13 +18,13 @@ // @author raver119@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_clipbyavgnorm) #include #include -namespace nd4j { +namespace sd { namespace ops { CONFIGURABLE_OP_IMPL(clipbyavgnorm, 1, 1, true, 1, 0) { @@ -42,7 +42,7 @@ CONFIGURABLE_OP_IMPL(clipbyavgnorm, 1, 1, true, 1, 0) { DECLARE_TYPES(clipbyavgnorm) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_FLOATS}); } diff --git a/libnd4j/include/ops/declarable/generic/transforms/clip_by_global_norm.cpp b/libnd4j/include/ops/declarable/generic/transforms/clip_by_global_norm.cpp index f36c2114e..99a01d390 100644 --- a/libnd4j/include/ops/declarable/generic/transforms/clip_by_global_norm.cpp +++ b/libnd4j/include/ops/declarable/generic/transforms/clip_by_global_norm.cpp @@ -18,13 +18,13 @@ // @author sgazeos@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_clip_by_global_norm) #include #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(clip_by_global_norm, 1, 2, true, 1, 0) { @@ -61,7 +61,7 @@ DECLARE_SHAPE_FN(clip_by_global_norm) { DECLARE_TYPES(clip_by_global_norm) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_FLOATS}); } diff --git a/libnd4j/include/ops/declarable/generic/transforms/clip_by_norm.cpp b/libnd4j/include/ops/declarable/generic/transforms/clip_by_norm.cpp index d558167eb..43b23ba18 100644 --- a/libnd4j/include/ops/declarable/generic/transforms/clip_by_norm.cpp +++ b/libnd4j/include/ops/declarable/generic/transforms/clip_by_norm.cpp @@ -18,13 +18,13 @@ // @author raver119@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_clipbynorm) #include #include -namespace nd4j { +namespace sd { namespace ops { CONFIGURABLE_OP_IMPL(clipbynorm, 1, 1, true, 1, 0) { @@ -63,7 +63,7 @@ namespace ops { DECLARE_TYPES(clipbynorm) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_FLOATS}); } diff --git a/libnd4j/include/ops/declarable/generic/transforms/clip_by_value.cpp b/libnd4j/include/ops/declarable/generic/transforms/clip_by_value.cpp index e4cb1eda5..4275e4837 100644 --- a/libnd4j/include/ops/declarable/generic/transforms/clip_by_value.cpp +++ b/libnd4j/include/ops/declarable/generic/transforms/clip_by_value.cpp @@ -18,13 +18,13 @@ // @author raver119@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_clipbyvalue) #include #include -namespace nd4j { +namespace sd { namespace ops { CONFIGURABLE_OP_IMPL(clipbyvalue, 1, 1, true, 2, 0) { auto input = INPUT_VARIABLE(0); @@ -45,7 +45,7 @@ namespace nd4j { DECLARE_TYPES(clipbyvalue) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_FLOATS}); } } diff --git a/libnd4j/include/ops/declarable/generic/transforms/concat.cpp b/libnd4j/include/ops/declarable/generic/transforms/concat.cpp index faa59fa6c..0b171b36f 100644 --- a/libnd4j/include/ops/declarable/generic/transforms/concat.cpp +++ b/libnd4j/include/ops/declarable/generic/transforms/concat.cpp @@ -23,7 +23,7 @@ #include #include -namespace nd4j { +namespace sd { namespace ops { @@ -38,7 +38,7 @@ CUSTOM_OP_IMPL(concat, -1, 1, false, 0, 0) { // first of all take into account possible presence of empty arrays // also if scalar is present -> copy its value to vector with length=1 - std::vector nonEmptyArrs; + std::vector nonEmptyArrs; std::vector arrsToDelete; int index = 0; bool allOfSameType = true; @@ -117,7 +117,7 @@ CUSTOM_OP_IMPL(concat, -1, 1, false, 0, 0) { DECLARE_TYPES(concat) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY); + ->setAllowedInputTypes(sd::DataType::ANY); // ->setSameMode(true); } @@ -243,7 +243,7 @@ DECLARE_SHAPE_FN(concat) { // if (_dimension < 0) // _dimension += first->rankOf(); - // if (nd4j::Environment::getInstance()->isDebugAndVerbose()) { + // if (sd::Environment::getInstance()->isDebugAndVerbose()) { // printf("Shape %i: ", 0); // shape::printShapeInfoLinear((Nd4jLong *) shapes[0]); // } @@ -261,12 +261,12 @@ DECLARE_SHAPE_FN(concat) { // oldScalars &= array->rankOf() == 2 && array->isScalar(); - // if (nd4j::Environment::getInstance()->isDebugAndVerbose()) { + // if (sd::Environment::getInstance()->isDebugAndVerbose()) { // printf("Shape %i: ", e); // shape::printShapeInfoLinear(array->shapeInfo()); // } // } - // if (nd4j::Environment::getInstance()->isDebugAndVerbose()) + // if (sd::Environment::getInstance()->isDebugAndVerbose()) // fflush(stdout); // if (oldScalars) { @@ -274,11 +274,11 @@ DECLARE_SHAPE_FN(concat) { // _dimension = 1; // } - // nd4j::SpecialMethods::concatCpuGeneric(_dimension, elements, buffers, shapes, output->getBuffer(), output->getShapeInfo()); + // sd::SpecialMethods::concatCpuGeneric(_dimension, elements, buffers, shapes, output->getBuffer(), output->getShapeInfo()); // STORE_RESULT(*output); - // if (nd4j::Environment::getInstance()->isDebugAndVerbose()) + // if (sd::Environment::getInstance()->isDebugAndVerbose()) // output->printShapeInfo("Concat result shape"); // delete[] buffers; @@ -413,7 +413,7 @@ CUSTOM_OP_IMPL(concat_bp, -1, -1, false, 0, 0) { DECLARE_TYPES(concat_bp) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_FLOATS}); } diff --git a/libnd4j/include/ops/declarable/generic/transforms/cumprod.cpp b/libnd4j/include/ops/declarable/generic/transforms/cumprod.cpp index e7cd1ccb9..c0b011f99 100644 --- a/libnd4j/include/ops/declarable/generic/transforms/cumprod.cpp +++ b/libnd4j/include/ops/declarable/generic/transforms/cumprod.cpp @@ -18,13 +18,13 @@ // @author raver119@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_cumprod) #include #include -namespace nd4j { +namespace sd { namespace ops { CONFIGURABLE_OP_IMPL(cumprod, 1, 1, true, 0, 2) { auto input = INPUT_VARIABLE(0); @@ -42,7 +42,7 @@ namespace nd4j { if (block.getIArguments()->size() == 2 && block.width() == 1) { // all at once case - nd4j::ops::helpers::prefix(block.launchContext(), scalar::Multiply, input, output, exclusive, reverse); + sd::ops::helpers::prefix(block.launchContext(), scalar::Multiply, input, output, exclusive, reverse); } else { std::vector dims(block.numI() - 2); @@ -59,7 +59,7 @@ namespace nd4j { if (dims[e] < 0) dims[e] += input->rankOf(); - nd4j::ops::helpers::prefix(block.launchContext(), scalar::Multiply, input, output, dims, exclusive, reverse); + sd::ops::helpers::prefix(block.launchContext(), scalar::Multiply, input, output, dims, exclusive, reverse); } return Status::OK(); @@ -67,7 +67,7 @@ namespace nd4j { DECLARE_TYPES(cumprod) { getOpDescriptor() - ->setAllowedInputTypes(0, nd4j::DataType::ANY) + ->setAllowedInputTypes(0, sd::DataType::ANY) ->setAllowedInputTypes(1, {ALL_INTS}) ->setAllowedOutputTypes({ALL_FLOATS}) ->setSameMode(true); @@ -75,7 +75,7 @@ namespace nd4j { DECLARE_TYPES(cumprod_bp) { getOpDescriptor() - ->setAllowedInputTypes(0, nd4j::DataType::ANY) + ->setAllowedInputTypes(0, sd::DataType::ANY) ->setAllowedInputTypes(1, {ALL_INTS, ALL_FLOATS}) // there is a case when axes given as IArgs ->setAllowedInputTypes(2, {ALL_FLOATS}) ->setAllowedOutputTypes({ALL_FLOATS}) @@ -103,35 +103,35 @@ namespace nd4j { dims[e] = INT_ARG(e + 2); } - nd4j::ops::helpers::prefix(block.launchContext(), scalar::Multiply, input, output, dims, exclusive, reverse); + sd::ops::helpers::prefix(block.launchContext(), scalar::Multiply, input, output, dims, exclusive, reverse); NDArray val = NDArray(output->dup()); gradOut->applyPairwiseTransform(pairwise::Multiply, *output, val); val.applyPairwiseTransform(pairwise::Divide, *input, val); if (!exclusive && !reverse) { if (dims.size()) - nd4j::ops::helpers::prefix(block.launchContext(), scalar::Add, &val, output, dims, true, false); + sd::ops::helpers::prefix(block.launchContext(), scalar::Add, &val, output, dims, true, false); else - nd4j::ops::helpers::prefix(block.launchContext(), scalar::Add, &val, output, false, true); + sd::ops::helpers::prefix(block.launchContext(), scalar::Add, &val, output, false, true); } else if (!exclusive && reverse){ if (dims.size()) - nd4j::ops::helpers::prefix(block.launchContext(), scalar::Add, &val, output, dims, false, false); + sd::ops::helpers::prefix(block.launchContext(), scalar::Add, &val, output, dims, false, false); else - nd4j::ops::helpers::prefix(block.launchContext(), scalar::Add, &val, output, false, false); + sd::ops::helpers::prefix(block.launchContext(), scalar::Add, &val, output, false, false); } else if (exclusive && !reverse) { if (dims.size()) - nd4j::ops::helpers::prefix(block.launchContext(), scalar::Add, &val, output, dims, true, true); + sd::ops::helpers::prefix(block.launchContext(), scalar::Add, &val, output, dims, true, true); else - nd4j::ops::helpers::prefix(block.launchContext(), scalar::Add, &val, output, true, true); + sd::ops::helpers::prefix(block.launchContext(), scalar::Add, &val, output, true, true); } else { if (dims.size()) - nd4j::ops::helpers::prefix(block.launchContext(), scalar::Add, &val, output, dims, true, false); + sd::ops::helpers::prefix(block.launchContext(), scalar::Add, &val, output, dims, true, false); else - nd4j::ops::helpers::prefix(block.launchContext(), scalar::Add, &val, output, true, false); + sd::ops::helpers::prefix(block.launchContext(), scalar::Add, &val, output, true, false); } return Status::OK(); diff --git a/libnd4j/include/ops/declarable/generic/transforms/cumsum.cpp b/libnd4j/include/ops/declarable/generic/transforms/cumsum.cpp index 866853c5e..97389fddb 100644 --- a/libnd4j/include/ops/declarable/generic/transforms/cumsum.cpp +++ b/libnd4j/include/ops/declarable/generic/transforms/cumsum.cpp @@ -18,13 +18,13 @@ // @author raver119@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_cumsum) #include #include -namespace nd4j { +namespace sd { namespace ops { CONFIGURABLE_OP_IMPL(cumsum, 1, 1, true, 0, 2) { @@ -43,7 +43,7 @@ CONFIGURABLE_OP_IMPL(cumsum, 1, 1, true, 0, 2) { if (block.getIArguments()->size() == 2 && block.width() == 1) { // all at once case - nd4j::ops::helpers::prefix(block.launchContext(), scalar::Add, input, output, exclusive, reverse); + sd::ops::helpers::prefix(block.launchContext(), scalar::Add, input, output, exclusive, reverse); } else { std::vector dims(block.numI() - 2); @@ -62,7 +62,7 @@ CONFIGURABLE_OP_IMPL(cumsum, 1, 1, true, 0, 2) { if (dims[e] < 0) dims[e] += input->rankOf(); - nd4j::ops::helpers::prefix(block.launchContext(), scalar::Add, input, output, dims, exclusive, reverse); + sd::ops::helpers::prefix(block.launchContext(), scalar::Add, input, output, dims, exclusive, reverse); } return Status::OK(); @@ -98,28 +98,28 @@ CUSTOM_OP_IMPL(cumsum_bp, 2, -1, true, 0, 2) { } if (!exclusive && !reverse) { if (dims.size()) - nd4j::ops::helpers::prefix(block.launchContext(), scalar::Add, gradOut, output, dims, false, true); + sd::ops::helpers::prefix(block.launchContext(), scalar::Add, gradOut, output, dims, false, true); else - nd4j::ops::helpers::prefix(block.launchContext(), scalar::Add, gradOut, output, false, true); + sd::ops::helpers::prefix(block.launchContext(), scalar::Add, gradOut, output, false, true); } else if (!exclusive && reverse){ if (dims.size()) - nd4j::ops::helpers::prefix(block.launchContext(), scalar::Add, gradOut, output, dims, false, false); + sd::ops::helpers::prefix(block.launchContext(), scalar::Add, gradOut, output, dims, false, false); else - nd4j::ops::helpers::prefix(block.launchContext(), scalar::Add, gradOut, output, false, false); + sd::ops::helpers::prefix(block.launchContext(), scalar::Add, gradOut, output, false, false); } else if (exclusive && !reverse) { if (dims.size()) - nd4j::ops::helpers::prefix(block.launchContext(), scalar::Add, gradOut, output, dims, true, true); + sd::ops::helpers::prefix(block.launchContext(), scalar::Add, gradOut, output, dims, true, true); else - nd4j::ops::helpers::prefix(block.launchContext(), scalar::Add, gradOut, output, true, true); + sd::ops::helpers::prefix(block.launchContext(), scalar::Add, gradOut, output, true, true); } else { if (dims.size()) - nd4j::ops::helpers::prefix(block.launchContext(), scalar::Add, gradOut, output, dims, true, false); + sd::ops::helpers::prefix(block.launchContext(), scalar::Add, gradOut, output, dims, true, false); else - nd4j::ops::helpers::prefix(block.launchContext(), scalar::Add, gradOut, output, true, false); + sd::ops::helpers::prefix(block.launchContext(), scalar::Add, gradOut, output, true, false); } return Status::OK(); diff --git a/libnd4j/include/ops/declarable/generic/transforms/eye.cpp b/libnd4j/include/ops/declarable/generic/transforms/eye.cpp index c5f3dbff6..41469468c 100644 --- a/libnd4j/include/ops/declarable/generic/transforms/eye.cpp +++ b/libnd4j/include/ops/declarable/generic/transforms/eye.cpp @@ -17,13 +17,13 @@ // // @author Yurii Shyrma (iuriish@yahoo.com), created on 22.01.2018 // -#include +#include #if NOT_EXCLUDED(OP_eye) #include #include -namespace nd4j { +namespace sd { namespace ops { @@ -44,7 +44,7 @@ namespace ops { std::vector params; - nd4j::DataType dtype = block.getTArguments()->empty() ? nd4j::DataType::FLOAT32 : nd4j::DataTypeUtils::fromInt(T_ARG(0)); + sd::DataType dtype = block.getTArguments()->empty() ? sd::DataType::FLOAT32 : sd::DataTypeUtils::fromInt(T_ARG(0)); if(block.width() == 0) { params = *block.getIArguments(); diff --git a/libnd4j/include/ops/declarable/generic/transforms/flatten.cpp b/libnd4j/include/ops/declarable/generic/transforms/flatten.cpp index b387a0970..19cc4f469 100644 --- a/libnd4j/include/ops/declarable/generic/transforms/flatten.cpp +++ b/libnd4j/include/ops/declarable/generic/transforms/flatten.cpp @@ -18,13 +18,13 @@ // @author raver119@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_) #include #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(flatten, -1, 1, false, 0, 1) { auto output = OUTPUT_VARIABLE(0); @@ -48,13 +48,13 @@ namespace nd4j { } DECLARE_TYPES(flatten) { - getOpDescriptor()->setAllowedInputTypes({ALL_INTS, ALL_FLOATS, nd4j::DataType::BOOL}); - getOpDescriptor()->setAllowedOutputTypes(0, {ALL_FLOATS, ALL_INTS, nd4j::DataType::BOOL}); + getOpDescriptor()->setAllowedInputTypes({ALL_INTS, ALL_FLOATS, sd::DataType::BOOL}); + getOpDescriptor()->setAllowedOutputTypes(0, {ALL_FLOATS, ALL_INTS, sd::DataType::BOOL}); } DECLARE_SHAPE_FN(flatten) { Nd4jLong length = 0; - nd4j::DataType dtype = ArrayOptions::dataType(inputShape->at(0)); + sd::DataType dtype = ArrayOptions::dataType(inputShape->at(0)); for (int e = 0; e < inputShape->size(); e++) { length += shape::length(inputShape->at(e)); REQUIRE_TRUE(dtype == ArrayOptions::dataType(inputShape->at(e)), 0, "Flatten: all input arrays must have the same datatype"); diff --git a/libnd4j/include/ops/declarable/generic/transforms/floor.cpp b/libnd4j/include/ops/declarable/generic/transforms/floor.cpp index 5a8559075..984708de5 100644 --- a/libnd4j/include/ops/declarable/generic/transforms/floor.cpp +++ b/libnd4j/include/ops/declarable/generic/transforms/floor.cpp @@ -18,12 +18,12 @@ // @author raver119@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_Floor) #include -namespace nd4j { +namespace sd { namespace ops { OP_IMPL(Floor, 1, 1, true) { auto first = INPUT_VARIABLE(0); @@ -39,7 +39,7 @@ namespace nd4j { DECLARE_TYPES(Floor) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setSameMode(true); } } diff --git a/libnd4j/include/ops/declarable/generic/transforms/gather.cpp b/libnd4j/include/ops/declarable/generic/transforms/gather.cpp index 61ed3bc65..79ce8ad29 100644 --- a/libnd4j/include/ops/declarable/generic/transforms/gather.cpp +++ b/libnd4j/include/ops/declarable/generic/transforms/gather.cpp @@ -18,7 +18,7 @@ // @author Shyrma Yurii (iuriish@yahoo.com), created on 16.11.2017 // -#include +#include #if NOT_EXCLUDED(OP_gather) #include @@ -26,7 +26,7 @@ #include -namespace nd4j { +namespace sd { namespace ops { diff --git a/libnd4j/include/ops/declarable/generic/transforms/gatherNd.cpp b/libnd4j/include/ops/declarable/generic/transforms/gatherNd.cpp index c889569e2..30b5b19ef 100644 --- a/libnd4j/include/ops/declarable/generic/transforms/gatherNd.cpp +++ b/libnd4j/include/ops/declarable/generic/transforms/gatherNd.cpp @@ -18,14 +18,14 @@ // @author Shyrma Yurii (iuriish@yahoo.com), created on 23.01.2018 // -#include +#include #if NOT_EXCLUDED(OP_gather_nd) #include #include #include -namespace nd4j { +namespace sd { namespace ops { diff --git a/libnd4j/include/ops/declarable/generic/transforms/hashcode.cpp b/libnd4j/include/ops/declarable/generic/transforms/hashcode.cpp index 123001dda..4196385c1 100644 --- a/libnd4j/include/ops/declarable/generic/transforms/hashcode.cpp +++ b/libnd4j/include/ops/declarable/generic/transforms/hashcode.cpp @@ -18,14 +18,14 @@ // @author raver119@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_hashcode) #include #include #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(hashcode, 1, 1, false, 0, 0) { REQUIRE_TRUE(block.width() == 1, 0, "hashcode: this op can't be applied along dimension"); @@ -41,7 +41,7 @@ namespace nd4j { }; DECLARE_SHAPE_FN(hashcode) { - return SHAPELIST(ConstantShapeHelper::getInstance()->scalarShapeInfo(nd4j::DataType::INT64)); + return SHAPELIST(ConstantShapeHelper::getInstance()->scalarShapeInfo(sd::DataType::INT64)); } @@ -49,7 +49,7 @@ namespace nd4j { getOpDescriptor() ->setAllowedInputTypes(0, {ALL_INTS, ALL_FLOATS}) ->setAllowedInputTypes(1, {ALL_INTS}) - ->setAllowedOutputTypes({nd4j::DataType::INT64}); + ->setAllowedOutputTypes({sd::DataType::INT64}); }; } } diff --git a/libnd4j/include/ops/declarable/generic/transforms/histogram.cpp b/libnd4j/include/ops/declarable/generic/transforms/histogram.cpp index ab5a70c4b..415361894 100644 --- a/libnd4j/include/ops/declarable/generic/transforms/histogram.cpp +++ b/libnd4j/include/ops/declarable/generic/transforms/histogram.cpp @@ -18,14 +18,14 @@ // @author raver119@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_histogram) #include #include #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(histogram, 1, 1, false, 0, 1) { auto input = INPUT_VARIABLE(0); @@ -43,7 +43,7 @@ namespace nd4j { DECLARE_SHAPE_FN(histogram) { auto numBins = INT_ARG(0); - return SHAPELIST(ConstantShapeHelper::getInstance()->vectorShapeInfo(numBins, nd4j::DataType::INT64)); + return SHAPELIST(ConstantShapeHelper::getInstance()->vectorShapeInfo(numBins, sd::DataType::INT64)); } diff --git a/libnd4j/include/ops/declarable/generic/transforms/histogram_fixed_width.cpp b/libnd4j/include/ops/declarable/generic/transforms/histogram_fixed_width.cpp index 529446e12..36175fc01 100644 --- a/libnd4j/include/ops/declarable/generic/transforms/histogram_fixed_width.cpp +++ b/libnd4j/include/ops/declarable/generic/transforms/histogram_fixed_width.cpp @@ -18,13 +18,13 @@ // @author Yurii Shyrma (iuriish@yahoo.com), created on 31.08.2018 // -#include +#include #if NOT_EXCLUDED(OP_histogram_fixed_width) #include #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(histogram_fixed_width, 2, 1, false, 0, 0) { @@ -48,7 +48,7 @@ CUSTOM_OP_IMPL(histogram_fixed_width, 2, 1, false, 0, 0) { DECLARE_TYPES(histogram_fixed_width) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_INDICES}); } diff --git a/libnd4j/include/ops/declarable/generic/transforms/invertPermutation.cpp b/libnd4j/include/ops/declarable/generic/transforms/invertPermutation.cpp index e4bbf8dff..3814106cf 100644 --- a/libnd4j/include/ops/declarable/generic/transforms/invertPermutation.cpp +++ b/libnd4j/include/ops/declarable/generic/transforms/invertPermutation.cpp @@ -18,13 +18,13 @@ // @author, Yurii Shyrma (iuriish@yahoo.com), created on 06.12.2017 // -#include +#include #if NOT_EXCLUDED(OP_invert_permutation) #include #include -namespace nd4j { +namespace sd { namespace ops { //////////////////////////////////////////////////////////////////////// @@ -44,7 +44,7 @@ DECLARE_SYN(InvertPermutation, invert_permutation); DECLARE_TYPES(invert_permutation) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setSameMode(true); } } diff --git a/libnd4j/include/ops/declarable/generic/transforms/layer_norm.cpp b/libnd4j/include/ops/declarable/generic/transforms/layer_norm.cpp index 7afe9b3ed..5643932cb 100644 --- a/libnd4j/include/ops/declarable/generic/transforms/layer_norm.cpp +++ b/libnd4j/include/ops/declarable/generic/transforms/layer_norm.cpp @@ -18,14 +18,14 @@ // @author Paul Dubs // -#include +#include #if NOT_EXCLUDED(OP_layer_norm) #include #include #include -namespace nd4j { +namespace sd { namespace ops { CONFIGURABLE_OP_IMPL(layer_norm, 2, 1, false, 0, -1) { @@ -48,18 +48,18 @@ namespace ops { std::vector longAxis = ArrayUtils::toLongVector(axis); - nd4j::ops::standardize standardizeOp; + sd::ops::standardize standardizeOp; std::vector inputs = {input}; std::vector outputs = {output}; std::vector targs = {}; std::vector bargs = {}; standardizeOp.execute(inputs, outputs, targs, longAxis, bargs); - // output->applyTrueBroadcast(nd4j::BroadcastOpsTuple::Multiply(), gain, output); - output->applyBroadcast(nd4j::broadcast::Multiply, {dimC}, *gain, *output); + // output->applyTrueBroadcast(sd::BroadcastOpsTuple::Multiply(), gain, output); + output->applyBroadcast(sd::broadcast::Multiply, {dimC}, *gain, *output); if(bias != nullptr) { - // output->applyTrueBroadcast(nd4j::BroadcastOpsTuple::Add(), bias, output); - // output->applyBroadcast(nd4j::broadcast::Add, {dimC}, bias); + // output->applyTrueBroadcast(sd::BroadcastOpsTuple::Add(), bias, output); + // output->applyBroadcast(sd::broadcast::Add, {dimC}, bias); helpers::addBias(block, *output, *bias, *output, isNCHW); } @@ -93,25 +93,25 @@ namespace ops { if(bias != nullptr) { REQUIRE_TRUE(bias->rankOf() == 1 && bias->sizeAt(0) == input->sizeAt(dimC), 0, "LAYER_NORM_BP OP: wrong shape of bias array, expected is {%i}, but got %s instead !", input->sizeAt(dimC), ShapeUtils::shapeAsString(bias).c_str()); - // eps->reduceAlongDimension(nd4j::reduce::Sum, *dLdb, {0}, true); - eps->reduceAlongDimension(nd4j::reduce::Sum, *dLdb, ShapeUtils::evalDimsToExclude(input->rankOf(), {dimC})); + // eps->reduceAlongDimension(sd::reduce::Sum, *dLdb, {0}, true); + eps->reduceAlongDimension(sd::reduce::Sum, *dLdb, ShapeUtils::evalDimsToExclude(input->rankOf(), {dimC})); } NDArray standardized(input->shapeInfo(), false, block.launchContext()); - nd4j::ops::standardize standardizeOp; + sd::ops::standardize standardizeOp; std::vector inputs = {input}; std::vector outputs = {&standardized}; std::vector targs = {}; std::vector bargs = {}; standardizeOp.execute(inputs, outputs, targs, longAxis, bargs); - standardized.applyPairwiseTransform(nd4j::pairwise::Multiply, *eps, standardized); - standardized.reduceAlongDimension(nd4j::reduce::Sum, *dLdg, ShapeUtils::evalDimsToExclude(input->rankOf(), {dimC})); + standardized.applyPairwiseTransform(sd::pairwise::Multiply, *eps, standardized); + standardized.reduceAlongDimension(sd::reduce::Sum, *dLdg, ShapeUtils::evalDimsToExclude(input->rankOf(), {dimC})); - nd4j::ops::standardize_bp standardizeBp; - // eps->applyTrueBroadcast(nd4j::BroadcastOpsTuple::Multiply(), gain, dLdx); - eps->applyBroadcast(nd4j::broadcast::Multiply, {dimC}, *gain, *dLdx); + sd::ops::standardize_bp standardizeBp; + // eps->applyTrueBroadcast(sd::BroadcastOpsTuple::Multiply(), gain, dLdx); + eps->applyBroadcast(sd::broadcast::Multiply, {dimC}, *gain, *dLdx); auto dLdx_tmp = dLdx->dup(); std::vector standardizeBpArgs = {input, &dLdx_tmp}; diff --git a/libnd4j/include/ops/declarable/generic/transforms/log1p.cpp b/libnd4j/include/ops/declarable/generic/transforms/log1p.cpp index ef9bdb925..797ca8b2a 100644 --- a/libnd4j/include/ops/declarable/generic/transforms/log1p.cpp +++ b/libnd4j/include/ops/declarable/generic/transforms/log1p.cpp @@ -18,12 +18,12 @@ // @author raver119@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_Log1p) #include -namespace nd4j { +namespace sd { namespace ops { OP_IMPL(Log1p, 1, 1, true) { auto x = INPUT_VARIABLE(0); @@ -40,7 +40,7 @@ namespace nd4j { DECLARE_TYPES(Log1p) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_FLOATS}); } } diff --git a/libnd4j/include/ops/declarable/generic/transforms/merge_add.cpp b/libnd4j/include/ops/declarable/generic/transforms/merge_add.cpp index c8a88c37e..a68a4ce02 100644 --- a/libnd4j/include/ops/declarable/generic/transforms/merge_add.cpp +++ b/libnd4j/include/ops/declarable/generic/transforms/merge_add.cpp @@ -18,13 +18,13 @@ // Created by raver119 on 24.11.17. // -#include +#include #if NOT_EXCLUDED(OP_mergeadd) #include #include -namespace nd4j { +namespace sd { namespace ops { OP_IMPL(mergeadd, -1, 1, false) { @@ -51,8 +51,8 @@ DECLARE_SYN(accumulate_n, mergeadd); DECLARE_TYPES(mergeadd) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) - ->setAllowedOutputTypes(nd4j::DataType::ANY); + ->setAllowedInputTypes(sd::DataType::ANY) + ->setAllowedOutputTypes(sd::DataType::ANY); } } } diff --git a/libnd4j/include/ops/declarable/generic/transforms/merge_avg.cpp b/libnd4j/include/ops/declarable/generic/transforms/merge_avg.cpp index d9f9dd0dc..53e46c16e 100644 --- a/libnd4j/include/ops/declarable/generic/transforms/merge_avg.cpp +++ b/libnd4j/include/ops/declarable/generic/transforms/merge_avg.cpp @@ -18,13 +18,13 @@ // Created by raver119 on 24.11.17. // -#include +#include #if NOT_EXCLUDED(OP_mergeavg) #include #include -namespace nd4j { +namespace sd { namespace ops { OP_IMPL(mergeavg, -1, 1, false) { diff --git a/libnd4j/include/ops/declarable/generic/transforms/merge_max.cpp b/libnd4j/include/ops/declarable/generic/transforms/merge_max.cpp index c6f53e444..7a41f4c1b 100644 --- a/libnd4j/include/ops/declarable/generic/transforms/merge_max.cpp +++ b/libnd4j/include/ops/declarable/generic/transforms/merge_max.cpp @@ -18,13 +18,13 @@ // Created by raver119 on 24.11.17. // -#include +#include #if NOT_EXCLUDED(OP_mergemax) #include #include -namespace nd4j { +namespace sd { namespace ops { OP_IMPL(mergemax, -1, 1, false) { @@ -47,8 +47,8 @@ DECLARE_SYN(MergeMax, mergemax); DECLARE_TYPES(mergemax) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) - ->setAllowedOutputTypes(nd4j::DataType::ANY); + ->setAllowedInputTypes(sd::DataType::ANY) + ->setAllowedOutputTypes(sd::DataType::ANY); } } diff --git a/libnd4j/include/ops/declarable/generic/transforms/merge_max_idx.cpp b/libnd4j/include/ops/declarable/generic/transforms/merge_max_idx.cpp index e48761f8f..7fe727452 100644 --- a/libnd4j/include/ops/declarable/generic/transforms/merge_max_idx.cpp +++ b/libnd4j/include/ops/declarable/generic/transforms/merge_max_idx.cpp @@ -18,13 +18,13 @@ // Created by raver119 on 24.11.17. // -#include +#include #if NOT_EXCLUDED(OP_mergemaxindex) #include #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(mergemaxindex, -1, 1, false, 0, 0) { diff --git a/libnd4j/include/ops/declarable/generic/transforms/mirrorPad.cpp b/libnd4j/include/ops/declarable/generic/transforms/mirrorPad.cpp index 2de8ee5a2..a4b934853 100644 --- a/libnd4j/include/ops/declarable/generic/transforms/mirrorPad.cpp +++ b/libnd4j/include/ops/declarable/generic/transforms/mirrorPad.cpp @@ -17,13 +17,13 @@ // // @author Yurii Shyrma (iuriish@yahoo.com), created on 07.06.2018 // -#include +#include #if NOT_EXCLUDED(OP_mirror_pad) #include #include -namespace nd4j { +namespace sd { namespace ops { diff --git a/libnd4j/include/ops/declarable/generic/transforms/pad.cpp b/libnd4j/include/ops/declarable/generic/transforms/pad.cpp index c6c8c8ff8..d5d38aaeb 100644 --- a/libnd4j/include/ops/declarable/generic/transforms/pad.cpp +++ b/libnd4j/include/ops/declarable/generic/transforms/pad.cpp @@ -18,14 +18,14 @@ // @author Shyrma Yurii (iuriish@yahoo.com), created on 06.11.2017. // -#include +#include #if NOT_EXCLUDED(OP_pad) #include #include #include -namespace nd4j { +namespace sd { namespace ops { @@ -36,16 +36,16 @@ CUSTOM_OP_IMPL(pad, 2, 1, false, 0, 1) { auto paddings = INPUT_VARIABLE(1); auto output = OUTPUT_VARIABLE(0); - const int rank = input->rankOf(); + const int rank = input->rankOf(); // input validation - std::string expectedPaddingsShape = ShapeUtils::shapeAsString({rank, 2}); - std::string currentPaddingsShape = ShapeUtils::shapeAsString(paddings); - REQUIRE_TRUE(expectedPaddingsShape == currentPaddingsShape, 0, "PAD op: wrong shape of paddings array, expected is %s, but got %s instead !", expectedPaddingsShape.c_str(), currentPaddingsShape.c_str()); + std::vector expectedPaddingsShape = {rank, 2}; + std::vector currentPaddingsShape = paddings->getShapeAsVector(); + REQUIRE_TRUE(expectedPaddingsShape == currentPaddingsShape, 0, "PAD op: wrong shape of paddings array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedPaddingsShape).c_str(), ShapeUtils::shapeAsString(currentPaddingsShape).c_str()); NDArray padValue(input->dataType(), block.launchContext()); - // in case of REFLECT and SYMMETRIC modes paddings must obey additional shape requirements + // in case of REFLECT and SYMMETRIC modes paddings must obey additional shape requirements if (INT_ARG(0) == 0) { // CONSTANT mode if(block.width() > 2) { REQUIRE_TRUE(input->dataType() == INPUT_VARIABLE(2)->dataType(), 0, "PAD op: data types of input and padValue arrays should be the same but got %i and %i correspondingly !", input->dataType(), INPUT_VARIABLE(2)->dataType()); @@ -68,16 +68,16 @@ CUSTOM_OP_IMPL(pad, 2, 1, false, 0, 1) { // std::vector dimensions(input->rankOf()); // std::iota(dimensions.begin(), dimensions.end(), 0); // fill with 0, 1, ... rank-1 - + // helpers::recursiveLoopForPad(INT_ARG(0), *input, *paddings, *output, dimensions, 0, 0, 0, padValue); helpers::pad(block.launchContext(), INT_ARG(0), *input, *paddings, *output, padValue); - + return Status::OK(); } DECLARE_TYPES(pad) { getOpDescriptor() - ->setAllowedInputTypes(0, nd4j::DataType::ANY) + ->setAllowedInputTypes(0, sd::DataType::ANY) ->setAllowedInputTypes(1, {DataType::INT32, DataType::INT64}) // INT32 with TF // ->setAllowedInputTypes(1, {DataType::INT32, DataType::INT64}) // INT32 with TF, but used also INT64 due long shapes ->setSameMode(true); @@ -85,27 +85,26 @@ DECLARE_TYPES(pad) { DECLARE_SHAPE_FN(pad) { - // check shape of paddings + // check shape of paddings auto inputShapeInfo = inputShape->at(0); auto paddings = INPUT_VARIABLE(1); - const int rank = inputShapeInfo[0]; + const int rank = inputShapeInfo[0]; // paddings validation - std::string expectedPaddingsShape = ShapeUtils::shapeAsString({rank, 2}); - std::string currentPaddingsShape = ShapeUtils::shapeAsString(paddings); - REQUIRE_TRUE(expectedPaddingsShape == currentPaddingsShape, 0, "PAD op: wrong shape of paddings array, expected is %s, but got %s instead !", expectedPaddingsShape.c_str(), currentPaddingsShape.c_str()); - + const std::vector expectedPaddingsShape = {rank, 2}; + const std::vector currentPaddingsShape = paddings->getShapeAsVector(); + REQUIRE_TRUE(expectedPaddingsShape == currentPaddingsShape, 0, "PAD op: wrong shape of paddings array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedPaddingsShape).c_str(), ShapeUtils::shapeAsString(currentPaddingsShape).c_str()); + Nd4jLong* outShapeInfo = nullptr; ALLOCATE(outShapeInfo, block.getWorkspace(), shape::shapeInfoLength(rank), Nd4jLong); outShapeInfo[0] = rank; for(int i=1; i <= rank; ++i) outShapeInfo[i] = inputShapeInfo[i] + paddings->e(i-1,0) + paddings->e(i-1,1); - + ShapeUtils::updateStridesAndType(outShapeInfo, inputShapeInfo, shape::order(inputShapeInfo)); ShapeDescriptor descriptor(outShapeInfo); RELEASE(outShapeInfo, block.getWorkspace()); return SHAPELIST(ConstantShapeHelper::getInstance()->createShapeInfo(descriptor)); - } diff --git a/libnd4j/include/ops/declarable/generic/transforms/repeat.cpp b/libnd4j/include/ops/declarable/generic/transforms/repeat.cpp index f3e7b84a5..99ab3d635 100644 --- a/libnd4j/include/ops/declarable/generic/transforms/repeat.cpp +++ b/libnd4j/include/ops/declarable/generic/transforms/repeat.cpp @@ -18,12 +18,12 @@ // @author Yurii Shyrma (iuriish@yahoo.com) // -#include +#include #if NOT_EXCLUDED(OP_repeat) #include -namespace nd4j { +namespace sd { namespace ops { ////////////////////////////////////////////////////////////////////////// @@ -50,7 +50,7 @@ CUSTOM_OP_IMPL(repeat, 1, 1, true, 0, -1) { DECLARE_TYPES(repeat) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setSameMode(true); } diff --git a/libnd4j/include/ops/declarable/generic/transforms/reverse.cpp b/libnd4j/include/ops/declarable/generic/transforms/reverse.cpp index 8047da41a..ceb953979 100644 --- a/libnd4j/include/ops/declarable/generic/transforms/reverse.cpp +++ b/libnd4j/include/ops/declarable/generic/transforms/reverse.cpp @@ -18,14 +18,14 @@ // @author Yurii Shyrma (iuriish@yahoo.com), created on 02.11.2017 // -#include +#include #if NOT_EXCLUDED(OP_reverse) #include #include -namespace nd4j { +namespace sd { namespace ops { CONFIGURABLE_OP_IMPL(reverse, 1, 1, true, 0, -2) { @@ -46,7 +46,8 @@ namespace ops { axis = *block.getIArguments(); if(axis.empty()) { // do not perform reversion - output->assign(input); + if (!block.isInplace()) + output->assign(input); } else { // check the consistency of input dimensions to reverse along @@ -92,7 +93,7 @@ namespace ops { DECLARE_TYPES(reverse_bp) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_FLOATS}); } diff --git a/libnd4j/include/ops/declarable/generic/transforms/reverseSequence.cpp b/libnd4j/include/ops/declarable/generic/transforms/reverseSequence.cpp index b3c2a93d4..c7dcc6e36 100644 --- a/libnd4j/include/ops/declarable/generic/transforms/reverseSequence.cpp +++ b/libnd4j/include/ops/declarable/generic/transforms/reverseSequence.cpp @@ -18,13 +18,13 @@ // Created by Yurii Shyrma on 25.01.2018 // -#include +#include #if NOT_EXCLUDED(OP_reverse_sequence) #include #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(reverse_sequence, 2, 1, false, 0, 2) { diff --git a/libnd4j/include/ops/declarable/generic/transforms/scatter_update.cpp b/libnd4j/include/ops/declarable/generic/transforms/scatter_update.cpp index 247e454bd..d15b4c859 100644 --- a/libnd4j/include/ops/declarable/generic/transforms/scatter_update.cpp +++ b/libnd4j/include/ops/declarable/generic/transforms/scatter_update.cpp @@ -18,13 +18,13 @@ // Created by raver119 on 24.11.17. // -#include +#include #if NOT_EXCLUDED(OP_scatter_update) #include #include -namespace nd4j { +namespace sd { namespace ops { /** * scatter update operation @@ -51,7 +51,7 @@ namespace nd4j { DECLARE_TYPES(scatter_update) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setSameMode(true); } } diff --git a/libnd4j/include/ops/declarable/generic/transforms/standardize.cpp b/libnd4j/include/ops/declarable/generic/transforms/standardize.cpp index 25efc1a73..f4e8a6f7a 100644 --- a/libnd4j/include/ops/declarable/generic/transforms/standardize.cpp +++ b/libnd4j/include/ops/declarable/generic/transforms/standardize.cpp @@ -18,14 +18,14 @@ // @author Paul Dubs // -#include +#include #if NOT_EXCLUDED(OP_standardize) #include #include -namespace nd4j { +namespace sd { namespace ops { CONFIGURABLE_OP_IMPL(standardize, 1, 1, true, 0, -2) { @@ -48,9 +48,9 @@ namespace ops { auto stdev = input->varianceAlongDimension(variance::SummaryStatsStandardDeviation, false, axis); stdev.reshapei(means.getShapeAsVector()); - input->applyTrueBroadcast(nd4j::BroadcastOpsTuple::Subtract(), means, *output, false); - output->applyTrueBroadcast(nd4j::BroadcastOpsTuple::Divide(), stdev, *output, false); - output->applyScalar(nd4j::scalar::ReplaceNans, 0, *output); + input->applyTrueBroadcast(sd::BroadcastOpsTuple::Subtract(), means, *output, false); + output->applyTrueBroadcast(sd::BroadcastOpsTuple::Divide(), stdev, *output, false); + output->applyScalar(sd::scalar::ReplaceNans, 0, *output); return Status::OK(); } @@ -84,7 +84,7 @@ namespace ops { auto stdev = input->varianceAlongDimension(variance::SummaryStatsStandardDeviation, false, axis); stdev.reshapei(means.getShapeAsVector()); - eps->applyTrueBroadcast(nd4j::BroadcastOpsTuple::Divide(), stdev, *output, false); + eps->applyTrueBroadcast(sd::BroadcastOpsTuple::Divide(), stdev, *output, false); NDArray dldu_sum = -output->reduceAlongDimension(reduce::Sum, axis, true); @@ -94,16 +94,16 @@ namespace ops { std::vector meanBpTArgs = {}; std::vector meanBpBArgs = {}; - nd4j::ops::reduce_mean_bp meanBp; + sd::ops::reduce_mean_bp meanBp; meanBp.execute(meanBpArgs, meanBpOutput, meanBpTArgs, longAxis, meanBpBArgs); *output += dldx_u; // (eps * (means - input) / (stdev * stdev)) NDArray tmp(eps->shapeInfo(), false, block.launchContext()); - means.applyTrueBroadcast(nd4j::BroadcastOpsTuple::Subtract(), *input, tmp, false); - tmp.applyPairwiseTransform(nd4j::pairwise::Multiply, *eps, tmp); - stdev.applyPairwiseTransform(nd4j::pairwise::Multiply, stdev, stdev); - tmp.applyTrueBroadcast(nd4j::BroadcastOpsTuple::Divide(), stdev, tmp, false); + means.applyTrueBroadcast(sd::BroadcastOpsTuple::Subtract(), *input, tmp, false); + tmp.applyPairwiseTransform(sd::pairwise::Multiply, *eps, tmp); + stdev.applyPairwiseTransform(sd::pairwise::Multiply, stdev, stdev); + tmp.applyTrueBroadcast(sd::BroadcastOpsTuple::Divide(), stdev, tmp, false); auto dlds_sum = tmp.reduceAlongDimension(reduce::Sum, axis, true); NDArray dldx_s(input->shapeInfo(), false, block.launchContext()); @@ -111,18 +111,18 @@ namespace ops { std::vector stdevBpOutput = {&dldx_s}; std::vector stdevBpTArgs = {}; std::vector stdevBpBArgs = {}; - nd4j::ops::reduce_stdev_bp stdevBp; + sd::ops::reduce_stdev_bp stdevBp; stdevBp.execute(stdevBpArgs, stdevBpOutput, stdevBpTArgs, longAxis, stdevBpBArgs); *output += dldx_s; - output->applyScalar(nd4j::scalar::ReplaceNans, 0, *output); + output->applyScalar(sd::scalar::ReplaceNans, 0, *output); return Status::OK(); } DECLARE_TYPES(standardize_bp) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_FLOATS}); } diff --git a/libnd4j/include/ops/declarable/generic/transforms/tile.cpp b/libnd4j/include/ops/declarable/generic/transforms/tile.cpp index 8ef1032d5..6041d1c41 100644 --- a/libnd4j/include/ops/declarable/generic/transforms/tile.cpp +++ b/libnd4j/include/ops/declarable/generic/transforms/tile.cpp @@ -19,13 +19,13 @@ // @author Yurii Shyrma (iuriish@yahoo.com) // -#include +#include #if NOT_EXCLUDED(OP_tile) #include #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(tile, 1, 1, false, 0, -2) { @@ -60,9 +60,9 @@ CUSTOM_OP_IMPL(tile, 1, 1, false, 0, -2) { } DECLARE_TYPES(tile) { - getOpDescriptor()->setAllowedInputTypes(0, nd4j::DataType::ANY) + getOpDescriptor()->setAllowedInputTypes(0, sd::DataType::ANY) ->setAllowedInputTypes(1, {ALL_INTS}) - ->setAllowedOutputTypes(nd4j::DataType::ANY); + ->setAllowedOutputTypes(sd::DataType::ANY); } diff --git a/libnd4j/include/ops/declarable/generic/transforms/trace.cpp b/libnd4j/include/ops/declarable/generic/transforms/trace.cpp index cac0ddfc0..fa9fd5f56 100644 --- a/libnd4j/include/ops/declarable/generic/transforms/trace.cpp +++ b/libnd4j/include/ops/declarable/generic/transforms/trace.cpp @@ -18,13 +18,13 @@ // @author Yurii Shyrma (iuriish@yahoo.com), created on 24.01.2018 // -#include +#include #if NOT_EXCLUDED(OP_trace) #include #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(trace, 1, 1, false, 0, 0) { diff --git a/libnd4j/include/ops/declarable/generic/transforms/tri.cpp b/libnd4j/include/ops/declarable/generic/transforms/tri.cpp index a6106f197..19144e2fb 100644 --- a/libnd4j/include/ops/declarable/generic/transforms/tri.cpp +++ b/libnd4j/include/ops/declarable/generic/transforms/tri.cpp @@ -21,7 +21,7 @@ #include -namespace nd4j { +namespace sd { namespace ops { diff --git a/libnd4j/include/ops/declarable/generic/transforms/triu.cpp b/libnd4j/include/ops/declarable/generic/transforms/triu.cpp index b382cbfb1..839828f62 100644 --- a/libnd4j/include/ops/declarable/generic/transforms/triu.cpp +++ b/libnd4j/include/ops/declarable/generic/transforms/triu.cpp @@ -22,7 +22,7 @@ #include -namespace nd4j { +namespace sd { namespace ops { diff --git a/libnd4j/include/ops/declarable/generic/tsne/cell_contains.cpp b/libnd4j/include/ops/declarable/generic/tsne/cell_contains.cpp index e72799a54..e176797b0 100644 --- a/libnd4j/include/ops/declarable/generic/tsne/cell_contains.cpp +++ b/libnd4j/include/ops/declarable/generic/tsne/cell_contains.cpp @@ -18,13 +18,13 @@ // @author George A. Shulinok +#include #if NOT_EXCLUDED(OP_cell_contains) #include #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(cell_contains, 3, 1, false, 0, 1) { @@ -40,13 +40,13 @@ namespace nd4j { DECLARE_TYPES(cell_contains) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) - ->setAllowedOutputTypes(nd4j::DataType::BOOL) + ->setAllowedInputTypes(sd::DataType::ANY) + ->setAllowedOutputTypes(sd::DataType::BOOL) ->setSameMode(false); } DECLARE_SHAPE_FN(cell_contains) { - return SHAPELIST(CONSTANT(ShapeBuilders::createScalarShapeInfo(nd4j::DataType::BOOL, block.workspace()))); + return SHAPELIST(CONSTANT(ShapeBuilders::createScalarShapeInfo(sd::DataType::BOOL, block.workspace()))); } } } diff --git a/libnd4j/include/ops/declarable/generic/tsne/edge_force.cpp b/libnd4j/include/ops/declarable/generic/tsne/edge_force.cpp index 75a984e66..1d409c51f 100644 --- a/libnd4j/include/ops/declarable/generic/tsne/edge_force.cpp +++ b/libnd4j/include/ops/declarable/generic/tsne/edge_force.cpp @@ -18,13 +18,13 @@ // @author George A. Shulinok , created on 4/18/2019. // -#include +#include #if NOT_EXCLUDED(OP_barnes_edge_force) #include #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(barnes_edge_forces, 4, 1, false, 0, 1) { diff --git a/libnd4j/include/ops/declarable/generic/tsne/gains.cpp b/libnd4j/include/ops/declarable/generic/tsne/gains.cpp index 8aa701435..4fb943483 100644 --- a/libnd4j/include/ops/declarable/generic/tsne/gains.cpp +++ b/libnd4j/include/ops/declarable/generic/tsne/gains.cpp @@ -18,13 +18,13 @@ // @author George A. Shulinok +#include #if NOT_EXCLUDED(OP_barnes_gains) #include #include -namespace nd4j { +namespace sd { namespace ops { OP_IMPL(barnes_gains, 3, 1, true) { @@ -40,7 +40,7 @@ namespace ops { DECLARE_TYPES(barnes_gains) { getOpDescriptor() - ->setAllowedInputTypes(nd4j::DataType::ANY) + ->setAllowedInputTypes(sd::DataType::ANY) ->setSameMode(true); } } diff --git a/libnd4j/include/ops/declarable/generic/tsne/symmetrized.cpp b/libnd4j/include/ops/declarable/generic/tsne/symmetrized.cpp index 135426aa1..82dd8c36e 100644 --- a/libnd4j/include/ops/declarable/generic/tsne/symmetrized.cpp +++ b/libnd4j/include/ops/declarable/generic/tsne/symmetrized.cpp @@ -18,13 +18,13 @@ // @author George A. Shulinok , created on 4/18/2019. // -#include +#include #if NOT_EXCLUDED(OP_barnes_symmetrized) #include #include -namespace nd4j { +namespace sd { namespace ops { NDArray* rowCountsPtr = nullptr; @@ -80,9 +80,9 @@ namespace ops { // outShapeInfo[2] = len; // ShapeUtils::updateStridesAndType(outShapeInfo, ArrayOptions::dataType(valPShapeInfo), 'c'); //outShapeInfo = ShapeBuilders::createVectorShapeInfo(ArrayOptions::dataType(valPShapeInfo), len, block.workspace()); - outShapeInfo = nd4j::ShapeBuilders::createShapeInfo(ArrayOptions::dataType(valPShapeInfo), 'c', {1, len}, block.getWorkspace()); - auto outColsShapeInfo = nd4j::ShapeBuilders::createShapeInfo(dataType, 'c', {1, len}, block.getWorkspace()); - auto outRowsShapeInfo = nd4j::ShapeBuilders::createShapeInfo(dataType, 'c', {1, N + 1}, block.getWorkspace()); + outShapeInfo = sd::ShapeBuilders::createShapeInfo(ArrayOptions::dataType(valPShapeInfo), 'c', {1, len}, block.getWorkspace()); + auto outColsShapeInfo = sd::ShapeBuilders::createShapeInfo(dataType, 'c', {1, len}, block.getWorkspace()); + auto outRowsShapeInfo = sd::ShapeBuilders::createShapeInfo(dataType, 'c', {1, N + 1}, block.getWorkspace()); return SHAPELIST(CONSTANT(outRowsShapeInfo), CONSTANT(outColsShapeInfo), CONSTANT(outShapeInfo)); } diff --git a/libnd4j/include/ops/declarable/generic/util/print_affinity.cpp b/libnd4j/include/ops/declarable/generic/util/print_affinity.cpp index 6b1514ab9..0103e8672 100644 --- a/libnd4j/include/ops/declarable/generic/util/print_affinity.cpp +++ b/libnd4j/include/ops/declarable/generic/util/print_affinity.cpp @@ -18,13 +18,13 @@ // @author raver119@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_print_affinity) #include #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(print_affinity, 1, 1, true, 0, 0) { // TODO: make this op compatible with ArrayList etc @@ -38,9 +38,9 @@ namespace nd4j { DECLARE_TYPES(print_affinity) { getOpDescriptor() - ->setAllowedInputTypes(0, nd4j::DataType::ANY) + ->setAllowedInputTypes(0, sd::DataType::ANY) ->setAllowedInputTypes(1, {ALL_STRINGS}) - ->setAllowedOutputTypes(0, nd4j::DataType::INT32); + ->setAllowedOutputTypes(0, sd::DataType::INT32); } DECLARE_SHAPE_FN(print_affinity) { diff --git a/libnd4j/include/ops/declarable/generic/util/print_variable.cpp b/libnd4j/include/ops/declarable/generic/util/print_variable.cpp index 6828b2f90..9d3369627 100644 --- a/libnd4j/include/ops/declarable/generic/util/print_variable.cpp +++ b/libnd4j/include/ops/declarable/generic/util/print_variable.cpp @@ -17,13 +17,13 @@ // // @author raver119@gmail.com // -#include +#include #if NOT_EXCLUDED(OP_print_variable) #include #include -namespace nd4j { +namespace sd { namespace ops { CUSTOM_OP_IMPL(print_variable, 1, 1, true, 0, 0) { // TODO: make this op compatible with ArrayList etc @@ -42,7 +42,7 @@ namespace nd4j { if (block.numB() > 0) printSpecial = B_ARG(0); - if (printSpecial && !nd4j::Environment::getInstance()->isCPU()) { + if (printSpecial && !sd::Environment::getInstance()->isCPU()) { // only specific backends support special printout. for cpu-based backends it's the same as regular print if (block.width() == 2) @@ -63,9 +63,9 @@ namespace nd4j { DECLARE_TYPES(print_variable) { getOpDescriptor() - ->setAllowedInputTypes(0, nd4j::DataType::ANY) + ->setAllowedInputTypes(0, sd::DataType::ANY) ->setAllowedInputTypes(1, {ALL_STRINGS}) - ->setAllowedOutputTypes(0, nd4j::DataType::INT32); + ->setAllowedOutputTypes(0, sd::DataType::INT32); } DECLARE_SHAPE_FN(print_variable) { diff --git a/libnd4j/include/ops/declarable/headers/BarnesHutTsne.h b/libnd4j/include/ops/declarable/headers/BarnesHutTsne.h index d3a4c042d..3f0d86e19 100644 --- a/libnd4j/include/ops/declarable/headers/BarnesHutTsne.h +++ b/libnd4j/include/ops/declarable/headers/BarnesHutTsne.h @@ -23,7 +23,7 @@ #include -namespace nd4j { +namespace sd { namespace ops { /** * This operation used as helper with BarnesHutTsne class diff --git a/libnd4j/include/ops/declarable/headers/activations.h b/libnd4j/include/ops/declarable/headers/activations.h index 9d0b22198..db9e8186a 100644 --- a/libnd4j/include/ops/declarable/headers/activations.h +++ b/libnd4j/include/ops/declarable/headers/activations.h @@ -24,7 +24,7 @@ #include -namespace nd4j { +namespace sd { namespace ops { /** * This is Sigmoid activation function implementation diff --git a/libnd4j/include/ops/declarable/headers/bitwise.h b/libnd4j/include/ops/declarable/headers/bitwise.h index cb395b496..b5f29896f 100644 --- a/libnd4j/include/ops/declarable/headers/bitwise.h +++ b/libnd4j/include/ops/declarable/headers/bitwise.h @@ -23,7 +23,7 @@ #include -namespace nd4j { +namespace sd { namespace ops { /** * This operation toggles individual bits of each element in array diff --git a/libnd4j/include/ops/declarable/headers/blas.h b/libnd4j/include/ops/declarable/headers/blas.h index d94d365dd..09215e113 100644 --- a/libnd4j/include/ops/declarable/headers/blas.h +++ b/libnd4j/include/ops/declarable/headers/blas.h @@ -22,7 +22,7 @@ #include -namespace nd4j { +namespace sd { namespace ops { /** diff --git a/libnd4j/include/ops/declarable/headers/boolean.h b/libnd4j/include/ops/declarable/headers/boolean.h index 21fe48202..75e95f630 100644 --- a/libnd4j/include/ops/declarable/headers/boolean.h +++ b/libnd4j/include/ops/declarable/headers/boolean.h @@ -23,7 +23,7 @@ #include -namespace nd4j { +namespace sd { namespace ops { /** diff --git a/libnd4j/include/ops/declarable/headers/broadcastable.h b/libnd4j/include/ops/declarable/headers/broadcastable.h index 9a2dc9f62..691a1b7b2 100644 --- a/libnd4j/include/ops/declarable/headers/broadcastable.h +++ b/libnd4j/include/ops/declarable/headers/broadcastable.h @@ -25,7 +25,7 @@ #include #include -namespace nd4j { +namespace sd { namespace ops { // TODO: make broadcastables separate class diff --git a/libnd4j/include/ops/declarable/headers/common.h b/libnd4j/include/ops/declarable/headers/common.h index 6b670e3be..d70e6beb8 100644 --- a/libnd4j/include/ops/declarable/headers/common.h +++ b/libnd4j/include/ops/declarable/headers/common.h @@ -22,9 +22,9 @@ #define LIBND4J_OPS_DECLARABLE_COMMON_H #include -#include +#include #include -#include +#include #include #include #include @@ -36,7 +36,7 @@ #include #include #include -#include +#include #include #endif \ No newline at end of file diff --git a/libnd4j/include/ops/declarable/headers/compat.h b/libnd4j/include/ops/declarable/headers/compat.h index 8ce73153e..37894517a 100644 --- a/libnd4j/include/ops/declarable/headers/compat.h +++ b/libnd4j/include/ops/declarable/headers/compat.h @@ -23,7 +23,7 @@ #include -namespace nd4j { +namespace sd { namespace ops { /** * This operation splits input string into pieces separated by delimiter diff --git a/libnd4j/include/ops/declarable/headers/convo.h b/libnd4j/include/ops/declarable/headers/convo.h index 89824c342..d00da07f2 100644 --- a/libnd4j/include/ops/declarable/headers/convo.h +++ b/libnd4j/include/ops/declarable/headers/convo.h @@ -23,7 +23,7 @@ #include -namespace nd4j { +namespace sd { namespace ops { /** diff --git a/libnd4j/include/ops/declarable/headers/datatypes.h b/libnd4j/include/ops/declarable/headers/datatypes.h index b82ab4ad6..e46753919 100644 --- a/libnd4j/include/ops/declarable/headers/datatypes.h +++ b/libnd4j/include/ops/declarable/headers/datatypes.h @@ -22,7 +22,7 @@ #include -namespace nd4j { +namespace sd { namespace ops { /** * This operation casts elements of input array to double data type diff --git a/libnd4j/include/ops/declarable/headers/images.h b/libnd4j/include/ops/declarable/headers/images.h index 14acd1877..41974901a 100644 --- a/libnd4j/include/ops/declarable/headers/images.h +++ b/libnd4j/include/ops/declarable/headers/images.h @@ -30,7 +30,7 @@ #include #include -namespace nd4j { +namespace sd { namespace ops { diff --git a/libnd4j/include/ops/declarable/headers/kernels.h b/libnd4j/include/ops/declarable/headers/kernels.h index 8fb2bab62..c4cc02cb5 100644 --- a/libnd4j/include/ops/declarable/headers/kernels.h +++ b/libnd4j/include/ops/declarable/headers/kernels.h @@ -23,7 +23,7 @@ #include -namespace nd4j { +namespace sd { namespace ops { #if NOT_EXCLUDED(OP_knn_mindistance) DECLARE_CUSTOM_OP(knn_mindistance, 3, 1, false, 0, 0); diff --git a/libnd4j/include/ops/declarable/headers/list.h b/libnd4j/include/ops/declarable/headers/list.h index 756895a1f..af4fb5706 100644 --- a/libnd4j/include/ops/declarable/headers/list.h +++ b/libnd4j/include/ops/declarable/headers/list.h @@ -23,7 +23,7 @@ #include -namespace nd4j { +namespace sd { namespace ops { // list operations, basically all around NDArrayList diff --git a/libnd4j/include/ops/declarable/headers/loss.h b/libnd4j/include/ops/declarable/headers/loss.h index 04d14532d..3f3625040 100644 --- a/libnd4j/include/ops/declarable/headers/loss.h +++ b/libnd4j/include/ops/declarable/headers/loss.h @@ -23,7 +23,7 @@ #include -namespace nd4j { +namespace sd { namespace ops { ////////////////////////////////////////////////////////////////////////// diff --git a/libnd4j/include/ops/declarable/headers/nlp.h b/libnd4j/include/ops/declarable/headers/nlp.h index 6da62e506..e12db1402 100644 --- a/libnd4j/include/ops/declarable/headers/nlp.h +++ b/libnd4j/include/ops/declarable/headers/nlp.h @@ -22,7 +22,7 @@ #define DEV_TESTS_NLP_H #include -namespace nd4j { +namespace sd { namespace ops { #if NOT_EXCLUDED(OP_skipgram) diff --git a/libnd4j/include/ops/declarable/headers/nn.h b/libnd4j/include/ops/declarable/headers/nn.h index 810733680..26699aa32 100644 --- a/libnd4j/include/ops/declarable/headers/nn.h +++ b/libnd4j/include/ops/declarable/headers/nn.h @@ -23,7 +23,7 @@ #include -namespace nd4j { +namespace sd { namespace ops { #if NOT_EXCLUDED(OP_softmax) @@ -175,7 +175,7 @@ namespace nd4j { * applies layer normalization to input * y = g * standardize(x) + b * - * see nd4j::ops::standardize + * see sd::ops::standardize * */ #if NOT_EXCLUDED(OP_layer_norm) diff --git a/libnd4j/include/ops/declarable/headers/parity_ops.h b/libnd4j/include/ops/declarable/headers/parity_ops.h index 1b5bda29a..81742fa3d 100644 --- a/libnd4j/include/ops/declarable/headers/parity_ops.h +++ b/libnd4j/include/ops/declarable/headers/parity_ops.h @@ -24,7 +24,7 @@ #include -namespace nd4j { +namespace sd { namespace ops { /** * This operation returns index of max element in a given NDArray (optionally: along given dimension(s)) @@ -1044,6 +1044,48 @@ namespace nd4j { DECLARE_CUSTOM_OP(logdet, 1, 1, false, 0, 0); #endif + /** + * matrix_solve_ls op (lstsq) - solves one or more linear least-squares problems. + * + * input params: + * 0 - the tensor with dimension (x * y * z * ::: * M * N) - left parts of equations + * 1 - the tensor with dimension (x * y * z * ::: * M * K) - right parts of equations + * + * float args: + * 0 - l2_regularizer (default 0. and only for 0 implemented) + * + * boolean args: + * 0 - fast - default is true (optional) - use Cholesky decomposition instead QR decomposition of matricies. + * + * return value: + * tensor with dimension (x * y * z * ::: * N * K) with solutions + * + */ + #if NOT_EXCLUDED(OP_lstsq) + DECLARE_CUSTOM_OP(lstsq, 2, 1, false, 0, 0); + #endif + + /* solve_ls - analog of lstsq op with another solution approach + * + * input params: + * 0 - the tensor with dimension (x * y * z * ::: * M * N) - left parts of equations + * 1 - the tensor with dimension (x * y * z * ::: * M * K) - right parts of equations + * + * float args: + * 0 - l2_regularizer (default 0. and only for 0 implemented) + * + * boolean args: + * 0 - fast - default is true (optional) - use Cholesky decomposition instead QR decomposition of matricies. + * + * return value: + * tensor with dimension (x * y * z * ::: * N * K) with solutions + * + * Note: if fast is false - then l2_regularizer arg is ignored and used lstsq method due QR decomposition + * */ + #if NOT_EXCLUDED(OP_solve_ls) + DECLARE_CUSTOM_OP(solve_ls, 2, 1, false, 0, 0); + #endif + /** * matrix_inverse op. - make inverse for all 2D square matricies found in the input tensor * @@ -1073,7 +1115,7 @@ namespace nd4j { * */ #if NOT_EXCLUDED(OP_triangular_solve) - DECLARE_CUSTOM_OP(triangular_solve, 2, 1, true, 0, 0); + DECLARE_CUSTOM_OP(triangular_solve, 2, 1, false, 0, 0); #endif /** diff --git a/libnd4j/include/ops/declarable/headers/random.h b/libnd4j/include/ops/declarable/headers/random.h index f52534411..367a41995 100644 --- a/libnd4j/include/ops/declarable/headers/random.h +++ b/libnd4j/include/ops/declarable/headers/random.h @@ -24,7 +24,7 @@ #include -namespace nd4j { +namespace sd { namespace ops { #if NOT_EXCLUDED(OP_set_seed) DECLARE_CUSTOM_OP(set_seed, -2, 1, false, 0, -2); diff --git a/libnd4j/include/ops/declarable/headers/recurrent.h b/libnd4j/include/ops/declarable/headers/recurrent.h index bf6aaa6bc..55138bb60 100644 --- a/libnd4j/include/ops/declarable/headers/recurrent.h +++ b/libnd4j/include/ops/declarable/headers/recurrent.h @@ -23,7 +23,7 @@ #include -namespace nd4j { +namespace sd { namespace ops { ////////////////////////////////////////////////////////////////////////// diff --git a/libnd4j/include/ops/declarable/headers/shape.h b/libnd4j/include/ops/declarable/headers/shape.h index c21cdb84d..7f9330342 100644 --- a/libnd4j/include/ops/declarable/headers/shape.h +++ b/libnd4j/include/ops/declarable/headers/shape.h @@ -23,7 +23,7 @@ #include -namespace nd4j { +namespace sd { namespace ops { #if NOT_EXCLUDED(OP_permute) DECLARE_CUSTOM_OP(permute, 1, 1, false, 0, -2); diff --git a/libnd4j/include/ops/declarable/headers/strings.h b/libnd4j/include/ops/declarable/headers/strings.h index 0849f118a..bd4b8b949 100644 --- a/libnd4j/include/ops/declarable/headers/strings.h +++ b/libnd4j/include/ops/declarable/headers/strings.h @@ -23,7 +23,7 @@ #include -namespace nd4j { +namespace sd { namespace ops { /** * This operation splits input string into pieces separated by delimiter diff --git a/libnd4j/include/ops/declarable/headers/tests.h b/libnd4j/include/ops/declarable/headers/tests.h index da3cd8ecf..cad12b3c8 100644 --- a/libnd4j/include/ops/declarable/headers/tests.h +++ b/libnd4j/include/ops/declarable/headers/tests.h @@ -19,7 +19,7 @@ // #include -namespace nd4j { +namespace sd { namespace ops { #if NOT_EXCLUDED(OP_test_output_reshape) DECLARE_OP(test_output_reshape, 1, 1, true); diff --git a/libnd4j/include/ops/declarable/headers/third_party.h b/libnd4j/include/ops/declarable/headers/third_party.h index 38639d038..705a02903 100644 --- a/libnd4j/include/ops/declarable/headers/third_party.h +++ b/libnd4j/include/ops/declarable/headers/third_party.h @@ -23,7 +23,7 @@ #include -namespace nd4j { +namespace sd { namespace ops { #if NOT_EXCLUDED(OP_firas_sparse) DECLARE_CUSTOM_OP(firas_sparse, 1, 1, false, 0, -1); diff --git a/libnd4j/include/ops/declarable/headers/transforms.h b/libnd4j/include/ops/declarable/headers/transforms.h index ab4e962a3..0e14037df 100644 --- a/libnd4j/include/ops/declarable/headers/transforms.h +++ b/libnd4j/include/ops/declarable/headers/transforms.h @@ -23,7 +23,7 @@ #include -namespace nd4j { +namespace sd { namespace ops { #if NOT_EXCLUDED(OP_clipbyvalue) DECLARE_CONFIGURABLE_OP(clipbyvalue, 1, 1, true, 2, 0); diff --git a/libnd4j/include/ops/declarable/headers/util.h b/libnd4j/include/ops/declarable/headers/util.h index aa1f52363..57b013f29 100644 --- a/libnd4j/include/ops/declarable/headers/util.h +++ b/libnd4j/include/ops/declarable/headers/util.h @@ -23,7 +23,7 @@ #include -namespace nd4j { +namespace sd { namespace ops { /** * This operation prints out NDArray content, either on host or device. diff --git a/libnd4j/include/ops/declarable/helpers/BarnesHutTsne.h b/libnd4j/include/ops/declarable/helpers/BarnesHutTsne.h index 9e2258a59..f52dd9ba4 100644 --- a/libnd4j/include/ops/declarable/helpers/BarnesHutTsne.h +++ b/libnd4j/include/ops/declarable/helpers/BarnesHutTsne.h @@ -23,7 +23,7 @@ #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { diff --git a/libnd4j/include/ops/declarable/helpers/activations.h b/libnd4j/include/ops/declarable/helpers/activations.h index 331170369..ab652ab24 100644 --- a/libnd4j/include/ops/declarable/helpers/activations.h +++ b/libnd4j/include/ops/declarable/helpers/activations.h @@ -23,27 +23,27 @@ #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { - ND4J_EXPORT void softMaxForVector(nd4j::LaunchContext * context, const NDArray &input, NDArray &output); + ND4J_EXPORT void softMaxForVector(sd::LaunchContext * context, const NDArray &input, NDArray &output); - ND4J_EXPORT void logSoftMaxForVector(nd4j::LaunchContext * context, const NDArray &input, NDArray &output); + ND4J_EXPORT void logSoftMaxForVector(sd::LaunchContext * context, const NDArray &input, NDArray &output); - ND4J_EXPORT void softmax(nd4j::LaunchContext * context, const NDArray &input, NDArray &output, const int dimension); + ND4J_EXPORT void softmax(sd::LaunchContext * context, const NDArray &input, NDArray &output, const int dimension); - ND4J_EXPORT void logSoftmax(nd4j::LaunchContext * context, const NDArray &input, NDArray &output, const int dimension); + ND4J_EXPORT void logSoftmax(sd::LaunchContext * context, const NDArray &input, NDArray &output, const int dimension); - ND4J_EXPORT void softmaxDerivative(nd4j::LaunchContext * context, const NDArray& input, NDArray& output, const int dimension); + ND4J_EXPORT void softmaxDerivative(sd::LaunchContext * context, const NDArray& input, NDArray& output, const int dimension); - ND4J_EXPORT void prelu(nd4j::LaunchContext * context, const NDArray &input, const NDArray &alpha, NDArray &output); + ND4J_EXPORT void prelu(sd::LaunchContext * context, const NDArray &input, const NDArray &alpha, NDArray &output); - ND4J_EXPORT void preluBP(nd4j::LaunchContext * context, const NDArray &input, const NDArray &alpha, const NDArray &dLdO, NDArray &dLdI, NDArray &dLdA); + ND4J_EXPORT void preluBP(sd::LaunchContext * context, const NDArray &input, const NDArray &alpha, const NDArray &dLdO, NDArray &dLdI, NDArray &dLdA); - ND4J_EXPORT void thresholdRelu(nd4j::LaunchContext * context, const NDArray &input, double threshold, NDArray &output); + ND4J_EXPORT void thresholdRelu(sd::LaunchContext * context, const NDArray &input, double threshold, NDArray &output); - ND4J_EXPORT void thresholdReluDerivative(nd4j::LaunchContext * context, NDArray *input, double threshold, NDArray* dLdO, NDArray *output); + ND4J_EXPORT void thresholdReluDerivative(sd::LaunchContext * context, NDArray *input, double threshold, NDArray* dLdO, NDArray *output); } } } diff --git a/libnd4j/include/ops/declarable/helpers/addBias.h b/libnd4j/include/ops/declarable/helpers/addBias.h index c754c07de..8eff731f7 100644 --- a/libnd4j/include/ops/declarable/helpers/addBias.h +++ b/libnd4j/include/ops/declarable/helpers/addBias.h @@ -24,7 +24,7 @@ #include #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { diff --git a/libnd4j/include/ops/declarable/helpers/adjust_hue.h b/libnd4j/include/ops/declarable/helpers/adjust_hue.h index afa7b2436..2d0e2f087 100644 --- a/libnd4j/include/ops/declarable/helpers/adjust_hue.h +++ b/libnd4j/include/ops/declarable/helpers/adjust_hue.h @@ -20,15 +20,15 @@ // @author Oleh Semeniv (oleg.semeniv@gmail.com) // -#include -#include +#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { - void adjustHue(nd4j::LaunchContext* context, const NDArray *input, const NDArray* deltaScalarArr, NDArray *output, const int dimC); + void adjustHue(sd::LaunchContext* context, const NDArray *input, const NDArray* deltaScalarArr, NDArray *output, const int dimC); @@ -39,8 +39,8 @@ FORCEINLINE _CUDA_HD void rgbToHsv(const T& r, const T& g, const T& b, T& h, T& // h values are in range [0, 360) // s and v values are in range [0, 1] - const T max = nd4j::math::nd4j_max(r, nd4j::math::nd4j_max(g, b)); - const T min = nd4j::math::nd4j_min(r, nd4j::math::nd4j_min(g, b)); + const T max = sd::math::nd4j_max(r, sd::math::nd4j_max(g, b)); + const T min = sd::math::nd4j_min(r, sd::math::nd4j_min(g, b)); const T c = max - min; const T _p6 = (T)1 / (T)6; // calculate h diff --git a/libnd4j/include/ops/declarable/helpers/adjust_saturation.h b/libnd4j/include/ops/declarable/helpers/adjust_saturation.h index cd0930cc0..25dc30f10 100644 --- a/libnd4j/include/ops/declarable/helpers/adjust_saturation.h +++ b/libnd4j/include/ops/declarable/helpers/adjust_saturation.h @@ -19,21 +19,21 @@ // @author Yurii Shyrma (iuriish@yahoo.com) // -#include -#include -#include +#include +#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { - void adjustSaturation(nd4j::LaunchContext* context, const NDArray *input, const NDArray* factorScalarArr, NDArray *output, const int dimC); + void adjustSaturation(sd::LaunchContext* context, const NDArray *input, const NDArray* factorScalarArr, NDArray *output, const int dimC); /* template static FORCEINLINE _CUDA_HD void rgb_to_hsv(T r, T g, T b, T* h, T* s, T* v) { - T vv = nd4j::math::nd4j_max(r, nd4j::math::nd4j_max(g, b)); - T range = vv - nd4j::math::nd4j_min(r, nd4j::math::nd4j_min(g, b)); + T vv = sd::math::nd4j_max(r, sd::math::nd4j_max(g, b)); + T range = vv - sd::math::nd4j_min(r, sd::math::nd4j_min(g, b)); if (vv > 0) { *s = range / vv; } else { @@ -72,7 +72,7 @@ namespace helpers { while (fmodu >= (T) 2.0f) fmodu -= (T) 2.0f; - T x = c * (1. - nd4j::math::nd4j_abs(fmodu - 1.)); + T x = c * (1. - sd::math::nd4j_abs(fmodu - 1.)); switch (h_category) { case 0: rr = c; diff --git a/libnd4j/include/ops/declarable/helpers/axis.h b/libnd4j/include/ops/declarable/helpers/axis.h index 42876b6f0..76c5a070d 100644 --- a/libnd4j/include/ops/declarable/helpers/axis.h +++ b/libnd4j/include/ops/declarable/helpers/axis.h @@ -19,10 +19,10 @@ // #ifndef __AXIS_H_HELPERS__ #define __AXIS_H_HELPERS__ -#include -#include +#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { diff --git a/libnd4j/include/ops/declarable/helpers/batched_gemm.h b/libnd4j/include/ops/declarable/helpers/batched_gemm.h index 5bf1c6c99..26651cf3c 100644 --- a/libnd4j/include/ops/declarable/helpers/batched_gemm.h +++ b/libnd4j/include/ops/declarable/helpers/batched_gemm.h @@ -19,9 +19,9 @@ // #include -#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { diff --git a/libnd4j/include/ops/declarable/helpers/batchnorm.h b/libnd4j/include/ops/declarable/helpers/batchnorm.h index 1df55f6f3..72bc69718 100644 --- a/libnd4j/include/ops/declarable/helpers/batchnorm.h +++ b/libnd4j/include/ops/declarable/helpers/batchnorm.h @@ -23,7 +23,7 @@ #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { diff --git a/libnd4j/include/ops/declarable/helpers/betaInc.h b/libnd4j/include/ops/declarable/helpers/betaInc.h index 9192bb1c1..4c37d7c5d 100644 --- a/libnd4j/include/ops/declarable/helpers/betaInc.h +++ b/libnd4j/include/ops/declarable/helpers/betaInc.h @@ -22,15 +22,15 @@ #define LIBND4J_BETAINC_H #include -#include "NDArray.h" +#include "array/NDArray.h" -namespace nd4j { +namespace sd { namespace ops { namespace helpers { const uint maxIter = MAX_NUM_THREADS /*articles propose 10000*/; // max number of loop iterations in function for continued fractions - void betaInc(nd4j::LaunchContext* context, const NDArray& a, const NDArray& b, const NDArray& x, NDArray& output); + void betaInc(sd::LaunchContext* context, const NDArray& a, const NDArray& b, const NDArray& x, NDArray& output); } diff --git a/libnd4j/include/ops/declarable/helpers/choose.h b/libnd4j/include/ops/declarable/helpers/choose.h index 2f2a67277..233c6b1ff 100644 --- a/libnd4j/include/ops/declarable/helpers/choose.h +++ b/libnd4j/include/ops/declarable/helpers/choose.h @@ -19,15 +19,15 @@ // #ifndef __CHOOSE_H_HELPERS__ #define __CHOOSE_H_HELPERS__ -#include -#include +#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { - void chooseFunctorArray(nd4j::LaunchContext * context, NDArray* arg, NDArray* comp, int mode, NDArray* result, NDArray* numResults); - void chooseFunctorScalar(nd4j::LaunchContext * context, NDArray* arg, double scalar, int mode, NDArray* result, NDArray* numResults); + void chooseFunctorArray(sd::LaunchContext * context, NDArray* arg, NDArray* comp, int mode, NDArray* result, NDArray* numResults); + void chooseFunctorScalar(sd::LaunchContext * context, NDArray* arg, double scalar, int mode, NDArray* result, NDArray* numResults); } } diff --git a/libnd4j/include/ops/declarable/helpers/col2im.h b/libnd4j/include/ops/declarable/helpers/col2im.h index 66d7a684a..39a29da85 100644 --- a/libnd4j/include/ops/declarable/helpers/col2im.h +++ b/libnd4j/include/ops/declarable/helpers/col2im.h @@ -23,11 +23,11 @@ #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { - ND4J_EXPORT void col2im(nd4j::LaunchContext & context, const NDArray& input, NDArray& output, const int sH, const int sW, const int pH, const int pW, const int iH, const int iW, const int dH, const int dW); + ND4J_EXPORT void col2im(sd::LaunchContext & context, const NDArray& input, NDArray& output, const int sH, const int sW, const int pH, const int pW, const int iH, const int iW, const int dH, const int dW); } diff --git a/libnd4j/include/ops/declarable/helpers/compare_elem.h b/libnd4j/include/ops/declarable/helpers/compare_elem.h index 016c73bdc..18faee328 100644 --- a/libnd4j/include/ops/declarable/helpers/compare_elem.h +++ b/libnd4j/include/ops/declarable/helpers/compare_elem.h @@ -19,13 +19,13 @@ #define LIBND4J_COMPARE_ELEM_H #include -#include "NDArray.h" +#include "array/NDArray.h" -namespace nd4j { +namespace sd { namespace ops { namespace helpers { - void compare_elem(nd4j::LaunchContext * context, NDArray* input, bool isStrictlyIncreasing, bool& output); + void compare_elem(sd::LaunchContext * context, NDArray* input, bool isStrictlyIncreasing, bool& output); } } } diff --git a/libnd4j/include/ops/declarable/helpers/confusion.h b/libnd4j/include/ops/declarable/helpers/confusion.h index a0debcedc..a4d27be4f 100644 --- a/libnd4j/include/ops/declarable/helpers/confusion.h +++ b/libnd4j/include/ops/declarable/helpers/confusion.h @@ -19,14 +19,14 @@ // #ifndef __CONFUSION_H_HELPERS__ #define __CONFUSION_H_HELPERS__ -#include -#include +#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { - void confusionFunctor(nd4j::LaunchContext * context, NDArray* labels, NDArray* predictions, NDArray* weights, NDArray* output); + void confusionFunctor(sd::LaunchContext * context, NDArray* labels, NDArray* predictions, NDArray* weights, NDArray* output); } } diff --git a/libnd4j/include/ops/declarable/helpers/convolutions.h b/libnd4j/include/ops/declarable/helpers/convolutions.h index e8bf735bc..6ba6136a4 100644 --- a/libnd4j/include/ops/declarable/helpers/convolutions.h +++ b/libnd4j/include/ops/declarable/helpers/convolutions.h @@ -21,13 +21,13 @@ #ifndef LIBND4J_CONVOLUTIONS_H #define LIBND4J_CONVOLUTIONS_H -#include +#include #include -#include +#include #include -namespace nd4j { +namespace sd { namespace ops { enum PoolingType { @@ -64,9 +64,9 @@ namespace nd4j { oW = (iW - ((kW - 1) * dW + 1) + 2 * pW) / sW + 1; } else if(paddingMode == 1) { // same - oD = (int) nd4j::math::nd4j_ceil(iD * 1. / sD); - oH = (int) nd4j::math::nd4j_ceil(iH * 1. / sH); - oW = (int) nd4j::math::nd4j_ceil(iW * 1. / sW); + oD = (int) sd::math::nd4j_ceil(iD * 1. / sD); + oH = (int) sd::math::nd4j_ceil(iH * 1. / sH); + oW = (int) sd::math::nd4j_ceil(iW * 1. / sW); } else { // causal @@ -254,39 +254,39 @@ namespace nd4j { // } // } - static void conv2d(nd4j::graph::Context &context, const NDArray* input, const NDArray* weights, const NDArray* bias, NDArray* output, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int paddingMode, const int isNCHW); + static void conv2d(sd::graph::Context &context, const NDArray* input, const NDArray* weights, const NDArray* bias, NDArray* output, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int paddingMode, const int isNCHW); - // static void conv2d(nd4j::graph::Context & block, const std::vector& inArrs, NDArray* output, const std::vector& intArgs); + // static void conv2d(sd::graph::Context & block, const std::vector& inArrs, NDArray* output, const std::vector& intArgs); - // static void conv2dBP(nd4j::graph::Context & block, const std::vector& inArrs, const std::vector& outArrs, const std::vector& intArgs); + // static void conv2dBP(sd::graph::Context & block, const std::vector& inArrs, const std::vector& outArrs, const std::vector& intArgs); - static void conv2dBP(nd4j::graph::Context & block, const NDArray* input, const NDArray* weights, const NDArray* bias, const NDArray* gradO, NDArray* gradI, NDArray* gradW, NDArray* gradB, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int paddingMode, const int isNCHW); + static void conv2dBP(sd::graph::Context & block, const NDArray* input, const NDArray* weights, const NDArray* bias, const NDArray* gradO, NDArray* gradI, NDArray* gradW, NDArray* gradB, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int paddingMode, const int isNCHW); - static void depthwiseConv2d(nd4j::graph::Context & block, const NDArray* input, const NDArray* weights, const NDArray* bias, NDArray* output, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int paddingMode, const int isNCHW); + static void depthwiseConv2d(sd::graph::Context & block, const NDArray* input, const NDArray* weights, const NDArray* bias, NDArray* output, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int paddingMode, const int isNCHW); - static void depthwiseConv2dBP(nd4j::graph::Context & block, const NDArray* input, const NDArray* weights, const NDArray* bias, const NDArray* gradO, NDArray* gradI, NDArray* gradW, NDArray* gradB, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int paddingMode, const int isNCHW); + static void depthwiseConv2dBP(sd::graph::Context & block, const NDArray* input, const NDArray* weights, const NDArray* bias, const NDArray* gradO, NDArray* gradI, NDArray* gradW, NDArray* gradB, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int paddingMode, const int isNCHW); - static void sconv2d(nd4j::graph::Context & block, const NDArray* input, const NDArray* weightsDepth, const NDArray* weightsPoint, const NDArray* bias, NDArray* output, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int paddingMode, const int isNCHW); + static void sconv2d(sd::graph::Context & block, const NDArray* input, const NDArray* weightsDepth, const NDArray* weightsPoint, const NDArray* bias, NDArray* output, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int paddingMode, const int isNCHW); - static void vol2col(nd4j::graph::Context & block, const NDArray& vol, NDArray& col, const int sD, const int sH, const int sW, const int pD, const int pH, const int pW, const int dD, const int dH, const int dW); + static void vol2col(sd::graph::Context & block, const NDArray& vol, NDArray& col, const int sD, const int sH, const int sW, const int pD, const int pH, const int pW, const int dD, const int dH, const int dW); - static void col2vol(nd4j::graph::Context & block, const NDArray& col, NDArray& vol, const int sD, const int sH, const int sW, const int pD, const int pH, const int pW, const int dD, const int dH, const int dW); + static void col2vol(sd::graph::Context & block, const NDArray& col, NDArray& vol, const int sD, const int sH, const int sW, const int pD, const int pH, const int pW, const int dD, const int dH, const int dW); - static void upsampling2d(nd4j::graph::Context & block, const NDArray& input, NDArray& output, const int factorH, const int factorW, const bool isNCHW); + static void upsampling2d(sd::graph::Context & block, const NDArray& input, NDArray& output, const int factorH, const int factorW, const bool isNCHW); - static void upsampling3d(nd4j::graph::Context & block, const NDArray& input, NDArray& output, const int factorD, const int factorH, const int factorW, const bool isNCDHW); + static void upsampling3d(sd::graph::Context & block, const NDArray& input, NDArray& output, const int factorD, const int factorH, const int factorW, const bool isNCDHW); - static void upsampling2dBP(nd4j::graph::Context & block, const NDArray& gradO, NDArray& gradI, const bool isNCHW); + static void upsampling2dBP(sd::graph::Context & block, const NDArray& gradO, NDArray& gradI, const bool isNCHW); - static void upsampling3dBP(nd4j::graph::Context & block, const NDArray& gradO, NDArray& gradI, const bool isNCDHW); + static void upsampling3dBP(sd::graph::Context & block, const NDArray& gradO, NDArray& gradI, const bool isNCDHW); - static void pooling2d(nd4j::graph::Context & block, const NDArray& input, NDArray& output, const int kH, const int kW, const int sH, const int sW, const int pH, const int pW, const int dH, const int dW, const PoolingType poolingMode, const int extraParam0); + static void pooling2d(sd::graph::Context & block, const NDArray& input, NDArray& output, const int kH, const int kW, const int sH, const int sW, const int pH, const int pW, const int dH, const int dW, const PoolingType poolingMode, const int extraParam0); - static void pooling3d(nd4j::graph::Context & block, const NDArray& input, NDArray& output, const int kD, const int kH, const int kW, const int sD, const int sH, const int sW, const int pD, const int pH, const int pW, const int dD, const int dH, const int dW, const int poolingMode, const int extraParam0); + static void pooling3d(sd::graph::Context & block, const NDArray& input, NDArray& output, const int kD, const int kH, const int kW, const int sD, const int sH, const int sW, const int pD, const int pH, const int pW, const int dD, const int dH, const int dW, const int poolingMode, const int extraParam0); - static void pooling2dBP(nd4j::graph::Context & block, const NDArray& input, const NDArray& gradO, NDArray& gradI, const int kH, const int kW, const int sH, const int sW, const int pH, const int pW, const int dH, const int dW, const int poolingMode, const int extraParam0); + static void pooling2dBP(sd::graph::Context & block, const NDArray& input, const NDArray& gradO, NDArray& gradI, const int kH, const int kW, const int sH, const int sW, const int pH, const int pW, const int dH, const int dW, const int poolingMode, const int extraParam0); - static void pooling3dBP(nd4j::graph::Context & block, const NDArray& input, const NDArray& gradO, NDArray& gradI, const int kD, const int kH, const int kW, const int sD, const int sH, const int sW, const int pD, const int pH, const int pW, const int dD, const int dH, const int dW, const int poolingMode, const int extraParam0); + static void pooling3dBP(sd::graph::Context & block, const NDArray& input, const NDArray& gradO, NDArray& gradI, const int kD, const int kH, const int kW, const int sD, const int sH, const int sW, const int pD, const int pH, const int pW, const int dD, const int dH, const int dW, const int poolingMode, const int extraParam0); }; } diff --git a/libnd4j/include/ops/declarable/helpers/cpu/BarnesHutTsne.cpp b/libnd4j/include/ops/declarable/helpers/cpu/BarnesHutTsne.cpp index baf19de10..5ac61964c 100644 --- a/libnd4j/include/ops/declarable/helpers/cpu/BarnesHutTsne.cpp +++ b/libnd4j/include/ops/declarable/helpers/cpu/BarnesHutTsne.cpp @@ -21,7 +21,7 @@ #include #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { @@ -30,7 +30,7 @@ namespace helpers { int* pRowCounts = reinterpret_cast(rowCounts.buffer()); int const* pRows = reinterpret_cast(rowP->getBuffer()); int const* pCols = reinterpret_cast(colP->getBuffer()); - for (int n = 0; n < N; n++) { + for (Nd4jLong n = 0; n < N; n++) { int begin = pRows[n];//->e(n); int end = pRows[n + 1];//rowP->e(n + 1); for (int i = begin; i < end; i++) { @@ -72,7 +72,7 @@ namespace helpers { int const* pRows = reinterpret_cast(rowP->getBuffer()); int* symRowP = reinterpret_cast(outputRows->buffer()); symRowP[0] = 0; - for (int n = 0; n < N; n++) + for (Nd4jLong n = 0; n < N; n++) symRowP[n + 1] = symRowP[n] + rowCounts->e(n); // outputRows->printBuffer("output rows"); @@ -86,7 +86,7 @@ namespace helpers { std::vector offset(N);// = NDArrayFactory::create('c', {N}); //PRAGMA_OMP_PARALLEL_FOR_SIMD_ARGS(schedule(guided) shared(offset)) - for (int n = 0; n < N; n++) { + for (Nd4jLong n = 0; n < N; n++) { int begin = pRows[n]; int bound = pRows[n + 1]; @@ -188,9 +188,9 @@ namespace helpers { // gains = gains.add(.2).muli(sign(yGrads)).neq(sign(yIncs)).castTo(Nd4j.defaultFloatingPointType()) // .addi(gains.mul(0.8).muli(sign(yGrads)).neq(sign(yIncs))); auto gainsInternal = LAMBDA_TTT(x, grad, eps) { -// return T((x + 2.) * nd4j::math::nd4j_sign(grad) != nd4j::math::nd4j_sign(eps)) + T(x * 0.8 * nd4j::math::nd4j_sign(grad) != nd4j::math::nd4j_sign(eps)); - //return T((x + 2.) * nd4j::math::nd4j_sign(grad) == nd4j::math::nd4j_sign(eps)) + T(x * 0.8 * nd4j::math::nd4j_sign(grad) == nd4j::math::nd4j_sign(eps)); - T res = nd4j::math::nd4j_sign(grad) != nd4j::math::nd4j_sign(eps) ? x + T(.2) : x * T(.8); +// return T((x + 2.) * sd::math::nd4j_sign(grad) != sd::math::nd4j_sign(eps)) + T(x * 0.8 * sd::math::nd4j_sign(grad) != sd::math::nd4j_sign(eps)); + //return T((x + 2.) * sd::math::nd4j_sign(grad) == sd::math::nd4j_sign(eps)) + T(x * 0.8 * sd::math::nd4j_sign(grad) == sd::math::nd4j_sign(eps)); + T res = sd::math::nd4j_sign(grad) != sd::math::nd4j_sign(eps) ? x + T(.2) : x * T(.8); if(res < .01) res = .01; return res; }; diff --git a/libnd4j/include/ops/declarable/helpers/cpu/activations.cpp b/libnd4j/include/ops/declarable/helpers/cpu/activations.cpp index 2e63c9d5e..de56650c8 100644 --- a/libnd4j/include/ops/declarable/helpers/cpu/activations.cpp +++ b/libnd4j/include/ops/declarable/helpers/cpu/activations.cpp @@ -20,64 +20,18 @@ // #include -#include +#include #include -#include +#include #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { -template -static void softMaxForVector_(void *input, Nd4jLong *inShapeInfo, void *output, Nd4jLong *outShapeInfo) { - - T* inBuff = reinterpret_cast(input); - T* outBuff = reinterpret_cast(output); - - T max = -DataTypeUtils::max(); - T sum = 0.; - int inEWS = shape::elementWiseStride(inShapeInfo); - int outEWS = shape::elementWiseStride(outShapeInfo); - int length = shape::length(inShapeInfo); - - if (inEWS >= 1 && outEWS >= 1) { - - if (inEWS == 1 && outEWS == 1) { - - for (int i = 0; i < length; i++) - max = nd4j::math::nd4j_max(max, inBuff[i]); - - for (int i = 0; i < length; i++) { - outBuff[i] = nd4j::math::nd4j_exp(inBuff[i] - max); - sum += outBuff[i]; - } - - PRAGMA_OMP_SIMD - for (int i = 0; i < length; i++) - outBuff[i] /= sum; - } - else { - - for (int i = 0; i < length; i++) - max = nd4j::math::nd4j_max(max, inBuff[i * inEWS]); - - for (int i = 0; i < length; i++) { - T r = nd4j::math::nd4j_exp(inBuff[i * inEWS] - max); - outBuff[i * outEWS] = r; - sum += r; - } - - PRAGMA_OMP_SIMD - for (int i = 0; i < length; i++) - outBuff[i * outEWS] /= sum; - } - } -} - /////////////////////////////////////////////////////////////////// template - void static _softMaxDerivForVector(nd4j::LaunchContext * context, const void *input, const Nd4jLong *inShapeInfo, void *output) { + void static _softMaxDerivForVector(sd::LaunchContext * context, const void *input, const Nd4jLong *inShapeInfo, void *output) { const T* inBuff = reinterpret_cast(input); T* outBuff = reinterpret_cast(output); @@ -88,12 +42,12 @@ static void softMaxForVector_(void *input, Nd4jLong *inShapeInfo, void *output, for (int i = 0; i < length; i++) { const Nd4jLong offset = shape::getIndexOffset(i, inShapeInfo); - max = nd4j::math::nd4j_max(max, inBuff[offset]); + max = sd::math::nd4j_max(max, inBuff[offset]); } for (int i = 0; i < length; i++) { const Nd4jLong offset = shape::getIndexOffset(i, inShapeInfo); - outBuff[offset] = nd4j::math::nd4j_exp(inBuff[offset] - max); + outBuff[offset] = sd::math::nd4j_exp(inBuff[offset] - max); sum += outBuff[offset]; } @@ -105,7 +59,7 @@ static void softMaxForVector_(void *input, Nd4jLong *inShapeInfo, void *output, } /////////////////////////////////////////////////////////////////// - void softmaxDerivative(nd4j::LaunchContext * context, const NDArray& input, NDArray& output, const int dimension) { + void softmaxDerivative(sd::LaunchContext * context, const NDArray& input, NDArray& output, const int dimension) { const int rank = input.rankOf(); int temp; @@ -123,16 +77,6 @@ static void softMaxForVector_(void *input, Nd4jLong *inShapeInfo, void *output, } } - /////////////////////////////////////////////////////////////////// -void softMaxForVector(nd4j::LaunchContext * context, const NDArray& input, NDArray& output) { - - if(!input.isVector() || !output.isVector()) - throw std::runtime_error("ops::helpers::softMaxForVector function: input and output arrays must be vectors !"); - - auto xType = input.dataType(); - BUILD_SINGLE_SELECTOR(xType, softMaxForVector_, (input.getBuffer(), input.getShapeInfo(), output.buffer(), output.shapeInfo()), FLOAT_TYPES); -} - /////////////////////////////////////////////////////////////////// template void logSoftMaxForVector_(void *input, Nd4jLong *inShapeInfo, void *output, Nd4jLong *outShapeInfo) { @@ -146,43 +90,43 @@ void softMaxForVector(nd4j::LaunchContext * context, const NDArray& input, NDArr auto length = shape::length(inShapeInfo); if (inEWS == 1) { - for (int i = 0; i < length; i++) - max = nd4j::math::nd4j_max(max, inBuff[i]); + for (Nd4jLong i = 0; i < length; i++) + max = sd::math::nd4j_max(max, inBuff[i]); PRAGMA_OMP_SIMD_SUM(sum) - for (int i = 0; i < length; i++) { - outBuff[i] = nd4j::math::nd4j_exp(inBuff[i] - max); + for (Nd4jLong i = 0; i < length; i++) { + outBuff[i] = sd::math::nd4j_exp(inBuff[i] - max); sum += outBuff[i]; } PRAGMA_OMP_SIMD - for (int i = 0; i < length; i++) { + for (Nd4jLong i = 0; i < length; i++) { outBuff[i] /= sum; - outBuff[i] = nd4j::math::nd4j_log(outBuff[i]); + outBuff[i] = sd::math::nd4j_log(outBuff[i]); } } else if (inEWS > 1) { PRAGMA_OMP_SIMD_MAX(max) - for (int i = 0; i < length; i++) - max = nd4j::math::nd4j_max(max, inBuff[i * inEWS]); + for (Nd4jLong i = 0; i < length; i++) + max = sd::math::nd4j_max(max, inBuff[i * inEWS]); PRAGMA_OMP_SIMD_SUM(sum) - for (int i = 0; i < length; i++) { - outBuff[i * inEWS] = nd4j::math::nd4j_exp(inBuff[i * inEWS] - max); + for (Nd4jLong i = 0; i < length; i++) { + outBuff[i * inEWS] = sd::math::nd4j_exp(inBuff[i * inEWS] - max); sum += outBuff[i * inEWS]; } PRAGMA_OMP_SIMD - for (int i = 0; i < length; i++) { + for (Nd4jLong i = 0; i < length; i++) { outBuff[i * inEWS] /= sum; - outBuff[i * inEWS] = nd4j::math::nd4j_log(outBuff[i * inEWS]); + outBuff[i * inEWS] = sd::math::nd4j_log(outBuff[i * inEWS]); } } } /////////////////////////////////////////////////////////////////// - void logSoftMaxForVector(nd4j::LaunchContext * context, const NDArray& input, NDArray& output) { + void logSoftMaxForVector(sd::LaunchContext * context, const NDArray& input, NDArray& output) { if(!input.isVector() || !output.isVector()) throw std::runtime_error("ops::helpers::logSoftMaxForVector function input and output arrays must be vectors !"); @@ -191,151 +135,9 @@ void softMaxForVector(nd4j::LaunchContext * context, const NDArray& input, NDArr BUILD_SINGLE_SELECTOR(xType, logSoftMaxForVector_, (input.getBuffer(), input.getShapeInfo(), output.buffer(), output.shapeInfo()), FLOAT_TYPES); } - template - void softmax_loop(T *input, T *output, Nd4jLong *offsets, Nd4jLong numOfSubArrs, uint32_t tadLen); - - template <> - FORCEINLINE void softmax_loop(float *input, float *output, Nd4jLong *offsets, Nd4jLong numOfSubArrs, uint32_t tadLen) { - auto func = PRAGMA_THREADS_FOR { - for (auto i = start; i < stop; i++) { - auto inBuff = input + offsets[i]; - auto outBuff = output + offsets[i]; - - float max = -DataTypeUtils::max(); - float sum = 0.f; - - #pragma omp simd reduction(max:max) - for (uint j = 0; j < tadLen; ++j) - max = nd4j::math::nd4j_max(max, inBuff[j]); - - #pragma omp simd reduction(+:sum) - for (uint j = 0; j < tadLen; ++j) { - float temp = nd4j::math::nd4j_exp(inBuff[j] - max); - outBuff[j] = temp; - sum += temp; - } - - #pragma omp simd - for (uint j = 0; j < tadLen; ++j) - outBuff[j] /= sum; - } - }; - - samediff::Threads::parallel_tad(func,0, numOfSubArrs); - } - - - template - FORCEINLINE void softmax_loop(T *input, T *output, Nd4jLong *offsets, Nd4jLong numOfSubArrs, uint32_t tadLen) { - auto func = PRAGMA_THREADS_FOR { - for (auto i = start; i < stop; i++) { - auto inBuff = input + offsets[i]; - auto outBuff = output + offsets[i]; - - T max = -DataTypeUtils::max(); - T sum(0.f); - - #pragma omp simd reduction(maxT:max) - for (uint j = 0; j < tadLen; ++j) - max = nd4j::math::nd4j_max(max, inBuff[j]); - - #pragma omp simd reduction(sumT:sum) - for (uint j = 0; j < tadLen; ++j) { - T temp = nd4j::math::nd4j_exp(inBuff[j] - max); - outBuff[j] = temp; - sum += temp; - } - - #pragma omp simd - for (uint j = 0; j < tadLen; ++j) - outBuff[j] /= sum; - } - }; - - samediff::Threads::parallel_tad(func,0, numOfSubArrs); - } ////////////////////////////////////////////////////////////////////////// -template -static void softmax_(nd4j::LaunchContext * context, const NDArray& input, NDArray& output, const int dimension) { - - const int rank = input.rankOf(); - - if(input.isVector()) { - - if(rank == 1 || input.sizeAt(dimension) != 1) - softMaxForVector_(input.getBuffer(), input.getShapeInfo(), output.buffer(), output.getShapeInfo()); - else - output = 1.; - } - else if(input.isSameShapeStrict(output)) { - - TadPack tadPack = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(input.getShapeInfo(), dimension); - Nd4jLong* tadShapeInfo = tadPack.primaryShapeInfo(); - Nd4jLong* tadOffsets = tadPack.primaryOffsets(); - const uint numOfSubArrs = tadPack.numberOfTads(); - const uint tadLen = shape::length(tadShapeInfo); - - if(shape::elementWiseStride(tadShapeInfo) == 1){ - T *inBuff = input.bufferAsT(); - T *outBuff = output.bufferAsT(); - - softmax_loop(inBuff, outBuff, tadOffsets, numOfSubArrs, tadLen); - } - else { - - uint inShapeInfoCast[MAX_RANK]; - bool canCast = nd4j::DataTypeUtils::castShapeInfo(tadShapeInfo, inShapeInfoCast); - - auto offsets = new Nd4jLong[tadLen]; - shape::calcOffsets(tadShapeInfo, offsets); - - auto func = PRAGMA_THREADS_FOR { - for (auto i = start; i < stop; i++) { - auto inBuff = input.bufferAsT() + tadOffsets[i]; - auto outBuff = output.bufferAsT() + tadOffsets[i]; - - T max = -DataTypeUtils::max(); - T sum = 0.f; - - for (uint j = 0; j < tadLen; ++j) - max = nd4j::math::nd4j_max(max, inBuff[offsets[j]]); - - for (uint j = 0; j < tadLen; ++j) { - T temp = nd4j::math::nd4j_exp(inBuff[offsets[j]] - max); - outBuff[offsets[j]] = temp; - sum += temp; - } - - for (uint j = 0; j < tadLen; ++j) - outBuff[offsets[j]] /= sum; - } - }; - - samediff::Threads::parallel_tad(func, 0, numOfSubArrs); - - delete []offsets; - } - } - else { - NDArray max = input.reduceAlongDimension(nd4j::reduce::Max, {dimension}, true); - input.applyTrueBroadcast(nd4j::BroadcastOpsTuple::Subtract(), max, output, false); - output.applyTransform(nd4j::transform::Exp, output); - NDArray sum = output.reduceAlongDimension(nd4j::reduce::Sum, {dimension}, true); - output /= sum; - } -} - - -/////////////////////////////////////////////////////////////////// -void softmax(nd4j::LaunchContext * context, const NDArray& input, NDArray& output, const int dimension) { - - BUILD_SINGLE_SELECTOR(input.dataType(), softmax_, (context, input, output, dimension), FLOAT_TYPES); -} - - -////////////////////////////////////////////////////////////////////////// -void prelu(nd4j::LaunchContext * context, const NDArray& input, const NDArray& alpha, NDArray& output) { +void prelu(sd::LaunchContext * context, const NDArray& input, const NDArray& alpha, NDArray& output) { const Nd4jLong inputLen = input.lengthOf(); const Nd4jLong* inputShapeInfo = input.getShapeInfo(); const Nd4jLong* alphaShapeInfo = alpha.getShapeInfo(); @@ -356,7 +158,7 @@ void prelu(nd4j::LaunchContext * context, const NDArray& input, const NDArray& a } ////////////////////////////////////////////////////////////////////////// -void preluBP(nd4j::LaunchContext * context, const NDArray& input, const NDArray& alpha, const NDArray& dLdO, NDArray& dLdI, NDArray& dLdA) { +void preluBP(sd::LaunchContext * context, const NDArray& input, const NDArray& alpha, const NDArray& dLdO, NDArray& dLdI, NDArray& dLdA) { const Nd4jLong inputLen = input.lengthOf(); const Nd4jLong* inputShapeInfo = input.getShapeInfo(); @@ -393,24 +195,24 @@ void preluBP(nd4j::LaunchContext * context, const NDArray& input, const NDArray& const_cast(input).applyLambda(routine, output); } - void thresholdRelu(nd4j::LaunchContext * context, NDArray const& input, double threshold, NDArray& output) { + void thresholdRelu(sd::LaunchContext * context, NDArray const& input, double threshold, NDArray& output) { BUILD_SINGLE_SELECTOR(input.dataType(), thresholdRelu_, (input, threshold, output), FLOAT_TYPES); } template - static void thresholdReluDerivative_(nd4j::LaunchContext * context, NDArray* input, double theta, NDArray* dLdO, NDArray* output) { + static void thresholdReluDerivative_(sd::LaunchContext * context, NDArray* input, double theta, NDArray* dLdO, NDArray* output) { auto derivative = LAMBDA_TT(_x, grO, theta) {if (_x > theta) return grO; else return static_cast(0); }; input->applyPairwiseLambda(*dLdO, derivative, *output); } - void thresholdReluDerivative(nd4j::LaunchContext * context, NDArray* input, double threshold, NDArray* dLdO, NDArray* output) { + void thresholdReluDerivative(sd::LaunchContext * context, NDArray* input, double threshold, NDArray* dLdO, NDArray* output) { BUILD_SINGLE_SELECTOR(input->dataType(), thresholdReluDerivative_, (context, input, threshold, dLdO, output), FLOAT_TYPES); } /////////////////////////////////////////////////////////////////// - void logSoftmax(nd4j::LaunchContext * context, const NDArray& input, NDArray& output, const int dimension) { + void logSoftmax(sd::LaunchContext * context, const NDArray& input, NDArray& output, const int dimension) { const int rank = input.rankOf(); @@ -432,10 +234,9 @@ void preluBP(nd4j::LaunchContext * context, const NDArray& input, const NDArray& } } -BUILD_SINGLE_TEMPLATE(template void thresholdReluDerivative_, (nd4j::LaunchContext * context, NDArray* input, double threshold, NDArray* dLdO, NDArray* output), FLOAT_TYPES); -BUILD_SINGLE_TEMPLATE(template void softmax_, (nd4j::LaunchContext * context, const NDArray& input, NDArray& output, const int dimension), FLOAT_TYPES); -BUILD_SINGLE_TEMPLATE(template void logSoftMaxForVector_, (void *input, Nd4jLong *inShapeInfo, void *output, Nd4jLong *outShapeInfo), FLOAT_TYPES); - BUILD_SINGLE_TEMPLATE(template void _softMaxDerivForVector, (nd4j::LaunchContext * context, const void *input, const Nd4jLong *inShapeInfo, void *output), FLOAT_TYPES); + BUILD_SINGLE_TEMPLATE(template void thresholdReluDerivative_, (sd::LaunchContext * context, NDArray* input, double threshold, NDArray* dLdO, NDArray* output), FLOAT_TYPES); + BUILD_SINGLE_TEMPLATE(template void logSoftMaxForVector_, (void *input, Nd4jLong *inShapeInfo, void *output, Nd4jLong *outShapeInfo), FLOAT_TYPES); + BUILD_SINGLE_TEMPLATE(template void _softMaxDerivForVector, (sd::LaunchContext * context, const void *input, const Nd4jLong *inShapeInfo, void *output), FLOAT_TYPES); } } diff --git a/libnd4j/include/ops/declarable/helpers/cpu/addBias.cpp b/libnd4j/include/ops/declarable/helpers/cpu/addBias.cpp index 39e51f6d7..68b8c6955 100644 --- a/libnd4j/include/ops/declarable/helpers/cpu/addBias.cpp +++ b/libnd4j/include/ops/declarable/helpers/cpu/addBias.cpp @@ -28,7 +28,7 @@ #include #include #include -#include +#include #include #if defined(__GNUC__) @@ -39,7 +39,7 @@ #define align32 #endif -namespace nd4j { +namespace sd { namespace ops { namespace helpers { @@ -129,19 +129,19 @@ namespace nd4j { static void channel_atTheEnd_stride1_C(const Nd4jLong*& x_strides, const Nd4jLong*& bases, T* x, const T* b, T* z, const bool& inplace, const Nd4jLong& start, const Nd4jLong& stop, const Nd4jLong& inc) { size_t loop_count = (stop - start) / inc; - nd4j::CoordsState cst; - size_t offset = nd4j::init_coords(cst, start, bases, x_strides); + sd::CoordsState cst; + size_t offset = sd::init_coords(cst, start, bases, x_strides); if (!inplace) { for (size_t i = 0; i < loop_count; i++) { _add(&(x[offset]), b, &(z[offset]), inc); - offset = nd4j::inc_coords(cst, offset); + offset = sd::inc_coords(cst, offset); } } else { for (size_t i = 0; i < loop_count; i++) { _add_inplace(&(x[offset]), b, inc); - offset = nd4j::inc_coords(cst, offset); + offset = sd::inc_coords(cst, offset); } } } @@ -157,8 +157,8 @@ namespace nd4j { } else { size_t loop_count = (stop - start) / inc; - nd4j::ZipCoordsState cst; - nd4j::zip_size_t offset = nd4j::init_coords(cst, start, bases, x_strides, z_strides); + sd::ZipCoordsState cst; + sd::zip_size_t offset = sd::init_coords(cst, start, bases, x_strides, z_strides); Nd4jLong x_stride = ZIP_STRIDE1(cst, constRank - 1); Nd4jLong z_stride = ZIP_STRIDE2(cst, constRank - 1); @@ -166,7 +166,7 @@ namespace nd4j { /* bases are equal with different strides , but the last one is 1. So we can still vectorize it */ for (size_t i = 0; i < loop_count; i++) { _add(&(x[offset.first]), b, &(z[offset.second]), inc); - offset = nd4j::inc_coords(cst, offset); + offset = sd::inc_coords(cst, offset); } } else { @@ -175,7 +175,7 @@ namespace nd4j { T* zz = &(z[offset.second]); for (size_t j = 0; j < inc; j++) zz[j * z_stride] = xx[j * x_stride] + b[j]; - offset = nd4j::inc_coords(cst, offset); + offset = sd::inc_coords(cst, offset); } } } @@ -215,21 +215,21 @@ namespace nd4j { static void channel_NC_stride1_C(const Nd4jLong*& x_strides, const Nd4jLong*& bases, T* x, const T2* b, T* z, const bool& inplace, const Nd4jLong yStrideC, const Nd4jLong& start, const Nd4jLong& stop, const Nd4jLong& inc) { size_t loop_count = (stop - start) / inc; - nd4j::CoordsState cst; - size_t offset = nd4j::init_coords(cst, start, bases, x_strides); + sd::CoordsState cst; + size_t offset = sd::init_coords(cst, start, bases, x_strides); if (!inplace) { for (size_t i = 0; i < loop_count; i++) { T yy = static_cast(b[COORDS(cst, 1) * yStrideC]); _add_broadcast(&(x[offset]), yy, &(z[offset]), inc); - offset = nd4j::inc_coords(cst, offset); + offset = sd::inc_coords(cst, offset); } } else { for (size_t i = 0; i < loop_count; i++) { T yy = static_cast(b[COORDS(cst, 1) * yStrideC]); _add_broadcast_inplace(&(x[offset]), yy, inc); - offset = nd4j::inc_coords(cst, offset); + offset = sd::inc_coords(cst, offset); } } } @@ -248,8 +248,8 @@ namespace nd4j { // (stop-start) % inc == 0 because we handled inside partitioning using the channel size size_t loop_count = (stop - start) / inc; - nd4j::ZipCoordsState cst; - nd4j::zip_size_t offset = nd4j::init_coords(cst, start, bases, x_strides, z_strides); + sd::ZipCoordsState cst; + sd::zip_size_t offset = sd::init_coords(cst, start, bases, x_strides, z_strides); Nd4jLong x_stride = ZIP_STRIDE1(cst, constRank - 1); Nd4jLong z_stride = ZIP_STRIDE2(cst, constRank - 1); if (same_order && z_stride == 1 && x_stride == 1) { @@ -257,7 +257,7 @@ namespace nd4j { for (size_t i = 0; i < loop_count; i++) { T yy = static_cast(b[ZIP_COORDS(cst, 1) * yStrideC]); _add_broadcast(&(x[offset.first]), yy, &(z[offset.second]), inc); - offset = nd4j::inc_coords(cst, offset); + offset = sd::inc_coords(cst, offset); } } else { @@ -267,7 +267,7 @@ namespace nd4j { T yy = static_cast(b[ZIP_COORDS(cst, 1) * yStrideC]); for (size_t j = 0; j < inc; j++) zz[j * z_stride] = xx[j * x_stride] + yy; - offset = nd4j::inc_coords(cst, offset); + offset = sd::inc_coords(cst, offset); } } } @@ -280,16 +280,16 @@ namespace nd4j { // (stop-start) % inc == 0 because we handled inside partitioning using the channel size size_t loop_count = (stop - start) / inc; - nd4j::CoordsState<1> cst; + sd::CoordsState<1> cst; //note: we had to manually pass index - size_t offset_p = nd4j::init_coords<2>(cst, start / inc, bases, x_strides); + size_t offset_p = sd::init_coords<2>(cst, start / inc, bases, x_strides); //partitioning was done using numHW, so we can increment from rank 2 if (inplaceOp) { for (size_t i = 0; i < loop_count; i++) { T yy = static_cast(b[COORDS(cst, 1) * yStrideC]); _add_broadcast_inplace(&(x[offset_p]), yy, inc); - offset_p = nd4j::inc_coords<2>(cst, offset_p); + offset_p = sd::inc_coords<2>(cst, offset_p); } } else { @@ -297,14 +297,14 @@ namespace nd4j { for (size_t i = 0; i < loop_count; i++) { T yy = static_cast(b[COORDS(cst, 1)]); _add_broadcast(&(x[offset_p]), yy, &(z[offset_p]), inc); - offset_p = nd4j::inc_coords<2>(cst, offset_p); + offset_p = sd::inc_coords<2>(cst, offset_p); } } else { for (size_t i = 0; i < loop_count; i++) { T yy = static_cast(b[COORDS(cst, 1) * yStrideC]); _add_broadcast(&(x[offset_p]), yy, &(z[offset_p]), inc); - offset_p = nd4j::inc_coords<2>(cst, offset_p); + offset_p = sd::inc_coords<2>(cst, offset_p); } } } @@ -316,20 +316,20 @@ namespace nd4j { { // (stop-start) % inc == 0 because we handled inside partitioning using the channel size size_t loop_count = (stop - start) / inc; - nd4j::CoordsState cst; - size_t offset_p = nd4j::init_coords(cst, start, bases, x_strides); + sd::CoordsState cst; + size_t offset_p = sd::init_coords(cst, start, bases, x_strides); if (!inplace) { for (size_t i = 0; i < loop_count; i++) { T yy = static_cast(b[COORDS(cst, b_index) * yStrideC]); _add_broadcast(&(x[offset_p]), yy, &(z[offset_p]), inc); - offset_p = nd4j::inc_coords(cst, offset_p); + offset_p = sd::inc_coords(cst, offset_p); } } else { for (size_t i = 0; i < loop_count; i++) { T yy = static_cast(b[COORDS(cst, b_index) * yStrideC]); _add_broadcast_inplace(&(x[offset_p]), yy, inc); - offset_p = nd4j::inc_coords(cst, offset_p); + offset_p = sd::inc_coords(cst, offset_p); } } } @@ -346,8 +346,8 @@ namespace nd4j { // (stop-start) % inc == 0 because we handled inside partitioning using the channel size size_t loop_count = (stop - start) / inc; - nd4j::ZipCoordsState cst; - nd4j::zip_size_t offset = nd4j::init_coords(cst, start, bases, x_strides, z_strides); + sd::ZipCoordsState cst; + sd::zip_size_t offset = sd::init_coords(cst, start, bases, x_strides, z_strides); Nd4jLong x_stride = ZIP_STRIDE1(cst, 0); Nd4jLong z_stride = ZIP_STRIDE2(cst, 0); if (same_order && z_stride == 1 && x_stride == 1) { @@ -355,7 +355,7 @@ namespace nd4j { for (size_t i = 0; i < loop_count; i++) { T yy = static_cast(b[ZIP_COORDS(cst, b_index) * yStrideC]); _add_broadcast(&(x[offset.first]), yy, &(z[offset.second]), inc); - offset = nd4j::inc_coords(cst, offset); + offset = sd::inc_coords(cst, offset); } } else { @@ -365,7 +365,7 @@ namespace nd4j { T yy = static_cast(b[ZIP_COORDS(cst, b_index) * yStrideC]); for (size_t j = 0; j < inc; j++) zz[j * z_stride] = xx[j * x_stride] + yy; - offset = nd4j::inc_coords(cst, offset); + offset = sd::inc_coords(cst, offset); } } } @@ -418,7 +418,7 @@ namespace nd4j { //for rank>5 if (rank > 5) { const int channelDim = isNCHW ? 1 : input.rankOf() - 1; // second or last - const_cast(input).applyBroadcast(nd4j::broadcast::Add, { channelDim }, bias, output); + const_cast(input).applyBroadcast(sd::broadcast::Add, { channelDim }, bias, output); return; } @@ -443,7 +443,7 @@ namespace nd4j { const X* bias_new; X* bias_extra = nullptr; size_t total_num = 1; - for (size_t i = 0; i < rank; i++) { + for (Nd4jLong i = 0; i < rank; i++) { total_num *= bases[i]; } Nd4jLong inc; @@ -482,7 +482,7 @@ namespace nd4j { if (isContinuous) { //we can choose other inc and index for that case //but for now lets choose all till the last one - uint32_t req_numThreads = nd4j::Environment::getInstance()->maxMasterThreads(); + uint32_t req_numThreads = sd::Environment::getInstance()->maxMasterThreads(); isContinuous = false; if (rank > 2) { if (req_numThreads < 2 || bases[rank - 1] >= req_numThreads) { @@ -574,7 +574,7 @@ namespace nd4j { for (size_t i = 0; i < 2; i++) { numNC *= bases[i]; } - for (size_t i = 2; i < rank; i++) { + for (Nd4jLong i = 2; i < rank; i++) { numHW *= bases[i]; } Nd4jLong total_num = numNC * numHW; @@ -582,7 +582,7 @@ namespace nd4j { if (order == 'c' && isContinuous) { //sometimes last dimension is too big and multithreading could suffer using unfair partitioning //so we will do it only when inc is smaller our value or multithreading turned off - uint32_t req_numThreads = nd4j::Environment::getInstance()->maxMasterThreads(); + uint32_t req_numThreads = sd::Environment::getInstance()->maxMasterThreads(); if (req_numThreads < 2 || numNC >= req_numThreads || inc <= 2 * 8196 || rank == 3) { inc = numHW; } @@ -635,7 +635,7 @@ namespace nd4j { } } ////////////////////////////////////////////////////////////////////////// - void addBias(nd4j::graph::Context& block, const NDArray& input, const NDArray& bias, NDArray& output, const bool isNCHW) { + void addBias(sd::graph::Context& block, const NDArray& input, const NDArray& bias, NDArray& output, const bool isNCHW) { // bias.rankOf() == 1 ? bias : bias.reshape(bias.ordering(), {bias.lengthOf()}) BUILD_DOUBLE_SELECTOR(input.dataType(), bias.dataType(), addBias_, (input, bias, output, isNCHW), FLOAT_TYPES, FLOAT_TYPES); diff --git a/libnd4j/include/ops/declarable/helpers/cpu/adjust_hue.cpp b/libnd4j/include/ops/declarable/helpers/cpu/adjust_hue.cpp index 5a22b02eb..078ebda10 100644 --- a/libnd4j/include/ops/declarable/helpers/cpu/adjust_hue.cpp +++ b/libnd4j/include/ops/declarable/helpers/cpu/adjust_hue.cpp @@ -23,7 +23,7 @@ #include #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { @@ -59,8 +59,8 @@ static void adjustHue_(const NDArray *input, const NDArray* deltaScalarArr, NDAr } else { - auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), dimC); - auto packZ = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), dimC); + auto packX = sd::ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), dimC); + auto packZ = sd::ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), dimC); const Nd4jLong numOfTads = packX.numberOfTads(); const Nd4jLong xDimCstride = input->stridesOf()[dimC]; @@ -92,13 +92,13 @@ static void adjustHue_(const NDArray *input, const NDArray* deltaScalarArr, NDAr } -void adjustHue(nd4j::LaunchContext* context, const NDArray *input, const NDArray* deltaScalarArr, NDArray *output, const int dimC) { +void adjustHue(sd::LaunchContext* context, const NDArray *input, const NDArray* deltaScalarArr, NDArray *output, const int dimC) { BUILD_SINGLE_SELECTOR(input->dataType(), adjustHue_, (input, deltaScalarArr, output, dimC), FLOAT_TYPES); } /* template -static void adjust_hue_single_(nd4j::LaunchContext * context, NDArray *array, NDArray *output, float delta, bool isNHWC) { +static void adjust_hue_single_(sd::LaunchContext * context, NDArray *array, NDArray *output, float delta, bool isNHWC) { // we're 100% sure it's 3 const int numChannels = 3; int tuples = array->lengthOf() / numChannels; @@ -166,7 +166,7 @@ static void adjust_hue_single_(nd4j::LaunchContext * context, NDArray *array, ND } } -void adjust_hue_(nd4j::LaunchContext * context, NDArray *array, NDArray *output, NDArray* delta, bool isNHWC) { +void adjust_hue_(sd::LaunchContext * context, NDArray *array, NDArray *output, NDArray* delta, bool isNHWC) { auto xType = array->dataType(); float d = delta->e(0); @@ -188,7 +188,7 @@ void adjust_hue_(nd4j::LaunchContext * context, NDArray *array, NDArray *output, } } -BUILD_SINGLE_TEMPLATE(template void adjust_hue_single_, (nd4j::LaunchContext * context, NDArray *array, NDArray *output, float delta, bool isNHWC);, FLOAT_TYPES); +BUILD_SINGLE_TEMPLATE(template void adjust_hue_single_, (sd::LaunchContext * context, NDArray *array, NDArray *output, float delta, bool isNHWC);, FLOAT_TYPES); */ diff --git a/libnd4j/include/ops/declarable/helpers/cpu/adjust_saturation.cpp b/libnd4j/include/ops/declarable/helpers/cpu/adjust_saturation.cpp index 594280ebe..c5c5cf9c6 100644 --- a/libnd4j/include/ops/declarable/helpers/cpu/adjust_saturation.cpp +++ b/libnd4j/include/ops/declarable/helpers/cpu/adjust_saturation.cpp @@ -25,7 +25,7 @@ #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { @@ -58,8 +58,8 @@ static void adjustSaturation_(const NDArray *input, const NDArray* factorScalarA samediff::Threads::parallel_for(func, 0, input->lengthOf(), 3); } else { - auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), dimC); - auto packZ = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), dimC); + auto packX = sd::ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), dimC); + auto packZ = sd::ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), dimC); const Nd4jLong numOfTads = packX.numberOfTads(); const Nd4jLong xDimCstride = input->stridesOf()[dimC]; @@ -89,14 +89,14 @@ static void adjustSaturation_(const NDArray *input, const NDArray* factorScalarA } -void adjustSaturation(nd4j::LaunchContext* context, const NDArray *input, const NDArray* factorScalarArr, NDArray *output, const int dimC) { +void adjustSaturation(sd::LaunchContext* context, const NDArray *input, const NDArray* factorScalarArr, NDArray *output, const int dimC) { BUILD_SINGLE_SELECTOR(input->dataType(), adjustSaturation_, (input, factorScalarArr, output, dimC), FLOAT_TYPES); } /* template -static void adjust_saturation_single_(nd4j::LaunchContext * context, NDArray *array, NDArray *output, float delta, bool isNHWC) { +static void adjust_saturation_single_(sd::LaunchContext * context, NDArray *array, NDArray *output, float delta, bool isNHWC) { // we're 100% sure it's 3 const int numChannels = 3; int tuples = array->lengthOf() / numChannels; @@ -114,7 +114,7 @@ static void adjust_saturation_single_(nd4j::LaunchContext * context, NDArray *ar T h, s, v; // Convert the RGB color to Hue/V-range. helpers::rgb_to_hsv(i[0], i[1], i[2], &h, &s, &v); - s = nd4j::math::nd4j_min((T) 1.0f, nd4j::math::nd4j_max((T) 0.0f, s * delta)); + s = sd::math::nd4j_min((T) 1.0f, sd::math::nd4j_max((T) 0.0f, s * delta)); // Convert the hue and v-range back into RGB. helpers::hsv_to_rgb(h, s, v, o, o + 1, o + 2); } @@ -143,7 +143,7 @@ static void adjust_saturation_single_(nd4j::LaunchContext * context, NDArray *ar T h, s, v; // Convert the RGB color to Hue/V-range. helpers::rgb_to_hsv(_ri[0], _gi[0], _bi[0], &h, &s, &v); - s = nd4j::math::nd4j_min((T) 1.0f, nd4j::math::nd4j_max((T) 0.0f, s * delta)); + s = sd::math::nd4j_min((T) 1.0f, sd::math::nd4j_max((T) 0.0f, s * delta)); // Convert the hue and v-range back into RGB. helpers::hsv_to_rgb(h, s, v, _ro, _go, _bo); } @@ -153,7 +153,7 @@ static void adjust_saturation_single_(nd4j::LaunchContext * context, NDArray *ar } } -void adjust_saturation(nd4j::LaunchContext * context, NDArray *array, NDArray *output, NDArray* delta, bool isNHWC) { +void adjust_saturation(sd::LaunchContext * context, NDArray *array, NDArray *output, NDArray* delta, bool isNHWC) { auto xType = array->dataType(); float d = delta->e(0); @@ -177,7 +177,7 @@ void adjust_saturation(nd4j::LaunchContext * context, NDArray *array, NDArray *o } } -BUILD_SINGLE_TEMPLATE(template void adjust_saturation_single_, (nd4j::LaunchContext * context, NDArray *array, NDArray *output, float delta, bool isNHWC), FLOAT_TYPES); +BUILD_SINGLE_TEMPLATE(template void adjust_saturation_single_, (sd::LaunchContext * context, NDArray *array, NDArray *output, float delta, bool isNHWC), FLOAT_TYPES); */ } diff --git a/libnd4j/include/ops/declarable/helpers/cpu/axis.cpp b/libnd4j/include/ops/declarable/helpers/cpu/axis.cpp index eb56acb9c..36f716630 100644 --- a/libnd4j/include/ops/declarable/helpers/cpu/axis.cpp +++ b/libnd4j/include/ops/declarable/helpers/cpu/axis.cpp @@ -21,13 +21,13 @@ #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { void adjustAxis(Nd4jLong rank, NDArray* axisVector, std::vector& output) { output.resize(axisVector->lengthOf()); - for (int e = 0; e < axisVector->lengthOf(); e++) { + for (Nd4jLong e = 0; e < axisVector->lengthOf(); e++) { auto ca = axisVector->e(e); if (ca < 0) ca += rank; @@ -37,7 +37,7 @@ namespace helpers { } void adjustAxis(Nd4jLong rank, std::vector &axisVector) { - for (int e = 0; e < axisVector.size(); e++) { + for (size_t e = 0; e < axisVector.size(); e++) { auto a = axisVector[e]; if (a < 0) axisVector[e] = a + rank; diff --git a/libnd4j/include/ops/declarable/helpers/cpu/batched_gemm.cpp b/libnd4j/include/ops/declarable/helpers/cpu/batched_gemm.cpp index c63dc3c1c..daaf4f71a 100644 --- a/libnd4j/include/ops/declarable/helpers/cpu/batched_gemm.cpp +++ b/libnd4j/include/ops/declarable/helpers/cpu/batched_gemm.cpp @@ -18,14 +18,14 @@ // @author raver119@gmail.com // -#include +#include #include #include #include #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { diff --git a/libnd4j/include/ops/declarable/helpers/cpu/batchnorm.cpp b/libnd4j/include/ops/declarable/helpers/cpu/batchnorm.cpp index aa9624600..2293fe843 100644 --- a/libnd4j/include/ops/declarable/helpers/cpu/batchnorm.cpp +++ b/libnd4j/include/ops/declarable/helpers/cpu/batchnorm.cpp @@ -19,12 +19,12 @@ // -#include +#include #include -#include +#include #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { @@ -64,9 +64,9 @@ static void batchnorm_(const NDArray* input, const NDArray* mean, const NDArray* Nd4jLong* xOffsets = new Nd4jLong[steps]; Nd4jLong* zOffsets = xzSameOffset ? xOffsets : new Nd4jLong[steps]; - Nd4jLong* auxBuff = new Nd4jLong[2 * input->rankOf()]; + int* auxBuff = new int[2 * input->rankOf()]; - for (int j = 0; j < lenSmall; ++j) { + for (Nd4jLong j = 0; j < lenSmall; ++j) { const bool isOwner = (j < info._numThreads) ? thread_id == j : thread_id == (j % info._numThreads); @@ -77,7 +77,7 @@ static void batchnorm_(const NDArray* input, const NDArray* mean, const NDArray* const auto varOffset = paramSameOffset ? meanOffset : shape::getIndexOffset(j, variance->getShapeInfo()); const auto meanVal = m[meanOffset]; - auto sigmaInvGam = static_cast(1) / nd4j::math::nd4j_sqrt(v[varOffset] + epsilon); + auto sigmaInvGam = static_cast(1) / sd::math::nd4j_sqrt(v[varOffset] + epsilon); if(g != nullptr) { const auto gammaOffset = paramSameOffset ? meanOffset : shape::getIndexOffset(j, gamma->getShapeInfo()); @@ -96,7 +96,7 @@ static void batchnorm_(const NDArray* input, const NDArray* mean, const NDArray* shape::outerArrayOffsets(zOffsets, j, output->getShapeInfo(), mean->getShapeInfo(), auxBuff, dimsToExclude.data()); PRAGMA_OMP_SIMD - for (uint i = 0; i < steps; ++i) + for (Nd4jLong i = 0; i < steps; ++i) z[zOffsets[i]] = (x[xOffsets[i]] - meanVal) * sigmaInvGam + betaVal; } @@ -139,40 +139,42 @@ static void batchnorm2_(const NDArray* input, const NDArray* mean, const NDArray auto func = PRAGMA_THREADS_FOR { - Nd4jLong coords[MAX_RANK]; + int xzCoords[MAX_RANK], minCoords[MAX_RANK]; + + for (uint i = 0, j = 0; i < xRank; ++i) + if(j < numAxes && i != axes[j]) + minCoords[i] = 0; + else + ++j; for (auto i = start; i < stop; i++) { - shape::index2coords(i, input->getShapeInfo(), coords); + shape::index2coordsCPU(start, i, input->getShapeInfo(), xzCoords); - const auto xOffset = shape::getOffset(input->getShapeInfo(), coords); - const auto zOffset = xzSameOffset ? xOffset : shape::getOffset(output->getShapeInfo(), coords); + const auto xOffset = shape::getOffset(input->getShapeInfo(), xzCoords); + const auto zOffset = xzSameOffset ? xOffset : shape::getOffset(output->getShapeInfo(), xzCoords); if(minRank == xRank) { - for (uint i = 0, j = 0; i < xRank; ++i) { - if(j < numAxes && i != axes[j]) - coords[i] = 0; - else - ++j; - } + for (uint j = 0; j < numAxes; ++j) + minCoords[axes[j]] = xzCoords[axes[j]]; } else // minRank = numAxes = 1 in this case - coords[0] = coords[axes[0]]; + minCoords[0] = xzCoords[axes[0]]; - const auto meanOffset = shape::getOffset(mean->getShapeInfo(), coords); - const auto varianceOffset = paramSameOffset ? meanOffset : shape::getOffset(variance->getShapeInfo(), coords); + const auto meanOffset = shape::getOffset(mean->getShapeInfo(), minCoords); + const auto varianceOffset = paramSameOffset ? meanOffset : shape::getOffset(variance->getShapeInfo(), minCoords); - T sigmaInvGam = 1. / nd4j::math::nd4j_sqrt(v[varianceOffset] + epsilon); + T sigmaInvGam = 1. / sd::math::nd4j_sqrt(v[varianceOffset] + epsilon); if(g != nullptr) { - const auto gammaOffset = paramSameOffset ? meanOffset : shape::getOffset(gamma->getShapeInfo(), coords); + const auto gammaOffset = paramSameOffset ? meanOffset : shape::getOffset(gamma->getShapeInfo(), minCoords); sigmaInvGam *= g[gammaOffset]; } z[zOffset] = (x[xOffset] - m[meanOffset]) * sigmaInvGam; if(b != nullptr) { - const auto betaOffset = paramSameOffset ? meanOffset : shape::getOffset(beta->getShapeInfo(), coords); + const auto betaOffset = paramSameOffset ? meanOffset : shape::getOffset(beta->getShapeInfo(), minCoords); z[zOffset] += b[betaOffset]; } } @@ -184,7 +186,7 @@ static void batchnorm2_(const NDArray* input, const NDArray* mean, const NDArray ////////////////////////////////////////////////////////////////////////// void batchnorm(const NDArray* input, const NDArray* mean, const NDArray* variance, const NDArray* gamma, const NDArray* beta, NDArray* output, const std::vector& axes, const double epsilon) { - // batchnorm2_ is slower + // batchnorm2_ is still slower ? BUILD_SINGLE_SELECTOR(input->dataType(), batchnorm_, (input, mean, variance, gamma, beta, output, axes, epsilon), FLOAT_TYPES); } diff --git a/libnd4j/include/ops/declarable/helpers/cpu/betaInc.cpp b/libnd4j/include/ops/declarable/helpers/cpu/betaInc.cpp index 5e80d12fb..ec06610b8 100644 --- a/libnd4j/include/ops/declarable/helpers/cpu/betaInc.cpp +++ b/libnd4j/include/ops/declarable/helpers/cpu/betaInc.cpp @@ -19,12 +19,12 @@ // #include -#include -#include -#include +#include +#include +#include #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { @@ -112,7 +112,7 @@ static T betaIncCore(T a, T b, T x) { /////////////////////////////////////////////////////////////////// template -static void betaIncForArray(nd4j::LaunchContext * context, const NDArray& a, const NDArray& b, const NDArray& x, NDArray& output) { +static void betaIncForArray(sd::LaunchContext * context, const NDArray& a, const NDArray& b, const NDArray& x, NDArray& output) { int xLen = x.lengthOf(); @@ -126,12 +126,12 @@ static void betaIncForArray(nd4j::LaunchContext * context, const NDArray& a, con /////////////////////////////////////////////////////////////////// // overload betaInc for arrays, shapes of a, b and x must be the same !!! -void betaInc(nd4j::LaunchContext * context, const NDArray& a, const NDArray& b, const NDArray& x, NDArray& output) { +void betaInc(sd::LaunchContext * context, const NDArray& a, const NDArray& b, const NDArray& x, NDArray& output) { auto xType = a.dataType(); BUILD_SINGLE_SELECTOR(xType, betaIncForArray, (context, a, b, x, output), FLOAT_TYPES); } -BUILD_SINGLE_TEMPLATE(template void betaIncForArray, (nd4j::LaunchContext * context, const NDArray& a, const NDArray& b, const NDArray& x, NDArray& output), FLOAT_TYPES); +BUILD_SINGLE_TEMPLATE(template void betaIncForArray, (sd::LaunchContext * context, const NDArray& a, const NDArray& b, const NDArray& x, NDArray& output), FLOAT_TYPES); } diff --git a/libnd4j/include/ops/declarable/helpers/cpu/col2im.cpp b/libnd4j/include/ops/declarable/helpers/cpu/col2im.cpp index 26f82bdd9..db6d27ffd 100644 --- a/libnd4j/include/ops/declarable/helpers/cpu/col2im.cpp +++ b/libnd4j/include/ops/declarable/helpers/cpu/col2im.cpp @@ -21,13 +21,13 @@ #include #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { // [bS, iC, kH, kW, oH, oW] is de-convoluted to [bS, iC, iH, iW] template -void col2im_(nd4j::LaunchContext & context, const NDArray& input, NDArray& output, const int sH, const int sW, const int pH, const int pW, const int iH, const int iW, const int dH, const int dW) { +void col2im_(sd::LaunchContext & context, const NDArray& input, NDArray& output, const int sH, const int sW, const int pH, const int pW, const int iH, const int iW, const int dH, const int dW) { auto imBuff = output.bufferAsT(); auto colBuff = input.bufferAsT(); @@ -65,8 +65,8 @@ void col2im_(nd4j::LaunchContext & context, const NDArray& input, NDArray& outp T *col, *im; int imRow, imCol; - for (uint b = start_x; b < stop_x; b += inc_x) { - for (uint c = start_y; c < stop_y; c += inc_y) { + for (auto b = start_x; b < stop_x; b += inc_x) { + for (auto c = start_y; c < stop_y; c += inc_y) { for (int kRow = 0; kRow < kH; ++kRow) { for (int kCol = 0; kCol < kW; ++kCol) { for (int colH = 0; colH < oH; ++colH) { @@ -96,7 +96,7 @@ void col2im_(nd4j::LaunchContext & context, const NDArray& input, NDArray& outp auto func = PRAGMA_THREADS_FOR { T *col, *im; - for (uint b = start; b < stop; b++) { + for (auto b = start; b < stop; b++) { T *im0 = imBuff + b * imStride0; T *col4 = colBuff + b * colStride0; for (int colH = 0; colH < oH; ++colH, col4 += colStride4) { @@ -132,7 +132,7 @@ void col2im_(nd4j::LaunchContext & context, const NDArray& input, NDArray& outp } -void col2im(nd4j::LaunchContext & context, const NDArray& input, NDArray& output, const int sH, const int sW, const int pH, const int pW, const int iH, const int iW, const int dH, const int dW) { +void col2im(sd::LaunchContext & context, const NDArray& input, NDArray& output, const int sH, const int sW, const int pH, const int pW, const int iH, const int iW, const int dH, const int dW) { BUILD_SINGLE_SELECTOR(input.dataType(), col2im_, (context, input, output, sH, sW, pH, pW, iH, iW, dH, dW), FLOAT_TYPES); } diff --git a/libnd4j/include/ops/declarable/helpers/cpu/compare_elem.cpp b/libnd4j/include/ops/declarable/helpers/cpu/compare_elem.cpp index e5e51d38f..12961fe92 100644 --- a/libnd4j/include/ops/declarable/helpers/cpu/compare_elem.cpp +++ b/libnd4j/include/ops/declarable/helpers/cpu/compare_elem.cpp @@ -17,7 +17,7 @@ #include #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { template @@ -25,8 +25,8 @@ namespace nd4j { auto length = shape::length(input->getShapeInfo()); int elementsPerThread = length / ELEMENT_THRESHOLD; - int num_threads = nd4j::math::nd4j_max(1, elementsPerThread); - num_threads = nd4j::math::nd4j_min(num_threads, omp_get_max_threads()); + int num_threads = sd::math::nd4j_max(1, elementsPerThread); + num_threads = sd::math::nd4j_min(num_threads, omp_get_max_threads()); Nd4jLong sumt = 0; if(isStrictlyIncreasing) { @@ -62,7 +62,7 @@ namespace nd4j { } - void compare_elem(nd4j::LaunchContext * context, NDArray *input, bool isStrictlyIncreasing, bool& output) { + void compare_elem(sd::LaunchContext * context, NDArray *input, bool isStrictlyIncreasing, bool& output) { auto xType = input->dataType(); BUILD_SINGLE_SELECTOR(xType, _compare_elem, (input, isStrictlyIncreasing, output), LIBND4J_TYPES); diff --git a/libnd4j/include/ops/declarable/helpers/cpu/compilation_units/crop_and_resize_0.cpp b/libnd4j/include/ops/declarable/helpers/cpu/compilation_units/crop_and_resize_0.cpp index 3177cca34..94e74cd84 100644 --- a/libnd4j/include/ops/declarable/helpers/cpu/compilation_units/crop_and_resize_0.cpp +++ b/libnd4j/include/ops/declarable/helpers/cpu/compilation_units/crop_and_resize_0.cpp @@ -21,7 +21,7 @@ #include #include "../crop_and_resize.hpp" -namespace nd4j { +namespace sd { namespace ops { namespace helpers { BUILD_TRIPLE_TEMPLATE(template void cropAndResizeFunctor_, (NDArray const *images, NDArray const *boxes, NDArray const *indices, NDArray const *cropSize, int method, double extrapolationVal, NDArray *crops), NUMERIC_TYPES_0, FLOAT_TYPES, INTEGER_TYPES); diff --git a/libnd4j/include/ops/declarable/helpers/cpu/compilation_units/crop_and_resize_1.cpp b/libnd4j/include/ops/declarable/helpers/cpu/compilation_units/crop_and_resize_1.cpp index cd9c00dc5..9820c1392 100644 --- a/libnd4j/include/ops/declarable/helpers/cpu/compilation_units/crop_and_resize_1.cpp +++ b/libnd4j/include/ops/declarable/helpers/cpu/compilation_units/crop_and_resize_1.cpp @@ -21,7 +21,7 @@ #include #include "../crop_and_resize.hpp" -namespace nd4j { +namespace sd { namespace ops { namespace helpers { BUILD_TRIPLE_TEMPLATE(template void cropAndResizeFunctor_, (NDArray const *images, NDArray const *boxes, NDArray const *indices, NDArray const *cropSize, int method, double extrapolationVal, NDArray *crops), NUMERIC_TYPES_1, FLOAT_TYPES, INTEGER_TYPES); diff --git a/libnd4j/include/ops/declarable/helpers/cpu/compilation_units/crop_and_resize_2.cpp b/libnd4j/include/ops/declarable/helpers/cpu/compilation_units/crop_and_resize_2.cpp index 3b126d288..2a78f285f 100644 --- a/libnd4j/include/ops/declarable/helpers/cpu/compilation_units/crop_and_resize_2.cpp +++ b/libnd4j/include/ops/declarable/helpers/cpu/compilation_units/crop_and_resize_2.cpp @@ -21,7 +21,7 @@ #include #include "../crop_and_resize.hpp" -namespace nd4j { +namespace sd { namespace ops { namespace helpers { BUILD_TRIPLE_TEMPLATE(template void cropAndResizeFunctor_, (NDArray const *images, NDArray const *boxes, NDArray const *indices, NDArray const *cropSize, int method, double extrapolationVal, NDArray *crops), NUMERIC_TYPES_2, FLOAT_TYPES, INTEGER_TYPES); diff --git a/libnd4j/include/ops/declarable/helpers/cpu/compilation_units/crop_and_resize_3.cpp b/libnd4j/include/ops/declarable/helpers/cpu/compilation_units/crop_and_resize_3.cpp index cca97a1ac..13757997a 100644 --- a/libnd4j/include/ops/declarable/helpers/cpu/compilation_units/crop_and_resize_3.cpp +++ b/libnd4j/include/ops/declarable/helpers/cpu/compilation_units/crop_and_resize_3.cpp @@ -21,7 +21,7 @@ #include #include "../crop_and_resize.hpp" -namespace nd4j { +namespace sd { namespace ops { namespace helpers { BUILD_TRIPLE_TEMPLATE(template void cropAndResizeFunctor_, (NDArray const *images, NDArray const *boxes, NDArray const *indices, NDArray const *cropSize, int method, double extrapolationVal, NDArray *crops), NUMERIC_TYPES_3, FLOAT_TYPES, INTEGER_TYPES); diff --git a/libnd4j/include/ops/declarable/helpers/cpu/compilation_units/crop_and_resize_4.cpp b/libnd4j/include/ops/declarable/helpers/cpu/compilation_units/crop_and_resize_4.cpp index 568492c08..ea3043eeb 100644 --- a/libnd4j/include/ops/declarable/helpers/cpu/compilation_units/crop_and_resize_4.cpp +++ b/libnd4j/include/ops/declarable/helpers/cpu/compilation_units/crop_and_resize_4.cpp @@ -21,7 +21,7 @@ #include #include "../crop_and_resize.hpp" -namespace nd4j { +namespace sd { namespace ops { namespace helpers { BUILD_TRIPLE_TEMPLATE(template void cropAndResizeFunctor_, (NDArray const *images, NDArray const *boxes, NDArray const *indices, NDArray const *cropSize, int method, double extrapolationVal, NDArray *crops), NUMERIC_TYPES_4, FLOAT_TYPES, INTEGER_TYPES); diff --git a/libnd4j/include/ops/declarable/helpers/cpu/compilation_units/crop_and_resize_5.cpp b/libnd4j/include/ops/declarable/helpers/cpu/compilation_units/crop_and_resize_5.cpp index 1491c9e1d..60c1ae906 100644 --- a/libnd4j/include/ops/declarable/helpers/cpu/compilation_units/crop_and_resize_5.cpp +++ b/libnd4j/include/ops/declarable/helpers/cpu/compilation_units/crop_and_resize_5.cpp @@ -21,7 +21,7 @@ #include #include "../crop_and_resize.hpp" -namespace nd4j { +namespace sd { namespace ops { namespace helpers { BUILD_TRIPLE_TEMPLATE(template void cropAndResizeFunctor_, (NDArray const *images, NDArray const *boxes, NDArray const *indices, NDArray const *cropSize, int method, double extrapolationVal, NDArray *crops), NUMERIC_TYPES_5, FLOAT_TYPES, INTEGER_TYPES); diff --git a/libnd4j/include/ops/declarable/helpers/cpu/compilation_units/crop_and_resize_6.cpp b/libnd4j/include/ops/declarable/helpers/cpu/compilation_units/crop_and_resize_6.cpp index 8517a39e9..6e33d5546 100644 --- a/libnd4j/include/ops/declarable/helpers/cpu/compilation_units/crop_and_resize_6.cpp +++ b/libnd4j/include/ops/declarable/helpers/cpu/compilation_units/crop_and_resize_6.cpp @@ -21,7 +21,7 @@ #include #include "../crop_and_resize.hpp" -namespace nd4j { +namespace sd { namespace ops { namespace helpers { BUILD_TRIPLE_TEMPLATE(template void cropAndResizeFunctor_, (NDArray const *images, NDArray const *boxes, NDArray const *indices, NDArray const *cropSize, int method, double extrapolationVal, NDArray *crops), NUMERIC_TYPES_6, FLOAT_TYPES, INTEGER_TYPES); diff --git a/libnd4j/include/ops/declarable/helpers/cpu/compilation_units/crop_and_resize_7.cpp b/libnd4j/include/ops/declarable/helpers/cpu/compilation_units/crop_and_resize_7.cpp index e12190170..ef4a199fd 100644 --- a/libnd4j/include/ops/declarable/helpers/cpu/compilation_units/crop_and_resize_7.cpp +++ b/libnd4j/include/ops/declarable/helpers/cpu/compilation_units/crop_and_resize_7.cpp @@ -21,7 +21,7 @@ #include #include "../crop_and_resize.hpp" -namespace nd4j { +namespace sd { namespace ops { namespace helpers { BUILD_TRIPLE_TEMPLATE(template void cropAndResizeFunctor_, (NDArray const *images, NDArray const *boxes, NDArray const *indices, NDArray const *cropSize, int method, double extrapolationVal, NDArray *crops), NUMERIC_TYPES_7, FLOAT_TYPES, INTEGER_TYPES); diff --git a/libnd4j/include/ops/declarable/helpers/cpu/compilation_units/crop_and_resize_8.cpp b/libnd4j/include/ops/declarable/helpers/cpu/compilation_units/crop_and_resize_8.cpp index c4ddd7066..71cd2ebb8 100644 --- a/libnd4j/include/ops/declarable/helpers/cpu/compilation_units/crop_and_resize_8.cpp +++ b/libnd4j/include/ops/declarable/helpers/cpu/compilation_units/crop_and_resize_8.cpp @@ -21,7 +21,7 @@ #include #include "../crop_and_resize.hpp" -namespace nd4j { +namespace sd { namespace ops { namespace helpers { BUILD_TRIPLE_TEMPLATE(template void cropAndResizeFunctor_, (NDArray const *images, NDArray const *boxes, NDArray const *indices, NDArray const *cropSize, int method, double extrapolationVal, NDArray *crops), NUMERIC_TYPES_8, FLOAT_TYPES, INTEGER_TYPES); diff --git a/libnd4j/include/ops/declarable/helpers/cpu/compilation_units/crop_and_resize_9.cpp b/libnd4j/include/ops/declarable/helpers/cpu/compilation_units/crop_and_resize_9.cpp index 38cf05787..e9db5c303 100644 --- a/libnd4j/include/ops/declarable/helpers/cpu/compilation_units/crop_and_resize_9.cpp +++ b/libnd4j/include/ops/declarable/helpers/cpu/compilation_units/crop_and_resize_9.cpp @@ -21,7 +21,7 @@ #include #include "../crop_and_resize.hpp" -namespace nd4j { +namespace sd { namespace ops { namespace helpers { BUILD_TRIPLE_TEMPLATE(template void cropAndResizeFunctor_, (NDArray const *images, NDArray const *boxes, NDArray const *indices, NDArray const *cropSize, int method, double extrapolationVal, NDArray *crops), NUMERIC_TYPES_9, FLOAT_TYPES, INTEGER_TYPES); diff --git a/libnd4j/include/ops/declarable/helpers/cpu/concat.cpp b/libnd4j/include/ops/declarable/helpers/cpu/concat.cpp index 1bdf0a6ad..9fd2b5b02 100644 --- a/libnd4j/include/ops/declarable/helpers/cpu/concat.cpp +++ b/libnd4j/include/ops/declarable/helpers/cpu/concat.cpp @@ -22,20 +22,20 @@ #include #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { ////////////////////////////////////////////////////////////////////////// template - static void concat_(const std::vector& inArrs, NDArray& output, const int axis) { - nd4j::SpecialMethods::concatCpuGeneric(inArrs, output, axis); + static void concat_(const std::vector& inArrs, NDArray& output, const int axis) { + sd::SpecialMethods::concatCpuGeneric(inArrs, output, axis); } - void concat(nd4j::LaunchContext * context, const std::vector& inArrs, NDArray& output, const int axis) { + void concat(sd::LaunchContext * context, const std::vector& inArrs, NDArray& output, const int axis) { BUILD_SINGLE_SELECTOR(output.dataType(), concat_,(inArrs, output, axis), LIBND4J_TYPES); } - BUILD_SINGLE_TEMPLATE(template void concat_, (const std::vector& inArrs, NDArray& output, const int axis), LIBND4J_TYPES); + BUILD_SINGLE_TEMPLATE(template void concat_, (const std::vector& inArrs, NDArray& output, const int axis), LIBND4J_TYPES); } } } \ No newline at end of file diff --git a/libnd4j/include/ops/declarable/helpers/cpu/confusion.cpp b/libnd4j/include/ops/declarable/helpers/cpu/confusion.cpp index 39449c7f8..685d80d2d 100644 --- a/libnd4j/include/ops/declarable/helpers/cpu/confusion.cpp +++ b/libnd4j/include/ops/declarable/helpers/cpu/confusion.cpp @@ -22,7 +22,7 @@ #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { @@ -43,7 +43,7 @@ namespace helpers { samediff::Threads::parallel_for(func, 0, lLen); } - void confusionFunctor(nd4j::LaunchContext * context, NDArray* labels, NDArray* predictions, NDArray* weights, NDArray* output) { + void confusionFunctor(sd::LaunchContext * context, NDArray* labels, NDArray* predictions, NDArray* weights, NDArray* output) { auto xType = output->dataType(); // weights can be null BUILD_SINGLE_SELECTOR(xType, _confusionFunctor, (labels, predictions, weights, output), NUMERIC_TYPES); diff --git a/libnd4j/include/ops/declarable/helpers/cpu/convolutions.cpp b/libnd4j/include/ops/declarable/helpers/cpu/convolutions.cpp index 51ddc0369..f852bed23 100644 --- a/libnd4j/include/ops/declarable/helpers/cpu/convolutions.cpp +++ b/libnd4j/include/ops/declarable/helpers/cpu/convolutions.cpp @@ -22,11 +22,11 @@ #include #include #include -#include -#include +#include +#include #include -namespace nd4j { +namespace sd { namespace ops { @@ -258,7 +258,7 @@ namespace nd4j { ////////////////////////////////////////////////////////////////////////// template - static void conv2d_(nd4j::graph::Context& block, const NDArray* input, const NDArray* weights, const NDArray* bias, NDArray* output, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int paddingMode, const int isNCHW) { + static void conv2d_(sd::graph::Context& block, const NDArray* input, const NDArray* weights, const NDArray* bias, NDArray* output, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int paddingMode, const int isNCHW) { // input [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW) // weights [kH, kW, iC, oC] always @@ -319,7 +319,7 @@ namespace nd4j { ////////////////////////////////////////////////////////////////////////// template - static void conv2dBP_(nd4j::graph::Context& block, const NDArray* input, const NDArray* weights, const NDArray* bias, const NDArray* gradO, NDArray* gradI, NDArray* gradW, NDArray* gradB, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int paddingMode, const int isNCHW) { + static void conv2dBP_(sd::graph::Context& block, const NDArray* input, const NDArray* weights, const NDArray* bias, const NDArray* gradO, NDArray* gradI, NDArray* gradW, NDArray* gradB, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int paddingMode, const int isNCHW) { // input [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW) // weights [kH, kW, iC, oC] always @@ -365,7 +365,7 @@ namespace nd4j { if(gradW) { auto ctx = block.launchContext(); helpers::im2col(*ctx, *input, columns, kH, kW, sH, sW, pH, pW, dH, dW, NDArrayFactory::create(0.f, input->getContext())); // [bS, iC, iH, iW] is convoluted to [bS, iC, kH, kW, oH, oW] - nd4j::MmulHelper::tensorDot(&columns, gradO, gradW, {0,4,5}, gradOaxesForDot, {2, 0, 1, 3}); // [bS, iC, kH, kW, oH, oW] x [bS, oH, oW, oC]/[bS, oC, oH, oW] = [iC, kH, kW, oC] + sd::MmulHelper::tensorDot(&columns, gradO, gradW, {0,4,5}, gradOaxesForDot, {2, 0, 1, 3}); // [bS, iC, kH, kW, oH, oW] x [bS, oH, oW, oC]/[bS, oC, oH, oW] = [iC, kH, kW, oC] } // ----- calculation of gradB ----- // @@ -379,7 +379,7 @@ namespace nd4j { } //----- calculation of gradI -----// - nd4j::MmulHelper::tensorDot(weights, gradO, &columns, {indWoC}, {indIOioC}, {2, 3, 1, 0, 4, 5}); // [kH, kW, iC, oC]/[oC, iC, kH, kW]] x [bS, oH, oW, oC]/[bS, oC, oH, oW] = [kH, kW, iC, bS, oH, oW] + sd::MmulHelper::tensorDot(weights, gradO, &columns, {indWoC}, {indIOioC}, {2, 3, 1, 0, 4, 5}); // [kH, kW, iC, oC]/[oC, iC, kH, kW]] x [bS, oH, oW, oC]/[bS, oC, oH, oW] = [kH, kW, iC, bS, oH, oW] helpers::col2im(*block.launchContext(), columns, *gradI, sH, sW, pH, pW, iH, iW, dH, dW); // [bS, iC, kH, kW, oH, oW] is de-convoluted to [bS, iC, iH, iW] @@ -391,7 +391,7 @@ namespace nd4j { ////////////////////////////////////////////////////////////////////////// template - static void depthwiseConv2d_(nd4j::graph::Context& block, const NDArray* input, const NDArray* weights, const NDArray* bias, NDArray* output, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int paddingMode, const int isNCHW) { + static void depthwiseConv2d_(sd::graph::Context& block, const NDArray* input, const NDArray* weights, const NDArray* bias, NDArray* output, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int paddingMode, const int isNCHW) { // input [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW) // weights [kH, kW, iC, mC] always @@ -499,7 +499,7 @@ namespace nd4j { // ----- calculation of gradW and gradB ----- // helpers::im2col(*input->getContext(), *input, columns, kH, kW, sH, sW, pH, pW, dH, dW, NDArrayFactory::create(0.f, input->getContext())); // [bS, iC, iH, iW] is convoluted to [bS, iC, kH, kW, oH, oW] - nd4j::MmulHelper::tensorDot(&columns, &gradOreshaped, gradW, modifColumns, modifGradO1, {{2,0,1,3},{iC,kH*kW,mC}}); // [iC, kW*kH, bS*oH*oW] x [iC, bS*oH*oW, mC] = [iC, kH*kW, mC] + sd::MmulHelper::tensorDot(&columns, &gradOreshaped, gradW, modifColumns, modifGradO1, {{2,0,1,3},{iC,kH*kW,mC}}); // [iC, kW*kH, bS*oH*oW] x [iC, bS*oH*oW, mC] = [iC, kH*kW, mC] // ----- calculation of gradB ----- // if(gradB) { @@ -513,7 +513,7 @@ namespace nd4j { } //----- calculation of gradI -----// - nd4j::MmulHelper::tensorDot(weights, gradO, &columns, {{2,0,1,3},{iC,kH*kW,mC}}, modifGradO2, modifColumns); // [iC, kH*kW, mC] x [iC, mC, bS*oH*oW] = [iC, kW*kH, bS*oH*oW] + sd::MmulHelper::tensorDot(weights, gradO, &columns, {{2,0,1,3},{iC,kH*kW,mC}}, modifGradO2, modifColumns); // [iC, kH*kW, mC] x [iC, mC, bS*oH*oW] = [iC, kW*kH, bS*oH*oW] helpers::col2im(*input->getContext(), columns, *gradI, sH, sW, pH, pW, iH, iW, dH, dW); // [bS, iC, kH, kW, oH, oW] is de-convoluted to [bS, iC, iH, iW] if(!isNCHW) { @@ -524,7 +524,7 @@ namespace nd4j { ////////////////////////////////////////////////////////////////////////// template - static void sconv2d_(nd4j::graph::Context& block, const NDArray* input, const NDArray* weightsDepth, const NDArray* weightsPoint, const NDArray* bias, NDArray* output, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int paddingMode, const int isNCHW) { + static void sconv2d_(sd::graph::Context& block, const NDArray* input, const NDArray* weightsDepth, const NDArray* weightsPoint, const NDArray* bias, NDArray* output, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int paddingMode, const int isNCHW) { // input [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW) // weightsDepth [kH, kW, iC, mC] always @@ -782,7 +782,7 @@ namespace nd4j { ////////////////////////////////////////////////////////////////////////// template - static void pooling2d_(nd4j::graph::Context& block, const NDArray& input, NDArray& output, const int kH, const int kW, const int sH, const int sW, const int pH, const int pW, const int dH, const int dW, const int poolingMode, const int extraParam0) { + static void pooling2d_(sd::graph::Context& block, const NDArray& input, NDArray& output, const int kH, const int kW, const int sH, const int sW, const int pH, const int pW, const int dH, const int dW, const int poolingMode, const int extraParam0) { // input is [bS, iC, iH, iW] // output is [bS, iC, oH, oW] T* out = output.bufferAsT(); @@ -832,13 +832,13 @@ namespace nd4j { wend = wstart + kWEff; if (hstart < 0) - hstart += dH * ((-hstart + dH - 1) / dH); // (Nd4jLong)nd4j::math::nd4j_ceil(static_cast(-hstart) / static_cast(dH)); + hstart += dH * ((-hstart + dH - 1) / dH); // (Nd4jLong)sd::math::nd4j_ceil(static_cast(-hstart) / static_cast(dH)); if (wstart < 0) - wstart += dW * ((-wstart + dW - 1) / dW); //(Nd4jLong)nd4j::math::nd4j_ceil(static_cast(-wstart) / static_cast(dW)); + wstart += dW * ((-wstart + dW - 1) / dW); //(Nd4jLong)sd::math::nd4j_ceil(static_cast(-wstart) / static_cast(dW)); if (hend > iH) - hend -= dH * ((hend - iH + dH - 1) / dH); //(Nd4jLong)nd4j::math::nd4j_ceil(static_cast(hend-iH) / static_cast(dH)); + hend -= dH * ((hend - iH + dH - 1) / dH); //(Nd4jLong)sd::math::nd4j_ceil(static_cast(hend-iH) / static_cast(dH)); if (wend > iW) - wend -= dW * ((wend - iW + dW - 1) / dW); //(Nd4jLong)nd4j::math::nd4j_ceil(static_cast(wend-iW) / static_cast(dW)); + wend -= dW * ((wend - iW + dW - 1) / dW); //(Nd4jLong)sd::math::nd4j_ceil(static_cast(wend-iW) / static_cast(dW)); hstart *= iStride2; hend *= iStride2; @@ -881,13 +881,13 @@ namespace nd4j { wend = wstart + kWEff; if (hstart < 0) - hstart += dH * ((-hstart + dH - 1) / dH); // (Nd4jLong)nd4j::math::nd4j_ceil(static_cast(-hstart) / static_cast(dH)); + hstart += dH * ((-hstart + dH - 1) / dH); // (Nd4jLong)sd::math::nd4j_ceil(static_cast(-hstart) / static_cast(dH)); if (wstart < 0) - wstart += dW * ((-wstart + dW - 1) / dW); //(Nd4jLong)nd4j::math::nd4j_ceil(static_cast(-wstart) / static_cast(dW)); + wstart += dW * ((-wstart + dW - 1) / dW); //(Nd4jLong)sd::math::nd4j_ceil(static_cast(-wstart) / static_cast(dW)); if (hend > iH) - hend -= dH * ((hend - iH + dH - 1) / dH); //(Nd4jLong)nd4j::math::nd4j_ceil(static_cast(hend-iH) / static_cast(dH)); + hend -= dH * ((hend - iH + dH - 1) / dH); //(Nd4jLong)sd::math::nd4j_ceil(static_cast(hend-iH) / static_cast(dH)); if (wend > iW) - wend -= dW * ((wend - iW + dW - 1) / dW); //(Nd4jLong)nd4j::math::nd4j_ceil(static_cast(wend-iW) / static_cast(dW)); + wend -= dW * ((wend - iW + dW - 1) / dW); //(Nd4jLong)sd::math::nd4j_ceil(static_cast(wend-iW) / static_cast(dW)); hstart *= iStride2; hend *= iStride2; @@ -935,13 +935,13 @@ namespace nd4j { wend = wstart + kWEff; if (hstart < 0) - hstart += dH * ((-hstart + dH - 1) / dH); // (Nd4jLong)nd4j::math::nd4j_ceil(static_cast(-hstart) / static_cast(dH)); + hstart += dH * ((-hstart + dH - 1) / dH); // (Nd4jLong)sd::math::nd4j_ceil(static_cast(-hstart) / static_cast(dH)); if (wstart < 0) - wstart += dW * ((-wstart + dW - 1) / dW); //(Nd4jLong)nd4j::math::nd4j_ceil(static_cast(-wstart) / static_cast(dW)); + wstart += dW * ((-wstart + dW - 1) / dW); //(Nd4jLong)sd::math::nd4j_ceil(static_cast(-wstart) / static_cast(dW)); if (hend > iH) - hend -= dH * ((hend - iH + dH - 1) / dH); //(Nd4jLong)nd4j::math::nd4j_ceil(static_cast(hend-iH) / static_cast(dH)); + hend -= dH * ((hend - iH + dH - 1) / dH); //(Nd4jLong)sd::math::nd4j_ceil(static_cast(hend-iH) / static_cast(dH)); if (wend > iW) - wend -= dW * ((wend - iW + dW - 1) / dW); //(Nd4jLong)nd4j::math::nd4j_ceil(static_cast(wend-iW) / static_cast(dW)); + wend -= dW * ((wend - iW + dW - 1) / dW); //(Nd4jLong)sd::math::nd4j_ceil(static_cast(wend-iW) / static_cast(dW)); hstart *= iStride2; hend *= iStride2; @@ -952,9 +952,9 @@ namespace nd4j { for (Nd4jLong kh = hstart; kh < hend; kh += iStep2) for (Nd4jLong kw = wstart; kw < wend; kw += iStep3) - sum += nd4j::math::nd4j_pow(nd4j::math::nd4j_abs(pIn[kh + kw]), extraParam0); + sum += sd::math::nd4j_pow(sd::math::nd4j_abs(pIn[kh + kw]), extraParam0); - sum = nd4j::math::nd4j_pow(sum, static_cast((T) 1.f) / extraParam0); + sum = sd::math::nd4j_pow(sum, static_cast((T) 1.f) / extraParam0); out[b * oStride0 + c * oStride1 + oh * oStride2 + ow * oStride3] = sum; } @@ -973,7 +973,7 @@ namespace nd4j { ////////////////////////////////////////////////////////////////////////// template - static void pooling3d_(nd4j::graph::Context& block, const NDArray& input, NDArray& output, const int kD, const int kH, const int kW, const int sD, const int sH, const int sW, const int pD, const int pH, const int pW, const int dD, const int dH, const int dW, const int poolingMode, const int extraParam0) { + static void pooling3d_(sd::graph::Context& block, const NDArray& input, NDArray& output, const int kD, const int kH, const int kW, const int sD, const int sH, const int sW, const int pD, const int pH, const int pW, const int dD, const int dH, const int dW, const int poolingMode, const int extraParam0) { // input is [bS, iC, iD, iH, iW] // output is [bS, iC, oD, oH, oW] T* out = output.bufferAsT(); @@ -1119,7 +1119,7 @@ namespace nd4j { sum += pIn[kd + kh + kw]; if (extraParam0 == 0) //Exclude padding - sum /= nd4j::math::nd4j_ceil(static_cast(dend - dstart) / static_cast(iStep2)) * nd4j::math::nd4j_ceil(static_cast(hend - hstart) / static_cast(iStep3)) * nd4j::math::nd4j_ceil(static_cast(wend - wstart) / static_cast(iStep4)); //Accounts for dilation + sum /= sd::math::nd4j_ceil(static_cast(dend - dstart) / static_cast(iStep2)) * sd::math::nd4j_ceil(static_cast(hend - hstart) / static_cast(iStep3)) * sd::math::nd4j_ceil(static_cast(wend - wstart) / static_cast(iStep4)); //Accounts for dilation else if (extraParam0 == 1) //Include padding sum /= kProd; @@ -1179,9 +1179,9 @@ namespace nd4j { for (Nd4jLong kd = dstart; kd < dend; kd += iStep2) for (Nd4jLong kh = hstart; kh < hend; kh += iStep3) for (Nd4jLong kw = wstart; kw < wend; kw += iStep4) - sum += nd4j::math::nd4j_pow(nd4j::math::nd4j_abs(pIn[kd + kh + kw]), extraParam0); + sum += sd::math::nd4j_pow(sd::math::nd4j_abs(pIn[kd + kh + kw]), extraParam0); - sum = nd4j::math::nd4j_pow(sum, (T) 1.f / extraParam0); + sum = sd::math::nd4j_pow(sum, (T) 1.f / extraParam0); out[b * oStride0 + c * oStride1 + od * oStride2 + oh * oStride3 + ow * oStride4] = sum; } @@ -1202,7 +1202,7 @@ namespace nd4j { ////////////////////////////////////////////////////////////////////////// template - static void pooling2dBP_(nd4j::graph::Context& block, const NDArray& input, const NDArray& gradO, NDArray& gradI, const int kH, const int kW, const int sH, const int sW, const int pH, const int pW, const int dH, const int dW, const int poolingMode, const int extraParam0) { + static void pooling2dBP_(sd::graph::Context& block, const NDArray& input, const NDArray& gradO, NDArray& gradI, const int kH, const int kW, const int sH, const int sW, const int pH, const int pW, const int dH, const int dW, const int poolingMode, const int extraParam0) { // input [bS, iC, iH, iW] // gradI [bS, iC, iH, iW] -> gradI is output in this function // gradO [bS, iC, oH, oW] @@ -1265,13 +1265,13 @@ namespace nd4j { wend = wstart + kWEff; if (hstart < 0) - hstart += dH * ((-hstart + dH - 1) / dH); // (Nd4jLong)nd4j::math::nd4j_ceil(static_cast(-hstart) / static_cast(dH)); + hstart += dH * ((-hstart + dH - 1) / dH); // (Nd4jLong)sd::math::nd4j_ceil(static_cast(-hstart) / static_cast(dH)); if (wstart < 0) - wstart += dW * ((-wstart + dW - 1) / dW); //(Nd4jLong)nd4j::math::nd4j_ceil(static_cast(-wstart) / static_cast(dW)); + wstart += dW * ((-wstart + dW - 1) / dW); //(Nd4jLong)sd::math::nd4j_ceil(static_cast(-wstart) / static_cast(dW)); if (hend > iH) - hend -= dH * ((hend - iH + dH - 1) / dH); //(Nd4jLong)nd4j::math::nd4j_ceil(static_cast(hend-iH) / static_cast(dH)); + hend -= dH * ((hend - iH + dH - 1) / dH); //(Nd4jLong)sd::math::nd4j_ceil(static_cast(hend-iH) / static_cast(dH)); if (wend > iW) - wend -= dW * ((wend - iW + dW - 1) / dW); //(Nd4jLong)nd4j::math::nd4j_ceil(static_cast(wend-iW) / static_cast(dW)); + wend -= dW * ((wend - iW + dW - 1) / dW); //(Nd4jLong)sd::math::nd4j_ceil(static_cast(wend-iW) / static_cast(dW)); sum = -DataTypeUtils::max(); valO = gO[b * oStride0 + c * oStride1 + oh * oStride2 + ow * oStride3]; @@ -1343,16 +1343,16 @@ namespace nd4j { if (hstart < 0) hstart += dH * ((-hstart + dH - 1) / - dH); // (Nd4jLong)nd4j::math::nd4j_ceil(static_cast(-hstart) / static_cast(dH)); + dH); // (Nd4jLong)sd::math::nd4j_ceil(static_cast(-hstart) / static_cast(dH)); if (wstart < 0) wstart += dW * ((-wstart + dW - 1) / - dW); //(Nd4jLong)nd4j::math::nd4j_ceil(static_cast(-wstart) / static_cast(dW)); + dW); //(Nd4jLong)sd::math::nd4j_ceil(static_cast(-wstart) / static_cast(dW)); if (hend > iH) hend -= dH * ((hend - iH + dH - 1) / - dH); //(Nd4jLong)nd4j::math::nd4j_ceil(static_cast(hend-iH) / static_cast(dH)); + dH); //(Nd4jLong)sd::math::nd4j_ceil(static_cast(hend-iH) / static_cast(dH)); if (wend > iW) wend -= dW * ((wend - iW + dW - 1) / - dW); //(Nd4jLong)nd4j::math::nd4j_ceil(static_cast(wend-iW) / static_cast(dW)); + dW); //(Nd4jLong)sd::math::nd4j_ceil(static_cast(wend-iW) / static_cast(dW)); hstart *= gIStride2; hend *= gIStride2; @@ -1362,9 +1362,9 @@ namespace nd4j { valO = gO[b * oStride0 + c * oStride1 + oh * oStride2 + ow * oStride3]; if ((int) extraParam0 == 0) //Exclude padding - valO /= static_cast(nd4j::math::nd4j_ceil( + valO /= static_cast(sd::math::nd4j_ceil( static_cast(hend - hstart) / static_cast(gIStep2))) * - static_cast(nd4j::math::nd4j_ceil( + static_cast(sd::math::nd4j_ceil( static_cast(wend - wstart) / static_cast(gIStep3))); //Accounts for dilation else if ((int) extraParam0 == 1) //Include padding @@ -1402,16 +1402,16 @@ namespace nd4j { if (hstart < 0) hstart += dH * ((-hstart + dH - 1) / - dH); // (Nd4jLong)nd4j::math::nd4j_ceil(static_cast(-hstart) / static_cast(dH)); + dH); // (Nd4jLong)sd::math::nd4j_ceil(static_cast(-hstart) / static_cast(dH)); if (wstart < 0) wstart += dW * ((-wstart + dW - 1) / - dW); //(Nd4jLong)nd4j::math::nd4j_ceil(static_cast(-wstart) / static_cast(dW)); + dW); //(Nd4jLong)sd::math::nd4j_ceil(static_cast(-wstart) / static_cast(dW)); if (hend > iH) hend -= dH * ((hend - iH + dH - 1) / - dH); //(Nd4jLong)nd4j::math::nd4j_ceil(static_cast(hend-iH) / static_cast(dH)); + dH); //(Nd4jLong)sd::math::nd4j_ceil(static_cast(hend-iH) / static_cast(dH)); if (wend > iW) wend -= dW * ((wend - iW + dW - 1) / - dW); //(Nd4jLong)nd4j::math::nd4j_ceil(static_cast(wend-iW) / static_cast(dW)); + dW); //(Nd4jLong)sd::math::nd4j_ceil(static_cast(wend-iW) / static_cast(dW)); sum = static_cast(0.f); valO = gO[b * oStride0 + c * oStride1 + oh * oStride2 + ow * oStride3]; @@ -1425,37 +1425,37 @@ namespace nd4j { for (Nd4jLong kh = hstart; kh < hend; kh += iStep2) for (Nd4jLong kw = wstart; kw < wend; kw += iStep3) - sum += nd4j::math::nd4j_pow( - nd4j::math::nd4j_abs(pIn[kh + kw]), extraParam0); + sum += sd::math::nd4j_pow( + sd::math::nd4j_abs(pIn[kh + kw]), extraParam0); - valO *= nd4j::math::nd4j_pow(sum, + valO *= sd::math::nd4j_pow(sum, ((T) 1. - extraParam0) / extraParam0); for (Nd4jLong kh = hstart; kh < hend; kh += iStep2) for (Nd4jLong kw = wstart; kw < wend; kw += iStep3) - pgI[kh + kw] += valO * nd4j::math::nd4j_pow( - nd4j::math::nd4j_abs(pIn[kh + kw]), extraParam0 - 1.f) * - nd4j::math::nd4j_sgn(pIn[kh + kw]); + pgI[kh + kw] += valO * sd::math::nd4j_pow( + sd::math::nd4j_abs(pIn[kh + kw]), extraParam0 - 1.f) * + sd::math::nd4j_sgn(pIn[kh + kw]); } else { for (Nd4jLong kh = hstart; kh < hend; kh += dH) for (Nd4jLong kw = wstart; kw < wend; kw += dW) - sum += nd4j::math::nd4j_pow( - nd4j::math::nd4j_abs(pIn[kh * iStride2 + kw * iStride3]), + sum += sd::math::nd4j_pow( + sd::math::nd4j_abs(pIn[kh * iStride2 + kw * iStride3]), extraParam0); - valO *= nd4j::math::nd4j_pow(sum, + valO *= sd::math::nd4j_pow(sum, ((T) 1. - extraParam0) / extraParam0); for (Nd4jLong kh = hstart; kh < hend; kh += dH) { for (Nd4jLong kw = wstart; kw < wend; kw += dW) { const auto inVal = pIn[kh * iStride2 + kw * iStride3]; pgI[kh * gIStride2 + kw * gIStride3] += valO * - nd4j::math::nd4j_pow( - nd4j::math::nd4j_abs( + sd::math::nd4j_pow( + sd::math::nd4j_abs( inVal), extraParam0 - 1.f) * - nd4j::math::nd4j_sgn( + sd::math::nd4j_sgn( inVal); } } @@ -1476,7 +1476,7 @@ namespace nd4j { ////////////////////////////////////////////////////////////////////////// template - static void pooling3dBP_(nd4j::graph::Context& block, const NDArray& input, const NDArray& gradO, NDArray& gradI, const int kD, const int kH, const int kW, const int sD, const int sH, const int sW, const int pD, const int pH, const int pW, const int dD, const int dH, const int dW, const int poolingMode, const int extraParam0) { + static void pooling3dBP_(sd::graph::Context& block, const NDArray& input, const NDArray& gradO, NDArray& gradI, const int kD, const int kH, const int kW, const int sD, const int sH, const int sW, const int pD, const int pH, const int pW, const int dD, const int dH, const int dW, const int poolingMode, const int extraParam0) { // input [bS, iC, iD, iH, iW] // gradI [bS, iC, iD, iH, iW] -> gradI is output in this function // gradO [bS, iC, oD, oH, oW] @@ -1530,13 +1530,13 @@ namespace nd4j { const bool sameStrides = iStride0 == gIStride0 && iStride1 == gIStride1 && iStride2 == gIStride2 && iStride3 == gIStride3 && iStride4 == gIStride4; if(poolingMode == 0) { // max - auto func = PRAGMA_THREADS_FOR_3D { + auto func = PRAGMA_THREADS_FOR_2D { Nd4jLong dstart, hstart, wstart, dend, hend, wend, maxKD, maxKH, maxKW; T sum, valO, *pIn, *pgI; - for (int b = start_x; b < stop_x; b += inc_x) { - for (int c = start_y; c < stop_y; c += inc_y) { - for (int od = start_z; od < stop_z; od += inc_z) { + for (int b = start_x; b < stop_x; b++) { + for (int c = start_y; c < stop_y; c++) { + for (int od = 0; od < oD; od++) { for (int oh = 0; oh < oH; ++oh) { for (int ow = 0; ow < oW; ++ow) { @@ -1618,17 +1618,17 @@ namespace nd4j { } }; - samediff::Threads::parallel_for(func, 0, bS, 1, 0, iC, 1, 0, oD, 1); + samediff::Threads::parallel_for(func, 0, bS, 1, 0, iC, 1); } /*************************************************************************/ else if(poolingMode == 1) { // avg - auto func = PRAGMA_THREADS_FOR_3D { + auto func = PRAGMA_THREADS_FOR_2D { Nd4jLong dstart, hstart, wstart, dend, hend, wend, maxKD, maxKH, maxKW; T sum, valO, *pIn, *pgI; - for (int b = start_x; b < stop_x; b += inc_x) { - for (int c = start_y; c < stop_y; c += inc_y) { - for (int od = start_z; od < stop_z; od += inc_z) { + for (int b = start_x; b < stop_x; b++) { + for (int c = start_y; c < stop_y; c++) { + for (int od = 0; od < oD; od++) { for (int oh = 0; oh < oH; ++oh) { for (int ow = 0; ow < oW; ++ow) { @@ -1664,7 +1664,7 @@ namespace nd4j { valO = gO[b * oStride0 + c * oStride1 + od * oStride2 + oh * oStride3 + ow * oStride4]; if (extraParam0 == 0) //Exclude padding - valO /= nd4j::math::nd4j_ceil(static_cast(dend - dstart) / static_cast(gIStep2)) * nd4j::math::nd4j_ceil(static_cast(hend - hstart) / static_cast(gIStep3)) * nd4j::math::nd4j_ceil(static_cast(wend - wstart) / static_cast(gIStep4)); //Accounts for dilation + valO /= sd::math::nd4j_ceil(static_cast(dend - dstart) / static_cast(gIStep2)) * sd::math::nd4j_ceil(static_cast(hend - hstart) / static_cast(gIStep3)) * sd::math::nd4j_ceil(static_cast(wend - wstart) / static_cast(gIStep4)); //Accounts for dilation else if (extraParam0 == 1) //Include padding valO /= kProd; @@ -1679,17 +1679,17 @@ namespace nd4j { } }; - samediff::Threads::parallel_for(func, 0, bS, 1, 0, iC, 1, 0, oD, 1); + samediff::Threads::parallel_for(func, 0, bS, 1, 0, iC, 1); } /*************************************************************************/ else if(poolingMode == 2) { // pnorm - auto func = PRAGMA_THREADS_FOR_3D { + auto func = PRAGMA_THREADS_FOR_2D { Nd4jLong dstart, hstart, wstart, dend, hend, wend, maxKD, maxKH, maxKW; T sum, valO, *pIn, *pgI; - for (int b = start_x; b < stop_x; b += inc_x) { - for (int c = start_y; c < stop_y; c += inc_y) { - for (int od = start_z; od < stop_z; od += inc_z) { + for (int b = start_x; b < stop_x; b++) { + for (int c = start_y; c < stop_y; c++) { + for (int od = 0; od < oD; od++) { for (int oh = 0; oh < oH; ++oh) { for (int ow = 0; ow < oW; ++ow) { @@ -1731,27 +1731,27 @@ namespace nd4j { for (Nd4jLong kd = dstart; kd < dend; kd += iStep2) for (Nd4jLong kh = hstart; kh < hend; kh += iStep3) for (Nd4jLong kw = wstart; kw < wend; kw += iStep4) - sum += nd4j::math::nd4j_pow(nd4j::math::nd4j_abs(pIn[kd + kh + kw]), extraParam0); + sum += sd::math::nd4j_pow(sd::math::nd4j_abs(pIn[kd + kh + kw]), extraParam0); - valO *= nd4j::math::nd4j_pow(sum, ((T) 1.f - extraParam0) / extraParam0); + valO *= sd::math::nd4j_pow(sum, ((T) 1.f - extraParam0) / extraParam0); for (Nd4jLong kd = dstart; kd < dend; kd += iStep2) for (Nd4jLong kh = hstart; kh < hend; kh += iStep3) for (Nd4jLong kw = wstart; kw < wend; kw += iStep4) - pgI[kd + kh + kw] += valO * nd4j::math::nd4j_pow(nd4j::math::nd4j_abs(pIn[kd + kh + kw]),extraParam0 - (T) 1.f) * nd4j::math::nd4j_sgn(pIn[kd + kh + kw]); + pgI[kd + kh + kw] += valO * sd::math::nd4j_pow(sd::math::nd4j_abs(pIn[kd + kh + kw]),extraParam0 - (T) 1.f) * sd::math::nd4j_sgn(pIn[kd + kh + kw]); } else { for (Nd4jLong kd = dstart; kd < dend; kd += dD) for (Nd4jLong kh = hstart; kh < hend; kh += dH) for (Nd4jLong kw = wstart; kw < wend; kw += dW) - sum += nd4j::math::nd4j_pow(nd4j::math::nd4j_abs(pIn[kd * iStride2 + kh * iStride3 + kw * iStride4]), extraParam0); + sum += sd::math::nd4j_pow(sd::math::nd4j_abs(pIn[kd * iStride2 + kh * iStride3 + kw * iStride4]), extraParam0); - valO *= nd4j::math::nd4j_pow(sum, ((T) 1.f - extraParam0) / extraParam0); + valO *= sd::math::nd4j_pow(sum, ((T) 1.f - extraParam0) / extraParam0); for (Nd4jLong kd = dstart; kd < dend; kd += dD) for (Nd4jLong kh = hstart; kh < hend; kh += dH) for (Nd4jLong kw = wstart; kw < wend; kw += dW) { const auto inVal = pIn[kD * iStride2 + kh * iStride3 + kw * iStride4]; - pgI[kd * gIStride2 + kh * gIStride3 + kw * gIStride4] += valO * nd4j::math::nd4j_pow(nd4j::math::nd4j_abs(inVal), extraParam0 - 1.f) * nd4j::math::nd4j_sgn(inVal); + pgI[kd * gIStride2 + kh * gIStride3 + kw * gIStride4] += valO * sd::math::nd4j_pow(sd::math::nd4j_abs(inVal), extraParam0 - 1.f) * sd::math::nd4j_sgn(inVal); } } } @@ -1761,7 +1761,7 @@ namespace nd4j { } }; - samediff::Threads::parallel_for(func, 0, bS, 1, 0, iC, 1, 0, oD, 1); + samediff::Threads::parallel_for(func, 0, bS, 1, 0, iC, 1); } else { nd4j_printf("ConvolutionUtils::pooling3dBP: pooling mode argument can take three values only: 0, 1, 2, but got %i instead !\n", poolingMode); @@ -1772,52 +1772,52 @@ namespace nd4j { - void ConvolutionUtils::conv2d(nd4j::graph::Context& block, const NDArray* input, const NDArray* weights, const NDArray* bias, NDArray* output, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int paddingMode, const int isNCHW) { + void ConvolutionUtils::conv2d(sd::graph::Context& block, const NDArray* input, const NDArray* weights, const NDArray* bias, NDArray* output, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int paddingMode, const int isNCHW) { BUILD_SINGLE_SELECTOR_TWICE(input->dataType(), conv2d_, (block, input, weights, bias, output, kH, kW, sH, sW, pH, pW, dH, dW, paddingMode, isNCHW), FLOAT_TYPES); } - void ConvolutionUtils::conv2dBP(nd4j::graph::Context& block, const NDArray* input, const NDArray* weights, const NDArray* bias, const NDArray* gradO, NDArray* gradI, NDArray* gradW, NDArray* gradB, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int paddingMode, const int isNCHW) { + void ConvolutionUtils::conv2dBP(sd::graph::Context& block, const NDArray* input, const NDArray* weights, const NDArray* bias, const NDArray* gradO, NDArray* gradI, NDArray* gradW, NDArray* gradB, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int paddingMode, const int isNCHW) { BUILD_SINGLE_SELECTOR_TWICE(input->dataType(), conv2dBP_, (block, input, weights, bias, gradO, gradI, gradW, gradB, kH, kW, sH, sW, pH, pW, dH, dW, paddingMode, isNCHW), FLOAT_TYPES); } - void ConvolutionUtils::depthwiseConv2d(nd4j::graph::Context& block, const NDArray* input, const NDArray* weights, const NDArray* bias, NDArray* output, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int paddingMode, const int isNCHW) { + void ConvolutionUtils::depthwiseConv2d(sd::graph::Context& block, const NDArray* input, const NDArray* weights, const NDArray* bias, NDArray* output, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int paddingMode, const int isNCHW) { BUILD_SINGLE_SELECTOR_TWICE(input->dataType(), depthwiseConv2d_, (block, input, weights, bias, output, kH, kW, sH, sW, pH, pW, dH, dW, paddingMode, isNCHW), FLOAT_TYPES); } - void ConvolutionUtils::depthwiseConv2dBP(nd4j::graph::Context& block, const NDArray* input, const NDArray* weights, const NDArray* bias, const NDArray* gradO, NDArray* gradI, NDArray* gradW, NDArray* gradB, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int paddingMode, const int isNCHW) { + void ConvolutionUtils::depthwiseConv2dBP(sd::graph::Context& block, const NDArray* input, const NDArray* weights, const NDArray* bias, const NDArray* gradO, NDArray* gradI, NDArray* gradW, NDArray* gradB, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int paddingMode, const int isNCHW) { BUILD_SINGLE_SELECTOR_TWICE(input->dataType(), depthwiseConv2dBP_, (input, weights, bias, gradO, gradI, gradW, gradB, kH, kW, sH, sW, pH, pW, dH, dW, paddingMode, isNCHW), FLOAT_TYPES); } - void ConvolutionUtils::sconv2d(nd4j::graph::Context& block, const NDArray* input, const NDArray* weightsDepth, const NDArray* weightsPoint, const NDArray* bias, NDArray* output, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int paddingMode, const int isNCHW) { + void ConvolutionUtils::sconv2d(sd::graph::Context& block, const NDArray* input, const NDArray* weightsDepth, const NDArray* weightsPoint, const NDArray* bias, NDArray* output, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int paddingMode, const int isNCHW) { BUILD_SINGLE_SELECTOR_TWICE(input->dataType(), sconv2d_, (block, input, weightsDepth, weightsPoint, bias, output, kH, kW, sH, sW, pH, pW, dH, dW, paddingMode, isNCHW), FLOAT_TYPES); } - void ConvolutionUtils::vol2col(nd4j::graph::Context& block, const NDArray& volume, NDArray& columns, const int sD, const int sH, const int sW, const int pD, const int pH, const int pW, const int dD, const int dH, const int dW) { + void ConvolutionUtils::vol2col(sd::graph::Context& block, const NDArray& volume, NDArray& columns, const int sD, const int sH, const int sW, const int pD, const int pH, const int pW, const int dD, const int dH, const int dW) { BUILD_SINGLE_SELECTOR(volume.dataType(), vol2col_, (volume, columns, sD, sH, sW, pD, pH, pW, dD, dH, dW), FLOAT_TYPES); } - void ConvolutionUtils::col2vol(nd4j::graph::Context& block, const NDArray& columns, NDArray& volume, const int sD, const int sH, const int sW, const int pD, const int pH, const int pW, const int dD, const int dH, const int dW) { + void ConvolutionUtils::col2vol(sd::graph::Context& block, const NDArray& columns, NDArray& volume, const int sD, const int sH, const int sW, const int pD, const int pH, const int pW, const int dD, const int dH, const int dW) { BUILD_SINGLE_SELECTOR(volume.dataType(), col2vol_, (columns, volume, sD, sH, sW, pD, pH, pW, dD, dH, dW), FLOAT_TYPES); } - void ConvolutionUtils::upsampling2d(nd4j::graph::Context& block, const NDArray& input, NDArray& output, const int factorH, const int factorW, const bool isNCHW) { + void ConvolutionUtils::upsampling2d(sd::graph::Context& block, const NDArray& input, NDArray& output, const int factorH, const int factorW, const bool isNCHW) { BUILD_SINGLE_SELECTOR(input.dataType(), upsampling2d_, (input, output, factorH, factorW, isNCHW), FLOAT_TYPES); } - void ConvolutionUtils::upsampling3d(nd4j::graph::Context& block, const NDArray& input, NDArray& output, const int factorD, const int factorH, const int factorW, const bool isNCDHW) { + void ConvolutionUtils::upsampling3d(sd::graph::Context& block, const NDArray& input, NDArray& output, const int factorD, const int factorH, const int factorW, const bool isNCDHW) { BUILD_SINGLE_SELECTOR(input.dataType(), upsampling3d_, (input, output, factorD, factorH, factorW, isNCDHW), FLOAT_TYPES); } - void ConvolutionUtils::upsampling2dBP(nd4j::graph::Context& block, const NDArray& gradO, NDArray& gradI, const bool isNCHW) { + void ConvolutionUtils::upsampling2dBP(sd::graph::Context& block, const NDArray& gradO, NDArray& gradI, const bool isNCHW) { BUILD_SINGLE_SELECTOR(gradO.dataType(), upsampling2dBP_, (gradO, gradI, isNCHW), FLOAT_TYPES); } - void ConvolutionUtils::upsampling3dBP(nd4j::graph::Context& block, const NDArray& gradO, NDArray& gradI, const bool isNCHW) { + void ConvolutionUtils::upsampling3dBP(sd::graph::Context& block, const NDArray& gradO, NDArray& gradI, const bool isNCHW) { BUILD_SINGLE_SELECTOR(gradO.dataType(), upsampling3dBP_, (gradO, gradI, isNCHW), FLOAT_TYPES); } - void ConvolutionUtils::pooling2d(nd4j::graph::Context& block, const NDArray& input, NDArray& output, const int kH, const int kW, const int sH, const int sW, const int pH, const int pW, const int dH, const int dW, const PoolingType poolingMode, const int extraParam0) { + void ConvolutionUtils::pooling2d(sd::graph::Context& block, const NDArray& input, NDArray& output, const int kH, const int kW, const int sH, const int sW, const int pH, const int pW, const int dH, const int dW, const PoolingType poolingMode, const int extraParam0) { BUILD_SINGLE_SELECTOR(input.dataType(), pooling2d_, (block, input, output, kH, kW, sH, sW, pH, pW, dH, dW, poolingMode, extraParam0), FLOAT_TYPES); } - void ConvolutionUtils::pooling3d(nd4j::graph::Context& block, const NDArray& input, NDArray& output, const int kD, const int kH, const int kW, const int sD, const int sH, const int sW, const int pD, const int pH, const int pW, const int dD, const int dH, const int dW, const int poolingMode, const int extraParam0) { + void ConvolutionUtils::pooling3d(sd::graph::Context& block, const NDArray& input, NDArray& output, const int kD, const int kH, const int kW, const int sD, const int sH, const int sW, const int pD, const int pH, const int pW, const int dD, const int dH, const int dW, const int poolingMode, const int extraParam0) { BUILD_SINGLE_SELECTOR(input.dataType(), pooling3d_, (block, input, output, kD, kH, kW, sD, sH, sW, pD, pH, pW, dD, dH, dW, poolingMode, extraParam0), FLOAT_TYPES); } - void ConvolutionUtils::pooling2dBP(nd4j::graph::Context& block, const NDArray& input, const NDArray& gradO, NDArray& gradI, const int kH, const int kW, const int sH, const int sW, const int pH, const int pW, const int dH, const int dW, const int poolingMode, const int extraParam0) { + void ConvolutionUtils::pooling2dBP(sd::graph::Context& block, const NDArray& input, const NDArray& gradO, NDArray& gradI, const int kH, const int kW, const int sH, const int sW, const int pH, const int pW, const int dH, const int dW, const int poolingMode, const int extraParam0) { BUILD_SINGLE_SELECTOR(input.dataType(), pooling2dBP_, (block, input, gradO, gradI, kH, kW, sH, sW, pH, pW, dH, dW, poolingMode, extraParam0), FLOAT_TYPES); } - void ConvolutionUtils::pooling3dBP(nd4j::graph::Context& block, const NDArray& input, const NDArray& gradO, NDArray& gradI, const int kD, const int kH, const int kW, const int sD, const int sH, const int sW, const int pD, const int pH, const int pW, const int dD, const int dH, const int dW, const int poolingMode, const int extraParam0) { + void ConvolutionUtils::pooling3dBP(sd::graph::Context& block, const NDArray& input, const NDArray& gradO, NDArray& gradI, const int kD, const int kH, const int kW, const int sD, const int sH, const int sW, const int pD, const int pH, const int pW, const int dD, const int dH, const int dW, const int poolingMode, const int extraParam0) { BUILD_SINGLE_SELECTOR(input.dataType(), pooling3dBP_, (block, input, gradO, gradI, kD, kH, kW, sD, sH, sW, pD, pH, pW, dD, dH, dW, poolingMode, extraParam0), FLOAT_TYPES); } } diff --git a/libnd4j/include/ops/declarable/helpers/cpu/crop_and_resize.cpp b/libnd4j/include/ops/declarable/helpers/cpu/crop_and_resize.cpp index 233699163..ab6503946 100644 --- a/libnd4j/include/ops/declarable/helpers/cpu/crop_and_resize.cpp +++ b/libnd4j/include/ops/declarable/helpers/cpu/crop_and_resize.cpp @@ -36,7 +36,7 @@ limitations under the License. #include #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { @@ -53,7 +53,7 @@ namespace nd4j { // \@param crops - output image batch (4D with given type) // void - cropAndResizeFunctor(nd4j::LaunchContext * context, NDArray const *images, NDArray const *boxes, + cropAndResizeFunctor(sd::LaunchContext * context, NDArray const *images, NDArray const *boxes, NDArray const *indices, NDArray const *cropSize, int method, double extrapolationVal, NDArray *crops) { BUILD_TRIPLE_SELECTOR(images->dataType(), boxes->dataType(), indices->dataType(), cropAndResizeFunctor_, (images, boxes, indices, cropSize, method, extrapolationVal, crops), NUMERIC_TYPES, FLOAT_TYPES, INTEGER_TYPES); diff --git a/libnd4j/include/ops/declarable/helpers/cpu/crop_and_resize.hpp b/libnd4j/include/ops/declarable/helpers/cpu/crop_and_resize.hpp index 1f55378c0..c7d29c471 100644 --- a/libnd4j/include/ops/declarable/helpers/cpu/crop_and_resize.hpp +++ b/libnd4j/include/ops/declarable/helpers/cpu/crop_and_resize.hpp @@ -21,7 +21,7 @@ #include #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { template @@ -64,8 +64,8 @@ namespace nd4j { continue; } if (method == 0 /* bilinear */) { - const int topYIndex = nd4j::math::p_floor(inY); - const int bottomYIndex = nd4j::math::p_ceil(inY); + const int topYIndex = sd::math::p_floor(inY); + const int bottomYIndex = sd::math::p_ceil(inY); const float y_lerp = inY - topYIndex; for (auto x = 0; x < cropWidth; ++x) { diff --git a/libnd4j/include/ops/declarable/helpers/cpu/cross.cpp b/libnd4j/include/ops/declarable/helpers/cpu/cross.cpp index 6a8523925..51af1840b 100644 --- a/libnd4j/include/ops/declarable/helpers/cpu/cross.cpp +++ b/libnd4j/include/ops/declarable/helpers/cpu/cross.cpp @@ -23,11 +23,11 @@ #include #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { -void crossBatched(nd4j::LaunchContext * context, NDArray *a, NDArray *b, NDArray *o) { +void crossBatched(sd::LaunchContext * context, NDArray *a, NDArray *b, NDArray *o) { auto _a = a->reshape(a->ordering(), {-1, 3}); auto _b = b->reshape(b->ordering(), {-1, 3}); auto _o = o->reshape(o->ordering(), {-1, 3}, false); diff --git a/libnd4j/include/ops/declarable/helpers/cpu/d_t_s.cpp b/libnd4j/include/ops/declarable/helpers/cpu/d_t_s.cpp index d3e524ff4..700e5b8dd 100644 --- a/libnd4j/include/ops/declarable/helpers/cpu/d_t_s.cpp +++ b/libnd4j/include/ops/declarable/helpers/cpu/d_t_s.cpp @@ -21,7 +21,7 @@ #include #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { @@ -93,7 +93,7 @@ namespace helpers { } } - void _depthToSpace(nd4j::LaunchContext * context, NDArray *input, NDArray *output, int block_size, bool isNHWC) { + void _depthToSpace(sd::LaunchContext * context, NDArray *input, NDArray *output, int block_size, bool isNHWC) { auto xType = input->dataType(); BUILD_SINGLE_SELECTOR(xType, __depthToSpace, (input, output, block_size, isNHWC), LIBND4J_TYPES); diff --git a/libnd4j/include/ops/declarable/helpers/cpu/diGamma.cpp b/libnd4j/include/ops/declarable/helpers/cpu/diGamma.cpp index 2a51b92a6..37abaf559 100644 --- a/libnd4j/include/ops/declarable/helpers/cpu/diGamma.cpp +++ b/libnd4j/include/ops/declarable/helpers/cpu/diGamma.cpp @@ -22,7 +22,7 @@ #include #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { @@ -38,7 +38,7 @@ static void diGamma_(const NDArray& x, NDArray& z) { samediff::Threads::parallel_for(func, 0, x.lengthOf()); } -void diGamma(nd4j::LaunchContext* context, const NDArray& x, NDArray& z) { +void diGamma(sd::LaunchContext* context, const NDArray& x, NDArray& z) { BUILD_SINGLE_SELECTOR(x.dataType(), diGamma_, (x, z), FLOAT_TYPES); } diff --git a/libnd4j/include/ops/declarable/helpers/cpu/diag.cpp b/libnd4j/include/ops/declarable/helpers/cpu/diag.cpp index f2f2033c1..670ad5322 100644 --- a/libnd4j/include/ops/declarable/helpers/cpu/diag.cpp +++ b/libnd4j/include/ops/declarable/helpers/cpu/diag.cpp @@ -18,10 +18,10 @@ // Created by GS on 4/6/2018. // -#include "ResultSet.h" +#include #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { @@ -38,7 +38,7 @@ static void _diagFunctor(const NDArray* input, NDArray* output) { output->p(i * (inLength + 1), (*input).e(i)); } - void diagFunctor(nd4j::LaunchContext * context, const NDArray* input, NDArray* output) { + void diagFunctor(sd::LaunchContext * context, const NDArray* input, NDArray* output) { auto xType = input->dataType(); BUILD_SINGLE_SELECTOR(xType, _diagFunctor, (input, output), LIBND4J_TYPES); @@ -46,7 +46,7 @@ static void _diagFunctor(const NDArray* input, NDArray* output) { BUILD_SINGLE_TEMPLATE(template void _diagFunctor, (const NDArray* input, NDArray* output);, LIBND4J_TYPES); -void diagPartFunctor(nd4j::LaunchContext * context, NDArray const* input, NDArray* output) { +void diagPartFunctor(sd::LaunchContext * context, NDArray const* input, NDArray* output) { const int outLen = output->lengthOf(); const int inLen = input->lengthOf(); int i(0), j(0); diff --git a/libnd4j/include/ops/declarable/helpers/cpu/dilation2d.cpp b/libnd4j/include/ops/declarable/helpers/cpu/dilation2d.cpp index 6e801b1fa..fbf071e28 100644 --- a/libnd4j/include/ops/declarable/helpers/cpu/dilation2d.cpp +++ b/libnd4j/include/ops/declarable/helpers/cpu/dilation2d.cpp @@ -22,7 +22,7 @@ #include #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { @@ -55,8 +55,8 @@ static void dilation2d_(NDArray *input, NDArray *weights, NDArray *output, const auto func = PRAGMA_THREADS_FOR_2D { - for (uint b = start_x; b < stop_x; b += inc_x) { - for (uint oh = start_y; oh < stop_y; oh += inc_y) { + for (auto b = start_x; b < stop_x; b += inc_x) { + for (auto oh = start_y; oh < stop_y; oh += inc_y) { for (uint ow = 0; ow < oW; ++ow) { for (uint c = 0; c < iC; ++c) { @@ -70,7 +70,7 @@ static void dilation2d_(NDArray *input, NDArray *weights, NDArray *output, const const int iw = ow * sW - pW + kw * dW; if (iw < 0 || iw >= iW) continue; - uint xCoords[4] = {b, (uint)ih, (uint)iw, c}; + uint xCoords[4] = { static_cast(b), static_cast(ih), static_cast(iw), c}; uint yCoords[3] = {kh, kw, c}; const X val = x[shape::getOffset(xShapeInfo, xCoords)] + y[shape::getOffset(yShapeInfo, yCoords)]; @@ -79,7 +79,7 @@ static void dilation2d_(NDArray *input, NDArray *weights, NDArray *output, const } } - uint zCoords[4] = {b, oh, ow, c}; + uint zCoords[4] = { static_cast(b), static_cast(oh), ow, c}; z[shape::getOffset(zShapeInfo, zCoords)] = static_cast(max); } } @@ -90,7 +90,7 @@ static void dilation2d_(NDArray *input, NDArray *weights, NDArray *output, const samediff::Threads::parallel_for(func, 0, bS, 1, 0, oH, 1); } -void dilation2d(nd4j::LaunchContext* context, NDArray *input, NDArray *weights, NDArray *output, const int sH, const int sW, const int pH, const int pW, const int dH, const int dW) { +void dilation2d(sd::LaunchContext* context, NDArray *input, NDArray *weights, NDArray *output, const int sH, const int sW, const int pH, const int pW, const int dH, const int dW) { BUILD_SINGLE_SELECTOR_TWICE(input->dataType(), dilation2d_, (input, weights, output, sH, sW, pH, pW, dH, dW), FLOAT_TYPES); } diff --git a/libnd4j/include/ops/declarable/helpers/cpu/dropout.cpp b/libnd4j/include/ops/declarable/helpers/cpu/dropout.cpp index a470f140a..54981dea5 100644 --- a/libnd4j/include/ops/declarable/helpers/cpu/dropout.cpp +++ b/libnd4j/include/ops/declarable/helpers/cpu/dropout.cpp @@ -19,19 +19,19 @@ // #include -#include +#include #include #include #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { template static void dropoutSimple(NDArray const* input, NDArray* output, double probValue, int seed) { - nd4j::graph::RandomGenerator nodeRng(3019L, seed); + sd::graph::RandomGenerator nodeRng(3019L, seed); int inLen = input->lengthOf(); auto func = PRAGMA_THREADS_FOR { @@ -50,7 +50,7 @@ namespace helpers { template int dropOutFunctor_(graph::Context& context, NDArray* input, NDArray* output, NDArray* reduceShape, int seed, double probValue) { //NativeOps native; - //nd4j::graph::RandomGenerator nodeRng(seed); //static int dropOutFunctor_(nd4j::random::RandomBuffer* rng, NDArray* input, NDArray* output, NDArray* reduceShape, int seed, double probValue) { + //sd::graph::RandomGenerator nodeRng(seed); //static int dropOutFunctor_(sd::random::RandomBuffer* rng, NDArray* input, NDArray* output, NDArray* reduceShape, int seed, double probValue) { //NativeOps native; //native.reSeedBuffer(nullptr, (long)seed, rng); //if (newRng ) @@ -63,7 +63,7 @@ namespace helpers { std::vector dims(reduceShape->lengthOf()); bool fit = true; - for( int i = 0; i < dims.size(); i++ ) { + for(auto i = 0; i < dims.size(); i++ ) { if (fit) { dims[i] = reduceShape->e(i); for (int e = 0; e < input->rankOf(); ++e) @@ -127,7 +127,7 @@ namespace helpers { // return ND4J_STATUS_BAD_RNG; //T probValueArr[] = {probValue, alpha, alpha1, beta}; //input->template applyRandom>(rng, nullptr, output, probValueArr); - nd4j::graph::RandomGenerator nodeRng(3019L, seed); + sd::graph::RandomGenerator nodeRng(3019L, seed); auto func = PRAGMA_THREADS_FOR { for (auto e = start; e < stop; e++) { diff --git a/libnd4j/include/ops/declarable/helpers/cpu/dynamic.cpp b/libnd4j/include/ops/declarable/helpers/cpu/dynamic.cpp index 0673a6f2b..2b6b4cd02 100644 --- a/libnd4j/include/ops/declarable/helpers/cpu/dynamic.cpp +++ b/libnd4j/include/ops/declarable/helpers/cpu/dynamic.cpp @@ -20,7 +20,7 @@ #include #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { @@ -53,7 +53,7 @@ namespace nd4j { outputs[i].second = 0; //PRAGMA_OMP_PARALLEL_FOR_IF(indices->lengthOf() > Environment::getInstance()->elementwiseThreshold()) - for (int e = 0; e < indices->lengthOf(); ++e) + for (Nd4jLong e = 0; e < indices->lengthOf(); ++e) if ((*indices).e(e) == i) listOutForCurrent.at(outputs[i].second++)->assign(listOfTensors.at(e)); } @@ -65,7 +65,7 @@ namespace nd4j { for (auto i = start; i < stop; i++) { outputs[i].first = outputList[i]; outputs[i].second = 0; - for (int e = 0; e < indices->lengthOf(); ++e) + for (Nd4jLong e = 0; e < indices->lengthOf(); ++e) if (indices->e(e) == i) outputs[i].first->p(outputs[i].second++, input->e(e)); } @@ -83,7 +83,7 @@ namespace nd4j { for (int e = 0; e < numOfData; e++) { auto data = inputs[e]; auto index = indices[e]; - for (int i = 0; i < index->lengthOf(); i++) { + for (Nd4jLong i = 0; i < index->lengthOf(); i++) { Nd4jLong pos = index->e(i); if (pos < 0) { nd4j_printf("dynamic_stitch: Index value should be non-negative. But %i was given", pos); @@ -100,7 +100,7 @@ namespace nd4j { } else { std::vector restDims(output->rankOf() - 1); - for (int i = restDims.size(); i > 0; i--) + for (auto i = restDims.size(); i > 0; i--) restDims[restDims.size() - i] = output->rankOf() - i; ResultSet listOfOutTensors = output->allTensorsAlongDimension(restDims); @@ -109,12 +109,12 @@ namespace nd4j { auto data = inputs[e]; auto index = indices[e]; std::vector sourceDims(data->rankOf() - index->rankOf()); - for (int i = sourceDims.size(); i > 0; i--) + for (auto i = sourceDims.size(); i > 0; i--) sourceDims[sourceDims.size() - i] = data->rankOf() - i; ResultSet listOfTensors = data->allTensorsAlongDimension(sourceDims) ; - for (int i = 0; i < index->lengthOf(); i++) { + for (Nd4jLong i = 0; i < index->lengthOf(); i++) { auto pos = index->e(i); if (pos < 0) { nd4j_printf("dynamic_stitch: Index value should be non-negative. But %i was given", pos); @@ -146,7 +146,7 @@ namespace nd4j { ResultSet listOfTensors = outputList[0]->allTensorsAlongDimension(sourceDims); - for (unsigned int i = 0; i < inputGradientList.size(); i++) { + for (auto i = 0; i < inputGradientList.size(); i++) { outputs[i].first = inputGradientList[i]; if (outputs[i].first->rankOf() < 1) continue; // skip empty gradient outs std::vector outDims(outputs[i].first->rankOf() - 1); @@ -158,7 +158,7 @@ namespace nd4j { outputs[i].second = 0; - for (int e = 0; e < indices->lengthOf(); ++e) + for (Nd4jLong e = 0; e < indices->lengthOf(); ++e) if (indices->e(e) == i) listOfTensors.at(e)->assign(listOutForCurrent.at(outputs[i].second++)); } @@ -171,7 +171,7 @@ namespace nd4j { for (auto i = start; i < stop; i++) { outputs[i].first = inputGradientList[i]; outputs[i].second = 0; - for (int e = 0; e < indices->lengthOf(); ++e) + for (Nd4jLong e = 0; e < indices->lengthOf(); ++e) if (indices->e(e) == i) output->p(e, outputs[i].first->e(outputs[i].second++)); } @@ -183,7 +183,7 @@ namespace nd4j { outputList[1]->assign(indices); } - void dynamicPartitionFunctor(nd4j::LaunchContext * context, NDArray const* input, NDArray const* indices, std::vector& outputList) { + void dynamicPartitionFunctor(sd::LaunchContext * context, NDArray const* input, NDArray const* indices, std::vector& outputList) { auto xType = input->dataType(); BUILD_SINGLE_SELECTOR(xType, _dynamicPartitionFunctor, (input, indices, outputList), LIBND4J_TYPES); @@ -194,19 +194,19 @@ namespace nd4j { throw std::runtime_error("Not umplemented yet"); } - int dynamicStitchFunctor(nd4j::LaunchContext * context, std::vector const& inputs, std::vector const& indices, NDArray* output){ + int dynamicStitchFunctor(sd::LaunchContext * context, std::vector const& inputs, std::vector const& indices, NDArray* output){ auto xType = inputs.at(0)->dataType(); BUILD_SINGLE_SELECTOR(xType, return _dynamicStitchFunctor, (inputs, indices, output), LIBND4J_TYPES); } - int dynamicStitchFunctorBP(nd4j::LaunchContext * context, std::vector const& inputs, std::vector const& indices, NDArray const* gradInput, std::vector& outputList) { + int dynamicStitchFunctorBP(sd::LaunchContext * context, std::vector const& inputs, std::vector const& indices, NDArray const* gradInput, std::vector& outputList) { auto xType = inputs.at(0)->dataType(); BUILD_SINGLE_SELECTOR(xType, return _dynamicStitchFunctorBP, (inputs, indices, gradInput, outputList), LIBND4J_TYPES); } - void dynamicPartitionFunctorBP(nd4j::LaunchContext * context, NDArray const* input, NDArray const* indices, std::vector const& inputGradientList, std::vector& outputList) { + void dynamicPartitionFunctorBP(sd::LaunchContext * context, NDArray const* input, NDArray const* indices, std::vector const& inputGradientList, std::vector& outputList) { auto xType = input->dataType(); BUILD_SINGLE_SELECTOR(xType, _dynamicPartitionFunctorBP, (input, indices, inputGradientList, outputList), LIBND4J_TYPES); diff --git a/libnd4j/include/ops/declarable/helpers/cpu/extract_patches.cpp b/libnd4j/include/ops/declarable/helpers/cpu/extract_patches.cpp index b2707ea5c..15ea569e8 100644 --- a/libnd4j/include/ops/declarable/helpers/cpu/extract_patches.cpp +++ b/libnd4j/include/ops/declarable/helpers/cpu/extract_patches.cpp @@ -21,7 +21,7 @@ #include #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { @@ -86,7 +86,7 @@ namespace helpers { } - void extractPatches(nd4j::LaunchContext * context, NDArray* images, NDArray* output, int sizeRow, int sizeCol, int stradeRow, int stradeCol, int rateRow, int rateCol, bool theSame){ + void extractPatches(sd::LaunchContext * context, NDArray* images, NDArray* output, int sizeRow, int sizeCol, int stradeRow, int stradeCol, int rateRow, int rateCol, bool theSame){ auto xType = images->dataType(); BUILD_SINGLE_SELECTOR(xType, _extractPatches, (images, output, sizeRow, sizeCol, stradeRow, stradeCol, rateRow, rateCol, theSame), LIBND4J_TYPES); diff --git a/libnd4j/include/ops/declarable/helpers/cpu/fake_quantization.cpp b/libnd4j/include/ops/declarable/helpers/cpu/fake_quantization.cpp index f18f48fac..d2c918da9 100644 --- a/libnd4j/include/ops/declarable/helpers/cpu/fake_quantization.cpp +++ b/libnd4j/include/ops/declarable/helpers/cpu/fake_quantization.cpp @@ -19,9 +19,9 @@ // #include -#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { @@ -47,7 +47,7 @@ namespace helpers { if (zeroPointFromMin > quantMaxF) { return static_cast(quantMax); } - return (uint16_t)nd4j::math::nd4j_round(zeroPointFromMin); + return (uint16_t)sd::math::nd4j_round(zeroPointFromMin); }(); // compute nudged min and max with computed nudged zero point *nudgedMin = (quantMinF - nudged_zero_point) * (*scale); @@ -102,7 +102,7 @@ namespace helpers { val = nudgedMax; // converse value with scale and shifted with nudged min val -= nudgedMin; - return (nd4j::math::nd4j_floor(val / scale + T(0.5f)) * scale + nudgedMin); + return (sd::math::nd4j_floor(val / scale + T(0.5f)) * scale + nudgedMin); }; input->applyLambda(fakeQuantizationWithMinMax, *output); diff --git a/libnd4j/include/ops/declarable/helpers/cpu/flatten.cpp b/libnd4j/include/ops/declarable/helpers/cpu/flatten.cpp index 30d4d3f7a..aadd74298 100644 --- a/libnd4j/include/ops/declarable/helpers/cpu/flatten.cpp +++ b/libnd4j/include/ops/declarable/helpers/cpu/flatten.cpp @@ -20,7 +20,7 @@ #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { @@ -45,12 +45,12 @@ namespace nd4j { auto xShapeInfo = inputs[e]->shapeInfo(); auto xLength = inputs[e]->lengthOf(); - for (uint i = 0; i < xLength; i++) + for (Nd4jLong i = 0; i < xLength; i++) z[i] = xBuffer[getIndexOffsetOrdered(i, xShapeInfo, order)]; } } - void flatten(nd4j::LaunchContext *context, std::vector &inputs, NDArray *output, char order) { + void flatten(sd::LaunchContext *context, std::vector &inputs, NDArray *output, char order) { BUILD_SINGLE_SELECTOR(output->dataType(), flatten_, (inputs, output, order), LIBND4J_TYPES); } } diff --git a/libnd4j/include/ops/declarable/helpers/cpu/gather.cpp b/libnd4j/include/ops/declarable/helpers/cpu/gather.cpp index ed844e84f..fb715a5e5 100644 --- a/libnd4j/include/ops/declarable/helpers/cpu/gather.cpp +++ b/libnd4j/include/ops/declarable/helpers/cpu/gather.cpp @@ -21,15 +21,15 @@ #include #include #include -#include -#include +#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { //////////////////////////////////////////////////////////////////////// -void gather(nd4j::LaunchContext * context, const NDArray* input, const NDArray* indices, NDArray* output, const std::vector& intArgs) { +void gather(sd::LaunchContext * context, const NDArray* input, const NDArray* indices, NDArray* output, const std::vector& intArgs) { int axis = intArgs.size() > 0 ? intArgs[0] : 0; const int inputRank = input->rankOf(); diff --git a/libnd4j/include/ops/declarable/helpers/cpu/gradient.cpp b/libnd4j/include/ops/declarable/helpers/cpu/gradient.cpp index f6756dd88..df5ee1afc 100644 --- a/libnd4j/include/ops/declarable/helpers/cpu/gradient.cpp +++ b/libnd4j/include/ops/declarable/helpers/cpu/gradient.cpp @@ -19,9 +19,9 @@ // #include -#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { template @@ -33,7 +33,7 @@ static void applyGradientDescent_(NDArray* input, NDArray* step, double weight, input->applyPairwiseLambda(*step, lambda, *output); } -void applyGradientDescent(nd4j::LaunchContext* context, NDArray* input, NDArray* step, double weight, NDArray* output) { +void applyGradientDescent(sd::LaunchContext* context, NDArray* input, NDArray* step, double weight, NDArray* output) { BUILD_SINGLE_SELECTOR(input->dataType(), applyGradientDescent_, (input, step, weight, output), FLOAT_TYPES); } BUILD_SINGLE_TEMPLATE(template void applyGradientDescent_, (NDArray* input, NDArray* step, double weight, NDArray* output), FLOAT_TYPES); diff --git a/libnd4j/include/ops/declarable/helpers/cpu/gru.cpp b/libnd4j/include/ops/declarable/helpers/cpu/gru.cpp index 3db5e5373..b00036b81 100644 --- a/libnd4j/include/ops/declarable/helpers/cpu/gru.cpp +++ b/libnd4j/include/ops/declarable/helpers/cpu/gru.cpp @@ -24,18 +24,18 @@ // "Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation" -#include +#include #include -#include -#include +#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { ////////////////////////////////////////////////////////////////////////// -void gruCell(nd4j::LaunchContext * context, const NDArray* x, const NDArray* hLast, const NDArray* W, const NDArray* Wc, +void gruCell(sd::LaunchContext * context, const NDArray* x, const NDArray* hLast, const NDArray* W, const NDArray* Wc, const NDArray* b, const NDArray* bc, NDArray* r, NDArray* u, NDArray* c, NDArray* h) { @@ -128,7 +128,7 @@ void gruCell(nd4j::LaunchContext * context, const NDArray* x, const NDArray* hLa } ////////////////////////////////////////////////////////////////////////// -void gruTimeLoop(nd4j::LaunchContext * context, const NDArray* x, const NDArray* hLast, const NDArray* Wx, const NDArray* Wh, const NDArray* b, NDArray* h) { +void gruTimeLoop(sd::LaunchContext * context, const NDArray* x, const NDArray* hLast, const NDArray* Wx, const NDArray* Wh, const NDArray* b, NDArray* h) { // x input [time, bS, iS] // hLast initial cell output (at time step = 0) [bS, nU] @@ -154,7 +154,7 @@ void gruTimeLoop(nd4j::LaunchContext * context, const NDArray* x, const NDArray* } ////////////////////////////////////////////////////////////////////////// -void gruCellBP(nd4j::LaunchContext* context, +void gruCellBP(sd::LaunchContext* context, const NDArray* x, const NDArray* hLast, const NDArray* W, const NDArray* Wc, const NDArray* b, const NDArray* bc, const NDArray* dLdr, const NDArray* dLdu, const NDArray* dLdc, const NDArray* dLdh, diff --git a/libnd4j/include/ops/declarable/helpers/cpu/hamming.cpp b/libnd4j/include/ops/declarable/helpers/cpu/hamming.cpp index fc6fc768b..10b6a27e0 100644 --- a/libnd4j/include/ops/declarable/helpers/cpu/hamming.cpp +++ b/libnd4j/include/ops/declarable/helpers/cpu/hamming.cpp @@ -22,7 +22,7 @@ #include #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { @@ -47,7 +47,7 @@ namespace nd4j { Nd4jLong distance = 0; auto lengthOf = x.lengthOf(); - int maxThreads = nd4j::math::nd4j_min(256, omp_get_max_threads()); + int maxThreads = sd::math::nd4j_min(256, omp_get_max_threads()); Nd4jLong intermediate[256]; // nullify temp values diff --git a/libnd4j/include/ops/declarable/helpers/cpu/hashcode.cpp b/libnd4j/include/ops/declarable/helpers/cpu/hashcode.cpp index beb48e382..5893b2c88 100644 --- a/libnd4j/include/ops/declarable/helpers/cpu/hashcode.cpp +++ b/libnd4j/include/ops/declarable/helpers/cpu/hashcode.cpp @@ -21,12 +21,12 @@ #include #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { template static void hashCode_(LaunchContext *context, NDArray &array, NDArray &result) { - auto blockSize = 32; + Nd4jLong blockSize = 32; auto length = array.lengthOf(); int numBlocks = length / blockSize + ((length % blockSize == 0) ? 0 : 1); auto tempA = NDArrayFactory::create('c', {numBlocks}, context); @@ -42,11 +42,11 @@ namespace nd4j { // we divide array into 32 element chunks, and store intermediate results once auto func = PRAGMA_THREADS_FOR { - for (auto b = 0; b < stop; b++) { + for (auto b = start; b < stop; b++) { auto blockBuffer = buffer + b * numBlocks; Nd4jLong r = 1; - for (int e = 0; e < blockSize && e + (b * numBlocks) < length; e++) { + for (Nd4jLong e = 0; e < blockSize && e + (b * numBlocks) < length; e++) { auto v = longBytes(blockBuffer[e]); r = 31 * r + v; } @@ -68,7 +68,7 @@ namespace nd4j { auto blockBuffer = tempBuffer + b * numBlocks; Nd4jLong r = 1; - for (int e = 0; e < blockSize && e + (b * numBlocks) < lastLength; e++) { + for (Nd4jLong e = 0; e < blockSize && e + (b * numBlocks) < lastLength; e++) { auto v = longBytes(blockBuffer[e]); r = 31 * r + v; } @@ -103,4 +103,3 @@ namespace nd4j { } } } - diff --git a/libnd4j/include/ops/declarable/helpers/cpu/histogram.cpp b/libnd4j/include/ops/declarable/helpers/cpu/histogram.cpp index 911230367..9fc6ddefb 100644 --- a/libnd4j/include/ops/declarable/helpers/cpu/histogram.cpp +++ b/libnd4j/include/ops/declarable/helpers/cpu/histogram.cpp @@ -20,7 +20,7 @@ #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { template @@ -49,7 +49,7 @@ namespace nd4j { } PRAGMA_OMP_SIMD - for (int x = 0; x < numBins; x++) { + for (Nd4jLong x = 0; x < numBins; x++) { result[x] += bins[x]; } @@ -58,7 +58,7 @@ namespace nd4j { } } - void histogramHelper(nd4j::LaunchContext *context, NDArray &input, NDArray &output) { + void histogramHelper(sd::LaunchContext *context, NDArray &input, NDArray &output) { Nd4jLong numBins = output.lengthOf(); double min_val = input.reduceNumber(reduce::SameOps::Min).e(0); double max_val = input.reduceNumber(reduce::SameOps::Max).e(0); diff --git a/libnd4j/include/ops/declarable/helpers/cpu/histogramFixedWidth.cpp b/libnd4j/include/ops/declarable/helpers/cpu/histogramFixedWidth.cpp index 1ffb59824..9376e80bf 100644 --- a/libnd4j/include/ops/declarable/helpers/cpu/histogramFixedWidth.cpp +++ b/libnd4j/include/ops/declarable/helpers/cpu/histogramFixedWidth.cpp @@ -20,7 +20,7 @@ #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { @@ -57,7 +57,7 @@ void histogramFixedWidth_(const NDArray& input, const NDArray& range, NDArray& o } } -void histogramFixedWidth(nd4j::LaunchContext * context, const NDArray& input, const NDArray& range, NDArray& output) { +void histogramFixedWidth(sd::LaunchContext * context, const NDArray& input, const NDArray& range, NDArray& output) { BUILD_SINGLE_SELECTOR(input.dataType(), histogramFixedWidth_, (input, range, output), LIBND4J_TYPES); } BUILD_SINGLE_TEMPLATE(template void histogramFixedWidth_, (const NDArray& input, const NDArray& range, NDArray& output), LIBND4J_TYPES); diff --git a/libnd4j/include/ops/declarable/helpers/cpu/im2col.cpp b/libnd4j/include/ops/declarable/helpers/cpu/im2col.cpp index 7be34e6ca..2129b4bee 100644 --- a/libnd4j/include/ops/declarable/helpers/cpu/im2col.cpp +++ b/libnd4j/include/ops/declarable/helpers/cpu/im2col.cpp @@ -22,13 +22,13 @@ #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { ////////////////////////////////////////////////////////////////////////// template -static void im2col_(nd4j::LaunchContext & context, const NDArray& input, NDArray& output, const int kH, const int kW, const int sH, const int sW, const int pH, const int pW, const int dH, const int dW, const NDArray& arrZeroPadVal) { +static void im2col_(sd::LaunchContext & context, const NDArray& input, NDArray& output, const int kH, const int kW, const int sH, const int sW, const int pH, const int pW, const int dH, const int dW, const NDArray& arrZeroPadVal) { // input [bS, iC, iH, iW] is convoluted to output [bS, iC, kH, kW, oH, oW] @@ -64,8 +64,8 @@ static void im2col_(nd4j::LaunchContext & context, const NDArray& input, NDArra if (shape::order(imShapeBuffer) == 'c' && shape::order(colShapeBuffer) == 'c' && shape::strideDescendingCAscendingF(imShapeBuffer) && shape::strideDescendingCAscendingF(colShapeBuffer)) { auto func = PRAGMA_THREADS_FOR_2D { - for (int b = start_x; b < stop_x; b++) { - for (int c = start_y; c < stop_y; c++) { + for (auto b = start_x; b < stop_x; b++) { + for (auto c = start_y; c < stop_y; c++) { for (int kRow = 0; kRow < kH; ++kRow) { for (int kCol = 0; kCol < kW; ++kCol) { for (int colH = 0; colH < oH; ++colH) { @@ -98,8 +98,8 @@ static void im2col_(nd4j::LaunchContext & context, const NDArray& input, NDArra T *col, *im; int imRow, imCol; - for (int b = start_x; b < stop_x; b += inc_x) { - for (int colH = start_y; colH < stop_y; colH += inc_y) { + for (auto b = start_x; b < stop_x; b += inc_x) { + for (auto colH = start_y; colH < stop_y; colH += inc_y) { for (int colW = 0; colW < oW; ++colW) { for (int c = 0; c < iC; ++c) { for (int kRow = 0; kRow < kH; ++kRow) { @@ -129,7 +129,7 @@ static void im2col_(nd4j::LaunchContext & context, const NDArray& input, NDArra } -void im2col(nd4j::LaunchContext & context, const NDArray& im, NDArray& col, const int kH, const int kW, const int sH, const int sW, const int pH, const int pW, const int dH, const int dW, const NDArray& arrZeroPadVal) { +void im2col(sd::LaunchContext & context, const NDArray& im, NDArray& col, const int kH, const int kW, const int sH, const int sW, const int pH, const int pW, const int dH, const int dW, const NDArray& arrZeroPadVal) { BUILD_SINGLE_SELECTOR(im.dataType(), im2col_, (context, im, col, kH, kW, sH, sW, pH, pW, dH, dW, arrZeroPadVal), FLOAT_TYPES); } diff --git a/libnd4j/include/ops/declarable/helpers/cpu/image_draw_bounding_boxes.cpp b/libnd4j/include/ops/declarable/helpers/cpu/image_draw_bounding_boxes.cpp index d9b018268..ee4faafb0 100644 --- a/libnd4j/include/ops/declarable/helpers/cpu/image_draw_bounding_boxes.cpp +++ b/libnd4j/include/ops/declarable/helpers/cpu/image_draw_bounding_boxes.cpp @@ -31,11 +31,11 @@ limitations under the License. // // @author sgazeos@gmail.com // -#include -#include +#include +#include #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { typedef std::vector> ColorTable_t; @@ -60,7 +60,7 @@ namespace helpers { return colorTable; } - void drawBoundingBoxesFunctor(nd4j::LaunchContext * context, NDArray* images, NDArray* boxes, NDArray* colors, NDArray* output) { + void drawBoundingBoxesFunctor(sd::LaunchContext * context, NDArray* images, NDArray* boxes, NDArray* colors, NDArray* output) { // images - batch of 3D images with BW (last dim = 1), RGB (last dim = 3) or RGBA (last dim = 4) channel set // boxes - batch of 2D bounds with last dim (y_start, x_start, y_end, x_end) to compute i and j as // floor((height - 1 ) * y_start) => rowStart, floor((height - 1) * y_end) => rowEnd @@ -95,13 +95,13 @@ namespace helpers { for (auto boxIndex = 0; boxIndex < numBoxes; ++boxIndex) { auto colorIndex = boxIndex % colorTable.size(); auto rowStart = Nd4jLong((height - 1) * boxes->t(batch, boxIndex, 0)); - auto rowStartBound = nd4j::math::nd4j_max(Nd4jLong(0), rowStart); + auto rowStartBound = sd::math::nd4j_max(Nd4jLong(0), rowStart); auto rowEnd = Nd4jLong((height - 1) * boxes->t(batch, boxIndex, 2)); - auto rowEndBound = nd4j::math::nd4j_min(Nd4jLong(height - 1), rowEnd); + auto rowEndBound = sd::math::nd4j_min(Nd4jLong(height - 1), rowEnd); auto colStart = Nd4jLong((width - 1) * boxes->t(batch, boxIndex, 1)); - auto colStartBound = nd4j::math::nd4j_max(Nd4jLong(0), colStart); + auto colStartBound = sd::math::nd4j_max(Nd4jLong(0), colStart); auto colEnd = Nd4jLong((width - 1) * boxes->t(batch, boxIndex, 3)); - auto colEndBound = nd4j::math::nd4j_min(Nd4jLong(width - 1), colEnd); + auto colEndBound = sd::math::nd4j_min(Nd4jLong(width - 1), colEnd); if (rowStart > rowEnd || colStart > colEnd) { nd4j_debug( diff --git a/libnd4j/include/ops/declarable/helpers/cpu/image_resize.cpp b/libnd4j/include/ops/declarable/helpers/cpu/image_resize.cpp index 23acab375..2f0f00779 100644 --- a/libnd4j/include/ops/declarable/helpers/cpu/image_resize.cpp +++ b/libnd4j/include/ops/declarable/helpers/cpu/image_resize.cpp @@ -38,7 +38,7 @@ limitations under the License. #include #include "../cross.h" -namespace nd4j { +namespace sd { namespace ops { namespace helpers { @@ -171,10 +171,10 @@ namespace helpers { for (auto k = start; k < stop; k++) { auto i = (outSize - k - 1); double const in = scaler(i, scale); - double const in_f = nd4j::math::nd4j_floor(in); - double const in_c = nd4j::math::nd4j_ceil(in); - interpolationData[i]._bottomIndex = nd4j::math::nd4j_max(static_cast(in_f), (Nd4jLong)0LL);//static_cast(in); - interpolationData[i]._topIndex = nd4j::math::nd4j_min(static_cast(in_c), inSize - 1); + double const in_f = sd::math::nd4j_floor(in); + double const in_c = sd::math::nd4j_ceil(in); + interpolationData[i]._bottomIndex = sd::math::nd4j_max(static_cast(in_f), (Nd4jLong)0LL);//static_cast(in); + interpolationData[i]._topIndex = sd::math::nd4j_min(static_cast(in_c), inSize - 1); interpolationData[i]._interpolarValue = in - in_f; } }; @@ -219,16 +219,16 @@ namespace helpers { auto func = PRAGMA_THREADS_FOR { for (auto batch = start; batch < stop; ++batch) { auto pInput = pInputBuf + batch * inBatchNumValues; - for (auto y = 0; y < outHeight; ++y) { + for (Nd4jLong y = 0; y < outHeight; ++y) { auto pOutput = pOutputBuf + (batch * outHeight + y) * outRowSize; const T* ysInputLowerPtr = pInput + ys[y]._bottomIndex * inRowSize; const T* ysInputUpperPtr = pInput + ys[y]._topIndex * inRowSize; double yVal = ys[y]._interpolarValue; - for (auto x = 0; x < outWidth; ++x) { + for (Nd4jLong x = 0; x < outWidth; ++x) { auto xsBottom = xsPtr[x]._bottomIndex; auto xsTop = xsPtr[x]._topIndex; auto xVal = xsPtr[x]._interpolarValue; - for (auto c = 0; c < channels; ++c) { + for (Nd4jLong c = 0; c < channels; ++c) { double topLeft(ysInputLowerPtr[xsBottom + c]); double topRight(ysInputLowerPtr[xsTop + c]); double bottomLeft(ysInputUpperPtr[xsBottom + c]); @@ -305,19 +305,19 @@ namespace helpers { auto func = PRAGMA_THREADS_FOR_2D { for (auto b = start_x; b < stop_x; b += inc_x) { for (auto y = start_y; y < stop_y; y += inc_y) { - auto posY = alignCorners ? static_cast(nd4j::math::p_round(scaler(y, st.heightScale))) : static_cast(nd4j::math::p_floor(scaler(y, st.heightScale))); - Nd4jLong inY = nd4j::math::nd4j_min(posY, inHeight - 1); + auto posY = alignCorners ? static_cast(sd::math::p_round(scaler(y, st.heightScale))) : static_cast(sd::math::p_floor(scaler(y, st.heightScale))); + Nd4jLong inY = sd::math::nd4j_min(posY, inHeight - 1); if (halfPixelCenter) { - inY = nd4j::math::nd4j_max(0LL, inY); + inY = sd::math::nd4j_max(0LL, inY); } - for (auto x = 0; x < outWidth; ++x) { - auto posX = alignCorners ? static_cast(nd4j::math::p_round(scaler(x, st.widthScale))) : static_cast(nd4j::math::p_floor(scaler(x, st.widthScale))); - Nd4jLong inX = nd4j::math::nd4j_min(posX,inWidth - 1); + for (Nd4jLong x = 0; x < outWidth; ++x) { + auto posX = alignCorners ? static_cast(sd::math::p_round(scaler(x, st.widthScale))) : static_cast(sd::math::p_floor(scaler(x, st.widthScale))); + Nd4jLong inX = sd::math::nd4j_min(posX,inWidth - 1); if (halfPixelCenter) { - inX = nd4j::math::nd4j_max(0LL, inX); + inX = sd::math::nd4j_max(0LL, inX); } // copy pixel over all channels - for (auto e = 0; e < channels; e++) + for (Nd4jLong e = 0; e < channels; e++) output->t(b, y, x, e) = images->t(b, inY, inX, e); } } @@ -355,13 +355,13 @@ namespace helpers { // NUMERIC_TYPES, FLOAT_TYPES); // } - int resizeBilinearFunctor(nd4j::LaunchContext * context, NDArray const *images, int const width, int const height, + int resizeBilinearFunctor(sd::LaunchContext * context, NDArray const *images, int const width, int const height, bool const alignCorners, bool const halfPixelCenter, NDArray *output) { BUILD_DOUBLE_SELECTOR(images->dataType(), output->dataType(), return resizeBilinearFunctor_, (images, width, height, alignCorners, halfPixelCenter, output), NUMERIC_TYPES, FLOAT_TYPES); return Status::OK(); } - int resizeNeighborFunctor(nd4j::LaunchContext * context, NDArray const *images, int const width, int const height, + int resizeNeighborFunctor(sd::LaunchContext * context, NDArray const *images, int const width, int const height, bool const alignCorners, bool const halfPixelCenter, NDArray *output) { BUILD_SINGLE_SELECTOR(images->dataType(), return resizeNeighborFunctor_, (images, width, height, alignCorners, halfPixelCenter, output), LIBND4J_TYPES); } @@ -451,12 +451,12 @@ namespace helpers { } template - int resizeBicubicFunctor_(nd4j::LaunchContext * context, NDArray const* image, int width, int height, + int resizeBicubicFunctor_(sd::LaunchContext * context, NDArray const* image, int width, int height, bool preserveAspectRatio, bool antialias, NDArray* output) { return ND4J_STATUS_OK; } - int resizeBicubicFunctor(nd4j::LaunchContext * context, NDArray const* image, int width, int height, + int resizeBicubicFunctor(sd::LaunchContext * context, NDArray const* image, int width, int height, bool preserveAspectRatio, bool antialias, NDArray* output) { BUILD_SINGLE_SELECTOR(image->dataType(), return resizeBicubicFunctor_, (context, image, width, height, preserveAspectRatio, antialias, output), NUMERIC_TYPES); @@ -613,7 +613,7 @@ namespace helpers { for (auto b = start; b < stop; ++b) { auto pInput = inputPtr + b * inBatchWidth; - for (auto y = 0; y < outHeight; ++y) { + for (Nd4jLong y = 0; y < outHeight; ++y) { auto pOutput = &pOutputY[(b * outHeight + y) * outWidth * numChannels]; WeightsAndIndices yWai; @@ -635,7 +635,7 @@ namespace helpers { F cached_value_0[4] = {0}; F cached_value_1[4] = {0}; F cached_value_2[4] = {0}; - for (auto x = 0; x < resizerState.outWidth; ++x) { + for (Nd4jLong x = 0; x < resizerState.outWidth; ++x) { const WeightsAndIndices &xWai = xWais[x]; // Shift values in cached_value_* to fill first '_advance' values. switch (xWai._advance) { @@ -712,7 +712,7 @@ namespace helpers { xWai._weight2, xWai._weight3); } } else { - for (auto x = 0; x < resizerState.outWidth; ++x) { + for (Nd4jLong x = 0; x < resizerState.outWidth; ++x) { const WeightsAndIndices &xWai = xWais[x]; // Shift values in cachedValue to fill first '_advance' values. switch (xWai._advance) { @@ -780,7 +780,7 @@ namespace helpers { // simplified bicubic resize without antialiasing // template - int resizeBicubicFunctorA_(nd4j::LaunchContext * context, NDArray const* image, int const width, int const height, + int resizeBicubicFunctorA_(sd::LaunchContext * context, NDArray const* image, int const width, int const height, bool const alignCorners, bool const halfPixelAlign, NDArray* output) { ImageResizerState st(alignCorners, halfPixelAlign); // align_corners, half_pixel_align int res = st.validateAndCreateOutput(image, width, height); @@ -789,7 +789,7 @@ namespace helpers { return res; } - int resizeBicubicFunctorA(nd4j::LaunchContext * context, NDArray const* image, int const width, int const height, + int resizeBicubicFunctorA(sd::LaunchContext * context, NDArray const* image, int const width, int const height, bool const alignCorners, bool const halfPixelAlign, NDArray* output) { BUILD_SINGLE_SELECTOR(image->dataType(), return resizeBicubicFunctorA_, (context, image, width, height, alignCorners, halfPixelAlign, output), NUMERIC_TYPES); } @@ -828,7 +828,7 @@ namespace helpers { float sum_0 = 0; float sum_1 = 0; float sum_2 = 0; - for (int i = 0; i < yPtrs.size(); ++i) { + for (size_t i = 0; i < yPtrs.size(); ++i) { const T* ptr = yPtrs[i].yPtr; float scaleX = xCache.startScale; Nd4jLong offset = 3 * boundIfNeeded(xCache.start, st.inWidth); @@ -879,7 +879,7 @@ namespace helpers { const auto numChannels = st.channels; for (Nd4jLong c = 0; c < numChannels; ++c) { float sum = 0; - for (int i = 0; i < yPtrs.size(); ++i) { + for (size_t i = 0; i < yPtrs.size(); ++i) { T const* ptr = yPtrs[i].yPtr; float scaleX = xCache.startScale; float sumY = static_cast(ptr[numChannels * boundIfNeeded(xCache.start, st.inWidth) + c]) * scaleX; @@ -954,7 +954,7 @@ namespace helpers { } template - int resizeAreaFunctor_(nd4j::LaunchContext* context, NDArray const* image, int const width, int const height, + int resizeAreaFunctor_(sd::LaunchContext* context, NDArray const* image, int const width, int const height, bool const alignCorners, NDArray* output) { ImageResizerState st(alignCorners, false); // Create resize info auto res = st.validateAndCalculateOutputSize(image, width, height); @@ -988,13 +988,13 @@ namespace helpers { return res; } - int resizeAreaFunctor(nd4j::LaunchContext * context, NDArray const* image, int const width, int const height, + int resizeAreaFunctor(sd::LaunchContext * context, NDArray const* image, int const width, int const height, bool const alignCorners, NDArray* output) { BUILD_SINGLE_SELECTOR(image->dataType(), return resizeAreaFunctor_, (context, image, width, height, alignCorners, output), NUMERIC_TYPES); } // ------------------------------------------------------------------------------------------------------------------ // - int resizeFunctor(nd4j::LaunchContext * context, NDArray const* image, int const width, int const height, + int resizeFunctor(sd::LaunchContext * context, NDArray const* image, int const width, int const height, ImageResizeMethods method, bool preserveAspectRatio, bool antialias, NDArray* output) { switch (method) { case kResizeBilinear: return resizeBilinearFunctor(context, image, width, height, false, false, output); break; diff --git a/libnd4j/include/ops/declarable/helpers/cpu/image_suppression.cpp b/libnd4j/include/ops/declarable/helpers/cpu/image_suppression.cpp index bf5acd01e..c3ad42db3 100644 --- a/libnd4j/include/ops/declarable/helpers/cpu/image_suppression.cpp +++ b/libnd4j/include/ops/declarable/helpers/cpu/image_suppression.cpp @@ -19,12 +19,12 @@ // #include -#include +#include #include #include #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { @@ -46,26 +46,26 @@ namespace helpers { std::vector selectedIndices(output->lengthOf(), 0); auto needToSuppressWithThreshold = [] (NDArray& boxes, int previousIndex, int nextIndex, T threshold) -> bool { if (previousIndex < 0 || nextIndex < 0) return true; - T minYPrev = nd4j::math::nd4j_min(boxes.t(previousIndex, 0), boxes.t(previousIndex, 2)); - T minXPrev = nd4j::math::nd4j_min(boxes.t(previousIndex, 1), boxes.t(previousIndex, 3)); - T maxYPrev = nd4j::math::nd4j_max(boxes.t(previousIndex, 0), boxes.t(previousIndex, 2)); - T maxXPrev = nd4j::math::nd4j_max(boxes.t(previousIndex, 1), boxes.t(previousIndex, 3)); - T minYNext = nd4j::math::nd4j_min(boxes.t(nextIndex, 0), boxes.t(nextIndex, 2)); - T minXNext = nd4j::math::nd4j_min(boxes.t(nextIndex, 1), boxes.t(nextIndex, 3)); - T maxYNext = nd4j::math::nd4j_max(boxes.t(nextIndex, 0), boxes.t(nextIndex, 2)); - T maxXNext = nd4j::math::nd4j_max(boxes.t(nextIndex, 1), boxes.t(nextIndex, 3)); + T minYPrev = sd::math::nd4j_min(boxes.t(previousIndex, 0), boxes.t(previousIndex, 2)); + T minXPrev = sd::math::nd4j_min(boxes.t(previousIndex, 1), boxes.t(previousIndex, 3)); + T maxYPrev = sd::math::nd4j_max(boxes.t(previousIndex, 0), boxes.t(previousIndex, 2)); + T maxXPrev = sd::math::nd4j_max(boxes.t(previousIndex, 1), boxes.t(previousIndex, 3)); + T minYNext = sd::math::nd4j_min(boxes.t(nextIndex, 0), boxes.t(nextIndex, 2)); + T minXNext = sd::math::nd4j_min(boxes.t(nextIndex, 1), boxes.t(nextIndex, 3)); + T maxYNext = sd::math::nd4j_max(boxes.t(nextIndex, 0), boxes.t(nextIndex, 2)); + T maxXNext = sd::math::nd4j_max(boxes.t(nextIndex, 1), boxes.t(nextIndex, 3)); T areaPrev = (maxYPrev - minYPrev) * (maxXPrev - minXPrev); T areaNext = (maxYNext - minYNext) * (maxXNext - minXNext); if (areaNext <= T(0.f) || areaPrev <= T(0.f)) return false; - T minIntersectionY = nd4j::math::nd4j_max(minYPrev, minYNext); - T minIntersectionX = nd4j::math::nd4j_max(minXPrev, minXNext); - T maxIntersectionY = nd4j::math::nd4j_min(maxYPrev, maxYNext); - T maxIntersectionX = nd4j::math::nd4j_min(maxXPrev, maxXNext); + T minIntersectionY = sd::math::nd4j_max(minYPrev, minYNext); + T minIntersectionX = sd::math::nd4j_max(minXPrev, minXNext); + T maxIntersectionY = sd::math::nd4j_min(maxYPrev, maxYNext); + T maxIntersectionX = sd::math::nd4j_min(maxXPrev, maxXNext); T intersectionArea = - nd4j::math::nd4j_max(T(maxIntersectionY - minIntersectionY), T(0.0f)) * - nd4j::math::nd4j_max(T(maxIntersectionX - minIntersectionX), T(0.0f)); + sd::math::nd4j_max(T(maxIntersectionY - minIntersectionY), T(0.0f)) * + sd::math::nd4j_max(T(maxIntersectionX - minIntersectionX), T(0.0f)); T intersectionValue = intersectionArea / (areaPrev + areaNext - intersectionArea); return intersectionValue > threshold; @@ -140,7 +140,7 @@ namespace helpers { //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// template static Nd4jLong - nonMaxSuppressionGeneric_(nd4j::LaunchContext* context, NDArray* boxes, NDArray* scores, int outputSize, + nonMaxSuppressionGeneric_(sd::LaunchContext* context, NDArray* boxes, NDArray* scores, int outputSize, float overlapThreshold, float scoreThreshold, NDArray* output, SimiliratyFunc f) { auto numBoxes = boxes->sizeAt(0); @@ -232,24 +232,24 @@ namespace helpers { } Nd4jLong - nonMaxSuppressionGeneric(nd4j::LaunchContext* context, NDArray* boxes, NDArray* scores, int maxSize, + nonMaxSuppressionGeneric(sd::LaunchContext* context, NDArray* boxes, NDArray* scores, int maxSize, double overlapThreshold, double scoreThreshold, NDArray* output) { BUILD_DOUBLE_SELECTOR(boxes->dataType(), output == nullptr?DataType::INT32:output->dataType(), return nonMaxSuppressionGeneric_, (context, boxes, scores, maxSize, overlapThreshold, scoreThreshold, output, similiratyOverlaps), FLOAT_TYPES, INTEGER_TYPES); return 0; } Nd4jLong - nonMaxSuppressionV3(nd4j::LaunchContext* context, NDArray* boxes, NDArray* scores, int maxSize, + nonMaxSuppressionV3(sd::LaunchContext* context, NDArray* boxes, NDArray* scores, int maxSize, double overlapThreshold, double scoreThreshold, NDArray* output) { BUILD_DOUBLE_SELECTOR(boxes->dataType(), output == nullptr?DataType::INT32:output->dataType(), return nonMaxSuppressionGeneric_, (context, boxes, scores, maxSize, overlapThreshold, scoreThreshold, output, similiratyV3), FLOAT_TYPES, INTEGER_TYPES); return 0; } - BUILD_DOUBLE_TEMPLATE(template Nd4jLong nonMaxSuppressionGeneric_, (nd4j::LaunchContext* context, NDArray* boxes, NDArray* scores, int maxSize, + BUILD_DOUBLE_TEMPLATE(template Nd4jLong nonMaxSuppressionGeneric_, (sd::LaunchContext* context, NDArray* boxes, NDArray* scores, int maxSize, float overlapThreshold, float scoreThreshold, NDArray* output, SimiliratyFunc similiratyFunc), FLOAT_TYPES, INTEGER_TYPES); void - nonMaxSuppression(nd4j::LaunchContext * context, NDArray* boxes, NDArray* scales, int maxSize, + nonMaxSuppression(sd::LaunchContext * context, NDArray* boxes, NDArray* scales, int maxSize, double overlapThreshold, double scoreThreshold, NDArray* output) { BUILD_SINGLE_SELECTOR(boxes->dataType(), nonMaxSuppressionV2_, (boxes, scales, maxSize, overlapThreshold, scoreThreshold, output), NUMERIC_TYPES); diff --git a/libnd4j/include/ops/declarable/helpers/cpu/imagesHelpers.cpp b/libnd4j/include/ops/declarable/helpers/cpu/imagesHelpers.cpp index b98e7f026..682677ef3 100644 --- a/libnd4j/include/ops/declarable/helpers/cpu/imagesHelpers.cpp +++ b/libnd4j/include/ops/declarable/helpers/cpu/imagesHelpers.cpp @@ -24,7 +24,7 @@ #include #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { @@ -51,22 +51,22 @@ static void rgbToGrs_(const NDArray& input, NDArray& output, const int dimC) { auto func = PRAGMA_THREADS_FOR{ - Nd4jLong coords[MAX_RANK]; - for (auto i = start; i < stop; i++) { - shape::index2coords(i, output.getShapeInfo(), coords); - const auto zOffset = shape::getOffset(output.getShapeInfo(), coords); - const auto xOffset0 = shape::getOffset(input.getShapeInfo(), coords); - const auto xOffset1 = xOffset0 + input.strideAt(dimC); - const auto xOffset2 = xOffset1 + input.strideAt(dimC); - z[zOffset] = 0.2989f*x[xOffset0] + 0.5870f*x[xOffset1] + 0.1140f*x[xOffset2]; - } - }; + int coords[MAX_RANK]; + for (auto i = start; i < stop; i++) { + shape::index2coordsCPU(start, i, output.getShapeInfo(), coords); + const auto zOffset = shape::getOffset(output.getShapeInfo(), coords); + const auto xOffset0 = shape::getOffset(input.getShapeInfo(), coords); + const auto xOffset1 = xOffset0 + input.strideAt(dimC); + const auto xOffset2 = xOffset1 + input.strideAt(dimC); + z[zOffset] = 0.2989f*x[xOffset0] + 0.5870f*x[xOffset1] + 0.1140f*x[xOffset2]; + } + }; - samediff::Threads::parallel_for(func, 0, output.lengthOf(), 1); - return; + samediff::Threads::parallel_for(func, 0, output.lengthOf(), 1); + return; } -void transformRgbGrs(nd4j::LaunchContext* context, const NDArray& input, NDArray& output, const int dimC) { +void transformRgbGrs(sd::LaunchContext* context, const NDArray& input, NDArray& output, const int dimC) { BUILD_SINGLE_SELECTOR(input.dataType(), rgbToGrs_, (input, output, dimC), NUMERIC_TYPES); } @@ -78,9 +78,9 @@ FORCEINLINE static void rgbToFromYuv_(const NDArray& input, NDArray& output, con const int rank = input.rankOf(); bool bSimple = (dimC == rank - 1 && 'c' == input.ordering() && 1 == input.ews() && 'c' == output.ordering() && 1 == output.ews()); - + if (bSimple) { - + auto func = PRAGMA_THREADS_FOR{ for (auto i = start; i < stop; i += increment) { op(x[i], x[i + 1], x[i + 2], z[i], z[i + 1], z[i + 2]); @@ -91,8 +91,8 @@ FORCEINLINE static void rgbToFromYuv_(const NDArray& input, NDArray& output, con return; } - auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(input.getShapeInfo(), dimC); - auto packZ = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(output.getShapeInfo(), dimC); + auto packX = sd::ConstantTadHelper::getInstance()->tadForDimensions(input.getShapeInfo(), dimC); + auto packZ = sd::ConstantTadHelper::getInstance()->tadForDimensions(output.getShapeInfo(), dimC); const Nd4jLong numOfTads = packX.numberOfTads(); const Nd4jLong xDimCstride = input.stridesOf()[dimC]; @@ -112,21 +112,21 @@ FORCEINLINE static void rgbToFromYuv_(const NDArray& input, NDArray& output, con template FORCEINLINE static void rgbYuv_(const NDArray& input, NDArray& output, const int dimC) { - auto op = nd4j::ops::helpers::rgbYuv; + auto op = sd::ops::helpers::rgbYuv; return rgbToFromYuv_(input, output, dimC, op); } -void transformRgbYuv(nd4j::LaunchContext* context, const NDArray& input, NDArray& output, const int dimC) { +void transformRgbYuv(sd::LaunchContext* context, const NDArray& input, NDArray& output, const int dimC) { BUILD_SINGLE_SELECTOR(input.dataType(), rgbYuv_, (input, output, dimC), FLOAT_TYPES); } template FORCEINLINE static void yuvRgb_(const NDArray& input, NDArray& output, const int dimC) { - auto op = nd4j::ops::helpers::yuvRgb; + auto op = sd::ops::helpers::yuvRgb; return rgbToFromYuv_(input, output, dimC, op); } -void transformYuvRgb(nd4j::LaunchContext* context, const NDArray& input, NDArray& output, const int dimC) { +void transformYuvRgb(sd::LaunchContext* context, const NDArray& input, NDArray& output, const int dimC) { BUILD_SINGLE_SELECTOR(input.dataType(), yuvRgb_, (input, output, dimC), FLOAT_TYPES); } @@ -149,8 +149,8 @@ FORCEINLINE static void tripleTransformer(const NDArray* input, NDArray* output, samediff::Threads::parallel_for(func, 0, input->lengthOf(), 3); } else { - auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), dimC); - auto packZ = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), dimC); + auto packX = sd::ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), dimC); + auto packZ = sd::ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), dimC); const Nd4jLong numOfTads = packX.numberOfTads(); const Nd4jLong xDimCstride = input->stridesOf()[dimC]; @@ -177,12 +177,12 @@ FORCEINLINE static void tripleTransformer(const NDArray* input, NDArray* output, const T* x = input->bufferAsT(); T* z = output->bufferAsT(); - // TODO: Use tensordot or other optimizied helpers to see if we can get better performance. + // TODO: Use tensordot or other optimizied helpers to see if we can get better performance. if (dimC == rank - 1 && input->ews() == 1 && output->ews() == 1 && input->ordering() == 'c' && output->ordering() == 'c') { auto func = PRAGMA_THREADS_FOR{ - for (auto i = start; i < stop; i += increment) { + for (auto i = start; i < stop; i += increment) { //simple M*v //tr.T*v.T // v * tr //rule: (AB)' =B'A' // v.shape (1,3) row vector T x0, x1, x2; @@ -192,15 +192,15 @@ FORCEINLINE static void tripleTransformer(const NDArray* input, NDArray* output, z[i] = x0 * tr[0][0] + x1 * tr[1][0] + x2 * tr[2][0]; z[i+1] = x0 * tr[0][1] + x1 * tr[1][1] + x2 * tr[2][1]; z[i+2] = x0 * tr[0][2] + x1 * tr[1][2] + x2 * tr[2][2]; - + } }; samediff::Threads::parallel_for(func, 0, input->lengthOf(), 3); } else { - auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), dimC); - auto packZ = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), dimC); + auto packX = sd::ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), dimC); + auto packZ = sd::ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), dimC); const Nd4jLong numOfTads = packX.numberOfTads(); const Nd4jLong xDimCstride = input->stridesOf()[dimC]; @@ -231,13 +231,13 @@ FORCEINLINE static void tripleTransformer(const NDArray* input, NDArray* output, template FORCEINLINE static void hsvRgb(const NDArray* input, NDArray* output, const int dimC) { - auto op = nd4j::ops::helpers::hsvToRgb; + auto op = sd::ops::helpers::hsvToRgb; return tripleTransformer(input, output, dimC, op); } template FORCEINLINE static void rgbHsv(const NDArray* input, NDArray* output, const int dimC) { - auto op = nd4j::ops::helpers::rgbToHsv; + auto op = sd::ops::helpers::rgbToHsv; return tripleTransformer(input, output, dimC, op); } @@ -266,19 +266,19 @@ FORCEINLINE static void yiqRgb(const NDArray* input, NDArray* output, const int -void transformHsvRgb(nd4j::LaunchContext* context, const NDArray* input, NDArray* output, const int dimC) { +void transformHsvRgb(sd::LaunchContext* context, const NDArray* input, NDArray* output, const int dimC) { BUILD_SINGLE_SELECTOR(input->dataType(), hsvRgb, (input, output, dimC), FLOAT_TYPES); } -void transformRgbHsv(nd4j::LaunchContext* context, const NDArray* input, NDArray* output, const int dimC) { +void transformRgbHsv(sd::LaunchContext* context, const NDArray* input, NDArray* output, const int dimC) { BUILD_SINGLE_SELECTOR(input->dataType(), rgbHsv, (input, output, dimC), FLOAT_TYPES); } -void transformYiqRgb(nd4j::LaunchContext* context, const NDArray* input, NDArray* output, const int dimC) { +void transformYiqRgb(sd::LaunchContext* context, const NDArray* input, NDArray* output, const int dimC) { BUILD_SINGLE_SELECTOR(input->dataType(), yiqRgb, (input, output, dimC), FLOAT_TYPES); } -void transformRgbYiq(nd4j::LaunchContext* context, const NDArray* input, NDArray* output, const int dimC) { +void transformRgbYiq(sd::LaunchContext* context, const NDArray* input, NDArray* output, const int dimC) { BUILD_SINGLE_SELECTOR(input->dataType(), rgbYiq, (input, output, dimC), FLOAT_TYPES); } diff --git a/libnd4j/include/ops/declarable/helpers/cpu/ismax.cpp b/libnd4j/include/ops/declarable/helpers/cpu/ismax.cpp index 1fea8e4fe..5a4bb28cc 100644 --- a/libnd4j/include/ops/declarable/helpers/cpu/ismax.cpp +++ b/libnd4j/include/ops/declarable/helpers/cpu/ismax.cpp @@ -25,7 +25,7 @@ #include #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { @@ -125,8 +125,8 @@ static void ismax_(const NDArray* input, NDArray* output, const std::vector //moving all dimensions (in sorted order) //to the back. //permuted version of the input shape info for setting up the tad problem - auto tadPack = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), const_cast(dimensions.data()), dimensionsLength); - auto tadPackZ = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), const_cast(dimensions.data()), dimensionsLength); + auto tadPack = sd::ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), const_cast(dimensions.data()), dimensionsLength); + auto tadPackZ = sd::ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), const_cast(dimensions.data()), dimensionsLength); auto tadShapeShapeInfo = tadPack.primaryShapeInfo(); @@ -137,8 +137,8 @@ static void ismax_(const NDArray* input, NDArray* output, const std::vector int tads = tadPack.numberOfTads(); int tadsPerThread = tads / TAD_THRESHOLD; - int num_threads = nd4j::math::nd4j_max(1, tadsPerThread); - num_threads = nd4j::math::nd4j_min(num_threads, omp_get_max_threads()); + int num_threads = sd::math::nd4j_max(1, tadsPerThread); + num_threads = sd::math::nd4j_min(num_threads, omp_get_max_threads()); auto tadEWS = shape::elementWiseStride(tadShapeShapeInfo); auto zEWS = shape::elementWiseStride(tadPackZ.primaryShapeInfo()); @@ -200,7 +200,7 @@ static void ismax_(const NDArray* input, NDArray* output, const std::vector } -void ismax(nd4j::LaunchContext * context, const NDArray *input, NDArray *output, const std::vector& dimensions) { +void ismax(sd::LaunchContext * context, const NDArray *input, NDArray *output, const std::vector& dimensions) { BUILD_DOUBLE_SELECTOR(input->dataType(), output->dataType(), ismax_, (input, output, dimensions), LIBND4J_TYPES, LIBND4J_TYPES); } diff --git a/libnd4j/include/ops/declarable/helpers/cpu/legacy_helper.cpp b/libnd4j/include/ops/declarable/helpers/cpu/legacy_helper.cpp index 4db975ddf..b2a0e537f 100644 --- a/libnd4j/include/ops/declarable/helpers/cpu/legacy_helper.cpp +++ b/libnd4j/include/ops/declarable/helpers/cpu/legacy_helper.cpp @@ -19,10 +19,10 @@ // #include -#include +#include #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { template @@ -34,7 +34,7 @@ namespace helpers { theFirst->applyPairwiseLambda(*theSecond, functor, *theFirst); } - void reluDerivative(nd4j::LaunchContext * context, NDArray* theFirst, NDArray* theSecond) { + void reluDerivative(sd::LaunchContext * context, NDArray* theFirst, NDArray* theSecond) { BUILD_SINGLE_SELECTOR(theFirst->dataType(), reluDerivative__, (theFirst, theSecond), FLOAT_TYPES); } @@ -64,7 +64,7 @@ namespace helpers { */ } - void reluDerivative(nd4j::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput) { + void reluDerivative(sd::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput) { BUILD_SINGLE_SELECTOR(theFirst->dataType(), reluDerivative_, (theFirst, theSecond, theOutput), FLOAT_TYPES); } @@ -77,7 +77,7 @@ namespace helpers { input->applyPairwiseLambda(*epsilon, functor, *output); } - void relu6Derivative(nd4j::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput) { + void relu6Derivative(sd::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput) { BUILD_SINGLE_SELECTOR(theFirst->dataType(), relu6Derivative_, (theFirst, theSecond, theOutput), FLOAT_TYPES); } @@ -93,7 +93,7 @@ namespace helpers { input->applyPairwiseLambda(*epsilon, functor, *output); } - void leakyReluDerivative(nd4j::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput, const float alpha) { + void leakyReluDerivative(sd::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput, const float alpha) { BUILD_SINGLE_SELECTOR(theFirst->dataType(), leakyReluDerivative_, (theFirst, theSecond, theOutput, alpha), FLOAT_TYPES); } @@ -103,13 +103,13 @@ namespace helpers { const T alphaT = static_cast(alpha); auto functor = LAMBDA_TT(x, y, alphaT){ - return y * nd4j::math::nd4j_eluderivative(x, alphaT); + return y * sd::math::nd4j_eluderivative(x, alphaT); }; input->applyPairwiseLambda(*epsilon, functor, *output); } - void eluDerivative(nd4j::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput, const float alpha) { + void eluDerivative(sd::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput, const float alpha) { BUILD_SINGLE_SELECTOR(theFirst->dataType(), eluDerivative_, (theFirst, theSecond, theOutput, alpha), FLOAT_TYPES); } @@ -122,7 +122,7 @@ namespace helpers { input->applyPairwiseLambda(*epsilon, functor, *output); } - void seluDerivative(nd4j::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput) { + void seluDerivative(sd::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput) { BUILD_SINGLE_SELECTOR(theFirst->dataType(), seluDerivative_, (theFirst, theSecond, theOutput), FLOAT_TYPES); } @@ -135,7 +135,7 @@ namespace helpers { input->applyPairwiseLambda(*epsilon, functor, *output); } - void cubeDerivative(nd4j::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput) { + void cubeDerivative(sd::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput) { BUILD_SINGLE_SELECTOR(theFirst->dataType(), cubeDerivative_, (theFirst, theSecond, theOutput), FLOAT_TYPES); } @@ -149,7 +149,7 @@ namespace helpers { input->applyPairwiseLambda(*epsilon, functor, *output); } - void reduceNorm1(nd4j::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput) { + void reduceNorm1(sd::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput) { BUILD_SINGLE_SELECTOR(theFirst->dataType(), reduceNorm1_, (theFirst, theSecond, theOutput), FLOAT_TYPES); } @@ -157,13 +157,13 @@ namespace helpers { template static void sigmCrossEntropy_(NDArray* logits, NDArray* labels, NDArray* output) { auto functor = LAMBDA_TT(x, y){ - return nd4j::math::nd4j_max(x, (T)0.f) - x * y + nd4j::math::nd4j_log((T)1.f + nd4j::math::nd4j_exp(-nd4j::math::nd4j_abs(x))); + return sd::math::nd4j_max(x, (T)0.f) - x * y + sd::math::nd4j_log((T)1.f + sd::math::nd4j_exp(-sd::math::nd4j_abs(x))); }; logits->applyPairwiseLambda(*labels, functor, *output); } - void sigmCrossEntropy(nd4j::LaunchContext * context, NDArray* logits, NDArray* labels, NDArray* output) { + void sigmCrossEntropy(sd::LaunchContext * context, NDArray* logits, NDArray* labels, NDArray* output) { BUILD_SINGLE_SELECTOR(logits->dataType(), sigmCrossEntropy_, (logits, labels, output), FLOAT_TYPES); } @@ -173,15 +173,15 @@ namespace helpers { // 1 - labels - 1 / (1 + exp(logits)) auto functor = LAMBDA_TT(x, y) { if(x <= 0) - return static_cast(1.) - y - static_cast(1.) / (static_cast(1.) + nd4j::math::nd4j_exp(x)); - auto e = nd4j::math::nd4j_exp(-x); + return static_cast(1.) - y - static_cast(1.) / (static_cast(1.) + sd::math::nd4j_exp(x)); + auto e = sd::math::nd4j_exp(-x); return static_cast(1.) - y - e / (static_cast(1.) + e); }; logits->applyPairwiseLambda(*labels, functor, *output); } - void sigmCrossEntropyGrad(nd4j::LaunchContext * context, NDArray* logits, NDArray* labels, NDArray* output) { + void sigmCrossEntropyGrad(sd::LaunchContext * context, NDArray* logits, NDArray* labels, NDArray* output) { BUILD_SINGLE_SELECTOR(logits->dataType(), sigmCrossEntropyGrad_, (logits, labels, output), FLOAT_TYPES); } @@ -189,14 +189,14 @@ namespace helpers { template static void tanhDerivative_(NDArray* input, NDArray* epsilon, NDArray* output) { auto functor = LAMBDA_TT(x, y){ - T th = nd4j::math::nd4j_tanh(x); + T th = sd::math::nd4j_tanh(x); return y * ((T)1.0f - (th * th)); }; input->applyPairwiseLambda(*epsilon, functor, *output); } - void tanhDerivative(nd4j::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput) { + void tanhDerivative(sd::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput) { BUILD_SINGLE_SELECTOR(theFirst->dataType(), tanhDerivative_, (theFirst, theSecond, theOutput), FLOAT_TYPES); } @@ -204,14 +204,14 @@ namespace helpers { template static void hardTanhDerivative_(NDArray* input, NDArray* epsilon, NDArray* output) { auto functor = LAMBDA_TT(x, y){ - T th = nd4j::math::nd4j_tanh(x); + T th = sd::math::nd4j_tanh(x); return y * simdOps::HardTanhDerivative::op(x, nullptr); }; input->applyPairwiseLambda(*epsilon, functor, *output); } - void hardTanhDerivative(nd4j::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput) { + void hardTanhDerivative(sd::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput) { BUILD_SINGLE_SELECTOR(theFirst->dataType(), hardTanhDerivative_, (theFirst, theSecond, theOutput), FLOAT_TYPES); } @@ -224,51 +224,51 @@ namespace helpers { input->applyPairwiseLambda(*epsilon, functor, *output); } - void rationalTanhDerivative(nd4j::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput) { + void rationalTanhDerivative(sd::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput) { BUILD_SINGLE_SELECTOR(theFirst->dataType(), rationalTanhDerivative_, (theFirst, theSecond, theOutput), FLOAT_TYPES); } template static void rectifiedTanhDerivative_(NDArray* input, NDArray* epsilon, NDArray* output) { auto functor = LAMBDA_TT(x, y){ - return x > (T) 0.0f ? y * (nd4j::math::nd4j_tanhderivative(x)) : (T) 0.0f; + return x > (T) 0.0f ? y * (sd::math::nd4j_tanhderivative(x)) : (T) 0.0f; }; input->applyPairwiseLambda(*epsilon, functor, *output); } - void rectifiedTanhDerivative(nd4j::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput) { + void rectifiedTanhDerivative(sd::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput) { BUILD_SINGLE_SELECTOR(theFirst->dataType(), rectifiedTanhDerivative_, (theFirst, theSecond, theOutput), FLOAT_TYPES); } - // X f = (X) 1.0f + nd4j::math::nd4j_abs(d1); + // X f = (X) 1.0f + sd::math::nd4j_abs(d1); // return (X) d2 * ((X) 1.0f / (f * f)); template static void softSignDerivative_(NDArray* input, NDArray* epsilon, NDArray* output) { auto functor = LAMBDA_TT(x, y){ - T ss = (T)1.f + nd4j::math::nd4j_abs(x); + T ss = (T)1.f + sd::math::nd4j_abs(x); return y * ((T) 1.0f / (ss * ss)); }; input->applyPairwiseLambda(*epsilon, functor, *output); } - void softSignDerivative(nd4j::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput) { + void softSignDerivative(sd::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput) { BUILD_SINGLE_SELECTOR(theFirst->dataType(), softSignDerivative_, (theFirst, theSecond, theOutput), FLOAT_TYPES); } template static void softPlusDerivative_(NDArray* input, NDArray* epsilon, NDArray* output) { auto functor = LAMBDA_TT(x, y){ - T p = nd4j::math::nd4j_pow(static_cast(M_E), x); + T p = sd::math::nd4j_pow(static_cast(M_E), x); return y * (p / (p + 1.)); }; input->applyPairwiseLambda(*epsilon, functor, *output); } - void softPlusDerivative(nd4j::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput) { + void softPlusDerivative(sd::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput) { BUILD_SINGLE_SELECTOR(theFirst->dataType(), softPlusDerivative_, (theFirst, theSecond, theOutput), FLOAT_TYPES); } /// @@ -278,14 +278,14 @@ namespace helpers { template static void sigmoidDerivative_(NDArray* input, NDArray* epsilon, NDArray* output) { auto functor = LAMBDA_TT(x, y){ - T s = nd4j::math::nd4j_sigmoid(x); + T s = sd::math::nd4j_sigmoid(x); return y * (s * ((T) 1.0f - s)); }; input->applyPairwiseLambda(*epsilon, functor, *output); } - void sigmoidDerivative(nd4j::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput) { + void sigmoidDerivative(sd::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput) { BUILD_SINGLE_SELECTOR(theFirst->dataType(), sigmoidDerivative_, (theFirst, theSecond, theOutput), FLOAT_TYPES); } @@ -298,7 +298,7 @@ namespace helpers { input->applyPairwiseLambda(*epsilon, functor, *output); } - void hardSigmoidDerivative(nd4j::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput) { + void hardSigmoidDerivative(sd::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput) { BUILD_SINGLE_SELECTOR(theFirst->dataType(), hardSigmoidDerivative_, (theFirst, theSecond, theOutput), FLOAT_TYPES); } @@ -334,11 +334,11 @@ namespace helpers { output->applyTransform(transform::Log, *output); } - void logSumExp(nd4j::LaunchContext * context, NDArray* input, NDArray* axis, NDArray* output) { + void logSumExp(sd::LaunchContext * context, NDArray* input, NDArray* axis, NDArray* output) { BUILD_SINGLE_SELECTOR(input->dataType(), logSumExp_, (input, axis, output), FLOAT_TYPES); } - void logSumExp(nd4j::LaunchContext * context, NDArray* input, NDArray* subtrah, NDArray* axis, NDArray* output) { + void logSumExp(sd::LaunchContext * context, NDArray* input, NDArray* subtrah, NDArray* axis, NDArray* output) { BUILD_SINGLE_SELECTOR(input->dataType(), logSumExp_, (input, subtrah, axis, output), FLOAT_TYPES); } @@ -351,15 +351,15 @@ static void weightedCrossEntropyWithLogitsFunctor_(NDArray const* targets, NDArr auto mainRoutineT1 = LAMBDA_TT(_x, _z, posWeight) { T targetWeight = (1. + (posWeight - (T)1.f) * _z); return (1. - _z) * _x + - targetWeight * (nd4j::math::nd4j_log((T)1.f + nd4j::math::nd4j_exp(-nd4j::math::nd4j_abs(_x))) + - nd4j::math::nd4j_max(-_x, T(0.f)) + targetWeight * (sd::math::nd4j_log((T)1.f + sd::math::nd4j_exp(-sd::math::nd4j_abs(_x))) + + sd::math::nd4j_max(-_x, T(0.f)) ); }; auto mainRoutineT2 = LAMBDA_TTT(_x, _z, _w) { return (((T)1.0 - _z) * _x) + - _w * (nd4j::math::nd4j_log(T(1.) + nd4j::math::nd4j_exp(-nd4j::math::nd4j_abs(_x))) + - nd4j::math::nd4j_max(-_x, T(0.f))); + _w * (sd::math::nd4j_log(T(1.) + sd::math::nd4j_exp(-sd::math::nd4j_abs(_x))) + + sd::math::nd4j_max(-_x, T(0.f))); }; @@ -377,7 +377,7 @@ static void weightedCrossEntropyWithLogitsFunctor_(NDArray const* targets, NDArr } } -void weightedCrossEntropyWithLogitsFunctor(nd4j::LaunchContext * context, NDArray const* targets, NDArray const* input, NDArray const* weights, NDArray* output) { +void weightedCrossEntropyWithLogitsFunctor(sd::LaunchContext * context, NDArray const* targets, NDArray const* input, NDArray const* weights, NDArray* output) { BUILD_SINGLE_SELECTOR(targets->dataType(), weightedCrossEntropyWithLogitsFunctor_, (targets, input, weights, output), FLOAT_TYPES); } diff --git a/libnd4j/include/ops/declarable/helpers/cpu/lgamma.cpp b/libnd4j/include/ops/declarable/helpers/cpu/lgamma.cpp index 2978a9d45..3b71f7ce9 100644 --- a/libnd4j/include/ops/declarable/helpers/cpu/lgamma.cpp +++ b/libnd4j/include/ops/declarable/helpers/cpu/lgamma.cpp @@ -22,7 +22,7 @@ #include #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { @@ -38,7 +38,7 @@ static void lgamma_(NDArray& x, NDArray& z) { x.applyLambda(lgammaProc, z); } -void lgamma(nd4j::LaunchContext* context, NDArray& x, NDArray& z) { +void lgamma(sd::LaunchContext* context, NDArray& x, NDArray& z) { BUILD_SINGLE_SELECTOR(x.dataType(), lgamma_, (x, z), FLOAT_TYPES); } diff --git a/libnd4j/include/ops/declarable/helpers/cpu/lrn.cpp b/libnd4j/include/ops/declarable/helpers/cpu/lrn.cpp index aeb9e38b0..31235d737 100644 --- a/libnd4j/include/ops/declarable/helpers/cpu/lrn.cpp +++ b/libnd4j/include/ops/declarable/helpers/cpu/lrn.cpp @@ -20,28 +20,28 @@ // #include -#include -#include +#include +#include #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { template -static int lrnFunctor_(nd4j::graph::Context& block, NDArray* input, NDArray* output, int depth, float bias, float alpha, float beta) { +static int lrnFunctor_(sd::graph::Context& block, NDArray* input, NDArray* output, int depth, float bias, float alpha, float beta) { nd4j_debug("MKL-DNN is not used for lrn!\n", 0); const int rank = input->rankOf(); - TadPack inTadPack = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), {rank - 1}); + TadPack inTadPack = sd::ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), {rank - 1}); TadPack outTadPack; if(shape::haveSameShapeAndStrides(input->getShapeInfo(), output->getShapeInfo())) outTadPack = inTadPack; else - outTadPack = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), {rank - 1}); + outTadPack = sd::ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), {rank - 1}); const Nd4jLong numOfTads = inTadPack.numberOfTads(); const Nd4jLong tadLen = input->sizeAt(-1); @@ -62,7 +62,7 @@ static int lrnFunctor_(nd4j::graph::Context& block, NDArray* input, NDArray* out if(inTadEws == 1 && outTadEws == 1) { auto func = PRAGMA_THREADS_FOR { - for (uint i = start; i < stop; i++) { + for (auto i = start; i < stop; i++) { const T *x = inBuff + inTadOffsets[i]; T *y = outBuff + outTadOffsets[i]; @@ -70,10 +70,10 @@ static int lrnFunctor_(nd4j::graph::Context& block, NDArray* input, NDArray* out // calculate squared sum of elements per each j-th element range [j - depth, j + depth + 1] // we store each squared sum in corresponding element of y array - for (uint j = 0; j < tadLen; ++j) { - const uint begin = nd4j::math::nd4j_max(0, j - depth); + for (Nd4jLong j = 0; j < tadLen; ++j) { + const uint begin = sd::math::nd4j_max(0, j - depth); const uint last = depth + j + 1; - const uint end = nd4j::math::nd4j_min(last, tadLen); + const uint end = sd::math::nd4j_min(last, tadLen); if (j == 0) { for (uint s = begin; s < end; ++s) @@ -91,7 +91,7 @@ static int lrnFunctor_(nd4j::graph::Context& block, NDArray* input, NDArray* out if (j != 0) prev = y[j]; - y[j] = x[j] / nd4j::math::nd4j_pow(tbias + alpha * prev, tbeta); + y[j] = x[j] / sd::math::nd4j_pow(tbias + alpha * prev, tbeta); } } }; @@ -100,7 +100,7 @@ static int lrnFunctor_(nd4j::graph::Context& block, NDArray* input, NDArray* out } else { auto func = PRAGMA_THREADS_FOR { - for (uint i = 0; i < numOfTads; ++i) { + for (Nd4jLong i = 0; i < numOfTads; ++i) { const T *x = inBuff + inTadOffsets[i]; T *y = outBuff + outTadOffsets[i]; @@ -108,10 +108,10 @@ static int lrnFunctor_(nd4j::graph::Context& block, NDArray* input, NDArray* out // calculate squared sum of elements per each j-th element range [j - depth, j + depth + 1] // we store each squared sum in corresponding element of y array - for (uint j = 0; j < tadLen; ++j) { - const uint begin = nd4j::math::nd4j_max(0, j - depth); + for (Nd4jLong j = 0; j < tadLen; ++j) { + const uint begin = sd::math::nd4j_max(0, j - depth); const uint last = depth + j + 1; - const uint end = nd4j::math::nd4j_min(last, tadLen); + const uint end = sd::math::nd4j_min(last, tadLen); if (j == 0) { for (uint s = begin; s < end; ++s) @@ -129,7 +129,7 @@ static int lrnFunctor_(nd4j::graph::Context& block, NDArray* input, NDArray* out if (j != 0) prev = y[j * outTadEws]; - y[j * outTadEws] = x[j * inTadEws] / nd4j::math::nd4j_pow(tbias + alpha * prev, tbeta); + y[j * outTadEws] = x[j * inTadEws] / sd::math::nd4j_pow(tbias + alpha * prev, tbeta); } } }; @@ -139,9 +139,9 @@ static int lrnFunctor_(nd4j::graph::Context& block, NDArray* input, NDArray* out return Status::OK(); } -BUILD_SINGLE_TEMPLATE(template int lrnFunctor_, (nd4j::graph::Context& block, NDArray* input, NDArray* output, int depth, float bias, float alpha, float beta), FLOAT_TYPES); +BUILD_SINGLE_TEMPLATE(template int lrnFunctor_, (sd::graph::Context& block, NDArray* input, NDArray* output, int depth, float bias, float alpha, float beta), FLOAT_TYPES); -int lrnFunctor(nd4j::graph::Context& block, NDArray* input, NDArray* output, int depth, double bias, double alpha, double beta) { +int lrnFunctor(sd::graph::Context& block, NDArray* input, NDArray* output, int depth, double bias, double alpha, double beta) { BUILD_SINGLE_SELECTOR(input->dataType(), return lrnFunctor_, (block, input, output, depth, bias, alpha, beta), FLOAT_TYPES); } @@ -151,13 +151,13 @@ static void lrnBP_(const NDArray& input, const NDArray& gradO, NDArray& gradI, c const int rank = input.rankOf(); - TadPack inTadPack = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(input.getShapeInfo(), {rank - 1}); + TadPack inTadPack = sd::ConstantTadHelper::getInstance()->tadForDimensions(input.getShapeInfo(), {rank - 1}); TadPack gradITadPack; if(shape::haveSameShapeAndStrides(input.getShapeInfo(), gradI.getShapeInfo())) gradITadPack = inTadPack; else - gradITadPack = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(gradI.getShapeInfo(), {rank - 1}); + gradITadPack = sd::ConstantTadHelper::getInstance()->tadForDimensions(gradI.getShapeInfo(), {rank - 1}); const Nd4jLong numOfTads = inTadPack.numberOfTads(); const Nd4jLong tadLen = input.sizeAt(-1); @@ -179,16 +179,16 @@ static void lrnBP_(const NDArray& input, const NDArray& gradO, NDArray& gradI, c if(inTadEws == 1 && gradITadEws == 1) { auto func = PRAGMA_THREADS_FOR { - for (uint i = start; i < stop; i++) { + for (auto i = start; i < stop; i++) { const X *x = inBuff + inTadOffsets[i]; Y *y = gradIBuff + gradITadOffsets[i]; // this loop calculates squared sum of elements per each j-th element range [j - depth, j + depth + 1] // we store each squared sum in corresponding element of y array - for (uint j = 0; j < tadLen; ++j) { - const uint begin = nd4j::math::nd4j_max(0, j - depth); + for (Nd4jLong j = 0; j < tadLen; ++j) { + const uint begin = sd::math::nd4j_max(0, j - depth); const uint last = depth + j + 1; - const uint end = nd4j::math::nd4j_min(last, tadLen); + const uint end = sd::math::nd4j_min(last, tadLen); if (j == 0) { y[0] = 0; @@ -208,24 +208,24 @@ static void lrnBP_(const NDArray& input, const NDArray& gradO, NDArray& gradI, c Y prev = 0; // second loop calculates derivatives using information gained in first loop above - for (uint j = 0; j < tadLen; ++j) { - const uint begin = nd4j::math::nd4j_max(0, j - depth); + for (Nd4jLong j = 0; j < tadLen; ++j) { + const uint begin = sd::math::nd4j_max(0, j - depth); const uint last = depth + j + 1; - const uint end = nd4j::math::nd4j_min(last, tadLen); + const uint end = sd::math::nd4j_min(last, tadLen); Y init = tbias + talpha * y[j]; if (j == 0) { for (uint s = begin; s < end; ++s) { - factor[s] = nd4j::math::nd4j_pow(tbias + talpha * y[s], -tbeta - 1); + factor[s] = sd::math::nd4j_pow(tbias + talpha * y[s], -tbeta - 1); prev = prev + x[s] * factor[s]; } y[0] = prev; } else if (begin == 0 && last <= tadLen) { - factor[end - 1] = nd4j::math::nd4j_pow(tbias + talpha * y[end - 1], -tbeta - 1); + factor[end - 1] = sd::math::nd4j_pow(tbias + talpha * y[end - 1], -tbeta - 1); y[j] = prev + x[end - 1] * factor[end - 1]; } else if (begin > 0 && last <= tadLen) { - factor[end - 1] = nd4j::math::nd4j_pow(tbias + talpha * y[end - 1], -tbeta - 1); + factor[end - 1] = sd::math::nd4j_pow(tbias + talpha * y[end - 1], -tbeta - 1); y[j] = prev + x[end - 1] * factor[end - 1] - x[begin - 1] * factor[begin - 1]; } else if (begin > 0 && last > tadLen) y[j] = prev - x[begin - 1] * factor[begin - 1]; @@ -247,16 +247,16 @@ static void lrnBP_(const NDArray& input, const NDArray& gradO, NDArray& gradI, c else { auto func = PRAGMA_THREADS_FOR { - for (uint i = start; i < stop; i++) { + for (auto i = start; i < stop; i++) { const X *x = inBuff + inTadOffsets[i]; Y *y = gradIBuff + gradITadOffsets[i]; // this loop calculates squared sum of elements per each j-th element range [j - depth, j + depth + 1] // we store each squared sum in corresponding element of y array - for (uint j = 0; j < tadLen; ++j) { - const uint begin = nd4j::math::nd4j_max(0, j - depth); + for (Nd4jLong j = 0; j < tadLen; ++j) { + const uint begin = sd::math::nd4j_max(0, j - depth); const uint last = depth + j + 1; - const uint end = nd4j::math::nd4j_min(last, tadLen); + const uint end = sd::math::nd4j_min(last, tadLen); if (j == 0) { y[0] = 0; @@ -280,25 +280,25 @@ static void lrnBP_(const NDArray& input, const NDArray& gradO, NDArray& gradI, c Y prev = 0; // second loop calculates derivatives using information gained in first loop above - for (uint j = 0; j < tadLen; ++j) { - const uint begin = nd4j::math::nd4j_max(0, j - depth); + for (Nd4jLong j = 0; j < tadLen; ++j) { + const uint begin = sd::math::nd4j_max(0, j - depth); const uint last = depth + j + 1; - const uint end = nd4j::math::nd4j_min(last, tadLen); + const uint end = sd::math::nd4j_min(last, tadLen); Y init = tbias + talpha * y[j * gradITadEws]; if (j == 0) { for (uint s = begin; s < end; ++s) { - factor[s] = nd4j::math::nd4j_pow(tbias + talpha * y[s * gradITadEws], -tbeta - 1); + factor[s] = sd::math::nd4j_pow(tbias + talpha * y[s * gradITadEws], -tbeta - 1); prev = prev + x[s * inTadEws] * factor[s]; } y[0] = prev; } else if (begin == 0 && last <= tadLen) { - factor[end - 1] = nd4j::math::nd4j_pow(tbias + talpha * y[(end - 1) * gradITadEws], + factor[end - 1] = sd::math::nd4j_pow(tbias + talpha * y[(end - 1) * gradITadEws], -tbeta - 1); y[j * gradITadEws] = prev + x[(end - 1) * inTadEws] * factor[end - 1]; } else if (begin > 0 && last <= tadLen) { - factor[end - 1] = nd4j::math::nd4j_pow(tbias + talpha * y[(end - 1) * gradITadEws], + factor[end - 1] = sd::math::nd4j_pow(tbias + talpha * y[(end - 1) * gradITadEws], -tbeta - 1); y[j * gradITadEws] = prev + x[(end - 1) * inTadEws] * factor[end - 1] - x[(begin - 1) * inTadEws] * factor[begin - 1]; @@ -323,7 +323,7 @@ static void lrnBP_(const NDArray& input, const NDArray& gradO, NDArray& gradI, c } -void lrnBP(nd4j::graph::Context& block, const NDArray& input, const NDArray& gradO, NDArray& gradI, const int depth, const float bias, const float alpha, const float beta) { +void lrnBP(sd::graph::Context& block, const NDArray& input, const NDArray& gradO, NDArray& gradI, const int depth, const float bias, const float alpha, const float beta) { BUILD_DOUBLE_SELECTOR(input.dataType(), gradO.dataType(), lrnBP_, (input, gradO, gradI, depth, bias, alpha, beta), FLOAT_TYPES, FLOAT_TYPES); } diff --git a/libnd4j/include/ops/declarable/helpers/cpu/lstm.cpp b/libnd4j/include/ops/declarable/helpers/cpu/lstm.cpp index 634d875d2..02d4c9855 100644 --- a/libnd4j/include/ops/declarable/helpers/cpu/lstm.cpp +++ b/libnd4j/include/ops/declarable/helpers/cpu/lstm.cpp @@ -27,22 +27,22 @@ #include -#include +#include #include #include #include #include #include -#include +#include #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { ////////////////////////////////////////////////////////////////////////// -void lstmCell(nd4j::LaunchContext * context, const NDArray* xt, const NDArray* ht_1, const NDArray* ct_1, const NDArray* Wx, const NDArray* Wh, const NDArray* Wc, const NDArray* Wp, const NDArray* b, +void lstmCell(sd::LaunchContext * context, const NDArray* xt, const NDArray* ht_1, const NDArray* ct_1, const NDArray* Wx, const NDArray* Wh, const NDArray* Wc, const NDArray* Wp, const NDArray* b, NDArray* ht, NDArray* ct, const std::vector& params) { // xt input [bS x nIn] @@ -124,9 +124,9 @@ static void fusedTanh(NDArray *z, NDArray *i, NDArray *c, const NDArray *cLast, auto h_ = h->bufferAsT(); auto func = PRAGMA_THREADS_FOR { - for (uint e = start; e < stop; e++) { + for (auto e = start; e < stop; e++) { c_[e] = z_[e] * i_[e] + (f_[e] * cLast_[e]); - h_[e] = nd4j::math::nd4j_tanh(c_[e]); + h_[e] = sd::math::nd4j_tanh(c_[e]); } }; diff --git a/libnd4j/include/ops/declarable/helpers/cpu/lstsq.cpp b/libnd4j/include/ops/declarable/helpers/cpu/lstsq.cpp new file mode 100644 index 000000000..554486bbf --- /dev/null +++ b/libnd4j/include/ops/declarable/helpers/cpu/lstsq.cpp @@ -0,0 +1,108 @@ +/******************************************************************************* + * Copyright (c) 2020 Konduit, K.K. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ + +// +// @author GS +// +#include +#include +#include +#include +#include + +#include +#include +#include +#include + +namespace sd { +namespace ops { +namespace helpers { + + template + static void fillRegularizer(NDArray& ioMatrix, double const value) { + auto lastDims = ioMatrix.allTensorsAlongDimension({-2, -1}); + auto rows = ioMatrix.sizeAt(-2); + //auto cols = ioMatrix.sizeAt(-1); + + for (auto x = 0; x < lastDims.size(); x++) { + for (auto r = 0; r < rows; r++) { + lastDims[x]->t(r,r) = (T)value; + } + } + + } + + template + int leastSquaresSolveFunctor_(sd::LaunchContext* context, NDArray const* leftInput, NDArray const* rightInput, double const l2Regularizer, bool const fast, NDArray* output) { + NDArray::preparePrimaryUse({output}, {leftInput, rightInput}); + if (fast) { // Cholesky decomposition approach + // Equation for solve A^T * Ax = A^T * b, so + // 1. Computing A2: + auto tAtShape = ShapeUtils::evalShapeForMatmul(leftInput->getShapeInfo(), leftInput->getShapeInfo(), true, false); + //tAtShape[tAtShape.size() - 2] = output->sizeAt(-2); + NDArray leftOutput('c', tAtShape, output->dataType(), context); + MmulHelper::matmul(leftInput, leftInput, &leftOutput, true, false); // Computing A2 = A^T * A + // 2. Computing B' = A^T * b + auto rightOutput = output->ulike(); + + MmulHelper::matmul(leftInput, rightInput, &rightOutput, true, false); // Computing B' = A^T * b + // 3. due l2Regularizer = 0, skip regularization ( indeed A' = A2 - l2Regularizer * I) + auto regularizer = leftOutput.ulike(); + fillRegularizer(regularizer, l2Regularizer);https://mangapark.net/ +// regularizer *= l2Regularizer; + leftOutput += regularizer; + // 4. Cholesky decomposition -- output matrix is square and lower triangular +// auto leftOutputT = leftOutput.ulike(); + auto err = helpers::cholesky(context, &leftOutput, &leftOutput, true); // inplace decomposition + if (err) return err; + // alternate moment: inverse lower triangular matrix to solve equation A'x = b' => L^Tx = L^-1 * b' + // solve one upper triangular system (to avoid float problems) + + // 5. Solve two triangular systems: + auto rightB = rightOutput.ulike(); + helpers::triangularSolveFunctor(context, &leftOutput, &rightOutput, true, false, &rightB); + helpers::adjointMatrix(context, &leftOutput, true, &leftOutput); //.transposei(); + helpers::triangularSolveFunctor(context, &leftOutput, &rightB, false, false, output); + // All done + } + else { // QR decomposition approach + // Equation for solve Rx = Q^T * b, where A = Q * R, where Q - orthogonal matrix, and R - upper triangular + // 1. QR decomposition + auto qShape = leftInput->getShapeAsVector(); + auto rShape = leftInput->getShapeAsVector(); + qShape[leftInput->rankOf() - 1] = leftInput->sizeAt(-2); + + NDArray Q(leftInput->ordering(), qShape, leftInput->dataType(), context);// = leftInput->ulike(); + NDArray R(leftInput->ordering(), rShape, leftInput->dataType(), context); // = rightInput->ulike(); + helpers::qr(context, leftInput, &Q, &R, true); + // 2. b` = Q^t * b: + auto rightOutput = rightInput->ulike(); + MmulHelper::matmul(&Q, rightInput, &rightOutput, true, false); + // 3. Solve triangular system + helpers::triangularSolveFunctor(context, &R, &rightOutput, false, false, output); + } + NDArray::registerPrimaryUse({output}, {leftInput, rightInput}); + return Status::OK(); + } + + int leastSquaresSolveFunctor(sd::LaunchContext* context, NDArray const* leftInput, NDArray const* rightInput, double const l2Regularizer, bool const fast, NDArray* output) { + BUILD_SINGLE_SELECTOR(leftInput->dataType(), return leastSquaresSolveFunctor_, (context, leftInput, rightInput, l2Regularizer, fast, output), FLOAT_TYPES); + } + +} +} +} diff --git a/libnd4j/include/ops/declarable/helpers/cpu/lup.cpp b/libnd4j/include/ops/declarable/helpers/cpu/lup.cpp index 7d2eb5051..8466631da 100644 --- a/libnd4j/include/ops/declarable/helpers/cpu/lup.cpp +++ b/libnd4j/include/ops/declarable/helpers/cpu/lup.cpp @@ -19,13 +19,13 @@ // #include -#include -#include -#include +#include +#include +#include #include #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { @@ -65,24 +65,30 @@ namespace helpers { template static void invertLowerMatrix_(NDArray* inputMatrix, NDArray* invertedMatrix) { int n = inputMatrix->rows(); - invertedMatrix->assign(0.f); - - // PRAGMA_OMP_PARALLEL_FOR_IF(n > Environment::getInstance()->elementwiseThreshold()) - for (int i = 0; i < n; i++) - invertedMatrix->p(i, i, 1.0f); + invertedMatrix->setIdentity(); if (inputMatrix->isIdentityMatrix()) return; - //PRAGMA_OMP_PARALLEL_FOR_IF(n > Environment::getInstance()->elementwiseThreshold()) - for (int i = 1; i < n; i++) - invertedMatrix->t(i, i - 1) = -inputMatrix->t(i, i - 1); + auto invertDiagonals = PRAGMA_THREADS_FOR { + for (int i = start; i < stop; i += increment) + invertedMatrix->t(i, i) /= inputMatrix->t(i, i); + }; - //PRAGMA_OMP_PARALLEL_FOR_SIMD - for (int i = 2; i < n; i++) { - for (int j = i - 2; j > -1; --j) + auto invertSubDiagonals = PRAGMA_THREADS_FOR { + for (int i = start; i < stop; i += increment) + invertedMatrix->t(i, i - 1) -= (inputMatrix->t(i, i - 1) * invertedMatrix->t(i - 1, i - 1) / inputMatrix->t(i, i)); + }; + + samediff::Threads::parallel_for(invertDiagonals, 0, n, 1); + samediff::Threads::parallel_for(invertSubDiagonals, 1, n, 1); + +// PRAGMA_OMP_PARALLEL_FOR_SIMD + for (int i = 1; i < n; i++) { + for (int j = 0; j < i - 1 ; j++) for (int k = 0; k < i; k++) - invertedMatrix->t(i, j) -= (invertedMatrix->t(k, j) * inputMatrix->t(i, k)); + invertedMatrix->t(i, j) -= ((invertedMatrix->t(k, j) * inputMatrix->t(i, k) / inputMatrix->t(i, i))); } + } BUILD_SINGLE_TEMPLATE(template void invertLowerMatrix_, (NDArray* inputMatrix, NDArray* invertedMatrix);, FLOAT_TYPES); @@ -100,18 +106,25 @@ namespace helpers { return; } - //PRAGMA_OMP_PARALLEL_FOR_IF(n > Environment::getInstance()->elementwiseThreshold()) - for (int i = 0; i < n; i++) - invertedMatrix->t(i, i) /= inputMatrix->t(i, i); + auto invertDiagonals = PRAGMA_THREADS_FOR { + for (auto i = start; i < stop; i += increment) + invertedMatrix->t(i, i) /= inputMatrix->t(i, i); + }; //PRAGMA_OMP_PARALLEL_FOR_IF(n > Environment::getInstance()->elementwiseThreshold()) - for (int i = 0; i < n - 1; i++) - invertedMatrix->t(i, i + 1) -= (inputMatrix->t(i, i + 1) * invertedMatrix->t(i + 1, i + 1) / inputMatrix->t(i, i)); + auto invertUpDiagonals = PRAGMA_THREADS_FOR { + for (auto i = start; i < stop; i += increment) + invertedMatrix->t(i, i + 1) -= (inputMatrix->t(i, i + 1) * invertedMatrix->t(i + 1, i + 1) / + inputMatrix->t(i, i)); + }; + + samediff::Threads::parallel_for(invertDiagonals, 0, n, 1); + samediff::Threads::parallel_for(invertUpDiagonals, 0, n - 1, 1); // PRAGMA_OMP_PARALLEL_FOR_SIMD - for (int i = n - 2; i > - 1; i--) { - for (int j = i + 2; j < n; j++) - for (int k = i; k < n; k++) + for (auto i = n - 2; i >= 0; i--) { + for (auto j = i + 2; j < n; j++) + for (auto k = i; k < n; k++) invertedMatrix->t(i, j) -= ((invertedMatrix->t(k, j) * inputMatrix->t(i, k) / inputMatrix->t(i, i))); } } @@ -143,8 +156,8 @@ namespace helpers { pivot = -1; //PRAGMA_OMP_PARALLEL_FOR //_ARGS(firstprivate(pivot,pivotValue)) for(int rowCounter = i; rowCounter < rowNum; rowCounter++ ) { - if (nd4j::math::nd4j_abs(compoundMatrix.t(rowCounter, i)) > pivotValue) { - pivotValue = nd4j::math::nd4j_abs(compoundMatrix.t(rowCounter, i)); + if (sd::math::nd4j_abs(compoundMatrix.t(rowCounter, i)) > pivotValue) { + pivotValue = sd::math::nd4j_abs(compoundMatrix.t(rowCounter, i)); pivot = rowCounter; } } @@ -199,15 +212,15 @@ namespace helpers { auto rowNum = shape::sizeAt(compoundShape, 0); Nd4jLong xInitial[] = {column, column}; auto xInitialIndex = shape::getOffset(compoundShape, xInitial, 0); - auto maxValue = T(0); //nd4j::math::nd4j_abs(compoundBuffer[xInitialIndex]); + auto maxValue = T(0); //sd::math::nd4j_abs(compoundBuffer[xInitialIndex]); auto result = -1; //auto loop = PRAGMA_THREADS_FOR { auto start = column, stop = rowNum, increment = 1; for (auto rowCounter = start; rowCounter < stop; rowCounter++) { Nd4jLong xPos[] = {rowCounter, column}; auto xIndex = shape::getOffset(compoundShape, xPos, 0); - if (nd4j::math::nd4j_abs(compoundBuffer[xIndex]) > maxValue) { - maxValue = nd4j::math::nd4j_max(maxValue, nd4j::math::nd4j_abs(compoundBuffer[xIndex])); + if (sd::math::nd4j_abs(compoundBuffer[xIndex]) > maxValue) { + maxValue = sd::math::nd4j_max(maxValue, sd::math::nd4j_abs(compoundBuffer[xIndex])); result = rowCounter; } } @@ -340,7 +353,7 @@ namespace helpers { return Status::OK(); } - int determinant(nd4j::LaunchContext * context, NDArray* input, NDArray* output) { + int determinant(sd::LaunchContext * context, NDArray* input, NDArray* output) { BUILD_SINGLE_SELECTOR(input->dataType(), return determinant_, (context, input, output), FLOAT_TYPES); } @@ -357,13 +370,13 @@ template } NDArray det = lup_(context, &matrix, (NDArray*)nullptr, (NDArray*)nullptr); if (det.e(0) != 0.f) - output->p(e, nd4j::math::nd4j_log(nd4j::math::nd4j_abs(det.t(0)))); + output->p(e, sd::math::nd4j_log(sd::math::nd4j_abs(det.t(0)))); } return ND4J_STATUS_OK; } - int logAbsDeterminant(nd4j::LaunchContext * context, NDArray* input, NDArray* output) { + int logAbsDeterminant(sd::LaunchContext * context, NDArray* input, NDArray* output) { BUILD_SINGLE_SELECTOR(input->dataType(), return logAbsDeterminant_, (context, input, output), FLOAT_TYPES); } @@ -391,7 +404,7 @@ template T det = lup_(context, &matrix, &compound, &permutation).template e(0); // FIXME: and how this is going to work on float16? - if (nd4j::math::nd4j_abs(det) < T(0.000001)) { + if (sd::math::nd4j_abs(det) < T(0.000001)) { nd4j_printf("matrix_inverse: The matrix %i has no inverse due determinant is %lf. Quiting...\n", e, det); matrix.printIndexedBuffer("Wrong matrix"); return ND4J_STATUS_VALIDATION; @@ -410,8 +423,8 @@ template invertLowerMatrix(&lowerMatrix, &upperMatrix); - nd4j::MmulHelper::mmul(&matrix, &upperMatrix, &compound, 1.0, 0.0); - nd4j::MmulHelper::mmul(&compound, &permutation, &matrix, 1.0, 0.0); + sd::MmulHelper::mmul(&matrix, &upperMatrix, &compound, 1.0, 0.0); + sd::MmulHelper::mmul(&compound, &permutation, &matrix, 1.0, 0.0); for (int k = e * n2, row = 0; k < (e + 1) * n2; k++) { output->t(k) = matrix.template t(row++); } @@ -420,12 +433,83 @@ template return Status::OK(); } - int inverse(nd4j::LaunchContext * context, NDArray* input, NDArray* output) { - BUILD_SINGLE_SELECTOR(input->dataType(), return inverse_, (context, input, output), FLOAT_TYPES); + template + static int lowerInverse_(LaunchContext *context, NDArray* input, NDArray* output) { + + auto n = input->sizeAt(-1); + auto n2 = n * n; + auto totalCount = output->lengthOf() / n2; + + output->assign(0.f); // fill up output tensor with zeros + auto matrix = NDArrayFactory::create('c', {n, n}, DataTypeUtils::fromT(), context); //, block.getWorkspace()); + auto compound = NDArrayFactory::create('c', {n, n}, DataTypeUtils::fromT(), context); //, block.getWorkspace()); + auto permutation = NDArrayFactory::create('c', {n, n}, DataTypeUtils::fromT(), context); + auto lowerMatrix = NDArrayFactory::create('c', {n, n}, DataTypeUtils::fromT(), context); + auto upperMatrix = NDArrayFactory::create('c', {n, n}, DataTypeUtils::fromT(), context); + +// auto batchLoop = PRAGMA_THREADS_FOR { + for (int e = 0; e < totalCount; e++) { + if (e) + matrix.assign(0.f); + + for (int k = e * n2, row = 0; k < (e + 1) * n2; k++) { + matrix.p(row++, input->e(k)); + } + T det = T(1.f); + for (auto i = 0; i < n; i++) { + det *= matrix. template t(i, i); + } + + // FIXME: and how this is going to work on float16? + if (sd::math::nd4j_abs(det) < T(0.000001)) { + nd4j_printf("matrix_inverse: The matrix %i has no inverse due determinant is %lf. Quiting...\n", e, det); + matrix.printIndexedBuffer("Wrong matrix"); + return ND4J_STATUS_VALIDATION; + } + lowerMatrix.nullify(); + invertLowerMatrix(&matrix, &lowerMatrix); + + for (int k = e * n2, row = 0; k < (e + 1) * n2; k++) { + output->t(k) = lowerMatrix.template t(row++); + } + } + + return Status::OK(); } template - static bool checkCholeskyInput_(nd4j::LaunchContext * context, NDArray const* input) { + static int upperInverse_(LaunchContext *context, NDArray* input, NDArray* output) { + + auto n = input->sizeAt(-1); + auto n2 = n * n; + + output->nullify(); // fill up output tensor with zeros +// auto matrix = NDArrayFactory::create('c', {n, n}, DataTypeUtils::fromT(), context); //, block.getWorkspace()); +// auto lowerMatrix = NDArrayFactory::create('c', {n, n}, DataTypeUtils::fromT(), context); +// auto upperMatrix = NDArrayFactory::create('c', {n, n}, DataTypeUtils::fromT(), context); + auto inputPart = input->allTensorsAlongDimension({-2, -1}); + auto outputPart = output->allTensorsAlongDimension({-2, -1}); + auto totalCount = outputPart.size(); //lengthOf() / n2; + for (int e = 0; e < totalCount; e++) { + invertUpperMatrix(inputPart.at(e), outputPart.at(e)); + } + return Status::OK(); + } + + int inverse(sd::LaunchContext * context, NDArray* input, NDArray* output) { + BUILD_SINGLE_SELECTOR(input->dataType(), return inverse_, (context, input, output), FLOAT_TYPES); + } + + int lowerInverseFunctor(sd::LaunchContext * context, NDArray* input, NDArray* output) { + BUILD_SINGLE_SELECTOR(input->dataType(), return lowerInverse_, (context, input, output), FLOAT_TYPES); + } + + int upperInverseFunctor(sd::LaunchContext * context, NDArray* input, NDArray* output) { + BUILD_SINGLE_SELECTOR(input->dataType(), return upperInverse_, (context, input, output), FLOAT_TYPES); + } + + template + static bool checkCholeskyInput_(sd::LaunchContext * context, NDArray const* input) { //std::unique_ptr matrix(NDArrayFactory::create_('c', {n, n}, input->dataType())); //, block.getWorkspace()); ResultSet lastMatrixList = input->allTensorsAlongDimension({input->rankOf() - 2, input->rankOf()-1}); for (size_t i = 0; i < lastMatrixList.size(); i++) { @@ -433,7 +517,7 @@ template // check for symmetric for (Nd4jLong r = 0; r < thisMatrix->rows(); r++) for (Nd4jLong c = 0; c < thisMatrix->columns(); c++) - if (nd4j::math::nd4j_abs(thisMatrix->e(r, c) - lastMatrixList.at(i)->e(c,r)) > DataTypeUtils::min()) return false; + if (sd::math::nd4j_abs(thisMatrix->e(r, c) - lastMatrixList.at(i)->e(c,r)) > DataTypeUtils::min()) return false; NDArray output = NDArrayFactory::create(0., context); if (ND4J_STATUS_OK != determinant(context, thisMatrix, &output)) return false; @@ -449,7 +533,7 @@ template return true; } - bool checkCholeskyInput(nd4j::LaunchContext * context, NDArray const* input) { + bool checkCholeskyInput(sd::LaunchContext * context, NDArray const* input) { BUILD_SINGLE_SELECTOR(input->dataType(), return checkCholeskyInput_, (context, input), FLOAT_TYPES); } @@ -484,7 +568,7 @@ template T diagonalSum = 0; for (Nd4jLong k = 0; k < col; ++k) diagonalSum += lowerMatrix->e(col, k) * lowerMatrix->e(col, k); - lowerMatrix->p(col, col, nd4j::math::nd4j_sqrt(matrix->e(col, col) - diagonalSum)); + lowerMatrix->p(col, col, sd::math::nd4j_sqrt(matrix->e(col, col) - diagonalSum)); //nd4j_printf("%i: ", col); //lowerMatrix->printIndexedBuffer("Lower matrix"); } @@ -496,7 +580,7 @@ template return ND4J_STATUS_OK; } - int cholesky(nd4j::LaunchContext * context, NDArray* input, NDArray* output, bool inplace) { + int cholesky(sd::LaunchContext * context, NDArray* input, NDArray* output, bool inplace) { BUILD_SINGLE_SELECTOR(input->dataType(), return cholesky_, (context, input, output, inplace), FLOAT_TYPES); } @@ -513,16 +597,16 @@ template for (Nd4jLong e = 0; e < totalCount; e++) { for (size_t i = 0; i < n; ++i) - output->t(e) += nd4j::math::nd4j_log(nd4j::math::nd4j_pow(matricies.at(e)->t(i, i), T(2))); + output->t(e) += sd::math::nd4j_log(sd::math::nd4j_pow(matricies.at(e)->t(i, i), T(2))); } return ND4J_STATUS_OK; } - int logdetFunctor(nd4j::LaunchContext * context, NDArray* input, NDArray* output) { + int logdetFunctor(sd::LaunchContext * context, NDArray* input, NDArray* output) { BUILD_SINGLE_SELECTOR(input->dataType(), return logdetFunctor_, (context, input, output), FLOAT_TYPES); } - int lup(nd4j::LaunchContext * context, NDArray* input, NDArray* compound, NDArray* permutation) { + int lup(sd::LaunchContext * context, NDArray* input, NDArray* compound, NDArray* permutation) { BUILD_DOUBLE_SELECTOR(input->dataType(), permutation->dataType(), lup_, (context, input, compound, permutation), FLOAT_NATIVE, INDEXING_TYPES); return Status::OK(); } diff --git a/libnd4j/include/ops/declarable/helpers/cpu/matrixSetDiag.cpp b/libnd4j/include/ops/declarable/helpers/cpu/matrixSetDiag.cpp index 25605d77e..60df150a9 100644 --- a/libnd4j/include/ops/declarable/helpers/cpu/matrixSetDiag.cpp +++ b/libnd4j/include/ops/declarable/helpers/cpu/matrixSetDiag.cpp @@ -18,11 +18,11 @@ // @author Yurii Shyrma (iuriish@yahoo.com) // -#include "ResultSet.h" +#include #include #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { @@ -49,9 +49,12 @@ void matrixSetDiag_(const NDArray& input, const NDArray& diagonal, NDArray& outp const auto xLen = input.lengthOf(); auto func = PRAGMA_THREADS_FOR { - Nd4jLong coords[MAX_RANK]; + + int coords[MAX_RANK]; + for (Nd4jLong i = 0; i < xLen; ++i) { - shape::index2coords(i, xShapeInfo, coords); + + shape::index2coordsCPU(start, i, xShapeInfo, coords); const auto xOffset = shape::getOffset(xShapeInfo, coords); const auto zOffset = areSameOffsets ? xOffset : shape::getOffset(zShapeInfo, coords); @@ -67,7 +70,7 @@ void matrixSetDiag_(const NDArray& input, const NDArray& diagonal, NDArray& outp } ////////////////////////////////////////////////////////////////////////// -void matrixSetDiag(nd4j::LaunchContext* context, const NDArray& input, const NDArray& diagonal, NDArray& output, const bool zeroPad) { +void matrixSetDiag(sd::LaunchContext* context, const NDArray& input, const NDArray& diagonal, NDArray& output, const bool zeroPad) { BUILD_SINGLE_SELECTOR(input.dataType(), matrixSetDiag_, (input, diagonal, output, zeroPad), LIBND4J_TYPES); } diff --git a/libnd4j/include/ops/declarable/helpers/cpu/matrix_band.cpp b/libnd4j/include/ops/declarable/helpers/cpu/matrix_band.cpp index fbab49e80..d83f0dab9 100644 --- a/libnd4j/include/ops/declarable/helpers/cpu/matrix_band.cpp +++ b/libnd4j/include/ops/declarable/helpers/cpu/matrix_band.cpp @@ -19,7 +19,7 @@ // #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { @@ -32,7 +32,7 @@ namespace helpers { Nd4jLong preLastDim = input->rankOf() - 2; ResultSet listOut = output->allTensorsAlongDimension({(int)preLastDim, (int)lastDim}); ResultSet listDiag = input->allTensorsAlongDimension({(int)preLastDim, (int)lastDim}); - for (Nd4jLong e = 0; e < listOut.size(); ++e) { + for (Nd4jLong e = 0; e < static_cast(listOut.size()); ++e) { NDArray* inputMatrix = listDiag.at(e); NDArray* outputMatrix = listOut.at(e); if (outputMatrix != inputMatrix) // if not inplace @@ -63,7 +63,7 @@ namespace helpers { } } - void matrixBandPart(nd4j::LaunchContext * context, NDArray* input, NDArray* output, Nd4jLong lowerBand, Nd4jLong upperBand) { + void matrixBandPart(sd::LaunchContext * context, NDArray* input, NDArray* output, Nd4jLong lowerBand, Nd4jLong upperBand) { BUILD_SINGLE_SELECTOR(input->dataType(), matrixBandPart_, (input, output, lowerBand, upperBand), FLOAT_TYPES); } BUILD_SINGLE_TEMPLATE(template void matrixBandPart_, (NDArray* input, NDArray* output, Nd4jLong lowerBand, Nd4jLong upperBand), FLOAT_TYPES); diff --git a/libnd4j/include/ops/declarable/helpers/cpu/matrix_diag_part.cpp b/libnd4j/include/ops/declarable/helpers/cpu/matrix_diag_part.cpp index 8a2048263..3271dc110 100644 --- a/libnd4j/include/ops/declarable/helpers/cpu/matrix_diag_part.cpp +++ b/libnd4j/include/ops/declarable/helpers/cpu/matrix_diag_part.cpp @@ -18,12 +18,12 @@ // Created by GS on 3/21/2018. // -#include "ResultSet.h" +#include #include -#include +#include #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { @@ -41,7 +41,7 @@ int _matrixDiagPart(const NDArray* input, NDArray* output) { nd4j_printf("matrix_diag_part: Input matrix has wrong shape.", ""); return ND4J_STATUS_VALIDATION; } - int lastDimension = nd4j::math::nd4j_min(input->sizeAt(-2), input->sizeAt(-1)); + int lastDimension = sd::math::nd4j_min(input->sizeAt(-2), input->sizeAt(-1)); // TODO: tune this properlys int lO = listOut.size(); @@ -56,7 +56,7 @@ int _matrixDiagPart(const NDArray* input, NDArray* output) { return Status::OK(); } - int matrixDiagPart(nd4j::LaunchContext * context, const NDArray* input, NDArray* output) { + int matrixDiagPart(sd::LaunchContext * context, const NDArray* input, NDArray* output) { BUILD_SINGLE_SELECTOR(input->dataType(), return _matrixDiagPart, (input, output), LIBND4J_TYPES); } diff --git a/libnd4j/include/ops/declarable/helpers/cpu/max_pooling.cpp b/libnd4j/include/ops/declarable/helpers/cpu/max_pooling.cpp index 6ebca9184..a458b5eff 100644 --- a/libnd4j/include/ops/declarable/helpers/cpu/max_pooling.cpp +++ b/libnd4j/include/ops/declarable/helpers/cpu/max_pooling.cpp @@ -22,12 +22,12 @@ #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { template - static void maxPoolingFunctor_(nd4j::graph::Context& block, NDArray* input, NDArray* values, std::vector const& params, NDArray* indices) { + static void maxPoolingFunctor_(sd::graph::Context& block, NDArray* input, NDArray* values, std::vector const& params, NDArray* indices) { int kY = params[0]; int kX = params[1]; @@ -72,7 +72,7 @@ namespace helpers { } - void maxPoolingFunctor(nd4j::LaunchContext * context, nd4j::graph::Context& block, NDArray* input, NDArray* values, std::vector const& params, NDArray* indices) { + void maxPoolingFunctor(sd::LaunchContext * context, sd::graph::Context& block, NDArray* input, NDArray* values, std::vector const& params, NDArray* indices) { BUILD_SINGLE_SELECTOR(input->dataType(), maxPoolingFunctor_, (block, input, values, params, indices), FLOAT_TYPES); } diff --git a/libnd4j/include/ops/declarable/helpers/cpu/meshgrid.cpp b/libnd4j/include/ops/declarable/helpers/cpu/meshgrid.cpp index a8a0d919d..336eacf20 100644 --- a/libnd4j/include/ops/declarable/helpers/cpu/meshgrid.cpp +++ b/libnd4j/include/ops/declarable/helpers/cpu/meshgrid.cpp @@ -23,14 +23,14 @@ #include #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { ////////////////////////////////////////////////////////////////////////// -void meshgrid(nd4j::LaunchContext * context, const std::vector& inArrs, const std::vector& outArrs, const bool swapFirst2Dims) { +void meshgrid(sd::LaunchContext * context, const std::vector& inArrs, const std::vector& outArrs, const bool swapFirst2Dims) { const int rank = inArrs.size(); int inIndices[MAX_RANK]; diff --git a/libnd4j/include/ops/declarable/helpers/cpu/minimax.cpp b/libnd4j/include/ops/declarable/helpers/cpu/minimax.cpp index 8d94d23ca..6174151d6 100644 --- a/libnd4j/include/ops/declarable/helpers/cpu/minimax.cpp +++ b/libnd4j/include/ops/declarable/helpers/cpu/minimax.cpp @@ -19,11 +19,11 @@ // #ifndef __MIN_I_MAX_H_HELPERS__ #define __MIN_I_MAX_H_HELPERS__ -#include -#include +#include +#include #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { @@ -161,11 +161,11 @@ namespace helpers { } } - void minimumBPFunctor(nd4j::LaunchContext * context, NDArray* x, NDArray* y, NDArray* epsNext, NDArray* gradX, NDArray* gradY) { + void minimumBPFunctor(sd::LaunchContext * context, NDArray* x, NDArray* y, NDArray* epsNext, NDArray* gradX, NDArray* gradY) { BUILD_SINGLE_SELECTOR(x->dataType(), minimumBPFunctor_, (x, y, epsNext, gradX, gradY), NUMERIC_TYPES); } - void maximumBPFunctor(nd4j::LaunchContext * context, NDArray* x, NDArray* y, NDArray* epsNext, NDArray* gradX, NDArray* gradY) { + void maximumBPFunctor(sd::LaunchContext * context, NDArray* x, NDArray* y, NDArray* epsNext, NDArray* gradX, NDArray* gradY) { BUILD_SINGLE_SELECTOR(x->dataType(), maximumBPFunctor_, (x, y, epsNext, gradX, gradY), NUMERIC_TYPES); } BUILD_SINGLE_TEMPLATE(template void minimumBPFunctor_, (NDArray* x, NDArray* y, NDArray* epsNext, NDArray* gradX, NDArray* gradY), NUMERIC_TYPES); diff --git a/libnd4j/include/ops/declarable/helpers/cpu/nth_element.cpp b/libnd4j/include/ops/declarable/helpers/cpu/nth_element.cpp index 20d8bd34f..2730d9e88 100644 --- a/libnd4j/include/ops/declarable/helpers/cpu/nth_element.cpp +++ b/libnd4j/include/ops/declarable/helpers/cpu/nth_element.cpp @@ -19,12 +19,12 @@ // #include -#include -#include +#include +#include #include #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { @@ -47,7 +47,7 @@ namespace helpers { else { // rank greater than 1 std::vector lastDims({input->rankOf() - 1});// = ShapeUtils::evalDimsToExclude(input->rankOf(), {input->rankOf() - 1}); - auto pack = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(sortedVals.shapeInfo(), lastDims); + auto pack = sd::ConstantTadHelper::getInstance()->tadForDimensions(sortedVals.shapeInfo(), lastDims); SpecialMethods::sortTadGeneric(sortedVals.buffer(), sortedVals.shapeInfo(), lastDims.data(), lastDims.size(), pack.primaryShapeInfo(), pack.primaryOffsets(), reverse); @@ -65,7 +65,7 @@ namespace helpers { } } - void nthElementFunctor(nd4j::LaunchContext *launchContext, NDArray* input, Nd4jLong n, NDArray* output, bool reverse) { + void nthElementFunctor(sd::LaunchContext *launchContext, NDArray* input, Nd4jLong n, NDArray* output, bool reverse) { BUILD_SINGLE_SELECTOR(input->dataType(), nthElementFunctor_, (input, n, output, reverse), LIBND4J_TYPES); } diff --git a/libnd4j/include/ops/declarable/helpers/cpu/one_hot.cpp b/libnd4j/include/ops/declarable/helpers/cpu/one_hot.cpp index 71beed7f9..d3f7add49 100644 --- a/libnd4j/include/ops/declarable/helpers/cpu/one_hot.cpp +++ b/libnd4j/include/ops/declarable/helpers/cpu/one_hot.cpp @@ -23,7 +23,7 @@ #include #include "../one_hot.h" -namespace nd4j { +namespace sd { namespace ops { namespace helpers { template @@ -31,7 +31,7 @@ namespace nd4j { auto output = reinterpret_cast(voutput); auto indices = reinterpret_cast(vindices); - auto tadPack = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(zShapeInfo, {axis}); + auto tadPack = sd::ConstantTadHelper::getInstance()->tadForDimensions(zShapeInfo, {axis}); auto iLen = static_cast(shape::length(iShapeInfo)); auto tLen = static_cast(shape::length(tadPack.primaryShapeInfo())); @@ -92,7 +92,7 @@ namespace nd4j { } } - void onehot(const nd4j::LaunchContext* context, const NDArray *indices, NDArray *output, const uint axis, const uint depth, const double on, const double off) { + void onehot(const sd::LaunchContext* context, const NDArray *indices, NDArray *output, const uint axis, const uint depth, const double on, const double off) { auto zType = output->dataType(); auto iType = indices->dataType(); diff --git a/libnd4j/include/ops/declarable/helpers/cpu/percentile.cpp b/libnd4j/include/ops/declarable/helpers/cpu/percentile.cpp index fa8061e54..3ffa4dd82 100644 --- a/libnd4j/include/ops/declarable/helpers/cpu/percentile.cpp +++ b/libnd4j/include/ops/declarable/helpers/cpu/percentile.cpp @@ -19,10 +19,10 @@ // #include -#include -#include "ResultSet.h" +#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { @@ -76,7 +76,7 @@ static void _percentile(const NDArray& input, NDArray& output, std::vector& } } - void percentile(nd4j::LaunchContext * context, const NDArray& input, NDArray& output, std::vector& axises, const float q, const int interpolation) { + void percentile(sd::LaunchContext * context, const NDArray& input, NDArray& output, std::vector& axises, const float q, const int interpolation) { BUILD_SINGLE_SELECTOR(input.dataType(), _percentile, (input, output, axises, q, interpolation), LIBND4J_TYPES); } diff --git a/libnd4j/include/ops/declarable/helpers/cpu/polyGamma.cpp b/libnd4j/include/ops/declarable/helpers/cpu/polyGamma.cpp index df80636ee..2c93cee08 100644 --- a/libnd4j/include/ops/declarable/helpers/cpu/polyGamma.cpp +++ b/libnd4j/include/ops/declarable/helpers/cpu/polyGamma.cpp @@ -20,10 +20,10 @@ #include #include -#include +#include #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { @@ -49,7 +49,7 @@ static FORCEINLINE T getFactorial(const int n) { ////////////////////////////////////////////////////////////////////////// // implementation is based on serial representation written in terms of the Hurwitz zeta function as polygamma = (-1)^{n+1} * n! * zeta(n+1, x) template -static FORCEINLINE T polyGammaScalar(nd4j::LaunchContext * context, const int n, const T x) { +static FORCEINLINE T polyGammaScalar(sd::LaunchContext * context, const int n, const T x) { // if (n < 0) // throw("polyGamma function: n must be >= 0 !"); @@ -67,7 +67,7 @@ static FORCEINLINE T polyGammaScalar(nd4j::LaunchContext * context, const int n, ////////////////////////////////////////////////////////////////////////// // calculate polygamma function for arrays template -static void polyGamma_(nd4j::LaunchContext * context, const NDArray& n, const NDArray& x, NDArray& output) { +static void polyGamma_(sd::LaunchContext * context, const NDArray& n, const NDArray& x, NDArray& output) { auto func = PRAGMA_THREADS_FOR { for (auto i = start; i < stop; i++) { @@ -83,11 +83,11 @@ static void polyGamma_(nd4j::LaunchContext * context, const NDArray& n, const ND samediff::Threads::parallel_for(func, 0, x.lengthOf()); } - void polyGamma(nd4j::LaunchContext * context, const NDArray& n, const NDArray& x, NDArray& output) { + void polyGamma(sd::LaunchContext * context, const NDArray& n, const NDArray& x, NDArray& output) { BUILD_SINGLE_SELECTOR(x.dataType(), polyGamma_, (context, n, x, output), FLOAT_TYPES); } -BUILD_SINGLE_TEMPLATE(template void polyGamma_, (nd4j::LaunchContext * context, const NDArray& n, const NDArray& x, NDArray& output), FLOAT_TYPES); +BUILD_SINGLE_TEMPLATE(template void polyGamma_, (sd::LaunchContext * context, const NDArray& n, const NDArray& x, NDArray& output), FLOAT_TYPES); diff --git a/libnd4j/include/ops/declarable/helpers/cpu/prefix.cpp b/libnd4j/include/ops/declarable/helpers/cpu/prefix.cpp index 43c65f14b..5307f841e 100644 --- a/libnd4j/include/ops/declarable/helpers/cpu/prefix.cpp +++ b/libnd4j/include/ops/declarable/helpers/cpu/prefix.cpp @@ -23,7 +23,7 @@ #include #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { template @@ -68,7 +68,7 @@ namespace nd4j { if (shape::elementWiseStride(xShapeInfo) == 1 && shape::elementWiseStride(zShapeInfo) == 1 && shape::order(xShapeInfo) == 'c' && shape::order(zShapeInfo) == 'c') { - for (int e = 0; e < length; e++) { + for (Nd4jLong e = 0; e < length; e++) { sum = op == scalar::Add ? simdOps::Add::op(sum, x[e]) : simdOps::Multiply::op(sum, x[e]); if (!exclusive) @@ -81,7 +81,7 @@ namespace nd4j { } else { - for (int e = 0; e < length; e++) { + for (Nd4jLong e = 0; e < length; e++) { auto xOffset = shape::getIndexOffset(e, xShapeInfo); auto zOffset = shape::getIndexOffset(e, zShapeInfo); @@ -116,11 +116,11 @@ namespace nd4j { prefix_(op, x->getBuffer(), x->getShapeInfo(), z->buffer(), z->shapeInfo(), exclusive, reverse); }; - void prefix(nd4j::LaunchContext * context, scalar::Ops op, const NDArray* x, NDArray* z, bool exclusive, bool reverse) { + void prefix(sd::LaunchContext * context, scalar::Ops op, const NDArray* x, NDArray* z, bool exclusive, bool reverse) { BUILD_SINGLE_SELECTOR(x->dataType(), prefix_, (op, x, z, exclusive, reverse), LIBND4J_TYPES); } - void prefix(nd4j::LaunchContext * context, scalar::Ops op, const NDArray* x, NDArray* z, const std::vector& dims, bool exclusive, bool reverse) { + void prefix(sd::LaunchContext * context, scalar::Ops op, const NDArray* x, NDArray* z, const std::vector& dims, bool exclusive, bool reverse) { BUILD_SINGLE_SELECTOR(x->dataType(), prefix_, (op, x, z, dims, exclusive, reverse), LIBND4J_TYPES); } diff --git a/libnd4j/include/ops/declarable/helpers/cpu/print_variable.cpp b/libnd4j/include/ops/declarable/helpers/cpu/print_variable.cpp index 293518be6..26a24a5af 100644 --- a/libnd4j/include/ops/declarable/helpers/cpu/print_variable.cpp +++ b/libnd4j/include/ops/declarable/helpers/cpu/print_variable.cpp @@ -20,7 +20,7 @@ #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { void print_special(LaunchContext &ctx, const NDArray &array, const std::string &message) { diff --git a/libnd4j/include/ops/declarable/helpers/cpu/qr.cpp b/libnd4j/include/ops/declarable/helpers/cpu/qr.cpp index 9e1980e54..2ea18a79d 100644 --- a/libnd4j/include/ops/declarable/helpers/cpu/qr.cpp +++ b/libnd4j/include/ops/declarable/helpers/cpu/qr.cpp @@ -20,9 +20,9 @@ #include #include #include -#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { @@ -43,8 +43,8 @@ namespace helpers { T const* vBuf = v.getDataBuffer()->primaryAsT(); T* resBuf = res.dataBuffer()->primaryAsT(); auto interloop = PRAGMA_THREADS_FOR_2D { - for (int i = start_x; i < n; i += inc_x) - for (int j = start_y; j < n; j += inc_y) + for (auto i = start_x; i < n; i += inc_x) + for (auto j = start_y; j < n; j += inc_y) resBuf[i * n + j] = -2 * vBuf[i] * vBuf[j] + (i == j ? T(1) : T(0)); }; @@ -63,7 +63,7 @@ namespace helpers { NDArray z = *matrix; NDArray e('c', {M}, DataTypeUtils::fromT()); // two internal buffers and scalar for squared norm - for (auto k = 0; k < N && k < M - 1; k++) { // loop for columns, but not further then row number + for (Nd4jLong k = 0; k < N && k < M - 1; k++) { // loop for columns, but not further then row number e.nullify(); z = matrixMinor(z, k); // minor computing for current column with given matrix z (initally is a input matrix) // z.printIndexedBuffer("Minor!!!"); @@ -87,7 +87,7 @@ namespace helpers { } resQ.assign(q[0]); // // MmulHelper::matmul(&q[0], matrix, &resR, false, false); - for (int i = 1; i < N && i < M - 1; i++) { + for (Nd4jLong i = 1; i < N && i < M - 1; i++) { auto tempResQ = resQ; MmulHelper::matmul(&q[i], &resQ, &tempResQ, false, false); // use mmulMxM? resQ = std::move(tempResQ); @@ -106,7 +106,7 @@ namespace helpers { } template - void qr_(NDArray* input, NDArray* outputQ, NDArray* outputR, bool const fullMatricies) { + void qr_(NDArray const* input, NDArray* outputQ, NDArray* outputR, bool const fullMatricies) { Nd4jLong lastDim = input->rankOf() - 1; Nd4jLong preLastDim = input->rankOf() - 2; ResultSet listOutQ(outputQ->allTensorsAlongDimension({(int)preLastDim, (int)lastDim})); @@ -123,7 +123,7 @@ namespace helpers { } - void qr(nd4j::LaunchContext* context, NDArray* input, NDArray* outputQ, NDArray* outputR, bool const fullMatricies) { + void qr(sd::LaunchContext* context, NDArray const* input, NDArray* outputQ, NDArray* outputR, bool const fullMatricies) { BUILD_SINGLE_SELECTOR(input->dataType(), qr_, (input, outputQ, outputR, fullMatricies), FLOAT_TYPES); } diff --git a/libnd4j/include/ops/declarable/helpers/cpu/random.cpp b/libnd4j/include/ops/declarable/helpers/cpu/random.cpp index ad04db307..b38101feb 100644 --- a/libnd4j/include/ops/declarable/helpers/cpu/random.cpp +++ b/libnd4j/include/ops/declarable/helpers/cpu/random.cpp @@ -22,12 +22,12 @@ //#include #include //#include -#include +#include #include #include #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { @@ -57,10 +57,10 @@ namespace helpers { T* outputBuf = output->dataBuffer()->primaryAsT(); PRAGMA_OMP_PARALLEL_FOR - for (auto k = 0; k < shift; k++) { + for (Nd4jLong k = 0; k < shift; k++) { auto pos = k * step; auto u = rng.relativeT(k, 0., 1.); - for (auto e = 0; e < step; e++) + for (Nd4jLong e = 0; e < step; e++) if (directOutput) { outputBuf[pos + e] = math::nd4j_igamma(copyAlpha->t(e), beta != nullptr ? copyBeta->t(e) * u : u); @@ -104,10 +104,10 @@ namespace helpers { bool directLa = lambda->ews() == 1 && lambda->ordering() == 'c'; bool directOut = output->ews() == 1 && output->ordering() == 'c'; PRAGMA_OMP_PARALLEL_FOR - for (auto k = 0; k < shift; k++) { + for (Nd4jLong k = 0; k < shift; k++) { auto pos = k * step; auto u = rng.relativeT(k, 0., 1.); - for (auto e = 0; e < step; e++) { + for (Nd4jLong e = 0; e < step; e++) { auto p = math::nd4j_exp(-lambda->t(e)); auto s = p; auto x = T(0.f); @@ -143,7 +143,7 @@ namespace helpers { RandomLauncher::fillUniform(context, rng, output, minVal, maxVal); else { PRAGMA_OMP_PARALLEL_FOR - for (auto i = 0; i < output->lengthOf(); i++) { + for (Nd4jLong i = 0; i < output->lengthOf(); i++) { output->t(i) = rng.relativeT(i, minVal, maxVal); } } @@ -184,9 +184,9 @@ namespace helpers { auto nSamplesPerBatch = nBatchIndex * numOfClassX * numOfSamples; auto nClassesPerSample = nSampleIndexInBatch * numOfClassX; - for (auto nClass = 0; nClass < numOfClassX; nClass += 1) { + for (Nd4jLong nClass = 0; nClass < numOfClassX; nClass += 1) { auto nIndex = nSamplesPerBatch + nClassesPerSample + nClass; - auto unifornLog = nd4j::math::nd4j_log(-nd4j::math::nd4j_log(rng.relativeT(nIndex, minVal, maxVal))); + auto unifornLog = sd::math::nd4j_log(-sd::math::nd4j_log(rng.relativeT(nIndex, minVal, maxVal))); Tx tValue = (xTad[nClass * xDimAstride] - unifornLog); if (tValue > Max) { Max = tValue; diff --git a/libnd4j/include/ops/declarable/helpers/cpu/random_crop.cpp b/libnd4j/include/ops/declarable/helpers/cpu/random_crop.cpp index c39e28928..365465f64 100644 --- a/libnd4j/include/ops/declarable/helpers/cpu/random_crop.cpp +++ b/libnd4j/include/ops/declarable/helpers/cpu/random_crop.cpp @@ -23,7 +23,7 @@ #include #include #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { @@ -50,7 +50,7 @@ namespace helpers { width = lastDim; } - for (int i = 0; i < input->lengthOf(); i += lastDim) { + for (Nd4jLong i = 0; i < input->lengthOf(); i += lastDim) { for (Nd4jLong k = startPos; k < width && pos < output->lengthOf(); k++) { output->p(pos++, input->e(i + k)); } diff --git a/libnd4j/include/ops/declarable/helpers/cpu/range.cpp b/libnd4j/include/ops/declarable/helpers/cpu/range.cpp index a14fb89f9..e4349ac8a 100644 --- a/libnd4j/include/ops/declarable/helpers/cpu/range.cpp +++ b/libnd4j/include/ops/declarable/helpers/cpu/range.cpp @@ -22,7 +22,7 @@ #include #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { @@ -45,7 +45,7 @@ static void _range(const NDArray& start, const NDArray& delta, NDArray& outVecto samediff::Threads::parallel_for(func, 0, len); } - void range(nd4j::LaunchContext * context, const NDArray& start, const NDArray& delta, NDArray& outVector) { + void range(sd::LaunchContext * context, const NDArray& start, const NDArray& delta, NDArray& outVector) { BUILD_SINGLE_SELECTOR(outVector.dataType(), _range, (start, delta, outVector), LIBND4J_TYPES); } diff --git a/libnd4j/include/ops/declarable/helpers/cpu/reverse.cpp b/libnd4j/include/ops/declarable/helpers/cpu/reverse.cpp index 4c80e3bf2..3d17fb62a 100644 --- a/libnd4j/include/ops/declarable/helpers/cpu/reverse.cpp +++ b/libnd4j/include/ops/declarable/helpers/cpu/reverse.cpp @@ -24,7 +24,7 @@ #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { @@ -38,7 +38,7 @@ inline void swap(T* arr, Nd4jLong from, Nd4jLong to) { // this legacy op is written by raver119@gmail.com template -static void reverseArray(nd4j::LaunchContext * context, void *vinArr, Nd4jLong *inShapeBuffer, void *voutArr, Nd4jLong *outShapeBuffer, int numOfElemsToReverse = 0) { +static void reverseArray(sd::LaunchContext * context, void *vinArr, Nd4jLong *inShapeBuffer, void *voutArr, Nd4jLong *outShapeBuffer, int numOfElemsToReverse = 0) { auto inArr = reinterpret_cast(vinArr); auto outArr = reinterpret_cast(voutArr); @@ -151,7 +151,7 @@ static void reverseArray(nd4j::LaunchContext * context, void *vinArr, Nd4jLong * /////////////////////////////////////////////////////////////////// template -static void reverseSequence_(nd4j::LaunchContext * context, const NDArray* input, const NDArray* seqLengths, NDArray* output, int seqDim, const int batchDim){ +static void reverseSequence_(sd::LaunchContext * context, const NDArray* input, const NDArray* seqLengths, NDArray* output, int seqDim, const int batchDim){ int posOfNonUnityDim = -1; if(input->isVector() || shape::isLikeVector(input->getShapeInfo(), posOfNonUnityDim)) { @@ -188,12 +188,12 @@ static void reverseSequence_(nd4j::LaunchContext * context, const NDArray* input } } - void reverseSequence(nd4j::LaunchContext * context, const NDArray* input, const NDArray* seqLengths, NDArray* output, int seqDim, const int batchDim) { + void reverseSequence(sd::LaunchContext * context, const NDArray* input, const NDArray* seqLengths, NDArray* output, int seqDim, const int batchDim) { BUILD_SINGLE_SELECTOR(input->dataType(), reverseSequence_, (context, input, seqLengths, output, seqDim, batchDim), LIBND4J_TYPES); } ////////////////////////////////////////////////////////////////////////// -void reverse(nd4j::LaunchContext * context, const NDArray* input, NDArray* output, const std::vector* intArgs, bool isBackProp) { +void reverse(sd::LaunchContext * context, const NDArray* input, NDArray* output, const std::vector* intArgs, bool isBackProp) { // we need to reverse axis only if that's new op std::vector dimensions = isBackProp ? ShapeUtils::evalDimsToExclude(input->rankOf(), *intArgs) : *intArgs; @@ -210,8 +210,8 @@ void reverse(nd4j::LaunchContext * context, const NDArray* input, NDArray* outpu } } -BUILD_SINGLE_TEMPLATE(template void reverseSequence_, (nd4j::LaunchContext * context, const NDArray* input, const NDArray* seqLengths, NDArray* output, int seqDim, const int batchDim), LIBND4J_TYPES); -BUILD_SINGLE_TEMPLATE(template void reverseArray, (nd4j::LaunchContext * context, void *inArr, Nd4jLong *inShapeBuffer, void *outArr, Nd4jLong *outShapeBuffer, int numOfElemsToReverse), LIBND4J_TYPES); +BUILD_SINGLE_TEMPLATE(template void reverseSequence_, (sd::LaunchContext * context, const NDArray* input, const NDArray* seqLengths, NDArray* output, int seqDim, const int batchDim), LIBND4J_TYPES); +BUILD_SINGLE_TEMPLATE(template void reverseArray, (sd::LaunchContext * context, void *inArr, Nd4jLong *inShapeBuffer, void *outArr, Nd4jLong *outShapeBuffer, int numOfElemsToReverse), LIBND4J_TYPES); } diff --git a/libnd4j/include/ops/declarable/helpers/cpu/roll.cpp b/libnd4j/include/ops/declarable/helpers/cpu/roll.cpp index 8bfc1ca1a..278f3bcf5 100644 --- a/libnd4j/include/ops/declarable/helpers/cpu/roll.cpp +++ b/libnd4j/include/ops/declarable/helpers/cpu/roll.cpp @@ -20,7 +20,7 @@ #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { @@ -50,7 +50,7 @@ namespace helpers { auto _e0 = output->e(e); auto _e1 = output->e(sourceIndex); - //nd4j::math::nd4j_swap((*output)(e), (*output)(sourceIndex)); + //sd::math::nd4j_swap((*output)(e), (*output)(sourceIndex)); output->p(e, _e1); output->p(sourceIndex, _e0); } @@ -65,7 +65,7 @@ namespace helpers { auto _e0 = output->e(destinationIndex); auto _e1 = output->e(sourceIndex); - //nd4j::math::nd4j_swap((*output)(destinationIndex), (*output)(sourceIndex)); + //sd::math::nd4j_swap((*output)(destinationIndex), (*output)(sourceIndex)); output->p(destinationIndex, _e1); output->p(sourceIndex, _e0); } @@ -77,7 +77,7 @@ namespace helpers { auto _e0 = output->e(i); auto _e1 = output->e(i + remainShift); - //nd4j::math::nd4j_swap((*output)(i), (*output)(i + remainShift)); + //sd::math::nd4j_swap((*output)(i), (*output)(i + remainShift)); output->p(i, _e1); output->p(i + remainShift, _e0); @@ -85,7 +85,7 @@ namespace helpers { } } - void rollFunctorFull(nd4j::LaunchContext * context, NDArray* input, NDArray* output, std::vector const& shifts, std::vector const& axes, bool inplace){ + void rollFunctorFull(sd::LaunchContext * context, NDArray* input, NDArray* output, std::vector const& shifts, std::vector const& axes, bool inplace){ if (!inplace) output->assign(input); @@ -110,7 +110,7 @@ namespace helpers { } else { std::vector dims(source->rankOf() - axe - 1); - for (int i = 0; i < dims.size(); ++i) + for (size_t i = 0; i < dims.size(); ++i) dims[i] = axe + 1 + i; ResultSet listOfTensors = source->allTensorsAlongDimension({dims}); @@ -151,7 +151,7 @@ namespace helpers { } } - void rollFunctorLinear(nd4j::LaunchContext * context, NDArray* input, NDArray* output, int shift, bool inplace){ + void rollFunctorLinear(sd::LaunchContext * context, NDArray* input, NDArray* output, int shift, bool inplace){ BUILD_SINGLE_SELECTOR(input->dataType(), rollFunctorLinear_, (input, output, shift, inplace), LIBND4J_TYPES); } diff --git a/libnd4j/include/ops/declarable/helpers/cpu/s_t_b.cpp b/libnd4j/include/ops/declarable/helpers/cpu/s_t_b.cpp index 09a628b84..6a854bba8 100644 --- a/libnd4j/include/ops/declarable/helpers/cpu/s_t_b.cpp +++ b/libnd4j/include/ops/declarable/helpers/cpu/s_t_b.cpp @@ -22,7 +22,7 @@ #include #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { @@ -55,9 +55,9 @@ static void batchToSpace_(const NDArray& input, NDArray& output, const uint crop // loop through output array auto func = PRAGMA_THREADS_FOR_3D { - for (uint b = start_x; b < stop_x; b += inc_x) { - for (uint h = start_y; h < stop_y; h += inc_y) { - for (uint w = start_z; w < stop_z; w += inc_z) { + for (auto b = start_x; b < stop_x; b += inc_x) { + for (auto h = start_y; h < stop_y; h += inc_y) { + for (auto w = start_z; w < stop_z; w += inc_z) { for (uint c = 0; c < iC; ++c) { const Nd4jLong xOffset = b * xShapeInfo[5] + h * xShapeInfo[6] + w * xShapeInfo[7] + c * xShapeInfo[8]; const Nd4jLong zOffset = b * zShapeInfo[5] + (h - cropBottom) * zShapeInfo[6] + (w - cropLeft) * zShapeInfo[7] + c * zShapeInfo[8]; @@ -75,7 +75,7 @@ static void batchToSpace_(const NDArray& input, NDArray& output, const uint crop BUILD_SINGLE_TEMPLATE(template void batchToSpace_, (const NDArray& input, NDArray& output, const uint cropBottom, const uint cropTop, const uint cropLeft, const uint cropRight), LIBND4J_TYPES); ////////////////////////////////////////////////////////////////////////// -void batchToSpace(nd4j::LaunchContext* context, const NDArray& input, NDArray& output, const uint cropBottom, const uint cropTop, const uint cropLeft, const uint cropRight, const uint blockSize) { +void batchToSpace(sd::LaunchContext* context, const NDArray& input, NDArray& output, const uint cropBottom, const uint cropTop, const uint cropLeft, const uint cropRight, const uint blockSize) { // [bS*blockSize*blockSize, H/blockSize, W/blockSize, iC] is rearranged/permuted to [bS, oH, oW, iC] // oH = H - cropTop - cropBottom @@ -113,18 +113,23 @@ static void batchToSpaceND_(const NDArray& input, const NDArray& crop, NDArray& // loop through input array auto func = PRAGMA_THREADS_FOR { - Nd4jLong coords[MAX_RANK]; + + int zCoords[MAX_RANK], xCoords[MAX_RANK]; + for (auto i = start; i < stop; i++) { - shape::index2coords(i, output.getShapeInfo(), coords); + shape::index2coordsCPU(start, i, output.getShapeInfo(), zCoords); - const auto zOffset = shape::getOffset(output.getShapeInfo(), coords); + memcpy(xCoords, zCoords, rank * sizeof(int)); // evaluate spatial coordinates for x for (uint j = 1; j <= numOfSpatialDims; ++j) - coords[j] += crop.e(j - 1, 0); // add crop left + xCoords[j] += crop.e(j - 1, 0); // add crop left - z[zOffset] = x[shape::getOffset(input.getShapeInfo(), coords)]; + const auto zOffset = shape::getOffset(output.getShapeInfo(), zCoords); + const auto xOffset = shape::getOffset(input.getShapeInfo(), xCoords); + + z[zOffset] = x[xOffset]; } }; @@ -134,7 +139,7 @@ static void batchToSpaceND_(const NDArray& input, const NDArray& crop, NDArray& BUILD_SINGLE_TEMPLATE(template void batchToSpaceND_, (const NDArray& input, const NDArray& crop, NDArray& output, const uint numOfSpatialDims), LIBND4J_TYPES); ////////////////////////////////////////////////////////////////////////// -void batchToSpaceND(nd4j::LaunchContext* context, const NDArray& input, const NDArray& blockShape, const NDArray& crop, NDArray& output) { +void batchToSpaceND(sd::LaunchContext* context, const NDArray& input, const NDArray& blockShape, const NDArray& crop, NDArray& output) { // 4D example, numOfSpatialDims = 2 - two spatial dimensions // [bS*blockShape[0]*blockShape[1], iH, iW, iC] is rearranged/permuted to [bS, iH*blockShape[0] - cropTop - cropBottom, iW*blockShape[1] - cropLeft - cropRight, iC] @@ -146,11 +151,11 @@ void batchToSpaceND(nd4j::LaunchContext* context, const NDArray& input, const ND std::vector temp(numOfSpatialDims + rank); - int i; + uint i; for(i = 0; i < numOfSpatialDims; ++i) temp[i] = blockShape.e(i); temp[i++] = output.sizeAt(0); - for(int j = 1; j < rank; ++i, ++j) + for(uint j = 1; j < rank; ++i, ++j) temp[i] = input.sizeAt(j); NDArray inputRearranged0 = input.reshape(input.ordering(), temp); @@ -163,7 +168,7 @@ void batchToSpaceND(nd4j::LaunchContext* context, const NDArray& input, const ND temp[2*i - 1] = numOfSpatialDims + i; temp[2*i] = i - 1; } - for(i = 2 * numOfSpatialDims + 1; i < temp.size(); ++i) + for(i = 2 * numOfSpatialDims + 1; i < static_cast(temp.size()); ++i) temp[i] = i; inputRearranged0.permutei(temp); @@ -216,8 +221,8 @@ static void spaceToBatch_(const NDArray& input, NDArray& output, const uint padB // loop through output array auto func = PRAGMA_THREADS_FOR_2D { - for (uint b = start_x; b < stop_x; b += inc_x) { - for (uint h = start_y; h < stop_y; h += inc_y) { + for (auto b = start_x; b < stop_x; b += inc_x) { + for (auto h = start_y; h < stop_y; h += inc_y) { for (uint w = 0; w < oW; ++w) { for (uint c = 0; c < iC; ++c) { @@ -240,7 +245,7 @@ static void spaceToBatch_(const NDArray& input, NDArray& output, const uint padB BUILD_SINGLE_TEMPLATE(template void spaceToBatch_, (const NDArray& input, NDArray& output, const uint padBottom, const uint padTop, const uint padLeft, const uint padRight), LIBND4J_TYPES); ////////////////////////////////////////////////////////////////////////// -void spaceToBatch(nd4j::LaunchContext* context, const NDArray& input, NDArray& output, const uint padBottom, const uint padTop, const uint padLeft, const uint padRight, const uint blockSize) { +void spaceToBatch(sd::LaunchContext* context, const NDArray& input, NDArray& output, const uint padBottom, const uint padTop, const uint padLeft, const uint padRight, const uint blockSize) { // [bS, iH, iW, iC] is rearranged/permuted to [bS*blockSize*blockSize, (iH + padBottom + padTop)/blockSize, (iW + padLeft + padRight)/blockSize, iC] @@ -299,11 +304,16 @@ static void spaceToBatchND_(const NDArray& input, const NDArray& padding, NDArra // loop through output array auto func = PRAGMA_THREADS_FOR { - Nd4jLong coords[MAX_RANK]; - for (auto i = start; i < stop; i++) { - shape::index2coords(i, output.getShapeInfo(), coords); - const auto zOffset = shape::getOffset(output.getShapeInfo(), coords); + int zCoords[MAX_RANK], xCoords[MAX_RANK]; + + for (auto i = start; i < stop; i++) { + + shape::index2coordsCPU(start, i, output.getShapeInfo(), zCoords); + + const auto zOffset = shape::getOffset(output.getShapeInfo(), zCoords); + + memcpy(xCoords, zCoords, rank * sizeof(int)); bool within = true; @@ -312,16 +322,16 @@ static void spaceToBatchND_(const NDArray& input, const NDArray& padding, NDArra const auto padLeft = padding.e(j - 1, 0); const auto padRight = padding.e(j - 1, 1); - within &= (coords[j] >= padLeft && coords[j] < output.sizeAt(j) - padRight); + within &= zCoords[j] >= padLeft && zCoords[j] < output.sizeAt(j) - padRight; if (!within) break; - coords[j] -= padLeft; // get coordinates for x + xCoords[j] = zCoords[j] - padLeft; // get coordinates for x } if (within) - z[zOffset] = x[shape::getOffset(input.getShapeInfo(), coords)]; + z[zOffset] = x[shape::getOffset(input.getShapeInfo(), xCoords)]; else z[zOffset] = 0.f; } @@ -333,7 +343,7 @@ static void spaceToBatchND_(const NDArray& input, const NDArray& padding, NDArra BUILD_SINGLE_TEMPLATE(template void spaceToBatchND_, (const NDArray& input, const NDArray& padding, NDArray& output, const uint numOfSpatialDims), LIBND4J_TYPES); ////////////////////////////////////////////////////////////////////////// -void spaceToBatchND(nd4j::LaunchContext* context, const NDArray& input, const NDArray& blockShape, const NDArray& padding, NDArray& output ) { +void spaceToBatchND(sd::LaunchContext* context, const NDArray& input, const NDArray& blockShape, const NDArray& padding, NDArray& output ) { // 4D example with two spatial dimensions // [bS, iH, iW, iC] is rearranged/permuted to [bS*blockShape[0]*blockShape[1], (iH + padBottom + padTop)/blockShape[0], (iW + padLeft + padRight)/blockShape[1], iC] @@ -426,13 +436,13 @@ void spaceToBatchND(nd4j::LaunchContext* context, const NDArray& input, const ND }; template - void _execute(nd4j::LaunchContext * context, void *vptrSpace, const Nd4jLong *space_shape, const Nd4jLong *space_strides, const Nd4jLong *block_shape, const Nd4jLong *pad_start, const Nd4jLong *block_offsets, void *vptrBatch, const Nd4jLong *batch_shape, const Nd4jLong *batch_strides) { + void _execute(sd::LaunchContext * context, void *vptrSpace, const Nd4jLong *space_shape, const Nd4jLong *space_strides, const Nd4jLong *block_shape, const Nd4jLong *pad_start, const Nd4jLong *block_offsets, void *vptrBatch, const Nd4jLong *batch_shape, const Nd4jLong *batch_strides) { auto ptrSpace = reinterpret_cast(vptrSpace); auto ptrBatch = reinterpret_cast(vptrBatch); SpaceToBatchHelper::run(ptrSpace, space_shape, space_strides, block_shape, pad_start, block_offsets, ptrBatch, batch_shape, batch_strides); }; - Nd4jStatus _spaceToBatch(nd4j::LaunchContext * context, int internal_block_dims, NDArray *input, NDArray *output, std::vector &internal_input_shape, std::vector &internal_output_shape, Nd4jLong *block_shape, Nd4jLong *paddings) { + Nd4jStatus _spaceToBatch(sd::LaunchContext * context, int internal_block_dims, NDArray *input, NDArray *output, std::vector &internal_input_shape, std::vector &internal_output_shape, Nd4jLong *block_shape, Nd4jLong *paddings) { auto in = input->reshape('c', internal_input_shape); auto out = output->reshape('c', internal_output_shape); switch (internal_block_dims) { @@ -456,7 +466,7 @@ void spaceToBatchND(nd4j::LaunchContext* context, const NDArray& input, const ND return Status::OK(); } - Nd4jStatus _batchToSpace(nd4j::LaunchContext * context, int internal_block_dims, NDArray *input, NDArray *output, std::vector &internal_input_shape, std::vector &internal_output_shape, Nd4jLong *block_shape, Nd4jLong *crops) { + Nd4jStatus _batchToSpace(sd::LaunchContext * context, int internal_block_dims, NDArray *input, NDArray *output, std::vector &internal_input_shape, std::vector &internal_output_shape, Nd4jLong *block_shape, Nd4jLong *crops) { auto in = input->reshape('c', internal_input_shape); auto out = output->reshape('c', internal_output_shape); switch (internal_block_dims) { @@ -488,7 +498,7 @@ void spaceToBatchND(nd4j::LaunchContext* context, const NDArray& input, const ND #define STB_BOOL (0, false),\ (1, true) - BUILD_TRIPLE_TEMPLATE(template void _execute, (nd4j::LaunchContext * context, void *ptrSpace, const Nd4jLong *space_shape, const Nd4jLong *space_strides, const Nd4jLong *block_shape, const Nd4jLong *pad_start, const Nd4jLong *block_offsets, void *ptrBatch, const Nd4jLong *batch_shape, const Nd4jLong *batch_strides), LIBND4J_TYPES, STB_DIM, STB_BOOL); + BUILD_TRIPLE_TEMPLATE(template void _execute, (sd::LaunchContext * context, void *ptrSpace, const Nd4jLong *space_shape, const Nd4jLong *space_strides, const Nd4jLong *block_shape, const Nd4jLong *pad_start, const Nd4jLong *block_offsets, void *ptrBatch, const Nd4jLong *batch_shape, const Nd4jLong *batch_strides), LIBND4J_TYPES, STB_DIM, STB_BOOL); #undef STB_BOOL #undef STB_DIM diff --git a/libnd4j/include/ops/declarable/helpers/cpu/s_t_d.cpp b/libnd4j/include/ops/declarable/helpers/cpu/s_t_d.cpp index 557d63fd3..32968b486 100644 --- a/libnd4j/include/ops/declarable/helpers/cpu/s_t_d.cpp +++ b/libnd4j/include/ops/declarable/helpers/cpu/s_t_d.cpp @@ -21,7 +21,7 @@ #include #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { template @@ -97,7 +97,7 @@ namespace helpers { } } - void _spaceTodepth(nd4j::LaunchContext * context, NDArray *input, NDArray *output, int block_size, bool isNHWC) { + void _spaceTodepth(sd::LaunchContext * context, NDArray *input, NDArray *output, int block_size, bool isNHWC) { BUILD_SINGLE_SELECTOR(input->dataType(), _spaceTodepth_, (input, output, block_size, isNHWC), LIBND4J_TYPES); } diff --git a/libnd4j/include/ops/declarable/helpers/cpu/scatter.cpp b/libnd4j/include/ops/declarable/helpers/cpu/scatter.cpp index 2de2b2d22..dd83a8618 100644 --- a/libnd4j/include/ops/declarable/helpers/cpu/scatter.cpp +++ b/libnd4j/include/ops/declarable/helpers/cpu/scatter.cpp @@ -23,7 +23,7 @@ #include #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { @@ -43,11 +43,11 @@ Nd4jLong checkIndices_(const NDArray& indices, const NDArray& output, const int auto func = PRAGMA_THREADS_FOR { - Nd4jLong xCoords[MAX_RANK]; + int xCoords[MAX_RANK]; for (auto i = start; i < stop; i++) { - shape::index2coords(i, xShapeInfo, xCoords); + shape::index2coordsCPU(start, i, xShapeInfo, xCoords); const Nd4jLong currentInd = x[shape::getOffset(xShapeInfo, xCoords)]; @@ -64,13 +64,13 @@ Nd4jLong checkIndices_(const NDArray& indices, const NDArray& output, const int } /////////////////////////////////////////////////////////////////// -Nd4jLong checkIndices(nd4j::LaunchContext *context, const NDArray& indices, const NDArray& output, const int axis) { +Nd4jLong checkIndices(sd::LaunchContext *context, const NDArray& indices, const NDArray& output, const int axis) { BUILD_SINGLE_SELECTOR(indices.dataType(), return checkIndices_, (indices, output, axis), INDEXING_TYPES); } /////////////////////////////////////////////////////////////////// -void scatter(nd4j::LaunchContext *context, pairwise::Ops op, const NDArray& indices, const NDArray& updates, NDArray& output, const bool lock) { +void scatter(sd::LaunchContext *context, pairwise::Ops op, const NDArray& indices, const NDArray& updates, NDArray& output, const bool lock) { const int outRank = output.rankOf(); const int indRank = indices.rankOf(); @@ -87,7 +87,7 @@ void scatter(nd4j::LaunchContext *context, pairwise::Ops op, const NDArray& ind } }; - samediff::Threads::parallel_tad(func, 0, indLen, 1, lock ? 1 : nd4j::Environment::getInstance()->maxThreads()); + samediff::Threads::parallel_tad(func, 0, indLen, 1, lock ? 1 : sd::Environment::getInstance()->maxThreads()); } else { // outRank > 1 @@ -107,12 +107,12 @@ void scatter(nd4j::LaunchContext *context, pairwise::Ops op, const NDArray& ind } }; - samediff::Threads::parallel_tad(func, 0, indLen, 1, lock ? 1 : nd4j::Environment::getInstance()->maxThreads()); + samediff::Threads::parallel_tad(func, 0, indLen, 1, lock ? 1 : sd::Environment::getInstance()->maxThreads()); } } /////////////////////////////////////////////////////////////////// -void scatterND(nd4j::LaunchContext *context, pairwise::Ops op, const NDArray& indices, const NDArray& updates, NDArray& output, const bool lock) { +void scatterND(sd::LaunchContext *context, pairwise::Ops op, const NDArray& indices, const NDArray& updates, NDArray& output, const bool lock) { const Nd4jLong indLen = indices.lengthOf(); const int outRank = output.rankOf(); @@ -129,7 +129,7 @@ void scatterND(nd4j::LaunchContext *context, pairwise::Ops op, const NDArray& i } }; - samediff::Threads::parallel_tad(func, 0, indLen, 1, lock ? 1 : nd4j::Environment::getInstance()->maxThreads()); + samediff::Threads::parallel_tad(func, 0, indLen, 1, lock ? 1 : sd::Environment::getInstance()->maxThreads()); } else { std::vector dimsToExcludeInd = ShapeUtils::evalDimsToExclude(indRank, {indRank-1}); @@ -154,11 +154,11 @@ void scatterND(nd4j::LaunchContext *context, pairwise::Ops op, const NDArray& i } }; - samediff::Threads::parallel_tad(func, 0, indLen / indLastDim, 1, lock ? 1 : nd4j::Environment::getInstance()->maxThreads()); + samediff::Threads::parallel_tad(func, 0, indLen / indLastDim, 1, lock ? 1 : sd::Environment::getInstance()->maxThreads()); } } -void scatterForLoss(nd4j::LaunchContext *context, const NDArray& indices, NDArray& updates, NDArray& output, const bool calcGrad) { +void scatterForLoss(sd::LaunchContext *context, const NDArray& indices, NDArray& updates, NDArray& output, const bool calcGrad) { // shapes of indices and output must be the same // shape of indices should be the same as updates shape with last dimension excluded diff --git a/libnd4j/include/ops/declarable/helpers/cpu/segment.cpp b/libnd4j/include/ops/declarable/helpers/cpu/segment.cpp index 08aafc98c..e57264e66 100644 --- a/libnd4j/include/ops/declarable/helpers/cpu/segment.cpp +++ b/libnd4j/include/ops/declarable/helpers/cpu/segment.cpp @@ -20,11 +20,11 @@ // #include -#include +#include #include #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { @@ -40,7 +40,7 @@ namespace helpers { for (Nd4jLong e = 1; e < indices->lengthOf(); e++) { if (idx == indices->e(e)) { // max - val = nd4j::math::nd4j_max(val, input->t(e)); + val = sd::math::nd4j_max(val, input->t(e)); } else { idx = indices->e(e); @@ -65,7 +65,7 @@ namespace helpers { if (indices->e(i) == idx) { for (Nd4jLong e = 0; e < maxT->lengthOf(); e++) { - maxT->t(e) = nd4j::math::nd4j_max(maxT->t(e), listOfTensors.at(i)->t(e)); + maxT->t(e) = sd::math::nd4j_max(maxT->t(e), listOfTensors.at(i)->t(e)); } } else { @@ -87,10 +87,10 @@ namespace helpers { if (input->isVector()) { T val = input->e(0); - for (int e = 1; e < indices->lengthOf(); e++) { + for (Nd4jLong e = 1; e < indices->lengthOf(); e++) { if (idx == indices->e(e)) { // min - val = nd4j::math::nd4j_min(val, input->t(e)); + val = sd::math::nd4j_min(val, input->t(e)); } else { idx = indices->e(e); @@ -115,8 +115,8 @@ namespace helpers { for (Nd4jLong i = 1; i < indices->lengthOf(); i++) { if (indices->e(i) == idx) { - for (int e = 0; e < minT->lengthOf(); e++) { - minT->p(e, nd4j::math::nd4j_min(minT->e(e), listOfTensors.at(i)->e(e))); + for (Nd4jLong e = 0; e < minT->lengthOf(); e++) { + minT->p(e, sd::math::nd4j_min(minT->e(e), listOfTensors.at(i)->e(e))); } } else { @@ -138,7 +138,7 @@ namespace helpers { T val = T(0.f); int count = 0; - for (int e = 0; e < indices->lengthOf(); e++) { + for (Nd4jLong e = 0; e < indices->lengthOf(); e++) { if (idx == indices->e(e)) { // mean val += input->e(e); @@ -166,7 +166,7 @@ namespace helpers { auto meanV = meanT->dup(); meanV.assign(listOfTensors.at(0)); - for (int i = 1; i < indices->lengthOf(); i++) { + for (Nd4jLong i = 1; i < indices->lengthOf(); i++) { if (indices->e(i) == idx) { auto func = PRAGMA_THREADS_FOR { for (auto e = start; e < stop; e++) { @@ -198,7 +198,7 @@ namespace helpers { if (input->isVector()) { T val = T(0.f); int count = 0; - for (int e = 0; e < indices->lengthOf(); e++) { + for (Nd4jLong e = 0; e < indices->lengthOf(); e++) { if (idx == indices->e(e)) { // sum val += input->t(e); @@ -220,7 +220,7 @@ namespace helpers { std::vector> outputs(numOfClasses); auto sumT = listOfOutTensors.at(idx); - for (int i = 0; i < indices->lengthOf(); i++) { + for (Nd4jLong i = 0; i < indices->lengthOf(); i++) { if (indices->e(i) == idx) { auto func = PRAGMA_THREADS_FOR { for (auto e = start; e < stop; e++) { @@ -248,7 +248,7 @@ namespace helpers { T val = input->e(0); int count = 0; - for (int e = 1; e < indices->lengthOf(); e++) { + for (Nd4jLong e = 1; e < indices->lengthOf(); e++) { if (idx == indices->e(e)) { // sum val *= input->e(e); @@ -269,7 +269,7 @@ namespace helpers { int numOfClasses = output->sizeAt(0); // number of classes auto sumT = listOfOutTensors.at(idx); sumT->assign(listOfTensors.at(0)); - for (int i = 1; i < indices->lengthOf(); i++) { + for (Nd4jLong i = 1; i < indices->lengthOf(); i++) { if (indices->e(i) == idx) { auto func = PRAGMA_THREADS_FOR { for (auto e = start; e < stop; e++) { @@ -291,29 +291,29 @@ namespace helpers { // static bool segmentIndicesValidate_(NDArray* indices, NDArray& aexpected, NDArray& anOutput) { // } - void segmentMaxFunctor(nd4j::LaunchContext * context, NDArray* input, NDArray* indices, NDArray* output) { + void segmentMaxFunctor(sd::LaunchContext * context, NDArray* input, NDArray* indices, NDArray* output) { BUILD_SINGLE_SELECTOR(input->dataType(), segmentMaxFunctor_, (input, indices, output), LIBND4J_TYPES); } - void segmentMinFunctor(nd4j::LaunchContext * context, NDArray* input, NDArray* indices, NDArray* output) { + void segmentMinFunctor(sd::LaunchContext * context, NDArray* input, NDArray* indices, NDArray* output) { BUILD_SINGLE_SELECTOR(input->dataType(), segmentMinFunctor_, (input, indices, output), LIBND4J_TYPES); } - void segmentMeanFunctor(nd4j::LaunchContext * context, NDArray* input, NDArray* indices, NDArray* output) { + void segmentMeanFunctor(sd::LaunchContext * context, NDArray* input, NDArray* indices, NDArray* output) { BUILD_SINGLE_SELECTOR(input->dataType(), segmentMeanFunctor_, (input, indices, output), LIBND4J_TYPES); } - void segmentSumFunctor(nd4j::LaunchContext * context, NDArray* input, NDArray* indices, NDArray* output) { + void segmentSumFunctor(sd::LaunchContext * context, NDArray* input, NDArray* indices, NDArray* output) { BUILD_SINGLE_SELECTOR(input->dataType(), segmentSumFunctor_, (input, indices, output), LIBND4J_TYPES); } - void segmentProdFunctor(nd4j::LaunchContext * context, NDArray* input, NDArray* indices, NDArray* output) { + void segmentProdFunctor(sd::LaunchContext * context, NDArray* input, NDArray* indices, NDArray* output) { BUILD_SINGLE_SELECTOR(input->dataType(), segmentProdFunctor_, (input, indices, output), LIBND4J_TYPES); } - bool segmentIndicesValidate(nd4j::LaunchContext * context, NDArray* indices, NDArray& expected, NDArray& output) { + bool segmentIndicesValidate(sd::LaunchContext * context, NDArray* indices, NDArray& expected, NDArray& output) { auto val = indices->e(0); - for (int e = 1; e < indices->lengthOf(); e++) { + for (Nd4jLong e = 1; e < indices->lengthOf(); e++) { output = indices->e(e); if (val.e(0) > output.e(0)) return false; @@ -333,7 +333,7 @@ namespace helpers { // Unsorted segment ops // -------------------------------------------------------------------------------------------------------------- // - bool unsortedSegmentIndicesValidate(nd4j::LaunchContext * context, NDArray* indices, Nd4jLong expected, Nd4jLong& output) { + bool unsortedSegmentIndicesValidate(sd::LaunchContext * context, NDArray* indices, Nd4jLong expected, Nd4jLong& output) { Nd4jLong val = indices->e(0); Nd4jLong maxInd = indices->argMax(); @@ -350,7 +350,7 @@ namespace helpers { // if input is a vector: (as if in doc sample) //int idx = static_cast((*indices)(0.)); - std::map> idxs;//(indices->lengthOf()); + MAP_IMPL> idxs;//(indices->lengthOf()); for (Nd4jLong e = 0; e < indices->lengthOf(); ++e) idxs[indices->e(e)].push_back(e); @@ -362,8 +362,8 @@ namespace helpers { for (auto fi = idxs.begin(); fi != idxs.end(); ++fi) { T val = input->e(fi->second.at(0)); - for (Nd4jLong idx = 1; idx < fi->second.size(); ++idx) { - val = nd4j::math::nd4j_max(val, input->e(fi->second.at(idx))); + for (Nd4jLong idx = 1; idx < static_cast(fi->second.size()); ++idx) { + val = sd::math::nd4j_max(val, input->e(fi->second.at(idx))); } output->p(fi->first, val); } @@ -380,10 +380,10 @@ namespace helpers { for (auto fi = idxs.begin(); fi != idxs.end(); ++fi) { auto outputT = listOfOutTensors.at(fi->first); outputT->assign(listOfTensors.at(fi->second.at(0))); - for (Nd4jLong idx = 1; idx < fi->second.size(); ++idx) { + for (Nd4jLong idx = 1; idx < static_cast(fi->second.size()); ++idx) { auto maxT = listOfTensors.at(fi->second.at(idx)); for (Nd4jLong e = 0; e < outputT->lengthOf(); ++e) { - T val = nd4j::math::nd4j_max(maxT->e(e), outputT->e(e)); + T val = sd::math::nd4j_max(maxT->e(e), outputT->e(e)); outputT->p(e, val); } @@ -391,7 +391,7 @@ namespace helpers { } } } - void unsortedSegmentMaxFunctor(nd4j::LaunchContext * context, NDArray* input, NDArray* indices, Nd4jLong numOfClasses, NDArray* output) { + void unsortedSegmentMaxFunctor(sd::LaunchContext * context, NDArray* input, NDArray* indices, Nd4jLong numOfClasses, NDArray* output) { BUILD_SINGLE_SELECTOR(input->dataType(), unsortedSegmentMaxFunctor_, (input, indices, numOfClasses, output), NUMERIC_TYPES); } BUILD_SINGLE_TEMPLATE(template void unsortedSegmentMaxFunctor_, (NDArray* input, NDArray* indices, Nd4jLong numOfClasses, NDArray* output), NUMERIC_TYPES); @@ -400,7 +400,7 @@ namespace helpers { static void unsortedSegmentMinFunctor_(NDArray* input, NDArray* indices, Nd4jLong numOfClasses, NDArray* output) { // if input is a vector: (as if in doc sample) //int idx = static_cast((*indices)(0.)); - std::map> idxs;//(indices->lengthOf()); + MAP_IMPL> idxs;//(indices->lengthOf()); for (Nd4jLong e = 0; e < indices->lengthOf(); ++e) idxs[indices->e(e)].push_back(e); @@ -415,7 +415,7 @@ namespace helpers { T val = input->t(fi->second.at(0)); for (size_t idx = 1; idx < fi->second.size(); ++idx) { - val = nd4j::math::nd4j_min(val, input->t(fi->second.at(idx))); + val = sd::math::nd4j_min(val, input->t(fi->second.at(idx))); } output->t(fi->first) = val; } @@ -432,11 +432,11 @@ namespace helpers { for (auto fi = idxs.begin(); fi != idxs.end(); ++fi) { auto outputT = listOfOutTensors.at(fi->first); outputT->assign(listOfTensors.at(fi->second.at(0))); - for (Nd4jLong idx = 1; idx < fi->second.size(); ++idx) { + for (size_t idx = 1; idx < fi->second.size(); ++idx) { auto minT = listOfTensors.at(fi->second.at(idx)); for (Nd4jLong e = 0; e < outputT->lengthOf(); ++e) { - outputT->t(e) = nd4j::math::nd4j_min(minT->t(e), outputT->t(e)); + outputT->t(e) = sd::math::nd4j_min(minT->t(e), outputT->t(e)); } } //outputT->assign(maxT); @@ -444,15 +444,15 @@ namespace helpers { } } - void unsortedSegmentMinFunctor(nd4j::LaunchContext * context, NDArray* input, NDArray* indices, Nd4jLong numOfClasses, NDArray* output) { + void unsortedSegmentMinFunctor(sd::LaunchContext * context, NDArray* input, NDArray* indices, Nd4jLong numOfClasses, NDArray* output) { BUILD_SINGLE_SELECTOR(input->dataType(), unsortedSegmentMinFunctor_, (input, indices, numOfClasses, output), NUMERIC_TYPES); } BUILD_SINGLE_TEMPLATE(template void unsortedSegmentMinFunctor_, (NDArray* input, NDArray* indices, Nd4jLong numOfClasses, NDArray* output), NUMERIC_TYPES); - void unsortedSegmentMeanFunctor(nd4j::LaunchContext * context, NDArray* input, NDArray* indices, Nd4jLong numOfClasses, NDArray* output) { - std::map> idxs;//(indices->lengthOf()); + void unsortedSegmentMeanFunctor(sd::LaunchContext * context, NDArray* input, NDArray* indices, Nd4jLong numOfClasses, NDArray* output) { + MAP_IMPL> idxs;//(indices->lengthOf()); for (Nd4jLong e = 0; e < indices->lengthOf(); ++e) idxs[indices->e(e)].push_back(e); @@ -493,8 +493,8 @@ namespace helpers { } } - void unsortedSegmentSumFunctor(nd4j::LaunchContext * context, NDArray* input, NDArray* indices, Nd4jLong numOfClasses, NDArray* output) { - std::map> idxs;//(indices->lengthOf()); + void unsortedSegmentSumFunctor(sd::LaunchContext * context, NDArray* input, NDArray* indices, Nd4jLong numOfClasses, NDArray* output) { + MAP_IMPL> idxs;//(indices->lengthOf()); for (Nd4jLong e = 0; e < indices->lengthOf(); ++e) idxs[indices->e(e)].push_back(e); @@ -534,7 +534,7 @@ namespace helpers { template void unsortedSegmentProdFunctor_(NDArray* input, NDArray* indices, Nd4jLong numOfClasses, NDArray* output) { - std::map> idxs;//(indices->lengthOf()); + MAP_IMPL> idxs;//(indices->lengthOf()); for (Nd4jLong e = 0; e < indices->lengthOf(); ++e) idxs[indices->e(e)].push_back(e); @@ -560,7 +560,7 @@ namespace helpers { for (auto fi = idxs.begin(); fi != idxs.end(); ++fi) { auto outputT = listOfOutTensors.at(fi->first); outputT->assign(listOfTensors.at(fi->second.at(0))); - for (Nd4jLong idx = 1; idx < fi->second.size(); ++idx) { + for (size_t idx = 1; idx < fi->second.size(); ++idx) { auto current = listOfTensors.at(fi->second.at(idx)); *outputT *= *current; @@ -569,13 +569,13 @@ namespace helpers { } } - void unsortedSegmentProdFunctor(nd4j::LaunchContext * context, NDArray* input, NDArray* indices, Nd4jLong numOfClasses, NDArray* output) { + void unsortedSegmentProdFunctor(sd::LaunchContext * context, NDArray* input, NDArray* indices, Nd4jLong numOfClasses, NDArray* output) { BUILD_SINGLE_SELECTOR(input->dataType(), unsortedSegmentProdFunctor_, (input, indices, numOfClasses, output), NUMERIC_TYPES); } BUILD_SINGLE_TEMPLATE(template void unsortedSegmentProdFunctor_, (NDArray* input, NDArray* indices, Nd4jLong numOfClasses, NDArray* output), NUMERIC_TYPES); - void unsortedSegmentSqrtNFunctor(nd4j::LaunchContext * context, NDArray* input, NDArray* indices, Nd4jLong numOfClasses, NDArray* output) { - std::map> idxs;//(indices->lengthOf()); + void unsortedSegmentSqrtNFunctor(sd::LaunchContext * context, NDArray* input, NDArray* indices, Nd4jLong numOfClasses, NDArray* output) { + MAP_IMPL> idxs;//(indices->lengthOf()); for (Nd4jLong e = 0; e < indices->lengthOf(); ++e) idxs[indices->e(e)].push_back(e); @@ -584,10 +584,10 @@ namespace helpers { if (input->isVector()) { // 1D case for (auto fi = idxs.begin(); fi != idxs.end(); ++fi) { double sumValue = input->e(fi->second.at(0)); - for (Nd4jLong idx = 1; idx < fi->second.size(); ++idx) { + for (size_t idx = 1; idx < fi->second.size(); ++idx) { sumValue += input->e(fi->second.at(idx)); } - output->p(fi->first, sumValue / nd4j::math::nd4j_sqrt(fi->second.size())); + output->p(fi->first, sumValue / sd::math::nd4j_sqrt(fi->second.size())); } } else { @@ -599,12 +599,12 @@ namespace helpers { for (auto fi = idxs.begin(); fi != idxs.end(); ++fi) { auto outputT = listOfOutTensors.at(fi->first); outputT->assign(listOfTensors.at(fi->second.at(0))); - for (Nd4jLong idx = 1; idx < fi->second.size(); ++idx) { + for (size_t idx = 1; idx < fi->second.size(); ++idx) { auto current = listOfTensors.at(fi->second.at(idx)); *outputT += *current; } //outputT->assign(maxT); - (*outputT) /= nd4j::math::nd4j_sqrt(fi->second.size()); + (*outputT) /= sd::math::nd4j_sqrt(fi->second.size()); } } } @@ -616,7 +616,7 @@ namespace helpers { // // segment max template - int segmentMaxFunctorBP_(nd4j::LaunchContext * context, NDArray* input, NDArray* indices, NDArray* gradOut, NDArray* output) { + int segmentMaxFunctorBP_(sd::LaunchContext * context, NDArray* input, NDArray* indices, NDArray* gradOut, NDArray* output) { //int numOfClasses = gradOut->sizeAt(0); // if input is a vector: (as if in doc sample) auto tempRes = gradOut->dup(); @@ -627,7 +627,7 @@ namespace helpers { auto func = PRAGMA_THREADS_FOR { for (auto e = start; e < stop; e++) { auto classNum = indices->e(e); - if (nd4j::math::nd4j_abs(tempRes.e(classNum) - input->e(e)) <= T(1.e-6)) + if (sd::math::nd4j_abs(tempRes.e(classNum) - input->e(e)) <= T(1.e-6)) output->p(e, gradOut->e(classNum)); } }; @@ -651,8 +651,8 @@ namespace helpers { auto currentOut = listOfOutTensors.at(i); auto currentGradOut = listOfGradOuts.at(classNum); - for (uint64_t e = 0; e < current->lengthOf(); e++) { - if (nd4j::math::nd4j_abs(listOfBPTensors.at(classNum)->e(e) - current->e(e)) <= T(1.e-6)) + for (Nd4jLong e = 0; e < current->lengthOf(); e++) { + if (sd::math::nd4j_abs(listOfBPTensors.at(classNum)->e(e) - current->e(e)) <= T(1.e-6)) currentOut->p(e, currentGradOut->e(e)); } } @@ -664,20 +664,20 @@ namespace helpers { return ND4J_STATUS_OK; } - int segmentMaxFunctorBP(nd4j::LaunchContext * context, NDArray* input, NDArray* indices, NDArray* gradOut, NDArray* output) { + int segmentMaxFunctorBP(sd::LaunchContext * context, NDArray* input, NDArray* indices, NDArray* gradOut, NDArray* output) { BUILD_SINGLE_SELECTOR(output->dataType(), return segmentMaxFunctorBP_, (context, input, indices, gradOut, output), NUMERIC_TYPES); } - BUILD_SINGLE_TEMPLATE(template int segmentMaxFunctorBP_, (nd4j::LaunchContext * context, NDArray* input, NDArray* indices, NDArray* gradOut, NDArray* output), NUMERIC_TYPES); + BUILD_SINGLE_TEMPLATE(template int segmentMaxFunctorBP_, (sd::LaunchContext * context, NDArray* input, NDArray* indices, NDArray* gradOut, NDArray* output), NUMERIC_TYPES); // segmen min - int segmentMinFunctorBP(nd4j::LaunchContext * context, NDArray* input, NDArray* indices, NDArray* gradOut, NDArray* output) { + int segmentMinFunctorBP(sd::LaunchContext * context, NDArray* input, NDArray* indices, NDArray* gradOut, NDArray* output) { NDArray tempRes = gradOut->dup(); segmentMinFunctor(context, input, indices, &tempRes); if (input->isVector()) { auto func = PRAGMA_THREADS_FOR { for (auto e = start; e < stop; e++) { auto classNum = indices->e(e); - if (nd4j::math::nd4j_abs(tempRes.e(classNum) - input->e(e)) < 1.e-5) + if (sd::math::nd4j_abs(tempRes.e(classNum) - input->e(e)) < 1.e-5) output->p(e, gradOut->e(classNum)); } }; @@ -703,8 +703,8 @@ namespace helpers { auto currentOut = listOfOutTensors.at(i); auto currentGradOut = listOfGradOuts.at(classNum); - for (int e = 0; e < current->lengthOf(); e++) { - if (nd4j::math::nd4j_abs(listOfBPTensors.at(classNum)->e(e) - current->e(e)) < + for (Nd4jLong e = 0; e < current->lengthOf(); e++) { + if (sd::math::nd4j_abs(listOfBPTensors.at(classNum)->e(e) - current->e(e)) < 1.e-5) currentOut->p(e, currentGradOut->e(e)); } @@ -717,9 +717,9 @@ namespace helpers { } // segmen mean - int segmentMeanFunctorBP(nd4j::LaunchContext * context, NDArray* input, NDArray* indices, NDArray* gradOut, NDArray* output) { + int segmentMeanFunctorBP(sd::LaunchContext * context, NDArray* input, NDArray* indices, NDArray* gradOut, NDArray* output) { int numClasses = output->sizeAt(0); - std::map classCount;//(numClasses); + MAP_IMPL classCount;//(numClasses); for (Nd4jLong count = 0; count < numClasses; ++count) { classCount[count] = 0; @@ -746,13 +746,13 @@ namespace helpers { int pos = 0; //auto func = [&](uint64_t thread_id, uint64_t start, uint64_t stop, uint64_t increment) -> void { - for (auto i = 0; i < indices->lengthOf(); i++) { + for (Nd4jLong i = 0; i < indices->lengthOf(); i++) { auto classNum = indices->e(i); auto current = listOfTensors.at(i); auto currentOut = listOfOutTensors.at(i); auto currentGradOut = listOfGradOuts.at(classNum); - for (int e = 0; e < current->lengthOf(); e++) { + for (Nd4jLong e = 0; e < current->lengthOf(); e++) { currentOut->p(e, currentGradOut->e(e) / classCount.at(classNum)); } } @@ -763,7 +763,7 @@ namespace helpers { return ND4J_STATUS_OK; } - int segmentSumFunctorBP(nd4j::LaunchContext * context, NDArray* input, NDArray* indices, NDArray* gradOut, NDArray* output) { + int segmentSumFunctorBP(sd::LaunchContext * context, NDArray* input, NDArray* indices, NDArray* gradOut, NDArray* output) { // int numClasses = output->sizeAt(0); // if input is a vector: (as if in doc sample) Nd4jLong idx = indices->e(0); @@ -781,7 +781,7 @@ namespace helpers { ResultSet listOfOutTensors = output->allTensorsAlongDimension(restDims); //auto func = PRAGMA_THREADS_FOR { - for (auto i = 0; i < indices->lengthOf(); i++) { + for (Nd4jLong i = 0; i < indices->lengthOf(); i++) { auto classNum = indices->e(i); auto current = listOfTensors.at(i); auto currentOut = listOfOutTensors.at(i); @@ -796,7 +796,7 @@ namespace helpers { return Status::OK(); } - int segmentProdFunctorBP(nd4j::LaunchContext * context, NDArray* input, NDArray* indices, NDArray* gradOut, NDArray* output) { + int segmentProdFunctorBP(sd::LaunchContext * context, NDArray* input, NDArray* indices, NDArray* gradOut, NDArray* output) { auto tempRes = gradOut->dup(); segmentProdFunctor(context, input, indices, &tempRes); if (input->isVector()) { @@ -817,7 +817,7 @@ namespace helpers { //std::vector> outputs(numOfClasses); //auto func = PRAGMA_THREADS_FOR { - for (auto i = 0; i < indices->lengthOf(); i++) { + for (Nd4jLong i = 0; i < indices->lengthOf(); i++) { auto classNum = indices->e(i); auto current = listOfTensors.at(i); auto currentOut = listOfOutTensors.at(i); @@ -839,7 +839,7 @@ namespace helpers { // -------------------------------------------------------------------------------------------------------------- // template - static int unsortedSegmentMaxFunctorBP_(nd4j::LaunchContext * context, NDArray* input, NDArray* indices, NDArray* gradOut, Nd4jLong numOfClasses, NDArray* output) { + static int unsortedSegmentMaxFunctorBP_(sd::LaunchContext * context, NDArray* input, NDArray* indices, NDArray* gradOut, Nd4jLong numOfClasses, NDArray* output) { // int numOfClasses = gradOut->sizeAt(0); // if input is a vector: (as if in doc sample) auto tempRes = gradOut->dup(); @@ -848,7 +848,7 @@ namespace helpers { for (Nd4jLong e = 0; e < input->lengthOf(); ++e) { Nd4jLong classNum = indices->e(e); - if (nd4j::math::nd4j_abs(tempRes.e(classNum) - input->e(e)) < 1.e-5) + if (sd::math::nd4j_abs(tempRes.e(classNum) - input->e(e)) < 1.e-5) output->p(e, gradOut->e(classNum)); } } @@ -860,13 +860,13 @@ namespace helpers { ResultSet listOfTensors = input->allTensorsAlongDimension(restDims); ResultSet listOfOutTensors = output->allTensorsAlongDimension(restDims); - for (int i = 0; i < indices->lengthOf(); i++) { + for (Nd4jLong i = 0; i < indices->lengthOf(); i++) { Nd4jLong classNum = indices->e(i); NDArray* current = listOfTensors.at(i); NDArray* currentOut = listOfOutTensors.at(i); NDArray* currentGradOut = listOfGradOuts.at(classNum); for (int e = 0; e < current->lengthOf(); e++) { - if (nd4j::math::nd4j_abs(listOfBPTensors.at(classNum)->e(e) - current->e(e)) < 1.e-5) + if (sd::math::nd4j_abs(listOfBPTensors.at(classNum)->e(e) - current->e(e)) < 1.e-5) currentOut->p(e, currentGradOut->e(e)); } } @@ -875,13 +875,13 @@ namespace helpers { return ND4J_STATUS_OK; } - int unsortedSegmentMaxFunctorBP(nd4j::LaunchContext * context, NDArray* input, NDArray* indices, NDArray* gradOut, Nd4jLong numOfClasses, NDArray* output) { + int unsortedSegmentMaxFunctorBP(sd::LaunchContext * context, NDArray* input, NDArray* indices, NDArray* gradOut, Nd4jLong numOfClasses, NDArray* output) { BUILD_SINGLE_SELECTOR(output->dataType(), return unsortedSegmentMaxFunctorBP_, (context, input, indices, gradOut, numOfClasses, output), NUMERIC_TYPES); } - BUILD_SINGLE_TEMPLATE(template int unsortedSegmentMaxFunctorBP_, (nd4j::LaunchContext * context, NDArray* input, NDArray* indices, NDArray* gradOut, Nd4jLong numOfClasses, NDArray* output), NUMERIC_TYPES); + BUILD_SINGLE_TEMPLATE(template int unsortedSegmentMaxFunctorBP_, (sd::LaunchContext * context, NDArray* input, NDArray* indices, NDArray* gradOut, Nd4jLong numOfClasses, NDArray* output), NUMERIC_TYPES); template - static int unsortedSegmentMinFunctorBP_(nd4j::LaunchContext * context, NDArray* input, NDArray* indices, NDArray* gradOut, Nd4jLong numOfClasses, NDArray* output) { + static int unsortedSegmentMinFunctorBP_(sd::LaunchContext * context, NDArray* input, NDArray* indices, NDArray* gradOut, Nd4jLong numOfClasses, NDArray* output) { auto tempRes = gradOut->dup(); unsortedSegmentMinFunctor(context, input, indices, numOfClasses, &tempRes); if (input->isVector()) { @@ -889,7 +889,7 @@ namespace helpers { auto func = PRAGMA_THREADS_FOR { for (auto e = start; e < stop; e++) { auto classNum = indices->e(e); - if (nd4j::math::nd4j_abs(tempRes.t(classNum) - input->t(e)) < 1.e-6) + if (sd::math::nd4j_abs(tempRes.t(classNum) - input->t(e)) < 1.e-6) output->t(e) = gradOut->t(classNum); } }; @@ -905,14 +905,14 @@ namespace helpers { ResultSet listOfOutTensors = output->allTensorsAlongDimension(restDims); //auto func = PRAGMA_THREADS_FOR { - for (auto i = 0; i < indices->lengthOf(); i++) { + for (Nd4jLong i = 0; i < indices->lengthOf(); i++) { auto classNum = indices->e(i); auto current = listOfTensors.at(i); auto currentOut = listOfOutTensors.at(i); auto currentGradOut = listOfGradOuts.at(classNum); - for (int e = 0; e < current->lengthOf(); e++) { - if (nd4j::math::nd4j_abs(listOfBPTensors.at(classNum)->t(e) - current->t(e)) < 1.e-6) + for (Nd4jLong e = 0; e < current->lengthOf(); e++) { + if (sd::math::nd4j_abs(listOfBPTensors.at(classNum)->t(e) - current->t(e)) < 1.e-6) currentOut->t(e) = currentGradOut->t(e); } } @@ -924,14 +924,14 @@ namespace helpers { return ND4J_STATUS_OK; } - int unsortedSegmentMinFunctorBP(nd4j::LaunchContext * context, NDArray* input, NDArray* indices, NDArray* gradOut, Nd4jLong numOfClasses, NDArray* output) { + int unsortedSegmentMinFunctorBP(sd::LaunchContext * context, NDArray* input, NDArray* indices, NDArray* gradOut, Nd4jLong numOfClasses, NDArray* output) { BUILD_SINGLE_SELECTOR(output->dataType(), return unsortedSegmentMinFunctorBP_, (context, input, indices, gradOut, numOfClasses, output), NUMERIC_TYPES); } - BUILD_SINGLE_TEMPLATE(template int unsortedSegmentMinFunctorBP_, (nd4j::LaunchContext * context, NDArray* input, NDArray* indices, NDArray* gradOut, Nd4jLong numOfClasses, NDArray* output), NUMERIC_TYPES); + BUILD_SINGLE_TEMPLATE(template int unsortedSegmentMinFunctorBP_, (sd::LaunchContext * context, NDArray* input, NDArray* indices, NDArray* gradOut, Nd4jLong numOfClasses, NDArray* output), NUMERIC_TYPES); - int unsortedSegmentMeanFunctorBP(nd4j::LaunchContext * context, NDArray* input, NDArray* indices, NDArray* gradOut, Nd4jLong numOfClasses, NDArray* output) { + int unsortedSegmentMeanFunctorBP(sd::LaunchContext * context, NDArray* input, NDArray* indices, NDArray* gradOut, Nd4jLong numOfClasses, NDArray* output) { - std::map classCount;//(numClasses); + MAP_IMPL classCount;//(numClasses); for (Nd4jLong count = 0; count < numOfClasses; ++count) { classCount[count] = 0; @@ -955,7 +955,7 @@ namespace helpers { ResultSet listOfTensors = input->allTensorsAlongDimension(restDims); ResultSet listOfOutTensors = output->allTensorsAlongDimension(restDims); - for (int i = 0; i < indices->lengthOf(); i++) { + for (Nd4jLong i = 0; i < indices->lengthOf(); i++) { Nd4jLong classNum = indices->e(i); NDArray* current = listOfTensors.at(i); NDArray* currentOut = listOfOutTensors.at(i); @@ -966,7 +966,7 @@ namespace helpers { return ND4J_STATUS_OK; } - int unsortedSegmentSumFunctorBP(nd4j::LaunchContext * context, NDArray* input, NDArray* indices, NDArray* gradOut, Nd4jLong numOfClasses, NDArray* output) { + int unsortedSegmentSumFunctorBP(sd::LaunchContext * context, NDArray* input, NDArray* indices, NDArray* gradOut, Nd4jLong numOfClasses, NDArray* output) { // if input is a vector: (as if in doc sample) Nd4jLong idx = indices->e(0); @@ -984,7 +984,7 @@ namespace helpers { ResultSet listOfOutTensors = output->allTensorsAlongDimension(restDims); //auto func = PRAGMA_THREADS_FOR { - for (auto i = 0; i < indices->lengthOf(); i++) { + for (Nd4jLong i = 0; i < indices->lengthOf(); i++) { auto classNum = indices->e(i); auto currentOut = listOfOutTensors.at(i); auto currentGradOut = listOfGradOuts.at(classNum); @@ -998,7 +998,7 @@ namespace helpers { return Status::OK(); } - int unsortedSegmentProdFunctorBP(nd4j::LaunchContext * context, NDArray* input, NDArray* indices, NDArray* gradOut, Nd4jLong numOfClasses, NDArray* output) { + int unsortedSegmentProdFunctorBP(sd::LaunchContext * context, NDArray* input, NDArray* indices, NDArray* gradOut, Nd4jLong numOfClasses, NDArray* output) { auto tempRes = gradOut->dup(); unsortedSegmentProdFunctor(context, input, indices, numOfClasses, &tempRes); @@ -1021,7 +1021,7 @@ namespace helpers { ResultSet listOfOutTensors = output->allTensorsAlongDimension(restDims); //auto func = PRAGMA_THREADS_FOR { - for (auto i = 0; i < indices->lengthOf(); i++) { + for (Nd4jLong i = 0; i < indices->lengthOf(); i++) { auto classNum = indices->e(i); auto current = listOfTensors.at(i); auto currentOut = listOfOutTensors.at(i); @@ -1039,8 +1039,8 @@ namespace helpers { } // template - int unsortedSegmentSqrtNFunctorBP(nd4j::LaunchContext * context, NDArray* input, NDArray* indices, NDArray* gradOut, Nd4jLong numOfClasses, NDArray* output) { - std::map classCount;//(numClasses); + int unsortedSegmentSqrtNFunctorBP(sd::LaunchContext * context, NDArray* input, NDArray* indices, NDArray* gradOut, Nd4jLong numOfClasses, NDArray* output) { + MAP_IMPL classCount;//(numClasses); for (Nd4jLong count = 0; count < numOfClasses; ++count) { classCount[count] = 0; @@ -1053,9 +1053,9 @@ namespace helpers { // if input is a vector: (as if in doc sample) if (input->isVector()) { //auto func = PRAGMA_THREADS_FOR { - for (auto e = 0; e < indices->lengthOf(); e++) { + for (Nd4jLong e = 0; e < indices->lengthOf(); e++) { auto classNum = indices->e(e); - output->p(e, gradOut->e(classNum) / nd4j::math::nd4j_sqrt(classCount[classNum])); + output->p(e, gradOut->e(classNum) / sd::math::nd4j_sqrt(classCount[classNum])); } //}; @@ -1069,14 +1069,14 @@ namespace helpers { ResultSet listOfOutTensors =output->allTensorsAlongDimension(restDims); //auto func = PRAGMA_THREADS_FOR { - for (auto i = 0; i < indices->lengthOf(); i++) { + for (Nd4jLong i = 0; i < indices->lengthOf(); i++) { auto classNum = indices->e(i); auto current = listOfTensors.at(i); auto currentOut = listOfOutTensors.at(i); auto currentGradOut = listOfGradOuts.at(classNum); for (int e = 0; e < current->lengthOf(); e++) { - currentOut->p(e, currentGradOut->e(e) / nd4j::math::nd4j_sqrt(classCount[classNum])); + currentOut->p(e, currentGradOut->e(e) / sd::math::nd4j_sqrt(classCount[classNum])); } } //}; diff --git a/libnd4j/include/ops/declarable/helpers/cpu/sequence_mask.cpp b/libnd4j/include/ops/declarable/helpers/cpu/sequence_mask.cpp index c175fd96d..8e25c4690 100644 --- a/libnd4j/include/ops/declarable/helpers/cpu/sequence_mask.cpp +++ b/libnd4j/include/ops/declarable/helpers/cpu/sequence_mask.cpp @@ -21,7 +21,7 @@ #include #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { @@ -37,7 +37,7 @@ namespace helpers { samediff::Threads::parallel_for(func, 0, maxIndex, 1, 0, input->lengthOf(), 1); } - void sequenceMask(nd4j::LaunchContext * context, NDArray* input, NDArray* output, int maxIndex) { + void sequenceMask(sd::LaunchContext * context, NDArray* input, NDArray* output, int maxIndex) { BUILD_DOUBLE_SELECTOR(input->dataType(), output->dataType(), sequenceMask_, (input, output, maxIndex), INTEGER_TYPES, LIBND4J_TYPES_EXTENDED); } diff --git a/libnd4j/include/ops/declarable/helpers/cpu/sg_cb.cpp b/libnd4j/include/ops/declarable/helpers/cpu/sg_cb.cpp index 05353bf5e..07cbca04e 100644 --- a/libnd4j/include/ops/declarable/helpers/cpu/sg_cb.cpp +++ b/libnd4j/include/ops/declarable/helpers/cpu/sg_cb.cpp @@ -19,12 +19,12 @@ // #include -#include +#include #include #define HS_MAX_EXP 6.0f -namespace nd4j { +namespace sd { namespace ops { namespace helpers { template @@ -172,7 +172,7 @@ namespace nd4j { // target is known in advance } else { randomValue = randomValue * (unsigned long long) 25214903917 + 11; - auto idx = nd4j::math::nd4j_abs((randomValue >> 16) % negLength); + auto idx = sd::math::nd4j_abs((randomValue >> 16) % negLength); irow = idx >= negLength ? -1 : static_cast(negTable[idx]); if (irow < 0 || irow >= vocabSize) irow = randomValue % (vocabSize - 1) + 1; @@ -247,7 +247,7 @@ namespace nd4j { // target is known in advance } else { randomValue = randomValue * (unsigned long long) 25214903917 + 11; - auto idx = nd4j::math::nd4j_abs((randomValue >> 16) % negLength); + auto idx = sd::math::nd4j_abs((randomValue >> 16) % negLength); irow = idx >= negLength ? -1 : static_cast(negTable[idx]); if (irow < 0 || irow >= vocabSize) irow = randomValue % (vocabSize - 1) + 1; @@ -276,7 +276,7 @@ namespace nd4j { int binarySearch(const int *haystack, const int needle, const int totalElements) { int firstIndex = 0; int lastIndex = totalElements - 1; - int halfIndex = nd4j::math::nd4j_floor((lastIndex + firstIndex) / (float) 2); + int halfIndex = sd::math::nd4j_floor((lastIndex + firstIndex) / (float) 2); while(haystack[halfIndex] != needle && firstIndex < lastIndex) { if (needle < haystack[halfIndex]) { @@ -284,7 +284,7 @@ namespace nd4j { } else if (needle > haystack[halfIndex]) { firstIndex = halfIndex + 1; } - halfIndex = nd4j::math::nd4j_floor((lastIndex + firstIndex) / (float) 2); + halfIndex = sd::math::nd4j_floor((lastIndex + firstIndex) / (float) 2); } return (haystack[halfIndex] == needle) ? halfIndex : -1; @@ -310,8 +310,8 @@ namespace nd4j { int irow = 0; unsigned long long randomValue = rv; for (int r = 0; r < nsRounds; r++) { - randomValue = nd4j::math::nd4j_abs(randomValue * (unsigned long long) 25214903917 + 11); - auto idx = nd4j::math::nd4j_abs((randomValue >> 16) % negLength); + randomValue = sd::math::nd4j_abs(randomValue * (unsigned long long) 25214903917 + 11); + auto idx = sd::math::nd4j_abs((randomValue >> 16) % negLength); irow = idx >= negLength ? -1 : static_cast(negTable[idx]); if (irow < 0 || irow >= vocabSize) @@ -378,7 +378,7 @@ namespace nd4j { int irow = 0; auto cShift = t * idxShift; - for (int e = 0; e < hsRounds; e++) { + for (Nd4jLong e = 0; e < hsRounds; e++) { irow = bIndices[e + cShift]; if (irow < 0 || irow >= vocabSize) continue; @@ -401,7 +401,7 @@ namespace nd4j { // target is known in advance } else { randomValue = randomValue * (unsigned long long) 25214903917 + 11; - auto idx = nd4j::math::nd4j_abs((randomValue >> 16) % negLength); + auto idx = sd::math::nd4j_abs((randomValue >> 16) % negLength); irow = idx >= negLength ? -1 : static_cast(negTable[idx]); if (irow < 0 || irow >= vocabSize) @@ -457,7 +457,7 @@ namespace nd4j { T sneu1[600]; T sneu1e[600]; - for (int e = start; e < stop; e++) { + for (auto e = start; e < stop; e++) { T *neu1 = vectorLength <= 600 ? sneu1 : new T[vectorLength]; T *neu1e = vectorLength <= 600 ? sneu1e : new T[vectorLength]; @@ -500,7 +500,7 @@ namespace nd4j { // hierarchic softmax step if (!indices.isEmpty()) { - for (int i = 0; i < numIndices; i++) { + for (Nd4jLong i = 0; i < numIndices; i++) { const int cIndex = bIndices[(e * numIndices) + i]; const int cCode = bCodes[(e * numIndices) + i]; @@ -526,7 +526,7 @@ namespace nd4j { // we're skipping rng on 0 step if (r != 0) { randomValue = randomValue * (unsigned long long) 25214903917 + 11; - auto idx = nd4j::math::nd4j_abs((randomValue >> 16) % negLength); + auto idx = sd::math::nd4j_abs((randomValue >> 16) % negLength); irow = idx >= negLength ? -1 : static_cast(negTable[idx]); if (irow < 0 || irow >= vocabSize) irow = randomValue % (vocabSize - 1) + 1; diff --git a/libnd4j/include/ops/declarable/helpers/cpu/shift.cpp b/libnd4j/include/ops/declarable/helpers/cpu/shift.cpp index 4b54c7362..9dfeac2ec 100644 --- a/libnd4j/include/ops/declarable/helpers/cpu/shift.cpp +++ b/libnd4j/include/ops/declarable/helpers/cpu/shift.cpp @@ -20,7 +20,7 @@ #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { template diff --git a/libnd4j/include/ops/declarable/helpers/cpu/softmax.cpp b/libnd4j/include/ops/declarable/helpers/cpu/softmax.cpp new file mode 100644 index 000000000..e2c0f5183 --- /dev/null +++ b/libnd4j/include/ops/declarable/helpers/cpu/softmax.cpp @@ -0,0 +1,254 @@ +/******************************************************************************* + * Copyright (c) 2015-2018 Skymind, Inc. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ + +// +// @author Yurii Shyrma (iuriish@yahoo.com), created on 19.04.2018 +// @author raver119@gmail.com +// + +#include +#include +#include +#include +#include + +namespace sd { + namespace ops { + namespace helpers { + + template + static void softMaxForVector_(void *input, Nd4jLong *inShapeInfo, void *output, Nd4jLong *outShapeInfo) { + + T* inBuff = reinterpret_cast(input); + T* outBuff = reinterpret_cast(output); + + T max = -DataTypeUtils::max(); + T sum = 0.; + int inEWS = shape::elementWiseStride(inShapeInfo); + int outEWS = shape::elementWiseStride(outShapeInfo); + int length = shape::length(inShapeInfo); + + if (inEWS >= 1 && outEWS >= 1) { + + if (inEWS == 1 && outEWS == 1) { + + for (int i = 0; i < length; i++) + max = sd::math::nd4j_max(max, inBuff[i]); + + for (int i = 0; i < length; i++) { + outBuff[i] = sd::math::nd4j_exp(inBuff[i] - max); + sum += outBuff[i]; + } + + for (int i = 0; i < length; i++) + outBuff[i] /= sum; + } + else { + + for (int i = 0; i < length; i++) + max = sd::math::nd4j_max(max, inBuff[i * inEWS]); + + for (int i = 0; i < length; i++) { + T r = sd::math::nd4j_exp(inBuff[i * inEWS] - max); + outBuff[i * outEWS] = r; + sum += r; + } + + for (int i = 0; i < length; i++) + outBuff[i * outEWS] /= sum; + } + } + } + + /////////////////////////////////////////////////////////////////// + void softMaxForVector(sd::LaunchContext * context, const NDArray& input, NDArray& output) { + + if(!input.isVector() || !output.isVector()) + throw std::runtime_error("ops::helpers::softMaxForVector function: input and output arrays must be vectors !"); + + auto xType = input.dataType(); + BUILD_SINGLE_SELECTOR(xType, softMaxForVector_, (input.getBuffer(), input.getShapeInfo(), output.buffer(), output.shapeInfo()), FLOAT_TYPES); + } + + template + void softmax_loop(T *input, T *output, Nd4jLong *offsets, Nd4jLong numOfSubArrs, uint32_t tadLen); +#ifdef _OPENMP + template <> + FORCEINLINE void softmax_loop(float *input, float *output, Nd4jLong *offsets, Nd4jLong numOfSubArrs, uint32_t tadLen) { +#pragma omp parallel for + for (Nd4jLong i = 0; i < numOfSubArrs; i++) { + auto inBuff = input + offsets[i]; + auto outBuff = output + offsets[i]; + + float max = -DataTypeUtils::max(); + float sum = 0.f; + +#pragma omp simd reduction(max:max) + for (uint j = 0; j < tadLen; ++j) + max = sd::math::nd4j_max(max, inBuff[j]); + +#pragma omp simd reduction(+:sum) + for (uint j = 0; j < tadLen; ++j) { + float temp = sd::math::nd4j_exp(inBuff[j] - max); + outBuff[j] = temp; + sum += temp; + } + + for (uint j = 0; j < tadLen; ++j) + outBuff[j] /= sum; + } + } +#else + template <> + FORCEINLINE void softmax_loop(float *input, float *output, Nd4jLong *offsets, Nd4jLong numOfSubArrs, uint32_t tadLen) { + auto func = PRAGMA_THREADS_FOR { + for (auto i = start; i < stop; i++) { + auto inBuff = input + offsets[i]; + auto outBuff = output + offsets[i]; + + float max = -DataTypeUtils::max(); + float sum = 0.f; + + for (uint j = 0; j < tadLen; ++j) + max = sd::math::nd4j_max(max, inBuff[j]); + + for (uint j = 0; j < tadLen; ++j) { + float temp = sd::math::nd4j_exp(inBuff[j] - max); + outBuff[j] = temp; + sum += temp; + } + + for (uint j = 0; j < tadLen; ++j) + outBuff[j] /= sum; + } + }; + + samediff::Threads::parallel_tad(func,0, numOfSubArrs); + } + +#endif + + + template + FORCEINLINE void softmax_loop(T *input, T *output, Nd4jLong *offsets, Nd4jLong numOfSubArrs, uint32_t tadLen) { + auto func = PRAGMA_THREADS_FOR { + for (auto i = start; i < stop; i++) { + auto inBuff = input + offsets[i]; + auto outBuff = output + offsets[i]; + + T max = -DataTypeUtils::max(); + T sum(0.f); + +#pragma omp simd reduction(maxT:max) + for (uint j = 0; j < tadLen; ++j) + max = sd::math::nd4j_max(max, inBuff[j]); + +#pragma omp simd reduction(sumT:sum) + for (uint j = 0; j < tadLen; ++j) { + T temp = sd::math::nd4j_exp(inBuff[j] - max); + outBuff[j] = temp; + sum += temp; + } + + for (uint j = 0; j < tadLen; ++j) + outBuff[j] /= sum; + } + }; + + samediff::Threads::parallel_tad(func,0, numOfSubArrs); + } + +////////////////////////////////////////////////////////////////////////// + template + static void softmax_(sd::LaunchContext * context, const NDArray& input, NDArray& output, const int dimension) { + + const int rank = input.rankOf(); + + if(input.isVector()) { + + if(rank == 1 || input.sizeAt(dimension) != 1) + softMaxForVector_(input.getBuffer(), input.getShapeInfo(), output.buffer(), output.getShapeInfo()); + else + output = 1.; + } + else if(input.isSameShapeStrict(output)) { + + TadPack tadPack = sd::ConstantTadHelper::getInstance()->tadForDimensions(input.getShapeInfo(), dimension); + Nd4jLong* tadShapeInfo = tadPack.primaryShapeInfo(); + Nd4jLong* tadOffsets = tadPack.primaryOffsets(); + const uint numOfSubArrs = tadPack.numberOfTads(); + const uint tadLen = shape::length(tadShapeInfo); + + if(shape::elementWiseStride(tadShapeInfo) == 1){ + T *inBuff = input.bufferAsT(); + T *outBuff = output.bufferAsT(); + + softmax_loop(inBuff, outBuff, tadOffsets, numOfSubArrs, tadLen); + } + else { + + uint inShapeInfoCast[MAX_RANK]; + bool canCast = sd::DataTypeUtils::castShapeInfo(tadShapeInfo, inShapeInfoCast); + + auto offsets = new Nd4jLong[tadLen]; + shape::calcOffsets(tadShapeInfo, offsets); + + auto func = PRAGMA_THREADS_FOR { + for (auto i = start; i < stop; i++) { + auto inBuff = input.bufferAsT() + tadOffsets[i]; + auto outBuff = output.bufferAsT() + tadOffsets[i]; + + T max = -DataTypeUtils::max(); + T sum = 0.f; + + for (uint j = 0; j < tadLen; ++j) + max = sd::math::nd4j_max(max, inBuff[offsets[j]]); + + for (uint j = 0; j < tadLen; ++j) { + T temp = sd::math::nd4j_exp(inBuff[offsets[j]] - max); + outBuff[offsets[j]] = temp; + sum += temp; + } + + for (uint j = 0; j < tadLen; ++j) + outBuff[offsets[j]] /= sum; + } + }; + + samediff::Threads::parallel_tad(func, 0, numOfSubArrs); + + delete []offsets; + } + } + else { + NDArray max = input.reduceAlongDimension(sd::reduce::Max, {dimension}, true); + input.applyTrueBroadcast(sd::BroadcastOpsTuple::Subtract(), max, output, false); + output.applyTransform(sd::transform::Exp, output); + NDArray sum = output.reduceAlongDimension(sd::reduce::Sum, {dimension}, true); + output /= sum; + } + } + + + /////////////////////////////////////////////////////////////////// + void softmax(sd::LaunchContext * context, const NDArray& input, NDArray& output, const int dimension) { + + BUILD_SINGLE_SELECTOR(input.dataType(), softmax_, (context, input, output, dimension), FLOAT_TYPES); + } + + } + } +} \ No newline at end of file diff --git a/libnd4j/include/ops/declarable/helpers/cpu/solve.cpp b/libnd4j/include/ops/declarable/helpers/cpu/solve.cpp index c8774f028..9a06975aa 100644 --- a/libnd4j/include/ops/declarable/helpers/cpu/solve.cpp +++ b/libnd4j/include/ops/declarable/helpers/cpu/solve.cpp @@ -17,9 +17,9 @@ // // @author GS // -#include -#include -#include +#include +#include +#include #include #include @@ -27,13 +27,13 @@ #include "../lup.h" #include "../solve.h" -namespace nd4j { +namespace sd { namespace ops { namespace helpers { // --------------------------------------------------------------------------------------------------------------------------------------- // template - static void adjointMatrix_(nd4j::LaunchContext* context, NDArray const* input, NDArray* output) { + static void adjointMatrix_(sd::LaunchContext* context, NDArray const* input, NDArray* output) { auto inputPart = input->allTensorsAlongDimension({-2, -1}); auto outputPart = output->allTensorsAlongDimension({-2, -1}); auto rows = input->sizeAt(-2); @@ -41,8 +41,8 @@ namespace helpers { auto batchLoop = PRAGMA_THREADS_FOR { for (auto batch = start; batch < stop; batch++) { - for (auto r = 0; r < rows; r++) { - for (auto c = 0; c < r; c++) { + for (Nd4jLong r = 0; r < rows; r++) { + for (Nd4jLong c = 0; c < r; c++) { math::nd4j_swap(outputPart[batch]->t(r, c) , outputPart[batch]->t(c, r)); } } @@ -53,7 +53,7 @@ namespace helpers { // --------------------------------------------------------------------------------------------------------------------------------------- // template - static int solveFunctor_(nd4j::LaunchContext * context, NDArray* leftInput, NDArray* rightInput, bool const adjoint, NDArray* output) { + static int solveFunctor_(sd::LaunchContext * context, NDArray* leftInput, NDArray* rightInput, bool const adjoint, NDArray* output) { // stage 1: LU decomposition batched auto leftOutput = leftInput->ulike(); @@ -66,7 +66,7 @@ namespace helpers { auto permutationsPart = permutations.allTensorsAlongDimension({-1}); for (auto batch = 0; batch < permutationsPart.size(); ++batch) { - for (auto row = 0; row < PPart[batch]->rows(); ++row) { + for (Nd4jLong row = 0; row < PPart[batch]->rows(); ++row) { PPart[batch]->t(row, permutationsPart[batch]->t(row)) = T(1.f); } } @@ -77,7 +77,7 @@ namespace helpers { MmulHelper::matmul(&P, rightInput, &rightPermuted, 0, 0); ResultSet leftLowerPart = leftLower.allTensorsAlongDimension({-2, -1}); for (auto i = 0; i < leftLowerPart.size(); i++) { - for (auto r = 0; r < leftLowerPart[i]->rows(); r++) + for (Nd4jLong r = 0; r < leftLowerPart[i]->rows(); r++) leftLowerPart[i]->t(r,r) = (T)1.f; } // stage 2: triangularSolveFunctor for Lower with given b @@ -89,11 +89,11 @@ namespace helpers { } // --------------------------------------------------------------------------------------------------------------------------------------- // - int solveFunctor(nd4j::LaunchContext * context, NDArray* leftInput, NDArray* rightInput, bool const adjoint, NDArray* output) { + int solveFunctor(sd::LaunchContext * context, NDArray* leftInput, NDArray* rightInput, bool const adjoint, NDArray* output) { BUILD_SINGLE_SELECTOR(leftInput->dataType(), return solveFunctor_, (context, leftInput, rightInput, adjoint, output), FLOAT_TYPES); } // --------------------------------------------------------------------------------------------------------------------------------------- // - void adjointMatrix(nd4j::LaunchContext* context, NDArray const* input, NDArray* output) { + void adjointMatrix(sd::LaunchContext* context, NDArray const* input, NDArray* output) { BUILD_SINGLE_SELECTOR(input->dataType(), adjointMatrix_, (context, input, output), FLOAT_TYPES); } // --------------------------------------------------------------------------------------------------------------------------------------- // diff --git a/libnd4j/include/ops/declarable/helpers/cpu/split.cpp b/libnd4j/include/ops/declarable/helpers/cpu/split.cpp new file mode 100644 index 000000000..2e30cdf0a --- /dev/null +++ b/libnd4j/include/ops/declarable/helpers/cpu/split.cpp @@ -0,0 +1,131 @@ +/******************************************************************************* + * Copyright (c) 2019-2020 Konduit K.K. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ + + // + // @author Oleh Semeniv (oleg.semeniv@gmail.com) + // + +#include +#include + +namespace sd { +namespace ops { +namespace helpers { + + + ////////////////////////////////////////////////////////////////////////// + template + static void split_(const NDArray& input, const std::vector& outArrs, const int axis) { + uint numSplits = outArrs.size(); + + const auto sizeofT = input.sizeOfT(); + + T* xBuff = input.bufferAsT(); + + bool luckCase1 = ((axis == 0 && input.ordering() == 'c') || (axis == input.rankOf() - 1 && input.ordering() == 'f')) && input.ews() == 1; + + if (luckCase1) { + for (uint i = 0; i < numSplits; ++i) { + luckCase1 &= outArrs[i]->ordering() == input.ordering() && outArrs[i]->ews() == 1; + if (!luckCase1) + break; + } + } + + if (luckCase1) { + + T* x = const_cast(xBuff); + for (uint i = 0; i < numSplits; ++i) { + const auto memAmountToCopy = outArrs[i]->lengthOf(); + memcpy(outArrs[i]->bufferAsT(), x, memAmountToCopy * sizeofT); + x += memAmountToCopy; + } + return; + } + + const bool isXcontin = input.strideAt(axis) == 1 && input.ordering() == 'c'; + bool areOutsContin = true; + bool allSameOrder = true; + + if (isXcontin) { + for (uint i = 0; i < numSplits; ++i) { + areOutsContin &= outArrs[i]->strideAt(axis) == 1; + allSameOrder &= outArrs[i]->ordering() == input.ordering(); + if (!areOutsContin || !allSameOrder) + break; + } + } + + const bool luckCase2 = isXcontin && areOutsContin && allSameOrder; + + if (luckCase2) { + + const auto xDim = input.sizeAt(axis); + + for (Nd4jLong i = 0; i < input.lengthOf() / xDim; ++i) { + + T* x = xBuff + xDim * i; + + for (uint j = 0; j < numSplits; ++j) { + const auto zDim = outArrs[j]->sizeAt(axis); + T* z = outArrs[j]->bufferAsT() + zDim * i; + memcpy(z, x, zDim * sizeofT); + z += zDim; + x += zDim; + } + } + + return; + } + + uint zDim = outArrs[0]->sizeAt(axis); + // general case + + auto func = PRAGMA_THREADS_FOR{ + + int coords[MAX_RANK], temp; + + for (auto i = start; i < stop; i += increment) { + + shape::index2coordsCPU(start, i, input.getShapeInfo(), coords); + const auto xOffset = shape::getOffset(input.getShapeInfo(), coords); + + uint outArrIdx = 0; + + temp = coords[axis]; + + while (coords[axis] >= zDim) { + coords[axis] -= zDim; + ++outArrIdx; + } + + T* z = outArrs[outArrIdx]->bufferAsT(); + const auto zOffset = shape::getOffset(outArrs[outArrIdx]->getShapeInfo(), coords); + z[zOffset] = xBuff[xOffset]; + + coords[axis] = temp; + } + }; + + samediff::Threads::parallel_for(func, 0, input.lengthOf()); + } + + void split(sd::LaunchContext* context, const NDArray& input, std::vector& outArrs, const int axis) { + BUILD_SINGLE_SELECTOR(input.dataType(), split_, (input, outArrs, axis), LIBND4J_TYPES); + } + } + } +} diff --git a/libnd4j/include/ops/declarable/helpers/cpu/sru.cpp b/libnd4j/include/ops/declarable/helpers/cpu/sru.cpp index d2dd3bf30..ecd5ead2b 100644 --- a/libnd4j/include/ops/declarable/helpers/cpu/sru.cpp +++ b/libnd4j/include/ops/declarable/helpers/cpu/sru.cpp @@ -21,11 +21,11 @@ // #include -#include -#include +#include +#include #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { @@ -46,7 +46,7 @@ static FORCEINLINE NDArray sigmoid(const NDArray& arr) { ////////////////////////////////////////////////////////////////////////// -void sruCell(nd4j::LaunchContext * context, const NDArray* x, const NDArray* c0, const NDArray* w, const NDArray* b, NDArray* h, NDArray* c) { +void sruCell(sd::LaunchContext * context, const NDArray* x, const NDArray* c0, const NDArray* w, const NDArray* b, NDArray* h, NDArray* c) { // x input [bS x inSize], bS - batch size, inSize - number of features // c0 previous cell state c [bS x inSize], that is at previous time step t-1 @@ -78,7 +78,7 @@ void sruCell(nd4j::LaunchContext * context, const NDArray* x, const NDArray* c0, ////////////////////////////////////////////////////////////////////////// -void sruTimeLoop(nd4j::LaunchContext * context, const NDArray* x, const NDArray* c0, const NDArray* w, const NDArray* b, NDArray* h, NDArray* c) { +void sruTimeLoop(sd::LaunchContext * context, const NDArray* x, const NDArray* c0, const NDArray* w, const NDArray* b, NDArray* h, NDArray* c) { // x input [bS x inSize x time] // c0 initial cell state (at time step = 0) [bS x inSize], @@ -168,12 +168,12 @@ static void sruBI_(NDArray* x, const NDArray* w, const NDArray* b, const NDArray for (Nd4jLong t = 0; t < time; ++t) { // evaluate sigmoids - T ft = (1.) / (1. + nd4j::math::nd4j_exp(-(pWiVal[1] + bF))); - T rt = (1.) / (1. + nd4j::math::nd4j_exp(-(pWiVal[2] + bR))); + T ft = (1.) / (1. + sd::math::nd4j_exp(-(pWiVal[1] + bF))); + T rt = (1.) / (1. + sd::math::nd4j_exp(-(pWiVal[2] + bR))); cur = (cur - *pWiVal) * ft + *pWiVal; *pCtVal = cur; - T val = nd4j::math::nd4j_tanh(cur); + T val = sd::math::nd4j_tanh(cur); *pHtVal = (val * maskVal - *pIVal) * rt + *pIVal; pIVal += ncolsRev; @@ -268,10 +268,10 @@ static void sruBIBP_(NDArray* x, const NDArray* w, const NDArray* b, const NDArr for (Nd4jLong t = 0; t < time; ++t) { // evaluate sigmoids - T ft = ((T) 1.) / ((T) 1. + nd4j::math::nd4j_exp(-(*(pWiVal + 1) + bF))); - T rt = ((T) 1.) / ((T) 1. + nd4j::math::nd4j_exp(-(*(pWiVal + 2) + bR))); + T ft = ((T) 1.) / ((T) 1. + sd::math::nd4j_exp(-(*(pWiVal + 1) + bF))); + T rt = ((T) 1.) / ((T) 1. + sd::math::nd4j_exp(-(*(pWiVal + 2) + bR))); - T val = nd4j::math::nd4j_tanh(*pStateVal); + T val = sd::math::nd4j_tanh(*pStateVal); T prevVal = (t < time - 1) ? (*(pStateVal - ncolsRev)) : (*(pInit + col)); // grad wrt input *pGradInputVal = *pInGradHtVal - (*pInGradHtVal) * rt; @@ -314,10 +314,10 @@ static void sruBIBP_(NDArray* x, const NDArray* w, const NDArray* b, const NDArr } -void sruBI(nd4j::LaunchContext * context, NDArray* x, const NDArray* w, const NDArray* b, const NDArray* c0, const NDArray* mask, NDArray* ht, NDArray* ct) { +void sruBI(sd::LaunchContext * context, NDArray* x, const NDArray* w, const NDArray* b, const NDArray* c0, const NDArray* mask, NDArray* ht, NDArray* ct) { BUILD_SINGLE_SELECTOR(x->dataType(), sruBI_, (x, w, b, c0, mask, ht, ct), FLOAT_TYPES); } -void sruBIBP(nd4j::LaunchContext * context, NDArray* x, const NDArray* w, const NDArray* b, const NDArray* c0, const NDArray* ct, const NDArray* inGradC0, const NDArray* inGradH, const NDArray* mask, NDArray* gradI, NDArray* gradW, NDArray* gradB, NDArray* gradC0) { +void sruBIBP(sd::LaunchContext * context, NDArray* x, const NDArray* w, const NDArray* b, const NDArray* c0, const NDArray* ct, const NDArray* inGradC0, const NDArray* inGradH, const NDArray* mask, NDArray* gradI, NDArray* gradW, NDArray* gradB, NDArray* gradC0) { BUILD_SINGLE_SELECTOR(x->dataType(), sruBIBP_, (x, w, b, c0, ct, inGradC0, inGradH, mask, gradI, gradW, gradB, gradC0), FLOAT_TYPES); } diff --git a/libnd4j/include/ops/declarable/helpers/cpu/stack.cpp b/libnd4j/include/ops/declarable/helpers/cpu/stack.cpp index a3d27702d..f8fc07201 100644 --- a/libnd4j/include/ops/declarable/helpers/cpu/stack.cpp +++ b/libnd4j/include/ops/declarable/helpers/cpu/stack.cpp @@ -15,53 +15,107 @@ ******************************************************************************/ // -// Created by Yurii Shyrma on 02.01.2018 +// @author Yurii Shyrma (iuriish@yahoo.com) // #include #include #include #include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { /////////////////////////////////////////////////////////////////// template -static void stack_(const std::vector& inArrs, NDArray* outArr, const int dim) { +static void stack_(const std::vector& inArrs, NDArray& output, const int dim) { + + const int numOfSubArrs = inArrs.size(); if(inArrs[0]->rankOf() == 0) { - int inSize = inArrs.size(); auto func = PRAGMA_THREADS_FOR { for (auto i = start; i < stop; i++) - outArr->p(i, inArrs[i]->t(0)); + output.p(i, inArrs[i]->t(0)); }; - samediff::Threads::parallel_for(func, 0, inSize); + samediff::Threads::parallel_for(func, 0, numOfSubArrs); } else { - std::vector dimsToExclude = ShapeUtils::evalDimsToExclude(outArr->rankOf(), {dim}); - auto list = outArr->allTensorsAlongDimension(dimsToExclude); // list.size() == block.width() - int listSize = list.size(); + auto zTadPack = ConstantTadHelper::getInstance()->tadForDimensions(output.getShapeInfo(), ShapeUtils::evalDimsToExclude(output.rankOf(), {dim})); + Nd4jLong* zTadShapeInfo = zTadPack.primaryShapeInfo(); + + auto func = PRAGMA_THREADS_FOR { + + for (auto i = start; i < stop; i++) { + + void* zBuff = output.bufferWithOffset(zTadPack.primaryOffsets()[i]); + + NativeOpExecutioner::execTransformAny(inArrs[0]->getContext(), transform::Assign, + inArrs[i]->getBuffer(), inArrs[i]->getShapeInfo(), nullptr/*input specialBuffer*/, nullptr/*input specialShapeInfo*/, + zBuff, zTadShapeInfo, nullptr/*output specialBuffer*/, nullptr/*output specialShapeInfo*/, + nullptr, nullptr, nullptr, false/*allowParallelism*/); + } + }; + + samediff::Threads::parallel_tad(func, 0, numOfSubArrs); + } + +} + +//////////////////////////////////////////////////////////////////////// +void stack(sd::LaunchContext * context, const std::vector& inArrs, NDArray& output, const int dim) { + BUILD_SINGLE_SELECTOR(output.dataType(), stack_, (inArrs, output, dim), LIBND4J_TYPES); +} +BUILD_SINGLE_TEMPLATE(template void stack_ , (const std::vector& inArrs, NDArray& output, const int dim), LIBND4J_TYPES); + + +/////////////////////////////////////////////////////////////////// +template +static void unstack_(const NDArray& input, const std::vector& outArrs, const int dim) { + + const int numOfSubArrs = outArrs.size(); + + if(outArrs[0]->rankOf() == 0) { auto func = PRAGMA_THREADS_FOR { for (auto i = start; i < stop; i++) - list.at(i)->assign(inArrs[i]); + outArrs[i]->p(0, input.t(i)); }; - samediff::Threads::parallel_tad(func, 0, listSize); + + samediff::Threads::parallel_for(func, 0, numOfSubArrs); + } + else { + + auto xTadPack = ConstantTadHelper::getInstance()->tadForDimensions(input.getShapeInfo(), ShapeUtils::evalDimsToExclude(input.rankOf(), {dim})); + Nd4jLong* xTadShapeInfo = xTadPack.primaryShapeInfo(); + + auto func = PRAGMA_THREADS_FOR { + for (auto i = start; i < stop; i++) { + + void* xBuff = input.bufferWithOffset(xTadPack.primaryOffsets()[i]); + + NativeOpExecutioner::execTransformAny(input.getContext(), transform::Assign, + xBuff, xTadShapeInfo, nullptr/*input specialBuffer*/, nullptr/*input specialShapeInfo*/, + outArrs[i]->getBuffer(), outArrs[i]->getShapeInfo(), nullptr/*output specialBuffer*/, nullptr/*output specialShapeInfo*/, + nullptr, nullptr, nullptr, false/*allowParallelism*/); + } + }; + + samediff::Threads::parallel_tad(func, 0, numOfSubArrs); } } - void stack(nd4j::LaunchContext * context, const std::vector& inArrs, NDArray* outArr, const int dim) { - BUILD_SINGLE_SELECTOR(outArr->dataType(), stack_, (inArrs, outArr, dim), LIBND4J_TYPES); - } - - BUILD_SINGLE_TEMPLATE(template void stack_ , (const std::vector& inArrs, NDArray* outArr, const int dim), LIBND4J_TYPES); +//////////////////////////////////////////////////////////////////////// +void unstack(sd::LaunchContext* context, const NDArray& input, const std::vector& outArrs, const int dim) { + BUILD_SINGLE_SELECTOR(input.dataType(), unstack_, (input, outArrs, dim), LIBND4J_TYPES); +} +BUILD_SINGLE_TEMPLATE(template void unstack_, (const NDArray& input, const std::vector& outArrs, const int dim), LIBND4J_TYPES); } } diff --git a/libnd4j/include/ops/declarable/helpers/cpu/svd.cpp b/libnd4j/include/ops/declarable/helpers/cpu/svd.cpp index 9d755f6b6..c4f99af3f 100644 --- a/libnd4j/include/ops/declarable/helpers/cpu/svd.cpp +++ b/libnd4j/include/ops/declarable/helpers/cpu/svd.cpp @@ -18,12 +18,12 @@ // @author Yurii Shyrma (iuriish@yahoo.com), created on 03.01.2018 // -#include -#include +#include +#include #include #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { @@ -972,7 +972,7 @@ static void svd_(const NDArray* x, const std::vector& outArrs, const b } } - void svd(nd4j::LaunchContext * context, const NDArray* x, const std::vector& outArrs, const bool fullUV, const bool calcUV, const int switchNum) { + void svd(sd::LaunchContext * context, const NDArray* x, const std::vector& outArrs, const bool fullUV, const bool calcUV, const int switchNum) { BUILD_SINGLE_SELECTOR(x->dataType(), svd_, (x, outArrs, fullUV, calcUV, switchNum), FLOAT_TYPES); } diff --git a/libnd4j/include/ops/declarable/helpers/cpu/toggle_bits.cpp b/libnd4j/include/ops/declarable/helpers/cpu/toggle_bits.cpp index 481575297..67b4e0f77 100644 --- a/libnd4j/include/ops/declarable/helpers/cpu/toggle_bits.cpp +++ b/libnd4j/include/ops/declarable/helpers/cpu/toggle_bits.cpp @@ -21,7 +21,7 @@ #include #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { template @@ -33,7 +33,7 @@ namespace nd4j { in.applyLambda(lambda, out); } - void __toggle_bits(nd4j::LaunchContext * context, NDArray& in, NDArray& out) { + void __toggle_bits(sd::LaunchContext * context, NDArray& in, NDArray& out) { BUILD_SINGLE_SELECTOR(in.dataType(), toggle_bits__, (in, out), INTEGER_TYPES); } } diff --git a/libnd4j/include/ops/declarable/helpers/cpu/top_k.cpp b/libnd4j/include/ops/declarable/helpers/cpu/top_k.cpp index c4b45b398..7e0b07da0 100644 --- a/libnd4j/include/ops/declarable/helpers/cpu/top_k.cpp +++ b/libnd4j/include/ops/declarable/helpers/cpu/top_k.cpp @@ -20,10 +20,10 @@ #include #include -#include +#include #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { @@ -39,7 +39,7 @@ namespace helpers { // } // ----------------------------------------------------------------------------------------------- // std::vector dimsToExclude(input->rankOf() - 1); - for (int d = 0; d < dimsToExclude.size(); ++d) + for (size_t d = 0; d < dimsToExclude.size(); ++d) dimsToExclude[d] = d; const Nd4jLong numOfSubArrs = ShapeUtils::getNumOfSubArrs(input->getShapeInfo(), dimsToExclude); @@ -72,7 +72,7 @@ namespace helpers { NDArray topValues = NDArrayFactory::create('c', {k}); NDArray sortedVals = NDArrayFactory::create('c', {k}); NDArray topIndices = NDArrayFactory::create('c', {k}); - for (Nd4jLong pos = 0; pos < k; ++pos) { + for (uint pos = 0; pos < k; ++pos) { topIndices.t(pos) = pos; topValues.t(pos) = trial.t(pos); } @@ -80,7 +80,7 @@ namespace helpers { sortedVals.assign(topValues);// = NDArrayFactory::create('c', {k}); //std::sort(sortedVals.begin(), sortedVals.end()); // sorted in ascending order SpecialMethods::sortGeneric(sortedVals.buffer(), sortedVals.shapeInfo(), false); - for (int i = k; i < width; ++i) { + for (Nd4jLong i = static_cast(k); i < width; ++i) { T val = trial.e(i); T minTopVal = sortedVals.t(0); if (minTopVal < val) { // value should be inserted to top k @@ -104,15 +104,15 @@ namespace helpers { if (needSort) { SpecialMethods::sortGeneric(topValues.buffer(), topValues.shapeInfo(), true); - for (int j = 0; j < width; j++) - for (int pos = 0; pos < k; ++pos) + for (Nd4jLong j = 0; j < width; j++) + for (uint pos = 0; pos < k; ++pos) if (topValues.t(pos) == trial.t(j)) topIndices.t(pos) = j; } else { // else sort by indices std::map sortValsMap; //std::vector> data(topValues.lengthOf()); - for (size_t e = 0; e < topValues.lengthOf(); ++e) { + for (Nd4jLong e = 0; e < topValues.lengthOf(); ++e) { sortValsMap[topIndices.t(e)] = topValues.t(e); } @@ -138,7 +138,7 @@ namespace helpers { // ----------------------------------------------------------------------------------------------- // template - static int inTopKFunctor_(nd4j::LaunchContext* context, const NDArray* input, const NDArray* target, NDArray* result, const uint k) { + static int inTopKFunctor_(sd::LaunchContext* context, const NDArray* input, const NDArray* target, NDArray* result, const uint k) { std::vector shapeI(input->rankOf()); for (int i = 0; i < input->rankOf() - 1; i++) @@ -152,7 +152,7 @@ namespace helpers { auto func = PRAGMA_THREADS_FOR { for (auto e = start; e < stop; e++) { bool found = false; - for (int j = 0; j < k; j++) { + for (uint j = 0; j < k; j++) { if (target->e(e) == indices->e(e * k + j)) { found = true; break; @@ -169,16 +169,16 @@ namespace helpers { } - int topKFunctor(nd4j::LaunchContext * context, const NDArray* input, NDArray* values, NDArray* indices, const uint k, bool needSort) { + int topKFunctor(sd::LaunchContext * context, const NDArray* input, NDArray* values, NDArray* indices, const uint k, bool needSort) { BUILD_SINGLE_SELECTOR(input->dataType(), return topKFunctor_, (input, values, indices, k, needSort), NUMERIC_TYPES); } - int inTopKFunctor(nd4j::LaunchContext * context, const NDArray* input, const NDArray* target, NDArray* result, const uint k) { + int inTopKFunctor(sd::LaunchContext * context, const NDArray* input, const NDArray* target, NDArray* result, const uint k) { BUILD_SINGLE_SELECTOR(input->dataType(), return inTopKFunctor_, (context, input, target, result, k), NUMERIC_TYPES); } BUILD_SINGLE_TEMPLATE(template int topKFunctor_, (const NDArray* input, NDArray* values, NDArray* indices, const uint k, bool needSort), NUMERIC_TYPES); - BUILD_SINGLE_TEMPLATE(template int inTopKFunctor_, (nd4j::LaunchContext * context, const NDArray* input, const NDArray* target, NDArray* result, const uint k), NUMERIC_TYPES); + BUILD_SINGLE_TEMPLATE(template int inTopKFunctor_, (sd::LaunchContext * context, const NDArray* input, const NDArray* target, NDArray* result, const uint k), NUMERIC_TYPES); } } } diff --git a/libnd4j/include/ops/declarable/helpers/cpu/transforms.cpp b/libnd4j/include/ops/declarable/helpers/cpu/transforms.cpp index 1f630e8e0..7169cca4a 100644 --- a/libnd4j/include/ops/declarable/helpers/cpu/transforms.cpp +++ b/libnd4j/include/ops/declarable/helpers/cpu/transforms.cpp @@ -23,20 +23,20 @@ #include #include #include -#include +#include #include #include -#include +#include #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { ////////////////////////////////////////////////////////////////////////// template -static void triuBP_(nd4j::LaunchContext * context, const NDArray& input, const NDArray& gradO, NDArray& gradI, const int diagonal) { +static void triuBP_(sd::LaunchContext * context, const NDArray& input, const NDArray& gradO, NDArray& gradI, const int diagonal) { auto dOdI = NDArray(&gradO); // dO/dI const_cast(input).fillAsTriangular(0, diagonal, dOdI.sizeAt(-1), dOdI, 'b'); @@ -54,7 +54,7 @@ static void triuBP_(nd4j::LaunchContext * context, const NDArray& input, const N gradI.assign(dOdI * gradO); // chain rule: dLoss/dI = dO/dI * dLoss/dO } - void triuBP(nd4j::LaunchContext * context, const NDArray& input, const NDArray& gradO, NDArray& gradI, const int diagonal) { + void triuBP(sd::LaunchContext * context, const NDArray& input, const NDArray& gradO, NDArray& gradI, const int diagonal) { BUILD_SINGLE_SELECTOR(gradO.dataType(), triuBP_, (context, input, gradO, gradI, diagonal), LIBND4J_TYPES); } @@ -71,13 +71,13 @@ static void trace_(const NDArray& input, NDArray& output) { samediff::Threads::parallel_for(func, 0, setOfSubArrs.size()); } - void trace(nd4j::LaunchContext * context, const NDArray& input, NDArray& output) { + void trace(sd::LaunchContext * context, const NDArray& input, NDArray& output) { BUILD_SINGLE_SELECTOR(input.dataType(), trace_, (input, output), LIBND4J_TYPES); } ////////////////////////////////////////////////////////////////////////// template -void randomShuffle_(NDArray& input, NDArray& output, nd4j::graph::RandomGenerator& rng, const bool isInplace) { +void randomShuffle_(NDArray& input, NDArray& output, sd::graph::RandomGenerator& rng, const bool isInplace) { // check edge cases first int temp; @@ -163,7 +163,7 @@ void randomShuffle_(NDArray& input, NDArray& output, nd4j::graph::RandomGenerato } - void randomShuffle(nd4j::LaunchContext * context, NDArray& input, NDArray& output, nd4j::graph::RandomGenerator& rng, const bool isInplace) { + void randomShuffle(sd::LaunchContext * context, NDArray& input, NDArray& output, sd::graph::RandomGenerator& rng, const bool isInplace) { BUILD_SINGLE_SELECTOR(input.dataType(), randomShuffle_, (input, output, rng, isInplace), LIBND4J_TYPES); } @@ -188,24 +188,35 @@ void pad_(const int mode, const NDArray& input, const NDArray& paddings, NDArray const T padVal = padValue.e(0); auto func = PRAGMA_THREADS_FOR { - Nd4jLong coords[MAX_RANK]; + + int zCoords[MAX_RANK], xCoords[MAX_RANK]; + for (auto i = start; i < stop; i++) { - shape::index2coords(i, output.getShapeInfo(), coords); - const auto zOffset = shape::getOffset(output.getShapeInfo(), coords); + + shape::index2coordsCPU(start, i, output.getShapeInfo(), zCoords); + const auto zOffset = shape::getOffset(output.getShapeInfo(), zCoords); + + memcpy(xCoords, zCoords, rank * sizeof(int)); bool within = true; + for (int j = rankMinusOne; j >= 0; --j) { - if (xShape[j] == zShape[j]) continue; + + if (xShape[j] == zShape[j]) + continue; + const auto left = paddings.e(j, 0); - if (coords[j] < left || coords[j] >= left + xShape[j]) { + + if (zCoords[j] < left || zCoords[j] >= left + xShape[j]) { within = false; break; } - else { coords[j] = coords[j] - left; } + else + xCoords[j] = zCoords[j] - left; } if (within) - z[zOffset] = x[shape::getOffset(input.getShapeInfo(), coords)]; + z[zOffset] = x[shape::getOffset(input.getShapeInfo(), xCoords)]; else z[zOffset] = padVal; } @@ -219,20 +230,30 @@ void pad_(const int mode, const NDArray& input, const NDArray& paddings, NDArray const Nd4jLong shift2 = mode == 1 ? 2 : 1; // REFLECT : SYMMETRIC auto func = PRAGMA_THREADS_FOR { - Nd4jLong coords[MAX_RANK]; + + int zCoords[MAX_RANK], xCoords[MAX_RANK]; + for (auto i = start; i < stop; i++) { - shape::index2coords(i, output.getShapeInfo(), coords); - const auto zOffset = shape::getOffset(output.getShapeInfo(), coords); + + shape::index2coordsCPU(start, i, output.getShapeInfo(), zCoords); + const auto zOffset = shape::getOffset(output.getShapeInfo(), zCoords); + + memcpy(xCoords, zCoords, rank * sizeof(int)); for (int j = rankMinusOne; j >= 0; --j) { - if (xShape[j] == zShape[j]) continue; - coords[j] = coords[j] - paddings.e(j, 0); // are ready to fill middle (within input dimension range) - if (coords[j] < 0) coords[j] = -coords[j] - shift1; // means fill from left - else if (coords[j] >= xShape[j]) coords[j] = 2 * xShape[j] - coords[j] - shift2; // means fill from right + if (xShape[j] == zShape[j]) + continue; + + xCoords[j] = zCoords[j] - paddings.e(j, 0); // are ready to fill middle (within input dimension range) + + if (xCoords[j] < 0) + xCoords[j] = -xCoords[j] - shift1; // means fill from left + else if (xCoords[j] >= xShape[j]) + xCoords[j] = 2 * xShape[j] - xCoords[j] - shift2; // means fill from right } - const auto xOffset = shape::getOffset(input.getShapeInfo(), coords); + const auto xOffset = shape::getOffset(input.getShapeInfo(), xCoords); z[zOffset] = x[xOffset]; } }; @@ -374,7 +395,7 @@ void pad_(const int mode, const NDArray& input, const NDArray& paddings, NDArray // } // } -void pad(nd4j::LaunchContext * context, const int mode, const NDArray& input, const NDArray& paddings, NDArray& output, NDArray const& padValue) { +void pad(sd::LaunchContext * context, const int mode, const NDArray& input, const NDArray& paddings, NDArray& output, NDArray const& padValue) { BUILD_SINGLE_SELECTOR(input.dataType(), pad_, (mode, input, paddings, output, padValue), LIBND4J_TYPES); } @@ -528,7 +549,7 @@ static void recursiveLoopForPad_(const int mode, NDArray& input, const NDArray& */ //////////////////////////////////////////////////////////////////////// -void invertPermutation(nd4j::LaunchContext * context, const NDArray& input, NDArray& output) { +void invertPermutation(sd::LaunchContext * context, const NDArray& input, NDArray& output) { std::set uniqueElems; const int length = input.lengthOf(); @@ -558,49 +579,41 @@ static void gatherND_(NDArray& input, NDArray& indices, NDArray& output) { const int xRank = input.rankOf(); const int yRank = indices.rankOf(); const int zRank = output.rankOf(); - const int maxRank = nd4j::math::nd4j_max(yRank, nd4j::math::nd4j_max(xRank, zRank)); + const int maxRank = sd::math::nd4j_max(yRank, sd::math::nd4j_max(xRank, zRank)); const Nd4jLong zLen = output.lengthOf(); - const int yLastDim = indices.sizeAt(-1); + const uint yLastDim = indices.sizeAt(-1); + + const int diff = zRank - xRank; + const bool bEqual = yLastDim == xRank; auto func = PRAGMA_THREADS_FOR { - Nd4jLong coords[MAX_RANK * 3]; + + int xCoords[MAX_RANK], zCoords[MAX_RANK], temp; + for (auto i = start; i < stop; i++) { - Nd4jLong *zCoordStart, *xCoordStart; - if (yLastDim == xRank) { - zCoordStart = coords; - xCoordStart = coords; - } else if (zRank >= xRank) { - zCoordStart = coords; - xCoordStart = coords + zRank - xRank; - } else { - zCoordStart = coords + xRank - zRank; - xCoordStart = coords; - } + shape::index2coordsCPU(start, i, output.getShapeInfo(), zCoords); - shape::index2coords(i, output.getShapeInfo(), zCoordStart); + const auto zOffset = shape::getOffset(output.getShapeInfo(), zCoords); - const auto zOffset = shape::getOffset(output.getShapeInfo(), zCoordStart); + temp = zCoords[yRank - 1]; + zCoords[yRank - 1] = 0; + const auto yOffset = shape::getOffset(indices.getShapeInfo(), zCoords); + zCoords[yRank - 1] = temp; - // last y coordinate - uint coordToRestore; - if (yLastDim != xRank) - coordToRestore = static_cast(zCoordStart[yRank - 1]); + if(bEqual) + memcpy(xCoords, zCoords, zRank * sizeof(int)); + else if(diff >= 0) + memcpy(xCoords, zCoords + diff, xRank * sizeof(int)); + else + memcpy(xCoords - diff, zCoords, zRank * sizeof(int)); - zCoordStart[yRank - 1] = 0; - const auto yOffset = shape::getOffset(indices.getShapeInfo(), zCoordStart); - - //restore z coordinate - if (yLastDim != xRank) - zCoordStart[yRank - 1] = coordToRestore; - - // construct coordinates for x for (uint j = 0; j < yLastDim; ++j) - xCoordStart[j] = y[yOffset + j * indices.stridesOf()[yRank - 1]]; // last stride + xCoords[j] = y[yOffset + j * indices.stridesOf()[yRank - 1]]; // last stride - const auto xOffset = shape::getOffset(input.getShapeInfo(), xCoordStart); + const auto xOffset = shape::getOffset(input.getShapeInfo(), xCoords); z[zOffset] = x[xOffset]; } @@ -610,7 +623,7 @@ static void gatherND_(NDArray& input, NDArray& indices, NDArray& output) { } //////////////////////////////////////////////////////////////////////// -void gatherND(nd4j::LaunchContext * context, NDArray& input, NDArray& indices, NDArray& output) { +void gatherND(sd::LaunchContext * context, NDArray& input, NDArray& indices, NDArray& output) { BUILD_DOUBLE_SELECTOR(input.dataType(), indices.dataType(), gatherND_, (input, indices, output), LIBND4J_TYPES, INDEXING_TYPES); } @@ -628,7 +641,7 @@ static void gather_(NDArray* input, const NDArray* indices, NDArray* output, con if (indices != nullptr) { - for(int i = 0; i < indices->lengthOf(); ++i) + for(Nd4jLong i = 0; i < indices->lengthOf(); ++i) if(indices->e(i) >= input->sizeAt(axis)) throw std::runtime_error("helpers::gather function: indices array contains wrong elements, each element must be smaller than corresponding dimension of input array !"); @@ -641,7 +654,7 @@ static void gather_(NDArray* input, const NDArray* indices, NDArray* output, con output->assign(scalarNDArray); } else { auto dimensions = ShapeUtils::evalDimsToExclude(input->rankOf(), {axis}); - auto tadPack = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), dimensions); + auto tadPack = sd::ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), dimensions); auto tadArr = NDArray(reinterpret_cast(reinterpret_cast(input->getBuffer()) + tadPack.primaryOffsets()[indices->e(0)]), tadPack.primaryShapeInfo(), output->getContext()); output->assign(&tadArr); @@ -704,7 +717,7 @@ static void gather_(NDArray* input, const NDArray* indices, NDArray* output, con } ////////////////////////////////////////////////////////////////////////// -void eye(nd4j::LaunchContext * context, NDArray& output) { +void eye(sd::LaunchContext * context, NDArray& output) { const int rank = output.rankOf(); auto arrs = output.allTensorsAlongDimension({rank-2, rank-1}); @@ -718,7 +731,7 @@ void eye(nd4j::LaunchContext * context, NDArray& output) { } ////////////////////////////////////////////////////////////////////////// -void scatterUpdate(nd4j::LaunchContext * context, NDArray& input, NDArray& updates, const std::vector* intArgs) { +void scatterUpdate(sd::LaunchContext * context, NDArray& input, NDArray& updates, const std::vector* intArgs) { int opCode = (*intArgs)[0]; int dimSize = (*intArgs)[1]; @@ -733,7 +746,7 @@ void scatterUpdate(nd4j::LaunchContext * context, NDArray& input, NDArray& updat // increasing counter to skip numIndices e++; std::vector indices; - for (; e < intArgs->size(); e++) + for (; e < static_cast(intArgs->size()); e++) indices.push_back((*intArgs)[e]); auto func = PRAGMA_THREADS_FOR { @@ -777,7 +790,7 @@ void scatterUpdate(nd4j::LaunchContext * context, NDArray& input, NDArray& updat ////////////////////////////////////////////////////////////////////////// -void scatterSimple(nd4j::LaunchContext * context, const int opId, NDArray& input, const NDArray& updates, const NDArray& indices, const std::vector& dimensions) { +void scatterSimple(sd::LaunchContext * context, const int opId, NDArray& input, const NDArray& updates, const NDArray& indices, const std::vector& dimensions) { // updates and indices have same length const Nd4jLong len = indices.lengthOf(); @@ -813,7 +826,7 @@ static void mergeMaxIndex_(const std::vector& inArrs, NDArray& output) T max = -DataTypeUtils::max(); Nd4jLong idx = 0; - for (int i = 0; i < numArgs; i++) { + for (Nd4jLong i = 0; i < numArgs; i++) { T v = inArrs[i]->e(e); if (v > max) { max = v; @@ -827,7 +840,7 @@ static void mergeMaxIndex_(const std::vector& inArrs, NDArray& output) samediff::Threads::parallel_for(func, 0, x->lengthOf()); } -void mergeMaxIndex(nd4j::LaunchContext * context, const std::vector& inArrs, NDArray& output) { +void mergeMaxIndex(sd::LaunchContext * context, const std::vector& inArrs, NDArray& output) { BUILD_SINGLE_SELECTOR(inArrs[0]->dataType(), mergeMaxIndex_, (inArrs, output), LIBND4J_TYPES); } @@ -841,7 +854,7 @@ static void mergeMax_(const std::vector& inArrs, NDArray& output) { auto func = PRAGMA_THREADS_FOR { for (auto e = start; e < stop; e++) { T max = -DataTypeUtils::max(); - for (int i = 0; i < numArgs; i++) { + for (Nd4jLong i = 0; i < numArgs; i++) { T v = inArrs[i]->e(e); if (v > max) max = v; @@ -853,7 +866,7 @@ static void mergeMax_(const std::vector& inArrs, NDArray& output) { samediff::Threads::parallel_for(func, 0, x->lengthOf()); } -void mergeMax(nd4j::LaunchContext * context, const std::vector& inArrs, NDArray& output) { +void mergeMax(sd::LaunchContext * context, const std::vector& inArrs, NDArray& output) { BUILD_SINGLE_SELECTOR(output.dataType(), mergeMax_, (inArrs, output), LIBND4J_TYPES); } @@ -867,7 +880,7 @@ static void mergeAvg_(const std::vector& inArrs, NDArray& output) { auto func = PRAGMA_THREADS_FOR { for (auto e = start; e < stop; e++) { T sum = 0.; - for (int i = 0; i < numArgs; i++) { + for (Nd4jLong i = 0; i < numArgs; i++) { T v = inArrs[i]->e(e); sum += v; } @@ -878,7 +891,7 @@ static void mergeAvg_(const std::vector& inArrs, NDArray& output) { samediff::Threads::parallel_for(func, 0, x->lengthOf()); } -void mergeAvg(nd4j::LaunchContext * context, const std::vector& inArrs, NDArray& output) { +void mergeAvg(sd::LaunchContext * context, const std::vector& inArrs, NDArray& output) { BUILD_SINGLE_SELECTOR(output.dataType(), mergeAvg_, (inArrs, output), LIBND4J_TYPES); } @@ -893,7 +906,7 @@ static void mergeAdd_(const std::vector& inArrs, NDArray& output) { auto func = PRAGMA_THREADS_FOR { for (auto e = start; e < stop; e++) { T sum = (T) 0.f; - for (int i = 0; i < numArgs; i++) + for (Nd4jLong i = 0; i < numArgs; i++) sum += inArrs[i]->e(e); output.p(e, sum); @@ -902,7 +915,7 @@ static void mergeAdd_(const std::vector& inArrs, NDArray& output) { samediff::Threads::parallel_for(func, 0, x->lengthOf()); } - void mergeAdd(nd4j::LaunchContext * context, const std::vector& inArrs, NDArray& output) { + void mergeAdd(sd::LaunchContext * context, const std::vector& inArrs, NDArray& output) { BUILD_SINGLE_SELECTOR(output.dataType(), mergeAdd_, (inArrs, output), LIBND4J_TYPES); } @@ -969,7 +982,7 @@ static void clipByNorm_(NDArray& input, NDArray& output, const std::vector& } ////////////////////////////////////////////////////////////////////////// -void clipByNorm(nd4j::LaunchContext * context, NDArray& input, NDArray& output, const std::vector& dimensions, const NDArray& clipNorm, const bool isInplace) { +void clipByNorm(sd::LaunchContext * context, NDArray& input, NDArray& output, const std::vector& dimensions, const NDArray& clipNorm, const bool isInplace) { BUILD_SINGLE_SELECTOR(output.dataType(), clipByNorm_, (input, output, dimensions, clipNorm, isInplace), FLOAT_TYPES); } @@ -983,7 +996,7 @@ void clipByNorm(nd4j::LaunchContext * context, NDArray& input, NDArray& output, template - static void clipByGlobalNorm_(std::vector const& inputs, double clipNorm, nd4j::memory::Workspace* workspace, std::vector& outputs, bool isInplace) { + static void clipByGlobalNorm_(std::vector const& inputs, double clipNorm, sd::memory::Workspace* workspace, std::vector& outputs, bool isInplace) { T globalNorm = 0; //NDArrayFactory::create(0, inputs[0]->getContext()); //sqrt(sum([l2norm(t)**2 for t in t_list])) // PRAGMA_OMP_PARALLEL_FOR_SIMD_REDUCTION(sumT : globalNorm) for (size_t i = 0; i < inputs.size(); i++) { @@ -992,8 +1005,8 @@ void clipByNorm(nd4j::LaunchContext * context, NDArray& input, NDArray& output, globalNorm += l2norm.t(0) * l2norm.t(0); } - //globalNorm.applyTransform(transform::Sqrt, nullptr, nullptr);// = nd4j::math::nd4j_sqrt(globalNorm); - auto normS = nd4j::math::nd4j_sqrt(globalNorm); + //globalNorm.applyTransform(transform::Sqrt, nullptr, nullptr);// = sd::math::nd4j_sqrt(globalNorm); + auto normS = sd::math::nd4j_sqrt(globalNorm); outputs[inputs.size()]->p(0, normS); const T factor = clipNorm / normS; @@ -1014,11 +1027,11 @@ void clipByNorm(nd4j::LaunchContext * context, NDArray& input, NDArray& output, } } } - void clipByGlobalNorm(nd4j::LaunchContext * context, std::vector const& inputs, double clipNorm, nd4j::memory::Workspace* workspace, std::vector& outputs, bool isInplace) { + void clipByGlobalNorm(sd::LaunchContext * context, std::vector const& inputs, double clipNorm, sd::memory::Workspace* workspace, std::vector& outputs, bool isInplace) { BUILD_SINGLE_SELECTOR(outputs[0]->dataType(), clipByGlobalNorm_, (inputs, clipNorm, workspace, outputs, isInplace), FLOAT_TYPES); } - BUILD_SINGLE_TEMPLATE(template void clipByGlobalNorm_, (std::vector const& inputs, double clipNorm, nd4j::memory::Workspace* workspace, std::vector& outputs, bool isInplace), FLOAT_TYPES); + BUILD_SINGLE_TEMPLATE(template void clipByGlobalNorm_, (std::vector const& inputs, double clipNorm, sd::memory::Workspace* workspace, std::vector& outputs, bool isInplace), FLOAT_TYPES); ////////////////////////////////////////////////////////////////////////// template @@ -1083,7 +1096,7 @@ static void clipByNormBP_(const NDArray& input, const NDArray& gradO, NDArray& g } } - void clipByNormBP(nd4j::LaunchContext * context, const NDArray& input, const NDArray& gradO, NDArray& gradI /*output*/, const std::vector& dimensions, const NDArray& clipNorm) { + void clipByNormBP(sd::LaunchContext * context, const NDArray& input, const NDArray& gradO, NDArray& gradI /*output*/, const std::vector& dimensions, const NDArray& clipNorm) { BUILD_SINGLE_SELECTOR(gradI.dataType(), clipByNormBP_, (input, gradO, gradI, dimensions, clipNorm), FLOAT_TYPES); } @@ -1126,7 +1139,7 @@ static void clipByAveraged_(NDArray& input, NDArray& output, const std::vector& dimensions, const NDArray& clipNorm, const bool isInplace) { + void clipByAveraged(sd::LaunchContext * context, NDArray& input, NDArray& output, const std::vector& dimensions, const NDArray& clipNorm, const bool isInplace) { BUILD_SINGLE_SELECTOR(input.dataType(), clipByAveraged_, (input, output, dimensions, clipNorm, isInplace), FLOAT_TYPES); } @@ -1151,7 +1164,7 @@ static void clipByAveraged_(NDArray& input, NDArray& output, const std::vector(routine, output); } - void clipByValue(nd4j::LaunchContext * context, NDArray& input, double leftBound, double rightBound, NDArray& output) { + void clipByValue(sd::LaunchContext * context, NDArray& input, double leftBound, double rightBound, NDArray& output) { BUILD_SINGLE_SELECTOR(input.dataType(), clipByValue_, (input, leftBound, rightBound, output), FLOAT_TYPES); } @@ -1188,10 +1201,12 @@ static void mirrorPad_(const NDArray& input, const NDArray& paddings, NDArray& o else { auto func = PRAGMA_THREADS_FOR { - Nd4jLong inIdx[MAX_RANK]; - Nd4jLong outIdx[MAX_RANK]; + + int inIdx[MAX_RANK], outIdx[MAX_RANK]; + for (auto i = start; i < stop; i++) { - shape::index2coords(i, output.getShapeInfo(), outIdx); + + shape::index2coordsCPU(start, i, output.getShapeInfo(), outIdx); for (int j = 0; j < rank; ++j) { const Nd4jLong inLen = input.sizeAt(j); @@ -1219,7 +1234,7 @@ static void mirrorPad_(const NDArray& input, const NDArray& paddings, NDArray& o } } - void mirrorPad(nd4j::LaunchContext * context, const NDArray& input, const NDArray& paddings, NDArray& output, const int mode) { + void mirrorPad(sd::LaunchContext * context, const NDArray& input, const NDArray& paddings, NDArray& output, const int mode) { BUILD_SINGLE_SELECTOR(input.dataType(), mirrorPad_, (input, paddings, output, mode), LIBND4J_TYPES); } @@ -1234,7 +1249,7 @@ static void tileBP_(const NDArray& gradO /*input*/, NDArray& gradI /*output*/, c const T* gradOBuff = reinterpret_cast(gradO.getBuffer()); const Nd4jLong gradILen = gradI.lengthOf(); const Nd4jLong gradOLen = gradO.lengthOf(); // gradOLen >= gradILen - const Nd4jLong gradIEWS = nd4j::math::nd4j_abs(gradI.ews()); + const Nd4jLong gradIEWS = sd::math::nd4j_abs(gradI.ews()); const Nd4jLong gradOEWS = gradO.ews(); // initial zeroing of gradI content @@ -1242,7 +1257,7 @@ static void tileBP_(const NDArray& gradO /*input*/, NDArray& gradI /*output*/, c memset(gradIBuff, 0, gradILen * sizeof(T)); else { //PRAGMA_OMP_PARALLEL_FOR_SIMD - for (int i = 0; i < gradILen * gradIEWS; i += gradIEWS) + for (Nd4jLong i = 0; i < gradILen * gradIEWS; i += gradIEWS) gradIBuff[i] = static_cast(0.f); } @@ -1274,7 +1289,7 @@ static void tileBP_(const NDArray& gradO /*input*/, NDArray& gradI /*output*/, c } } -void tileBP(nd4j::LaunchContext * context, const NDArray& gradO /*input*/, NDArray& gradI /*output*/, const std::vector reps) { +void tileBP(sd::LaunchContext * context, const NDArray& gradO /*input*/, NDArray& gradI /*output*/, const std::vector reps) { BUILD_SINGLE_SELECTOR(gradI.dataType(), tileBP_, (gradO, gradI, reps), FLOAT_TYPES); } diff --git a/libnd4j/include/ops/declarable/helpers/cpu/triangular_solve.cpp b/libnd4j/include/ops/declarable/helpers/cpu/triangular_solve.cpp index c825a8fee..bcf406392 100644 --- a/libnd4j/include/ops/declarable/helpers/cpu/triangular_solve.cpp +++ b/libnd4j/include/ops/declarable/helpers/cpu/triangular_solve.cpp @@ -17,12 +17,12 @@ // // @author GS // -#include -#include +#include +#include #include #include "../triangular_solve.h" -namespace nd4j { +namespace sd { namespace ops { namespace helpers { /* @@ -39,14 +39,14 @@ namespace helpers { * * */ template - static void lowerTriangularSolve(nd4j::LaunchContext * context, NDArray* leftInput, NDArray* rightInput, bool adjoint, NDArray* output) { + static void lowerTriangularSolve(sd::LaunchContext * context, NDArray* leftInput, NDArray* rightInput, bool adjoint, NDArray* output) { auto rows = leftInput->rows(); auto cols = rightInput->columns(); //output->t(0,0) = rightInput->t(0,0) / leftInput->t(0,0); - for (auto r = 0; r < rows; r++) { - for (auto j = 0; j < cols; j++) { + for (Nd4jLong r = 0; r < rows; r++) { + for (Nd4jLong j = 0; j < cols; j++) { auto sum = rightInput->t(r, j); - for (auto c = 0; c < r; c++) { + for (Nd4jLong c = 0; c < r; c++) { sum -= leftInput->t(r, c) * output->t(c, j); } output->t(r, j) = sum / leftInput->t(r, r); @@ -69,13 +69,13 @@ namespace helpers { * */ template - static void upperTriangularSolve(nd4j::LaunchContext * context, NDArray* leftInput, NDArray* rightInput, bool adjoint, NDArray* output) { + static void upperTriangularSolve(sd::LaunchContext * context, NDArray* leftInput, NDArray* rightInput, bool adjoint, NDArray* output) { auto rows = leftInput->rows(); auto cols = rightInput->columns(); - for (auto r = rows; r > 0; r--) { - for (auto j = 0; j < cols; j++) { + for (Nd4jLong r = rows; r > 0; r--) { + for (Nd4jLong j = 0; j < cols; j++) { auto sum = rightInput->t(r - 1, j); - for (auto c = r; c < rows; c++) { + for (Nd4jLong c = r; c < rows; c++) { sum -= leftInput->t(r - 1, c) * output->t(c, j); } output->t(r - 1, j) = sum / leftInput->t(r - 1, r - 1); @@ -84,7 +84,7 @@ namespace helpers { } template - static int triangularSolveFunctor_(nd4j::LaunchContext * context, NDArray* leftInput, NDArray* rightInput, bool lower, bool adjoint, NDArray* output) { + static int triangularSolveFunctor_(sd::LaunchContext * context, NDArray* leftInput, NDArray* rightInput, bool lower, bool adjoint, NDArray* output) { auto leftPart = leftInput->allTensorsAlongDimension({-2, -1}); auto rightPart = rightInput->allTensorsAlongDimension({-2, -1}); auto outputPart = output->allTensorsAlongDimension({-2, -1}); @@ -105,7 +105,7 @@ namespace helpers { } template - static void adjointTriangularMatrix_(nd4j::LaunchContext* context, NDArray const* input, bool const lower, NDArray* output) { + static void adjointTriangularMatrix_(sd::LaunchContext* context, NDArray const* input, bool const lower, NDArray* output) { auto inputPart = input->allTensorsAlongDimension({-2, -1}); auto outputPart = output->allTensorsAlongDimension({-2, -1}); auto cols = input->sizeAt(-1); @@ -114,14 +114,14 @@ namespace helpers { auto batchLoop = PRAGMA_THREADS_FOR { for (auto batch = start; batch < stop; batch++) { if (!lower) { - for (auto r = 0; r < rows; r++) { - for (auto c = 0; c <= r; c++) { + for (Nd4jLong r = 0; r < rows; r++) { + for (Nd4jLong c = 0; c <= r; c++) { outputPart[batch]->t(r, c) = inputPart[batch]->t(c, r); } } } else { - for (auto r = 0; r < rows; r++) { - for (auto c = r; c < cols; c++) { + for (Nd4jLong r = 0; r < rows; r++) { + for (Nd4jLong c = r; c < cols; c++) { outputPart[batch]->t(r, c) = inputPart[batch]->t(c, r); } } @@ -131,11 +131,11 @@ namespace helpers { samediff::Threads::parallel_tad(batchLoop, 0, inputPart.size(), 1); } - int triangularSolveFunctor(nd4j::LaunchContext * context, NDArray* leftInput, NDArray* rightInput, bool lower, bool adjoint, NDArray* output) { + int triangularSolveFunctor(sd::LaunchContext * context, NDArray* leftInput, NDArray* rightInput, bool lower, bool adjoint, NDArray* output) { BUILD_SINGLE_SELECTOR(leftInput->dataType(), return triangularSolveFunctor_, (context, leftInput, rightInput, lower, adjoint, output), FLOAT_NATIVE); } - void adjointMatrix(nd4j::LaunchContext* context, NDArray const* input, bool const lower, NDArray* output) { + void adjointMatrix(sd::LaunchContext* context, NDArray const* input, bool const lower, NDArray* output) { BUILD_SINGLE_SELECTOR(input->dataType(), adjointTriangularMatrix_, (context, input, lower, output), FLOAT_NATIVE); } } diff --git a/libnd4j/include/ops/declarable/helpers/cpu/weights.cpp b/libnd4j/include/ops/declarable/helpers/cpu/weights.cpp index 2d4bfee21..ebdfc674b 100644 --- a/libnd4j/include/ops/declarable/helpers/cpu/weights.cpp +++ b/libnd4j/include/ops/declarable/helpers/cpu/weights.cpp @@ -20,13 +20,13 @@ #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { template static void adjustWeights_(NDArray* input, NDArray* weights, NDArray* output, int minLength, int maxLength) { - for (int e = 0; e < input->lengthOf(); e++) { + for (Nd4jLong e = 0; e < input->lengthOf(); e++) { int val = input->e(e); if (val < maxLength) { if (weights != nullptr) @@ -37,7 +37,7 @@ namespace helpers { } } - void adjustWeights(nd4j::LaunchContext * context, NDArray* input, NDArray* weights, NDArray* output, int minLength, int maxLength) { + void adjustWeights(sd::LaunchContext * context, NDArray* input, NDArray* weights, NDArray* output, int minLength, int maxLength) { BUILD_SINGLE_SELECTOR(output->dataType(), adjustWeights_, (input, weights, output, minLength, maxLength), LIBND4J_TYPES); } diff --git a/libnd4j/include/ops/declarable/helpers/cpu/zeta.cpp b/libnd4j/include/ops/declarable/helpers/cpu/zeta.cpp index 90ef634c1..d127fc166 100644 --- a/libnd4j/include/ops/declarable/helpers/cpu/zeta.cpp +++ b/libnd4j/include/ops/declarable/helpers/cpu/zeta.cpp @@ -21,7 +21,7 @@ #include #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { @@ -58,7 +58,7 @@ static FORCEINLINE T zetaScalarSlow(const T x, const T q) { ////////////////////////////////////////////////////////////////////////// // calculate the Hurwitz zeta function for arrays template -static void zeta_(nd4j::LaunchContext * context, const NDArray& x, const NDArray& q, NDArray &z) { +static void zeta_(sd::LaunchContext * context, const NDArray& x, const NDArray& q, NDArray &z) { //auto result = NDArray(&x, false, context); int xLen = x.lengthOf(); @@ -71,11 +71,11 @@ static void zeta_(nd4j::LaunchContext * context, const NDArray& x, const NDArray samediff::Threads::parallel_for(func, 0, xLen); } -void zeta(nd4j::LaunchContext * context, const NDArray& x, const NDArray& q, NDArray& z) { +void zeta(sd::LaunchContext * context, const NDArray& x, const NDArray& q, NDArray& z) { BUILD_SINGLE_SELECTOR(x.dataType(), zeta_, (context, x, q, z), FLOAT_TYPES); } -BUILD_SINGLE_TEMPLATE(template void zeta_, (nd4j::LaunchContext * context, const NDArray& x, const NDArray& q, NDArray& z), FLOAT_TYPES); +BUILD_SINGLE_TEMPLATE(template void zeta_, (sd::LaunchContext * context, const NDArray& x, const NDArray& q, NDArray& z), FLOAT_TYPES); } } diff --git a/libnd4j/include/ops/declarable/helpers/crop_and_resize.h b/libnd4j/include/ops/declarable/helpers/crop_and_resize.h index 3926dbfb0..cff96f93e 100644 --- a/libnd4j/include/ops/declarable/helpers/crop_and_resize.h +++ b/libnd4j/include/ops/declarable/helpers/crop_and_resize.h @@ -22,17 +22,17 @@ #ifndef SD_CROP_AND_RESIZE_H #define SD_CROP_AND_RESIZE_H -#include -#include +#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { template void cropAndResizeFunctor_(NDArray const *images, NDArray const *boxes, NDArray const *indices, NDArray const *cropSize, int method, double extrapolationVal, NDArray *crops); - void cropAndResizeFunctor(nd4j::LaunchContext * context, NDArray const* images, NDArray const* boxes, NDArray const* indices, NDArray const* cropSize, int method, double extrapolationVal, NDArray* crops); + void cropAndResizeFunctor(sd::LaunchContext * context, NDArray const* images, NDArray const* boxes, NDArray const* indices, NDArray const* cropSize, int method, double extrapolationVal, NDArray* crops); } } } diff --git a/libnd4j/include/ops/declarable/helpers/cross.h b/libnd4j/include/ops/declarable/helpers/cross.h index 3ea80966b..bd1e2a61d 100644 --- a/libnd4j/include/ops/declarable/helpers/cross.h +++ b/libnd4j/include/ops/declarable/helpers/cross.h @@ -21,13 +21,13 @@ #include #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { -void crossBatched(nd4j::LaunchContext * context, NDArray *a, NDArray *b, NDArray *o); +void crossBatched(sd::LaunchContext * context, NDArray *a, NDArray *b, NDArray *o); -void FORCEINLINE cross(nd4j::LaunchContext * context, NDArray *a, NDArray *b, NDArray *o) { +void FORCEINLINE cross(sd::LaunchContext * context, NDArray *a, NDArray *b, NDArray *o) { if (a->isR()) { auto a0 = a->e(0); @@ -56,7 +56,7 @@ void FORCEINLINE cross(nd4j::LaunchContext * context, NDArray *a, NDArray *b, ND } } - void FORCEINLINE _crossBatched(nd4j::LaunchContext * context, NDArray *a, NDArray *b, NDArray *o) { + void FORCEINLINE _crossBatched(sd::LaunchContext * context, NDArray *a, NDArray *b, NDArray *o) { auto a_ = a->reshape(a->ordering(), {-1, 3}); auto b_ = b->reshape(b->ordering(), {-1, 3}); auto o_ = o->reshape(o->ordering(), {-1, 3}, false); @@ -80,7 +80,7 @@ void FORCEINLINE cross(nd4j::LaunchContext * context, NDArray *a, NDArray *b, ND samediff::Threads::parallel_tad(func, 0, tads); } - void weightedCrossEntropyWithLogitsFunctor(nd4j::LaunchContext * context, NDArray const* targets, NDArray const* input, NDArray const* weights, NDArray* output); + void weightedCrossEntropyWithLogitsFunctor(sd::LaunchContext * context, NDArray const* targets, NDArray const* input, NDArray const* weights, NDArray* output); } } } \ No newline at end of file diff --git a/libnd4j/include/ops/declarable/helpers/cuda/BarnesHutTsne.cu b/libnd4j/include/ops/declarable/helpers/cuda/BarnesHutTsne.cu index 4c746f244..71eef3386 100644 --- a/libnd4j/include/ops/declarable/helpers/cuda/BarnesHutTsne.cu +++ b/libnd4j/include/ops/declarable/helpers/cuda/BarnesHutTsne.cu @@ -20,7 +20,7 @@ #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// @@ -233,13 +233,13 @@ namespace helpers { BUILD_SINGLE_TEMPLATE(template void barnes_edge_forces_, (const NDArray* rowP, NDArray const* colP, NDArray const* valP, int N, NDArray const* data, NDArray* output), FLOAT_TYPES); //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// -// gains - run a function T((x + 2.) * nd4j::math::nd4j_sign(grad) != nd4j::math::nd4j_sign(eps)) + T(x * 0.8 * nd4j::math::nd4j_sign(grad) != nd4j::math::nd4j_sign(eps)); +// gains - run a function T((x + 2.) * sd::math::nd4j_sign(grad) != sd::math::nd4j_sign(eps)) + T(x * 0.8 * sd::math::nd4j_sign(grad) != sd::math::nd4j_sign(eps)); // for all members in input and put all in output // template void barnes_gains_(NDArray* input, NDArray* gradX, NDArray* epsilon, NDArray* output) { auto gainsInternal = LAMBDA_TTT(x, grad, eps) { - T res = nd4j::math::nd4j_sign(grad) != nd4j::math::nd4j_sign(eps) ? x + T(.2) : x * T(.8); + T res = sd::math::nd4j_sign(grad) != sd::math::nd4j_sign(eps) ? x + T(.2) : x * T(.8); if(res < .01) res = .01; return res; }; diff --git a/libnd4j/include/ops/declarable/helpers/cuda/activations.cu b/libnd4j/include/ops/declarable/helpers/cuda/activations.cu index 7bddb00fe..4243c6e04 100644 --- a/libnd4j/include/ops/declarable/helpers/cuda/activations.cu +++ b/libnd4j/include/ops/declarable/helpers/cuda/activations.cu @@ -19,14 +19,14 @@ // @author raver119@gmail.com // -#include +#include #include -#include +#include #include -#include +#include #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { @@ -52,7 +52,7 @@ __global__ void preluCuda(const void *vx, const Nd4jLong *xShapeInfo, __syncthreads(); const auto tid = blockIdx.x * blockDim.x + threadIdx.x; - Nd4jLong coords[MAX_RANK]; + int coords[MAX_RANK]; for (int i = tid; i < xzLen; i += blockDim.x * gridDim.x) { shape::index2coords(i, xShapeInfo, coords); @@ -79,7 +79,7 @@ linkage void preluCudaLauncher(const int blocksPerGrid, const int threadsPerBloc } /////////////////////////////////////////////////////////////////// -void prelu(nd4j::LaunchContext * context, const NDArray& input, const NDArray& alpha, NDArray& output) { +void prelu(sd::LaunchContext * context, const NDArray& input, const NDArray& alpha, NDArray& output) { PointersManager manager(context, "prelu"); @@ -124,7 +124,7 @@ __global__ linkage void preluBPCuda(const void *vIn, const Nd4jLong *inShapeI __syncthreads(); const auto tid = blockIdx.x * blockDim.x + threadIdx.x; - Nd4jLong coords[MAX_RANK]; + int coords[MAX_RANK]; for (int i = tid; i < inLen; i += totalThreads) { shape::index2coords(i, inShapeInfo, coords); @@ -147,7 +147,7 @@ __global__ linkage void preluBPCuda(const void *vIn, const Nd4jLong *inShapeI dLdI[dLdIOffset] = grO * alpha[alphaOffset]; - nd4j::math::atomics::nd4j_atomicAdd(&dLdA[dLdAOffset], static_cast(grO * xVal)); + sd::math::atomics::nd4j_atomicAdd(&dLdA[dLdAOffset], static_cast(grO * xVal)); } else dLdI[dLdIOffset] = grO; @@ -162,7 +162,7 @@ __host__ linkage void preluBPCudaLauncher(const int blocksPerGrid, const int thr } ////////////////////////////////////////////////////////////////////////// -void preluBP(nd4j::LaunchContext* context, const NDArray& input, const NDArray& alpha, const NDArray& dLdO, NDArray& dLdI, NDArray& dLdA) { +void preluBP(sd::LaunchContext* context, const NDArray& input, const NDArray& alpha, const NDArray& dLdO, NDArray& dLdI, NDArray& dLdA) { dLdA.nullify(); PointersManager manager(context, "preluBP"); @@ -211,7 +211,7 @@ __device__ void softMaxForVectorCuda(const void *vx, const Nd4jLong *xShapeInfo, const Nd4jLong elemIdx = i * blockDim.x + threadIdx.x; if(elemIdx < len) { const Nd4jLong xOffset = shape::getIndexOffset(elemIdx, xShapeInfo); - shmem[threadIdx.x] = (threadIdx.x != 0) ? x[xOffset] : nd4j::math::nd4j_max(x[xOffset], temp); // take into account max element evaluated on previous iteration and stored in temp + shmem[threadIdx.x] = (threadIdx.x != 0) ? x[xOffset] : sd::math::nd4j_max(x[xOffset], temp); // take into account max element evaluated on previous iteration and stored in temp } else shmem[threadIdx.x] = -DataTypeUtils::max(); // FIXME: what if T is unsigned ?? @@ -220,7 +220,7 @@ __device__ void softMaxForVectorCuda(const void *vx, const Nd4jLong *xShapeInfo, for (int s = blockDim.x / 2; s > 0; s /= 2) { if(threadIdx.x < s) - shmem[threadIdx.x] = nd4j::math::nd4j_max(shmem[threadIdx.x], shmem[threadIdx.x + s]); + shmem[threadIdx.x] = sd::math::nd4j_max(shmem[threadIdx.x], shmem[threadIdx.x + s]); __syncthreads(); } @@ -238,7 +238,7 @@ __device__ void softMaxForVectorCuda(const void *vx, const Nd4jLong *xShapeInfo, if(elemIdx < len) { const Nd4jLong xOffset = shape::getIndexOffset(elemIdx, xShapeInfo); const Nd4jLong zOffset = shape::getIndexOffset(elemIdx, zShapeInfo); - z[zOffset] = nd4j::math::nd4j_exp(x[xOffset] - max); + z[zOffset] = sd::math::nd4j_exp(x[xOffset] - max); shmem[threadIdx.x] = (threadIdx.x != 0) ? z[zOffset] : (z[zOffset] + temp); // take into account sum element evaluated on previous iteration and stored in temp } else @@ -302,7 +302,7 @@ static void softMaxCudaLauncher(const int blocksPerGrid, const int threadsPerBlo ////////////////////////////////////////////////////////////////////////// -void softmax(nd4j::LaunchContext * context, const NDArray& input, NDArray& output, const int dimension) { +void softmax(sd::LaunchContext * context, const NDArray& input, NDArray& output, const int dimension) { if(!input.isActualOnDeviceSide()) input.syncToDevice(); const int rank = input.rankOf(); @@ -321,8 +321,8 @@ void softmax(nd4j::LaunchContext * context, const NDArray& input, NDArray& outpu } else { - auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(input.getShapeInfo(), {dimension}); - auto packZ = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(output.getShapeInfo(), {dimension}); + auto packX = sd::ConstantTadHelper::getInstance()->tadForDimensions(input.getShapeInfo(), {dimension}); + auto packZ = sd::ConstantTadHelper::getInstance()->tadForDimensions(output.getShapeInfo(), {dimension}); const int threadsPerBlock = MAX_NUM_THREADS / 4; const int blocksPerGrid = packZ.numberOfTads(); @@ -374,7 +374,7 @@ __global__ void logSoftMaxForVectorCuda(const void *vx, const Nd4jLong *xzShape const Nd4jLong elemIdx = i * blockDim.x + threadIdx.x; if(elemIdx < len) { const Nd4jLong offset = shape::getIndexOffset(elemIdx, xzShapeInfo); - shmem[threadIdx.x] = (threadIdx.x != 0) ? x[offset] : nd4j::math::nd4j_max(x[offset], temp); // take into account max element evaluated on previous iteration and stored in temp + shmem[threadIdx.x] = (threadIdx.x != 0) ? x[offset] : sd::math::nd4j_max(x[offset], temp); // take into account max element evaluated on previous iteration and stored in temp } else shmem[threadIdx.x] = -DataTypeUtils::max(); // FIXME: what if T is unsigned ?? @@ -383,7 +383,7 @@ __global__ void logSoftMaxForVectorCuda(const void *vx, const Nd4jLong *xzShape for (int s = blockDim.x / 2; s > 0; s /= 2) { if(threadIdx.x < s) - shmem[threadIdx.x] = nd4j::math::nd4j_max(shmem[threadIdx.x], shmem[threadIdx.x + s]); + shmem[threadIdx.x] = sd::math::nd4j_max(shmem[threadIdx.x], shmem[threadIdx.x + s]); __syncthreads(); } @@ -400,7 +400,7 @@ __global__ void logSoftMaxForVectorCuda(const void *vx, const Nd4jLong *xzShape const Nd4jLong elemIdx = i * blockDim.x + threadIdx.x; if(elemIdx < len) { const Nd4jLong offset = shape::getIndexOffset(elemIdx, xzShapeInfo); - z[offset] = nd4j::math::nd4j_exp(x[offset] - max); + z[offset] = sd::math::nd4j_exp(x[offset] - max); shmem[threadIdx.x] = (threadIdx.x != 0) ? z[offset] : (z[offset] + temp); // take into account sum element evaluated on previous iteration and stored in temp } else @@ -422,7 +422,7 @@ __global__ void logSoftMaxForVectorCuda(const void *vx, const Nd4jLong *xzShape const Nd4jLong elemIdx = i * blockDim.x + threadIdx.x; if(elemIdx >= len) continue; const Nd4jLong offset = shape::getIndexOffset(elemIdx, xzShapeInfo); - z[offset] = nd4j::math::nd4j_log(z[offset] / shmem[0]); + z[offset] = sd::math::nd4j_log(z[offset] / shmem[0]); } } @@ -434,7 +434,7 @@ linkage void logSoftMaxForVectorCudaLauncher(const cudaStream_t* stream, const v } ////////////////////////////////////////////////////////////////////////// -void logSoftmax(nd4j::LaunchContext * context, const NDArray& input, NDArray& output, const int dimension) { +void logSoftmax(sd::LaunchContext * context, const NDArray& input, NDArray& output, const int dimension) { if(!input.isActualOnDeviceSide()) input.syncToDevice(); const int rank = input.rankOf(); @@ -493,7 +493,7 @@ __global__ linkage void softMaxDerivForVectorCuda(const void *vx, const Nd4jLong const Nd4jLong elemIdx = i * blockDim.x + threadIdx.x; if(elemIdx < len) { const Nd4jLong offset = shape::getIndexOffset(elemIdx, xzShapeInfo); - shmem[threadIdx.x] = (threadIdx.x != 0) ? x[offset] : nd4j::math::nd4j_max(x[offset], temp); // take into account max element evaluated on previous iteration and stored in temp + shmem[threadIdx.x] = (threadIdx.x != 0) ? x[offset] : sd::math::nd4j_max(x[offset], temp); // take into account max element evaluated on previous iteration and stored in temp } else shmem[threadIdx.x] = -DataTypeUtils::max(); // FIXME: what if T is unsigned ?? @@ -502,7 +502,7 @@ __global__ linkage void softMaxDerivForVectorCuda(const void *vx, const Nd4jLong for (int s = blockDim.x / 2; s > 0; s /= 2) { if(threadIdx.x < s) - shmem[threadIdx.x] = nd4j::math::nd4j_max(shmem[threadIdx.x], shmem[threadIdx.x + s]); + shmem[threadIdx.x] = sd::math::nd4j_max(shmem[threadIdx.x], shmem[threadIdx.x + s]); __syncthreads(); } @@ -519,7 +519,7 @@ __global__ linkage void softMaxDerivForVectorCuda(const void *vx, const Nd4jLong const Nd4jLong elemIdx = i * blockDim.x + threadIdx.x; if(elemIdx < len) { const Nd4jLong offset = shape::getIndexOffset(elemIdx, xzShapeInfo); - z[offset] = nd4j::math::nd4j_exp(x[offset] - max); + z[offset] = sd::math::nd4j_exp(x[offset] - max); shmem[threadIdx.x] = (threadIdx.x != 0) ? z[offset] : (z[offset] + temp); // take into account sum element evaluated on previous iteration and stored in temp } else @@ -554,7 +554,7 @@ linkage void softMaxDerivForVectorCudaLauncher(const cudaStream_t* stream, const } /////////////////////////////////////////////////////////////////// -void softmaxDerivative(nd4j::LaunchContext * context, const NDArray& input, NDArray& output, const int dimension) { +void softmaxDerivative(sd::LaunchContext * context, const NDArray& input, NDArray& output, const int dimension) { if(!input.isActualOnDeviceSide()) input.syncToDevice(); const int rank = input.rankOf(); @@ -590,7 +590,7 @@ void softmaxDerivative(nd4j::LaunchContext * context, const NDArray& input, NDAr const_cast(input).applyLambda(routine, output); } - void thresholdRelu(nd4j::LaunchContext * context, NDArray const& input, double threshold, NDArray& output) { + void thresholdRelu(sd::LaunchContext * context, NDArray const& input, double threshold, NDArray& output) { BUILD_SINGLE_SELECTOR(input.dataType(), thresholdRelu_, (input, threshold, output), FLOAT_TYPES); } @@ -601,7 +601,7 @@ void softmaxDerivative(nd4j::LaunchContext * context, const NDArray& input, NDAr input->applyPairwiseLambda(*dLdO, derivative, *output); } - void thresholdReluDerivative(nd4j::LaunchContext * context, NDArray* input, double threshold, NDArray* dLdO, NDArray* output) { + void thresholdReluDerivative(sd::LaunchContext * context, NDArray* input, double threshold, NDArray* dLdO, NDArray* output) { BUILD_SINGLE_SELECTOR(input->dataType(), thresholdReluDerivative_, (input, threshold, dLdO, output), FLOAT_TYPES); } diff --git a/libnd4j/include/ops/declarable/helpers/cuda/addBias.cu b/libnd4j/include/ops/declarable/helpers/cuda/addBias.cu index 1c0bc1925..0878a1c77 100644 --- a/libnd4j/include/ops/declarable/helpers/cuda/addBias.cu +++ b/libnd4j/include/ops/declarable/helpers/cuda/addBias.cu @@ -20,9 +20,9 @@ #include -#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { @@ -45,7 +45,7 @@ __global__ static void addBiasCuda( const void* vx, const Nd4jLong* xShapeInfo, X* z = reinterpret_cast(vz); __shared__ int rank, channelPosition, posOfNonUnityDim; - __shared__ Nd4jLong *sharedMem, len; + __shared__ Nd4jLong len, *sharedMem; __shared__ bool xzSameOffsets, xzAreSame; if (threadIdx.x == 0) { @@ -91,19 +91,55 @@ static void addBiasCudaLauncher(const int blocksPerGrid, const int threadsPerBlo addBiasCuda<<>>(vx, xShapeInfo, vy, yShapeInfo, vz, zShapeInfo, isNCHW); } +template +__global__ static void addBias2DCuda( const void* vx, + const void* vy, + void* vz, + uint32_t blocks, uint32_t length) { + + auto y = reinterpret_cast(vy); + + for (uint32_t b = blockIdx.x; b < blocks; b += gridDim.x) { + auto x = reinterpret_cast(vx) + length * b; + auto z = reinterpret_cast(vz) + length * b; + + for (uint32_t e = threadIdx.x; e < length; e += blockDim.x) { + z[e] = x[e] + y[e]; + } + } +} + +template +static void addBias2DCudaLauncher(const cudaStream_t *stream, const void* vx, + const void* vy, + void* vz, + uint32_t blocks, uint32_t length) { + + addBias2DCuda<<<256, 1024, 128, *stream>>>(vx, vy, vz, blocks, length); +} + ////////////////////////////////////////////////////////////////////////// -void addBias(nd4j::graph::Context& block, const NDArray& input, const NDArray& bias, NDArray& output, const bool isNCHW) { +void addBias(sd::graph::Context& block, const NDArray& input, const NDArray& bias, NDArray& output, const bool isNCHW) { PointersManager manager(block.launchContext(), "addBias"); - - const int threadsPerBlock = MAX_NUM_THREADS/2; - const int blocksPerGrid = (input.lengthOf() + threadsPerBlock - 1) / threadsPerBlock; - const int sharedMem = input.rankOf() * sizeof(Nd4jLong) * threadsPerBlock + 128; - NDArray::prepareSpecialUse({&output}, {&input, &bias}); - BUILD_DOUBLE_SELECTOR(input.dataType(), bias.dataType(), addBiasCudaLauncher, (blocksPerGrid, threadsPerBlock, sharedMem, block.launchContext()->getCudaStream(), input.getSpecialBuffer(), input.getSpecialShapeInfo(), bias.getSpecialBuffer(), bias.getSpecialShapeInfo(), output.specialBuffer(), output.specialShapeInfo(), isNCHW), FLOAT_TYPES, FLOAT_TYPES); - NDArray::registerSpecialUse({&output}, {&input, &bias}); + if (input.rankOf() == 2 && bias.rankOf() == 1 && input.ordering() == 'c' && output.ordering() == 'c' && input.ews() == 1 && bias.ews() == 1 && input.sizeAt(1) == bias.sizeAt(0)) { + BUILD_DOUBLE_SELECTOR(input.dataType(), bias.dataType(), addBias2DCudaLauncher, + (block.launchContext()->getCudaStream(), input.getSpecialBuffer(), bias.getSpecialBuffer(), output.specialBuffer(), input.sizeAt(0), bias.sizeAt(0)), + FLOAT_TYPES, FLOAT_TYPES); + } else { + // default case + const int threadsPerBlock = MAX_NUM_THREADS / 4; + const int blocksPerGrid = (input.lengthOf() + threadsPerBlock - 1) / threadsPerBlock; + const int sharedMem = input.rankOf() * sizeof(Nd4jLong) * threadsPerBlock + 128; + + + BUILD_DOUBLE_SELECTOR(input.dataType(), bias.dataType(), addBiasCudaLauncher, + (blocksPerGrid, threadsPerBlock, sharedMem, block.launchContext()->getCudaStream(), input.getSpecialBuffer(), input.getSpecialShapeInfo(), bias.getSpecialBuffer(), bias.getSpecialShapeInfo(), output.specialBuffer(), output.specialShapeInfo(), isNCHW), + FLOAT_TYPES, FLOAT_TYPES); + } + NDArray::registerSpecialUse({&output}, {&input, &bias}); manager.synchronize(); } diff --git a/libnd4j/include/ops/declarable/helpers/cuda/adjust_hue.cu b/libnd4j/include/ops/declarable/helpers/cuda/adjust_hue.cu index 5712887da..0b3681663 100644 --- a/libnd4j/include/ops/declarable/helpers/cuda/adjust_hue.cu +++ b/libnd4j/include/ops/declarable/helpers/cuda/adjust_hue.cu @@ -21,9 +21,9 @@ #include #include -#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { @@ -79,10 +79,10 @@ static _CUDA_H void adjustHueCudaLauncher(const int blocksPerGrid, const int thr } //////////////////////////////////////////////////////////////////////// -void adjustHue(nd4j::LaunchContext* context, const NDArray *input, const NDArray* deltaScalarArr, NDArray *output, const int dimC) { +void adjustHue(sd::LaunchContext* context, const NDArray *input, const NDArray* deltaScalarArr, NDArray *output, const int dimC) { - auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), {dimC}); - auto packZ = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), {dimC}); + auto packX = sd::ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), {dimC}); + auto packZ = sd::ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), {dimC}); const Nd4jLong numOfTads = packX.numberOfTads(); @@ -166,15 +166,15 @@ static void _CUDA_G adjustHueSingleNCHWKernel(void *xBuffer, Nd4jLong *xTadShape } template -static void _adjust_hue_single(nd4j::LaunchContext * context, NDArray *array, NDArray *output, float delta, bool isNHWC) { +static void _adjust_hue_single(sd::LaunchContext * context, NDArray *array, NDArray *output, float delta, bool isNHWC) { // numChannels is always 3 auto tuples = array->lengthOf() / 3; if (isNHWC) { adjustHueSingleNHWCKernel<<<256, 256, 1024, *context->getCudaStream()>>>(array->specialBuffer(), array->specialShapeInfo(), output->specialBuffer(), output->specialShapeInfo(), tuples, delta); } else { // TODO: check this one - auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(array->getShapeInfo(), {1, 2}); - auto packZ = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), {1, 2}); + auto packX = sd::ConstantTadHelper::getInstance()->tadForDimensions(array->getShapeInfo(), {1, 2}); + auto packZ = sd::ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), {1, 2}); auto tadLength = shape::length(packX.primaryShapeInfo()); @@ -184,7 +184,7 @@ static void _adjust_hue_single(nd4j::LaunchContext * context, NDArray *array, ND template -static void _adjust_hue_batch(nd4j::LaunchContext * context, NDArray *array, NDArray *output, float delta, bool isNHWC) { +static void _adjust_hue_batch(sd::LaunchContext * context, NDArray *array, NDArray *output, float delta, bool isNHWC) { auto xType = array->dataType(); // numChannels is always 3 @@ -195,8 +195,8 @@ static void _adjust_hue_batch(nd4j::LaunchContext * context, NDArray *array, NDA BUILD_SINGLE_SELECTOR(xType, _adjust_hue_single, (context, array, output, delta, isNHWC);, FLOAT_TYPES); } else { // TODO: check this one - auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(array->getShapeInfo(), {0, 2, 3}); - auto packZ = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), {0, 2, 3}); + auto packX = sd::ConstantTadHelper::getInstance()->tadForDimensions(array->getShapeInfo(), {0, 2, 3}); + auto packZ = sd::ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), {0, 2, 3}); auto tadLength = shape::length(packX.primaryShapeInfo()); @@ -204,7 +204,7 @@ static void _adjust_hue_batch(nd4j::LaunchContext * context, NDArray *array, NDA } } -void _adjust_hue(nd4j::LaunchContext * context, NDArray *array, NDArray *output, NDArray* delta, bool isNHWC) { +void _adjust_hue(sd::LaunchContext * context, NDArray *array, NDArray *output, NDArray* delta, bool isNHWC) { auto xType = array->dataType(); float d = delta->e(0); diff --git a/libnd4j/include/ops/declarable/helpers/cuda/adjust_saturation.cu b/libnd4j/include/ops/declarable/helpers/cuda/adjust_saturation.cu index b801765b2..f2da480cb 100644 --- a/libnd4j/include/ops/declarable/helpers/cuda/adjust_saturation.cu +++ b/libnd4j/include/ops/declarable/helpers/cuda/adjust_saturation.cu @@ -22,10 +22,10 @@ #include #include #include -#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { @@ -81,10 +81,10 @@ static _CUDA_H void adjustSaturationCudaLauncher(const int blocksPerGrid, const } //////////////////////////////////////////////////////////////////////// -void adjustSaturation(nd4j::LaunchContext* context, const NDArray *input, const NDArray* factorScalarArr, NDArray *output, const int dimC) { +void adjustSaturation(sd::LaunchContext* context, const NDArray *input, const NDArray* factorScalarArr, NDArray *output, const int dimC) { - auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), {dimC}); - auto packZ = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), {dimC}); + auto packX = sd::ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), {dimC}); + auto packZ = sd::ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), {dimC}); const Nd4jLong numOfTads = packX.numberOfTads(); @@ -117,7 +117,7 @@ static void _CUDA_G adjustSaturationSingleNHWCKernel(void *xBuffer, Nd4jLong *xS T h, s, v; // Convert the RGB color to Hue/V-range. helpers::rgb_to_hsv(i[0], i[1], i[2], &h, &s, &v); - s = nd4j::math::nd4j_min((T) 1.0f, nd4j::math::nd4j_max((T) 0.0f, s * delta)); + s = sd::math::nd4j_min((T) 1.0f, sd::math::nd4j_max((T) 0.0f, s * delta)); // Convert the hue and v-range back into RGB. helpers::hsv_to_rgb(h, s, v, o, o + 1, o + 2); @@ -150,22 +150,22 @@ static void _CUDA_G adjustSaturationSingleNCHWKernel(void *xBuffer, Nd4jLong *xT T h, s, v; // Convert the RGB color to Hue/V-range. helpers::rgb_to_hsv(_ri[0], _gi[0], _bi[0], &h, &s, &v); - s = nd4j::math::nd4j_min((T) 1.0f, nd4j::math::nd4j_max((T) 0.0f, s * delta)); + s = sd::math::nd4j_min((T) 1.0f, sd::math::nd4j_max((T) 0.0f, s * delta)); // Convert the hue and v-range back into RGB. helpers::hsv_to_rgb(h, s, v, _ro, _go, _bo); } } template -static void _adjust_saturation_single(nd4j::LaunchContext * context, NDArray *array, NDArray *output, float delta, bool isNHWC) { +static void _adjust_saturation_single(sd::LaunchContext * context, NDArray *array, NDArray *output, float delta, bool isNHWC) { // numChannels is always 3 auto tuples = array->lengthOf() / 3; if (isNHWC) { adjustSaturationSingleNHWCKernel<<<256, 256, 1024, *context->getCudaStream()>>>(array->specialBuffer(), array->specialShapeInfo(), output->specialBuffer(), output->specialShapeInfo(), tuples, delta); } else { - auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(array->getShapeInfo(), {1, 2}); - auto packZ = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), {1, 2}); + auto packX = sd::ConstantTadHelper::getInstance()->tadForDimensions(array->getShapeInfo(), {1, 2}); + auto packZ = sd::ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), {1, 2}); auto tadLength = shape::length(packX.primaryShapeInfo()); @@ -174,7 +174,7 @@ static void _adjust_saturation_single(nd4j::LaunchContext * context, NDArray *ar } template -static void _adjust_saturation_batch(nd4j::LaunchContext * context, NDArray *array, NDArray *output, float delta, bool isNHWC) { +static void _adjust_saturation_batch(sd::LaunchContext * context, NDArray *array, NDArray *output, float delta, bool isNHWC) { auto xType = array->dataType(); // numChannels is always 3 @@ -185,8 +185,8 @@ static void _adjust_saturation_batch(nd4j::LaunchContext * context, NDArray *arr BUILD_SINGLE_SELECTOR(xType, _adjust_saturation_single, (context, array, output, delta, isNHWC);, FLOAT_TYPES); } else { // TODO: check this one - auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(array->getShapeInfo(), {0, 2, 3}); - auto packZ = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), {0, 2, 3}); + auto packX = sd::ConstantTadHelper::getInstance()->tadForDimensions(array->getShapeInfo(), {0, 2, 3}); + auto packZ = sd::ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), {0, 2, 3}); auto tadLength = shape::length(packX.primaryShapeInfo()); @@ -194,7 +194,7 @@ static void _adjust_saturation_batch(nd4j::LaunchContext * context, NDArray *arr } } -void adjust_saturation(nd4j::LaunchContext * context, NDArray *array, NDArray *output, NDArray* delta, bool isNHWC) { +void adjust_saturation(sd::LaunchContext * context, NDArray *array, NDArray *output, NDArray* delta, bool isNHWC) { auto xType = array->dataType(); float d = delta->e(0); diff --git a/libnd4j/include/ops/declarable/helpers/cuda/axis.cu b/libnd4j/include/ops/declarable/helpers/cuda/axis.cu index 1236ae495..1dd00f688 100644 --- a/libnd4j/include/ops/declarable/helpers/cuda/axis.cu +++ b/libnd4j/include/ops/declarable/helpers/cuda/axis.cu @@ -21,7 +21,7 @@ #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { diff --git a/libnd4j/include/ops/declarable/helpers/cuda/batched_gemm.cu b/libnd4j/include/ops/declarable/helpers/cuda/batched_gemm.cu index 99fbd33a8..3e3110b0e 100644 --- a/libnd4j/include/ops/declarable/helpers/cuda/batched_gemm.cu +++ b/libnd4j/include/ops/declarable/helpers/cuda/batched_gemm.cu @@ -21,14 +21,14 @@ #include #include -#include -#include +#include +#include #include #include -#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { @@ -92,7 +92,7 @@ void bgemm(const std::vector& vA, const std::vector& vB, std pCbuffs[i] = pC[i]->getSpecialBuffer(); } - nd4j::LaunchContext* context = vA[0]->getContext(); + sd::LaunchContext* context = vA[0]->getContext(); PointersManager manager(context, "helpers::bgemm cuda"); const void** aBuffers = reinterpret_cast(manager.replicatePointer(pAbuffs.data(), bS * sizeof(void*))); diff --git a/libnd4j/include/ops/declarable/helpers/cuda/batchnorm.cu b/libnd4j/include/ops/declarable/helpers/cuda/batchnorm.cu index eedbe1fdf..2daac26c3 100644 --- a/libnd4j/include/ops/declarable/helpers/cuda/batchnorm.cu +++ b/libnd4j/include/ops/declarable/helpers/cuda/batchnorm.cu @@ -21,11 +21,11 @@ #include #include -#include -#include -#include +#include +#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { @@ -67,7 +67,7 @@ namespace helpers { // const auto meanOffset = shape::getIndexOffset(i, meanShapeInfo); // const auto varianceOffset = shape::getIndexOffset(i, varianceShapeInfo); -// T sigmaInvGam = 1. / nd4j::math::nd4j_sqrt(variance[varianceOffset] + epsilon); +// T sigmaInvGam = 1. / sd::math::nd4j_sqrt(variance[varianceOffset] + epsilon); // if(gamma != nullptr) // sigmaInvGam *= gamma[shape::getIndexOffset(i, gammaShapeInfo)]; @@ -124,7 +124,7 @@ __global__ static void batchnormCuda2(const void* vx, const Nd4jLong* xShapeInfo } __syncthreads(); - Nd4jLong coords[MAX_RANK]; + int coords[MAX_RANK]; const auto tid = blockIdx.x * blockDim.x + threadIdx.x; @@ -149,7 +149,7 @@ __global__ static void batchnormCuda2(const void* vx, const Nd4jLong* xShapeInfo const auto meanOffset = shape::getOffset(meanShapeInfo, coords); const auto varianceOffset = shape::getOffset(varianceShapeInfo, coords); - T sigmaInvGam = 1. / nd4j::math::nd4j_sqrt(variance[varianceOffset] + epsilon); + T sigmaInvGam = 1. / sd::math::nd4j_sqrt(variance[varianceOffset] + epsilon); if(gamma != nullptr) { const auto gammaOffset = shape::getOffset(gammaShapeInfo, coords); @@ -201,8 +201,8 @@ void batchnorm(const NDArray* input, const NDArray* mean, const NDArray* varianc // std::vector dimsToExclude = ShapeUtils::evalDimsToExclude(input->rankOf(), axes); - // auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), dimsToExclude); - // auto packZ = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(output->shapeInfo(), dimsToExclude); + // auto packX = sd::ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), dimsToExclude); + // auto packZ = sd::ConstantTadHelper::getInstance()->tadForDimensions(output->shapeInfo(), dimsToExclude); // const int threadsPerBlock = MAX_NUM_THREADS / 2; // const int blocksPerGrid = (mean->lengthOf() + threadsPerBlock - 1) / threadsPerBlock; diff --git a/libnd4j/include/ops/declarable/helpers/cuda/betaInc.cu b/libnd4j/include/ops/declarable/helpers/cuda/betaInc.cu index 267ae21c2..f1407f9e8 100644 --- a/libnd4j/include/ops/declarable/helpers/cuda/betaInc.cu +++ b/libnd4j/include/ops/declarable/helpers/cuda/betaInc.cu @@ -19,11 +19,11 @@ // #include -#include +#include #include -#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { @@ -170,7 +170,7 @@ static void betaIncForArrayCudaLauncher(const int blocksPerGrid, const int threa /////////////////////////////////////////////////////////////////// // overload betaInc for arrays, shapes of a, b and x must be the same !!! -void betaInc(nd4j::LaunchContext* context, const NDArray& a, const NDArray& b, const NDArray& x, NDArray& output) { +void betaInc(sd::LaunchContext* context, const NDArray& a, const NDArray& b, const NDArray& x, NDArray& output) { const int threadsPerBlock = maxIter; const int blocksPerGrid = output.lengthOf(); diff --git a/libnd4j/include/ops/declarable/helpers/cuda/col2im.cu b/libnd4j/include/ops/declarable/helpers/cuda/col2im.cu index f924fdb75..878ce3a6a 100644 --- a/libnd4j/include/ops/declarable/helpers/cuda/col2im.cu +++ b/libnd4j/include/ops/declarable/helpers/cuda/col2im.cu @@ -20,9 +20,9 @@ // #include -#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { @@ -69,8 +69,8 @@ static __global__ void col2imCuda(const void* columns, const Nd4jLong* colShapeI const uint colHstart = (imH < kH) ? 0 : (imH - kH) / sH + 1; const uint colWstart = (imW < kW) ? 0 : (imW - kW) / sW + 1; - const uint colHend = nd4j::math::nd4j_min(imH / sH + 1, oH); - const uint colWend = nd4j::math::nd4j_min(imW / sW + 1, oW); + const uint colHend = sd::math::nd4j_min(imH / sH + 1, oH); + const uint colWend = sd::math::nd4j_min(imW / sW + 1, oW); T val = 0; @@ -140,10 +140,10 @@ __global__ static void col2imCuda2(const void *columns, void *image, const Nd4jL // compute the start and end of the output // These are the indexes for dimensions ??? in the 6d col matrix int w_col_start = (w_im < kWeff) ? 0 : (w_im - kWeff) / sW + 1; - int w_col_end = nd4j::math::nd4j_min(w_im / sW + 1, oW); + int w_col_end = sd::math::nd4j_min(w_im / sW + 1, oW); int h_col_start = (h_im < kHeff) ? 0 : (h_im - kHeff) / sH + 1; - int h_col_end = nd4j::math::nd4j_min(h_im / sH + 1, oH); + int h_col_end = sd::math::nd4j_min(h_im / sH + 1, oH); //Iterate over col entries in the 6d array... these are added up for (int colH = h_col_start; colH < h_col_end; colH += 1) { @@ -184,7 +184,7 @@ static void col2imCudaLauncher(const int blocksPerGrid, const int threadsPerBloc } ////////////////////////////////////////////////////////////////////////// -void col2im(nd4j::LaunchContext& context, const NDArray& col, NDArray& im, const int sH, const int sW, const int pH, const int pW, const int iH, const int iW, const int dH, const int dW) { +void col2im(sd::LaunchContext& context, const NDArray& col, NDArray& im, const int sH, const int sW, const int pH, const int pW, const int iH, const int iW, const int dH, const int dW) { PointersManager manager(&context, "col2im"); diff --git a/libnd4j/include/ops/declarable/helpers/cuda/compare_elem.cu b/libnd4j/include/ops/declarable/helpers/cuda/compare_elem.cu index d2792b630..8d0bede62 100644 --- a/libnd4j/include/ops/declarable/helpers/cuda/compare_elem.cu +++ b/libnd4j/include/ops/declarable/helpers/cuda/compare_elem.cu @@ -16,7 +16,7 @@ #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { @@ -107,21 +107,21 @@ namespace nd4j { } template - static void _compare_elem(nd4j::LaunchContext * context, NDArray *input, bool isStrictlyIncreasing, bool& output) { + static void _compare_elem(sd::LaunchContext * context, NDArray *input, bool isStrictlyIncreasing, bool& output) { auto z = NDArrayFactory::create(false, context); const int numThreads = 256; - const int numBlocks = nd4j::math::nd4j_min(128, nd4j::math::nd4j_max(1, input->lengthOf() / numThreads)); + const int numBlocks = sd::math::nd4j_min(128, sd::math::nd4j_max(1, input->lengthOf() / numThreads)); comparator<<getCudaStream()>>>(input->specialBuffer(), input->specialShapeInfo(), input->lengthOf(), isStrictlyIncreasing, context->getReductionPointer(), reinterpret_cast(z.specialBuffer())); z.tickWriteDevice(); - nd4j::DebugHelper::checkErrorCode(context->getCudaStream(), "is_strictly_increasing"); + sd::DebugHelper::checkErrorCode(context->getCudaStream(), "is_strictly_increasing"); output = z.e(0); } - void compare_elem(nd4j::LaunchContext * context, NDArray *input, bool isStrictlyIncreasing, bool& output) { + void compare_elem(sd::LaunchContext * context, NDArray *input, bool isStrictlyIncreasing, bool& output) { auto xType = input->dataType(); input->syncToDevice(); @@ -129,7 +129,7 @@ namespace nd4j { } - BUILD_SINGLE_TEMPLATE(template void _compare_elem, (nd4j::LaunchContext * context, NDArray *A, bool isStrictlyIncreasing, bool& output);, LIBND4J_TYPES); + BUILD_SINGLE_TEMPLATE(template void _compare_elem, (sd::LaunchContext * context, NDArray *A, bool isStrictlyIncreasing, bool& output);, LIBND4J_TYPES); } } } diff --git a/libnd4j/include/ops/declarable/helpers/cuda/concat.cu b/libnd4j/include/ops/declarable/helpers/cuda/concat.cu index b455ff659..10e1d132c 100644 --- a/libnd4j/include/ops/declarable/helpers/cuda/concat.cu +++ b/libnd4j/include/ops/declarable/helpers/cuda/concat.cu @@ -23,13 +23,13 @@ #include #include #include -#include +#include #include #include -#include -#include +#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { @@ -51,7 +51,7 @@ __global__ static void concatCuda(void* pVx, void* pxShapeInfo, void* vz, Nd4jL const auto tid = blockIdx.x * blockDim.x + threadIdx.x; - Nd4jLong coords[MAX_RANK]; + int coords[MAX_RANK]; for (uint64_t i = tid; i < zLen; i += totalThreads) { shape::index2coords(i, zShapeInfo, coords); @@ -83,14 +83,12 @@ __host__ static void concatCudaLauncher(const int blocksPerGrid, const int threa BUILD_SINGLE_TEMPLATE(template void concatCudaLauncher, (const int blocksPerGrid, const int threadsPerBlock, const int sharedMem, const cudaStream_t *stream, void* pVx, void* pxShapeInfo, void* vz, Nd4jLong* zShapeInfo, const int axis), LIBND4J_TYPES); ////////////////////////////////////////////////////////////////////////// -void concat(nd4j::LaunchContext * context, const std::vector& inArrs, NDArray& output, const int axis) { +void concat(sd::LaunchContext * context, const std::vector& inArrs, NDArray& output, const int axis) { const int numOfInArrs = inArrs.size(); const auto sizeofT = output.sizeOfT(); - for(int i = 0; i < numOfInArrs; ++i) - inArrs[i]->syncToDevice(); - output.syncToDevice(); + NDArray::prepareSpecialUse({&output}, inArrs); bool luckCase1 = ((axis == 0 && output.ordering() == 'c') || (axis == output.rankOf() - 1 && output.ordering() == 'f')) && output.ews() == 1; @@ -122,43 +120,48 @@ void concat(nd4j::LaunchContext * context, const std::vector& inArrs, return; } - const bool isZcontin = output.strideAt(axis) == 1; - bool areInputsContin = true; - bool allSameOrder = true; + // const bool isZcontin = output.strideAt(axis) == 1; + // bool areInputsContin = true; + // bool allSameOrder = true; + // std::vector strideOfContigStride(numOfInArrs); - if(isZcontin) { - for (uint i = 0; i < inArrs.size(); ++i) { - areInputsContin &= inArrs[i]->strideAt(axis) == 1; - allSameOrder &= output.ordering() == inArrs[i]->ordering(); - if(!areInputsContin || !allSameOrder) - break; - } - } + // if(isZcontin) { - const bool luckCase2 = isZcontin && areInputsContin && allSameOrder; + // for (uint i = 0; i < inArrs.size(); ++i) { - if(luckCase2) { // for example {2,1,3} + {2,5,3} + {2,10,3} = {2,16,3}, here axis 1 shoud have stride = 1 for all inputs arrays and output array + // areInputsContin &= inArrs[i]->strideAt(axis) == 1; + // allSameOrder &= output.ordering() == inArrs[i]->ordering(); + // if(!areInputsContin || !allSameOrder) + // break; - const uint zDim = output.sizeAt(axis); + // strideOfContigStride[i] = shape::strideOverContigAxis(axis, inArrs[i]->getShapeInfo()); + // } + // } - for (uint i = 0; i < output.lengthOf() / zDim; ++i) { + // const bool luckCase2 = isZcontin && areInputsContin && allSameOrder; - const auto iShift = i * sizeofT; - void* z = static_cast(output.getSpecialBuffer()) + zDim * iShift; + // if(luckCase2) { // for example {2,1,3} + {2,5,3} + {2,10,3} = {2,16,3}, here axis 1 shoud have stride = 1 for all inputs arrays and output array - for (uint j = 0; j < numOfInArrs; ++j) { - const auto xDim = inArrs[j]->sizeAt(axis); - void* x = static_cast(inArrs[j]->getSpecialBuffer()) + xDim * iShift; - const auto memSizeToCopy = xDim * sizeofT; - cudaMemcpyAsync(z, x, memSizeToCopy, cudaMemcpyDeviceToDevice, *context->getCudaStream()); - z = static_cast(z) + memSizeToCopy; - } - } + // const auto zStep = shape::strideOverContigAxis(axis, output.getShapeInfo()); - if(cudaStreamSynchronize(*context->getCudaStream()) != 0) - throw std::runtime_error("concat cuda: luckCase2 failed!"); - } - else { // general (slower) case + // for (uint i = 0; i < output.lengthOf() / output.sizeAt(axis); ++i) { + + // const auto iShift = i * sizeofT; + // void* z = static_cast(output.getSpecialBuffer()) + zStep * iShift; + + // for (uint j = 0; j < numOfInArrs; ++j) { + // const auto xDim = inArrs[j]->sizeAt(axis); + // void* x = static_cast(inArrs[j]->getSpecialBuffer()) + strideOfContigStride[j] * iShift; + // const auto memSizeToCopy = xDim * sizeofT; + // cudaMemcpyAsync(z, x, memSizeToCopy, cudaMemcpyDeviceToDevice, *context->getCudaStream()); + // z = static_cast(z) + memSizeToCopy; + // } + // } + + // if(cudaStreamSynchronize(*context->getCudaStream()) != 0) + // throw std::runtime_error("concat cuda: luckCase2 failed!"); + // } + // else { // general (slower) case const int threadsPerBlock = 256; const int blocksPerGrid = 512; @@ -181,11 +184,9 @@ void concat(nd4j::LaunchContext * context, const std::vector& inArrs, BUILD_SINGLE_SELECTOR(inArrs[0]->dataType(), concatCudaLauncher, (blocksPerGrid, threadsPerBlock, sharedMem, context->getCudaStream(), dInBuffers, dInShapeInfo, output.specialBuffer(), output.specialShapeInfo(), axis), LIBND4J_TYPES); manager.synchronize(); - } + // } - for(int i = 0; i < numOfInArrs; ++i) - inArrs[i]->tickReadDevice(); - output.tickWriteDevice(); + NDArray::registerSpecialUse({&output}, inArrs); } } diff --git a/libnd4j/include/ops/declarable/helpers/cuda/confusion.cu b/libnd4j/include/ops/declarable/helpers/cuda/confusion.cu index 3738d7770..edb7538d4 100644 --- a/libnd4j/include/ops/declarable/helpers/cuda/confusion.cu +++ b/libnd4j/include/ops/declarable/helpers/cuda/confusion.cu @@ -19,12 +19,12 @@ // #include -#include -#include -#include +#include +#include +#include #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { @@ -65,20 +65,20 @@ namespace helpers { } template - void _confusionFunctor(nd4j::LaunchContext * context, NDArray* labels, NDArray* predictions, NDArray* weights, NDArray* output) { + void _confusionFunctor(sd::LaunchContext * context, NDArray* labels, NDArray* predictions, NDArray* weights, NDArray* output) { auto stream = context->getCudaStream(); - auto pack = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(output->shapeInfo(), 1); + auto pack = sd::ConstantTadHelper::getInstance()->tadForDimensions(output->shapeInfo(), 1); PointersManager manager(context, "helpers::confusion"); - Nd4jLong* labelsLongBuffer = labels->dataType() == nd4j::DataType::INT64?(Nd4jLong*)labels->specialBuffer():nullptr; - Nd4jLong* predictionLongBuffer = predictions->dataType() == nd4j::DataType::INT64?(Nd4jLong*)predictions->specialBuffer():nullptr; + Nd4jLong* labelsLongBuffer = labels->dataType() == sd::DataType::INT64?(Nd4jLong*)labels->specialBuffer():nullptr; + Nd4jLong* predictionLongBuffer = predictions->dataType() == sd::DataType::INT64?(Nd4jLong*)predictions->specialBuffer():nullptr; if (labelsLongBuffer == nullptr) { auto err = cudaMalloc(&labelsLongBuffer, labels->lengthOf() * sizeof(Nd4jLong)); if (err != 0) - throw nd4j::cuda_exception::build("Cannot allocate memory for labels long buffer", err); + throw sd::cuda_exception::build("Cannot allocate memory for labels long buffer", err); // copy with type conversion copyBuffers<<<256, 512, 1024, *stream>>>(labelsLongBuffer, labels->getSpecialBuffer(), labels->lengthOf()); } @@ -86,7 +86,7 @@ namespace helpers { if (predictionLongBuffer == nullptr) { auto err = cudaMalloc(&predictionLongBuffer, predictions->lengthOf() * sizeof(Nd4jLong)); if (err != 0) - throw nd4j::cuda_exception::build("Cannot allocate memory for predictions long buffer", err); + throw sd::cuda_exception::build("Cannot allocate memory for predictions long buffer", err); // copy with type conversion copyBuffers<<<256, 512, 1024, *stream>>>(predictionLongBuffer, predictions->getSpecialBuffer(), predictions->lengthOf()); } @@ -100,17 +100,17 @@ namespace helpers { if (predictionLongBuffer != predictions->getSpecialBuffer()) { cudaError_t err = cudaFree(predictionLongBuffer); if (err != 0) - throw nd4j::cuda_exception::build("Cannot deallocate memory for predictions long buffer", err); + throw sd::cuda_exception::build("Cannot deallocate memory for predictions long buffer", err); } if (labelsLongBuffer != labels->getSpecialBuffer()) { cudaError_t err = cudaFree(labelsLongBuffer); if (err != 0) - throw nd4j::cuda_exception::build("Cannot deallocate memory for labels long buffer", err); + throw sd::cuda_exception::build("Cannot deallocate memory for labels long buffer", err); } } - void confusionFunctor(nd4j::LaunchContext * context, NDArray* labels, NDArray* predictions, NDArray* weights, NDArray* output) { + void confusionFunctor(sd::LaunchContext * context, NDArray* labels, NDArray* predictions, NDArray* weights, NDArray* output) { auto xType = predictions->dataType(); auto zType = output->dataType(); // weights can be null NDArray::prepareSpecialUse({output}, {labels, predictions, weights}); diff --git a/libnd4j/include/ops/declarable/helpers/cuda/convolutions.cu b/libnd4j/include/ops/declarable/helpers/cuda/convolutions.cu index 39732b024..76ba2e1df 100644 --- a/libnd4j/include/ops/declarable/helpers/cuda/convolutions.cu +++ b/libnd4j/include/ops/declarable/helpers/cuda/convolutions.cu @@ -24,12 +24,12 @@ #include #include #include -#include -#include -#include -#include +#include +#include +#include +#include -namespace nd4j { +namespace sd { namespace ops { ////////////////////////////////////////////////////////////////////////// @@ -99,7 +99,7 @@ static void vol2colCudaLauncher(const int blocksPerGrid, const int threadsPerBlo } ////////////////////////////////////////////////////////////////////////// -void ConvolutionUtils::vol2col(nd4j::graph::Context& block, const NDArray& vol, NDArray& col, const int sD, const int sH, const int sW, const int pD, const int pH, const int pW, const int dD, const int dH, const int dW) { +void ConvolutionUtils::vol2col(sd::graph::Context& block, const NDArray& vol, NDArray& col, const int sD, const int sH, const int sW, const int pD, const int pH, const int pW, const int dD, const int dH, const int dW) { PointersManager manager(block.launchContext(), "vol2col"); @@ -161,9 +161,9 @@ static __global__ void col2volCuda(const void* columns, const Nd4jLong* colShape const uint colHstart = (imH < kH) ? 0 : (imH - kH) / sH + 1; const uint colWstart = (imW < kW) ? 0 : (imW - kW) / sW + 1; - const uint colDend = nd4j::math::nd4j_min(imD / sD + 1, oD); - const uint colHend = nd4j::math::nd4j_min(imH / sH + 1, oH); - const uint colWend = nd4j::math::nd4j_min(imW / sW + 1, oW); + const uint colDend = sd::math::nd4j_min(imD / sD + 1, oD); + const uint colHend = sd::math::nd4j_min(imH / sH + 1, oH); + const uint colWend = sd::math::nd4j_min(imW / sW + 1, oW); T val = 0; @@ -200,7 +200,7 @@ static void col2volCudaLauncher(const int blocksPerGrid, const int threadsPerBlo } ////////////////////////////////////////////////////////////////////////// -void ConvolutionUtils::col2vol(nd4j::graph::Context& block, const NDArray& col, NDArray& vol, const int sD, const int sH, const int sW, const int pD, const int pH, const int pW, const int dD, const int dH, const int dW) { +void ConvolutionUtils::col2vol(sd::graph::Context& block, const NDArray& col, NDArray& vol, const int sD, const int sH, const int sW, const int pD, const int pH, const int pW, const int dD, const int dH, const int dW) { PointersManager manager(block.launchContext(), "col2vol"); @@ -217,7 +217,7 @@ void ConvolutionUtils::col2vol(nd4j::graph::Context& block, const NDArray& col, ////////////////////////////////////////////////////////////////////////// template -static void conv2d_(nd4j::graph::Context& block, const NDArray* input, const NDArray* weights, const NDArray* bias, NDArray* output, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int paddingMode, const int isNCHW) { +static void conv2d_(sd::graph::Context& block, const NDArray* input, const NDArray* weights, const NDArray* bias, NDArray* output, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int paddingMode, const int isNCHW) { // input [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW) // weights [kH, kW, iC, oC] always @@ -275,13 +275,13 @@ static void conv2d_(nd4j::graph::Context& block, const NDArray* input, const NDA } ////////////////////////////////////////////////////////////////////////// -void ConvolutionUtils::conv2d(nd4j::graph::Context& block, const NDArray* input, const NDArray* weights, const NDArray* bias, NDArray* output, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int paddingMode, const int isNCHW) { +void ConvolutionUtils::conv2d(sd::graph::Context& block, const NDArray* input, const NDArray* weights, const NDArray* bias, NDArray* output, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int paddingMode, const int isNCHW) { BUILD_SINGLE_SELECTOR_TWICE(input->dataType(), conv2d_, (block, input, weights, bias, output, kH, kW, sH, sW, pH, pW, dH, dW, paddingMode, isNCHW), FLOAT_TYPES); } ////////////////////////////////////////////////////////////////////////// template -static void depthwiseConv2d_(nd4j::graph::Context& block, const NDArray* input, const NDArray* weights, const NDArray* bias, NDArray* output, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int paddingMode, const int isNCHW) { +static void depthwiseConv2d_(sd::graph::Context& block, const NDArray* input, const NDArray* weights, const NDArray* bias, NDArray* output, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int paddingMode, const int isNCHW) { // input [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW) // weights [kH, kW, iC, mC] always @@ -336,13 +336,13 @@ static void depthwiseConv2d_(nd4j::graph::Context& block, const NDArray* input, } ////////////////////////////////////////////////////////////////////////// -void ConvolutionUtils::depthwiseConv2d(nd4j::graph::Context& block, const NDArray* input, const NDArray* weights, const NDArray* bias, NDArray* output, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int paddingMode, const int isNCHW) { +void ConvolutionUtils::depthwiseConv2d(sd::graph::Context& block, const NDArray* input, const NDArray* weights, const NDArray* bias, NDArray* output, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int paddingMode, const int isNCHW) { BUILD_SINGLE_SELECTOR_TWICE(input->dataType(), depthwiseConv2d_, (block, input, weights, bias, output, kH, kW, sH, sW, pH, pW, dH, dW, paddingMode, isNCHW), FLOAT_TYPES); } ////////////////////////////////////////////////////////////////////////// template -static void sconv2d_(nd4j::graph::Context& block, const NDArray* input, const NDArray* weightsDepth, const NDArray* weightsPoint, const NDArray* bias, NDArray* output, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int paddingMode, const int isNCHW) { +static void sconv2d_(sd::graph::Context& block, const NDArray* input, const NDArray* weightsDepth, const NDArray* weightsPoint, const NDArray* bias, NDArray* output, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int paddingMode, const int isNCHW) { // input [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW) // weightsDepth [kH, kW, iC, mC] always @@ -381,7 +381,7 @@ static void sconv2d_(nd4j::graph::Context& block, const NDArray* input, const ND } ////////////////////////////////////////////////////////////////////////// -void ConvolutionUtils::sconv2d(nd4j::graph::Context& block, const NDArray* input, const NDArray* weightsDepth, const NDArray* weightsPoint, const NDArray* bias, NDArray* output, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int paddingMode, const int isNCHW) { +void ConvolutionUtils::sconv2d(sd::graph::Context& block, const NDArray* input, const NDArray* weightsDepth, const NDArray* weightsPoint, const NDArray* bias, NDArray* output, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int paddingMode, const int isNCHW) { BUILD_SINGLE_SELECTOR_TWICE(input->dataType(), sconv2d_, (block, input, weightsDepth, weightsPoint, bias, output, kH, kW, sH, sW, pH, pW, dH, dW, paddingMode, isNCHW), FLOAT_TYPES); } @@ -438,24 +438,24 @@ static __global__ void avgPooling2dCuda(const void *vx, const Nd4jLong *xShapeIn int wend = wstart + kWEff; if(hstart < 0){ - int f = nd4j::math::nd4j_ceil((Z) -hstart / (Z)dH); + int f = sd::math::nd4j_ceil((Z) -hstart / (Z)dH); hstart += f * dH; } if(wstart < 0){ - int f = nd4j::math::nd4j_ceil((Z) -wstart / (Z) dW); + int f = sd::math::nd4j_ceil((Z) -wstart / (Z) dW); wstart += f * dW; } if(hend > iH){ - int f = nd4j::math::nd4j_ceil((Z) (hend-iH) / (Z) dH); + int f = sd::math::nd4j_ceil((Z) (hend-iH) / (Z) dH); hend -= f * dH; } if(wend > iW){ - int f = nd4j::math::nd4j_ceil((Z) (wend-iW) / (Z) dW); + int f = sd::math::nd4j_ceil((Z) (wend-iW) / (Z) dW); wend -= f * dW; } //Accounts for dilation - int pool_size = nd4j::math::nd4j_ceil((double) (hend-hstart) / (double) dH) * nd4j::math::nd4j_ceil((double) (wend-wstart) / (double) dW); + int pool_size = sd::math::nd4j_ceil((double) (hend-hstart) / (double) dH) * sd::math::nd4j_ceil((double) (wend-wstart) / (double) dW); Z sum = 0.0f; @@ -475,7 +475,7 @@ static __global__ void avgPooling2dCuda(const void *vx, const Nd4jLong *xShapeIn ////////////////////////////////////////////////////////////////////////// template -static void avgPooling2dCudaLauncher(nd4j::LaunchContext & block, void *vx, Nd4jLong *vxShapeInfo, void *vz, Nd4jLong *vzShapeInfo, const int kH, const int kW, const int sH, const int sW, const int pH, const int pW, const int dH, const int dW, const int extraParam0) { +static void avgPooling2dCudaLauncher(sd::LaunchContext & block, void *vx, Nd4jLong *vxShapeInfo, void *vz, Nd4jLong *vzShapeInfo, const int kH, const int kW, const int sH, const int sW, const int pH, const int pW, const int dH, const int dW, const int extraParam0) { avgPooling2dCuda<<<512, 512, 4192, *block.getCudaStream()>>>(vx, vxShapeInfo, vz, vzShapeInfo, kH, kW, sH, sW, pH, pW, dH, dW, extraParam0); } @@ -533,24 +533,24 @@ static __global__ void pnormPooling2dCuda(const void *vx, const Nd4jLong *xShape int wend = wstart + kWEff; if (hstart < 0) { - int f = nd4j::math::nd4j_ceil((Z) -hstart / (Z) dH); + int f = sd::math::nd4j_ceil((Z) -hstart / (Z) dH); hstart += f * dH; } if (wstart < 0) { - int f = nd4j::math::nd4j_ceil((Z) -wstart / (Z) dW); + int f = sd::math::nd4j_ceil((Z) -wstart / (Z) dW); wstart += f * dW; } if (hend > iH) { - int f = nd4j::math::nd4j_ceil((Z) (hend - iH) / (Z) dH); + int f = sd::math::nd4j_ceil((Z) (hend - iH) / (Z) dH); hend -= f * dH; } if (wend > iW) { - int f = nd4j::math::nd4j_ceil((Z) (wend - iW) / (Z) dW); + int f = sd::math::nd4j_ceil((Z) (wend - iW) / (Z) dW); wend -= f * dW; } //Accounts for dilation - int pool_size = nd4j::math::nd4j_ceil((double) (hend - hstart) / (double) dH) * - nd4j::math::nd4j_ceil((double) (wend - wstart) / (double) dW); + int pool_size = sd::math::nd4j_ceil((double) (hend - hstart) / (double) dH) * + sd::math::nd4j_ceil((double) (wend - wstart) / (double) dW); Z sum = 0.f; @@ -558,15 +558,15 @@ static __global__ void pnormPooling2dCuda(const void *vx, const Nd4jLong *xShape for (int h = hstart; h < hend; h += dH) for (int w = wstart; w < wend; w += dW) - sum += nd4j::math::nd4j_pow(static_cast(nd4j::math::nd4j_abs(inSlice[h * strideY + w * strideX])), extraParam0); + sum += sd::math::nd4j_pow(static_cast(sd::math::nd4j_abs(inSlice[h * strideY + w * strideX])), extraParam0); - z[n * strideOB + c * strideOC + pw * strideOX + ph * strideOY] = nd4j::math::nd4j_pow(sum, (Z) 1.0f / extraParam0); + z[n * strideOB + c * strideOC + pw * strideOX + ph * strideOY] = sd::math::nd4j_pow(sum, (Z) 1.0f / extraParam0); } } ////////////////////////////////////////////////////////////////////////// template -static void pnormPooling2dCudaLauncher(nd4j::LaunchContext & block, void *vx, Nd4jLong *vxShapeInfo, void *vz, Nd4jLong *vzShapeInfo, const int kH, const int kW, const int sH, const int sW, const int pH, const int pW, const int dH, const int dW, const int extraParam0) { +static void pnormPooling2dCudaLauncher(sd::LaunchContext & block, void *vx, Nd4jLong *vxShapeInfo, void *vz, Nd4jLong *vzShapeInfo, const int kH, const int kW, const int sH, const int sW, const int pH, const int pW, const int dH, const int dW, const int extraParam0) { pnormPooling2dCuda<<<512, 512, 4192, *block.getCudaStream()>>>(vx, vxShapeInfo, vz, vzShapeInfo, kH, kW, sH, sW, pH, pW, dH, dW, extraParam0); } @@ -624,25 +624,25 @@ static __global__ void maxPooling2dCuda(const void *vx, const Nd4jLong *xShapeIn int wend = wstart + kWEff; if(hstart < 0){ - int f = nd4j::math::nd4j_ceil((Z) -hstart / (Z)dH); + int f = sd::math::nd4j_ceil((Z) -hstart / (Z)dH); hstart += f * dH; } if(wstart < 0){ - int f = nd4j::math::nd4j_ceil((Z) -wstart / (Z) dW); + int f = sd::math::nd4j_ceil((Z) -wstart / (Z) dW); wstart += f * dW; } if(hend > iH){ - int f = nd4j::math::nd4j_ceil((Z) (hend-iH) / (Z) dH); + int f = sd::math::nd4j_ceil((Z) (hend-iH) / (Z) dH); hend -= f * dH; } if(wend > iW){ - int f = nd4j::math::nd4j_ceil((Z) (wend-iW) / (Z) dW); + int f = sd::math::nd4j_ceil((Z) (wend-iW) / (Z) dW); wend -= f * dW; } //Accounts for dilation - int pool_size = nd4j::math::nd4j_ceil((double) (hend-hstart) / (double) dH) * nd4j::math::nd4j_ceil((double) (wend-wstart) / (double) dW); + int pool_size = sd::math::nd4j_ceil((double) (hend-hstart) / (double) dH) * sd::math::nd4j_ceil((double) (wend-wstart) / (double) dW); - Z max = -nd4j::DataTypeUtils::max(); + Z max = -sd::DataTypeUtils::max(); const X *inSlice = x + (n * strideB + c * strideC); @@ -660,12 +660,12 @@ static __global__ void maxPooling2dCuda(const void *vx, const Nd4jLong *xShapeIn ////////////////////////////////////////////////////////////////////////// template -static void maxPooling2dCudaLauncher(nd4j::LaunchContext & block, void *vx, Nd4jLong *vxShapeInfo, void *vz, Nd4jLong *vzShapeInfo, const int kH, const int kW, const int sH, const int sW, const int pH, const int pW, const int dH, const int dW, const int extraParam0) { +static void maxPooling2dCudaLauncher(sd::LaunchContext & block, void *vx, Nd4jLong *vxShapeInfo, void *vz, Nd4jLong *vzShapeInfo, const int kH, const int kW, const int sH, const int sW, const int pH, const int pW, const int dH, const int dW, const int extraParam0) { maxPooling2dCuda<<<512, 512, 4192, *block.getCudaStream()>>>(vx, vxShapeInfo, vz, vzShapeInfo, kH, kW, sH, sW, pH, pW, dH, dW, extraParam0); } ////////////////////////////////////////////////////////////////////////// -void ConvolutionUtils::pooling2d(nd4j::graph::Context& block, const NDArray& input, NDArray& output, const int kH, const int kW, const int sH, const int sW, const int pH, const int pW, const int dH, const int dW, const PoolingType poolingMode, const int extraParam0) { +void ConvolutionUtils::pooling2d(sd::graph::Context& block, const NDArray& input, NDArray& output, const int kH, const int kW, const int sH, const int sW, const int pH, const int pW, const int dH, const int dW, const PoolingType poolingMode, const int extraParam0) { if(!input.isActualOnDeviceSide()) input.syncToDevice(); @@ -706,7 +706,7 @@ __global__ static void pooling3dCuda(const void* vx, const Nd4jLong* xShapeInfo, T* z = reinterpret_cast(vz); __shared__ int rank, kDeff, kHeff, kWeff, iD, iH, iW, kProd; - __shared__ Nd4jLong *sharedMem, zLen; + __shared__ Nd4jLong zLen, *sharedMem; if (threadIdx.x == 0) { extern __shared__ unsigned char shmem[]; @@ -789,7 +789,7 @@ __global__ static void pooling3dCuda(const void* vx, const Nd4jLong* xShapeInfo, uint a = (dend - dstart) / dD + ((dend - dstart) % dD == 0 ? 0 : 1); uint b = (hend - hstart) / dH + ((hend - hstart) % dH == 0 ? 0 : 1); uint c = (wend - wstart) / dW + ((wend - wstart) % dW == 0 ? 0 : 1); - sum /= static_cast(a * b * c); // /= nd4j::math::nd4j_ceil(static_cast(dend - dstart) / static_cast(dD)) * nd4j::math::nd4j_ceil(static_cast(hend - hstart) / static_cast(dH)) * nd4j::math::nd4j_ceil(static_cast(wend - wstart) / static_cast(dW)); //Accounts for dilation + sum /= static_cast(a * b * c); // /= sd::math::nd4j_ceil(static_cast(dend - dstart) / static_cast(dD)) * sd::math::nd4j_ceil(static_cast(hend - hstart) / static_cast(dH)) * sd::math::nd4j_ceil(static_cast(wend - wstart) / static_cast(dW)); //Accounts for dilation } else if (extraParam0 == 1) //Include padding sum /= kProd; @@ -804,9 +804,9 @@ __global__ static void pooling3dCuda(const void* vx, const Nd4jLong* xShapeInfo, for (coords[2] = dstart; coords[2] < dend; coords[2] += dD) for (coords[3] = hstart; coords[3] < hend; coords[3] += dH) for (coords[4] = wstart; coords[4] < wend; coords[4] += dW) - sum += nd4j::math::nd4j_pow(nd4j::math::nd4j_abs(x[shape::getOffset(xShapeInfo, coords)]), extraParam0); + sum += sd::math::nd4j_pow(sd::math::nd4j_abs(x[shape::getOffset(xShapeInfo, coords)]), extraParam0); - sum = nd4j::math::nd4j_pow(sum, (T) 1.f / extraParam0); + sum = sd::math::nd4j_pow(sum, (T) 1.f / extraParam0); z[zOffset] = sum; } @@ -829,7 +829,7 @@ static void pooling3dCudaLauncher(const int blocksPerGrid, const int threadsPerB } ////////////////////////////////////////////////////////////////////////// -void ConvolutionUtils::pooling3d(nd4j::graph::Context& block, const NDArray& input, NDArray& output, const int kD, const int kH, const int kW, const int sD, const int sH, const int sW, const int pD, const int pH, const int pW, const int dD, const int dH, const int dW, const int poolingMode, const int extraParam0) { +void ConvolutionUtils::pooling3d(sd::graph::Context& block, const NDArray& input, NDArray& output, const int kD, const int kH, const int kW, const int sD, const int sH, const int sW, const int pD, const int pH, const int pW, const int dD, const int dH, const int dW, const int poolingMode, const int extraParam0) { PointersManager manager(block.launchContext(), "pooling3d"); @@ -858,7 +858,7 @@ __global__ static void pooling2dBPCuda(const void* vx, const Nd4jLong* xShapeInf Nd4jLong coord2, coord3; __shared__ int rank, kHeff, kWeff, iH, iW, kProd; - __shared__ Nd4jLong *sharedMem, yLen; + __shared__ Nd4jLong yLen, *sharedMem; if (threadIdx.x == 0) { extern __shared__ unsigned char shmem[]; @@ -923,7 +923,7 @@ __global__ static void pooling2dBPCuda(const void* vx, const Nd4jLong* xShapeInf coords[2] = coord2; coords[3] = coord3; auto zOffset = shape::getOffset(zShapeInfo, coords); - nd4j::math::atomics::nd4j_atomicAdd(&z[zOffset], y[yOffset]); + sd::math::atomics::nd4j_atomicAdd(&z[zOffset], y[yOffset]); //z[zOffset] += y[yOffset]; } break; @@ -934,13 +934,13 @@ __global__ static void pooling2dBPCuda(const void* vx, const Nd4jLong* xShapeInf T val = y[yOffset]; if (extraParam0 == 0) //Exclude padding - val /= nd4j::math::nd4j_ceil(static_cast(hend - hstart) / static_cast(dH)) * nd4j::math::nd4j_ceil(static_cast(wend - wstart) / static_cast(dW)); //Accounts for dilation + val /= sd::math::nd4j_ceil(static_cast(hend - hstart) / static_cast(dH)) * sd::math::nd4j_ceil(static_cast(wend - wstart) / static_cast(dW)); //Accounts for dilation else if (extraParam0 == 1) //Include padding val /= kProd; for (coords[2] = hstart; coords[2] < hend; coords[2] += dH) for (coords[3] = wstart; coords[3] < wend; coords[3] += dW) - nd4j::math::atomics::nd4j_atomicAdd(&z[shape::getOffset(zShapeInfo, coords)], val); + sd::math::atomics::nd4j_atomicAdd(&z[shape::getOffset(zShapeInfo, coords)], val); } break; @@ -952,15 +952,15 @@ __global__ static void pooling2dBPCuda(const void* vx, const Nd4jLong* xShapeInf for (coords[2] = hstart; coords[2] < hend; coords[2] += dH) for (coords[3] = wstart; coords[3] < wend; coords[3] += dW) - sum += nd4j::math::nd4j_pow(nd4j::math::nd4j_abs(x[shape::getOffset(xShapeInfo, coords)]), extraParam0); + sum += sd::math::nd4j_pow(sd::math::nd4j_abs(x[shape::getOffset(xShapeInfo, coords)]), extraParam0); - val *= nd4j::math::nd4j_pow(sum, ((T)1.f - extraParam0) / extraParam0); + val *= sd::math::nd4j_pow(sum, ((T)1.f - extraParam0) / extraParam0); for (coords[2] = hstart; coords[2] < hend; coords[2] += dH) { for (coords[3] = wstart; coords[3] < wend; coords[3] += dW) { const auto xOffset = shape::getOffset(xShapeInfo, coords); const auto zOffset = shape::getOffset(zShapeInfo, coords); - nd4j::math::atomics::nd4j_atomicAdd(&z[zOffset], val * nd4j::math::nd4j_pow(nd4j::math::nd4j_abs(x[xOffset]), extraParam0 - 1.f) * nd4j::math::nd4j_sgn(x[xOffset])); + sd::math::atomics::nd4j_atomicAdd(&z[zOffset], val * sd::math::nd4j_pow(sd::math::nd4j_abs(x[xOffset]), extraParam0 - 1.f) * sd::math::nd4j_sgn(x[xOffset])); } } } @@ -984,7 +984,7 @@ static void pooling2dBPCudaLauncher(const int blocksPerGrid, const int threadsPe } ////////////////////////////////////////////////////////////////////////// -void ConvolutionUtils::pooling2dBP(nd4j::graph::Context& block, const NDArray& input, const NDArray& gradO, NDArray& gradI, const int kH, const int kW, const int sH, const int sW, const int pH, const int pW, const int dH, const int dW, const int poolingMode, const int extraParam0) { +void ConvolutionUtils::pooling2dBP(sd::graph::Context& block, const NDArray& input, const NDArray& gradO, NDArray& gradI, const int kH, const int kW, const int sH, const int sW, const int pH, const int pW, const int dH, const int dW, const int poolingMode, const int extraParam0) { // initial zeroing of gradI gradI.nullify(); @@ -1017,7 +1017,7 @@ __global__ static void pooling3dBPCuda(const void* vx, const Nd4jLong* xShapeInf Nd4jLong coord2, coord3, coord4; __shared__ int rank, kDeff, kHeff, kWeff, iD, iH, iW, kProd; - __shared__ Nd4jLong *sharedMem, yLen; + __shared__ Nd4jLong yLen, *sharedMem; if (threadIdx.x == 0) { extern __shared__ unsigned char shmem[]; @@ -1092,7 +1092,7 @@ __global__ static void pooling3dBPCuda(const void* vx, const Nd4jLong* xShapeInf coords[2] = coord2; coords[3] = coord3; coords[4] = coord4; - nd4j::math::atomics::nd4j_atomicAdd(&z[shape::getOffset(zShapeInfo, coords)], y[yOffset]); + sd::math::atomics::nd4j_atomicAdd(&z[shape::getOffset(zShapeInfo, coords)], y[yOffset]); } break; @@ -1102,14 +1102,14 @@ __global__ static void pooling3dBPCuda(const void* vx, const Nd4jLong* xShapeInf T val = y[yOffset]; if (extraParam0 == 0) //Exclude padding - val /= nd4j::math::nd4j_ceil(static_cast(dend - dstart) / static_cast(dD)) * nd4j::math::nd4j_ceil(static_cast(hend - hstart) / static_cast(dH)) * nd4j::math::nd4j_ceil(static_cast(wend - wstart) / static_cast(dW)); //Accounts for dilation + val /= sd::math::nd4j_ceil(static_cast(dend - dstart) / static_cast(dD)) * sd::math::nd4j_ceil(static_cast(hend - hstart) / static_cast(dH)) * sd::math::nd4j_ceil(static_cast(wend - wstart) / static_cast(dW)); //Accounts for dilation else if (extraParam0 == 1) //Include padding val /= kProd; for (coords[2] = dstart; coords[2] < dend; coords[2] += dD) for (coords[3] = hstart; coords[3] < hend; coords[3] += dH) for (coords[4] = wstart; coords[4] < wend; coords[4] += dW) - nd4j::math::atomics::nd4j_atomicAdd(&z[shape::getOffset(zShapeInfo, coords)], val); + sd::math::atomics::nd4j_atomicAdd(&z[shape::getOffset(zShapeInfo, coords)], val); } break; @@ -1122,16 +1122,16 @@ __global__ static void pooling3dBPCuda(const void* vx, const Nd4jLong* xShapeInf for (coords[2] = dstart; coords[2] < dend; coords[2] += dD) for (coords[3] = hstart; coords[3] < hend; coords[3] += dH) for (coords[4] = wstart; coords[4] < wend; coords[4] += dW) - sum += nd4j::math::nd4j_pow(nd4j::math::nd4j_abs(x[shape::getOffset(xShapeInfo, coords)]), extraParam0); + sum += sd::math::nd4j_pow(sd::math::nd4j_abs(x[shape::getOffset(xShapeInfo, coords)]), extraParam0); - val *= nd4j::math::nd4j_pow(sum, ((T)1.f - extraParam0) / extraParam0); + val *= sd::math::nd4j_pow(sum, ((T)1.f - extraParam0) / extraParam0); for (coords[2] = dstart; coords[2] < dend; coords[2] += dD) { for (coords[3] = hstart; coords[3] < hend; coords[3] += dH) { for (coords[4] = wstart; coords[4] < wend; coords[4] += dW) { const auto xOffset = shape::getOffset(xShapeInfo, coords); const auto zOffset = shape::getOffset(zShapeInfo, coords); - nd4j::math::atomics::nd4j_atomicAdd(&z[zOffset], val * nd4j::math::nd4j_pow(nd4j::math::nd4j_abs(x[xOffset]), extraParam0 - 1.f) * nd4j::math::nd4j_sgn(x[xOffset])); + sd::math::atomics::nd4j_atomicAdd(&z[zOffset], val * sd::math::nd4j_pow(sd::math::nd4j_abs(x[xOffset]), extraParam0 - 1.f) * sd::math::nd4j_sgn(x[xOffset])); } } } @@ -1156,7 +1156,7 @@ static void pooling3dBPCudaLauncher(const int blocksPerGrid, const int threadsPe } ////////////////////////////////////////////////////////////////////////// -void ConvolutionUtils::pooling3dBP(nd4j::graph::Context& block, const NDArray& input, const NDArray& gradO, NDArray& gradI, const int kD, const int kH, const int kW, const int sD, const int sH, const int sW, const int pD, const int pH, const int pW, const int dD, const int dH, const int dW, const int poolingMode, const int extraParam0) { +void ConvolutionUtils::pooling3dBP(sd::graph::Context& block, const NDArray& input, const NDArray& gradO, NDArray& gradI, const int kD, const int kH, const int kW, const int sD, const int sH, const int sW, const int pD, const int pH, const int pW, const int dD, const int dH, const int dW, const int poolingMode, const int extraParam0) { // initial zeroing of gradI gradI.nullify(); @@ -1176,7 +1176,7 @@ void ConvolutionUtils::pooling3dBP(nd4j::graph::Context& block, const NDArray& i ////////////////////////////////////////////////////////////////////////// template -static void conv2dBP_(nd4j::graph::Context& block, const NDArray* input, const NDArray* weights, const NDArray* bias, const NDArray* gradO, NDArray* gradI, NDArray* gradW, NDArray* gradB, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int paddingMode, const int isNCHW) { +static void conv2dBP_(sd::graph::Context& block, const NDArray* input, const NDArray* weights, const NDArray* bias, const NDArray* gradO, NDArray* gradI, NDArray* gradW, NDArray* gradB, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int paddingMode, const int isNCHW) { // input [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW) // weights [kH, kW, iC, oC] always @@ -1220,7 +1220,7 @@ static void conv2dBP_(nd4j::graph::Context& block, const NDArray* input, const N if(gradW) { auto ctx = block.launchContext(); helpers::im2col(*ctx, *input, columns, kH, kW, sH, sW, pH, pW, dH, dW, NDArrayFactory::create(0.f, input->getContext())); // [bS, iC, iH, iW] is convoluted to [bS, iC, kH, kW, oH, oW] - nd4j::MmulHelper::tensorDot(&columns, gradO, gradW, {0,4,5}, gradOaxesForDot, {2, 0, 1, 3}); // [bS, iC, kH, kW, oH, oW] x [bS, oH, oW, oC]/[bS, oC, oH, oW] = [iC, kH, kW, oC] + sd::MmulHelper::tensorDot(&columns, gradO, gradW, {0,4,5}, gradOaxesForDot, {2, 0, 1, 3}); // [bS, iC, kH, kW, oH, oW] x [bS, oH, oW, oC]/[bS, oC, oH, oW] = [iC, kH, kW, oC] } // ----- calculation of gradB ----- // @@ -1234,7 +1234,7 @@ static void conv2dBP_(nd4j::graph::Context& block, const NDArray* input, const N } //----- calculation of gradI -----// - nd4j::MmulHelper::tensorDot(weights, gradO, &columns, {indWoC}, {indIOioC}, {2, 3, 1, 0, 4, 5}); // [kH, kW, iC, oC]/[oC, iC, kH, kW]] x [bS, oH, oW, oC]/[bS, oC, oH, oW] = [kH, kW, iC, bS, oH, oW] + sd::MmulHelper::tensorDot(weights, gradO, &columns, {indWoC}, {indIOioC}, {2, 3, 1, 0, 4, 5}); // [kH, kW, iC, oC]/[oC, iC, kH, kW]] x [bS, oH, oW, oC]/[bS, oC, oH, oW] = [kH, kW, iC, bS, oH, oW] helpers::col2im(*block.launchContext(), columns, *gradI, sH, sW, pH, pW, iH, iW, dH, dW); // [bS, iC, kH, kW, oH, oW] is de-convoluted to [bS, iC, iH, iW] @@ -1245,7 +1245,7 @@ static void conv2dBP_(nd4j::graph::Context& block, const NDArray* input, const N } ////////////////////////////////////////////////////////////////////////// -void ConvolutionUtils::conv2dBP(nd4j::graph::Context& block, const NDArray* input, const NDArray* weights, const NDArray* bias, const NDArray* gradO, NDArray* gradI, NDArray* gradW, NDArray* gradB, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int paddingMode, const int isNCHW) { +void ConvolutionUtils::conv2dBP(sd::graph::Context& block, const NDArray* input, const NDArray* weights, const NDArray* bias, const NDArray* gradO, NDArray* gradI, NDArray* gradW, NDArray* gradB, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int paddingMode, const int isNCHW) { BUILD_SINGLE_SELECTOR_TWICE(input->dataType(), conv2dBP_, (block, input, weights, bias, gradO, gradI, gradW, gradB, kH, kW, sH, sW, pH, pW, dH, dW, paddingMode, isNCHW), FLOAT_TYPES); } @@ -1303,7 +1303,7 @@ static void depthwiseConv2dBP_(const NDArray* input, const NDArray* weights, con // ----- calculation of gradW and gradB ----- // helpers::im2col(*input->getContext(), *input, columns, kH, kW, sH, sW, pH, pW, dH, dW, NDArrayFactory::create(0.f, input->getContext())); // [bS, iC, iH, iW] is convoluted to [bS, iC, kH, kW, oH, oW] - nd4j::MmulHelper::tensorDot(&columns, &gradOreshaped, gradW, modifColumns, modifGradO1, {{2,0,1,3},{iC,kH*kW,mC}}); // [iC, kW*kH, bS*oH*oW] x [iC, bS*oH*oW, mC] = [iC, kH*kW, mC] + sd::MmulHelper::tensorDot(&columns, &gradOreshaped, gradW, modifColumns, modifGradO1, {{2,0,1,3},{iC,kH*kW,mC}}); // [iC, kW*kH, bS*oH*oW] x [iC, bS*oH*oW, mC] = [iC, kH*kW, mC] // ----- calculation of gradB ----- // if(gradB) { @@ -1316,7 +1316,7 @@ static void depthwiseConv2dBP_(const NDArray* input, const NDArray* weights, con } //----- calculation of gradI -----// - nd4j::MmulHelper::tensorDot(weights, gradO, &columns, {{2,0,1,3},{iC,kH*kW,mC}}, modifGradO2, modifColumns); // [iC, kH*kW, mC] x [iC, mC, bS*oH*oW] = [iC, kW*kH, bS*oH*oW] + sd::MmulHelper::tensorDot(weights, gradO, &columns, {{2,0,1,3},{iC,kH*kW,mC}}, modifGradO2, modifColumns); // [iC, kH*kW, mC] x [iC, mC, bS*oH*oW] = [iC, kW*kH, bS*oH*oW] helpers::col2im(*input->getContext(), columns, *gradI, sH, sW, pH, pW, iH, iW, dH, dW); // [bS, iC, kH, kW, oH, oW] is de-convoluted to [bS, iC, iH, iW] if(!isNCHW) { @@ -1326,7 +1326,7 @@ static void depthwiseConv2dBP_(const NDArray* input, const NDArray* weights, con } ////////////////////////////////////////////////////////////////////////// -void ConvolutionUtils::depthwiseConv2dBP(nd4j::graph::Context& block, const NDArray* input, const NDArray* weights, const NDArray* bias, const NDArray* gradO, NDArray* gradI, NDArray* gradW, NDArray* gradB, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int paddingMode, const int isNCHW) { +void ConvolutionUtils::depthwiseConv2dBP(sd::graph::Context& block, const NDArray* input, const NDArray* weights, const NDArray* bias, const NDArray* gradO, NDArray* gradI, NDArray* gradW, NDArray* gradB, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int paddingMode, const int isNCHW) { BUILD_SINGLE_SELECTOR_TWICE(input->dataType(), depthwiseConv2dBP_, (input, weights, bias, gradO, gradI, gradW, gradB, kH, kW, sH, sW, pH, pW, dH, dW, paddingMode, isNCHW), FLOAT_TYPES); } @@ -1342,7 +1342,7 @@ __global__ static void upsampling2dCuda(const void* vx, const Nd4jLong* xShapeIn T* z = reinterpret_cast(vz); __shared__ int rank, dimIH; - __shared__ Nd4jLong *sharedMem, zLen; + __shared__ Nd4jLong zLen, *sharedMem; if (threadIdx.x == 0) { extern __shared__ unsigned char shmem[]; @@ -1384,7 +1384,7 @@ static void upsampling2dCudaLauncher(const int blocksPerGrid, const int threadsP } ////////////////////////////////////////////////////////////////////////// -void ConvolutionUtils::upsampling2d(nd4j::graph::Context& block, const NDArray& input, NDArray& output, const int factorH, const int factorW, const bool isNCHW) { +void ConvolutionUtils::upsampling2d(sd::graph::Context& block, const NDArray& input, NDArray& output, const int factorH, const int factorW, const bool isNCHW) { PointersManager manager(block.launchContext(), "upsampling2d"); @@ -1410,7 +1410,7 @@ __global__ static void upsampling3dCuda(const void* vx, const Nd4jLong* xShapeIn T* z = reinterpret_cast(vz); __shared__ int rank, dimID; - __shared__ Nd4jLong *sharedMem, zLen; + __shared__ Nd4jLong zLen, *sharedMem; if (threadIdx.x == 0) { extern __shared__ unsigned char shmem[]; @@ -1453,7 +1453,7 @@ static void upsampling3dCudaLauncher(const int blocksPerGrid, const int threadsP } ////////////////////////////////////////////////////////////////////////// -void ConvolutionUtils::upsampling3d(nd4j::graph::Context& block, const NDArray& input, NDArray& output, const int factorD, const int factorH, const int factorW, const bool isNCDHW) { +void ConvolutionUtils::upsampling3d(sd::graph::Context& block, const NDArray& input, NDArray& output, const int factorD, const int factorH, const int factorW, const bool isNCDHW) { PointersManager manager(block.launchContext(), "upsampling3d"); @@ -1480,7 +1480,7 @@ __global__ static void upsampling2dBPCuda(const void* vx, const Nd4jLong* xShape __shared__ int rank, dimIH; __shared__ uint factorH, factorW; - __shared__ Nd4jLong *sharedMem, zLen; + __shared__ Nd4jLong zLen, *sharedMem; if (threadIdx.x == 0) { extern __shared__ unsigned char shmem[]; @@ -1527,7 +1527,7 @@ static void upsampling2dBPCudaLauncher(const int blocksPerGrid, const int thread } ////////////////////////////////////////////////////////////////////////// -void ConvolutionUtils::upsampling2dBP(nd4j::graph::Context& block, const NDArray& gradO, NDArray& gradI, const bool isNCHW) { +void ConvolutionUtils::upsampling2dBP(sd::graph::Context& block, const NDArray& gradO, NDArray& gradI, const bool isNCHW) { PointersManager manager(block.launchContext(), "upsampling2d_bp"); @@ -1554,7 +1554,7 @@ __global__ static void upsampling3dBPCuda(const void* vx, const Nd4jLong* xShape __shared__ int rank, dimID; __shared__ uint factorD, factorH, factorW; - __shared__ Nd4jLong *sharedMem, zLen; + __shared__ Nd4jLong zLen, *sharedMem; if (threadIdx.x == 0) { extern __shared__ unsigned char shmem[]; @@ -1604,7 +1604,7 @@ static void upsampling3dBPCudaLauncher(const int blocksPerGrid, const int thread } ////////////////////////////////////////////////////////////////////////// -void ConvolutionUtils::upsampling3dBP(nd4j::graph::Context& block, const NDArray& gradO, NDArray& gradI, const bool isNCDHW) { +void ConvolutionUtils::upsampling3dBP(sd::graph::Context& block, const NDArray& gradO, NDArray& gradI, const bool isNCDHW) { PointersManager manager(block.launchContext(), "upsampling3d_bp"); diff --git a/libnd4j/include/ops/declarable/helpers/cuda/cross.cu b/libnd4j/include/ops/declarable/helpers/cuda/cross.cu index 1cd771b98..d7694641c 100644 --- a/libnd4j/include/ops/declarable/helpers/cuda/cross.cu +++ b/libnd4j/include/ops/declarable/helpers/cuda/cross.cu @@ -20,10 +20,10 @@ #include -#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { @@ -36,8 +36,8 @@ __global__ static void crossCuda(const void* vx, const Nd4jLong* xShapeInfo, __shared__ const T* x; __shared__ const T* y; __shared__ T* z; - __shared__ int rank; - __shared__ Nd4jLong lenWithoutLastDim, totalThreads, *sharedMem; + __shared__ int rank, *sharedMem; + __shared__ Nd4jLong lenWithoutLastDim, totalThreads; if (threadIdx.x == 0) { x = reinterpret_cast(vx); @@ -45,7 +45,7 @@ __global__ static void crossCuda(const void* vx, const Nd4jLong* xShapeInfo, z = reinterpret_cast(vz); extern __shared__ unsigned char shmem[]; - sharedMem = reinterpret_cast(shmem); + sharedMem = reinterpret_cast(shmem); totalThreads = gridDim.x * blockDim.x; rank = shape::rank(xShapeInfo); @@ -102,11 +102,11 @@ __host__ static void crossCudaLauncher(const int blocksPerGrid, const int thread BUILD_SINGLE_TEMPLATE(template void crossCudaLauncher, (const int blocksPerGrid, const int threadsPerBlock, const int sharedMem, const cudaStream_t *stream, const void* vx, const Nd4jLong* xShapeInfo, const void* vy, const Nd4jLong* yShapeInfo, void* vz, const Nd4jLong* zShapeInfo), NUMERIC_TYPES); -void crossBatched(nd4j::LaunchContext* context, NDArray *x, NDArray *y, NDArray *z) { +void crossBatched(sd::LaunchContext* context, NDArray *x, NDArray *y, NDArray *z) { const int threadsPerBlock = MAX_NUM_THREADS / 4; const int blocksPerGrid = (x->lengthOf() / x->sizeAt(-1) + threadsPerBlock - 1) / threadsPerBlock; - const int sharedMem = sizeof(Nd4jLong) * threadsPerBlock * x->rankOf() + 128; + const int sharedMem = sizeof(int) * threadsPerBlock * x->rankOf() + 128; PointersManager manager(context, "cross"); diff --git a/libnd4j/include/ops/declarable/helpers/cuda/d_t_s.cu b/libnd4j/include/ops/declarable/helpers/cuda/d_t_s.cu index b9aa4339b..35103d18b 100644 --- a/libnd4j/include/ops/declarable/helpers/cuda/d_t_s.cu +++ b/libnd4j/include/ops/declarable/helpers/cuda/d_t_s.cu @@ -20,7 +20,7 @@ #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { @@ -88,11 +88,11 @@ namespace helpers { template - static void __depthToSpace(nd4j::LaunchContext * context, NDArray *input, NDArray *output, int block_size, bool isNHWC) { + static void __depthToSpace(sd::LaunchContext * context, NDArray *input, NDArray *output, int block_size, bool isNHWC) { depthToSpaceKernel<<<512, 512, 1024, *context->getCudaStream()>>>(input->specialBuffer(), input->specialShapeInfo(), output->specialBuffer(), output->specialShapeInfo(), block_size, isNHWC); } - void _depthToSpace(nd4j::LaunchContext * context, NDArray *input, NDArray *output, int block_size, bool isNHWC) { + void _depthToSpace(sd::LaunchContext * context, NDArray *input, NDArray *output, int block_size, bool isNHWC) { auto xType = input->dataType(); NDArray::prepareSpecialUse({output}, {input}); @@ -101,7 +101,7 @@ namespace helpers { NDArray::registerSpecialUse({output}, {input}); } - BUILD_SINGLE_TEMPLATE(template void __depthToSpace, (nd4j::LaunchContext * context, NDArray *input, NDArray *output, int block_size, bool isNHWC);, LIBND4J_TYPES); + BUILD_SINGLE_TEMPLATE(template void __depthToSpace, (sd::LaunchContext * context, NDArray *input, NDArray *output, int block_size, bool isNHWC);, LIBND4J_TYPES); } } diff --git a/libnd4j/include/ops/declarable/helpers/cuda/diGamma.cu b/libnd4j/include/ops/declarable/helpers/cuda/diGamma.cu index 3edd59ecc..a6d06be17 100644 --- a/libnd4j/include/ops/declarable/helpers/cuda/diGamma.cu +++ b/libnd4j/include/ops/declarable/helpers/cuda/diGamma.cu @@ -20,9 +20,9 @@ // #include -#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { @@ -60,7 +60,7 @@ static void diGammaCudaLauncher(const int blocksPerGrid, const int threadsPerBlo } /////////////////////////////////////////////////////////////////// -void diGamma(nd4j::LaunchContext* context, const NDArray& x, NDArray& z) { +void diGamma(sd::LaunchContext* context, const NDArray& x, NDArray& z) { int threadsPerBlock = MAX_NUM_THREADS / 2; int blocksPerGrid = (z.lengthOf() + threadsPerBlock - 1) / threadsPerBlock; diff --git a/libnd4j/include/ops/declarable/helpers/cuda/diag.cu b/libnd4j/include/ops/declarable/helpers/cuda/diag.cu index 265e47776..87fd2aa98 100644 --- a/libnd4j/include/ops/declarable/helpers/cuda/diag.cu +++ b/libnd4j/include/ops/declarable/helpers/cuda/diag.cu @@ -18,10 +18,10 @@ // Created by GS on 4/6/2018. // -#include "ResultSet.h" +#include #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// @@ -90,7 +90,7 @@ static __global__ void diagFunctorKernel(void* outputBuffer, Nd4jLong* outputSha // Returns a batched matrix tensor with new batched diagonal values. // for detailed explanations please take a look on web page: https://www.tensorflow.org/api_docs/python/tf/matrix_set_diag template - static void _diagFunctor(nd4j::LaunchContext * context, const NDArray* input, NDArray* output) { + static void _diagFunctor(sd::LaunchContext * context, const NDArray* input, NDArray* output) { auto stream = context->getCudaStream(); auto inputLength = input->lengthOf(); dim3 launchDims(256, 512, 8192); @@ -101,18 +101,18 @@ static __global__ void diagFunctorKernel(void* outputBuffer, Nd4jLong* outputSha //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// // diagFunctor - caller for diag functor processor - void diagFunctor(nd4j::LaunchContext * context, const NDArray* input, NDArray* output) { + void diagFunctor(sd::LaunchContext * context, const NDArray* input, NDArray* output) { auto xType = input->dataType(); BUILD_SINGLE_SELECTOR(xType, _diagFunctor, (context, input, output), LIBND4J_TYPES); } - BUILD_SINGLE_TEMPLATE(template void _diagFunctor, (nd4j::LaunchContext * context, const NDArray* input, NDArray* output);, LIBND4J_TYPES); + BUILD_SINGLE_TEMPLATE(template void _diagFunctor, (sd::LaunchContext * context, const NDArray* input, NDArray* output);, LIBND4J_TYPES); //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// // diagPartFunctor - caller for diag part functor kernel template - void _diagPartFunctor(nd4j::LaunchContext * context, NDArray const* input, NDArray* output) { + void _diagPartFunctor(sd::LaunchContext * context, NDArray const* input, NDArray* output) { const int outLen = output->lengthOf(); const int inLen = input->lengthOf(); auto stream = context->getCudaStream(); @@ -126,7 +126,7 @@ static __global__ void diagFunctorKernel(void* outputBuffer, Nd4jLong* outputSha //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// // diagPartFunctor - caller for diag part functor processor - void diagPartFunctor(nd4j::LaunchContext * context, NDArray const* input, NDArray* output) { + void diagPartFunctor(sd::LaunchContext * context, NDArray const* input, NDArray* output) { auto zType = output->dataType(); BUILD_SINGLE_SELECTOR(zType, _diagPartFunctor, (context, input, output), NUMERIC_TYPES); diff --git a/libnd4j/include/ops/declarable/helpers/cuda/dilation2d.cu b/libnd4j/include/ops/declarable/helpers/cuda/dilation2d.cu index 92aa4c55a..c05b5fb6d 100644 --- a/libnd4j/include/ops/declarable/helpers/cuda/dilation2d.cu +++ b/libnd4j/include/ops/declarable/helpers/cuda/dilation2d.cu @@ -20,9 +20,9 @@ #include #include -#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { @@ -43,13 +43,13 @@ __global__ static void dilation2dCuda(const void* vx, const Nd4jLong* xShapeInfo const X* y = reinterpret_cast(vy); Z* z = reinterpret_cast(vz); - __shared__ int xzRank, yRank; + __shared__ int xzRank, yRank, *sharedMem; __shared__ uint iH, iW, kH, kW; - __shared__ Nd4jLong *sharedMem, zLen; + __shared__ Nd4jLong zLen; if (threadIdx.x == 0) { extern __shared__ unsigned char shmem[]; - sharedMem = reinterpret_cast(shmem); + sharedMem = reinterpret_cast(shmem); zLen = shape::length(zShapeInfo); @@ -113,13 +113,13 @@ static void dilation2dCudaLauncher(const int blocksPerGrid, const int threadsPer dilation2dCuda<<>>(vx, xShapeInfo, vy, yShapeInfo, vz, zShapeInfo, sH, sW, pH, pW, dH, dW); } -void dilation2d(nd4j::LaunchContext* context, NDArray *input, NDArray *weights, NDArray *output, const int sH, const int sW, const int pH, const int pW, const int dH, const int dW) { +void dilation2d(sd::LaunchContext* context, NDArray *input, NDArray *weights, NDArray *output, const int sH, const int sW, const int pH, const int pW, const int dH, const int dW) { PointersManager manager(context, "dilation2d"); const int threadsPerBlock = MAX_NUM_THREADS / 2; const int blocksPerGrid = (output->lengthOf() + threadsPerBlock - 1) / threadsPerBlock; - const int sharedMem = (weights->rankOf() + output->rankOf()) * sizeof(Nd4jLong) * threadsPerBlock + 128; + const int sharedMem = (weights->rankOf() + output->rankOf()) * sizeof(int) * threadsPerBlock + 128; NDArray::prepareSpecialUse({output}, {input, weights}); BUILD_SINGLE_SELECTOR_TWICE(input->dataType(), dilation2dCudaLauncher, (blocksPerGrid, threadsPerBlock, sharedMem, context->getCudaStream(), input->getSpecialBuffer(), input->getSpecialShapeInfo(), weights->getSpecialBuffer(), weights->getSpecialShapeInfo(), output->specialBuffer(), output->specialShapeInfo(), sH, sW, pH, pW, dH, dW), FLOAT_TYPES); diff --git a/libnd4j/include/ops/declarable/helpers/cuda/dropout.cu b/libnd4j/include/ops/declarable/helpers/cuda/dropout.cu index 9b2a42d8f..aee79caa7 100644 --- a/libnd4j/include/ops/declarable/helpers/cuda/dropout.cu +++ b/libnd4j/include/ops/declarable/helpers/cuda/dropout.cu @@ -19,17 +19,17 @@ // #include -#include +#include #include #include -#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { template - static __global__ void dropoutSimpleKernel(void const* inputBuf, Nd4jLong const* inputShape, void* outputBuf, Nd4jLong* outputShape, double probVal, int inLen, nd4j::graph::RandomGenerator* nodeRng) { + static __global__ void dropoutSimpleKernel(void const* inputBuf, Nd4jLong const* inputShape, void* outputBuf, Nd4jLong* outputShape, double probVal, int inLen, sd::graph::RandomGenerator* nodeRng) { auto tid = blockIdx.x * blockDim.x + threadIdx.x; auto step = blockDim.x * gridDim.x; T const* input = reinterpret_cast(inputBuf); @@ -46,18 +46,18 @@ namespace helpers { } template - static void dropoutSimple(nd4j::LaunchContext* context, NDArray const* input, NDArray* output, double probValue, int seed) { - nd4j::graph::RandomGenerator nodeRng(3019L, seed); + static void dropoutSimple(sd::LaunchContext* context, NDArray const* input, NDArray* output, double probValue, int seed) { + sd::graph::RandomGenerator nodeRng(3019L, seed); int inLen = input->lengthOf(); - nd4j::graph::RandomGenerator* dRandom; + sd::graph::RandomGenerator* dRandom; auto stream = context->getCudaStream(); NDArray::prepareSpecialUse({output}, {input}); - auto err = cudaMalloc(&dRandom, sizeof(nd4j::graph::RandomGenerator)); + auto err = cudaMalloc(&dRandom, sizeof(sd::graph::RandomGenerator)); if (err) { throw cuda_exception::build("helpers::dropoutSimple: Cannot allocate device memory for random generator.", err); } - err = cudaMemcpy(dRandom, &nodeRng, sizeof(nd4j::graph::RandomGenerator), cudaMemcpyHostToDevice); + err = cudaMemcpy(dRandom, &nodeRng, sizeof(sd::graph::RandomGenerator), cudaMemcpyHostToDevice); if (err) { throw cuda_exception::build("helpers::dropoutSimple: Cannot set up device memory for random generator.", err); } @@ -165,7 +165,7 @@ namespace helpers { } template - static __global__ void alphaDropoutSimpleKernel(void const* inputBuf, Nd4jLong const* inputShape, void* outputBuf, Nd4jLong* outputShape, double probValue, double alpha, double alpha1, double beta, int inLen, nd4j::graph::RandomGenerator* nodeRng) { + static __global__ void alphaDropoutSimpleKernel(void const* inputBuf, Nd4jLong const* inputShape, void* outputBuf, Nd4jLong* outputShape, double probValue, double alpha, double alpha1, double beta, int inLen, sd::graph::RandomGenerator* nodeRng) { auto tid = blockIdx.x * blockDim.x + threadIdx.x; auto step = blockDim.x * gridDim.x; T const* input = reinterpret_cast(inputBuf); @@ -178,15 +178,15 @@ namespace helpers { } } template - static void alphaDropoutSimple(nd4j::LaunchContext* context, NDArray const* input, NDArray* output, int seed, double probValue, double alpha, double alpha1, double beta) { - nd4j::graph::RandomGenerator nodeRng(3019L, seed), *dRandom; + static void alphaDropoutSimple(sd::LaunchContext* context, NDArray const* input, NDArray* output, int seed, double probValue, double alpha, double alpha1, double beta) { + sd::graph::RandomGenerator nodeRng(3019L, seed), *dRandom; auto stream = context->getCudaStream(); - auto err = cudaMalloc(&dRandom, sizeof(nd4j::graph::RandomGenerator)); + auto err = cudaMalloc(&dRandom, sizeof(sd::graph::RandomGenerator)); NDArray::prepareSpecialUse({output}, {input}); if (err) { throw cuda_exception::build("helpers::alphaDropoutSimple: Cannot allocate device memory for random generator.", err); } - err = cudaMemcpy(dRandom, &nodeRng, sizeof(nd4j::graph::RandomGenerator), cudaMemcpyHostToDevice); + err = cudaMemcpy(dRandom, &nodeRng, sizeof(sd::graph::RandomGenerator), cudaMemcpyHostToDevice); if (err) { throw cuda_exception::build("helpers::alphaDropoutSimple: Cannot set up device memory for random generator.", err); } diff --git a/libnd4j/include/ops/declarable/helpers/cuda/dynamic.cu b/libnd4j/include/ops/declarable/helpers/cuda/dynamic.cu index c70283997..a80d838be 100644 --- a/libnd4j/include/ops/declarable/helpers/cuda/dynamic.cu +++ b/libnd4j/include/ops/declarable/helpers/cuda/dynamic.cu @@ -21,7 +21,7 @@ #include #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { @@ -110,7 +110,7 @@ namespace nd4j { } template - static void _dynamicPartitionFunctor(nd4j::LaunchContext * context, NDArray const* input, NDArray const* indices, std::vector& outputList) { + static void _dynamicPartitionFunctor(sd::LaunchContext * context, NDArray const* input, NDArray const* indices, std::vector& outputList) { std::vector> outputs(outputList.size()); int sourceDimsLen = input->rankOf() - indices->rankOf(); @@ -230,7 +230,7 @@ namespace nd4j { } template - static int _dynamicStitchFunctor(nd4j::LaunchContext * context, std::vector const& inputs, std::vector const& indices, NDArray* output){ + static int _dynamicStitchFunctor(sd::LaunchContext * context, std::vector const& inputs, std::vector const& indices, NDArray* output){ int inputSize = inputs.size(); @@ -307,7 +307,7 @@ namespace nd4j { } - void dynamicPartitionFunctor(nd4j::LaunchContext * context, NDArray const* input, NDArray const* indices, std::vector& outputList) { + void dynamicPartitionFunctor(sd::LaunchContext * context, NDArray const* input, NDArray const* indices, std::vector& outputList) { auto xType = input->dataType(); auto yType = indices->dataType(); @@ -328,7 +328,7 @@ namespace nd4j { throw std::runtime_error("Not umplemented yet"); } - int dynamicStitchFunctor(nd4j::LaunchContext * context, std::vector const& inputs, std::vector const& indices, NDArray* output){ + int dynamicStitchFunctor(sd::LaunchContext * context, std::vector const& inputs, std::vector const& indices, NDArray* output){ auto xType = inputs.at(0)->dataType(); auto yType = indices.at(0)->dataType(); @@ -352,13 +352,13 @@ namespace nd4j { return Status::OK(); } - int dynamicStitchFunctorBP(nd4j::LaunchContext * context, std::vector const& inputs, std::vector const& indices, NDArray const* gradInput, std::vector& outputList) { + int dynamicStitchFunctorBP(sd::LaunchContext * context, std::vector const& inputs, std::vector const& indices, NDArray const* gradInput, std::vector& outputList) { auto xType = inputs.at(0)->dataType(); BUILD_SINGLE_SELECTOR(xType, return _dynamicStitchFunctorBP, (inputs, indices, gradInput, outputList), NUMERIC_TYPES); } - void dynamicPartitionFunctorBP(nd4j::LaunchContext * context, NDArray const* input, NDArray const* indices, std::vector const& inputGradientList, std::vector& outputList) { + void dynamicPartitionFunctorBP(sd::LaunchContext * context, NDArray const* input, NDArray const* indices, std::vector const& inputGradientList, std::vector& outputList) { auto xType = input->dataType(); BUILD_SINGLE_SELECTOR(xType, _dynamicPartitionFunctorBP, (input, indices, inputGradientList, outputList), NUMERIC_TYPES); diff --git a/libnd4j/include/ops/declarable/helpers/cuda/extract_patches.cu b/libnd4j/include/ops/declarable/helpers/cuda/extract_patches.cu index 9470f21be..3a0ea9240 100644 --- a/libnd4j/include/ops/declarable/helpers/cuda/extract_patches.cu +++ b/libnd4j/include/ops/declarable/helpers/cuda/extract_patches.cu @@ -25,7 +25,7 @@ #include #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// @@ -92,7 +92,7 @@ namespace helpers { //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// template - static void _extractPatches(nd4j::LaunchContext * context, NDArray* images, NDArray* output, int sizeRow, int sizeCol, int strideRow, int strideCol, int rateRow, int rateCol, bool theSame){ + static void _extractPatches(sd::LaunchContext * context, NDArray* images, NDArray* output, int sizeRow, int sizeCol, int strideRow, int strideCol, int rateRow, int rateCol, bool theSame){ NDArray::prepareSpecialUse({output}, {images}); std::vector restDims({1, 2, 3}); // the first and the last dims // 3D matricies - 2D matricies of vectors (if last dim is greater than 1) @@ -114,8 +114,8 @@ namespace helpers { if (sizeCol * rateCol < 3) colCast = 0; - auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(images->getShapeInfo(), restDims.data(), restDims.size()); - auto packZ = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), restDims.data(), restDims.size()); + auto packX = sd::ConstantTadHelper::getInstance()->tadForDimensions(images->getShapeInfo(), restDims.data(), restDims.size()); + auto packZ = sd::ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), restDims.data(), restDims.size()); int batchCount = packX.numberOfTads(); PointersManager manager(context, "helpers::extractPatches"); @@ -132,11 +132,11 @@ namespace helpers { manager.synchronize(); NDArray::registerSpecialUse({output}, {images}); } - BUILD_SINGLE_TEMPLATE(template void _extractPatches, (nd4j::LaunchContext * context, NDArray* input, NDArray* output, int sizeRow, int sizeCol, int stradeRow, int stradeCol, int rateRow, int rateCol, bool theSame), LIBND4J_TYPES); + BUILD_SINGLE_TEMPLATE(template void _extractPatches, (sd::LaunchContext * context, NDArray* input, NDArray* output, int sizeRow, int sizeCol, int stradeRow, int stradeCol, int rateRow, int rateCol, bool theSame), LIBND4J_TYPES); - void extractPatches(nd4j::LaunchContext * context, NDArray* images, NDArray* output, int sizeRow, int sizeCol, int stradeRow, int stradeCol, int rateRow, int rateCol, bool theSame){ + void extractPatches(sd::LaunchContext * context, NDArray* images, NDArray* output, int sizeRow, int sizeCol, int stradeRow, int stradeCol, int rateRow, int rateCol, bool theSame){ auto xType = images->dataType(); BUILD_SINGLE_SELECTOR(xType, _extractPatches, (context, images, output, sizeRow, sizeCol, stradeRow, stradeCol, rateRow, rateCol, theSame), LIBND4J_TYPES); diff --git a/libnd4j/include/ops/declarable/helpers/cuda/fake_quantization.cu b/libnd4j/include/ops/declarable/helpers/cuda/fake_quantization.cu index cbdff509d..262b1fe3e 100644 --- a/libnd4j/include/ops/declarable/helpers/cuda/fake_quantization.cu +++ b/libnd4j/include/ops/declarable/helpers/cuda/fake_quantization.cu @@ -19,9 +19,9 @@ // #include -#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// @@ -47,7 +47,7 @@ namespace helpers { if (zeroPointFromMin > quantMaxF) { return static_cast(quantMax); } - return nd4j::math::nd4j_round(zeroPointFromMin); + return sd::math::nd4j_round(zeroPointFromMin); }(); *nudgedMax = (quantMaxF - static_cast(nudgedZeroPoint)) * (*scale); *nudgedMin = (quantMinF - static_cast(nudgedZeroPoint)) * (*scale); diff --git a/libnd4j/include/ops/declarable/helpers/cuda/flatten.cu b/libnd4j/include/ops/declarable/helpers/cuda/flatten.cu index df4e25130..3600104e1 100644 --- a/libnd4j/include/ops/declarable/helpers/cuda/flatten.cu +++ b/libnd4j/include/ops/declarable/helpers/cuda/flatten.cu @@ -21,13 +21,13 @@ #include #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { template void _CUDA_G flattenKernel(void **xBuffers, Nd4jLong **xShapeInfos, Nd4jLong *offsets, Nd4jLong numInputs, void *zBuffer, Nd4jLong *zShapeInfo, char order) { - Nd4jLong xCoord[MAX_RANK]; + int xCoord[MAX_RANK]; // each block of threads works on 1 input array for (Nd4jLong e = blockIdx.x; e < numInputs; e += gridDim.x) { @@ -44,7 +44,7 @@ namespace nd4j { } template - void flatten_(nd4j::LaunchContext *context, std::vector &inputs, NDArray *output, char order) { + void flatten_(sd::LaunchContext *context, std::vector &inputs, NDArray *output, char order) { PointersManager pm(context, "flatten"); std::vector hdBuffers(inputs.size()); @@ -72,7 +72,7 @@ namespace nd4j { pm.synchronize(); } - void flatten(nd4j::LaunchContext *context, std::vector &inputs, NDArray *output, char order) { + void flatten(sd::LaunchContext *context, std::vector &inputs, NDArray *output, char order) { // FIXME: we want NDArrayFactory::prepareSpecialUse here eventually for (auto v:inputs) v->syncToDevice(); diff --git a/libnd4j/include/ops/declarable/helpers/cuda/gather.cu b/libnd4j/include/ops/declarable/helpers/cuda/gather.cu index f6d8acc77..03d2f35d8 100644 --- a/libnd4j/include/ops/declarable/helpers/cuda/gather.cu +++ b/libnd4j/include/ops/declarable/helpers/cuda/gather.cu @@ -21,10 +21,10 @@ #include #include -#include -#include +#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { @@ -105,7 +105,7 @@ __host__ static void gatherCudaLauncher(const cudaStream_t *stream, const int nu } ////////////////////////////////////////////////////////////////////// -void gather(nd4j::LaunchContext * context, const NDArray* input, const NDArray* indices, NDArray* output, const std::vector& intArgs) { +void gather(sd::LaunchContext * context, const NDArray* input, const NDArray* indices, NDArray* output, const std::vector& intArgs) { const int inputRank = input->rankOf(); const int numOfIntArgs = intArgs.size(); diff --git a/libnd4j/include/ops/declarable/helpers/cuda/gather_nd.cu b/libnd4j/include/ops/declarable/helpers/cuda/gather_nd.cu index 6b3bf5135..21ab1ff98 100644 --- a/libnd4j/include/ops/declarable/helpers/cuda/gather_nd.cu +++ b/libnd4j/include/ops/declarable/helpers/cuda/gather_nd.cu @@ -23,13 +23,13 @@ #include #include #include -#include +#include #include #include -#include -#include +#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { /////////////////////////////////////////////////////////////////// @@ -53,7 +53,7 @@ namespace nd4j { xRank = shape::rank(xShapeInfo); yRank = shape::rank(yShapeInfo); zRank = shape::rank(zShapeInfo); - maxRank = nd4j::math::nd4j_max(yRank, nd4j::math::nd4j_max(xRank, zRank)); + maxRank = sd::math::nd4j_max(yRank, sd::math::nd4j_max(xRank, zRank)); zLen = shape::length(zShapeInfo); yLastDim = yShapeInfo[yRank]; @@ -121,9 +121,9 @@ namespace nd4j { } /////////////////////////////////////////////////////////////////// - void gatherND(nd4j::LaunchContext * context, NDArray& input, NDArray& indices, NDArray& output) { + void gatherND(sd::LaunchContext * context, NDArray& input, NDArray& indices, NDArray& output) { - const int maxRank = nd4j::math::nd4j_max(indices.rankOf(), nd4j::math::nd4j_max(input.rankOf(), output.rankOf())); + const int maxRank = sd::math::nd4j_max(indices.rankOf(), sd::math::nd4j_max(input.rankOf(), output.rankOf())); const int threadsPerBlock = 256; const int blocksPerGrid = (output.lengthOf() + threadsPerBlock - 1) / threadsPerBlock; diff --git a/libnd4j/include/ops/declarable/helpers/cuda/gradient.cu b/libnd4j/include/ops/declarable/helpers/cuda/gradient.cu index a12b43973..f165d88b7 100644 --- a/libnd4j/include/ops/declarable/helpers/cuda/gradient.cu +++ b/libnd4j/include/ops/declarable/helpers/cuda/gradient.cu @@ -19,9 +19,9 @@ // #include -#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { template @@ -34,7 +34,7 @@ void applyGradientDescent_(LaunchContext* context, NDArray* input, NDArray* step input->applyPairwiseLambda(*step, lambda, *output); } -void applyGradientDescent(nd4j::LaunchContext* context, NDArray* input, NDArray* step, double weight, NDArray* output) { +void applyGradientDescent(sd::LaunchContext* context, NDArray* input, NDArray* step, double weight, NDArray* output) { BUILD_SINGLE_SELECTOR(input->dataType(), applyGradientDescent_, (context, input, step, weight, output), FLOAT_TYPES); } BUILD_SINGLE_TEMPLATE(template void applyGradientDescent_, (LaunchContext* context, NDArray* input, NDArray* step, double weight, NDArray* output), FLOAT_TYPES); diff --git a/libnd4j/include/ops/declarable/helpers/cuda/gru.cu b/libnd4j/include/ops/declarable/helpers/cuda/gru.cu index 82ab9d764..bd4e878e3 100644 --- a/libnd4j/include/ops/declarable/helpers/cuda/gru.cu +++ b/libnd4j/include/ops/declarable/helpers/cuda/gru.cu @@ -27,15 +27,15 @@ #include #include #include -#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { ////////////////////////////////////////////////////////////////////////// -void gruCell(nd4j::LaunchContext * context, const NDArray* x, const NDArray* hLast, const NDArray* W, const NDArray* Wc, +void gruCell(sd::LaunchContext * context, const NDArray* x, const NDArray* hLast, const NDArray* W, const NDArray* Wc, const NDArray* b, const NDArray* bc, NDArray* r, NDArray* u, NDArray* c, NDArray* h) { @@ -128,7 +128,7 @@ void gruCell(nd4j::LaunchContext * context, const NDArray* x, const NDArray* hLa } ////////////////////////////////////////////////////////////////////////// -void gruTimeLoop(nd4j::LaunchContext * context, const NDArray* x, const NDArray* hLast, const NDArray* Wx, const NDArray* Wh, const NDArray* b, NDArray* h) { +void gruTimeLoop(sd::LaunchContext * context, const NDArray* x, const NDArray* hLast, const NDArray* Wx, const NDArray* Wh, const NDArray* b, NDArray* h) { // x input [time, bS, iS] // hLast initial cell output (at time step = 0) [bS, nU] @@ -154,7 +154,7 @@ void gruTimeLoop(nd4j::LaunchContext * context, const NDArray* x, const NDArray* } ////////////////////////////////////////////////////////////////////////// -void gruCellBP(nd4j::LaunchContext* context, +void gruCellBP(sd::LaunchContext* context, const NDArray* x, const NDArray* hLast, const NDArray* W, const NDArray* Wc, const NDArray* b, const NDArray* bc, const NDArray* dLdr, const NDArray* dLdu, const NDArray* dLdc, const NDArray* dLdh, diff --git a/libnd4j/include/ops/declarable/helpers/cuda/hamming.cu b/libnd4j/include/ops/declarable/helpers/cuda/hamming.cu index 9802ff231..f88ec6003 100644 --- a/libnd4j/include/ops/declarable/helpers/cuda/hamming.cu +++ b/libnd4j/include/ops/declarable/helpers/cuda/hamming.cu @@ -21,7 +21,7 @@ #include #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { template @@ -52,7 +52,7 @@ namespace nd4j { __syncthreads(); // now we accumulate values - auto numItems = nd4j::math::nd4j_min(blockDim.x, length); + auto numItems = sd::math::nd4j_min(blockDim.x, length); auto floorPow2 = numItems; if (floorPow2 & (floorPow2 - 1)) { @@ -77,7 +77,7 @@ namespace nd4j { // FIXME: do we really want atomicAdd on global memory here // and store them to output if (threadIdx.x == 0 && shared[0] > 0) - nd4j::math::atomics::nd4j_atomicAdd(&z[0], static_cast(shared[threadIdx.x])); + sd::math::atomics::nd4j_atomicAdd(&z[0], static_cast(shared[threadIdx.x])); } template diff --git a/libnd4j/include/ops/declarable/helpers/cuda/hashcode.cu b/libnd4j/include/ops/declarable/helpers/cuda/hashcode.cu index ac56b69d5..1c4ca9152 100644 --- a/libnd4j/include/ops/declarable/helpers/cuda/hashcode.cu +++ b/libnd4j/include/ops/declarable/helpers/cuda/hashcode.cu @@ -21,7 +21,7 @@ #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { template diff --git a/libnd4j/include/ops/declarable/helpers/cuda/histogram.cu b/libnd4j/include/ops/declarable/helpers/cuda/histogram.cu index a4bcbb311..51af14fc4 100644 --- a/libnd4j/include/ops/declarable/helpers/cuda/histogram.cu +++ b/libnd4j/include/ops/declarable/helpers/cuda/histogram.cu @@ -19,9 +19,9 @@ // #include -#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { template @@ -54,7 +54,7 @@ namespace nd4j { int idx = int((dx[e] - *min_val) / binSize); idx = math::nd4j_max(idx, 0); //atomicMax(&idx, 0);//atomicMax(&idx, 0); idx = math::nd4j_min(idx, int(numBins - 1)); //atomicMin(&idx, int(numBins - 1)); - nd4j::math::atomics::nd4j_atomicAdd(&bins[idx], (Z)1); + sd::math::atomics::nd4j_atomicAdd(&bins[idx], (Z)1); } __syncthreads(); // at this point all bins in shared memory are calculated, so we aggregate them now via threadfence trick @@ -108,9 +108,9 @@ namespace nd4j { } template - static void histogram_(nd4j::LaunchContext *context, void *xBuffer, Nd4jLong *xShapeInfo, Nd4jLong *dxShapeInfo, void *zBuffer, Nd4jLong *zShapeInfo, Nd4jLong numBins, void* min_val, void* max_val) { + static void histogram_(sd::LaunchContext *context, void *xBuffer, Nd4jLong *xShapeInfo, Nd4jLong *dxShapeInfo, void *zBuffer, Nd4jLong *zShapeInfo, Nd4jLong numBins, void* min_val, void* max_val) { int numThreads = 256; - int numBlocks = nd4j::math::nd4j_max(256, nd4j::math::nd4j_min(1, shape::length(xShapeInfo) / numThreads)); + int numBlocks = sd::math::nd4j_max(256, sd::math::nd4j_min(1, shape::length(xShapeInfo) / numThreads)); int workspaceSize = numBlocks * numBins; auto tmp = NDArrayFactory::create('c', {workspaceSize}); @@ -119,7 +119,7 @@ namespace nd4j { cudaStreamSynchronize(*context->getCudaStream()); } - void histogramHelper(nd4j::LaunchContext *context, NDArray &input, NDArray &output) { + void histogramHelper(sd::LaunchContext *context, NDArray &input, NDArray &output) { Nd4jLong numBins = output.lengthOf(); NDArray::registerSpecialUse({&output}, {&input}); diff --git a/libnd4j/include/ops/declarable/helpers/cuda/histogramFixedWidth.cu b/libnd4j/include/ops/declarable/helpers/cuda/histogramFixedWidth.cu index 07d7bcd93..e39f9b438 100644 --- a/libnd4j/include/ops/declarable/helpers/cuda/histogramFixedWidth.cu +++ b/libnd4j/include/ops/declarable/helpers/cuda/histogramFixedWidth.cu @@ -19,10 +19,10 @@ // #include -#include -#include +#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { @@ -66,7 +66,7 @@ __global__ static void histogramFixedWidthCuda( const void* vx, const Nd4jLong* else zIndex = static_cast((value - leftEdge) / binWidth); - nd4j::math::atomics::nd4j_atomicAdd(&z[shape::getIndexOffset(zIndex, zShapeInfo)], 1); + sd::math::atomics::nd4j_atomicAdd(&z[shape::getIndexOffset(zIndex, zShapeInfo)], 1); } } @@ -81,7 +81,7 @@ __host__ static void histogramFixedWidthCudaLauncher(const cudaStream_t *stream, } //////////////////////////////////////////////////////////////////////// -void histogramFixedWidth(nd4j::LaunchContext* context, const NDArray& input, const NDArray& range, NDArray& output) { +void histogramFixedWidth(sd::LaunchContext* context, const NDArray& input, const NDArray& range, NDArray& output) { // firstly initialize output with zeros output.nullify(); @@ -137,13 +137,13 @@ void histogramFixedWidth(nd4j::LaunchContext* context, const NDArray& input, con // currInd = 0; // else if(value >= lastButOneEdge) // currInd = outputLength - 1; -// nd4j::math::atomics::nd4j_atomicAdd(&z[currInd], 1LL); +// sd::math::atomics::nd4j_atomicAdd(&z[currInd], 1LL); // } // } // template -// void histogramFixedWidth_(nd4j::LaunchContext * context, const NDArray& input, const NDArray& range, NDArray& output) { +// void histogramFixedWidth_(sd::LaunchContext * context, const NDArray& input, const NDArray& range, NDArray& output) { // const int nbins = output.lengthOf(); // auto stream = context->getCudaStream(); // // firstly initialize output with zeros @@ -191,10 +191,10 @@ void histogramFixedWidth(nd4j::LaunchContext* context, const NDArray& input, con // // } // } -// void histogramFixedWidth(nd4j::LaunchContext * context, const NDArray& input, const NDArray& range, NDArray& output) { +// void histogramFixedWidth(sd::LaunchContext * context, const NDArray& input, const NDArray& range, NDArray& output) { // BUILD_SINGLE_SELECTOR(input.dataType(), histogramFixedWidth_, (context, input, range, output), LIBND4J_TYPES); // } -// BUILD_SINGLE_TEMPLATE(template void histogramFixedWidth_, (nd4j::LaunchContext * context, const NDArray& input, const NDArray& range, NDArray& output), LIBND4J_TYPES); +// BUILD_SINGLE_TEMPLATE(template void histogramFixedWidth_, (sd::LaunchContext * context, const NDArray& input, const NDArray& range, NDArray& output), LIBND4J_TYPES); } } diff --git a/libnd4j/include/ops/declarable/helpers/cuda/im2col.cu b/libnd4j/include/ops/declarable/helpers/cuda/im2col.cu index 62fcd0588..0dbca8c47 100644 --- a/libnd4j/include/ops/declarable/helpers/cuda/im2col.cu +++ b/libnd4j/include/ops/declarable/helpers/cuda/im2col.cu @@ -19,9 +19,9 @@ // #include -#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { @@ -40,12 +40,12 @@ __global__ static void im2colCuda(const void *image, void *columns, const auto im = reinterpret_cast(image); auto col = reinterpret_cast(columns); - __shared__ Nd4jLong colLen, *sharedMem, iH, iW; - __shared__ int imRank, colRank; + __shared__ Nd4jLong colLen, iH, iW; + __shared__ int imRank, colRank, *sharedMem; if (threadIdx.x == 0) { extern __shared__ unsigned char shmem[]; - sharedMem = reinterpret_cast(shmem); + sharedMem = reinterpret_cast(shmem); colRank = 6; imRank = 4; @@ -80,12 +80,12 @@ __global__ static void im2colCuda(const void *image, void *columns, ////////////////////////////////////////////////////////////////////////// template -static void im2colCudaLauncher(const int blocksPerGrid, const int threadsPerBlock, nd4j::LaunchContext & context, const void *image, void *columns, const Nd4jLong *imShapeInfo, const Nd4jLong *colShapeInfo, int sH, int sW, int pH, int pW, int dH, int dW, double zeroPadVal) { - im2colCuda<<>>(image, columns, imShapeInfo, colShapeInfo, sH, sW, pH, pW, dH, dW, zeroPadVal); +static void im2colCudaLauncher(const int blocksPerGrid, const int threadsPerBlock, sd::LaunchContext & context, const void *image, void *columns, const Nd4jLong *imShapeInfo, const Nd4jLong *colShapeInfo, int sH, int sW, int pH, int pW, int dH, int dW, double zeroPadVal) { + im2colCuda<<>>(image, columns, imShapeInfo, colShapeInfo, sH, sW, pH, pW, dH, dW, zeroPadVal); } ////////////////////////////////////////////////////////////////////////// -void im2col(nd4j::LaunchContext& context, const NDArray& image, NDArray& columns, const int kH, const int kW, const int sH, const int sW, const int pH, const int pW, const int dH, const int dW, const NDArray& arrZeroPadVal) { +void im2col(sd::LaunchContext& context, const NDArray& image, NDArray& columns, const int kH, const int kW, const int sH, const int sW, const int pH, const int pW, const int dH, const int dW, const NDArray& arrZeroPadVal) { PointersManager manager(&context, "im2col"); diff --git a/libnd4j/include/ops/declarable/helpers/cuda/image_draw_bounding_boxes.cu b/libnd4j/include/ops/declarable/helpers/cuda/image_draw_bounding_boxes.cu index 9b05f891a..9817471bb 100644 --- a/libnd4j/include/ops/declarable/helpers/cuda/image_draw_bounding_boxes.cu +++ b/libnd4j/include/ops/declarable/helpers/cuda/image_draw_bounding_boxes.cu @@ -17,10 +17,10 @@ // // @author sgazeos@gmail.com // -#include -#include +#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { @@ -58,22 +58,22 @@ namespace helpers { // box with shape //auto internalBox = &boxes[b * colorSetSize * 4 + c * 4];//(*boxes)(b, {0})(c, {0});//internalBoxes->at(c); auto colorIndex = boxIndex % colorTableLen;//colorSet->at(c); -// auto rowStart = nd4j::math::nd4j_max(Nd4jLong (0), Nd4jLong ((height - 1) * internalBox[0])); -// auto rowEnd = nd4j::math::nd4j_min(Nd4jLong (height - 1), Nd4jLong ((height - 1) * internalBox[2])); -// auto colStart = nd4j::math::nd4j_max(Nd4jLong (0), Nd4jLong ((width - 1) * internalBox[1])); -// auto colEnd = nd4j::math::nd4j_min(Nd4jLong(width - 1), Nd4jLong ((width - 1) * internalBox[3])); +// auto rowStart = sd::math::nd4j_max(Nd4jLong (0), Nd4jLong ((height - 1) * internalBox[0])); +// auto rowEnd = sd::math::nd4j_min(Nd4jLong (height - 1), Nd4jLong ((height - 1) * internalBox[2])); +// auto colStart = sd::math::nd4j_max(Nd4jLong (0), Nd4jLong ((width - 1) * internalBox[1])); +// auto colEnd = sd::math::nd4j_min(Nd4jLong(width - 1), Nd4jLong ((width - 1) * internalBox[3])); Nd4jLong indices0[] = {batch, boxIndex, 0}; Nd4jLong indices1[] = {batch, boxIndex, 1}; Nd4jLong indices2[] = {batch, boxIndex, 2}; Nd4jLong indices3[] = {batch, boxIndex, 3}; auto rowStart = Nd4jLong ((height - 1) * boxes[shape::getOffset(boxesShape, indices0, 0)]); - auto rowStartBound = nd4j::math::nd4j_max(Nd4jLong (0), rowStart); + auto rowStartBound = sd::math::nd4j_max(Nd4jLong (0), rowStart); auto rowEnd = Nd4jLong ((height - 1) * boxes[shape::getOffset(boxesShape, indices2, 0)]); - auto rowEndBound = nd4j::math::nd4j_min(Nd4jLong (height - 1), rowEnd); + auto rowEndBound = sd::math::nd4j_min(Nd4jLong (height - 1), rowEnd); auto colStart = Nd4jLong ((width - 1) * boxes[shape::getOffset(boxesShape, indices1, 0)]); - auto colStartBound = nd4j::math::nd4j_max(Nd4jLong (0), colStart); + auto colStartBound = sd::math::nd4j_max(Nd4jLong (0), colStart); auto colEnd = Nd4jLong ((width - 1) * boxes[shape::getOffset(boxesShape, indices3, 0)]); - auto colEndBound = nd4j::math::nd4j_min(Nd4jLong(width - 1), colEnd); + auto colEndBound = sd::math::nd4j_min(Nd4jLong(width - 1), colEnd); if (rowStart > rowEnd || colStart > colEnd) { // printf("helpers::drawBoundingBoxesFunctor: Bounding box (%lld, %lld, %lld, %lld) is inverted " // "and will not be drawn\n", rowStart, colStart, rowEnd, colEnd); @@ -137,7 +137,7 @@ namespace helpers { } template - void drawBoundingBoxesH(nd4j::LaunchContext* context, NDArray const* images, NDArray const* boxes, NDArray const* colors, NDArray* output) { + void drawBoundingBoxesH(sd::LaunchContext* context, NDArray const* images, NDArray const* boxes, NDArray const* colors, NDArray* output) { auto batchSize = images->sizeAt(0); auto height = images->sizeAt(1); auto width = images->sizeAt(2); @@ -158,7 +158,7 @@ namespace helpers { outputBuf, output->specialShapeInfo(), batchSize, width, height, channels, boxSize, colorsTable.lengthOf()); } - void drawBoundingBoxesFunctor(nd4j::LaunchContext * context, NDArray* images, NDArray* boxes, NDArray* colors, NDArray* output) { + void drawBoundingBoxesFunctor(sd::LaunchContext * context, NDArray* images, NDArray* boxes, NDArray* colors, NDArray* output) { // images - batch of 3D images with BW (last dim = 1), RGB (last dim = 3) or RGBA (last dim = 4) channel set // boxes - batch of 2D bounds with last dim (y_start, x_start, y_end, x_end) to compute i and j as // floor((height - 1 ) * y_start) => rowStart, floor((height - 1) * y_end) => rowEnd @@ -171,7 +171,7 @@ namespace helpers { BUILD_SINGLE_SELECTOR(output->dataType(), drawBoundingBoxesH, (context, images, boxes, colors, output), FLOAT_TYPES); NDArray::registerSpecialUse({output}, {images, boxes, colors}); } - BUILD_SINGLE_TEMPLATE(template void drawBoundingBoxesH, (nd4j::LaunchContext* context, NDArray const* images, NDArray const* boxes, NDArray const* colors, NDArray* output), FLOAT_TYPES); + BUILD_SINGLE_TEMPLATE(template void drawBoundingBoxesH, (sd::LaunchContext* context, NDArray const* images, NDArray const* boxes, NDArray const* colors, NDArray* output), FLOAT_TYPES); } } } diff --git a/libnd4j/include/ops/declarable/helpers/cuda/image_resize.cu b/libnd4j/include/ops/declarable/helpers/cuda/image_resize.cu index 6096f3a85..6a045bc8d 100644 --- a/libnd4j/include/ops/declarable/helpers/cuda/image_resize.cu +++ b/libnd4j/include/ops/declarable/helpers/cuda/image_resize.cu @@ -34,9 +34,9 @@ limitations under the License. // #include -#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { @@ -100,12 +100,12 @@ namespace helpers { for (Nd4jLong i = outSize - tid; i >= 0; i -= step) { double in = scaler(i, scale); // interpolationData[i].bottomIndex = static_cast(in); -// interpolationData[i].topIndex = nd4j::math::nd4j_min(interpolationData[i].bottomIndex + 1, inSize - 1); +// interpolationData[i].topIndex = sd::math::nd4j_min(interpolationData[i].bottomIndex + 1, inSize - 1); // interpolationData[i].interpolarValue = in - interpolationData[i].bottomIndex; - double const in_f = nd4j::math::p_floor(in); - double const in_c = nd4j::math::p_ceil(in); - interpolationData[i].bottomIndex = nd4j::math::nd4j_max(static_cast(in_f), (Nd4jLong)0LL);//static_cast(in); - interpolationData[i].topIndex = nd4j::math::nd4j_min(static_cast(in_c), inSize - 1); + double const in_f = sd::math::p_floor(in); + double const in_c = sd::math::p_ceil(in); + interpolationData[i].bottomIndex = sd::math::nd4j_max(static_cast(in_f), (Nd4jLong)0LL);//static_cast(in); + interpolationData[i].topIndex = sd::math::nd4j_min(static_cast(in_c), inSize - 1); interpolationData[i].interpolarValue = in - in_f; if (channels) { @@ -117,7 +117,7 @@ namespace helpers { //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// // resize image with bilinear interpolation algorithm // - static void resizeImage(nd4j::LaunchContext* context, NDArray const* images, Nd4jLong batchSize, Nd4jLong inHeight, Nd4jLong inWidth, Nd4jLong outHeight, + static void resizeImage(sd::LaunchContext* context, NDArray const* images, Nd4jLong batchSize, Nd4jLong inHeight, Nd4jLong inWidth, Nd4jLong outHeight, Nd4jLong outWidth, Nd4jLong channels, BilinearInterpolationData* xs_, BilinearInterpolationData* ys_, @@ -162,7 +162,7 @@ namespace helpers { //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// // resize image with template - static void resizeImage_(nd4j::LaunchContext* context, NDArray const* images, Nd4jLong batchSize, Nd4jLong inHeight, Nd4jLong inWidth, Nd4jLong outHeight, + static void resizeImage_(sd::LaunchContext* context, NDArray const* images, Nd4jLong batchSize, Nd4jLong inHeight, Nd4jLong inWidth, Nd4jLong outHeight, Nd4jLong outWidth, Nd4jLong channels, BilinearInterpolationData* xs_, BilinearInterpolationData* ys_, @@ -187,7 +187,7 @@ namespace helpers { //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// template - static int resizeBilinearFunctor_(nd4j::LaunchContext* context, NDArray const* images, int const width, + static int resizeBilinearFunctor_(sd::LaunchContext* context, NDArray const* images, int const width, int const height, bool const alignCorners, bool const halfPixelCenter, NDArray* output) { const Nd4jLong batchSize = images->sizeAt(0); const Nd4jLong inHeight = images->sizeAt(1); @@ -263,19 +263,19 @@ namespace helpers { { auto b = blockIdx.x; for (int y = threadIdx.x; y < outHeight; y += blockDim.x) { - auto posY = alignCorners ? static_cast(nd4j::math::p_round(halfPixelCenters?((float)y + 0.5f) * heightScale:(float)y * heightScale)) : static_cast(nd4j::math::p_floor( + auto posY = alignCorners ? static_cast(sd::math::p_round(halfPixelCenters?((float)y + 0.5f) * heightScale:(float)y * heightScale)) : static_cast(sd::math::p_floor( halfPixelCenters?((float)y + 0.5f) * heightScale:(float)y * heightScale)); - Nd4jLong inY = nd4j::math::nd4j_min(posY, inHeight - 1); + Nd4jLong inY = sd::math::nd4j_min(posY, inHeight - 1); if (halfPixelCenters) { - inY = nd4j::math::nd4j_max(0LL, inY); + inY = sd::math::nd4j_max(0LL, inY); } for (int x = threadIdx.y; x < outWidth; x += blockDim.y) { - auto posX = alignCorners ? static_cast(nd4j::math::p_round(halfPixelCenters?((float)x + 0.5f) * widthScale:(float)x * widthScale)) : static_cast(nd4j::math::p_floor( + auto posX = alignCorners ? static_cast(sd::math::p_round(halfPixelCenters?((float)x + 0.5f) * widthScale:(float)x * widthScale)) : static_cast(sd::math::p_floor( halfPixelCenters?((float)x + 0.5f) * widthScale:(float)x * widthScale)); - Nd4jLong inX = nd4j::math::nd4j_min(posX, inWidth - 1); + Nd4jLong inX = sd::math::nd4j_min(posX, inWidth - 1); if (halfPixelCenters) { - inX = nd4j::math::nd4j_max(0LL, inX); + inX = sd::math::nd4j_max(0LL, inX); } auto start = blockIdx.z * blockDim.z + threadIdx.z; @@ -298,7 +298,7 @@ namespace helpers { // resizeNeighborFunctor - main algorithm by nearest neighbor // template - int resizeNeighborFunctor_(nd4j::LaunchContext* context, NDArray const* images, int const width, int const height, + int resizeNeighborFunctor_(sd::LaunchContext* context, NDArray const* images, int const width, int const height, bool const alignCorners, bool const halfPixelCenters, NDArray* output) { const Nd4jLong batchSize = images->sizeAt(0); const Nd4jLong inHeight = images->sizeAt(1); @@ -339,7 +339,7 @@ namespace helpers { //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// // resizeImage - resize bilinear algorithm caller // - void resizeImage(nd4j::LaunchContext* context, NDArray const* images, Nd4jLong batchSize, Nd4jLong inHeight, + void resizeImage(sd::LaunchContext* context, NDArray const* images, Nd4jLong batchSize, Nd4jLong inHeight, Nd4jLong inWidth, Nd4jLong outHeight, Nd4jLong outWidth, Nd4jLong channels, BilinearInterpolationData* xs_, BilinearInterpolationData* ys_, NDArray* output) { BUILD_DOUBLE_SELECTOR(images->dataType(), output->dataType(), @@ -347,28 +347,28 @@ namespace helpers { xs_, ys_, output), NUMERIC_TYPES, FLOAT_TYPES); } - BUILD_DOUBLE_TEMPLATE(template void resizeImage_,(nd4j::LaunchContext* context, NDArray const* images, + BUILD_DOUBLE_TEMPLATE(template void resizeImage_,(sd::LaunchContext* context, NDArray const* images, Nd4jLong batchSize, Nd4jLong inHeight, Nd4jLong inWidth, Nd4jLong outHeight, Nd4jLong outWidth, Nd4jLong channels, BilinearInterpolationData* xs_, BilinearInterpolationData* ys_, NDArray* output), NUMERIC_TYPES, FLOAT_TYPES); //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// - int resizeBilinearFunctor(nd4j::LaunchContext* context, NDArray const* images, int width, int height, + int resizeBilinearFunctor(sd::LaunchContext* context, NDArray const* images, int width, int height, bool const alignCorners, bool const halfPixelCenter, NDArray* output) { BUILD_DOUBLE_SELECTOR(images->dataType(), output->dataType(), return resizeBilinearFunctor_, (context, images, width, height, alignCorners, halfPixelCenter, output), NUMERIC_TYPES, FLOAT_TYPES); } -// BUILD_SINGLE_TEMPLATE(template int resizeBilinearFunctor_, (nd4j::LaunchContext* context, +// BUILD_SINGLE_TEMPLATE(template int resizeBilinearFunctor_, (sd::LaunchContext* context, // NDArray const* images, int const width, int const height, bool const alignCorners, // bool const halfPixelCenter, NDArray* output), LIBND4J_TYPES); //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// - int resizeNeighborFunctor(nd4j::LaunchContext* context, NDArray const* images, int const width, int const height, + int resizeNeighborFunctor(sd::LaunchContext* context, NDArray const* images, int const width, int const height, bool const alignCorners, bool const halfPixelCenter, NDArray* output) { BUILD_SINGLE_SELECTOR(images->dataType(), return resizeNeighborFunctor_, (context, images, width, height, alignCorners, halfPixelCenter, output), LIBND4J_TYPES); } -// BUILD_SINGLE_TEMPLATE(template int resizeNeighborFunctor_, (nd4j::LaunchContext* context, NDArray const* images, +// BUILD_SINGLE_TEMPLATE(template int resizeNeighborFunctor_, (sd::LaunchContext* context, NDArray const* images, // int width, int height, bool const alignCorners, bool const halfPixelCenter, NDArray* output), LIBND4J_TYPES); //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// @@ -915,17 +915,17 @@ namespace helpers { } //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// template - int resizeBicubicFunctor_(nd4j::LaunchContext * context, NDArray const* image, int width, int height, + int resizeBicubicFunctor_(sd::LaunchContext * context, NDArray const* image, int width, int height, bool preserveAspectRatio, bool antialias, NDArray* output) { return Status::OK(); } - int resizeBicubicFunctor(nd4j::LaunchContext * context, NDArray const* image, int width, int height, + int resizeBicubicFunctor(sd::LaunchContext * context, NDArray const* image, int width, int height, bool preserveAspectRatio, bool antialias, NDArray* output) { BUILD_SINGLE_SELECTOR(image->dataType(), return resizeBicubicFunctor_, (context, image, width, height, preserveAspectRatio, antialias, output), NUMERIC_TYPES); } - BUILD_SINGLE_TEMPLATE(template int resizeBicubicFunctor_, (nd4j::LaunchContext * context, NDArray const* image, int width, int height, + BUILD_SINGLE_TEMPLATE(template int resizeBicubicFunctor_, (sd::LaunchContext * context, NDArray const* image, int width, int height, bool preserveAspectRatio, bool antialias, NDArray* output), NUMERIC_TYPES); // ------------------------------------------------------------------------------------------------------------------ // struct CachedInterpolation { @@ -1124,7 +1124,7 @@ namespace helpers { } // ------------------------------------------------------------------------------------------------------------------ // template - int resizeAreaFunctor_(nd4j::LaunchContext* context, NDArray const* image, int const width, int const height, + int resizeAreaFunctor_(sd::LaunchContext* context, NDArray const* image, int const width, int const height, bool const alignCorners, NDArray* output) { ImageResizerState st(alignCorners, false); // Create resize info @@ -1144,7 +1144,7 @@ namespace helpers { return res; } - int resizeAreaFunctor(nd4j::LaunchContext * context, NDArray const* image, int const width, int const height, + int resizeAreaFunctor(sd::LaunchContext * context, NDArray const* image, int const width, int const height, bool const alignCorners, NDArray* output) { BUILD_SINGLE_SELECTOR(image->dataType(), return resizeAreaFunctor_, (context, image, width, height, alignCorners, output), NUMERIC_TYPES); } @@ -1153,7 +1153,7 @@ namespace helpers { // simplified bicubic resize without antialiasing // template - int resizeBicubicFunctorA_(nd4j::LaunchContext * context, NDArray const* image, int width, int height, + int resizeBicubicFunctorA_(sd::LaunchContext * context, NDArray const* image, int width, int height, bool const alignCorners, bool const halfPixelCenters, NDArray* output) { ImageResizerState st(alignCorners, halfPixelCenters); // align_corners, half_pixel_align @@ -1166,16 +1166,16 @@ namespace helpers { return res; } - int resizeBicubicFunctorA(nd4j::LaunchContext * context, NDArray const* image, int width, int height, + int resizeBicubicFunctorA(sd::LaunchContext * context, NDArray const* image, int width, int height, bool const alignCorners, bool const halfPixelCenters, NDArray* output) { BUILD_SINGLE_SELECTOR(image->dataType(), return resizeBicubicFunctorA_, (context, image, width, height, alignCorners, halfPixelCenters, output), NUMERIC_TYPES); } - BUILD_SINGLE_TEMPLATE(template int resizeBicubicFunctorA_, (nd4j::LaunchContext * context, + BUILD_SINGLE_TEMPLATE(template int resizeBicubicFunctorA_, (sd::LaunchContext * context, NDArray const* image, int width, int height, bool const alignCorners, bool const halfPixelCenters, NDArray* output), NUMERIC_TYPES); //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// - int resizeFunctor(nd4j::LaunchContext * context, NDArray const* image, int width, int height, + int resizeFunctor(sd::LaunchContext * context, NDArray const* image, int width, int height, ImageResizeMethods method, bool preserveAspectRatio, bool antialias, NDArray* output) { switch (method) { case kResizeBilinear: return resizeBilinearFunctor(context, image, width, height, false, false, output); break; @@ -1240,8 +1240,8 @@ namespace helpers { } if (method == 0 /* bilinear */) { - const int topYIndex = nd4j::math::p_floor(inY); - const int bottomYIndex = nd4j::math::p_ceil(inY); + const int topYIndex = sd::math::p_floor(inY); + const int bottomYIndex = sd::math::p_ceil(inY); const float y_lerp = inY - topYIndex; for (int x = 0; x < cropWidth; ++x) { @@ -1326,7 +1326,7 @@ namespace helpers { // crops - output (4D tensor - [batch, outWidth, outHeight, pixels]) // template - void cropAndResizeFunctor_(nd4j::LaunchContext* context, NDArray const *images, NDArray const *boxes, NDArray const *indices, + void cropAndResizeFunctor_(sd::LaunchContext* context, NDArray const *images, NDArray const *boxes, NDArray const *indices, NDArray const *cropSize, int method, double extrapolationVal, NDArray *crops) { const int batchSize = images->sizeAt(0); const int imageHeight = images->sizeAt(1); @@ -1354,13 +1354,13 @@ namespace helpers { } //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// - void cropAndResizeFunctor(nd4j::LaunchContext * context, NDArray const *images, NDArray const *boxes, NDArray const *indices, NDArray const *cropSize, int method, double extrapolationVal, NDArray *crops) { + void cropAndResizeFunctor(sd::LaunchContext * context, NDArray const *images, NDArray const *boxes, NDArray const *indices, NDArray const *cropSize, int method, double extrapolationVal, NDArray *crops) { BUILD_TRIPLE_SELECTOR(images->dataType(), boxes->dataType(), indices->dataType(), cropAndResizeFunctor_, (context, images, boxes, indices, cropSize, method, extrapolationVal, crops), NUMERIC_TYPES, FLOAT_TYPES, INTEGER_TYPES); // } BUILD_TRIPLE_TEMPLATE(template void cropAndResizeFunctor_, - (nd4j::LaunchContext * context, NDArray const* images, NDArray const* boxes, NDArray const* indices, NDArray const* cropSize, int method, double extrapolationVal, NDArray* crops), + (sd::LaunchContext * context, NDArray const* images, NDArray const* boxes, NDArray const* indices, NDArray const* cropSize, int method, double extrapolationVal, NDArray* crops), NUMERIC_TYPES, FLOAT_TYPES, INTEGER_TYPES); } } diff --git a/libnd4j/include/ops/declarable/helpers/cuda/image_suppression.cu b/libnd4j/include/ops/declarable/helpers/cuda/image_suppression.cu index be2a71e8c..5ed534cb6 100644 --- a/libnd4j/include/ops/declarable/helpers/cuda/image_suppression.cu +++ b/libnd4j/include/ops/declarable/helpers/cuda/image_suppression.cu @@ -19,12 +19,12 @@ // #include -#include -#include -#include +#include +#include +#include #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// @@ -50,14 +50,14 @@ namespace helpers { // we have rectangle with given max values. Compute vexes of rectangle first - T minYPrev = nd4j::math::nd4j_min(boxes[shape::getOffset(boxesShape, previous0)], boxes[shape::getOffset(boxesShape, previous2)]); - T minXPrev = nd4j::math::nd4j_min(boxes[shape::getOffset(boxesShape, previous1)], boxes[shape::getOffset(boxesShape, previous3)]); - T maxYPrev = nd4j::math::nd4j_max(boxes[shape::getOffset(boxesShape, previous0)], boxes[shape::getOffset(boxesShape, previous2)]); - T maxXPrev = nd4j::math::nd4j_max(boxes[shape::getOffset(boxesShape, previous1)], boxes[shape::getOffset(boxesShape, previous3)]); - T minYNext = nd4j::math::nd4j_min(boxes[shape::getOffset(boxesShape, next0)], boxes[shape::getOffset(boxesShape, next2)]); - T minXNext = nd4j::math::nd4j_min(boxes[shape::getOffset(boxesShape, next1)], boxes[shape::getOffset(boxesShape, next3)]); - T maxYNext = nd4j::math::nd4j_max(boxes[shape::getOffset(boxesShape, next0)], boxes[shape::getOffset(boxesShape, next2)]); - T maxXNext = nd4j::math::nd4j_max(boxes[shape::getOffset(boxesShape, next1)], boxes[shape::getOffset(boxesShape, next3)]); + T minYPrev = sd::math::nd4j_min(boxes[shape::getOffset(boxesShape, previous0)], boxes[shape::getOffset(boxesShape, previous2)]); + T minXPrev = sd::math::nd4j_min(boxes[shape::getOffset(boxesShape, previous1)], boxes[shape::getOffset(boxesShape, previous3)]); + T maxYPrev = sd::math::nd4j_max(boxes[shape::getOffset(boxesShape, previous0)], boxes[shape::getOffset(boxesShape, previous2)]); + T maxXPrev = sd::math::nd4j_max(boxes[shape::getOffset(boxesShape, previous1)], boxes[shape::getOffset(boxesShape, previous3)]); + T minYNext = sd::math::nd4j_min(boxes[shape::getOffset(boxesShape, next0)], boxes[shape::getOffset(boxesShape, next2)]); + T minXNext = sd::math::nd4j_min(boxes[shape::getOffset(boxesShape, next1)], boxes[shape::getOffset(boxesShape, next3)]); + T maxYNext = sd::math::nd4j_max(boxes[shape::getOffset(boxesShape, next0)], boxes[shape::getOffset(boxesShape, next2)]); + T maxXNext = sd::math::nd4j_max(boxes[shape::getOffset(boxesShape, next1)], boxes[shape::getOffset(boxesShape, next3)]); // compute areas for comparation T areaPrev = (maxYPrev - minYPrev) * (maxXPrev - minXPrev); @@ -67,13 +67,13 @@ namespace helpers { if (areaNext <= T(0.f) || areaPrev <= T(0.f)) return false; // compute intersection of rectangles - T minIntersectionY = nd4j::math::nd4j_max(minYPrev, minYNext); - T minIntersectionX = nd4j::math::nd4j_max(minXPrev, minXNext); - T maxIntersectionY = nd4j::math::nd4j_min(maxYPrev, maxYNext); - T maxIntersectionX = nd4j::math::nd4j_min(maxXPrev, maxXNext); + T minIntersectionY = sd::math::nd4j_max(minYPrev, minYNext); + T minIntersectionX = sd::math::nd4j_max(minXPrev, minXNext); + T maxIntersectionY = sd::math::nd4j_min(maxYPrev, maxYNext); + T maxIntersectionX = sd::math::nd4j_min(maxXPrev, maxXNext); T intersectionArea = - nd4j::math::nd4j_max(T(maxIntersectionY - minIntersectionY), T(0.0f)) * - nd4j::math::nd4j_max(T(maxIntersectionX - minIntersectionX), T(0.0f)); + sd::math::nd4j_max(T(maxIntersectionY - minIntersectionY), T(0.0f)) * + sd::math::nd4j_max(T(maxIntersectionX - minIntersectionX), T(0.0f)); T intersectionValue = intersectionArea / (areaPrev + areaNext - intersectionArea); // final check return intersectionValue > threshold; @@ -92,14 +92,14 @@ namespace helpers { // we have rectangle with given max values. Compute vexes of rectangle first - T minYPrev = nd4j::math::nd4j_min(boxes[shape::getOffset(boxesShape, previous0)], boxes[shape::getOffset(boxesShape, previous2)]); - T minXPrev = nd4j::math::nd4j_min(boxes[shape::getOffset(boxesShape, previous1)], boxes[shape::getOffset(boxesShape, previous3)]); - T maxYPrev = nd4j::math::nd4j_max(boxes[shape::getOffset(boxesShape, previous0)], boxes[shape::getOffset(boxesShape, previous2)]); - T maxXPrev = nd4j::math::nd4j_max(boxes[shape::getOffset(boxesShape, previous1)], boxes[shape::getOffset(boxesShape, previous3)]); - T minYNext = nd4j::math::nd4j_min(boxes[shape::getOffset(boxesShape, next0)], boxes[shape::getOffset(boxesShape, next2)]); - T minXNext = nd4j::math::nd4j_min(boxes[shape::getOffset(boxesShape, next1)], boxes[shape::getOffset(boxesShape, next3)]); - T maxYNext = nd4j::math::nd4j_max(boxes[shape::getOffset(boxesShape, next0)], boxes[shape::getOffset(boxesShape, next2)]); - T maxXNext = nd4j::math::nd4j_max(boxes[shape::getOffset(boxesShape, next1)], boxes[shape::getOffset(boxesShape, next3)]); + T minYPrev = sd::math::nd4j_min(boxes[shape::getOffset(boxesShape, previous0)], boxes[shape::getOffset(boxesShape, previous2)]); + T minXPrev = sd::math::nd4j_min(boxes[shape::getOffset(boxesShape, previous1)], boxes[shape::getOffset(boxesShape, previous3)]); + T maxYPrev = sd::math::nd4j_max(boxes[shape::getOffset(boxesShape, previous0)], boxes[shape::getOffset(boxesShape, previous2)]); + T maxXPrev = sd::math::nd4j_max(boxes[shape::getOffset(boxesShape, previous1)], boxes[shape::getOffset(boxesShape, previous3)]); + T minYNext = sd::math::nd4j_min(boxes[shape::getOffset(boxesShape, next0)], boxes[shape::getOffset(boxesShape, next2)]); + T minXNext = sd::math::nd4j_min(boxes[shape::getOffset(boxesShape, next1)], boxes[shape::getOffset(boxesShape, next3)]); + T maxYNext = sd::math::nd4j_max(boxes[shape::getOffset(boxesShape, next0)], boxes[shape::getOffset(boxesShape, next2)]); + T maxXNext = sd::math::nd4j_max(boxes[shape::getOffset(boxesShape, next1)], boxes[shape::getOffset(boxesShape, next3)]); // compute areas for comparation T areaPrev = (maxYPrev - minYPrev) * (maxXPrev - minXPrev); @@ -109,13 +109,13 @@ namespace helpers { if (areaNext <= T(0.f) || areaPrev <= T(0.f)) return false; // compute intersection of rectangles - T minIntersectionY = nd4j::math::nd4j_max(minYPrev, minYNext); - T minIntersectionX = nd4j::math::nd4j_max(minXPrev, minXNext); - T maxIntersectionY = nd4j::math::nd4j_min(maxYPrev, maxYNext); - T maxIntersectionX = nd4j::math::nd4j_min(maxXPrev, maxXNext); + T minIntersectionY = sd::math::nd4j_max(minYPrev, minYNext); + T minIntersectionX = sd::math::nd4j_max(minXPrev, minXNext); + T maxIntersectionY = sd::math::nd4j_min(maxYPrev, maxYNext); + T maxIntersectionX = sd::math::nd4j_min(maxXPrev, maxXNext); T intersectionArea = - nd4j::math::nd4j_max(T(maxIntersectionY - minIntersectionY), T(0.0f)) * - nd4j::math::nd4j_max(T(maxIntersectionX - minIntersectionX), T(0.0f)); + sd::math::nd4j_max(T(maxIntersectionY - minIntersectionY), T(0.0f)) * + sd::math::nd4j_max(T(maxIntersectionX - minIntersectionX), T(0.0f)); T intersectionValue = intersectionArea / (areaPrev + areaNext - intersectionArea); // final check return intersectionValue; @@ -185,7 +185,7 @@ namespace helpers { // nonMaxSuppressionV2 algorithm - given from TF NonMaxSuppressionV2 implementation // template - static void nonMaxSuppressionV2_(nd4j::LaunchContext* context, NDArray* boxes, NDArray* scales, int maxSize, double threshold, double scoreThreshold, NDArray* output) { + static void nonMaxSuppressionV2_(sd::LaunchContext* context, NDArray* boxes, NDArray* scales, int maxSize, double threshold, double scoreThreshold, NDArray* output) { auto stream = context->getCudaStream(); NDArray::prepareSpecialUse({output}, {boxes, scales}); std::unique_ptr indices(NDArrayFactory::create_('c', {scales->lengthOf()})); // - 1, scales->lengthOf()); //, scales->getContext()); @@ -335,7 +335,7 @@ namespace helpers { //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// template static Nd4jLong - nonMaxSuppressionGeneric_(nd4j::LaunchContext* context, NDArray* boxes, NDArray* scores, int outputSize, + nonMaxSuppressionGeneric_(sd::LaunchContext* context, NDArray* boxes, NDArray* scores, int outputSize, double overlapThreshold, double scoreThreshold, NDArray* output, bool simple) { auto stream = context->getCudaStream(); if (output) @@ -386,14 +386,14 @@ namespace helpers { return res; } //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// - void nonMaxSuppression(nd4j::LaunchContext * context, NDArray* boxes, NDArray* scales, int maxSize, double threshold, double scoreThreshold, NDArray* output) { + void nonMaxSuppression(sd::LaunchContext * context, NDArray* boxes, NDArray* scales, int maxSize, double threshold, double scoreThreshold, NDArray* output) { BUILD_DOUBLE_SELECTOR(boxes->dataType(), output->dataType(), nonMaxSuppressionV2_, (context, boxes, scales, maxSize, threshold, scoreThreshold, output), FLOAT_TYPES, INDEXING_TYPES); } //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// - Nd4jLong nonMaxSuppressionGeneric(nd4j::LaunchContext * context, NDArray* boxes, NDArray* scales, int maxSize, double threshold, double scoreThreshold, NDArray* output) { + Nd4jLong nonMaxSuppressionGeneric(sd::LaunchContext * context, NDArray* boxes, NDArray* scales, int maxSize, double threshold, double scoreThreshold, NDArray* output) { BUILD_DOUBLE_SELECTOR(boxes->dataType(), output ? output->dataType():DataType::INT32, return nonMaxSuppressionGeneric_, (context, boxes, scales, maxSize, threshold, scoreThreshold, output, true), FLOAT_TYPES, INDEXING_TYPES); @@ -401,7 +401,7 @@ namespace helpers { } Nd4jLong - nonMaxSuppressionV3(nd4j::LaunchContext* context, NDArray* boxes, NDArray* scores, int maxSize, + nonMaxSuppressionV3(sd::LaunchContext* context, NDArray* boxes, NDArray* scores, int maxSize, double overlapThreshold, double scoreThreshold, NDArray* output) { BUILD_DOUBLE_SELECTOR(boxes->dataType(), output ? output->dataType():DataType::INT32, return nonMaxSuppressionGeneric_, (context, boxes, scores, maxSize, overlapThreshold, scoreThreshold, output, false), diff --git a/libnd4j/include/ops/declarable/helpers/cuda/imagesHelpers.cu b/libnd4j/include/ops/declarable/helpers/cuda/imagesHelpers.cu index 35393c48c..54f306ef7 100644 --- a/libnd4j/include/ops/declarable/helpers/cuda/imagesHelpers.cu +++ b/libnd4j/include/ops/declarable/helpers/cuda/imagesHelpers.cu @@ -19,14 +19,14 @@ // @author Oleh Semeniv (oleg.semeniv@gmail.com) // -#include +#include #include #include #include -#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { @@ -67,10 +67,10 @@ linkage void rgbToYuvCudaLauncher(const int blocksPerGrid, const int threadsPerB } /////////////////////////////////////////////////////////////////// -void transformRgbYuv(nd4j::LaunchContext* context, const NDArray& input, NDArray& output, const int dimC) { +void transformRgbYuv(sd::LaunchContext* context, const NDArray& input, NDArray& output, const int dimC) { - auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(input.getShapeInfo(), { dimC }); - auto packZ = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(output.getShapeInfo(), { dimC }); + auto packX = sd::ConstantTadHelper::getInstance()->tadForDimensions(input.getShapeInfo(), { dimC }); + auto packZ = sd::ConstantTadHelper::getInstance()->tadForDimensions(output.getShapeInfo(), { dimC }); const Nd4jLong numOfTads = packX.numberOfTads(); @@ -122,10 +122,10 @@ linkage void yuvToRgbCudaLauncher(const int blocksPerGrid, const int threadsPerB } /////////////////////////////////////////////////////////////////// -void transformYuvRgb(nd4j::LaunchContext* context, const NDArray& input, NDArray& output, const int dimC) { +void transformYuvRgb(sd::LaunchContext* context, const NDArray& input, NDArray& output, const int dimC) { - auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(input.getShapeInfo(), { dimC }); - auto packZ = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(output.getShapeInfo(), { dimC }); + auto packX = sd::ConstantTadHelper::getInstance()->tadForDimensions(input.getShapeInfo(), { dimC }); + auto packZ = sd::ConstantTadHelper::getInstance()->tadForDimensions(output.getShapeInfo(), { dimC }); const Nd4jLong numOfTads = packX.numberOfTads(); @@ -149,19 +149,19 @@ __global__ void rgbToGrsCuda(const void *vx, const Nd4jLong *xShapeInfo, void *v const auto x = reinterpret_cast(vx); auto z = reinterpret_cast(vz); - __shared__ Nd4jLong zLen, *sharedMem; - __shared__ int rank; // xRank == zRank + __shared__ Nd4jLong zLen; + __shared__ int rank, *sharedMem; // xRank == zRank if (threadIdx.x == 0) { extern __shared__ unsigned char shmem[]; - sharedMem = reinterpret_cast(shmem); + sharedMem = reinterpret_cast(shmem); zLen = shape::length(zShapeInfo); rank = shape::rank(zShapeInfo); } __syncthreads(); - Nd4jLong* coords = sharedMem + threadIdx.x * rank; + auto coords = sharedMem + threadIdx.x * rank; for (Nd4jLong i = blockIdx.x * blockDim.x + threadIdx.x; i < zLen; i += gridDim.x * blockDim.x) { @@ -191,13 +191,13 @@ linkage void rgbToGrsCudaLauncher(const int blocksPerGrid, const int threadsPerB } /////////////////////////////////////////////////////////////////// -void transformRgbGrs(nd4j::LaunchContext* context, const NDArray& input, NDArray& output, const int dimC) { +void transformRgbGrs(sd::LaunchContext* context, const NDArray& input, NDArray& output, const int dimC) { PointersManager manager(context, "rgbToGrs"); const int threadsPerBlock = MAX_NUM_THREADS / 4; const int blocksPerGrid = (input.lengthOf() + threadsPerBlock - 1) / threadsPerBlock; - const int sharedMem = input.rankOf() * sizeof(Nd4jLong) * threadsPerBlock + 128; + const int sharedMem = input.rankOf() * sizeof(int) * threadsPerBlock + 128; NDArray::prepareSpecialUse({&output}, {&input}); BUILD_SINGLE_SELECTOR(input.dataType(), rgbToGrsCudaLauncher, (blocksPerGrid, threadsPerBlock, sharedMem, context->getCudaStream(), input.getSpecialBuffer(), input.getSpecialShapeInfo(), output.getSpecialBuffer(), output.getSpecialShapeInfo(), dimC), NUMERIC_TYPES); @@ -285,10 +285,10 @@ static _CUDA_H void rgbToHsvCudaLauncher(const int blocksPerGrid, const int thre } /////////////////////////////////////////////////////////////////// -void transformHsvRgb(nd4j::LaunchContext* context, const NDArray* input, NDArray* output, const int dimC) { +void transformHsvRgb(sd::LaunchContext* context, const NDArray* input, NDArray* output, const int dimC) { - auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), {dimC}); - auto packZ = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), {dimC}); + auto packX = sd::ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), {dimC}); + auto packZ = sd::ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), {dimC}); const Nd4jLong numOfTads = packX.numberOfTads(); @@ -305,9 +305,9 @@ void transformHsvRgb(nd4j::LaunchContext* context, const NDArray* input, NDArray } /////////////////////////////////////////////////////////////////// -void transformRgbHsv(nd4j::LaunchContext* context, const NDArray* input, NDArray* output, const int dimC) { - auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), {dimC}); - auto packZ = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), {dimC}); +void transformRgbHsv(sd::LaunchContext* context, const NDArray* input, NDArray* output, const int dimC) { + auto packX = sd::ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), {dimC}); + auto packZ = sd::ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), {dimC}); const Nd4jLong numOfTads = packX.numberOfTads(); @@ -388,9 +388,9 @@ __global__ void tripleTransformerCuda(const void *vx, const Nd4jLong *xShapeInfo template -static void rgbYiq(nd4j::LaunchContext* context, const NDArray* input, NDArray* output, const int dimC) { - auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), dimC); - auto packZ = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), dimC); +static void rgbYiq(sd::LaunchContext* context, const NDArray* input, NDArray* output, const int dimC) { + auto packX = sd::ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), dimC); + auto packZ = sd::ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), dimC); NDArray::prepareSpecialUse({output}, {input}); return tripleTransformerCuda<<<256, 256, 8192, *context->getCudaStream()>>>(input->getSpecialBuffer(), input->getSpecialShapeInfo(), packX.platformShapeInfo(), packX.platformOffsets(), output->specialBuffer(), output->specialShapeInfo(), packZ.platformShapeInfo(), packZ.platformOffsets(), dimC, 1, packZ.numberOfTads()); @@ -398,20 +398,20 @@ static void rgbYiq(nd4j::LaunchContext* context, const NDArray* input, NDArray* } template -FORCEINLINE static void yiqRgb(nd4j::LaunchContext* context, const NDArray* input, NDArray* output, const int dimC) { - auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), dimC); - auto packZ = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), dimC); +FORCEINLINE static void yiqRgb(sd::LaunchContext* context, const NDArray* input, NDArray* output, const int dimC) { + auto packX = sd::ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), dimC); + auto packZ = sd::ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), dimC); NDArray::prepareSpecialUse({output}, {input}); return tripleTransformerCuda<<<256, 256, 8192, *context->getCudaStream()>>>(input->getSpecialBuffer(), input->getSpecialShapeInfo(), packX.platformShapeInfo(), packX.platformOffsets(), output->specialBuffer(), output->specialShapeInfo(), packZ.platformShapeInfo(), packZ.platformOffsets(), dimC, 2, packZ.numberOfTads()); NDArray::registerSpecialUse({output}, {input}); } -void transformYiqRgb(nd4j::LaunchContext* context, const NDArray* input, NDArray* output, const int dimC) { +void transformYiqRgb(sd::LaunchContext* context, const NDArray* input, NDArray* output, const int dimC) { BUILD_SINGLE_SELECTOR(input->dataType(), yiqRgb, (context, input, output, dimC), FLOAT_TYPES); } -void transformRgbYiq(nd4j::LaunchContext* context, const NDArray* input, NDArray* output, const int dimC) { +void transformRgbYiq(sd::LaunchContext* context, const NDArray* input, NDArray* output, const int dimC) { BUILD_SINGLE_SELECTOR(input->dataType(), rgbYiq, (context, input, output, dimC), FLOAT_TYPES); } diff --git a/libnd4j/include/ops/declarable/helpers/cuda/ismax.cu b/libnd4j/include/ops/declarable/helpers/cuda/ismax.cu index bf6a943fa..27f4f35f2 100644 --- a/libnd4j/include/ops/declarable/helpers/cuda/ismax.cu +++ b/libnd4j/include/ops/declarable/helpers/cuda/ismax.cu @@ -24,16 +24,16 @@ #include #include #include -#include -#include +#include +#include #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { template -static void ismax_(nd4j::LaunchContext * context, const NDArray* input, NDArray* output, const std::vector& dimensions) { +static void ismax_(sd::LaunchContext * context, const NDArray* input, NDArray* output, const std::vector& dimensions) { auto stream = context->getCudaStream(); auto xRank = input->rankOf(); @@ -61,7 +61,7 @@ static void ismax_(nd4j::LaunchContext * context, const NDArray* input, NDArray* int dimensionLength = dimensions.size(); std::vector copy(dimensions); - auto packZ = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), copy.data(), copy.size()); + auto packZ = sd::ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), copy.data(), copy.size()); // we launch legacy IndexMax op, to get indices of max values along dimension auto indexMaxArr = input->applyIndexReduce(indexreduce::IndexMax, dimensions); @@ -76,7 +76,7 @@ static void ismax_(nd4j::LaunchContext * context, const NDArray* input, NDArray* } -void ismax(nd4j::LaunchContext * context, const NDArray *input, NDArray *output, const std::vector& dimensions) { +void ismax(sd::LaunchContext * context, const NDArray *input, NDArray *output, const std::vector& dimensions) { NDArray::prepareSpecialUse({output}, {input}); BUILD_SINGLE_SELECTOR(input->dataType(), ismax_, (context, input, output, dimensions), LIBND4J_TYPES); @@ -84,7 +84,7 @@ void ismax(nd4j::LaunchContext * context, const NDArray *input, NDArray *output, NDArray::registerSpecialUse({output}, {input}); } -BUILD_SINGLE_TEMPLATE(template void ismax_, (nd4j::LaunchContext * context, const NDArray *input, NDArray *output, const std::vector& dimensions), LIBND4J_TYPES); +BUILD_SINGLE_TEMPLATE(template void ismax_, (sd::LaunchContext * context, const NDArray *input, NDArray *output, const std::vector& dimensions), LIBND4J_TYPES); } } diff --git a/libnd4j/include/ops/declarable/helpers/cuda/legacy/relu.cu b/libnd4j/include/ops/declarable/helpers/cuda/legacy/relu.cu index a3d24111a..ab65ed96b 100644 --- a/libnd4j/include/ops/declarable/helpers/cuda/legacy/relu.cu +++ b/libnd4j/include/ops/declarable/helpers/cuda/legacy/relu.cu @@ -20,10 +20,10 @@ #include #include -#include -#include +#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { @@ -36,7 +36,7 @@ namespace nd4j { theFirst->applyPairwiseLambda(*theSecond, functor, *theFirst); } - void reluDerivative(nd4j::LaunchContext * context, NDArray* theFirst, NDArray* theSecond) { + void reluDerivative(sd::LaunchContext * context, NDArray* theFirst, NDArray* theSecond) { BUILD_SINGLE_SELECTOR(theFirst->dataType(), reluDerivative__, (theFirst, theSecond), FLOAT_TYPES); } @@ -49,7 +49,7 @@ namespace nd4j { input->applyPairwiseLambda(*epsilon, functor, *output); } - void reluDerivative(nd4j::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput) { + void reluDerivative(sd::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput) { BUILD_SINGLE_SELECTOR(theFirst->dataType(), reluDerivative_, (theFirst, theSecond, theOutput), FLOAT_TYPES); } @@ -62,7 +62,7 @@ namespace nd4j { input->applyPairwiseLambda(*epsilon, functor, *output); } - void relu6Derivative(nd4j::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput) { + void relu6Derivative(sd::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput) { BUILD_SINGLE_SELECTOR(theFirst->dataType(), relu6Derivative_, (theFirst, theSecond, theOutput), FLOAT_TYPES); } @@ -78,7 +78,7 @@ namespace nd4j { input->applyPairwiseLambda(*epsilon, functor, *output); } - void leakyReluDerivative(nd4j::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput, const float alpha) { + void leakyReluDerivative(sd::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput, const float alpha) { BUILD_SINGLE_SELECTOR(theFirst->dataType(), leakyReluDerivative_, (theFirst, theSecond, theOutput, alpha), FLOAT_TYPES); } @@ -88,13 +88,13 @@ namespace nd4j { const T alphaT = static_cast(alpha); auto functor = LAMBDA_TT(x, y, alphaT){ - return y * nd4j::math::nd4j_eluderivative(x, alphaT); + return y * sd::math::nd4j_eluderivative(x, alphaT); }; input->applyPairwiseLambda(*epsilon, functor, *output); } - void eluDerivative(nd4j::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput, const float alpha) { + void eluDerivative(sd::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput, const float alpha) { BUILD_SINGLE_SELECTOR(theFirst->dataType(), eluDerivative_, (theFirst, theSecond, theOutput, alpha), FLOAT_TYPES); } @@ -107,7 +107,7 @@ namespace nd4j { input->applyPairwiseLambda(*epsilon, functor, *output); } - void seluDerivative(nd4j::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput) { + void seluDerivative(sd::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput) { BUILD_SINGLE_SELECTOR(theFirst->dataType(), seluDerivative_, (theFirst, theSecond, theOutput), FLOAT_TYPES); } } diff --git a/libnd4j/include/ops/declarable/helpers/cuda/legacy/tanh.cu b/libnd4j/include/ops/declarable/helpers/cuda/legacy/tanh.cu index afd07cd48..56a57614f 100644 --- a/libnd4j/include/ops/declarable/helpers/cuda/legacy/tanh.cu +++ b/libnd4j/include/ops/declarable/helpers/cuda/legacy/tanh.cu @@ -19,25 +19,25 @@ // #include -#include +#include #include -#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { //////////////////////////////////////////////////////////////////////// template linkage void tanhDerivative_(NDArray* input, NDArray* epsilon, NDArray* output) { auto functor = LAMBDA_TT(x, y){ - T th = nd4j::math::nd4j_tanh(x); + T th = sd::math::nd4j_tanh(x); return y * ((T)1.0f - (th * th)); }; input->applyPairwiseLambda(*epsilon, functor, *output); } - void tanhDerivative(nd4j::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput) { + void tanhDerivative(sd::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput) { BUILD_SINGLE_SELECTOR(theFirst->dataType(), tanhDerivative_, (theFirst, theSecond, theOutput), FLOAT_TYPES); } @@ -45,14 +45,14 @@ namespace nd4j { template linkage void hardTanhDerivative_(NDArray* input, NDArray* epsilon, NDArray* output) { auto functor = LAMBDA_TT(x, y){ - T th = nd4j::math::nd4j_tanh(x); + T th = sd::math::nd4j_tanh(x); return y * simdOps::HardTanhDerivative::op(x, nullptr); }; input->applyPairwiseLambda(*epsilon, functor, *output); } - void hardTanhDerivative(nd4j::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput) { + void hardTanhDerivative(sd::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput) { BUILD_SINGLE_SELECTOR(theFirst->dataType(), hardTanhDerivative_, (theFirst, theSecond, theOutput), FLOAT_TYPES); } @@ -65,20 +65,20 @@ namespace nd4j { input->applyPairwiseLambda(*epsilon, functor, *output); } - void rationalTanhDerivative(nd4j::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput) { + void rationalTanhDerivative(sd::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput) { BUILD_SINGLE_SELECTOR(theFirst->dataType(), rationalTanhDerivative_, (theFirst, theSecond, theOutput), FLOAT_TYPES); } template linkage void rectifiedTanhDerivative_(NDArray* input, NDArray* epsilon, NDArray* output) { auto functor = LAMBDA_TT(x, y){ - return x > (T) 0.0f ? y * (nd4j::math::nd4j_tanhderivative(x)) : (T) 0.0f; + return x > (T) 0.0f ? y * (sd::math::nd4j_tanhderivative(x)) : (T) 0.0f; }; input->applyPairwiseLambda(*epsilon, functor, *output); } - void rectifiedTanhDerivative(nd4j::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput) { + void rectifiedTanhDerivative(sd::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput) { BUILD_SINGLE_SELECTOR(theFirst->dataType(), rectifiedTanhDerivative_, (theFirst, theSecond, theOutput), FLOAT_TYPES); } } diff --git a/libnd4j/include/ops/declarable/helpers/cuda/legacy_helper.cu b/libnd4j/include/ops/declarable/helpers/cuda/legacy_helper.cu index fb4a94abb..181050919 100644 --- a/libnd4j/include/ops/declarable/helpers/cuda/legacy_helper.cu +++ b/libnd4j/include/ops/declarable/helpers/cuda/legacy_helper.cu @@ -19,11 +19,11 @@ // #include -#include -#include +#include +#include #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { @@ -38,7 +38,7 @@ namespace helpers { } //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// - void cubeDerivative(nd4j::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput) { + void cubeDerivative(sd::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput) { BUILD_SINGLE_SELECTOR(theFirst->dataType(), cubeDerivative_, (theFirst, theSecond, theOutput), FLOAT_TYPES); } @@ -54,7 +54,7 @@ namespace helpers { } //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// - void reduceNorm1(nd4j::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput) { + void reduceNorm1(sd::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput) { BUILD_SINGLE_SELECTOR(theFirst->dataType(), reduceNorm1_, (theFirst, theSecond, theOutput), FLOAT_TYPES); } @@ -63,14 +63,14 @@ namespace helpers { template linkage void sigmCrossEntropy_(NDArray* logits, NDArray* labels, NDArray* output) { auto functor = LAMBDA_TT(x, y){ - return nd4j::math::nd4j_max(x, (T)0.f) - x * y + nd4j::math::nd4j_log((T)1.f + nd4j::math::nd4j_exp(-nd4j::math::nd4j_abs(x))); + return sd::math::nd4j_max(x, (T)0.f) - x * y + sd::math::nd4j_log((T)1.f + sd::math::nd4j_exp(-sd::math::nd4j_abs(x))); }; logits->applyPairwiseLambda(*labels, functor, *output); } //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// - void sigmCrossEntropy(nd4j::LaunchContext * context, NDArray* logits, NDArray* labels, NDArray* output) { + void sigmCrossEntropy(sd::LaunchContext * context, NDArray* logits, NDArray* labels, NDArray* output) { BUILD_SINGLE_SELECTOR(logits->dataType(), sigmCrossEntropy_, (logits, labels, output), FLOAT_TYPES); } @@ -81,26 +81,26 @@ namespace helpers { // 1 - labels - 1 / (1 + exp(logits)) auto functor = LAMBDA_TT(x, y) { if(x <= 0) - return static_cast(1.) - y - static_cast(1.) / (static_cast(1.) + nd4j::math::nd4j_exp(x)); - auto e = nd4j::math::nd4j_exp(-x); + return static_cast(1.) - y - static_cast(1.) / (static_cast(1.) + sd::math::nd4j_exp(x)); + auto e = sd::math::nd4j_exp(-x); return static_cast(1.) - y - e / (static_cast(1.) + e); }; logits->applyPairwiseLambda(*labels, functor, *output); } //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// - void sigmCrossEntropyGrad(nd4j::LaunchContext * context, NDArray* logits, NDArray* labels, NDArray* output) { + void sigmCrossEntropyGrad(sd::LaunchContext * context, NDArray* logits, NDArray* labels, NDArray* output) { BUILD_SINGLE_SELECTOR(logits->dataType(), sigmCrossEntropyGrad_, (logits, labels, output), FLOAT_TYPES); } //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// - // X f = (X) 1.0f + nd4j::math::nd4j_abs(d1); + // X f = (X) 1.0f + sd::math::nd4j_abs(d1); // return (X) d2 * ((X) 1.0f / (f * f)); // template linkage void softSignDerivative_(NDArray* input, NDArray* epsilon, NDArray* output) { auto functor = LAMBDA_TT(x, y){ - T ss = (T)1.f + nd4j::math::nd4j_abs(x); + T ss = (T)1.f + sd::math::nd4j_abs(x); return y * ((T) 1.0f / (ss * ss)); }; @@ -108,7 +108,7 @@ namespace helpers { } //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// - void softSignDerivative(nd4j::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput) { + void softSignDerivative(sd::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput) { BUILD_SINGLE_SELECTOR(theFirst->dataType(), softSignDerivative_, (theFirst, theSecond, theOutput), FLOAT_TYPES); } @@ -116,14 +116,14 @@ namespace helpers { template linkage void softPlusDerivative_(NDArray* input, NDArray* epsilon, NDArray* output) { auto functor = LAMBDA_TT(x, y){ - T p = nd4j::math::nd4j_pow(static_cast(M_E), x); + T p = sd::math::nd4j_pow(static_cast(M_E), x); return y * (p / (p + 1.)); }; input->applyPairwiseLambda(*epsilon, functor, *output); } - void softPlusDerivative(nd4j::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput) { + void softPlusDerivative(sd::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput) { BUILD_SINGLE_SELECTOR(theFirst->dataType(), softPlusDerivative_, (theFirst, theSecond, theOutput), FLOAT_TYPES); } //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// @@ -134,14 +134,14 @@ namespace helpers { template linkage void sigmoidDerivative_(NDArray* input, NDArray* epsilon, NDArray* output) { auto functor = LAMBDA_TT(x, y){ - T s = nd4j::math::nd4j_sigmoid(x); + T s = sd::math::nd4j_sigmoid(x); return y * (s * ((T) 1.0f - s)); }; input->applyPairwiseLambda(*epsilon, functor, *output); } - void sigmoidDerivative(nd4j::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput) { + void sigmoidDerivative(sd::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput) { BUILD_SINGLE_SELECTOR(theFirst->dataType(), sigmoidDerivative_, (theFirst, theSecond, theOutput), FLOAT_TYPES); } @@ -154,7 +154,7 @@ namespace helpers { input->applyPairwiseLambda(*epsilon, functor, *output); } - void hardSigmoidDerivative(nd4j::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput) { + void hardSigmoidDerivative(sd::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput) { BUILD_SINGLE_SELECTOR(theFirst->dataType(), hardSigmoidDerivative_, (theFirst, theSecond, theOutput), FLOAT_TYPES); } @@ -192,12 +192,12 @@ namespace helpers { } //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// - void logSumExp(nd4j::LaunchContext * context, NDArray* input, NDArray* axis, NDArray* output) { + void logSumExp(sd::LaunchContext * context, NDArray* input, NDArray* axis, NDArray* output) { BUILD_SINGLE_SELECTOR(input->dataType(), logSumExp_, (input, axis, output), FLOAT_TYPES); } //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// - void logSumExp(nd4j::LaunchContext * context, NDArray* input, NDArray* subtrah, NDArray* axis, NDArray* output) { + void logSumExp(sd::LaunchContext * context, NDArray* input, NDArray* subtrah, NDArray* axis, NDArray* output) { BUILD_SINGLE_SELECTOR(input->dataType(), logSumExp_, (input, subtrah, axis, output), FLOAT_TYPES); } @@ -210,15 +210,15 @@ namespace helpers { auto mainRoutineT1 = LAMBDA_TT(_x, _z, posWeight) { T targetWeight = (1. + (posWeight - (T)1.f) * _z); return (1. - _z) * _x + - targetWeight * (nd4j::math::nd4j_log((T)1.f + nd4j::math::nd4j_exp(-nd4j::math::nd4j_abs(_x))) + - nd4j::math::nd4j_max(-_x, T(0.f)) + targetWeight * (sd::math::nd4j_log((T)1.f + sd::math::nd4j_exp(-sd::math::nd4j_abs(_x))) + + sd::math::nd4j_max(-_x, T(0.f)) ); }; auto mainRoutineT2 = LAMBDA_TTT(_x, _z, _w) { return (((T)1.0 - _z) * _x) + - _w * (nd4j::math::nd4j_log(T(1.) + nd4j::math::nd4j_exp(-nd4j::math::nd4j_abs(_x))) + - nd4j::math::nd4j_max(-_x, T(0.f))); + _w * (sd::math::nd4j_log(T(1.) + sd::math::nd4j_exp(-sd::math::nd4j_abs(_x))) + + sd::math::nd4j_max(-_x, T(0.f))); }; @@ -236,7 +236,7 @@ namespace helpers { } } //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// - void weightedCrossEntropyWithLogitsFunctor(nd4j::LaunchContext * context, NDArray const* targets, NDArray const* input, NDArray const* weights, NDArray* output) { + void weightedCrossEntropyWithLogitsFunctor(sd::LaunchContext * context, NDArray const* targets, NDArray const* input, NDArray const* weights, NDArray* output) { NDArray::prepareSpecialUse({output}, {targets, input, weights}); BUILD_SINGLE_SELECTOR(targets->dataType(), weightedCrossEntropyWithLogitsFunctor_, (targets, input, weights, output), FLOAT_TYPES); diff --git a/libnd4j/include/ops/declarable/helpers/cuda/lgamma.cu b/libnd4j/include/ops/declarable/helpers/cuda/lgamma.cu index ea9901f0a..2de455d0f 100644 --- a/libnd4j/include/ops/declarable/helpers/cuda/lgamma.cu +++ b/libnd4j/include/ops/declarable/helpers/cuda/lgamma.cu @@ -23,7 +23,7 @@ //#include //#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { @@ -39,7 +39,7 @@ void lgamma_(NDArray& x, NDArray& z) { x.applyLambda(lgammaProc, z); } -void lgamma(nd4j::LaunchContext* context, NDArray& x, NDArray& z) { +void lgamma(sd::LaunchContext* context, NDArray& x, NDArray& z) { BUILD_SINGLE_SELECTOR(x.dataType(), lgamma_, (x, z), FLOAT_TYPES); } diff --git a/libnd4j/include/ops/declarable/helpers/cuda/lrn.cu b/libnd4j/include/ops/declarable/helpers/cuda/lrn.cu index ca2bda4a4..57bb205a9 100644 --- a/libnd4j/include/ops/declarable/helpers/cuda/lrn.cu +++ b/libnd4j/include/ops/declarable/helpers/cuda/lrn.cu @@ -19,10 +19,10 @@ // #include -#include -#include +#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { @@ -50,15 +50,15 @@ namespace helpers { shared[threadIdx.x] = x[threadIdx.x * xEws]; __syncthreads(); - const uint begin = nd4j::math::nd4j_max(0, threadIdx.x - depth); + const uint begin = sd::math::nd4j_max(0, threadIdx.x - depth); const uint last = depth + threadIdx.x + 1; - const uint end = nd4j::math::nd4j_min(last, tadLength); + const uint end = sd::math::nd4j_min(last, tadLength); T prev = 0.; for (int s = begin; s < end; s++) prev = prev + shared[s] * shared[s]; - z[threadIdx.x * zEws] = shared[threadIdx.x] / nd4j::math::nd4j_pow(tbias + alpha * prev, tbeta); + z[threadIdx.x * zEws] = shared[threadIdx.x] / sd::math::nd4j_pow(tbias + alpha * prev, tbeta); } } @@ -85,9 +85,9 @@ namespace helpers { auto x = reinterpret_cast(vx) + xTadOffsets[i]; auto z = reinterpret_cast(vz) + zTadOffsets[i]; - const uint begin = nd4j::math::nd4j_max(0, threadIdx.x - depth); + const uint begin = sd::math::nd4j_max(0, threadIdx.x - depth); const uint last = depth + threadIdx.x + 1; - const uint end = nd4j::math::nd4j_min(last, tadLength); + const uint end = sd::math::nd4j_min(last, tadLength); // load everything into shared memory sharedX[threadIdx.x] = x[threadIdx.x * xEws]; @@ -104,7 +104,7 @@ namespace helpers { Z prev = 0.f; for (uint s = begin; s < end; ++s) { - factor[s] = nd4j::math::nd4j_pow(tbias + talpha * sharedY[s], -tbeta - 1); + factor[s] = sd::math::nd4j_pow(tbias + talpha * sharedY[s], -tbeta - 1); prev = prev + sharedX[s] * factor[s]; } @@ -114,13 +114,13 @@ namespace helpers { template - static void lrnBP_(nd4j::graph::Context& block, const NDArray& input, const NDArray& gradO, NDArray& gradI, const int depth, const float bias, const float alpha, const float beta) { + static void lrnBP_(sd::graph::Context& block, const NDArray& input, const NDArray& gradO, NDArray& gradI, const int depth, const float bias, const float alpha, const float beta) { auto rank = input.rankOf(); auto packX = ConstantTadHelper::getInstance()->tadForDimensions(input.getShapeInfo(), {rank - 1}); auto packZ = ConstantTadHelper::getInstance()->tadForDimensions(gradI.getShapeInfo(), {rank - 1}); const auto tadLength = shape::length(packX.primaryShapeInfo()); - const int numBlocks = nd4j::math::nd4j_min(1024, packX.numberOfTads()); + const int numBlocks = sd::math::nd4j_min(1024, packX.numberOfTads()); const int numThreads = tadLength; if (tadLength > 1024 || tadLength < 1) @@ -132,7 +132,7 @@ namespace helpers { gradI *= gradO; } - void lrnBP(nd4j::graph::Context& block, const NDArray& input, const NDArray& gradO, NDArray& gradI, const int depth, const float bias, const float alpha, const float beta) { + void lrnBP(sd::graph::Context& block, const NDArray& input, const NDArray& gradO, NDArray& gradI, const int depth, const float bias, const float alpha, const float beta) { input.syncToDevice(); gradO.syncToDevice(); @@ -142,13 +142,13 @@ namespace helpers { } template - static void lrnFunctor_(nd4j::graph::Context& block, NDArray* input, NDArray* output, int depth, double bias, double alpha, double beta) { + static void lrnFunctor_(sd::graph::Context& block, NDArray* input, NDArray* output, int depth, double bias, double alpha, double beta) { auto rank = input->rankOf(); auto packX = ConstantTadHelper::getInstance()->tadForDimensions(input->shapeInfo(), {rank - 1}); auto packZ = ConstantTadHelper::getInstance()->tadForDimensions(output->shapeInfo(), {rank - 1}); const auto tadLength = shape::length(packX.primaryShapeInfo()); - const int numBlocks = nd4j::math::nd4j_min(1024, packX.numberOfTads()); + const int numBlocks = sd::math::nd4j_min(1024, packX.numberOfTads()); const int numThreads = tadLength; if (tadLength > 1024 || tadLength < 1) @@ -157,7 +157,7 @@ namespace helpers { lrnKernel<<getCudaStream()>>>(input->specialBuffer(), packX.platformShapeInfo(), packX.platformOffsets(), output->specialBuffer(), packZ.platformShapeInfo(), packZ.platformOffsets(), packX.numberOfTads(), tadLength, depth, bias, alpha, beta); } - int lrnFunctor(nd4j::graph::Context& block, NDArray* input, NDArray* output, int depth, double bias, double alpha, double beta) { + int lrnFunctor(sd::graph::Context& block, NDArray* input, NDArray* output, int depth, double bias, double alpha, double beta) { input->syncToDevice(); BUILD_SINGLE_SELECTOR(input->dataType(), lrnFunctor_, (block, input, output, depth, bias, alpha, beta), FLOAT_TYPES); diff --git a/libnd4j/include/ops/declarable/helpers/cuda/lstm.cu b/libnd4j/include/ops/declarable/helpers/cuda/lstm.cu index 2204c9189..af0c413d6 100644 --- a/libnd4j/include/ops/declarable/helpers/cuda/lstm.cu +++ b/libnd4j/include/ops/declarable/helpers/cuda/lstm.cu @@ -34,14 +34,14 @@ #include #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { ////////////////////////////////////////////////////////////////////////// -void lstmCell(nd4j::LaunchContext * context, const NDArray* xt, const NDArray* ht_1, const NDArray* ct_1, const NDArray* Wx, const NDArray* Wh, const NDArray* Wc, const NDArray* Wp, const NDArray* b, +void lstmCell(sd::LaunchContext * context, const NDArray* xt, const NDArray* ht_1, const NDArray* ct_1, const NDArray* Wx, const NDArray* Wh, const NDArray* Wc, const NDArray* Wp, const NDArray* b, NDArray* ht, NDArray* ct, const std::vector& params) { // xt input [bS x nIn] diff --git a/libnd4j/include/ops/declarable/helpers/cuda/lstsq.cu b/libnd4j/include/ops/declarable/helpers/cuda/lstsq.cu new file mode 100644 index 000000000..a3b029c0b --- /dev/null +++ b/libnd4j/include/ops/declarable/helpers/cuda/lstsq.cu @@ -0,0 +1,115 @@ +/******************************************************************************* + * Copyright (c) 2020 Konduit, K.K. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ + +// +// @author GS +// +#include +#include +#include +#include +#include + +#include +#include +#include +#include + +namespace sd { +namespace ops { +namespace helpers { + + template + static __global__ void fillRegularizerKernel(T* ioMatrixData, Nd4jLong* ioMatrixShape, Nd4jLong* ioMatrixTads, Nd4jLong* ioMatrixOffsets, Nd4jLong batchSize, Nd4jLong rows, T const value) { + + for (auto x = blockIdx.x; x < batchSize; x += gridDim.x) { + auto z = ioMatrixData + ioMatrixOffsets[x]; + for (auto r = threadIdx.x; r < rows; r += blockDim.x) { + Nd4jLong pos[] = {r,r}; + auto zIndex = shape::getOffset(ioMatrixTads, pos); + z[zIndex] = value; + } + } + + } + + template + static void fillRegularizer(sd::LaunchContext* context, NDArray& ioMatrix, double const value) { + auto lastDimsTads = ConstantTadHelper::getInstance()->tadForDimensions(ioMatrix.shapeInfo(), {-2, -1}); + auto stream = context->getCudaStream(); + auto rows = ioMatrix.sizeAt(-2); + //auto cols = ioMatrix.sizeAt(-1); + fillRegularizerKernel<<<256, 256, 128, *stream>>>(ioMatrix.dataBuffer()->specialAsT(), ioMatrix.specialShapeInfo(), lastDimsTads.specialShapeInfo(), lastDimsTads.specialOffsets(), lastDimsTads.numberOfTads(), rows, (T)value); + + } + + template + int leastSquaresSolveFunctor_(sd::LaunchContext* context, NDArray const* leftInput, NDArray const* rightInput, double const l2Regularizer, bool const fast, NDArray* output) { + if (fast) { // Cholesky decomposition approach + // Equation for solve A^T * Ax = A^T * b, so + // 1. Computing A2: + auto tAtShape = ShapeUtils::evalShapeForMatmul(leftInput->getShapeInfo(), leftInput->getShapeInfo(), true, false); + //tAtShape[tAtShape.size() - 2] = output->sizeAt(-2); + NDArray leftOutput(leftInput->ordering(), tAtShape, output->dataType(), context); + MmulHelper::matmul(leftInput, leftInput, &leftOutput, true, false); // Computing A2 = A^T * A + // 2. Computing B' = A^T * b + auto rightOutput = output->ulike(); + + MmulHelper::matmul(leftInput, rightInput, &rightOutput, true, false); // Computing B' = A^T * b + // 3. Regularization ( indeed A' = A2 - l2Regularizer * I) + if (l2Regularizer != 0.0) { + auto regularizer = leftOutput.ulike(); regularizer.nullify(); + fillRegularizer(context, regularizer, (T)l2Regularizer); + leftOutput += regularizer; + } + + // 4. Cholesky decomposition -- output matrix is square and lower triangular + helpers::cholesky(context, &leftOutput, &leftOutput, true); // inplace decomposition + // 5. Solve two triangular systems: + auto rightB = rightOutput.ulike(); rightB.nullify(); + + helpers::triangularSolveFunctor(context, &leftOutput, &rightOutput, true, false, &rightB); + + helpers::adjointMatrix(context, &leftOutput, true, &leftOutput); + helpers::triangularSolveFunctor(context, &leftOutput, &rightB, false, false, output); + // All done + } + else { // QR decomposition approach + // Equation for solve Rx = Q^T * b, where A = Q * R, where Q - orthogonal matrix, and R - upper triangular + // 1. QR decomposition + auto qShape = leftInput->getShapeAsVector(); + auto rShape = leftInput->getShapeAsVector(); + qShape[leftInput->rankOf() - 1] = leftInput->sizeAt(-2); + + NDArray Q(leftInput->ordering(), qShape, leftInput->dataType(), context);// = leftInput->ulike(); + NDArray R(leftInput->ordering(), rShape, leftInput->dataType(), context); // = rightInput->ulike(); + helpers::qr(context, leftInput, &Q, &R, true); + // 2. b` = Q^t * b: + auto rightOutput = rightInput->ulike(); + MmulHelper::matmul(&Q, rightInput, &rightOutput, true, false); + // 3. Solve triangular system + helpers::triangularSolveFunctor(context, &R, &rightOutput, false, false, output); + } + return Status::OK(); + } + + int leastSquaresSolveFunctor(sd::LaunchContext* context, NDArray const* leftInput, NDArray const* rightInput, double const l2Regularizer, bool const fast, NDArray* output) { + BUILD_SINGLE_SELECTOR(leftInput->dataType(), return leastSquaresSolveFunctor_, (context, leftInput, rightInput, l2Regularizer, fast, output), FLOAT_TYPES); + } + +} +} +} diff --git a/libnd4j/include/ops/declarable/helpers/cuda/lup.cu b/libnd4j/include/ops/declarable/helpers/cuda/lup.cu index 3e8def28a..7630694e1 100644 --- a/libnd4j/include/ops/declarable/helpers/cuda/lup.cu +++ b/libnd4j/include/ops/declarable/helpers/cuda/lup.cu @@ -19,17 +19,17 @@ // #include -#include -#include -#include -#include -#include +#include +#include +#include +#include +#include //#include #include -#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { @@ -401,7 +401,7 @@ namespace helpers { } } else { - NDArray permutVector('c', {n}, nd4j::DataType::INT32, context); + NDArray permutVector('c', {n}, sd::DataType::INT32, context); int* permutationBuf = permutVector.dataBuffer()->specialAsT(); status = cusolverDnDgetrf( cusolverH, @@ -548,14 +548,14 @@ namespace helpers { auto rowNum = shape::sizeAt(compoundShape, 0); Nd4jLong xInitial[] = {column, column}; auto xInitialIndex = shape::getOffset(compoundShape, xInitial, 0); - auto maxValue = T(0); //nd4j::math::nd4j_abs(compoundBuffer[xInitialIndex]); + auto maxValue = T(0); //sd::math::nd4j_abs(compoundBuffer[xInitialIndex]); auto result = -1LL; for (auto rowCounter = column; rowCounter < rowNum; rowCounter++) { Nd4jLong xPos[] = {rowCounter, column}; auto xIndex = shape::getOffset(compoundShape, xPos, 0); - if (nd4j::math::nd4j_abs(compoundBuffer[xIndex]) > maxValue) { - maxValue = nd4j::math::nd4j_max(maxValue, nd4j::math::nd4j_abs(compoundBuffer[xIndex])); + if (sd::math::nd4j_abs(compoundBuffer[xIndex]) > maxValue) { + maxValue = sd::math::nd4j_max(maxValue, sd::math::nd4j_abs(compoundBuffer[xIndex])); result = rowCounter; } } @@ -603,7 +603,7 @@ namespace helpers { output->assign(input); // fill up output tensor with zeros // output->tickWriteDevice(); - permutationVectors->applyTrueBroadcast(nd4j::BroadcastOpsTuple::Assign(), iota, *permutationVectors, true, nullptr); + permutationVectors->applyTrueBroadcast(sd::BroadcastOpsTuple::Assign(), iota, *permutationVectors, true, nullptr); // permutationVectors->tickWriteDevice(); auto tads = ConstantTadHelper::getInstance()->tadForDimensions(output->shapeInfo(), {-2, -1}); auto permutaionTads = ConstantTadHelper::getInstance()->tadForDimensions(output->shapeInfo(), {-1}); @@ -621,7 +621,7 @@ namespace helpers { } // ------------------------------------------------------------------------------------------------------------------ // template - static int determinant_(nd4j::LaunchContext *context, NDArray *input, NDArray *output) { + static int determinant_(sd::LaunchContext *context, NDArray *input, NDArray *output) { Nd4jLong n = input->sizeAt(-1); Nd4jLong n2 = n * n; std::vector dims(); @@ -659,7 +659,7 @@ namespace helpers { return Status::OK(); } - int determinant(nd4j::LaunchContext *context, NDArray *input, NDArray *output) { + int determinant(sd::LaunchContext *context, NDArray *input, NDArray *output) { NDArray::prepareSpecialUse({output}, {input}); BUILD_SINGLE_SELECTOR(input->dataType(), return determinant_, (context, input, output), FLOAT_NATIVE); NDArray::registerSpecialUse({output}, {input}); @@ -708,7 +708,7 @@ namespace helpers { return ND4J_STATUS_OK; } - int logAbsDeterminant(nd4j::LaunchContext *context, NDArray *input, NDArray *output) { + int logAbsDeterminant(sd::LaunchContext *context, NDArray *input, NDArray *output) { NDArray::prepareSpecialUse({output}, {input}); BUILD_SINGLE_SELECTOR(input->dataType(), return logAbsDeterminant_, (context, input, output), FLOAT_NATIVE); NDArray::registerSpecialUse({output}, {input}); @@ -747,7 +747,7 @@ namespace helpers { } template - static int inverse_(nd4j::LaunchContext *context, NDArray *input, NDArray *output) { + static int inverse_(sd::LaunchContext *context, NDArray *input, NDArray *output) { auto n = input->sizeAt(-1); auto n2 = n * n; auto dtype = DataTypeUtils::fromT(); //input->dataType(); @@ -758,10 +758,10 @@ namespace helpers { NDArray lower = NDArrayFactory::create('c', {n, n}, dtype, context); NDArray compound = NDArrayFactory::create('c', {n, n}, dtype, context); NDArray permutation = NDArrayFactory::create('c', {n, n}, dtype, context); - auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), + auto packX = sd::ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), {input->rankOf() - 2, input->rankOf() - 1}); - auto packZ = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), + auto packZ = sd::ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), {output->rankOf() - 2, output->rankOf() - 1}); auto stream = context->getCudaStream(); @@ -787,7 +787,7 @@ namespace helpers { // compound.printIndexedBuffer("Lower Inverted"); // matrix.tickWriteDevice(); // compound.tickWriteDevice(); - nd4j::MmulHelper::mmul(&matrix, &compound, &upper, 1.0, 0.0); + sd::MmulHelper::mmul(&matrix, &compound, &upper, 1.0, 0.0); upper.tickWriteDevice(); // upper.printIndexedBuffer("Full inverted"); returnMatrix <<<1, n2, 1024, *stream>>>(output->specialBuffer(), output->specialShapeInfo(), upper.specialBuffer(), upper.specialShapeInfo(), i * n2, n); @@ -795,13 +795,13 @@ namespace helpers { return Status::OK(); } - int inverse(nd4j::LaunchContext *context, NDArray *input, NDArray *output) { + int inverse(sd::LaunchContext *context, NDArray *input, NDArray *output) { NDArray::prepareSpecialUse({output}, {input}); BUILD_SINGLE_SELECTOR(input->dataType(), return inverse_, (context, input, output), FLOAT_NATIVE); NDArray::registerSpecialUse({output}, {input}); } - bool checkCholeskyInput(nd4j::LaunchContext *context, NDArray const *input) { + bool checkCholeskyInput(sd::LaunchContext *context, NDArray const *input) { return true; } @@ -848,7 +848,7 @@ namespace helpers { throw cuda_exception::build("helpers::cholesky_: Cannot create solver handle", status); } F **dArrayBatch = nullptr; - auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(tempOutput.getShapeInfo(), + auto packX = sd::ConstantTadHelper::getInstance()->tadForDimensions(tempOutput.getShapeInfo(), {tempOutput.rankOf() - 2, tempOutput.rankOf() - 1}); const Nd4jLong batchSize = packX.numberOfTads(); @@ -933,12 +933,12 @@ namespace helpers { return Status::OK(); } - int cholesky(nd4j::LaunchContext *context, NDArray *input, NDArray *output, bool inplace) { + int cholesky(sd::LaunchContext *context, NDArray *input, NDArray *output, bool inplace) { // BUILD_SINGLE_SELECTOR(input->dataType(), return cholesky_, (context, input, output, inplace), FLOAT_TYPES); return cholesky_(context, input, output, inplace); } // BUILD_SINGLE_TEMPLATE(template int cholesky_, (LaunchContext* context, NDArray* input, NDArray* output, bool inplace), FLOAT_TYPES); - BUILD_SINGLE_TEMPLATE(template int inverse_, (nd4j::LaunchContext * context, NDArray * input, NDArray * output), + BUILD_SINGLE_TEMPLATE(template int inverse_, (sd::LaunchContext * context, NDArray * input, NDArray * output), FLOAT_NATIVE); template @@ -968,7 +968,7 @@ namespace helpers { } template - int logdetFunctor_(nd4j::LaunchContext *context, NDArray *input, NDArray *output) { + int logdetFunctor_(sd::LaunchContext *context, NDArray *input, NDArray *output) { NDArray::prepareSpecialUse({output}, {input}); auto n2 = input->sizeAt(-1) * input->sizeAt(-2); auto stream = context->getCudaStream(); @@ -979,7 +979,7 @@ namespace helpers { auto outputBuf = output->dataBuffer()->specialAsT(); //reinterpret_cast(output->specialBuffer()); // + e * n2; // + e * n2; auto inputBuf = tempOutput.dataBuffer()->specialAsT(); //reinterpret_cast(tempOutput.specialBuffer()); output->nullify(); - auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(tempOutput.getShapeInfo(), + auto packX = sd::ConstantTadHelper::getInstance()->tadForDimensions(tempOutput.getShapeInfo(), {tempOutput.rankOf() - 2, tempOutput.rankOf() - 1}); logDetKernel <<<128, 512, 256, *stream>>>(inputBuf, tempOutput.specialShapeInfo(), @@ -990,7 +990,7 @@ namespace helpers { return Status::OK(); } - int logdetFunctor(nd4j::LaunchContext *context, NDArray *input, NDArray *output) { + int logdetFunctor(sd::LaunchContext *context, NDArray *input, NDArray *output) { BUILD_SINGLE_SELECTOR(output->dataType(), return logdetFunctor_, (context, input, output), FLOAT_NATIVE); } @@ -1003,7 +1003,7 @@ namespace helpers { } // BUILD_SINGLE_TEMPLATE(template int logdetFunctor_, -// (nd4j::LaunchContext * context, NDArray * input, NDArray * output), FLOAT_NATIVE); +// (sd::LaunchContext * context, NDArray * input, NDArray * output), FLOAT_NATIVE); } } } diff --git a/libnd4j/include/ops/declarable/helpers/cuda/matrixSetDiag.cu b/libnd4j/include/ops/declarable/helpers/cuda/matrixSetDiag.cu index a3c754cf5..e5773abf5 100644 --- a/libnd4j/include/ops/declarable/helpers/cuda/matrixSetDiag.cu +++ b/libnd4j/include/ops/declarable/helpers/cuda/matrixSetDiag.cu @@ -18,11 +18,11 @@ // @author Yurii Shyrma (iuriish@yahoo.com) // -#include "ResultSet.h" +#include #include -#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { @@ -39,14 +39,14 @@ __global__ static void matrixSetDiagCuda(const void* vx, const Nd4jLong* xShapeI const auto y = reinterpret_cast(vy); auto z = reinterpret_cast(vz); - __shared__ int xRank; // xRank = zRank, xRank = yRank + 1 - __shared__ Nd4jLong xLen, *sharedMem; // xLen = zLen + __shared__ int xRank, *sharedMem; // xRank = zRank, xRank = yRank + 1 + __shared__ Nd4jLong xLen; // xLen = zLen __shared__ bool areSameOffsets; if (threadIdx.x == 0) { extern __shared__ unsigned char shmem[]; - sharedMem = reinterpret_cast(shmem); + sharedMem = reinterpret_cast(shmem); areSameOffsets = shape::haveSameShapeAndStrides(xShapeInfo, zShapeInfo); // shapes are definitely the same, but strides might not @@ -56,7 +56,7 @@ __global__ static void matrixSetDiagCuda(const void* vx, const Nd4jLong* xShapeI __syncthreads(); - auto coords = sharedMem + threadIdx.x * xRank; // we provide (xRank * sizeof(Nd4jLong) * threadIdx.x) amount of shared memory per each thread + auto coords = sharedMem + threadIdx.x * xRank; // we provide (xRank * sizeof(int) * threadIdx.x) amount of shared memory per each thread const auto tid = blockIdx.x * blockDim.x + threadIdx.x; for (Nd4jLong i = tid; i < xLen; i += gridDim.x * blockDim.x) { @@ -82,11 +82,11 @@ static void matrixSetDiagCudaLauncher(const int blocksPerGrid, const int threads } /////////////////////////////////////////////////////////////////// -void matrixSetDiag(nd4j::LaunchContext* context, const NDArray& input, const NDArray& diagonal, NDArray& output, const bool zeroPad) { +void matrixSetDiag(sd::LaunchContext* context, const NDArray& input, const NDArray& diagonal, NDArray& output, const bool zeroPad) { const int threadsPerBlock = MAX_NUM_THREADS / 2; const int blocksPerGrid = (input.lengthOf() + threadsPerBlock - 1) / threadsPerBlock; - const int sharedMem = threadsPerBlock * sizeof(Nd4jLong) * input.rankOf() + 128; + const int sharedMem = threadsPerBlock * sizeof(int) * input.rankOf() + 128; PointersManager manager(context, "matrixSetDiag"); diff --git a/libnd4j/include/ops/declarable/helpers/cuda/matrix_band.cu b/libnd4j/include/ops/declarable/helpers/cuda/matrix_band.cu index 3b7ea9551..3c1305391 100644 --- a/libnd4j/include/ops/declarable/helpers/cuda/matrix_band.cu +++ b/libnd4j/include/ops/declarable/helpers/cuda/matrix_band.cu @@ -18,12 +18,12 @@ // @author George A. Shulinok // #include -#include -#include -#include +#include +#include +#include #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// @@ -83,15 +83,15 @@ namespace helpers { // matrixBandPart_ - main algorithm caller // template - void matrixBandPart_(nd4j::LaunchContext * context, NDArray* input, NDArray* output, Nd4jLong lowerBand, Nd4jLong upperBand) { + void matrixBandPart_(sd::LaunchContext * context, NDArray* input, NDArray* output, Nd4jLong lowerBand, Nd4jLong upperBand) { dim3 launchDims(256, 512, 8192); auto stream = context->getCudaStream(); std::vector lastDims({input->rankOf() - 2, input->rankOf() - 1}); std::vector dimsToExclude = ShapeUtils::evalDimsToExclude(input->rankOf(), lastDims); - auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), lastDims); - auto packZ = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), lastDims); + auto packX = sd::ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), lastDims); + auto packZ = sd::ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), lastDims); const Nd4jLong numTads = packX.numberOfTads(); @@ -103,10 +103,10 @@ namespace helpers { } //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// - void matrixBandPart(nd4j::LaunchContext * context, NDArray* input, NDArray* output, Nd4jLong lowerBand, Nd4jLong upperBand) { + void matrixBandPart(sd::LaunchContext * context, NDArray* input, NDArray* output, Nd4jLong lowerBand, Nd4jLong upperBand) { BUILD_SINGLE_SELECTOR(input->dataType(), matrixBandPart_, (context, input, output, lowerBand, upperBand), FLOAT_TYPES); } - BUILD_SINGLE_TEMPLATE(template void matrixBandPart_, (nd4j::LaunchContext * context, NDArray* input, NDArray* output, Nd4jLong lowerBand, Nd4jLong upperBand), FLOAT_TYPES); + BUILD_SINGLE_TEMPLATE(template void matrixBandPart_, (sd::LaunchContext * context, NDArray* input, NDArray* output, Nd4jLong lowerBand, Nd4jLong upperBand), FLOAT_TYPES); } } } diff --git a/libnd4j/include/ops/declarable/helpers/cuda/matrix_diag_part.cu b/libnd4j/include/ops/declarable/helpers/cuda/matrix_diag_part.cu index 5a95eeb83..7d78d0323 100644 --- a/libnd4j/include/ops/declarable/helpers/cuda/matrix_diag_part.cu +++ b/libnd4j/include/ops/declarable/helpers/cuda/matrix_diag_part.cu @@ -18,16 +18,16 @@ // Created by GS on 3/21/2018. // -#include "ResultSet.h" +#include #include -#include -#include -#include -#include -#include +#include +#include +#include +#include +#include #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { @@ -54,7 +54,7 @@ namespace helpers { // for detailed explanations please take a look on web page: https://www.tensorflow.org/api_docs/python/tf/matrix_set_diag // template - int _matrixDiagPart(nd4j::LaunchContext * context, const NDArray* input, NDArray* output) { + int _matrixDiagPart(sd::LaunchContext * context, const NDArray* input, NDArray* output) { auto stream = context->getCudaStream(); auto listOut = output->allTensorsAlongDimension({output->rankOf() - 1}); auto listDiag = input->allTensorsAlongDimension({input->rankOf() - 2, input->rankOf() - 1}); @@ -63,7 +63,7 @@ namespace helpers { nd4j_printf("matrix_diag_part: Input matrix has wrong shape.", ""); return ND4J_STATUS_VALIDATION; } - Nd4jLong lastDimension = nd4j::math::nd4j_min(input->sizeAt(-2), input->sizeAt(-1)); + Nd4jLong lastDimension = sd::math::nd4j_min(input->sizeAt(-2), input->sizeAt(-1)); std::vector dimsToExclude = ShapeUtils::evalDimsToExclude(output->rankOf(), {output->rankOf() - 1}); const Nd4jLong numTads = ShapeUtils::getNumOfSubArrs(input->getShapeInfo(), dimsToExclude); //this->tensorsAlongDimension({dimension}); @@ -71,8 +71,8 @@ namespace helpers { //tadOnlyInputShapeInfo, tadInputOffsets, tadOnlyOutputShapeInfo, tadOutputOffsets; std::vector outputDims({output->rankOf() - 1}); std::vector inputDims({input->rankOf() - 2, input->rankOf() - 1}); - auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), inputDims); - auto packZ = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), outputDims); + auto packX = sd::ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), inputDims); + auto packZ = sd::ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), outputDims); if (!output->isActualOnDeviceSide()) @@ -91,11 +91,11 @@ namespace helpers { //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// // caller for _matrixDiagPart // - int matrixDiagPart(nd4j::LaunchContext * context, const NDArray* input, NDArray* output) { + int matrixDiagPart(sd::LaunchContext * context, const NDArray* input, NDArray* output) { BUILD_SINGLE_SELECTOR(input->dataType(), return _matrixDiagPart, (context, input, output), LIBND4J_TYPES); } - BUILD_SINGLE_TEMPLATE(template int _matrixDiagPart, (nd4j::LaunchContext * context, const NDArray* input, NDArray* output), LIBND4J_TYPES); + BUILD_SINGLE_TEMPLATE(template int _matrixDiagPart, (sd::LaunchContext * context, const NDArray* input, NDArray* output), LIBND4J_TYPES); } } diff --git a/libnd4j/include/ops/declarable/helpers/cuda/max_pooling.cu b/libnd4j/include/ops/declarable/helpers/cuda/max_pooling.cu index aa129ee8e..b809647c1 100644 --- a/libnd4j/include/ops/declarable/helpers/cuda/max_pooling.cu +++ b/libnd4j/include/ops/declarable/helpers/cuda/max_pooling.cu @@ -22,7 +22,7 @@ #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { @@ -38,7 +38,7 @@ namespace helpers { } template - static void maxPoolingFunctor_(nd4j::graph::Context& block, NDArray* input, NDArray* values, std::vector const& params, NDArray* indices) { + static void maxPoolingFunctor_(sd::graph::Context& block, NDArray* input, NDArray* values, std::vector const& params, NDArray* indices) { int kY = params[0]; int kX = params[1]; @@ -85,9 +85,9 @@ namespace helpers { } } - void maxPoolingFunctor(nd4j::LaunchContext * context, nd4j::graph::Context& block, NDArray* input, NDArray* values, std::vector const& params, NDArray* indices) { + void maxPoolingFunctor(sd::LaunchContext * context, sd::graph::Context& block, NDArray* input, NDArray* values, std::vector const& params, NDArray* indices) { NDArray::prepareSpecialUse({values, indices}, {input}); - auto yType = indices == nullptr ? nd4j::DataType::INT64 : indices->dataType(); + auto yType = indices == nullptr ? sd::DataType::INT64 : indices->dataType(); BUILD_DOUBLE_SELECTOR(input->dataType(), yType, maxPoolingFunctor_, (block, input, values, params, indices), FLOAT_TYPES, INDEXING_TYPES); NDArray::registerSpecialUse({values, indices}, {input}); } diff --git a/libnd4j/include/ops/declarable/helpers/cuda/maximum.cu b/libnd4j/include/ops/declarable/helpers/cuda/maximum.cu index b93563de2..c4c8783ff 100644 --- a/libnd4j/include/ops/declarable/helpers/cuda/maximum.cu +++ b/libnd4j/include/ops/declarable/helpers/cuda/maximum.cu @@ -18,12 +18,12 @@ // @author sgazeos@gmail.com // -#include -#include +#include +#include #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { @@ -94,7 +94,7 @@ namespace nd4j { } } - void maximumBPFunctor(nd4j::LaunchContext * context, NDArray* x, NDArray* y, NDArray* epsNext, NDArray* gradX, NDArray* gradY) { + void maximumBPFunctor(sd::LaunchContext * context, NDArray* x, NDArray* y, NDArray* epsNext, NDArray* gradX, NDArray* gradY) { NDArray::prepareSpecialUse({gradX, gradY}, {x, y, epsNext}); BUILD_SINGLE_SELECTOR(x->dataType(), maximumBPFunctor_, (x, y, epsNext, gradX, gradY), NUMERIC_TYPES); diff --git a/libnd4j/include/ops/declarable/helpers/cuda/merge.cu b/libnd4j/include/ops/declarable/helpers/cuda/merge.cu index 14fda24ec..b448fbd35 100644 --- a/libnd4j/include/ops/declarable/helpers/cuda/merge.cu +++ b/libnd4j/include/ops/declarable/helpers/cuda/merge.cu @@ -23,13 +23,13 @@ #include #include #include -#include +#include #include #include -#include -#include +#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { ////////////////////////////////////////////////////////////////////////// @@ -60,7 +60,7 @@ namespace nd4j { } template - static void mergeMaxIndex_(nd4j::LaunchContext * context, const std::vector& inArrs, NDArray& output) { + static void mergeMaxIndex_(sd::LaunchContext * context, const std::vector& inArrs, NDArray& output) { std::vector inBuffers(inArrs.size()); std::vector inShapes(inArrs.size()); @@ -80,7 +80,7 @@ namespace nd4j { manager.synchronize(); } - void mergeMaxIndex(nd4j::LaunchContext * context, const std::vector& inArrs, NDArray& output) { + void mergeMaxIndex(sd::LaunchContext * context, const std::vector& inArrs, NDArray& output) { NDArray::prepareSpecialUse({&output}, {}); for (auto v:inArrs) v->syncToDevice(); @@ -116,7 +116,7 @@ namespace nd4j { } template - static void mergeMax_(nd4j::LaunchContext * context, const std::vector& inArrs, NDArray& output) { + static void mergeMax_(sd::LaunchContext * context, const std::vector& inArrs, NDArray& output) { std::vector inBuffers(inArrs.size()); std::vector inShapes(inArrs.size()); @@ -136,7 +136,7 @@ namespace nd4j { manager.synchronize(); } - void mergeMax(nd4j::LaunchContext * context, const std::vector& inArrs, NDArray& output) { + void mergeMax(sd::LaunchContext * context, const std::vector& inArrs, NDArray& output) { NDArray::prepareSpecialUse({&output}, {}); for (auto v:inArrs) v->syncToDevice(); @@ -168,7 +168,7 @@ namespace nd4j { } template - static void mergeAvg_(nd4j::LaunchContext * context, const std::vector& inArrs, NDArray& output) { + static void mergeAvg_(sd::LaunchContext * context, const std::vector& inArrs, NDArray& output) { std::vector inBuffers(inArrs.size()); std::vector inShapes(inArrs.size()); @@ -188,7 +188,7 @@ namespace nd4j { manager.synchronize(); } - void mergeAvg(nd4j::LaunchContext * context, const std::vector& inArrs, NDArray& output) { + void mergeAvg(sd::LaunchContext * context, const std::vector& inArrs, NDArray& output) { NDArray::prepareSpecialUse({&output}, {}); for (auto v:inArrs) v->syncToDevice(); @@ -221,7 +221,7 @@ namespace nd4j { } template - static void mergeAdd_(nd4j::LaunchContext * context, const std::vector& inArrs, NDArray& output) { + static void mergeAdd_(sd::LaunchContext * context, const std::vector& inArrs, NDArray& output) { std::vector inBuffers(inArrs.size()); std::vector inShapes(inArrs.size()); @@ -240,9 +240,9 @@ namespace nd4j { manager.synchronize(); } - BUILD_SINGLE_TEMPLATE(template void mergeAdd_, (nd4j::LaunchContext * context, const std::vector& inArrs, NDArray& output), NUMERIC_TYPES); + BUILD_SINGLE_TEMPLATE(template void mergeAdd_, (sd::LaunchContext * context, const std::vector& inArrs, NDArray& output), NUMERIC_TYPES); - void mergeAdd(nd4j::LaunchContext * context, const std::vector& inArrs, NDArray& output) { + void mergeAdd(sd::LaunchContext * context, const std::vector& inArrs, NDArray& output) { NDArray::prepareSpecialUse({&output}, {}); for (auto v:inArrs) v->syncToDevice(); diff --git a/libnd4j/include/ops/declarable/helpers/cuda/meshgrid.cu b/libnd4j/include/ops/declarable/helpers/cuda/meshgrid.cu index 399447c9a..53570a0ba 100644 --- a/libnd4j/include/ops/declarable/helpers/cuda/meshgrid.cu +++ b/libnd4j/include/ops/declarable/helpers/cuda/meshgrid.cu @@ -25,7 +25,7 @@ #include #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { @@ -78,7 +78,7 @@ namespace helpers { } template - static void meshgrid_(nd4j::LaunchContext * context, const std::vector& inArrs, const std::vector& outArrs, const bool swapFirst2Dims) { + static void meshgrid_(sd::LaunchContext * context, const std::vector& inArrs, const std::vector& outArrs, const bool swapFirst2Dims) { const int rank = inArrs.size(); int inIndices[MAX_RANK]; std::iota(inIndices, inIndices + rank, 0); @@ -134,7 +134,7 @@ namespace helpers { } ////////////////////////////////////////////////////////////////////////// - void meshgrid(nd4j::LaunchContext * context, const std::vector& inArrs, const std::vector& outArrs, const bool swapFirst2Dims) { + void meshgrid(sd::LaunchContext * context, const std::vector& inArrs, const std::vector& outArrs, const bool swapFirst2Dims) { BUILD_SINGLE_SELECTOR(inArrs.at(0)->dataType(), meshgrid_, (context, inArrs, outArrs, swapFirst2Dims), NUMERIC_TYPES); diff --git a/libnd4j/include/ops/declarable/helpers/cuda/minimum.cu b/libnd4j/include/ops/declarable/helpers/cuda/minimum.cu index 90142091f..b43bb418e 100644 --- a/libnd4j/include/ops/declarable/helpers/cuda/minimum.cu +++ b/libnd4j/include/ops/declarable/helpers/cuda/minimum.cu @@ -19,11 +19,11 @@ // #ifndef __MIN_I_MAX_H_HELPERS__ #define __MIN_I_MAX_H_HELPERS__ -#include -#include +#include +#include #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { @@ -95,7 +95,7 @@ namespace nd4j { } - void minimumBPFunctor(nd4j::LaunchContext * context, NDArray* x, NDArray* y, NDArray* epsNext, NDArray* gradX, NDArray* gradY) { + void minimumBPFunctor(sd::LaunchContext * context, NDArray* x, NDArray* y, NDArray* epsNext, NDArray* gradX, NDArray* gradY) { NDArray::prepareSpecialUse({gradX, gradY}, {x, y, epsNext}); BUILD_SINGLE_SELECTOR(x->dataType(), minimumBPFunctor_, (x, y, epsNext, gradX, gradY), NUMERIC_TYPES); diff --git a/libnd4j/include/ops/declarable/helpers/cuda/nth_element.cu b/libnd4j/include/ops/declarable/helpers/cuda/nth_element.cu index bafbde2e2..4f26ef397 100644 --- a/libnd4j/include/ops/declarable/helpers/cuda/nth_element.cu +++ b/libnd4j/include/ops/declarable/helpers/cuda/nth_element.cu @@ -19,13 +19,13 @@ // #include -#include -#include -#include -#include +#include +#include +#include +#include #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { @@ -50,7 +50,7 @@ namespace helpers { } template - void nthElementFunctor_(nd4j::LaunchContext * context, NDArray* input, Nd4jLong n, NDArray* output, bool reverse) { + void nthElementFunctor_(sd::LaunchContext * context, NDArray* input, Nd4jLong n, NDArray* output, bool reverse) { NDArray::prepareSpecialUse({output}, {input}); NDArray sortedVals(*input); @@ -66,7 +66,7 @@ namespace helpers { else { // rank greater than 1 std::vector lastDims({input->rankOf() - 1});// = ShapeUtils::evalDimsToExclude(input->rankOf(), {input->rankOf() - 1}); - auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(sortedVals.getShapeInfo(), lastDims); + auto packX = sd::ConstantTadHelper::getInstance()->tadForDimensions(sortedVals.getShapeInfo(), lastDims); auto pTadShape = packX.specialShapeInfo(); auto pTadShapeH = packX.primaryShapeInfo(); @@ -79,7 +79,7 @@ namespace helpers { } NDArray::registerSpecialUse({output}, {input}); } - void nthElementFunctor(nd4j::LaunchContext * context, NDArray* input, Nd4jLong n, NDArray* output, bool reverse) { + void nthElementFunctor(sd::LaunchContext * context, NDArray* input, Nd4jLong n, NDArray* output, bool reverse) { BUILD_SINGLE_SELECTOR(input->dataType(), nthElementFunctor_, (context, input, n, output, reverse), LIBND4J_TYPES); } diff --git a/libnd4j/include/ops/declarable/helpers/cuda/one_hot.cu b/libnd4j/include/ops/declarable/helpers/cuda/one_hot.cu index c0d1d95dc..f1b87c1aa 100644 --- a/libnd4j/include/ops/declarable/helpers/cuda/one_hot.cu +++ b/libnd4j/include/ops/declarable/helpers/cuda/one_hot.cu @@ -23,14 +23,14 @@ #include #include #include -#include +#include #include #include -#include -#include +#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { @@ -86,7 +86,7 @@ static void onehotCudaLauncher(const int blocksPerGrid, const int threadsPerBloc } /////////////////////////////////////////////////////////////////// -void onehot(const nd4j::LaunchContext* context, const NDArray *indices, NDArray *output, const uint axis, const uint depth, const double on, const double off) { +void onehot(const sd::LaunchContext* context, const NDArray *indices, NDArray *output, const uint axis, const uint depth, const double on, const double off) { const auto xType = indices->dataType(); const auto zType = output->dataType(); diff --git a/libnd4j/include/ops/declarable/helpers/cuda/pad.cu b/libnd4j/include/ops/declarable/helpers/cuda/pad.cu index aede6243a..fc4d96ce0 100644 --- a/libnd4j/include/ops/declarable/helpers/cuda/pad.cu +++ b/libnd4j/include/ops/declarable/helpers/cuda/pad.cu @@ -23,13 +23,13 @@ #include #include #include -#include +#include #include #include -#include -#include +#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { /////////////////////////////////////////////////////////////////// @@ -126,7 +126,7 @@ namespace nd4j { } /////////////////////////////////////////////////////////////////// - void pad(nd4j::LaunchContext * context, const int mode, const NDArray& input, const NDArray& paddings, NDArray& output, const NDArray& padValue) { + void pad(sd::LaunchContext * context, const int mode, const NDArray& input, const NDArray& paddings, NDArray& output, const NDArray& padValue) { PointersManager manager(context, "pad"); @@ -232,7 +232,7 @@ namespace nd4j { } template - static void mirrorPad_(nd4j::LaunchContext * context, const NDArray& input, const NDArray& paddings, NDArray& output, const int mode) { + static void mirrorPad_(sd::LaunchContext * context, const NDArray& input, const NDArray& paddings, NDArray& output, const int mode) { // mode: 0 - REFLECT, else - SYMMETRIC const int reflBorder = (bool)mode ? 1 : 0; const int rank = input.rankOf(); @@ -248,16 +248,16 @@ namespace nd4j { const Nd4jLong len = 2*(inLen-1) + leftSide + reflBorder; mirrorPadLinearKernel<<<256, 512, 256, *stream>>>(input.getSpecialBuffer(), input.getSpecialShapeInfo(), output.specialBuffer(), output.specialShapeInfo(), leftSide, leftSideCorrected, inLen, len, outLen); - nd4j::DebugHelper::checkErrorCode(stream, "helpers::mirrorPadLinearKernel(...) failed"); + sd::DebugHelper::checkErrorCode(stream, "helpers::mirrorPadLinearKernel(...) failed"); } else { mirrorPadKernel<<<256, 256, 8192, *stream>>>(input.getSpecialBuffer(), input.getSpecialShapeInfo(), output.specialBuffer(), output.specialShapeInfo(), outLen, paddings.getSpecialBuffer(), paddings.getSpecialShapeInfo(), reflBorder); - nd4j::DebugHelper::checkErrorCode(stream, "helpers::mirrorPadKernel(...) failed"); + sd::DebugHelper::checkErrorCode(stream, "helpers::mirrorPadKernel(...) failed"); } NDArray::registerSpecialUse({&output}, {&input, &paddings}); } - void mirrorPad(nd4j::LaunchContext * context, const NDArray& input, const NDArray& paddings, NDArray& output, const int mode) { + void mirrorPad(sd::LaunchContext * context, const NDArray& input, const NDArray& paddings, NDArray& output, const int mode) { BUILD_DOUBLE_SELECTOR(input.dataType(), paddings.dataType(), mirrorPad_, (context, input, paddings, output, mode), LIBND4J_TYPES, INDEXING_TYPES); } diff --git a/libnd4j/include/ops/declarable/helpers/cuda/percentile.cu b/libnd4j/include/ops/declarable/helpers/cuda/percentile.cu index 79c9024f5..ebb067251 100644 --- a/libnd4j/include/ops/declarable/helpers/cuda/percentile.cu +++ b/libnd4j/include/ops/declarable/helpers/cuda/percentile.cu @@ -20,12 +20,12 @@ // #include -#include +#include #include #include -#include "ResultSet.h" +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { @@ -84,7 +84,7 @@ namespace helpers { template - static void _percentile(nd4j::LaunchContext * context, const NDArray& input, NDArray& output, std::vector& axis, const float q, const int interpolation) { + static void _percentile(sd::LaunchContext * context, const NDArray& input, NDArray& output, std::vector& axis, const float q, const int interpolation) { const int inputRank = input.rankOf(); if(axis.empty()) @@ -116,10 +116,10 @@ namespace helpers { percentileKernel<<<256, 512, 1024, *context->getCudaStream()>>>(tempArray.specialBuffer(), packX.platformShapeInfo(), packX.platformOffsets(), packX.numberOfTads(), tadLength, output.specialBuffer(), output.specialShapeInfo(), output.lengthOf(), position); - nd4j::DebugHelper::checkErrorCode(context->getCudaStream(), "percentile"); + sd::DebugHelper::checkErrorCode(context->getCudaStream(), "percentile"); } - void percentile(nd4j::LaunchContext * context, const NDArray& input, NDArray& output, std::vector& axises, const float q, const int interpolation) { + void percentile(sd::LaunchContext * context, const NDArray& input, NDArray& output, std::vector& axises, const float q, const int interpolation) { NDArray::prepareSpecialUse({&output}, {&input}); BUILD_SINGLE_SELECTOR(input.dataType(), _percentile, (context, input, output, axises, q, interpolation), LIBND4J_TYPES); @@ -127,7 +127,7 @@ namespace helpers { NDArray::registerSpecialUse({&output}, {&input}); } - BUILD_SINGLE_TEMPLATE(template void _percentile, (nd4j::LaunchContext * context, const NDArray& input, NDArray& output, std::vector& axises, const float q, const int interpolation), LIBND4J_TYPES); + BUILD_SINGLE_TEMPLATE(template void _percentile, (sd::LaunchContext * context, const NDArray& input, NDArray& output, std::vector& axises, const float q, const int interpolation), LIBND4J_TYPES); } } diff --git a/libnd4j/include/ops/declarable/helpers/cuda/polyGamma.cu b/libnd4j/include/ops/declarable/helpers/cuda/polyGamma.cu index 6a62246a3..2f96d96e7 100644 --- a/libnd4j/include/ops/declarable/helpers/cuda/polyGamma.cu +++ b/libnd4j/include/ops/declarable/helpers/cuda/polyGamma.cu @@ -20,9 +20,9 @@ #include #include -#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { @@ -83,7 +83,7 @@ static void polyGammaCudaLauncher(const int blocksPerGrid, const int threadsPerB } /////////////////////////////////////////////////////////////////// -void polyGamma(nd4j::LaunchContext * context, const NDArray& n, const NDArray& x, NDArray& z) { +void polyGamma(sd::LaunchContext * context, const NDArray& n, const NDArray& x, NDArray& z) { NDArray::prepareSpecialUse({&z}, {&n, &x}); diff --git a/libnd4j/include/ops/declarable/helpers/cuda/prefix.cu b/libnd4j/include/ops/declarable/helpers/cuda/prefix.cu index 52dd8b815..3d1fd104a 100644 --- a/libnd4j/include/ops/declarable/helpers/cuda/prefix.cu +++ b/libnd4j/include/ops/declarable/helpers/cuda/prefix.cu @@ -19,12 +19,12 @@ // #include -#include -#include -#include +#include +#include +#include #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { @@ -146,10 +146,10 @@ static void prefixPerBlockCudaLauncher(const int blocksPerGrid, const int thread } /////////////////////////////////////////////////////////////////// -void prefix(nd4j::LaunchContext * context, scalar::Ops op, const NDArray* x, NDArray* z, const std::vector& dims, bool exclusive, bool reverse) { +void prefix(sd::LaunchContext * context, scalar::Ops op, const NDArray* x, NDArray* z, const std::vector& dims, bool exclusive, bool reverse) { - auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(x->getShapeInfo(), dims); - auto packZ = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(z->getShapeInfo(), dims); + auto packX = sd::ConstantTadHelper::getInstance()->tadForDimensions(x->getShapeInfo(), dims); + auto packZ = sd::ConstantTadHelper::getInstance()->tadForDimensions(z->getShapeInfo(), dims); const Nd4jLong numTads = packX.numberOfTads(); const Nd4jLong tadLen = x->lengthOf() / numTads; @@ -168,7 +168,7 @@ void prefix(nd4j::LaunchContext * context, scalar::Ops op, const NDArray* x, NDA } /////////////////////////////////////////////////////////////////// -void prefix(nd4j::LaunchContext * context, scalar::Ops op, const NDArray* x, NDArray* z, bool exclusive, bool reverse) { +void prefix(sd::LaunchContext * context, scalar::Ops op, const NDArray* x, NDArray* z, bool exclusive, bool reverse) { prefix(context, op, x, z, {}, exclusive, reverse); } diff --git a/libnd4j/include/ops/declarable/helpers/cuda/print_variable.cu b/libnd4j/include/ops/declarable/helpers/cuda/print_variable.cu index 88d2b5937..a518ddd72 100644 --- a/libnd4j/include/ops/declarable/helpers/cuda/print_variable.cu +++ b/libnd4j/include/ops/declarable/helpers/cuda/print_variable.cu @@ -21,7 +21,7 @@ #include #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { template diff --git a/libnd4j/include/ops/declarable/helpers/cuda/qr.cu b/libnd4j/include/ops/declarable/helpers/cuda/qr.cu index 29d8924db..d0d5fddd5 100644 --- a/libnd4j/include/ops/declarable/helpers/cuda/qr.cu +++ b/libnd4j/include/ops/declarable/helpers/cuda/qr.cu @@ -18,10 +18,10 @@ // @author George A. Shulinok // #include -#include -#include +#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { @@ -151,7 +151,7 @@ namespace helpers { } template - void qr_(LaunchContext* context, NDArray* input, NDArray* outputQ, NDArray* outputR, bool const fullMatricies) { + void qr_(LaunchContext* context, NDArray const* input, NDArray* outputQ, NDArray* outputR, bool const fullMatricies) { Nd4jLong lastDim = input->rankOf() - 1; Nd4jLong preLastDim = input->rankOf() - 2; @@ -170,7 +170,7 @@ namespace helpers { NDArray::registerSpecialUse({outputQ, outputR}, {input}); } - void qr(nd4j::LaunchContext* context, NDArray* input, NDArray* outputQ, NDArray* outputR, bool const fullMatricies) { + void qr(sd::LaunchContext* context, NDArray const* input, NDArray* outputQ, NDArray* outputR, bool const fullMatricies) { BUILD_SINGLE_SELECTOR(input->dataType(), qr_, (context, input, outputQ, outputR, fullMatricies), FLOAT_TYPES); } diff --git a/libnd4j/include/ops/declarable/helpers/cuda/random.cu b/libnd4j/include/ops/declarable/helpers/cuda/random.cu index 1e290bc56..59f22d878 100644 --- a/libnd4j/include/ops/declarable/helpers/cuda/random.cu +++ b/libnd4j/include/ops/declarable/helpers/cuda/random.cu @@ -24,13 +24,13 @@ #include #include #include -#include -#include -#include +#include +#include +#include #include -#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { @@ -291,7 +291,7 @@ __global__ static void fillMultiNomialCuda_(graph::RandomGenerator* devRng, cons for (Nd4jLong nClass = 0; nClass < numOfClassX; nClass++) { Nd4jLong nIndex = nSamplesPerBatch + nClassPerSamples + nClass; - X tValue = (xTad[nClass * xDimAstride] - nd4j::math::nd4j_log(-nd4j::math::nd4j_log(devRng->relativeT(nIndex, minVal, maxVal)))); + X tValue = (xTad[nClass * xDimAstride] - sd::math::nd4j_log(-sd::math::nd4j_log(devRng->relativeT(nIndex, minVal, maxVal)))); if (tValue > Max) { Max = tValue; arg = nClass; diff --git a/libnd4j/include/ops/declarable/helpers/cuda/random_crop.cu b/libnd4j/include/ops/declarable/helpers/cuda/random_crop.cu index d2c83d0f4..0489103f9 100644 --- a/libnd4j/include/ops/declarable/helpers/cuda/random_crop.cu +++ b/libnd4j/include/ops/declarable/helpers/cuda/random_crop.cu @@ -23,7 +23,7 @@ #include #include #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { diff --git a/libnd4j/include/ops/declarable/helpers/cuda/range.cu b/libnd4j/include/ops/declarable/helpers/cuda/range.cu index 3a5504905..668518d82 100644 --- a/libnd4j/include/ops/declarable/helpers/cuda/range.cu +++ b/libnd4j/include/ops/declarable/helpers/cuda/range.cu @@ -21,7 +21,7 @@ #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { @@ -38,11 +38,11 @@ namespace helpers { ////////////////////////////////////////////////////////////////////////// // be careful: outVector must have c-order and ews = 1 !!! template - static void _range(nd4j::LaunchContext * context, const NDArray& start, const NDArray& delta, NDArray& outVector) { + static void _range(sd::LaunchContext * context, const NDArray& start, const NDArray& delta, NDArray& outVector) { global_range<<<512, 512, 2048, *context->getCudaStream()>>>(outVector.getSpecialBuffer(), outVector.lengthOf(), start.e(0), delta.e(0)); } - void range(nd4j::LaunchContext * context, const NDArray& start, const NDArray& delta, NDArray& outVector) { + void range(sd::LaunchContext * context, const NDArray& start, const NDArray& delta, NDArray& outVector) { NDArray::prepareSpecialUse({&outVector}, {&start, &delta}); BUILD_SINGLE_SELECTOR(outVector.dataType(), _range, (context, start, delta, outVector), LIBND4J_TYPES); NDArray::registerSpecialUse({&outVector}, {&start, &delta}); diff --git a/libnd4j/include/ops/declarable/helpers/cuda/reverse.cu b/libnd4j/include/ops/declarable/helpers/cuda/reverse.cu index 15335d57e..793d90f91 100644 --- a/libnd4j/include/ops/declarable/helpers/cuda/reverse.cu +++ b/libnd4j/include/ops/declarable/helpers/cuda/reverse.cu @@ -21,12 +21,12 @@ #include #include #include -#include -#include -#include +#include +#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { @@ -141,13 +141,13 @@ namespace helpers { } template - static void reverseTad(nd4j::LaunchContext * context, const NDArray* input, NDArray* output, Nd4jLong *inputTadShape, Nd4jLong *inputTadOffsets, Nd4jLong *outputTadShape, Nd4jLong *outputTadOffsets, uint64_t tadLength) { + static void reverseTad(sd::LaunchContext * context, const NDArray* input, NDArray* output, Nd4jLong *inputTadShape, Nd4jLong *inputTadOffsets, Nd4jLong *outputTadShape, Nd4jLong *outputTadOffsets, uint64_t tadLength) { auto stream = context->getCudaStream(); reverseTadKernel<<<256, 512, 8192, *stream>>>(input->getSpecialBuffer(), input->getSpecialShapeInfo(), output->specialBuffer(), output->specialShapeInfo(), inputTadShape, inputTadOffsets, outputTadShape, outputTadOffsets, input->lengthOf(), tadLength, input->lengthOf() / tadLength); } template - static void reverseArray(nd4j::LaunchContext * context, const NDArray* input, NDArray* output, Nd4jLong numOfElemsToReverse) { + static void reverseArray(sd::LaunchContext * context, const NDArray* input, NDArray* output, Nd4jLong numOfElemsToReverse) { auto stream = context->getCudaStream(); Nd4jLong numOfReverse = numOfElemsToReverse; if (numOfElemsToReverse == 0) @@ -159,7 +159,7 @@ namespace helpers { /////////////////////////////////////////////////////////////////// template - static void reverseSequence_(nd4j::LaunchContext * context, const NDArray* input, const NDArray* seqLengths, NDArray* output, int seqDim, const int batchDim){ + static void reverseSequence_(sd::LaunchContext * context, const NDArray* input, const NDArray* seqLengths, NDArray* output, int seqDim, const int batchDim){ int posOfNonUnityDim = -1; seqLengths->syncToHost(); auto stream = context->getCudaStream(); @@ -198,7 +198,7 @@ namespace helpers { } } - void reverseSequence(nd4j::LaunchContext * context, const NDArray* input, const NDArray* seqLengths, NDArray* output, int seqDim, const int batchDim) { + void reverseSequence(sd::LaunchContext * context, const NDArray* input, const NDArray* seqLengths, NDArray* output, int seqDim, const int batchDim) { NDArray::prepareSpecialUse({output}, {input, seqLengths}); // if op isn't inplace - copy original data into output array @@ -210,12 +210,12 @@ namespace helpers { } ////////////////////////////////////////////////////////////////////////// - void reverse(nd4j::LaunchContext * context, const NDArray* input, NDArray* output, const std::vector* intArgs, bool isBackProp) { + void reverse(sd::LaunchContext * context, const NDArray* input, NDArray* output, const std::vector* intArgs, bool isBackProp) { // we need to reverse axis only if that's new op std::vector dimensions = isBackProp ? ShapeUtils::evalDimsToExclude(input->rankOf(), *intArgs) : *intArgs; std::vector axis = ShapeUtils::evalDimsToExclude(input->rankOf(), dimensions); - auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), dimensions); - auto packZ = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), dimensions); + auto packX = sd::ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), dimensions); + auto packZ = sd::ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), dimensions); @@ -230,7 +230,7 @@ namespace helpers { NDArray::registerSpecialUse({output}, {input}); } -BUILD_SINGLE_TEMPLATE(template void reverseArray, (nd4j::LaunchContext * context, const NDArray *inArr, NDArray *outArr, Nd4jLong numOfElemsToReverse), LIBND4J_TYPES); +BUILD_SINGLE_TEMPLATE(template void reverseArray, (sd::LaunchContext * context, const NDArray *inArr, NDArray *outArr, Nd4jLong numOfElemsToReverse), LIBND4J_TYPES); } } diff --git a/libnd4j/include/ops/declarable/helpers/cuda/roll.cu b/libnd4j/include/ops/declarable/helpers/cuda/roll.cu index bc53946d3..d014b9115 100644 --- a/libnd4j/include/ops/declarable/helpers/cuda/roll.cu +++ b/libnd4j/include/ops/declarable/helpers/cuda/roll.cu @@ -22,7 +22,7 @@ #include #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { @@ -308,7 +308,7 @@ namespace helpers { } } - void rollFunctorFull(nd4j::LaunchContext * context, NDArray* input, NDArray* output, std::vector const& shifts, std::vector const& axes, bool inplace){ + void rollFunctorFull(sd::LaunchContext * context, NDArray* input, NDArray* output, std::vector const& shifts, std::vector const& axes, bool inplace){ input->syncToDevice(); BUILD_SINGLE_SELECTOR(input->dataType(), rollFunctorFull_, (input, output, shifts, axes, inplace), LIBND4J_TYPES); @@ -316,7 +316,7 @@ namespace helpers { output->tickWriteDevice(); } - void rollFunctorLinear(nd4j::LaunchContext * context, NDArray* input, NDArray* output, int shift, bool inplace){ + void rollFunctorLinear(sd::LaunchContext * context, NDArray* input, NDArray* output, int shift, bool inplace){ input->syncToDevice(); BUILD_SINGLE_SELECTOR(input->dataType(), rollFunctorLinear_, (input, output, shift, inplace), LIBND4J_TYPES); diff --git a/libnd4j/include/ops/declarable/helpers/cuda/s_t_b.cu b/libnd4j/include/ops/declarable/helpers/cuda/s_t_b.cu index f3bee349b..5784699d0 100644 --- a/libnd4j/include/ops/declarable/helpers/cuda/s_t_b.cu +++ b/libnd4j/include/ops/declarable/helpers/cuda/s_t_b.cu @@ -20,9 +20,9 @@ // #include -#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { @@ -43,12 +43,12 @@ __global__ static void batchToSpaceCuda(const void* vx, const Nd4jLong* xShapeIn const auto x = reinterpret_cast(vx); auto z = reinterpret_cast(vz); - __shared__ int rank; - __shared__ Nd4jLong zLen, *sharedMem; + __shared__ int rank, *sharedMem; + __shared__ Nd4jLong zLen; if (threadIdx.x == 0) { extern __shared__ unsigned char shmem[]; - sharedMem = reinterpret_cast(shmem); + sharedMem = reinterpret_cast(shmem); rank = shape::rank(zShapeInfo); zLen = shape::length(zShapeInfo); @@ -84,7 +84,7 @@ static void batchToSpaceCudaLauncher(const int blocksPerGrid, const int threadsP BUILD_SINGLE_TEMPLATE(template void batchToSpaceCudaLauncher, (const int blocksPerGrid, const int threadsPerBlock, const int sharedMem, const cudaStream_t *stream, const void* vx, const Nd4jLong* xShapeInfo, void* vz, const Nd4jLong* zShapeInfo, const uint cropBottom, const uint cropLeft), LIBND4J_TYPES); /////////////////////////////////////////////////////////////////// -void batchToSpace(nd4j::LaunchContext* context, const NDArray& input, NDArray& output, const uint cropBottom, const uint cropTop, const uint cropLeft, const uint cropRight, const uint blockSize) { +void batchToSpace(sd::LaunchContext* context, const NDArray& input, NDArray& output, const uint cropBottom, const uint cropTop, const uint cropLeft, const uint cropRight, const uint blockSize) { // [bS*blockSize*blockSize, H/blockSize, W/blockSize, iC] is rearranged/permuted to [bS, oH, oW, iC] // oH = H - cropTop - cropBottom @@ -103,7 +103,7 @@ void batchToSpace(nd4j::LaunchContext* context, const NDArray& input, NDArray& o const int threadsPerBlock = MAX_NUM_THREADS / 2; const int blocksPerGrid = (output.lengthOf() + threadsPerBlock - 1) / threadsPerBlock; - const int sharedMem = threadsPerBlock * sizeof(Nd4jLong) * output.rankOf() + 128; + const int sharedMem = threadsPerBlock * sizeof(int) * output.rankOf() + 128; PointersManager manager(context, "batchToSpace"); @@ -138,13 +138,13 @@ __global__ static void batchToSpaceNDCuda(const void* vx, const Nd4jLong* xShape const auto y = reinterpret_cast(vy); auto z = reinterpret_cast(vz); - __shared__ int rank; - __shared__ Nd4jLong zLen, *sharedMem; + __shared__ int rank, *sharedMem; + __shared__ Nd4jLong zLen; if (threadIdx.x == 0) { extern __shared__ unsigned char shmem[]; - sharedMem = reinterpret_cast(shmem); + sharedMem = reinterpret_cast(shmem); rank = shape::rank(zShapeInfo); zLen = shape::length(zShapeInfo); @@ -181,7 +181,7 @@ static void batchToSpaceNDCudaLauncher(const int blocksPerGrid, const int thread BUILD_DOUBLE_TEMPLATE(template void batchToSpaceNDCudaLauncher, (const int blocksPerGrid, const int threadsPerBlock, const int sharedMem, const cudaStream_t *stream, const void* vx, const Nd4jLong* xShapeInfo, const void* vy, const Nd4jLong* yShapeInfo, void* vz, const Nd4jLong* zShapeInfo, const uint numOfSpatialDims), LIBND4J_TYPES, INTEGER_TYPES); ////////////////////////////////////////////////////////////////////////// -void batchToSpaceND(nd4j::LaunchContext* context, const NDArray& input, const NDArray& blockShape, const NDArray& crop, NDArray& output) { +void batchToSpaceND(sd::LaunchContext* context, const NDArray& input, const NDArray& blockShape, const NDArray& crop, NDArray& output) { // 4D example, numOfSpatialDims = 2 - two spatial dimensions // [bS*blockShape[0]*blockShape[1], iH, iW, iC] is rearranged/permuted to [bS, iH*blockShape[0] - cropTop - cropBottom, iW*blockShape[1] - cropLeft - cropRight, iC] @@ -234,7 +234,7 @@ void batchToSpaceND(nd4j::LaunchContext* context, const NDArray& input, const ND const int threadsPerBlock = MAX_NUM_THREADS / 4; const int blocksPerGrid = (output.lengthOf() + threadsPerBlock - 1) / threadsPerBlock; - const int sharedMem = threadsPerBlock * sizeof(Nd4jLong) * output.rankOf() + 128; + const int sharedMem = threadsPerBlock * sizeof(int) * output.rankOf() + 128; PointersManager manager(context, "batchToSpaceND"); @@ -264,12 +264,12 @@ __global__ static void spaceToBatchCuda(const void* vx, const Nd4jLong* xShapeIn const auto x = reinterpret_cast(vx); auto z = reinterpret_cast(vz); - __shared__ int rank; - __shared__ Nd4jLong zLen, *sharedMem; + __shared__ int rank, *sharedMem; + __shared__ Nd4jLong zLen; if (threadIdx.x == 0) { extern __shared__ unsigned char shmem[]; - sharedMem = reinterpret_cast(shmem); + sharedMem = reinterpret_cast(shmem); rank = shape::rank(zShapeInfo); zLen = shape::length(zShapeInfo); @@ -309,7 +309,7 @@ static void spaceToBatchCudaLauncher(const int blocksPerGrid, const int threadsP BUILD_SINGLE_TEMPLATE(template void spaceToBatchCudaLauncher, (const int blocksPerGrid, const int threadsPerBlock, const int sharedMem, const cudaStream_t *stream, const void* vx, const Nd4jLong* xShapeInfo, void* vz, const Nd4jLong* zShapeInfo, const uint padBottom, const uint padTop, const uint padLeft, const uint padRight), LIBND4J_TYPES); /////////////////////////////////////////////////////////////////// -void spaceToBatch(nd4j::LaunchContext* context, const NDArray& input, NDArray& output, const uint padBottom, const uint padTop, const uint padLeft, const uint padRight, const uint blockSize) { +void spaceToBatch(sd::LaunchContext* context, const NDArray& input, NDArray& output, const uint padBottom, const uint padTop, const uint padLeft, const uint padRight, const uint blockSize) { // [bS, iH, iW, iC] is rearranged/permuted to [bS*blockSize*blockSize, (iH + padBottom + padTop)/blockSize, (iW + padLeft + padRight)/blockSize, iC] @@ -326,7 +326,7 @@ void spaceToBatch(nd4j::LaunchContext* context, const NDArray& input, NDArray& o const int threadsPerBlock = MAX_NUM_THREADS / 2; const int blocksPerGrid = (output.lengthOf() + threadsPerBlock - 1) / threadsPerBlock; - const int sharedMem = threadsPerBlock * sizeof(Nd4jLong) * output.rankOf() + 128; + const int sharedMem = threadsPerBlock * sizeof(int) * output.rankOf() + 128; PointersManager manager(context, "spaceToBatch"); @@ -364,13 +364,13 @@ __global__ static void spaceToBatchNDCuda(const void* vx, const Nd4jLong* xShape const auto y = reinterpret_cast(vy); auto z = reinterpret_cast(vz); - __shared__ int rank; // xRank = zRank, yRank = 2; - __shared__ Nd4jLong zLen, totalThreads, *sharedMem; + __shared__ int rank, *sharedMem; // xRank = zRank, yRank = 2; + __shared__ Nd4jLong zLen, totalThreads; if (threadIdx.x == 0) { extern __shared__ unsigned char shmem[]; - sharedMem = reinterpret_cast(shmem); + sharedMem = reinterpret_cast(shmem); rank = shape::rank(zShapeInfo); zLen = shape::length(zShapeInfo); @@ -420,7 +420,7 @@ static void spaceToBatchNDCudaLauncher(const int blocksPerGrid, const int thread BUILD_DOUBLE_TEMPLATE(template void spaceToBatchNDCudaLauncher, (const int blocksPerGrid, const int threadsPerBlock, const int sharedMem, const cudaStream_t *stream, const void* vx, const Nd4jLong* xShapeInfo, const void* vy, const Nd4jLong* yShapeInfo, void* vz, const Nd4jLong* zShapeInfo, const uint numOfSpatialDims), LIBND4J_TYPES, INTEGER_TYPES); ////////////////////////////////////////////////////////////////////////// -void spaceToBatchND(nd4j::LaunchContext* context, const NDArray& input, const NDArray& blockShape, const NDArray& padding, NDArray& output ) { +void spaceToBatchND(sd::LaunchContext* context, const NDArray& input, const NDArray& blockShape, const NDArray& padding, NDArray& output ) { // 4D example with two spatial dimensions // [bS, iH, iW, iC] is rearranged/permuted to [bS*blockShape[0]*blockShape[1], (iH + padBottom + padTop)/blockShape[0], (iW + padLeft + padRight)/blockShape[1], iC] @@ -473,7 +473,7 @@ void spaceToBatchND(nd4j::LaunchContext* context, const NDArray& input, const ND const int threadsPerBlock = MAX_NUM_THREADS / 4; const int blocksPerGrid = (output.lengthOf() + threadsPerBlock - 1) / threadsPerBlock; - const int sharedMem = threadsPerBlock * sizeof(Nd4jLong) * output.rankOf() + 128; + const int sharedMem = threadsPerBlock * sizeof(int) * output.rankOf() + 128; PointersManager manager(context, "spaceToBatchND"); @@ -523,13 +523,13 @@ void spaceToBatchND(nd4j::LaunchContext* context, const NDArray& input, const ND }; template - void _execute(nd4j::LaunchContext * context, void *vptrSpace, const Nd4jLong *space_shape, const Nd4jLong *space_strides, const Nd4jLong *block_shape, const Nd4jLong *pad_start, const Nd4jLong *block_offsets, void *vptrBatch, const Nd4jLong *batch_shape, const Nd4jLong *batch_strides) { + void _execute(sd::LaunchContext * context, void *vptrSpace, const Nd4jLong *space_shape, const Nd4jLong *space_strides, const Nd4jLong *block_shape, const Nd4jLong *pad_start, const Nd4jLong *block_offsets, void *vptrBatch, const Nd4jLong *batch_shape, const Nd4jLong *batch_strides) { auto ptrSpace = reinterpret_cast(vptrSpace); auto ptrBatch = reinterpret_cast(vptrBatch); SpaceToBatchHelper::run(ptrSpace, space_shape, space_strides, block_shape, pad_start, block_offsets, ptrBatch, batch_shape, batch_strides); }; - Nd4jStatus _batchToSpace(nd4j::LaunchContext * context, int internal_block_dims, NDArray *input, NDArray *output, std::vector &internal_input_shape, std::vector &internal_output_shape, Nd4jLong *block_shape, Nd4jLong *crops) { + Nd4jStatus _batchToSpace(sd::LaunchContext * context, int internal_block_dims, NDArray *input, NDArray *output, std::vector &internal_input_shape, std::vector &internal_output_shape, Nd4jLong *block_shape, Nd4jLong *crops) { return Status::OK(); } @@ -542,7 +542,7 @@ void spaceToBatchND(nd4j::LaunchContext* context, const NDArray& input, const ND #define STB_BOOL (0, false),\ (1, true) - BUILD_TRIPLE_TEMPLATE(template void _execute, (nd4j::LaunchContext * context, void *ptrSpace, const Nd4jLong *space_shape, const Nd4jLong *space_strides, const Nd4jLong *block_shape, const Nd4jLong *pad_start, const Nd4jLong *block_offsets, void *ptrBatch, const Nd4jLong *batch_shape, const Nd4jLong *batch_strides), LIBND4J_TYPES, STB_DIM, STB_BOOL); + BUILD_TRIPLE_TEMPLATE(template void _execute, (sd::LaunchContext * context, void *ptrSpace, const Nd4jLong *space_shape, const Nd4jLong *space_strides, const Nd4jLong *block_shape, const Nd4jLong *pad_start, const Nd4jLong *block_offsets, void *ptrBatch, const Nd4jLong *batch_shape, const Nd4jLong *batch_strides), LIBND4J_TYPES, STB_DIM, STB_BOOL); #undef STB_BOOL #undef STB_DIM diff --git a/libnd4j/include/ops/declarable/helpers/cuda/s_t_d.cu b/libnd4j/include/ops/declarable/helpers/cuda/s_t_d.cu index 1e02a54ba..a5ae42e78 100644 --- a/libnd4j/include/ops/declarable/helpers/cuda/s_t_d.cu +++ b/libnd4j/include/ops/declarable/helpers/cuda/s_t_d.cu @@ -20,7 +20,7 @@ #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { template @@ -90,17 +90,17 @@ namespace helpers { } template - static void _spaceTodepth_(nd4j::LaunchContext * context, NDArray *input, NDArray *output, int block_size, bool isNHWC) { + static void _spaceTodepth_(sd::LaunchContext * context, NDArray *input, NDArray *output, int block_size, bool isNHWC) { spaceToDepthKernel<<<512, 512, 1024, *context->getCudaStream()>>>(input->specialBuffer(), input->specialShapeInfo(), output->specialBuffer(), output->specialShapeInfo(), block_size, isNHWC); } - void _spaceTodepth(nd4j::LaunchContext * context, NDArray *input, NDArray *output, int block_size, bool isNHWC) { + void _spaceTodepth(sd::LaunchContext * context, NDArray *input, NDArray *output, int block_size, bool isNHWC) { NDArray::prepareSpecialUse({output}, {input}); BUILD_SINGLE_SELECTOR(input->dataType(), _spaceTodepth_, (context, input, output, block_size, isNHWC), LIBND4J_TYPES); NDArray::registerSpecialUse({output}, {input}); } - BUILD_SINGLE_TEMPLATE(template void _spaceTodepth_, (nd4j::LaunchContext * context, NDArray *input, NDArray *output, int block_size, bool isNHWC), LIBND4J_TYPES); + BUILD_SINGLE_TEMPLATE(template void _spaceTodepth_, (sd::LaunchContext * context, NDArray *input, NDArray *output, int block_size, bool isNHWC), LIBND4J_TYPES); } } diff --git a/libnd4j/include/ops/declarable/helpers/cuda/scatter.cu b/libnd4j/include/ops/declarable/helpers/cuda/scatter.cu index ec85efddf..364ad83d2 100644 --- a/libnd4j/include/ops/declarable/helpers/cuda/scatter.cu +++ b/libnd4j/include/ops/declarable/helpers/cuda/scatter.cu @@ -22,12 +22,12 @@ #include #include #include -#include +#include #include #include #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { @@ -63,13 +63,13 @@ __global__ static void checkIndicesCuda(const void *vx, const Nd4jLong *xShapeIn if(currentInd >= shape::sizeAt(zShapeInfo, axis == -1 ? xCoords[xRank-1] : axis)) { printf("checkIndices cuda: out of range element %lld at index %lld \n", currentInd, i); - nd4j::math::atomics::nd4j_atomicAdd(&numOfBadIndxPerBlock, 1); + sd::math::atomics::nd4j_atomicAdd(&numOfBadIndxPerBlock, 1); } } __syncthreads(); if (threadIdx.x == 0 && numOfBadIndxPerBlock != 0) - nd4j::math::atomics::nd4j_atomicAdd(y, numOfBadIndxPerBlock); + sd::math::atomics::nd4j_atomicAdd(y, numOfBadIndxPerBlock); } /////////////////////////////////////////////////////////////////// @@ -82,7 +82,7 @@ static void checkIndicesCudaLauncher(const int blocksPerGrid, const int threadsP /////////////////////////////////////////////////////////////////// -Nd4jLong checkIndices(nd4j::LaunchContext *context, const NDArray& indices, const NDArray& output, const int axis) { +Nd4jLong checkIndices(sd::LaunchContext *context, const NDArray& indices, const NDArray& output, const int axis) { const int threadsPerBlock = MAX_NUM_THREADS / 2; const int blocksPerGrid = (indices.lengthOf() + threadsPerBlock - 1) / threadsPerBlock; @@ -93,7 +93,7 @@ Nd4jLong checkIndices(nd4j::LaunchContext *context, const NDArray& indices, cons PointersManager manager(context, "scatterNDcheckIndices"); // scalar, initial value = 0 - NDArray numOfBadIndx(nd4j::DataType::INT64, context, true); + NDArray numOfBadIndx(sd::DataType::INT64, context, true); NDArray::prepareSpecialUse({&numOfBadIndx}, {&indices}); BUILD_SINGLE_SELECTOR(xType, checkIndicesCudaLauncher, (blocksPerGrid, threadsPerBlock, sharedMem, context->getCudaStream(), indices.getSpecialBuffer(), indices.getSpecialShapeInfo(), reinterpret_cast(numOfBadIndx.getSpecialBuffer()), output.getSpecialShapeInfo(), axis), INDEXING_TYPES); @@ -334,7 +334,7 @@ static void scatterCudaLauncher(const int blocksPerGrid, const int threadsPerBlo /////////////////////////////////////////////////////////////////// -void scatter(nd4j::LaunchContext *context, pairwise::Ops op, const NDArray& indices, const NDArray& updates, NDArray& output, const bool lock) { +void scatter(sd::LaunchContext *context, pairwise::Ops op, const NDArray& indices, const NDArray& updates, NDArray& output, const bool lock) { const auto xType = indices.dataType(); const auto yType = updates.dataType(); @@ -596,7 +596,7 @@ static void scatterNDCudaLauncher(const int blocksPerGrid, const int threadsPerB } /////////////////////////////////////////////////////////////////// -void scatterND(nd4j::LaunchContext *context, pairwise::Ops op, const NDArray& indices, const NDArray& updates, NDArray& output, const bool lock) { +void scatterND(sd::LaunchContext *context, pairwise::Ops op, const NDArray& indices, const NDArray& updates, NDArray& output, const bool lock) { const int xRank = indices.rankOf(); const int yRank = updates.rankOf(); @@ -628,12 +628,12 @@ __global__ void scatterForLossCuda(const void *vx, const Nd4jLong *xShapeInfo, auto y = reinterpret_cast(vy); auto z = reinterpret_cast(vz); - __shared__ Nd4jLong xLen, *sharedMem; - __shared__ int xRank; // xRank = zRank, yRank = xRank + 1 + __shared__ Nd4jLong xLen; + __shared__ int xRank, *sharedMem; // xRank = zRank, yRank = xRank + 1 if (threadIdx.x == 0) { extern __shared__ unsigned char shmem[]; - sharedMem = reinterpret_cast(shmem); + sharedMem = reinterpret_cast(shmem); xLen = shape::length(xShapeInfo); xRank = shape::rank(xShapeInfo); @@ -670,7 +670,7 @@ static void scatterForLossCudaLauncher(const int blocksPerGrid, const int thread } /////////////////////////////////////////////////////////////////// -void scatterForLoss(nd4j::LaunchContext* context, const NDArray& indices, NDArray& updates, NDArray& output, const bool calcGrad) { +void scatterForLoss(sd::LaunchContext* context, const NDArray& indices, NDArray& updates, NDArray& output, const bool calcGrad) { // shapes of indices and output must be the same // shape of indices should be the same as updates shape with last dimension excluded, for example if updates is {a,b,c} then indices should be {a,b} @@ -678,7 +678,7 @@ void scatterForLoss(nd4j::LaunchContext* context, const NDArray& indices, NDArra const int threadsPerBlock = MAX_NUM_THREADS / 2; const int blocksPerGrid = (indices.lengthOf() + threadsPerBlock - 1) / threadsPerBlock; - const int sharedMem = updates.rankOf() * sizeof(Nd4jLong) * threadsPerBlock + 128; + const int sharedMem = updates.rankOf() * sizeof(int) * threadsPerBlock + 128; if(calcGrad) { NDArray::prepareSpecialUse({&updates}, {&indices}); @@ -736,13 +736,13 @@ __global__ static void scatterLockCuda(const int opCode, std::vector yTadDims(sizeOfUpdDims); std::iota(yTadDims.begin(), yTadDims.end(), 0); - auto packY = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(updates.getShapeInfo(), ShapeUtils::evalDimsToExclude(updates.rankOf(), yTadDims)); - auto packZ = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(output.getShapeInfo(), zTadDims); + auto packY = sd::ConstantTadHelper::getInstance()->tadForDimensions(updates.getShapeInfo(), ShapeUtils::evalDimsToExclude(updates.rankOf(), yTadDims)); + auto packZ = sd::ConstantTadHelper::getInstance()->tadForDimensions(output.getShapeInfo(), zTadDims); const Nd4jLong zTadLen = shape::length(packZ.primaryShapeInfo()); const Nd4jLong yTadLen = shape::length(packY.primaryShapeInfo()); - const auto threadsPerBlock = nd4j::math::nd4j_max(32, nd4j::math::nd4j_min(zTadLen, 1024)); + const auto threadsPerBlock = sd::math::nd4j_max(32, sd::math::nd4j_min(zTadLen, 1024)); const auto blocksPerGrid = indices.lengthOf(); const auto xType = indices.dataType(); @@ -959,12 +959,12 @@ __global__ static void scatterLockCuda(const int opCode, template - void scatter_(nd4j::LaunchContext *context, pairwise::Ops op, const NDArray& indices, const NDArray& updates, NDArray& output, const bool lock) { + void scatter_(sd::LaunchContext *context, pairwise::Ops op, const NDArray& indices, const NDArray& updates, NDArray& output, const bool lock) { std::vector dims = {0}; auto inverted = ShapeUtils::evalDimsToExclude(output.rankOf(), dims); - auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(output.getShapeInfo(), inverted); - auto packY = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(updates.getShapeInfo(), inverted); + auto packX = sd::ConstantTadHelper::getInstance()->tadForDimensions(output.getShapeInfo(), inverted); + auto packY = sd::ConstantTadHelper::getInstance()->tadForDimensions(updates.getShapeInfo(), inverted); auto psX = packX.specialShapeInfo(); auto psY = packY.specialShapeInfo(); @@ -981,7 +981,7 @@ __global__ static void scatterLockCuda(const int opCode, if (tadLengthX != tadLengthY) throw std::runtime_error("scatter: Lengths of TADs must be equal"); - auto blockSize = nd4j::math::nd4j_max(32, nd4j::math::nd4j_min(tadLengthX, 1024)); + auto blockSize = sd::math::nd4j_max(32, sd::math::nd4j_min(tadLengthX, 1024)); if (lock) scatterCuda<<<512, blockSize, 1024, *context->getCudaStream()>>>(op, indices.lengthOf(), output.getSpecialBuffer(), psX, poX, updates.getSpecialBuffer(), psY, poY, reinterpret_cast(indices.getSpecialBuffer()), tadLengthX, tadLengthY); @@ -1016,9 +1016,9 @@ const int xLastDim = indices.sizeAt(-1); zTadDims[i] = zRank - 1 - j; } - auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(indices.getShapeInfo(), {xRank - 1}); - auto packY = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(updates.getShapeInfo(), yTadDims); - auto packZ = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(output.getShapeInfo(), zTadDims); + auto packX = sd::ConstantTadHelper::getInstance()->tadForDimensions(indices.getShapeInfo(), {xRank - 1}); + auto packY = sd::ConstantTadHelper::getInstance()->tadForDimensions(updates.getShapeInfo(), yTadDims); + auto packZ = sd::ConstantTadHelper::getInstance()->tadForDimensions(output.getShapeInfo(), zTadDims); const int threadsPerBlock = MAX_NUM_THREADS / 4; const int blocksPerGrid = packZ.numberOfTads(); diff --git a/libnd4j/include/ops/declarable/helpers/cuda/scatter_simple.cu b/libnd4j/include/ops/declarable/helpers/cuda/scatter_simple.cu index 37a465144..277a1f587 100644 --- a/libnd4j/include/ops/declarable/helpers/cuda/scatter_simple.cu +++ b/libnd4j/include/ops/declarable/helpers/cuda/scatter_simple.cu @@ -23,13 +23,13 @@ #include #include #include -#include +#include #include #include -#include -#include +#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { template @@ -48,7 +48,7 @@ namespace nd4j { template - void scatterSimple_(nd4j::LaunchContext * context, const int opId, NDArray& input, const NDArray& updates, const NDArray& indices, const std::vector& dimensions) { + void scatterSimple_(sd::LaunchContext * context, const int opId, NDArray& input, const NDArray& updates, const NDArray& indices, const std::vector& dimensions) { auto dims = ShapeUtils::evalDimsToExclude(input.rankOf(), dimensions); auto packX = ConstantTadHelper::getInstance()->tadForDimensions(input.getShapeInfo(), dims); @@ -61,7 +61,7 @@ namespace nd4j { } - void scatterSimple(nd4j::LaunchContext * context, const int opId, NDArray& input, const NDArray& updates, const NDArray& indices, const std::vector& dimensions) { + void scatterSimple(sd::LaunchContext * context, const int opId, NDArray& input, const NDArray& updates, const NDArray& indices, const std::vector& dimensions) { auto xType = input.dataType(); auto yType = indices.dataType(); diff --git a/libnd4j/include/ops/declarable/helpers/cuda/scatter_update.cu b/libnd4j/include/ops/declarable/helpers/cuda/scatter_update.cu index 1ad55a111..748a2e6a3 100644 --- a/libnd4j/include/ops/declarable/helpers/cuda/scatter_update.cu +++ b/libnd4j/include/ops/declarable/helpers/cuda/scatter_update.cu @@ -23,13 +23,13 @@ #include #include #include -#include +#include #include #include -#include -#include +#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { /////////////////////////////////////////////////////////////////// @@ -104,7 +104,7 @@ namespace nd4j { ////////////////////////////////////////////////////////////////////////// - void scatterUpdate(nd4j::LaunchContext* context, NDArray& input, NDArray& updates, const std::vector* intArgs) { + void scatterUpdate(sd::LaunchContext* context, NDArray& input, NDArray& updates, const std::vector* intArgs) { const int opCode = (*intArgs)[0]; const int numOfDims = (*intArgs)[1]; @@ -117,7 +117,7 @@ namespace nd4j { auto packX = ConstantTadHelper::getInstance()->tadForDimensions(input.getShapeInfo(), tadDimensions); auto packY = ConstantTadHelper::getInstance()->tadForDimensions(updates.getShapeInfo(), tadDimensions); - NDArray indices(const_cast(intArgs->data()) + numOfDims + 3, 'c', {numOfInd}, nd4j::DataType::INT32, context); + NDArray indices(const_cast(intArgs->data()) + numOfDims + 3, 'c', {numOfInd}, sd::DataType::INT32, context); PointersManager manager(context, "scatterUpdate"); diff --git a/libnd4j/include/ops/declarable/helpers/cuda/segment.cu b/libnd4j/include/ops/declarable/helpers/cuda/segment.cu index 4aa5c762d..796dd6a1e 100644 --- a/libnd4j/include/ops/declarable/helpers/cuda/segment.cu +++ b/libnd4j/include/ops/declarable/helpers/cuda/segment.cu @@ -20,14 +20,14 @@ #include #include -#include +#include #include #include #include -#include -#include +#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { @@ -39,7 +39,7 @@ namespace helpers { return true; } - bool segmentIndicesValidate(nd4j::LaunchContext* context , NDArray* indices, NDArray& expected, NDArray& output) { + bool segmentIndicesValidate(sd::LaunchContext* context , NDArray* indices, NDArray& expected, NDArray& output) { BUILD_DOUBLE_SELECTOR(output.dataType(), indices->dataType(), return segmentIndicesValidate_, (indices, expected, output), NUMERIC_TYPES, INDEXING_TYPES); } @@ -59,14 +59,14 @@ namespace helpers { auto start = threadIdx.x + blockIdx.x * blockDim.x; auto step = gridDim.x * blockDim.x; for (int e = start; e < len && onlyTrue; e += step) { - nd4j::math::atomics::nd4j_atomicMax(found, indices[e]); + sd::math::atomics::nd4j_atomicMax(found, indices[e]); if (expected < *found) onlyTrue = false; } } template - static bool unsortedSegmentIndicesValidate_(nd4j::LaunchContext* context , NDArray* indices, Nd4jLong expected, Nd4jLong& output) { + static bool unsortedSegmentIndicesValidate_(sd::LaunchContext* context , NDArray* indices, Nd4jLong expected, Nd4jLong& output) { output = expected; I found = output; I exp = expected; @@ -81,7 +81,7 @@ namespace helpers { return expected == output; } - bool unsortedSegmentIndicesValidate(nd4j::LaunchContext* context , NDArray* indices, Nd4jLong expected, Nd4jLong& output) { + bool unsortedSegmentIndicesValidate(sd::LaunchContext* context , NDArray* indices, Nd4jLong expected, Nd4jLong& output) { BUILD_SINGLE_SELECTOR(indices->dataType(), return unsortedSegmentIndicesValidate_, (context, indices, expected, output), INDEXING_TYPES); } @@ -105,8 +105,8 @@ namespace helpers { for (auto j = tid; j < idxLen; j += step) { auto pos = idxBuf[j]; - nd4j::math::atomics::nd4j_atomicMin(&classesRangesStart[pos], (int)j); - nd4j::math::atomics::nd4j_atomicAdd(&classesRangesLenghts[pos], 1); + sd::math::atomics::nd4j_atomicMin(&classesRangesStart[pos], (int)j); + sd::math::atomics::nd4j_atomicAdd(&classesRangesLenghts[pos], 1); } } diff --git a/libnd4j/include/ops/declarable/helpers/cuda/segment_max.cu b/libnd4j/include/ops/declarable/helpers/cuda/segment_max.cu index 9585642dd..208bf764b 100644 --- a/libnd4j/include/ops/declarable/helpers/cuda/segment_max.cu +++ b/libnd4j/include/ops/declarable/helpers/cuda/segment_max.cu @@ -21,14 +21,14 @@ #include #include -#include +#include #include #include #include -#include -#include +#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { @@ -70,7 +70,7 @@ namespace nd4j { for (auto e = start + threadIdx.x + 1; e < finish; e += blockDim.x) { auto xIndex = shape::getIndexOffset(e, inputShape); - nd4j::math::atomics::nd4j_atomicMax(&z[zIndex], x[xIndex]); + sd::math::atomics::nd4j_atomicMax(&z[zIndex], x[xIndex]); } } // -------------------------------------------------------------------------------------------------------------- // @@ -108,7 +108,7 @@ namespace nd4j { auto xIndex = shape::getIndexOffset(e, inputShape); auto yIndex = shape::getIndexOffset(e, indicesShape); if (y[yIndex] == segment) { - nd4j::math::atomics::nd4j_atomicMax(&z[zIndex], x[xIndex]); + sd::math::atomics::nd4j_atomicMax(&z[zIndex], x[xIndex]); } } } @@ -142,7 +142,7 @@ namespace nd4j { for (auto e = threadIdx.x; e < len; e += blockDim.x) { auto xIndex = shape::getIndexOffset(e, inputTads); auto zIndex = shape::getIndexOffset(e, outputTads); - nd4j::math::atomics::nd4j_atomicMax(&z[zIndex], x[xIndex]); + sd::math::atomics::nd4j_atomicMax(&z[zIndex], x[xIndex]); //z[zIndex] = x[xIndex]; } } @@ -151,7 +151,7 @@ namespace nd4j { auto xIndex = shape::getIndexOffset(e, inputTads); auto zIndex = shape::getIndexOffset(e, outputTads); if (lengths[segment]) - nd4j::math::atomics::nd4j_atomicMax(&z[zIndex], x[xIndex]); + sd::math::atomics::nd4j_atomicMax(&z[zIndex], x[xIndex]); } } } @@ -185,8 +185,8 @@ namespace nd4j { } else { std::vector dimensions = ShapeUtils::evalDimsToExclude(input->rankOf(), {0}); - auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), dimensions); - auto packZ = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), dimensions); + auto packX = sd::ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), dimensions); + auto packZ = sd::ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), dimensions); Nd4jLong* inputTads = packX.specialShapeInfo(); Nd4jLong* inputTadOffsets = packX.specialOffsets(); Nd4jLong* outputTads = packZ.specialShapeInfo(); @@ -196,7 +196,7 @@ namespace nd4j { NDArray::registerSpecialUse({output}, {input, indices, &classesRangesBegs, &classesRangesLens}); } // -------------------------------------------------------------------------------------------------------------- // - void segmentMaxFunctor(nd4j::LaunchContext* context , NDArray* input, NDArray* indices, NDArray* output) { + void segmentMaxFunctor(sd::LaunchContext* context , NDArray* input, NDArray* indices, NDArray* output) { NDArray::prepareSpecialUse({output}, {input, indices}); BUILD_DOUBLE_SELECTOR(input->dataType(), indices->dataType(), segmentMaxFunctor_, (context, input, indices, output), NUMERIC_TYPES, INDEXING_TYPES); NDArray::registerSpecialUse({output}, {input, indices}); @@ -204,7 +204,7 @@ namespace nd4j { // -------------------------------------------------------------------------------------------------------------- // template - static void unsortedSegmentMaxFunctor_(nd4j::LaunchContext* context, NDArray* input, NDArray* indices, Nd4jLong numOfClasses, NDArray* output) { + static void unsortedSegmentMaxFunctor_(sd::LaunchContext* context, NDArray* input, NDArray* indices, Nd4jLong numOfClasses, NDArray* output) { auto stream = context->getCudaStream(); // NDArray classes = NDArrayFactory::create('c', {numOfClasses, 2}); output->assign(DataTypeUtils::infOrMax()); @@ -212,7 +212,7 @@ namespace nd4j { NDArray classesRangesBegs = NDArrayFactory::create('c', {numOfClasses}); NDArray classesRangesLens = NDArrayFactory::create('c', {numOfClasses}); // NDArray row = NDArrayFactory::create('c', {1, 2}, {(int)indices->lengthOf(), (int)0}); -// classes.applyTrueBroadcast(nd4j::BroadcastOpsTuple::Assign(), row, classes); +// classes.applyTrueBroadcast(sd::BroadcastOpsTuple::Assign(), row, classes); classesRangesBegs.assign(indices->lengthOf()); classesRangesLens.assign(0); dim3 dims(numOfClasses, indices->lengthOf(), numOfClasses * 32 + 32); @@ -226,8 +226,8 @@ namespace nd4j { } else { std::vector dimensions = ShapeUtils::evalDimsToExclude(input->rankOf(), {0}); - auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), dimensions); - auto packZ = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), dimensions); + auto packX = sd::ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), dimensions); + auto packZ = sd::ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), dimensions); Nd4jLong* inputTads = packX.specialShapeInfo(); Nd4jLong* inputTadOffsets = packX.specialOffsets(); Nd4jLong* outputTads = packZ.specialShapeInfo(); @@ -239,7 +239,7 @@ namespace nd4j { } // -------------------------------------------------------------------------------------------------------------- // - void unsortedSegmentMaxFunctor(nd4j::LaunchContext* context, NDArray* input, NDArray* indices, Nd4jLong numOfClasses, NDArray* output) { + void unsortedSegmentMaxFunctor(sd::LaunchContext* context, NDArray* input, NDArray* indices, Nd4jLong numOfClasses, NDArray* output) { NDArray::prepareSpecialUse({output}, {input, indices}); output->nullify(); BUILD_DOUBLE_SELECTOR(input->dataType(), indices->dataType(), unsortedSegmentMaxFunctor_, (context, input, indices, numOfClasses, output), NUMERIC_TYPES, INDEXING_TYPES); @@ -283,7 +283,7 @@ namespace nd4j { auto gradOffsetI = shape::getIndexOffset(classIndex, forwardShape); auto gradOffsetO = shape::getIndexOffset(classIndex, epsShape); - if (nd4j::math::nd4j_abs(gradIn[gradOffsetI] - x[xOffset]) <= T(1.e-6)) { + if (sd::math::nd4j_abs(gradIn[gradOffsetI] - x[xOffset]) <= T(1.e-6)) { z[zOffset] = gradOut[gradOffsetO]; } } @@ -326,14 +326,14 @@ namespace nd4j { T* outGrad = gradOut + gradOutOffsets[segment]; for (auto e = threadIdx.x; e < currentLen; e += blockDim.x) { - if (nd4j::math::nd4j_abs(in[e] - current[e]) <= T(1.e-6)) + if (sd::math::nd4j_abs(in[e] - current[e]) <= T(1.e-6)) currentOut[e] = outGrad[e]; } } } // -------------------------------------------------------------------------------------------------------------- // template - int segmentMaxFunctorBP_(nd4j::LaunchContext* context , NDArray* input, NDArray* indices, NDArray* gradOut, NDArray* output) { + int segmentMaxFunctorBP_(sd::LaunchContext* context , NDArray* input, NDArray* indices, NDArray* gradOut, NDArray* output) { //int numOfClasses = gradOut->sizeAt(0); // if input is a vector: (as if in doc sample) auto stream = context->getCudaStream(); @@ -349,10 +349,10 @@ namespace nd4j { } else { std::vector dimensions = ShapeUtils::evalDimsToExclude(input->rankOf(), {0}); - auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), dimensions); - auto packZ = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), dimensions); - auto packGradIn = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(tempRes.getShapeInfo(), dimensions); - auto packGradOut = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(gradOut->getShapeInfo(), dimensions); + auto packX = sd::ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), dimensions); + auto packZ = sd::ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), dimensions); + auto packGradIn = sd::ConstantTadHelper::getInstance()->tadForDimensions(tempRes.getShapeInfo(), dimensions); + auto packGradOut = sd::ConstantTadHelper::getInstance()->tadForDimensions(gradOut->getShapeInfo(), dimensions); Nd4jLong* inputTads = packX.specialShapeInfo(); Nd4jLong* inputTadOffsets = packX.specialOffsets(); Nd4jLong* outputTads = packZ.specialShapeInfo(); @@ -372,7 +372,7 @@ namespace nd4j { return Status::OK(); } // -------------------------------------------------------------------------------------------------------------- // - int segmentMaxFunctorBP(nd4j::LaunchContext* context , NDArray* input, NDArray* indices, NDArray* gradOut, NDArray* output) { + int segmentMaxFunctorBP(sd::LaunchContext* context , NDArray* input, NDArray* indices, NDArray* gradOut, NDArray* output) { NDArray::prepareSpecialUse({output}, {input, indices, gradOut}); BUILD_DOUBLE_SELECTOR(output->dataType(), indices->dataType(), return segmentMaxFunctorBP_, (context, input, indices, gradOut, output), FLOAT_TYPES, INDEXING_TYPES); @@ -381,7 +381,7 @@ namespace nd4j { // -------------------------------------------------------------------------------------------------------------- // template - static int unsortedSegmentMaxFunctorBP_(nd4j::LaunchContext* context, NDArray* input, NDArray* indices, NDArray* gradOut, Nd4jLong numOfClasses, NDArray* output) { + static int unsortedSegmentMaxFunctorBP_(sd::LaunchContext* context, NDArray* input, NDArray* indices, NDArray* gradOut, Nd4jLong numOfClasses, NDArray* output) { //int numOfClasses = gradOut->sizeAt(0); // if input is a vector: (as if in doc sample) auto stream = context->getCudaStream(); @@ -397,10 +397,10 @@ namespace nd4j { } else { std::vector dimensions = ShapeUtils::evalDimsToExclude(input->rankOf(), {0}); - auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), dimensions); - auto packZ = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), dimensions); - auto packGradIn = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(tempRes.getShapeInfo(), dimensions); - auto packGradOut = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(gradOut->getShapeInfo(), dimensions); + auto packX = sd::ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), dimensions); + auto packZ = sd::ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), dimensions); + auto packGradIn = sd::ConstantTadHelper::getInstance()->tadForDimensions(tempRes.getShapeInfo(), dimensions); + auto packGradOut = sd::ConstantTadHelper::getInstance()->tadForDimensions(gradOut->getShapeInfo(), dimensions); Nd4jLong* inputTads = packX.specialShapeInfo(); Nd4jLong* inputTadOffsets = packX.specialOffsets(); Nd4jLong* outputTads = packZ.specialShapeInfo(); @@ -420,7 +420,7 @@ namespace nd4j { return Status::OK(); } // -------------------------------------------------------------------------------------------------------------- // - int unsortedSegmentMaxFunctorBP(nd4j::LaunchContext* context , NDArray* input, NDArray* indices, NDArray* gradOut, Nd4jLong numOfClasses, NDArray* output) { + int unsortedSegmentMaxFunctorBP(sd::LaunchContext* context , NDArray* input, NDArray* indices, NDArray* gradOut, Nd4jLong numOfClasses, NDArray* output) { NDArray::prepareSpecialUse({output}, {input, indices, gradOut}); BUILD_DOUBLE_SELECTOR(output->dataType(), indices->dataType(), return unsortedSegmentMaxFunctorBP_, (context, input, indices, gradOut, numOfClasses, output), FLOAT_TYPES, INDEXING_TYPES); NDArray::registerSpecialUse({output}, {input, indices, gradOut}); diff --git a/libnd4j/include/ops/declarable/helpers/cuda/segment_mean.cu b/libnd4j/include/ops/declarable/helpers/cuda/segment_mean.cu index dc958f79c..fa8882190 100644 --- a/libnd4j/include/ops/declarable/helpers/cuda/segment_mean.cu +++ b/libnd4j/include/ops/declarable/helpers/cuda/segment_mean.cu @@ -20,14 +20,14 @@ #include #include -#include +#include #include #include #include -#include -#include +#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { // -------------------------------------------------------------------------------------------------------------- // @@ -67,7 +67,7 @@ namespace helpers { for (auto e = start + threadIdx.x + 1; e < finish; e += blockDim.x) { auto xIndex = shape::getIndexOffset(e, inputShape); if (lengths[segment]) - nd4j::math::atomics::nd4j_atomicAdd(&z[zIndex], T(x[xIndex] / lengths[segment])); + sd::math::atomics::nd4j_atomicAdd(&z[zIndex], T(x[xIndex] / lengths[segment])); } } // -------------------------------------------------------------------------------------------------------------- // @@ -108,7 +108,7 @@ namespace helpers { auto xIndex = shape::getIndexOffset(e, inputShape); auto yIndex = shape::getIndexOffset(e, indicesShape); if (y[yIndex] == segment && e != starts[segment]) { - nd4j::math::atomics::nd4j_atomicAdd(&z[zIndex], T(x[xIndex]/T(lengths[segment]))); + sd::math::atomics::nd4j_atomicAdd(&z[zIndex], T(x[xIndex]/T(lengths[segment]))); } } } @@ -139,7 +139,7 @@ namespace helpers { for (auto e = threadIdx.x; e < len; e += blockDim.x) { auto xIndex = shape::getIndexOffset(e, inputTads); auto zIndex = shape::getIndexOffset(e, outputTads); - nd4j::math::atomics::nd4j_atomicAdd(&z[zIndex], T(x[xIndex]/lengths[segment])); + sd::math::atomics::nd4j_atomicAdd(&z[zIndex], T(x[xIndex]/lengths[segment])); } } else { @@ -147,7 +147,7 @@ namespace helpers { auto xIndex = shape::getIndexOffset(e, inputTads); auto zIndex = shape::getIndexOffset(e, outputTads); if (lengths[segment]) - nd4j::math::atomics::nd4j_atomicAdd(&z[zIndex], T(x[xIndex]/lengths[segment])); + sd::math::atomics::nd4j_atomicAdd(&z[zIndex], T(x[xIndex]/lengths[segment])); } } } @@ -174,8 +174,8 @@ namespace helpers { } else { std::vector dimensions = ShapeUtils::evalDimsToExclude(input->rankOf(), {0}); - auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), dimensions); - auto packZ = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), dimensions); + auto packX = sd::ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), dimensions); + auto packZ = sd::ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), dimensions); Nd4jLong* inputTads = packX.specialShapeInfo(); Nd4jLong* inputTadOffsets = packX.specialOffsets(); Nd4jLong* outputTads = packZ.specialShapeInfo(); @@ -186,7 +186,7 @@ namespace helpers { } // -------------------------------------------------------------------------------------------------------------- // - void segmentMeanFunctor(nd4j::LaunchContext* context , NDArray* input, NDArray* indices, NDArray* output) { + void segmentMeanFunctor(sd::LaunchContext* context , NDArray* input, NDArray* indices, NDArray* output) { NDArray::prepareSpecialUse({output}, {input, indices}); BUILD_DOUBLE_SELECTOR(output->dataType(), indices->dataType(), segmentMeanFunctor_, (context, input, indices, output), NUMERIC_TYPES, INDEXING_TYPES); NDArray::registerSpecialUse({output}, {input, indices}); @@ -194,14 +194,14 @@ namespace helpers { // -------------------------------------------------------------------------------------------------------------- // template - static void unsortedSegmentMeanFunctor_(nd4j::LaunchContext* context, NDArray* input, NDArray* indices, Nd4jLong numOfClasses, NDArray* output) { + static void unsortedSegmentMeanFunctor_(sd::LaunchContext* context, NDArray* input, NDArray* indices, Nd4jLong numOfClasses, NDArray* output) { auto stream = context->getCudaStream(); // NDArray classes = NDArrayFactory::create('c', {numOfClasses, 2}); NDArray classesRangesBegs = NDArrayFactory::create('c', {numOfClasses}); NDArray classesRangesLens = NDArrayFactory::create('c', {numOfClasses}); // NDArray row = NDArrayFactory::create('c', {1, 2}, {(int)indices->lengthOf(), (int)0}); -// classes.applyTrueBroadcast(nd4j::BroadcastOpsTuple::Assign(), &row, &classes); +// classes.applyTrueBroadcast(sd::BroadcastOpsTuple::Assign(), &row, &classes); classesRangesBegs.assign(indices->lengthOf()); classesRangesLens.assign(0); dim3 dims(numOfClasses, indices->lengthOf(), numOfClasses * 32 + 32); @@ -216,8 +216,8 @@ namespace helpers { else { output->assign(0); std::vector dimensions = ShapeUtils::evalDimsToExclude(input->rankOf(), {0}); - auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), dimensions); - auto packZ = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), dimensions); + auto packX = sd::ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), dimensions); + auto packZ = sd::ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), dimensions); Nd4jLong* inputTads = packX.specialShapeInfo(); Nd4jLong* inputTadOffsets = packX.specialOffsets(); Nd4jLong* outputTads = packZ.specialShapeInfo(); @@ -228,7 +228,7 @@ namespace helpers { } // -------------------------------------------------------------------------------------------------------------- // - void unsortedSegmentMeanFunctor(nd4j::LaunchContext* context , NDArray* input, NDArray* indices, Nd4jLong numOfClasses, NDArray* output) { + void unsortedSegmentMeanFunctor(sd::LaunchContext* context , NDArray* input, NDArray* indices, Nd4jLong numOfClasses, NDArray* output) { NDArray::prepareSpecialUse({output}, {input, indices}); BUILD_DOUBLE_SELECTOR(input->dataType(), indices->dataType(), unsortedSegmentMeanFunctor_, (context, input, indices, numOfClasses, output), NUMERIC_TYPES, INDEXING_TYPES); @@ -310,7 +310,7 @@ namespace helpers { // -------------------------------------------------------------------------------------------------------------- // // backrop for mean template - int segmentMeanFunctorBP_(nd4j::LaunchContext* context , NDArray* input, NDArray* indices, NDArray* gradOut, NDArray* output) { + int segmentMeanFunctorBP_(sd::LaunchContext* context , NDArray* input, NDArray* indices, NDArray* gradOut, NDArray* output) { auto stream = context->getCudaStream(); NDArray::prepareSpecialUse({output}, {input, indices, gradOut}); auto numClasses = indices->e(indices->lengthOf() - 1) + 1; @@ -333,10 +333,10 @@ namespace helpers { } else { std::vector dimensions = ShapeUtils::evalDimsToExclude(input->rankOf(), {0}); - auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), dimensions); - auto packZ = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), dimensions); -// auto packGradIn = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(tempRes.getShapeInfo(), dimensions); - auto packGradOut = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(gradOut->getShapeInfo(), dimensions); + auto packX = sd::ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), dimensions); + auto packZ = sd::ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), dimensions); +// auto packGradIn = sd::ConstantTadHelper::getInstance()->tadForDimensions(tempRes.getShapeInfo(), dimensions); + auto packGradOut = sd::ConstantTadHelper::getInstance()->tadForDimensions(gradOut->getShapeInfo(), dimensions); Nd4jLong* inputTads = packX.specialShapeInfo(); Nd4jLong* inputTadOffsets = packX.specialOffsets(); Nd4jLong* outputTads = packZ.specialShapeInfo(); @@ -354,7 +354,7 @@ namespace helpers { } // -------------------------------------------------------------------------------------------------------------- // // segmen mean bp main - int segmentMeanFunctorBP(nd4j::LaunchContext* context , NDArray* input, NDArray* indices, NDArray* gradOut, NDArray* output) { + int segmentMeanFunctorBP(sd::LaunchContext* context , NDArray* input, NDArray* indices, NDArray* gradOut, NDArray* output) { NDArray::prepareSpecialUse({output}, {input, indices, gradOut}); BUILD_DOUBLE_SELECTOR(output->dataType(), indices->dataType(), return segmentMeanFunctorBP_, (context, input, indices, gradOut, output), FLOAT_TYPES, INDEXING_TYPES); @@ -363,7 +363,7 @@ namespace helpers { // -------------------------------------------------------------------------------------------------------------- // template - static int unsortedSegmentMeanFunctorBP_(nd4j::LaunchContext* context , NDArray* input, NDArray* indices, NDArray* gradOut, Nd4jLong numOfClasses, NDArray* output) { + static int unsortedSegmentMeanFunctorBP_(sd::LaunchContext* context , NDArray* input, NDArray* indices, NDArray* gradOut, Nd4jLong numOfClasses, NDArray* output) { auto stream = context->getCudaStream(); NDArray::prepareSpecialUse({output}, {input, indices, gradOut}); auto numClasses = indices->e(indices->lengthOf() - 1) + 1; @@ -386,10 +386,10 @@ namespace helpers { } else { std::vector dimensions = ShapeUtils::evalDimsToExclude(input->rankOf(), {0}); - auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), dimensions); - auto packZ = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), dimensions); -// auto packGradIn = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(tempRes.getShapeInfo(), dimensions); - auto packGradOut = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(gradOut->getShapeInfo(), dimensions); + auto packX = sd::ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), dimensions); + auto packZ = sd::ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), dimensions); +// auto packGradIn = sd::ConstantTadHelper::getInstance()->tadForDimensions(tempRes.getShapeInfo(), dimensions); + auto packGradOut = sd::ConstantTadHelper::getInstance()->tadForDimensions(gradOut->getShapeInfo(), dimensions); Nd4jLong* inputTads = packX.specialShapeInfo(); Nd4jLong* inputTadOffsets = packX.specialOffsets(); Nd4jLong* outputTads = packZ.specialShapeInfo(); @@ -406,7 +406,7 @@ namespace helpers { return Status::OK(); } // -------------------------------------------------------------------------------------------------------------- // - int unsortedSegmentMeanFunctorBP(nd4j::LaunchContext* context , NDArray* input, NDArray* indices, NDArray* gradOut, Nd4jLong numOfClasses, NDArray* output) { + int unsortedSegmentMeanFunctorBP(sd::LaunchContext* context , NDArray* input, NDArray* indices, NDArray* gradOut, Nd4jLong numOfClasses, NDArray* output) { NDArray::prepareSpecialUse({output}, {input, indices, gradOut}); BUILD_DOUBLE_SELECTOR(output->dataType(), indices->dataType(), return unsortedSegmentMeanFunctorBP_, (context, input, indices, gradOut, numOfClasses, output), FLOAT_TYPES, INDEXING_TYPES); NDArray::registerSpecialUse({output}, {input, indices, gradOut}); diff --git a/libnd4j/include/ops/declarable/helpers/cuda/segment_min.cu b/libnd4j/include/ops/declarable/helpers/cuda/segment_min.cu index 506cfaa41..b83d37567 100644 --- a/libnd4j/include/ops/declarable/helpers/cuda/segment_min.cu +++ b/libnd4j/include/ops/declarable/helpers/cuda/segment_min.cu @@ -20,14 +20,14 @@ #include #include -#include +#include #include #include #include -#include -#include +#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { // -------------------------------------------------------------------------------------------------------------- // @@ -68,7 +68,7 @@ namespace helpers { for (auto e = start + threadIdx.x + 1; e < finish; e += blockDim.x) { auto xIndex = shape::getIndexOffset(e, inputShape); - nd4j::math::atomics::nd4j_atomicMin(&z[zIndex], x[xIndex]); + sd::math::atomics::nd4j_atomicMin(&z[zIndex], x[xIndex]); } } @@ -111,7 +111,7 @@ namespace helpers { auto xIndex = shape::getIndexOffset(e, inputShape); auto yIndex = shape::getIndexOffset(e, indicesShape); if (y[yIndex] == segment) { - nd4j::math::atomics::nd4j_atomicMin(&z[zIndex], x[xIndex]); + sd::math::atomics::nd4j_atomicMin(&z[zIndex], x[xIndex]); } } } @@ -142,7 +142,7 @@ namespace helpers { for (auto e = threadIdx.x; e < len; e += blockDim.x) { auto xIndex = shape::getIndexOffset(e, inputTads); auto zIndex = shape::getIndexOffset(e, outputTads); - nd4j::math::atomics::nd4j_atomicMin(&z[zIndex], x[xIndex]); + sd::math::atomics::nd4j_atomicMin(&z[zIndex], x[xIndex]); } } else { @@ -150,7 +150,7 @@ namespace helpers { auto xIndex = shape::getIndexOffset(e, inputTads); auto zIndex = shape::getIndexOffset(e, outputTads); // if (lengths[indices[idx]]) - nd4j::math::atomics::nd4j_atomicMin(&z[zIndex], x[xIndex]); + sd::math::atomics::nd4j_atomicMin(&z[zIndex], x[xIndex]); } } } @@ -176,8 +176,8 @@ namespace helpers { } else { std::vector dimensions = ShapeUtils::evalDimsToExclude(input->rankOf(), {0}); - auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), dimensions); - auto packZ = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), dimensions); + auto packX = sd::ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), dimensions); + auto packZ = sd::ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), dimensions); Nd4jLong* inputTads = packX.specialShapeInfo(); Nd4jLong* inputTadOffsets = packX.specialOffsets(); Nd4jLong* outputTads = packZ.specialShapeInfo(); @@ -189,7 +189,7 @@ namespace helpers { } // -------------------------------------------------------------------------------------------------------------- // - void segmentMinFunctor(nd4j::LaunchContext* context , NDArray* input, NDArray* indices, NDArray* output) { + void segmentMinFunctor(sd::LaunchContext* context , NDArray* input, NDArray* indices, NDArray* output) { NDArray::prepareSpecialUse({output}, {input, indices}); output->nullify(); BUILD_DOUBLE_SELECTOR(input->dataType(), indices->dataType(), segmentMinFunctor_, (context, input, indices, output), NUMERIC_TYPES, INDEXING_TYPES); @@ -199,13 +199,13 @@ namespace helpers { // -------------------------------------------------------------------------------------------------------------- // template - static void unsortedSegmentMinFunctor_(nd4j::LaunchContext* context, NDArray* input, NDArray* indices, Nd4jLong numOfClasses, NDArray* output) { + static void unsortedSegmentMinFunctor_(sd::LaunchContext* context, NDArray* input, NDArray* indices, Nd4jLong numOfClasses, NDArray* output) { auto stream = context->getCudaStream(); // NDArray classes = NDArrayFactory::create('c', {numOfClasses, 2}); NDArray classesRangesBegs = NDArrayFactory::create('c', {numOfClasses}); NDArray classesRangesLens = NDArrayFactory::create('c', {numOfClasses}); // NDArray row = NDArrayFactory::create('c', {1, 2}, {(int)indices->lengthOf(), (int)0}); -// classes.applyTrueBroadcast(nd4j::BroadcastOpsTuple::Assign(), &row, &classes); +// classes.applyTrueBroadcast(sd::BroadcastOpsTuple::Assign(), &row, &classes); output->assign(DataTypeUtils::infOrMax()); classesRangesBegs.assign(indices->lengthOf()); classesRangesLens.assign(0); @@ -221,8 +221,8 @@ namespace helpers { else { output->assign(DataTypeUtils::max()); std::vector dimensions = ShapeUtils::evalDimsToExclude(input->rankOf(), {0}); - auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), dimensions); - auto packZ = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), dimensions); + auto packX = sd::ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), dimensions); + auto packZ = sd::ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), dimensions); Nd4jLong* inputTads = packX.specialShapeInfo(); Nd4jLong* inputTadOffsets = packX.specialOffsets(); Nd4jLong* outputTads = packZ.specialShapeInfo(); @@ -234,7 +234,7 @@ namespace helpers { } // -------------------------------------------------------------------------------------------------------------- // - void unsortedSegmentMinFunctor(nd4j::LaunchContext* context , NDArray* input, NDArray* indices, Nd4jLong numOfClasses, NDArray* output) { + void unsortedSegmentMinFunctor(sd::LaunchContext* context , NDArray* input, NDArray* indices, Nd4jLong numOfClasses, NDArray* output) { NDArray::prepareSpecialUse({output}, {input, indices}); output->nullify(); BUILD_DOUBLE_SELECTOR(input->dataType(), indices->dataType(), unsortedSegmentMinFunctor_, (context, input, indices, numOfClasses, output), @@ -276,7 +276,7 @@ namespace helpers { auto gradOffsetI = shape::getIndexOffset(classIndex, forwardShape); auto gradOffsetO = shape::getIndexOffset(classIndex, epsShape); - if (nd4j::math::nd4j_abs(gradIn[gradOffsetI] - x[xOffset]) <= T(1.e-6)) { + if (sd::math::nd4j_abs(gradIn[gradOffsetI] - x[xOffset]) <= T(1.e-6)) { z[zOffset] = gradOut[gradOffsetO]; } } @@ -319,7 +319,7 @@ namespace helpers { T* outGrad = gradOut + gradOutOffsets[segment]; for (auto e = threadIdx.x; e < currentLen; e += blockDim.x) { - if (nd4j::math::nd4j_abs(in[e] - current[e]) <= T(1.e-6)) + if (sd::math::nd4j_abs(in[e] - current[e]) <= T(1.e-6)) currentOut[e] = outGrad[e]; } } @@ -327,7 +327,7 @@ namespace helpers { // -------------------------------------------------------------------------------------------------------------- // template - int segmentMinFunctorBP_(nd4j::LaunchContext* context , NDArray* input, NDArray* indices, NDArray* gradOut, NDArray* output) { + int segmentMinFunctorBP_(sd::LaunchContext* context , NDArray* input, NDArray* indices, NDArray* gradOut, NDArray* output) { //int numOfClasses = gradOut->sizeAt(0); // if input is a vector: (as if in doc sample) auto stream = context->getCudaStream(); @@ -344,10 +344,10 @@ namespace helpers { } else { std::vector dimensions = ShapeUtils::evalDimsToExclude(input->rankOf(), {0}); - auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), dimensions); - auto packZ = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), dimensions); - auto packGradIn = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(tempRes.getShapeInfo(), dimensions); - auto packGradOut = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(gradOut->getShapeInfo(), dimensions); + auto packX = sd::ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), dimensions); + auto packZ = sd::ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), dimensions); + auto packGradIn = sd::ConstantTadHelper::getInstance()->tadForDimensions(tempRes.getShapeInfo(), dimensions); + auto packGradOut = sd::ConstantTadHelper::getInstance()->tadForDimensions(gradOut->getShapeInfo(), dimensions); Nd4jLong* inputTads = packX.specialShapeInfo(); Nd4jLong* inputTadOffsets = packX.specialOffsets(); Nd4jLong* outputTads = packZ.specialShapeInfo(); @@ -368,7 +368,7 @@ namespace helpers { } // -------------------------------------------------------------------------------------------------------------- // // segmen min - int segmentMinFunctorBP(nd4j::LaunchContext* context , NDArray* input, NDArray* indices, NDArray* gradOut, NDArray* output) { + int segmentMinFunctorBP(sd::LaunchContext* context , NDArray* input, NDArray* indices, NDArray* gradOut, NDArray* output) { NDArray::prepareSpecialUse({output}, {input, indices, gradOut}); BUILD_DOUBLE_SELECTOR(output->dataType(), indices->dataType(), return segmentMinFunctorBP_, (context, input, indices, gradOut, output), FLOAT_TYPES, INDEXING_TYPES); @@ -376,7 +376,7 @@ namespace helpers { } template - static int unsortedSegmentMinFunctorBP_(nd4j::LaunchContext* context, NDArray* input, NDArray* indices, NDArray* gradOut, Nd4jLong numOfClasses, NDArray* output) { + static int unsortedSegmentMinFunctorBP_(sd::LaunchContext* context, NDArray* input, NDArray* indices, NDArray* gradOut, Nd4jLong numOfClasses, NDArray* output) { //int numOfClasses = gradOut->sizeAt(0); // if input is a vector: (as if in doc sample) auto stream = context->getCudaStream(); @@ -392,10 +392,10 @@ namespace helpers { } else { std::vector dimensions = ShapeUtils::evalDimsToExclude(input->rankOf(), {0}); - auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), dimensions); - auto packZ = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), dimensions); - auto packGradIn = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(tempRes.getShapeInfo(), dimensions); - auto packGradOut = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(gradOut->getShapeInfo(), dimensions); + auto packX = sd::ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), dimensions); + auto packZ = sd::ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), dimensions); + auto packGradIn = sd::ConstantTadHelper::getInstance()->tadForDimensions(tempRes.getShapeInfo(), dimensions); + auto packGradOut = sd::ConstantTadHelper::getInstance()->tadForDimensions(gradOut->getShapeInfo(), dimensions); Nd4jLong* inputTads = packX.specialShapeInfo(); Nd4jLong* inputTadOffsets = packX.specialOffsets(); Nd4jLong* outputTads = packZ.specialShapeInfo(); @@ -415,7 +415,7 @@ namespace helpers { return Status::OK(); } // -------------------------------------------------------------------------------------------------------------- // - int unsortedSegmentMinFunctorBP(nd4j::LaunchContext* context , NDArray* input, NDArray* indices, NDArray* gradOut, Nd4jLong numOfClasses, NDArray* output) { + int unsortedSegmentMinFunctorBP(sd::LaunchContext* context , NDArray* input, NDArray* indices, NDArray* gradOut, Nd4jLong numOfClasses, NDArray* output) { NDArray::prepareSpecialUse({output}, {input, indices, gradOut}); BUILD_DOUBLE_SELECTOR(output->dataType(), indices->dataType(), return unsortedSegmentMinFunctorBP_, (context, input, indices, gradOut, numOfClasses, output), FLOAT_TYPES, INDEXING_TYPES); NDArray::registerSpecialUse({output}, {input, indices, gradOut}); diff --git a/libnd4j/include/ops/declarable/helpers/cuda/segment_prod.cu b/libnd4j/include/ops/declarable/helpers/cuda/segment_prod.cu index 613aa548b..baec75b9e 100644 --- a/libnd4j/include/ops/declarable/helpers/cuda/segment_prod.cu +++ b/libnd4j/include/ops/declarable/helpers/cuda/segment_prod.cu @@ -20,14 +20,14 @@ #include #include -#include +#include #include #include #include -#include -#include +#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { // -------------------------------------------------------------------------------------------------------------- // @@ -59,7 +59,7 @@ namespace helpers { } for (auto e = start + threadIdx.x; e < finish; e += blockDim.x) { auto xIndex = shape::getIndexOffset(e, inputShape); - nd4j::math::atomics::nd4j_atomicMul(&z[segment], x[xIndex]); + sd::math::atomics::nd4j_atomicMul(&z[segment], x[xIndex]); } } @@ -84,7 +84,7 @@ namespace helpers { if (lengths[segment] == 0) { continue; } - nd4j::math::atomics::nd4j_atomicMul(&output[zIndex], input[xIndex]); + sd::math::atomics::nd4j_atomicMul(&output[zIndex], input[xIndex]); } } // -------------------------------------------------------------------------------------------------------------- // @@ -112,14 +112,14 @@ namespace helpers { for (auto e = threadIdx.x; e < len; e += blockDim.x) { auto xIndex = shape::getIndexOffset(e, inputTads); auto zIndex = shape::getIndexOffset(e, outputTads); - nd4j::math::atomics::nd4j_atomicMul(&z[zIndex], x[xIndex]); + sd::math::atomics::nd4j_atomicMul(&z[zIndex], x[xIndex]); } } } // -------------------------------------------------------------------------------------------------------------- // template - static void segmentProdFunctor_(nd4j::LaunchContext* context, NDArray* input, NDArray* indices, NDArray* output) { + static void segmentProdFunctor_(sd::LaunchContext* context, NDArray* input, NDArray* indices, NDArray* output) { auto stream = context->getCudaStream(); Nd4jLong numClasses = indices->e(indices->lengthOf() - 1) + 1; NDArray classesRangesLens = NDArrayFactory::create('c', {numClasses}); @@ -138,8 +138,8 @@ namespace helpers { } else { std::vector dimensions = ShapeUtils::evalDimsToExclude(input->rankOf(), {0}); - auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), dimensions); - auto packZ = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), dimensions); + auto packX = sd::ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), dimensions); + auto packZ = sd::ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), dimensions); Nd4jLong* inputTads = packX.specialShapeInfo(); Nd4jLong* inputTadOffsets = packX.specialOffsets(); Nd4jLong* outputTads = packZ.specialShapeInfo(); @@ -149,7 +149,7 @@ namespace helpers { } // -------------------------------------------------------------------------------------------------------------- // - void segmentProdFunctor(nd4j::LaunchContext* context , NDArray* input, NDArray* indices, NDArray* output) { + void segmentProdFunctor(sd::LaunchContext* context , NDArray* input, NDArray* indices, NDArray* output) { NDArray::prepareSpecialUse({output}, {input, indices}); BUILD_DOUBLE_SELECTOR(output->dataType(), indices->dataType(), segmentProdFunctor_, (context, input, indices, output), NUMERIC_TYPES, INDEXING_TYPES); NDArray::registerSpecialUse({output}, {input, indices}); @@ -157,13 +157,13 @@ namespace helpers { // -------------------------------------------------------------------------------------------------------------- // template - static void unsortedSegmentProdFunctor_(nd4j::LaunchContext* context, NDArray* input, NDArray* indices, Nd4jLong numOfClasses, NDArray* output) { + static void unsortedSegmentProdFunctor_(sd::LaunchContext* context, NDArray* input, NDArray* indices, Nd4jLong numOfClasses, NDArray* output) { auto stream = context->getCudaStream(); // NDArray classes = NDArrayFactory::create('c', {numOfClasses, 2}); NDArray classesRangesBegs = NDArrayFactory::create('c', {numOfClasses}); NDArray classesRangesLens = NDArrayFactory::create('c', {numOfClasses}); // NDArray row = NDArrayFactory::create('c', {1, 2}, {(int)indices->lengthOf(), (int)0}); -// classes.applyTrueBroadcast(nd4j::BroadcastOpsTuple::Assign(), &row, &classes); +// classes.applyTrueBroadcast(sd::BroadcastOpsTuple::Assign(), &row, &classes); classesRangesBegs.assign(indices->lengthOf()); classesRangesLens.assign(0); dim3 dims(numOfClasses, indices->lengthOf(), numOfClasses * 32 + 32); @@ -181,8 +181,8 @@ namespace helpers { } else { std::vector dimensions = ShapeUtils::evalDimsToExclude(input->rankOf(), {0}); - auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), dimensions); - auto packZ = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), dimensions); + auto packX = sd::ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), dimensions); + auto packZ = sd::ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), dimensions); Nd4jLong* inputTads = packX.specialShapeInfo(); Nd4jLong* inputTadOffsets = packX.specialOffsets(); Nd4jLong* outputTads = packZ.specialShapeInfo(); @@ -193,7 +193,7 @@ namespace helpers { } // -------------------------------------------------------------------------------------------------------------- // - void unsortedSegmentProdFunctor(nd4j::LaunchContext* context , NDArray* input, NDArray* indices, Nd4jLong numOfClasses, NDArray* output) { + void unsortedSegmentProdFunctor(sd::LaunchContext* context , NDArray* input, NDArray* indices, Nd4jLong numOfClasses, NDArray* output) { NDArray::prepareSpecialUse({output}, {input, indices}); BUILD_DOUBLE_SELECTOR(input->dataType(), indices->dataType(), unsortedSegmentProdFunctor_, (context, input, indices, numOfClasses, output), NUMERIC_TYPES, INDEXING_TYPES); @@ -283,7 +283,7 @@ namespace helpers { // -------------------------------------------------------------------------------------------------------------- // template - int segmentProdFunctorBP_(nd4j::LaunchContext* context , NDArray* input, NDArray* indices, NDArray* gradOut, NDArray* output) { + int segmentProdFunctorBP_(sd::LaunchContext* context , NDArray* input, NDArray* indices, NDArray* gradOut, NDArray* output) { auto stream = context->getCudaStream(); NDArray tempRes(gradOut->ordering(), gradOut->getShapeAsVector(), DataTypeUtils::fromT(), context);//->shapeInfo(), context); segmentProdFunctor_(context, input, indices, &tempRes); @@ -297,10 +297,10 @@ namespace helpers { } else { std::vector dimensions = ShapeUtils::evalDimsToExclude(input->rankOf(), {0}); - auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), dimensions); - auto packZ = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), dimensions); - auto packGradIn = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(tempRes.getShapeInfo(), dimensions); - auto packGradOut = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(gradOut->getShapeInfo(), dimensions); + auto packX = sd::ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), dimensions); + auto packZ = sd::ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), dimensions); + auto packGradIn = sd::ConstantTadHelper::getInstance()->tadForDimensions(tempRes.getShapeInfo(), dimensions); + auto packGradOut = sd::ConstantTadHelper::getInstance()->tadForDimensions(gradOut->getShapeInfo(), dimensions); Nd4jLong* inputTads = packX.specialShapeInfo(); Nd4jLong* inputTadOffsets = packX.specialOffsets(); Nd4jLong* outputTads = packZ.specialShapeInfo(); @@ -322,7 +322,7 @@ namespace helpers { // -------------------------------------------------------------------------------------------------------------- // - int segmentProdFunctorBP(nd4j::LaunchContext* context , NDArray* input, NDArray* indices, NDArray* gradOut, NDArray* output) { + int segmentProdFunctorBP(sd::LaunchContext* context , NDArray* input, NDArray* indices, NDArray* gradOut, NDArray* output) { NDArray::prepareSpecialUse({output}, {input, indices, gradOut}); BUILD_DOUBLE_SELECTOR(output->dataType(), indices->dataType(), return segmentProdFunctorBP_, (context, input, indices, gradOut, output), FLOAT_TYPES, INDEXING_TYPES); @@ -332,7 +332,7 @@ namespace helpers { // -------------------------------------------------------------------------------------------------------------- // template - static int unsortedSegmentProdFunctorBP_(nd4j::LaunchContext* context , NDArray* input, NDArray* indices, NDArray* gradOut, Nd4jLong numOfClasses, NDArray* output) { + static int unsortedSegmentProdFunctorBP_(sd::LaunchContext* context , NDArray* input, NDArray* indices, NDArray* gradOut, Nd4jLong numOfClasses, NDArray* output) { auto stream = context->getCudaStream(); NDArray tempRes(gradOut->ordering(), gradOut->getShapeAsVector(), DataTypeUtils::fromT(), context);//->shapeInfo(), context); @@ -347,10 +347,10 @@ namespace helpers { } else { std::vector dimensions = ShapeUtils::evalDimsToExclude(input->rankOf(), {0}); - auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), dimensions); - auto packZ = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), dimensions); - auto packGradIn = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(tempRes.getShapeInfo(), dimensions); - auto packGradOut = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(gradOut->getShapeInfo(), dimensions); + auto packX = sd::ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), dimensions); + auto packZ = sd::ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), dimensions); + auto packGradIn = sd::ConstantTadHelper::getInstance()->tadForDimensions(tempRes.getShapeInfo(), dimensions); + auto packGradOut = sd::ConstantTadHelper::getInstance()->tadForDimensions(gradOut->getShapeInfo(), dimensions); Nd4jLong* inputTads = packX.specialShapeInfo(); Nd4jLong* inputTadOffsets = packX.specialOffsets(); Nd4jLong* outputTads = packZ.specialShapeInfo(); @@ -371,7 +371,7 @@ namespace helpers { } // -------------------------------------------------------------------------------------------------------------- // - int unsortedSegmentProdFunctorBP(nd4j::LaunchContext* context , NDArray* input, NDArray* indices, NDArray* gradOut, Nd4jLong numOfClasses, NDArray* output) { + int unsortedSegmentProdFunctorBP(sd::LaunchContext* context , NDArray* input, NDArray* indices, NDArray* gradOut, Nd4jLong numOfClasses, NDArray* output) { NDArray::prepareSpecialUse({output}, {input, indices, gradOut}); BUILD_DOUBLE_SELECTOR(output->dataType(), indices->dataType(), return unsortedSegmentProdFunctorBP_, (context, input, indices, gradOut, numOfClasses, output), FLOAT_TYPES, INDEXING_TYPES); NDArray::registerSpecialUse({output}, {input, indices, gradOut}); diff --git a/libnd4j/include/ops/declarable/helpers/cuda/segment_sqrtn.cu b/libnd4j/include/ops/declarable/helpers/cuda/segment_sqrtn.cu index 4daa0f4ab..7d85a0ea6 100644 --- a/libnd4j/include/ops/declarable/helpers/cuda/segment_sqrtn.cu +++ b/libnd4j/include/ops/declarable/helpers/cuda/segment_sqrtn.cu @@ -20,14 +20,14 @@ #include #include -#include +#include #include #include #include -#include -#include +#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { // -------------------------------------------------------------------------------------------------------------- // @@ -51,7 +51,7 @@ namespace helpers { if (lengths[segment] == 0) continue; auto xIndex = shape::getIndexOffset(idx, inputShape); - nd4j::math::atomics::nd4j_atomicAdd(&output[zIndex], input[xIndex] / nd4j::math::nd4j_sqrt(lengths[segment])); + sd::math::atomics::nd4j_atomicAdd(&output[zIndex], input[xIndex] / sd::math::nd4j_sqrt(lengths[segment])); } } // -------------------------------------------------------------------------------------------------------------- // @@ -77,19 +77,19 @@ namespace helpers { for (auto e = threadIdx.x; e < len; e += blockDim.x) { auto xIndex = shape::getIndexOffset(e, inputTads); auto zIndex = shape::getIndexOffset(e, outputTads); - nd4j::math::atomics::nd4j_atomicAdd(&z[zIndex], x[xIndex] / nd4j::math::nd4j_sqrt(lengths[segment])); + sd::math::atomics::nd4j_atomicAdd(&z[zIndex], x[xIndex] / sd::math::nd4j_sqrt(lengths[segment])); } } } // -------------------------------------------------------------------------------------------------------------- // template - static void unsortedSegmentSqrtNFunctor_(nd4j::LaunchContext* context, NDArray* input, NDArray* indices, Nd4jLong numOfClasses, NDArray* output) { + static void unsortedSegmentSqrtNFunctor_(sd::LaunchContext* context, NDArray* input, NDArray* indices, Nd4jLong numOfClasses, NDArray* output) { auto stream = context->getCudaStream(); // NDArray classes = NDArrayFactory::create('c', {numOfClasses, 2}); NDArray classesRangesBegs = NDArrayFactory::create('c', {numOfClasses}); NDArray classesRangesLens = NDArrayFactory::create('c', {numOfClasses}); // NDArray row = NDArrayFactory::create('c', {1, 2}, {(int)indices->lengthOf(), (int)0}); -// classes.applyTrueBroadcast(nd4j::BroadcastOpsTuple::Assign(), &row, &classes); +// classes.applyTrueBroadcast(sd::BroadcastOpsTuple::Assign(), &row, &classes); classesRangesBegs.assign(indices->lengthOf()); classesRangesLens.assign(0); // dim3 dims(numOfClasses, indices->lengthOf(), numOfClasses * 32 + 32); @@ -108,8 +108,8 @@ namespace helpers { else { output->nullify(); std::vector dimensions = ShapeUtils::evalDimsToExclude(input->rankOf(), {0}); - auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), dimensions); - auto packZ = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), dimensions); + auto packX = sd::ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), dimensions); + auto packZ = sd::ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), dimensions); Nd4jLong* inputTads = packX.specialShapeInfo(); Nd4jLong* inputTadOffsets = packX.specialOffsets(); Nd4jLong* outputTads = packZ.specialShapeInfo(); @@ -121,7 +121,7 @@ namespace helpers { } } // -------------------------------------------------------------------------------------------------------------- // - void unsortedSegmentSqrtNFunctor(nd4j::LaunchContext* context , NDArray* input, NDArray* indices, Nd4jLong numOfClasses, NDArray* output) { + void unsortedSegmentSqrtNFunctor(sd::LaunchContext* context , NDArray* input, NDArray* indices, Nd4jLong numOfClasses, NDArray* output) { NDArray::prepareSpecialUse({output}, {input, indices}); BUILD_DOUBLE_SELECTOR(input->dataType(), indices->dataType(), unsortedSegmentSqrtNFunctor_, (context, input, indices, numOfClasses, output), FLOAT_TYPES, INDEXING_TYPES); @@ -203,7 +203,7 @@ namespace helpers { // -------------------------------------------------------------------------------------------------------------- // template - static int unsortedSegmentSqrtNFunctorBP_(nd4j::LaunchContext* context , NDArray* input, NDArray* indices, NDArray* gradOut, Nd4jLong numOfClasses, NDArray* output) { + static int unsortedSegmentSqrtNFunctorBP_(sd::LaunchContext* context , NDArray* input, NDArray* indices, NDArray* gradOut, Nd4jLong numOfClasses, NDArray* output) { auto stream = context->getCudaStream(); NDArray::prepareSpecialUse({output}, {input, indices, gradOut}); auto numClasses = indices->e(indices->lengthOf() - 1) + 1; @@ -226,10 +226,10 @@ namespace helpers { } else { std::vector dimensions = ShapeUtils::evalDimsToExclude(input->rankOf(), {0}); - auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), dimensions); - auto packZ = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), dimensions); -// auto packGradIn = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(tempRes.getShapeInfo(), dimensions); - auto packGradOut = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(gradOut->getShapeInfo(), dimensions); + auto packX = sd::ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), dimensions); + auto packZ = sd::ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), dimensions); +// auto packGradIn = sd::ConstantTadHelper::getInstance()->tadForDimensions(tempRes.getShapeInfo(), dimensions); + auto packGradOut = sd::ConstantTadHelper::getInstance()->tadForDimensions(gradOut->getShapeInfo(), dimensions); Nd4jLong* inputTads = packX.specialShapeInfo(); Nd4jLong* inputTadOffsets = packX.specialOffsets(); Nd4jLong* outputTads = packZ.specialShapeInfo(); @@ -247,7 +247,7 @@ namespace helpers { return Status::OK(); } // -------------------------------------------------------------------------------------------------------------- // - int unsortedSegmentSqrtNFunctorBP(nd4j::LaunchContext* context , NDArray* input, NDArray* indices, NDArray* gradOut, Nd4jLong numOfClasses, NDArray* output) { + int unsortedSegmentSqrtNFunctorBP(sd::LaunchContext* context , NDArray* input, NDArray* indices, NDArray* gradOut, Nd4jLong numOfClasses, NDArray* output) { NDArray::prepareSpecialUse({output}, {input, indices, gradOut}); BUILD_DOUBLE_SELECTOR(output->dataType(), indices->dataType(), return unsortedSegmentSqrtNFunctorBP_, (context, input, indices, gradOut, numOfClasses, output), FLOAT_TYPES, INDEXING_TYPES); NDArray::registerSpecialUse({output}, {input, indices, gradOut}); diff --git a/libnd4j/include/ops/declarable/helpers/cuda/segment_sum.cu b/libnd4j/include/ops/declarable/helpers/cuda/segment_sum.cu index cf4ddd942..6e1e3fca8 100644 --- a/libnd4j/include/ops/declarable/helpers/cuda/segment_sum.cu +++ b/libnd4j/include/ops/declarable/helpers/cuda/segment_sum.cu @@ -20,14 +20,14 @@ #include #include -#include +#include #include #include #include -#include -#include +#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { // -------------------------------------------------------------------------------------------------------------- // @@ -70,7 +70,7 @@ namespace helpers { for (auto e = start + threadIdx.x + 1; e < finish; e += blockDim.x) { auto xIndex = shape::getIndexOffset(e, inputShape); - nd4j::math::atomics::nd4j_atomicAdd(&z[zIndex], x[xIndex]); + sd::math::atomics::nd4j_atomicAdd(&z[zIndex], x[xIndex]); } } // -------------------------------------------------------------------------------------------------------------- // @@ -112,7 +112,7 @@ namespace helpers { auto xIndex = shape::getIndexOffset(e, inputShape); auto yIndex = shape::getIndexOffset(e, indicesShape); if (y[yIndex] == segment && e != starts[segment]) { - nd4j::math::atomics::nd4j_atomicAdd(&z[zIndex], x[xIndex]); + sd::math::atomics::nd4j_atomicAdd(&z[zIndex], x[xIndex]); } } } @@ -143,7 +143,7 @@ namespace helpers { for (auto e = threadIdx.x; e < len; e += blockDim.x) { auto xIndex = shape::getIndexOffset(e, inputTads); auto zIndex = shape::getIndexOffset(e, outputTads); - nd4j::math::atomics::nd4j_atomicAdd(&z[zIndex], x[xIndex]); + sd::math::atomics::nd4j_atomicAdd(&z[zIndex], x[xIndex]); } } else { @@ -151,7 +151,7 @@ namespace helpers { auto xIndex = shape::getIndexOffset(e, inputTads); auto zIndex = shape::getIndexOffset(e, outputTads); if (lengths[indices[idx]]) - nd4j::math::atomics::nd4j_atomicAdd(&z[zIndex], x[xIndex]); + sd::math::atomics::nd4j_atomicAdd(&z[zIndex], x[xIndex]); } } } @@ -159,7 +159,7 @@ namespace helpers { // -------------------------------------------------------------------------------------------------------------- // template - static void segmentSumFunctor_(nd4j::LaunchContext* context, NDArray* input, NDArray* indices, NDArray* output) { + static void segmentSumFunctor_(sd::LaunchContext* context, NDArray* input, NDArray* indices, NDArray* output) { auto stream = context->getCudaStream(); Nd4jLong numClasses = indices->e(indices->lengthOf() - 1) + 1; NDArray classesRangesLens = NDArrayFactory::create('c', {numClasses}); @@ -178,8 +178,8 @@ namespace helpers { } else { std::vector dimensions = ShapeUtils::evalDimsToExclude(input->rankOf(), {0}); - auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), dimensions); - auto packZ = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), dimensions); + auto packX = sd::ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), dimensions); + auto packZ = sd::ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), dimensions); Nd4jLong* inputTads = packX.specialShapeInfo(); Nd4jLong* inputTadOffsets = packX.specialOffsets(); Nd4jLong* outputTads = packZ.specialShapeInfo(); @@ -189,7 +189,7 @@ namespace helpers { } // -------------------------------------------------------------------------------------------------------------- // - void segmentSumFunctor(nd4j::LaunchContext* context , NDArray* input, NDArray* indices, NDArray* output) { + void segmentSumFunctor(sd::LaunchContext* context , NDArray* input, NDArray* indices, NDArray* output) { NDArray::prepareSpecialUse({output}, {input, indices}); output->nullify(); BUILD_DOUBLE_SELECTOR(input->dataType(), indices->dataType(), segmentSumFunctor_, (context, input, indices, output), NUMERIC_TYPES, INDEXING_TYPES); @@ -198,13 +198,13 @@ namespace helpers { // -------------------------------------------------------------------------------------------------------------- // template - static void unsortedSegmentSumFunctor_(nd4j::LaunchContext* context, NDArray* input, NDArray* indices, Nd4jLong numOfClasses, NDArray* output) { + static void unsortedSegmentSumFunctor_(sd::LaunchContext* context, NDArray* input, NDArray* indices, Nd4jLong numOfClasses, NDArray* output) { auto stream = context->getCudaStream(); // NDArray classes = NDArrayFactory::create('c', {numOfClasses, 2}); NDArray classesRangesBegs = NDArrayFactory::create('c', {numOfClasses}); NDArray classesRangesLens = NDArrayFactory::create('c', {numOfClasses}); // NDArray row = NDArrayFactory::create('c', {1, 2}, {(int)indices->lengthOf(), (int)0}); -// classes.applyTrueBroadcast(nd4j::BroadcastOpsTuple::Assign(), &row, &classes); +// classes.applyTrueBroadcast(sd::BroadcastOpsTuple::Assign(), &row, &classes); classesRangesBegs.assign(indices->lengthOf()); classesRangesLens.assign(0); dim3 dims(numOfClasses, indices->lengthOf(), (numOfClasses + 1) * 64); @@ -219,8 +219,8 @@ namespace helpers { else { output->assign(0); std::vector dimensions = ShapeUtils::evalDimsToExclude(input->rankOf(), {0}); - auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), dimensions); - auto packZ = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), dimensions); + auto packX = sd::ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), dimensions); + auto packZ = sd::ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), dimensions); Nd4jLong* inputTads = packX.specialShapeInfo(); Nd4jLong* inputTadOffsets = packX.specialOffsets(); Nd4jLong* outputTads = packZ.specialShapeInfo(); @@ -231,7 +231,7 @@ namespace helpers { } // -------------------------------------------------------------------------------------------------------------- // - void unsortedSegmentSumFunctor(nd4j::LaunchContext* context , NDArray* input, NDArray* indices, Nd4jLong numOfClasses, NDArray* output) { + void unsortedSegmentSumFunctor(sd::LaunchContext* context , NDArray* input, NDArray* indices, Nd4jLong numOfClasses, NDArray* output) { NDArray::prepareSpecialUse({output}, {input, indices}); output->nullify(); BUILD_DOUBLE_SELECTOR(input->dataType(), indices->dataType(), unsortedSegmentSumFunctor_, (context, input, indices, numOfClasses, output), @@ -315,7 +315,7 @@ namespace helpers { } // -------------------------------------------------------------------------------------------------------------- // template - int segmentSumFunctorBP_(nd4j::LaunchContext* context , NDArray* input, NDArray* indices, NDArray* gradOut, NDArray* output) { + int segmentSumFunctorBP_(sd::LaunchContext* context , NDArray* input, NDArray* indices, NDArray* gradOut, NDArray* output) { auto stream = context->getCudaStream(); NDArray::prepareSpecialUse({output}, {input, indices, gradOut}); if (input->isVector()) { @@ -327,9 +327,9 @@ namespace helpers { } else { std::vector dimensions = ShapeUtils::evalDimsToExclude(input->rankOf(), {0}); - auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), dimensions); - auto packZ = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), dimensions); - auto packGradOut = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(gradOut->getShapeInfo(), dimensions); + auto packX = sd::ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), dimensions); + auto packZ = sd::ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), dimensions); + auto packGradOut = sd::ConstantTadHelper::getInstance()->tadForDimensions(gradOut->getShapeInfo(), dimensions); Nd4jLong* inputTads = packX.specialShapeInfo(); Nd4jLong* inputTadOffsets = packX.specialOffsets(); Nd4jLong* outputTads = packZ.specialShapeInfo(); @@ -348,7 +348,7 @@ namespace helpers { } // -------------------------------------------------------------------------------------------------------------- // - int segmentSumFunctorBP(nd4j::LaunchContext* context , NDArray* input, NDArray* indices, NDArray* gradOut, NDArray* output) { + int segmentSumFunctorBP(sd::LaunchContext* context , NDArray* input, NDArray* indices, NDArray* gradOut, NDArray* output) { NDArray::prepareSpecialUse({output}, {input, indices, gradOut}); BUILD_DOUBLE_SELECTOR(output->dataType(), indices->dataType(), return segmentSumFunctorBP_, (context, input, indices, gradOut, output), FLOAT_TYPES, INDEXING_TYPES); @@ -356,7 +356,7 @@ namespace helpers { } template - static int unsortedSegmentSumFunctorBP_(nd4j::LaunchContext* context , NDArray* input, NDArray* indices, NDArray* gradOut, Nd4jLong numOfClasses, NDArray* output) { + static int unsortedSegmentSumFunctorBP_(sd::LaunchContext* context , NDArray* input, NDArray* indices, NDArray* gradOut, Nd4jLong numOfClasses, NDArray* output) { auto stream = context->getCudaStream(); NDArray::prepareSpecialUse({output}, {input, indices, gradOut}); if (input->isVector()) { @@ -368,9 +368,9 @@ namespace helpers { } else { std::vector dimensions = ShapeUtils::evalDimsToExclude(input->rankOf(), {0}); - auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), dimensions); - auto packZ = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), dimensions); - auto packGradOut = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(gradOut->getShapeInfo(), dimensions); + auto packX = sd::ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), dimensions); + auto packZ = sd::ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), dimensions); + auto packGradOut = sd::ConstantTadHelper::getInstance()->tadForDimensions(gradOut->getShapeInfo(), dimensions); Nd4jLong* inputTads = packX.specialShapeInfo(); Nd4jLong* inputTadOffsets = packX.specialOffsets(); Nd4jLong* outputTads = packZ.specialShapeInfo(); @@ -388,7 +388,7 @@ namespace helpers { return Status::OK(); } // -------------------------------------------------------------------------------------------------------------- // - int unsortedSegmentSumFunctorBP(nd4j::LaunchContext* context , NDArray* input, NDArray* indices, NDArray* gradOut, Nd4jLong numOfClasses, NDArray* output) { + int unsortedSegmentSumFunctorBP(sd::LaunchContext* context , NDArray* input, NDArray* indices, NDArray* gradOut, Nd4jLong numOfClasses, NDArray* output) { NDArray::prepareSpecialUse({output}, {input, indices, gradOut}); BUILD_DOUBLE_SELECTOR(output->dataType(), indices->dataType(), return unsortedSegmentSumFunctorBP_, (context, input, indices, gradOut, numOfClasses, output), FLOAT_TYPES, INDEXING_TYPES); NDArray::registerSpecialUse({output}, {input, indices, gradOut}); diff --git a/libnd4j/include/ops/declarable/helpers/cuda/sequence_mask.cu b/libnd4j/include/ops/declarable/helpers/cuda/sequence_mask.cu index 6b33a384e..b06797753 100644 --- a/libnd4j/include/ops/declarable/helpers/cuda/sequence_mask.cu +++ b/libnd4j/include/ops/declarable/helpers/cuda/sequence_mask.cu @@ -20,7 +20,7 @@ #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { @@ -54,11 +54,11 @@ namespace helpers { NDArray::registerSpecialUse({output}, {input}); } - void sequenceMask(nd4j::LaunchContext * context, NDArray* input, NDArray* output, int maxIndex) { + void sequenceMask(sd::LaunchContext * context, NDArray* input, NDArray* output, int maxIndex) { BUILD_DOUBLE_SELECTOR(input->dataType(), output->dataType(), sequenceMask_, (context, input, output, maxIndex), INTEGER_TYPES, LIBND4J_TYPES_EXTENDED); } - BUILD_DOUBLE_TEMPLATE(template void sequenceMask_, (nd4j::LaunchContext* context, NDArray* input, NDArray* output, int maxIndex), INTEGER_TYPES, LIBND4J_TYPES_EXTENDED); + BUILD_DOUBLE_TEMPLATE(template void sequenceMask_, (sd::LaunchContext* context, NDArray* input, NDArray* output, int maxIndex), INTEGER_TYPES, LIBND4J_TYPES_EXTENDED); } } } \ No newline at end of file diff --git a/libnd4j/include/ops/declarable/helpers/cuda/sg_cb.cu b/libnd4j/include/ops/declarable/helpers/cuda/sg_cb.cu index 24116ec63..f85a855b7 100644 --- a/libnd4j/include/ops/declarable/helpers/cuda/sg_cb.cu +++ b/libnd4j/include/ops/declarable/helpers/cuda/sg_cb.cu @@ -19,12 +19,12 @@ // #include -#include -#include +#include +#include #define HS_MAX_EXP 6.0f -namespace nd4j { +namespace sd { namespace ops { namespace helpers { template @@ -129,7 +129,7 @@ namespace nd4j { int binarySearch(const int *haystack, const int needle, const int totalElements) { int firstIndex = 0; int lastIndex = totalElements - 1; - int halfIndex = nd4j::math::nd4j_floor((lastIndex + firstIndex) / (float) 2); + int halfIndex = sd::math::nd4j_floor((lastIndex + firstIndex) / (float) 2); while(haystack[halfIndex] != needle && firstIndex < lastIndex) { if (needle < haystack[halfIndex]) { @@ -137,7 +137,7 @@ namespace nd4j { } else if (needle > haystack[halfIndex]) { firstIndex = halfIndex + 1; } - halfIndex = nd4j::math::nd4j_floor((lastIndex + firstIndex) / (float) 2); + halfIndex = sd::math::nd4j_floor((lastIndex + firstIndex) / (float) 2); } return (haystack[halfIndex] == needle) ? halfIndex : -1; @@ -198,7 +198,7 @@ namespace nd4j { // target is known in advance } else { randomValue = randomValue * (unsigned long long) 25214903917 + 11; - auto idx = nd4j::math::nd4j_abs((randomValue >> 16) % negLength); + auto idx = sd::math::nd4j_abs((randomValue >> 16) % negLength); irow = idx >= negLength ? -1 : negTableV.e(idx); if (irow < 0 || irow >= vocabSize) irow = randomValue % (vocabSize - 1) + 1; @@ -306,7 +306,7 @@ namespace nd4j { // target is known in advance } else { randomValue = randomValue * (unsigned long long) 25214903917 + 11; - auto idx = nd4j::math::nd4j_abs((randomValue >> 16) % negLength); + auto idx = sd::math::nd4j_abs((randomValue >> 16) % negLength); irow = idx >= negLength ? -1 : static_cast(negTable[idx]); if (irow < 0 || irow >= vocabSize) @@ -470,7 +470,7 @@ namespace nd4j { // target is known in advance } else { randomValue = randomValue * (unsigned long long) 25214903917 + 11; - auto idx = nd4j::math::nd4j_abs((randomValue >> 16) % negLength); + auto idx = sd::math::nd4j_abs((randomValue >> 16) % negLength); irow = idx >= negLength ? -1 : static_cast(negTable[idx]); if (irow < 0 || irow >= vocabSize) irow = randomValue % (vocabSize - 1) + 1; @@ -672,7 +672,7 @@ namespace nd4j { // we're skipping rng on 0 step if (r != 0) { randomValue = randomValue * (unsigned long long) 25214903917 + 11; - auto idx = nd4j::math::nd4j_abs((randomValue >> 16) % negLength); + auto idx = sd::math::nd4j_abs((randomValue >> 16) % negLength); irow = idx >= negLength ? -1 : static_cast(negTable[idx]); if (irow < 0 || irow >= vocabSize) irow = randomValue % (vocabSize - 1) + 1; diff --git a/libnd4j/include/ops/declarable/helpers/cuda/shift.cu b/libnd4j/include/ops/declarable/helpers/cuda/shift.cu index 8ba3d40ce..c69285ef2 100644 --- a/libnd4j/include/ops/declarable/helpers/cuda/shift.cu +++ b/libnd4j/include/ops/declarable/helpers/cuda/shift.cu @@ -20,7 +20,7 @@ #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { template diff --git a/libnd4j/include/ops/declarable/helpers/cuda/solve.cu b/libnd4j/include/ops/declarable/helpers/cuda/solve.cu index 6437b80bd..74823483e 100644 --- a/libnd4j/include/ops/declarable/helpers/cuda/solve.cu +++ b/libnd4j/include/ops/declarable/helpers/cuda/solve.cu @@ -18,18 +18,18 @@ // @author GS // -#include -#include -#include -#include +#include +#include +#include +#include #include -#include +#include #include "../triangular_solve.h" #include "../lup.h" #include "../solve.h" -namespace nd4j { +namespace sd { namespace ops { namespace helpers { @@ -63,7 +63,7 @@ namespace nd4j { } template - static int solveFunctor_(nd4j::LaunchContext * context, NDArray* leftInput, NDArray* rightInput, + static int solveFunctor_(sd::LaunchContext * context, NDArray* leftInput, NDArray* rightInput, bool adjoint, NDArray* output) { NDArray::prepareSpecialUse({output}, {leftInput, rightInput}); // stage 1: LU decomposition batched @@ -94,7 +94,7 @@ namespace nd4j { return Status::OK(); } - int solveFunctor(nd4j::LaunchContext * context, NDArray* leftInput, NDArray* rightInput, bool adjoint, NDArray* output) { + int solveFunctor(sd::LaunchContext * context, NDArray* leftInput, NDArray* rightInput, bool adjoint, NDArray* output) { BUILD_SINGLE_SELECTOR(leftInput->dataType(), return solveFunctor_, (context, leftInput, rightInput, adjoint, output), FLOAT_TYPES); } @@ -118,7 +118,7 @@ namespace nd4j { } template - static void adjointMatrix_(nd4j::LaunchContext* context, NDArray const* input, NDArray* output) { + static void adjointMatrix_(sd::LaunchContext* context, NDArray const* input, NDArray* output) { NDArray::prepareSpecialUse({output}, {input}); auto inputTads = ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), {-2, -1}); auto outputTads = ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), {-2, -1}); @@ -131,7 +131,7 @@ namespace nd4j { NDArray::registerSpecialUse({output}, {input}); } - void adjointMatrix(nd4j::LaunchContext* context, NDArray const* input, NDArray* output) { + void adjointMatrix(sd::LaunchContext* context, NDArray const* input, NDArray* output) { BUILD_SINGLE_SELECTOR(input->dataType(), adjointMatrix_, (context, input, output), FLOAT_TYPES); } diff --git a/libnd4j/include/ops/declarable/helpers/cuda/split.cu b/libnd4j/include/ops/declarable/helpers/cuda/split.cu new file mode 100644 index 000000000..5690d786c --- /dev/null +++ b/libnd4j/include/ops/declarable/helpers/cuda/split.cu @@ -0,0 +1,192 @@ +/******************************************************************************* + * Copyright (c) 2015-2018 Skymind, Inc. + * Copyright (c) 2019 Konduit K.K. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ + +// +// @author Yurii Shyrma (iuriish@yahoo.com) +// + + +#include +#include +#include +#include +#include +#include +#include +#include +#include + +namespace sd { +namespace ops { +namespace helpers { + + +/////////////////////////////////////////////////////////////////// +template +__global__ static void splitCuda(const void* vx, const Nd4jLong* xShapeInfo, void* pVz, const Nd4jLong* zTadShapeInfo, const int axis) { + + const T* x = reinterpret_cast(vx); + + __shared__ Nd4jLong xLen, totalThreads; + __shared__ int xRank, zDim; + + if (threadIdx.x == 0) { + xLen = shape::length(xShapeInfo); + xRank = shape::rank(xShapeInfo); + zDim = shape::shapeOf(zTadShapeInfo)[axis]; // same for all input arrays + totalThreads = gridDim.x * blockDim.x; + } + __syncthreads(); + + const auto tid = blockIdx.x * blockDim.x + threadIdx.x; + + int coords[MAX_RANK]; + + for (uint64_t i = tid; i < xLen; i += totalThreads) { + + shape::index2coords(i, xShapeInfo, coords); + + const auto xOffset = shape::getOffset(xShapeInfo, coords); + + auto *z = reinterpret_cast(reinterpret_cast(pVz)[coords[axis] / zDim]); + + coords[axis] %= zDim; + + const auto zOffset = shape::getOffset(zTadShapeInfo, coords); + + z[zOffset] = x[xOffset]; + } +} + +/////////////////////////////////////////////////////////////////// +template +__host__ static void splitCudaLauncher(const int blocksPerGrid, const int threadsPerBlock, const cudaStream_t *stream, + const void* vx, const Nd4jLong* xShapeInfo, void* pVz, const Nd4jLong* zTadShapeInfo, const int axis) { + + splitCuda<<>>(vx, xShapeInfo, pVz, zTadShapeInfo, axis); +} +BUILD_SINGLE_TEMPLATE(template void splitCudaLauncher, (const int blocksPerGrid, const int threadsPerBlock, const cudaStream_t *stream, const void* vx, const Nd4jLong* xShapeInfo, void* pVz, const Nd4jLong* zTadShapeInfo, const int axis), LIBND4J_TYPES); + +////////////////////////////////////////////////////////////////////////// +void split(sd::LaunchContext* context, const NDArray& input, std::vector& outArrs, const int axis) { + + const int numOfSubArrs = outArrs.size(); + const auto sizeofT = input.sizeOfT(); + + for(int i = 0; i < numOfSubArrs; ++i) + outArrs[i]->syncToDevice(); + input.syncToDevice(); + + bool luckCase1 = ((axis == 0 && input.ordering() == 'c') || (axis == input.rankOf() - 1 && input.ordering() == 'f')) && input.ews() == 1; + + if(luckCase1) { + for (uint i = 0; i < numOfSubArrs; ++i) { + luckCase1 &= outArrs[i]->ordering() == input.ordering() && outArrs[i]->ews() == 1; + if(!luckCase1) + break; + } + } + + if(luckCase1) { // for example {1,10} + {2,10} + {3,10} = {6, 10} order c; or {10,1} + {10,2} + {10,3} = {10, 6} order f + + void* x = static_cast(input.getSpecialBuffer()); + + for (uint i = 0; i < numOfSubArrs; ++i) { + const auto memAmountToCopy = outArrs[i]->lengthOf() * sizeofT; + cudaMemcpyAsync(static_cast(outArrs[i]->getSpecialBuffer()), x, memAmountToCopy, cudaMemcpyDeviceToDevice, *context->getCudaStream()); + x = static_cast(x) + memAmountToCopy; + } + + if(cudaStreamSynchronize(*context->getCudaStream()) != 0) + throw std::runtime_error("split cuda: luckCase1 failed!"); + + for(int i = 0; i < numOfSubArrs; ++i) + outArrs[i]->tickWriteDevice(); + input.tickReadDevice(); + + return; + } + + // const bool isXcontin = input.strideAt(axis) == 1; + // bool areOutputsContin = true; + // bool allSameOrder = true; + // std::vector strideOfContigStride(outArrs.size()); + + // if(isXcontin) { + + // for (uint i = 0; i < outArrs.size(); ++i) { + + // areOutputsContin &= outArrs[i]->strideAt(axis) == 1; + // allSameOrder &= input.ordering() == outArrs[i]->ordering(); + // if(!areOutputsContin || !allSameOrder) + // break; + + // strideOfContigStride[i] = shape::strideOverContigAxis(axis, outArrs[i]->getShapeInfo()); + // } + // } + + // const bool luckCase2 = isXcontin && areOutputsContin && allSameOrder; + + // if(luckCase2) { // for example {2,1,3} + {2,5,3} + {2,10,3} = {2,16,3}, here axis 1 shoud have stride = 1 for all inputs arrays and input array + + // const auto xStep = shape::strideOverContigAxis(axis, input.getShapeInfo()); + // const auto zDim = outArrs[0]->sizeAt(axis); // same for all outArrs + + // for (uint i = 0; i < input.lengthOf() / input.sizeAt(axis); ++i) { + + // const auto iShift = i * sizeofT; + // void* x = static_cast(input.getSpecialBuffer()) + xStep * iShift; + + // for (uint j = 0; j < numOfSubArrs; ++j) { + // void* z = static_cast(outArrs[j]->getSpecialBuffer()) + strideOfContigStride[j] * iShift; + // const auto memSizeToCopy = zDim * sizeofT; + // cudaMemcpyAsync(z, x, memSizeToCopy, cudaMemcpyDeviceToDevice, *context->getCudaStream()); + // x = static_cast(x) + memSizeToCopy; + // } + // } + + // if(cudaStreamSynchronize(*context->getCudaStream()) != 0) + // throw std::runtime_error("split cuda: luckCase2 failed!"); + // } + // else { // general (slower) case + + const int threadsPerBlock = MAX_NUM_THREADS / 2; + const int blocksPerGrid = (input.lengthOf() + threadsPerBlock - 1) / threadsPerBlock; + + // prepare arrays of pointers on buffers and shapes + std::vector hOutBuffers(numOfSubArrs); + + for(int i = 0; i < numOfSubArrs; ++i) + hOutBuffers[i] = outArrs[i]->getSpecialBuffer(); + + PointersManager manager(context, "helpers::split"); + + void* dOutBuffers = manager.replicatePointer(hOutBuffers.data(), hOutBuffers.size() * sizeof(void*)); + + BUILD_SINGLE_SELECTOR(input.dataType(), splitCudaLauncher, (blocksPerGrid, threadsPerBlock, context->getCudaStream(), input.getSpecialBuffer(), input.getSpecialShapeInfo(), dOutBuffers, outArrs[0]->specialShapeInfo(), axis), LIBND4J_TYPES); + + manager.synchronize(); + // } + + for(int i = 0; i < numOfSubArrs; ++i) + outArrs[i]->tickWriteDevice(); + input.tickReadDevice(); +} + +} +} +} \ No newline at end of file diff --git a/libnd4j/include/ops/declarable/helpers/cuda/sru.cu b/libnd4j/include/ops/declarable/helpers/cuda/sru.cu index 76530269c..518525ecf 100644 --- a/libnd4j/include/ops/declarable/helpers/cuda/sru.cu +++ b/libnd4j/include/ops/declarable/helpers/cuda/sru.cu @@ -21,11 +21,11 @@ // #include -#include -#include -#include +#include +#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { @@ -45,7 +45,7 @@ namespace helpers { ////////////////////////////////////////////////////////////////////////// -void sruCell(nd4j::LaunchContext * context, const NDArray* x, const NDArray* c0, const NDArray* w, const NDArray* b, NDArray* h, NDArray* c) { +void sruCell(sd::LaunchContext * context, const NDArray* x, const NDArray* c0, const NDArray* w, const NDArray* b, NDArray* h, NDArray* c) { // x input [bS x inSize], bS - batch size, inSize - number of features // c0 previous cell state c [bS x inSize], that is at previous time step t-1 @@ -76,7 +76,7 @@ void sruCell(nd4j::LaunchContext * context, const NDArray* x, const NDArray* c0, } ////////////////////////////////////////////////////////////////////////// -void sruTimeLoop(nd4j::LaunchContext * context, const NDArray* x, const NDArray* c0, const NDArray* w, const NDArray* b, NDArray* h, NDArray* c) { +void sruTimeLoop(sd::LaunchContext * context, const NDArray* x, const NDArray* c0, const NDArray* w, const NDArray* b, NDArray* h, NDArray* c) { // x input [bS x inSize x time] // c0 initial cell state (at time step = 0) [bS x inSize], @@ -135,13 +135,13 @@ __global__ static void sruBICuda(const void* vx, const Nd4jLong* xShapeInfo, const int rank = 3; - __shared__ int time, K; - __shared__ Nd4jLong len, totalThreads, *sharedMem; + __shared__ int time, K, *sharedMem; + __shared__ Nd4jLong len, totalThreads; if (threadIdx.x == 0) { extern __shared__ unsigned char shmem[]; - sharedMem = reinterpret_cast(shmem); + sharedMem = reinterpret_cast(shmem); time = xShapeInfo[1]; K = xShapeInfo[3] / 2; @@ -152,7 +152,7 @@ __global__ static void sruBICuda(const void* vx, const Nd4jLong* xShapeInfo, __syncthreads(); const auto tid = blockIdx.x * blockDim.x + threadIdx.x; - Nd4jLong* coords = sharedMem + threadIdx.x * rank; + auto coords = sharedMem + threadIdx.x * rank; if(tid >= len) return; @@ -189,12 +189,12 @@ __global__ static void sruBICuda(const void* vx, const Nd4jLong* xShapeInfo, for (uint t = 0; t < time; ++t) { // evaluate sigmoids - T ft = (1.f)/(1.f + nd4j::math::nd4j_exp(-(wi[wiOffset1] + bF))); - T rt = (1.f)/(1.f + nd4j::math::nd4j_exp(-(wi[wiOffset2] + bR))); + T ft = (1.f)/(1.f + sd::math::nd4j_exp(-(wi[wiOffset1] + bF))); + T rt = (1.f)/(1.f + sd::math::nd4j_exp(-(wi[wiOffset2] + bR))); c0Val = (c0Val - wi[wiOffset0]) * ft + wi[wiOffset0]; ct[ctOffset] = c0Val; - T val = nd4j::math::nd4j_tanh(c0Val); + T val = sd::math::nd4j_tanh(c0Val); T xVal = x[xOffset]; ht[htOffset] = (val * maskVal - xVal) * rt + xVal; @@ -232,7 +232,7 @@ static void sruBICudaLauncher(const int blocksPerGrid, const int threadsPerBlock } ////////////////////////////////////////////////////////////////////////// -void sruBI(nd4j::LaunchContext * context, NDArray* x, const NDArray* w, const NDArray* b, const NDArray* c0, const NDArray* mask, NDArray* ht, NDArray* ct) { +void sruBI(sd::LaunchContext * context, NDArray* x, const NDArray* w, const NDArray* b, const NDArray* c0, const NDArray* mask, NDArray* ht, NDArray* ct) { // x = x * mask if(mask) @@ -245,7 +245,7 @@ void sruBI(nd4j::LaunchContext * context, NDArray* x, const NDArray* w, const ND const int threadsPerBlock = MAX_NUM_THREADS / 4; const int blocksPerGrid = (x->sizeAt(1) * x->sizeAt(2) + threadsPerBlock - 1) / threadsPerBlock; // loop through last two dimensions of x array -> bS, 2*K - const int sharedMem = threadsPerBlock * sizeof(Nd4jLong) * x->rankOf() + 128; + const int sharedMem = threadsPerBlock * sizeof(int) * x->rankOf() + 128; NDArray::prepareSpecialUse({ht, ct}, {x, &wi, b, c0, mask}); BUILD_SINGLE_SELECTOR(x->dataType(), sruBICudaLauncher, (blocksPerGrid, threadsPerBlock, sharedMem, context->getCudaStream(), x->getSpecialBuffer(), x->getSpecialShapeInfo(), wi.getSpecialBuffer(), wi.getSpecialShapeInfo(), b->getSpecialBuffer(), b->getSpecialShapeInfo(), c0->getSpecialBuffer(), c0->getSpecialShapeInfo(), mask ? mask->getSpecialBuffer() : nullptr, mask ? mask->getSpecialShapeInfo() : nullptr, ht->specialBuffer(), ht->specialShapeInfo(), ct->specialBuffer(), ct->specialShapeInfo()), FLOAT_TYPES); @@ -340,13 +340,13 @@ __global__ static void sruBIBPCuda(const void* vx, const Nd4jLong* xShapeI const int rank = 3; - __shared__ int time, K; - __shared__ Nd4jLong len, totalThreads, *sharedMem; + __shared__ int time, K, *sharedMem; + __shared__ Nd4jLong len, totalThreads; if (threadIdx.x == 0) { extern __shared__ unsigned char shmem[]; - sharedMem = reinterpret_cast(shmem); + sharedMem = reinterpret_cast(shmem); time = xShapeInfo[1]; K = xShapeInfo[3] / 2; @@ -358,7 +358,7 @@ __global__ static void sruBIBPCuda(const void* vx, const Nd4jLong* xShapeI __syncthreads(); const auto tid = blockIdx.x * blockDim.x + threadIdx.x; - Nd4jLong* coords = sharedMem + threadIdx.x * rank; + auto coords = sharedMem + threadIdx.x * rank; if(tid >= len) return; @@ -408,10 +408,10 @@ __global__ static void sruBIBPCuda(const void* vx, const Nd4jLong* xShapeI for (uint t = 0; t < time; ++t) { // evaluate sigmoids - T ft = (1.f)/(1.f + nd4j::math::nd4j_exp(-(wi[wiOffset1] + bF))); - T rt = (1.f)/(1.f + nd4j::math::nd4j_exp(-(wi[wiOffset2] + bR))); + T ft = (1.f)/(1.f + sd::math::nd4j_exp(-(wi[wiOffset1] + bF))); + T rt = (1.f)/(1.f + sd::math::nd4j_exp(-(wi[wiOffset2] + bR))); - T val = nd4j::math::nd4j_tanh(ct[ctOffset]); + T val = sd::math::nd4j_tanh(ct[ctOffset]); T prevVal; if(t < time-1) @@ -491,7 +491,7 @@ static void sruBIBPCudaLauncher(const int blocksPerGrid, const int threadsPerBlo BUILD_SINGLE_TEMPLATE(template void sruBIBPCudaLauncher, (const int blocksPerGrid, const int threadsPerBlock, const int sharedMem, const cudaStream_t *stream, const void* vx, const Nd4jLong* xShapeInfo, const void* vwi, const Nd4jLong* wiShapeInfo, const void* vb, const Nd4jLong* bShapeInfo, const void* vc0, const Nd4jLong* c0ShapeInfo, const void* vmask, const Nd4jLong* maskShapeInfo, const void* vct, const Nd4jLong* ctShapeInfo, const void* vgradHt, const Nd4jLong* gradHtShapeInfo, const void* vgradCt, const Nd4jLong* gradCtShapeInfo, void* vgradI, const Nd4jLong* gradIShapeInfo, void* vgradWi, const Nd4jLong* gradWiShapeInfo, void* vgradB, const Nd4jLong* gradBShapeInfo, void* vgradC0, const Nd4jLong* gradC0ShapeInfo), FLOAT_TYPES); ////////////////////////////////////////////////////////////////////////// -void sruBIBP(nd4j::LaunchContext* context, NDArray* x, const NDArray* w, const NDArray* b, const NDArray* c0, const NDArray* ct, +void sruBIBP(sd::LaunchContext* context, NDArray* x, const NDArray* w, const NDArray* b, const NDArray* c0, const NDArray* ct, const NDArray* gradCt, const NDArray* gradHt, const NDArray* mask, NDArray* gradI, NDArray* gradW, NDArray* gradB, NDArray* gradC0) { @@ -513,7 +513,7 @@ void sruBIBP(nd4j::LaunchContext* context, NDArray* x, const NDArray* w, const N const int threadsPerBlock = MAX_NUM_THREADS / 4; const int blocksPerGrid = (x->sizeAt(1) * x->sizeAt(2) + threadsPerBlock - 1) / threadsPerBlock; // loop through last two dimensions of x array -> bS, 2*K - const int sharedMem = threadsPerBlock * sizeof(Nd4jLong) * x->rankOf() + 128; + const int sharedMem = threadsPerBlock * sizeof(int) * x->rankOf() + 128; NDArray::prepareSpecialUse({gradI, &gradWi, &gradBias, gradC0}, {x, &wi, b, c0, ct, gradCt, gradHt, mask}); BUILD_SINGLE_SELECTOR(x->dataType(), sruBIBPCudaLauncher, (blocksPerGrid, threadsPerBlock, sharedMem, context->getCudaStream(), x->getSpecialBuffer(), x->getSpecialShapeInfo(), wi.getSpecialBuffer(), wi.getSpecialShapeInfo(), b->getSpecialBuffer(), b->getSpecialShapeInfo(), c0->getSpecialBuffer(), c0->getSpecialShapeInfo(), mask ? mask->getSpecialBuffer() : nullptr, mask ? mask->getSpecialShapeInfo() : nullptr, ct->getSpecialBuffer(), ct->getSpecialShapeInfo(), gradHt->getSpecialBuffer(), gradHt->getSpecialShapeInfo(), gradCt->getSpecialBuffer(), gradCt->getSpecialShapeInfo(), gradI->specialBuffer(), gradI->specialShapeInfo(), gradWi.specialBuffer(), gradWi.specialShapeInfo(), gradBias.specialBuffer(), gradBias.specialShapeInfo(), gradC0->specialBuffer(), gradC0->specialShapeInfo()), FLOAT_TYPES); diff --git a/libnd4j/include/ops/declarable/helpers/cuda/stack.cu b/libnd4j/include/ops/declarable/helpers/cuda/stack.cu index e88f5ade8..89859ae1d 100644 --- a/libnd4j/include/ops/declarable/helpers/cuda/stack.cu +++ b/libnd4j/include/ops/declarable/helpers/cuda/stack.cu @@ -21,89 +21,343 @@ #include #include #include -#include -#include -#include -#include +#include +#include +#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { - template - static __global__ void stackKernel(void** inputList, void** inputShapeList, int inputListLength, Nd4jLong arrLen, void* vz, const Nd4jLong* zShapeInfo, Nd4jLong* tadShape, Nd4jLong *tadOffsets) { +/////////////////////////////////////////////////////////////////// +template +static __global__ void stackScalarsCuda(void* pVx, void* vz, const Nd4jLong* zShapeInfo) { - T* z = reinterpret_cast(vz); + T* z = reinterpret_cast(vz); - if(tadShape == nullptr) { // scalar case + __shared__ Nd4jLong zLen, totalThreads; - for (Nd4jLong i = blockIdx.x * blockDim.x + threadIdx.x; i < inputListLength; i += gridDim.x * blockDim.x) - z[shape::getIndexOffset(i, zShapeInfo)] = reinterpret_cast(inputList[i])[0]; - } - else { + if (threadIdx.x == 0) { + zLen = shape::length(zShapeInfo); + totalThreads = gridDim.x * blockDim.x; + } + __syncthreads(); - for (int t = blockIdx.x; t < inputListLength; t += gridDim.x) { + const auto tid = blockIdx.x * blockDim.x + threadIdx.x; - auto tZ = z + tadOffsets[t]; - auto tX = reinterpret_cast(inputList[t]); - auto xShapeInfo = reinterpret_cast(inputShapeList[t]); + for (Nd4jLong i = tid; i < zLen; i += totalThreads) { - for (int e = threadIdx.x; e < arrLen; e += blockDim.x) - tZ[shape::getIndexOffset(e, tadShape)] = tX[shape::getIndexOffset(e, xShapeInfo)]; - } - } - } + const T *x = reinterpret_cast(reinterpret_cast(pVx)[i]); + z[shape::getIndexOffset(i, zShapeInfo)] = *x; + } +} - /////////////////////////////////////////////////////////////////// - template - static void stack_(nd4j::LaunchContext * context, const std::vector& inArrs, NDArray* outArr, const int dim) { - const bool scalarCase = inArrs[0]->isScalar(); +/////////////////////////////////////////////////////////////////// +template +__host__ static void stackScalarsCudaLauncher(const int blocksPerGrid, const int threadsPerBlock, const cudaStream_t *stream, + void* pVx, void* vz, const Nd4jLong* zShapeInfo) { - const int threadsPerBlock = MAX_NUM_THREADS / 2; - const int blocksPerGrid = scalarCase ? (outArr->lengthOf() + threadsPerBlock - 1) / threadsPerBlock : inArrs.size(); + stackScalarsCuda<<>>(pVx, vz, zShapeInfo); +} - NDArray::prepareSpecialUse({outArr}, {}); +/////////////////////////////////////////////////////////////////// +template +static void stack_(sd::LaunchContext* context, const std::vector& inArrs, NDArray& output, const int dim) { - // FIXME: !!! - for (auto v:inArrs) - NDArray::prepareSpecialUse({}, {v}); + const int numOfSubArrs = inArrs.size(); - std::vector inputList(inArrs.size()); - std::vector inputShapeList(inArrs.size()); + NDArray::prepareSpecialUse({&output}, inArrs); - for (size_t i = 0; i < inputList.size(); ++i) { - inputList[i] = inArrs[i]->getSpecialBuffer(); - inputShapeList[i] = inArrs[i]->getSpecialShapeInfo(); - } + if(inArrs[0]->rankOf() == 0) { - PointersManager manager(context, "helpers::stack"); - auto dInBuffers = (void **) manager.replicatePointer(inputList.data(), inputList.size() * sizeof(Nd4jLong*)); - auto dInShapeInfo = (void **) manager.replicatePointer(inputShapeList.data(), inputShapeList.size() * sizeof(Nd4jLong*)); + std::vector hInBuffers(numOfSubArrs); + + for(int i = 0; i < numOfSubArrs; ++i) + hInBuffers[i] = inArrs[i]->getSpecialBuffer(); + + PointersManager manager(context, "helpers::stack cuda"); + + void* dInBuffers = manager.replicatePointer(hInBuffers.data(), hInBuffers.size() * sizeof(void*)); + + const int threadsPerBlock = MAX_NUM_THREADS / 2; + const int blocksPerGrid = (output.lengthOf() + threadsPerBlock - 1) / threadsPerBlock; + + stackScalarsCudaLauncher(blocksPerGrid, threadsPerBlock, context->getCudaStream(), dInBuffers, output.specialBuffer(), output.specialShapeInfo()); - if(scalarCase) { - stackKernel<<getCudaStream()>>>((void**)dInBuffers, (void**)dInShapeInfo, inputList.size(), inArrs[0]->lengthOf(), outArr->specialBuffer(), outArr->getSpecialShapeInfo(), nullptr, nullptr); - } - else { - std::vector axis = ShapeUtils::evalDimsToExclude(outArr->rankOf(), {dim}); - auto packZ = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(outArr->getShapeInfo(), axis); - stackKernel<<getCudaStream()>>>((void**)dInBuffers, (void**)dInShapeInfo, inputList.size(), inArrs[0]->lengthOf(), outArr->specialBuffer(), nullptr, packZ.specialShapeInfo(), packZ.specialOffsets()); - } manager.synchronize(); + } + else { - NDArray::registerSpecialUse({outArr}, {}); + auto zTadPack = ConstantTadHelper::getInstance()->tadForDimensions(output.getShapeInfo(), ShapeUtils::evalDimsToExclude(output.rankOf(), {dim})); + Nd4jLong* zTadShapeInfo = zTadPack.primaryShapeInfo(); - // FIXME: !!! - for (auto v:inArrs) - NDArray::registerSpecialUse({}, {v}); - } + for (uint i = 0; i < numOfSubArrs; ++i) { - void stack(nd4j::LaunchContext * context, const std::vector& inArrs, NDArray* outArr, const int dim) { - BUILD_SINGLE_SELECTOR(outArr->dataType(), stack_, (context, inArrs, outArr, dim), LIBND4J_TYPES); - } + void* zBuff = output.specialBufferWithOffset(zTadPack.primaryOffsets()[i]); - BUILD_SINGLE_TEMPLATE(template void stack_ , (nd4j::LaunchContext * context, const std::vector& inArrs, NDArray* outArr, const int dim), LIBND4J_TYPES); + NativeOpExecutioner::execTransformAny(context, transform::Assign, + nullptr, inArrs[i]->getShapeInfo(), inArrs[i]->getSpecialBuffer(), inArrs[i]->getSpecialShapeInfo(), + nullptr, zTadShapeInfo, zBuff, zTadPack.specialShapeInfo(), + nullptr, nullptr, nullptr, false/*allowParallelism*/); + } + } + + NDArray::registerSpecialUse({&output}, inArrs); +} + +//////////////////////////////////////////////////////////////////////// +void stack(sd::LaunchContext* context, const std::vector& inArrs, NDArray& output, const int dim) { + BUILD_SINGLE_SELECTOR(output.dataType(), stack_, (context, inArrs, output, dim), LIBND4J_TYPES); +} +BUILD_SINGLE_TEMPLATE(template void stack_ , (sd::LaunchContext* context, const std::vector& inArrs, NDArray& output, const int dim), LIBND4J_TYPES); + + +/////////////////////////////////////////////////////////////////// +template +static __global__ void unstackScalarsCuda(const void* vx, const Nd4jLong* xShapeInfo, void* pVz) { + + const T* x = reinterpret_cast(vx); + + __shared__ Nd4jLong xLen, totalThreads; + + if (threadIdx.x == 0) { + xLen = shape::length(xShapeInfo); + totalThreads = gridDim.x * blockDim.x; + } + __syncthreads(); + + const auto tid = blockIdx.x * blockDim.x + threadIdx.x; + + for (Nd4jLong i = tid; i < xLen; i += totalThreads) { + + T* z = reinterpret_cast(reinterpret_cast(pVz)[i]); + *z = x[shape::getIndexOffset(i, xShapeInfo)]; + } +} + + +/////////////////////////////////////////////////////////////////// +template +__host__ static void unstackScalarsCudaLauncher(const int blocksPerGrid, const int threadsPerBlock, const cudaStream_t *stream, + const void* vx, const Nd4jLong* xShapeInfo, void* pVz) { + + unstackScalarsCuda<<>>(vx, xShapeInfo, pVz); +} + +/////////////////////////////////////////////////////////////////// +template +static void unstack_(sd::LaunchContext* context, const NDArray& input, const std::vector& outArrs, const int dim) { + + const int numOfSubArrs = outArrs.size(); + + // NDArray::prepareSpecialUse(outArrs, {&input}); + input.syncToDevice(); + for (const auto a : outArrs) + a->getDataBuffer()->allocateSpecial(); + + + if(outArrs[0]->rankOf() == 0) { + + std::vector hOutBuffers(numOfSubArrs); + + for(int i = 0; i < numOfSubArrs; ++i) + hOutBuffers[i] = outArrs[i]->getSpecialBuffer(); + + PointersManager manager(context, "helpers::unstack cuda"); + + void* dOutBuffers = manager.replicatePointer(hOutBuffers.data(), hOutBuffers.size() * sizeof(void*)); + + const int threadsPerBlock = MAX_NUM_THREADS / 2; + const int blocksPerGrid = (input.lengthOf() + threadsPerBlock - 1) / threadsPerBlock; + + unstackScalarsCudaLauncher(blocksPerGrid, threadsPerBlock, context->getCudaStream(), input.getSpecialBuffer(), input.getSpecialShapeInfo(), dOutBuffers); + + manager.synchronize(); + } + else { + + auto xTadPack = ConstantTadHelper::getInstance()->tadForDimensions(input.getShapeInfo(), ShapeUtils::evalDimsToExclude(input.rankOf(), {dim})); + Nd4jLong* xTadShapeInfo = xTadPack.primaryShapeInfo(); + + for (uint i = 0; i < numOfSubArrs; ++i) { + + void* xBuff = input.specialBufferWithOffset(xTadPack.primaryOffsets()[i]); + + NativeOpExecutioner::execTransformAny(input.getContext(), transform::Assign, + nullptr, xTadShapeInfo, xBuff, xTadPack.specialShapeInfo(), + nullptr, outArrs[i]->getShapeInfo(), outArrs[i]->specialBuffer(), outArrs[i]->specialShapeInfo(), + nullptr, nullptr, nullptr, false/*allowParallelism*/); + } + } + + // NDArray::registerSpecialUse(outArrs, {&input}); + input.tickReadDevice(); + for (const auto p : outArrs) + p->tickWriteDevice(); +} + +//////////////////////////////////////////////////////////////////////// +void unstack(sd::LaunchContext* context, const NDArray& input, const std::vector& outArrs, const int dim) { + BUILD_SINGLE_SELECTOR(input.dataType(), unstack_, (context, input, outArrs, dim), LIBND4J_TYPES); +} +BUILD_SINGLE_TEMPLATE(template void unstack_, (sd::LaunchContext* context, const NDArray& input, const std::vector& outArrs, const int dim), LIBND4J_TYPES); + +/////////////////////////////////////////////////////////////////// +// template +// static __global__ void unstackCuda(const void* vx, const Nd4jLong* xShapeInfo, void* pVz, const Nd4jLong* zTadShapeInfo, const int axis) { + +// const T* x = reinterpret_cast(vx); +// __shared__ Nd4jLong xLen, totalThreads; +// __shared__ int xRank; + +// if (threadIdx.x == 0) { +// xLen = shape::length(xShapeInfo); +// xRank = shape::rank(xShapeInfo); +// totalThreads = gridDim.x * blockDim.x; +// } +// __syncthreads(); + +// const auto tid = blockIdx.x * blockDim.x + threadIdx.x; + +// Nd4jLong coords[MAX_RANK]; + +// for (uint64_t i = tid; i < xLen; i += totalThreads) { + +// shape::index2coords(i, xShapeInfo, coords); + +// const auto xOffset = shape::getOffset(xShapeInfo, coords); + +// T *z = reinterpret_cast(reinterpret_cast(pVz)[coords[axis]]); + +// for (uint j = axis; j < xRank - 1; ++j) // shift coords staring from axis position +// coords[j] = coords[j + 1]; + +// const auto zOffset = shape::getOffset(zTadShapeInfo, coords); + +// z[zOffset] = x[xOffset]; +// } +// } + +// /////////////////////////////////////////////////////////////////// +// template +// __host__ static void unstackCudaLauncher(const int blocksPerGrid, const int threadsPerBlock, const cudaStream_t *stream, +// const void* vx, const Nd4jLong* xShapeInfo, void* pVz, const Nd4jLong* zTadShapeInfo, const int axis) { + +// unstackCuda<<>>(vx, xShapeInfo, pVz, zTadShapeInfo, axis); +// } +// BUILD_SINGLE_TEMPLATE(template void unstackCudaLauncher, (const int blocksPerGrid, const int threadsPerBlock, const cudaStream_t *stream, const void* vx, const Nd4jLong* xShapeInfo, void* pVz, const Nd4jLong* zTadShapeInfo, const int axis), LIBND4J_TYPES); + + +// /////////////////////////////////////////////////////////////////// +// void unstack(sd::LaunchContext* context, const NDArray& input, const std::vector& outArrs, const int axis) { + +// const int threadsPerBlock = MAX_NUM_THREADS / 2; +// const int blocksPerGrid = (input.lengthOf() + threadsPerBlock - 1) / threadsPerBlock; + +// const int numOfSubArrs = outArrs.size(); + +// std::vector hOutBuffers(numOfSubArrs); + +// for(int i = 0; i < numOfSubArrs; ++i) +// hOutBuffers[i] = outArrs[i]->getSpecialBuffer(); + +// PointersManager manager(context, "helpers::unstack"); + +// void* dOutBuffers = manager.replicatePointer(hOutBuffers.data(), hOutBuffers.size() * sizeof(void*)); + +// for(uint i = 0; i < numOfSubArrs; ++i) +// outArrs[i]->syncToDevice(); +// input.syncToDevice(); + +// BUILD_SINGLE_SELECTOR(input.dataType(), unstackCudaLauncher, (blocksPerGrid, threadsPerBlock, context->getCudaStream(), input.getSpecialBuffer(), input.getSpecialShapeInfo(), dOutBuffers, outArrs[0]->getSpecialShapeInfo(), axis), LIBND4J_TYPES); + +// manager.synchronize(); + +// for(uint i = 0; i < numOfSubArrs; ++i) +// outArrs[i]->tickReadDevice(); +// input.tickWriteDevice(); +// } + + +// /////////////////////////////////////////////////////////////////// +// template +// static __global__ void stackCuda(void* pVx, const Nd4jLong* xTadShapeInfo, void* vz, const Nd4jLong* zShapeInfo, const int axis) { + +// T* z = reinterpret_cast(vz); + +// __shared__ Nd4jLong zLen, totalThreads; +// __shared__ int zRank; + +// if (threadIdx.x == 0) { +// zLen = shape::length(zShapeInfo); +// zRank = shape::rank(zShapeInfo); +// totalThreads = gridDim.x * blockDim.x; +// } +// __syncthreads(); + +// const auto tid = blockIdx.x * blockDim.x + threadIdx.x; + +// Nd4jLong coords[MAX_RANK]; + +// for (uint64_t i = tid; i < zLen; i += totalThreads) { + +// shape::index2coords(i, zShapeInfo, coords); + +// const auto zOffset = shape::getOffset(zShapeInfo, coords); + +// const T *x = reinterpret_cast(reinterpret_cast(pVx)[coords[axis]]); + +// for (uint j = axis; j < zRank - 1; ++j) // shift coords staring from axis position +// coords[j] = coords[j + 1]; + +// const auto xOffset = shape::getOffset(xTadShapeInfo, coords); + +// z[zOffset] = x[xOffset]; +// } +// } + +// /////////////////////////////////////////////////////////////////// +// template +// __host__ static void stackCudaLauncher(const int blocksPerGrid, const int threadsPerBlock, const cudaStream_t *stream, +// void* pVx, const Nd4jLong* xTadShapeInfo, void* vz, const Nd4jLong* zShapeInfo, const int axis) { + +// stackCuda<<>>(pVx, xTadShapeInfo, vz, zShapeInfo, axis); +// } +// BUILD_SINGLE_TEMPLATE(template void stackCudaLauncher, (const int blocksPerGrid, const int threadsPerBlock, const cudaStream_t *stream, void* pVx, const Nd4jLong* xTadShapeInfo, void* vz, const Nd4jLong* zShapeInfo, const int axis), LIBND4J_TYPES); + + +// /////////////////////////////////////////////////////////////////// +// void stack(sd::LaunchContext* context, const std::vector& inArrs, NDArray& output, const int axis) { + +// const int threadsPerBlock = MAX_NUM_THREADS / 2; +// const int blocksPerGrid = (output.lengthOf() + threadsPerBlock - 1) / threadsPerBlock; + +// const int numOfSubArrs = inArrs.size(); + +// std::vector hInBuffers(numOfSubArrs); + +// for(int i = 0; i < numOfSubArrs; ++i) +// hInBuffers[i] = inArrs[i]->getSpecialBuffer(); + +// PointersManager manager(context, "helpers::stack"); + +// void* dInBuffers = manager.replicatePointer(hInBuffers.data(), hInBuffers.size() * sizeof(void*)); + +// for(uint i = 0; i < numOfSubArrs; ++i) +// inArrs[i]->syncToDevice(); +// output.syncToDevice(); + +// BUILD_SINGLE_SELECTOR(output.dataType(), stackCudaLauncher, (blocksPerGrid, threadsPerBlock, context->getCudaStream(), dInBuffers, inArrs[0]->getSpecialShapeInfo(), output.getSpecialBuffer(), output.getSpecialShapeInfo(), axis), LIBND4J_TYPES); + +// manager.synchronize(); + +// for(uint i = 0; i < numOfSubArrs; ++i) +// inArrs[i]->tickReadDevice(); +// output.tickWriteDevice(); +// } } } diff --git a/libnd4j/include/ops/declarable/helpers/cuda/svd.cu b/libnd4j/include/ops/declarable/helpers/cuda/svd.cu index 4d1b18eef..44f924bf0 100644 --- a/libnd4j/include/ops/declarable/helpers/cuda/svd.cu +++ b/libnd4j/include/ops/declarable/helpers/cuda/svd.cu @@ -18,15 +18,15 @@ // @author Yurii Shyrma (iuriish@yahoo.com) // -#include +#include #include #include #include #include -#include -#include +#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { @@ -100,7 +100,7 @@ static void inverseColumnSignCudaLauncher(const int blocksPerGrid, const int thr BUILD_SINGLE_TEMPLATE(template void inverseColumnSignCudaLauncher, (const int blocksPerGrid, const int threadsPerBlock, const int sharedMem, const cudaStream_t* stream, void* vu, const Nd4jLong* uShapeInfo, void* vv, const Nd4jLong* vShapeInfo), FLOAT_TYPES); ////////////////////////////////////////////////////////////////////////// -static void svdQR(nd4j::LaunchContext* context, const NDArray* A, NDArray* S, NDArray* U, NDArray* VT, const bool fullUV, const bool calcUV) { +static void svdQR(sd::LaunchContext* context, const NDArray* A, NDArray* S, NDArray* U, NDArray* VT, const bool fullUV, const bool calcUV) { // since cusa api cusolverDnDgesvd/cusolverDnSgesvd have following constrain on input matrix A: A_rows >= A_columns && A_order = 'f' // we make this function to have deal with 2 valid cases only: @@ -124,19 +124,19 @@ static void svdQR(nd4j::LaunchContext* context, const NDArray* A, NDArray* S, ND if(m < n) throw std::runtime_error("svdQR: due to cuda api input constrains given shape of A array are not valid !"); - if(ShapeUtils::shapeAsString({minDim}) != ShapeUtils::shapeAsString(S)) + if(std::vector({minDim}) != S->getShapeAsVector()) throw std::runtime_error("svdQR: wrong shape of S array !"); if(calcUV) { - if(fullUV && ShapeUtils::shapeAsString({m,m}) != ShapeUtils::shapeAsString(U)) + if(fullUV && std::vector({m,m}) != U->getShapeAsVector()) throw std::runtime_error("svdQR: wrong shape of U array !"); - else if(!fullUV && ShapeUtils::shapeAsString({m,minDim}) != ShapeUtils::shapeAsString(U)) + else if(!fullUV && std::vector({m,minDim}) != U->getShapeAsVector()) throw std::runtime_error("svdQR: wrong shape of U array !"); - if(fullUV && ShapeUtils::shapeAsString({n,n}) != ShapeUtils::shapeAsString(VT)) + if(fullUV && std::vector({n,n}) != VT->getShapeAsVector()) throw std::runtime_error("svdQR: wrong shape of VT array !"); - else if(!fullUV && ShapeUtils::shapeAsString({minDim,n}) != ShapeUtils::shapeAsString(VT)) + else if(!fullUV && std::vector({minDim,n}) != VT->getShapeAsVector()) throw std::runtime_error("svdQR: wrong shape of VT array !"); } @@ -266,7 +266,7 @@ static void svdQR(nd4j::LaunchContext* context, const NDArray* A, NDArray* S, ND } ////////////////////////////////////////////////////////////////////////// -static void svdJcb(nd4j::LaunchContext* context, const NDArray* A, NDArray* S, NDArray* U, NDArray* V, const bool fullUV, const bool calcUV) { +static void svdJcb(sd::LaunchContext* context, const NDArray* A, NDArray* S, NDArray* U, NDArray* V, const bool fullUV, const bool calcUV) { // A [m, n] // S [n] @@ -280,19 +280,19 @@ static void svdJcb(nd4j::LaunchContext* context, const NDArray* A, NDArray* S, N int n = A->sizeAt(1); const int minDim = m < n ? m : n; - if(ShapeUtils::shapeAsString({minDim}) != ShapeUtils::shapeAsString(S)) + if(std::vector({minDim}) != S->getShapeAsVector()) throw std::runtime_error("svdJcb: wrong shape of S array !"); if(calcUV) { - if(fullUV && ShapeUtils::shapeAsString({m,m}) != ShapeUtils::shapeAsString(U)) + if(fullUV && std::vector({m,m}) != U->getShapeAsVector()) throw std::runtime_error("svdJcb: wrong shape of U array !"); - else if(!fullUV && ShapeUtils::shapeAsString({m,minDim}) != ShapeUtils::shapeAsString(U)) + else if(!fullUV && std::vector({m,minDim}) != U->getShapeAsVector()) throw std::runtime_error("svdJcb: wrong shape of U array !"); - if(fullUV && ShapeUtils::shapeAsString({n,n}) != ShapeUtils::shapeAsString(V)) + if(fullUV && std::vector({n,n}) != V->getShapeAsVector()) throw std::runtime_error("svdJcb: wrong shape of V array !"); - else if(!fullUV && ShapeUtils::shapeAsString({n,minDim}) != ShapeUtils::shapeAsString(V)) + else if(!fullUV && std::vector({n,minDim}) != V->getShapeAsVector()) throw std::runtime_error("svdJcb: wrong shape of V array !"); } @@ -455,7 +455,7 @@ static void svdJcb(nd4j::LaunchContext* context, const NDArray* A, NDArray* S, N } ////////////////////////////////////////////////////////////////////////// -static void svdBatched(nd4j::LaunchContext* context, const NDArray* A, NDArray* S, NDArray* U, NDArray* V, const bool fullUV, const bool calcUV) { +static void svdBatched(sd::LaunchContext* context, const NDArray* A, NDArray* S, NDArray* U, NDArray* V, const bool fullUV, const bool calcUV) { // A [..., m, n] // S [..., n] @@ -628,7 +628,7 @@ static void svdBatched(nd4j::LaunchContext* context, const NDArray* A, NDArray* } //////////////////////////////////////////////////////////////////// -void svd(nd4j::LaunchContext* context, const NDArray* x, const std::vector& outArrs, const bool fullUV, const bool calcUV, const int switchNum) { +void svd(sd::LaunchContext* context, const NDArray* x, const std::vector& outArrs, const bool fullUV, const bool calcUV, const int switchNum) { NDArray* S = outArrs[0]; NDArray* U = outArrs[1]; diff --git a/libnd4j/include/ops/declarable/helpers/cuda/toggle_bits.cu b/libnd4j/include/ops/declarable/helpers/cuda/toggle_bits.cu index bc1171efe..2138e1188 100644 --- a/libnd4j/include/ops/declarable/helpers/cuda/toggle_bits.cu +++ b/libnd4j/include/ops/declarable/helpers/cuda/toggle_bits.cu @@ -21,7 +21,7 @@ #include #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { template @@ -34,7 +34,7 @@ namespace nd4j { } BUILD_SINGLE_TEMPLATE(template void toggle_bits__, (NDArray &in, NDArray &out), INTEGER_TYPES); - void __toggle_bits(nd4j::LaunchContext * context, NDArray& in, NDArray& out) { + void __toggle_bits(sd::LaunchContext * context, NDArray& in, NDArray& out) { BUILD_SINGLE_SELECTOR(in.dataType(), toggle_bits__, (in, out), INTEGER_TYPES); } } diff --git a/libnd4j/include/ops/declarable/helpers/cuda/top_k.cu b/libnd4j/include/ops/declarable/helpers/cuda/top_k.cu index 520a6115d..b344f570e 100644 --- a/libnd4j/include/ops/declarable/helpers/cuda/top_k.cu +++ b/libnd4j/include/ops/declarable/helpers/cuda/top_k.cu @@ -19,10 +19,10 @@ // #include -#include -#include +#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { @@ -87,11 +87,11 @@ static void inTopKCudaLauncher(const int blocksPerGrid, const int threadsPerBloc } /////////////////////////////////////////////////////////////////// -int inTopKFunctor(nd4j::LaunchContext * context, const NDArray* predictions, const NDArray* targets, NDArray* output, const uint k) { +int inTopKFunctor(sd::LaunchContext * context, const NDArray* predictions, const NDArray* targets, NDArray* output, const uint k) { PointersManager manager(context, "in_top_k"); - const auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(predictions->getShapeInfo(), {1}); + const auto packX = sd::ConstantTadHelper::getInstance()->tadForDimensions(predictions->getShapeInfo(), {1}); const int threadsPerBlock = MAX_NUM_THREADS; const int blocksPerGrid = static_cast(packX.numberOfTads()); @@ -241,7 +241,7 @@ int inTopKFunctor(nd4j::LaunchContext * context, const NDArray* predictions, con template - static int topKFunctor_(nd4j::LaunchContext * context, const NDArray* input, NDArray* values, NDArray* indices, const uint k, bool needSort) { + static int topKFunctor_(sd::LaunchContext * context, const NDArray* input, NDArray* values, NDArray* indices, const uint k, bool needSort) { auto packX = ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), {input->rankOf() - 1}); auto packI = ConstantTadHelper::getInstance()->tadForDimensions(indices->shapeInfo(), {input->rankOf() - 1}); @@ -266,7 +266,7 @@ int inTopKFunctor(nd4j::LaunchContext * context, const NDArray* predictions, con return Status::OK(); } - int topKFunctor(nd4j::LaunchContext * context, const NDArray* input, NDArray* values, NDArray* indices, const uint k, bool needSort) { + int topKFunctor(sd::LaunchContext * context, const NDArray* input, NDArray* values, NDArray* indices, const uint k, bool needSort) { input->syncToDevice(); BUILD_DOUBLE_SELECTOR(input->dataType(), indices->dataType(), topKFunctor_, (context, input, values, indices, k, needSort), LIBND4J_TYPES, INDEXING_TYPES); diff --git a/libnd4j/include/ops/declarable/helpers/cuda/transforms.cu b/libnd4j/include/ops/declarable/helpers/cuda/transforms.cu index 764b6abbf..b4dcfb2f6 100644 --- a/libnd4j/include/ops/declarable/helpers/cuda/transforms.cu +++ b/libnd4j/include/ops/declarable/helpers/cuda/transforms.cu @@ -24,13 +24,13 @@ #include #include #include -#include +#include #include #include -#include -#include +#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { @@ -71,7 +71,7 @@ __host__ static void invertPermutationCudaLauncher(const int blocksPerGrid, cons } //////////////////////////////////////////////////////////////////////// -void invertPermutation(nd4j::LaunchContext* context, const NDArray& input, NDArray& output) { +void invertPermutation(sd::LaunchContext* context, const NDArray& input, NDArray& output) { const int threadsPerBlock = MAX_NUM_THREADS; const int blocksPerGrid = (input.lengthOf() + threadsPerBlock - 1) / threadsPerBlock; @@ -93,13 +93,13 @@ __global__ static void traceCuda(const void* vx, const Nd4jLong* xShapeInfo, voi auto z = reinterpret_cast(vz); __shared__ T* sharedMem; - __shared__ int xRank, zRank; // xRank = zRank + 2 - __shared__ Nd4jLong xLen, zLen, *coordsMem; + __shared__ int xRank, zRank, *coordsMem; // xRank = zRank + 2 + __shared__ Nd4jLong xLen, zLen; if (threadIdx.x == 0) { extern __shared__ unsigned char shmem[]; sharedMem = reinterpret_cast(shmem); - coordsMem = reinterpret_cast(shmem + blockDim.x * sizeof(T)); + coordsMem = reinterpret_cast(shmem + blockDim.x * sizeof(T)); xRank = shape::rank(xShapeInfo); zRank = shape::rank(zShapeInfo); @@ -109,7 +109,7 @@ __global__ static void traceCuda(const void* vx, const Nd4jLong* xShapeInfo, voi } __syncthreads(); - Nd4jLong* coords = coordsMem + threadIdx.x * xRank; + auto coords = coordsMem + threadIdx.x * xRank; for (uint m = blockIdx.x; m < zLen; m += gridDim.x) { // one block per each element of z, that is per each matrix @@ -153,14 +153,14 @@ static void traceCudaLauncher(const int blocksPerGrid, const int threadsPerBlock /////////////////////////////////////////////////////////////////// -void trace(nd4j::LaunchContext* context, const NDArray& input, NDArray& output) { +void trace(sd::LaunchContext* context, const NDArray& input, NDArray& output) { PointersManager manager(context, "trace"); const uint diagLen = input.sizeAt(-1) < input.sizeAt(-2) ? input.sizeAt(-1) : input.sizeAt(-2); const int threadsPerBlock = MAX_NUM_THREADS / 4; const int blocksPerGrid = (output.lengthOf() + threadsPerBlock - 1) / threadsPerBlock; - const int sharedMem = threadsPerBlock * (sizeof(Nd4jLong) * input.rankOf() + input.sizeOfT()) + 128; + const int sharedMem = threadsPerBlock * (sizeof(int) * input.rankOf() + input.sizeOfT()) + 128; NDArray::prepareSpecialUse({&output}, {&input}); BUILD_SINGLE_SELECTOR(input.dataType(), traceCudaLauncher, (blocksPerGrid, threadsPerBlock, sharedMem, context->getCudaStream(), input.getSpecialBuffer(), input.getSpecialShapeInfo(), output.specialBuffer(), output.specialShapeInfo(), diagLen), LIBND4J_TYPES); @@ -177,13 +177,13 @@ __global__ static void triuBPCuda(const void* vx, const Nd4jLong* xShapeInfo, vo const auto x = reinterpret_cast(vx); // gradO auto z = reinterpret_cast(vz); // gradI - __shared__ int rank, areSameOffsets; // xRank = zRank - __shared__ Nd4jLong len, totalThreads, *sharedMem; // xLen = zLen + __shared__ int rank, areSameOffsets, *sharedMem; // xRank = zRank + __shared__ Nd4jLong len, totalThreads; // xLen = zLen if (threadIdx.x == 0) { extern __shared__ unsigned char shmem[]; - sharedMem = reinterpret_cast(shmem); + sharedMem = reinterpret_cast(shmem); areSameOffsets = shape::haveSameShapeAndStrides(xShapeInfo, zShapeInfo); rank = shape::rank(xShapeInfo); len = shape::length(zShapeInfo); @@ -217,11 +217,11 @@ static void triuBPCudaLauncher(const int blocksPerGrid, const int threadsPerBloc } /////////////////////////////////////////////////////////////////// -void triuBP(nd4j::LaunchContext* context, const NDArray& input, const NDArray& gradO, NDArray& gradI, const int diagonal) { +void triuBP(sd::LaunchContext* context, const NDArray& input, const NDArray& gradO, NDArray& gradI, const int diagonal) { const int threadsPerBlock = MAX_NUM_THREADS / 4; const int blocksPerGrid = (gradO.lengthOf() + threadsPerBlock - 1) / threadsPerBlock; - const int sharedMem = threadsPerBlock * sizeof(Nd4jLong) * gradO.rankOf() + 128; + const int sharedMem = threadsPerBlock * sizeof(int) * gradO.rankOf() + 128; PointersManager manager(context, "triuBP"); @@ -240,13 +240,13 @@ __global__ static void tileBPCuda(const void* vx, const Nd4jLong* xShapeInfo, vo const auto x = reinterpret_cast(vx); // gradO auto z = reinterpret_cast(vz); // gradI - __shared__ int xRank, zRank; // xRank >= zRank - __shared__ Nd4jLong numOfXOffsets, zLen, totalThreads, *sharedMem; // xLen >= zLen + __shared__ int xRank, zRank, *sharedMem; // xRank >= zRank + __shared__ Nd4jLong numOfXOffsets, zLen, totalThreads; // xLen >= zLen if (threadIdx.x == 0) { extern __shared__ unsigned char shmem[]; - sharedMem = reinterpret_cast(shmem); + sharedMem = reinterpret_cast(shmem); xRank = shape::rank(zShapeInfo); zLen = shape::length(zShapeInfo); @@ -283,13 +283,13 @@ static void tileBPCudaLauncher(const int blocksPerGrid, const int threadsPerBloc ////////////////////////////////////////////////////////////////////////// -void tileBP(nd4j::LaunchContext * context, const NDArray& gradO /*input*/, NDArray& gradI /*output*/, const std::vector reps) { +void tileBP(sd::LaunchContext * context, const NDArray& gradO /*input*/, NDArray& gradI /*output*/, const std::vector reps) { - NDArray memBuff('c', gradO.getShapeAsVector(), nd4j::DataType::INT64, context); // empty auxiliary array for storing device memory which will be used in kernel calculations + NDArray memBuff('c', gradO.getShapeAsVector(), sd::DataType::INT64, context); // empty auxiliary array for storing device memory which will be used in kernel calculations const int threadsPerBlock = MAX_NUM_THREADS / 4; const int blocksPerGrid = (gradI.lengthOf() + threadsPerBlock - 1) / threadsPerBlock; - const int sharedMem = threadsPerBlock * sizeof(Nd4jLong) * 2 * gradO.rankOf() + 128; + const int sharedMem = threadsPerBlock * sizeof(int) * 2 * gradO.rankOf() + 128; PointersManager manager(context, "tileBP"); @@ -528,7 +528,7 @@ static void clipByNormBPCudaLauncher(const int blocksPerGrid, const int threadsP BUILD_DOUBLE_TEMPLATE(template void clipByNormBPCudaLauncher, (const int blocksPerGrid, const int threadsPerBlock, const int sharedMem, const cudaStream_t *stream, const void *vx, const Nd4jLong *xShapeInfo, const Nd4jLong* xTadOffsets, const void *vy, const Nd4jLong *yShapeInfo, const Nd4jLong* yTadOffsets, void *vz, const Nd4jLong *zShapeInfo, const Nd4jLong* zTadOffsets, void* vreducBuff, const double clipNormVal), FLOAT_TYPES, FLOAT_TYPES); ////////////////////////////////////////////////////////////////////////// -void clipByNormBP(nd4j::LaunchContext* context, const NDArray& input, const NDArray& gradO, NDArray& gradI /*output*/, const std::vector& dimensions, const NDArray& clipNorm) { +void clipByNormBP(sd::LaunchContext* context, const NDArray& input, const NDArray& gradO, NDArray& gradI /*output*/, const std::vector& dimensions, const NDArray& clipNorm) { PointersManager manager(context, "clipByNormBP"); @@ -564,7 +564,7 @@ void clipByNormBP(nd4j::LaunchContext* context, const NDArray& input, const NDAr } template - static __global__ void swapShuffleKernel(T* input, Nd4jLong* shape, Nd4jLong firstDim, nd4j::graph::RandomGenerator* rng) { + static __global__ void swapShuffleKernel(T* input, Nd4jLong* shape, Nd4jLong firstDim, sd::graph::RandomGenerator* rng) { auto tid = blockIdx.x * blockDim.x; auto step = blockDim.x * gridDim.x; @@ -582,7 +582,7 @@ void clipByNormBP(nd4j::LaunchContext* context, const NDArray& input, const NDAr } } template - static __global__ void fillShuffleKernel(T* input, Nd4jLong* inputShape, T* output, Nd4jLong* outputShape, Nd4jLong firstDim, int* indices, nd4j::graph::RandomGenerator* rng) { + static __global__ void fillShuffleKernel(T* input, Nd4jLong* inputShape, T* output, Nd4jLong* outputShape, Nd4jLong firstDim, int* indices, sd::graph::RandomGenerator* rng) { // PRAGMA_OMP_PARALLEL_FOR_IF((firstDim-1) > Environment::getInstance()->tadThreshold()) auto tid = blockIdx.x * blockDim.x; @@ -602,7 +602,7 @@ void clipByNormBP(nd4j::LaunchContext* context, const NDArray& input, const NDAr } ////////////////////////////////////////////////////////////////////////// template - void randomShuffle_(nd4j::LaunchContext * context, NDArray& input, NDArray& output, nd4j::graph::RandomGenerator& rng, const bool isInplace) { + void randomShuffle_(sd::LaunchContext * context, NDArray& input, NDArray& output, sd::graph::RandomGenerator& rng, const bool isInplace) { // check edge cases first int temp; @@ -616,9 +616,9 @@ void clipByNormBP(nd4j::LaunchContext* context, const NDArray& input, const NDAr else if (input.isVector() || shape::isLikeVector(input.getShapeInfo(), temp)) { // apply Fisher-Yates shuffle - nd4j::graph::RandomGenerator* dRandom = nullptr; - cudaMalloc(&dRandom, sizeof(nd4j::graph::RandomGenerator)); - cudaMemcpy(dRandom, &rng, sizeof(nd4j::graph::RandomGenerator), cudaMemcpyHostToDevice); + sd::graph::RandomGenerator* dRandom = nullptr; + cudaMalloc(&dRandom, sizeof(sd::graph::RandomGenerator)); + cudaMemcpy(dRandom, &rng, sizeof(sd::graph::RandomGenerator), cudaMemcpyHostToDevice); T* inputBuf = reinterpret_cast(input.specialBuffer()); if(isInplace) { swapShuffleKernel<<<128, 256, 1024, *stream>>>(inputBuf, input.specialShapeInfo(), firstDim, dRandom); @@ -679,15 +679,15 @@ void clipByNormBP(nd4j::LaunchContext* context, const NDArray& input, const NDAr } - void randomShuffle(nd4j::LaunchContext * context, NDArray& input, NDArray& output, nd4j::graph::RandomGenerator& rng, const bool isInplace) { + void randomShuffle(sd::LaunchContext * context, NDArray& input, NDArray& output, sd::graph::RandomGenerator& rng, const bool isInplace) { BUILD_SINGLE_SELECTOR(input.dataType(), randomShuffle_, (context, input, output, rng, isInplace), LIBND4J_TYPES); } - BUILD_SINGLE_TEMPLATE(template void randomShuffle_, (nd4j::LaunchContext * context, NDArray& input, NDArray& output, nd4j::graph::RandomGenerator& rng, const bool isInplace), LIBND4J_TYPES); + BUILD_SINGLE_TEMPLATE(template void randomShuffle_, (sd::LaunchContext * context, NDArray& input, NDArray& output, sd::graph::RandomGenerator& rng, const bool isInplace), LIBND4J_TYPES); ////////////////////////////////////////////////////////////////////////// - void eye(nd4j::LaunchContext * context, NDArray& output) { + void eye(sd::LaunchContext * context, NDArray& output) { output.setIdentity(); } @@ -743,7 +743,7 @@ void clipByNormBP(nd4j::LaunchContext* context, const NDArray& input, const NDAr ////////////////////////////////////////////////////////////////////////// template - static void clipByNorm_(nd4j::LaunchContext * context, NDArray& input, NDArray& output, const std::vector& dimensions, NDArray const& clipNormA, const bool isInplace) { + static void clipByNorm_(sd::LaunchContext * context, NDArray& input, NDArray& output, const std::vector& dimensions, NDArray const& clipNormA, const bool isInplace) { const int rank = input.rankOf(); auto norm2 = input.reduceAlongDimension(reduce::Norm2, dimensions); clipNormA.syncToHost(); @@ -762,8 +762,8 @@ void clipByNormBP(nd4j::LaunchContext* context, const NDArray& input, const NDAr std::vector dimsToExclude = ShapeUtils::evalDimsToExclude(rank, dimensions); const Nd4jLong numOfSubArrs = ShapeUtils::getNumOfSubArrs(input.getShapeInfo(), dimsToExclude); - auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(input.getShapeInfo(), dimensions); - //auto packZ = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(output.getShapeInfo(), dimsToExclude); + auto packX = sd::ConstantTadHelper::getInstance()->tadForDimensions(input.getShapeInfo(), dimensions); + //auto packZ = sd::ConstantTadHelper::getInstance()->tadForDimensions(output.getShapeInfo(), dimsToExclude); T* inputBuffer = reinterpret_cast(input.specialBuffer()); T* norm2buf = reinterpret_cast(norm2.specialBuffer()); @@ -785,8 +785,8 @@ void clipByNormBP(nd4j::LaunchContext* context, const NDArray& input, const NDAr std::vector dimsToExclude = ShapeUtils::evalDimsToExclude(rank, dimensions); const Nd4jLong numOfSubArrs = ShapeUtils::getNumOfSubArrs(input.getShapeInfo(), dimsToExclude); - auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(input.getShapeInfo(), dimensions); - auto packZ = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(output.getShapeInfo(), dimensions); + auto packX = sd::ConstantTadHelper::getInstance()->tadForDimensions(input.getShapeInfo(), dimensions); + auto packZ = sd::ConstantTadHelper::getInstance()->tadForDimensions(output.getShapeInfo(), dimensions); T* inputBuffer = reinterpret_cast(input.specialBuffer()); T* norm2buf = reinterpret_cast(norm2.specialBuffer()); T* outputBuffer = reinterpret_cast(output.specialBuffer()); @@ -796,14 +796,14 @@ void clipByNormBP(nd4j::LaunchContext* context, const NDArray& input, const NDAr } } - void clipByNorm(nd4j::LaunchContext * context, NDArray& input, NDArray& output, const std::vector& dimensions, const NDArray& clipNorm, const bool isInplace) { + void clipByNorm(sd::LaunchContext * context, NDArray& input, NDArray& output, const std::vector& dimensions, const NDArray& clipNorm, const bool isInplace) { BUILD_SINGLE_SELECTOR(output.dataType(), clipByNorm_, (context, input, output, dimensions, clipNorm, isInplace), FLOAT_TYPES); } - BUILD_SINGLE_TEMPLATE(template void clipByNorm_, (nd4j::LaunchContext * context, NDArray& input, NDArray& output, const std::vector& dimensions, const NDArray& clipNorm, const bool isInplace), FLOAT_TYPES); + BUILD_SINGLE_TEMPLATE(template void clipByNorm_, (sd::LaunchContext * context, NDArray& input, NDArray& output, const std::vector& dimensions, const NDArray& clipNorm, const bool isInplace), FLOAT_TYPES); template - void clipByGlobalNorm_(nd4j::LaunchContext * context, std::vector const& inputs, double clipNorm, nd4j::memory::Workspace* workspace, std::vector& outputs, bool isInplace) { + void clipByGlobalNorm_(sd::LaunchContext * context, std::vector const& inputs, double clipNorm, sd::memory::Workspace* workspace, std::vector& outputs, bool isInplace) { NDArray globalNorm = NDArrayFactory::create(0, inputs[0]->getContext()); //sqrt(sum([l2norm(t)**2 for t in t_list])) for (auto i = 0; i < inputs.size(); i++) { @@ -812,7 +812,7 @@ void clipByNormBP(nd4j::LaunchContext* context, const NDArray& input, const NDAr globalNorm += l2norm * l2norm; } - globalNorm.applyTransform(transform::Sqrt, globalNorm); // = nd4j::math::nd4j_sqrt(globalNorm); + globalNorm.applyTransform(transform::Sqrt, globalNorm); // = sd::math::nd4j_sqrt(globalNorm); outputs[inputs.size()]->p(0, globalNorm); globalNorm.syncToHost(); const T factor = static_cast(clipNorm) / globalNorm.e(0); @@ -833,16 +833,16 @@ void clipByNormBP(nd4j::LaunchContext* context, const NDArray& input, const NDAr } } - void clipByGlobalNorm(nd4j::LaunchContext * context, std::vector const& inputs, double clipNorm, nd4j::memory::Workspace* workspace, std::vector& outputs, bool isInplace) { + void clipByGlobalNorm(sd::LaunchContext * context, std::vector const& inputs, double clipNorm, sd::memory::Workspace* workspace, std::vector& outputs, bool isInplace) { BUILD_SINGLE_SELECTOR(outputs[0]->dataType(), clipByGlobalNorm_, (context, inputs, clipNorm, workspace, outputs, isInplace), FLOAT_TYPES); } - BUILD_SINGLE_TEMPLATE(template void clipByGlobalNorm_, (nd4j::LaunchContext * context, std::vector const& inputs, double clipNorm, nd4j::memory::Workspace* workspace, std::vector& outputs, bool isInplace), FLOAT_TYPES); + BUILD_SINGLE_TEMPLATE(template void clipByGlobalNorm_, (sd::LaunchContext * context, std::vector const& inputs, double clipNorm, sd::memory::Workspace* workspace, std::vector& outputs, bool isInplace), FLOAT_TYPES); ////////////////////////////////////////////////////////////////////////// template - static void clipByAveraged_(nd4j::LaunchContext * context, NDArray& input, NDArray& output, const std::vector& dimensions, const NDArray& clipNorm, const bool isInplace) { + static void clipByAveraged_(sd::LaunchContext * context, NDArray& input, NDArray& output, const std::vector& dimensions, const NDArray& clipNorm, const bool isInplace) { auto cn = clipNorm.e(0); if (dimensions.size() == 0) { // all-reduce @@ -877,11 +877,11 @@ void clipByNormBP(nd4j::LaunchContext* context, const NDArray& input, const NDAr } } - void clipByAveraged(nd4j::LaunchContext * context, NDArray& input, NDArray& output, const std::vector& dimensions, const NDArray& clipNorm, const bool isInplace) { + void clipByAveraged(sd::LaunchContext * context, NDArray& input, NDArray& output, const std::vector& dimensions, const NDArray& clipNorm, const bool isInplace) { BUILD_SINGLE_SELECTOR(input.dataType(), clipByAveraged_, (context, input, output, dimensions, clipNorm, isInplace), FLOAT_TYPES); } - BUILD_SINGLE_TEMPLATE(template void clipByAveraged_, (nd4j::LaunchContext * context, NDArray& input, NDArray& output, const std::vector& dimensions, const NDArray& clipNorm, const bool isInplace), FLOAT_TYPES); + BUILD_SINGLE_TEMPLATE(template void clipByAveraged_, (sd::LaunchContext * context, NDArray& input, NDArray& output, const std::vector& dimensions, const NDArray& clipNorm, const bool isInplace), FLOAT_TYPES); /* if (d1 > params[1]) @@ -923,7 +923,7 @@ void clipByNormBP(nd4j::LaunchContext* context, const NDArray& input, const NDAr } template - static void clipByValue_(nd4j::LaunchContext * context, NDArray& input, double leftBound, double rightBound, NDArray& output) { + static void clipByValue_(sd::LaunchContext * context, NDArray& input, double leftBound, double rightBound, NDArray& output) { auto stream = context->getCudaStream(); if (!input.isActualOnDeviceSide()) input.syncToDevice(); @@ -932,11 +932,11 @@ void clipByNormBP(nd4j::LaunchContext* context, const NDArray& input, const NDAr NDArray::registerSpecialUse({&output}, {&input}); } - void clipByValue(nd4j::LaunchContext * context, NDArray& input, double leftBound, double rightBound, NDArray& output) { + void clipByValue(sd::LaunchContext * context, NDArray& input, double leftBound, double rightBound, NDArray& output) { BUILD_SINGLE_SELECTOR(input.dataType(), clipByValue_, (context, input, leftBound, rightBound, output), FLOAT_TYPES); } - BUILD_SINGLE_TEMPLATE(template void clipByValue_, (nd4j::LaunchContext * context, NDArray& input, double leftBound, double rightBound, NDArray& output);, FLOAT_TYPES); + BUILD_SINGLE_TEMPLATE(template void clipByValue_, (sd::LaunchContext * context, NDArray& input, double leftBound, double rightBound, NDArray& output);, FLOAT_TYPES); } } diff --git a/libnd4j/include/ops/declarable/helpers/cuda/triangular_solve.cu b/libnd4j/include/ops/declarable/helpers/cuda/triangular_solve.cu index 6f5fe6b8c..e34fd11f8 100644 --- a/libnd4j/include/ops/declarable/helpers/cuda/triangular_solve.cu +++ b/libnd4j/include/ops/declarable/helpers/cuda/triangular_solve.cu @@ -18,13 +18,13 @@ // @author GS // -#include -#include +#include +#include #include -#include +#include #include "../triangular_solve.h" -namespace nd4j { +namespace sd { namespace ops { namespace helpers { /* @@ -138,7 +138,7 @@ namespace nd4j { } template - static int triangularSolveFunctor_(nd4j::LaunchContext * context, NDArray* leftInput, NDArray* rightInput, + static int triangularSolveFunctor_(sd::LaunchContext * context, NDArray* leftInput, NDArray* rightInput, bool lower, bool adjoint, NDArray* output) { NDArray::prepareSpecialUse({output}, {leftInput, rightInput}); auto leftTads = ConstantTadHelper::getInstance()->tadForDimensions(leftInput->getShapeInfo(), {-2, -1}); @@ -161,7 +161,7 @@ namespace nd4j { } - int triangularSolveFunctor(nd4j::LaunchContext * context, NDArray* leftInput, NDArray* rightInput, bool lower, bool adjoint, NDArray* output) { + int triangularSolveFunctor(sd::LaunchContext * context, NDArray* leftInput, NDArray* rightInput, bool lower, bool adjoint, NDArray* output) { BUILD_SINGLE_SELECTOR(leftInput->dataType(), return triangularSolveFunctor_, (context, leftInput, rightInput, lower, adjoint, output), FLOAT_NATIVE); } @@ -207,7 +207,7 @@ namespace nd4j { } template - static void adjointTriangularMatrix_(nd4j::LaunchContext* context, NDArray const* input, bool const lower, + static void adjointTriangularMatrix_(sd::LaunchContext* context, NDArray const* input, bool const lower, NDArray* output) { auto inputTads = ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), {-2, -1}); @@ -225,7 +225,7 @@ namespace nd4j { } } - void adjointMatrix(nd4j::LaunchContext* context, NDArray const* input, bool const lower, NDArray* output) { + void adjointMatrix(sd::LaunchContext* context, NDArray const* input, bool const lower, NDArray* output) { BUILD_SINGLE_SELECTOR(input->dataType(), adjointTriangularMatrix_, (context, input, lower, output), FLOAT_NATIVE); } diff --git a/libnd4j/include/ops/declarable/helpers/cuda/weights.cu b/libnd4j/include/ops/declarable/helpers/cuda/weights.cu index 622732d7d..b543fa1c2 100644 --- a/libnd4j/include/ops/declarable/helpers/cuda/weights.cu +++ b/libnd4j/include/ops/declarable/helpers/cuda/weights.cu @@ -20,7 +20,7 @@ #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { @@ -48,13 +48,13 @@ namespace helpers { Nd4jLong yOffset = shape::getIndexOffset(e, weightsShape); //atomicAdd(); //*reinterpret_cast(outputBuffer) += reinterpret_cast(weightsBuffer)[yOffset]; - nd4j::math::atomics::nd4j_atomicAdd(reinterpret_cast(outputBuffer), reinterpret_cast(weightsBuffer)[yOffset]); //output->p(val, output->e(val) + 1); + sd::math::atomics::nd4j_atomicAdd(reinterpret_cast(outputBuffer), reinterpret_cast(weightsBuffer)[yOffset]); //output->p(val, output->e(val) + 1); // atomicAdd(reinterpret_cast(outputBuffer), reinterpret_cast(weightsBuffer)[yOffset]); //output->p(val, output->e(val) + 1); } else { //*reinterpret_cast(outputBuffer) += int(1); //printf("outputBuffer[0] = %d\n", static_cast(*(reinterpret_cast(outputBuffer)))); - nd4j::math::atomics::nd4j_atomicAdd(reinterpret_cast(outputBuffer), T(1)); //output->p(val, output->e(val) + 1); + sd::math::atomics::nd4j_atomicAdd(reinterpret_cast(outputBuffer), T(1)); //output->p(val, output->e(val) + 1); // atomicAdd(reinterpret_cast(outputBuffer), int(1)); //output->p(val, output->e(val) + 1); // printf("outputBuffer[%ld] = %d\n", zOffset, static_cast(*(reinterpret_cast(outputBuffer) + zOffset))); } @@ -92,7 +92,7 @@ namespace helpers { } template - static void adjustWeights_(nd4j::LaunchContext * context, NDArray* input, NDArray* weights, NDArray* output, int minLength, int maxLength) { + static void adjustWeights_(sd::LaunchContext * context, NDArray* input, NDArray* weights, NDArray* output, int minLength, int maxLength) { // for (int e = 0; e < input->lengthOf(); e++) { // int val = input->e(e); // if (val < maxLength) { @@ -109,11 +109,11 @@ namespace helpers { output->specialBuffer(), output->specialShapeInfo(), minLength, maxLength); } - void adjustWeights(nd4j::LaunchContext * context, NDArray* input, NDArray* weights, NDArray* output, int minLength, int maxLength) { + void adjustWeights(sd::LaunchContext * context, NDArray* input, NDArray* weights, NDArray* output, int minLength, int maxLength) { BUILD_SINGLE_SELECTOR(output->dataType(), adjustWeights_, (context, input, weights, output, minLength, maxLength), GENERIC_NUMERIC_TYPES); } - BUILD_SINGLE_TEMPLATE(template void adjustWeights_, (nd4j::LaunchContext * context, NDArray* input, NDArray* weights, NDArray* output, int minLength, int maxLength), GENERIC_NUMERIC_TYPES); + BUILD_SINGLE_TEMPLATE(template void adjustWeights_, (sd::LaunchContext * context, NDArray* input, NDArray* weights, NDArray* output, int minLength, int maxLength), GENERIC_NUMERIC_TYPES); } } } \ No newline at end of file diff --git a/libnd4j/include/ops/declarable/helpers/cuda/zeta.cu b/libnd4j/include/ops/declarable/helpers/cuda/zeta.cu index ada547ac3..43f0ee8d1 100644 --- a/libnd4j/include/ops/declarable/helpers/cuda/zeta.cu +++ b/libnd4j/include/ops/declarable/helpers/cuda/zeta.cu @@ -20,7 +20,7 @@ #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { @@ -61,7 +61,7 @@ static void zetaCudaLauncher(const int blocksPerGrid, const int threadsPerBlock, zetaCuda<<>>(vx, xShapeInfo, vq, qShapeInfo, vz, zShapeInfo); } -void zeta(nd4j::LaunchContext * context, const NDArray& x, const NDArray& q, NDArray& z) { +void zeta(sd::LaunchContext * context, const NDArray& x, const NDArray& q, NDArray& z) { if(!x.isActualOnDeviceSide()) x.syncToDevice(); if(!q.isActualOnDeviceSide()) q.syncToDevice(); diff --git a/libnd4j/include/ops/declarable/helpers/d_t_s.h b/libnd4j/include/ops/declarable/helpers/d_t_s.h index b27e6d8af..20c11ec24 100644 --- a/libnd4j/include/ops/declarable/helpers/d_t_s.h +++ b/libnd4j/include/ops/declarable/helpers/d_t_s.h @@ -19,13 +19,13 @@ // #include -#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { - void _depthToSpace(nd4j::LaunchContext * context, NDArray *input, NDArray *output, int block_size, bool isNHWC); + void _depthToSpace(sd::LaunchContext * context, NDArray *input, NDArray *output, int block_size, bool isNHWC); } } } \ No newline at end of file diff --git a/libnd4j/include/ops/declarable/helpers/diag.h b/libnd4j/include/ops/declarable/helpers/diag.h index 48c2e1f71..af84eec01 100644 --- a/libnd4j/include/ops/declarable/helpers/diag.h +++ b/libnd4j/include/ops/declarable/helpers/diag.h @@ -19,15 +19,15 @@ // #ifndef __DIAG_H_HELPERS__ #define __DIAG_H_HELPERS__ -#include -#include +#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { - void diagFunctor(nd4j::LaunchContext * context, NDArray const* input, NDArray* output); - void diagPartFunctor(nd4j::LaunchContext * context, NDArray const* input, NDArray* output); + void diagFunctor(sd::LaunchContext * context, NDArray const* input, NDArray* output); + void diagPartFunctor(sd::LaunchContext * context, NDArray const* input, NDArray* output); } } diff --git a/libnd4j/include/ops/declarable/helpers/dilation2d.h b/libnd4j/include/ops/declarable/helpers/dilation2d.h index d6908c0f8..a26fe10f1 100644 --- a/libnd4j/include/ops/declarable/helpers/dilation2d.h +++ b/libnd4j/include/ops/declarable/helpers/dilation2d.h @@ -20,15 +20,15 @@ #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { ////////////////////////////////////////////////////////////////////// -void dilation2d(nd4j::LaunchContext* context, NDArray *input, NDArray *weights, NDArray *output, const int sH, const int sW, const int pH, const int pW, const int dH, const int dW); +void dilation2d(sd::LaunchContext* context, NDArray *input, NDArray *weights, NDArray *output, const int sH, const int sW, const int pH, const int pW, const int dH, const int dW); ////////////////////////////////////////////////////////////////////// -FORCEINLINE Nd4jStatus outputSize(nd4j::LaunchContext * context, const int inSize, const int k, const int d, const int s, bool isSameMode, int *outSize, int *padding_before, int *padding_after) { +FORCEINLINE Nd4jStatus outputSize(sd::LaunchContext * context, const int inSize, const int k, const int d, const int s, bool isSameMode, int *outSize, int *padding_before, int *padding_after) { if (s <= 0) return Status::THROW("Dilation2D: Stride must be > 0"); @@ -38,7 +38,7 @@ FORCEINLINE Nd4jStatus outputSize(nd4j::LaunchContext * context, const int inSiz int kEff = (k - 1) * d + 1; if (isSameMode) { *outSize = (inSize + s - 1) / s; - const int padding_needed = nd4j::math::nd4j_max(0, (*outSize - 1) * s + kEff -inSize); + const int padding_needed = sd::math::nd4j_max(0, (*outSize - 1) * s + kEff -inSize); *padding_before = padding_needed / 2; *padding_after = padding_needed - *padding_before; @@ -54,7 +54,7 @@ FORCEINLINE Nd4jStatus outputSize(nd4j::LaunchContext * context, const int inSiz } ////////////////////////////////////////////////////////////////////// -FORCEINLINE Nd4jStatus dilation_hw(nd4j::LaunchContext * context, Nd4jLong *in, Nd4jLong *wh, std::vector &strides, std::vector &rates, bool isSameMode, int *sH, int *sW, int *pH, int *pW, int *dH, int *dW, int *oH, int *oW) { +FORCEINLINE Nd4jStatus dilation_hw(sd::LaunchContext * context, Nd4jLong *in, Nd4jLong *wh, std::vector &strides, std::vector &rates, bool isSameMode, int *sH, int *sW, int *pH, int *pW, int *dH, int *dW, int *oH, int *oW) { const int iH = shape::sizeAt(in, 1); const int iW = shape::sizeAt(in, 2); const int iC = shape::sizeAt(in, 3); diff --git a/libnd4j/include/ops/declarable/helpers/dropout.h b/libnd4j/include/ops/declarable/helpers/dropout.h index 1825efbd9..052b68f33 100644 --- a/libnd4j/include/ops/declarable/helpers/dropout.h +++ b/libnd4j/include/ops/declarable/helpers/dropout.h @@ -19,11 +19,11 @@ // #ifndef __DROP_OUT_HELPERS__ #define __DROP_OUT_HELPERS__ -#include -#include +#include +#include #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { diff --git a/libnd4j/include/ops/declarable/helpers/dynamic.h b/libnd4j/include/ops/declarable/helpers/dynamic.h index 64656a2bc..29452cb8f 100644 --- a/libnd4j/include/ops/declarable/helpers/dynamic.h +++ b/libnd4j/include/ops/declarable/helpers/dynamic.h @@ -20,20 +20,20 @@ #ifndef __DYNAMIC_H_HELPERS__ #define __DYNAMIC_H_HELPERS__ -#include -#include +#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { - void dynamicPartitionFunctor(nd4j::LaunchContext * context, NDArray const* input, NDArray const* indices, std::vector& outputList); + void dynamicPartitionFunctor(sd::LaunchContext * context, NDArray const* input, NDArray const* indices, std::vector& outputList); - int dynamicStitchFunctor(nd4j::LaunchContext * context, std::vector const& inputs, std::vector const& indices, NDArray* output); + int dynamicStitchFunctor(sd::LaunchContext * context, std::vector const& inputs, std::vector const& indices, NDArray* output); - void dynamicPartitionFunctorBP(nd4j::LaunchContext * context, NDArray const* input, NDArray const* indices, std::vector const& gradientInputList, std::vector& outputList); + void dynamicPartitionFunctorBP(sd::LaunchContext * context, NDArray const* input, NDArray const* indices, std::vector const& gradientInputList, std::vector& outputList); - int dynamicStitchFunctorBP(nd4j::LaunchContext * context, std::vector const& inputs, std::vector const& indices, NDArray const* gradientInput, std::vector& outputList); + int dynamicStitchFunctorBP(sd::LaunchContext * context, std::vector const& inputs, std::vector const& indices, NDArray const* gradientInput, std::vector& outputList); } } } diff --git a/libnd4j/include/ops/declarable/helpers/extract_patches.h b/libnd4j/include/ops/declarable/helpers/extract_patches.h index 2b0d46a90..63d5e94f4 100644 --- a/libnd4j/include/ops/declarable/helpers/extract_patches.h +++ b/libnd4j/include/ops/declarable/helpers/extract_patches.h @@ -19,14 +19,14 @@ // #ifndef __EXTRACT_PATCHES_H_HELPERS__ #define __EXTRACT_PATCHES_H_HELPERS__ -#include -#include +#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { - void extractPatches(nd4j::LaunchContext * context, NDArray* images, NDArray* output, int sizeRow, int sizeCol, int stradeRow, int stradeCol, int rateRow, int rateCol, bool theSame); + void extractPatches(sd::LaunchContext * context, NDArray* images, NDArray* output, int sizeRow, int sizeCol, int stradeRow, int stradeCol, int rateRow, int rateCol, bool theSame); } } diff --git a/libnd4j/include/ops/declarable/helpers/fake_quantization.h b/libnd4j/include/ops/declarable/helpers/fake_quantization.h index cadd8be7c..b5f4dff00 100644 --- a/libnd4j/include/ops/declarable/helpers/fake_quantization.h +++ b/libnd4j/include/ops/declarable/helpers/fake_quantization.h @@ -19,10 +19,10 @@ // #ifndef __FAKE_QUANTIZATION_H_HELPERS__ #define __FAKE_QUANTIZATION_H_HELPERS__ -#include -#include +#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { diff --git a/libnd4j/include/ops/declarable/helpers/flatten.h b/libnd4j/include/ops/declarable/helpers/flatten.h index 0513e45ea..da6253dfa 100644 --- a/libnd4j/include/ops/declarable/helpers/flatten.h +++ b/libnd4j/include/ops/declarable/helpers/flatten.h @@ -22,15 +22,15 @@ #define DEV_TESTS_FLATTEN_H #include -#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { ////////////////////////////////////////////////////////////////////// -void flatten(nd4j::LaunchContext *context, std::vector &inputs, NDArray *output, char order); +void flatten(sd::LaunchContext *context, std::vector &inputs, NDArray *output, char order); ////////////////////////////////////////////////////////////////////// diff --git a/libnd4j/include/ops/declarable/helpers/gammaMathFunc.h b/libnd4j/include/ops/declarable/helpers/gammaMathFunc.h index 2ad540409..2f99f3777 100644 --- a/libnd4j/include/ops/declarable/helpers/gammaMathFunc.h +++ b/libnd4j/include/ops/declarable/helpers/gammaMathFunc.h @@ -23,17 +23,17 @@ #define LIBND4J_GAMMAMATHFUNC_H #include -#include "NDArray.h" +#include "array/NDArray.h" -namespace nd4j { +namespace sd { namespace ops { namespace helpers { // calculate the digamma function for each element for array - void diGamma(nd4j::LaunchContext* context, const NDArray& x, NDArray& z); + void diGamma(sd::LaunchContext* context, const NDArray& x, NDArray& z); // calculate the polygamma function - void polyGamma(nd4j::LaunchContext* context, const NDArray& n, const NDArray& x, NDArray& z); + void polyGamma(sd::LaunchContext* context, const NDArray& n, const NDArray& x, NDArray& z); // calculate the digamma function for one element // implementation is based on serial representation written in terms of the Hurwitz zeta function as polygamma = (-1)^{n+1} * n! * zeta(n+1, x) @@ -47,7 +47,7 @@ namespace helpers { if(x == xInt) // integer return DataTypeUtils::infOrMax(); else - return diGammaScalar(1 - x) - M_PI / nd4j::math::nd4j_tan(M_PI * x); // use reflection formula psi(1-x) = psi(x) + pi*cot(pi*x) + return diGammaScalar(1 - x) - M_PI / sd::math::nd4j_tan(M_PI * x); // use reflection formula psi(1-x) = psi(x) + pi*cot(pi*x) } // positive integer @@ -61,7 +61,7 @@ namespace helpers { // positive half-integer if(x - xInt == 0.5 && xInt <= 20) { // psi(n+0.5) = -Euler_Mascheroni_const - 2*ln(2) + sum_from_k=1_to_n( 2/(2*k-1) ) , for n = 1,2,3,...inf, we use this formula only for n <= 20 to avoid time consuming sum calculation for bigger n - T result = -0.577215664901532 - 2 * nd4j::math::nd4j_log(2); + T result = -0.577215664901532 - 2 * sd::math::nd4j_log(2); for (uint i = 1; i <= xInt; ++i) { result += static_cast(2) / (2*i - 1); } @@ -78,7 +78,7 @@ namespace helpers { // psi(x) = log(x) - 1/(2*x) - 1/(12*x^2) + 1/(120*x^4) - 1/(252*x^6) + 1/(240*x^8) - 5/(660*x^10) + 691/(32760*x^12) - 1/(12*x^14) + ... if(x >= (sizeof(T) > 4 ? 1.e16 : 1.e8)) // if x is too big take into account only log(x) - return nd4j::math::nd4j_log(x); + return sd::math::nd4j_log(x); // coefficients used in truncated asymptotic expansion formula const T coeffs[7] = {-(T)1/12, (T)1/120, -(T)1/252, (T)1/240, -(T)5/660, (T)691/32760, -(T)1/12}; @@ -89,7 +89,7 @@ namespace helpers { for (int i = 6; i >= 0; --i) result = (result + coeffs[i]) * x2Inv; - return result + nd4j::math::nd4j_log(x) - static_cast(0.5) / x; + return result + sd::math::nd4j_log(x) - static_cast(0.5) / x; } } diff --git a/libnd4j/include/ops/declarable/helpers/gather.h b/libnd4j/include/ops/declarable/helpers/gather.h index b2eca1bc0..f38708385 100644 --- a/libnd4j/include/ops/declarable/helpers/gather.h +++ b/libnd4j/include/ops/declarable/helpers/gather.h @@ -21,13 +21,13 @@ #ifndef LIBND4J_GATHER_H #define LIBND4J_GATHER_H -#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { - void gather(nd4j::LaunchContext * context, const NDArray* input, const NDArray* indices, NDArray* output, const std::vector& intArgs); + void gather(sd::LaunchContext * context, const NDArray* input, const NDArray* indices, NDArray* output, const std::vector& intArgs); } } diff --git a/libnd4j/include/ops/declarable/helpers/gradient.h b/libnd4j/include/ops/declarable/helpers/gradient.h index 3850338e1..583396cf3 100644 --- a/libnd4j/include/ops/declarable/helpers/gradient.h +++ b/libnd4j/include/ops/declarable/helpers/gradient.h @@ -19,17 +19,17 @@ // #ifndef __GRADIENT_H_HELPERS__ #define __GRADIENT_H_HELPERS__ -#include -#include +#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { /* * applyGradientDescent: calculate z = x - y * w. * */ - void applyGradientDescent(nd4j::LaunchContext* context, NDArray* input, NDArray* step, double weight, NDArray* output); + void applyGradientDescent(sd::LaunchContext* context, NDArray* input, NDArray* step, double weight, NDArray* output); } } diff --git a/libnd4j/include/ops/declarable/helpers/gru.h b/libnd4j/include/ops/declarable/helpers/gru.h index 87e1786fb..3fecfa71b 100644 --- a/libnd4j/include/ops/declarable/helpers/gru.h +++ b/libnd4j/include/ops/declarable/helpers/gru.h @@ -23,17 +23,17 @@ #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { - void gruCell(nd4j::LaunchContext * context, const NDArray* x, const NDArray* hLast, const NDArray* Wru, const NDArray* Wc, + void gruCell(sd::LaunchContext * context, const NDArray* x, const NDArray* hLast, const NDArray* Wru, const NDArray* Wc, const NDArray* bru, const NDArray* bc, NDArray* r, NDArray* u, NDArray* c, NDArray* h); - void gruTimeLoop(nd4j::LaunchContext * context, const NDArray* x, const NDArray* h0, const NDArray* Wx, const NDArray* Wh, const NDArray* b, NDArray* h); + void gruTimeLoop(sd::LaunchContext * context, const NDArray* x, const NDArray* h0, const NDArray* Wx, const NDArray* Wh, const NDArray* b, NDArray* h); - void gruCellBP(nd4j::LaunchContext* context, const NDArray* x, const NDArray* hLast, const NDArray* W, const NDArray* Wc, const NDArray* b, const NDArray* bc, const NDArray* dLdr, const NDArray* dLdu, const NDArray* dLdc, const NDArray* dLdh, NDArray* dLdx, NDArray* dLdhLast, NDArray* dLdW, NDArray* dLdWc, NDArray* dLdb, NDArray* dLdbc); + void gruCellBP(sd::LaunchContext* context, const NDArray* x, const NDArray* hLast, const NDArray* W, const NDArray* Wc, const NDArray* b, const NDArray* bc, const NDArray* dLdr, const NDArray* dLdu, const NDArray* dLdc, const NDArray* dLdh, NDArray* dLdx, NDArray* dLdhLast, NDArray* dLdW, NDArray* dLdWc, NDArray* dLdb, NDArray* dLdbc); } } diff --git a/libnd4j/include/ops/declarable/helpers/hamming.h b/libnd4j/include/ops/declarable/helpers/hamming.h index 3ca3a6933..6450d7882 100644 --- a/libnd4j/include/ops/declarable/helpers/hamming.h +++ b/libnd4j/include/ops/declarable/helpers/hamming.h @@ -21,7 +21,7 @@ #ifndef SD_HAMMING_H #define SD_HAMMING_H -namespace nd4j { +namespace sd { namespace ops { namespace helpers { void hamming(LaunchContext *context, NDArray &x, NDArray &y, NDArray &output); diff --git a/libnd4j/include/ops/declarable/helpers/hashcode.h b/libnd4j/include/ops/declarable/helpers/hashcode.h index a7fa5bc83..730249d1a 100644 --- a/libnd4j/include/ops/declarable/helpers/hashcode.h +++ b/libnd4j/include/ops/declarable/helpers/hashcode.h @@ -23,7 +23,7 @@ #include "helpers.h" -namespace nd4j { +namespace sd { namespace ops { namespace helpers { template diff --git a/libnd4j/include/ops/declarable/helpers/helpers.h b/libnd4j/include/ops/declarable/helpers/helpers.h index f3aebc7b7..c36387e6e 100644 --- a/libnd4j/include/ops/declarable/helpers/helpers.h +++ b/libnd4j/include/ops/declarable/helpers/helpers.h @@ -21,17 +21,17 @@ #ifndef LIBND4J_OPS_HELPERS_H #define LIBND4J_OPS_HELPERS_H -#include -#include +#include +#include #include #include #include #include -#include +#include #include #include -#include -#include +#include +#include #ifdef __CUDACC__ #include @@ -42,7 +42,7 @@ #include #include -#include +#include #endif // CUDACC diff --git a/libnd4j/include/ops/declarable/helpers/histogram.h b/libnd4j/include/ops/declarable/helpers/histogram.h index b6556599f..b9738ef07 100644 --- a/libnd4j/include/ops/declarable/helpers/histogram.h +++ b/libnd4j/include/ops/declarable/helpers/histogram.h @@ -21,12 +21,12 @@ #ifndef LIBND4J_HISTOGRAM_H #define LIBND4J_HISTOGRAM_H -#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { - void histogramHelper(nd4j::LaunchContext *context, NDArray &input, NDArray &output); + void histogramHelper(sd::LaunchContext *context, NDArray &input, NDArray &output); } } } diff --git a/libnd4j/include/ops/declarable/helpers/histogramFixedWidth.h b/libnd4j/include/ops/declarable/helpers/histogramFixedWidth.h index 17fc84e27..40ba6ffec 100644 --- a/libnd4j/include/ops/declarable/helpers/histogramFixedWidth.h +++ b/libnd4j/include/ops/declarable/helpers/histogramFixedWidth.h @@ -23,11 +23,11 @@ #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { -void histogramFixedWidth(nd4j::LaunchContext * context, const NDArray& input, const NDArray& range, NDArray& output); +void histogramFixedWidth(sd::LaunchContext * context, const NDArray& input, const NDArray& range, NDArray& output); } diff --git a/libnd4j/include/ops/declarable/helpers/im2col.h b/libnd4j/include/ops/declarable/helpers/im2col.h index f484c9bc4..6b61535f9 100644 --- a/libnd4j/include/ops/declarable/helpers/im2col.h +++ b/libnd4j/include/ops/declarable/helpers/im2col.h @@ -23,11 +23,11 @@ #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { - ND4J_EXPORT void im2col(nd4j::LaunchContext & context, const NDArray& im, NDArray& col, const int kH, const int kW, const int sH, const int sW, const int pH, const int pW, const int dH, const int dW, const NDArray& arrZeroPadVal); + ND4J_EXPORT void im2col(sd::LaunchContext & context, const NDArray& im, NDArray& col, const int kH, const int kW, const int sH, const int sW, const int pH, const int pW, const int dH, const int dW, const NDArray& arrZeroPadVal); } } } diff --git a/libnd4j/include/ops/declarable/helpers/image_draw_bounding_boxes.h b/libnd4j/include/ops/declarable/helpers/image_draw_bounding_boxes.h index dd61d9532..758a02e31 100644 --- a/libnd4j/include/ops/declarable/helpers/image_draw_bounding_boxes.h +++ b/libnd4j/include/ops/declarable/helpers/image_draw_bounding_boxes.h @@ -19,14 +19,14 @@ // #ifndef __IMAGE_DRAW_BOUNDING_BOXES_H_HELPERS__ #define __IMAGE_DRAW_BOUNDING_BOXES_H_HELPERS__ -#include -#include +#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { - void drawBoundingBoxesFunctor(nd4j::LaunchContext * context, NDArray* images, NDArray* boxes, NDArray* colors, NDArray* output); + void drawBoundingBoxesFunctor(sd::LaunchContext * context, NDArray* images, NDArray* boxes, NDArray* colors, NDArray* output); } } diff --git a/libnd4j/include/ops/declarable/helpers/image_resize.h b/libnd4j/include/ops/declarable/helpers/image_resize.h index decac3db9..c11e94ed4 100644 --- a/libnd4j/include/ops/declarable/helpers/image_resize.h +++ b/libnd4j/include/ops/declarable/helpers/image_resize.h @@ -20,10 +20,10 @@ // #ifndef __IMAGE_RESIZE_HELPERS__ #define __IMAGE_RESIZE_HELPERS__ -#include -#include +#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { @@ -37,18 +37,18 @@ namespace helpers { kResizeArea }; - int resizeBilinearFunctor(nd4j::LaunchContext * context, NDArray const* image, int const width, int const height, + int resizeBilinearFunctor(sd::LaunchContext * context, NDArray const* image, int const width, int const height, bool const alignCorners, bool const halfPixelCenter, NDArray* output); - int resizeNeighborFunctor(nd4j::LaunchContext * context, NDArray const* image, int const width, int const height, + int resizeNeighborFunctor(sd::LaunchContext * context, NDArray const* image, int const width, int const height, bool const alignCorners, bool const halfPixelCenter, NDArray* output); - int resizeBicubicFunctor(nd4j::LaunchContext * context, NDArray const* image, int const width, int const height, + int resizeBicubicFunctor(sd::LaunchContext * context, NDArray const* image, int const width, int const height, bool preserveAspectRatio, bool antialias, NDArray* output); - int resizeBicubicFunctorA(nd4j::LaunchContext * context, NDArray const* image, int const width, int const height, + int resizeBicubicFunctorA(sd::LaunchContext * context, NDArray const* image, int const width, int const height, bool const alignCorners, bool const halfPixelAlign, NDArray* output); - int resizeAreaFunctor(nd4j::LaunchContext * context, NDArray const* image, int const width, int const height, + int resizeAreaFunctor(sd::LaunchContext * context, NDArray const* image, int const width, int const height, bool const alignCorners, NDArray* output); - int resizeFunctor(nd4j::LaunchContext * context, NDArray const* image, int const width, int const height, + int resizeFunctor(sd::LaunchContext * context, NDArray const* image, int const width, int const height, ImageResizeMethods method, bool preserveAspectRatio, bool antialias, NDArray* output); } } diff --git a/libnd4j/include/ops/declarable/helpers/image_suppression.h b/libnd4j/include/ops/declarable/helpers/image_suppression.h index 4013b72b3..a8d2027b8 100644 --- a/libnd4j/include/ops/declarable/helpers/image_suppression.h +++ b/libnd4j/include/ops/declarable/helpers/image_suppression.h @@ -19,18 +19,18 @@ // #ifndef __IMAGE_SUPPRESSION_H_HELPERS__ #define __IMAGE_SUPPRESSION_H_HELPERS__ -#include -#include +#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { - void nonMaxSuppression(nd4j::LaunchContext * context, NDArray* boxes, NDArray* scales, int maxSize, + void nonMaxSuppression(sd::LaunchContext * context, NDArray* boxes, NDArray* scales, int maxSize, double overlapThreshold, double scoreThreshold, NDArray* output); - Nd4jLong nonMaxSuppressionV3(nd4j::LaunchContext * context, NDArray* boxes, NDArray* scales, int maxSize, + Nd4jLong nonMaxSuppressionV3(sd::LaunchContext * context, NDArray* boxes, NDArray* scales, int maxSize, double overlapThreshold, double scoreThreshold, NDArray* output); - Nd4jLong nonMaxSuppressionGeneric(nd4j::LaunchContext* context, NDArray* boxes, NDArray* scores, int maxSize, + Nd4jLong nonMaxSuppressionGeneric(sd::LaunchContext* context, NDArray* boxes, NDArray* scores, int maxSize, double overlapThreshold, double scoreThreshold, NDArray* output); } diff --git a/libnd4j/include/ops/declarable/helpers/imagesHelpers.h b/libnd4j/include/ops/declarable/helpers/imagesHelpers.h index 0ae8ba072..3a1666c7a 100644 --- a/libnd4j/include/ops/declarable/helpers/imagesHelpers.h +++ b/libnd4j/include/ops/declarable/helpers/imagesHelpers.h @@ -24,25 +24,25 @@ #ifndef LIBND4J_HELPERS_IMAGES_H #define LIBND4J_HELPERS_IMAGES_H -#include -#include -#include +#include +#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { - void transformRgbGrs(nd4j::LaunchContext* context, const NDArray& input, NDArray& output, const int dimC); + void transformRgbGrs(sd::LaunchContext* context, const NDArray& input, NDArray& output, const int dimC); - void transformHsvRgb(nd4j::LaunchContext* context, const NDArray* input, NDArray* output, const int dimC); + void transformHsvRgb(sd::LaunchContext* context, const NDArray* input, NDArray* output, const int dimC); - void transformRgbHsv(nd4j::LaunchContext* context, const NDArray* input, NDArray* output, const int dimC); - void transformYuvRgb(nd4j::LaunchContext* context, const NDArray& input, NDArray& output, const int dimC); - void transformRgbYuv(nd4j::LaunchContext* context, const NDArray& input, NDArray& output, const int dimC); + void transformRgbHsv(sd::LaunchContext* context, const NDArray* input, NDArray* output, const int dimC); + void transformYuvRgb(sd::LaunchContext* context, const NDArray& input, NDArray& output, const int dimC); + void transformRgbYuv(sd::LaunchContext* context, const NDArray& input, NDArray& output, const int dimC); - void transformYiqRgb(nd4j::LaunchContext* context, const NDArray* input, NDArray* output, const int dimC); + void transformYiqRgb(sd::LaunchContext* context, const NDArray* input, NDArray* output, const int dimC); - void transformRgbYiq(nd4j::LaunchContext* context, const NDArray* input, NDArray* output, const int dimC); + void transformRgbYiq(sd::LaunchContext* context, const NDArray* input, NDArray* output, const int dimC); } } } diff --git a/libnd4j/include/ops/declarable/helpers/impl/choose.cpp b/libnd4j/include/ops/declarable/helpers/impl/choose.cpp index a75298af6..2f574a52e 100644 --- a/libnd4j/include/ops/declarable/helpers/impl/choose.cpp +++ b/libnd4j/include/ops/declarable/helpers/impl/choose.cpp @@ -19,21 +19,21 @@ // #include -#include +#include #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { template - static nd4j::NDArray* processCondition_(int mode,nd4j::NDArray *arg, nd4j::NDArray *comp, nd4j::NDArray& compScalar); + static sd::NDArray* processCondition_(int mode,sd::NDArray *arg, sd::NDArray *comp, sd::NDArray& compScalar); template static T processElementCondition(int mode,T d1,T d2); template - nd4j::NDArray* processCondition_(int mode,nd4j::NDArray *arg, nd4j::NDArray *comp, nd4j::NDArray *output, nd4j::NDArray *numResult, nd4j::NDArray& compScalar) { + sd::NDArray* processCondition_(int mode,sd::NDArray *arg, sd::NDArray *comp, sd::NDArray *output, sd::NDArray *numResult, sd::NDArray& compScalar) { //Convert to straight ndarray based on input @@ -42,8 +42,8 @@ namespace helpers { if (comp->isScalar()) { //Other input for compare could be an ndarray or a secondary scalar //for comparison -// nd4j::NDArray arg1 = *arg; -// nd4j::NDArray comp1 = *comp; +// sd::NDArray arg1 = *arg; +// sd::NDArray comp1 = *comp; for (Nd4jLong i = 0; i < arg->lengthOf(); i++) { T result2 = processElementCondition(mode, arg->e(i), comp->e(0)); if(result2 > static_cast(0)) { @@ -56,7 +56,7 @@ namespace helpers { // REQUIRE_TRUE(comp.isSameShape(arg)); //Other input for compare could be an ndarray or a secondary scalar //for comparison - nd4j::NDArray arg1 = *arg; + sd::NDArray arg1 = *arg; for (Nd4jLong i = 0; i < arg->lengthOf(); i++) { T result2 = processElementCondition(mode, arg->e(i), comp->e(i)); if(result2 > static_cast(0)) { @@ -69,7 +69,7 @@ namespace helpers { } else { - // nd4j::NDArray arg1 = *arg; + // sd::NDArray arg1 = *arg; //Other input for compare could be an ndarray or a secondary scalar //for comparison for (Nd4jLong i = 0; i < arg->lengthOf(); i++) { @@ -88,7 +88,7 @@ namespace helpers { return output; } - nd4j::NDArray* processCondition(nd4j::LaunchContext * context, int mode,nd4j::NDArray *arg, nd4j::NDArray *comp, nd4j::NDArray *output, nd4j::NDArray *numResult, nd4j::NDArray& compScalar) { + sd::NDArray* processCondition(sd::LaunchContext * context, int mode,sd::NDArray *arg, sd::NDArray *comp, sd::NDArray *output, sd::NDArray *numResult, sd::NDArray& compScalar) { arg->syncToHost(); if (comp != nullptr) @@ -118,7 +118,7 @@ namespace helpers { compScalar.syncToDevice(); } - BUILD_SINGLE_TEMPLATE(template NDArray* processCondition_, (int mode,nd4j::NDArray *arg, nd4j::NDArray *comp, nd4j::NDArray *output, nd4j::NDArray *numResult, nd4j::NDArray& compScalar), FLOAT_TYPES); + BUILD_SINGLE_TEMPLATE(template NDArray* processCondition_, (int mode,sd::NDArray *arg, sd::NDArray *comp, sd::NDArray *output, sd::NDArray *numResult, sd::NDArray& compScalar), FLOAT_TYPES); template T processElementCondition(int mode,T d1,T d2) { @@ -129,7 +129,7 @@ namespace helpers { } - void chooseFunctorArray(nd4j::LaunchContext * context, NDArray* arg, NDArray* comp, int mode, NDArray* result, NDArray* numResults) { + void chooseFunctorArray(sd::LaunchContext * context, NDArray* arg, NDArray* comp, int mode, NDArray* result, NDArray* numResults) { if(arg->isScalar() || comp->isScalar()) { if(arg->isScalar()) { processCondition(context, mode,comp,nullptr,result,numResults, *arg); @@ -144,7 +144,7 @@ namespace helpers { } } - void chooseFunctorScalar(nd4j::LaunchContext * context, NDArray* arg, double scalar, int mode, NDArray* result, NDArray* numResults) { + void chooseFunctorScalar(sd::LaunchContext * context, NDArray* arg, double scalar, int mode, NDArray* result, NDArray* numResults) { auto scalarA = NDArrayFactory::create(scalar); processCondition(context, mode, arg, nullptr,result, numResults, scalarA); } diff --git a/libnd4j/include/ops/declarable/helpers/impl/knn_mindistance.cpp b/libnd4j/include/ops/declarable/helpers/impl/knn_mindistance.cpp index 71711832d..d3880c730 100644 --- a/libnd4j/include/ops/declarable/helpers/impl/knn_mindistance.cpp +++ b/libnd4j/include/ops/declarable/helpers/impl/knn_mindistance.cpp @@ -20,7 +20,7 @@ #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { template @@ -41,13 +41,13 @@ namespace nd4j { T h = high[e]; if (!(l <= p || h <= p)) { if (p < l) - res += nd4j::math::nd4j_pow((p - o), po); + res += sd::math::nd4j_pow((p - o), po); else - res += nd4j::math::nd4j_pow((p - h), po); + res += sd::math::nd4j_pow((p - h), po); } } - output[0] = nd4j::math::nd4j_pow(res, (T) 0.5f); + output[0] = sd::math::nd4j_pow(res, (T) 0.5f); } void knn_mindistance(const NDArray &input, const NDArray &lowest, const NDArray &highest, NDArray &output) { diff --git a/libnd4j/include/ops/declarable/helpers/impl/listdiff.cpp b/libnd4j/include/ops/declarable/helpers/impl/listdiff.cpp index c840f6960..6d937dc0f 100644 --- a/libnd4j/include/ops/declarable/helpers/impl/listdiff.cpp +++ b/libnd4j/include/ops/declarable/helpers/impl/listdiff.cpp @@ -22,7 +22,7 @@ #include //#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { template @@ -39,7 +39,7 @@ namespace helpers { return saved; } - Nd4jLong listDiffCount(nd4j::LaunchContext * context, NDArray* values, NDArray* keep) { + Nd4jLong listDiffCount(sd::LaunchContext * context, NDArray* values, NDArray* keep) { auto xType = values->dataType(); NDArray::preparePrimaryUse({},{values, keep}); @@ -71,7 +71,7 @@ namespace helpers { if (saved.size() == 0) { -// if (nd4j::ops::conditionHelper(__FILE__, __LINE__, false, 0, "ListDiff: search returned no results") != 0) +// if (sd::ops::conditionHelper(__FILE__, __LINE__, false, 0, "ListDiff: search returned no results") != 0) nd4j_printf("ListDiff: search returned no results", ""); throw std::invalid_argument("Op validation failed"); } else { @@ -95,7 +95,7 @@ namespace helpers { return Status::OK(); } - int listDiffFunctor(nd4j::LaunchContext * context, NDArray* values, NDArray* keep, NDArray* output1, NDArray* output2) { + int listDiffFunctor(sd::LaunchContext * context, NDArray* values, NDArray* keep, NDArray* output1, NDArray* output2) { auto xType = values->dataType(); NDArray::preparePrimaryUse({output1, output2}, {values, keep}); diff --git a/libnd4j/include/ops/declarable/helpers/impl/lstm.cpp b/libnd4j/include/ops/declarable/helpers/impl/lstm.cpp index d80b0d323..4ab585e26 100644 --- a/libnd4j/include/ops/declarable/helpers/impl/lstm.cpp +++ b/libnd4j/include/ops/declarable/helpers/impl/lstm.cpp @@ -27,15 +27,15 @@ #include -#include +#include #include #include #include #include #include -#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { @@ -102,7 +102,7 @@ namespace nd4j { ////////////////////////////////////////////////////////////////////////// - void lstmTimeLoop(nd4j::LaunchContext * context, const NDArray* x, const NDArray* h0, const NDArray* c0, const NDArray* Wx, const NDArray* Wh, const NDArray* Wc, const NDArray* Wp, const NDArray* b, + void lstmTimeLoop(sd::LaunchContext * context, const NDArray* x, const NDArray* h0, const NDArray* c0, const NDArray* Wx, const NDArray* Wh, const NDArray* Wc, const NDArray* Wp, const NDArray* b, NDArray* h, NDArray* c, const std::vector& params) { // x input [time x bS x nIn] diff --git a/libnd4j/include/ops/declarable/helpers/impl/lstmLayer.cpp b/libnd4j/include/ops/declarable/helpers/impl/lstmLayer.cpp index 2b65d0c8e..435a3e32d 100644 --- a/libnd4j/include/ops/declarable/helpers/impl/lstmLayer.cpp +++ b/libnd4j/include/ops/declarable/helpers/impl/lstmLayer.cpp @@ -34,9 +34,9 @@ // #include // #include // #include -// #include +// #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { @@ -232,13 +232,13 @@ void lstmLayerTimeLoop(const NDArray* x, const NDArray* Wx, const NDArray* Wr, if(!h) { // seqLen and h are absent lstmLayerCell(xSet->at(0), Wx, Wr, b, h0, c0, Wp, params, ht, ct); // first time step - for (int t = 1; t < sL; ++t) + for (Nd4jLong t = 1; t < sL; ++t) lstmLayerCell(xSet->at(t), Wx, Wr, b, ht, ct, Wp, params, ht, ct); // rest time steps } else { // seqLen is absent and h is present lstmLayerCell(xSet->at(0), Wx, Wr, b, h0, c0, Wp, params, hSet->at(0), ct); // first time step - for (int t = 1; t < sL; ++t) + for (Nd4jLong t = 1; t < sL; ++t) lstmLayerCell(xSet->at(t), Wx, Wr, b, hSet->at(t - 1), ct, Wp, params, hSet->at(t), ct); // rest time steps if(hL) @@ -249,7 +249,7 @@ void lstmLayerTimeLoop(const NDArray* x, const NDArray* Wx, const NDArray* Wr, if(!h) { // seqLen is present and h is absent - for (int e = 0; e < bS; ++e) { + for (Nd4jLong e = 0; e < bS; ++e) { const int limit = seqLen->e(e); @@ -272,7 +272,7 @@ void lstmLayerTimeLoop(const NDArray* x, const NDArray* Wx, const NDArray* Wr, } else { // seqLen and h are present - for (int e = 0; e < bS; ++e) { + for (Nd4jLong e = 0; e < bS; ++e) { int limit = seqLen->e(e); @@ -312,13 +312,13 @@ void lstmLayerTimeLoop(const NDArray* x, const NDArray* Wx, const NDArray* Wr, if(!h) { // seqLen and h are absent lstmLayerCell(xSet->at(sL - 1), Wx, Wr, b, h0, c0, Wp, params, ht, ct); // first time step - for (int t = sL - 2; t >= 0; --t) + for (Nd4jLong t = sL - 2; t >= 0; --t) lstmLayerCell(xSet->at(t), Wx, Wr, b, ht, ct, Wp, params, ht, ct); // rest time steps } else { // seqLen is absent and h is present lstmLayerCell(xSet->at(sL - 1), Wx, Wr, b, h0, c0, Wp, params, hSet->at(sL - 1), ct); // first time step - for (int t = sL - 2; t >= 0; --t) + for (Nd4jLong t = sL - 2; t >= 0; --t) lstmLayerCell(xSet->at(t), Wx, Wr, b, hSet->at(t + 1), ct, Wp, params, hSet->at(t), ct); // rest time steps if(hL) @@ -329,7 +329,7 @@ void lstmLayerTimeLoop(const NDArray* x, const NDArray* Wx, const NDArray* Wr, if(!h) { // h is absent and seqLen is present - for (int e = 0; e < bS; ++e) { + for (Nd4jLong e = 0; e < bS; ++e) { const int limit = seqLen->e(e); @@ -344,7 +344,7 @@ void lstmLayerTimeLoop(const NDArray* x, const NDArray* Wx, const NDArray* Wr, auto ind = getBatchTimeTotalIndex(dataFormat, sL, bS, sL - 1, e); lstmLayerCell(xSet->at(ind), Wx, Wr, b, h0Set->at(e), c0Set->at(e), Wp, params, htSet->at(e), ctSet->at(e)); // first time step - for (int t = sL - 2; t >= sL - limit; --t) { + for (Nd4jLong t = sL - 2; t >= sL - limit; --t) { ind = getBatchTimeTotalIndex(dataFormat, sL, bS, t, e); lstmLayerCell(xSet->at(ind), Wx, Wr, b, htSet->at(e), ctSet->at(e), Wp, params, htSet->at(e), ctSet->at(e)); // rest time steps } @@ -352,7 +352,7 @@ void lstmLayerTimeLoop(const NDArray* x, const NDArray* Wx, const NDArray* Wr, } else { // seqLen and h are present - for (int e = 0; e < bS; ++e) { + for (Nd4jLong e = 0; e < bS; ++e) { int limit = seqLen->e(e); @@ -371,7 +371,7 @@ void lstmLayerTimeLoop(const NDArray* x, const NDArray* Wx, const NDArray* Wr, auto indPrev = getBatchTimeTotalIndex(dataFormat, sL, bS, sL - 1, e); lstmLayerCell(xSet->at(indPrev), Wx, Wr, b, h0Set->at(e), c0Set->at(e), Wp, params, hSet->at(indPrev), ctSet->at(e)); // first time step - for (int t = sL - 2; t >= sL - limit; --t) { + for (Nd4jLong t = sL - 2; t >= sL - limit; --t) { auto indCurr = getBatchTimeTotalIndex(dataFormat, sL, bS, t, e); lstmLayerCell(xSet->at(indCurr), Wx, Wr, b, hSet->at(indPrev), ctSet->at(e), Wp, params, hSet->at(indCurr), ctSet->at(e)); // rest time steps indPrev = indCurr; @@ -388,7 +388,7 @@ void lstmLayerTimeLoop(const NDArray* x, const NDArray* Wx, const NDArray* Wr, if(!h) { // h is absent and seqLen is present - for (int e = 0; e < bS; ++e) { + for (Nd4jLong e = 0; e < bS; ++e) { const int limit = seqLen->e(e); @@ -411,7 +411,7 @@ void lstmLayerTimeLoop(const NDArray* x, const NDArray* Wx, const NDArray* Wr, } else { // seqLen and h are present - for (int e = 0; e < bS; ++e) { + for (Nd4jLong e = 0; e < bS; ++e) { int limit = seqLen->e(e); diff --git a/libnd4j/include/ops/declarable/helpers/impl/multiUnique.cpp b/libnd4j/include/ops/declarable/helpers/impl/multiUnique.cpp index cfa2fe53a..ff9ae3144 100644 --- a/libnd4j/include/ops/declarable/helpers/impl/multiUnique.cpp +++ b/libnd4j/include/ops/declarable/helpers/impl/multiUnique.cpp @@ -21,18 +21,18 @@ #include #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// - bool multiUnique(std::vector const& inputList, nd4j::memory::Workspace *workspace) { + bool multiUnique(std::vector const& inputList, sd::memory::Workspace *workspace) { Nd4jLong length = 0; std::vector reshaped(inputList.size()); int pos = 0; Nd4jLong axis = 0; Context cContext(1); for (auto array: inputList) { - if (array->dataType() != nd4j::DataType::INT32) + if (array->dataType() != sd::DataType::INT32) throw std::runtime_error("multiUnique: this op support INT32 data type only."); reshaped[pos] = array->reshape(array->ordering(), {-1}); @@ -41,25 +41,24 @@ namespace helpers { length += array->lengthOf(); pos++; } - NDArray arrayFull('c', {length}, nd4j::DataType::INT32); + NDArray arrayFull('c', {length}, sd::DataType::INT32); cContext.setOutputArray(0, &arrayFull); cContext.setIArguments(&axis, 1); - nd4j::ops::concat opConcat; + sd::ops::concat opConcat; auto cResult = opConcat.execute(&cContext); if (Status::OK() != cResult) throw std::runtime_error("multiUnique: cannot execute concat op properly."); - nd4j::ops::unique opUnique; + sd::ops::unique opUnique; auto uResult = opUnique.evaluate({&arrayFull}); - if (Status::OK() != uResult->status()) + if (Status::OK() != uResult.status()) throw std::runtime_error("multiUnique: cannot execute unique op properly."); - auto uniqueVals = uResult->at(0); + auto uniqueVals = uResult.at(0); bool res = uniqueVals->lengthOf() == arrayFull.lengthOf(); - delete uResult; return res; } diff --git a/libnd4j/include/ops/declarable/helpers/impl/rnn.cpp b/libnd4j/include/ops/declarable/helpers/impl/rnn.cpp index 3c65f740d..f910f07ed 100644 --- a/libnd4j/include/ops/declarable/helpers/impl/rnn.cpp +++ b/libnd4j/include/ops/declarable/helpers/impl/rnn.cpp @@ -24,13 +24,13 @@ #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { ////////////////////////////////////////////////////////////////////////// -void rnnCell(nd4j::LaunchContext * context, const NDArray* xt, const NDArray* Wx, const NDArray* Wh, const NDArray* b, const NDArray* hPrev, NDArray* ht) { +void rnnCell(sd::LaunchContext * context, const NDArray* xt, const NDArray* Wx, const NDArray* Wh, const NDArray* b, const NDArray* hPrev, NDArray* ht) { // xt input [bS x iS] // Wx input-to-hidden weights, [iS x nU] @@ -46,7 +46,7 @@ void rnnCell(nd4j::LaunchContext * context, const NDArray* xt, const NDArray* Wx } ////////////////////////////////////////////////////////////////////////// -void rnnTimeLoop(nd4j::LaunchContext * context, const NDArray* x, const NDArray* Wx, const NDArray* Wh, const NDArray* b, const NDArray* h0, const NDArray* maxTimeStep, NDArray* h, NDArray* hFinal) { +void rnnTimeLoop(sd::LaunchContext * context, const NDArray* x, const NDArray* Wx, const NDArray* Wh, const NDArray* b, const NDArray* h0, const NDArray* maxTimeStep, NDArray* h, NDArray* hFinal) { // x input [time x bS x iS] // Wx input-to-hidden weights, [iS x nU] diff --git a/libnd4j/include/ops/declarable/helpers/impl/sparse_to_dense.cpp b/libnd4j/include/ops/declarable/helpers/impl/sparse_to_dense.cpp index e21499314..36044907e 100644 --- a/libnd4j/include/ops/declarable/helpers/impl/sparse_to_dense.cpp +++ b/libnd4j/include/ops/declarable/helpers/impl/sparse_to_dense.cpp @@ -23,7 +23,7 @@ #include #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { template @@ -32,7 +32,7 @@ namespace nd4j { auto indices = reinterpret_cast(vindices); auto output = reinterpret_cast(voutput); - Nd4jLong coords[MAX_RANK]; + int coords[MAX_RANK]; uint64_t pos = 0; for (uint64_t e = 0L; e < length; e++) { // indices come in blocks @@ -80,7 +80,7 @@ namespace nd4j { valueCoords[e] = indices.e(e); // write results individually - for (uint64_t e = 0; e < numElements; e++) { + for (Nd4jLong e = 0; e < numElements; e++) { auto vIndex = shape::coords2index(output.shapeInfo(), valueCoords.data()); auto cLength = 0L; std::string str; diff --git a/libnd4j/include/ops/declarable/helpers/impl/unique.cpp b/libnd4j/include/ops/declarable/helpers/impl/unique.cpp index 3bcdea865..c67b713c2 100644 --- a/libnd4j/include/ops/declarable/helpers/impl/unique.cpp +++ b/libnd4j/include/ops/declarable/helpers/impl/unique.cpp @@ -19,10 +19,11 @@ // #include -#include +#include #include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { @@ -32,7 +33,7 @@ namespace helpers { std::vector values; - for (int e = 0; e < input->lengthOf(); e++) { + for (Nd4jLong e = 0; e < input->lengthOf(); e++) { T v = input->e(e); if (std::find(values.begin(), values.end(), v) == values.end()) { values.push_back(v); @@ -42,21 +43,20 @@ namespace helpers { return count; } - Nd4jLong uniqueCount(nd4j::LaunchContext * context, NDArray* input) { + Nd4jLong uniqueCount(sd::LaunchContext * context, NDArray* input) { BUILD_SINGLE_SELECTOR(input->dataType(), return uniqueCount_, (input), LIBND4J_TYPES); } BUILD_SINGLE_TEMPLATE(template Nd4jLong uniqueCount_, (NDArray* input), LIBND4J_TYPES); - template static Nd4jStatus uniqueFunctor_(NDArray* input, NDArray* values, NDArray* indices, NDArray* counts) { std::vector valuesVector; - std::map indicesMap; - std::map countsMap; + MAP_IMPL indicesMap; + MAP_IMPL countsMap; - for (int e = 0; e < input->lengthOf(); e++) { + for (Nd4jLong e = 0; e < input->lengthOf(); e++) { T v = input->e(e); if (std::find(valuesVector.begin(), valuesVector.end(), v) == valuesVector.end()) { valuesVector.push_back(v); @@ -77,7 +77,7 @@ namespace helpers { }; samediff::Threads::parallel_for(func, 0, values->lengthOf()); - for (int e = 0; e < indices->lengthOf(); e++) { + for (Nd4jLong e = 0; e < indices->lengthOf(); e++) { auto posI = std::find(valuesVector.begin(), valuesVector.end(), input->e(e)); auto dist = std::distance(valuesVector.begin(), posI); indices->p(e, Nd4jLong(dist));//indicesMap[(*input)(e)]; @@ -86,7 +86,7 @@ namespace helpers { return Status::OK(); } - Nd4jStatus uniqueFunctor(nd4j::LaunchContext * context, NDArray* input, NDArray* values, NDArray* indices, NDArray* counts) { + Nd4jStatus uniqueFunctor(sd::LaunchContext * context, NDArray* input, NDArray* values, NDArray* indices, NDArray* counts) { input->syncToHost(); values->syncToHost(); indices->syncToHost(); diff --git a/libnd4j/include/ops/declarable/helpers/impl/where.cpp b/libnd4j/include/ops/declarable/helpers/impl/where.cpp index 5ca54ced9..df8fd1074 100644 --- a/libnd4j/include/ops/declarable/helpers/impl/where.cpp +++ b/libnd4j/include/ops/declarable/helpers/impl/where.cpp @@ -21,7 +21,7 @@ #include #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { template @@ -29,11 +29,14 @@ namespace nd4j { NDArrayList list(0, true); int cnt = 0; - Nd4jLong idx[MAX_RANK]; - for (int e = 0; e < condition.lengthOf(); e++) { - shape::index2coords(e, condition.getShapeInfo(), idx); + int idx[MAX_RANK]; + + for (Nd4jLong e = 0; e < condition.lengthOf(); e++) { + + shape::index2coordsCPU(0, e, condition.getShapeInfo(), idx); auto offset = shape::getOffset(condition.getShapeInfo(), idx); + if (condition.e(offset)) { auto array = NDArrayFactory::create_('c', {1, condition.rankOf()}, output.dataType(), output.getContext()); for (int f = 0; f < condition.rankOf(); f++) @@ -49,7 +52,7 @@ namespace nd4j { } BUILD_SINGLE_TEMPLATE(template void __where,(NDArray &condition, NDArray& output, memory::Workspace *workspace), LIBND4J_TYPES); - void _where(nd4j::LaunchContext * context, NDArray &condition, NDArray& output, memory::Workspace *workspace) { + void _where(sd::LaunchContext * context, NDArray &condition, NDArray& output, memory::Workspace *workspace) { condition.syncToHost(); BUILD_SINGLE_SELECTOR(output.dataType(), __where, (condition, output, workspace), LIBND4J_TYPES); output.syncToDevice(); diff --git a/libnd4j/include/ops/declarable/helpers/ismax.h b/libnd4j/include/ops/declarable/helpers/ismax.h index 831cd592f..6052b3624 100644 --- a/libnd4j/include/ops/declarable/helpers/ismax.h +++ b/libnd4j/include/ops/declarable/helpers/ismax.h @@ -24,11 +24,11 @@ #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { - void ismax(nd4j::LaunchContext * context, const NDArray* input, NDArray* output, const std::vector& dimensions); + void ismax(sd::LaunchContext * context, const NDArray* input, NDArray* output, const std::vector& dimensions); } } diff --git a/libnd4j/include/ops/declarable/helpers/knn.h b/libnd4j/include/ops/declarable/helpers/knn.h index a2de9c71c..3a3494a12 100644 --- a/libnd4j/include/ops/declarable/helpers/knn.h +++ b/libnd4j/include/ops/declarable/helpers/knn.h @@ -23,7 +23,7 @@ #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { void knn_mindistance(const NDArray &input, const NDArray &lowest, const NDArray &highest, NDArray &output); diff --git a/libnd4j/include/ops/declarable/helpers/legacy_helpers.h b/libnd4j/include/ops/declarable/helpers/legacy_helpers.h index dfe338864..e3191425d 100644 --- a/libnd4j/include/ops/declarable/helpers/legacy_helpers.h +++ b/libnd4j/include/ops/declarable/helpers/legacy_helpers.h @@ -19,9 +19,9 @@ // #ifndef __H_LEGACY_HELPERS__ #define __H_LEGACY_HELPERS__ -#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { /* @@ -43,27 +43,27 @@ namespace helpers { FORCEINLINE void sigmoidDerivative(NDArray* theFirst, NDArray* theSecond, NDArray* theOutput); FORCEINLINE void hardSigmoidDerivative(NDArray* theFirst, NDArray* theSecond, NDArray* theOutput); */ - void reluDerivative(nd4j::LaunchContext * context, NDArray* theFirst, NDArray* theSecond); - void reluDerivative(nd4j::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput); - void relu6Derivative(nd4j::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput); - void leakyReluDerivative(nd4j::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput, const float alpha); - void eluDerivative(nd4j::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput, const float alpha); - void seluDerivative(nd4j::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput); - void cubeDerivative(nd4j::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput); - void reduceNorm1(nd4j::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput); - void sigmCrossEntropy(nd4j::LaunchContext * context, NDArray* logits, NDArray* lablels, NDArray* theOutput); - void sigmCrossEntropyGrad(nd4j::LaunchContext * context, NDArray* logits, NDArray* lablels, NDArray* theOutput); - void tanhDerivative(nd4j::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput); - void hardTanhDerivative(nd4j::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput); - void rationalTanhDerivative(nd4j::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput); - void rectifiedTanhDerivative(nd4j::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput); - void softSignDerivative(nd4j::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput); - void softPlusDerivative(nd4j::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput); - void sigmoidDerivative(nd4j::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput); - void hardSigmoidDerivative(nd4j::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput); - void logSumExp(nd4j::LaunchContext * context, NDArray* input, NDArray* axis, NDArray* output); - void logSumExp(nd4j::LaunchContext * context, NDArray* input, NDArray* subtrah, NDArray* axis, NDArray* output); - void weightedCrossEntropyWithLogitsFunctor(nd4j::LaunchContext * context, NDArray const* targets, NDArray const* input, NDArray const* weights, NDArray* output); + void reluDerivative(sd::LaunchContext * context, NDArray* theFirst, NDArray* theSecond); + void reluDerivative(sd::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput); + void relu6Derivative(sd::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput); + void leakyReluDerivative(sd::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput, const float alpha); + void eluDerivative(sd::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput, const float alpha); + void seluDerivative(sd::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput); + void cubeDerivative(sd::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput); + void reduceNorm1(sd::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput); + void sigmCrossEntropy(sd::LaunchContext * context, NDArray* logits, NDArray* lablels, NDArray* theOutput); + void sigmCrossEntropyGrad(sd::LaunchContext * context, NDArray* logits, NDArray* lablels, NDArray* theOutput); + void tanhDerivative(sd::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput); + void hardTanhDerivative(sd::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput); + void rationalTanhDerivative(sd::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput); + void rectifiedTanhDerivative(sd::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput); + void softSignDerivative(sd::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput); + void softPlusDerivative(sd::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput); + void sigmoidDerivative(sd::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput); + void hardSigmoidDerivative(sd::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput); + void logSumExp(sd::LaunchContext * context, NDArray* input, NDArray* axis, NDArray* output); + void logSumExp(sd::LaunchContext * context, NDArray* input, NDArray* subtrah, NDArray* axis, NDArray* output); + void weightedCrossEntropyWithLogitsFunctor(sd::LaunchContext * context, NDArray const* targets, NDArray const* input, NDArray const* weights, NDArray* output); } } } diff --git a/libnd4j/include/ops/declarable/helpers/lgamma.h b/libnd4j/include/ops/declarable/helpers/lgamma.h index 48bcf1d73..184e33556 100644 --- a/libnd4j/include/ops/declarable/helpers/lgamma.h +++ b/libnd4j/include/ops/declarable/helpers/lgamma.h @@ -23,14 +23,14 @@ #define __LIBND4J_L_GAMMA__H__ #include -#include "NDArray.h" +#include "array/NDArray.h" -namespace nd4j { +namespace sd { namespace ops { namespace helpers { // calculate the digamma function for each element for array - void lgamma(nd4j::LaunchContext* context, NDArray& x, NDArray& z); + void lgamma(sd::LaunchContext* context, NDArray& x, NDArray& z); } } diff --git a/libnd4j/include/ops/declarable/helpers/listdiff.h b/libnd4j/include/ops/declarable/helpers/listdiff.h index 99e83f5af..227eccac8 100644 --- a/libnd4j/include/ops/declarable/helpers/listdiff.h +++ b/libnd4j/include/ops/declarable/helpers/listdiff.h @@ -20,14 +20,14 @@ #ifndef __LIST_DIFF_HELPERS__ #define __LIST_DIFF_HELPERS__ //#include -#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { - int listDiffFunctor(nd4j::LaunchContext * context, NDArray* values, NDArray* keep, NDArray* output1, NDArray* output2); - Nd4jLong listDiffCount(nd4j::LaunchContext * context, NDArray* values, NDArray* keep); + int listDiffFunctor(sd::LaunchContext * context, NDArray* values, NDArray* keep, NDArray* output1, NDArray* output2); + Nd4jLong listDiffCount(sd::LaunchContext * context, NDArray* values, NDArray* keep); } } } diff --git a/libnd4j/include/ops/declarable/helpers/lrn.h b/libnd4j/include/ops/declarable/helpers/lrn.h index bbec42586..f8c9089c7 100644 --- a/libnd4j/include/ops/declarable/helpers/lrn.h +++ b/libnd4j/include/ops/declarable/helpers/lrn.h @@ -19,17 +19,17 @@ // #ifndef __LRN_H_HELPERS__ #define __LRN_H_HELPERS__ -#include -#include +#include +#include #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { - int lrnFunctor(nd4j::graph::Context& block, NDArray* input, NDArray* output, int depth, double bias, double alpha, double beta); + int lrnFunctor(sd::graph::Context& block, NDArray* input, NDArray* output, int depth, double bias, double alpha, double beta); - void lrnBP(nd4j::graph::Context& block, const NDArray& input, const NDArray& gradO, NDArray& gradI, const int depth, const float bias, const float alpha, const float beta); + void lrnBP(sd::graph::Context& block, const NDArray& input, const NDArray& gradO, NDArray& gradI, const int depth, const float bias, const float alpha, const float beta); } } diff --git a/libnd4j/include/ops/declarable/helpers/lstm.h b/libnd4j/include/ops/declarable/helpers/lstm.h index 9c0df2fa5..6eb5886f3 100644 --- a/libnd4j/include/ops/declarable/helpers/lstm.h +++ b/libnd4j/include/ops/declarable/helpers/lstm.h @@ -23,7 +23,7 @@ #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { @@ -59,10 +59,10 @@ namespace helpers { } } - void lstmCell(nd4j::LaunchContext * context, const NDArray* xt, const NDArray* ht_1, const NDArray* ct_1, const NDArray* Wx, const NDArray* Wh, const NDArray* Wc, const NDArray* Wp, const NDArray* b, + void lstmCell(sd::LaunchContext * context, const NDArray* xt, const NDArray* ht_1, const NDArray* ct_1, const NDArray* Wx, const NDArray* Wh, const NDArray* Wc, const NDArray* Wp, const NDArray* b, NDArray* ht, NDArray* ct, const std::vector& params); - void lstmTimeLoop(nd4j::LaunchContext * context, const NDArray* x, const NDArray* h0, const NDArray* c0, const NDArray* Wx, const NDArray* Wh, const NDArray* Wc, const NDArray* Wp, const NDArray* b, + void lstmTimeLoop(sd::LaunchContext * context, const NDArray* x, const NDArray* h0, const NDArray* c0, const NDArray* Wx, const NDArray* Wh, const NDArray* Wc, const NDArray* Wp, const NDArray* b, NDArray* h, NDArray* c, const std::vector& params); void lstmBlockCell(const NDArray* xt, const NDArray* cLast, const NDArray* yLast, diff --git a/libnd4j/include/ops/declarable/helpers/lstmBlock.h b/libnd4j/include/ops/declarable/helpers/lstmBlock.h index ff30fe29d..7df9bb795 100644 --- a/libnd4j/include/ops/declarable/helpers/lstmBlock.h +++ b/libnd4j/include/ops/declarable/helpers/lstmBlock.h @@ -23,7 +23,7 @@ #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { diff --git a/libnd4j/include/ops/declarable/helpers/lstmLayer.h b/libnd4j/include/ops/declarable/helpers/lstmLayer.h index a52c2c0e5..dfa9268b4 100644 --- a/libnd4j/include/ops/declarable/helpers/lstmLayer.h +++ b/libnd4j/include/ops/declarable/helpers/lstmLayer.h @@ -24,7 +24,7 @@ #include #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { diff --git a/libnd4j/include/ops/declarable/helpers/lstsq.h b/libnd4j/include/ops/declarable/helpers/lstsq.h new file mode 100644 index 000000000..9cc629383 --- /dev/null +++ b/libnd4j/include/ops/declarable/helpers/lstsq.h @@ -0,0 +1,34 @@ +/******************************************************************************* + * Copyright (c) Konduit K.K. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ + +// +// @author GS +// +#ifndef __LST_SQ_SOLVE__H_HELPERS__ +#define __LST_SQ_SOLVE__H_HELPERS__ + +#include +#include + +namespace sd { +namespace ops { +namespace helpers { + + int leastSquaresSolveFunctor(sd::LaunchContext* context, NDArray const* leftInput, NDArray const* rightInput, double const l2Regularizer, bool const fast, NDArray* output); +} +} +} +#endif diff --git a/libnd4j/include/ops/declarable/helpers/lup.h b/libnd4j/include/ops/declarable/helpers/lup.h index ae10e6136..1e58c2e3f 100644 --- a/libnd4j/include/ops/declarable/helpers/lup.h +++ b/libnd4j/include/ops/declarable/helpers/lup.h @@ -19,23 +19,25 @@ // #ifndef __LUP_H_HELPERS__ #define __LUP_H_HELPERS__ -#include -#include +#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { - int lup(nd4j::LaunchContext* context, NDArray* input, NDArray* lu, NDArray* permutation); - void lu(nd4j::LaunchContext *context, NDArray* input, NDArray* output, NDArray* permutation); - int determinant(nd4j::LaunchContext * context, NDArray* input, NDArray* output); - int logAbsDeterminant(nd4j::LaunchContext * context, NDArray* input, NDArray* output); + int lup(sd::LaunchContext* context, NDArray* input, NDArray* lu, NDArray* permutation); + void lu(sd::LaunchContext *context, NDArray* input, NDArray* output, NDArray* permutation); + int determinant(sd::LaunchContext * context, NDArray* input, NDArray* output); + int logAbsDeterminant(sd::LaunchContext * context, NDArray* input, NDArray* output); - int inverse(nd4j::LaunchContext * context, NDArray* input, NDArray* output); + int inverse(sd::LaunchContext * context, NDArray* input, NDArray* output); + int upperInverseFunctor(sd::LaunchContext* context, NDArray* input, NDArray* output); + int lowerInverseFunctor(sd::LaunchContext* context, NDArray* input, NDArray* output); - bool checkCholeskyInput(nd4j::LaunchContext * context, NDArray const* input); - int cholesky(nd4j::LaunchContext * context, NDArray* input, NDArray* output, bool inplace = false); - int logdetFunctor(nd4j::LaunchContext * context, NDArray* input, NDArray* output); + bool checkCholeskyInput(sd::LaunchContext * context, NDArray const* input); + int cholesky(sd::LaunchContext * context, NDArray* input, NDArray* output, bool inplace = false); + int logdetFunctor(sd::LaunchContext * context, NDArray* input, NDArray* output); } } } diff --git a/libnd4j/include/ops/declarable/helpers/matmul.h b/libnd4j/include/ops/declarable/helpers/matmul.h index 2e7cce13f..2cce162d8 100644 --- a/libnd4j/include/ops/declarable/helpers/matmul.h +++ b/libnd4j/include/ops/declarable/helpers/matmul.h @@ -21,13 +21,13 @@ #ifndef LIBND4J_HELPERS_MATMUL_H #define LIBND4J_HELPERS_MATMUL_H -#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { - void _matmul(nd4j::LaunchContext * context, NDArray *A, NDArray *B, NDArray *C, int transA, int transB, double alpha = 1., double beta = 0.); + void _matmul(sd::LaunchContext * context, NDArray *A, NDArray *B, NDArray *C, int transA, int transB, double alpha = 1., double beta = 0.); } } } diff --git a/libnd4j/include/ops/declarable/helpers/matrixSetDiag.h b/libnd4j/include/ops/declarable/helpers/matrixSetDiag.h index fb7d57d18..332c3134b 100644 --- a/libnd4j/include/ops/declarable/helpers/matrixSetDiag.h +++ b/libnd4j/include/ops/declarable/helpers/matrixSetDiag.h @@ -22,13 +22,13 @@ #define LIBND4J_MATRIXSETDIAG_H #include -#include "NDArray.h" +#include "array/NDArray.h" -namespace nd4j { +namespace sd { namespace ops { namespace helpers { - void matrixSetDiag(nd4j::LaunchContext* context, const NDArray& input, const NDArray& diagonal, NDArray& output, const bool zeroPad); + void matrixSetDiag(sd::LaunchContext* context, const NDArray& input, const NDArray& diagonal, NDArray& output, const bool zeroPad); } } diff --git a/libnd4j/include/ops/declarable/helpers/matrix_band.h b/libnd4j/include/ops/declarable/helpers/matrix_band.h index f63ae6680..f997e4d56 100644 --- a/libnd4j/include/ops/declarable/helpers/matrix_band.h +++ b/libnd4j/include/ops/declarable/helpers/matrix_band.h @@ -19,14 +19,14 @@ // #ifndef __MATRIX_BAND_H_HELPERS__ #define __MATRIX_BAND_H_HELPERS__ -#include -#include +#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { - void matrixBandPart(nd4j::LaunchContext * context, NDArray* input, NDArray* output, Nd4jLong lowerBand, Nd4jLong upperBand); + void matrixBandPart(sd::LaunchContext * context, NDArray* input, NDArray* output, Nd4jLong lowerBand, Nd4jLong upperBand); } diff --git a/libnd4j/include/ops/declarable/helpers/matrix_diag_part.h b/libnd4j/include/ops/declarable/helpers/matrix_diag_part.h index 43ab84091..fd25c636c 100644 --- a/libnd4j/include/ops/declarable/helpers/matrix_diag_part.h +++ b/libnd4j/include/ops/declarable/helpers/matrix_diag_part.h @@ -19,14 +19,14 @@ // #ifndef __MATRIX_DIAG_PART_HELPERS__ #define __MATRIX_DIAG_PART_HELPERS__ -#include -#include +#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { - int matrixDiagPart(nd4j::LaunchContext * context, NDArray const* input, NDArray* output); + int matrixDiagPart(sd::LaunchContext * context, NDArray const* input, NDArray* output); } } diff --git a/libnd4j/include/ops/declarable/helpers/max_pooling.h b/libnd4j/include/ops/declarable/helpers/max_pooling.h index fda2560bf..a3750798b 100644 --- a/libnd4j/include/ops/declarable/helpers/max_pooling.h +++ b/libnd4j/include/ops/declarable/helpers/max_pooling.h @@ -19,15 +19,15 @@ // #ifndef __MAX_POOLING_HELPERS__ #define __MAX_POOLING_HELPERS__ -#include -#include +#include +#include #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { - void maxPoolingFunctor(nd4j::LaunchContext * context, nd4j::graph::Context& block, NDArray* input, NDArray* values, std::vector const& params, NDArray* indices); + void maxPoolingFunctor(sd::LaunchContext * context, sd::graph::Context& block, NDArray* input, NDArray* values, std::vector const& params, NDArray* indices); } } } diff --git a/libnd4j/include/ops/declarable/helpers/meshgrid.h b/libnd4j/include/ops/declarable/helpers/meshgrid.h index e39e54e0a..e6c385029 100644 --- a/libnd4j/include/ops/declarable/helpers/meshgrid.h +++ b/libnd4j/include/ops/declarable/helpers/meshgrid.h @@ -23,11 +23,11 @@ #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { - void meshgrid(nd4j::LaunchContext * context, const std::vector& inArrs, const std::vector& outArrs, const bool swapFirst2Dims); + void meshgrid(sd::LaunchContext * context, const std::vector& inArrs, const std::vector& outArrs, const bool swapFirst2Dims); } } diff --git a/libnd4j/include/ops/declarable/helpers/minimax.h b/libnd4j/include/ops/declarable/helpers/minimax.h index 6cb2f60b6..f619a20f6 100644 --- a/libnd4j/include/ops/declarable/helpers/minimax.h +++ b/libnd4j/include/ops/declarable/helpers/minimax.h @@ -19,15 +19,15 @@ // #ifndef __MIN_I_MAX_H_HELPERS__ #define __MIN_I_MAX_H_HELPERS__ -#include -#include +#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { - void minimumBPFunctor(nd4j::LaunchContext * context, NDArray* x, NDArray* y, NDArray* epsNext, NDArray* gradX, NDArray* gradY); - void maximumBPFunctor(nd4j::LaunchContext * context, NDArray* x, NDArray* y, NDArray* epsNext, NDArray* gradX, NDArray* gradY); + void minimumBPFunctor(sd::LaunchContext * context, NDArray* x, NDArray* y, NDArray* epsNext, NDArray* gradX, NDArray* gradY); + void maximumBPFunctor(sd::LaunchContext * context, NDArray* x, NDArray* y, NDArray* epsNext, NDArray* gradX, NDArray* gradY); } } diff --git a/libnd4j/include/ops/declarable/helpers/multiUnique.h b/libnd4j/include/ops/declarable/helpers/multiUnique.h index 12fa6db10..3119901c1 100644 --- a/libnd4j/include/ops/declarable/helpers/multiUnique.h +++ b/libnd4j/include/ops/declarable/helpers/multiUnique.h @@ -19,14 +19,14 @@ // #ifndef __MULTI_UNIQUE_H_HELPERS__ #define __MULTI_UNIQUE_H_HELPERS__ -#include -#include +#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { - ND4J_EXPORT bool multiUnique(std::vector const& inputList, nd4j::memory::Workspace* workspace = nullptr); + ND4J_EXPORT bool multiUnique(std::vector const& inputList, sd::memory::Workspace* workspace = nullptr); } } diff --git a/libnd4j/include/ops/declarable/helpers/nth_element.h b/libnd4j/include/ops/declarable/helpers/nth_element.h index 486c7489f..1a2c28719 100644 --- a/libnd4j/include/ops/declarable/helpers/nth_element.h +++ b/libnd4j/include/ops/declarable/helpers/nth_element.h @@ -19,14 +19,14 @@ // #ifndef __NTH_ELEMENT__H_HELPERS__ #define __NTH_ELEMENT__H_HELPERS__ -#include -#include +#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { - void nthElementFunctor(nd4j::LaunchContext * context, NDArray* input, Nd4jLong n, NDArray* output, bool reverse); + void nthElementFunctor(sd::LaunchContext * context, NDArray* input, Nd4jLong n, NDArray* output, bool reverse); } } diff --git a/libnd4j/include/ops/declarable/helpers/one_hot.h b/libnd4j/include/ops/declarable/helpers/one_hot.h index f91ed8cc9..1fefd7c44 100644 --- a/libnd4j/include/ops/declarable/helpers/one_hot.h +++ b/libnd4j/include/ops/declarable/helpers/one_hot.h @@ -21,14 +21,14 @@ #ifndef DEV_TESTS_ONE_HOT_H #define DEV_TESTS_ONE_HOT_H -#include -#include +#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { - void onehot(const nd4j::LaunchContext* context, const NDArray *indices, NDArray *output, const uint axis, const uint depth, const double on, const double off); + void onehot(const sd::LaunchContext* context, const NDArray *indices, NDArray *output, const uint axis, const uint depth, const double on, const double off); } } diff --git a/libnd4j/include/ops/declarable/helpers/percentile.h b/libnd4j/include/ops/declarable/helpers/percentile.h index a2859c9b6..5eddb42b2 100644 --- a/libnd4j/include/ops/declarable/helpers/percentile.h +++ b/libnd4j/include/ops/declarable/helpers/percentile.h @@ -22,13 +22,13 @@ #define LIBND4J_PERCENTILE_H #include -#include "NDArray.h" +#include "array/NDArray.h" -namespace nd4j { +namespace sd { namespace ops { namespace helpers { - void percentile(nd4j::LaunchContext * context, const NDArray& input, NDArray& output, std::vector& axises, const float q, const int interpolation); + void percentile(sd::LaunchContext * context, const NDArray& input, NDArray& output, std::vector& axises, const float q, const int interpolation); } diff --git a/libnd4j/include/ops/declarable/helpers/prefix.h b/libnd4j/include/ops/declarable/helpers/prefix.h index 50b692623..757c5c94f 100644 --- a/libnd4j/include/ops/declarable/helpers/prefix.h +++ b/libnd4j/include/ops/declarable/helpers/prefix.h @@ -21,20 +21,20 @@ #ifndef LIBND4J_PREFIX_HELPER_H #define LIBND4J_PREFIX_HELPER_H -#include +#include #include #include -#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { // template - // void prefix(nd4j::LaunchContext * context, nd4j::scalar::Ops op, void* x, Nd4jLong *xShapeInfo, void* z, Nd4jLong* zShapeInfo, bool exclusive, bool reverse); + // void prefix(sd::LaunchContext * context, sd::scalar::Ops op, void* x, Nd4jLong *xShapeInfo, void* z, Nd4jLong* zShapeInfo, bool exclusive, bool reverse); - void prefix(nd4j::LaunchContext* context, nd4j::scalar::Ops op, const NDArray* x, NDArray* z, bool exclusive, bool reverse); + void prefix(sd::LaunchContext* context, sd::scalar::Ops op, const NDArray* x, NDArray* z, bool exclusive, bool reverse); - void prefix(nd4j::LaunchContext* context, nd4j::scalar::Ops op, const NDArray* x, NDArray* z, const std::vector& dims, bool exclusive, bool reverse); + void prefix(sd::LaunchContext* context, sd::scalar::Ops op, const NDArray* x, NDArray* z, const std::vector& dims, bool exclusive, bool reverse); } } } diff --git a/libnd4j/include/ops/declarable/helpers/print_variable.h b/libnd4j/include/ops/declarable/helpers/print_variable.h index 3521e38b9..46cf4ee01 100644 --- a/libnd4j/include/ops/declarable/helpers/print_variable.h +++ b/libnd4j/include/ops/declarable/helpers/print_variable.h @@ -23,7 +23,7 @@ #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { void print_special(LaunchContext &ctx, const NDArray &array, const std::string &message = {}); diff --git a/libnd4j/include/ops/declarable/helpers/qr.h b/libnd4j/include/ops/declarable/helpers/qr.h index 33649e7c8..05de6ca40 100644 --- a/libnd4j/include/ops/declarable/helpers/qr.h +++ b/libnd4j/include/ops/declarable/helpers/qr.h @@ -19,14 +19,14 @@ // #ifndef __QR__H_HELPERS__ #define __QR__H_HELPERS__ -#include -#include +#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { - void qr(nd4j::LaunchContext * context, NDArray* input, NDArray* outputQ, NDArray* outputR, bool const fullMatricies); + void qr(sd::LaunchContext * context, NDArray const* input, NDArray* outputQ, NDArray* outputR, bool const fullMatricies); } diff --git a/libnd4j/include/ops/declarable/helpers/random.h b/libnd4j/include/ops/declarable/helpers/random.h index c97aae118..5ee75e141 100644 --- a/libnd4j/include/ops/declarable/helpers/random.h +++ b/libnd4j/include/ops/declarable/helpers/random.h @@ -22,12 +22,12 @@ // #ifndef __RANDOM_HELPERS__ #define __RANDOM_HELPERS__ -#include -#include +#include +#include #include #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { diff --git a/libnd4j/include/ops/declarable/helpers/random_crop.h b/libnd4j/include/ops/declarable/helpers/random_crop.h index dba950210..f4d36a850 100644 --- a/libnd4j/include/ops/declarable/helpers/random_crop.h +++ b/libnd4j/include/ops/declarable/helpers/random_crop.h @@ -19,12 +19,12 @@ // #ifndef __RANDOM_CROP_HELPERS__ #define __RANDOM_CROP_HELPERS__ -#include -#include +#include +#include #include #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { diff --git a/libnd4j/include/ops/declarable/helpers/range.h b/libnd4j/include/ops/declarable/helpers/range.h index 8381e2a2c..13155fd70 100644 --- a/libnd4j/include/ops/declarable/helpers/range.h +++ b/libnd4j/include/ops/declarable/helpers/range.h @@ -23,12 +23,12 @@ #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { // be careful: outVector must have c-order and ews = 1 !!! - void range(nd4j::LaunchContext * context, const NDArray& start, const NDArray& delta, NDArray& outVector); + void range(sd::LaunchContext * context, const NDArray& start, const NDArray& delta, NDArray& outVector); } } diff --git a/libnd4j/include/ops/declarable/helpers/reverse.h b/libnd4j/include/ops/declarable/helpers/reverse.h index 28045cbb6..d85d017ba 100644 --- a/libnd4j/include/ops/declarable/helpers/reverse.h +++ b/libnd4j/include/ops/declarable/helpers/reverse.h @@ -23,13 +23,13 @@ #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { - void reverseSequence(nd4j::LaunchContext * context, const NDArray* input, const NDArray* seqLengths, NDArray* output, int seqDim, const int batchDim); + void reverseSequence(sd::LaunchContext * context, const NDArray* input, const NDArray* seqLengths, NDArray* output, int seqDim, const int batchDim); - void reverse(nd4j::LaunchContext * context, const NDArray* input, NDArray* output, const std::vector* intArgs, bool isBackProp); + void reverse(sd::LaunchContext * context, const NDArray* input, NDArray* output, const std::vector* intArgs, bool isBackProp); diff --git a/libnd4j/include/ops/declarable/helpers/rnn.h b/libnd4j/include/ops/declarable/helpers/rnn.h index cb0d6d6e4..32f49fe2e 100644 --- a/libnd4j/include/ops/declarable/helpers/rnn.h +++ b/libnd4j/include/ops/declarable/helpers/rnn.h @@ -23,14 +23,14 @@ #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { - void rnnCell(nd4j::LaunchContext * context, const NDArray* xt, const NDArray* Wx, const NDArray* Wh, const NDArray* b, const NDArray* ht_1, NDArray* ht); + void rnnCell(sd::LaunchContext * context, const NDArray* xt, const NDArray* Wx, const NDArray* Wh, const NDArray* b, const NDArray* ht_1, NDArray* ht); - void rnnTimeLoop(nd4j::LaunchContext * context, const NDArray* x, const NDArray* Wx, const NDArray* Wh, const NDArray* b, const NDArray* h0, const NDArray* maxTimeStep, NDArray* h, NDArray* hFinal); + void rnnTimeLoop(sd::LaunchContext * context, const NDArray* x, const NDArray* Wx, const NDArray* Wh, const NDArray* b, const NDArray* h0, const NDArray* maxTimeStep, NDArray* h, NDArray* hFinal); } } diff --git a/libnd4j/include/ops/declarable/helpers/roll.h b/libnd4j/include/ops/declarable/helpers/roll.h index b20367c0d..3e637dbc4 100644 --- a/libnd4j/include/ops/declarable/helpers/roll.h +++ b/libnd4j/include/ops/declarable/helpers/roll.h @@ -21,12 +21,12 @@ #include #ifndef __HELPERS__ROLL__H__ #define __HELPERS__ROLL__H__ -namespace nd4j { +namespace sd { namespace ops { namespace helpers { - void rollFunctorLinear(nd4j::LaunchContext * context, NDArray* input, NDArray* output, int shift, bool inplace = false); + void rollFunctorLinear(sd::LaunchContext * context, NDArray* input, NDArray* output, int shift, bool inplace = false); - void rollFunctorFull(nd4j::LaunchContext * context, NDArray* input, NDArray* output, std::vector const& shifts, std::vector const& axes, bool inplace = false); + void rollFunctorFull(sd::LaunchContext * context, NDArray* input, NDArray* output, std::vector const& shifts, std::vector const& axes, bool inplace = false); } } } diff --git a/libnd4j/include/ops/declarable/helpers/s_t_b.h b/libnd4j/include/ops/declarable/helpers/s_t_b.h index 35e57a1e8..1147f05ab 100644 --- a/libnd4j/include/ops/declarable/helpers/s_t_b.h +++ b/libnd4j/include/ops/declarable/helpers/s_t_b.h @@ -23,27 +23,27 @@ #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { - void batchToSpace(nd4j::LaunchContext* context, const NDArray& input, NDArray& output, const uint cropBottom, const uint cropTop, const uint cropLeft, const uint cropRight, const uint blockSize); + void batchToSpace(sd::LaunchContext* context, const NDArray& input, NDArray& output, const uint cropBottom, const uint cropTop, const uint cropLeft, const uint cropRight, const uint blockSize); - void spaceToBatch(nd4j::LaunchContext* context, const NDArray& input, NDArray& output, const uint padBottom, const uint padTop, const uint padLeft, const uint padRight, const uint blockSize); + void spaceToBatch(sd::LaunchContext* context, const NDArray& input, NDArray& output, const uint padBottom, const uint padTop, const uint padLeft, const uint padRight, const uint blockSize); - void spaceToBatchND(nd4j::LaunchContext* context, const NDArray& input, const NDArray& blockShape, const NDArray& padding, NDArray& output); + void spaceToBatchND(sd::LaunchContext* context, const NDArray& input, const NDArray& blockShape, const NDArray& padding, NDArray& output); - void batchToSpaceND(nd4j::LaunchContext* context, const NDArray& input, const NDArray& blockShape, const NDArray& crop, NDArray& output); + void batchToSpaceND(sd::LaunchContext* context, const NDArray& input, const NDArray& blockShape, const NDArray& crop, NDArray& output); /* // this method MUST be platform-specific template - void _execute(nd4j::LaunchContext * context, void *ptrSpace, const Nd4jLong *space_shape, const Nd4jLong *space_strides, const Nd4jLong *block_shape, const Nd4jLong *pad_start, const Nd4jLong *block_offsets, void *ptrBatch, const Nd4jLong *batch_shape, const Nd4jLong *batch_strides); + void _execute(sd::LaunchContext * context, void *ptrSpace, const Nd4jLong *space_shape, const Nd4jLong *space_strides, const Nd4jLong *block_shape, const Nd4jLong *pad_start, const Nd4jLong *block_offsets, void *ptrBatch, const Nd4jLong *batch_shape, const Nd4jLong *batch_strides); template - FORCEINLINE void _prepare(nd4j::LaunchContext * context, NDArray * space, NDArray *batch, const Nd4jLong block_array[NUM_BLOCK_DIMS], const Nd4jLong padding_array[NUM_BLOCK_DIMS * 2]) { + FORCEINLINE void _prepare(sd::LaunchContext * context, NDArray * space, NDArray *batch, const Nd4jLong block_array[NUM_BLOCK_DIMS], const Nd4jLong padding_array[NUM_BLOCK_DIMS * 2]) { Nd4jLong pad_start[NUM_BLOCK_DIMS]; Nd4jLong block_shape[NUM_BLOCK_DIMS]; @@ -83,9 +83,9 @@ namespace helpers { } }; - Nd4jStatus _spaceToBatch(nd4j::LaunchContext * context, int internal_block_dims, NDArray *input, NDArray *output, std::vector &internal_input_shape, std::vector &internal_output_shape, Nd4jLong *block_shape, Nd4jLong *paddings); + Nd4jStatus _spaceToBatch(sd::LaunchContext * context, int internal_block_dims, NDArray *input, NDArray *output, std::vector &internal_input_shape, std::vector &internal_output_shape, Nd4jLong *block_shape, Nd4jLong *paddings); - Nd4jStatus _batchToSpace(nd4j::LaunchContext * context, int internal_block_dims, NDArray *input, NDArray *output, std::vector &internal_input_shape, std::vector &internal_output_shape, Nd4jLong *block_shape, Nd4jLong *crops); + Nd4jStatus _batchToSpace(sd::LaunchContext * context, int internal_block_dims, NDArray *input, NDArray *output, std::vector &internal_input_shape, std::vector &internal_output_shape, Nd4jLong *block_shape, Nd4jLong *crops); */ } } diff --git a/libnd4j/include/ops/declarable/helpers/s_t_d.h b/libnd4j/include/ops/declarable/helpers/s_t_d.h index 64764935e..6dbc64f21 100644 --- a/libnd4j/include/ops/declarable/helpers/s_t_d.h +++ b/libnd4j/include/ops/declarable/helpers/s_t_d.h @@ -19,12 +19,12 @@ // #include -#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { - void _spaceTodepth(nd4j::LaunchContext * context, NDArray *input, NDArray *output, int block_size, bool isNHWC); + void _spaceTodepth(sd::LaunchContext * context, NDArray *input, NDArray *output, int block_size, bool isNHWC); } } } \ No newline at end of file diff --git a/libnd4j/include/ops/declarable/helpers/scatter.h b/libnd4j/include/ops/declarable/helpers/scatter.h index b470285ff..6e456ff97 100644 --- a/libnd4j/include/ops/declarable/helpers/scatter.h +++ b/libnd4j/include/ops/declarable/helpers/scatter.h @@ -21,18 +21,18 @@ #ifndef DEV_TESTS_SCATTER_H #define DEV_TESTS_SCATTER_H -#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { - void scatter(nd4j::LaunchContext* context, pairwise::Ops op, const NDArray& indices, const NDArray& updates, NDArray& output, const bool lock); + void scatter(sd::LaunchContext* context, pairwise::Ops op, const NDArray& indices, const NDArray& updates, NDArray& output, const bool lock); - void scatterND(nd4j::LaunchContext* context, pairwise::Ops op, const NDArray& indices, const NDArray& updates, NDArray& output, const bool lock); + void scatterND(sd::LaunchContext* context, pairwise::Ops op, const NDArray& indices, const NDArray& updates, NDArray& output, const bool lock); - void scatterForLoss(nd4j::LaunchContext* context, const NDArray& indices, NDArray& updates, NDArray& output, const bool calcGrad); + void scatterForLoss(sd::LaunchContext* context, const NDArray& indices, NDArray& updates, NDArray& output, const bool calcGrad); - Nd4jLong checkIndices(nd4j::LaunchContext *context, const NDArray& indices, const NDArray& output, const int axis = -1); + Nd4jLong checkIndices(sd::LaunchContext *context, const NDArray& indices, const NDArray& output, const int axis = -1); } } } diff --git a/libnd4j/include/ops/declarable/helpers/segment.h b/libnd4j/include/ops/declarable/helpers/segment.h index 8064d69c6..2433313ff 100644 --- a/libnd4j/include/ops/declarable/helpers/segment.h +++ b/libnd4j/include/ops/declarable/helpers/segment.h @@ -21,60 +21,60 @@ // #ifndef __SEGMENT_HELPERS__ #define __SEGMENT_HELPERS__ -#include -#include +#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { - bool segmentIndicesValidate(nd4j::LaunchContext * context, NDArray* indices, NDArray& expected, NDArray& output); + bool segmentIndicesValidate(sd::LaunchContext * context, NDArray* indices, NDArray& expected, NDArray& output); - bool unsortedSegmentIndicesValidate(nd4j::LaunchContext * context, NDArray* indices, Nd4jLong numOfClasses, Nd4jLong& output); + bool unsortedSegmentIndicesValidate(sd::LaunchContext * context, NDArray* indices, Nd4jLong numOfClasses, Nd4jLong& output); - void segmentMaxFunctor(nd4j::LaunchContext * context, NDArray* input, NDArray* indices, NDArray* output); + void segmentMaxFunctor(sd::LaunchContext * context, NDArray* input, NDArray* indices, NDArray* output); - void segmentMinFunctor(nd4j::LaunchContext * context, NDArray* input, NDArray* indices, NDArray* output); + void segmentMinFunctor(sd::LaunchContext * context, NDArray* input, NDArray* indices, NDArray* output); - void segmentMeanFunctor(nd4j::LaunchContext * context, NDArray* input, NDArray* indices, NDArray* output); + void segmentMeanFunctor(sd::LaunchContext * context, NDArray* input, NDArray* indices, NDArray* output); - void segmentSumFunctor(nd4j::LaunchContext * context, NDArray* input, NDArray* indices, NDArray* output); + void segmentSumFunctor(sd::LaunchContext * context, NDArray* input, NDArray* indices, NDArray* output); - void segmentProdFunctor(nd4j::LaunchContext * context, NDArray* input, NDArray* indices, NDArray* output); + void segmentProdFunctor(sd::LaunchContext * context, NDArray* input, NDArray* indices, NDArray* output); - void unsortedSegmentSqrtNFunctor(nd4j::LaunchContext * context, NDArray* input, NDArray* indices, Nd4jLong numOfClasses, NDArray* output); + void unsortedSegmentSqrtNFunctor(sd::LaunchContext * context, NDArray* input, NDArray* indices, Nd4jLong numOfClasses, NDArray* output); - void unsortedSegmentMaxFunctor(nd4j::LaunchContext * context, NDArray* input, NDArray* indices, Nd4jLong numOfClasses, NDArray* output); + void unsortedSegmentMaxFunctor(sd::LaunchContext * context, NDArray* input, NDArray* indices, Nd4jLong numOfClasses, NDArray* output); - void unsortedSegmentMinFunctor(nd4j::LaunchContext * context, NDArray* input, NDArray* indices, Nd4jLong numOfClasses, NDArray* output); + void unsortedSegmentMinFunctor(sd::LaunchContext * context, NDArray* input, NDArray* indices, Nd4jLong numOfClasses, NDArray* output); - void unsortedSegmentMeanFunctor(nd4j::LaunchContext * context, NDArray* input, NDArray* indices, Nd4jLong numOfClasses, NDArray* output); + void unsortedSegmentMeanFunctor(sd::LaunchContext * context, NDArray* input, NDArray* indices, Nd4jLong numOfClasses, NDArray* output); - void unsortedSegmentSumFunctor(nd4j::LaunchContext * context, NDArray* input, NDArray* indices, Nd4jLong numOfClasses, NDArray* output); + void unsortedSegmentSumFunctor(sd::LaunchContext * context, NDArray* input, NDArray* indices, Nd4jLong numOfClasses, NDArray* output); - void unsortedSegmentProdFunctor(nd4j::LaunchContext * context, NDArray* input, NDArray* indices, Nd4jLong numOfClasses, NDArray* output); + void unsortedSegmentProdFunctor(sd::LaunchContext * context, NDArray* input, NDArray* indices, Nd4jLong numOfClasses, NDArray* output); - int segmentMaxFunctorBP(nd4j::LaunchContext * context, NDArray* input, NDArray* indices, NDArray* gradOut, NDArray* output); + int segmentMaxFunctorBP(sd::LaunchContext * context, NDArray* input, NDArray* indices, NDArray* gradOut, NDArray* output); - int segmentMinFunctorBP(nd4j::LaunchContext * context, NDArray* input, NDArray* indices, NDArray* gradOut, NDArray* output); + int segmentMinFunctorBP(sd::LaunchContext * context, NDArray* input, NDArray* indices, NDArray* gradOut, NDArray* output); - int segmentMeanFunctorBP(nd4j::LaunchContext * context, NDArray* input, NDArray* indices, NDArray* gradOut, NDArray* output); + int segmentMeanFunctorBP(sd::LaunchContext * context, NDArray* input, NDArray* indices, NDArray* gradOut, NDArray* output); - int segmentSumFunctorBP(nd4j::LaunchContext * context, NDArray* input, NDArray* indices, NDArray* gradOut, NDArray* output); + int segmentSumFunctorBP(sd::LaunchContext * context, NDArray* input, NDArray* indices, NDArray* gradOut, NDArray* output); - int segmentProdFunctorBP(nd4j::LaunchContext * context, NDArray* input, NDArray* indices, NDArray* gradOut, NDArray* output); + int segmentProdFunctorBP(sd::LaunchContext * context, NDArray* input, NDArray* indices, NDArray* gradOut, NDArray* output); - int unsortedSegmentSqrtNFunctorBP(nd4j::LaunchContext * context, NDArray* input, NDArray* indices, NDArray* gradOut, Nd4jLong numOfClasses, NDArray* output); + int unsortedSegmentSqrtNFunctorBP(sd::LaunchContext * context, NDArray* input, NDArray* indices, NDArray* gradOut, Nd4jLong numOfClasses, NDArray* output); - int unsortedSegmentMaxFunctorBP(nd4j::LaunchContext * context, NDArray* input, NDArray* indices, NDArray* gradOut, Nd4jLong numOfClasses, NDArray* output); + int unsortedSegmentMaxFunctorBP(sd::LaunchContext * context, NDArray* input, NDArray* indices, NDArray* gradOut, Nd4jLong numOfClasses, NDArray* output); - int unsortedSegmentMinFunctorBP(nd4j::LaunchContext * context, NDArray* input, NDArray* indices, NDArray* gradOut, Nd4jLong numOfClasses, NDArray* output); + int unsortedSegmentMinFunctorBP(sd::LaunchContext * context, NDArray* input, NDArray* indices, NDArray* gradOut, Nd4jLong numOfClasses, NDArray* output); - int unsortedSegmentMeanFunctorBP(nd4j::LaunchContext * context, NDArray* input, NDArray* indices, NDArray* gradOut, Nd4jLong numOfClasses, NDArray* output); + int unsortedSegmentMeanFunctorBP(sd::LaunchContext * context, NDArray* input, NDArray* indices, NDArray* gradOut, Nd4jLong numOfClasses, NDArray* output); - int unsortedSegmentSumFunctorBP(nd4j::LaunchContext * context, NDArray* input, NDArray* indices, NDArray* gradOut, Nd4jLong numOfClasses, NDArray* output); + int unsortedSegmentSumFunctorBP(sd::LaunchContext * context, NDArray* input, NDArray* indices, NDArray* gradOut, Nd4jLong numOfClasses, NDArray* output); - int unsortedSegmentProdFunctorBP(nd4j::LaunchContext * context, NDArray* input, NDArray* indices, NDArray* gradOut, Nd4jLong numOfClasses, NDArray* output); + int unsortedSegmentProdFunctorBP(sd::LaunchContext * context, NDArray* input, NDArray* indices, NDArray* gradOut, Nd4jLong numOfClasses, NDArray* output); } } diff --git a/libnd4j/include/ops/declarable/helpers/segment_common.h b/libnd4j/include/ops/declarable/helpers/segment_common.h index 2d0ec5b8b..b0a92b8b3 100644 --- a/libnd4j/include/ops/declarable/helpers/segment_common.h +++ b/libnd4j/include/ops/declarable/helpers/segment_common.h @@ -21,10 +21,10 @@ // #ifndef __SEGMENT_COMMON_HELPERS__ #define __SEGMENT_COMMON_HELPERS__ -#include -#include +#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { void fillUpSegments(NDArray* indices, Nd4jLong numClasses, NDArray& classesRangesBegs, NDArray& classesRangesLens); diff --git a/libnd4j/include/ops/declarable/helpers/sequence_mask.h b/libnd4j/include/ops/declarable/helpers/sequence_mask.h index 9711b7cde..491640b9e 100644 --- a/libnd4j/include/ops/declarable/helpers/sequence_mask.h +++ b/libnd4j/include/ops/declarable/helpers/sequence_mask.h @@ -19,14 +19,14 @@ // #ifndef __SEQUENCE_MASK_HELPERS__ #define __SEQUENCE_MASK_HELPERS__ -#include -#include +#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { - void sequenceMask(nd4j::LaunchContext * context, NDArray* input, NDArray* output, int maxIndex); + void sequenceMask(sd::LaunchContext * context, NDArray* input, NDArray* output, int maxIndex); } } diff --git a/libnd4j/include/ops/declarable/helpers/sg_cb.h b/libnd4j/include/ops/declarable/helpers/sg_cb.h index 8aa616fa6..6b0824a81 100644 --- a/libnd4j/include/ops/declarable/helpers/sg_cb.h +++ b/libnd4j/include/ops/declarable/helpers/sg_cb.h @@ -21,11 +21,11 @@ #ifndef DEV_TESTS_SG_CB_H #define DEV_TESTS_SG_CB_H -#include +#include #include -#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { void skipgram(NDArray &syn0, NDArray &syn1, NDArray &syn1Neg, NDArray &expTable, NDArray &negTable, NDArray &target, NDArray &ngStarter, int nsRounds, NDArray &indices, NDArray &codes, NDArray &alpha, NDArray &randomValue, NDArray &inferenceVector, const bool preciseMode, const int numWorkers); diff --git a/libnd4j/include/ops/declarable/helpers/shift.h b/libnd4j/include/ops/declarable/helpers/shift.h index e07a0e992..f1b21741c 100644 --- a/libnd4j/include/ops/declarable/helpers/shift.h +++ b/libnd4j/include/ops/declarable/helpers/shift.h @@ -21,11 +21,11 @@ #ifndef DEV_TESTS_SHIFT_H #define DEV_TESTS_SHIFT_H -#include +#include #include -#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { void rshift_bits(LaunchContext* launchContext, NDArray &x, NDArray &z, uint32_t shift); diff --git a/libnd4j/include/ops/declarable/helpers/solve.h b/libnd4j/include/ops/declarable/helpers/solve.h index d097fa217..17234f313 100644 --- a/libnd4j/include/ops/declarable/helpers/solve.h +++ b/libnd4j/include/ops/declarable/helpers/solve.h @@ -19,15 +19,15 @@ // #ifndef __SOLVE__H_HELPERS__ #define __SOLVE__H_HELPERS__ -#include -#include +#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { - int solveFunctor(nd4j::LaunchContext* context, NDArray* leftInput, NDArray* rightInput, bool adjoint, NDArray* output); - void adjointMatrix(nd4j::LaunchContext* context, NDArray const* input, NDArray* output); + int solveFunctor(sd::LaunchContext* context, NDArray* leftInput, NDArray* rightInput, bool adjoint, NDArray* output); + void adjointMatrix(sd::LaunchContext* context, NDArray const* input, NDArray* output); } } } diff --git a/libnd4j/include/ops/declarable/helpers/sparse_to_dense.h b/libnd4j/include/ops/declarable/helpers/sparse_to_dense.h index 8d00639de..541621257 100644 --- a/libnd4j/include/ops/declarable/helpers/sparse_to_dense.h +++ b/libnd4j/include/ops/declarable/helpers/sparse_to_dense.h @@ -23,7 +23,7 @@ #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { void compat_sparse_to_dense(const NDArray &values, const NDArray &indices, NDArray *def, NDArray &output); diff --git a/libnd4j/include/ops/declarable/helpers/sru.h b/libnd4j/include/ops/declarable/helpers/sru.h index 3a89ccd66..639247278 100644 --- a/libnd4j/include/ops/declarable/helpers/sru.h +++ b/libnd4j/include/ops/declarable/helpers/sru.h @@ -23,18 +23,18 @@ #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { - void sruCell(nd4j::LaunchContext * context, const NDArray* x, const NDArray* c0, const NDArray* w, const NDArray* b, NDArray* h, NDArray* c); + void sruCell(sd::LaunchContext * context, const NDArray* x, const NDArray* c0, const NDArray* w, const NDArray* b, NDArray* h, NDArray* c); - void sruTimeLoop(nd4j::LaunchContext * context, const NDArray* x, const NDArray* c0, const NDArray* w, const NDArray* b, NDArray* h, NDArray* c); + void sruTimeLoop(sd::LaunchContext * context, const NDArray* x, const NDArray* c0, const NDArray* w, const NDArray* b, NDArray* h, NDArray* c); - void sruBI(nd4j::LaunchContext * context, NDArray* x, const NDArray* w, const NDArray* b, const NDArray* c0, const NDArray* mask, NDArray* ht, NDArray* ct); + void sruBI(sd::LaunchContext * context, NDArray* x, const NDArray* w, const NDArray* b, const NDArray* c0, const NDArray* mask, NDArray* ht, NDArray* ct); - void sruBIBP(nd4j::LaunchContext * context, NDArray* x, const NDArray* w, const NDArray* b, const NDArray* c0, const NDArray* ct, const NDArray* inGradC0, const NDArray* inGradH, const NDArray* mask, + void sruBIBP(sd::LaunchContext * context, NDArray* x, const NDArray* w, const NDArray* b, const NDArray* c0, const NDArray* ct, const NDArray* inGradC0, const NDArray* inGradH, const NDArray* mask, NDArray* gradI, NDArray* gradWeights, NDArray* gradB, NDArray* gradC0); } } diff --git a/libnd4j/include/ops/declarable/helpers/stack.h b/libnd4j/include/ops/declarable/helpers/stack.h index 49115ef8e..0ab486a5d 100644 --- a/libnd4j/include/ops/declarable/helpers/stack.h +++ b/libnd4j/include/ops/declarable/helpers/stack.h @@ -22,13 +22,14 @@ #define LIBND4J_STACK_H #include -#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { -void stack(nd4j::LaunchContext * context, const std::vector& inArrs, NDArray* outArr, const int dim); +void stack (sd::LaunchContext* context, const std::vector& inArrs, NDArray& outArr, const int dim); +void unstack(sd::LaunchContext* context, const NDArray& input, const std::vector& outArrs, const int dim); } diff --git a/libnd4j/include/ops/declarable/helpers/svd.h b/libnd4j/include/ops/declarable/helpers/svd.h index 42689c319..027807191 100644 --- a/libnd4j/include/ops/declarable/helpers/svd.h +++ b/libnd4j/include/ops/declarable/helpers/svd.h @@ -22,15 +22,15 @@ #define LIBND4J_SVD_HELPER_H #include -#include "NDArray.h" +#include "array/NDArray.h" -namespace nd4j { +namespace sd { namespace ops { namespace helpers { ////////////////////////////////////////////////////////////////////////// // svd operation, this function is not method of SVD class, it is standalone function -void svd(nd4j::LaunchContext* context, const NDArray* x, const std::vector& outArrs, const bool fullUV, const bool calcUV, const int switchNum); +void svd(sd::LaunchContext* context, const NDArray* x, const std::vector& outArrs, const bool fullUV, const bool calcUV, const int switchNum); } diff --git a/libnd4j/include/ops/declarable/helpers/toggle_bits.h b/libnd4j/include/ops/declarable/helpers/toggle_bits.h index bf3481e2c..6d8ffe44a 100644 --- a/libnd4j/include/ops/declarable/helpers/toggle_bits.h +++ b/libnd4j/include/ops/declarable/helpers/toggle_bits.h @@ -23,13 +23,13 @@ #ifndef DEV_TESTS_TOGGLE_BITS_H #define DEV_TESTS_TOGGLE_BITS_H -namespace nd4j { +namespace sd { namespace ops { namespace helpers { template - static void toggle_bits__(nd4j::LaunchContext * context, NDArray& in, NDArray& out); + static void toggle_bits__(sd::LaunchContext * context, NDArray& in, NDArray& out); - void __toggle_bits(nd4j::LaunchContext * context, NDArray& in, NDArray& out); + void __toggle_bits(sd::LaunchContext * context, NDArray& in, NDArray& out); } } } diff --git a/libnd4j/include/ops/declarable/helpers/top_k.h b/libnd4j/include/ops/declarable/helpers/top_k.h index 5ce7c93fb..6a459f925 100644 --- a/libnd4j/include/ops/declarable/helpers/top_k.h +++ b/libnd4j/include/ops/declarable/helpers/top_k.h @@ -19,16 +19,16 @@ // #ifndef __TOP_K_HELPERS__ #define __TOP_K_HELPERS__ -#include -#include +#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { - int topKFunctor(nd4j::LaunchContext * context, const NDArray* input, NDArray* values, NDArray* indices, const uint k, bool needSort); + int topKFunctor(sd::LaunchContext * context, const NDArray* input, NDArray* values, NDArray* indices, const uint k, bool needSort); - int inTopKFunctor(nd4j::LaunchContext * context, const NDArray* predictions, const NDArray* targets, NDArray* output, const uint k); + int inTopKFunctor(sd::LaunchContext * context, const NDArray* predictions, const NDArray* targets, NDArray* output, const uint k); } } diff --git a/libnd4j/include/ops/declarable/helpers/transforms.h b/libnd4j/include/ops/declarable/helpers/transforms.h index 82ff4a73e..8678c16fd 100644 --- a/libnd4j/include/ops/declarable/helpers/transforms.h +++ b/libnd4j/include/ops/declarable/helpers/transforms.h @@ -25,54 +25,60 @@ #include #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { - void triuBP(nd4j::LaunchContext * context, const NDArray& input, const NDArray& gradO, NDArray& gradI, const int diagonal); + void triuBP(sd::LaunchContext * context, const NDArray& input, const NDArray& gradO, NDArray& gradI, const int diagonal); - void trace(nd4j::LaunchContext * context, const NDArray& input, NDArray& output); + void trace(sd::LaunchContext * context, const NDArray& input, NDArray& output); - void randomShuffle(nd4j::LaunchContext * context, NDArray& input, NDArray& output, nd4j::graph::RandomGenerator& rng, const bool isInplace); + void randomShuffle(sd::LaunchContext * context, NDArray& input, NDArray& output, sd::graph::RandomGenerator& rng, const bool isInplace); // auxiliary function which serves for recursion purpose and is used in pad operation // void recursiveLoopForPad(const int mode, NDArray& input, const NDArray& paddings, NDArray& output, std::vector dimensions, int dim, int inIdx, int outIdx, NDArray& padValue); - void pad(nd4j::LaunchContext * context, const int mode, const NDArray& input, const NDArray& paddings, NDArray& output, NDArray const& padValue); + void pad(sd::LaunchContext * context, const int mode, const NDArray& input, const NDArray& paddings, NDArray& output, NDArray const& padValue); - void invertPermutation(nd4j::LaunchContext * context, const NDArray& input, NDArray& output); + void invertPermutation(sd::LaunchContext * context, const NDArray& input, NDArray& output); - void gatherND(nd4j::LaunchContext * context, NDArray& input, NDArray& indices, NDArray& output); + void gatherND(sd::LaunchContext * context, NDArray& input, NDArray& indices, NDArray& output); - void gather(nd4j::LaunchContext * context, NDArray* input, const NDArray* indices, NDArray* output, const std::vector& intArgs); + void gather(sd::LaunchContext * context, NDArray* input, const NDArray* indices, NDArray* output, const std::vector& intArgs); - void eye(nd4j::LaunchContext * context, NDArray& output); + void eye(sd::LaunchContext * context, NDArray& output); - void scatterUpdate(nd4j::LaunchContext * context, NDArray& operand, NDArray& updates, const std::vector* intArgs); + void scatterUpdate(sd::LaunchContext * context, NDArray& operand, NDArray& updates, const std::vector* intArgs); - void scatterSimple(nd4j::LaunchContext * context, const int opId, NDArray& input, const NDArray& updates, const NDArray& indices, const std::vector& dimensions); + void scatterSimple(sd::LaunchContext * context, const int opId, NDArray& input, const NDArray& updates, const NDArray& indices, const std::vector& dimensions); - void mergeMaxIndex(nd4j::LaunchContext * context, const std::vector& inArrs, NDArray& output); + void mergeMaxIndex(sd::LaunchContext * context, const std::vector& inArrs, NDArray& output); - void mergeMax(nd4j::LaunchContext * context, const std::vector& inArrs, NDArray& output); + void mergeMax(sd::LaunchContext * context, const std::vector& inArrs, NDArray& output); - void mergeAvg(nd4j::LaunchContext * context, const std::vector& inArrs, NDArray& output); + void mergeAvg(sd::LaunchContext * context, const std::vector& inArrs, NDArray& output); - void mergeAdd(nd4j::LaunchContext * context, const std::vector& inArrs, NDArray& output); + void mergeAdd(sd::LaunchContext * context, const std::vector& inArrs, NDArray& output); - void clipByNorm(nd4j::LaunchContext * context, NDArray& input, NDArray& output, const std::vector& dimensions, const NDArray& clipNorm, const bool isInplace); - void clipByGlobalNorm(nd4j::LaunchContext * context, std::vector const& inputs, double clipNorm, nd4j::memory::Workspace* workspace, std::vector& outputs, bool isInplace); + void clipByNorm(sd::LaunchContext * context, NDArray& input, NDArray& output, const std::vector& dimensions, const NDArray& clipNorm, const bool isInplace); + void clipByGlobalNorm(sd::LaunchContext * context, std::vector const& inputs, double clipNorm, sd::memory::Workspace* workspace, std::vector& outputs, bool isInplace); - void clipByNormBP(nd4j::LaunchContext * context, const NDArray& input, const NDArray& gradO, NDArray& gradI /*output*/, const std::vector& dimensions, const NDArray& clipNorm); + void clipByNormBP(sd::LaunchContext * context, const NDArray& input, const NDArray& gradO, NDArray& gradI /*output*/, const std::vector& dimensions, const NDArray& clipNorm); - void clipByAveraged(nd4j::LaunchContext * context, NDArray& input, NDArray& output, const std::vector& dimensions, const NDArray& clipNorm, const bool isInplace); - void clipByValue(nd4j::LaunchContext * context, NDArray& input, double leftBound, double rightBound, NDArray& output); + void clipByAveraged(sd::LaunchContext * context, NDArray& input, NDArray& output, const std::vector& dimensions, const NDArray& clipNorm, const bool isInplace); + void clipByValue(sd::LaunchContext * context, NDArray& input, double leftBound, double rightBound, NDArray& output); - void mirrorPad(nd4j::LaunchContext * context, const NDArray& input, const NDArray& paddings, NDArray& output, const int mode); + void mirrorPad(sd::LaunchContext * context, const NDArray& input, const NDArray& paddings, NDArray& output, const int mode); - void concat(nd4j::LaunchContext * context, const std::vector& inArrs, NDArray& output, const int axis); + void clipByValue(sd::LaunchContext * context, NDArray& input, double leftBound, double rightBound, NDArray& output); - void tileBP(nd4j::LaunchContext * context, const NDArray& gradO /*input*/, NDArray& gradI /*output*/, const std::vector reps); + void mirrorPad(sd::LaunchContext * context, const NDArray& input, const NDArray& paddings, NDArray& output, const int mode); + + void concat(sd::LaunchContext * context, const std::vector& inArrs, NDArray& output, const int axis); + + void tileBP(sd::LaunchContext * context, const NDArray& gradO /*input*/, NDArray& gradI /*output*/, const std::vector reps); + + void split(sd::LaunchContext* context, const NDArray& input, std::vector& outArrs, const int axis); } } } diff --git a/libnd4j/include/ops/declarable/helpers/triangular_solve.h b/libnd4j/include/ops/declarable/helpers/triangular_solve.h index a40a3e144..73965f8c5 100644 --- a/libnd4j/include/ops/declarable/helpers/triangular_solve.h +++ b/libnd4j/include/ops/declarable/helpers/triangular_solve.h @@ -19,15 +19,15 @@ // #ifndef __TRIANGULAR_SOLVE__H_HELPERS__ #define __TRIANGULAR_SOLVE__H_HELPERS__ -#include -#include +#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { - int triangularSolveFunctor(nd4j::LaunchContext* context, NDArray* leftInput, NDArray* rightInput, bool lower, bool adjoint, NDArray* output); - void adjointMatrix(nd4j::LaunchContext* context, NDArray const* input, bool const lower, NDArray* output); + int triangularSolveFunctor(sd::LaunchContext* context, NDArray* leftInput, NDArray* rightInput, bool lower, bool adjoint, NDArray* output); + void adjointMatrix(sd::LaunchContext* context, NDArray const* input, bool const lower, NDArray* output); } } } diff --git a/libnd4j/include/ops/declarable/helpers/unique.h b/libnd4j/include/ops/declarable/helpers/unique.h index 8116d7ec9..8898be585 100644 --- a/libnd4j/include/ops/declarable/helpers/unique.h +++ b/libnd4j/include/ops/declarable/helpers/unique.h @@ -20,16 +20,16 @@ #ifndef __UNIQUE_H_HELPERS__ #define __UNIQUE_H_HELPERS__ -#include -#include +#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { - Nd4jLong uniqueCount(nd4j::LaunchContext * context, NDArray* input); + Nd4jLong uniqueCount(sd::LaunchContext * context, NDArray* input); - Nd4jStatus uniqueFunctor(nd4j::LaunchContext * context, NDArray* input, NDArray* values, NDArray* indices, NDArray* counts); + Nd4jStatus uniqueFunctor(sd::LaunchContext * context, NDArray* input, NDArray* values, NDArray* indices, NDArray* counts); } } diff --git a/libnd4j/include/ops/declarable/helpers/weights.h b/libnd4j/include/ops/declarable/helpers/weights.h index 367c85b5e..66246b641 100644 --- a/libnd4j/include/ops/declarable/helpers/weights.h +++ b/libnd4j/include/ops/declarable/helpers/weights.h @@ -19,14 +19,14 @@ // #ifndef __WEIGHTS_H_HELPERS__ #define __WEIGHTS_H_HELPERS__ -#include -#include +#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { - void adjustWeights(nd4j::LaunchContext * context, NDArray* input, NDArray* weights, NDArray* output, int minLength, int maxLength); + void adjustWeights(sd::LaunchContext * context, NDArray* input, NDArray* weights, NDArray* output, int minLength, int maxLength); } } diff --git a/libnd4j/include/ops/declarable/helpers/where.h b/libnd4j/include/ops/declarable/helpers/where.h index 09df7ebc9..2c9588462 100644 --- a/libnd4j/include/ops/declarable/helpers/where.h +++ b/libnd4j/include/ops/declarable/helpers/where.h @@ -23,10 +23,10 @@ #include -namespace nd4j { +namespace sd { namespace ops { namespace helpers { - void _where(nd4j::LaunchContext * context, NDArray &condition, NDArray& output, memory::Workspace *workspace); + void _where(sd::LaunchContext * context, NDArray &condition, NDArray& output, memory::Workspace *workspace); } } } diff --git a/libnd4j/include/ops/declarable/helpers/zeta.h b/libnd4j/include/ops/declarable/helpers/zeta.h index 1f4a02747..7aee45f1c 100644 --- a/libnd4j/include/ops/declarable/helpers/zeta.h +++ b/libnd4j/include/ops/declarable/helpers/zeta.h @@ -22,15 +22,15 @@ #define LIBND4J_ZETA_H #include -#include "NDArray.h" +#include "array/NDArray.h" -namespace nd4j { +namespace sd { namespace ops { namespace helpers { // calculate the Hurwitz zeta function for arrays - void zeta(nd4j::LaunchContext * context, const NDArray& x, const NDArray& q, NDArray& output); + void zeta(sd::LaunchContext * context, const NDArray& x, const NDArray& q, NDArray& output); diff --git a/libnd4j/include/ops/declarable/impl/BooleanOp.cpp b/libnd4j/include/ops/declarable/impl/BooleanOp.cpp index ea4838f01..00079f9ae 100644 --- a/libnd4j/include/ops/declarable/impl/BooleanOp.cpp +++ b/libnd4j/include/ops/declarable/impl/BooleanOp.cpp @@ -21,9 +21,9 @@ #include "ops/declarable/BooleanOp.h" #include #include -#include +#include -namespace nd4j { +namespace sd { namespace ops { BooleanOp::BooleanOp(const char *name, int numInputs, bool scalar) : DeclarableOp::DeclarableOp(name, numInputs, scalar) { // @@ -32,11 +32,11 @@ namespace nd4j { /** * Output shape of any BooleanOp is ALWAYS scalar */ - ShapeList *BooleanOp::calculateOutputShape(ShapeList *inputShape, nd4j::graph::Context &block) { + ShapeList *BooleanOp::calculateOutputShape(ShapeList *inputShape, sd::graph::Context &block) { return SHAPELIST(ConstantShapeHelper::getInstance()->scalarShapeInfo(DataType::BOOL)); } - bool BooleanOp::verify(nd4j::graph::Context &block) { + bool BooleanOp::verify(sd::graph::Context &block) { // check if scalar or not // validation? @@ -81,7 +81,7 @@ namespace nd4j { return true; } - Nd4jStatus nd4j::ops::BooleanOp::execute(Context* block) { + Nd4jStatus sd::ops::BooleanOp::execute(Context* block) { // basic validation: ensure inputs are set REQUIRE_OK(this->validateNonEmptyInput(*block)); @@ -115,7 +115,7 @@ namespace nd4j { return ND4J_STATUS_KERNEL_FAILURE; } - bool BooleanOp::verify(const std::vector &args) { + bool BooleanOp::verify(const std::vector &args) { VariableSpace variableSpace; int cnt = -1; diff --git a/libnd4j/include/ops/declarable/impl/BroadcastableOp.cpp b/libnd4j/include/ops/declarable/impl/BroadcastableOp.cpp index 7d696c8ef..eb691b84d 100644 --- a/libnd4j/include/ops/declarable/impl/BroadcastableOp.cpp +++ b/libnd4j/include/ops/declarable/impl/BroadcastableOp.cpp @@ -18,24 +18,24 @@ // Created by raver on 6/6/2018. // -#include -#include +#include +#include #include #include -namespace nd4j { +namespace sd { namespace ops { BroadcastableOp::BroadcastableOp(const char *name, int numTArgs, int numIArgs) : DeclarableCustomOp::DeclarableCustomOp(2, 1, name, false, numTArgs, numIArgs) { // } - ShapeList *BroadcastableOp::calculateOutputShape(ShapeList *inputShape, nd4j::graph::Context &block) { + ShapeList *BroadcastableOp::calculateOutputShape(ShapeList *inputShape, sd::graph::Context &block) { auto shapeList = SHAPELIST(); auto x = inputShape->at(0); auto y = inputShape->at(1); auto outputs = _descriptor->getOutputTypesForOutput(0); - nd4j::DataType dtype = block.dataType(0); - if (block.dataType(0) != nd4j::DataType::BOOL && !(outputs.size() == 1 && outputs[0] == nd4j::DataType::BOOL)) { + sd::DataType dtype = block.dataType(0); + if (block.dataType(0) != sd::DataType::BOOL && !(outputs.size() == 1 && outputs[0] == sd::DataType::BOOL)) { if (Environment::getInstance()->isExperimentalBuild()) { if (shape::length(y) > shape::length(x)) { dtype = DataTypeUtils::pickPairwiseResultType(y, x); @@ -46,7 +46,7 @@ namespace nd4j { dtype = ArrayOptions::dataType(x); } } else - dtype = nd4j::DataType::BOOL; + dtype = sd::DataType::BOOL; if(shape::isEmpty(x) || shape::isEmpty(y)) { // this is edge case, [3, 4] + [] = [] diff --git a/libnd4j/include/ops/declarable/impl/DeclarableCustomOp.cpp b/libnd4j/include/ops/declarable/impl/DeclarableCustomOp.cpp index 1fd57c867..d6227af0c 100644 --- a/libnd4j/include/ops/declarable/impl/DeclarableCustomOp.cpp +++ b/libnd4j/include/ops/declarable/impl/DeclarableCustomOp.cpp @@ -21,9 +21,9 @@ #include #include -namespace nd4j { +namespace sd { namespace ops { - DeclarableCustomOp::DeclarableCustomOp(int numInputs, int numOutputs, const char *opName, bool allowsInplace, int tArgs, int iArgs) : nd4j::ops::DeclarableOp(numInputs, numOutputs, opName, allowsInplace, tArgs, iArgs) { + DeclarableCustomOp::DeclarableCustomOp(int numInputs, int numOutputs, const char *opName, bool allowsInplace, int tArgs, int iArgs) : sd::ops::DeclarableOp(numInputs, numOutputs, opName, allowsInplace, tArgs, iArgs) { // } } diff --git a/libnd4j/include/ops/declarable/impl/DeclarableListOp.cpp b/libnd4j/include/ops/declarable/impl/DeclarableListOp.cpp index 624d6dbef..13aa763f8 100644 --- a/libnd4j/include/ops/declarable/impl/DeclarableListOp.cpp +++ b/libnd4j/include/ops/declarable/impl/DeclarableListOp.cpp @@ -24,7 +24,7 @@ #include #include -namespace nd4j { +namespace sd { namespace ops { DeclarableListOp::DeclarableListOp(int numInputs, int numOutputs, const char* opName, int tArgs, int iArgs) : DeclarableOp::DeclarableOp(numInputs, numOutputs, opName, false, tArgs, iArgs) { // This kind of operations work with sets: NDArrayList @@ -44,14 +44,14 @@ namespace nd4j { * @param block * @return */ - ShapeList* DeclarableListOp::calculateOutputShape(ShapeList* inputShape, nd4j::graph::Context& block) { + ShapeList* DeclarableListOp::calculateOutputShape(ShapeList* inputShape, sd::graph::Context& block) { // TODO: ensure this method isn't ever called auto newShape = ConstantShapeHelper::getInstance()->createShapeInfo(block.dataType(), 'c', {1, 1}); return SHAPELIST(newShape); } - nd4j::NDArray* nd4j::ops::DeclarableListOp::getZ(Context& block, int inputId) { + sd::NDArray* sd::ops::DeclarableListOp::getZ(Context& block, int inputId) { //nd4j_printf("wow\n",""); return nullptr; } @@ -64,7 +64,7 @@ namespace nd4j { block.pushNDArrayListToVariableSpace(block.getNodeId(), 0, arrayList); } - ResultSet* DeclarableListOp::execute(NDArrayList* list, std::initializer_list inputs, std::initializer_list tArgs, std::initializer_list iArgs) { + ResultSet DeclarableListOp::execute(NDArrayList* list, std::initializer_list inputs, std::initializer_list tArgs, std::initializer_list iArgs) { std::vector ins(inputs); std::vector tas(tArgs); std::vector ias(iArgs); @@ -94,7 +94,7 @@ namespace nd4j { return status; } - ResultSet* DeclarableListOp::execute(NDArrayList* list, std::vector& inputs, std::vector& tArgs, std::vector& iArgs) { + ResultSet DeclarableListOp::execute(NDArrayList* list, std::vector& inputs, std::vector& tArgs, std::vector& iArgs) { VariableSpace varSpace; int nodeId = 119; @@ -132,8 +132,8 @@ namespace nd4j { Nd4jStatus result = this->validateAndExecute(block); - auto res = new ResultSet(); - res->setStatus(result); + ResultSet res; + res.setStatus(result); for (int e = 0; e < DataTypeUtils::max(); e++) { std::pair pair(1, e); @@ -143,10 +143,10 @@ namespace nd4j { auto arr = var->getNDArray(); if (arr->isAttached()) { auto d = arr->detach(); - res->push_back(d); + res.push_back(d); } else { var->markRemovable(false); - res->push_back(arr); + res.push_back(arr); } } } else diff --git a/libnd4j/include/ops/declarable/impl/DeclarableOp.cpp b/libnd4j/include/ops/declarable/impl/DeclarableOp.cpp index 9724b6ba5..2c1d52348 100644 --- a/libnd4j/include/ops/declarable/impl/DeclarableOp.cpp +++ b/libnd4j/include/ops/declarable/impl/DeclarableOp.cpp @@ -19,17 +19,17 @@ // #include -#include +#include #include -#include +#include #include -#include +#include #include #include #include #include -namespace nd4j { +namespace sd { namespace ops { Nd4jStatus conditionHelper(const char *file, int line, int condition, int argNumber, const char *format, ...) { if (!condition) { @@ -53,22 +53,27 @@ namespace nd4j { DeclarableOp::DeclarableOp(const char *name, bool isLogical) { _descriptor = new OpDescriptor(name, isLogical); + _name = name; } DeclarableOp::DeclarableOp(const char *name, int numInputs, bool scalar) { _descriptor = new OpDescriptor(numInputs, name, scalar); + _name = name; } DeclarableOp::DeclarableOp(int numInputs, int numOutputs, const char *opName, bool allowsInplace) { _descriptor = new OpDescriptor(numInputs, numOutputs, opName, allowsInplace); + _name = opName; } DeclarableOp::DeclarableOp(int numInputs, int numOutputs, const char *opName, bool allowsInplace, bool divergent) { _descriptor = new OpDescriptor(numInputs, numOutputs, opName, allowsInplace, divergent); + _name = opName; } DeclarableOp::DeclarableOp(int numInputs, int numOutputs, const char *opName, bool allowsInplace, int tArgs, int iArgs) { _descriptor = new OpDescriptor(numInputs, numOutputs, opName, allowsInplace, tArgs, iArgs); + _name = opName; } DeclarableOp::~DeclarableOp() { @@ -92,7 +97,7 @@ namespace nd4j { } - nd4j::NDArray* nd4j::ops::DeclarableOp::getZ(Context& ctx, int inputId) { + sd::NDArray* sd::ops::DeclarableOp::getZ(Context& ctx, int inputId) { NDArray* z = nullptr; if (ctx.isFastPath()) { @@ -136,11 +141,14 @@ namespace nd4j { return z; } - int nd4j::ops::DeclarableOp::prepareOutputs(Context &ctx) { + int sd::ops::DeclarableOp::prepareOutputs(Context &ctx) { auto workspace = ctx.getWorkspace(); GraphProfile *prof = nullptr; NodeProfile *node = nullptr; std::chrono::time_point inputEnd, inputStart, shapeStart, shapeEnd, arrayStart, arrayEnd; + bool canUseFastPath = true; + + auto fp = ctx.isFastPath(); if (Environment::getInstance()->isProfiling()) { if (ctx.getVariableSpace() != nullptr && ctx.getVariableSpace()->flowPath() != nullptr) { @@ -151,7 +159,7 @@ namespace nd4j { if (ctx.isInplace()) { if (Environment::getInstance()->isProfiling() && node != nullptr) { - if (ctx.isFastPath()) { + if (fp) { // } else { for (auto p: *ctx.inputs()) { @@ -166,11 +174,48 @@ namespace nd4j { } } + // if that's not fp, we can still propagate inputs and outputs + if (!fp) { + int cnt = 0; + auto id = ctx.nodeId(); + auto vs = ctx.getVariableSpace(); + for (auto p: *ctx.inputs()) { + auto var = ctx.variable(p); + if (var->variableType() == VariableType::NDARRAY) { + NDArray *array = var->getNDArray(); + ctx.setInputArray(cnt, array); + ctx.setOutputArray(cnt, array); + + + // in case of this override we might need to update outputs in the Graph VariableSpace as well + if (vs != nullptr) { + if (vs->hasVariable(id, cnt)) { + auto v2 = vs->getVariable(id, cnt); + if (!v2->hasNDArray()) { + v2->setNDArray(array); + v2->markRemovable(false); + + } + } else { + auto v2 = vs->putVariable(id, cnt, array); + v2->markRemovable(false); + } + } + + cnt++; + } else { + canUseFastPath = false; + } + } + } + + if (!canUseFastPath) + ctx.forbidFastPath(true); + // do nothing, getZ result will do the trick return static_cast(ctx.width()); } else { // if op is not inplace - we should pre-allocate arrays - ShapeList inSha; int results = 0; @@ -179,11 +224,12 @@ namespace nd4j { int cntIn = 0; // we build list of input shapes - if (ctx.isFastPath()) { + if (fp) { for (const auto p:ctx.fastpath_in()) { inSha.push_back(p == nullptr ? nullptr : p->getShapeInfo()); } } else { + int arrCnt = 0; for (auto p: *ctx.inputs()) { auto var = ctx.variable(p); if (var->variableType() == VariableType::NDARRAY) { @@ -192,13 +238,19 @@ namespace nd4j { throw unresolved_input_exception::build("Variable wasn't resolved prior shape calculation", p); inSha.push_back(array->getShapeInfo()); + + // we're also filling ctx with arrays + if (canUseFastPath) + ctx.setInputArray(arrCnt++, array); + } else { + canUseFastPath = false; } cntIn++; } } // if we override shape function, we'll return size of fastPath - if (ctx.isFastPath() && ctx.shapeFunctionOverride()) { + if (fp && ctx.shapeFunctionOverride()) { return (int) ctx.fastpath_out().size(); } @@ -232,8 +284,9 @@ namespace nd4j { } int cnt = 0; + for (auto out: *outSha->asVector()) { - if (!ctx.isFastPath()) { + if (!fp) { // we need to check, if Z is really needed std::pair pair(ctx.nodeId(), cnt++); @@ -244,11 +297,17 @@ namespace nd4j { auto outArr = new NDArray(out, true, ctx.launchContext()); ctx.pushNDArrayToVariableSpace(pair, outArr); + + if (canUseFastPath) + ctx.setOutputArray(pair.second, outArr); } else { // validate/compare shapes here. existent vs provided in outSha auto var = ctx.variable(pair); auto shape = var->getNDArray()->shapeInfo(); + if (canUseFastPath) + ctx.setOutputArray(pair.second, var->getNDArray()); + if (!shape::equalsSoft(out, shape) || shape::isEmpty(out) != shape::isEmpty(shape)) { auto eShape = ShapeUtils::shapeAsString(out); auto aShape = ShapeUtils::shapeAsString(shape); @@ -265,7 +324,7 @@ namespace nd4j { //checking out data type equality if (ArrayOptions::dataType(out) != ArrayOptions::dataType(shape)) { std::string msg = "Provided array [" + StringUtils::valueToString(pair.second) + "] has unexpected data type"; - throw nd4j::datatype_exception::build(msg, ArrayOptions::dataType(out), ArrayOptions::dataType(shape)); + throw sd::datatype_exception::build(msg, ArrayOptions::dataType(out), ArrayOptions::dataType(shape)); } */ } @@ -289,20 +348,13 @@ namespace nd4j { nd4j_printf("Expected vs provided shape mismatch %s vs %s at index %i\n", eShape.c_str(), aShape.c_str(), idx); throw std::runtime_error("Expected vs provided shape mismatch"); } - - /* - * FIXME: we want to uncomment this eventually, and check data types equality - //checking out data type equality - if (ArrayOptions::dataType(out) != array->dataType()) { - std::string msg = "Provided array [" + StringUtils::valueToString(idx) + "] has unexpected data type"; - throw nd4j::datatype_exception::build(msg, ArrayOptions::dataType(out), array->dataType()); - } - */ } } } - //outSha->destroy(); + if (!canUseFastPath) + ctx.forbidFastPath(true); + delete outSha; // saving arrayTime @@ -316,15 +368,15 @@ namespace nd4j { } } - void nd4j::ops::DeclarableOp::storeResult(Context &block, int outputNumber, NDArray* array) { + void sd::ops::DeclarableOp::storeResult(Context &block, int outputNumber, NDArray* array) { this->storeResult(block, outputNumber, *array); } - void nd4j::ops::DeclarableOp::storeResult(nd4j::graph::Context &ctx, int outputNumber, NDArray& array) { + void sd::ops::DeclarableOp::storeResult(sd::graph::Context &ctx, int outputNumber, NDArray& array) { ctx.pushNDArrayToVariableSpace(ctx.nodeId(), outputNumber, &array, !ctx.isInplace()); } - bool nd4j::ops::DeclarableOp::allocateResult(Context& block, Nd4jLong* shape) { + bool sd::ops::DeclarableOp::allocateResult(Context& block, Nd4jLong* shape) { auto var = block.variable(block.getNodeId(), 0); auto workspace = block.getWorkspace(); @@ -352,7 +404,7 @@ namespace nd4j { } - bool nd4j::ops::DeclarableOp::allocateResult(Context& block, std::initializer_list& shape, char order) { + bool sd::ops::DeclarableOp::allocateResult(Context& block, std::initializer_list& shape, char order) { auto var = block.variable(block.getNodeId(), 0); auto workspace = block.getWorkspace(); @@ -369,7 +421,7 @@ namespace nd4j { return true; } - Nd4jStatus nd4j::ops::DeclarableOp::validateDataTypes(Context& block) { + Nd4jStatus sd::ops::DeclarableOp::validateDataTypes(Context& block) { _registrator.lock(); if (!_registered) { _registered = true; @@ -379,7 +431,7 @@ namespace nd4j { // rolling over inputs first int cnt = 0, inT = 0; - std::vector inputTypes(block.width()); + std::vector inputTypes(block.width()); if (block.isFastPath()) { for (auto array: block.fastpath_in()) { if (array == nullptr) @@ -399,7 +451,7 @@ namespace nd4j { auto var = block.variable(p); // we're not checking validity, if ANY types were explicitly allowed - //if (block.dataType(cnt) == nd4j::DataType::ANY) + //if (block.dataType(cnt) == sd::DataType::ANY) // continue; // only validating non-null variables @@ -531,7 +583,7 @@ namespace nd4j { return ND4J_STATUS_OK; } - Nd4jStatus nd4j::ops::DeclarableOp::execute(Context* block) { + Nd4jStatus sd::ops::DeclarableOp::execute(Context* block) { nd4j_debug("Executing op: [%s]\n", this->getOpName()->c_str()); std::chrono::time_point timeEnter, timeStart, timeEnd; @@ -564,7 +616,7 @@ namespace nd4j { bool hasHelper = false; // platform helpers use might be forbidden for various reasons, so we'll check it out first - if (block->helpersAllowed() && nd4j::Environment::getInstance()->helpersAllowed()) { + if (block->helpersAllowed() && sd::Environment::getInstance()->helpersAllowed()) { // if we have platform-specific helper for this op - invoke it if (OpRegistrator::getInstance()->hasHelper(this->getOpHash(), block->engine())) { auto helper = OpRegistrator::getInstance()->getPlatformHelper(this->getOpHash(), block->engine()); @@ -586,7 +638,7 @@ namespace nd4j { block->setInnerTime(outerTime); } - if (Environment::getInstance()->isProfiling() && !block->isFastPath()) { + if (Environment::getInstance()->isProfiling() && block->getVariableSpace() != nullptr) { auto fp = block->getVariableSpace()->flowPath(); if (fp != nullptr) { auto p = fp->profile(); @@ -602,7 +654,7 @@ namespace nd4j { // now we print out all outputs for this node - if (nd4j::Environment::getInstance()->isDebugAndVerbose()) { + if (sd::Environment::getInstance()->isDebugAndVerbose()) { auto vs = block->getVariableSpace(); for (int e = 0; e < numOutputs; e++) { @@ -670,7 +722,7 @@ namespace nd4j { */ } - Nd4jStatus nd4j::ops::DeclarableOp::validateArguments(Context& block) { + Nd4jStatus sd::ops::DeclarableOp::validateArguments(Context& block) { /* * We're checking number of T and I arguments. If number of args is finite number - we check strict equality * If number of args is variable (-1), but variables MUST be present - we check for non-zero number of arguments @@ -703,7 +755,7 @@ namespace nd4j { return ND4J_STATUS_OK; } - Nd4jStatus nd4j::ops::DeclarableOp::validateInputDimensions(Context& block, int rank) { + Nd4jStatus sd::ops::DeclarableOp::validateInputDimensions(Context& block, int rank) { if (block.width() == 0) return ND4J_STATUS_OK; @@ -721,19 +773,19 @@ namespace nd4j { return ND4J_STATUS_OK; } - Nd4jStatus nd4j::ops::DeclarableOp::validateInput2D(Context& block) { + Nd4jStatus sd::ops::DeclarableOp::validateInput2D(Context& block) { return validateInputDimensions(block, 2); } - Nd4jStatus nd4j::ops::DeclarableOp::validateInput3D(Context& block) { + Nd4jStatus sd::ops::DeclarableOp::validateInput3D(Context& block) { return validateInputDimensions(block, 3); } - Nd4jStatus nd4j::ops::DeclarableOp::validateInput4D(Context& block) { + Nd4jStatus sd::ops::DeclarableOp::validateInput4D(Context& block) { return validateInputDimensions(block, 4); } - Nd4jStatus nd4j::ops::DeclarableOp::validateNonEmptyInput(Context& block) { + Nd4jStatus sd::ops::DeclarableOp::validateNonEmptyInput(Context& block) { if (this->getOpDescriptor()->getNumberOfInputs() == -2 || this->getOpDescriptor()->getNumberOfInputs() == 0) return Status::OK(); @@ -778,7 +830,7 @@ namespace nd4j { return ND4J_STATUS_OK; } - Nd4jStatus nd4j::ops::DeclarableOp::validateOrdersMatch(Context& block) { + Nd4jStatus sd::ops::DeclarableOp::validateOrdersMatch(Context& block) { if (block.width() == 0) return ND4J_STATUS_OK; @@ -793,7 +845,7 @@ namespace nd4j { return ND4J_STATUS_OK; } - Nd4jStatus nd4j::ops::DeclarableOp::execute(nd4j::graph::RandomGenerator& rng, const std::vector& inputs, const std::vector& outputs, const std::vector& tArgs, const std::vector& iArgs, const std::vector& bArgs, const std::vector& dArgs, bool isInplace, nd4j::DataType type) { + Nd4jStatus sd::ops::DeclarableOp::execute(sd::graph::RandomGenerator& rng, const std::vector& inputs, const std::vector& outputs, const std::vector& tArgs, const std::vector& iArgs, const std::vector& bArgs, const std::vector& dArgs, bool isInplace, sd::DataType type) { VariableSpace variableSpace; FlowPath fp; variableSpace.setFlowPath(&fp); @@ -846,16 +898,16 @@ namespace nd4j { } Nd4jStatus DeclarableOp::execute(const std::vector &inputs, const std::vector &outputs) { - return execute(inputs, outputs, std::vector(), std::vector(), std::vector(), std::vector()); + return execute(inputs, outputs, std::vector(), std::vector(), std::vector(), std::vector()); } template <> Nd4jStatus DeclarableOp::execute(const std::vector &inputs, const std::vector &outputs, std::initializer_list tArgs) { - return execute(inputs, outputs, tArgs, std::vector(), std::vector(), std::vector()); + return execute(inputs, outputs, tArgs, std::vector(), std::vector(), std::vector()); } template <> - Nd4jStatus DeclarableOp::execute(const std::vector &inputs, const std::vector &outputs, std::initializer_list dArgs) { + Nd4jStatus DeclarableOp::execute(const std::vector &inputs, const std::vector &outputs, std::initializer_list dArgs) { return execute(inputs, outputs, std::vector(), std::vector(), std::vector(), dArgs); } @@ -865,12 +917,12 @@ namespace nd4j { for (auto v:tArgs) realArgs.emplace_back(v); - return execute(inputs, outputs, realArgs, std::vector(), std::vector(), std::vector()); + return execute(inputs, outputs, realArgs, std::vector(), std::vector(), std::vector()); } template <> Nd4jStatus DeclarableOp::execute(const std::vector &inputs, const std::vector &outputs, std::initializer_list iArgs) { - return execute(inputs, outputs, std::vector(), iArgs, std::vector(), std::vector()); + return execute(inputs, outputs, std::vector(), iArgs, std::vector(), std::vector()); } template <> @@ -879,15 +931,15 @@ namespace nd4j { for (auto v:iArgs) realArgs.emplace_back(v); - return execute(inputs, outputs, std::vector(), realArgs, std::vector(), std::vector()); + return execute(inputs, outputs, std::vector(), realArgs, std::vector(), std::vector()); } template <> Nd4jStatus DeclarableOp::execute(const std::vector &inputs, const std::vector &outputs, std::initializer_list bArgs) { - return execute(inputs, outputs, std::vector(), std::vector(), bArgs, std::vector()); + return execute(inputs, outputs, std::vector(), std::vector(), bArgs, std::vector()); } - Nd4jStatus DeclarableOp::execute(const std::vector &inputs, const std::vector &outputs, const std::vector &tArgs, const std::vector &iArgs, const std::vector &bArgs, const std::vector &dArgs, bool isInplace) { + Nd4jStatus DeclarableOp::execute(const std::vector &inputs, const std::vector &outputs, const std::vector &tArgs, const std::vector &iArgs, const std::vector &bArgs, const std::vector &dArgs, bool isInplace) { Context ctx(1); for (int e = 0; e < inputs.size(); e++) { @@ -910,49 +962,49 @@ namespace nd4j { return execute(&ctx); } - nd4j::ResultSet *DeclarableOp::evaluate(const std::vector &inputs) { - return evaluate(inputs, std::vector(), std::vector(), std::vector(), std::vector()); + sd::ResultSet DeclarableOp::evaluate(const std::vector &inputs) { + return evaluate(inputs, std::vector(), std::vector(), std::vector(), std::vector()); } template <> - nd4j::ResultSet *DeclarableOp::evaluate(const std::vector &inputs, std::initializer_list iArgs) { + sd::ResultSet DeclarableOp::evaluate(const std::vector &inputs, std::initializer_list iArgs) { std::vector realArgs; for (auto v:iArgs) realArgs.emplace_back(v); - return evaluate(inputs, std::vector(), realArgs, std::vector(), std::vector()); + return evaluate(inputs, std::vector(), realArgs, std::vector(), std::vector()); } template <> - nd4j::ResultSet *DeclarableOp::evaluate(const std::vector &inputs, std::initializer_list iArgs) { - return evaluate(inputs, std::vector(), iArgs, std::vector(), std::vector()); + sd::ResultSet DeclarableOp::evaluate(const std::vector &inputs, std::initializer_list iArgs) { + return evaluate(inputs, std::vector(), iArgs, std::vector(), std::vector()); } template <> - nd4j::ResultSet *DeclarableOp::evaluate(const std::vector &inputs, std::initializer_list tArgs) { + sd::ResultSet DeclarableOp::evaluate(const std::vector &inputs, std::initializer_list tArgs) { std::vector realArgs; for (auto v:tArgs) realArgs.emplace_back(v); - return evaluate(inputs, realArgs, std::vector(), std::vector(), std::vector()); + return evaluate(inputs, realArgs, std::vector(), std::vector(), std::vector()); } template <> - nd4j::ResultSet *DeclarableOp::evaluate(const std::vector &inputs, std::initializer_list tArgs) { - return evaluate(inputs, tArgs, std::vector(), std::vector(), std::vector()); + sd::ResultSet DeclarableOp::evaluate(const std::vector &inputs, std::initializer_list tArgs) { + return evaluate(inputs, tArgs, std::vector(), std::vector(), std::vector()); } template <> - nd4j::ResultSet *DeclarableOp::evaluate(const std::vector &inputs, std::initializer_list bArgs) { - return evaluate(inputs, std::vector(), std::vector(), bArgs, std::vector()); + sd::ResultSet DeclarableOp::evaluate(const std::vector &inputs, std::initializer_list bArgs) { + return evaluate(inputs, std::vector(), std::vector(), bArgs, std::vector()); } template <> - nd4j::ResultSet *DeclarableOp::evaluate(const std::vector &inputs, std::initializer_list bArgs) { + sd::ResultSet DeclarableOp::evaluate(const std::vector &inputs, std::initializer_list bArgs) { return evaluate(inputs, std::vector(), std::vector(), std::vector(), bArgs); } - nd4j::ResultSet *DeclarableOp::evaluate(const std::vector &inputs, const std::vector &tArgs, const std::vector &iArgs, const std::vector &bArgs, const std::vector &dArgs, bool isInplace) { + sd::ResultSet DeclarableOp::evaluate(const std::vector &inputs, const std::vector &tArgs, const std::vector &iArgs, const std::vector &bArgs, const std::vector &dArgs, bool isInplace) { VariableSpace variableSpace; //ResultSet arrayList; FlowPath fp; @@ -971,7 +1023,7 @@ namespace nd4j { } Context block(1, &variableSpace, false); - block.setDataType(0, nd4j::DataType::FLOAT32); + block.setDataType(0, sd::DataType::FLOAT32); block.fillInputs(in); block.markInplace(isInplace); // block.setRNG(ProviderRNG::getInstance().getRNG()); @@ -989,40 +1041,45 @@ namespace nd4j { block.getDArguments()->push_back(dArgs.at(e)); Nd4jStatus status = this->execute(&block); - auto arrayList = new ResultSet(); + ResultSet arrayList; if (isInplace) - arrayList->setNonRemovable(); + arrayList.setNonRemovable(); - arrayList->setStatus(status); + arrayList.setStatus(status); if (status != ND4J_STATUS_OK) return arrayList; - - for (int e = 0; e < DataTypeUtils::max(); e++) { - std::pair pair(1, e); - if (variableSpace.hasVariable(pair)) { - auto var = variableSpace.getVariable(pair); - auto arr = var->getNDArray(); - if (!arr->isAttached()) { - var->markRemovable(false); - arr->setContext(nd4j::LaunchContext ::defaultContext()); - arrayList->push_back(arr); - } else { - arrayList->push_back(arr->detach()); - } - } else - break; + if (!isInplace) { + for (int e = 0; e < DataTypeUtils::max(); e++) { + std::pair pair(1, e); + if (variableSpace.hasVariable(pair)) { + auto var = variableSpace.getVariable(pair); + auto arr = var->getNDArray(); + if (!arr->isAttached()) { + var->markRemovable(false); + arr->setContext(sd::LaunchContext::defaultContext()); + arrayList.push_back(arr); + } else { + arrayList.push_back(arr->detach()); + } + } else + break; + } + } else { + for (auto v:inputs) { + arrayList.push_back(v); + } } return arrayList; } - nd4j::ResultSet* nd4j::ops::DeclarableOp::execute(const nd4j::OpArgsHolder& holder, bool isInplace) { + sd::ResultSet sd::ops::DeclarableOp::execute(const sd::OpArgsHolder& holder, bool isInplace) { // FIXME: add DArgs to OpArgsHolder - return evaluate(holder.getInArrs(), holder.getTArgs(), holder.getIArgs(), holder.getBArgs(), std::vector(), isInplace); + return evaluate(holder.getInArrs(), holder.getTArgs(), holder.getIArgs(), holder.getBArgs(), std::vector(), isInplace); } - Nd4jStatus nd4j::ops::DeclarableOp::validateInputDimensionsMatch(Context& block) { + Nd4jStatus sd::ops::DeclarableOp::validateInputDimensionsMatch(Context& block) { if (block.width() == 0) return ND4J_STATUS_OK; @@ -1036,7 +1093,7 @@ namespace nd4j { return ND4J_STATUS_OK; } - Nd4jStatus nd4j::ops::DeclarableOp::validateInputLengthMatch(Context& block) { + Nd4jStatus sd::ops::DeclarableOp::validateInputLengthMatch(Context& block) { if (block.width() == 0) return ND4J_STATUS_OK; @@ -1060,7 +1117,7 @@ namespace nd4j { /* template - int* nd4j::ops::DeclarableOp::calculateOutputShape(int* inputShape, nd4j::graph::Block& block) { + int* sd::ops::DeclarableOp::calculateOutputShape(int* inputShape, sd::graph::Block& block) { // default implementation suits transform, so just returns the same shape int* newshape; diff --git a/libnd4j/include/ops/declarable/impl/DeclarableReductionOp.cpp b/libnd4j/include/ops/declarable/impl/DeclarableReductionOp.cpp index 98a60b28b..4f6646694 100644 --- a/libnd4j/include/ops/declarable/impl/DeclarableReductionOp.cpp +++ b/libnd4j/include/ops/declarable/impl/DeclarableReductionOp.cpp @@ -25,13 +25,13 @@ #include #include -namespace nd4j { +namespace sd { namespace ops { - DeclarableReductionOp::DeclarableReductionOp(int numInputs, int numOutputs, const char *opName, bool allowsInplace, int tArgs, int iArgs) : nd4j::ops::DeclarableOp(numInputs, numOutputs, opName, allowsInplace, tArgs, iArgs) { + DeclarableReductionOp::DeclarableReductionOp(int numInputs, int numOutputs, const char *opName, bool allowsInplace, int tArgs, int iArgs) : sd::ops::DeclarableOp(numInputs, numOutputs, opName, allowsInplace, tArgs, iArgs) { // } - nd4j::ShapeList* DeclarableReductionOp::calculateOutputShape(nd4j::ShapeList* inputShape, nd4j::graph::Context& block) { + sd::ShapeList* DeclarableReductionOp::calculateOutputShape(sd::ShapeList* inputShape, sd::graph::Context& block) { // int numDims = INT_ARG(0); std::vector dims; if (inputShape->size() > 1) { @@ -51,7 +51,7 @@ namespace nd4j { std::sort(dims.begin(), dims.end()); // special case - output is scalar - if (dims.size() == 0 || (dims.size() == 1 && dims.at(0) == nd4j::DataTypeUtils::max())) { + if (dims.size() == 0 || (dims.size() == 1 && dims.at(0) == sd::DataTypeUtils::max())) { auto newShape = ConstantShapeHelper::getInstance()->scalarShapeInfo(block.dataType()); return SHAPELIST(newShape); } diff --git a/libnd4j/include/ops/declarable/impl/LegacyBroadcastBoolOp.cpp b/libnd4j/include/ops/declarable/impl/LegacyBroadcastBoolOp.cpp index 0cd91e3b5..03f34d269 100644 --- a/libnd4j/include/ops/declarable/impl/LegacyBroadcastBoolOp.cpp +++ b/libnd4j/include/ops/declarable/impl/LegacyBroadcastBoolOp.cpp @@ -19,13 +19,13 @@ // #include -#include +#include #include #include -#include +#include -namespace nd4j { +namespace sd { namespace ops { Nd4jStatus LegacyBroadcastBoolOp::validateAndExecute(Context &block) { auto x = INPUT_VARIABLE(0); @@ -41,7 +41,7 @@ namespace nd4j { int opNum = block.opNum() < 0 ? this->_opNum : block.opNum(); - auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(x->getShapeInfo(), dims); + auto packX = sd::ConstantTadHelper::getInstance()->tadForDimensions(x->getShapeInfo(), dims); PointersManager manager(block.launchContext(), "LegacyBroadcastBoolOp"); auto pTadShape = Environment::getInstance()->isCPU() ? packX.primaryShapeInfo() : packX.specialShapeInfo(); //(Nd4jLong *) manager.replicatePointer(tad.tadOnlyShapeInfo, shape::shapeInfoByteLength(tad.tadOnlyShapeInfo)); @@ -57,7 +57,7 @@ namespace nd4j { else { // this is rare, but possible use case - X and Z might have different shapes/strides/orders. In this case we prepare and pass separate TAD info - auto packZ = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(z->getShapeInfo(), dims); + auto packZ = sd::ConstantTadHelper::getInstance()->tadForDimensions(z->getShapeInfo(), dims); auto zTadShape = Environment::getInstance()->isCPU() ? packZ.primaryShapeInfo() : packZ.specialShapeInfo(); //(Nd4jLong *) manager.replicatePointer(tadZ.tadOnlyShapeInfo, shape::shapeInfoByteLength(tadZ.tadOnlyShapeInfo)); auto zTadOffsets = Environment::getInstance()->isCPU() ? packZ.primaryOffsets() : packZ.specialOffsets(); //(Nd4jLong *) manager.replicatePointer(tadZ.tadOffsets, tadZ.numTads * sizeof(Nd4jLong)); @@ -89,7 +89,7 @@ namespace nd4j { /** * If external NDArray wasn't specified - the same shape is returned by all broadcast ops. */ - ShapeList* LegacyBroadcastBoolOp::calculateOutputShape(ShapeList *inputShape, nd4j::graph::Context &block) { + ShapeList* LegacyBroadcastBoolOp::calculateOutputShape(ShapeList *inputShape, sd::graph::Context &block) { auto inShape = inputShape->at(0); return SHAPELIST(ConstantShapeHelper::getInstance()->createShapeInfo(ShapeDescriptor(inShape, DataType::BOOL))); } diff --git a/libnd4j/include/ops/declarable/impl/LegacyBroadcastOp.cpp b/libnd4j/include/ops/declarable/impl/LegacyBroadcastOp.cpp index 4dee50c13..0297df28a 100644 --- a/libnd4j/include/ops/declarable/impl/LegacyBroadcastOp.cpp +++ b/libnd4j/include/ops/declarable/impl/LegacyBroadcastOp.cpp @@ -23,9 +23,9 @@ #include #include #include -#include +#include -namespace nd4j { +namespace sd { namespace ops { Nd4jStatus LegacyBroadcastOp::validateAndExecute(Context &block) { auto x = INPUT_VARIABLE(0); @@ -47,7 +47,7 @@ namespace nd4j { int opNum = block.opNum() < 0 ? this->_opNum : block.opNum(); - auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(x->getShapeInfo(), dims); + auto packX = sd::ConstantTadHelper::getInstance()->tadForDimensions(x->getShapeInfo(), dims); auto tadLen = shape::length(packX.primaryShapeInfo()); REQUIRE_TRUE(tadLen == y->lengthOf(), 0, "Length of broadcast TAD should be equal to length of Y operand, but got [%i] vs [%i]",tadLen, (int) y->lengthOf()); @@ -62,7 +62,7 @@ namespace nd4j { z->buffer(), z->shapeInfo(), z->specialBuffer(), z->specialShapeInfo(), dims.data(), dims.size(), pTadShape, pTadOffsets, pTadShape, pTadOffsets); else { // this is rare, but possible use case - X and Z might have different shapes/strides/orders. In this case we prepare and pass separate TAD info - auto packZ = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(z->getShapeInfo(), dims); + auto packZ = sd::ConstantTadHelper::getInstance()->tadForDimensions(z->getShapeInfo(), dims); auto zTadShape = Environment::getInstance()->isCPU() ? packZ.primaryShapeInfo() : packZ.specialShapeInfo(); //(Nd4jLong *) manager.replicatePointer(tadZ.tadOnlyShapeInfo, shape::shapeInfoByteLength(tadZ.tadOnlyShapeInfo)); auto zTadOffsets = Environment::getInstance()->isCPU() ? packZ.primaryOffsets() : packZ.specialOffsets(); //(Nd4jLong *) manager.replicatePointer(tadZ.tadOffsets, tadZ.numTads * sizeof(Nd4jLong)); @@ -94,7 +94,7 @@ namespace nd4j { /** * If external NDArray wasn't specified - the same shape is returned by all broadcast ops. */ - ShapeList* LegacyBroadcastOp::calculateOutputShape(ShapeList *inputShape, nd4j::graph::Context &block) { + ShapeList* LegacyBroadcastOp::calculateOutputShape(ShapeList *inputShape, sd::graph::Context &block) { auto inShape = inputShape->at(0); // FIXME: remove memcpy diff --git a/libnd4j/include/ops/declarable/impl/LegacyIndexReduceOp.cpp b/libnd4j/include/ops/declarable/impl/LegacyIndexReduceOp.cpp index c63139bb2..c92577f3b 100644 --- a/libnd4j/include/ops/declarable/impl/LegacyIndexReduceOp.cpp +++ b/libnd4j/include/ops/declarable/impl/LegacyIndexReduceOp.cpp @@ -21,11 +21,11 @@ #include #include #include -#include +#include #include -namespace nd4j { +namespace sd { namespace ops { LegacyIndexReduceOp::LegacyIndexReduceOp() : LegacyOp::LegacyOp(1){ // @@ -39,7 +39,7 @@ namespace nd4j { return new LegacyIndexReduceOp(this->_opNum); } - ShapeList *LegacyIndexReduceOp::calculateOutputShape(ShapeList *inputShape, nd4j::graph::Context &block) { + ShapeList *LegacyIndexReduceOp::calculateOutputShape(ShapeList *inputShape, sd::graph::Context &block) { auto inShape = inputShape->at(0); Nd4jLong *newShape; @@ -139,7 +139,7 @@ namespace nd4j { if (dims.size() > 1) std::sort(dims.begin(), dims.end()); - auto tadPack = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(x->shapeInfo(), dims); + auto tadPack = sd::ConstantTadHelper::getInstance()->tadForDimensions(x->shapeInfo(), dims); NativeOpExecutioner::execIndexReduce(block.launchContext(), opNum, x->getBuffer(), x->getShapeInfo(), x->getSpecialBuffer(), x->getSpecialShapeInfo(), @@ -175,7 +175,7 @@ namespace nd4j { REQUIRE_TRUE(axis.size() > 0, 0, "Some dimensions required for reduction!"); - auto tadPack = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(x->shapeInfo(), axis); + auto tadPack = sd::ConstantTadHelper::getInstance()->tadForDimensions(x->shapeInfo(), axis); NativeOpExecutioner::execIndexReduce(block.launchContext(), opNum, x->getBuffer(), x->getShapeInfo(), x->getSpecialBuffer(), x->getSpecialShapeInfo(), diff --git a/libnd4j/include/ops/declarable/impl/LegacyOp.cpp b/libnd4j/include/ops/declarable/impl/LegacyOp.cpp index e9920c409..c179488df 100644 --- a/libnd4j/include/ops/declarable/impl/LegacyOp.cpp +++ b/libnd4j/include/ops/declarable/impl/LegacyOp.cpp @@ -21,7 +21,7 @@ #include -namespace nd4j { +namespace sd { namespace ops { LegacyOp::LegacyOp(int numInputs) : DeclarableOp::DeclarableOp(numInputs , 1, "LegacyOp", false) { _numInputs = numInputs; diff --git a/libnd4j/include/ops/declarable/impl/LegacyPairwiseTransformBoolOp.cpp b/libnd4j/include/ops/declarable/impl/LegacyPairwiseTransformBoolOp.cpp index 86eee11d5..eb75141a9 100644 --- a/libnd4j/include/ops/declarable/impl/LegacyPairwiseTransformBoolOp.cpp +++ b/libnd4j/include/ops/declarable/impl/LegacyPairwiseTransformBoolOp.cpp @@ -22,7 +22,7 @@ #include -namespace nd4j { +namespace sd { namespace ops { LegacyPairwiseTransformBoolOp::LegacyPairwiseTransformBoolOp() : LegacyOp::LegacyOp(2) { // just a no-op @@ -43,11 +43,8 @@ namespace nd4j { NDArray::prepareSpecialUse({z}, {x, y}); - if (!x->isSameShape(y)) { - std::string sx = ShapeUtils::shapeAsString(x); - std::string sy = ShapeUtils::shapeAsString(y); - REQUIRE_TRUE(x->isSameShape(y) || y->isScalar(), 0, "Node_%i: For Pairwise transforms shapes of both operands should be equal but got %s vs %s", block.getNodeId(), sx.c_str(), sy.c_str()); - } + if (!x->isSameShape(y)) + REQUIRE_TRUE(x->isSameShape(y) || y->isScalar(), 0, "Node_%i: For Pairwise transforms shapes of both operands should be equal but got %s vs %s", block.getNodeId(), ShapeUtils::shapeAsString(x).c_str(), ShapeUtils::shapeAsString(y).c_str()); int opNum = block.opNum() < 0 ? this->_opNum : block.opNum(); @@ -68,7 +65,7 @@ namespace nd4j { /** * Output shape of PWT operations always the same as input[0] shape, no exclusions. */ - ShapeList *LegacyPairwiseTransformBoolOp::calculateOutputShape(ShapeList *inputShape, nd4j::graph::Context &block) { + ShapeList *LegacyPairwiseTransformBoolOp::calculateOutputShape(ShapeList *inputShape, sd::graph::Context &block) { auto inShape = inputShape->at(0); return SHAPELIST(ConstantShapeHelper::getInstance()->createShapeInfo(ShapeDescriptor(inShape, DataType::BOOL))); } diff --git a/libnd4j/include/ops/declarable/impl/LegacyPairwiseTransformOp.cpp b/libnd4j/include/ops/declarable/impl/LegacyPairwiseTransformOp.cpp index 49f896be1..7f6eecb19 100644 --- a/libnd4j/include/ops/declarable/impl/LegacyPairwiseTransformOp.cpp +++ b/libnd4j/include/ops/declarable/impl/LegacyPairwiseTransformOp.cpp @@ -22,7 +22,7 @@ #include -namespace nd4j { +namespace sd { namespace ops { LegacyPairwiseTransformOp::LegacyPairwiseTransformOp() : LegacyOp::LegacyOp(2) { this->getOpDescriptor()->allowInplace(true); @@ -43,11 +43,8 @@ namespace nd4j { NDArray::prepareSpecialUse({z}, {x, y}); - if (!x->isSameShape(y)) { - std::string sx = ShapeUtils::shapeAsString(x); - std::string sy = ShapeUtils::shapeAsString(y); - REQUIRE_TRUE(x->isSameShape(y) || y->isScalar(), 0, "Node_%i: For Pairwise transforms shapes of both operands should be equal but got %s vs %s", block.getNodeId(), sx.c_str(), sy.c_str()); - } + if (!x->isSameShape(y)) + REQUIRE_TRUE(x->isSameShape(y) || y->isScalar(), 0, "Node_%i: For Pairwise transforms shapes of both operands should be equal but got %s vs %s", block.getNodeId(), ShapeUtils::shapeAsString(x).c_str(), ShapeUtils::shapeAsString(y).c_str()); int opNum = block.opNum() < 0 ? this->_opNum : block.opNum(); @@ -68,7 +65,7 @@ namespace nd4j { /** * Output shape of PWT operations always the same as input[0] shape, no exclusions. */ - ShapeList *LegacyPairwiseTransformOp::calculateOutputShape(ShapeList *inputShape, nd4j::graph::Context &block) { + ShapeList *LegacyPairwiseTransformOp::calculateOutputShape(ShapeList *inputShape, sd::graph::Context &block) { auto inShape = inputShape->at(0); Nd4jLong *newShape; diff --git a/libnd4j/include/ops/declarable/impl/LegacyRandomOp.cpp b/libnd4j/include/ops/declarable/impl/LegacyRandomOp.cpp index 731c5a5f9..eeb80f403 100644 --- a/libnd4j/include/ops/declarable/impl/LegacyRandomOp.cpp +++ b/libnd4j/include/ops/declarable/impl/LegacyRandomOp.cpp @@ -20,12 +20,12 @@ #include #include -#include -#include -#include +#include +#include +#include #include -namespace nd4j { +namespace sd { namespace ops { LegacyRandomOp::LegacyRandomOp() : LegacyOp::LegacyOp(1) { // just a no-op @@ -61,7 +61,7 @@ namespace nd4j { (12, randomOps::AlphaDropOut) */ switch(opNum) { - case nd4j::random::UniformDistribution: { + case sd::random::UniformDistribution: { // uniform distribution T from, to; if (block.width() > 2) { @@ -87,7 +87,7 @@ namespace nd4j { //OVERWRITE_RESULT(z); } break; - case nd4j::random::DropOut: { + case sd::random::DropOut: { auto z = OUTPUT_VARIABLE(0); T prob; @@ -108,13 +108,13 @@ namespace nd4j { RandomLauncher::applyDropOut(block.launchContext(), block.randomGenerator(), z, prob); } break; - case nd4j::random::DropOutInverted: { + case sd::random::DropOutInverted: { auto z = OUTPUT_VARIABLE(0); - nd4j::ops::dropout op; + sd::ops::dropout op; return op.execute(&block); } break; - case nd4j::random::GaussianDistribution: { + case sd::random::GaussianDistribution: { // gaussian distribution T mean, stdev; if (block.width() > 2) { @@ -146,7 +146,7 @@ namespace nd4j { //OVERWRITE_RESULT(z); } break; - case nd4j::random::BernoulliDistribution: { + case sd::random::BernoulliDistribution: { // bernoulli distribution T prob; if (block.width() > 1) { @@ -174,7 +174,7 @@ namespace nd4j { //OVERWRITE_RESULT(z); } break; - case nd4j::random::BinomialDistributionEx: { + case sd::random::BinomialDistributionEx: { // BinomialEx distribution T prob; int trials; @@ -207,7 +207,7 @@ namespace nd4j { //OVERWRITE_RESULT(z); } break; - case nd4j::random::LogNormalDistribution: { + case sd::random::LogNormalDistribution: { // lognorm distribution T mean, stdev; if (block.width() > 2) { @@ -239,7 +239,7 @@ namespace nd4j { //OVERWRITE_RESULT(z); } break; - case nd4j::random::TruncatedNormalDistribution: { + case sd::random::TruncatedNormalDistribution: { // truncated norm distribution T mean, stdev; if (block.width() > 2) { @@ -266,12 +266,9 @@ namespace nd4j { auto z = OUTPUT_VARIABLE(0); // NDArrayFactory::create_('c', shape, block.getWorkspace()); RandomLauncher::fillTruncatedNormal(block.launchContext(), block.randomGenerator(), z, mean, stdev); - - // FIXME: !!! - //OVERWRITE_RESULT(z); } break; - case nd4j::random::AlphaDropOut: { + case sd::random::AlphaDropOut: { auto z = OUTPUT_VARIABLE(0); T prob, a, b, pa; @@ -304,7 +301,7 @@ namespace nd4j { RandomLauncher::applyAlphaDropOut(block.launchContext(), block.randomGenerator(), z, prob, a, b, pa); } break; - case nd4j::random::Linspace: { + case sd::random::Linspace: { auto z = OUTPUT_VARIABLE(0); auto start = INPUT_VARIABLE(0); auto finish = INPUT_VARIABLE(1); @@ -334,7 +331,7 @@ namespace nd4j { * But these ops already have CustomOp implementations. * */ - ShapeList *LegacyRandomOp::calculateOutputShape(ShapeList *inputShape, nd4j::graph::Context &block) { + ShapeList *LegacyRandomOp::calculateOutputShape(ShapeList *inputShape, sd::graph::Context &block) { auto inShape = inputShape->at(0); auto xType = ArrayOptions::dataType(inShape); Nd4jLong *newShape; @@ -357,20 +354,20 @@ namespace nd4j { return DeclarableOp::execute(block); } - nd4j::ResultSet* LegacyRandomOp::execute(nd4j::graph::RandomGenerator& rng, std::initializer_list inputs, std::initializer_list tArgs, std::initializer_list iArgs, bool isInplace) { + sd::ResultSet LegacyRandomOp::execute(sd::graph::RandomGenerator& rng, std::initializer_list inputs, std::initializer_list tArgs, std::initializer_list iArgs, bool isInplace) { std::vector ins(inputs); std::vector tas(tArgs); std::vector ias(iArgs); return this->execute(rng, ins, tas, ias, isInplace); } - nd4j::ResultSet* LegacyRandomOp::execute(nd4j::graph::RandomGenerator& rng, std::vector& inputs, std::vector& tArgs, std::vector& iArgs, bool isInplace) { + sd::ResultSet LegacyRandomOp::execute(sd::graph::RandomGenerator& rng, std::vector& inputs, std::vector& tArgs, std::vector& iArgs, bool isInplace) { VariableSpace variableSpace; - auto arrayList = new ResultSet(); + ResultSet arrayList; //ResultSet arrayList; if (isInplace) - arrayList->setNonRemovable(); + arrayList.setNonRemovable(); int cnt = -1; std::vector in; @@ -398,7 +395,7 @@ namespace nd4j { block.getIArguments()->emplace_back(iArgs.at(e)); Nd4jStatus status = this->execute(&block); - arrayList->setStatus(status); + arrayList.setStatus(status); if (status != ND4J_STATUS_OK) return arrayList; @@ -410,9 +407,9 @@ namespace nd4j { auto arr = var->getNDArray(); if (!arr->isAttached()) { var->markRemovable(false); - arrayList->push_back(arr); + arrayList.push_back(arr); } else { - arrayList->push_back(arr->detach()); + arrayList.push_back(arr->detach()); } } else break; @@ -421,6 +418,32 @@ namespace nd4j { return arrayList; } + Nd4jStatus LegacyRandomOp::validateDataTypes(Context& block) { + if (block.isFastPath()) { + // in this case we'll roll through pre-defined outputs + auto fpo = block.fastpath_out(); + for (auto v:fpo) { + if (v != nullptr) { + if (!v->isR()) + return ND4J_STATUS_BAD_ARGUMENTS; + } + } + } else { + std::pair pair(block.nodeId(), 0); + if (block.getVariableSpace()->hasVariable(pair)) { + auto var = block.variable(pair); + if (!var->hasNDArray()) + return ND4J_STATUS_BAD_ARGUMENTS; + + auto arr = var->getNDArray(); + if (!arr->isR()) + return ND4J_STATUS_BAD_ARGUMENTS; + } + } + + return Status::OK(); + } + BUILD_SINGLE_TEMPLATE(template Nd4jStatus LegacyRandomOp::validateAndExecute_, (Context&), FLOAT_TYPES); } -} \ No newline at end of file +} diff --git a/libnd4j/include/ops/declarable/impl/LegacyReduce3Op.cpp b/libnd4j/include/ops/declarable/impl/LegacyReduce3Op.cpp index 684f09262..7143c3bbd 100644 --- a/libnd4j/include/ops/declarable/impl/LegacyReduce3Op.cpp +++ b/libnd4j/include/ops/declarable/impl/LegacyReduce3Op.cpp @@ -24,7 +24,7 @@ #include #include -namespace nd4j { +namespace sd { namespace ops { Nd4jStatus LegacyReduce3Op::validateAndExecute(Context &block) { auto x = INPUT_VARIABLE(0); @@ -40,7 +40,7 @@ namespace nd4j { ExtraArguments extras(*block.getTArguments()); PointersManager manager(block.launchContext(), "LegacyReduce3Op"); - if (x->isSameShape(y) && (block.getIArguments()->size() == 0 || (block.getIArguments()->size() == 1 && INT_ARG(0) == nd4j::DataTypeUtils::max()))) { + if (x->isSameShape(y) && (block.getIArguments()->size() == 0 || (block.getIArguments()->size() == 1 && INT_ARG(0) == sd::DataTypeUtils::max()))) { // reduce3 to scalar NativeOpExecutioner::execReduce3Scalar(block.launchContext(), opNum, x->buffer(), x->shapeInfo(), x->specialBuffer(), x->specialShapeInfo(), extras.argumentsAsT(z->dataType()), @@ -52,8 +52,8 @@ namespace nd4j { if (dims[e] < 0) dims[e] += x->rankOf(); - auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(x->getShapeInfo(), dims); - auto packZ = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(z->getShapeInfo(), dims); + auto packX = sd::ConstantTadHelper::getInstance()->tadForDimensions(x->getShapeInfo(), dims); + auto packZ = sd::ConstantTadHelper::getInstance()->tadForDimensions(z->getShapeInfo(), dims); REQUIRE_TRUE(dims.size() > 0, 0, "Some dimensions requuired for reduction!"); @@ -92,13 +92,13 @@ namespace nd4j { * For all reductions rules are simple: either you return scalar, or you return reduced NDArray. * It solely depends on input shape, and requested dimensions */ - ShapeList *LegacyReduce3Op::calculateOutputShape(ShapeList *inputShape, nd4j::graph::Context &block) { + ShapeList *LegacyReduce3Op::calculateOutputShape(ShapeList *inputShape, sd::graph::Context &block) { auto xShape = inputShape->at(0); auto yShape = inputShape->at(1); Nd4jLong *zShape = nullptr; - if (shape::equalsSoft(xShape, yShape) && (block.getIArguments()->size() == 0 || (block.getIArguments()->size() == 1 && INT_ARG(0) == nd4j::DataTypeUtils::max()))) { + if (shape::equalsSoft(xShape, yShape) && (block.getIArguments()->size() == 0 || (block.getIArguments()->size() == 1 && INT_ARG(0) == sd::DataTypeUtils::max()))) { // reduce3 to scalar case ALLOCATE(zShape, block.getWorkspace(), shape::shapeInfoLength(2), Nd4jLong); zShape[0] = 2; diff --git a/libnd4j/include/ops/declarable/impl/LegacyReduceBoolOp.cpp b/libnd4j/include/ops/declarable/impl/LegacyReduceBoolOp.cpp index 12a25537d..433e173fc 100644 --- a/libnd4j/include/ops/declarable/impl/LegacyReduceBoolOp.cpp +++ b/libnd4j/include/ops/declarable/impl/LegacyReduceBoolOp.cpp @@ -21,11 +21,11 @@ #include #include #include -#include +#include #include #include -namespace nd4j { +namespace sd { namespace ops { LegacyReduceBoolOp::LegacyReduceBoolOp() : LegacyOp::LegacyOp(1) { // @@ -61,7 +61,7 @@ namespace nd4j { allAxes = true; if ((axis.empty()) || - (axis.size() == 1 && axis[0] == nd4j::DataTypeUtils::max()) || allAxes) { + (axis.size() == 1 && axis[0] == sd::DataTypeUtils::max()) || allAxes) { // scalar NativeOpExecutioner::execReduceBoolScalar(block.launchContext(), opNum, x->getBuffer(), x->getShapeInfo(), x->specialBuffer(), x->specialShapeInfo(), extras.argumentsAsT(x->dataType()), z->buffer(), z->shapeInfo(), z->specialBuffer(), z->specialShapeInfo()); @@ -75,7 +75,7 @@ namespace nd4j { REQUIRE_TRUE(dims.size() > 0, 0, "Some dimensions required for reduction!"); - auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(x->getShapeInfo(), dims); + auto packX = sd::ConstantTadHelper::getInstance()->tadForDimensions(x->getShapeInfo(), dims); auto pTadShape = Environment::getInstance()->isCPU() ? packX.primaryShapeInfo() : packX.specialShapeInfo(); //manager.replicatePointer(tad.tadOnlyShapeInfo, shape::shapeInfoByteLength(tad.tadOnlyShapeInfo)); auto pTadOffsets = Environment::getInstance()->isCPU() ? packX.primaryOffsets() : packX.specialOffsets(); //manager.replicatePointer(tad.tadOffsets, tad.numTads * sizeof(Nd4jLong)); @@ -101,7 +101,7 @@ namespace nd4j { dims[e] = f >= 0 ? f : f += x->rankOf(); } - if ((block.getIArguments()->size() == 1 && INT_ARG(0) == nd4j::DataTypeUtils::max()) || allAxes) { + if ((block.getIArguments()->size() == 1 && INT_ARG(0) == sd::DataTypeUtils::max()) || allAxes) { // scalar NativeOpExecutioner::execReduceBoolScalar(block.launchContext(), opNum, x->getBuffer(), x->getShapeInfo(), x->specialBuffer(), x->specialShapeInfo(), extras.argumentsAsT(x->dataType()), z->buffer(), z->shapeInfo(), z->specialBuffer(), z->specialShapeInfo()); } else { @@ -111,7 +111,7 @@ namespace nd4j { REQUIRE_TRUE(dims.size() > 0, 0, "Some dimensions required for reduction!"); - auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(x->getShapeInfo(), dims); + auto packX = sd::ConstantTadHelper::getInstance()->tadForDimensions(x->getShapeInfo(), dims); auto pTadShape = Environment::getInstance()->isCPU() ? packX.primaryShapeInfo() : packX.specialShapeInfo(); //(Nd4jLong *) manager.replicatePointer(tad.tadOnlyShapeInfo, shape::shapeInfoByteLength(tad.tadOnlyShapeInfo)); auto pTadOffsets = Environment::getInstance()->isCPU() ? packX.primaryOffsets() : packX.specialOffsets(); //(Nd4jLong *) manager.replicatePointer(tad.tadOffsets, tad.numTads * sizeof(Nd4jLong)); @@ -129,7 +129,7 @@ namespace nd4j { * For all reductions rules are simple: either you return scalar, or you return reduced NDArray. * It solely depends on input shape, and requested dimensions */ - ShapeList *LegacyReduceBoolOp::calculateOutputShape(ShapeList *inputShape, nd4j::graph::Context &block) { + ShapeList *LegacyReduceBoolOp::calculateOutputShape(ShapeList *inputShape, sd::graph::Context &block) { auto inShape = inputShape->at(0); Nd4jLong *newShape; diff --git a/libnd4j/include/ops/declarable/impl/LegacyReduceFloatOp.cpp b/libnd4j/include/ops/declarable/impl/LegacyReduceFloatOp.cpp index 2765e1b3f..23f863ba2 100644 --- a/libnd4j/include/ops/declarable/impl/LegacyReduceFloatOp.cpp +++ b/libnd4j/include/ops/declarable/impl/LegacyReduceFloatOp.cpp @@ -21,11 +21,11 @@ #include #include #include -#include +#include #include #include -namespace nd4j { +namespace sd { namespace ops { LegacyReduceFloatOp::LegacyReduceFloatOp() : LegacyOp::LegacyOp(1) { // @@ -61,7 +61,7 @@ namespace nd4j { allAxes = true; // _axis.(block.getIArguments()->size() == 0) || - // (block.getIArguments()->size() == 1 && INT_ARG(0) == nd4j::DataTypeUtils::max()) + // (block.getIArguments()->size() == 1 && INT_ARG(0) == sd::DataTypeUtils::max()) if (block.getAxis()->empty() || allAxes) { // scalar NativeOpExecutioner::execReduceFloatScalar(block.launchContext(), opNum, x->getBuffer(), x->getShapeInfo(), x->specialBuffer(), x->specialShapeInfo(), @@ -76,7 +76,7 @@ namespace nd4j { REQUIRE_TRUE(dims.size() > 0, 0, "Some dimensions required for reduction!"); - auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(x->getShapeInfo(), dims); + auto packX = sd::ConstantTadHelper::getInstance()->tadForDimensions(x->getShapeInfo(), dims); auto pTadShape = Environment::getInstance()->isCPU() ? packX.primaryShapeInfo() : packX.specialShapeInfo(); //manager.replicatePointer(tad.tadOnlyShapeInfo, shape::shapeInfoByteLength(tad.tadOnlyShapeInfo)); auto pTadOffsets = Environment::getInstance()->isCPU() ? packX.primaryOffsets() : packX.specialOffsets(); //manager.replicatePointer(tad.tadOffsets, tad.numTads * sizeof(Nd4jLong)); @@ -102,14 +102,14 @@ namespace nd4j { dims[e] = f >= 0 ? f : f += x->rankOf(); } - if ((block.getIArguments()->size() == 1 && INT_ARG(0) == nd4j::DataTypeUtils::max()) || allAxes) { + if ((block.getIArguments()->size() == 1 && INT_ARG(0) == sd::DataTypeUtils::max()) || allAxes) { // scalar NativeOpExecutioner::execReduceFloatScalar(block.launchContext(), opNum, x->buffer(), x->shapeInfo(), x->specialBuffer(), x->specialShapeInfo(), extras.argumentsAsT(x->dataType()), z->buffer(), z->shapeInfo(), z->specialBuffer(), z->specialShapeInfo()); } else { // TAD REQUIRE_TRUE(dims.size() > 0, 0, "Some dimensions required for reduction!"); - auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(x->getShapeInfo(), dims); + auto packX = sd::ConstantTadHelper::getInstance()->tadForDimensions(x->getShapeInfo(), dims); auto pTadShape = Environment::getInstance()->isCPU() ? packX.primaryShapeInfo() : packX.specialShapeInfo(); //(Nd4jLong *) manager.replicatePointer(tad.tadOnlyShapeInfo, shape::shapeInfoByteLength(tad.tadOnlyShapeInfo)); auto pTadOffsets = Environment::getInstance()->isCPU() ? packX.primaryOffsets() : packX.specialOffsets(); //(Nd4jLong *) manager.replicatePointer(tad.tadOffsets, tad.numTads * sizeof(Nd4jLong)); @@ -130,7 +130,7 @@ namespace nd4j { * For all reductions rules are simple: either you return scalar, or you return reduced NDArray. * It solely depends on input shape, and requested dimensions */ - ShapeList *LegacyReduceFloatOp::calculateOutputShape(ShapeList *inputShape, nd4j::graph::Context &block) { + ShapeList *LegacyReduceFloatOp::calculateOutputShape(ShapeList *inputShape, sd::graph::Context &block) { auto inShape = inputShape->at(0); Nd4jLong *newShape; diff --git a/libnd4j/include/ops/declarable/impl/LegacyReduceLongOp.cpp b/libnd4j/include/ops/declarable/impl/LegacyReduceLongOp.cpp index 836564c79..17cba4227 100644 --- a/libnd4j/include/ops/declarable/impl/LegacyReduceLongOp.cpp +++ b/libnd4j/include/ops/declarable/impl/LegacyReduceLongOp.cpp @@ -21,11 +21,11 @@ #include #include #include -#include +#include #include #include -namespace nd4j { +namespace sd { namespace ops { LegacyReduceLongOp::LegacyReduceLongOp() : LegacyOp::LegacyOp(1) { // @@ -61,7 +61,7 @@ namespace nd4j { allAxes = true; if ((axis.empty()) || - (axis.size() == 1 && axis[0] == nd4j::DataTypeUtils::max()) || allAxes) { + (axis.size() == 1 && axis[0] == sd::DataTypeUtils::max()) || allAxes) { // scalar NativeOpExecutioner::execReduceLongScalar(block.launchContext(), opNum, x->getBuffer(), x->getShapeInfo(), x->specialBuffer(), x->specialShapeInfo(), extras.argumentsAsT(x->dataType()), z->buffer(), z->shapeInfo(), z->specialBuffer(), z->specialShapeInfo()); @@ -78,7 +78,7 @@ namespace nd4j { REQUIRE_TRUE(dims.size() > 0, 0, "Some dimensions required for reduction!"); - auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(x->getShapeInfo(), dims); + auto packX = sd::ConstantTadHelper::getInstance()->tadForDimensions(x->getShapeInfo(), dims); auto pTadShape = Environment::getInstance()->isCPU() ? packX.primaryShapeInfo() : packX.specialShapeInfo(); //(Nd4jLong *) manager.replicatePointer(tad.tadOnlyShapeInfo, shape::shapeInfoByteLength(tad.tadOnlyShapeInfo)); auto pTadOffsets = Environment::getInstance()->isCPU() ? packX.primaryOffsets() : packX.specialOffsets(); //(Nd4jLong *) manager.replicatePointer(tad.tadOffsets, tad.numTads * sizeof(Nd4jLong)); @@ -104,14 +104,14 @@ namespace nd4j { dims[e] = f >= 0 ? f : f += x->rankOf(); } - if ((block.getIArguments()->size() == 1 && INT_ARG(0) == nd4j::DataTypeUtils::max()) || allAxes) { + if ((block.getIArguments()->size() == 1 && INT_ARG(0) == sd::DataTypeUtils::max()) || allAxes) { // scalar NativeOpExecutioner::execReduceLongScalar(block.launchContext(), opNum, x->buffer(), x->shapeInfo(), x->specialBuffer(), x->specialShapeInfo(), extras.argumentsAsT(x->dataType()), z->buffer(), z->shapeInfo(), z->specialBuffer(), z->specialShapeInfo()); } else { // TAD REQUIRE_TRUE(dims.size() > 0, 0, "Some dimensions required for reduction!"); - auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(x->getShapeInfo(), dims); + auto packX = sd::ConstantTadHelper::getInstance()->tadForDimensions(x->getShapeInfo(), dims); auto pTadShape = Environment::getInstance()->isCPU() ? packX.primaryShapeInfo() : packX.specialShapeInfo(); //(Nd4jLong *) manager.replicatePointer(tad.tadOnlyShapeInfo, shape::shapeInfoByteLength(tad.tadOnlyShapeInfo)); auto pTadOffsets = Environment::getInstance()->isCPU() ? packX.primaryOffsets() : packX.specialOffsets(); //(Nd4jLong *) manager.replicatePointer(tad.tadOffsets, tad.numTads * sizeof(Nd4jLong)); @@ -130,7 +130,7 @@ namespace nd4j { * For all reductions rules are simple: either you return scalar, or you return reduced NDArray. * It solely depends on input shape, and requested dimensions */ - ShapeList *LegacyReduceLongOp::calculateOutputShape(ShapeList *inputShape, nd4j::graph::Context &block) { + ShapeList *LegacyReduceLongOp::calculateOutputShape(ShapeList *inputShape, sd::graph::Context &block) { auto inShape = inputShape->at(0); Nd4jLong *newShape; diff --git a/libnd4j/include/ops/declarable/impl/LegacyReduceOp.cpp b/libnd4j/include/ops/declarable/impl/LegacyReduceOp.cpp index 8fac021d9..46be149c6 100644 --- a/libnd4j/include/ops/declarable/impl/LegacyReduceOp.cpp +++ b/libnd4j/include/ops/declarable/impl/LegacyReduceOp.cpp @@ -22,7 +22,7 @@ #include #include #ifdef LEGACY_REDUCE_SAME_ONLY -namespace nd4j { +namespace sd { namespace ops { LegacyReduceOp::LegacyReduceOp() : LegacyOp::LegacyOp(1) { // @@ -140,7 +140,7 @@ namespace nd4j { * For all reductions rules are simple: either you return scalar, or you return reduced NDArray. * It solely depends on input shape, and requested dimensions */ - ShapeList *LegacyReduceOp::calculateOutputShape(ShapeList *inputShape, nd4j::graph::Context &block) { + ShapeList *LegacyReduceOp::calculateOutputShape(ShapeList *inputShape, sd::graph::Context &block) { auto inShape = inputShape->at(0); Nd4jLong *newShape; diff --git a/libnd4j/include/ops/declarable/impl/LegacyReduceSameOp.cpp b/libnd4j/include/ops/declarable/impl/LegacyReduceSameOp.cpp index 2340f39b0..3c96bca70 100644 --- a/libnd4j/include/ops/declarable/impl/LegacyReduceSameOp.cpp +++ b/libnd4j/include/ops/declarable/impl/LegacyReduceSameOp.cpp @@ -21,11 +21,11 @@ #include #include #include -#include +#include #include #include -namespace nd4j { +namespace sd { namespace ops { LegacyReduceSameOp::LegacyReduceSameOp() : LegacyOp::LegacyOp(1) { // @@ -73,7 +73,7 @@ namespace nd4j { REQUIRE_TRUE(dims.size() > 0, 0, "Some dimensions required for reduction!"); - auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(x->getShapeInfo(), dims); + auto packX = sd::ConstantTadHelper::getInstance()->tadForDimensions(x->getShapeInfo(), dims); auto pTadShape = Environment::getInstance()->isCPU() ? packX.primaryShapeInfo() : packX.specialShapeInfo(); //(Nd4jLong *) manager.replicatePointer(tad.tadOnlyShapeInfo, shape::shapeInfoByteLength(tad.tadOnlyShapeInfo)); auto pTadOffsets = Environment::getInstance()->isCPU() ? packX.primaryOffsets() : packX.specialOffsets(); //(Nd4jLong *) manager.replicatePointer(tad.tadOffsets, tad.numTads * sizeof(Nd4jLong)); @@ -99,14 +99,14 @@ namespace nd4j { dims[e] = f >= 0 ? f : f += x->rankOf(); } - if ((block.getIArguments()->size() == 1 && INT_ARG(0) == nd4j::DataTypeUtils::max()) || allAxes) { + if ((block.getIArguments()->size() == 1 && INT_ARG(0) == sd::DataTypeUtils::max()) || allAxes) { // scalar NativeOpExecutioner::execReduceSameScalar(block.launchContext(), opNum, x->buffer(), x->shapeInfo(), x->specialBuffer(), x->specialShapeInfo(), extras.argumentsAsT(z->dataType()), z->buffer(), z->shapeInfo(), z->specialBuffer(), z->specialShapeInfo()); } else { // TAD REQUIRE_TRUE(dims.size() > 0, 0, "Some dimensions required for reduction!"); - auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(x->getShapeInfo(), dims); + auto packX = sd::ConstantTadHelper::getInstance()->tadForDimensions(x->getShapeInfo(), dims); auto pTadShape = Environment::getInstance()->isCPU() ? packX.primaryShapeInfo() : packX.specialShapeInfo(); //(Nd4jLong *) manager.replicatePointer(tad.tadOnlyShapeInfo, shape::shapeInfoByteLength(tad.tadOnlyShapeInfo)); auto pTadOffsets = Environment::getInstance()->isCPU() ? packX.primaryOffsets() : packX.specialOffsets(); //(Nd4jLong *) manager.replicatePointer(tad.tadOffsets, tad.numTads * sizeof(Nd4jLong)); @@ -126,7 +126,7 @@ namespace nd4j { * For all reductions rules are simple: either you return scalar, or you return reduced NDArray. * It solely depends on input shape, and requested dimensions */ - ShapeList *LegacyReduceSameOp::calculateOutputShape(ShapeList *inputShape, nd4j::graph::Context &block) { + ShapeList *LegacyReduceSameOp::calculateOutputShape(ShapeList *inputShape, sd::graph::Context &block) { auto inShape = inputShape->at(0); Nd4jLong *newShape; diff --git a/libnd4j/include/ops/declarable/impl/LegacyScalarBoolOp.cpp b/libnd4j/include/ops/declarable/impl/LegacyScalarBoolOp.cpp index 040cde77c..46728ede1 100644 --- a/libnd4j/include/ops/declarable/impl/LegacyScalarBoolOp.cpp +++ b/libnd4j/include/ops/declarable/impl/LegacyScalarBoolOp.cpp @@ -19,11 +19,11 @@ // #include -#include -#include +#include +#include -namespace nd4j { +namespace sd { namespace ops { LegacyScalarBoolOp::LegacyScalarBoolOp() : LegacyOp::LegacyOp(1) { // no-op @@ -41,7 +41,7 @@ namespace nd4j { _scalar = new NDArray(scalar.dup(scalar.ordering())); } - ShapeList *LegacyScalarBoolOp::calculateOutputShape(ShapeList *inputShape, nd4j::graph::Context &block) { + ShapeList *LegacyScalarBoolOp::calculateOutputShape(ShapeList *inputShape, sd::graph::Context &block) { auto inShape = inputShape->at(0); Nd4jLong *newShape; diff --git a/libnd4j/include/ops/declarable/impl/LegacyScalarOp.cpp b/libnd4j/include/ops/declarable/impl/LegacyScalarOp.cpp index 856bfdeaf..de104a11d 100644 --- a/libnd4j/include/ops/declarable/impl/LegacyScalarOp.cpp +++ b/libnd4j/include/ops/declarable/impl/LegacyScalarOp.cpp @@ -19,11 +19,11 @@ // #include -#include -#include +#include +#include -namespace nd4j { +namespace sd { namespace ops { LegacyScalarOp::LegacyScalarOp() : LegacyOp::LegacyOp(1) { this->getOpDescriptor()->allowInplace(true); @@ -41,7 +41,7 @@ namespace nd4j { _scalar = new NDArray(scalar.dup(scalar.ordering())); } - ShapeList *LegacyScalarOp::calculateOutputShape(ShapeList *inputShape, nd4j::graph::Context &block) { + ShapeList *LegacyScalarOp::calculateOutputShape(ShapeList *inputShape, sd::graph::Context &block) { auto inShape = inputShape->at(0); Nd4jLong *newShape; @@ -70,7 +70,7 @@ namespace nd4j { } else if (block.getTArguments()->size() > 0) { auto y = NDArrayFactory::create(x->dataType(), T_ARG(0), block.launchContext()); - x->applyScalarArr(static_cast(opNum), y, *z); + x->applyScalarArr(static_cast(opNum), y, *z); // NDArray::prepareSpecialUse({z}, {x, &y}); // NativeOpExecutioner::execScalar(block.launchContext(), opNum, x->getBuffer(), x->getShapeInfo(), x->specialBuffer(), x->specialShapeInfo(), z->getBuffer(), z->getShapeInfo(), z->specialBuffer(), z->specialShapeInfo(), y.buffer(), y.shapeInfo(), y.specialBuffer(), y.specialShapeInfo(), extras.argumentsAsT(z->dataType(), 1)); diff --git a/libnd4j/include/ops/declarable/impl/LegacyStatsOp.cpp b/libnd4j/include/ops/declarable/impl/LegacyStatsOp.cpp index 08ebb80de..74f82d162 100644 --- a/libnd4j/include/ops/declarable/impl/LegacyStatsOp.cpp +++ b/libnd4j/include/ops/declarable/impl/LegacyStatsOp.cpp @@ -25,7 +25,7 @@ #include -namespace nd4j { +namespace sd { namespace ops { Nd4jStatus LegacyStatsOp::validateAndExecute(Context &block) { auto x = INPUT_VARIABLE(0); @@ -44,7 +44,7 @@ namespace nd4j { ExtraArguments extras(*block.getTArguments()); PointersManager manager(block.launchContext(),"LegacyStatsOp"); - if (block.getIArguments()->size() == 1 || (block.getIArguments()->size() == 2 && INT_ARG(1) == nd4j::DataTypeUtils::max())) { + if (block.getIArguments()->size() == 1 || (block.getIArguments()->size() == 2 && INT_ARG(1) == sd::DataTypeUtils::max())) { // scalar NativeOpExecutioner::execSummaryStatsScalar(block.launchContext(), opNum, x->getBuffer(), x->getShapeInfo(), x->specialBuffer(), x->specialShapeInfo(), extras.argumentsAsT(z->dataType()), z->getBuffer(), z->getShapeInfo(), z->specialBuffer(), z->specialShapeInfo(), biasCorrected); @@ -58,7 +58,7 @@ namespace nd4j { REQUIRE_TRUE(dims.size() > 0, 0, "Some dimensions requuired for reduction!"); - auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(x->getShapeInfo(), dims); + auto packX = sd::ConstantTadHelper::getInstance()->tadForDimensions(x->getShapeInfo(), dims); auto pTadShape = Environment::getInstance()->isCPU() ? packX.primaryShapeInfo() : packX.specialShapeInfo(); //(Nd4jLong *) manager.replicatePointer(tad.tadOnlyShapeInfo, shape::shapeInfoByteLength(tad.tadOnlyShapeInfo)); auto pTadOffsets = Environment::getInstance()->isCPU() ? packX.primaryOffsets() : packX.specialOffsets(); //(Nd4jLong *) manager.replicatePointer(tad.tadOffsets, tad.numTads * sizeof(Nd4jLong)); @@ -89,11 +89,11 @@ namespace nd4j { * For all reductions rules are simple: either you return scalar, or you return reduced NDArray. * It solely depends on input shape, and requested dimensions */ - ShapeList *LegacyStatsOp::calculateOutputShape(ShapeList *inputShape, nd4j::graph::Context &block) { + ShapeList *LegacyStatsOp::calculateOutputShape(ShapeList *inputShape, sd::graph::Context &block) { auto inShape = inputShape->at(0); Nd4jLong *newShape; - if (block.getIArguments()->size() == 0 || (block.getIArguments()->size() == 1 && INT_ARG(0) == nd4j::DataTypeUtils::max())) { + if (block.getIArguments()->size() == 0 || (block.getIArguments()->size() == 1 && INT_ARG(0) == sd::DataTypeUtils::max())) { // in this case we just return scalar ALLOCATE(newShape, block.getWorkspace(), shape::shapeInfoLength(2), Nd4jLong); newShape[0] = 2; diff --git a/libnd4j/include/ops/declarable/impl/LegacyTransformAnyOp.cpp b/libnd4j/include/ops/declarable/impl/LegacyTransformAnyOp.cpp index 2f08565e6..def577eb3 100644 --- a/libnd4j/include/ops/declarable/impl/LegacyTransformAnyOp.cpp +++ b/libnd4j/include/ops/declarable/impl/LegacyTransformAnyOp.cpp @@ -20,10 +20,10 @@ #include -#include +#include -namespace nd4j { +namespace sd { namespace ops { LegacyTransformAnyOp::LegacyTransformAnyOp() : LegacyOp::LegacyOp(1) { // just a no-op @@ -62,7 +62,7 @@ namespace nd4j { * But these ops already have CustomOp implementations. * */ - ShapeList *LegacyTransformAnyOp::calculateOutputShape(ShapeList *inputShape, nd4j::graph::Context &block) { + ShapeList *LegacyTransformAnyOp::calculateOutputShape(ShapeList *inputShape, sd::graph::Context &block) { auto inShape = inputShape->at(0); Nd4jLong *newShape; diff --git a/libnd4j/include/ops/declarable/impl/LegacyTransformBoolOp.cpp b/libnd4j/include/ops/declarable/impl/LegacyTransformBoolOp.cpp index 96af1f632..99b856b8a 100644 --- a/libnd4j/include/ops/declarable/impl/LegacyTransformBoolOp.cpp +++ b/libnd4j/include/ops/declarable/impl/LegacyTransformBoolOp.cpp @@ -20,10 +20,10 @@ #include -#include +#include -namespace nd4j { +namespace sd { namespace ops { LegacyTransformBoolOp::LegacyTransformBoolOp() : LegacyOp::LegacyOp(1) { // just a no-op @@ -63,7 +63,7 @@ namespace nd4j { * But these ops already have CustomOp implementations. * */ - ShapeList *LegacyTransformBoolOp::calculateOutputShape(ShapeList *inputShape, nd4j::graph::Context &block) { + ShapeList *LegacyTransformBoolOp::calculateOutputShape(ShapeList *inputShape, sd::graph::Context &block) { auto inShape = inputShape->at(0); return SHAPELIST(ConstantShapeHelper::getInstance()->createShapeInfo(ShapeDescriptor(inShape, DataType::BOOL))); } diff --git a/libnd4j/include/ops/declarable/impl/LegacyTransformFloatOp.cpp b/libnd4j/include/ops/declarable/impl/LegacyTransformFloatOp.cpp index e5f4f62e9..f0795b7bb 100644 --- a/libnd4j/include/ops/declarable/impl/LegacyTransformFloatOp.cpp +++ b/libnd4j/include/ops/declarable/impl/LegacyTransformFloatOp.cpp @@ -20,10 +20,10 @@ #include -#include +#include -namespace nd4j { +namespace sd { namespace ops { LegacyTransformFloatOp::LegacyTransformFloatOp() : LegacyOp::LegacyOp(1) { // just a no-op @@ -62,7 +62,7 @@ namespace nd4j { * But these ops already have CustomOp implementations. * */ - ShapeList *LegacyTransformFloatOp::calculateOutputShape(ShapeList *inputShape, nd4j::graph::Context &block) { + ShapeList *LegacyTransformFloatOp::calculateOutputShape(ShapeList *inputShape, sd::graph::Context &block) { auto inShape = inputShape->at(0); Nd4jLong *newShape; diff --git a/libnd4j/include/ops/declarable/impl/LegacyTransformOp.cpp b/libnd4j/include/ops/declarable/impl/LegacyTransformOp.cpp index de8248d25..b073d9df1 100644 --- a/libnd4j/include/ops/declarable/impl/LegacyTransformOp.cpp +++ b/libnd4j/include/ops/declarable/impl/LegacyTransformOp.cpp @@ -20,10 +20,10 @@ #include -#include +#include #ifdef ONLY_SAME_TRANSFORM -namespace nd4j { +namespace sd { namespace ops { LegacyTransformOp::LegacyTransformOp() : LegacyOp::LegacyOp(1) { // just a no-op @@ -55,7 +55,7 @@ namespace nd4j { * But these ops already have CustomOp implementations. * */ - ShapeList *LegacyTransformOp::calculateOutputShape(ShapeList *inputShape, nd4j::graph::Context &block) { + ShapeList *LegacyTransformOp::calculateOutputShape(ShapeList *inputShape, sd::graph::Context &block) { auto inShape = inputShape->at(0); Nd4jLong *newShape; diff --git a/libnd4j/include/ops/declarable/impl/LegacyTransformSameOp.cpp b/libnd4j/include/ops/declarable/impl/LegacyTransformSameOp.cpp index 6b097c3af..0d827787e 100644 --- a/libnd4j/include/ops/declarable/impl/LegacyTransformSameOp.cpp +++ b/libnd4j/include/ops/declarable/impl/LegacyTransformSameOp.cpp @@ -20,10 +20,10 @@ #include -#include +#include -namespace nd4j { +namespace sd { namespace ops { LegacyTransformSameOp::LegacyTransformSameOp() : LegacyOp::LegacyOp(1) { this->getOpDescriptor()->allowInplace(true); @@ -62,7 +62,7 @@ namespace nd4j { * But these ops already have CustomOp implementations. * */ - ShapeList *LegacyTransformSameOp::calculateOutputShape(ShapeList *inputShape, nd4j::graph::Context &block) { + ShapeList *LegacyTransformSameOp::calculateOutputShape(ShapeList *inputShape, sd::graph::Context &block) { auto inShape = inputShape->at(0); Nd4jLong *newShape; diff --git a/libnd4j/include/ops/declarable/impl/LegacyTransformStrictOp.cpp b/libnd4j/include/ops/declarable/impl/LegacyTransformStrictOp.cpp index a390a458c..f36853579 100644 --- a/libnd4j/include/ops/declarable/impl/LegacyTransformStrictOp.cpp +++ b/libnd4j/include/ops/declarable/impl/LegacyTransformStrictOp.cpp @@ -20,10 +20,10 @@ #include -#include +#include -namespace nd4j { +namespace sd { namespace ops { LegacyTransformStrictOp::LegacyTransformStrictOp() : LegacyOp::LegacyOp(1) { this->getOpDescriptor()->allowInplace(true); @@ -61,7 +61,7 @@ namespace nd4j { * But these ops already have CustomOp implementations. * */ - ShapeList *LegacyTransformStrictOp::calculateOutputShape(ShapeList *inputShape, nd4j::graph::Context &block) { + ShapeList *LegacyTransformStrictOp::calculateOutputShape(ShapeList *inputShape, sd::graph::Context &block) { auto inShape = inputShape->at(0); Nd4jLong *newShape; diff --git a/libnd4j/include/ops/declarable/impl/LogicOp.cpp b/libnd4j/include/ops/declarable/impl/LogicOp.cpp index 7873defe4..ae24b5631 100644 --- a/libnd4j/include/ops/declarable/impl/LogicOp.cpp +++ b/libnd4j/include/ops/declarable/impl/LogicOp.cpp @@ -20,19 +20,19 @@ #include "ops/declarable/LogicOp.h" -namespace nd4j { +namespace sd { namespace ops { LogicOp::LogicOp(const char *name) : DeclarableOp::DeclarableOp(name, true) { // just using DeclarableOp constructor //this->_descriptor-> } - Nd4jStatus LogicOp::validateAndExecute(nd4j::graph::Context &block) { + Nd4jStatus LogicOp::validateAndExecute(sd::graph::Context &block) { nd4j_logger("WARNING: LogicOps should NOT be ever called\n", ""); return ND4J_STATUS_BAD_INPUT; } - ShapeList* LogicOp::calculateOutputShape(ShapeList *inputShape, nd4j::graph::Context &block) { + ShapeList* LogicOp::calculateOutputShape(ShapeList *inputShape, sd::graph::Context &block) { // FIXME: we probably want these ops to evaluate scopes return SHAPELIST(); } diff --git a/libnd4j/include/ops/declarable/impl/OpDescriptor.cpp b/libnd4j/include/ops/declarable/impl/OpDescriptor.cpp index 417fc0605..398c11729 100644 --- a/libnd4j/include/ops/declarable/impl/OpDescriptor.cpp +++ b/libnd4j/include/ops/declarable/impl/OpDescriptor.cpp @@ -20,7 +20,7 @@ #include -namespace nd4j { +namespace sd { namespace ops { OpDescriptor::OpDescriptor(const char * opName, bool isLogic) { @@ -33,8 +33,8 @@ namespace nd4j { _numOutputs = 1; _opName = opName; - _hash = nd4j::ops::HashHelper::getInstance()->getLongHash(_opName); - _opClass = nd4j::graph::OpClass_CONDITIONAL; + _hash = sd::ops::HashHelper::getInstance()->getLongHash(_opName); + _opClass = sd::graph::OpClass_CONDITIONAL; _scalar = isScalar; } @@ -44,8 +44,8 @@ namespace nd4j { _numOutputs = 1; _opName = opName; - _hash = nd4j::ops::HashHelper::getInstance()->getLongHash(_opName); - _opClass = nd4j::graph::OpClass_CONDITIONAL; + _hash = sd::ops::HashHelper::getInstance()->getLongHash(_opName); + _opClass = sd::graph::OpClass_CONDITIONAL; _scalar = isScalar; } @@ -77,11 +77,11 @@ namespace nd4j { std::string tmp(opName); _opName = tmp; _allowsInplace = allowsInplace; - _hash = nd4j::ops::HashHelper::getInstance()->getLongHash(tmp); + _hash = sd::ops::HashHelper::getInstance()->getLongHash(tmp); _divergent = false; // just default value - _opClass = nd4j::graph::OpClass_TRANSFORM; + _opClass = sd::graph::OpClass_TRANSFORM; } // constructor for configurable op @@ -159,12 +159,12 @@ namespace nd4j { return _inputType; } - OpDescriptor* OpDescriptor::setAllowedInputTypes(const std::initializer_list &dtypes) { + OpDescriptor* OpDescriptor::setAllowedInputTypes(const std::initializer_list &dtypes) { _allowedIns = dtypes; return this; } - OpDescriptor* OpDescriptor::setAllowedOutputTypes(const std::initializer_list &dtypes) { + OpDescriptor* OpDescriptor::setAllowedOutputTypes(const std::initializer_list &dtypes) { _allowedOuts = dtypes; return this; } @@ -174,24 +174,24 @@ namespace nd4j { return this; } - OpDescriptor* OpDescriptor::setAllowedInputTypes(const nd4j::DataType dtype) { + OpDescriptor* OpDescriptor::setAllowedInputTypes(const sd::DataType dtype) { _allowedIns.clear(); _allowedIns.emplace_back(dtype); return this; } - OpDescriptor* OpDescriptor::setAllowedOutputTypes(const nd4j::DataType dtype) { + OpDescriptor* OpDescriptor::setAllowedOutputTypes(const sd::DataType dtype) { _allowedOuts.clear(); _allowedOuts.emplace_back(dtype); return this; } - OpDescriptor* OpDescriptor::setInputType(const int idx, const nd4j::DataType dtype) { + OpDescriptor* OpDescriptor::setInputType(const int idx, const sd::DataType dtype) { _inputTypes[idx] = { dtype }; return this; } - OpDescriptor* OpDescriptor::setOutputType(const int idx, const nd4j::DataType dtype) { + OpDescriptor* OpDescriptor::setOutputType(const int idx, const sd::DataType dtype) { _outputTypes[idx] = { dtype }; return this; } @@ -201,17 +201,17 @@ namespace nd4j { return this; } - OpDescriptor* OpDescriptor::setAllowedInputTypes(int index, const std::vector &dtype) { + OpDescriptor* OpDescriptor::setAllowedInputTypes(int index, const std::vector &dtype) { _inputTypes[index] = dtype; return this; } - OpDescriptor* OpDescriptor::setAllowedOutputTypes(int index, const std::vector &dtype) { + OpDescriptor* OpDescriptor::setAllowedOutputTypes(int index, const std::vector &dtype) { _outputTypes[index] = dtype; return this; } - OpDescriptor* OpDescriptor::setAllowedInputTypes(int index, nd4j::DataType dtype) { + OpDescriptor* OpDescriptor::setAllowedInputTypes(int index, sd::DataType dtype) { if (_inputTypes.count(index) == 0) _inputTypes[index] = {dtype}; else @@ -220,7 +220,7 @@ namespace nd4j { return this; } - OpDescriptor* OpDescriptor::setAllowedOutputTypes(int index, nd4j::DataType dtype) { + OpDescriptor* OpDescriptor::setAllowedOutputTypes(int index, sd::DataType dtype) { if (_outputTypes.count(index) == 0) _outputTypes[index] = {dtype}; else @@ -229,7 +229,7 @@ namespace nd4j { return this; } - bool OpDescriptor::checkDataTypesMatch(nd4j::DataType needle, std::vector &haystack) const { + bool OpDescriptor::checkDataTypesMatch(sd::DataType needle, std::vector &haystack) const { // if haystack is empty - INHERIT is occurs - any type is perfect? if (haystack.empty()) return true; @@ -238,7 +238,7 @@ namespace nd4j { if (std::find(haystack.begin(), haystack.end(), needle) == haystack.end()) { // if direct input match failed - we're checking for ANY as allowed input - if (std::find(haystack.begin(), haystack.end(), nd4j::DataType::ANY) == haystack.end()) + if (std::find(haystack.begin(), haystack.end(), sd::DataType::ANY) == haystack.end()) return false; else return true; @@ -247,7 +247,7 @@ namespace nd4j { } } - bool OpDescriptor::checkInputMatch(int index, nd4j::DataType dataType) { + bool OpDescriptor::checkInputMatch(int index, sd::DataType dataType) { // we check for per-input types first if (_inputTypes.empty() || _inputTypes.count(index) == 0) { // checking global input types @@ -260,7 +260,7 @@ namespace nd4j { return true; } - bool OpDescriptor::checkOutputMatch(int index, nd4j::DataType dataType) { + bool OpDescriptor::checkOutputMatch(int index, sd::DataType dataType) { // we check for per-output types first if (_outputTypes.empty() || _outputTypes.count(index) == 0) { @@ -279,23 +279,23 @@ namespace nd4j { } bool OpDescriptor::isInherit(int index) { - if (std::find(_allowedOuts.begin(), _allowedOuts.end(), nd4j::DataType::INHERIT) != _allowedOuts.end()) + if (std::find(_allowedOuts.begin(), _allowedOuts.end(), sd::DataType::INHERIT) != _allowedOuts.end()) return true; if (_outputTypes.count(index) > 0) { auto vec = _outputTypes[index]; - if (std::find(vec.begin(), vec.end(), nd4j::DataType::INHERIT) != vec.end()) + if (std::find(vec.begin(), vec.end(), sd::DataType::INHERIT) != vec.end()) return true; } return false; } - std::vector OpDescriptor::getOutputTypesForOutput(int index) { + std::vector OpDescriptor::getOutputTypesForOutput(int index) { if (_outputTypes.count(index) > 0) return _outputTypes.at(index); else - return std::vector(); + return std::vector(); } } } \ No newline at end of file diff --git a/libnd4j/include/ops/declarable/impl/OpRegistrator.cpp b/libnd4j/include/ops/declarable/impl/OpRegistrator.cpp index 09e4ec58f..65d694dea 100644 --- a/libnd4j/include/ops/declarable/impl/OpRegistrator.cpp +++ b/libnd4j/include/ops/declarable/impl/OpRegistrator.cpp @@ -23,7 +23,7 @@ #include #include -namespace nd4j { +namespace sd { namespace ops { /////////////////////////////// @@ -42,7 +42,7 @@ namespace nd4j { std::string newName(name); std::string oldName(oname); - OpRegistrator::getInstance()->updateMSVC(nd4j::ops::HashHelper::getInstance()->getLongHash(newName), oldName); + OpRegistrator::getInstance()->updateMSVC(sd::ops::HashHelper::getInstance()->getLongHash(newName), oldName); return; } OpRegistrator::getInstance()->registerOperation(name, ptr); @@ -53,7 +53,7 @@ namespace nd4j { OpRegistrator* OpRegistrator::getInstance() { if (!_INSTANCE) - _INSTANCE = new nd4j::ops::OpRegistrator(); + _INSTANCE = new sd::ops::OpRegistrator(); return _INSTANCE; } @@ -130,7 +130,7 @@ namespace nd4j { _locker.lock(); if (!isInit) { - for (std::map::iterator it=_declarablesD.begin(); it!=_declarablesD.end(); ++it) { + for (MAP_IMPL::iterator it=_declarablesD.begin(); it!=_declarablesD.end(); ++it) { std::string op = it->first + ":" + local_to_string(it->second->getOpDescriptor()->getHash()) + ":" + local_to_string(it->second->getOpDescriptor()->getNumberOfInputs()) + ":" @@ -151,13 +151,13 @@ namespace nd4j { } - bool OpRegistrator::registerOperation(const char* name, nd4j::ops::DeclarableOp* op) { + bool OpRegistrator::registerOperation(const char* name, sd::ops::DeclarableOp* op) { std::string str(name); - std::pair pair(str, op); + std::pair pair(str, op); _declarablesD.insert(pair); - auto hash = nd4j::ops::HashHelper::getInstance()->getLongHash(str); - std::pair pair2(hash, op); + auto hash = sd::ops::HashHelper::getInstance()->getLongHash(str); + std::pair pair2(hash, op); _declarablesLD.insert(pair2); return true; } @@ -167,12 +167,12 @@ namespace nd4j { * * @param op */ - bool OpRegistrator::registerOperation(nd4j::ops::DeclarableOp *op) { + bool OpRegistrator::registerOperation(sd::ops::DeclarableOp *op) { _uniqueD.emplace_back(op); return registerOperation(op->getOpName()->c_str(), op); } - void OpRegistrator::registerHelper(nd4j::ops::platforms::PlatformHelper* op) { + void OpRegistrator::registerHelper(sd::ops::platforms::PlatformHelper* op) { std::pair p = {op->hash(), op->engine()}; if (_helpersLH.count(p) > 0) throw std::runtime_error("Tried to double register PlatformHelper"); @@ -181,14 +181,14 @@ namespace nd4j { nd4j_debug("Adding helper for op \"%s\": [%lld - %i]\n", op->name().c_str(), op->hash(), (int) op->engine()); - std::pair, nd4j::ops::platforms::PlatformHelper*> pair({op->name(), op->engine()}, op); + std::pair, sd::ops::platforms::PlatformHelper*> pair({op->name(), op->engine()}, op); _helpersH.insert(pair); - std::pair, nd4j::ops::platforms::PlatformHelper*> pair2(p, op); + std::pair, sd::ops::platforms::PlatformHelper*> pair2(p, op); _helpersLH.insert(pair2); } - nd4j::ops::DeclarableOp* OpRegistrator::getOperation(const char *name) { + sd::ops::DeclarableOp* OpRegistrator::getOperation(const char *name) { std::string str(name); return getOperation(str); } @@ -199,7 +199,7 @@ namespace nd4j { * @param name * @return */ - nd4j::ops::DeclarableOp *OpRegistrator::getOperation(Nd4jLong hash) { + sd::ops::DeclarableOp *OpRegistrator::getOperation(Nd4jLong hash) { if (!_declarablesLD.count(hash)) { if (!_msvc.count(hash)) { nd4j_printf("Unknown D operation requested by hash: [%lld]\n", hash); @@ -211,7 +211,7 @@ namespace nd4j { auto op = _declarablesD.at(str); auto oHash = op->getOpDescriptor()->getHash(); - std::pair pair(oHash, op); + std::pair pair(oHash, op); _declarablesLD.insert(pair); _locker.unlock(); @@ -221,7 +221,7 @@ namespace nd4j { return _declarablesLD.at(hash); } - nd4j::ops::DeclarableOp *OpRegistrator::getOperation(std::string& name) { + sd::ops::DeclarableOp *OpRegistrator::getOperation(std::string& name) { if (!_declarablesD.count(name)) { nd4j_debug("Unknown operation requested: [%s]\n", name.c_str()); return nullptr; @@ -230,7 +230,7 @@ namespace nd4j { return _declarablesD.at(name); } - nd4j::ops::platforms::PlatformHelper* OpRegistrator::getPlatformHelper(Nd4jLong hash, samediff::Engine engine) { + sd::ops::platforms::PlatformHelper* OpRegistrator::getPlatformHelper(Nd4jLong hash, samediff::Engine engine) { std::pair p = {hash, engine}; if (_helpersLH.count(p) == 0) throw std::runtime_error("Requested helper can't be found"); @@ -257,7 +257,23 @@ namespace nd4j { return result; } - nd4j::ops::OpRegistrator* nd4j::ops::OpRegistrator::_INSTANCE = 0; + sd::ops::OpRegistrator* sd::ops::OpRegistrator::_INSTANCE = 0; + } +} + +namespace std { + size_t hash>::operator()(const std::pair& k) const { + using std::hash; + auto res = std::hash()(k.first); + res ^= std::hash()((int) k.second) + 0x9e3779b9 + (res << 6) + (res >> 2); + return res; + } + + size_t hash>::operator()(const std::pair& k) const { + using std::hash; + auto res = std::hash()(k.first); + res ^= std::hash()((int) k.second) + 0x9e3779b9 + (res << 6) + (res >> 2); + return res; } } diff --git a/libnd4j/include/ops/declarable/impl/OpTuple.cpp b/libnd4j/include/ops/declarable/impl/OpTuple.cpp index 126f864a2..fc43739e8 100644 --- a/libnd4j/include/ops/declarable/impl/OpTuple.cpp +++ b/libnd4j/include/ops/declarable/impl/OpTuple.cpp @@ -20,38 +20,38 @@ #include "ops/declarable/OpTuple.h" -nd4j::ops::OpTuple::OpTuple(const char *opName) { +sd::ops::OpTuple::OpTuple(const char *opName) { _opName = opName; } -nd4j::ops::OpTuple::OpTuple(const char *opName, std::initializer_list &&inputs, std::initializer_list &&tArgs, std::initializer_list &&iArgs) { +sd::ops::OpTuple::OpTuple(const char *opName, std::initializer_list &&inputs, std::initializer_list &&tArgs, std::initializer_list &&iArgs) { _opName = opName; _inputs = inputs; _iArgs = iArgs; _tArgs = tArgs; } -nd4j::ops::OpTuple::~OpTuple() { +sd::ops::OpTuple::~OpTuple() { for (auto v: _inputs) delete v; } -nd4j::ops::OpTuple *nd4j::ops::OpTuple::addInput(nd4j::NDArray *array) { +sd::ops::OpTuple *sd::ops::OpTuple::addInput(sd::NDArray *array) { _inputs.emplace_back(array); return this; } -nd4j::ops::OpTuple *nd4j::ops::OpTuple::addOutput(nd4j::NDArray *array) { +sd::ops::OpTuple *sd::ops::OpTuple::addOutput(sd::NDArray *array) { _outputs.emplace_back(array); return this; } -nd4j::ops::OpTuple *nd4j::ops::OpTuple::setTArgs(std::initializer_list tArgs) { +sd::ops::OpTuple *sd::ops::OpTuple::setTArgs(std::initializer_list tArgs) { _tArgs = tArgs; return this; } -nd4j::ops::OpTuple *nd4j::ops::OpTuple::setIArgs(std::initializer_list iArgs) { +sd::ops::OpTuple *sd::ops::OpTuple::setIArgs(std::initializer_list iArgs) { _iArgs = iArgs; return this; } diff --git a/libnd4j/include/ops/declarable/impl/PlatformHelper.cpp b/libnd4j/include/ops/declarable/impl/PlatformHelper.cpp index 86c84b0fb..fe0928ce6 100644 --- a/libnd4j/include/ops/declarable/impl/PlatformHelper.cpp +++ b/libnd4j/include/ops/declarable/impl/PlatformHelper.cpp @@ -21,7 +21,7 @@ #include "../PlatformHelper.h" #include -namespace nd4j { +namespace sd { namespace ops { namespace platforms { PlatformHelper::PlatformHelper(const char *name, samediff::Engine engine) { @@ -31,7 +31,7 @@ namespace nd4j { _engine = engine; } - nd4j::NDArray *PlatformHelper::getZ(graph::Context &ctx, int inputId) { + sd::NDArray *PlatformHelper::getZ(graph::Context &ctx, int inputId) { NDArray *z = nullptr; if (ctx.isFastPath()) { diff --git a/libnd4j/include/ops/declarable/platform/cudnn/avgpool2d.cu b/libnd4j/include/ops/declarable/platform/cudnn/avgpool2d.cu index 8ff0bafb1..015c08172 100644 --- a/libnd4j/include/ops/declarable/platform/cudnn/avgpool2d.cu +++ b/libnd4j/include/ops/declarable/platform/cudnn/avgpool2d.cu @@ -22,7 +22,7 @@ #include "cudnnUtils.h" #include -namespace nd4j { +namespace sd { namespace ops { namespace platforms { diff --git a/libnd4j/include/ops/declarable/platform/cudnn/avgpool3d.cu b/libnd4j/include/ops/declarable/platform/cudnn/avgpool3d.cu index 878f306b3..aeaaa6516 100644 --- a/libnd4j/include/ops/declarable/platform/cudnn/avgpool3d.cu +++ b/libnd4j/include/ops/declarable/platform/cudnn/avgpool3d.cu @@ -22,7 +22,7 @@ #include "cudnnUtils.h" #include -namespace nd4j { +namespace sd { namespace ops { namespace platforms { diff --git a/libnd4j/include/ops/declarable/platform/cudnn/batchnorm.cu b/libnd4j/include/ops/declarable/platform/cudnn/batchnorm.cu index 1177d1a3c..8d0b1301a 100644 --- a/libnd4j/include/ops/declarable/platform/cudnn/batchnorm.cu +++ b/libnd4j/include/ops/declarable/platform/cudnn/batchnorm.cu @@ -22,7 +22,7 @@ #include "cudnnUtils.h" #include -namespace nd4j { +namespace sd { namespace ops { namespace platforms { @@ -44,7 +44,7 @@ static void batchnormCUDNN(const LaunchContext* context, auto handle = reinterpret_cast(context->getCuDnnHandle()); cudnnStatus_t err = cudnnSetStream(*handle, *context->getCudaStream()); - if (err != 0) throw nd4j::cuda_exception::build("conv2dCUDNN: can't set stream for cuDNN", err); + if (err != 0) throw sd::cuda_exception::build("conv2dCUDNN: can't set stream for cuDNN", err); const std::vector xShape = input->getShapeAsVectorInt(); // input and output have same shapes @@ -77,7 +77,7 @@ static void batchnormCUDNN(const LaunchContext* context, err = cudnnSetTensorNdDescriptorEx(x, format, dataType, xRank, xShape.data()); else err = cudnnSetTensorNdDescriptor(x, dataType, xRank, xShape.data(), xStrides.data()); - if (err != 0) throw nd4j::cuda_exception::build("batchnormCUDNN: cudnnSetTensorNdDescriptor/cudnnSetTensorNdDescriptorEx for input failed", err); + if (err != 0) throw sd::cuda_exception::build("batchnormCUDNN: cudnnSetTensorNdDescriptor/cudnnSetTensorNdDescriptorEx for input failed", err); // output descriptor cudnnTensorDescriptor_t z; @@ -86,7 +86,7 @@ static void batchnormCUDNN(const LaunchContext* context, err = cudnnSetTensorNdDescriptorEx(z, format, dataType, xRank, xShape.data()); else err = cudnnSetTensorNdDescriptor(z, dataType, xRank, xShape.data(), zStrides.data()); - if (err != 0) throw nd4j::cuda_exception::build("batchnormCUDNN: cudnnSetTensorNdDescriptor/cudnnSetTensorNdDescriptorEx for output failed", err); + if (err != 0) throw sd::cuda_exception::build("batchnormCUDNN: cudnnSetTensorNdDescriptor/cudnnSetTensorNdDescriptorEx for output failed", err); // mean, variance, gamma and beta descriptor, the same descriptor for all of them cudnnTensorDescriptor_t params; @@ -95,7 +95,7 @@ static void batchnormCUDNN(const LaunchContext* context, err = cudnnSetTensorNdDescriptorEx(params, format, dataType, xRank, paramsShape.data()); else err = cudnnSetTensorNdDescriptor(params, dataType, xRank, paramsShape.data(), paramsStrides.data()); - if (err != 0) throw nd4j::cuda_exception::build("batchnormCUDNN: cudnnSetTensorNdDescriptor/cudnnSetTensorNdDescriptorEx for mean/variance/gamma/beta failed", err); + if (err != 0) throw sd::cuda_exception::build("batchnormCUDNN: cudnnSetTensorNdDescriptor/cudnnSetTensorNdDescriptorEx for mean/variance/gamma/beta failed", err); // provide scaling parameters const float alpha32(1), beta32(0); @@ -114,7 +114,7 @@ static void batchnormCUDNN(const LaunchContext* context, gamma->getSpecialBuffer(), beta->getSpecialBuffer(), mean->getSpecialBuffer(), variance->getSpecialBuffer(), epsilon); - if (err != 0) throw nd4j::cuda_exception::build("batchnormCUDNN: cudnnBatchNormalizationForwardInference failed", err); + if (err != 0) throw sd::cuda_exception::build("batchnormCUDNN: cudnnBatchNormalizationForwardInference failed", err); auto cudaErr = cudaStreamSynchronize(*context->getCudaStream()); if (cudaErr != 0) @@ -139,7 +139,7 @@ static void batchnormBpCUDNN(const LaunchContext* context, auto handle = reinterpret_cast(context->getCuDnnHandle()); cudnnStatus_t err = cudnnSetStream(*handle, *context->getCudaStream()); - if (err != 0) throw nd4j::cuda_exception::build("batchnormBpCUDNN: can't set stream for cuDNN", err); + if (err != 0) throw sd::cuda_exception::build("batchnormBpCUDNN: can't set stream for cuDNN", err); const std::vector xShape = input->getShapeAsVectorInt(); // input and output have same shapes @@ -174,7 +174,7 @@ static void batchnormBpCUDNN(const LaunchContext* context, err = cudnnSetTensorNdDescriptorEx(x, format, dataType, xRank, xShape.data()); else err = cudnnSetTensorNdDescriptor(x, dataType, xRank, xShape.data(), xStrides.data()); - if (err != 0) throw nd4j::cuda_exception::build("batchnormBpCUDNN: cudnnSetTensorNdDescriptor/cudnnSetTensorNdDescriptorEx for input failed", err); + if (err != 0) throw sd::cuda_exception::build("batchnormBpCUDNN: cudnnSetTensorNdDescriptor/cudnnSetTensorNdDescriptorEx for input failed", err); // gradO descriptor cudnnTensorDescriptor_t dz; @@ -183,7 +183,7 @@ static void batchnormBpCUDNN(const LaunchContext* context, err = cudnnSetTensorNdDescriptorEx(dz, format, dataType, xRank, xShape.data()); else err = cudnnSetTensorNdDescriptor(dz, dataType, xRank, xShape.data(), dzStrides.data()); - if (err != 0) throw nd4j::cuda_exception::build("batchnormBpCUDNN: cudnnSetTensorNdDescriptor/cudnnSetTensorNdDescriptorEx for gradO failed", err); + if (err != 0) throw sd::cuda_exception::build("batchnormBpCUDNN: cudnnSetTensorNdDescriptor/cudnnSetTensorNdDescriptorEx for gradO failed", err); // gradI descriptor cudnnTensorDescriptor_t dx; @@ -192,7 +192,7 @@ static void batchnormBpCUDNN(const LaunchContext* context, err = cudnnSetTensorNdDescriptorEx(dx, format, dataType, xRank, xShape.data()); else err = cudnnSetTensorNdDescriptor(dx, dataType, xRank, xShape.data(), dxStrides.data()); - if (err != 0) throw nd4j::cuda_exception::build("batchnormBpCUDNN: cudnnSetTensorNdDescriptor/cudnnSetTensorNdDescriptorEx for gradI failed", err); + if (err != 0) throw sd::cuda_exception::build("batchnormBpCUDNN: cudnnSetTensorNdDescriptor/cudnnSetTensorNdDescriptorEx for gradI failed", err); // mean, variance, gamma, gradG and gradB descriptor, the same descriptor for all of them cudnnTensorDescriptor_t params; @@ -201,7 +201,7 @@ static void batchnormBpCUDNN(const LaunchContext* context, err = cudnnSetTensorNdDescriptorEx(params, format, dataType, xRank, paramsShape.data()); else err = cudnnSetTensorNdDescriptor(params, dataType, xRank, paramsShape.data(), paramsStrides.data()); - if (err != 0) throw nd4j::cuda_exception::build("batchnormBpCUDNN: cudnnSetTensorNdDescriptor/cudnnSetTensorNdDescriptorEx for mean/variance/gamma/gradG/gradB failed", err); + if (err != 0) throw sd::cuda_exception::build("batchnormBpCUDNN: cudnnSetTensorNdDescriptor/cudnnSetTensorNdDescriptorEx for mean/variance/gamma/gradG/gradB failed", err); // provide scaling parameters const float alpha32(1), beta32(0); @@ -223,7 +223,7 @@ static void batchnormBpCUDNN(const LaunchContext* context, epsilon, nullptr/*mean->getSpecialBuffer()*/, nullptr/*variance->getSpecialBuffer()*/); - if (err != 0) throw nd4j::cuda_exception::build("batchnormBpCUDNN: cudnnBatchNormalizationBackward failed", err); + if (err != 0) throw sd::cuda_exception::build("batchnormBpCUDNN: cudnnBatchNormalizationBackward failed", err); auto cudaErr = cudaStreamSynchronize(*context->getCudaStream()); if (cudaErr != 0) diff --git a/libnd4j/include/ops/declarable/platform/cudnn/conv2d.cu b/libnd4j/include/ops/declarable/platform/cudnn/conv2d.cu index 234dbffb7..b58cc40f3 100644 --- a/libnd4j/include/ops/declarable/platform/cudnn/conv2d.cu +++ b/libnd4j/include/ops/declarable/platform/cudnn/conv2d.cu @@ -23,7 +23,7 @@ #include "cudnnUtils.h" #include -namespace nd4j { +namespace sd { namespace ops { namespace platforms { @@ -42,7 +42,7 @@ static void conv2dCUDNN(const LaunchContext* context, auto handle = reinterpret_cast(context->getCuDnnHandle()); cudnnStatus_t err = cudnnSetStream(*handle, *context->getCudaStream()); - if (err != 0) throw nd4j::cuda_exception::build("conv2dCUDNN: can't set stream for cuDNN", err); + if (err != 0) throw sd::cuda_exception::build("conv2dCUDNN: can't set stream for cuDNN", err); cudnnTensorFormat_t format = isNCHW ? CUDNN_TENSOR_NCHW : CUDNN_TENSOR_NHWC; @@ -53,13 +53,13 @@ static void conv2dCUDNN(const LaunchContext* context, err = cudnnSetTensor4dDescriptor(x, format, cudnnDataType(input->dataType()), bS, iC, iH, iW); else err = cudnnSetTensor4dDescriptorEx(x, cudnnDataType(input->dataType()), bS, iC, iH, iW, input->strideAt(0), input->strideAt(indIOioC), input->strideAt(indIiH), input->strideAt(indIiH + 1)); - if (err != 0) throw nd4j::cuda_exception::build("conv2dCUDNN: cudnnSetTensor4dDescriptor/cudnnSetTensor4dDescriptorEx for input failed", err); + if (err != 0) throw sd::cuda_exception::build("conv2dCUDNN: cudnnSetTensor4dDescriptor/cudnnSetTensor4dDescriptorEx for input failed", err); // weights descriptor cudnnFilterDescriptor_t w; cudnnCreateFilterDescriptor(&w); err = cudnnSetFilter4dDescriptor(w, cudnnDataType(weights->dataType()), CUDNN_TENSOR_NCHW, oC, iC, kH, kW); - if(err != 0) throw nd4j::cuda_exception::build("conv2dCUDNN: cudnnSetFilter4dDescriptor failed", err); + if(err != 0) throw sd::cuda_exception::build("conv2dCUDNN: cudnnSetFilter4dDescriptor failed", err); // output descriptor cudnnTensorDescriptor_t z; @@ -68,27 +68,27 @@ static void conv2dCUDNN(const LaunchContext* context, err = cudnnSetTensor4dDescriptor(z, format, cudnnDataType(output->dataType()), bS, oC, oH, oW); else err = cudnnSetTensor4dDescriptorEx(z, cudnnDataType(output->dataType()), bS, oC, oH, oW, output->strideAt(0), output->strideAt(indIOioC), output->strideAt(indOoH), output->strideAt(indOoH + 1)); - if (err != 0) throw nd4j::cuda_exception::build("conv2dCUDNN: cudnnSetTensor4dDescriptor/cudnnSetTensor4dDescriptorEx for output failed", err); + if (err != 0) throw sd::cuda_exception::build("conv2dCUDNN: cudnnSetTensor4dDescriptor/cudnnSetTensor4dDescriptorEx for output failed", err); // description of convolution cudnnConvolutionDescriptor_t conv; cudnnCreateConvolutionDescriptor(&conv); err = cudnnSetConvolution2dDescriptor(conv, pH, pW, sH, sW, dH, dW, CUDNN_CROSS_CORRELATION, cudnnDataType(output->dataType())); - if (err != 0) throw nd4j::cuda_exception::build("conv2dCUDNN: cudnnSetConvolution2dDescriptor failed", err); + if (err != 0) throw sd::cuda_exception::build("conv2dCUDNN: cudnnSetConvolution2dDescriptor failed", err); // algorithm description cudnnConvolutionFwdAlgo_t algo; err = cudnnGetConvolutionForwardAlgorithm(*handle, x, w, conv, z, CUDNN_CONVOLUTION_FWD_PREFER_FASTEST, 0, &algo); - if (err != 0) throw nd4j::cuda_exception::build("conv2dCUDNN: cudnnGetConvolutionForwardAlgorithm failed", err); + if (err != 0) throw sd::cuda_exception::build("conv2dCUDNN: cudnnGetConvolutionForwardAlgorithm failed", err); // allocate auxiliary device memory, abbreviation ws means workspace size_t wsSize; err = cudnnGetConvolutionForwardWorkspaceSize(*handle, x, w, conv, z, algo, &wsSize); - if (err != 0) throw nd4j::cuda_exception::build("conv2dCUDNN: cudnnGetConvolutionForwardWorkspaceSize failed", err); + if (err != 0) throw sd::cuda_exception::build("conv2dCUDNN: cudnnGetConvolutionForwardWorkspaceSize failed", err); void* wsData; auto cudaErr = cudaMalloc(&wsData, wsSize); - if (cudaErr != 0) throw nd4j::cuda_exception::build("conv2dCUDNN: cudaMalloc for auxiliary workspace memory failed", cudaErr); + if (cudaErr != 0) throw sd::cuda_exception::build("conv2dCUDNN: cudaMalloc for auxiliary workspace memory failed", cudaErr); // provide scaling parameters const float alpha32(1), beta32(0); @@ -100,7 +100,7 @@ static void conv2dCUDNN(const LaunchContext* context, // run calculation err = cudnnConvolutionForward(*handle, alpha, x, input->getSpecialBuffer(), w, weights->getSpecialBuffer(), conv, algo, wsData, wsSize, beta, z, output->specialBuffer()); - if (err != 0) throw nd4j::cuda_exception::build("conv2dCUDNN: cudnnConvolutionForward failed", err); + if (err != 0) throw sd::cuda_exception::build("conv2dCUDNN: cudnnConvolutionForward failed", err); // add bias if it is present if (bias != nullptr) { @@ -108,9 +108,9 @@ static void conv2dCUDNN(const LaunchContext* context, cudnnTensorDescriptor_t b; cudnnCreateTensorDescriptor(&b); err = cudnnSetTensor4dDescriptor(b, format, cudnnDataType(bias->dataType()), 1, isNCHW ? bias->lengthOf() : 1, 1, isNCHW ? 1: bias->lengthOf()); - if (err != 0) throw nd4j::cuda_exception::build("conv2dCUDNN: cudnnSetTensor4dDescriptor for bias failed", err); + if (err != 0) throw sd::cuda_exception::build("conv2dCUDNN: cudnnSetTensor4dDescriptor for bias failed", err); err = cudnnAddTensor(*handle, alpha, b, bias->getSpecialBuffer(), alpha, z, output->specialBuffer()); - if (err != 0) throw nd4j::cuda_exception::build("conv2dCUDNN: cudnnAddTensor bias failed", err); + if (err != 0) throw sd::cuda_exception::build("conv2dCUDNN: cudnnAddTensor bias failed", err); } // cudaErr = cudaStreamSynchronize(*context->getCudaStream()); @@ -118,7 +118,7 @@ static void conv2dCUDNN(const LaunchContext* context, // throw cuda_exception::build("conv2dCUDNN: cudaStreamSynchronize failed !", cudaErr); cudaErr = cudaFree(wsData); - if (cudaErr != 0) throw nd4j::cuda_exception::build("conv2dCUDNN: cudaFree for auxiliary workspace memory failed", cudaErr); + if (cudaErr != 0) throw sd::cuda_exception::build("conv2dCUDNN: cudaFree for auxiliary workspace memory failed", cudaErr); NDArray::registerSpecialUse({output}, {input, weights, bias}); } @@ -139,7 +139,7 @@ static void conv2dBpCUDNN(const LaunchContext* context, auto handle = reinterpret_cast(context->getCuDnnHandle()); cudnnStatus_t err = cudnnSetStream(*handle, *context->getCudaStream()); - if (err != 0) throw nd4j::cuda_exception::build("conv2dBpCUDNN: can't set stream for cuDNN", err); + if (err != 0) throw sd::cuda_exception::build("conv2dBpCUDNN: can't set stream for cuDNN", err); cudnnTensorFormat_t format = isNCHW ? CUDNN_TENSOR_NCHW : CUDNN_TENSOR_NHWC; @@ -150,7 +150,7 @@ static void conv2dBpCUDNN(const LaunchContext* context, err = cudnnSetTensor4dDescriptor(x, format, cudnnDataType(input->dataType()), bS, iC, iH, iW); else err = cudnnSetTensor4dDescriptorEx(x, cudnnDataType(input->dataType()), bS, iC, iH, iW, input->strideAt(0), input->strideAt(indIOioC), input->strideAt(indIiH), input->strideAt(indIiH + 1)); - if (err != 0) throw nd4j::cuda_exception::build("conv2dBpCUDNN: cudnnSetTensor4dDescriptor/cudnnSetTensor4dDescriptorEx for input failed", err); + if (err != 0) throw sd::cuda_exception::build("conv2dBpCUDNN: cudnnSetTensor4dDescriptor/cudnnSetTensor4dDescriptorEx for input failed", err); // gradO descriptor cudnnTensorDescriptor_t dz; @@ -159,7 +159,7 @@ static void conv2dBpCUDNN(const LaunchContext* context, err = cudnnSetTensor4dDescriptor(dz, format, cudnnDataType(gradO->dataType()), bS, oC, oH, oW); else err = cudnnSetTensor4dDescriptorEx(dz, cudnnDataType(gradO->dataType()), bS, oC, oH, oW, gradO->strideAt(0), gradO->strideAt(indIOioC), gradO->strideAt(indOoH), gradO->strideAt(indOoH + 1)); - if (err != 0) throw nd4j::cuda_exception::build("conv2dBpCUDNN: cudnnSetTensor4dDescriptor/cudnnSetTensor4dDescriptorEx for gradO failed", err); + if (err != 0) throw sd::cuda_exception::build("conv2dBpCUDNN: cudnnSetTensor4dDescriptor/cudnnSetTensor4dDescriptorEx for gradO failed", err); // gradI descriptor cudnnTensorDescriptor_t dx; @@ -168,45 +168,45 @@ static void conv2dBpCUDNN(const LaunchContext* context, err = cudnnSetTensor4dDescriptor(dx, format, cudnnDataType(gradI->dataType()), bS, iC, iH, iW); else err = cudnnSetTensor4dDescriptorEx(dx, cudnnDataType(gradI->dataType()), bS, iC, iH, iW, gradI->strideAt(0), gradI->strideAt(indIOioC), gradI->strideAt(indIiH), gradI->strideAt(indIiH + 1)); - if (err != 0) throw nd4j::cuda_exception::build("conv2dBpCUDNN: cudnnSetTensor4dDescriptor/cudnnSetTensor4dDescriptorEx for gradI failed", err); + if (err != 0) throw sd::cuda_exception::build("conv2dBpCUDNN: cudnnSetTensor4dDescriptor/cudnnSetTensor4dDescriptorEx for gradI failed", err); // gradW descriptor cudnnFilterDescriptor_t dw; cudnnCreateFilterDescriptor(&dw); err = cudnnSetFilter4dDescriptor(dw, cudnnDataType(gradW->dataType()), CUDNN_TENSOR_NCHW, oC, iC, kH, kW); - if(err != 0) throw nd4j::cuda_exception::build("conv2dBpCUDNN: cudnnSetFilter4dDescriptor gradW failed", err); + if(err != 0) throw sd::cuda_exception::build("conv2dBpCUDNN: cudnnSetFilter4dDescriptor gradW failed", err); // description of convolution cudnnConvolutionDescriptor_t conv; cudnnCreateConvolutionDescriptor(&conv); err = cudnnSetConvolution2dDescriptor(conv, pH, pW, sH, sW, dH, dW, CUDNN_CROSS_CORRELATION, cudnnDataType(gradO->dataType())); - if (err != 0) throw nd4j::cuda_exception::build("conv2dBpCUDNN: cudnnSetConvolution2dDescriptor failed", err); + if (err != 0) throw sd::cuda_exception::build("conv2dBpCUDNN: cudnnSetConvolution2dDescriptor failed", err); // gradW algorithm description cudnnConvolutionBwdFilterAlgo_t algoGradW; err = cudnnGetConvolutionBackwardFilterAlgorithm(*handle, x, dz, conv, dw, CUDNN_CONVOLUTION_BWD_FILTER_PREFER_FASTEST, 0, &algoGradW); - if (err != 0) throw nd4j::cuda_exception::build("conv2dBpCUDNN: cudnnGetConvolutionBackwardFilterAlgorithm failed", err); + if (err != 0) throw sd::cuda_exception::build("conv2dBpCUDNN: cudnnGetConvolutionBackwardFilterAlgorithm failed", err); // gradI algorithm description cudnnConvolutionBwdDataAlgo_t algoGradI; err = cudnnGetConvolutionBackwardDataAlgorithm(*handle, dw, dz, conv, x, CUDNN_CONVOLUTION_BWD_DATA_PREFER_FASTEST, 0, &algoGradI); - if (err != 0) throw nd4j::cuda_exception::build("conv2dBpCUDNN: cudnnGetConvolutionBackwardDataAlgorithm failed", err); + if (err != 0) throw sd::cuda_exception::build("conv2dBpCUDNN: cudnnGetConvolutionBackwardDataAlgorithm failed", err); // allocate auxiliary device memory for gradW calculation, abbreviation ws means workspace size_t wsGradWSize; err = cudnnGetConvolutionBackwardFilterWorkspaceSize(*handle, x, dz, conv, dw, algoGradW, &wsGradWSize); - if (err != 0) throw nd4j::cuda_exception::build("conv2dBpCUDNN: cudnnGetConvolutionBackwardFilterWorkspaceSize failed", err); + if (err != 0) throw sd::cuda_exception::build("conv2dBpCUDNN: cudnnGetConvolutionBackwardFilterWorkspaceSize failed", err); void* wsGradWData; auto cudaErr = cudaMalloc(&wsGradWData, wsGradWSize); - if (cudaErr != 0) throw nd4j::cuda_exception::build("conv2dBpCUDNN: cudaMalloc for auxiliary workspace memory wsGradWData failed", cudaErr); + if (cudaErr != 0) throw sd::cuda_exception::build("conv2dBpCUDNN: cudaMalloc for auxiliary workspace memory wsGradWData failed", cudaErr); // allocate auxiliary device memory for gradI calculation, abbreviation ws means workspace size_t wsGradISize; err = cudnnGetConvolutionBackwardDataWorkspaceSize(*handle, dw, dz, conv, dx, algoGradI, &wsGradISize); - if (err != 0) throw nd4j::cuda_exception::build("conv2dBpCUDNN: cudnnGetConvolutionBackwardDataWorkspaceSize failed", err); + if (err != 0) throw sd::cuda_exception::build("conv2dBpCUDNN: cudnnGetConvolutionBackwardDataWorkspaceSize failed", err); void* wsGradIData; cudaErr = cudaMalloc(&wsGradIData, wsGradISize); - if (cudaErr != 0) throw nd4j::cuda_exception::build("conv2dBpCUDNN: cudaMalloc for auxiliary workspace memory wsGradIData failed", cudaErr); + if (cudaErr != 0) throw sd::cuda_exception::build("conv2dBpCUDNN: cudaMalloc for auxiliary workspace memory wsGradIData failed", cudaErr); // provide scaling parameters const float alpha32(1), beta32(0); @@ -221,28 +221,28 @@ static void conv2dBpCUDNN(const LaunchContext* context, cudnnTensorDescriptor_t db; cudnnCreateTensorDescriptor(&db); err = cudnnSetTensor4dDescriptor(db, format, cudnnDataType(gradB->dataType()), 1, isNCHW ? gradB->lengthOf() : 1, 1, isNCHW ? 1: gradB->lengthOf()); - if (err != 0) throw nd4j::cuda_exception::build("conv2dBpCUDNN: cudnnSetTensor4dDescriptor for gradB failed", err); + if (err != 0) throw sd::cuda_exception::build("conv2dBpCUDNN: cudnnSetTensor4dDescriptor for gradB failed", err); err = cudnnConvolutionBackwardBias(*handle, alpha, dz, gradO->getSpecialBuffer(), beta, db, gradB->getSpecialBuffer()); - if (err != 0) throw nd4j::cuda_exception::build("conv2dBpCUDNN: cudnnConvolutionBackwardBias failed", err); + if (err != 0) throw sd::cuda_exception::build("conv2dBpCUDNN: cudnnConvolutionBackwardBias failed", err); } // run calculation for gradW err = cudnnConvolutionBackwardFilter(*handle, alpha, x, input->getSpecialBuffer(), dz, gradO->getSpecialBuffer(), conv, algoGradW, wsGradWData, wsGradWSize, beta, dw, gradW->getSpecialBuffer()); - if (err != 0) throw nd4j::cuda_exception::build("conv2dBpCUDNN: cudnnConvolutionBackwardFilter failed", err); + if (err != 0) throw sd::cuda_exception::build("conv2dBpCUDNN: cudnnConvolutionBackwardFilter failed", err); // run calculation for gradI err = cudnnConvolutionBackwardData(*handle, alpha, dw, weights->getSpecialBuffer(), dz, gradO->getSpecialBuffer(), conv, algoGradI, wsGradIData, wsGradISize, beta, dx, gradI->getSpecialBuffer()); - if (err != 0) throw nd4j::cuda_exception::build("conv2dBpCUDNN: cudnnConvolutionBackwardData failed", err); + if (err != 0) throw sd::cuda_exception::build("conv2dBpCUDNN: cudnnConvolutionBackwardData failed", err); // cudaErr = cudaStreamSynchronize(*context->getCudaStream()); // if (cudaErr != 0) // throw cuda_exception::build("conv2dBpCUDNN: cudaStreamSynchronize failed !", cudaErr); cudaErr = cudaFree(wsGradWData); - if (cudaErr != 0) throw nd4j::cuda_exception::build("conv2dBpCUDNN: cudaFree for auxiliary workspace memory wsGradWData failed", cudaErr); + if (cudaErr != 0) throw sd::cuda_exception::build("conv2dBpCUDNN: cudaFree for auxiliary workspace memory wsGradWData failed", cudaErr); cudaErr = cudaFree(wsGradIData); - if (cudaErr != 0) throw nd4j::cuda_exception::build("conv2dBpCUDNN: cudaFree for auxiliary workspace memory wsGradIData failed", cudaErr); + if (cudaErr != 0) throw sd::cuda_exception::build("conv2dBpCUDNN: cudaFree for auxiliary workspace memory wsGradIData failed", cudaErr); NDArray::registerSpecialUse({gradI, gradW, gradB}, {input, weights, gradO}); } @@ -422,7 +422,7 @@ PLATFORM_CHECK(conv2d_bp, ENGINE_CUDA) { // auto handle = reinterpret_cast(block.launchContext()->getCuDnnHandle()); // auto res = cudnnSetStream(*handle, *block.launchContext()->getCudaStream()); // if (res != 0) -// throw nd4j::cuda_exception::build("Can't set stream for cuDNN", res); +// throw sd::cuda_exception::build("Can't set stream for cuDNN", res); // auto input = INPUT_VARIABLE(0); // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW) // auto weights = INPUT_VARIABLE(1); // [kH, kW, iC, oC] always @@ -456,39 +456,39 @@ PLATFORM_CHECK(conv2d_bp, ENGINE_CUDA) { // cudnnCreateTensorDescriptor(&src); // res = cudnnSetTensor4dDescriptorEx(src, dtype, input->sizeAt(0), input->sizeAt(1), input->sizeAt(2), input->sizeAt(3), input->strideAt(0), input->strideAt(1), input->strideAt(2), input->strideAt(3)); // if (res != 0) -// throw nd4j::cuda_exception::build("cudnnSetTensor4dDescriptorEx src failed", res); +// throw sd::cuda_exception::build("cudnnSetTensor4dDescriptorEx src failed", res); // // TODO: we definitely want NHWC here as well // cudnnFilterDescriptor_t wght; // cudnnCreateFilterDescriptor(&wght); // res = cudnnSetFilter4dDescriptor(wght, dtype, CUDNN_TENSOR_NCHW, oC, iC, kH, kW); // if (res != 0) -// throw nd4j::cuda_exception::build("cudnnSetFilter4dDescriptor failed", res); +// throw sd::cuda_exception::build("cudnnSetFilter4dDescriptor failed", res); // cudnnConvolutionDescriptor_t cdc; // cudnnCreateConvolutionDescriptor(&cdc); // res = cudnnSetConvolution2dDescriptor(cdc, pH, pW, sH, sW, dH, dW, CUDNN_CROSS_CORRELATION, dtype); // if (res != 0) -// throw nd4j::cuda_exception::build("cudnnSetConvolution2dDescriptor failed", res); +// throw sd::cuda_exception::build("cudnnSetConvolution2dDescriptor failed", res); // cudnnTensorDescriptor_t dst; // cudnnCreateTensorDescriptor(&dst); // res = cudnnSetTensor4dDescriptorEx(dst, dtype, output->sizeAt(0), output->sizeAt(1), output->sizeAt(2), output->sizeAt(3), output->strideAt(0), output->strideAt(1), output->strideAt(2), output->strideAt(3)); // if (res != 0) -// throw nd4j::cuda_exception::build("cudnnSetTensor4dDescriptorEx dst failed", res); +// throw sd::cuda_exception::build("cudnnSetTensor4dDescriptorEx dst failed", res); // // TODO: workspace algorithms are supposed to be faster, so we should use it here if we have enough memory // cudnnConvolutionFwdAlgo_t algo; // res = cudnnGetConvolutionForwardAlgorithm(*handle, src, wght, cdc, dst, CUDNN_CONVOLUTION_FWD_NO_WORKSPACE, 0, &algo); // if (res != 0) -// throw nd4j::cuda_exception::build("cudnnGetConvolutionForwardAlgorithm failed", res); +// throw sd::cuda_exception::build("cudnnGetConvolutionForwardAlgorithm failed", res); // // TODO: should be float if dtype is half/float, and double otherwise // float alpha = 1.0f; // float beta = 0.0f; // res = cudnnConvolutionForward(*handle, &alpha, src, input->specialBuffer(), wght, weights->specialBuffer(), cdc, algo, nullptr, 0, &beta, dst, output->specialBuffer()); // if (res != 0) -// throw nd4j::cuda_exception::build("cudnnConvolutionForward failed", res); +// throw sd::cuda_exception::build("cudnnConvolutionForward failed", res); // if (bias != nullptr) { @@ -497,16 +497,16 @@ PLATFORM_CHECK(conv2d_bp, ENGINE_CUDA) { // if (isNCHW) { // res = cudnnSetTensor4dDescriptor(bs, CUDNN_TENSOR_NCHW, dtype, 1, bias->lengthOf(), 1, 1); // if (res != 0) -// throw nd4j::cuda_exception::build("cudnnSetTensor4dDescriptorEx bias NHWC failed", res); +// throw sd::cuda_exception::build("cudnnSetTensor4dDescriptorEx bias NHWC failed", res); // } else { // res = cudnnSetTensor4dDescriptor(bs, CUDNN_TENSOR_NHWC, dtype, 1, 1, 1, bias->lengthOf()); // if (res != 0) -// throw nd4j::cuda_exception::build("cudnnSetTensor4dDescriptorEx bias NHWC failed", res); +// throw sd::cuda_exception::build("cudnnSetTensor4dDescriptorEx bias NHWC failed", res); // } // res = cudnnAddTensor(*handle, &alpha, bs, bias->specialBuffer(), &alpha, dst, output->specialBuffer()); // if (res != 0) -// throw nd4j::cuda_exception::build("cudnnAddTensor failed", res); +// throw sd::cuda_exception::build("cudnnAddTensor failed", res); // } diff --git a/libnd4j/include/ops/declarable/platform/cudnn/conv3d.cu b/libnd4j/include/ops/declarable/platform/cudnn/conv3d.cu index 9d30ff04c..1e86aaa07 100644 --- a/libnd4j/include/ops/declarable/platform/cudnn/conv3d.cu +++ b/libnd4j/include/ops/declarable/platform/cudnn/conv3d.cu @@ -23,7 +23,7 @@ #include "cudnnUtils.h" #include -namespace nd4j { +namespace sd { namespace ops { namespace platforms { @@ -44,7 +44,7 @@ static void conv3dCUDNN(const LaunchContext* context, auto handle = reinterpret_cast(context->getCuDnnHandle()); cudnnStatus_t err = cudnnSetStream(*handle, *context->getCudaStream()); - if (err != 0) throw nd4j::cuda_exception::build("conv3dCUDNN: can't set stream for cuDNN", err); + if (err != 0) throw sd::cuda_exception::build("conv3dCUDNN: can't set stream for cuDNN", err); const std::vector pads = {pD, pH, pW}; const std::vector filtStrides = {sD, sH, sW}; @@ -67,13 +67,13 @@ static void conv3dCUDNN(const LaunchContext* context, err = cudnnSetTensorNdDescriptorEx(x, format, cudnnDataType(input->dataType()), numDims, xShape.data()); else err = cudnnSetTensorNdDescriptor(x, cudnnDataType(input->dataType()), numDims, xShape.data(), xStrides.data()); - if (err != 0) throw nd4j::cuda_exception::build("conv3dCUDNN: cudnnSetTensorNdDescriptor/cudnnSetTensorNdDescriptorEx for input failed", err); + if (err != 0) throw sd::cuda_exception::build("conv3dCUDNN: cudnnSetTensorNdDescriptor/cudnnSetTensorNdDescriptorEx for input failed", err); // weights descriptor cudnnFilterDescriptor_t w; cudnnCreateFilterDescriptor(&w); err = cudnnSetFilterNdDescriptor(w, cudnnDataType(weights->dataType()), CUDNN_TENSOR_NCHW, numDims, wShape.data()); - if(err != 0) throw nd4j::cuda_exception::build("conv3dCUDNN: cudnnSetFilterNdDescriptor failed", err); + if(err != 0) throw sd::cuda_exception::build("conv3dCUDNN: cudnnSetFilterNdDescriptor failed", err); // output descriptor cudnnTensorDescriptor_t z; @@ -82,26 +82,26 @@ static void conv3dCUDNN(const LaunchContext* context, err = cudnnSetTensorNdDescriptorEx(z, format, cudnnDataType(output->dataType()), numDims, zShape.data()); else err = cudnnSetTensorNdDescriptor(z, cudnnDataType(output->dataType()), numDims, zShape.data(), zStrides.data()); - if (err != 0) throw nd4j::cuda_exception::build("conv3dCUDNN: cudnnSetTensorNdDescriptor/cudnnSetTensorNdDescriptorEx for output failed", err); + if (err != 0) throw sd::cuda_exception::build("conv3dCUDNN: cudnnSetTensorNdDescriptor/cudnnSetTensorNdDescriptorEx for output failed", err); // description of convolution cudnnConvolutionDescriptor_t conv; cudnnCreateConvolutionDescriptor(&conv); err = cudnnSetConvolutionNdDescriptor(conv, numDims-2, pads.data(), filtStrides.data(), dilations.data(), CUDNN_CROSS_CORRELATION, cudnnDataType(output->dataType())); - if (err != 0) throw nd4j::cuda_exception::build("conv3dCUDNN: cudnnSetConvolutionNdDescriptor failed", err); + if (err != 0) throw sd::cuda_exception::build("conv3dCUDNN: cudnnSetConvolutionNdDescriptor failed", err); // algorithm description cudnnConvolutionFwdAlgo_t algo; err = cudnnGetConvolutionForwardAlgorithm(*handle, x, w, conv, z, CUDNN_CONVOLUTION_FWD_PREFER_FASTEST, 0, &algo); - if (err != 0) throw nd4j::cuda_exception::build("conv3dCUDNN: cudnnGetConvolutionForwardAlgorithm failed", err); + if (err != 0) throw sd::cuda_exception::build("conv3dCUDNN: cudnnGetConvolutionForwardAlgorithm failed", err); // allocate auxiliary device memory, abbreviation ws means workspace size_t wsSize; err = cudnnGetConvolutionForwardWorkspaceSize(*handle, x, w, conv, z, algo, &wsSize); - if (err != 0) throw nd4j::cuda_exception::build("conv3dCUDNN: cudnnGetConvolutionForwardWorkspaceSize failed", err); + if (err != 0) throw sd::cuda_exception::build("conv3dCUDNN: cudnnGetConvolutionForwardWorkspaceSize failed", err); void* wsData; auto cudaErr = cudaMalloc(&wsData, wsSize); - if (cudaErr != 0) throw nd4j::cuda_exception::build("conv3dCUDNN: cudaMalloc for auxiliary workspace memory failed", cudaErr); + if (cudaErr != 0) throw sd::cuda_exception::build("conv3dCUDNN: cudaMalloc for auxiliary workspace memory failed", cudaErr); // provide scaling parameters const float alpha32(1), beta32(0); @@ -113,7 +113,7 @@ static void conv3dCUDNN(const LaunchContext* context, // run calculation err = cudnnConvolutionForward(*handle, alpha, x, input->getSpecialBuffer(), w, weights->getSpecialBuffer(), conv, algo, wsData, wsSize, beta, z, output->specialBuffer()); - if (err != 0) throw nd4j::cuda_exception::build("conv3dCUDNN: cudnnConvolutionForward failed", err); + if (err != 0) throw sd::cuda_exception::build("conv3dCUDNN: cudnnConvolutionForward failed", err); // add bias if it is present if (bias != nullptr) { @@ -121,9 +121,9 @@ static void conv3dCUDNN(const LaunchContext* context, cudnnTensorDescriptor_t b; cudnnCreateTensorDescriptor(&b); err = cudnnSetTensorNdDescriptorEx(b, format, cudnnDataType(bias->dataType()), numDims, bShape.data()); - if (err != 0) throw nd4j::cuda_exception::build("conv3dCUDNN: cudnnSetTensorNdDescriptor for bias failed", err); + if (err != 0) throw sd::cuda_exception::build("conv3dCUDNN: cudnnSetTensorNdDescriptor for bias failed", err); err = cudnnAddTensor(*handle, alpha, b, bias->getSpecialBuffer(), alpha, z, output->specialBuffer()); - if (err != 0) throw nd4j::cuda_exception::build("conv3dCUDNN: cudnnAddTensor bias failed", err); + if (err != 0) throw sd::cuda_exception::build("conv3dCUDNN: cudnnAddTensor bias failed", err); } // cudaErr = cudaStreamSynchronize(*context->getCudaStream()); @@ -131,7 +131,7 @@ static void conv3dCUDNN(const LaunchContext* context, // throw cuda_exception::build("conv3dCUDNN: cudaStreamSynchronize failed !", cudaErr); cudaErr = cudaFree(wsData); - if (cudaErr != 0) throw nd4j::cuda_exception::build("conv3dCUDNN: cudaFree for auxiliary workspace memory failed", cudaErr); + if (cudaErr != 0) throw sd::cuda_exception::build("conv3dCUDNN: cudaFree for auxiliary workspace memory failed", cudaErr); NDArray::registerSpecialUse({output}, {input, weights, bias}); } @@ -154,7 +154,7 @@ static void conv3dBpCUDNN(const LaunchContext* context, auto handle = reinterpret_cast(context->getCuDnnHandle()); cudnnStatus_t err = cudnnSetStream(*handle, *context->getCudaStream()); - if (err != 0) throw nd4j::cuda_exception::build("conv3dBpCUDNN: can't set stream for cuDNN", err); + if (err != 0) throw sd::cuda_exception::build("conv3dBpCUDNN: can't set stream for cuDNN", err); const std::vector pads = {pD, pH, pW}; const std::vector filtStrides = {sD, sH, sW}; @@ -178,7 +178,7 @@ static void conv3dBpCUDNN(const LaunchContext* context, err = cudnnSetTensorNdDescriptorEx(x, format, cudnnDataType(input->dataType()), numDims, xShape.data()); else err = cudnnSetTensorNdDescriptor(x, cudnnDataType(input->dataType()), numDims, xShape.data(), xStrides.data()); - if (err != 0) throw nd4j::cuda_exception::build("conv3dBpCUDNN: cudnnSetTensorNdDescriptor/cudnnSetTensorNdDescriptorEx for input failed", err); + if (err != 0) throw sd::cuda_exception::build("conv3dBpCUDNN: cudnnSetTensorNdDescriptor/cudnnSetTensorNdDescriptorEx for input failed", err); // gradO descriptor cudnnTensorDescriptor_t dz; @@ -187,7 +187,7 @@ static void conv3dBpCUDNN(const LaunchContext* context, err = cudnnSetTensorNdDescriptorEx(dz, format, cudnnDataType(gradO->dataType()), numDims, dzShape.data()); else err = cudnnSetTensorNdDescriptor(dz, cudnnDataType(gradO->dataType()), numDims, dzShape.data(), dzStrides.data()); - if (err != 0) throw nd4j::cuda_exception::build("conv3dBpCUDNN: cudnnSetTensorNdDescriptor/cudnnSetTensorNdDescriptorEx for gradO failed", err); + if (err != 0) throw sd::cuda_exception::build("conv3dBpCUDNN: cudnnSetTensorNdDescriptor/cudnnSetTensorNdDescriptorEx for gradO failed", err); // gradI descriptor cudnnTensorDescriptor_t dx; @@ -196,45 +196,45 @@ static void conv3dBpCUDNN(const LaunchContext* context, err = cudnnSetTensorNdDescriptorEx(dx, format, cudnnDataType(gradI->dataType()), numDims, xShape.data()); else err = cudnnSetTensorNdDescriptor(dx, cudnnDataType(gradI->dataType()), numDims, xShape.data(), dxStrides.data()); - if (err != 0) throw nd4j::cuda_exception::build("conv3dBpCUDNN: cudnnSetTensorNdDescriptor/cudnnSetTensorNdDescriptorEx for gradI failed", err); + if (err != 0) throw sd::cuda_exception::build("conv3dBpCUDNN: cudnnSetTensorNdDescriptor/cudnnSetTensorNdDescriptorEx for gradI failed", err); // gradW descriptor cudnnFilterDescriptor_t dw; cudnnCreateFilterDescriptor(&dw); err = cudnnSetFilterNdDescriptor(dw, cudnnDataType(gradW->dataType()), CUDNN_TENSOR_NCHW, numDims, wShape.data()); - if(err != 0) throw nd4j::cuda_exception::build("conv3dBpCUDNN: cudnnSetFilterNdDescriptor failed", err); + if(err != 0) throw sd::cuda_exception::build("conv3dBpCUDNN: cudnnSetFilterNdDescriptor failed", err); // description of convolution cudnnConvolutionDescriptor_t conv; cudnnCreateConvolutionDescriptor(&conv); err = cudnnSetConvolutionNdDescriptor(conv, numDims-2, pads.data(), filtStrides.data(), dilations.data(), CUDNN_CROSS_CORRELATION, cudnnDataType(gradO->dataType())); - if (err != 0) throw nd4j::cuda_exception::build("conv3dBpCUDNN: cudnnSetConvolutionNdDescriptor failed", err); + if (err != 0) throw sd::cuda_exception::build("conv3dBpCUDNN: cudnnSetConvolutionNdDescriptor failed", err); // gradW algorithm description cudnnConvolutionBwdFilterAlgo_t algoGradW; err = cudnnGetConvolutionBackwardFilterAlgorithm(*handle, x, dz, conv, dw, CUDNN_CONVOLUTION_BWD_FILTER_PREFER_FASTEST, 0, &algoGradW); - if (err != 0) throw nd4j::cuda_exception::build("conv3dBpCUDNN: cudnnGetConvolutionBackwardFilterAlgorithm failed", err); + if (err != 0) throw sd::cuda_exception::build("conv3dBpCUDNN: cudnnGetConvolutionBackwardFilterAlgorithm failed", err); // gradI algorithm description cudnnConvolutionBwdDataAlgo_t algoGradI; err = cudnnGetConvolutionBackwardDataAlgorithm(*handle, dw, dz, conv, x, CUDNN_CONVOLUTION_BWD_DATA_PREFER_FASTEST, 0, &algoGradI); - if (err != 0) throw nd4j::cuda_exception::build("conv3dBpCUDNN: cudnnGetConvolutionBackwardDataAlgorithm failed", err); + if (err != 0) throw sd::cuda_exception::build("conv3dBpCUDNN: cudnnGetConvolutionBackwardDataAlgorithm failed", err); // allocate auxiliary device memory for gradW calculation, abbreviation ws means workspace size_t wsGradWSize; err = cudnnGetConvolutionBackwardFilterWorkspaceSize(*handle, x, dz, conv, dw, algoGradW, &wsGradWSize); - if (err != 0) throw nd4j::cuda_exception::build("conv3dBpCUDNN: cudnnGetConvolutionBackwardFilterWorkspaceSize failed", err); + if (err != 0) throw sd::cuda_exception::build("conv3dBpCUDNN: cudnnGetConvolutionBackwardFilterWorkspaceSize failed", err); void* wsGradWData; auto cudaErr = cudaMalloc(&wsGradWData, wsGradWSize); - if (cudaErr != 0) throw nd4j::cuda_exception::build("conv3dBpCUDNN: cudaMalloc for auxiliary workspace memory wsGradWData failed", cudaErr); + if (cudaErr != 0) throw sd::cuda_exception::build("conv3dBpCUDNN: cudaMalloc for auxiliary workspace memory wsGradWData failed", cudaErr); // allocate auxiliary device memory for gradI calculation, abbreviation ws means workspace size_t wsGradISize; err = cudnnGetConvolutionBackwardDataWorkspaceSize(*handle, dw, dz, conv, dx, algoGradI, &wsGradISize); - if (err != 0) throw nd4j::cuda_exception::build("conv3dBpCUDNN: cudnnGetConvolutionBackwardDataWorkspaceSize failed", err); + if (err != 0) throw sd::cuda_exception::build("conv3dBpCUDNN: cudnnGetConvolutionBackwardDataWorkspaceSize failed", err); void* wsGradIData; cudaErr = cudaMalloc(&wsGradIData, wsGradISize); - if (cudaErr != 0) throw nd4j::cuda_exception::build("conv3dBpCUDNN: cudaMalloc for auxiliary workspace memory wsGradIData failed", cudaErr); + if (cudaErr != 0) throw sd::cuda_exception::build("conv3dBpCUDNN: cudaMalloc for auxiliary workspace memory wsGradIData failed", cudaErr); // provide scaling parameters const float alpha32(1), beta32(0); @@ -250,28 +250,28 @@ static void conv3dBpCUDNN(const LaunchContext* context, cudnnTensorDescriptor_t db; cudnnCreateTensorDescriptor(&db); err = cudnnSetTensorNdDescriptorEx(db, format, cudnnDataType(gradB->dataType()), numDims, dbShape.data()); - if (err != 0) throw nd4j::cuda_exception::build("conv3dBpCUDNN: cudnnSetTensorNdDescriptor for gradB failed", err); + if (err != 0) throw sd::cuda_exception::build("conv3dBpCUDNN: cudnnSetTensorNdDescriptor for gradB failed", err); err = cudnnConvolutionBackwardBias(*handle, alpha, dz, gradO->getSpecialBuffer(), beta, db, gradB->getSpecialBuffer()); - if (err != 0) throw nd4j::cuda_exception::build("conv3dBpCUDNN: cudnnConvolutionBackwardBias failed", err); + if (err != 0) throw sd::cuda_exception::build("conv3dBpCUDNN: cudnnConvolutionBackwardBias failed", err); } // run calculation for gradW err = cudnnConvolutionBackwardFilter(*handle, alpha, x, input->getSpecialBuffer(), dz, gradO->getSpecialBuffer(), conv, algoGradW, wsGradWData, wsGradWSize, beta, dw, gradW->getSpecialBuffer()); - if (err != 0) throw nd4j::cuda_exception::build("conv3dBpCUDNN: cudnnConvolutionBackwardFilter failed", err); + if (err != 0) throw sd::cuda_exception::build("conv3dBpCUDNN: cudnnConvolutionBackwardFilter failed", err); // run calculation for gradI err = cudnnConvolutionBackwardData(*handle, alpha, dw, weights->getSpecialBuffer(), dz, gradO->getSpecialBuffer(), conv, algoGradI, wsGradIData, wsGradISize, beta, dx, gradI->getSpecialBuffer()); - if (err != 0) throw nd4j::cuda_exception::build("conv3dBpCUDNN: cudnnConvolutionBackwardData failed", err); + if (err != 0) throw sd::cuda_exception::build("conv3dBpCUDNN: cudnnConvolutionBackwardData failed", err); // cudaErr = cudaStreamSynchronize(*context->getCudaStream()); // if (cudaErr != 0) // throw cuda_exception::build("conv3dBpCUDNN: cudaStreamSynchronize failed !", cudaErr); cudaErr = cudaFree(wsGradWData); - if (cudaErr != 0) throw nd4j::cuda_exception::build("conv3dBpCUDNN: cudaFree for auxiliary workspace memory wsGradWData failed", cudaErr); + if (cudaErr != 0) throw sd::cuda_exception::build("conv3dBpCUDNN: cudaFree for auxiliary workspace memory wsGradWData failed", cudaErr); cudaErr = cudaFree(wsGradIData); - if (cudaErr != 0) throw nd4j::cuda_exception::build("conv3dBpCUDNN: cudaFree for auxiliary workspace memory wsGradIData failed", cudaErr); + if (cudaErr != 0) throw sd::cuda_exception::build("conv3dBpCUDNN: cudaFree for auxiliary workspace memory wsGradIData failed", cudaErr); NDArray::registerSpecialUse({gradI, gradW, gradB}, {input, weights, gradO}); } diff --git a/libnd4j/include/ops/declarable/platform/cudnn/cudnnUtils.cu b/libnd4j/include/ops/declarable/platform/cudnn/cudnnUtils.cu index 02a302e61..22b0f9b1c 100644 --- a/libnd4j/include/ops/declarable/platform/cudnn/cudnnUtils.cu +++ b/libnd4j/include/ops/declarable/platform/cudnn/cudnnUtils.cu @@ -22,7 +22,7 @@ #include "cudnnUtils.h" #include -namespace nd4j { +namespace sd { namespace ops { namespace platforms { @@ -128,7 +128,7 @@ void pooling2dCUDNN(const LaunchContext* context, auto handle = reinterpret_cast(context->getCuDnnHandle()); cudnnStatus_t err = cudnnSetStream(*handle, *context->getCudaStream()); - if (err != 0) throw nd4j::cuda_exception::build("pooling2dCUDNN: can't set stream for cuDNN", err); + if (err != 0) throw sd::cuda_exception::build("pooling2dCUDNN: can't set stream for cuDNN", err); cudnnTensorFormat_t format = isNCHW ? CUDNN_TENSOR_NCHW : CUDNN_TENSOR_NHWC; @@ -139,7 +139,7 @@ void pooling2dCUDNN(const LaunchContext* context, err = cudnnSetTensor4dDescriptor(x, format, cudnnDataType(input->dataType()), bS, iC, iH, iW); else err = cudnnSetTensor4dDescriptorEx(x, cudnnDataType(input->dataType()), bS, iC, iH, iW, input->strideAt(0), input->strideAt(indIOioC), input->strideAt(indIiH), input->strideAt(indIiH + 1)); - if (err != 0) throw nd4j::cuda_exception::build("pooling2dCUDNN: cudnnSetTensor4dDescriptor/cudnnSetTensor4dDescriptorEx for input failed", err); + if (err != 0) throw sd::cuda_exception::build("pooling2dCUDNN: cudnnSetTensor4dDescriptor/cudnnSetTensor4dDescriptorEx for input failed", err); // output descriptor cudnnTensorDescriptor_t z; @@ -148,13 +148,13 @@ void pooling2dCUDNN(const LaunchContext* context, err = cudnnSetTensor4dDescriptor(z, format, cudnnDataType(output->dataType()), bS, oC, oH, oW); else err = cudnnSetTensor4dDescriptorEx(z, cudnnDataType(output->dataType()), bS, oC, oH, oW, output->strideAt(0), output->strideAt(indIOioC), output->strideAt(indOoH), output->strideAt(indOoH + 1)); - if (err != 0) throw nd4j::cuda_exception::build("pooling2dCUDNN: cudnnSetTensor4dDescriptor/cudnnSetTensor4dDescriptorEx for output failed", err); + if (err != 0) throw sd::cuda_exception::build("pooling2dCUDNN: cudnnSetTensor4dDescriptor/cudnnSetTensor4dDescriptorEx for output failed", err); // description of pooling cudnnPoolingDescriptor_t pooling; cudnnCreatePoolingDescriptor(&pooling); err = cudnnSetPooling2dDescriptor(pooling, mode, CUDNN_PROPAGATE_NAN, kH, kW, pH, pW, sH, sW); - if (err != 0) throw nd4j::cuda_exception::build("pooling2dCUDNN: cudnnSetPooling2dDescriptor failed", err); + if (err != 0) throw sd::cuda_exception::build("pooling2dCUDNN: cudnnSetPooling2dDescriptor failed", err); // provide scaling parameters const float alpha32(1), beta32(0); @@ -166,7 +166,7 @@ void pooling2dCUDNN(const LaunchContext* context, // run calculation err = cudnnPoolingForward(*handle, pooling, alpha, x, input->getSpecialBuffer(), beta, z, output->specialBuffer()); - if (err != 0) throw nd4j::cuda_exception::build("pooling2dCUDNN: cudnnPoolingForward failed", err); + if (err != 0) throw sd::cuda_exception::build("pooling2dCUDNN: cudnnPoolingForward failed", err); auto cudaErr = cudaStreamSynchronize(*context->getCudaStream()); if (cudaErr != 0) @@ -191,7 +191,7 @@ void pooling2dBpCUDNN(const LaunchContext* context, auto handle = reinterpret_cast(context->getCuDnnHandle()); cudnnStatus_t err = cudnnSetStream(*handle, *context->getCudaStream()); - if (err != 0) throw nd4j::cuda_exception::build("pooling2dBpCUDNN: can't set stream for cuDNN", err); + if (err != 0) throw sd::cuda_exception::build("pooling2dBpCUDNN: can't set stream for cuDNN", err); cudnnTensorFormat_t format = isNCHW ? CUDNN_TENSOR_NCHW : CUDNN_TENSOR_NHWC; @@ -202,7 +202,7 @@ void pooling2dBpCUDNN(const LaunchContext* context, err = cudnnSetTensor4dDescriptor(x, format, cudnnDataType(input->dataType()), bS, iC, iH, iW); else err = cudnnSetTensor4dDescriptorEx(x, cudnnDataType(input->dataType()), bS, iC, iH, iW, input->strideAt(0), input->strideAt(indIOioC), input->strideAt(indIiH), input->strideAt(indIiH + 1)); - if (err != 0) throw nd4j::cuda_exception::build("pooling2dBpCUDNN: cudnnSetTensor4dDescriptor/cudnnSetTensor4dDescriptorEx for input/gradI failed", err); + if (err != 0) throw sd::cuda_exception::build("pooling2dBpCUDNN: cudnnSetTensor4dDescriptor/cudnnSetTensor4dDescriptorEx for input/gradI failed", err); // gradO descriptor cudnnTensorDescriptor_t dz; @@ -211,13 +211,13 @@ void pooling2dBpCUDNN(const LaunchContext* context, err = cudnnSetTensor4dDescriptor(dz, format, cudnnDataType(gradO->dataType()), bS, oC, oH, oW); else err = cudnnSetTensor4dDescriptorEx(dz, cudnnDataType(gradO->dataType()), bS, oC, oH, oW, gradO->strideAt(0), gradO->strideAt(indIOioC), gradO->strideAt(indOoH), gradO->strideAt(indOoH + 1)); - if (err != 0) throw nd4j::cuda_exception::build("pooling2dBpCUDNN: cudnnSetTensor4dDescriptor/cudnnSetTensor4dDescriptorEx for gradO failed", err); + if (err != 0) throw sd::cuda_exception::build("pooling2dBpCUDNN: cudnnSetTensor4dDescriptor/cudnnSetTensor4dDescriptorEx for gradO failed", err); // description of pooling cudnnPoolingDescriptor_t pooling; cudnnCreatePoolingDescriptor(&pooling); err = cudnnSetPooling2dDescriptor(pooling, mode, CUDNN_PROPAGATE_NAN, kH, kW, pH, pW, sH, sW); - if (err != 0) throw nd4j::cuda_exception::build("pooling2dBpCUDNN: cudnnSetPooling2dDescriptor failed", err); + if (err != 0) throw sd::cuda_exception::build("pooling2dBpCUDNN: cudnnSetPooling2dDescriptor failed", err); // provide scaling parameters const float alpha32(1), beta32(0); @@ -229,7 +229,7 @@ void pooling2dBpCUDNN(const LaunchContext* context, // run calculation for gradI err = cudnnPoolingBackward(*handle, pooling, alpha, dz, gradO->getSpecialBuffer(), dz, gradO->getSpecialBuffer(), x, input->getSpecialBuffer(), beta, x, gradI->getSpecialBuffer()); - if (err != 0) throw nd4j::cuda_exception::build("pooling2dBpCUDNN: cudnnPoolingBackward failed", err); + if (err != 0) throw sd::cuda_exception::build("pooling2dBpCUDNN: cudnnPoolingBackward failed", err); auto cudaErr = cudaStreamSynchronize(*context->getCudaStream()); if (cudaErr != 0) @@ -249,7 +249,7 @@ void pooling3dCUDNN(const LaunchContext* context, auto handle = reinterpret_cast(context->getCuDnnHandle()); cudnnStatus_t err = cudnnSetStream(*handle, *context->getCudaStream()); - if (err != 0) throw nd4j::cuda_exception::build("pooling3dCUDNN: can't set stream for cuDNN", err); + if (err != 0) throw sd::cuda_exception::build("pooling3dCUDNN: can't set stream for cuDNN", err); const int numDims = 5; @@ -276,7 +276,7 @@ void pooling3dCUDNN(const LaunchContext* context, err = cudnnSetTensorNdDescriptorEx(x, format, cudnnDataType(input->dataType()), numDims, xShape); else err = cudnnSetTensorNdDescriptor(x, cudnnDataType(input->dataType()), numDims, xShape, xStrides); - if (err != 0) throw nd4j::cuda_exception::build("pooling3dCUDNN: cudnnSetTensorNdDescriptor/cudnnSetTensorNdDescriptorEx for input failed", err); + if (err != 0) throw sd::cuda_exception::build("pooling3dCUDNN: cudnnSetTensorNdDescriptor/cudnnSetTensorNdDescriptorEx for input failed", err); // output descriptor cudnnTensorDescriptor_t z; @@ -285,13 +285,13 @@ void pooling3dCUDNN(const LaunchContext* context, err = cudnnSetTensorNdDescriptorEx(z, format, cudnnDataType(output->dataType()), numDims, zShape); else err = cudnnSetTensorNdDescriptor(z, cudnnDataType(output->dataType()), numDims, zShape, zStrides); - if (err != 0) throw nd4j::cuda_exception::build("pooling3dCUDNN: cudnnSetTensorNdDescriptor/cudnnSetTensorNdDescriptorEx for output failed", err); + if (err != 0) throw sd::cuda_exception::build("pooling3dCUDNN: cudnnSetTensorNdDescriptor/cudnnSetTensorNdDescriptorEx for output failed", err); // description of pooling cudnnPoolingDescriptor_t pooling; cudnnCreatePoolingDescriptor(&pooling); err = cudnnSetPoolingNdDescriptor(pooling, mode, CUDNN_PROPAGATE_NAN, numDims - 2, kSizes, pSizes, sSizes); - if (err != 0) throw nd4j::cuda_exception::build("pooling3dCUDNN: cudnnSetPoolingNdDescriptor failed", err); + if (err != 0) throw sd::cuda_exception::build("pooling3dCUDNN: cudnnSetPoolingNdDescriptor failed", err); // provide scaling parameters const float alpha32(1), beta32(0); @@ -303,7 +303,7 @@ void pooling3dCUDNN(const LaunchContext* context, // run calculation err = cudnnPoolingForward(*handle, pooling, alpha, x, input->getSpecialBuffer(), beta, z, output->specialBuffer()); - if (err != 0) throw nd4j::cuda_exception::build("pooling3dCUDNN: cudnnPoolingForward failed", err); + if (err != 0) throw sd::cuda_exception::build("pooling3dCUDNN: cudnnPoolingForward failed", err); auto cudaErr = cudaStreamSynchronize(*context->getCudaStream()); if (cudaErr != 0) @@ -324,7 +324,7 @@ void pooling3dBpCUDNN(const LaunchContext* context, auto handle = reinterpret_cast(context->getCuDnnHandle()); cudnnStatus_t err = cudnnSetStream(*handle, *context->getCudaStream()); - if (err != 0) throw nd4j::cuda_exception::build("pooling3dBpCUDNN: can't set stream for cuDNN", err); + if (err != 0) throw sd::cuda_exception::build("pooling3dBpCUDNN: can't set stream for cuDNN", err); const int numDims = 5; @@ -351,7 +351,7 @@ void pooling3dBpCUDNN(const LaunchContext* context, err = cudnnSetTensorNdDescriptorEx(x, format, cudnnDataType(input->dataType()), numDims, xShape); else err = cudnnSetTensorNdDescriptor(x, cudnnDataType(input->dataType()), numDims, xShape, xStrides); - if (err != 0) throw nd4j::cuda_exception::build("pooling3dBpCUDNN: cudnnSetTensorNdDescriptor/cudnnSetTensorNdDescriptorEx for input/gradI failed", err); + if (err != 0) throw sd::cuda_exception::build("pooling3dBpCUDNN: cudnnSetTensorNdDescriptor/cudnnSetTensorNdDescriptorEx for input/gradI failed", err); // gradO descriptor cudnnTensorDescriptor_t dz; @@ -360,13 +360,13 @@ void pooling3dBpCUDNN(const LaunchContext* context, err = cudnnSetTensorNdDescriptorEx(dz, format, cudnnDataType(gradO->dataType()), numDims, dzShape); else err = cudnnSetTensorNdDescriptor(dz, cudnnDataType(gradO->dataType()), numDims, dzShape, dzStrides); - if (err != 0) throw nd4j::cuda_exception::build("pooling3dBpCUDNN: cudnnSetTensorNdDescriptor/cudnnSetTensorNdDescriptorEx for gradO failed", err); + if (err != 0) throw sd::cuda_exception::build("pooling3dBpCUDNN: cudnnSetTensorNdDescriptor/cudnnSetTensorNdDescriptorEx for gradO failed", err); // description of pooling cudnnPoolingDescriptor_t pooling; cudnnCreatePoolingDescriptor(&pooling); err = cudnnSetPoolingNdDescriptor(pooling, mode, CUDNN_PROPAGATE_NAN, numDims - 2, kSizes, pSizes, sSizes); - if (err != 0) throw nd4j::cuda_exception::build("pooling3dBpCUDNN: cudnnSetPoolingNdDescriptor failed", err); + if (err != 0) throw sd::cuda_exception::build("pooling3dBpCUDNN: cudnnSetPoolingNdDescriptor failed", err); // provide scaling parameters const float alpha32(1), beta32(0); @@ -383,11 +383,11 @@ void pooling3dBpCUDNN(const LaunchContext* context, // run ff calculation err = cudnnPoolingForward(*handle, pooling, alpha, x, input->getSpecialBuffer(), beta, dz, temp.specialBuffer()); - if (err != 0) throw nd4j::cuda_exception::build("pooling3dCUDNN: cudnnPoolingForward failed", err); + if (err != 0) throw sd::cuda_exception::build("pooling3dCUDNN: cudnnPoolingForward failed", err); // run bp calculation for gradI err = cudnnPoolingBackward(*handle, pooling, alpha, dz, temp.getSpecialBuffer(), dz, gradO->getSpecialBuffer(), x, input->getSpecialBuffer(), beta, x, gradI->getSpecialBuffer()); - if (err != 0) throw nd4j::cuda_exception::build("pooling2dBpCUDNN: cudnnPoolingBackward failed", err); + if (err != 0) throw sd::cuda_exception::build("pooling2dBpCUDNN: cudnnPoolingBackward failed", err); NDArray::registerSpecialUse({gradI}, {input, gradO, &temp}); } @@ -397,7 +397,7 @@ void pooling3dBpCUDNN(const LaunchContext* context, // run bp calculation for gradI err = cudnnPoolingBackward(*handle, pooling, alpha, dz, gradO->getSpecialBuffer(), dz, gradO->getSpecialBuffer(), x, input->getSpecialBuffer(), beta, x, gradI->getSpecialBuffer()); - if (err != 0) throw nd4j::cuda_exception::build("pooling2dBpCUDNN: cudnnPoolingBackward failed", err); + if (err != 0) throw sd::cuda_exception::build("pooling2dBpCUDNN: cudnnPoolingBackward failed", err); NDArray::registerSpecialUse({gradI}, {input, gradO}); } diff --git a/libnd4j/include/ops/declarable/platform/cudnn/cudnnUtils.h b/libnd4j/include/ops/declarable/platform/cudnn/cudnnUtils.h index 5c46fb7b0..3379979a3 100644 --- a/libnd4j/include/ops/declarable/platform/cudnn/cudnnUtils.h +++ b/libnd4j/include/ops/declarable/platform/cudnn/cudnnUtils.h @@ -23,14 +23,14 @@ #include #include -#include +#include #include #include -#include +#include #include -namespace nd4j { +namespace sd { namespace ops { namespace platforms { @@ -59,17 +59,17 @@ namespace platforms { DECLARE_PLATFORM(maxpool3dnew_bp, ENGINE_CUDA); ////////////////////////////////////////////////////////////////////////// -FORCEINLINE cudnnDataType_t cudnnDataType(nd4j::DataType dataType) { +FORCEINLINE cudnnDataType_t cudnnDataType(sd::DataType dataType) { switch (dataType) { - case nd4j::DataType::FLOAT32: + case sd::DataType::FLOAT32: return CUDNN_DATA_FLOAT; - case nd4j::DataType::DOUBLE: + case sd::DataType::DOUBLE: return CUDNN_DATA_DOUBLE; - case nd4j::DataType::HALF: + case sd::DataType::HALF: return CUDNN_DATA_HALF; - case nd4j::DataType::INT32: + case sd::DataType::INT32: return CUDNN_DATA_INT32; - case nd4j::DataType::INT8: + case sd::DataType::INT8: return CUDNN_DATA_INT8; default: throw datatype_exception::build("Unsupported data type", dataType); diff --git a/libnd4j/include/ops/declarable/platform/cudnn/depthwiseConv2d.cu b/libnd4j/include/ops/declarable/platform/cudnn/depthwiseConv2d.cu index d328fa92b..ae07ce944 100644 --- a/libnd4j/include/ops/declarable/platform/cudnn/depthwiseConv2d.cu +++ b/libnd4j/include/ops/declarable/platform/cudnn/depthwiseConv2d.cu @@ -22,7 +22,7 @@ #include "cudnnUtils.h" #include -namespace nd4j { +namespace sd { namespace ops { namespace platforms { @@ -51,7 +51,7 @@ static void depthwiseConv2dCUDNN(const LaunchContext* context, auto handle = reinterpret_cast(context->getCuDnnHandle()); cudnnStatus_t err = cudnnSetStream(*handle, *context->getCudaStream()); - if (err != 0) throw nd4j::cuda_exception::build("depthwiseConv2dCUDNN: can't set stream for cuDNN", err); + if (err != 0) throw sd::cuda_exception::build("depthwiseConv2dCUDNN: can't set stream for cuDNN", err); cudnnTensorFormat_t format = isNCHW ? CUDNN_TENSOR_NCHW : CUDNN_TENSOR_NHWC; @@ -62,13 +62,13 @@ static void depthwiseConv2dCUDNN(const LaunchContext* context, err = cudnnSetTensor4dDescriptor(x, format, cudnnDataType(input->dataType()), bS, iC, iH, iW); else err = cudnnSetTensor4dDescriptorEx(x, cudnnDataType(input->dataType()), bS, iC, iH, iW, input->strideAt(0), input->strideAt(indIOioC), input->strideAt(indIiH), input->strideAt(indIiH + 1)); - if (err != 0) throw nd4j::cuda_exception::build("depthwiseConv2dCUDNN: cudnnSetTensor4dDescriptor/cudnnSetTensor4dDescriptorEx for input failed", err); + if (err != 0) throw sd::cuda_exception::build("depthwiseConv2dCUDNN: cudnnSetTensor4dDescriptor/cudnnSetTensor4dDescriptorEx for input failed", err); // weights descriptor cudnnFilterDescriptor_t w; cudnnCreateFilterDescriptor(&w); err = cudnnSetFilter4dDescriptor(w, cudnnDataType(weights->dataType()), CUDNN_TENSOR_NCHW, iC, mC, kH, kW); - if(err != 0) throw nd4j::cuda_exception::build("depthwiseConv2dCUDNN: cudnnSetFilter4dDescriptor failed", err); + if(err != 0) throw sd::cuda_exception::build("depthwiseConv2dCUDNN: cudnnSetFilter4dDescriptor failed", err); // output descriptor cudnnTensorDescriptor_t z; @@ -77,28 +77,28 @@ static void depthwiseConv2dCUDNN(const LaunchContext* context, err = cudnnSetTensor4dDescriptor(z, format, cudnnDataType(output->dataType()), bS, oC, oH, oW); else err = cudnnSetTensor4dDescriptorEx(z, cudnnDataType(output->dataType()), bS, oC, oH, oW, output->strideAt(0), output->strideAt(indIOioC), output->strideAt(indOoH), output->strideAt(indOoH + 1)); - if (err != 0) throw nd4j::cuda_exception::build("depthwiseConv2dCUDNN: cudnnSetTensor4dDescriptor/cudnnSetTensor4dDescriptorEx for output failed", err); + if (err != 0) throw sd::cuda_exception::build("depthwiseConv2dCUDNN: cudnnSetTensor4dDescriptor/cudnnSetTensor4dDescriptorEx for output failed", err); // description of convolution cudnnConvolutionDescriptor_t conv; cudnnCreateConvolutionDescriptor(&conv); err = cudnnSetConvolution2dDescriptor(conv, pH, pW, sH, sW, dH, dW, CUDNN_CROSS_CORRELATION, cudnnDataType(output->dataType())); - if (err != 0) throw nd4j::cuda_exception::build("depthwiseConv2dCUDNN: cudnnSetConvolution2dDescriptor failed", err); + if (err != 0) throw sd::cuda_exception::build("depthwiseConv2dCUDNN: cudnnSetConvolution2dDescriptor failed", err); err = cudnnSetConvolutionGroupCount(conv, iC); // set number of groups (depthwise mode) in description of convolution, groupCount == iC - if (err != 0) throw nd4j::cuda_exception::build("depthwiseConv2dCUDNN: cudnnSetConvolutionGroupCount failed", err); + if (err != 0) throw sd::cuda_exception::build("depthwiseConv2dCUDNN: cudnnSetConvolutionGroupCount failed", err); // algorithm description cudnnConvolutionFwdAlgo_t algo; err = cudnnGetConvolutionForwardAlgorithm(*handle, x, w, conv, z, CUDNN_CONVOLUTION_FWD_PREFER_FASTEST, 0, &algo); - if (err != 0) throw nd4j::cuda_exception::build("depthwiseConv2dCUDNN: cudnnGetConvolutionForwardAlgorithm failed", err); + if (err != 0) throw sd::cuda_exception::build("depthwiseConv2dCUDNN: cudnnGetConvolutionForwardAlgorithm failed", err); // allocate auxiliary device memory, abbreviation ws means workspace size_t wsSize; err = cudnnGetConvolutionForwardWorkspaceSize(*handle, x, w, conv, z, algo, &wsSize); - if (err != 0) throw nd4j::cuda_exception::build("depthwiseConv2dCUDNN: cudnnGetConvolutionForwardWorkspaceSize failed", err); + if (err != 0) throw sd::cuda_exception::build("depthwiseConv2dCUDNN: cudnnGetConvolutionForwardWorkspaceSize failed", err); void* wsData; auto cudaErr = cudaMalloc(&wsData, wsSize); - if (cudaErr != 0) throw nd4j::cuda_exception::build("depthwiseConv2dCUDNN: cudaMalloc for auxiliary workspace memory failed", cudaErr); + if (cudaErr != 0) throw sd::cuda_exception::build("depthwiseConv2dCUDNN: cudaMalloc for auxiliary workspace memory failed", cudaErr); // provide scaling parameters const float alpha32(1), beta32(0); @@ -110,7 +110,7 @@ static void depthwiseConv2dCUDNN(const LaunchContext* context, // run calculation err = cudnnConvolutionForward(*handle, alpha, x, input->getSpecialBuffer(), w, weights->getSpecialBuffer(), conv, algo, wsData, wsSize, beta, z, output->specialBuffer()); - if (err != 0) throw nd4j::cuda_exception::build("depthwiseConv2dCUDNN: cudnnConvolutionForward failed", err); + if (err != 0) throw sd::cuda_exception::build("depthwiseConv2dCUDNN: cudnnConvolutionForward failed", err); // add bias if it is present if (bias != nullptr) { @@ -118,9 +118,9 @@ static void depthwiseConv2dCUDNN(const LaunchContext* context, cudnnTensorDescriptor_t b; cudnnCreateTensorDescriptor(&b); err = cudnnSetTensor4dDescriptor(b, format, cudnnDataType(bias->dataType()), 1, isNCHW ? bias->lengthOf() : 1, 1, isNCHW ? 1: bias->lengthOf()); - if (err != 0) throw nd4j::cuda_exception::build("depthwiseConv2dCUDNN: cudnnSetTensor4dDescriptor for bias failed", err); + if (err != 0) throw sd::cuda_exception::build("depthwiseConv2dCUDNN: cudnnSetTensor4dDescriptor for bias failed", err); err = cudnnAddTensor(*handle, alpha, b, bias->getSpecialBuffer(), alpha, z, output->specialBuffer()); - if (err != 0) throw nd4j::cuda_exception::build("depthwiseConv2dCUDNN: cudnnAddTensor bias failed", err); + if (err != 0) throw sd::cuda_exception::build("depthwiseConv2dCUDNN: cudnnAddTensor bias failed", err); } // cudaErr = cudaStreamSynchronize(*context->getCudaStream()); @@ -128,7 +128,7 @@ static void depthwiseConv2dCUDNN(const LaunchContext* context, // throw cuda_exception::build("depthwiseConv2dCUDNN: cudaStreamSynchronize failed !", cudaErr); cudaErr = cudaFree(wsData); - if (cudaErr != 0) throw nd4j::cuda_exception::build("depthwiseConv2dCUDNN: cudaFree for auxiliary workspace memory failed", cudaErr); + if (cudaErr != 0) throw sd::cuda_exception::build("depthwiseConv2dCUDNN: cudaFree for auxiliary workspace memory failed", cudaErr); NDArray::registerSpecialUse({output}, {input, weights, bias}); } @@ -158,7 +158,7 @@ static void depthwiseConv2dBpCUDNN(const LaunchContext* context, auto handle = reinterpret_cast(context->getCuDnnHandle()); cudnnStatus_t err = cudnnSetStream(*handle, *context->getCudaStream()); - if (err != 0) throw nd4j::cuda_exception::build("depthwiseConv2dBpCUDNN: can't set stream for cuDNN", err); + if (err != 0) throw sd::cuda_exception::build("depthwiseConv2dBpCUDNN: can't set stream for cuDNN", err); cudnnTensorFormat_t format = isNCHW ? CUDNN_TENSOR_NCHW : CUDNN_TENSOR_NHWC; @@ -169,7 +169,7 @@ static void depthwiseConv2dBpCUDNN(const LaunchContext* context, err = cudnnSetTensor4dDescriptor(x, format, cudnnDataType(input->dataType()), bS, iC, iH, iW); else err = cudnnSetTensor4dDescriptorEx(x, cudnnDataType(input->dataType()), bS, iC, iH, iW, input->strideAt(0), input->strideAt(indIOioC), input->strideAt(indIiH), input->strideAt(indIiH + 1)); - if (err != 0) throw nd4j::cuda_exception::build("depthwiseConv2dBpCUDNN: cudnnSetTensor4dDescriptor/cudnnSetTensor4dDescriptorEx for input failed", err); + if (err != 0) throw sd::cuda_exception::build("depthwiseConv2dBpCUDNN: cudnnSetTensor4dDescriptor/cudnnSetTensor4dDescriptorEx for input failed", err); // gradO descriptor cudnnTensorDescriptor_t dz; @@ -178,7 +178,7 @@ static void depthwiseConv2dBpCUDNN(const LaunchContext* context, err = cudnnSetTensor4dDescriptor(dz, format, cudnnDataType(gradO->dataType()), bS, oC, oH, oW); else err = cudnnSetTensor4dDescriptorEx(dz, cudnnDataType(gradO->dataType()), bS, oC, oH, oW, gradO->strideAt(0), gradO->strideAt(indIOioC), gradO->strideAt(indOoH), gradO->strideAt(indOoH + 1)); - if (err != 0) throw nd4j::cuda_exception::build("depthwiseConv2dBpCUDNN: cudnnSetTensor4dDescriptor/cudnnSetTensor4dDescriptorEx for gradO failed", err); + if (err != 0) throw sd::cuda_exception::build("depthwiseConv2dBpCUDNN: cudnnSetTensor4dDescriptor/cudnnSetTensor4dDescriptorEx for gradO failed", err); // gradI descriptor cudnnTensorDescriptor_t dx; @@ -187,47 +187,47 @@ static void depthwiseConv2dBpCUDNN(const LaunchContext* context, err = cudnnSetTensor4dDescriptor(dx, format, cudnnDataType(gradI->dataType()), bS, iC, iH, iW); else err = cudnnSetTensor4dDescriptorEx(dx, cudnnDataType(gradI->dataType()), bS, iC, iH, iW, gradI->strideAt(0), gradI->strideAt(indIOioC), gradI->strideAt(indIiH), gradI->strideAt(indIiH + 1)); - if (err != 0) throw nd4j::cuda_exception::build("depthwiseConv2dBpCUDNN: cudnnSetTensor4dDescriptor/cudnnSetTensor4dDescriptorEx for gradI failed", err); + if (err != 0) throw sd::cuda_exception::build("depthwiseConv2dBpCUDNN: cudnnSetTensor4dDescriptor/cudnnSetTensor4dDescriptorEx for gradI failed", err); // gradW descriptor cudnnFilterDescriptor_t dw; cudnnCreateFilterDescriptor(&dw); err = cudnnSetFilter4dDescriptor(dw, cudnnDataType(gradW->dataType()), CUDNN_TENSOR_NCHW, iC, mC, kH, kW); - if(err != 0) throw nd4j::cuda_exception::build("depthwiseConv2dBpCUDNN: cudnnSetFilter4dDescriptor gradW failed", err); + if(err != 0) throw sd::cuda_exception::build("depthwiseConv2dBpCUDNN: cudnnSetFilter4dDescriptor gradW failed", err); // description of convolution cudnnConvolutionDescriptor_t conv; cudnnCreateConvolutionDescriptor(&conv); err = cudnnSetConvolution2dDescriptor(conv, pH, pW, sH, sW, dH, dW, CUDNN_CROSS_CORRELATION, cudnnDataType(gradO->dataType())); - if (err != 0) throw nd4j::cuda_exception::build("depthwiseConv2dBpCUDNN: cudnnSetConvolution2dDescriptor failed", err); + if (err != 0) throw sd::cuda_exception::build("depthwiseConv2dBpCUDNN: cudnnSetConvolution2dDescriptor failed", err); err = cudnnSetConvolutionGroupCount(conv, iC); // set number of groups (depthwise mode) in description of convolution, groupCount == iC - if (err != 0) throw nd4j::cuda_exception::build("depthwiseConv2dBpCUDNN: cudnnSetConvolutionGroupCount failed", err); + if (err != 0) throw sd::cuda_exception::build("depthwiseConv2dBpCUDNN: cudnnSetConvolutionGroupCount failed", err); // gradW algorithm description cudnnConvolutionBwdFilterAlgo_t algoGradW; err = cudnnGetConvolutionBackwardFilterAlgorithm(*handle, x, dz, conv, dw, CUDNN_CONVOLUTION_BWD_FILTER_PREFER_FASTEST, 0, &algoGradW); - if (err != 0) throw nd4j::cuda_exception::build("depthwiseConv2dBpCUDNN: cudnnGetConvolutionBackwardFilterAlgorithm failed", err); + if (err != 0) throw sd::cuda_exception::build("depthwiseConv2dBpCUDNN: cudnnGetConvolutionBackwardFilterAlgorithm failed", err); // gradI algorithm description cudnnConvolutionBwdDataAlgo_t algoGradI; err = cudnnGetConvolutionBackwardDataAlgorithm(*handle, dw, dz, conv, x, CUDNN_CONVOLUTION_BWD_DATA_PREFER_FASTEST, 0, &algoGradI); - if (err != 0) throw nd4j::cuda_exception::build("depthwiseConv2dBpCUDNN: cudnnGetConvolutionBackwardDataAlgorithm failed", err); + if (err != 0) throw sd::cuda_exception::build("depthwiseConv2dBpCUDNN: cudnnGetConvolutionBackwardDataAlgorithm failed", err); // allocate auxiliary device memory for gradW calculation, abbreviation ws means workspace size_t wsGradWSize; err = cudnnGetConvolutionBackwardFilterWorkspaceSize(*handle, x, dz, conv, dw, algoGradW, &wsGradWSize); - if (err != 0) throw nd4j::cuda_exception::build("depthwiseConv2dBpCUDNN: cudnnGetConvolutionBackwardFilterWorkspaceSize failed", err); + if (err != 0) throw sd::cuda_exception::build("depthwiseConv2dBpCUDNN: cudnnGetConvolutionBackwardFilterWorkspaceSize failed", err); void* wsGradWData; auto cudaErr = cudaMalloc(&wsGradWData, wsGradWSize); - if (cudaErr != 0) throw nd4j::cuda_exception::build("depthwiseConv2dBpCUDNN: cudaMalloc for auxiliary workspace memory wsGradWData failed", cudaErr); + if (cudaErr != 0) throw sd::cuda_exception::build("depthwiseConv2dBpCUDNN: cudaMalloc for auxiliary workspace memory wsGradWData failed", cudaErr); // allocate auxiliary device memory for gradI calculation, abbreviation ws means workspace size_t wsGradISize; err = cudnnGetConvolutionBackwardDataWorkspaceSize(*handle, dw, dz, conv, dx, algoGradI, &wsGradISize); - if (err != 0) throw nd4j::cuda_exception::build("depthwiseConv2dBpCUDNN: cudnnGetConvolutionBackwardDataWorkspaceSize failed", err); + if (err != 0) throw sd::cuda_exception::build("depthwiseConv2dBpCUDNN: cudnnGetConvolutionBackwardDataWorkspaceSize failed", err); void* wsGradIData; cudaErr = cudaMalloc(&wsGradIData, wsGradISize); - if (cudaErr != 0) throw nd4j::cuda_exception::build("depthwiseConv2dBpCUDNN: cudaMalloc for auxiliary workspace memory wsGradIData failed", cudaErr); + if (cudaErr != 0) throw sd::cuda_exception::build("depthwiseConv2dBpCUDNN: cudaMalloc for auxiliary workspace memory wsGradIData failed", cudaErr); // provide scaling parameters const float alpha32(1), beta32(0); @@ -242,28 +242,28 @@ static void depthwiseConv2dBpCUDNN(const LaunchContext* context, cudnnTensorDescriptor_t db; cudnnCreateTensorDescriptor(&db); err = cudnnSetTensor4dDescriptor(db, format, cudnnDataType(gradB->dataType()), 1, isNCHW ? gradB->lengthOf() : 1, 1, isNCHW ? 1: gradB->lengthOf()); - if (err != 0) throw nd4j::cuda_exception::build("depthwiseConv2dBpCUDNN: cudnnSetTensor4dDescriptor for gradB failed", err); + if (err != 0) throw sd::cuda_exception::build("depthwiseConv2dBpCUDNN: cudnnSetTensor4dDescriptor for gradB failed", err); err = cudnnConvolutionBackwardBias(*handle, alpha, dz, gradO->getSpecialBuffer(), beta, db, gradB->getSpecialBuffer()); - if (err != 0) throw nd4j::cuda_exception::build("depthwiseConv2dBpCUDNN: cudnnConvolutionBackwardBias failed", err); + if (err != 0) throw sd::cuda_exception::build("depthwiseConv2dBpCUDNN: cudnnConvolutionBackwardBias failed", err); } // run calculation for gradW err = cudnnConvolutionBackwardFilter(*handle, alpha, x, input->getSpecialBuffer(), dz, gradO->getSpecialBuffer(), conv, algoGradW, wsGradWData, wsGradWSize, beta, dw, gradW->getSpecialBuffer()); - if (err != 0) throw nd4j::cuda_exception::build("depthwiseConv2dBpCUDNN: cudnnConvolutionBackwardFilter failed", err); + if (err != 0) throw sd::cuda_exception::build("depthwiseConv2dBpCUDNN: cudnnConvolutionBackwardFilter failed", err); // run calculation for gradI err = cudnnConvolutionBackwardData(*handle, alpha, dw, weights->getSpecialBuffer(), dz, gradO->getSpecialBuffer(), conv, algoGradI, wsGradIData, wsGradISize, beta, dx, gradI->getSpecialBuffer()); - if (err != 0) throw nd4j::cuda_exception::build("depthwiseConv2dBpCUDNN: cudnnConvolutionBackwardData failed", err); + if (err != 0) throw sd::cuda_exception::build("depthwiseConv2dBpCUDNN: cudnnConvolutionBackwardData failed", err); // cudaErr = cudaStreamSynchronize(*context->getCudaStream()); // if (cudaErr != 0) // throw cuda_exception::build("depthwiseConv2dBpCUDNN: cudaStreamSynchronize failed !", cudaErr); cudaErr = cudaFree(wsGradWData); - if (cudaErr != 0) throw nd4j::cuda_exception::build("depthwiseConv2dBpCUDNN: cudaFree for auxiliary workspace memory wsGradWData failed", cudaErr); + if (cudaErr != 0) throw sd::cuda_exception::build("depthwiseConv2dBpCUDNN: cudaFree for auxiliary workspace memory wsGradWData failed", cudaErr); cudaErr = cudaFree(wsGradIData); - if (cudaErr != 0) throw nd4j::cuda_exception::build("depthwiseConv2dBpCUDNN: cudaFree for auxiliary workspace memory wsGradIData failed", cudaErr); + if (cudaErr != 0) throw sd::cuda_exception::build("depthwiseConv2dBpCUDNN: cudaFree for auxiliary workspace memory wsGradIData failed", cudaErr); NDArray::registerSpecialUse({gradI, gradW, gradB}, {input, weights, gradO}); } diff --git a/libnd4j/include/ops/declarable/platform/cudnn/maxpool2d.cu b/libnd4j/include/ops/declarable/platform/cudnn/maxpool2d.cu index 6d5affe79..841faa0d3 100644 --- a/libnd4j/include/ops/declarable/platform/cudnn/maxpool2d.cu +++ b/libnd4j/include/ops/declarable/platform/cudnn/maxpool2d.cu @@ -22,7 +22,7 @@ #include "cudnnUtils.h" #include -namespace nd4j { +namespace sd { namespace ops { namespace platforms { diff --git a/libnd4j/include/ops/declarable/platform/cudnn/maxpool3d.cu b/libnd4j/include/ops/declarable/platform/cudnn/maxpool3d.cu index fc2e38577..82e7b9f84 100644 --- a/libnd4j/include/ops/declarable/platform/cudnn/maxpool3d.cu +++ b/libnd4j/include/ops/declarable/platform/cudnn/maxpool3d.cu @@ -22,7 +22,7 @@ #include "cudnnUtils.h" #include -namespace nd4j { +namespace sd { namespace ops { namespace platforms { diff --git a/libnd4j/include/ops/declarable/platform/mkldnn/avgpooling2d.cpp b/libnd4j/include/ops/declarable/platform/mkldnn/avgpooling2d.cpp index 4c8a582f0..9df7bedf3 100644 --- a/libnd4j/include/ops/declarable/platform/mkldnn/avgpooling2d.cpp +++ b/libnd4j/include/ops/declarable/platform/mkldnn/avgpooling2d.cpp @@ -22,7 +22,7 @@ #include #include -#include +#include #include #include "mkldnnUtils.h" @@ -31,7 +31,7 @@ using namespace dnnl; using namespace samediff; -namespace nd4j { +namespace sd { namespace ops { namespace platforms { @@ -78,7 +78,7 @@ PLATFORM_CHECK(avgpool2d, ENGINE_CPU) { auto input = INPUT_VARIABLE(0); auto output = OUTPUT_VARIABLE(0); - return block.isUseMKLDNN() && nd4j::MKLDNNStream::isSupported({input, output}); + return block.isUseMKLDNN() && sd::MKLDNNStream::isSupported({input, output}); } ////////////////////////////////////////////////////////////////////////// @@ -125,7 +125,7 @@ PLATFORM_CHECK(avgpool2d_bp, ENGINE_CPU) { auto input = INPUT_VARIABLE(0); auto output = OUTPUT_VARIABLE(0); - return block.isUseMKLDNN() && nd4j::MKLDNNStream::isSupported({input, output}); + return block.isUseMKLDNN() && sd::MKLDNNStream::isSupported({input, output}); } diff --git a/libnd4j/include/ops/declarable/platform/mkldnn/avgpooling3d.cpp b/libnd4j/include/ops/declarable/platform/mkldnn/avgpooling3d.cpp index 39e85de98..e8582658e 100644 --- a/libnd4j/include/ops/declarable/platform/mkldnn/avgpooling3d.cpp +++ b/libnd4j/include/ops/declarable/platform/mkldnn/avgpooling3d.cpp @@ -22,7 +22,7 @@ #include #include -#include +#include #include #include "mkldnnUtils.h" @@ -30,7 +30,7 @@ using namespace dnnl; -namespace nd4j { +namespace sd { namespace ops { namespace platforms { @@ -78,7 +78,7 @@ PLATFORM_CHECK(avgpool3dnew, ENGINE_CPU) { auto input = INPUT_VARIABLE(0); auto output = OUTPUT_VARIABLE(0); - return block.isUseMKLDNN() && nd4j::MKLDNNStream::isSupported({input, output}); + return block.isUseMKLDNN() && sd::MKLDNNStream::isSupported({input, output}); } ////////////////////////////////////////////////////////////////////// @@ -130,7 +130,7 @@ PLATFORM_CHECK(avgpool3dnew_bp, ENGINE_CPU) { auto input = INPUT_VARIABLE(0); auto output = OUTPUT_VARIABLE(0); - return block.isUseMKLDNN() && nd4j::MKLDNNStream::isSupported({input, output}); + return block.isUseMKLDNN() && sd::MKLDNNStream::isSupported({input, output}); } diff --git a/libnd4j/include/ops/declarable/platform/mkldnn/batchnorm.cpp b/libnd4j/include/ops/declarable/platform/mkldnn/batchnorm.cpp index f63690e81..cc52e90b3 100644 --- a/libnd4j/include/ops/declarable/platform/mkldnn/batchnorm.cpp +++ b/libnd4j/include/ops/declarable/platform/mkldnn/batchnorm.cpp @@ -23,15 +23,15 @@ #include #include -#include +#include #include #include "mkldnnUtils.h" #include -#include +#include -namespace nd4j { +namespace sd { namespace ops { namespace platforms { @@ -90,32 +90,13 @@ static void batchnormMKLDNN(const NDArray* x, const NDArray* mean, const NDArray // x dnnl::memory::desc x_mkl_md = dnnl::memory::desc(dims, type, format); dnnl::memory::desc x_user_md = dnnl::memory::desc(dims, type, format); - if(x->ews() != 1 || x->ordering() != 'c') { - x_user_md.data.format_kind = dnnl_blocked; // overrides format - x_user_md.data.format_desc.blocking.strides[0] = x->strideAt(0); - x_user_md.data.format_desc.blocking.strides[1] = x->strideAt(1); - if(xRank > 2) { - x_user_md.data.format_desc.blocking.strides[2] = x->strideAt(2); - x_user_md.data.format_desc.blocking.strides[3] = x->strideAt(3); - } - if(xRank > 4) - x_user_md.data.format_desc.blocking.strides[4] = x->strideAt(4); - } + mkldnnUtils::setBlockStrides(x, xRank, x_user_md); // z, output dnnl::memory::desc z_mkl_md = dnnl::memory::desc(dims, type, dnnl::memory::format_tag::any); dnnl::memory::desc z_user_md = dnnl::memory::desc(dims, type, format); - if(z->ews() != 1 || z->ordering() != 'c') { - z_user_md.data.format_kind = dnnl_blocked; // overrides format - z_user_md.data.format_desc.blocking.strides[0] = z->strideAt(0); - z_user_md.data.format_desc.blocking.strides[1] = z->strideAt(1); - if(xRank > 2) { - z_user_md.data.format_desc.blocking.strides[2] = z->strideAt(2); - z_user_md.data.format_desc.blocking.strides[3] = z->strideAt(3); - } - if(xRank > 4) - z_user_md.data.format_desc.blocking.strides[4] = z->strideAt(4); - } + + mkldnnUtils::setBlockStrides(z, xRank, z_user_md); auto engine = mkldnnUtils::getEngine(LaunchContext::defaultContext()->engine()); @@ -131,14 +112,9 @@ static void batchnormMKLDNN(const NDArray* x, const NDArray* mean, const NDArray // provide memory and check whether reorder is required // x - auto x_user_mem = dnnl::memory(x_user_md, engine, x->getBuffer()); - const bool xReorder = op_ff_prim_desc.src_desc() != x_user_mem.get_desc(); - auto x_mkl_mem = xReorder ? dnnl::memory(op_ff_prim_desc.src_desc(), engine) : x_user_mem; - if (xReorder) - dnnl::reorder(x_user_mem, x_mkl_mem).execute(stream, x_user_mem, x_mkl_mem); - args[DNNL_ARG_SRC] = x_mkl_mem; - - // z + mkldnnUtils::loadDataToMklStream(x, engine, stream, args, x_user_md, op_ff_prim_desc.src_desc(), DNNL_ARG_SRC); + + // z auto z_user_mem = dnnl::memory(z_user_md, engine, z->getBuffer()); const bool zReorder = op_ff_prim_desc.dst_desc() != z_user_mem.get_desc(); auto z_mkl_mem = zReorder ? dnnl::memory(op_ff_prim_desc.dst_desc(), engine) : z_user_mem; @@ -230,47 +206,20 @@ static void batchnormBackPropMKLDNN(const NDArray* x, const NDArray* mean, const // x dnnl::memory::desc x_mkl_md = dnnl::memory::desc(dims, type, format); dnnl::memory::desc x_user_md = dnnl::memory::desc(dims, type, format); - if(x->ews() != 1 || x->ordering() != 'c') { - x_user_md.data.format_kind = dnnl_blocked; // overrides format - x_user_md.data.format_desc.blocking.strides[0] = x->strideAt(0); - x_user_md.data.format_desc.blocking.strides[1] = x->strideAt(1); - if(xRank > 2) { - x_user_md.data.format_desc.blocking.strides[2] = x->strideAt(2); - x_user_md.data.format_desc.blocking.strides[3] = x->strideAt(3); - } - if(xRank > 4) - x_user_md.data.format_desc.blocking.strides[4] = x->strideAt(4); - } + mkldnnUtils::setBlockStrides(x, xRank, x_user_md); + // dLdO dnnl::memory::desc dLdO_mkl_md = dnnl::memory::desc(dims, type, dnnl::memory::format_tag::any); dnnl::memory::desc dLdO_user_md = dnnl::memory::desc(dims, type, format); - if(dLdO->ews() != 1 || dLdO->ordering() != 'c') { - dLdO_user_md.data.format_kind = dnnl_blocked; // overrides format - dLdO_user_md.data.format_desc.blocking.strides[0] = dLdO->strideAt(0); - dLdO_user_md.data.format_desc.blocking.strides[1] = dLdO->strideAt(1); - if(xRank > 2) { - dLdO_user_md.data.format_desc.blocking.strides[2] = dLdO->strideAt(2); - dLdO_user_md.data.format_desc.blocking.strides[3] = dLdO->strideAt(3); - } - if(xRank > 4) - dLdO_user_md.data.format_desc.blocking.strides[4] = dLdO->strideAt(4); - } + + mkldnnUtils::setBlockStrides(dLdO, xRank, dLdO_user_md); // dLdI dnnl::memory::desc dLdI_mkl_md = dnnl::memory::desc(dims, type, dnnl::memory::format_tag::any); dnnl::memory::desc dLdI_user_md = dnnl::memory::desc(dims, type, format); - if(dLdI->ews() != 1 || dLdI->ordering() != 'c') { - dLdI_user_md.data.format_kind = dnnl_blocked; // overrides format - dLdI_user_md.data.format_desc.blocking.strides[0] = dLdI->strideAt(0); - dLdI_user_md.data.format_desc.blocking.strides[1] = dLdI->strideAt(1); - if(xRank > 2) { - dLdI_user_md.data.format_desc.blocking.strides[2] = dLdI->strideAt(2); - dLdI_user_md.data.format_desc.blocking.strides[3] = dLdI->strideAt(3); - } - if(xRank > 4) - dLdI_user_md.data.format_desc.blocking.strides[4] = dLdI->strideAt(4); - } + + mkldnnUtils::setBlockStrides(dLdI, xRank, dLdI_user_md); auto engine = mkldnnUtils::getEngine(LaunchContext::defaultContext()->engine()); @@ -290,20 +239,10 @@ static void batchnormBackPropMKLDNN(const NDArray* x, const NDArray* mean, const // provide memory and check whether reorder is required // x - auto x_user_mem = dnnl::memory(x_user_md, engine, x->getBuffer()); - const bool xReorder = op_bp_prim_desc.src_desc() != x_user_mem.get_desc(); - auto x_mkl_mem = xReorder ? dnnl::memory(op_bp_prim_desc.src_desc(), engine) : x_user_mem; - if (xReorder) - dnnl::reorder(x_user_mem, x_mkl_mem).execute(stream, x_user_mem, x_mkl_mem); - args[DNNL_ARG_SRC] = x_mkl_mem; + mkldnnUtils::loadDataToMklStream(x, engine, stream, args, x_user_md, op_bp_prim_desc.src_desc(), DNNL_ARG_SRC); // dLdO - auto dLdO_user_mem = dnnl::memory(dLdO_user_md, engine, dLdO->getBuffer()); - const bool dLdOReorder = op_bp_prim_desc.diff_dst_desc() != dLdO_user_mem.get_desc(); - auto dLdO_mkl_mem = dLdOReorder ? dnnl::memory(op_bp_prim_desc.diff_dst_desc(), engine) : dLdO_user_mem; - if (dLdOReorder) - dnnl::reorder(dLdO_user_mem, dLdO_mkl_mem).execute(stream, dLdO_user_mem, dLdO_mkl_mem); - args[DNNL_ARG_DIFF_DST] = dLdO_mkl_mem; + mkldnnUtils::loadDataToMklStream(dLdO, engine, stream, args, dLdO_user_md, op_bp_prim_desc.diff_dst_desc(), DNNL_ARG_DIFF_DST); // mean auto mean_mkl_mem = dnnl::memory(op_bp_prim_desc.mean_desc(), engine, mean->getBuffer()); @@ -369,7 +308,7 @@ static void batchnormBackPropMKLDNN(const NDArray* x, const NDArray* mean, const // x - mean NDArray xMinusMean(x); // empty array with same shape as x - const_cast(x)->applyBroadcast(nd4j::broadcast::Subtract, axes, *mean, xMinusMean); + const_cast(x)->applyBroadcast(sd::broadcast::Subtract, axes, *mean, xMinusMean); // stdInv NDArray stdInv = *variance + epsilon; @@ -377,30 +316,30 @@ static void batchnormBackPropMKLDNN(const NDArray* x, const NDArray* mean, const stdInv.applyTransform(transform::Sqrt, stdInv); // 1 / (variance + epsilon)^0.5 // dfdm / N - auto dfdm = dLdO->reduceAlongDimension(nd4j::reduce::Sum, excludedAxes); + auto dfdm = dLdO->reduceAlongDimension(sd::reduce::Sum, excludedAxes); dfdm *= stdInv; dfdm *= -Ninv; // dvdm / 2 NDArray dvdm(mean); // empty array with same shape as mean - xMinusMean.reduceAlongDimension(nd4j::reduce::Sum, dvdm, excludedAxes); + xMinusMean.reduceAlongDimension(sd::reduce::Sum, dvdm, excludedAxes); dvdm *= -Ninv; // (2/N)*dfdv NDArray dfdv(variance); // empty array with same shape as variance - (xMinusMean * *dLdO).reduceAlongDimension(nd4j::reduce::Sum, dfdv, excludedAxes); + (xMinusMean * *dLdO).reduceAlongDimension(sd::reduce::Sum, dfdv, excludedAxes); dfdv *= stdInv*stdInv*stdInv; dfdv *= -Ninv; // dvdm/2 + (x - m) - xMinusMean.applyBroadcast(nd4j::broadcast::Add, axes, dvdm, xMinusMean); + xMinusMean.applyBroadcast(sd::broadcast::Add, axes, dvdm, xMinusMean); // dfdv * (dvdm/2 + (x - m)) - xMinusMean.applyBroadcast(nd4j::broadcast::Multiply, axes, dfdv, xMinusMean); + xMinusMean.applyBroadcast(sd::broadcast::Multiply, axes, dfdv, xMinusMean); // add dfdm / N - xMinusMean.applyBroadcast(nd4j::broadcast::Add, axes, dfdm, xMinusMean); + xMinusMean.applyBroadcast(sd::broadcast::Add, axes, dfdm, xMinusMean); // * gamma auto gamma = (*weights)({0,1, 0,0}); - xMinusMean.applyBroadcast(nd4j::broadcast::Multiply, axes, gamma, xMinusMean); + xMinusMean.applyBroadcast(sd::broadcast::Multiply, axes, gamma, xMinusMean); *dLdI += xMinusMean; } @@ -644,7 +583,7 @@ PLATFORM_CHECK(batchnorm, ENGINE_CPU) { // axes.push_back(input->rankOf() - 1); // return block.isUseMKLDNN() && -// nd4j::MKLDNNStream::isSupported({input, mean, variance, gamma, beta, output}) && +// sd::MKLDNNStream::isSupported({input, mean, variance, gamma, beta, output}) && // axes.size() == 1; // } diff --git a/libnd4j/include/ops/declarable/platform/mkldnn/conv2d.cpp b/libnd4j/include/ops/declarable/platform/mkldnn/conv2d.cpp index 2d88a73ef..9d236d293 100644 --- a/libnd4j/include/ops/declarable/platform/mkldnn/conv2d.cpp +++ b/libnd4j/include/ops/declarable/platform/mkldnn/conv2d.cpp @@ -22,7 +22,7 @@ #include #include -#include +#include #include #include "mkldnnUtils.h" @@ -30,7 +30,7 @@ using namespace dnnl; -namespace nd4j { +namespace sd { namespace ops { namespace platforms { @@ -67,13 +67,7 @@ static void conv2dMKLDNN(const NDArray *input, const NDArray *weights, // input dnnl::memory::desc x_mkl_md = dnnl::memory::desc(xDims, type, dnnl::memory::format_tag::any); dnnl::memory::desc x_user_md = dnnl::memory::desc(xDims, type, xzFrmat); - if(input->ews() != 1 || input->ordering() != 'c') { - x_user_md.data.format_kind = dnnl_blocked; // overrides format - x_user_md.data.format_desc.blocking.strides[0] = input->strideAt(0); - x_user_md.data.format_desc.blocking.strides[1] = input->strideAt(1); - x_user_md.data.format_desc.blocking.strides[2] = input->strideAt(2); - x_user_md.data.format_desc.blocking.strides[3] = input->strideAt(3); - } + mkldnnUtils::setBlockStrides(input, 4, x_user_md); // weights dnnl::memory::desc w_mkl_md = dnnl::memory::desc(wDims, type, dnnl::memory::format_tag::any); @@ -92,13 +86,8 @@ static void conv2dMKLDNN(const NDArray *input, const NDArray *weights, // output dnnl::memory::desc z_mkl_md = dnnl::memory::desc(zDims, type, dnnl::memory::format_tag::any); dnnl::memory::desc z_user_md = dnnl::memory::desc(zDims, type, xzFrmat); - if(output->ews() != 1 || output->ordering() != 'c') { - z_user_md.data.format_kind = dnnl_blocked; // overrides format - z_user_md.data.format_desc.blocking.strides[0] = output->strideAt(0); - z_user_md.data.format_desc.blocking.strides[1] = output->strideAt(1); - z_user_md.data.format_desc.blocking.strides[2] = output->strideAt(2); - z_user_md.data.format_desc.blocking.strides[3] = output->strideAt(3); - } + + mkldnnUtils::setBlockStrides(output, 4, z_user_md); auto engine = mkldnnUtils::getEngine(LaunchContext::defaultContext()->engine()); @@ -114,20 +103,10 @@ static void conv2dMKLDNN(const NDArray *input, const NDArray *weights, // provide memory buffers and check whether reorder is required // input - auto x_user_mem = dnnl::memory(x_user_md, engine, input->getBuffer()); - const bool xReorder = op_prim_desc.src_desc() != x_user_mem.get_desc(); - auto x_mkl_mem = xReorder ? dnnl::memory(op_prim_desc.src_desc(), engine) : x_user_mem; - if (xReorder) - dnnl::reorder(x_user_mem, x_mkl_mem).execute(stream, x_user_mem, x_mkl_mem); - args[DNNL_ARG_SRC] = x_mkl_mem; + mkldnnUtils::loadDataToMklStream(input, engine, stream, args, x_user_md, op_prim_desc.src_desc(), DNNL_ARG_SRC); // weights - auto w_user_mem = dnnl::memory(w_user_md, engine, weights->getBuffer()); - const bool wReorder = op_prim_desc.weights_desc() != w_user_mem.get_desc(); - auto w_mkl_mem = wReorder ? dnnl::memory(op_prim_desc.weights_desc(), engine) : w_user_mem; - if (wReorder) - dnnl::reorder(w_user_mem, w_mkl_mem).execute(stream, w_user_mem, w_mkl_mem); - args[DNNL_ARG_WEIGHTS] = w_mkl_mem; + mkldnnUtils::loadDataToMklStream(weights, engine, stream, args, w_user_md, op_prim_desc.weights_desc(), DNNL_ARG_WEIGHTS); // bias if(bias != nullptr) { @@ -185,13 +164,7 @@ static void conv2dBpMKLDNN(const NDArray *input, const NDArray *weights, const N // input dnnl::memory::desc x_mkl_md = dnnl::memory::desc(xDims, type, dnnl::memory::format_tag::any); dnnl::memory::desc x_user_md = dnnl::memory::desc(xDims, type, xzFrmat); - if(input->ews() != 1 || input->ordering() != 'c') { - x_user_md.data.format_kind = dnnl_blocked; // overrides format - x_user_md.data.format_desc.blocking.strides[0] = input->strideAt(0); - x_user_md.data.format_desc.blocking.strides[1] = input->strideAt(1); - x_user_md.data.format_desc.blocking.strides[2] = input->strideAt(2); - x_user_md.data.format_desc.blocking.strides[3] = input->strideAt(3); - } + mkldnnUtils::setBlockStrides(input, 4, x_user_md); // weights dnnl::memory::desc w_mkl_md = dnnl::memory::desc(wDims, type, dnnl::memory::format_tag::any); @@ -205,25 +178,13 @@ static void conv2dBpMKLDNN(const NDArray *input, const NDArray *weights, const N // gradO dnnl::memory::desc gradO_mkl_md = dnnl::memory::desc(zDims, type, dnnl::memory::format_tag::any); dnnl::memory::desc gradO_user_md = dnnl::memory::desc(zDims, type, xzFrmat); - if(gradO->ews() != 1 || gradO->ordering() != 'c') { - gradO_user_md.data.format_kind = dnnl_blocked; // overrides format - gradO_user_md.data.format_desc.blocking.strides[0] = gradO->strideAt(0); - gradO_user_md.data.format_desc.blocking.strides[1] = gradO->strideAt(1); - gradO_user_md.data.format_desc.blocking.strides[2] = gradO->strideAt(2); - gradO_user_md.data.format_desc.blocking.strides[3] = gradO->strideAt(3); - } - + mkldnnUtils::setBlockStrides(gradO, 4, gradO_user_md); + // gradI dnnl::memory::desc gradI_mkl_md = dnnl::memory::desc(xDims, type, dnnl::memory::format_tag::any); dnnl::memory::desc gradI_user_md = dnnl::memory::desc(xDims, type, xzFrmat); - if(gradI->ews() != 1 || gradI->ordering() != 'c') { - gradI_user_md.data.format_kind = dnnl_blocked; // overrides format - gradI_user_md.data.format_desc.blocking.strides[0] = gradI->strideAt(0); - gradI_user_md.data.format_desc.blocking.strides[1] = gradI->strideAt(1); - gradI_user_md.data.format_desc.blocking.strides[2] = gradI->strideAt(2); - gradI_user_md.data.format_desc.blocking.strides[3] = gradI->strideAt(3); - } - + mkldnnUtils::setBlockStrides(gradI, 4, gradI_user_md); + // gradW dnnl::memory::desc gradW_mkl_md = dnnl::memory::desc(wDims, type, dnnl::memory::format_tag::any); dnnl::memory::desc gradW_user_md = dnnl::memory::desc(wDims, type, wFormat); @@ -260,20 +221,10 @@ static void conv2dBpMKLDNN(const NDArray *input, const NDArray *weights, const N // provide memory buffers and check whether reorder is required // input - auto x_user_mem = dnnl::memory(x_user_md, engine, input->getBuffer()); - const bool xReorder = op_weights_bp_prim_desc.src_desc() != x_user_mem.get_desc(); - auto x_mkl_mem = xReorder ? dnnl::memory(op_weights_bp_prim_desc.src_desc(), engine) : x_user_mem; - if (xReorder) - dnnl::reorder(x_user_mem, x_mkl_mem).execute(stream, x_user_mem, x_mkl_mem); - args[DNNL_ARG_SRC] = x_mkl_mem; + mkldnnUtils::loadDataToMklStream(input, engine, stream, args, x_user_md, op_weights_bp_prim_desc.src_desc(), DNNL_ARG_SRC); // weights - auto w_user_mem = dnnl::memory(w_user_md, engine, weights->getBuffer()); - const bool wReorder = op_data_bp_prim_desc.weights_desc() != w_user_mem.get_desc(); - auto w_mkl_mem = wReorder ? dnnl::memory(op_data_bp_prim_desc.weights_desc(), engine) : w_user_mem; - if (wReorder) - dnnl::reorder(w_user_mem, w_mkl_mem).execute(stream, w_user_mem, w_mkl_mem); - args[DNNL_ARG_WEIGHTS] = w_mkl_mem; + mkldnnUtils::loadDataToMklStream(weights, engine, stream, args, w_user_md, op_data_bp_prim_desc.weights_desc(), DNNL_ARG_WEIGHTS); // gradO auto gradO_user_mem = dnnl::memory(gradO_user_md, engine, gradO->getBuffer()); @@ -327,7 +278,7 @@ static void conv2dBpMKLDNN(const NDArray *input, const NDArray *weights, const N /* ////////////////////////////////////////////////////////////////////// -static void conv2dMKLDNN(nd4j::graph::Context &block, const NDArray *input, const NDArray *weights, +static void conv2dMKLDNN(sd::graph::Context &block, const NDArray *input, const NDArray *weights, const NDArray *bias, NDArray *output, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int paddingMode, const int isNCHW) { @@ -404,7 +355,7 @@ static void conv2dMKLDNN(nd4j::graph::Context &block, const NDArray *input, cons } ////////////////////////////////////////////////////////////////////// -static void conv2dBpMKLDNN(nd4j::graph::Context &block, +static void conv2dBpMKLDNN(sd::graph::Context &block, const NDArray *input, const NDArray *weights, const NDArray *bias, const NDArray *gradO, NDArray *gradI, NDArray *gradW, NDArray *gradB, const int kH, const int kW, const int sH,const int sW, int pH, int pW, const int dH, const int dW, @@ -577,8 +528,8 @@ PLATFORM_CHECK(conv2d, ENGINE_CPU) { auto weights = INPUT_VARIABLE(1); // conv2d is only available for float32 dtype - return block.isUseMKLDNN() && input->dataType() == nd4j::DataType::FLOAT32 && - weights->dataType() == nd4j::DataType::FLOAT32; + return block.isUseMKLDNN() && input->dataType() == sd::DataType::FLOAT32 && + weights->dataType() == sd::DataType::FLOAT32; } ////////////////////////////////////////////////////////////////////// @@ -639,7 +590,7 @@ PLATFORM_CHECK(conv2d_bp, ENGINE_CPU) { return block.isUseMKLDNN() && - nd4j::MKLDNNStream::isSupported({input, weights, bias, gradO, gradI, gradW, gradB}); + sd::MKLDNNStream::isSupported({input, weights, bias, gradO, gradI, gradW, gradB}); } diff --git a/libnd4j/include/ops/declarable/platform/mkldnn/conv3d.cpp b/libnd4j/include/ops/declarable/platform/mkldnn/conv3d.cpp index 7c10b0d1e..6c0575378 100644 --- a/libnd4j/include/ops/declarable/platform/mkldnn/conv3d.cpp +++ b/libnd4j/include/ops/declarable/platform/mkldnn/conv3d.cpp @@ -21,7 +21,7 @@ #include #include -#include +#include #include #include "mkldnnUtils.h" @@ -29,7 +29,7 @@ using namespace dnnl; -namespace nd4j { +namespace sd { namespace ops { namespace platforms { @@ -70,14 +70,7 @@ static void conv3dMKLDNN(const NDArray *input, const NDArray *weights, // input dnnl::memory::desc x_mkl_md = dnnl::memory::desc(xDims, type, dnnl::memory::format_tag::any); dnnl::memory::desc x_user_md = dnnl::memory::desc(xDims, type, xzFrmat); - if(input->ews() != 1 || input->ordering() != 'c') { - x_user_md.data.format_kind = dnnl_blocked; // overrides format - x_user_md.data.format_desc.blocking.strides[0] = input->strideAt(0); - x_user_md.data.format_desc.blocking.strides[1] = input->strideAt(1); - x_user_md.data.format_desc.blocking.strides[2] = input->strideAt(2); - x_user_md.data.format_desc.blocking.strides[3] = input->strideAt(3); - x_user_md.data.format_desc.blocking.strides[4] = input->strideAt(4); - } + mkldnnUtils::setBlockStrides(input, 5, x_user_md); // weights dnnl::memory::desc w_mkl_md = dnnl::memory::desc(wDims, type, dnnl::memory::format_tag::any); @@ -97,14 +90,7 @@ static void conv3dMKLDNN(const NDArray *input, const NDArray *weights, // output dnnl::memory::desc z_mkl_md = dnnl::memory::desc(zDims, type, dnnl::memory::format_tag::any); dnnl::memory::desc z_user_md = dnnl::memory::desc(zDims, type, xzFrmat); - if(output->ews() != 1 || output->ordering() != 'c') { - z_user_md.data.format_kind = dnnl_blocked; // overrides format - z_user_md.data.format_desc.blocking.strides[0] = output->strideAt(0); - z_user_md.data.format_desc.blocking.strides[1] = output->strideAt(1); - z_user_md.data.format_desc.blocking.strides[2] = output->strideAt(2); - z_user_md.data.format_desc.blocking.strides[3] = output->strideAt(3); - z_user_md.data.format_desc.blocking.strides[4] = output->strideAt(4); - } + mkldnnUtils::setBlockStrides(output, 5, z_user_md); auto engine = mkldnnUtils::getEngine(LaunchContext::defaultContext()->engine()); @@ -120,21 +106,11 @@ static void conv3dMKLDNN(const NDArray *input, const NDArray *weights, // provide memory buffers and check whether reorder is required // input - auto x_user_mem = dnnl::memory(x_user_md, engine, input->getBuffer()); - const bool xReorder = op_prim_desc.src_desc() != x_user_mem.get_desc(); - auto x_mkl_mem = xReorder ? dnnl::memory(op_prim_desc.src_desc(), engine) : x_user_mem; - if (xReorder) - dnnl::reorder(x_user_mem, x_mkl_mem).execute(stream, x_user_mem, x_mkl_mem); - args[DNNL_ARG_SRC] = x_mkl_mem; + mkldnnUtils::loadDataToMklStream(input, engine, stream, args, x_user_md, op_prim_desc.src_desc(), DNNL_ARG_SRC); // weights - auto w_user_mem = dnnl::memory(w_user_md, engine, weights->getBuffer()); - const bool wReorder = op_prim_desc.weights_desc() != w_user_mem.get_desc(); - auto w_mkl_mem = wReorder ? dnnl::memory(op_prim_desc.weights_desc(), engine) : w_user_mem; - if (wReorder) - dnnl::reorder(w_user_mem, w_mkl_mem).execute(stream, w_user_mem, w_mkl_mem); - args[DNNL_ARG_WEIGHTS] = w_mkl_mem; - + mkldnnUtils::loadDataToMklStream(weights, engine, stream, args, w_user_md, op_prim_desc.weights_desc(), DNNL_ARG_WEIGHTS); + // bias if(bias != nullptr) { auto b_mkl_mem = dnnl::memory(b_mkl_md, engine, bias->getBuffer()); @@ -194,14 +170,7 @@ static void conv3dBpMKLDNN(const NDArray *input, const NDArray *weights, const N // input dnnl::memory::desc x_mkl_md = dnnl::memory::desc(xDims, type, dnnl::memory::format_tag::any); dnnl::memory::desc x_user_md = dnnl::memory::desc(xDims, type, xzFrmat); - if(input->ews() != 1 || input->ordering() != 'c') { - x_user_md.data.format_kind = dnnl_blocked; // overrides format - x_user_md.data.format_desc.blocking.strides[0] = input->strideAt(0); - x_user_md.data.format_desc.blocking.strides[1] = input->strideAt(1); - x_user_md.data.format_desc.blocking.strides[2] = input->strideAt(2); - x_user_md.data.format_desc.blocking.strides[3] = input->strideAt(3); - x_user_md.data.format_desc.blocking.strides[4] = input->strideAt(4); - } + mkldnnUtils::setBlockStrides(input, 5, x_user_md); // weights dnnl::memory::desc w_mkl_md = dnnl::memory::desc(wDims, type, dnnl::memory::format_tag::any); @@ -216,26 +185,14 @@ static void conv3dBpMKLDNN(const NDArray *input, const NDArray *weights, const N // gradO dnnl::memory::desc gradO_mkl_md = dnnl::memory::desc(zDims, type, dnnl::memory::format_tag::any); dnnl::memory::desc gradO_user_md = dnnl::memory::desc(zDims, type, xzFrmat); - if(gradO->ews() != 1 || gradO->ordering() != 'c') { - gradO_user_md.data.format_kind = dnnl_blocked; // overrides format - gradO_user_md.data.format_desc.blocking.strides[0] = gradO->strideAt(0); - gradO_user_md.data.format_desc.blocking.strides[1] = gradO->strideAt(1); - gradO_user_md.data.format_desc.blocking.strides[2] = gradO->strideAt(2); - gradO_user_md.data.format_desc.blocking.strides[3] = gradO->strideAt(3); - gradO_user_md.data.format_desc.blocking.strides[4] = gradO->strideAt(4); - } + + mkldnnUtils::setBlockStrides(gradO, 5, gradO_user_md); // gradI dnnl::memory::desc gradI_mkl_md = dnnl::memory::desc(xDims, type, dnnl::memory::format_tag::any); dnnl::memory::desc gradI_user_md = dnnl::memory::desc(xDims, type, xzFrmat); - if(gradI->ews() != 1 || gradI->ordering() != 'c') { - gradI_user_md.data.format_kind = dnnl_blocked; // overrides format - gradI_user_md.data.format_desc.blocking.strides[0] = gradI->strideAt(0); - gradI_user_md.data.format_desc.blocking.strides[1] = gradI->strideAt(1); - gradI_user_md.data.format_desc.blocking.strides[2] = gradI->strideAt(2); - gradI_user_md.data.format_desc.blocking.strides[3] = gradI->strideAt(3); - gradI_user_md.data.format_desc.blocking.strides[4] = gradI->strideAt(4); - } + + mkldnnUtils::setBlockStrides(gradI, 5, gradI_user_md); // gradW dnnl::memory::desc gradW_mkl_md = dnnl::memory::desc(wDims, type, dnnl::memory::format_tag::any); @@ -274,20 +231,10 @@ static void conv3dBpMKLDNN(const NDArray *input, const NDArray *weights, const N // provide memory buffers and check whether reorder is required // input - auto x_user_mem = dnnl::memory(x_user_md, engine, input->getBuffer()); - const bool xReorder = op_weights_bp_prim_desc.src_desc() != x_user_mem.get_desc(); - auto x_mkl_mem = xReorder ? dnnl::memory(op_weights_bp_prim_desc.src_desc(), engine) : x_user_mem; - if (xReorder) - dnnl::reorder(x_user_mem, x_mkl_mem).execute(stream, x_user_mem, x_mkl_mem); - args[DNNL_ARG_SRC] = x_mkl_mem; + mkldnnUtils::loadDataToMklStream(input, engine, stream, args, x_user_md, op_weights_bp_prim_desc.src_desc(), DNNL_ARG_SRC); // weights - auto w_user_mem = dnnl::memory(w_user_md, engine, weights->getBuffer()); - const bool wReorder = op_data_bp_prim_desc.weights_desc() != w_user_mem.get_desc(); - auto w_mkl_mem = wReorder ? dnnl::memory(op_data_bp_prim_desc.weights_desc(), engine) : w_user_mem; - if (wReorder) - dnnl::reorder(w_user_mem, w_mkl_mem).execute(stream, w_user_mem, w_mkl_mem); - args[DNNL_ARG_WEIGHTS] = w_mkl_mem; + mkldnnUtils::loadDataToMklStream(weights, engine, stream, args, w_user_md, op_data_bp_prim_desc.weights_desc(), DNNL_ARG_WEIGHTS); // gradO auto gradO_user_mem = dnnl::memory(gradO_user_md, engine, gradO->getBuffer()); @@ -342,7 +289,7 @@ static void conv3dBpMKLDNN(const NDArray *input, const NDArray *weights, const N /* ////////////////////////////////////////////////////////////////////// -static void conv3dMKLDNN(nd4j::graph::Context &block, +static void conv3dMKLDNN(sd::graph::Context &block, const NDArray *input, const NDArray *weights, const NDArray *bias, NDArray *output, const int kD, const int kH, const int kW, const int sD, const int sH, const int sW, int pD, int pH, int pW, const int dD, const int dH, const int dW, @@ -416,7 +363,7 @@ static void conv3dMKLDNN(nd4j::graph::Context &block, ////////////////////////////////////////////////////////////////////// -static void conv3dBpMKLDNN(nd4j::graph::Context &block, +static void conv3dBpMKLDNN(sd::graph::Context &block, const NDArray *input, const NDArray *weights, const NDArray *bias, const NDArray *gradO, NDArray *gradI, NDArray *gradW, NDArray *gradB, const int kD, const int kH, const int kW, const int sD, const int sH, const int sW, int pD, int pH, int pW, const int dD, const int dH, const int dW, @@ -584,7 +531,7 @@ PLATFORM_CHECK(conv3dnew, ENGINE_CPU) { auto bias = block.width() > 2 ? INPUT_VARIABLE(2) : nullptr; // [oC] auto output = OUTPUT_VARIABLE(0); // [bS, oD, oH, oW, oC] (NDHWC) or [bS, oC, oD, oH, oW] (NCDHW) - return block.isUseMKLDNN() && nd4j::MKLDNNStream::isSupported({input, weights, bias, output}); + return block.isUseMKLDNN() && sd::MKLDNNStream::isSupported({input, weights, bias, output}); } ////////////////////////////////////////////////////////////////////// @@ -651,7 +598,7 @@ PLATFORM_CHECK(conv3dnew_bp, ENGINE_CPU) { auto gradB = block.width() > 3 ? OUTPUT_VARIABLE(2) : nullptr; // [oC] return block.isUseMKLDNN() && - nd4j::MKLDNNStream::isSupported({input, weights, bias, gradO, gradI, gradW, gradB}); + sd::MKLDNNStream::isSupported({input, weights, bias, gradO, gradI, gradW, gradB}); } diff --git a/libnd4j/include/ops/declarable/platform/mkldnn/deconv2d.cpp b/libnd4j/include/ops/declarable/platform/mkldnn/deconv2d.cpp index 1879ef8fb..1ee177e6a 100644 --- a/libnd4j/include/ops/declarable/platform/mkldnn/deconv2d.cpp +++ b/libnd4j/include/ops/declarable/platform/mkldnn/deconv2d.cpp @@ -20,14 +20,14 @@ #include #include -#include +#include #include #include "mkldnnUtils.h" #include -namespace nd4j { +namespace sd { namespace ops { namespace platforms { @@ -88,13 +88,7 @@ static void deconv2dMKLDNN(const NDArray* input, const NDArray* weights, const N // input dnnl::memory::desc x_mkl_md = dnnl::memory::desc(xDims, xType, dnnl::memory::format_tag::any); dnnl::memory::desc x_user_md = dnnl::memory::desc(xDims, xType, xFormat); - if(input->ews() != 1 || input->ordering() != 'c') { - x_user_md.data.format_kind = dnnl_blocked; // overrides format - x_user_md.data.format_desc.blocking.strides[0] = input->strideAt(0); - x_user_md.data.format_desc.blocking.strides[1] = input->strideAt(1); - x_user_md.data.format_desc.blocking.strides[2] = input->strideAt(2); - x_user_md.data.format_desc.blocking.strides[3] = input->strideAt(3); - } + mkldnnUtils::setBlockStrides(input, 4, x_user_md); // weights dnnl::memory::desc w_mkl_md = dnnl::memory::desc(wDims, wType, dnnl::memory::format_tag::any); @@ -113,13 +107,7 @@ static void deconv2dMKLDNN(const NDArray* input, const NDArray* weights, const N // output dnnl::memory::desc z_mkl_md = dnnl::memory::desc(zDims, zType, dnnl::memory::format_tag::any); dnnl::memory::desc z_user_md = dnnl::memory::desc(zDims, zType, xFormat); - if(output->ews() != 1 || output->ordering() != 'c') { - z_user_md.data.format_kind = dnnl_blocked; // overrides format - z_user_md.data.format_desc.blocking.strides[0] = output->strideAt(0); - z_user_md.data.format_desc.blocking.strides[1] = output->strideAt(1); - z_user_md.data.format_desc.blocking.strides[2] = output->strideAt(2); - z_user_md.data.format_desc.blocking.strides[3] = output->strideAt(3); - } + mkldnnUtils::setBlockStrides(output, 4, z_user_md); auto engine = mkldnnUtils::getEngine(LaunchContext::defaultContext()->engine()); @@ -136,20 +124,10 @@ static void deconv2dMKLDNN(const NDArray* input, const NDArray* weights, const N // provide memory buffers and check whether reorder is required // input - auto x_user_mem = dnnl::memory(x_user_md, engine, input->getBuffer()); - const bool xReorder = op_prim_desc.src_desc() != x_user_mem.get_desc(); - auto x_mkl_mem = xReorder ? dnnl::memory(op_prim_desc.src_desc(), engine) : x_user_mem; - if (xReorder) - dnnl::reorder(x_user_mem, x_mkl_mem).execute(stream, x_user_mem, x_mkl_mem); - args[DNNL_ARG_SRC] = x_mkl_mem; + mkldnnUtils::loadDataToMklStream(input, engine, stream, args, x_user_md, op_prim_desc.src_desc(), DNNL_ARG_SRC); // weights - auto w_user_mem = dnnl::memory(w_user_md, engine, weights->getBuffer()); - const bool wReorder = op_prim_desc.weights_desc() != w_user_mem.get_desc(); - auto w_mkl_mem = wReorder ? dnnl::memory(op_prim_desc.weights_desc(), engine) : w_user_mem; - if (wReorder) - dnnl::reorder(w_user_mem, w_mkl_mem).execute(stream, w_user_mem, w_mkl_mem); - args[DNNL_ARG_WEIGHTS] = w_mkl_mem; + mkldnnUtils::loadDataToMklStream(weights, engine, stream, args, w_user_md, op_prim_desc.weights_desc(), DNNL_ARG_WEIGHTS); // bias if(bias != nullptr) { @@ -216,13 +194,7 @@ static void deconv2dBpMKLDNN(const NDArray* input, const NDArray* weights, const // input dnnl::memory::desc x_mkl_md = dnnl::memory::desc(xDims, xType, dnnl::memory::format_tag::any); dnnl::memory::desc x_user_md = dnnl::memory::desc(xDims, xType, xFormat); - if(input->ews() != 1 || input->ordering() != 'c') { - x_user_md.data.format_kind = dnnl_blocked; // overrides format - x_user_md.data.format_desc.blocking.strides[0] = input->strideAt(0); - x_user_md.data.format_desc.blocking.strides[1] = input->strideAt(1); - x_user_md.data.format_desc.blocking.strides[2] = input->strideAt(2); - x_user_md.data.format_desc.blocking.strides[3] = input->strideAt(3); - } + mkldnnUtils::setBlockStrides(input, 4, x_user_md); // weights dnnl::memory::desc w_mkl_md = dnnl::memory::desc(wDims, wType, dnnl::memory::format_tag::any); @@ -236,24 +208,12 @@ static void deconv2dBpMKLDNN(const NDArray* input, const NDArray* weights, const // gradO dnnl::memory::desc gradO_mkl_md = dnnl::memory::desc(zDims, gradOType, dnnl::memory::format_tag::any); dnnl::memory::desc gradO_user_md = dnnl::memory::desc(zDims, gradOType, xFormat); - if(gradO->ews() != 1 || gradO->ordering() != 'c') { - gradO_user_md.data.format_kind = dnnl_blocked; // overrides format - gradO_user_md.data.format_desc.blocking.strides[0] = gradO->strideAt(0); - gradO_user_md.data.format_desc.blocking.strides[1] = gradO->strideAt(1); - gradO_user_md.data.format_desc.blocking.strides[2] = gradO->strideAt(2); - gradO_user_md.data.format_desc.blocking.strides[3] = gradO->strideAt(3); - } + mkldnnUtils::setBlockStrides(gradO, 4, gradO_user_md); // gradI dnnl::memory::desc gradI_mkl_md = dnnl::memory::desc(xDims, gradIType, dnnl::memory::format_tag::any); dnnl::memory::desc gradI_user_md = dnnl::memory::desc(xDims, gradIType, xFormat); - if(gradI->ews() != 1 || gradI->ordering() != 'c') { - gradI_user_md.data.format_kind = dnnl_blocked; // overrides format - gradI_user_md.data.format_desc.blocking.strides[0] = gradI->strideAt(0); - gradI_user_md.data.format_desc.blocking.strides[1] = gradI->strideAt(1); - gradI_user_md.data.format_desc.blocking.strides[2] = gradI->strideAt(2); - gradI_user_md.data.format_desc.blocking.strides[3] = gradI->strideAt(3); - } + mkldnnUtils::setBlockStrides(gradI, 4, gradI_user_md); // gradW dnnl::memory::desc gradW_mkl_md = dnnl::memory::desc(wDims, gradWType, dnnl::memory::format_tag::any); @@ -291,20 +251,10 @@ static void deconv2dBpMKLDNN(const NDArray* input, const NDArray* weights, const // provide memory buffers and check whether reorder is required // input - auto x_user_mem = dnnl::memory(x_user_md, engine, input->getBuffer()); - const bool xReorder = op_weights_bp_prim_desc.src_desc() != x_user_mem.get_desc(); - auto x_mkl_mem = xReorder ? dnnl::memory(op_weights_bp_prim_desc.src_desc(), engine) : x_user_mem; - if (xReorder) - dnnl::reorder(x_user_mem, x_mkl_mem).execute(stream, x_user_mem, x_mkl_mem); - args[DNNL_ARG_SRC] = x_mkl_mem; + mkldnnUtils::loadDataToMklStream(input, engine, stream, args, x_user_md, op_weights_bp_prim_desc.src_desc(), DNNL_ARG_SRC); // weights - auto w_user_mem = dnnl::memory(w_user_md, engine, weights->getBuffer()); - const bool wReorder = op_data_bp_prim_desc.weights_desc() != w_user_mem.get_desc(); - auto w_mkl_mem = wReorder ? dnnl::memory(op_data_bp_prim_desc.weights_desc(), engine) : w_user_mem; - if (wReorder) - dnnl::reorder(w_user_mem, w_mkl_mem).execute(stream, w_user_mem, w_mkl_mem); - args[DNNL_ARG_WEIGHTS] = w_mkl_mem; + mkldnnUtils::loadDataToMklStream(weights, engine, stream, args, w_user_md, op_data_bp_prim_desc.weights_desc(), DNNL_ARG_WEIGHTS); // gradO auto gradO_user_mem = dnnl::memory(gradO_user_md, engine, gradO->getBuffer()); diff --git a/libnd4j/include/ops/declarable/platform/mkldnn/deconv2d_tf.cpp b/libnd4j/include/ops/declarable/platform/mkldnn/deconv2d_tf.cpp index 7c6582ab4..e7283e1d3 100644 --- a/libnd4j/include/ops/declarable/platform/mkldnn/deconv2d_tf.cpp +++ b/libnd4j/include/ops/declarable/platform/mkldnn/deconv2d_tf.cpp @@ -20,13 +20,13 @@ #include #include -#include +#include #include #include "mkldnnUtils.h" #include -namespace nd4j { +namespace sd { namespace ops { namespace platforms { @@ -76,24 +76,12 @@ static void deconv2TFdBackPropMKLDNN(const NDArray* weights, const NDArray* grad // gradO dnnl::memory::desc gradO_mkl_md = dnnl::memory::desc(zDims, gradOType, dnnl::memory::format_tag::any); dnnl::memory::desc gradO_user_md = dnnl::memory::desc(zDims, gradOType, xFormat); - if(gradO->ews() != 1 || gradO->ordering() != 'c') { - gradO_user_md.data.format_kind = dnnl_blocked; // overrides format - gradO_user_md.data.format_desc.blocking.strides[0] = gradO->strideAt(0); - gradO_user_md.data.format_desc.blocking.strides[1] = gradO->strideAt(1); - gradO_user_md.data.format_desc.blocking.strides[2] = gradO->strideAt(2); - gradO_user_md.data.format_desc.blocking.strides[3] = gradO->strideAt(3); - } + mkldnnUtils::setBlockStrides(gradO, 4, gradO_user_md); // gradI dnnl::memory::desc gradI_mkl_md = dnnl::memory::desc(xDims, gradIType, dnnl::memory::format_tag::any); dnnl::memory::desc gradI_user_md = dnnl::memory::desc(xDims, gradIType, xFormat); - if(gradI->ews() != 1 || gradI->ordering() != 'c') { - gradI_user_md.data.format_kind = dnnl_blocked; // overrides format - gradI_user_md.data.format_desc.blocking.strides[0] = gradI->strideAt(0); - gradI_user_md.data.format_desc.blocking.strides[1] = gradI->strideAt(1); - gradI_user_md.data.format_desc.blocking.strides[2] = gradI->strideAt(2); - gradI_user_md.data.format_desc.blocking.strides[3] = gradI->strideAt(3); - } + mkldnnUtils::setBlockStrides(gradI, 4, gradI_user_md); auto engine = mkldnnUtils::getEngine(LaunchContext::defaultContext()->engine()); @@ -113,20 +101,10 @@ static void deconv2TFdBackPropMKLDNN(const NDArray* weights, const NDArray* grad // provide memory buffers and check whether reorder is required // weights - auto w_user_mem = dnnl::memory(w_user_md, engine, weights->getBuffer()); - const bool wReorder = op_data_bp_prim_desc.weights_desc() != w_user_mem.get_desc(); - auto w_mkl_mem = wReorder ? dnnl::memory(op_data_bp_prim_desc.weights_desc(), engine) : w_user_mem; - if (wReorder) - dnnl::reorder(w_user_mem, w_mkl_mem).execute(stream, w_user_mem, w_mkl_mem); - args[DNNL_ARG_WEIGHTS] = w_mkl_mem; + mkldnnUtils::loadDataToMklStream(weights, engine, stream, args, w_user_md, op_data_bp_prim_desc.weights_desc(), DNNL_ARG_WEIGHTS); // gradO - auto gradO_user_mem = dnnl::memory(gradO_user_md, engine, gradO->getBuffer()); - const bool gradOReorder = op_data_bp_prim_desc.diff_dst_desc() != gradO_user_mem.get_desc(); - auto gradO_mkl_mem = gradOReorder ? dnnl::memory(op_data_bp_prim_desc.diff_dst_desc(), engine) : gradO_user_mem; - if (gradOReorder) - dnnl::reorder(gradO_user_mem, gradO_mkl_mem).execute(stream, gradO_user_mem, gradO_mkl_mem); - args[DNNL_ARG_DIFF_DST] = gradO_mkl_mem; + mkldnnUtils::loadDataToMklStream(gradO, engine, stream, args, gradO_user_md, op_data_bp_prim_desc.diff_dst_desc(), DNNL_ARG_DIFF_DST); // gradI auto gradI_user_mem = dnnl::memory(gradI_user_md, engine, gradI->getBuffer()); @@ -146,8 +124,6 @@ static void deconv2TFdBackPropMKLDNN(const NDArray* weights, const NDArray* grad // shape::printArray(z_mkl_mem.map_data(),8); } - - ////////////////////////////////////////////////////////////////////////// PLATFORM_IMPL(deconv2d_tf, ENGINE_CPU) { diff --git a/libnd4j/include/ops/declarable/platform/mkldnn/deconv3d.cpp b/libnd4j/include/ops/declarable/platform/mkldnn/deconv3d.cpp index 5daab8228..dc50288a0 100644 --- a/libnd4j/include/ops/declarable/platform/mkldnn/deconv3d.cpp +++ b/libnd4j/include/ops/declarable/platform/mkldnn/deconv3d.cpp @@ -20,14 +20,14 @@ #include #include -#include +#include #include #include "mkldnnUtils.h" #include -namespace nd4j { +namespace sd { namespace ops { namespace platforms { @@ -89,14 +89,7 @@ static void deconv3dMKLDNN(const NDArray* input, const NDArray* weights, const N // input dnnl::memory::desc x_mkl_md = dnnl::memory::desc(xDims, xType, dnnl::memory::format_tag::any); dnnl::memory::desc x_user_md = dnnl::memory::desc(xDims, xType, xFormat); - if(input->ews() != 1 || input->ordering() != 'c') { - x_user_md.data.format_kind = dnnl_blocked; // overrides format - x_user_md.data.format_desc.blocking.strides[0] = input->strideAt(0); - x_user_md.data.format_desc.blocking.strides[1] = input->strideAt(1); - x_user_md.data.format_desc.blocking.strides[2] = input->strideAt(2); - x_user_md.data.format_desc.blocking.strides[3] = input->strideAt(3); - x_user_md.data.format_desc.blocking.strides[4] = input->strideAt(4); - } + mkldnnUtils::setBlockStrides(input, 5, x_user_md); // weights dnnl::memory::desc w_mkl_md = dnnl::memory::desc(wDims, wType, dnnl::memory::format_tag::any); @@ -116,14 +109,7 @@ static void deconv3dMKLDNN(const NDArray* input, const NDArray* weights, const N // output dnnl::memory::desc z_mkl_md = dnnl::memory::desc(zDims, zType, dnnl::memory::format_tag::any); dnnl::memory::desc z_user_md = dnnl::memory::desc(zDims, zType, xFormat); - if(output->ews() !=1 || output->ordering() != 'c') { - z_user_md.data.format_kind = dnnl_blocked; // overrides format - z_user_md.data.format_desc.blocking.strides[0] = output->strideAt(0); - z_user_md.data.format_desc.blocking.strides[1] = output->strideAt(1); - z_user_md.data.format_desc.blocking.strides[2] = output->strideAt(2); - z_user_md.data.format_desc.blocking.strides[3] = output->strideAt(3); - z_user_md.data.format_desc.blocking.strides[4] = output->strideAt(4); - } + mkldnnUtils::setBlockStrides(output, 5, z_user_md); auto engine = mkldnnUtils::getEngine(LaunchContext::defaultContext()->engine()); @@ -140,20 +126,10 @@ static void deconv3dMKLDNN(const NDArray* input, const NDArray* weights, const N // provide memory buffers and check whether reorder is required // input - auto x_user_mem = dnnl::memory(x_user_md, engine, input->getBuffer()); - const bool xReorder = op_prim_desc.src_desc() != x_user_mem.get_desc(); - auto x_mkl_mem = xReorder ? dnnl::memory(op_prim_desc.src_desc(), engine) : x_user_mem; - if (xReorder) - dnnl::reorder(x_user_mem, x_mkl_mem).execute(stream, x_user_mem, x_mkl_mem); - args[DNNL_ARG_SRC] = x_mkl_mem; + mkldnnUtils::loadDataToMklStream(input, engine, stream, args, x_user_md, op_prim_desc.src_desc(), DNNL_ARG_SRC); // weights - auto w_user_mem = dnnl::memory(w_user_md, engine, weights->getBuffer()); - const bool wReorder = op_prim_desc.weights_desc() != w_user_mem.get_desc(); - auto w_mkl_mem = wReorder ? dnnl::memory(op_prim_desc.weights_desc(), engine) : w_user_mem; - if (wReorder) - dnnl::reorder(w_user_mem, w_mkl_mem).execute(stream, w_user_mem, w_mkl_mem); - args[DNNL_ARG_WEIGHTS] = w_mkl_mem; + mkldnnUtils::loadDataToMklStream(weights, engine, stream, args, w_user_md, op_prim_desc.weights_desc(), DNNL_ARG_WEIGHTS); // bias if(bias != nullptr) { @@ -223,14 +199,7 @@ static void deconv3dBackPropMKLDNN(const NDArray* input, const NDArray* weights, // input dnnl::memory::desc x_mkl_md = dnnl::memory::desc(xDims, xType, dnnl::memory::format_tag::any); dnnl::memory::desc x_user_md = dnnl::memory::desc(xDims, xType, xFormat); - if(input->ews() != 1 || input->ordering() != 'c') { - x_user_md.data.format_kind = dnnl_blocked; // overrides format - x_user_md.data.format_desc.blocking.strides[0] = input->strideAt(0); - x_user_md.data.format_desc.blocking.strides[1] = input->strideAt(1); - x_user_md.data.format_desc.blocking.strides[2] = input->strideAt(2); - x_user_md.data.format_desc.blocking.strides[3] = input->strideAt(3); - x_user_md.data.format_desc.blocking.strides[4] = input->strideAt(4); - } + mkldnnUtils::setBlockStrides(input, 5, x_user_md); // weights dnnl::memory::desc w_mkl_md = dnnl::memory::desc(wDims, wType, dnnl::memory::format_tag::any); @@ -245,26 +214,12 @@ static void deconv3dBackPropMKLDNN(const NDArray* input, const NDArray* weights, // gradO dnnl::memory::desc gradO_mkl_md = dnnl::memory::desc(zDims, gradOType, dnnl::memory::format_tag::any); dnnl::memory::desc gradO_user_md = dnnl::memory::desc(zDims, gradOType, xFormat); - if(gradO->ews() != 1 || gradO->ordering() != 'c') { - gradO_user_md.data.format_kind = dnnl_blocked; // overrides format - gradO_user_md.data.format_desc.blocking.strides[0] = gradO->strideAt(0); - gradO_user_md.data.format_desc.blocking.strides[1] = gradO->strideAt(1); - gradO_user_md.data.format_desc.blocking.strides[2] = gradO->strideAt(2); - gradO_user_md.data.format_desc.blocking.strides[3] = gradO->strideAt(3); - gradO_user_md.data.format_desc.blocking.strides[4] = gradO->strideAt(4); - } + mkldnnUtils::setBlockStrides(gradO, 5, gradO_user_md); // gradI dnnl::memory::desc gradI_mkl_md = dnnl::memory::desc(xDims, gradIType, dnnl::memory::format_tag::any); dnnl::memory::desc gradI_user_md = dnnl::memory::desc(xDims, gradIType, xFormat); - if(gradI->ews() != 1 || gradI->ordering() != 'c') { - gradI_user_md.data.format_kind = dnnl_blocked; // overrides format - gradI_user_md.data.format_desc.blocking.strides[0] = gradI->strideAt(0); - gradI_user_md.data.format_desc.blocking.strides[1] = gradI->strideAt(1); - gradI_user_md.data.format_desc.blocking.strides[2] = gradI->strideAt(2); - gradI_user_md.data.format_desc.blocking.strides[3] = gradI->strideAt(3); - gradI_user_md.data.format_desc.blocking.strides[4] = gradI->strideAt(4); - } + mkldnnUtils::setBlockStrides(gradI, 5, gradI_user_md); // gradW dnnl::memory::desc gradW_mkl_md = dnnl::memory::desc(wDims, gradWType, wFormat); @@ -304,20 +259,10 @@ static void deconv3dBackPropMKLDNN(const NDArray* input, const NDArray* weights, // provide memory buffers and check whether reorder is required // input - auto x_user_mem = dnnl::memory(x_user_md, engine, input->getBuffer()); - const bool xReorder = op_weights_bp_prim_desc.src_desc() != x_user_mem.get_desc(); - auto x_mkl_mem = xReorder ? dnnl::memory(op_weights_bp_prim_desc.src_desc(), engine) : x_user_mem; - if (xReorder) - dnnl::reorder(x_user_mem, x_mkl_mem).execute(stream, x_user_mem, x_mkl_mem); - args[DNNL_ARG_SRC] = x_mkl_mem; + mkldnnUtils::loadDataToMklStream(input, engine, stream, args, x_user_md, op_weights_bp_prim_desc.src_desc(), DNNL_ARG_SRC); // weights - auto w_user_mem = dnnl::memory(w_user_md, engine, weights->getBuffer()); - const bool wReorder = op_data_bp_prim_desc.weights_desc() != w_user_mem.get_desc(); - auto w_mkl_mem = wReorder ? dnnl::memory(op_data_bp_prim_desc.weights_desc(), engine) : w_user_mem; - if (wReorder) - dnnl::reorder(w_user_mem, w_mkl_mem).execute(stream, w_user_mem, w_mkl_mem); - args[DNNL_ARG_WEIGHTS] = w_mkl_mem; + mkldnnUtils::loadDataToMklStream(weights, engine, stream, args, w_user_md, op_data_bp_prim_desc.weights_desc(), DNNL_ARG_WEIGHTS); // gradO auto gradO_user_mem = dnnl::memory(gradO_user_md, engine, gradO->getBuffer()); diff --git a/libnd4j/include/ops/declarable/platform/mkldnn/depthwiseConv2d.cpp b/libnd4j/include/ops/declarable/platform/mkldnn/depthwiseConv2d.cpp index 4da2c2cb0..ae4409923 100644 --- a/libnd4j/include/ops/declarable/platform/mkldnn/depthwiseConv2d.cpp +++ b/libnd4j/include/ops/declarable/platform/mkldnn/depthwiseConv2d.cpp @@ -21,14 +21,14 @@ #include #include -#include +#include #include #include #include "mkldnnUtils.h" using namespace dnnl; -namespace nd4j { +namespace sd { namespace ops { namespace platforms { @@ -98,13 +98,7 @@ static void depthwiseConv2dMKLDNN(const NDArray* input, const NDArray* weights, // input dnnl::memory::desc x_mkl_md = dnnl::memory::desc(xDims, xType, dnnl::memory::format_tag::any); dnnl::memory::desc x_user_md = dnnl::memory::desc(xDims, xType, xzFrmat); - if(input->ews() != 1 || input->ordering() != 'c') { - x_user_md.data.format_kind = dnnl_blocked; // overrides format - x_user_md.data.format_desc.blocking.strides[0] = input->strideAt(0); - x_user_md.data.format_desc.blocking.strides[1] = input->strideAt(1); // do permutation NHWC -> NCHW - x_user_md.data.format_desc.blocking.strides[2] = input->strideAt(2); - x_user_md.data.format_desc.blocking.strides[3] = input->strideAt(3); - } + mkldnnUtils::setBlockStrides(input, 4, x_user_md); // weights, make permute [kH, kW, iC, mC] -> [iC, mC, 1, kH, kW]; dnnl::memory::desc w_mkl_md = dnnl::memory::desc(wDims, wType, dnnl::memory::format_tag::any); @@ -124,13 +118,7 @@ static void depthwiseConv2dMKLDNN(const NDArray* input, const NDArray* weights, // output dnnl::memory::desc z_mkl_md = dnnl::memory::desc(zDims, zType, dnnl::memory::format_tag::any); dnnl::memory::desc z_user_md = dnnl::memory::desc(zDims, zType, xzFrmat); - if(output->ews() != 1 || output->ordering() != 'c') { - z_user_md.data.format_kind = dnnl_blocked; // overrides format - z_user_md.data.format_desc.blocking.strides[0] = output->strideAt(0); - z_user_md.data.format_desc.blocking.strides[1] = output->strideAt(1); // do permutation NHWC -> NCHW - z_user_md.data.format_desc.blocking.strides[2] = output->strideAt(2); - z_user_md.data.format_desc.blocking.strides[3] = output->strideAt(3); - } + mkldnnUtils::setBlockStrides(output, 4, z_user_md); auto engine = mkldnnUtils::getEngine(LaunchContext::defaultContext()->engine()); @@ -147,20 +135,10 @@ static void depthwiseConv2dMKLDNN(const NDArray* input, const NDArray* weights, // provide memory buffers and check whether reorder is required // input - auto x_user_mem = dnnl::memory(x_user_md, engine, input->getBuffer()); - const bool xReorder = op_prim_desc.src_desc() != x_user_mem.get_desc(); - auto x_mkl_mem = xReorder ? dnnl::memory(op_prim_desc.src_desc(), engine) : x_user_mem; - if (xReorder) - dnnl::reorder(x_user_mem, x_mkl_mem).execute(stream, x_user_mem, x_mkl_mem); - args[DNNL_ARG_SRC] = x_mkl_mem; + mkldnnUtils::loadDataToMklStream(input, engine, stream, args, x_user_md, op_prim_desc.src_desc(), DNNL_ARG_SRC); // weights - auto w_user_mem = dnnl::memory(w_user_md, engine, weights->getBuffer()); - const bool wReorder = op_prim_desc.weights_desc() != w_user_mem.get_desc(); - auto w_mkl_mem = wReorder ? dnnl::memory(op_prim_desc.weights_desc(), engine) : w_user_mem; - if (wReorder) - dnnl::reorder(w_user_mem, w_mkl_mem).execute(stream, w_user_mem, w_mkl_mem); - args[DNNL_ARG_WEIGHTS] = w_mkl_mem; + mkldnnUtils::loadDataToMklStream(weights, engine, stream, args, w_user_md, op_prim_desc.weights_desc(), DNNL_ARG_WEIGHTS); // bias if(bias != nullptr) { @@ -235,13 +213,7 @@ static void depthwiseConv2dNackPropMKLDNN(const NDArray* input, const NDArray* w // input dnnl::memory::desc x_mkl_md = dnnl::memory::desc(xDims, xType, dnnl::memory::format_tag::any); dnnl::memory::desc x_user_md = dnnl::memory::desc(xDims, xType, xzFrmat); - if(input->ews() != 1 || input->ordering() != 'c') { - x_user_md.data.format_kind = dnnl_blocked; // overrides format - x_user_md.data.format_desc.blocking.strides[0] = input->strideAt(0); - x_user_md.data.format_desc.blocking.strides[1] = input->strideAt(1); - x_user_md.data.format_desc.blocking.strides[2] = input->strideAt(2); - x_user_md.data.format_desc.blocking.strides[3] = input->strideAt(3); - } + mkldnnUtils::setBlockStrides(input, 4, x_user_md); // weights, make permute [kH, kW, iC, mC] -> [iC, mC, 1, kH, kW]; dnnl::memory::desc w_mkl_md = dnnl::memory::desc(wDims, wType, dnnl::memory::format_tag::any); @@ -256,24 +228,12 @@ static void depthwiseConv2dNackPropMKLDNN(const NDArray* input, const NDArray* w // gradO dnnl::memory::desc gradO_mkl_md = dnnl::memory::desc(zDims, gradOType, dnnl::memory::format_tag::any); dnnl::memory::desc gradO_user_md = dnnl::memory::desc(zDims, gradOType, xzFrmat); - if(gradO->ews() != 1 || gradO->ordering() != 'c') { - gradO_user_md.data.format_kind = dnnl_blocked; // overrides format - gradO_user_md.data.format_desc.blocking.strides[0] = gradO->strideAt(0); - gradO_user_md.data.format_desc.blocking.strides[1] = gradO->strideAt(1); - gradO_user_md.data.format_desc.blocking.strides[2] = gradO->strideAt(2); - gradO_user_md.data.format_desc.blocking.strides[3] = gradO->strideAt(3); - } + mkldnnUtils::setBlockStrides(gradO, 4, gradO_user_md); // gradI dnnl::memory::desc gradI_mkl_md = dnnl::memory::desc(xDims, gradIType, dnnl::memory::format_tag::any); dnnl::memory::desc gradI_user_md = dnnl::memory::desc(xDims, gradIType, xzFrmat); - if(gradI->ews() != 1 || gradI->ordering() != 'c') { - gradI_user_md.data.format_kind = dnnl_blocked; // overrides format - gradI_user_md.data.format_desc.blocking.strides[0] = gradI->strideAt(0); - gradI_user_md.data.format_desc.blocking.strides[1] = gradI->strideAt(1); - gradI_user_md.data.format_desc.blocking.strides[2] = gradI->strideAt(2); - gradI_user_md.data.format_desc.blocking.strides[3] = gradI->strideAt(3); - } + mkldnnUtils::setBlockStrides(gradI, 4, gradI_user_md); // gradW, make permute [kH, kW, iC, mC] -> [iC, mC, 1, kH, kW]; dnnl::memory::desc gradW_mkl_md = dnnl::memory::desc(wDims, gradWType, dnnl::memory::format_tag::any); @@ -312,20 +272,10 @@ static void depthwiseConv2dNackPropMKLDNN(const NDArray* input, const NDArray* w // provide memory buffers and check whether reorder is required // input - auto x_user_mem = dnnl::memory(x_user_md, engine, input->getBuffer()); - const bool xReorder = op_weights_bp_prim_desc.src_desc() != x_user_mem.get_desc(); - auto x_mkl_mem = xReorder ? dnnl::memory(op_weights_bp_prim_desc.src_desc(), engine) : x_user_mem; - if (xReorder) - dnnl::reorder(x_user_mem, x_mkl_mem).execute(stream, x_user_mem, x_mkl_mem); - args[DNNL_ARG_SRC] = x_mkl_mem; + mkldnnUtils::loadDataToMklStream(input, engine, stream, args, x_user_md, op_weights_bp_prim_desc.src_desc(), DNNL_ARG_SRC); // weights - auto w_user_mem = dnnl::memory(w_user_md, engine, weights->getBuffer()); - const bool wReorder = op_data_bp_prim_desc.weights_desc() != w_user_mem.get_desc(); - auto w_mkl_mem = wReorder ? dnnl::memory(op_data_bp_prim_desc.weights_desc(), engine) : w_user_mem; - if (wReorder) - dnnl::reorder(w_user_mem, w_mkl_mem).execute(stream, w_user_mem, w_mkl_mem); - args[DNNL_ARG_WEIGHTS] = w_mkl_mem; + mkldnnUtils::loadDataToMklStream(weights, engine, stream, args, w_user_md, op_data_bp_prim_desc.weights_desc(), DNNL_ARG_WEIGHTS); // gradO auto gradO_user_mem = dnnl::memory(gradO_user_md, engine, gradO->getBuffer()); diff --git a/libnd4j/include/ops/declarable/platform/mkldnn/lrn.cpp b/libnd4j/include/ops/declarable/platform/mkldnn/lrn.cpp index a0f2f6151..583ab0852 100644 --- a/libnd4j/include/ops/declarable/platform/mkldnn/lrn.cpp +++ b/libnd4j/include/ops/declarable/platform/mkldnn/lrn.cpp @@ -21,7 +21,7 @@ #include #include -#include +#include #include #include "mkldnnUtils.h" @@ -29,7 +29,7 @@ using namespace dnnl; -namespace nd4j { +namespace sd { namespace ops { namespace platforms { PLATFORM_IMPL(lrn, ENGINE_CPU) { @@ -86,7 +86,7 @@ namespace nd4j { auto input = INPUT_VARIABLE(0); auto output = OUTPUT_VARIABLE(0); - return block.isUseMKLDNN() && nd4j::MKLDNNStream::isSupported({input, output}); + return block.isUseMKLDNN() && sd::MKLDNNStream::isSupported({input, output}); } } } diff --git a/libnd4j/include/ops/declarable/platform/mkldnn/lstmLayer.cpp b/libnd4j/include/ops/declarable/platform/mkldnn/lstmLayer.cpp index 26aeacaa3..c4d987054 100644 --- a/libnd4j/include/ops/declarable/platform/mkldnn/lstmLayer.cpp +++ b/libnd4j/include/ops/declarable/platform/mkldnn/lstmLayer.cpp @@ -23,7 +23,7 @@ using namespace dnnl; -namespace nd4j { +namespace sd { namespace ops { namespace platforms { @@ -272,29 +272,14 @@ static void lstmLayerMKLDNN(const NDArray* x, const NDArray* Wx, const NDArray* // provide memory and check whether reorder is required // x - auto x_user_mem = dnnl::memory(x_user_md, engine, x->getBuffer()); - const bool xReorder = lstm_prim_desc.src_layer_desc() != x_user_mem.get_desc(); - auto x_lstm_mem = xReorder ? dnnl::memory(lstm_prim_desc.src_layer_desc(), engine) : x_user_mem; - if (xReorder) - reorder(x_user_mem, x_lstm_mem).execute(stream, x_user_mem, x_lstm_mem); - args[DNNL_ARG_SRC_LAYER] = x_lstm_mem; - + mkldnnUtils::loadDataToMklStream(x, engine, stream, args, x_user_md, lstm_prim_desc.src_layer_desc(), DNNL_ARG_SRC_LAYER); + // wx - auto wx_user_mem = dnnl::memory(wx_user_md, engine, Wx->getBuffer()); - const bool wxReorder = lstm_prim_desc.weights_layer_desc()!= wx_user_mem.get_desc(); - auto wx_lstm_mem = wxReorder ? dnnl::memory(lstm_prim_desc.weights_layer_desc(), engine) : wx_user_mem; - if (wxReorder) - reorder(wx_user_mem, wx_lstm_mem).execute(stream, wx_user_mem, wx_lstm_mem); - args[DNNL_ARG_WEIGHTS_LAYER] = wx_lstm_mem; + mkldnnUtils::loadDataToMklStream(Wx, engine, stream, args, wx_user_md, lstm_prim_desc.weights_layer_desc(), DNNL_ARG_WEIGHTS_LAYER); // wr - auto wr_user_mem = dnnl::memory(wr_user_md, engine, Wr->getBuffer()); - const bool wrReorder = lstm_prim_desc.weights_iter_desc() != wr_user_mem.get_desc(); - auto wr_lstm_mem = wxReorder ? dnnl::memory(lstm_prim_desc.weights_iter_desc(), engine) : wr_user_mem; - if (wrReorder) - reorder(wr_user_mem, wr_lstm_mem).execute(stream, wr_user_mem, wr_lstm_mem); - args[DNNL_ARG_WEIGHTS_ITER] = wr_lstm_mem; - + mkldnnUtils::loadDataToMklStream(Wr, engine, stream, args, wr_user_md, lstm_prim_desc.weights_iter_desc(), DNNL_ARG_WEIGHTS_ITER); + // h auto h_user_mem = dnnl::memory(h_user_md, engine, h->getBuffer()); const bool hReorder = lstm_prim_desc.dst_layer_desc() != h_user_mem.get_desc(); @@ -303,32 +288,17 @@ static void lstmLayerMKLDNN(const NDArray* x, const NDArray* Wx, const NDArray* // b if(b) { - auto b_user_mem = dnnl::memory(b_user_md, engine, b->getBuffer()); - const bool bReorder = lstm_prim_desc.bias_desc() != b_user_mem.get_desc(); - auto b_lstm_mem = bReorder ? dnnl::memory(lstm_prim_desc.bias_desc(), engine) : b_user_mem; - if (bReorder) - reorder(b_user_mem, b_lstm_mem).execute(stream, b_user_mem, b_lstm_mem); - args[DNNL_ARG_BIAS] = b_lstm_mem; + mkldnnUtils::loadDataToMklStream(b, engine, stream, args, b_user_md, lstm_prim_desc.bias_desc(), DNNL_ARG_BIAS); } // hI if(hI) { - auto hI_user_mem = dnnl::memory(hI_user_md, engine, hI->getBuffer()); - const bool hIReorder = lstm_prim_desc.src_iter_desc() != hI_user_mem.get_desc(); - auto hI_lstm_mem = hIReorder ? dnnl::memory(lstm_prim_desc.src_iter_desc(), engine) : hI_user_mem; - if (hIReorder) - reorder(hI_user_mem, hI_lstm_mem).execute(stream, hI_user_mem, hI_lstm_mem); - args[DNNL_ARG_SRC_ITER] = hI_lstm_mem; + mkldnnUtils::loadDataToMklStream(hI, engine, stream, args, hI_user_md, lstm_prim_desc.src_iter_desc(), DNNL_ARG_SRC_ITER); } // cI if(cI) { - auto cI_user_mem = dnnl::memory(cI_user_md, engine, cI->getBuffer()); - const bool cIReorder = lstm_prim_desc.src_iter_c_desc() != cI_user_mem.get_desc(); - auto cI_lstm_mem = cIReorder ? dnnl::memory(lstm_prim_desc.src_iter_c_desc(), engine) : cI_user_mem; - if (cIReorder) - reorder(cI_user_mem, cI_lstm_mem).execute(stream, cI_user_mem, cI_lstm_mem); - args[DNNL_ARG_SRC_ITER_C] = cI_lstm_mem; + mkldnnUtils::loadDataToMklStream(cI, engine, stream, args, cI_user_md, lstm_prim_desc.src_iter_c_desc(), DNNL_ARG_SRC_ITER_C); } bool hLReorder(false), cLReorder(false); diff --git a/libnd4j/include/ops/declarable/platform/mkldnn/matmul.cpp b/libnd4j/include/ops/declarable/platform/mkldnn/matmul.cpp index 53d18e3cd..805507277 100644 --- a/libnd4j/include/ops/declarable/platform/mkldnn/matmul.cpp +++ b/libnd4j/include/ops/declarable/platform/mkldnn/matmul.cpp @@ -20,14 +20,14 @@ #include #include -#include +#include #include #include "mkldnnUtils.h" #include -namespace nd4j { +namespace sd { namespace ops { namespace platforms { @@ -163,21 +163,25 @@ static void matmulMKLDNN(const NDArray* x, const NDArray* y, NDArray* z, const b // provide memory buffers and check whether reorder is required // input + mkldnnUtils::loadDataToMklStream(xTR, engine, stream, args, x_user_md, op_prim_desc.src_desc(), DNNL_ARG_SRC); + /* auto x_user_mem = dnnl::memory(x_user_md, engine, xTR->getBuffer()); const bool xReorder = op_prim_desc.src_desc() != x_user_mem.get_desc(); auto x_mkl_mem = xReorder ? dnnl::memory(op_prim_desc.src_desc(), engine) : x_user_mem; if (xReorder) dnnl::reorder(x_user_mem, x_mkl_mem).execute(stream, x_user_mem, x_mkl_mem); args[DNNL_ARG_SRC] = x_mkl_mem; - +*/ // y + mkldnnUtils::loadDataToMklStream(yTR, engine, stream, args, y_user_md, op_prim_desc.weights_desc(), DNNL_ARG_WEIGHTS); + /* auto y_user_mem = dnnl::memory(y_user_md, engine, yTR->getBuffer()); const bool yReorder = op_prim_desc.weights_desc() != y_user_mem.get_desc(); auto y_mkl_mem = yReorder ? dnnl::memory(op_prim_desc.weights_desc(), engine) : y_user_mem; if (yReorder) dnnl::reorder(y_user_mem, y_mkl_mem).execute(stream, y_user_mem, y_mkl_mem); args[DNNL_ARG_WEIGHTS] = y_mkl_mem; - +*/ // z auto z_user_mem = dnnl::memory(z_user_md, engine, zR->getBuffer()); const bool zReorder = op_prim_desc.dst_desc() != z_user_mem.get_desc(); diff --git a/libnd4j/include/ops/declarable/platform/mkldnn/maxpooling2d.cpp b/libnd4j/include/ops/declarable/platform/mkldnn/maxpooling2d.cpp index 3e7979f2f..1b60684a1 100644 --- a/libnd4j/include/ops/declarable/platform/mkldnn/maxpooling2d.cpp +++ b/libnd4j/include/ops/declarable/platform/mkldnn/maxpooling2d.cpp @@ -22,7 +22,7 @@ #include #include -#include +#include #include #include "mkldnnUtils.h" @@ -30,7 +30,7 @@ using namespace dnnl; -namespace nd4j { +namespace sd { namespace ops { namespace platforms { @@ -75,7 +75,7 @@ PLATFORM_CHECK(maxpool2d, ENGINE_CPU) { auto input = INPUT_VARIABLE(0); auto output = OUTPUT_VARIABLE(0); - return block.isUseMKLDNN() && nd4j::MKLDNNStream::isSupported({input, output}); + return block.isUseMKLDNN() && sd::MKLDNNStream::isSupported({input, output}); } ////////////////////////////////////////////////////////////////////////// @@ -119,7 +119,7 @@ PLATFORM_CHECK(maxpool2d_bp, ENGINE_CPU) { auto input = INPUT_VARIABLE(0); auto output = OUTPUT_VARIABLE(0); - return block.isUseMKLDNN() && nd4j::MKLDNNStream::isSupported({input, output}); + return block.isUseMKLDNN() && sd::MKLDNNStream::isSupported({input, output}); } } diff --git a/libnd4j/include/ops/declarable/platform/mkldnn/maxpooling3d.cpp b/libnd4j/include/ops/declarable/platform/mkldnn/maxpooling3d.cpp index 7f6e95418..fbd17d882 100644 --- a/libnd4j/include/ops/declarable/platform/mkldnn/maxpooling3d.cpp +++ b/libnd4j/include/ops/declarable/platform/mkldnn/maxpooling3d.cpp @@ -21,7 +21,7 @@ #include #include -#include +#include #include #include "mkldnnUtils.h" @@ -29,7 +29,7 @@ using namespace dnnl; -namespace nd4j { +namespace sd { namespace ops { namespace platforms { @@ -76,7 +76,7 @@ PLATFORM_CHECK(maxpool3dnew, ENGINE_CPU) { auto input = INPUT_VARIABLE(0); auto output = OUTPUT_VARIABLE(0); - return block.isUseMKLDNN() && nd4j::MKLDNNStream::isSupported({input, output}); + return block.isUseMKLDNN() && sd::MKLDNNStream::isSupported({input, output}); } ////////////////////////////////////////////////////////////////////////// @@ -125,7 +125,7 @@ PLATFORM_CHECK(maxpool3dnew_bp, ENGINE_CPU) { auto input = INPUT_VARIABLE(0); auto output = OUTPUT_VARIABLE(0); - return block.isUseMKLDNN() && nd4j::MKLDNNStream::isSupported({input, output}); + return block.isUseMKLDNN() && sd::MKLDNNStream::isSupported({input, output}); } } diff --git a/libnd4j/include/ops/declarable/platform/mkldnn/mkldnnUtils.cpp b/libnd4j/include/ops/declarable/platform/mkldnn/mkldnnUtils.cpp index 02bba4300..1c6974ea8 100644 --- a/libnd4j/include/ops/declarable/platform/mkldnn/mkldnnUtils.cpp +++ b/libnd4j/include/ops/declarable/platform/mkldnn/mkldnnUtils.cpp @@ -25,9 +25,58 @@ using namespace dnnl; -namespace nd4j { +namespace sd { namespace mkldnnUtils { +////////////////////////////////////////////////////////////////////// +void getDims(const NDArray* array, const int rank, dnnl::memory::dims& mklDims){ + + std::vector vDims(rank); + for (auto i = 0; i < rank; i++) { + vDims[i] = array->sizeAt(i); + } + mklDims = dnnl::memory::dims(vDims); +} +////////////////////////////////////////////////////////////////////// +dnnl::memory::format_tag getFormat(const int rank){ + if (2 == rank) { + return dnnl::memory::format_tag::ab; + } + else if (3 == rank) { + return dnnl::memory::format_tag::abc; + } + else if (4 == rank) { + return dnnl::memory::format_tag::abcd; + } + else if (5 == rank) { + return dnnl::memory::format_tag::abcde; + } + else if (6 == rank) { + return dnnl::memory::format_tag::abcdef; + } + return dnnl::memory::format_tag::a; // 1 == dataSetRank +} +////////////////////////////////////////////////////////////////////// +void setBlockStrides(const NDArray* array, const int rank, dnnl::memory::desc& mklMd){ + if (array->ews() != 1 || array->ordering() != 'c') { + mklMd.data.format_kind = dnnl_blocked; // overrides format + for (auto i = 0; i < rank; ++i) { + mklMd.data.format_desc.blocking.strides[i] = array->strideAt(i); + } + } +} +//////////////////////////////////////////////////////////////////////////////////////////////// +void loadDataToMklStream(const NDArray* array, dnnl::engine& engine, dnnl::stream& stream, + std::unordered_map& args, dnnl::memory::desc& user_md, dnnl::memory::desc primitive_md, int DNNL_ARG ){ + + auto user_mem = dnnl::memory(user_md, engine, array->getBuffer()); + const bool bReorder = primitive_md != user_mem.get_desc(); + auto mkl_mem = bReorder ? dnnl::memory(primitive_md, engine) : user_mem; + if (bReorder) + dnnl::reorder(user_mem, mkl_mem).execute(stream, user_mem, mkl_mem); + args[DNNL_ARG] = mkl_mem; +} + ////////////////////////////////////////////////////////////////////// void poolingMKLDNN(const NDArray *input, NDArray *output, const int kD, const int kH, const int kW, @@ -113,12 +162,7 @@ void poolingMKLDNN(const NDArray *input, NDArray *output, // provide memory buffers and check whether reorder is required // input - auto x_user_mem = dnnl::memory(x_user_md, engine, input->getBuffer()); - const bool xReorder = op_prim_desc.src_desc() != x_user_mem.get_desc(); - auto x_mkl_mem = xReorder ? dnnl::memory(op_prim_desc.src_desc(), engine) : x_user_mem; - if (xReorder) - dnnl::reorder(x_user_mem, x_mkl_mem).execute(stream, x_user_mem, x_mkl_mem); - args[DNNL_ARG_SRC] = x_mkl_mem; + mkldnnUtils::loadDataToMklStream(input, engine, stream, args, x_user_md, op_prim_desc.src_desc(), DNNL_ARG_SRC); // output auto z_user_mem = dnnl::memory(z_user_md, engine, output->getBuffer()); @@ -236,13 +280,8 @@ void poolingBpMKLDNN(const NDArray *input, const NDArray *gradO, NDArray *gradI, std::unordered_map args; // gradO - auto gradO_user_mem = dnnl::memory(gradO_user_md, engine, gradO->getBuffer()); - const bool gradOReorder = op_bp_prim_desc.diff_dst_desc() != gradO_user_mem.get_desc(); - auto gradO_mkl_mem = gradOReorder ? dnnl::memory(op_bp_prim_desc.diff_dst_desc(), engine) : gradO_user_mem; - if (gradOReorder) - dnnl::reorder(gradO_user_mem, gradO_mkl_mem).execute(stream, gradO_user_mem, gradO_mkl_mem); - args[DNNL_ARG_DIFF_DST] = gradO_mkl_mem; - + mkldnnUtils::loadDataToMklStream(gradO, engine, stream, args, gradO_user_md, op_bp_prim_desc.diff_dst_desc(), DNNL_ARG_DIFF_DST); + // gradI auto gradI_user_mem = dnnl::memory(gradI_user_md, engine, gradI->getBuffer()); const bool gradIReorder = op_bp_prim_desc.diff_src_desc() != gradI_user_mem.get_desc(); @@ -252,13 +291,8 @@ void poolingBpMKLDNN(const NDArray *input, const NDArray *gradO, NDArray *gradI, if(mode == algorithm::pooling_max) { // input - auto x_user_mem = dnnl::memory(x_user_md, engine, input->getBuffer()); - const bool xReorder = op_ff_prim_desc.src_desc() != x_user_mem.get_desc(); - auto x_mkl_mem = xReorder ? dnnl::memory(op_ff_prim_desc.src_desc(), engine) : x_user_mem; - if (xReorder) - dnnl::reorder(x_user_mem, x_mkl_mem).execute(stream, x_user_mem, x_mkl_mem); - args[DNNL_ARG_SRC] = x_mkl_mem; - + mkldnnUtils::loadDataToMklStream(input, engine, stream, args, x_user_md, op_ff_prim_desc.src_desc(), DNNL_ARG_SRC); + // z auto z_mkl_mem = dnnl::memory(op_ff_prim_desc.dst_desc(), engine); args[DNNL_ARG_DST] = z_mkl_mem; diff --git a/libnd4j/include/ops/declarable/platform/mkldnn/mkldnnUtils.h b/libnd4j/include/ops/declarable/platform/mkldnn/mkldnnUtils.h index 10adf533d..29b5ebf2a 100644 --- a/libnd4j/include/ops/declarable/platform/mkldnn/mkldnnUtils.h +++ b/libnd4j/include/ops/declarable/platform/mkldnn/mkldnnUtils.h @@ -14,26 +14,27 @@ * SPDX-License-Identifier: Apache-2.0 ******************************************************************************/ -// -// @author saudet -// @author Yurii Shyrma (iuriish@yahoo.com) -// + // + // @author saudet + // @author Yurii Shyrma (iuriish@yahoo.com) + // #ifndef DEV_TESTS_MKLDNNUTILS_H #define DEV_TESTS_MKLDNNUTILS_H -#include -#include + +#include +#include #include -#include +#include #include #include -#include +#include using namespace samediff; -namespace nd4j{ +namespace sd { namespace ops { namespace platforms { /** @@ -86,64 +87,108 @@ namespace nd4j{ DECLARE_PLATFORM(depthwise_conv2d_bp, ENGINE_CPU); DECLARE_PLATFORM(matmul, ENGINE_CPU); + + DECLARE_PLATFORM(softmax, ENGINE_CPU); + + DECLARE_PLATFORM(softmax_bp, ENGINE_CPU); + + DECLARE_PLATFORM(tanh, ENGINE_CPU); + + DECLARE_PLATFORM(tanh_bp, ENGINE_CPU); + } } namespace mkldnnUtils { - void poolingMKLDNN(const NDArray *input, NDArray *output, const int kD, const int kH, const int kW, const int sD, const int sH, const int sW, const int pD, const int pH, const int pW, const int isNCHW, const dnnl::algorithm mode); + void poolingMKLDNN(const NDArray* input, NDArray* output, const int kD, const int kH, const int kW, const int sD, const int sH, const int sW, const int pD, const int pH, const int pW, const int isNCHW, const dnnl::algorithm mode); - void poolingBpMKLDNN(const NDArray *input, const NDArray *gradO, NDArray *gradI, const int kD, const int kH, const int kW, const int sD, const int sH, const int sW, const int pD, const int pH, const int pW, const int isNCHW, const dnnl::algorithm mode); + void poolingBpMKLDNN(const NDArray* input, const NDArray* gradO, NDArray* gradI, const int kD, const int kH, const int kW, const int sD, const int sH, const int sW, const int pD, const int pH, const int pW, const int isNCHW, const dnnl::algorithm mode); void getMKLDNNMemoryDescLrn(const NDArray* src, const NDArray* diff_src, const NDArray* dst, - dnnl::memory::desc* lrn_src_md, dnnl::memory::desc* lrn_diff_src_md, dnnl::memory::desc* lrn_dst_md, - dnnl::memory::desc* user_src_md, dnnl::memory::desc* user_diff_src_md, dnnl::memory::desc* user_dst_md, int axis); + dnnl::memory::desc* lrn_src_md, dnnl::memory::desc* lrn_diff_src_md, dnnl::memory::desc* lrn_dst_md, + dnnl::memory::desc* user_src_md, dnnl::memory::desc* user_diff_src_md, dnnl::memory::desc* user_dst_md, int axis); - dnnl::engine& getEngine(void *ptr); + dnnl::engine& getEngine(void* ptr); + + /** + * This function creates memory dimentions + * @param const pointer to array + * @param const array rank + * @param reference to memory dimentions + */ + void getDims(const NDArray* array, const int rank, dnnl::memory::dims& mklDims); + /** + * This function generate memory format tag based on rank + * @param const array rank + * @return memory format + */ + dnnl::memory::format_tag getFormat(const int rank); + /** + * This function generate memory format tag based on rank + * @param const pointer to dataset + * @param const dataset rank + * @param reference to memory descriptor + * @return memory format + */ + void setBlockStrides(const NDArray* array, const int rank, dnnl::memory::desc& mklMd); + ////////////////////////////////////////////////////////////////////// + /** + * This function load and reorder user memory to mkl + * @param const pointer to dataset + * @param reference to mkl engine + * @param reference to mkl stream + * @param reference to args container for dnnl + * @param reference to user memory description + * @param primitive memory descriptor + * @param dnnl arg activation enumerator + */ + void loadDataToMklStream(const NDArray* array, dnnl::engine& engine, dnnl::stream& stream, + std::unordered_map& args, dnnl::memory::desc& user_md, dnnl::memory::desc primitive_md, int DNNL_ARG); /** * Utility methods for MKLDNN */ -/* void getMKLDNNMemoryDescConv2d( - int kH, int kW, int sH, int sW, int pH, int pW, int dH, int dW, const int paddingMode, bool isNCHW, - int bS, int iC, int iH, int iW, int oC, int oH, int oW, const NDArray* src, const NDArray* diff_src, - const NDArray* weights, const NDArray* diff_weights, const NDArray* bias, const NDArray* dst, - dnnl::memory::desc* conv_src_md, dnnl::memory::desc* conv_diff_src_md, dnnl::memory::desc* conv_weights_md, - dnnl::memory::desc* conv_diff_weights_md, dnnl::memory::desc* conv_bias_md, dnnl::memory::desc* conv_dst_md, - dnnl::memory::desc* user_src_md, dnnl::memory::desc* user_diff_src_md, dnnl::memory::desc* user_weights_md, - dnnl::memory::desc* user_diff_weights_md, dnnl::memory::desc* user_bias_md, dnnl::memory::desc* user_dst_md, - dnnl::memory::dims& conv_strides, dnnl::memory::dims& conv_padding, dnnl::memory::dims& conv_padding_r, dnnl::memory::dims& conv_dilation); + /* void getMKLDNNMemoryDescConv2d( + int kH, int kW, int sH, int sW, int pH, int pW, int dH, int dW, const int paddingMode, bool isNCHW, + int bS, int iC, int iH, int iW, int oC, int oH, int oW, const NDArray* src, const NDArray* diff_src, + const NDArray* weights, const NDArray* diff_weights, const NDArray* bias, const NDArray* dst, + dnnl::memory::desc* conv_src_md, dnnl::memory::desc* conv_diff_src_md, dnnl::memory::desc* conv_weights_md, + dnnl::memory::desc* conv_diff_weights_md, dnnl::memory::desc* conv_bias_md, dnnl::memory::desc* conv_dst_md, + dnnl::memory::desc* user_src_md, dnnl::memory::desc* user_diff_src_md, dnnl::memory::desc* user_weights_md, + dnnl::memory::desc* user_diff_weights_md, dnnl::memory::desc* user_bias_md, dnnl::memory::desc* user_dst_md, + dnnl::memory::dims& conv_strides, dnnl::memory::dims& conv_padding, dnnl::memory::dims& conv_padding_r, dnnl::memory::dims& conv_dilation); - void getMKLDNNMemoryDescConv3d( - int kD, int kH, int kW, int sD, int sH, int sW, int pD, int pH, int pW, int dD, int dH, int dW, bool isSameMode, bool isNCDHW, - int bS, int iC, int iD, int iH, int iW, int oC, int oD, int oH, int oW, const NDArray* src, const NDArray* diff_src, - const NDArray* weights, const NDArray* diff_weights, const NDArray* bias, const NDArray* dst, - dnnl::memory::desc* conv_src_md, dnnl::memory::desc* conv_diff_src_md, dnnl::memory::desc* conv_weights_md, - dnnl::memory::desc* conv_diff_weights_md, dnnl::memory::desc* conv_bias_md, dnnl::memory::desc* conv_dst_md, - dnnl::memory::desc* user_src_md, dnnl::memory::desc* user_diff_src_md, dnnl::memory::desc* user_weights_md, - dnnl::memory::desc* user_diff_weights_md, dnnl::memory::desc* user_bias_md, dnnl::memory::desc* user_dst_md, - dnnl::memory::dims& conv_strides, dnnl::memory::dims& conv_padding, dnnl::memory::dims& conv_padding_r, dnnl::memory::dims& conv_dilation); + void getMKLDNNMemoryDescConv3d( + int kD, int kH, int kW, int sD, int sH, int sW, int pD, int pH, int pW, int dD, int dH, int dW, bool isSameMode, bool isNCDHW, + int bS, int iC, int iD, int iH, int iW, int oC, int oD, int oH, int oW, const NDArray* src, const NDArray* diff_src, + const NDArray* weights, const NDArray* diff_weights, const NDArray* bias, const NDArray* dst, + dnnl::memory::desc* conv_src_md, dnnl::memory::desc* conv_diff_src_md, dnnl::memory::desc* conv_weights_md, + dnnl::memory::desc* conv_diff_weights_md, dnnl::memory::desc* conv_bias_md, dnnl::memory::desc* conv_dst_md, + dnnl::memory::desc* user_src_md, dnnl::memory::desc* user_diff_src_md, dnnl::memory::desc* user_weights_md, + dnnl::memory::desc* user_diff_weights_md, dnnl::memory::desc* user_bias_md, dnnl::memory::desc* user_dst_md, + dnnl::memory::dims& conv_strides, dnnl::memory::dims& conv_padding, dnnl::memory::dims& conv_padding_r, dnnl::memory::dims& conv_dilation); - void getMKLDNNMemoryDescPool2d( - int kH, int kW, int sH, int sW, int pH, int pW, int dH, int dW, int poolingMode, int extraParam0, bool isNCHW, - int bS, int iC, int iH, int iW, int oC, int oH, int oW, - const NDArray* src, const NDArray* diff_src, const NDArray* dst, dnnl::algorithm& algorithm, - dnnl::memory::desc* pool_src_md, dnnl::memory::desc* pool_diff_src_md, dnnl::memory::desc* pool_dst_md, - dnnl::memory::desc* user_src_md, dnnl::memory::desc* user_diff_src_md, dnnl::memory::desc* user_dst_md, - dnnl::memory::dims& pool_strides, dnnl::memory::dims& pool_kernel, dnnl::memory::dims& pool_padding, dnnl::memory::dims& pool_padding_r); + void getMKLDNNMemoryDescPool2d( + int kH, int kW, int sH, int sW, int pH, int pW, int dH, int dW, int poolingMode, int extraParam0, bool isNCHW, + int bS, int iC, int iH, int iW, int oC, int oH, int oW, + const NDArray* src, const NDArray* diff_src, const NDArray* dst, dnnl::algorithm& algorithm, + dnnl::memory::desc* pool_src_md, dnnl::memory::desc* pool_diff_src_md, dnnl::memory::desc* pool_dst_md, + dnnl::memory::desc* user_src_md, dnnl::memory::desc* user_diff_src_md, dnnl::memory::desc* user_dst_md, + dnnl::memory::dims& pool_strides, dnnl::memory::dims& pool_kernel, dnnl::memory::dims& pool_padding, dnnl::memory::dims& pool_padding_r); - void getMKLDNNMemoryDescPool3d( - int kD, int kH, int kW, int sD, int sH, int sW, int pD, int pH, int pW, int dD, int dH, int dW, int poolingMode, int extraParam0, bool isNCDHW, - int bS, int iC, int iD, int iH, int iW, int oC, int oD, int oH, int oW, - const NDArray* src, const NDArray* diff_src, const NDArray* dst, dnnl::algorithm& algorithm, - dnnl::memory::desc* pool_src_md, dnnl::memory::desc* pool_diff_src_md, dnnl::memory::desc* pool_dst_md, - dnnl::memory::desc* user_src_md, dnnl::memory::desc* user_diff_src_md, dnnl::memory::desc* user_dst_md, - dnnl::memory::dims& pool_strides, dnnl::memory::dims& pool_kernel, dnnl::memory::dims& pool_padding, dnnl::memory::dims& pool_padding_r); + void getMKLDNNMemoryDescPool3d( + int kD, int kH, int kW, int sD, int sH, int sW, int pD, int pH, int pW, int dD, int dH, int dW, int poolingMode, int extraParam0, bool isNCDHW, + int bS, int iC, int iD, int iH, int iW, int oC, int oD, int oH, int oW, + const NDArray* src, const NDArray* diff_src, const NDArray* dst, dnnl::algorithm& algorithm, + dnnl::memory::desc* pool_src_md, dnnl::memory::desc* pool_diff_src_md, dnnl::memory::desc* pool_dst_md, + dnnl::memory::desc* user_src_md, dnnl::memory::desc* user_diff_src_md, dnnl::memory::desc* user_dst_md, + dnnl::memory::dims& pool_strides, dnnl::memory::dims& pool_kernel, dnnl::memory::dims& pool_padding, dnnl::memory::dims& pool_padding_r); - void getMKLDNNMemoryDescBatchNorm(const NDArray* src, const NDArray* diff_src, const NDArray* dst, - dnnl::memory::desc* batchnorm_src_md, dnnl::memory::desc* batchnorm_diff_src_md, dnnl::memory::desc* batchnorm_dst_md, - dnnl::memory::desc* user_src_md, dnnl::memory::desc* user_diff_src_md, dnnl::memory::desc* user_dst_md, int axis); -*/ + void getMKLDNNMemoryDescBatchNorm(const NDArray* src, const NDArray* diff_src, const NDArray* dst, + dnnl::memory::desc* batchnorm_src_md, dnnl::memory::desc* batchnorm_diff_src_md, dnnl::memory::desc* batchnorm_dst_md, + dnnl::memory::desc* user_src_md, dnnl::memory::desc* user_diff_src_md, dnnl::memory::desc* user_dst_md, int axis); + */ } } diff --git a/libnd4j/include/ops/declarable/platform/mkldnn/softmax.cpp b/libnd4j/include/ops/declarable/platform/mkldnn/softmax.cpp new file mode 100644 index 000000000..d67d205da --- /dev/null +++ b/libnd4j/include/ops/declarable/platform/mkldnn/softmax.cpp @@ -0,0 +1,274 @@ +/******************************************************************************* + * Copyright (c) 2019-2020 Konduit K.K. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ + + // + // @author Oleg Semeniv + // + // + +#include +#include +#include +#include +#include "mkldnnUtils.h" + +using namespace dnnl; + +namespace sd { + namespace ops { + namespace platforms { + + + ////////////////////////////////////////////////////////////////////// + static void softmaxMKLDNN(const NDArray* x, NDArray* z, const int axis) { + + const auto xRank = x->rankOf(); + dnnl::memory::dims xShape, zShape; + + mkldnnUtils::getDims(x, xRank, xShape); + mkldnnUtils::getDims(z, xRank, zShape); + + + dnnl::memory::format_tag format = mkldnnUtils::getFormat(xRank); + // optimized cases + if (2 == xRank && 0 == axis) { + format = dnnl::memory::format_tag::ba; + } + else if (4 == xRank && 1 == axis && (x->sizeAt(2) * x->sizeAt(3)) > 1) { + format = dnnl::memory::format_tag::acdb; + } + + dnnl::memory::data_type xType = dnnl::memory::data_type::f32; + + dnnl::memory::desc x_mkl_md = dnnl::memory::desc(xShape, xType, format); + dnnl::memory::desc x_user_md = dnnl::memory::desc(xShape, xType, format); + mkldnnUtils::setBlockStrides(x, xRank, x_user_md); + + // z + dnnl::memory::desc z_mkl_md = dnnl::memory::desc(zShape, xType, format); + dnnl::memory::desc z_user_md = dnnl::memory::desc(zShape, xType, format); + mkldnnUtils::setBlockStrides(z, xRank, z_user_md); + + auto engine = mkldnnUtils::getEngine(LaunchContext::defaultContext()->engine()); + + // Create attributes (to handle alpha and beta if necessary) + dnnl::primitive_attr attr; // it is empty since we have usual values for alpha (=1) and beta (=0) + + // operation primitive description + dnnl::softmax_forward::desc op_desc(dnnl::prop_kind::forward_inference, x_mkl_md, axis); + + dnnl::softmax_forward::primitive_desc op_prim_desc(op_desc, attr, engine); + + // arguments (memory buffers) necessary for calculations + std::unordered_map args; + + dnnl::stream stream(engine); + + // provide memory buffers and check whether reorder is required + + // input + mkldnnUtils::loadDataToMklStream(x, engine, stream, args, x_user_md, op_prim_desc.src_desc(), DNNL_ARG_SRC); + + // z + auto z_user_mem = dnnl::memory(z_user_md, engine, z->getBuffer()); + const bool zReorder = op_prim_desc.dst_desc() != z_user_mem.get_desc(); + auto z_mkl_mem = zReorder ? dnnl::memory(op_prim_desc.dst_desc(), engine) : z_user_mem; + args[DNNL_ARG_DST] = z_mkl_mem; + + // run calculations + dnnl::softmax_forward(op_prim_desc).execute(stream, args); + + // reorder outputs if necessary + if (zReorder) + dnnl::reorder(z_mkl_mem, z_user_mem).execute(stream, z_mkl_mem, z_user_mem); + + stream.wait(); + } + + + PLATFORM_IMPL(softmax, ENGINE_CPU) { + + auto input = INPUT_VARIABLE(0); + auto output = OUTPUT_VARIABLE(0); + + const int rank = input->rankOf(); + int dim = block.getIArguments()->size() > 0 ? INT_ARG(0) : rank - 1; + + if (dim < 0) { + dim += rank; + } + + REQUIRE_TRUE(dim < rank && dim >= 0, 0, "SOFTMAX_MKLDNN OP: the value of input integer parameter (dimension) must be less than input array rank %i, but got dimension = %i instead !", rank, dim); + + REQUIRE_TRUE(rank <= 6, 0, "SOFTMAX_MKLDNN OP: the rank of input must be less or qual 6, but got rank = %i instead !", rank); + + // mkldnnSoftMax + softmaxMKLDNN(input, output, dim); + + return Status::OK(); + } + + PLATFORM_CHECK(softmax, ENGINE_CPU) { + + auto x = INPUT_VARIABLE(0); + auto z = OUTPUT_VARIABLE(0); + + const DataType xType = x->dataType(); + const DataType zType = z->dataType(); + + const int xRank = x->rankOf(); + bool bSupportedRanks = (xRank > 2 && xRank < 7); + /* + Source Destination + f32 f32 + */ + return !x->isEmpty() && block.isUseMKLDNN() && bSupportedRanks && (xType == DataType::FLOAT32 && zType == DataType::FLOAT32); + + } + + ////////////////////////////////////////////////////////////////////// + static void softmaxBpMKLDNN(const NDArray* x, const NDArray* dLdz, NDArray* dLdx, const int axis) { + + const auto xRank = x->rankOf(); + const auto dLdzRank = dLdz->rankOf(); + + dnnl::memory::dims xShape, dLdxShape, dLdzShape; + + mkldnnUtils::getDims(x, xRank, xShape); + mkldnnUtils::getDims(dLdx, xRank, dLdxShape); + mkldnnUtils::getDims(dLdz, dLdzRank, dLdzShape); + + dnnl::memory::format_tag format = mkldnnUtils::getFormat(xRank); + + // x + dnnl::memory::desc x_mkl_md = dnnl::memory::desc(xShape, dnnl::memory::data_type::f32, format); + dnnl::memory::desc x_user_md = dnnl::memory::desc(xShape, dnnl::memory::data_type::f32, format); + mkldnnUtils::setBlockStrides(x, xRank, x_user_md); + + // dLdx + dnnl::memory::desc dLdx_mkl_md = dnnl::memory::desc(dLdxShape, dnnl::memory::data_type::f32, format); + dnnl::memory::desc dLdx_user_md = dnnl::memory::desc(dLdxShape, dnnl::memory::data_type::f32, format); + mkldnnUtils::setBlockStrides(dLdx, xRank, dLdx_user_md); + // todo if mkl does not support broadcast we can remove this + format = mkldnnUtils::getFormat(dLdzRank); + + // dLdz + dnnl::memory::desc dLdz_mkl_md = dnnl::memory::desc(dLdzShape, dnnl::memory::data_type::f32, format); + dnnl::memory::desc dLdz_user_md = dnnl::memory::desc(dLdzShape, dnnl::memory::data_type::f32, format); + mkldnnUtils::setBlockStrides(dLdz, dLdzRank, dLdz_user_md); + + auto engine = mkldnnUtils::getEngine(LaunchContext::defaultContext()->engine()); + + // operation primitive description + // forward description + dnnl::softmax_forward::desc op_ff_desc(dnnl::prop_kind::forward_inference, x_mkl_md, axis); + dnnl::softmax_forward::primitive_desc op_ff_prim_desc(op_ff_desc, engine); + + // backward description + dnnl::softmax_backward::desc op_bp_desc(dLdz_mkl_md, dLdx_mkl_md, axis); + dnnl::softmax_backward::primitive_desc op_bp_prim_desc(op_bp_desc, engine, op_ff_prim_desc); + + // arguments (memory buffers) necessary for calculations + std::unordered_map argsbp, argsff; + + dnnl::stream stream(engine); + + // provide memory buffers and check whether reorder is required for forward + // input + mkldnnUtils::loadDataToMklStream(x, engine, stream, argsff, x_user_md, op_ff_prim_desc.src_desc(), DNNL_ARG_SRC); + + // dLdx + auto dLdx_user_mem = dnnl::memory(dLdx_user_md, engine, dLdx->getBuffer()); + const bool dLdxReorder = op_ff_prim_desc.dst_desc() != dLdx_user_mem.get_desc(); + auto dLdx_mkl_mem = dLdxReorder ? dnnl::memory(op_ff_prim_desc.dst_desc(), engine) : dLdx_user_mem; + argsff[DNNL_ARG_DST] = dLdx_mkl_mem; + + // check and arg set for backprob + argsbp[DNNL_ARG_DIFF_SRC] = dLdx_mkl_mem; + argsbp[DNNL_ARG_DST] = dLdx_mkl_mem; + // dLdz + mkldnnUtils::loadDataToMklStream(dLdz, engine, stream, argsbp, dLdz_user_md, op_bp_prim_desc.diff_dst_desc(), DNNL_ARG_DIFF_DST); + + // run calculations forward + dnnl::softmax_forward(op_ff_prim_desc).execute(stream, argsff); + + // run calculations backward + dnnl::softmax_backward(op_bp_prim_desc).execute(stream, argsbp); + + // reorder outputs if necessary + if (dLdxReorder) + dnnl::reorder(dLdx_mkl_mem, dLdx_user_mem).execute(stream, dLdx_mkl_mem, dLdx_user_mem); + + stream.wait(); + } + + + PLATFORM_IMPL(softmax_bp, ENGINE_CPU) { + + auto input = INPUT_VARIABLE(0); + auto dLdz = INPUT_VARIABLE(1); + auto dLdx = OUTPUT_VARIABLE(0); + + const int rank = input->rankOf(); + const int dLdzRank = dLdz->rankOf(); + int dim = block.getIArguments()->size() > 0 ? INT_ARG(0) : rank - 1; + + if (dim < 0) { + dim += rank; + } + + REQUIRE_TRUE(dim < rank && dim >= 0, 0, "SOFTMAX_MKLDNN_BP OP: the value of input integer parameter (dimension) must be less than input array rank %i, but got dimension = %i instead !", rank, dim); + + REQUIRE_TRUE(rank <= 6 && dLdzRank <= 6, 0, "SOFTMAX_MKLDNN_BP OP: the rank of input and dLdz must be less or qual 6, but got input rank = %i and dLdz rank rank = %i instead !", rank, dLdzRank); + + // mkldnnSoftMax + softmaxBpMKLDNN(input, dLdz, dLdx, dim); + + return Status::OK(); + } + + PLATFORM_CHECK(softmax_bp, ENGINE_CPU) { + + auto x = INPUT_VARIABLE(0); + auto dLdz = INPUT_VARIABLE(1); + auto dLdx = OUTPUT_VARIABLE(0); + + const DataType xType = x->dataType(); + const DataType dLdzType = dLdz->dataType(); + const DataType dLdxType = dLdx->dataType(); + + const int xRank = x->rankOf(); + const int dLdzRank = dLdz->rankOf(); + + bool bSupportedRanks = xRank < 7 && dLdzRank == xRank && (!x->isEmpty() && !dLdz->isEmpty()); + + if (bSupportedRanks) { + for (int i = 0; i < xRank; i++) { + if (x->sizeAt(i) != dLdz->sizeAt(i)) { + bSupportedRanks = false; + break; + } + } + } + + //Source Destination + //f32 f32 + return block.isUseMKLDNN() && bSupportedRanks && (xType == DataType::FLOAT32 && dLdzType == DataType::FLOAT32 && dLdxType == DataType::FLOAT32); + } + + } + } +} diff --git a/libnd4j/include/ops/declarable/platform/mkldnn/tanh.cpp b/libnd4j/include/ops/declarable/platform/mkldnn/tanh.cpp new file mode 100644 index 000000000..5a3ab0f57 --- /dev/null +++ b/libnd4j/include/ops/declarable/platform/mkldnn/tanh.cpp @@ -0,0 +1,236 @@ +/******************************************************************************* + * Copyright (c) 2019-2020 Konduit K.K. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ + + // + // @author Oleg Semeniv + // + // + +#include +#include +#include +#include +#include "mkldnnUtils.h" + +using namespace dnnl; + +namespace sd { + namespace ops { + namespace platforms { + + ////////////////////////////////////////////////////////////////////// + static void tanhMKLDNN(const NDArray* x, NDArray* z) { + + const auto xRank = x->rankOf(); + dnnl::memory::dims xShape, zShape; + + mkldnnUtils::getDims(x, xRank, xShape); + mkldnnUtils::getDims(z, xRank, zShape); + + dnnl::memory::format_tag format = mkldnnUtils::getFormat(xRank); + + dnnl::memory::desc x_mkl_md = dnnl::memory::desc(xShape, dnnl::memory::data_type::f32, format); + dnnl::memory::desc x_user_md = dnnl::memory::desc(xShape, dnnl::memory::data_type::f32, format); + mkldnnUtils::setBlockStrides(x, xRank, x_user_md); + + // z + dnnl::memory::desc z_mkl_md = dnnl::memory::desc(zShape, dnnl::memory::data_type::f32, format); + dnnl::memory::desc z_user_md = dnnl::memory::desc(zShape, dnnl::memory::data_type::f32, format); + mkldnnUtils::setBlockStrides(z, xRank, z_user_md); + + auto engine = mkldnnUtils::getEngine(LaunchContext::defaultContext()->engine()); + + // Create attributes (to handle alpha and beta if necessary) + dnnl::primitive_attr attr; // it is empty since we have usual values for alpha (=1) and beta (=0) + + // operation primitive description + dnnl::eltwise_forward::desc op_desc(dnnl::prop_kind::forward_inference, algorithm::eltwise_tanh, x_mkl_md, 0, 0); + + dnnl::eltwise_forward::primitive_desc op_prim_desc(op_desc, attr, engine); + + // arguments (memory buffers) necessary for calculations + std::unordered_map args; + + dnnl::stream stream(engine); + + // provide memory buffers and check whether reorder is required + // input + mkldnnUtils::loadDataToMklStream(x, engine, stream, args, x_user_md, op_prim_desc.src_desc(), DNNL_ARG_SRC); + + // z + auto z_user_mem = dnnl::memory(z_user_md, engine, z->getBuffer()); + const bool zReorder = op_prim_desc.dst_desc() != z_user_mem.get_desc(); + auto z_mkl_mem = zReorder ? dnnl::memory(op_prim_desc.dst_desc(), engine) : z_user_mem; + args[DNNL_ARG_DST] = z_mkl_mem; + + // run calculations + dnnl::eltwise_forward(op_prim_desc).execute(stream, args); + + // reorder outputs if necessary + if (zReorder) + dnnl::reorder(z_mkl_mem, z_user_mem).execute(stream, z_mkl_mem, z_user_mem); + + stream.wait(); + } + + + PLATFORM_IMPL(tanh, ENGINE_CPU) { + + auto input = INPUT_VARIABLE(0); + auto output = OUTPUT_VARIABLE(0); + const int rank = input->rankOf(); + REQUIRE_TRUE(rank <= 6, 0, "TANH_MKLDNN OP: the rank of input must be less or qual 6, but got rank = %i instead !", rank); + + // mkldnnTanh + tanhMKLDNN(input, output); + + return Status::OK(); + } + + PLATFORM_CHECK(tanh, ENGINE_CPU) { + + auto x = INPUT_VARIABLE(0); + auto z = OUTPUT_VARIABLE(0); + + const DataType xType = x->dataType(); + const DataType zType = z->dataType(); + + const int xRank = x->rankOf(); + bool bSupportedRanks = !x->isEmpty() && xRank < 7 && (xType == DataType::FLOAT32 && zType == DataType::FLOAT32); + /* + Source Destination + f32 f32 + */ + return block.isUseMKLDNN() && bSupportedRanks; + } + + + ////////////////////////////////////////////////////////////////////// + static void tanhBpMKLDNN(const NDArray* x, const NDArray* dLdz, NDArray* dLdx) { + + const auto xRank = x->rankOf(); + dnnl::memory::dims xShape, dLdzShape, dLdxShape; + + mkldnnUtils::getDims(x, xRank, xShape); + mkldnnUtils::getDims(dLdz, xRank, dLdzShape); + mkldnnUtils::getDims(dLdx, xRank, dLdxShape); + + dnnl::memory::format_tag format = mkldnnUtils::getFormat(xRank); + + dnnl::memory::desc x_mkl_md = dnnl::memory::desc(xShape, dnnl::memory::data_type::f32, format); + dnnl::memory::desc x_user_md = dnnl::memory::desc(xShape, dnnl::memory::data_type::f32, format); + mkldnnUtils::setBlockStrides(x, xRank, x_user_md); + + // dLdz + dnnl::memory::desc dLdz_mkl_md = dnnl::memory::desc(xShape, dnnl::memory::data_type::f32, format); + dnnl::memory::desc dLdz_user_md = dnnl::memory::desc(xShape, dnnl::memory::data_type::f32, format); + mkldnnUtils::setBlockStrides(dLdz, xRank, dLdz_user_md); + + // dLdx + dnnl::memory::desc dLdx_mkl_md = dnnl::memory::desc(xShape, dnnl::memory::data_type::f32, format); + dnnl::memory::desc dLdx_user_md = dnnl::memory::desc(xShape, dnnl::memory::data_type::f32, format); + mkldnnUtils::setBlockStrides(dLdx, xRank, dLdx_user_md); + + auto engine = mkldnnUtils::getEngine(LaunchContext::defaultContext()->engine()); + + // arguments (memory buffers) necessary for calculations + std::unordered_map args; + + dnnl::stream stream(engine); + + // operation primitive description + // forward + dnnl::eltwise_forward::desc op_ff_desc(dnnl::prop_kind::forward_inference, algorithm::eltwise_tanh, x_mkl_md, 0, 0); + dnnl::eltwise_forward::primitive_desc op_ff_prim_desc(op_ff_desc, engine); + + // backward description + dnnl::eltwise_backward::desc op_desc(algorithm::eltwise_tanh, dLdz_mkl_md, x_mkl_md, 0, 0); + dnnl::eltwise_backward::primitive_desc op_prim_desc(op_desc, engine, op_ff_prim_desc); + + // provide memory buffers and check whether reorder is required for forward + // input + mkldnnUtils::loadDataToMklStream(x, engine, stream, args, x_user_md, op_prim_desc.src_desc(), DNNL_ARG_SRC); + + // dLdz + mkldnnUtils::loadDataToMklStream(dLdz, engine, stream, args, dLdz_user_md, op_prim_desc.diff_dst_desc(), DNNL_ARG_DIFF_DST); + + // dLdx + auto dLdx_user_mem = dnnl::memory(dLdx_user_md, engine, dLdx->getBuffer()); + const bool dLdxReorder = op_prim_desc.diff_src_desc() != dLdx_user_mem.get_desc(); + auto dLdx_mkl_mem = dLdxReorder ? dnnl::memory(op_prim_desc.diff_src_desc(), engine) : dLdx_user_mem; + args[DNNL_ARG_DIFF_SRC] = dLdx_mkl_mem; + + // run calculations backward + dnnl::eltwise_backward(op_prim_desc).execute(stream, args); + + // reorder outputs if necessary + if (dLdxReorder) + dnnl::reorder(dLdx_mkl_mem, dLdx_user_mem).execute(stream, dLdx_mkl_mem, dLdx_user_mem); + + stream.wait(); + } + + + PLATFORM_IMPL(tanh_bp, ENGINE_CPU) { + + auto input = INPUT_VARIABLE(0); + auto dLdz = INPUT_VARIABLE(1); + auto dLdx = OUTPUT_VARIABLE(0); + + const int rank = input->rankOf(); + const int dLdzRank = dLdz->rankOf(); + + REQUIRE_TRUE(rank <= 6 && dLdzRank <= 6, 0, "TANH_BP_MKLDNN OP: the rank of input and dLdz must be less or qual 6, but got input rank = %i and dLdz rank rank = %i instead !", rank, dLdzRank); + + // mkldnnSoftMax + tanhBpMKLDNN(input, dLdz, dLdx); + + return Status::OK(); + } + + PLATFORM_CHECK(tanh_bp, ENGINE_CPU) { + + auto x = INPUT_VARIABLE(0); + auto dLdz = INPUT_VARIABLE(1); + auto dLdx = OUTPUT_VARIABLE(0); + + const DataType xType = x->dataType(); + const DataType dLdzType = dLdz->dataType(); + const DataType dLdxType = dLdx->dataType(); + + const int xRank = x->rankOf(); + const int dLdzRank = dLdz->rankOf(); + + bool bSupportedRanks = xRank < 7 && dLdzRank == xRank && (!x->isEmpty() && !dLdz->isEmpty()); + bSupportedRanks &= (xType == DataType::FLOAT32 && dLdzType == DataType::FLOAT32 && dLdxType == DataType::FLOAT32); + + if (bSupportedRanks) { + for (int i = 0; i < xRank; i++) { + if (x->sizeAt(i) != dLdz->sizeAt(i)) { + bSupportedRanks = false; + break; + } + } + } + + //Source Destination + //f32 f32 + return block.isUseMKLDNN() && bSupportedRanks; + } + + } + } +} diff --git a/libnd4j/include/ops/gemm.h b/libnd4j/include/ops/gemm.h index c967425c7..23f1636a2 100644 --- a/libnd4j/include/ops/gemm.h +++ b/libnd4j/include/ops/gemm.h @@ -22,11 +22,11 @@ #define LIBND4J_GEMM_H #include -#include -#include +#include +#include -namespace nd4j { +namespace sd { namespace blas { template static void * transpose(int orderSource, int orderTarget, int rows, int cols, void *source); @@ -42,7 +42,7 @@ namespace nd4j { }; template - class GEMV : public nd4j::blas::GEMM{ + class GEMV : public sd::blas::GEMM{ public: static void op(int TRANS, int M, int N, double alpha, void* vA, int lda, void* vX, int incx, double beta, void* vY, int incy ); }; diff --git a/libnd4j/include/ops/impl/BroadcastBoolOpsTuple.cpp b/libnd4j/include/ops/impl/BroadcastBoolOpsTuple.cpp index 31fa9cc66..7e903346b 100644 --- a/libnd4j/include/ops/impl/BroadcastBoolOpsTuple.cpp +++ b/libnd4j/include/ops/impl/BroadcastBoolOpsTuple.cpp @@ -19,8 +19,8 @@ // #include -namespace nd4j { - BroadcastBoolOpsTuple BroadcastBoolOpsTuple::custom(nd4j::scalar::BoolOps scalar, nd4j::pairwise::BoolOps pairwise, nd4j::broadcast::BoolOps broadcast) { +namespace sd { + BroadcastBoolOpsTuple BroadcastBoolOpsTuple::custom(sd::scalar::BoolOps scalar, sd::pairwise::BoolOps pairwise, sd::broadcast::BoolOps broadcast) { BroadcastBoolOpsTuple t(scalar, pairwise, broadcast); return t; } diff --git a/libnd4j/include/ops/impl/BroadcastIntOpsTuple.cpp b/libnd4j/include/ops/impl/BroadcastIntOpsTuple.cpp index 607572b59..5680b8056 100644 --- a/libnd4j/include/ops/impl/BroadcastIntOpsTuple.cpp +++ b/libnd4j/include/ops/impl/BroadcastIntOpsTuple.cpp @@ -19,8 +19,8 @@ // #include -namespace nd4j { - BroadcastIntOpsTuple BroadcastIntOpsTuple::custom(nd4j::scalar::IntOps scalar, nd4j::pairwise::IntOps pairwise, nd4j::broadcast::IntOps broadcast) { +namespace sd { + BroadcastIntOpsTuple BroadcastIntOpsTuple::custom(sd::scalar::IntOps scalar, sd::pairwise::IntOps pairwise, sd::broadcast::IntOps broadcast) { BroadcastIntOpsTuple t(scalar, pairwise, broadcast); return t; } diff --git a/libnd4j/include/ops/impl/BroadcastOpsTuple.cpp b/libnd4j/include/ops/impl/BroadcastOpsTuple.cpp index 26cda74a4..71afe8260 100644 --- a/libnd4j/include/ops/impl/BroadcastOpsTuple.cpp +++ b/libnd4j/include/ops/impl/BroadcastOpsTuple.cpp @@ -19,48 +19,48 @@ // #include -namespace nd4j { - BroadcastOpsTuple BroadcastOpsTuple::custom(nd4j::scalar::Ops scalar, nd4j::pairwise::Ops pairwise, nd4j::broadcast::Ops broadcast) { +namespace sd { + BroadcastOpsTuple BroadcastOpsTuple::custom(sd::scalar::Ops scalar, sd::pairwise::Ops pairwise, sd::broadcast::Ops broadcast) { BroadcastOpsTuple t(scalar, pairwise, broadcast); return t; } BroadcastOpsTuple BroadcastOpsTuple::Add() { - return custom(nd4j::scalar::Add, nd4j::pairwise::Add, nd4j::broadcast::Add); + return custom(sd::scalar::Add, sd::pairwise::Add, sd::broadcast::Add); } BroadcastOpsTuple BroadcastOpsTuple::Assign() { - return custom(nd4j::scalar::CopyPws, nd4j::pairwise::CopyPws, nd4j::broadcast::CopyPws); + return custom(sd::scalar::CopyPws, sd::pairwise::CopyPws, sd::broadcast::CopyPws); } BroadcastOpsTuple BroadcastOpsTuple::Divide() { - return custom(nd4j::scalar::Divide, nd4j::pairwise::Divide, nd4j::broadcast::Divide); + return custom(sd::scalar::Divide, sd::pairwise::Divide, sd::broadcast::Divide); } BroadcastOpsTuple BroadcastOpsTuple::DivideNoNan() { - return custom(nd4j::scalar::DivideNoNan, nd4j::pairwise::DivideNoNan, nd4j::broadcast::DivideNoNan); + return custom(sd::scalar::DivideNoNan, sd::pairwise::DivideNoNan, sd::broadcast::DivideNoNan); } BroadcastOpsTuple BroadcastOpsTuple::Multiply() { - return custom(nd4j::scalar::Multiply, nd4j::pairwise::Multiply, nd4j::broadcast::Multiply); + return custom(sd::scalar::Multiply, sd::pairwise::Multiply, sd::broadcast::Multiply); } BroadcastOpsTuple BroadcastOpsTuple::Subtract() { - return custom(nd4j::scalar::Subtract, nd4j::pairwise::Subtract, nd4j::broadcast::Subtract); + return custom(sd::scalar::Subtract, sd::pairwise::Subtract, sd::broadcast::Subtract); } BroadcastOpsTuple BroadcastOpsTuple::IGamma() { - return custom(nd4j::scalar::IGamma, nd4j::pairwise::IGamma, nd4j::broadcast::IGamma); + return custom(sd::scalar::IGamma, sd::pairwise::IGamma, sd::broadcast::IGamma); } BroadcastOpsTuple BroadcastOpsTuple::IGammac() { - return custom(nd4j::scalar::IGammac, nd4j::pairwise::IGammac, nd4j::broadcast::IGammac); + return custom(sd::scalar::IGammac, sd::pairwise::IGammac, sd::broadcast::IGammac); } BroadcastOpsTuple BroadcastOpsTuple::Pow() { - return custom(nd4j::scalar::Pow, nd4j::pairwise::Pow, nd4j::broadcast::Pow); + return custom(sd::scalar::Pow, sd::pairwise::Pow, sd::broadcast::Pow); } BroadcastOpsTuple BroadcastOpsTuple::PowDerivative() { - return custom(nd4j::scalar::PowDerivative, nd4j::pairwise::PowDerivative, nd4j::broadcast::PowDerivative); + return custom(sd::scalar::PowDerivative, sd::pairwise::PowDerivative, sd::broadcast::PowDerivative); } } diff --git a/libnd4j/include/ops/impl/compilation_units/specials_double_0.cpp b/libnd4j/include/ops/impl/compilation_units/specials_double_0.cpp index 8ef8032bb..e9d262f58 100644 --- a/libnd4j/include/ops/impl/compilation_units/specials_double_0.cpp +++ b/libnd4j/include/ops/impl/compilation_units/specials_double_0.cpp @@ -21,7 +21,7 @@ #include "../specials_double.hpp" -namespace nd4j { +namespace sd { BUILD_DOUBLE_TEMPLATE(template class DoubleMethods, , LIBND4J_TYPES, LIBND4J_TYPES_0); BUILD_DOUBLE_TEMPLATE(template void SpecialTypeConverter::convertGeneric, (Nd4jPointer * extras, void *dx, Nd4jLong N, void *dz), LIBND4J_TYPES, LIBND4J_TYPES); diff --git a/libnd4j/include/ops/impl/compilation_units/specials_double_1.cpp b/libnd4j/include/ops/impl/compilation_units/specials_double_1.cpp index 5bb518d76..a61a98870 100644 --- a/libnd4j/include/ops/impl/compilation_units/specials_double_1.cpp +++ b/libnd4j/include/ops/impl/compilation_units/specials_double_1.cpp @@ -21,6 +21,6 @@ #include "../specials_double.hpp" -namespace nd4j { +namespace sd { BUILD_DOUBLE_TEMPLATE(template class DoubleMethods, , LIBND4J_TYPES, LIBND4J_TYPES_1); } \ No newline at end of file diff --git a/libnd4j/include/ops/impl/compilation_units/specials_double_2.cpp b/libnd4j/include/ops/impl/compilation_units/specials_double_2.cpp index 27b68e732..89deb3d9c 100644 --- a/libnd4j/include/ops/impl/compilation_units/specials_double_2.cpp +++ b/libnd4j/include/ops/impl/compilation_units/specials_double_2.cpp @@ -21,6 +21,6 @@ #include "../specials_double.hpp" -namespace nd4j { +namespace sd { BUILD_DOUBLE_TEMPLATE(template class DoubleMethods, , LIBND4J_TYPES, LIBND4J_TYPES_2); } \ No newline at end of file diff --git a/libnd4j/include/ops/impl/compilation_units/specials_double_3.cpp b/libnd4j/include/ops/impl/compilation_units/specials_double_3.cpp index 80e2258c7..7690749bf 100644 --- a/libnd4j/include/ops/impl/compilation_units/specials_double_3.cpp +++ b/libnd4j/include/ops/impl/compilation_units/specials_double_3.cpp @@ -21,6 +21,6 @@ #include "../specials_double.hpp" -namespace nd4j { +namespace sd { BUILD_DOUBLE_TEMPLATE(template class DoubleMethods, , LIBND4J_TYPES, LIBND4J_TYPES_3); } \ No newline at end of file diff --git a/libnd4j/include/ops/impl/compilation_units/specials_double_4.cpp b/libnd4j/include/ops/impl/compilation_units/specials_double_4.cpp index e34b0c528..505ea9921 100644 --- a/libnd4j/include/ops/impl/compilation_units/specials_double_4.cpp +++ b/libnd4j/include/ops/impl/compilation_units/specials_double_4.cpp @@ -21,6 +21,6 @@ #include "../specials_double.hpp" -namespace nd4j { +namespace sd { BUILD_DOUBLE_TEMPLATE(template class DoubleMethods, , LIBND4J_TYPES, LIBND4J_TYPES_4); } \ No newline at end of file diff --git a/libnd4j/include/ops/impl/compilation_units/specials_double_5.cpp b/libnd4j/include/ops/impl/compilation_units/specials_double_5.cpp index 96797cc98..caa9d2dfa 100644 --- a/libnd4j/include/ops/impl/compilation_units/specials_double_5.cpp +++ b/libnd4j/include/ops/impl/compilation_units/specials_double_5.cpp @@ -21,6 +21,6 @@ #include "../specials_double.hpp" -namespace nd4j { +namespace sd { BUILD_DOUBLE_TEMPLATE(template class DoubleMethods, , LIBND4J_TYPES, LIBND4J_TYPES_5); } \ No newline at end of file diff --git a/libnd4j/include/ops/impl/compilation_units/specials_double_6.cpp b/libnd4j/include/ops/impl/compilation_units/specials_double_6.cpp index 70c7f3990..9646534a9 100644 --- a/libnd4j/include/ops/impl/compilation_units/specials_double_6.cpp +++ b/libnd4j/include/ops/impl/compilation_units/specials_double_6.cpp @@ -21,6 +21,6 @@ #include "../specials_double.hpp" -namespace nd4j { +namespace sd { BUILD_DOUBLE_TEMPLATE(template class DoubleMethods, , LIBND4J_TYPES, LIBND4J_TYPES_6); } \ No newline at end of file diff --git a/libnd4j/include/ops/impl/compilation_units/specials_double_7.cpp b/libnd4j/include/ops/impl/compilation_units/specials_double_7.cpp index e2d1df0e9..3230c1fbc 100644 --- a/libnd4j/include/ops/impl/compilation_units/specials_double_7.cpp +++ b/libnd4j/include/ops/impl/compilation_units/specials_double_7.cpp @@ -21,6 +21,6 @@ #include "../specials_double.hpp" -namespace nd4j { +namespace sd { BUILD_DOUBLE_TEMPLATE(template class DoubleMethods, , LIBND4J_TYPES, LIBND4J_TYPES_7); } \ No newline at end of file diff --git a/libnd4j/include/ops/impl/compilation_units/specials_double_8.cpp b/libnd4j/include/ops/impl/compilation_units/specials_double_8.cpp index 25e14d39f..a56b335b6 100644 --- a/libnd4j/include/ops/impl/compilation_units/specials_double_8.cpp +++ b/libnd4j/include/ops/impl/compilation_units/specials_double_8.cpp @@ -21,6 +21,6 @@ #include "../specials_double.hpp" -namespace nd4j { +namespace sd { BUILD_DOUBLE_TEMPLATE(template class DoubleMethods, , LIBND4J_TYPES, LIBND4J_TYPES_8); } \ No newline at end of file diff --git a/libnd4j/include/ops/impl/compilation_units/specials_double_9.cpp b/libnd4j/include/ops/impl/compilation_units/specials_double_9.cpp index f3b4cbcb6..bb13c0415 100644 --- a/libnd4j/include/ops/impl/compilation_units/specials_double_9.cpp +++ b/libnd4j/include/ops/impl/compilation_units/specials_double_9.cpp @@ -21,6 +21,6 @@ #include "../specials_double.hpp" -namespace nd4j { +namespace sd { BUILD_DOUBLE_TEMPLATE(template class DoubleMethods, , LIBND4J_TYPES, LIBND4J_TYPES_9); } \ No newline at end of file diff --git a/libnd4j/include/ops/impl/compilation_units/specials_single_0.cpp b/libnd4j/include/ops/impl/compilation_units/specials_single_0.cpp index 4d1575123..f74717f05 100644 --- a/libnd4j/include/ops/impl/compilation_units/specials_single_0.cpp +++ b/libnd4j/include/ops/impl/compilation_units/specials_single_0.cpp @@ -21,6 +21,6 @@ #include "../specials_single.hpp" -namespace nd4j { +namespace sd { BUILD_SINGLE_TEMPLATE(template class SpecialMethods, , LIBND4J_TYPES_0); } \ No newline at end of file diff --git a/libnd4j/include/ops/impl/compilation_units/specials_single_1.cpp b/libnd4j/include/ops/impl/compilation_units/specials_single_1.cpp index b50c487b7..cbacbb60e 100644 --- a/libnd4j/include/ops/impl/compilation_units/specials_single_1.cpp +++ b/libnd4j/include/ops/impl/compilation_units/specials_single_1.cpp @@ -21,6 +21,6 @@ #include "../specials_single.hpp" -namespace nd4j { +namespace sd { BUILD_SINGLE_TEMPLATE(template class SpecialMethods, , LIBND4J_TYPES_1); } \ No newline at end of file diff --git a/libnd4j/include/ops/impl/compilation_units/specials_single_2.cpp b/libnd4j/include/ops/impl/compilation_units/specials_single_2.cpp index 972b936dd..b1c7c0db6 100644 --- a/libnd4j/include/ops/impl/compilation_units/specials_single_2.cpp +++ b/libnd4j/include/ops/impl/compilation_units/specials_single_2.cpp @@ -21,6 +21,6 @@ #include "../specials_single.hpp" -namespace nd4j { +namespace sd { BUILD_SINGLE_TEMPLATE(template class SpecialMethods, , LIBND4J_TYPES_2); } \ No newline at end of file diff --git a/libnd4j/include/ops/impl/compilation_units/specials_single_3.cpp b/libnd4j/include/ops/impl/compilation_units/specials_single_3.cpp index 9eb99b238..d340500e5 100644 --- a/libnd4j/include/ops/impl/compilation_units/specials_single_3.cpp +++ b/libnd4j/include/ops/impl/compilation_units/specials_single_3.cpp @@ -21,6 +21,6 @@ #include "../specials_single.hpp" -namespace nd4j { +namespace sd { BUILD_SINGLE_TEMPLATE(template class SpecialMethods, , LIBND4J_TYPES_3); } \ No newline at end of file diff --git a/libnd4j/include/ops/impl/compilation_units/specials_single_4.cpp b/libnd4j/include/ops/impl/compilation_units/specials_single_4.cpp index 6558d7284..b8ea2a933 100644 --- a/libnd4j/include/ops/impl/compilation_units/specials_single_4.cpp +++ b/libnd4j/include/ops/impl/compilation_units/specials_single_4.cpp @@ -21,6 +21,6 @@ #include "../specials_single.hpp" -namespace nd4j { +namespace sd { BUILD_SINGLE_TEMPLATE(template class SpecialMethods, , LIBND4J_TYPES_4); } \ No newline at end of file diff --git a/libnd4j/include/ops/impl/compilation_units/specials_single_5.cpp b/libnd4j/include/ops/impl/compilation_units/specials_single_5.cpp index d89652899..cc3fe3f0b 100644 --- a/libnd4j/include/ops/impl/compilation_units/specials_single_5.cpp +++ b/libnd4j/include/ops/impl/compilation_units/specials_single_5.cpp @@ -21,6 +21,6 @@ #include "../specials_single.hpp" -namespace nd4j { +namespace sd { BUILD_SINGLE_TEMPLATE(template class SpecialMethods, , LIBND4J_TYPES_5); } \ No newline at end of file diff --git a/libnd4j/include/ops/impl/compilation_units/specials_single_6.cpp b/libnd4j/include/ops/impl/compilation_units/specials_single_6.cpp index 40c9598ee..4e0b96a82 100644 --- a/libnd4j/include/ops/impl/compilation_units/specials_single_6.cpp +++ b/libnd4j/include/ops/impl/compilation_units/specials_single_6.cpp @@ -21,6 +21,6 @@ #include "../specials_single.hpp" -namespace nd4j { +namespace sd { BUILD_SINGLE_TEMPLATE(template class SpecialMethods, , LIBND4J_TYPES_6); } \ No newline at end of file diff --git a/libnd4j/include/ops/impl/compilation_units/specials_single_7.cpp b/libnd4j/include/ops/impl/compilation_units/specials_single_7.cpp index e49ace221..e8bd8d950 100644 --- a/libnd4j/include/ops/impl/compilation_units/specials_single_7.cpp +++ b/libnd4j/include/ops/impl/compilation_units/specials_single_7.cpp @@ -21,6 +21,6 @@ #include "../specials_single.hpp" -namespace nd4j { +namespace sd { BUILD_SINGLE_TEMPLATE(template class SpecialMethods, , LIBND4J_TYPES_7); } \ No newline at end of file diff --git a/libnd4j/include/ops/impl/compilation_units/specials_single_8.cpp b/libnd4j/include/ops/impl/compilation_units/specials_single_8.cpp index 973b25edc..b2581352e 100644 --- a/libnd4j/include/ops/impl/compilation_units/specials_single_8.cpp +++ b/libnd4j/include/ops/impl/compilation_units/specials_single_8.cpp @@ -21,6 +21,6 @@ #include "../specials_single.hpp" -namespace nd4j { +namespace sd { BUILD_SINGLE_TEMPLATE(template class SpecialMethods, , LIBND4J_TYPES_8); } \ No newline at end of file diff --git a/libnd4j/include/ops/impl/compilation_units/specials_single_9.cpp b/libnd4j/include/ops/impl/compilation_units/specials_single_9.cpp index b3bf0beeb..5105affa8 100644 --- a/libnd4j/include/ops/impl/compilation_units/specials_single_9.cpp +++ b/libnd4j/include/ops/impl/compilation_units/specials_single_9.cpp @@ -21,6 +21,6 @@ #include "../specials_single.hpp" -namespace nd4j { +namespace sd { BUILD_SINGLE_TEMPLATE(template class SpecialMethods, , LIBND4J_TYPES_9); } \ No newline at end of file diff --git a/libnd4j/include/ops/impl/gemm.cpp b/libnd4j/include/ops/impl/gemm.cpp index efd57a7c5..0c4ab167c 100644 --- a/libnd4j/include/ops/impl/gemm.cpp +++ b/libnd4j/include/ops/impl/gemm.cpp @@ -19,12 +19,12 @@ // Modified by GS on 3/9/2018 // -#include +#include #include -#include +#include #include -namespace nd4j { +namespace sd { namespace blas { template @@ -95,7 +95,7 @@ namespace nd4j { for (int k = 0; k < K; k++) { aIdx = (transAFlag ? linearIndexC(M, K, r, k) : linearIndexF(M, K, r, k)); bIdx = (transBFlag ? linearIndexC(K, N, k, c) : linearIndexF(K, N, k, c)); - dot += static_cast(alpha) * static_cast(A[aIdx]) * static_cast(B[bIdx]);//A[aIdx]nd4j::math::nd4j_dot(aX, bX, K) * alpha; + dot += static_cast(alpha) * static_cast(A[aIdx]) * static_cast(B[bIdx]);//A[aIdx]sd::math::nd4j_dot(aX, bX, K) * alpha; } } @@ -127,14 +127,14 @@ namespace nd4j { auto y = reinterpret_cast(vY); auto z = reinterpret_cast(vZ); - auto aT = TRANS == CblasTrans ? reinterpret_cast(nd4j::blas::transpose(CblasColMajor, CblasRowMajor, M, N, reinterpret_cast(x))) : x; + auto aT = TRANS == CblasTrans ? reinterpret_cast(sd::blas::transpose(CblasColMajor, CblasRowMajor, M, N, reinterpret_cast(x))) : x; auto func = PRAGMA_THREADS_FOR { for (auto r = start; r < stop; r++) { int aIdx = linearIndexC(M, N, r, 0); auto aX = aT + aIdx; - auto dot = nd4j::math::nd4j_dot(aX, y, lda) * static_cast(alpha); + auto dot = sd::math::nd4j_dot(aX, y, lda) * static_cast(alpha); z[r] = beta == 0.0f ? dot : dot + static_cast(beta) * z[r]; } }; diff --git a/libnd4j/include/ops/impl/specials_double.hpp b/libnd4j/include/ops/impl/specials_double.hpp index 73f50c772..96f7d2db2 100644 --- a/libnd4j/include/ops/impl/specials_double.hpp +++ b/libnd4j/include/ops/impl/specials_double.hpp @@ -20,17 +20,17 @@ // -#include +#include #include #include -#include -#include -#include +#include +#include +#include #include #include #include -namespace nd4j { +namespace sd { template diff --git a/libnd4j/include/ops/impl/specials_single.hpp b/libnd4j/include/ops/impl/specials_single.hpp index ad63ee490..ed86315f7 100644 --- a/libnd4j/include/ops/impl/specials_single.hpp +++ b/libnd4j/include/ops/impl/specials_single.hpp @@ -20,17 +20,17 @@ // -#include +#include #include #include -#include -#include -#include +#include +#include +#include #include #include #include -namespace nd4j { +namespace sd { /** * Concatneate multi array of the same shape together * along a particular dimension @@ -100,7 +100,7 @@ namespace nd4j { // auto func = PRAGMA_THREADS_FOR { // for (auto i = start; i < stop; i += increment) { // auto temp = output(indices[i], true); -// nd4j::TransformLoops::template loopTransform>( inArrs[i]->bufferAsT(), inArrs[i]->getShapeInfo(), temp.bufferAsT(), temp.getShapeInfo(), nullptr, 0, 1); +// sd::TransformLoops::template loopTransform>( inArrs[i]->bufferAsT(), inArrs[i]->getShapeInfo(), temp.bufferAsT(), temp.getShapeInfo(), nullptr, 0, 1); // } // }; @@ -108,7 +108,7 @@ namespace nd4j { // } template -void SpecialMethods::concatCpuGeneric(const std::vector& inArrs, NDArray& output, const int axis) { +void SpecialMethods::concatCpuGeneric(const std::vector& inArrs, NDArray& output, const int axis) { const int numOfInArrs = inArrs.size(); const auto sizeofT = output.sizeOfT(); @@ -136,52 +136,60 @@ void SpecialMethods::concatCpuGeneric(const std::vector& inArrs, ND return; } - const bool isZcontin = output.strideAt(axis) == 1 && output.ordering() == 'c'; - bool areInputsContin = true; - bool allSameOrder = true; + // const bool isZcontin = output.strideAt(axis) == 1; + // bool areInputsContin = true; + // bool allSameOrder = true; + // std::vector strideOfContigStride(numOfInArrs); - if(isZcontin) { - for (uint i = 0; i < numOfInArrs; ++i) { - areInputsContin &= inArrs[i]->strideAt(axis) == 1; - allSameOrder &= inArrs[i]->ordering() == output.ordering(); - if(!areInputsContin || !allSameOrder) - break; - } - } + // if(isZcontin) { - const bool luckCase2 = isZcontin && areInputsContin && allSameOrder; + // for (uint i = 0; i < numOfInArrs; ++i) { - if(luckCase2) { // for example {2,1,3} + {2,5,3} + {2,10,3} = {2,16,3}, here axis 1 shoud have stride = 1 for all inputs arrays and output array + // areInputsContin &= inArrs[i]->strideAt(axis) == 1; + // allSameOrder &= inArrs[i]->ordering() == output.ordering(); + // if(!areInputsContin || !allSameOrder) + // break; - const uint zDim = output.sizeAt(axis); + // strideOfContigStride[i] = shape::strideOverContigAxis(axis, inArrs[i]->getShapeInfo()); + // } + // } - for (uint i = 0; i < output.lengthOf() / zDim; ++i) { - T* z = zBuff + zDim * i; + // const bool luckCase2 = isZcontin && areInputsContin && allSameOrder; - for (uint j = 0; j < inArrs.size(); ++j) { - const auto xDim = inArrs[j]->sizeAt(axis); - const T* x = inArrs[j]->bufferAsT() + xDim * i; - memcpy(z, x, xDim * sizeofT); - z += xDim; - } - } + // if(luckCase2) { // for example {2,1,3} + {2,5,3} + {2,10,3} = {2,16,3}, here axis 1 shoud have stride = 1 for all inputs arrays and output array - return; - } + // const auto zStep = shape::strideOverContigAxis(axis, output.getShapeInfo()); + + // for (uint i = 0; i < output.lengthOf() / output.sizeAt(axis); ++i) { + + // T* z = zBuff + zStep * i; + + // for (uint j = 0; j < inArrs.size(); ++j) { + // const auto xDim = inArrs[j]->sizeAt(axis); + // const T* x = inArrs[j]->bufferAsT() + strideOfContigStride[j] * i; + // memcpy(z, x, xDim * sizeofT); + // z += xDim; + // } + // } + + // return; + // } // general case auto func = PRAGMA_THREADS_FOR { - Nd4jLong coords[MAX_RANK]; + int coords[MAX_RANK], temp; for (auto i = start; i < stop; i += increment) { - shape::index2coords(i, output.getShapeInfo(), coords); + shape::index2coordsCPU(start, i, output.getShapeInfo(), coords); + const auto zOffset = shape::getOffset(output.getShapeInfo(), coords); uint inArrIdx = 0; uint xDim = inArrs[inArrIdx]->sizeAt(axis); + temp = coords[axis]; while (coords[axis] >= xDim) { coords[axis] -= xDim; xDim = inArrs[++inArrIdx]->sizeAt(axis); @@ -191,6 +199,8 @@ void SpecialMethods::concatCpuGeneric(const std::vector& inArrs, ND const auto xOffset = shape::getOffset(inArrs[inArrIdx]->getShapeInfo(), coords); zBuff[zOffset] = x[xOffset]; + + coords[axis] = temp; } }; @@ -204,20 +214,121 @@ void SpecialMethods::concatCpuGeneric(const std::vector& inArrs, ND template void SpecialMethods::concatCpuGeneric(int dimension, int numArrays, Nd4jPointer *data, Nd4jPointer *inputShapeInfo, void *vresult, Nd4jLong *resultShapeInfo) { auto result = reinterpret_cast(vresult); - std::vector inputs(numArrays); + std::vector inputs(numArrays); NDArray output(static_cast(result), static_cast(resultShapeInfo)); for(int i = 0; i < numArrays; ++i) inputs[i] = new NDArray(static_cast(data[i]), static_cast(inputShapeInfo[i])); - nd4j::SpecialMethods::concatCpuGeneric(inputs, output, dimension); + sd::SpecialMethods::concatCpuGeneric(inputs, output, dimension); for(int i = 0; i < numArrays; ++i) delete inputs[i]; } +template +void SpecialMethods::splitCpuGeneric(const NDArray& input, const std::vector& outArrs, const int axis) { + + int numSplits = outArrs.size(); + + const auto sizeofT = input.sizeOfT(); + + T* xBuff = input.bufferAsT(); + + bool luckCase1 = ((axis == 0 && input.ordering() == 'c') || (axis == input.rankOf() - 1 && input.ordering() == 'f')) && input.ews() == 1; + + if (luckCase1) { + for (uint i = 0; i < numSplits; ++i) { + luckCase1 &= outArrs[i]->ordering() == input.ordering() && outArrs[i]->ews() == 1; + if (!luckCase1) + break; + } + } + + if (luckCase1) { + + T* x = const_cast(xBuff); + for (uint i = 0; i < numSplits; ++i) { + const auto memAmountToCopy = outArrs[i]->lengthOf(); + memcpy(outArrs[i]->bufferAsT(), x, memAmountToCopy * sizeofT); + x += memAmountToCopy; + } + return; + } + + // const bool isXcontin = input.strideAt(axis) == 1; + // bool areOutsContin = true; + // bool allSameOrder = true; + // std::vector strideOfContigStride(numSplits); + + // if (isXcontin) { + + // for (uint i = 0; i < numSplits; ++i) { + + // areOutsContin &= outArrs[i]->strideAt(axis) == 1; + // allSameOrder &= outArrs[i]->ordering() == input.ordering(); + // if (!areOutsContin || !allSameOrder) + // break; + + // strideOfContigStride[i] = shape::strideOverContigAxis(axis, outArrs[i]->getShapeInfo()); + // } + // } + + // const bool luckCase2 = isXcontin && areOutsContin && allSameOrder; + + // if (luckCase2) { + + // const auto xStep = shape::strideOverContigAxis(axis, input.getShapeInfo()); + + // for (uint i = 0; i < input.lengthOf() / input.sizeAt(axis); ++i) { + + // T* x = xBuff + xStep * i; + + // for (uint j = 0; j < numSplits; ++j) { + // const auto zDim = outArrs[j]->sizeAt(axis); + // T* z = outArrs[j]->bufferAsT() + strideOfContigStride[j] * i; + // memcpy(z, x, zDim * sizeofT); + // x += zDim; + // } + // } + + // return; + // } + + uint zDim = outArrs[0]->sizeAt(axis); + // general case + + auto func = PRAGMA_THREADS_FOR{ + + int coords[MAX_RANK], temp; + + for (auto i = start; i < stop; i += increment) { + + shape::index2coordsCPU(start, i, input.getShapeInfo(), coords); + const auto xOffset = shape::getOffset(input.getShapeInfo(), coords); + + uint outArrIdx = 0; + temp = coords[axis]; + + while (coords[axis] >= zDim) { + coords[axis] -= zDim; + ++outArrIdx; + } + + T* z = outArrs[outArrIdx]->bufferAsT(); + const auto zOffset = shape::getOffset(outArrs[outArrIdx]->getShapeInfo(), coords); + z[zOffset] = xBuff[xOffset]; + + coords[axis] = temp; + } + }; + + samediff::Threads::parallel_for(func, 0, input.lengthOf()); +} + + /** * This kernel accumulates X arrays, and stores result into Z * @@ -487,7 +598,7 @@ PRAGMA_OMP_SINGLE_ARGS(nowait) continue; T val = dx[e]; - T abs = nd4j::math::nd4j_abs(val); + T abs = sd::math::nd4j_abs(val); int bitId = e % 16; diff --git a/libnd4j/include/ops/impl/specials_sparse.cpp b/libnd4j/include/ops/impl/specials_sparse.cpp index 6a76c9209..c782ccf18 100644 --- a/libnd4j/include/ops/impl/specials_sparse.cpp +++ b/libnd4j/include/ops/impl/specials_sparse.cpp @@ -19,8 +19,8 @@ // #include -#include -#include +#include +#include #include #include #ifdef _OPENMP @@ -29,7 +29,7 @@ #include #include -namespace nd4j { +namespace sd { namespace sparse { template diff --git a/libnd4j/include/ops/meta_ops.h b/libnd4j/include/ops/meta_ops.h index 53be344b6..a2120477d 100644 --- a/libnd4j/include/ops/meta_ops.h +++ b/libnd4j/include/ops/meta_ops.h @@ -18,8 +18,8 @@ #ifndef FUSED_OPS_H_ #define FUSED_OPS_H_ -#include -#include +#include +#include #include diff --git a/libnd4j/include/ops/ops.h b/libnd4j/include/ops/ops.h index 1b54889d4..e49165e78 100644 --- a/libnd4j/include/ops/ops.h +++ b/libnd4j/include/ops/ops.h @@ -18,11 +18,11 @@ #ifndef OPS_H_ #define OPS_H_ -#include +#include #include #include #include -#include +#include #include #include @@ -228,23 +228,23 @@ namespace simdOps { op_def static Z op(X z, Y c) { auto zz = static_cast(z); auto zc = static_cast(c); - return (nd4j::math::nd4j_exp(c) - zz * zc + (zz * nd4j::math::nd4j_log(z) - zz + static_cast(0.5f) * nd4j::math::nd4j_log(static_cast(DOUBLE_PI_X) * zz))); + return (sd::math::nd4j_exp(c) - zz * zc + (zz * sd::math::nd4j_log(z) - zz + static_cast(0.5f) * sd::math::nd4j_log(static_cast(DOUBLE_PI_X) * zz))); } op_def static Z op(X z, Y c, Z *params) { auto zz = static_cast(z); auto zc = static_cast(c); - return (nd4j::math::nd4j_exp(c) - zz * zc + (zz * nd4j::math::nd4j_log(z) - zz + static_cast(0.5f) * nd4j::math::nd4j_log(static_cast(DOUBLE_PI_X) * zz))); + return (sd::math::nd4j_exp(c) - zz * zc + (zz * sd::math::nd4j_log(z) - zz + static_cast(0.5f) * sd::math::nd4j_log(static_cast(DOUBLE_PI_X) * zz))); } op_def static Z op(X z) { auto zz = static_cast(z); - return (zz * nd4j::math::nd4j_log(z) - zz + static_cast(0.5f) * nd4j::math::nd4j_log(static_cast(DOUBLE_PI_X) * zz)); + return (zz * sd::math::nd4j_log(z) - zz + static_cast(0.5f) * sd::math::nd4j_log(static_cast(DOUBLE_PI_X) * zz)); } // op for MetaOps op_def static X op(X z, Y *params) { - return (nd4j::math::nd4j_exp(params[0]) - z * params[0] + (z * nd4j::math::nd4j_log(z) - z + static_cast(0.5f) * nd4j::math::nd4j_log(DOUBLE_PI_X * z))); + return (sd::math::nd4j_exp(params[0]) - z * params[0] + (z * sd::math::nd4j_log(z) - z + static_cast(0.5f) * sd::math::nd4j_log(DOUBLE_PI_X * z))); } }; @@ -255,13 +255,13 @@ namespace simdOps { op_def static Z op(X z, Y c) { auto zz = static_cast(z); auto zc = static_cast(c); - return (nd4j::math::nd4j_exp(c) - zz * zc); + return (sd::math::nd4j_exp(c) - zz * zc); } op_def static Z op(X z, Y c, Z *params) { auto zz = static_cast(z); auto zc = static_cast(c); - return (nd4j::math::nd4j_exp(c) - zz * zc); + return (sd::math::nd4j_exp(c) - zz * zc); } op_def static Z op(X z) { @@ -270,7 +270,7 @@ namespace simdOps { // op for MetaOps op_def static Z op(X z, Y *params) { - return (nd4j::math::nd4j_exp(params[0]) - static_cast(z) * static_cast(params[0])); + return (sd::math::nd4j_exp(params[0]) - static_cast(z) * static_cast(params[0])); } }; @@ -383,20 +383,20 @@ namespace simdOps { class FloorDiv { public: op_def static Z op(X d1, Y d2) { - return nd4j::math::nd4j_floor(static_cast(d1 / d2)); + return sd::math::nd4j_floor(static_cast(d1 / d2)); } op_def static Z op(X d1, Y d2, Z *params) { - return nd4j::math::nd4j_floor(static_cast(d1 / d2)); + return sd::math::nd4j_floor(static_cast(d1 / d2)); } op_def static Z op(X d1) { - return nd4j::math::nd4j_floor(static_cast(d1)); + return sd::math::nd4j_floor(static_cast(d1)); } // op for MetaOps op_def static Z op(X d1, Y *params) { - return nd4j::math::nd4j_floor(static_cast(d1 / params[0])); + return sd::math::nd4j_floor(static_cast(d1 / params[0])); } }; @@ -458,11 +458,11 @@ namespace simdOps { class Remainder { public: op_def static Z op(X d1, Y d2) { - return nd4j::math::nd4j_remainder(d1, d2); + return sd::math::nd4j_remainder(d1, d2); } op_def static Z op(X d1, Y d2, Z *params) { - return nd4j::math::nd4j_remainder(d1, d2); + return sd::math::nd4j_remainder(d1, d2); } op_def static Z op(X d1) { @@ -471,7 +471,7 @@ namespace simdOps { // op for MetaOps op_def static Z op(X d1, Y *params) { - return nd4j::math::nd4j_remainder(d1, params[0]); + return sd::math::nd4j_remainder(d1, params[0]); } }; @@ -479,11 +479,11 @@ namespace simdOps { class FMod { public: op_def static Z op(X d1, Y d2) { - return nd4j::math::nd4j_fmod(d1, d2); + return sd::math::nd4j_fmod(d1, d2); } op_def static Z op(X d1, Y d2, Z *params) { - return nd4j::math::nd4j_fmod(d1, d2); + return sd::math::nd4j_fmod(d1, d2); } op_def static Z op(X d1) { @@ -492,7 +492,7 @@ namespace simdOps { // op for MetaOps op_def static Z op(X d1, Y *params) { - return nd4j::math::nd4j_fmod(d1, params[0]); + return sd::math::nd4j_fmod(d1, params[0]); } }; @@ -500,13 +500,13 @@ namespace simdOps { class FloorMod { public: op_def static Z op(X d1, Y d2) { - auto m = nd4j::math::nd4j_fmod(d1, d2); - return (d1 < static_cast(0)) == (d2 < static_cast(0)) ? m : nd4j::math::nd4j_fmod(m + static_cast(d2), d2); + auto m = sd::math::nd4j_fmod(d1, d2); + return (d1 < static_cast(0)) == (d2 < static_cast(0)) ? m : sd::math::nd4j_fmod(m + static_cast(d2), d2); } op_def static Z op(X d1, Y d2, Z *params) { - auto m = nd4j::math::nd4j_fmod(d1, d2); - return (d1 < static_cast(0.0f)) == (d2 < static_cast(0)) ? m : nd4j::math::nd4j_fmod(m + static_cast(d2), d2); + auto m = sd::math::nd4j_fmod(d1, d2); + return (d1 < static_cast(0.0f)) == (d2 < static_cast(0)) ? m : sd::math::nd4j_fmod(m + static_cast(d2), d2); } op_def static Z op(X d1) { @@ -722,7 +722,7 @@ namespace simdOps { public: op_def static X op(X d1, X d2) { - return d1 << d2 | d1 >> ((sizeof(X) * 8) - d2); + return sd::math::nd4j_rotl(d1, d2); } op_def static X op(X d1, X d2, X *params) { @@ -735,7 +735,7 @@ namespace simdOps { public: op_def static X op(X d1, X d2) { - return d1 >> d2 | d1 << ((sizeof(X) * 8) - d2); + return sd::math::nd4j_rotr(d1, d2); } op_def static X op(X d1, X d2, X *params) { @@ -969,7 +969,7 @@ namespace simdOps { op_def static Z op(X d1, X d2) { X diff = d1 - d2; - X absDiff = nd4j::math::nd4j_abs(diff); + X absDiff = sd::math::nd4j_abs(diff); if (absDiff <= static_cast(MIN_V)) return static_cast(1); return static_cast(0); @@ -1100,7 +1100,7 @@ namespace simdOps { no_op_exec_special_same_cuda op_def static X op(X d1, X *params) { - return nd4j::math::nd4j_abs(d1); + return sd::math::nd4j_abs(d1); } }; @@ -1112,7 +1112,7 @@ namespace simdOps { no_op_exec_special_same_cuda op_def static X op(X d1, X *params) { - return nd4j::math::nd4j_ceil(d1); + return sd::math::nd4j_ceil(d1); } }; @@ -1124,7 +1124,7 @@ namespace simdOps { no_op_exec_special_same_cuda op_def static X op(X d1, X *params) { - return nd4j::math::nd4j_cos(d1); + return sd::math::nd4j_cos(d1); } }; @@ -1136,7 +1136,7 @@ namespace simdOps { no_op_exec_special_same_cuda op_def static X op(X d1, X *params) { - return nd4j::math::nd4j_exp(d1); + return sd::math::nd4j_exp(d1); } }; @@ -1178,7 +1178,7 @@ namespace simdOps { no_op_exec_special_same_cuda op_def static X op(X d1, X *params) { - return nd4j::math::nd4j_floor(d1); + return sd::math::nd4j_floor(d1); } }; @@ -1190,7 +1190,7 @@ namespace simdOps { no_op_exec_special_same_cuda op_def static X op(X d1, X *params) { - return nd4j::math::nd4j_log(d1); + return sd::math::nd4j_log(d1); } }; @@ -1201,7 +1201,7 @@ namespace simdOps { no_op_exec_special_same_cuda op_def static X op(X d1, X *params) { - return nd4j::math::nd4j_log(1 + d1); + return sd::math::nd4j_log(1 + d1); } }; @@ -1210,7 +1210,7 @@ namespace simdOps { public: op_def static Z op(X d1, Y d2, Z *params) { - return nd4j::math::nd4j_log(d1) / nd4j::math::nd4j_log(d2) ; + return sd::math::nd4j_log(d1) / sd::math::nd4j_log(d2) ; } }; @@ -1222,7 +1222,7 @@ namespace simdOps { op_def static X op(X d1, X *params) { if (d1 <= static_cast(0)) - return static_cast(nd4j::DataTypeUtils::min()); + return static_cast(sd::DataTypeUtils::min()); else return d1; } }; @@ -1235,7 +1235,7 @@ namespace simdOps { op_def static X op(X d1, X *params) { if (d1 <= static_cast(0)) - return nd4j::DataTypeUtils::min(); + return sd::DataTypeUtils::min(); else return d1; } }; @@ -1270,7 +1270,7 @@ namespace simdOps { no_op_exec_special_same_cuda op_def static X op(X d1, X *params) { - return nd4j::math::nd4j_erf(d1); + return sd::math::nd4j_erf(d1); } }; @@ -1282,7 +1282,7 @@ namespace simdOps { no_op_exec_special_same_cuda op_def static X op(X d1, X *params) { - return nd4j::math::nd4j_erfc(d1); + return sd::math::nd4j_erfc(d1); } }; @@ -1307,11 +1307,11 @@ namespace simdOps { no_op_exec_special_cuda op_def static Z op(X d1, Z *params) { - return nd4j::math::nd4j_pow(d1, static_cast(2)); + return sd::math::nd4j_pow(d1, static_cast(2)); } op_def static Z op(X d1) { - return nd4j::math::nd4j_pow(d1, static_cast(2)); + return sd::math::nd4j_pow(d1, static_cast(2)); } }; @@ -1323,7 +1323,7 @@ namespace simdOps { no_op_exec_special_cuda op_def static Z op(X d1, Y d2) { - return nd4j::math::nd4j_re(d1, d2); + return sd::math::nd4j_re(d1, d2); } op_def static Z op(X d1, Y d2, Z *params) { @@ -1343,7 +1343,7 @@ namespace simdOps { op_def static Z op(X d1, Y d2, Z *params) { X threshold = params[0]; - return nd4j::math::nd4j_re(d1, d2) > threshold ? static_cast(1) : static_cast(0); + return sd::math::nd4j_re(d1, d2) > threshold ? static_cast(1) : static_cast(0); } op_def static Z op(X d1) { @@ -1361,13 +1361,13 @@ namespace simdOps { X d2 = params[0]; X thresholdRelative = params[1]; X thresholdAbsolute = params[2]; - return nd4j::math::nd4j_re(d1, d2) > thresholdRelative ? (nd4j::math::nd4j_abs(d1 - static_cast(d2)) < thresholdAbsolute ? static_cast(0) : static_cast(1)) : static_cast(0); + return sd::math::nd4j_re(d1, d2) > thresholdRelative ? (sd::math::nd4j_abs(d1 - static_cast(d2)) < thresholdAbsolute ? static_cast(0) : static_cast(1)) : static_cast(0); } op_def static Z op(X d1, Y d2, Z *params) { X thresholdRelative = params[0]; X thresholdAbsolute = params[1]; - return nd4j::math::nd4j_re(d1, d2) > thresholdRelative ? (nd4j::math::nd4j_abs(d1 - static_cast(d2)) < thresholdAbsolute ? static_cast(0) : static_cast(1)) : static_cast(0); + return sd::math::nd4j_re(d1, d2) > thresholdRelative ? (sd::math::nd4j_abs(d1 - static_cast(d2)) < thresholdAbsolute ? static_cast(0) : static_cast(1)) : static_cast(0); } op_def static Z op(X d1) { @@ -1382,15 +1382,15 @@ namespace simdOps { no_op_exec_special_cuda op_def static Z op(X d1, Z *params) { - return nd4j::math::nd4j_pow(params[0], d1); + return sd::math::nd4j_pow(params[0], d1); } op_def static Z op(X d1, Y d2) { - return nd4j::math::nd4j_pow(d2, d1); + return sd::math::nd4j_pow(d2, d1); } op_def static Z op(X d1, Y d2, Z *params) { - return nd4j::math::nd4j_pow(d2, d1); + return sd::math::nd4j_pow(d2, d1); } op_def static Z op(X d1) { @@ -1405,15 +1405,15 @@ namespace simdOps { no_op_exec_special_cuda op_def static Z op(X d1, Z *params) { - return nd4j::math::nd4j_pow(d1, params[0]); + return sd::math::nd4j_pow(d1, params[0]); } op_def static Z op(X d1, Y d2) { - return nd4j::math::nd4j_pow(d1, d2); + return sd::math::nd4j_pow(d1, d2); } op_def static Z op(X d1, Y d2, Z *params) { - return nd4j::math::nd4j_pow(d1, d2); + return sd::math::nd4j_pow(d1, d2); } op_def static Z op(X d1) { @@ -1429,15 +1429,15 @@ namespace simdOps { no_op_exec_special_cuda op_def static Z op(X d1, Z *params) { - return params[0] * nd4j::math::nd4j_pow(d1, static_cast(params[0]) - static_cast(1.f)); + return params[0] * sd::math::nd4j_pow(d1, static_cast(params[0]) - static_cast(1.f)); } op_def static Z op(X d1, Y d2) { - return static_cast(d2) * nd4j::math::nd4j_pow(d1, static_cast(d2) - static_cast(1.f)); + return static_cast(d2) * sd::math::nd4j_pow(d1, static_cast(d2) - static_cast(1.f)); } op_def static Z op(X d1, Y d2, Z *params) { - return static_cast(d2) * nd4j::math::nd4j_pow(d1, static_cast(d2) - static_cast(1.f)); + return static_cast(d2) * sd::math::nd4j_pow(d1, static_cast(d2) - static_cast(1.f)); } op_def static Z op(X d1) { @@ -1453,15 +1453,15 @@ namespace simdOps { no_op_exec_special_cuda op_def static Z op(X d1, Z *params) { - return nd4j::math::nd4j_igamma(d1, params[0]); + return sd::math::nd4j_igamma(d1, params[0]); } op_def static Z op(X d1, Y d2) { - return nd4j::math::nd4j_igamma(d1, d2); + return sd::math::nd4j_igamma(d1, d2); } op_def static Z op(X d1, Y d2, Z *params) { - return nd4j::math::nd4j_igamma(d1, d2); + return sd::math::nd4j_igamma(d1, d2); } op_def static Z op(X d1) { @@ -1476,15 +1476,15 @@ namespace simdOps { no_op_exec_special_cuda op_def static Z op(X d1, Z *params) { - return nd4j::math::nd4j_igammac(d1, params[0]); + return sd::math::nd4j_igammac(d1, params[0]); } op_def static Z op(X d1, Y d2) { - return nd4j::math::nd4j_igammac(d1, d2); + return sd::math::nd4j_igammac(d1, d2); } op_def static Z op(X d1, Y d2, Z *params) { - return nd4j::math::nd4j_igammac(d1, d2); + return sd::math::nd4j_igammac(d1, d2); } op_def static Z op(X d1) { @@ -1499,7 +1499,7 @@ namespace simdOps { no_op_exec_special_same_cuda op_def static X op(X d1, X *params) { - return nd4j::math::nd4j_round(d1); + return sd::math::nd4j_round(d1); } }; @@ -1513,7 +1513,7 @@ namespace simdOps { no_op_exec_special_accumulation_cuda op_def static Z op(X d1, X *params) { - return nd4j::math::nd4j_isnan(d1) ? static_cast(1) : static_cast(0); + return sd::math::nd4j_isnan(d1) ? static_cast(1) : static_cast(0); } op_def static X startingValue(const X *input) { @@ -1543,7 +1543,7 @@ namespace simdOps { no_op_exec_special_same_cuda op_def static X op(X d1, X *params) { - return nd4j::math::nd4j_exp(d1) - static_cast(1); + return sd::math::nd4j_exp(d1) - static_cast(1); } }; @@ -1617,7 +1617,7 @@ namespace simdOps { no_op_exec_special_accumulation_cuda op_def static Z op(X d1, X *params) { - return nd4j::math::nd4j_isinf(d1) ? static_cast(1) : static_cast(0); + return sd::math::nd4j_isinf(d1) ? static_cast(1) : static_cast(0); } op_def static X startingValue(const X *input) { @@ -1650,7 +1650,7 @@ namespace simdOps { no_op_exec_special_accumulation_cuda op_def static Z op(X d1, X *params) { - return nd4j::math::nd4j_isfin(d1) ? static_cast(0) : static_cast(1); + return sd::math::nd4j_isfin(d1) ? static_cast(0) : static_cast(1); } op_def static X startingValue(const X *input) { @@ -1683,7 +1683,7 @@ namespace simdOps { no_op_exec_special_accumulation_cuda op_def static Z op(X d1, X *params) { - return nd4j::math::nd4j_isfin(d1) ? static_cast(1) : static_cast(0); + return sd::math::nd4j_isfin(d1) ? static_cast(1) : static_cast(0); } op_def static X startingValue(const X *input) { @@ -1742,7 +1742,7 @@ namespace simdOps { no_op_exec_special_same_cuda op_def static X op(X d1, X *params) { - return d1 * nd4j::math::nd4j_sigmoid(d1); + return d1 * sd::math::nd4j_sigmoid(d1); } }; @@ -1753,7 +1753,7 @@ namespace simdOps { no_op_exec_special_same_cuda op_def static X op(X d1, X *params) { - return d1 * nd4j::math::nd4j_tanh(nd4j::math::nd4j_softplus(d1)); + return d1 * sd::math::nd4j_tanh(sd::math::nd4j_softplus(d1)); } }; @@ -1764,11 +1764,11 @@ namespace simdOps { no_op_exec_special_same_cuda op_def static X op(X d1, X *params) { - auto ex = nd4j::math::nd4j_exp(d1); + auto ex = sd::math::nd4j_exp(d1); auto e2x = ex * ex; auto e3x = ex * ex * ex; - return (ex * (4 * (d1 + 1) + 4 * e2x + e3x + ex *(4 * d1 + 6))) / nd4j::math::nd4j_pow((2 * ex + e2x + 2), (X) 2.f); + return (ex * (4 * (d1 + 1) + 4 * e2x + e3x + ex *(4 * d1 + 6))) / sd::math::nd4j_pow((2 * ex + e2x + 2), (X) 2.f); } }; @@ -1779,7 +1779,7 @@ namespace simdOps { no_op_exec_special_same_cuda op_def static X op(X d1, X *params) { - return d1 * nd4j::math::nd4j_sigmoid(static_cast(1.702f) * d1); + return d1 * sd::math::nd4j_sigmoid(static_cast(1.702f) * d1); } }; @@ -1790,9 +1790,9 @@ namespace simdOps { no_op_exec_special_same_cuda op_def static X op(X d1, X *params) { - auto sp = nd4j::math::nd4j_sqrt(static_cast(2) / static_cast(M_PI)); - auto xp = d1 + nd4j::math::nd4j_pow(static_cast(0.044715) * d1, static_cast(3)); - return (d1 / static_cast(2)) * (static_cast(1) + nd4j::math::nd4j_tanh(sp * xp)); + auto sp = sd::math::nd4j_sqrt(static_cast(2) / static_cast(M_PI)); + auto xp = d1 + sd::math::nd4j_pow(static_cast(0.044715) * d1, static_cast(3)); + return (d1 / static_cast(2)) * (static_cast(1) + sd::math::nd4j_tanh(sp * xp)); } }; @@ -1804,9 +1804,9 @@ namespace simdOps { op_def static X op(X d1, X *params) { auto x17 = static_cast(1.702f) * d1; - auto ep = nd4j::math::nd4j_pow(static_cast(M_E), x17); + auto ep = sd::math::nd4j_pow(static_cast(M_E), x17); // (E^(1.702 x) (1. + E^(1.702 x) + 1.702 x))/(1. + E^(1.702 x))^2 - return (ep * (static_cast(1.f) + ep + x17)) / nd4j::math::nd4j_pow((static_cast(1.f) + ep), 2); + return (ep * (static_cast(1.f) + ep + x17)) / sd::math::nd4j_pow((static_cast(1.f) + ep), 2); } }; @@ -1818,12 +1818,12 @@ namespace simdOps { op_def static X op(X d1, X *params) { auto x79 = static_cast(0.797885) * d1; - auto x03 = nd4j::math::nd4j_pow(static_cast(0.0356774) * d1, 3); + auto x03 = sd::math::nd4j_pow(static_cast(0.0356774) * d1, 3); auto x39 = static_cast(0.398942) * d1; - auto x05 = nd4j::math::nd4j_pow(static_cast(0.0535161) * d1, 3); - auto scz = nd4j::math::nd4j_sech(x79 + x03); + auto x05 = sd::math::nd4j_pow(static_cast(0.0535161) * d1, 3); + auto scz = sd::math::nd4j_sech(x79 + x03); // 0.5 + (0.398942 x + 0.0535161 x^3) Sech[0.797885 x + 0.0356774 x^3]^2 + 0.5 Tanh[0.797885 x + 0.0356774 x^3] - return static_cast(0.5) + (x39 + x05) * (scz * scz) + static_cast(0.5) * nd4j::math::nd4j_tanh(x79 + x03); + return static_cast(0.5) + (x39 + x05) * (scz * scz) + static_cast(0.5) * sd::math::nd4j_tanh(x79 + x03); } }; @@ -1835,8 +1835,8 @@ namespace simdOps { no_op_exec_special_same_cuda op_def static X op(X d1, X *params) { - X ex = nd4j::math::nd4j_pow(static_cast(M_E), d1); - return (ex * (d1 + ex + static_cast(1.f))) / nd4j::math::nd4j_pow((ex + static_cast(1.f)) , static_cast(2.f)); + X ex = sd::math::nd4j_pow(static_cast(M_E), d1); + return (ex * (d1 + ex + static_cast(1.f))) / sd::math::nd4j_pow((ex + static_cast(1.f)) , static_cast(2.f)); } }; @@ -1848,7 +1848,7 @@ namespace simdOps { no_op_exec_special_same_cuda op_def static X op(X d1, X *params) { - return nd4j::math::nd4j_log(nd4j::math::nd4j_sigmoid(d1)); + return sd::math::nd4j_log(sd::math::nd4j_sigmoid(d1)); } }; @@ -1859,7 +1859,7 @@ namespace simdOps { no_op_exec_special_same_cuda op_def static X op(X d1, X *params) { - X ex = nd4j::math::nd4j_pow(M_E, d1); + X ex = sd::math::nd4j_pow(M_E, d1); return static_cast(1.f) / (ex + static_cast(1.f)); } }; @@ -1871,7 +1871,7 @@ namespace simdOps { no_op_exec_special_same_cuda op_def static X op(X d1, X *params) { - return nd4j::math::nd4j_sigmoid(d1); + return sd::math::nd4j_sigmoid(d1); } }; @@ -1893,7 +1893,7 @@ namespace simdOps { no_op_exec_special_same_cuda op_def static X op(X d1, X *params) { - return nd4j::math::nd4j_sigmoidderivative(d1); + return sd::math::nd4j_sigmoidderivative(d1); } }; @@ -1905,7 +1905,7 @@ namespace simdOps { no_op_exec_special_same_cuda op_def static X op(X d1, X *params) { - return nd4j::math::nd4j_min(static_cast(1), nd4j::math::nd4j_max(static_cast(0), (static_cast(0.2f)) * d1 + static_cast(0.5f))); + return sd::math::nd4j_min(static_cast(1), sd::math::nd4j_max(static_cast(0), (static_cast(0.2f)) * d1 + static_cast(0.5f))); } }; @@ -1936,11 +1936,11 @@ namespace simdOps { if (static_cast(d1) >= min && static_cast(d1) <= max) return d1; if (min == static_cast(0) && max == static_cast(1)) { - auto val = static_cast(1) / (static_cast(1) + nd4j::math::nd4j_exp(-d1)); - return (nd4j::math::nd4j_floor(val * (max - min)) + min); + auto val = static_cast(1) / (static_cast(1) + sd::math::nd4j_exp(-d1)); + return (sd::math::nd4j_floor(val * (max - min)) + min); } - return (nd4j::math::nd4j_floor(d1 * (max - min)) + min); + return (sd::math::nd4j_floor(d1 * (max - min)) + min); } }; @@ -1952,7 +1952,7 @@ namespace simdOps { no_op_exec_special_same_cuda op_def static X op(X d1, X *params) { - return nd4j::math::nd4j_sin(d1); + return sd::math::nd4j_sin(d1); } }; @@ -1974,7 +1974,7 @@ namespace simdOps { no_op_exec_special_cuda op_def static Z op(X d1, Z *params) { - return nd4j::math::nd4j_sqrt(d1); + return sd::math::nd4j_sqrt(d1); } }; @@ -1985,7 +1985,7 @@ namespace simdOps { no_op_exec_special_cuda op_def static Z op(X d1, Z *params) { - return static_cast(1) / nd4j::math::nd4j_sqrt(d1); + return static_cast(1) / sd::math::nd4j_sqrt(d1); } }; @@ -1996,7 +1996,7 @@ namespace simdOps { no_op_exec_special_same_cuda op_def static X op(X d1, X *params) { - return nd4j::math::nd4j_rint(d1); + return sd::math::nd4j_rint(d1); } }; @@ -2008,7 +2008,7 @@ namespace simdOps { no_op_exec_special_same_cuda op_def static X op(X d1, X *params) { - return nd4j::math::nd4j_softplus(d1); + return sd::math::nd4j_softplus(d1); } }; @@ -2047,7 +2047,7 @@ namespace simdOps { // keep 2/3 as runtime variable, to match precision auto dis = (static_cast(2) / static_cast(3)) * d1; - auto tanh = nd4j::math::nd4j_sgn(dis) * (static_cast(1) - (static_cast(1) / (static_cast(1) + static_cast(nd4j::math::nd4j_abs(dis)) + nd4j::math::nd4j_pow(dis, static_cast(2)) + static_cast(1.41645f) * nd4j::math::nd4j_pow(dis, static_cast(4)) ))); + auto tanh = sd::math::nd4j_sgn(dis) * (static_cast(1) - (static_cast(1) / (static_cast(1) + static_cast(sd::math::nd4j_abs(dis)) + sd::math::nd4j_pow(dis, static_cast(2)) + static_cast(1.41645f) * sd::math::nd4j_pow(dis, static_cast(4)) ))); return static_cast(1.7159f) * tanh; } }; @@ -2061,9 +2061,9 @@ namespace simdOps { op_def static X op(X d1, X *params) { auto dis = (static_cast(2.f) / static_cast(3.f)) * d1; - auto a = static_cast(1.f) + nd4j::math::nd4j_abs(dis) + nd4j::math::nd4j_pow(dis, static_cast(2.f)) + static_cast(1.41645f) * nd4j::math::nd4j_pow(dis, static_cast(4)); + auto a = static_cast(1.f) + sd::math::nd4j_abs(dis) + sd::math::nd4j_pow(dis, static_cast(2.f)) + static_cast(1.41645f) * sd::math::nd4j_pow(dis, static_cast(4)); - auto tDeriv = (static_cast(1.f) + nd4j::math::nd4j_sign(dis) * (static_cast(2.f) * dis + static_cast(4.f) * static_cast(1.41645f) * nd4j::math::nd4j_pow(dis, static_cast(3)))) / (a * a); + auto tDeriv = (static_cast(1.f) + sd::math::nd4j_sign(dis) * (static_cast(2.f) * dis + static_cast(4.f) * static_cast(1.41645f) * sd::math::nd4j_pow(dis, static_cast(3)))) / (a * a); return static_cast(1.7159f) * (static_cast(2.f) / static_cast(3.f)) * tDeriv; } @@ -2076,7 +2076,7 @@ namespace simdOps { no_op_exec_special_same_cuda op_def static X op(X d1, X *params) { - return nd4j::math::nd4j_tanh(d1); + return sd::math::nd4j_tanh(d1); } }; @@ -2087,7 +2087,7 @@ namespace simdOps { no_op_exec_special_same_cuda op_def static X op(X d1, X *params) { - return params[0] * nd4j::math::nd4j_tanh(params[1] * d1); + return params[0] * sd::math::nd4j_tanh(params[1] * d1); } }; @@ -2098,7 +2098,7 @@ namespace simdOps { no_op_exec_special_same_cuda op_def static X op(X d1, X *params) { - return nd4j::math::nd4j_max(static_cast(0), nd4j::math::nd4j_tanh(d1)); + return sd::math::nd4j_max(static_cast(0), sd::math::nd4j_tanh(d1)); } }; @@ -2109,7 +2109,7 @@ namespace simdOps { no_op_exec_special_same_cuda op_def static X op(X d1, X *params) { - return d1 > static_cast(0.f) ? nd4j::math::nd4j_tanhderivative(d1) : static_cast(0.f); + return d1 > static_cast(0.f) ? sd::math::nd4j_tanhderivative(d1) : static_cast(0.f); } }; @@ -2120,7 +2120,7 @@ namespace simdOps { no_op_exec_special_same_cuda op_def static X op(X d1, X *params) { - return nd4j::math::nd4j_atanh(d1); + return sd::math::nd4j_atanh(d1); } }; @@ -2131,7 +2131,7 @@ namespace simdOps { no_op_exec_special_same_cuda op_def static X op(X d1, X *params) { - return nd4j::math::nd4j_tanhderivative(d1); + return sd::math::nd4j_tanhderivative(d1); } }; @@ -2165,7 +2165,7 @@ namespace simdOps { no_op_exec_special_same_cuda op_def static X op(X d1, X *params) { - return nd4j::math::nd4j_acos(d1); + return sd::math::nd4j_acos(d1); } }; @@ -2176,7 +2176,7 @@ namespace simdOps { no_op_exec_special_same_cuda op_def static X op(X d1, X *params) { - return nd4j::math::nd4j_asinh(d1); + return sd::math::nd4j_asinh(d1); } }; @@ -2187,7 +2187,7 @@ namespace simdOps { no_op_exec_special_same_cuda op_def static X op(X d1, X *params) { - return static_cast(1.f) / (nd4j::math::nd4j_sqrt(nd4j::math::nd4j_pow(d1, static_cast(2.f)) + static_cast(1.f))); + return static_cast(1.f) / (sd::math::nd4j_sqrt(sd::math::nd4j_pow(d1, static_cast(2.f)) + static_cast(1.f))); } }; @@ -2198,7 +2198,7 @@ namespace simdOps { no_op_exec_special_same_cuda op_def static X op(X d1, X *params) { - return nd4j::math::nd4j_acosh(d1); + return sd::math::nd4j_acosh(d1); } }; @@ -2210,7 +2210,7 @@ namespace simdOps { no_op_exec_special_same_cuda op_def static X op(X d1, X *params) { - return static_cast(1.f) / (nd4j::math::nd4j_sqrt(d1 - static_cast(1.f)) * nd4j::math::nd4j_sqrt(d1 + static_cast(1.f))); + return static_cast(1.f) / (sd::math::nd4j_sqrt(d1 - static_cast(1.f)) * sd::math::nd4j_sqrt(d1 + static_cast(1.f))); } }; @@ -2236,7 +2236,7 @@ namespace simdOps { no_op_exec_special_same_cuda op_def static X op(X d1, X *params) { - return nd4j::math::nd4j_softsign(d1); + return sd::math::nd4j_softsign(d1); } }; @@ -2248,7 +2248,7 @@ namespace simdOps { no_op_exec_special_same_cuda op_def static X op(X d1, X *params) { - return nd4j::math::nd4j_softsignderivative(d1); + return sd::math::nd4j_softsignderivative(d1); } }; @@ -2268,9 +2268,9 @@ namespace simdOps { switch (mode) { case 0: // equals - return nd4j::math::nd4j_abs(d1 - compare) <= eps ? true : false; + return sd::math::nd4j_abs(d1 - compare) <= eps ? true : false; case 1: // not equals - return nd4j::math::nd4j_abs(d1 - compare) > eps ? true : false; + return sd::math::nd4j_abs(d1 - compare) > eps ? true : false; case 2: // less_than return d1 < compare ? true : false; case 3: // greater_than @@ -2280,27 +2280,27 @@ namespace simdOps { case 5: // greater_or_equals_than return d1 >= compare ? true : false; case 6: // abs_less_than - return nd4j::math::nd4j_abs(d1) < compare ? true : false; + return sd::math::nd4j_abs(d1) < compare ? true : false; case 7: // abs_greater_than - return nd4j::math::nd4j_abs(d1) > compare ? true : false; + return sd::math::nd4j_abs(d1) > compare ? true : false; case 8: // is inf - return nd4j::math::nd4j_isinf(d1) ? true : false; + return sd::math::nd4j_isinf(d1) ? true : false; case 9: // is nan - return nd4j::math::nd4j_isnan(d1) ? true : false; + return sd::math::nd4j_isnan(d1) ? true : false; case 10: return (d1 == compare) ? true : false; case 11: return (d1 != compare) ? true : false; case 12: // abs_greater_or_equals_than - return nd4j::math::nd4j_abs(d1) >= compare ? true : false; + return sd::math::nd4j_abs(d1) >= compare ? true : false; case 13: // abs_less_or_equals_than - return nd4j::math::nd4j_abs(d1) <= compare ? true : false; + return sd::math::nd4j_abs(d1) <= compare ? true : false; case 14: // isFinite - return !(nd4j::math::nd4j_isinf(d1) || nd4j::math::nd4j_isnan(d1)); + return !(sd::math::nd4j_isinf(d1) || sd::math::nd4j_isnan(d1)); case 15: // isInfinite - return nd4j::math::nd4j_isinf(d1) || nd4j::math::nd4j_isnan(d1); + return sd::math::nd4j_isinf(d1) || sd::math::nd4j_isnan(d1); default: printf("Undefined match condition: [%i]\n", mode); } @@ -2333,9 +2333,9 @@ namespace simdOps { op_def static Z op(X d1, X compare, X eps, int mode) { switch (mode) { case 0: // equals - return nd4j::math::nd4j_abs(d1 - compare) <= eps ? 1 : 0; + return sd::math::nd4j_abs(d1 - compare) <= eps ? 1 : 0; case 1: // not equals - return nd4j::math::nd4j_abs(d1 - compare) > eps ? 1 : 0; + return sd::math::nd4j_abs(d1 - compare) > eps ? 1 : 0; case 2: // less_than return d1 < compare ? 1 : 0; case 3: // greater_than @@ -2345,27 +2345,27 @@ namespace simdOps { case 5: // greater_or_equals_than return d1 >= compare ? 1 : 0; case 6: // abs_less_than - return nd4j::math::nd4j_abs(d1) < compare ? 1 : 0; + return sd::math::nd4j_abs(d1) < compare ? 1 : 0; case 7: // abs_greater_than - return nd4j::math::nd4j_abs(d1) > compare ? 1 : 0; + return sd::math::nd4j_abs(d1) > compare ? 1 : 0; case 8: // is inf - return nd4j::math::nd4j_isinf(d1) ? 1 : 0; + return sd::math::nd4j_isinf(d1) ? 1 : 0; case 9: // is nan - return nd4j::math::nd4j_isnan(d1) ? 1 : 0; + return sd::math::nd4j_isnan(d1) ? 1 : 0; case 10: return (d1 == compare) ? 1 : 0; case 11: return (d1 != compare) ? 1 : 0; case 12: // abs_greater_or_equals_than - return nd4j::math::nd4j_abs(d1) >= compare ? 1 : 0; + return sd::math::nd4j_abs(d1) >= compare ? 1 : 0; case 13: // abs_less_or_equals_than - return nd4j::math::nd4j_abs(d1) <= compare ? 1 : 0; + return sd::math::nd4j_abs(d1) <= compare ? 1 : 0; case 14: // isFinite - return !(nd4j::math::nd4j_isinf(d1) || nd4j::math::nd4j_isnan(d1)) ? 1 : 0; + return !(sd::math::nd4j_isinf(d1) || sd::math::nd4j_isnan(d1)) ? 1 : 0; case 15: // isInfinite - return nd4j::math::nd4j_isinf(d1) || nd4j::math::nd4j_isnan(d1) ? 1 : 0; + return sd::math::nd4j_isinf(d1) || sd::math::nd4j_isnan(d1) ? 1 : 0; default: printf("Undefined match condition: [%i]\n", mode); } @@ -2404,7 +2404,7 @@ namespace simdOps { no_op_exec_special_same_cuda op_def static Z op(X d1, Y d2, Z *params) { - return nd4j::math::nd4j_elu(d1, static_cast(d2)); + return sd::math::nd4j_elu(d1, static_cast(d2)); } }; @@ -2416,7 +2416,7 @@ namespace simdOps { no_op_exec_special_same_cuda op_def static Z op(X d1, Y d2, Z *params) { - return nd4j::math::nd4j_eluderivative(d1, static_cast(d2)); + return sd::math::nd4j_eluderivative(d1, static_cast(d2)); } }; @@ -2474,7 +2474,7 @@ namespace simdOps { no_op_exec_special_same_cuda op_def static X op(X d1, X *params) { - return d1 > static_cast(0.0f) ? static_cast(SELU_LAMBDA) * static_cast(d1) : static_cast(SELU_LAMBDA) * (static_cast(SELU_ALPHA) * nd4j::math::nd4j_exp(d1) - static_cast(SELU_ALPHA)); + return d1 > static_cast(0.0f) ? static_cast(SELU_LAMBDA) * static_cast(d1) : static_cast(SELU_LAMBDA) * (static_cast(SELU_ALPHA) * sd::math::nd4j_exp(d1) - static_cast(SELU_ALPHA)); } }; @@ -2485,7 +2485,7 @@ namespace simdOps { no_op_exec_special_same_cuda op_def static X op(X d1, X *params) { - return d1 > static_cast(0.f) ? static_cast(SELU_LAMBDA) : static_cast(SELU_ALPHA) * static_cast(SELU_LAMBDA) * nd4j::math::nd4j_exp(d1); + return d1 > static_cast(0.f) ? static_cast(SELU_LAMBDA) : static_cast(SELU_ALPHA) * static_cast(SELU_LAMBDA) * sd::math::nd4j_exp(d1); } }; @@ -2511,7 +2511,7 @@ namespace simdOps { no_op_exec_special_same_cuda op_def static X op(X d1, X *params) { - return nd4j::math::nd4j_asin(d1); + return sd::math::nd4j_asin(d1); } }; @@ -2522,7 +2522,7 @@ namespace simdOps { no_op_exec_special_same_cuda op_def static X op(X d1, X *params) { - return nd4j::math::nd4j_sinh(d1); + return sd::math::nd4j_sinh(d1); } }; @@ -2533,7 +2533,7 @@ namespace simdOps { no_op_exec_special_same_cuda op_def static X op(X d1, X *params) { - return nd4j::math::nd4j_cosh(d1); + return sd::math::nd4j_cosh(d1); } }; @@ -2544,7 +2544,7 @@ namespace simdOps { no_op_exec_special_same_cuda op_def static X op(X d1, X *params) { - return nd4j::math::nd4j_cosh(d1); + return sd::math::nd4j_cosh(d1); } }; @@ -2556,7 +2556,7 @@ namespace simdOps { no_op_exec_special_same_cuda op_def static X op(X d1, X *params) { - return nd4j::math::nd4j_tan(d1); + return sd::math::nd4j_tan(d1); } }; @@ -2567,7 +2567,7 @@ namespace simdOps { no_op_exec_special_same_cuda op_def static X op(X d1, X *params) { - return static_cast(1.f) / nd4j::math::nd4j_pow(nd4j::math::nd4j_cos(d1), static_cast(2.0f)); + return static_cast(1.f) / sd::math::nd4j_pow(sd::math::nd4j_cos(d1), static_cast(2.0f)); } }; @@ -2578,7 +2578,7 @@ namespace simdOps { no_op_exec_special_same_cuda op_def static X op(X d1, X *params) { - return nd4j::math::nd4j_atan(d1); + return sd::math::nd4j_atan(d1); } }; @@ -2589,7 +2589,7 @@ namespace simdOps { no_op_exec_special_cuda op_def static Z op(X d1, Y d2) { - return nd4j::math::nd4j_atan2(d2, d1); + return sd::math::nd4j_atan2(d2, d1); } op_def static Z op(X d1, Y d2, Z *params) { @@ -2706,11 +2706,11 @@ namespace simdOps { op_def static X op(X d1, X *extraParams) { auto f1 = static_cast(d1); - return static_cast(nd4j::math::nd4j_pow(f1, 3) - + nd4j::math::nd4j_log(f1) * nd4j::math::nd4j_sin(f1) - / nd4j::math::nd4j_tanh(static_cast(M_E) * static_cast(M_PI) * f1) - * nd4j::math::nd4j_sqrt(static_cast(M_PI) / f1) - - nd4j::math::nd4j_atan(static_cast(M_E) / f1)); + return static_cast(sd::math::nd4j_pow(f1, 3) + + sd::math::nd4j_log(f1) * sd::math::nd4j_sin(f1) + / sd::math::nd4j_tanh(static_cast(M_E) * static_cast(M_PI) * f1) + * sd::math::nd4j_sqrt(static_cast(M_PI) / f1) + - sd::math::nd4j_atan(static_cast(M_E) / f1)); } op_def static X postProcess(X reduction, Nd4jLong n, X *extraParams) { @@ -2741,7 +2741,7 @@ namespace simdOps { op_def static Z op(X d1, Z *extraParams) { auto p = d1 * d1; - return static_cast(p) * nd4j::math::nd4j_log(p); + return static_cast(p) * sd::math::nd4j_log(p); } op_def static Z postProcess(Z reduction, Nd4jLong n, Z *extraParams) { @@ -2771,12 +2771,12 @@ namespace simdOps { } op_def static Z op(X d1, Z *extraParams) { - return static_cast(d1) * nd4j::math::nd4j_log(d1); + return static_cast(d1) * sd::math::nd4j_log(d1); } op_def static Z postProcess(Z reduction, Nd4jLong n, Z *extraParams) { //entropy is -sum(p(x) * log(p(x))); log entropy is log of this - return nd4j::math::nd4j_log(-reduction); + return sd::math::nd4j_log(-reduction); } }; @@ -2801,7 +2801,7 @@ namespace simdOps { } op_def static Z op(X d1, Z *extraParams) { - return static_cast(d1) * nd4j::math::nd4j_log(d1); + return static_cast(d1) * sd::math::nd4j_log(d1); } op_def static Z postProcess(Z reduction, Nd4jLong n, Z *extraParams) { @@ -2823,19 +2823,19 @@ namespace simdOps { } op_def static X merge(X old, X opOutput, X *extraParams) { - return nd4j::math::nd4j_abs(opOutput) + nd4j::math::nd4j_abs(old); + return sd::math::nd4j_abs(opOutput) + sd::math::nd4j_abs(old); } op_def static X update(X old, X opOutput, X *extraParams) { - return nd4j::math::nd4j_abs(opOutput) + nd4j::math::nd4j_abs(old); + return sd::math::nd4j_abs(opOutput) + sd::math::nd4j_abs(old); } op_def static X op(X d1, X *extraParams) { - return nd4j::math::nd4j_abs(d1); + return sd::math::nd4j_abs(d1); } op_def static X postProcess(X reduction, Nd4jLong n, X *extraParams) { - return nd4j::math::nd4j_abs(reduction); + return sd::math::nd4j_abs(reduction); } }; @@ -3039,11 +3039,11 @@ namespace simdOps { op_def static Z op(X d1, Z *extraParams) { auto f1 = static_cast(d1); - return static_cast(nd4j::math::nd4j_pow(f1, 3) - + nd4j::math::nd4j_log(f1) * nd4j::math::nd4j_sin(f1) - / nd4j::math::nd4j_tanh(static_cast(M_E) * static_cast(M_PI) * f1) - * nd4j::math::nd4j_sqrt(static_cast(M_PI) / f1) - - nd4j::math::nd4j_atan(static_cast(M_E) / f1)); + return static_cast(sd::math::nd4j_pow(f1, 3) + + sd::math::nd4j_log(f1) * sd::math::nd4j_sin(f1) + / sd::math::nd4j_tanh(static_cast(M_E) * static_cast(M_PI) * f1) + * sd::math::nd4j_sqrt(static_cast(M_PI) / f1) + - sd::math::nd4j_atan(static_cast(M_E) / f1)); } op_def static Z postProcess(Z reduction, Nd4jLong n, Z *extraParams) { @@ -3065,7 +3065,7 @@ namespace simdOps { } op_def static Z merge(Z old, Z opOutput, Z *extraParams) { - return nd4j::math::nd4j_abs(opOutput) + nd4j::math::nd4j_abs(old); + return sd::math::nd4j_abs(opOutput) + sd::math::nd4j_abs(old); } op_def static Z update(Z old, Z opOutput, Z *extraParams) { @@ -3073,11 +3073,11 @@ namespace simdOps { } op_def static Z op(X d1, Z *extraParams) { - return nd4j::math::nd4j_abs(d1); + return sd::math::nd4j_abs(d1); } op_def static Z postProcess(Z reduction, Nd4jLong n, Z *extraParams) { - return nd4j::math::nd4j_abs(reduction) / static_cast(n); + return sd::math::nd4j_abs(reduction) / static_cast(n); } }; @@ -3090,23 +3090,23 @@ namespace simdOps { const static functions::ReduceType reduceType = functions::ReduceType::MAX; op_def static X startingValue(const X *input) { - return -nd4j::DataTypeUtils::infOrMax(); + return -sd::DataTypeUtils::infOrMax(); } op_def static X merge(X old, X opOutput, X *extraParams) { - return nd4j::math::nd4j_max(old, opOutput); + return sd::math::nd4j_max(old, opOutput); } op_def static X update(X old, X opOutput, X *extraParams) { - return nd4j::math::nd4j_max(opOutput, old); + return sd::math::nd4j_max(opOutput, old); } op_def static X op(X d1, X d2, X *params) { - return nd4j::math::nd4j_max(d1, d2); + return sd::math::nd4j_max(d1, d2); } op_def static X op(X d1, X d2) { - return nd4j::math::nd4j_max(d1, d2); + return sd::math::nd4j_max(d1, d2); } // FIXME: this signature overlaps with MetaOp @@ -3131,7 +3131,7 @@ namespace simdOps { auto z1 = static_cast(d1); auto z2 = static_cast(d2); - if (nd4j::math::nd4j_abs(z1) > nd4j::math::nd4j_abs(z2)) + if (sd::math::nd4j_abs(z1) > sd::math::nd4j_abs(z2)) return z1; else return z2; @@ -3150,7 +3150,7 @@ namespace simdOps { auto z1 = static_cast(d1); auto z2 = static_cast(d2); - if (nd4j::math::nd4j_abs(z1) < nd4j::math::nd4j_abs(z2)) + if (sd::math::nd4j_abs(z1) < sd::math::nd4j_abs(z2)) return z1; else return z2; @@ -3161,11 +3161,11 @@ namespace simdOps { class MaxPairwise { public: op_def static Z op(X d1, Y d2, Z *params) { - return nd4j::math::nd4j_max(static_cast(d1), static_cast(d2)); + return sd::math::nd4j_max(static_cast(d1), static_cast(d2)); } op_def static Z op(X d1, Y d2) { - return nd4j::math::nd4j_max(static_cast(d1), static_cast(d2)); + return sd::math::nd4j_max(static_cast(d1), static_cast(d2)); } }; @@ -3174,11 +3174,11 @@ namespace simdOps { class MinPairwise { public: op_def static Z op(X d1, Y d2, Z *params) { - return nd4j::math::nd4j_min(static_cast(d1), static_cast(d2)); + return sd::math::nd4j_min(static_cast(d1), static_cast(d2)); } op_def static Z op(X d1, Y d2) { - return nd4j::math::nd4j_min(static_cast(d1), static_cast(d2)); + return sd::math::nd4j_min(static_cast(d1), static_cast(d2)); } }; @@ -3195,28 +3195,28 @@ namespace simdOps { } op_def static X merge(X old, X opOutput, X *extraParams) { - return nd4j::math::nd4j_max(nd4j::math::nd4j_abs(old), nd4j::math::nd4j_abs(opOutput)); + return sd::math::nd4j_max(sd::math::nd4j_abs(old), sd::math::nd4j_abs(opOutput)); } op_def static X update(X old, X opOutput, X *extraParams) { - return nd4j::math::nd4j_max(nd4j::math::nd4j_abs(opOutput), nd4j::math::nd4j_abs(old)); + return sd::math::nd4j_max(sd::math::nd4j_abs(opOutput), sd::math::nd4j_abs(old)); } op_def static X op(X d1, X d2, X *params) { - return nd4j::math::nd4j_max(nd4j::math::nd4j_abs(d1), nd4j::math::nd4j_abs(d2)); + return sd::math::nd4j_max(sd::math::nd4j_abs(d1), sd::math::nd4j_abs(d2)); } op_def static X op(X d1, X d2) { - return nd4j::math::nd4j_abs(d1) > nd4j::math::nd4j_abs(d2) ? d1 : d2; + return sd::math::nd4j_abs(d1) > sd::math::nd4j_abs(d2) ? d1 : d2; } // FIXME: this signature overlaps with MetaOp op_def static X op(X d1, X *extraParams) { - return nd4j::math::nd4j_abs(d1); + return sd::math::nd4j_abs(d1); } op_def static X postProcess(X reduction, Nd4jLong n, X *extraParams) { - return nd4j::math::nd4j_abs(reduction); + return sd::math::nd4j_abs(reduction); } }; @@ -3234,28 +3234,28 @@ namespace simdOps { } op_def static X merge(X old, X opOutput, X *extraParams) { - return nd4j::math::nd4j_min(nd4j::math::nd4j_abs(old), nd4j::math::nd4j_abs(opOutput)); + return sd::math::nd4j_min(sd::math::nd4j_abs(old), sd::math::nd4j_abs(opOutput)); } op_def static X update(X old, X opOutput, X *extraParams) { - return nd4j::math::nd4j_min(nd4j::math::nd4j_abs(opOutput), nd4j::math::nd4j_abs(old)); + return sd::math::nd4j_min(sd::math::nd4j_abs(opOutput), sd::math::nd4j_abs(old)); } op_def static X op(X d1, X d2, X *params) { - return nd4j::math::nd4j_min(nd4j::math::nd4j_abs(d1), nd4j::math::nd4j_abs(d2)); + return sd::math::nd4j_min(sd::math::nd4j_abs(d1), sd::math::nd4j_abs(d2)); } op_def static X op(X d1, X d2) { - return nd4j::math::nd4j_min(nd4j::math::nd4j_abs(d1), nd4j::math::nd4j_abs(d2)); + return sd::math::nd4j_min(sd::math::nd4j_abs(d1), sd::math::nd4j_abs(d2)); } // FIXME: this signature overlaps with MetaOp op_def static X op(X d1, X *extraParams) { - return nd4j::math::nd4j_abs(d1); + return sd::math::nd4j_abs(d1); } op_def static X postProcess(X reduction, Nd4jLong n, X *extraParams) { - return nd4j::math::nd4j_abs(reduction); + return sd::math::nd4j_abs(reduction); } }; @@ -3268,23 +3268,23 @@ namespace simdOps { const static functions::ReduceType reduceType = functions::ReduceType::MIN; op_def static X startingValue(const X *input) { - return nd4j::DataTypeUtils::infOrMax(); + return sd::DataTypeUtils::infOrMax(); } op_def static X merge(X old, X opOutput, X *extraParams) { - return nd4j::math::nd4j_min(old, opOutput); + return sd::math::nd4j_min(old, opOutput); } op_def static X update(X old, X opOutput, X *extraParams) { - return nd4j::math::nd4j_min(opOutput, old); + return sd::math::nd4j_min(opOutput, old); } op_def static X op(X d1, X d2, X *params) { - return nd4j::math::nd4j_min(d1, d2); + return sd::math::nd4j_min(d1, d2); } op_def static X op(X d1, X d2) { - return nd4j::math::nd4j_min(d1, d2); + return sd::math::nd4j_min(d1, d2); } // FIXME: this signature overlaps with MetaOp @@ -3321,7 +3321,7 @@ namespace simdOps { } op_def static Z op(X d1, Z *extraParams) { - return static_cast(nd4j::math::nd4j_abs(d1)); + return static_cast(sd::math::nd4j_abs(d1)); } op_def static Z postProcess(Z reduction, Nd4jLong n, Z *extraParams) { @@ -3353,7 +3353,7 @@ namespace simdOps { op_def static Z postProcess(Z reduction, Nd4jLong n, Z *extraParams) { - return nd4j::math::nd4j_sqrt(reduction); + return sd::math::nd4j_sqrt(reduction); } op_def static Z op(X d1, Z *extraParams) { @@ -3413,12 +3413,12 @@ namespace simdOps { } op_def static Z op(X d1, Z *extraParams) { - X v = nd4j::math::nd4j_abs(d1); + X v = sd::math::nd4j_abs(d1); return static_cast(v * v); } op_def static Z postProcess(Z reduction, Nd4jLong n, Z *extraParams) { - return nd4j::math::nd4j_sqrt(reduction); + return sd::math::nd4j_sqrt(reduction); } }; @@ -3443,11 +3443,11 @@ namespace simdOps { } op_def static Z op(X d1, Z *extraParams) { - return nd4j::math::nd4j_pow(nd4j::math::nd4j_abs(d1), extraParams[0]); + return sd::math::nd4j_pow(sd::math::nd4j_abs(d1), extraParams[0]); } op_def static Z postProcess(Z reduction, Nd4jLong n, Z *extraParams) { - return nd4j::math::nd4j_pow(reduction, static_cast(1.0f) / extraParams[0]); + return sd::math::nd4j_pow(reduction, static_cast(1.0f) / extraParams[0]); } }; @@ -3469,8 +3469,8 @@ namespace simdOps { } op_def static Z update(Z old, Z opOutput, Z *extraParams) { - return nd4j::math::nd4j_max(nd4j::math::nd4j_abs(old), - nd4j::math::nd4j_abs(opOutput)); + return sd::math::nd4j_max(sd::math::nd4j_abs(old), + sd::math::nd4j_abs(opOutput)); } op_def static Z op(X d1, Z *extraParams) { @@ -3478,7 +3478,7 @@ namespace simdOps { } op_def static Z postProcess(Z reduction, Nd4jLong n, Z *extraParams) { - return nd4j::math::nd4j_max(nd4j::math::nd4j_abs(reduction), nd4j::math::nd4j_abs(reduction)); + return sd::math::nd4j_max(sd::math::nd4j_abs(reduction), sd::math::nd4j_abs(reduction)); } }; @@ -3511,7 +3511,7 @@ namespace simdOps { op_def static Z postProcess(X reduction, Nd4jLong n, Z *extraParams) { // T bias = extraParams[1]; - // return (reduction - (nd4j::math::nd4j_pow(bias, static_cast(2.0f)) / static_cast(n))) / (n - 1) + // return (reduction - (sd::math::nd4j_pow(bias, static_cast(2.0f)) / static_cast(n))) / (n - 1) return static_cast(reduction) / static_cast(n - 1); } }; @@ -3548,7 +3548,7 @@ namespace simdOps { op_def static Z postProcess(X reduction, Nd4jLong n, Z *extraParams) { Z ret = Variance::postProcess(reduction, n, extraParams); - Z sqrtRet = nd4j::math::nd4j_sqrt(ret); + Z sqrtRet = sd::math::nd4j_sqrt(ret); return sqrtRet; } }; @@ -3572,7 +3572,7 @@ namespace simdOps { } op_def static Y postProcess(Y reduction, Nd4jLong n, Y *extraParams) { - return reduction / (nd4j::math::nd4j_sqrt(extraParams[0]) * nd4j::math::nd4j_sqrt(extraParams[1])); + return reduction / (sd::math::nd4j_sqrt(extraParams[0]) * sd::math::nd4j_sqrt(extraParams[1])); } op_def static Y op(X d1, X d2, Y *extraParams) { @@ -3588,8 +3588,8 @@ namespace simdOps { #ifdef __CUDACC__ static _CUDA_D inline Y opAtomic(X d1, X d2, Y *extraParams) { - nd4j::math::atomics::nd4j_atomicAdd(&extraParams[0],static_cast(d1 * d1)); - nd4j::math::atomics::nd4j_atomicAdd(&extraParams[1],static_cast(d2 * d2)); + sd::math::atomics::nd4j_atomicAdd(&extraParams[0],static_cast(d1 * d1)); + sd::math::atomics::nd4j_atomicAdd(&extraParams[1],static_cast(d2 * d2)); return static_cast(d1 * d2); } @@ -3630,11 +3630,11 @@ namespace simdOps { } op_def static Y num(X d1, X d2) { - return nd4j::math::nd4j_min(d1, d2); + return sd::math::nd4j_min(d1, d2); } op_def static Y denom(X d1, X d2) { - return nd4j::math::nd4j_max(d1, d2); + return sd::math::nd4j_max(d1, d2); } op_def static Y op(X d1, X d2, Y *extraParams) { @@ -3651,8 +3651,8 @@ namespace simdOps { #ifdef __CUDACC__ __device__ static inline Y opAtomic(X d1, X d2, Y *extraParams) { - nd4j::math::atomics::nd4j_atomicAdd(&extraParams[0],num(d1, d2)); - nd4j::math::atomics::nd4j_atomicAdd(&extraParams[1], denom(d1, d2)); + sd::math::atomics::nd4j_atomicAdd(&extraParams[0],num(d1, d2)); + sd::math::atomics::nd4j_atomicAdd(&extraParams[1], denom(d1, d2)); return static_cast(0.0f); } @@ -3735,12 +3735,12 @@ namespace simdOps { } op_def static Y postProcess(Y reduction, Nd4jLong n, Y *extraParams) { - return (static_cast(1.0f)) - (reduction / (nd4j::math::nd4j_sqrt(extraParams[0]) * nd4j::math::nd4j_sqrt(extraParams[1]))); + return (static_cast(1.0f)) - (reduction / (sd::math::nd4j_sqrt(extraParams[0]) * sd::math::nd4j_sqrt(extraParams[1]))); } op_def static Y op(X d1, X d2, Y *extraParams) { - extraParams[0] += static_cast(nd4j::math::nd4j_abs(d1) * nd4j::math::nd4j_abs(d1)); - extraParams[1] += static_cast(nd4j::math::nd4j_abs(d2) * nd4j::math::nd4j_abs(d2)); + extraParams[0] += static_cast(sd::math::nd4j_abs(d1) * sd::math::nd4j_abs(d1)); + extraParams[1] += static_cast(sd::math::nd4j_abs(d2) * sd::math::nd4j_abs(d2)); return (d1 * d2); } @@ -3751,8 +3751,8 @@ namespace simdOps { #ifdef __CUDACC__ static _CUDA_D inline Y opAtomic(X d1, X d2, Y *extraParams) { - nd4j::math::atomics::nd4j_atomicAdd(&extraParams[0], nd4j::math::nd4j_abs(d1) * nd4j::math::nd4j_abs(d1)); - nd4j::math::atomics::nd4j_atomicAdd(&extraParams[1], nd4j::math::nd4j_abs(d2) * nd4j::math::nd4j_abs(d2)); + sd::math::atomics::nd4j_atomicAdd(&extraParams[0], sd::math::nd4j_abs(d1) * sd::math::nd4j_abs(d1)); + sd::math::atomics::nd4j_atomicAdd(&extraParams[1], sd::math::nd4j_abs(d2) * sd::math::nd4j_abs(d2)); return (d1 * d2); } @@ -3843,8 +3843,8 @@ namespace simdOps { } op_def static Z op(X d1, X d2, Z *extraParamsRef) { - double eps = nd4j::math::nd4j_abs(extraParamsRef[2]); - return static_cast(!nd4j::math::nd4j_eq(d1, d2, eps)); + double eps = sd::math::nd4j_abs(extraParamsRef[2]); + return static_cast(!sd::math::nd4j_eq(d1, d2, eps)); } @@ -3886,7 +3886,7 @@ namespace simdOps { } op_def static Y postProcess(Y reduction, Nd4jLong n, Y *extraParamsRef) { - return nd4j::math::nd4j_sqrt(reduction); + return sd::math::nd4j_sqrt(reduction); } op_def static Y op(X d1, X d2, Y *extraParamsRef) { @@ -3936,7 +3936,7 @@ namespace simdOps { } op_def static Y op(X d1, X d2, Y *extraParamsRef) { - return nd4j::math::nd4j_abs(d1 - d2); + return sd::math::nd4j_abs(d1 - d2); } op_def static Y update(Y old, Y opOutput, Y *extraParamsRef) { @@ -3968,12 +3968,12 @@ namespace simdOps { class IndexAbsoluteMax { public: static _CUDA_HD inline functions::indexreduce::IndexValue op(functions::indexreduce::IndexValue val, X *extraParams) { - return nd4j::math::nd4j_abs(val); + return sd::math::nd4j_abs(val); } static _CUDA_HD inline functions::indexreduce::IndexValue update(functions::indexreduce::IndexValue &old, functions::indexreduce::IndexValue &opOutput, X *extraParams) { - opOutput.value = nd4j::math::nd4j_abs(opOutput.value); - old.value = nd4j::math::nd4j_abs(old.value); + opOutput.value = sd::math::nd4j_abs(opOutput.value); + old.value = sd::math::nd4j_abs(old.value); if (opOutput.value > old.value) return opOutput; #ifdef __CUDACC__ @@ -3989,7 +3989,7 @@ namespace simdOps { static _CUDA_HD inline functions::indexreduce::IndexValue merge( functions::indexreduce::IndexValue f1, functions::indexreduce::IndexValue f2, X *extraParams) { - if (nd4j::math::nd4j_abs(f1.value) > nd4j::math::nd4j_abs(f2.value)) + if (sd::math::nd4j_abs(f1.value) > sd::math::nd4j_abs(f2.value)) return f2; return f1; } @@ -4048,7 +4048,7 @@ namespace simdOps { } static _CUDA_HD inline X startingValue(const X *input) { - return -nd4j::DataTypeUtils::infOrMax(); + return -sd::DataTypeUtils::infOrMax(); } static _CUDA_HD inline functions::indexreduce::IndexValue startingIndexValue(X *input) { @@ -4107,7 +4107,7 @@ namespace simdOps { } static _CUDA_HD inline X startingValue(const X *input) { - return -nd4j::DataTypeUtils::infOrMax(); + return -sd::DataTypeUtils::infOrMax(); } static _CUDA_HD inline functions::indexreduce::IndexValue startingIndexValue(X *input) { @@ -4175,7 +4175,7 @@ namespace simdOps { } static _CUDA_HD inline X startingValue(const X *input) { - return -nd4j::DataTypeUtils::infOrMax(); + return -sd::DataTypeUtils::infOrMax(); } static _CUDA_HD inline functions::indexreduce::IndexValue startingIndexValue(X *input) { @@ -4201,7 +4201,7 @@ namespace simdOps { } static _CUDA_HD inline X startingValue(const X *input) { - return nd4j::DataTypeUtils::infOrMax(); + return sd::DataTypeUtils::infOrMax(); } static _CUDA_HD inline functions::indexreduce::IndexValue startingIndexValue(X *input) { @@ -4212,8 +4212,8 @@ namespace simdOps { } static _CUDA_HD inline functions::indexreduce::IndexValue update(functions::indexreduce::IndexValue &old, functions::indexreduce::IndexValue &opOutput, X *extraParams) { - opOutput.value = nd4j::math::nd4j_abs(opOutput.value); - old.value = nd4j::math::nd4j_abs(old.value); + opOutput.value = sd::math::nd4j_abs(opOutput.value); + old.value = sd::math::nd4j_abs(old.value); if (opOutput.value < old.value) return opOutput; @@ -4230,7 +4230,7 @@ namespace simdOps { static _CUDA_HD inline functions::indexreduce::IndexValue merge( functions::indexreduce::IndexValue f1, functions::indexreduce::IndexValue f2, X *extraParams) { - if (nd4j::math::nd4j_abs(f1.value) < nd4j::math::nd4j_abs(f2.value)) + if (sd::math::nd4j_abs(f1.value) < sd::math::nd4j_abs(f2.value)) return f2; return f1; } @@ -4257,7 +4257,7 @@ namespace simdOps { } static _CUDA_HD inline X startingValue(const X *input) { - return nd4j::DataTypeUtils::infOrMax(); + return sd::DataTypeUtils::infOrMax(); } static _CUDA_HD inline functions::indexreduce::IndexValue startingIndexValue(X *input) { @@ -4328,11 +4328,11 @@ namespace simdOps { if (biasCorrected) { auto ret = static_cast(val.varianceBiasCorrected()); if (ret < static_cast(0.0f)) - return nd4j::math::nd4j_sqrt(val.variance()); + return sd::math::nd4j_sqrt(val.variance()); else - return nd4j::math::nd4j_sqrt(ret); + return sd::math::nd4j_sqrt(ret); } - return nd4j::math::nd4j_sqrt(val.variance()); + return sd::math::nd4j_sqrt(val.variance()); } static _CUDA_HD inline functions::summarystats::SummaryStatsData op(functions::summarystats::SummaryStatsData d1, Z *extraParams) { @@ -4352,7 +4352,7 @@ namespace simdOps { #ifdef __CUDACC__ X length = params[1]; X tid = blockIdx.x * blockDim.x + threadIdx.x; - X rnd = nd4j::math::nd4j_abs(nd4j::math::nd4j_cos(static_cast(clock64()) * static_cast(tid) + static_cast(length) * static_cast(tid))); + X rnd = sd::math::nd4j_abs(sd::math::nd4j_cos(static_cast(clock64()) * static_cast(tid) + static_cast(length) * static_cast(tid))); #else X rnd = static_cast(rand() / RAND_MAX); #endif @@ -4374,7 +4374,7 @@ namespace simdOps { #ifdef __CUDACC__ X length = params[1]; X tid = blockIdx.x * blockDim.x + threadIdx.x; - X rnd = nd4j::math::nd4j_abs(nd4j::math::nd4j_cos(static_cast(clock64()) * static_cast(tid) + static_cast(length) * static_cast(tid))); + X rnd = sd::math::nd4j_abs(sd::math::nd4j_cos(static_cast(clock64()) * static_cast(tid) + static_cast(length) * static_cast(tid))); #else X rnd = static_cast(rand() / RAND_MAX); #endif @@ -4390,7 +4390,7 @@ namespace simdOps { no_op_exec_special_cuda op_def static Z op(X d1, Y d2, Z *params) { - return nd4j::math::nd4j_isnan(d1) ? static_cast(d2) : static_cast(d1) ; + return sd::math::nd4j_isnan(d1) ? static_cast(d2) : static_cast(d1) ; } }; @@ -4406,12 +4406,12 @@ namespace simdOps { auto eps = params[2]; int mode = (int) params[3]; if (mode == 0) // equals - if (nd4j::math::nd4j_abs(zd1 - compare) <= eps) + if (sd::math::nd4j_abs(zd1 - compare) <= eps) return zd2; else return zd1; else if (mode == 1) // not equals eps - if (nd4j::math::nd4j_abs(zd1 - compare) > eps) + if (sd::math::nd4j_abs(zd1 - compare) > eps) return zd2; else return zd1; @@ -4436,22 +4436,22 @@ namespace simdOps { else return zd1; else if (mode == 6) // abs_less_than - if (nd4j::math::nd4j_abs(zd1) < compare) + if (sd::math::nd4j_abs(zd1) < compare) return zd2; else return zd1; else if (mode == 7) // abs_greater_than - if (nd4j::math::nd4j_abs(zd1) > compare) + if (sd::math::nd4j_abs(zd1) > compare) return zd2; else return zd1; else if (mode == 8) // is inf - if (nd4j::math::nd4j_isinf(zd1)) + if (sd::math::nd4j_isinf(zd1)) return zd2; else return zd1; else if (mode == 9) // is nan - if (nd4j::math::nd4j_isnan(zd1)) + if (sd::math::nd4j_isnan(zd1)) return zd2; else return zd1; @@ -4466,12 +4466,12 @@ namespace simdOps { else return zd1; else if (mode == 12) // abs_greater_or_equals_than - if (nd4j::math::nd4j_abs(zd1) >= compare) + if (sd::math::nd4j_abs(zd1) >= compare) return zd2; else return zd1; else if (mode == 13) // abs_less_or_equals_than - if (nd4j::math::nd4j_abs(zd1) <= compare) + if (sd::math::nd4j_abs(zd1) <= compare) return zd2; else return zd1; @@ -4494,12 +4494,12 @@ namespace simdOps { auto eps = params[2]; auto mode = static_cast(params[3]); if (mode == 0) // equals - if (nd4j::math::nd4j_abs(d2 - compare) <= eps) + if (sd::math::nd4j_abs(d2 - compare) <= eps) return d2; else return d1; else if (mode == 1) // not equals - if (nd4j::math::nd4j_abs(d2 - compare) > eps) + if (sd::math::nd4j_abs(d2 - compare) > eps) return d2; else return d1; @@ -4524,22 +4524,22 @@ namespace simdOps { else return d1; else if (mode == 6) // abs_less_than - if (nd4j::math::nd4j_abs(d2) < compare) + if (sd::math::nd4j_abs(d2) < compare) return d2; else return d1; else if (mode == 7) // abs_greater_than - if (nd4j::math::nd4j_abs(d2) > compare) + if (sd::math::nd4j_abs(d2) > compare) return d2; else return d1; else if (mode == 8) // is inf - if (nd4j::math::nd4j_isinf(d2)) + if (sd::math::nd4j_isinf(d2)) return d2; else return d1; else if (mode == 9) // is nan - if (nd4j::math::nd4j_isnan(d2)) + if (sd::math::nd4j_isnan(d2)) return d2; else return d1; @@ -4554,12 +4554,12 @@ namespace simdOps { else return d1; else if (mode == 12) // abs_greater_or_equals_than - if (nd4j::math::nd4j_abs(d1) >= compare) + if (sd::math::nd4j_abs(d1) >= compare) return d2; else return d1; else if (mode == 13) // abs_less_or_equals_than - if (nd4j::math::nd4j_abs(d1) <= compare) + if (sd::math::nd4j_abs(d1) <= compare) return d2; else return d1; @@ -4585,17 +4585,17 @@ namespace simdOps { // with mode == 0 we do set if d1 equals to compare, and with mode == 1 - we go otherwise int mode = (int) params[3]; if (mode == 0) // equals - if (nd4j::math::nd4j_abs(d1 - compare) <= eps) + if (sd::math::nd4j_abs(d1 - compare) <= eps) return set; else return d1; - //return nd4j::math::nd4j_abs(d1 - compare) <= eps ? set : d1; + //return sd::math::nd4j_abs(d1 - compare) <= eps ? set : d1; else if (mode == 1) // not equals - if (nd4j::math::nd4j_abs(d1 - compare) > eps) + if (sd::math::nd4j_abs(d1 - compare) > eps) return set; else return d1; - //return nd4j::math::nd4j_abs(d1 - compare) > eps ? set : d1; + //return sd::math::nd4j_abs(d1 - compare) > eps ? set : d1; else if (mode == 2) // less_than if (d1 < compare) return set; @@ -4617,22 +4617,22 @@ namespace simdOps { else return d1; else if (mode == 6) // abs_less_than - if (nd4j::math::nd4j_abs(d1) < compare) + if (sd::math::nd4j_abs(d1) < compare) return set; else return d1; else if (mode == 7) // abs_greater_than - if (nd4j::math::nd4j_abs(d1) > compare) + if (sd::math::nd4j_abs(d1) > compare) return set; else return d1; else if (mode == 8) // is inf - if (nd4j::math::nd4j_isinf(d1)) + if (sd::math::nd4j_isinf(d1)) return set; else return d1; else if (mode == 9) // is nan - if (nd4j::math::nd4j_isnan(d1)) + if (sd::math::nd4j_isnan(d1)) return set; else return d1; @@ -4647,12 +4647,12 @@ namespace simdOps { else return d1; else if (mode == 12) // abs_greater_or_equals_than - if (nd4j::math::nd4j_abs(d1) >= compare) + if (sd::math::nd4j_abs(d1) >= compare) return set; else return d1; else if (mode == 13) // abs_less_or_equals_than - if (nd4j::math::nd4j_abs(d1) <= compare) + if (sd::math::nd4j_abs(d1) <= compare) return set; else return d1; diff --git a/libnd4j/include/ops/random_ops.h b/libnd4j/include/ops/random_ops.h index 8eb25c84c..844f88ed3 100644 --- a/libnd4j/include/ops/random_ops.h +++ b/libnd4j/include/ops/random_ops.h @@ -28,9 +28,9 @@ #endif // since we can't inherit/overwrite static methods - we just define default impls -#define method_idx random_def T op(Nd4jLong idx, Nd4jLong length, nd4j::graph::RandomGenerator* rng, T *extraParams) { return -1.0f; } -#define method_X random_def T op(T valueX, Nd4jLong idx, Nd4jLong length, nd4j::graph::RandomGenerator* rng, T *extraParams) { return -2.0f; } -#define method_XY random_def T op(T valueX, T valueY, Nd4jLong idx, Nd4jLong length, nd4j::graph::RandomGenerator* rng, T *extraParams) { return -3.0f; } +#define method_idx random_def T op(Nd4jLong idx, Nd4jLong length, sd::graph::RandomGenerator* rng, T *extraParams) { return -1.0f; } +#define method_X random_def T op(T valueX, Nd4jLong idx, Nd4jLong length, sd::graph::RandomGenerator* rng, T *extraParams) { return -2.0f; } +#define method_XY random_def T op(T valueX, T valueY, Nd4jLong idx, Nd4jLong length, sd::graph::RandomGenerator* rng, T *extraParams) { return -3.0f; } #define no_exec_special static const bool requiresSpecial = false; static inline void specialOp(Nd4jPointer state, T *x, Nd4jLong *xShapeBuffer, T *y, Nd4jLong *yShapeBuffer, T *z, Nd4jLong *zShapeBuffer, T *extraArguments) { } @@ -59,7 +59,7 @@ namespace randomOps { method_idx method_X - random_def T op(T valueX, T valueY, Nd4jLong idx, Nd4jLong length, nd4j::graph::RandomGenerator *helper, T *extraParams) { + random_def T op(T valueX, T valueY, Nd4jLong idx, Nd4jLong length, sd::graph::RandomGenerator *helper, T *extraParams) { T threshold = extraParams[0]; T randVal = helper->relativeT(idx); @@ -80,7 +80,7 @@ namespace randomOps { method_XY method_X - random_def T op(Nd4jLong idx, Nd4jLong length, nd4j::graph::RandomGenerator *helper, T *extraParams) { + random_def T op(Nd4jLong idx, Nd4jLong length, sd::graph::RandomGenerator *helper, T *extraParams) { return helper->relativeT(idx, extraParams[0], extraParams[1]); } }; @@ -96,11 +96,11 @@ namespace randomOps { method_XY - random_def T op(Nd4jLong idx, Nd4jLong length, nd4j::graph::RandomGenerator *helper, T *extraParams) { + random_def T op(Nd4jLong idx, Nd4jLong length, sd::graph::RandomGenerator *helper, T *extraParams) { return extraParams[0] >= helper->relativeT(idx) ? (T) 1.0f : (T) 0.0f; } - random_def T op(T valueX, Nd4jLong idx, Nd4jLong length, nd4j::graph::RandomGenerator *helper, T *extraParams) { + random_def T op(T valueX, Nd4jLong idx, Nd4jLong length, sd::graph::RandomGenerator *helper, T *extraParams) { return valueX >= helper->relativeT(idx) ? (T) 1.0f : (T) 0.0f; } }; @@ -117,15 +117,15 @@ namespace randomOps { method_XY - random_def T op(Nd4jLong idx, Nd4jLong length, nd4j::graph::RandomGenerator *helper, T *extraParams) { + random_def T op(Nd4jLong idx, Nd4jLong length, sd::graph::RandomGenerator *helper, T *extraParams) { T lambda = extraParams[0]; - T x = helper->relativeT(idx, -nd4j::DataTypeUtils::template max() / 10 , nd4j::DataTypeUtils::template max() / 10); - return x <= (T)0.f ? (T)0.f : (T)1.f - nd4j::math::nd4j_pow((T) M_E, -(lambda * x)); + T x = helper->relativeT(idx, -sd::DataTypeUtils::template max() / 10 , sd::DataTypeUtils::template max() / 10); + return x <= (T)0.f ? (T)0.f : (T)1.f - sd::math::nd4j_pow((T) M_E, -(lambda * x)); } - random_def T op(T valueX, Nd4jLong idx, Nd4jLong length, nd4j::graph::RandomGenerator *helper, T *extraParams) { + random_def T op(T valueX, Nd4jLong idx, Nd4jLong length, sd::graph::RandomGenerator *helper, T *extraParams) { T lambda = extraParams[0]; - return valueX <= (T)0.f ? (T)0.f : (T)1.f - nd4j::math::nd4j_pow((T) M_E, -(lambda * valueX)); + return valueX <= (T)0.f ? (T)0.f : (T)1.f - sd::math::nd4j_pow((T) M_E, -(lambda * valueX)); } }; @@ -137,15 +137,15 @@ namespace randomOps { method_XY - random_def T op(Nd4jLong idx, Nd4jLong length, nd4j::graph::RandomGenerator *helper, T *extraParams) { + random_def T op(Nd4jLong idx, Nd4jLong length, sd::graph::RandomGenerator *helper, T *extraParams) { T lambda = extraParams[0]; - T x = helper->relativeT(idx, -nd4j::DataTypeUtils::template max() / 10 , nd4j::DataTypeUtils::template max() / 10); - return x <= (T)0.f ? (T)0.f : nd4j::math::nd4j_igammac(nd4j::math::nd4j_floor(x), lambda); + T x = helper->relativeT(idx, -sd::DataTypeUtils::template max() / 10 , sd::DataTypeUtils::template max() / 10); + return x <= (T)0.f ? (T)0.f : sd::math::nd4j_igammac(sd::math::nd4j_floor(x), lambda); } - random_def T op(T valueX, Nd4jLong idx, Nd4jLong length, nd4j::graph::RandomGenerator *helper, T *extraParams) { + random_def T op(T valueX, Nd4jLong idx, Nd4jLong length, sd::graph::RandomGenerator *helper, T *extraParams) { T lambda = extraParams[0]; - return valueX <= (T)0.f ? (T)0.f : (T)nd4j::math::nd4j_igammac(nd4j::math::nd4j_floor(valueX), lambda); + return valueX <= (T)0.f ? (T)0.f : (T)sd::math::nd4j_igammac(sd::math::nd4j_floor(valueX), lambda); } }; @@ -157,17 +157,17 @@ namespace randomOps { method_XY - random_def T op(Nd4jLong idx, Nd4jLong length, nd4j::graph::RandomGenerator *helper, T *extraParams) { + random_def T op(Nd4jLong idx, Nd4jLong length, sd::graph::RandomGenerator *helper, T *extraParams) { T alpha = extraParams[0]; T beta = extraParams[1]; - T x = helper->relativeT(idx, -nd4j::DataTypeUtils::template max() / 10 , nd4j::DataTypeUtils::template max() / 10); - return x <= (T)0.f ? (T)0.f : nd4j::math::nd4j_igamma(alpha, x * beta); + T x = helper->relativeT(idx, -sd::DataTypeUtils::template max() / 10 , sd::DataTypeUtils::template max() / 10); + return x <= (T)0.f ? (T)0.f : sd::math::nd4j_igamma(alpha, x * beta); } - random_def T op(T valueX, Nd4jLong idx, Nd4jLong length, nd4j::graph::RandomGenerator *helper, T *extraParams) { + random_def T op(T valueX, Nd4jLong idx, Nd4jLong length, sd::graph::RandomGenerator *helper, T *extraParams) { T alpha = extraParams[0]; T beta = extraParams[1]; - return valueX <= (T)0.f ? (T)0.f : nd4j::math::nd4j_igamma(alpha, beta * valueX); + return valueX <= (T)0.f ? (T)0.f : sd::math::nd4j_igamma(alpha, beta * valueX); } }; @@ -185,7 +185,7 @@ namespace randomOps { method_XY // please note: prob is chance to retain original value - random_def T op(T valueX, Nd4jLong idx, Nd4jLong length, nd4j::graph::RandomGenerator *helper, T *extraParams) { + random_def T op(T valueX, Nd4jLong idx, Nd4jLong length, sd::graph::RandomGenerator *helper, T *extraParams) { T randVal = helper->relativeT(idx); return randVal >= extraParams[0] ? (T) 0.0f : valueX; } @@ -202,7 +202,7 @@ namespace randomOps { method_XY // please note: prob is chance to retain original value - random_def T op(T valueX, Nd4jLong idx, Nd4jLong length, nd4j::graph::RandomGenerator *helper, T *extraParams) { + random_def T op(T valueX, Nd4jLong idx, Nd4jLong length, sd::graph::RandomGenerator *helper, T *extraParams) { T randVal = helper->relativeT(idx); // extraParams[0] == p // [1] = a @@ -226,7 +226,7 @@ namespace randomOps { method_XY // please note: prob is chance to retain original value - random_def T op(T valueX, Nd4jLong idx, Nd4jLong length, nd4j::graph::RandomGenerator *helper, T *extraParams) { + random_def T op(T valueX, Nd4jLong idx, Nd4jLong length, sd::graph::RandomGenerator *helper, T *extraParams) { T prob = extraParams[0]; T randVal = helper->relativeT(idx); return randVal >= prob ? (T) 0.0f : valueX / prob; @@ -244,7 +244,7 @@ namespace randomOps { method_X method_XY - random_def T op(Nd4jLong idx, Nd4jLong length, nd4j::graph::RandomGenerator *helper, T *extraParams) { + random_def T op(Nd4jLong idx, Nd4jLong length, sd::graph::RandomGenerator *helper, T *extraParams) { T from = extraParams[0]; T to = extraParams[1]; T step = extraParams[2]; @@ -266,15 +266,15 @@ namespace randomOps { method_XY - random_def T op(Nd4jLong idx, Nd4jLong length, nd4j::graph::RandomGenerator *helper, T *extraParams) { + random_def T op(Nd4jLong idx, Nd4jLong length, sd::graph::RandomGenerator *helper, T *extraParams) { T lambda = extraParams[0]; - T x = helper->relativeT(idx, nd4j::DataTypeUtils::template min(), (T)1.f); - return -nd4j::math::nd4j_log((T)1.f - x) / lambda; + T x = helper->relativeT(idx, sd::DataTypeUtils::template min(), (T)1.f); + return -sd::math::nd4j_log((T)1.f - x) / lambda; } - random_def T op(T valueX, Nd4jLong idx, Nd4jLong length, nd4j::graph::RandomGenerator *helper, T *extraParams) { + random_def T op(T valueX, Nd4jLong idx, Nd4jLong length, sd::graph::RandomGenerator *helper, T *extraParams) { T lambda = extraParams[0]; - return -nd4j::math::nd4j_log((T)1.f - valueX) / lambda; // valueX must be within (0, 1] + return -sd::math::nd4j_log((T)1.f - valueX) / lambda; // valueX must be within (0, 1] } }; diff --git a/libnd4j/include/ops/special_random_ops.h b/libnd4j/include/ops/special_random_ops.h index 354f8e328..50a50752e 100644 --- a/libnd4j/include/ops/special_random_ops.h +++ b/libnd4j/include/ops/special_random_ops.h @@ -24,7 +24,7 @@ #include #include #include -#include +#include #include namespace randomOps { @@ -63,16 +63,16 @@ namespace randomOps { __shared__ char yOrder; __shared__ char zOrder; - __shared__ nd4j::graph::RandomGenerator *rng; + __shared__ sd::graph::RandomGenerator *rng; __shared__ unsigned char *cB; __shared__ unsigned char *dB; - __shared__ nd4j::graph::RandomGenerator *devRng; + __shared__ sd::graph::RandomGenerator *devRng; if (threadIdx.x == 0) { extern __shared__ unsigned char shmem[]; - rng = (nd4j::graph::RandomGenerator*) shmem; + rng = (sd::graph::RandomGenerator*) shmem; cB = shmem; - devRng = reinterpret_cast (state); + devRng = reinterpret_cast (state); dB = reinterpret_cast (state); xLength = shape::length(xShapeBuffer); @@ -89,7 +89,7 @@ namespace randomOps { __syncthreads(); // using this loop instead of memcpy - for (int e = threadIdx.x; e < sizeof(nd4j::graph::RandomGenerator); e+= blockDim.x) + for (int e = threadIdx.x; e < sizeof(sd::graph::RandomGenerator); e+= blockDim.x) cB[e] = dB[e]; __syncthreads(); @@ -148,8 +148,8 @@ namespace randomOps { * Z will hold results */ - //nd4j::random::RandomBuffer *buffer = reinterpret_cast (state); - nd4j::graph::RandomGenerator* rng = reinterpret_cast(state); + //sd::random::RandomBuffer *buffer = reinterpret_cast (state); + sd::graph::RandomGenerator* rng = reinterpret_cast(state); // TODO: we probably might want to skip this sum, and state that probabilities array should be real probabilities, i.e. should sum to 1.0 //T probSum = extraArguments[0]; @@ -162,8 +162,8 @@ namespace randomOps { auto zEWS = shape::elementWiseStride(zShapeBuffer); int elementsPerThread = zLength / TAD_THRESHOLD; - int _threads = nd4j::math::nd4j_max(1, elementsPerThread); - _threads = nd4j::math::nd4j_min(_threads, nd4j::Environment::getInstance()->maxThreads()); + int _threads = sd::math::nd4j_max(1, elementsPerThread); + _threads = sd::math::nd4j_min(_threads, sd::Environment::getInstance()->maxThreads()); if (zEWS >= 1 && xEWS >= 1 && yEWS >= 1) { auto func = PRAGMA_THREADS_FOR { @@ -244,19 +244,19 @@ namespace randomOps { __shared__ T *tZ; - __shared__ nd4j::graph::RandomGenerator* rng; + __shared__ sd::graph::RandomGenerator* rng; __shared__ unsigned char *cB; __shared__ unsigned char *dB; - __shared__ nd4j::graph::RandomGenerator *devRng; + __shared__ sd::graph::RandomGenerator *devRng; if (threadIdx.x == 0) { extern __shared__ unsigned char shmem[]; - rng = reinterpret_cast(shmem); + rng = reinterpret_cast(shmem); cB = shmem; - devRng = reinterpret_cast (state); + devRng = reinterpret_cast (state); dB = reinterpret_cast (state); - tZ = reinterpret_cast(shmem + sizeof(nd4j::graph::RandomGenerator)); + tZ = reinterpret_cast(shmem + sizeof(sd::graph::RandomGenerator)); zLength = shape::length(zShapeBuffer); zEWS = shape::elementWiseStride(zShapeBuffer); @@ -274,7 +274,7 @@ namespace randomOps { __syncthreads(); // using this loop instead of memcpy - for (int e = threadIdx.x; e < sizeof(nd4j::graph::RandomGenerator); e+= blockDim.x) + for (int e = threadIdx.x; e < sizeof(sd::graph::RandomGenerator); e+= blockDim.x) cB[e] = dB[e]; __syncthreads(); @@ -292,11 +292,11 @@ namespace randomOps { T realMean0 = y == z ? mean : y[e * yEWS]; - z[e * zEWS] = (nd4j::math::nd4j_sqrt(t * nd4j::math::nd4j_log(r0)) * nd4j::math::nd4j_cos(two_pi * r1)) * stddev + realMean0; + z[e * zEWS] = (sd::math::nd4j_sqrt(t * sd::math::nd4j_log(r0)) * sd::math::nd4j_cos(two_pi * r1)) * stddev + realMean0; if (epm < zLength) { T realMean1 = y == z ? mean : y[epm * yEWS]; - z[epm * zEWS] = (nd4j::math::nd4j_sqrt(t * nd4j::math::nd4j_log(r0)) * nd4j::math::nd4j_sin(two_pi * r1)) * stddev + realMean1; + z[epm * zEWS] = (sd::math::nd4j_sqrt(t * sd::math::nd4j_log(r0)) * sd::math::nd4j_sin(two_pi * r1)) * stddev + realMean1; } } } @@ -314,16 +314,16 @@ namespace randomOps { auto middle = zLength % 2 + zLength / 2; int elementsPerThread = middle / TAD_THRESHOLD; - int _threads = nd4j::math::nd4j_max(1, elementsPerThread); - _threads = nd4j::math::nd4j_min(_threads, nd4j::Environment::getInstance()->maxThreads()); + int _threads = sd::math::nd4j_max(1, elementsPerThread); + _threads = sd::math::nd4j_min(_threads, sd::Environment::getInstance()->maxThreads()); int span = (middle / _threads) + 8; // we're enforcing even chunks, since it's mandatory for this algorithm span -= span % 2; - //nd4j::random::RandomBuffer *buffer = reinterpret_cast (state); - nd4j::graph::RandomGenerator* rng = reinterpret_cast(state); + //sd::random::RandomBuffer *buffer = reinterpret_cast (state); + sd::graph::RandomGenerator* rng = reinterpret_cast(state); const T mean = extraArguments[0]; const T stddev = extraArguments[1]; @@ -339,14 +339,14 @@ namespace randomOps { T realMean0 = y == z ? mean : y[e * yEWS]; - auto z0 = (nd4j::math::nd4j_sqrt(static_cast(-2.0f) * nd4j::math::nd4j_log(r0)) * - nd4j::math::nd4j_cos(two_pi * r1)) * stddev + realMean0; + auto z0 = (sd::math::nd4j_sqrt(static_cast(-2.0f) * sd::math::nd4j_log(r0)) * + sd::math::nd4j_cos(two_pi * r1)) * stddev + realMean0; z[e * zEWS] = z0; if (epm < zLength) { T realMean1 = y == z ? mean : y[epm * yEWS]; - auto z1 = (nd4j::math::nd4j_sqrt(static_cast(-2.0f) * nd4j::math::nd4j_log(r0)) * - nd4j::math::nd4j_sin(two_pi * r1)) * stddev + realMean1; + auto z1 = (sd::math::nd4j_sqrt(static_cast(-2.0f) * sd::math::nd4j_log(r0)) * + sd::math::nd4j_sin(two_pi * r1)) * stddev + realMean1; z[epm * zEWS] = z1; } } @@ -381,15 +381,15 @@ namespace randomOps { __shared__ int yEWS; __shared__ int zEWS; - __shared__ nd4j::graph::RandomGenerator* rng; + __shared__ sd::graph::RandomGenerator* rng; __shared__ unsigned char *cB; __shared__ unsigned char *dB; - __shared__ nd4j::graph::RandomGenerator *devRng; + __shared__ sd::graph::RandomGenerator *devRng; if (threadIdx.x == 0) { extern __shared__ unsigned char shmem[]; - rng = reinterpret_cast(shmem); + rng = reinterpret_cast(shmem); cB = shmem; - devRng = reinterpret_cast(state); + devRng = reinterpret_cast(state); dB = reinterpret_cast (state); zLength = shape::length(zShapeBuffer); @@ -399,7 +399,7 @@ namespace randomOps { __syncthreads(); // using this loop instead of memcpy - for (int e = threadIdx.x; e < sizeof(nd4j::graph::RandomGenerator); e+= blockDim.x) + for (int e = threadIdx.x; e < sizeof(sd::graph::RandomGenerator); e+= blockDim.x) cB[e] = dB[e]; __syncthreads(); @@ -433,12 +433,12 @@ namespace randomOps { auto zEWS = shape::elementWiseStride(zShapeBuffer); int elementsPerThread = zLength / TAD_THRESHOLD; - int _threads = nd4j::math::nd4j_max(1, elementsPerThread); - _threads = nd4j::math::nd4j_min(_threads, nd4j::Environment::getInstance()->maxThreads()); + int _threads = sd::math::nd4j_max(1, elementsPerThread); + _threads = sd::math::nd4j_min(_threads, sd::Environment::getInstance()->maxThreads()); T prob = extraArguments[1]; - nd4j::graph::RandomGenerator* rng = reinterpret_cast(state); + sd::graph::RandomGenerator* rng = reinterpret_cast(state); auto func = PRAGMA_THREADS_FOR { for (auto e = start; e < stop; e++) { @@ -488,15 +488,15 @@ namespace randomOps { __shared__ int yEWS; __shared__ int zEWS; - __shared__ nd4j::graph::RandomGenerator* rng; + __shared__ sd::graph::RandomGenerator* rng; __shared__ unsigned char *cB; __shared__ unsigned char *dB; - __shared__ nd4j::graph::RandomGenerator *devRng; + __shared__ sd::graph::RandomGenerator *devRng; if (threadIdx.x == 0) { extern __shared__ unsigned char shmem[]; - rng = (nd4j::graph::RandomGenerator*) shmem; + rng = (sd::graph::RandomGenerator*) shmem; cB = shmem; - devRng = reinterpret_cast (state); + devRng = reinterpret_cast (state); dB = reinterpret_cast (state); zLength = shape::length(zShapeBuffer); @@ -506,7 +506,7 @@ namespace randomOps { __syncthreads(); // using this loop instead of memcpy - for (int e = threadIdx.x; e < sizeof(nd4j::graph::RandomGenerator); e+= blockDim.x) + for (int e = threadIdx.x; e < sizeof(sd::graph::RandomGenerator); e+= blockDim.x) cB[e] = dB[e]; __syncthreads(); @@ -541,13 +541,13 @@ namespace randomOps { auto zEWS = shape::elementWiseStride(zShapeBuffer); int elementsPerThread = zLength / TAD_THRESHOLD; - int _threads = nd4j::math::nd4j_max(1, elementsPerThread); - _threads = nd4j::math::nd4j_min(_threads, nd4j::Environment::getInstance()->maxThreads()); + int _threads = sd::math::nd4j_max(1, elementsPerThread); + _threads = sd::math::nd4j_min(_threads, sd::Environment::getInstance()->maxThreads()); T prob = extraArguments[1]; - //nd4j::random::RandomBuffer *buffer = reinterpret_cast (state); - nd4j::graph::RandomGenerator* rng = reinterpret_cast(state); + //sd::random::RandomBuffer *buffer = reinterpret_cast (state); + sd::graph::RandomGenerator* rng = reinterpret_cast(state); auto func = PRAGMA_THREADS_FOR { for (auto e = start; e < stop; e++) { @@ -577,7 +577,7 @@ namespace randomOps { template class TruncatedNormalDistribution { private: - static inline _CUDA_HD T step(nd4j::graph::RandomGenerator* rng, T mean, T stddev, Nd4jLong e, Nd4jLong middle, T& z) { + static inline _CUDA_HD T step(sd::graph::RandomGenerator* rng, T mean, T stddev, Nd4jLong e, Nd4jLong middle, T& z) { auto epm = e + middle; const T two_pi = static_cast(2.0f) * static_cast(3.14159265358979323846); const T epsilon = static_cast(1.e-5f); @@ -587,12 +587,12 @@ namespace randomOps { T realMean0 = mean; - auto z0 = (nd4j::math::nd4j_sqrt(static_cast(-2.0f) * nd4j::math::nd4j_log(r0)) * nd4j::math::nd4j_cos(two_pi * r1)) * stddev + realMean0; + auto z0 = (sd::math::nd4j_sqrt(static_cast(-2.0f) * sd::math::nd4j_log(r0)) * sd::math::nd4j_cos(two_pi * r1)) * stddev + realMean0; z = z0; if (epm < middle) { T realMean1 = mean; - auto z1 = (nd4j::math::nd4j_sqrt(static_cast(-2.0f) * nd4j::math::nd4j_log(r0)) * - nd4j::math::nd4j_sin(two_pi * r1)) * stddev + realMean1; + auto z1 = (sd::math::nd4j_sqrt(static_cast(-2.0f) * sd::math::nd4j_log(r0)) * + sd::math::nd4j_sin(two_pi * r1)) * stddev + realMean1; z = z1; } return z; @@ -619,20 +619,20 @@ namespace randomOps { __shared__ T *tZ; - __shared__ nd4j::graph::RandomGenerator* rng; + __shared__ sd::graph::RandomGenerator* rng; __shared__ unsigned char *cB; __shared__ unsigned char *dB; - __shared__ nd4j::graph::RandomGenerator* devRng; + __shared__ sd::graph::RandomGenerator* devRng; __shared__ Nd4jLong middle; if (threadIdx.x == 0) { extern __shared__ unsigned char shmem[]; - rng = reinterpret_cast(shmem); + rng = reinterpret_cast(shmem); cB = shmem; - devRng = reinterpret_cast (state); + devRng = reinterpret_cast (state); dB = reinterpret_cast (state); - tZ = reinterpret_cast(shmem + sizeof(nd4j::graph::RandomGenerator)); + tZ = reinterpret_cast(shmem + sizeof(sd::graph::RandomGenerator)); zLength = shape::length(zShapeBuffer); zEWS = shape::elementWiseStride(zShapeBuffer); @@ -650,7 +650,7 @@ namespace randomOps { __syncthreads(); // using this loop instead of memcpy - for (int e = threadIdx.x; e < sizeof(nd4j::graph::RandomGenerator); e+= blockDim.x) + for (int e = threadIdx.x; e < sizeof(sd::graph::RandomGenerator); e+= blockDim.x) cB[e] = dB[e]; __syncthreads(); @@ -660,13 +660,13 @@ namespace randomOps { GaussianDistribution::specialOpCuda(state, x, xShapeBuffer, y, yShapeBuffer, z, zShapeBuffer, extraArguments); __syncthreads(); - T ds = nd4j::math::nd4j_abs(stddev) * static_cast(2.0f); + T ds = sd::math::nd4j_abs(stddev) * static_cast(2.0f); for (Nd4jLong e = tid; e < zLength; e += step) { if (z[e] > mean + ds || z[e] < mean - ds) { z[e] = TruncatedNormalDistribution::step(rng, mean, stddev, e, middle, z[e]); if (z[e] > mean + ds || z[e] < mean - ds) - z[e] = mean + nd4j::DataTypeUtils::min(); + z[e] = mean + sd::DataTypeUtils::min(); } } } @@ -678,14 +678,14 @@ namespace randomOps { Nd4jLong zLength = shape::length(zShapeBuffer); //auto yEWS = shape::elementWiseStride(yShapeBuffer); //auto zEWS = shape::elementWiseStride(zShapeBuffer); - nd4j::graph::RandomGenerator* rng = reinterpret_cast(state); + sd::graph::RandomGenerator* rng = reinterpret_cast(state); T mean = extraArguments[0]; T stddev = extraArguments[1]; - T ds = nd4j::math::nd4j_abs(stddev) * (T) 2.0f; + T ds = sd::math::nd4j_abs(stddev) * (T) 2.0f; Nd4jLong middle = zLength / 2 + (zLength % 2); int elementsPerThread = middle / TAD_THRESHOLD; - int _threads = nd4j::math::nd4j_max(1, elementsPerThread); - _threads = nd4j::math::nd4j_min(_threads, nd4j::Environment::getInstance()->maxThreads()); + int _threads = sd::math::nd4j_max(1, elementsPerThread); + _threads = sd::math::nd4j_min(_threads, sd::Environment::getInstance()->maxThreads()); const T epsilon = static_cast(1e-5); @@ -695,7 +695,7 @@ namespace randomOps { z[e] = step(rng, mean, stddev, e, middle, z[e]); if (z[e] > mean + ds || z[e] < mean - ds) - z[e] = mean + nd4j::DataTypeUtils::min(); + z[e] = mean + sd::DataTypeUtils::min(); } } }; @@ -731,20 +731,20 @@ namespace randomOps { __shared__ T *tZ; - __shared__ nd4j::graph::RandomGenerator* rng; + __shared__ sd::graph::RandomGenerator* rng; __shared__ unsigned char *cB; __shared__ unsigned char *dB; - __shared__ nd4j::graph::RandomGenerator* devRng; + __shared__ sd::graph::RandomGenerator* devRng; if (threadIdx.x == 0) { extern __shared__ unsigned char shmem[]; - rng = reinterpret_cast(state); + rng = reinterpret_cast(state); cB = shmem; - devRng = reinterpret_cast(state); + devRng = reinterpret_cast(state); dB = reinterpret_cast (state); - tZ = reinterpret_cast(shmem + sizeof(nd4j::graph::RandomGenerator)); + tZ = reinterpret_cast(shmem + sizeof(sd::graph::RandomGenerator)); zLength = shape::length(zShapeBuffer); zEWS = shape::elementWiseStride(zShapeBuffer); @@ -762,7 +762,7 @@ namespace randomOps { __syncthreads(); // using this loop instead of memcpy - for (int e = threadIdx.x; e < sizeof(nd4j::graph::RandomGenerator); e+= blockDim.x) + for (int e = threadIdx.x; e < sizeof(sd::graph::RandomGenerator); e+= blockDim.x) cB[e] = dB[e]; __syncthreads(); @@ -780,11 +780,11 @@ namespace randomOps { T realMean = y == z ? mean : y[e * yEWS]; - z[e *zEWS] = nd4j::math::nd4j_exp((nd4j::math::nd4j_sqrt(static_cast(-2.0f) * nd4j::math::nd4j_log(r0)) * nd4j::math::nd4j_cos(two_pi * r1)) * stddev + realMean); + z[e *zEWS] = sd::math::nd4j_exp((sd::math::nd4j_sqrt(static_cast(-2.0f) * sd::math::nd4j_log(r0)) * sd::math::nd4j_cos(two_pi * r1)) * stddev + realMean); if (epm < zLength) { realMean = y == z ? mean : y[epm * yEWS]; - z[epm *zEWS] = nd4j::math::nd4j_exp((nd4j::math::nd4j_sqrt(static_cast(-2.0f) * nd4j::math::nd4j_log(r0)) * nd4j::math::nd4j_sin(two_pi * r1)) * stddev + realMean); + z[epm *zEWS] = sd::math::nd4j_exp((sd::math::nd4j_sqrt(static_cast(-2.0f) * sd::math::nd4j_log(r0)) * sd::math::nd4j_sin(two_pi * r1)) * stddev + realMean); } } } @@ -801,16 +801,16 @@ namespace randomOps { auto middle = zLength % 2 == 0 ? zLength / 2 : zLength / 2 + 1; int elementsPerThread = middle / TAD_THRESHOLD; - int _threads = nd4j::math::nd4j_max(1, elementsPerThread); - _threads = nd4j::math::nd4j_min(_threads, nd4j::Environment::getInstance()->maxThreads()); + int _threads = sd::math::nd4j_max(1, elementsPerThread); + _threads = sd::math::nd4j_min(_threads, sd::Environment::getInstance()->maxThreads()); int span = (zLength / _threads) + 8; // we're enforcing even chunks, since it's mandatory for this algorithm span -= span % 2; -// auto buffer = reinterpret_cast (state); - nd4j::graph::RandomGenerator* rng = reinterpret_cast(state); +// auto buffer = reinterpret_cast (state); + sd::graph::RandomGenerator* rng = reinterpret_cast(state); const T mean = extraArguments[0]; const T stddev = extraArguments[1]; @@ -827,11 +827,11 @@ namespace randomOps { T realMean = y == z ? mean : y[e * yEWS]; - z[e * zEWS] = nd4j::math::nd4j_exp((nd4j::math::nd4j_sqrt(static_cast(-2.0f) * nd4j::math::nd4j_log(r0)) * nd4j::math::nd4j_cos(two_pi * r1)) * stddev + realMean); + z[e * zEWS] = sd::math::nd4j_exp((sd::math::nd4j_sqrt(static_cast(-2.0f) * sd::math::nd4j_log(r0)) * sd::math::nd4j_cos(two_pi * r1)) * stddev + realMean); if (epm < zLength) { realMean = y == z ? mean : y[epm * yEWS]; - z[epm * zEWS] = nd4j::math::nd4j_exp((nd4j::math::nd4j_sqrt(static_cast(-2.0f) * nd4j::math::nd4j_log(r0)) * nd4j::math::nd4j_sin(two_pi * r1)) * stddev + realMean); + z[epm * zEWS] = sd::math::nd4j_exp((sd::math::nd4j_sqrt(static_cast(-2.0f) * sd::math::nd4j_log(r0)) * sd::math::nd4j_sin(two_pi * r1)) * stddev + realMean); } } }; diff --git a/libnd4j/include/ops/specials.h b/libnd4j/include/ops/specials.h index d8030db0b..c250d72f6 100644 --- a/libnd4j/include/ops/specials.h +++ b/libnd4j/include/ops/specials.h @@ -27,10 +27,10 @@ #define TAD_THRESHOLD 2 #endif -#include +#include #include -namespace nd4j { +namespace sd { class NDArray; //FIXME: get rid of this redefinition @@ -50,8 +50,9 @@ namespace nd4j { template class ND4J_EXPORT SpecialMethods { public: - static void concatCpuGeneric(const std::vector& inArrs, NDArray& output, const int axis); + static void concatCpuGeneric(const std::vector& inArrs, NDArray& output, const int axis); static void concatCpuGeneric(int dimension, int numArrays, Nd4jPointer *data, Nd4jPointer *inputShapeInfo, void *result, Nd4jLong *resultShapeInfo); + static void splitCpuGeneric(const NDArray& input, const std::vector& outArrs, const int axis); static void accumulateGeneric(void **x, void *z, Nd4jLong *zShapeInfo, int n, const Nd4jLong length); static void averageGeneric(void **x, void *z, Nd4jLong *zShapeInfo, int n, const Nd4jLong length, bool propagate); @@ -67,6 +68,7 @@ namespace nd4j { static void decodeBitmapGeneric(void *dx, Nd4jLong N, void *dz, Nd4jLong *zShapeInfo); static Nd4jLong encodeBitmapGeneric(void *dx, Nd4jLong *zShapeInfo, Nd4jLong N, int *dz, float threshold); + }; template diff --git a/libnd4j/include/ops/specials_sparse.h b/libnd4j/include/ops/specials_sparse.h index 741c9cce8..cd0e2f6b5 100644 --- a/libnd4j/include/ops/specials_sparse.h +++ b/libnd4j/include/ops/specials_sparse.h @@ -23,9 +23,9 @@ #ifndef LIBND4J_SPECIALS_SPARSE_H #define LIBND4J_SPECIALS_SPARSE_H -#include +#include -namespace nd4j { +namespace sd { namespace sparse { template diff --git a/libnd4j/include/performance/benchmarking/BenchmarkSuit.h b/libnd4j/include/performance/benchmarking/BenchmarkSuit.h index 1a77dbd9f..7805e570e 100644 --- a/libnd4j/include/performance/benchmarking/BenchmarkSuit.h +++ b/libnd4j/include/performance/benchmarking/BenchmarkSuit.h @@ -22,12 +22,12 @@ #define LIBND4J_BENCHMARKSUIT_H #include -#include -#include -#include -#include +#include +#include +#include +#include -namespace nd4j { +namespace sd { class ND4J_EXPORT BenchmarkSuit { public: BenchmarkSuit() = default; diff --git a/libnd4j/include/performance/benchmarking/FullBenchmarkSuit.h b/libnd4j/include/performance/benchmarking/FullBenchmarkSuit.h index dc2b63a4d..6b2314b96 100644 --- a/libnd4j/include/performance/benchmarking/FullBenchmarkSuit.h +++ b/libnd4j/include/performance/benchmarking/FullBenchmarkSuit.h @@ -23,7 +23,7 @@ #include -namespace nd4j { +namespace sd { class FullBenchmarkSuit : public BenchmarkSuit { public: std::string runSuit() override; diff --git a/libnd4j/include/performance/benchmarking/LightBenchmarkSuit.h b/libnd4j/include/performance/benchmarking/LightBenchmarkSuit.h index 35215d032..65a74b1fe 100644 --- a/libnd4j/include/performance/benchmarking/LightBenchmarkSuit.h +++ b/libnd4j/include/performance/benchmarking/LightBenchmarkSuit.h @@ -23,7 +23,7 @@ #include -namespace nd4j { +namespace sd { class LightBenchmarkSuit : public BenchmarkSuit { public: std::string runSuit() override; diff --git a/libnd4j/include/performance/benchmarking/impl/FullBenchmarkSuit.cpp b/libnd4j/include/performance/benchmarking/impl/FullBenchmarkSuit.cpp index b4960bc90..0eabe959a 100644 --- a/libnd4j/include/performance/benchmarking/impl/FullBenchmarkSuit.cpp +++ b/libnd4j/include/performance/benchmarking/impl/FullBenchmarkSuit.cpp @@ -78,7 +78,7 @@ int limit3 = 1; #endif -namespace nd4j { +namespace sd { static std::string layerNormBenchmark() { std::string output; @@ -126,7 +126,7 @@ namespace nd4j { return ctx; }; - nd4j::ops::layer_norm layerNorm; + sd::ops::layer_norm layerNorm; DeclarableBenchmark benchmark(layerNorm, "layer norm"); output += helper.runOperationSuit(&benchmark, generator, batch, "Layer Norm"); @@ -141,7 +141,7 @@ namespace nd4j { BoolParameters ncdhw("ncdhw"); //1 = ndhwc ParametersBatch batch({&ncdhw}); - nd4j::ops::maxpool3dnew maxpool3Dnew; + sd::ops::maxpool3dnew maxpool3Dnew; DeclarableBenchmark benchmark(maxpool3Dnew, "maxPool3d"); #ifdef _RELEASE @@ -207,7 +207,7 @@ namespace nd4j { BoolParameters ncdhw("ncdhw"); //1 = ndhwc ParametersBatch batch({&ncdhw}); - nd4j::ops::conv3dnew conv3Dnew; + sd::ops::conv3dnew conv3Dnew; DeclarableBenchmark benchmark(conv3Dnew, "conv3d"); #ifdef _RELEASE @@ -284,7 +284,7 @@ namespace nd4j { #endif ParametersBatch batch({&format, &mb, &nInOut}); - nd4j::ops::lstmBlock lstmBlock; + sd::ops::lstmBlock lstmBlock; DeclarableBenchmark benchmark(lstmBlock, "lstm"); int seqLength = 32; @@ -414,7 +414,7 @@ namespace nd4j { return ctx; }; - nd4j::ops::batchnorm batchnorm; + sd::ops::batchnorm batchnorm; DeclarableBenchmark benchmark(batchnorm, "batchnorm"); output += helper.runOperationSuit(&benchmark, generator, batch, "Batch Normalization"); @@ -471,11 +471,11 @@ namespace nd4j { return ctx; }; - nd4j::ops::avgpool2d avgpool2d; + sd::ops::avgpool2d avgpool2d; DeclarableBenchmark benchmark1(avgpool2d, "avgpool"); output += helper.runOperationSuit(&benchmark1, generator, batch, "Average Pooling 2d Operation"); - nd4j::ops::maxpool2d maxpool2d; + sd::ops::maxpool2d maxpool2d; DeclarableBenchmark benchmark2(maxpool2d, "maxpool"); output += helper.runOperationSuit(&benchmark2, generator, batch, "Max Pooling 2d Operation"); return output; @@ -497,7 +497,7 @@ namespace nd4j { PredefinedParameters hw("hw", {8}); #endif ParametersBatch batch({&nhwc, &k, &c, &hw}); - nd4j::ops::conv2d conv2d; + sd::ops::conv2d conv2d; DeclarableBenchmark benchmark(conv2d, "conv2d"); auto generator = PARAMETRIC_D() { @@ -573,27 +573,27 @@ namespace nd4j { return ctx; }; - nd4j::ops::LegacyRandomOp unif(random::UniformDistribution); + sd::ops::LegacyRandomOp unif(random::UniformDistribution); DeclarableBenchmark dbU(unif, "uniform"); output += helper.runOperationSuit(&dbU, gen01, batch, "Uniform Distribution"); - nd4j::ops::LegacyRandomOp gaussian(random::GaussianDistribution); + sd::ops::LegacyRandomOp gaussian(random::GaussianDistribution); DeclarableBenchmark dbG(gaussian, "gaussian"); output += helper.runOperationSuit(&dbG, gen01, batch, "Gaussian Distribution"); - nd4j::ops::LegacyRandomOp trunc(random::TruncatedNormalDistribution); + sd::ops::LegacyRandomOp trunc(random::TruncatedNormalDistribution); DeclarableBenchmark dbTU(unif, "trunc.norm"); output += helper.runOperationSuit(&dbTU, gen01, batch, "Truncated Normal Distribution"); - nd4j::ops::LegacyRandomOp ln(random::LogNormalDistribution); + sd::ops::LegacyRandomOp ln(random::LogNormalDistribution); DeclarableBenchmark dbLN(ln, "uniform"); output += helper.runOperationSuit(&dbLN, gen01, batch, "Log Normal Distribution"); - nd4j::ops::LegacyRandomOp bernoulli(random::BernoulliDistribution); + sd::ops::LegacyRandomOp bernoulli(random::BernoulliDistribution); DeclarableBenchmark dbB(bernoulli, "bernoulli"); output += helper.runOperationSuit(&dbB, gen05, batch, "Bernoulli Distribution"); - nd4j::ops::LegacyRandomOp dropout(random::BernoulliDistribution); + sd::ops::LegacyRandomOp dropout(random::BernoulliDistribution); DeclarableBenchmark dbD(dropout, "dropout"); output += helper.runOperationSuit(&dbD, gen05, batch, "Dropout"); @@ -764,7 +764,7 @@ namespace nd4j { return ctx; }; - nd4j::ops::matmul mmul; + sd::ops::matmul mmul; DeclarableBenchmark benchmark(mmul, "mmul (batch)"); output += helper.runOperationSuit(&benchmark, generator, b, "MMul (batch)"); @@ -822,7 +822,7 @@ namespace nd4j { ParametersBatch batch({&length}); //Gather 1D tests - 1d ref, 1d indices, 1d updates -> 1d output - nd4j::ops::scatter_upd scatter_update1; + sd::ops::scatter_upd scatter_update1; DeclarableBenchmark sa1d(scatter_update1, "scatter_update1d"); auto generator = PARAMETRIC_D() { auto ctx = new Context(1); @@ -856,7 +856,7 @@ namespace nd4j { IntPowerParameters rows("rows", 2, 8, gatherOpPowLimit2, 4); //2^10 to 2^16 in steps of 2: 2^10, ..., 2^20 PredefinedParameters cols("cols", {32}); ParametersBatch batch2({&rows, &cols}); - nd4j::ops::scatter_upd scatter_update2; + sd::ops::scatter_upd scatter_update2; DeclarableBenchmark sa2d(scatter_update2, "scatter_update2d"); auto generator2 = PARAMETRIC_D() { auto ctx = new Context(1); @@ -891,7 +891,7 @@ namespace nd4j { IntPowerParameters sz0("sz0", 2, 8, gatherOpPowLimit3, 4); PredefinedParameters sz1("sz1", {32}); ParametersBatch batch3({&sz0, &sz1}); - nd4j::ops::scatter_upd scatter_update3; + sd::ops::scatter_upd scatter_update3; DeclarableBenchmark sa3d(scatter_update3, "scatter3d"); auto generator3 = PARAMETRIC_D() { auto ctx = new Context(1); @@ -932,7 +932,7 @@ namespace nd4j { ParametersBatch batch({&length}); //Gather 1D tests - 1d input, 1d indices -> 1d output - nd4j::ops::gather gather1; + sd::ops::gather gather1; DeclarableBenchmark gather1d(gather1, "gather1d"); auto generator = PARAMETRIC_D() { auto ctx = new Context(1); @@ -962,7 +962,7 @@ namespace nd4j { IntPowerParameters rows("rows", 2, 8, gatherOpPowLimit2, 4); //2^10 to 2^20 in steps of 2: 2^10, ..., 2^20 PredefinedParameters cols("cols", {32}); ParametersBatch batch2({&rows, &cols}); - nd4j::ops::gather gather2; + sd::ops::gather gather2; DeclarableBenchmark gather2d(gather2, "gather2d"); auto generator2 = PARAMETRIC_D() { auto ctx = new Context(1); @@ -994,7 +994,7 @@ namespace nd4j { IntPowerParameters sz0("sz0", 2, 8, gatherOpPowLimit3, 4); //2^8 to 2^16 in steps of 4 PredefinedParameters sz1("sz1", {32}); ParametersBatch batch3({&sz0, &sz1}); - nd4j::ops::gather gather3; + sd::ops::gather gather3; DeclarableBenchmark gather3d(gather3, "gather3d"); auto generator3 = PARAMETRIC_D() { auto ctx = new Context(1); @@ -1166,7 +1166,7 @@ namespace nd4j { name += "Broadcast Matrix Add (Custom) - Rank"; name += std::to_string(rank); - nd4j::ops::add op; + sd::ops::add op; DeclarableBenchmark benchmark(op, "add"); output += helper.runOperationSuit(&benchmark, generator, b, name.c_str()); } @@ -1207,7 +1207,7 @@ namespace nd4j { }; std::string s("add"); - nd4j::ops::add op; + sd::ops::add op; DeclarableBenchmark benchmark(op, "add"); output += helper.runOperationSuit(&benchmark, generator, batch, "Broadcast (Custom) Add - 2d"); return output; @@ -1432,7 +1432,7 @@ namespace nd4j { return ctx; }; - nd4j::ops::argmax opArgmax; + sd::ops::argmax opArgmax; DeclarableBenchmark dbArgmax(opArgmax, "stridedArgmax"); output += helper.runOperationSuit(&dbArgmax, generator3, batch, "Strided Argmax"); return output; @@ -1508,7 +1508,7 @@ namespace nd4j { std::string s5("Argmax Along Dimension - "); s5 += std::to_string(length[i]); - nd4j::ops::argmax opArgmax; + sd::ops::argmax opArgmax; DeclarableBenchmark dbArgmax(opArgmax, "Argmax"); output += helper.runOperationSuit(&dbArgmax, generator3, batch, s5.c_str()); } @@ -1537,7 +1537,7 @@ namespace nd4j { output += helper.runOperationSuit(&rbSum, (const std::function)(generator), batch, "Sum - Full Array Reduction"); //Index reduction - nd4j::ops::argmax opArgmax; + sd::ops::argmax opArgmax; DeclarableBenchmark dbArgmax(opArgmax, "Argmax"); auto generator3 = PARAMETRIC_D(){ auto ctx = new Context(1); @@ -1571,7 +1571,7 @@ namespace nd4j { if(p.getIntParam("inplace") == 1){ z.push_back(view); } else { - z.push_back(NDArrayFactory::create_('c', {r,r})); + z.push_back(NDArrayFactory::create_('c', {view->sizeAt(0),view->sizeAt(1)})); } delete arr; }; @@ -1640,7 +1640,7 @@ namespace nd4j { output += helper.runOperationSuit(&erf, generator, batch, "Error Function (Erf)"); ParametersBatch batch2({&length}); - nd4j::ops::polygamma op1; + sd::ops::polygamma op1; DeclarableBenchmark pg(op1, "polygamma"); auto generator2 = PARAMETRIC_D() { auto ctx = new Context(1); @@ -1657,7 +1657,7 @@ namespace nd4j { IntPowerParameters lengthBetaInc("length", 2, 10, heavyPowLimit, 4); //2^10 to 2^22 in steps of 4 ParametersBatch batch3({&lengthBetaInc}); - nd4j::ops::betainc op2; + sd::ops::betainc op2; DeclarableBenchmark binc(op2, "betainc"); auto generator3 = PARAMETRIC_D() { auto ctx = new Context(1); diff --git a/libnd4j/include/performance/benchmarking/impl/LightBenchmarkSuit.cpp b/libnd4j/include/performance/benchmarking/impl/LightBenchmarkSuit.cpp index 9e179db7f..99a1b05bf 100644 --- a/libnd4j/include/performance/benchmarking/impl/LightBenchmarkSuit.cpp +++ b/libnd4j/include/performance/benchmarking/impl/LightBenchmarkSuit.cpp @@ -32,7 +32,7 @@ #endif -namespace nd4j { +namespace sd { template static std::string transformBenchmark() { @@ -262,7 +262,7 @@ namespace nd4j { output += helper.runOperationSuit(&rbMax, (const std::function)(generator), batch, "Maximum - Full Array Reduction"); //Index reduction - nd4j::ops::argmax opArgmax; + sd::ops::argmax opArgmax; DeclarableBenchmark dbArgmax(opArgmax, "Argmax"); auto generator3 = PARAMETRIC_D(){ auto ctx = new Context(1); @@ -353,7 +353,7 @@ namespace nd4j { std::string s5("Argmax Along Dimension - "); s5 += std::to_string(length[i]); - nd4j::ops::argmax opArgmax; + sd::ops::argmax opArgmax; DeclarableBenchmark dbArgmax(opArgmax, "Argmax"); output += helper.runOperationSuit(&dbArgmax, generator3, batch, s5.c_str()); } @@ -371,7 +371,7 @@ namespace nd4j { PredefinedParameters k("k", {2, 3}); ParametersBatch batch({&nhwc, &k}); - nd4j::ops::conv2d conv2d; + sd::ops::conv2d conv2d; DeclarableBenchmark benchmark(conv2d, "conv2d"); int hw = 64; @@ -462,11 +462,11 @@ namespace nd4j { return ctx; }; - nd4j::ops::avgpool2d avgpool2d; + sd::ops::avgpool2d avgpool2d; DeclarableBenchmark benchmark1(avgpool2d, "avgpool"); output += helper.runOperationSuit(&benchmark1, generator, batch, "Average Pool 2d"); - nd4j::ops::maxpool2d maxpool2d; + sd::ops::maxpool2d maxpool2d; DeclarableBenchmark benchmark2(maxpool2d, "maxpool"); output += helper.runOperationSuit(&benchmark2, generator, batch, "Max Pool 2d"); return output; @@ -483,7 +483,7 @@ namespace nd4j { int n = 128; ParametersBatch batch({&format, &mb}); - nd4j::ops::lstmBlock lstmBlock; + sd::ops::lstmBlock lstmBlock; DeclarableBenchmark benchmark(lstmBlock, "lstm"); int seqLength = 8; @@ -585,7 +585,7 @@ namespace nd4j { }; std::string s("add"); - nd4j::ops::add op; + sd::ops::add op; DeclarableBenchmark benchmark(op, "add"); output += helper.runOperationSuit(&benchmark, generator, batch, "Broadcast (Custom) Add - 2d"); return output; @@ -593,9 +593,9 @@ namespace nd4j { std::string LightBenchmarkSuit::runSuit() { #ifdef RELEASE_BUILD - std::vector dtypes({nd4j::DataType::FLOAT32, nd4j::DataType::HALF}); + std::vector dtypes({sd::DataType::FLOAT32, sd::DataType::HALF}); #else - std::vector dtypes({nd4j::DataType::FLOAT32}); + std::vector dtypes({sd::DataType::FLOAT32}); #endif std::string result; @@ -609,7 +609,7 @@ namespace nd4j { nd4j_printf("Running LightBenchmarkSuite.pairwiseBenchmark [%s]\n", DataTypeUtils::asString(t).c_str()); BUILD_SINGLE_SELECTOR(t, result += pairwiseBenchmark, (), LIBND4J_TYPES); -/* + nd4j_printf("Running LightBenchmarkSuite.reduceFullBenchmark [%s]\n", DataTypeUtils::asString(t).c_str()); BUILD_SINGLE_SELECTOR(t, result += reduceFullBenchmark, (), LIBND4J_TYPES); @@ -627,13 +627,13 @@ namespace nd4j { nd4j_printf("Running LightBenchmarkSuite.lstmBenchmark [%s]\n", DataTypeUtils::asString(t).c_str()); BUILD_SINGLE_SELECTOR(t, result += lstmBenchmark, (), LIBND4J_TYPES); - */ + } nd4j_printf("Running LightBenchmarkSuite.broadcast2d\n", ""); - //result += broadcast2d(); + result += broadcast2d(); nd4j_printf("Running LightBenchmarkSuite.mismatchedOrderAssign\n", ""); - //result += mismatchedOrderAssign(); + result += mismatchedOrderAssign(); return result; } diff --git a/libnd4j/include/samediff.h b/libnd4j/include/samediff.h new file mode 100644 index 000000000..4907c9802 --- /dev/null +++ b/libnd4j/include/samediff.h @@ -0,0 +1,39 @@ +/******************************************************************************* + * Copyright (c) 2020 Konduit K.K. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ + +// +// @author raver119@gmail.com +// + +#ifndef _SAMEDIFF_H +#define _SAMEDIFF_H + +/** + * This file is a basic include that pulls everything in + */ + +// basic NDArray-related includes +#include +#include + +// basic Graph-related includes +#include +#include + +// ML ops includes +#include + +#endif //S_SAMEDIFF_H diff --git a/libnd4j/blas/BlasVersionHelper.h b/libnd4j/include/system/BlasVersionHelper.h similarity index 96% rename from libnd4j/blas/BlasVersionHelper.h rename to libnd4j/include/system/BlasVersionHelper.h index 93e8d75e3..7cc97a26c 100644 --- a/libnd4j/blas/BlasVersionHelper.h +++ b/libnd4j/include/system/BlasVersionHelper.h @@ -21,11 +21,11 @@ #ifndef SAMEDIFF_BLASVERSIONHELPER_H #define SAMEDIFF_BLASVERSIONHELPER_H -#include +#include #include #include -namespace nd4j { +namespace sd { class ND4J_EXPORT BlasVersionHelper { public: int _blasMajorVersion = 0; diff --git a/libnd4j/blas/Environment.h b/libnd4j/include/system/Environment.h similarity index 93% rename from libnd4j/blas/Environment.h rename to libnd4j/include/system/Environment.h index 5bef3f1e4..392e70871 100644 --- a/libnd4j/blas/Environment.h +++ b/libnd4j/include/system/Environment.h @@ -23,13 +23,13 @@ #include #include -#include +#include #include #include #include -#include +#include -namespace nd4j{ +namespace sd{ class ND4J_EXPORT Environment { private: std::atomic _tadThreshold; @@ -38,7 +38,7 @@ namespace nd4j{ std::atomic _debug; std::atomic _leaks; std::atomic _profile; - std::atomic _dataType; + std::atomic _dataType; std::atomic _precBoost; std::atomic _useMKLDNN{true}; std::atomic _allowHelpers{true}; @@ -51,6 +51,8 @@ namespace nd4j{ std::atomic _maxTotalSpecialMemory{-1}; std::atomic _maxDeviceMemory{-1}; + bool _blasFallback = false; + #ifdef __ND4J_EXPERIMENTAL__ const bool _experimental = true; #else @@ -85,6 +87,8 @@ namespace nd4j{ void setLeaksDetector(bool reallyDetect); bool helpersAllowed(); void allowHelpers(bool reallyAllow); + + bool blasFallback(); int tadThreshold(); void setTadThreshold(int threshold); @@ -125,8 +129,8 @@ namespace nd4j{ bool isUseMKLDNN() { return _useMKLDNN.load(); } void setUseMKLDNN(bool useMKLDNN) { _useMKLDNN.store(useMKLDNN); } - nd4j::DataType defaultFloatDataType(); - void setDefaultFloatDataType(nd4j::DataType dtype); + sd::DataType defaultFloatDataType(); + void setDefaultFloatDataType(sd::DataType dtype); bool precisionBoostAllowed(); void allowPrecisionBoost(bool reallyAllow); diff --git a/libnd4j/include/buffer.h b/libnd4j/include/system/buffer.h similarity index 98% rename from libnd4j/include/buffer.h rename to libnd4j/include/system/buffer.h index 79197753d..5072965ca 100755 --- a/libnd4j/include/buffer.h +++ b/libnd4j/include/system/buffer.h @@ -28,15 +28,15 @@ #include #include #endif -#include +#include #include #include #include -#include +#include //Question: Should the indexes here really be int? Isn't size_t or Nd4jLong more appropriate? -namespace nd4j { +namespace sd { namespace buffer { /** * Represents both a cpu and gpu @@ -288,7 +288,7 @@ __host__ void copyDataFromGpu(Buffer **buffer, cudaStream_t stream) { #ifdef __CUDACC__ template -__host__ void printArr(nd4j::buffer::Buffer *buff) { +__host__ void printArr(sd::buffer::Buffer *buff) { for (int i = 0; i < buff->length; i++) { printf("Buffer[%d] was %f\n", i, buff->data[i]); } diff --git a/libnd4j/include/dll.h b/libnd4j/include/system/dll.h similarity index 97% rename from libnd4j/include/dll.h rename to libnd4j/include/system/dll.h index 91d5a7677..71098f8bf 100644 --- a/libnd4j/include/dll.h +++ b/libnd4j/include/system/dll.h @@ -21,7 +21,7 @@ #ifndef NATIVEOPERATIONS_DLL_H #define NATIVEOPERATIONS_DLL_H -#include +#include #ifdef _WIN32 //#include diff --git a/libnd4j/include/enum_boilerplate.h b/libnd4j/include/system/enum_boilerplate.h similarity index 99% rename from libnd4j/include/enum_boilerplate.h rename to libnd4j/include/system/enum_boilerplate.h index 1d549527b..2acb1536a 100644 --- a/libnd4j/include/enum_boilerplate.h +++ b/libnd4j/include/system/enum_boilerplate.h @@ -21,7 +21,7 @@ #ifndef LIBND4J_ENUM_BOILERPLATE_H #define LIBND4J_ENUM_BOILERPLATE_H -#include +#include #define EN_1(WHAT, OP_PAIR) WHAT(OP_PAIR) diff --git a/libnd4j/include/msvc.h b/libnd4j/include/system/msvc.h similarity index 100% rename from libnd4j/include/msvc.h rename to libnd4j/include/system/msvc.h diff --git a/libnd4j/include/nd4jmalloc.h b/libnd4j/include/system/nd4jmalloc.h similarity index 100% rename from libnd4j/include/nd4jmalloc.h rename to libnd4j/include/system/nd4jmalloc.h diff --git a/libnd4j/include/nd4jmemset.h b/libnd4j/include/system/nd4jmemset.h similarity index 100% rename from libnd4j/include/nd4jmemset.h rename to libnd4j/include/system/nd4jmemset.h diff --git a/libnd4j/include/op_boilerplate.h b/libnd4j/include/system/op_boilerplate.h similarity index 96% rename from libnd4j/include/op_boilerplate.h rename to libnd4j/include/system/op_boilerplate.h index 8487f0264..b4df39a29 100644 --- a/libnd4j/include/op_boilerplate.h +++ b/libnd4j/include/system/op_boilerplate.h @@ -65,8 +65,8 @@ #ifndef OP_BOILERPLATE_HH #define OP_BOILERPLATE_HH -#include -#include +#include +#include #include #include @@ -118,8 +118,8 @@ #endif -#define ELEMENT_THRESHOLD nd4j::Environment::getInstance()->elementwiseThreshold() -#define TAD_THRESHOLD nd4j::Environment::getInstance()->tadThreshold() +#define ELEMENT_THRESHOLD sd::Environment::getInstance()->elementwiseThreshold() +#define TAD_THRESHOLD sd::Environment::getInstance()->tadThreshold() #define SHAPELIST(...) new ShapeList({__VA_ARGS__}, block.workspace() != nullptr) @@ -129,8 +129,8 @@ #define PRINT_FIRST(...) printf(__VA_ARGS__); fflush(stdout) #endif -#define DEBUG_CALL(STREAM) if (nd4j::Environment::getInstance()->isDebug()) { cudaError_t tRes = cudaStreamSynchronize(*STREAM); checkCudaErrors(tRes); if (tRes != 0) { throw std::runtime_error(); }; } -#define DEBUG_KERNEL(STREAM, OP_NUM) if (nd4j::Environment::getInstance()->isDebug()) { cudaError_t tRes = cudaStreamSynchronize(*STREAM); checkCudaErrors(tRes); if (tRes != 0) {std::string tFile(__FILE__); std::string tOp = "Kernel OpNum failed: [" + nd4j::StringUtils::valueToString(OP_NUM) + std::string("]; File: ") + tFile + std::string(":") + nd4j::StringUtils::valueToString(__LINE__); throw std::runtime_error(tOp.c_str()); }; } +#define DEBUG_CALL(STREAM) if (sd::Environment::getInstance()->isDebug()) { cudaError_t tRes = cudaStreamSynchronize(*STREAM); checkCudaErrors(tRes); if (tRes != 0) { throw std::runtime_error(); }; } +#define DEBUG_KERNEL(STREAM, OP_NUM) if (sd::Environment::getInstance()->isDebug()) { cudaError_t tRes = cudaStreamSynchronize(*STREAM); checkCudaErrors(tRes); if (tRes != 0) {std::string tFile(__FILE__); std::string tOp = "Kernel OpNum failed: [" + sd::StringUtils::valueToString(OP_NUM) + std::string("]; File: ") + tFile + std::string(":") + sd::StringUtils::valueToString(__LINE__); throw std::runtime_error(tOp.c_str()); }; } #define LAUNCH(A, B, C, D) <<>> @@ -1228,8 +1228,8 @@ /// graph definitions -#define REQUIRE_OK(A) if (nd4j::ops::resultHelper( (A), #A, __FILE__, __LINE__ ) != 0) return ND4J_STATUS_VALIDATION; -#define REQUIRE_TRUE(...) if (nd4j::ops::conditionHelper(__FILE__, __LINE__, __VA_ARGS__) != 0) throw std::invalid_argument("Op validation failed"); +#define REQUIRE_OK(A) if (sd::ops::resultHelper( (A), #A, __FILE__, __LINE__ ) != 0) return ND4J_STATUS_VALIDATION; +#define REQUIRE_TRUE(COND, ...) if (!(COND)) { if (sd::ops::conditionHelper(__FILE__, __LINE__, COND, __VA_ARGS__) != 0) throw std::invalid_argument("Op validation failed");}; #define DECLARE_ENTRY(NAME, ...) template struct ND4J_EXPORT __registratorFloat>; \ template struct ND4J_EXPORT __registratorHalf>; \ @@ -1243,13 +1243,13 @@ #define NOT_EXCLUDED(NAME) 1>0 #else // for now we don't want minifier mechanics working -//#define NOT_EXCLUDED(NAME) defined(LIBND4J_ALL_OPS) || defined(NAME) +//#define NOT_EXCLUDED(NAME) defined(SD_ALL_OPS) || defined(NAME) #define NOT_EXCLUDED(NAME) 1>0 #endif #ifdef __JAVACPP_HACK__ #define REGISTER_H(NAME) -#elif defined(LIBND4J_ALL_OPS) +#elif defined(SD_ALL_OPS) #define REGISTER_H(NAME) #else #define REGISTER_H(NAME) template \ @@ -1259,12 +1259,12 @@ OpRegistrator::getInstance()->registerOperation(ptr); \ }\ };\ - static nd4j::ops::__registrator_##NAME zzz_register_opd_##NAME; + static sd::ops::__registrator_##NAME zzz_register_opd_##NAME; #endif #ifdef __JAVACPP_HACK__ #define REGISTER_C(NAME) -#elif defined(LIBND4J_ALL_OPS) +#elif defined(SD_ALL_OPS) #define REGISTER_C(NAME) template \ struct __registrator_##NAME {\ __registrator_##NAME() {\ @@ -1272,63 +1272,63 @@ OpRegistrator::getInstance()->registerOperation(ptr); \ }\ };\ - static nd4j::ops::__registrator_##NAME zzz_register_opd_##NAME; + static sd::ops::__registrator_##NAME zzz_register_opd_##NAME; #else #define REGISTER_C(NAME) #endif -#define DECLARE_OP(NAME, NIN, NOUT, INPLACEABLE) class ND4J_EXPORT NAME: public nd4j::ops::DeclarableOp { \ +#define DECLARE_OP(NAME, NIN, NOUT, INPLACEABLE) class ND4J_EXPORT NAME: public sd::ops::DeclarableOp { \ public:\ NAME(); \ - nd4j::ShapeList* calculateOutputShape(nd4j::ShapeList* inputShape, nd4j::graph::Context& block); \ + sd::ShapeList* calculateOutputShape(sd::ShapeList* inputShape, sd::graph::Context& block); \ protected: \ void registerTypes(); \ - Nd4jStatus validateAndExecute(nd4j::graph::Context& block); \ + Nd4jStatus validateAndExecute(sd::graph::Context& block); \ };\ REGISTER_H(NAME) -#define DECLARE_BOOLEAN_OP(NAME, NIN, SCALAR) class ND4J_EXPORT NAME: public nd4j::ops::BooleanOp { \ +#define DECLARE_BOOLEAN_OP(NAME, NIN, SCALAR) class ND4J_EXPORT NAME: public sd::ops::BooleanOp { \ public:\ NAME(); \ protected: \ void registerTypes(); \ - Nd4jStatus validateAndExecute(nd4j::graph::Context& block); \ + Nd4jStatus validateAndExecute(sd::graph::Context& block); \ }; \ REGISTER_H(NAME) -#define BOOLEAN_OP_IMPL(NAME, NIN, SCALAR) NAME::NAME() : nd4j::ops::BooleanOp(#NAME, NIN, SCALAR) { }; \ +#define BOOLEAN_OP_IMPL(NAME, NIN, SCALAR) NAME::NAME() : sd::ops::BooleanOp(#NAME, NIN, SCALAR) { }; \ REGISTER_C(NAME) \ - Nd4jStatus nd4j::ops::NAME::validateAndExecute(nd4j::graph::Context& block) + Nd4jStatus sd::ops::NAME::validateAndExecute(sd::graph::Context& block) -#define DECLARE_LIST_OP(NAME, NIN, NOUT, TARGS, IARGS) class ND4J_EXPORT NAME: public nd4j::ops::DeclarableListOp { \ +#define DECLARE_LIST_OP(NAME, NIN, NOUT, TARGS, IARGS) class ND4J_EXPORT NAME: public sd::ops::DeclarableListOp { \ public:\ NAME(); \ protected: \ - Nd4jStatus validateAndExecute(nd4j::graph::Context& block); \ + Nd4jStatus validateAndExecute(sd::graph::Context& block); \ };\ REGISTER_H(NAME) -#define LIST_OP_IMPL(NAME, NIN, NOUT, TARGS, IARGS) NAME::NAME() : nd4j::ops::DeclarableListOp(NIN, NOUT, #NAME, TARGS, IARGS) { }; \ +#define LIST_OP_IMPL(NAME, NIN, NOUT, TARGS, IARGS) NAME::NAME() : sd::ops::DeclarableListOp(NIN, NOUT, #NAME, TARGS, IARGS) { }; \ REGISTER_C(NAME) \ - Nd4jStatus nd4j::ops::NAME::validateAndExecute(nd4j::graph::Context& block) + Nd4jStatus sd::ops::NAME::validateAndExecute(sd::graph::Context& block) -#define DECLARE_LOGIC_OP(NAME) class ND4J_EXPORT NAME: public nd4j::ops::LogicOp { \ +#define DECLARE_LOGIC_OP(NAME) class ND4J_EXPORT NAME: public sd::ops::LogicOp { \ public:\ NAME(); \ protected: \ - Nd4jStatus validateAndExecute(nd4j::graph::Context& block); \ + Nd4jStatus validateAndExecute(sd::graph::Context& block); \ };\ REGISTER_H(NAME) -#define LOGIC_OP_IMPL(NAME) NAME::NAME() : nd4j::ops::LogicOp(#NAME) { }; \ +#define LOGIC_OP_IMPL(NAME) NAME::NAME() : sd::ops::LogicOp(#NAME) { }; \ REGISTER_C(NAME) \ - Nd4jStatus nd4j::ops::NAME::validateAndExecute(nd4j::graph::Context& block) { return nd4j::ops::LogicOp::validateAndExecute(block); }; + Nd4jStatus sd::ops::NAME::validateAndExecute(sd::graph::Context& block) { return sd::ops::LogicOp::validateAndExecute(block); }; -#define OP_IMPL(NAME, NIN, NOUT, INPLACEABLE) NAME::NAME() : nd4j::ops::DeclarableOp(NIN, NOUT, #NAME, INPLACEABLE) { }; \ +#define OP_IMPL(NAME, NIN, NOUT, INPLACEABLE) NAME::NAME() : sd::ops::DeclarableOp(NIN, NOUT, #NAME, INPLACEABLE) { }; \ REGISTER_C(NAME) \ - nd4j::ShapeList* nd4j::ops::NAME::calculateOutputShape(nd4j::ShapeList* inputShape, nd4j::graph::Context& block) { \ + sd::ShapeList* sd::ops::NAME::calculateOutputShape(sd::ShapeList* inputShape, sd::graph::Context& block) { \ auto shapeList = SHAPELIST(); \ auto opLimit = this->getOpDescriptor()->getNumberOfOutputs() < 1 ? block.width() : this->getOpDescriptor()->getNumberOfOutputs(); \ for (int e = 0; e < opLimit; e++) { \ @@ -1337,7 +1337,7 @@ } \ return shapeList; \ } \ - Nd4jStatus nd4j::ops::NAME::validateAndExecute(nd4j::graph::Context& block) + Nd4jStatus sd::ops::NAME::validateAndExecute(sd::graph::Context& block) #define DECLARE_SYN(NAME, ORIGINAL) template \ @@ -1347,26 +1347,26 @@ if (ptr == nullptr) { \ std::string newName(name); \ std::string oldName(oname); \ - OpRegistrator::getInstance()->updateMSVC(nd4j::ops::HashHelper::getInstance()->getLongHash(newName), oldName);\ + OpRegistrator::getInstance()->updateMSVC(sd::ops::HashHelper::getInstance()->getLongHash(newName), oldName);\ return;\ }\ OpRegistrator::getInstance()->registerOperation(name, ptr);\ }\ };\ - static nd4j::ops::__registratorSynonym_##NAME zzz_register_opd_##NAME(#NAME, #ORIGINAL) + static sd::ops::__registratorSynonym_##NAME zzz_register_opd_##NAME(#NAME, #ORIGINAL) -#define DECLARE_DIVERGENT_OP(NAME, NIN, NOUT, INPLACEABLE) class ND4J_EXPORT NAME: public nd4j::ops::DeclarableOp { \ +#define DECLARE_DIVERGENT_OP(NAME, NIN, NOUT, INPLACEABLE) class ND4J_EXPORT NAME: public sd::ops::DeclarableOp { \ public:\ NAME(); \ - nd4j::ShapeList* calculateOutputShape(nd4j::ShapeList* inputShape, nd4j::graph::Context& block); \ + sd::ShapeList* calculateOutputShape(sd::ShapeList* inputShape, sd::graph::Context& block); \ protected: \ - Nd4jStatus validateAndExecute(nd4j::graph::Context& block); \ + Nd4jStatus validateAndExecute(sd::graph::Context& block); \ };\ REGISTER_H(NAME) -#define DIVERGENT_OP_IMPL(NAME, NIN, NOUT, INPLACEABLE) NAME::NAME() : nd4j::ops::DeclarableOp(NIN, NOUT, #NAME, INPLACEABLE, true) { }; \ +#define DIVERGENT_OP_IMPL(NAME, NIN, NOUT, INPLACEABLE) NAME::NAME() : sd::ops::DeclarableOp(NIN, NOUT, #NAME, INPLACEABLE, true) { }; \ REGISTER_C(NAME) \ - nd4j::ShapeList* nd4j::ops::NAME::calculateOutputShape(nd4j::ShapeList* inputShape, nd4j::graph::Context& block) { \ + sd::ShapeList* sd::ops::NAME::calculateOutputShape(sd::ShapeList* inputShape, sd::graph::Context& block) { \ auto shapeList = SHAPELIST(); \ auto opLimit = this->getOpDescriptor()->getNumberOfOutputs() < 1 ? block.width() : this->getOpDescriptor()->getNumberOfOutputs(); \ for (int e = 0; e < opLimit; e++) { \ @@ -1376,21 +1376,21 @@ } \ return shapeList; \ } \ - Nd4jStatus nd4j::ops::NAME::validateAndExecute(nd4j::graph::Context& block) + Nd4jStatus sd::ops::NAME::validateAndExecute(sd::graph::Context& block) -#define DECLARE_CONFIGURABLE_OP(NAME, NIN, NOUT, INPLACEABLE, TARGS, IARGS) class ND4J_EXPORT NAME: public nd4j::ops::DeclarableOp { \ +#define DECLARE_CONFIGURABLE_OP(NAME, NIN, NOUT, INPLACEABLE, TARGS, IARGS) class ND4J_EXPORT NAME: public sd::ops::DeclarableOp { \ public:\ NAME(); \ - nd4j::ShapeList* calculateOutputShape(nd4j::ShapeList* inputShape, nd4j::graph::Context& block); \ + sd::ShapeList* calculateOutputShape(sd::ShapeList* inputShape, sd::graph::Context& block); \ protected: \ void registerTypes(); \ - Nd4jStatus validateAndExecute(nd4j::graph::Context& block); \ + Nd4jStatus validateAndExecute(sd::graph::Context& block); \ };\ REGISTER_H(NAME) -#define CONFIGURABLE_OP_IMPL(NAME, NIN, NOUT, INPLACEABLE, TARGS, IARGS) NAME::NAME() : nd4j::ops::DeclarableOp(NIN, NOUT, #NAME, INPLACEABLE, TARGS, IARGS) { }; \ +#define CONFIGURABLE_OP_IMPL(NAME, NIN, NOUT, INPLACEABLE, TARGS, IARGS) NAME::NAME() : sd::ops::DeclarableOp(NIN, NOUT, #NAME, INPLACEABLE, TARGS, IARGS) { }; \ REGISTER_C(NAME) \ - nd4j::ShapeList* nd4j::ops::NAME::calculateOutputShape(nd4j::ShapeList* inputShape, nd4j::graph::Context& block) { \ + sd::ShapeList* sd::ops::NAME::calculateOutputShape(sd::ShapeList* inputShape, sd::graph::Context& block) { \ auto shapeList = SHAPELIST(); \ auto opLimit = this->getOpDescriptor()->getNumberOfOutputs() < 1 ? block.width() : this->getOpDescriptor()->getNumberOfOutputs(); \ for (int e = 0; e < opLimit; e++) { \ @@ -1399,9 +1399,9 @@ } \ return shapeList; \ } \ - Nd4jStatus nd4j::ops::NAME::validateAndExecute(Context& block) + Nd4jStatus sd::ops::NAME::validateAndExecute(Context& block) -#define DECLARE_REDUCTION_OP(NAME, NIN, NOUT, INPLACEABLE, TARGS, IARGS) class ND4J_EXPORT NAME: public nd4j::ops::DeclarableReductionOp { \ +#define DECLARE_REDUCTION_OP(NAME, NIN, NOUT, INPLACEABLE, TARGS, IARGS) class ND4J_EXPORT NAME: public sd::ops::DeclarableReductionOp { \ public:\ NAME(); \ protected: \ @@ -1410,34 +1410,34 @@ };\ REGISTER_H(NAME) -#define REDUCTION_OP_IMPL(NAME, NIN, NOUT, INPLACEABLE, TARGS, IARGS) NAME::NAME() : nd4j::ops::DeclarableReductionOp(NIN, NOUT, #NAME, INPLACEABLE, TARGS, IARGS) { }; \ +#define REDUCTION_OP_IMPL(NAME, NIN, NOUT, INPLACEABLE, TARGS, IARGS) NAME::NAME() : sd::ops::DeclarableReductionOp(NIN, NOUT, #NAME, INPLACEABLE, TARGS, IARGS) { }; \ REGISTER_C(NAME) \ - Nd4jStatus nd4j::ops::NAME::validateAndExecute(nd4j::graph::Context& block) + Nd4jStatus sd::ops::NAME::validateAndExecute(sd::graph::Context& block) -#define DECLARE_CUSTOM_OP(NAME, NIN, NOUT, INPLACEABLE, TARGS, IARGS) class ND4J_EXPORT NAME: public nd4j::ops::DeclarableCustomOp { \ +#define DECLARE_CUSTOM_OP(NAME, NIN, NOUT, INPLACEABLE, TARGS, IARGS) class ND4J_EXPORT NAME: public sd::ops::DeclarableCustomOp { \ protected: \ void registerTypes(); \ Nd4jStatus validateAndExecute(Context& block); \ public:\ NAME(); \ - nd4j::ShapeList* calculateOutputShape(nd4j::ShapeList* inputShape, nd4j::graph::Context& block); \ + sd::ShapeList* calculateOutputShape(sd::ShapeList* inputShape, sd::graph::Context& block); \ };\ REGISTER_H(NAME) -#define CUSTOM_OP_IMPL(NAME, NIN, NOUT, INPLACEABLE, TARGS, IARGS) NAME::NAME(): nd4j::ops::DeclarableCustomOp(NIN, NOUT, #NAME, INPLACEABLE, TARGS, IARGS) { }; \ +#define CUSTOM_OP_IMPL(NAME, NIN, NOUT, INPLACEABLE, TARGS, IARGS) NAME::NAME(): sd::ops::DeclarableCustomOp(NIN, NOUT, #NAME, INPLACEABLE, TARGS, IARGS) { }; \ REGISTER_C(NAME) \ - Nd4jStatus nd4j::ops::NAME::validateAndExecute(nd4j::graph::Context& block) + Nd4jStatus sd::ops::NAME::validateAndExecute(sd::graph::Context& block) // this declaration MUST follow DECLARE_CUSTOM_OP -#define DECLARE_SHAPE_FN(NAME) nd4j::ShapeList* nd4j::ops::NAME::calculateOutputShape(nd4j::ShapeList* inputShape, nd4j::graph::Context& block) +#define DECLARE_SHAPE_FN(NAME) sd::ShapeList* sd::ops::NAME::calculateOutputShape(sd::ShapeList* inputShape, sd::graph::Context& block) -#define DECLARE_SAME_TYPE(NAME) void nd4j::ops::NAME::registerTypes() {this->getOpDescriptor()->setSameMode(true);} +#define DECLARE_SAME_TYPE(NAME) void sd::ops::NAME::registerTypes() {this->getOpDescriptor()->setSameMode(true);} -#define DECLARE_TYPES(NAME) void nd4j::ops::NAME::registerTypes() +#define DECLARE_TYPES(NAME) void sd::ops::NAME::registerTypes() -#define DECLARE_BROADCASTABLE_OP(NAME,TARGS, IARGS) class ND4J_EXPORT NAME: public nd4j::ops::BroadcastableOp { \ +#define DECLARE_BROADCASTABLE_OP(NAME,TARGS, IARGS) class ND4J_EXPORT NAME: public sd::ops::BroadcastableOp { \ protected: \ void registerTypes(); \ Nd4jStatus validateAndExecute(Context& block); \ @@ -1446,17 +1446,17 @@ };\ REGISTER_H(NAME) -#define BROADCASTABLE_OP_IMPL(NAME, TARGS, IARGS) NAME::NAME(): nd4j::ops::BroadcastableOp(#NAME, TARGS, IARGS) { }; \ +#define BROADCASTABLE_OP_IMPL(NAME, TARGS, IARGS) NAME::NAME(): sd::ops::BroadcastableOp(#NAME, TARGS, IARGS) { }; \ REGISTER_C(NAME) \ - Nd4jStatus nd4j::ops::NAME::validateAndExecute(nd4j::graph::Context& block) + Nd4jStatus sd::ops::NAME::validateAndExecute(sd::graph::Context& block) #define DECLARE_DEVICE_OP(NAME, NIN, NOUT, INPLACEABLE, TARGS, IARGS) #define REPLICATE_SHAPE(SRC, TGT) if (shape::order(SRC) == 'c')\ - shape::shapeBuffer(shape::rank(SRC), nd4j::ArrayOptions::dataType(SRC), shape::shapeOf(SRC), TGT);\ + shape::shapeBuffer(shape::rank(SRC), sd::ArrayOptions::dataType(SRC), shape::shapeOf(SRC), TGT);\ else \ - shape::shapeBufferFortran(shape::rank(SRC), nd4j::ArrayOptions::dataType(SRC), shape::shapeOf(SRC), TGT);\ + shape::shapeBufferFortran(shape::rank(SRC), sd::ArrayOptions::dataType(SRC), shape::shapeOf(SRC), TGT);\ #ifdef __CUDABLAS__ @@ -1464,14 +1464,14 @@ #ifdef _RELEASE // we intentionally add 8 tail bytes here to avoid problems with atomic operations -#define ALLOCATE_SPECIAL(VARIABLE, WORKSPACE, LENGTH, TT) if (WORKSPACE == nullptr) {auto erc_##VARIABLE = cudaMalloc(reinterpret_cast(&VARIABLE), LENGTH * sizeof(TT) + 8); if (erc_##VARIABLE != 0) {throw cuda_exception::build("[DEVICE] allocation failed", erc_##VARIABLE);} else { }; } else {VARIABLE = reinterpret_cast(WORKSPACE->allocateBytes(nd4j::memory::MemoryType::DEVICE, LENGTH * sizeof(TT) + 8)); } +#define ALLOCATE_SPECIAL(VARIABLE, WORKSPACE, LENGTH, TT) if (WORKSPACE == nullptr) {auto erc_##VARIABLE = cudaMalloc(reinterpret_cast(&VARIABLE), LENGTH * sizeof(TT) + 8); if (erc_##VARIABLE != 0) {throw cuda_exception::build("[DEVICE] allocation failed", erc_##VARIABLE);} else { }; } else {VARIABLE = reinterpret_cast(WORKSPACE->allocateBytes(sd::memory::MemoryType::DEVICE, LENGTH * sizeof(TT) + 8)); } #define RELEASE_SPECIAL(VARIABLE, WORKSPACE) if (VARIABLE != nullptr) {if (WORKSPACE == nullptr) { auto erc_##VARIABLE = cudaFree(reinterpret_cast(VARIABLE)); if (erc_##VARIABLE != 0) {throw cuda_exception::build("[DEVICE] deallocation failed", erc_##VARIABLE);}; }; }; #else // we intentionally add 8 tail bytes here to avoid problems with atomic operations -#define ALLOCATE_SPECIAL(VARIABLE, WORKSPACE, LENGTH, TT) if (WORKSPACE == nullptr) {auto erc_##VARIABLE = cudaMalloc(reinterpret_cast(&VARIABLE), LENGTH * sizeof(TT) + 8); if (erc_##VARIABLE != 0) {throw cuda_exception::build("[DEVICE] allocation failed", erc_##VARIABLE);} else { nd4j::memory::MemoryTracker::getInstance()->countIn(nd4j::memory::MemoryType::DEVICE, VARIABLE, LENGTH * sizeof(TT)); }; } else {VARIABLE = reinterpret_cast(WORKSPACE->allocateBytes(nd4j::memory::MemoryType::DEVICE, LENGTH * sizeof(TT) + 8)); } -#define RELEASE_SPECIAL(VARIABLE, WORKSPACE) if (VARIABLE != nullptr) {if (WORKSPACE == nullptr) { nd4j::memory::MemoryTracker::getInstance()->countOut(VARIABLE); auto erc_##VARIABLE = cudaFree(reinterpret_cast(VARIABLE)); if (erc_##VARIABLE != 0) {throw cuda_exception::build("[DEVICE] deallocation failed", erc_##VARIABLE);}; }; }; +#define ALLOCATE_SPECIAL(VARIABLE, WORKSPACE, LENGTH, TT) if (WORKSPACE == nullptr) {auto erc_##VARIABLE = cudaMalloc(reinterpret_cast(&VARIABLE), LENGTH * sizeof(TT) + 8); if (erc_##VARIABLE != 0) {throw cuda_exception::build("[DEVICE] allocation failed", erc_##VARIABLE);} else { sd::memory::MemoryTracker::getInstance()->countIn(sd::memory::MemoryType::DEVICE, VARIABLE, LENGTH * sizeof(TT)); }; } else {VARIABLE = reinterpret_cast(WORKSPACE->allocateBytes(sd::memory::MemoryType::DEVICE, LENGTH * sizeof(TT) + 8)); } +#define RELEASE_SPECIAL(VARIABLE, WORKSPACE) if (VARIABLE != nullptr) {if (WORKSPACE == nullptr) { sd::memory::MemoryTracker::getInstance()->countOut(VARIABLE); auto erc_##VARIABLE = cudaFree(reinterpret_cast(VARIABLE)); if (erc_##VARIABLE != 0) {throw cuda_exception::build("[DEVICE] deallocation failed", erc_##VARIABLE);}; }; }; #endif @@ -1489,8 +1489,8 @@ #else -#define ALLOCATE(VARIABLE, WORKSPACE, LENGTH, TT) if (WORKSPACE == nullptr) {VARIABLE = new TT[LENGTH]; nd4j::memory::MemoryTracker::getInstance()->countIn(nd4j::memory::MemoryType::HOST, VARIABLE, LENGTH * sizeof(TT)); } else {VARIABLE = reinterpret_cast(WORKSPACE->allocateBytes(LENGTH * sizeof(TT))); }; memset(VARIABLE, 0, LENGTH * sizeof(TT)); -#define RELEASE(VARIABLE, WORKSPACE) if (WORKSPACE == nullptr) { nd4j::memory::MemoryTracker::getInstance()->countOut(VARIABLE); delete[] VARIABLE;}; +#define ALLOCATE(VARIABLE, WORKSPACE, LENGTH, TT) if (WORKSPACE == nullptr) {VARIABLE = new TT[LENGTH]; sd::memory::MemoryTracker::getInstance()->countIn(sd::memory::MemoryType::HOST, VARIABLE, LENGTH * sizeof(TT)); } else {VARIABLE = reinterpret_cast(WORKSPACE->allocateBytes(LENGTH * sizeof(TT))); }; memset(VARIABLE, 0, LENGTH * sizeof(TT)); +#define RELEASE(VARIABLE, WORKSPACE) if (WORKSPACE == nullptr) { sd::memory::MemoryTracker::getInstance()->countOut(VARIABLE); delete[] VARIABLE;}; #endif @@ -1512,9 +1512,9 @@ #define UNSTASH(NAME) block.getStash()->extractArray(block.getNodeId(), NAME); #define INPUT_VARIABLE(INDEX) block.array(INDEX) -#define OUTPUT_VARIABLE(INDEX) reinterpret_cast(this->getZ(block, INDEX)) +#define OUTPUT_VARIABLE(INDEX) reinterpret_cast(this->getZ(block, INDEX)) -#define INPUT_LIST(INDEX) reinterpret_cast(block.getVariable(INDEX)->getNDArrayList()) +#define INPUT_LIST(INDEX) reinterpret_cast(block.getVariable(INDEX)->getNDArrayList()) #define D_ARG(INDEX) block.getDArguments()->at(INDEX) #define INT_ARG(INDEX) block.getIArguments()->at(INDEX) @@ -1559,7 +1559,7 @@ #endif // CUDACC -#define CHECK_ALLOC(PTR, MSG, BYTES) if (PTR == nullptr) { throw nd4j::allocation_exception::build(MSG, BYTES); }; +#define CHECK_ALLOC(PTR, MSG, BYTES) if (PTR == nullptr) { throw sd::allocation_exception::build(MSG, BYTES); }; diff --git a/libnd4j/include/op_enums.h b/libnd4j/include/system/op_enums.h similarity index 97% rename from libnd4j/include/op_enums.h rename to libnd4j/include/system/op_enums.h index 8a100153f..ad16d281e 100644 --- a/libnd4j/include/op_enums.h +++ b/libnd4j/include/system/op_enums.h @@ -23,10 +23,10 @@ #define LIBND4J_OP_ENUMS_H #include -#include -#include +#include +#include -namespace nd4j { +namespace sd { namespace random { enum Ops { BUILD_ENUMERATION(RANDOM_OPS) diff --git a/libnd4j/include/openmp_pragmas.h b/libnd4j/include/system/openmp_pragmas.h similarity index 100% rename from libnd4j/include/openmp_pragmas.h rename to libnd4j/include/system/openmp_pragmas.h diff --git a/libnd4j/include/optype.h b/libnd4j/include/system/optype.h similarity index 100% rename from libnd4j/include/optype.h rename to libnd4j/include/system/optype.h diff --git a/libnd4j/include/pairwise_util.h b/libnd4j/include/system/pairwise_util.h similarity index 98% rename from libnd4j/include/pairwise_util.h rename to libnd4j/include/system/pairwise_util.h index e0ed79a7e..d9e0965c8 100755 --- a/libnd4j/include/pairwise_util.h +++ b/libnd4j/include/system/pairwise_util.h @@ -32,12 +32,12 @@ #define omp_set_num_threads(threads) #endif -#include +#include #include -#include -#include -#include -#include +#include +#include +#include +#include #ifdef _OPENMP #include #endif @@ -274,8 +274,8 @@ public: BlockInformation(Nd4jLong length, int threshold) { threads = length / threshold; - threads = (1 < threads)?threads:1;//nd4j::math::nd4j_max(1, threads); - threads = (threads < omp_get_max_threads())?threads:omp_get_max_threads();//nd4j::math::nd4j_min(threads, omp_get_max_threads()); + threads = (1 < threads)?threads:1;//sd::math::nd4j_max(1, threads); + threads = (threads < omp_get_max_threads())?threads:omp_get_max_threads();//sd::math::nd4j_min(threads, omp_get_max_threads()); items = length / threads; remainder = length % threads; diff --git a/libnd4j/include/platform_boilerplate.h b/libnd4j/include/system/platform_boilerplate.h similarity index 97% rename from libnd4j/include/platform_boilerplate.h rename to libnd4j/include/system/platform_boilerplate.h index 5c73a1b38..bdbb1a051 100644 --- a/libnd4j/include/platform_boilerplate.h +++ b/libnd4j/include/system/platform_boilerplate.h @@ -44,7 +44,7 @@ } \ }; \ static __registratorPlatformHelper_##CNAME platformHelper_##CNAME; \ - Nd4jStatus PLATFORM_##CNAME::invokeHelper(nd4j::graph::Context &block) + Nd4jStatus PLATFORM_##CNAME::invokeHelper(sd::graph::Context &block) #define PLATFORM_IMPL(NAME, ENGINE) PLATFORM_IMPL_F(NAME, ENGINE, NAME ##_## ENGINE) diff --git a/libnd4j/include/play.h b/libnd4j/include/system/play.h similarity index 86% rename from libnd4j/include/play.h rename to libnd4j/include/system/play.h index d0fecee82..9e121d88b 100644 --- a/libnd4j/include/play.h +++ b/libnd4j/include/system/play.h @@ -22,7 +22,7 @@ #define LIBND4J_PLAY_H //#include -#include +#include /* #define DATA_TYPES \ (DATA_FLOAT, float) ,\ @@ -71,10 +71,10 @@ DECLARE_PLATFORM(conv2d, ENGINE_CPU) */ /* #define NATIVE_LAYERS \ - (0, nd4j::layers::DenseLayer) -// (1, nd4j::layers::ConvolutionLayer) ,\ -// (2, nd4j::layers::Pooling2DLayer) ,\ -// (3, nd4j::layers::LSTMLayer) + (0, sd::layers::DenseLayer) +// (1, sd::layers::ConvolutionLayer) ,\ +// (2, sd::layers::Pooling2DLayer) ,\ +// (3, sd::layers::LSTMLayer) */ @@ -114,26 +114,26 @@ EXECUTE_NOE((x, y, extras), OPS_A(PAIRWISE_TRANSFORM_OPS)) //EXECUTE_NOE((x, extras), OPS_A(SCALAR_OPS)) -//BUILD_CALL_1(template void nd4j::NDArray::applyTransform, float16, (NDArray* a, float16* b), TRANSFORM_OPS) +//BUILD_CALL_1(template void sd::NDArray::applyTransform, float16, (NDArray* a, float16* b), TRANSFORM_OPS) -//BUILD_CALL_1(template void nd4j::NDArray::applyPairwiseTransform, float16, (NDArray* other, float16* extraParams), PAIRWISE_TRANSFORM_OPS) +//BUILD_CALL_1(template void sd::NDArray::applyPairwiseTransform, float16, (NDArray* other, float16* extraParams), PAIRWISE_TRANSFORM_OPS) //BUILD_TRACKER(TRANSFORM, ACTIVATIONS) -//BUILD_CALL_1(template void nd4j::NDArray::applyScalar, float16, (float16 scalar, NDArray* target, float16 *extraParams) , ACTIVATIONS); +//BUILD_CALL_1(template void sd::NDArray::applyScalar, float16, (float16 scalar, NDArray* target, float16 *extraParams) , ACTIVATIONS); /* #define DECLARE_OP(NAME, NIN, NOUT) DECLARE_OP_UNIQ(__COUNTER__, NAME, NIN, NOUT) #define DECLARE_OP_UNIQ(CTR, NAME, NIN, NOUT) template \ - class NAME: public nd4j::ops::DeclarableOp { \ + class NAME: public sd::ops::DeclarableOp { \ public:\ - NAME() : nd4j::ops::DeclarableOp(NIN, NOUT, #NAME) { } \ + NAME() : sd::ops::DeclarableOp(NIN, NOUT, #NAME) { } \ protected: \ Nd4jStatus validateAndExecute(Block& block); \ };\ template \ - Nd4jStatus nd4j::ops::NAME::validateAndExecute(Block& block) + Nd4jStatus sd::ops::NAME::validateAndExecute(Block& block) */ -//#define END_OP(NAME) }; static nd4j::ops::__registrator> register_op##Name; +//#define END_OP(NAME) }; static sd::ops::__registrator> register_op##Name; //#DECLARE_OP(Concat, -1, 1) diff --git a/libnd4j/include/pointercast.h b/libnd4j/include/system/pointercast.h similarity index 85% rename from libnd4j/include/pointercast.h rename to libnd4j/include/system/pointercast.h index e080b33b6..2c64d608e 100644 --- a/libnd4j/include/pointercast.h +++ b/libnd4j/include/system/pointercast.h @@ -21,7 +21,7 @@ #ifndef NATIVEOPERATIONS_POINTERCAST_H #define NATIVEOPERATIONS_POINTERCAST_H -#include +#include #include typedef void* Nd4jPointer; @@ -60,5 +60,28 @@ typedef int Nd4jStatus; #define ND4J_STATUS_MAYBE 119 +#ifdef _MSC_VER + +#include +#define MAP_IMPL std::map + +#elif __clang__ + +#include +#define MAP_IMPL std::unordered_map + +#elif __GNUC__ + +#include +#define MAP_IMPL std::unordered_map + +#else + +#include +#define MAP_IMPL std::unordered_map + +#endif + + #endif //NATIVEOPERATIONS_POINTERCAST_H diff --git a/libnd4j/include/type_boilerplate.h b/libnd4j/include/system/type_boilerplate.h similarity index 99% rename from libnd4j/include/type_boilerplate.h rename to libnd4j/include/system/type_boilerplate.h index af0fe369d..997fcab22 100644 --- a/libnd4j/include/type_boilerplate.h +++ b/libnd4j/include/system/type_boilerplate.h @@ -631,12 +631,12 @@ #define RANDOMTRIPLE(NAME, SIGNATURE, TYPES_X, TYPES_Y, TYPE_Z) _RANDOMTRIPLE(NAME, SIGNATURE, TYPE_Z, TYPES_X, TYPES_Y) -#define BROADCAST(NAME) nd4j::BroadcastOpsTuple::custom(nd4j::scalar::NAME, nd4j::pairwise::NAME, nd4j::broadcast::NAME) -#define BROADCAST_BOOL(NAME) nd4j::BroadcastBoolOpsTuple::custom(nd4j::scalar::NAME, nd4j::pairwise::NAME, nd4j::broadcast::NAME) +#define BROADCAST(NAME) sd::BroadcastOpsTuple::custom(sd::scalar::NAME, sd::pairwise::NAME, sd::broadcast::NAME) +#define BROADCAST_BOOL(NAME) sd::BroadcastBoolOpsTuple::custom(sd::scalar::NAME, sd::pairwise::NAME, sd::broadcast::NAME) -#define ALL_STRINGS nd4j::DataType::UTF8, nd4j::DataType::UTF16, nd4j::DataType::UTF32 -#define ALL_INDICES nd4j::DataType::INT32, nd4j::DataType::INT64 -#define ALL_INTS nd4j::DataType::INT8, nd4j::DataType::UINT8, nd4j::DataType::INT16, nd4j::DataType::UINT16, nd4j::DataType::INT32, nd4j::DataType::UINT32, nd4j::DataType::INT64, nd4j::DataType::UINT64 -#define ALL_FLOATS nd4j::DataType::HALF, nd4j::DataType::FLOAT32, nd4j::DataType::DOUBLE, nd4j::DataType::BFLOAT16 +#define ALL_STRINGS sd::DataType::UTF8, sd::DataType::UTF16, sd::DataType::UTF32 +#define ALL_INDICES sd::DataType::INT32, sd::DataType::INT64 +#define ALL_INTS sd::DataType::INT8, sd::DataType::UINT8, sd::DataType::INT16, sd::DataType::UINT16, sd::DataType::INT32, sd::DataType::UINT32, sd::DataType::INT64, sd::DataType::UINT64 +#define ALL_FLOATS sd::DataType::HALF, sd::DataType::FLOAT32, sd::DataType::DOUBLE, sd::DataType::BFLOAT16 #endif //TESTS_CPU_TYPE_BOILERPLATE_H diff --git a/libnd4j/include/util.h b/libnd4j/include/system/util.h similarity index 97% rename from libnd4j/include/util.h rename to libnd4j/include/system/util.h index 153f7f4ae..aa2055606 100644 --- a/libnd4j/include/util.h +++ b/libnd4j/include/system/util.h @@ -30,7 +30,7 @@ #include #endif -#include "pointercast.h" +#include "system/pointercast.h" static inline Nd4jLong microTime() { #ifdef WIN32 diff --git a/libnd4j/include/types/bfloat16.h b/libnd4j/include/types/bfloat16.h index 847c2ebda..a05909816 100644 --- a/libnd4j/include/types/bfloat16.h +++ b/libnd4j/include/types/bfloat16.h @@ -43,7 +43,7 @@ #define local_def inline #endif -//namespace nd4j +//namespace sd //{ struct bfloat16 { diff --git a/libnd4j/include/types/float16.h b/libnd4j/include/types/float16.h index 4aa0d5d66..761e66f1b 100644 --- a/libnd4j/include/types/float16.h +++ b/libnd4j/include/types/float16.h @@ -20,7 +20,7 @@ #include #include #include -#include +#include #if defined(__INTEL_COMPILER) || defined(SD_F16C) #include #endif diff --git a/libnd4j/include/types/float8.h b/libnd4j/include/types/float8.h index d7e4b80a3..6dc03bba4 100644 --- a/libnd4j/include/types/float8.h +++ b/libnd4j/include/types/float8.h @@ -33,10 +33,10 @@ #endif */ -#include +#include -namespace nd4j { +namespace sd { typedef struct { unsigned char x; diff --git a/libnd4j/include/types/impl/float8.cpp b/libnd4j/include/types/impl/float8.cpp index f5c6d2e9a..b36846e35 100644 --- a/libnd4j/include/types/impl/float8.cpp +++ b/libnd4j/include/types/impl/float8.cpp @@ -20,7 +20,7 @@ #include -namespace nd4j { +namespace sd { /* template float8::float8(const float& rhs); diff --git a/libnd4j/include/types/impl/int16.cpp b/libnd4j/include/types/impl/int16.cpp index 50529aa97..67f90e9d8 100644 --- a/libnd4j/include/types/impl/int16.cpp +++ b/libnd4j/include/types/impl/int16.cpp @@ -20,7 +20,7 @@ #include -namespace nd4j { +namespace sd { /* template int16::int16(const float& rhs); diff --git a/libnd4j/include/types/impl/int8.cpp b/libnd4j/include/types/impl/int8.cpp index 6b0d5a659..030695f96 100644 --- a/libnd4j/include/types/impl/int8.cpp +++ b/libnd4j/include/types/impl/int8.cpp @@ -20,7 +20,7 @@ #include -namespace nd4j { +namespace sd { /* template int8::int8(const float& rhs); diff --git a/libnd4j/include/types/impl/pair.cpp b/libnd4j/include/types/impl/pair.cpp index 910bf6274..767bfa630 100644 --- a/libnd4j/include/types/impl/pair.cpp +++ b/libnd4j/include/types/impl/pair.cpp @@ -20,7 +20,7 @@ #include -namespace nd4j { +namespace sd { Pair::Pair(int first, int second) { _first = first; _second = second; diff --git a/libnd4j/include/types/impl/triple.cpp b/libnd4j/include/types/impl/triple.cpp index b01afb680..0b39d4bac 100644 --- a/libnd4j/include/types/impl/triple.cpp +++ b/libnd4j/include/types/impl/triple.cpp @@ -20,7 +20,7 @@ #include -namespace nd4j { +namespace sd { int Triple::first() const { return _first; } diff --git a/libnd4j/include/types/impl/uint16.cpp b/libnd4j/include/types/impl/uint16.cpp index c15f71021..5b858222d 100644 --- a/libnd4j/include/types/impl/uint16.cpp +++ b/libnd4j/include/types/impl/uint16.cpp @@ -18,10 +18,10 @@ // @author raver119@gmail.com // -#include +#include #include -namespace nd4j { +namespace sd { /* diff --git a/libnd4j/include/types/impl/uint8.cpp b/libnd4j/include/types/impl/uint8.cpp index 6e9423f97..a6d25c9d3 100644 --- a/libnd4j/include/types/impl/uint8.cpp +++ b/libnd4j/include/types/impl/uint8.cpp @@ -20,7 +20,7 @@ #include -namespace nd4j { +namespace sd { /* template uint8::uint8(const float& rhs); diff --git a/libnd4j/include/types/impl/utf8string.cpp b/libnd4j/include/types/impl/utf8string.cpp index 8cfeecb5c..a7df7cc28 100644 --- a/libnd4j/include/types/impl/utf8string.cpp +++ b/libnd4j/include/types/impl/utf8string.cpp @@ -21,7 +21,7 @@ #include #include -namespace nd4j { +namespace sd { utf8string::~utf8string() { if (_allocated) delete[] _buffer; diff --git a/libnd4j/include/types/int16.h b/libnd4j/include/types/int16.h index 695ba64fa..25a771381 100644 --- a/libnd4j/include/types/int16.h +++ b/libnd4j/include/types/int16.h @@ -22,10 +22,10 @@ #define LIBND4J_INT16_H #include -#include +#include -namespace nd4j { +namespace sd { float _CUDA_HD FORCEINLINE cpu_int162float(int16_t data); int16_t _CUDA_HD FORCEINLINE cpu_float2int16(float data); diff --git a/libnd4j/include/types/int8.h b/libnd4j/include/types/int8.h index eb7f5a3c8..19e1b91e1 100644 --- a/libnd4j/include/types/int8.h +++ b/libnd4j/include/types/int8.h @@ -22,10 +22,10 @@ #define LIBND4J_INT8_H #include -#include +#include -namespace nd4j { +namespace sd { float _CUDA_HD FORCEINLINE cpu_int82float(int8_t data); int8_t _CUDA_HD FORCEINLINE cpu_float2int8(float data); diff --git a/libnd4j/include/types/pair.h b/libnd4j/include/types/pair.h index 28d25f5ea..0471c45ed 100644 --- a/libnd4j/include/types/pair.h +++ b/libnd4j/include/types/pair.h @@ -21,9 +21,9 @@ #ifndef LIBND4J_PAIR_H #define LIBND4J_PAIR_H -#include +#include -namespace nd4j { +namespace sd { class ND4J_EXPORT Pair { protected: int _first = 0; diff --git a/libnd4j/include/types/triple.h b/libnd4j/include/types/triple.h index 8084c48e8..0a5310265 100644 --- a/libnd4j/include/types/triple.h +++ b/libnd4j/include/types/triple.h @@ -22,9 +22,9 @@ #define LIBND4J_TRIPLE_H -#include +#include -namespace nd4j { +namespace sd { class ND4J_EXPORT Triple { protected: int _first = 0; diff --git a/libnd4j/include/types/types.h b/libnd4j/include/types/types.h index 7322c6bd5..7717c8019 100644 --- a/libnd4j/include/types/types.h +++ b/libnd4j/include/types/types.h @@ -21,7 +21,7 @@ #ifndef LIBND4J_TYPES_H #define LIBND4J_TYPES_H -#include +#include #include #include #include @@ -30,175 +30,199 @@ #include #include #include -#include +#include #define LIBND4J_STRINGTYPES \ - (nd4j::DataType::UTF8, std::string),\ - (nd4j::DataType::UTF16, std::u16string), \ - (nd4j::DataType::UTF32, std::u32string) + (sd::DataType::UTF8, std::string),\ + (sd::DataType::UTF16, std::u16string), \ + (sd::DataType::UTF32, std::u32string) #define LIBND4J_TYPES \ - (nd4j::DataType::BFLOAT16, bfloat16),\ - (nd4j::DataType::HALF, float16), \ - (nd4j::DataType::FLOAT32, float), \ - (nd4j::DataType::DOUBLE, double), \ - (nd4j::DataType::BOOL, bool), \ - (nd4j::DataType::INT8, int8_t), \ - (nd4j::DataType::UINT8, uint8_t), \ - (nd4j::DataType::UINT16, uint16_t), \ - (nd4j::DataType::UINT32, uint32_t), \ - (nd4j::DataType::UINT64, uint64_t), \ - (nd4j::DataType::INT16, int16_t), \ - (nd4j::DataType::INT32, int32_t), \ - (nd4j::DataType::INT64, Nd4jLong) + (sd::DataType::BFLOAT16, bfloat16),\ + (sd::DataType::HALF, float16), \ + (sd::DataType::FLOAT32, float), \ + (sd::DataType::DOUBLE, double), \ + (sd::DataType::BOOL, bool), \ + (sd::DataType::INT8, int8_t), \ + (sd::DataType::UINT8, uint8_t), \ + (sd::DataType::UINT16, uint16_t), \ + (sd::DataType::UINT32, uint32_t), \ + (sd::DataType::UINT64, uint64_t), \ + (sd::DataType::INT16, int16_t), \ + (sd::DataType::INT32, int32_t), \ + (sd::DataType::INT64, Nd4jLong) #define LIBND4J_TYPES_EXTENDED \ - (nd4j::DataType::HALF, float16), \ - (nd4j::DataType::FLOAT32, float), \ - (nd4j::DataType::DOUBLE, double), \ - (nd4j::DataType::BOOL, bool), \ - (nd4j::DataType::INT8, int8_t), \ - (nd4j::DataType::UINT8, uint8_t), \ - (nd4j::DataType::INT16, int16_t), \ - (nd4j::DataType::INT32, int32_t), \ - (nd4j::DataType::INT64, Nd4jLong), \ - (nd4j::DataType::UINT16, uint16_t), \ - (nd4j::DataType::UINT64, Nd4jULong), \ - (nd4j::DataType::UINT32, uint32_t), \ - (nd4j::DataType::BFLOAT16, bfloat16) + (sd::DataType::HALF, float16), \ + (sd::DataType::FLOAT32, float), \ + (sd::DataType::DOUBLE, double), \ + (sd::DataType::BOOL, bool), \ + (sd::DataType::INT8, int8_t), \ + (sd::DataType::UINT8, uint8_t), \ + (sd::DataType::INT16, int16_t), \ + (sd::DataType::INT32, int32_t), \ + (sd::DataType::INT64, Nd4jLong), \ + (sd::DataType::UINT16, uint16_t), \ + (sd::DataType::UINT64, Nd4jULong), \ + (sd::DataType::UINT32, uint32_t), \ + (sd::DataType::BFLOAT16, bfloat16) #define BOOL_TYPES \ - (nd4j::DataType::BOOL, bool) + (sd::DataType::BOOL, bool) #define LONG_TYPES \ - (nd4j::DataType::INT64, Nd4jLong),\ - (nd4j::DataType::UINT64, uint64_t) + (sd::DataType::INT64, Nd4jLong),\ + (sd::DataType::UINT64, uint64_t) #define FLOAT_TYPES \ - (nd4j::DataType::BFLOAT16, bfloat16) ,\ - (nd4j::DataType::HALF, float16), \ - (nd4j::DataType::FLOAT32, float), \ - (nd4j::DataType::DOUBLE, double) + (sd::DataType::BFLOAT16, bfloat16) ,\ + (sd::DataType::HALF, float16), \ + (sd::DataType::FLOAT32, float), \ + (sd::DataType::DOUBLE, double) #define INDEXING_TYPES \ - (nd4j::DataType::INT32, int32_t), \ - (nd4j::DataType::INT64, Nd4jLong) + (sd::DataType::INT32, int32_t), \ + (sd::DataType::INT64, Nd4jLong) #define FLOAT_NATIVE \ - (nd4j::DataType::FLOAT32, float), \ - (nd4j::DataType::DOUBLE, double) + (sd::DataType::FLOAT32, float), \ + (sd::DataType::DOUBLE, double) #define FLOAT_TYPES_0 \ - (nd4j::DataType::HALF, float16) + (sd::DataType::HALF, float16) #define FLOAT_TYPES_1 \ - (nd4j::DataType::FLOAT32, float) + (sd::DataType::FLOAT32, float) #define FLOAT_TYPES_2 \ - (nd4j::DataType::DOUBLE, double) + (sd::DataType::DOUBLE, double) #define FLOAT_TYPES_3 \ - (nd4j::DataType::BFLOAT16, bfloat16) + (sd::DataType::BFLOAT16, bfloat16) #define LIBND4J_TYPES_0 \ - (nd4j::DataType::HALF, float16) + (sd::DataType::HALF, float16) #define LIBND4J_TYPES_1 \ - (nd4j::DataType::FLOAT32, float) + (sd::DataType::FLOAT32, float) #define LIBND4J_TYPES_2 \ - (nd4j::DataType::DOUBLE, double) + (sd::DataType::DOUBLE, double) #define LIBND4J_TYPES_3 \ - (nd4j::DataType::BOOL, bool) + (sd::DataType::BOOL, bool) #define LIBND4J_TYPES_4 \ - (nd4j::DataType::INT8, int8_t) + (sd::DataType::INT8, int8_t) #define LIBND4J_TYPES_5 \ - (nd4j::DataType::UINT8, uint8_t) + (sd::DataType::UINT8, uint8_t) #define LIBND4J_TYPES_6 \ - (nd4j::DataType::INT16, int16_t),\ - (nd4j::DataType::UINT16, uint16_t) + (sd::DataType::INT16, int16_t),\ + (sd::DataType::UINT16, uint16_t) #define LIBND4J_TYPES_7 \ - (nd4j::DataType::INT32, int32_t), \ - (nd4j::DataType::UINT32, uint32_t) + (sd::DataType::INT32, int32_t), \ + (sd::DataType::UINT32, uint32_t) #define LIBND4J_TYPES_8 \ - (nd4j::DataType::INT64, Nd4jLong),\ - (nd4j::DataType::UINT64, uint64_t) + (sd::DataType::INT64, Nd4jLong),\ + (sd::DataType::UINT64, uint64_t) #define LIBND4J_TYPES_9 \ - (nd4j::DataType::BFLOAT16, bfloat16) + (sd::DataType::BFLOAT16, bfloat16) #define INTEGER_TYPES \ - (nd4j::DataType::INT8, int8_t), \ - (nd4j::DataType::UINT8, uint8_t), \ - (nd4j::DataType::UINT16, uint16_t), \ - (nd4j::DataType::UINT32, uint32_t), \ - (nd4j::DataType::UINT64, uint64_t), \ - (nd4j::DataType::INT16, int16_t), \ - (nd4j::DataType::INT32, int32_t), \ - (nd4j::DataType::INT64, Nd4jLong) + (sd::DataType::INT8, int8_t), \ + (sd::DataType::UINT8, uint8_t), \ + (sd::DataType::UINT16, uint16_t), \ + (sd::DataType::UINT32, uint32_t), \ + (sd::DataType::UINT64, uint64_t), \ + (sd::DataType::INT16, int16_t), \ + (sd::DataType::INT32, int32_t), \ + (sd::DataType::INT64, Nd4jLong) + +#define INTEGER_TYPES_0 \ + (sd::DataType::INT8, int8_t) + +#define INTEGER_TYPES_1 \ + (sd::DataType::UINT8, uint8_t) + +#define INTEGER_TYPES_2 \ + (sd::DataType::UINT16, uint16_t) + +#define INTEGER_TYPES_3 \ + (sd::DataType::UINT32, uint32_t) + +#define INTEGER_TYPES_4 \ + (sd::DataType::UINT64, uint64_t) + +#define INTEGER_TYPES_5 \ + (sd::DataType::INT16, int16_t) + +#define INTEGER_TYPES_6 \ + (sd::DataType::INT32, int32_t) + +#define INTEGER_TYPES_7 \ + (sd::DataType::INT64, Nd4jLong) #define NUMERIC_TYPES \ - (nd4j::DataType::HALF, float16), \ - (nd4j::DataType::FLOAT32, float), \ - (nd4j::DataType::DOUBLE, double), \ - (nd4j::DataType::INT8, int8_t), \ - (nd4j::DataType::UINT8, uint8_t), \ - (nd4j::DataType::UINT16, uint16_t), \ - (nd4j::DataType::UINT32, uint32_t), \ - (nd4j::DataType::UINT64, uint64_t), \ - (nd4j::DataType::INT16, int16_t), \ - (nd4j::DataType::INT32, int32_t), \ - (nd4j::DataType::INT64, Nd4jLong), \ - (nd4j::DataType::BFLOAT16, bfloat16) + (sd::DataType::HALF, float16), \ + (sd::DataType::FLOAT32, float), \ + (sd::DataType::DOUBLE, double), \ + (sd::DataType::INT8, int8_t), \ + (sd::DataType::UINT8, uint8_t), \ + (sd::DataType::UINT16, uint16_t), \ + (sd::DataType::UINT32, uint32_t), \ + (sd::DataType::UINT64, uint64_t), \ + (sd::DataType::INT16, int16_t), \ + (sd::DataType::INT32, int32_t), \ + (sd::DataType::INT64, Nd4jLong), \ + (sd::DataType::BFLOAT16, bfloat16) #define NUMERIC_TYPES_0 \ - (nd4j::DataType::HALF, float16) + (sd::DataType::HALF, float16) #define NUMERIC_TYPES_1 \ - (nd4j::DataType::FLOAT32, float) + (sd::DataType::FLOAT32, float) #define NUMERIC_TYPES_2 \ - (nd4j::DataType::DOUBLE, double) + (sd::DataType::DOUBLE, double) #define NUMERIC_TYPES_3 \ - (nd4j::DataType::INT8, int8_t), \ - (nd4j::DataType::BFLOAT16, bfloat16) + (sd::DataType::INT8, int8_t), \ + (sd::DataType::BFLOAT16, bfloat16) #define NUMERIC_TYPES_4 \ - (nd4j::DataType::UINT8, uint8_t) + (sd::DataType::UINT8, uint8_t) #define NUMERIC_TYPES_5 \ - (nd4j::DataType::UINT16, uint16_t) + (sd::DataType::UINT16, uint16_t) #define NUMERIC_TYPES_6 \ - (nd4j::DataType::UINT32, uint32_t) + (sd::DataType::UINT32, uint32_t) #define NUMERIC_TYPES_7 \ - (nd4j::DataType::UINT64, uint64_t) + (sd::DataType::UINT64, uint64_t) #define NUMERIC_TYPES_8 \ - (nd4j::DataType::INT16, int16_t) + (sd::DataType::INT16, int16_t) #define NUMERIC_TYPES_9 \ - (nd4j::DataType::INT32, int32_t), \ - (nd4j::DataType::INT64, Nd4jLong) + (sd::DataType::INT32, int32_t), \ + (sd::DataType::INT64, Nd4jLong) #define GENERIC_NUMERIC_TYPES \ - (nd4j::DataType::HALF, float16), \ - (nd4j::DataType::FLOAT32, float), \ - (nd4j::DataType::DOUBLE, double), \ - (nd4j::DataType::INT32, int32_t), \ - (nd4j::DataType::INT64, Nd4jLong), \ - (nd4j::DataType::BFLOAT16, bfloat16) + (sd::DataType::HALF, float16), \ + (sd::DataType::FLOAT32, float), \ + (sd::DataType::DOUBLE, double), \ + (sd::DataType::INT32, int32_t), \ + (sd::DataType::INT64, Nd4jLong), \ + (sd::DataType::BFLOAT16, bfloat16) #ifdef __ND4J_EXPERIMENTAL__ @@ -436,9 +460,7 @@ #define PAIRWISE_TYPES_4 \ (int16_t, int16_t, int16_t) , \ -(int16_t, bool, int16_t),\ -(uint16_t, uint16_t, uint16_t) ,\ -(uint16_t, bool, uint16_t) +(int16_t, bool, int16_t) #define PAIRWISE_TYPES_5 \ (uint8_t, uint8_t, uint8_t) , \ @@ -446,23 +468,31 @@ #define PAIRWISE_TYPES_6 \ (int, int, int) ,\ -(int, bool, int) ,\ -(uint32_t, uint32_t, uint32_t) ,\ -(uint32_t, bool, uint32_t) +(int, bool, int) #define PAIRWISE_TYPES_7 \ (bool, bool, bool) #define PAIRWISE_TYPES_8 \ (Nd4jLong, Nd4jLong, Nd4jLong) ,\ -(Nd4jLong, bool, Nd4jLong) ,\ -(uint64_t, uint64_t, uint64_t) ,\ -(uint64_t, bool, uint64_t) +(Nd4jLong, bool, Nd4jLong) #define PAIRWISE_TYPES_9 \ (bfloat16, bfloat16, bfloat16) , \ (bfloat16, bool, bfloat16) +#define PAIRWISE_TYPES_10 \ +(uint64_t, uint64_t, uint64_t) ,\ +(uint64_t, bool, uint64_t) + +#define PAIRWISE_TYPES_11 \ +(uint32_t, uint32_t, uint32_t) ,\ +(uint32_t, bool, uint32_t) + +#define PAIRWISE_TYPES_12 \ +(uint16_t, uint16_t, uint16_t) ,\ +(uint16_t, bool, uint16_t) + #endif #endif //LIBND4J_TYPES_H diff --git a/libnd4j/include/types/u64.h b/libnd4j/include/types/u64.h index 0e9e63145..908a9ba1c 100644 --- a/libnd4j/include/types/u64.h +++ b/libnd4j/include/types/u64.h @@ -21,11 +21,11 @@ #define LIBND4J_U64_H #include -#include +#include #include -namespace nd4j { +namespace sd { typedef struct { int16_t _v0; int16_t _v1; diff --git a/libnd4j/include/types/uint16.h b/libnd4j/include/types/uint16.h index 7daa755e5..5fee50e7a 100644 --- a/libnd4j/include/types/uint16.h +++ b/libnd4j/include/types/uint16.h @@ -22,10 +22,10 @@ #define LIBND4J_UINT16_H #include -#include +#include -namespace nd4j { +namespace sd { uint16_t _CUDA_HD FORCEINLINE cpu_float2uint16(float data); float _CUDA_HD FORCEINLINE cpu_uint162float(uint16_t data); diff --git a/libnd4j/include/types/uint8.h b/libnd4j/include/types/uint8.h index 7feab81a7..a2505c9ab 100644 --- a/libnd4j/include/types/uint8.h +++ b/libnd4j/include/types/uint8.h @@ -22,10 +22,10 @@ #define LIBND4J_UINT8_H #include -#include +#include -namespace nd4j { +namespace sd { float _CUDA_HD FORCEINLINE cpu_uint82float(uint8_t data); uint8_t _CUDA_HD FORCEINLINE cpu_float2uint8(float data); diff --git a/libnd4j/include/types/utf8string.h b/libnd4j/include/types/utf8string.h index b3a794cee..ed25c6e10 100644 --- a/libnd4j/include/types/utf8string.h +++ b/libnd4j/include/types/utf8string.h @@ -22,9 +22,9 @@ #define DEV_TESTS_UTF8STRING_H #include -#include +#include -namespace nd4j { +namespace sd { struct ND4J_EXPORT utf8string { private: bool _allocated = false; diff --git a/libnd4j/minifier/minifier.cpp b/libnd4j/minifier/minifier.cpp index 071dacc17..7846c1846 100644 --- a/libnd4j/minifier/minifier.cpp +++ b/libnd4j/minifier/minifier.cpp @@ -23,12 +23,12 @@ #endif #include #include "graphopt.h" -#include +#include #include #include -using namespace nd4j::ops; -using namespace nd4j::graph; +using namespace sd::ops; +using namespace sd::graph; int main(int argc, char *argv[]) { diff --git a/libnd4j/server/CMakeLists.txt b/libnd4j/server/CMakeLists.txt index 7477e25b1..da9c36962 100644 --- a/libnd4j/server/CMakeLists.txt +++ b/libnd4j/server/CMakeLists.txt @@ -45,7 +45,7 @@ else() endif() else() set(CMAKE_CXX_FLAGS " -g -O0 -fPIC -std=c++11 -fmax-errors=2") - if (CPU_BLAS) + if (SD_CPU) SET( CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fsanitize=address") endif() endif() @@ -53,7 +53,7 @@ endif() # tests are always compiled with all ops included -SET( CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -DLIBND4J_ALL_OPS=true") +SET( CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -DSD_ALL_OPS=true") if ("${CMAKE_CXX_COMPILER_ID}" STREQUAL "Clang") # using Clang @@ -117,7 +117,7 @@ find_package(GRPC REQUIRED) message("gRPC found, building GraphServer") add_executable(GraphServer ./GraphServer.cpp ../include/graph/generated/graph.grpc.fb.cc ../blas/cpu/NativeOps.cpp ../blas/cpu/GraphExecutioner.cpp ../blas/cpu/NativeOpExecutioner.cpp ../blas/cpu/NDArray.cpp - ../include/cnpy/cnpy.cpp ../include/nd4jmemset.h ../include/nd4jmalloc.h + ../include/cnpy/cnpy.cpp ../include/system/nd4jmemset.h ../include/system/nd4jmalloc.h ../blas/Environment.cpp ../blas/Environment.h ${LOOPS_SOURCES} ${ARRAY_SOURCES} ${TYPES_SOURCES} ${MEMORY_SOURCES} ${GRAPH_SOURCES} ${CUSTOMOPS_SOURCES} ${INDEXING_SOURCES} ${HELPERS_SOURCES} ${CUSTOMOPS_HELPERS_SOURCES} ${OPS_SOURCES}) diff --git a/libnd4j/server/GraphServer.cpp b/libnd4j/server/GraphServer.cpp index 10d0f1575..a9e8c3ddc 100644 --- a/libnd4j/server/GraphServer.cpp +++ b/libnd4j/server/GraphServer.cpp @@ -20,7 +20,7 @@ #include "GraphServer.h" #include -#include +#include #include #include #include @@ -33,7 +33,7 @@ -namespace nd4j { +namespace sd { namespace graph { grpc::Status GraphInferenceServerImpl::RegisterGraph( grpc::ServerContext *context, const flatbuffers::grpc::Message *request_msg, flatbuffers::grpc::Message *response_msg) { auto flat_graph = request_msg->GetRoot(); @@ -75,7 +75,7 @@ namespace nd4j { assert(response_msg->Verify()); return grpc::Status::OK; - } catch (nd4j::graph::unknown_graph_exception &e) { + } catch (sd::graph::unknown_graph_exception &e) { grpc::string gmsg(e.message()); return grpc::Status(grpc::StatusCode::NOT_FOUND, gmsg); } catch (std::runtime_error &e) { @@ -100,7 +100,7 @@ namespace nd4j { assert(response_msg->Verify()); return grpc::Status::OK; - } catch (nd4j::graph::unknown_graph_exception &e) { + } catch (sd::graph::unknown_graph_exception &e) { grpc::string gmsg(e.message()); return grpc::Status(grpc::StatusCode::NOT_FOUND, gmsg); } @@ -118,13 +118,13 @@ namespace nd4j { assert(response_msg->Verify()); return grpc::Status::OK; - } catch (nd4j::graph::no_results_exception &e) { + } catch (sd::graph::no_results_exception &e) { grpc::string gmsg(e.message()); return grpc::Status(grpc::StatusCode::INTERNAL, gmsg); - } catch (nd4j::graph::unknown_graph_exception &e) { + } catch (sd::graph::unknown_graph_exception &e) { grpc::string gmsg(e.message()); return grpc::Status(grpc::StatusCode::NOT_FOUND, gmsg); - } catch (nd4j::graph::graph_execution_exception &e) { + } catch (sd::graph::graph_execution_exception &e) { grpc::string gmsg(e.message()); return grpc::Status(grpc::StatusCode::INTERNAL, gmsg); } catch (std::runtime_error &e) { @@ -139,10 +139,10 @@ void RunServer(int port) { assert(port > 0 && port < 65535); std::string server_address("0.0.0.0:"); - server_address += nd4j::StringUtils::valueToString(port); + server_address += sd::StringUtils::valueToString(port); - nd4j::graph::GraphInferenceServerImpl service; - auto registrator = nd4j::ops::OpRegistrator::getInstance(); + sd::graph::GraphInferenceServerImpl service; + auto registrator = sd::ops::OpRegistrator::getInstance(); grpc::ServerBuilder builder; builder.AddListeningPort(server_address, grpc::InsecureServerCredentials()); @@ -181,7 +181,7 @@ int main(int argc, char *argv[]) { if(cmdOptionExists(argv, argv+argc, "-f")) { auto file = getCmdOption(argv, argv + argc, "-f"); auto graph = GraphExecutioner::importFromFlatBuffers(file); - nd4j::graph::GraphHolder::getInstance()->registerGraph(0L, graph); + sd::graph::GraphHolder::getInstance()->registerGraph(0L, graph); } RunServer(port); diff --git a/libnd4j/server/GraphServer.h b/libnd4j/server/GraphServer.h index 4e9c539d6..0dceacf25 100644 --- a/libnd4j/server/GraphServer.h +++ b/libnd4j/server/GraphServer.h @@ -20,13 +20,13 @@ #include -#include +#include #include #include #include -namespace nd4j { +namespace sd { namespace graph { class GraphInferenceServerImpl final : public GraphInferenceServer::Service { private: diff --git a/libnd4j/tests_cpu/layers_tests/ArrayOptionsTests.cpp b/libnd4j/tests_cpu/layers_tests/ArrayOptionsTests.cpp index 6ddae6019..18551909c 100644 --- a/libnd4j/tests_cpu/layers_tests/ArrayOptionsTests.cpp +++ b/libnd4j/tests_cpu/layers_tests/ArrayOptionsTests.cpp @@ -20,9 +20,9 @@ #include "testlayers.h" #include -#include +#include -using namespace nd4j; +using namespace sd; class ArrayOptionsTests : public testing::Test { @@ -69,42 +69,42 @@ TEST_F(ArrayOptionsTests, TestShape_Basic_4) { auto dtype = ArrayOptions::dataType(shape); ASSERT_FALSE(ArrayOptions::isSparseArray(shape)); - ASSERT_TRUE(nd4j::DataType::HALF == ArrayOptions::dataType(shape)); - ASSERT_EQ(nd4j::ArrayType::DENSE, ArrayOptions::arrayType(shape)); - ASSERT_EQ(nd4j::SpaceType::QUANTIZED, ArrayOptions::spaceType(shape)); + ASSERT_TRUE(sd::DataType::HALF == ArrayOptions::dataType(shape)); + ASSERT_EQ(sd::ArrayType::DENSE, ArrayOptions::arrayType(shape)); + ASSERT_EQ(sd::SpaceType::QUANTIZED, ArrayOptions::spaceType(shape)); } TEST_F(ArrayOptionsTests, TestShape_Basic_5) { ArrayOptions::setPropertyBits(shape, {ARRAY_SPARSE, ARRAY_INT, ARRAY_CSC}); ASSERT_TRUE(ArrayOptions::isSparseArray(shape)); - ASSERT_TRUE(nd4j::DataType::INT32 == ArrayOptions::dataType(shape)); - ASSERT_EQ(nd4j::SparseType::CSC, ArrayOptions::sparseType(shape)); + ASSERT_TRUE(sd::DataType::INT32 == ArrayOptions::dataType(shape)); + ASSERT_EQ(sd::SparseType::CSC, ArrayOptions::sparseType(shape)); } TEST_F(ArrayOptionsTests, TestShape_Basic_6) { ArrayOptions::setPropertyBits(shape, {ARRAY_EMPTY, ARRAY_INT, ARRAY_CSC}); - ASSERT_EQ(nd4j::ArrayType::EMPTY, ArrayOptions::arrayType(shape)); + ASSERT_EQ(sd::ArrayType::EMPTY, ArrayOptions::arrayType(shape)); } TEST_F(ArrayOptionsTests, TestShape_Basic_7) { - ArrayOptions::setDataType(shape, nd4j::DataType::FLOAT32); - ArrayOptions::setDataType(shape, nd4j::DataType::FLOAT32); + ArrayOptions::setDataType(shape, sd::DataType::FLOAT32); + ArrayOptions::setDataType(shape, sd::DataType::FLOAT32); - ASSERT_EQ(nd4j::DataType::FLOAT32, ArrayOptions::dataType(shape)); + ASSERT_EQ(sd::DataType::FLOAT32, ArrayOptions::dataType(shape)); } TEST_F(ArrayOptionsTests, TestShape_Basic_8) { - ArrayOptions::setDataType(shape, nd4j::DataType::DOUBLE); - ArrayOptions::setDataType(shape, nd4j::DataType::FLOAT32); + ArrayOptions::setDataType(shape, sd::DataType::DOUBLE); + ArrayOptions::setDataType(shape, sd::DataType::FLOAT32); - ASSERT_EQ(nd4j::DataType::FLOAT32, ArrayOptions::dataType(shape)); + ASSERT_EQ(sd::DataType::FLOAT32, ArrayOptions::dataType(shape)); } TEST_F(ArrayOptionsTests, TestShape_Basic_9) { - ArrayOptions::setDataType(shape, nd4j::DataType::FLOAT32); - ArrayOptions::setDataType(shape, nd4j::DataType::DOUBLE); + ArrayOptions::setDataType(shape, sd::DataType::FLOAT32); + ArrayOptions::setDataType(shape, sd::DataType::DOUBLE); - ASSERT_EQ(nd4j::DataType::DOUBLE, ArrayOptions::dataType(shape)); + ASSERT_EQ(sd::DataType::DOUBLE, ArrayOptions::dataType(shape)); } \ No newline at end of file diff --git a/libnd4j/tests_cpu/layers_tests/AtomicTests.cu b/libnd4j/tests_cpu/layers_tests/AtomicTests.cu index fdf543026..bd024ef3b 100644 --- a/libnd4j/tests_cpu/layers_tests/AtomicTests.cu +++ b/libnd4j/tests_cpu/layers_tests/AtomicTests.cu @@ -20,14 +20,14 @@ #include "testlayers.h" #include -#include +#include #include -#include +#include #include #include -using namespace nd4j; +using namespace sd; class AtomicTests : public testing::Test { @@ -48,16 +48,16 @@ static _CUDA_G void multiplyKernel(void *vbuffer, uint64_t length, void *vresult auto rem = e % 4; auto i = (e - rem) / 4; - nd4j::math::atomics::nd4j_atomicMul(&result[i], buffer[e]); + sd::math::atomics::nd4j_atomicMul(&result[i], buffer[e]); } } template static void multiplyLauncher(void *vbuffer, uint64_t length, void *vresult) { - multiplyKernel<<<256, 256, 1024, *nd4j::LaunchContext::defaultContext()->getCudaStream()>>>(vbuffer, length, vresult); - auto err = cudaStreamSynchronize(*nd4j::LaunchContext::defaultContext()->getCudaStream()); + multiplyKernel<<<256, 256, 1024, *sd::LaunchContext::defaultContext()->getCudaStream()>>>(vbuffer, length, vresult); + auto err = cudaStreamSynchronize(*sd::LaunchContext::defaultContext()->getCudaStream()); if (err != 0) - nd4j::cuda_exception::build("multiply failed", err); + sd::cuda_exception::build("multiply failed", err); } template @@ -71,16 +71,16 @@ static _CUDA_G void sumKernel(void *vbuffer, uint64_t length, void *vresult) { auto rem = e % 4; auto i = (e - rem) / 4; - nd4j::math::atomics::nd4j_atomicAdd(&result[i], buffer[e]); + sd::math::atomics::nd4j_atomicAdd(&result[i], buffer[e]); } } template static void sumLauncher(void *vbuffer, uint64_t length, void *vresult) { - sumKernel<<<256, 256, 1024, *nd4j::LaunchContext::defaultContext()->getCudaStream()>>>(vbuffer, length, vresult); - auto err = cudaStreamSynchronize(*nd4j::LaunchContext::defaultContext()->getCudaStream()); + sumKernel<<<256, 256, 1024, *sd::LaunchContext::defaultContext()->getCudaStream()>>>(vbuffer, length, vresult); + auto err = cudaStreamSynchronize(*sd::LaunchContext::defaultContext()->getCudaStream()); if (err != 0) - nd4j::cuda_exception::build("sum failed", err); + sd::cuda_exception::build("sum failed", err); } template @@ -94,16 +94,16 @@ static _CUDA_G void subKernel(void *vbuffer, uint64_t length, void *vresult) { auto rem = e % 4; auto i = (e - rem) / 4; - nd4j::math::atomics::nd4j_atomicSub(&result[i], buffer[e]); + sd::math::atomics::nd4j_atomicSub(&result[i], buffer[e]); } } template static void subLauncher(void *vbuffer, uint64_t length, void *vresult) { - subKernel<<<256, 256, 1024, *nd4j::LaunchContext::defaultContext()->getCudaStream()>>>(vbuffer, length, vresult); - auto err = cudaStreamSynchronize(*nd4j::LaunchContext::defaultContext()->getCudaStream()); + subKernel<<<256, 256, 1024, *sd::LaunchContext::defaultContext()->getCudaStream()>>>(vbuffer, length, vresult); + auto err = cudaStreamSynchronize(*sd::LaunchContext::defaultContext()->getCudaStream()); if (err != 0) - nd4j::cuda_exception::build("sub failed", err); + sd::cuda_exception::build("sub failed", err); } template @@ -117,16 +117,16 @@ static _CUDA_G void divKernel(void *vbuffer, uint64_t length, void *vresult) { auto rem = e % 4; auto i = (e - rem) / 4; - nd4j::math::atomics::nd4j_atomicDiv(&result[i], buffer[e]); + sd::math::atomics::nd4j_atomicDiv(&result[i], buffer[e]); } } template static void divLauncher(void *vbuffer, uint64_t length, void *vresult) { - divKernel<<<256, 256, 1024, *nd4j::LaunchContext::defaultContext()->getCudaStream()>>>(vbuffer, length, vresult); - auto err = cudaStreamSynchronize(*nd4j::LaunchContext::defaultContext()->getCudaStream()); + divKernel<<<256, 256, 1024, *sd::LaunchContext::defaultContext()->getCudaStream()>>>(vbuffer, length, vresult); + auto err = cudaStreamSynchronize(*sd::LaunchContext::defaultContext()->getCudaStream()); if (err != 0) - nd4j::cuda_exception::build("div failed", err); + sd::cuda_exception::build("div failed", err); } static void multiplyHost(NDArray &input, NDArray &output) { @@ -146,7 +146,7 @@ static void divHost(NDArray &input, NDArray &output) { } TEST_F(AtomicTests, test_multiply) { - std::vector dtypes = {nd4j::DataType::FLOAT32, nd4j::DataType::DOUBLE, nd4j::DataType::INT16, nd4j::DataType::HALF}; + std::vector dtypes = {sd::DataType::FLOAT32, sd::DataType::DOUBLE, sd::DataType::INT16, sd::DataType::HALF}; for (auto t:dtypes) { nd4j_printf("Trying data type [%s]\n", DataTypeUtils::asString(t).c_str()); @@ -164,7 +164,7 @@ TEST_F(AtomicTests, test_multiply) { } TEST_F(AtomicTests, test_multiply_2) { - std::vector dtypes = {nd4j::DataType::FLOAT32, nd4j::DataType::DOUBLE, nd4j::DataType::HALF, nd4j::DataType::BFLOAT16}; + std::vector dtypes = {sd::DataType::FLOAT32, sd::DataType::DOUBLE, sd::DataType::HALF, sd::DataType::BFLOAT16}; for (auto t:dtypes) { nd4j_printf("Trying data type [%s]\n", DataTypeUtils::asString(t).c_str()); @@ -183,7 +183,7 @@ TEST_F(AtomicTests, test_multiply_2) { } TEST_F(AtomicTests, test_sum) { - std::vector dtypes = {nd4j::DataType::FLOAT32, nd4j::DataType::DOUBLE, nd4j::DataType::BFLOAT16, nd4j::DataType::HALF, nd4j::DataType::INT16}; + std::vector dtypes = {sd::DataType::FLOAT32, sd::DataType::DOUBLE, sd::DataType::BFLOAT16, sd::DataType::HALF, sd::DataType::INT16}; for (auto t:dtypes) { nd4j_printf("Trying data type [%s]\n", DataTypeUtils::asString(t).c_str()); @@ -202,7 +202,7 @@ TEST_F(AtomicTests, test_sum) { } TEST_F(AtomicTests, test_sub) { - std::vector dtypes = {nd4j::DataType::FLOAT32, nd4j::DataType::DOUBLE, nd4j::DataType::HALF}; + std::vector dtypes = {sd::DataType::FLOAT32, sd::DataType::DOUBLE, sd::DataType::HALF}; for (auto t:dtypes) { nd4j_printf("Trying data type [%s]\n", DataTypeUtils::asString(t).c_str()); @@ -222,7 +222,7 @@ TEST_F(AtomicTests, test_sub) { } TEST_F(AtomicTests, test_div) { - std::vector dtypes = {nd4j::DataType::FLOAT32, nd4j::DataType::DOUBLE, nd4j::DataType::BFLOAT16, nd4j::DataType::HALF}; + std::vector dtypes = {sd::DataType::FLOAT32, sd::DataType::DOUBLE, sd::DataType::BFLOAT16, sd::DataType::HALF}; for (auto t:dtypes) { nd4j_printf("Trying data type [%s]\n", DataTypeUtils::asString(t).c_str()); diff --git a/libnd4j/tests_cpu/layers_tests/AttentionTests.cpp b/libnd4j/tests_cpu/layers_tests/AttentionTests.cpp index aa9d941ea..ab6d50b53 100644 --- a/libnd4j/tests_cpu/layers_tests/AttentionTests.cpp +++ b/libnd4j/tests_cpu/layers_tests/AttentionTests.cpp @@ -20,13 +20,13 @@ #include "testlayers.h" #include -#include +#include #include -#include +#include #include -using namespace nd4j; +using namespace sd; class AttentionTests : public testing::Test { @@ -42,11 +42,9 @@ TEST_F(AttentionTests, basic_dot_product_attention) { auto values = NDArrayFactory::create('c', {10, 4, 3}); auto queries = NDArrayFactory::create('c', {10, 4, 1}); - nd4j::ops::dot_product_attention op; + sd::ops::dot_product_attention op; auto result = op.evaluate({&queries, &keys, &values}, {1, 0}); - ASSERT_EQ(Status::OK(), result->status()); - - delete result; + ASSERT_EQ(Status::OK(), result.status()); } /* @@ -57,7 +55,7 @@ TEST_F(AttentionTests, basic_dot_product_attention_bp) { auto queries = NDArrayFactory::create('c', {10, 4, 1}); auto eps = NDArrayFactory::create('c', {10, 4, 1}); - nd4j::ops::dot_product_attention_bp op; + sd::ops::dot_product_attention_bp op; auto result = op.execute({&queries, &keys, &values, &eps}, {}, {1, 0}, {}); ASSERT_EQ(Status::OK(), result->status()); @@ -70,11 +68,9 @@ TEST_F(AttentionTests, basic_dot_product_attention_with_weights) { auto values = NDArrayFactory::create('c', {10, 4, 3}); auto queries = NDArrayFactory::create('c', {10, 4, 1}); - nd4j::ops::dot_product_attention op; + sd::ops::dot_product_attention op; auto result = op.evaluate({&queries, &keys, &values}, {1, 1}); - ASSERT_EQ(Status::OK(), result->status()); - - delete result; + ASSERT_EQ(Status::OK(), result.status()); } TEST_F(AttentionTests, basic_dot_product_attention_with_mask) { @@ -84,11 +80,9 @@ TEST_F(AttentionTests, basic_dot_product_attention_with_mask) { auto mask = NDArrayFactory::create('c', {10, 3}); mask.assign(1.); - nd4j::ops::dot_product_attention op; + sd::ops::dot_product_attention op; auto result = op.evaluate({&queries, &keys, &values, &mask}, {1, 0}); - ASSERT_EQ(Status::OK(), result->status()); - - delete result; + ASSERT_EQ(Status::OK(), result.status()); } /* @@ -101,7 +95,7 @@ TEST_F(AttentionTests, basic_dot_product_attention_bp_with_mask) { auto mask = NDArrayFactory::create('c', {10, 3}); mask.assign(1.); - nd4j::ops::dot_product_attention_bp op; + sd::ops::dot_product_attention_bp op; auto result = op.execute({&queries, &keys, &values, &eps, &mask}, {}, {1, 0}, {}); ASSERT_EQ(Status::OK(), result->status()); @@ -116,11 +110,9 @@ TEST_F(AttentionTests, multi_head_input_dot_product_attention_with_mask) { auto mask = NDArrayFactory::create('c', {2, 3}); mask.assign(1.); - nd4j::ops::dot_product_attention op; + sd::ops::dot_product_attention op; auto result = op.evaluate({&queries, &keys, &values, &mask}, {1, 0}); - ASSERT_EQ(Status::OK(), result->status()); - - delete result; + ASSERT_EQ(Status::OK(), result.status()); } /* @@ -133,7 +125,7 @@ TEST_F(AttentionTests, multi_head_input_dot_product_attention_bp_with_mask) { auto mask = NDArrayFactory::create('c', {2, 3}); mask.assign(1.); - nd4j::ops::dot_product_attention_bp op; + sd::ops::dot_product_attention_bp op; auto result = op.execute({&queries, &keys, &values, &eps, &mask}, {}, {1, 0}, {}); ASSERT_EQ(Status::OK(), result->status()); @@ -152,11 +144,9 @@ TEST_F(AttentionTests, basic_multi_head_dot_product_attention) { auto Wq = NDArrayFactory::create('c', {2, 3, 4}); auto Wo = NDArrayFactory::create('c', {2* 3, 4}); - nd4j::ops::multi_head_dot_product_attention op; + sd::ops::multi_head_dot_product_attention op; auto result = op.evaluate({&queries, &keys, &values, &Wk, &Wv, &Wq, &Wo}, {1, 0}); - ASSERT_EQ(Status::OK(), result->status()); - - delete result; + ASSERT_EQ(Status::OK(), result.status()); } /* @@ -174,7 +164,7 @@ TEST_F(AttentionTests, basic_multi_head_dot_product_bp_attention) { auto eps = NDArrayFactory::create('c', {10, 7, 2}); - nd4j::ops::multi_head_dot_product_attention_bp op; + sd::ops::multi_head_dot_product_attention_bp op; auto result = op.execute({&queries, &keys, &values, &Wk, &Wv, &Wq, &Wo, &eps}, {}, {1, 0}, {}); ASSERT_EQ(Status::OK(), result->status()); @@ -196,11 +186,9 @@ TEST_F(AttentionTests, basic_multi_head_dot_product_attention_with_mask) { mask.assign(1.); - nd4j::ops::multi_head_dot_product_attention op; + sd::ops::multi_head_dot_product_attention op; auto result = op.evaluate({&queries, &keys, &values, &Wk, &Wv, &Wq, &Wo, &mask}, {1, 0}); - ASSERT_EQ(Status::OK(), result->status()); - - delete result; + ASSERT_EQ(Status::OK(), result.status()); } /* @@ -221,7 +209,7 @@ TEST_F(AttentionTests, basic_multi_head_dot_product_bp_attention_with_mask) { mask.assign(1.); - nd4j::ops::multi_head_dot_product_attention_bp op; + sd::ops::multi_head_dot_product_attention_bp op; auto result = op.execute({&queries, &keys, &values, &Wk, &Wv, &Wq, &Wo, &eps, &mask}, {}, {1, 0}, {}); ASSERT_EQ(Status::OK(), result->status()); diff --git a/libnd4j/tests_cpu/layers_tests/BackpropTests.cpp b/libnd4j/tests_cpu/layers_tests/BackpropTests.cpp index 88bd9b286..5c528f072 100644 --- a/libnd4j/tests_cpu/layers_tests/BackpropTests.cpp +++ b/libnd4j/tests_cpu/layers_tests/BackpropTests.cpp @@ -21,9 +21,9 @@ #include "testlayers.h" #include -using namespace nd4j; -using namespace nd4j::ops; -using namespace nd4j::graph; +using namespace sd; +using namespace sd::ops; +using namespace sd::graph; class BackpropTests : public testing::Test { public: @@ -32,20 +32,18 @@ public: TEST_F(BackpropTests, Test_Add_1) { - NDArray x('c', {2, 3, 4}, nd4j::DataType::FLOAT32); - NDArray y('c', {3, 4}, nd4j::DataType::FLOAT32); - NDArray e('c', {2, 3, 4}, nd4j::DataType::FLOAT32); + NDArray x('c', {2, 3, 4}, sd::DataType::FLOAT32); + NDArray y('c', {3, 4}, sd::DataType::FLOAT32); + NDArray e('c', {2, 3, 4}, sd::DataType::FLOAT32); - nd4j::ops::add_bp op; + sd::ops::add_bp op; auto result = op.evaluate({&x, &y, &e}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto eps = result->at(0); - auto grad = result->at(1); + auto eps = result.at(0); + auto grad = result.at(1); ASSERT_TRUE(x.isSameShape(eps)); ASSERT_TRUE(y.isSameShape(grad)); - - delete result; } \ No newline at end of file diff --git a/libnd4j/tests_cpu/layers_tests/BitwiseUtilsTests.cpp b/libnd4j/tests_cpu/layers_tests/BitwiseUtilsTests.cpp index 487f7ba9b..4174637e2 100644 --- a/libnd4j/tests_cpu/layers_tests/BitwiseUtilsTests.cpp +++ b/libnd4j/tests_cpu/layers_tests/BitwiseUtilsTests.cpp @@ -20,12 +20,12 @@ #include "testlayers.h" #include -#include -#include +#include +#include #include -using namespace nd4j; -using namespace nd4j::graph; +using namespace sd; +using namespace sd::graph; class BitwiseUtilsTests : public testing::Test { public: diff --git a/libnd4j/tests_cpu/layers_tests/BooleanOpsTests.cpp b/libnd4j/tests_cpu/layers_tests/BooleanOpsTests.cpp index 38aada40f..199cb88eb 100644 --- a/libnd4j/tests_cpu/layers_tests/BooleanOpsTests.cpp +++ b/libnd4j/tests_cpu/layers_tests/BooleanOpsTests.cpp @@ -21,9 +21,9 @@ #include "testlayers.h" #include -using namespace nd4j; -using namespace nd4j::ops; -using namespace nd4j::graph; +using namespace sd; +using namespace sd::ops; +using namespace sd::graph; class BooleanOpsTests : public testing::Test { public: @@ -35,7 +35,7 @@ TEST_F(BooleanOpsTests, LtTest_1) { auto x = NDArrayFactory::create_(1.0f); auto y = NDArrayFactory::create_(2.0f); - nd4j::ops::lt_scalar op; + sd::ops::lt_scalar op; ASSERT_TRUE(op.verify({x, y})); @@ -48,7 +48,7 @@ TEST_F(BooleanOpsTests, LtTest_2) { auto x = NDArrayFactory::create_(2.0f); auto y = NDArrayFactory::create_(1.0f); - nd4j::ops::lt_scalar op; + sd::ops::lt_scalar op; ASSERT_FALSE(op.verify({x, y})); @@ -60,7 +60,7 @@ TEST_F(BooleanOpsTests, LtTest_2) { TEST_F(BooleanOpsTests, Is_non_decreasing_1) { auto x = NDArrayFactory::create('c', {2 , 2}, {1, 2, 4, 4}); - nd4j::ops::is_non_decreasing op; + sd::ops::is_non_decreasing op; ASSERT_TRUE(op.verify({&x})); @@ -69,7 +69,7 @@ TEST_F(BooleanOpsTests, Is_non_decreasing_1) { TEST_F(BooleanOpsTests, Is_non_decreasing_2) { auto x = NDArrayFactory::create('c', {2 , 2}, {1, 2, 4, 3}); - nd4j::ops::is_non_decreasing op; + sd::ops::is_non_decreasing op; ASSERT_FALSE(op.verify({&x})); @@ -78,7 +78,7 @@ TEST_F(BooleanOpsTests, Is_non_decreasing_2) { TEST_F(BooleanOpsTests, Is_strictly_increasing_1) { auto x = NDArrayFactory::create('c', {2 , 2}, {1, 2, 4, 5}); - nd4j::ops::is_strictly_increasing op; + sd::ops::is_strictly_increasing op; ASSERT_TRUE(op.verify({&x})); @@ -87,7 +87,7 @@ TEST_F(BooleanOpsTests, Is_strictly_increasing_1) { TEST_F(BooleanOpsTests, Is_strictly_increasing_2) { auto x = NDArrayFactory::create('c', {2 , 2}, {1, 2, 3, 3}); - nd4j::ops::is_strictly_increasing op; + sd::ops::is_strictly_increasing op; ASSERT_FALSE(op.verify({&x})); @@ -96,7 +96,7 @@ TEST_F(BooleanOpsTests, Is_strictly_increasing_2) { TEST_F(BooleanOpsTests, Is_strictly_increasing_3) { auto x = NDArrayFactory::create('c', {2 , 2}, {1, 2, 4, 3}); - nd4j::ops::is_strictly_increasing op; + sd::ops::is_strictly_increasing op; ASSERT_FALSE(op.verify({&x})); } @@ -105,7 +105,7 @@ TEST_F(BooleanOpsTests, Is_strictly_increasing_5) { auto x = NDArrayFactory::create('c', {64, 512}); x.linspace(1.0); - nd4j::ops::is_strictly_increasing op; + sd::ops::is_strictly_increasing op; ASSERT_TRUE(op.verify({&x})); } @@ -116,7 +116,7 @@ TEST_F(BooleanOpsTests, Is_strictly_increasing_6) { x.p(18, 1000323.f); - nd4j::ops::is_strictly_increasing op; + sd::ops::is_strictly_increasing op; ASSERT_FALSE(op.verify({&x})); } @@ -124,7 +124,7 @@ TEST_F(BooleanOpsTests, Is_strictly_increasing_6) { TEST_F(BooleanOpsTests, Is_numeric_tensor_1) { auto x = NDArrayFactory::create('c', {2 , 2}, {1.f, 2.f, 4.f, 3.f}); - nd4j::ops::is_numeric_tensor op; + sd::ops::is_numeric_tensor op; ASSERT_TRUE(op.verify({&x})); } @@ -134,16 +134,15 @@ TEST_F(BooleanOpsTests, test_where_1) { auto y = NDArrayFactory::create('c', {6}, { 2, -3, 1, 1, -2, 1 }); auto e = NDArrayFactory::create('c', {3}, { 4, 8, 5 }); - nd4j::ops::choose op; + sd::ops::choose op; auto result = op.evaluate({&x, &y}, {3}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); //z->printIndexedBuffer("z"); ASSERT_EQ(e, *z); - - delete result; } + diff --git a/libnd4j/tests_cpu/layers_tests/BroadcastableOpsTests.cpp b/libnd4j/tests_cpu/layers_tests/BroadcastableOpsTests.cpp index 9b6d06ec6..51c6e2375 100644 --- a/libnd4j/tests_cpu/layers_tests/BroadcastableOpsTests.cpp +++ b/libnd4j/tests_cpu/layers_tests/BroadcastableOpsTests.cpp @@ -20,12 +20,12 @@ #include "testlayers.h" -#include -#include +#include +#include #include -using namespace nd4j; -using namespace nd4j::graph; +using namespace sd; +using namespace sd::graph; class BroadcastableOpsTests : public testing::Test { public: @@ -34,9 +34,9 @@ public: TEST_F(BroadcastableOpsTests, Test_Add_1) { - NDArray x('c', {5, 5}, nd4j::DataType::FLOAT32); - NDArray y('c', {1, 5}, nd4j::DataType::FLOAT32); - NDArray exp('c', {5, 5}, nd4j::DataType::FLOAT32); + NDArray x('c', {5, 5}, sd::DataType::FLOAT32); + NDArray y('c', {1, 5}, sd::DataType::FLOAT32); + NDArray exp('c', {5, 5}, sd::DataType::FLOAT32); x.linspace(1); y.linspace(1); exp.linspace(1); @@ -45,12 +45,12 @@ TEST_F(BroadcastableOpsTests, Test_Add_1) { exp.applyBroadcast(broadcast::Add, {1}, y, exp); - nd4j::ops::add op; + sd::ops::add op; auto result = op.evaluate({&x, &y}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); //exp.printIndexedBuffer("E A"); //z->printIndexedBuffer("Z"); @@ -58,7 +58,6 @@ TEST_F(BroadcastableOpsTests, Test_Add_1) { ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; } @@ -72,17 +71,15 @@ TEST_F(BroadcastableOpsTests, Test_Multiply_1) { exp.applyBroadcast(broadcast::Multiply, {1}, y, exp); - nd4j::ops::multiply op; + sd::ops::multiply op; auto result = op.evaluate({&x, &y}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - - delete result; } @@ -97,17 +94,16 @@ TEST_F(BroadcastableOpsTests, Test_SquaredSubtract_1) { exp.applyBroadcast(broadcast::SquaredSubtract, {1}, y, exp); - nd4j::ops::squaredsubtract op; + sd::ops::squaredsubtract op; auto result = op.evaluate({&x, &y}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; } @@ -116,17 +112,15 @@ TEST_F(BroadcastableOpsTests, Test_ScalarBroadcast_1) { auto y = NDArrayFactory::create('c', {1, 3}, {0, 1, 2}); auto exp = NDArrayFactory::create('c', {1,3}, {1, 0, -1}); - nd4j::ops::subtract op; + sd::ops::subtract op; auto result = op.evaluate({&x, &y}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - - delete result; } @@ -135,17 +129,15 @@ TEST_F(BroadcastableOpsTests, Test_ScalarBroadcast_2) { auto y = NDArrayFactory::create('c', {1, 3}, {0, 1, 2}); auto exp = NDArrayFactory::create('c', {1,3}, {1, 2, 3}); - nd4j::ops::add op; + sd::ops::add op; auto result = op.evaluate({&x, &y}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - - delete result; } @@ -154,16 +146,14 @@ TEST_F(BroadcastableOpsTests, Test_Maximum_1) { auto row = NDArrayFactory::create('c', {1, 3}, {2, 2, 2}); auto exp = NDArrayFactory::create('c', {2, 3}, {2, 2, 2, 2, 3, 2}); - nd4j::ops::maximum op; + sd::ops::maximum op; auto result = op.evaluate({&x, &row}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - - delete result; } @@ -172,22 +162,21 @@ TEST_F(BroadcastableOpsTests, Test_Minimum_1) { auto col = NDArrayFactory::create('c', {2, 1}, {2, 1}); auto exp = NDArrayFactory::create('c', {2, 3}, {1, 2, 1, 1, 1, 1}); - nd4j::ops::minimum op; + sd::ops::minimum op; auto result = op.evaluate({&x, &col}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; } TEST_F(BroadcastableOpsTests, Test_Shape_1) { - nd4j::ops::minimum op; + sd::ops::minimum op; Nd4jLong shapeX[] = {2, 2, 5, 5, 1, 8192, 1, 99}; Nd4jLong shapeY[] = {2, 2, 5, 5, 1, 8192, 1, 99}; @@ -204,7 +193,7 @@ TEST_F(BroadcastableOpsTests, Test_Shape_1) { } TEST_F(BroadcastableOpsTests, Test_Shape_2) { - nd4j::ops::minimum op; + sd::ops::minimum op; Nd4jLong shapeX[] = {2, 1, 1, 1, 1, 8192, 1, 99}; Nd4jLong shapeY[] = {2, 2, 5, 5, 1, 8192, 1, 99}; @@ -222,7 +211,7 @@ TEST_F(BroadcastableOpsTests, Test_Shape_2) { TEST_F(BroadcastableOpsTests, Test_Shape_3) { - nd4j::ops::minimum op; + sd::ops::minimum op; Nd4jLong shapeX[] = {2, 5, 3, 1, 1, 8192, 1, 99}; Nd4jLong shapeY[] = {2, 1, 3, 3, 1, 8192, 1, 99}; @@ -240,7 +229,7 @@ TEST_F(BroadcastableOpsTests, Test_Shape_3) { TEST_F(BroadcastableOpsTests, Test_Shape_4) { - nd4j::ops::minimum op; + sd::ops::minimum op; Nd4jLong shapeX[] = {2, 5, 3, 1, 1, 8192, 1, 99}; Nd4jLong shapeY[] = {2, 5, 1, 1, 1, 8192, 1, 99}; @@ -259,7 +248,7 @@ TEST_F(BroadcastableOpsTests, Test_Shape_4) { // (2,1,3) + (4,3) = (2,4,3) TEST_F(BroadcastableOpsTests, Test_Shape_5) { - nd4j::ops::minimum op; + sd::ops::minimum op; Nd4jLong shapeX[] = {3, 2, 1, 3, 3, 3, 1, 8192, 1, 99}; Nd4jLong shapeY[] = {2, 4, 3, 3, 1, 8192, 1, 99}; @@ -281,16 +270,15 @@ TEST_F(BroadcastableOpsTests, Test_Scalar_Add_1) { auto y = NDArrayFactory::create(2.0f); auto exp = NDArrayFactory::create('c', {2, 2}, {3, 4, 5, 6}); - nd4j::ops::add op; + sd::ops::add op; auto result = op.evaluate({&x, &y}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; } @@ -303,7 +291,7 @@ TEST_F(BroadcastableOpsTests, Test_Inplace_Output_1) { y.assign(1.0f); e.assign(1.0f); - nd4j::ops::add op; + sd::ops::add op; auto result = op.execute({&x, &y}, {&o}, {}, {}, {}); ASSERT_EQ(Status::OK(), result); @@ -331,13 +319,11 @@ TEST_F(BroadcastableOpsTests, Test_Subtract_2) { auto y = NDArrayFactory::create('c', {2}, {0.0f, 1.0f}); auto e = NDArrayFactory::create('c', {2}, {1.0f, 0.0f}); - nd4j::ops::subtract op; + sd::ops::subtract op; auto result = op.evaluate({&x, &y}); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(e.equalsTo(z)); - - delete result; } TEST_F(BroadcastableOpsTests, Test_Subtract_3) { @@ -346,7 +332,7 @@ TEST_F(BroadcastableOpsTests, Test_Subtract_3) { auto z = NDArrayFactory::create('c', {2}, {0.0f, 0.0f}); auto e = NDArrayFactory::create('c', {2}, {1.0f, 0.0f}); - nd4j::ops::subtract op; + sd::ops::subtract op; auto result = op.execute({&x, &y}, {&z}, {}, {}, {}); ASSERT_EQ(Status::OK(), result); @@ -509,15 +495,14 @@ TEST_F(BroadcastableOpsTests, Test_Multiply_7) { auto y = NDArrayFactory::create('c', {1}, {4.f}); auto e = NDArrayFactory::create('c', {1}, {8.f}); - nd4j::ops::multiply op; + sd::ops::multiply op; auto result = op.evaluate({&x, &y}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(e.equalsTo(z)); - delete result; } TEST_F(BroadcastableOpsTests, Test_Multiply_8) { @@ -525,15 +510,13 @@ TEST_F(BroadcastableOpsTests, Test_Multiply_8) { auto y = NDArrayFactory::create('c', {1, 1}, {4.f}); auto e = NDArrayFactory::create('c', {1, 1}, {8.f}); - nd4j::ops::multiply op; + sd::ops::multiply op; auto result = op.evaluate({&x, &y}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(e.equalsTo(z)); - - delete result; } ////////////////////////////////////////////////////////////////////// @@ -541,10 +524,10 @@ TEST_F(BroadcastableOpsTests, broadcast_add_1) { NDArray x('c', {4}, {1,1,1,1}); NDArray y('c', {1,4}, {1,2,3,4}); - NDArray z('c', {1,4}, nd4j::DataType::DOUBLE); - NDArray exp('c', {1,4}, {2,3,4,5}, nd4j::DataType::DOUBLE); + NDArray z('c', {1,4}, sd::DataType::DOUBLE); + NDArray exp('c', {1,4}, {2,3,4,5}, sd::DataType::DOUBLE); - nd4j::ops::add op; + sd::ops::add op; auto status = op.execute({&x, &y}, {&z}); ASSERT_EQ(ND4J_STATUS_OK, status); @@ -556,10 +539,10 @@ TEST_F(BroadcastableOpsTests, broadcast_equals_1) { NDArray x('c', {1,4}, {1,2,3,4}); NDArray y('c', {3,4}, {0,0,0,0, 1,2,3,4, 1,2,3,4}); - NDArray z('c', {3,4}, nd4j::DataType::BOOL); - NDArray exp('c', {3,4}, {0,0,0,0, 1,1,1,1, 1,1,1,1}, nd4j::DataType::BOOL); + NDArray z('c', {3,4}, sd::DataType::BOOL); + NDArray exp('c', {3,4}, {0,0,0,0, 1,1,1,1, 1,1,1,1}, sd::DataType::BOOL); - nd4j::ops::equals op; + sd::ops::equals op; auto status = op.execute({&x, &y}, {&z}); // z.printIndexedBuffer(); @@ -571,11 +554,11 @@ TEST_F(BroadcastableOpsTests, broadcast_equals_1) { TEST_F(BroadcastableOpsTests, broadcast_empty_1) { NDArray y('c', {3,4}, {0,0,0,0, 1,2,3,4, 1,2,3,4}); - NDArray x(nd4j::DataType::DOUBLE, y.getContext(), false); - NDArray z(nd4j::DataType::DOUBLE, y.getContext(), false); - NDArray zExp(nd4j::DataType::DOUBLE, y.getContext(), false); + NDArray x(sd::DataType::DOUBLE, y.getContext(), false); + NDArray z(sd::DataType::DOUBLE, y.getContext(), false); + NDArray zExp(sd::DataType::DOUBLE, y.getContext(), false); - nd4j::ops::multiply op; + sd::ops::multiply op; auto status = op.execute({&x, &y}, {&z}, {}, {}, {}); ASSERT_EQ(ND4J_STATUS_OK, status); @@ -589,7 +572,7 @@ TEST_F(BroadcastableOpsTests, broadcast_empty_2) { NDArray x = NDArrayFactory::create('c', {0, 4}); NDArray e = NDArrayFactory::create('c', {0, 4});; - nd4j::ops::multiply op; + sd::ops::multiply op; auto status = op.execute({&x, &y}, {&x}, {}, {}, {}); ASSERT_EQ(ND4J_STATUS_OK, status); @@ -600,20 +583,18 @@ TEST_F(BroadcastableOpsTests, broadcast_empty_2) { TEST_F(BroadcastableOpsTests, broadcast_empty_3) { NDArray x = NDArrayFactory::create('c', {1, 0, 2}); - NDArray y('c', {}, std::vector{0.1}, nd4j::DataType::FLOAT32); + NDArray y('c', {}, std::vector{0.1}, sd::DataType::FLOAT32); NDArray e = NDArrayFactory::create('c', {1, 0, 2});; - nd4j::ops::maximum op; + sd::ops::maximum op; auto result = op.evaluate({&x, &y}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(e.isSameShape(z)); ASSERT_TRUE(e.equalsTo(*z)); - - delete result; } TEST_F(BroadcastableOpsTests, broadcast_empty_4) { @@ -622,17 +603,16 @@ TEST_F(BroadcastableOpsTests, broadcast_empty_4) { NDArray y = NDArrayFactory::create('c', {1, 0, 2}); NDArray e = NDArrayFactory::create('c', {1, 0, 2});; - nd4j::ops::maximum op; + sd::ops::maximum op; auto result = op.evaluate({&x, &y}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(e.isSameShape(z)); ASSERT_TRUE(e.equalsTo(*z)); - delete result; } TEST_F(BroadcastableOpsTests, broadcast_empty_5) { @@ -641,17 +621,16 @@ TEST_F(BroadcastableOpsTests, broadcast_empty_5) { NDArray y = NDArrayFactory::create('c', {1, 0, 2}); NDArray e = NDArrayFactory::create('c', {1, 0, 2});; - nd4j::ops::realdiv op; + sd::ops::realdiv op; auto result = op.evaluate({&x, &y}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(e.isSameShape(z)); ASSERT_TRUE(e.equalsTo(*z)); - delete result; } TEST_F(BroadcastableOpsTests, broadcast_empty_6) { @@ -660,17 +639,16 @@ TEST_F(BroadcastableOpsTests, broadcast_empty_6) { NDArray y = NDArrayFactory::create('c', {1, 2}, {2, 2}); NDArray e = NDArrayFactory::create('c', {1, 0, 2});; - nd4j::ops::realdiv op; + sd::ops::realdiv op; auto result = op.evaluate({&x, &y}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(e.isSameShape(z)); ASSERT_TRUE(e.equalsTo(*z)); - delete result; } TEST_F(BroadcastableOpsTests, broadcast_empty_7) { @@ -679,28 +657,26 @@ TEST_F(BroadcastableOpsTests, broadcast_empty_7) { NDArray y = NDArrayFactory::create('c', {1, 2, 0}); NDArray e = NDArrayFactory::create('c', {1, 0, 2, 0});; - nd4j::ops::realdiv op; + sd::ops::realdiv op; auto result = op.evaluate({&x, &y}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(e.isSameShape(z)); ASSERT_TRUE(e.equalsTo(*z)); - - delete result; } TEST_F(BroadcastableOpsTests, broadcast_bool_empty_1) { NDArray y('c', {3,4}, {0,0,0,0, 1,2,3,4, 1,2,3,4}); - NDArray x(nd4j::DataType::DOUBLE, y.getContext(), false); - NDArray z(nd4j::DataType::BOOL, y.getContext(), false); - NDArray zExp(nd4j::DataType::BOOL, y.getContext(), false); + NDArray x(sd::DataType::DOUBLE, y.getContext(), false); + NDArray z(sd::DataType::BOOL, y.getContext(), false); + NDArray zExp(sd::DataType::BOOL, y.getContext(), false); - nd4j::ops::greater op; + sd::ops::greater op; auto status = op.execute({&x, &y}, {&z}, {}, {}, {}); ASSERT_EQ(ND4J_STATUS_OK, status); @@ -715,32 +691,30 @@ TEST_F(BroadcastableOpsTests, broadcast_bool_empty_2) { NDArray e = NDArrayFactory::create('c', {0, 4});; - nd4j::ops::greater op; + sd::ops::greater op; auto result = op.evaluate({&x, &y}); - auto z = result->at(0); + auto z = result.at(0); // z->printShapeInfo("z"); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); ASSERT_TRUE(e.isSameShape(z)); ASSERT_TRUE(e.equalsTo(*z)); - - delete result; } TEST_F(BroadcastableOpsTests, broadcast_bool_1) { - NDArray x('c', {3, 1, 2}, nd4j::DataType::FLOAT32); - NDArray y('c', {2, 2}, nd4j::DataType::FLOAT32); - NDArray z('c', {3, 2, 2}, nd4j::DataType::BOOL); - NDArray e('c', {3, 2, 2}, nd4j::DataType::BOOL); + NDArray x('c', {3, 1, 2}, sd::DataType::FLOAT32); + NDArray y('c', {2, 2}, sd::DataType::FLOAT32); + NDArray z('c', {3, 2, 2}, sd::DataType::BOOL); + NDArray e('c', {3, 2, 2}, sd::DataType::BOOL); x.assign(4.f); y.assign(2.f); e.assign(true); - nd4j::ops::greater op; + sd::ops::greater op; auto status = op.execute({&x, &y}, {&z}); @@ -754,16 +728,16 @@ TEST_F(BroadcastableOpsTests, broadcast_bool_1) { TEST_F(BroadcastableOpsTests, broadcast_bool_2) { - NDArray x('c', {3, 1, 2}, nd4j::DataType::FLOAT32); - NDArray y('c', {2, 2}, nd4j::DataType::FLOAT32); - NDArray z('c', {3, 2, 2}, nd4j::DataType::BOOL); - NDArray e('c', {3, 2, 2}, nd4j::DataType::BOOL); + NDArray x('c', {3, 1, 2}, sd::DataType::FLOAT32); + NDArray y('c', {2, 2}, sd::DataType::FLOAT32); + NDArray z('c', {3, 2, 2}, sd::DataType::BOOL); + NDArray e('c', {3, 2, 2}, sd::DataType::BOOL); x.assign(1.f); y.assign(2.f); e.assign(false); - nd4j::ops::equals op; + sd::ops::equals op; auto status = op.execute({&x, &y}, {&z}, {}, {}, {}); @@ -779,12 +753,12 @@ TEST_F(BroadcastableOpsTests, broadcast_bool_3) { auto x = NDArrayFactory::create(0); auto y = NDArrayFactory::create('c', {3}, {2, 1, 2}); - NDArray z('c', {3}, nd4j::DataType::BOOL); - NDArray e('c', {3}, nd4j::DataType::BOOL); + NDArray z('c', {3}, sd::DataType::BOOL); + NDArray e('c', {3}, sd::DataType::BOOL); e.assign(true); - nd4j::ops::less op; + sd::ops::less op; auto status = op.execute({&x, &y}, {&z}, {}, {}, {}); ASSERT_EQ(ND4J_STATUS_OK, status); @@ -796,16 +770,16 @@ TEST_F(BroadcastableOpsTests, broadcast_bool_3) { } TEST_F(BroadcastableOpsTests, broadcast_2) { - NDArray x('c', {3, 1, 2}, nd4j::DataType::FLOAT32); - NDArray y('c', {2, 2}, nd4j::DataType::FLOAT32); - NDArray z('c', {3, 2, 2}, nd4j::DataType::FLOAT32); - NDArray e('c', {3, 2, 2}, nd4j::DataType::FLOAT32); + NDArray x('c', {3, 1, 2}, sd::DataType::FLOAT32); + NDArray y('c', {2, 2}, sd::DataType::FLOAT32); + NDArray z('c', {3, 2, 2}, sd::DataType::FLOAT32); + NDArray e('c', {3, 2, 2}, sd::DataType::FLOAT32); x = 4.f; y = 2.f; e = -2.f; - nd4j::ops::reversesubtract op; // z = y - x; + sd::ops::reversesubtract op; // z = y - x; auto status = op.execute({&x, &y}, {&z}, {}, {}, {}); @@ -820,10 +794,10 @@ TEST_F(BroadcastableOpsTests, broadcast_2) { TEST_F(BroadcastableOpsTests, broadcast_3) { auto x = NDArrayFactory::create(0); auto y = NDArrayFactory::create('c', {3}, {2, 1, 2}); - NDArray z('c', {3}, nd4j::DataType::INT32); + NDArray z('c', {3}, sd::DataType::INT32); auto e = NDArrayFactory::create('c', {3}, {2, 1, 2}); - nd4j::ops::add op; + sd::ops::add op; auto status = op.execute({&x, &y}, {&z}, {}, {}, {}); ASSERT_EQ(ND4J_STATUS_OK, status); @@ -850,7 +824,7 @@ TEST_F(BroadcastableOpsTests, test_bert_multiply_1) { ctx.setInputArray(1, &y); ctx.setOutputArray(0, &z); - nd4j::ops::multiply op; + sd::ops::multiply op; auto status = op.execute(&ctx); ASSERT_EQ(Status::OK(), status); diff --git a/libnd4j/tests_cpu/layers_tests/BrodcastTests.cpp b/libnd4j/tests_cpu/layers_tests/BrodcastTests.cpp index bc2ae2152..ed97c3137 100644 --- a/libnd4j/tests_cpu/layers_tests/BrodcastTests.cpp +++ b/libnd4j/tests_cpu/layers_tests/BrodcastTests.cpp @@ -19,7 +19,7 @@ // #include "testinclude.h" -#include +#include class BroadcastMultiDimTest : public testing::Test { public: @@ -54,7 +54,7 @@ TEST_F(BroadcastMultiDimTest,MultimDimTest) { tad->tadOnlyShapeInfo, //tadShapeInfo tad->tadOffsets, //tadOffset tad->tadOnlyShapeInfo, //tadShapeInfoZ - tad->tadOffsets, nd4j::LoopKind::COMMON, 0, tad->numTads); //tadOffsetZ + tad->tadOffsets, sd::LoopKind::COMMON, 0, tad->numTads); //tadOffsetZ for(int i = 0; i < 30; i++) { ASSERT_EQ(dataAssertion[i],result[i]); } diff --git a/libnd4j/tests_cpu/layers_tests/CMakeLists.txt b/libnd4j/tests_cpu/layers_tests/CMakeLists.txt index 17ae714cd..07f473dbc 100644 --- a/libnd4j/tests_cpu/layers_tests/CMakeLists.txt +++ b/libnd4j/tests_cpu/layers_tests/CMakeLists.txt @@ -1,5 +1,5 @@ include_directories(${gtest_SOURCE_DIR}/include ${gtest_SOURCE_DIR}) -include_directories(../../include ../../layers ../../include/helpers ../../include/exceptions ../../include/execution ../../include/array ../../include/memory ../../include/loops ../../include/graph ../../include/ops ../../include/types ../../include/cnpy ../../blas ../lib/googletest-release-1.8.0/googletest/include) +include_directories(${CMAKE_CURRENT_SOURCE_DIR}/include) if(LINUX) link_directories(/usr/local/lib) link_directories(/usr/lib) @@ -21,7 +21,7 @@ if(WIN32) endforeach() endif() -if (CUDA_BLAS) +if (SD_CUDA) find_package(CUDA) message("Tests CUDA include directory: ${CUDA_INCLUDE_DIRS}") include_directories(${CUDA_INCLUDE_DIRS}) @@ -44,11 +44,11 @@ endif() if (APPLE) set(CMAKE_CXX_FLAGS " -fPIC -fmax-errors=2 -D__APPLE_OS__=true") elseif(WIN32) - if (CPU_BLAS) + if (SD_CPU) set(CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE} -fPIC -march=native -mtune=native -O3") endif() - if (CPU_BLAS AND LINUX) + if (SD_CPU AND LINUX) set(CMAKE_CXX_FLAGS " -fPIC -fmax-errors=2") endif() else() @@ -60,7 +60,7 @@ else() set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -march=native -mtune=native") endif() - if (CPU_BLAS) + if (SD_CPU AND SD_SANITIZE) set(CMAKE_CXX_FLAGS_DEBUG "${CMAKE_CXX_FLAGS} -fsanitize=address") else() # CUDA? @@ -69,7 +69,7 @@ endif() # tests are always compiled with all ops included -SET( CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -DLIBND4J_ALL_OPS=true -DBUILD_TESTS=true") +SET( CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -DSD_ALL_OPS=true -DBUILD_TESTS=true") if ("${CMAKE_CXX_COMPILER_ID}" STREQUAL "Clang") # using Clang @@ -109,9 +109,9 @@ else() message("OPENMP NOT FOUND") endif() -if (CPU_BLAS) +if (SD_CPU) file(GLOB_RECURSE TEST_SOURCES false ./*.cpp ./*.h) -elseif (CUDA_BLAS) +elseif (SD_CUDA) file(GLOB_RECURSE TEST_SOURCES false ./*.cpp ./*.cu ./*.h) endif() @@ -124,14 +124,14 @@ foreach (TMP_PATH ${TEST_SOURCES}) endif () endforeach(TMP_PATH) -if (CPU_BLAS) +if (SD_CPU) if (NOT BLAS_LIBRARIES) set(BLAS_LIBRARIES "") endif() add_executable(runtests ${TEST_SOURCES}) - target_link_libraries(runtests ${LIBND4J_NAME}static ${MKLDNN_LIBRARIES} ${OPENBLAS_LIBRARIES} ${MKLDNN} ${BLAS_LIBRARIES} ${CPU_FEATURES} gtest gtest_main) -elseif(CUDA_BLAS) + target_link_libraries(runtests ${SD_LIBRARY_NAME}static ${MKLDNN_LIBRARIES} ${OPENBLAS_LIBRARIES} ${MKLDNN} ${BLAS_LIBRARIES} ${CPU_FEATURES} gtest gtest_main) +elseif(SD_CUDA) add_executable(runtests ${TEST_SOURCES}) @@ -148,5 +148,5 @@ elseif(CUDA_BLAS) message("CUDNN library: ${CUDNN}") endif() - target_link_libraries(runtests ${LIBND4J_NAME}static ${CUDA_LIBRARIES} ${CUDA_CUBLAS_LIBRARIES} ${CUDA_cusolver_LIBRARY} ${CUDNN} ${MKLDNN} gtest gtest_main) + target_link_libraries(runtests ${SD_LIBRARY_NAME}static ${CUDA_LIBRARIES} ${CUDA_CUBLAS_LIBRARIES} ${CUDA_cusolver_LIBRARY} ${CUDNN} ${MKLDNN} gtest gtest_main) endif() \ No newline at end of file diff --git a/libnd4j/tests_cpu/layers_tests/CnpyTests.cpp b/libnd4j/tests_cpu/layers_tests/CnpyTests.cpp index 086da26c5..ea8025592 100644 --- a/libnd4j/tests_cpu/layers_tests/CnpyTests.cpp +++ b/libnd4j/tests_cpu/layers_tests/CnpyTests.cpp @@ -20,7 +20,7 @@ #include "testinclude.h" #include -#include +#include class FileTest : public testing::Test { @@ -38,28 +38,28 @@ TEST_F(HeaderTest, test_dataTypes_1) { std::string header("0NUMPY6789{'descr': '>f4"); - ASSERT_EQ(nd4j::DataType::FLOAT32, dataTypeFromNpyHeader(const_cast(header.data()))); + ASSERT_EQ(sd::DataType::FLOAT32, dataTypeFromNpyHeader(const_cast(header.data()))); } TEST_F(HeaderTest, test_dataTypes_2) { std::string header("0NUMPY6789{'descr': '>f8"); - ASSERT_EQ(nd4j::DataType::DOUBLE, dataTypeFromNpyHeader(const_cast(header.data()))); + ASSERT_EQ(sd::DataType::DOUBLE, dataTypeFromNpyHeader(const_cast(header.data()))); } TEST_F(HeaderTest, test_dataTypes_3) { std::string header("0NUMPY6789{'descr': '(header.data()))); + ASSERT_EQ(sd::DataType::INT32, dataTypeFromNpyHeader(const_cast(header.data()))); } TEST_F(HeaderTest, test_dataTypes_4) { std::string header("0NUMPY6789{'descr': '>u2"); - ASSERT_EQ(nd4j::DataType::UINT16, dataTypeFromNpyHeader(const_cast(header.data()))); + ASSERT_EQ(sd::DataType::UINT16, dataTypeFromNpyHeader(const_cast(header.data()))); } /* diff --git a/libnd4j/tests_cpu/layers_tests/ConditionalTests.cpp b/libnd4j/tests_cpu/layers_tests/ConditionalTests.cpp index 60ba4733c..00752ca0f 100644 --- a/libnd4j/tests_cpu/layers_tests/ConditionalTests.cpp +++ b/libnd4j/tests_cpu/layers_tests/ConditionalTests.cpp @@ -19,13 +19,13 @@ // #include "testlayers.h" -#include -#include -#include +#include +#include +#include #include -using namespace nd4j; -using namespace nd4j::graph; +using namespace sd; +using namespace sd::graph; class ConditionalTests : public testing::Test { public: @@ -75,7 +75,7 @@ TEST_F(ConditionalTests, BasicTests_1) { auto nodeC0 = new Node(OpType_REDUCE_SAME, reduce::Sum, 7, {-1}); nodeC0->setScopeInfo(1, "scopeCondition"); - nd4j::ops::eq_scalar op; + sd::ops::eq_scalar op; auto nodeC1 = new Node(&op, 8, {7, -4}); nodeC1->setScopeInfo(1, "scopeCondition"); @@ -110,7 +110,7 @@ TEST_F(ConditionalTests, BasicTests_1) { * Condition is False */ TEST_F(ConditionalTests, Flat_Test_1) { - nd4j::ops::identity op0; + sd::ops::identity op0; auto graph = GraphExecutioner::importFromFlatBuffers("./resources/simpleif_0_1.fb"); auto varSpace = graph->getVariableSpace(); @@ -141,7 +141,7 @@ TEST_F(ConditionalTests, Flat_Test_1) { TEST_F(ConditionalTests, Flat_Test_2) { Environment::getInstance()->setDebug(true); Environment::getInstance()->setVerbose(true); - nd4j::ops::identity op0; + sd::ops::identity op0; auto graph = GraphExecutioner::importFromFlatBuffers("./resources/simpleif_0.fb"); auto varSpace = graph->getVariableSpace(); @@ -169,7 +169,7 @@ TEST_F(ConditionalTests, Flat_Test_2) { * Condition is false here, so there loop will be skipped */ TEST_F(ConditionalTests, Flat_Test_3) { - nd4j::ops::identity op0; + sd::ops::identity op0; auto graph = GraphExecutioner::importFromFlatBuffers("./resources/simplewhile_0_3.fb"); auto varSpace = graph->getVariableSpace(); @@ -196,7 +196,7 @@ TEST_F(ConditionalTests, Flat_Test_3) { * just one cycle in body */ TEST_F(ConditionalTests, Flat_Test_4) { - nd4j::ops::identity op0; + sd::ops::identity op0; auto graph = GraphExecutioner::importFromFlatBuffers("./resources/simplewhile_0_4.fb"); auto varSpace = graph->getVariableSpace(); @@ -225,7 +225,7 @@ TEST_F(ConditionalTests, Flat_Test_4) { * just two cycles in body */ TEST_F(ConditionalTests, Flat_Test_5) { - nd4j::ops::identity op0; + sd::ops::identity op0; auto graph = GraphExecutioner::importFromFlatBuffers("./resources/simplewhile_0_4.fb"); auto varSpace = graph->getVariableSpace(); @@ -253,7 +253,7 @@ TEST_F(ConditionalTests, Flat_Test_5) { * While loop with multiple variables */ TEST_F(ConditionalTests, Flat_Test_6) { - nd4j::ops::identity op0; + sd::ops::identity op0; auto graph = GraphExecutioner::importFromFlatBuffers("./resources/simplewhile_1.fb"); auto varSpace = graph->getVariableSpace(); @@ -280,7 +280,7 @@ TEST_F(ConditionalTests, Flat_Test_6) { } TEST_F(ConditionalTests, Flat_Test_7) { - nd4j::ops::identity op0; + sd::ops::identity op0; auto graph = GraphExecutioner::importFromFlatBuffers("./resources/simplewhile_1.fb"); auto varSpace = graph->getVariableSpace(); diff --git a/libnd4j/tests_cpu/layers_tests/ConstantShapeHelperTests.cpp b/libnd4j/tests_cpu/layers_tests/ConstantShapeHelperTests.cpp index e025aaead..5b747ab5b 100644 --- a/libnd4j/tests_cpu/layers_tests/ConstantShapeHelperTests.cpp +++ b/libnd4j/tests_cpu/layers_tests/ConstantShapeHelperTests.cpp @@ -20,14 +20,14 @@ #include "testlayers.h" #include -#include -#include +#include +#include #include #include -using namespace nd4j; -using namespace nd4j::ops; -using namespace nd4j::graph; +using namespace sd; +using namespace sd::ops; +using namespace sd::graph; class ConstantShapeHelperTests : public testing::Test { public: @@ -76,7 +76,7 @@ TEST_F(ConstantTadHelperTests, test_cachedAmount_1) { } TEST_F(ConstantShapeHelperTests, basic_test_1) { - auto ptr = ShapeBuilders::createShapeInfo(nd4j::DataType::BFLOAT16, 'f', {5, 10, 15}); + auto ptr = ShapeBuilders::createShapeInfo(sd::DataType::BFLOAT16, 'f', {5, 10, 15}); ShapeDescriptor descriptor(ptr); ShapeDescriptor descriptor2(ptr); @@ -85,7 +85,7 @@ TEST_F(ConstantShapeHelperTests, basic_test_1) { ASSERT_EQ(1, descriptor.ews()); ASSERT_EQ(3, descriptor.rank()); ASSERT_EQ('f', descriptor.order()); - ASSERT_EQ(nd4j::DataType::BFLOAT16, descriptor.dataType()); + ASSERT_EQ(sd::DataType::BFLOAT16, descriptor.dataType()); ASSERT_FALSE(descriptor.isEmpty()); ASSERT_FALSE(ConstantShapeHelper::getInstance()->checkBufferExistenceForShapeInfo(descriptor)); @@ -104,6 +104,25 @@ TEST_F(ConstantShapeHelperTests, basic_test_1) { delete []ptr; } +TEST_F(ConstantShapeHelperTests, stress_test_1) { + + for (auto x = 0; x < 1000; x++) { + auto ptr = ShapeBuilders::createShapeInfo(sd::DataType::FLOAT32, 'c', {5, x + 10, x + 1}); + ShapeDescriptor descriptor(ptr); + ConstantShapeHelper::getInstance()->createShapeInfo(descriptor); + delete [] ptr; + } + ShapeDescriptor aShape(sd::DataType::FLOAT32, 'c', {(Nd4jLong)5, (Nd4jLong)382, (Nd4jLong)373}); +// nd4j_printf("%d\n", ConstantShapeHelper::getInstance()->cachedEntriesForDevice(0)); + + auto timeStart = std::chrono::system_clock::now(); + ASSERT_TRUE(ConstantShapeHelper::getInstance()->checkBufferExistenceForShapeInfo(aShape)); + auto timeEnd = std::chrono::system_clock::now(); + + auto outerTime = std::chrono::duration_cast(timeEnd - timeStart).count(); + nd4j_printf("Total time (us) %lld\n", outerTime); +} + TEST_F(ConstantShapeHelperTests, basic_test_3) { auto array = NDArrayFactory::create_('c', {128}); @@ -126,7 +145,7 @@ TEST_F(ConstantShapeHelperTests, basic_test_4) { #ifdef __CUDABLAS__ ASSERT_TRUE(dup->specialShapeInfo() != nullptr); - PointersManager manager(nd4j::LaunchContext ::defaultContext(), "test"); + PointersManager manager(sd::LaunchContext ::defaultContext(), "test"); // manager.printDevContentOnDev(dup->specialShapeInfo(), shape::shapeInfoLength(2), 0); #endif @@ -150,8 +169,8 @@ TEST_F(ConstantShapeHelperTests, basic_test_5) { } TEST_F(ConstantShapeHelperTests, basic_test_6) { - ShapeDescriptor descriptorA(nd4j::DataType::INT32, 'c', {}); - ShapeDescriptor descriptorB(nd4j::DataType::FLOAT32, 'c', {10, 10}); + ShapeDescriptor descriptorA(sd::DataType::INT32, 'c', {}); + ShapeDescriptor descriptorB(sd::DataType::FLOAT32, 'c', {10, 10}); // ASSERT_FALSE(descriptorA < descriptorB); // ASSERT_TRUE(descriptorB < descriptorA); @@ -176,14 +195,14 @@ TEST_F(ConstantHelperTests, basic_test_1) { ConstantDescriptor descriptor({1, 2, 3}); - ConstantDataBuffer* fBuffer = ConstantHelper::getInstance()->constantBuffer(descriptor, nd4j::DataType::FLOAT32); + ConstantDataBuffer* fBuffer = ConstantHelper::getInstance()->constantBuffer(descriptor, sd::DataType::FLOAT32); auto fPtr = fBuffer->primaryAsT(); ASSERT_NEAR(1.f, fPtr[0], 1e-5); ASSERT_NEAR(2.f, fPtr[1], 1e-5); ASSERT_NEAR(3.f, fPtr[2], 1e-5); - auto iBuffer = ConstantHelper::getInstance()->constantBuffer(descriptor, nd4j::DataType::INT32); + auto iBuffer = ConstantHelper::getInstance()->constantBuffer(descriptor, sd::DataType::INT32); auto iPtr = iBuffer->primaryAsT(); ASSERT_EQ(1, iPtr[0]); @@ -196,14 +215,14 @@ TEST_F(ConstantHelperTests, basic_test_2) { double array[] = {1., 2., 3.}; ConstantDescriptor descriptor(array, 3); - ConstantDataBuffer* fBuffer = ConstantHelper::getInstance()->constantBuffer(descriptor, nd4j::DataType::FLOAT32); + ConstantDataBuffer* fBuffer = ConstantHelper::getInstance()->constantBuffer(descriptor, sd::DataType::FLOAT32); auto fPtr = fBuffer->primaryAsT(); ASSERT_NEAR(1.f, fPtr[0], 1e-5); ASSERT_NEAR(2.f, fPtr[1], 1e-5); ASSERT_NEAR(3.f, fPtr[2], 1e-5); - auto iBuffer = ConstantHelper::getInstance()->constantBuffer(descriptor, nd4j::DataType::INT32); + auto iBuffer = ConstantHelper::getInstance()->constantBuffer(descriptor, sd::DataType::INT32); auto iPtr = iBuffer->primaryAsT(); ASSERT_EQ(1, iPtr[0]); diff --git a/libnd4j/tests_cpu/layers_tests/ContextTests.cpp b/libnd4j/tests_cpu/layers_tests/ContextTests.cpp index 13316fe8d..57d9ce88d 100644 --- a/libnd4j/tests_cpu/layers_tests/ContextTests.cpp +++ b/libnd4j/tests_cpu/layers_tests/ContextTests.cpp @@ -21,9 +21,9 @@ #include "testlayers.h" #include -using namespace nd4j; -using namespace nd4j::ops; -using namespace nd4j::graph; +using namespace sd; +using namespace sd::ops; +using namespace sd::graph; class ContextTests : public testing::Test { public: @@ -327,7 +327,7 @@ TEST_F(ContextTests, test_short_context_2) { ASSERT_EQ(2, ctx.width()); - nd4j::ops::add op; + sd::ops::add op; op.execute(&ctx); ASSERT_EQ(exp, z); @@ -345,7 +345,7 @@ TEST_F(ContextTests, test_short_context_3) { ASSERT_EQ(2, ctx.width()); - nd4j::ops::add op; + sd::ops::add op; op.execute(&ctx); ASSERT_EQ(1, ctx.fastpath_out().size()); diff --git a/libnd4j/tests_cpu/layers_tests/ConvolutionTests1.cpp b/libnd4j/tests_cpu/layers_tests/ConvolutionTests1.cpp index a6b99f976..b296a8903 100644 --- a/libnd4j/tests_cpu/layers_tests/ConvolutionTests1.cpp +++ b/libnd4j/tests_cpu/layers_tests/ConvolutionTests1.cpp @@ -22,23 +22,23 @@ #define LIBND4J_CONVOLUTIONTESTS1_H #include "testlayers.h" -#include -#include -#include +#include +#include +#include #include #include #include #include #include -#include -#include +#include +#include #ifdef HAVE_MKLDNN #include #endif -using namespace nd4j; -using namespace nd4j::graph; +using namespace sd; +using namespace sd::graph; class ConvolutionTests1 : public testing::Test { public: @@ -102,7 +102,7 @@ TYPED_TEST(TypedConvolutionTests1, conv2d_1) { // is NHWC block->getIArguments()->push_back(0); - nd4j::ops::conv2d op; + sd::ops::conv2d op; Nd4jStatus status = op.execute(block); ASSERT_EQ(ND4J_STATUS_OK, status); @@ -139,16 +139,14 @@ TYPED_TEST(TypedConvolutionTests1, conv2d_2) { weights.assign(2.0); input.linspace(1); - nd4j::ops::conv2d op; + sd::ops::conv2d op; auto result = op.evaluate({&input, &weights}, {}, {1, 1, 1, 1, 0, 0, 1, 1, 0, 0}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - - delete result; } ////////////////////////////////////////////////////////////////////// @@ -171,16 +169,16 @@ TYPED_TEST(TypedConvolutionTests1, conv2d_3) { input = 2.; weights.linspace(0.1, 0.1); - nd4j::ops::conv2d op; + sd::ops::conv2d op; auto results = op.evaluate({&input, &weights}, {}, {kH,kW, sH,sW, pH,pW, dH,dW, paddingMode, dataFormat}); - auto output = results->at(0); + auto output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expOutput.isSameShape(output)); ASSERT_TRUE(expOutput.equalsTo(output)); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -200,16 +198,14 @@ TYPED_TEST(TypedConvolutionTests1, conv2d_4) { input = 2.; weights.linspace(0.1, 0.1); - nd4j::ops::conv2d op; + sd::ops::conv2d op; auto results = op.evaluate({&input, &weights}, {}, {kH,kW, sH,sW, pH,pW, dH,dW, paddingMode, dataFormat}); - auto output = results->at(0); + auto output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expOutput.isSameShape(output)); ASSERT_TRUE(expOutput.equalsTo(output)); - - delete results; } ////////////////////////////////////////////////////////////////////// @@ -230,18 +226,16 @@ TYPED_TEST(TypedConvolutionTests1, conv2d_5) { weights.linspace(0.1, 0.1); weights.permutei({2,3,1,0}); - nd4j::ops::conv2d op; + sd::ops::conv2d op; auto results = op.evaluate({&input, &weights, &bias}, {}, {kH,kW, sH,sW, pH,pW, dH,dW, paddingMode, dataFormat}); - auto output = results->at(0); + auto output = results.at(0); // output->printIndexedBuffer(); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expOutput.isSameShape(output)); ASSERT_TRUE(expOutput.equalsTo(output)); - - delete results; } ////////////////////////////////////////////////////////////////////// @@ -249,11 +243,9 @@ TYPED_TEST(TypedConvolutionTests1, conv2d_6) { auto input = NDArrayFactory::create('c', {54, 1, 12, 12}); auto weights = NDArrayFactory::create('c', {1, 2, 12, 2}); - nd4j::ops::conv2d op; + sd::ops::conv2d op; auto result = op.evaluate({&input, &weights}, {}, {-1,-1, 1,1, 0,0, 1,1, 1,1}); - ASSERT_EQ(Status::OK(), result->status()); - - delete result; + ASSERT_EQ(Status::OK(), result.status()); } ////////////////////////////////////////////////////////////////////// @@ -270,13 +262,13 @@ TYPED_TEST(TypedConvolutionTests1, conv2d_7) { input = 5.; weights = 3.; - nd4j::ops::conv2d op; + sd::ops::conv2d op; auto results = op.evaluate({&input, &weights}, {}, {kH,kW, sH,sW, pH,pW, dH,dW, paddingMode, dataFormat}); - auto* output = results->at(0); + auto* output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -304,18 +296,17 @@ TEST_F(ConvolutionTests1, conv2d_8) { 1.179925, 1.909109, 2.009143, 2.299778, 1.957207, 1.779718, 2.480604, 1.529086, 1.748063, 1.952856, 2.029487, 2.699131, 1.879842, 1.471205, 2.150177, 2.039078, 1.933456, 1.764169, 2.584944, 2.521004, 1.744296, 1.707578, 2.237938, 2.325231, 0.984485, 1.766936, 1.590640, 1.347524, 1.404648, 1.422042, 1.709862, 1.155412}); - nd4j::ops::conv2d op; + sd::ops::conv2d op; auto results = op.evaluate({&input, &weights, &bias}, {kH,kW, sH,sW, pH,pW, dH,dW, paddingMode, dataFormat}); - auto output = results->at(0); + auto output = results.at(0); // output->printBuffer(); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expOutput.isSameShape(output)); ASSERT_TRUE(expOutput.equalsTo(output)); - delete results; } ////////////////////////////////////////////////////////////////////// @@ -368,7 +359,7 @@ TEST_F(ConvolutionTests1, sconv2d_1) { // NOT same mode block->getIArguments()->push_back(0); - nd4j::ops::sconv2d op; + sd::ops::sconv2d op; Nd4jStatus status = op.execute(block); @@ -417,11 +408,11 @@ TYPED_TEST(TypedConvolutionTests1, sconv2d_2) { weightsD.applyScalar(scalar::Divide, 100.0, weightsD); weightsP.applyScalar(scalar::Divide, 100.0, weightsP); - nd4j::ops::sconv2d op; + sd::ops::sconv2d op; auto resultFF = op.evaluate({&input, &weightsD, &weightsP}, {5, 5, 1, 1, 0, 0, 1, 1, 0, 0}); - auto z = resultFF->at(0); + auto z = resultFF.at(0); //z->printShapeInfo("FF shape"); @@ -430,8 +421,6 @@ TYPED_TEST(TypedConvolutionTests1, sconv2d_2) { //expFF.printBuffer("e"); //z->printBuffer("z"); ASSERT_TRUE(z->equalsTo(&expFF, 1e-3)); - - delete resultFF; } TYPED_TEST(TypedConvolutionTests1, sconv2d_3) { @@ -451,11 +440,11 @@ TYPED_TEST(TypedConvolutionTests1, sconv2d_3) { auto expOutput = NDArrayFactory::create('c', {3, 2, 8, 8}); - nd4j::ops::sconv2d op; + sd::ops::sconv2d op; Nd4jStatus status = op.execute({&input, &weightsD, &weightsP, &bias}, {&output}, {1, 1, 1, 1, 0, 0, 1, 1, 0}); auto result = op.evaluate({&input, &weightsD, &weightsP, &bias}, {1, 1, 1, 1, 0, 0, 1, 1, 0}); - auto z = result->at(0); + auto z = result.at(0); //printf("\n"); //output.printBuffer("output"); @@ -463,8 +452,6 @@ TYPED_TEST(TypedConvolutionTests1, sconv2d_3) { //ASSERT_TRUE(expOutput.isSameShape(z)); - - delete result; } ////////////////////////////////////////////////////////////////////// @@ -492,16 +479,15 @@ TEST_F(ConvolutionTests1, sconv2d_4) { 2.096277, 1.178815, 1.637460, 1.254187, 1.491076, 0.968625, 0.986342, 2.116042, 1.536920, 1.504321, 1.490398, 2.136795, 1.351860, 1.148578, 1.817408, 1.327139, 1.288620, 0.962232, 0.980667, 1.623775, 1.417320, 1.845710, 1.237095, 1.762792, 1.352515}); - nd4j::ops::sconv2d op; + sd::ops::sconv2d op; auto results = op.evaluate({&input, &weightsD, &weightsP, &biases}, {kH,kW, sH,sW, pH,pW, dH,dW, paddingMode, dataFormat}); - auto* output = results->at(0); + auto* output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expOutput.isSameShape(output)); ASSERT_TRUE(expOutput.equalsTo(output)); - delete results; } TYPED_TEST(TypedConvolutionTests1, conv2D_BP_Bias_1) { @@ -529,15 +515,15 @@ TYPED_TEST(TypedConvolutionTests1, conv2D_BP_Bias_1) { epsilonNext.linspace(1); weights.permutei({2,3,1,0}); - nd4j::ops::conv2d_bp op; + sd::ops::conv2d_bp op; auto results = op.evaluate({&input, &weights, &bias, &epsilonNext}, {}, {3, 3, 1, 1, 0, 0, 1, 1, 1}, {}); - ASSERT_TRUE(results->size() == 3); + ASSERT_TRUE(results.size() == 3); - auto epsilon = results->at(0); - auto gradW = results->at(1); - auto gradB = results->at(2); + auto epsilon = results.at(0); + auto gradW = results.at(1); + auto gradB = results.at(2); ASSERT_TRUE(expWGrad.isSameShape(gradW)); @@ -556,7 +542,7 @@ TYPED_TEST(TypedConvolutionTests1, conv2D_BP_Bias_1) { ASSERT_TRUE(expBGrad.equalsTo(gradB)); - delete results; + } @@ -579,14 +565,14 @@ TYPED_TEST(TypedConvolutionTests1, conv2D_BP_NoBias_1) { epsilonNext.linspace(1); weights.permutei({2,3,1,0}); - nd4j::ops::conv2d_bp op; + sd::ops::conv2d_bp op; auto results = op.evaluate({&input, &weights, &epsilonNext}, {}, {3, 3, 1, 1, 0, 0, 1, 1, 1}, {}); - ASSERT_TRUE(results->size() == 2); + ASSERT_TRUE(results.size() == 2); - auto epsilon = results->at(0); - auto gradW = results->at(1); + auto epsilon = results.at(0); + auto gradW = results.at(1); ASSERT_TRUE(expWGrad.isSameShape(gradW)); @@ -601,7 +587,7 @@ TYPED_TEST(TypedConvolutionTests1, conv2D_BP_NoBias_1) { //epsilon->printBuffer("Result buffer"); ASSERT_TRUE(expEps.equalsTo(epsilon)); - delete results; + } TYPED_TEST(TypedConvolutionTests1, sconv2d_conv2d_1) { @@ -663,27 +649,24 @@ TYPED_TEST(TypedConvolutionTests1, sconv2d_conv2d_1) { input.linspace(1); - nd4j::ops::sconv2d op; + sd::ops::sconv2d op; auto resultFF = op.evaluate({&input, &weightsD}, {}, {5, 5, 1, 1, 0, 0, 1, 1, 0}, {}); - auto z = resultFF->at(0); + auto z = resultFF.at(0); ASSERT_TRUE(z->isSameShape(&expFF)); ASSERT_TRUE(z->equalsTo(&expFF, 1)); - nd4j::ops::conv2d op2d; + sd::ops::conv2d op2d; // weightsP.printShapeInfo(); auto result2D = op2d.evaluate({z, &weightsP}, {}, {1, 1, 1, 1, 0, 0, 1, 1, 0, 0}, {}); - auto z2d = result2D->at(0); + auto z2d = result2D.at(0); // z2d->printBuffer(); ASSERT_TRUE(z2d->isSameShape(&exp2FF)); ASSERT_TRUE(z2d->equalsTo(&exp2FF)); - - delete resultFF; - delete result2D; } TEST_F(ConvolutionTests1, TestDeconv_bp_1) { @@ -694,10 +677,10 @@ TEST_F(ConvolutionTests1, TestDeconv_bp_1) { int dataFormat = 0; // 1-NHWC, 0-NCHW - NDArray input('c', {bS, iC, iH, iW}, nd4j::DataType::FLOAT32); - NDArray bias('c', {oC}, nd4j::DataType::FLOAT32); - NDArray weights('c',{kH,kW,oC,iC}, {1,3,5,2,4,6}, nd4j::DataType::FLOAT32); - NDArray gradO('c', {bS, oC, oH, oW},nd4j::DataType::FLOAT32); + NDArray input('c', {bS, iC, iH, iW}, sd::DataType::FLOAT32); + NDArray bias('c', {oC}, sd::DataType::FLOAT32); + NDArray weights('c',{kH,kW,oC,iC}, {1,3,5,2,4,6}, sd::DataType::FLOAT32); + NDArray gradO('c', {bS, oC, oH, oW},sd::DataType::FLOAT32); NDArray expGradI('c', {bS, iC, iH, iW}, {35.f, 38.f, 41.f, 44.f, 47.f, 50.f, 53.f, 56.f, 59.f, 62.f, 65.f, 68.f, 71.f, 74.f, 77.f, 80.f, 71.f, 78.f, 85.f, 92.f, 99.f, 106.f, 113.f, 120.f, 127.f, 134.f, 141.f, 148.f, 155.f, 162.f, 169.f, @@ -707,24 +690,24 @@ TEST_F(ConvolutionTests1, TestDeconv_bp_1) { 481.f, 492.f, 503.f, 514.f, 525.f, 536.f, 547.f, 558.f, 569.f, 580.f, 591.f, 602.f, 613.f, 624.f, 227.f, 230.f, 233.f, 236.f, 239.f, 242.f, 245.f, 248.f, 251.f, 254.f, 257.f, 260.f, 263.f, 266.f, 269.f, 272.f, 519.f, 526.f, 533.f, 540.f, 547.f, 554.f, 561.f, 568.f, 575.f, 582.f, 589.f, 596.f, 603.f, 610.f, 617.f, 624.f, 811.f, 822.f, 833.f, 844.f, 855.f, - 866.f, 877.f, 888.f, 899.f, 910.f, 921.f, 932.f, 943.f, 954.f, 965.f, 976.f}, nd4j::DataType::FLOAT32); - NDArray expGradW('c', {kH, kW, oC, iC}, {160008., 191112., 222216., 203400., 246792., 290184.f}, nd4j::DataType::FLOAT32); - NDArray expGradB('c', {oC}, {1944.f, 2712.f}, nd4j::DataType::FLOAT32); + 866.f, 877.f, 888.f, 899.f, 910.f, 921.f, 932.f, 943.f, 954.f, 965.f, 976.f}, sd::DataType::FLOAT32); + NDArray expGradW('c', {kH, kW, oC, iC}, {160008., 191112., 222216., 203400., 246792., 290184.f}, sd::DataType::FLOAT32); + NDArray expGradB('c', {oC}, {1944.f, 2712.f}, sd::DataType::FLOAT32); input.linspace(1); bias.linspace(1); gradO.linspace(1); - nd4j::ops::deconv2d_bp op; + sd::ops::deconv2d_bp op; auto results = op.evaluate({&input, &weights, &bias, &gradO}, {kH,kW, sH,sW, pH,pW, dH,dW, paddingMode, dataFormat}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); - - auto gradI = results->at(0); - auto gradW = results->at(1); - auto gradB = results->at(2); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); + auto gradI = results.at(0); + auto gradW = results.at(1); + auto gradB = results.at(2); + ASSERT_TRUE(expGradI.isSameShape(gradI)); ASSERT_TRUE(expGradI.equalsTo(gradI)); @@ -734,8 +717,8 @@ TEST_F(ConvolutionTests1, TestDeconv_bp_1) { ASSERT_TRUE(expGradB.isSameShape(gradB)); ASSERT_TRUE(expGradB.equalsTo(gradB)); - delete results; } + TEST_F(ConvolutionTests1, TestDeconv_bp_2) { /* Input shape: @@ -769,13 +752,13 @@ TEST_F(ConvolutionTests1, TestDeconv_bp_2) { bias.linspace(1); epsilon.linspace(1); - nd4j::ops::deconv2d_bp op; + sd::ops::deconv2d_bp op; auto result = op.evaluate({&input, &weights, &bias, &epsilon}, {}, {2, 2, 1, 1, 0, 0, 2, 2, 0}); ASSERT_EQ(ND4J_STATUS_OK, result->status()); - delete result;*/ + */ } TYPED_TEST(TypedConvolutionTests1, Test_Conv1D_ff_1) { auto input = NDArrayFactory::create('c', {2, 2, 6}); @@ -790,27 +773,27 @@ TYPED_TEST(TypedConvolutionTests1, Test_Conv1D_ff_1) { input.linspace(1); bias.linspace(1); - nd4j::ops::conv1d op; + sd::ops::conv1d op; auto result_FF = op.evaluate({&input, &weights, &bias}, {}, {2, 1, 0, 1, 0, 0}); - ASSERT_EQ(ND4J_STATUS_OK, result_FF->status()); + ASSERT_EQ(ND4J_STATUS_OK, result_FF.status()); - auto z = result_FF->at(0); + auto z = result_FF.at(0); ASSERT_TRUE(expFF.isSameShape(z)); ASSERT_TRUE(expFF.equalsTo(z)); - nd4j::ops::conv1d_bp op_bp; + sd::ops::conv1d_bp op_bp; auto epsilonNxt = new NDArray(z->dup()); epsilonNxt->linspace(1); auto result_BP = op_bp.evaluate({&input, &weights, &bias, epsilonNxt}, {}, {2, 1, 0, 1, 0, 0}); - ASSERT_EQ(ND4J_STATUS_OK, result_BP->status()); + ASSERT_EQ(ND4J_STATUS_OK, result_BP.status()); - auto eps = result_BP->at(0); - auto gradW = result_BP->at(1); - auto gradB = result_BP->at(2); + auto eps = result_BP.at(0); + auto gradW = result_BP.at(1); + auto gradB = result_BP.at(2); ASSERT_TRUE(expEps.isSameShape(eps)); ASSERT_TRUE(expGW.isSameShape(gradW)); @@ -820,8 +803,6 @@ TYPED_TEST(TypedConvolutionTests1, Test_Conv1D_ff_1) { ASSERT_TRUE(expGW.equalsTo(gradW)); ASSERT_TRUE(expGB.equalsTo(gradB)); - delete result_FF; - delete result_BP; delete epsilonNxt; } @@ -832,14 +813,13 @@ TYPED_TEST(TypedConvolutionTests1, Test_Conv1D_ff_2) { input.linspace(1); - nd4j::ops::conv1d op; + sd::ops::conv1d op; auto result = op.evaluate({&input, &weights}, {}, {2, 1, 0, 1, 1,0}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); - delete result; } ////////////////////////////////////////////////////////////////////// @@ -859,16 +839,15 @@ TEST_F(ConvolutionTests1, conv1d_causal_1) { input.linspace(1., 1.); weights.linspace(0.1, 0.1); - nd4j::ops::conv1d op; + sd::ops::conv1d op; auto results = op.evaluate({&input, &weights, &bias}, {kW, sW, pW, dW, paddingMode, dataFormat}); - auto output = results->at(0); + auto output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expOutput.isSameShape(output)); ASSERT_TRUE(expOutput.equalsTo(output)); - delete results; } ////////////////////////////////////////////////////////////////////// @@ -891,16 +870,15 @@ TEST_F(ConvolutionTests1, conv1d_causal_2) { input.linspace(1., 1.); weights.linspace(0.1, 0.1); - nd4j::ops::conv1d op; + sd::ops::conv1d op; auto results = op.evaluate({&input, &weights, &bias}, {kW, sW, pW, dW, paddingMode, dataFormat}); - auto output = results->at(0); + auto output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expOutput.isSameShape(output)); ASSERT_TRUE(expOutput.equalsTo(output)); - delete results; } ////////////////////////////////////////////////////////////////////// @@ -922,16 +900,15 @@ TEST_F(ConvolutionTests1, conv1d_causal_3) { input.linspace(1., 1.); weights.linspace(0.1, 0.1); - nd4j::ops::conv1d op; + sd::ops::conv1d op; auto results = op.evaluate({&input, &weights, &bias}, {kW, sW, pW, dW, paddingMode, dataFormat}); - auto output = results->at(0); + auto output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expOutput.isSameShape(output)); ASSERT_TRUE(expOutput.equalsTo(output)); - delete results; } ////////////////////////////////////////////////////////////////////// @@ -953,16 +930,15 @@ TEST_F(ConvolutionTests1, conv1d_causal_4) { input.linspace(1., 1.); weights.linspace(0.1, 0.1); - nd4j::ops::conv1d op; + sd::ops::conv1d op; auto results = op.evaluate({&input, &weights, &bias}, {kW, sW, pW, dW, paddingMode, dataFormat}); - auto output = results->at(0); + auto output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expOutput.isSameShape(output)); ASSERT_TRUE(expOutput.equalsTo(output)); - delete results; } ////////////////////////////////////////////////////////////////////// @@ -984,16 +960,15 @@ TEST_F(ConvolutionTests1, conv1d_causal_5) { input.linspace(1., 1.); weights.linspace(0.1, 0.1); - nd4j::ops::conv1d op; + sd::ops::conv1d op; auto results = op.evaluate({&input, &weights, &bias}, {kW, sW, pW, dW, paddingMode, dataFormat}); - auto output = results->at(0); + auto output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expOutput.isSameShape(output)); ASSERT_TRUE(expOutput.equalsTo(output)); - delete results; } ////////////////////////////////////////////////////////////////////// @@ -1015,16 +990,15 @@ TEST_F(ConvolutionTests1, conv1d_causal_6) { input.linspace(1., 1.); weights.linspace(0.1, 0.1); - nd4j::ops::conv1d op; + sd::ops::conv1d op; auto results = op.evaluate({&input, &weights, &bias}, {kW, sW, pW, dW, paddingMode, dataFormat}); - auto output = results->at(0); + auto output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expOutput.isSameShape(output)); ASSERT_TRUE(expOutput.equalsTo(output)); - delete results; } ////////////////////////////////////////////////////////////////////// @@ -1035,28 +1009,27 @@ TEST_F(ConvolutionTests1, conv1d_causal_7) { int paddingMode = 2; // CAUSAL int dataFormat = 1; // 1-NHWC, 0-NCHW - NDArray input('c', {bS, iW, iC}, nd4j::DataType::FLOAT32); - NDArray weights('c', {kW, iC, oC}, nd4j::DataType::FLOAT32); + NDArray input('c', {bS, iW, iC}, sd::DataType::FLOAT32); + NDArray weights('c', {kW, iC, oC}, sd::DataType::FLOAT32); NDArray expOutput('c', {bS, oW, oC}, {11.000000, 11.600000, 12.200000, 12.800000, 30.099998, 32.200001, 34.299999, 36.400002, 49.899998, 53.800003, 57.699997, 61.599998, 69.699997, 75.400002, 81.099998, 86.800003, 89.500000, 97.000000, 104.500000, 112.000000, 109.300003, 118.600006, 127.899994, 137.199997, 129.100006, 140.199997, 151.300003, 162.399994, 148.899994, 161.800003, 174.699997, 187.600006, 133.399994, 141.200012, 149.000000, 156.800003, 188.500000, 205.000000, 221.500000, 238.000000, 208.299988, 226.600006, 244.899994, 263.200012, 228.100006, 248.200012, 268.299988, 288.399994, 247.899994, 269.799988, 291.700012, - 313.600006, 267.700012, 291.399994, 315.100006, 338.799988, 287.500000, 313.000000, 338.500000, 364.000000, 307.299988, 334.600006, 361.899994, 389.200012}, nd4j::DataType::FLOAT32); + 313.600006, 267.700012, 291.399994, 315.100006, 338.799988, 287.500000, 313.000000, 338.500000, 364.000000, 307.299988, 334.600006, 361.899994, 389.200012}, sd::DataType::FLOAT32); input.linspace(1., 1.); weights.linspace(0.1, 0.1); - nd4j::ops::conv1d op; + sd::ops::conv1d op; auto results = op.evaluate({&input, &weights}, {kW, sW, pW, dW, paddingMode, dataFormat}); - auto output = results->at(0); + auto output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expOutput.isSameShape(output)); ASSERT_TRUE(expOutput.equalsTo(output)); - delete results; } ////////////////////////////////////////////////////////////////////// @@ -1067,29 +1040,28 @@ TEST_F(ConvolutionTests1, conv1d_causal_8) { int paddingMode = 2; // CAUSAL int dataFormat = 1; // 1-NHWC, 0-NCHW - NDArray input('c', {bS, iW, iC}, nd4j::DataType::FLOAT32); - NDArray weights('c', {kW, iC, oC}, nd4j::DataType::FLOAT32); + NDArray input('c', {bS, iW, iC}, sd::DataType::FLOAT32); + NDArray weights('c', {kW, iC, oC}, sd::DataType::FLOAT32); NDArray expOutput('c', {bS, oW, oC}, {11.000000, 11.600000, 12.200000, 12.800000, 26.299999, 27.799999, 29.299999, 30.799999, 45.399998, 48.399998, 51.400002, 54.400005, 65.199997, 70.000000, 74.800003, 79.600006, 85.000000, 91.600006, 98.199997, 104.800003, 104.799995, 113.199997, 121.600006, 130.000000, 124.599998, 134.800003, 145.000000, 155.200012, 144.399994, 156.399994, 168.399994, 180.400009, 133.400009, 141.199997, 149.000000, 156.800003, 148.699997, 157.400009, 166.099991, 174.800003, 203.800003, 221.200012, 238.599991, 256.000000, 223.599991, 242.799988, 262.000000, 281.200012, 243.399994, 264.399994, 285.399994, 306.399994, 263.199982, 286.000000, 308.799988, 331.600006, 283.000000, 307.600006, 332.200012, - 356.800018, 302.799988, 329.199982, 355.600006, 382.000000}, nd4j::DataType::FLOAT32); + 356.800018, 302.799988, 329.199982, 355.600006, 382.000000}, sd::DataType::FLOAT32); input.linspace(1., 1.); weights.linspace(0.1, 0.1); - nd4j::ops::conv1d op; + sd::ops::conv1d op; auto results = op.evaluate({&input, &weights}, {kW, sW, pW, dW, paddingMode, dataFormat}); - auto output = results->at(0); + auto output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expOutput.isSameShape(output)); ASSERT_TRUE(expOutput.equalsTo(output)); - delete results; } ////////////////////////////////////////////////////////////////////// @@ -1112,8 +1084,8 @@ TEST_F(ConvolutionTests1, conv1d_causal_bp_1) { const OpArgsHolder argsHolderFF({&input, &weights, &bias}, {}, {kW, sW, pW, dW, paddingMode, dataFormat}); const OpArgsHolder argsHolderBP({&input, &weights, &bias, &gradO}, {}, {kW, sW, pW, dW, paddingMode, dataFormat}); - nd4j::ops::conv1d opFF; - nd4j::ops::conv1d_bp opBP; + sd::ops::conv1d opFF; + sd::ops::conv1d_bp opBP; const bool isGradCorrect = GradCheck::checkGrad(opFF, opBP, argsHolderFF, argsHolderBP); @@ -1128,16 +1100,14 @@ TEST_F(ConvolutionTests1, Test_Dilation2D_1) { input.linspace(1); weights.linspace(1); - nd4j::ops::dilation2d op; + sd::ops::dilation2d op; auto result = op.evaluate({&input, &weights}, {1, 1,2,2,1, 1,2,2,1}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - - delete result; } TEST_F(ConvolutionTests1, Test_Dilation2D_2) { @@ -1148,16 +1118,15 @@ TEST_F(ConvolutionTests1, Test_Dilation2D_2) { input.linspace(1); weights.linspace(1); - nd4j::ops::dilation2d op; + sd::ops::dilation2d op; auto result = op.evaluate({&input, &weights}, {0, 1,2,2,1, 1,2,2,1}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; } ////////////////////////////////////////////////////////////////////// @@ -1187,12 +1156,12 @@ TYPED_TEST(TypedConvolutionTests1, conv2d_bp_test1) { weights.linspace(0.1, 0.1); gradO.linspace(0.01, 0.01); - nd4j::ops::conv2d_bp op; + sd::ops::conv2d_bp op; auto results = op.evaluate({&input, &weights, &bias, &gradO}, {}, {kH,kW, sH,sW, pH,pW, dH,dW, paddingMode, dataFormat}); - auto gradI = results->at(0); - auto gradW = results->at(1); + auto gradI = results.at(0); + auto gradW = results.at(1); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expGradI.isSameShape(gradI)); ASSERT_TRUE(expGradI.equalsTo(gradI)); @@ -1200,7 +1169,6 @@ TYPED_TEST(TypedConvolutionTests1, conv2d_bp_test1) { ASSERT_TRUE(expGradW.isSameShape(gradW)); ASSERT_TRUE(expGradW.equalsTo(gradW)); - delete results; } ////////////////////////////////////////////////////////////////////// @@ -1230,20 +1198,18 @@ TYPED_TEST(TypedConvolutionTests1, conv2d_bp_test2) { weights.linspace(0.1, 0.1); gradO.linspace(0.01, 0.01); - nd4j::ops::conv2d_bp op; + sd::ops::conv2d_bp op; auto results = op.evaluate({&input, &weights, &bias, &gradO}, {}, {kH,kW, sH,sW, pH,pW, dH,dW, paddingMode, dataFormat}); - auto gradI = results->at(0); - auto gradW = results->at(1); + auto gradI = results.at(0); + auto gradW = results.at(1); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expGradI.isSameShape(gradI)); ASSERT_TRUE(expGradI.equalsTo(gradI)); ASSERT_TRUE(expGradW.isSameShape(gradW)); ASSERT_TRUE(expGradW.equalsTo(gradW)); - - delete results; } ////////////////////////////////////////////////////////////////////// @@ -1275,13 +1241,13 @@ TYPED_TEST(TypedConvolutionTests1, conv2d_bp_test3) { weights.permutei({2,3,1,0}); expGradW.permutei({2,3,1,0}); - nd4j::ops::conv2d_bp op; + sd::ops::conv2d_bp op; auto results = op.evaluate({&input, &weights, &bias, &gradO}, {}, {kH,kW, sH,sW, pH,pW, dH,dW, paddingMode, dataFormat}); - auto gradI = results->at(0); - auto gradW = results->at(1); - auto gradB = results->at(2); + auto gradI = results.at(0); + auto gradW = results.at(1); + auto gradB = results.at(2); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expGradI.isSameShape(gradI)); ASSERT_TRUE(expGradI.equalsTo(gradI)); @@ -1292,7 +1258,7 @@ TYPED_TEST(TypedConvolutionTests1, conv2d_bp_test3) { ASSERT_TRUE(expGradB.isSameShape(gradB)); ASSERT_TRUE(expGradB.equalsTo(gradB)); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -1303,21 +1269,21 @@ TEST_F(ConvolutionTests1, conv2d_bp_4) { int paddingMode = 1; // 1-SAME, 0-VALID; int dataFormat = 0; // 1-NHWC, 0-NCHW - NDArray input('c', {bS, iC, iH, iW}, nd4j::DataType::FLOAT32); - NDArray weights('c', {kH, kW, iC, oC}, nd4j::DataType::FLOAT32); - NDArray bias('c', {oC}, {1,2,3}, nd4j::DataType::FLOAT32); - NDArray gradO('c', {bS, oC, oH, oW}, nd4j::DataType::FLOAT32); + NDArray input('c', {bS, iC, iH, iW}, sd::DataType::FLOAT32); + NDArray weights('c', {kH, kW, iC, oC}, sd::DataType::FLOAT32); + NDArray bias('c', {oC}, {1,2,3}, sd::DataType::FLOAT32); + NDArray gradO('c', {bS, oC, oH, oW}, sd::DataType::FLOAT32); - NDArray gradI('c', {bS, iC, iH, iW}, nd4j::DataType::FLOAT32); - NDArray gradW('c', {kH, kW, iC, oC}, nd4j::DataType::FLOAT32); - NDArray gradB('c', {oC}, nd4j::DataType::FLOAT32); + NDArray gradI('c', {bS, iC, iH, iW}, sd::DataType::FLOAT32); + NDArray gradW('c', {kH, kW, iC, oC}, sd::DataType::FLOAT32); + NDArray gradB('c', {oC}, sd::DataType::FLOAT32); input = 2.; weights.linspace(0.1, 0.1); gradO.linspace(0.01, 0.01); - nd4j::ops::conv2d_bp op; + sd::ops::conv2d_bp op; auto status = op.execute({&input, &weights, &bias, &gradO}, {&gradI, &gradW, &gradB}, {}, {kH,kW, sH,sW, pH,pW, dH,dW, paddingMode, dataFormat}, {}); ASSERT_EQ(Status::OK(), status); @@ -1357,19 +1323,19 @@ TYPED_TEST(TypedConvolutionTests1, conv3d_bp_test1) { weights.linspace(0.1, 0.1); gradO.linspace(0.01, 0.01); - nd4j::ops::conv3dnew_bp op; + sd::ops::conv3dnew_bp op; auto results = op.evaluate({&input, &weights, &bias, &gradO}, {kD,kH,kW, sD,sH,sW, pD,pH,pW, dD,dH,dW, paddingMode, dataFormat}); - auto gradI = results->at(0); - auto gradW = results->at(1); + auto gradI = results.at(0); + auto gradW = results.at(1); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expGradI.isSameShape(gradI)); ASSERT_TRUE(expGradI.equalsTo(gradI)); ASSERT_TRUE(expGradW.isSameShape(gradW)); ASSERT_TRUE(expGradW.equalsTo(gradW)); - delete results; + } @@ -1405,19 +1371,19 @@ TYPED_TEST(TypedConvolutionTests1, conv3d_bp_test2) { weights.linspace(0.1, 0.1); gradO.linspace(0.01, 0.01); - nd4j::ops::conv3dnew_bp op; + sd::ops::conv3dnew_bp op; auto results = op.evaluate({&input, &weights, &bias, &gradO}, {kD,kH,kW, sD,sH,sW, pD,pH,pW, dD,dH,dW, paddingMode, dataFormat}); - auto gradI = results->at(0); - auto gradW = results->at(1); + auto gradI = results.at(0); + auto gradW = results.at(1); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expGradI.isSameShape(gradI)); ASSERT_TRUE(expGradI.equalsTo(gradI)); ASSERT_TRUE(expGradW.isSameShape(gradW)); ASSERT_TRUE(expGradW.equalsTo(gradW)); - delete results; + } @@ -1458,13 +1424,13 @@ TYPED_TEST(TypedConvolutionTests1, conv3d_bp_test3) { weights.permutei({2, 3, 4, 1, 0}); expGradW.permutei({2, 3, 4, 1, 0}); - nd4j::ops::conv3dnew_bp op; + sd::ops::conv3dnew_bp op; auto results = op.evaluate({&input, &weights, &bias, &gradO}, {kD,kH,kW, sD,sH,sW, pD,pH,pW, dD,dH,dW, paddingMode, dataFormat}); - auto* gradI = results->at(0); - auto* gradW = results->at(1); - auto* gradB = results->at(2); + auto* gradI = results.at(0); + auto* gradW = results.at(1); + auto* gradB = results.at(2); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expGradI.isSameShape(gradI)); ASSERT_TRUE(expGradI.equalsTo(gradI)); @@ -1475,7 +1441,7 @@ TYPED_TEST(TypedConvolutionTests1, conv3d_bp_test3) { ASSERT_TRUE(expGradB.isSameShape(gradB)); ASSERT_TRUE(expGradB.equalsTo(gradB)); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -1493,20 +1459,20 @@ TEST_F(ConvolutionTests1, depthwise_conv2d_bp_test1) { auto gradO = NDArrayFactory::create('c', {bS, oH, oW, oC}); NDArray expGradI('c', {bS, iH, iW, iC},{0.07 , 0.19 , 0.348, 0.652, 0.588, 0.956, 0.387, 0.687, 1.326, 2.022, 1.878, 2.67 , 1.071, 1.515, 2.982, 3.966, 3.534, 4.614, 1.606, 1.982, 3.932, 4.748, 4.428, 5.308, - 1.126, 1.63 , 3.228, 4.3 , 3.468, 4.604, 3.123, 3.999, 7.95 , 9.798, 8.502, 10.446, 3.807, 4.827, 9.606, 11.742,10.158, 12.39 , 4.198, 4.958, 9.884, 11.468,10.38 , 12.028}, nd4j::DataType::FLOAT32); + 1.126, 1.63 , 3.228, 4.3 , 3.468, 4.604, 3.123, 3.999, 7.95 , 9.798, 8.502, 10.446, 3.807, 4.827, 9.606, 11.742,10.158, 12.39 , 4.198, 4.958, 9.884, 11.468,10.38 , 12.028}, sd::DataType::FLOAT32); - NDArray expGradW('c', {kH, kW, iC, mC},{19.08, 19.44,19.8 , 20.16,12.24, 12.48,12.72, 12.96,22.56, 23.04,23.52, 24. ,14.4 , 14.72,15.04, 15.36,14.76, 15.12,15.48, 15.84, 9.36, 9.6 , 9.84, 10.08}, nd4j::DataType::FLOAT32); + NDArray expGradW('c', {kH, kW, iC, mC},{19.08, 19.44,19.8 , 20.16,12.24, 12.48,12.72, 12.96,22.56, 23.04,23.52, 24. ,14.4 , 14.72,15.04, 15.36,14.76, 15.12,15.48, 15.84, 9.36, 9.6 , 9.84, 10.08}, sd::DataType::FLOAT32); input = 2.; weights.linspace(0.1, 0.1); gradO.linspace(0.01, 0.01); - nd4j::ops::depthwise_conv2d_bp op; + sd::ops::depthwise_conv2d_bp op; auto results = op.evaluate({&input, &weights, &bias, &gradO}, {kH,kW, sH,sW, pH,pW, dH,dW, paddingMode, dataFormat}); - auto* gradI = results->at(0); - auto* gradW = results->at(1); + auto* gradI = results.at(0); + auto* gradW = results.at(1); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expGradI.isSameShape(gradI)); ASSERT_TRUE(expGradI.equalsTo(gradI)); @@ -1514,7 +1480,6 @@ TEST_F(ConvolutionTests1, depthwise_conv2d_bp_test1) { ASSERT_TRUE(expGradW.isSameShape(gradW)); ASSERT_TRUE(expGradW.equalsTo(gradW)); - delete results; } ////////////////////////////////////////////////////////////////////// @@ -1532,19 +1497,19 @@ TEST_F(ConvolutionTests1, depthwise_conv2d_bp_test2) { auto gradO = NDArrayFactory::create('c', {bS, oH, oW, oC}); NDArray expGradI('c', {bS, iH, iW, iC},{0.005, 0.025,0.034, 0.106,0.061, 0.113,0.058, 0.162,0.292, 0.564,0.298, 0.466,0.234, 0.402,0.772, 1.172,0.602, 0.834,0.333, 0.449,0.882, 1.146,0.581, 0.729, - 0.053, 0.137,0.258, 0.458,0.237, 0.353,0.41 , 0.642,1.252, 1.78 ,0.906, 1.202,1.098, 1.394,2.756, 3.412,1.722, 2.082,0.893, 1.073,2.13 , 2.522,1.269, 1.481}, nd4j::DataType::FLOAT32); - NDArray expGradW('c', {kH, kW, iC, mC},{2.4 , 2.56,2.72, 2.88,2.4 , 2.56,2.72, 2.88,2.4 , 2.56,2.72, 2.88,2.4 , 2.56,2.72, 2.88,2.4 , 2.56,2.72, 2.88,2.4 , 2.56,2.72, 2.88}, nd4j::DataType::FLOAT32); + 0.053, 0.137,0.258, 0.458,0.237, 0.353,0.41 , 0.642,1.252, 1.78 ,0.906, 1.202,1.098, 1.394,2.756, 3.412,1.722, 2.082,0.893, 1.073,2.13 , 2.522,1.269, 1.481}, sd::DataType::FLOAT32); + NDArray expGradW('c', {kH, kW, iC, mC},{2.4 , 2.56,2.72, 2.88,2.4 , 2.56,2.72, 2.88,2.4 , 2.56,2.72, 2.88,2.4 , 2.56,2.72, 2.88,2.4 , 2.56,2.72, 2.88,2.4 , 2.56,2.72, 2.88}, sd::DataType::FLOAT32); input = 2.; weights.linspace(0.1, 0.1); gradO.linspace(0.01, 0.01); - nd4j::ops::depthwise_conv2d_bp op; + sd::ops::depthwise_conv2d_bp op; auto results = op.evaluate({&input, &weights, &bias, &gradO}, {kH,kW, sH,sW, pH,pW, dH,dW, paddingMode, dataFormat}); - auto* gradI = results->at(0); - auto* gradW = results->at(1); + auto* gradI = results.at(0); + auto* gradW = results.at(1); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expGradI.isSameShape(gradI)); ASSERT_TRUE(expGradI.equalsTo(gradI)); @@ -1552,7 +1517,6 @@ TEST_F(ConvolutionTests1, depthwise_conv2d_bp_test2) { ASSERT_TRUE(expGradW.isSameShape(gradW)); ASSERT_TRUE(expGradW.equalsTo(gradW)); - delete results; } ////////////////////////////////////////////////////////////////////// @@ -1581,10 +1545,10 @@ TEST_F(ConvolutionTests1, depthwise_conv2d_bp_test4) { int paddingMode = 1; // 1-SAME, 0-VALID; int dataFormat = 1; // 1-NHWC, 0-NCHW - NDArray input('c', {bS, iH, iW, iC}, nd4j::DataType::FLOAT32); - NDArray weights('c', {kH, kW, iC, mC}, nd4j::DataType::FLOAT32); - NDArray gradO('c', {bS, oH, oW, oC}, nd4j::DataType::FLOAT32); - NDArray bias('c', {oC}, nd4j::DataType::FLOAT32); + NDArray input('c', {bS, iH, iW, iC}, sd::DataType::FLOAT32); + NDArray weights('c', {kH, kW, iC, mC}, sd::DataType::FLOAT32); + NDArray gradO('c', {bS, oH, oW, oC}, sd::DataType::FLOAT32); + NDArray bias('c', {oC}, sd::DataType::FLOAT32); input.linspace(-10, 0.1); weights.linspace(-2, 0.1); @@ -1596,23 +1560,23 @@ TEST_F(ConvolutionTests1, depthwise_conv2d_bp_test4) { -117.209999, -134.399994, -151.769989, 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-939.019958, -976.640076, -1014.379944, -1052.239990, -1090.219971, -838.400024, -876.140015, -913.999939, -951.979919, -990.080017, -1028.299927, -1066.640015, -1105.099976, -849.919983, -888.140015, -926.479980, -964.939941, -1003.520081, -1042.219971, -1081.040039, -1119.979980, -861.440063, -900.140015, -938.960022,-977.899963, -1016.960022, -1056.140015, -1095.440063, -1134.859985, -872.960022, -912.140015, -951.439941, -990.859985, -1030.400024, -1070.060059, -1109.839844, -1149.739990, -884.479980, -924.140015, -963.919922, -1003.819946, -1043.839966, -1083.979980, -1124.239990, -1164.619995, -896.000000, -936.140015, -976.399963, -1016.780029, -1057.280029, -1097.899902, -1138.640015, -1179.500122, -705.919983, -733.000000, -760.159912, -787.400024, -814.719971, -842.119995, -869.599976, -897.160034}, nd4j::DataType::FLOAT32); + -788.250000, -843.239990, -898.410034, -524.639954, -579.449951, -634.440002, -689.609985, -744.960022, -800.489990, -856.200012, -912.090027, -533.280029, -588.810059, -644.520081, -700.409973, -756.480042, -812.730103, -869.159912, -925.769958, -505.920013, -543.420044, -581.040039, -618.780029, -656.640015, -694.620056, -732.719971, -770.940002, -447.359985, -471.559998, -495.840027, -520.200012, -544.640015, -569.159973, -593.760010, -618.440002, -815.359985, -852.140015, -889.040039, -926.059937, -963.200073, -1000.460022, -1037.839966, -1075.339966, -826.879944, -864.139954, -901.519958, -939.019958, -976.640076, -1014.379944, -1052.239990, -1090.219971, -838.400024, -876.140015, -913.999939, -951.979919, -990.080017, -1028.299927, -1066.640015, -1105.099976, -849.919983, -888.140015, -926.479980, -964.939941, -1003.520081, -1042.219971, -1081.040039, -1119.979980, -861.440063, -900.140015, -938.960022,-977.899963, -1016.960022, -1056.140015, -1095.440063, -1134.859985, -872.960022, -912.140015, -951.439941, -990.859985, -1030.400024, -1070.060059, -1109.839844, -1149.739990, -884.479980, -924.140015, -963.919922, -1003.819946, -1043.839966, -1083.979980, -1124.239990, -1164.619995, -896.000000, -936.140015, -976.399963, -1016.780029, -1057.280029, -1097.899902, -1138.640015, -1179.500122, -705.919983, -733.000000, -760.159912, -787.400024, -814.719971, -842.119995, -869.599976, -897.160034}, sd::DataType::FLOAT32); NDArray expGradW('c', {kH, kW, iC, mC},{-104306.421875, -104786.734375, -105268.687500, -105752.250000, -106237.421875, -106724.242188, -107212.671875, -107702.734375, -116289.593750, -116823.296875, -117358.781250, -117896.109375, -118435.210938, -118976.109375, -119518.796875, -120063.296875, -104824.789062, -105305.117188, -105787.070312, -106270.640625, -106755.843750, -107242.640625, -107731.078125, -108221.117188, -126744.000000, -127277.710938, -127813.187500, -128350.484375, -128889.601562, -129430.515625, -129973.210938, -130517.703125, -140944.000000, -141536.984375, -142131.984375, -142729.000000, -143328.000000, - -143929.015625, -144532.000000, -145137.000000, -126744.000000, -127277.710938, -127813.187500, -128350.484375, -128889.601562, -129430.515625, -129973.210938, -130517.703125, -104824.789062, -105305.117188, -105787.070312, -106270.640625, -106755.843750, -107242.640625, -107731.078125, -108221.117188, -116289.593750, -116823.296875, -117358.781250, -117896.109375, -118435.210938, -118976.109375, -119518.796875, -120063.296875, -104306.421875, -104786.734375, -105268.687500, -105752.250000, -106237.421875, -106724.242188, -107212.671875, -107702.734375}, nd4j::DataType::FLOAT32); + -143929.015625, -144532.000000, -145137.000000, -126744.000000, -127277.710938, -127813.187500, -128350.484375, -128889.601562, -129430.515625, -129973.210938, -130517.703125, -104824.789062, -105305.117188, -105787.070312, -106270.640625, -106755.843750, -107242.640625, -107731.078125, -108221.117188, -116289.593750, -116823.296875, -117358.781250, -117896.109375, -118435.210938, -118976.109375, -119518.796875, -120063.296875, -104306.421875, -104786.734375, -105268.687500, -105752.250000, -106237.421875, -106724.242188, -107212.671875, -107702.734375}, sd::DataType::FLOAT32); - NDArray expGradB('c', {oC}, {-2960., -2970., -2980., -2990., -3000., -3010., -3020., -3030.}, nd4j::DataType::FLOAT32); + NDArray expGradB('c', {oC}, {-2960., -2970., -2980., -2990., -3000., -3010., -3020., -3030.}, sd::DataType::FLOAT32); - nd4j::ops::depthwise_conv2d_bp op; - ResultSet* results = op.evaluate({&input, &weights, &bias, &gradO}, {kH,kW, sH,sW, pH,pW, dH,dW, paddingMode, dataFormat}); - NDArray* gradI = results->at(0); - NDArray* gradW = results->at(1); - NDArray* gradB = results->at(2); + sd::ops::depthwise_conv2d_bp op; + auto results = op.evaluate({&input, &weights, &bias, &gradO}, {kH,kW, sH,sW, pH,pW, dH,dW, paddingMode, dataFormat}); + NDArray* gradI = results.at(0); + NDArray* gradW = results.at(1); + NDArray* gradB = results.at(2); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expGradI.isSameShape(gradI)); ASSERT_TRUE(expGradI.equalsTo(gradI)); @@ -1623,7 +1587,6 @@ TEST_F(ConvolutionTests1, depthwise_conv2d_bp_test4) { ASSERT_TRUE(expGradB.isSameShape(gradB)); ASSERT_TRUE(expGradB.equalsTo(gradB)); - delete results; } ////////////////////////////////////////////////////////////////////// @@ -1635,10 +1598,10 @@ TEST_F(ConvolutionTests1, depthwise_conv2d_bp_test5) { int paddingMode = 1; // 1-SAME, 0-VALID; int dataFormat = 0; // 1-NHWC, 0-NCHW - NDArray input('c', {bS, iC, iH, iW}, nd4j::DataType::FLOAT32); - NDArray weights('c', {kH, kW, iC, mC}, nd4j::DataType::FLOAT32); - NDArray gradO('c', {bS, oC, oH, oW}, nd4j::DataType::FLOAT32); - NDArray bias('c', {oC}, nd4j::DataType::FLOAT32); + NDArray input('c', {bS, iC, iH, iW}, sd::DataType::FLOAT32); + NDArray weights('c', {kH, kW, iC, mC}, sd::DataType::FLOAT32); + NDArray gradO('c', {bS, oC, oH, oW}, sd::DataType::FLOAT32); + NDArray bias('c', {oC}, sd::DataType::FLOAT32); input.linspace(-10, 0.1); weights.linspace(-2, 0.1); @@ -1650,24 +1613,24 @@ TEST_F(ConvolutionTests1, depthwise_conv2d_bp_test5) { -33.879997, -34.059994, -34.239994, -34.419994, -57.299995, -129.209991, -269.969971, -271.319977, -272.670044, -274.019989, -275.369995, -276.720001, -278.070007, -279.420013, -239.369980, -135.809998, -283.470001, -284.820007, -286.169983, -287.520020, -288.869995, -290.220001, -291.570038, -292.919983, -250.770004, -142.410004, -296.969971, -298.320007, -299.669983, -301.020020, -302.369995, -303.719971, -305.070007, -306.419983, -262.169983, -149.009995, -310.470001, -311.820007, -313.170013, -314.519989, -315.869995, -317.220001, -318.570007, -319.919983, -273.570007, -155.610016, -323.969971, -325.320038, -326.669983, -328.020020, -329.369965, -330.719971, -332.070007, -333.419983, -284.970001, -162.209991, -337.469971, -338.820007, -340.169983, -341.519958, -342.869995, -344.220001, -345.570007, -346.920013, -296.369995, -168.809998, -350.970001, -352.320007, -353.669983, -355.019989, -356.369995, -357.719971, -359.070038, -360.419983, -307.769989, -175.410004, -364.469971, -365.820007, -367.169983, -368.520020, -369.869995, -371.219971, -372.570007, -373.919983, -319.169983, -260.179993, -459.399994, -461.019958, -462.639984, -464.260010, -465.880005, -467.500000, -469.119995, -470.739990, -361.459991, 2.480003, -69.520004, -69.760025, -70.000000, -70.239990, -70.479996, -70.720001, -70.960007, -71.200005, -97.839996, -213.840012, -432.960022, -434.400055, -435.840027, -437.279999, -438.720001, -440.160065, -441.599976, -443.040039, -372.480011, -221.040009, -447.360016, -448.800018, -450.239990, -451.679993, -453.119995, -454.559967, -456.000061, -457.440033, -384.480011, -228.239990, -461.759979, -463.200012, -464.639984, -466.079956, -467.520081, -468.960052, -470.399963, -471.839996, -396.479980, -235.440002, -476.159912, -477.600006, -479.040039, -480.479980, -481.919952, -483.360046, -484.800079, -486.239990, -408.480042, -242.639999, -490.559967, -491.999969, -493.440063, -494.880035, -496.319946, -497.759979, -499.200012, -500.639984, -420.480011, -249.840012, -504.960052, -506.399963, -507.839996, -509.280029, -510.720001, -512.159973, -513.599976, -515.040039, -432.480011, -257.040009, -519.360046, -520.800049, -522.239990, -523.680054, -525.120056, -526.559998, -527.999939, -529.440002, -444.480011, -264.239990, -533.760010, -535.200012, -536.640015, -538.079956, -539.520020, -540.960022, -542.399963, -543.839966, -456.479980, -367.599976, -644.559998, -646.239929, -647.920044, -649.599976, -651.280029, -652.960022, -654.640076, -656.320007, -501.200043, -13.740002, -117.880005, -118.179993, -118.479996, -118.780014, -119.080002, -119.379990, -119.680008, -119.979996, -146.379990, -310.470001, -613.950012, -615.479980, -617.010071, -618.539978, -620.069946, -621.599976, -623.130005, -624.660034, -517.589966, -318.269958, -629.250000, -630.779968, -632.309937, -633.840027, -635.369995, -636.899902, -638.429993, -639.959961, -530.190063, -326.070038, -644.550049, -646.079956, -647.609985, -649.140015, -650.669922, -652.200012, -653.729980, -655.260010, -542.789978, -333.870026, -659.849976, -661.380005, -662.910034, -664.439941, -665.970093, -667.500000, -669.029968, -670.559937, -555.390015, -341.669983, -675.149902, -676.679993, -678.209961, -679.740051, -681.270020, -682.800049, -684.329956, -685.859985, -567.989990, -349.470001, -690.450012, -691.979980, -693.510010, -695.039978, -696.569946, -698.099976, -699.630005, -701.160034, -580.589966, -357.269958, -705.750000, -707.279968, -708.809937, -710.340027, -711.869995, -713.399902, -714.929993, -716.459961, -593.190002, -365.070038, -721.050049, -722.579956, -724.109985, -725.640015, -727.169922, -728.700012, -730.229980, -731.760010, -605.789978, -483.019958, -841.719971, -843.460022, -845.200073, -846.939941, -848.680054, -850.419983, -852.159973, -853.899963, -648.940002, -37.960014, -178.240021, -178.599976, -178.959991, -179.320007, -179.679993, -180.039978, -180.399994, -180.759964, -202.919983, -419.099915, -812.939941, -814.559937, -816.179993, -817.800049, -819.419922, -821.040039, -822.660034, -824.279968, -674.699951, -427.500031, -829.140015, -830.759949, -832.380005, -833.999939, -835.619995, -837.240051, -838.859924, -840.479980, -687.899963, -435.899994, -845.339966, -846.959961, -848.579956, -850.200012, -851.819885, -853.439941, -855.059937, -856.679993, -701.100037, -444.299927, -861.540039, -863.160034, -864.779968, -866.399963, -868.020020, -869.640015, -871.259949, -872.880005, -714.299988, -452.700012, -877.740051, -879.359924, -880.979980, -882.599915, -884.219971, -885.839966, -887.459961, -889.079956, -727.500000, -461.099915, -893.939941, -895.559937, -897.179993, -898.800049, -900.419922, -902.040039, -903.660034, -905.279968, -740.700012, -469.499969, -910.140015, -911.759949, -913.380005, -914.999939, -916.620056, -918.239990, -919.860046, -921.479919, -753.899963, -477.899902, -926.339905, -927.959961, -929.579956, -931.200012, -932.819946, -934.439880, -936.059937, -937.679932, -767.100037, -606.439941, -1050.880005, -1052.680054, -1054.479980, -1056.280029, -1058.079956, -1059.880005, -1061.679932, -1063.479980, -804.679993, -70.180008, -250.600006, -251.019958, -251.440033, -251.860001, -252.280029, -252.700043, -253.120026, -253.540039, -267.459991, -539.730042, -1029.929932, -1031.640137, -1033.350098, -1035.060059, -1036.770020, -1038.479980, -1040.190063, -1041.900024, -843.809998, -548.729980, -1047.030029, -1048.740112, -1050.449829, -1052.160034, -1053.870117, -1055.580078, -1057.289917, -1059.000122, -857.609985, -557.729980, - -1064.130005, -1065.840088, -1067.550049, -1069.260010, -1070.969849, -1072.679932, -1074.390137, -1076.100098, -871.410034, -566.729980, -1081.229980, -1082.940063, -1084.650024, -1086.359985, -1088.069946, -1089.780029, -1091.489990, -1093.199951, -885.210022, -575.729980, -1098.329956, -1100.040039, -1101.750122, -1103.460205, -1105.170166, -1106.879883, -1108.589966, -1110.300049, -899.010071, -584.730042, -1115.429932, -1117.140137, -1118.850098, -1120.560059, -1122.270020, -1123.979980, -1125.689941, -1127.400024, -912.810059, -593.730042, -1132.530029, -1134.240234, -1135.949951, -1137.659912, -1139.370117, -1141.079956, -1142.790039, -1144.500122, -926.610046, -602.730042, -1149.629883, -1151.339966, -1153.050049, -1154.760132, -1156.469971, -1158.179810, -1159.890137, -1161.600098, -940.410034, -737.859985, -1272.040039, -1273.899902, -1275.760010, -1277.619995, -1279.479980, -1281.340088, -1283.200195, -1285.060059, -968.420044}, nd4j::DataType::FLOAT32); + -1064.130005, -1065.840088, -1067.550049, -1069.260010, -1070.969849, -1072.679932, -1074.390137, -1076.100098, -871.410034, -566.729980, -1081.229980, -1082.940063, -1084.650024, -1086.359985, -1088.069946, -1089.780029, -1091.489990, -1093.199951, -885.210022, -575.729980, -1098.329956, -1100.040039, -1101.750122, -1103.460205, -1105.170166, -1106.879883, -1108.589966, -1110.300049, -899.010071, -584.730042, -1115.429932, -1117.140137, -1118.850098, -1120.560059, -1122.270020, -1123.979980, -1125.689941, -1127.400024, -912.810059, -593.730042, -1132.530029, -1134.240234, -1135.949951, -1137.659912, -1139.370117, -1141.079956, -1142.790039, -1144.500122, -926.610046, -602.730042, -1149.629883, -1151.339966, -1153.050049, -1154.760132, -1156.469971, -1158.179810, -1159.890137, -1161.600098, -940.410034, -737.859985, -1272.040039, -1273.899902, -1275.760010, -1277.619995, -1279.479980, -1281.340088, -1283.200195, -1285.060059, -968.420044}, sd::DataType::FLOAT32); NDArray expGradW('c', {kH, kW, iC, mC}, {-2586.600586, -2505.600098, -18624.595703, -50943.605469, -99462.601562, -164181.609375, -245100.609375, -342219.625000, -2880.149902, -2790.150146, -20700.152344, -56610.148438, -110520.156250, -182430.156250, -272340.156250, -380250.125000, -2594.701416, -2513.699951, -18632.699219, -50951.695312, -99470.695312, -164189.703125, -245108.687500, -342227.750000, -3043.501465, -2953.500244, -20863.500000, -56773.492188, -110683.515625, -182593.515625, -272503.531250, -380413.562500, -3383.499756, -3283.500000, -23183.501953, -63083.500000, -122983.500000, -202883.515625, -302783.531250, -422683.468750, -3043.501465, -2953.500244, -20863.500000, -56773.492188, -110683.515625, -182593.515625, -272503.531250, -380413.562500, - -2594.701416, -2513.699951, -18632.699219, -50951.695312, -99470.695312, -164189.703125, -245108.687500, -342227.750000, -2880.149902, -2790.150146, -20700.152344, -56610.148438, -110520.156250, -182430.156250, -272340.156250, -380250.125000, -2586.600586, -2505.600098, -18624.595703, -50943.605469, -99462.601562, -164181.609375, -245100.609375, -342219.625000}, nd4j::DataType::FLOAT32); + -2594.701416, -2513.699951, -18632.699219, -50951.695312, -99470.695312, -164189.703125, -245108.687500, -342227.750000, -2880.149902, -2790.150146, -20700.152344, -56610.148438, -110520.156250, -182430.156250, -272340.156250, -380250.125000, -2586.600586, -2505.600098, -18624.595703, -50943.605469, -99462.601562, -164181.609375, -245100.609375, -342219.625000}, sd::DataType::FLOAT32); - NDArray expGradB('c', {oC}, {505., -495., -1495., -2495., -3495., -4494.999512, -5495., -6495.}, nd4j::DataType::FLOAT32); + NDArray expGradB('c', {oC}, {505., -495., -1495., -2495., -3495., -4494.999512, -5495., -6495.}, sd::DataType::FLOAT32); - nd4j::ops::depthwise_conv2d_bp op; - ResultSet* results = op.evaluate({&input, &weights, &bias, &gradO}, {kH,kW, sH,sW, pH,pW, dH,dW, paddingMode, dataFormat}); - NDArray* gradI = results->at(0); - NDArray* gradW = results->at(1); - NDArray* gradB = results->at(2); + sd::ops::depthwise_conv2d_bp op; + auto results = op.evaluate({&input, &weights, &bias, &gradO}, {kH,kW, sH,sW, pH,pW, dH,dW, paddingMode, dataFormat}); + NDArray* gradI = results.at(0); + NDArray* gradW = results.at(1); + NDArray* gradB = results.at(2); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expGradI.isSameShape(gradI)); ASSERT_TRUE(expGradI.equalsTo(gradI)); @@ -1678,7 +1641,6 @@ TEST_F(ConvolutionTests1, depthwise_conv2d_bp_test5) { ASSERT_TRUE(expGradB.isSameShape(gradB)); ASSERT_TRUE(expGradB.equalsTo(gradB)); - delete results; } ////////////////////////////////////////////////////////////////////// @@ -1705,12 +1667,12 @@ TEST_F(ConvolutionTests1, depthwise_conv2d_bp_test6) { weights.linspace(0.1, 0.1); gradO.linspace(0.01, 0.01); - nd4j::ops::depthwise_conv2d_bp op; + sd::ops::depthwise_conv2d_bp op; auto results = op.evaluate({&input, &weights, &bias, &gradO}, {kH,kW, sH,sW, pH,pW, dH,dW, paddingMode, dataFormat}); - auto* gradI = results->at(0); - auto* gradW = results->at(1); + auto* gradI = results.at(0); + auto* gradW = results.at(1); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expGradI.isSameShape(gradI)); ASSERT_TRUE(expGradI.equalsTo(gradI)); @@ -1718,7 +1680,6 @@ TEST_F(ConvolutionTests1, depthwise_conv2d_bp_test6) { ASSERT_TRUE(expGradW.isSameShape(gradW)); ASSERT_TRUE(expGradW.equalsTo(gradW)); - delete results; } ////////////////////////////////////////////////////////////////////// @@ -1741,15 +1702,14 @@ TYPED_TEST(TypedConvolutionTests1, conv3d_test1) { input = 2.; weights = 1.; - nd4j::ops::conv3dnew op; + sd::ops::conv3dnew op; auto results = op.evaluate({&input, &weights}, {}, {kD,kH,kW, sD,sH,sW, pD,pH,pW, dD,dH,dW, paddingMode, dataFormat}); - auto* output = results->at(0); + auto* output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; } @@ -1773,15 +1733,14 @@ TYPED_TEST(TypedConvolutionTests1, conv3d_test2) { input = 2.; weights.linspace(0.1, 0.1); - nd4j::ops::conv3dnew op; + sd::ops::conv3dnew op; auto results = op.evaluate({&input, &weights}, {}, {kD,kH,kW, sD,sH,sW, pD,pH,pW, dD,dH,dW, paddingMode, dataFormat}); - auto* output = results->at(0); + auto* output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; } ////////////////////////////////////////////////////////////////////// @@ -1800,15 +1759,14 @@ TYPED_TEST(TypedConvolutionTests1, conv3d_test3) { input = 2.; weights.linspace(0.1, 0.1); - nd4j::ops::conv3dnew op; + sd::ops::conv3dnew op; auto results = op.evaluate({&input, &weights}, {}, {kD,kH,kW, sD,sH,sW, pD,pH,pW, dD,dH,dW, paddingMode, dataFormat}); - auto* output = results->at(0); + auto* output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; } @@ -1826,15 +1784,14 @@ TYPED_TEST(TypedConvolutionTests1, conv3d_test4) { weights = 0.5; expected = 48.; - nd4j::ops::conv3dnew op; + sd::ops::conv3dnew op; auto results = op.evaluate({&input, &weights}, {}, {kD,kH,kW, sD,sH,sW, pD,pH,pW, dD,dH,dW, paddingMode, dataFormat}); - auto* output = results->at(0); + auto* output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; } //////////////////////////////////////////////////////////////////// @@ -1854,17 +1811,16 @@ TYPED_TEST(TypedConvolutionTests1, conv3d_test5) { expected = 49.; bias = 1.; - nd4j::ops::conv3dnew op; + sd::ops::conv3dnew op; auto results = op.evaluate({&input, &weights, &bias}, {}, {kD,kH,kW, sD,sH,sW, pD,pH,pW, dD,dH,dW, paddingMode, dataFormat}); - auto* output = results->at(0); + auto* output = results.at(0); // output->printIndexedBuffer(); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; } //////////////////////////////////////////////////////////////////// @@ -1883,17 +1839,16 @@ TYPED_TEST(TypedConvolutionTests1, conv3d_test6) { input = 2.; weights = 0.5; - nd4j::ops::conv3dnew op; + sd::ops::conv3dnew op; auto results = op.evaluate({&input, &weights, &bias}, {}, {kD,kH,kW, sD,sH,sW, pD,pH,pW, dD,dH,dW, paddingMode, dataFormat}); - auto* output = results->at(0); + auto* output = results.at(0); // output->printIndexedBuffer(); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; } //////////////////////////////////////////////////////////////////// @@ -1914,17 +1869,17 @@ TYPED_TEST(TypedConvolutionTests1, conv3d_test7) { weights.linspace(0.1, 0.1); weights.permutei({2, 3, 4, 1, 0}); - nd4j::ops::conv3dnew op; + sd::ops::conv3dnew op; auto results = op.evaluate({&input, &weights, &bias}, {}, {kD,kH,kW, sD,sH,sW, pD,pH,pW, dD,dH,dW, paddingMode, dataFormat}); - auto* output = results->at(0); + auto* output = results.at(0); // output->printIndexedBuffer(); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; + } //////////////////////////////////////////////////////////////////// @@ -1943,15 +1898,15 @@ TYPED_TEST(TypedConvolutionTests1, conv3d_test8) { weights.linspace(0.1, 0.1); weights.permutei({2, 3, 4, 1, 0}); - nd4j::ops::conv3dnew op; + sd::ops::conv3dnew op; auto results = op.evaluate({&input, &weights}, {}, {kD,kH,kW, sD,sH,sW, pD,pH,pW, dD,dH,dW, paddingMode, dataFormat}); - auto* output = results->at(0); + auto* output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -1960,15 +1915,13 @@ TYPED_TEST(TypedConvolutionTests1, conv3d_test9) { auto y = NDArrayFactory::create('c', {2, 5, 5, 3, 4}); auto e = NDArrayFactory::create('c', {4, 1, 7, 10, 4}); - nd4j::ops::conv3dnew op; + sd::ops::conv3dnew op; auto result = op.evaluate({&x, &y}, {}, {2,5,5, 5,4,3, 0,0,0, 1,1,1, 1,1}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(e.isSameShape(z)); - - delete result; } TYPED_TEST(TypedConvolutionTests1, conv3d_test10) { @@ -1976,9 +1929,9 @@ TYPED_TEST(TypedConvolutionTests1, conv3d_test10) { auto w = NDArrayFactory::create('c', {2, 5, 5, 3, 4}); auto exp = NDArrayFactory::create('c', {4, 1, 7, 10, 4}); - nd4j::ops::conv3dnew op; + sd::ops::conv3dnew op; auto result = op.evaluate({&x, &w}, {}, {2,5,5, 5,4,3, 0,0,0, 1,1,1, 1,1}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); ShapeList shapeList({x.shapeInfo(), w.shapeInfo()}); ContextPrototype proto; @@ -2007,13 +1960,11 @@ TYPED_TEST(TypedConvolutionTests1, conv3d_test10) { auto s = shapes->at(0); - auto z = result->at(0); + auto z = result.at(0); // z->printShapeInfo("z shape"); ASSERT_TRUE(exp.isSameShape(z)); - delete result; - shapes->destroy(); delete shapes; } @@ -2038,15 +1989,14 @@ TYPED_TEST(TypedConvolutionTests1, pointwise_conv2d_test1) { weights.linspace(0.1, 0.1); bias = 1.; - nd4j::ops::pointwise_conv2d op; + sd::ops::pointwise_conv2d op; auto results = op.evaluate({&input, &weights, &bias}, {}, {dataFormat}); - auto* output = results->at(0); + auto* output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expOutput.isSameShape(output)); ASSERT_TRUE(expOutput.equalsTo(output)); - delete results; } ////////////////////////////////////////////////////////////////////// @@ -2062,13 +2012,12 @@ TYPED_TEST(TypedConvolutionTests1, conv3d_test11) { input = 2.; weights = 1.; - nd4j::ops::conv3dnew op; + sd::ops::conv3dnew op; auto results = op.evaluate({&input, &weights}, {}, {kD,kH,kW, sD,sH,sW, pD,pH,pW, dD,dH,dW, paddingMode, dataFormat}); - auto* output = results->at(0); + auto* output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); - delete results; } ////////////////////////////////////////////////////////////////////// @@ -2086,14 +2035,13 @@ TYPED_TEST(TypedConvolutionTests1, conv3d_test12) { input = 2.; weights = 1.; - nd4j::ops::conv3dnew op; + sd::ops::conv3dnew op; auto results = op.evaluate({&input, &weights}, {}, {kD,kH,kW, sD,sH,sW, pD,pH,pW, dD,dH,dW, paddingMode, dataFormat}); - auto* output = results->at(0); + auto* output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(output->isSameShape(&expected)); - delete results; } ////////////////////////////////////////////////////////////////////// @@ -2102,8 +2050,8 @@ TEST_F(ConvolutionTests1, vol2col_test1) { int bS=2, iD=2,iH=3,iW=2, iC=3,oC=2, kD=2,kH=3,kW=2, sD=1,sH=1,sW=1, pD=0,pH=0,pW=0, dD=1,dH=1,dW=1; int oD=2,oH=3,oW=2; - NDArray volume('c', {bS, iC, iD, iH, iW}, nd4j::DataType::FLOAT32); - NDArray columns('c', {bS, iC, kD, kH, kW, oD, oH, oW}, nd4j::DataType::FLOAT32); + NDArray volume('c', {bS, iC, iD, iH, iW}, sd::DataType::FLOAT32); + NDArray columns('c', {bS, iC, kD, kH, kW, oD, oH, oW}, sd::DataType::FLOAT32); columns = -1.; volume.linspace(1); @@ -2121,10 +2069,10 @@ TEST_F(ConvolutionTests1, vol2col_test1) { 53., 54., 0., 0., 0., 0., 59., 60., 0., 0., 0., 0., 54., 0., 0., 0., 0., 0., 60., 0., 0., 0., 0., 0., 55., 56., 57., 58., 59., 60., 0., 0.,0., 0., 0., 0., 56., 0., 58., 0., 60., 0., 0., 0., 0., 0., 0., 0., 57., 58., 59., 60., 0., 0., 0., 0., 0., 0., 0., 0., 58., 0., 60., 0., 0., 0., 0., 0., 0., 0., 0., 0., 59., 60., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 60., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 61., 62., 63., 64., 65., 66., 67., 68., 69., 70., 71., 72., 62., 0., 64., 0., 66., 0., 68., 0., 70., 0., 72., 0., 63., 64., 65., 66., 0., 0., 69., 70., 71., 72., 0., 0., 64., 0., 66., 0., 0., 0., 70., 0., 72., 0., 0., 0., 65., 66., 0., 0., 0., 0., 71., 72., 0., 0., 0., 0., 66., 0., 0., 0., 0., 0., 72., 0., 0., 0., 0., 0., 67., 68., 69., 70., 71., 72., 0., 0., 0., 0., 0., 0., 68., 0., 70., 0., 72., 0., 0., 0., 0., 0., 0., -0., 69., 70., 71., 72., 0., 0., 0., 0., 0., 0., 0., 0., 70., 0., 72., 0., 0., 0., 0., 0., 0., 0., 0., 0., 71., 72., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 72., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.}, nd4j::DataType::FLOAT32); +0., 69., 70., 71., 72., 0., 0., 0., 0., 0., 0., 0., 0., 70., 0., 72., 0., 0., 0., 0., 0., 0., 0., 0., 0., 71., 72., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 72., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.}, sd::DataType::FLOAT32); graph::Context context(1); - nd4j::ops::ConvolutionUtils::vol2col(context, volume, columns, sD, sH, sW, pD, pH, pW, dD, dH, dW); + sd::ops::ConvolutionUtils::vol2col(context, volume, columns, sD, sH, sW, pD, pH, pW, dD, dH, dW); // columns.printBuffer(); ASSERT_TRUE(columns.equalsTo(columnsExpected)); @@ -2160,7 +2108,7 @@ TEST_F(ConvolutionTests1, vol2col_test2) { 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 70.f, 0.f, 72.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 71.f, 72.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 72.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f}); graph::Context context(1); - nd4j::ops::ConvolutionUtils::vol2col(context, volume, columns, sD, sH, sW, pD, pH, pW, dD, dH, dW); + sd::ops::ConvolutionUtils::vol2col(context, volume, columns, sD, sH, sW, pD, pH, pW, dD, dH, dW); // columns.printBuffer(); ASSERT_TRUE(columns.equalsTo(columnsExpected)); @@ -2181,7 +2129,7 @@ TEST_F(ConvolutionTests1, col2im_test1) { auto imageExpected = NDArrayFactory::create('c', {bS, iC, iH, iW}, {1.f, 7.f, 12.f, 34.f, 17.f, 39.f, 44.f, 98.f, 33.f, 71.f, 76.f, 162.f, 49.f, 103.f, 108.f, 226.f}); LaunchContext ctx; - nd4j::ops::helpers::col2im(ctx, columns, image, sH, sW, pH, pW, iH, iW, dH, dW); + sd::ops::helpers::col2im(ctx, columns, image, sH, sW, pH, pW, iH, iW, dH, dW); ASSERT_TRUE(image.equalsTo(imageExpected)); } @@ -2204,15 +2152,14 @@ TEST_F(ConvolutionTests1, upsampling2d_test1) { 25.f, 26.f, 27.f, 25.f, 26.f, 27.f, 25.f, 26.f, 27.f, 28.f, 29.f, 30.f, 28.f, 29.f, 30.f, 28.f, 29.f, 30.f, 25.f, 26.f, 27.f, 25.f, 26.f, 27.f, 25.f, 26.f, 27.f, 28.f, 29.f, 30.f, 28.f, 29.f, 30.f, 28.f, 29.f, 30.f, 31.f, 32.f, 33.f, 31.f, 32.f, 33.f, 31.f, 32.f, 33.f, 34.f, 35.f, 36.f, 34.f, 35.f, 36.f, 34.f, 35.f, 36.f, 31.f, 32.f, 33.f, 31.f, 32.f, 33.f, 31.f, 32.f, 33.f, 34.f, 35.f, 36.f, 34.f, 35.f, 36.f, 34.f, 35.f, 36.f}); - nd4j::ops::upsampling2d op; + sd::ops::upsampling2d op; auto results = op.evaluate({&input}, {factorH, factorW, isNCHW}); - auto* output = results->at(0); + auto* output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expOutput.isSameShape(output)); ASSERT_TRUE(expOutput.equalsTo(output)); - delete results; } ////////////////////////////////////////////////////////////////////// @@ -2232,16 +2179,15 @@ TEST_F(ConvolutionTests1, upsampling2d_test2) { 29.f, 29.f, 29.f, 30.f, 30.f, 30.f, 29.f, 29.f, 29.f, 30.f, 30.f, 30.f, 31.f, 31.f, 31.f, 32.f, 32.f, 32.f, 31.f, 31.f, 31.f, 32.f, 32.f, 32.f, 33.f, 33.f, 33.f, 34.f, 34.f, 34.f, 33.f, 33.f, 33.f, 34.f, 34.f, 34.f, 35.f, 35.f, 35.f, 36.f, 36.f, 36.f, 35.f, 35.f, 35.f, 36.f, 36.f, 36.f}); - nd4j::ops::upsampling2d op; + sd::ops::upsampling2d op; auto results = op.evaluate({&input}, {factorH, factorW, isNCHW}); - auto* output = results->at(0); + auto* output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expOutput.isSameShape(output)); ASSERT_TRUE(expOutput.equalsTo(output)); - delete results; } ////////////////////////////////////////////////////////////////////// @@ -2270,15 +2216,14 @@ TEST_F(ConvolutionTests1, upsampling3d_test1) { 67.f, 68.f, 69.f, 67.f, 68.f, 69.f, 70.f, 71.f, 72.f, 70.f, 71.f, 72.f, 61.f, 62.f, 63.f, 61.f, 62.f, 63.f, 64.f, 65.f, 66.f, 64.f, 65.f, 66.f, 61.f, 62.f, 63.f, 61.f, 62.f, 63.f, 64.f, 65.f, 66.f, 64.f, 65.f, 66.f, 61.f, 62.f, 63.f, 61.f, 62.f, 63.f, 64.f, 65.f, 66.f, 64.f, 65.f, 66.f, 67.f, 68.f, 69.f, 67.f, 68.f, 69.f, 70.f, 71.f, 72.f, 70.f, 71.f, 72.f, 67.f, 68.f, 69.f, 67.f, 68.f, 69.f, 70.f, 71.f, 72.f, 70.f, 71.f, 72.f, 67.f, 68.f, 69.f, 67.f, 68.f, 69.f, 70.f, 71.f, 72.f, 70.f, 71.f, 72.f}); - nd4j::ops::upsampling3d op; + sd::ops::upsampling3d op; auto results = op.evaluate({&input}, {factorD, factorH, factorW, isNCDHW}); - auto* output = results->at(0); + auto* output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expOutput.isSameShape(output)); ASSERT_TRUE(expOutput.equalsTo(output)); - delete results; } ////////////////////////////////////////////////////////////////////// @@ -2304,16 +2249,15 @@ TEST_F(ConvolutionTests1, upsampling3d_test2) { 61.f, 61.f, 62.f, 62.f, 61.f, 61.f, 62.f, 62.f, 61.f, 61.f, 62.f, 62.f, 63.f, 63.f, 64.f, 64.f, 63.f, 63.f, 64.f, 64.f, 63.f, 63.f, 64.f, 64.f, 61.f, 61.f, 62.f, 62.f, 61.f, 61.f, 62.f, 62.f, 61.f, 61.f, 62.f, 62.f, 63.f, 63.f, 64.f, 64.f, 63.f, 63.f, 64.f, 64.f, 63.f, 63.f, 64.f, 64.f, 65.f, 65.f, 66.f, 66.f, 65.f, 65.f, 66.f, 66.f, 65.f, 65.f, 66.f, 66.f, 67.f, 67.f, 68.f, 68.f, 67.f, 67.f, 68.f, 68.f, 67.f, 67.f, 68.f, 68.f, 65.f, 65.f, 66.f, 66.f, 65.f, 65.f, 66.f, 66.f, 65.f, 65.f, 66.f, 66.f, 67.f, 67.f, 68.f, 68.f, 67.f, 67.f, 68.f, 68.f, 67.f, 67.f, 68.f, 68.f, 69.f, 69.f, 70.f, 70.f, 69.f, 69.f, 70.f, 70.f, 69.f, 69.f, 70.f, 70.f, 71.f, 71.f, 72.f, 72.f, 71.f, 71.f, 72.f, 72.f, 71.f, 71.f, 72.f, 72.f, 69.f, 69.f, 70.f, 70.f, 69.f, 69.f, 70.f, 70.f, 69.f, 69.f, 70.f, 70.f, 71.f, 71.f, 72.f, 72.f, 71.f, 71.f, 72.f, 72.f, 71.f, 71.f, 72.f, 72.f}); - nd4j::ops::upsampling3d op; + sd::ops::upsampling3d op; auto results = op.evaluate({&input}, {factorD, factorH, factorW, isNCDHW}); - auto* output = results->at(0); + auto* output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expOutput.isSameShape(output)); ASSERT_TRUE(expOutput.equalsTo(output)); - delete results; } @@ -2331,15 +2275,13 @@ TEST_F(ConvolutionTests1, upsampling3d_bp_test1) { auto expGradI = NDArrayFactory::create('c', {bS, iC, iD, iH, iW}); expGradI = 8.; - nd4j::ops::upsampling3d_bp op; + sd::ops::upsampling3d_bp op; auto results = op.evaluate({&input, &gradO}, {isNCDHW}); - auto* gradI = results->at(0); + auto* gradI = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expGradI.isSameShape(gradI)); ASSERT_TRUE(expGradI.equalsTo(gradI)); - - delete results; } TYPED_TEST(TypedConvolutionTests1, conv2D_input_BP_test1) { @@ -2357,18 +2299,17 @@ TYPED_TEST(TypedConvolutionTests1, conv2D_input_BP_test1) { epsilonNext.linspace(1); weights.permutei({2,3,1,0}); - nd4j::ops::conv2d_input_bp op; + sd::ops::conv2d_input_bp op; auto results = op.evaluate({&inputShape, &weights, &epsilonNext}, {}, {3, 3, 1, 1, 0, 0, 1, 1, 1}); - ASSERT_TRUE(results->size() == 1); + ASSERT_TRUE(results.size() == 1); - auto epsilon = results->at(0); + auto epsilon = results.at(0); ASSERT_TRUE(shapeArr.isSameShape(epsilon)); ASSERT_TRUE(expEps.equalsTo(epsilon)); - delete results; } ////////////////////////////////////////////////////////////////////// @@ -2378,7 +2319,7 @@ TEST_F(ConvolutionTests1, upsampling3d_bp_test3) { const int factorD=2, factorH=2, factorW=2; const int isNCDHW = 1; // data format, default is NCHW - NDArray input('c', {bS, iC, iD, iH, iW}, nd4j::DataType::FLOAT32); + NDArray input('c', {bS, iC, iD, iH, iW}, sd::DataType::FLOAT32); NDArray gradO('c', {bS, iC, iD*factorD, iH*factorH, iW*factorW}, {0.6793504, 0.35508695, 0.84278935, 0.20031333, 0.7014987, 0.31069338, 0.44793984, 0.93800974, 0.32667395, 0.15187258, 0.38331753, 0.78212297, 0.1988072, 0.7985636, 0.1632634, 0.14696825, 0.26089668, 0.13505761, 0.7562093, 0.27545404, 0.36908787, 0.09282647, 0.83649176, 0.26841334, 0.09506222, 0.31279507, 0.13591796, 0.5175439, @@ -2415,23 +2356,22 @@ TEST_F(ConvolutionTests1, upsampling3d_bp_test3) { 0.10535406, 0.66438645, 0.4372345, 0.93851465, 0.8635335, 0.3405871, 0.45652762, 0.3636232, 0.52931345, 0.20154329, 0.07698499, 0.6125804, 0.3583082, 0.3894796, 0.32601944, 0.5237369, 0.66683626, 0.08541841, 0.4815708, 0.11897489, 0.97555137, 0.3602705, 0.9620871, 0.6361821, 0.71167386, 0.5134439, 0.57761437, 0.58598644, 0.39387667, 0.6966405, 0.46841687, 0.85788506, 0.9957087, 0.051309288, 0.24846801, 0.55938333, - 0.10230542, 0.9370694, 0.57527155, 0.54656035, 0.28896323, 0.51303476, 0.8865, 0.38641605, 0.9836358}, nd4j::DataType::FLOAT32); + 0.10230542, 0.9370694, 0.57527155, 0.54656035, 0.28896323, 0.51303476, 0.8865, 0.38641605, 0.9836358}, sd::DataType::FLOAT32); NDArray expGradI('c', {bS, iC, iD, iH, iW}, {3.510932, 3.4310975, 3.538762, 4.148549, 2.8380678, 2.5431657, 3.3928843, 3.228055, 3.1467278, 3.2603023, 5.611751, 4.334653, 3.3697734, 4.603307, 4.4357986, 4.32991, 3.0532732, 3.1370173, 4.181534, 2.9965065, 2.8553872, 5.2719016, 4.5671935, 3.7027276, 3.3517184, 5.2544537, 3.5107024, 4.1496124, 3.9333878, 3.1798909, 3.1446428, 3.0932689, 3.9730802, 3.0466917, 4.9675374, 4.769673, 3.766952, 3.6375027, 3.6492167, 4.9440994, 3.8379507, 3.467589, 4.719474, 3.1295977, 4.5177174, 4.2760015, 2.8443856, - 4.225355, 4.377341, 4.4398847, 4.710785, 4.4199953, 3.928307, 4.8769503}, nd4j::DataType::FLOAT32); + 4.225355, 4.377341, 4.4398847, 4.710785, 4.4199953, 3.928307, 4.8769503}, sd::DataType::FLOAT32); - nd4j::ops::upsampling3d_bp op; + sd::ops::upsampling3d_bp op; auto results = op.evaluate({&input, &gradO}, {isNCDHW}); - auto* gradI = results->at(0); + auto* gradI = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expGradI.isSameShape(gradI)); ASSERT_TRUE(expGradI.equalsTo(gradI)); - delete results; } @@ -2456,16 +2396,15 @@ TEST_F(ConvolutionTests1, deconv2d_test1) { input = 0.5; weights.linspace(0.1, 0.1); - nd4j::ops::deconv2d op; + sd::ops::deconv2d op; auto results = op.evaluate({&input, &weights}, {kH,kW, sH,sW, pH,pW, dH,dW, paddingMode, dataFormat}); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); - auto output = results->at(0); + auto output = results.at(0); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete results; } ////////////////////////////////////////////////////////////////////// @@ -2489,15 +2428,14 @@ TEST_F(ConvolutionTests1, deconv2d_test2) { input = 0.5; weights.linspace(0.1, 0.1); - nd4j::ops::deconv2d op; + sd::ops::deconv2d op; auto results = op.evaluate({&input, &weights}, {kH,kW, sH,sW, pH,pW, dH,dW, paddingMode, dataFormat}); - auto output = results->at(0); + auto output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete results; } ////////////////////////////////////////////////////////////////////// @@ -2521,23 +2459,22 @@ TEST_F(ConvolutionTests1, deconv2d_test3) { weights.linspace(0.1, 0.1); bias = 0.2; - nd4j::ops::deconv2d op; + sd::ops::deconv2d op; auto results = op.evaluate({&input, &weights}, {kH,kW, sH,sW, pH,pW, dH,dW, paddingMode, dataFormat}); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); - auto output = results->at(0); + auto output = results.at(0); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete results; } ////////////////////////////////////////////////////////////////////// TEST_F(ConvolutionTests1, deconv2d_test4) { - NDArray input('c', {2, 3, 4, 4}, nd4j::DataType::FLOAT32); - NDArray weights('c', {3, 3, 5, 5}, nd4j::DataType::FLOAT32); + NDArray input('c', {2, 3, 4, 4}, sd::DataType::FLOAT32); + NDArray weights('c', {3, 3, 5, 5}, sd::DataType::FLOAT32); NDArray exp('c', {2,3,8,8}, {6276.0,12831.0,19668.0,26790.0,27012.0,20703.0,14100.0,7200.0,13719.0,28023.0,42918.0,58410.0,58902.0,45105.0,30693.0,15660.0,22389.0,45696.0,69930.0,95100.0,95910.0,73386.0,49899.0,25440.0,32346.0,65970.0, 100884.0,137100.0,138276.0,105726.0,71838.0,36600.0,33726.0,68790.0,105204.0,142980.0,144156.0,110226.0,74898.0,38160.0,27555.0,56154.0,85806.0,116520.0,117474.0,89748.0,60933.0,31020.0,19917.0,40557.0,61926.0, 84030.0,84714.0,64671.0,43875.0,22320.0,10752.0,21879.0,33384.0,45270.0,45636.0,34815.0,23604.0,12000.0,7551.0,15456.0,23718.0,32340.0,32562.0,24978.0,17025.0,8700.0,16569.0,33873.0,51918.0,70710.0,71202.0, @@ -2550,23 +2487,22 @@ TEST_F(ConvolutionTests1, deconv2d_test4) { 268338.0,180882.0,91440.0,66867.0,135210.0,205038.0,276360.0,279042.0,211572.0,142581.0,72060.0,46845.0,94701.0,143574.0,193470.0,195306.0,148047.0,99747.0,50400.0,24576.0,49671.0,75288.0,101430.0,102372.0,77583.0, 52260.0,26400.0,22095.0,44688.0,67782.0,91380.0,92178.0,69906.0,47121.0,23820.0,46377.0,93777.0,142206.0,191670.0,193314.0,146571.0,98775.0,49920.0,72906.0,147387.0,223452.0,301110.0,303648.0,230175.0,155082.0, 78360.0,101742.0,205638.0,311700.0,419940.0,423420.0,320898.0,216162.0,109200.0,106002.0,214218.0,324660.0,437340.0,440820.0,334038.0,224982.0,113640.0,83292.0,168285.0,254988.0,343410.0,346092.0,262197.0,176556.0, - 89160.0,58095.0,117351.0,177774.0,239370.0,241206.0,182697.0,122997.0,62100.0,30351.0,61296.0,92838.0,124980.0,125922.0,95358.0,64185.0,32400.0,26970.0,54513.0,82632.0,111330.0,112128.0,84981.0,57246.0,28920.0,56427.0,114027.0,172806.0,232770.0,234414.0,177621.0,119625.0,60420.0,88431.0,178662.0,270702.0,364560.0,367098.0,278100.0,187257.0,94560.0,123042.0,248538.0,376500.0,506940.0,510420.0,386598.0,260262.0,131400.0,127302.0,257118.0,389460.0,524340.0,527820.0,399738.0,269082.0,135840.0,99717.0,201360.0,304938.0,410460.0,413142.0,312822.0,210531.0,106260.0,69345.0,140001.0,211974.0,285270.0,287106.0,217347.0,146247.0,73800.0,36126.0,72921.0,110388.0,148530.0,149472.0,113133.0,76110.0,38400.0}, nd4j::DataType::FLOAT32); + 89160.0,58095.0,117351.0,177774.0,239370.0,241206.0,182697.0,122997.0,62100.0,30351.0,61296.0,92838.0,124980.0,125922.0,95358.0,64185.0,32400.0,26970.0,54513.0,82632.0,111330.0,112128.0,84981.0,57246.0,28920.0,56427.0,114027.0,172806.0,232770.0,234414.0,177621.0,119625.0,60420.0,88431.0,178662.0,270702.0,364560.0,367098.0,278100.0,187257.0,94560.0,123042.0,248538.0,376500.0,506940.0,510420.0,386598.0,260262.0,131400.0,127302.0,257118.0,389460.0,524340.0,527820.0,399738.0,269082.0,135840.0,99717.0,201360.0,304938.0,410460.0,413142.0,312822.0,210531.0,106260.0,69345.0,140001.0,211974.0,285270.0,287106.0,217347.0,146247.0,73800.0,36126.0,72921.0,110388.0,148530.0,149472.0,113133.0,76110.0,38400.0}, sd::DataType::FLOAT32); input.linspace(1); weights.linspace(1); weights.permutei({2,3,1,0}); - nd4j::ops::deconv2d op; + sd::ops::deconv2d op; auto result = op.evaluate({&input, &weights}, {5, 5, 1, 1, 0, 0, 1, 1, 0, 0}); - auto z = result->at(0); + auto z = result.at(0); // z->printShapeInfo(); // z->printBuffer(); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; } ////////////////////////////////////////////////////////////////////// @@ -2583,7 +2519,7 @@ TEST_F(ConvolutionTests1, deconv2d_test5) { weights.linspace(1); weights.permutei({2,3,1,0}); - nd4j::ops::deconv2d op; + sd::ops::deconv2d op; auto result = op.execute({&input, &weights}, {&z}, {5, 5, 1, 1, 0, 0, 1, 1, 0, 0}); ASSERT_EQ(ND4J_STATUS_OK, result); @@ -2614,17 +2550,16 @@ TYPED_TEST(TypedConvolutionTests1, deconv2d_test6) { input.linspace(1); - nd4j::ops::deconv2d op; + sd::ops::deconv2d op; auto results = op.evaluate({&input, &weights}, {kH,kW, sH,sW, pH,pW, dH,dW, paddingMode, dataFormat}); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); - auto output = results->at(0); + auto output = results.at(0); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete results; } TEST_F(ConvolutionTests1, deconv2d_test7) { @@ -2638,18 +2573,16 @@ TEST_F(ConvolutionTests1, deconv2d_test7) { input.linspace(1); bias.linspace(1); - nd4j::ops::deconv2d op; + sd::ops::deconv2d op; auto result = op.evaluate({&input, &weights, &bias}, {1, 1, 1, 1, 0, 0, 1, 1, 1, 0}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto output = result->at(0); + auto output = result.at(0); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - - delete result; } ////////////////////////////////////////////////////////////////////// @@ -2682,17 +2615,16 @@ TEST_F(ConvolutionTests1, deconv2d_test8) { 1.235054, 1.201363, 1.222816, 1.623673, 1.590317, 1.322463, 1.206481, 1.466262, 0.974741, 0.922343, 1.367100, 1.087943, 1.084952, 1.586691, 1.133576, 1.405098, 1.471922, 1.484062, 1.212039, 1.144419, 1.266123}); - nd4j::ops::deconv2d op; + sd::ops::deconv2d op; auto results = op.evaluate({&input, &weights, &bias}, {kH,kW, sH,sW, pH,pW, dH,dW, paddingMode, dataFormat}); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); - auto output = results->at(0); + auto output = results.at(0); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete results; } ////////////////////////////////////////////////////////////////////// @@ -2717,15 +2649,14 @@ TYPED_TEST(TypedConvolutionTests1, deconv2d_tf_test1) { input = 0.5; weights.linspace(0.1, 0.1); - nd4j::ops::deconv2d_tf op; + sd::ops::deconv2d_tf op; auto results = op.evaluate({&outShape, &weights, &input}, {kH,kW, sH,sW, pH,pW, dH,dW, paddingMode, dataFormat}); - auto output = results->at(0); + auto output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete results; } diff --git a/libnd4j/tests_cpu/layers_tests/ConvolutionTests2.cpp b/libnd4j/tests_cpu/layers_tests/ConvolutionTests2.cpp index 0c63f527e..2c3351175 100644 --- a/libnd4j/tests_cpu/layers_tests/ConvolutionTests2.cpp +++ b/libnd4j/tests_cpu/layers_tests/ConvolutionTests2.cpp @@ -23,19 +23,19 @@ #define LIBND4J_CONVOLUTIONTESTS2_H #include "testlayers.h" -#include -#include -#include +#include +#include +#include #include #include #include #include #include -#include -#include +#include +#include -using namespace nd4j; -using namespace nd4j::graph; +using namespace sd; +using namespace sd::graph; class ConvolutionTests2 : public testing::Test { public: @@ -67,7 +67,7 @@ TEST_F(ConvolutionTests2, im2col_1) { int paddingMode = 0; // 1-SAME, 0-VALID; - NDArray image('c', {bS, iC, iH, iW}, nd4j::DataType::DOUBLE); + NDArray image('c', {bS, iC, iH, iW}, sd::DataType::DOUBLE); NDArray expected('c', {bS, iC, kH, kW, oH, oW}, {1, 2, 4, 5, 2, 3, 5, 6, 4, 5, 7, 8, 5, 6, 8, 9, 7, 8, 10, 11, 8, 9, 11, 12, 13, 14, 16, 17, 14, 15, 17, 18, 16, 17, 19, 20, 17, 18, 20, 21, 19, 20, 22, 23, 20, 21, 23, 24, 25, 26, 28, 29, 26, 27, 29, 30, 28, 29, 31, 32, 29, 30, 32, 33, 31, 32, 34, 35, 32, 33, 35, 36, 37, 38, 40, 41, 38, 39, 41, 42, 40, 41, 43, @@ -78,16 +78,15 @@ TEST_F(ConvolutionTests2, im2col_1) { image.linspace(1, 1); - nd4j::ops::im2col op; + sd::ops::im2col op; auto results = op.evaluate({&image}, {kH,kW, sH,sW, pH,pW, dH,dW, paddingMode}); - auto column = results->at(0); + auto column = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expected.isSameShape(column)); ASSERT_TRUE(expected.equalsTo(column)); - delete results; } template @@ -121,15 +120,14 @@ TYPED_TEST(TypedConvolutionTests2, deconv2d_tf_test2) { input = 0.5; weights.linspace(0.1, 0.1); - nd4j::ops::deconv2d_tf op; + sd::ops::deconv2d_tf op; auto results = op.evaluate({&outShape, &weights, &input}, {}, {kH,kW, sH,sW, pH,pW, dH,dW, paddingMode, dataFormat}); - auto output = results->at(0); + auto output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete results; } ////////////////////////////////////////////////////////////////////// @@ -139,13 +137,12 @@ TYPED_TEST(TypedConvolutionTests2, Test_DeConv2D_TF_1) { auto input2 = NDArrayFactory::create('c', {12, 4, 4, 16}); auto exp = NDArrayFactory::create('c', {12, 5, 5, 32}); - nd4j::ops::deconv2d_tf op; + sd::ops::deconv2d_tf op; auto result = op.evaluate({&input0, &input1, &input2}, {}, {2, 2, 1, 1, 0, 0, 1, 1, 0, 1}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - ASSERT_EQ(exp, *result->at(0)); + ASSERT_EQ(exp, *result.at(0)); - delete result; } ////////////////////////////////////////////////////////////////////// @@ -169,16 +166,15 @@ TYPED_TEST(TypedConvolutionTests2, Test_DeConv2D_TF_2) { auto input2 = NDArrayFactory::create('c', {3, 4, 4, 5}, {0.98114507f, 0.96400015f, 0.58669623f, 0.60073098f, 0.75425418f, 0.44258752f, 0.76373084f, 0.96593234f, 0.34067846f, 0.57962620f, 0.77517051f, 0.97472977f, 0.79237527f, 0.68690428f, 0.21719366f, 0.79959206f, 0.84814187f, 0.22496814f, 0.08646965f, 0.31110474f, 0.79813162f, 0.19661444f, 0.57760099f, 0.72138960f, 0.15244268f, 0.87687051f, 0.11130344f, 0.01087698f, 0.34817841f, 0.54992017f, 0.23443850f, 0.31725614f, 0.59755220f, 0.20364695f, 0.00531392f, 0.23403114f, 0.07442912f, 0.83707647f, 0.89291743f, 0.09044587f, 0.69041462f, 0.29904183f, 0.61904680f, 0.85306847f, 0.34467042f, 0.95839152f, 0.54517124f, 0.29640937f, 0.94855959f, 0.95970016f, 0.94045145f, 0.95510301f, 0.34666505f, 0.34717010f, 0.69245678f, 0.71669175f, 0.59043738f, 0.64924132f, 0.06033522f, 0.60185199f, 0.04690073f, 0.59241154f, 0.40229547f, 0.23002481f, 0.45161195f, 0.73743778f, 0.93209113f, 0.37294358f, 0.50177744f, 0.15072501f, 0.26146917f, 0.05252146f, 0.04758931f, 0.76448288f, 0.85149045f, 0.08840467f, 0.07692576f, 0.33180160f, 0.27241259f, 0.74834620f, 0.56453640f, 0.23057286f, 0.68429752f, 0.11961551f, 0.39045977f, 0.44356094f, 0.77018807f, 0.07984410f, 0.47926806f, 0.26165759f, 0.18606064f, 0.89972877f, 0.17962874f, 0.47273120f, 0.64641705f, 0.61890443f, 0.58730015f, 0.25937832f, 0.35231561f, 0.10243882f, 0.17459193f, 0.95906995f, 0.09227025f, 0.30003223f, 0.41601210f, 0.38269713f, 0.84799751f, 0.59295173f, 0.76277990f, 0.68910424f, 0.37672606f, 0.40675461f, 0.94346058f, 0.91438505f, 0.84728183f, 0.64367667f, 0.74899979f, 0.60570691f, 0.16417363f, 0.68852426f, 0.85486889f, 0.22585792f, 0.86953176f, 0.07465519f, 0.93096301f, 0.38008822f, 0.38752587f, 0.44004038f, 0.13170612f, 0.94541045f, 0.89349973f, 0.69245307f, 0.94978877f, 0.98776658f, 0.79445884f, 0.30607409f, 0.58264961f, 0.37980538f, 0.41810784f, 0.48903038f, 0.51615888f, 0.57682794f, 0.82481897f, 0.78341080f, 0.48446465f, 0.17447931f, 0.71125424f, 0.30263851f, 0.70675352f, 0.03215584f, 0.92381065f, 0.22343694f, 0.08851149f, 0.91402490f, 0.70074717f, 0.30912192f, 0.37723206f, 0.97579397f, 0.23554587f, 0.95939133f, 0.41565709f, 0.01741416f, 0.58362787f, 0.22106662f, 0.89065537f, 0.31900249f, 0.41280911f, 0.67947610f, 0.04545590f, 0.15352812f, 0.85412524f, 0.84933222f, 0.80000225f, 0.93147073f, 0.70094105f, 0.69269875f, 0.95282194f, 0.65913582f, 0.79186874f, 0.59855248f, 0.39707430f, 0.95126239f, 0.15618217f, 0.33446689f, 0.98123758f, 0.84770758f, 0.98081012f, 0.54427413f, 0.18728519f, 0.89792955f, 0.53360126f, 0.72812986f, 0.13307744f, 0.51217443f, 0.66708084f, 0.29416915f, 0.31298995f, 0.39155037f, 0.29288291f, 0.87063305f, 0.61759154f, 0.73723332f, 0.37167635f, 0.82122716f, 0.22937430f, 0.76570536f, 0.47911792f, 0.02826214f, 0.94277323f, 0.59945469f, 0.19042060f, 0.68173155f, 0.82771295f, 0.95649538f, 0.40833101f, 0.90838542f, 0.55245881f, 0.49011012f, 0.36773444f, 0.34513527f, 0.42050683f, 0.16113964f, 0.30969388f, 0.27174174f, 0.12117655f, 0.35270175f, 0.81967867f, 0.63723136f, 0.84309389f, 0.71822576f, 0.84883484f, 0.32306117f, 0.08176457f, 0.56175486f, 0.34892198f, 0.09306929f, 0.85437582f, 0.13925577f, 0.48629188f, 0.29923539f}); auto exp = NDArrayFactory::create('c', {3, 8, 8, 16}, {5.98743296f, -2.83037376f, -0.87943113f, 1.41339970f, 1.32433391f, -1.20299149f, -0.02893090f, 2.05326009f, 1.19417048f, 5.58212376f, 3.28139353f, 1.19237995f, -1.09431255f, -2.55264497f, 3.11014652f, 6.81296825f, -2.09029293f, -4.32068443f, -0.52808392f, -1.97968531f, -0.18673831f, 0.84605980f, 4.55825520f, 2.71503139f, 0.15210046f, 0.85310984f, -3.82062817f, 2.76470995f, 3.69004202f, -1.45017099f, -2.59361267f, -1.35094655f, 7.24145126f, -5.25432396f, 0.19920218f, -4.30596399f, 1.35318923f, -3.88142037f, 3.67493343f, 2.25931478f, 2.87630725f, 1.66349852f, 6.21347952f, 0.94105923f, -1.61742055f, -2.35699606f, 0.12850338f, 1.79141688f, -2.09535933f, -6.35418081f, -0.06303531f, -4.38615131f, 0.48237842f, 0.26528549f, 3.38231516f, 3.76315165f, -0.40254810f, -0.23716694f, -6.13381910f, -0.41950428f, -0.89680839f, -1.46491277f, -1.98541689f, -0.99357355f, 5.58237648f, -2.38937521f, -0.00872564f, -2.37138414f, 4.91117287f, -4.51916361f, 0.97943687f, 2.91052818f, -2.50362611f, 1.70252812f, 5.04137802f, 3.57108784f, -1.87532270f, -3.66677809f, -2.38861251f, 5.55765152f, -7.27571774f, -1.68887305f, -0.72266489f, -4.42809057f, -0.92118186f, 1.02381468f, 4.44284725f, 5.17150497f, -0.42438728f, 2.02693963f, -1.36484981f, -1.47912180f, 0.26649538f, -0.02091765f, -2.86906910f, -3.03046989f, 1.35122132f, -3.21707630f, 2.21112418f, 0.24121630f, 3.96940088f, -7.66105747f, 2.76352382f, -0.99061489f, -2.16720009f, -1.63170409f, 1.12701774f, -1.02415371f, -0.90435314f, -1.51372027f, -0.76884907f, 0.39066136f, -0.89562428f, -2.03204703f, 1.28074932f, -2.14551091f, -2.36843777f, 0.46580017f, 0.75451565f, -0.00336730f, -1.06597757f, 3.27195978f, -0.41307712f, -0.10376054f, -1.34102952f, -2.22901654f, 2.31929803f, 1.40851438f, -2.23774385f, 0.20417206f, -1.12153268f, -0.13188094f, -3.96649432f, 2.10269976f, 0.49845099f, 6.18937683f, -0.51783508f, -0.48048639f, -1.92970264f, 3.16670656f, 1.13355756f, -0.07890664f, 1.31536257f, -0.43924797f, -0.04562932f, -0.87974954f, 0.75411212f, -2.39745235f, -3.97132111f, 0.37202546f, -2.40399146f, -1.50796390f, -3.08302689f, 0.23075986f, -0.94316757f, 1.34948587f, 0.58591264f, 2.18529797f, 7.97652435f, 2.32798409f, -4.09404373f, 0.89634895f, 0.77697754f, -0.65091681f, -7.05506849f, 5.86194515f, 2.51394033f, 4.69959354f, 0.20835471f, 3.18049693f, -1.29682434f, 3.70832396f, -0.48123091f, -1.67904007f, -1.35418940f, 1.58435583f, -1.13851106f, -1.19225955f, 0.59713769f, -5.80462933f, -7.45143986f, -1.08658695f, 1.03244078f, -1.75307107f, -7.07100582f, 3.85825157f, 1.62127817f, 2.32572675f, 0.56171900f, -0.80591971f, 3.98835945f, 0.15742642f, -2.97832179f, 0.13821673f, -0.72556758f, -0.84936106f, -7.28444147f, 3.94134307f, 0.80779338f, 7.47784615f, 8.23335075f, 4.80595016f, -4.89574575f, 4.03362942f, -6.67522192f, -4.55204487f, 2.12511182f, -2.70781207f, -1.57226098f, -3.08408356f, -0.30812448f, -5.32870674f, -5.13238287f, 0.49605465f, -0.55042171f, 0.46324944f, -3.83545256f, -0.12562510f, -0.20978995f, -0.13068712f, -1.92144060f, -1.68787408f, 5.45581436f, -0.79583496f, -2.38866687f, -3.90546346f, -0.47028148f, -0.14319679f, -3.37016582f, 2.00905991f, -1.21345615f, 1.81376505f, 7.73004007f, 0.74310112f, -4.64536428f, 3.78111577f, -9.05182457f, -0.10674095f, 1.53476238f, 0.63345337f, -0.40907967f, -1.44729769f, -1.87145400f, -2.46623540f, 1.07472968f, 0.77390999f, -3.93438888f, 4.49174690f, -0.96686655f, 1.92278123f, 0.30049133f, -0.02388665f, -1.99777114f, -3.23885751f, 5.87784004f, 2.13776040f, 3.56758308f, -3.37774134f, -3.67526293f, 1.63700044f, -1.69959962f, -0.99112594f, 6.03103638f, 1.67399430f, -1.28699589f, 7.16759014f, 12.63490295f, 3.62937450f, -4.75982571f, 2.17861104f, -2.03065681f, 4.30207729f, -0.46797156f, -2.96022511f, -6.02702332f, 3.09229851f, -1.39771092f, -0.03471333f, 3.22175527f, 5.63565636f, 1.78195477f, -0.63545251f, -3.99497652f, 1.46043062f, 4.60050488f, -2.96651959f, -2.03159475f, -1.52386189f, -0.15129802f, -3.90390921f, -0.63852370f, 0.79210538f, 2.35288715f, -5.55609035f, 5.36427498f, -0.60248077f, -0.26181316f, 5.04884720f, 8.53192806f, 5.05080223f, -6.56371737f, 1.52260923f, -7.13623667f, 6.49414349f, 2.33445597f, -4.11490965f, -6.44347477f, -0.47079402f, -0.63467920f, 2.60399365f, 1.05958164f, 3.66901422f, -1.05657935f, 1.88611507f, -6.37475634f, 2.01480770f, 3.36020517f, -5.11001921f, -0.46132171f, 2.16525555f, 4.21938848f, -2.08346295f, 2.86168146f, 1.26987600f, 6.76066971f, -7.84916353f, 4.11700916f, 0.47985530f, -4.60113716f, 7.42062473f, 6.37472820f, 4.37820530f, -7.12197018f, 0.01357239f, -7.90392113f, 8.32131577f, -0.87593079f, -0.16994858f, -5.86345863f, -0.20697471f, -1.37845206f, 1.63819647f, 1.59720242f, -0.74357712f, -1.88725603f, -1.98357940f, -8.57950306f, -4.10104513f, 3.57231879f, -2.89855957f, -0.11263305f, 2.78033924f, 1.53078973f, -2.93089223f, 0.73189604f, 3.20563078f, 3.92601013f, -5.21916151f, 0.89163935f, -0.42978728f, -6.70888853f, 4.56477976f, 1.20105875f, 3.83393812f, -6.27205181f, 4.05993128f, -7.35513067f, 1.60660768f, -1.21052051f, 1.58191252f, -1.37899971f, -1.20117283f, 2.93301678f, 1.06302834f, 1.38993621f, -1.66884089f, -3.34452581f, 1.04498529f, -4.10412455f, -4.03310585f, 1.61513603f, -1.09388447f, 2.11451387f, -0.94192362f, -0.23287666f, 5.88265705f, -0.83010495f, -2.15317154f, -0.60276151f, -1.49265075f, 3.93397975f, 5.45194483f, 1.45161700f, -2.57401872f, -5.59288931f, 4.29170895f, 1.87151814f, 0.08362055f, -0.28767288f, 1.17675185f, 0.85266006f, 1.30549634f, -5.60830832f, 0.19398519f, -0.83982587f, 1.75940764f, -5.46077394f, 1.64495635f, 0.17102760f, -0.54459631f, -2.21975255f, -0.37443402f, -2.08474159f, 1.85959935f, 11.19680309f, -0.18611598f, -2.59765387f, 3.06330776f, -1.52183700f, -4.88415241f, -0.75097847f, 2.58201051f, 7.40885210f, 3.58994508f, 1.62457407f, 3.12514591f, -4.36833286f, 1.39830995f, 3.61003447f, -0.63837433f, -3.62661815f, 3.78898096f, 2.92802262f, 5.87374496f, -4.38554621f, -2.53411579f, -2.87311554f, -1.31391978f, -4.26736879f, 3.45099425f, 1.58769250f, 1.73341393f, -1.08842182f, 2.27120280f, -1.78938174f, -2.29940319f, 7.07046986f, 0.51426595f, -6.22928905f, 5.28968811f, 2.31827855f, -4.20915890f, -1.27249205f, 5.92120600f, 3.19458675f, 7.09252501f, 3.96577907f, 6.41484213f, -4.66009521f, 10.00181389f, 0.51108456f, -4.62243366f, -5.18351841f, 2.12961674f, 5.10694027f, 7.29412317f, 0.15912467f, -3.38902974f, -4.01918602f, -2.17383957f, 0.13118666f, 0.27872476f, -0.92317247f, 3.51440644f, 1.84171486f, 1.03378081f, 1.30569839f, -2.09583759f, 9.03952980f, -0.55187917f, -2.04549074f, 1.08294606f, -2.65263700f, -2.93977118f, 1.88909876f, 0.96043622f, 1.76579499f, 3.14314699f, 5.86394691f, 7.36944389f, -7.04524136f, 6.68673229f, -5.52591467f, -2.19745898f, -4.32036924f, 0.52971321f, 2.26268244f, 6.91575766f, -0.94590527f, -3.98923349f, -0.12266219f, 0.24294075f, -1.07783222f, 1.87989080f, -3.57109427f, 1.61553633f, 0.42486978f, 0.75852054f, -6.19481468f, -3.80570698f, 2.39946675f, -1.93851781f, -5.42234039f, -6.34092760f, -2.52374983f, -1.85044456f, 3.92693520f, 0.40042299f, 4.69742584f, 5.40483189f, -1.02398944f, 8.89605045f, 0.64680403f, 0.89943957f, 0.76993859f, -1.88244629f, 1.90714884f, 3.10836840f, -0.17064989f, 0.84892416f, -6.94988108f, 1.92141032f, -1.36458397f, 6.39284658f, 0.45201308f, 2.58823442f, 6.33375788f, -4.76916075f, -8.45738983f, -0.48962492f, 2.40652561f, 4.56602001f, -3.34420681f, 1.86862195f, -7.01420689f, -6.94657421f, -2.47419310f, -4.61693668f, -0.18822384f, -0.36949772f, 2.01374269f, 4.11018658f, -5.11564064f, 8.04294395f, 2.88567662f, -2.87645102f, -1.23238611f, -5.91409397f, -0.62205851f, 1.38689423f, -0.01120412f, 5.25955677f, -1.98474956f, -3.72012186f, 3.00445986f, 4.99141550f, 2.97457719f, 2.70827627f, 6.04544449f, -0.20756161f, -10.87035751f, 0.80454814f, 0.33568168f, -2.48132324f, -2.84452009f, 2.63126230f, 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&input2}, {}, {7,7, 2,2, 0,0, 1,1, 1,1}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; } ////////////////////////////////////////////////////////////////////// @@ -188,15 +184,13 @@ TEST_F(ConvolutionTests2, Test_Dilation2D_Again_1) { auto exp = NDArrayFactory::create('c', {4, 64, 43, 4}); - nd4j::ops::dilation2d op; + sd::ops::dilation2d op; auto result = op.evaluate({&x, &w}, {}, {1, 1,5,7,1, 1,2,3,1}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); - - delete result; } ////////////////////////////////////////////////////////////////////// @@ -204,11 +198,10 @@ TEST_F(ConvolutionTests2, Test_Dilation2D_Again_2) { auto x = NDArrayFactory::create('c', {4, 26, 19, 4}); auto w = NDArrayFactory::create('c', {11, 7, 4}); - nd4j::ops::dilation2d op; + sd::ops::dilation2d op; auto result = op.evaluate({&x, &w}, {}, {0, 1,2,3,1, 1,3,2,1}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - delete result; } TYPED_TEST(TypedConvolutionTests2, sconv2d_bp_1) { @@ -245,14 +238,14 @@ TYPED_TEST(TypedConvolutionTests2, sconv2d_bp_1) { weightsP.applyScalar(scalar::Divide, 100.0, weightsP); epsilonNext.applyScalar(scalar::Divide, 100.0, epsilonNext); - nd4j::ops::sconv2d_bp op; + sd::ops::sconv2d_bp op; auto resultBP = op.evaluate({&input, &epsilonNext, &weightsD, &weightsP },{}, {5, 5, 1, 1, 0, 0, 1, 1, 0}, {}); - ASSERT_EQ(3, resultBP->size()); + ASSERT_EQ(3, resultBP.size()); - auto _epsilon = resultBP->at(0); - auto _gradWD = resultBP->at(1); - auto _gradWP = resultBP->at(2); + auto _epsilon = resultBP.at(0); + auto _gradWD = resultBP.at(1); + auto _gradWP = resultBP.at(2); //_gradWP->printBuffer("gradWP"); @@ -273,7 +266,6 @@ TYPED_TEST(TypedConvolutionTests2, sconv2d_bp_1) { ASSERT_TRUE(_epsilon->equalsTo(&expE)); - delete resultBP; } ////////////////////////////////////////////////////////////////////// @@ -285,11 +277,11 @@ TYPED_TEST(TypedConvolutionTests2, sconv2d_bp_2) { int paddingMode = 0; // 1-SAME, 0-VALID; int dataFormat = 0; // 1-NHWC, 0-NCHW - NDArray input('c', {bS, iC, iH, iW}, typeid(TypeParam) == typeid(float) ? nd4j::DataType::FLOAT32 : nd4j::DataType::DOUBLE); - NDArray gradO('c', {bS, oC, oH, oW}, typeid(TypeParam) == typeid(float) ? nd4j::DataType::FLOAT32 : nd4j::DataType::DOUBLE); - NDArray weightsDepth('c', {kH, kW, iC, mC}, typeid(TypeParam) == typeid(float) ? nd4j::DataType::FLOAT32 : nd4j::DataType::DOUBLE); - NDArray weightsPoint('f', {1, 1, iC*mC, oC}, typeid(TypeParam) == typeid(float) ? nd4j::DataType::FLOAT32 : nd4j::DataType::DOUBLE); - NDArray bias('c', {1,oC}, {0.5, 0.5}, typeid(TypeParam) == typeid(float) ? nd4j::DataType::FLOAT32 : nd4j::DataType::DOUBLE); + NDArray input('c', {bS, iC, iH, iW}, typeid(TypeParam) == typeid(float) ? sd::DataType::FLOAT32 : sd::DataType::DOUBLE); + NDArray gradO('c', {bS, oC, oH, oW}, typeid(TypeParam) == typeid(float) ? sd::DataType::FLOAT32 : sd::DataType::DOUBLE); + NDArray weightsDepth('c', {kH, kW, iC, mC}, typeid(TypeParam) == typeid(float) ? sd::DataType::FLOAT32 : sd::DataType::DOUBLE); + NDArray weightsPoint('f', {1, 1, iC*mC, oC}, typeid(TypeParam) == typeid(float) ? sd::DataType::FLOAT32 : sd::DataType::DOUBLE); + NDArray bias('c', {1,oC}, {0.5, 0.5}, typeid(TypeParam) == typeid(float) ? sd::DataType::FLOAT32 : sd::DataType::DOUBLE); NDArray gradI(&input); NDArray gradWD(&weightsDepth); @@ -301,7 +293,7 @@ TYPED_TEST(TypedConvolutionTests2, sconv2d_bp_2) { weightsPoint.linspace(0.15, 0.1); gradO.linspace(0.01, 0.01); - nd4j::ops::sconv2d_bp op; + sd::ops::sconv2d_bp op; Nd4jStatus status = op.execute({&input, &gradO, &weightsDepth, & weightsPoint, &bias}, {&gradI, &gradWD, &gradWP, &gradB}, {}, {kH,kW, sH,sW, pH,pW, dH,dW, paddingMode, dataFormat}, {}); @@ -341,17 +333,15 @@ TYPED_TEST(TypedConvolutionTests2, sconv2d_bp_3) { auto epsilon = NDArrayFactory::create('c', {3, 3, 16, 16}); - nd4j::ops::sconv2d_bp op; + sd::ops::sconv2d_bp op; auto result = op.evaluate({&input, &epsilonNext, &weightsD, &weightsP}, {}, {2, 2, 1, 1, 0, 0, 2, 2, 0}); - auto eps = result->at(0); - auto gWD = result->at(1); - auto gWP = result->at(2); + auto eps = result.at(0); + auto gWD = result.at(1); + auto gWP = result.at(2); ASSERT_TRUE(epsilon.isSameShape(eps)); - - delete result; } ////////////////////////////////////////////////////////////////////// @@ -377,12 +367,12 @@ TYPED_TEST(TypedConvolutionTests2, sconv2d_bp_4) { weightsDepth.linspace(0.1, 0.1); gradO.linspace(0.01, 0.01); - nd4j::ops::sconv2d_bp op; + sd::ops::sconv2d_bp op; auto results = op.evaluate({&input, &gradO, &weightsDepth, &bias}, {}, {kH,kW, sH,sW, pH,pW, dH,dW, paddingMode, dataFormat}); - auto* gradI = results->at(0); - auto* gradWD = results->at(1); + auto* gradI = results.at(0); + auto* gradWD = results.at(1); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expGradI.isSameShape(gradI)); ASSERT_TRUE(expGradI.equalsTo(gradI)); @@ -390,7 +380,6 @@ TYPED_TEST(TypedConvolutionTests2, sconv2d_bp_4) { ASSERT_TRUE(expGradW.isSameShape(gradWD)); ASSERT_TRUE(expGradW.equalsTo(gradWD)); - delete results; } ////////////////////////////////////////////////////////////////////// @@ -418,7 +407,7 @@ TEST_F(ConvolutionTests2, sconv2d_bp_5) { weightsDepth.linspace(-0.5, 0.1); gradO.linspace(0.01, 0.01); - nd4j::ops::sconv2d_bp op; + sd::ops::sconv2d_bp op; auto status = op.execute({&input, &gradO, &weightsDepth, &weightsPoint, &bias}, {&gradI, &gradWD, &gradWP, &gradB}, {}, {kH,kW, sH,sW, pH,pW, dH,dW, paddingMode, dataFormat}, {}); ASSERT_EQ(Status::OK(), status); } @@ -430,11 +419,11 @@ TEST_F(ConvolutionTests2, im2col_bp_1) { int oH=12,oW=12; // [bS, iC, kH, kW, oH, oW] is de-convoluted to [bS, iC, iH, iW] - NDArray input('c', {bS, iC, iH, iW}, nd4j::DataType::DOUBLE); - NDArray gradO('c', {bS, iC, kH, kW, oH, oW}, nd4j::DataType::DOUBLE); - NDArray gradI('c', {bS, iC, iH, iW}, nd4j::DataType::DOUBLE); // output + NDArray input('c', {bS, iC, iH, iW}, sd::DataType::DOUBLE); + NDArray gradO('c', {bS, iC, kH, kW, oH, oW}, sd::DataType::DOUBLE); + NDArray gradI('c', {bS, iC, iH, iW}, sd::DataType::DOUBLE); // output - nd4j::ops::im2col_bp op; + sd::ops::im2col_bp op; Nd4jStatus status = op.execute({&input, &gradO}, {&gradI}, {}, {kH, kW, sH, sW, pH, pW, dH, dW, 1}, {}); ASSERT_EQ(ND4J_STATUS_OK, status); @@ -462,17 +451,16 @@ TEST_F(ConvolutionTests2, deconv3d_test1) { input = 0.5; weights.linspace(0.1, 0.1); - nd4j::ops::deconv3d op; + sd::ops::deconv3d op; auto results = op.evaluate({&input, &weights}, {}, {kD,kH,kW, sD,sH,sW, pD,pH,pW, dD,dH,dW, paddingMode, dataFormat}, {}); - auto output = results->at(0); + auto output = results.at(0); // output->printBuffer(); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete results; } ////////////////////////////////////////////////////////////////////// @@ -496,15 +484,14 @@ TEST_F(ConvolutionTests2, deconv3d_test2) { input = 0.5; weights.linspace(0.1, 0.1); - nd4j::ops::deconv3d op; + sd::ops::deconv3d op; auto results = op.evaluate({&input, &weights}, {}, {kD,kH,kW, sD,sH,sW, pD,pH,pW, dD,dH,dW, paddingMode, dataFormat}, {}); - auto output = results->at(0); + auto output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete results; } ////////////////////////////////////////////////////////////////////// @@ -529,15 +516,14 @@ TEST_F(ConvolutionTests2, deconv3d_test3) { weights.linspace(0.1, 0.1); weights.permutei({2, 3, 4, 1, 0}); - nd4j::ops::deconv3d op; + sd::ops::deconv3d op; auto results = op.evaluate({&input, &weights}, {}, {kD,kH,kW, sD,sH,sW, pD,pH,pW, dD,dH,dW, paddingMode, dataFormat}, {}); - auto output = results->at(0); + auto output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete results; } ////////////////////////////////////////////////////////////////////// @@ -556,15 +542,14 @@ TEST_F(ConvolutionTests2, deconv3d_test4) { weights.linspace(0.1, 0.1); weights.permutei({2, 3, 4, 1, 0}); - nd4j::ops::deconv3d op; + sd::ops::deconv3d op; auto results = op.evaluate({&input, &weights}, {}, {kD,kH,kW, sD,sH,sW, pD,pH,pW, dD,dH,dW, paddingMode, dataFormat}, {}); - auto output = results->at(0); + auto output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete results; } ////////////////////////////////////////////////////////////////////// @@ -599,16 +584,15 @@ TEST_F(ConvolutionTests2, deconv3d_test5) { weights.linspace(0.1, 0.1); bias = 0.2; - nd4j::ops::deconv3d op; + sd::ops::deconv3d op; auto results = op.evaluate({&input, &weights}, {}, {kD,kH,kW, sD,sH,sW, pD,pH,pW, dD,dH,dW, paddingMode, dataFormat}); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); - auto output = results->at(0); + auto output = results.at(0); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete results; } ////////////////////////////////////////////////////////////////////// @@ -624,22 +608,22 @@ TEST_F(ConvolutionTests2, deconv3d_bp_test1) { auto bias = NDArrayFactory::create('c', {iC}); auto gradO = NDArrayFactory::create('c', {bS, iD, iH, iW, iC}); - NDArray expGradI('c', {bS, oD, oH, oW, oC}, {62., 67.6, 68.4, 74.8, 81.2, 89.2, 87.6, 96.4, 119.6, 132.4, 126., 139.6, 138.8, 154., 145.2, 161.2}, nd4j::DataType::FLOAT32); - NDArray expGradW('c', {kD, kH, kW, iC, oC}, {28., 28., 32., 32., 40., 40., 44., 44., 64, 64., 68., 68., 76., 76., 80., 80.}, nd4j::DataType::FLOAT32); - NDArray expGradB('c', {iC}, std::vector{364.5}, nd4j::DataType::FLOAT32); + NDArray expGradI('c', {bS, oD, oH, oW, oC}, {62., 67.6, 68.4, 74.8, 81.2, 89.2, 87.6, 96.4, 119.6, 132.4, 126., 139.6, 138.8, 154., 145.2, 161.2}, sd::DataType::FLOAT32); + NDArray expGradW('c', {kD, kH, kW, iC, oC}, {28., 28., 32., 32., 40., 40., 44., 44., 64, 64., 68., 68., 76., 76., 80., 80.}, sd::DataType::FLOAT32); + NDArray expGradB('c', {iC}, std::vector{364.5}, sd::DataType::FLOAT32); input = 0.5; weights.linspace(0.1, 0.1); gradO.linspace(0.5); - nd4j::ops::deconv3d_bp op; + sd::ops::deconv3d_bp op; auto results = op.evaluate({&input, &weights, &bias, &gradO}, {}, {kD,kH,kW, sD,sH,sW, pD,pH,pW, dD,dH,dW, paddingMode, dataFormat}, {}); - auto gradI = results->at(0); - auto gradW = results->at(1); - auto gradB = results->at(2); + auto gradI = results.at(0); + auto gradW = results.at(1); + auto gradB = results.at(2); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expGradI.isSameShape(gradI)); ASSERT_TRUE(expGradI.equalsTo(gradI)); @@ -650,8 +634,7 @@ TEST_F(ConvolutionTests2, deconv3d_bp_test1) { ASSERT_TRUE(expGradB.isSameShape(gradB)); ASSERT_TRUE(expGradB.equalsTo(gradB)); - delete results; -} + } ////////////////////////////////////////////////////////////////////// TEST_F(ConvolutionTests2, deconv3d_bp_test2) { @@ -665,20 +648,20 @@ TEST_F(ConvolutionTests2, deconv3d_bp_test2) { auto weights = NDArrayFactory::create('c', {kD, kH, kW, iC, oC}); auto gradO = NDArrayFactory::create('c', {bS, iD, iH, iW, iC}); - NDArray expGradI('c', {bS, oD, oH, oW, oC}, {34, 37.2, 16.6, 18.4, 15.4, 17.4, 7.1, 8.2, 10.6, 13., 4.3, 5.6, 2.9, 4.3, 0.75, 1.5}, nd4j::DataType::FLOAT32); - NDArray expGradW('c', {kD, kH, kW, iC, oC}, {16, 16, 9, 9, 10, 10, 5.5, 5.5, 12, 12, 6.5, 6.5, 7, 7, 3.75, 3.75}, nd4j::DataType::FLOAT32); + NDArray expGradI('c', {bS, oD, oH, oW, oC}, {34, 37.2, 16.6, 18.4, 15.4, 17.4, 7.1, 8.2, 10.6, 13., 4.3, 5.6, 2.9, 4.3, 0.75, 1.5}, sd::DataType::FLOAT32); + NDArray expGradW('c', {kD, kH, kW, iC, oC}, {16, 16, 9, 9, 10, 10, 5.5, 5.5, 12, 12, 6.5, 6.5, 7, 7, 3.75, 3.75}, sd::DataType::FLOAT32); input = 0.5; weights.linspace(0.1, 0.1); gradO.linspace(0.5); - nd4j::ops::deconv3d_bp op; + sd::ops::deconv3d_bp op; auto results = op.evaluate({&input, &weights, &gradO}, {}, {kD,kH,kW, sD,sH,sW, pD,pH,pW, dD,dH,dW, paddingMode, dataFormat}, {}); - auto gradI = results->at(0); - auto gradW = results->at(1); + auto gradI = results.at(0); + auto gradW = results.at(1); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expGradI.isSameShape(gradI)); ASSERT_TRUE(expGradI.equalsTo(gradI)); @@ -686,7 +669,6 @@ TEST_F(ConvolutionTests2, deconv3d_bp_test2) { ASSERT_TRUE(expGradW.isSameShape(gradW)); ASSERT_TRUE(expGradW.equalsTo(gradW)); - delete results; } ////////////////////////////////////////////////////////////////////// @@ -701,19 +683,19 @@ TEST_F(ConvolutionTests2, deconv3d_bp_test3) { auto weights = NDArrayFactory::create('c', {kD, kH, kW, iC, oC}, {0.1f, 0.9f, 0.2f, 0.1f, 0.3f, 1.1f, 0.4f, 1.2f, 0.5f, 1.3f, 0.6f, 1.4f, 0.7f, 1.5f, 0.8f, 1.6f}); auto gradO = NDArrayFactory::create('c', {bS, iC, iD, iH, iW}); - NDArray expGradI('c', {bS, oD, oH, oW, oC}, {33.8, 37.4, 44.6, 48.2, 66.2, 69.8, 77., 80.6, 77.25, 86.35, 104.55, 113.65, 159.15, 168.25, 186.45, 195.55}, nd4j::DataType::FLOAT32); - NDArray expGradW('c', {kD, kH, kW, iC, oC}, {28., 28, 32, 32, 40, 40, 44, 44, 64, 64, 68, 68, 76, 76, 80, 80.}, nd4j::DataType::FLOAT32); + NDArray expGradI('c', {bS, oD, oH, oW, oC}, {33.8, 37.4, 44.6, 48.2, 66.2, 69.8, 77., 80.6, 77.25, 86.35, 104.55, 113.65, 159.15, 168.25, 186.45, 195.55}, sd::DataType::FLOAT32); + NDArray expGradW('c', {kD, kH, kW, iC, oC}, {28., 28, 32, 32, 40, 40, 44, 44, 64, 64, 68, 68, 76, 76, 80, 80.}, sd::DataType::FLOAT32); input = 0.5; gradO.linspace(0.5); - nd4j::ops::deconv3d_bp op; + sd::ops::deconv3d_bp op; auto results = op.evaluate({&input, &weights, &gradO}, {}, {kD,kH,kW, sD,sH,sW, pD,pH,pW, dD,dH,dW, paddingMode, dataFormat}, {}); - auto gradI = results->at(0); - auto gradW = results->at(1); + auto gradI = results.at(0); + auto gradW = results.at(1); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expGradI.isSameShape(gradI)); ASSERT_TRUE(expGradI.equalsTo(gradI)); @@ -721,7 +703,6 @@ TEST_F(ConvolutionTests2, deconv3d_bp_test3) { ASSERT_TRUE(expGradW.isSameShape(gradW)); ASSERT_TRUE(expGradW.equalsTo(gradW)); - delete results; } ////////////////////////////////////////////////////////////////////// @@ -736,27 +717,25 @@ TEST_F(ConvolutionTests2, deconv3d_bp_test4) { auto weights = NDArrayFactory::create('c', {kD, kH, kW, iC, oC}, {0.1f, 0.9f, 0.2f, 0.1f, 0.3f, 1.1f, 0.4f, 1.2f, 0.5f, 1.3f, 0.6f, 1.4f, 0.7f, 1.5f, 0.8f, 1.6f}); auto gradO = NDArrayFactory::create('c', {bS, iC, iD, iH, iW}); - NDArray expGradI('c', {bS, oC, oD, oH, oW}, {0.4, 1.55, 1.05, 2.3, 5.7, 3.2, 1.5, 3.35, 1.75, 3.8, 8.3, 4.3, 9.0, 18.6, 9.2, 4.4, 8.7, 4.1, 1.8, 3.55, 1.65, 3.5, 6.5, 2.8, 1.3, 2.15, 0.75, 0.8, 3.15, 2.25, 4.7, 12.1, 7.2, 3.5, 8.15, 4.55, 7.8, 17.9, 9.9, 19.75, 42.85, 23.6, 9.35, 21.55, 12.9, 5.4, 11.55, 6.05, 8.25, 20.75, 13.2, 0.65, 6.6, 6.75}, nd4j::DataType::FLOAT32); - NDArray expGradW('c', {kD, kH, kW, iC, oC}, {16.0, 16.0, 16.0, 16.0, 16.0, 16.0, 16.0, 16.0, 16.0, 16.0, 16.0, 16.0, 16.0, 16.0, 16.0, 16.}, nd4j::DataType::FLOAT32); + NDArray expGradI('c', {bS, oC, oD, oH, oW}, {0.4, 1.55, 1.05, 2.3, 5.7, 3.2, 1.5, 3.35, 1.75, 3.8, 8.3, 4.3, 9.0, 18.6, 9.2, 4.4, 8.7, 4.1, 1.8, 3.55, 1.65, 3.5, 6.5, 2.8, 1.3, 2.15, 0.75, 0.8, 3.15, 2.25, 4.7, 12.1, 7.2, 3.5, 8.15, 4.55, 7.8, 17.9, 9.9, 19.75, 42.85, 23.6, 9.35, 21.55, 12.9, 5.4, 11.55, 6.05, 8.25, 20.75, 13.2, 0.65, 6.6, 6.75}, sd::DataType::FLOAT32); + NDArray expGradW('c', {kD, kH, kW, iC, oC}, {16.0, 16.0, 16.0, 16.0, 16.0, 16.0, 16.0, 16.0, 16.0, 16.0, 16.0, 16.0, 16.0, 16.0, 16.0, 16.}, sd::DataType::FLOAT32); input = 0.5; gradO.linspace(0.5); - nd4j::ops::deconv3d_bp op; + sd::ops::deconv3d_bp op; auto results = op.evaluate({&input, &weights, &gradO}, {}, {kD,kH,kW, sD,sH,sW, pD,pH,pW, dD,dH,dW, paddingMode, dataFormat}, {}); - auto gradI = results->at(0); - auto gradW = results->at(1); + auto gradI = results.at(0); + auto gradW = results.at(1); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expGradI.isSameShape(gradI)); ASSERT_TRUE(expGradI.equalsTo(gradI)); ASSERT_TRUE(expGradW.isSameShape(gradW)); ASSERT_TRUE(expGradW.equalsTo(gradW)); - - delete results; } ////////////////////////////////////////////////////////////////////// @@ -775,12 +754,12 @@ TEST_F(ConvolutionTests2, maxpool2d_1) { std::vector* argI = block->getIArguments(); *argI = {kH,kW, sH,sW, pH,pW, dH,dW, 0}; // 0,1 - kernel Height/Width; 2,3 - stride Height/Width; 4,5 - pad Height/Width; 6,7 - dilation Height/Width; 8 - same mode; - nd4j::ops::maxpool2d pooling; + sd::ops::maxpool2d pooling; Nd4jStatus status = pooling.execute(block); ASSERT_EQ(ND4J_STATUS_OK, status); auto result = variableSpace->getVariable(block->getNodeId())->getNDArray(); - // result->printShapeInfo(); + // result.printShapeInfo(); ASSERT_TRUE(exp.isSameShape(result)); delete variableSpace; @@ -819,12 +798,12 @@ TEST_F(ConvolutionTests2, maxpool2d_2) { std::vector* argI = block->getIArguments(); *argI = {kH,kW, sH,sW, pH,pW, dH,dW, 0}; // 0,1 - kernel Height/Width; 2,3 - stride Height/Width; 4,5 - pad Height/Width; 6,7 - dilation Height/Width; 8 - same mode; - nd4j::ops::maxpool2d pooling; + sd::ops::maxpool2d pooling; Nd4jStatus status = pooling.execute(block); ASSERT_EQ(ND4J_STATUS_OK, status); auto result = variableSpace->getVariable(block->getNodeId())->getNDArray(); - // result->printShapeInfo(); + // result.printShapeInfo(); ASSERT_TRUE(exp.isSameShape(result)); delete variableSpace; @@ -846,8 +825,8 @@ TEST_F(ConvolutionTests2, maxpool2d_3) { const int pW = 0; const int dH = 1; const int dW = 1; - const int oH = (int) nd4j::math::nd4j_ceil(iH * 1.f / sH); - const int oW = (int) nd4j::math::nd4j_ceil(iW * 1.f / sW); + const int oH = (int) sd::math::nd4j_ceil(iH * 1.f / sH); + const int oW = (int) sd::math::nd4j_ceil(iW * 1.f / sW); auto x = NDArrayFactory::create_('c', {bS,iD,iH,iW}); @@ -863,12 +842,12 @@ TEST_F(ConvolutionTests2, maxpool2d_3) { std::vector* argI = block->getIArguments(); *argI = {kH,kW, sH,sW, pH,pW, dH,dW, 1}; // 0,1 - kernel Height/Width; 2,3 - stride Height/Width; 4,5 - pad Height/Width; 6,7 - dilation Height/Width; 8 - same mode; - nd4j::ops::maxpool2d pooling; + sd::ops::maxpool2d pooling; Nd4jStatus status = pooling.execute(block); ASSERT_EQ(ND4J_STATUS_OK, status); auto result = variableSpace->getVariable(block->getNodeId())->getNDArray(); - // result->printShapeInfo(); + // result.printShapeInfo(); ASSERT_TRUE(exp.isSameShape(result)); delete variableSpace; @@ -907,12 +886,12 @@ TEST_F(ConvolutionTests2, maxpool2d_4) { std::vector* argI = block->getIArguments(); *argI = {kH,kW, sH,sW, pH,pW, dH,dW, 0}; // 0,1 - kernel Height/Width; 2,3 - stride Height/Width; 4,5 - pad Height/Width; 6,7 - dilation Height/Width; 8 - same mode; - nd4j::ops::maxpool2d pooling; + sd::ops::maxpool2d pooling; Nd4jStatus status = pooling.execute(block); ASSERT_EQ(ND4J_STATUS_OK, status); auto result = variableSpace->getVariable(block->getNodeId())->getNDArray(); - // result->printShapeInfo(); + // result.printShapeInfo(); ASSERT_TRUE(exp.isSameShape(result)); delete variableSpace; @@ -934,8 +913,8 @@ TEST_F(ConvolutionTests2, maxpool2d_5) { const int pW = 0; const int dH = 1; const int dW = 1; - const int oH = (int) nd4j::math::nd4j_ceil(iH * 1.f / sH); - const int oW = (int) nd4j::math::nd4j_ceil(iW * 1.f / sW); + const int oH = (int) sd::math::nd4j_ceil(iH * 1.f / sH); + const int oW = (int) sd::math::nd4j_ceil(iW * 1.f / sW); auto x = NDArrayFactory::create_('c', {bS,iD,iH,iW}); @@ -951,12 +930,12 @@ TEST_F(ConvolutionTests2, maxpool2d_5) { std::vector* argI = block->getIArguments(); *argI = {kH,kW, sH,sW, pH,pW, dH,dW, 1}; // 0,1 - kernel Height/Width; 2,3 - stride Height/Width; 4,5 - pad Height/Width; 6,7 - dilation Height/Width; 8 - same mode; - nd4j::ops::maxpool2d pooling; + sd::ops::maxpool2d pooling; Nd4jStatus status = pooling.execute(block); ASSERT_EQ(ND4J_STATUS_OK, status); auto result = variableSpace->getVariable(block->getNodeId())->getNDArray(); - // result->printShapeInfo(); + // result.printShapeInfo(); ASSERT_TRUE(exp.isSameShape(result)); delete variableSpace; @@ -970,17 +949,16 @@ TYPED_TEST(TypedConvolutionTests2, maxpool2d_6) { x.linspace(1); - nd4j::ops::maxpool2d op; + sd::ops::maxpool2d op; auto result = op.evaluate({&x}, {}, {2, 2, 2, 2, 0, 0, 1, 1, 1, 1, 1}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; } ////////////////////////////////////////////////////////////////////// @@ -990,17 +968,16 @@ TYPED_TEST(TypedConvolutionTests2, maxpool2d_7) { x.linspace(1); - nd4j::ops::maxpool2d op; + sd::ops::maxpool2d op; auto result = op.evaluate({&x}, {}, {2, 2, 2, 2, 0, 0, 1, 1, 0, 1, 1}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; } ////////////////////////////////////////////////////////////////////// @@ -1010,17 +987,15 @@ TYPED_TEST(TypedConvolutionTests2, maxpool2d_8) { x.linspace(1); - nd4j::ops::maxpool2d op; + sd::ops::maxpool2d op; auto result = op.evaluate({&x}, {}, {2, 2, 2, 2, 0, 0, 1, 1, 0, 1, 0}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - - delete result; } ////////////////////////////////////////////////////////////////////// @@ -1040,14 +1015,13 @@ TYPED_TEST(TypedConvolutionTests2, maxpool2d_9) { auto input = NDArrayFactory::create('c', {bS, iC, iH, iW}); - nd4j::ops::maxpool2d op; + sd::ops::maxpool2d op; auto results = op.evaluate({&input}, {}, {kH,kW, sH,sW, pH,pW, dH,dW, isSameMode, 1, 0}); - auto output = results->at(0); + auto output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(output->isSameShape({bS, iC, oH, oW})); - delete results; } ////////////////////////////////////////////////////////////////////// @@ -1067,32 +1041,29 @@ TYPED_TEST(TypedConvolutionTests2, maxpool2d_10) { 0.7135856f, 0.9613717f, 0.9613717f, 0.78289545f, 0.9613717f, 0.9613717f, 0.78289545f, 0.7997134f, 0.8536445f, 0.8536445f, 0.7997134f, 0.85019743f, 0.85019743f, 0.85722464f, 0.85722464f, 0.85019743f}); - nd4j::ops::maxpool2d op; + sd::ops::maxpool2d op; auto results = op.evaluate({&input}, {}, {kH,kW, sH,sW, pH,pW, dH,dW, paddingMode}); - auto* output = results->at(0); + auto* output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expOutput.isSameShape(output)); ASSERT_TRUE(expOutput.equalsTo(output)); - - delete results; } ////////////////////////////////////////////////////////////////////// TEST_F(ConvolutionTests2, maxpool2d_11) { - NDArray input('c', {1,1,4,5}, nd4j::DataType::FLOAT32); - NDArray z('c', {1,1,4,5}, nd4j::DataType::FLOAT32); + NDArray input('c', {1,1,4,5}, sd::DataType::FLOAT32); + NDArray z('c', {1,1,4,5}, sd::DataType::FLOAT32); input.linspace(1.); - nd4j::ops::maxpool2d op; + sd::ops::maxpool2d op; auto results = op.evaluate({&input}, {}, {2,2, 1,1, 1,1, 2,2, 1,0,0}); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); - delete results; } ////////////////////////////////////////////////////////////////////// @@ -1109,15 +1080,14 @@ TYPED_TEST(TypedConvolutionTests2, avgpool3d_test1) { 154.5f,155.5f,157.5f,158.5f,166.5f,167.5f,169.5f,170.5f,190.5f,191.5f,193.5f,194.5f,202.5f,203.5f,205.5f,206.5f}); input.linspace(1.); - nd4j::ops::avgpool3dnew op; + sd::ops::avgpool3dnew op; auto results = op.evaluate({&input}, {}, {kD,kH,kW, sD,sH,sW, pD,pH,pW, dD,dH,dW, paddingMode, 1, dataFormat}); - auto output = results->at(0); + auto output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; } ////////////////////////////////////////////////////////////////////// @@ -1137,15 +1107,14 @@ TYPED_TEST(TypedConvolutionTests2, avgpool3d_test2) { 187.f, 188.f, 189.f, 190.f, 191.f, 192.f, 191.5f, 192.5f, 193.5f, 191.5f, 192.5f, 193.5f, 194.5f, 195.5f, 196.5f, 196.f, 197.f, 198.f, 200.5f, 201.5f, 202.5f, 203.5f, 204.5f, 205.5f, 205.f, 206.f, 207.f, 205.f, 206.f, 207.f, 208.f, 209.f, 210.f, 209.5f, 210.5f, 211.5f}); input.linspace(1.); - nd4j::ops::avgpool3dnew op; + sd::ops::avgpool3dnew op; auto results = op.evaluate({&input}, {}, {kD,kH,kW, sD,sH,sW, pD,pH,pW, dD,dH,dW, paddingMode, 0, dataFormat}); - auto output = results->at(0); + auto output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; } ////////////////////////////////////////////////////////////////////// @@ -1162,15 +1131,14 @@ TYPED_TEST(TypedConvolutionTests2, avgpool3d_test3) { 173.5f, 174.5f, 175.5f, 176.5f, 177.5f, 178.5f, 182.5f, 183.5f, 184.5f, 185.5f, 186.5f, 187.5f}); input.linspace(1.); - nd4j::ops::avgpool3dnew op; + sd::ops::avgpool3dnew op; auto results = op.evaluate({&input}, {}, {kD,kH,kW, sD,sH,sW, pD,pH,pW, dD,dH,dW, paddingMode, 1, dataFormat}); - auto output = results->at(0); + auto output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; } ////////////////////////////////////////////////////////////////////// @@ -1202,15 +1170,14 @@ TYPED_TEST(TypedConvolutionTests2, avgpool3d_test4) { 68.833336f, 138.00f, 138.666672f, 69.50f, 34.416668f, 69.00f, 69.333336f, 34.75f, 52.00f, 104.25f, 104.75f, 52.50f, 52.75f, 105.75f, 106.25f, 53.25f, 35.416668f, 71.00f, 71.333336f, 35.75f}); input.linspace(1.); - nd4j::ops::avgpool3dnew op; + sd::ops::avgpool3dnew op; auto results = op.evaluate({&input}, {}, {kD,kH,kW, sD,sH,sW, pD,pH,pW, dD,dH,dW, paddingMode, 1, dataFormat}); - auto output = results->at(0); + auto output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; } ////////////////////////////////////////////////////////////////////// @@ -1226,15 +1193,13 @@ TYPED_TEST(TypedConvolutionTests2, maxpool3d_test1) { 128.f, 129.f, 131.f, 132.f, 140.f, 141.f, 143.f, 144.f, 164.f, 165.f, 167.f, 168.f, 176.f, 177.f, 179.f, 180.f, 200.f, 201.f, 203.f, 204.f, 212.f, 213.f, 215.f, 216.f}); input.linspace(1.); - nd4j::ops::maxpool3dnew op; + sd::ops::maxpool3dnew op; auto results = op.evaluate({&input}, {}, {kD,kH,kW, sD,sH,sW, pD,pH,pW, dD,dH,dW, paddingMode, 1, dataFormat}); - auto output = results->at(0); + auto output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - - delete results; } ////////////////////////////////////////////////////////////////////// @@ -1254,15 +1219,13 @@ TYPED_TEST(TypedConvolutionTests2, maxpool3d_test2) { 193.f, 194.f, 195.f, 196.f, 197.f, 198.f, 196.f, 197.f, 198.f, 202.f, 203.f, 204.f, 205.f, 206.f, 207.f, 205.f, 206.f, 207.f, 211.f, 212.f, 213.f, 214.f, 215.f, 216.f, 214.f, 215.f, 216.f, 211.f, 212.f, 213.f, 214.f, 215.f, 216.f, 214.f, 215.f, 216.f}); input.linspace(1.); - nd4j::ops::maxpool3dnew op; + sd::ops::maxpool3dnew op; auto results = op.evaluate({&input}, {}, {kD,kH,kW, sD,sH,sW, pD,pH,pW, dD,dH,dW, paddingMode, 1, dataFormat}); - auto output = results->at(0); + auto output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - - delete results; } ////////////////////////////////////////////////////////////////////// @@ -1278,15 +1241,14 @@ TYPED_TEST(TypedConvolutionTests2, maxpool3d_test3) { 166.f, 167.f, 168.f, 169.f, 170.f, 171.f, 175.f, 176.f, 177.f, 178.f, 179.f, 180.f, 202.f, 203.f, 204.f, 205.f, 206.f, 207.f, 211.f, 212.f, 213.f, 214.f, 215.f, 216.f}); input.linspace(1.); - nd4j::ops::maxpool3dnew op; + sd::ops::maxpool3dnew op; auto results = op.evaluate({&input}, {}, {kD,kH,kW, sD,sH,sW, pD,pH,pW, dD,dH,dW, paddingMode, 1, dataFormat}); - auto output = results->at(0); + auto output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; } ////////////////////////////////////////////////////////////////////// @@ -1308,15 +1270,13 @@ TYPED_TEST(TypedConvolutionTests2, maxpool3d_test4) { 196.f, 197.f, 198.f, 198.f, 199.f, 200.f, 201.f, 201.f, 202.f, 203.f, 204.f, 204.f, 202.f, 203.f, 204.f, 204.f, 208.f, 209.f, 210.f, 210.f, 211.f, 212.f, 213.f, 213.f, 214.f, 215.f, 216.f, 216.f, 214.f, 215.f, 216.f, 216.f, 208.f, 209.f, 210.f, 210.f, 211.f, 212.f, 213.f, 213.f, 214.f, 215.f, 216.f, 216.f, 214.f, 215.f, 216.f, 216.f}); input.linspace(1.); - nd4j::ops::maxpool3dnew op; + sd::ops::maxpool3dnew op; auto results = op.evaluate({&input}, {}, {kD,kH,kW, sD,sH,sW, pD,pH,pW, dD,dH,dW, paddingMode, 1, dataFormat}); - auto output = results->at(0); + auto output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - - delete results; } ////////////////////////////////////////////////////////////////////// @@ -1341,15 +1301,14 @@ TYPED_TEST(TypedConvolutionTests2, avgpool3d_bp_test1) { input.linspace(1.); gradO = 2.; - nd4j::ops::avgpool3dnew_bp op; + sd::ops::avgpool3dnew_bp op; auto results = op.evaluate({&input, &gradO}, {}, {kD,kH,kW, sD,sH,sW, pD,pH,pW, dD,dH,dW, paddingMode, 1, dataFormat}); - auto output = results->at(0); + auto output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; } @@ -1375,17 +1334,16 @@ TYPED_TEST(TypedConvolutionTests2, avgpool3d_bp_test2) { input.linspace(1.); gradO = 2.; - nd4j::ops::avgpool3dnew_bp op; + sd::ops::avgpool3dnew_bp op; auto results = op.evaluate({&input, &gradO}, {}, {kD,kH,kW, sD,sH,sW, pD,pH,pW, dD,dH,dW, paddingMode, 1, dataFormat}); - auto output = results->at(0); + auto output = results.at(0); // output->printBuffer(); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; } @@ -1410,15 +1368,13 @@ TYPED_TEST(TypedConvolutionTests2, avgpool3d_bp_test3) { input.linspace(1.); gradO = 2.; - nd4j::ops::avgpool3dnew_bp op; + sd::ops::avgpool3dnew_bp op; auto results = op.evaluate({&input, &gradO}, {}, {kD,kH,kW, sD,sH,sW, pD,pH,pW, dD,dH,dW, paddingMode, 0, dataFormat}); - auto output = results->at(0); + auto output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - - delete results; } ////////////////////////////////////////////////////////////////////// @@ -1442,15 +1398,14 @@ TYPED_TEST(TypedConvolutionTests2, avgpool3d_bp_test4) { input.linspace(1.); gradO = 2.; - nd4j::ops::avgpool3dnew_bp op; + sd::ops::avgpool3dnew_bp op; auto results = op.evaluate({&input, &gradO}, {}, {kD,kH,kW, sD,sH,sW, pD,pH,pW, dD,dH,dW, paddingMode, 0, dataFormat}); - auto output = results->at(0); + auto output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; } ////////////////////////////////////////////////////////////////////// @@ -1473,15 +1428,13 @@ TYPED_TEST(TypedConvolutionTests2, maxpool3d_bp_test1) { input.linspace(1.); gradO.linspace(0.1, 0.1); - nd4j::ops::maxpool3dnew_bp op; + sd::ops::maxpool3dnew_bp op; auto results = op.evaluate({&input, &gradO}, {}, {kD,kH,kW, sD,sH,sW, pD,pH,pW, dD,dH,dW, paddingMode, 1, dataFormat}); - auto output = results->at(0); + auto output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - - delete results; } ////////////////////////////////////////////////////////////////////// @@ -1506,15 +1459,13 @@ TYPED_TEST(TypedConvolutionTests2, maxpool3d_bp_test2) { input.linspace(1.); gradO.linspace(0.1, 0.1); - nd4j::ops::maxpool3dnew_bp op; + sd::ops::maxpool3dnew_bp op; auto results = op.evaluate({&input, &gradO}, {}, {kD,kH,kW, sD,sH,sW, pD,pH,pW, dD,dH,dW, paddingMode, 1, dataFormat}); - auto output = results->at(0); + auto output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - - delete results; } ////////////////////////////////////////////////////////////////////// @@ -1538,15 +1489,14 @@ TYPED_TEST(TypedConvolutionTests2, maxpool3d_bp_test3) { input.linspace(1.); gradO.linspace(0.1, 0.1); - nd4j::ops::maxpool3dnew_bp op; + sd::ops::maxpool3dnew_bp op; auto results = op.evaluate({&input, &gradO}, {}, {kD,kH,kW, sD,sH,sW, pD,pH,pW, dD,dH,dW, paddingMode, 1, dataFormat}); - auto output = results->at(0); + auto output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; } @@ -1570,15 +1520,14 @@ TYPED_TEST(TypedConvolutionTests2, maxpool3d_bp_test4) { input.linspace(1.); gradO.linspace(0.1, 0.1); - nd4j::ops::maxpool3dnew_bp op; + sd::ops::maxpool3dnew_bp op; auto results = op.evaluate({&input, &gradO}, {}, {kD,kH,kW, sD,sH,sW, pD,pH,pW, dD,dH,dW, paddingMode, 1, dataFormat}); - auto output = results->at(0); + auto output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; } ////////////////////////////////////////////////////////////////////// @@ -1599,7 +1548,7 @@ TEST_F(ConvolutionTests2, maxpool2d_bp_1) { std::vector* argI = block->getIArguments(); *argI = {kH,kW, sH,sW, pH,pW, dW,dH, 0, 0, 0}; // 0,1 - kernel Height/Width; 2,3 - stride Height/Width; 4,5 - pad Height/Width; 6,7 - dilation Height/Width; 8 - same mode; - nd4j::ops::maxpool2d_bp bp; + sd::ops::maxpool2d_bp bp; Nd4jStatus status = bp.execute(block); ASSERT_EQ(ND4J_STATUS_OK, status); @@ -1629,14 +1578,13 @@ TEST_F(ConvolutionTests2, maxpool2d_bp_2) { std::initializer_list argI = {kH,kW, sH,sW, pH,pW, dW,dH, 0, 0, 0}; // 0,1 - kernel Height/Width; 2,3 - stride Height/Width; 4,5 - pad Height/Width; 6,7 - dilation Height/Width; 8 - same mode; - nd4j::ops::maxpool2d_bp op; + sd::ops::maxpool2d_bp op; auto results = op.evaluate({&input, &epsilon}, {}, argI); - auto output = results->at(0); + auto output = results.at(0); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; } ////////////////////////////////////////////////////////////////////// @@ -1655,15 +1603,13 @@ TYPED_TEST(TypedConvolutionTests2, maxpool2d_bp_3) { input.linspace(1.); gradO.linspace(0.1, 0.1); - nd4j::ops::maxpool2d_bp op; + sd::ops::maxpool2d_bp op; auto results = op.evaluate({&input, &gradO}, {}, {kH,kW, sH,sW, pH,pW, dH,dW, paddingMode, 1, dataFormat}); - auto output = results->at(0); + auto output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - - delete results; } ////////////////////////////////////////////////////////////////////// @@ -1682,15 +1628,14 @@ TYPED_TEST(TypedConvolutionTests2, maxpool2d_bp_4) { input.linspace(1.); gradO.linspace(0.1, 0.1); - nd4j::ops::maxpool2d_bp op; + sd::ops::maxpool2d_bp op; auto results = op.evaluate({&input, &gradO}, {}, {kH,kW, sH,sW, pH,pW, dH,dW, paddingMode, 1, dataFormat}); - auto output = results->at(0); + auto output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; } ////////////////////////////////////////////////////////////////////// @@ -1709,15 +1654,14 @@ TYPED_TEST(TypedConvolutionTests2, maxpool2d_bp_5) { input.linspace(1.); gradO.linspace(0.1, 0.1); - nd4j::ops::maxpool2d_bp op; + sd::ops::maxpool2d_bp op; auto results = op.evaluate({&input, &gradO}, {}, {kH,kW, sH,sW, pH,pW, dH,dW, paddingMode, 1, dataFormat}); - auto output = results->at(0); + auto output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; } ////////////////////////////////////////////////////////////////////// @@ -1736,15 +1680,14 @@ TYPED_TEST(TypedConvolutionTests2, maxpool2d_bp_6) { input.linspace(1.); gradO.linspace(0.1, 0.1); - nd4j::ops::maxpool2d_bp op; + sd::ops::maxpool2d_bp op; auto results = op.evaluate({&input, &gradO}, {}, {kH,kW, sH,sW, pH,pW, dH,dW, paddingMode, 1, dataFormat}); - auto output = results->at(0); + auto output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; } ////////////////////////////////////////////////////////////////////// @@ -1761,15 +1704,14 @@ TEST_F(ConvolutionTests2, maxpool2d_bp_7) { input.linspace(1.); gradO.linspace(0.1, 0.1); - nd4j::ops::maxpool2d_bp op; + sd::ops::maxpool2d_bp op; auto results = op.evaluate({&input, &gradO}, {}, {kH,kW, sH,sW, pH,pW, dH,dW, paddingMode, 1, dataFormat}); - // auto output = results->at(0); + // auto output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); // ASSERT_TRUE(expected.isSameShape(output)); // ASSERT_TRUE(expected.equalsTo(output)); - delete results; } ////////////////////////////////////////////////////////////////////// @@ -1790,7 +1732,7 @@ TEST_F(ConvolutionTests2, avgpool2d_bp_1) { std::vector* argI = block->getIArguments(); *argI = {kH,kW, sH,sW, pH,pW, dW,dH, 0, 1, 0}; // 0,1 - kernel Height/Width; 2,3 - stride Height/Width; 4,5 - pad Height/Width; 6,7 - dilation Height/Width; 8 - same mode, 9 - extraParam0 (unnecessary for avg mode), 10 - data format - nd4j::ops::avgpool2d_bp bp; + sd::ops::avgpool2d_bp bp; Nd4jStatus status = bp.execute(block); ASSERT_EQ(ND4J_STATUS_OK, status); @@ -1819,14 +1761,13 @@ TYPED_TEST(TypedConvolutionTests2, avgpool2d_bp_2) { std::initializer_list argI = {kH,kW, sH,sW, pH,pW, dW,dH, 1, 1, 0}; - nd4j::ops::avgpool2d_bp op; + sd::ops::avgpool2d_bp op; auto results = op.evaluate({&input, &epsilon}, {}, argI); - auto output = results->at(0); + auto output = results.at(0); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; } ////////////////////////////////////////////////////////////////////// @@ -1848,15 +1789,14 @@ TYPED_TEST(TypedConvolutionTests2, avgpool2d_bp_3) { input.linspace(1.); gradO.linspace(0.1, 0.1); - nd4j::ops::avgpool2d_bp op; + sd::ops::avgpool2d_bp op; auto results = op.evaluate({&input, &gradO}, {}, {kH,kW, sH,sW, pH,pW, dH,dW, paddingMode, 1, dataFormat}); - auto output = results->at(0); + auto output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; } @@ -1879,15 +1819,14 @@ TYPED_TEST(TypedConvolutionTests2, avgpool2d_bp_4) { input.linspace(1.); gradO.linspace(0.1, 0.1); - nd4j::ops::avgpool2d_bp op; + sd::ops::avgpool2d_bp op; auto results = op.evaluate({&input, &gradO}, {}, {kH,kW, sH,sW, pH,pW, dH,dW, paddingMode, 1, dataFormat}); - auto output = results->at(0); + auto output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; } @@ -1908,15 +1847,14 @@ TYPED_TEST(TypedConvolutionTests2, avgpool2d_bp_5) { input.linspace(1.); gradO.linspace(0.1, 0.1); - nd4j::ops::avgpool2d_bp op; + sd::ops::avgpool2d_bp op; auto results = op.evaluate({&input, &gradO}, {}, {kH,kW, sH,sW, pH,pW, dH,dW, paddingMode, 0, dataFormat}); - auto output = results->at(0); + auto output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; } @@ -1937,15 +1875,14 @@ TYPED_TEST(TypedConvolutionTests2, avgpool2d_bp_6) { input.linspace(1.); gradO.linspace(0.1, 0.1); - nd4j::ops::avgpool2d_bp op; + sd::ops::avgpool2d_bp op; auto results = op.evaluate({&input, &gradO}, {}, {kH,kW, sH,sW, pH,pW, dH,dW, paddingMode, 1, dataFormat}); - auto output = results->at(0); + auto output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; } ////////////////////////////////////////////////////////////////////// @@ -1968,7 +1905,7 @@ TEST_F(ConvolutionTests2, pnormpool2d_bp_1) { std::vector* argT = block->getTArguments(); *argT = {0.000001}; - nd4j::ops::pnormpool2d_bp bp; + sd::ops::pnormpool2d_bp bp; Nd4jStatus status = bp.execute(block); ASSERT_EQ(ND4J_STATUS_OK, status); @@ -2001,15 +1938,14 @@ TYPED_TEST(TypedConvolutionTests2, pnormpool2d_bp_2) { input.linspace(1.); gradO.linspace(0.1, 0.1); - nd4j::ops::pnormpool2d_bp op; + sd::ops::pnormpool2d_bp op; auto results = op.evaluate({&input, &gradO}, {eps}, {kH,kW, sH,sW, pH,pW, dH,dW, paddingMode, pnorm, dataFormat}); - auto output = results->at(0); + auto output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; } ////////////////////////////////////////////////////////////////////// @@ -2034,15 +1970,14 @@ TYPED_TEST(TypedConvolutionTests2, pnormpool2d_bp_3) { input.linspace(1.); gradO.linspace(0.1, 0.1); - nd4j::ops::pnormpool2d_bp op; + sd::ops::pnormpool2d_bp op; auto results = op.evaluate({&input, &gradO}, {eps}, {kH,kW, sH,sW, pH,pW, dH,dW, paddingMode, pnorm, dataFormat}); - auto output = results->at(0); + auto output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; } @@ -2060,15 +1995,14 @@ TEST_F(ConvolutionTests2, upsampling2d_bp_1) { auto expGradI = NDArrayFactory::create('c', {bS, iC, iH, iW}); expGradI = 4.; - nd4j::ops::upsampling2d_bp op; + sd::ops::upsampling2d_bp op; auto results = op.evaluate({&input, &gradO}, {}, {isNCHW}); - auto* gradI = results->at(0); + auto* gradI = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expGradI.isSameShape(gradI)); ASSERT_TRUE(expGradI.equalsTo(gradI)); - delete results; } ////////////////////////////////////////////////////////////////////// @@ -2085,15 +2019,14 @@ TEST_F(ConvolutionTests2, upsampling2d_bp_2) { auto expGradI = NDArrayFactory::create('c', {bS, iH, iW, iC}); expGradI = 4.; - nd4j::ops::upsampling2d_bp op; + sd::ops::upsampling2d_bp op; auto results = op.evaluate({&input, &gradO}, {}, {isNCHW}); - auto* gradI = results->at(0); + auto* gradI = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expGradI.isSameShape(gradI)); ASSERT_TRUE(expGradI.equalsTo(gradI)); - delete results; } ////////////////////////////////////////////////////////////////////// @@ -2103,7 +2036,7 @@ TEST_F(ConvolutionTests2, upsampling2d_bp_3) { const int factorH=2, factorW=2; const int isNCHW = 1; // data format, default is NCHW - NDArray input('c', {bS, iC, iH, iW}, nd4j::DataType::FLOAT32); + NDArray input('c', {bS, iC, iH, iW}, sd::DataType::FLOAT32); NDArray gradO('c', {bS, iC, iH*factorH, iW*factorW}, {0.6793504, 0.35508695, 0.84278935, 0.20031333, 0.7014987, 0.31069338, 0.44793984, 0.93800974, 0.32667395, 0.15187258, 0.38331753, 0.78212297, 0.1988072, 0.7985636, 0.1632634, 0.14696825, 0.26089668, 0.13505761, @@ -2111,20 +2044,19 @@ TEST_F(ConvolutionTests2, upsampling2d_bp_3) { 0.061735712, 0.39643127, 0.248016, 0.5489592, 0.115046196, 0.8143622, 0.7215636, 0.40449402, 0.29908907, 0.4038839, 0.9883108, 0.022296403, 0.927782, 0.3184157, 0.0685462, 0.28453344, 0.23272, 0.35214192, 0.058909304, 0.7112212, 0.6744568, 0.19694561, 0.6994972, 0.0743224, 0.42042503, 0.5842631, 0.14957358, 0.44640633, 0.72307247, 0.06448108, 0.48307765, 0.8759956, 0.5698191, 0.4458631, 0.5277549, - 0.016646361, 0.753678, 0.14063567, 0.7541292, 0.16193217, 0.7750374, 0.3326449, 0.11739397}, nd4j::DataType::FLOAT32); + 0.016646361, 0.753678, 0.14063567, 0.7541292, 0.16193217, 0.7750374, 0.3326449, 0.11739397}, sd::DataType::FLOAT32); NDArray expGradI('c', {bS, iC, iH, iW}, {2.4203868, 1.5216494, 2.1776323, 2.0290341, 0.772146, 1.5008594, 1.0523045, 1.3174672, 1.9263644, - 1.090545, 1.9094483, 1.3611296, 2.1195147, 2.0659215, 1.0423062, 2.3405795, 1.9105877, 1.2203633}, nd4j::DataType::FLOAT32); + 1.090545, 1.9094483, 1.3611296, 2.1195147, 2.0659215, 1.0423062, 2.3405795, 1.9105877, 1.2203633}, sd::DataType::FLOAT32); - nd4j::ops::upsampling2d_bp op; + sd::ops::upsampling2d_bp op; auto results = op.evaluate({&input, &gradO}, {}, {isNCHW}); - auto* gradI = results->at(0); + auto* gradI = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expGradI.isSameShape(gradI)); ASSERT_TRUE(expGradI.equalsTo(gradI)); - delete results; } ////////////////////////////////////////////////////////////////////// @@ -2147,16 +2079,14 @@ TYPED_TEST(TypedConvolutionTests2, depthwise_conv2d_1) { input = 2.; weights.linspace(0.1, 0.1); - nd4j::ops::depthwise_conv2d op; + sd::ops::depthwise_conv2d op; auto results = op.evaluate({&input, &weights}, {}, {kH,kW, sH,sW, pH,pW, dH,dW, paddingMode, dataFormat}); - auto* output = results->at(0); + auto* output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expOutput.isSameShape(output)); ASSERT_TRUE(expOutput.equalsTo(output)); - - delete results; } ////////////////////////////////////////////////////////////////////// @@ -2177,16 +2107,15 @@ TEST_F(ConvolutionTests2, depthwise_conv2d_2) { input = 2.; weights.linspace(0.1, 0.1); - nd4j::ops::depthwise_conv2d op; + sd::ops::depthwise_conv2d op; auto results = op.evaluate({&input, &weights}, {}, {kH,kW, sH,sW, pH,pW, dH,dW, paddingMode, dataFormat}); - auto* output = results->at(0); + auto* output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expOutput.isSameShape(output)); ASSERT_TRUE(expOutput.equalsTo(output)); - delete results; } @@ -2203,22 +2132,21 @@ TEST_F(ConvolutionTests2, depthwise_conv2d_3) { auto weights = NDArrayFactory::create('c', {mC, iC, kH, kW}); auto biases = NDArrayFactory::create('c', {iC*mC}, {1.f,2.f,3.f,4.f}); - NDArray expOutput('c', {bS, oC, oH, oW},{5.2, 5.2, 5.2, 5.2,20.6,20.6,20.6,20.6,14.4,14.4,14.4,14.4,29.8,29.8,29.8,29.8, 5.2, 5.2, 5.2, 5.2,20.6,20.6,20.6,20.6,14.4,14.4,14.4,14.4,29.8,29.8,29.8,29.8}, nd4j::DataType::FLOAT32); + NDArray expOutput('c', {bS, oC, oH, oW},{5.2, 5.2, 5.2, 5.2,20.6,20.6,20.6,20.6,14.4,14.4,14.4,14.4,29.8,29.8,29.8,29.8, 5.2, 5.2, 5.2, 5.2,20.6,20.6,20.6,20.6,14.4,14.4,14.4,14.4,29.8,29.8,29.8,29.8}, sd::DataType::FLOAT32); input = 2.; weights.linspace(0.1, 0.1); weights.permutei({2,3,1,0}); - nd4j::ops::depthwise_conv2d op; + sd::ops::depthwise_conv2d op; auto results = op.evaluate({&input, &weights, &biases}, {}, {kH,kW, sH,sW, pH,pW, dH,dW, paddingMode, dataFormat}); - auto* output = results->at(0); + auto* output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expOutput.isSameShape(output)); ASSERT_TRUE(expOutput.equalsTo(output)); - delete results; } ////////////////////////////////////////////////////////////////////// @@ -2233,14 +2161,14 @@ TEST_F(ConvolutionTests2, depthwise_conv2d_4) { const float unique = -1000000; - NDArray input('c', {bS, iH, iW, iC}, nd4j::DataType::FLOAT32); - NDArray weights('c', {kH, kW, iC, mC}, nd4j::DataType::FLOAT32); - NDArray output('c', {bS, oH, oW, oC}, nd4j::DataType::FLOAT32); + NDArray input('c', {bS, iH, iW, iC}, sd::DataType::FLOAT32); + NDArray weights('c', {kH, kW, iC, mC}, sd::DataType::FLOAT32); + NDArray output('c', {bS, oH, oW, oC}, sd::DataType::FLOAT32); input.linspace(0.1, 0.0001); weights = 0.5; output = unique; - nd4j::ops::depthwise_conv2d op; + sd::ops::depthwise_conv2d op; Nd4jStatus status = op.execute({&input, &weights}, {&output} , {}, {kH,kW, sH,sW, pH,pW, dH,dW, paddingMode, dataFormat}, {}); ASSERT_EQ(Status::OK(), status); @@ -2261,21 +2189,20 @@ TEST_F(ConvolutionTests2, depthwise_conv2d_5) { auto input = NDArrayFactory::create('c', {bS, iH, iW, iC}); auto weights = NDArrayFactory::create('c', {kH, kW, iC, mC}); - NDArray expOutput('c', {bS, oH, oW, oC}, {10., 12., 14., 16., 8., 9., 22., 24., 26., 28., 14., 15., 14., 15., 16., 17., 8.5, 9.}, nd4j::DataType::FLOAT32); + NDArray expOutput('c', {bS, oH, oW, oC}, {10., 12., 14., 16., 8., 9., 22., 24., 26., 28., 14., 15., 14., 15., 16., 17., 8.5, 9.}, sd::DataType::FLOAT32); input.linspace(1.); weights = 0.5; - nd4j::ops::depthwise_conv2d op; + sd::ops::depthwise_conv2d op; auto results = op.evaluate({&input, &weights}, {}, {kH,kW, sH,sW, pH,pW, dH,dW, paddingMode, dataFormat}); - auto output = results->at(0); + auto output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expOutput.isSameShape(output)); ASSERT_TRUE(expOutput.equalsTo(output)); - delete results; } ////////////////////////////////////////////////////////////////////// @@ -2287,24 +2214,23 @@ TEST_F(ConvolutionTests2, depthwise_conv2d_6) { int paddingMode = 1; // 1-SAME, 0-VALID; int dataFormat = 1; // 1-NHWC, 0-NCHW - NDArray input('c', {bS, iH, iW, iC}, nd4j::DataType::FLOAT32); - NDArray weights('c', {kH, kW, iC, mC}, nd4j::DataType::FLOAT32); + NDArray input('c', {bS, iH, iW, iC}, sd::DataType::FLOAT32); + NDArray weights('c', {kH, kW, iC, mC}, sd::DataType::FLOAT32); - NDArray expOutput('c', {bS, oH, oW, oC}, {20., 24.,28., 32.,16., 18.,44., 48.,52., 56.,28., 30.,28., 30.,32., 34.,17., 18.}, nd4j::DataType::FLOAT32); + NDArray expOutput('c', {bS, oH, oW, oC}, {20., 24.,28., 32.,16., 18.,44., 48.,52., 56.,28., 30.,28., 30.,32., 34.,17., 18.}, sd::DataType::FLOAT32); input.linspace(1.); weights = 1.; - nd4j::ops::depthwise_conv2d op; + sd::ops::depthwise_conv2d op; auto results = op.evaluate({&input, &weights}, {}, {kH,kW, sH,sW, pH,pW, dH,dW, paddingMode, dataFormat}); - NDArray* output = results->at(0); + NDArray* output = results.at(0); // output.printIndexedBuffer(); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expOutput.isSameShape(output)); ASSERT_TRUE(expOutput.equalsTo(output)); - delete results; } ////////////////////////////////////////////////////////////////////// @@ -2318,27 +2244,26 @@ TEST_F(ConvolutionTests2, depthwise_conv2d_7) { NDArray input('c', {bS, iC, iH, iW}, {0.6793503761291504, 0.35508695244789124, 0.842789351940155, 0.20031332969665527, 0.7014986872673035, 0.3106933832168579, 0.44793984293937683, 0.9380097389221191, 0.3266739547252655, 0.15187257528305054, 0.3833175301551819, 0.7821229696273804, - 0.19880719482898712, 0.7985635995864868, 0.16326339542865753, 0.14696824550628662, 0.2608966827392578, 0.13505761325359344}, nd4j::DataType::FLOAT32); - NDArray weights('c', {kH, kW, iC, mC}, {0.1308445781469345, 0.6442840099334717, 0.5698848366737366, 0.19896849989891052}, nd4j::DataType::FLOAT32); - NDArray biases('c', {1,iC*mC}, {0.6123566627502441, 0.37637925148010254, 0.17464971542358398, 0.4270855486392975}, nd4j::DataType::FLOAT32); + 0.19880719482898712, 0.7985635995864868, 0.16326339542865753, 0.14696824550628662, 0.2608966827392578, 0.13505761325359344}, sd::DataType::FLOAT32); + NDArray weights('c', {kH, kW, iC, mC}, {0.1308445781469345, 0.6442840099334717, 0.5698848366737366, 0.19896849989891052}, sd::DataType::FLOAT32); + NDArray biases('c', {1,iC*mC}, {0.6123566627502441, 0.37637925148010254, 0.17464971542358398, 0.4270855486392975}, sd::DataType::FLOAT32); NDArray expOutput('c', {bS, oC, oH, oW}, {0.7012459761288241, 0.6588178652487691, 0.722631079971582, 0.6385665758716108, 0.7041439625563628, 0.6530092074102978, 0.670967162534851, 0.735090151337225, 0.6551001785478623, 0.8140738359624038, 0.6051560970782859, 0.9193749546773375, 0.5054379267801892, 0.8283436386757472, 0.5765540302788565, 0.6649797296980537, 0.9807239274294943, 0.586850056971322, 0.261199593183985, 0.3930965634902499, 0.6203697362284615, 0.28794692117826504, 0.6297390019475202, 0.26769104886224415, 0.25840469001015975, 0.3233307788551656, 0.25161700129415276, 0.4573034071191504, 0.5033536625992294, 0.5827033826425385, - 0.4666419179635315, 0.585974550122895, 0.4595698215161401, 0.45632759998045813, 0.4789957702325296, 0.4539577593482922}, nd4j::DataType::FLOAT32); + 0.4666419179635315, 0.585974550122895, 0.4595698215161401, 0.45632759998045813, 0.4789957702325296, 0.4539577593482922}, sd::DataType::FLOAT32); - nd4j::ops::depthwise_conv2d op; + sd::ops::depthwise_conv2d op; auto results = op.evaluate({&input, &weights, &biases}, {}, {kH,kW, sH,sW, pH,pW, dH,dW, paddingMode, dataFormat}); - auto* output = results->at(0); + auto* output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expOutput.isSameShape(output)); ASSERT_TRUE(expOutput.equalsTo(output)); - delete results; } ////////////////////////////////////////////////////////////////////// @@ -2350,8 +2275,8 @@ TEST_F(ConvolutionTests2, depthwise_conv2d_8) { int paddingMode = 1; // 1-SAME, 0-VALID; int dataFormat = 1; // 1-NHWC, 0-NCHW - NDArray input('c', {bS, iH, iW, iC}, nd4j::DataType::FLOAT32); - NDArray weights('c', {kH, kW, iC, mC}, nd4j::DataType::FLOAT32); + NDArray input('c', {bS, iH, iW, iC}, sd::DataType::FLOAT32); + NDArray weights('c', {kH, kW, iC, mC}, sd::DataType::FLOAT32); NDArray expOutput('c', {bS, oH, oW, oC}, {-42.879997, -43.959999, -44.959999, -45.879997, -46.720005, -47.480003, -48.160000, -48.760002, -43.519997, -45.139999, -46.639996, -48.020000, -49.280003, -50.419998, -51.440006, -52.340000, -31.999998, -33.139999, -34.160000, -35.060001, -35.840004, -36.500004, -37.039997, -37.459999, -20.480000, -21.139997, -21.680000, -22.100000, -22.399998, -22.579998, -22.639996, -22.580002, -8.960000, -9.139998, -9.200002, -9.140001, -8.960001, -8.660000, -8.240002, -7.700001, 2.560000, 2.860002, 3.279998, 3.820000, 4.480001, 5.260000, 6.160001, 7.180000, 14.080000, 14.860000, 15.759998, 16.779999, 17.920002, 19.180000, 20.560001, 22.059998, @@ -2362,22 +2287,20 @@ TEST_F(ConvolutionTests2, depthwise_conv2d_8) { 214.889999, 222.720001, 230.730011, 238.919998, 247.290009, 201.119995, 209.129990, 217.319992, 225.690002, 234.240005, 242.970001, 251.880005, 260.970001, 209.760010, 218.489990, 227.399994, 236.490005, 245.760010, 255.209991, 264.839996, 274.649994, 218.399994, 227.850006, 237.479996, 247.289993, 257.279999, 267.449982, 277.799988, 288.330017, 227.040009, 237.209991, 247.559998, 258.089996, 268.800018, 279.690002, 290.760010, 302.010010, 235.679993, 246.570007, 257.639984, 268.889984, 280.320007, 291.929993, 303.720001, 315.690002, 244.320007, 255.929993, 267.720001, 279.690002, 291.839996, 304.169983, 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753.449951, 254.400009, 277.020020, 299.760010, 322.619995, 345.600006, 368.700012, 391.919983, 415.260010, 446.399994, 470.459961, 494.640015, 518.940002, 543.360046, 567.900024, 592.559998, 617.340027, 538.080017, 574.170044, 610.440002, 646.890015, 683.520020, 720.329956, 757.320007, 794.489990, 546.719971, 583.530029, 620.520020, 657.690002, 695.040039, 732.570007, 770.279968, 808.169983, 555.359985, 592.889954, 630.599976, 668.489990, 706.559998, 744.809998, 783.239990, 821.849976, 564.000000, 602.250000, 640.679993, 679.289978, 718.080017, 757.050049, 796.199951, 835.530029, 572.640015, 611.609985, 650.760010, 690.089966, 729.600037, 769.289978, 809.160034, 849.210083, 581.279968, 620.970032, 660.839966, 700.889954, 741.119995, 781.529968, 822.119995, 862.890015, 589.919983, 630.330017, 670.919983, 711.690002, 752.640015, 793.770020, 835.079956, 876.570007, 598.559998, 639.690002, 681.000000, 722.490051, 764.160034, 806.010010, 848.039978, 890.250061, 292.799988, 320.220001, 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384.090027, 287.520020, 302.730011, 318.119995, 333.690002, 349.440002, 365.369995, 381.479980, 397.770020, 296.160004, 312.089996, 328.199982, 344.489990, 360.960022, 377.609985, 394.440002, 411.449982, 304.799988, 321.450012, 338.280029, 355.289978, 372.480011, 389.850006, 407.399994, 425.130005, 313.440002, 330.809998, 348.359985, 366.089996, 384.000000, 402.090027, 420.359985, 438.809998, 322.079987, 340.169983, 358.440002, 376.889984, 395.520020, 414.329987, 433.320007, 452.489990, 330.720001, 349.530029, 368.520020, 387.690002, 407.039978, 426.570007, 446.279999, 466.170013, 339.360016, 358.890015, 378.599976, 398.490021, 418.559998, 438.809998, 459.239990, 479.849976, 177.600006, 190.619995, 203.759995, 217.020004, 230.399994, 243.899994, 257.519989, 271.260010, 292.799988, 307.260010, 321.839996, 336.539978, 351.360016, 366.299988, 381.359985, 396.540009, 365.279999, 386.970001, 408.839996, 430.889984, 453.120026, 475.529968, 498.119995, 520.890015, 373.920013, 396.329987, 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555.359985, 592.889954, 630.599976, 668.489990, 706.559998, 744.809998, 783.239990, 821.849976, 564.000000, 602.250000, 640.679993, 679.289978, 718.080017, 757.050049, 796.199951, 835.530029, 572.640015, 611.609985, 650.760010, 690.089966, 729.600037, 769.289978, 809.160034, 849.210083, 581.279968, 620.970032, 660.839966, 700.889954, 741.119995, 781.529968, 822.119995, 862.890015, 589.919983, 630.330017, 670.919983, 711.690002, 752.640015, 793.770020, 835.079956, 876.570007, 598.559998, 639.690002, 681.000000, 722.490051, 764.160034, 806.010010, 848.039978, 890.250061, 292.799988, 320.220001, 347.760010, 375.419983, 403.200012, 431.100006, 459.119995, 487.260010, 523.199951, 552.059998, 581.040039, 610.139954, 639.360046, 668.699951, 698.159973, 727.739990, 624.479980, 667.770020, 711.239990, 754.890015, 798.719971, 842.729980, 886.919983, 931.290039, 633.119995, 677.130005, 721.319946, 765.690002, 810.239990, 854.969971, 899.880005, 944.969971, 641.760010, 686.489990, 731.400024, 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287.980011, 31.359993, 68.139999, 105.039993, 142.059982, 179.200027, 216.459991, 253.839996, 291.339996, 31.360008, 68.619995, 106.000000, 143.499985, 181.119995, 218.860001, 256.719971, 294.700012, 31.360001, 69.099991, 106.959984, 144.939987, 183.040009, 221.260010, 259.600006, 298.059998, 31.360008, 69.579971, 107.920006, 146.379990, 184.960007, 223.660004, 262.479980, 301.419983, 31.360001, 70.059975, 108.880020, 147.819977, 186.880020, 226.059998, 265.359985, 304.779999, -83.840004, -58.040001, -32.159988, -6.200012, 19.840012, 45.959984, 72.159996, 98.440010}, sd::DataType::FLOAT32); input.linspace(-10, 0.1); weights.linspace(-2, 0.1); - nd4j::ops::depthwise_conv2d op; + sd::ops::depthwise_conv2d op; auto results = op.evaluate({&input, &weights}, {}, {kH,kW, sH,sW, pH,pW, dH,dW, paddingMode, dataFormat}); - auto output = results->at(0); + auto output = results.at(0); // output->printBuffer(); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expOutput.isSameShape(output)); ASSERT_TRUE(expOutput.equalsTo(output)); - - delete results; } ////////////////////////////////////////////////////////////////////// @@ -2389,8 +2312,8 @@ TEST_F(ConvolutionTests2, depthwise_conv2d_9) { int paddingMode = 1; // 1-SAME, 0-VALID; int dataFormat = 0; // 1-NHWC, 0-NCHW - NDArray input('c', {bS, iC, iH, iW}, nd4j::DataType::FLOAT32); - NDArray weights('c', {kH, kW, iC, mC}, nd4j::DataType::FLOAT32); + NDArray input('c', {bS, iC, iH, iW}, sd::DataType::FLOAT32); + NDArray weights('c', {kH, kW, iC, mC}, sd::DataType::FLOAT32); NDArray expOutput('c', {bS, oC, oH, oW}, {-103.360001, -131.440002, -130.000000, -128.559998, -127.120003, -125.680000, -124.240005, -122.799995, -121.360001, -66.720001,-76.199997, -81.239998, -80.160004, -79.080002, -78.000000, -76.919998, -75.840004, -74.760002, -73.680000, -29.400002, -66.599998, -70.440002, -69.360001, -68.279999, -67.199997, -66.120003, -65.040001, -63.959999, 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909.119995, 910.739990, 912.359985, 913.979980, 915.599976, 917.219971, 918.839966, 478.859985, 749.579956, 923.699951, 925.319946, 926.940002, 928.559998, 930.179993, 931.799988, 933.419983, 935.039978, 487.260010, 762.779968, 939.900024, 941.520020, 943.140015, 944.760010, 946.380005, 948.000000, 949.619995, 951.239990, 495.659973, 775.979980, 956.099976, 957.719971, 959.339966, 960.959961, 962.579956, 964.199951, 965.819946, 967.440002, 504.059998, 236.679977, 214.880005, 215.239990, 215.599991, 215.959991, 216.319992, 216.679993, 217.040009, 217.399994, 49.959995, 850.180054, 1131.079956, 1132.939941, 1134.800049, 1136.660034, 1138.520020, 1140.380005, 1142.239990, 1144.100098, 665.060059, 852.209961, 1059.689941, 1061.399902, 1063.110107, 1064.820068, 1066.530029, 1068.239990, 1069.950073, 1071.660034, 566.370056, 866.010010, 1076.790039, 1078.500000, 1080.209961, 1081.920044, 1083.630005, 1085.339966, 1087.050049, 1088.760010, 575.369995, 879.809998, 1093.890015, 1095.599976, 1097.310059, 1099.020020, 1100.729980, 1102.439941, 1104.149902, 1105.859985, 584.369995, 893.609985, 1110.989990, 1112.699951, 1114.410034, 1116.120117, 1117.830078, 1119.540039, 1121.250000, 1122.959961, 593.370056, 907.410034, 1128.089966, 1129.800049, 1131.510010, 1133.220093, 1134.929932, 1136.639893, 1138.349976, 1140.060059, 602.369995, 921.209961, 1145.189941, 1146.900024, 1148.609985, 1150.320068, 1152.030029, 1153.739990, 1155.449951, 1157.160034, 611.370056, 935.010010, 1162.290039, 1164.000000, 1165.709961, 1167.420044, 1169.130005, 1170.839966, 1172.550049, 1174.260010, 620.369995, 948.809998, 1179.390015, 1181.099976, 1182.810059, 1184.520020, 1186.229980, 1187.939941, 1189.650024, 1191.359985, 629.370056, 304.099976, 292.039978, 292.460022, 292.880005, 293.300018, 293.720001, 294.140015, 294.559998, 294.980042, 85.700005}, sd::DataType::FLOAT32); input.linspace(-10, 0.1); weights.linspace(-2, 0.1); - nd4j::ops::depthwise_conv2d op; + sd::ops::depthwise_conv2d op; auto results = op.evaluate({&input, &weights}, {}, {kH,kW, sH,sW, pH,pW, dH,dW, paddingMode, dataFormat}); - auto output = results->at(0); + auto output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expOutput.isSameShape(output)); ASSERT_TRUE(expOutput.equalsTo(output, 1e-4)); - delete results; } #endif //LIBND4J_CONVOLUTIONTESTS2_H \ No newline at end of file diff --git a/libnd4j/tests_cpu/layers_tests/CuDnnTests.cu b/libnd4j/tests_cpu/layers_tests/CuDnnTests.cu index 02e1040aa..47892c973 100644 --- a/libnd4j/tests_cpu/layers_tests/CuDnnTests.cu +++ b/libnd4j/tests_cpu/layers_tests/CuDnnTests.cu @@ -21,7 +21,7 @@ #include "testlayers.h" #include -#include +#include #include #include #include @@ -32,14 +32,14 @@ #endif -using namespace nd4j; +using namespace sd; class CuDnnTests : public testing::Test { public: }; -static void printer(std::initializer_list helpers) { +static void printer(std::initializer_list helpers) { for (auto v:helpers) { nd4j_printf("Initialized [%s]\n", v->name().c_str()); @@ -50,22 +50,22 @@ static void printer(std::initializer_list TEST_F(CuDnnTests, helpers_includer) { // we need this block, to make sure all helpers are still available within binary, and not optimized out by linker #ifdef HAVE_CUDNN - nd4j::ops::platforms::PLATFORM_conv2d_ENGINE_CUDA conv2d; - nd4j::ops::platforms::PLATFORM_conv2d_bp_ENGINE_CUDA conv2d_bp; - nd4j::ops::platforms::PLATFORM_conv3dnew_ENGINE_CUDA conv3dnew; - nd4j::ops::platforms::PLATFORM_conv3dnew_bp_ENGINE_CUDA conv3dnew_bp; - nd4j::ops::platforms::PLATFORM_depthwise_conv2d_ENGINE_CUDA depthwise_conv2d; - nd4j::ops::platforms::PLATFORM_depthwise_conv2d_bp_ENGINE_CUDA depthwise_conv2d_bp; - nd4j::ops::platforms::PLATFORM_batchnorm_ENGINE_CUDA batchnorm; - nd4j::ops::platforms::PLATFORM_batchnorm_bp_ENGINE_CUDA batchnorm_bp; - nd4j::ops::platforms::PLATFORM_avgpool2d_ENGINE_CUDA avgpool2d; - nd4j::ops::platforms::PLATFORM_avgpool2d_bp_ENGINE_CUDA avgpool2d_bp; - nd4j::ops::platforms::PLATFORM_maxpool2d_ENGINE_CUDA maxpool2d; - nd4j::ops::platforms::PLATFORM_maxpool2d_bp_ENGINE_CUDA maxpool2d_bp; - nd4j::ops::platforms::PLATFORM_avgpool3dnew_ENGINE_CUDA avgpool3dnew; - nd4j::ops::platforms::PLATFORM_avgpool3dnew_bp_ENGINE_CUDA avgpool3dnew_bp; - nd4j::ops::platforms::PLATFORM_maxpool3dnew_ENGINE_CUDA maxpool3dnew; - nd4j::ops::platforms::PLATFORM_maxpool3dnew_bp_ENGINE_CUDA maxpool3dnew_bp; + sd::ops::platforms::PLATFORM_conv2d_ENGINE_CUDA conv2d; + sd::ops::platforms::PLATFORM_conv2d_bp_ENGINE_CUDA conv2d_bp; + sd::ops::platforms::PLATFORM_conv3dnew_ENGINE_CUDA conv3dnew; + sd::ops::platforms::PLATFORM_conv3dnew_bp_ENGINE_CUDA conv3dnew_bp; + sd::ops::platforms::PLATFORM_depthwise_conv2d_ENGINE_CUDA depthwise_conv2d; + sd::ops::platforms::PLATFORM_depthwise_conv2d_bp_ENGINE_CUDA depthwise_conv2d_bp; + sd::ops::platforms::PLATFORM_batchnorm_ENGINE_CUDA batchnorm; + sd::ops::platforms::PLATFORM_batchnorm_bp_ENGINE_CUDA batchnorm_bp; + sd::ops::platforms::PLATFORM_avgpool2d_ENGINE_CUDA avgpool2d; + sd::ops::platforms::PLATFORM_avgpool2d_bp_ENGINE_CUDA avgpool2d_bp; + sd::ops::platforms::PLATFORM_maxpool2d_ENGINE_CUDA maxpool2d; + sd::ops::platforms::PLATFORM_maxpool2d_bp_ENGINE_CUDA maxpool2d_bp; + sd::ops::platforms::PLATFORM_avgpool3dnew_ENGINE_CUDA avgpool3dnew; + sd::ops::platforms::PLATFORM_avgpool3dnew_bp_ENGINE_CUDA avgpool3dnew_bp; + sd::ops::platforms::PLATFORM_maxpool3dnew_ENGINE_CUDA maxpool3dnew; + sd::ops::platforms::PLATFORM_maxpool3dnew_bp_ENGINE_CUDA maxpool3dnew_bp; @@ -115,7 +115,7 @@ TEST_F(CuDnnTests, mixed_helpers_test_1) { weights.syncToHost(); bias.syncToHost(); - nd4j::ops::conv2d op; + sd::ops::conv2d op; // cuDNN part Context cuda(1); diff --git a/libnd4j/tests_cpu/layers_tests/CudaBasicsTests1.cu b/libnd4j/tests_cpu/layers_tests/CudaBasicsTests1.cu index 0a22272d8..a0104e637 100644 --- a/libnd4j/tests_cpu/layers_tests/CudaBasicsTests1.cu +++ b/libnd4j/tests_cpu/layers_tests/CudaBasicsTests1.cu @@ -19,25 +19,25 @@ // #include "testlayers.h" -#include -#include -#include -#include +#include +#include +#include +#include #include #include -#include -#include -#include +#include +#include +#include #include #include #include -#include -#include -#include +#include +#include +#include #include -using namespace nd4j; -using namespace nd4j::graph; +using namespace sd; +using namespace sd::graph; class CudaBasicsTests1 : public testing::Test { public: @@ -128,15 +128,15 @@ TEST_F(CudaBasicsTests1, TestPairwise_1) { //////////////////////////////////////////////////////////////////////////// TEST_F(CudaBasicsTests1, execIndexReduceScalar_1) { - NDArray x1('c', {2,2}, {0, 1, 2, 3}, nd4j::DataType::INT32); - NDArray x2('c', {2,2}, {0.5, 1.5, -4.5, 3.5}, nd4j::DataType::BFLOAT16); - NDArray x3('c', {2,2}, {0, -1, 0, 1}, nd4j::DataType::BOOL); + NDArray x1('c', {2,2}, {0, 1, 2, 3}, sd::DataType::INT32); + NDArray x2('c', {2,2}, {0.5, 1.5, -4.5, 3.5}, sd::DataType::BFLOAT16); + NDArray x3('c', {2,2}, {0, -1, 0, 1}, sd::DataType::BOOL); - NDArray scalar('c', {}, std::vector{0}, nd4j::DataType::INT64); + NDArray scalar('c', {}, std::vector{0}, sd::DataType::INT64); - NDArray exp1('c', {}, std::vector{3}, nd4j::DataType::INT64); - NDArray exp2('c', {}, std::vector{2}, nd4j::DataType::INT64); - NDArray exp3('c', {}, std::vector{1}, nd4j::DataType::INT64); + NDArray exp1('c', {}, std::vector{3}, sd::DataType::INT64); + NDArray exp2('c', {}, std::vector{2}, sd::DataType::INT64); + NDArray exp3('c', {}, std::vector{1}, sd::DataType::INT64); void *dX1, *dX2, *dX3, *dZ; Nd4jLong *dX1ShapeInfo, *dX2ShapeInfo, *dX3ShapeInfo, *dZShapeInfo; @@ -180,7 +180,7 @@ TEST_F(CudaBasicsTests1, execIndexReduceScalar_1) { /***************************************/ NativeOpExecutioner::execIndexReduceScalar(&lc, - nd4j::indexreduce::IndexAbsoluteMax, + sd::indexreduce::IndexAbsoluteMax, x1.buffer(), x1.getShapeInfo(), dX1, dX1ShapeInfo, nullptr, @@ -202,7 +202,7 @@ TEST_F(CudaBasicsTests1, execIndexReduceScalar_1) { /***************************************/ NativeOpExecutioner::execIndexReduceScalar(&lc, - nd4j::indexreduce::IndexAbsoluteMax, + sd::indexreduce::IndexAbsoluteMax, nullptr, x2.getShapeInfo(), dX2, dX2ShapeInfo, nullptr, @@ -222,7 +222,7 @@ TEST_F(CudaBasicsTests1, execIndexReduceScalar_1) { // ************************************* NativeOpExecutioner::execIndexReduceScalar(&lc, - nd4j::indexreduce::IndexAbsoluteMax, + sd::indexreduce::IndexAbsoluteMax, nullptr, x3.getShapeInfo(), dX3, dX3ShapeInfo, nullptr, @@ -257,16 +257,16 @@ TEST_F(CudaBasicsTests1, execReduce3Scalar_1) { if (!Environment::getInstance()->isExperimentalBuild()) return; - NDArray x1('c', {2,2}, {1,2,3,4}, nd4j::DataType::INT32); - NDArray x2('c', {2,2}, {-1,-2,-3,-4}, nd4j::DataType::INT32); - NDArray x3('c', {2,2}, {1.5,1.5,1.5,1.5}, nd4j::DataType::DOUBLE); - NDArray x4('c', {2,2}, {1,2,3,4}, nd4j::DataType::DOUBLE); + NDArray x1('c', {2,2}, {1,2,3,4}, sd::DataType::INT32); + NDArray x2('c', {2,2}, {-1,-2,-3,-4}, sd::DataType::INT32); + NDArray x3('c', {2,2}, {1.5,1.5,1.5,1.5}, sd::DataType::DOUBLE); + NDArray x4('c', {2,2}, {1,2,3,4}, sd::DataType::DOUBLE); - NDArray exp1('c', {}, std::vector{-30.f}, nd4j::DataType::FLOAT32); - NDArray exp2('c', {}, std::vector{15.}, nd4j::DataType::DOUBLE); + NDArray exp1('c', {}, std::vector{-30.f}, sd::DataType::FLOAT32); + NDArray exp2('c', {}, std::vector{15.}, sd::DataType::DOUBLE); - NDArray scalar1('c', {}, std::vector{100.f}, nd4j::DataType::FLOAT32); - NDArray scalar2('c', {}, std::vector{100.}, nd4j::DataType::DOUBLE); + NDArray scalar1('c', {}, std::vector{100.f}, sd::DataType::FLOAT32); + NDArray scalar2('c', {}, std::vector{100.}, sd::DataType::DOUBLE); void *dX1, *dX2, *dX3, *dX4, *dZ1, *dZ2; Nd4jLong *dX1ShapeInfo, *dX3ShapeInfo, *dZ1ShapeInfo, *dZ2ShapeInfo; @@ -316,7 +316,7 @@ TEST_F(CudaBasicsTests1, execReduce3Scalar_1) { /***************************************/ - NativeOpExecutioner::execReduce3Scalar(&lc, nd4j::reduce3::Dot,nullptr, x1.getShapeInfo(),dX1, dX1ShapeInfo, nullptr, nullptr, x2.getShapeInfo(),dX2, dX1ShapeInfo,nullptr, scalar1.getShapeInfo(),dZ1, dZ1ShapeInfo); + NativeOpExecutioner::execReduce3Scalar(&lc, sd::reduce3::Dot,nullptr, x1.getShapeInfo(),dX1, dX1ShapeInfo, nullptr, nullptr, x2.getShapeInfo(),dX2, dX1ShapeInfo,nullptr, scalar1.getShapeInfo(),dZ1, dZ1ShapeInfo); cudaResult = cudaStreamSynchronize(stream); ASSERT_EQ(0, cudaResult); @@ -333,7 +333,7 @@ TEST_F(CudaBasicsTests1, execReduce3Scalar_1) { /***************************************/ - NativeOpExecutioner::execReduce3Scalar(&lc, nd4j::reduce3::Dot,nullptr, x3.getShapeInfo(),dX3, dX3ShapeInfo, nullptr, nullptr, x4.getShapeInfo(),dX4, dX3ShapeInfo,nullptr, scalar2.getShapeInfo(),dZ2, dZ2ShapeInfo); + NativeOpExecutioner::execReduce3Scalar(&lc, sd::reduce3::Dot,nullptr, x3.getShapeInfo(),dX3, dX3ShapeInfo, nullptr, nullptr, x4.getShapeInfo(),dX4, dX3ShapeInfo,nullptr, scalar2.getShapeInfo(),dZ2, dZ2ShapeInfo); cudaResult = cudaStreamSynchronize(stream); ASSERT_EQ(0, cudaResult); @@ -360,11 +360,11 @@ TEST_F(CudaBasicsTests1, execReduce3Scalar_1) { //////////////////////////////////////////////////////////////////////////// TEST_F(CudaBasicsTests1, execReduce3_1) { - NDArray x('c', {2,2}, {1,2,3,4}, nd4j::DataType::INT32); - NDArray y('c', {2,2}, {-1,-2,-3,-4}, nd4j::DataType::INT32); + NDArray x('c', {2,2}, {1,2,3,4}, sd::DataType::INT32); + NDArray y('c', {2,2}, {-1,-2,-3,-4}, sd::DataType::INT32); - NDArray exp('c', {}, std::vector{-30.f}, nd4j::DataType::FLOAT32); - NDArray z('c', {}, std::vector{100.f}, nd4j::DataType::FLOAT32); + NDArray exp('c', {}, std::vector{-30.f}, sd::DataType::FLOAT32); + NDArray z('c', {}, std::vector{100.f}, sd::DataType::FLOAT32); std::vector dimensions = {0, 1}; @@ -386,7 +386,7 @@ TEST_F(CudaBasicsTests1, execReduce3_1) { cudaResult = allocateDeviceMem(lc, devicePtrs, hostData); ASSERT_EQ(0, cudaResult); // call cuda kernel which calculates result - NativeOpExecutioner::execReduce3(&lc, nd4j::reduce3::Dot, + NativeOpExecutioner::execReduce3(&lc, sd::reduce3::Dot, nullptr, x.getShapeInfo(), x.specialBuffer(), x.specialShapeInfo(), nullptr, nullptr, y.getShapeInfo(), y.specialBuffer(), y.specialShapeInfo(), @@ -412,11 +412,11 @@ TEST_F(CudaBasicsTests1, execReduce3_1) { //////////////////////////////////////////////////////////////////////////// TEST_F(CudaBasicsTests1, execReduce3_2) { - NDArray x('c', {2,2}, {1.5,1.5,1.5,1.5}, nd4j::DataType::DOUBLE); - NDArray y('c', {2,2}, {1,2,3,4}, nd4j::DataType::DOUBLE); + NDArray x('c', {2,2}, {1.5,1.5,1.5,1.5}, sd::DataType::DOUBLE); + NDArray y('c', {2,2}, {1,2,3,4}, sd::DataType::DOUBLE); - NDArray exp('c', {}, std::vector{15.}, nd4j::DataType::DOUBLE); - NDArray z('c', {}, std::vector{100.}, nd4j::DataType::DOUBLE); + NDArray exp('c', {}, std::vector{15.}, sd::DataType::DOUBLE); + NDArray z('c', {}, std::vector{100.}, sd::DataType::DOUBLE); std::vector dimensions = {0, 1}; @@ -435,7 +435,7 @@ TEST_F(CudaBasicsTests1, execReduce3_2) { cudaResult = allocateDeviceMem(lc, devicePtrs, hostData); ASSERT_EQ(0, cudaResult); // call cuda kernel which calculates result - NativeOpExecutioner::execReduce3(&lc, nd4j::reduce3::Dot, + NativeOpExecutioner::execReduce3(&lc, sd::reduce3::Dot, nullptr, x.getShapeInfo(), x.specialBuffer(), x.specialShapeInfo(), nullptr, nullptr, y.getShapeInfo(), y.specialBuffer(), y.specialShapeInfo(), @@ -461,11 +461,11 @@ TEST_F(CudaBasicsTests1, execReduce3_2) { //////////////////////////////////////////////////////////////////////////// TEST_F(CudaBasicsTests1, execReduce3_3) { - NDArray x('c', {2,3}, {1,2,3,4,5,6}, nd4j::DataType::INT32); - NDArray y('c', {2,3}, {-6,-5,-4,-3,-2,-1}, nd4j::DataType::INT32); + NDArray x('c', {2,3}, {1,2,3,4,5,6}, sd::DataType::INT32); + NDArray y('c', {2,3}, {-6,-5,-4,-3,-2,-1}, sd::DataType::INT32); - NDArray exp('c', {3}, {-18,-20,-18}, nd4j::DataType::FLOAT32); - NDArray z('c', {3}, {100,100,100}, nd4j::DataType::FLOAT32); + NDArray exp('c', {3}, {-18,-20,-18}, sd::DataType::FLOAT32); + NDArray z('c', {3}, {100,100,100}, sd::DataType::FLOAT32); std::vector dimensions = {0}; @@ -501,7 +501,7 @@ TEST_F(CudaBasicsTests1, execReduce3_3) { cudaResult = allocateDeviceMem(lc, devicePtrs, hostData); ASSERT_EQ(0, cudaResult); // call cuda kernel which calculates result - NativeOpExecutioner::execReduce3(&lc, nd4j::reduce3::Dot, + NativeOpExecutioner::execReduce3(&lc, sd::reduce3::Dot, nullptr, x.getShapeInfo(), x.specialBuffer(), x.specialShapeInfo(), nullptr, nullptr, y.getShapeInfo(), y.specialBuffer(), y.specialShapeInfo(), @@ -527,11 +527,11 @@ TEST_F(CudaBasicsTests1, execReduce3_3) { //////////////////////////////////////////////////////////////////////////// TEST_F(CudaBasicsTests1, execReduce3_4) { - NDArray x('c', {2,3}, {1,2,3,4,5,6}, nd4j::DataType::DOUBLE); - NDArray y('c', {2,3}, {1.5,1.5,1.5,1.5,1.5,1.5}, nd4j::DataType::DOUBLE); + NDArray x('c', {2,3}, {1,2,3,4,5,6}, sd::DataType::DOUBLE); + NDArray y('c', {2,3}, {1.5,1.5,1.5,1.5,1.5,1.5}, sd::DataType::DOUBLE); - NDArray exp('c', {2}, {9,22.5}, nd4j::DataType::DOUBLE); - NDArray z('c', {2}, {100,100}, nd4j::DataType::DOUBLE); + NDArray exp('c', {2}, {9,22.5}, sd::DataType::DOUBLE); + NDArray z('c', {2}, {100,100}, sd::DataType::DOUBLE); std::vector dimensions = {1}; @@ -567,7 +567,7 @@ TEST_F(CudaBasicsTests1, execReduce3_4) { cudaResult = allocateDeviceMem(lc, devicePtrs, hostData); ASSERT_EQ(0, cudaResult); // call cuda kernel which calculates result - NativeOpExecutioner::execReduce3(&lc, nd4j::reduce3::Dot, + NativeOpExecutioner::execReduce3(&lc, sd::reduce3::Dot, nullptr, x.getShapeInfo(), x.specialBuffer(), x.specialShapeInfo(), nullptr, nullptr, y.getShapeInfo(), y.specialBuffer(), y.specialShapeInfo(), @@ -593,11 +593,11 @@ TEST_F(CudaBasicsTests1, execReduce3_4) { //////////////////////////////////////////////////////////////////////////// TEST_F(CudaBasicsTests1, execReduce3_5) { - NDArray x('c', {2,2,3}, {1.5,1.5,1.5,1.5,1.5,1.5,1.5,1.5,1.5,1.5,1.5,1.5}, nd4j::DataType::FLOAT32); - NDArray y('c', {2,2,3}, {1,2,3,4,5,6,7,8,9,10,11,12}, nd4j::DataType::FLOAT32); + NDArray x('c', {2,2,3}, {1.5,1.5,1.5,1.5,1.5,1.5,1.5,1.5,1.5,1.5,1.5,1.5}, sd::DataType::FLOAT32); + NDArray y('c', {2,2,3}, {1,2,3,4,5,6,7,8,9,10,11,12}, sd::DataType::FLOAT32); - NDArray exp('c', {2,3}, {7.5, 10.5, 13.5, 25.5, 28.5, 31.5}, nd4j::DataType::FLOAT32); - NDArray z('c', {2,3}, {100,100,100,100,100,100}, nd4j::DataType::FLOAT32); + NDArray exp('c', {2,3}, {7.5, 10.5, 13.5, 25.5, 28.5, 31.5}, sd::DataType::FLOAT32); + NDArray z('c', {2,3}, {100,100,100,100,100,100}, sd::DataType::FLOAT32); std::vector dimensions = {1}; @@ -633,7 +633,7 @@ TEST_F(CudaBasicsTests1, execReduce3_5) { cudaResult = allocateDeviceMem(lc, devicePtrs, hostData); ASSERT_EQ(0, cudaResult); // call cuda kernel which calculates result - NativeOpExecutioner::execReduce3(&lc, nd4j::reduce3::Dot, + NativeOpExecutioner::execReduce3(&lc, sd::reduce3::Dot, nullptr, x.getShapeInfo(), x.specialBuffer(), x.specialShapeInfo(), nullptr, nullptr, y.getShapeInfo(), y.specialBuffer(), y.specialShapeInfo(), @@ -659,11 +659,11 @@ TEST_F(CudaBasicsTests1, execReduce3_5) { //////////////////////////////////////////////////////////////////////////// TEST_F(CudaBasicsTests1, execReduce3All_1) { - NDArray x('c', {2,2}, {1,2,3,4}, nd4j::DataType::INT32); - NDArray y('c', {2,3}, {-1,1,-1,1,-1,1}, nd4j::DataType::INT32); + NDArray x('c', {2,2}, {1,2,3,4}, sd::DataType::INT32); + NDArray y('c', {2,3}, {-1,1,-1,1,-1,1}, sd::DataType::INT32); - NDArray exp('c', {2,3}, {2,-2,2,2,-2,2}, nd4j::DataType::FLOAT32); - NDArray z('c', {2,3}, {100,100,100,100,100,100}, nd4j::DataType::FLOAT32); + NDArray exp('c', {2,3}, {2,-2,2,2,-2,2}, sd::DataType::FLOAT32); + NDArray z('c', {2,3}, {100,100,100,100,100,100}, sd::DataType::FLOAT32); std::vector dimensions = {0}; @@ -699,7 +699,7 @@ TEST_F(CudaBasicsTests1, execReduce3All_1) { cudaResult = allocateDeviceMem(lc, devicePtrs, hostData); ASSERT_EQ(0, cudaResult); // call cuda kernel which calculates result - NativeOpExecutioner::execReduce3All(&lc, nd4j::reduce3::Dot, + NativeOpExecutioner::execReduce3All(&lc, sd::reduce3::Dot, nullptr, x.getShapeInfo(), x.specialBuffer(), x.specialShapeInfo(), nullptr, nullptr, y.getShapeInfo(), y.specialBuffer(), y.specialShapeInfo(), @@ -725,11 +725,11 @@ TEST_F(CudaBasicsTests1, execReduce3All_1) { //////////////////////////////////////////////////////////////////////////// TEST_F(CudaBasicsTests1, execReduce3All_2) { - NDArray x('c', {2,2}, {1,2,3,4}, nd4j::DataType::DOUBLE); - NDArray y('c', {2,3}, {1.5,1.5,1.5,1.5,1.5,1.5}, nd4j::DataType::DOUBLE); + NDArray x('c', {2,2}, {1,2,3,4}, sd::DataType::DOUBLE); + NDArray y('c', {2,3}, {1.5,1.5,1.5,1.5,1.5,1.5}, sd::DataType::DOUBLE); - NDArray exp('c', {2,3}, {6,6,6,9,9,9}, nd4j::DataType::DOUBLE); - NDArray z('c', {2,3}, {100,100,100,100,100,100,},nd4j::DataType::DOUBLE); + NDArray exp('c', {2,3}, {6,6,6,9,9,9}, sd::DataType::DOUBLE); + NDArray z('c', {2,3}, {100,100,100,100,100,100,},sd::DataType::DOUBLE); std::vector dimensions = {0}; @@ -765,7 +765,7 @@ TEST_F(CudaBasicsTests1, execReduce3All_2) { cudaResult = allocateDeviceMem(lc, devicePtrs, hostData); ASSERT_EQ(0, cudaResult); // call cuda kernel which calculates result - NativeOpExecutioner::execReduce3All(&lc, nd4j::reduce3::Dot, + NativeOpExecutioner::execReduce3All(&lc, sd::reduce3::Dot, nullptr, x.getShapeInfo(), x.specialBuffer(), x.specialShapeInfo(), nullptr, nullptr, y.getShapeInfo(), y.specialBuffer(), y.specialShapeInfo(), @@ -791,10 +791,10 @@ TEST_F(CudaBasicsTests1, execReduce3All_2) { //////////////////////////////////////////////////////////////////////////// TEST_F(CudaBasicsTests1, execIndexReduce_1) { - NDArray x('c', {2,3}, {100,100,100,100,100,100}, nd4j::DataType::DOUBLE); + NDArray x('c', {2,3}, {100,100,100,100,100,100}, sd::DataType::DOUBLE); x.linspace(-2.); x.syncToDevice(); - NDArray exp('c', {2}, {2, 2}, nd4j::DataType::INT64); - NDArray z('c', {2}, {100,100}, nd4j::DataType::INT64); + NDArray exp('c', {2}, {2, 2}, sd::DataType::INT64); + NDArray z('c', {2}, {100,100}, sd::DataType::INT64); std::vector dimensions = {1}; @@ -822,7 +822,7 @@ TEST_F(CudaBasicsTests1, execIndexReduce_1) { cudaResult = allocateDeviceMem(lc, devicePtrs, hostData); ASSERT_EQ(0, cudaResult); // call cuda kernel which calculates result - NativeOpExecutioner::execIndexReduce(&lc, nd4j::indexreduce::IndexMax, + NativeOpExecutioner::execIndexReduce(&lc, sd::indexreduce::IndexMax, nullptr, x.getShapeInfo(), x.specialBuffer(), x.specialShapeInfo(), nullptr, nullptr, z.getShapeInfo(), z.specialBuffer(), z.specialShapeInfo(), @@ -852,10 +852,10 @@ TEST_F(CudaBasicsTests1, execIndexReduce_2) { 100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100, 100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100, 100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100, - 100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100}, nd4j::DataType::FLOAT32); + 100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100}, sd::DataType::FLOAT32); x.linspace(-2.f); x.syncToDevice(); - NDArray exp('c', {2,5}, {11,11,11,11,11,11,11,11,11,11}, nd4j::DataType::INT64); - NDArray z('c', {2,5}, {100,100,100,100,100,100,100,100,100,100}, nd4j::DataType::INT64); + NDArray exp('c', {2,5}, {11,11,11,11,11,11,11,11,11,11}, sd::DataType::INT64); + NDArray z('c', {2,5}, {100,100,100,100,100,100,100,100,100,100}, sd::DataType::INT64); std::vector dimensions = {1,2}; @@ -884,7 +884,7 @@ TEST_F(CudaBasicsTests1, execIndexReduce_2) { cudaResult = allocateDeviceMem(lc, devicePtrs, hostData); ASSERT_EQ(0, cudaResult); // call cuda kernel which calculates result - NativeOpExecutioner::execIndexReduce(&lc, nd4j::indexreduce::IndexMax, + NativeOpExecutioner::execIndexReduce(&lc, sd::indexreduce::IndexMax, nullptr, x.getShapeInfo(), x.specialBuffer(), x.specialShapeInfo(), nullptr, nullptr, z.getShapeInfo(), z.specialBuffer(), z.specialShapeInfo(), @@ -914,10 +914,10 @@ TEST_F(CudaBasicsTests1, execIndexReduce_3) { 100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100, 100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100, 100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100, - 100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100}, nd4j::DataType::DOUBLE); + 100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100}, sd::DataType::DOUBLE); x.linspace(-2.); x.syncToDevice(); - NDArray exp('c', {3}, {39, 39, 39}, nd4j::DataType::INT64); - NDArray z('c', {3}, {100,100,100}, nd4j::DataType::INT64); + NDArray exp('c', {3}, {39, 39, 39}, sd::DataType::INT64); + NDArray z('c', {3}, {100,100,100}, sd::DataType::INT64); std::vector dimensions = {0,2,3}; @@ -945,7 +945,7 @@ TEST_F(CudaBasicsTests1, execIndexReduce_3) { cudaResult = allocateDeviceMem(lc, devicePtrs, hostData); ASSERT_EQ(0, cudaResult); // call cuda kernel which calculates result - NativeOpExecutioner::execIndexReduce(&lc, nd4j::indexreduce::IndexMax, + NativeOpExecutioner::execIndexReduce(&lc, sd::indexreduce::IndexMax, nullptr, x.getShapeInfo(), x.specialBuffer(), x.specialShapeInfo(), nullptr, nullptr, z.getShapeInfo(), z.specialBuffer(), z.specialShapeInfo(), @@ -973,10 +973,10 @@ TEST_F(CudaBasicsTests1, execScalar_1) { if (!Environment::getInstance()->isExperimentalBuild()) return; - NDArray x('c', {2,3}, {0,1,2,3,4,5}, nd4j::DataType::INT64); - NDArray exp('c',{2,3}, {0,0,1,1,2,2}, nd4j::DataType::INT64); - NDArray scalar('c',{}, std::vector{2.f}, nd4j::DataType::FLOAT32); - NDArray z('c', {2,3}, {100,100,100,100,100,100}, nd4j::DataType::INT64); + NDArray x('c', {2,3}, {0,1,2,3,4,5}, sd::DataType::INT64); + NDArray exp('c',{2,3}, {0,0,1,1,2,2}, sd::DataType::INT64); + NDArray scalar('c',{}, std::vector{2.f}, sd::DataType::FLOAT32); + NDArray z('c', {2,3}, {100,100,100,100,100,100}, sd::DataType::INT64); // create cuda stream and LaunchContext cudaError_t cudaResult; @@ -985,7 +985,7 @@ TEST_F(CudaBasicsTests1, execScalar_1) { LaunchContext lc(&stream); // call cuda kernel which calculates result - NativeOpExecutioner::execScalar(&lc, nd4j::scalar::Divide, + NativeOpExecutioner::execScalar(&lc, sd::scalar::Divide, nullptr, x.getShapeInfo(), x.specialBuffer(), x.specialShapeInfo(), nullptr, z.getShapeInfo(), z.specialBuffer(), z.specialShapeInfo(), nullptr, scalar.getShapeInfo(), scalar.specialBuffer(), scalar.specialShapeInfo(), @@ -1008,10 +1008,10 @@ TEST_F(CudaBasicsTests1, execScalar_2) { if (!Environment::getInstance()->isExperimentalBuild()) return; - NDArray x('c', {2,3}, {-1,-2,-3,-4,-5,-6}, nd4j::DataType::INT64); - NDArray exp('c',{2,3}, {10,10,10,10,10,10}, nd4j::DataType::FLOAT32); - NDArray scalar('c',{}, std::vector{10.f}, nd4j::DataType::FLOAT32); - NDArray z('c', {2,3}, {100,100,100,100,100,100}, nd4j::DataType::FLOAT32); + NDArray x('c', {2,3}, {-1,-2,-3,-4,-5,-6}, sd::DataType::INT64); + NDArray exp('c',{2,3}, {10,10,10,10,10,10}, sd::DataType::FLOAT32); + NDArray scalar('c',{}, std::vector{10.f}, sd::DataType::FLOAT32); + NDArray z('c', {2,3}, {100,100,100,100,100,100}, sd::DataType::FLOAT32); // create cuda stream and LaunchContext cudaError_t cudaResult; @@ -1020,7 +1020,7 @@ TEST_F(CudaBasicsTests1, execScalar_2) { LaunchContext lc(&stream); // call cuda kernel which calculates result - NativeOpExecutioner::execScalar(&lc, nd4j::scalar::CopyPws, + NativeOpExecutioner::execScalar(&lc, sd::scalar::CopyPws, nullptr, x.getShapeInfo(), x.specialBuffer(), x.specialShapeInfo(), nullptr, z.getShapeInfo(), z.specialBuffer(), z.specialShapeInfo(), nullptr, scalar.getShapeInfo(), scalar.specialBuffer(), scalar.specialShapeInfo(), @@ -1044,10 +1044,10 @@ TEST_F(CudaBasicsTests1, execScalar_3) { if (!Environment::getInstance()->isExperimentalBuild()) return; - NDArray x('c', {2,3,2}, {0,1,2,3,4,5,6,7,8,9,10,11}, nd4j::DataType::INT64); - NDArray scalars('c',{2,2}, {1,2,3,4}, nd4j::DataType::FLOAT32); - NDArray exp('c', {2,3,2}, {0,0,2,1,4,2, 2,1,2,2,3,2}, nd4j::DataType::INT64); - NDArray z('c', {2,3,2}, {100,100,100,100,100,100,100,100,100,100,100,100}, nd4j::DataType::INT64); + NDArray x('c', {2,3,2}, {0,1,2,3,4,5,6,7,8,9,10,11}, sd::DataType::INT64); + NDArray scalars('c',{2,2}, {1,2,3,4}, sd::DataType::FLOAT32); + NDArray exp('c', {2,3,2}, {0,0,2,1,4,2, 2,1,2,2,3,2}, sd::DataType::INT64); + NDArray z('c', {2,3,2}, {100,100,100,100,100,100,100,100,100,100,100,100}, sd::DataType::INT64); std::vector dimensions = {1}; @@ -1075,7 +1075,7 @@ TEST_F(CudaBasicsTests1, execScalar_3) { cudaResult = allocateDeviceMem(lc, devicePtrs, hostData); ASSERT_EQ(0, cudaResult); // call cuda kernel which calculates result - NativeOpExecutioner::execScalar(&lc, nd4j::scalar::Divide, + NativeOpExecutioner::execScalar(&lc, sd::scalar::Divide, nullptr, x.getShapeInfo(), x.specialBuffer(), x.specialShapeInfo(), nullptr, nullptr, z.getShapeInfo(), z.specialBuffer(), z.specialShapeInfo(), @@ -1102,10 +1102,10 @@ TEST_F(CudaBasicsTests1, execScalar_3) { //////////////////////////////////////////////////////////////////////////// TEST_F(CudaBasicsTests1, execScalarBool_1) { - NDArray x('c', {2,3}, {-1,-2,0,1,2,3}, nd4j::DataType::BFLOAT16); - NDArray scalar('c',{}, std::vector{0}, nd4j::DataType::BFLOAT16); - NDArray exp('c',{2,3}, {0,0,0,1,1,1}, nd4j::DataType::BOOL); - NDArray z('c', {2,3}, {100,100,100,100,100,100,}, nd4j::DataType::BOOL); + NDArray x('c', {2,3}, {-1,-2,0,1,2,3}, sd::DataType::BFLOAT16); + NDArray scalar('c',{}, std::vector{0}, sd::DataType::BFLOAT16); + NDArray exp('c',{2,3}, {0,0,0,1,1,1}, sd::DataType::BOOL); + NDArray z('c', {2,3}, {100,100,100,100,100,100,}, sd::DataType::BOOL); // create cuda stream and LaunchContext cudaError_t cudaResult; @@ -1115,7 +1115,7 @@ TEST_F(CudaBasicsTests1, execScalarBool_1) { // call cuda kernel which calculates result // call cuda kernel which calculates result - NativeOpExecutioner::execScalarBool(&lc, nd4j::scalar::GreaterThan, + NativeOpExecutioner::execScalarBool(&lc, sd::scalar::GreaterThan, nullptr, x.getShapeInfo(), x.specialBuffer(), x.specialShapeInfo(), nullptr, z.getShapeInfo(), z.specialBuffer(), z.specialShapeInfo(), nullptr, scalar.getShapeInfo(), scalar.specialBuffer(), scalar.specialShapeInfo(), @@ -1135,10 +1135,10 @@ TEST_F(CudaBasicsTests1, execScalarBool_1) { //////////////////////////////////////////////////////////////////////////// TEST_F(CudaBasicsTests1, execScalarBool_2) { - NDArray x('c', {2,3}, {0,1,2,3,4,5}, nd4j::DataType::FLOAT32); - NDArray scalars('c',{2}, {-1,4}, nd4j::DataType::FLOAT32); - NDArray exp('c', {2,3}, {1,1,1,0,0,1}, nd4j::DataType::BOOL); - NDArray z('c', {2,3}, {100,100,100,100,100,100}, nd4j::DataType::BOOL); + NDArray x('c', {2,3}, {0,1,2,3,4,5}, sd::DataType::FLOAT32); + NDArray scalars('c',{2}, {-1,4}, sd::DataType::FLOAT32); + NDArray exp('c', {2,3}, {1,1,1,0,0,1}, sd::DataType::BOOL); + NDArray z('c', {2,3}, {100,100,100,100,100,100}, sd::DataType::BOOL); std::vector dimensions = {1}; @@ -1165,7 +1165,7 @@ TEST_F(CudaBasicsTests1, execScalarBool_2) { cudaResult = allocateDeviceMem(lc, devicePtrs, hostData); ASSERT_EQ(0, cudaResult); // call cuda kernel which calculates result - NativeOpExecutioner::execScalarBool(&lc, nd4j::scalar::GreaterThan, + NativeOpExecutioner::execScalarBool(&lc, sd::scalar::GreaterThan, nullptr, x.getShapeInfo(), x.specialBuffer(), x.specialShapeInfo(), nullptr, nullptr, z.getShapeInfo(), z.specialBuffer(), z.specialShapeInfo(), @@ -1195,10 +1195,10 @@ TEST_F(CudaBasicsTests1, execBroadcast_1) { if (!Environment::getInstance()->isExperimentalBuild()) return; - NDArray x('c', {2,3,4}, {100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100}, nd4j::DataType::INT32); - NDArray y('c', {3}, {10, 20, 30}, nd4j::DataType::INT64); - NDArray z('c', {2,3,4}, {100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100}, nd4j::DataType::INT32); - NDArray exp('c', {2,3,4}, {10, 11, 12, 13,24, 25, 26, 27,38, 39, 40, 41,22, 23, 24, 25,36, 37, 38, 39,50, 51, 52, 53}, nd4j::DataType::INT32); + NDArray x('c', {2,3,4}, {100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100}, sd::DataType::INT32); + NDArray y('c', {3}, {10, 20, 30}, sd::DataType::INT64); + NDArray z('c', {2,3,4}, {100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100}, sd::DataType::INT32); + NDArray exp('c', {2,3,4}, {10, 11, 12, 13,24, 25, 26, 27,38, 39, 40, 41,22, 23, 24, 25,36, 37, 38, 39,50, 51, 52, 53}, sd::DataType::INT32); x.linspace(0); x.syncToDevice(); std::vector dimensions = {1}; @@ -1226,7 +1226,7 @@ TEST_F(CudaBasicsTests1, execBroadcast_1) { cudaResult = allocateDeviceMem(lc, devicePtrs, hostData); ASSERT_EQ(0, cudaResult); // call cuda kernel which calculates result - NativeOpExecutioner::execBroadcast(&lc, nd4j::broadcast::Add, + NativeOpExecutioner::execBroadcast(&lc, sd::broadcast::Add, nullptr, x.getShapeInfo(), x.specialBuffer(), x.specialShapeInfo(), nullptr, y.getShapeInfo(), y.specialBuffer(), y.specialShapeInfo(), nullptr, z.getShapeInfo(), z.specialBuffer(), z.specialShapeInfo(), @@ -1255,10 +1255,10 @@ TEST_F(CudaBasicsTests1, execBroadcast_2) { if (!Environment::getInstance()->isExperimentalBuild()) return; - NDArray x('c', {2,3,4}, {100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100}, nd4j::DataType::INT32); - NDArray y('c', {2,4}, {10,20,30,40,50,60,70,80}, nd4j::DataType::FLOAT32); - NDArray z('c', {2,3,4}, {100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100}, nd4j::DataType::FLOAT32); - NDArray exp('c', {2,3,4}, {10., 21., 32., 43., 14., 25., 36., 47., 18., 29., 40., 51., 62., 73., 84., 95., 66., 77., 88., 99., 70., 81., 92., 103}, nd4j::DataType::FLOAT32); + NDArray x('c', {2,3,4}, {100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100}, sd::DataType::INT32); + NDArray y('c', {2,4}, {10,20,30,40,50,60,70,80}, sd::DataType::FLOAT32); + NDArray z('c', {2,3,4}, {100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100}, sd::DataType::FLOAT32); + NDArray exp('c', {2,3,4}, {10., 21., 32., 43., 14., 25., 36., 47., 18., 29., 40., 51., 62., 73., 84., 95., 66., 77., 88., 99., 70., 81., 92., 103}, sd::DataType::FLOAT32); x.linspace(0); x.syncToDevice(); std::vector dimensions = {0,2}; @@ -1286,7 +1286,7 @@ TEST_F(CudaBasicsTests1, execBroadcast_2) { cudaResult = allocateDeviceMem(lc, devicePtrs, hostData); ASSERT_EQ(0, cudaResult); // call cuda kernel which calculates result - NativeOpExecutioner::execBroadcast(&lc, nd4j::broadcast::Add, + NativeOpExecutioner::execBroadcast(&lc, sd::broadcast::Add, nullptr, x.getShapeInfo(), x.specialBuffer(), x.specialShapeInfo(), nullptr, y.getShapeInfo(), y.specialBuffer(), y.specialShapeInfo(), nullptr, z.getShapeInfo(), z.specialBuffer(), z.specialShapeInfo(), @@ -1312,10 +1312,10 @@ TEST_F(CudaBasicsTests1, execBroadcast_2) { //////////////////////////////////////////////////////////////////////////// TEST_F(CudaBasicsTests1, execBroadcastBool_1) { - NDArray x('c', {2,3,4}, {100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100}, nd4j::DataType::INT32); - NDArray y('c', {3}, {2, 12, 22}, nd4j::DataType::INT32); - NDArray z('c', {2,3,4}, {100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,}, nd4j::DataType::BOOL); - NDArray exp('c', {2,3,4}, {0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0}, nd4j::DataType::BOOL); + NDArray x('c', {2,3,4}, {100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100}, sd::DataType::INT32); + NDArray y('c', {3}, {2, 12, 22}, sd::DataType::INT32); + NDArray z('c', {2,3,4}, {100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,}, sd::DataType::BOOL); + NDArray exp('c', {2,3,4}, {0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0}, sd::DataType::BOOL); x.linspace(1); x.syncToDevice(); std::vector dimensions = {1}; @@ -1343,7 +1343,7 @@ TEST_F(CudaBasicsTests1, execBroadcastBool_1) { cudaResult = allocateDeviceMem(lc, devicePtrs, hostData); ASSERT_EQ(0, cudaResult); // call cuda kernel which calculates result - NativeOpExecutioner::execBroadcastBool(&lc, nd4j::broadcast::EqualTo, + NativeOpExecutioner::execBroadcastBool(&lc, sd::broadcast::EqualTo, nullptr, x.getShapeInfo(), x.specialBuffer(), x.specialShapeInfo(), nullptr, y.getShapeInfo(), y.specialBuffer(), y.specialShapeInfo(), nullptr, z.getShapeInfo(), z.specialBuffer(), z.specialShapeInfo(), @@ -1370,10 +1370,10 @@ TEST_F(CudaBasicsTests1, execBroadcastBool_1) { //////////////////////////////////////////////////////////////////////////// TEST_F(CudaBasicsTests1, execBroadcastBool_2) { - NDArray x('c', {2,3,4}, {100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100},nd4j::DataType::FLOAT32); - NDArray y('c', {2,4}, {1,10,10,15,20,20,20,24}, nd4j::DataType::FLOAT32); - NDArray z('c', {2,3,4}, {100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100}, nd4j::DataType::BOOL); - NDArray exp('c', {2,3,4}, {1, 0, 0, 0,0, 0, 0, 0,0, 1, 0, 0,0, 0, 0, 0,0, 0, 0, 0,0, 0, 0, 1}, nd4j::DataType::BOOL); + NDArray x('c', {2,3,4}, {100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100},sd::DataType::FLOAT32); + NDArray y('c', {2,4}, {1,10,10,15,20,20,20,24}, sd::DataType::FLOAT32); + NDArray z('c', {2,3,4}, {100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100}, sd::DataType::BOOL); + NDArray exp('c', {2,3,4}, {1, 0, 0, 0,0, 0, 0, 0,0, 1, 0, 0,0, 0, 0, 0,0, 0, 0, 0,0, 0, 0, 1}, sd::DataType::BOOL); x.linspace(1); x.syncToDevice(); std::vector dimensions = {0,2}; @@ -1402,7 +1402,7 @@ TEST_F(CudaBasicsTests1, execBroadcastBool_2) { cudaResult = allocateDeviceMem(lc, devicePtrs, hostData); ASSERT_EQ(0, cudaResult); // call cuda kernel which calculates result - NativeOpExecutioner::execBroadcastBool(&lc, nd4j::broadcast::EqualTo, + NativeOpExecutioner::execBroadcastBool(&lc, sd::broadcast::EqualTo, nullptr, x.getShapeInfo(), x.specialBuffer(), x.specialShapeInfo(), nullptr, y.getShapeInfo(), y.specialBuffer(), y.specialShapeInfo(), nullptr, z.getShapeInfo(), z.specialBuffer(), z.specialShapeInfo(), @@ -1432,10 +1432,10 @@ TEST_F(CudaBasicsTests1, execPairwiseTransform_1) { if (!Environment::getInstance()->isExperimentalBuild()) return; - NDArray x('c', {2,2,2}, {1,5,3,7,2,6,4,8}, nd4j::DataType::INT32); - NDArray y('c', {4,2}, {0.1,0.2,0.3,0.4,1.5,0.6,0.7,1.8}, nd4j::DataType::DOUBLE); - NDArray z('c', {8}, {100,100,100,100,100,100,100,100}, nd4j::DataType::INT32); - NDArray exp('c', {8}, {0,1,2,3,3,5,6,6}, nd4j::DataType::INT32); + NDArray x('c', {2,2,2}, {1,5,3,7,2,6,4,8}, sd::DataType::INT32); + NDArray y('c', {4,2}, {0.1,0.2,0.3,0.4,1.5,0.6,0.7,1.8}, sd::DataType::DOUBLE); + NDArray z('c', {8}, {100,100,100,100,100,100,100,100}, sd::DataType::INT32); + NDArray exp('c', {8}, {0,1,2,3,3,5,6,6}, sd::DataType::INT32); x.permutei({2,1,0}); // -> {1,2,3,4,5,6,7,8} x.syncShape(); @@ -1446,7 +1446,7 @@ TEST_F(CudaBasicsTests1, execPairwiseTransform_1) { LaunchContext lc(&stream); // call cuda kernel which calculates result - NativeOpExecutioner::execPairwiseTransform(&lc, nd4j::pairwise::Subtract, + NativeOpExecutioner::execPairwiseTransform(&lc, sd::pairwise::Subtract, nullptr, x.getShapeInfo(), x.specialBuffer(), x.specialShapeInfo(), nullptr, y.getShapeInfo(), y.specialBuffer(), y.specialShapeInfo(), nullptr, z.getShapeInfo(), z.specialBuffer(), z.specialShapeInfo(), @@ -1466,10 +1466,10 @@ TEST_F(CudaBasicsTests1, execPairwiseTransform_1) { //////////////////////////////////////////////////////////////////////////// TEST_F(CudaBasicsTests1, execPairwiseBoolTransform_1) { - NDArray x('c', {2,2,2}, {1,5,3,7,2,6,4,8}, nd4j::DataType::INT64); - NDArray y('c', {4,2}, {0,2,0,4,0,6,0,8}, nd4j::DataType::INT64); - NDArray z('c', {8}, {100,100,100,100,100,100,100,100}, nd4j::DataType::BOOL); - NDArray exp('c', {8}, {0,1,0,1,0,1,0,1}, nd4j::DataType::BOOL); + NDArray x('c', {2,2,2}, {1,5,3,7,2,6,4,8}, sd::DataType::INT64); + NDArray y('c', {4,2}, {0,2,0,4,0,6,0,8}, sd::DataType::INT64); + NDArray z('c', {8}, {100,100,100,100,100,100,100,100}, sd::DataType::BOOL); + NDArray exp('c', {8}, {0,1,0,1,0,1,0,1}, sd::DataType::BOOL); x.permutei({2,1,0}); // -> {1,2,3,4,5,6,7,8} x.syncShape(); @@ -1480,7 +1480,7 @@ TEST_F(CudaBasicsTests1, execPairwiseBoolTransform_1) { LaunchContext lc(&stream); // call cuda kernel which calculates result - NativeOpExecutioner::execPairwiseBoolTransform(&lc, nd4j::pairwise::EqualTo, + NativeOpExecutioner::execPairwiseBoolTransform(&lc, sd::pairwise::EqualTo, nullptr, x.getShapeInfo(), x.specialBuffer(), x.specialShapeInfo(), nullptr, y.getShapeInfo(), y.specialBuffer(), y.specialShapeInfo(), nullptr, z.getShapeInfo(), z.specialBuffer(), z.specialShapeInfo(), @@ -1501,9 +1501,9 @@ TEST_F(CudaBasicsTests1, execPairwiseBoolTransform_1) { //////////////////////////////////////////////////////////////////////////// TEST_F(CudaBasicsTests1, execTransformFloat_1) { - NDArray x('c', {2,2}, {0, 6.25, 2.25, 12.25}, nd4j::DataType::DOUBLE); - NDArray z('c', {4}, {100,100,100,100}, nd4j::DataType::FLOAT32); - NDArray exp('c', {4}, {0, 1.5, 2.5, 3.5}, nd4j::DataType::FLOAT32); + NDArray x('c', {2,2}, {0, 6.25, 2.25, 12.25}, sd::DataType::DOUBLE); + NDArray z('c', {4}, {100,100,100,100}, sd::DataType::FLOAT32); + NDArray exp('c', {4}, {0, 1.5, 2.5, 3.5}, sd::DataType::FLOAT32); x.permutei({1,0}); x.syncShape(); @@ -1514,7 +1514,7 @@ TEST_F(CudaBasicsTests1, execTransformFloat_1) { LaunchContext lc(&stream); // call cuda kernel which calculates result - NativeOpExecutioner::execTransformFloat(&lc, nd4j::transform::Sqrt, + NativeOpExecutioner::execTransformFloat(&lc, sd::transform::Sqrt, nullptr, x.getShapeInfo(), x.specialBuffer(), x.specialShapeInfo(), nullptr, z.getShapeInfo(), z.specialBuffer(), z.specialShapeInfo(), nullptr, nullptr, nullptr); @@ -1533,9 +1533,9 @@ TEST_F(CudaBasicsTests1, execTransformFloat_1) { //////////////////////////////////////////////////////////////////////////// TEST_F(CudaBasicsTests1, execTransformFloat_2) { - NDArray x('c', {1,4}, {0, 4, 9, 16}, nd4j::DataType::INT64); - NDArray z('c', {2,2}, {100,100,100,100}, nd4j::DataType::DOUBLE); - NDArray exp('c', {2,2}, {0, 2, 3, 4}, nd4j::DataType::DOUBLE); + NDArray x('c', {1,4}, {0, 4, 9, 16}, sd::DataType::INT64); + NDArray z('c', {2,2}, {100,100,100,100}, sd::DataType::DOUBLE); + NDArray exp('c', {2,2}, {0, 2, 3, 4}, sd::DataType::DOUBLE); // create cuda stream and LaunchContext cudaError_t cudaResult; @@ -1544,7 +1544,7 @@ TEST_F(CudaBasicsTests1, execTransformFloat_2) { LaunchContext lc(&stream); // call cuda kernel which calculates result - NativeOpExecutioner::execTransformFloat(&lc, nd4j::transform::Sqrt, + NativeOpExecutioner::execTransformFloat(&lc, sd::transform::Sqrt, nullptr, x.getShapeInfo(), x.specialBuffer(), x.specialShapeInfo(), nullptr, z.getShapeInfo(), z.specialBuffer(), z.specialShapeInfo(), nullptr, nullptr, nullptr); @@ -1563,9 +1563,9 @@ TEST_F(CudaBasicsTests1, execTransformFloat_2) { //////////////////////////////////////////////////////////////////////////// TEST_F(CudaBasicsTests1, execTransformAny_1) { - NDArray x('c', {2,2}, {0, 6.25, 2.25, 12.25}, nd4j::DataType::DOUBLE); - NDArray z('c', {4,1}, {100,100,100,100}, nd4j::DataType::INT32); - NDArray exp('c', {4,1}, {0, 2, 6, 12}, nd4j::DataType::INT32); + NDArray x('c', {2,2}, {0, 6.25, 2.25, 12.25}, sd::DataType::DOUBLE); + NDArray z('c', {4,1}, {100,100,100,100}, sd::DataType::INT32); + NDArray exp('c', {4,1}, {0, 2, 6, 12}, sd::DataType::INT32); x.permutei({1,0}); // create cuda stream and LaunchContext @@ -1575,7 +1575,7 @@ TEST_F(CudaBasicsTests1, execTransformAny_1) { LaunchContext lc(&stream); // call cuda kernel which calculates result - NativeOpExecutioner::execTransformAny(&lc, nd4j::transform::Assign, + NativeOpExecutioner::execTransformAny(&lc, sd::transform::Assign, nullptr, x.getShapeInfo(), x.specialBuffer(), x.specialShapeInfo(), nullptr, z.getShapeInfo(), z.specialBuffer(), z.specialShapeInfo(), nullptr, nullptr, nullptr); @@ -1594,9 +1594,9 @@ TEST_F(CudaBasicsTests1, execTransformAny_1) { //////////////////////////////////////////////////////////////////////////// TEST_F(CudaBasicsTests1, execTransformAny_2) { - NDArray x('c', {1,4}, {0, 6.25, 2.25, 12.25}, nd4j::DataType::BFLOAT16); - NDArray z('c', {2,2}, {100,100,100,100}, nd4j::DataType::FLOAT32); - NDArray exp('c', {2,2}, {0, 6.25, 2.25, 12.25}, nd4j::DataType::FLOAT32); + NDArray x('c', {1,4}, {0, 6.25, 2.25, 12.25}, sd::DataType::BFLOAT16); + NDArray z('c', {2,2}, {100,100,100,100}, sd::DataType::FLOAT32); + NDArray exp('c', {2,2}, {0, 6.25, 2.25, 12.25}, sd::DataType::FLOAT32); // create cuda stream and LaunchContext cudaError_t cudaResult; @@ -1605,7 +1605,7 @@ TEST_F(CudaBasicsTests1, execTransformAny_2) { LaunchContext lc(&stream); // call cuda kernel which calculates result - NativeOpExecutioner::execTransformAny(&lc, nd4j::transform::Assign, + NativeOpExecutioner::execTransformAny(&lc, sd::transform::Assign, nullptr, x.getShapeInfo(), x.specialBuffer(), x.specialShapeInfo(), nullptr, z.getShapeInfo(), z.specialBuffer(), z.specialShapeInfo(), nullptr, nullptr, nullptr); @@ -1624,9 +1624,9 @@ TEST_F(CudaBasicsTests1, execTransformAny_2) { //////////////////////////////////////////////////////////////////////////// TEST_F(CudaBasicsTests1, execTransformStrict_1) { - NDArray x('c', {2,3}, {0,2,4,1,3,5}, nd4j::DataType::DOUBLE); - NDArray z('c', {3,2}, {100,100,100,100,100,100}, nd4j::DataType::DOUBLE); - NDArray exp('c', {3,2}, {0, 3, 12, 27, 48, 75}, nd4j::DataType::DOUBLE); + NDArray x('c', {2,3}, {0,2,4,1,3,5}, sd::DataType::DOUBLE); + NDArray z('c', {3,2}, {100,100,100,100,100,100}, sd::DataType::DOUBLE); + NDArray exp('c', {3,2}, {0, 3, 12, 27, 48, 75}, sd::DataType::DOUBLE); x.permutei({1,0}); // create cuda stream and LaunchContext @@ -1636,7 +1636,7 @@ TEST_F(CudaBasicsTests1, execTransformStrict_1) { LaunchContext lc(&stream); // call cuda kernel which calculates result - NativeOpExecutioner::execTransformStrict(&lc, nd4j::transform::CubeDerivative, + NativeOpExecutioner::execTransformStrict(&lc, sd::transform::CubeDerivative, nullptr, x.getShapeInfo(), x.specialBuffer(), x.specialShapeInfo(), nullptr, z.getShapeInfo(), z.specialBuffer(), z.specialShapeInfo(), nullptr, nullptr, nullptr); @@ -1655,9 +1655,9 @@ TEST_F(CudaBasicsTests1, execTransformStrict_1) { //////////////////////////////////////////////////////////////////////////// TEST_F(CudaBasicsTests1, execTransformStrict_2) { - NDArray x('c', {6}, {0,1,2,3,4,5}, nd4j::DataType::FLOAT32); - NDArray z('c', {3,2}, {100,100,100,100,100,100}, nd4j::DataType::FLOAT32); - NDArray exp('c', {3,2}, {0, 3, 12, 27, 48, 75}, nd4j::DataType::FLOAT32); + NDArray x('c', {6}, {0,1,2,3,4,5}, sd::DataType::FLOAT32); + NDArray z('c', {3,2}, {100,100,100,100,100,100}, sd::DataType::FLOAT32); + NDArray exp('c', {3,2}, {0, 3, 12, 27, 48, 75}, sd::DataType::FLOAT32); // create cuda stream and LaunchContext cudaError_t cudaResult; @@ -1666,7 +1666,7 @@ TEST_F(CudaBasicsTests1, execTransformStrict_2) { LaunchContext lc(&stream); // call cuda kernel which calculates result - NativeOpExecutioner::execTransformStrict(&lc, nd4j::transform::CubeDerivative, + NativeOpExecutioner::execTransformStrict(&lc, sd::transform::CubeDerivative, nullptr, x.getShapeInfo(), x.specialBuffer(), x.specialShapeInfo(), nullptr, z.getShapeInfo(), z.specialBuffer(), z.specialShapeInfo(), nullptr, nullptr, nullptr); @@ -1685,9 +1685,9 @@ TEST_F(CudaBasicsTests1, execTransformStrict_2) { //////////////////////////////////////////////////////////////////////////// TEST_F(CudaBasicsTests1, execTransformSame_1) { - NDArray x('c', {2,3}, {0,2.5,4.5,1.5,3.5,5.5}, nd4j::DataType::DOUBLE); - NDArray z('c', {1,6}, {100,100,100,100,100,100}, nd4j::DataType::DOUBLE); - NDArray exp('c', {1,6}, {0,2.25,6.25,12.25,20.25,30.25}, nd4j::DataType::DOUBLE); + NDArray x('c', {2,3}, {0,2.5,4.5,1.5,3.5,5.5}, sd::DataType::DOUBLE); + NDArray z('c', {1,6}, {100,100,100,100,100,100}, sd::DataType::DOUBLE); + NDArray exp('c', {1,6}, {0,2.25,6.25,12.25,20.25,30.25}, sd::DataType::DOUBLE); x.permutei({1,0}); // create cuda stream and LaunchContext @@ -1697,7 +1697,7 @@ TEST_F(CudaBasicsTests1, execTransformSame_1) { LaunchContext lc(&stream); // call cuda kernel which calculates result - NativeOpExecutioner::execTransformSame(&lc, nd4j::transform::Square, + NativeOpExecutioner::execTransformSame(&lc, sd::transform::Square, nullptr, x.getShapeInfo(), x.specialBuffer(), x.specialShapeInfo(), nullptr, z.getShapeInfo(), z.specialBuffer(), z.specialShapeInfo(), nullptr, nullptr, nullptr); @@ -1716,9 +1716,9 @@ TEST_F(CudaBasicsTests1, execTransformSame_1) { //////////////////////////////////////////////////////////////////////////// TEST_F(CudaBasicsTests1, execTransformSame_2) { - NDArray x('c', {6}, {0,1,2,3,4,5}, nd4j::DataType::INT32); - NDArray z('c', {3,2}, {100,100,100,100,100,100}, nd4j::DataType::INT32); - NDArray exp('c', {3,2}, {0,1,4,9,16,25}, nd4j::DataType::INT32); + NDArray x('c', {6}, {0,1,2,3,4,5}, sd::DataType::INT32); + NDArray z('c', {3,2}, {100,100,100,100,100,100}, sd::DataType::INT32); + NDArray exp('c', {3,2}, {0,1,4,9,16,25}, sd::DataType::INT32); // create cuda stream and LaunchContext cudaError_t cudaResult; @@ -1727,7 +1727,7 @@ TEST_F(CudaBasicsTests1, execTransformSame_2) { LaunchContext lc(&stream); // call cuda kernel which calculates result - NativeOpExecutioner::execTransformSame(&lc, nd4j::transform::Square, + NativeOpExecutioner::execTransformSame(&lc, sd::transform::Square, nullptr, x.getShapeInfo(), x.specialBuffer(), x.specialShapeInfo(), nullptr, z.getShapeInfo(), z.specialBuffer(), z.specialShapeInfo(), nullptr, nullptr, nullptr); @@ -1746,9 +1746,9 @@ TEST_F(CudaBasicsTests1, execTransformSame_2) { //////////////////////////////////////////////////////////////////////////// TEST_F(CudaBasicsTests1, execTransformBool_1) { - NDArray x('c', {2,3}, {0,2,4,-1,-3,-5}, nd4j::DataType::DOUBLE); - NDArray z('c', {1,6}, {100,100,100,100,100,100}, nd4j::DataType::BOOL); - NDArray exp('c', {1,6}, {0,0,1,0,1,0}, nd4j::DataType::BOOL); + NDArray x('c', {2,3}, {0,2,4,-1,-3,-5}, sd::DataType::DOUBLE); + NDArray z('c', {1,6}, {100,100,100,100,100,100}, sd::DataType::BOOL); + NDArray exp('c', {1,6}, {0,0,1,0,1,0}, sd::DataType::BOOL); x.permutei({1,0}); // create cuda stream and LaunchContext @@ -1758,7 +1758,7 @@ TEST_F(CudaBasicsTests1, execTransformBool_1) { LaunchContext lc(&stream); // call cuda kernel which calculates result - NativeOpExecutioner::execTransformBool(&lc, nd4j::transform::IsPositive, + NativeOpExecutioner::execTransformBool(&lc, sd::transform::IsPositive, nullptr, x.getShapeInfo(), x.specialBuffer(), x.specialShapeInfo(), nullptr, z.getShapeInfo(), z.specialBuffer(), z.specialShapeInfo(), nullptr, nullptr, nullptr); @@ -1777,9 +1777,9 @@ TEST_F(CudaBasicsTests1, execTransformBool_1) { //////////////////////////////////////////////////////////////////////////// TEST_F(CudaBasicsTests1, execTransformBool_2) { - NDArray x('c', {6}, {0,-1,2,-3,4,-5}, nd4j::DataType::INT32); - NDArray z('c', {3,2}, {100,100,100,100,100,100}, nd4j::DataType::BOOL); - NDArray exp('c', {3,2}, {0,0,1,0,1,0}, nd4j::DataType::BOOL); + NDArray x('c', {6}, {0,-1,2,-3,4,-5}, sd::DataType::INT32); + NDArray z('c', {3,2}, {100,100,100,100,100,100}, sd::DataType::BOOL); + NDArray exp('c', {3,2}, {0,0,1,0,1,0}, sd::DataType::BOOL); // create cuda stream and LaunchContext cudaError_t cudaResult; @@ -1788,7 +1788,7 @@ TEST_F(CudaBasicsTests1, execTransformBool_2) { LaunchContext lc(&stream); // call cuda kernel which calculates result - NativeOpExecutioner::execTransformBool(&lc, nd4j::transform::IsPositive, + NativeOpExecutioner::execTransformBool(&lc, sd::transform::IsPositive, nullptr, x.getShapeInfo(), x.specialBuffer(), x.specialShapeInfo(), nullptr, z.getShapeInfo(), z.specialBuffer(), z.specialShapeInfo(), nullptr, nullptr, nullptr); @@ -1807,9 +1807,9 @@ TEST_F(CudaBasicsTests1, execTransformBool_2) { //////////////////////////////////////////////////////////////////////////// TEST_F(CudaBasicsTests1, execReduceFloat_1) { - NDArray x('c', {2,3,4}, {-5,-4,-3,-2,-1,0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18}, nd4j::DataType::INT32); - NDArray z('c', {3}, {100,100,100}, nd4j::DataType::FLOAT32); - NDArray exp('c', {3}, {2.5, 6.5, 10.5}, nd4j::DataType::FLOAT32); + NDArray x('c', {2,3,4}, {-5,-4,-3,-2,-1,0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18}, sd::DataType::INT32); + NDArray z('c', {3}, {100,100,100}, sd::DataType::FLOAT32); + NDArray exp('c', {3}, {2.5, 6.5, 10.5}, sd::DataType::FLOAT32); x.permutei({2,1,0}); std::vector dimensions = {0,2}; @@ -1837,7 +1837,7 @@ TEST_F(CudaBasicsTests1, execReduceFloat_1) { cudaResult = allocateDeviceMem(lc, devicePtrs, hostData); ASSERT_EQ(0, cudaResult); // call cuda kernel which calculates result - NativeOpExecutioner::execReduceFloat(&lc, nd4j::reduce::Mean, + NativeOpExecutioner::execReduceFloat(&lc, sd::reduce::Mean, nullptr, x.getShapeInfo(), x.specialBuffer(), x.specialShapeInfo(), nullptr, nullptr, z.getShapeInfo(), z.specialBuffer(), z.specialShapeInfo(), @@ -1862,9 +1862,9 @@ TEST_F(CudaBasicsTests1, execReduceFloat_1) { //////////////////////////////////////////////////////////////////////////// TEST_F(CudaBasicsTests1, execReduceFloat_2) { - NDArray x('c', {2,3,4}, {-5,-4,-3,-2,-1,0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18}, nd4j::DataType::INT32); - NDArray z('c', {2,4}, {100,100,100,100,100,100,100,100}, nd4j::DataType::DOUBLE); - NDArray exp('c', {2,4}, {-1., 0., 1., 2.,11., 12., 13., 14.}, nd4j::DataType::DOUBLE); + NDArray x('c', {2,3,4}, {-5,-4,-3,-2,-1,0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18}, sd::DataType::INT32); + NDArray z('c', {2,4}, {100,100,100,100,100,100,100,100}, sd::DataType::DOUBLE); + NDArray exp('c', {2,4}, {-1., 0., 1., 2.,11., 12., 13., 14.}, sd::DataType::DOUBLE); std::vector dimensions = {1}; @@ -1891,7 +1891,7 @@ TEST_F(CudaBasicsTests1, execReduceFloat_2) { cudaResult = allocateDeviceMem(lc, devicePtrs, hostData); ASSERT_EQ(0, cudaResult); // call cuda kernel which calculates result - NativeOpExecutioner::execReduceFloat(&lc, nd4j::reduce::Mean, + NativeOpExecutioner::execReduceFloat(&lc, sd::reduce::Mean, nullptr, x.getShapeInfo(), x.specialBuffer(), x.specialShapeInfo(), nullptr, nullptr, z.getShapeInfo(), z.specialBuffer(), z.specialShapeInfo(), @@ -1916,9 +1916,9 @@ TEST_F(CudaBasicsTests1, execReduceFloat_2) { //////////////////////////////////////////////////////////////////////////// TEST_F(CudaBasicsTests1, execReduceSame_1) { - NDArray x('c', {2,3,4}, {-5,-4,-3,-2,-1,0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18}, nd4j::DataType::INT32); - NDArray z('c', {3}, {100,100,100}, nd4j::DataType::INT32); - NDArray exp('c', {3}, {20, 52, 84}, nd4j::DataType::INT32); + NDArray x('c', {2,3,4}, {-5,-4,-3,-2,-1,0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18}, sd::DataType::INT32); + NDArray z('c', {3}, {100,100,100}, sd::DataType::INT32); + NDArray exp('c', {3}, {20, 52, 84}, sd::DataType::INT32); x.permutei({2,1,0}); std::vector dimensions = {0,2}; @@ -1946,7 +1946,7 @@ TEST_F(CudaBasicsTests1, execReduceSame_1) { cudaResult = allocateDeviceMem(lc, devicePtrs, hostData); ASSERT_EQ(0, cudaResult); // call cuda kernel which calculates result - NativeOpExecutioner::execReduceSame(&lc, nd4j::reduce::Sum, + NativeOpExecutioner::execReduceSame(&lc, sd::reduce::Sum, nullptr, x.getShapeInfo(), x.specialBuffer(), x.specialShapeInfo(), nullptr, nullptr, z.getShapeInfo(), z.specialBuffer(), z.specialShapeInfo(), @@ -1971,9 +1971,9 @@ TEST_F(CudaBasicsTests1, execReduceSame_1) { //////////////////////////////////////////////////////////////////////////// TEST_F(CudaBasicsTests1, execReduceSame_2) { - NDArray x('c', {2,3,4}, {-5,-4,-3,-2,-1,0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18}, nd4j::DataType::FLOAT32); - NDArray z('c', {2,4}, {100,100,100,100,100,100,100,100}, nd4j::DataType::FLOAT32); - NDArray exp('c', {2,4}, {-3., 0., 3., 6.,33., 36., 39., 42.}, nd4j::DataType::FLOAT32); + NDArray x('c', {2,3,4}, {-5,-4,-3,-2,-1,0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18}, sd::DataType::FLOAT32); + NDArray z('c', {2,4}, {100,100,100,100,100,100,100,100}, sd::DataType::FLOAT32); + NDArray exp('c', {2,4}, {-3., 0., 3., 6.,33., 36., 39., 42.}, sd::DataType::FLOAT32); std::vector dimensions = {1}; @@ -2000,7 +2000,7 @@ TEST_F(CudaBasicsTests1, execReduceSame_2) { cudaResult = allocateDeviceMem(lc, devicePtrs, hostData); ASSERT_EQ(0, cudaResult); // call cuda kernel which calculates result - NativeOpExecutioner::execReduceSame(&lc, nd4j::reduce::Sum, + NativeOpExecutioner::execReduceSame(&lc, sd::reduce::Sum, nullptr, x.getShapeInfo(), x.specialBuffer(), x.specialShapeInfo(), nullptr, nullptr, z.getShapeInfo(), z.specialBuffer(), z.specialShapeInfo(), @@ -2025,9 +2025,9 @@ TEST_F(CudaBasicsTests1, execReduceSame_2) { //////////////////////////////////////////////////////////////////////////// TEST_F(CudaBasicsTests1, execReduceBool_1) { - NDArray x('c', {2,3,4}, {-5,-4,-3,-2,-1,0,1,2,3,4,5,6,-7,-8,-9,-10,-11,-12,-13,-14,-15,-16,-17,-18}, nd4j::DataType::INT32); - NDArray z('c', {3}, {100,100,100}, nd4j::DataType::BOOL); - NDArray exp('c', {3}, {0, 1, 1}, nd4j::DataType::BOOL); + NDArray x('c', {2,3,4}, {-5,-4,-3,-2,-1,0,1,2,3,4,5,6,-7,-8,-9,-10,-11,-12,-13,-14,-15,-16,-17,-18}, sd::DataType::INT32); + NDArray z('c', {3}, {100,100,100}, sd::DataType::BOOL); + NDArray exp('c', {3}, {0, 1, 1}, sd::DataType::BOOL); x.permutei({2,1,0}); @@ -2056,7 +2056,7 @@ TEST_F(CudaBasicsTests1, execReduceBool_1) { cudaResult = allocateDeviceMem(lc, devicePtrs, hostData); ASSERT_EQ(0, cudaResult); // call cuda kernel which calculates result - NativeOpExecutioner::execReduceBool(&lc, nd4j::reduce::IsPositive, + NativeOpExecutioner::execReduceBool(&lc, sd::reduce::IsPositive, nullptr, x.getShapeInfo(), x.specialBuffer(), x.specialShapeInfo(), nullptr, nullptr, z.getShapeInfo(), z.specialBuffer(), z.specialShapeInfo(), @@ -2081,9 +2081,9 @@ TEST_F(CudaBasicsTests1, execReduceBool_1) { //////////////////////////////////////////////////////////////////////////// TEST_F(CudaBasicsTests1, execReduceBool_2) { - NDArray x('c', {2,3,4}, {-5,-4,-3,-2,-1,0,1,2,3,4,5,6,-7,-8,-9,-10,-11,-12,-13,-14,-15,-16,-17,-18}, nd4j::DataType::FLOAT32); - NDArray z('c', {2,4}, {100,100,100,100,100,100,100,100}, nd4j::DataType::BOOL); - NDArray exp('c', {2,4}, {1, 1, 1, 1, 0, 0, 0, 0}, nd4j::DataType::BOOL); + NDArray x('c', {2,3,4}, {-5,-4,-3,-2,-1,0,1,2,3,4,5,6,-7,-8,-9,-10,-11,-12,-13,-14,-15,-16,-17,-18}, sd::DataType::FLOAT32); + NDArray z('c', {2,4}, {100,100,100,100,100,100,100,100}, sd::DataType::BOOL); + NDArray exp('c', {2,4}, {1, 1, 1, 1, 0, 0, 0, 0}, sd::DataType::BOOL); std::vector dimensions = {1}; @@ -2110,7 +2110,7 @@ TEST_F(CudaBasicsTests1, execReduceBool_2) { cudaResult = allocateDeviceMem(lc, devicePtrs, hostData); ASSERT_EQ(0, cudaResult); // call cuda kernel which calculates result - NativeOpExecutioner::execReduceBool(&lc, nd4j::reduce::IsPositive, + NativeOpExecutioner::execReduceBool(&lc, sd::reduce::IsPositive, nullptr, x.getShapeInfo(), x.specialBuffer(), x.specialShapeInfo(), nullptr, nullptr, z.getShapeInfo(), z.specialBuffer(), z.specialShapeInfo(), @@ -2135,9 +2135,9 @@ TEST_F(CudaBasicsTests1, execReduceBool_2) { //////////////////////////////////////////////////////////////////////////// TEST_F(CudaBasicsTests1, execReduceLong_1) { - NDArray x('c', {2,3,4}, {-5,0,-3,0,-1,0,1,2,3,4,5,6,7,0,9,10,11,0,13,14,0,16,0,18}, nd4j::DataType::INT32); - NDArray z('c', {3}, {100,100,100}, nd4j::DataType::INT64); - NDArray exp('c', {3}, {5,6,6}, nd4j::DataType::INT64); + NDArray x('c', {2,3,4}, {-5,0,-3,0,-1,0,1,2,3,4,5,6,7,0,9,10,11,0,13,14,0,16,0,18}, sd::DataType::INT32); + NDArray z('c', {3}, {100,100,100}, sd::DataType::INT64); + NDArray exp('c', {3}, {5,6,6}, sd::DataType::INT64); x.permutei({2,1,0}); std::vector dimensions = {0,2}; @@ -2165,7 +2165,7 @@ TEST_F(CudaBasicsTests1, execReduceLong_1) { cudaResult = allocateDeviceMem(lc, devicePtrs, hostData); ASSERT_EQ(0, cudaResult); // call cuda kernel which calculates result - NativeOpExecutioner::execReduceLong(&lc, nd4j::reduce::CountNonZero, + NativeOpExecutioner::execReduceLong(&lc, sd::reduce::CountNonZero, nullptr, x.getShapeInfo(), x.specialBuffer(), x.specialShapeInfo(), nullptr, nullptr, z.getShapeInfo(), z.specialBuffer(), z.specialShapeInfo(), @@ -2190,9 +2190,9 @@ TEST_F(CudaBasicsTests1, execReduceLong_1) { //////////////////////////////////////////////////////////////////////////// TEST_F(CudaBasicsTests1, execReduceLong_2) { - NDArray x('c', {2,3,4}, {-5,0,-3,0,-1,0,1,2,3,4,5,6,7,0,9,10,11,0,13,14,0,16,0,18}, nd4j::DataType::FLOAT32); - NDArray z('c', {2,4}, {100,100,100,100,100,100,100,100}, nd4j::DataType::INT64); - NDArray exp('c', {2,4}, {3, 1, 3, 2, 2, 1, 2, 3}, nd4j::DataType::INT64); + NDArray x('c', {2,3,4}, {-5,0,-3,0,-1,0,1,2,3,4,5,6,7,0,9,10,11,0,13,14,0,16,0,18}, sd::DataType::FLOAT32); + NDArray z('c', {2,4}, {100,100,100,100,100,100,100,100}, sd::DataType::INT64); + NDArray exp('c', {2,4}, {3, 1, 3, 2, 2, 1, 2, 3}, sd::DataType::INT64); std::vector dimensions = {1}; @@ -2219,7 +2219,7 @@ TEST_F(CudaBasicsTests1, execReduceLong_2) { cudaResult = allocateDeviceMem(lc, devicePtrs, hostData); ASSERT_EQ(0, cudaResult); // call cuda kernel which calculates result - NativeOpExecutioner::execReduceLong(&lc, nd4j::reduce::CountNonZero, + NativeOpExecutioner::execReduceLong(&lc, sd::reduce::CountNonZero, nullptr, x.getShapeInfo(), x.specialBuffer(), x.specialShapeInfo(), nullptr, nullptr, z.getShapeInfo(), z.specialBuffer(), z.specialShapeInfo(), @@ -2244,9 +2244,9 @@ TEST_F(CudaBasicsTests1, execReduceLong_2) { //////////////////////////////////////////////////////////////////////////// TEST_F(CudaBasicsTests1, execReduceFloatScalar_1) { - NDArray x('c', {2,3,4}, {-5,-4,-3,-2,-1,0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18}, nd4j::DataType::INT32); - NDArray z('c', {}, std::vector{100}, nd4j::DataType::FLOAT32); - NDArray exp('c', {}, std::vector{6.5}, nd4j::DataType::FLOAT32); + NDArray x('c', {2,3,4}, {-5,-4,-3,-2,-1,0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18}, sd::DataType::INT32); + NDArray z('c', {}, std::vector{100}, sd::DataType::FLOAT32); + NDArray exp('c', {}, std::vector{6.5}, sd::DataType::FLOAT32); x.permutei({2,1,0}); // create cuda stream and LaunchContext @@ -2262,7 +2262,7 @@ TEST_F(CudaBasicsTests1, execReduceFloatScalar_1) { lc.setAllocationPointer(allocationPointer); // call cuda kernel which calculates result - NativeOpExecutioner::execReduceFloatScalar(&lc, nd4j::reduce::Mean, + NativeOpExecutioner::execReduceFloatScalar(&lc, sd::reduce::Mean, nullptr, x.getShapeInfo(), x.specialBuffer(), x.specialShapeInfo(), nullptr, nullptr, z.getShapeInfo(), z.specialBuffer(), z.specialShapeInfo()); @@ -2281,9 +2281,9 @@ TEST_F(CudaBasicsTests1, execReduceFloatScalar_1) { //////////////////////////////////////////////////////////////////////////// TEST_F(CudaBasicsTests1, execReduceFloatScalar_2) { - NDArray x('c', {2,3,4}, {-5,-4,-3,-2,-1,0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18}, nd4j::DataType::INT32); - NDArray z('c', {}, std::vector{100}, nd4j::DataType::DOUBLE); - NDArray exp('c', {}, std::vector{6.5}, nd4j::DataType::DOUBLE); + NDArray x('c', {2,3,4}, {-5,-4,-3,-2,-1,0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18}, sd::DataType::INT32); + NDArray z('c', {}, std::vector{100}, sd::DataType::DOUBLE); + NDArray exp('c', {}, std::vector{6.5}, sd::DataType::DOUBLE); // create cuda stream and LaunchContext cudaError_t cudaResult; @@ -2298,7 +2298,7 @@ TEST_F(CudaBasicsTests1, execReduceFloatScalar_2) { lc.setAllocationPointer(allocationPointer); // call cuda kernel which calculates result - NativeOpExecutioner::execReduceFloatScalar(&lc, nd4j::reduce::Mean, + NativeOpExecutioner::execReduceFloatScalar(&lc, sd::reduce::Mean, nullptr, x.getShapeInfo(), x.specialBuffer(), x.specialShapeInfo(), nullptr, nullptr, z.getShapeInfo(), z.specialBuffer(), z.specialShapeInfo()); @@ -2317,9 +2317,9 @@ TEST_F(CudaBasicsTests1, execReduceFloatScalar_2) { //////////////////////////////////////////////////////////////////////////// TEST_F(CudaBasicsTests1, execReduceSameScalar_1) { - NDArray x('c', {2,3,4}, {-5,-4,-3,-2,-1,0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18}, nd4j::DataType::INT32); - NDArray z('c', {}, std::vector{100}, nd4j::DataType::INT32); - NDArray exp('c', {}, std::vector{156}, nd4j::DataType::INT32); + NDArray x('c', {2,3,4}, {-5,-4,-3,-2,-1,0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18}, sd::DataType::INT32); + NDArray z('c', {}, std::vector{100}, sd::DataType::INT32); + NDArray exp('c', {}, std::vector{156}, sd::DataType::INT32); x.permutei({2,1,0}); // create cuda stream and LaunchContext @@ -2335,7 +2335,7 @@ TEST_F(CudaBasicsTests1, execReduceSameScalar_1) { lc.setAllocationPointer(allocationPointer); // call cuda kernel which calculates result - NativeOpExecutioner::execReduceSameScalar(&lc, nd4j::reduce::Sum, + NativeOpExecutioner::execReduceSameScalar(&lc, sd::reduce::Sum, nullptr, x.getShapeInfo(), x.specialBuffer(), x.specialShapeInfo(), nullptr, nullptr, z.getShapeInfo(), z.specialBuffer(), z.specialShapeInfo()); @@ -2354,9 +2354,9 @@ TEST_F(CudaBasicsTests1, execReduceSameScalar_1) { //////////////////////////////////////////////////////////////////////////// TEST_F(CudaBasicsTests1, execReduceSameScalar_2) { - NDArray x('c', {2,3,4}, {-5,-4,-3,-2,-1,0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18}, nd4j::DataType::DOUBLE); - NDArray z('c', {}, std::vector{100}, nd4j::DataType::DOUBLE); - NDArray exp('c', {}, std::vector{156}, nd4j::DataType::DOUBLE); + NDArray x('c', {2,3,4}, {-5,-4,-3,-2,-1,0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18}, sd::DataType::DOUBLE); + NDArray z('c', {}, std::vector{100}, sd::DataType::DOUBLE); + NDArray exp('c', {}, std::vector{156}, sd::DataType::DOUBLE); // create cuda stream and LaunchContext cudaError_t cudaResult; @@ -2371,7 +2371,7 @@ TEST_F(CudaBasicsTests1, execReduceSameScalar_2) { lc.setAllocationPointer(allocationPointer); // call cuda kernel which calculates result - NativeOpExecutioner::execReduceSameScalar(&lc, nd4j::reduce::Sum, + NativeOpExecutioner::execReduceSameScalar(&lc, sd::reduce::Sum, nullptr, x.getShapeInfo(), x.specialBuffer(), x.specialShapeInfo(), nullptr, nullptr, z.getShapeInfo(), z.specialBuffer(), z.specialShapeInfo()); @@ -2390,9 +2390,9 @@ TEST_F(CudaBasicsTests1, execReduceSameScalar_2) { //////////////////////////////////////////////////////////////////////////// TEST_F(CudaBasicsTests1, execReduceBoolScalar_1) { - NDArray x('c', {2,3,4}, {-5,-4,-3,-2,-1,0,1,2,3,4,5,6,-7,-8,-9,-10,-11,-12,-13,-14,-15,-16,-17,-18}, nd4j::DataType::INT32); - NDArray z('c', {}, std::vector{100}, nd4j::DataType::BOOL); - NDArray exp('c', {}, std::vector{1}, nd4j::DataType::BOOL); + NDArray x('c', {2,3,4}, {-5,-4,-3,-2,-1,0,1,2,3,4,5,6,-7,-8,-9,-10,-11,-12,-13,-14,-15,-16,-17,-18}, sd::DataType::INT32); + NDArray z('c', {}, std::vector{100}, sd::DataType::BOOL); + NDArray exp('c', {}, std::vector{1}, sd::DataType::BOOL); x.permutei({2,1,0}); x.syncShape(); @@ -2409,7 +2409,7 @@ TEST_F(CudaBasicsTests1, execReduceBoolScalar_1) { lc.setAllocationPointer(allocationPointer); // call cuda kernel which calculates result - NativeOpExecutioner::execReduceBoolScalar(&lc, nd4j::reduce::IsPositive, + NativeOpExecutioner::execReduceBoolScalar(&lc, sd::reduce::IsPositive, nullptr, x.getShapeInfo(), x.specialBuffer(), x.specialShapeInfo(), nullptr, nullptr, z.getShapeInfo(), z.specialBuffer(), z.specialShapeInfo()); @@ -2428,9 +2428,9 @@ TEST_F(CudaBasicsTests1, execReduceBoolScalar_1) { //////////////////////////////////////////////////////////////////////////// TEST_F(CudaBasicsTests1, execReduceBoolScalar_2) { - NDArray x('c', {2,3,4}, {-5,-4,-3,-2,-1,0,1,2,3,4,5,6,-7,-8,-9,-10,-11,-12,-13,-14,-15,-16,-17,-18}, nd4j::DataType::DOUBLE); - NDArray z('c', {}, std::vector{100}, nd4j::DataType::BOOL); - NDArray exp('c', {}, std::vector{1}, nd4j::DataType::BOOL); + NDArray x('c', {2,3,4}, {-5,-4,-3,-2,-1,0,1,2,3,4,5,6,-7,-8,-9,-10,-11,-12,-13,-14,-15,-16,-17,-18}, sd::DataType::DOUBLE); + NDArray z('c', {}, std::vector{100}, sd::DataType::BOOL); + NDArray exp('c', {}, std::vector{1}, sd::DataType::BOOL); // create cuda stream and LaunchContext cudaError_t cudaResult; @@ -2445,7 +2445,7 @@ TEST_F(CudaBasicsTests1, execReduceBoolScalar_2) { lc.setAllocationPointer(allocationPointer); // call cuda kernel which calculates result - NativeOpExecutioner::execReduceBoolScalar(&lc, nd4j::reduce::IsPositive, + NativeOpExecutioner::execReduceBoolScalar(&lc, sd::reduce::IsPositive, nullptr, x.getShapeInfo(), x.specialBuffer(), x.specialShapeInfo(), nullptr, nullptr, z.getShapeInfo(), z.specialBuffer(), z.specialShapeInfo()); @@ -2464,9 +2464,9 @@ TEST_F(CudaBasicsTests1, execReduceBoolScalar_2) { //////////////////////////////////////////////////////////////////////////// TEST_F(CudaBasicsTests1, execReduceLongScalar_1) { - NDArray x('c', {2,3,4}, {-5,0,-3,0,-1,0,1,2,3,4,5,6,7,0,9,10,11,0,13,14,0,16,0,18}, nd4j::DataType::INT32); - NDArray z('c', {}, std::vector{100}, nd4j::DataType::INT64); - NDArray exp('c', {}, std::vector{17}, nd4j::DataType::INT64); + NDArray x('c', {2,3,4}, {-5,0,-3,0,-1,0,1,2,3,4,5,6,7,0,9,10,11,0,13,14,0,16,0,18}, sd::DataType::INT32); + NDArray z('c', {}, std::vector{100}, sd::DataType::INT64); + NDArray exp('c', {}, std::vector{17}, sd::DataType::INT64); x.permutei({2,1,0}); x.syncShape(); @@ -2483,7 +2483,7 @@ TEST_F(CudaBasicsTests1, execReduceLongScalar_1) { lc.setAllocationPointer(allocationPointer); // call cuda kernel which calculates result - NativeOpExecutioner::execReduceLongScalar(&lc, nd4j::reduce::CountNonZero, + NativeOpExecutioner::execReduceLongScalar(&lc, sd::reduce::CountNonZero, nullptr, x.getShapeInfo(), x.specialBuffer(), x.specialShapeInfo(), nullptr, nullptr, z.getShapeInfo(), z.specialBuffer(), z.specialShapeInfo()); @@ -2502,9 +2502,9 @@ TEST_F(CudaBasicsTests1, execReduceLongScalar_1) { //////////////////////////////////////////////////////////////////////////// TEST_F(CudaBasicsTests1, execReduceLongScalar_2) { - NDArray x('c', {2,3,4}, {-5,0,-3,0,-1,0,1,2,3,4,5,6,7,0,9,10,11,0,13,14,0,16,0,18}, nd4j::DataType::DOUBLE); - NDArray z('c', {}, std::vector{100}, nd4j::DataType::INT64); - NDArray exp('c', {}, std::vector{17}, nd4j::DataType::INT64); + NDArray x('c', {2,3,4}, {-5,0,-3,0,-1,0,1,2,3,4,5,6,7,0,9,10,11,0,13,14,0,16,0,18}, sd::DataType::DOUBLE); + NDArray z('c', {}, std::vector{100}, sd::DataType::INT64); + NDArray exp('c', {}, std::vector{17}, sd::DataType::INT64); // create cuda stream and LaunchContext cudaError_t cudaResult; @@ -2519,7 +2519,7 @@ TEST_F(CudaBasicsTests1, execReduceLongScalar_2) { lc.setAllocationPointer(allocationPointer); // call cuda kernel which calculates result - NativeOpExecutioner::execReduceLongScalar(&lc, nd4j::reduce::CountNonZero, + NativeOpExecutioner::execReduceLongScalar(&lc, sd::reduce::CountNonZero, nullptr, x.getShapeInfo(), x.specialBuffer(), x.specialShapeInfo(), nullptr, nullptr, z.getShapeInfo(), z.specialBuffer(), z.specialShapeInfo()); @@ -2538,10 +2538,10 @@ TEST_F(CudaBasicsTests1, execReduceLongScalar_2) { //////////////////////////////////////////////////////////////////////////// TEST_F(CudaBasicsTests1, execReduce3TAD_1) { - NDArray x('c', {2,2,3}, {-5,-4,-3,-2,-1,0,1,2,3,4,5,6}, nd4j::DataType::FLOAT32); - NDArray y('c', {2,2}, {1,2,3,4}, nd4j::DataType::FLOAT32); - NDArray exp('c', {3}, {10,20,30}, nd4j::DataType::DOUBLE); - NDArray z('c', {3}, {100,100,100}, nd4j::DataType::DOUBLE); + NDArray x('c', {2,2,3}, {-5,-4,-3,-2,-1,0,1,2,3,4,5,6}, sd::DataType::FLOAT32); + NDArray y('c', {2,2}, {1,2,3,4}, sd::DataType::FLOAT32); + NDArray exp('c', {3}, {10,20,30}, sd::DataType::DOUBLE); + NDArray z('c', {3}, {100,100,100}, sd::DataType::DOUBLE); std::vector dimensions = {0,1}; auto packX = ConstantTadHelper::getInstance()->tadForDimensions(x.shapeInfo(), dimensions); @@ -2551,7 +2551,7 @@ TEST_F(CudaBasicsTests1, execReduce3TAD_1) { y.syncToDevice(); PointersManager pm(context, "execReduce3TAD_1"); // call cuda kernel which calculates result - NativeOpExecutioner::execReduce3TAD(context, nd4j::reduce3::Dot, + NativeOpExecutioner::execReduce3TAD(context, sd::reduce3::Dot, nullptr, x.getShapeInfo(), x.specialBuffer(), x.specialShapeInfo(), nullptr, nullptr, y.getShapeInfo(), y.specialBuffer(), y.specialShapeInfo(), @@ -2571,10 +2571,10 @@ TEST_F(CudaBasicsTests1, execReduce3TAD_1) { //////////////////////////////////////////////////////////////////////////// TEST_F(CudaBasicsTests1, execReduce3TAD_2) { - NDArray x('c', {2,2,3}, {-5,-4,-3,-2,-1,0,1,2,3,4,5,6}, nd4j::DataType::INT64); - NDArray y('c', {2,3}, {1,2,3,4,5,6}, nd4j::DataType::INT64); - NDArray exp('c', {2}, {10,73}, nd4j::DataType::FLOAT32); - NDArray z('c', {2}, {100,100}, nd4j::DataType::FLOAT32); + NDArray x('c', {2,2,3}, {-5,-4,-3,-2,-1,0,1,2,3,4,5,6}, sd::DataType::INT64); + NDArray y('c', {2,3}, {1,2,3,4,5,6}, sd::DataType::INT64); + NDArray exp('c', {2}, {10,73}, sd::DataType::FLOAT32); + NDArray z('c', {2}, {100,100}, sd::DataType::FLOAT32); std::vector dimensions = {0,2}; @@ -2602,7 +2602,7 @@ TEST_F(CudaBasicsTests1, execReduce3TAD_2) { cudaResult = allocateDeviceMem(lc, devicePtrs, hostData); ASSERT_EQ(0, cudaResult); // call cuda kernel which calculates result - NativeOpExecutioner::execReduce3TAD(&lc, nd4j::reduce3::Dot, + NativeOpExecutioner::execReduce3TAD(&lc, sd::reduce3::Dot, nullptr, x.getShapeInfo(), x.specialBuffer(), x.specialShapeInfo(), nullptr, nullptr, y.getShapeInfo(), y.specialBuffer(), y.specialShapeInfo(), @@ -2627,10 +2627,10 @@ TEST_F(CudaBasicsTests1, execReduce3TAD_2) { //////////////////////////////////////////////////////////////////////////// TEST_F(CudaBasicsTests1, execReduce3TAD_3) { - NDArray x('c', {2,2,3}, {-5,-4,-3,-2,-1,0,1,2,3,4,5,6}, nd4j::DataType::INT64); - NDArray y('c', {3}, {1,2,3}, nd4j::DataType::INT64); - NDArray exp('c', {2,2}, {-22,-4,14,32}, nd4j::DataType::FLOAT32); - NDArray z('c', {2,2}, {100,100,100,100}, nd4j::DataType::FLOAT32); + NDArray x('c', {2,2,3}, {-5,-4,-3,-2,-1,0,1,2,3,4,5,6}, sd::DataType::INT64); + NDArray y('c', {3}, {1,2,3}, sd::DataType::INT64); + NDArray exp('c', {2,2}, {-22,-4,14,32}, sd::DataType::FLOAT32); + NDArray z('c', {2,2}, {100,100,100,100}, sd::DataType::FLOAT32); std::vector dimensions = {2}; @@ -2658,7 +2658,7 @@ TEST_F(CudaBasicsTests1, execReduce3TAD_3) { cudaResult = allocateDeviceMem(lc, devicePtrs, hostData); ASSERT_EQ(0, cudaResult); // call cuda kernel which calculates result - NativeOpExecutioner::execReduce3TAD(&lc, nd4j::reduce3::Dot, + NativeOpExecutioner::execReduce3TAD(&lc, sd::reduce3::Dot, nullptr, x.getShapeInfo(), x.specialBuffer(), x.specialShapeInfo(), nullptr, nullptr, y.getShapeInfo(), y.specialBuffer(), y.specialShapeInfo(), @@ -2683,10 +2683,10 @@ TEST_F(CudaBasicsTests1, execReduce3TAD_3) { //////////////////////////////////////////////////////////////////////////// TEST_F(CudaBasicsTests1, execReduce3TAD_4) { - NDArray x('c', {2,2,3}, {-5,-4,-3,-2,-1,0,1,2,3,4,5,6}, nd4j::DataType::DOUBLE); - NDArray y('c', {2,2,3}, {10,20,30,40,50,60,70,80,90,100,110,120}, nd4j::DataType::DOUBLE); - NDArray exp('c', {}, std::vector{1820}, nd4j::DataType::FLOAT32); - NDArray z('c', {}, std::vector{100}, nd4j::DataType::FLOAT32); + NDArray x('c', {2,2,3}, {-5,-4,-3,-2,-1,0,1,2,3,4,5,6}, sd::DataType::DOUBLE); + NDArray y('c', {2,2,3}, {10,20,30,40,50,60,70,80,90,100,110,120}, sd::DataType::DOUBLE); + NDArray exp('c', {}, std::vector{1820}, sd::DataType::FLOAT32); + NDArray z('c', {}, std::vector{100}, sd::DataType::FLOAT32); std::vector dimensions = {0,1,2}; @@ -2713,7 +2713,7 @@ TEST_F(CudaBasicsTests1, execReduce3TAD_4) { cudaResult = allocateDeviceMem(lc, devicePtrs, hostData); ASSERT_EQ(0, cudaResult); // call cuda kernel which calculates result - NativeOpExecutioner::execReduce3TAD(&lc, nd4j::reduce3::Dot, + NativeOpExecutioner::execReduce3TAD(&lc, sd::reduce3::Dot, nullptr, x.getShapeInfo(), x.specialBuffer(), x.specialShapeInfo(), nullptr, nullptr, y.getShapeInfo(), y.specialBuffer(), y.specialShapeInfo(), @@ -2738,9 +2738,9 @@ TEST_F(CudaBasicsTests1, execReduce3TAD_4) { //////////////////////////////////////////////////////////////////////////// TEST_F(CudaBasicsTests1, execSummaryStats_1) { - NDArray x('c', {2,2,3}, {-5,-4,-3,-2,-1,0,1,2,3,4,5,6}, nd4j::DataType::INT64); - NDArray exp('c', {}, std::vector{3.605551}, nd4j::DataType::FLOAT32); - NDArray z('c', {}, std::vector{100}, nd4j::DataType::FLOAT32); + NDArray x('c', {2,2,3}, {-5,-4,-3,-2,-1,0,1,2,3,4,5,6}, sd::DataType::INT64); + NDArray exp('c', {}, std::vector{3.605551}, sd::DataType::FLOAT32); + NDArray z('c', {}, std::vector{100}, sd::DataType::FLOAT32); // create cuda stream and LaunchContext cudaError_t cudaResult; @@ -2752,7 +2752,7 @@ TEST_F(CudaBasicsTests1, execSummaryStats_1) { lc.setReductionPointer(reductionPointer); // call cuda kernel which calculates result - NativeOpExecutioner::execSummaryStats(&lc, nd4j::variance::SummaryStatsStandardDeviation, + NativeOpExecutioner::execSummaryStats(&lc, sd::variance::SummaryStatsStandardDeviation, nullptr, x.getShapeInfo(), x.specialBuffer(), x.specialShapeInfo(), nullptr, nullptr, z.getShapeInfo(), z.specialBuffer(), z.specialShapeInfo(), @@ -2772,9 +2772,9 @@ TEST_F(CudaBasicsTests1, execSummaryStats_1) { //////////////////////////////////////////////////////////////////////////// TEST_F(CudaBasicsTests1, execSummaryStats_2) { - NDArray x('c', {2,2,3}, {-5,-4,-3,-20,-1,0,1,2,3,4,5,6}, nd4j::DataType::DOUBLE); - NDArray exp('c', {2}, {3.405877, 9.715966}, nd4j::DataType::FLOAT32); - NDArray z('c', {2}, {100,100}, nd4j::DataType::FLOAT32); + NDArray x('c', {2,2,3}, {-5,-4,-3,-20,-1,0,1,2,3,4,5,6}, sd::DataType::DOUBLE); + NDArray exp('c', {2}, {3.405877, 9.715966}, sd::DataType::FLOAT32); + NDArray z('c', {2}, {100,100}, sd::DataType::FLOAT32); std::vector dimensions = {0,2}; @@ -2801,7 +2801,7 @@ TEST_F(CudaBasicsTests1, execSummaryStats_2) { cudaResult = allocateDeviceMem(lc, devicePtrs, hostData); ASSERT_EQ(0, cudaResult); // call cuda kernel which calculates result - NativeOpExecutioner::execSummaryStats(&lc, nd4j::variance::SummaryStatsStandardDeviation, + NativeOpExecutioner::execSummaryStats(&lc, sd::variance::SummaryStatsStandardDeviation, nullptr, x.getShapeInfo(), x.specialBuffer(), x.specialShapeInfo(), nullptr, nullptr, z.getShapeInfo(), z.specialBuffer(), z.specialShapeInfo(), @@ -2826,9 +2826,9 @@ TEST_F(CudaBasicsTests1, execSummaryStats_2) { //////////////////////////////////////////////////////////////////////////// TEST_F(CudaBasicsTests1, execSummaryStats_3) { - NDArray x('c', {2,2,3}, {-5,-4,-3,-20,-1,0,1,2,3,4,5,6}, nd4j::DataType::DOUBLE); - NDArray exp('c', {2}, {10.606602, 2.121320}, nd4j::DataType::FLOAT32); - NDArray z('c', {2}, {100,100}, nd4j::DataType::FLOAT32); + NDArray x('c', {2,2,3}, {-5,-4,-3,-20,-1,0,1,2,3,4,5,6}, sd::DataType::DOUBLE); + NDArray exp('c', {2}, {10.606602, 2.121320}, sd::DataType::FLOAT32); + NDArray z('c', {2}, {100,100}, sd::DataType::FLOAT32); std::vector dimensions = {1}; @@ -2855,7 +2855,7 @@ TEST_F(CudaBasicsTests1, execSummaryStats_3) { cudaResult = allocateDeviceMem(lc, devicePtrs, hostData); ASSERT_EQ(0, cudaResult); // call cuda kernel which calculates result - NativeOpExecutioner::execSummaryStats(&lc, nd4j::variance::SummaryStatsStandardDeviation, + NativeOpExecutioner::execSummaryStats(&lc, sd::variance::SummaryStatsStandardDeviation, nullptr, x.getShapeInfo(), x.specialBuffer(), x.specialShapeInfo(), nullptr, nullptr, z.getShapeInfo(), z.specialBuffer(), z.specialShapeInfo(), @@ -2880,9 +2880,9 @@ TEST_F(CudaBasicsTests1, execSummaryStats_3) { //////////////////////////////////////////////////////////////////////////// TEST_F(CudaBasicsTests1, execSummaryStatsScalar_1) { - NDArray x('c', {2,2,3}, {-5,-4,-3,-2,-1,0,1,2,3,4,5,6}, nd4j::DataType::INT64); - NDArray exp('c', {}, std::vector{3.605551}, nd4j::DataType::FLOAT32); - NDArray z('c', {}, std::vector{100}, nd4j::DataType::FLOAT32); + NDArray x('c', {2,2,3}, {-5,-4,-3,-2,-1,0,1,2,3,4,5,6}, sd::DataType::INT64); + NDArray exp('c', {}, std::vector{3.605551}, sd::DataType::FLOAT32); + NDArray z('c', {}, std::vector{100}, sd::DataType::FLOAT32); // create cuda stream and LaunchContext cudaError_t cudaResult; @@ -2894,7 +2894,7 @@ TEST_F(CudaBasicsTests1, execSummaryStatsScalar_1) { lc.setReductionPointer(reductionPointer); // call cuda kernel which calculates result - NativeOpExecutioner::execSummaryStatsScalar(&lc, nd4j::variance::SummaryStatsStandardDeviation, + NativeOpExecutioner::execSummaryStatsScalar(&lc, sd::variance::SummaryStatsStandardDeviation, nullptr, x.getShapeInfo(), x.specialBuffer(), x.specialShapeInfo(), nullptr, nullptr, z.getShapeInfo(), z.specialBuffer(), z.specialShapeInfo(), @@ -2914,11 +2914,11 @@ TEST_F(CudaBasicsTests1, execSummaryStatsScalar_1) { ////////////////////////////////////////////////////////////////////////// TEST_F(CudaBasicsTests1, execRandom_1) { -// NDArray z('c', {10}, {100,0,0,0,0,0,0,0,0,0}, nd4j::DataType::DOUBLE); - NDArray z('c', {10}, {100,0,0,0,0,0,0,0,0,100}, nd4j::DataType::FLOAT32); - NDArray exp('c', {10}, {0.050942, -0.183229, -0.093921, 0.075469, 0.257166, -0.254838, 0.342227, -0.682188, -0.004345, 0.464633}, nd4j::DataType::FLOAT32); +// NDArray z('c', {10}, {100,0,0,0,0,0,0,0,0,0}, sd::DataType::DOUBLE); + NDArray z('c', {10}, {100,0,0,0,0,0,0,0,0,100}, sd::DataType::FLOAT32); + NDArray exp('c', {10}, {0.050942, -0.183229, -0.093921, 0.075469, 0.257166, -0.254838, 0.342227, -0.682188, -0.004345, 0.464633}, sd::DataType::FLOAT32); - nd4j::graph::RandomGenerator gen(119,5); + sd::graph::RandomGenerator gen(119,5); cudaError_t cudaResult; NDArray* array = &z; @@ -2942,7 +2942,7 @@ TEST_F(CudaBasicsTests1, execRandom_1) { // // // ::execRandom(extraPointers, random::GaussianDistribution, &gen, z.buffer(), z.shapeInfo(), z.specialBuffer(), z.specialShapeInfo(), &extra); // // call cuda kernel which calculates result -// NativeOpExecutioner::execRandom(&lc, nd4j::random::GaussianDistribution, +// NativeOpExecutioner::execRandom(&lc, sd::random::GaussianDistribution, // &gen, // nullptr, z.getShapeInfo(), z.specialBuffer(), z.specialShapeInfo(), // nullptr, z.getShapeInfo(), z.specialBuffer(), z.specialShapeInfo(), @@ -2968,12 +2968,12 @@ TEST_F(CudaBasicsTests1, execRandom_1) { ////////////////////////////////////////////////////////////////////////// TEST_F(CudaBasicsTests1, execRandom_2) { - NDArray x('c', {10}, {0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1}, nd4j::DataType::DOUBLE); - NDArray z('c', {2,5}, {100,100,100,100,100,100,100,100,100,100}, nd4j::DataType::DOUBLE); - NDArray exp('c', {10}, {0., 0., 0.3, 0., 0.5, 0., 0.7, 0., 0., 1.}, nd4j::DataType::DOUBLE); + NDArray x('c', {10}, {0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1}, sd::DataType::DOUBLE); + NDArray z('c', {2,5}, {100,100,100,100,100,100,100,100,100,100}, sd::DataType::DOUBLE); + NDArray exp('c', {10}, {0., 0., 0.3, 0., 0.5, 0., 0.7, 0., 0., 1.}, sd::DataType::DOUBLE); ExtraArguments extraArguments({0.7}); - nd4j::graph::RandomGenerator gen(119,5); + sd::graph::RandomGenerator gen(119,5); // // prepare input arrays for prepareDataForCuda function // std::vector> hostData; @@ -2990,7 +2990,7 @@ TEST_F(CudaBasicsTests1, execRandom_2) { // cudaResult = allocateDeviceMem(lc, devicePtrs, hostData); ASSERT_EQ(0, cudaResult); // call cuda kernel which calculates result - NativeOpExecutioner::execRandom(lc, nd4j::random::DropOut, + NativeOpExecutioner::execRandom(lc, sd::random::DropOut, &gen, nullptr, x.getShapeInfo(), x.specialBuffer(), x.specialShapeInfo(), nullptr, z.getShapeInfo(), z.specialBuffer(), z.specialShapeInfo(), @@ -3013,11 +3013,11 @@ TEST_F(CudaBasicsTests1, execRandom_2) { ////////////////////////////////////////////////////////////////////////// TEST_F(CudaBasicsTests1, execRandom_3) { - NDArray z('c', {10}, {100,100,100,100,100,100,100,100,100,100}, nd4j::DataType::DOUBLE); - NDArray exp('c', {10}, {2.373649, 2.239791, 1.887353, 2.488636, 2.068904, 2.281399, 1.828228, 2.228222, 2.490847, 1.669537}, nd4j::DataType::DOUBLE); + NDArray z('c', {10}, {100,100,100,100,100,100,100,100,100,100}, sd::DataType::DOUBLE); + NDArray exp('c', {10}, {2.373649, 2.239791, 1.887353, 2.488636, 2.068904, 2.281399, 1.828228, 2.228222, 2.490847, 1.669537}, sd::DataType::DOUBLE); std::vector extraArguments = {1.5, 2.5}; - nd4j::graph::RandomGenerator gen(119,5); + sd::graph::RandomGenerator gen(119,5); // prepare input arrays for prepareDataForCuda function std::vector> hostData; @@ -3034,7 +3034,7 @@ TEST_F(CudaBasicsTests1, execRandom_3) { cudaResult = allocateDeviceMem(lc, devicePtrs, hostData); ASSERT_EQ(0, cudaResult); // call cuda kernel which calculates result - NativeOpExecutioner::execRandom(&lc, nd4j::random::UniformDistribution, + NativeOpExecutioner::execRandom(&lc, sd::random::UniformDistribution, &gen, nullptr, z.getShapeInfo(), z.specialBuffer(), z.specialShapeInfo(), devicePtrs[0]); @@ -3056,12 +3056,12 @@ TEST_F(CudaBasicsTests1, execRandom_3) { ////////////////////////////////////////////////////////////////////////// TEST_F(CudaBasicsTests1, execRandom_4) { - NDArray z('c', {2,5}, {1,2,3,4,5,6,7,8,9,10}, nd4j::DataType::FLOAT32); - NDArray exp('c', {10}, {2.373649, 2.281399, 2.239791, 1.828228, 1.887353, 2.228222, 2.488636, 2.490847, 2.068904, 1.669537}, nd4j::DataType::FLOAT32); + NDArray z('c', {2,5}, {1,2,3,4,5,6,7,8,9,10}, sd::DataType::FLOAT32); + NDArray exp('c', {10}, {2.373649, 2.281399, 2.239791, 1.828228, 1.887353, 2.228222, 2.488636, 2.490847, 2.068904, 1.669537}, sd::DataType::FLOAT32); z.permutei({1,0}); ExtraArguments extraArguments({1.5, 2.5}); - nd4j::graph::RandomGenerator gen(119,5); + sd::graph::RandomGenerator gen(119,5); // // prepare input arrays for prepareDataForCuda function // std::vector> hostData; @@ -3079,7 +3079,7 @@ TEST_F(CudaBasicsTests1, execRandom_4) { auto context = z.getContext(); PointersManager pm(context, "execRandom4"); // call cuda kernel which calculates result - NativeOpExecutioner::execRandom(context, nd4j::random::UniformDistribution, + NativeOpExecutioner::execRandom(context, sd::random::UniformDistribution, &gen, nullptr, z.getShapeInfo(), z.specialBuffer(), z.specialShapeInfo(), extraArguments.argumentsAsT(z.dataType())); diff --git a/libnd4j/tests_cpu/layers_tests/CudaBasicsTests2.cu b/libnd4j/tests_cpu/layers_tests/CudaBasicsTests2.cu index 027dcdd42..b425ffcbb 100644 --- a/libnd4j/tests_cpu/layers_tests/CudaBasicsTests2.cu +++ b/libnd4j/tests_cpu/layers_tests/CudaBasicsTests2.cu @@ -19,20 +19,20 @@ // #include "testlayers.h" -#include -#include -#include -#include +#include +#include +#include +#include #include #include -#include -#include -#include +#include +#include +#include #include -using namespace nd4j; -using namespace nd4j::graph; +using namespace sd; +using namespace sd::graph; class CudaBasicsTests2 : public testing::Test { public: @@ -51,13 +51,13 @@ TEST_F(CudaBasicsTests2, mmulMxM_1) { const Nd4jLong K = 4; const Nd4jLong N = 5; - NDArray a('f', {M,K}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, nd4j::DataType::FLOAT32); - NDArray b('f', {K,N}, {1,-2,3,-4,5,-6,7,-8,9,-10,11,-12,13,-14,15,-16,17,-18,19,-20}, nd4j::DataType::FLOAT32); - NDArray c('f', {M,N}, nd4j::DataType::FLOAT32); + NDArray a('f', {M,K}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, sd::DataType::FLOAT32); + NDArray b('f', {K,N}, {1,-2,3,-4,5,-6,7,-8,9,-10,11,-12,13,-14,15,-16,17,-18,19,-20}, sd::DataType::FLOAT32); + NDArray c('f', {M,N}, sd::DataType::FLOAT32); - NDArray exp('f', {M,N}, {0.1, 0.3, 0.5, 2.5, 2.7, 2.9, 4.9, 5.1, 5.3, 7.3, 7.5, 7.7, 9.7, 9.9, 10.1}, nd4j::DataType::FLOAT32); + NDArray exp('f', {M,N}, {0.1, 0.3, 0.5, 2.5, 2.7, 2.9, 4.9, 5.1, 5.3, 7.3, 7.5, 7.7, 9.7, 9.9, 10.1}, sd::DataType::FLOAT32); - nd4j::MmulHelper::mmul(&a, &b, &c, 1., 0.); + sd::MmulHelper::mmul(&a, &b, &c, 1., 0.); // c.printIndexedBuffer(); ASSERT_TRUE(c.equalsTo(&exp)); @@ -70,12 +70,12 @@ TEST_F(CudaBasicsTests2, mmulMxM_2) { const Nd4jLong K = 4; const Nd4jLong N = 5; - NDArray a('c', {M,K}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, nd4j::DataType::DOUBLE); - NDArray b('f', {K,N}, {1,-2,3,-4,5,-6,7,-8,9,-10,11,-12,13,-14,15,-16,17,-18,19,-20}, nd4j::DataType::DOUBLE); - NDArray c('f', {M,N}, nd4j::DataType::DOUBLE); - NDArray exp('f', {M,N}, {-1.6, -0.7, 0.2, -0.8, 0.1, 1., -0., 0.9, 1.8, 0.8, 1.7, 2.6, 1.6, 2.5, 3.4}, nd4j::DataType::DOUBLE); + NDArray a('c', {M,K}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, sd::DataType::DOUBLE); + NDArray b('f', {K,N}, {1,-2,3,-4,5,-6,7,-8,9,-10,11,-12,13,-14,15,-16,17,-18,19,-20}, sd::DataType::DOUBLE); + NDArray c('f', {M,N}, sd::DataType::DOUBLE); + NDArray exp('f', {M,N}, {-1.6, -0.7, 0.2, -0.8, 0.1, 1., -0., 0.9, 1.8, 0.8, 1.7, 2.6, 1.6, 2.5, 3.4}, sd::DataType::DOUBLE); - nd4j::MmulHelper::mmul(&a, &b, &c, 1., 0.); + sd::MmulHelper::mmul(&a, &b, &c, 1., 0.); ASSERT_TRUE(c.equalsTo(&exp)); } @@ -87,13 +87,13 @@ TEST_F(CudaBasicsTests2, mmulMxM_3) { const Nd4jLong K = 4; const Nd4jLong N = 5; - NDArray a('f', {M,K}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, nd4j::DataType::DOUBLE); - NDArray b('c', {K,N}, {1,-2,3,-4,5,-6,7,-8,9,-10,11,-12,13,-14,15,-16,17,-18,19,-20}, nd4j::DataType::DOUBLE); - NDArray c('f', {M,N}, nd4j::DataType::DOUBLE); + NDArray a('f', {M,K}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, sd::DataType::DOUBLE); + NDArray b('c', {K,N}, {1,-2,3,-4,5,-6,7,-8,9,-10,11,-12,13,-14,15,-16,17,-18,19,-20}, sd::DataType::DOUBLE); + NDArray c('f', {M,N}, sd::DataType::DOUBLE); - NDArray exp('f', {M,N}, {-1.9, -0.9, 0.1, 1.3, 0.3, -0.7, -0.7, 0.3, 1.3, 0.1, -0.9, -1.9, 0.5, 1.5, 2.5}, nd4j::DataType::DOUBLE); + NDArray exp('f', {M,N}, {-1.9, -0.9, 0.1, 1.3, 0.3, -0.7, -0.7, 0.3, 1.3, 0.1, -0.9, -1.9, 0.5, 1.5, 2.5}, sd::DataType::DOUBLE); - nd4j::MmulHelper::mmul(&a, &b, &c, 1., 0.); + sd::MmulHelper::mmul(&a, &b, &c, 1., 0.); ASSERT_TRUE(c.equalsTo(&exp)); } @@ -105,13 +105,13 @@ TEST_F(CudaBasicsTests2, mmulMxM_4) { const Nd4jLong K = 4; const Nd4jLong N = 5; - NDArray a('f', {M,K}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, nd4j::DataType::DOUBLE); - NDArray b('f', {K,N}, {1,-2,3,-4,5,-6,7,-8,9,-10,11,-12,13,-14,15,-16,17,-18,19,-20}, nd4j::DataType::DOUBLE); - NDArray c('c', {M,N}, nd4j::DataType::DOUBLE); + NDArray a('f', {M,K}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, sd::DataType::DOUBLE); + NDArray b('f', {K,N}, {1,-2,3,-4,5,-6,7,-8,9,-10,11,-12,13,-14,15,-16,17,-18,19,-20}, sd::DataType::DOUBLE); + NDArray c('c', {M,N}, sd::DataType::DOUBLE); - NDArray exp('c', {M,N}, {0.1, 2.5, 4.9, 7.3, 9.7,0.3, 2.7, 5.1, 7.5, 9.9,0.5, 2.9, 5.3, 7.7, 10.1}, nd4j::DataType::DOUBLE); + NDArray exp('c', {M,N}, {0.1, 2.5, 4.9, 7.3, 9.7,0.3, 2.7, 5.1, 7.5, 9.9,0.5, 2.9, 5.3, 7.7, 10.1}, sd::DataType::DOUBLE); - nd4j::MmulHelper::mmul(&a, &b, &c, 1., 0.); + sd::MmulHelper::mmul(&a, &b, &c, 1., 0.); ASSERT_TRUE(c.equalsTo(&exp)); @@ -119,7 +119,7 @@ TEST_F(CudaBasicsTests2, mmulMxM_4) { // NDArray* pB = b.permute({1,0}); // NDArray* pC = c.permute({1,0}); - // nd4j::MmulHelper::mmul(pB, pA, pC, 1., 0.); + // sd::MmulHelper::mmul(pB, pA, pC, 1., 0.); // ASSERT_TRUE(c.equalsTo(&exp)); // delete pA; @@ -134,13 +134,13 @@ TEST_F(CudaBasicsTests2, mmulMxM_5) { const Nd4jLong K = 4; const Nd4jLong N = 5; - NDArray a('c', {M,K}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, nd4j::DataType::DOUBLE); - NDArray b('c', {K,N}, {1,-2,3,-4,5,-6,7,-8,9,-10,11,-12,13,-14,15,-16,17,-18,19,-20}, nd4j::DataType::DOUBLE); - NDArray c('f', {M,N}, nd4j::DataType::DOUBLE); + NDArray a('c', {M,K}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, sd::DataType::DOUBLE); + NDArray b('c', {K,N}, {1,-2,3,-4,5,-6,7,-8,9,-10,11,-12,13,-14,15,-16,17,-18,19,-20}, sd::DataType::DOUBLE); + NDArray c('f', {M,N}, sd::DataType::DOUBLE); - NDArray exp('f', {M,N}, {-8.8, -4.3, 0.2, 8.6, 4.1, -0.4, -8.4, -3.9, 0.6, 8.2, 3.7, -0.8, -8.0, -3.5, 1.}, nd4j::DataType::DOUBLE); + NDArray exp('f', {M,N}, {-8.8, -4.3, 0.2, 8.6, 4.1, -0.4, -8.4, -3.9, 0.6, 8.2, 3.7, -0.8, -8.0, -3.5, 1.}, sd::DataType::DOUBLE); - nd4j::MmulHelper::mmul(&a, &b, &c, 1., 0.); + sd::MmulHelper::mmul(&a, &b, &c, 1., 0.); ASSERT_TRUE(c.equalsTo(&exp)); } @@ -152,13 +152,13 @@ TEST_F(CudaBasicsTests2, mmulMxM_6) { const Nd4jLong K = 4; const Nd4jLong N = 5; - NDArray a('c', {M,K}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, nd4j::DataType::DOUBLE); - NDArray b('f', {K,N}, {1,-2,3,-4,5,-6,7,-8,9,-10,11,-12,13,-14,15,-16,17,-18,19,-20}, nd4j::DataType::DOUBLE); - NDArray c('c', {M,N}, nd4j::DataType::DOUBLE); + NDArray a('c', {M,K}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, sd::DataType::DOUBLE); + NDArray b('f', {K,N}, {1,-2,3,-4,5,-6,7,-8,9,-10,11,-12,13,-14,15,-16,17,-18,19,-20}, sd::DataType::DOUBLE); + NDArray c('c', {M,N}, sd::DataType::DOUBLE); - NDArray exp('c', {M,N}, {-1.6, -0.8, -0.0, 0.8, 1.6, -0.7, 0.1, 0.9, 1.7, 2.5, 0.2, 1.0, 1.8, 2.6, 3.4}, nd4j::DataType::DOUBLE); + NDArray exp('c', {M,N}, {-1.6, -0.8, -0.0, 0.8, 1.6, -0.7, 0.1, 0.9, 1.7, 2.5, 0.2, 1.0, 1.8, 2.6, 3.4}, sd::DataType::DOUBLE); - nd4j::MmulHelper::mmul(&a, &b, &c, 1., 0.); + sd::MmulHelper::mmul(&a, &b, &c, 1., 0.); ASSERT_TRUE(c.equalsTo(&exp)); } @@ -170,13 +170,13 @@ TEST_F(CudaBasicsTests2, mmulMxM_7) { const Nd4jLong K = 4; const Nd4jLong N = 5; - NDArray a('f', {M,K}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, nd4j::DataType::DOUBLE); - NDArray b('c', {K,N}, {1,-2,3,-4,5,-6,7,-8,9,-10,11,-12,13,-14,15,-16,17,-18,19,-20}, nd4j::DataType::DOUBLE); - NDArray c('c', {M,N}, nd4j::DataType::DOUBLE); + NDArray a('f', {M,K}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, sd::DataType::DOUBLE); + NDArray b('c', {K,N}, {1,-2,3,-4,5,-6,7,-8,9,-10,11,-12,13,-14,15,-16,17,-18,19,-20}, sd::DataType::DOUBLE); + NDArray c('c', {M,N}, sd::DataType::DOUBLE); - NDArray exp('c', {M,N}, {-1.9, 1.3, -0.7, 0.1, 0.5, -0.9, 0.3, 0.3, -0.9, 1.5, 0.1, -0.7, 1.3, -1.9, 2.5}, nd4j::DataType::DOUBLE); + NDArray exp('c', {M,N}, {-1.9, 1.3, -0.7, 0.1, 0.5, -0.9, 0.3, 0.3, -0.9, 1.5, 0.1, -0.7, 1.3, -1.9, 2.5}, sd::DataType::DOUBLE); - nd4j::MmulHelper::mmul(&a, &b, &c, 1., 0.); + sd::MmulHelper::mmul(&a, &b, &c, 1., 0.); ASSERT_TRUE(c.equalsTo(&exp)); } @@ -188,13 +188,13 @@ TEST_F(CudaBasicsTests2, mmulMxM_8) { const Nd4jLong K = 4; const Nd4jLong N = 5; - NDArray a('c', {M,K}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, nd4j::DataType::DOUBLE); - NDArray b('c', {K,N}, {1,-2,3,-4,5,-6,7,-8,9,-10,11,-12,13,-14,15,-16,17,-18,19,-20}, nd4j::DataType::DOUBLE); - NDArray c('c', {M,N}, nd4j::DataType::DOUBLE); + NDArray a('c', {M,K}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, sd::DataType::DOUBLE); + NDArray b('c', {K,N}, {1,-2,3,-4,5,-6,7,-8,9,-10,11,-12,13,-14,15,-16,17,-18,19,-20}, sd::DataType::DOUBLE); + NDArray c('c', {M,N}, sd::DataType::DOUBLE); - NDArray exp('c', {M,N}, {-8.8, 8.6, -8.4, 8.2, -8.0, -4.3, 4.1, -3.9, 3.7, -3.5, 0.2, -0.4, 0.6, -0.8, 1.}, nd4j::DataType::DOUBLE); + NDArray exp('c', {M,N}, {-8.8, 8.6, -8.4, 8.2, -8.0, -4.3, 4.1, -3.9, 3.7, -3.5, 0.2, -0.4, 0.6, -0.8, 1.}, sd::DataType::DOUBLE); - nd4j::MmulHelper::mmul(&a, &b, &c, 1., 0.); + sd::MmulHelper::mmul(&a, &b, &c, 1., 0.); ASSERT_TRUE(c.equalsTo(&exp)); } @@ -206,13 +206,13 @@ TEST_F(CudaBasicsTests2, mmulMxM_9) { const Nd4jLong K = 4; const Nd4jLong N = 5; - NDArray a('c', {M,K}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, nd4j::DataType::FLOAT32); - NDArray b('c', {K,N}, {1,-2,3,-4,5,-6,7,-8,9,-10,11,-12,13,-14,15,-16,17,-18,19,-20}, nd4j::DataType::FLOAT32); - NDArray c('c', {M,N}, nd4j::DataType::FLOAT32); + NDArray a('c', {M,K}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, sd::DataType::FLOAT32); + NDArray b('c', {K,N}, {1,-2,3,-4,5,-6,7,-8,9,-10,11,-12,13,-14,15,-16,17,-18,19,-20}, sd::DataType::FLOAT32); + NDArray c('c', {M,N}, sd::DataType::FLOAT32); - NDArray exp('c', {M,N}, {-8.8, 8.6, -8.4, 8.2, -8.0, -4.3, 4.1, -3.9, 3.7, -3.5, 0.2, -0.4, 0.6, -0.8, 1.}, nd4j::DataType::FLOAT32); + NDArray exp('c', {M,N}, {-8.8, 8.6, -8.4, 8.2, -8.0, -4.3, 4.1, -3.9, 3.7, -3.5, 0.2, -0.4, 0.6, -0.8, 1.}, sd::DataType::FLOAT32); - nd4j::MmulHelper::mmul(&a, &b, &c, 1., 0.); + sd::MmulHelper::mmul(&a, &b, &c, 1., 0.); ASSERT_TRUE(c.equalsTo(&exp)); } @@ -224,13 +224,13 @@ TEST_F(CudaBasicsTests2, mmulMxM_10) { const Nd4jLong K = 4; const Nd4jLong N = 5; - NDArray a('f', {M,K}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, nd4j::DataType::FLOAT32); - NDArray b('f', {K,N}, {1,-2,3,-4,5,-6,7,-8,9,-10,11,-12,13,-14,15,-16,17,-18,19,-20}, nd4j::DataType::FLOAT32); - NDArray c('f', {M,N}, nd4j::DataType::FLOAT32); + NDArray a('f', {M,K}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, sd::DataType::FLOAT32); + NDArray b('f', {K,N}, {1,-2,3,-4,5,-6,7,-8,9,-10,11,-12,13,-14,15,-16,17,-18,19,-20}, sd::DataType::FLOAT32); + NDArray c('f', {M,N}, sd::DataType::FLOAT32); - NDArray exp('f', {M,N}, {0.1, 0.3, 0.5, 2.5, 2.7, 2.9, 4.9, 5.1, 5.3, 7.3, 7.5, 7.7, 9.7, 9.9, 10.1}, nd4j::DataType::FLOAT32); + NDArray exp('f', {M,N}, {0.1, 0.3, 0.5, 2.5, 2.7, 2.9, 4.9, 5.1, 5.3, 7.3, 7.5, 7.7, 9.7, 9.9, 10.1}, sd::DataType::FLOAT32); - nd4j::MmulHelper::mmul(&a, &b, &c, 1., 0.); + sd::MmulHelper::mmul(&a, &b, &c, 1., 0.); // c.printIndexedBuffer(); ASSERT_TRUE(c.equalsTo(&exp)); @@ -243,13 +243,13 @@ TEST_F(CudaBasicsTests2, mmulMxM_11) { const Nd4jLong K = 4; const Nd4jLong N = 5; - NDArray a('f', {M,K}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, nd4j::DataType::FLOAT32); - NDArray b('c', {K,N}, {1,-2,3,-4,5,-6,7,-8,9,-10,11,-12,13,-14,15,-16,17,-18,19,-20}, nd4j::DataType::FLOAT32); - NDArray c('f', {M,N}, nd4j::DataType::FLOAT32); + NDArray a('f', {M,K}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, sd::DataType::FLOAT32); + NDArray b('c', {K,N}, {1,-2,3,-4,5,-6,7,-8,9,-10,11,-12,13,-14,15,-16,17,-18,19,-20}, sd::DataType::FLOAT32); + NDArray c('f', {M,N}, sd::DataType::FLOAT32); - NDArray exp('f', {M,N}, {-1.9, -0.9, 0.1, 1.3, 0.3, -0.7, -0.7, 0.3, 1.3, 0.1, -0.9, -1.9, 0.5, 1.5, 2.5}, nd4j::DataType::FLOAT32); + NDArray exp('f', {M,N}, {-1.9, -0.9, 0.1, 1.3, 0.3, -0.7, -0.7, 0.3, 1.3, 0.1, -0.9, -1.9, 0.5, 1.5, 2.5}, sd::DataType::FLOAT32); - nd4j::MmulHelper::mmul(&a, &b, &c, 1., 0.); + sd::MmulHelper::mmul(&a, &b, &c, 1., 0.); ASSERT_TRUE(c.equalsTo(&exp)); } @@ -265,13 +265,13 @@ TEST_F(CudaBasicsTests2, mmulMxM_12) { const Nd4jLong K = 4; const Nd4jLong N = 4; - NDArray a('f', {M,K}, {1.,2,3,4,5,6,7,8,9,2,3,2,1,0,4,7.}, nd4j::DataType::INT8); - NDArray b('f', {K,N}, {-2,-3,0,1,5,-6,7,-8,9,-1,2,-2,3,-4,5,-6.}, nd4j::DataType::INT8); - NDArray c('f', {M,N}, nd4j::DataType::FLOAT32); + NDArray a('f', {M,K}, {1.,2,3,4,5,6,7,8,9,2,3,2,1,0,4,7.}, sd::DataType::INT8); + NDArray b('f', {K,N}, {-2,-3,0,1,5,-6,7,-8,9,-1,2,-2,3,-4,5,-6.}, sd::DataType::INT8); + NDArray c('f', {M,N}, sd::DataType::FLOAT32); - NDArray exp('f', {M,N}, {-16., -22., -23., -25., 30., -12., -38., -70., 20., 16., 18., 18., 22., -8., -28., -52.}, nd4j::DataType::FLOAT32); + NDArray exp('f', {M,N}, {-16., -22., -23., -25., 30., -12., -38., -70., 20., 16., 18., 18., 22., -8., -28., -52.}, sd::DataType::FLOAT32); - nd4j::MmulHelper::mmul(&a, &b, &c, 1., 0.); + sd::MmulHelper::mmul(&a, &b, &c, 1., 0.); // c.printBuffer(); ASSERT_TRUE(c.equalsTo(&exp)); @@ -288,13 +288,13 @@ TEST_F(CudaBasicsTests2, mmulMxM_13) { const Nd4jLong K = 4; const Nd4jLong N = 5; - NDArray a('f', {M,K}, {1.,2,3,4,5,6,7,8,9,10,11,12}, nd4j::DataType::INT8); - NDArray b('c', {K,N}, {-2,-3,0,1,5,-6,7,-8,9,-10,11,-12,13,-14,15,-16,17,-18,19,-20}, nd4j::DataType::INT8); - NDArray c('f', {M,N}, nd4j::DataType::FLOAT32); + NDArray a('f', {M,K}, {1.,2,3,4,5,6,7,8,9,10,11,12}, sd::DataType::INT8); + NDArray b('c', {K,N}, {-2,-3,0,1,5,-6,7,-8,9,-10,11,-12,13,-14,15,-16,17,-18,19,-20}, sd::DataType::INT8); + NDArray c('f', {M,N}, sd::DataType::FLOAT32); - NDArray exp('f', {M,N}, {-109., -122., -135., 111., 120., 129., -121., -134., -147., 129., 144., 159., -130., -140., -150.}, nd4j::DataType::FLOAT32); + NDArray exp('f', {M,N}, {-109., -122., -135., 111., 120., 129., -121., -134., -147., 129., 144., 159., -130., -140., -150.}, sd::DataType::FLOAT32); - nd4j::MmulHelper::mmul(&a, &b, &c, 1., 0.); + sd::MmulHelper::mmul(&a, &b, &c, 1., 0.); ASSERT_TRUE(c.equalsTo(&exp)); } @@ -310,13 +310,13 @@ TEST_F(CudaBasicsTests2, mmulMxM_14) { const Nd4jLong K = 4; const Nd4jLong N = 5; - NDArray a('c', {M,K}, {1.,2,3,4,5,6,7,8,9,10,11,12}, nd4j::DataType::INT8); - NDArray b('c', {K,N}, {-2,-3,0,1,5,-6,7,-8,9,-10,11,-12,13,-14,15,-16,17,-18,19,-20}, nd4j::DataType::INT8); - NDArray c('c', {M,N}, nd4j::DataType::FLOAT32); + NDArray a('c', {M,K}, {1.,2,3,4,5,6,7,8,9,10,11,12}, sd::DataType::INT8); + NDArray b('c', {K,N}, {-2,-3,0,1,5,-6,7,-8,9,-10,11,-12,13,-14,15,-16,17,-18,19,-20}, sd::DataType::INT8); + NDArray c('c', {M,N}, sd::DataType::FLOAT32); - NDArray exp('c', {M,N}, {-45., 43., -49., 53., -50., -97., 79., -101., 113., -90., -149., 115., -153., 173., -130.}, nd4j::DataType::FLOAT32); + NDArray exp('c', {M,N}, {-45., 43., -49., 53., -50., -97., 79., -101., 113., -90., -149., 115., -153., 173., -130.}, sd::DataType::FLOAT32); - nd4j::MmulHelper::mmul(&a, &b, &c, 1., 0.); + sd::MmulHelper::mmul(&a, &b, &c, 1., 0.); ASSERT_TRUE(c.equalsTo(&exp)); } @@ -332,13 +332,13 @@ TEST_F(CudaBasicsTests2, mmulMxM_15) { const Nd4jLong K = 4; const Nd4jLong N = 5; - NDArray a('f', {M,K}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, nd4j::DataType::HALF); - NDArray b('f', {K,N}, {1,-2,3,-4,5,-6,7,-8,9,-10,11,-12,13,-14,15,-16,17,-18,19,-20}, nd4j::DataType::HALF); - NDArray c('f', {M,N}, nd4j::DataType::FLOAT32); + NDArray a('f', {M,K}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, sd::DataType::HALF); + NDArray b('f', {K,N}, {1,-2,3,-4,5,-6,7,-8,9,-10,11,-12,13,-14,15,-16,17,-18,19,-20}, sd::DataType::HALF); + NDArray c('f', {M,N}, sd::DataType::FLOAT32); - NDArray exp('f', {M,N}, {0.1, 0.3, 0.5, 2.5, 2.7, 2.9, 4.9, 5.1, 5.3, 7.3, 7.5, 7.7, 9.7, 9.9, 10.1}, nd4j::DataType::FLOAT32); + NDArray exp('f', {M,N}, {0.1, 0.3, 0.5, 2.5, 2.7, 2.9, 4.9, 5.1, 5.3, 7.3, 7.5, 7.7, 9.7, 9.9, 10.1}, sd::DataType::FLOAT32); - nd4j::MmulHelper::mmul(&a, &b, &c, 1., 0.); + sd::MmulHelper::mmul(&a, &b, &c, 1., 0.); // c.printBuffer(); ASSERT_TRUE(c.equalsTo(&exp, 0.01)); @@ -355,13 +355,13 @@ TEST_F(CudaBasicsTests2, mmulMxM_16) { const Nd4jLong K = 4; const Nd4jLong N = 5; - NDArray a('f', {M,K}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, nd4j::DataType::HALF); - NDArray b('c', {K,N}, {1,-2,3,-4,5,-6,7,-8,9,-10,11,-12,13,-14,15,-16,17,-18,19,-20}, nd4j::DataType::HALF); - NDArray c('f', {M,N}, nd4j::DataType::FLOAT32); + NDArray a('f', {M,K}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, sd::DataType::HALF); + NDArray b('c', {K,N}, {1,-2,3,-4,5,-6,7,-8,9,-10,11,-12,13,-14,15,-16,17,-18,19,-20}, sd::DataType::HALF); + NDArray c('f', {M,N}, sd::DataType::FLOAT32); - NDArray exp('f', {M,N}, {-1.9, -0.9, 0.1, 1.3, 0.3, -0.7, -0.7, 0.3, 1.3, 0.1, -0.9, -1.9, 0.5, 1.5, 2.5}, nd4j::DataType::FLOAT32); + NDArray exp('f', {M,N}, {-1.9, -0.9, 0.1, 1.3, 0.3, -0.7, -0.7, 0.3, 1.3, 0.1, -0.9, -1.9, 0.5, 1.5, 2.5}, sd::DataType::FLOAT32); - nd4j::MmulHelper::mmul(&a, &b, &c, 1., 0.); + sd::MmulHelper::mmul(&a, &b, &c, 1., 0.); ASSERT_TRUE(c.equalsTo(&exp, 0.01)); } @@ -377,13 +377,13 @@ TEST_F(CudaBasicsTests2, mmulMxM_17) { const Nd4jLong K = 4; const Nd4jLong N = 5; - NDArray a('c', {M,K}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, nd4j::DataType::HALF); - NDArray b('c', {K,N}, {1,-2,3,-4,5,-6,7,-8,9,-10,11,-12,13,-14,15,-16,17,-18,19,-20}, nd4j::DataType::HALF); - NDArray c('c', {M,N}, nd4j::DataType::FLOAT32); + NDArray a('c', {M,K}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, sd::DataType::HALF); + NDArray b('c', {K,N}, {1,-2,3,-4,5,-6,7,-8,9,-10,11,-12,13,-14,15,-16,17,-18,19,-20}, sd::DataType::HALF); + NDArray c('c', {M,N}, sd::DataType::FLOAT32); - NDArray exp('c', {M,N}, {-8.8, 8.6, -8.4, 8.2, -8.0, -4.3, 4.1, -3.9, 3.7, -3.5, 0.2, -0.4, 0.6, -0.8, 1.}, nd4j::DataType::FLOAT32); + NDArray exp('c', {M,N}, {-8.8, 8.6, -8.4, 8.2, -8.0, -4.3, 4.1, -3.9, 3.7, -3.5, 0.2, -0.4, 0.6, -0.8, 1.}, sd::DataType::FLOAT32); - nd4j::MmulHelper::mmul(&a, &b, &c, 1., 0.); + sd::MmulHelper::mmul(&a, &b, &c, 1., 0.); ASSERT_TRUE(c.equalsTo(&exp, 0.01)); } @@ -399,13 +399,13 @@ TEST_F(CudaBasicsTests2, mmulMxM_18) { const Nd4jLong K = 4; const Nd4jLong N = 5; - NDArray a('f', {M,K}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, nd4j::DataType::HALF); - NDArray b('f', {K,N}, {1,-2,3,-4,5,-6,7,-8,9,-10,11,-12,13,-14,15,-16,17,-18,19,-20}, nd4j::DataType::HALF); - NDArray c('f', {M,N}, nd4j::DataType::HALF); + NDArray a('f', {M,K}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, sd::DataType::HALF); + NDArray b('f', {K,N}, {1,-2,3,-4,5,-6,7,-8,9,-10,11,-12,13,-14,15,-16,17,-18,19,-20}, sd::DataType::HALF); + NDArray c('f', {M,N}, sd::DataType::HALF); - NDArray exp('f', {M,N}, {0.1, 0.3, 0.5, 2.5, 2.7, 2.9, 4.9, 5.1, 5.3, 7.3, 7.5, 7.7, 9.7, 9.9, 10.1}, nd4j::DataType::HALF); + NDArray exp('f', {M,N}, {0.1, 0.3, 0.5, 2.5, 2.7, 2.9, 4.9, 5.1, 5.3, 7.3, 7.5, 7.7, 9.7, 9.9, 10.1}, sd::DataType::HALF); - nd4j::MmulHelper::mmul(&a, &b, &c, 1., 0.); + sd::MmulHelper::mmul(&a, &b, &c, 1., 0.); ASSERT_TRUE(c.equalsTo(&exp, 1e-1)); } @@ -421,13 +421,13 @@ TEST_F(CudaBasicsTests2, mmulMxM_19) { const Nd4jLong K = 4; const Nd4jLong N = 5; - NDArray a('f', {M,K}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, nd4j::DataType::HALF); - NDArray b('c', {K,N}, {1,-2,3,-4,5,-6,7,-8,9,-10,11,-12,13,-14,15,-16,17,-18,19,-20}, nd4j::DataType::HALF); - NDArray c('f', {M,N}, nd4j::DataType::HALF); + NDArray a('f', {M,K}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, sd::DataType::HALF); + NDArray b('c', {K,N}, {1,-2,3,-4,5,-6,7,-8,9,-10,11,-12,13,-14,15,-16,17,-18,19,-20}, sd::DataType::HALF); + NDArray c('f', {M,N}, sd::DataType::HALF); - NDArray exp('f', {M,N}, {-1.9, -0.9, 0.1, 1.3, 0.3, -0.7, -0.7, 0.3, 1.3, 0.1, -0.9, -1.9, 0.5, 1.5, 2.5}, nd4j::DataType::HALF); + NDArray exp('f', {M,N}, {-1.9, -0.9, 0.1, 1.3, 0.3, -0.7, -0.7, 0.3, 1.3, 0.1, -0.9, -1.9, 0.5, 1.5, 2.5}, sd::DataType::HALF); - nd4j::MmulHelper::mmul(&a, &b, &c, 1., 0.); + sd::MmulHelper::mmul(&a, &b, &c, 1., 0.); ASSERT_TRUE(c.equalsTo(&exp, 1e-1)); } @@ -443,13 +443,13 @@ TEST_F(CudaBasicsTests2, mmulMxM_20) { const Nd4jLong K = 4; const Nd4jLong N = 5; - NDArray a('c', {M,K}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, nd4j::DataType::HALF); - NDArray b('c', {K,N}, {1,-2,3,-4,5,-6,7,-8,9,-10,11,-12,13,-14,15,-16,17,-18,19,-20}, nd4j::DataType::HALF); - NDArray c('c', {M,N}, nd4j::DataType::HALF); + NDArray a('c', {M,K}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, sd::DataType::HALF); + NDArray b('c', {K,N}, {1,-2,3,-4,5,-6,7,-8,9,-10,11,-12,13,-14,15,-16,17,-18,19,-20}, sd::DataType::HALF); + NDArray c('c', {M,N}, sd::DataType::HALF); - NDArray exp('c', {M,N}, {-8.8, 8.6, -8.4, 8.2, -8.0, -4.3, 4.1, -3.9, 3.7, -3.5, 0.2, -0.4, 0.6, -0.8, 1.}, nd4j::DataType::HALF); + NDArray exp('c', {M,N}, {-8.8, 8.6, -8.4, 8.2, -8.0, -4.3, 4.1, -3.9, 3.7, -3.5, 0.2, -0.4, 0.6, -0.8, 1.}, sd::DataType::HALF); - nd4j::MmulHelper::mmul(&a, &b, &c, 1., 0.); + sd::MmulHelper::mmul(&a, &b, &c, 1., 0.); ASSERT_TRUE(c.equalsTo(&exp, 1e-1)); } @@ -461,13 +461,13 @@ TEST_F(CudaBasicsTests2, mmulMxM_21) { const Nd4jLong K = 4; const Nd4jLong N = 5; - NDArray a('c', {M,K}, {1.,2,3,4,5,6,7,8,9,10,11,12}, nd4j::DataType::INT8); - NDArray b('c', {K,N}, {-2,-3,0,1,5,-6,7,-8,9,-10,11,-12,13,-14,15,-16,17,-18,19,-20}, nd4j::DataType::FLOAT32); - NDArray c('c', {M,N}, nd4j::DataType::DOUBLE); + NDArray a('c', {M,K}, {1.,2,3,4,5,6,7,8,9,10,11,12}, sd::DataType::INT8); + NDArray b('c', {K,N}, {-2,-3,0,1,5,-6,7,-8,9,-10,11,-12,13,-14,15,-16,17,-18,19,-20}, sd::DataType::FLOAT32); + NDArray c('c', {M,N}, sd::DataType::DOUBLE); - NDArray exp('c', {M,N}, {-45., 43., -49., 53., -50., -97., 79., -101., 113., -90., -149., 115., -153., 173., -130.}, nd4j::DataType::DOUBLE); + NDArray exp('c', {M,N}, {-45., 43., -49., 53., -50., -97., 79., -101., 113., -90., -149., 115., -153., 173., -130.}, sd::DataType::DOUBLE); - nd4j::MmulHelper::mmul(&a, &b, &c, 1., 0.); + sd::MmulHelper::mmul(&a, &b, &c, 1., 0.); ASSERT_TRUE(c.equalsTo(&exp)); } @@ -479,13 +479,13 @@ TEST_F(CudaBasicsTests2, mmulMxM_22) { const Nd4jLong K = 4; const Nd4jLong N = 5; - NDArray a('c', {M,K}, {1.,2,3,4,5,6,7,8,9,10,11,12}, nd4j::DataType::FLOAT32); - NDArray b('c', {K,N}, {-2,-3,0,1,5,-6,7,-8,9,-10,11,-12,13,-14,15,-16,17,-18,19,-20}, nd4j::DataType::HALF); - NDArray c('c', {M,N}, nd4j::DataType::FLOAT32); + NDArray a('c', {M,K}, {1.,2,3,4,5,6,7,8,9,10,11,12}, sd::DataType::FLOAT32); + NDArray b('c', {K,N}, {-2,-3,0,1,5,-6,7,-8,9,-10,11,-12,13,-14,15,-16,17,-18,19,-20}, sd::DataType::HALF); + NDArray c('c', {M,N}, sd::DataType::FLOAT32); - NDArray exp('c', {M,N}, {-45., 43., -49., 53., -50., -97., 79., -101., 113., -90., -149., 115., -153., 173., -130.}, nd4j::DataType::FLOAT32); + NDArray exp('c', {M,N}, {-45., 43., -49., 53., -50., -97., 79., -101., 113., -90., -149., 115., -153., 173., -130.}, sd::DataType::FLOAT32); - nd4j::MmulHelper::mmul(&a, &b, &c, 1., 0.); + sd::MmulHelper::mmul(&a, &b, &c, 1., 0.); // c.printBuffer(); ASSERT_TRUE(c.equalsTo(&exp)); @@ -498,13 +498,13 @@ TEST_F(CudaBasicsTests2, mmulMxM_23) { const Nd4jLong K = 4; const Nd4jLong N = 5; - NDArray a('c', {M,K}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, nd4j::DataType::HALF); - NDArray b('c', {K,N}, {1,-2,3,-4,5,-6,7,-8,9,-10,11,-12,13,-14,15,-16,17,-18,19,-20}, nd4j::DataType::HALF); - NDArray c('c', {M,N}, nd4j::DataType::DOUBLE); + NDArray a('c', {M,K}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, sd::DataType::HALF); + NDArray b('c', {K,N}, {1,-2,3,-4,5,-6,7,-8,9,-10,11,-12,13,-14,15,-16,17,-18,19,-20}, sd::DataType::HALF); + NDArray c('c', {M,N}, sd::DataType::DOUBLE); - NDArray exp('c', {M,N}, {-8.8, 8.6, -8.4, 8.2, -8.0, -4.3, 4.1, -3.9, 3.7, -3.5, 0.2, -0.4, 0.6, -0.8, 1.}, nd4j::DataType::DOUBLE); + NDArray exp('c', {M,N}, {-8.8, 8.6, -8.4, 8.2, -8.0, -4.3, 4.1, -3.9, 3.7, -3.5, 0.2, -0.4, 0.6, -0.8, 1.}, sd::DataType::DOUBLE); - nd4j::MmulHelper::mmul(&a, &b, &c, 1., 0.); + sd::MmulHelper::mmul(&a, &b, &c, 1., 0.); ASSERT_TRUE(c.equalsTo(&exp, 0.01)); } @@ -516,13 +516,13 @@ TEST_F(CudaBasicsTests2, mmulMxM_24) { const Nd4jLong K = 4; const Nd4jLong N = 5; - NDArray a('c', {M,K}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, nd4j::DataType::HALF); - NDArray b('c', {K,N}, {1,-2,3,-4,5,-6,7,-8,9,-10,11,-12,13,-14,15,-16,17,-18,19,-20}, nd4j::DataType::FLOAT32); - NDArray c('c', {M,N}, nd4j::DataType::DOUBLE); + NDArray a('c', {M,K}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, sd::DataType::HALF); + NDArray b('c', {K,N}, {1,-2,3,-4,5,-6,7,-8,9,-10,11,-12,13,-14,15,-16,17,-18,19,-20}, sd::DataType::FLOAT32); + NDArray c('c', {M,N}, sd::DataType::DOUBLE); - NDArray exp('c', {M,N}, {-8.8, 8.6, -8.4, 8.2, -8.0, -4.3, 4.1, -3.9, 3.7, -3.5, 0.2, -0.4, 0.6, -0.8, 1.}, nd4j::DataType::DOUBLE); + NDArray exp('c', {M,N}, {-8.8, 8.6, -8.4, 8.2, -8.0, -4.3, 4.1, -3.9, 3.7, -3.5, 0.2, -0.4, 0.6, -0.8, 1.}, sd::DataType::DOUBLE); - nd4j::MmulHelper::mmul(&a, &b, &c, 1., 0.); + sd::MmulHelper::mmul(&a, &b, &c, 1., 0.); ASSERT_TRUE(c.equalsTo(&exp, 0.01)); } @@ -534,13 +534,13 @@ TEST_F(CudaBasicsTests2, mmulMxM_25) { const Nd4jLong K = 4; const Nd4jLong N = 5; - NDArray a('c', {M,K}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, nd4j::DataType::DOUBLE); - NDArray b('c', {K,N}, {1,-2,3,-4,5,-6,7,-8,9,-10,11,-12,13,-14,15,-16,17,-18,19,-20}, nd4j::DataType::FLOAT32); - NDArray c('c', {M,N}, nd4j::DataType::HALF); + NDArray a('c', {M,K}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, sd::DataType::DOUBLE); + NDArray b('c', {K,N}, {1,-2,3,-4,5,-6,7,-8,9,-10,11,-12,13,-14,15,-16,17,-18,19,-20}, sd::DataType::FLOAT32); + NDArray c('c', {M,N}, sd::DataType::HALF); - NDArray exp('c', {M,N}, {-8.8, 8.6, -8.4, 8.2, -8.0, -4.3, 4.1, -3.9, 3.7, -3.5, 0.2, -0.4, 0.6, -0.8, 1.}, nd4j::DataType::HALF); + NDArray exp('c', {M,N}, {-8.8, 8.6, -8.4, 8.2, -8.0, -4.3, 4.1, -3.9, 3.7, -3.5, 0.2, -0.4, 0.6, -0.8, 1.}, sd::DataType::HALF); - nd4j::MmulHelper::mmul(&a, &b, &c, 1., 0.); + sd::MmulHelper::mmul(&a, &b, &c, 1., 0.); ASSERT_TRUE(c.equalsTo(&exp, 0.01)); } @@ -553,13 +553,13 @@ TEST_F(CudaBasicsTests2, mmulMxM_26) { const Nd4jLong N = 5; // 3x4 * 4x5 = 3x5 - NDArray a('c', {M,K}, {1.,2,3,4,5,6,7,8,9,10,11,12}, nd4j::DataType::INT64); - NDArray b('c', {K,N}, {-2,-3,0,1,5,-6,7,-8,9,-10,11,-12,13,-14,15,-16,17,-18,19,-20}, nd4j::DataType::FLOAT32); - NDArray c('c', {M,N}, nd4j::DataType::DOUBLE); + NDArray a('c', {M,K}, {1.,2,3,4,5,6,7,8,9,10,11,12}, sd::DataType::INT64); + NDArray b('c', {K,N}, {-2,-3,0,1,5,-6,7,-8,9,-10,11,-12,13,-14,15,-16,17,-18,19,-20}, sd::DataType::FLOAT32); + NDArray c('c', {M,N}, sd::DataType::DOUBLE); - NDArray exp('c', {M,N}, {-45., 43., -49., 53., -50., -97., 79., -101., 113., -90., -149., 115., -153., 173., -130.}, nd4j::DataType::DOUBLE); + NDArray exp('c', {M,N}, {-45., 43., -49., 53., -50., -97., 79., -101., 113., -90., -149., 115., -153., 173., -130.}, sd::DataType::DOUBLE); - nd4j::MmulHelper::mmul(&a, &b, &c, 1., 0.); + sd::MmulHelper::mmul(&a, &b, &c, 1., 0.); // c.printBuffer(); ASSERT_TRUE(c.equalsTo(&exp)); @@ -572,13 +572,13 @@ TEST_F(CudaBasicsTests2, mmulMxM_27) { const Nd4jLong K = 4; const Nd4jLong N = 5; - NDArray a('f', {M,K}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, nd4j::DataType::HALF); - NDArray b('f', {K,N}, {1,-2,3,-4,5,-6,7,-8,9,-10,11,-12,13,-14,15,-16,17,-18,19,-20}, nd4j::DataType::DOUBLE); - NDArray c('f', {M,N}, nd4j::DataType::FLOAT32); + NDArray a('f', {M,K}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, sd::DataType::HALF); + NDArray b('f', {K,N}, {1,-2,3,-4,5,-6,7,-8,9,-10,11,-12,13,-14,15,-16,17,-18,19,-20}, sd::DataType::DOUBLE); + NDArray c('f', {M,N}, sd::DataType::FLOAT32); - NDArray exp('f', {M,N}, {0.1, 0.3, 0.5, 2.5, 2.7, 2.9, 4.9, 5.1, 5.3, 7.3, 7.5, 7.7, 9.7, 9.9, 10.1}, nd4j::DataType::FLOAT32); + NDArray exp('f', {M,N}, {0.1, 0.3, 0.5, 2.5, 2.7, 2.9, 4.9, 5.1, 5.3, 7.3, 7.5, 7.7, 9.7, 9.9, 10.1}, sd::DataType::FLOAT32); - nd4j::MmulHelper::mmul(&a, &b, &c, 1., 0.); + sd::MmulHelper::mmul(&a, &b, &c, 1., 0.); // c.printBuffer(); ASSERT_TRUE(c.equalsTo(&exp, 0.01)); @@ -591,13 +591,13 @@ TEST_F(CudaBasicsTests2, mmulMxM_28) { const Nd4jLong K = 4; const Nd4jLong N = 5; - NDArray a('c', {M,K}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, nd4j::DataType::FLOAT32); - NDArray b('f', {K,N}, {1,-2,3,-4,5,-6,7,-8,9,-10,11,-12,13,-14,15,-16,17,-18,19,-20}, nd4j::DataType::DOUBLE); - NDArray c('f', {M,N}, nd4j::DataType::FLOAT32); + NDArray a('c', {M,K}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, sd::DataType::FLOAT32); + NDArray b('f', {K,N}, {1,-2,3,-4,5,-6,7,-8,9,-10,11,-12,13,-14,15,-16,17,-18,19,-20}, sd::DataType::DOUBLE); + NDArray c('f', {M,N}, sd::DataType::FLOAT32); - NDArray exp('f', {M,N}, {-1.6, -0.7, 0.2, -0.8, 0.1, 1., -0., 0.9, 1.8, 0.8, 1.7, 2.6, 1.6, 2.5, 3.4}, nd4j::DataType::FLOAT32); + NDArray exp('f', {M,N}, {-1.6, -0.7, 0.2, -0.8, 0.1, 1., -0., 0.9, 1.8, 0.8, 1.7, 2.6, 1.6, 2.5, 3.4}, sd::DataType::FLOAT32); - nd4j::MmulHelper::mmul(&a, &b, &c, 1., 0.); + sd::MmulHelper::mmul(&a, &b, &c, 1., 0.); ASSERT_TRUE(c.equalsTo(&exp)); } @@ -609,13 +609,13 @@ TEST_F(CudaBasicsTests2, mmulMxV_1) { const Nd4jLong M = 3; const Nd4jLong N = 4; - NDArray a('f', {M,N}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, nd4j::DataType::DOUBLE); - NDArray x('f', {N}, {1,-2,3,-4}, nd4j::DataType::DOUBLE); - NDArray y('f', {M}, nd4j::DataType::DOUBLE); + NDArray a('f', {M,N}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, sd::DataType::DOUBLE); + NDArray x('f', {N}, {1,-2,3,-4}, sd::DataType::DOUBLE); + NDArray y('f', {M}, sd::DataType::DOUBLE); - NDArray exp('f', {M}, {0.1, 0.3, 0.5}, nd4j::DataType::DOUBLE); + NDArray exp('f', {M}, {0.1, 0.3, 0.5}, sd::DataType::DOUBLE); - nd4j::MmulHelper::mmul(&a, &x, &y, 1., 0.); + sd::MmulHelper::mmul(&a, &x, &y, 1., 0.); ASSERT_TRUE(y.equalsTo(&exp)); } @@ -625,13 +625,13 @@ TEST_F(CudaBasicsTests2, mmulMxV_2) { const Nd4jLong M = 3; const Nd4jLong N = 4; - NDArray a('c', {M,N}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, nd4j::DataType::DOUBLE); - NDArray x('f', {N}, {1,-2,3,-4}, nd4j::DataType::DOUBLE); - NDArray y('f', {M}, nd4j::DataType::DOUBLE); + NDArray a('c', {M,N}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, sd::DataType::DOUBLE); + NDArray x('f', {N}, {1,-2,3,-4}, sd::DataType::DOUBLE); + NDArray y('f', {M}, sd::DataType::DOUBLE); - NDArray exp('f', {M}, {-1.6, -0.7, 0.2}, nd4j::DataType::DOUBLE); + NDArray exp('f', {M}, {-1.6, -0.7, 0.2}, sd::DataType::DOUBLE); - nd4j::MmulHelper::mmul(&a, &x, &y, 1., 0.); + sd::MmulHelper::mmul(&a, &x, &y, 1., 0.); ASSERT_TRUE(y.equalsTo(&exp)); } @@ -641,13 +641,13 @@ TEST_F(CudaBasicsTests2, mmulMxV_3) { const Nd4jLong M = 3; const Nd4jLong N = 4; - NDArray a('c', {M,N}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, nd4j::DataType::DOUBLE); - NDArray x('c', {N}, {1,-2,3,-4}, nd4j::DataType::DOUBLE); - NDArray y('f', {M}, nd4j::DataType::DOUBLE); + NDArray a('c', {M,N}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, sd::DataType::DOUBLE); + NDArray x('c', {N}, {1,-2,3,-4}, sd::DataType::DOUBLE); + NDArray y('f', {M}, sd::DataType::DOUBLE); - NDArray exp('f', {M}, {-1.6, -0.7, 0.2}, nd4j::DataType::DOUBLE); + NDArray exp('f', {M}, {-1.6, -0.7, 0.2}, sd::DataType::DOUBLE); - nd4j::MmulHelper::mmul(&a, &x, &y, 1., 0.); + sd::MmulHelper::mmul(&a, &x, &y, 1., 0.); ASSERT_TRUE(y.equalsTo(&exp)); } @@ -657,13 +657,13 @@ TEST_F(CudaBasicsTests2, mmulMxV_4) { const Nd4jLong M = 3; const Nd4jLong N = 4; - NDArray a('c', {M,N}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, nd4j::DataType::DOUBLE); - NDArray x('c', {N}, {1,-2,3,-4}, nd4j::DataType::DOUBLE); - NDArray y('c', {M}, nd4j::DataType::DOUBLE); + NDArray a('c', {M,N}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, sd::DataType::DOUBLE); + NDArray x('c', {N}, {1,-2,3,-4}, sd::DataType::DOUBLE); + NDArray y('c', {M}, sd::DataType::DOUBLE); - NDArray exp('c', {M}, {-1.6, -0.7, 0.2}, nd4j::DataType::DOUBLE); + NDArray exp('c', {M}, {-1.6, -0.7, 0.2}, sd::DataType::DOUBLE); - nd4j::MmulHelper::mmul(&a, &x, &y, 1., 0.); + sd::MmulHelper::mmul(&a, &x, &y, 1., 0.); ASSERT_TRUE(y.equalsTo(&exp)); } @@ -673,13 +673,13 @@ TEST_F(CudaBasicsTests2, mmulMxV_5) { const Nd4jLong M = 3; const Nd4jLong N = 4; - NDArray a('f', {M,N}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, nd4j::DataType::DOUBLE); - NDArray x('c', {N}, {1,-2,3,-4}, nd4j::DataType::DOUBLE); - NDArray y('c', {M}, nd4j::DataType::DOUBLE); + NDArray a('f', {M,N}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, sd::DataType::DOUBLE); + NDArray x('c', {N}, {1,-2,3,-4}, sd::DataType::DOUBLE); + NDArray y('c', {M}, sd::DataType::DOUBLE); - NDArray exp('c', {M}, {0.1, 0.3, 0.5}, nd4j::DataType::DOUBLE); + NDArray exp('c', {M}, {0.1, 0.3, 0.5}, sd::DataType::DOUBLE); - nd4j::MmulHelper::mmul(&a, &x, &y, 1., 0.); + sd::MmulHelper::mmul(&a, &x, &y, 1., 0.); ASSERT_TRUE(y.equalsTo(&exp)); } @@ -689,14 +689,14 @@ TEST_F(CudaBasicsTests2, mmulMxV_6) { const Nd4jLong M = 3; const Nd4jLong N = 4; - NDArray a('f', {M,N}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, nd4j::DataType::DOUBLE); - NDArray temp('f', {M,N,5}, {16,2,-6,7,2,-2,4,-7,6,4,4,6,-3,1,3,9,1,4,9,10,-10,-3,-8,7,-7,-7,6,9,7,-6,8,7,-3,-3,4,-2,5,-3,-3,4,6,-5,-1,7,-5,4,-10,-1,8,0,-7,4,-10,-7,-8,-9,2,9,7,9}, nd4j::DataType::DOUBLE); + NDArray a('f', {M,N}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, sd::DataType::DOUBLE); + NDArray temp('f', {M,N,5}, {16,2,-6,7,2,-2,4,-7,6,4,4,6,-3,1,3,9,1,4,9,10,-10,-3,-8,7,-7,-7,6,9,7,-6,8,7,-3,-3,4,-2,5,-3,-3,4,6,-5,-1,7,-5,4,-10,-1,8,0,-7,4,-10,-7,-8,-9,2,9,7,9}, sd::DataType::DOUBLE); NDArray x = temp(6, {0,2}); - NDArray y('f', {M}, nd4j::DataType::DOUBLE); + NDArray y('f', {M}, sd::DataType::DOUBLE); - NDArray exp('f', {M}, {5.5, 5.1, 4.7}, nd4j::DataType::DOUBLE); + NDArray exp('f', {M}, {5.5, 5.1, 4.7}, sd::DataType::DOUBLE); - nd4j::MmulHelper::mmul(&a, &x, &y, 1., 0.); + sd::MmulHelper::mmul(&a, &x, &y, 1., 0.); ASSERT_TRUE(y.equalsTo(&exp)); } @@ -706,15 +706,15 @@ TEST_F(CudaBasicsTests2, mmulMxV_7) { const Nd4jLong M = 3; const Nd4jLong N = 4; - NDArray a('f', {N,M}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, nd4j::DataType::DOUBLE); + NDArray a('f', {N,M}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, sd::DataType::DOUBLE); a.permutei({1,0}); - NDArray temp('f', {M,N,5}, {16,2,-6,7,2,-2,4,-7,6,4,4,6,-3,1,3,9,1,4,9,10,-10,-3,-8,7,-7,-7,6,9,7,-6,8,7,-3,-3,4,-2,5,-3,-3,4,6,-5,-1,7,-5,4,-10,-1,8,0,-7,4,-10,-7,-8,-9,2,9,7,9}, nd4j::DataType::DOUBLE); + NDArray temp('f', {M,N,5}, {16,2,-6,7,2,-2,4,-7,6,4,4,6,-3,1,3,9,1,4,9,10,-10,-3,-8,7,-7,-7,6,9,7,-6,8,7,-3,-3,4,-2,5,-3,-3,4,6,-5,-1,7,-5,4,-10,-1,8,0,-7,4,-10,-7,-8,-9,2,9,7,9}, sd::DataType::DOUBLE); NDArray x = temp(6, {0,2}); - NDArray y('f', {M}, nd4j::DataType::DOUBLE); + NDArray y('f', {M}, sd::DataType::DOUBLE); - NDArray exp('f', {M}, {5.1, 3.3, 1.5}, nd4j::DataType::DOUBLE); + NDArray exp('f', {M}, {5.1, 3.3, 1.5}, sd::DataType::DOUBLE); - nd4j::MmulHelper::mmul(&a, &x, &y, 1., 0.); + sd::MmulHelper::mmul(&a, &x, &y, 1., 0.); ASSERT_TRUE(y.equalsTo(&exp)); } @@ -724,15 +724,15 @@ TEST_F(CudaBasicsTests2, mmulMxV_8) { const Nd4jLong M = 3; const Nd4jLong N = 4; - NDArray a('f', {N,M}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, nd4j::DataType::DOUBLE); + NDArray a('f', {N,M}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, sd::DataType::DOUBLE); a.permutei({1,0}); - NDArray temp('f', {N,M,5}, {16,2,-6,7,2,-2,4,-7,6,4,4,6,-3,1,3,9,1,4,9,10,-10,-3,-8,7,-7,-7,6,9,7,-6,8,7,-3,-3,4,-2,5,-3,-3,4,6,-5,-1,7,-5,4,-10,-1,8,0,-7,4,-10,-7,-8,-9,2,9,7,9}, nd4j::DataType::DOUBLE); + NDArray temp('f', {N,M,5}, {16,2,-6,7,2,-2,4,-7,6,4,4,6,-3,1,3,9,1,4,9,10,-10,-3,-8,7,-7,-7,6,9,7,-6,8,7,-3,-3,4,-2,5,-3,-3,4,6,-5,-1,7,-5,4,-10,-1,8,0,-7,4,-10,-7,-8,-9,2,9,7,9}, sd::DataType::DOUBLE); NDArray x = temp(4, {1,2}); - NDArray y('f', {M}, nd4j::DataType::DOUBLE); + NDArray y('f', {M}, sd::DataType::DOUBLE); - NDArray exp('f', {M}, {6.2, 4.5, 1.7}, nd4j::DataType::DOUBLE); + NDArray exp('f', {M}, {6.2, 4.5, 1.7}, sd::DataType::DOUBLE); - nd4j::MmulHelper::mmul(&a, &x, &y, 1., 0.); + sd::MmulHelper::mmul(&a, &x, &y, 1., 0.); ASSERT_TRUE(y.equalsTo(&exp)); } @@ -742,15 +742,15 @@ TEST_F(CudaBasicsTests2, mmulMxV_9) { const Nd4jLong M = 3; const Nd4jLong N = 4; - NDArray a('f', {N,M}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, nd4j::DataType::DOUBLE); + NDArray a('f', {N,M}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, sd::DataType::DOUBLE); a.permutei({1,0}); - NDArray temp('f', {5,M,N}, {16,2,-6,7,2,-2,4,-7,6,4,4,6,-3,1,3,9,1,4,9,10,-10,-3,-8,7,-7,-7,6,9,7,-6,8,7,-3,-3,4,-2,5,-3,-3,4,6,-5,-1,7,-5,4,-10,-1,8,0,-7,4,-10,-7,-8,-9,2,9,7,9}, nd4j::DataType::DOUBLE); + NDArray temp('f', {5,M,N}, {16,2,-6,7,2,-2,4,-7,6,4,4,6,-3,1,3,9,1,4,9,10,-10,-3,-8,7,-7,-7,6,9,7,-6,8,7,-3,-3,4,-2,5,-3,-3,4,6,-5,-1,7,-5,4,-10,-1,8,0,-7,4,-10,-7,-8,-9,2,9,7,9}, sd::DataType::DOUBLE); NDArray x = temp(3, {0,1}); - NDArray y('f', {M}, nd4j::DataType::DOUBLE); + NDArray y('f', {M}, sd::DataType::DOUBLE); - NDArray exp('f', {M}, {1.5, 1.8, 1.5}, nd4j::DataType::DOUBLE); + NDArray exp('f', {M}, {1.5, 1.8, 1.5}, sd::DataType::DOUBLE); - nd4j::MmulHelper::mmul(&a, &x, &y, 1., 0.); + sd::MmulHelper::mmul(&a, &x, &y, 1., 0.); ASSERT_TRUE(y.equalsTo(&exp)); } @@ -760,15 +760,15 @@ TEST_F(CudaBasicsTests2, mmulMxV_10) { const Nd4jLong M = 3; const Nd4jLong N = 4; - NDArray a('c', {N,M}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, nd4j::DataType::DOUBLE); + NDArray a('c', {N,M}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, sd::DataType::DOUBLE); a.permutei({1,0}); - NDArray temp('f', {5,M,N}, {16,2,-6,7,2,-2,4,-7,6,4,4,6,-3,1,3,9,1,4,9,10,-10,-3,-8,7,-7,-7,6,9,7,-6,8,7,-3,-3,4,-2,5,-3,-3,4,6,-5,-1,7,-5,4,-10,-1,8,0,-7,4,-10,-7,-8,-9,2,9,7,9}, nd4j::DataType::DOUBLE); + NDArray temp('f', {5,M,N}, {16,2,-6,7,2,-2,4,-7,6,4,4,6,-3,1,3,9,1,4,9,10,-10,-3,-8,7,-7,-7,6,9,7,-6,8,7,-3,-3,4,-2,5,-3,-3,4,6,-5,-1,7,-5,4,-10,-1,8,0,-7,4,-10,-7,-8,-9,2,9,7,9}, sd::DataType::DOUBLE); NDArray x = temp(2, {0,1}); - NDArray y('f', {M}, nd4j::DataType::DOUBLE); + NDArray y('f', {M}, sd::DataType::DOUBLE); - NDArray exp('f', {M}, {-0.3, 0.3, 0.9}, nd4j::DataType::DOUBLE); + NDArray exp('f', {M}, {-0.3, 0.3, 0.9}, sd::DataType::DOUBLE); - nd4j::MmulHelper::mmul(&a, &x, &y, 1., 0.); + sd::MmulHelper::mmul(&a, &x, &y, 1., 0.); ASSERT_TRUE(y.equalsTo(&exp)); } @@ -778,15 +778,15 @@ TEST_F(CudaBasicsTests2, mmulMxV_11) { const Nd4jLong M = 3; const Nd4jLong N = 4; - NDArray a('c', {N,M}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, nd4j::DataType::DOUBLE); + NDArray a('c', {N,M}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, sd::DataType::DOUBLE); a.permutei({1,0}); - NDArray temp('c', {5,N,M}, {16,2,-6,7,2,-2,4,-7,6,4,4,6,-3,1,3,9,1,4,9,10,-10,-3,-8,7,-7,-7,6,9,7,-6,8,7,-3,-3,4,-2,5,-3,-3,4,6,-5,-1,7,-5,4,-10,-1,8,0,-7,4,-10,-7,-8,-9,2,9,7,9}, nd4j::DataType::DOUBLE); + NDArray temp('c', {5,N,M}, {16,2,-6,7,2,-2,4,-7,6,4,4,6,-3,1,3,9,1,4,9,10,-10,-3,-8,7,-7,-7,6,9,7,-6,8,7,-3,-3,4,-2,5,-3,-3,4,6,-5,-1,7,-5,4,-10,-1,8,0,-7,4,-10,-7,-8,-9,2,9,7,9}, sd::DataType::DOUBLE); NDArray x = temp(13, {0,2}); - NDArray y('f', {M}, nd4j::DataType::DOUBLE); + NDArray y('f', {M}, sd::DataType::DOUBLE); - NDArray exp('f', {M}, {-12.1, -10.9, -9.7}, nd4j::DataType::DOUBLE); + NDArray exp('f', {M}, {-12.1, -10.9, -9.7}, sd::DataType::DOUBLE); - nd4j::MmulHelper::mmul(&a, &x, &y, 1., 0.); + sd::MmulHelper::mmul(&a, &x, &y, 1., 0.); ASSERT_TRUE(y.equalsTo(&exp)); } @@ -796,15 +796,15 @@ TEST_F(CudaBasicsTests2, mmulMxV_12) { const Nd4jLong M = 3; const Nd4jLong N = 4; - NDArray a('c', {N,M}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, nd4j::DataType::DOUBLE); + NDArray a('c', {N,M}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, sd::DataType::DOUBLE); a.permutei({1,0}); - NDArray temp('c', {5,N,M}, {16,2,-6,7,2,-2,4,-7,6,4,4,6,-3,1,3,9,1,4,9,10,-10,-3,-8,7,-7,-7,6,9,7,-6,8,7,-3,-3,4,-2,5,-3,-3,4,6,-5,-1,7,-5,4,-10,-1,8,0,-7,4,-10,-7,-8,-9,2,9,7,9}, nd4j::DataType::DOUBLE); + NDArray temp('c', {5,N,M}, {16,2,-6,7,2,-2,4,-7,6,4,4,6,-3,1,3,9,1,4,9,10,-10,-3,-8,7,-7,-7,6,9,7,-6,8,7,-3,-3,4,-2,5,-3,-3,4,6,-5,-1,7,-5,4,-10,-1,8,0,-7,4,-10,-7,-8,-9,2,9,7,9}, sd::DataType::DOUBLE); NDArray x = temp(10, {0,2}); - NDArray y('c', {M}, nd4j::DataType::DOUBLE); + NDArray y('c', {M}, sd::DataType::DOUBLE); - NDArray exp('c', {M}, {3.3, 3.3, 3.3}, nd4j::DataType::DOUBLE); + NDArray exp('c', {M}, {3.3, 3.3, 3.3}, sd::DataType::DOUBLE); - nd4j::MmulHelper::mmul(&a, &x, &y, 1., 0.); + sd::MmulHelper::mmul(&a, &x, &y, 1., 0.); ASSERT_TRUE(y.equalsTo(&exp)); } @@ -814,15 +814,15 @@ TEST_F(CudaBasicsTests2, mmulMxV_13) { const Nd4jLong M = 3; const Nd4jLong N = 4; - NDArray a('c', {N,M}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, nd4j::DataType::DOUBLE); + NDArray a('c', {N,M}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, sd::DataType::DOUBLE); a.permutei({1,0}); - NDArray temp('f', {5,M,N}, {16,2,-6,7,2,-2,4,-7,6,4,4,6,-3,1,3,9,1,4,9,10,-10,-3,-8,7,-7,-7,6,9,7,-6,8,7,-3,-3,4,-2,5,-3,-3,4,6,-5,-1,7,-5,4,-10,-1,8,0,-7,4,-10,-7,-8,-9,2,9,7,9}, nd4j::DataType::DOUBLE); + NDArray temp('f', {5,M,N}, {16,2,-6,7,2,-2,4,-7,6,4,4,6,-3,1,3,9,1,4,9,10,-10,-3,-8,7,-7,-7,6,9,7,-6,8,7,-3,-3,4,-2,5,-3,-3,4,6,-5,-1,7,-5,4,-10,-1,8,0,-7,4,-10,-7,-8,-9,2,9,7,9}, sd::DataType::DOUBLE); NDArray x = temp(2, {0,1}, true); - NDArray y('f', {M}, nd4j::DataType::DOUBLE); + NDArray y('f', {M}, sd::DataType::DOUBLE); - NDArray exp('f', {M}, {-0.3, 0.3, 0.9}, nd4j::DataType::DOUBLE); + NDArray exp('f', {M}, {-0.3, 0.3, 0.9}, sd::DataType::DOUBLE); - nd4j::MmulHelper::mmul(&a, &x, &y, 1., 0.); + sd::MmulHelper::mmul(&a, &x, &y, 1., 0.); ASSERT_TRUE(y.equalsTo(&exp)); } @@ -832,15 +832,15 @@ TEST_F(CudaBasicsTests2, mmulMxV_14) { const Nd4jLong M = 3; const Nd4jLong N = 4; - NDArray a('c', {N,M}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, nd4j::DataType::DOUBLE); + NDArray a('c', {N,M}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, sd::DataType::DOUBLE); a.permutei({1,0}); - NDArray temp('c', {5,N,M}, {16,2,-6,7,2,-2,4,-7,6,4,4,6,-3,1,3,9,1,4,9,10,-10,-3,-8,7,-7,-7,6,9,7,-6,8,7,-3,-3,4,-2,5,-3,-3,4,6,-5,-1,7,-5,4,-10,-1,8,0,-7,4,-10,-7,-8,-9,2,9,7,9}, nd4j::DataType::DOUBLE); + NDArray temp('c', {5,N,M}, {16,2,-6,7,2,-2,4,-7,6,4,4,6,-3,1,3,9,1,4,9,10,-10,-3,-8,7,-7,-7,6,9,7,-6,8,7,-3,-3,4,-2,5,-3,-3,4,6,-5,-1,7,-5,4,-10,-1,8,0,-7,4,-10,-7,-8,-9,2,9,7,9}, sd::DataType::DOUBLE); NDArray x = temp(10, {0,2}, true); - NDArray y('c', {M}, nd4j::DataType::DOUBLE); + NDArray y('c', {M}, sd::DataType::DOUBLE); - NDArray exp('c', {M}, {3.3, 3.3, 3.3}, nd4j::DataType::DOUBLE); + NDArray exp('c', {M}, {3.3, 3.3, 3.3}, sd::DataType::DOUBLE); - nd4j::MmulHelper::mmul(&a, &x, &y, 1., 0.); + sd::MmulHelper::mmul(&a, &x, &y, 1., 0.); ASSERT_TRUE(y.equalsTo(&exp)); } @@ -850,15 +850,15 @@ TEST_F(CudaBasicsTests2, mmulMxV_15) { const Nd4jLong M = 3; const Nd4jLong N = 4; - NDArray a('c', {N,M}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, nd4j::DataType::DOUBLE); + NDArray a('c', {N,M}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, sd::DataType::DOUBLE); a.permutei({1,0}); - NDArray temp('f', {5,M,N}, {16,2,-6,7,2,-2,4,-7,6,4,4,6,-3,1,3,9,1,4,9,10,-10,-3,-8,7,-7,-7,6,9,7,-6,8,7,-3,-3,4,-2,5,-3,-3,4,6,-5,-1,7,-5,4,-10,-1,8,0,-7,4,-10,-7,-8,-9,2,9,7,9}, nd4j::DataType::DOUBLE); + NDArray temp('f', {5,M,N}, {16,2,-6,7,2,-2,4,-7,6,4,4,6,-3,1,3,9,1,4,9,10,-10,-3,-8,7,-7,-7,6,9,7,-6,8,7,-3,-3,4,-2,5,-3,-3,4,6,-5,-1,7,-5,4,-10,-1,8,0,-7,4,-10,-7,-8,-9,2,9,7,9}, sd::DataType::DOUBLE); NDArray x = temp(2, {0,1}); NDArray y = temp(17, {0,2}); - NDArray exp('f', {M}, {-0.3, 0.3, 0.9}, nd4j::DataType::DOUBLE); + NDArray exp('f', {M}, {-0.3, 0.3, 0.9}, sd::DataType::DOUBLE); - nd4j::MmulHelper::mmul(&a, &x, &y, 1., 0.); + sd::MmulHelper::mmul(&a, &x, &y, 1., 0.); ASSERT_TRUE(y.equalsTo(&exp)); } @@ -868,16 +868,16 @@ TEST_F(CudaBasicsTests2, mmulMxV_16) { const Nd4jLong M = 3; const Nd4jLong N = 4; - NDArray a('c', {N,M}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, nd4j::DataType::DOUBLE); + NDArray a('c', {N,M}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, sd::DataType::DOUBLE); a.permutei({1,0}); - NDArray temp('f', {5,M,N}, {16,2,-6,7,2,-2,4,-7,6,4,4,6,-3,1,3,9,1,4,9,10,-10,-3,-8,7,-7,-7,6,9,7,-6,8,7,-3,-3,4,-2,5,-3,-3,4,6,-5,-1,7,-5,4,-10,-1,8,0,-7,4,-10,-7,-8,-9,2,9,7,9}, nd4j::DataType::DOUBLE); - NDArray temp1('c', {5,M,N}, {16,2,-6,7,2,-2,4,-7,6,4,4,6,-3,1,3,9,1,4,9,10,-10,-3,-8,7,-7,-7,6,9,7,-6,8,7,-3,-3,4,-2,5,-3,-3,4,6,-5,-1,7,-5,4,-10,-1,8,0,-7,4,-10,-7,-8,-9,2,9,7,9}, nd4j::DataType::DOUBLE); + NDArray temp('f', {5,M,N}, {16,2,-6,7,2,-2,4,-7,6,4,4,6,-3,1,3,9,1,4,9,10,-10,-3,-8,7,-7,-7,6,9,7,-6,8,7,-3,-3,4,-2,5,-3,-3,4,6,-5,-1,7,-5,4,-10,-1,8,0,-7,4,-10,-7,-8,-9,2,9,7,9}, sd::DataType::DOUBLE); + NDArray temp1('c', {5,M,N}, {16,2,-6,7,2,-2,4,-7,6,4,4,6,-3,1,3,9,1,4,9,10,-10,-3,-8,7,-7,-7,6,9,7,-6,8,7,-3,-3,4,-2,5,-3,-3,4,6,-5,-1,7,-5,4,-10,-1,8,0,-7,4,-10,-7,-8,-9,2,9,7,9}, sd::DataType::DOUBLE); NDArray x = temp(2, {0,1}); NDArray y = temp1(17, {0,2}); - NDArray exp('c', {M}, {-0.3, 0.3, 0.9}, nd4j::DataType::DOUBLE); + NDArray exp('c', {M}, {-0.3, 0.3, 0.9}, sd::DataType::DOUBLE); - nd4j::MmulHelper::mmul(&a, &x, &y, 1., 0.); + sd::MmulHelper::mmul(&a, &x, &y, 1., 0.); ASSERT_TRUE(y.equalsTo(&exp)); } @@ -887,16 +887,16 @@ TEST_F(CudaBasicsTests2, mmulMxV_17) { const Nd4jLong M = 3; const Nd4jLong N = 4; - NDArray a('c', {N,M}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, nd4j::DataType::DOUBLE); + NDArray a('c', {N,M}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, sd::DataType::DOUBLE); a.permutei({1,0}); - NDArray temp('f', {5,M,N}, {16,2,-6,7,2,-2,4,-7,6,4,4,6,-3,1,3,9,1,4,9,10,-10,-3,-8,7,-7,-7,6,9,7,-6,8,7,-3,-3,4,-2,5,-3,-3,4,6,-5,-1,7,-5,4,-10,-1,8,0,-7,4,-10,-7,-8,-9,2,9,7,9}, nd4j::DataType::DOUBLE); + NDArray temp('f', {5,M,N}, {16,2,-6,7,2,-2,4,-7,6,4,4,6,-3,1,3,9,1,4,9,10,-10,-3,-8,7,-7,-7,6,9,7,-6,8,7,-3,-3,4,-2,5,-3,-3,4,6,-5,-1,7,-5,4,-10,-1,8,0,-7,4,-10,-7,-8,-9,2,9,7,9}, sd::DataType::DOUBLE); NDArray x = temp(2, {0,1}); NDArray y = temp(17, {0,2}, true); // y.printShapeInfo(); - NDArray exp('f', {1,M,1}, {-0.3, 0.3, 0.9}, nd4j::DataType::DOUBLE); + NDArray exp('f', {1,M,1}, {-0.3, 0.3, 0.9}, sd::DataType::DOUBLE); - nd4j::MmulHelper::mmul(&a, &x, &y, 1., 0.); + sd::MmulHelper::mmul(&a, &x, &y, 1., 0.); ASSERT_TRUE(y.equalsTo(&exp)); } @@ -906,16 +906,16 @@ TEST_F(CudaBasicsTests2, mmulMxV_18) { const Nd4jLong M = 3; const Nd4jLong N = 4; - NDArray a('c', {N,M}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, nd4j::DataType::DOUBLE); + NDArray a('c', {N,M}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, sd::DataType::DOUBLE); a.permutei({1,0}); - NDArray temp('f', {5,M,N}, {16,2,-6,7,2,-2,4,-7,6,4,4,6,-3,1,3,9,1,4,9,10,-10,-3,-8,7,-7,-7,6,9,7,-6,8,7,-3,-3,4,-2,5,-3,-3,4,6,-5,-1,7,-5,4,-10,-1,8,0,-7,4,-10,-7,-8,-9,2,9,7,9}, nd4j::DataType::DOUBLE); - NDArray temp1('c', {5,M,N}, {16,2,-6,7,2,-2,4,-7,6,4,4,6,-3,1,3,9,1,4,9,10,-10,-3,-8,7,-7,-7,6,9,7,-6,8,7,-3,-3,4,-2,5,-3,-3,4,6,-5,-1,7,-5,4,-10,-1,8,0,-7,4,-10,-7,-8,-9,2,9,7,9}, nd4j::DataType::DOUBLE); + NDArray temp('f', {5,M,N}, {16,2,-6,7,2,-2,4,-7,6,4,4,6,-3,1,3,9,1,4,9,10,-10,-3,-8,7,-7,-7,6,9,7,-6,8,7,-3,-3,4,-2,5,-3,-3,4,6,-5,-1,7,-5,4,-10,-1,8,0,-7,4,-10,-7,-8,-9,2,9,7,9}, sd::DataType::DOUBLE); + NDArray temp1('c', {5,M,N}, {16,2,-6,7,2,-2,4,-7,6,4,4,6,-3,1,3,9,1,4,9,10,-10,-3,-8,7,-7,-7,6,9,7,-6,8,7,-3,-3,4,-2,5,-3,-3,4,6,-5,-1,7,-5,4,-10,-1,8,0,-7,4,-10,-7,-8,-9,2,9,7,9}, sd::DataType::DOUBLE); NDArray x = temp(2, {0,1},true); NDArray y = temp1(17, {0,2},true); - NDArray exp('c', {1,M,1}, {-0.3, 0.3, 0.9}, nd4j::DataType::DOUBLE); + NDArray exp('c', {1,M,1}, {-0.3, 0.3, 0.9}, sd::DataType::DOUBLE); - nd4j::MmulHelper::mmul(&a, &x, &y, 1., 0.); + sd::MmulHelper::mmul(&a, &x, &y, 1., 0.); ASSERT_TRUE(y.equalsTo(&exp)); } @@ -926,13 +926,13 @@ TEST_F(CudaBasicsTests2, mmulMxV_19) { const Nd4jLong M = 3; const Nd4jLong N = 4; - NDArray a('f', {M,N}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, nd4j::DataType::FLOAT32); - NDArray x('f', {N}, {1,-2,3,-4}, nd4j::DataType::DOUBLE); - NDArray y('f', {M}, nd4j::DataType::FLOAT32); + NDArray a('f', {M,N}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, sd::DataType::FLOAT32); + NDArray x('f', {N}, {1,-2,3,-4}, sd::DataType::DOUBLE); + NDArray y('f', {M}, sd::DataType::FLOAT32); - NDArray exp('f', {M}, {0.1, 0.3, 0.5}, nd4j::DataType::FLOAT32); + NDArray exp('f', {M}, {0.1, 0.3, 0.5}, sd::DataType::FLOAT32); - nd4j::MmulHelper::mmul(&a, &x, &y, 1., 0.); + sd::MmulHelper::mmul(&a, &x, &y, 1., 0.); ASSERT_TRUE(y.equalsTo(&exp)); } @@ -942,12 +942,12 @@ TEST_F(CudaBasicsTests2, mmulMxV_20) { const Nd4jLong M = 3; const Nd4jLong N = 4; - NDArray a('c', {M,N}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, nd4j::DataType::FLOAT32); - NDArray x('f', {N}, {1,-2,3,-4}, nd4j::DataType::DOUBLE); - NDArray y('f', {M}, nd4j::DataType::FLOAT32); - NDArray exp('f', {M}, {-1.6, -0.7, 0.2}, nd4j::DataType::FLOAT32); + NDArray a('c', {M,N}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, sd::DataType::FLOAT32); + NDArray x('f', {N}, {1,-2,3,-4}, sd::DataType::DOUBLE); + NDArray y('f', {M}, sd::DataType::FLOAT32); + NDArray exp('f', {M}, {-1.6, -0.7, 0.2}, sd::DataType::FLOAT32); - nd4j::MmulHelper::mmul(&a, &x, &y, 1., 0.); + sd::MmulHelper::mmul(&a, &x, &y, 1., 0.); ASSERT_TRUE(y.equalsTo(&exp)); } @@ -957,12 +957,12 @@ TEST_F(CudaBasicsTests2, mmulMxV_21) { const Nd4jLong M = 3; const Nd4jLong N = 4; - NDArray a('c', {M,N}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, nd4j::DataType::FLOAT32); - NDArray x('c', {N}, {1,-2,3,-4}, nd4j::DataType::DOUBLE); - NDArray y('c', {M}, nd4j::DataType::FLOAT32); - NDArray exp('c', {M}, {-1.6, -0.7, 0.2}, nd4j::DataType::FLOAT32); + NDArray a('c', {M,N}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, sd::DataType::FLOAT32); + NDArray x('c', {N}, {1,-2,3,-4}, sd::DataType::DOUBLE); + NDArray y('c', {M}, sd::DataType::FLOAT32); + NDArray exp('c', {M}, {-1.6, -0.7, 0.2}, sd::DataType::FLOAT32); - nd4j::MmulHelper::mmul(&a, &x, &y, 1., 0.); + sd::MmulHelper::mmul(&a, &x, &y, 1., 0.); ASSERT_TRUE(y.equalsTo(&exp)); } @@ -972,14 +972,14 @@ TEST_F(CudaBasicsTests2, mmulMxV_22) { const Nd4jLong M = 3; const Nd4jLong N = 4; - NDArray a('f', {M,N}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, nd4j::DataType::FLOAT32); - NDArray temp('f', {M,N,5}, {16,2,-6,7,2,-2,4,-7,6,4,4,6,-3,1,3,9,1,4,9,10,-10,-3,-8,7,-7,-7,6,9,7,-6,8,7,-3,-3,4,-2,5,-3,-3,4,6,-5,-1,7,-5,4,-10,-1,8,0,-7,4,-10,-7,-8,-9,2,9,7,9}, nd4j::DataType::DOUBLE); + NDArray a('f', {M,N}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, sd::DataType::FLOAT32); + NDArray temp('f', {M,N,5}, {16,2,-6,7,2,-2,4,-7,6,4,4,6,-3,1,3,9,1,4,9,10,-10,-3,-8,7,-7,-7,6,9,7,-6,8,7,-3,-3,4,-2,5,-3,-3,4,6,-5,-1,7,-5,4,-10,-1,8,0,-7,4,-10,-7,-8,-9,2,9,7,9}, sd::DataType::DOUBLE); NDArray x = temp(6, {0,2}); - NDArray y('f', {M}, nd4j::DataType::FLOAT32); + NDArray y('f', {M}, sd::DataType::FLOAT32); - NDArray exp('f', {M}, {5.5, 5.1, 4.7}, nd4j::DataType::FLOAT32); + NDArray exp('f', {M}, {5.5, 5.1, 4.7}, sd::DataType::FLOAT32); - nd4j::MmulHelper::mmul(&a, &x, &y, 1., 0.); + sd::MmulHelper::mmul(&a, &x, &y, 1., 0.); ASSERT_TRUE(y.equalsTo(&exp)); } @@ -989,15 +989,15 @@ TEST_F(CudaBasicsTests2, mmulMxV_23) { const Nd4jLong M = 3; const Nd4jLong N = 4; - NDArray a('f', {N,M}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, nd4j::DataType::FLOAT32); + NDArray a('f', {N,M}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, sd::DataType::FLOAT32); a.permutei({1,0}); - NDArray temp('f', {5,M,N}, {16,2,-6,7,2,-2,4,-7,6,4,4,6,-3,1,3,9,1,4,9,10,-10,-3,-8,7,-7,-7,6,9,7,-6,8,7,-3,-3,4,-2,5,-3,-3,4,6,-5,-1,7,-5,4,-10,-1,8,0,-7,4,-10,-7,-8,-9,2,9,7,9}, nd4j::DataType::DOUBLE); + NDArray temp('f', {5,M,N}, {16,2,-6,7,2,-2,4,-7,6,4,4,6,-3,1,3,9,1,4,9,10,-10,-3,-8,7,-7,-7,6,9,7,-6,8,7,-3,-3,4,-2,5,-3,-3,4,6,-5,-1,7,-5,4,-10,-1,8,0,-7,4,-10,-7,-8,-9,2,9,7,9}, sd::DataType::DOUBLE); NDArray x = temp(3, {0,1}); - NDArray y('f', {M}, nd4j::DataType::FLOAT32); + NDArray y('f', {M}, sd::DataType::FLOAT32); - NDArray exp('f', {M}, {1.5, 1.8, 1.5}, nd4j::DataType::FLOAT32); + NDArray exp('f', {M}, {1.5, 1.8, 1.5}, sd::DataType::FLOAT32); - nd4j::MmulHelper::mmul(&a, &x, &y, 1., 0.); + sd::MmulHelper::mmul(&a, &x, &y, 1., 0.); ASSERT_TRUE(y.equalsTo(&exp)); } @@ -1007,14 +1007,14 @@ TEST_F(CudaBasicsTests2, mmulMxV_24) { const Nd4jLong M = 3; const Nd4jLong N = 4; - NDArray a('f', {M,N}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, nd4j::DataType::FLOAT32); - NDArray temp('f', {M,N,5}, {16,2,-6,7,2,-2,4,-7,6,4,4,6,-3,1,3,9,1,4,9,10,-10,-3,-8,7,-7,-7,6,9,7,-6,8,7,-3,-3,4,-2,5,-3,-3,4,6,-5,-1,7,-5,4,-10,-1,8,0,-7,4,-10,-7,-8,-9,2,9,7,9}, nd4j::DataType::DOUBLE); + NDArray a('f', {M,N}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, sd::DataType::FLOAT32); + NDArray temp('f', {M,N,5}, {16,2,-6,7,2,-2,4,-7,6,4,4,6,-3,1,3,9,1,4,9,10,-10,-3,-8,7,-7,-7,6,9,7,-6,8,7,-3,-3,4,-2,5,-3,-3,4,6,-5,-1,7,-5,4,-10,-1,8,0,-7,4,-10,-7,-8,-9,2,9,7,9}, sd::DataType::DOUBLE); NDArray x = temp(6, {0,2},true); - NDArray y('f', {M}, nd4j::DataType::FLOAT32); + NDArray y('f', {M}, sd::DataType::FLOAT32); - NDArray exp('f', {M}, {5.5, 5.1, 4.7}, nd4j::DataType::FLOAT32); + NDArray exp('f', {M}, {5.5, 5.1, 4.7}, sd::DataType::FLOAT32); - nd4j::MmulHelper::mmul(&a, &x, &y, 1., 0.); + sd::MmulHelper::mmul(&a, &x, &y, 1., 0.); ASSERT_TRUE(y.equalsTo(&exp)); } @@ -1024,15 +1024,15 @@ TEST_F(CudaBasicsTests2, mmulMxV_25) { const Nd4jLong M = 3; const Nd4jLong N = 4; - NDArray a('f', {N,M}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, nd4j::DataType::FLOAT32); + NDArray a('f', {N,M}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, sd::DataType::FLOAT32); a.permutei({1,0}); - NDArray temp('f', {5,M,N}, {16,2,-6,7,2,-2,4,-7,6,4,4,6,-3,1,3,9,1,4,9,10,-10,-3,-8,7,-7,-7,6,9,7,-6,8,7,-3,-3,4,-2,5,-3,-3,4,6,-5,-1,7,-5,4,-10,-1,8,0,-7,4,-10,-7,-8,-9,2,9,7,9}, nd4j::DataType::DOUBLE); + NDArray temp('f', {5,M,N}, {16,2,-6,7,2,-2,4,-7,6,4,4,6,-3,1,3,9,1,4,9,10,-10,-3,-8,7,-7,-7,6,9,7,-6,8,7,-3,-3,4,-2,5,-3,-3,4,6,-5,-1,7,-5,4,-10,-1,8,0,-7,4,-10,-7,-8,-9,2,9,7,9}, sd::DataType::DOUBLE); NDArray x = temp(3, {0,1}, true); - NDArray y('f', {M}, nd4j::DataType::FLOAT32); + NDArray y('f', {M}, sd::DataType::FLOAT32); - NDArray exp('f', {M}, {1.5, 1.8, 1.5}, nd4j::DataType::FLOAT32); + NDArray exp('f', {M}, {1.5, 1.8, 1.5}, sd::DataType::FLOAT32); - nd4j::MmulHelper::mmul(&a, &x, &y, 1., 0.); + sd::MmulHelper::mmul(&a, &x, &y, 1., 0.); ASSERT_TRUE(y.equalsTo(&exp)); } @@ -1042,16 +1042,16 @@ TEST_F(CudaBasicsTests2, mmulMxV_26) { const Nd4jLong M = 3; const Nd4jLong N = 4; - NDArray a('c', {N,M}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, nd4j::DataType::FLOAT32); + NDArray a('c', {N,M}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, sd::DataType::FLOAT32); a.permutei({1,0}); - NDArray temp('f', {5,M,N}, {16,2,-6,7,2,-2,4,-7,6,4,4,6,-3,1,3,9,1,4,9,10,-10,-3,-8,7,-7,-7,6,9,7,-6,8,7,-3,-3,4,-2,5,-3,-3,4,6,-5,-1,7,-5,4,-10,-1,8,0,-7,4,-10,-7,-8,-9,2,9,7,9}, nd4j::DataType::DOUBLE); - NDArray temp1('c', {5,M,N}, {16,2,-6,7,2,-2,4,-7,6,4,4,6,-3,1,3,9,1,4,9,10,-10,-3,-8,7,-7,-7,6,9,7,-6,8,7,-3,-3,4,-2,5,-3,-3,4,6,-5,-1,7,-5,4,-10,-1,8,0,-7,4,-10,-7,-8,-9,2,9,7,9}, nd4j::DataType::FLOAT32); + NDArray temp('f', {5,M,N}, {16,2,-6,7,2,-2,4,-7,6,4,4,6,-3,1,3,9,1,4,9,10,-10,-3,-8,7,-7,-7,6,9,7,-6,8,7,-3,-3,4,-2,5,-3,-3,4,6,-5,-1,7,-5,4,-10,-1,8,0,-7,4,-10,-7,-8,-9,2,9,7,9}, sd::DataType::DOUBLE); + NDArray temp1('c', {5,M,N}, {16,2,-6,7,2,-2,4,-7,6,4,4,6,-3,1,3,9,1,4,9,10,-10,-3,-8,7,-7,-7,6,9,7,-6,8,7,-3,-3,4,-2,5,-3,-3,4,6,-5,-1,7,-5,4,-10,-1,8,0,-7,4,-10,-7,-8,-9,2,9,7,9}, sd::DataType::FLOAT32); NDArray x = temp(2, {0,1}); NDArray y = temp1(17, {0,2}); - NDArray exp('c', {M}, {-0.3, 0.3, 0.9}, nd4j::DataType::FLOAT32); + NDArray exp('c', {M}, {-0.3, 0.3, 0.9}, sd::DataType::FLOAT32); - nd4j::MmulHelper::mmul(&a, &x, &y, 1., 0.); + sd::MmulHelper::mmul(&a, &x, &y, 1., 0.); ASSERT_TRUE(y.equalsTo(&exp)); } @@ -1061,16 +1061,16 @@ TEST_F(CudaBasicsTests2, mmulMxV_27) { const Nd4jLong M = 3; const Nd4jLong N = 4; - NDArray a('c', {N,M}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, nd4j::DataType::FLOAT32); + NDArray a('c', {N,M}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, sd::DataType::FLOAT32); a.permutei({1,0}); - NDArray temp('f', {5,M,N}, {16,2,-6,7,2,-2,4,-7,6,4,4,6,-3,1,3,9,1,4,9,10,-10,-3,-8,7,-7,-7,6,9,7,-6,8,7,-3,-3,4,-2,5,-3,-3,4,6,-5,-1,7,-5,4,-10,-1,8,0,-7,4,-10,-7,-8,-9,2,9,7,9}, nd4j::DataType::DOUBLE); - NDArray temp1('c', {5,M,N}, {16,2,-6,7,2,-2,4,-7,6,4,4,6,-3,1,3,9,1,4,9,10,-10,-3,-8,7,-7,-7,6,9,7,-6,8,7,-3,-3,4,-2,5,-3,-3,4,6,-5,-1,7,-5,4,-10,-1,8,0,-7,4,-10,-7,-8,-9,2,9,7,9}, nd4j::DataType::FLOAT32); + NDArray temp('f', {5,M,N}, {16,2,-6,7,2,-2,4,-7,6,4,4,6,-3,1,3,9,1,4,9,10,-10,-3,-8,7,-7,-7,6,9,7,-6,8,7,-3,-3,4,-2,5,-3,-3,4,6,-5,-1,7,-5,4,-10,-1,8,0,-7,4,-10,-7,-8,-9,2,9,7,9}, sd::DataType::DOUBLE); + NDArray temp1('c', {5,M,N}, {16,2,-6,7,2,-2,4,-7,6,4,4,6,-3,1,3,9,1,4,9,10,-10,-3,-8,7,-7,-7,6,9,7,-6,8,7,-3,-3,4,-2,5,-3,-3,4,6,-5,-1,7,-5,4,-10,-1,8,0,-7,4,-10,-7,-8,-9,2,9,7,9}, sd::DataType::FLOAT32); NDArray x = temp(2, {0,1},true); NDArray y = temp1(17, {0,2},true); - NDArray exp('c', {1,M,1}, {-0.3, 0.3, 0.9}, nd4j::DataType::FLOAT32); + NDArray exp('c', {1,M,1}, {-0.3, 0.3, 0.9}, sd::DataType::FLOAT32); - nd4j::MmulHelper::mmul(&a, &x, &y, 1., 0.); + sd::MmulHelper::mmul(&a, &x, &y, 1., 0.); ASSERT_TRUE(y.equalsTo(&exp)); } @@ -1080,14 +1080,14 @@ TEST_F(CudaBasicsTests2, mmulMxV_28) { const Nd4jLong M = 3; const Nd4jLong N = 4; - NDArray a('c', {M,N}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, nd4j::DataType::FLOAT32); - NDArray temp('f', {M,N,5}, {16,2,-6,7,2,-2,4,-7,6,4,4,6,-3,1,3,9,1,4,9,10,-10,-3,-8,7,-7,-7,6,9,7,-6,8,7,-3,-3,4,-2,5,-3,-3,4,6,-5,-1,7,-5,4,-10,-1,8,0,-7,4,-10,-7,-8,-9,2,9,7,9}, nd4j::DataType::DOUBLE); + NDArray a('c', {M,N}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, sd::DataType::FLOAT32); + NDArray temp('f', {M,N,5}, {16,2,-6,7,2,-2,4,-7,6,4,4,6,-3,1,3,9,1,4,9,10,-10,-3,-8,7,-7,-7,6,9,7,-6,8,7,-3,-3,4,-2,5,-3,-3,4,6,-5,-1,7,-5,4,-10,-1,8,0,-7,4,-10,-7,-8,-9,2,9,7,9}, sd::DataType::DOUBLE); NDArray x = temp(6, {0,2}); - NDArray y('f', {M}, nd4j::DataType::FLOAT32); + NDArray y('f', {M}, sd::DataType::FLOAT32); - NDArray exp('f', {M}, {5.1, 3.3, 1.5}, nd4j::DataType::FLOAT32); + NDArray exp('f', {M}, {5.1, 3.3, 1.5}, sd::DataType::FLOAT32); - nd4j::MmulHelper::mmul(&a, &x, &y, 1., 0.); + sd::MmulHelper::mmul(&a, &x, &y, 1., 0.); ASSERT_TRUE(y.equalsTo(&exp)); } @@ -1096,13 +1096,13 @@ TEST_F(CudaBasicsTests2, mmulDot_1) { const Nd4jLong N = 4; - NDArray x('c', {N}, {1, 2, 3, 4}, nd4j::DataType::INT32); - NDArray y('f', {N}, {0.1, 0.2, 0.3, 0.4}, nd4j::DataType::FLOAT32); - NDArray z(nd4j::DataType::DOUBLE); + NDArray x('c', {N}, {1, 2, 3, 4}, sd::DataType::INT32); + NDArray y('f', {N}, {0.1, 0.2, 0.3, 0.4}, sd::DataType::FLOAT32); + NDArray z(sd::DataType::DOUBLE); - NDArray exp('c', {}, {3}, nd4j::DataType::DOUBLE); + NDArray exp('c', {}, {3}, sd::DataType::DOUBLE); - nd4j::MmulHelper::mmul(&x, &y, &z); + sd::MmulHelper::mmul(&x, &y, &z); ASSERT_TRUE(z.equalsTo(&exp)); } @@ -1111,13 +1111,13 @@ TEST_F(CudaBasicsTests2, mmulDot_2) { const Nd4jLong N = 4; - NDArray x('c', {1,1,N}, {1,2, 3, 4}, nd4j::DataType::INT32); - NDArray y('f', {1,1,N,1,1,1}, {0.1, 0.2, 0.3, 0.4}, nd4j::DataType::FLOAT32); - NDArray z(nd4j::DataType::DOUBLE); + NDArray x('c', {1,1,N}, {1,2, 3, 4}, sd::DataType::INT32); + NDArray y('f', {1,1,N,1,1,1}, {0.1, 0.2, 0.3, 0.4}, sd::DataType::FLOAT32); + NDArray z(sd::DataType::DOUBLE); - NDArray exp('c', {}, {3}, nd4j::DataType::DOUBLE); + NDArray exp('c', {}, {3}, sd::DataType::DOUBLE); - nd4j::MmulHelper::mmul(&x, &y, &z); + sd::MmulHelper::mmul(&x, &y, &z); ASSERT_TRUE(z.equalsTo(&exp)); } @@ -1126,15 +1126,15 @@ TEST_F(CudaBasicsTests2, mmulDot_3) { const Nd4jLong N = 4; - NDArray xBig('c', {4,2}, {1, 0, 2, 0, 3, 0, 4, 0}, nd4j::DataType::INT32); - NDArray yBig('c', {4,3}, {0.1, 0, 0, 0.2, 0, 0, 0.3, 0, 0, 0.4, 0,0}, nd4j::DataType::FLOAT32); + NDArray xBig('c', {4,2}, {1, 0, 2, 0, 3, 0, 4, 0}, sd::DataType::INT32); + NDArray yBig('c', {4,3}, {0.1, 0, 0, 0.2, 0, 0, 0.3, 0, 0, 0.4, 0,0}, sd::DataType::FLOAT32); NDArray x = xBig(0, {1}, true); NDArray y = yBig(0, {1}, true); - NDArray z(nd4j::DataType::DOUBLE); + NDArray z(sd::DataType::DOUBLE); - NDArray exp('c', {}, {3}, nd4j::DataType::DOUBLE); + NDArray exp('c', {}, {3}, sd::DataType::DOUBLE); - nd4j::MmulHelper::mmul(&x, &y, &z); + sd::MmulHelper::mmul(&x, &y, &z); ASSERT_TRUE(z.equalsTo(&exp)); } @@ -1143,15 +1143,15 @@ TEST_F(CudaBasicsTests2, mmulDot_4) { const Nd4jLong N = 4; - NDArray xBig('f', {4,2}, {1, 2, 3, 4, 0, 0, 0, 0}, nd4j::DataType::INT32); - NDArray yBig('c', {4,3}, {0.1, 0, 0, 0.2, 0, 0, 0.3, 0, 0, 0.4, 0,0}, nd4j::DataType::FLOAT32); + NDArray xBig('f', {4,2}, {1, 2, 3, 4, 0, 0, 0, 0}, sd::DataType::INT32); + NDArray yBig('c', {4,3}, {0.1, 0, 0, 0.2, 0, 0, 0.3, 0, 0, 0.4, 0,0}, sd::DataType::FLOAT32); NDArray x = xBig(0, {1}, true); NDArray y = yBig(0, {1}); - NDArray z(nd4j::DataType::DOUBLE); + NDArray z(sd::DataType::DOUBLE); - NDArray exp('c', {}, {3}, nd4j::DataType::DOUBLE); + NDArray exp('c', {}, {3}, sd::DataType::DOUBLE); - nd4j::MmulHelper::mmul(&x, &y, &z); + sd::MmulHelper::mmul(&x, &y, &z); ASSERT_TRUE(z.equalsTo(&exp)); } */ \ No newline at end of file diff --git a/libnd4j/tests_cpu/layers_tests/CudaExtraArgumentsTests.cu b/libnd4j/tests_cpu/layers_tests/CudaExtraArgumentsTests.cu index fd8493db8..30d58946c 100644 --- a/libnd4j/tests_cpu/layers_tests/CudaExtraArgumentsTests.cu +++ b/libnd4j/tests_cpu/layers_tests/CudaExtraArgumentsTests.cu @@ -24,7 +24,7 @@ #include #include -using namespace nd4j; +using namespace sd; class CudaExtraArgumentsTests : public testing::Test { public: diff --git a/libnd4j/tests_cpu/layers_tests/CudaLaunchHelperTests.cpp b/libnd4j/tests_cpu/layers_tests/CudaLaunchHelperTests.cpp index 7d3753dbd..66cc024bf 100644 --- a/libnd4j/tests_cpu/layers_tests/CudaLaunchHelperTests.cpp +++ b/libnd4j/tests_cpu/layers_tests/CudaLaunchHelperTests.cpp @@ -21,8 +21,8 @@ #include "testlayers.h" #include -using namespace nd4j; -using namespace nd4j::graph; +using namespace sd; +using namespace sd::graph; class CudaLaunchHelperTests : public testing::Test { public: diff --git a/libnd4j/tests_cpu/layers_tests/DataBufferTests.cpp b/libnd4j/tests_cpu/layers_tests/DataBufferTests.cpp index 03d7bb38e..42ab543b1 100644 --- a/libnd4j/tests_cpu/layers_tests/DataBufferTests.cpp +++ b/libnd4j/tests_cpu/layers_tests/DataBufferTests.cpp @@ -19,9 +19,9 @@ // #include "testlayers.h" -#include -#include -#include +#include +#include +#include #include #include #include @@ -29,9 +29,9 @@ #include #include -using namespace nd4j; -using namespace nd4j::graph; -using namespace nd4j::memory; +using namespace sd; +using namespace sd::graph; +using namespace sd::memory; class DataBufferTests : public testing::Test { public: diff --git a/libnd4j/tests_cpu/layers_tests/DataBufferTestsCuda.cu b/libnd4j/tests_cpu/layers_tests/DataBufferTestsCuda.cu index 4f309cff5..730ade824 100644 --- a/libnd4j/tests_cpu/layers_tests/DataBufferTestsCuda.cu +++ b/libnd4j/tests_cpu/layers_tests/DataBufferTestsCuda.cu @@ -19,9 +19,9 @@ // #include "testlayers.h" -#include -#include -#include +#include +#include +#include #include #include #include @@ -29,9 +29,9 @@ #include #include -using namespace nd4j; -using namespace nd4j::graph; -using namespace nd4j::memory; +using namespace sd; +using namespace sd::graph; +using namespace sd::memory; class DataBufferTestsCuda : public testing::Test { public: diff --git a/libnd4j/tests_cpu/layers_tests/DataTypesValidationTests.cpp b/libnd4j/tests_cpu/layers_tests/DataTypesValidationTests.cpp index 9ee23c36d..83f3a15f5 100644 --- a/libnd4j/tests_cpu/layers_tests/DataTypesValidationTests.cpp +++ b/libnd4j/tests_cpu/layers_tests/DataTypesValidationTests.cpp @@ -19,9 +19,9 @@ // #include "testlayers.h" -#include -#include -#include +#include +#include +#include #include #include #include @@ -29,8 +29,8 @@ #include #include -using namespace nd4j; -using namespace nd4j::graph; +using namespace sd; +using namespace sd::graph; class DataTypesValidationTests : public testing::Test { public: @@ -45,12 +45,10 @@ TEST_F(DataTypesValidationTests, Basic_Test_1) { weights.assign(2.0); input.linspace(1); - nd4j::ops::conv2d op; + sd::ops::conv2d op; auto result = op.evaluate({&input, &weights}, {1, 1, 1, 1, 0, 0, 1, 1, 0, 0}); - ASSERT_EQ(ND4J_STATUS_VALIDATION, result->status()); - - delete result; + ASSERT_EQ(ND4J_STATUS_VALIDATION, result.status()); } TEST_F(DataTypesValidationTests, Basic_Test_2) { @@ -61,15 +59,14 @@ TEST_F(DataTypesValidationTests, Basic_Test_2) { weights.assign(2.0); input.linspace(1); - nd4j::ops::conv2d op; + sd::ops::conv2d op; auto result = op.evaluate({&input, &weights}, {1, 1, 1, 1, 0, 0, 1, 1, 0, 0}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.equalsTo(z)); - delete result; } @@ -82,7 +79,7 @@ TEST_F(DataTypesValidationTests, Basic_Test_3) { weights.assign(2.0); input.linspace(1); - nd4j::ops::conv2d op; + sd::ops::conv2d op; auto result = op.execute({&input, &weights}, {&out}, {}, {1, 1, 1, 1, 0, 0, 1, 1, 0, 0}, {}); ASSERT_EQ(Status::OK(), result); @@ -98,7 +95,7 @@ TEST_F(DataTypesValidationTests, Basic_Test_4) { weights.assign(2.0); input.linspace(1); - nd4j::ops::conv2d op; + sd::ops::conv2d op; auto result = op.execute({&input, &weights}, {&out}, {}, {1, 1, 1, 1, 0, 0, 1, 1, 0, 0}, {}); ASSERT_EQ(ND4J_STATUS_VALIDATION, result); } @@ -138,7 +135,7 @@ TEST_F(DataTypesValidationTests, test_bits_hamming_distance_1) { ctx.setInputArray(1, &y); ctx.setOutputArray(0, &z); - nd4j::ops::bits_hamming_distance op; + sd::ops::bits_hamming_distance op; auto status = op.execute(&ctx); ASSERT_NE(Status::OK(), status); } @@ -153,7 +150,7 @@ TEST_F(DataTypesValidationTests, test_bits_hamming_distance_2) { ctx.setInputArray(1, &y); ctx.setOutputArray(0, &z); - nd4j::ops::bits_hamming_distance op; + sd::ops::bits_hamming_distance op; auto status = op.execute(&ctx); ASSERT_EQ(Status::OK(), status); } \ No newline at end of file diff --git a/libnd4j/tests_cpu/layers_tests/DeclarableOpsTests1.cpp b/libnd4j/tests_cpu/layers_tests/DeclarableOpsTests1.cpp index 507a507af..28240cc10 100644 --- a/libnd4j/tests_cpu/layers_tests/DeclarableOpsTests1.cpp +++ b/libnd4j/tests_cpu/layers_tests/DeclarableOpsTests1.cpp @@ -14,26 +14,26 @@ * SPDX-License-Identifier: Apache-2.0 ******************************************************************************/ -// -// @author raver119@gmail.com -// + // + // @author raver119@gmail.com + // #include "testlayers.h" -#include +#include #include -#include -#include +#include +#include #include #include #include -#include +#include #include -#include +#include #include #include -using namespace nd4j; -using namespace nd4j::graph; +using namespace sd; +using namespace sd::graph; class DeclarableOpsTests1 : public testing::Test { public: @@ -51,15 +51,15 @@ public: const int pW = 0; // padding width const int dH = 2; // dilation height const int dW = 2; // dilation width - const int oH = (iH - kH - (kH-1)*(dH-1) + 2*pH)/sH + 1; // output height - const int oW = (iW - kW - (kW-1)*(dW-1) + 2*pW)/sW + 1; // output width + const int oH = (iH - kH - (kH - 1) * (dH - 1) + 2 * pH) / sH + 1; // output height + const int oW = (iW - kW - (kW - 1) * (dW - 1) + 2 * pW) / sW + 1; // output width DeclarableOpsTests1() { - nd4j::memory::MemoryTracker::getInstance()->reset(); + sd::memory::MemoryTracker::getInstance()->reset(); } ~DeclarableOpsTests1() { - nd4j::memory::MemoryTracker::getInstance()->summarize(); + sd::memory::MemoryTracker::getInstance()->summarize(); } }; @@ -80,8 +80,8 @@ public: const int pW = 0; // padding width const int dH = 2; // dilation height const int dW = 2; // dilation width - const int oH = (iH - kH - (kH-1)*(dH-1) + 2*pH)/sH + 1; // output height - const int oW = (iW - kW - (kW-1)*(dW-1) + 2*pW)/sW + 1; // output width + const int oH = (iH - kH - (kH - 1) * (dH - 1) + 2 * pH) / sH + 1; // output height + const int oW = (iW - kW - (kW - 1) * (dW - 1) + 2 * pW) / sW + 1; // output width TypedDeclarableOpsTests1() { printf("\n"); @@ -93,15 +93,15 @@ TYPED_TEST_CASE(TypedDeclarableOpsTests1, TestingTypes); ////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests1, BasicInitialization1) { - auto concat = new nd4j::ops::concat(); + auto concat = new sd::ops::concat(); std::string expName("concat"); ASSERT_EQ(expName, *(concat->getOpName())); - auto x0 = NDArrayFactory::create_('c', {1, 5}); - auto x1 = NDArrayFactory::create_('c', {1, 5}); - auto x2 = NDArrayFactory::create_('c', {1, 5}); - auto x3 = NDArrayFactory::create_('c', {1, 5}); - auto x4 = NDArrayFactory::create_('c', {1, 5}); + auto x0 = NDArrayFactory::create_('c', { 1, 5 }); + auto x1 = NDArrayFactory::create_('c', { 1, 5 }); + auto x2 = NDArrayFactory::create_('c', { 1, 5 }); + auto x3 = NDArrayFactory::create_('c', { 1, 5 }); + auto x4 = NDArrayFactory::create_('c', { 1, 5 }); x0->assign(1.0f); x1->assign(1.0f); @@ -123,7 +123,7 @@ TEST_F(DeclarableOpsTests1, BasicInitialization1) { Context block(1, variableSpace); block.getIArguments()->push_back(1); - block.fillInputs({-1, -2, -3, -4, -5}); + block.fillInputs({ -1, -2, -3, -4, -5 }); ASSERT_FALSE(nodeVar->hasNDArray()); @@ -144,7 +144,7 @@ TEST_F(DeclarableOpsTests1, BasicInitialization1) { ////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests1, BasicInitialization2) { - auto op = nd4j::ops::OpRegistrator::getInstance()->getOperation("concat"); + auto op = sd::ops::OpRegistrator::getInstance()->getOperation("concat"); ASSERT_TRUE(op != nullptr); std::string expName("concat"); @@ -156,80 +156,80 @@ TEST_F(DeclarableOpsTests1, BasicInitialization2) { ////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests1, ApplyGradientDescent_1) { - auto x = NDArrayFactory::create('c', {3,4}, {1,2,3,4,5,6,7,8,9,10,11,12}); - auto y = NDArrayFactory::create('c', {3,4}, {0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0,1.1,1.2}); - auto exp = NDArrayFactory::create('c', {3,4}); + auto x = NDArrayFactory::create('c', { 3,4 }, { 1,2,3,4,5,6,7,8,9,10,11,12 }); + auto y = NDArrayFactory::create('c', { 3,4 }, { 0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0,1.1,1.2 }); + auto exp = NDArrayFactory::create('c', { 3,4 }); exp.linspace(0.9, 0.9); - nd4j::ops::apply_sgd op; - auto result = op.evaluate({&x, &y}, {1.}, {}); - ASSERT_EQ(result->status(), ND4J_STATUS_OK); - auto z = result->at(0); + sd::ops::apply_sgd op; + auto result = op.evaluate({ &x, &y }, { 1. }, {}); + ASSERT_EQ(result.status(), ND4J_STATUS_OK); + auto z = result.at(0); ASSERT_TRUE(z->equalsTo(exp)); - delete result; + } ////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests1, AssignBroadcastTest_1) { - auto x = NDArrayFactory::create('c', {3,4}, {1,2,3,4,5,6,7,8,9,10,11,12}); - auto y = NDArrayFactory::create('c', {1,4}, {0.1,0.2,0.3,0.4}); - auto exp = NDArrayFactory::create('c', {3,4}, {0.1, 0.2, 0.3, 0.4, 0.1, 0.2, 0.3, 0.4, 0.1, 0.2, 0.3, 0.4}); - nd4j::ops::assign op; - auto result = op.evaluate({&x, &y}); - ASSERT_EQ(result->status(), ND4J_STATUS_OK); - auto z = result->at(0); + auto x = NDArrayFactory::create('c', { 3,4 }, { 1,2,3,4,5,6,7,8,9,10,11,12 }); + auto y = NDArrayFactory::create('c', { 1,4 }, { 0.1,0.2,0.3,0.4 }); + auto exp = NDArrayFactory::create('c', { 3,4 }, { 0.1, 0.2, 0.3, 0.4, 0.1, 0.2, 0.3, 0.4, 0.1, 0.2, 0.3, 0.4 }); + sd::ops::assign op; + auto result = op.evaluate({ &x, &y }); + ASSERT_EQ(result.status(), ND4J_STATUS_OK); + auto z = result.at(0); ASSERT_TRUE(z->equalsTo(exp)); - delete result; + } ////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests1, AssignBroadcastTest_2) { - auto x = NDArrayFactory::create('c', {3,4}, {1,2,3,4,5,6,7,8,9,10,11,12}); - auto y = NDArrayFactory::create('c', {1,4}, {0.1,0.2,0.3,0.4}); - auto eps = NDArrayFactory::create('c', {3,4}, {1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4}); - auto exp1 = NDArrayFactory::create('c', {3,4}); // zero - auto exp2 = NDArrayFactory::create('c', {1,4}, {3, 6, 9, 12}); - nd4j::ops::assign_bp op; - auto result = op.evaluate({&x, &y, &eps}); - ASSERT_EQ(result->status(), ND4J_STATUS_OK); - auto z1 = result->at(0); - auto z2 = result->at(1); + auto x = NDArrayFactory::create('c', { 3,4 }, { 1,2,3,4,5,6,7,8,9,10,11,12 }); + auto y = NDArrayFactory::create('c', { 1,4 }, { 0.1,0.2,0.3,0.4 }); + auto eps = NDArrayFactory::create('c', { 3,4 }, { 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4 }); + auto exp1 = NDArrayFactory::create('c', { 3,4 }); // zero + auto exp2 = NDArrayFactory::create('c', { 1,4 }, { 3, 6, 9, 12 }); + sd::ops::assign_bp op; + auto result = op.evaluate({ &x, &y, &eps }); + ASSERT_EQ(result.status(), ND4J_STATUS_OK); + auto z1 = result.at(0); + auto z2 = result.at(1); ASSERT_TRUE(z1->equalsTo(exp1)); ASSERT_TRUE(z2->equalsTo(exp2)); - delete result; + } ////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests1, AXpY_Test_1) { - auto x = NDArrayFactory::create('c', {3,4}, {1,2,3,4,5,6,7,8,9,10,11,12}); - auto y = NDArrayFactory::create('c', {3,4}, {1,2,3,4,5,6,7,8,9,10,11,12}); - auto exp = NDArrayFactory::create('c', {3,4}); + auto x = NDArrayFactory::create('c', { 3,4 }, { 1,2,3,4,5,6,7,8,9,10,11,12 }); + auto y = NDArrayFactory::create('c', { 3,4 }, { 1,2,3,4,5,6,7,8,9,10,11,12 }); + auto exp = NDArrayFactory::create('c', { 3,4 }); exp.linspace(3, 3); - nd4j::ops::axpy op; - auto result = op.evaluate({&x, &y}, {2.}); - ASSERT_EQ(result->status(), ND4J_STATUS_OK); - auto z = result->at(0); + sd::ops::axpy op; + auto result = op.evaluate({ &x, &y }, { 2. }); + ASSERT_EQ(result.status(), ND4J_STATUS_OK); + auto z = result.at(0); ASSERT_TRUE(z->equalsTo(exp)); - delete result; + } TEST_F(DeclarableOpsTests1, BasicInitialization3) { - auto op1 = nd4j::ops::OpRegistrator::getInstance()->getOperation("concat"); + auto op1 = sd::ops::OpRegistrator::getInstance()->getOperation("concat"); std::string expName("concat"); - auto hash = nd4j::ops::HashHelper::getInstance()->getLongHash(expName); + auto hash = sd::ops::HashHelper::getInstance()->getLongHash(expName); - auto op2 = nd4j::ops::OpRegistrator::getInstance()->getOperation(hash); + auto op2 = sd::ops::OpRegistrator::getInstance()->getOperation(hash); ASSERT_TRUE(op1 == op2); } TEST_F(DeclarableOpsTests1, SynonymInitialization2) { - auto op = nd4j::ops::OpRegistrator::getInstance()->getOperation("Mul"); - auto op2 = nd4j::ops::OpRegistrator::getInstance()->getOperation("multiply"); + auto op = sd::ops::OpRegistrator::getInstance()->getOperation("Mul"); + auto op2 = sd::ops::OpRegistrator::getInstance()->getOperation("multiply"); ASSERT_TRUE(op != nullptr); std::string expName("multiply"); @@ -240,105 +240,105 @@ TEST_F(DeclarableOpsTests1, SynonymInitialization2) { TEST_F(DeclarableOpsTests1, TestTensorMmul1) { - NDArray x('c', {2, 3, 4}, nd4j::DataType::FLOAT32); - NDArray y('c', {2, 3, 4}, nd4j::DataType::FLOAT32); + NDArray x('c', { 2, 3, 4 }, sd::DataType::FLOAT32); + NDArray y('c', { 2, 3, 4 }, sd::DataType::FLOAT32); x.linspace(1); y.linspace(1); - NDArray exp('c', {2, 2}, {650.0, 1586.0, 1586.0, 4250.0}, nd4j::DataType::FLOAT32); + NDArray exp('c', { 2, 2 }, { 650.0, 1586.0, 1586.0, 4250.0 }, sd::DataType::FLOAT32); - nd4j::ops::tensormmul op; - auto results = op.evaluate({&x, &y}, {}, {2,1,2,2,1,2}); + sd::ops::tensormmul op; + auto results = op.evaluate({ &x, &y }, {}, { 2,1,2,2,1,2 }); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *out = results->at(0); + auto* out = results.at(0); ASSERT_TRUE(exp.isSameShape(out)); ASSERT_TRUE(exp.equalsTo(out)); - delete results; + } TEST_F(DeclarableOpsTests1, TestTensorDot2) { - NDArray x('f', {2, 3, 4}, {1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24.}, nd4j::DataType::FLOAT32); - NDArray y('f', {2, 3, 4}, {1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24.}, nd4j::DataType::FLOAT32); + NDArray x('f', { 2, 3, 4 }, { 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24. }, sd::DataType::FLOAT32); + NDArray y('f', { 2, 3, 4 }, { 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24. }, sd::DataType::FLOAT32); - NDArray exp('c', {2, 2}, {2300.0, 2444.0, 2444.0, 2600.0}, nd4j::DataType::FLOAT32); + NDArray exp('c', { 2, 2 }, { 2300.0, 2444.0, 2444.0, 2600.0 }, sd::DataType::FLOAT32); - nd4j::ops::tensormmul op; - auto results = op.evaluate({&x, &y}, {}, {2,1,2,2,1,2}); + sd::ops::tensormmul op; + auto results = op.evaluate({ &x, &y }, {}, { 2,1,2,2,1,2 }); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *out = results->at(0); + auto* out = results.at(0); ASSERT_TRUE(exp.isSameShape(out)); ASSERT_TRUE(exp.equalsTo(out)); - delete results; + } TEST_F(DeclarableOpsTests1, TestTensorDot3) { - NDArray x('c', {2, 3, 4}, {1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24.}, nd4j::DataType::FLOAT32); - NDArray y('f', {2, 3, 4}, {1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24.}, nd4j::DataType::FLOAT32); + NDArray x('c', { 2, 3, 4 }, { 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24. }, sd::DataType::FLOAT32); + NDArray y('f', { 2, 3, 4 }, { 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24. }, sd::DataType::FLOAT32); - NDArray exp('f', {2, 2}, {1090.0, 2818.0, 1168.0, 3040.0}, nd4j::DataType::FLOAT32); + NDArray exp('f', { 2, 2 }, { 1090.0, 2818.0, 1168.0, 3040.0 }, sd::DataType::FLOAT32); - nd4j::ops::tensormmul op; - auto results = op.evaluate({&x, &y}, {}, {2,1,2,2,1,2}); + sd::ops::tensormmul op; + auto results = op.evaluate({ &x, &y }, {}, { 2,1,2,2,1,2 }); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *out = results->at(0); + auto* out = results.at(0); ASSERT_TRUE(exp.isSameShape(out)); ASSERT_TRUE(exp.equalsTo(out)); - delete results; + } TEST_F(DeclarableOpsTests1, TestTensorDot4) { - NDArray x('f', {2, 3, 4}, {1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24.}, nd4j::DataType::FLOAT32); - NDArray y('c', {2, 3, 4}, {1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24.}, nd4j::DataType::FLOAT32); + NDArray x('f', { 2, 3, 4 }, { 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24. }, sd::DataType::FLOAT32); + NDArray y('c', { 2, 3, 4 }, { 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24. }, sd::DataType::FLOAT32); - NDArray exp('f', {2, 2}, {1090.0, 1168.0, 2818.0, 3040.0}, nd4j::DataType::FLOAT32); + NDArray exp('f', { 2, 2 }, { 1090.0, 1168.0, 2818.0, 3040.0 }, sd::DataType::FLOAT32); - nd4j::ops::tensormmul op; - auto results = op.evaluate({&x, &y}, {}, {2,1,2,2,1,2}); + sd::ops::tensormmul op; + auto results = op.evaluate({ &x, &y }, {}, { 2,1,2,2,1,2 }); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *out = results->at(0); + auto* out = results.at(0); ASSERT_TRUE(exp.isSameShape(out)); ASSERT_TRUE(exp.equalsTo(out)); - delete results; + } //////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests1, TestTensorDot5) { - auto x = NDArrayFactory::create('c', {2,3,4}, {1,3,5,7,9,11,13,15, 1,3,5,7,9,11,13,15, 1,3,5,7,9,11,13,15}); - auto y = NDArrayFactory::create('c', {2,4,3}, {2,4,6,8,10,12,14,16, 2,4,6,8,10,12,14,16, 2,4,6,8,10,12,14,16}); - auto expected = NDArrayFactory::create('c', {2,4,2,4}, {44,110,160, 66,132, 38, 88,154, 68,170,224,102,204, 82,136,238, 92,230,288,138,276,126,184,322, 116,290,352,174,348,170,232,406, 76,190,160,114,228,182,152,266, 100,250,224,150,300,226,200,350, 124,310,288,186,372,270,248,434, 148,370,352,222,444,314,296,518}); + auto x = NDArrayFactory::create('c', { 2,3,4 }, { 1,3,5,7,9,11,13,15, 1,3,5,7,9,11,13,15, 1,3,5,7,9,11,13,15 }); + auto y = NDArrayFactory::create('c', { 2,4,3 }, { 2,4,6,8,10,12,14,16, 2,4,6,8,10,12,14,16, 2,4,6,8,10,12,14,16 }); + auto expected = NDArrayFactory::create('c', { 2,4,2,4 }, { 44,110,160, 66,132, 38, 88,154, 68,170,224,102,204, 82,136,238, 92,230,288,138,276,126,184,322, 116,290,352,174,348,170,232,406, 76,190,160,114,228,182,152,266, 100,250,224,150,300,226,200,350, 124,310,288,186,372,270,248,434, 148,370,352,222,444,314,296,518 }); - nd4j::ops::tensormmul op; - auto results = op.evaluate({&x, &y}, {}, {1,1,1,2}); + sd::ops::tensormmul op; + auto results = op.evaluate({ &x, &y }, {}, { 1,1,1,2 }); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto* result = results.at(0); ASSERT_TRUE(expected.isSameShape(result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; + } @@ -346,70 +346,70 @@ TEST_F(DeclarableOpsTests1, TestTensorDot5) { //////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests1, TestTensorDot6) { - auto x = NDArrayFactory::create('c', {2,3,4}, {1,3,5,7,9,11,13,15, 1,3,5,7,9,11,13,15, 1,3,5,7,9,11,13,15}); - auto y = NDArrayFactory::create('f', {2,4,3}, {2,4,6,8,10,12,14,16, 2,4,6,8,10,12,14,16, 2,4,6,8,10,12,14,16}); - auto expected = NDArrayFactory::create('c', {2,4,2,4}, {22, 66,110,154, 44, 88,132,176, 34,102,170,238, 68,136,204,272, 46,138,230,322, 92,184,276,368, 58,174,290,406,116,232,348,464, 38,114,190,266, 76,152,228,304, 50,150,250,350,100,200,300,400, 62,186,310,434,124,248,372,496, 74,222,370,518,148,296,444,592}); + auto x = NDArrayFactory::create('c', { 2,3,4 }, { 1,3,5,7,9,11,13,15, 1,3,5,7,9,11,13,15, 1,3,5,7,9,11,13,15 }); + auto y = NDArrayFactory::create('f', { 2,4,3 }, { 2,4,6,8,10,12,14,16, 2,4,6,8,10,12,14,16, 2,4,6,8,10,12,14,16 }); + auto expected = NDArrayFactory::create('c', { 2,4,2,4 }, { 22, 66,110,154, 44, 88,132,176, 34,102,170,238, 68,136,204,272, 46,138,230,322, 92,184,276,368, 58,174,290,406,116,232,348,464, 38,114,190,266, 76,152,228,304, 50,150,250,350,100,200,300,400, 62,186,310,434,124,248,372,496, 74,222,370,518,148,296,444,592 }); - nd4j::ops::tensormmul op; - auto results = op.evaluate({&x, &y}, {}, {1,1,1,2}); + sd::ops::tensormmul op; + auto results = op.evaluate({ &x, &y }, {}, { 1,1,1,2 }); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto* result = results.at(0); ASSERT_TRUE(expected.isSameShape(result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; + } //////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests1, TestTensorDot7) { - auto x = NDArrayFactory::create('f', {2,3,4}, {1,3,5,7,9,11,13,15, 1,3,5,7,9,11,13,15, 1,3,5,7,9,11,13,15}); - auto y = NDArrayFactory::create('c', {2,4,3}, {2,4,6,8,10,12,14,16, 2,4,6,8,10,12,14,16, 2,4,6,8,10,12,14,16}); - auto expected = NDArrayFactory::create('c', {2,4,2,4}, {76,166,112,106,196, 62,136,226, 60,174,208, 98,212,230,136,250, 76,214,336,122,260,174,168,306, 124,286,240,178,340,150,232,394, 100,226,176,142,268,106,184,310, 84,234,272,134,284,274,184,334, 100,274,400,158,332,218,216,390, 148,346,304,214,412,194,280,478}); + auto x = NDArrayFactory::create('f', { 2,3,4 }, { 1,3,5,7,9,11,13,15, 1,3,5,7,9,11,13,15, 1,3,5,7,9,11,13,15 }); + auto y = NDArrayFactory::create('c', { 2,4,3 }, { 2,4,6,8,10,12,14,16, 2,4,6,8,10,12,14,16, 2,4,6,8,10,12,14,16 }); + auto expected = NDArrayFactory::create('c', { 2,4,2,4 }, { 76,166,112,106,196, 62,136,226, 60,174,208, 98,212,230,136,250, 76,214,336,122,260,174,168,306, 124,286,240,178,340,150,232,394, 100,226,176,142,268,106,184,310, 84,234,272,134,284,274,184,334, 100,274,400,158,332,218,216,390, 148,346,304,214,412,194,280,478 }); - nd4j::ops::tensormmul op; - auto results = op.evaluate({&x, &y}, {}, {1,1,1,2}); + sd::ops::tensormmul op; + auto results = op.evaluate({ &x, &y }, {}, { 1,1,1,2 }); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto* result = results.at(0); ASSERT_TRUE(expected.isSameShape(result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; + } //////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests1, TestTensorDot8) { - auto x = NDArrayFactory::create('f', {2,3,4}, {1,3,5,7,9,11,13,15, 1,3,5,7,9,11,13,15, 1,3,5,7,9,11,13,15}); - auto y = NDArrayFactory::create('f', {2,4,3}, {2,4,6,8,10,12,14,16, 2,4,6,8,10,12,14,16, 2,4,6,8,10,12,14,16}); - auto expected = NDArrayFactory::create('c', {2,4,2,4}, {30, 90,150,210, 60,120,180,240, 38,114,190,266, 76,152,228,304, 46,138,230,322, 92,184,276,368, 54,162,270,378,108,216,324,432, 42,126,210,294, 84,168,252,336, 50,150,250,350,100,200,300,400, 58,174,290,406,116,232,348,464, 66,198,330,462,132,264,396,528}); + auto x = NDArrayFactory::create('f', { 2,3,4 }, { 1,3,5,7,9,11,13,15, 1,3,5,7,9,11,13,15, 1,3,5,7,9,11,13,15 }); + auto y = NDArrayFactory::create('f', { 2,4,3 }, { 2,4,6,8,10,12,14,16, 2,4,6,8,10,12,14,16, 2,4,6,8,10,12,14,16 }); + auto expected = NDArrayFactory::create('c', { 2,4,2,4 }, { 30, 90,150,210, 60,120,180,240, 38,114,190,266, 76,152,228,304, 46,138,230,322, 92,184,276,368, 54,162,270,378,108,216,324,432, 42,126,210,294, 84,168,252,336, 50,150,250,350,100,200,300,400, 58,174,290,406,116,232,348,464, 66,198,330,462,132,264,396,528 }); - nd4j::ops::tensormmul op; - auto results = op.evaluate({&x, &y}, {}, {1,1,1,2}); + sd::ops::tensormmul op; + auto results = op.evaluate({ &x, &y }, {}, { 1,1,1,2 }); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto* result = results.at(0); ASSERT_TRUE(expected.isSameShape(result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; + } //////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests1, TestTensorDot9) { - // NDArray z('f',{2,2,3}, nd4j::DataType::DOUBLE); + // NDArray z('f',{2,2,3}, sd::DataType::DOUBLE); // z.linspace(1); // z.printShapeInfo(); // z.printIndexedBuffer(); @@ -417,42 +417,42 @@ TEST_F(DeclarableOpsTests1, TestTensorDot9) { // z.printShapeInfo(); // z.printIndexedBuffer(); - auto x = NDArrayFactory::create('f', {2,3,4}, {1,3,5,7,9,11,13,15, 1,3,5,7,9,11,13,15, 1,3,5,7,9,11,13,15}); - auto y = NDArrayFactory::create('f', {2,4,3}, {2,4,6,8,10,12,14,16, 2,4,6,8,10,12,14,16, 2,4,6,8,10,12,14,16}); - auto expected = NDArrayFactory::create('c', {3,4,4,3}, {14, 14, 14, 30, 30, 30, 46, 46, 46, 62, 62, 62, 86, 86, 86,198,198,198,310,310,310,422,422,422, 62, 62, 62,142,142,142,222,222,222,302,302,302, 38, 38, 38, 86, 86, 86,134,134,134,182,182,182, 38, 38, 38, 86, 86, 86,134,134,134,182,182,182, 14, 14, 14, 30, 30, 30, 46, 46, 46, 62, 62, 62, 86, 86, 86,198,198,198,310,310,310,422,422,422, 62, 62, 62,142,142,142,222,222,222,302,302,302, 62, 62, 62,142,142,142,222,222,222,302,302,302, 38, 38, 38, 86, 86, 86,134,134,134,182,182,182, 14, 14, 14, 30, 30, 30, 46, 46, 46, 62, 62, 62, 86, 86, 86,198,198,198,310,310,310,422,422,422}); + auto x = NDArrayFactory::create('f', { 2,3,4 }, { 1,3,5,7,9,11,13,15, 1,3,5,7,9,11,13,15, 1,3,5,7,9,11,13,15 }); + auto y = NDArrayFactory::create('f', { 2,4,3 }, { 2,4,6,8,10,12,14,16, 2,4,6,8,10,12,14,16, 2,4,6,8,10,12,14,16 }); + auto expected = NDArrayFactory::create('c', { 3,4,4,3 }, { 14, 14, 14, 30, 30, 30, 46, 46, 46, 62, 62, 62, 86, 86, 86,198,198,198,310,310,310,422,422,422, 62, 62, 62,142,142,142,222,222,222,302,302,302, 38, 38, 38, 86, 86, 86,134,134,134,182,182,182, 38, 38, 38, 86, 86, 86,134,134,134,182,182,182, 14, 14, 14, 30, 30, 30, 46, 46, 46, 62, 62, 62, 86, 86, 86,198,198,198,310,310,310,422,422,422, 62, 62, 62,142,142,142,222,222,222,302,302,302, 62, 62, 62,142,142,142,222,222,222,302,302,302, 38, 38, 38, 86, 86, 86,134,134,134,182,182,182, 14, 14, 14, 30, 30, 30, 46, 46, 46, 62, 62, 62, 86, 86, 86,198,198,198,310,310,310,422,422,422 }); - nd4j::ops::tensormmul op; - auto results = op.evaluate({&x, &y}, {}, {1,0,1,0}); + sd::ops::tensormmul op; + auto results = op.evaluate({ &x, &y }, {}, { 1,0,1,0 }); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto* result = results.at(0); ASSERT_TRUE(expected.isSameShape(result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; + } //////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests1, TestTensorDot10) { - auto x = NDArrayFactory::create('f', {2,3,4}, {1,3,5,7,9,11,13,15, 1,3,5,7,9,11,13,15, 1,3,5,7,9,11,13,15}); - auto y = NDArrayFactory::create('f', {2,4,3}, {2,4,6,8,10,12,14,16, 2,4,6,8,10,12,14,16, 2,4,6,8,10,12,14,16}); - auto expected = NDArrayFactory::create('c', {4,4}, {114,258,402,546, 138,314,490,666, 162,370,578,786, 186,426,666,906}); + auto x = NDArrayFactory::create('f', { 2,3,4 }, { 1,3,5,7,9,11,13,15, 1,3,5,7,9,11,13,15, 1,3,5,7,9,11,13,15 }); + auto y = NDArrayFactory::create('f', { 2,4,3 }, { 2,4,6,8,10,12,14,16, 2,4,6,8,10,12,14,16, 2,4,6,8,10,12,14,16 }); + auto expected = NDArrayFactory::create('c', { 4,4 }, { 114,258,402,546, 138,314,490,666, 162,370,578,786, 186,426,666,906 }); - nd4j::ops::tensormmul op; - auto results = op.evaluate({&x, &y}, {}, {2,0,1, 2,0,2}); + sd::ops::tensormmul op; + auto results = op.evaluate({ &x, &y }, {}, { 2,0,1, 2,0,2 }); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto* result = results.at(0); ASSERT_TRUE(expected.isSameShape(result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; + } @@ -460,144 +460,144 @@ TEST_F(DeclarableOpsTests1, TestTensorDot10) { //////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests1, TestTensorDot11) { - auto x = NDArrayFactory::create('c', {2,3,4}, {1,3,5,7,9,11,13,15, 1,3,5,7,9,11,13,15, 1,3,5,7,9,11,13,15}); - auto y = NDArrayFactory::create('f', {2,4,3}, {2,4,6,8,10,12,14,16, 2,4,6,8,10,12,14,16, 2,4,6,8,10,12,14,16}); - auto expected = NDArrayFactory::create('c', {4,4}, {98,218,338,458, 134,302,470,638, 170,386,602,818, 206,470,734,998}); + auto x = NDArrayFactory::create('c', { 2,3,4 }, { 1,3,5,7,9,11,13,15, 1,3,5,7,9,11,13,15, 1,3,5,7,9,11,13,15 }); + auto y = NDArrayFactory::create('f', { 2,4,3 }, { 2,4,6,8,10,12,14,16, 2,4,6,8,10,12,14,16, 2,4,6,8,10,12,14,16 }); + auto expected = NDArrayFactory::create('c', { 4,4 }, { 98,218,338,458, 134,302,470,638, 170,386,602,818, 206,470,734,998 }); - nd4j::ops::tensormmul op; - auto results = op.evaluate({&x, &y}, {}, {2,0,1, 2,0,2}); + sd::ops::tensormmul op; + auto results = op.evaluate({ &x, &y }, {}, { 2,0,1, 2,0,2 }); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto* result = results.at(0); ASSERT_TRUE(expected.isSameShape(result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; + } //////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests1, TestTensorDot12) { - auto x = NDArrayFactory::create('c', {2,3,4}, {1,3,5,7,9,11,13,15, 1,3,5,7,9,11,13,15, 1,3,5,7,9,11,13,15}); - auto y = NDArrayFactory::create('c', {2,4,3}, {2,4,6,8,10,12,14,16, 2,4,6,8,10,12,14,16, 2,4,6,8,10,12,14,16}); - auto expected = NDArrayFactory::create('c', {4,4}, {272,292,312,332, 368,396,424,452, 464,500,536,572, 560,604,648,692}); + auto x = NDArrayFactory::create('c', { 2,3,4 }, { 1,3,5,7,9,11,13,15, 1,3,5,7,9,11,13,15, 1,3,5,7,9,11,13,15 }); + auto y = NDArrayFactory::create('c', { 2,4,3 }, { 2,4,6,8,10,12,14,16, 2,4,6,8,10,12,14,16, 2,4,6,8,10,12,14,16 }); + auto expected = NDArrayFactory::create('c', { 4,4 }, { 272,292,312,332, 368,396,424,452, 464,500,536,572, 560,604,648,692 }); - nd4j::ops::tensormmul op; - auto results = op.evaluate({&x, &y}, {}, {2,0,1, 2,0,2}); + sd::ops::tensormmul op; + auto results = op.evaluate({ &x, &y }, {}, { 2,0,1, 2,0,2 }); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto* result = results.at(0); ASSERT_TRUE(expected.isSameShape(result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; + } //////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests1, TestTensorDot13) { - auto x = NDArrayFactory::create('c', {2,3,4}, {1,3,5,7,9,11,13,15, 1,3,5,7,9,11,13,15, 1,3,5,7,9,11,13,15}); - auto y = NDArrayFactory::create('c', {4,2,3}, {2,4,6,8,10,12,14,16, 2,4,6,8,10,12,14,16, 2,4,6,8,10,12,14,16}); - auto expected = NDArrayFactory::create('c', {3,3}, {640,560,640, 576,624,576, 640,560,640}); + auto x = NDArrayFactory::create('c', { 2,3,4 }, { 1,3,5,7,9,11,13,15, 1,3,5,7,9,11,13,15, 1,3,5,7,9,11,13,15 }); + auto y = NDArrayFactory::create('c', { 4,2,3 }, { 2,4,6,8,10,12,14,16, 2,4,6,8,10,12,14,16, 2,4,6,8,10,12,14,16 }); + auto expected = NDArrayFactory::create('c', { 3,3 }, { 640,560,640, 576,624,576, 640,560,640 }); - nd4j::ops::tensormmul op; - auto results = op.evaluate({&x, &y}, {}, {2,0,2, 2,1,0}); + sd::ops::tensormmul op; + auto results = op.evaluate({ &x, &y }, {}, { 2,0,2, 2,1,0 }); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto* result = results.at(0); ASSERT_TRUE(expected.isSameShape(result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; + } //////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests1, TestTensorDot14) { - auto x = NDArrayFactory::create('f', {2,3,4}, {1,3,5,7,9,11,13,15, 1,3,5,7,9,11,13,15, 1,3,5,7,9,11,13,15}); - auto y = NDArrayFactory::create('c', {4,2,3}, {2,4,6,8,10,12,14,16, 2,4,6,8,10,12,14,16, 2,4,6,8,10,12,14,16}); - auto expected = NDArrayFactory::create('c', {3,3}, {648,600,520, 648,536,648, 520,600,648}); + auto x = NDArrayFactory::create('f', { 2,3,4 }, { 1,3,5,7,9,11,13,15, 1,3,5,7,9,11,13,15, 1,3,5,7,9,11,13,15 }); + auto y = NDArrayFactory::create('c', { 4,2,3 }, { 2,4,6,8,10,12,14,16, 2,4,6,8,10,12,14,16, 2,4,6,8,10,12,14,16 }); + auto expected = NDArrayFactory::create('c', { 3,3 }, { 648,600,520, 648,536,648, 520,600,648 }); - nd4j::ops::tensormmul op; - auto results = op.evaluate({&x, &y}, {}, {2,0,2, 2,1,0}); + sd::ops::tensormmul op; + auto results = op.evaluate({ &x, &y }, {}, { 2,0,2, 2,1,0 }); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto* result = results.at(0); ASSERT_TRUE(expected.isSameShape(result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; + } //////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests1, TestTensorDot15) { - auto x = NDArrayFactory::create('f', {2,3,4}, {1,3,5,7,9,11,13,15, 1,3,5,7,9,11,13,15, 1,3,5,7,9,11,13,15}); - auto y = NDArrayFactory::create('f', {4,2,3}, {2,4,6,8,10,12,14,16, 2,4,6,8,10,12,14,16, 2,4,6,8,10,12,14,16}); - auto expected = NDArrayFactory::create('c', {3,3}, {624,624,624, 656,656,656, 624,624,624}); + auto x = NDArrayFactory::create('f', { 2,3,4 }, { 1,3,5,7,9,11,13,15, 1,3,5,7,9,11,13,15, 1,3,5,7,9,11,13,15 }); + auto y = NDArrayFactory::create('f', { 4,2,3 }, { 2,4,6,8,10,12,14,16, 2,4,6,8,10,12,14,16, 2,4,6,8,10,12,14,16 }); + auto expected = NDArrayFactory::create('c', { 3,3 }, { 624,624,624, 656,656,656, 624,624,624 }); - nd4j::ops::tensormmul op; - auto results = op.evaluate({&x, &y}, {}, {2,0,2, 2,1,0}); + sd::ops::tensormmul op; + auto results = op.evaluate({ &x, &y }, {}, { 2,0,2, 2,1,0 }); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto* result = results.at(0); ASSERT_TRUE(expected.isSameShape(result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; + } //////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests1, TestTensorDot16) { - NDArray x('c', {1}, std::vector{2}, nd4j::DataType::FLOAT32); - NDArray y('c', {2,1,2}, {1,2,3,4}, nd4j::DataType::FLOAT32); - NDArray exp('c', {2,2}, {2,4,6,8}, nd4j::DataType::FLOAT32); + NDArray x('c', { 1 }, std::vector{2}, sd::DataType::FLOAT32); + NDArray y('c', { 2,1,2 }, { 1,2,3,4 }, sd::DataType::FLOAT32); + NDArray exp('c', { 2,2 }, { 2,4,6,8 }, sd::DataType::FLOAT32); - nd4j::ops::tensormmul op; - auto results = op.evaluate({&x, &y}, {}, {1,0, 1,1}); + sd::ops::tensormmul op; + auto results = op.evaluate({ &x, &y }, {}, { 1,0, 1,1 }); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto* result = results.at(0); ASSERT_TRUE(exp.isSameShape(result)); ASSERT_TRUE(exp.equalsTo(result)); - delete results; + } //////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests1, TestTensorDot17) { - NDArray x('f', {16,16}, nd4j::DataType::FLOAT32); - NDArray y('f', {1000,16}, nd4j::DataType::FLOAT32); - NDArray z('c', {16,1000}, nd4j::DataType::FLOAT32); + NDArray x('f', { 16,16 }, sd::DataType::FLOAT32); + NDArray y('f', { 1000,16 }, sd::DataType::FLOAT32); + NDArray z('c', { 16,1000 }, sd::DataType::FLOAT32); - nd4j::ops::tensormmul op; - auto status = op.execute({&x, &y}, {&z}, {}, {1,1, 1,1}, {}); + sd::ops::tensormmul op; + auto status = op.execute({ &x, &y }, { &z }, {}, { 1,1, 1,1 }, {}); ASSERT_EQ(ND4J_STATUS_OK, status); } ////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests1, DivergentCheck1) { - auto op = nd4j::ops::OpRegistrator::getInstance()->getOperation("switch"); + auto op = sd::ops::OpRegistrator::getInstance()->getOperation("switch"); ASSERT_TRUE(op != nullptr); std::string expName("Switch"); @@ -609,9 +609,9 @@ TEST_F(DeclarableOpsTests1, DivergentCheck1) { ////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests1, AddMatrices1) { - auto x = NDArrayFactory::create_ ('c', {5, 3}); - auto y = NDArrayFactory::create_ ('c', {5, 3}); - auto exp = NDArrayFactory::create_('c', {5, 3}); + auto x = NDArrayFactory::create_('c', { 5, 3 }); + auto y = NDArrayFactory::create_('c', { 5, 3 }); + auto exp = NDArrayFactory::create_('c', { 5, 3 }); x->assign(2); y->assign(1); exp->assign(3); @@ -620,9 +620,9 @@ TEST_F(DeclarableOpsTests1, AddMatrices1) { variableSpace->putVariable(-1, x); variableSpace->putVariable(-2, y); auto block = new Context(1, variableSpace, true); - block->fillInputs({-1, -2}); + block->fillInputs({ -1, -2 }); - nd4j::ops::add addOp; + sd::ops::add addOp; addOp.execute(block); @@ -637,9 +637,9 @@ TEST_F(DeclarableOpsTests1, AddMatrices1) { ////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests1, AddVectorVector1) { - auto x = NDArrayFactory::create_ ('c', {1, 15}); - auto y = NDArrayFactory::create_ ('c', {1, 15}); - auto exp = NDArrayFactory::create_('c', {1, 15}); + auto x = NDArrayFactory::create_('c', { 1, 15 }); + auto y = NDArrayFactory::create_('c', { 1, 15 }); + auto exp = NDArrayFactory::create_('c', { 1, 15 }); x->assign(2); y->assign(1); exp->assign(3); @@ -648,9 +648,9 @@ TEST_F(DeclarableOpsTests1, AddVectorVector1) { variableSpace->putVariable(-1, x); variableSpace->putVariable(-2, y); auto block = new Context(1, variableSpace, true); - block->fillInputs({-1, -2}); + block->fillInputs({ -1, -2 }); - nd4j::ops::add addOp; + sd::ops::add addOp; addOp.execute(block); @@ -664,9 +664,9 @@ TEST_F(DeclarableOpsTests1, AddVectorVector1) { ////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests1, AddMatrixScalar1) { - auto x = NDArrayFactory::create_('c', {5, 3}); - auto y = NDArrayFactory::create_('c', {1, 1}); - auto exp = NDArrayFactory::create('c', {5, 3}); + auto x = NDArrayFactory::create_('c', { 5, 3 }); + auto y = NDArrayFactory::create_('c', { 1, 1 }); + auto exp = NDArrayFactory::create('c', { 5, 3 }); x->assign(2); y->assign(1); exp.assign(3); @@ -675,9 +675,9 @@ TEST_F(DeclarableOpsTests1, AddMatrixScalar1) { variableSpace->putVariable(-1, x); variableSpace->putVariable(-2, y); auto block = new Context(1, variableSpace, true); - block->fillInputs({-1, -2}); + block->fillInputs({ -1, -2 }); - nd4j::ops::add addOp; + sd::ops::add addOp; addOp.execute(block); @@ -690,9 +690,9 @@ TEST_F(DeclarableOpsTests1, AddMatrixScalar1) { ////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests1, AddScalarScalar1) { - auto x = NDArrayFactory::create_('c', {1, 1}); - auto y = NDArrayFactory::create_('c', {1, 1}); - auto exp = NDArrayFactory::create('c', {1, 1}); + auto x = NDArrayFactory::create_('c', { 1, 1 }); + auto y = NDArrayFactory::create_('c', { 1, 1 }); + auto exp = NDArrayFactory::create('c', { 1, 1 }); x->assign(2); y->assign(1); exp.assign(3); @@ -701,9 +701,9 @@ TEST_F(DeclarableOpsTests1, AddScalarScalar1) { variableSpace->putVariable(-1, x); variableSpace->putVariable(-2, y); auto block = new Context(1, variableSpace, true); - block->fillInputs({-1, -2}); + block->fillInputs({ -1, -2 }); - nd4j::ops::add addOp; + sd::ops::add addOp; addOp.execute(block); @@ -716,9 +716,9 @@ TEST_F(DeclarableOpsTests1, AddScalarScalar1) { ////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests1, SubtractMatrices1) { - auto x = NDArrayFactory::create_('c', {5, 3}); - auto y = NDArrayFactory::create_('c', {5, 3}); - auto exp = NDArrayFactory::create('c', {5, 3}); + auto x = NDArrayFactory::create_('c', { 5, 3 }); + auto y = NDArrayFactory::create_('c', { 5, 3 }); + auto exp = NDArrayFactory::create('c', { 5, 3 }); x->assign(3); y->assign(1); exp.assign(2); @@ -727,9 +727,9 @@ TEST_F(DeclarableOpsTests1, SubtractMatrices1) { variableSpace->putVariable(-1, x); variableSpace->putVariable(-2, y); auto block = new Context(1, variableSpace, true); - block->fillInputs({-1, -2}); + block->fillInputs({ -1, -2 }); - nd4j::ops::subtract subOp; + sd::ops::subtract subOp; subOp.execute(block); @@ -743,9 +743,9 @@ TEST_F(DeclarableOpsTests1, SubtractMatrices1) { ////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests1, SubtractTest_1) { - auto x = NDArrayFactory::create_('c', {1, 6}); - auto y = NDArrayFactory::create_('c', {1, 6}); - auto exp = NDArrayFactory::create('c', {1, 6}); + auto x = NDArrayFactory::create_('c', { 1, 6 }); + auto y = NDArrayFactory::create_('c', { 1, 6 }); + auto exp = NDArrayFactory::create('c', { 1, 6 }); x->assign(3); y->assign(1); exp.assign(2); @@ -754,9 +754,9 @@ TEST_F(DeclarableOpsTests1, SubtractTest_1) { variableSpace->putVariable(-1, x); variableSpace->putVariable(-2, y); auto block = new Context(1, variableSpace, true); - block->fillInputs({-1, -2}); + block->fillInputs({ -1, -2 }); - nd4j::ops::subtract subOp; + sd::ops::subtract subOp; subOp.execute(block); @@ -769,67 +769,67 @@ TEST_F(DeclarableOpsTests1, SubtractTest_1) { ////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests1, SubtractTest_2) { - auto x = NDArrayFactory::create('c', {3, 4, 5, 1}); - auto y = NDArrayFactory::create('c', {1, 6}); -// auto y({6}, {1,1,1,1,1,1}); - auto exp = NDArrayFactory::create('c', {3, 4, 5, 6}); + auto x = NDArrayFactory::create('c', { 3, 4, 5, 1 }); + auto y = NDArrayFactory::create('c', { 1, 6 }); + // auto y({6}, {1,1,1,1,1,1}); + auto exp = NDArrayFactory::create('c', { 3, 4, 5, 6 }); x.assign(3); y.assign(1); exp.assign(2); - nd4j::ops::subtract subOp; + sd::ops::subtract subOp; - auto res = subOp.evaluate({&x, &y}); + auto res = subOp.evaluate({ &x, &y }); - ASSERT_TRUE(res->status() == ND4J_STATUS_OK); + ASSERT_TRUE(res.status() == ND4J_STATUS_OK); - ASSERT_TRUE(res->at(0)->equalsTo(&exp)); + ASSERT_TRUE(res.at(0)->equalsTo(&exp)); - delete res; + } TEST_F(DeclarableOpsTests1, TestRng1) { -/* - Nd4jLong *buffer = new Nd4jLong[100000]; + /* + Nd4jLong *buffer = new Nd4jLong[100000]; - nd4j::random::RandomBuffer *rng = (nd4j::random::RandomBuffer *) initRandom(nullptr, 123, 100000, (Nd4jPointer) buffer); + sd::random::RandomBuffer *rng = (sd::random::RandomBuffer *) initRandom(nullptr, 123, 100000, (Nd4jPointer) buffer); - if (rng == nullptr) - throw std::runtime_error("RNG initialization failed"); + if (rng == nullptr) + throw std::runtime_error("RNG initialization failed"); - auto x = NDArrayFactory::create_('c', {5, 3}); - auto variableSpace = new VariableSpace(); - variableSpace->putVariable(-1, x); - auto block = new Context(1, variableSpace, true); - block->fillInputs({-1}); - block->setRNG(rng); - block->getTArguments()->push_back(0.0f); - block->getTArguments()->push_back(1.0f); + auto x = NDArrayFactory::create_('c', {5, 3}); + auto variableSpace = new VariableSpace(); + variableSpace->putVariable(-1, x); + auto block = new Context(1, variableSpace, true); + block->fillInputs({-1}); + block->setRNG(rng); + block->getTArguments()->push_back(0.0f); + block->getTArguments()->push_back(1.0f); - nd4j::ops::randomuniform uniform; + sd::ops::randomuniform uniform; - Nd4jStatus status = uniform.execute(block); + Nd4jStatus status = uniform.execute(block); - ASSERT_EQ(ND4J_STATUS_OK, status); + ASSERT_EQ(ND4J_STATUS_OK, status); - ASSERT_TRUE(x->sumNumber() > 0.0); + ASSERT_TRUE(x->sumNumber() > 0.0); - destroyRandom((Nd4jPointer) rng); - delete[] buffer; + destroyRandom((Nd4jPointer) rng); + delete[] buffer; - delete variableSpace; - delete block; - */ + delete variableSpace; + delete block; + */ } ////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests1, MergeSumTest1) { - auto x = NDArrayFactory::create_('c', {5, 5}); - auto y = NDArrayFactory::create_('c', {5, 5}); - auto z = NDArrayFactory::create_('c', {5, 5}); - auto exp = NDArrayFactory::create('c', {5, 5}); + auto x = NDArrayFactory::create_('c', { 5, 5 }); + auto y = NDArrayFactory::create_('c', { 5, 5 }); + auto z = NDArrayFactory::create_('c', { 5, 5 }); + auto exp = NDArrayFactory::create('c', { 5, 5 }); x->assign(3); y->assign(1); z->assign(2); @@ -839,11 +839,11 @@ TEST_F(DeclarableOpsTests1, MergeSumTest1) { variableSpace->putVariable(-1, x); variableSpace->putVariable(-2, y); variableSpace->putVariable(-3, z); - variableSpace->putVariable(1, new Variable(NDArrayFactory::create_('c', {5, 5}))); + variableSpace->putVariable(1, new Variable(NDArrayFactory::create_('c', { 5, 5 }))); auto block = new Context(1, variableSpace, false); - block->fillInputs({-1, -2, -3}); + block->fillInputs({ -1, -2, -3 }); - nd4j::ops::mergeadd merge; + sd::ops::mergeadd merge; merge.execute(block); @@ -859,8 +859,8 @@ TEST_F(DeclarableOpsTests1, MergeSumTest1) { ////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests1, ClipByValue1) { - auto x = NDArrayFactory::create_('c', {5, 5}); - auto exp = NDArrayFactory::create('c', {5, 5}); + auto x = NDArrayFactory::create_('c', { 5, 5 }); + auto exp = NDArrayFactory::create('c', { 5, 5 }); x->assign(4); x->p(0, -1); x->p(1, 2); @@ -874,9 +874,9 @@ TEST_F(DeclarableOpsTests1, ClipByValue1) { auto block = new Context(1, variableSpace, true); block->getTArguments()->push_back(0.0f); block->getTArguments()->push_back(3.0f); - block->fillInputs({-1}); + block->fillInputs({ -1 }); - nd4j::ops::clipbyvalue clip; + sd::ops::clipbyvalue clip; clip.execute(block); @@ -890,26 +890,26 @@ TEST_F(DeclarableOpsTests1, ClipByValue1) { ////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests1, MergeAvgTest1) { - auto x = NDArrayFactory::create_('c', {5, 5}); - auto y = NDArrayFactory::create_('c', {5, 5}); - auto z = NDArrayFactory::create_('c', {5, 5}); - auto exp = NDArrayFactory::create('c', {5, 5}); + auto x = NDArrayFactory::create_('c', { 5, 5 }); + auto y = NDArrayFactory::create_('c', { 5, 5 }); + auto z = NDArrayFactory::create_('c', { 5, 5 }); + auto exp = NDArrayFactory::create('c', { 5, 5 }); x->assign(3); y->assign(1); z->assign(2); exp.assign(2); - auto zu = NDArrayFactory::create('c', {5, 5}); + auto zu = NDArrayFactory::create('c', { 5, 5 }); auto variableSpace = new VariableSpace(); variableSpace->putVariable(-1, x); variableSpace->putVariable(-2, y); variableSpace->putVariable(-3, z); - variableSpace->putVariable(1, new Variable(NDArrayFactory::create_('c', {5, 5}))); + variableSpace->putVariable(1, new Variable(NDArrayFactory::create_('c', { 5, 5 }))); auto block = new Context(1, variableSpace, false); - block->fillInputs({-1, -2, -3}); + block->fillInputs({ -1, -2, -3 }); - nd4j::ops::mergeavg merge; + sd::ops::mergeavg merge; merge.execute(block); @@ -925,9 +925,9 @@ TEST_F(DeclarableOpsTests1, MergeAvgTest1) { ////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests1, SubtractVectorVector1) { - auto x = NDArrayFactory::create_('c', {1, 15}); - auto y = NDArrayFactory::create_('c', {1, 15}); - auto exp = NDArrayFactory::create('c', {1, 15}); + auto x = NDArrayFactory::create_('c', { 1, 15 }); + auto y = NDArrayFactory::create_('c', { 1, 15 }); + auto exp = NDArrayFactory::create('c', { 1, 15 }); x->assign(3); y->assign(1); exp.assign(2); @@ -936,9 +936,9 @@ TEST_F(DeclarableOpsTests1, SubtractVectorVector1) { variableSpace->putVariable(-1, x); variableSpace->putVariable(-2, y); auto block = new Context(1, variableSpace, true); - block->fillInputs({-1, -2}); + block->fillInputs({ -1, -2 }); - nd4j::ops::subtract subOp; + sd::ops::subtract subOp; subOp.execute(block); @@ -953,9 +953,9 @@ TEST_F(DeclarableOpsTests1, SubtractVectorVector1) { ////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests1, SubtractMatrixScalar1) { - auto x = NDArrayFactory::create_('c', {5, 3}); - auto y = NDArrayFactory::create_('c', {1, 1}); - auto exp = NDArrayFactory::create('c', {5, 3}); + auto x = NDArrayFactory::create_('c', { 5, 3 }); + auto y = NDArrayFactory::create_('c', { 1, 1 }); + auto exp = NDArrayFactory::create('c', { 5, 3 }); x->assign(3); y->assign(1); exp.assign(2); @@ -964,9 +964,9 @@ TEST_F(DeclarableOpsTests1, SubtractMatrixScalar1) { variableSpace->putVariable(-1, x); variableSpace->putVariable(-2, y); auto block = new Context(1, variableSpace, true); - block->fillInputs({-1, -2}); + block->fillInputs({ -1, -2 }); - nd4j::ops::subtract subOp; + sd::ops::subtract subOp; subOp.execute(block); @@ -980,9 +980,9 @@ TEST_F(DeclarableOpsTests1, SubtractMatrixScalar1) { ////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests1, SubtractScalarScalar1) { - auto x = NDArrayFactory::create_('c', {1, 1}); - auto y = NDArrayFactory::create_('c', {1, 1}); - auto exp = NDArrayFactory::create('c', {1, 1}); + auto x = NDArrayFactory::create_('c', { 1, 1 }); + auto y = NDArrayFactory::create_('c', { 1, 1 }); + auto exp = NDArrayFactory::create('c', { 1, 1 }); x->assign(3); y->assign(1); exp.assign(2); @@ -991,9 +991,9 @@ TEST_F(DeclarableOpsTests1, SubtractScalarScalar1) { variableSpace->putVariable(-1, x); variableSpace->putVariable(-2, y); auto block = new Context(1, variableSpace, true); - block->fillInputs({-1, -2}); + block->fillInputs({ -1, -2 }); - nd4j::ops::subtract subOp; + sd::ops::subtract subOp; subOp.execute(block); @@ -1006,9 +1006,9 @@ TEST_F(DeclarableOpsTests1, SubtractScalarScalar1) { ////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests1, ReverseSubtractMatrices1) { - auto x = NDArrayFactory::create_('c', {5, 3}); - auto y = NDArrayFactory::create_('c', {5, 3}); - auto exp = NDArrayFactory::create('c', {5, 3}); + auto x = NDArrayFactory::create_('c', { 5, 3 }); + auto y = NDArrayFactory::create_('c', { 5, 3 }); + auto exp = NDArrayFactory::create('c', { 5, 3 }); x->assign(3.f); y->assign(1.f); exp.assign(-2.f); @@ -1017,9 +1017,9 @@ TEST_F(DeclarableOpsTests1, ReverseSubtractMatrices1) { variableSpace->putVariable(-1, x); variableSpace->putVariable(-2, y); auto block = new Context(1, variableSpace, true); - block->fillInputs({-1, -2}); + block->fillInputs({ -1, -2 }); - nd4j::ops::reversesubtract subOp; + sd::ops::reversesubtract subOp; subOp.execute(block); @@ -1032,30 +1032,30 @@ TEST_F(DeclarableOpsTests1, ReverseSubtractMatrices1) { ////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests1, ReverseSubtractTest_1) { - auto x = NDArrayFactory::create('c', {1, 6}); - auto y = NDArrayFactory::create('c', {1, 6}); - auto exp = NDArrayFactory::create('c', {1, 6}); + auto x = NDArrayFactory::create('c', { 1, 6 }); + auto y = NDArrayFactory::create('c', { 1, 6 }); + auto exp = NDArrayFactory::create('c', { 1, 6 }); x.assign(3.f); y.assign(1.f); exp.assign(-2.f); - nd4j::ops::reversesubtract subOp; + sd::ops::reversesubtract subOp; - auto res = subOp.evaluate({&x, &y}); + auto res = subOp.evaluate({ &x, &y }); - ASSERT_TRUE(res->status() == ND4J_STATUS_OK); - ASSERT_TRUE(res->at(0)->equalsTo(&exp)); + ASSERT_TRUE(res.status() == ND4J_STATUS_OK); + ASSERT_TRUE(res.at(0)->equalsTo(&exp)); - delete res; + } ////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests1, ReverseSubtractTest_2) { -// auto x('c', {1, 6}); - auto x = NDArrayFactory::create('c', {1, 6}); - auto y = NDArrayFactory::create('c', {3, 4, 5, 1}); - auto exp = NDArrayFactory::create('c', {3, 4, 5, 6}); + // auto x('c', {1, 6}); + auto x = NDArrayFactory::create('c', { 1, 6 }); + auto y = NDArrayFactory::create('c', { 3, 4, 5, 1 }); + auto exp = NDArrayFactory::create('c', { 3, 4, 5, 6 }); auto z(exp); x.assign(3.f); y.assign(1.f); @@ -1064,45 +1064,45 @@ TEST_F(DeclarableOpsTests1, ReverseSubtractTest_2) { ASSERT_TRUE(exp.equalsTo(&z)); - nd4j::ops::reversesubtract subOp; + sd::ops::reversesubtract subOp; - auto res = subOp.evaluate({&x, &y}); + auto res = subOp.evaluate({ &x, &y }); - ASSERT_TRUE(res->status() == ND4J_STATUS_OK); - ASSERT_TRUE(res->at(0)->equalsTo(&exp)); + ASSERT_TRUE(res.status() == ND4J_STATUS_OK); + ASSERT_TRUE(res.at(0)->equalsTo(&exp)); - delete res; + } ////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests1, ReverseSubtractTest_3) { -// auto x('c', {1, 6}); - auto x = NDArrayFactory::create('c', {6}); - auto y = NDArrayFactory::create('c', {3, 4, 5, 1}); - auto exp = NDArrayFactory::create('c', {3, 4, 5, 6}); + // auto x('c', {1, 6}); + auto x = NDArrayFactory::create('c', { 6 }); + auto y = NDArrayFactory::create('c', { 3, 4, 5, 1 }); + auto exp = NDArrayFactory::create('c', { 3, 4, 5, 6 }); auto z(exp); x.assign(1); y.assign(3); exp.assign(2); x.applyTrueBroadcast(BROADCAST(ReverseSubtract), y, z, true); ASSERT_TRUE(z.equalsTo(&exp)); - nd4j::ops::reversesubtract subOp; + sd::ops::reversesubtract subOp; - auto res = subOp.evaluate({&x, &y}); - ASSERT_TRUE(res->status() == ND4J_STATUS_OK); - ASSERT_TRUE(res->at(0)->equalsTo(&exp)); + auto res = subOp.evaluate({ &x, &y }); + ASSERT_TRUE(res.status() == ND4J_STATUS_OK); + ASSERT_TRUE(res.at(0)->equalsTo(&exp)); - delete res; + } ////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests1, ReverseModTest_1) { -// auto x('c', {1, 6}); - auto x = NDArrayFactory::create('c', {6}); - auto y = NDArrayFactory::create('c', {3, 4, 5, 1}); - auto exp = NDArrayFactory::create('c', {3, 4, 5, 6}); + // auto x('c', {1, 6}); + auto x = NDArrayFactory::create('c', { 6 }); + auto y = NDArrayFactory::create('c', { 3, 4, 5, 1 }); + auto exp = NDArrayFactory::create('c', { 3, 4, 5, 6 }); auto z(exp); x.assign(2.); y.assign(9.f); @@ -1113,24 +1113,24 @@ TEST_F(DeclarableOpsTests1, ReverseModTest_1) { x.applyTrueBroadcast(BROADCAST(ReverseMod), y, exp, true); ASSERT_TRUE(exp.equalsTo(&z)); - nd4j::ops::reversemod subOp; + sd::ops::reversemod subOp; - auto res = subOp.evaluate({&x, &y}); + auto res = subOp.evaluate({ &x, &y }); - ASSERT_TRUE(res->status() == ND4J_STATUS_OK); - ASSERT_TRUE(res->at(0)->equalsTo(&exp)); + ASSERT_TRUE(res.status() == ND4J_STATUS_OK); + ASSERT_TRUE(res.at(0)->equalsTo(&exp)); ASSERT_TRUE(exp.equalsTo(&z)); - delete res; + } ////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests1, ReverseModTest_2) { -// auto x('c', {1, 6}); - auto x = NDArrayFactory::create('c', {3, 4, 5}); - auto y = NDArrayFactory::create('c', {3, 4, 5}); - auto exp = NDArrayFactory::create('c', {3, 4, 5}); + // auto x('c', {1, 6}); + auto x = NDArrayFactory::create('c', { 3, 4, 5 }); + auto y = NDArrayFactory::create('c', { 3, 4, 5 }); + auto exp = NDArrayFactory::create('c', { 3, 4, 5 }); auto z(exp); x.assign(2.f); y.assign(9.f); @@ -1140,22 +1140,22 @@ TEST_F(DeclarableOpsTests1, ReverseModTest_2) { x.applyTrueBroadcast(BROADCAST(ReverseMod), y, exp, true); ASSERT_TRUE(z.equalsTo(&exp)); - nd4j::ops::reversemod subOp; + sd::ops::reversemod subOp; - auto res = subOp.evaluate({&x, &y}); + auto res = subOp.evaluate({ &x, &y }); - ASSERT_TRUE(res->status() == ND4J_STATUS_OK); - ASSERT_TRUE(res->at(0)->equalsTo(&exp)); + ASSERT_TRUE(res.status() == ND4J_STATUS_OK); + ASSERT_TRUE(res.at(0)->equalsTo(&exp)); - delete res; + } ////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests1, ReverseSubtractVectorVector1) { - auto x = NDArrayFactory::create_ ('c', {1, 15}); - auto y = NDArrayFactory::create_ ('c', {1, 15}); - auto exp = NDArrayFactory::create_ ('c', {1, 15}); + auto x = NDArrayFactory::create_('c', { 1, 15 }); + auto y = NDArrayFactory::create_('c', { 1, 15 }); + auto exp = NDArrayFactory::create_('c', { 1, 15 }); x->assign(3); y->assign(1); exp->assign(-2); @@ -1164,9 +1164,9 @@ TEST_F(DeclarableOpsTests1, ReverseSubtractVectorVector1) { variableSpace->putVariable(-1, x); variableSpace->putVariable(-2, y); auto block = new Context(1, variableSpace, true); - block->fillInputs({-1, -2}); + block->fillInputs({ -1, -2 }); - nd4j::ops::reversesubtract subOp; + sd::ops::reversesubtract subOp; subOp.execute(block); @@ -1181,9 +1181,9 @@ TEST_F(DeclarableOpsTests1, ReverseSubtractVectorVector1) { ////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests1, ReverseSubtractMatrixScalar1) { - auto x = NDArrayFactory::create_ ('c', {5, 3}); - auto y = NDArrayFactory::create_ ('c', {1, 1}); - auto exp = NDArrayFactory::create_('c', {5, 3}); + auto x = NDArrayFactory::create_('c', { 5, 3 }); + auto y = NDArrayFactory::create_('c', { 1, 1 }); + auto exp = NDArrayFactory::create_('c', { 5, 3 }); x->assign(3); y->assign(1); exp->assign(-2); @@ -1192,9 +1192,9 @@ TEST_F(DeclarableOpsTests1, ReverseSubtractMatrixScalar1) { variableSpace->putVariable(-1, x); variableSpace->putVariable(-2, y); auto block = new Context(1, variableSpace, true); - block->fillInputs({-1, -2}); + block->fillInputs({ -1, -2 }); - nd4j::ops::reversesubtract subOp; + sd::ops::reversesubtract subOp; subOp.execute(block); @@ -1209,9 +1209,9 @@ TEST_F(DeclarableOpsTests1, ReverseSubtractMatrixScalar1) { ////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests1, ReverseSubtractScalarScalar1) { - auto x = NDArrayFactory::create_ ('c', {1, 1}); - auto y = NDArrayFactory::create_ ('c', {1, 1}); - auto exp = NDArrayFactory::create_('c', {1, 1}); + auto x = NDArrayFactory::create_('c', { 1, 1 }); + auto y = NDArrayFactory::create_('c', { 1, 1 }); + auto exp = NDArrayFactory::create_('c', { 1, 1 }); x->assign(3); y->assign(1); exp->assign(-2); @@ -1220,9 +1220,9 @@ TEST_F(DeclarableOpsTests1, ReverseSubtractScalarScalar1) { variableSpace->putVariable(-1, x); variableSpace->putVariable(-2, y); auto block = new Context(1, variableSpace, true); - block->fillInputs({-1, -2}); + block->fillInputs({ -1, -2 }); - nd4j::ops::reversesubtract subOp; + sd::ops::reversesubtract subOp; subOp.execute(block); @@ -1236,9 +1236,9 @@ TEST_F(DeclarableOpsTests1, ReverseSubtractScalarScalar1) { ////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests1, MultiplyMatrices1) { - auto x = NDArrayFactory::create_ ('c', {5, 3}); - auto y = NDArrayFactory::create_ ('c', {5, 3}); - auto exp = NDArrayFactory::create_('c', {5, 3}); + auto x = NDArrayFactory::create_('c', { 5, 3 }); + auto y = NDArrayFactory::create_('c', { 5, 3 }); + auto exp = NDArrayFactory::create_('c', { 5, 3 }); x->assign(2); y->assign(3); exp->assign(6); @@ -1247,9 +1247,9 @@ TEST_F(DeclarableOpsTests1, MultiplyMatrices1) { variableSpace->putVariable(-1, x); variableSpace->putVariable(-2, y); auto block = new Context(1, variableSpace, true); - block->fillInputs({-1, -2}); + block->fillInputs({ -1, -2 }); - nd4j::ops::multiply mul; + sd::ops::multiply mul; mul.execute(block); @@ -1263,9 +1263,9 @@ TEST_F(DeclarableOpsTests1, MultiplyMatrices1) { ////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests1, MultiplyVectorVector1) { - auto x = NDArrayFactory::create_ ('c', {1, 15}); - auto y = NDArrayFactory::create_ ('c', {1, 15}); - auto exp = NDArrayFactory::create_('c', {1, 15}); + auto x = NDArrayFactory::create_('c', { 1, 15 }); + auto y = NDArrayFactory::create_('c', { 1, 15 }); + auto exp = NDArrayFactory::create_('c', { 1, 15 }); x->assign(2); y->assign(3); exp->assign(6); @@ -1274,9 +1274,9 @@ TEST_F(DeclarableOpsTests1, MultiplyVectorVector1) { variableSpace->putVariable(-1, x); variableSpace->putVariable(-2, y); auto block = new Context(1, variableSpace, true); - block->fillInputs({-1, -2}); + block->fillInputs({ -1, -2 }); - nd4j::ops::multiply mul; + sd::ops::multiply mul; mul.execute(block); @@ -1290,9 +1290,9 @@ TEST_F(DeclarableOpsTests1, MultiplyVectorVector1) { ////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests1, MultiplyMatrixScalar) { - auto x = NDArrayFactory::create_ ('c', {5, 3}); - auto y = NDArrayFactory::create_ ('c', {1, 1}); - auto exp = NDArrayFactory::create_('c', {5, 3}); + auto x = NDArrayFactory::create_('c', { 5, 3 }); + auto y = NDArrayFactory::create_('c', { 1, 1 }); + auto exp = NDArrayFactory::create_('c', { 5, 3 }); x->assign(2); y->assign(3); exp->assign(6); @@ -1301,9 +1301,9 @@ TEST_F(DeclarableOpsTests1, MultiplyMatrixScalar) { variableSpace->putVariable(-1, x); variableSpace->putVariable(-2, y); auto block = new Context(1, variableSpace, true); - block->fillInputs({-1, -2}); + block->fillInputs({ -1, -2 }); - nd4j::ops::multiply mul; + sd::ops::multiply mul; mul.execute(block); @@ -1317,9 +1317,9 @@ TEST_F(DeclarableOpsTests1, MultiplyMatrixScalar) { ////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests1, MultiplyScalarScalar1) { - auto x = NDArrayFactory::create_ ('c', {1, 1}); - auto y = NDArrayFactory::create_ ('c', {1, 1}); - auto exp = NDArrayFactory::create_('c', {1, 1}); + auto x = NDArrayFactory::create_('c', { 1, 1 }); + auto y = NDArrayFactory::create_('c', { 1, 1 }); + auto exp = NDArrayFactory::create_('c', { 1, 1 }); x->assign(2); y->assign(3); exp->assign(6); @@ -1328,9 +1328,9 @@ TEST_F(DeclarableOpsTests1, MultiplyScalarScalar1) { variableSpace->putVariable(-1, x); variableSpace->putVariable(-2, y); auto block = new Context(1, variableSpace, true); - block->fillInputs({-1, -2}); + block->fillInputs({ -1, -2 }); - nd4j::ops::multiply mul; + sd::ops::multiply mul; mul.execute(block); @@ -1344,20 +1344,20 @@ TEST_F(DeclarableOpsTests1, MultiplyScalarScalar1) { ////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests1, TestSoftMax_bp_1) { - auto input = NDArrayFactory::create_('c', {2, 2}); + auto input = NDArrayFactory::create_('c', { 2, 2 }); for (int e = 0; e < input->lengthOf(); e++) - input->p(e, e+1); + input->p(e, e + 1); - auto epsilon = NDArrayFactory::create_('c', {2, 2}); + auto epsilon = NDArrayFactory::create_('c', { 2, 2 }); epsilon->p(0, 0.1f); epsilon->p(1, 0.2f); epsilon->p(2, 0.3f); epsilon->p(3, 0.4f); - auto output = NDArrayFactory::create_('c', {2, 2}); + auto output = NDArrayFactory::create_('c', { 2, 2 }); output->assign(1.0f); - auto exp = NDArrayFactory::create_('c', {2, 2}); + auto exp = NDArrayFactory::create_('c', { 2, 2 }); exp->p(0, -0.019661194f); exp->p(1, 0.019661194f); exp->p(2, -0.019661194f); @@ -1370,9 +1370,9 @@ TEST_F(DeclarableOpsTests1, TestSoftMax_bp_1) { //variableSpace->putVariable(42, exp); auto block = new Context(1, variableSpace, false); - block->fillInputs({-1, -2}); + block->fillInputs({ -1, -2 }); - nd4j::ops::softmax_bp op; + sd::ops::softmax_bp op; Nd4jStatus status = op.execute(block); ASSERT_EQ(ND4J_STATUS_OK, status); @@ -1388,90 +1388,90 @@ TEST_F(DeclarableOpsTests1, TestSoftMax_bp_1) { ////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests1, BroadcastDivideTest_1) { - auto x = NDArrayFactory::create('c', {3, 4, 5, 1}); - auto y = NDArrayFactory::create('c', {1, 6}); - auto exp = NDArrayFactory::create('c', {3, 4, 5, 6}); + auto x = NDArrayFactory::create('c', { 3, 4, 5, 1 }); + auto y = NDArrayFactory::create('c', { 1, 6 }); + auto exp = NDArrayFactory::create('c', { 3, 4, 5, 6 }); x.assign(6); y.assign(2); exp.assign(3); - nd4j::ops::divide div; + sd::ops::divide div; - auto res = div.evaluate({&x, &y}); + auto res = div.evaluate({ &x, &y }); - ASSERT_EQ(res->status(), ND4J_STATUS_OK); - ASSERT_TRUE(res->at(0)->equalsTo(exp)); + ASSERT_EQ(res.status(), ND4J_STATUS_OK); + ASSERT_TRUE(res.at(0)->equalsTo(exp)); - delete res; + } ////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests1, BroadcastDivideTest_2) { - auto x = NDArrayFactory::create('c', {3, 4, 5, 1}); - auto y = NDArrayFactory::create('c', {1, 6}); - auto exp = NDArrayFactory::create('c', {3, 4, 5, 6}); + auto x = NDArrayFactory::create('c', { 3, 4, 5, 1 }); + auto y = NDArrayFactory::create('c', { 1, 6 }); + auto exp = NDArrayFactory::create('c', { 3, 4, 5, 6 }); x.assign(6); y.assign(2); exp.assign(3); - nd4j::ops::divide_no_nan div; - auto res = div.evaluate({&x, &y}); + sd::ops::divide_no_nan div; + auto res = div.evaluate({ &x, &y }); - ASSERT_EQ(res->status(), ND4J_STATUS_OK); - ASSERT_TRUE(res->at(0)->equalsTo(exp)); + ASSERT_EQ(res.status(), ND4J_STATUS_OK); + ASSERT_TRUE(res.at(0)->equalsTo(exp)); - delete res; + } ////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests1, BroadcastDivideTest_3) { - auto x = NDArrayFactory::create({6,6,6,6,6}); - auto y = NDArrayFactory::create({3,3,0,3,3}); - auto exp = NDArrayFactory::create({2, 2, 0, 2, 2}); + auto x = NDArrayFactory::create({ 6,6,6,6,6 }); + auto y = NDArrayFactory::create({ 3,3,0,3,3 }); + auto exp = NDArrayFactory::create({ 2, 2, 0, 2, 2 }); - nd4j::ops::divide_no_nan div; - auto res = div.evaluate({&x, &y}); + sd::ops::divide_no_nan div; + auto res = div.evaluate({ &x, &y }); - ASSERT_EQ(res->status(), ND4J_STATUS_OK); - ASSERT_TRUE(res->at(0)->equalsTo(exp)); + ASSERT_EQ(res.status(), ND4J_STATUS_OK); + ASSERT_TRUE(res.at(0)->equalsTo(exp)); - delete res; + } ////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests1, BroadcastReverseDivideTest_1) { - auto x = NDArrayFactory::create('c', {3, 4, 5, 1}); - auto y = NDArrayFactory::create('c', {1, 6}); - auto exp = NDArrayFactory::create('c', {3, 4, 5, 6}); + auto x = NDArrayFactory::create('c', { 3, 4, 5, 1 }); + auto y = NDArrayFactory::create('c', { 1, 6 }); + auto exp = NDArrayFactory::create('c', { 3, 4, 5, 6 }); x.assign(3.f); y.assign(6.f); exp.assign(2.f); - nd4j::ops::reversedivide div; + sd::ops::reversedivide div; - auto res = div.evaluate({&x, &y}); + auto res = div.evaluate({ &x, &y }); - ASSERT_EQ(res->status(), ND4J_STATUS_OK); + ASSERT_EQ(res.status(), ND4J_STATUS_OK); - ASSERT_TRUE(res->at(0)->equalsTo(exp)); + ASSERT_TRUE(res.at(0)->equalsTo(exp)); auto z(exp); x.applyTrueBroadcast(BROADCAST(ReverseDivide), y, z, true); y.applyTrueBroadcast(BROADCAST(Divide), x, exp, true); ASSERT_TRUE(z.equalsTo(&exp)); - delete res; + } ////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests1, DivideMatrices1) { - auto x = NDArrayFactory::create_ ('c', {5, 3}); - auto y = NDArrayFactory::create_ ('c', {5, 3}); - auto exp = NDArrayFactory::create_('c', {5, 3}); + auto x = NDArrayFactory::create_('c', { 5, 3 }); + auto y = NDArrayFactory::create_('c', { 5, 3 }); + auto exp = NDArrayFactory::create_('c', { 5, 3 }); x->assign(6); y->assign(2); exp->assign(3); @@ -1480,9 +1480,9 @@ TEST_F(DeclarableOpsTests1, DivideMatrices1) { variableSpace->putVariable(-1, x); variableSpace->putVariable(-2, y); auto block = new Context(1, variableSpace, true); - block->fillInputs({-1, -2}); + block->fillInputs({ -1, -2 }); - nd4j::ops::divide div; + sd::ops::divide div; div.execute(block); @@ -1496,9 +1496,9 @@ TEST_F(DeclarableOpsTests1, DivideMatrices1) { ////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests1, DivideVectorVector1) { - auto x = NDArrayFactory::create_('c', {1, 15}); - auto y = NDArrayFactory::create_('c', {1, 15}); - auto exp = NDArrayFactory::create('c', {1, 15}); + auto x = NDArrayFactory::create_('c', { 1, 15 }); + auto y = NDArrayFactory::create_('c', { 1, 15 }); + auto exp = NDArrayFactory::create('c', { 1, 15 }); x->assign(6); y->assign(2); exp.assign(3); @@ -1507,9 +1507,9 @@ TEST_F(DeclarableOpsTests1, DivideVectorVector1) { variableSpace->putVariable(-1, x); variableSpace->putVariable(-2, y); auto block = new Context(1, variableSpace, true); - block->fillInputs({-1, -2}); + block->fillInputs({ -1, -2 }); - nd4j::ops::divide div; + sd::ops::divide div; div.execute(block); @@ -1522,9 +1522,9 @@ TEST_F(DeclarableOpsTests1, DivideVectorVector1) { ////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests1, DivideMatrixScalar1) { - auto x = NDArrayFactory::create_('c', {5, 3}); - auto y = NDArrayFactory::create_('c', {1, 1}); - auto exp = NDArrayFactory::create('c', {5, 3}); + auto x = NDArrayFactory::create_('c', { 5, 3 }); + auto y = NDArrayFactory::create_('c', { 1, 1 }); + auto exp = NDArrayFactory::create('c', { 5, 3 }); x->assign(6); y->assign(2); exp.assign(3); @@ -1533,9 +1533,9 @@ TEST_F(DeclarableOpsTests1, DivideMatrixScalar1) { variableSpace->putVariable(-1, x); variableSpace->putVariable(-2, y); auto block = new Context(1, variableSpace, true); - block->fillInputs({-1, -2}); + block->fillInputs({ -1, -2 }); - nd4j::ops::divide div; + sd::ops::divide div; div.execute(block); @@ -1549,9 +1549,9 @@ TEST_F(DeclarableOpsTests1, DivideMatrixScalar1) { ////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests1, DivideScalarScalar1) { - auto x = NDArrayFactory::create_('c', {5, 1}); - auto y = NDArrayFactory::create_('c', {5, 1}); - auto exp = NDArrayFactory::create('c', {5, 1}); + auto x = NDArrayFactory::create_('c', { 5, 1 }); + auto y = NDArrayFactory::create_('c', { 5, 1 }); + auto exp = NDArrayFactory::create('c', { 5, 1 }); x->assign(6); y->assign(2); exp.assign(3); @@ -1560,9 +1560,9 @@ TEST_F(DeclarableOpsTests1, DivideScalarScalar1) { variableSpace->putVariable(-1, x); variableSpace->putVariable(-2, y); auto block = new Context(1, variableSpace, true); - block->fillInputs({-1, -2}); + block->fillInputs({ -1, -2 }); - nd4j::ops::divide div; + sd::ops::divide div; div.execute(block); @@ -1575,9 +1575,9 @@ TEST_F(DeclarableOpsTests1, DivideScalarScalar1) { ////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests1, ReverseDivideMatrices1) { - auto x = NDArrayFactory::create_('c', {5, 3}); - auto y = NDArrayFactory::create_('c', {5, 3}); - auto exp = NDArrayFactory::create('c', {5, 3}); + auto x = NDArrayFactory::create_('c', { 5, 3 }); + auto y = NDArrayFactory::create_('c', { 5, 3 }); + auto exp = NDArrayFactory::create('c', { 5, 3 }); x->assign(2); y->assign(6); exp.assign(3); @@ -1586,9 +1586,9 @@ TEST_F(DeclarableOpsTests1, ReverseDivideMatrices1) { variableSpace->putVariable(-1, x); variableSpace->putVariable(-2, y); auto block = new Context(1, variableSpace, true); - block->fillInputs({-1, -2}); + block->fillInputs({ -1, -2 }); - nd4j::ops::reversedivide div; + sd::ops::reversedivide div; div.execute(block); @@ -1601,9 +1601,9 @@ TEST_F(DeclarableOpsTests1, ReverseDivideMatrices1) { ////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests1, ReverseDivideVectorVector1) { - auto x = NDArrayFactory::create_('c', {1, 15}); - auto y = NDArrayFactory::create_('c', {1, 15}); - auto exp = NDArrayFactory::create('c', {1, 15}); + auto x = NDArrayFactory::create_('c', { 1, 15 }); + auto y = NDArrayFactory::create_('c', { 1, 15 }); + auto exp = NDArrayFactory::create('c', { 1, 15 }); x->assign(2); y->assign(6); exp.assign(3); @@ -1612,9 +1612,9 @@ TEST_F(DeclarableOpsTests1, ReverseDivideVectorVector1) { variableSpace->putVariable(-1, x); variableSpace->putVariable(-2, y); auto block = new Context(1, variableSpace, true); - block->fillInputs({-1, -2}); + block->fillInputs({ -1, -2 }); - nd4j::ops::reversedivide div; + sd::ops::reversedivide div; div.execute(block); @@ -1627,9 +1627,9 @@ TEST_F(DeclarableOpsTests1, ReverseDivideVectorVector1) { ////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests1, ReverseDivideMatrixScalar1) { - auto x = NDArrayFactory::create_('c', {5, 3}); - auto y = NDArrayFactory::create_('c', {1, 1}); - auto exp = NDArrayFactory::create('c', {5, 3}); + auto x = NDArrayFactory::create_('c', { 5, 3 }); + auto y = NDArrayFactory::create_('c', { 1, 1 }); + auto exp = NDArrayFactory::create('c', { 5, 3 }); x->assign(2); y->assign(6); exp.assign(3); @@ -1638,9 +1638,9 @@ TEST_F(DeclarableOpsTests1, ReverseDivideMatrixScalar1) { variableSpace->putVariable(-1, x); variableSpace->putVariable(-2, y); auto block = new Context(1, variableSpace, true); - block->fillInputs({-1, -2}); + block->fillInputs({ -1, -2 }); - nd4j::ops::reversedivide div; + sd::ops::reversedivide div; div.execute(block); @@ -1653,9 +1653,9 @@ TEST_F(DeclarableOpsTests1, ReverseDivideMatrixScalar1) { ////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests1, ReverseDivideScalarScalar1) { - auto x = NDArrayFactory::create_('c', {1, 1}); - auto y = NDArrayFactory::create_('c', {1, 1}); - auto exp = NDArrayFactory::create('c', {1, 1}); + auto x = NDArrayFactory::create_('c', { 1, 1 }); + auto y = NDArrayFactory::create_('c', { 1, 1 }); + auto exp = NDArrayFactory::create('c', { 1, 1 }); x->assign(2); y->assign(6); exp.assign(3); @@ -1664,9 +1664,9 @@ TEST_F(DeclarableOpsTests1, ReverseDivideScalarScalar1) { variableSpace->putVariable(-1, x); variableSpace->putVariable(-2, y); auto block = new Context(1, variableSpace, true); - block->fillInputs({-1, -2}); + block->fillInputs({ -1, -2 }); - nd4j::ops::reversedivide div; + sd::ops::reversedivide div; div.execute(block); @@ -1678,8 +1678,8 @@ TEST_F(DeclarableOpsTests1, ReverseDivideScalarScalar1) { ////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests1, Reshapeas1) { - const std::vector xShape = {5,4,3}; - const std::vector yShape = {3,5,4}; + const std::vector xShape = { 5,4,3 }; + const std::vector yShape = { 3,5,4 }; auto x = NDArrayFactory::create_('f', xShape); auto y = NDArrayFactory::create_('f', yShape); @@ -1689,9 +1689,9 @@ TEST_F(DeclarableOpsTests1, Reshapeas1) { variableSpace->putVariable(-1, x); variableSpace->putVariable(-2, y); auto block = new Context(1, variableSpace, true); - block->fillInputs({-1, -2}); + block->fillInputs({ -1, -2 }); - nd4j::ops::reshapeas reshape; + sd::ops::reshapeas reshape; reshape.execute(block); @@ -1703,24 +1703,24 @@ TEST_F(DeclarableOpsTests1, Reshapeas1) { TEST_F(DeclarableOpsTests1, Test_Cast_1) { // TODO: right now there's no real cast implementation, but genera idea should be the same: arrays equality to be expected - auto x = NDArrayFactory::create('c', {5, 5}); - auto yExp = NDArrayFactory::create('c', {5, 5}); + auto x = NDArrayFactory::create('c', { 5, 5 }); + auto yExp = NDArrayFactory::create('c', { 5, 5 }); x.linspace(1); yExp.linspace(1); - nd4j::ops::cast op; + sd::ops::cast op; - auto result = op.evaluate({&x}, {}, {3}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + auto result = op.evaluate({ &x }, {}, { 3 }); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(yExp.equalsTo(z)); - delete result; + } ////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests1, TestRegistrator1) { - auto res = nd4j::ops::OpRegistrator::getInstance()->getAllCustomOperations(); + auto res = sd::ops::OpRegistrator::getInstance()->getAllCustomOperations(); } // ////////////////////////////////////////////////////////////////////// @@ -1738,7 +1738,7 @@ TEST_F(DeclarableOpsTests1, TestRegistrator1) { // z->assign(120.0f); // std::string opName("add"); -// auto hash = nd4j::ops::HashHelper::getInstance()->getInstance()->getLongHash(opName); +// auto hash = sd::ops::HashHelper::getInstance()->getInstance()->getLongHash(opName); // auto inputBuffers = new Nd4jPointer[2]; // auto inputShapes = new Nd4jPointer[2]; @@ -1788,7 +1788,7 @@ TEST_F(DeclarableOpsTests1, TestRegistrator1) { // std::string opName("add"); -// auto hash = nd4j::ops::HashHelper::getInstance()->getInstance()->getLongHash(opName); +// auto hash = sd::ops::HashHelper::getInstance()->getInstance()->getLongHash(opName); // auto inputBuffers = new Nd4jPointer[2]; // auto inputShapes = new Nd4jPointer[2]; @@ -1824,12 +1824,12 @@ TEST_F(DeclarableOpsTests1, TestGemv1) { /* auto xBuffer = new float[15]{1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 7.f, 8.f, 9.f, 10.f, 11.f, 12.f, 13.f, 14.f, 15.f}; auto xShape = new Nd4jLong[8] {2, 5, 3, 3, 1, 0, 1, 99}; - ArrayOptions::setDataType(xShape, nd4j::DataType::FLOAT32); + ArrayOptions::setDataType(xShape, sd::DataType::FLOAT32); auto x = new NDArray(xBuffer, xShape); auto yBuffer = new float[3]{2.f, 4.f, 6.f}; auto yShape = new Nd4jLong[8] {2, 3, 1, 1, 1, 0, 1, 99}; - ArrayOptions::setDataType(yShape, nd4j::DataType::FLOAT32); + ArrayOptions::setDataType(yShape, sd::DataType::FLOAT32); auto y = new NDArray(yBuffer, yShape); @@ -1838,7 +1838,7 @@ TEST_F(DeclarableOpsTests1, TestGemv1) { auto expBuffer = new float[5]{28.00f,64.00f,100.00f,136.00f,172.00f}; auto exp = new NDArray(expBuffer, z->getShapeInfo()); - nd4j::blas::GEMV::op('f', x->rows(), x->columns(), 1.0f, x->getBuffer(), y->rows(), y->getBuffer(), 1, 0.0, z->getBuffer(), 1); + sd::blas::GEMV::op('f', x->rows(), x->columns(), 1.0f, x->getBuffer(), y->rows(), y->getBuffer(), 1, 0.0, z->getBuffer(), 1); ASSERT_TRUE(z->equalsTo(exp)); @@ -1850,8 +1850,8 @@ TEST_F(DeclarableOpsTests1, TestGemv1) { ////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests1, Reshape2) { - const std::vector xShape = {5,4,3}; - const std::vector yShape = {3,5,4}; + const std::vector xShape = { 5,4,3 }; + const std::vector yShape = { 3,5,4 }; auto x = NDArrayFactory::create_('c', xShape); auto y = NDArrayFactory::create_('c', yShape); @@ -1861,14 +1861,14 @@ TEST_F(DeclarableOpsTests1, Reshape2) { variableSpace->putVariable(1, new Variable()); auto block = new Context(1, variableSpace, false); - block->fillInputs({-1}); + block->fillInputs({ -1 }); std::vector* arguments = block->getIArguments(); arguments->push_back(-y->ordering()); arguments->push_back(3); arguments->push_back(5); arguments->push_back(4); - nd4j::ops::reshape reshape; + sd::ops::reshape reshape; Nd4jStatus status = reshape.execute(block); ASSERT_EQ(ND4J_STATUS_OK, status); @@ -1882,92 +1882,92 @@ TEST_F(DeclarableOpsTests1, Reshape2) { } TEST_F(DeclarableOpsTests1, Reshape3) { - auto x = NDArrayFactory::create('c', {3, 4, 5}); + auto x = NDArrayFactory::create('c', { 3, 4, 5 }); - nd4j::ops::reshape op; - auto result = op.evaluate({&x}, {}, {-99, 3, 4, 5}); + sd::ops::reshape op; + auto result = op.evaluate({ &x }, {}, { -99, 3, 4, 5 }); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(x.isSameShape(z)); - delete result; + } TEST_F(DeclarableOpsTests1, Reshape4) { - auto x = NDArrayFactory::create('c', {3, 4, 5}); + auto x = NDArrayFactory::create('c', { 3, 4, 5 }); - nd4j::ops::reshape op; - auto result = op.evaluate({&x}, {}, {3, 4, 5}); + sd::ops::reshape op; + auto result = op.evaluate({ &x }, {}, { 3, 4, 5 }); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(x.isSameShape(z)); - delete result; + } TEST_F(DeclarableOpsTests1, Reshape5) { - auto x = NDArrayFactory::create('c', {3, 4, 5}); + auto x = NDArrayFactory::create('c', { 3, 4, 5 }); - nd4j::ops::reshape op; - auto result = op.evaluate({&x}, {}, {5, 4, 3}); + sd::ops::reshape op; + auto result = op.evaluate({ &x }, {}, { 5, 4, 3 }); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - delete result; + } -TEST_F(DeclarableOpsTests1, Reshape6){ - auto x = NDArrayFactory::create('c', {3, 4, 5}); - auto exp = NDArrayFactory::create('c', {4, 15}); +TEST_F(DeclarableOpsTests1, Reshape6) { + auto x = NDArrayFactory::create('c', { 3, 4, 5 }); + auto exp = NDArrayFactory::create('c', { 4, 15 }); - nd4j::ops::reshape op; - auto result = op.evaluate({&x}, {}, {4, -1}); + sd::ops::reshape op; + auto result = op.evaluate({ &x }, {}, { 4, -1 }); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(z->isSameShape(exp)); - delete result; + } -TEST_F(DeclarableOpsTests1, Reshape7){ - auto x = NDArrayFactory::create('c', {3, 4, 5}); - auto exp = NDArrayFactory::create('c', {60}); +TEST_F(DeclarableOpsTests1, Reshape7) { + auto x = NDArrayFactory::create('c', { 3, 4, 5 }); + auto exp = NDArrayFactory::create('c', { 60 }); - nd4j::ops::reshape op; - auto result = op.evaluate({&x}, {}, {-1}); + sd::ops::reshape op; + auto result = op.evaluate({ &x }, {}, { -1 }); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(z->isSameShape(exp)); - delete result; + } ////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests1, Transpose1) { - auto x = NDArrayFactory::create_('c', {3,5,2}); - auto exp = NDArrayFactory::create_('c', {2,5,3}); + auto x = NDArrayFactory::create_('c', { 3,5,2 }); + auto exp = NDArrayFactory::create_('c', { 2,5,3 }); auto variableSpace = new VariableSpace(); variableSpace->putVariable(-1, x); variableSpace->putVariable(1, new Variable()); auto block = new Context(1, variableSpace, false); // not-in-place - block->fillInputs({-1}); - nd4j::ops::transpose transpose; + block->fillInputs({ -1 }); + sd::ops::transpose transpose; Nd4jStatus status = transpose.execute(block); ASSERT_EQ(ND4J_STATUS_OK, status); @@ -1988,12 +1988,12 @@ TEST_F(DeclarableOpsTests1, Transpose1) { // not-in-place TEST_F(DeclarableOpsTests1, Permute1) { - Nd4jLong shapeX[] = {3, 5,10,15, 150,15,1, 0,1,99}; - Nd4jLong shapeExp[] = {3, 15,5,10, 50,10,1, 0,1,99}; - const std::vector perm = {2, 0, 1}; + Nd4jLong shapeX[] = { 3, 5,10,15, 150,15,1, 0,1,99 }; + Nd4jLong shapeExp[] = { 3, 15,5,10, 50,10,1, 0,1,99 }; + const std::vector perm = { 2, 0, 1 }; - ArrayOptions::setDataType(shapeX, nd4j::DataType::FLOAT32); - ArrayOptions::setDataType(shapeExp, nd4j::DataType::FLOAT32); + ArrayOptions::setDataType(shapeX, sd::DataType::FLOAT32); + ArrayOptions::setDataType(shapeExp, sd::DataType::FLOAT32); auto x = new NDArray(shapeX, true); auto exp = new NDArray(shapeExp, true); @@ -2003,11 +2003,11 @@ TEST_F(DeclarableOpsTests1, Permute1) { variableSpace->putVariable(1, new Variable()); auto block = new Context(1, variableSpace, false); // not-in-place - block->fillInputs({-1}); + block->fillInputs({ -1 }); auto arguments = block->getIArguments(); *arguments = perm; // set dimensions to be permuted - nd4j::ops::permute permute; + sd::ops::permute permute; Nd4jStatus status = permute.execute(block); auto result = variableSpace->getVariable(block->getNodeId())->getNDArray(); @@ -2021,13 +2021,13 @@ TEST_F(DeclarableOpsTests1, Permute1) { ////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests1, TestArgumentsValidation1) { - Nd4jLong shapeX[] = {3, 5, 10, 15, 150, 15, 1, 0, 1, 99}; - Nd4jLong shapeExp[] = {3, 15, 5, 10, 1, 150, 15, 0, -1, 99}; + Nd4jLong shapeX[] = { 3, 5, 10, 15, 150, 15, 1, 0, 1, 99 }; + Nd4jLong shapeExp[] = { 3, 15, 5, 10, 1, 150, 15, 0, -1, 99 }; - ArrayOptions::setDataType(shapeX, nd4j::DataType::FLOAT32); - ArrayOptions::setDataType(shapeExp, nd4j::DataType::FLOAT32); + ArrayOptions::setDataType(shapeX, sd::DataType::FLOAT32); + ArrayOptions::setDataType(shapeExp, sd::DataType::FLOAT32); - const std::vector perm = {2, 0, 1}; + const std::vector perm = { 2, 0, 1 }; auto x = new NDArray(shapeX); auto exp = new NDArray(shapeExp); @@ -2036,9 +2036,9 @@ TEST_F(DeclarableOpsTests1, TestArgumentsValidation1) { variableSpace->putVariable(1, new Variable()); auto block = new Context(1, variableSpace, false); // not-in-place - block->fillInputs({-1}); + block->fillInputs({ -1 }); - nd4j::ops::im2col permute; + sd::ops::im2col permute; Nd4jStatus status = permute.execute(block); ASSERT_TRUE(status != 0); @@ -2050,18 +2050,18 @@ TEST_F(DeclarableOpsTests1, TestArgumentsValidation1) { ////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests1, TestReductionShape1) { - auto input = NDArrayFactory::create_('c', {4, 5, 5, 10, 10}); + auto input = NDArrayFactory::create_('c', { 4, 5, 5, 10, 10 }); auto variableSpace = new VariableSpace(); variableSpace->putVariable(-1, input); auto block = new Context(1, variableSpace, false); // not-in-place - block->fillInputs({-1}); + block->fillInputs({ -1 }); // kernel params block->getIArguments()->push_back(MAX_INT); - nd4j::ops::testreduction testop; + sd::ops::testreduction testop; auto inP = new Nd4jLong[shape::shapeInfoLength(input->getShapeInfo())]; memcpy(inP, input->getShapeInfo(), shape::shapeInfoByteLength(input->rankOf())); @@ -2070,10 +2070,10 @@ TEST_F(DeclarableOpsTests1, TestReductionShape1) { auto shapes = testop.calculateOutputShape(inshape, *block); - ASSERT_EQ(1,shapes->size()); - ASSERT_EQ(0,shapes->at(0)[0]); // scalar shape has rank 0 - ASSERT_EQ(8192,shapes->at(0)[1]); - ASSERT_EQ(1,shapes->at(0)[2]); + ASSERT_EQ(1, shapes->size()); + ASSERT_EQ(0, shapes->at(0)[0]); // scalar shape has rank 0 + ASSERT_EQ(8192, shapes->at(0)[1]); + ASSERT_EQ(1, shapes->at(0)[2]); delete[] inP; delete variableSpace; @@ -2085,13 +2085,13 @@ TEST_F(DeclarableOpsTests1, TestReductionShape1) { ////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests1, TestReductionShape2) { - auto input = NDArrayFactory::create_('c', {4, 5, 5, 10, 10}); + auto input = NDArrayFactory::create_('c', { 4, 5, 5, 10, 10 }); auto variableSpace = new VariableSpace(); variableSpace->putVariable(-1, input); auto block = new Context(1, variableSpace, false); // not-in-place - block->fillInputs({-1}); + block->fillInputs({ -1 }); // kernel params //block->getIArguments()->push_back(4); @@ -2100,14 +2100,14 @@ TEST_F(DeclarableOpsTests1, TestReductionShape2) { block->getIArguments()->push_back(3); block->getIArguments()->push_back(4); - nd4j::ops::testreduction testop; + sd::ops::testreduction testop; auto inshapes = new ShapeList(input->getShapeInfo()); auto shapes = testop.calculateOutputShape(inshapes, *block); - ASSERT_EQ(1,shapes->size()); - ASSERT_EQ(1,shapes->at(0)[0]); - ASSERT_EQ(4,shapes->at(0)[1]); - ASSERT_EQ(1,shapes->at(0)[2]); + ASSERT_EQ(1, shapes->size()); + ASSERT_EQ(1, shapes->at(0)[0]); + ASSERT_EQ(4, shapes->at(0)[1]); + ASSERT_EQ(1, shapes->at(0)[2]); delete variableSpace; delete block; @@ -2117,21 +2117,21 @@ TEST_F(DeclarableOpsTests1, TestReductionShape2) { ////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests1, TestCustomShape1) { - auto input = NDArrayFactory::create_('c', {2, 3, 4}); + auto input = NDArrayFactory::create_('c', { 2, 3, 4 }); auto variableSpace = new VariableSpace(); variableSpace->putVariable(-1, input); auto block = new Context(1, variableSpace, false); // not-in-place - block->fillInputs({-1}); + block->fillInputs({ -1 }); - nd4j::ops::testcustom test; + sd::ops::testcustom test; auto inshapes = new ShapeList(input->getShapeInfo()); auto shapes = test.calculateOutputShape(inshapes, *block); - ASSERT_EQ(input->getShapeInfo()[0] , shapes->at(0)[0]); + ASSERT_EQ(input->getShapeInfo()[0], shapes->at(0)[0]); ASSERT_EQ(input->getShapeInfo()[1] * 2, shapes->at(0)[1]); ASSERT_EQ(input->getShapeInfo()[2] * 2, shapes->at(0)[2]); ASSERT_EQ(input->getShapeInfo()[3] * 2, shapes->at(0)[3]); @@ -2167,7 +2167,7 @@ TEST_F(DeclarableOpsTests1, Sum1) { std::vector* arguments = block->getIArguments(); *arguments = dimensions; - nd4j::ops::sum sum; + sd::ops::sum sum; Nd4jStatus status = sum.execute(block); auto result = variableSpace->getVariable(block->getNodeId())->getNDArray(); @@ -2183,8 +2183,8 @@ TEST_F(DeclarableOpsTests1, Sum1) { ////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests1, Pnormpool2d1) { - auto x = NDArrayFactory::create_('c', {bS,iD,iH,iW}); - auto exp = NDArrayFactory::create('c',{bS,iD,oH,oW}); + auto x = NDArrayFactory::create_('c', { bS,iD,iH,iW }); + auto exp = NDArrayFactory::create('c', { bS,iD,oH,oW }); // auto z('c',{bS,iD,oH,oW}); auto variableSpace = new VariableSpace(); @@ -2192,11 +2192,11 @@ TEST_F(DeclarableOpsTests1, Pnormpool2d1) { // variableSpace->putVariable(1, &z); auto block = new Context(1, variableSpace, false); - block->fillInputs({-1}); + block->fillInputs({ -1 }); std::vector* argI = block->getIArguments(); - *argI = {kH,kW, sH,sW, pH,pW, dW,dH, 0, 1, 0}; // 0,1 - kernel Height/Width; 2,3 - stride Height/Width; 4,5 - pad Height/Width; 6,7 - dilation Height/Width; 8 - same mode; 9 - extraParam0 for pnorm case; + *argI = { kH,kW, sH,sW, pH,pW, dW,dH, 0, 1, 0 }; // 0,1 - kernel Height/Width; 2,3 - stride Height/Width; 4,5 - pad Height/Width; 6,7 - dilation Height/Width; 8 - same mode; 9 - extraParam0 for pnorm case; - nd4j::ops::pnormpool2d pooling; + sd::ops::pnormpool2d pooling; Nd4jStatus status = pooling.execute(block); ASSERT_EQ(ND4J_STATUS_OK, status); @@ -2213,7 +2213,7 @@ TEST_F(DeclarableOpsTests1, IsMax1) { float xBuff[] = {1,2,3,4,5,6,7,8,9}; Nd4jLong xShape[] = {2,3,3,3,1,0,1,99}; bool expBuff[] = {0,0,1,0,0,1,0,0,1}; - ArrayOptions::setDataType(xShape, nd4j::DataType::BOOL); + ArrayOptions::setDataType(xShape, sd::DataType::BOOL); auto x = new NDArray(xBuff, xShape); NDArray exp(expBuff, xShape); @@ -2227,7 +2227,7 @@ TEST_F(DeclarableOpsTests1, IsMax1) { // *argI = {1}; // dimensions argI->push_back(1); // = {1}; // dimensions - nd4j::ops::ismax ismaxOp; + sd::ops::ismax ismaxOp; Nd4jStatus status = ismaxOp.execute(block); ASSERT_EQ(ND4J_STATUS_OK, status); @@ -2242,78 +2242,78 @@ TEST_F(DeclarableOpsTests1, IsMax1) { ////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests1, IsMax1) { - NDArray x('c', {3, 3}, nd4j::DataType::FLOAT32); -// NDArray exp('c', {3, 3}, nd4j::DataType::BOOL); - NDArray exp('c', {3, 3}, nd4j::DataType::FLOAT32); + NDArray x('c', { 3, 3 }, sd::DataType::FLOAT32); + // NDArray exp('c', {3, 3}, sd::DataType::BOOL); + NDArray exp('c', { 3, 3 }, sd::DataType::FLOAT32); x.linspace(1); exp.p(0, 2, true); exp.p(1, 2, true); exp.p(2, 2, true); - nd4j::ops::ismax ismaxOp; - auto result = ismaxOp.evaluate({&x}, {}, {1}); + sd::ops::ismax ismaxOp; + auto result = ismaxOp.evaluate({ &x }, {}, { 1 }); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto res = result->at(0); + auto res = result.at(0); //res->printIndexedBuffer("IS_MAX"); ASSERT_TRUE(exp.equalsTo(res)); - delete result; + } ////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests1, IsMax2) { - NDArray x('c', {3, 3}, nd4j::DataType::FLOAT32); -// NDArray exp('c', {3, 3}, nd4j::DataType::BOOL); - NDArray exp('c', {3, 3}, nd4j::DataType::FLOAT32); + NDArray x('c', { 3, 3 }, sd::DataType::FLOAT32); + // NDArray exp('c', {3, 3}, sd::DataType::BOOL); + NDArray exp('c', { 3, 3 }, sd::DataType::FLOAT32); x.linspace(1); //exp.p(0, 2, true); //exp.p(1, 2, true); exp.p(2, 2, true); - nd4j::ops::ismax ismaxOp; - auto result = ismaxOp.evaluate({&x}, {}, {0, 1}); + sd::ops::ismax ismaxOp; + auto result = ismaxOp.evaluate({ &x }, {}, { 0, 1 }); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto res = result->at(0); + auto res = result.at(0); //res->printIndexedBuffer("IS_MAX"); ASSERT_TRUE(exp.equalsTo(res)); - delete result; + } ////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests1, IsMax3) { - NDArray x = NDArrayFactory::create(120.f); //('c', {3, 3}, nd4j::DataType::FLOAT32); -// NDArray exp('c', {3, 3}, nd4j::DataType::BOOL); - NDArray exp = NDArrayFactory::create(1.f);//, nd4j::DataType::FLOAT32); //'c', {3, 3}, nd4j::DataType::FLOAT32); + NDArray x = NDArrayFactory::create(120.f); //('c', {3, 3}, sd::DataType::FLOAT32); +// NDArray exp('c', {3, 3}, sd::DataType::BOOL); + NDArray exp = NDArrayFactory::create(1.f);//, sd::DataType::FLOAT32); //'c', {3, 3}, sd::DataType::FLOAT32); x.linspace(1); //exp.p(0, 2, true); //exp.p(1, 2, true); //exp.p(2, 2, true); - nd4j::ops::ismax ismaxOp; - auto result = ismaxOp.evaluate({&x}, {}, {0}); + sd::ops::ismax ismaxOp; + auto result = ismaxOp.evaluate({ &x }, {}, { 0 }); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto res = result->at(0); + auto res = result.at(0); //res->printIndexedBuffer("IS_MAX"); ASSERT_TRUE(exp.equalsTo(res)); - delete result; + } ////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests1, IsMax4) { - auto x = NDArrayFactory::create('c', {6}, {0, 0, 0, 2, 2, 0}); - auto z = NDArrayFactory::create('c', {6}); - auto e = NDArrayFactory::create('c', {6}, {false, false, false, true, false, false}); + auto x = NDArrayFactory::create('c', { 6 }, { 0, 0, 0, 2, 2, 0 }); + auto z = NDArrayFactory::create('c', { 6 }); + auto e = NDArrayFactory::create('c', { 6 }, { false, false, false, true, false, false }); - nd4j::ops::ismax op; - auto result = op.execute({&x}, {&z}); + sd::ops::ismax op; + auto result = op.execute({ &x }, { &z }); ASSERT_EQ(Status::OK(), result); ASSERT_EQ(e, z); @@ -2326,13 +2326,13 @@ TEST_F(DeclarableOpsTests1, IsMax4) { // const int K = 3; // const int N = 4; -// NDArray input('c', {bS,K,N}, nd4j::DataType::DOUBLE); -// NDArray weights('c', {3*K,K}, nd4j::DataType::DOUBLE); -// NDArray bias('c', {1,2*K}, nd4j::DataType::DOUBLE); -// NDArray init('c', {bS,K}, nd4j::DataType::DOUBLE); -// NDArray mask('c', {bS,K}, nd4j::DataType::DOUBLE); -// NDArray expState('c', {bS,K,N}, {0.847983, 0.874549, 0.896109, 0.913715, 0.847983, 0.874549, 0.896109, 0.913715, 0.847983, 0.874549, 0.896109, 0.913715, 0.847983, 0.874549, 0.896109, 0.913715, 0.847983, 0.874549, 0.896109, 0.913715, 0.847983, 0.874549, 0.896109, 0.913715}, nd4j::DataType::DOUBLE); -// NDArray expOut('c', {bS,K,N}, {1.090533, 1.174509, 1.252403, 1.324656, 1.090533, 1.174509, 1.252403, 1.324656, 1.090533, 1.174509, 1.252403, 1.324656, 1.090533, 1.174509, 1.252403, 1.324656, 1.090533, 1.174509, 1.252403, 1.324656, 1.090533, 1.174509, 1.252403, 1.324656}, nd4j::DataType::DOUBLE); +// NDArray input('c', {bS,K,N}, sd::DataType::DOUBLE); +// NDArray weights('c', {3*K,K}, sd::DataType::DOUBLE); +// NDArray bias('c', {1,2*K}, sd::DataType::DOUBLE); +// NDArray init('c', {bS,K}, sd::DataType::DOUBLE); +// NDArray mask('c', {bS,K}, sd::DataType::DOUBLE); +// NDArray expState('c', {bS,K,N}, {0.847983, 0.874549, 0.896109, 0.913715, 0.847983, 0.874549, 0.896109, 0.913715, 0.847983, 0.874549, 0.896109, 0.913715, 0.847983, 0.874549, 0.896109, 0.913715, 0.847983, 0.874549, 0.896109, 0.913715, 0.847983, 0.874549, 0.896109, 0.913715}, sd::DataType::DOUBLE); +// NDArray expOut('c', {bS,K,N}, {1.090533, 1.174509, 1.252403, 1.324656, 1.090533, 1.174509, 1.252403, 1.324656, 1.090533, 1.174509, 1.252403, 1.324656, 1.090533, 1.174509, 1.252403, 1.324656, 1.090533, 1.174509, 1.252403, 1.324656, 1.090533, 1.174509, 1.252403, 1.324656}, sd::DataType::DOUBLE); // input.assign(1.5); // weights.assign(0.5); @@ -2340,19 +2340,19 @@ TEST_F(DeclarableOpsTests1, IsMax4) { // init.assign(1.); // mask.assign(1.); -// nd4j::ops::sru_old op; +// sd::ops::sru_old op; // auto results = op.execute({&input, &weights, &bias, &init, &mask}, {}, {}); -// ASSERT_TRUE(results->size() == 2); +// ASSERT_TRUE(results.size() == 2); -// auto state = results->at(0); -// auto output = results->at(1); +// auto state = results.at(0); +// auto output = results.at(1); // // state->printBuffer(); // // expState.printIndexedBuffer("EXP STATE"); // // state->printIndexedBuffer("OUT STATE"); // ASSERT_TRUE(expState.equalsTo(state)); // ASSERT_TRUE(expOut.equalsTo(output)); -// delete results; +// // } ////////////////////////////////////////////////////////////////// @@ -2362,31 +2362,31 @@ TEST_F(DeclarableOpsTests1, sru_test1) { const int K = 3; const int N = 4; - NDArray input('c', {bS,K,N}, nd4j::DataType::DOUBLE); - NDArray weights('c', {3*K,K}, nd4j::DataType::DOUBLE); - NDArray bias('c', {2*K}, nd4j::DataType::DOUBLE); - NDArray init('c', {bS,K}, nd4j::DataType::DOUBLE); - NDArray mask('c', {bS,K}, nd4j::DataType::DOUBLE); - NDArray expState('c', {bS,K,N}, {1.090533, 1.174509, 1.252403, 1.324656, 1.090533, 1.174509, 1.252403, 1.324656, 1.090533, 1.174509, 1.252403, 1.324656, 1.090533, 1.174509, 1.252403, 1.324656, 1.090533, 1.174509, 1.252403, 1.324656, 1.090533, 1.174509, 1.252403, 1.324656}, nd4j::DataType::DOUBLE); - NDArray expOut('c', {bS,K,N}, {0.847983, 0.874549, 0.896109, 0.913715, 0.847983, 0.874549, 0.896109, 0.913715, 0.847983, 0.874549, 0.896109, 0.913715, 0.847983, 0.874549, 0.896109, 0.913715, 0.847983, 0.874549, 0.896109, 0.913715, 0.847983, 0.874549, 0.896109, 0.913715}, nd4j::DataType::DOUBLE); + NDArray input('c', { bS,K,N }, sd::DataType::DOUBLE); + NDArray weights('c', { 3 * K,K }, sd::DataType::DOUBLE); + NDArray bias('c', { 2 * K }, sd::DataType::DOUBLE); + NDArray init('c', { bS,K }, sd::DataType::DOUBLE); + NDArray mask('c', { bS,K }, sd::DataType::DOUBLE); + NDArray expState('c', { bS,K,N }, { 1.090533, 1.174509, 1.252403, 1.324656, 1.090533, 1.174509, 1.252403, 1.324656, 1.090533, 1.174509, 1.252403, 1.324656, 1.090533, 1.174509, 1.252403, 1.324656, 1.090533, 1.174509, 1.252403, 1.324656, 1.090533, 1.174509, 1.252403, 1.324656 }, sd::DataType::DOUBLE); + NDArray expOut('c', { bS,K,N }, { 0.847983, 0.874549, 0.896109, 0.913715, 0.847983, 0.874549, 0.896109, 0.913715, 0.847983, 0.874549, 0.896109, 0.913715, 0.847983, 0.874549, 0.896109, 0.913715, 0.847983, 0.874549, 0.896109, 0.913715, 0.847983, 0.874549, 0.896109, 0.913715 }, sd::DataType::DOUBLE); input.assign(1.5); weights.assign(0.5); - bias.assign(0.3) ; + bias.assign(0.3); init.assign(1.); mask.assign(1.); - nd4j::ops::sru op; - auto results = op.evaluate({&input, &weights, &bias, &init, &mask}); - ASSERT_TRUE(results->size() == 2); + sd::ops::sru op; + auto results = op.evaluate({ &input, &weights, &bias, &init, &mask }); + ASSERT_TRUE(results.size() == 2); - auto output = results->at(0); - auto state = results->at(1); + auto output = results.at(0); + auto state = results.at(1); ASSERT_TRUE(expState.equalsTo(state)); ASSERT_TRUE(expOut.equalsTo(output)); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -2395,50 +2395,50 @@ TEST_F(DeclarableOpsTests1, sru_bp) { const int bS = 2; const int K = 3; const int N = 4; - std::vector expGradXBuff = {-0.0259303, -0.03869125, -0.0302272, -0.02299165, -0.0259303, -0.03869125, -0.0302272, -0.02299165, -0.0259303, -0.03869125, -0.0302272, -0.02299165, -0.0259303, -0.03869125, -0.0302272, -0.02299165, -0.0259303, -0.03869125, -0.0302272, -0.02299165, -0.0259303, -0.03869125, -0.0302272, -0.02299165}; - std::vector expGradWBuff = {0.42526005,0.42526005,0.42526005, 0.42526005,0.42526005,0.42526005, 0.42526005,0.42526005,0.42526005, -0.5282811 , -0.5282811 , -0.5282811 , -0.5282811 , -0.5282811 , -0.5282811 , -0.5282811 , -0.5282811 , -0.5282811 , -0.15967215, -0.15967215, -0.15967215, -0.15967215, -0.15967215, -0.15967215, -0.15967215, -0.15967215, -0.15967215, 0.42526005,0.42526005,0.42526005, 0.42526005,0.42526005,0.42526005, 0.42526005,0.42526005,0.42526005, -0.5282811 , -0.5282811 , -0.5282811 , -0.5282811 , -0.5282811 , -0.5282811 , -0.5282811 , -0.5282811 , -0.5282811 , -0.15967215, -0.15967215, -0.15967215, -0.15967215, -0.15967215, -0.15967215, -0.15967215, -0.15967215, -0.15967215}; - std::vector expGradBBuff = {-0.7043748, -0.7043748, -0.7043748, -0.2128962, -0.2128962, -0.2128962}; - std::vector expGradInitBuff = {1.1421, 1.1421, 1.1421, 1.1421, 1.1421, 1.1421}; - std::vector stateBuff = {0.847983, 0.874549, 0.896109, 0.913715, 0.847983, 0.874549, 0.896109, 0.913715, 0.847983, 0.874549, 0.896109, 0.913715, 0.847983, 0.874549, 0.896109, 0.913715, 0.847983, 0.874549, 0.896109, 0.913715, 0.847983, 0.874549, 0.896109, 0.913715}; + std::vector expGradXBuff = { -0.0259303, -0.03869125, -0.0302272, -0.02299165, -0.0259303, -0.03869125, -0.0302272, -0.02299165, -0.0259303, -0.03869125, -0.0302272, -0.02299165, -0.0259303, -0.03869125, -0.0302272, -0.02299165, -0.0259303, -0.03869125, -0.0302272, -0.02299165, -0.0259303, -0.03869125, -0.0302272, -0.02299165 }; + std::vector expGradWBuff = { 0.42526005,0.42526005,0.42526005, 0.42526005,0.42526005,0.42526005, 0.42526005,0.42526005,0.42526005, -0.5282811 , -0.5282811 , -0.5282811 , -0.5282811 , -0.5282811 , -0.5282811 , -0.5282811 , -0.5282811 , -0.5282811 , -0.15967215, -0.15967215, -0.15967215, -0.15967215, -0.15967215, -0.15967215, -0.15967215, -0.15967215, -0.15967215, 0.42526005,0.42526005,0.42526005, 0.42526005,0.42526005,0.42526005, 0.42526005,0.42526005,0.42526005, -0.5282811 , -0.5282811 , -0.5282811 , -0.5282811 , -0.5282811 , -0.5282811 , -0.5282811 , -0.5282811 , -0.5282811 , -0.15967215, -0.15967215, -0.15967215, -0.15967215, -0.15967215, -0.15967215, -0.15967215, -0.15967215, -0.15967215 }; + std::vector expGradBBuff = { -0.7043748, -0.7043748, -0.7043748, -0.2128962, -0.2128962, -0.2128962 }; + std::vector expGradInitBuff = { 1.1421, 1.1421, 1.1421, 1.1421, 1.1421, 1.1421 }; + std::vector stateBuff = { 0.847983, 0.874549, 0.896109, 0.913715, 0.847983, 0.874549, 0.896109, 0.913715, 0.847983, 0.874549, 0.896109, 0.913715, 0.847983, 0.874549, 0.896109, 0.913715, 0.847983, 0.874549, 0.896109, 0.913715, 0.847983, 0.874549, 0.896109, 0.913715 }; - auto input = NDArrayFactory::create('c', {bS,K,N}); - auto weights = NDArrayFactory::create('c', {3*K,K}); - auto bias = NDArrayFactory::create('c', {1,2*K}); - auto init = NDArrayFactory::create('c', {bS,K}); - auto mask = NDArrayFactory::create('c', {bS,K}); - auto state = NDArrayFactory::create('c', {bS,K,N}, stateBuff); - auto inGradCt = NDArrayFactory::create('c', {bS,K}); - auto inGradH = NDArrayFactory::create('c', {bS,K,N}); + auto input = NDArrayFactory::create('c', { bS,K,N }); + auto weights = NDArrayFactory::create('c', { 3 * K,K }); + auto bias = NDArrayFactory::create('c', { 1,2 * K }); + auto init = NDArrayFactory::create('c', { bS,K }); + auto mask = NDArrayFactory::create('c', { bS,K }); + auto state = NDArrayFactory::create('c', { bS,K,N }, stateBuff); + auto inGradCt = NDArrayFactory::create('c', { bS,K }); + auto inGradH = NDArrayFactory::create('c', { bS,K,N }); - auto expGradX = NDArrayFactory::create('c', {bS,K,N}, expGradXBuff); - auto expGradW = NDArrayFactory::create('c', {bS,3*K,K}, expGradWBuff); - auto expGradB = NDArrayFactory::create('c', {1,2*K}, expGradBBuff); - auto expGradInit = NDArrayFactory::create('c', {bS,K}, expGradInitBuff); + auto expGradX = NDArrayFactory::create('c', { bS,K,N }, expGradXBuff); + auto expGradW = NDArrayFactory::create('c', { bS,3 * K,K }, expGradWBuff); + auto expGradB = NDArrayFactory::create('c', { 1,2 * K }, expGradBBuff); + auto expGradInit = NDArrayFactory::create('c', { bS,K }, expGradInitBuff); input.assign(1.5); weights.assign(0.5); - bias.assign(0.3) ; + bias.assign(0.3); mask.assign(1.); init.assign(1.); inGradCt.assign(0.5); inGradH.assign(0.5); - nd4j::ops::sru_bp bp; - auto resultsBP = bp.evaluate({&input, &weights, &bias, &init, &state, &inGradCt, &inGradH, &mask}, {}, {}); - ASSERT_TRUE(resultsBP->size() == 4); + sd::ops::sru_bp bp; + auto resultsBP = bp.evaluate({ &input, &weights, &bias, &init, &state, &inGradCt, &inGradH, &mask }, {}, {}); + ASSERT_TRUE(resultsBP.size() == 4); - auto gradX = resultsBP->at(0); - auto gradW = resultsBP->at(1); - auto gradB = resultsBP->at(2); - auto gradInit = resultsBP->at(3); + auto gradX = resultsBP.at(0); + auto gradW = resultsBP.at(1); + auto gradB = resultsBP.at(2); + auto gradInit = resultsBP.at(3); // expGradX.printBuffer("Exp GRAD"); // gradX->printBuffer("Res GRAD"); - ASSERT_TRUE(expGradX.equalsTo(gradX,1e-4)); + ASSERT_TRUE(expGradX.equalsTo(gradX, 1e-4)); ASSERT_TRUE(expGradW.equalsTo(gradW)); ASSERT_TRUE(expGradB.equalsTo(gradB)); ASSERT_TRUE(expGradInit.equalsTo(gradInit)); - delete resultsBP; + } ////////////////////////////////////////////////////////////////// @@ -2448,33 +2448,33 @@ TEST_F(DeclarableOpsTests1, sru_bi_1) { const int K = 3; const int N = 4; - NDArray input('c', {N,bS,2*K}, nd4j::DataType::DOUBLE); - NDArray weights('c', {2*K,6*K}, nd4j::DataType::DOUBLE); - NDArray bias('c', {4*K}, nd4j::DataType::DOUBLE); - NDArray init('c', {bS,2*K}, nd4j::DataType::DOUBLE); - NDArray mask('c', {bS,2*K}, nd4j::DataType::DOUBLE); - NDArray expState('c', {N,bS,2*K}, {1.02857, 1.02857, 1.02857, 1.11288, 1.11288, 1.11288, 1.02857, 1.02857, 1.02857, 1.11288, 1.11288, 1.11288, 1.0569, 1.0569, 1.0569, 1.08501, 1.08501, 1.08501, 1.0569, 1.0569, 1.0569, 1.08501, 1.08501, 1.08501, 1.08501, 1.08501, 1.08501, 1.0569, 1.0569, 1.0569, 1.08501, 1.08501, 1.08501, 1.0569, 1.0569, 1.0569, 1.11288, 1.11288, 1.11288, 1.02857, 1.02857, 1.02857, 1.11288, 1.11288, 1.11288, 1.02857, 1.02857, 1.02857}); - NDArray expOut('c', {N,bS,2*K}, {0.779265, 0.779265, 0.779265, 0.810752, 0.810752, 0.810752, 0.779265, 0.779265, 0.779265, 0.810752, 0.810752, 0.810752, 0.790317, 0.790317, 0.790317, 0.800804, 0.800804, 0.800804, 0.790317, 0.790317, 0.790317, 0.800804, 0.800804, 0.800804, 0.800804, 0.800804, 0.800804, 0.790317, 0.790317, 0.790317, 0.800804, 0.800804, 0.800804, 0.790317, 0.790317, 0.790317, 0.810752, 0.810752, 0.810752, 0.779265, 0.779265, 0.779265, 0.810752, 0.810752, 0.810752, 0.779265, 0.779265, 0.779265}); + NDArray input('c', { N,bS,2 * K }, sd::DataType::DOUBLE); + NDArray weights('c', { 2 * K,6 * K }, sd::DataType::DOUBLE); + NDArray bias('c', { 4 * K }, sd::DataType::DOUBLE); + NDArray init('c', { bS,2 * K }, sd::DataType::DOUBLE); + NDArray mask('c', { bS,2 * K }, sd::DataType::DOUBLE); + NDArray expState('c', { N,bS,2 * K }, { 1.02857, 1.02857, 1.02857, 1.11288, 1.11288, 1.11288, 1.02857, 1.02857, 1.02857, 1.11288, 1.11288, 1.11288, 1.0569, 1.0569, 1.0569, 1.08501, 1.08501, 1.08501, 1.0569, 1.0569, 1.0569, 1.08501, 1.08501, 1.08501, 1.08501, 1.08501, 1.08501, 1.0569, 1.0569, 1.0569, 1.08501, 1.08501, 1.08501, 1.0569, 1.0569, 1.0569, 1.11288, 1.11288, 1.11288, 1.02857, 1.02857, 1.02857, 1.11288, 1.11288, 1.11288, 1.02857, 1.02857, 1.02857 }); + NDArray expOut('c', { N,bS,2 * K }, { 0.779265, 0.779265, 0.779265, 0.810752, 0.810752, 0.810752, 0.779265, 0.779265, 0.779265, 0.810752, 0.810752, 0.810752, 0.790317, 0.790317, 0.790317, 0.800804, 0.800804, 0.800804, 0.790317, 0.790317, 0.790317, 0.800804, 0.800804, 0.800804, 0.800804, 0.800804, 0.800804, 0.790317, 0.790317, 0.790317, 0.800804, 0.800804, 0.800804, 0.790317, 0.790317, 0.790317, 0.810752, 0.810752, 0.810752, 0.779265, 0.779265, 0.779265, 0.810752, 0.810752, 0.810752, 0.779265, 0.779265, 0.779265 }); input.assign(1.5); weights.assign(0.5); - bias.assign(0.3) ; + bias.assign(0.3); init.assign(1.); mask.assign(1.); - nd4j::ops::sru_bi op; - auto results = op.evaluate({&input, &weights, &bias, &init, &mask}, {}, {}); - ASSERT_TRUE(results->size() == 2); + sd::ops::sru_bi op; + auto results = op.evaluate({ &input, &weights, &bias, &init, &mask }, {}, {}); + ASSERT_TRUE(results.size() == 2); - auto output = results->at(0); - auto state = results->at(1); + auto output = results.at(0); + auto state = results.at(1); // state->printBuffer(); // output->printBuffer(); ASSERT_TRUE(expState.equalsTo(state)); ASSERT_TRUE(expOut.equalsTo(output)); - delete results; + } TEST_F(DeclarableOpsTests1, sru_bi_bp_1) { @@ -2482,1276 +2482,988 @@ TEST_F(DeclarableOpsTests1, sru_bi_bp_1) { const int bS = 2; const int K = 3; const int N = 3; - std::vector expGradXBuff = {0.00408129, 0.00408129, 0.00408129, 0.00408129, 0.00408129, 0.00408129, 0.00408129, 0.00408129, 0.00408129, 0.00408129, 0.00408129, 0.00408129, 0.00408129, 0.00408129, 0.00408129, 0.00408129, 0.00408129, 0.00408129, 0.00408129, 0.00408129, 0.00408129, 0.00408129, 0.00408129, 0.00408129, 0.00408129, 0.00408129, 0.00408129, 0.00408129, 0.00408129, 0.00408129, 0.00408129, 0.00408129, 0.00408129, 0.00408129, 0.00408129, 0.00408129}; - std::vector expGradInitBuff = {1.05121, 1.05121, 1.05121, 1.02676, 1.02676, 1.02676, 1.05121, 1.05121, 1.05121, 1.02676, 1.02676, 1.02676}; - std::vector expGradWBuff = {0.02595354,-0.090096 ,-0.00882456,0.02595354,-0.090096 ,-0.0088245, 0.02595354,-0.090096 ,-0.00882456,0.01651665,-0.0559437,-0.0084390, 0.01651665,-0.0559437,-0.00843906,0.01651665,-0.0559437,-0.00843906, 0.02595354,-0.090096 ,-0.00882456,0.02595354,-0.090096 ,-0.0088245, 0.02595354,-0.090096 ,-0.00882456,0.01651665,-0.0559437,-0.0084390, 0.01651665,-0.0559437,-0.00843906,0.01651665,-0.0559437,-0.00843906, 0.02595354,-0.090096 ,-0.00882456,0.02595354,-0.090096 ,-0.0088245, 0.02595354,-0.090096 ,-0.00882456,0.01651665,-0.0559437,-0.0084390, 0.01651665,-0.0559437,-0.00843906,0.01651665,-0.0559437,-0.00843906, 0.02595354,-0.090096 ,-0.00882456,0.02595354,-0.090096 ,-0.0088245, 0.02595354,-0.090096 ,-0.00882456,0.01651665,-0.0559437,-0.0084390, 0.01651665,-0.0559437,-0.00843906,0.01651665,-0.0559437,-0.00843906, 0.02595354,-0.090096 ,-0.00882456,0.02595354,-0.090096 ,-0.0088245, 0.02595354,-0.090096 ,-0.00882456,0.01651665,-0.0559437,-0.0084390, 0.01651665,-0.0559437,-0.00843906,0.01651665,-0.0559437,-0.00843906, 0.02595354,-0.090096 ,-0.00882456,0.02595354,-0.090096 ,-0.0088245, 0.02595354,-0.090096 ,-0.00882456,0.01651665,-0.0559437,-0.0084390, 0.01651665,-0.0559437,-0.00843906,0.01651665,-0.0559437,-0.00843906, 0.02124567,-0.0731508,-0.00868926,0.02124567,-0.0731508,-0.0086892, 0.02124567,-0.0731508,-0.00868926,0.02084955,-0.0712011,-0.0085608, 0.02084955,-0.0712011,-0.00856086,0.02084955,-0.0712011,-0.00856086, 0.02124567,-0.0731508,-0.00868926,0.02124567,-0.0731508,-0.0086892, 0.02124567,-0.0731508,-0.00868926,0.02084955,-0.0712011,-0.0085608, 0.02084955,-0.0712011,-0.00856086,0.02084955,-0.0712011,-0.00856086, 0.02124567,-0.0731508,-0.00868926,0.02124567,-0.0731508,-0.0086892, 0.02124567,-0.0731508,-0.00868926,0.02084955,-0.0712011,-0.0085608, 0.02084955,-0.0712011,-0.00856086,0.02084955,-0.0712011,-0.00856086, 0.02124567,-0.0731508,-0.00868926,0.02124567,-0.0731508,-0.0086892, 0.02124567,-0.0731508,-0.00868926,0.02084955,-0.0712011,-0.0085608, 0.02084955,-0.0712011,-0.00856086,0.02084955,-0.0712011,-0.00856086, 0.02124567,-0.0731508,-0.00868926,0.02124567,-0.0731508,-0.0086892, 0.02124567,-0.0731508,-0.00868926,0.02084955,-0.0712011,-0.0085608, 0.02084955,-0.0712011,-0.00856086,0.02084955,-0.0712011,-0.00856086, 0.02124567,-0.0731508,-0.00868926,0.02124567,-0.0731508,-0.0086892, 0.02124567,-0.0731508,-0.00868926,0.02084955,-0.0712011,-0.0085608, 0.02084955,-0.0712011,-0.00856086,0.02084955,-0.0712011,-0.00856086, 0.01671156,-0.0570699,-0.00856086,0.01671156,-0.0570699,-0.0085608, 0.01671156,-0.0570699,-0.00856086,0.02534988,-0.0880002,-0.0086892, 0.02534988,-0.0880002,-0.00868926,0.02534988,-0.0880002,-0.00868926, 0.01671156,-0.0570699,-0.00856086,0.01671156,-0.0570699,-0.0085608, 0.01671156,-0.0570699,-0.00856086,0.02534988,-0.0880002,-0.0086892, 0.02534988,-0.0880002,-0.00868926,0.02534988,-0.0880002,-0.00868926, 0.01671156,-0.0570699,-0.00856086,0.01671156,-0.0570699,-0.0085608, 0.01671156,-0.0570699,-0.00856086,0.02534988,-0.0880002,-0.0086892, 0.02534988,-0.0880002,-0.00868926,0.02534988,-0.0880002,-0.00868926, 0.01671156,-0.0570699,-0.00856086,0.01671156,-0.0570699,-0.0085608, 0.01671156,-0.0570699,-0.00856086,0.02534988,-0.0880002,-0.0086892, 0.02534988,-0.0880002,-0.00868926,0.02534988,-0.0880002,-0.00868926, 0.01671156,-0.0570699,-0.00856086,0.01671156,-0.0570699,-0.0085608, 0.01671156,-0.0570699,-0.00856086,0.02534988,-0.0880002,-0.0086892, 0.02534988,-0.0880002,-0.00868926,0.02534988,-0.0880002,-0.00868926, 0.01671156,-0.0570699,-0.00856086,0.01671156,-0.0570699,-0.0085608, 0.01671156,-0.0570699,-0.00856086,0.02534988,-0.0880002,-0.0086892, 0.02534988,-0.0880002,-0.00868926,0.02534988,-0.0880002,-0.00868926}; - std::vector expGradBBuff = {-0.0734389, -0.0734389, -0.0734389, -0.0717151, -0.0717151, -0.0717151, -0.0734389, -0.0734389, -0.0734389, -0.0717151, -0.0717151, -0.0717151, -0.00869156, -0.00869156, -0.00869156, -0.00856306, -0.00856306, -0.00856306, -0.00869156, -0.00869156, -0.00869156, -0.00856306, -0.00856306, -0.00856306}; - std::vector stateBuff = {1.028569, 1.028569, 1.028569, 1.112884, 1.112884, 1.112884, 1.028569, 1.028569, 1.028569, 1.112884,1.112884, 1.112884, 1.056905, 1.056905, 1.056905, 1.085009, 1.085009, 1.085009, 1.056905, 1.056905,1.056905, 1.085009, 1.085009, 1.085009, 1.085009, 1.085009, 1.085009, 1.056905, 1.056905, 1.056905,1.085009, 1.085009, 1.085009, 1.056905, 1.056905, 1.056905}; + std::vector expGradXBuff = { 0.00408129, 0.00408129, 0.00408129, 0.00408129, 0.00408129, 0.00408129, 0.00408129, 0.00408129, 0.00408129, 0.00408129, 0.00408129, 0.00408129, 0.00408129, 0.00408129, 0.00408129, 0.00408129, 0.00408129, 0.00408129, 0.00408129, 0.00408129, 0.00408129, 0.00408129, 0.00408129, 0.00408129, 0.00408129, 0.00408129, 0.00408129, 0.00408129, 0.00408129, 0.00408129, 0.00408129, 0.00408129, 0.00408129, 0.00408129, 0.00408129, 0.00408129 }; + std::vector expGradInitBuff = { 1.05121, 1.05121, 1.05121, 1.02676, 1.02676, 1.02676, 1.05121, 1.05121, 1.05121, 1.02676, 1.02676, 1.02676 }; + std::vector expGradWBuff = { 0.02595354,-0.090096 ,-0.00882456,0.02595354,-0.090096 ,-0.0088245, 0.02595354,-0.090096 ,-0.00882456,0.01651665,-0.0559437,-0.0084390, 0.01651665,-0.0559437,-0.00843906,0.01651665,-0.0559437,-0.00843906, 0.02595354,-0.090096 ,-0.00882456,0.02595354,-0.090096 ,-0.0088245, 0.02595354,-0.090096 ,-0.00882456,0.01651665,-0.0559437,-0.0084390, 0.01651665,-0.0559437,-0.00843906,0.01651665,-0.0559437,-0.00843906, 0.02595354,-0.090096 ,-0.00882456,0.02595354,-0.090096 ,-0.0088245, 0.02595354,-0.090096 ,-0.00882456,0.01651665,-0.0559437,-0.0084390, 0.01651665,-0.0559437,-0.00843906,0.01651665,-0.0559437,-0.00843906, 0.02595354,-0.090096 ,-0.00882456,0.02595354,-0.090096 ,-0.0088245, 0.02595354,-0.090096 ,-0.00882456,0.01651665,-0.0559437,-0.0084390, 0.01651665,-0.0559437,-0.00843906,0.01651665,-0.0559437,-0.00843906, 0.02595354,-0.090096 ,-0.00882456,0.02595354,-0.090096 ,-0.0088245, 0.02595354,-0.090096 ,-0.00882456,0.01651665,-0.0559437,-0.0084390, 0.01651665,-0.0559437,-0.00843906,0.01651665,-0.0559437,-0.00843906, 0.02595354,-0.090096 ,-0.00882456,0.02595354,-0.090096 ,-0.0088245, 0.02595354,-0.090096 ,-0.00882456,0.01651665,-0.0559437,-0.0084390, 0.01651665,-0.0559437,-0.00843906,0.01651665,-0.0559437,-0.00843906, 0.02124567,-0.0731508,-0.00868926,0.02124567,-0.0731508,-0.0086892, 0.02124567,-0.0731508,-0.00868926,0.02084955,-0.0712011,-0.0085608, 0.02084955,-0.0712011,-0.00856086,0.02084955,-0.0712011,-0.00856086, 0.02124567,-0.0731508,-0.00868926,0.02124567,-0.0731508,-0.0086892, 0.02124567,-0.0731508,-0.00868926,0.02084955,-0.0712011,-0.0085608, 0.02084955,-0.0712011,-0.00856086,0.02084955,-0.0712011,-0.00856086, 0.02124567,-0.0731508,-0.00868926,0.02124567,-0.0731508,-0.0086892, 0.02124567,-0.0731508,-0.00868926,0.02084955,-0.0712011,-0.0085608, 0.02084955,-0.0712011,-0.00856086,0.02084955,-0.0712011,-0.00856086, 0.02124567,-0.0731508,-0.00868926,0.02124567,-0.0731508,-0.0086892, 0.02124567,-0.0731508,-0.00868926,0.02084955,-0.0712011,-0.0085608, 0.02084955,-0.0712011,-0.00856086,0.02084955,-0.0712011,-0.00856086, 0.02124567,-0.0731508,-0.00868926,0.02124567,-0.0731508,-0.0086892, 0.02124567,-0.0731508,-0.00868926,0.02084955,-0.0712011,-0.0085608, 0.02084955,-0.0712011,-0.00856086,0.02084955,-0.0712011,-0.00856086, 0.02124567,-0.0731508,-0.00868926,0.02124567,-0.0731508,-0.0086892, 0.02124567,-0.0731508,-0.00868926,0.02084955,-0.0712011,-0.0085608, 0.02084955,-0.0712011,-0.00856086,0.02084955,-0.0712011,-0.00856086, 0.01671156,-0.0570699,-0.00856086,0.01671156,-0.0570699,-0.0085608, 0.01671156,-0.0570699,-0.00856086,0.02534988,-0.0880002,-0.0086892, 0.02534988,-0.0880002,-0.00868926,0.02534988,-0.0880002,-0.00868926, 0.01671156,-0.0570699,-0.00856086,0.01671156,-0.0570699,-0.0085608, 0.01671156,-0.0570699,-0.00856086,0.02534988,-0.0880002,-0.0086892, 0.02534988,-0.0880002,-0.00868926,0.02534988,-0.0880002,-0.00868926, 0.01671156,-0.0570699,-0.00856086,0.01671156,-0.0570699,-0.0085608, 0.01671156,-0.0570699,-0.00856086,0.02534988,-0.0880002,-0.0086892, 0.02534988,-0.0880002,-0.00868926,0.02534988,-0.0880002,-0.00868926, 0.01671156,-0.0570699,-0.00856086,0.01671156,-0.0570699,-0.0085608, 0.01671156,-0.0570699,-0.00856086,0.02534988,-0.0880002,-0.0086892, 0.02534988,-0.0880002,-0.00868926,0.02534988,-0.0880002,-0.00868926, 0.01671156,-0.0570699,-0.00856086,0.01671156,-0.0570699,-0.0085608, 0.01671156,-0.0570699,-0.00856086,0.02534988,-0.0880002,-0.0086892, 0.02534988,-0.0880002,-0.00868926,0.02534988,-0.0880002,-0.00868926, 0.01671156,-0.0570699,-0.00856086,0.01671156,-0.0570699,-0.0085608, 0.01671156,-0.0570699,-0.00856086,0.02534988,-0.0880002,-0.0086892, 0.02534988,-0.0880002,-0.00868926,0.02534988,-0.0880002,-0.00868926 }; + std::vector expGradBBuff = { -0.0734389, -0.0734389, -0.0734389, -0.0717151, -0.0717151, -0.0717151, -0.0734389, -0.0734389, -0.0734389, -0.0717151, -0.0717151, -0.0717151, -0.00869156, -0.00869156, -0.00869156, -0.00856306, -0.00856306, -0.00856306, -0.00869156, -0.00869156, -0.00869156, -0.00856306, -0.00856306, -0.00856306 }; + std::vector stateBuff = { 1.028569, 1.028569, 1.028569, 1.112884, 1.112884, 1.112884, 1.028569, 1.028569, 1.028569, 1.112884,1.112884, 1.112884, 1.056905, 1.056905, 1.056905, 1.085009, 1.085009, 1.085009, 1.056905, 1.056905,1.056905, 1.085009, 1.085009, 1.085009, 1.085009, 1.085009, 1.085009, 1.056905, 1.056905, 1.056905,1.085009, 1.085009, 1.085009, 1.056905, 1.056905, 1.056905 }; - auto input = NDArrayFactory::create('c', {N,bS,2*K}); - auto weights = NDArrayFactory::create('c', {2*K,6*K}); - auto bias = NDArrayFactory::create('c', {4*K}); - auto init = NDArrayFactory::create('c', {bS,2*K}); - auto mask = NDArrayFactory::create('c', {bS,2*K}); - NDArray state('c', {N,bS,2*K}, stateBuff); - auto inGradCt = NDArrayFactory::create('c', {bS,2*K}); - auto inGradH = NDArrayFactory::create('c', {N,bS,2*K}); + auto input = NDArrayFactory::create('c', { N,bS,2 * K }); + auto weights = NDArrayFactory::create('c', { 2 * K,6 * K }); + auto bias = NDArrayFactory::create('c', { 4 * K }); + auto init = NDArrayFactory::create('c', { bS,2 * K }); + auto mask = NDArrayFactory::create('c', { bS,2 * K }); + NDArray state('c', { N,bS,2 * K }, stateBuff); + auto inGradCt = NDArrayFactory::create('c', { bS,2 * K }); + auto inGradH = NDArrayFactory::create('c', { N,bS,2 * K }); - NDArray gradBias('c', {bS,4*K}, expGradBBuff); + NDArray gradBias('c', { bS,4 * K }, expGradBBuff); - NDArray expGradX('c', {N,bS,2*K}, expGradXBuff); - NDArray expGradW('c', {N,2*K,6*K}, expGradWBuff); - auto expGradB = NDArrayFactory::create('c', {4*K}); - gradBias.reduceAlongDimension(reduce::Sum, expGradB, {0}); // [bS, 4K] -> [4K] - NDArray expGradInit('c', {bS,2*K}, expGradInitBuff); + NDArray expGradX('c', { N,bS,2 * K }, expGradXBuff); + NDArray expGradW('c', { N,2 * K,6 * K }, expGradWBuff); + auto expGradB = NDArrayFactory::create('c', { 4 * K }); + gradBias.reduceAlongDimension(reduce::Sum, expGradB, { 0 }); // [bS, 4K] -> [4K] + NDArray expGradInit('c', { bS,2 * K }, expGradInitBuff); input.assign(1.5); weights.assign(0.5); - bias.assign(0.3) ; + bias.assign(0.3); mask.assign(1.); init.assign(1.); inGradCt.assign(0.5); inGradH.assign(0.5); - nd4j::ops::sru_bi_bp bp; - auto resultsBP = bp.evaluate({&input, &weights, &bias, &init, &state, &inGradCt, &inGradH, &mask}, {}, {}); - ASSERT_TRUE(resultsBP->size() == 4); + sd::ops::sru_bi_bp bp; + auto resultsBP = bp.evaluate({ &input, &weights, &bias, &init, &state, &inGradCt, &inGradH, &mask }, {}, {}); + ASSERT_TRUE(resultsBP.size() == 4); - auto gradX = resultsBP->at(0); - auto gradW = resultsBP->at(1); - auto gradB = resultsBP->at(2); - auto gradInit = resultsBP->at(3); + auto gradX = resultsBP.at(0); + auto gradW = resultsBP.at(1); + auto gradB = resultsBP.at(2); + auto gradInit = resultsBP.at(3); ASSERT_TRUE(expGradX.equalsTo(gradX)); ASSERT_TRUE(expGradW.equalsTo(gradW)); ASSERT_TRUE(expGradB.equalsTo(gradB)); ASSERT_TRUE(expGradInit.equalsTo(gradInit)); - delete resultsBP; + } TEST_F(DeclarableOpsTests1, ArgMax1) { - auto x = NDArrayFactory::create('c', {3, 5}); + auto x = NDArrayFactory::create('c', { 3, 5 }); x.linspace(1); - auto exp = NDArrayFactory::create('c', {3}); + auto exp = NDArrayFactory::create('c', { 3 }); exp.assign(4); - nd4j::ops::argmax op; + sd::ops::argmax op; - auto result = op.evaluate({&x}, {}, {1}); + auto result = op.evaluate({ &x }, {}, { 1 }); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } TEST_F(DeclarableOpsTests1, ArgMax2) { - auto x = NDArrayFactory::create('c', {3, 5}); + auto x = NDArrayFactory::create('c', { 3, 5 }); x.linspace(1); - auto exp = NDArrayFactory::create('c', {5}); + auto exp = NDArrayFactory::create('c', { 5 }); exp.assign(2); - nd4j::ops::argmax op; + sd::ops::argmax op; - auto result = op.evaluate({&x}, {}, {0}); + auto result = op.evaluate({ &x }, {}, { 0 }); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } TEST_F(DeclarableOpsTests1, ArgMax3) { - auto x = NDArrayFactory::create('c', {3, 5}); - auto dim = NDArrayFactory::create('c', {1, 1}, {0.}); + auto x = NDArrayFactory::create('c', { 3, 5 }); + auto dim = NDArrayFactory::create('c', { 1, 1 }, { 0. }); x.linspace(1); - auto exp = NDArrayFactory::create('c', {5}); + auto exp = NDArrayFactory::create('c', { 5 }); exp.assign(2); - nd4j::ops::argmax op; + sd::ops::argmax op; - auto result = op.evaluate({&x, &dim}, {}, {}); + auto result = op.evaluate({ &x, &dim }, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } TEST_F(DeclarableOpsTests1, ArgMax4) { - auto x = NDArrayFactory::create('c', {3, 5}); - auto dim = NDArrayFactory::create('c', {1, 1}, {1}); + auto x = NDArrayFactory::create('c', { 3, 5 }); + auto dim = NDArrayFactory::create('c', { 1, 1 }, { 1 }); x.linspace(1); - auto exp = NDArrayFactory::create('c', {3}); + auto exp = NDArrayFactory::create('c', { 3 }); exp.assign(4); - nd4j::ops::argmax op; + sd::ops::argmax op; - auto result = op.evaluate({&x, &dim}, {}, {}); + auto result = op.evaluate({ &x, &dim }, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } TEST_F(DeclarableOpsTests1, ArgMax5) { - auto x = NDArrayFactory::create('c', {3, 5}); - auto dim = NDArrayFactory::create('c', {1, 2}, {0, 1}); + auto x = NDArrayFactory::create('c', { 3, 5 }); + auto dim = NDArrayFactory::create('c', { 1, 2 }, { 0, 1 }); x.linspace(1); auto exp = NDArrayFactory::create(14); - nd4j::ops::argmax op; + sd::ops::argmax op; - auto result = op.evaluate({&x, &dim}, {}, {}); + auto result = op.evaluate({ &x, &dim }, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } TEST_F(DeclarableOpsTests1, ArgMax6) { - auto x = NDArrayFactory::create('c', {3, 4, 5}); + auto x = NDArrayFactory::create('c', { 3, 4, 5 }); auto dim = NDArrayFactory::create(-1.f); x.linspace(1); - nd4j::ops::argmax op; + sd::ops::argmax op; - auto expected = op.evaluate({&x}, {}, {2}); - ASSERT_EQ(Status::OK(), expected->status()); - auto exp = expected->at(0); + auto expected = op.evaluate({ &x }, {}, { 2 }); + ASSERT_EQ(Status::OK(), expected.status()); + auto exp = expected.at(0); - auto result = op.evaluate({&x, &dim}, {}, {}); - ASSERT_EQ(Status::OK(), result->status()); + auto result = op.evaluate({ &x, &dim }, {}, {}); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_EQ(*exp, *z); - - delete result; - delete expected; } TEST_F(DeclarableOpsTests1, ArgMin1) { - auto x = NDArrayFactory::create('c', {3, 5}); + auto x = NDArrayFactory::create('c', { 3, 5 }); x.linspace(1); -// auto exp('c', {3, 1}); - auto exp = NDArrayFactory::create('c', {3}); + // auto exp('c', {3, 1}); + auto exp = NDArrayFactory::create('c', { 3 }); exp.assign(0.0f); - nd4j::ops::argmin op; + sd::ops::argmin op; - auto result = op.evaluate({&x}, {}, {1}); + auto result = op.evaluate({ &x }, {}, { 1 }); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } TEST_F(DeclarableOpsTests1, SquareTests1) { - auto x = NDArrayFactory::create('c', {3, 5}); + auto x = NDArrayFactory::create('c', { 3, 5 }); x.linspace(1); - auto exp = NDArrayFactory::create('c', {3, 5}); + auto exp = NDArrayFactory::create('c', { 3, 5 }); exp.linspace(1); exp *= exp; - nd4j::ops::square op; + sd::ops::square op; - auto result = op.evaluate({&x}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + auto result = op.evaluate({ &x }, {}, {}); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } TEST_F(DeclarableOpsTests1, OneHotTests_1) { - auto indices = NDArrayFactory::create('c', {1, 4}, {0.0f, 2.0f, -1.0f, 1.0f}); + auto indices = NDArrayFactory::create('c', { 1, 4 }, { 0.0f, 2.0f, -1.0f, 1.0f }); - auto exp = NDArrayFactory::create('c', {1, 4, 3}, {1.f, 0.f, 0.f, 0.f, 0.f, 1.f, 0.f, 0.f, 0.f, 0.f, 1.f, 0.f}); + auto exp = NDArrayFactory::create('c', { 1, 4, 3 }, { 1.f, 0.f, 0.f, 0.f, 0.f, 1.f, 0.f, 0.f, 0.f, 0.f, 1.f, 0.f }); - nd4j::ops::onehot op; + sd::ops::onehot op; - auto result = op.evaluate({&indices}, {1.0f, 0.0f}, {-1, 3}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + auto result = op.evaluate({ &indices }, { 1.0f, 0.0f }, { -1, 3 }); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); // z->printBuffer(); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } TEST_F(DeclarableOpsTests1, OneHotTests_2) { - auto indices = NDArrayFactory::create('c', {2, 2}, {0.f, 2.f, 1.f, -1.f}); + auto indices = NDArrayFactory::create('c', { 2, 2 }, { 0.f, 2.f, 1.f, -1.f }); - auto exp = NDArrayFactory::create('c', {2, 2, 3}, {1.f, 0.f, 0.f, 0.f, 0.f, 1.f, 0.f, 1.f, 0.f, 0.f, 0.f, 0.f}); + auto exp = NDArrayFactory::create('c', { 2, 2, 3 }, { 1.f, 0.f, 0.f, 0.f, 0.f, 1.f, 0.f, 1.f, 0.f, 0.f, 0.f, 0.f }); - nd4j::ops::onehot op; - auto result = op.evaluate({&indices}, {1.0f, 0.0f}, {-1, 3}); + sd::ops::onehot op; + auto result = op.evaluate({ &indices }, { 1.0f, 0.0f }, { -1, 3 }); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } TEST_F(DeclarableOpsTests1, OneHotTests_3) { - auto indices = NDArrayFactory::create('c', {4}, {0.0f, 2.0f, -1.0f, 1.0f}); + auto indices = NDArrayFactory::create('c', { 4 }, { 0.0f, 2.0f, -1.0f, 1.0f }); - auto exp = NDArrayFactory::create('c', {4, 3}, {1.f, 0.f, 0.f, 0.f, 0.f, 1.f, 0.f, 0.f, 0.f, 0.f, 1.f, 0.f}); + auto exp = NDArrayFactory::create('c', { 4, 3 }, { 1.f, 0.f, 0.f, 0.f, 0.f, 1.f, 0.f, 0.f, 0.f, 0.f, 1.f, 0.f }); - nd4j::ops::onehot op; + sd::ops::onehot op; - auto result = op.evaluate({&indices}, {1.0f, 0.0f}, {-1, 3}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + auto result = op.evaluate({ &indices }, { 1.0f, 0.0f }, { -1, 3 }); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); // z->printIndexedBuffer("z"); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } TEST_F(DeclarableOpsTests1, OneHotTests_4) { - auto indices = NDArrayFactory::create('c', {4}, {0.0f, 2.0f, -1.0f, 1.0f}); + auto indices = NDArrayFactory::create('c', { 4 }, { 0.0f, 2.0f, -1.0f, 1.0f }); auto depth = NDArrayFactory::create(3.0f); - auto exp = NDArrayFactory::create('c', {4, 3}, {1.f, 0.f, 0.f, 0.f, 0.f, 1.f, 0.f, 0.f, 0.f, 0.f, 1.f, 0.f}); + auto exp = NDArrayFactory::create('c', { 4, 3 }, { 1.f, 0.f, 0.f, 0.f, 0.f, 1.f, 0.f, 0.f, 0.f, 0.f, 1.f, 0.f }); - nd4j::ops::onehot op; + sd::ops::onehot op; - auto result = op.evaluate({&indices, &depth}, {1.0f, 0.0f}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + auto result = op.evaluate({ &indices, &depth }, { 1.0f, 0.0f }, {}); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } TEST_F(DeclarableOpsTests1, OneHotTests_5) { - auto indices = NDArrayFactory::create('c', {4}, {0.0f, 2.0f, -1.0f, 1.0f}); + auto indices = NDArrayFactory::create('c', { 4 }, { 0.0f, 2.0f, -1.0f, 1.0f }); auto depth = NDArrayFactory::create(3.0f); auto on = NDArrayFactory::create(1.0f); auto off = NDArrayFactory::create(0.0f); - auto exp = NDArrayFactory::create('c', {4, 3}, {1.f, 0.f, 0.f, 0.f, 0.f, 1.f, 0.f, 0.f, 0.f, 0.f, 1.f, 0.f}); + auto exp = NDArrayFactory::create('c', { 4, 3 }, { 1.f, 0.f, 0.f, 0.f, 0.f, 1.f, 0.f, 0.f, 0.f, 0.f, 1.f, 0.f }); - nd4j::ops::onehot op; + sd::ops::onehot op; - auto result = op.evaluate({&indices, &depth, &on, &off}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + auto result = op.evaluate({ &indices, &depth, &on, &off }, {}, {}); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } TEST_F(DeclarableOpsTests1, OneHotTests_6) { - auto indices = NDArrayFactory::create('c', {3}, {0.f, 1.f, 2.f}); - auto e = NDArrayFactory::create('c', {3, 3}, {1.f, 0.f, 0.f, 0.f, 1.f, 0.f, 0.f, 0.f, 1.f}); + auto indices = NDArrayFactory::create('c', { 3 }, { 0.f, 1.f, 2.f }); + auto e = NDArrayFactory::create('c', { 3, 3 }, { 1.f, 0.f, 0.f, 0.f, 1.f, 0.f, 0.f, 0.f, 1.f }); - nd4j::ops::onehot op; - auto result = op.evaluate({&indices}, {1.0, 0.0}, {0, 3}); - auto z = result->at(0); + sd::ops::onehot op; + auto result = op.evaluate({ &indices }, { 1.0, 0.0 }, { 0, 3 }); + auto z = result.at(0); ASSERT_EQ(e, *z); - delete result; + } TEST_F(DeclarableOpsTests1, OneHotTests_7) { - auto indices = NDArrayFactory::create('c', {3}, {0, 1, 2}); - auto e = NDArrayFactory::create('c', {3, 3}, {1., 0., 0., 0., 1., 0., 0., 0., 1.}); + auto indices = NDArrayFactory::create('c', { 3 }, { 0, 1, 2 }); + auto e = NDArrayFactory::create('c', { 3, 3 }, { 1., 0., 0., 0., 1., 0., 0., 0., 1. }); - nd4j::ops::onehot op; - auto result = op.evaluate({&indices}, {1.0, 0.0}, {0, 3}, {}, {nd4j::DataType::HALF}, false); - auto z = result->at(0); + sd::ops::onehot op; + auto result = op.evaluate({ &indices }, { 1.0, 0.0 }, { 0, 3 }, {}, { sd::DataType::HALF }, false); + auto z = result.at(0); ASSERT_EQ(e, *z); - delete result; + } TEST_F(DeclarableOpsTests1, FillAs_1) { - auto x = NDArrayFactory::create('c', {2, 2}); + auto x = NDArrayFactory::create('c', { 2, 2 }); x.assign(117); float scalar = 119.f; - nd4j::ops::fill_as op; - auto result = op.evaluate({&x}, {scalar}, {}); + sd::ops::fill_as op; + auto result = op.evaluate({ &x }, { scalar }, {}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - ASSERT_TRUE(x.isSameShape(result->at(0))); + ASSERT_TRUE(x.isSameShape(result.at(0))); - ASSERT_NEAR(scalar, result->at(0)->meanNumber().e(0), 1e-5f); + ASSERT_NEAR(scalar, result.at(0)->meanNumber().e(0), 1e-5f); - delete result; + } ////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests1, LRN1) { - nd4j::ops::lrn lrn; + sd::ops::lrn lrn; lrn.getOpName(); } -////////////////////////////////////////////////////////////////////// -TEST_F(DeclarableOpsTests1, Stack_1) { - - float buff1[] = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10,11,12}; - float buff2[] = {13,14,16,16,17,18,19,20,21,22,23,24}; - float expBuff[] = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10,11,12,13,14,16,16,17,18,19,20,21,22,23,24}; - Nd4jLong shape1[] = {2, 3, 4, 4, 1, 0, 1, 99}; - Nd4jLong shape2[] = {2, 3, 4, 4, 1, 0, 1, 99}; - Nd4jLong expShape[] = {3, 2, 3, 4, 12, 4, 1, 0, 1, 99}; - ArrayOptions::setDataType(shape1, nd4j::DataType::FLOAT32); - ArrayOptions::setDataType(shape2, nd4j::DataType::FLOAT32); - ArrayOptions::setDataType(expShape, nd4j::DataType::FLOAT32); - - NDArray input1(buff1, shape1); - NDArray input2(buff2, shape2); - NDArray expected(expBuff, expShape); - - nd4j::ops::stack op; - auto results = op.evaluate({&input1, &input2}, {}, {0}); - auto output = results->at(0); - - ASSERT_TRUE(expected.isSameShapeStrict(*output)); - ASSERT_TRUE(expected.equalsTo(output)); - - delete results; - -} - -////////////////////////////////////////////////////////////////////// -TEST_F(DeclarableOpsTests1, Stack_2) { - - float buff1[] = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10,11,12}; - float buff2[] = {13,14,16,16,17,18,19,20,21,22,23,24}; - float expBuff[] = {1,2,3,4, 13, 14, 16, 16, 5,6,7,8, 17, 18, 19, 20, 9, 10, 11, 12, 21, 22, 23, 24}; - Nd4jLong shape1[] = {2, 3, 4, 4, 1, 0, 1, 99}; - Nd4jLong shape2[] = {2, 3, 4, 4, 1, 0, 1, 99}; - Nd4jLong expShape[] = {3, 3, 2, 4, 8, 4, 1, 0, 1, 99}; - ArrayOptions::setDataType(shape1, nd4j::DataType::FLOAT32); - ArrayOptions::setDataType(shape2, nd4j::DataType::FLOAT32); - ArrayOptions::setDataType(expShape, nd4j::DataType::FLOAT32); - - NDArray input1(buff1, shape1); - NDArray input2(buff2, shape2); - NDArray expected(expBuff, expShape); - - nd4j::ops::stack op; - auto results = op.evaluate({&input1, &input2}, {}, {1}); - auto output = results->at(0); - - ASSERT_TRUE(expected.isSameShapeStrict(*output)); - ASSERT_TRUE(expected.equalsTo(output)); - - delete results; -} - - -////////////////////////////////////////////////////////////////////// -TEST_F(DeclarableOpsTests1, Stack_3) { - - float buff1[] = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10,11,12}; - float buff2[] = {13,14,16,16,17,18,19,20,21,22,23,24}; - float expBuff[] = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10,11,12,13,14,16,16,17,18,19,20,21,22,23,24}; - Nd4jLong shape1[] = {2, 1, 12, 12, 1, 0, 1, 99}; - Nd4jLong shape2[] = {2, 1, 12, 12, 1, 0, 1, 99}; - Nd4jLong expShape[] = {3, 2, 1, 12, 12, 12, 1, 0, 1, 99}; - ArrayOptions::setDataType(shape1, nd4j::DataType::FLOAT32); - ArrayOptions::setDataType(shape2, nd4j::DataType::FLOAT32); - ArrayOptions::setDataType(expShape, nd4j::DataType::FLOAT32); - - NDArray input1(buff1, shape1); - NDArray input2(buff2, shape2); - NDArray expected(expBuff, expShape); - - nd4j::ops::stack op; - auto results = op.evaluate({&input1, &input2}, {}, {0}); - auto output = results->at(0); - - ASSERT_TRUE(expected.isSameShapeStrict(*output)); - ASSERT_TRUE(expected.equalsTo(output)); - - delete results; -} - -////////////////////////////////////////////////////////////////////// -TEST_F(DeclarableOpsTests1, Stack_4) { - - float buff1[] = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10,11,12}; - float buff2[] = {13,14,16,16,17,18,19,20,21,22,23,24}; - float expBuff[] = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10,11,12,13,14,16,16,17,18,19,20,21,22,23,24}; - Nd4jLong shape1[] = {2, 1, 12, 12, 1, 0, 1, 99}; - Nd4jLong shape2[] = {2, 1, 12, 12, 1, 0, 1, 99}; - Nd4jLong expShape[] = {3, 1, 2, 12, 24, 12, 1, 0, 1, 99}; - ArrayOptions::setDataType(shape1, nd4j::DataType::FLOAT32); - ArrayOptions::setDataType(shape2, nd4j::DataType::FLOAT32); - ArrayOptions::setDataType(expShape, nd4j::DataType::FLOAT32); - - NDArray input1(buff1, shape1); - NDArray input2(buff2, shape2); - NDArray expected(expBuff, expShape); - - nd4j::ops::stack op; - auto results = op.evaluate({&input1, &input2}, {}, {1}); - auto output = results->at(0); - - ASSERT_TRUE(expected.isSameShapeStrict(*output)); - ASSERT_TRUE(expected.equalsTo(output)); - - delete results; -} - -////////////////////////////////////////////////////////////////////// -TEST_F(DeclarableOpsTests1, Stack_5) { - - float buff1[] = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10,11,12}; - float buff2[] = {13,14,16,16,17,18,19,20,21,22,23,24}; - float expBuff[] = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10,11,12,13,14,16,16,17,18,19,20,21,22,23,24}; - Nd4jLong shape1[] = {2, 12, 1, 1,1, 0, 1, 99}; - Nd4jLong shape2[] = {2, 12, 1, 1,1, 0, 1, 99}; - Nd4jLong expShape[] = {3, 2, 12, 1, 12, 1, 1, 0, 1, 99}; - ArrayOptions::setDataType(shape1, nd4j::DataType::FLOAT32); - ArrayOptions::setDataType(shape2, nd4j::DataType::FLOAT32); - ArrayOptions::setDataType(expShape, nd4j::DataType::FLOAT32); - - NDArray input1(buff1, shape1); - NDArray input2(buff2, shape2); - NDArray expected(expBuff, expShape); - - nd4j::ops::stack op; - auto results = op.evaluate({&input1, &input2}, {}, {0}); - auto output = results->at(0); - - ASSERT_TRUE(expected.isSameShapeStrict(*output)); - ASSERT_TRUE(expected.equalsTo(output)); - - delete results; -} - -////////////////////////////////////////////////////////////////////// -TEST_F(DeclarableOpsTests1, Stack_6) { - - float buff1[] = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10,11,12}; - float buff2[] = {13,14,16,16,17,18,19,20,21,22,23,24}; - float expBuff[] = {1 ,13 ,2 ,14 ,3 ,16 ,4 ,16 ,5 ,17 ,6 ,18 ,7 ,19 ,8 ,20 ,9 ,21 ,10 ,22 ,11 ,23 ,12 ,24}; - Nd4jLong shape1[] = {2, 12, 1, 1, 12, 0, 1, 99}; - Nd4jLong shape2[] = {2, 12, 1, 1, 12, 0, 1, 99}; - Nd4jLong expShape[] = {3, 12, 2, 1, 2, 1, 1, 0, 1, 99}; - ArrayOptions::setDataType(shape1, nd4j::DataType::FLOAT32); - ArrayOptions::setDataType(shape2, nd4j::DataType::FLOAT32); - ArrayOptions::setDataType(expShape, nd4j::DataType::FLOAT32); - - NDArray input1(buff1, shape1); - NDArray input2(buff2, shape2); - NDArray expected(expBuff, expShape); - - nd4j::ops::stack op; - auto results = op.evaluate({&input1, &input2}, {}, {1}); - auto output = results->at(0); - - ASSERT_TRUE(expected.isSameShapeStrict(*output)); - ASSERT_TRUE(expected.equalsTo(output)); - - delete results; -} - - -////////////////////////////////////////////////////////////////////// -TEST_F(DeclarableOpsTests1, Stack_7) { - - float buff1[] = {1}; - float expBuff[] = {1, 1, 1}; - Nd4jLong shape1[] = {2, 1, 1, 1, 1, 0, 1, 99}; - Nd4jLong expShape[] = {3, 3, 1, 1, 1, 1, 1, 0, 1, 99}; - ArrayOptions::setDataType(shape1, nd4j::DataType::FLOAT32); - ArrayOptions::setDataType(expShape, nd4j::DataType::FLOAT32); - - NDArray input1(buff1, shape1); - NDArray expected(expBuff, expShape); - - nd4j::ops::stack op; - auto results = op.evaluate({&input1, &input1, &input1}, {}, {0}); - auto output = results->at(0); - - ASSERT_TRUE(expected.isSameShapeStrict(*output)); - ASSERT_TRUE(expected.equalsTo(output)); - - delete results; -} - -////////////////////////////////////////////////////////////////////// -TEST_F(DeclarableOpsTests1, Stack_8) { - - float buff1[] = {1}; - float expBuff[] = {1, 1, 1}; - Nd4jLong shape1[] = {1, 1, 1, 0, 1, 99}; - Nd4jLong expShape[] = {2, 3, 1, 1, 1, 0, 1, 99}; - ArrayOptions::setDataType(shape1, nd4j::DataType::FLOAT32); - ArrayOptions::setDataType(expShape, nd4j::DataType::FLOAT32); - - NDArray input1(buff1, shape1); - NDArray expected(expBuff, expShape); - - nd4j::ops::stack op; - auto results = op.evaluate({&input1, &input1, &input1}, {}, {0}); - auto output = results->at(0); - - ASSERT_TRUE(expected.isSameShapeStrict(*output)); - ASSERT_TRUE(expected.equalsTo(output)); - - delete results; -} - -////////////////////////////////////////////////////////////////////// -TEST_F(DeclarableOpsTests1, Stack_9) { - - float buff1[] = {1}; - float expBuff[] = {1, 1, 1}; - Nd4jLong shape1[] = {2, 1, 1, 1, 1, 0, 1, 99}; - Nd4jLong expShape[] = {3, 1, 3, 1, 3, 1, 1, 0, 1, 99}; - ArrayOptions::setDataType(shape1, nd4j::DataType::FLOAT32); - ArrayOptions::setDataType(expShape, nd4j::DataType::FLOAT32); - - NDArray input1(buff1, shape1); - NDArray expected(expBuff, expShape); - - nd4j::ops::stack op; - auto results = op.evaluate({&input1, &input1, &input1}, {}, {1}); - auto output = results->at(0); - - ASSERT_TRUE(expected.isSameShapeStrict(*output)); - ASSERT_TRUE(expected.equalsTo(output)); - - delete results; -} - -////////////////////////////////////////////////////////////////////// -TEST_F(DeclarableOpsTests1, Stack_10) { - - float buff1[] = {1}; - float expBuff[] = {1, 1, 1}; - Nd4jLong shape1[] = {1, 1, 1, 0, 1, 99}; - Nd4jLong expShape[] = {2, 1, 3, 3, 1, 0, 1, 99}; - ArrayOptions::setDataType(shape1, nd4j::DataType::FLOAT32); - ArrayOptions::setDataType(expShape, nd4j::DataType::FLOAT32); - - NDArray input1(buff1, shape1); - NDArray expected(expBuff, expShape); - - nd4j::ops::stack op; - auto results = op.evaluate({&input1, &input1, &input1}, {}, {1}); - auto output = results->at(0); - - //expected.printShapeInfo("exp"); - //output->printShapeInfo("out"); - - ASSERT_TRUE(expected.isSameShapeStrict(*output)); - ASSERT_TRUE(expected.equalsTo(output)); - - delete results; -} - -TEST_F(DeclarableOpsTests1, Stack_11) { - - float buff1[] = {1}; - float expBuff[] = {1, 1, 1}; - Nd4jLong shape1[] = {1, 1, 1, 0, 1, 99}; - Nd4jLong expShape[] = {2, 3, 1, 1, 1, 0, 1, 99}; - ArrayOptions::setDataType(shape1, nd4j::DataType::FLOAT32); - ArrayOptions::setDataType(expShape, nd4j::DataType::FLOAT32); - - NDArray input1(buff1, shape1); - NDArray expected(expBuff, expShape); - - nd4j::ops::stack op; - auto results = op.evaluate({&input1, &input1, &input1}, {}, {}); - auto output = results->at(0); - - ASSERT_TRUE(expected.isSameShapeStrict(*output)); - ASSERT_TRUE(expected.equalsTo(output)); - - delete results; -} - - TEST_F(DeclarableOpsTests1, Test_Range_Integer_1) { - auto exp = NDArrayFactory::create('c', {4}); + auto exp = NDArrayFactory::create('c', { 4 }); exp.linspace(1); - nd4j::ops::range op; + sd::ops::range op; - auto result = op.evaluate({}, {}, {1, 5, 1}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + auto result = op.evaluate({}, {}, { 1, 5, 1 }); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - ASSERT_EQ(1, result->size()); + ASSERT_EQ(1, result.size()); - auto array = result->at(0); + auto array = result.at(0); // array->printIndexedBuffer("Range integer 1"); ASSERT_TRUE(exp.isSameShape(array)); ASSERT_TRUE(exp.equalsTo(array)); - delete result; + } TEST_F(DeclarableOpsTests1, Test_Range_Integer_2) { - auto exp = NDArrayFactory::create('c', {4}); + auto exp = NDArrayFactory::create('c', { 4 }); exp.linspace(1); - auto start = NDArrayFactory::create('c', {1, 1}); - auto stop = NDArrayFactory::create('c', {1, 1}); - auto step = NDArrayFactory::create('c', {1, 1}); + auto start = NDArrayFactory::create('c', { 1, 1 }); + auto stop = NDArrayFactory::create('c', { 1, 1 }); + auto step = NDArrayFactory::create('c', { 1, 1 }); start.p(0, 1.f); stop.p(0, 5.f); step.p(0, 1.f); - nd4j::ops::range op; + sd::ops::range op; - auto result = op.evaluate({&start, &stop, &step}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + auto result = op.evaluate({ &start, &stop, &step }, {}, {}); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - ASSERT_EQ(1, result->size()); + ASSERT_EQ(1, result.size()); - auto array = result->at(0); + auto array = result.at(0); ASSERT_TRUE(exp.isSameShape(array)); ASSERT_TRUE(exp.equalsTo(array)); - delete result; + } TEST_F(DeclarableOpsTests1, Test_Range_Integer_3) { - auto exp = NDArrayFactory::create('c', {4}); + auto exp = NDArrayFactory::create('c', { 4 }); exp.linspace(1); - nd4j::ops::range op; + sd::ops::range op; - auto result = op.evaluate({}, {1.f, 5.f, 1.f}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + auto result = op.evaluate({}, { 1.f, 5.f, 1.f }, {}); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - ASSERT_EQ(1, result->size()); + ASSERT_EQ(1, result.size()); - auto array = result->at(0); + auto array = result.at(0); ASSERT_TRUE(exp.isSameShape(array)); ASSERT_TRUE(exp.equalsTo(array)); - delete result; + } ////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests1, softmax_test1) { - auto input = NDArrayFactory::create('c', {3, 3}, {-1, 1, -2, 2, -3, 3, -4, 4, 5}); - auto expOutput = NDArrayFactory::create('c', {3, 3}, {1.14195199e-01, 8.43794734e-01, 4.20100661e-02, 2.68454951e-01, 1.80883523e-03, 7.29736214e-01, 9.02116571e-05, 2.68917160e-01, 7.30992629e-01}); - nd4j::ops::softmax op; - auto results = op.evaluate({&input}, {}, {}, {}); - auto z = results->at(0); + NDArray input('c', { 3, 3 }, { -1.f, 1.f, -2.f, 2.f, -3.f, 3.f, -4.f, 4.f, 5.f }, sd::DataType::FLOAT32); - ASSERT_EQ(Status::OK(), results->status()); + NDArray expOutput('c', { 3, 3 }, { 1.14195199e-01, 8.43794734e-01, 4.20100661e-02, 2.68454951e-01, 1.80883523e-03, 7.29736214e-01, 9.02116571e-05, 2.68917160e-01, 7.30992629e-01 }, sd::DataType::FLOAT32); + + sd::ops::softmax op; + auto results = op.evaluate({ &input }, {}, {}, {}); + auto z = results.at(0); + + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expOutput.isSameShape(z)); ASSERT_TRUE(expOutput.equalsTo(z)); - delete results; + } ////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests1, softmax_test2) { - auto input = NDArrayFactory::create('c', {3, 3, 3}, {-1, 1, -2, 2, -3, 3, -4, 4, -5,5 ,-6,6, -7,7, -8,8, -9,9, -10,10, -11,11, -12,12, -13,13, 14}); - auto expOutput = NDArrayFactory::create('c', {3, 3, 3}, {4.73142e-02,4.73847e-02,6.69062e-03, 9.50330e-01,8.67881e-04,9.92976e-01, 2.35563e-03,9.51747e-01,3.33106e-04, 4.74259e-02,2.26032e-06,4.74259e-02, 2.91395e-07,9.99998e-01,3.94360e-08, 9.52574e-01,1.12535e-07,9.52574e-01, 7.58256e-10,4.74259e-02,1.22325e-11, 1.00000e+00,1.32293e-11,1.19203e-01, 3.77513e-11,9.52574e-01,8.80797e-01}); + NDArray input('c', { 3, 3, 3 }, { -1, 1, -2, 2, -3, 3, -4, 4, -5,5 ,-6,6, -7,7, -8,8, -9,9, -10,10, -11,11, -12,12, -13,13, 14 }, sd::DataType::FLOAT32); + NDArray expOutput('c', { 3, 3, 3 }, { 4.73142e-02,4.73847e-02,6.69062e-03, 9.50330e-01,8.67881e-04,9.92976e-01, 2.35563e-03,9.51747e-01,3.33106e-04, 4.74259e-02,2.26032e-06,4.74259e-02, 2.91395e-07,9.99998e-01,3.94360e-08, 9.52574e-01,1.12535e-07,9.52574e-01, 7.58256e-10,4.74259e-02,1.22325e-11, 1.00000e+00,1.32293e-11,1.19203e-01, 3.77513e-11,9.52574e-01,8.80797e-01 }, sd::DataType::FLOAT32); - nd4j::ops::softmax op; - auto results = op.evaluate({&input}, {}, {1}, {}); - auto z = results->at(0); + sd::ops::softmax op; + auto results = op.evaluate({ &input }, {}, { 1 }, {}); + auto z = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expOutput.isSameShape(z)); ASSERT_TRUE(expOutput.equalsTo(z)); - delete results; + } ////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests1, softmax_test3) { - auto input = NDArrayFactory::create('c', {3, 3, 3}, {-1, 1, -2, 2, -3, 3, -4, 4, -5,5 ,-6,6, -7,7, -8,8, -9,9, -10,10, -11,11, -12,12, -13,13, 14}); - auto expOutput = NDArrayFactory::create('c', {3, 3, 3}, {2.47262e-03,1.23395e-04,3.35350e-04, 1.23395e-04,4.53979e-05,1.23395e-04, 6.14417e-06,1.23395e-04,5.56530e-09, 9.97527e-01,1.12521e-07,9.99665e-01, 1.52281e-08,9.99955e-01,2.06090e-09, 9.99994e-01,2.78912e-10,6.69285e-03, 3.05146e-07,9.99876e-01,4.13855e-08, 9.99877e-01,5.60254e-09,9.99877e-01, 7.58251e-10,9.99877e-01,9.93307e-01}); + NDArray input('c', { 3, 3, 3 }, { -1, 1, -2, 2, -3, 3, -4, 4, -5,5 ,-6,6, -7,7, -8,8, -9,9, -10,10, -11,11, -12,12, -13,13, 14 }, sd::DataType::FLOAT32); + NDArray expOutput('c', { 3, 3, 3 }, { 2.47262e-03,1.23395e-04,3.35350e-04, 1.23395e-04,4.53979e-05,1.23395e-04, 6.14417e-06,1.23395e-04,5.56530e-09, 9.97527e-01,1.12521e-07,9.99665e-01, 1.52281e-08,9.99955e-01,2.06090e-09, 9.99994e-01,2.78912e-10,6.69285e-03, 3.05146e-07,9.99876e-01,4.13855e-08, 9.99877e-01,5.60254e-09,9.99877e-01, 7.58251e-10,9.99877e-01,9.93307e-01 }, sd::DataType::FLOAT32); - nd4j::ops::softmax op; - auto results = op.evaluate({&input}, {}, {0}, {}); - auto z = results->at(0); + sd::ops::softmax op; + auto results = op.evaluate({ &input }, {}, { 0 }, {}); + auto z = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expOutput.isSameShape(z)); ASSERT_TRUE(expOutput.equalsTo(z)); - delete results; + } ////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests1, softmax_test4) { - auto input = NDArrayFactory::create('c', {1, 5}, {-1, 1, -2, 2, 3}); - auto expOutput = NDArrayFactory::create('c', {1, 5}, {0.01198,0.08855,0.00441,0.24072,0.65434}); + NDArray input('c', { 1, 5 }, { -1, 1, -2, 2, 3 }, sd::DataType::FLOAT32); + NDArray expOutput('c', { 1, 5 }, { 0.01198,0.08855,0.00441,0.24072,0.65434 }, sd::DataType::FLOAT32); - nd4j::ops::softmax op; - auto results = op.evaluate({&input}, {}, {1}, {}); - auto z = results->at(0); + sd::ops::softmax op; + auto results = op.evaluate({ &input }, {}, { 1 }, {}); + auto z = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expOutput.isSameShape(z)); ASSERT_TRUE(expOutput.equalsTo(z)); - delete results; + } ////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests1, softmax_test5) { - auto input = NDArrayFactory::create('c', {1, 5}, {-1, 1, -2, 2, 3}); - auto expOutput = NDArrayFactory::create('c', {1, 5}, {1,1,1,1,1}); + NDArray input('c', { 1, 5 }, { -1, 1, -2, 2, 3 }, sd::DataType::FLOAT32); + NDArray expOutput('c', { 1, 5 }, { 1,1,1,1,1 }, sd::DataType::FLOAT32); - nd4j::ops::softmax op; - auto results = op.evaluate({&input}, {}, {0}); - auto z = results->at(0); + sd::ops::softmax op; + auto results = op.evaluate({ &input }, {}, { 0 }); + auto z = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expOutput.isSameShape(z)); ASSERT_TRUE(expOutput.equalsTo(z)); - delete results; + } ////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests1, softmax_test6) { - auto input = NDArrayFactory::create('c', {5, 1}, {-1, 1, -2, 2, 3}); - auto expOutput = NDArrayFactory::create('c', {5, 1}, {0.01198,0.08855,0.00441,0.24072,0.65434}); + NDArray input('c', { 5, 1 }, { -1, 1, -2, 2, 3 }, sd::DataType::FLOAT32); + NDArray expOutput('c', { 5, 1 }, { 0.01198,0.08855,0.00441,0.24072,0.65434 }, sd::DataType::FLOAT32); - nd4j::ops::softmax op; - auto results = op.evaluate({&input}, {}, {0}, {}); - auto z = results->at(0); + sd::ops::softmax op; + auto results = op.evaluate({ &input }, {}, { 0 }, {}); + auto z = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expOutput.isSameShape(z)); ASSERT_TRUE(expOutput.equalsTo(z)); - delete results; + } ////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests1, softmax_test7) { - auto input = NDArrayFactory::create('c', {5, 1}, {-1, 1, -2, 2, 3}); - auto expOutput = NDArrayFactory::create('c', {5, 1}, {1,1,1,1,1}); + NDArray input('c', { 5, 1 }, { -1, 1, -2, 2, 3 }, sd::DataType::FLOAT32); + NDArray expOutput('c', { 5, 1 }, { 1,1,1,1,1 }, sd::DataType::FLOAT32); - nd4j::ops::softmax op; - auto results = op.evaluate({&input}, {}, {1}, {}); - auto z = results->at(0); + sd::ops::softmax op; + auto results = op.evaluate({ &input }, {}, { 1 }, {}); + auto z = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expOutput.isSameShape(z)); ASSERT_TRUE(expOutput.equalsTo(z)); - delete results; + } ////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests1, softmax_test8) { - auto input = NDArrayFactory::create('c', {5}, {-1, 1, -2, 2, 3}); - auto expOutput = NDArrayFactory::create('c', {5}, {0.01198,0.08855,0.00441,0.24072,0.65434}); + NDArray input('c', { 5 }, { -1, 1, -2, 2, 3 }, sd::DataType::FLOAT32); + NDArray expOutput('c', { 5 }, { 0.01198,0.08855,0.00441,0.24072,0.65434 }, sd::DataType::FLOAT32); - nd4j::ops::softmax op; - auto results = op.evaluate({&input}, {}, {}, {}); - auto z = results->at(0); + sd::ops::softmax op; + auto results = op.evaluate({ &input }, {}, {}, {}); + auto z = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expOutput.isSameShape(z)); ASSERT_TRUE(expOutput.equalsTo(z)); - delete results; + } ////////////////////////////////////////////////////////////////////// -TEST_F(DeclarableOpsTests1, Test_Stack_Edge_1) { - float inBuff[] = {1.0f, 2.0f, 3.0f}; - float expBuff[] = {1.0f, 2.0f, 3.0f}; +TEST_F(DeclarableOpsTests1, softmax_test9) { + NDArray input('c', { 2, 2, 2, 2 }, { -1, 1, -2, 2, -3, 3, -4, 4, -5,5 ,-6,6, -7,7, -8,8 }, sd::DataType::FLOAT32); + NDArray expOutput('c', { 2, 2, 2, 2 }, { 0.731059, 0.268941, 0.268941, 0.731059, 0.731059, 0.268941, 0.268941, 0.731059, 0.731059, 0.268941, 0.268941, 0.731059, 0.731059, 0.268941, 0.268941, 0.731059 }, sd::DataType::FLOAT32); - auto input = NDArrayFactory::create(inBuff, 'c', {1, 3}); + sd::ops::softmax op; + auto results = op.evaluate({ &input }, {}, { 2 }, {}); + auto z = results.at(0); - auto exp = NDArrayFactory::create(expBuff, 'c', {1, 1, 3}); + ASSERT_EQ(Status::OK(), results.status()); + ASSERT_TRUE(expOutput.isSameShape(z)); + ASSERT_TRUE(expOutput.equalsTo(z)); - nd4j::ops::stack op; + +} +////////////////////////////////////////////////////////////////////// +TEST_F(DeclarableOpsTests1, softmax_test10) { + NDArray input('c', { 2, 2, 2, 2, 2 }, { -1, 1, -2, 2, -3, 3, -4, 4, -5,5 ,-6,6, -7,7, -8,8, -9,9, -10,10, -11,11, -12,12, -13,13, 14, -14, 15, -15, 16,-16 }, sd::DataType::FLOAT32); + NDArray expOutput('c', { 2, 2, 2, 2, 2 }, { 0.119203, 0.880797, 0.017986, 0.982014, 0.002473, 0.997527, 0.000335, 0.999665, 0.000045, 0.999955, 0.000006, 0.999994, 0.000001, 0.999999, 0.000000, 1.000000, 0.000000, 1.000000, 0.000000, 1.000000, 0.000000, 1.000000, 0.000000, 1.000000, 0.000000, 1.000000, 1.000000, 0.000000, 1.000000, 0.000000, 1.000000, 0.00000 }, sd::DataType::FLOAT32); - auto result = op.evaluate({&input}, {}, {0}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + sd::ops::softmax op; + auto results = op.evaluate({ &input }, {}, { 4 }, {}); + auto z = results.at(0); - auto z = result->at(0); + ASSERT_EQ(Status::OK(), results.status()); + ASSERT_TRUE(expOutput.isSameShape(z)); + ASSERT_TRUE(expOutput.equalsTo(z)); - ASSERT_TRUE(exp.isSameShape(z)); - ASSERT_TRUE(exp.equalsTo(z)); + +} +////////////////////////////////////////////////////////////////////// +TEST_F(DeclarableOpsTests1, softmax_test11) { + NDArray input('c', { 2, 2, 2, 2, 2, 2 }, { -1, 1, -2, 2, -3, 3, -4, 4, -5,5 ,-6,6, -7,7, -8,8, -9,9, -10,10, -11,11, -12,12, -13,13, 14, -14, 15, -15, 16,-16, -2.1, 2.1, -2.2, 2.2, -2.3, 2.3, -2.4, 2.4, -2.5,2.5 ,-2.6,2.6, -2.7,2.7, -2.8,2.8, -2.9,2.9, -3.0,3.0, -3.1,3.1, -3.2,3.2, -3.3,3.3, 3.4, -3.4, 3.5, -3.5, 3.6,-3.6 }, sd::DataType::FLOAT32); + NDArray expOutput('c', { 2, 2, 2, 2, 2, 2 }, { 0.731059, 0.268941, 0.268941, 0.731059, 0.731059, 0.268941, 0.268941, 0.731059, 0.731059, 0.268941, 0.268941, 0.731059, 0.731059, 0.268941, 0.268941, 0.731059, 0.731059, 0.268941, 0.268941, 0.731059, 0.731059, 0.268941, 0.268941, 0.731059, 0.000000, 1.000000, 1.000000, 0.000000, 0.268941, 0.731059, 0.731059, 0.268941, 0.524979, 0.475021, 0.475021, 0.524979, 0.524979, 0.475021, 0.475021, 0.524979, 0.524979, 0.475021, 0.475021, 0.524979, 0.524979, 0.475021, 0.475021, 0.524979, 0.524979, 0.475021, 0.475021, 0.524979, 0.524979, 0.475021, 0.475021, 0.524979, 0.001229, 0.998771, 0.998771, 0.001229, 0.475021, 0.524979, 0.524979, 0.475021 }, sd::DataType::FLOAT32); - delete result; + sd::ops::softmax op; + auto results = op.evaluate({ &input }, {}, { 4 }, {}); + auto z = results.at(0); + + ASSERT_EQ(Status::OK(), results.status()); + ASSERT_TRUE(expOutput.isSameShape(z)); + ASSERT_TRUE(expOutput.equalsTo(z)); + + } ////////////////////////////////////////////////////////////////////// -TEST_F(DeclarableOpsTests1, Test_Stack_Edge_2) { - float inBuff[] = {1.0f, 2.0f, 3.0f}; - float expBuff[] = {1.0f, 2.0f, 3.0f}; +TEST_F(DeclarableOpsTests1, softmax_test12) { + NDArray input('f', { 2, 2, 2, 2, 2, 2 }, { -1, 1, -2, 2, -3, 3, -4, 4, -5,5 ,-6,6, -7,7, -8,8, -9,9, -10,10, -11,11, -12,12, -13,13, 14, -14, 15, -15, 16,-16, -2.1, 2.1, -2.2, 2.2, -2.3, 2.3, -2.4, 2.4, -2.5,2.5 ,-2.6,2.6, -2.7,2.7, -2.8,2.8, -2.9,2.9, -3.0,3.0, -3.1,3.1, -3.2,3.2, -3.3,3.3, 3.4, -3.4, 3.5, -3.5, 3.6,-3.6 }, sd::DataType::FLOAT32); + NDArray exp('c', { 2, 2, 2, 2, 2, 2 }, { 0.982014, 0.598688, 0.982014, 0.598688, 0.017986, 0.401312, 0.017986, 0.401312, 0.982014, 0.598688, 0.000000, 0.001359, 0.017986, 0.401312, 1.000000, 0.998641, 0.982014, 0.598688, 0.000000, 0.001659, 0.017986, 0.401312, 1.000000, 0.998341, 0.982014, 0.598688, 0.000000, 0.001113, 0.017986, 0.401312, 1.000000, 0.998887, 0.017986, 0.401312, 0.017986, 0.401312, 0.982014, 0.598688, 0.982014, 0.598688, 0.017986, 0.401312, 1.000000, 0.998641, 0.982014, 0.598688, 0.000000, 0.001359, 0.017986, 0.401312, 1.000000, 0.998341, 0.982014, 0.598688, 0.000000, 0.001659, 0.017986, 0.401312, 1.000000, 0.998887, 0.982014, 0.598688, 0.000000, 0.001113 }, sd::DataType::FLOAT32); - auto input = NDArrayFactory::create(inBuff, 'c', {1, 1, 3}); + auto expOutput = NDArray('f', { 2, 2, 2, 2, 2, 2 }, sd::DataType::FLOAT32); + expOutput.assign(exp); - auto exp = NDArrayFactory::create(expBuff, 'c', {1, 1, 1, 3}); + sd::ops::softmax op; + auto results = op.evaluate({ &input }, {}, { 3 }, {}); + auto z = results.at(0); - nd4j::ops::stack op; + ASSERT_EQ(Status::OK(), results.status()); + ASSERT_TRUE(expOutput.isSameShape(z)); + ASSERT_TRUE(expOutput.equalsTo(z)); - auto result = op.evaluate({&input}, {}, {0}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); - - auto z = result->at(0); - - ASSERT_TRUE(exp.isSameShape(z)); - ASSERT_TRUE(exp.equalsTo(z)); - - delete result; + } - ////////////////////////////////////////////////////////////////////// -TEST_F(DeclarableOpsTests1, Test_Stack_Edge_3) { - float inBuff[] = {1.0f, 2.0f, 3.0f}; - float expBuff[] = {1.0f, 2.0f, 3.0f}; +TEST_F(DeclarableOpsTests1, Reverse_1) { - auto input = NDArrayFactory::create(inBuff, 'c', {1, 3}); - - auto exp = NDArrayFactory::create(expBuff, 'c', {1, 1, 3}); - - nd4j::ops::stack op; - - auto result = op.evaluate({&input}, {}, {1}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); - - auto z = result->at(0); - - //z->printShapeInfo(); - - ASSERT_TRUE(exp.isSameShape(z)); - ASSERT_TRUE(exp.equalsTo(z)); - - delete result; -} - -////////////////////////////////////////////////////////////////////// -TEST_F(DeclarableOpsTests1, Reverse_1 ) { - - float inBuff[] = {1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24}; - float expBuff[] = {24., 23., 22., 21., 20., 19., 18., 17., 16., 15., 14., 13., 12., 11., 10., 9., 8., 7., 6., 5., 4., 3., 2., 1.}; - Nd4jLong shapeInfo[] = {3, 2, 3, 4, 12, 4, 1, 0, 1, 99}; - ArrayOptions::setDataType(shapeInfo, nd4j::DataType::FLOAT32); + float inBuff[] = { 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24 }; + float expBuff[] = { 24., 23., 22., 21., 20., 19., 18., 17., 16., 15., 14., 13., 12., 11., 10., 9., 8., 7., 6., 5., 4., 3., 2., 1. }; + Nd4jLong shapeInfo[] = { 3, 2, 3, 4, 12, 4, 1, 0, 1, 99 }; + ArrayOptions::setDataType(shapeInfo, sd::DataType::FLOAT32); NDArray input(inBuff, shapeInfo); NDArray expected(expBuff, shapeInfo); NDArray output(shapeInfo); - nd4j::ops::reverse op; - auto results = op.evaluate({&input}, {}, {0,1,2}); + sd::ops::reverse op; + auto results = op.evaluate({ &input }, {}, { 0,1,2 }); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto result = results->at(0); + auto result = results.at(0); ASSERT_TRUE(expected.isSameShapeStrict(*result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; + } ////////////////////////////////////////////////////////////////////// -TEST_F(DeclarableOpsTests1, Reverse_2 ) { +TEST_F(DeclarableOpsTests1, Reverse_2) { - float inBuff[] = {1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24}; - float expBuff[] = {1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24}; - Nd4jLong shapeInfo[] = {3, 2, 3, 4, 12, 4, 1, 0, 1, 99}; - ArrayOptions::setDataType(shapeInfo, nd4j::DataType::FLOAT32); + float inBuff[] = { 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24 }; + float expBuff[] = { 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24 }; + Nd4jLong shapeInfo[] = { 3, 2, 3, 4, 12, 4, 1, 0, 1, 99 }; + ArrayOptions::setDataType(shapeInfo, sd::DataType::FLOAT32); NDArray input(inBuff, shapeInfo); NDArray expected(expBuff, shapeInfo); NDArray output(shapeInfo); - nd4j::ops::reverse op; - auto results = op.evaluate({&input}, {}, {}, {}, {}, true); + sd::ops::reverse op; + auto results = op.evaluate({ &input }, {}, {}, {}, {}, true); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto result = results->at(0); + auto result = results.at(0); ASSERT_TRUE(expected.isSameShapeStrict(input)); ASSERT_TRUE(expected.equalsTo(&input)); - delete results; + } ////////////////////////////////////////////////////////////////////// -TEST_F(DeclarableOpsTests1, Reverse_3 ) { +TEST_F(DeclarableOpsTests1, Reverse_3) { - float inBuff[] = {1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24}; - float expBuff[] = {12., 11., 10., 9., 8., 7., 6., 5., 4., 3., 2., 1., 24., 23., 22., 21., 20., 19., 18., 17., 16., 15., 14., 13.}; - Nd4jLong shapeInfo[] = {3, 2, 3, 4, 12, 4, 1, 0, 1, 99}; - ArrayOptions::setDataType(shapeInfo, nd4j::DataType::FLOAT32); + float inBuff[] = { 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24 }; + float expBuff[] = { 12., 11., 10., 9., 8., 7., 6., 5., 4., 3., 2., 1., 24., 23., 22., 21., 20., 19., 18., 17., 16., 15., 14., 13. }; + Nd4jLong shapeInfo[] = { 3, 2, 3, 4, 12, 4, 1, 0, 1, 99 }; + ArrayOptions::setDataType(shapeInfo, sd::DataType::FLOAT32); NDArray input(inBuff, shapeInfo); NDArray expected(expBuff, shapeInfo); NDArray output(shapeInfo); - nd4j::ops::reverse op; - auto results = op.evaluate({&input}, {}, {1,2}); + sd::ops::reverse op; + auto results = op.evaluate({ &input }, {}, { 1,2 }); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto result = results->at(0); + auto result = results.at(0); // result->printBuffer(); ASSERT_TRUE(expected.isSameShapeStrict(*result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; + } ////////////////////////////////////////////////////////////////////// -TEST_F(DeclarableOpsTests1, Reverse_4 ) { +TEST_F(DeclarableOpsTests1, Reverse_4) { - float inBuff[] = {1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24}; - float expBuff[] = {16,15,14,13,20,19,18,17,24,23,22,21,4,3,2,1,8,7,6,5,12,11,10,9,}; - Nd4jLong shapeInfo[] = {3, 2, 3, 4, 12, 4, 1, 0, 1, 99}; - ArrayOptions::setDataType(shapeInfo, nd4j::DataType::FLOAT32); + float inBuff[] = { 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24 }; + float expBuff[] = { 16,15,14,13,20,19,18,17,24,23,22,21,4,3,2,1,8,7,6,5,12,11,10,9, }; + Nd4jLong shapeInfo[] = { 3, 2, 3, 4, 12, 4, 1, 0, 1, 99 }; + ArrayOptions::setDataType(shapeInfo, sd::DataType::FLOAT32); NDArray input(inBuff, shapeInfo); NDArray expected(expBuff, shapeInfo); NDArray output(shapeInfo); - nd4j::ops::reverse op; - auto results = op.evaluate({&input}, {}, {0,2}); + sd::ops::reverse op; + auto results = op.evaluate({ &input }, {}, { 0,2 }); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto result = results->at(0); + auto result = results.at(0); // result->printBuffer(); ASSERT_TRUE(expected.isSameShapeStrict(*result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; + } ////////////////////////////////////////////////////////////////////// -TEST_F(DeclarableOpsTests1, Reverse_5 ) { +TEST_F(DeclarableOpsTests1, Reverse_5) { - float inBuff[] = {1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24}; - float expBuff[] = {21., 22., 23., 24., 17., 18., 19., 20., 13., 14., 15., 16., 9., 10., 11., 12., 5., 6., 7., 8., 1., 2., 3., 4.}; - Nd4jLong shapeInfo[] = {3, 2, 3, 4, 12, 4, 1, 0, 1, 99}; - ArrayOptions::setDataType(shapeInfo, nd4j::DataType::FLOAT32); + float inBuff[] = { 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24 }; + float expBuff[] = { 21., 22., 23., 24., 17., 18., 19., 20., 13., 14., 15., 16., 9., 10., 11., 12., 5., 6., 7., 8., 1., 2., 3., 4. }; + Nd4jLong shapeInfo[] = { 3, 2, 3, 4, 12, 4, 1, 0, 1, 99 }; + ArrayOptions::setDataType(shapeInfo, sd::DataType::FLOAT32); NDArray input(inBuff, shapeInfo); NDArray expected(expBuff, shapeInfo); NDArray output(shapeInfo); - nd4j::ops::reverse op; - auto results = op.evaluate({&input}, {}, {0,1}); + sd::ops::reverse op; + auto results = op.evaluate({ &input }, {}, { 0,1 }); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto result = results->at(0); + auto result = results.at(0); ASSERT_TRUE(expected.isSameShapeStrict(*result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; + } //////////////////////////////////////////////////////////////////// -TEST_F(DeclarableOpsTests1, Reverse_6 ) { +TEST_F(DeclarableOpsTests1, Reverse_6) { - float inBuff[] = {1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24}; - float expBuff[] = {4., 3., 2., 1., 8., 7., 6., 5., 12., 11., 10., 9., 16., 15., 14., 13., 20., 19., 18., 17., 24., 23., 22., 21.}; - Nd4jLong shapeInfo[] = {3, 2, 3, 4, 12, 4, 1, 0, 1, 99}; - ArrayOptions::setDataType(shapeInfo, nd4j::DataType::FLOAT32); + float inBuff[] = { 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24 }; + float expBuff[] = { 4., 3., 2., 1., 8., 7., 6., 5., 12., 11., 10., 9., 16., 15., 14., 13., 20., 19., 18., 17., 24., 23., 22., 21. }; + Nd4jLong shapeInfo[] = { 3, 2, 3, 4, 12, 4, 1, 0, 1, 99 }; + ArrayOptions::setDataType(shapeInfo, sd::DataType::FLOAT32); NDArray input(inBuff, shapeInfo); NDArray expected(expBuff, shapeInfo); NDArray output(shapeInfo); - nd4j::ops::reverse op; - auto results = op.evaluate({&input}, {}, {2}, {}, {}, true); + sd::ops::reverse op; + auto results = op.evaluate({ &input }, {}, { 2 }, {}, {}, true); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto result = results->at(0); + auto result = results.at(0); // result->printBuffer(); ASSERT_TRUE(expected.isSameShapeStrict(input)); ASSERT_TRUE(expected.equalsTo(&input)); - delete results; + } //////////////////////////////////////////////////////////////////// -TEST_F(DeclarableOpsTests1, Reverse_7 ) { +TEST_F(DeclarableOpsTests1, Reverse_7) { - float inBuff[] = {1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24}; - float expBuff[] = {9., 10., 11., 12., 5., 6., 7., 8., 1., 2., 3., 4., 21., 22., 23., 24., 17., 18., 19., 20., 13., 14., 15., 16.}; - Nd4jLong shapeInfo[] = {3, 2, 3, 4, 12, 4, 1, 0, 1, 99}; - ArrayOptions::setDataType(shapeInfo, nd4j::DataType::FLOAT32); + float inBuff[] = { 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24 }; + float expBuff[] = { 9., 10., 11., 12., 5., 6., 7., 8., 1., 2., 3., 4., 21., 22., 23., 24., 17., 18., 19., 20., 13., 14., 15., 16. }; + Nd4jLong shapeInfo[] = { 3, 2, 3, 4, 12, 4, 1, 0, 1, 99 }; + ArrayOptions::setDataType(shapeInfo, sd::DataType::FLOAT32); NDArray input(inBuff, shapeInfo); NDArray expected(expBuff, shapeInfo); NDArray output(shapeInfo); - nd4j::ops::reverse op; - auto results = op.evaluate({&input}, {}, {1}); + sd::ops::reverse op; + auto results = op.evaluate({ &input }, {}, { 1 }); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto result = results->at(0); + auto result = results.at(0); //expected.printIndexedBuffer("E"); //result->printIndexedBuffer("R"); ASSERT_TRUE(expected.isSameShapeStrict(*result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; + } ////////////////////////////////////////////////////////////////////// -TEST_F(DeclarableOpsTests1, Reverse_8 ) { +TEST_F(DeclarableOpsTests1, Reverse_8) { - float inBuff[] = {1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24}; - float expBuff[] = {12., 11., 10., 9., 8., 7., 6., 5., 4., 3., 2., 1., 24., 23., 22., 21., 20., 19., 18., 17., 16., 15., 14., 13.}; - Nd4jLong shapeInfo[] = {3, 2, 3, 4, 12, 4, 1, 0, 1, 99}; - ArrayOptions::setDataType(shapeInfo, nd4j::DataType::FLOAT32); + float inBuff[] = { 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24 }; + float expBuff[] = { 12., 11., 10., 9., 8., 7., 6., 5., 4., 3., 2., 1., 24., 23., 22., 21., 20., 19., 18., 17., 16., 15., 14., 13. }; + Nd4jLong shapeInfo[] = { 3, 2, 3, 4, 12, 4, 1, 0, 1, 99 }; + ArrayOptions::setDataType(shapeInfo, sd::DataType::FLOAT32); NDArray input(inBuff, shapeInfo); NDArray expected(expBuff, shapeInfo); NDArray output(shapeInfo); - nd4j::ops::reverse op; - auto results = op.evaluate({&input}, {}, {2,1}); + sd::ops::reverse op; + auto results = op.evaluate({ &input }, {}, { 2,1 }); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto result = results->at(0); + auto result = results.at(0); // result->printBuffer(); ASSERT_TRUE(expected.isSameShapeStrict(*result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; + } //////////////////////////////////////////////////////////////////// -TEST_F(DeclarableOpsTests1, Reverse_9 ) { +TEST_F(DeclarableOpsTests1, Reverse_9) { - float inBuff[] = {1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24}; - float expBuff[] = {13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12.}; - Nd4jLong shapeInfo[] = {3, 2, 3, 4, 12, 4, 1, 0, 1, 99}; - ArrayOptions::setDataType(shapeInfo, nd4j::DataType::FLOAT32); + float inBuff[] = { 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24 }; + float expBuff[] = { 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12. }; + Nd4jLong shapeInfo[] = { 3, 2, 3, 4, 12, 4, 1, 0, 1, 99 }; + ArrayOptions::setDataType(shapeInfo, sd::DataType::FLOAT32); NDArray input(inBuff, shapeInfo); NDArray expected(expBuff, shapeInfo); NDArray output(shapeInfo); - nd4j::ops::reverse op; - auto results = op.evaluate({&input}, {}, {0}); + sd::ops::reverse op; + auto results = op.evaluate({ &input }, {}, { 0 }); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto result = results->at(0); + auto result = results.at(0); ASSERT_TRUE(expected.isSameShapeStrict(*result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; + } -TEST_F(DeclarableOpsTests1, Reverse_10 ) { - auto x = NDArrayFactory::create('c', {4, 3}, {1.5375735, 0.1592365, 0.09966054, 0.677872, 1.144433, -1.0355669, 0.48456487, -0.67863184, 0.85020787, 0.13950661, 0.20998026, -1.1660044}); - auto i = NDArrayFactory::create('c', {1}, {-1}); - auto e = NDArrayFactory::create('c', {4, 3}, {0.09966054, 0.1592365, 1.5375735, -1.0355669, 1.144433, 0.677872,0.85020787, -0.67863184, 0.48456487, -1.1660044, 0.20998026, 0.13950661}); +TEST_F(DeclarableOpsTests1, Reverse_10) { + auto x = NDArrayFactory::create('c', { 4, 3 }, { 1.5375735, 0.1592365, 0.09966054, 0.677872, 1.144433, -1.0355669, 0.48456487, -0.67863184, 0.85020787, 0.13950661, 0.20998026, -1.1660044 }); + auto i = NDArrayFactory::create('c', { 1 }, { -1 }); + auto e = NDArrayFactory::create('c', { 4, 3 }, { 0.09966054, 0.1592365, 1.5375735, -1.0355669, 1.144433, 0.677872,0.85020787, -0.67863184, 0.48456487, -1.1660044, 0.20998026, 0.13950661 }); - nd4j::ops::reverse op; - auto result = op.evaluate({&x, &i}, {}, {}, {}); + sd::ops::reverse op; + auto result = op.evaluate({ &x, &i }, {}, {}, {}); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(e.isSameShape(z)); ASSERT_TRUE(e.equalsTo(z)); - delete result; + } ////////////////////////////////////////////////////////////////////// -TEST_F(DeclarableOpsTests1, Reverse_11 ) { +TEST_F(DeclarableOpsTests1, Reverse_11) { - auto input = NDArrayFactory::create('c', {2,3,4}); - auto expected = NDArrayFactory::create('c', {2,3,4}, {24.f, 23.f, 22.f, 21.f, 20.f, 19.f, 18.f, 17.f, 16.f, + auto input = NDArrayFactory::create('c', { 2,3,4 }); + auto expected = NDArrayFactory::create('c', { 2,3,4 }, { 24.f, 23.f, 22.f, 21.f, 20.f, 19.f, 18.f, 17.f, 16.f, 15.f, 14.f, 13.f, 12.f, 11.f, 10.f, 9.f, 8.f, 7.f, - 6.f, 5.f, 4.f, 3.f, 2.f, 1.f}); + 6.f, 5.f, 4.f, 3.f, 2.f, 1.f }); input.linspace(1); - nd4j::ops::reverse op; - auto results = op.evaluate({&input}, {}, {0, 1, 2}); + sd::ops::reverse op; + auto results = op.evaluate({ &input }, {}, { 0, 1, 2 }); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto result = results->at(0); + auto result = results.at(0); ASSERT_TRUE(expected.isSameShapeStrict(*result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; + } ////////////////////////////////////////////////////////////////////// -TEST_F(DeclarableOpsTests1, Reverse_12 ) { +TEST_F(DeclarableOpsTests1, Reverse_12) { - auto input = NDArrayFactory::create({0.f, 1.f, 2.f, 3.f, 4.f}); - auto expected = NDArrayFactory::create({4.f, 3.f, 2.f, 1.f, 0.f}); + auto input = NDArrayFactory::create({ 0.f, 1.f, 2.f, 3.f, 4.f }); + auto expected = NDArrayFactory::create({ 4.f, 3.f, 2.f, 1.f, 0.f }); //input.linspace(1); - nd4j::ops::reverse op; - auto results = op.evaluate({&input}, {}, {0}); + sd::ops::reverse op; + auto results = op.evaluate({ &input }, {}, { 0 }); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto result = results->at(0); + auto result = results.at(0); //result->printIndexedBuffer("Result reverse"); //expected.printIndexedBuffer("Expected reverse"); ASSERT_TRUE(expected.isSameShapeStrict(*result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; + } ////////////////////////////////////////////////////////////////////// -TEST_F(DeclarableOpsTests1, Reverse_13 ) { +TEST_F(DeclarableOpsTests1, Reverse_13) { - auto input = NDArrayFactory::create({0.f, 1.f, 2.f, 3.f, 4.f}); - auto expected = NDArrayFactory::create({4.f, 3.f, 2.f, 1.f, 0.f}); + auto input = NDArrayFactory::create({ 0.f, 1.f, 2.f, 3.f, 4.f }); + auto expected = NDArrayFactory::create({ 4.f, 3.f, 2.f, 1.f, 0.f }); //input.linspace(1); - nd4j::ops::reverse op; - auto results = op.evaluate({&input}, {}, {-1}); + sd::ops::reverse op; + auto results = op.evaluate({ &input }, {}, { -1 }); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto result = results->at(0); + auto result = results.at(0); ASSERT_TRUE(expected.isSameShapeStrict(*result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; + } ////////////////////////////////////////////////////////////////////// -TEST_F(DeclarableOpsTests1, Reverse_14 ) { +TEST_F(DeclarableOpsTests1, Reverse_14) { - auto input = NDArrayFactory::create({0.f, 1.f, 2.f, 3.f, 4.f}); - auto expected = NDArrayFactory::create({0.f, 1.f, 2.f, 3.f, 4.f}); + auto input = NDArrayFactory::create({ 0.f, 1.f, 2.f, 3.f, 4.f }); + auto expected = NDArrayFactory::create({ 0.f, 1.f, 2.f, 3.f, 4.f }); //input.linspace(1); - nd4j::ops::reverse op; - auto results = op.evaluate({&input}, {}, {}, {}); + sd::ops::reverse op; + auto results = op.evaluate({ &input }, {}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto result = results->at(0); + auto result = results.at(0); ASSERT_TRUE(expected.isSameShapeStrict(*result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; + } TEST_F(DeclarableOpsTests1, Test_Expose_1) { - auto input0 = NDArrayFactory::create('c', {2, 3}, {1, 2, 3, 6, 5, 4}); - auto input1 = NDArrayFactory::create('c', {2, 3}, {3, 2, 1, 4, 5, 6}); + auto input0 = NDArrayFactory::create('c', { 2, 3 }, { 1, 2, 3, 6, 5, 4 }); + auto input1 = NDArrayFactory::create('c', { 2, 3 }, { 3, 2, 1, 4, 5, 6 }); - nd4j::ops::expose op; + sd::ops::expose op; - auto result = op.evaluate({&input0, &input1}); + auto result = op.evaluate({ &input0, &input1 }); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z0 = result->at(0); - auto z1 = result->at(1); + auto z0 = result.at(0); + auto z1 = result.at(1); ASSERT_TRUE(input0.equalsTo(z0)); ASSERT_TRUE(input1.equalsTo(z1)); - delete result; + } TEST_F(DeclarableOpsTests1, Test_Expose_2) { @@ -3767,7 +3479,7 @@ TEST_F(DeclarableOpsTests1, Test_Expose_2) { Context block(1, &variableSpace); block.pickInput(-1); - nd4j::ops::expose op; + sd::ops::expose op; auto result = op.execute(&block); ASSERT_EQ(ND4J_STATUS_OK, result); @@ -3784,7 +3496,6 @@ TEST_F(DeclarableOpsTests1, Test_Expose_2) { } TEST_F(DeclarableOpsTests1, Test_Release) { - auto x = NDArrayFactory::create('c', {8, 8}); + auto x = NDArrayFactory::create('c', { 8, 8 }); // x.printShapeInfo("x shape"); } - diff --git a/libnd4j/tests_cpu/layers_tests/DeclarableOpsTests10.cpp b/libnd4j/tests_cpu/layers_tests/DeclarableOpsTests10.cpp index 484719a45..03e5ae53f 100644 --- a/libnd4j/tests_cpu/layers_tests/DeclarableOpsTests10.cpp +++ b/libnd4j/tests_cpu/layers_tests/DeclarableOpsTests10.cpp @@ -22,12 +22,12 @@ #include "testlayers.h" #include -#include +#include #include -#include +#include -using namespace nd4j; +using namespace sd; class DeclarableOpsTests10 : public testing::Test { @@ -59,16 +59,14 @@ TEST_F(DeclarableOpsTests10, Test_ArgMax_1) { x.linspace(1.0); - nd4j::ops::argmax op; + sd::ops::argmax op; auto result = op.evaluate({&x}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = *result->at(0); + auto z = *result.at(0); ASSERT_EQ(e, z); - - delete result; } TEST_F(DeclarableOpsTests10, Test_ArgMax_2) { @@ -78,18 +76,16 @@ TEST_F(DeclarableOpsTests10, Test_ArgMax_2) { x.linspace(1.0); - nd4j::ops::argmax op; + sd::ops::argmax op; auto result = op.evaluate({&x, &y}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = *result->at(0); + auto z = *result.at(0); //z.printIndexedBuffer("z"); //z.printShapeInfo("z shape"); ASSERT_EQ(e, z); - - delete result; } TEST_F(DeclarableOpsTests10, Test_And_1) { @@ -97,13 +93,11 @@ TEST_F(DeclarableOpsTests10, Test_And_1) { auto y = NDArrayFactory::create('c', {4}, {0, 0, 0, 1}); auto e = NDArrayFactory::create('c', {4}, {0, 0, 0, 1}); - nd4j::ops::boolean_and op; + sd::ops::boolean_and op; auto result = op.evaluate({&x, &y}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - ASSERT_EQ(e, *result->at(0)); - - delete result; + ASSERT_EQ(e, *result.at(0)); } TEST_F(DeclarableOpsTests10, Test_Or_1) { @@ -111,13 +105,11 @@ TEST_F(DeclarableOpsTests10, Test_Or_1) { auto y = NDArrayFactory::create('c', {4}, {0, 0, 0, 1}); auto e = NDArrayFactory::create('c', {4}, {1, 1, 0, 1}); - nd4j::ops::boolean_or op; + sd::ops::boolean_or op; auto result = op.evaluate({&x, &y}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - ASSERT_EQ(e, *result->at(0)); - - delete result; + ASSERT_EQ(e, *result.at(0)); } TEST_F(DeclarableOpsTests10, Test_Not_1) { @@ -126,27 +118,24 @@ TEST_F(DeclarableOpsTests10, Test_Not_1) { // auto e = NDArrayFactory::create('c', {4}, {1, 1, 1, 0}); auto e = NDArrayFactory::create('c', {4}, {false, false, true, false}); - nd4j::ops::boolean_not op; + sd::ops::boolean_not op; auto result = op.evaluate({&x, &y}); - ASSERT_EQ(Status::OK(), result->status()); - auto res = result->at(0); + ASSERT_EQ(Status::OK(), result.status()); + auto res = result.at(0); ASSERT_TRUE(e.equalsTo(res)); - - delete result; } TEST_F(DeclarableOpsTests10, Test_Size_at_1) { auto x = NDArrayFactory::create('c', {10, 20, 30}); auto e = NDArrayFactory::create(20); - nd4j::ops::size_at op; + sd::ops::size_at op; auto result = op.evaluate({&x}, {1}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - ASSERT_EQ(e, *result->at(0)); + ASSERT_EQ(e, *result.at(0)); - delete result; } //////////////////////////////////////////////////////////////////////////////// @@ -159,13 +148,12 @@ TEST_F(DeclarableOpsTests10, MirrorPad_SGO_Test_1) { auto exp = NDArrayFactory::create({2., 1., 2., 3., 4., 5., 4.}); - nd4j::ops::mirror_pad op; + sd::ops::mirror_pad op; auto res = op.evaluate({&in, &pad}, {10.0}, {0}); - ASSERT_EQ(res->status(), ND4J_STATUS_OK); + ASSERT_EQ(res.status(), ND4J_STATUS_OK); - ASSERT_TRUE(exp.equalsTo(res->at(0))); - delete res; + ASSERT_TRUE(exp.equalsTo(res.at(0))); } //////////////////////////////////////////////////////////////////////////////// @@ -174,15 +162,15 @@ TEST_F(DeclarableOpsTests10, Unique_SGO_Test_1) { auto expIdx = NDArrayFactory::create({0, 1, 0, 2, 0, 3, 4, 1, 4, 1}); auto exp = NDArrayFactory::create({3., 4., 1., 0., 2.}); - nd4j::ops::unique op; + sd::ops::unique op; auto res = op.evaluate({&input}, {}, {}); - ASSERT_EQ(res->status(), ND4J_STATUS_OK); - auto res1 = res->at(0); - auto res2 = res->at(1); + ASSERT_EQ(res.status(), ND4J_STATUS_OK); + auto res1 = res.at(0); + auto res2 = res.at(1); ASSERT_TRUE(exp.equalsTo(res1)); ASSERT_TRUE(expIdx.equalsTo(res2)); - delete res; + } //////////////////////////////////////////////////////////////////////////////// @@ -191,15 +179,15 @@ TEST_F(DeclarableOpsTests10, Where_SGO_Test_1) { //auto expIdx({0., 1., 0., 2., 0., 3., 4., 1., 4., 1.}); auto exp = NDArrayFactory::create('c', {6, 2}, {0LL, 0LL, 1LL, 0LL, 1LL, 1LL, 2LL, 0LL, 2LL, 1LL, 2LL, 2LL}); - nd4j::ops::Where op; + sd::ops::Where op; auto res = op.evaluate({&input}, {}, {}); - ASSERT_TRUE(res->status() == ND4J_STATUS_OK); - auto resA = res->at(0); + ASSERT_TRUE(res.status() == ND4J_STATUS_OK); + auto resA = res.at(0); ASSERT_TRUE(exp.isSameShape(resA)); ASSERT_TRUE(exp.equalsTo(resA)); -// ASSERT_TRUE(expIdx.equalsTo(res->at(1))); - delete res; +// ASSERT_TRUE(expIdx.equalsTo(res.at(1))); + } //////////////////////////////////////////////////////////////////////////////// @@ -208,15 +196,15 @@ TEST_F(DeclarableOpsTests10, Where_SGO_Test_02) { //auto expIdx({0., 1., 0., 2., 0., 3., 4., 1., 4., 1.}); auto exp = NDArrayFactory::create('c', {5, 3}, {0LL, 0LL, 0LL, 0LL, 1LL, 1LL, 1LL, 0LL, 0LL, 1LL, 0LL, 1LL, 1LL, 1LL, 0LL}); - nd4j::ops::Where op; + sd::ops::Where op; auto res = op.evaluate({&input}, {}, {}); - ASSERT_TRUE(res->status() == ND4J_STATUS_OK); - auto resA = res->at(0); + ASSERT_TRUE(res.status() == ND4J_STATUS_OK); + auto resA = res.at(0); ASSERT_TRUE(exp.equalsTo(resA)); ASSERT_TRUE(exp.isSameShape(resA)); -// ASSERT_TRUE(expIdx.equalsTo(res->at(1))); - delete res; +// ASSERT_TRUE(expIdx.equalsTo(res.at(1))); + } //////////////////////////////////////////////////////////////////////////////// @@ -226,21 +214,21 @@ TEST_F(DeclarableOpsTests10, WhereNP_SGO_Test_1) { auto exp1 = NDArrayFactory::create({0, 0, 1, 1, 1}); auto exp2 = NDArrayFactory::create({0, 1, 0, 0, 1}); auto exp3 = NDArrayFactory::create({0, 1, 0, 1, 0}); - nd4j::ops::where_np op; + sd::ops::where_np op; auto res = op.evaluate({&cond3d}, {}, {}); - ASSERT_TRUE(res->size() == 3); - ASSERT_EQ(res->status(), ND4J_STATUS_OK); - auto res1 = res->at(0); - auto res2 = res->at(1); - auto res3 = res->at(2); + ASSERT_TRUE(res.size() == 3); + ASSERT_EQ(res.status(), ND4J_STATUS_OK); + auto res1 = res.at(0); + auto res2 = res.at(1); + auto res3 = res.at(2); // res1->printShapeInfo("Res1 shape"); res1->printBuffer("Res1"); // res2->printShapeInfo("Res2 shape"); res2->printBuffer("Res2"); // res3->printShapeInfo("Res3 shape"); res3->printBuffer("Res3"); ASSERT_TRUE(exp1.equalsTo(res1)); ASSERT_TRUE(exp2.equalsTo(res2)); ASSERT_TRUE(exp3.equalsTo(res3)); - //ASSERT_TRUE(expIdx.equalsTo(res->at(1))); - delete res; + //ASSERT_TRUE(expIdx.equalsTo(res.at(1))); + } //////////////////////////////////////////////////////////////////////////////// @@ -250,14 +238,14 @@ TEST_F(DeclarableOpsTests10, WhereNP_SGO_Test_2) { // auto expIdx({0, 1, 0, 2, 0, 3, 4, 1, 4, 1}); auto exp1 = NDArrayFactory::create({0, 0, 0, 1, 1, 1, 1, 1, 2, 2, 2, 2}); auto exp2 = NDArrayFactory::create({0, 1, 4, 0, 1, 2, 3, 4, 1, 2, 3, 4}); - nd4j::ops::where_np op; + sd::ops::where_np op; auto res = op.evaluate({&cond2d}, {}, {}); - ASSERT_TRUE(res->size() == 2); - ASSERT_TRUE(res->status() == ND4J_STATUS_OK); - ASSERT_TRUE(exp1.equalsTo(res->at(0))); - ASSERT_TRUE(exp2.equalsTo(res->at(1))); - //ASSERT_TRUE(expIdx.equalsTo(res->at(1))); - delete res; + ASSERT_TRUE(res.size() == 2); + ASSERT_TRUE(res.status() == ND4J_STATUS_OK); + ASSERT_TRUE(exp1.equalsTo(res.at(0))); + ASSERT_TRUE(exp2.equalsTo(res.at(1))); + //ASSERT_TRUE(expIdx.equalsTo(res.at(1))); + } //////////////////////////////////////////////////////////////////////////////// @@ -266,16 +254,16 @@ TEST_F(DeclarableOpsTests10, Where_SGO_Test_2) { //auto expIdx({0., 1., 0., 2., 0., 3., 4., 1., 4., 1.}); auto exp = NDArrayFactory::create('c', {4,1}, {0, 2, 3, 4}); - nd4j::ops::Where op; + sd::ops::Where op; auto res = op.evaluate({&input}); - ASSERT_TRUE(res->status() == ND4J_STATUS_OK); - auto resA = res->at(0); + ASSERT_TRUE(res.status() == ND4J_STATUS_OK); + auto resA = res.at(0); // resA->printIndexedBuffer("Result A"); // resA->printShapeInfo("ShapeA"); ASSERT_TRUE(exp.equalsTo(resA)); ASSERT_TRUE(exp.isSameShape(resA)); -// ASSERT_TRUE(expIdx.equalsTo(res->at(1))); - delete res; +// ASSERT_TRUE(expIdx.equalsTo(res.at(1))); + } //////////////////////////////////////////////////////////////////////////////// @@ -284,16 +272,16 @@ TEST_F(DeclarableOpsTests10, Where_SGO_Test_3) { //auto expIdx({0., 1., 0., 2., 0., 3., 4., 1., 4., 1.}); auto exp = NDArrayFactory::create('c', {4, 2}, {0, 0, 2, 0, 3, 0, 4, 0}); - nd4j::ops::Where op; + sd::ops::Where op; auto res = op.evaluate({&input}, {}, {}); - ASSERT_TRUE(res->status() == ND4J_STATUS_OK); - auto resA = res->at(0); + ASSERT_TRUE(res.status() == ND4J_STATUS_OK); + auto resA = res.at(0); //resA->printIndexedBuffer("Result A"); //resA->printShapeInfo("ShapeA"); ASSERT_TRUE(exp.equalsTo(resA)); ASSERT_TRUE(exp.isSameShape(resA)); -// ASSERT_TRUE(expIdx.equalsTo(res->at(1))); - delete res; +// ASSERT_TRUE(expIdx.equalsTo(res.at(1))); + } //////////////////////////////////////////////////////////////////////////////// @@ -302,17 +290,17 @@ TEST_F(DeclarableOpsTests10, Where_SGO_Test_4) { //auto expIdx({0., 1., 0., 2., 0., 3., 4., 1., 4., 1.}); auto exp = NDArrayFactory::create('c', {4, 2}, {0, 0, 2, 0, 3, 0, 4, 0}); - nd4j::ops::Where op; + sd::ops::Where op; auto res = op.evaluate({&input}, {}, {}); - ASSERT_TRUE(res->status() == ND4J_STATUS_OK); - auto resA = res->at(0); + ASSERT_TRUE(res.status() == ND4J_STATUS_OK); + auto resA = res.at(0); ASSERT_TRUE(resA->isEmpty()); //resA->printIndexedBuffer("Result A"); //resA->printShapeInfo("ShapeA"); //ASSERT_TRUE(exp.equalsTo(resA)); //ASSERT_TRUE(exp.isSameShape(resA)); -// ASSERT_TRUE(expIdx.equalsTo(res->at(1))); - delete res; +// ASSERT_TRUE(expIdx.equalsTo(res.at(1))); + } //////////////////////////////////////////////////////////////////////////////// @@ -321,16 +309,16 @@ TEST_F(DeclarableOpsTests10, Where_SGO_Test_5) { //auto expIdx({0., 1., 0., 2., 0., 3., 4., 1., 4., 1.}); auto exp = NDArrayFactory::create('c', {3, 1}, {0, 3, 4}); - nd4j::ops::Where op; + sd::ops::Where op; auto res = op.evaluate({&input}, {}, {}); - ASSERT_TRUE(res->status() == ND4J_STATUS_OK); - auto resA = res->at(0); + ASSERT_TRUE(res.status() == ND4J_STATUS_OK); + auto resA = res.at(0); //ASSERT_TRUE(resA->isEmpty()); ASSERT_TRUE(exp.equalsTo(resA)); ASSERT_TRUE(exp.isSameShape(resA)); -// ASSERT_TRUE(expIdx.equalsTo(res->at(1))); - delete res; +// ASSERT_TRUE(expIdx.equalsTo(res.at(1))); + } //////////////////////////////////////////////////////////////////////////////// @@ -339,17 +327,17 @@ TEST_F(DeclarableOpsTests10, WhereNP_SGO_Test_4) { //auto expIdx({0., 1., 0., 2., 0., 3., 4., 1., 4., 1.}); auto exp = NDArrayFactory::create('c', {4, 2}, {0, 0, 2, 0, 3, 0, 4, 0}); - nd4j::ops::where_np op; + sd::ops::where_np op; auto res = op.evaluate({&input}, {}, {}); - ASSERT_TRUE(res->status() == ND4J_STATUS_OK); - auto resA = res->at(0); + ASSERT_TRUE(res.status() == ND4J_STATUS_OK); + auto resA = res.at(0); ASSERT_TRUE(resA->isEmpty()); //resA->printIndexedBuffer("Result A"); //resA->printShapeInfo("ShapeA"); //ASSERT_TRUE(exp.equalsTo(resA)); //ASSERT_TRUE(exp.isSameShape(resA)); -// ASSERT_TRUE(expIdx.equalsTo(res->at(1))); - delete res; +// ASSERT_TRUE(expIdx.equalsTo(res.at(1))); + } //////////////////////////////////////////////////////////////////////////////// @@ -360,14 +348,13 @@ TEST_F(DeclarableOpsTests10, CosineDistance_SGO_Test_1) { auto weights = NDArrayFactory::create('c', {2, 1}, {0., 1.}); auto exp = NDArrayFactory::create(0.6); - nd4j::ops::cosine_distance_loss op; + sd::ops::cosine_distance_loss op; auto res = op.evaluate({&predictions, &weights, &labels}, {}, {3, 1}); - ASSERT_TRUE(res->status() == ND4J_STATUS_OK); - auto resA = res->at(0); + ASSERT_TRUE(res.status() == ND4J_STATUS_OK); + auto resA = res.at(0); ASSERT_TRUE(exp.equalsTo(resA)); - delete res; } //////////////////////////////////////////////////////////////////////////////// @@ -378,14 +365,13 @@ TEST_F(DeclarableOpsTests10, CosineDistance_SGO_Test_2) { auto weights = NDArrayFactory::create('c', {2, 1}, {0., 1.}); auto exp = NDArrayFactory::create(0.6); - nd4j::ops::cosine_distance_loss op; + sd::ops::cosine_distance_loss op; auto res = op.evaluate({&predictions, &weights, &labels}, {}, {2, 1}); - ASSERT_TRUE(res->status() == ND4J_STATUS_OK); - auto resA = res->at(0); + ASSERT_TRUE(res.status() == ND4J_STATUS_OK); + auto resA = res.at(0); ASSERT_TRUE(exp.equalsTo(resA)); - delete res; } /////////////////////////////////////////////////////////////////// @@ -401,15 +387,13 @@ TEST_F(DeclarableOpsTests10, TestMarixBandPart_Test_1) { exp.p(0, 2, 0, 0.); exp.p(1, 2, 0, 0.); - nd4j::ops::matrix_band_part op; + sd::ops::matrix_band_part op; auto results = op.evaluate({&x}, {}, {1, 1}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); - //results->at(0)->printIndexedBuffer("MBP Test1"); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); + //results.at(0)->printIndexedBuffer("MBP Test1"); //exp.printIndexedBuffer("MBP Expec"); - ASSERT_TRUE(exp.equalsTo(results->at(0))); - - delete results; + ASSERT_TRUE(exp.equalsTo(results.at(0))); } ////////////////////////////////////////////////////////////////////////////// @@ -421,15 +405,13 @@ TEST_F(DeclarableOpsTests10, atan2_test1) { auto exp = NDArrayFactory::create('c', {2,3,4}, {-2.04201, -2.03663, -2.03009, -2.02199,-2.01166, -1.99808, -1.97941, -1.95217,-1.90875, -1.8292 , -1.6416 , -0.942 , 0.33172, 0.69614, 0.81846, 0.87776, 0.91253, 0.93533, 0.95141, 0.96336, 0.97259, 0.97993, 0.98591, 1.01266,}); - nd4j::ops::tf_atan2 op; + sd::ops::tf_atan2 op; auto result = op.evaluate({&y, &x}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); - auto z = result->at(0); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - - delete result; } ////////////////////////////////////////////////////////////////////////////// @@ -441,18 +423,16 @@ TEST_F(DeclarableOpsTests10, atan2_test2) { auto exp = NDArrayFactory::create('c', {2,3,4}, {-2.38008, -2.30149, -2.22748, -2.1232 ,-1.96979, -1.73736, -1.3973 , -0.98279,-0.61088, -0.34685, -0.17256, -0.0555 , 3.11208, 2.99987, 2.83399, 2.57869, 2.207 , 1.77611, 1.41664, 1.17298, 1.01458, 0.90829, 0.8336 , 0.77879}); - nd4j::ops::tf_atan2 op; + sd::ops::tf_atan2 op; auto result = op.evaluate({&y, &x}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); - auto z = result->at(0); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); + auto z = result.at(0); // z->printIndexedBuffer(); - // x.applyTrueBroadcast(nd4j::BroadcastOpsTuple::custom(scalar::Atan2, pairwise::Atan2, broadcast::Atan2), &y, &z, true); + // x.applyTrueBroadcast(sd::BroadcastOpsTuple::custom(scalar::Atan2, pairwise::Atan2, broadcast::Atan2), &y, &z, true); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - - delete result; } ////////////////////////////////////////////////////////////////////////////// @@ -464,15 +444,13 @@ TEST_F(DeclarableOpsTests10, atan2_test3) { auto exp = NDArrayFactory::create('c', {2,3,4}, {-2.33231, -2.41089, -2.48491, -2.58919,-2.74259, -2.97502, 2.9681 , 2.55359, 2.18167, 1.91765, 1.74335, 1.62629, -1.54128, -1.42907, -1.2632 , -1.00789,-0.63621, -0.20531, 0.15416, 0.39782, 0.55622, 0.6625 , 0.7372 , 0.79201}); - nd4j::ops::tf_atan2 op; + sd::ops::tf_atan2 op; auto result = op.evaluate({&x, &y}, {}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); - auto z = result->at(0); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - - delete result; } ////////////////////////////////////////////////////////////////////////////// @@ -484,15 +462,13 @@ TEST_F(DeclarableOpsTests10, atan2_test4) { auto exp = NDArrayFactory::create('c', {2,3,4}, {-2.45527, -2.36165, -2.24628, -2.10492,-2.1703 , -1.86945, -1.50321, -1.15359,-0.25062, -0.17373, -0.13273, -0.10733, 3.05688, 3.03942, 3.01293, 2.9681 , 2.18167, 1.87635, 1.50156, 1.14451, 1.13674, 0.97626, 0.84423, 0.7372 }); - nd4j::ops::tf_atan2 op; + sd::ops::tf_atan2 op; auto result = op.evaluate({&x, &y}, {}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); - auto z = result->at(0); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - - delete result; } ////////////////////////////////////////////////////////////////////////////// @@ -504,15 +480,13 @@ TEST_F(DeclarableOpsTests10, atan2_test5) { auto exp = NDArrayFactory::create('c', {2,3,4}, {-2.25712, -2.35074, -2.46611, -2.60747,-2.54209, -2.84294, 3.07401, 2.72438, 1.82141, 1.74453, 1.70353, 1.67813, -1.48608, -1.46862, -1.44214, -1.3973 ,-0.61088, -0.30556, 0.06924, 0.42629, 0.43405, 0.59453, 0.72657, 0.8336 }); - nd4j::ops::tf_atan2 op; + sd::ops::tf_atan2 op; auto result = op.evaluate({&y, &x}, {}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); - auto z = result->at(0); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - - delete result; } ////////////////////////////////////////////////////////////////////////////// @@ -523,15 +497,13 @@ TEST_F(DeclarableOpsTests10, atan2_test6) { auto exp = NDArrayFactory::create('c', {1,3,4}, {-2.25712, -1.68608, -1.44214, -0.54006,-2.77695, -2.16855, 0.34972, 0.24585, 2.71267, 1.74453, 1.45312, 0.8336 }); - nd4j::ops::tf_atan2 op; + sd::ops::tf_atan2 op; auto result = op.evaluate({&y, &x}, {}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); - auto z = result->at(0); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - - delete result; } ////////////////////////////////////////////////////////////////////////////// @@ -545,16 +517,14 @@ TEST_F(DeclarableOpsTests10, IGamma_Test1) { 0.0066205109, 0.022211598, 0.040677428, 0.059117373, 0.0000039433403, 0.000086064574, 0.000436067, 0.0012273735}); - nd4j::ops::igamma op; + sd::ops::igamma op; auto result = op.evaluate({&y, &x}, {}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); - auto z = result->at(0); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); + auto z = result.at(0); // z->printBuffer("OUtput"); // exp.printBuffer("EXpect"); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - - delete result; } ////////////////////////////////////////////////////////////////////////////// @@ -567,16 +537,14 @@ TEST_F(DeclarableOpsTests10, IGamma_Test2) { 0.993379, 0.977788, 0.959323, 0.940883, 0.999996, 0.999914, 0.999564, 0.998773}); - nd4j::ops::igammac op; + sd::ops::igammac op; auto result = op.evaluate({&y, &x}, {}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); - auto z = result->at(0); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); + auto z = result.at(0); // z->printBuffer("OUtput"); // exp.printBuffer("EXpect"); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - - delete result; } ////////////////////////////////////////////////////////////////////////////// @@ -590,16 +558,16 @@ TEST_F(DeclarableOpsTests10, LGamma_Test1) { 0.28468287, 0.4348206 , 0.6931472 }); - nd4j::ops::lgamma op; + sd::ops::lgamma op; auto result = op.evaluate({&x}, {}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); - auto z = result->at(0); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); + auto z = result.at(0); // z->printBuffer("OUtput"); // exp.printBuffer("EXpect"); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } ////////////////////////////////////////////////////////////////////////////// @@ -609,17 +577,15 @@ TEST_F(DeclarableOpsTests10, range_test10) { limit = 5.; auto exp = NDArrayFactory::create('c', {5}, {0.,1.,2.,3.,4.}); - nd4j::ops::range op; + sd::ops::range op; auto result = op.evaluate({&limit}, {}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - - delete result; } ////////////////////////////////////////////////////////////////////////////// @@ -631,17 +597,15 @@ TEST_F(DeclarableOpsTests10, range_test11) { start = 0.5; auto exp = NDArrayFactory::create('c', {5}, {0.5,1.5,2.5,3.5,4.5}); - nd4j::ops::range op; + sd::ops::range op; auto result = op.evaluate({&start, &limit}, {}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - - delete result; } ////////////////////////////////////////////////////////////////////////////// @@ -649,17 +613,15 @@ TEST_F(DeclarableOpsTests10, range_test12) { auto exp = NDArrayFactory::create('c', {9}, {0.5f, 1.f , 1.5f, 2.f , 2.5f, 3.f , 3.5f, 4.f , 4.5f}); - nd4j::ops::range op; + sd::ops::range op; auto result = op.evaluate({}, {0.5, 5, 0.5}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - - delete result; } ////////////////////////////////////////////////////////////////////////////// @@ -670,29 +632,26 @@ TEST_F(DeclarableOpsTests10, top_k_permuted_test1) { auto expSorted = NDArrayFactory::create({9., 8., 7., 6., 5.}); // Sorted = False - nd4j::ops::top_k op; + sd::ops::top_k op; auto result = op.evaluate({&x}, {}, {4}, {false}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); - auto zI = result->at(1); + auto z = result.at(0); + auto zI = result.at(1); ASSERT_TRUE(expUnsorted.isSameShape(z)); ASSERT_TRUE(expUnsorted.equalsTo(z)); auto result2 = op.evaluate({&x}, {}, {5}, {true}); - ASSERT_EQ(ND4J_STATUS_OK, result2->status()); + ASSERT_EQ(ND4J_STATUS_OK, result2.status()); - z = result2->at(0); - zI = result2->at(1); + z = result2.at(0); + zI = result2.at(1); ASSERT_TRUE(expSorted.isSameShape(z)); ASSERT_TRUE(expSorted.equalsTo(z)); - - delete result; - delete result2; } ////////////////////////////////////////////////////////////////////////////// @@ -703,29 +662,26 @@ TEST_F(DeclarableOpsTests10, top_k_permuted_test2) { auto expSorted = NDArrayFactory::create({9., 8., 7., 6., 5.}); // Sorted = False - nd4j::ops::top_k op; + sd::ops::top_k op; auto result = op.evaluate({&x}, {}, {5}, {false}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); - auto zI = result->at(1); + auto z = result.at(0); + auto zI = result.at(1); ASSERT_TRUE(expUnsorted.isSameShape(z)); ASSERT_TRUE(expUnsorted.equalsTo(z)); auto result2 = op.evaluate({&x}, {}, {5}, {true}); - ASSERT_EQ(ND4J_STATUS_OK, result2->status()); + ASSERT_EQ(ND4J_STATUS_OK, result2.status()); - z = result2->at(0); - zI = result2->at(1); + z = result2.at(0); + zI = result2.at(1); ASSERT_TRUE(expSorted.isSameShape(z)); ASSERT_TRUE(expSorted.equalsTo(z)); - - delete result; - delete result2; } /////////////////////////////////////////////////////////////////// @@ -737,17 +693,15 @@ TEST_F(DeclarableOpsTests10, sparse_softmax_cross_entropy_loss_with_logits_test1 logits.linspace(0.1, 0.1); - nd4j::ops::sparse_softmax_cross_entropy_loss_with_logits op; + sd::ops::sparse_softmax_cross_entropy_loss_with_logits op; auto results = op.evaluate({&labels, &logits}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto output = results->at(0); + auto output = results.at(0); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - - delete results; } /////////////////////////////////////////////////////////////////// @@ -759,39 +713,35 @@ TEST_F(DeclarableOpsTests10, sparse_softmax_cross_entropy_loss_with_logits_test2 logits.linspace(0.1, 0.1); - nd4j::ops::sparse_softmax_cross_entropy_loss_with_logits op; + sd::ops::sparse_softmax_cross_entropy_loss_with_logits op; auto results = op.evaluate({&labels, &logits}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto output = results->at(0); + auto output = results.at(0); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - - delete results; } /////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests10, sparse_softmax_cross_entropy_loss_with_logits_test3) { - NDArray labels('c', {1}, std::vector{0}, nd4j::DataType::INT32); + NDArray labels('c', {1}, std::vector{0}, sd::DataType::INT32); auto logits = NDArrayFactory::create('c', {1,3}); auto expected = NDArrayFactory::create('c', {1}, {1.20194}); logits.linspace(0.1, 0.1); - nd4j::ops::sparse_softmax_cross_entropy_loss_with_logits op; + sd::ops::sparse_softmax_cross_entropy_loss_with_logits op; auto results = op.evaluate({&labels, &logits}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto output = results->at(0); + auto output = results.at(0); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - - delete results; } /////////////////////////////////////////////////////////////////// @@ -803,65 +753,15 @@ TEST_F(DeclarableOpsTests10, sparse_softmax_cross_entropy_loss_with_logits_test4 logits.linspace(0.1, 0.1); - nd4j::ops::sparse_softmax_cross_entropy_loss_with_logits op; + sd::ops::sparse_softmax_cross_entropy_loss_with_logits op; auto results = op.evaluate({&labels, &logits}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto output = results->at(0); + auto output = results.at(0); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - - delete results; -} - -/////////////////////////////////////////////////////////////////// -TEST_F(DeclarableOpsTests10, split_test4) { - - auto input = NDArrayFactory::create('c', {10},{1.f,2.f,3.f,4.f,5.f,6.f,7.f,8.f,9.f,10.f}); - auto axis = NDArrayFactory::create(-1); - auto exp1 = NDArrayFactory::create('c', {5}, {1.f,2.f,3.f,4.f,5.f}); - auto exp2 = NDArrayFactory::create('c', {5}, {6.f,7.f,8.f,9.f,10.f}); - - nd4j::ops::split op; - auto results = op.evaluate({&input, &axis}, {}, {2}, {}); - - ASSERT_EQ(ND4J_STATUS_OK, results->status()); - - auto out1 = results->at(0); - auto out2 = results->at(1); - - ASSERT_TRUE(exp1.isSameShape(out1)); - ASSERT_TRUE(exp2.isSameShape(out2)); - ASSERT_TRUE(exp1.equalsTo(out1)); - ASSERT_TRUE(exp2.equalsTo(out2)); - - delete results; -} - - -/////////////////////////////////////////////////////////////////// -TEST_F(DeclarableOpsTests10, split_test5) { - - auto input = NDArrayFactory::create('c', {3,8},{1.f,2.f,3.f,4.f,5.f,6.f,7.f,8.f,9.f,10.f,11.f,12.f,13.f,14.f,15.f,16.f,17.f,18.f,19.f,20.f,21.f,22.f,23.f,24.f}); - auto exp1 = NDArrayFactory::create('c', {3,4}, {1.f,2.f,3.f,4.f, 9.f,10.f,11.f,12.f, 17.f,18.f,19.f,20.f}); - auto exp2 = NDArrayFactory::create('c', {3,4}, {5.f,6.f,7.f,8.f, 13.f,14.f,15.f,16.f, 21.f,22.f,23.f,24.f}); - - nd4j::ops::split op; - auto results = op.evaluate({&input}, {}, {2,-1},{}); - - ASSERT_EQ(ND4J_STATUS_OK, results->status()); - - auto out1 = results->at(0); - auto out2 = results->at(1); - - ASSERT_TRUE(exp1.isSameShape(out1)); - ASSERT_TRUE(exp2.isSameShape(out2)); - ASSERT_TRUE(exp1.equalsTo(out1)); - ASSERT_TRUE(exp2.equalsTo(out2)); - - delete results; } /////////////////////////////////////////////////////////////////// @@ -871,17 +771,15 @@ TEST_F(DeclarableOpsTests10, histogram_fixed_width_test1) { auto range = NDArrayFactory::create('c', {2}, {0, 5}); auto exp = NDArrayFactory::create('c', {5}, {2, 1, 1, 0, 2}); - nd4j::ops::histogram_fixed_width op; + sd::ops::histogram_fixed_width op; auto results = op.evaluate({&input, &range}, {}, {5}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto out = results->at(0); + auto out = results.at(0); ASSERT_TRUE(exp.isSameShape(out)); ASSERT_TRUE(exp.equalsTo(out)); - - delete results; } /////////////////////////////////////////////////////////////////// @@ -891,17 +789,15 @@ TEST_F(DeclarableOpsTests10, histogram_fixed_width_test2) { auto range = NDArrayFactory::create('c', {2}, {0, 5}); auto exp = NDArrayFactory::create('c', {5}, {5, 2, 5, 3, 9}); - nd4j::ops::histogram_fixed_width op; + sd::ops::histogram_fixed_width op; auto results = op.evaluate({&input, &range}, {}, {5}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto out = results->at(0); + auto out = results.at(0); ASSERT_TRUE(exp.isSameShape(out)); ASSERT_TRUE(exp.equalsTo(out)); - - delete results; } /////////////////////////////////////////////////////////////////// @@ -911,17 +807,15 @@ TEST_F(DeclarableOpsTests10, histogram_fixed_width_test3) { auto range = NDArrayFactory::create('c', {1,2,1}, {0, 5}); auto exp = NDArrayFactory::create('c', {5}, {5, 2, 5, 4, 8}); - nd4j::ops::histogram_fixed_width op; + sd::ops::histogram_fixed_width op; auto results = op.evaluate({&input, &range}, {}, {5}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto out = results->at(0); + auto out = results.at(0); ASSERT_TRUE(exp.isSameShape(out)); ASSERT_TRUE(exp.equalsTo(out)); - - delete results; } /////////////////////////////////////////////////////////////////// @@ -936,17 +830,15 @@ TEST_F(DeclarableOpsTests10, histogram_fixed_width_test4) { auto range = NDArrayFactory::create('c', {1,2}, {0, 50}); auto exp = NDArrayFactory::create('c', {5}, {22, 17, 24, 19, 18}); - nd4j::ops::histogram_fixed_width op; + sd::ops::histogram_fixed_width op; auto results = op.evaluate({&input, &range}, {}, {5}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto out = results->at(0); + auto out = results.at(0); ASSERT_TRUE(exp.isSameShape(out)); ASSERT_TRUE(exp.equalsTo(out)); - - delete results; } /////////////////////////////////////////////////////////////////// @@ -962,18 +854,16 @@ TEST_F(DeclarableOpsTests10, histogram_fixed_width_test5) { // auto exp = NDArrayFactory::create('c', {5}, {23, 19, 20, 23, 15}); // 23, 15, 24, 17, 21 auto exp = NDArrayFactory::create('c', {5}, {23, 15, 24, 17, 21}); - nd4j::ops::histogram_fixed_width op; + sd::ops::histogram_fixed_width op; auto results = op.evaluate({&input, &range}, {}, {5}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *out = results->at(0); + auto *out = results.at(0); ASSERT_TRUE(exp.isSameShape(out)); // out->printBuffer("5HIST"); ASSERT_TRUE(exp.equalsTo(out)); - - delete results; } /////////////////////////////////////////////////////////////////// @@ -985,19 +875,17 @@ TEST_F(DeclarableOpsTests10, histogram_fixed_width_test6) { auto exp = NDArrayFactory::create('c', {5}, {3, 1, 2, 0, 1}); - nd4j::ops::histogram_fixed_width op; + sd::ops::histogram_fixed_width op; auto results = op.evaluate({&input, &range, &bins}, {}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto out = results->at(0); + auto out = results.at(0); // out->printShapeInfo(); // out->printIndexedBuffer(); ASSERT_TRUE(exp.isSameShape(out)); ASSERT_TRUE(exp.equalsTo(out)); - - delete results; } /////////////////////////////////////////////////////////////////// @@ -1009,17 +897,15 @@ TEST_F(DeclarableOpsTests10, NTH_Element_Test_1) { //input.linspace(1.f); - nd4j::ops::nth_element op; + sd::ops::nth_element op; auto results = op.evaluate({&input, &n}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - NDArray* output = results->at(0); + NDArray* output = results.at(0); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - - delete results; } /////////////////////////////////////////////////////////////////// @@ -1031,17 +917,15 @@ TEST_F(DeclarableOpsTests10, NTH_Element_Test_2) { // input.linspace(1.f); - nd4j::ops::nth_element op; + sd::ops::nth_element op; auto results = op.evaluate({&input, &n}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - NDArray* output = results->at(0); + NDArray* output = results.at(0); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - - delete results; } /////////////////////////////////////////////////////////////////// @@ -1053,17 +937,15 @@ TEST_F(DeclarableOpsTests10, NTH_Element_Test_3) { //input.linspace(1.f); - nd4j::ops::nth_element op; + sd::ops::nth_element op; auto results = op.evaluate({&input, &n}, {}, {1}); // with reverse = true - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - NDArray* output = results->at(0); + NDArray* output = results.at(0); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - - delete results; } /////////////////////////////////////////////////////////////////// @@ -1075,17 +957,15 @@ TEST_F(DeclarableOpsTests10, NTH_Element_Test_4) { //input.linspace(1.f); - nd4j::ops::nth_element op; + sd::ops::nth_element op; auto results = op.evaluate({&input, &n}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - NDArray* output = results->at(0); + NDArray* output = results.at(0); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - - delete results; } /////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests10, NTH_Element_Test_04) { @@ -1096,17 +976,15 @@ TEST_F(DeclarableOpsTests10, NTH_Element_Test_04) { input.linspace(1.f); - nd4j::ops::nth_element op; + sd::ops::nth_element op; auto results = op.evaluate({&input, &n}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - NDArray* output = results->at(0); + NDArray* output = results.at(0); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - - delete results; } /////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests10, NTH_Element_Test_5) { @@ -1117,17 +995,15 @@ TEST_F(DeclarableOpsTests10, NTH_Element_Test_5) { // input.linspace(1.f); - nd4j::ops::nth_element op; + sd::ops::nth_element op; auto results = op.evaluate({&input, &n}, {}, {1}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - NDArray* output = results->at(0); + NDArray* output = results.at(0); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - - delete results; } /////////////////////////////////////////////////////////////////// @@ -1139,16 +1015,14 @@ TEST_F(DeclarableOpsTests10, NTH_Element_Test_6) { // input.linspace(1.f); - nd4j::ops::nth_element op; + sd::ops::nth_element op; auto results = op.evaluate({&input, &n}, {}, {0}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - NDArray* output = results->at(0); + NDArray* output = results.at(0); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - - delete results; } /////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests10, NTH_Element_Test_06) { @@ -1159,16 +1033,14 @@ TEST_F(DeclarableOpsTests10, NTH_Element_Test_06) { // input.linspace(1.f); - nd4j::ops::nth_element op; + sd::ops::nth_element op; auto results = op.evaluate({&input, &n}, {}, {1}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - NDArray* output = results->at(0); + NDArray* output = results.at(0); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - - delete results; } /////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests10, NTH_Element_Test_7) { @@ -1185,17 +1057,15 @@ TEST_F(DeclarableOpsTests10, NTH_Element_Test_7) { //input.linspace(1.f); - nd4j::ops::nth_element op; + sd::ops::nth_element op; auto results = op.evaluate({&input, &n}, {}, {0}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - NDArray* output = results->at(0); + NDArray* output = results.at(0); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - - delete results; } /////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests10, NTH_Element_Test_8) { @@ -1212,17 +1082,15 @@ TEST_F(DeclarableOpsTests10, NTH_Element_Test_8) { //input.linspace(1.f); - nd4j::ops::nth_element op; + sd::ops::nth_element op; auto results = op.evaluate({&input, &n}, {}, {1}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - NDArray* output = results->at(0); + NDArray* output = results.at(0); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - - delete results; } /////////////////////////////////////////////////////////////////// @@ -1234,17 +1102,15 @@ TEST_F(DeclarableOpsTests10, broadcast_to_test1) { input.linspace(1.f); - nd4j::ops::broadcast_to op; + sd::ops::broadcast_to op; auto results = op.evaluate({&input, &shape}, {}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *output = results->at(0); + auto *output = results.at(0); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - - delete results; } /////////////////////////////////////////////////////////////////// @@ -1256,17 +1122,15 @@ TEST_F(DeclarableOpsTests10, broadcast_to_test2) { input.linspace(1.f); - nd4j::ops::broadcast_to op; + sd::ops::broadcast_to op; auto results = op.evaluate({&input, &shape}, {}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *output = results->at(0); + auto *output = results.at(0); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - - delete results; } /////////////////////////////////////////////////////////////////// @@ -1278,17 +1142,15 @@ TEST_F(DeclarableOpsTests10, broadcast_to_test3) { input.linspace(1.f); - nd4j::ops::broadcast_to op; + sd::ops::broadcast_to op; auto results = op.evaluate({&input, &shape}, {}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *output = results->at(0); + auto *output = results.at(0); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - - delete results; } /////////////////////////////////////////////////////////////////// @@ -1298,17 +1160,15 @@ TEST_F(DeclarableOpsTests10, broadcast_to_test4) { auto shape = NDArrayFactory::create('c', {2}, {3.f, 3.f}); auto exp = NDArrayFactory::create('c', {3,3}, {10.f, 10.f, 10.f,10.f, 10.f, 10.f, 10.f, 10.f, 10.f}); - nd4j::ops::broadcast_to op; + sd::ops::broadcast_to op; auto results = op.evaluate({&input, &shape}, {}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *output = results->at(0); + auto *output = results.at(0); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - - delete results; } /////////////////////////////////////////////////////////////////// @@ -1318,17 +1178,15 @@ TEST_F(DeclarableOpsTests10, broadcast_to_test5) { auto shape = NDArrayFactory::create('c', {1}, {3.f}); auto exp = NDArrayFactory::create('c', {3}, {10.f, 10.f, 10.f}); - nd4j::ops::broadcast_to op; + sd::ops::broadcast_to op; auto results = op.evaluate({&input, &shape}, {}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *output = results->at(0); + auto *output = results.at(0); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - - delete results; } /////////////////////////////////////////////////////////////////// @@ -1338,17 +1196,15 @@ TEST_F(DeclarableOpsTests10, broadcast_to_test6) { auto shape = NDArrayFactory::create(1.f); auto exp = NDArrayFactory::create('c', {1}, {10.f}); - nd4j::ops::broadcast_to op; + sd::ops::broadcast_to op; auto results = op.evaluate({&input, &shape}, {}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *output = results->at(0); + auto *output = results.at(0); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - - delete results; } /////////////////////////////////////////////////////////////////// @@ -1358,17 +1214,15 @@ TEST_F(DeclarableOpsTests10, broadcast_to_test7) { auto shape = NDArrayFactory::create(1); auto exp = NDArrayFactory::create('c', {1}, {10.}); - nd4j::ops::broadcast_to op; + sd::ops::broadcast_to op; auto results = op.evaluate({&input, &shape}, {}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *output = results->at(0); + auto *output = results.at(0); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - - delete results; } /////////////////////////////////////////////////////////////////// @@ -1380,17 +1234,15 @@ TEST_F(DeclarableOpsTests10, broadcast_to_test8) { input.linspace(1.f); - nd4j::ops::broadcast_to op; + sd::ops::broadcast_to op; auto results = op.evaluate({&input, &shape}, {}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *output = results->at(0); + auto *output = results.at(0); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - - delete results; } /////////////////////////////////////////////////////////////////// @@ -1402,17 +1254,15 @@ TEST_F(DeclarableOpsTests10, broadcast_to_test9) { 1.f, 1.f, 1.f,2.f, 2.f, 2.f,3.f, 3.f, 3.f,4.f, 4.f, 4.f,5.f, 5.f, 5.f}); input.linspace(1.f); - nd4j::ops::broadcast_to op; + sd::ops::broadcast_to op; auto results = op.evaluate({&input, &shape}, {}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *output = results->at(0); + auto *output = results.at(0); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - - delete results; } /////////////////////////////////////////////////////////////////// @@ -1424,17 +1274,15 @@ TEST_F(DeclarableOpsTests10, broadcast_to_test10) { 1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 7.f, 8.f, 9.f,10.f, 11.f, 12.f,13.f, 14.f, 15.f}); input.linspace(1.f); - nd4j::ops::broadcast_to op; + sd::ops::broadcast_to op; auto results = op.evaluate({&input, &shape}, {}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *output = results->at(0); + auto *output = results.at(0); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - - delete results; } //////////////////////////////////////////////////////////////////// @@ -1480,18 +1328,18 @@ TEST_F(DeclarableOpsTests10, ImageResizeBilinear_Test1) { //input = 1.f; input.linspace(1); - nd4j::ops::resize_bilinear op; + sd::ops::resize_bilinear op; auto results = op.evaluate({&input}, {}, {10, 10}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - NDArray* result = results->at(0); + NDArray* result = results.at(0); - //result->printIndexedBuffer("Resized to 10x10"); + //result.printIndexedBuffer("Resized to 10x10"); //expected.printIndexedBuffer("Expect for 10x10"); ASSERT_TRUE(expected.isSameShape(result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; + } //////////////////////////////////////////////////////////////////// @@ -1502,15 +1350,13 @@ TEST_F(DeclarableOpsTests10, ImageResizeBilinear_Test_11) { input.assign(0.8f); //linspace(1); auto size = NDArrayFactory::create({65,65}); auto ex = NDArrayFactory::create('c', {1,65,65,256}); - nd4j::ops::resize_bilinear op; + sd::ops::resize_bilinear op; auto results = op.evaluate({&input, &size}, {}, {}, {false}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - NDArray* result = results->at(0); + NDArray* result = results.at(0); ASSERT_NE(*result, ex); - - delete results; } //////////////////////////////////////////////////////////////////// @@ -1521,15 +1367,13 @@ TEST_F(DeclarableOpsTests10, ImageResizeBilinear_Test_12) { input.assign(0.8f); //linspace(1); auto size = NDArrayFactory::create({65,65}); auto ex = NDArrayFactory::create('c', {1,65,65,256}); - nd4j::ops::resize_bilinear op; + sd::ops::resize_bilinear op; auto results = op.evaluate({&input, &size}, {}, {}, {true}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - NDArray* result = results->at(0); + NDArray* result = results.at(0); ASSERT_NE(*result, ex); - - delete results; } TEST_F(DeclarableOpsTests10, ImageResizeBilinear_Test1_1) { @@ -1565,18 +1409,18 @@ TEST_F(DeclarableOpsTests10, ImageResizeBilinear_Test1_1) { //input = 1.f; input.linspace(1); - nd4j::ops::resize_bilinear op; + sd::ops::resize_bilinear op; auto results = op.evaluate({&input}, {}, {4, 5}, {false, true}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - NDArray* result = results->at(0); + NDArray* result = results.at(0); -// result->printIndexedBuffer("Resized to 4x5 bilinear with half pixels"); +// result.printIndexedBuffer("Resized to 4x5 bilinear with half pixels"); //expected.printIndexedBuffer("Expect for 10x10"); ASSERT_TRUE(expected.isSameShape(result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; + } TEST_F(DeclarableOpsTests10, ImageResizeBilinear_Test1_2) { @@ -1612,18 +1456,18 @@ TEST_F(DeclarableOpsTests10, ImageResizeBilinear_Test1_2) { //input = 1.f; input.linspace(1); - nd4j::ops::resize_bilinear op; + sd::ops::resize_bilinear op; auto results = op.evaluate({&input}, {}, {4, 5}, {false, true}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - NDArray* result = results->at(0); + NDArray* result = results.at(0); -// result->printBuffer("Resized to 4x5"); +// result.printBuffer("Resized to 4x5"); // expected.printBuffer("Expect for 4x5"); ASSERT_TRUE(expected.isSameShape(result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; + } TEST_F(DeclarableOpsTests10, ImageResizeBilinear_Test01) { @@ -1668,20 +1512,20 @@ TEST_F(DeclarableOpsTests10, ImageResizeBilinear_Test01) { //input = 1.f; input.linspace(1); - nd4j::ops::resize_bilinear op; + sd::ops::resize_bilinear op; auto results = op.evaluate({&input}, {}, {10, 10}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - NDArray* result = results->at(0); + NDArray* result = results.at(0); - //result->printIndexedBuffer("Resized to 10x10"); + //result.printIndexedBuffer("Resized to 10x10"); //expected.printIndexedBuffer("Expect for 10x10"); -// result->printShapeInfo("Output shape"); +// result.printShapeInfo("Output shape"); // expected.printShapeInfo("Expect shape"); ASSERT_TRUE(expected.isSameShape(result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; + } TEST_F(DeclarableOpsTests10, ImageResizeBilinear_Test02) { @@ -1823,20 +1667,20 @@ TEST_F(DeclarableOpsTests10, ImageResizeBilinear_Test02) { }); //input.linspace(1); - nd4j::ops::resize_bilinear op; + sd::ops::resize_bilinear op; auto results = op.evaluate({&input}, {}, {9, 9}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - NDArray* result = results->at(0); + NDArray* result = results.at(0); -// result->printBuffer("Resized to 9x9"); +// result.printBuffer("Resized to 9x9"); // expected.printBuffer("Expect for 9x9"); -// result->printShapeInfo("Output shape"); +// result.printShapeInfo("Output shape"); // expected.printShapeInfo("Expect shape"); ASSERT_TRUE(expected.isSameShape(result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; + } //////////////////////////////////////////////////////////////////// @@ -1882,17 +1726,15 @@ TEST_F(DeclarableOpsTests10, ImageResizeBilinear_Test2) { //input = 1.f; input.linspace(1); - nd4j::ops::resize_bilinear op; + sd::ops::resize_bilinear op; auto results = op.evaluate({&input, &size}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - NDArray* result = results->at(0); + NDArray* result = results.at(0); ASSERT_TRUE(expected.isSameShape(result)); ASSERT_TRUE(expected.equalsTo(result)); - - delete results; } //////////////////////////////////////////////////////////////////// @@ -2012,17 +1854,15 @@ TEST_F(DeclarableOpsTests10, ImageResizeBilinear_Test3) { //input = 1.f; input.linspace(1); - nd4j::ops::resize_bilinear op; + sd::ops::resize_bilinear op; auto results = op.evaluate({&input}, {}, {10, 10}, {true}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - NDArray* result = results->at(0); + NDArray* result = results.at(0); ASSERT_TRUE(expected.isSameShape(result)); ASSERT_TRUE(expected.equalsTo(result)); - - delete results; } //////////////////////////////////////////////////////////////////// @@ -2141,19 +1981,17 @@ TEST_F(DeclarableOpsTests10, ImageResizeBilinear_Test4) { //input = 1.f; input.linspace(1); - nd4j::ops::resize_bilinear op; + sd::ops::resize_bilinear op; auto results = op.evaluate({&input, &size}, {}, {}, {true}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - NDArray* result = results->at(0); -// result->printIndexedBuffer("Resized to 10x10"); + NDArray* result = results.at(0); +// result.printIndexedBuffer("Resized to 10x10"); // expected.printIndexedBuffer("Expected of 10x10"); -// result->printShapeInfo("Resized to 10x10 shape"); +// result.printShapeInfo("Resized to 10x10 shape"); ASSERT_TRUE(expected.isSameShape(result)); ASSERT_TRUE(expected.equalsTo(result)); - - delete results; } //////////////////////////////////////////////////////////////////// @@ -2165,13 +2003,13 @@ TEST_F(DeclarableOpsTests10, LinSpace_Test1) { NDArray expect = NDArrayFactory::create({1., 1.5, 2., 2.5, 3., 3.5, 4., 4.5, 5., 5.5, 6., 6.5, 7., 7.5, 8., 8.5, 9., 9.5, 10., 10.5, 11., 11.5, 12.}); - nd4j::ops::lin_space op; + sd::ops::lin_space op; auto result = op.evaluate({&start, &finish, &num}, {}, {}); - ASSERT_EQ(result->status(), ND4J_STATUS_OK); - auto res = result->at(0); + ASSERT_EQ(result.status(), ND4J_STATUS_OK); + auto res = result.at(0); ASSERT_TRUE(expect.equalsTo(res)); - delete result; + } //////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests10, ImageResizeNeighbor_Test1) { @@ -2207,19 +2045,17 @@ TEST_F(DeclarableOpsTests10, ImageResizeNeighbor_Test1) { //input = 1.f; input.linspace(1); - nd4j::ops::resize_nearest_neighbor op; + sd::ops::resize_nearest_neighbor op; auto results = op.evaluate({&input}, {}, {4, 5}, {false, false}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - NDArray* result = results->at(0); + NDArray* result = results.at(0); -// result->printIndexedBuffer("Resized to 4x5"); +// result.printIndexedBuffer("Resized to 4x5"); // expected.printIndexedBuffer("Expect for 4x5"); ASSERT_TRUE(expected.isSameShape(result)); ASSERT_TRUE(expected.equalsTo(result)); - - delete results; } TEST_F(DeclarableOpsTests10, ImageResizeNeighbor_Test1_1) { @@ -2255,19 +2091,17 @@ TEST_F(DeclarableOpsTests10, ImageResizeNeighbor_Test1_1) { //input = 1.f; input.linspace(1); - nd4j::ops::resize_nearest_neighbor op; + sd::ops::resize_nearest_neighbor op; auto results = op.evaluate({&input}, {}, {4, 5}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - NDArray* result = results->at(0); + NDArray* result = results.at(0); -// result->printIndexedBuffer("Resized to 4x5"); +// result.printIndexedBuffer("Resized to 4x5"); // expected.printIndexedBuffer("Expect for 4x5"); ASSERT_TRUE(expected.isSameShape(result)); ASSERT_TRUE(expected.equalsTo(result)); - - delete results; } TEST_F(DeclarableOpsTests10, ImageResizeNeighbor_Test1_1_1) { @@ -2303,19 +2137,17 @@ TEST_F(DeclarableOpsTests10, ImageResizeNeighbor_Test1_1_1) { //input = 1.f; input.linspace(1); - nd4j::ops::resize_nearest_neighbor op; + sd::ops::resize_nearest_neighbor op; auto results = op.evaluate({&input}, {}, {4,5}, {false, true}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - NDArray* result = results->at(0); + NDArray* result = results.at(0); -// result->printIndexedBuffer("Resized to 4x5"); +// result.printIndexedBuffer("Resized to 4x5"); // expected.printBuffer("Expect for 4x5"); ASSERT_TRUE(expected.isSameShape(result)); ASSERT_TRUE(expected.equalsTo(result)); - - delete results; } TEST_F(DeclarableOpsTests10, ImageResizeNeighbor_Test01) { @@ -2350,19 +2182,17 @@ TEST_F(DeclarableOpsTests10, ImageResizeNeighbor_Test01) { //input = 1.f; input.linspace(1); - nd4j::ops::resize_nearest_neighbor op; + sd::ops::resize_nearest_neighbor op; auto results = op.evaluate({&input}, {}, {4, 5}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - NDArray* result = results->at(0); + NDArray* result = results.at(0); - //result->printIndexedBuffer("Resized to 4x5"); + //result.printIndexedBuffer("Resized to 4x5"); //expected.printIndexedBuffer("Expect for 4x5"); ASSERT_TRUE(expected.isSameShape(result)); ASSERT_TRUE(expected.equalsTo(result)); - - delete results; } //////////////////////////////////////////////////////////////////// @@ -2372,18 +2202,16 @@ TEST_F(DeclarableOpsTests10, ReduceLogSumExpTest_1) { NDArray expected = NDArrayFactory::create(2.5206409f); - nd4j::ops::reduce_logsumexp op; + sd::ops::reduce_logsumexp op; auto results = op.evaluate({&input}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto result = results->at(0); + auto result = results.at(0); ASSERT_TRUE(expected.isSameShapeStrict(*result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; - } //////////////////////////////////////////////////////////////////// @@ -2393,18 +2221,18 @@ TEST_F(DeclarableOpsTests10, ReduceLogSumExpTest_2) { NDArray expected = NDArrayFactory::create({1.0986123f, 1.8619947f, 1.0986123f}); - nd4j::ops::reduce_logsumexp op; + sd::ops::reduce_logsumexp op; auto results = op.evaluate({&input}, {}, {0}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto result = results->at(0); -// result->printIndexedBuffer("REDUCE_LOGSUMEXP"); + auto result = results.at(0); +// result.printIndexedBuffer("REDUCE_LOGSUMEXP"); // expected.printIndexedBuffer("LSE EXPECTED"); ASSERT_TRUE(expected.isSameShapeStrict(*result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; + } //////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests10, ReduceLogSumExpTest_3) { @@ -2413,18 +2241,16 @@ TEST_F(DeclarableOpsTests10, ReduceLogSumExpTest_3) { NDArray expected = NDArrayFactory::create('c', {1,3}, {1.0986123f, 1.8619947f, 1.0986123f}); - nd4j::ops::reduce_logsumexp op; + sd::ops::reduce_logsumexp op; auto results = op.evaluate({&input}, {1.f}, {0}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto result = results->at(0); -// result->printIndexedBuffer("REDUCE_LOGSUMEXP"); + auto result = results.at(0); +// result.printIndexedBuffer("REDUCE_LOGSUMEXP"); // expected.printIndexedBuffer("LSE EXPECTED"); ASSERT_TRUE(expected.isSameShapeStrict(*result)); ASSERT_TRUE(expected.equalsTo(result)); - - delete results; } //////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests10, Image_NonMaxSuppressing_1) { @@ -2434,18 +2260,18 @@ TEST_F(DeclarableOpsTests10, Image_NonMaxSuppressing_1) { NDArray expected = NDArrayFactory::create('c', {3}, {2, 1, 0}); boxes.linspace(1.f); - nd4j::ops::non_max_suppression op; + sd::ops::non_max_suppression op; auto results = op.evaluate({&boxes, &scores}, {}, {3}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - NDArray* result = results->at(0); - //result->printIndexedBuffer("OOOOUUUUTTT"); + NDArray* result = results.at(0); + //result.printIndexedBuffer("OOOOUUUUTTT"); ASSERT_TRUE(expected.isSameShapeStrict(*result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; + } //////////////////////////////////////////////////////////////////// @@ -2456,17 +2282,15 @@ TEST_F(DeclarableOpsTests10, Image_NonMaxSuppressing_2) { NDArray scales = NDArrayFactory::create('c', {6}, {0.9f, .75f, .6f, .95f, .5f, .3f}); //3, 0, 1, 2, 4, 5 NDArray expected = NDArrayFactory::create('c', {3}, {3,0,5}); - nd4j::ops::non_max_suppression op; + sd::ops::non_max_suppression op; auto results = op.evaluate({&boxes, &scales}, {0.5}, {3}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - NDArray* result = results->at(0); -// result->printBuffer("NonMaxSuppression OUtput2"); + NDArray* result = results.at(0); +// result.printBuffer("NonMaxSuppression OUtput2"); ASSERT_TRUE(expected.isSameShapeStrict(*result)); ASSERT_TRUE(expected.equalsTo(result)); - - delete results; } //////////////////////////////////////////////////////////////////// @@ -2478,17 +2302,15 @@ TEST_F(DeclarableOpsTests10, Image_NonMaxSuppressing_3) { NDArray scales = NDArrayFactory::create('c', {3}, {0.0029f, 0.8135f, 0.4873f}); //3, 0, 1, 2, 4, 5 NDArray expected = NDArrayFactory::create('c', {1}, {1}); - nd4j::ops::non_max_suppression op; + sd::ops::non_max_suppression op; auto results = op.evaluate({&boxes, &scales}, {0.5, 0.5}, {2}); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); - NDArray* result = results->at(0); -// result->printBuffer("NonMaxSuppression OUtput3"); + NDArray* result = results.at(0); +// result.printBuffer("NonMaxSuppression OUtput3"); ASSERT_TRUE(expected.isSameShapeStrict(*result)); ASSERT_TRUE(expected.equalsTo(result)); - - delete results; } TEST_F(DeclarableOpsTests10, Image_NonMaxSuppressing_4) { @@ -2501,17 +2323,15 @@ TEST_F(DeclarableOpsTests10, Image_NonMaxSuppressing_4) { NDArray maxSize = NDArrayFactory::create(2); NDArray threshold = NDArrayFactory::create(0.5f); NDArray scoreThreshold = NDArrayFactory::create(0.5); - nd4j::ops::non_max_suppression op; + sd::ops::non_max_suppression op; auto results = op.evaluate({&boxes, &scales, &maxSize, &threshold, &scoreThreshold}, {}, {}); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); - NDArray* result = results->at(0); -// result->printBuffer("NonMaxSuppression OUtput4"); + NDArray* result = results.at(0); +// result.printBuffer("NonMaxSuppression OUtput4"); ASSERT_TRUE(expected.isSameShapeStrict(*result)); ASSERT_TRUE(expected.equalsTo(result)); - - delete results; } TEST_F(DeclarableOpsTests10, Image_NonMaxSuppressing_5) { @@ -2523,17 +2343,15 @@ TEST_F(DeclarableOpsTests10, Image_NonMaxSuppressing_5) { NDArray maxSize = NDArrayFactory::create(2); NDArray threshold = NDArrayFactory::create(0.5f); NDArray scoreThreshold = NDArrayFactory::create(-DataTypeUtils::infOrMax()); - nd4j::ops::non_max_suppression op; + sd::ops::non_max_suppression op; auto results = op.evaluate({&boxes, &scales, &maxSize, &threshold, &scoreThreshold}, {}, {}); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); - NDArray* result = results->at(0); -// result->printBuffer("NonMaxSuppression OUtput4"); + NDArray* result = results.at(0); +// result.printBuffer("NonMaxSuppression OUtput4"); ASSERT_TRUE(expected.isSameShapeStrict(*result)); ASSERT_TRUE(expected.equalsTo(result)); - - delete results; } TEST_F(DeclarableOpsTests10, Image_NonMaxSuppressing_6) { @@ -2546,18 +2364,16 @@ TEST_F(DeclarableOpsTests10, Image_NonMaxSuppressing_6) { NDArray maxSize = NDArrayFactory::create(2); NDArray threshold = NDArrayFactory::create(0.5f); NDArray scoreThreshold = NDArrayFactory::create(-DataTypeUtils::infOrMax()); - nd4j::ops::non_max_suppression_v3 op; + sd::ops::non_max_suppression_v3 op; auto results = op.evaluate({&boxes, &scales, &maxSize, &threshold, &scoreThreshold}, {}, {}); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); - NDArray* result = results->at(0); -// result->printBuffer("NonMaxSuppression OUtput6"); -// result->printShapeInfo("Ouput6 shape is"); + NDArray* result = results.at(0); +// result.printBuffer("NonMaxSuppression OUtput6"); +// result.printShapeInfo("Ouput6 shape is"); ASSERT_TRUE(expected.isSameShapeStrict(*result)); ASSERT_TRUE(expected.equalsTo(result)); - - delete results; } TEST_F(DeclarableOpsTests10, Image_NonMaxSuppressing_06) { @@ -2570,18 +2386,16 @@ TEST_F(DeclarableOpsTests10, Image_NonMaxSuppressing_06) { NDArray maxSize = NDArrayFactory::create(2); NDArray threshold = NDArrayFactory::create(0.5f); NDArray scoreThreshold = NDArrayFactory::create(-DataTypeUtils::infOrMax()); - nd4j::ops::non_max_suppression_v3 op; + sd::ops::non_max_suppression_v3 op; auto results = op.evaluate({&boxes, &scales, &maxSize, &threshold, &scoreThreshold}, {}, {}); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); - NDArray* result = results->at(0); -// result->printBuffer("NonMaxSuppression OUtput06"); -// result->printShapeInfo("Ouput06 shape is"); + NDArray* result = results.at(0); +// result.printBuffer("NonMaxSuppression OUtput06"); +// result.printShapeInfo("Ouput06 shape is"); ASSERT_TRUE(expected.isSameShapeStrict(*result)); ASSERT_TRUE(expected.equalsTo(result)); - - delete results; } TEST_F(DeclarableOpsTests10, Image_NonMaxSuppressing_7) { @@ -2593,17 +2407,15 @@ TEST_F(DeclarableOpsTests10, Image_NonMaxSuppressing_7) { NDArray maxSize = NDArrayFactory::create(0); NDArray threshold = NDArrayFactory::create(0.5f); NDArray scoreThreshold = NDArrayFactory::create(0.5f); - nd4j::ops::non_max_suppression_v3 op; + sd::ops::non_max_suppression_v3 op; auto results = op.evaluate({&boxes, &scales, &maxSize, &threshold, &scoreThreshold}, {}, {}); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); - NDArray* result = results->at(0); -// result->printBuffer("NonMaxSuppression OUtput7"); -// result->printShapeInfo("Ouput6 shape is"); + NDArray* result = results.at(0); +// result.printBuffer("NonMaxSuppression OUtput7"); +// result.printShapeInfo("Ouput6 shape is"); ASSERT_TRUE(result->isEmpty()); - - delete results; } //////////////////////////////////////////////////////////////////// @@ -2618,17 +2430,15 @@ TEST_F(DeclarableOpsTests10, Image_NonMaxSuppressingOverlap_1) { NDArray max_num = NDArrayFactory::create(3); NDArray expected = NDArrayFactory::create('c', {1,}, {3}); - nd4j::ops::non_max_suppression_overlaps op; + sd::ops::non_max_suppression_overlaps op; auto results = op.evaluate({&boxes, &scores, &max_num}, {0.5, 0.}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - NDArray* result = results->at(0); -// result->printBuffer("NonMaxSuppressionOverlap1 Output"); + NDArray* result = results.at(0); +// result.printBuffer("NonMaxSuppressionOverlap1 Output"); ASSERT_TRUE(expected.isSameShapeStrict(*result)); ASSERT_TRUE(expected.equalsTo(result)); - - delete results; } //////////////////////////////////////////////////////////////////// @@ -2643,17 +2453,15 @@ TEST_F(DeclarableOpsTests10, Image_NonMaxSuppressingOverlap_2) { NDArray max_num = NDArrayFactory::create(3); NDArray expected = NDArrayFactory::create('c', {3,}, {1,1,1}); - nd4j::ops::non_max_suppression_overlaps op; + sd::ops::non_max_suppression_overlaps op; auto results = op.evaluate({&boxes, &scores, &max_num}, {0.5, 0.}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - NDArray* result = results->at(0); -// result->printBuffer("NonMaxSuppressionOverlap Output"); + NDArray* result = results.at(0); +// result.printBuffer("NonMaxSuppressionOverlap Output"); ASSERT_TRUE(expected.isSameShapeStrict(*result)); ASSERT_TRUE(expected.equalsTo(result)); - - delete results; } //////////////////////////////////////////////////////////////////// @@ -2668,17 +2476,15 @@ TEST_F(DeclarableOpsTests10, Image_NonMaxSuppressingOverlap_3) { NDArray max_num = NDArrayFactory::create(5); NDArray expected = NDArrayFactory::create('c', {5,}, {1,1,1,1,1}); - nd4j::ops::non_max_suppression_overlaps op; + sd::ops::non_max_suppression_overlaps op; auto results = op.evaluate({&boxes, &scores, &max_num}, {0.5, 0.}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - NDArray* result = results->at(0); -// result->printBuffer("NonMaxSuppressionOverlap Output"); + NDArray* result = results.at(0); +// result.printBuffer("NonMaxSuppressionOverlap Output"); ASSERT_TRUE(expected.isSameShapeStrict(*result)); ASSERT_TRUE(expected.equalsTo(result)); - - delete results; } //////////////////////////////////////////////////////////////////// @@ -2692,18 +2498,16 @@ TEST_F(DeclarableOpsTests10, Image_CropAndResize_1) { //NDArray ('c', {6}, {0.9f, .75f, .6f, .95f, .5f, .3f}); NDArray expected = NDArrayFactory::create('c', {1,1,1,1}, {2.5f}); - nd4j::ops::crop_and_resize op; + sd::ops::crop_and_resize op; auto results = op.evaluate({&images, &boxes, &boxI, &cropSize}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto result = results->at(0); -// result->printIndexedBuffer("Cropped and Resized"); + auto result = results.at(0); +// result.printIndexedBuffer("Cropped and Resized"); ASSERT_TRUE(expected.isSameShapeStrict(*result)); ASSERT_TRUE(expected.equalsTo(result)); - - delete results; } //////////////////////////////////////////////////////////////////// @@ -2717,89 +2521,81 @@ TEST_F(DeclarableOpsTests10, Image_CropAndResize_2) { //NDArray ('c', {6}, {0.9f, .75f, .6f, .95f, .5f, .3f}); NDArray expected = NDArrayFactory::create('c', {1,1,1,1}, {4.f}); - nd4j::ops::crop_and_resize op; + sd::ops::crop_and_resize op; auto results = op.evaluate({&images, &boxes, &boxI, &cropSize}, {}, {1}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto result = results->at(0); + auto result = results.at(0); ASSERT_TRUE(expected.isSameShapeStrict(*result)); ASSERT_TRUE(expected.equalsTo(result)); - - delete results; } //////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests10, Image_CropAndResize_3) { - NDArray images ('c', {1,2,2,1}, {1,2,3,4}, nd4j::DataType::FLOAT32); - NDArray boxes('c', {1,4}, {0,0,1,1}, nd4j::DataType::FLOAT32); - NDArray boxI('c', {1}, std::vector{0}, nd4j::DataType::INT64); + NDArray images ('c', {1,2,2,1}, {1,2,3,4}, sd::DataType::FLOAT32); + NDArray boxes('c', {1,4}, {0,0,1,1}, sd::DataType::FLOAT32); + NDArray boxI('c', {1}, std::vector{0}, sd::DataType::INT64); NDArray cropSize = NDArrayFactory::create({3, 3}); //NDArray ('c', {6}, {0.9f, .75f, .6f, .95f, .5f, .3f}); - NDArray expected('c', {1,3,3,1}, {1.f, 1.5f, 2., 2.f, 2.5f, 3.f, 3.f, 3.5f, 4.f}, nd4j::DataType::FLOAT32); + NDArray expected('c', {1,3,3,1}, {1.f, 1.5f, 2., 2.f, 2.5f, 3.f, 3.f, 3.5f, 4.f}, sd::DataType::FLOAT32); - nd4j::ops::crop_and_resize op; + sd::ops::crop_and_resize op; auto results = op.evaluate({&images, &boxes, &boxI, &cropSize}, {}, {0}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto result = results->at(0); + auto result = results.at(0); ASSERT_TRUE(expected.isSameShapeStrict(*result)); ASSERT_TRUE(expected.equalsTo(result)); - - delete results; } //////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests10, Image_CropAndResize_4) { - NDArray images('c', {1,2,2,1}, {1, 2, 3, 4}, nd4j::DataType::FLOAT32); - NDArray boxes('c', {1,4}, {0,0,1,1}, nd4j::DataType::FLOAT32); - NDArray boxI('c', {1}, std::vector({0.}), nd4j::DataType::INT32); + NDArray images('c', {1,2,2,1}, {1, 2, 3, 4}, sd::DataType::FLOAT32); + NDArray boxes('c', {1,4}, {0,0,1,1}, sd::DataType::FLOAT32); + NDArray boxI('c', {1}, std::vector({0.}), sd::DataType::INT32); NDArray cropSize = NDArrayFactory::create({3, 3}); //NDArray ('c', {6}, {0.9f, .75f, .6f, .95f, .5f, .3f}); - NDArray expected('c', {1,3,3,1}, {1.f, 2.f, 2.f, 3.f, 4, 4.f, 3.f, 4.f, 4.f}, nd4j::DataType::FLOAT32); + NDArray expected('c', {1,3,3,1}, {1.f, 2.f, 2.f, 3.f, 4, 4.f, 3.f, 4.f, 4.f}, sd::DataType::FLOAT32); - nd4j::ops::crop_and_resize op; + sd::ops::crop_and_resize op; auto results = op.evaluate({&images, &boxes, &boxI, &cropSize}, {}, {1}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto result = results->at(0); - // result->printIndexedBuffer("Cropped and Resized"); + auto result = results.at(0); + // result.printIndexedBuffer("Cropped and Resized"); ASSERT_TRUE(expected.isSameShapeStrict(*result)); ASSERT_TRUE(expected.equalsTo(result)); - - delete results; } //////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests10, Image_CropAndResize_5) { - NDArray images('c', {1, 100, 100, 3}, nd4j::DataType::FLOAT32); - NDArray boxes('c', {1,4}, {0,0,1,1}, nd4j::DataType::FLOAT32); - NDArray boxI('c', {2}, {1,1}, nd4j::DataType::INT32); + NDArray images('c', {1, 100, 100, 3}, sd::DataType::FLOAT32); + NDArray boxes('c', {1,4}, {0,0,1,1}, sd::DataType::FLOAT32); + NDArray boxI('c', {2}, {1,1}, sd::DataType::INT32); NDArray cropSize = NDArrayFactory::create({10, 10}); //NDArray ('c', {6}, {0.9f, .75f, .6f, .95f, .5f, .3f}); - NDArray expected('c', {1, 10, 10,3}, nd4j::DataType::FLOAT32); + NDArray expected('c', {1, 10, 10,3}, sd::DataType::FLOAT32); - nd4j::ops::crop_and_resize op; + sd::ops::crop_and_resize op; auto results = op.evaluate({&images, &boxes, &boxI, &cropSize}, {}, {1}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto result = results->at(0); + auto result = results.at(0); ASSERT_TRUE(expected.isSameShapeStrict(*result)); //ASSERT_TRUE(expected.equalsTo(result)); - - delete results; } //////////////////////////////////////////////////////////////////// @@ -2825,19 +2621,17 @@ TEST_F(DeclarableOpsTests10, Image_DrawBoundingBoxes_1) { 106.f, 107.f, 108.f, 109.f, 110.f, 111.f, 112.f, 113.f, 114.f, 115.f, 116.f, 117.f, 118.f, 119.f, 120.f }); images.linspace(1.); - nd4j::ops::draw_bounding_boxes op; + sd::ops::draw_bounding_boxes op; auto results = op.evaluate({&images, &boxes, &colors}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto result = results->at(0); + auto result = results.at(0); result->syncToHost(); -// result->printBuffer("Bounded boxes"); +// result.printBuffer("Bounded boxes"); // expected.printBuffer("Bounded expec"); ASSERT_TRUE(expected.isSameShapeStrict(*result)); ASSERT_TRUE(expected.equalsTo(result)); - - delete results; } //////////////////////////////////////////////////////////////////// @@ -2858,19 +2652,17 @@ TEST_F(DeclarableOpsTests10, Image_DrawBoundingBoxes_2) { 64.1f , 65.1f, 66.1f, 67.1f, 68.1f , 69.1f, 70.1f , 71.1f , 72.1f , 73.1f , 74.1f, 75.1f, 76.1f, 77.1f , 78.1f, 79.1f , 80.1f , 81.1f }); images.linspace(1.1); - nd4j::ops::draw_bounding_boxes op; + sd::ops::draw_bounding_boxes op; auto results = op.evaluate({&images, &boxes, &colors}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto result = results->at(0); -// result->syncToHost(); -// result->printBuffer("Bounded boxes 2"); + auto result = results.at(0); +// result.syncToHost(); +// result.printBuffer("Bounded boxes 2"); // expected.printBuffer("Bounded expec 2"); ASSERT_TRUE(expected.isSameShapeStrict(*result)); ASSERT_TRUE(expected.equalsTo(result)); - - delete results; } //////////////////////////////////////////////////////////////////// @@ -2911,43 +2703,39 @@ TEST_F(DeclarableOpsTests10, Image_DrawBoundingBoxes_3) { 0.7234f, 0.269f , 0.0062f, 0.0327f, 0.0644f, 0.8428f, 0.9441f, 0.9441f, 0.9441f, 0.3491f, 0.5793f, 0.573f , 0.1822f, 0.642f , 0.9143f}); - nd4j::ops::draw_bounding_boxes op; + sd::ops::draw_bounding_boxes op; auto results = op.evaluate({&images, &boxes, &colors}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto result = results->at(0); -// result->printBuffer("Boxes3 output"); + auto result = results.at(0); +// result.printBuffer("Boxes3 output"); // expected.printBuffer("Boxes3 expect"); -// result->syncToHost(); -// result->printBuffer("Bounded boxes 2"); +// result.syncToHost(); +// result.printBuffer("Bounded boxes 2"); // expected.printBuffer("Bounded expec 2"); ASSERT_TRUE(expected.isSameShapeStrict(*result)); ASSERT_TRUE(expected.equalsTo(result)); - - delete results; } //////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests10, FakeQuantWithMinMaxVars_Test_1) { - NDArray x('c', {2,3}, {-63.80f, -63.75f, -63.70f, -63.5f, 0.0f, 0.1f}, nd4j::DataType::FLOAT32); - NDArray exp('c', {2,3}, {-63.75f, -63.75f, -63.75f, -63.5f, 0.f, 0.f}, nd4j::DataType::FLOAT32); - NDArray min('c', {}, std::vector{-63.65f}, nd4j::DataType::FLOAT32); - NDArray max('c', {}, std::vector{0.1f}, nd4j::DataType::FLOAT32); + NDArray x('c', {2,3}, {-63.80f, -63.75f, -63.70f, -63.5f, 0.0f, 0.1f}, sd::DataType::FLOAT32); + NDArray exp('c', {2,3}, {-63.75f, -63.75f, -63.75f, -63.5f, 0.f, 0.f}, sd::DataType::FLOAT32); + NDArray min('c', {}, std::vector{-63.65f}, sd::DataType::FLOAT32); + NDArray max('c', {}, std::vector{0.1f}, sd::DataType::FLOAT32); - nd4j::ops::fake_quant_with_min_max_vars op; + sd::ops::fake_quant_with_min_max_vars op; auto results = op.evaluate({&x, &min, &max}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto result = results->at(0); -// result->printBuffer("Quantized"); + auto result = results.at(0); +// result.printBuffer("Quantized"); // exp.printBuffer("Expected"); ASSERT_TRUE(exp.isSameShapeStrict(*result)); ASSERT_TRUE(exp.equalsTo(result)); - - delete results; } //////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests10, FakeQuantWithMinMaxVars_Test_2) { @@ -2957,17 +2745,15 @@ TEST_F(DeclarableOpsTests10, FakeQuantWithMinMaxVars_Test_2) { NDArray min = NDArrayFactory::create(-63.65); NDArray max = NDArrayFactory::create(0.1); - nd4j::ops::fake_quant_with_min_max_vars op; + sd::ops::fake_quant_with_min_max_vars op; auto results = op.evaluate({&x, &min, &max}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto result = results->at(0); - // result->printIndexedBuffer("Quantized2"); + auto result = results.at(0); + // result.printIndexedBuffer("Quantized2"); ASSERT_TRUE(exp.isSameShapeStrict(*result)); ASSERT_TRUE(exp.equalsTo(result)); - - delete results; } //////////////////////////////////////////////////////////////////// @@ -2978,17 +2764,15 @@ TEST_F(DeclarableOpsTests10, FakeQuantWithMinMaxVars_Test_3) { NDArray min = NDArrayFactory::create('c', {1},{-63.65}); NDArray max = NDArrayFactory::create('c', {1}, {0.1}); - nd4j::ops::fake_quant_with_min_max_vars_per_channel op; + sd::ops::fake_quant_with_min_max_vars_per_channel op; auto results = op.evaluate({&x, &min, &max}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto result = results->at(0); - // result->printIndexedBuffer("Quantized2"); + auto result = results.at(0); + // result.printIndexedBuffer("Quantized2"); ASSERT_TRUE(exp.isSameShapeStrict(*result)); ASSERT_TRUE(exp.equalsTo(result)); - - delete results; } TEST_F(DeclarableOpsTests10, FakeQuantWithMinMaxVars_Test_03) { @@ -3002,18 +2786,17 @@ TEST_F(DeclarableOpsTests10, FakeQuantWithMinMaxVars_Test_03) { NDArray min = NDArrayFactory::create({-0.2283f, -0.0719f, -0.0154f, -0.5162f, -0.3567f}); NDArray max = NDArrayFactory::create({0.9441f, 0.5957f, 0.8669f, 0.3502f, 0.5100f}); - nd4j::ops::fake_quant_with_min_max_vars_per_channel op; + sd::ops::fake_quant_with_min_max_vars_per_channel op; auto results = op.evaluate({&x, &min, &max}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto result = results->at(0); -// result->printIndexedBuffer("Quantized03"); + auto result = results.at(0); +// result.printIndexedBuffer("Quantized03"); ASSERT_TRUE(exp.isSameShapeStrict(*result)); ASSERT_TRUE(exp.equalsTo(result)); - - delete results; } + TEST_F(DeclarableOpsTests10, FakeQuantWithMinMaxVars_Test_03_1) { NDArray x = NDArrayFactory::create('c', {3,5}, {0.7788f,0.8012f, 0.7244f, 0.2309f,0.7271f, 0.1804f, 0.5056f, 0.8925f, 0.5461f, 0.9234f, @@ -3025,17 +2808,15 @@ TEST_F(DeclarableOpsTests10, FakeQuantWithMinMaxVars_Test_03_1) { NDArray min = NDArrayFactory::create({-0.2283f, -0.0719f, -0.0154f, -0.5162f, -0.3567f}); NDArray max = NDArrayFactory::create({0.9441f, 0.5957f, 0.8669f, 0.3502f, 0.5100f}); - nd4j::ops::fake_quant_with_min_max_vars_per_channel op; + sd::ops::fake_quant_with_min_max_vars_per_channel op; auto results = op.evaluate({&x, &min, &max}, {}, {8}, {true}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto result = results->at(0); -// result->printIndexedBuffer("Quantized03_1"); + auto result = results.at(0); +// result.printIndexedBuffer("Quantized03_1"); ASSERT_TRUE(exp.isSameShapeStrict(*result)); ASSERT_TRUE(exp.equalsTo(result)); - - delete results; } TEST_F(DeclarableOpsTests10, FakeQuantWithMinMaxVars_Test_03_2) { @@ -3049,17 +2830,15 @@ TEST_F(DeclarableOpsTests10, FakeQuantWithMinMaxVars_Test_03_2) { NDArray min = NDArrayFactory::create({-0.2283f, -0.0719f, -0.0154f, -0.5162f, -0.3567f}); NDArray max = NDArrayFactory::create({0.9441f, 0.5957f, 0.8669f, 0.3502f, 0.5100f}); - nd4j::ops::fake_quant_with_min_max_vars_per_channel op; + sd::ops::fake_quant_with_min_max_vars_per_channel op; auto results = op.evaluate({&x, &min, &max}, {}, {6}, {true}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto result = results->at(0); + auto result = results.at(0); result->printIndexedBuffer("Quantized03_2"); ASSERT_TRUE(exp.isSameShapeStrict(*result)); ASSERT_TRUE(exp.equalsTo(result)); - - delete results; } TEST_F(DeclarableOpsTests10, FakeQuantWithMinMaxVars_Test_03_3) { @@ -3072,17 +2851,15 @@ TEST_F(DeclarableOpsTests10, FakeQuantWithMinMaxVars_Test_03_3) { NDArray min = NDArrayFactory::create({-0.2283f, -0.0719f, -0.0154f, -0.5162f, -0.3567f}); NDArray max = NDArrayFactory::create({0.9441f, 0.5957f, 0.8669f, 0.3502f, 0.5100f}); - nd4j::ops::fake_quant_with_min_max_vars_per_channel op; + sd::ops::fake_quant_with_min_max_vars_per_channel op; auto results = op.evaluate({&x, &min, &max}, {}, {6}, {false}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto result = results->at(0); + auto result = results.at(0); result->printIndexedBuffer("Quantized03_3"); ASSERT_TRUE(exp.isSameShapeStrict(*result)); ASSERT_TRUE(exp.equalsTo(result)); - - delete results; } //////////////////////////////////////////////////////////////////// @@ -3111,20 +2888,18 @@ TEST_F(DeclarableOpsTests10, FakeQuantWithMinMaxVars_Test_4) { NDArray min = NDArrayFactory::create({20.f, 20.f, 20.f}); NDArray max = NDArrayFactory::create({65.f, 70.f, 90.f}); x.linspace(1.); - nd4j::ops::fake_quant_with_min_max_vars_per_channel op; + sd::ops::fake_quant_with_min_max_vars_per_channel op; auto results = op.evaluate({&x, &min, &max}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto result = results->at(0); -// result->printBuffer("Quantized per channels 4"); + auto result = results.at(0); +// result.printBuffer("Quantized per channels 4"); // exp.printBuffer("Quantized per channest E"); // auto diff = *result - exp; // diff.printIndexedBuffer("Difference"); ASSERT_TRUE(exp.isSameShapeStrict(*result)); ASSERT_TRUE(exp.equalsTo(result)); - - delete results; } TEST_F(DeclarableOpsTests10, FakeQuantWithMinMaxVars_Test_5) { @@ -3164,21 +2939,19 @@ TEST_F(DeclarableOpsTests10, FakeQuantWithMinMaxVars_Test_5) { NDArray min = NDArrayFactory::create({-20.f, -19.f, -18.f, -17.f}); NDArray max = NDArrayFactory::create({20.f, 21.f, 22.f, 23.f}); x.linspace(-60.); - nd4j::ops::fake_quant_with_min_max_vars_per_channel op; + sd::ops::fake_quant_with_min_max_vars_per_channel op; auto results = op.evaluate({&x, &min, &max}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto result = results->at(0); -// result->printBuffer("Quantized per channels 5"); + auto result = results.at(0); +// result.printBuffer("Quantized per channels 5"); // exp.printBuffer("Quantized per channest E"); // auto diff = *result - exp; // diff.printIndexedBuffer("Difference"); ASSERT_TRUE(exp.isSameShapeStrict(*result)); ASSERT_TRUE(exp.equalsTo(result)); - - delete results; } TEST_F(DeclarableOpsTests10, FakeQuantWithMinMaxVars_Test_6) { @@ -3198,21 +2971,19 @@ TEST_F(DeclarableOpsTests10, FakeQuantWithMinMaxVars_Test_6) { NDArray min = NDArrayFactory::create('c', {5}, {-0.2283f, -0.0719f, -0.0154f, -0.5162f, -0.3567f}); NDArray max = NDArrayFactory::create('c', {5}, {0.9441f, 0.5957f, 0.8669f, 0.3502f, 0.5100f}); // x.linspace(-60.); - nd4j::ops::fake_quant_with_min_max_vars_per_channel op; + sd::ops::fake_quant_with_min_max_vars_per_channel op; auto results = op.evaluate({&x, &min, &max}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto result = results->at(0); -// result->printBuffer("Quantized per channels 5"); + auto result = results.at(0); +// result.printBuffer("Quantized per channels 5"); // exp.printBuffer("Quantized per channest E"); // auto diff = *result - exp; // diff.printIndexedBuffer("Difference"); ASSERT_TRUE(exp.isSameShapeStrict(*result)); ASSERT_TRUE(exp.equalsTo(result)); - - delete results; } ////////////////////////////////////////////////////////////////// @@ -3244,18 +3015,16 @@ TEST_F(DeclarableOpsTests10, FakeQuantWithMinMaxVars_Test_7) { NDArray min = NDArrayFactory::create('c', {1},{0.0f}); NDArray max = NDArrayFactory::create('c', {1}, {1.f}); x.linspace(0., 0.01); - nd4j::ops::fake_quant_with_min_max_vars op; + sd::ops::fake_quant_with_min_max_vars op; auto results = op.evaluate({&x, &min, &max}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto result = results->at(0); -// result->printBuffer("Quantized7"); + auto result = results.at(0); +// result.printBuffer("Quantized7"); // exp.printBuffer("Expected 7"); ASSERT_TRUE(exp.isSameShapeStrict(*result)); ASSERT_TRUE(exp.equalsTo(result)); - - delete results; } ////////////////////////////////////////////////////////////////// @@ -3269,32 +3038,30 @@ TEST_F(DeclarableOpsTests10, FakeQuantWithMinMaxVars_Test_8) { NDArray min = NDArrayFactory::create('c', {1},{0.0f}); NDArray max = NDArrayFactory::create('c', {1}, {1.f}); x.linspace(0., 0.1); - nd4j::ops::fake_quant_with_min_max_vars op; + sd::ops::fake_quant_with_min_max_vars op; auto results = op.evaluate({&x, &min, &max}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto result = results->at(0); + auto result = results.at(0); // x.printBuffer("SourInput8"); -// result->printBuffer("Quantized8"); +// result.printBuffer("Quantized8"); // exp.printBuffer("Expected 8"); ASSERT_TRUE(exp.isSameShapeStrict(*result)); ASSERT_TRUE(exp.equalsTo(result)); - - delete results; } /////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests10, bool_broadcast_test_1) { - NDArray arr1('c', {2,2,1}, {1, 2, 3, 4}, nd4j::DataType::INT32); - NDArray arr2('c', { 2,2}, {0, 1, 0, 4}, nd4j::DataType::INT32); + NDArray arr1('c', {2,2,1}, {1, 2, 3, 4}, sd::DataType::INT32); + NDArray arr2('c', { 2,2}, {0, 1, 0, 4}, sd::DataType::INT32); - NDArray expd('c', {2,2,2}, {false, true, false, false, false, false, false, true}, nd4j::DataType::BOOL); + NDArray expd('c', {2,2,2}, {false, true, false, false, false, false, false, true}, sd::DataType::BOOL); - NDArray result('c', {2,2,2}, nd4j::DataType::BOOL); + NDArray result('c', {2,2,2}, sd::DataType::BOOL); - arr1.applyTrueBroadcast(nd4j::BroadcastBoolOpsTuple::custom(scalar::EqualTo, pairwise::EqualTo, broadcast::EqualTo), arr2, result, true); + arr1.applyTrueBroadcast(sd::BroadcastBoolOpsTuple::custom(scalar::EqualTo, pairwise::EqualTo, broadcast::EqualTo), arr2, result, true); // result.printIndexedBuffer(); // expd.printIndexedBuffer(); @@ -3305,14 +3072,14 @@ TEST_F(DeclarableOpsTests10, bool_broadcast_test_1) { /////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests10, printIndexedTest_1) { - NDArray arr('c', {2,2,2,2}, {1, 2, 3, 4, 5, 6, 7, 8,9, 10, 11, 12, 13, 14, 15, 16}, nd4j::DataType::INT32); -// NDArray arr2('c', { 2,2}, {0, 1, 0, 4}, nd4j::DataType::INT32); + NDArray arr('c', {2,2,2,2}, {1, 2, 3, 4, 5, 6, 7, 8,9, 10, 11, 12, 13, 14, 15, 16}, sd::DataType::INT32); +// NDArray arr2('c', { 2,2}, {0, 1, 0, 4}, sd::DataType::INT32); -// NDArray expd('c', {2,2,2}, {0,1,0,0, 0,0,0,1}, nd4j::DataType::BOOL); +// NDArray expd('c', {2,2,2}, {0,1,0,0, 0,0,0,1}, sd::DataType::BOOL); -// NDArray result('c', {2,2,2}, nd4j::DataType::BOOL); +// NDArray result('c', {2,2,2}, sd::DataType::BOOL); -// arr1.applyTrueBroadcast(nd4j::BroadcastBoolOpsTuple::custom(scalar::EqualTo, pairwise::EqualTo, broadcast::EqualTo), &arr2, &result, true, nullptr); +// arr1.applyTrueBroadcast(sd::BroadcastBoolOpsTuple::custom(scalar::EqualTo, pairwise::EqualTo, broadcast::EqualTo), &arr2, &result, true, nullptr); // result.printIndexedBuffer(); // expd.printIndexedBuffer(); @@ -3350,4 +3117,3 @@ TEST_F(DeclarableOpsTests10, printIndexedTest_1) { printf("]\n"); } - diff --git a/libnd4j/tests_cpu/layers_tests/DeclarableOpsTests11.cpp b/libnd4j/tests_cpu/layers_tests/DeclarableOpsTests11.cpp index aeecaccef..b8c89322c 100644 --- a/libnd4j/tests_cpu/layers_tests/DeclarableOpsTests11.cpp +++ b/libnd4j/tests_cpu/layers_tests/DeclarableOpsTests11.cpp @@ -21,11 +21,12 @@ #include "testlayers.h" #include -#include +#include #include -#include +#include +#include -using namespace nd4j; +using namespace sd; class DeclarableOpsTests11 : public testing::Test { @@ -42,19 +43,19 @@ TEST_F(DeclarableOpsTests11, test_listdiff_1) { auto x = NDArrayFactory::create('c', {4}, {0, 1, 2, 3}); auto y = NDArrayFactory::create('c',{2}, {3, 1}); - nd4j::ops::listdiff op; + sd::ops::listdiff op; auto result = op.evaluate({&x, &y}, {}, {}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - delete result; + } /////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests11, log_loss_grad_test1) { - NDArray labels('c', {2,3,4}, nd4j::DataType::DOUBLE); - NDArray predictions('c', {2,3,4}, nd4j::DataType::DOUBLE); - NDArray weights('c', {2,3,4}, nd4j::DataType::DOUBLE); + NDArray labels('c', {2,3,4}, sd::DataType::DOUBLE); + NDArray predictions('c', {2,3,4}, sd::DataType::DOUBLE); + NDArray weights('c', {2,3,4}, sd::DataType::DOUBLE); NDArray dLdpExp('c', {2,3,4}, {-12.49997,-13.04346, -13.63635, -14.28571,-14.99999,-15.78947, -16.66666, -17.64705,-18.75 ,-20. , -21.42857, -23.07692, -24.99999,-27.27272, -29.99999, -33.33332,-37.49999,-42.85713, -49.99998, -59.99998,-74.99995,-99.99992,-149.99986,-299.99911}); @@ -67,14 +68,14 @@ TEST_F(DeclarableOpsTests11, log_loss_grad_test1) { labels.linspace(1); weights.assign(0.5); - nd4j::ops::log_loss_grad op; + sd::ops::log_loss_grad op; auto results = op.evaluate({&predictions, &weights, &labels}, {1e-7}, {0}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *dLdp = results->at(0); - auto *dLdw = results->at(1); - auto *dLdl = results->at(2); + auto *dLdp = results.at(0); + auto *dLdw = results.at(1); + auto *dLdl = results.at(2); ASSERT_TRUE(dLdpExp.isSameShape(dLdp)); ASSERT_TRUE(dLdpExp.equalsTo(dLdp)); @@ -82,16 +83,14 @@ TEST_F(DeclarableOpsTests11, log_loss_grad_test1) { ASSERT_TRUE(dLdwExp.equalsTo(dLdw)); ASSERT_TRUE(dLdlExp.isSameShape(dLdl)); ASSERT_TRUE(dLdlExp.equalsTo(dLdl)); - - delete results; } /////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests11, log_loss_grad_test2) { - NDArray labels('c', {2,3,4}, nd4j::DataType::DOUBLE); - NDArray predictions('c', {2,3,4}, nd4j::DataType::DOUBLE); - NDArray weights('c', {2,1,4}, nd4j::DataType::DOUBLE); + NDArray labels('c', {2,3,4}, sd::DataType::DOUBLE); + NDArray predictions('c', {2,3,4}, sd::DataType::DOUBLE); + NDArray weights('c', {2,1,4}, sd::DataType::DOUBLE); NDArray dLdwExp('c', {2,1,4}, {15.99805, 16.72406, 16.27746, 14.83754,-44.97147,-59.99582,-79.28771,-107.35497}); @@ -99,25 +98,23 @@ TEST_F(DeclarableOpsTests11, log_loss_grad_test2) { labels.linspace(1); weights.assign(0.5); - nd4j::ops::log_loss_grad op; + sd::ops::log_loss_grad op; auto results = op.evaluate({&predictions, &weights, &labels}, {1e-7}, {0}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *dLdw = results->at(1); + auto *dLdw = results.at(1); ASSERT_TRUE(dLdwExp.isSameShape(dLdw)); ASSERT_TRUE(dLdwExp.equalsTo(dLdw)); - - delete results; } /////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests11, log_loss_grad_test3) { - NDArray labels('c', {2,3,4}, nd4j::DataType::DOUBLE); - NDArray predictions('c', {2,3,4}, nd4j::DataType::DOUBLE); - NDArray weights(nd4j::DataType::DOUBLE); + NDArray labels('c', {2,3,4}, sd::DataType::DOUBLE); + NDArray predictions('c', {2,3,4}, sd::DataType::DOUBLE); + NDArray weights(sd::DataType::DOUBLE); NDArray dLdpExp('c', {2,3,4}, {-12.49997,-13.04346, -13.63635, -14.28571,-14.99999,-15.78947, -16.66666, -17.64705,-18.75 ,-20. , -21.42857, -23.07692, -24.99999,-27.27272, -29.99999, -33.33332,-37.49999,-42.85713, -49.99998, -59.99998,-74.99995,-99.99992,-149.99986,-299.99911}); @@ -129,14 +126,14 @@ TEST_F(DeclarableOpsTests11, log_loss_grad_test3) { labels.linspace(1); weights.assign(0.5); - nd4j::ops::log_loss_grad op; + sd::ops::log_loss_grad op; auto results = op.evaluate({&predictions, &weights, &labels}, {1e-7}, {1}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *dLdp = results->at(0); - auto *dLdw = results->at(1); - auto *dLdl = results->at(2); + auto *dLdp = results.at(0); + auto *dLdw = results.at(1); + auto *dLdl = results.at(2); ASSERT_TRUE(dLdpExp.isSameShape(dLdp)); ASSERT_TRUE(dLdpExp.equalsTo(dLdp)); @@ -144,16 +141,14 @@ TEST_F(DeclarableOpsTests11, log_loss_grad_test3) { ASSERT_TRUE(dLdwExp.equalsTo(dLdw)); ASSERT_TRUE(dLdlExp.isSameShape(dLdl)); ASSERT_TRUE(dLdlExp.equalsTo(dLdl)); - - delete results; } /////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests11, log_loss_grad_test4) { - NDArray labels('c', {2,3,4}, nd4j::DataType::DOUBLE); - NDArray predictions('c', {2,3,4}, nd4j::DataType::DOUBLE); - NDArray weights('c', {1,3,1}, nd4j::DataType::DOUBLE); + NDArray labels('c', {2,3,4}, sd::DataType::DOUBLE); + NDArray predictions('c', {2,3,4}, sd::DataType::DOUBLE); + NDArray weights('c', {1,3,1}, sd::DataType::DOUBLE); NDArray dLdwExp('c', {1,3,1}, {4.8876 , -46.29156, -186.36887}); @@ -161,27 +156,25 @@ TEST_F(DeclarableOpsTests11, log_loss_grad_test4) { labels.linspace(1); weights.assign(0.5); - nd4j::ops::log_loss_grad op; + sd::ops::log_loss_grad op; auto results = op.evaluate({&predictions, &weights, &labels}, {1e-7}, {1}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *dLdw = results->at(1); + auto *dLdw = results.at(1); // dLdw->printIndexedBuffer(); // dLdw->printShapeInfo(); ASSERT_TRUE(dLdwExp.isSameShape(dLdw)); ASSERT_TRUE(dLdwExp.equalsTo(dLdw)); - - delete results; } /////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests11, log_loss_grad_test5) { - NDArray labels('c', {2,3,4}, nd4j::DataType::DOUBLE); - NDArray predictions('c', {2,3,4}, nd4j::DataType::DOUBLE); - NDArray weights('c', {2,3,4}, nd4j::DataType::DOUBLE); + NDArray labels('c', {2,3,4}, sd::DataType::DOUBLE); + NDArray predictions('c', {2,3,4}, sd::DataType::DOUBLE); + NDArray weights('c', {2,3,4}, sd::DataType::DOUBLE); NDArray dLdpExp('c', {2,3,4}, {-1.04166,-1.08696, -1.13636, -1.19048,-1.25 ,-1.31579, -1.38889, -1.47059,-1.5625 ,-1.66667, -1.78571, -1.92308, -2.08333,-2.27273, -2.5 , -2.77778,-3.125 ,-3.57143, -4.16667, -5. ,-6.25 ,-8.33333,-12.49999,-24.99993}); @@ -194,14 +187,14 @@ TEST_F(DeclarableOpsTests11, log_loss_grad_test5) { labels.linspace(1); weights.assign(0.5); - nd4j::ops::log_loss_grad op; + sd::ops::log_loss_grad op; auto results = op.evaluate({&predictions, &weights, &labels}, {1e-7}, {2}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *dLdp = results->at(0); - auto *dLdw = results->at(1); - auto *dLdl = results->at(2); + auto *dLdp = results.at(0); + auto *dLdw = results.at(1); + auto *dLdl = results.at(2); ASSERT_TRUE(dLdpExp.isSameShape(dLdp)); ASSERT_TRUE(dLdpExp.equalsTo(dLdp)); @@ -209,16 +202,14 @@ TEST_F(DeclarableOpsTests11, log_loss_grad_test5) { ASSERT_TRUE(dLdwExp.equalsTo(dLdw)); ASSERT_TRUE(dLdlExp.isSameShape(dLdl)); ASSERT_TRUE(dLdlExp.equalsTo(dLdl)); - - delete results; } /////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests11, log_loss_grad_test6) { - NDArray labels('c', {2,3,4}, nd4j::DataType::DOUBLE); - NDArray predictions('c', {2,3,4}, nd4j::DataType::DOUBLE); - NDArray weights('c', {1,3,1}, nd4j::DataType::DOUBLE); + NDArray labels('c', {2,3,4}, sd::DataType::DOUBLE); + NDArray predictions('c', {2,3,4}, sd::DataType::DOUBLE); + NDArray weights('c', {1,3,1}, sd::DataType::DOUBLE); NDArray dLdwExp('c', {1,3,1}, {6.73432, 2.46939,-9.20372}); @@ -226,25 +217,23 @@ TEST_F(DeclarableOpsTests11, log_loss_grad_test6) { labels.linspace(1); weights.assign(0.5); - nd4j::ops::log_loss_grad op; + sd::ops::log_loss_grad op; auto results = op.evaluate({&predictions, &weights, &labels}, {1e-7}, {2}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *dLdw = results->at(1); + auto *dLdw = results.at(1); ASSERT_TRUE(dLdwExp.isSameShape(dLdw)); ASSERT_TRUE(dLdwExp.equalsTo(dLdw)); - - delete results; } /////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests11, log_loss_grad_test7) { - NDArray labels('c', {2,3,4}, nd4j::DataType::DOUBLE); - NDArray predictions('c', {2,3,4}, nd4j::DataType::DOUBLE); - NDArray weights(nd4j::DataType::DOUBLE); + NDArray labels('c', {2,3,4}, sd::DataType::DOUBLE); + NDArray predictions('c', {2,3,4}, sd::DataType::DOUBLE); + NDArray weights(sd::DataType::DOUBLE); NDArray dLdwExp('c', {}, std::vector{0.}); @@ -252,25 +241,23 @@ TEST_F(DeclarableOpsTests11, log_loss_grad_test7) { labels.linspace(1); weights.assign(0.5); - nd4j::ops::log_loss_grad op; + sd::ops::log_loss_grad op; auto results = op.evaluate({&predictions, &weights, &labels}, {1e-7}, {2}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *dLdw = results->at(1); + auto *dLdw = results.at(1); ASSERT_TRUE(dLdwExp.isSameShape(dLdw)); ASSERT_TRUE(dLdwExp.equalsTo(dLdw)); - - delete results; } /////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests11, log_loss_grad_test8) { - NDArray labels('c', {2,3,4}, nd4j::DataType::DOUBLE); - NDArray predictions('c', {2,3,4}, nd4j::DataType::DOUBLE); - NDArray weights('c', {2,3,4}, nd4j::DataType::DOUBLE); + NDArray labels('c', {2,3,4}, sd::DataType::DOUBLE); + NDArray predictions('c', {2,3,4}, sd::DataType::DOUBLE); + NDArray weights('c', {2,3,4}, sd::DataType::DOUBLE); NDArray dLdpExp('c', {2,3,4}, {0. , 0. , 0. , 0. ,-1.5 ,-1.57895, -1.66667, -1.76471,-1.875 ,-2. , -2.14286, -2.30769, -2.5 ,-2.72727, -3. , -3.33333,-3.75 ,-4.28571, -5. , -6. ,-7.49999,-9.99999,-14.99999,-29.99991}); @@ -287,14 +274,14 @@ TEST_F(DeclarableOpsTests11, log_loss_grad_test8) { weights.p(2, 0.); weights.p(3, 0.); - nd4j::ops::log_loss_grad op; + sd::ops::log_loss_grad op; auto results = op.evaluate({&predictions, &weights, &labels}, {1e-7}, {2}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *dLdp = results->at(0); - auto *dLdw = results->at(1); - auto *dLdl = results->at(2); + auto *dLdp = results.at(0); + auto *dLdw = results.at(1); + auto *dLdl = results.at(2); ASSERT_TRUE(dLdpExp.isSameShape(dLdp)); ASSERT_TRUE(dLdpExp.equalsTo(dLdp)); @@ -302,16 +289,14 @@ TEST_F(DeclarableOpsTests11, log_loss_grad_test8) { ASSERT_TRUE(dLdwExp.equalsTo(dLdw)); ASSERT_TRUE(dLdlExp.isSameShape(dLdl)); ASSERT_TRUE(dLdlExp.equalsTo(dLdl)); - - delete results; } /////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests11, log_loss_grad_test9) { - NDArray labels('c', {2,3,4}, nd4j::DataType::DOUBLE); - NDArray predictions('c', {2,3,4}, nd4j::DataType::DOUBLE); - NDArray weights('c', {2,3,4}, nd4j::DataType::DOUBLE); + NDArray labels('c', {2,3,4}, sd::DataType::DOUBLE); + NDArray predictions('c', {2,3,4}, sd::DataType::DOUBLE); + NDArray weights('c', {2,3,4}, sd::DataType::DOUBLE); NDArray dLdpExp('c', {2,3,4}, {-0.52083,-0.54348,-0.56818, -0.59524,-0.625 ,-0.65789,-0.69444, -0.73529,-0.78125,-0.83333,-0.89286, -0.96154, -1.04167,-1.13636,-1.25 , -1.38889,-1.5625 ,-1.78571,-2.08333, -2.5 ,-3.125 ,-4.16666,-6.24999,-12.49996}); @@ -324,14 +309,14 @@ TEST_F(DeclarableOpsTests11, log_loss_grad_test9) { labels.linspace(1); weights.assign(0.5); - nd4j::ops::log_loss_grad op; + sd::ops::log_loss_grad op; auto results = op.evaluate({&predictions, &weights, &labels}, {1e-7}, {3}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *dLdp = results->at(0); - auto *dLdw = results->at(1); - auto *dLdl = results->at(2); + auto *dLdp = results.at(0); + auto *dLdw = results.at(1); + auto *dLdl = results.at(2); ASSERT_TRUE(dLdpExp.isSameShape(dLdp)); ASSERT_TRUE(dLdpExp.equalsTo(dLdp)); @@ -339,16 +324,14 @@ TEST_F(DeclarableOpsTests11, log_loss_grad_test9) { ASSERT_TRUE(dLdwExp.equalsTo(dLdw)); ASSERT_TRUE(dLdlExp.isSameShape(dLdl)); ASSERT_TRUE(dLdlExp.equalsTo(dLdl)); - - delete results; } /////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests11, log_loss_grad_test10) { - NDArray labels('c', {2,3,4}, nd4j::DataType::DOUBLE); - NDArray predictions('c', {2,3,4}, nd4j::DataType::DOUBLE); - NDArray weights('c', {1,1}, nd4j::DataType::DOUBLE); + NDArray labels('c', {2,3,4}, sd::DataType::DOUBLE); + NDArray predictions('c', {2,3,4}, sd::DataType::DOUBLE); + NDArray weights('c', {1,1}, sd::DataType::DOUBLE); NDArray dLdwExp('c', {1,1}, std::vector{-9.49054}); @@ -356,25 +339,23 @@ TEST_F(DeclarableOpsTests11, log_loss_grad_test10) { labels.linspace(1); weights.assign(0.5); - nd4j::ops::log_loss_grad op; + sd::ops::log_loss_grad op; auto results = op.evaluate({&predictions, &weights, &labels}, {1e-7}, {3}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *dLdw = results->at(1); + auto *dLdw = results.at(1); ASSERT_TRUE(dLdwExp.isSameShape(dLdw)); ASSERT_TRUE(dLdwExp.equalsTo(dLdw)); - - delete results; } /////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests11, log_loss_grad_test11) { - NDArray labels('c', {2,3,4}, nd4j::DataType::DOUBLE); - NDArray predictions('c', {2,3,4}, nd4j::DataType::DOUBLE); - NDArray weights('c', {1,3,1}, nd4j::DataType::DOUBLE); + NDArray labels('c', {2,3,4}, sd::DataType::DOUBLE); + NDArray predictions('c', {2,3,4}, sd::DataType::DOUBLE); + NDArray weights('c', {1,3,1}, sd::DataType::DOUBLE); NDArray dLdwExp('c', {1,3,1}, {0.20365,-1.92882,-7.76537}); @@ -382,25 +363,23 @@ TEST_F(DeclarableOpsTests11, log_loss_grad_test11) { labels.linspace(1); weights.assign(0.5); - nd4j::ops::log_loss_grad op; + sd::ops::log_loss_grad op; auto results = op.evaluate({&predictions, &weights, &labels}, {1e-7}, {3}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *dLdw = results->at(1); + auto *dLdw = results.at(1); ASSERT_TRUE(dLdwExp.isSameShape(dLdw)); ASSERT_TRUE(dLdwExp.equalsTo(dLdw)); - - delete results; } /////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests11, log_loss_grad_test12) { - NDArray labels('c', {2,3,4}, nd4j::DataType::DOUBLE); - NDArray predictions('c', {2,3,4}, nd4j::DataType::DOUBLE); - NDArray weights('c', {2,3,4}, nd4j::DataType::DOUBLE); + NDArray labels('c', {2,3,4}, sd::DataType::DOUBLE); + NDArray predictions('c', {2,3,4}, sd::DataType::DOUBLE); + NDArray weights('c', {2,3,4}, sd::DataType::DOUBLE); NDArray dLdpExp('c', {2,3,4}, { 0. , 0. , 0. , 0. ,-0.75 ,-0.789473,-0.833333, -0.882353,-0.9375 ,-1. ,-1.071428, -1.153846, -1.25 ,-1.363636,-1.5 , -1.666666,-1.875 ,-2.142857,-2.499999, -2.999999,-3.749997,-4.999997,-7.499993,-14.999956}); @@ -419,14 +398,14 @@ TEST_F(DeclarableOpsTests11, log_loss_grad_test12) { weights.t(3) = 0.; - nd4j::ops::log_loss_grad op; + sd::ops::log_loss_grad op; auto results = op.evaluate({&predictions, &weights, &labels}, {1e-7}, {3}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *dLdp = results->at(0); - auto *dLdw = results->at(1); - auto *dLdl = results->at(2); + auto *dLdp = results.at(0); + auto *dLdw = results.at(1); + auto *dLdl = results.at(2); ASSERT_TRUE(dLdpExp.isSameShape(dLdp)); ASSERT_TRUE(dLdpExp.equalsTo(dLdp)); @@ -434,16 +413,14 @@ TEST_F(DeclarableOpsTests11, log_loss_grad_test12) { ASSERT_TRUE(dLdwExp.equalsTo(dLdw)); ASSERT_TRUE(dLdlExp.isSameShape(dLdl)); ASSERT_TRUE(dLdlExp.equalsTo(dLdl)); - - delete results; } /////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests11, log_loss_grad_test13) { - NDArray labels('c', {2,3,4}, nd4j::DataType::DOUBLE); - NDArray predictions('c', {2,3,4}, nd4j::DataType::DOUBLE); - NDArray weights('c', {2,3,1}, nd4j::DataType::DOUBLE); + NDArray labels('c', {2,3,4}, sd::DataType::DOUBLE); + NDArray predictions('c', {2,3,4}, sd::DataType::DOUBLE); + NDArray weights('c', {2,3,1}, sd::DataType::DOUBLE); NDArray dLdpExp('c', {2,3,4}, {0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , -2.08333,-2.27273, -2.5 , -2.77778,-3.125 ,-3.57143, -4.16667, -5. ,-6.25 ,-8.33333,-12.49999,-24.99993}); @@ -458,14 +435,14 @@ TEST_F(DeclarableOpsTests11, log_loss_grad_test13) { weights.t(1) = 0.; weights.t(2) = 0.; - nd4j::ops::log_loss_grad op; + sd::ops::log_loss_grad op; auto results = op.evaluate({&predictions, &weights, &labels}, {1e-7}, {3}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *dLdp = results->at(0); - auto *dLdw = results->at(1); - auto *dLdl = results->at(2); + auto *dLdp = results.at(0); + auto *dLdw = results.at(1); + auto *dLdl = results.at(2); ASSERT_TRUE(dLdpExp.isSameShape(dLdp)); ASSERT_TRUE(dLdpExp.equalsTo(dLdp)); @@ -473,8 +450,6 @@ TEST_F(DeclarableOpsTests11, log_loss_grad_test13) { ASSERT_TRUE(dLdwExp.equalsTo(dLdw)); ASSERT_TRUE(dLdlExp.isSameShape(dLdl)); ASSERT_TRUE(dLdlExp.equalsTo(dLdl)); - - delete results; } TEST_F(DeclarableOpsTests11, ImageResizeBicubic_Test1) { @@ -641,17 +616,16 @@ TEST_F(DeclarableOpsTests11, ImageResizeBicubic_Test1) { 49.902905f, 50.092834f, 50.262653f, 50.329483f, 50.30638f, 50.25057f}); auto size = NDArrayFactory::create({30, 30}); - nd4j::ops::resize_bicubic op; + sd::ops::resize_bicubic op; auto results = op.evaluate({&input, &size}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); - NDArray* result = results->at(0); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); + NDArray* result = results.at(0); -// result->printBuffer("Resized to 30x30"); +// result.printBuffer("Resized to 30x30"); // expected.printBuffer("Expect for 30x30"); ASSERT_TRUE(expected.isSameShape(result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; } TEST_F(DeclarableOpsTests11, ImageResizeBicubic_Test2) { @@ -715,18 +689,17 @@ TEST_F(DeclarableOpsTests11, ImageResizeBicubic_Test2) { }); //input = 1.f; input.linspace(1); auto size = NDArrayFactory::create({10, 8}); - nd4j::ops::resize_bicubic op; + sd::ops::resize_bicubic op; auto results = op.evaluate({&input, &size}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - NDArray* result = results->at(0); + NDArray* result = results.at(0); -// result->printBuffer("Resized to 10x8"); +// result.printBuffer("Resized to 10x8"); // expected.printBuffer("Expect for 10x8"); ASSERT_TRUE(expected.isSameShape(result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; } TEST_F(DeclarableOpsTests11, ImageResizeBicubic_Test3) { @@ -752,18 +725,17 @@ TEST_F(DeclarableOpsTests11, ImageResizeBicubic_Test3) { }); input.linspace(1); auto size = NDArrayFactory::create({6, 6}); - nd4j::ops::resize_bicubic op; + sd::ops::resize_bicubic op; auto results = op.evaluate({&input, &size}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - NDArray* result = results->at(0); + NDArray* result = results.at(0); -// result->printBuffer("Resized to 6x6"); +// result.printBuffer("Resized to 6x6"); // expected.printBuffer("Expect for 6x6"); ASSERT_TRUE(expected.isSameShape(result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; } TEST_F(DeclarableOpsTests11, ImageResizeBicubic_Test4) { @@ -789,18 +761,17 @@ TEST_F(DeclarableOpsTests11, ImageResizeBicubic_Test4) { }); input.linspace(1); auto size = NDArrayFactory::create({6, 8}); - nd4j::ops::resize_bicubic op; + sd::ops::resize_bicubic op; auto results = op.evaluate({&input, &size}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - NDArray* result = results->at(0); + NDArray* result = results.at(0); -// result->printBuffer("Resized to 6x8"); +// result.printBuffer("Resized to 6x8"); // expected.printBuffer("Expect for 6x8"); ASSERT_TRUE(expected.isSameShape(result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; } TEST_F(DeclarableOpsTests11, ImageResizeBicubic_Test5) { @@ -832,18 +803,17 @@ TEST_F(DeclarableOpsTests11, ImageResizeBicubic_Test5) { }); input.linspace(1); auto size = NDArrayFactory::create({8, 8}); - nd4j::ops::resize_bicubic op; + sd::ops::resize_bicubic op; auto results = op.evaluate({&input, &size}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - NDArray* result = results->at(0); + NDArray* result = results.at(0); -// result->printBuffer("Resized to 8x8"); +// result.printBuffer("Resized to 8x8"); // expected.printBuffer("Expect for 8x8"); ASSERT_TRUE(expected.isSameShape(result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; } TEST_F(DeclarableOpsTests11, ImageResizeBicubic_Test6) { @@ -962,17 +932,16 @@ TEST_F(DeclarableOpsTests11, ImageResizeBicubic_Test6) { }); auto size = NDArrayFactory::create({30, 30}); - nd4j::ops::resize_bicubic op; + sd::ops::resize_bicubic op; auto results = op.evaluate({&input, &size}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); - NDArray* result = results->at(0); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); + NDArray* result = results.at(0); -// result->printBuffer("Resized to 30x30"); +// result.printBuffer("Resized to 30x30"); // expected.printBuffer("Expect for 30x30"); ASSERT_TRUE(expected.isSameShape(result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; } TEST_F(DeclarableOpsTests11, ImageResizeBicubic_Test7) { @@ -1020,18 +989,17 @@ TEST_F(DeclarableOpsTests11, ImageResizeBicubic_Test7) { 0.9139262f, 0.92068815f }); auto size = NDArrayFactory::create({9, 9}); - nd4j::ops::resize_bicubic op; + sd::ops::resize_bicubic op; auto results = op.evaluate({&input, &size}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - NDArray* result = results->at(0); + NDArray* result = results.at(0); -// result->printBuffer("Resized to 9x9"); +// result.printBuffer("Resized to 9x9"); // expected.printBuffer("Expect for 9x9"); ASSERT_TRUE(expected.isSameShape(result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; } TEST_F(DeclarableOpsTests11, ImageResizeBicubic_Test8) { @@ -1073,18 +1041,17 @@ TEST_F(DeclarableOpsTests11, ImageResizeBicubic_Test8) { }); auto size = NDArrayFactory::create({9, 9}); - nd4j::ops::resize_bicubic op; + sd::ops::resize_bicubic op; auto results = op.evaluate({&input, &size}, {}, {}, {true, false}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - NDArray* result = results->at(0); + NDArray* result = results.at(0); -// result->printBuffer("Resized to 9x9"); +// result.printBuffer("Resized to 9x9"); // testData.printBuffer("Expect for 9x9"); ASSERT_TRUE(testData.isSameShape(result)); ASSERT_TRUE(testData.equalsTo(result)); - delete results; } TEST_F(DeclarableOpsTests11, ImageResizeArea_Test1) { @@ -1134,18 +1101,17 @@ TEST_F(DeclarableOpsTests11, ImageResizeArea_Test1) { 33.f, 34.f, 35.f, 36.f }); input.linspace(1); auto size = NDArrayFactory::create({6, 6}); - nd4j::ops::resize_area op; + sd::ops::resize_area op; auto results = op.evaluate({&input, &size}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - NDArray* result = results->at(0); + NDArray* result = results.at(0); -// result->printBuffer("Area Resized to 6x6"); +// result.printBuffer("Area Resized to 6x6"); // expected.printBuffer("Area Expect for 6x6"); ASSERT_TRUE(expected.isSameShape(result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; } TEST_F(DeclarableOpsTests11, ImageResizeArea_Test2) { @@ -1161,18 +1127,17 @@ TEST_F(DeclarableOpsTests11, ImageResizeArea_Test2) { }); input.linspace(1); auto size = NDArrayFactory::create({6, 6}); - nd4j::ops::resize_area op; + sd::ops::resize_area op; auto results = op.evaluate({&input, &size}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - NDArray* result = results->at(0); + NDArray* result = results.at(0); -// result->printBuffer("Area Resized to 6x6"); +// result.printBuffer("Area Resized to 6x6"); // expected.printBuffer("Area Expect for 6x6"); ASSERT_TRUE(expected.isSameShape(result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; } @@ -1189,18 +1154,17 @@ TEST_F(DeclarableOpsTests11, ImageResizeArea_Test3) { }); input.linspace(1); auto size = NDArrayFactory::create({6, 6}); - nd4j::ops::resize_area op; + sd::ops::resize_area op; auto results = op.evaluate({&input, &size}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - NDArray* result = results->at(0); + NDArray* result = results.at(0); -// result->printBuffer("Area Resized to 6x6"); +// result.printBuffer("Area Resized to 6x6"); // expected.printBuffer("Area Expect for 6x6"); ASSERT_TRUE(expected.isSameShape(result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; } TEST_F(DeclarableOpsTests11, ImageResizeArea_Test4) { @@ -1227,18 +1191,17 @@ TEST_F(DeclarableOpsTests11, ImageResizeArea_Test4) { }); //input.linspace(1); auto size = NDArrayFactory::create({6, 6}); - nd4j::ops::resize_area op; + sd::ops::resize_area op; auto results = op.evaluate({&input, &size}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - NDArray* result = results->at(0); + NDArray* result = results.at(0); -// result->printBuffer("Area Resized to 6x6"); +// result.printBuffer("Area Resized to 6x6"); // expected.printBuffer("Area Expect for 6x6"); ASSERT_TRUE(expected.isSameShape(result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; } TEST_F(DeclarableOpsTests11, ImageResizeArea_Test5) { @@ -1265,18 +1228,17 @@ TEST_F(DeclarableOpsTests11, ImageResizeArea_Test5) { }); //input.linspace(1); auto size = NDArrayFactory::create({6, 6}); - nd4j::ops::resize_area op; + sd::ops::resize_area op; auto results = op.evaluate({&input, &size}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - NDArray* result = results->at(0); + NDArray* result = results.at(0); -// result->printBuffer("Area Resized to 6x6"); +// result.printBuffer("Area Resized to 6x6"); // expected.printBuffer("Area Expect for 6x6"); ASSERT_TRUE(expected.isSameShape(result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; } TEST_F(DeclarableOpsTests11, ImageResizeArea_Test6) { @@ -1303,18 +1265,17 @@ TEST_F(DeclarableOpsTests11, ImageResizeArea_Test6) { }); //input.linspace(1); auto size = NDArrayFactory::create({6, 6}); - nd4j::ops::resize_area op; + sd::ops::resize_area op; auto results = op.evaluate({&input, &size}, {}, {}, {true}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - NDArray* result = results->at(0); + NDArray* result = results.at(0); -// result->printBuffer("Area Resized to 6x6"); +// result.printBuffer("Area Resized to 6x6"); // expected.printBuffer("Area Expect for 6x6"); ASSERT_TRUE(expected.isSameShape(result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; } TEST_F(DeclarableOpsTests11, ImageResizeArea_Test7) { @@ -1341,18 +1302,17 @@ TEST_F(DeclarableOpsTests11, ImageResizeArea_Test7) { }); //input.linspace(1); // auto size = NDArrayFactory::create({6, 6}); - nd4j::ops::resize_area op; + sd::ops::resize_area op; auto results = op.evaluate({&input}, {}, {6, 6}, {true}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - NDArray* result = results->at(0); + NDArray* result = results.at(0); -// result->printBuffer("Area Resized to 6x6"); +// result.printBuffer("Area Resized to 6x6"); // expected.printBuffer("Area Expect for 6x6"); ASSERT_TRUE(expected.isSameShape(result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; } TEST_F(DeclarableOpsTests11, ImageResizeArea_Test8) { @@ -1371,18 +1331,17 @@ TEST_F(DeclarableOpsTests11, ImageResizeArea_Test8) { }); //input.linspace(1); // auto size = NDArrayFactory::create({6, 6}); - nd4j::ops::resize_area op; + sd::ops::resize_area op; auto results = op.evaluate({&input}, {}, {6, 6}, {true}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - NDArray* result = results->at(0); + NDArray* result = results.at(0); -// result->printBuffer("Area Resized to 6x6"); +// result.printBuffer("Area Resized to 6x6"); // expected.printBuffer("Area Expect for 6x6"); ASSERT_TRUE(expected.isSameShape(result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; } /////////////////////////////////////////////////////////////////// @@ -1398,18 +1357,17 @@ TEST_F(DeclarableOpsTests11, ImageResizeArea_Test9) { }); //input.linspace(1); auto size = NDArrayFactory::create({10, 10}); - nd4j::ops::resize_area op; + sd::ops::resize_area op; auto results = op.evaluate({&input, &size}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - NDArray* result = results->at(0); + NDArray* result = results.at(0); -// result->printBuffer("Area Resized to 10x10"); +// result.printBuffer("Area Resized to 10x10"); // expected.printBuffer("Area Expect for 6x6"); ASSERT_TRUE(expected.isSameShape(result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; } /////////////////////////////////////////////////////////////////// @@ -1425,18 +1383,17 @@ TEST_F(DeclarableOpsTests11, ImageResizeArea_Test10) { }); //input.linspace(1); //auto size = NDArrayFactory::create({10, 10}); - nd4j::ops::resize_area op; + sd::ops::resize_area op; auto results = op.evaluate({&input}, {}, {10, 10}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - NDArray* result = results->at(0); + NDArray* result = results.at(0); -// result->printBuffer("Area Resized to 10x10"); +// result.printBuffer("Area Resized to 10x10"); // expected.printBuffer("Area Expect for 6x6"); ASSERT_TRUE(expected.isSameShape(result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; } /////////////////////////////////////////////////////////////////// @@ -1452,18 +1409,17 @@ TEST_F(DeclarableOpsTests11, ImageResizeArea_Test11) { // }); //input.linspace(1); //auto size = NDArrayFactory::create({10, 10}); - nd4j::ops::resize_area op; + sd::ops::resize_area op; auto results = op.evaluate({&input}, {}, {6, 9}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - NDArray* result = results->at(0); + NDArray* result = results.at(0); -// result->printBuffer("Area Resized to 6x9"); +// result.printBuffer("Area Resized to 6x9"); // expected.printBuffer("Area Expect for 6x6"); // ASSERT_TRUE(expected.isSameShape(result)); // ASSERT_TRUE(expected.equalsTo(result)); - delete results; } /////////////////////////////////////////////////////////////////// @@ -1479,18 +1435,17 @@ TEST_F(DeclarableOpsTests11, ImageResizeArea_Test12) { // }); //input.linspace(1); //auto size = NDArrayFactory::create({10, 10}); - nd4j::ops::resize_area op; + sd::ops::resize_area op; auto results = op.evaluate({&input}, {}, {10, 15}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - NDArray* result = results->at(0); + NDArray* result = results.at(0); -// result->printBuffer("Area Resized to 6x9"); +// result.printBuffer("Area Resized to 6x9"); // expected.printBuffer("Area Expect for 6x6"); // ASSERT_TRUE(expected.isSameShape(result)); // ASSERT_TRUE(expected.equalsTo(result)); - delete results; } /////////////////////////////////////////////////////////////////// @@ -1506,18 +1461,17 @@ TEST_F(DeclarableOpsTests11, ImageResizeArea_Test13) { // }); //input.linspace(1); //auto size = NDArrayFactory::create({10, 10}); - nd4j::ops::resize_area op; + sd::ops::resize_area op; auto results = op.evaluate({&input}, {}, {9, 9}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - NDArray* result = results->at(0); + NDArray* result = results.at(0); -// result->printBuffer("Area Resized to 8x8"); +// result.printBuffer("Area Resized to 8x8"); // expected.printBuffer("Area Expect for 6x6"); // ASSERT_TRUE(expected.isSameShape(result)); // ASSERT_TRUE(expected.equalsTo(result)); - delete results; } TEST_F(DeclarableOpsTests11, ImageResizeArea_Test14) { @@ -1537,17 +1491,16 @@ TEST_F(DeclarableOpsTests11, ImageResizeArea_Test14) { 25.f }); //input.linspace(1); // auto size = NDArrayFactory::create({6, 6}); - nd4j::ops::resize_area op; + sd::ops::resize_area op; auto results = op.evaluate({&input, &size}, {}, {false}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - NDArray* result = results->at(0); -// result->printBuffer("Area Resized to 8x7"); + NDArray* result = results.at(0); +// result.printBuffer("Area Resized to 8x7"); // expected.printBuffer("Area Expect for 8x7"); ASSERT_TRUE(expected.isSameShape(result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; } TEST_F(DeclarableOpsTests11, ImageResizeArea_Test15) { @@ -1566,17 +1519,16 @@ TEST_F(DeclarableOpsTests11, ImageResizeArea_Test15) { 22.999998f , 23.800001f , 24.399984f , 25.f }); - nd4j::ops::resize_area op; + sd::ops::resize_area op; auto results = op.evaluate({&input}, {}, {8, 7}, {false}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - NDArray* result = results->at(0); -// result->printBuffer("Area Resized to 8x7"); + NDArray* result = results.at(0); +// result.printBuffer("Area Resized to 8x7"); // expected.printBuffer("Area Expect for 8x7"); ASSERT_TRUE(expected.isSameShape(result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; } /////////////////////////////////////////////////////////////////// @@ -1615,16 +1567,15 @@ TEST_F(DeclarableOpsTests11, Solve_Test_1) { 7.625f, 3.25f, 5.f }); - nd4j::ops::solve op; + sd::ops::solve op; auto res = op.evaluate({&a, &b}); - ASSERT_EQ(res->status(), ND4J_STATUS_OK); - auto z = res->at(0); + ASSERT_EQ(res.status(), ND4J_STATUS_OK); + auto z = res.at(0); // z->printIndexedBuffer("Solve of 3x3"); ASSERT_TRUE(exp.equalsTo(z)); - delete res; } //////////////////////////////////////////////////////////////////////////////// @@ -1645,16 +1596,16 @@ TEST_F(DeclarableOpsTests11, Solve_Test_2) { -3.3333333f, 3.6666666f, 0.333333f, 1.3333333f }); - nd4j::ops::solve op; + sd::ops::solve op; auto res = op.evaluate({&a, &b}); - ASSERT_EQ(res->status(), ND4J_STATUS_OK); - auto z = res->at(0); + ASSERT_EQ(res.status(), ND4J_STATUS_OK); + auto z = res.at(0); // z->printIndexedBuffer("Solve 4x4"); ASSERT_TRUE(exp.equalsTo(z)); - delete res; + } //////////////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests11, Solve_Test_3) { @@ -1682,16 +1633,16 @@ TEST_F(DeclarableOpsTests11, Solve_Test_3) { 1.333333f, -0.6666667f, 2.6666667f, -1.3333333f }); - nd4j::ops::solve op; + sd::ops::solve op; auto res = op.evaluate({&a, &b}); - ASSERT_EQ(res->status(), ND4J_STATUS_OK); - auto z = res->at(0); + ASSERT_EQ(res.status(), ND4J_STATUS_OK); + auto z = res.at(0); // z->printIndexedBuffer("Solve 4x4"); ASSERT_TRUE(exp.equalsTo(z)); - delete res; + } //////////////////////////////////////////////////////////////////////////////// @@ -1714,17 +1665,17 @@ TEST_F(DeclarableOpsTests11, Solve_Test_4) { 0.81915987f, 0.72049433f, 0.2643504f, 0.44472617f }); - nd4j::ops::solve op; + sd::ops::solve op; auto res = op.evaluate({&a, &b}); - ASSERT_EQ(res->status(), ND4J_STATUS_OK); - auto z = res->at(0); + ASSERT_EQ(res.status(), ND4J_STATUS_OK); + auto z = res.at(0); // z->printBuffer("4 Solve 4x4"); // exp.printBuffer("4 Expec 4x4"); ASSERT_TRUE(exp.equalsTo(z)); - delete res; + } //////////////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests11, Solve_Test_4_1) { @@ -1743,17 +1694,17 @@ TEST_F(DeclarableOpsTests11, Solve_Test_4_1) { 0.4400987f, 0.2766527f, 0.6394467f, 0.79696566f }); - nd4j::ops::solve op; + sd::ops::solve op; auto res = op.evaluate({&a, &b}, {true}); - ASSERT_EQ(res->status(), ND4J_STATUS_OK); - auto z = res->at(0); + ASSERT_EQ(res.status(), ND4J_STATUS_OK); + auto z = res.at(0); // z->printBuffer("4 Solve 4x4"); // exp.printBuffer("4 Expec 4x4"); ASSERT_TRUE(exp.equalsTo(z)); - delete res; + } //////////////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests11, Solve_Test_4_2) { @@ -1776,17 +1727,17 @@ TEST_F(DeclarableOpsTests11, Solve_Test_4_2) { -0.30013534f, -0.53690606f, -0.47959247f }); - nd4j::ops::triangular_solve op; + sd::ops::triangular_solve op; auto res = op.evaluate({&a, &b}, {true, false}); - ASSERT_EQ(res->status(), ND4J_STATUS_OK); - auto z = res->at(0); + ASSERT_EQ(res.status(), ND4J_STATUS_OK); + auto z = res.at(0); // z->printBuffer("4_2 Triangular_Solve 3x3"); // exp.printBuffer("4_2 Triangular_Expec 3x3"); ASSERT_TRUE(exp.equalsTo(z)); - delete res; + } //////////////////////////////////////////////////////////////////////////////// @@ -1810,17 +1761,17 @@ TEST_F(DeclarableOpsTests11, Solve_Test_4_3) { 1.1904413f, 1.3938838f, 1.3926021f }); - nd4j::ops::triangular_solve op; + sd::ops::triangular_solve op; auto res = op.evaluate({&a, &b}, {true, true}); - ASSERT_EQ(res->status(), ND4J_STATUS_OK); - auto z = res->at(0); + ASSERT_EQ(res.status(), ND4J_STATUS_OK); + auto z = res.at(0); // z->printBuffer("4_3 Triangular_Solve 3x3"); // exp.printBuffer("4_3 Triangular_Expec 3x3"); ASSERT_TRUE(exp.equalsTo(z)); - delete res; + } //////////////////////////////////////////////////////////////////////////////// @@ -1844,17 +1795,17 @@ TEST_F(DeclarableOpsTests11, Solve_Test_4_4) { -0.4398522f, -1.1899745f, -1.1392052f }); - nd4j::ops::solve op; + sd::ops::solve op; auto res = op.evaluate({&a, &b}, {false}); - ASSERT_EQ(res->status(), ND4J_STATUS_OK); - auto z = res->at(0); + ASSERT_EQ(res.status(), ND4J_STATUS_OK); + auto z = res.at(0); // z->printBuffer("4_4 Solve 3x3"); // exp.printBuffer("4_4 Expec 3x3"); ASSERT_TRUE(exp.equalsTo(z)); - delete res; + } //////////////////////////////////////////////////////////////////////////////// @@ -1878,17 +1829,17 @@ TEST_F(DeclarableOpsTests11, Solve_Test_4_5) { -0.8774802f, -1.2155888f, -1.8049058f }); - nd4j::ops::solve op; + sd::ops::solve op; auto res = op.evaluate({&a, &b}, {true, true}); - ASSERT_EQ(res->status(), ND4J_STATUS_OK); - auto z = res->at(0); + ASSERT_EQ(res.status(), ND4J_STATUS_OK); + auto z = res.at(0); // z->printBuffer("4_5 Solve 3x3"); // exp.printBuffer("4_5 Expec 3x3"); ASSERT_TRUE(exp.equalsTo(z)); - delete res; + } //////////////////////////////////////////////////////////////////////////////// @@ -1912,17 +1863,17 @@ TEST_F(DeclarableOpsTests11, Solve_Test_4_6) { 0.01692283f, -0.04538865f, -0.09868701f }); - nd4j::ops::triangular_solve op; + sd::ops::triangular_solve op; auto res = op.evaluate({&a, &b}, {false, true}); - ASSERT_EQ(res->status(), ND4J_STATUS_OK); - auto z = res->at(0); + ASSERT_EQ(res.status(), ND4J_STATUS_OK); + auto z = res.at(0); - z->printBuffer("4_6 Solve 3x3"); - exp.printBuffer("4_6 Expec 3x3"); +// z->printBuffer("4_6 Solve 3x3"); +// exp.printBuffer("4_6 Expec 3x3"); ASSERT_TRUE(exp.equalsTo(z)); - delete res; + } //////////////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests11, Solve_Test_4_7) { @@ -1949,17 +1900,17 @@ TEST_F(DeclarableOpsTests11, Solve_Test_4_7) { 0.01692283f, -0.04538865f, -0.09868701f }); - nd4j::ops::triangular_solve op; + sd::ops::triangular_solve op; auto res = op.evaluate({&a, &b}, {true, false}); - ASSERT_EQ(res->status(), ND4J_STATUS_OK); - auto z = res->at(0); + ASSERT_EQ(res.status(), ND4J_STATUS_OK); + auto z = res.at(0); - z->printBuffer("4_7 Solve 3x3"); - exp.printBuffer("4_7 Expec 3x3"); +// z->printBuffer("4_7 Solve 3x3"); +// exp.printBuffer("4_7 Expec 3x3"); ASSERT_TRUE(exp.equalsTo(z)); - delete res; + } //////////////////////////////////////////////////////////////////////////////// @@ -1983,24 +1934,136 @@ TEST_F(DeclarableOpsTests11, Solve_Test_5) { -0.8774802f, -1.2155888f, -1.8049058f }); - nd4j::ops::solve op; + sd::ops::solve op; auto res = op.evaluate({&a, &b}, {true}); - ASSERT_EQ(res->status(), ND4J_STATUS_OK); - auto z = res->at(0); + ASSERT_EQ(res.status(), ND4J_STATUS_OK); + auto z = res.at(0); - z->printBuffer("4 Solve 4x4"); - exp.printBuffer("4 Expec 4x4"); +// z->printBuffer("4 Solve 4x4"); +// exp.printBuffer("4 Expec 4x4"); ASSERT_TRUE(exp.equalsTo(z)); - delete res; + } +//////////////////////////////////////////////////////////////////////////////// +TEST_F(DeclarableOpsTests11, SolveLS_Test_1) { + + auto a = NDArrayFactory::create('c', {2,2, 2}, { + 1.f, 2.f, 3.f, 4.f, + 5.f, 6.f, 7.f, 8.f + }); + + auto b = NDArrayFactory::create('c', {2, 2, 1}, { + 3.f, 7.f, 11.f, 15.f + }); + + auto exp = NDArrayFactory::create('c', {2, 2, 1}, { + 0.8311695f, 1.0909086f, 0.9205573f, 1.0630057f + }); + + sd::ops::lstsq op; + + auto res = op.evaluate({&a, &b}, {0.5}, {}, {true}); + ASSERT_EQ(res.status(), ND4J_STATUS_OK); + auto z = res.at(0); + +// z->printIndexedBuffer("LS Solve 2x2"); +// exp.printIndexedBuffer("LS Expec 2x2"); + + ASSERT_TRUE(exp.equalsTo(z, 1.e-4)); +} + +TEST_F(DeclarableOpsTests11, SolveLS_Test_2) { + + auto a = NDArrayFactory::create('c', {2,2, 2}, { + 1.f, 2.f, 3.f, 4.f, + 5.f, 6.f, 7.f, 8.f + }); + + auto b = NDArrayFactory::create('c', {2, 2, 1}, { + 3.f, 7.f, 11.f, 15.f + }); + + auto exp = NDArrayFactory::create('c', {2, 2, 1}, { + 0.8311695f, 1.0909086f, 0.9205573f, 1.0630057f + }); + + sd::ops::lstsq op; + + auto res = op.evaluate({&a, &b}, {0.5}, {}, {true}); + ASSERT_EQ(res.status(), ND4J_STATUS_OK); + auto z = res.at(0); + +// z->printIndexedBuffer("2LS Solve 2x2"); +// exp.printIndexedBuffer("2LS Expec 2x2"); + + ASSERT_TRUE(exp.equalsTo(z, 1.e-4)); +} + +//////////////////////////////////////////////////////////////////////////////// +TEST_F(DeclarableOpsTests11, Cholesky_Test_2x2x2) { + + auto a = NDArrayFactory::create('c', {2,2, 2}, { + 10.f, 14.f, + 14.f, 20.f, + + 74.f, 86.f, + 86.f, 100.f + }); + + auto exp = NDArrayFactory::create('c', {2, 2, 2}, { + 3.1622777f, 0.f, 4.427189f, 0.6324552f, + 8.602325f, 0.f, 9.997296f, 0.23252854f + }); + + sd::ops::cholesky op; + + auto res = op.evaluate({&a}); + ASSERT_EQ(res.status(), ND4J_STATUS_OK); + auto z = res.at(0); + + z->printIndexedBuffer("L matrix is"); + exp.printIndexedBuffer("L expected is"); + + ASSERT_TRUE(exp.equalsTo(z)); + +} + +//////////////////////////////////////////////////////////////////////////////// +TEST_F(DeclarableOpsTests11, Cholesky_Test_2x2x2_2) { + + auto a = NDArrayFactory::create('c', {2,2, 2}, { + 10.5f, 14.f, + 14.f, 20.5f, + + 74.5f, 86.f, + 86.f, 100.5f + }); + + auto exp = NDArrayFactory::create('c', {2, 2, 2}, { + 3.2403703f, 0.f, 4.3204937f, 1.3540066f, + 8.631338f, 0.f, 9.963693f, 1.1067207f + }); + + sd::ops::cholesky op; + + auto res = op.evaluate({&a}); + ASSERT_EQ(res.status(), ND4J_STATUS_OK); + auto z = res.at(0); + +// z->printIndexedBuffer("L matrix is"); +// exp.printIndexedBuffer("L expected is"); + MmulHelper::matmul(z, z, &exp, false, true); + ASSERT_TRUE(exp.equalsTo(a)); +} + /////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests11, mean_sqerr_loss_grad_test1) { - NDArray labels('c', {2,3,4}, nd4j::DataType::DOUBLE); - NDArray predictions('c', {2,3,4}, nd4j::DataType::DOUBLE); - NDArray weights('c', {2,3,4}, nd4j::DataType::DOUBLE); + NDArray labels('c', {2,3,4}, sd::DataType::DOUBLE); + NDArray predictions('c', {2,3,4}, sd::DataType::DOUBLE); + NDArray weights('c', {2,3,4}, sd::DataType::DOUBLE); NDArray dLdpExp('c', {2,3,4}, {-0.96, -1.92, -2.88, -3.84, -4.8 , -5.76, -6.72, -7.68, -8.64, -9.6 ,-10.56,-11.52, -12.48,-13.44,-14.4 ,-15.36,-16.32,-17.28,-18.24,-19.2 ,-20.16,-21.12,-22.08,-23.04}); @@ -2011,14 +2074,14 @@ TEST_F(DeclarableOpsTests11, mean_sqerr_loss_grad_test1) { labels.linspace(1); weights.assign(0.5); - nd4j::ops::mean_sqerr_loss_grad op; + sd::ops::mean_sqerr_loss_grad op; auto results = op.evaluate({&predictions, &weights, &labels}, {}, {0}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto dLdp = results->at(0); - auto dLdw = results->at(1); - auto dLdl = results->at(2); + auto dLdp = results.at(0); + auto dLdw = results.at(1); + auto dLdl = results.at(2); ASSERT_TRUE(dLdpExp.isSameShape(dLdp)); ASSERT_TRUE(dLdpExp.equalsTo(dLdp)); @@ -2027,15 +2090,14 @@ TEST_F(DeclarableOpsTests11, mean_sqerr_loss_grad_test1) { ASSERT_TRUE(dLdpExp.isSameShape(-*dLdl)); ASSERT_TRUE(dLdpExp.equalsTo(-*dLdl)); - delete results; } /////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests11, mean_sqerr_loss_grad_test2) { - NDArray labels('c', {2,3,4}, nd4j::DataType::DOUBLE); - NDArray predictions('c', {2,3,4}, nd4j::DataType::DOUBLE); - NDArray weights('c', {2,1,4}, nd4j::DataType::DOUBLE); + NDArray labels('c', {2,3,4}, sd::DataType::DOUBLE); + NDArray predictions('c', {2,3,4}, sd::DataType::DOUBLE); + NDArray weights('c', {2,1,4}, sd::DataType::DOUBLE); NDArray dLdwExp('c', {2,1,4}, {98.61121,129.024 , 164.9664 , 206.4384 , 828.51837,925.28644,1027.58398,1135.41113}); @@ -2043,25 +2105,24 @@ TEST_F(DeclarableOpsTests11, mean_sqerr_loss_grad_test2) { labels.linspace(1); weights.assign(0.5); - nd4j::ops::mean_sqerr_loss_grad op; + sd::ops::mean_sqerr_loss_grad op; auto results = op.evaluate({&predictions, &weights, &labels}, {}, {0}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *dLdw = results->at(1); + auto *dLdw = results.at(1); ASSERT_TRUE(dLdwExp.isSameShape(dLdw)); ASSERT_TRUE(dLdwExp.equalsTo(dLdw)); - delete results; } /////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests11, mean_sqerr_loss_grad_test3) { - NDArray labels('c', {2,3,4}, nd4j::DataType::DOUBLE); - NDArray predictions('c', {2,3,4}, nd4j::DataType::DOUBLE); - NDArray weights(nd4j::DataType::DOUBLE); + NDArray labels('c', {2,3,4}, sd::DataType::DOUBLE); + NDArray predictions('c', {2,3,4}, sd::DataType::DOUBLE); + NDArray weights(sd::DataType::DOUBLE); NDArray dLdpExp('c', {2,3,4}, {-0.96, -1.92, -2.88, -3.84, -4.8 , -5.76, -6.72, -7.68, -8.64, -9.6 ,-10.56,-11.52, -12.48,-13.44,-14.4 ,-15.36,-16.32,-17.28,-18.24,-19.2 ,-20.16,-21.12,-22.08,-23.04}); @@ -2071,14 +2132,14 @@ TEST_F(DeclarableOpsTests11, mean_sqerr_loss_grad_test3) { labels.linspace(1); weights.assign(0.5); - nd4j::ops::mean_sqerr_loss_grad op; + sd::ops::mean_sqerr_loss_grad op; auto results = op.evaluate({&predictions, &weights, &labels}, {}, {1}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *dLdp = results->at(0); - auto *dLdw = results->at(1); - auto *dLdl = results->at(2); + auto *dLdp = results.at(0); + auto *dLdw = results.at(1); + auto *dLdl = results.at(2); ASSERT_TRUE(dLdpExp.isSameShape(dLdp)); ASSERT_TRUE(dLdpExp.equalsTo(dLdp)); @@ -2087,15 +2148,14 @@ TEST_F(DeclarableOpsTests11, mean_sqerr_loss_grad_test3) { ASSERT_TRUE(dLdpExp.isSameShape(-*dLdl)); ASSERT_TRUE(dLdpExp.equalsTo(-*dLdl)); - delete results; } /////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests11, mean_sqerr_loss_grad_test4) { - NDArray labels('c', {2,3,4}, nd4j::DataType::DOUBLE); - NDArray predictions('c', {2,3,4}, nd4j::DataType::DOUBLE); - NDArray weights('c', {1,3,1}, nd4j::DataType::DOUBLE); + NDArray labels('c', {2,3,4}, sd::DataType::DOUBLE); + NDArray predictions('c', {2,3,4}, sd::DataType::DOUBLE); + NDArray weights('c', {1,3,1}, sd::DataType::DOUBLE); NDArray dLdwExp('c', {1,3,1}, {807.32153, 1426.63684, 2281.88159}); @@ -2103,25 +2163,24 @@ TEST_F(DeclarableOpsTests11, mean_sqerr_loss_grad_test4) { labels.linspace(1); weights.assign(0.5); - nd4j::ops::mean_sqerr_loss_grad op; + sd::ops::mean_sqerr_loss_grad op; auto results = op.evaluate({&predictions, &weights, &labels}, {}, {1}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *dLdw = results->at(1); + auto *dLdw = results.at(1); ASSERT_TRUE(dLdwExp.isSameShape(dLdw)); ASSERT_TRUE(dLdwExp.equalsTo(dLdw)); - delete results; } /////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests11, mean_sqerr_loss_grad_test5) { - NDArray labels('c', {2,3,4}, nd4j::DataType::DOUBLE); - NDArray predictions('c', {2,3,4}, nd4j::DataType::DOUBLE); - NDArray weights('c', {2,3,4}, nd4j::DataType::DOUBLE); + NDArray labels('c', {2,3,4}, sd::DataType::DOUBLE); + NDArray predictions('c', {2,3,4}, sd::DataType::DOUBLE); + NDArray weights('c', {2,3,4}, sd::DataType::DOUBLE); NDArray dLdpExp('c', {2,3,4}, {-0.08,-0.16,-0.24,-0.32,-0.4 ,-0.48,-0.56,-0.64,-0.72,-0.8 ,-0.88,-0.96, -1.04,-1.12,-1.2 ,-1.28,-1.36,-1.44,-1.52,-1.6 ,-1.68,-1.76,-1.84,-1.92}); @@ -2132,14 +2191,14 @@ TEST_F(DeclarableOpsTests11, mean_sqerr_loss_grad_test5) { labels.linspace(1); weights.assign(0.5); - nd4j::ops::mean_sqerr_loss_grad op; + sd::ops::mean_sqerr_loss_grad op; auto results = op.evaluate({&predictions, &weights, &labels}, {}, {2}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *dLdp = results->at(0); - auto *dLdw = results->at(1); - auto *dLdl = results->at(2); + auto *dLdp = results.at(0); + auto *dLdw = results.at(1); + auto *dLdl = results.at(2); ASSERT_TRUE(dLdpExp.isSameShape(dLdp)); ASSERT_TRUE(dLdpExp.equalsTo(dLdp)); @@ -2148,15 +2207,14 @@ TEST_F(DeclarableOpsTests11, mean_sqerr_loss_grad_test5) { ASSERT_TRUE(dLdpExp.isSameShape(-*dLdl)); ASSERT_TRUE(dLdpExp.equalsTo(-*dLdl)); - delete results; } /////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests11, mean_sqerr_loss_grad_test6) { - NDArray labels('c', {2,3,4}, nd4j::DataType::DOUBLE); - NDArray predictions('c', {2,3,4}, nd4j::DataType::DOUBLE); - NDArray weights('c', {1,3,1}, nd4j::DataType::DOUBLE); + NDArray labels('c', {2,3,4}, sd::DataType::DOUBLE); + NDArray predictions('c', {2,3,4}, sd::DataType::DOUBLE); + NDArray weights('c', {1,3,1}, sd::DataType::DOUBLE); NDArray dLdwExp('c', {1,3,1}, {-58.16319, -6.5536 , 64.71682}); @@ -2164,25 +2222,24 @@ TEST_F(DeclarableOpsTests11, mean_sqerr_loss_grad_test6) { labels.linspace(1); weights.assign(0.5); - nd4j::ops::mean_sqerr_loss_grad op; + sd::ops::mean_sqerr_loss_grad op; auto results = op.evaluate({&predictions, &weights, &labels}, {}, {2}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *dLdw = results->at(1); + auto *dLdw = results.at(1); ASSERT_TRUE(dLdwExp.isSameShape(dLdw)); ASSERT_TRUE(dLdwExp.equalsTo(dLdw)); - delete results; } /////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests11, mean_sqerr_loss_grad_test7) { - NDArray labels('c', {2,3,4}, nd4j::DataType::DOUBLE); - NDArray predictions('c', {2,3,4}, nd4j::DataType::DOUBLE); - NDArray weights(nd4j::DataType::DOUBLE); + NDArray labels('c', {2,3,4}, sd::DataType::DOUBLE); + NDArray predictions('c', {2,3,4}, sd::DataType::DOUBLE); + NDArray weights(sd::DataType::DOUBLE); NDArray dLdwExp('c', {}, std::vector{0.}); @@ -2190,25 +2247,24 @@ TEST_F(DeclarableOpsTests11, mean_sqerr_loss_grad_test7) { labels.linspace(1); weights.assign(0.5); - nd4j::ops::mean_sqerr_loss_grad op; + sd::ops::mean_sqerr_loss_grad op; auto results = op.evaluate({&predictions, &weights, &labels}, {}, {2}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *dLdw = results->at(1); + auto *dLdw = results.at(1); ASSERT_TRUE(dLdwExp.isSameShape(dLdw)); ASSERT_TRUE(dLdwExp.equalsTo(dLdw)); - delete results; } /////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests11, mean_sqerr_loss_grad_test8) { - NDArray labels('c', {2,3,4}, nd4j::DataType::DOUBLE); - NDArray predictions('c', {2,3,4}, nd4j::DataType::DOUBLE); - NDArray weights('c', {2,3,4}, nd4j::DataType::DOUBLE); + NDArray labels('c', {2,3,4}, sd::DataType::DOUBLE); + NDArray predictions('c', {2,3,4}, sd::DataType::DOUBLE); + NDArray weights('c', {2,3,4}, sd::DataType::DOUBLE); NDArray dLdpExp('c', {2,3,4}, {0. ,0. ,0. ,0. ,-0.48 ,-0.576,-0.672,-0.768,-0.864,-0.96 ,-1.056,-1.152, -1.248,-1.344,-1.44 ,-1.536,-1.632,-1.728,-1.824,-1.92 ,-2.016,-2.112,-2.208,-2.304}); @@ -2223,14 +2279,14 @@ TEST_F(DeclarableOpsTests11, mean_sqerr_loss_grad_test8) { weights.p(2, 0.); weights.p(3, 0.); - nd4j::ops::mean_sqerr_loss_grad op; + sd::ops::mean_sqerr_loss_grad op; auto results = op.evaluate({&predictions, &weights, &labels}, {}, {2}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *dLdp = results->at(0); - auto *dLdw = results->at(1); - auto *dLdl = results->at(2); + auto *dLdp = results.at(0); + auto *dLdw = results.at(1); + auto *dLdl = results.at(2); ASSERT_TRUE(dLdpExp.isSameShape(dLdp)); ASSERT_TRUE(dLdpExp.equalsTo(dLdp)); @@ -2239,15 +2295,14 @@ TEST_F(DeclarableOpsTests11, mean_sqerr_loss_grad_test8) { ASSERT_TRUE(dLdpExp.isSameShape(-*dLdl)); ASSERT_TRUE(dLdpExp.equalsTo(-*dLdl)); - delete results; } /////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests11, mean_sqerr_loss_grad_test9) { - NDArray labels('c', {2,3,4}, nd4j::DataType::DOUBLE); - NDArray predictions('c', {2,3,4}, nd4j::DataType::DOUBLE); - NDArray weights('c', {2,3,4}, nd4j::DataType::DOUBLE); + NDArray labels('c', {2,3,4}, sd::DataType::DOUBLE); + NDArray predictions('c', {2,3,4}, sd::DataType::DOUBLE); + NDArray weights('c', {2,3,4}, sd::DataType::DOUBLE); NDArray dLdpExp('c', {2,3,4}, {-0.04,-0.08,-0.12,-0.16,-0.2 ,-0.24,-0.28,-0.32,-0.36,-0.4 ,-0.44,-0.48, -0.52,-0.56,-0.6 ,-0.64,-0.68,-0.72,-0.76,-0.8 ,-0.84,-0.88,-0.92,-0.96}); @@ -2258,14 +2313,14 @@ TEST_F(DeclarableOpsTests11, mean_sqerr_loss_grad_test9) { labels.linspace(1); weights.assign(0.5); - nd4j::ops::mean_sqerr_loss_grad op; + sd::ops::mean_sqerr_loss_grad op; auto results = op.evaluate({&predictions, &weights, &labels}, {}, {3}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *dLdp = results->at(0); - auto *dLdw = results->at(1); - auto *dLdl = results->at(2); + auto *dLdp = results.at(0); + auto *dLdw = results.at(1); + auto *dLdl = results.at(2); ASSERT_TRUE(dLdpExp.isSameShape(dLdp)); ASSERT_TRUE(dLdpExp.equalsTo(dLdp)); @@ -2274,15 +2329,14 @@ TEST_F(DeclarableOpsTests11, mean_sqerr_loss_grad_test9) { ASSERT_TRUE(dLdpExp.isSameShape(-*dLdl)); ASSERT_TRUE(dLdpExp.equalsTo(-*dLdl)); - delete results; } /////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests11, mean_sqerr_loss_grad_test10) { - NDArray labels('c', {2,3,4}, nd4j::DataType::DOUBLE); - NDArray predictions('c', {2,3,4}, nd4j::DataType::DOUBLE); - NDArray weights('c', {1,1}, nd4j::DataType::DOUBLE); + NDArray labels('c', {2,3,4}, sd::DataType::DOUBLE); + NDArray predictions('c', {2,3,4}, sd::DataType::DOUBLE); + NDArray weights('c', {1,1}, sd::DataType::DOUBLE); NDArray dLdwExp('c', {1,1}, std::vector{188.16}); @@ -2290,25 +2344,24 @@ TEST_F(DeclarableOpsTests11, mean_sqerr_loss_grad_test10) { labels.linspace(1); weights.assign(0.5); - nd4j::ops::mean_sqerr_loss_grad op; + sd::ops::mean_sqerr_loss_grad op; auto results = op.evaluate({&predictions, &weights, &labels}, {}, {3}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *dLdw = results->at(1); + auto *dLdw = results.at(1); ASSERT_TRUE(dLdwExp.isSameShape(dLdw)); ASSERT_TRUE(dLdwExp.equalsTo(dLdw)); - delete results; } /////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests11, mean_sqerr_loss_grad_test11) { - NDArray labels('c', {2,3,4}, nd4j::DataType::DOUBLE); - NDArray predictions('c', {2,3,4}, nd4j::DataType::DOUBLE); - NDArray weights('c', {1,3,1}, nd4j::DataType::DOUBLE); + NDArray labels('c', {2,3,4}, sd::DataType::DOUBLE); + NDArray predictions('c', {2,3,4}, sd::DataType::DOUBLE); + NDArray weights('c', {1,3,1}, sd::DataType::DOUBLE); NDArray dLdwExp('c', {1,3,1}, {33.6384 ,59.4432 ,95.07841}); @@ -2316,25 +2369,24 @@ TEST_F(DeclarableOpsTests11, mean_sqerr_loss_grad_test11) { labels.linspace(1); weights.assign(0.5); - nd4j::ops::mean_sqerr_loss_grad op; + sd::ops::mean_sqerr_loss_grad op; auto results = op.evaluate({&predictions, &weights, &labels}, {}, {3}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *dLdw = results->at(1); + auto *dLdw = results.at(1); ASSERT_TRUE(dLdwExp.isSameShape(dLdw)); ASSERT_TRUE(dLdwExp.equalsTo(dLdw)); - delete results; } /////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests11, mean_sqerr_loss_grad_test12) { - NDArray labels('c', {2,3,4}, nd4j::DataType::DOUBLE); - NDArray predictions('c', {2,3,4}, nd4j::DataType::DOUBLE); - NDArray weights('c', {2,3,4}, nd4j::DataType::DOUBLE); + NDArray labels('c', {2,3,4}, sd::DataType::DOUBLE); + NDArray predictions('c', {2,3,4}, sd::DataType::DOUBLE); + NDArray weights('c', {2,3,4}, sd::DataType::DOUBLE); NDArray dLdpExp('c', {2,3,4}, {0.,0.,0.,0., -0.24 ,-0.288,-0.336,-0.384,-0.432,-0.48 ,-0.528,-0.576, -0.624,-0.672,-0.72 ,-0.768,-0.816,-0.864,-0.912,-0.96 ,-1.008,-1.056,-1.104,-1.152}); @@ -2349,14 +2401,14 @@ TEST_F(DeclarableOpsTests11, mean_sqerr_loss_grad_test12) { weights.t(2) = 0.; weights.t(3) = 0.; - nd4j::ops::mean_sqerr_loss_grad op; + sd::ops::mean_sqerr_loss_grad op; auto results = op.evaluate({&predictions, &weights, &labels}, {}, {3}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *dLdp = results->at(0); - auto *dLdw = results->at(1); - auto *dLdl = results->at(2); + auto *dLdp = results.at(0); + auto *dLdw = results.at(1); + auto *dLdl = results.at(2); ASSERT_TRUE(dLdpExp.isSameShape(dLdp)); ASSERT_TRUE(dLdpExp.equalsTo(dLdp)); @@ -2365,15 +2417,14 @@ TEST_F(DeclarableOpsTests11, mean_sqerr_loss_grad_test12) { ASSERT_TRUE(dLdpExp.isSameShape(-*dLdl)); ASSERT_TRUE(dLdpExp.equalsTo(-*dLdl)); - delete results; } /////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests11, mean_sqerr_loss_grad_test13) { - NDArray labels('c', {2,3,4}, nd4j::DataType::DOUBLE); - NDArray predictions('c', {2,3,4}, nd4j::DataType::DOUBLE); - NDArray weights('c', {2,3,1}, nd4j::DataType::DOUBLE); + NDArray labels('c', {2,3,4}, sd::DataType::DOUBLE); + NDArray predictions('c', {2,3,4}, sd::DataType::DOUBLE); + NDArray weights('c', {2,3,1}, sd::DataType::DOUBLE); NDArray dLdpExp('c', {2,3,4}, {0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., -1.04,-1.12,-1.2 ,-1.28,-1.36,-1.44,-1.52,-1.6 ,-1.68,-1.76,-1.84,-1.92}); @@ -2386,14 +2437,14 @@ TEST_F(DeclarableOpsTests11, mean_sqerr_loss_grad_test13) { weights.t(1) = 0.; weights.t(2) = 0.; - nd4j::ops::mean_sqerr_loss_grad op; + sd::ops::mean_sqerr_loss_grad op; auto results = op.evaluate({&predictions, &weights, &labels}, {}, {3}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *dLdp = results->at(0); - auto *dLdw = results->at(1); - auto *dLdl = results->at(2); + auto *dLdp = results.at(0); + auto *dLdw = results.at(1); + auto *dLdl = results.at(2); ASSERT_TRUE(dLdpExp.isSameShape(dLdp)); ASSERT_TRUE(dLdpExp.equalsTo(dLdp)); @@ -2402,30 +2453,29 @@ TEST_F(DeclarableOpsTests11, mean_sqerr_loss_grad_test13) { ASSERT_TRUE(dLdpExp.isSameShape(-*dLdl)); ASSERT_TRUE(dLdpExp.equalsTo(-*dLdl)); - delete results; } TEST_F(DeclarableOpsTests11, SquaredSubtractTest_Test1) { auto x = NDArrayFactory::create('c', {4}, {0, 1, 2, 3}); auto y = NDArrayFactory::create('c',{4}, {3, 2, 1, 0}); auto exp = NDArrayFactory::create('c', {4}, {9, 1,1, 9}); - nd4j::ops::squaredsubtract op; + sd::ops::squaredsubtract op; auto result = op.evaluate({&x, &y}, {}, {}); - ASSERT_EQ(Status::OK(), result->status()); - ASSERT_TRUE(exp.equalsTo(result->at(0))); + ASSERT_EQ(Status::OK(), result.status()); + ASSERT_TRUE(exp.equalsTo(result.at(0))); - delete result; + } TEST_F(DeclarableOpsTests11, SquaredSubtractTest_Test2) { auto x = NDArrayFactory::create('c', {2, 4}, {0, 1, 2, 3, 0, 1, 2, 3}); auto y = NDArrayFactory::create('c',{4}, {3, 2, 1, 0}); auto exp = NDArrayFactory::create('c', {2, 4}, {9, 1,1, 9, 9, 1, 1, 9}); - nd4j::ops::squaredsubtract op; + sd::ops::squaredsubtract op; auto result = op.evaluate({&x, &y}, {}, {}); - ASSERT_EQ(Status::OK(), result->status()); - ASSERT_TRUE(exp.equalsTo(result->at(0))); - delete result; + ASSERT_EQ(Status::OK(), result.status()); + ASSERT_TRUE(exp.equalsTo(result.at(0))); + } TEST_F(DeclarableOpsTests11, SquaredSubtractTest_Test3) { @@ -2433,19 +2483,19 @@ TEST_F(DeclarableOpsTests11, SquaredSubtractTest_Test3) { auto y = NDArrayFactory::create('c',{4}, {3, 2, 1, 0}); auto exp = NDArrayFactory::create('c', {2, 4}, {-6, -4, 6, 24, -30, -12, 14, 48}); auto eps = NDArrayFactory::create('c', {2, 4}, {1,2,3,4,5,6,7,8}); - nd4j::ops::squaredsubtract_bp op; + sd::ops::squaredsubtract_bp op; auto result = op.evaluate({&x, &y, &eps}, {}, {}); - ASSERT_EQ(Status::OK(), result->status()); - ASSERT_TRUE(exp.equalsTo(result->at(0))); - delete result; + ASSERT_EQ(Status::OK(), result.status()); + ASSERT_TRUE(exp.equalsTo(result.at(0))); + } /////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests11, absolute_difference_loss_grad_test1) { - NDArray labels('c', {2,3,4}, nd4j::DataType::DOUBLE); - NDArray predictions('c', {2,3,4}, nd4j::DataType::DOUBLE); - NDArray weights('c', {2,3,4}, nd4j::DataType::DOUBLE); + NDArray labels('c', {2,3,4}, sd::DataType::DOUBLE); + NDArray predictions('c', {2,3,4}, sd::DataType::DOUBLE); + NDArray weights('c', {2,3,4}, sd::DataType::DOUBLE); NDArray dLdpExp('c', {2,3,4}, {-0.5,-0.5,-0.5,-0.5,-0.5,-0.5,-0.5,-0.5,-0.5,-0.5,-0.5,-0.5, -0.5,-0.5,-0.5,-0.5,-0.5,-0.5,-0.5,-0.5,-0.5,-0.5,-0.5,-0.5}); @@ -2456,14 +2506,14 @@ TEST_F(DeclarableOpsTests11, absolute_difference_loss_grad_test1) { labels.linspace(1); weights.assign(0.5); - nd4j::ops::absolute_difference_loss_grad op; + sd::ops::absolute_difference_loss_grad op; auto results = op.evaluate({&predictions, &weights, &labels}, {}, {0}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto dLdp = results->at(0); - auto dLdw = results->at(1); - auto dLdl = results->at(2); + auto dLdp = results.at(0); + auto dLdw = results.at(1); + auto dLdl = results.at(2); ASSERT_TRUE(dLdpExp.isSameShape(dLdp)); ASSERT_TRUE(dLdpExp.equalsTo(dLdp)); @@ -2471,16 +2521,14 @@ TEST_F(DeclarableOpsTests11, absolute_difference_loss_grad_test1) { ASSERT_TRUE(dLdwExp.equalsTo(dLdw)); ASSERT_TRUE(dLdpExp.isSameShape(-*dLdl)); ASSERT_TRUE(dLdpExp.equalsTo(-*dLdl)); - - delete results; } /////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests11, absolute_difference_loss_grad_test2) { - NDArray labels('c', {2,3,4}, nd4j::DataType::DOUBLE); - NDArray predictions('c', {2,3,4}, nd4j::DataType::DOUBLE); - NDArray weights('c', {2,1,4}, nd4j::DataType::DOUBLE); + NDArray labels('c', {2,3,4}, sd::DataType::DOUBLE); + NDArray predictions('c', {2,3,4}, sd::DataType::DOUBLE); + NDArray weights('c', {2,1,4}, sd::DataType::DOUBLE); NDArray dLdwExp('c', {2,1,4}, {14.4 , 17.28, 20.16, 23.04, 48.96, 51.84, 54.72, 57.6}); @@ -2488,25 +2536,24 @@ TEST_F(DeclarableOpsTests11, absolute_difference_loss_grad_test2) { labels.linspace(1); weights.assign(0.5); - nd4j::ops::absolute_difference_loss_grad op; + sd::ops::absolute_difference_loss_grad op; auto results = op.evaluate({&predictions, &weights, &labels}, {}, {0}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *dLdw = results->at(1); + auto *dLdw = results.at(1); ASSERT_TRUE(dLdwExp.isSameShape(dLdw)); ASSERT_TRUE(dLdwExp.equalsTo(dLdw)); - delete results; } /////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests11, absolute_difference_loss_grad_test3) { - NDArray labels('c', {2,3,4}, nd4j::DataType::DOUBLE); - NDArray predictions('c', {2,3,4}, nd4j::DataType::DOUBLE); - NDArray weights(nd4j::DataType::DOUBLE); + NDArray labels('c', {2,3,4}, sd::DataType::DOUBLE); + NDArray predictions('c', {2,3,4}, sd::DataType::DOUBLE); + NDArray weights(sd::DataType::DOUBLE); NDArray dLdpExp('c', {2,3,4}, {-0.5,-0.5,-0.5,-0.5,-0.5,-0.5,-0.5,-0.5,-0.5,-0.5,-0.5,-0.5, -0.5,-0.5,-0.5,-0.5,-0.5,-0.5,-0.5,-0.5,-0.5,-0.5,-0.5,-0.5}); @@ -2516,14 +2563,14 @@ TEST_F(DeclarableOpsTests11, absolute_difference_loss_grad_test3) { labels.linspace(1); weights.assign(0.5); - nd4j::ops::absolute_difference_loss_grad op; + sd::ops::absolute_difference_loss_grad op; auto results = op.evaluate({&predictions, &weights, &labels}, {}, {1}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *dLdp = results->at(0); - auto *dLdw = results->at(1); - auto *dLdl = results->at(2); + auto *dLdp = results.at(0); + auto *dLdw = results.at(1); + auto *dLdl = results.at(2); ASSERT_TRUE(dLdpExp.isSameShape(dLdp)); ASSERT_TRUE(dLdpExp.equalsTo(dLdp)); @@ -2532,15 +2579,14 @@ TEST_F(DeclarableOpsTests11, absolute_difference_loss_grad_test3) { ASSERT_TRUE(dLdpExp.isSameShape(-*dLdl)); ASSERT_TRUE(dLdpExp.equalsTo(-*dLdl)); - delete results; } /////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests11, absolute_difference_loss_grad_test4) { - NDArray labels('c', {2,3,4}, nd4j::DataType::DOUBLE); - NDArray predictions('c', {2,3,4}, nd4j::DataType::DOUBLE); - NDArray weights('c', {1,3,1}, nd4j::DataType::DOUBLE); + NDArray labels('c', {2,3,4}, sd::DataType::DOUBLE); + NDArray predictions('c', {2,3,4}, sd::DataType::DOUBLE); + NDArray weights('c', {1,3,1}, sd::DataType::DOUBLE); NDArray dLdwExp('c', {1,3,1}, {65.28, 96., 126.72001}); @@ -2548,25 +2594,24 @@ TEST_F(DeclarableOpsTests11, absolute_difference_loss_grad_test4) { labels.linspace(1); weights.assign(0.5); - nd4j::ops::absolute_difference_loss_grad op; + sd::ops::absolute_difference_loss_grad op; auto results = op.evaluate({&predictions, &weights, &labels}, {}, {1}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *dLdw = results->at(1); + auto *dLdw = results.at(1); ASSERT_TRUE(dLdwExp.isSameShape(dLdw)); ASSERT_TRUE(dLdwExp.equalsTo(dLdw)); - delete results; } /////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests11, absolute_difference_loss_grad_test5) { - NDArray labels('c', {2,3,4}, nd4j::DataType::DOUBLE); - NDArray predictions('c', {2,3,4}, nd4j::DataType::DOUBLE); - NDArray weights('c', {2,3,4}, nd4j::DataType::DOUBLE); + NDArray labels('c', {2,3,4}, sd::DataType::DOUBLE); + NDArray predictions('c', {2,3,4}, sd::DataType::DOUBLE); + NDArray weights('c', {2,3,4}, sd::DataType::DOUBLE); NDArray dLdpExp('c', {2,3,4}, {-0.04167,-0.04167,-0.04167,-0.04167,-0.04167,-0.04167,-0.04167,-0.04167,-0.04167,-0.04167,-0.04167,-0.04167, -0.04167,-0.04167,-0.04167,-0.04167,-0.04167,-0.04167,-0.04167,-0.04167,-0.04167,-0.04167,-0.04167,-0.04167}); @@ -2577,14 +2622,14 @@ TEST_F(DeclarableOpsTests11, absolute_difference_loss_grad_test5) { labels.linspace(1); weights.assign(0.5); - nd4j::ops::absolute_difference_loss_grad op; + sd::ops::absolute_difference_loss_grad op; auto results = op.evaluate({&predictions, &weights, &labels}, {}, {2}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *dLdp = results->at(0); - auto *dLdw = results->at(1); - auto *dLdl = results->at(2); + auto *dLdp = results.at(0); + auto *dLdw = results.at(1); + auto *dLdl = results.at(2); ASSERT_TRUE(dLdpExp.isSameShape(dLdp)); ASSERT_TRUE(dLdpExp.equalsTo(dLdp)); @@ -2593,15 +2638,14 @@ TEST_F(DeclarableOpsTests11, absolute_difference_loss_grad_test5) { ASSERT_TRUE(dLdpExp.isSameShape(-*dLdl)); ASSERT_TRUE(dLdpExp.equalsTo(-*dLdl)); - delete results; } /////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests11, absolute_difference_loss_grad_test6) { - NDArray labels('c', {2,3,4}, nd4j::DataType::DOUBLE); - NDArray predictions('c', {2,3,4}, nd4j::DataType::DOUBLE); - NDArray weights('c', {1,3,1}, nd4j::DataType::DOUBLE); + NDArray labels('c', {2,3,4}, sd::DataType::DOUBLE); + NDArray predictions('c', {2,3,4}, sd::DataType::DOUBLE); + NDArray weights('c', {1,3,1}, sd::DataType::DOUBLE); NDArray dLdwExp('c', {1,3,1}, {-2.56, 0., 2.56}); @@ -2609,25 +2653,24 @@ TEST_F(DeclarableOpsTests11, absolute_difference_loss_grad_test6) { labels.linspace(1); weights.assign(0.5); - nd4j::ops::absolute_difference_loss_grad op; + sd::ops::absolute_difference_loss_grad op; auto results = op.evaluate({&predictions, &weights, &labels}, {}, {2}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *dLdw = results->at(1); + auto *dLdw = results.at(1); ASSERT_TRUE(dLdwExp.isSameShape(dLdw)); ASSERT_TRUE(dLdwExp.equalsTo(dLdw)); - delete results; } /////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests11, absolute_difference_loss_grad_test7) { - NDArray labels('c', {2,3,4}, nd4j::DataType::DOUBLE); - NDArray predictions('c', {2,3,4}, nd4j::DataType::DOUBLE); - NDArray weights(nd4j::DataType::DOUBLE); + NDArray labels('c', {2,3,4}, sd::DataType::DOUBLE); + NDArray predictions('c', {2,3,4}, sd::DataType::DOUBLE); + NDArray weights(sd::DataType::DOUBLE); NDArray dLdwExp('c', {}, std::vector{0.}); @@ -2635,25 +2678,24 @@ TEST_F(DeclarableOpsTests11, absolute_difference_loss_grad_test7) { labels.linspace(1); weights.assign(0.5); - nd4j::ops::absolute_difference_loss_grad op; + sd::ops::absolute_difference_loss_grad op; auto results = op.evaluate({&predictions, &weights, &labels}, {}, {2}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *dLdw = results->at(1); + auto *dLdw = results.at(1); ASSERT_TRUE(dLdwExp.isSameShape(dLdw)); ASSERT_TRUE(dLdwExp.equalsTo(dLdw)); - delete results; } /////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests11, absolute_difference_loss_grad_test8) { - NDArray labels('c', {2,3,4}, nd4j::DataType::DOUBLE); - NDArray predictions('c', {2,3,4}, nd4j::DataType::DOUBLE); - NDArray weights('c', {2,3,4}, nd4j::DataType::DOUBLE); + NDArray labels('c', {2,3,4}, sd::DataType::DOUBLE); + NDArray predictions('c', {2,3,4}, sd::DataType::DOUBLE); + NDArray weights('c', {2,3,4}, sd::DataType::DOUBLE); NDArray dLdpExp('c', {2,3,4}, {-0. ,-0. ,-0. ,-0. ,-0.05,-0.05,-0.05,-0.05,-0.05,-0.05,-0.05,-0.05, -0.05,-0.05,-0.05,-0.05,-0.05,-0.05,-0.05,-0.05,-0.05,-0.05,-0.05,-0.05}); @@ -2668,14 +2710,14 @@ TEST_F(DeclarableOpsTests11, absolute_difference_loss_grad_test8) { weights.p(2, 0.); weights.p(3, 0.); - nd4j::ops::absolute_difference_loss_grad op; + sd::ops::absolute_difference_loss_grad op; auto results = op.evaluate({&predictions, &weights, &labels}, {}, {2}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *dLdp = results->at(0); - auto *dLdw = results->at(1); - auto *dLdl = results->at(2); + auto *dLdp = results.at(0); + auto *dLdw = results.at(1); + auto *dLdl = results.at(2); ASSERT_TRUE(dLdpExp.isSameShape(dLdp)); ASSERT_TRUE(dLdpExp.equalsTo(dLdp)); @@ -2684,15 +2726,14 @@ TEST_F(DeclarableOpsTests11, absolute_difference_loss_grad_test8) { ASSERT_TRUE(dLdpExp.isSameShape(-*dLdl)); ASSERT_TRUE(dLdpExp.equalsTo(-*dLdl)); - delete results; } /////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests11, absolute_difference_loss_grad_test9) { - NDArray labels('c', {2,3,4}, nd4j::DataType::DOUBLE); - NDArray predictions('c', {2,3,4}, nd4j::DataType::DOUBLE); - NDArray weights('c', {2,3,4}, nd4j::DataType::DOUBLE); + NDArray labels('c', {2,3,4}, sd::DataType::DOUBLE); + NDArray predictions('c', {2,3,4}, sd::DataType::DOUBLE); + NDArray weights('c', {2,3,4}, sd::DataType::DOUBLE); NDArray dLdpExp('c', {2,3,4}, {-0.02083, -0.02083, -0.02083, -0.02083,-0.02083, -0.02083, -0.02083, -0.02083,-0.02083, -0.02083, -0.02083, -0.02083, -0.02083, -0.02083, -0.02083, -0.02083,-0.02083, -0.02083, -0.02083, -0.02083,-0.02083, -0.02083, -0.02083, -0.02083}); @@ -2703,14 +2744,14 @@ TEST_F(DeclarableOpsTests11, absolute_difference_loss_grad_test9) { labels.linspace(1); weights.assign(0.5); - nd4j::ops::absolute_difference_loss_grad op; + sd::ops::absolute_difference_loss_grad op; auto results = op.evaluate({&predictions, &weights, &labels}, {}, {3}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *dLdp = results->at(0); - auto *dLdw = results->at(1); - auto *dLdl = results->at(2); + auto *dLdp = results.at(0); + auto *dLdw = results.at(1); + auto *dLdl = results.at(2); ASSERT_TRUE(dLdpExp.isSameShape(dLdp)); ASSERT_TRUE(dLdpExp.equalsTo(dLdp)); @@ -2719,15 +2760,14 @@ TEST_F(DeclarableOpsTests11, absolute_difference_loss_grad_test9) { ASSERT_TRUE(dLdpExp.isSameShape(-*dLdl)); ASSERT_TRUE(dLdpExp.equalsTo(-*dLdl)); - delete results; } /////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests11, absolute_difference_loss_grad_test10) { - NDArray labels('c', {2,3,4}, nd4j::DataType::DOUBLE); - NDArray predictions('c', {2,3,4}, nd4j::DataType::DOUBLE); - NDArray weights('c', {1,1}, nd4j::DataType::DOUBLE); + NDArray labels('c', {2,3,4}, sd::DataType::DOUBLE); + NDArray predictions('c', {2,3,4}, sd::DataType::DOUBLE); + NDArray weights('c', {1,1}, sd::DataType::DOUBLE); NDArray dLdwExp('c', {1,1}, std::vector{12.}); @@ -2735,25 +2775,24 @@ TEST_F(DeclarableOpsTests11, absolute_difference_loss_grad_test10) { labels.linspace(1); weights.assign(0.5); - nd4j::ops::absolute_difference_loss_grad op; + sd::ops::absolute_difference_loss_grad op; auto results = op.evaluate({&predictions, &weights, &labels}, {}, {3}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *dLdw = results->at(1); + auto *dLdw = results.at(1); ASSERT_TRUE(dLdwExp.isSameShape(dLdw)); ASSERT_TRUE(dLdwExp.equalsTo(dLdw)); - delete results; } /////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests11, absolute_difference_loss_grad_test11) { - NDArray labels('c', {2,3,4}, nd4j::DataType::DOUBLE); - NDArray predictions('c', {2,3,4}, nd4j::DataType::DOUBLE); - NDArray weights('c', {1,3,1}, nd4j::DataType::DOUBLE); + NDArray labels('c', {2,3,4}, sd::DataType::DOUBLE); + NDArray predictions('c', {2,3,4}, sd::DataType::DOUBLE); + NDArray weights('c', {1,3,1}, sd::DataType::DOUBLE); NDArray dLdwExp('c', {1,3,1}, {2.72, 4., 5.28}); @@ -2761,25 +2800,24 @@ TEST_F(DeclarableOpsTests11, absolute_difference_loss_grad_test11) { labels.linspace(1); weights.assign(0.5); - nd4j::ops::absolute_difference_loss_grad op; + sd::ops::absolute_difference_loss_grad op; auto results = op.evaluate({&predictions, &weights, &labels}, {}, {3}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *dLdw = results->at(1); + auto *dLdw = results.at(1); ASSERT_TRUE(dLdwExp.isSameShape(dLdw)); ASSERT_TRUE(dLdwExp.equalsTo(dLdw)); - delete results; } /////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests11, absolute_difference_loss_grad_test12) { - NDArray labels('c', {2,3,4}, nd4j::DataType::DOUBLE); - NDArray predictions('c', {2,3,4}, nd4j::DataType::DOUBLE); - NDArray weights('c', {2,3,4}, nd4j::DataType::DOUBLE); + NDArray labels('c', {2,3,4}, sd::DataType::DOUBLE); + NDArray predictions('c', {2,3,4}, sd::DataType::DOUBLE); + NDArray weights('c', {2,3,4}, sd::DataType::DOUBLE); NDArray dLdpExp('c', {2,3,4}, {0., 0., 0., 0., -0.025, -0.025, -0.025, -0.025,-0.025, -0.025, -0.025, -0.025, -0.025, -0.025, -0.025, -0.025,-0.025, -0.025, -0.025, -0.025,-0.025, -0.025, -0.025, -0.025}); @@ -2794,14 +2832,14 @@ TEST_F(DeclarableOpsTests11, absolute_difference_loss_grad_test12) { weights.t(2) = 0.; weights.t(3) = 0.; - nd4j::ops::absolute_difference_loss_grad op; + sd::ops::absolute_difference_loss_grad op; auto results = op.evaluate({&predictions, &weights, &labels}, {}, {3}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *dLdp = results->at(0); - auto *dLdw = results->at(1); - auto *dLdl = results->at(2); + auto *dLdp = results.at(0); + auto *dLdw = results.at(1); + auto *dLdl = results.at(2); ASSERT_TRUE(dLdpExp.isSameShape(dLdp)); ASSERT_TRUE(dLdpExp.equalsTo(dLdp)); @@ -2810,15 +2848,14 @@ TEST_F(DeclarableOpsTests11, absolute_difference_loss_grad_test12) { ASSERT_TRUE(dLdpExp.isSameShape(-*dLdl)); ASSERT_TRUE(dLdpExp.equalsTo(-*dLdl)); - delete results; } /////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests11, absolute_difference_loss_grad_test13) { - NDArray labels('c', {2,3,4}, nd4j::DataType::DOUBLE); - NDArray predictions('c', {2,3,4}, nd4j::DataType::DOUBLE); - NDArray weights('c', {2,3,1}, nd4j::DataType::DOUBLE); + NDArray labels('c', {2,3,4}, sd::DataType::DOUBLE); + NDArray predictions('c', {2,3,4}, sd::DataType::DOUBLE); + NDArray weights('c', {2,3,1}, sd::DataType::DOUBLE); NDArray dLdpExp('c', {2,3,4}, {0., 0., 0., 0., 0., 0., 0., 0.,0., 0., 0., 0., -0.04167, -0.04167, -0.04167, -0.04167,-0.04167, -0.04167, -0.04167, -0.04167,-0.04167, -0.04167, -0.04167, -0.04167}); @@ -2831,14 +2868,14 @@ TEST_F(DeclarableOpsTests11, absolute_difference_loss_grad_test13) { weights.t(1) = 0.; weights.t(2) = 0.; - nd4j::ops::absolute_difference_loss_grad op; + sd::ops::absolute_difference_loss_grad op; auto results = op.evaluate({&predictions, &weights, &labels}, {}, {3}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *dLdp = results->at(0); - auto *dLdw = results->at(1); - auto *dLdl = results->at(2); + auto *dLdp = results.at(0); + auto *dLdw = results.at(1); + auto *dLdl = results.at(2); ASSERT_TRUE(dLdpExp.isSameShape(dLdp)); ASSERT_TRUE(dLdpExp.equalsTo(dLdp)); @@ -2847,78 +2884,73 @@ TEST_F(DeclarableOpsTests11, absolute_difference_loss_grad_test13) { ASSERT_TRUE(dLdpExp.isSameShape(-*dLdl)); ASSERT_TRUE(dLdpExp.equalsTo(-*dLdl)); - delete results; } /////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests11, BFloat16_Test_1) { NDArray x = NDArrayFactory::create('c', {2,3,4}); - NDArray y = NDArrayFactory::create('c', {2,3,4});//('c', {2,3,4}, nd4j::DataType::BFLOAT16); - NDArray exp = NDArrayFactory::create('c', {2,3,4});//('c', {2,3,4}, nd4j::DataType::BFLOAT16); + NDArray y = NDArrayFactory::create('c', {2,3,4});//('c', {2,3,4}, sd::DataType::BFLOAT16); + NDArray exp = NDArrayFactory::create('c', {2,3,4});//('c', {2,3,4}, sd::DataType::BFLOAT16); x.linspace(1); y.linspace(1); exp.linspace(2,2); - nd4j::ops::add op; + sd::ops::add op; auto results = op.evaluate({&x, &y}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto res = results->at(0); + auto res = results.at(0); ASSERT_TRUE(res->equalsTo(exp)); - delete results; } /////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests11, BFloat16_Test_2) { NDArray x = NDArrayFactory::create('c', {2,3,4}); - NDArray y = NDArrayFactory::create('c', {2,3,4});//('c', {2,3,4}, nd4j::DataType::BFLOAT16); - NDArray exp = NDArrayFactory::create('c', {2,3,4});//('c', {2,3,4}, nd4j::DataType::BFLOAT16); + NDArray y = NDArrayFactory::create('c', {2,3,4});//('c', {2,3,4}, sd::DataType::BFLOAT16); + NDArray exp = NDArrayFactory::create('c', {2,3,4});//('c', {2,3,4}, sd::DataType::BFLOAT16); x.linspace(1); y.linspace(1); exp.linspace(2,2); - nd4j::ops::add op; + sd::ops::add op; auto results = op.evaluate({&x, &y}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto res = results->at(0); + auto res = results.at(0); ASSERT_TRUE(res->equalsTo(exp)); - delete results; } /////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests11, BFloat16_Test_3) { - NDArray x('c', {2,3,4}, nd4j::DataType::BFLOAT16); - NDArray y('c', {2,3,4}, nd4j::DataType::BFLOAT16); - NDArray exp('c', {2,3,4}, nd4j::DataType::BFLOAT16); + NDArray x('c', {2,3,4}, sd::DataType::BFLOAT16); + NDArray y('c', {2,3,4}, sd::DataType::BFLOAT16); + NDArray exp('c', {2,3,4}, sd::DataType::BFLOAT16); x.linspace(1); y.linspace(1); exp.linspace(2,2); - nd4j::ops::add op; + sd::ops::add op; auto results = op.evaluate({&x, &y}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto res = results->at(0); + auto res = results.at(0); ASSERT_TRUE(res->equalsTo(exp)); - - delete results; } /////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests11, sigm_cross_entropy_loss_grad_test1) { - NDArray labels('c', {2,3,4}, nd4j::DataType::DOUBLE); - NDArray logits('c', {2,3,4}, nd4j::DataType::DOUBLE); - NDArray weights('c', {2,3,4}, nd4j::DataType::DOUBLE); + NDArray labels('c', {2,3,4}, sd::DataType::DOUBLE); + NDArray logits('c', {2,3,4}, sd::DataType::DOUBLE); + NDArray weights('c', {2,3,4}, sd::DataType::DOUBLE); NDArray dLdpExp('c', {2,3,4}, {-0.25999, -0.755 , -1.25 , -1.745 , -2.24001, -2.73502, -3.23004, -3.72508, -4.22014, -4.71523, -5.21034, -5.70548, -6.20066, -6.69587, -7.19113, -7.68643, -8.18177, -8.67717, -9.17262, -9.66813,-10.1637 ,-10.65932,-11.15501,-11.65077}); @@ -2931,14 +2963,14 @@ TEST_F(DeclarableOpsTests11, sigm_cross_entropy_loss_grad_test1) { labels.linspace(1); weights.assign(0.5); - nd4j::ops::sigm_cross_entropy_loss_grad op; + sd::ops::sigm_cross_entropy_loss_grad op; auto results = op.evaluate({&logits, &weights, &labels}, {0.}, {0}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *dLdp = results->at(0); - auto *dLdw = results->at(1); - auto *dLdl = results->at(2); + auto *dLdp = results.at(0); + auto *dLdw = results.at(1); + auto *dLdl = results.at(2); ASSERT_TRUE(dLdpExp.isSameShape(dLdp)); ASSERT_TRUE(dLdpExp.equalsTo(dLdp)); @@ -2947,15 +2979,14 @@ TEST_F(DeclarableOpsTests11, sigm_cross_entropy_loss_grad_test1) { ASSERT_TRUE(dLdlExp.isSameShape(dLdl)); ASSERT_TRUE(dLdlExp.equalsTo(dLdl)); - delete results; } /////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests11, sigm_cross_entropy_loss_grad_test2) { - NDArray labels('c', {2,3,4}, nd4j::DataType::DOUBLE); - NDArray logits('c', {2,3,4}, nd4j::DataType::DOUBLE); - NDArray weights('c', {2,1,4}, nd4j::DataType::DOUBLE); + NDArray labels('c', {2,3,4}, sd::DataType::DOUBLE); + NDArray logits('c', {2,3,4}, sd::DataType::DOUBLE); + NDArray weights('c', {2,1,4}, sd::DataType::DOUBLE); NDArray dLdpExp('c', {2,3,4}, {-0.18499,-0.53 ,-0.875 ,-1.22 ,-1.56501,-1.91002,-2.25504,-2.60008,-2.94514,-3.29023,-3.63534,-3.98048, -4.32566,-4.67087,-5.01613,-5.36143,-5.70677,-6.05217,-6.39762,-6.74313,-7.0887 ,-7.43432,-7.78001,-8.12577}); @@ -2967,14 +2998,14 @@ TEST_F(DeclarableOpsTests11, sigm_cross_entropy_loss_grad_test2) { labels.linspace(1); weights.assign(0.5); - nd4j::ops::sigm_cross_entropy_loss_grad op; + sd::ops::sigm_cross_entropy_loss_grad op; auto results = op.evaluate({&logits, &weights, &labels}, {0.3}, {0}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *dLdp = results->at(0); - auto *dLdw = results->at(1); - auto *dLdl = results->at(2); + auto *dLdp = results.at(0); + auto *dLdw = results.at(1); + auto *dLdl = results.at(2); ASSERT_TRUE(dLdpExp.isSameShape(dLdp)); ASSERT_TRUE(dLdpExp.equalsTo(dLdp)); @@ -2983,15 +3014,14 @@ TEST_F(DeclarableOpsTests11, sigm_cross_entropy_loss_grad_test2) { ASSERT_TRUE(dLdlExp.isSameShape(dLdl)); ASSERT_TRUE(dLdlExp.equalsTo(dLdl)); - delete results; } /////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests11, sigm_cross_entropy_loss_grad_test3) { - NDArray labels('c', {2,3,4}, nd4j::DataType::DOUBLE); - NDArray logits('c', {2,3,4}, nd4j::DataType::DOUBLE); - NDArray weights(nd4j::DataType::DOUBLE); + NDArray labels('c', {2,3,4}, sd::DataType::DOUBLE); + NDArray logits('c', {2,3,4}, sd::DataType::DOUBLE); + NDArray weights(sd::DataType::DOUBLE); NDArray dLdpExp('c', {2,3,4}, {-0.18499,-0.53 ,-0.875 ,-1.22 ,-1.56501,-1.91002,-2.25504,-2.60008,-2.94514,-3.29023,-3.63534,-3.98048, -4.32566,-4.67087,-5.01613,-5.36143,-5.70677,-6.05217,-6.39762,-6.74313,-7.0887 ,-7.43432,-7.78001,-8.12577}); @@ -3003,14 +3033,14 @@ TEST_F(DeclarableOpsTests11, sigm_cross_entropy_loss_grad_test3) { labels.linspace(1); weights.assign(0.5); - nd4j::ops::sigm_cross_entropy_loss_grad op; + sd::ops::sigm_cross_entropy_loss_grad op; auto results = op.evaluate({&logits, &weights, &labels}, {0.3}, {1}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *dLdp = results->at(0); - auto *dLdw = results->at(1); - auto *dLdl = results->at(2); + auto *dLdp = results.at(0); + auto *dLdw = results.at(1); + auto *dLdl = results.at(2); ASSERT_TRUE(dLdpExp.isSameShape(dLdp)); ASSERT_TRUE(dLdpExp.equalsTo(dLdp)); @@ -3019,15 +3049,14 @@ TEST_F(DeclarableOpsTests11, sigm_cross_entropy_loss_grad_test3) { ASSERT_TRUE(dLdlExp.isSameShape(dLdl)); ASSERT_TRUE(dLdlExp.equalsTo(dLdl)); - delete results; } /////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests11, sigm_cross_entropy_loss_grad_test4) { - NDArray labels('c', {2,3,4}, nd4j::DataType::DOUBLE); - NDArray logits('c', {2,3,4}, nd4j::DataType::DOUBLE); - NDArray weights('c', {1,3,1}, nd4j::DataType::DOUBLE); + NDArray labels('c', {2,3,4}, sd::DataType::DOUBLE); + NDArray logits('c', {2,3,4}, sd::DataType::DOUBLE); + NDArray weights('c', {1,3,1}, sd::DataType::DOUBLE); NDArray dLdwExp('c', {1,3,1}, {-12.54779,-28.13393,-50.83936}); @@ -3035,25 +3064,24 @@ TEST_F(DeclarableOpsTests11, sigm_cross_entropy_loss_grad_test4) { labels.linspace(1); weights.assign(0.5); - nd4j::ops::sigm_cross_entropy_loss_grad op; + sd::ops::sigm_cross_entropy_loss_grad op; auto results = op.evaluate({&logits, &weights, &labels}, {0.3}, {1}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *dLdw = results->at(1); + auto *dLdw = results.at(1); ASSERT_TRUE(dLdwExp.isSameShape(dLdw)); ASSERT_TRUE(dLdwExp.equalsTo(dLdw)); - delete results; } /////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests11, sigm_cross_entropy_loss_grad_test5) { - NDArray labels('c', {2,3,4}, nd4j::DataType::DOUBLE); - NDArray logits('c', {2,3,4}, nd4j::DataType::DOUBLE); - NDArray weights('c', {2,3,4}, nd4j::DataType::DOUBLE); + NDArray labels('c', {2,3,4}, sd::DataType::DOUBLE); + NDArray logits('c', {2,3,4}, sd::DataType::DOUBLE); + NDArray weights('c', {2,3,4}, sd::DataType::DOUBLE); NDArray dLdpExp('c', {2,3,4}, {-0.01542,-0.04417,-0.07292,-0.10167,-0.13042,-0.15917,-0.18792,-0.21667,-0.24543,-0.27419,-0.30294,-0.33171, -0.36047,-0.38924,-0.41801,-0.44679,-0.47556,-0.50435,-0.53314,-0.56193,-0.59072,-0.61953,-0.64833,-0.67715}); @@ -3066,14 +3094,14 @@ TEST_F(DeclarableOpsTests11, sigm_cross_entropy_loss_grad_test5) { labels.linspace(1); weights.assign(0.5); - nd4j::ops::sigm_cross_entropy_loss_grad op; + sd::ops::sigm_cross_entropy_loss_grad op; auto results = op.evaluate({&logits, &weights, &labels}, {0.3}, {2}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *dLdp = results->at(0); - auto *dLdw = results->at(1); - auto *dLdl = results->at(2); + auto *dLdp = results.at(0); + auto *dLdw = results.at(1); + auto *dLdl = results.at(2); ASSERT_TRUE(dLdpExp.isSameShape(dLdp)); ASSERT_TRUE(dLdpExp.equalsTo(dLdp)); @@ -3082,15 +3110,14 @@ TEST_F(DeclarableOpsTests11, sigm_cross_entropy_loss_grad_test5) { ASSERT_TRUE(dLdlExp.isSameShape(dLdl)); ASSERT_TRUE(dLdlExp.equalsTo(dLdl)); - delete results; } /////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests11, sigm_cross_entropy_loss_grad_test6) { - NDArray labels('c', {2,3,4}, nd4j::DataType::DOUBLE); - NDArray logits('c', {2,3,4}, nd4j::DataType::DOUBLE); - NDArray weights('c', {1,3,1}, nd4j::DataType::DOUBLE); + NDArray labels('c', {2,3,4}, sd::DataType::DOUBLE); + NDArray logits('c', {2,3,4}, sd::DataType::DOUBLE); + NDArray weights('c', {1,3,1}, sd::DataType::DOUBLE); NDArray dLdwExp('c', {1,3,1}, {1.4966 , 0.19776,-1.69436}); @@ -3098,25 +3125,24 @@ TEST_F(DeclarableOpsTests11, sigm_cross_entropy_loss_grad_test6) { labels.linspace(1); weights.assign(0.5); - nd4j::ops::sigm_cross_entropy_loss_grad op; + sd::ops::sigm_cross_entropy_loss_grad op; auto results = op.evaluate({&logits, &weights, &labels}, {0.3}, {2}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *dLdw = results->at(1); + auto *dLdw = results.at(1); ASSERT_TRUE(dLdwExp.isSameShape(dLdw)); ASSERT_TRUE(dLdwExp.equalsTo(dLdw)); - delete results; } /////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests11, sigm_cross_entropy_loss_grad_test7) { - NDArray labels('c', {2,3,4}, nd4j::DataType::DOUBLE); - NDArray logits('c', {2,3,4}, nd4j::DataType::DOUBLE); - NDArray weights(nd4j::DataType::DOUBLE); + NDArray labels('c', {2,3,4}, sd::DataType::DOUBLE); + NDArray logits('c', {2,3,4}, sd::DataType::DOUBLE); + NDArray weights(sd::DataType::DOUBLE); NDArray dLdwExp('c', {}, std::vector{0.}); @@ -3124,25 +3150,24 @@ TEST_F(DeclarableOpsTests11, sigm_cross_entropy_loss_grad_test7) { labels.linspace(1); weights.assign(0.5); - nd4j::ops::sigm_cross_entropy_loss_grad op; + sd::ops::sigm_cross_entropy_loss_grad op; auto results = op.evaluate({&logits, &weights, &labels}, {0.3}, {2}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *dLdw = results->at(1); + auto *dLdw = results.at(1); ASSERT_TRUE(dLdwExp.isSameShape(dLdw)); ASSERT_TRUE(dLdwExp.equalsTo(dLdw)); - delete results; } /////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests11, sigm_cross_entropy_loss_grad_test8) { - NDArray labels('c', {2,3,4}, nd4j::DataType::DOUBLE); - NDArray logits('c', {2,3,4}, nd4j::DataType::DOUBLE); - NDArray weights('c', {2,3,4}, nd4j::DataType::DOUBLE); + NDArray labels('c', {2,3,4}, sd::DataType::DOUBLE); + NDArray logits('c', {2,3,4}, sd::DataType::DOUBLE); + NDArray weights('c', {2,3,4}, sd::DataType::DOUBLE); NDArray dLdpExp('c', {2,3,4}, { 0. , 0. , 0. , 0. ,-0.1565 ,-0.191 ,-0.2255 ,-0.26001,-0.29451,-0.32902,-0.36353,-0.39805, -0.43257,-0.46709,-0.50161,-0.53614,-0.57068,-0.60522,-0.63976,-0.67431,-0.70887,-0.74343,-0.778 ,-0.81258}); @@ -3158,14 +3183,14 @@ TEST_F(DeclarableOpsTests11, sigm_cross_entropy_loss_grad_test8) { weights.p(2, 0.); weights.p(3, 0.); - nd4j::ops::sigm_cross_entropy_loss_grad op; + sd::ops::sigm_cross_entropy_loss_grad op; auto results = op.evaluate({&logits, &weights, &labels}, {0.3}, {2}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *dLdp = results->at(0); - auto *dLdw = results->at(1); - auto *dLdl = results->at(2); + auto *dLdp = results.at(0); + auto *dLdw = results.at(1); + auto *dLdl = results.at(2); ASSERT_TRUE(dLdpExp.isSameShape(dLdp)); ASSERT_TRUE(dLdpExp.equalsTo(dLdp)); @@ -3174,15 +3199,14 @@ TEST_F(DeclarableOpsTests11, sigm_cross_entropy_loss_grad_test8) { ASSERT_TRUE(dLdlExp.isSameShape(dLdl)); ASSERT_TRUE(dLdlExp.equalsTo(dLdl)); - delete results; } /////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests11, sigm_cross_entropy_loss_grad_test9) { - NDArray labels('c', {2,3,4}, nd4j::DataType::DOUBLE); - NDArray logits('c', {2,3,4}, nd4j::DataType::DOUBLE); - NDArray weights('c', {2,3,4}, nd4j::DataType::DOUBLE); + NDArray labels('c', {2,3,4}, sd::DataType::DOUBLE); + NDArray logits('c', {2,3,4}, sd::DataType::DOUBLE); + NDArray weights('c', {2,3,4}, sd::DataType::DOUBLE); NDArray dLdpExp('c', {2,3,4}, {-0.00771, -0.02208, -0.03646, -0.05083,-0.06521, -0.07958, -0.09396, -0.10834,-0.12271, -0.13709, -0.15147, -0.16585, -0.18024, -0.19462, -0.20901, -0.22339,-0.23778, -0.25217, -0.26657, -0.28096,-0.29536, -0.30976, -0.32417, -0.33857}); @@ -3194,14 +3218,14 @@ TEST_F(DeclarableOpsTests11, sigm_cross_entropy_loss_grad_test9) { labels.linspace(1); weights.assign(0.5); - nd4j::ops::sigm_cross_entropy_loss_grad op; + sd::ops::sigm_cross_entropy_loss_grad op; auto results = op.evaluate({&logits, &weights, &labels}, {0.3}, {3}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *dLdp = results->at(0); - auto *dLdw = results->at(1); - auto *dLdl = results->at(2); + auto *dLdp = results.at(0); + auto *dLdw = results.at(1); + auto *dLdl = results.at(2); ASSERT_TRUE(dLdpExp.isSameShape(dLdp)); ASSERT_TRUE(dLdpExp.equalsTo(dLdp)); @@ -3210,15 +3234,14 @@ TEST_F(DeclarableOpsTests11, sigm_cross_entropy_loss_grad_test9) { ASSERT_TRUE(dLdlExp.isSameShape(dLdl)); ASSERT_TRUE(dLdlExp.equalsTo(dLdl)); - delete results; } /////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests11, sigm_cross_entropy_loss_grad_test10) { - NDArray labels('c', {2,3,4}, nd4j::DataType::DOUBLE); - NDArray logits('c', {2,3,4}, nd4j::DataType::DOUBLE); - NDArray weights('c', {1,1}, nd4j::DataType::DOUBLE); + NDArray labels('c', {2,3,4}, sd::DataType::DOUBLE); + NDArray logits('c', {2,3,4}, sd::DataType::DOUBLE); + NDArray weights('c', {1,1}, sd::DataType::DOUBLE); NDArray dLdwExp('c', {1,1}, std::vector{-3.81338}); @@ -3226,25 +3249,24 @@ TEST_F(DeclarableOpsTests11, sigm_cross_entropy_loss_grad_test10) { labels.linspace(1); weights.assign(0.5); - nd4j::ops::sigm_cross_entropy_loss_grad op; + sd::ops::sigm_cross_entropy_loss_grad op; auto results = op.evaluate({&logits, &weights, &labels}, {0.3}, {3}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *dLdw = results->at(1); + auto *dLdw = results.at(1); ASSERT_TRUE(dLdwExp.isSameShape(dLdw)); ASSERT_TRUE(dLdwExp.equalsTo(dLdw)); - delete results; } /////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests11, sigm_cross_entropy_loss_grad_test11) { - NDArray labels('c', {2,3,4}, nd4j::DataType::DOUBLE); - NDArray logits('c', {2,3,4}, nd4j::DataType::DOUBLE); - NDArray weights('c', {1,3,1}, nd4j::DataType::DOUBLE); + NDArray labels('c', {2,3,4}, sd::DataType::DOUBLE); + NDArray logits('c', {2,3,4}, sd::DataType::DOUBLE); + NDArray weights('c', {1,3,1}, sd::DataType::DOUBLE); NDArray dLdwExp('c', {1,3,1}, {-0.52282,-1.17225,-2.11831}); @@ -3252,25 +3274,24 @@ TEST_F(DeclarableOpsTests11, sigm_cross_entropy_loss_grad_test11) { labels.linspace(1); weights.assign(0.5); - nd4j::ops::sigm_cross_entropy_loss_grad op; + sd::ops::sigm_cross_entropy_loss_grad op; auto results = op.evaluate({&logits, &weights, &labels}, {0.3}, {3}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *dLdw = results->at(1); + auto *dLdw = results.at(1); ASSERT_TRUE(dLdwExp.isSameShape(dLdw)); ASSERT_TRUE(dLdwExp.equalsTo(dLdw)); - delete results; } /////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests11, sigm_cross_entropy_loss_grad_test12) { - NDArray labels('c', {2,3,4}, nd4j::DataType::DOUBLE); - NDArray logits('c', {2,3,4}, nd4j::DataType::DOUBLE); - NDArray weights('c', {2,3,4}, nd4j::DataType::DOUBLE); + NDArray labels('c', {2,3,4}, sd::DataType::DOUBLE); + NDArray logits('c', {2,3,4}, sd::DataType::DOUBLE); + NDArray weights('c', {2,3,4}, sd::DataType::DOUBLE); NDArray dLdpExp('c', {2,3,4}, {0. , 0. , 0. , 0. ,-0.07825, -0.0955 , -0.11275, -0.13 ,-0.14726, -0.16451, -0.18177, -0.19902, -0.21628, -0.23354, -0.25081, -0.26807,-0.28534, -0.30261, -0.31988, -0.33716,-0.35443, -0.37172, -0.389 , -0.40629}); @@ -3287,14 +3308,14 @@ TEST_F(DeclarableOpsTests11, sigm_cross_entropy_loss_grad_test12) { weights.t(3) = 0.; - nd4j::ops::sigm_cross_entropy_loss_grad op; + sd::ops::sigm_cross_entropy_loss_grad op; auto results = op.evaluate({&logits, &weights, &labels}, {0.3}, {3}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *dLdp = results->at(0); - auto *dLdw = results->at(1); - auto *dLdl = results->at(2); + auto *dLdp = results.at(0); + auto *dLdw = results.at(1); + auto *dLdl = results.at(2); ASSERT_TRUE(dLdpExp.isSameShape(dLdp)); ASSERT_TRUE(dLdpExp.equalsTo(dLdp)); @@ -3303,15 +3324,14 @@ TEST_F(DeclarableOpsTests11, sigm_cross_entropy_loss_grad_test12) { ASSERT_TRUE(dLdlExp.isSameShape(dLdl)); ASSERT_TRUE(dLdlExp.equalsTo(dLdl)); - delete results; } /////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests11, sigm_cross_entropy_loss_grad_test13) { - NDArray labels('c', {2,3,4}, nd4j::DataType::DOUBLE); - NDArray logits('c', {2,3,4}, nd4j::DataType::DOUBLE); - NDArray weights('c', {2,3,1}, nd4j::DataType::DOUBLE); + NDArray labels('c', {2,3,4}, sd::DataType::DOUBLE); + NDArray logits('c', {2,3,4}, sd::DataType::DOUBLE); + NDArray weights('c', {2,3,1}, sd::DataType::DOUBLE); NDArray dLdpExp('c', {2,3,4}, {0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , -0.36047, -0.38924, -0.41801, -0.44679,-0.47556, -0.50435, -0.53314, -0.56193,-0.59072, -0.61953, -0.64833, -0.67715}); @@ -3325,14 +3345,14 @@ TEST_F(DeclarableOpsTests11, sigm_cross_entropy_loss_grad_test13) { weights.t(1) = 0.; weights.t(2) = 0.; - nd4j::ops::sigm_cross_entropy_loss_grad op; + sd::ops::sigm_cross_entropy_loss_grad op; auto results = op.evaluate({&logits, &weights, &labels}, {0.3}, {3}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *dLdp = results->at(0); - auto *dLdw = results->at(1); - auto *dLdl = results->at(2); + auto *dLdp = results.at(0); + auto *dLdw = results.at(1); + auto *dLdl = results.at(2); ASSERT_TRUE(dLdpExp.isSameShape(dLdp)); ASSERT_TRUE(dLdpExp.equalsTo(dLdp)); @@ -3341,78 +3361,72 @@ TEST_F(DeclarableOpsTests11, sigm_cross_entropy_loss_grad_test13) { ASSERT_TRUE(dLdlExp.isSameShape(dLdl)); ASSERT_TRUE(dLdlExp.equalsTo(dLdl)); - delete results; } /////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests11, BFloat16_Test_4) { NDArray x = NDArrayFactory::create('c', {2,3,4}); - NDArray y = NDArrayFactory::create('c', {2,3,4});//('c', {2,3,4}, nd4j::DataType::BFLOAT16); - NDArray exp = NDArrayFactory::create('c', {2,3,4});//('c', {2,3,4}, nd4j::DataType::BFLOAT16); + NDArray y = NDArrayFactory::create('c', {2,3,4});//('c', {2,3,4}, sd::DataType::BFLOAT16); + NDArray exp = NDArrayFactory::create('c', {2,3,4});//('c', {2,3,4}, sd::DataType::BFLOAT16); x.linspace(1); y.linspace(1); exp.linspace(2,2); - nd4j::ops::add op; + sd::ops::add op; auto results = op.evaluate({&x, &y}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto res = results->at(0); + auto res = results.at(0); ASSERT_TRUE(res->equalsTo(exp)); - - delete results; } /////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests11, BFloat16_Test_5) { NDArray x = NDArrayFactory::create('c', {2,3,4}); - NDArray y = NDArrayFactory::create('c', {2,3,4});//('c', {2,3,4}, nd4j::DataType::BFLOAT16); - NDArray exp = NDArrayFactory::create('c', {2,3,4});//('c', {2,3,4}, nd4j::DataType::BFLOAT16); + NDArray y = NDArrayFactory::create('c', {2,3,4});//('c', {2,3,4}, sd::DataType::BFLOAT16); + NDArray exp = NDArrayFactory::create('c', {2,3,4});//('c', {2,3,4}, sd::DataType::BFLOAT16); x.linspace(2, 2); y.linspace(1); exp.linspace(1); - nd4j::ops::subtract op; + sd::ops::subtract op; auto results = op.evaluate({&x, &y}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto res = results->at(0); + auto res = results.at(0); ASSERT_TRUE(res->equalsTo(exp)); - delete results; } /////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests11, BFloat16_Test_6) { NDArray x = NDArrayFactory::create('c', {2,3,4}); - NDArray y = NDArrayFactory::create('c', {2,3,4});//('c', {2,3,4}, nd4j::DataType::BFLOAT16); - NDArray exp = NDArrayFactory::create('c', {2,3,4});//('c', {2,3,4}, nd4j::DataType::BFLOAT16); + NDArray y = NDArrayFactory::create('c', {2,3,4});//('c', {2,3,4}, sd::DataType::BFLOAT16); + NDArray exp = NDArrayFactory::create('c', {2,3,4});//('c', {2,3,4}, sd::DataType::BFLOAT16); x.linspace(2, 2); y.linspace(1); exp.linspace(1); - nd4j::ops::subtract op; + sd::ops::subtract op; auto results = op.evaluate({&x, &y}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto res = results->at(0); + auto res = results.at(0); ASSERT_TRUE(res->equalsTo(exp)); - - delete results; } ///////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests11, softmax_cross_entropy_loss_grad_test1) { - NDArray labels('c', {2,4}, {0,0,1,0, 0,1,0,0}, nd4j::DataType::INT32); - NDArray logits('c', {2,4}, nd4j::DataType::DOUBLE); - NDArray weights('c', {2}, nd4j::DataType::DOUBLE); + NDArray labels('c', {2,4}, {0,0,1,0, 0,1,0,0}, sd::DataType::INT32); + NDArray logits('c', {2,4}, sd::DataType::DOUBLE); + NDArray weights('c', {2}, sd::DataType::DOUBLE); NDArray dLdpExp('c', {2,4}, {0.1176, 0.1224, -0.3726, 0.1326, 0.1176, -0.3776, 0.1274, 0.1326}); NDArray dLdwExp('c', {2}, {1.36729, 1.40729}); @@ -3420,30 +3434,28 @@ TEST_F(DeclarableOpsTests11, softmax_cross_entropy_loss_grad_test1) { logits.linspace(-0.08, 0.04); weights.assign(0.5); - nd4j::ops::softmax_cross_entropy_loss_grad op; + sd::ops::softmax_cross_entropy_loss_grad op; auto results = op.evaluate({&logits, &weights, &labels}, {0.}, {0}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *dLdp = results->at(0); - auto *dLdw = results->at(1); - auto *dLdl = results->at(2); + auto *dLdp = results.at(0); + auto *dLdw = results.at(1); + auto *dLdl = results.at(2); ASSERT_TRUE(dLdpExp.isSameShape(dLdp)); ASSERT_TRUE(dLdpExp.equalsTo(dLdp)); ASSERT_TRUE(dLdwExp.isSameShape(dLdw)); ASSERT_TRUE(dLdwExp.equalsTo(dLdw)); - - delete results; } ///////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests11, softmax_cross_entropy_loss_grad_test2) { - NDArray labels('c', {4}, {0,0,1,0}, nd4j::DataType::INT32); - NDArray logits('c', {4}, nd4j::DataType::DOUBLE); - NDArray weights('c', {1}, nd4j::DataType::DOUBLE); + NDArray labels('c', {4}, {0,0,1,0}, sd::DataType::INT32); + NDArray logits('c', {4}, sd::DataType::DOUBLE); + NDArray weights('c', {1}, sd::DataType::DOUBLE); NDArray dLdpExp('c', {4}, {0.125, 0.125, -0.375, 0.125}); NDArray dLdwExp('c', {1}, std::vector{1.38629}); @@ -3451,30 +3463,28 @@ TEST_F(DeclarableOpsTests11, softmax_cross_entropy_loss_grad_test2) { logits = 2.; weights.assign(0.5); - nd4j::ops::softmax_cross_entropy_loss_grad op; + sd::ops::softmax_cross_entropy_loss_grad op; auto results = op.evaluate({&logits, &weights, &labels}, {0.}, {1}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *dLdp = results->at(0); - auto *dLdw = results->at(1); - auto *dLdl = results->at(2); + auto *dLdp = results.at(0); + auto *dLdw = results.at(1); + auto *dLdl = results.at(2); ASSERT_TRUE(dLdpExp.isSameShape(dLdp)); ASSERT_TRUE(dLdpExp.equalsTo(dLdp)); ASSERT_TRUE(dLdwExp.isSameShape(dLdw)); ASSERT_TRUE(dLdwExp.equalsTo(dLdw)); - - delete results; } ///////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests11, softmax_cross_entropy_loss_grad_test3) { - NDArray labels('c', {4}, {0,0,1,0}, nd4j::DataType::INT32); - NDArray logits('c', {4}, nd4j::DataType::DOUBLE); - NDArray weights('c', {}, std::vector{0}, nd4j::DataType::DOUBLE); + NDArray labels('c', {4}, {0,0,1,0}, sd::DataType::INT32); + NDArray logits('c', {4}, sd::DataType::DOUBLE); + NDArray weights('c', {}, std::vector{0}, sd::DataType::DOUBLE); NDArray dLdpExp('c', {4}, {0.125, 0.125, -0.375, 0.125}); NDArray dLdwExp('c', {}, std::vector{1.38629}); @@ -3482,30 +3492,28 @@ TEST_F(DeclarableOpsTests11, softmax_cross_entropy_loss_grad_test3) { logits = 2.; weights.assign(0.5); - nd4j::ops::softmax_cross_entropy_loss_grad op; + sd::ops::softmax_cross_entropy_loss_grad op; auto results = op.evaluate({&logits, &weights, &labels}, {0.}, {1}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *dLdp = results->at(0); - auto *dLdw = results->at(1); - auto *dLdl = results->at(2); + auto *dLdp = results.at(0); + auto *dLdw = results.at(1); + auto *dLdl = results.at(2); ASSERT_TRUE(dLdpExp.isSameShape(dLdp)); ASSERT_TRUE(dLdpExp.equalsTo(dLdp)); ASSERT_TRUE(dLdwExp.isSameShape(dLdw)); ASSERT_TRUE(dLdwExp.equalsTo(dLdw)); - - delete results; } ///////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests11, softmax_cross_entropy_loss_grad_test4) { - NDArray labels('c', {4}, {0,0,1,0}, nd4j::DataType::INT32); - NDArray logits('c', {4}, nd4j::DataType::DOUBLE); - NDArray weights('c', {}, std::vector{0}, nd4j::DataType::DOUBLE); + NDArray labels('c', {4}, {0,0,1,0}, sd::DataType::INT32); + NDArray logits('c', {4}, sd::DataType::DOUBLE); + NDArray weights('c', {}, std::vector{0}, sd::DataType::DOUBLE); NDArray dLdpExp('c', {4}, {0.23521, 0.2448 , -0.7452 , 0.26519}); NDArray dLdwExp('c', {}, std::vector{0.}); @@ -3513,30 +3521,29 @@ TEST_F(DeclarableOpsTests11, softmax_cross_entropy_loss_grad_test4) { logits.linspace(-0.08, 0.04); weights = 0.5; - nd4j::ops::softmax_cross_entropy_loss_grad op; + sd::ops::softmax_cross_entropy_loss_grad op; auto results = op.evaluate({&logits, &weights, &labels}, {0.}, {2}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *dLdp = results->at(0); - auto *dLdw = results->at(1); - auto *dLdl = results->at(2); + auto *dLdp = results.at(0); + auto *dLdw = results.at(1); + auto *dLdl = results.at(2); ASSERT_TRUE(dLdpExp.isSameShape(dLdp)); ASSERT_TRUE(dLdpExp.equalsTo(dLdp)); ASSERT_TRUE(dLdwExp.isSameShape(dLdw)); ASSERT_TRUE(dLdwExp.equalsTo(dLdw)); - delete results; } ///////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests11, softmax_cross_entropy_loss_grad_test5) { - NDArray labels('c', {4}, {0,0,1,0}, nd4j::DataType::INT32); - NDArray logits('c', {4}, nd4j::DataType::DOUBLE); - NDArray weights('c', {1}, nd4j::DataType::DOUBLE); + NDArray labels('c', {4}, {0,0,1,0}, sd::DataType::INT32); + NDArray logits('c', {4}, sd::DataType::DOUBLE); + NDArray weights('c', {1}, sd::DataType::DOUBLE); NDArray dLdpExp('c', {4}, {0.1176, 0.1224, -0.3726, 0.1326}); NDArray dLdwExp('c', {1}, std::vector{1.36729}); @@ -3544,30 +3551,29 @@ TEST_F(DeclarableOpsTests11, softmax_cross_entropy_loss_grad_test5) { logits.linspace(-0.08, 0.04); weights = 0.5; - nd4j::ops::softmax_cross_entropy_loss_grad op; + sd::ops::softmax_cross_entropy_loss_grad op; auto results = op.evaluate({&logits, &weights, &labels}, {0.}, {3}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *dLdp = results->at(0); - auto *dLdw = results->at(1); - auto *dLdl = results->at(2); + auto *dLdp = results.at(0); + auto *dLdw = results.at(1); + auto *dLdl = results.at(2); ASSERT_TRUE(dLdpExp.isSameShape(dLdp)); ASSERT_TRUE(dLdpExp.equalsTo(dLdp)); ASSERT_TRUE(dLdwExp.isSameShape(dLdw)); ASSERT_TRUE(dLdwExp.equalsTo(dLdw)); - delete results; } ///////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests11, softmax_cross_entropy_loss_grad_test6) { - NDArray labels('c', {2,4}, {0,0,1,0, 0,1,0,0}, nd4j::DataType::INT32); - NDArray logits('c', {2,4}, nd4j::DataType::DOUBLE); - NDArray weights('c', {2}, nd4j::DataType::DOUBLE); + NDArray labels('c', {2,4}, {0,0,1,0, 0,1,0,0}, sd::DataType::INT32); + NDArray logits('c', {2,4}, sd::DataType::DOUBLE); + NDArray weights('c', {2}, sd::DataType::DOUBLE); NDArray dLdpExp('c', {2,4}, {0.0801, 0.0849, -0.2601, 0.0951, 0.0801, -0.2651, 0.0899, 0.0951}); NDArray dLdwExp('c', {2}, {-0.014000, 0.014000}); @@ -3575,29 +3581,28 @@ TEST_F(DeclarableOpsTests11, softmax_cross_entropy_loss_grad_test6) { logits.linspace(-0.08, 0.04); weights.assign(0.5); - nd4j::ops::softmax_cross_entropy_loss_grad op; + sd::ops::softmax_cross_entropy_loss_grad op; auto results = op.evaluate({&logits, &weights, &labels}, {0.3}, {2}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *dLdp = results->at(0); - auto *dLdw = results->at(1); - auto *dLdl = results->at(2); + auto *dLdp = results.at(0); + auto *dLdw = results.at(1); + auto *dLdl = results.at(2); ASSERT_TRUE(dLdpExp.isSameShape(dLdp)); ASSERT_TRUE(dLdpExp.equalsTo(dLdp)); ASSERT_TRUE(dLdwExp.isSameShape(dLdw)); ASSERT_TRUE(dLdwExp.equalsTo(dLdw)); - delete results; } ///////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests11, softmax_cross_entropy_loss_grad_test7) { - NDArray labels('c', {2,3,4}, {1,0,0,0, 0,1,0,0, 0,0,1,0, 0,0,0,1, 1,0,0,0, 0,1,0,0}, nd4j::DataType::INT32); - NDArray logits('c', {2,3,4}, nd4j::DataType::DOUBLE); + NDArray labels('c', {2,3,4}, {1,0,0,0, 0,1,0,0, 0,0,1,0, 0,0,0,1, 1,0,0,0, 0,1,0,0}, sd::DataType::INT32); + NDArray logits('c', {2,3,4}, sd::DataType::DOUBLE); NDArray weights('c', {1,3}, {0.5, 0., 1.5}); NDArray dLdpExp('c', {2,3,4}, {-0.0956 , 0.0306 , 0.03185, 0.03315, 0.,-0., 0., 0., 0.0882 , 0.0918 ,-0.27945, 0.09945, @@ -3606,22 +3611,21 @@ TEST_F(DeclarableOpsTests11, softmax_cross_entropy_loss_grad_test7) { logits.linspace(-0.08, 0.04); - nd4j::ops::softmax_cross_entropy_loss_grad op; + sd::ops::softmax_cross_entropy_loss_grad op; auto results = op.evaluate({&logits, &weights, &labels}, {0.}, {3}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *dLdp = results->at(0); - auto *dLdw = results->at(1); - auto *dLdl = results->at(2); + auto *dLdp = results.at(0); + auto *dLdw = results.at(1); + auto *dLdl = results.at(2); ASSERT_TRUE(dLdpExp.isSameShape(dLdp)); ASSERT_TRUE(dLdpExp.equalsTo(dLdp)); ASSERT_TRUE(dLdwExp.isSameShape(dLdw)); ASSERT_TRUE(dLdwExp.equalsTo(dLdw)); - delete results; } ///////////////////////////////////////////////////////////////// @@ -3629,10 +3633,10 @@ TEST_F(DeclarableOpsTests11, softmax_cross_entropy_loss_grad_test8) { NDArray labels('c', {2,3,4,5}, {1,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,1,1,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0, 0,0,1,0,0,0,0,0,1,1,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,1,1,0,0,0,0,0,1, - 0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,1,1,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,1,0}, nd4j::DataType::INT32); + 0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,1,1,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,1,0}, sd::DataType::INT32); - NDArray logits('c', {2,3,4,5}, nd4j::DataType::DOUBLE); - NDArray weights('c', {1,1,4}, nd4j::DataType::DOUBLE); + NDArray logits('c', {2,3,4,5}, sd::DataType::DOUBLE); + NDArray weights('c', {1,1,4}, sd::DataType::DOUBLE); NDArray dLdpExp('c', {2,3,4,5}, {-0.03399, 0.00799, 0.00832, 0.00866, 0.00901, 0.00768,-0.03367, 0.00832, 0.00866, 0.00901, 0.00768, 0.00799,-0.03335, 0.00866, 0.00901, 0.00768, 0.00799, 0.00832,-0.03301, 0.00901, 0.00768, 0.00799, 0.00832, 0.00866,-0.03265,-0.03399, @@ -3648,15 +3652,15 @@ TEST_F(DeclarableOpsTests11, softmax_cross_entropy_loss_grad_test8) { logits.linspace(-0.08, 0.04); weights.assign(0.5); - nd4j::ops::softmax_cross_entropy_loss_grad op; + sd::ops::softmax_cross_entropy_loss_grad op; auto results = op.evaluate({&logits, &weights, &labels}, {0.}, {2}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *dLdp = results->at(0); - auto *dLdw = results->at(1); - auto *dLdl = results->at(2); + auto *dLdp = results.at(0); + auto *dLdw = results.at(1); + auto *dLdl = results.at(2); // dLdp->printIndexedBuffer(); @@ -3665,7 +3669,6 @@ TEST_F(DeclarableOpsTests11, softmax_cross_entropy_loss_grad_test8) { ASSERT_TRUE(dLdwExp.isSameShape(dLdw)); ASSERT_TRUE(dLdwExp.equalsTo(dLdw)); - delete results; } //////////////////////////////////////////////////////////////////////////////// @@ -3674,7 +3677,7 @@ TEST_F(DeclarableOpsTests11, SafeDivideMixed_Test1) { NDArray labels('c', {2, 3}, {1.0, 2.0, 3.0, -1.0, 2.0, 1.0}); auto sumDiff = labels.reduceAlongDimension(reduce::Sum, {1}, true); - NDArray numOfNonZero(sumDiff.getShapeInfo(), nd4j::DataType::INT64, false); + NDArray numOfNonZero(sumDiff.getShapeInfo(), sd::DataType::INT64, false); numOfNonZero.assign(1); sumDiff.applyPairwiseTransform(pairwise::SafeDivide, numOfNonZero, sumDiff); } @@ -3683,71 +3686,68 @@ TEST_F(DeclarableOpsTests11, SafeDivideMixed_Test1) { TEST_F(DeclarableOpsTests11, softmaxCrossEntropyWithLogits_grad_test1) { NDArray labels('c', {2,3,4}, {1,0,0,0, 0,1,0,0, 0,0,1,0, 0,0,0,1, 1,0,0,0, 0,1,0,0}); - NDArray logits('c', {2,3,4}, nd4j::DataType::DOUBLE); + NDArray logits('c', {2,3,4}, sd::DataType::DOUBLE); NDArray dLdpExp('c', {2,3,4}, {-0.76479, 0.2448, 0.2548, 0.26519, 0.23521,-0.7552, 0.2548, 0.26519, 0.23521, 0.2448,-0.7452, 0.26519, 0.23521, 0.2448, 0.2548,-0.73481,-0.76479, 0.2448, 0.2548, 0.26519, 0.23521,-0.7552, 0.2548, 0.26519}); logits.linspace(-0.08, 0.04); - nd4j::ops::softmax_cross_entropy_loss_with_logits_grad op; + sd::ops::softmax_cross_entropy_loss_with_logits_grad op; auto results = op.evaluate({&logits, &labels}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *dLdp = results->at(0); + auto *dLdp = results.at(0); ASSERT_TRUE(dLdpExp.isSameShape(dLdp)); ASSERT_TRUE(dLdpExp.equalsTo(dLdp)); - delete results; } ///////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests11, softmaxCrossEntropyWithLogits_grad_test2) { NDArray labels('c', {2,3,4}, {1,0,0,0, 0,1,0,1, 0,0,1,0, 0,0,0,1, 1,0,1,0, 0,1,0,0}); - NDArray logits('c', {2,3,4}, nd4j::DataType::DOUBLE); + NDArray logits('c', {2,3,4}, sd::DataType::DOUBLE); NDArray dLdpExp('c', {2,3,4}, {-0.71836, 0.28164, 0.28164, 0.28164, 0.33051, -0.66949, 0.33051, -0.66949, 0.38785, 0.38785, -0.61215, 0.38785, 0.28164, 0.28164, 0.28164, -0.71836,-0.66949, 0.33051, -0.66949, 0.33051, 0.38785, -0.61215, 0.38785, 0.38785}); logits.linspace(-0.08, 0.04); - nd4j::ops::softmax_cross_entropy_loss_with_logits_grad op; + sd::ops::softmax_cross_entropy_loss_with_logits_grad op; auto results = op.evaluate({&logits, &labels}, {}, {1}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *dLdp = results->at(0); + auto *dLdp = results.at(0); ASSERT_TRUE(dLdpExp.isSameShape(dLdp)); ASSERT_TRUE(dLdpExp.equalsTo(dLdp)); - delete results; } ///////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests11, softmaxCrossEntropyWithLogits_grad_test3) { NDArray labels('c', {2,3}, {1,0,0, 0,1,1}); - NDArray logits('c', {2,3}, nd4j::DataType::DOUBLE); + NDArray logits('c', {2,3}, sd::DataType::DOUBLE); NDArray dLdpExp('c', {2,3}, {-0.52996, 0.47004, 0.47004, 0.52996, -0.47004, -0.47004}); logits.linspace(-0.08, 0.04); - nd4j::ops::softmax_cross_entropy_loss_with_logits_grad op; + sd::ops::softmax_cross_entropy_loss_with_logits_grad op; auto results = op.evaluate({&logits, &labels}, {}, {0}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *dLdp = results->at(0); + auto *dLdp = results.at(0); ASSERT_TRUE(dLdpExp.isSameShape(dLdp)); ASSERT_TRUE(dLdpExp.equalsTo(dLdp)); - delete results; } ///////////////////////////////////////////////////////////////// @@ -3758,18 +3758,17 @@ TEST_F(DeclarableOpsTests11, softmaxCrossEntropyWithLogits_grad_test4) { NDArray dLdpExp('c', {2,1}, {0., 0.}); - nd4j::ops::softmax_cross_entropy_loss_with_logits_grad op; + sd::ops::softmax_cross_entropy_loss_with_logits_grad op; auto results = op.evaluate({&logits, &labels}, {}, {1}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *dLdp = results->at(0); + auto *dLdp = results.at(0); ASSERT_TRUE(dLdpExp.isSameShape(dLdp)); ASSERT_TRUE(dLdpExp.equalsTo(dLdp)); - delete results; } ///////////////////////////////////////////////////////////////// @@ -3780,18 +3779,17 @@ TEST_F(DeclarableOpsTests11, softmaxCrossEntropyWithLogits_grad_test5) { NDArray dLdpExp('c', {2,1}, {-0.51999, 0.51999}); - nd4j::ops::softmax_cross_entropy_loss_with_logits_grad op; + sd::ops::softmax_cross_entropy_loss_with_logits_grad op; auto results = op.evaluate({&logits, &labels}, {}, {0}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *dLdp = results->at(0); + auto *dLdp = results.at(0); ASSERT_TRUE(dLdpExp.isSameShape(dLdp)); ASSERT_TRUE(dLdpExp.equalsTo(dLdp)); - delete results; } ///////////////////////////////////////////////////////////////// @@ -3802,18 +3800,17 @@ TEST_F(DeclarableOpsTests11, softmaxCrossEntropyWithLogits_grad_test6) { NDArray dLdpExp('c', {1,2}, {0, 0.}); - nd4j::ops::softmax_cross_entropy_loss_with_logits_grad op; + sd::ops::softmax_cross_entropy_loss_with_logits_grad op; auto results = op.evaluate({&logits, &labels}, {}, {0}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *dLdp = results->at(0); + auto *dLdp = results.at(0); ASSERT_TRUE(dLdpExp.isSameShape(dLdp)); ASSERT_TRUE(dLdpExp.equalsTo(dLdp)); - delete results; } ///////////////////////////////////////////////////////////////// @@ -3824,18 +3821,17 @@ TEST_F(DeclarableOpsTests11, softmaxCrossEntropyWithLogits_grad_test7) { NDArray dLdpExp('c', {2}, {0.48001, -0.48001}); - nd4j::ops::softmax_cross_entropy_loss_with_logits_grad op; + sd::ops::softmax_cross_entropy_loss_with_logits_grad op; auto results = op.evaluate({&logits, &labels}, {}, {0}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *dLdp = results->at(0); + auto *dLdp = results.at(0); ASSERT_TRUE(dLdpExp.isSameShape(dLdp)); ASSERT_TRUE(dLdpExp.equalsTo(dLdp)); - delete results; } ///////////////////////////////////////////////////////////////// @@ -3846,161 +3842,152 @@ TEST_F(DeclarableOpsTests11, softmaxCrossEntropyWithLogits_grad_test8) { NDArray dLdpExp('c', {1}, std::vector{0}); - nd4j::ops::softmax_cross_entropy_loss_with_logits_grad op; + sd::ops::softmax_cross_entropy_loss_with_logits_grad op; auto results = op.evaluate({&logits, &labels}, {}, {0}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *dLdp = results->at(0); + auto *dLdp = results.at(0); ASSERT_TRUE(dLdpExp.isSameShape(dLdp)); ASSERT_TRUE(dLdpExp.equalsTo(dLdp)); - delete results; } ///////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests11, Multiply_BP_Test1) { - NDArray x('c', {3,4,5}, nd4j::DataType::DOUBLE); - NDArray y('c', {1,1,1}, nd4j::DataType::DOUBLE); + NDArray x('c', {3,4,5}, sd::DataType::DOUBLE); + NDArray y('c', {1,1,1}, sd::DataType::DOUBLE); - NDArray dLdp('c', {3,4,5}, nd4j::DataType::DOUBLE); - NDArray dLdpExp('c', {3,4,5}, nd4j::DataType::DOUBLE); + NDArray dLdp('c', {3,4,5}, sd::DataType::DOUBLE); + NDArray dLdpExp('c', {3,4,5}, sd::DataType::DOUBLE); x.assign(1.0);//linspace(0.1, 0.1); y.assign(1.0); dLdp.assign(1.0); dLdpExp.assign(1.0); - nd4j::ops::multiply_bp op; + sd::ops::multiply_bp op; auto results = op.evaluate({&x, &y, &dLdp}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *dLdo = results->at(0); + auto *dLdo = results.at(0); ASSERT_TRUE(dLdpExp.isSameShape(dLdo)); ASSERT_TRUE(dLdpExp.equalsTo(dLdo)); - delete results; } ///////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests11, sparseSoftmaxCrossEntropyWithLogits_grad_test1) { - NDArray labels('c', {2}, {2,1}, nd4j::DataType::INT64); - NDArray logits('c', {2,3}, nd4j::DataType::DOUBLE); + NDArray labels('c', {2}, {2,1}, sd::DataType::INT64); + NDArray logits('c', {2,3}, sd::DataType::DOUBLE); NDArray dLdpExp('c', {2,3}, {0.30061, 0.33222, -0.63283, 0.30061, -0.66778, 0.36717}); logits.linspace(0.1, 0.1); - nd4j::ops::sparse_softmax_cross_entropy_loss_with_logits_grad op; + sd::ops::sparse_softmax_cross_entropy_loss_with_logits_grad op; auto results = op.evaluate({&labels, &logits}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *dLdp = results->at(0); + auto *dLdp = results.at(0); ASSERT_TRUE(dLdpExp.isSameShape(dLdp)); ASSERT_TRUE(dLdpExp.equalsTo(dLdp)); - delete results; } ///////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests11, sparseSoftmaxCrossEntropyWithLogits_grad_test2) { - NDArray labels('c', {2}, {0,1}, nd4j::DataType::INT64); - NDArray logits('c', {2,3}, nd4j::DataType::DOUBLE); + NDArray labels('c', {2}, {0,1}, sd::DataType::INT64); + NDArray logits('c', {2,3}, sd::DataType::DOUBLE); NDArray dLdpExp('c', {2,3}, {-0.69939, 0.33222, 0.36717, 0.30061, -0.66778, 0.36717}); logits.linspace(-0.1, 0.1); - nd4j::ops::sparse_softmax_cross_entropy_loss_with_logits_grad op; + sd::ops::sparse_softmax_cross_entropy_loss_with_logits_grad op; auto results = op.evaluate({&labels, &logits}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *dLdp = results->at(0); + auto *dLdp = results.at(0); ASSERT_TRUE(dLdpExp.isSameShape(dLdp)); ASSERT_TRUE(dLdpExp.equalsTo(dLdp)); - delete results; } ///////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests11, sparseSoftmaxCrossEntropyWithLogits_grad_test3) { - NDArray labels('c', {}, std::vector{1}, nd4j::DataType::INT64); + NDArray labels('c', {}, std::vector{1}, sd::DataType::INT64); NDArray logits('c', {2}, {-0.2, 0.3}); NDArray dLdpExp('c', {2}, {0.37754, -0.37754}); - nd4j::ops::sparse_softmax_cross_entropy_loss_with_logits_grad op; + sd::ops::sparse_softmax_cross_entropy_loss_with_logits_grad op; auto results = op.evaluate({&labels, &logits}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *dLdp = results->at(0); + auto *dLdp = results.at(0); ASSERT_TRUE(dLdpExp.isSameShape(dLdp)); ASSERT_TRUE(dLdpExp.equalsTo(dLdp)); - delete results; } ///////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests11, sparseSoftmaxCrossEntropyWithLogits_grad_test4) { - NDArray labels('c', {2,3}, {0,1,1, 3,3,2}, nd4j::DataType::INT64); - NDArray logits('c', {2,3,4}, nd4j::DataType::DOUBLE); + NDArray labels('c', {2,3}, {0,1,1, 3,3,2}, sd::DataType::INT64); + NDArray logits('c', {2,3,4}, sd::DataType::DOUBLE); NDArray dLdpExp('c', {2,3,4}, {-0.78616, 0.23633, 0.26118, 0.28865, 0.21384, -0.76367, 0.26118, 0.28865, 0.21384, -0.76367, 0.26118, 0.28865, 0.21384, 0.23633, 0.26118, -0.71135, 0.21384, 0.23633, 0.26118, -0.71135, 0.21384, 0.23633, -0.73882, 0.28865}); logits.linspace(-0.5, 0.1); - nd4j::ops::sparse_softmax_cross_entropy_loss_with_logits_grad op; + sd::ops::sparse_softmax_cross_entropy_loss_with_logits_grad op; auto results = op.evaluate({&labels, &logits}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *dLdp = results->at(0); + auto *dLdp = results.at(0); ASSERT_TRUE(dLdpExp.isSameShape(dLdp)); ASSERT_TRUE(dLdpExp.equalsTo(dLdp)); - delete results; } ///////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests11, sparseSoftmaxCrossEntropyWithLogits_grad_test5) { - NDArray labels('c', {1,1}, std::vector({0}), nd4j::DataType::INT64); + NDArray labels('c', {1,1}, std::vector({0}), sd::DataType::INT64); NDArray logits('c', {1,1,2}, {-0.3,0.2}); NDArray dLdpExp('c', {1,1,2}, {-0.62246, 0.62246}); - nd4j::ops::sparse_softmax_cross_entropy_loss_with_logits_grad op; + sd::ops::sparse_softmax_cross_entropy_loss_with_logits_grad op; auto results = op.evaluate({&labels, &logits}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *dLdp = results->at(0); + auto *dLdp = results.at(0); ASSERT_TRUE(dLdpExp.isSameShape(dLdp)); ASSERT_TRUE(dLdpExp.equalsTo(dLdp)); - delete results; } - - diff --git a/libnd4j/tests_cpu/layers_tests/DeclarableOpsTests12.cpp b/libnd4j/tests_cpu/layers_tests/DeclarableOpsTests12.cpp index e5eaa9a6a..615f95bbd 100644 --- a/libnd4j/tests_cpu/layers_tests/DeclarableOpsTests12.cpp +++ b/libnd4j/tests_cpu/layers_tests/DeclarableOpsTests12.cpp @@ -21,13 +21,14 @@ #include "testlayers.h" #include -#include +#include #include -#include -#include +#include +#include #include +#include -using namespace nd4j; +using namespace sd; class DeclarableOpsTests12 : public testing::Test { @@ -43,14 +44,14 @@ TEST_F(DeclarableOpsTests12, test_any_validation_1) { auto x = NDArrayFactory::create('c', {2, 1}, {1.0, 2.0}); auto y = NDArrayFactory::create('c', {2}, {1, 0}); - nd4j::ops::transpose op; + sd::ops::transpose op; auto result = op.evaluate({&x, &y}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_EQ(x.dataType(), z->dataType()); - delete result; + } @@ -58,8 +59,8 @@ TEST_F(DeclarableOpsTests12, test_any_validation_1) { TEST_F(DeclarableOpsTests12, cosine_distance_loss_grad_test1) { NDArray labels('c', {2,4}, {0,1,1,0,1,0,1,0}); - NDArray predictions('c', {2,4}, nd4j::DataType::DOUBLE); - NDArray weights('c', {2,1}, nd4j::DataType::DOUBLE); + NDArray predictions('c', {2,4}, sd::DataType::DOUBLE); + NDArray weights('c', {2,1}, sd::DataType::DOUBLE); NDArray dLdpExp('c', {2,4}, {-0. , -0.5, -0.5, -0., -0.5, -0. , -0.5, -0.}); NDArray dLdwExp('c', {2,1}, {1.2, -0.2}); @@ -67,30 +68,30 @@ TEST_F(DeclarableOpsTests12, cosine_distance_loss_grad_test1) { predictions.linspace(-0.4, 0.2); weights.assign(0.5); - nd4j::ops::cosine_distance_loss_grad op; + sd::ops::cosine_distance_loss_grad op; auto results = op.evaluate({&predictions, &weights, &labels}, {}, {0, -1}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *dLdp = results->at(0); - auto *dLdw = results->at(1); - auto *dLdl = results->at(2); + auto *dLdp = results.at(0); + auto *dLdw = results.at(1); + auto *dLdl = results.at(2); ASSERT_TRUE(dLdpExp.isSameShape(dLdp)); ASSERT_TRUE(dLdpExp.equalsTo(dLdp)); ASSERT_TRUE(dLdwExp.isSameShape(dLdw)); ASSERT_TRUE(dLdwExp.equalsTo(dLdw)); - delete results; + } ///////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests12, cosine_distance_loss_grad_test2) { NDArray labels('c', {2,4}, {-0.1, 0.3, 2, -1.4, 2.5, -3, 1.2, 2.2}); - NDArray predictions('c', {2,4}, nd4j::DataType::DOUBLE); - NDArray weights('c', {1,4}, nd4j::DataType::DOUBLE); + NDArray predictions('c', {2,4}, sd::DataType::DOUBLE); + NDArray weights('c', {1,4}, sd::DataType::DOUBLE); NDArray dLdpExp('c', {2,4}, {0.05, -0.15, -1. , 0.7 ,-1.25, 1.5 , -0.6 , -1.1 }); NDArray dLdwExp('c', {1,4}, {-0.04, 2.86, 0.04, -0.92}); @@ -99,15 +100,15 @@ TEST_F(DeclarableOpsTests12, cosine_distance_loss_grad_test2) { predictions.linspace(-0.4, 0.2); weights.assign(0.5); - nd4j::ops::cosine_distance_loss_grad op; + sd::ops::cosine_distance_loss_grad op; auto results = op.evaluate({&predictions, &weights, &labels}, {}, {0, 0}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *dLdp = results->at(0); - auto *dLdw = results->at(1); - auto *dLdl = results->at(2); + auto *dLdp = results.at(0); + auto *dLdw = results.at(1); + auto *dLdl = results.at(2); ASSERT_TRUE(dLdpExp.isSameShape(dLdp)); ASSERT_TRUE(dLdpExp.equalsTo(dLdp)); @@ -116,15 +117,15 @@ TEST_F(DeclarableOpsTests12, cosine_distance_loss_grad_test2) { ASSERT_TRUE(dLdlExp.isSameShape(dLdl)); ASSERT_TRUE(dLdlExp.equalsTo(dLdl)); - delete results; + } ///////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests12, cosine_distance_loss_grad_test3) { NDArray labels('c', {4}, {-0.1, 0.3, 2, -1.4}); - NDArray predictions('c', {4}, nd4j::DataType::DOUBLE); - NDArray weights('c', {1}, nd4j::DataType::DOUBLE); + NDArray predictions('c', {4}, sd::DataType::DOUBLE); + NDArray weights('c', {1}, sd::DataType::DOUBLE); NDArray dLdpExp('c', {4}, {0.05, -0.15, -1., 0.7}); NDArray dLdwExp('c', {1}, std::vector{1.3}); @@ -133,15 +134,15 @@ TEST_F(DeclarableOpsTests12, cosine_distance_loss_grad_test3) { predictions.linspace(-0.4, 0.2); weights.assign(0.5); - nd4j::ops::cosine_distance_loss_grad op; + sd::ops::cosine_distance_loss_grad op; auto results = op.evaluate({&predictions, &weights, &labels}, {}, {0, 0}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *dLdp = results->at(0); - auto *dLdw = results->at(1); - auto *dLdl = results->at(2); + auto *dLdp = results.at(0); + auto *dLdw = results.at(1); + auto *dLdl = results.at(2); ASSERT_TRUE(dLdpExp.isSameShape(dLdp)); ASSERT_TRUE(dLdpExp.equalsTo(dLdp)); @@ -150,15 +151,15 @@ TEST_F(DeclarableOpsTests12, cosine_distance_loss_grad_test3) { ASSERT_TRUE(dLdlExp.isSameShape(dLdl)); ASSERT_TRUE(dLdlExp.equalsTo(dLdl)); - delete results; + } ///////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests12, cosine_distance_loss_grad_test4) { NDArray labels('c', {1,4}, {-0.1, 0.3, 2, -1.4}); - NDArray predictions('c', {1,4}, nd4j::DataType::DOUBLE); - NDArray weights('c', {}, std::vector{0.}, nd4j::DataType::DOUBLE); + NDArray predictions('c', {1,4}, sd::DataType::DOUBLE); + NDArray weights('c', {}, std::vector{0.}, sd::DataType::DOUBLE); NDArray dLdpExp('c', {1,4}, {0.05, -0.15, -1., 0.7}); NDArray dLdwExp('c', {}, std::vector{1.3}); @@ -167,15 +168,15 @@ TEST_F(DeclarableOpsTests12, cosine_distance_loss_grad_test4) { predictions.linspace(-0.4, 0.2); weights.assign(0.5); - nd4j::ops::cosine_distance_loss_grad op; + sd::ops::cosine_distance_loss_grad op; auto results = op.evaluate({&predictions, &weights, &labels}, {}, {1, 1}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *dLdp = results->at(0); - auto *dLdw = results->at(1); - auto *dLdl = results->at(2); + auto *dLdp = results.at(0); + auto *dLdw = results.at(1); + auto *dLdl = results.at(2); ASSERT_TRUE(dLdpExp.isSameShape(dLdp)); ASSERT_TRUE(dLdpExp.equalsTo(dLdp)); @@ -184,16 +185,16 @@ TEST_F(DeclarableOpsTests12, cosine_distance_loss_grad_test4) { ASSERT_TRUE(dLdlExp.isSameShape(dLdl)); ASSERT_TRUE(dLdlExp.equalsTo(dLdl)); - delete results; + } ///////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests12, cosine_distance_loss_grad_test5) { - NDArray labels('c', {4}, {-0.1, 0.3, 2, -1.4}, nd4j::DataType::DOUBLE); - NDArray predictions('c', {4}, nd4j::DataType::DOUBLE); - NDArray weights('c', {1,1}, nd4j::DataType::DOUBLE); + NDArray labels('c', {4}, {-0.1, 0.3, 2, -1.4}, sd::DataType::DOUBLE); + NDArray predictions('c', {4}, sd::DataType::DOUBLE); + NDArray weights('c', {1,1}, sd::DataType::DOUBLE); NDArray dLdpExp('c', {4}, {0.1, -0.3, -2. , 1.4}); NDArray dLdwExp('c', {1,1}, std::vector{0.}); @@ -202,15 +203,15 @@ TEST_F(DeclarableOpsTests12, cosine_distance_loss_grad_test5) { predictions.linspace(-0.4, 0.2); weights = 0.5; - nd4j::ops::cosine_distance_loss_grad op; + sd::ops::cosine_distance_loss_grad op; auto results = op.evaluate({&predictions, &weights, &labels}, {}, {2, 0}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *dLdp = results->at(0); - auto *dLdw = results->at(1); - auto *dLdl = results->at(2); + auto *dLdp = results.at(0); + auto *dLdw = results.at(1); + auto *dLdl = results.at(2); ASSERT_TRUE(dLdpExp.isSameShape(dLdp)); ASSERT_TRUE(dLdpExp.equalsTo(dLdp)); @@ -219,15 +220,15 @@ TEST_F(DeclarableOpsTests12, cosine_distance_loss_grad_test5) { ASSERT_TRUE(dLdlExp.isSameShape(dLdl)); ASSERT_TRUE(dLdlExp.equalsTo(dLdl)); - delete results; + } ///////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests12, cosine_distance_loss_grad_test6) { - NDArray labels('c', {4,1}, {-0.1, 0.3, 2, -1.4}, nd4j::DataType::DOUBLE); - NDArray predictions('c', {4,1}, nd4j::DataType::DOUBLE); - NDArray weights('c', {4,1}, nd4j::DataType::DOUBLE); + NDArray labels('c', {4,1}, {-0.1, 0.3, 2, -1.4}, sd::DataType::DOUBLE); + NDArray predictions('c', {4,1}, sd::DataType::DOUBLE); + NDArray weights('c', {4,1}, sd::DataType::DOUBLE); NDArray dLdpExp('c', {4,1}, {0.0125, -0.0375, -0.25 , 0.175}); NDArray dLdwExp('c', {4,1}, {0.24 , 0.265, 0.25 , 0.32}); @@ -236,15 +237,15 @@ TEST_F(DeclarableOpsTests12, cosine_distance_loss_grad_test6) { predictions.linspace(-0.4, 0.2); weights = 0.5; - nd4j::ops::cosine_distance_loss_grad op; + sd::ops::cosine_distance_loss_grad op; auto results = op.evaluate({&predictions, &weights, &labels}, {}, {3, 1}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *dLdp = results->at(0); - auto *dLdw = results->at(1); - auto *dLdl = results->at(2); + auto *dLdp = results.at(0); + auto *dLdw = results.at(1); + auto *dLdl = results.at(2); ASSERT_TRUE(dLdpExp.isSameShape(dLdp)); ASSERT_TRUE(dLdpExp.equalsTo(dLdp)); @@ -253,15 +254,15 @@ TEST_F(DeclarableOpsTests12, cosine_distance_loss_grad_test6) { ASSERT_TRUE(dLdlExp.isSameShape(dLdl)); ASSERT_TRUE(dLdlExp.equalsTo(dLdl)); - delete results; + } ///////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests12, cosine_distance_loss_grad_test7) { NDArray labels('c', {2,3,4}, {-0.1, 0.3, 2, -1.4, 2.5, -3, 1.2, 2.2,-0.1, 0.3, 2, -3.4, 2.5, -3, 1.2, 2.2,-0.2, 0.3, 2, -1.4, 2.7, -3, 1.2, 4.2}); - NDArray predictions('c', {2,3,4}, nd4j::DataType::DOUBLE); - NDArray weights('c', {1,3,1}, nd4j::DataType::DOUBLE); + NDArray predictions('c', {2,3,4}, sd::DataType::DOUBLE); + NDArray weights('c', {1,3,1}, sd::DataType::DOUBLE); NDArray dLdpExp('c', {2,3,4}, {0.00833, -0.025 , -0.16667, 0.11667,-0.20833, 0.25 , -0.1 , -0.18333, 0.00833, -0.025 , -0.16667, 0.28333, -0.20833, 0.25 , -0.1 , -0.18333, 0.01667, -0.025 , -0.16667, 0.11667,-0.225 , 0.25 , -0.1 , -0.35 }); @@ -272,15 +273,15 @@ TEST_F(DeclarableOpsTests12, cosine_distance_loss_grad_test7) { predictions.linspace(-0.4, 0.2); weights = 0.5; - nd4j::ops::cosine_distance_loss_grad op; + sd::ops::cosine_distance_loss_grad op; auto results = op.evaluate({&predictions, &weights, &labels}, {}, {2, 0}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *dLdp = results->at(0); - auto *dLdw = results->at(1); - auto *dLdl = results->at(2); + auto *dLdp = results.at(0); + auto *dLdw = results.at(1); + auto *dLdl = results.at(2); ASSERT_TRUE(dLdpExp.isSameShape(dLdp)); ASSERT_TRUE(dLdpExp.equalsTo(dLdp)); @@ -289,15 +290,15 @@ TEST_F(DeclarableOpsTests12, cosine_distance_loss_grad_test7) { ASSERT_TRUE(dLdlExp.isSameShape(dLdl)); ASSERT_TRUE(dLdlExp.equalsTo(dLdl)); - delete results; + } ///////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests12, cosine_distance_loss_grad_test8) { NDArray labels('c', {2,3,4}, {-0.1, 0.3, 2, -1.4, 2.5, -3, 1.2, 2.2,-0.1, 0.3, 2, -3.4, 2.5, -3, 1.2, 2.2,-0.2, 0.3, 2, -1.4, 2.7, -3, 1.2, 4.2}); - NDArray predictions('c', {2,3,4}, nd4j::DataType::DOUBLE); - NDArray weights('c', {2,1,1}, nd4j::DataType::DOUBLE); + NDArray predictions('c', {2,3,4}, sd::DataType::DOUBLE); + NDArray weights('c', {2,1,1}, sd::DataType::DOUBLE); NDArray dLdpExp('c', {2,3,4}, {0.00625, -0.01875, -0.125 , 0.0875,-0.15625, 0.1875 , -0.075 , -0.1375, 0.00625, -0.01875, -0.125 , 0.2125, -0.15625, 0.1875 , -0.075 , -0.1375, 0.0125 , -0.01875, -0.125 , 0.0875,-0.16875, 0.1875 , -0.075 , -0.2625}); @@ -308,15 +309,15 @@ TEST_F(DeclarableOpsTests12, cosine_distance_loss_grad_test8) { predictions.linspace(-0.4, 0.2); weights = 0.5; - nd4j::ops::cosine_distance_loss_grad op; + sd::ops::cosine_distance_loss_grad op; auto results = op.evaluate({&predictions, &weights, &labels}, {}, {3, 1}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *dLdp = results->at(0); - auto *dLdw = results->at(1); - auto *dLdl = results->at(2); + auto *dLdp = results.at(0); + auto *dLdw = results.at(1); + auto *dLdl = results.at(2); ASSERT_TRUE(dLdpExp.isSameShape(dLdp)); ASSERT_TRUE(dLdpExp.equalsTo(dLdp)); @@ -325,15 +326,15 @@ TEST_F(DeclarableOpsTests12, cosine_distance_loss_grad_test8) { ASSERT_TRUE(dLdlExp.isSameShape(dLdl)); ASSERT_TRUE(dLdlExp.equalsTo(dLdl)); - delete results; + } ///////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests12, cosine_distance_loss_grad_test9) { NDArray labels('c', {2,3,4}, {-0.1, 0.3, 2, -1.4, 2.5, -3, 1.2, 2.2,-0.1, 0.3, 2, -3.4, 2.5, -3, 1.2, 2.2,-0.2, 0.3, 2, -1.4, 2.7, -3, 1.2, 4.2}); - NDArray predictions('c', {2,3,4}, nd4j::DataType::DOUBLE); - NDArray weights('c', {2,3,1}, nd4j::DataType::DOUBLE); + NDArray predictions('c', {2,3,4}, sd::DataType::DOUBLE); + NDArray weights('c', {2,3,1}, sd::DataType::DOUBLE); NDArray dLdpExp('c', {2,3,4}, {0.05, -0.15, -1. , 0.7,-1.25, 1.5 , -0.6 , -1.1, 0.05, -0.15, -1. , 1.7, -1.25, 1.5 , -0.6 , -1.1, 0.1 , -0.15, -1. , 0.7,-1.35, 1.5 , -0.6 , -2.1}); @@ -344,15 +345,15 @@ TEST_F(DeclarableOpsTests12, cosine_distance_loss_grad_test9) { predictions.linspace(-0.4, 0.2); weights = 0.5; - nd4j::ops::cosine_distance_loss_grad op; + sd::ops::cosine_distance_loss_grad op; auto results = op.evaluate({&predictions, &weights, &labels}, {}, {0, 2}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *dLdp = results->at(0); - auto *dLdw = results->at(1); - auto *dLdl = results->at(2); + auto *dLdp = results.at(0); + auto *dLdw = results.at(1); + auto *dLdl = results.at(2); ASSERT_TRUE(dLdpExp.isSameShape(dLdp)); ASSERT_TRUE(dLdpExp.equalsTo(dLdp)); @@ -361,23 +362,23 @@ TEST_F(DeclarableOpsTests12, cosine_distance_loss_grad_test9) { ASSERT_TRUE(dLdlExp.isSameShape(dLdl)); ASSERT_TRUE(dLdlExp.equalsTo(dLdl)); - delete results; + } ///////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests12, hinge_loss_14) { - NDArray logits('c', {3,4}, nd4j::DataType::DOUBLE); + NDArray logits('c', {3,4}, sd::DataType::DOUBLE); NDArray weights('c', {}, std::vector{1.}); NDArray labels('c', {3,4}, {0,1,1,0,1,0,1,0,1,0,1,0}); - NDArray output('c', {}, std::vector{0.}, nd4j::DataType::DOUBLE); + NDArray output('c', {}, std::vector{0.}, sd::DataType::DOUBLE); logits.linspace(1.); weights.assign(1.); - nd4j::ops::hinge_loss op; + sd::ops::hinge_loss op; Nd4jStatus status = op.execute({&logits, &weights, &labels}, {&output}, {}, {1}, {}); ASSERT_EQ(ND4J_STATUS_OK, status); @@ -388,17 +389,17 @@ TEST_F(DeclarableOpsTests12, hinge_loss_14) { ///////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests12, TestDivideBP_1) { - NDArray x('c', {3,4}, nd4j::DataType::DOUBLE); + NDArray x('c', {3,4}, sd::DataType::DOUBLE); NDArray y = NDArrayFactory::create(2.); - NDArray eps('c', {3,4}, nd4j::DataType::DOUBLE); + NDArray eps('c', {3,4}, sd::DataType::DOUBLE); - NDArray output1('c', {3, 4}, nd4j::DataType::DOUBLE); - NDArray output2(nd4j::DataType::DOUBLE); + NDArray output1('c', {3, 4}, sd::DataType::DOUBLE); + NDArray output2(sd::DataType::DOUBLE); x.linspace(2., 2.); eps.linspace(1.); - nd4j::ops::divide_bp op; + sd::ops::divide_bp op; Nd4jStatus status = op.execute({&x, &y, &eps}, {&output1, &output2}, {}, {}, {}); ASSERT_EQ(ND4J_STATUS_OK, status); @@ -408,20 +409,20 @@ TEST_F(DeclarableOpsTests12, TestDivideBP_1) { ///////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests12, TestDivideBP_2) { - NDArray x('c', {3,4}, nd4j::DataType::DOUBLE); + NDArray x('c', {3,4}, sd::DataType::DOUBLE); NDArray y = NDArrayFactory::create('c', {3,4}); - NDArray eps('c', {3,4}, nd4j::DataType::DOUBLE); - NDArray exp1('c', {3,4}, nd4j::DataType::DOUBLE); - NDArray exp2('c', {3,4}, nd4j::DataType::DOUBLE); - NDArray output1('c', {3, 4}, nd4j::DataType::DOUBLE); - NDArray output2('c', {3, 4}, nd4j::DataType::DOUBLE); + NDArray eps('c', {3,4}, sd::DataType::DOUBLE); + NDArray exp1('c', {3,4}, sd::DataType::DOUBLE); + NDArray exp2('c', {3,4}, sd::DataType::DOUBLE); + NDArray output1('c', {3, 4}, sd::DataType::DOUBLE); + NDArray output2('c', {3, 4}, sd::DataType::DOUBLE); exp1.assign(1.); exp2.assign(-2.); x.linspace(2., 2.); y.linspace(1.); eps.linspace(1.); - nd4j::ops::divide_bp op; + sd::ops::divide_bp op; Nd4jStatus status = op.execute({&x, &y, &eps}, std::vector{&output1, &output2}, {}, {}, {}); ASSERT_EQ(ND4J_STATUS_OK, status); @@ -432,17 +433,17 @@ TEST_F(DeclarableOpsTests12, TestDivideBP_2) { ///////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests12, TestReverseDivideBP_1) { - NDArray x('c', {3,4}, nd4j::DataType::DOUBLE); + NDArray x('c', {3,4}, sd::DataType::DOUBLE); NDArray y = NDArrayFactory::create(2.); - NDArray eps('c', {3,4}, nd4j::DataType::DOUBLE); + NDArray eps('c', {3,4}, sd::DataType::DOUBLE); - NDArray output1('c', {3, 4}, nd4j::DataType::DOUBLE); - NDArray output2(nd4j::DataType::DOUBLE); + NDArray output1('c', {3, 4}, sd::DataType::DOUBLE); + NDArray output2(sd::DataType::DOUBLE); x.linspace(2., 2.); eps.linspace(1.); - nd4j::ops::reversedivide_bp op; + sd::ops::reversedivide_bp op; Nd4jStatus status = op.execute({&y, &x, &eps}, std::vector{&output2, &output1}, {}, {}, {}); ASSERT_EQ(ND4J_STATUS_OK, status); @@ -452,21 +453,21 @@ TEST_F(DeclarableOpsTests12, TestReverseDivideBP_1) { ///////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests12, TestReverseDivideBP_2) { - NDArray x('c', {3,4}, nd4j::DataType::DOUBLE); + NDArray x('c', {3,4}, sd::DataType::DOUBLE); NDArray y = NDArrayFactory::create('c', {3,4}); - NDArray eps('c', {3,4}, nd4j::DataType::DOUBLE); - NDArray exp1('c', {3,4}, nd4j::DataType::DOUBLE); - NDArray exp2('c', {3,4}, nd4j::DataType::DOUBLE); + NDArray eps('c', {3,4}, sd::DataType::DOUBLE); + NDArray exp1('c', {3,4}, sd::DataType::DOUBLE); + NDArray exp2('c', {3,4}, sd::DataType::DOUBLE); - NDArray output1('c', {3, 4}, nd4j::DataType::DOUBLE); - NDArray output2('c', {3, 4}, nd4j::DataType::DOUBLE); + NDArray output1('c', {3, 4}, sd::DataType::DOUBLE); + NDArray output2('c', {3, 4}, sd::DataType::DOUBLE); x.linspace(2., 2.); y.linspace(1.); eps.linspace(1.); exp1.assign(1.); exp2.assign(-2.); - nd4j::ops::reversedivide_bp op; + sd::ops::reversedivide_bp op; Nd4jStatus status = op.execute({&y, &x, &eps}, std::vector{&output2, &output1}, {}, {}, {}); ASSERT_EQ(ND4J_STATUS_OK, status); @@ -477,19 +478,19 @@ TEST_F(DeclarableOpsTests12, TestReverseDivideBP_2) { ///////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests12, TestSliceBP_1) { - NDArray x('c', {3,4}, nd4j::DataType::DOUBLE); - NDArray eps('c', {2,2}, nd4j::DataType::DOUBLE); + NDArray x('c', {3,4}, sd::DataType::DOUBLE); + NDArray eps('c', {2,2}, sd::DataType::DOUBLE); NDArray exp('c', {3,4}, {0., 0., 0., 0., 0., 1.,1., 0., 0., 1., 1., 0.}); - //NDArray exp2('c', {3,4}, nd4j::DataType::DOUBLE); + //NDArray exp2('c', {3,4}, sd::DataType::DOUBLE); - NDArray output('c', {3, 4}, nd4j::DataType::DOUBLE); - //NDArray output2('c', {3, 4}, nd4j::DataType::DOUBLE); + NDArray output('c', {3, 4}, sd::DataType::DOUBLE); + //NDArray output2('c', {3, 4}, sd::DataType::DOUBLE); output.assign(119.113); x.linspace(1.); eps.assign(1.); //exp1.assign(1.); //exp2.assign(-2.); - nd4j::ops::slice_bp op; + sd::ops::slice_bp op; Nd4jStatus status = op.execute({&x, &eps}, {&output}, {}, {1,1,2,2}, {}); ASSERT_EQ(ND4J_STATUS_OK, status); @@ -500,20 +501,20 @@ TEST_F(DeclarableOpsTests12, TestSliceBP_1) { ///////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests12, TestConfusionZero_1) { - NDArray x('c', {2}, {1,2}, nd4j::DataType::INT64); - NDArray i('c', {2}, {0,2}, nd4j::DataType::INT64); - //NDArray eps('c', {2,2}, nd4j::DataType::DOUBLE); - NDArray exp('c', {4,4}, {0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0}, nd4j::DataType::INT64); - //NDArray exp2('c', {3,4}, nd4j::DataType::DOUBLE); + NDArray x('c', {2}, {1,2}, sd::DataType::INT64); + NDArray i('c', {2}, {0,2}, sd::DataType::INT64); + //NDArray eps('c', {2,2}, sd::DataType::DOUBLE); + NDArray exp('c', {4,4}, {0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0}, sd::DataType::INT64); + //NDArray exp2('c', {3,4}, sd::DataType::DOUBLE); - NDArray output('c', {4, 4}, nd4j::DataType::INT64); - //NDArray output2('c', {3, 4}, nd4j::DataType::DOUBLE); + NDArray output('c', {4, 4}, sd::DataType::INT64); + //NDArray output2('c', {3, 4}, sd::DataType::DOUBLE); output.assign(119.113); x.linspace(1.); //eps.assign(1.); //exp1.assign(1.); //exp2.assign(-2.); - nd4j::ops::confusion_matrix op; + sd::ops::confusion_matrix op; Nd4jStatus status = op.execute({&x, &i}, {&output}, {}, {4}, {}); ASSERT_EQ(ND4J_STATUS_OK, status); @@ -524,21 +525,21 @@ TEST_F(DeclarableOpsTests12, TestConfusionZero_1) { ///////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests12, TestMaximumBP_1) { - NDArray x('c', {3,4}, nd4j::DataType::DOUBLE); - NDArray y('c', {3,4}, nd4j::DataType::DOUBLE); - NDArray eps('c', {3,4}, nd4j::DataType::DOUBLE); - NDArray exp1('c', {3,4}, {0, 0, 0, 0, 0, 0, 7, 8, 9, 10, 11, 12}, nd4j::DataType::DOUBLE); - NDArray exp2('c', {3,4}, {1, 2, 3, 4, 5, 6, 0, 0, 0, 0, 0, 0}, nd4j::DataType::DOUBLE); + NDArray x('c', {3,4}, sd::DataType::DOUBLE); + NDArray y('c', {3,4}, sd::DataType::DOUBLE); + NDArray eps('c', {3,4}, sd::DataType::DOUBLE); + NDArray exp1('c', {3,4}, {0, 0, 0, 0, 0, 0, 7, 8, 9, 10, 11, 12}, sd::DataType::DOUBLE); + NDArray exp2('c', {3,4}, {1, 2, 3, 4, 5, 6, 0, 0, 0, 0, 0, 0}, sd::DataType::DOUBLE); - NDArray output1('c', {3, 4}, nd4j::DataType::DOUBLE); - NDArray output2('c', {3, 4}, nd4j::DataType::DOUBLE); + NDArray output1('c', {3, 4}, sd::DataType::DOUBLE); + NDArray output2('c', {3, 4}, sd::DataType::DOUBLE); output1.assign(119); x.linspace(1.); y.linspace(12., -1.); eps.linspace(1.); //exp1.assign(1.); //exp2.assign(-2.); - nd4j::ops::maximum_bp op; + sd::ops::maximum_bp op; Nd4jStatus status = op.execute({&x, &y, &eps}, std::vector{&output1, &output2}, {}, {}, {}); ASSERT_EQ(ND4J_STATUS_OK, status); @@ -549,21 +550,21 @@ TEST_F(DeclarableOpsTests12, TestMaximumBP_1) { ///////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests12, TestMinimumBP_1) { - NDArray x('c', {3,4}, nd4j::DataType::DOUBLE); - NDArray y('c', {3,4}, nd4j::DataType::DOUBLE); - NDArray eps('c', {3,4}, nd4j::DataType::DOUBLE); - NDArray exp1('c', {3,4}, {0, 0, 0, 0, 0, 0, 7, 8, 9, 10, 11, 12}, nd4j::DataType::DOUBLE); - NDArray exp2('c', {3,4}, {1, 2, 3, 4, 5, 6, 0, 0, 0, 0, 0, 0}, nd4j::DataType::DOUBLE); + NDArray x('c', {3,4}, sd::DataType::DOUBLE); + NDArray y('c', {3,4}, sd::DataType::DOUBLE); + NDArray eps('c', {3,4}, sd::DataType::DOUBLE); + NDArray exp1('c', {3,4}, {0, 0, 0, 0, 0, 0, 7, 8, 9, 10, 11, 12}, sd::DataType::DOUBLE); + NDArray exp2('c', {3,4}, {1, 2, 3, 4, 5, 6, 0, 0, 0, 0, 0, 0}, sd::DataType::DOUBLE); - NDArray output1('c', {3, 4}, nd4j::DataType::DOUBLE); - NDArray output2('c', {3, 4}, nd4j::DataType::DOUBLE); + NDArray output1('c', {3, 4}, sd::DataType::DOUBLE); + NDArray output2('c', {3, 4}, sd::DataType::DOUBLE); output1.assign(119); x.linspace(1.); y.linspace(12., -1.); eps.linspace(1.); //exp1.assign(1.); //exp2.assign(-2.); - nd4j::ops::minimum_bp op; + sd::ops::minimum_bp op; Nd4jStatus status = op.execute({&x, &y, &eps}, std::vector{&output2, &output1}, {}, {}, {}); ASSERT_EQ(ND4J_STATUS_OK, status); @@ -575,34 +576,34 @@ TEST_F(DeclarableOpsTests12, TestMinimumBP_1) { ///////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests12, reverse_test15) { - NDArray x('c', {5}, {1,2,3,4,5}, nd4j::DataType::DOUBLE); - NDArray axis('c', {}, std::vector{0}, nd4j::DataType::INT32); - NDArray z('c', {5}, nd4j::DataType::DOUBLE); - NDArray exp('c', {5}, {5,4,3,2,1}, nd4j::DataType::DOUBLE); + NDArray x('c', {5}, {1,2,3,4,5}, sd::DataType::DOUBLE); + NDArray axis('c', {}, std::vector{0}, sd::DataType::INT32); + NDArray z('c', {5}, sd::DataType::DOUBLE); + NDArray exp('c', {5}, {5,4,3,2,1}, sd::DataType::DOUBLE); - nd4j::ops::reverse op; + sd::ops::reverse op; // auto result = op.execute({&x, &axis}, {}, {1}, {}); Nd4jStatus status = op.execute({&x, &axis}, {&z}, {}, {1}, {}); - // auto z = result->at(0); + // auto z = result.at(0); // z->printIndexedBuffer(); ASSERT_EQ(Status::OK(), status); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - // delete result; + // } ///////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests12, mirrorPad_test17) { - NDArray x('c', {2,3}, {1,2,3,4,5,6}, nd4j::DataType::DOUBLE); - NDArray padding('c', {2,2}, {1,1,2,2}, nd4j::DataType::INT64); - NDArray z('c', {4,7}, nd4j::DataType::DOUBLE); - NDArray exp1('c', {4,7}, {6, 5, 4, 5, 6, 5, 4,3, 2, 1, 2, 3, 2, 1,6, 5, 4, 5, 6, 5, 4,3, 2, 1, 2, 3, 2, 1}, nd4j::DataType::DOUBLE); - NDArray exp2('c', {4,7}, {2, 1, 1, 2, 3, 3, 2,2, 1, 1, 2, 3, 3, 2,5, 4, 4, 5, 6, 6, 5,5, 4, 4, 5, 6, 6, 5}, nd4j::DataType::DOUBLE); + NDArray x('c', {2,3}, {1,2,3,4,5,6}, sd::DataType::DOUBLE); + NDArray padding('c', {2,2}, {1,1,2,2}, sd::DataType::INT64); + NDArray z('c', {4,7}, sd::DataType::DOUBLE); + NDArray exp1('c', {4,7}, {6, 5, 4, 5, 6, 5, 4,3, 2, 1, 2, 3, 2, 1,6, 5, 4, 5, 6, 5, 4,3, 2, 1, 2, 3, 2, 1}, sd::DataType::DOUBLE); + NDArray exp2('c', {4,7}, {2, 1, 1, 2, 3, 3, 2,2, 1, 1, 2, 3, 3, 2,5, 4, 4, 5, 6, 6, 5,5, 4, 4, 5, 6, 6, 5}, sd::DataType::DOUBLE); - nd4j::ops::mirror_pad op; + sd::ops::mirror_pad op; Nd4jStatus status = op.execute({&x, &padding}, {&z}, {}, {0}, {}); // reflect ASSERT_EQ(Status::OK(), status); @@ -620,12 +621,12 @@ TEST_F(DeclarableOpsTests12, mirrorPad_test17) { ///////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests12, mirrorPad_test18) { - NDArray x('c', {3}, {1,2,3}, nd4j::DataType::DOUBLE); - NDArray padding('c', {1, 2}, {1,1}, nd4j::DataType::INT32); - NDArray z('c', {5}, nd4j::DataType::DOUBLE); - NDArray exp('c', {5}, {2,1,2,3,2}, nd4j::DataType::DOUBLE); + NDArray x('c', {3}, {1,2,3}, sd::DataType::DOUBLE); + NDArray padding('c', {1, 2}, {1,1}, sd::DataType::INT32); + NDArray z('c', {5}, sd::DataType::DOUBLE); + NDArray exp('c', {5}, {2,1,2,3,2}, sd::DataType::DOUBLE); - nd4j::ops::mirror_pad op; + sd::ops::mirror_pad op; Nd4jStatus status = op.execute({&x, &padding}, {&z}, {}, {0}, {}); // reflect ASSERT_EQ(Status::OK(), status); @@ -648,7 +649,7 @@ TEST_F(DeclarableOpsTests12, relu_1) { 0.133276, 0.326284, 0.102804, -0.133276, -0.326284, -0.102804,0.426913, 0.256251, 0.305241, -0.426913, -0.256251, -0.305241, 0.177977, 0.841799, 0.800615, -0.177977, -0.841799, -0.800615,0.001991, 0.518389, 0.439322, -0.001991, -0.518389, -0.439322, 0.166846, 0.508224, 0.486687, -0.166846, -0.508224, -0.486687,0.167493, 0.930932, 0.868717, -0.167493, -0.930932, -0.868717, - 0.174864, 0.444607, 0.445000, -0.174864, -0.444607, -0.445000}, nd4j::DataType::FLOAT32); + 0.174864, 0.444607, 0.445000, -0.174864, -0.444607, -0.445000}, sd::DataType::FLOAT32); NDArray expected('c', {1,5,5,6}, { 0.557449, 0.768277, 1.094015, 0., 0., 0., 0.563735, 0.900299, 0.789979, 0., 0., 0., 0.142528, 0.959611, 0.877506, 0., 0., 0., 0.448742, 0.995377, 1.171543, 0., 0., 0., @@ -662,11 +663,11 @@ TEST_F(DeclarableOpsTests12, relu_1) { 0.133276, 0.326284, 0.102804, 0., 0., 0., 0.426913, 0.256251, 0.305241, 0., 0., 0., 0.177977, 0.841799, 0.800615, 0., 0., 0., 0.001991, 0.518389, 0.439322, 0., 0., 0., 0.166846, 0.508224, 0.486687, 0., 0., 0., 0.167493, 0.930932, 0.868717, 0., 0., 0., - 0.174864, 0.444607, 0.445000, 0., 0., 0.}, nd4j::DataType::FLOAT32); + 0.174864, 0.444607, 0.445000, 0., 0., 0.}, sd::DataType::FLOAT32); - NDArray z('c', {1,5,5,6}, nd4j::DataType::FLOAT32); + NDArray z('c', {1,5,5,6}, sd::DataType::FLOAT32); - nd4j::ops::relu op; + sd::ops::relu op; Nd4jStatus status = op.execute({&input}, {&z}, {0}, {}, {}); ASSERT_EQ(ND4J_STATUS_OK, status); @@ -678,98 +679,98 @@ TEST_F(DeclarableOpsTests12, relu_1) { //////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests12, multiUnique_1) { - NDArray input1('c', {3,5}, {1,2,3,4,5,6,7,8,9,10,11,12,13,14,15}, nd4j::DataType::INT32); - NDArray input2('c', {3,4}, {1,2,3,4,5,6,7,8,9,10,11,12}, nd4j::DataType::INT32); - NDArray input3('c', {2,3}, {10,11,12,13,14,15}, nd4j::DataType::INT32); - NDArray input4('c', {1,5}, {7,8,9,10,11}, nd4j::DataType::INT32); - NDArray input5('c', {5,3}, {1,2,3,4,5,6,7,8,9,10,11,12,13,14,15}, nd4j::DataType::INT32); + NDArray input1('c', {3,5}, {1,2,3,4,5,6,7,8,9,10,11,12,13,14,15}, sd::DataType::INT32); + NDArray input2('c', {3,4}, {1,2,3,4,5,6,7,8,9,10,11,12}, sd::DataType::INT32); + NDArray input3('c', {2,3}, {10,11,12,13,14,15}, sd::DataType::INT32); + NDArray input4('c', {1,5}, {7,8,9,10,11}, sd::DataType::INT32); + NDArray input5('c', {5,3}, {1,2,3,4,5,6,7,8,9,10,11,12,13,14,15}, sd::DataType::INT32); - //NDArray indices('c', {1}, {2}, nd4j::DataType::INT32); - //NDArray expected('c', {1,5}, {11, 12, 13, 14, 15.}, nd4j::DataType::FLOAT32); + //NDArray indices('c', {1}, {2}, sd::DataType::INT32); + //NDArray expected('c', {1,5}, {11, 12, 13, 14, 15.}, sd::DataType::FLOAT32); std::vector arrayList({&input1, &input2, &input3, &input4, &input5}); - ASSERT_FALSE(nd4j::ops::helpers::multiUnique(arrayList)); + ASSERT_FALSE(sd::ops::helpers::multiUnique(arrayList)); } //////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests12, multiUnique_2) { - NDArray input1('c', {3,5}, {1,2,3,4,5,6,7,8,9,10,11,12,13,14,15}, nd4j::DataType::INT32); - NDArray input2('c', {3,4}, {21,22,23,24,25,26,27,28,29,210,211,212}, nd4j::DataType::INT32); - NDArray input3('c', {2,3}, {310,311,312,313,314,315}, nd4j::DataType::INT32); - NDArray input4('c', {1,5}, {47,48,49,410,411}, nd4j::DataType::INT32); - NDArray input5('c', {5,3}, {51,52,53,54,55,56,57,58,59,510,511,512,513,514,515}, nd4j::DataType::INT32); + NDArray input1('c', {3,5}, {1,2,3,4,5,6,7,8,9,10,11,12,13,14,15}, sd::DataType::INT32); + NDArray input2('c', {3,4}, {21,22,23,24,25,26,27,28,29,210,211,212}, sd::DataType::INT32); + NDArray input3('c', {2,3}, {310,311,312,313,314,315}, sd::DataType::INT32); + NDArray input4('c', {1,5}, {47,48,49,410,411}, sd::DataType::INT32); + NDArray input5('c', {5,3}, {51,52,53,54,55,56,57,58,59,510,511,512,513,514,515}, sd::DataType::INT32); - //NDArray indices('c', {1}, {2}, nd4j::DataType::INT32); - //NDArray expected('c', {1,5}, {11, 12, 13, 14, 15.}, nd4j::DataType::FLOAT32); + //NDArray indices('c', {1}, {2}, sd::DataType::INT32); + //NDArray expected('c', {1,5}, {11, 12, 13, 14, 15.}, sd::DataType::FLOAT32); std::vector arrayList({&input1, &input2, &input3, &input4, &input5}); - ASSERT_TRUE(nd4j::ops::helpers::multiUnique(arrayList)); + ASSERT_TRUE(sd::ops::helpers::multiUnique(arrayList)); } //////////////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests12, reduceMeanBp_4) { NDArray x('c', {3,5}, {1,2,3,4,5,6,7,8,9,10,11,12,13,14,15}); - NDArray gradO('c', {5}, nd4j::DataType::DOUBLE); - NDArray exp('c', {3,5}, nd4j::DataType::DOUBLE); + NDArray gradO('c', {5}, sd::DataType::DOUBLE); + NDArray exp('c', {3,5}, sd::DataType::DOUBLE); gradO = 1.; exp = 0.333333; - nd4j::ops::reduce_mean_bp op; + sd::ops::reduce_mean_bp op; auto result = op.evaluate({&x, &gradO}, {}, {0}); - auto output = result->at(0); + auto output = result.at(0); // output->printShapeInfo(); // output->printIndexedBuffer(); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests12, reduceMeanBp_5) { NDArray x('c', {3,5}, {1,2,3,4,5,6,7,8,9,10,11,12,13,14,15}); - NDArray gradO('c', {3}, nd4j::DataType::DOUBLE); - NDArray exp('c', {3,5}, nd4j::DataType::DOUBLE); + NDArray gradO('c', {3}, sd::DataType::DOUBLE); + NDArray exp('c', {3,5}, sd::DataType::DOUBLE); gradO = 1.; exp = 0.2; - nd4j::ops::reduce_mean_bp op; + sd::ops::reduce_mean_bp op; auto result = op.evaluate({&x, &gradO}, {}, {1}); - auto output = result->at(0); + auto output = result.at(0); // output->printShapeInfo(); // output->printIndexedBuffer(); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests12, reduceSqnormBp_1) { - NDArray x('c', {8,6,4}, nd4j::DataType::DOUBLE); - NDArray gradO('c', {8,6,1}, nd4j::DataType::DOUBLE); + NDArray x('c', {8,6,4}, sd::DataType::DOUBLE); + NDArray gradO('c', {8,6,1}, sd::DataType::DOUBLE); - nd4j::ops::reduce_sqnorm_bp op; + sd::ops::reduce_sqnorm_bp op; auto result = op.evaluate({&x, &gradO}, {1}, {2}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - delete result; + } //////////////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests12, pullRows_1) { NDArray x('c', {5, 1}, {0,1,2,3,4}); - NDArray z('c', {4, 1}, nd4j::DataType::DOUBLE); + NDArray z('c', {4, 1}, sd::DataType::DOUBLE); NDArray exp('c', {4, 1}, {0,2,3,4}); Nd4jLong indexes[] = {0,2,3,4}; @@ -778,8 +779,8 @@ TEST_F(DeclarableOpsTests12, pullRows_1) { std::vector dims = {1}; - auto xTadPack = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(x.getShapeInfo(), dims); - auto zTadPack = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(z.getShapeInfo(), dims); + auto xTadPack = sd::ConstantTadHelper::getInstance()->tadForDimensions(x.getShapeInfo(), dims); + auto zTadPack = sd::ConstantTadHelper::getInstance()->tadForDimensions(z.getShapeInfo(), dims); Nd4jPointer nativeStart[2]; @@ -805,7 +806,7 @@ TEST_F(DeclarableOpsTests12, pullRows_2) { NDArray* y = new NDArray(arr.dup('c')); NDArray x = (*y)({0,0, 0,1}, true); // view, points on first column of y, shape is {5,1} - NDArray z('c', {4, 1}, nd4j::DataType::DOUBLE); + NDArray z('c', {4, 1}, sd::DataType::DOUBLE); NDArray exp('c', {4, 1}, {0,2,3,4}); Nd4jLong indexes[] = {0,2,3,4}; @@ -814,8 +815,8 @@ TEST_F(DeclarableOpsTests12, pullRows_2) { std::vector dims = {1}; - auto xTadPack = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(x.getShapeInfo(), dims); - auto zTadPack = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(z.getShapeInfo(), dims); + auto xTadPack = sd::ConstantTadHelper::getInstance()->tadForDimensions(x.getShapeInfo(), dims); + auto zTadPack = sd::ConstantTadHelper::getInstance()->tadForDimensions(z.getShapeInfo(), dims); Nd4jPointer nativeStart[2]; #ifdef __CUDABLAS__ @@ -836,15 +837,15 @@ TEST_F(DeclarableOpsTests12, pullRows_2) { ////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests12, softmax_9) { - NDArray arrC('c', {5,2}, {-0.1, 0.2, -0.3, 0.4, -0.5, 0.6, -0.7, 0.8, -0.9, 1}, nd4j::DataType::FLOAT32); + NDArray arrC('c', {5,2}, {-0.1, 0.2, -0.3, 0.4, -0.5, 0.6, -0.7, 0.8, -0.9, 1}, sd::DataType::FLOAT32); NDArray* arrF = new NDArray(arrC.dup('f')); - NDArray outCC('c', {5,2}, nd4j::DataType::FLOAT32); - NDArray outCF('f', {5,2}, nd4j::DataType::FLOAT32); - NDArray outFC('c', {5,2}, nd4j::DataType::FLOAT32); - NDArray outFF('c', {5,2}, nd4j::DataType::FLOAT32); + NDArray outCC('c', {5,2}, sd::DataType::FLOAT32); + NDArray outCF('f', {5,2}, sd::DataType::FLOAT32); + NDArray outFC('c', {5,2}, sd::DataType::FLOAT32); + NDArray outFF('c', {5,2}, sd::DataType::FLOAT32); - nd4j::ops::softmax op; + sd::ops::softmax op; auto status1 = op.execute({&arrC}, {&outCC}, {}, {}, {}); ASSERT_EQ(ND4J_STATUS_OK, status1); auto status2 = op.execute({&arrC}, {&outCF}, {}, {}, {}); @@ -871,7 +872,7 @@ TEST_F(DeclarableOpsTests12, maxpool_bp_half_1) { auto y = NDArrayFactory::create('c', {2, 3, 10, 1}, {0.0f, -0.13391113f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, -0.1751709f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.51904297f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.5107422f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f}); auto z = NDArrayFactory::create('c', {2, 3, 10, 1}); - nd4j::ops::maxpool2d_bp op; + sd::ops::maxpool2d_bp op; Context ctx(1); Nd4jLong iArgs[] = {5,1,1, 2,2,0, 1,1,1, 0,0}; ctx.setIArguments(iArgs, 11); @@ -911,14 +912,14 @@ TEST_F(DeclarableOpsTests12, lrn_bp_1) { input.linspace(1); gradO = 1; - nd4j::ops::lrn_bp op; + sd::ops::lrn_bp op; auto results = op.evaluate({&input, &gradO}, {1., 1., 1}, {5}); - auto gradI = results->at(0); + auto gradI = results.at(0); ASSERT_EQ(*gradI, exp); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -942,14 +943,14 @@ TEST_F(DeclarableOpsTests12, lrn_bp_2) { input.linspace(-10, 0.1); gradO = 1; - nd4j::ops::lrn_bp op; + sd::ops::lrn_bp op; auto results = op.evaluate({&input, &gradO}, {1., 1., 1}, {2}); - auto gradI = results->at(0); + auto gradI = results.at(0); ASSERT_EQ(*gradI, exp); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -973,14 +974,14 @@ TEST_F(DeclarableOpsTests12, lrn_bp_3) { input.linspace(-10, 0.1); gradO = 1; - nd4j::ops::lrn_bp op; + sd::ops::lrn_bp op; auto results = op.evaluate({&input, &gradO}, {1., 1., 1}, {7}); - auto gradI = results->at(0); + auto gradI = results.at(0); ASSERT_EQ(*gradI, exp); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -1004,14 +1005,14 @@ TEST_F(DeclarableOpsTests12, lrn_bp_4) { input.linspace(-10, 0.1); gradO = 1; - nd4j::ops::lrn_bp op; + sd::ops::lrn_bp op; auto results = op.evaluate({&input, &gradO}, {1., 1., 1}, {12}); - auto gradI = results->at(0); + auto gradI = results.at(0); ASSERT_EQ(*gradI, exp); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -1027,14 +1028,14 @@ TEST_F(DeclarableOpsTests12, lrn_bp_5) { // gradO.linspace(0.1, 0.1); gradO = 1; - nd4j::ops::lrn_bp op; + sd::ops::lrn_bp op; auto results = op.evaluate({&input, &gradO}, {1., 1., 0.5}, {2}); - auto gradI = results->at(0); + auto gradI = results.at(0); ASSERT_EQ(*gradI, exp); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -1046,14 +1047,14 @@ TEST_F(DeclarableOpsTests12, lrn_bp_6) { // gradO.linspace(-1.5, 0.1); gradO = 1; - nd4j::ops::lrn_bp op; + sd::ops::lrn_bp op; auto results = op.evaluate({&input, &gradO}, {1., 2., 0.5}, {10}); - auto gradI = results->at(0); + auto gradI = results.at(0); ASSERT_EQ(*gradI, exp); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -1068,8 +1069,8 @@ TEST_F(DeclarableOpsTests12, lrn_bp_7) { const OpArgsHolder argsHolderFF({&input}, {1,2,0.5}, {2}); const OpArgsHolder argsHolderBP({&input, &gradO}, {1,2,0.5}, {2}); - nd4j::ops::lrn opFF; - nd4j::ops::lrn_bp opBP; + sd::ops::lrn opFF; + sd::ops::lrn_bp opBP; const bool isGradCorrect = GradCheck::checkGrad(opFF, opBP, argsHolderFF, argsHolderBP); @@ -1085,8 +1086,8 @@ TEST_F(DeclarableOpsTests12, lrn_bp_8) { const OpArgsHolder argsHolderFF({&input}, {1,2,0.5}, {2}); const OpArgsHolder argsHolderBP({&input, &gradO}, {1,2,0.5}, {2}); - nd4j::ops::lrn opFF; - nd4j::ops::lrn_bp opBP; + sd::ops::lrn opFF; + sd::ops::lrn_bp opBP; const bool isGradCorrect = GradCheck::checkGrad(opFF, opBP, argsHolderFF, argsHolderBP); @@ -1100,17 +1101,17 @@ TEST_F(DeclarableOpsTests12, lrn_bp_9) { NDArray gradO('c', {1,1,1,5}, {1, 1, 1, 1, 1}); NDArray exp('c', {1,1,1,5}, {0.1084472 , 0.03816165, 0.00978456, -0.01859251,-0.02511311}); - nd4j::ops::lrn_bp op; + sd::ops::lrn_bp op; auto results = op.evaluate({&input, &gradO}, {1., 2., 0.5}, {3}); - auto gradI = results->at(0); + auto gradI = results.at(0); // for (int i = 0; i < exp.lengthOf(); ++i) // printf("%10.5f %10.5f\n", exp.e(i), gradI->e(i)); ASSERT_EQ(*gradI, exp); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -1120,14 +1121,14 @@ TEST_F(DeclarableOpsTests12, lrn_bp_10) { NDArray gradO('c', {1,1,1,1}, std::vector{1}); NDArray exp('c', {1,1,1,1}, std::vector{0.19245008}); - nd4j::ops::lrn_bp op; + sd::ops::lrn_bp op; auto results = op.evaluate({&input, &gradO}, {1., 2., 0.5}, {1}); - auto gradI = results->at(0); + auto gradI = results.at(0); ASSERT_EQ(*gradI, exp); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -1141,14 +1142,14 @@ TEST_F(DeclarableOpsTests12, lrn_1) { input.linspace(-20, 1); - nd4j::ops::lrn op; + sd::ops::lrn op; auto results = op.evaluate({&input}, {1., 2., 0.5}, {2}); - auto output = results->at(0); + auto output = results.at(0); ASSERT_EQ(*output, exp); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -1157,13 +1158,13 @@ TEST_F(DeclarableOpsTests12, lrn_2) { NDArray input('c', {1,1,1,5}, {1, 2., 3, 4, 5}); NDArray exp('c', {1,1,1,5}, {0.09530295, 0.1906059 , 0.28590885, 0.3812118 , 0.47651473}); - nd4j::ops::lrn op; + sd::ops::lrn op; auto results = op.evaluate({&input}, {0.1, 2., 0.5}, {5}); - auto output = results->at(0); + auto output = results.at(0); ASSERT_EQ(*output, exp); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -1172,13 +1173,13 @@ TEST_F(DeclarableOpsTests12, lrn_3) { NDArray input('c', {1,1,1,1}, std::vector{1.}); NDArray exp('c', {1,1,1,1}, std::vector{0.69006556}); - nd4j::ops::lrn op; + sd::ops::lrn op; auto results = op.evaluate({&input}, {0.1, 2., 0.5}, {5}); - auto output = results->at(0); + auto output = results.at(0); ASSERT_EQ(*output, exp); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -1187,13 +1188,13 @@ TEST_F(DeclarableOpsTests12, lrn_4) { NDArray input('c', {1,1,1,1}, std::vector{1.}); NDArray exp('c', {1,1,1,1}, std::vector{0.69006556}); - nd4j::ops::lrn op; + sd::ops::lrn op; auto results = op.evaluate({&input}, {0.1, 2., 0.5}, {0}); - auto output = results->at(0); + auto output = results.at(0); ASSERT_EQ(*output, exp); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -1202,25 +1203,25 @@ TEST_F(DeclarableOpsTests12, lrn_5) { NDArray input('c', {1,1,1,5}, {1, 2., 3, 4, 5}); NDArray exp('c', {1,1,1,5}, {0.69006556, 0.70272833, 0.7051508 , 0.7060045 , 0.7064008}); - nd4j::ops::lrn op; + sd::ops::lrn op; auto results = op.evaluate({&input}, {0.1, 2., 0.5}, {0}); - auto output = results->at(0); + auto output = results.at(0); ASSERT_EQ(*output, exp); - delete results; + } ////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests12, inTopK_1) { NDArray x('c', {4, 5}, {11.0, 14.0, 6.0, 9.0, 3.5, 7.0, 21.0, 3.0, 15.0, 6.0, 9.0, 3.5, 7.0, 11.0, 13.0, 5.0, 16.0, 9.0, 13.5, 7.0}); - NDArray y('c', {4}, {0., 0, 0, 0}, nd4j::DataType::INT64); - NDArray z('c', {4}, {1., 1, 1, 1}, nd4j::DataType::BOOL); + NDArray y('c', {4}, {0., 0, 0, 0}, sd::DataType::INT64); + NDArray z('c', {4}, {1., 1, 1, 1}, sd::DataType::BOOL); - NDArray expV('c', {4}, {1., 0, 0, 0}, nd4j::DataType::BOOL); + NDArray expV('c', {4}, {1., 0, 0, 0}, sd::DataType::BOOL); - nd4j::ops::in_top_k op; + sd::ops::in_top_k op; Nd4jStatus status = op.execute({&x, &y, }, {&z}, {}, {2}, {}); // z.printIndexedBuffer(); @@ -1242,14 +1243,14 @@ TEST_F(DeclarableOpsTests12, inTopK_2) { input.linspace(1); idx.linspace(1); - nd4j::ops::in_top_k op; + sd::ops::in_top_k op; auto res = op.evaluate({&input, &idx}, {}, {1}); - ASSERT_EQ(res->status(), ND4J_STATUS_OK); - //res->at(0)->printIndexedBuffer("IN_TOP_K output"); - ASSERT_TRUE(res->at(0)->equalsTo(&exp)); - delete res; + ASSERT_EQ(res.status(), ND4J_STATUS_OK); + //res.at(0)->printIndexedBuffer("IN_TOP_K output"); + ASSERT_TRUE(res.at(0)->equalsTo(&exp)); + } ////////////////////////////////////////////////////////////////////// @@ -1258,18 +1259,18 @@ TEST_F(DeclarableOpsTests12, inTopK_3) { auto y = NDArrayFactory::create('c', {2}, {1, 1}); auto expV = NDArrayFactory::create('c', {2}, {true, false}); - nd4j::ops::in_top_k op; + sd::ops::in_top_k op; auto result = op.evaluate({&x, &y}, {}, {2}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); - ASSERT_EQ(1, result->size()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); + ASSERT_EQ(1, result.size()); - auto v = result->at(0); + auto v = result.at(0); ASSERT_TRUE(expV.isSameShape(v)); ASSERT_TRUE(expV.equalsTo(v)); - delete result; + } ////////////////////////////////////////////////////////////////////// @@ -1278,18 +1279,18 @@ TEST_F(DeclarableOpsTests12, inTopK_4) { auto y = NDArrayFactory::create('c', {6}, {0, 0, 0, 0, 0, 0}); auto expV = NDArrayFactory::create('c', {6}, {true, false, true, false, false, true}); - nd4j::ops::in_top_k op; + sd::ops::in_top_k op; auto result = op.evaluate({&x, &y}, {}, {2}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); - ASSERT_EQ(1, result->size()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); + ASSERT_EQ(1, result.size()); - auto v = result->at(0); + auto v = result.at(0); ASSERT_TRUE(expV.isSameShape(v)); ASSERT_TRUE(expV.equalsTo(v)); - delete result; + } @@ -1299,18 +1300,18 @@ TEST_F(DeclarableOpsTests12, inTopK_5) { auto y = NDArrayFactory::create('f', {6}, {0, 0, 0, 0, 0, 0}); auto expV = NDArrayFactory::create('f', {6}, {true, false, false, false, false, false }); - nd4j::ops::in_top_k op; + sd::ops::in_top_k op; auto result = op.evaluate({&x, &y}, {}, {2}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); - ASSERT_EQ(1, result->size()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); + ASSERT_EQ(1, result.size()); - auto v = result->at(0); + auto v = result.at(0); ASSERT_TRUE(expV.isSameShape(v)); ASSERT_TRUE(expV.equalsTo(v)); - delete result; + } //////////////////////////////////////////////////////////////////// @@ -1319,42 +1320,42 @@ TEST_F(DeclarableOpsTests12, cube_1) { NDArray x('c', {2, 3}, {1., 2., 3., 4., 5, 6}); NDArray exp('c', {2, 3}, {1., 8., 27., 64., 125, 216}); - nd4j::ops::cube op; + sd::ops::cube op; auto result = op.evaluate({&x}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } //////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests12, cube_bp_1) { NDArray x('c', {2, 3}, {1., 2., 3., 4., 5, 6}); - NDArray gradO('c', {2, 3}, nd4j::DataType::DOUBLE); + NDArray gradO('c', {2, 3}, sd::DataType::DOUBLE); NDArray exp('c', {2, 3}, {1.5, 6., 13.5, 24., 37.5, 54}); gradO = 0.5; - nd4j::ops::cube_bp op; + sd::ops::cube_bp op; auto result = op.evaluate({&x, &gradO}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); // z->printIndexedBuffer(); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } //////////////////////////////////////////////////////////////////// @@ -1362,22 +1363,22 @@ TEST_F(DeclarableOpsTests12, cube_bp_1) { TEST_F(DeclarableOpsTests12, pad_tests1) { - NDArray input('c', {2,3}, {1,2,3,4,5,6}, nd4j::DataType::FLOAT32); - NDArray paddings('c', {2,2}, {1,1,2,2}, nd4j::DataType::INT32); - NDArray expected('c', {4,7}, {0,0,0,0,0,0,0, 0,0,1,2,3,0,0, 0,0,4,5,6,0,0, 0,0,0,0,0,0,0}, nd4j::DataType::FLOAT32); + NDArray input('c', {2,3}, {1,2,3,4,5,6}, sd::DataType::FLOAT32); + NDArray paddings('c', {2,2}, {1,1,2,2}, sd::DataType::INT32); + NDArray expected('c', {4,7}, {0,0,0,0,0,0,0, 0,0,1,2,3,0,0, 0,0,4,5,6,0,0, 0,0,0,0,0,0,0}, sd::DataType::FLOAT32); - nd4j::ops::pad op; + sd::ops::pad op; auto results = op.evaluate({&input, &paddings}, {}, {0}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto result = results->at(0); + auto result = results.at(0); // result->printIndexedBuffer(); ASSERT_TRUE(expected.isSameShapeStrict(*result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; + } @@ -1393,18 +1394,18 @@ TEST_F(DeclarableOpsTests12, pad_tests2) { auto paddings = NDArrayFactory::create(padBuff, 'c', {2,2}); auto expected = NDArrayFactory::create(expBuff, 'c', {4,7}); - nd4j::ops::pad op; + sd::ops::pad op; auto results = op.evaluate({&input, &paddings}, {}, {1}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto result = results->at(0); + auto result = results.at(0); // result->printIndexedBuffer(); ASSERT_TRUE(expected.isSameShapeStrict(*result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; + } @@ -1420,18 +1421,18 @@ TEST_F(DeclarableOpsTests12, pad_tests3) { auto paddings = NDArrayFactory::create(padBuff, 'c', {2,2}); auto expected = NDArrayFactory::create(expBuff, 'c', {4,7}); - nd4j::ops::pad op; + sd::ops::pad op; auto results = op.evaluate({&input, &paddings}, {}, {2}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto result = results->at(0); + auto result = results.at(0); // result->printIndexedBuffer(); ASSERT_TRUE(expected.isSameShapeStrict(*result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; + } @@ -1451,12 +1452,12 @@ TEST_F(DeclarableOpsTests12, pad_tests4) { auto paddings = NDArrayFactory::create(padBuff, 'c', {3,2}); auto expected = NDArrayFactory::create(expBuff, 'c', {4,7,7}); - nd4j::ops::pad op; + sd::ops::pad op; auto results = op.evaluate({&input, &paddings}, {}, {0}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto result = results->at(0); + auto result = results.at(0); // result->printIndexedBuffer(); ASSERT_TRUE(expected.isSameShapeStrict(*result)); @@ -1469,7 +1470,7 @@ TEST_F(DeclarableOpsTests12, pad_tests4) { // printf("%i : %f, %f\n", i, one, two); // } - delete results; + } @@ -1485,18 +1486,18 @@ TEST_F(DeclarableOpsTests12, pad_tests5) { auto paddings = NDArrayFactory::create(padBuff, 'c', {3,2}); auto expected = NDArrayFactory::create(expBuff, 'c', {4,7,7}); - nd4j::ops::pad op; + sd::ops::pad op; auto results = op.evaluate({&input, &paddings}, {}, {1}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto result = results->at(0); + auto result = results.at(0); // result->printIndexedBuffer(); ASSERT_TRUE(expected.isSameShapeStrict(*result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; + } @@ -1512,18 +1513,18 @@ TEST_F(DeclarableOpsTests12, pad_tests6) { auto paddings = NDArrayFactory::create(padBuff, 'c', {3,2}); auto expected = NDArrayFactory::create(expBuff, 'c', {4,7,7}); - nd4j::ops::pad op; + sd::ops::pad op; auto results = op.evaluate({&input, &paddings}, {}, {2}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto result = results->at(0); + auto result = results.at(0); // result->printIndexedBuffer(); ASSERT_TRUE(expected.isSameShapeStrict(*result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; + } //////////////////////////////////////////////////////////////////// @@ -1538,18 +1539,18 @@ TEST_F(DeclarableOpsTests12, pad_tests7) auto paddings = NDArrayFactory::create(padBuff, 'c', {4, 2}); auto expected = NDArrayFactory::create(expBuff, 'c', {4, 4, 4, 4}); - nd4j::ops::pad op; + sd::ops::pad op; auto results = op.evaluate({&input, &paddings}, {}, {0}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); // result->printIndexedBuffer(); ASSERT_TRUE(expected.isSameShapeStrict(*result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; + } //////////////////////////////////////////////////////////////////// @@ -1564,18 +1565,18 @@ TEST_F(DeclarableOpsTests12, pad_tests8) auto paddings = NDArrayFactory::create(padBuff, 'c', {4, 2}); auto expected = NDArrayFactory::create(expBuff, 'c', {4, 4, 4, 4}); - nd4j::ops::pad op; + sd::ops::pad op; auto results = op.evaluate({&input, &paddings}, {}, {1}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); // result->printIndexedBuffer(); ASSERT_TRUE(expected.isSameShapeStrict(*result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; + } ////////////////////////////////////////////////////////////////// @@ -1590,18 +1591,18 @@ TEST_F(DeclarableOpsTests12, pad_tests9) auto paddings = NDArrayFactory::create(padBuff, 'c', {4, 2}); auto expected = NDArrayFactory::create(expBuff, 'c', {4, 4, 4, 4}); - nd4j::ops::pad op; + sd::ops::pad op; auto results = op.evaluate({&input, &paddings}, {}, {2}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); // result->printIndexedBuffer(); ASSERT_TRUE(expected.isSameShapeStrict(*result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; + } //////////////////////////////////////////////////////////////////// @@ -1613,17 +1614,17 @@ TEST_F(DeclarableOpsTests12, pad_tests10) { input = 1.f; //input.assign(1.); - nd4j::ops::pad op; + sd::ops::pad op; auto results = op.evaluate({&input, &paddings}, {}, {0}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto result = results->at(0); + auto result = results.at(0); ASSERT_TRUE(expected.isSameShapeStrict(*result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; + } //////////////////////////////////////////////////////////////////// @@ -1635,17 +1636,17 @@ TEST_F(DeclarableOpsTests12, pad_tests11) { input.linspace(1.f); - nd4j::ops::pad op; + sd::ops::pad op; auto results = op.evaluate({&input, &paddings}, {}, {1}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto result = results->at(0); + auto result = results.at(0); ASSERT_TRUE(expected.isSameShapeStrict(*result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; + } //////////////////////////////////////////////////////////////////// @@ -1663,18 +1664,18 @@ TEST_F(DeclarableOpsTests12, pad_tests12) { 101.,102.,103.,104.,105.,106.,107.,108.,109.,110.,111.,112.,113.,114.,115.,116.,117.,118.,119.,120.,116.,117.,118.,119.,120.}); input.linspace(1.f); - nd4j::ops::pad op; + sd::ops::pad op; auto results = op.evaluate({&input, &paddings}, {}, {2}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto result = results->at(0); + auto result = results.at(0); // result->printIndexedBuffer(); ASSERT_TRUE(expected.isSameShapeStrict(*result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; + } //////////////////////////////////////////////////////////////////// @@ -1685,18 +1686,18 @@ TEST_F(DeclarableOpsTests12, pad_tests13) { auto expected = NDArrayFactory::create('c', {10}, {3., 2., 1., 2., 3., 4., 5., 4., 3., 2.}); input.linspace(1.f); - nd4j::ops::pad op; + sd::ops::pad op; auto results = op.evaluate({&input, &paddings}, {}, {1}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto result = results->at(0); + auto result = results.at(0); // result->printIndexedBuffer(); ASSERT_TRUE(expected.isSameShapeStrict(*result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; + } //////////////////////////////////////////////////////////////////// @@ -1707,17 +1708,17 @@ TEST_F(DeclarableOpsTests12, pad_tests14) { auto expected = NDArrayFactory::create('c', {1,10}, {2., 1., 1., 2., 3., 4., 5., 5., 4., 3.}); input.linspace(1.f); - nd4j::ops::pad op; + sd::ops::pad op; auto results = op.evaluate({&input, &paddings}, {}, {2}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto result = results->at(0); + auto result = results.at(0); ASSERT_TRUE(expected.isSameShapeStrict(*result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; + } //////////////////////////////////////////////////////////////////// @@ -1728,17 +1729,17 @@ TEST_F(DeclarableOpsTests12, pad_tests15) { auto expected = NDArrayFactory::create('c', {3,5}, {1., 2., 3., 4., 5., 1., 2., 3., 4., 5., 1., 2., 3., 4., 5.}); input.linspace(1.f); - nd4j::ops::pad op; + sd::ops::pad op; auto results = op.evaluate({&input, &paddings}, {}, {2}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto result = results->at(0); + auto result = results.at(0); ASSERT_TRUE(expected.isSameShapeStrict(*result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; + } //////////////////////////////////////////////////////////////////// @@ -1749,17 +1750,17 @@ TEST_F(DeclarableOpsTests12, pad_tests16) { auto expected = NDArrayFactory::create('c', {10,1}, {3., 2., 1., 2., 3., 4., 5., 4., 3., 2.}); input.linspace(1.f); - nd4j::ops::pad op; + sd::ops::pad op; auto results = op.evaluate({&input, &paddings}, {}, {1}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto result = results->at(0); + auto result = results.at(0); ASSERT_TRUE(expected.isSameShapeStrict(*result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; + } //////////////////////////////////////////////////////////////////// @@ -1770,17 +1771,17 @@ TEST_F(DeclarableOpsTests12, pad_tests17) { auto expected = NDArrayFactory::create('c', {5,2}, {1.,1., 2.,2., 3.,3., 4.,4., 5.,5.}); input.linspace(1.f); - nd4j::ops::pad op; + sd::ops::pad op; auto results = op.evaluate({&input, &paddings}, {}, {2}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto result = results->at(0); + auto result = results.at(0); ASSERT_TRUE(expected.isSameShapeStrict(*result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; + } //////////////////////////////////////////////////////////////////// @@ -1791,17 +1792,17 @@ TEST_F(DeclarableOpsTests12, pad_tests18) { auto expected = NDArrayFactory::create('c', {5}, {1.,2.,3.,4.,5.}); input.linspace(1.f); - nd4j::ops::pad op; + sd::ops::pad op; auto results = op.evaluate({&input, &paddings}, {}, {1}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto result = results->at(0); + auto result = results.at(0); ASSERT_TRUE(expected.isSameShapeStrict(*result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; + } //////////////////////////////////////////////////////////////////// @@ -1812,17 +1813,17 @@ TEST_F(DeclarableOpsTests12, pad_tests19) { auto expected = NDArrayFactory::create('c', {5,1}, {1., 2., 3., 4., 5.}); input.linspace(1.f); - nd4j::ops::pad op; + sd::ops::pad op; auto results = op.evaluate({&input, &paddings}, {}, {1}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto result = results->at(0); + auto result = results.at(0); ASSERT_TRUE(expected.isSameShapeStrict(*result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; + } //////////////////////////////////////////////////////////////////// @@ -1833,17 +1834,17 @@ TEST_F(DeclarableOpsTests12, pad_tests20) { auto expected = NDArrayFactory::create('c', {1,5}, {1., 2., 3., 4., 5.}); input.linspace(1.f); - nd4j::ops::pad op; + sd::ops::pad op; auto results = op.evaluate({&input, &paddings}, {}, {1}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto result = results->at(0); + auto result = results.at(0); ASSERT_TRUE(expected.isSameShapeStrict(*result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; + } //////////////////////////////////////////////////////////////////// @@ -1855,18 +1856,18 @@ TEST_F(DeclarableOpsTests12, pad_tests21) { 11.,12.,13.,14.,15.,11.,12.,13.,14.,15.,11.,12.,13.,14.,15.,11.,12.,13.,14.,15.}); input.linspace(1.f); - nd4j::ops::pad op; + sd::ops::pad op; auto results = op.evaluate({&input, &paddings}, {}, {2}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto result = results->at(0); + auto result = results.at(0); // result->printIndexedBuffer(); ASSERT_TRUE(expected.isSameShapeStrict(*result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; + } //////////////////////////////////////////////////////////////////// @@ -1878,18 +1879,18 @@ TEST_F(DeclarableOpsTests12, pad_tests22) { input.linspace(1.f); - nd4j::ops::pad op; + sd::ops::pad op; auto results = op.evaluate({&input, &paddings}, {}, {0}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto result = results->at(0); + auto result = results.at(0); // result->printIndexedBuffer(); ASSERT_TRUE(expected.isSameShapeStrict(*result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; + } //////////////////////////////////////////////////////////////////// @@ -1901,19 +1902,19 @@ TEST_F(DeclarableOpsTests12, pad_tests23) { input.linspace(1.f); - nd4j::ops::pad op; + sd::ops::pad op; auto results = op.evaluate({&input, &paddings}, {}, {0}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto result = results->at(0); + auto result = results.at(0); // result->printShapeInfo("r"); // expected.printShapeInfo("e"); ASSERT_TRUE(expected.isSameShapeStrict(*result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; + } //////////////////////////////////////////////////////////////////// @@ -1925,17 +1926,17 @@ TEST_F(DeclarableOpsTests12, pad_tests24) { input.linspace(1.f); - nd4j::ops::pad op; + sd::ops::pad op; auto results = op.evaluate({&input, &paddings}, {}, {0}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto result = results->at(0); + auto result = results.at(0); ASSERT_TRUE(expected.isSameShapeStrict(*result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; + } //////////////////////////////////////////////////////////////////// @@ -1947,17 +1948,17 @@ TEST_F(DeclarableOpsTests12, pad_tests25) { input.linspace(1.f); - nd4j::ops::pad op; + sd::ops::pad op; auto results = op.evaluate({&input, &paddings}, {}, {2}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto result = results->at(0); + auto result = results.at(0); ASSERT_TRUE(expected.isSameShapeStrict(*result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; + } //////////////////////////////////////////////////////////////////// @@ -1969,29 +1970,29 @@ TEST_F(DeclarableOpsTests12, pad_tests26) { input.linspace(1.f); - nd4j::ops::pad op; + sd::ops::pad op; auto results = op.evaluate({&input, &paddings}, {}, {0}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto result = results->at(0); + auto result = results.at(0); ASSERT_TRUE(expected.isSameShapeStrict(*result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; + } //////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests12, pad_tests27) { - NDArray input('c', {2,3}, nd4j::DataType::FLOAT32); - NDArray paddings('c', {2,2}, {0,0,0,1}, nd4j::DataType::INT32); - NDArray exp('c', {2,4}, {1,1,1,0,1,1,1,0}, nd4j::DataType::FLOAT32); - NDArray z('c', {2,4}, nd4j::DataType::FLOAT32); + NDArray input('c', {2,3}, sd::DataType::FLOAT32); + NDArray paddings('c', {2,2}, {0,0,0,1}, sd::DataType::INT32); + NDArray exp('c', {2,4}, {1,1,1,0,1,1,1,0}, sd::DataType::FLOAT32); + NDArray z('c', {2,4}, sd::DataType::FLOAT32); input = 1.; - nd4j::ops::pad op; + sd::ops::pad op; Nd4jStatus status = op.execute({&input, &paddings}, {&z}, {0}, {0}, {}); // constant // z.printIndexedBuffer(); @@ -2003,16 +2004,16 @@ TEST_F(DeclarableOpsTests12, pad_tests27) { //////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests12, pad_tests28) { - NDArray input('c', {1,111,111,32}, nd4j::DataType::FLOAT32); - NDArray paddings('c', {4,2}, {0,0,0,1,0,1,0,0}, nd4j::DataType::INT32); - NDArray z('c', {1,112,112,32}, nd4j::DataType::FLOAT32); + NDArray input('c', {1,111,111,32}, sd::DataType::FLOAT32); + NDArray paddings('c', {4,2}, {0,0,0,1,0,1,0,0}, sd::DataType::INT32); + NDArray z('c', {1,112,112,32}, sd::DataType::FLOAT32); input = 1.; - nd4j::ops::pad op; + sd::ops::pad op; Nd4jStatus status = op.execute({&input, &paddings}, {&z}, {0}, {0}, {}); // constant // z.printIndexedBuffer(); - NDArray sum = z.reduceNumber(nd4j::reduce::Sum); + NDArray sum = z.reduceNumber(sd::reduce::Sum); ASSERT_EQ(ND4J_STATUS_OK, status); ASSERT_EQ(sum.e(0), 111*111*32); @@ -2028,12 +2029,12 @@ TEST_F(DeclarableOpsTests12, pad_tests29) { auto exp = NDArrayFactory::create({10., 1., 1., 1., 1., 1., 10.}); - nd4j::ops::pad op; + sd::ops::pad op; auto res = op.evaluate({&in, &pad}, {10.0}, {0}); - ASSERT_EQ(res->status(), ND4J_STATUS_OK); - ASSERT_TRUE(exp.equalsTo(res->at(0))); - delete res; + ASSERT_EQ(res.status(), ND4J_STATUS_OK); + ASSERT_TRUE(exp.equalsTo(res.at(0))); + } @@ -2045,12 +2046,12 @@ TEST_F(DeclarableOpsTests12, pad_tests30) { auto exp = NDArrayFactory::create({1., 1., 11., 111., 11., 1., 1.}); - nd4j::ops::pad op; + sd::ops::pad op; auto res = op.evaluate({&in, &pad}, {10.0}, {2}); - ASSERT_EQ(res->status(), ND4J_STATUS_OK); - ASSERT_TRUE(exp.equalsTo(res->at(0))); - delete res; + ASSERT_EQ(res.status(), ND4J_STATUS_OK); + ASSERT_TRUE(exp.equalsTo(res.at(0))); + } //////////////////////////////////////////////////////////////////////////////// @@ -2063,12 +2064,12 @@ TEST_F(DeclarableOpsTests12, pad_tests31) { auto exp = NDArrayFactory::create({11., 1., 11., 111., 1111., 11111., 1111.}); - nd4j::ops::pad op; + sd::ops::pad op; auto res = op.evaluate({&in, &pad}, {10.0}, {1}); - ASSERT_EQ(res->status(), ND4J_STATUS_OK); - ASSERT_TRUE(exp.equalsTo(res->at(0))); - delete res; + ASSERT_EQ(res.status(), ND4J_STATUS_OK); + ASSERT_TRUE(exp.equalsTo(res.at(0))); + } /////////////////////////////////////////////////////////////////////////////// @@ -2079,12 +2080,12 @@ TEST_F(DeclarableOpsTests12, pad_tests32) { auto exp = NDArrayFactory::create('c', {6,8}, {2, 1, 1, 2, 3, 3, 2, 1, 2, 1, 1, 2, 3, 3, 2, 1, 5, 4, 4, 5, 6, 6, 5, 4, 8, 7, 7, 8, 9, 9, 8, 7, 8, 7, 7, 8, 9, 9, 8, 7, 5, 4, 4, 5, 6, 6, 5, 4}); - nd4j::ops::pad op; + sd::ops::pad op; auto res = op.evaluate({&in, &pad}, {10.0}, {2}); - ASSERT_EQ(res->status(), ND4J_STATUS_OK); - ASSERT_TRUE(exp.equalsTo(res->at(0))); - delete res; + ASSERT_EQ(res.status(), ND4J_STATUS_OK); + ASSERT_TRUE(exp.equalsTo(res.at(0))); + } /////////////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests12, pad_tests33) { @@ -2102,23 +2103,23 @@ TEST_F(DeclarableOpsTests12, pad_tests33) { 19,18,17,17,18,19,20,20,19,18., 23,22,21,21,22,23,24,24,23,22., 23,22,21,21,22,23,24,24,23,22., 19,18,17,17,18,19,20,20,19,18., 15,14,13,13,14,15,16,16,15,14., 7,6,5,5,6,7,8,8,7,6., 3,2,1,1,2,3,4,4,3,2., 3,2,1,1,2,3,4,4,3,2., 7,6,5,5,6,7,8,8,7,6., 11,10,9,9,10,11,12,12,11,10., 11,10,9,9,10,11,12,12,11,10., 7,6,5,5,6,7,8,8,7,6., 3,2,1,1,2,3,4,4,3,2.}); - nd4j::ops::pad op; + sd::ops::pad op; auto res = op.evaluate({&in, &pad}, {10.0}, {2}); - ASSERT_EQ(res->status(), ND4J_STATUS_OK); - ASSERT_TRUE(exp.equalsTo(res->at(0))); - delete res; + ASSERT_EQ(res.status(), ND4J_STATUS_OK); + ASSERT_TRUE(exp.equalsTo(res.at(0))); + } //////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests12, pad_tests34) { - NDArray input('c', {5}, {0.778786, 0.801198, 0.724375, 0.230894, 0.727141}, nd4j::DataType::FLOAT32); - NDArray paddings('c', {1,2}, {1,1}, nd4j::DataType::INT32); - NDArray expected('c', {7}, {10., 0.778786, 0.801198, 0.724375, 0.230894, 0.727141, 10.}, nd4j::DataType::FLOAT32); - NDArray z('c', {7}, nd4j::DataType::FLOAT32); + NDArray input('c', {5}, {0.778786, 0.801198, 0.724375, 0.230894, 0.727141}, sd::DataType::FLOAT32); + NDArray paddings('c', {1,2}, {1,1}, sd::DataType::INT32); + NDArray expected('c', {7}, {10., 0.778786, 0.801198, 0.724375, 0.230894, 0.727141, 10.}, sd::DataType::FLOAT32); + NDArray z('c', {7}, sd::DataType::FLOAT32); - nd4j::ops::pad op; + sd::ops::pad op; Nd4jStatus status = op.execute({&input, &paddings}, {&z}, {10}, {0}, {}); // constant ASSERT_EQ(ND4J_STATUS_OK, status); @@ -2138,18 +2139,18 @@ TEST_F(DeclarableOpsTests12, Pad_1) { auto paddings = NDArrayFactory::create(padBuff, 'c', {2,2}); auto expected = NDArrayFactory::create(expBuff, 'c', {4,7}); - nd4j::ops::pad op; + sd::ops::pad op; auto results = op.evaluate({&input, &paddings}, {}, {0}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto result = results->at(0); + auto result = results.at(0); // result->printIndexedBuffer(); ASSERT_TRUE(expected.isSameShapeStrict(*result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; + } @@ -2165,18 +2166,18 @@ TEST_F(DeclarableOpsTests12, Pad_2) { auto paddings = NDArrayFactory::create(padBuff, 'c', {2,2}); auto expected = NDArrayFactory::create(expBuff, 'c', {4,7}); - nd4j::ops::pad op; + sd::ops::pad op; auto results = op.evaluate({&input, &paddings}, {}, {1}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto result = results->at(0); + auto result = results.at(0); // result->printIndexedBuffer(); ASSERT_TRUE(expected.isSameShapeStrict(*result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; + } @@ -2192,18 +2193,18 @@ TEST_F(DeclarableOpsTests12, Pad_3) { auto paddings = NDArrayFactory::create(padBuff, 'c', {2,2}); auto expected = NDArrayFactory::create(expBuff, 'c', {4,7}); - nd4j::ops::pad op; + sd::ops::pad op; auto results = op.evaluate({&input, &paddings}, {}, {2}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto result = results->at(0); + auto result = results.at(0); // result->printIndexedBuffer(); ASSERT_TRUE(expected.isSameShapeStrict(*result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; + } @@ -2219,18 +2220,18 @@ TEST_F(DeclarableOpsTests12, Pad_4) { auto paddings = NDArrayFactory::create(padBuff, 'c', {3,2}); auto expected = NDArrayFactory::create(expBuff, 'c', {4,7,7}); - nd4j::ops::pad op; + sd::ops::pad op; auto results = op.evaluate({&input, &paddings}, {}, {0}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto result = results->at(0); + auto result = results.at(0); // result->printIndexedBuffer(); ASSERT_TRUE(expected.isSameShapeStrict(*result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; + } @@ -2246,18 +2247,18 @@ TEST_F(DeclarableOpsTests12, Pad_5) { auto paddings = NDArrayFactory::create(padBuff, 'c', {3,2}); auto expected = NDArrayFactory::create(expBuff, 'c', {4,7,7}); - nd4j::ops::pad op; + sd::ops::pad op; auto results = op.evaluate({&input, &paddings}, {}, {1}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto result = results->at(0); + auto result = results.at(0); // result->printIndexedBuffer(); ASSERT_TRUE(expected.isSameShapeStrict(*result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; + } @@ -2273,18 +2274,18 @@ TEST_F(DeclarableOpsTests12, Pad_6) { auto paddings = NDArrayFactory::create(padBuff, 'c', {3,2}); auto expected = NDArrayFactory::create(expBuff, 'c', {4,7,7}); - nd4j::ops::pad op; + sd::ops::pad op; auto results = op.evaluate({&input, &paddings}, {}, {2}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto result = results->at(0); + auto result = results.at(0); // result->printIndexedBuffer(); ASSERT_TRUE(expected.isSameShapeStrict(*result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; + } //////////////////////////////////////////////////////////////////// @@ -2299,18 +2300,18 @@ TEST_F(DeclarableOpsTests12, Pad_7) auto paddings = NDArrayFactory::create(padBuff, 'c', {4, 2}); auto expected = NDArrayFactory::create(expBuff, 'c', {4, 4, 4, 4}); - nd4j::ops::pad op; + sd::ops::pad op; auto results = op.evaluate({&input, &paddings}, {}, {0}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); // result->printIndexedBuffer(); ASSERT_TRUE(expected.isSameShapeStrict(*result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; + } //////////////////////////////////////////////////////////////////// @@ -2325,18 +2326,18 @@ TEST_F(DeclarableOpsTests12, Pad_8) auto paddings = NDArrayFactory::create(padBuff, 'c', {4, 2}); auto expected = NDArrayFactory::create(expBuff, 'c', {4, 4, 4, 4}); - nd4j::ops::pad op; + sd::ops::pad op; auto results = op.evaluate({&input, &paddings}, {}, {1}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); // result->printIndexedBuffer(); ASSERT_TRUE(expected.isSameShapeStrict(*result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; + } ////////////////////////////////////////////////////////////////// @@ -2351,37 +2352,37 @@ TEST_F(DeclarableOpsTests12, Pad_9) auto paddings = NDArrayFactory::create(padBuff, 'c', {4, 2}); auto expected = NDArrayFactory::create(expBuff, 'c', {4, 4, 4, 4}); - nd4j::ops::pad op; + sd::ops::pad op; auto results = op.evaluate({&input, &paddings}, {}, {2}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); // result->printIndexedBuffer(); ASSERT_TRUE(expected.isSameShapeStrict(*result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; + } TEST_F(DeclarableOpsTests12, Test_Expose_1) { auto input0 = NDArrayFactory::create('c', {2, 3}, {1, 2, 3, 6, 5, 4}); auto input1 = NDArrayFactory::create('c', {2, 3}, {3, 2, 1, 4, 5, 6}); - nd4j::ops::expose op; + sd::ops::expose op; auto result = op.evaluate({&input0, &input1}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z0 = result->at(0); - auto z1 = result->at(1); + auto z0 = result.at(0); + auto z1 = result.at(1); ASSERT_TRUE(input0.equalsTo(z0)); ASSERT_TRUE(input1.equalsTo(z1)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -2394,14 +2395,14 @@ TEST_F(DeclarableOpsTests12, Pad_SGO_Test_1) { auto exp = NDArrayFactory::create({10., 1., 1., 1., 1., 1., 10.}); - nd4j::ops::pad op; + sd::ops::pad op; auto res = op.evaluate({&in, &pad}, {10.0}, {0}); - ASSERT_EQ(res->status(), ND4J_STATUS_OK); - // res->at(0)->printIndexedBuffer("PAD_SGO"); + ASSERT_EQ(res.status(), ND4J_STATUS_OK); + // res.at(0)->printIndexedBuffer("PAD_SGO"); // exp.printIndexedBuffer("PAD_EXP"); - ASSERT_TRUE(exp.equalsTo(res->at(0))); - delete res; + ASSERT_TRUE(exp.equalsTo(res.at(0))); + } //////////////////////////////////////////////////////////////////////////////// @@ -2410,19 +2411,19 @@ TEST_F(DeclarableOpsTests12, LU_Test_1) { auto in = NDArrayFactory::create('c', {3,3}, {1., 2., 3., 0., 2., 3., 0., 0., 7.}); auto exp = NDArrayFactory::create('c', {3,3}, {1., 2., 3., 0., 2., 3., 0., 0., 7}); auto pExp = NDArrayFactory::create('c', {3}, {0, 1, 2}); - nd4j::ops::lu op; + sd::ops::lu op; auto res = op.evaluate({&in}); - ASSERT_EQ(res->status(), ND4J_STATUS_OK); - auto z = res->at(0); - auto p = res->at(1); + ASSERT_EQ(res.status(), ND4J_STATUS_OK); + auto z = res.at(0); + auto p = res.at(1); // z->printIndexedBuffer("Triangulars"); // p->printIndexedBuffer("Permutaions"); ASSERT_TRUE(exp.equalsTo(z)); ASSERT_TRUE(pExp.equalsTo(p)); - delete res; + } //////////////////////////////////////////////////////////////////////////////// @@ -2431,17 +2432,17 @@ TEST_F(DeclarableOpsTests12, LU_Test_2) { auto expLU = NDArrayFactory::create('c', {3,3}, {4., 5., 6., 0.25, -1.25, -1.5, 0.5, -0.4, -3.6}); auto expP = NDArrayFactory::create({2, 0, 1}); - nd4j::ops::lu op; + sd::ops::lu op; auto res = op.evaluate({&in}); - ASSERT_EQ(res->status(), ND4J_STATUS_OK); - auto z = res->at(0); - auto p = res->at(1); + ASSERT_EQ(res.status(), ND4J_STATUS_OK); + auto z = res.at(0); + auto p = res.at(1); // z->printIndexedBuffer("Triangulars2"); // p->printIndexedBuffer("Permutaions2"); ASSERT_TRUE(expLU.equalsTo(z)); ASSERT_TRUE(expP.equalsTo(p)); - delete res; + } //////////////////////////////////////////////////////////////////////////////// @@ -2454,17 +2455,17 @@ TEST_F(DeclarableOpsTests12, LU_Test_3) { 0.09090909, 0.3448276, 0.34482753}); auto expP = NDArrayFactory::create({2, 1, 0}); - nd4j::ops::lu op; + sd::ops::lu op; auto res = op.evaluate({&in}); - ASSERT_EQ(res->status(), ND4J_STATUS_OK); - auto z = res->at(0); - auto p = res->at(1); + ASSERT_EQ(res.status(), ND4J_STATUS_OK); + auto z = res.at(0); + auto p = res.at(1); // z->printIndexedBuffer("Triangulars3"); // p->printIndexedBuffer("Permutaions3"); ASSERT_TRUE(expLU.equalsTo(z)); ASSERT_TRUE(expP.equalsTo(p)); - delete res; + } //////////////////////////////////////////////////////////////////////////////// @@ -2496,19 +2497,19 @@ TEST_F(DeclarableOpsTests12, LU_Test_4) { }); auto expP = NDArrayFactory::create({1, 2, 7, 3, 6, 8, 5, 4, 0, 9}); - nd4j::ops::lu op; + sd::ops::lu op; auto res = op.evaluate({&in}); - ASSERT_EQ(res->status(), ND4J_STATUS_OK); - auto z = res->at(0); - auto p = res->at(1); + ASSERT_EQ(res.status(), ND4J_STATUS_OK); + auto z = res.at(0); + auto p = res.at(1); // z->printBuffer("Triangulars4"); // expLU.printBuffer("TriangulExp4"); // p->printBuffer("Permutaions4"); ASSERT_TRUE(expLU.equalsTo(z)); ASSERT_TRUE(expP.equalsTo(p)); - delete res; + } TEST_F(DeclarableOpsTests12, LU_Test_5) { @@ -2566,19 +2567,19 @@ TEST_F(DeclarableOpsTests12, LU_Test_5) { 1, 2, 7, 3, 6, 8, 5, 4, 0, 9, 1, 2, 7, 3, 6, 8, 5, 4, 0, 9 }); - nd4j::ops::lu op; + sd::ops::lu op; auto res = op.evaluate({&in}); - ASSERT_EQ(res->status(), ND4J_STATUS_OK); - auto z = res->at(0); - auto p = res->at(1); + ASSERT_EQ(res.status(), ND4J_STATUS_OK); + auto z = res.at(0); + auto p = res.at(1); // z->printBuffer("Triangulars5"); // expLU.printBuffer("TriangulExp5"); // p->printBuffer("Permutaions5"); ASSERT_TRUE(expLU.equalsTo(z)); ASSERT_TRUE(expP.equalsTo(p)); - delete res; + } //////////////////////////////////////////////////////////////////////////////// @@ -2587,16 +2588,16 @@ TEST_F(DeclarableOpsTests12, LU_Test_1_2) { auto in = NDArrayFactory::create('c', {2, 3,3}, {1., 2., 3., 0., 2., 3., 0., 0., 7.,1., 2., 3., 0., 2., 3., 0., 0., 7.}); auto exp = NDArrayFactory::create('c', {2, 3,3}, {1., 2., 3., 0., 2., 3., 0., 0., 7, 1., 2., 3., 0., 2., 3., 0., 0., 7.}); - nd4j::ops::lu op; + sd::ops::lu op; auto res = op.evaluate({&in}); - ASSERT_EQ(res->status(), ND4J_STATUS_OK); - auto z = res->at(0); - auto p = res->at(1); + ASSERT_EQ(res.status(), ND4J_STATUS_OK); + auto z = res.at(0); + auto p = res.at(1); // z->printIndexedBuffer("Triangulars (2,3,3)"); // p->printIndexedBuffer("Permutaions (2,3,3)"); - ASSERT_TRUE(exp.equalsTo(res->at(0))); - delete res; + ASSERT_TRUE(exp.equalsTo(res.at(0))); + } //////////////////////////////////////////////////////////////////////////////// @@ -2615,18 +2616,18 @@ TEST_F(DeclarableOpsTests12, LU_Test_3_2) { }); auto expP = NDArrayFactory::create('c', {2,3}, {2, 1, 0, 2, 1, 0}); - nd4j::ops::lu op; + sd::ops::lu op; auto res = op.evaluate({&in}); - ASSERT_EQ(res->status(), ND4J_STATUS_OK); - auto z = res->at(0); - auto p = res->at(1); + ASSERT_EQ(res.status(), ND4J_STATUS_OK); + auto z = res.at(0); + auto p = res.at(1); // z->printIndexedBuffer("Triangulars3_2"); // p->printIndexedBuffer("Permutaions3_2"); ASSERT_TRUE(expLU.equalsTo(z)); ASSERT_TRUE(expP.equalsTo(p)); - delete res; + } //////////////////////////////////////////////////////////////////////////////// @@ -2643,18 +2644,18 @@ TEST_F(DeclarableOpsTests12, LU_Test_3_3) { 0.30769232, 0.619403, 9.029851}); auto expP = NDArrayFactory::create('c', {2,3}, {2, 1, 0, 0, 2, 1}); - nd4j::ops::lu op; + sd::ops::lu op; auto res = op.evaluate({&in}); - ASSERT_EQ(res->status(), ND4J_STATUS_OK); - auto z = res->at(0); - auto p = res->at(1); + ASSERT_EQ(res.status(), ND4J_STATUS_OK); + auto z = res.at(0); + auto p = res.at(1); // z->printIndexedBuffer("Triangulars3_3"); // p->printIndexedBuffer("Permutaions3_3"); ASSERT_TRUE(expLU.equalsTo(z)); ASSERT_TRUE(expP.equalsTo(p)); - delete res; + } //////////////////////////////////////////////////////////////////////////////// @@ -2671,18 +2672,18 @@ TEST_F(DeclarableOpsTests12, LU_Test_4_1) { }); auto expP = NDArrayFactory::create('c', {2,2}, {0, 1, 0, 1}); - nd4j::ops::lu op; + sd::ops::lu op; auto res = op.evaluate({&in}); - ASSERT_EQ(res->status(), ND4J_STATUS_OK); - auto z = res->at(0); - auto p = res->at(1); + ASSERT_EQ(res.status(), ND4J_STATUS_OK); + auto z = res.at(0); + auto p = res.at(1); // z->printIndexedBuffer("Triangulars4_1"); // p->printIndexedBuffer("Permutaions4_1"); ASSERT_TRUE(expLU.equalsTo(z)); ASSERT_TRUE(expP.equalsTo(p)); - delete res; + } //////////////////////////////////////////////////////////////////////////////// @@ -2699,18 +2700,19 @@ TEST_F(DeclarableOpsTests12, LU_Test_4_2) { }); auto expP = NDArrayFactory::create('c', {2,2}, {0, 1, 0, 1}); - nd4j::ops::lu op; + sd::ops::lu op; + + auto res = op.evaluate({&in}, {}, {sd::DataType::INT64}); + ASSERT_EQ(res.status(), ND4J_STATUS_OK); + auto z = res.at(0); + auto p = res.at(1); - auto res = op.evaluate({&in}, {}, {nd4j::DataType::INT64}); - ASSERT_EQ(res->status(), ND4J_STATUS_OK); - auto z = res->at(0); - auto p = res->at(1); // z->printIndexedBuffer("Triangulars4_2"); // p->printIndexedBuffer("Permutaions4_2"); ASSERT_TRUE(expLU.equalsTo(z)); ASSERT_TRUE(expP.equalsTo(p)); - delete res; + } //////////////////////////////////////////////////////////////////////////////// @@ -2725,28 +2727,27 @@ TEST_F(DeclarableOpsTests12, QR_Test_1) { auto expR = NDArrayFactory::create('c', {5,3}, { -14.177447, -20.666622, 13.401566, 0., -175.04254, 70.080315, 0., 0., 35.201546, 0., 0., 0., 0., 0., 0. }); - nd4j::ops::qr op; + sd::ops::qr op; auto res = op.evaluate({&in}, {}, {}, {true}); - ASSERT_EQ(res->status(), ND4J_STATUS_OK); - auto q = res->at(0); - auto r = res->at(1); + ASSERT_EQ(res.status(), ND4J_STATUS_OK); + auto q = res.at(0); + auto r = res.at(1); // q->printIndexedBuffer("Orthogonal 5x5"); // expQ.printBuffer("Orthogonal Exp"); // r->printIndexedBuffer("Upper triangular 5x3"); // expR.printBuffer("Upper triangular Exp"); // q->printShapeInfo("Q shape"); // r->printShapeInfo("R shape"); - nd4j::ops::matmul opMul; + sd::ops::matmul opMul; auto res2 = opMul.evaluate({q, r}); //MmulHelper::matmul(q, r, &in, false, false); - auto exp = res2->at(0);//->printIndexedBuffer("Result as result"); + auto exp = res2.at(0);//->printIndexedBuffer("Result as result"); ASSERT_TRUE(exp->isSameShape(in)); // ASSERT_TRUE(q->isSameShape(expQ)); //ASSERT_TRUE(expQ.equalsTo(q)); ASSERT_TRUE(exp->equalsTo(in)); - delete res2; - delete res; + } @@ -2772,28 +2773,27 @@ TEST_F(DeclarableOpsTests12, QR_Test_1_1) { -14.177447, -20.666622, 13.401566, 0., -175.04254, 70.080315, 0., 0., 35.201546, 0., 0., 0., 0., 0., 0., -14.177447, -20.666622, 13.401566, 0., -175.04254, 70.080315, 0., 0., 35.201546, 0., 0., 0., 0., 0., 0. }); - nd4j::ops::qr op; + sd::ops::qr op; auto res = op.evaluate({&in}, {}, {}, {true}); - ASSERT_EQ(res->status(), ND4J_STATUS_OK); - auto q = res->at(0); - auto r = res->at(1); + ASSERT_EQ(res.status(), ND4J_STATUS_OK); + auto q = res.at(0); + auto r = res.at(1); // q->printIndexedBuffer("Orthogonal 5x5"); // expQ.printBuffer("Orthogonal Exp"); // r->printIndexedBuffer("Upper triangular 5x3"); // expR.printBuffer("Upper triangular Exp"); // q->printShapeInfo("Q shape"); // r->printShapeInfo("R shape"); - nd4j::ops::matmul opMul; + sd::ops::matmul opMul; auto res2 = opMul.evaluate({q, r}); //MmulHelper::matmul(q, r, &in, false, false); - auto exp = res2->at(0);//->printIndexedBuffer("Result as result"); + auto exp = res2.at(0);//->printIndexedBuffer("Result as result"); ASSERT_TRUE(exp->isSameShape(in)); // ASSERT_TRUE(q->isSameShape(expQ)); //ASSERT_TRUE(expQ.equalsTo(q)); ASSERT_TRUE(exp->equalsTo(in)); - delete res2; - delete res; + } @@ -2804,22 +2804,21 @@ TEST_F(DeclarableOpsTests12, QR_Test_2) { auto expQ = NDArrayFactory::create('c', {5, 3}, {0.8464148,0.3912908,-0.3431241,-0.42320737, -0.9040873,0.02927014,0.28213826, -0.17042054, -0.93285596,0.07053456, -0.01404065,0.00109937,-0.14106913,0.0166551,0.10577161}); auto expR = NDArrayFactory::create('c', {3,3}, {-14.177447,-20.666622,13.401566,0.,-175.04254,70.080315,0.,0.,35.201546}); - nd4j::ops::qr op; + sd::ops::qr op; auto res = op.evaluate({&in}, {}, {}, {false}); - ASSERT_EQ(res->status(), ND4J_STATUS_OK); - auto q = res->at(0); - auto r = res->at(1); + ASSERT_EQ(res.status(), ND4J_STATUS_OK); + auto q = res.at(0); + auto r = res.at(1); ASSERT_TRUE(q->isSameShape(expQ)); ASSERT_TRUE(r->isSameShape(expR)); - nd4j::ops::matmul opMul; + sd::ops::matmul opMul; auto res2 = opMul.evaluate({q, r}); //MmulHelper::matmul(q, r, &in, false, false); - auto exp = res2->at(0);//->printIndexedBuffer("Result as result"); + auto exp = res2.at(0);//->printIndexedBuffer("Result as result"); ASSERT_TRUE(exp->isSameShape(in)); ASSERT_TRUE(exp->equalsTo(in)); - delete res2; - delete res; + } //////////////////////////////////////////////////////////////////////////////// @@ -2839,16 +2838,16 @@ TEST_F(DeclarableOpsTests12, TriangularSolve_Test_1) { auto exp = NDArrayFactory::create('c', {4, 1}, { 1.333333f, -0.6666667f, 2.6666667f, -1.3333333f }); - nd4j::ops::triangular_solve op; + sd::ops::triangular_solve op; auto res = op.evaluate({&a, &b}); - ASSERT_EQ(res->status(), ND4J_STATUS_OK); - auto z = res->at(0); + ASSERT_EQ(res.status(), ND4J_STATUS_OK); + auto z = res.at(0); // z->printIndexedBuffer("TriangularSolve"); ASSERT_TRUE(exp.equalsTo(z)); - delete res; + } //////////////////////////////////////////////////////////////////////////////// @@ -2868,16 +2867,16 @@ TEST_F(DeclarableOpsTests12, TriangularSolve_Test_2) { auto exp = NDArrayFactory::create('c', {4, 1}, { 2.f, 4.f, 1.f, 1.3333333f }); - nd4j::ops::triangular_solve op; + sd::ops::triangular_solve op; auto res = op.evaluate({&a, &b}); - ASSERT_EQ(res->status(), ND4J_STATUS_OK); - auto z = res->at(0); + ASSERT_EQ(res.status(), ND4J_STATUS_OK); + auto z = res.at(0); // z->printIndexedBuffer("TriangularSolve"); ASSERT_TRUE(exp.equalsTo(z)); - delete res; + } //////////////////////////////////////////////////////////////////////////////// @@ -2905,16 +2904,16 @@ TEST_F(DeclarableOpsTests12, TriangularSolve_Test_3) { 1.333333f, -0.6666667f, 2.6666667f, -1.3333333f }); - nd4j::ops::triangular_solve op; + sd::ops::triangular_solve op; auto res = op.evaluate({&a, &b}); - ASSERT_EQ(res->status(), ND4J_STATUS_OK); - auto z = res->at(0); + ASSERT_EQ(res.status(), ND4J_STATUS_OK); + auto z = res.at(0); // z->printIndexedBuffer("TriangularSolve"); ASSERT_TRUE(exp.equalsTo(z)); - delete res; + } //////////////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests12, TriangularSolve_Test_4) { @@ -2934,16 +2933,16 @@ TEST_F(DeclarableOpsTests12, TriangularSolve_Test_4) { -3.3333333f, 3.6666666f, 0.333333f, 1.3333333f }); - nd4j::ops::triangular_solve op; + sd::ops::triangular_solve op; - auto res = op.evaluate({&a, &b}, {}, {}, {false}); - ASSERT_EQ(res->status(), ND4J_STATUS_OK); - auto z = res->at(0); + auto res = op.evaluate({&a, &b}, {false}); + ASSERT_EQ(res.status(), ND4J_STATUS_OK); + auto z = res.at(0); // z->printIndexedBuffer("TriangularSolve"); ASSERT_TRUE(exp.equalsTo(z)); - delete res; + } //////////////////////////////////////////////////////////////////////////////// @@ -2964,19 +2963,155 @@ TEST_F(DeclarableOpsTests12, TriangularSolve_Test_5) { 1.f, 1.f, 1.f, 1.f }); - nd4j::ops::triangular_solve op; + sd::ops::triangular_solve op; - auto res = op.evaluate({&a, &b}, {}, {}, {false, true}); - ASSERT_EQ(res->status(), ND4J_STATUS_OK); - auto z = res->at(0); + auto res = op.evaluate({&a, &b}, {false, true}); + ASSERT_EQ(res.status(), ND4J_STATUS_OK); + auto z = res.at(0); // z->printIndexedBuffer("TriangularSolve with adjoint"); ASSERT_TRUE(exp.equalsTo(z)); - delete res; + } //////////////////////////////////////////////////////////////////////////////// +TEST_F(DeclarableOpsTests12, SolveLs_Test_1) { + + auto a = NDArrayFactory::create('c', {4, 4}, { + 3.f, 0.f, 0.f, 0.f, + 2.f, 1.f, 0.f, 0.f, + 1.f, 0.f, 1.f, 0.f, + 1.f, 1.f, 1.f, 1.f + }); + + auto b = NDArrayFactory::create('c', {4, 1}, { + 4.f, 2.f, 4.f, 2.f + }); + + auto exp = NDArrayFactory::create('c', {4, 1}, { + 1.333333f, -0.6666667f, 2.6666667f, -1.3333333f }); + + sd::ops::lstsq op; + + auto res = op.evaluate({&a, &b}); + ASSERT_EQ(res.status(), ND4J_STATUS_OK); + auto z = res.at(0); + +// z->printIndexedBuffer("MatrixSolveLS"); + MmulHelper::matmul(&a, z, &exp, false, false); + + ASSERT_TRUE(exp.equalsTo(b)); + +} + +//////////////////////////////////////////////////////////////////////////////// +TEST_F(DeclarableOpsTests12, SolveLs_Test_2) { + + auto a = NDArrayFactory::create('c', {3, 3}, { + 1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 11.f, 8.f, 21.f + }); + + auto b = NDArrayFactory::create('c', {3, 1}, { 1.f, 2.f, 3.f }); + + auto exp = NDArrayFactory::create('c', {3, 1}, { -0.24999914f, 0.4999994f, 0.08333314f }); + + sd::ops::lstsq op; + + auto res = op.evaluate({&a, &b}); + ASSERT_EQ(res.status(), ND4J_STATUS_OK); + auto z = res.at(0); + + MmulHelper::matmul(&a, z, &exp, false, false); + +// z->printIndexedBuffer("MatrixSolveLS2"); + + ASSERT_TRUE(exp.equalsTo(b)); + +} + +//////////////////////////////////////////////////////////////////////////////// +TEST_F(DeclarableOpsTests12, SolveLs_Test_3) { + + auto a = NDArrayFactory::create('c', {3, 4}, { + 1.f,1.f,0.f,0.f,-1.f,1.f,0.f,0.f,1.f,1.f,-1.f,-1.f + }); + + auto b = NDArrayFactory::create('c', {3, 1}, { 1.f, 2.f, 3.f }); + + auto exp = NDArrayFactory::create('c', {3, 1}, { -0.5f, 1.5f, -2.f }); + + sd::ops::lstsq op; + + auto res = op.evaluate({&a, &b}); + ASSERT_EQ(res.status(), ND4J_STATUS_OK); + auto z = res.at(0); + +// z->printIndexedBuffer("MatrixSolveLS3"); + MmulHelper::matmul(&a, z, &exp, false, false); + ASSERT_TRUE(exp.equalsTo(b)); + +} + +//////////////////////////////////////////////////////////////////////////////// +TEST_F(DeclarableOpsTests12, SolveLs_Test_4) { + + auto a = NDArrayFactory::create('c', {3, 4}, { + 1.f,1.f,0.f,0.f,-1.f,1.f,0.f,0.f,1.f,1.f,-1.f,-1.f + }); + + auto b = NDArrayFactory::create('c', {3, 1}, { 1.f, 2.f, 3.f }); + + auto exp = NDArrayFactory::create('c', {4, 1}, { -0.5f, 1.5f, -2.f, 0.f}); + + sd::ops::lstsq op; + + auto res = op.evaluate({&a, &b}, {false}); + ASSERT_EQ(res.status(), ND4J_STATUS_OK); + auto z = res.at(0); +// z->printIndexedBuffer("Output_12.4"); +// z->printShapeInfo("Output_12.4 shape"); +// MmulHelper::matmul(&a, z, &exp, false, false); + +// z->printIndexedBuffer("MatrixSolveLS4"); + + ASSERT_TRUE(exp.equalsTo(z)); + +} + +//////////////////////////////////////////////////////////////////////////////// +TEST_F(DeclarableOpsTests12, SolveLs_Test_5) { + + auto a = NDArrayFactory::create('c', {1, 0, 3, 4}); + auto b = NDArrayFactory::create('c', {1, 0, 3, 1}); + + sd::ops::lstsq op; + + auto res = op.evaluate({&a, &b}, {false}); + ASSERT_EQ(res.status(), ND4J_STATUS_OK); + auto z = res.at(0); + ASSERT_TRUE(z->isEmpty()); + + +} + +//////////////////////////////////////////////////////////////////////////////// +TEST_F(DeclarableOpsTests12, Solve_Test_6) { + + auto a = NDArrayFactory::create('c', {1, 0, 3, 3}); + auto b = NDArrayFactory::create('c', {1, 0, 3, 1}); + + sd::ops::solve op; + + auto res = op.evaluate({&a, &b}, {true}); + ASSERT_EQ(res.status(), ND4J_STATUS_OK); + auto z = res.at(0); + ASSERT_TRUE(z->isEmpty()); + + +} + +////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests12, TriangularSolve_Test_6) { auto a = NDArrayFactory::create('c', {4, 4}, { @@ -2994,14 +3129,14 @@ TEST_F(DeclarableOpsTests12, TriangularSolve_Test_6) { 1.f,0.2f, 1.f,0.8f, 1.f,0.4f, 1.f,1.2f }); - nd4j::ops::triangular_solve op; + sd::ops::triangular_solve op; auto res = op.evaluate({&a, &b}, {}, {}, {false, true}); - ASSERT_EQ(res->status(), ND4J_STATUS_OK); - auto z = res->at(0); + ASSERT_EQ(res.status(), ND4J_STATUS_OK); + auto z = res.at(0); z->printIndexedBuffer("TriangularSolve with adjoint"); ASSERT_TRUE(exp.equalsTo(z)); - delete res; -} \ No newline at end of file + +} diff --git a/libnd4j/tests_cpu/layers_tests/DeclarableOpsTests13.cpp b/libnd4j/tests_cpu/layers_tests/DeclarableOpsTests13.cpp index e964d397d..20665e5d3 100644 --- a/libnd4j/tests_cpu/layers_tests/DeclarableOpsTests13.cpp +++ b/libnd4j/tests_cpu/layers_tests/DeclarableOpsTests13.cpp @@ -21,13 +21,13 @@ #include "testlayers.h" #include -#include +#include #include -#include +#include #include -#include +#include -using namespace nd4j; +using namespace sd; class DeclarableOpsTests13 : public testing::Test { @@ -57,53 +57,47 @@ TEST_F(DeclarableOpsTests13, test_pow_1) { auto y = NDArrayFactory::create('c', {2}, {3, 3}); auto e = NDArrayFactory::create('c', {2, 2}, {8.f, 8.f, 8.f, 8.f}); - nd4j::ops::Pow op; + sd::ops::Pow op; auto result = op.evaluate({&x, &y}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_EQ(e, *z); - - delete result; } TEST_F(DeclarableOpsTests13, test_empty_range_1) { auto start = NDArrayFactory::create(0); auto limit = NDArrayFactory::create(0); - nd4j::ops::range op; + sd::ops::range op; auto result = op.evaluate({&start, &limit}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(z->isEmpty()); - delete result; + } TEST_F(DeclarableOpsTests13, test_empty_range_2) { - nd4j::ops::range op; + sd::ops::range op; auto result = op.evaluate({}, {1.0, 1.0}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(z->isEmpty()); - - delete result; } TEST_F(DeclarableOpsTests13, test_empty_range_3) { - nd4j::ops::range op; + sd::ops::range op; auto result = op.evaluate({}, {1, 1}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(z->isEmpty()); - - delete result; } TEST_F(DeclarableOpsTests13, test_argmax_edge_1) { @@ -115,7 +109,7 @@ TEST_F(DeclarableOpsTests13, test_argmax_edge_1) { ctx->setInputArray(1, NDArrayFactory::create_(0), true); //Axis 0 - nd4j::ops::argmax op; + sd::ops::argmax op; auto result = op.execute(ctx); ASSERT_EQ(Status::OK(), result); @@ -142,7 +136,7 @@ TEST_F(DeclarableOpsTests13, test_listdiff_1) { auto od = NDArrayFactory::create('c', {2}); auto oi = NDArrayFactory::create('c', {2}); - nd4j::ops::listdiff op; + sd::ops::listdiff op; auto result = op.execute({&x, &y}, std::vector{&od, &oi}, {}, {}, {}); ASSERT_EQ(Status::OK(), result); } @@ -151,11 +145,9 @@ TEST_F(DeclarableOpsTests13, test_greater_1) { auto x = NDArrayFactory::create('c', {3, 1}); auto y = NDArrayFactory::create('c', {1, 4}); - nd4j::ops::greater op; + sd::ops::greater op; auto result = op.evaluate({&x, &y}); - ASSERT_EQ(Status::OK(), result->status()); - - delete result; + ASSERT_EQ(Status::OK(), result.status()); } TEST_F(DeclarableOpsTests13, test_eval_reduction_shape_1) { @@ -164,24 +156,22 @@ TEST_F(DeclarableOpsTests13, test_eval_reduction_shape_1) { auto y = NDArrayFactory::create('c', {1}, {axis}); auto exp = NDArrayFactory::create('c', {2}, {1, 2}); - nd4j::ops::evaluate_reduction_shape op; + sd::ops::evaluate_reduction_shape op; auto result = op.evaluate({&x, &y}, {true}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_EQ(exp, *z); - - delete result; } TEST_F(DeclarableOpsTests13, test_or_1) { - NDArray x('c', {4}, {false, true, false, true}, nd4j::DataType::BOOL); - NDArray y('c', {4}, {false, false, true, true}, nd4j::DataType::BOOL); - NDArray e('c', {4}, {false, true, true, true}, nd4j::DataType::BOOL); + NDArray x('c', {4}, {false, true, false, true}, sd::DataType::BOOL); + NDArray y('c', {4}, {false, false, true, true}, sd::DataType::BOOL); + NDArray e('c', {4}, {false, true, true, true}, sd::DataType::BOOL); - NDArray z('c', {4}, nd4j::DataType::BOOL); + NDArray z('c', {4}, sd::DataType::BOOL); x.applyPairwiseTransform(pairwise::Or, y, z); @@ -217,13 +207,11 @@ TEST_F(DeclarableOpsTests13, BarnesHutTsne_GainsTest_1) { auto y = NDArrayFactory::create('c', {2,3}, {1,-2,3, -4, 5, -6}); auto eps = NDArrayFactory::create('c', {2,3}, {-0.1, 0.2, -0.3, 0.4, -0.5, 0.6}); auto exp = NDArrayFactory::create('c', {2,3}, {1.2,2.2,3.2,4.2,5.2,6.2}); - nd4j::ops::barnes_gains op; + sd::ops::barnes_gains op; auto result = op.evaluate({&x, &y, &eps}); - ASSERT_EQ(result->status(), Status::OK()); - //result->at(0)->printBuffer("Gains out"); - ASSERT_TRUE(exp.equalsTo(result->at(0))); - - delete result; + ASSERT_EQ(result.status(), Status::OK()); + //result.at(0)->printBuffer("Gains out"); + ASSERT_TRUE(exp.equalsTo(result.at(0))); } TEST_F(DeclarableOpsTests13, BarnesHutTsne_GainsTest_2) { @@ -231,14 +219,13 @@ TEST_F(DeclarableOpsTests13, BarnesHutTsne_GainsTest_2) { auto y = NDArrayFactory::create('c', {2,3}, {1, -2, 3, -4, 5, -6}); auto eps = NDArrayFactory::create('c', {2,3}, {-0.1, 0.2, -0.3, 0.4, -0.5, 0.6}); auto exp = NDArrayFactory::create('c', {2,3}, {1.2, 0.01, 3.2, 0.01, 5.2, 0.01}); - nd4j::ops::barnes_gains op; + sd::ops::barnes_gains op; auto result = op.evaluate({&x, &y, &eps}, {}, {}); - ASSERT_EQ(result->status(), Status::OK()); - //result->at(0)->printBuffer("Gains out"); - ASSERT_TRUE(exp.equalsTo(result->at(0))); + ASSERT_EQ(result.status(), Status::OK()); + //result.at(0)->printBuffer("Gains out"); + ASSERT_TRUE(exp.equalsTo(result.at(0))); //ASSERT_EQ(e, z); - delete result; } TEST_F(DeclarableOpsTests13, BarnesHutTsne_GainsTest_3) { @@ -246,12 +233,12 @@ TEST_F(DeclarableOpsTests13, BarnesHutTsne_GainsTest_3) { auto y = NDArrayFactory::create('c', {2,3}, {-0.1,-2,3, -4, -0.5, -6}); auto eps = NDArrayFactory::create('c', {2,3}, {-0.1, 0.2, -0.3, 0.4, -0.5, 0.6}); auto exp = NDArrayFactory::create('c', {2,3}, {0.01, 2.2, 0.01, 4.2, 0.01, 6.2}); - nd4j::ops::barnes_gains op; + sd::ops::barnes_gains op; auto result = op.evaluate({&x, &y, &eps}, {}, {}); - ASSERT_EQ(result->status(), Status::OK()); - //result->at(0)->printBuffer("Gains out"); - ASSERT_TRUE(exp.equalsTo(result->at(0))); - delete result; + ASSERT_EQ(result.status(), Status::OK()); + //result.at(0)->printBuffer("Gains out"); + ASSERT_TRUE(exp.equalsTo(result.at(0))); + } TEST_F(DeclarableOpsTests13, BarnesHutTsne_EdgeForceTest_1) { @@ -268,14 +255,14 @@ TEST_F(DeclarableOpsTests13, BarnesHutTsne_EdgeForceTest_1) { // auto y = NDArrayFactory::create('c', {2,3}, {-0.1,-2,3, -4, -0.5, -6}); // auto eps = NDArrayFactory::create('c', {2,3}, {-0.1, 0.2, -0.3, 0.4, -0.5, 0.6}); // auto exp = NDArrayFactory::create('c', {2,3}, {1, 2, 1, 2, 2, 2}); - nd4j::ops::barnes_edge_forces op; + sd::ops::barnes_edge_forces op; auto result = op.evaluate({&rows, &cols, &vals, &data}, {}, {1}); - ASSERT_EQ(result->status(), Status::OK()); - //result->at(0)->printBuffer("Output"); - ASSERT_TRUE(exp1.equalsTo(result->at(0))); - delete result; + ASSERT_EQ(result.status(), Status::OK()); + //result.at(0)->printBuffer("Output"); + ASSERT_TRUE(exp1.equalsTo(result.at(0))); + } TEST_F(DeclarableOpsTests13, BarnesHutTsne_EdgeForceTest_2) { @@ -292,14 +279,14 @@ TEST_F(DeclarableOpsTests13, BarnesHutTsne_EdgeForceTest_2) { // auto y = NDArrayFactory::create('c', {2,3}, {-0.1,-2,3, -4, -0.5, -6}); // auto eps = NDArrayFactory::create('c', {2,3}, {-0.1, 0.2, -0.3, 0.4, -0.5, 0.6}); // auto exp = NDArrayFactory::create('c', {2,3}, {1, 2, 1, 2, 2, 2}); - nd4j::ops::barnes_edge_forces op; + sd::ops::barnes_edge_forces op; auto result = op.evaluate({&rows, &cols, &vals, &data}, {}, {2}); - ASSERT_EQ(result->status(), Status::OK()); - //result->at(0)->printBuffer("Output"); - ASSERT_TRUE(exp.equalsTo(result->at(0))); - delete result; + ASSERT_EQ(result.status(), Status::OK()); + //result.at(0)->printBuffer("Output"); + ASSERT_TRUE(exp.equalsTo(result.at(0))); + } TEST_F(DeclarableOpsTests13, BarnesHutTsne_EdgeForceTest_3) { @@ -316,16 +303,16 @@ TEST_F(DeclarableOpsTests13, BarnesHutTsne_EdgeForceTest_3) { // auto y = NDArrayFactory::create('c', {2,3}, {-0.1,-2,3, -4, -0.5, -6}); // auto eps = NDArrayFactory::create('c', {2,3}, {-0.1, 0.2, -0.3, 0.4, -0.5, 0.6}); // auto exp = NDArrayFactory::create('c', {2,3}, {1, 2, 1, 2, 2, 2}); - nd4j::ops::barnes_edge_forces op; + sd::ops::barnes_edge_forces op; auto result = op.evaluate({&rows, &cols, &vals, &data}, {}, {11}); //nd4j_printf("rows %lld, cols %lld, vals %lld, res full %lld\n", rows.lengthOf(), cols.lengthOf(), vals.lengthOf(), exp1.lengthOf()); - ASSERT_EQ(result->status(), Status::OK()); - //result->at(0)->printBuffer("Output"); + ASSERT_EQ(result.status(), Status::OK()); + //result.at(0)->printBuffer("Output"); //exp.printBuffer("Expect"); - //result->at(0)->printShapeInfo("Shape output"); - ASSERT_TRUE(exp.equalsTo(result->at(0))); - delete result; + //result.at(0)->printShapeInfo("Shape output"); + ASSERT_TRUE(exp.equalsTo(result.at(0))); + } TEST_F(DeclarableOpsTests13, BarnesHutTsne_symmetrized_1) { @@ -339,13 +326,11 @@ TEST_F(DeclarableOpsTests13, BarnesHutTsne_symmetrized_1) { // auto y = NDArrayFactory::create('c', {2,3}, {-0.1,-2,3, -4, -0.5, -6}); // auto eps = NDArrayFactory::create('c', {2,3}, {-0.1, 0.2, -0.3, 0.4, -0.5, 0.6}); // auto exp = NDArrayFactory::create('c', {2,3}, {1, 2, 1, 2, 2, 2}); - nd4j::ops::barnes_symmetrized op; + sd::ops::barnes_symmetrized op; auto result = op.evaluate({&rows, &cols, &vals}, {}, {1}); - ASSERT_EQ(result->status(), Status::OK()); - //result->at(2)->printBuffer("Symmetrized1"); - ASSERT_TRUE(exp.equalsTo(result->at(2))); - - delete result; + ASSERT_EQ(result.status(), Status::OK()); + //result.at(2)->printBuffer("Symmetrized1"); + ASSERT_TRUE(exp.equalsTo(result.at(2))); } TEST_F(DeclarableOpsTests13, BarnesHutTsne_symmetrized_2) { @@ -358,13 +343,13 @@ TEST_F(DeclarableOpsTests13, BarnesHutTsne_symmetrized_2) { // auto y = NDArrayFactory::create('c', {2,3}, {-0.1,-2,3, -4, -0.5, -6}); // auto eps = NDArrayFactory::create('c', {2,3}, {-0.1, 0.2, -0.3, 0.4, -0.5, 0.6}); // auto exp = NDArrayFactory::create('c', {2,3}, {1, 2, 1, 2, 2, 2}); - nd4j::ops::barnes_symmetrized op; + sd::ops::barnes_symmetrized op; auto result = op.evaluate({&rows, &cols, &vals}, {}, {3}); - ASSERT_EQ(result->status(), Status::OK()); - //result->at(2)->printBuffer("Symmetrized2"); - // ASSERT_TRUE(exp[i]->equalsTo(result->at(i))); - ASSERT_TRUE(exp.equalsTo(result->at(2))); - delete result; + ASSERT_EQ(result.status(), Status::OK()); + //result.at(2)->printBuffer("Symmetrized2"); + // ASSERT_TRUE(exp[i]->equalsTo(result.at(i))); + ASSERT_TRUE(exp.equalsTo(result.at(2))); + } TEST_F(DeclarableOpsTests13, BarnesHutTsne_symmetrized_3) { @@ -377,14 +362,14 @@ TEST_F(DeclarableOpsTests13, BarnesHutTsne_symmetrized_3) { // auto y = NDArrayFactory::create('c', {2,3}, {-0.1,-2,3, -4, -0.5, -6}); // auto eps = NDArrayFactory::create('c', {2,3}, {-0.1, 0.2, -0.3, 0.4, -0.5, 0.6}); // auto exp = NDArrayFactory::create('c', {2,3}, {1, 2, 1, 2, 2, 2}); - nd4j::ops::barnes_symmetrized op; + sd::ops::barnes_symmetrized op; auto result = op.evaluate({&rows, &cols, &vals}, {}, {11}); - ASSERT_EQ(result->status(), Status::OK()); - //result->at(2)->printBuffer("Symmetrized3"); + ASSERT_EQ(result.status(), Status::OK()); + //result.at(2)->printBuffer("Symmetrized3"); //exp.printBuffer("EXPect symm3"); - // ASSERT_TRUE(exp[i]->equalsTo(result->at(i))); - //ASSERT_TRUE(exp.equalsTo(result->at(0))); - delete result; + // ASSERT_TRUE(exp[i]->equalsTo(result.at(i))); + //ASSERT_TRUE(exp.equalsTo(result.at(0))); + } TEST_F(DeclarableOpsTests13, BarnesHutTsne_symmetrized_4) { @@ -401,19 +386,19 @@ TEST_F(DeclarableOpsTests13, BarnesHutTsne_symmetrized_4) { // auto y = NDArrayFactory::create('c', {2,3}, {-0.1,-2,3, -4, -0.5, -6}); // auto eps = NDArrayFactory::create('c', {2,3}, {-0.1, 0.2, -0.3, 0.4, -0.5, 0.6}); // auto exp = NDArrayFactory::create('c', {2,3}, {1, 2, 1, 2, 2, 2}); - nd4j::ops::barnes_symmetrized op; + sd::ops::barnes_symmetrized op; auto result = op.evaluate({&rows, &cols, &vals}, {}, {11}); - ASSERT_EQ(result->status(), Status::OK()); - auto res = result->at(2); + ASSERT_EQ(result.status(), Status::OK()); + auto res = result.at(2); // res->printBuffer("Symmetrized4"); // exp4.printBuffer("Expected sym"); // nd4j_printf("Total res is {1, %lld}\n", res->lengthOf()); // nd4j_printf("Expected is {1, %lld}\n", exp4.lengthOf()); //exp.printBuffer("EXPect symm3"); - // ASSERT_TRUE(exp[i]->equalsTo(result->at(i))); + // ASSERT_TRUE(exp[i]->equalsTo(result.at(i))); ASSERT_TRUE(exp4.equalsTo(res)); - delete result; + } TEST_F(DeclarableOpsTests13, CellContains_test_1) { @@ -427,214 +412,201 @@ TEST_F(DeclarableOpsTests13, CellContains_test_1) { // auto y = NDArrayFactory::create('c', {2,3}, {-0.1,-2,3, -4, -0.5, -6}); // auto eps = NDArrayFactory::create('c', {2,3}, {-0.1, 0.2, -0.3, 0.4, -0.5, 0.6}); // auto exp = NDArrayFactory::create('c', {2,3}, {1, 2, 1, 2, 2, 2}); - nd4j::ops::cell_contains op; + sd::ops::cell_contains op; auto result = op.evaluate({&corners, &width, &point}, {}, {5}); - ASSERT_EQ(result->status(), Status::OK()); - ASSERT_TRUE(result->at(0)->e(0)); - //result->at(2)->printBuffer("Symmetrized3"); + ASSERT_EQ(result.status(), Status::OK()); + ASSERT_TRUE(result.at(0)->e(0)); + //result.at(2)->printBuffer("Symmetrized3"); //exp.printBuffer("EXPect symm3"); - // ASSERT_TRUE(exp[i]->equalsTo(result->at(i))); - //ASSERT_TRUE(exp.equalsTo(result->at(0))); - delete result; + // ASSERT_TRUE(exp[i]->equalsTo(result.at(i))); + //ASSERT_TRUE(exp.equalsTo(result.at(0))); + } //////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests13, adjustHue_1) { - NDArray input('c', {2,2,3}, {0,100,56, 17,220,5, 150,97,230, 255,2,13}, nd4j::DataType::FLOAT32); + NDArray input('c', {2,2,3}, {0,100,56, 17,220,5, 150,97,230, 255,2,13}, sd::DataType::FLOAT32); NDArray factor = NDArrayFactory::create(0.5); - NDArray exp ('c', {2,2,3}, {100,0,44, 208,5,220, 177,230,97, 2,255,244}, nd4j::DataType::FLOAT32); + NDArray exp ('c', {2,2,3}, {100,0,44, 208,5,220, 177,230,97, 2,255,244}, sd::DataType::FLOAT32); - nd4j::ops::adjust_hue op; - std::unique_ptr results (op.evaluate({&input, &factor}, {}, {2})); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + sd::ops::adjust_hue op; + auto results (op.evaluate({&input, &factor}, {}, {2})); - auto result = results->at(0); - // result->printIndexedBuffer(); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); + + auto result = results.at(0); + // result.printIndexedBuffer(); ASSERT_TRUE(exp.isSameShape(result)); ASSERT_TRUE(exp.equalsTo(result)); - - } //////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests13, adjustHue_2) { - NDArray input('c', { 2,2,3 }, { 0.f,100.f / 255.f,56.f / 255.f, 17.f / 255.f,220.f / 255.f,5.f / 255.f, 150.f / 255.f,97.f / 255.f,230.f / 255.f, 255.f / 255.f,2.f / 255.f,13.f / 255.f }, nd4j::DataType::FLOAT32); - NDArray exp('c', { 2,2,3 }, { 4.f / 255.f,100.f / 255.f,0.f, 146.f / 255.f,220.f / 255.f,5.f / 255.f, 97.f / 255.f,123.8f / 255.f,230.f / 255.f, 255.f / 255.f,2.f / 255.f,164.8f / 255.f }, nd4j::DataType::FLOAT32); + NDArray input('c', { 2,2,3 }, { 0.f,100.f / 255.f,56.f / 255.f, 17.f / 255.f,220.f / 255.f,5.f / 255.f, 150.f / 255.f,97.f / 255.f,230.f / 255.f, 255.f / 255.f,2.f / 255.f,13.f / 255.f }, sd::DataType::FLOAT32); + NDArray exp('c', { 2,2,3 }, { 4.f / 255.f,100.f / 255.f,0.f, 146.f / 255.f,220.f / 255.f,5.f / 255.f, 97.f / 255.f,123.8f / 255.f,230.f / 255.f, 255.f / 255.f,2.f / 255.f,164.8f / 255.f }, sd::DataType::FLOAT32); - nd4j::ops::adjust_hue op; - std::unique_ptr results(op.evaluate({&input}, {0.9}, {2})); + sd::ops::adjust_hue op; + auto results(op.evaluate({&input}, {0.9}, {2})); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto result = results->at(0); + auto result = results.at(0); ASSERT_TRUE(exp.isSameShape(result)); ASSERT_TRUE(exp.equalsTo(result)); - - } //////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests13, adjustHue_3) { - NDArray input('c', {2,2,3}, {0,100,56, 17,220,5, 150,97,230, 255,2,13}, nd4j::DataType::FLOAT32); - NDArray exp ('c', {2,2,3}, {0.,84.,100., 5.,220.,122.0001, 229.8,97.,230., 255.,142.8002,2.}, nd4j::DataType::FLOAT32); + NDArray input('c', {2,2,3}, {0,100,56, 17,220,5, 150,97,230, 255,2,13}, sd::DataType::FLOAT32); + NDArray exp ('c', {2,2,3}, {0.,84.,100., 5.,220.,122.0001, 229.8,97.,230., 255.,142.8002,2.}, sd::DataType::FLOAT32); - nd4j::ops::adjust_hue op; - std::unique_ptr results(op.evaluate({&input}, {-0.9}, {2})); + sd::ops::adjust_hue op; + auto results(op.evaluate({&input}, {-0.9}, {2})); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto result = results->at(0); + auto result = results.at(0); ASSERT_TRUE(exp.isSameShape(result)); ASSERT_TRUE(exp.equalsTo(result)); - - } //////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests13, adjustHue_4) { - NDArray input('c', {2,3,2}, {0,17, 100,220, 56,5, 150,255, 97,2, 230,13}, nd4j::DataType::FLOAT32); - NDArray exp ('c', {2,3,2}, {100,208, 0,5, 44,220, 177,2, 230,255, 97,244}, nd4j::DataType::FLOAT32); + NDArray input('c', {2,3,2}, {0,17, 100,220, 56,5, 150,255, 97,2, 230,13}, sd::DataType::FLOAT32); + NDArray exp ('c', {2,3,2}, {100,208, 0,5, 44,220, 177,2, 230,255, 97,244}, sd::DataType::FLOAT32); - nd4j::ops::adjust_hue op; - std::unique_ptr results(op.evaluate({&input}, {0.5}, {1})); + sd::ops::adjust_hue op; + auto results(op.evaluate({&input}, {0.5}, {1})); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto result = results->at(0); + auto result = results.at(0); ASSERT_TRUE(exp.isSameShape(result)); ASSERT_TRUE(exp.equalsTo(result)); - - } //////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests13, adjustHue_5) { - NDArray input('c', {3,2,2}, {0,17, 150,255, 100,220, 97,2, 56,5, 230,13}, nd4j::DataType::FLOAT32); - NDArray exp ('c', {3,2,2}, {100,208, 177,2, 0,5, 230,255, 44,220, 97,244}, nd4j::DataType::FLOAT32); + NDArray input('c', {3,2,2}, {0,17, 150,255, 100,220, 97,2, 56,5, 230,13}, sd::DataType::FLOAT32); + NDArray exp ('c', {3,2,2}, {100,208, 177,2, 0,5, 230,255, 44,220, 97,244}, sd::DataType::FLOAT32); - nd4j::ops::adjust_hue op; - std::unique_ptr results(op.evaluate({&input}, {0.5}, {0})); + sd::ops::adjust_hue op; + auto results(op.evaluate({&input}, {0.5}, {0})); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto result = results->at(0); + auto result = results.at(0); ASSERT_TRUE(exp.isSameShape(result)); ASSERT_TRUE(exp.equalsTo(result)); - } //////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests13, adjustSaturation_1) { - NDArray input('c', {2,2,3}, {0,100,56, 17,220,5, 150,97,230, 255,2,13}, nd4j::DataType::FLOAT32); + NDArray input('c', {2,2,3}, {0,100,56, 17,220,5, 150,97,230, 255,2,13}, sd::DataType::FLOAT32); NDArray factor = NDArrayFactory::create(0.5); - NDArray exp ('c', {2,2,3}, {50,100,78, 118.5,220,112.5, 190,163.5,230, 255,128.5,134}, nd4j::DataType::FLOAT32); + NDArray exp ('c', {2,2,3}, {50,100,78, 118.5,220,112.5, 190,163.5,230, 255,128.5,134}, sd::DataType::FLOAT32); - nd4j::ops::adjust_saturation op; + sd::ops::adjust_saturation op; auto results = op.evaluate({&input, &factor}, {}, {2}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto result = results->at(0); + auto result = results.at(0); ASSERT_TRUE(exp.isSameShape(result)); ASSERT_TRUE(exp.equalsTo(result)); - delete results; } //////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests13, adjustSaturation_2) { - NDArray input('c', {2,2,3}, {0,100,56, 17,220,5, 150,97,230, 255,2,13}, nd4j::DataType::DOUBLE); - NDArray exp ('c', {2,2,3}, {0.,100.,56., 12.279087,220.,0., 91.654228,0.,230., 255.,0.,11.087015}, nd4j::DataType::DOUBLE); + NDArray input('c', {2,2,3}, {0,100,56, 17,220,5, 150,97,230, 255,2,13}, sd::DataType::DOUBLE); + NDArray exp ('c', {2,2,3}, {0.,100.,56., 12.279087,220.,0., 91.654228,0.,230., 255.,0.,11.087015}, sd::DataType::DOUBLE); - nd4j::ops::adjust_saturation op; + sd::ops::adjust_saturation op; auto results = op.evaluate({&input}, {10}, {2}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto result = results->at(0); -// result->printIndexedBuffer("Result2"); + auto result = results.at(0); +// result.printIndexedBuffer("Result2"); // exp.printIndexedBuffer("Expect2"); ASSERT_TRUE(exp.isSameShape(result)); ASSERT_TRUE(exp.equalsTo(result)); - delete results; } //////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests13, adjustSaturation_3) { - NDArray input('c', {2,2,3}, {0,100,56, 17,220,5, 150,97,230, 255,2,13}, nd4j::DataType::FLOAT32); - NDArray exp ('c', {2,2,3}, {100.,100.,100., 220.,220.,220., 230.,230.,230., 255., 255., 255.}, nd4j::DataType::FLOAT32); + NDArray input('c', {2,2,3}, {0,100,56, 17,220,5, 150,97,230, 255,2,13}, sd::DataType::FLOAT32); + NDArray exp ('c', {2,2,3}, {100.,100.,100., 220.,220.,220., 230.,230.,230., 255., 255., 255.}, sd::DataType::FLOAT32); - nd4j::ops::adjust_saturation op; + sd::ops::adjust_saturation op; auto results = op.evaluate({&input}, {-10}, {2}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto result = results->at(0); + auto result = results.at(0); ASSERT_TRUE(exp.isSameShape(result)); ASSERT_TRUE(exp.equalsTo(result)); - delete results; } //////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests13, adjustSaturation_4) { - NDArray input('c', {2,3,2}, {0,17, 100,220, 56,5, 150,255, 97,2, 230,13}, nd4j::DataType::FLOAT32); - NDArray exp ('c', {2,3,2}, {50,118.5, 100,220, 78,112.5, 190,255, 163.5,128.5, 230,134}, nd4j::DataType::FLOAT32); + NDArray input('c', {2,3,2}, {0,17, 100,220, 56,5, 150,255, 97,2, 230,13}, sd::DataType::FLOAT32); + NDArray exp ('c', {2,3,2}, {50,118.5, 100,220, 78,112.5, 190,255, 163.5,128.5, 230,134}, sd::DataType::FLOAT32); - nd4j::ops::adjust_saturation op; + sd::ops::adjust_saturation op; auto results = op.evaluate({&input}, {0.5}, {1}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto result = results->at(0); - // result->printIndexedBuffer(); + auto result = results.at(0); + // result.printIndexedBuffer(); ASSERT_TRUE(exp.isSameShape(result)); ASSERT_TRUE(exp.equalsTo(result)); - delete results; } //////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests13, adjustSaturation_5) { - NDArray input('c', {3,2,2}, {0,17, 150,255, 100,220, 97,2, 56,5, 230,13}, nd4j::DataType::FLOAT32); - NDArray exp ('c', {3,2,2}, {50,118.5, 190,255, 100,220, 163.5,128.5, 78,112.5, 230,134}, nd4j::DataType::FLOAT32); + NDArray input('c', {3,2,2}, {0,17, 150,255, 100,220, 97,2, 56,5, 230,13}, sd::DataType::FLOAT32); + NDArray exp ('c', {3,2,2}, {50,118.5, 190,255, 100,220, 163.5,128.5, 78,112.5, 230,134}, sd::DataType::FLOAT32); - nd4j::ops::adjust_saturation op; + sd::ops::adjust_saturation op; auto results = op.evaluate({&input}, {0.5}, {0}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto result = results->at(0); + auto result = results.at(0); ASSERT_TRUE(exp.isSameShape(result)); ASSERT_TRUE(exp.equalsTo(result)); - delete results; } @@ -645,15 +617,14 @@ TEST_F(DeclarableOpsTests13, shift_bits_1) { x.assign(32); e.assign(512); - nd4j::ops::shift_bits op; + sd::ops::shift_bits op; auto result = op.evaluate({&x, &y}, {}, {}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_EQ(e, *z); - delete result; } TEST_F(DeclarableOpsTests13, rshift_bits_1) { @@ -663,15 +634,14 @@ TEST_F(DeclarableOpsTests13, rshift_bits_1) { x.assign(512); e.assign(32); - nd4j::ops::rshift_bits op; + sd::ops::rshift_bits op; auto result = op.evaluate({&x, &y}, {}, {}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_EQ(e, *z); - delete result; } TEST_F(DeclarableOpsTests13, cyclic_shift_bits_1) { @@ -681,15 +651,14 @@ TEST_F(DeclarableOpsTests13, cyclic_shift_bits_1) { x.assign(32); e.assign(512); - nd4j::ops::cyclic_shift_bits op; + sd::ops::cyclic_shift_bits op; auto result = op.evaluate({&x, &y}, {}, {}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_EQ(e, *z); - delete result; } TEST_F(DeclarableOpsTests13, cyclic_rshift_bits_1) { @@ -699,15 +668,14 @@ TEST_F(DeclarableOpsTests13, cyclic_rshift_bits_1) { x.assign(512); e.assign(32); - nd4j::ops::cyclic_rshift_bits op; + sd::ops::cyclic_rshift_bits op; auto result = op.evaluate({&x, &y}, {}, {}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_EQ(e, *z); - delete result; } TEST_F(DeclarableOpsTests13, shift_bits_2) { @@ -718,15 +686,14 @@ TEST_F(DeclarableOpsTests13, shift_bits_2) { y.assign(4); e.assign(512); - nd4j::ops::shift_bits op; + sd::ops::shift_bits op; auto result = op.evaluate({&x, &y}, {}, {}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_EQ(e, *z); - delete result; } TEST_F(DeclarableOpsTests13, rshift_bits_2) { @@ -737,15 +704,15 @@ TEST_F(DeclarableOpsTests13, rshift_bits_2) { y.assign(4); e.assign(32); - nd4j::ops::rshift_bits op; + sd::ops::rshift_bits op; auto result = op.evaluate({&x, &y}, {}, {}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_EQ(e, *z); - delete result; + } TEST_F(DeclarableOpsTests13, cyclic_shift_bits_2) { @@ -756,15 +723,14 @@ TEST_F(DeclarableOpsTests13, cyclic_shift_bits_2) { y.assign(4); e.assign(512); - nd4j::ops::cyclic_shift_bits op; + sd::ops::cyclic_shift_bits op; auto result = op.evaluate({&x, &y}, {}, {}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_EQ(e, *z); - delete result; } TEST_F(DeclarableOpsTests13, cyclic_rshift_bits_2) { @@ -775,15 +741,14 @@ TEST_F(DeclarableOpsTests13, cyclic_rshift_bits_2) { y.assign(4); e.assign(32); - nd4j::ops::cyclic_rshift_bits op; + sd::ops::cyclic_rshift_bits op; auto result = op.evaluate({&x, &y}, {}, {}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_EQ(e, *z); - delete result; } TEST_F(DeclarableOpsTests13, shift_bits_3) { auto x = NDArrayFactory::create('c', {5, 5}); @@ -793,75 +758,72 @@ TEST_F(DeclarableOpsTests13, shift_bits_3) { y.assign(4); e.assign(512); - nd4j::ops::shift_bits op; + sd::ops::shift_bits op; auto result = op.evaluate({&x, &y}, {}, {}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_EQ(e, *z); - delete result; } //////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests13, space_to_batch_nd_1) { - NDArray x('c', {1, 2, 2, 2, 3}, nd4j::DataType::FLOAT32); - NDArray blockShape('c', {3}, {2, 2, 2} , nd4j::DataType::INT32); // three spatial dimensions - NDArray paddings('c', {3, 2}, std::vector{0, 0, 0, 0, 0, 0} , nd4j::DataType::INT32); + NDArray x('c', {1, 2, 2, 2, 3}, sd::DataType::FLOAT32); + NDArray blockShape('c', {3}, {2, 2, 2} , sd::DataType::INT32); // three spatial dimensions + NDArray paddings('c', {3, 2}, std::vector{0, 0, 0, 0, 0, 0} , sd::DataType::INT32); - NDArray exp('c', {8, 1, 1, 1, 3}, nd4j::DataType::FLOAT32); + NDArray exp('c', {8, 1, 1, 1, 3}, sd::DataType::FLOAT32); x.linspace(1); exp.linspace(1); - nd4j::ops::space_to_batch_nd op; + sd::ops::space_to_batch_nd op; auto result = op.evaluate({&x, &blockShape, &paddings}, {}, {}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; } //////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests13, space_to_batch_nd_2) { - NDArray x('c', {2, 2,4,3, 1}, nd4j::DataType::FLOAT32); - NDArray blockShape('c', {3}, {2, 2, 3} , nd4j::DataType::INT32); // three spatial dimensions - NDArray paddings('c', {3, 2}, {0,0, 0,2, 2,1} , nd4j::DataType::INT32); + NDArray x('c', {2, 2,4,3, 1}, sd::DataType::FLOAT32); + NDArray blockShape('c', {3}, {2, 2, 3} , sd::DataType::INT32); // three spatial dimensions + NDArray paddings('c', {3, 2}, {0,0, 0,2, 2,1} , sd::DataType::INT32); NDArray exp('c', {24, 1,3,2, 1}, { 0, 2, 0, 8, 0, 0, 0, 26, 0, 32, 0, 0, 0, 3, 0, 9, 0, 0, 0, 27, 0, 33, 0, 0, 1, 0, 7, 0, 0, 0, 25, 0, 31, 0, 0, 0, 0, 5, 0, 11, 0, 0, 0, 29, 0, 35, 0, 0, 0, 6, 0, 12, 0, 0, 0, 30, 0, 36, 0, 0, 4, 0, 10, 0, 0, 0, 28, 0, 34, 0, 0, 0, 0, 14, 0, 20, 0, 0, 0, 38, 0, 44, 0, 0, 0, 15, 0, 21, 0, 0, 0, 39, 0, 45, 0, 0, 13, 0, 19, 0, 0, 0, 37, 0, 43, 0, 0, 0, 0, 17, 0, 23, 0, 0, 0, 41, 0, 47, 0, 0, 0, 18, - 0, 24, 0, 0, 0, 42, 0, 48, 0, 0, 16, 0, 22, 0, 0, 0, 40, 0, 46, 0, 0, 0}, nd4j::DataType::FLOAT32); + 0, 24, 0, 0, 0, 42, 0, 48, 0, 0, 16, 0, 22, 0, 0, 0, 40, 0, 46, 0, 0, 0}, sd::DataType::FLOAT32); x.linspace(1); - nd4j::ops::space_to_batch_nd op; + sd::ops::space_to_batch_nd op; auto result = op.evaluate({&x, &blockShape, &paddings}, {}, {}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); // z->printBuffer(); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; } //////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests13, space_to_batch_nd_3) { - NDArray x('c', {2, 2,4,3, 1}, nd4j::DataType::FLOAT32); - NDArray blockShape('c', {3}, {2, 2, 3} , nd4j::DataType::INT32); // three spatial dimensions - NDArray paddings('c', {3, 2}, {1,1, 0,2, 2,1} , nd4j::DataType::INT32); + NDArray x('c', {2, 2,4,3, 1}, sd::DataType::FLOAT32); + NDArray blockShape('c', {3}, {2, 2, 3} , sd::DataType::INT32); // three spatial dimensions + NDArray paddings('c', {3, 2}, {1,1, 0,2, 2,1} , sd::DataType::INT32); NDArray exp('c', {24, 2,3,2, 1}, { 0, 0, 0, 0, 0, 0, 0, 14, 0, 20, 0, 0, 0, 0, 0, 0, 0, 0, 0, 38, 0, 44, 0, 0, 0, 0, 0, 0, 0, 0, 0, 15, 0, 21, 0, 0, 0, 0, 0, 0, 0, 0, 0, 39, 0, 45, 0, 0, 0, 0, 0, 0, 0, 0, 13, 0, 19, 0, 0, 0, 0, 0, 0, 0, @@ -871,129 +833,124 @@ TEST_F(DeclarableOpsTests13, space_to_batch_nd_3) { 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 27, 0, 33, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 25, 0, 31, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 11, 0, 0, 0, 0, 0, 0, 0, 0, 0, 29, 0, 35, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 12, 0, 0, 0, 0, 0, 0, 0, 0, 0, 30, 0, 36, 0, 0, - 0, 0, 0, 0, 0, 0, 4, 0, 10, 0, 0, 0, 0, 0, 0, 0, 0, 0, 28, 0, 34, 0, 0, 0, 0, 0, 0, 0, 0, 0}, nd4j::DataType::FLOAT32); + 0, 0, 0, 0, 0, 0, 4, 0, 10, 0, 0, 0, 0, 0, 0, 0, 0, 0, 28, 0, 34, 0, 0, 0, 0, 0, 0, 0, 0, 0}, sd::DataType::FLOAT32); x.linspace(1); - nd4j::ops::space_to_batch_nd op; + sd::ops::space_to_batch_nd op; auto result = op.evaluate({&x, &blockShape, &paddings}, {}, {}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); // z->printBuffer(); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; } //////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests13, batch_to_space_nd_1) { - NDArray x('c', {8, 1, 1, 1, 3}, nd4j::DataType::FLOAT32); + NDArray x('c', {8, 1, 1, 1, 3}, sd::DataType::FLOAT32); - NDArray blockShape('c', {3}, {2., 2, 2} , nd4j::DataType::INT32); // three spatial dimensions - NDArray crop('c', {3, 2}, {0., 0, 0, 0, 0, 0} , nd4j::DataType::INT32); + NDArray blockShape('c', {3}, {2., 2, 2} , sd::DataType::INT32); // three spatial dimensions + NDArray crop('c', {3, 2}, {0., 0, 0, 0, 0, 0} , sd::DataType::INT32); - NDArray exp('c', {1, 2, 2, 2, 3}, nd4j::DataType::FLOAT32); + NDArray exp('c', {1, 2, 2, 2, 3}, sd::DataType::FLOAT32); x.linspace(1); exp.linspace(1); - nd4j::ops::batch_to_space_nd op; + sd::ops::batch_to_space_nd op; auto result = op.evaluate({&x, &blockShape, &crop}, {}, {}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; } //////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests13, batch_to_space_nd_2) { - NDArray x('c', {24, 1,3,2, 1}, nd4j::DataType::FLOAT32); - NDArray blockShape('c', {3}, {2, 2, 3} , nd4j::DataType::INT32); // three spatial dimensions - NDArray crop('c', {3, 2}, {0,0, 0,2, 2,1} , nd4j::DataType::INT32); + NDArray x('c', {24, 1,3,2, 1}, sd::DataType::FLOAT32); + NDArray blockShape('c', {3}, {2, 2, 3} , sd::DataType::INT32); // three spatial dimensions + NDArray crop('c', {3, 2}, {0,0, 0,2, 2,1} , sd::DataType::INT32); NDArray exp('c', {2, 2,4,3, 1}, {25, 2, 14, 61, 38, 50, 27, 4, 16, 63, 40, 52, 97, 74, 86, 133, 110, 122, 99, 76, 88, 135, 112, 124, - 31, 8, 20, 67, 44, 56, 33, 10, 22, 69, 46, 58, 103, 80, 92, 139, 116, 128, 105, 82, 94, 141, 118, 130}, nd4j::DataType::FLOAT32); + 31, 8, 20, 67, 44, 56, 33, 10, 22, 69, 46, 58, 103, 80, 92, 139, 116, 128, 105, 82, 94, 141, 118, 130}, sd::DataType::FLOAT32); x.linspace(1); - nd4j::ops::batch_to_space_nd op; + sd::ops::batch_to_space_nd op; auto result = op.evaluate({&x, &blockShape, &crop}, {}, {}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); // z->printBuffer(); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; } //////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests13, batch_to_space_nd_3) { - NDArray x('c', {24, 2,3,2, 1}, nd4j::DataType::FLOAT32); - NDArray blockShape('c', {3}, {2, 2, 3} , nd4j::DataType::INT32); // three spatial dimensions - NDArray crop('c', {3, 2}, {1,1, 0,2, 2,1} , nd4j::DataType::INT32); + NDArray x('c', {24, 2,3,2, 1}, sd::DataType::FLOAT32); + NDArray blockShape('c', {3}, {2, 2, 3} , sd::DataType::INT32); // three spatial dimensions + NDArray crop('c', {3, 2}, {1,1, 0,2, 2,1} , sd::DataType::INT32); NDArray exp('c', {2, 2,4,3, 1}, {193, 146, 170, 265, 218, 242, 195, 148, 172, 267, 220, 244, 55, 8, 32, 127, 80, 104, 57, 10, 34, 129, 82, - 106, 205, 158, 182, 277, 230, 254, 207, 160, 184, 279, 232, 256, 67, 20, 44, 139, 92, 116, 69, 22, 46, 141, 94, 118}, nd4j::DataType::FLOAT32); + 106, 205, 158, 182, 277, 230, 254, 207, 160, 184, 279, 232, 256, 67, 20, 44, 139, 92, 116, 69, 22, 46, 141, 94, 118}, sd::DataType::FLOAT32); x.linspace(1); - nd4j::ops::batch_to_space_nd op; + sd::ops::batch_to_space_nd op; auto result = op.evaluate({&x, &blockShape, &crop}, {}, {}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); // z->printBuffer(); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; } ////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests13, mergemax_1) { - NDArray x1('c', {5, 5}, nd4j::DataType::FLOAT32); - NDArray x2('c', {5, 5}, nd4j::DataType::FLOAT32); - NDArray x3('c', {5, 5}, nd4j::DataType::FLOAT32); - NDArray e('c', {5, 5}, nd4j::DataType::FLOAT32); + NDArray x1('c', {5, 5}, sd::DataType::FLOAT32); + NDArray x2('c', {5, 5}, sd::DataType::FLOAT32); + NDArray x3('c', {5, 5}, sd::DataType::FLOAT32); + NDArray e('c', {5, 5}, sd::DataType::FLOAT32); x1.assign(3); x2.assign(1); x3.assign(2); e.assign(3); - nd4j::ops::mergemax op; + sd::ops::mergemax op; auto result = op.evaluate({&x1, &x2, &x3}, {}, {}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); // z->printBuffer(); ASSERT_TRUE(e.isSameShape(z)); ASSERT_TRUE(e.equalsTo(z)); - delete result; } ////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests13, mergemax_2) { - NDArray x1('c', {1, 3}, {0., 1, 2}, nd4j::DataType::FLOAT32); - NDArray x2('c', {1, 1}, std::vector{1.}, nd4j::DataType::FLOAT32); - NDArray out('c', {1, 3}, {-1., -1, -1}, nd4j::DataType::FLOAT32); + NDArray x1('c', {1, 3}, {0., 1, 2}, sd::DataType::FLOAT32); + NDArray x2('c', {1, 1}, std::vector{1.}, sd::DataType::FLOAT32); + NDArray out('c', {1, 3}, {-1., -1, -1}, sd::DataType::FLOAT32); - nd4j::ops::mergemax op; + sd::ops::mergemax op; auto status = op.execute({&x1, &x2}, {&out}, {}, {}, {}); ASSERT_EQ(20, status); @@ -1026,12 +983,12 @@ TEST_F(DeclarableOpsTests13, lstmLayer_1) { const double cellClip = 0; // do not apply clipping - NDArray x('c', {sL, bS, nIn}, nd4j::DataType::FLOAT32); - NDArray Wx('c', {nIn, 4*nOut}, nd4j::DataType::FLOAT32); - NDArray Wr('c', {nOut, 4*nOut}, nd4j::DataType::FLOAT32); - NDArray b('c', {4*nOut}, nd4j::DataType::FLOAT32); - NDArray hI('c', {bS, nOut}, nd4j::DataType::FLOAT32); - NDArray cI('c', {bS, nOut}, nd4j::DataType::FLOAT32); + NDArray x('c', {sL, bS, nIn}, sd::DataType::FLOAT32); + NDArray Wx('c', {nIn, 4*nOut}, sd::DataType::FLOAT32); + NDArray Wr('c', {nOut, 4*nOut}, sd::DataType::FLOAT32); + NDArray b('c', {4*nOut}, sd::DataType::FLOAT32); + NDArray hI('c', {bS, nOut}, sd::DataType::FLOAT32); + NDArray cI('c', {bS, nOut}, sd::DataType::FLOAT32); x.linspace(0.5, 0.5); Wx = 0.003; @@ -1052,13 +1009,13 @@ TEST_F(DeclarableOpsTests13, lstmLayer_1) { auto expClast = NDArrayFactory::create('c', {bS, nOut}, {1.1589154f, 1.1589154f, 1.1589154f, 1.1892855f, 1.1892855f, 1.1892855f, 1.219861f, 1.219861f, 1.219861f}); - nd4j::ops::lstmLayer op; + sd::ops::lstmLayer op; auto results = op.evaluate({&x, &Wx, &Wr, &b, &hI, &cI}, tArgs, iArgs, bArgs); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *h = results->at(0); - auto *cL = results->at(2); + auto *h = results.at(0); + auto *cL = results.at(2); ASSERT_TRUE(expH.isSameShape(h)); ASSERT_TRUE(expH.equalsTo(h)); @@ -1066,7 +1023,6 @@ TEST_F(DeclarableOpsTests13, lstmLayer_1) { ASSERT_TRUE(expClast.isSameShape(cL)); ASSERT_TRUE(expClast.equalsTo(cL)); - delete results; } /////////////////////////////////////////////////////////////////// @@ -1096,12 +1052,12 @@ TEST_F(DeclarableOpsTests13, lstmLayer_2) { const double cellClip = 0; // do not apply clipping - NDArray x('c', {bS, sL, nIn}, nd4j::DataType::FLOAT32); - NDArray Wx('c', {nIn, 4*nOut}, nd4j::DataType::FLOAT32); - NDArray Wr('c', {nOut, 4*nOut}, nd4j::DataType::FLOAT32); - NDArray b('c', {4*nOut}, nd4j::DataType::FLOAT32); - NDArray hI('c', {bS, nOut}, nd4j::DataType::FLOAT32); - NDArray cI('c', {bS, nOut}, nd4j::DataType::FLOAT32); + NDArray x('c', {bS, sL, nIn}, sd::DataType::FLOAT32); + NDArray Wx('c', {nIn, 4*nOut}, sd::DataType::FLOAT32); + NDArray Wr('c', {nOut, 4*nOut}, sd::DataType::FLOAT32); + NDArray b('c', {4*nOut}, sd::DataType::FLOAT32); + NDArray hI('c', {bS, nOut}, sd::DataType::FLOAT32); + NDArray cI('c', {bS, nOut}, sd::DataType::FLOAT32); x.linspace(0.5, 0.5); Wx = 0.003; @@ -1120,13 +1076,13 @@ TEST_F(DeclarableOpsTests13, lstmLayer_2) { auto expClast = NDArrayFactory::create('c', {bS, nOut}, {0.996965f, 0.996965f, 0.996965f, 1.146756f, 1.146756f, 1.146756f, 1.301922f, 1.301922f, 1.301922f}); - nd4j::ops::lstmLayer op; + sd::ops::lstmLayer op; auto results = op.evaluate({&x, &Wx, &Wr, &b, &hI, &cI}, tArgs, iArgs, bArgs); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *h = results->at(0); - auto *cL = results->at(2); + auto *h = results.at(0); + auto *cL = results.at(2); ASSERT_TRUE(expH.isSameShape(h)); ASSERT_TRUE(expH.equalsTo(h)); @@ -1134,7 +1090,6 @@ TEST_F(DeclarableOpsTests13, lstmLayer_2) { ASSERT_TRUE(expClast.isSameShape(cL)); ASSERT_TRUE(expClast.equalsTo(cL)); - delete results; } /////////////////////////////////////////////////////////////////// @@ -1164,12 +1119,12 @@ TEST_F(DeclarableOpsTests13, lstmLayer_3) { const double cellClip = 0; // do not apply clipping - NDArray x('c', {sL,bS, nIn}, nd4j::DataType::FLOAT32); - NDArray Wx('c', {nIn, 4*nOut}, nd4j::DataType::FLOAT32); - NDArray Wr('c', {nOut, 4*nOut}, nd4j::DataType::FLOAT32); - NDArray b('c', {4*nOut}, nd4j::DataType::FLOAT32); - NDArray hI('c', {bS, nOut}, nd4j::DataType::FLOAT32); - NDArray cI('c', {bS, nOut}, nd4j::DataType::FLOAT32); + NDArray x('c', {sL,bS, nIn}, sd::DataType::FLOAT32); + NDArray Wx('c', {nIn, 4*nOut}, sd::DataType::FLOAT32); + NDArray Wr('c', {nOut, 4*nOut}, sd::DataType::FLOAT32); + NDArray b('c', {4*nOut}, sd::DataType::FLOAT32); + NDArray hI('c', {bS, nOut}, sd::DataType::FLOAT32); + NDArray cI('c', {bS, nOut}, sd::DataType::FLOAT32); x.linspace(0.5, 0.5); Wx = 0.003; @@ -1184,19 +1139,19 @@ TEST_F(DeclarableOpsTests13, lstmLayer_3) { NDArray expH('c', {sL, bS, nOut}, {0.493883f, 0.493883f, 0.493883f, 0.510990f, 0.510990f, 0.510990f, 0.534701f, 0.534701f, 0.534701f, 0.549139f, 0.549139f, 0.549139f, 0.571900f, 0.571900f, 0.571900f, 0.583561f, 0.583561f, 0.583561f, 0.605106f, 0.605106f, - 0.605106f, 0.614114f, 0.614114f, 0.614114f, 0.635354f, 0.635354f, 0.635354f, 0.642045f, 0.642045f, 0.642045f}, nd4j::DataType::FLOAT32); + 0.605106f, 0.614114f, 0.614114f, 0.614114f, 0.635354f, 0.635354f, 0.635354f, 0.642045f, 0.642045f, 0.642045f}, sd::DataType::FLOAT32); - NDArray expHL('c', {bS, nOut}, {0.493883f, 0.493883f, 0.493883f, 0.510990f, 0.510990f, 0.510990f}, nd4j::DataType::FLOAT32); - NDArray expCL('c', {bS, nOut}, {1.061274f, 1.061274f, 1.061274f, 1.115888f, 1.115888f, 1.115888f}, nd4j::DataType::FLOAT32); + NDArray expHL('c', {bS, nOut}, {0.493883f, 0.493883f, 0.493883f, 0.510990f, 0.510990f, 0.510990f}, sd::DataType::FLOAT32); + NDArray expCL('c', {bS, nOut}, {1.061274f, 1.061274f, 1.061274f, 1.115888f, 1.115888f, 1.115888f}, sd::DataType::FLOAT32); - nd4j::ops::lstmLayer op; + sd::ops::lstmLayer op; auto results = op.evaluate({&x, &Wx, &Wr, &b, &hI, &cI}, tArgs, iArgs, bArgs); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto h = results->at(0); - auto hL = results->at(1); - auto cL = results->at(2); + auto h = results.at(0); + auto hL = results.at(1); + auto cL = results.at(2); ASSERT_TRUE(expH.isSameShape(h)); ASSERT_TRUE(expH.equalsTo(h)); @@ -1207,7 +1162,6 @@ TEST_F(DeclarableOpsTests13, lstmLayer_3) { ASSERT_TRUE(expCL.isSameShape(cL)); ASSERT_TRUE(expCL.equalsTo(cL)); - delete results; } @@ -1237,12 +1191,12 @@ TEST_F(DeclarableOpsTests13, lstmLayer_4) { const double cellClip = 0; // do not apply clipping - NDArray x('c', {sL, bS, nIn}, nd4j::DataType::FLOAT32); - NDArray Wx('c', {2,nIn, 4*nOut}, nd4j::DataType::FLOAT32); - NDArray Wr('c', {2,nOut, 4*nOut}, nd4j::DataType::FLOAT32); - NDArray b('c', {2,4*nOut}, nd4j::DataType::FLOAT32); - NDArray hI('c', {2,bS, nOut}, nd4j::DataType::FLOAT32); - NDArray cI('c', {2,bS, nOut}, nd4j::DataType::FLOAT32); + NDArray x('c', {sL, bS, nIn}, sd::DataType::FLOAT32); + NDArray Wx('c', {2,nIn, 4*nOut}, sd::DataType::FLOAT32); + NDArray Wr('c', {2,nOut, 4*nOut}, sd::DataType::FLOAT32); + NDArray b('c', {2,4*nOut}, sd::DataType::FLOAT32); + NDArray hI('c', {2,bS, nOut}, sd::DataType::FLOAT32); + NDArray cI('c', {2,bS, nOut}, sd::DataType::FLOAT32); x.linspace(0.5, 0.5); Wx({0,1, 0,0, 0,0}) = 0.003f; @@ -1267,21 +1221,21 @@ TEST_F(DeclarableOpsTests13, lstmLayer_4) { -0.123305f, -0.123305f, -0.123305f, 0.560640f, 0.560640f, 0.560640f, -0.120862f, -0.120862f, -0.120862f, 0.550714f, 0.550714f, 0.550714f, -0.156223f, -0.156223f, -0.156223f, 0.565308f, 0.565308f, 0.565308f, -0.152313f, -0.152313f, -0.152313f, 0.563741f, 0.563741f, 0.563741f, -0.234128f, -0.234128f, -0.234128f, - 0.578676f, 0.578676f, 0.578676f, -0.228917f, -0.228917f, -0.228917f}, nd4j::DataType::FLOAT32); + 0.578676f, 0.578676f, 0.578676f, -0.228917f, -0.228917f, -0.228917f}, sd::DataType::FLOAT32); NDArray expHL('c', {2,bS, nOut}, {0.563741f, 0.563741f, 0.563741f, 0.578676f, 0.578676f, 0.578676f, -0.107642f, - -0.107642f, -0.107642f, -0.106937f, -0.106937f, -0.106937f}, nd4j::DataType::FLOAT32); + -0.107642f, -0.107642f, -0.106937f, -0.106937f, -0.106937f}, sd::DataType::FLOAT32); NDArray expCL('c', {2,bS, nOut}, {1.217757f, 1.217757f, 1.217757f, 1.272398f, 1.272398f, 1.272398f, -0.295768f, - -0.295768f, -0.295768f, -0.298453f, -0.298453f, -0.298453f}, nd4j::DataType::FLOAT32); + -0.295768f, -0.295768f, -0.298453f, -0.298453f, -0.298453f}, sd::DataType::FLOAT32); - nd4j::ops::lstmLayer op; + sd::ops::lstmLayer op; auto results = op.evaluate({&x, &Wx, &Wr, &b, &hI, &cI}, tArgs, iArgs, bArgs); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto h = results->at(0); - auto hL = results->at(1); - auto cL = results->at(2); + auto h = results.at(0); + auto hL = results.at(1); + auto cL = results.at(2); ASSERT_TRUE(expH.isSameShape(h)); ASSERT_TRUE(expH.equalsTo(h)); @@ -1291,8 +1245,7 @@ TEST_F(DeclarableOpsTests13, lstmLayer_4) { ASSERT_TRUE(expCL.isSameShape(cL)); ASSERT_TRUE(expCL.equalsTo(cL)); - - delete results; + } /////////////////////////////////////////////////////////////////// @@ -1321,12 +1274,12 @@ TEST_F(DeclarableOpsTests13, lstmLayer_5) { const double cellClip = 0; // do not apply clipping - NDArray x('c', {bS, sL, nIn}, nd4j::DataType::FLOAT32); - NDArray Wx('c', {2,nIn, 4*nOut}, nd4j::DataType::FLOAT32); - NDArray Wr('c', {2,nOut, 4*nOut}, nd4j::DataType::FLOAT32); - NDArray b('c', {2,4*nOut}, nd4j::DataType::FLOAT32); - NDArray hI('c', {2,bS, nOut}, nd4j::DataType::FLOAT32); - NDArray cI('c', {2,bS, nOut}, nd4j::DataType::FLOAT32); + NDArray x('c', {bS, sL, nIn}, sd::DataType::FLOAT32); + NDArray Wx('c', {2,nIn, 4*nOut}, sd::DataType::FLOAT32); + NDArray Wr('c', {2,nOut, 4*nOut}, sd::DataType::FLOAT32); + NDArray b('c', {2,4*nOut}, sd::DataType::FLOAT32); + NDArray hI('c', {2,bS, nOut}, sd::DataType::FLOAT32); + NDArray cI('c', {2,bS, nOut}, sd::DataType::FLOAT32); x.linspace(0.5, 0.5); Wx({0,1, 0,0, 0,0}) = 0.003; @@ -1349,21 +1302,21 @@ TEST_F(DeclarableOpsTests13, lstmLayer_5) { 0.526881f, 0.526881f, 0.526881f, -0.12883f, -0.12883f, -0.12883f, 0.515882f, 0.515882f, 0.515882f, -0.16868f, -0.16868f, -0.16868f, 0.51409f, 0.51409f, 0.51409f, -0.255185f, -0.255185f, -0.255185f, 0.614599f, 0.614599f, 0.614599f, -0.102739f, -0.102739f, -0.102739f, 0.599572f, 0.599572f, 0.599572f, -0.105802f, -0.105802f, -0.105802f, 0.591089f, 0.591089f, 0.591089f, -0.116681f, -0.116681f, -0.116681f, - 0.588694f, 0.588694f, 0.588694f, -0.149201f, -0.149201f, -0.149201f, 0.591492f, 0.591492f, 0.591492f, -0.228917f, -0.228917f, -0.228917f}, nd4j::DataType::FLOAT32); + 0.588694f, 0.588694f, 0.588694f, -0.149201f, -0.149201f, -0.149201f, 0.591492f, 0.591492f, 0.591492f, -0.228917f, -0.228917f, -0.228917f}, sd::DataType::FLOAT32); NDArray expHL('c', {2,bS, nOut}, {0.51409f, 0.51409f, 0.51409f, 0.591492f, 0.591492f, 0.591492f, - -0.107659f, -0.107659f, -0.107659f, -0.102739f, -0.102739f, -0.102739f}, nd4j::DataType::FLOAT32); + -0.107659f, -0.107659f, -0.107659f, -0.102739f, -0.102739f, -0.102739f}, sd::DataType::FLOAT32); NDArray expCL('c', {2,bS, nOut}, {1.07293f , 1.07293f , 1.07293f, 1.346609f, 1.346609f, 1.346609f, - -0.295811f, -0.295811f, -0.295811f, -0.305394f, -0.305394f, -0.305394f}, nd4j::DataType::FLOAT32); + -0.295811f, -0.295811f, -0.295811f, -0.305394f, -0.305394f, -0.305394f}, sd::DataType::FLOAT32); - nd4j::ops::lstmLayer op; + sd::ops::lstmLayer op; auto results = op.evaluate({&x, &Wx, &Wr, &b, &hI, &cI}, tArgs, iArgs, bArgs); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto h = results->at(0); - auto hL = results->at(1); - auto cL = results->at(2); + auto h = results.at(0); + auto hL = results.at(1); + auto cL = results.at(2); // h->printBuffer(); // hL->printBuffer(); @@ -1378,7 +1331,6 @@ TEST_F(DeclarableOpsTests13, lstmLayer_5) { ASSERT_TRUE(expCL.isSameShape(cL)); ASSERT_TRUE(expCL.equalsTo(cL)); - delete results; } /////////////////////////////////////////////////////////////////// @@ -1407,12 +1359,12 @@ TEST_F(DeclarableOpsTests13, lstmLayer_6) { const double cellClip = 0; // do not apply clipping - NDArray x('c', {sL, bS, nIn}, nd4j::DataType::FLOAT32); - NDArray Wx('c', {2,nIn, 4*nOut}, nd4j::DataType::FLOAT32); - NDArray Wr('c', {2,nOut, 4*nOut}, nd4j::DataType::FLOAT32); - NDArray b('c', {2,4*nOut}, nd4j::DataType::FLOAT32); - NDArray hI('c', {2,bS, nOut}, nd4j::DataType::FLOAT32); - NDArray cI('c', {2,bS, nOut}, nd4j::DataType::FLOAT32); + NDArray x('c', {sL, bS, nIn}, sd::DataType::FLOAT32); + NDArray Wx('c', {2,nIn, 4*nOut}, sd::DataType::FLOAT32); + NDArray Wr('c', {2,nOut, 4*nOut}, sd::DataType::FLOAT32); + NDArray b('c', {2,4*nOut}, sd::DataType::FLOAT32); + NDArray hI('c', {2,bS, nOut}, sd::DataType::FLOAT32); + NDArray cI('c', {2,bS, nOut}, sd::DataType::FLOAT32); x.linspace(0.5, 0.5); Wx({0,1, 0,0, 0,0}) = 0.003f; @@ -1433,23 +1385,23 @@ TEST_F(DeclarableOpsTests13, lstmLayer_6) { NDArray expH('c', {sL, bS, nOut}, { 0.470019f, 0.470019f, 0.470019f, 0.478352f, 0.478352f, 0.478352f, 0.444871f, 0.444871f, 0.444871f, 0.457060f, 0.457060f, 0.457060f, 0.424090f, 0.424090f, 0.424090f, 0.439778f, 0.439778f, 0.439778f, 0.394491f, 0.394491f, - 0.394491f, 0.412995f, 0.412995f, 0.412995f, 0.329613f, 0.329613f, 0.329613f, 0.349760f, 0.349760f, 0.349760f}, nd4j::DataType::FLOAT32); + 0.394491f, 0.412995f, 0.412995f, 0.412995f, 0.329613f, 0.329613f, 0.329613f, 0.349760f, 0.349760f, 0.349760f}, sd::DataType::FLOAT32); NDArray expHL('c', {2,bS, nOut}, {0.563741f, 0.563741f, 0.563741f, 0.578676f, 0.578676f, 0.578676f, -0.107642f, -0.107642f, -0.107642f, -0.106937f, -0.106937f, -0.106937f}, - nd4j::DataType::FLOAT32); + sd::DataType::FLOAT32); NDArray expCL('c', {2,bS, nOut}, {1.217757f, 1.217757f, 1.217757f, 1.272398f, 1.272398f, 1.272398f, -0.295768f, -0.295768f, -0.295768f, -0.298453f, -0.298453f, -0.298453f}, - nd4j::DataType::FLOAT32); + sd::DataType::FLOAT32); - nd4j::ops::lstmLayer op; + sd::ops::lstmLayer op; auto results = op.evaluate({&x, &Wx, &Wr, &b, &hI, &cI}, tArgs, iArgs, bArgs); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto h = results->at(0); - auto hL = results->at(1); - auto cL = results->at(2); + auto h = results.at(0); + auto hL = results.at(1); + auto cL = results.at(2); ASSERT_TRUE(expH.isSameShape(h)); ASSERT_TRUE(expH.equalsTo(h)); @@ -1460,7 +1412,6 @@ TEST_F(DeclarableOpsTests13, lstmLayer_6) { ASSERT_TRUE(expCL.isSameShape(cL)); ASSERT_TRUE(expCL.equalsTo(cL)); - delete results; } /////////////////////////////////////////////////////////////////// @@ -1491,13 +1442,13 @@ TEST_F(DeclarableOpsTests13, lstmLayer_7) { const double cellClip = 0; // do not apply clipping - NDArray x('c', {sL, bS, nIn}, nd4j::DataType::FLOAT32); - NDArray Wx('c', {nIn, 4*nOut}, nd4j::DataType::FLOAT32); - NDArray Wr('c', {nOut, 4*nOut}, nd4j::DataType::FLOAT32); - NDArray b('c', {4*nOut}, nd4j::DataType::FLOAT32); - NDArray hI('c', {bS, nOut}, nd4j::DataType::FLOAT32); - NDArray cI('c', {bS, nOut}, nd4j::DataType::FLOAT32); - NDArray Wp('c', {3*nOut}, nd4j::DataType::FLOAT32); + NDArray x('c', {sL, bS, nIn}, sd::DataType::FLOAT32); + NDArray Wx('c', {nIn, 4*nOut}, sd::DataType::FLOAT32); + NDArray Wr('c', {nOut, 4*nOut}, sd::DataType::FLOAT32); + NDArray b('c', {4*nOut}, sd::DataType::FLOAT32); + NDArray hI('c', {bS, nOut}, sd::DataType::FLOAT32); + NDArray cI('c', {bS, nOut}, sd::DataType::FLOAT32); + NDArray Wp('c', {3*nOut}, sd::DataType::FLOAT32); x.linspace(0.5, 0.5); Wx = 0.003; @@ -1513,19 +1464,19 @@ TEST_F(DeclarableOpsTests13, lstmLayer_7) { NDArray expH('c', {sL, bS, nOut}, {0.55533 , 0.55533 , 0.55533 , 0.562925, 0.562925, 0.562925, 0.531795, 0.531795, 0.531795, 0.542556, 0.542556, 0.542556, 0.521466, 0.521466, 0.521466, 0.534638, 0.534638, 0.534638, 0.524805, 0.524805, - 0.524805, 0.539187, 0.539187, 0.539187, 0.538309, 0.538309, 0.538309, 0.552923, 0.552923, 0.552923}, nd4j::DataType::FLOAT32); + 0.524805, 0.539187, 0.539187, 0.539187, 0.538309, 0.538309, 0.538309, 0.552923, 0.552923, 0.552923}, sd::DataType::FLOAT32); - NDArray expHL('c', {bS, nOut}, {0.538309, 0.538309, 0.538309,0.552923, 0.552923, 0.552923}, nd4j::DataType::FLOAT32); - NDArray expCL('c', {bS, nOut}, {1.147089, 1.147089, 1.147089,1.197228, 1.197228, 1.197228}, nd4j::DataType::FLOAT32); + NDArray expHL('c', {bS, nOut}, {0.538309, 0.538309, 0.538309,0.552923, 0.552923, 0.552923}, sd::DataType::FLOAT32); + NDArray expCL('c', {bS, nOut}, {1.147089, 1.147089, 1.147089,1.197228, 1.197228, 1.197228}, sd::DataType::FLOAT32); - nd4j::ops::lstmLayer op; + sd::ops::lstmLayer op; auto results = op.evaluate({&x, &Wx, &Wr, &b, &hI, &cI, &Wp}, tArgs, iArgs, bArgs); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto h = results->at(0); - auto hL = results->at(1); - auto cL = results->at(2); + auto h = results.at(0); + auto hL = results.at(1); + auto cL = results.at(2); ASSERT_TRUE(expH.isSameShape(h)); ASSERT_TRUE(expH.equalsTo(h)); @@ -1536,7 +1487,7 @@ TEST_F(DeclarableOpsTests13, lstmLayer_7) { ASSERT_TRUE(expCL.isSameShape(cL)); ASSERT_TRUE(expCL.equalsTo(cL)); - delete results; + #endif } @@ -1568,13 +1519,13 @@ TEST_F(DeclarableOpsTests13, lstmLayer_8) { const double cellClip = 1.; // do not apply clipping - NDArray x('c', {sL, bS, nIn}, nd4j::DataType::FLOAT32); - NDArray Wx('c', {nIn, 4*nOut}, nd4j::DataType::FLOAT32); - NDArray Wr('c', {nOut, 4*nOut}, nd4j::DataType::FLOAT32); - NDArray b('c', {4*nOut}, nd4j::DataType::FLOAT32); - NDArray hI('c', {bS, nOut}, nd4j::DataType::FLOAT32); - NDArray cI('c', {bS, nOut}, nd4j::DataType::FLOAT32); - NDArray Wp('c', {3*nOut}, nd4j::DataType::FLOAT32); + NDArray x('c', {sL, bS, nIn}, sd::DataType::FLOAT32); + NDArray Wx('c', {nIn, 4*nOut}, sd::DataType::FLOAT32); + NDArray Wr('c', {nOut, 4*nOut}, sd::DataType::FLOAT32); + NDArray b('c', {4*nOut}, sd::DataType::FLOAT32); + NDArray hI('c', {bS, nOut}, sd::DataType::FLOAT32); + NDArray cI('c', {bS, nOut}, sd::DataType::FLOAT32); + NDArray Wp('c', {3*nOut}, sd::DataType::FLOAT32); x.linspace(0.5, 0.5); Wx = 0.003; @@ -1591,19 +1542,19 @@ TEST_F(DeclarableOpsTests13, lstmLayer_8) { NDArray expH('c', {sL, bS, nOut}, { 0.436221f, 0.436221f, 0.436221f, 0.450573f, 0.450573f, 0.450573f, 0.463602f, 0.463602f, 0.463602f, 0.474674f, 0.474674f, 0.474674f, 0.484039f, 0.484039f, 0.484039f, 0.490679f, 0.490679f, 0.490679f, 0.494871f, 0.494871f, 0.494871f, 0.499028f, 0.499028f, 0.499028f, - 0.504649f, 0.504649f, 0.504649f, 0.508719f, 0.508719f, 0.508719f}, nd4j::DataType::FLOAT32); + 0.504649f, 0.504649f, 0.504649f, 0.508719f, 0.508719f, 0.508719f}, sd::DataType::FLOAT32); - NDArray expHL('c', {bS, nOut}, {0.436221f, 0.436221f, 0.436221f, 0.450573f, 0.450573f, 0.450573f}, nd4j::DataType::FLOAT32); - NDArray expCL('c', {bS, nOut}, {0.879804f, 0.879804f, 0.879804f, 0.914666f, 0.914666f, 0.914666f}, nd4j::DataType::FLOAT32); + NDArray expHL('c', {bS, nOut}, {0.436221f, 0.436221f, 0.436221f, 0.450573f, 0.450573f, 0.450573f}, sd::DataType::FLOAT32); + NDArray expCL('c', {bS, nOut}, {0.879804f, 0.879804f, 0.879804f, 0.914666f, 0.914666f, 0.914666f}, sd::DataType::FLOAT32); - nd4j::ops::lstmLayer op; + sd::ops::lstmLayer op; auto results = op.evaluate({&x, &Wx, &Wr, &b, &hI, &cI, &Wp}, tArgs, iArgs, bArgs); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto h = results->at(0); - auto hL = results->at(1); - auto cL = results->at(2); + auto h = results.at(0); + auto hL = results.at(1); + auto cL = results.at(2); ASSERT_TRUE(expH.isSameShape(h)); ASSERT_TRUE(expH.equalsTo(h)); @@ -1614,7 +1565,7 @@ TEST_F(DeclarableOpsTests13, lstmLayer_8) { ASSERT_TRUE(expCL.isSameShape(cL)); ASSERT_TRUE(expCL.equalsTo(cL)); - delete results; + #endif } @@ -1645,13 +1596,13 @@ TEST_F(DeclarableOpsTests13, lstmLayer_9) { const double cellClip = 0; // do not apply clipping - NDArray x('c', {sL, bS, nIn}, nd4j::DataType::FLOAT32); - NDArray Wx('c', {2,nIn, 4*nOut}, nd4j::DataType::FLOAT32); - NDArray Wr('c', {2,nOut, 4*nOut}, nd4j::DataType::FLOAT32); - NDArray b('c', {2,4*nOut}, nd4j::DataType::FLOAT32); - NDArray hI('c', {2,bS, nOut}, nd4j::DataType::FLOAT32); - NDArray cI('c', {2,bS, nOut}, nd4j::DataType::FLOAT32); - NDArray Wp('c', {2,3*nOut}, nd4j::DataType::FLOAT32); + NDArray x('c', {sL, bS, nIn}, sd::DataType::FLOAT32); + NDArray Wx('c', {2,nIn, 4*nOut}, sd::DataType::FLOAT32); + NDArray Wr('c', {2,nOut, 4*nOut}, sd::DataType::FLOAT32); + NDArray b('c', {2,4*nOut}, sd::DataType::FLOAT32); + NDArray hI('c', {2,bS, nOut}, sd::DataType::FLOAT32); + NDArray cI('c', {2,bS, nOut}, sd::DataType::FLOAT32); + NDArray Wp('c', {2,3*nOut}, sd::DataType::FLOAT32); x.linspace(0.5, 0.5); Wx({0,1, 0,0, 0,0}) = 0.003; @@ -1676,21 +1627,21 @@ TEST_F(DeclarableOpsTests13, lstmLayer_9) { 0.531795f, 0.531795f, 0.531795f, -0.107456f, -0.107456f, -0.107456f, 0.542556f, 0.542556f, 0.542556f, -0.106139f, -0.106139f, -0.106139f, 0.521466f, 0.521466f, 0.521466f, -0.11681f, -0.11681f, -0.11681f, 0.534638f, 0.534638f, 0.534638f, -0.11458f, -0.11458f, -0.11458f, 0.524805f, 0.524805f, 0.524805f, -0.145177f, -0.145177f, -0.145177f, 0.539187f, 0.539187f, 0.539187f, -0.14157f, -0.14157f, -0.14157f, - 0.538309f, 0.538309f, 0.538309f, -0.218056f, -0.218056f, -0.218056f, 0.552923f, 0.552923f, 0.552923f, -0.213068f, -0.213068f, -0.213068f}, nd4j::DataType::FLOAT32); + 0.538309f, 0.538309f, 0.538309f, -0.218056f, -0.218056f, -0.218056f, 0.552923f, 0.552923f, 0.552923f, -0.213068f, -0.213068f, -0.213068f}, sd::DataType::FLOAT32); NDArray expHL('c', {2,bS, nOut}, {0.538309f, 0.538309f, 0.538309f, 0.552923f, 0.552923f, 0.552923f, -0.104502f, -0.104502f, -0.104502f, - -0.103843f, -0.103843f, -0.103843f}, nd4j::DataType::FLOAT32); + -0.103843f, -0.103843f, -0.103843f}, sd::DataType::FLOAT32); NDArray expCL('c', {2,bS, nOut}, {1.147089f, 1.147089f, 1.147089f, 1.197228f, 1.197228f, 1.197228f, -0.289425f, -0.289425f, -0.289425f, - -0.292174f, -0.292174f, -0.292174f}, nd4j::DataType::FLOAT32); + -0.292174f, -0.292174f, -0.292174f}, sd::DataType::FLOAT32); - nd4j::ops::lstmLayer op; + sd::ops::lstmLayer op; auto results = op.evaluate({&x, &Wx, &Wr, &b, &hI, &cI, &Wp}, tArgs, iArgs, bArgs); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto h = results->at(0); - auto hL = results->at(1); - auto cL = results->at(2); + auto h = results.at(0); + auto hL = results.at(1); + auto cL = results.at(2); ASSERT_TRUE(expH.isSameShape(h)); ASSERT_TRUE(expH.equalsTo(h)); @@ -1701,7 +1652,7 @@ TEST_F(DeclarableOpsTests13, lstmLayer_9) { ASSERT_TRUE(expCL.isSameShape(cL)); ASSERT_TRUE(expCL.equalsTo(cL)); - delete results; + #endif } @@ -1732,14 +1683,14 @@ TEST_F(DeclarableOpsTests13, lstmLayer_10) { const double cellClip = 0; // do not apply clipping - NDArray x('c', {sL, bS, nIn}, nd4j::DataType::FLOAT32); - NDArray Wx('c', {nIn, 4*nOut}, nd4j::DataType::FLOAT32); - NDArray Wr('c', {nOut, 4*nOut}, nd4j::DataType::FLOAT32); - NDArray b('c', {4*nOut}, nd4j::DataType::FLOAT32); - NDArray hI('c', {bS, nOut}, nd4j::DataType::FLOAT32); - NDArray cI('c', {bS, nOut}, nd4j::DataType::FLOAT32); - NDArray seqLen('c', {bS}, {0,1,2,3,5}, nd4j::DataType::FLOAT32); - NDArray Wp('c', {3*nOut}, nd4j::DataType::FLOAT32); + NDArray x('c', {sL, bS, nIn}, sd::DataType::FLOAT32); + NDArray Wx('c', {nIn, 4*nOut}, sd::DataType::FLOAT32); + NDArray Wr('c', {nOut, 4*nOut}, sd::DataType::FLOAT32); + NDArray b('c', {4*nOut}, sd::DataType::FLOAT32); + NDArray hI('c', {bS, nOut}, sd::DataType::FLOAT32); + NDArray cI('c', {bS, nOut}, sd::DataType::FLOAT32); + NDArray seqLen('c', {bS}, {0,1,2,3,5}, sd::DataType::FLOAT32); + NDArray Wp('c', {3*nOut}, sd::DataType::FLOAT32); x.linspace(0.5, 0.5); Wx = 0.003; @@ -1763,19 +1714,19 @@ TEST_F(DeclarableOpsTests13, lstmLayer_10) { 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.692315f, 0.692315f, 0.692315f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f}, - nd4j::DataType::FLOAT32); + sd::DataType::FLOAT32); - NDArray expHL('c', {bS, nOut}, {0.f, 0.f, 0.f, 0.562925f, 0.562925f, 0.562925f, 0.576568f, 0.576568f, 0.576568f, 0.611224f, 0.611224f, 0.611224f, 0.692315f, 0.692315f, 0.692315f}, nd4j::DataType::FLOAT32); - NDArray expCL('c', {bS, nOut}, {0.f, 0.f, 0.f, 1.534275f, 1.534275f, 1.534275f, 1.40183f, 1.40183f, 1.40183f, 1.449675f, 1.449675f, 1.449675f, 1.767702f, 1.767702f, 1.767702f}, nd4j::DataType::FLOAT32); + NDArray expHL('c', {bS, nOut}, {0.f, 0.f, 0.f, 0.562925f, 0.562925f, 0.562925f, 0.576568f, 0.576568f, 0.576568f, 0.611224f, 0.611224f, 0.611224f, 0.692315f, 0.692315f, 0.692315f}, sd::DataType::FLOAT32); + NDArray expCL('c', {bS, nOut}, {0.f, 0.f, 0.f, 1.534275f, 1.534275f, 1.534275f, 1.40183f, 1.40183f, 1.40183f, 1.449675f, 1.449675f, 1.449675f, 1.767702f, 1.767702f, 1.767702f}, sd::DataType::FLOAT32); - nd4j::ops::lstmLayer op; + sd::ops::lstmLayer op; auto results = op.evaluate({&x, &Wx, &Wr, &b, &seqLen, &hI, &cI, &Wp}, tArgs, iArgs, bArgs); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto h = results->at(0); - auto hL = results->at(1); - auto cL = results->at(2); + auto h = results.at(0); + auto hL = results.at(1); + auto cL = results.at(2); ASSERT_TRUE(expH.isSameShape(h)); ASSERT_TRUE(expH.equalsTo(h)); @@ -1786,7 +1737,7 @@ TEST_F(DeclarableOpsTests13, lstmLayer_10) { ASSERT_TRUE(expCL.isSameShape(cL)); ASSERT_TRUE(expCL.equalsTo(cL)); - delete results; + #endif } @@ -1817,14 +1768,14 @@ TEST_F(DeclarableOpsTests13, lstmLayer_11) { const double cellClip = 0; // do not apply clipping - NDArray x('c', {sL, bS, nIn}, nd4j::DataType::FLOAT32); - NDArray Wx('c', {nIn, 4*nOut}, nd4j::DataType::FLOAT32); - NDArray Wr('c', {nOut, 4*nOut}, nd4j::DataType::FLOAT32); - NDArray b('c', {4*nOut}, nd4j::DataType::FLOAT32); - NDArray hI('c', {bS, nOut}, nd4j::DataType::FLOAT32); - NDArray cI('c', {bS, nOut}, nd4j::DataType::FLOAT32); - NDArray seqLen('c', {bS}, {0,1,2,3,5}, nd4j::DataType::FLOAT32); - NDArray Wp('c', {3*nOut}, nd4j::DataType::FLOAT32); + NDArray x('c', {sL, bS, nIn}, sd::DataType::FLOAT32); + NDArray Wx('c', {nIn, 4*nOut}, sd::DataType::FLOAT32); + NDArray Wr('c', {nOut, 4*nOut}, sd::DataType::FLOAT32); + NDArray b('c', {4*nOut}, sd::DataType::FLOAT32); + NDArray hI('c', {bS, nOut}, sd::DataType::FLOAT32); + NDArray cI('c', {bS, nOut}, sd::DataType::FLOAT32); + NDArray seqLen('c', {bS}, {0,1,2,3,5}, sd::DataType::FLOAT32); + NDArray Wp('c', {3*nOut}, sd::DataType::FLOAT32); x.linspace(0.5, 0.5); Wx = 0.003f; @@ -1843,19 +1794,19 @@ TEST_F(DeclarableOpsTests13, lstmLayer_11) { 0.61209f, 0.61209f,0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.652042f, 0.652042f, 0.652042f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.677708f, 0.677708f, 0.677708f, 0.684177f, 0.684177f, 0.684177f, 0.f, 0.f, 0.f,0.f, 0.f, 0.f, 0.699627f, 0.699627f, 0.699627f, 0.705371f, 0.705371f, 0.705371f, 0.710989f, 0.710989f, 0.710989f, 0., 0., 0., 0.719014, 0.719014, 0.719014, 0.724087, - 0.724087f, 0.724087f, 0.729084f, 0.729084f, 0.729084f, 0.734004f, 0.734004f, 0.734004f }, nd4j::DataType::FLOAT32); + 0.724087f, 0.724087f, 0.729084f, 0.729084f, 0.729084f, 0.734004f, 0.734004f, 0.734004f }, sd::DataType::FLOAT32); - NDArray expHL('c', {bS, nOut}, {0.f, 0.f, 0.f, 0.719014f, 0.719014f, 0.719014f, 0.699627f, 0.699627f, 0.699627f, 0.677708f, 0.677708f, 0.677708f, 0.61209f, 0.61209f, 0.61209f}, nd4j::DataType::FLOAT32); - NDArray expCL('c', {bS, nOut}, {0.f, 0.f, 0.f, 2.092814f, 2.092814f, 2.092814f, 2.08832f, 2.08832f, 2.08832f, 2.009851f, 2.009851f, 2.009851f, 1.646034f, 1.646034f, 1.646034f}, nd4j::DataType::FLOAT32); + NDArray expHL('c', {bS, nOut}, {0.f, 0.f, 0.f, 0.719014f, 0.719014f, 0.719014f, 0.699627f, 0.699627f, 0.699627f, 0.677708f, 0.677708f, 0.677708f, 0.61209f, 0.61209f, 0.61209f}, sd::DataType::FLOAT32); + NDArray expCL('c', {bS, nOut}, {0.f, 0.f, 0.f, 2.092814f, 2.092814f, 2.092814f, 2.08832f, 2.08832f, 2.08832f, 2.009851f, 2.009851f, 2.009851f, 1.646034f, 1.646034f, 1.646034f}, sd::DataType::FLOAT32); - nd4j::ops::lstmLayer op; + sd::ops::lstmLayer op; auto results = op.evaluate({&x, &Wx, &Wr, &b, &seqLen, &hI, &cI, &Wp}, tArgs, iArgs, bArgs); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto h = results->at(0); - auto hL = results->at(1); - auto cL = results->at(2); + auto h = results.at(0); + auto hL = results.at(1); + auto cL = results.at(2); ASSERT_TRUE(expH.isSameShape(h)); ASSERT_TRUE(expH.equalsTo(h)); @@ -1866,7 +1817,7 @@ TEST_F(DeclarableOpsTests13, lstmLayer_11) { ASSERT_TRUE(expCL.isSameShape(cL)); ASSERT_TRUE(expCL.equalsTo(cL)); - delete results; + #endif } @@ -1897,14 +1848,14 @@ TEST_F(DeclarableOpsTests13, lstmLayer_12) { const double cellClip = 0; // do not apply clipping - NDArray x('c', {sL, bS, nIn}, nd4j::DataType::FLOAT32); - NDArray Wx('c', {2,nIn, 4*nOut}, nd4j::DataType::FLOAT32); - NDArray Wr('c', {2,nOut, 4*nOut}, nd4j::DataType::FLOAT32); - NDArray b('c', {2,4*nOut}, nd4j::DataType::FLOAT32); - NDArray hI('c', {2,bS, nOut}, nd4j::DataType::FLOAT32); - NDArray cI('c', {2,bS, nOut}, nd4j::DataType::FLOAT32); - NDArray seqLen('c', {bS}, {0,1,2,3,5}, nd4j::DataType::FLOAT32); - NDArray Wp('c', {2,3*nOut}, nd4j::DataType::FLOAT32); + NDArray x('c', {sL, bS, nIn}, sd::DataType::FLOAT32); + NDArray Wx('c', {2,nIn, 4*nOut}, sd::DataType::FLOAT32); + NDArray Wr('c', {2,nOut, 4*nOut}, sd::DataType::FLOAT32); + NDArray b('c', {2,4*nOut}, sd::DataType::FLOAT32); + NDArray hI('c', {2,bS, nOut}, sd::DataType::FLOAT32); + NDArray cI('c', {2,bS, nOut}, sd::DataType::FLOAT32); + NDArray seqLen('c', {bS}, {0,1,2,3,5}, sd::DataType::FLOAT32); + NDArray Wp('c', {2,3*nOut}, sd::DataType::FLOAT32); x.linspace(0.5, 0.5); Wx({0,1, 0,0, 0,0}) = 0.003f; @@ -1932,21 +1883,21 @@ TEST_F(DeclarableOpsTests13, lstmLayer_12) { 0.621298, 0.621298, 0.621298, -0.090626, -0.090626, -0.090626, 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.655858, 0.655858, 0.655858, -0.098015, -0.098015, -0.098015, 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.692315, 0.692315, 0.692315, -0.143704, -0.143704, -0.143704, 0., 0., 0., 0., 0., 0., - 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.}, nd4j::DataType::FLOAT32); + 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.}, sd::DataType::FLOAT32); NDArray expHL('c', {2,bS, nOut}, {0.f, 0.f, 0.f, 0.562925f, 0.562925f, 0.562925f, 0.576568f, 0.576568f, 0.576568f, 0.611224f, 0.611224f, 0.611224f, 0.692315f, 0.692315f, 0.692315f, - 0.f, 0.f, 0.f, -0.25361f, -0.25361f, -0.25361f, -0.157103f, -0.157103f, -0.157103f, -0.116502f, -0.116502f, -0.116502f, -0.100025f, -0.100025f, -0.100025f}, nd4j::DataType::FLOAT32); + 0.f, 0.f, 0.f, -0.25361f, -0.25361f, -0.25361f, -0.157103f, -0.157103f, -0.157103f, -0.116502f, -0.116502f, -0.116502f, -0.100025f, -0.100025f, -0.100025f}, sd::DataType::FLOAT32); NDArray expCL('c', {2,bS, nOut}, {0.f, 0.f, 0.f, 1.534275f, 1.534275f, 1.534275f, 1.40183f, 1.40183f, 1.40183f, 1.449675f, 1.449675f, 1.449675f, 1.767702f, 1.767702f, 1.767702f, - 0.f, 0.f, 0.f, -0.86636f, -0.86636f, -0.86636f, -0.470245f, -0.470245f, -0.470245f, -0.341856f, -0.341856f, -0.341856f, -0.294986f, -0.294986f, -0.294986f}, nd4j::DataType::FLOAT32); + 0.f, 0.f, 0.f, -0.86636f, -0.86636f, -0.86636f, -0.470245f, -0.470245f, -0.470245f, -0.341856f, -0.341856f, -0.341856f, -0.294986f, -0.294986f, -0.294986f}, sd::DataType::FLOAT32); - nd4j::ops::lstmLayer op; + sd::ops::lstmLayer op; auto results = op.evaluate({&x, &Wx, &Wr, &b, &seqLen, &hI, &cI, &Wp}, tArgs, iArgs, bArgs); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto h = results->at(0); - auto hL = results->at(1); - auto cL = results->at(2); + auto h = results.at(0); + auto hL = results.at(1); + auto cL = results.at(2); ASSERT_TRUE(expH.isSameShape(h)); ASSERT_TRUE(expH.equalsTo(h)); @@ -1957,7 +1908,7 @@ TEST_F(DeclarableOpsTests13, lstmLayer_12) { ASSERT_TRUE(expCL.isSameShape(cL)); ASSERT_TRUE(expCL.equalsTo(cL)); - delete results; + #endif } @@ -1965,29 +1916,29 @@ TEST_F(DeclarableOpsTests13, lstmLayer_12) { //////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests13, batchnorm_test1) { - NDArray input ('c', {2,4}, nd4j::DataType::FLOAT32); - NDArray mean ('c', {4}, {1.05f, 1.15f, 1.2f, 1.3f}, nd4j::DataType::FLOAT32); - NDArray variance('c', {4}, {0.5f, 0.7f, 0.9f, 1.1f}, nd4j::DataType::FLOAT32); - NDArray gamma ('c', {4}, {-1.2f, 1.3f, -1.4f, 1.5f}, nd4j::DataType::FLOAT32); - NDArray beta ('c', {4}, {10.f, 20.f, -10.f, -20.f}, nd4j::DataType::FLOAT32); + NDArray input ('c', {2,4}, sd::DataType::FLOAT32); + NDArray mean ('c', {4}, {1.05f, 1.15f, 1.2f, 1.3f}, sd::DataType::FLOAT32); + NDArray variance('c', {4}, {0.5f, 0.7f, 0.9f, 1.1f}, sd::DataType::FLOAT32); + NDArray gamma ('c', {4}, {-1.2f, 1.3f, -1.4f, 1.5f}, sd::DataType::FLOAT32); + NDArray beta ('c', {4}, {10.f, 20.f, -10.f, -20.f}, sd::DataType::FLOAT32); - NDArray expected('c', {2,4}, {11.61218734f, 18.52390321f, -8.67185076f, -21.28716864f, 10.93337162f, 19.14541765f, -9.26213931f, -20.71509369f}, nd4j::DataType::FLOAT32); + NDArray expected('c', {2,4}, {11.61218734f, 18.52390321f, -8.67185076f, -21.28716864f, 10.93337162f, 19.14541765f, -9.26213931f, -20.71509369f}, sd::DataType::FLOAT32); input.linspace(0.1, 0.1); - nd4j::ops::batchnorm op; + sd::ops::batchnorm op; auto results = op.evaluate({&input, &mean, &variance, &gamma, &beta}, {1e-5}, {1,1}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto output = results->at(0); + auto output = results.at(0); // output->printBuffer(); ASSERT_TRUE(expected.isSameShapeStrict(*output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; + } //////////////////////////////////////////////////////////////////// @@ -2008,19 +1959,18 @@ TYPED_TEST(TypedDeclarableOpsTests13, batchnorm_test2) { gamma.assign(1.2); beta.assign(1.); - nd4j::ops::batchnorm op; + sd::ops::batchnorm op; auto results = op.evaluate({&input, &mean, &variance, &gamma, &beta}, {1e-5}, {1,1}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto output = results->at(0); + auto output = results.at(0); // output->printBuffer(); ASSERT_TRUE(expected.isSameShapeStrict(*output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; } //////////////////////////////////////////////////////////////////// @@ -2037,18 +1987,17 @@ TYPED_TEST(TypedDeclarableOpsTests13, batchnorm_test3) { input.linspace(0.1, 0.1); - nd4j::ops::batchnorm op; + sd::ops::batchnorm op; auto results = op.evaluate({&input, &mean, &variance, &gamma, &beta}, {1e-5}, {1,1,1}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto output = results->at(0); + auto output = results.at(0); ASSERT_TRUE(expected.isSameShapeStrict(*output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; } //////////////////////////////////////////////////////////////////// @@ -2065,96 +2014,94 @@ TYPED_TEST(TypedDeclarableOpsTests13, batchnorm_test4) { input.linspace(0.1, 0.1); - nd4j::ops::batchnorm op; + sd::ops::batchnorm op; auto results = op.evaluate({&input, &mean, &variance, &gamma, &beta}, {1e-5}, {1,1,0,2}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto output = results->at(0); + auto output = results.at(0); ASSERT_TRUE(expected.isSameShapeStrict(*output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; } //////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests13, batchnorm_test5) { - NDArray input ('c', {2,4,2,2}, nd4j::DataType::FLOAT32); - NDArray mean ('c', {4}, {1.05f, 1.15f, 1.2f, 1.3f}, nd4j::DataType::FLOAT32); - NDArray variance('c', {4}, {0.5f, 0.7f, 0.9f, 1.1f}, nd4j::DataType::FLOAT32); - NDArray gamma ('c', {4}, {-1.2f, 1.3f, -1.4f, 1.5f}, nd4j::DataType::FLOAT32); - NDArray beta ('c', {4}, {10.f, 20.f, -10.f, -20.f}, nd4j::DataType::FLOAT32); + NDArray input ('c', {2,4,2,2}, sd::DataType::FLOAT32); + NDArray mean ('c', {4}, {1.05f, 1.15f, 1.2f, 1.3f}, sd::DataType::FLOAT32); + NDArray variance('c', {4}, {0.5f, 0.7f, 0.9f, 1.1f}, sd::DataType::FLOAT32); + NDArray gamma ('c', {4}, {-1.2f, 1.3f, -1.4f, 1.5f}, sd::DataType::FLOAT32); + NDArray beta ('c', {4}, {10.f, 20.f, -10.f, -20.f}, sd::DataType::FLOAT32); NDArray expected('c', {2,4,2,2}, { 11.612187f, 11.442483f, 11.272779f, 11.103076f, 18.990039f, 19.145418f, 19.300796f, 19.456175f, -9.557284f, -9.704856f, -9.852428f, -10.f, -20.f, -19.856981f, -19.713963f, -19.570944f, 8.896924f, 8.727221f, 8.557517f, 8.387813f, 21.476097f, 21.631475f, 21.786854f, 21.942233f, -11.918438f, - -12.06601f, -12.213582f, -12.361154f, -17.7117f, -17.568681f, -17.425663f, -17.282644f}, nd4j::DataType::FLOAT32); + -12.06601f, -12.213582f, -12.361154f, -17.7117f, -17.568681f, -17.425663f, -17.282644f}, sd::DataType::FLOAT32); input.linspace(0.1, 0.1); - nd4j::ops::batchnorm op; + sd::ops::batchnorm op; auto results = op.evaluate({&input, &mean, &variance, &gamma, &beta}, {1e-5}, {1, 1, 1}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto output = results->at(0); + auto output = results.at(0); // output->printBuffer(); ASSERT_TRUE(expected.isSameShapeStrict(*output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; } //////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests13, batchnorm_test6) { - NDArray input ('c', {2,2,2,4}, nd4j::DataType::FLOAT32); - NDArray mean ('c', {4}, {1.05f, 1.15f, 1.2f, 1.3f}, nd4j::DataType::FLOAT32); - NDArray variance('c', {4}, {0.5f, 0.7f, 0.9, 1.1f}, nd4j::DataType::FLOAT32); - NDArray gamma ('c', {4}, {-1.2f, 1.3f, -1.4f, 1.5f}, nd4j::DataType::FLOAT32); - NDArray beta ('c', {4}, {10.f, 20.f, -10.f, -20.f}, nd4j::DataType::FLOAT32); + NDArray input ('c', {2,2,2,4}, sd::DataType::FLOAT32); + NDArray mean ('c', {4}, {1.05f, 1.15f, 1.2f, 1.3f}, sd::DataType::FLOAT32); + NDArray variance('c', {4}, {0.5f, 0.7f, 0.9, 1.1f}, sd::DataType::FLOAT32); + NDArray gamma ('c', {4}, {-1.2f, 1.3f, -1.4f, 1.5f}, sd::DataType::FLOAT32); + NDArray beta ('c', {4}, {10.f, 20.f, -10.f, -20.f}, sd::DataType::FLOAT32); NDArray expected('c', {2,2,2,4}, {11.612187f, 18.523903f, -8.671851f, -21.287169f, 10.933372f, 19.145418f, -9.262139f, -20.715094f, 10.254556f, 19.766932f, -9.852428f, -20.143019f, 9.57574f, 20.388447f, -10.442716f, -19.570944f, 8.896924f, 21.009961f, -11.033005f, -18.998869f, 8.218109f, 21.631475f, -11.623294f, -18.426794f, 7.539293f, 22.25299f, - -12.213582f, -17.854719f, 6.860477f, 22.874504f, -12.803871f, -17.282644f}, nd4j::DataType::FLOAT32); + -12.213582f, -17.854719f, 6.860477f, 22.874504f, -12.803871f, -17.282644f}, sd::DataType::FLOAT32); input.linspace(0.1, 0.1); - nd4j::ops::batchnorm op; + sd::ops::batchnorm op; auto results = op.evaluate({&input, &mean, &variance, &gamma, &beta}, {1e-5}, {1,1,3}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto output = results->at(0); + auto output = results.at(0); ASSERT_TRUE(expected.isSameShapeStrict(*output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; + } //////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests13, batchnorm_test7) { - NDArray input1('c', {3,3,15,15}, nd4j::DataType::FLOAT32); - NDArray input2('c', {3,15,15,3}, nd4j::DataType::FLOAT32); + NDArray input1('c', {3,3,15,15}, sd::DataType::FLOAT32); + NDArray input2('c', {3,15,15,3}, sd::DataType::FLOAT32); input2.permutei({0,3,1,2}); - NDArray mean ('c', {3}, {0., 0, 0}, nd4j::DataType::FLOAT32); - NDArray variance('c', {3}, {1., 1, 1}, nd4j::DataType::FLOAT32); - NDArray gamma ('c', {3}, {1., 1, 1}, nd4j::DataType::FLOAT32); - NDArray beta ('c', {3}, {0., 0, 0}, nd4j::DataType::FLOAT32); + NDArray mean ('c', {3}, {0., 0, 0}, sd::DataType::FLOAT32); + NDArray variance('c', {3}, {1., 1, 1}, sd::DataType::FLOAT32); + NDArray gamma ('c', {3}, {1., 1, 1}, sd::DataType::FLOAT32); + NDArray beta ('c', {3}, {0., 0, 0}, sd::DataType::FLOAT32); - NDArray out1('c', {3,3,15,15}, nd4j::DataType::FLOAT32); - NDArray out2('c', {3,3,15,15}, nd4j::DataType::FLOAT32); + NDArray out1('c', {3,3,15,15}, sd::DataType::FLOAT32); + NDArray out2('c', {3,3,15,15}, sd::DataType::FLOAT32); input1.linspace(-1012, 1); input2.assign(input1); - nd4j::ops::batchnorm op; + sd::ops::batchnorm op; auto res1 = op.execute({&input1, &mean, &variance, &gamma, &beta}, {&out1}, {1e-5}, {1,1,1}, {}); ASSERT_EQ(ND4J_STATUS_OK, res1); @@ -2168,12 +2115,12 @@ TEST_F(DeclarableOpsTests13, batchnorm_test7) { //////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests13, batchnorm_test8) { - NDArray input('c', {2,3,4,5}, nd4j::DataType::FLOAT32); + NDArray input('c', {2,3,4,5}, sd::DataType::FLOAT32); - NDArray mean ('c', {1,3,4,5}, nd4j::DataType::FLOAT32); - NDArray variance('c', {1,3,4,5}, nd4j::DataType::FLOAT32); - NDArray gamma ('c', {1,3,4,5}, nd4j::DataType::FLOAT32); - NDArray beta ('c', {1,3,4,5}, nd4j::DataType::FLOAT32); + NDArray mean ('c', {1,3,4,5}, sd::DataType::FLOAT32); + NDArray variance('c', {1,3,4,5}, sd::DataType::FLOAT32); + NDArray gamma ('c', {1,3,4,5}, sd::DataType::FLOAT32); + NDArray beta ('c', {1,3,4,5}, sd::DataType::FLOAT32); NDArray expected('c', {2,3,4,5}, {-105.019394, -103.322357, -101.625313, -99.928276, -98.231239, -96.534195, -94.837158, -93.140121, -91.443077, -89.746040, -88.049004, -86.351959, -84.654922, -82.957886, -81.260841, -79.563805, -77.866768, -76.169724, -74.472687, -72.775650, -71.078606, -69.381569, -67.684532, -65.987488, -64.290451, -62.593414, @@ -2183,7 +2130,7 @@ TEST_F(DeclarableOpsTests13, batchnorm_test8) { 6.985196, 8.682236, 10.379274, 12.076314, 13.773354, 15.470392, 17.167431, 18.864471, 20.561510, 22.258549, 23.955589, 25.652628, 27.349667, 29.046707, 30.743744, 32.440784, 34.137825, 35.834862, 37.531902, 39.228943, 40.925980, 42.623020, 44.320061, 46.017097, 47.714138, 49.411179, 51.108215, 52.805256, 54.502296, 56.199333, 57.896374, 59.593414, 61.290451, 62.987488, 64.684532, 66.381569, 68.078606, 69.775650, 71.472687, 73.169724, 74.866768, 76.563805, 78.260841, 79.957886, 81.654922, - 83.351959, 85.049004, 86.746040, 88.443077, 90.140121, 91.837158, 93.534195, 95.231239, 96.928276}, nd4j::DataType::FLOAT32); + 83.351959, 85.049004, 86.746040, 88.443077, 90.140121, 91.837158, 93.534195, 95.231239, 96.928276}, sd::DataType::FLOAT32); input.linspace(-60, 1); mean.assign(1.); @@ -2191,29 +2138,28 @@ TEST_F(DeclarableOpsTests13, batchnorm_test8) { gamma.assign(1.2); beta.assign(-1.5); - nd4j::ops::batchnorm op; + sd::ops::batchnorm op; auto results = op.evaluate({&input, &mean, &variance, &gamma, &beta}, {1e-5}, {1,1, 1,2,3}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto output = results->at(0); + auto output = results.at(0); ASSERT_TRUE(expected.isSameShape(*output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; } //////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests13, batchnorm_test9) { - NDArray input('c', {2,3,3,3,3}, nd4j::DataType::FLOAT32); + NDArray input('c', {2,3,3,3,3}, sd::DataType::FLOAT32); - NDArray mean ('c', {1,3,3,3,3}, nd4j::DataType::FLOAT32); - NDArray variance('c', {1,3,3,3,3}, nd4j::DataType::FLOAT32); - NDArray gamma ('c', {1,3,3,3,3}, nd4j::DataType::FLOAT32); - NDArray beta ('c', {1,3,3,3,3}, nd4j::DataType::FLOAT32); + NDArray mean ('c', {1,3,3,3,3}, sd::DataType::FLOAT32); + NDArray variance('c', {1,3,3,3,3}, sd::DataType::FLOAT32); + NDArray gamma ('c', {1,3,3,3,3}, sd::DataType::FLOAT32); + NDArray beta ('c', {1,3,3,3,3}, sd::DataType::FLOAT32); NDArray expected('c', {2,3,3,3,3}, {-138.960175, -137.263138, -135.566101, -133.869064, -132.172028, -130.474976, -128.777954, -127.080902, -125.383865, -123.686829, -121.989784, -120.292747, -118.595711, -116.898666, -115.201630, -113.504593, -111.807549, -110.110512, -108.413475, -106.716431, -105.019394, -103.322357, -101.625313, -99.928276, @@ -2226,7 +2172,7 @@ TEST_F(DeclarableOpsTests13, batchnorm_test9) { 40.925980, 42.623020, 44.320061, 46.017097, 47.714138, 49.411179, 51.108215, 52.805256, 54.502296, 56.199333, 57.896374, 59.593414, 61.290451, 62.987488, 64.684532, 66.381569, 68.078606, 69.775650, 71.472687, 73.169724, 74.866768, 76.563805, 78.260841, 79.957886, 81.654922, 83.351959, 85.049004, 86.746040, 88.443077, 90.140121, 91.837158, 93.534195, 95.231239, 96.928276, 98.625313, 100.322357, 102.019394, 103.716431, 105.413475, 107.110512, 108.807549, 110.504593, 112.201630, 113.898666, - 115.595711, 117.292747, 118.989784, 120.686829, 122.383865, 124.080902, 125.777946, 127.474976, 129.172028, 130.869064, 132.566101, 134.263138}, nd4j::DataType::FLOAT32); + 115.595711, 117.292747, 118.989784, 120.686829, 122.383865, 124.080902, 125.777946, 127.474976, 129.172028, 130.869064, 132.566101, 134.263138}, sd::DataType::FLOAT32); input.linspace(-80, 1); mean.assign(1.); @@ -2234,34 +2180,34 @@ TEST_F(DeclarableOpsTests13, batchnorm_test9) { gamma.assign(1.2); beta.assign(-1.5); - nd4j::ops::batchnorm op; + sd::ops::batchnorm op; auto results = op.evaluate({&input, &mean, &variance, &gamma, &beta}, {1e-5}, {1,1, 1,2,3,4}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto output = results->at(0); + auto output = results.at(0); // output->printBuffer(); ASSERT_TRUE(expected.isSameShape(*output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; + } //////////////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests13, batchnorm_bp_test1) { - NDArray input ('c', {2,3,4}, nd4j::DataType::FLOAT32); - NDArray mean ('c', {4}, {1.1, 1.2, 1.3, 1.4}, nd4j::DataType::FLOAT32); - NDArray variance('c', {4}, nd4j::DataType::FLOAT32); - NDArray gamma ('c', {4}, nd4j::DataType::FLOAT32); - NDArray beta ('c', {4}, nd4j::DataType::FLOAT32); - NDArray gradO ('c', {2,3,4}, nd4j::DataType::FLOAT32); + NDArray input ('c', {2,3,4}, sd::DataType::FLOAT32); + NDArray mean ('c', {4}, {1.1, 1.2, 1.3, 1.4}, sd::DataType::FLOAT32); + NDArray variance('c', {4}, sd::DataType::FLOAT32); + NDArray gamma ('c', {4}, sd::DataType::FLOAT32); + NDArray beta ('c', {4}, sd::DataType::FLOAT32); + NDArray gradO ('c', {2,3,4}, sd::DataType::FLOAT32); - NDArray expdLdI('c', {2,3,4}, {-0.000056, -0.000056, -0.000056, -0.000056, -0.000034, -0.000034, -0.000034, -0.000034, -0.000011, -0.000011, -0.000011, -0.000011, 0.000011, 0.000011, 0.000011, 0.000011, 0.000034, 0.000034, 0.000034, 0.000034, 0.000056, 0.000056, 0.000056, 0.000056}, nd4j::DataType::FLOAT32); - NDArray expdLdG('c', {4}, {6.148104, 6.148104, 6.148105, 6.148105}, nd4j::DataType::FLOAT32); - NDArray expdLdB('c', {4}, {3.6, 4.5, 5.4, 6.3}, nd4j::DataType::FLOAT32); + NDArray expdLdI('c', {2,3,4}, {-0.000056, -0.000056, -0.000056, -0.000056, -0.000034, -0.000034, -0.000034, -0.000034, -0.000011, -0.000011, -0.000011, -0.000011, 0.000011, 0.000011, 0.000011, 0.000011, 0.000034, 0.000034, 0.000034, 0.000034, 0.000056, 0.000056, 0.000056, 0.000056}, sd::DataType::FLOAT32); + NDArray expdLdG('c', {4}, {6.148104, 6.148104, 6.148105, 6.148105}, sd::DataType::FLOAT32); + NDArray expdLdB('c', {4}, {3.6, 4.5, 5.4, 6.3}, sd::DataType::FLOAT32); input.linspace(0.1, 0.1); variance.assign(0.46666667); @@ -2269,15 +2215,15 @@ TEST_F(DeclarableOpsTests13, batchnorm_bp_test1) { beta.assign(1.); // has no effect on gradient calculations gradO.linspace(-0.9, 0.15); - nd4j::ops::batchnorm_bp op; + sd::ops::batchnorm_bp op; auto results = op.evaluate({&input, &mean, &variance, &gamma, &beta, &gradO}, {1e-5}, {1,1}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto dLdI = results->at(0); - auto dLdG = results->at(3); - auto dLdB = results->at(4); + auto dLdI = results.at(0); + auto dLdG = results.at(3); + auto dLdB = results.at(4); ASSERT_TRUE(expdLdI.isSameShapeStrict(*dLdI)); ASSERT_TRUE(expdLdI.equalsTo(dLdI)); @@ -2288,39 +2234,39 @@ TEST_F(DeclarableOpsTests13, batchnorm_bp_test1) { ASSERT_TRUE(expdLdB.isSameShapeStrict(*dLdB)); ASSERT_TRUE(expdLdB.equalsTo(dLdB)); - delete results; + } //////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests13, batchnorm_bp_test2) { - NDArray input ('c', {2,3,4}, nd4j::DataType::FLOAT32); - NDArray mean ('c', {3}, {1.05, 1.1, 1.15}, nd4j::DataType::FLOAT32); - NDArray variance('c', {3}, {0.5, 0.6, 0.7}, nd4j::DataType::FLOAT32); - NDArray gamma ('c', {3}, {1.2, 1.3, 1.4}, nd4j::DataType::FLOAT32); - NDArray beta ('c', {3}, nd4j::DataType::FLOAT32); - NDArray gradO ('c', {2,3,4}, nd4j::DataType::FLOAT32); + NDArray input ('c', {2,3,4}, sd::DataType::FLOAT32); + NDArray mean ('c', {3}, {1.05, 1.1, 1.15}, sd::DataType::FLOAT32); + NDArray variance('c', {3}, {0.5, 0.6, 0.7}, sd::DataType::FLOAT32); + NDArray gamma ('c', {3}, {1.2, 1.3, 1.4}, sd::DataType::FLOAT32); + NDArray beta ('c', {3}, sd::DataType::FLOAT32); + NDArray gradO ('c', {2,3,4}, sd::DataType::FLOAT32); NDArray expdLdI('c', {2,3,4}, {-0.601415, -0.521226, -0.441037, -0.360849, -0.456306, -0.395465, -0.334624, -0.273784, 0.396631, 0.343747, 0.290863, 0.237978, 0.360849, 0.441037, 0.521226, 0.601415, 0.273784, 0.334625, 0.395465, 0.456306, -0.237978, - -0.290863, -0.343746, -0.396631}, nd4j::DataType::FLOAT32); - NDArray expdLdG('c', {3}, {5.81236 , 7.048771, 12.155388}, nd4j::DataType::FLOAT32); - NDArray expdLdB('c', {3}, {1.8, 6.6, 11.4}, nd4j::DataType::FLOAT32); + -0.290863, -0.343746, -0.396631}, sd::DataType::FLOAT32); + NDArray expdLdG('c', {3}, {5.81236 , 7.048771, 12.155388}, sd::DataType::FLOAT32); + NDArray expdLdB('c', {3}, {1.8, 6.6, 11.4}, sd::DataType::FLOAT32); input.linspace(0.1, 0.1); // beta.assign(1.); // has no effect on gradient calculations gradO.linspace(-0.9, 0.15); - nd4j::ops::batchnorm_bp op; + sd::ops::batchnorm_bp op; auto results = op.evaluate({&input, &mean, &variance, &gamma, &beta, &gradO}, {1e-5}, {1,1,1}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto dLdI = results->at(0); - auto dLdG = results->at(3); - auto dLdB = results->at(4); + auto dLdI = results.at(0); + auto dLdG = results.at(3); + auto dLdB = results.at(4); ASSERT_TRUE(expdLdI.isSameShapeStrict(*dLdI)); ASSERT_TRUE(expdLdI.equalsTo(dLdI)); @@ -2331,38 +2277,37 @@ TEST_F(DeclarableOpsTests13, batchnorm_bp_test2) { ASSERT_TRUE(expdLdB.isSameShapeStrict(*dLdB)); ASSERT_TRUE(expdLdB.equalsTo(dLdB)); - delete results; } //////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests13, batchnorm_bp_test3) { - NDArray input ('c', {2,3,4}, nd4j::DataType::FLOAT32); - NDArray mean ('c', {2,1,4}, {1.05, 1.1, 1.15, 1.2, 1.25, 1.3, 1.35, 1.4}, nd4j::DataType::FLOAT32); - NDArray variance('c', {2,1,4}, {0.5, 0.6, 0.7, 0.8, 0.9, 1., 1.1, 1.2}, nd4j::DataType::FLOAT32); - NDArray gamma ('c', {2,1,4}, {1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9}, nd4j::DataType::FLOAT32); - NDArray beta ('c', {2,1,4}, nd4j::DataType::FLOAT32); - NDArray gradO ('c', {2,3,4}, nd4j::DataType::FLOAT32); + NDArray input ('c', {2,3,4}, sd::DataType::FLOAT32); + NDArray mean ('c', {2,1,4}, {1.05, 1.1, 1.15, 1.2, 1.25, 1.3, 1.35, 1.4}, sd::DataType::FLOAT32); + NDArray variance('c', {2,1,4}, {0.5, 0.6, 0.7, 0.8, 0.9, 1., 1.1, 1.2}, sd::DataType::FLOAT32); + NDArray gamma ('c', {2,1,4}, {1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9}, sd::DataType::FLOAT32); + NDArray beta ('c', {2,1,4}, sd::DataType::FLOAT32); + NDArray gradO ('c', {2,3,4}, sd::DataType::FLOAT32); NDArray expdLdI('c', {2,3,4}, {-0.577002, -0.744041, -0.850999, -0.922373, -0.000000, -0.000000, -0.000000, -0.000000, 0.577002, 0.744041, 0.850999, 0.922373, -0.386037, -0.350205, -0.312047, -0.271737, -0.000000, -0.000000, - -0.000000, -0.000000, 0.386037, 0.350205, 0.312047, 0.271736}, nd4j::DataType::FLOAT32); - NDArray expdLdG('c', {2,1,4}, {1.378844, 0.910144, 0.573706, 0.335408, 2.640487, 2.954985, 3.289431, 3.64234 }, nd4j::DataType::FLOAT32); - NDArray expdLdB('c', {2,1,4}, {-0.9 , -0.45, 0. , 0.45, 4.5 , 4.95, 5.4 , 5.85}, nd4j::DataType::FLOAT32); + -0.000000, -0.000000, 0.386037, 0.350205, 0.312047, 0.271736}, sd::DataType::FLOAT32); + NDArray expdLdG('c', {2,1,4}, {1.378844, 0.910144, 0.573706, 0.335408, 2.640487, 2.954985, 3.289431, 3.64234 }, sd::DataType::FLOAT32); + NDArray expdLdB('c', {2,1,4}, {-0.9 , -0.45, 0. , 0.45, 4.5 , 4.95, 5.4 , 5.85}, sd::DataType::FLOAT32); input.linspace(0.1, 0.1); // beta.assign(1.); // has no effect on gradient calculations gradO.linspace(-0.9, 0.15); - nd4j::ops::batchnorm_bp op; + sd::ops::batchnorm_bp op; auto results = op.evaluate({&input, &mean, &variance, &gamma, &beta, &gradO}, {1e-5}, {1,1,0,2}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto dLdI = results->at(0); - auto dLdG = results->at(3); - auto dLdB = results->at(4); + auto dLdI = results.at(0); + auto dLdG = results.at(3); + auto dLdB = results.at(4); ASSERT_TRUE(expdLdI.isSameShapeStrict(*dLdI)); ASSERT_TRUE(expdLdI.equalsTo(dLdI)); @@ -2373,35 +2318,34 @@ TEST_F(DeclarableOpsTests13, batchnorm_bp_test3) { ASSERT_TRUE(expdLdB.isSameShapeStrict(*dLdB)); ASSERT_TRUE(expdLdB.equalsTo(dLdB)); - delete results; } //////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests13, batchnorm_bp_test4) { - NDArray input ('c', {2,4}, nd4j::DataType::FLOAT32); - NDArray mean ('c', {4}, {1.05, 1.15, 1.2, 1.3}, nd4j::DataType::FLOAT32); - NDArray variance('c', {4}, {0.5, 0.7, 0.9, 1.1}, nd4j::DataType::FLOAT32); - NDArray gamma ('c', {4}, {-1.2, 1.3, -1.4, 1.5}, nd4j::DataType::FLOAT32); - NDArray beta ('c', {4}, nd4j::DataType::FLOAT32); - NDArray gradO ('c', {2,4}, nd4j::DataType::FLOAT32); + NDArray input ('c', {2,4}, sd::DataType::FLOAT32); + NDArray mean ('c', {4}, {1.05, 1.15, 1.2, 1.3}, sd::DataType::FLOAT32); + NDArray variance('c', {4}, {0.5, 0.7, 0.9, 1.1}, sd::DataType::FLOAT32); + NDArray gamma ('c', {4}, {-1.2, 1.3, -1.4, 1.5}, sd::DataType::FLOAT32); + NDArray beta ('c', {4}, sd::DataType::FLOAT32); + NDArray gradO ('c', {2,4}, sd::DataType::FLOAT32); - NDArray expdLdI('c', {2,4}, {0.162923, -0.289673, 0.354174, -0.386151, -0.162923, 0.289673, -0.354174, 0.386151}, nd4j::DataType::FLOAT32); - NDArray expdLdG('c', {4}, {1.442483, 0.950200, 0.569207, 0.314641}, nd4j::DataType::FLOAT32); - NDArray expdLdB('c', {4}, {-1.2, -0.9, -0.6, -0.3}, nd4j::DataType::FLOAT32); + NDArray expdLdI('c', {2,4}, {0.162923, -0.289673, 0.354174, -0.386151, -0.162923, 0.289673, -0.354174, 0.386151}, sd::DataType::FLOAT32); + NDArray expdLdG('c', {4}, {1.442483, 0.950200, 0.569207, 0.314641}, sd::DataType::FLOAT32); + NDArray expdLdB('c', {4}, {-1.2, -0.9, -0.6, -0.3}, sd::DataType::FLOAT32); input.linspace(0.1, 0.1); gradO.linspace(-0.9, 0.15); - nd4j::ops::batchnorm_bp op; + sd::ops::batchnorm_bp op; auto results = op.evaluate({&input, &mean, &variance, &gamma, &beta, &gradO}, {1e-5}, {1,1}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto dLdI = results->at(0); - auto dLdG = results->at(3); - auto dLdB = results->at(4); + auto dLdI = results.at(0); + auto dLdG = results.at(3); + auto dLdB = results.at(4); ASSERT_TRUE(expdLdI.isSameShapeStrict(*dLdI)); ASSERT_TRUE(expdLdI.equalsTo(dLdI)); @@ -2411,8 +2355,6 @@ TEST_F(DeclarableOpsTests13, batchnorm_bp_test4) { ASSERT_TRUE(expdLdB.isSameShapeStrict(*dLdB)); ASSERT_TRUE(expdLdB.equalsTo(dLdB)); - - delete results; } //////////////////////////////////////////////////////////////////// @@ -2421,31 +2363,31 @@ TEST_F(DeclarableOpsTests13, batchnorm_bp_test5) { #if defined(HAVE_CUDNN) return; #endif - NDArray input ('c', {2,4,2,2}, nd4j::DataType::FLOAT32); - NDArray mean ('c', {4}, {1.05, 1.15, 1.2, 1.3}, nd4j::DataType::FLOAT32); - NDArray variance('c', {4}, {0.5, 0.7, 0.9, 1.1}, nd4j::DataType::FLOAT32); - NDArray gamma ('c', {4}, {-1.2, 1.3, -1.4, 1.5}, nd4j::DataType::FLOAT32); - NDArray beta ('c', {4}, nd4j::DataType::FLOAT32); - NDArray gradO ('c', {2,4,2,2}, nd4j::DataType::FLOAT32); + NDArray input ('c', {2,4,2,2}, sd::DataType::FLOAT32); + NDArray mean ('c', {4}, {1.05, 1.15, 1.2, 1.3}, sd::DataType::FLOAT32); + NDArray variance('c', {4}, {0.5, 0.7, 0.9, 1.1}, sd::DataType::FLOAT32); + NDArray gamma ('c', {4}, {-1.2, 1.3, -1.4, 1.5}, sd::DataType::FLOAT32); + NDArray beta ('c', {4}, sd::DataType::FLOAT32); + NDArray gradO ('c', {2,4,2,2}, sd::DataType::FLOAT32); NDArray expdLdI('c', {2,4,2,2}, {-0.737512, -0.659880, -0.582247, -0.504614, 0.561404, 0.502309, 0.443214, 0.384118, -1.168243, -1.045270, -0.922297, -0.799324, 1.899026, 1.699128, 1.499231, 1.299333, 0.504614, 0.582247, 0.659880, 0.737512, -0.384118, - -0.443214, -0.502308, -0.561404, 0.799324, 0.922297, 1.045270, 1.168243, -1.299334, -1.499231, -1.699129, -1.899026}, nd4j::DataType::FLOAT32); - NDArray expdLdG('c', {4}, {11.073181, 12.585667, 17.708657, 24.313186}, nd4j::DataType::FLOAT32); - NDArray expdLdB('c', {4}, {4.2, 9. , 13.8, 18.6}, nd4j::DataType::FLOAT32); + -0.443214, -0.502308, -0.561404, 0.799324, 0.922297, 1.045270, 1.168243, -1.299334, -1.499231, -1.699129, -1.899026}, sd::DataType::FLOAT32); + NDArray expdLdG('c', {4}, {11.073181, 12.585667, 17.708657, 24.313186}, sd::DataType::FLOAT32); + NDArray expdLdB('c', {4}, {4.2, 9. , 13.8, 18.6}, sd::DataType::FLOAT32); input.linspace(0.1, 0.1); gradO.linspace(-0.9, 0.15); - nd4j::ops::batchnorm_bp op; + sd::ops::batchnorm_bp op; auto results = op.evaluate({&input, &mean, &variance, &gamma, &beta, &gradO}, {1e-5}, {1,1,1}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto dLdI = results->at(0); - auto dLdG = results->at(3); - auto dLdB = results->at(4); + auto dLdI = results.at(0); + auto dLdG = results.at(3); + auto dLdB = results.at(4); ASSERT_TRUE(expdLdI.isSameShapeStrict(*dLdI)); ASSERT_TRUE(expdLdI.equalsTo(dLdI)); @@ -2455,8 +2397,6 @@ return; ASSERT_TRUE(expdLdB.isSameShapeStrict(*dLdB)); ASSERT_TRUE(expdLdB.equalsTo(dLdB)); - - delete results; } //////////////////////////////////////////////////////////////////// @@ -2466,31 +2406,31 @@ TEST_F(DeclarableOpsTests13, batchnorm_bp_test6) { return; #endif - NDArray input ('c', {2,2,2,4}, nd4j::DataType::FLOAT32); - NDArray mean ('c', {4}, {1.05, 1.15, 1.2, 1.3}, nd4j::DataType::FLOAT32); - NDArray variance('c', {4}, {0.5, 0.7, 0.9, 1.1}, nd4j::DataType::FLOAT32); - NDArray gamma ('c', {4}, {-1.2, 1.3, -1.4, 1.5}, nd4j::DataType::FLOAT32); - NDArray beta ('c', {4}, nd4j::DataType::FLOAT32); - NDArray gradO ('c', {2,2,2,4}, nd4j::DataType::FLOAT32); + NDArray input ('c', {2,2,2,4}, sd::DataType::FLOAT32); + NDArray mean ('c', {4}, {1.05, 1.15, 1.2, 1.3}, sd::DataType::FLOAT32); + NDArray variance('c', {4}, {0.5, 0.7, 0.9, 1.1}, sd::DataType::FLOAT32); + NDArray gamma ('c', {4}, {-1.2, 1.3, -1.4, 1.5}, sd::DataType::FLOAT32); + NDArray beta ('c', {4}, sd::DataType::FLOAT32); + NDArray gradO ('c', {2,2,2,4}, sd::DataType::FLOAT32); NDArray expdLdI('c', {2,2,2,4}, {-4.989124, 2.540357, -1.515022, 0.791769, -3.563660, 1.814540, -1.082159, 0.565549, -2.138196, 1.088724, -0.649295, 0.339329, -0.712732, 0.362908, -0.216432, 0.113110, 0.712732, -0.362908, 0.216432, -0.113110, 2.138195, -1.088724, 0.649295, - -0.339330, 3.563660,-1.814540, 1.082159, -0.565549, 4.989125, -2.540356, 1.515022, -0.791770}, nd4j::DataType::FLOAT32); - NDArray expdLdG('c', {4}, {20.364472, 17.856588, 16.949714, 15.903684}, nd4j::DataType::FLOAT32); - NDArray expdLdB('c', {4}, {9.6, 10.8, 12. , 13.2}, nd4j::DataType::FLOAT32); + -0.339330, 3.563660,-1.814540, 1.082159, -0.565549, 4.989125, -2.540356, 1.515022, -0.791770}, sd::DataType::FLOAT32); + NDArray expdLdG('c', {4}, {20.364472, 17.856588, 16.949714, 15.903684}, sd::DataType::FLOAT32); + NDArray expdLdB('c', {4}, {9.6, 10.8, 12. , 13.2}, sd::DataType::FLOAT32); input.linspace(0.1, 0.1); gradO.linspace(-0.9, 0.15); - nd4j::ops::batchnorm_bp op; + sd::ops::batchnorm_bp op; auto results = op.evaluate({&input, &mean, &variance, &gamma, &beta, &gradO}, {1e-5}, {1,1,3}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto dLdI = results->at(0); - auto dLdG = results->at(3); - auto dLdB = results->at(4); + auto dLdI = results.at(0); + auto dLdG = results.at(3); + auto dLdB = results.at(4); ASSERT_TRUE(expdLdI.isSameShapeStrict(*dLdI)); ASSERT_TRUE(expdLdI.equalsTo(dLdI)); @@ -2500,8 +2440,6 @@ return; ASSERT_TRUE(expdLdB.isSameShapeStrict(*dLdB)); ASSERT_TRUE(expdLdB.equalsTo(dLdB)); - - delete results; } //////////////////////////////////////////////////////////////////// @@ -2511,34 +2449,34 @@ TEST_F(DeclarableOpsTests13, batchnorm_bp_test7) { return; #endif - NDArray input ('c', {2,2,2,2,4}, nd4j::DataType::FLOAT32); - NDArray mean ('c', {4}, {1.05, 1.15, 1.2, 1.3}, nd4j::DataType::FLOAT32); - NDArray variance('c', {4}, {0.5, 0.7, 0.9, 1.1}, nd4j::DataType::FLOAT32); - NDArray gamma ('c', {4}, {-1.2, 1.3, -1.4, 1.5}, nd4j::DataType::FLOAT32); - NDArray beta ('c', {4}, nd4j::DataType::FLOAT32); - NDArray gradO ('c', {2,2,2,2,4}, nd4j::DataType::FLOAT32); + NDArray input ('c', {2,2,2,2,4}, sd::DataType::FLOAT32); + NDArray mean ('c', {4}, {1.05, 1.15, 1.2, 1.3}, sd::DataType::FLOAT32); + NDArray variance('c', {4}, {0.5, 0.7, 0.9, 1.1}, sd::DataType::FLOAT32); + NDArray gamma ('c', {4}, {-1.2, 1.3, -1.4, 1.5}, sd::DataType::FLOAT32); + NDArray beta ('c', {4}, sd::DataType::FLOAT32); + NDArray gradO ('c', {2,2,2,2,4}, sd::DataType::FLOAT32); NDArray expdLdI('c', {2,2,2,2,4}, {-119.435059, 78.159744, -58.732986, 46.630123, -103.510391, 67.738441, -50.901920, 40.412773, -87.585716, 57.317142, -43.070854, 34.195419, -71.661041, 46.895844, -35.239792, 27.978071, -55.736359, 36.474548, -27.408726, 21.760721, -39.811687, 26.053242, -19.577662, 15.543370, -23.887009, 15.631950, -11.746595, 9.326023, -7.962326, 5.210644, -3.915531, 3.108671, 7.962341, -5.210655, 3.915535, -3.108677, 23.887032, -15.631958, 11.746601, -9.326031, 39.811691, -26.053246, 19.577671, -15.543377, 55.736382, -36.474548, 27.408726, -21.760731, 71.661064, -46.895851, 35.239788, - -27.978077, 87.585732, -57.317154, 43.070866, -34.195431, 103.510384, -67.738464, 50.901920, -40.412777, 119.435097, -78.159744, 58.732998, -46.630131}, nd4j::DataType::FLOAT32); - NDArray expdLdG('c', {4}, {282.38734 , 244.542027, 224.140995, 207.548793}, nd4j::DataType::FLOAT32); - NDArray expdLdB('c', {4}, {57.6, 60. , 62.4, 64.8}, nd4j::DataType::FLOAT32); + -27.978077, 87.585732, -57.317154, 43.070866, -34.195431, 103.510384, -67.738464, 50.901920, -40.412777, 119.435097, -78.159744, 58.732998, -46.630131}, sd::DataType::FLOAT32); + NDArray expdLdG('c', {4}, {282.38734 , 244.542027, 224.140995, 207.548793}, sd::DataType::FLOAT32); + NDArray expdLdB('c', {4}, {57.6, 60. , 62.4, 64.8}, sd::DataType::FLOAT32); input.linspace(0.1, 0.1); gradO.linspace(-0.9, 0.15); - nd4j::ops::batchnorm_bp op; + sd::ops::batchnorm_bp op; auto results = op.evaluate({&input, &mean, &variance, &gamma, &beta, &gradO}, {1e-5}, {1,1,4}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto dLdI = results->at(0); - auto dLdG = results->at(3); - auto dLdB = results->at(4); + auto dLdI = results.at(0); + auto dLdG = results.at(3); + auto dLdB = results.at(4); // dLdI->printBuffer(); @@ -2551,7 +2489,7 @@ return; ASSERT_TRUE(expdLdB.isSameShapeStrict(*dLdB)); ASSERT_TRUE(expdLdB.equalsTo(dLdB)); - delete results; + } //////////////////////////////////////////////////////////////////// @@ -2561,33 +2499,33 @@ TEST_F(DeclarableOpsTests13, batchnorm_bp_test8) { return; #endif - NDArray input ('c', {2,4,2,2,2}, nd4j::DataType::FLOAT32); - NDArray mean ('c', {4}, {1.05, 1.15, 1.2, 1.3}, nd4j::DataType::FLOAT32); - NDArray variance('c', {4}, {0.5, 0.7, 0.9, 1.1}, nd4j::DataType::FLOAT32); - NDArray gamma ('c', {4}, {-1.2, 1.3, -1.4, 1.5}, nd4j::DataType::FLOAT32); - NDArray beta ('c', {4}, nd4j::DataType::FLOAT32); - NDArray gradO ('c', {2,4,2,2,2}, nd4j::DataType::FLOAT32); + NDArray input ('c', {2,4,2,2,2}, sd::DataType::FLOAT32); + NDArray mean ('c', {4}, {1.05, 1.15, 1.2, 1.3}, sd::DataType::FLOAT32); + NDArray variance('c', {4}, {0.5, 0.7, 0.9, 1.1}, sd::DataType::FLOAT32); + NDArray gamma ('c', {4}, {-1.2, 1.3, -1.4, 1.5}, sd::DataType::FLOAT32); + NDArray beta ('c', {4}, sd::DataType::FLOAT32); + NDArray gradO ('c', {2,4,2,2,2}, sd::DataType::FLOAT32); NDArray expdLdI('c', {2,4,2,2,2}, {-34.373802, -32.611046, -30.848286, -29.085529, -27.322769, -25.560009, -23.797251, -22.034491, 36.146996, 34.293301, 32.439610, 30.585917, 28.732227, 26.878534, 25.024841, 23.171150, -42.876553, -40.677757, -38.478958, -36.280159, -34.081367, -31.882565, -29.683767, -27.484968, 50.674446, 48.075760, 45.477066, 42.878380, 40.279686, 37.681000, 35.082310, 32.483616, 22.034489, 23.797249, 25.560009, 27.322765, 29.085526, 30.848286, 32.611046, 34.373802, -23.171146, -25.024837, -26.878536, -28.732231, -30.585918, -32.439613, -34.293297, -36.146996, 27.484982, 29.683773, - 31.882572, 34.081364, 36.280178, 38.478970, 40.677776, 42.876560, -32.483627, -35.082329, -37.681023, -40.279701, -42.878403, -45.477081, -48.075775, -50.674484}, nd4j::DataType::FLOAT32); - NDArray expdLdG('c', {4}, {134.490365, 179.785003, 248.933114, 330.087248}, nd4j::DataType::FLOAT32); - NDArray expdLdB('c', {4}, {32.4, 51.6, 70.8, 90.}, nd4j::DataType::FLOAT32); + 31.882572, 34.081364, 36.280178, 38.478970, 40.677776, 42.876560, -32.483627, -35.082329, -37.681023, -40.279701, -42.878403, -45.477081, -48.075775, -50.674484}, sd::DataType::FLOAT32); + NDArray expdLdG('c', {4}, {134.490365, 179.785003, 248.933114, 330.087248}, sd::DataType::FLOAT32); + NDArray expdLdB('c', {4}, {32.4, 51.6, 70.8, 90.}, sd::DataType::FLOAT32); input.linspace(0.1, 0.1); gradO.linspace(-0.9, 0.15); - nd4j::ops::batchnorm_bp op; + sd::ops::batchnorm_bp op; auto results = op.evaluate({&input, &mean, &variance, &gamma, &beta, &gradO}, {1e-5}, {1,1,1}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto dLdI = results->at(0); - auto dLdG = results->at(3); - auto dLdB = results->at(4); + auto dLdI = results.at(0); + auto dLdG = results.at(3); + auto dLdB = results.at(4); // dLdI->printBuffer(); @@ -2600,24 +2538,23 @@ return; ASSERT_TRUE(expdLdB.isSameShapeStrict(*dLdB)); ASSERT_TRUE(expdLdB.equalsTo(dLdB)); - delete results; } //////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests13, batchnorm_bp_test9) { - NDArray input ('c', {2,4,2,2}, nd4j::DataType::FLOAT32); - NDArray mean ('c', {4}, nd4j::DataType::FLOAT32); - NDArray variance('c', {4}, nd4j::DataType::FLOAT32); - NDArray gamma ('c', {4}, {-1.2, 1.3, -1.4, 1.5}, nd4j::DataType::FLOAT32); - NDArray beta ('c', {4}, nd4j::DataType::FLOAT32); - NDArray gradO ('c', {2,4,2,2}, nd4j::DataType::FLOAT32); + NDArray input ('c', {2,4,2,2}, sd::DataType::FLOAT32); + NDArray mean ('c', {4}, sd::DataType::FLOAT32); + NDArray variance('c', {4}, sd::DataType::FLOAT32); + NDArray gamma ('c', {4}, {-1.2, 1.3, -1.4, 1.5}, sd::DataType::FLOAT32); + NDArray beta ('c', {4}, sd::DataType::FLOAT32); + NDArray gradO ('c', {2,4,2,2}, sd::DataType::FLOAT32); NDArray expdLdI('c', {2,4,2,2}, {0.032378, 0.028967, 0.025558, 0.022147, -0.035056, -0.031364, -0.027669, -0.024006, 0.037742, 0.033766, 0.029791, 0.025818, -0.040429, -0.036172, -0.031913, -0.027656, -0.022155, -0.025564, -0.028974, -0.032359, 0.023982, 0.027677, 0.031373, 0.035063, - -0.025822, -0.029794, -0.033770, -0.037747, 0.027653, 0.031913, 0.036168, 0.040426}, nd4j::DataType::FLOAT32); - NDArray expdLdG('c', {4}, {9.685875, 9.685880, 9.685887, 9.685891}, nd4j::DataType::FLOAT32); - NDArray expdLdB('c', {4}, {4.2, 9. , 13.8, 18.6}, nd4j::DataType::FLOAT32); + -0.025822, -0.029794, -0.033770, -0.037747, 0.027653, 0.031913, 0.036168, 0.040426}, sd::DataType::FLOAT32); + NDArray expdLdG('c', {4}, {9.685875, 9.685880, 9.685887, 9.685891}, sd::DataType::FLOAT32); + NDArray expdLdB('c', {4}, {4.2, 9. , 13.8, 18.6}, sd::DataType::FLOAT32); input.linspace(1,0.01); gradO.linspace(-0.9, 0.15); @@ -2626,22 +2563,22 @@ TEST_F(DeclarableOpsTests13, batchnorm_bp_test9) { PointersManager manager(input.getContext(), "DeclarableOpsTests13.batchnorm_bp_test9"); std::vector dimensions = {0,2,3}; int* dims = reinterpret_cast(manager.replicatePointer(dimensions.data(), dimensions.size() * sizeof(int))); - input.reduceAlongDimension(nd4j::reduce::Mean, mean, dimensions); + input.reduceAlongDimension(sd::reduce::Mean, mean, dimensions); NDArray::prepareSpecialUse({&variance}, {&input}); - auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(input.getShapeInfo(), dimensions); + auto packX = sd::ConstantTadHelper::getInstance()->tadForDimensions(input.getShapeInfo(), dimensions); NativeOpExecutioner::execSummaryStats(input.getContext(), 0,input.getBuffer(), input.getShapeInfo(),input.getSpecialBuffer(), input.getSpecialShapeInfo(),nullptr,variance.getBuffer(), variance.getShapeInfo(),variance.getSpecialBuffer(), variance.getSpecialShapeInfo(), dims, dimensions.size(),packX.platformShapeInfo(), packX.platformOffsets(),false); manager.synchronize(); NDArray::registerSpecialUse({&variance}, {&input}); - nd4j::ops::batchnorm_bp op; + sd::ops::batchnorm_bp op; auto results = op.evaluate({&input, &mean, &variance, &gamma, &beta, &gradO}, {1e-5}, {1,1,1}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto dLdI = results->at(0); - auto dLdG = results->at(3); - auto dLdB = results->at(4); + auto dLdI = results.at(0); + auto dLdG = results.at(3); + auto dLdB = results.at(4); ASSERT_TRUE(expdLdI.isSameShapeStrict(*dLdI)); ASSERT_TRUE(expdLdI.equalsTo(dLdI, 1e-4)); @@ -2652,24 +2589,23 @@ TEST_F(DeclarableOpsTests13, batchnorm_bp_test9) { ASSERT_TRUE(expdLdB.isSameShapeStrict(*dLdB)); ASSERT_TRUE(expdLdB.equalsTo(dLdB)); - delete results; } //////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests13, batchnorm_bp_test10) { - NDArray input ('c', {2,2,2,4}, nd4j::DataType::FLOAT32); - NDArray mean ('c', {4}, nd4j::DataType::FLOAT32); - NDArray variance('c', {4}, nd4j::DataType::FLOAT32); - NDArray gamma ('c', {4}, {-1.2, 1.3, -1.4, 1.5}, nd4j::DataType::FLOAT32); - NDArray beta ('c', {4}, nd4j::DataType::FLOAT32); - NDArray gradO ('c', {2,2,2,4}, nd4j::DataType::FLOAT32); + NDArray input ('c', {2,2,2,4}, sd::DataType::FLOAT32); + NDArray mean ('c', {4}, sd::DataType::FLOAT32); + NDArray variance('c', {4}, sd::DataType::FLOAT32); + NDArray gamma ('c', {4}, {-1.2, 1.3, -1.4, 1.5}, sd::DataType::FLOAT32); + NDArray beta ('c', {4}, sd::DataType::FLOAT32); + NDArray gradO ('c', {2,2,2,4}, sd::DataType::FLOAT32); NDArray expdLdI('c', {2,2,2,4}, {0.032634, -0.035423, 0.038110, -0.040864, 0.023302, -0.025294, 0.027213, -0.029205, 0.013996, -0.015192, 0.016343, -0.017519, 0.004664, -0.005062, 0.005445, -0.005833, -0.004668, 0.005067, -0.005452, 0.005824, -0.013974, 0.015171, - -0.016325, 0.017508, -0.023309, 0.025301, -0.027221, 0.029197, -0.032639, 0.035428, -0.038118, 0.040878}, nd4j::DataType::FLOAT32); - NDArray expdLdG('c', {4}, {10.991656, 10.991631, 10.991643, 10.991632}, nd4j::DataType::FLOAT32); - NDArray expdLdB('c', {4}, {9.6, 10.8, 12., 13.2}, nd4j::DataType::FLOAT32); + -0.016325, 0.017508, -0.023309, 0.025301, -0.027221, 0.029197, -0.032639, 0.035428, -0.038118, 0.040878}, sd::DataType::FLOAT32); + NDArray expdLdG('c', {4}, {10.991656, 10.991631, 10.991643, 10.991632}, sd::DataType::FLOAT32); + NDArray expdLdB('c', {4}, {9.6, 10.8, 12., 13.2}, sd::DataType::FLOAT32); input.linspace(1,0.01); gradO.linspace(-0.9, 0.15); @@ -2678,22 +2614,22 @@ TEST_F(DeclarableOpsTests13, batchnorm_bp_test10) { PointersManager manager(input.getContext(), "DeclarableOpsTests13.batchnorm_bp_test9"); std::vector dimensions = {0,1,2}; int* dims = reinterpret_cast(manager.replicatePointer(dimensions.data(), dimensions.size() * sizeof(int))); - input.reduceAlongDimension(nd4j::reduce::Mean, mean, dimensions); + input.reduceAlongDimension(sd::reduce::Mean, mean, dimensions); NDArray::prepareSpecialUse({&variance}, {&input}); - auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(input.getShapeInfo(), dimensions); + auto packX = sd::ConstantTadHelper::getInstance()->tadForDimensions(input.getShapeInfo(), dimensions); NativeOpExecutioner::execSummaryStats(input.getContext(), 0,input.getBuffer(), input.getShapeInfo(),input.getSpecialBuffer(), input.getSpecialShapeInfo(),nullptr,variance.getBuffer(), variance.getShapeInfo(),variance.getSpecialBuffer(), variance.getSpecialShapeInfo(), dims, dimensions.size(),packX.platformShapeInfo(), packX.platformOffsets(),false); manager.synchronize(); NDArray::registerSpecialUse({&variance}, {&input}); - nd4j::ops::batchnorm_bp op; + sd::ops::batchnorm_bp op; auto results = op.evaluate({&input, &mean, &variance, &gamma, &beta, &gradO}, {1e-5}, {1,1,3}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto dLdI = results->at(0); - auto dLdG = results->at(3); - auto dLdB = results->at(4); + auto dLdI = results.at(0); + auto dLdG = results.at(3); + auto dLdB = results.at(4); ASSERT_TRUE(expdLdI.isSameShapeStrict(*dLdI)); ASSERT_TRUE(expdLdI.equalsTo(dLdI, 1e-4)); @@ -2704,18 +2640,17 @@ TEST_F(DeclarableOpsTests13, batchnorm_bp_test10) { ASSERT_TRUE(expdLdB.isSameShapeStrict(*dLdB)); ASSERT_TRUE(expdLdB.equalsTo(dLdB)); - delete results; } //////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests13, batchnorm_bp_test11) { - NDArray input ('c', {2,3,4,5}, nd4j::DataType::FLOAT32); - NDArray mean ('c', {1,3,4,5}, nd4j::DataType::FLOAT32); - NDArray variance('c', {1,3,4,5}, nd4j::DataType::FLOAT32); - NDArray gamma ('c', {1,3,4,5}, nd4j::DataType::FLOAT32); - NDArray beta ('c', {1,3,4,5}, nd4j::DataType::FLOAT32); - NDArray gradO ('c', {2,3,4,5}, nd4j::DataType::FLOAT32); + NDArray input ('c', {2,3,4,5}, sd::DataType::FLOAT32); + NDArray mean ('c', {1,3,4,5}, sd::DataType::FLOAT32); + NDArray variance('c', {1,3,4,5}, sd::DataType::FLOAT32); + NDArray gamma ('c', {1,3,4,5}, sd::DataType::FLOAT32); + NDArray beta ('c', {1,3,4,5}, sd::DataType::FLOAT32); + NDArray gradO ('c', {2,3,4,5}, sd::DataType::FLOAT32); NDArray expdLdI('c', {2,3,4,5}, {0.004981, 0.004818, 0.004652, 0.004483, 0.004319, 0.004153, 0.003985, 0.003832, 0.003661, 0.003505, 0.003340, 0.003171, 0.003001, 0.002837, 0.002670, 0.002505, 0.002337, 0.002167, 0.002003, 0.001835, 0.001666, 0.001499, 0.001327, 0.001162, 0.000996, 0.000830, 0.000664, 0.000498, @@ -2725,14 +2660,14 @@ TEST_F(DeclarableOpsTests13, batchnorm_bp_test11) { -0.003836, -0.003661, -0.003505, -0.003338, -0.003171, -0.003004, -0.002837, -0.002670, -0.002503, -0.002337, -0.002170, -0.002003, -0.001835, -0.001664, -0.001499, -0.001328, -0.001162, -0.000996, -0.000829, -0.000664, -0.000498, -0.000332, -0.000166, 0.0, 0.000166, 0.000334, 0.000500, 0.000668, 0.000834, 0.001003, 0.001170, 0.001337, 0.001502, 0.001669, 0.001838, 0.002005, 0.002172, 0.002330, 0.002496, 0.002669, - 0.002836, 0.003002, 0.003162, 0.003328, 0.003495, 0.003670, 0.003828, 0.003992, 0.004158, 0.004324, 0.004522, 0.004689, 0.004843}, nd4j::DataType::FLOAT32); + 0.002836, 0.003002, 0.003162, 0.003328, 0.003495, 0.003670, 0.003828, 0.003992, 0.004158, 0.004324, 0.004522, 0.004689, 0.004843}, sd::DataType::FLOAT32); NDArray expdLdG('c', {1,3,4,5}, {8.999503, 8.999502, 8.999502, 8.999503, 8.999502, 8.999503, 8.999503, 8.999499, 8.999501, 8.999498, 8.999498, 8.999498, 8.999498, 8.999498, 8.999498, 8.999498, 8.999498, 8.999498, 8.999498, 8.999499, 8.999501, 8.999500, 8.999503, 8.999503, 8.999503, 8.999504, 8.999503, 8.999503, 8.999504, 8.999503, 8.999504, 8.999504, 8.999499, 8.999500, 8.999497, 8.999498, 8.999496, 8.999496, 8.999496, 8.999498, 8.999498, 8.999496, 8.999496, 8.999496, 8.999501, - 8.999501, 8.999499, 8.999499, 8.999499, 8.999501, 8.999501, 8.999501, 8.999499, 8.999500, 8.999501, 8.999501, 8.999501, 8.999495, 8.999495, 8.999497}, nd4j::DataType::FLOAT32); + 8.999501, 8.999499, 8.999499, 8.999499, 8.999501, 8.999501, 8.999501, 8.999499, 8.999500, 8.999501, 8.999501, 8.999501, 8.999495, 8.999495, 8.999497}, sd::DataType::FLOAT32); NDArray expdLdB('c', {1,3,4,5}, {7.2, 7.5, 7.8, 8.1, 8.4, 8.7, 9.0, 9.3, 9.6, 9.9, 10.2, 10.5, 10.8, 11.1, 11.4, 11.7, 12.0, 12.3, 12.6, 12.9, 13.2, 13.5, 13.8, 14.1, 14.4, 14.7, 15.0, 15.3, 15.6, 15.9, 16.2, 16.5, 16.8, 17.1, 17.4, 17.7, 18.0, 18.3, 18.6, 18.9, 19.2, 19.5, 19.8, 20.1, 20.4, 20.7, 21.0, 21.3, 21.6, 21.9, 22.2, 22.5, - 22.8, 23.1, 23.4, 23.7, 24.0, 24.3, 24.6, 24.9}, nd4j::DataType::FLOAT32); + 22.8, 23.1, 23.4, 23.7, 24.0, 24.3, 24.6, 24.9}, sd::DataType::FLOAT32); input.linspace(1,0.01); gradO.linspace(-0.9, 0.15); @@ -2742,22 +2677,22 @@ TEST_F(DeclarableOpsTests13, batchnorm_bp_test11) { PointersManager manager(input.getContext(), "DeclarableOpsTests13.batchnorm_bp_test9"); std::vector dimensions = {0}; int* dims = reinterpret_cast(manager.replicatePointer(dimensions.data(), dimensions.size() * sizeof(int))); - input.reduceAlongDimension(nd4j::reduce::Mean, mean, dimensions, true); + input.reduceAlongDimension(sd::reduce::Mean, mean, dimensions, true); NDArray::prepareSpecialUse({&variance}, {&input}); - auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(input.getShapeInfo(), dimensions); + auto packX = sd::ConstantTadHelper::getInstance()->tadForDimensions(input.getShapeInfo(), dimensions); NativeOpExecutioner::execSummaryStats(input.getContext(), 0,input.getBuffer(), input.getShapeInfo(),input.getSpecialBuffer(), input.getSpecialShapeInfo(),nullptr,variance.getBuffer(), variance.getShapeInfo(),variance.getSpecialBuffer(), variance.getSpecialShapeInfo(), dims, dimensions.size(),packX.platformShapeInfo(), packX.platformOffsets(),false); manager.synchronize(); NDArray::registerSpecialUse({&variance}, {&input}); - nd4j::ops::batchnorm_bp op; + sd::ops::batchnorm_bp op; auto results = op.evaluate({&input, &mean, &variance, &gamma, &beta, &gradO}, {1e-5}, {1,1, 1,2,3}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto dLdI = results->at(0); - auto dLdG = results->at(3); - auto dLdB = results->at(4); + auto dLdI = results.at(0); + auto dLdG = results.at(3); + auto dLdB = results.at(4); ASSERT_TRUE(expdLdI.isSameShapeStrict(*dLdI)); ASSERT_TRUE(expdLdI.equalsTo(dLdI, 1e-4)); @@ -2768,5 +2703,4 @@ TEST_F(DeclarableOpsTests13, batchnorm_bp_test11) { ASSERT_TRUE(expdLdB.isSameShapeStrict(*dLdB)); ASSERT_TRUE(expdLdB.equalsTo(dLdB)); - delete results; -} \ No newline at end of file +} diff --git a/libnd4j/tests_cpu/layers_tests/DeclarableOpsTests14.cpp b/libnd4j/tests_cpu/layers_tests/DeclarableOpsTests14.cpp index 3672a4c20..b3a710be9 100644 --- a/libnd4j/tests_cpu/layers_tests/DeclarableOpsTests14.cpp +++ b/libnd4j/tests_cpu/layers_tests/DeclarableOpsTests14.cpp @@ -21,12 +21,12 @@ #include "testlayers.h" #include -#include +#include #include -#include +#include -using namespace nd4j; +using namespace sd; class DeclarableOpsTests14 : public testing::Test { @@ -43,15 +43,15 @@ TEST_F(DeclarableOpsTests14, Test_Validation_Edge_1) { auto exp = NDArrayFactory::create('c', {2, 2}, Environment::getInstance()->defaultFloatDataType()); exp.assign(4.0f); - nd4j::ops::fill op; + sd::ops::fill op; auto result = op.evaluate({&x}, {4.0f}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_EQ(exp, *z); - delete result; + } TEST_F(DeclarableOpsTests14, Test_Reshape_CF_1) { @@ -61,13 +61,13 @@ TEST_F(DeclarableOpsTests14, Test_Reshape_CF_1) { auto r = x.reshape('c', {3, 2});; r.streamline('f'); - nd4j::ops::reshape op; + sd::ops::reshape op; auto result = op.evaluate({&x}, {3, 2}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); - delete result; + } TEST_F(DeclarableOpsTests14, Test_Inf_Comparison_1) { @@ -100,15 +100,15 @@ TEST_F(DeclarableOpsTests14, Multiply_test) { y.assign(1.0); e.assign(1.0); - nd4j::ops::multiply op; + sd::ops::multiply op; auto result = op.evaluate({&x, &y}); - auto f = result->at(0); + auto f = result.at(0); NDArray r = *f; ASSERT_EQ(e, r); ASSERT_EQ(e, *f); - delete result; + } } @@ -117,14 +117,14 @@ TEST_F(DeclarableOpsTests14, Test_EvalReductionShape_1) { auto y = NDArrayFactory::create('c', {1}, {1}); auto e = NDArrayFactory::create('c', {2}, {5, 4}); - nd4j::ops::evaluate_reduction_shape op; + sd::ops::evaluate_reduction_shape op; auto result = op.evaluate({&x, &y}, {}, {}, {false, false}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_EQ(e, *z); - delete result; + } TEST_F(DeclarableOpsTests14, Test_EvalReductionShape_2) { @@ -132,14 +132,14 @@ TEST_F(DeclarableOpsTests14, Test_EvalReductionShape_2) { auto y = NDArrayFactory::create('c', {1}, {1}); auto e = NDArrayFactory::create('c', {3}, {5, 1, 4}); - nd4j::ops::evaluate_reduction_shape op; + sd::ops::evaluate_reduction_shape op; auto result = op.evaluate({&x, &y}, {}, {}, {true, false}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_EQ(e, *z); - delete result; + } TEST_F(DeclarableOpsTests14, Test_Reduce_Min_Small_0) { @@ -147,7 +147,7 @@ TEST_F(DeclarableOpsTests14, Test_Reduce_Min_Small_0) { auto z = NDArrayFactory::create('c', {4}); auto e = NDArrayFactory::create('c', {4}, {-999.f, 0.2236f, -2.1340f, 0.0962f}); - nd4j::ops::reduce_min op; + sd::ops::reduce_min op; op.execute({&x}, {&z}, {}, {0}, {}); //z.printIndexedBuffer("Z"); @@ -160,7 +160,7 @@ TEST_F(DeclarableOpsTests14, Test_Reduce_Min_Small_1) { auto z = NDArrayFactory::create('c', {3}); auto e = NDArrayFactory::create('c', {3}, {-999.f, -0.7301f, -2.1340f}); - nd4j::ops::reduce_min op; + sd::ops::reduce_min op; op.execute({&x}, {&z}, {}, {1}, {}); //z.printIndexedBuffer("Z"); @@ -173,7 +173,7 @@ TEST_F(DeclarableOpsTests14, Test_Diag_Zeros_1) { auto z = NDArrayFactory::create('c', {2, 2}, {-119, -119, -119, -119}); auto exp = NDArrayFactory::create('c', {2, 2}, {1, 0, 0, 2}); - nd4j::ops::diag op; + sd::ops::diag op; auto status = op.execute({&x}, {&z}, {}, {}, {}); ASSERT_EQ(Status::OK(), status); @@ -187,13 +187,13 @@ TEST_F(DeclarableOpsTests14, Test_scalar_broadcast_1) { e.assign(1.0); - nd4j::ops::add op; + sd::ops::add op; auto result = op.evaluate({&x, &y}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - ASSERT_EQ(e, *result->at(0)); + ASSERT_EQ(e, *result.at(0)); - delete result; + } TEST_F(DeclarableOpsTests14, Test_scalar_broadcast_2) { @@ -204,27 +204,27 @@ TEST_F(DeclarableOpsTests14, Test_scalar_broadcast_2) { e.assign(-1.0f); - nd4j::ops::subtract op; + sd::ops::subtract op; auto result = op.evaluate({&x, &y}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - ASSERT_EQ(e, *result->at(0)); + ASSERT_EQ(e, *result.at(0)); - delete result; + } TEST_F(DeclarableOpsTests14, test_empty_fill_1) { auto x = NDArrayFactory::empty(); auto y = NDArrayFactory::create(1); - nd4j::ops::fill op; + sd::ops::fill op; auto result = op.evaluate({&x, &y}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_EQ(y, *z); - delete result; + } TEST_F(DeclarableOpsTests14, test_lstmBlockCell_1) { @@ -245,95 +245,33 @@ TEST_F(DeclarableOpsTests14, test_lstmBlockCell_1) { auto z5 = NDArrayFactory::create('c', {1, 3}); auto z6 = NDArrayFactory::create('c', {1, 3}); - nd4j::ops::lstmBlockCell op; + sd::ops::lstmBlockCell op; auto result = op.execute({&a, &b, &c, &d, &e, &f, &g, &h}, {&z0, &z1, &z2, &z3, &z4, &z5, &z6}, {1.0, -1.0}, {0}, {}); ASSERT_EQ(Status::OK(), result); } -TEST_F(DeclarableOpsTests14, test_empty_stack_1) { - auto x = NDArrayFactory::create('c', {0}); - auto e = NDArrayFactory::create('c', {1, 0}); - - nd4j::ops::stack op; - auto result = op.evaluate({&x}, {}, {0}); - ASSERT_EQ(Status::OK(), result->status()); - - auto z = result->at(0); - ASSERT_EQ(e, *z); - nd4j::ops::reduce_min sumOp; - auto res2 = sumOp.evaluate({&e}, {1.}, {1}); - ASSERT_EQ(res2->status(), Status::OK()); - auto out = res2->at(0); - - ASSERT_EQ(out->e(0), DataTypeUtils::infOrMax()); - delete res2; - delete result; -} - -TEST_F(DeclarableOpsTests14, test_empty_stack_2) { - auto x = NDArrayFactory::empty(); - auto e = NDArrayFactory::create('c', {0}); - - nd4j::ops::stack op; - auto result = op.evaluate({&x}, {}, {0}); - ASSERT_EQ(Status::OK(), result->status()); - - auto z = result->at(0); - ASSERT_EQ(e, *z); - - delete result; -} - -TEST_F(DeclarableOpsTests14, test_empty_stack_3) { - auto x = NDArrayFactory::empty(); - auto e = NDArrayFactory::create('c', {2, 0}); - - nd4j::ops::stack op; - auto result = op.evaluate({&x, &x}, {}, {0}); - ASSERT_EQ(Status::OK(), result->status()); - - auto z = result->at(0); - ASSERT_EQ(e, *z); - - delete result; -} - -TEST_F(DeclarableOpsTests14, test_empty_stack_4) { - auto x = NDArrayFactory::create('c', {0}); - auto e = NDArrayFactory::create('c', {2, 0}); - - nd4j::ops::stack op; - auto result = op.evaluate({&x, &x}, {}, {0}); - ASSERT_EQ(Status::OK(), result->status()); - - auto z = result->at(0); - ASSERT_EQ(e, *z); - - delete result; -} - TEST_F(DeclarableOpsTests14, test_empty_reduce_min_1) { auto e = NDArrayFactory::create('c', {1, 0}); - nd4j::ops::reduce_min sumOp; + sd::ops::reduce_min sumOp; auto res2 = sumOp.evaluate({&e}, {1.}, {1}); - ASSERT_EQ(res2->status(), Status::OK()); - auto out = res2->at(0); + ASSERT_EQ(res2.status(), Status::OK()); + auto out = res2.at(0); ASSERT_EQ(out->e(0), DataTypeUtils::infOrMax()); - delete res2; + } TEST_F(DeclarableOpsTests14, test_empty_reduce_max_1) { auto e = NDArrayFactory::create('c', {1, 0}); - nd4j::ops::reduce_max sumOp; + sd::ops::reduce_max sumOp; auto res2 = sumOp.evaluate({&e}, {1.}, {1}); - ASSERT_EQ(res2->status(), Status::OK()); - auto out = res2->at(0); + ASSERT_EQ(res2.status(), Status::OK()); + auto out = res2.at(0); ASSERT_EQ(out->e(0), -DataTypeUtils::infOrMax()); - delete res2; + } TEST_F(DeclarableOpsTests14, test_empty_reduce_sum_1) { @@ -343,12 +281,12 @@ TEST_F(DeclarableOpsTests14, test_empty_reduce_sum_1) { #endif auto e = NDArrayFactory::create('c', {1, 0}); - nd4j::ops::reduce_sum sumOp; + sd::ops::reduce_sum sumOp; auto res2 = sumOp.evaluate({&e}, {1.}, {1}); - ASSERT_EQ(res2->status(), Status::OK()); - auto out = res2->at(0); + ASSERT_EQ(res2.status(), Status::OK()); + auto out = res2.at(0); ASSERT_EQ(out->e(0), 0.f); - delete res2; + } TEST_F(DeclarableOpsTests14, test_empty_reduce_mean_1) { @@ -358,14 +296,14 @@ TEST_F(DeclarableOpsTests14, test_empty_reduce_mean_1) { #endif auto e = NDArrayFactory::create('c', {1, 0}); - nd4j::ops::reduce_mean sumOp; + sd::ops::reduce_mean sumOp; auto res2 = sumOp.evaluate({&e}, {1.}, {1}); - ASSERT_EQ(res2->status(), Status::OK()); - auto out = res2->at(0); + ASSERT_EQ(res2.status(), Status::OK()); + auto out = res2.at(0); // out->printShapeInfo("ReduceMean empty shape with keep dims"); // out->printIndexedBuffer("ReduceMean scalar"); ASSERT_TRUE(std::isnan(out->e(0))); - delete res2; + } TEST_F(DeclarableOpsTests14, Test_StridedSliceZeros_1) { @@ -378,15 +316,15 @@ TEST_F(DeclarableOpsTests14, Test_StridedSliceZeros_1) { matrix.linspace(1); - nd4j::ops::strided_slice op; + sd::ops::strided_slice op; auto result = op.evaluate({&matrix, &b, &e, &s}, {}, {0, 0, 0, 0, 0}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); - delete result; + } TEST_F(DeclarableOpsTests14, Test_StridedSliceZeros_2) { @@ -399,15 +337,15 @@ TEST_F(DeclarableOpsTests14, Test_StridedSliceZeros_2) { matrix.linspace(1); - nd4j::ops::strided_slice op; + sd::ops::strided_slice op; auto result = op.evaluate({&matrix, &b, &e, &s}, {}, {0, 0, 0, 0, 1}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); - delete result; + } TEST_F(DeclarableOpsTests14, test_empty_argmax_1) { @@ -415,24 +353,24 @@ TEST_F(DeclarableOpsTests14, test_empty_argmax_1) { auto y = NDArrayFactory::create(0); auto e = NDArrayFactory::create('c', {0}); - nd4j::ops::argmax op; - //nd4j::ops::reduce_max op; + sd::ops::argmax op; + //sd::ops::reduce_max op; auto result = op.evaluate({&x, &y}, {}, {}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_EQ(e, *z); - delete result; + } TEST_F(DeclarableOpsTests14, test_empty_argmax_2) { auto x = NDArrayFactory::create('c', {1, 0}); auto y = NDArrayFactory::create(1); - nd4j::ops::argmax op; + sd::ops::argmax op; try { auto result = op.execute({&x, &y}, {&y}, {}, {}, {}); ASSERT_TRUE(false); @@ -444,16 +382,16 @@ TEST_F(DeclarableOpsTests14, test_empty_argmax_2) { TEST_F(DeclarableOpsTests14, test_empty_tanh_5) { auto x = NDArrayFactory::create('c', {32, 0}); - nd4j::ops::tanh op; + sd::ops::tanh op; auto result = op.evaluate({&x}, {}, {}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(x.isSameShape(z)); ASSERT_EQ(x, *z); - delete result; + } ////////////////////////////////////////////////////////////////////// @@ -462,16 +400,16 @@ TEST_F(DeclarableOpsTests14, repeat_1) { NDArray x('c', {2, 3}, {1, 2, 3, 4, 5, 6}); NDArray e('c', {4, 3}, {1, 2, 3, 1, 2, 3, 4, 5, 6, 4, 5, 6}); - nd4j::ops::repeat op; + sd::ops::repeat op; auto result = op.evaluate({&x}, {}, {2, 0}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(e.isSameShape(z)); ASSERT_TRUE(e.equalsTo(z)); - delete result; + } ////////////////////////////////////////////////////////////////////// @@ -480,16 +418,16 @@ TEST_F(DeclarableOpsTests14, repeat_2) { NDArray x('c', {2, 3}, {1, 2, 3, 4, 5, 6}); NDArray e('c', {2, 6}, {1, 1, 2, 2, 3, 3,4, 4, 5, 5, 6, 6}); - nd4j::ops::repeat op; + sd::ops::repeat op; auto result = op.evaluate({&x}, {}, {2, 1}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(e.isSameShape(z)); ASSERT_TRUE(e.equalsTo(z)); - delete result; + } ////////////////////////////////////////////////////////////////////// @@ -498,16 +436,16 @@ TEST_F(DeclarableOpsTests14, repeat_3) { NDArray x('c', {2, 3}, {1, 2, 3, 4, 5, 6}); NDArray e('c', {2, 6}, {1, 2, 2, 3, 3, 3,4, 5, 5, 6, 6, 6}); - nd4j::ops::repeat op; + sd::ops::repeat op; auto result = op.evaluate({&x}, {}, {1,2,3, 1}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(e.isSameShape(z)); ASSERT_TRUE(e.equalsTo(z)); - delete result; + } ////////////////////////////////////////////////////////////////////// @@ -516,16 +454,16 @@ TEST_F(DeclarableOpsTests14, repeat_4) { NDArray x('c', {2, 3}, {1, 2, 3, 4, 5, 6}); NDArray e('c', {7, 3}, {1, 2, 3, 1, 2, 3, 1, 2, 3, 4, 5, 6, 4, 5, 6, 4, 5, 6, 4, 5, 6}); - nd4j::ops::repeat op; + sd::ops::repeat op; auto result = op.evaluate({&x}, {}, {3,4, 0}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(e.isSameShape(z)); ASSERT_TRUE(e.equalsTo(z)); - delete result; + } ////////////////////////////////////////////////////////////////////// @@ -534,68 +472,68 @@ TEST_F(DeclarableOpsTests14, repeat_5) { NDArray x('c', {2, 3, 4}, {1, 2, 3, 4, 5, 6, 7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24}); NDArray e('c', {2, 4, 4}, {1, 2, 3, 4, 5, 6, 7, 8, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 17, 18, 19, 20, 21, 22, 23, 24}); - nd4j::ops::repeat op; + sd::ops::repeat op; auto result = op.evaluate({&x}, {}, {1,2,1, 1}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(e.isSameShape(z)); ASSERT_TRUE(e.equalsTo(z)); - delete result; + } ///////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests14, Test_broadcast_SpecialCaseTest) { - auto y = NDArray('c', { 3 }, nd4j::DataType::FLOAT32); - auto x = NDArray('c', { 5, 2, 1 }, nd4j::DataType::FLOAT32); + auto y = NDArray('c', { 3 }, sd::DataType::FLOAT32); + auto x = NDArray('c', { 5, 2, 1 }, sd::DataType::FLOAT32); - auto e = NDArray('c', { 5, 2, 3 }, { 2., 2., 2., 3., 3., 3., 4., 4., 4., 5., 5., 5., 6., 6., 6., 7., 7., 7., 8., 8., 8., 9., 9., 9., 10., 10., 10., 11., 11., 11. }, nd4j::DataType::FLOAT32); + auto e = NDArray('c', { 5, 2, 3 }, { 2., 2., 2., 3., 3., 3., 4., 4., 4., 5., 5., 5., 6., 6., 6., 7., 7., 7., 8., 8., 8., 9., 9., 9., 10., 10., 10., 11., 11., 11. }, sd::DataType::FLOAT32); y.assign(1.0); x.linspace(1.0); - nd4j::ops::add op; + sd::ops::add op; auto result = op.evaluate({ &x, &y }); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto res = *result->at(0); + auto res = *result.at(0); ASSERT_EQ(e, res); - delete result; + } ///////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests14, Test_broadcast_SpecialCaseTest2) { - auto y = NDArray('c', { 1, 3 }, nd4j::DataType::FLOAT32); - auto x = NDArray('c', { 5, 2, 1 }, nd4j::DataType::FLOAT32); + auto y = NDArray('c', { 1, 3 }, sd::DataType::FLOAT32); + auto x = NDArray('c', { 5, 2, 1 }, sd::DataType::FLOAT32); - auto e = NDArray('c', { 5, 2, 3 }, { 2., 2., 2., 3., 3., 3., 4., 4., 4., 5., 5., 5., 6., 6., 6., 7., 7., 7., 8., 8., 8., 9., 9., 9., 10., 10., 10., 11., 11., 11. }, nd4j::DataType::FLOAT32); + auto e = NDArray('c', { 5, 2, 3 }, { 2., 2., 2., 3., 3., 3., 4., 4., 4., 5., 5., 5., 6., 6., 6., 7., 7., 7., 8., 8., 8., 9., 9., 9., 10., 10., 10., 11., 11., 11. }, sd::DataType::FLOAT32); y.assign(1.0); x.linspace(1.0); - nd4j::ops::add op; + sd::ops::add op; auto result = op.evaluate({ &x, &y }); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto res = *result->at(0); + auto res = *result.at(0); ASSERT_EQ(e, res); - delete result; + } /////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests14, Test_broadcast_SpecialCaseTest3) { - auto x = NDArray('c', { 3, 5, 1 }, nd4j::DataType::FLOAT32); - auto y = NDArray('c', { 3, 1, 4 }, nd4j::DataType::FLOAT32); - auto z = NDArray('c', { 3, 5, 4 }, nd4j::DataType::FLOAT32); + auto x = NDArray('c', { 3, 5, 1 }, sd::DataType::FLOAT32); + auto y = NDArray('c', { 3, 1, 4 }, sd::DataType::FLOAT32); + auto z = NDArray('c', { 3, 5, 4 }, sd::DataType::FLOAT32); // recieved by main algorithm - auto e = NDArray('c', { 3, 5, 4 }, { 10., 11., 12., 13., 20., 22., 24., 26., 30., 33., 36., 39., 40., 44., 48., 52., 50., 55., 60., 65., 84., 90., 96., 102., 98., 105., 112., 119., 112., 120., 128., 136., 126., 135., 144., 153., 140., 150., 160., 170., 198., 209., 220., 231., 216., 228., 240., 252., 234., 247., 260., 273., 252., 266., 280., 294., 270., 285., 300., 315. }, nd4j::DataType::FLOAT32); + auto e = NDArray('c', { 3, 5, 4 }, { 10., 11., 12., 13., 20., 22., 24., 26., 30., 33., 36., 39., 40., 44., 48., 52., 50., 55., 60., 65., 84., 90., 96., 102., 98., 105., 112., 119., 112., 120., 128., 136., 126., 135., 144., 153., 140., 150., 160., 170., 198., 209., 220., 231., 216., 228., 240., 252., 234., 247., 260., 273., 252., 266., 280., 294., 270., 285., 300., 315. }, sd::DataType::FLOAT32); x.linspace(1.f); y.linspace(10.f); @@ -607,11 +545,11 @@ TEST_F(DeclarableOpsTests14, Test_broadcast_SpecialCaseTest3) { /////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests14, Test_broadcast_SpecialCaseTest4) { - auto x = NDArray('c', { 2, 3, 5, 1 }, nd4j::DataType::FLOAT32); - auto y = NDArray('c', { 2, 3, 1, 4 }, nd4j::DataType::FLOAT32); - auto z = NDArray('c', { 2, 3, 5, 4 }, nd4j::DataType::FLOAT32); + auto x = NDArray('c', { 2, 3, 5, 1 }, sd::DataType::FLOAT32); + auto y = NDArray('c', { 2, 3, 1, 4 }, sd::DataType::FLOAT32); + auto z = NDArray('c', { 2, 3, 5, 4 }, sd::DataType::FLOAT32); // recieved by main algorithm - auto e = NDArray('c', { 2, 3, 5, 4 }, { 10., 11., 12., 13.,20., 22., 24., 26.,30., 33., 36., 39.,40., 44., 48., 52.,50., 55., 60., 65.,84., 90., 96., 102.,98., 105., 112., 119.,112., 120., 128., 136.,126., 135., 144., 153.,140., 150., 160., 170.,198., 209., 220., 231.,216., 228., 240., 252.,234., 247., 260., 273.,252., 266., 280., 294.,270., 285., 300., 315.,352., 368., 384., 400.,374., 391., 408., 425.,396., 414., 432., 450.,418., 437., 456., 475.,440., 460., 480., 500.,546., 567., 588., 609.,572., 594., 616., 638.,598., 621., 644., 667.,624., 648., 672., 696.,650., 675., 700., 725.,780., 806., 832., 858.,810., 837., 864., 891.,840., 868., 896., 924.,870., 899., 928., 957.,900., 930., 960., 990. }, nd4j::DataType::FLOAT32); + auto e = NDArray('c', { 2, 3, 5, 4 }, { 10., 11., 12., 13.,20., 22., 24., 26.,30., 33., 36., 39.,40., 44., 48., 52.,50., 55., 60., 65.,84., 90., 96., 102.,98., 105., 112., 119.,112., 120., 128., 136.,126., 135., 144., 153.,140., 150., 160., 170.,198., 209., 220., 231.,216., 228., 240., 252.,234., 247., 260., 273.,252., 266., 280., 294.,270., 285., 300., 315.,352., 368., 384., 400.,374., 391., 408., 425.,396., 414., 432., 450.,418., 437., 456., 475.,440., 460., 480., 500.,546., 567., 588., 609.,572., 594., 616., 638.,598., 621., 644., 667.,624., 648., 672., 696.,650., 675., 700., 725.,780., 806., 832., 858.,810., 837., 864., 891.,840., 868., 896., 924.,870., 899., 928., 957.,900., 930., 960., 990. }, sd::DataType::FLOAT32); x.linspace(1.f); y.linspace(10.f); z.assign(0.f); @@ -622,11 +560,11 @@ TEST_F(DeclarableOpsTests14, Test_broadcast_SpecialCaseTest4) { /////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests14, Test_broadcast_SpecialCaseTest5) { - auto x = NDArray('c', { 3, 5, 1 }, nd4j::DataType::FLOAT32); - auto y = NDArray('c', { 3, 1, 4 }, nd4j::DataType::FLOAT32); - auto z = NDArray('c', { 3, 5, 4 }, nd4j::DataType::FLOAT32); + auto x = NDArray('c', { 3, 5, 1 }, sd::DataType::FLOAT32); + auto y = NDArray('c', { 3, 1, 4 }, sd::DataType::FLOAT32); + auto z = NDArray('c', { 3, 5, 4 }, sd::DataType::FLOAT32); // recieved by main algorithm - auto e = NDArray('c', { 3, 5, 4 }, { 0.1, 0.090909, 0.083333, 0.076923,0.2, 0.181818, 0.166667, 0.153846,0.3, 0.272727, 0.250000, 0.230769,0.4, 0.363636, 0.333333, 0.307692,0.5, 0.454545, 0.416667, 0.384615, 0.428571, 0.400000, 0.375000, 0.352941, 0.500000, 0.466667, 0.437500, 0.411765, 0.571429, 0.533333, 0.500000, 0.470588, 0.642857, 0.600000, 0.562500, 0.529412, 0.714286, 0.666667, 0.625000, 0.588235, 0.611111, 0.578947, 0.550000, 0.523810, 0.666667, 0.631579, 0.600000, 0.571429, 0.722222, 0.684211, 0.650000, 0.619048, 0.777778, 0.736842, 0.700000, 0.666667, 0.833333, 0.789474, 0.750000, 0.714286 }, nd4j::DataType::FLOAT32); + auto e = NDArray('c', { 3, 5, 4 }, { 0.1, 0.090909, 0.083333, 0.076923,0.2, 0.181818, 0.166667, 0.153846,0.3, 0.272727, 0.250000, 0.230769,0.4, 0.363636, 0.333333, 0.307692,0.5, 0.454545, 0.416667, 0.384615, 0.428571, 0.400000, 0.375000, 0.352941, 0.500000, 0.466667, 0.437500, 0.411765, 0.571429, 0.533333, 0.500000, 0.470588, 0.642857, 0.600000, 0.562500, 0.529412, 0.714286, 0.666667, 0.625000, 0.588235, 0.611111, 0.578947, 0.550000, 0.523810, 0.666667, 0.631579, 0.600000, 0.571429, 0.722222, 0.684211, 0.650000, 0.619048, 0.777778, 0.736842, 0.700000, 0.666667, 0.833333, 0.789474, 0.750000, 0.714286 }, sd::DataType::FLOAT32); x.linspace(1.f); y.linspace(10.f); z.assign(0.f); @@ -637,11 +575,11 @@ TEST_F(DeclarableOpsTests14, Test_broadcast_SpecialCaseTest5) { /////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests14, Test_broadcast_SpecialCaseTest6) { - auto x = NDArray('c', { 2, 3, 5, 1 }, nd4j::DataType::FLOAT32); - auto y = NDArray('c', { 2, 3, 1, 4 }, nd4j::DataType::FLOAT32); - auto z = NDArray('c', { 2, 3, 5, 4 }, nd4j::DataType::FLOAT32); + auto x = NDArray('c', { 2, 3, 5, 1 }, sd::DataType::FLOAT32); + auto y = NDArray('c', { 2, 3, 1, 4 }, sd::DataType::FLOAT32); + auto z = NDArray('c', { 2, 3, 5, 4 }, sd::DataType::FLOAT32); // recieved by main algorithm - auto e = NDArray('c', { 2, 3, 5, 4 }, { 0.1, 0.090909, 0.083333, 0.076923,0.2, 0.181818, 0.166667, 0.153846,0.3, 0.272727, 0.250000, 0.230769,0.4, 0.363636, 0.333333, 0.307692,0.5, 0.454545, 0.416667, 0.384615, 0.428571, 0.400000, 0.375000, 0.352941, 0.500000, 0.466667, 0.437500, 0.411765, 0.571429, 0.533333, 0.500000, 0.470588, 0.642857, 0.600000, 0.562500, 0.529412, 0.714286, 0.666667, 0.625000, 0.588235,0.611111, 0.578947, 0.550000, 0.523810,0.666667, 0.631579, 0.600000, 0.571429,0.722222, 0.684211, 0.650000, 0.619048,0.777778, 0.736842, 0.700000, 0.666667,0.833333, 0.789474, 0.750000, 0.714286, 0.727273, 0.695652, 0.666667, 0.64, 0.772727, 0.739130, 0.708333, 0.68, 0.818182, 0.782609, 0.750000, 0.72, 0.863636, 0.826087, 0.791667, 0.76, 0.909091, 0.869565, 0.833333, 0.80, 0.807692, 0.777778, 0.750000, 0.724138, 0.846154, 0.814815, 0.785714, 0.758621, 0.884615, 0.851852, 0.821429, 0.793103, 0.923077, 0.888889, 0.857143, 0.827586, 0.961538, 0.925926, 0.892857, 0.862069, 0.866667, 0.838710, 0.812500, 0.787879, 0.900000, 0.870968, 0.843750, 0.818182, 0.933333, 0.903226, 0.875000, 0.848485, 0.966667, 0.935484, 0.906250, 0.878788, 1.000000, 0.967742, 0.937500, 0.909091 }, nd4j::DataType::FLOAT32); + auto e = NDArray('c', { 2, 3, 5, 4 }, { 0.1, 0.090909, 0.083333, 0.076923,0.2, 0.181818, 0.166667, 0.153846,0.3, 0.272727, 0.250000, 0.230769,0.4, 0.363636, 0.333333, 0.307692,0.5, 0.454545, 0.416667, 0.384615, 0.428571, 0.400000, 0.375000, 0.352941, 0.500000, 0.466667, 0.437500, 0.411765, 0.571429, 0.533333, 0.500000, 0.470588, 0.642857, 0.600000, 0.562500, 0.529412, 0.714286, 0.666667, 0.625000, 0.588235,0.611111, 0.578947, 0.550000, 0.523810,0.666667, 0.631579, 0.600000, 0.571429,0.722222, 0.684211, 0.650000, 0.619048,0.777778, 0.736842, 0.700000, 0.666667,0.833333, 0.789474, 0.750000, 0.714286, 0.727273, 0.695652, 0.666667, 0.64, 0.772727, 0.739130, 0.708333, 0.68, 0.818182, 0.782609, 0.750000, 0.72, 0.863636, 0.826087, 0.791667, 0.76, 0.909091, 0.869565, 0.833333, 0.80, 0.807692, 0.777778, 0.750000, 0.724138, 0.846154, 0.814815, 0.785714, 0.758621, 0.884615, 0.851852, 0.821429, 0.793103, 0.923077, 0.888889, 0.857143, 0.827586, 0.961538, 0.925926, 0.892857, 0.862069, 0.866667, 0.838710, 0.812500, 0.787879, 0.900000, 0.870968, 0.843750, 0.818182, 0.933333, 0.903226, 0.875000, 0.848485, 0.966667, 0.935484, 0.906250, 0.878788, 1.000000, 0.967742, 0.937500, 0.909091 }, sd::DataType::FLOAT32); x.linspace(1.f); y.linspace(10.f); @@ -654,11 +592,11 @@ TEST_F(DeclarableOpsTests14, Test_broadcast_SpecialCaseTest6) { /////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests14, Test_broadcast_SpecialCaseTest7) { - auto x = NDArray('c', { 3, 5, 1 }, nd4j::DataType::FLOAT32); - auto y = NDArray('c', { 3, 1, 4 }, nd4j::DataType::FLOAT32); - auto z = NDArray('c', { 3, 5, 4 }, nd4j::DataType::FLOAT32); + auto x = NDArray('c', { 3, 5, 1 }, sd::DataType::FLOAT32); + auto y = NDArray('c', { 3, 1, 4 }, sd::DataType::FLOAT32); + auto z = NDArray('c', { 3, 5, 4 }, sd::DataType::FLOAT32); // recieved by main algorithm - auto e = NDArray('c', { 3, 5, 4 }, { -9., -10., -11., -12.,-8., -9., -10., -11., -7., -8., -9., -10.,-6., -7., -8., -9.,-5., -6., -7., -8.,-8., -9., -10., -11.,-7., -8., -9., -10.,-6., -7., -8., -9.,-5., -6., -7., -8.,-4., -5., -6., -7.,-7., -8.000000, -9.000000, -10.00,-6.000000, -7.000000, -8.000000, -9.000,-5.000000, -6.000000, -7.000000, -8.000,-4.000000, -5.000000, -6.000000, -7.000,-3.000000, -4.000000, -5.000000, -6.000 }, nd4j::DataType::FLOAT32); + auto e = NDArray('c', { 3, 5, 4 }, { -9., -10., -11., -12.,-8., -9., -10., -11., -7., -8., -9., -10.,-6., -7., -8., -9.,-5., -6., -7., -8.,-8., -9., -10., -11.,-7., -8., -9., -10.,-6., -7., -8., -9.,-5., -6., -7., -8.,-4., -5., -6., -7.,-7., -8.000000, -9.000000, -10.00,-6.000000, -7.000000, -8.000000, -9.000,-5.000000, -6.000000, -7.000000, -8.000,-4.000000, -5.000000, -6.000000, -7.000,-3.000000, -4.000000, -5.000000, -6.000 }, sd::DataType::FLOAT32); x.linspace(1.f); y.linspace(10.f); z.assign(0.f); @@ -669,11 +607,11 @@ TEST_F(DeclarableOpsTests14, Test_broadcast_SpecialCaseTest7) { /////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests14, Test_broadcast_SpecialCaseTest8) { - auto x = NDArray('c', { 2, 3, 5, 1 }, nd4j::DataType::FLOAT32); - auto y = NDArray('c', { 2, 3, 1, 4 }, nd4j::DataType::FLOAT32); - auto z = NDArray('c', { 2, 3, 5, 4 }, nd4j::DataType::FLOAT32); + auto x = NDArray('c', { 2, 3, 5, 1 }, sd::DataType::FLOAT32); + auto y = NDArray('c', { 2, 3, 1, 4 }, sd::DataType::FLOAT32); + auto z = NDArray('c', { 2, 3, 5, 4 }, sd::DataType::FLOAT32); // recieved by main algorithm - auto e = NDArray('c', { 2, 3, 5, 4 }, { -9.0, -10., -11., -12.,-8., -9., -10., -11.0,-7., -8., -9., -10.,-6., -7., -8., -9.,-5., -6., -7., -8.,-8., -9., -10., -11.,-7., -8., -9., -10.,-6., -7., -8., -9.,-5., -6., -7., -8.,-4., -5., -6., -7.,-7., -8., -9., -10.,-6., -7., -8., -9.,-5., -6., -7., -8.,-4., -5., -6., -7.,-3., -4., -5., -6.,-6., -7., -8., -9.,-5., -6., -7., -8.,-4., -5., -6., -7.,-3., -4., -5., -6.,-2., -3., -4., -5.,-5., -6., -7., -8.,-4., -5., -6., -7.,-3., -4., -5., -6.,-2., -3., -4., -5.,-1., -2., -3., -4.,-4., -5., -6., -7.,-3., -4., -5., -6.,-2., -3., -4., -5.,-1., -2., -3., -4., 0., -1., -2., -3. }, nd4j::DataType::FLOAT32); + auto e = NDArray('c', { 2, 3, 5, 4 }, { -9.0, -10., -11., -12.,-8., -9., -10., -11.0,-7., -8., -9., -10.,-6., -7., -8., -9.,-5., -6., -7., -8.,-8., -9., -10., -11.,-7., -8., -9., -10.,-6., -7., -8., -9.,-5., -6., -7., -8.,-4., -5., -6., -7.,-7., -8., -9., -10.,-6., -7., -8., -9.,-5., -6., -7., -8.,-4., -5., -6., -7.,-3., -4., -5., -6.,-6., -7., -8., -9.,-5., -6., -7., -8.,-4., -5., -6., -7.,-3., -4., -5., -6.,-2., -3., -4., -5.,-5., -6., -7., -8.,-4., -5., -6., -7.,-3., -4., -5., -6.,-2., -3., -4., -5.,-1., -2., -3., -4.,-4., -5., -6., -7.,-3., -4., -5., -6.,-2., -3., -4., -5.,-1., -2., -3., -4., 0., -1., -2., -3. }, sd::DataType::FLOAT32); x.linspace(1.f); y.linspace(10.f); @@ -693,15 +631,15 @@ TEST_F(DeclarableOpsTests14, matmul_test1) { x.linspace(1.); y.linspace(0.5, 0.5); - nd4j::ops::matmul op; + sd::ops::matmul op; auto results = op.evaluate({&x, &y}, {}, {}); - auto z = results->at(0); + auto z = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete results; + } @@ -715,15 +653,15 @@ TEST_F(DeclarableOpsTests14, matmul_test2) { x.linspace(1.); y.linspace(0.5, 0.5); - nd4j::ops::matmul op; + sd::ops::matmul op; auto results = op.evaluate({&x, &y}, {}, {}); - auto z = results->at(0); + auto z = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -736,15 +674,15 @@ TEST_F(DeclarableOpsTests14, matmul_test3) { x.linspace(1.); y.linspace(0.5, 0.5); - nd4j::ops::matmul op; + sd::ops::matmul op; auto results = op.evaluate({&x, &y}, {}, {}); - auto z = results->at(0); + auto z = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete results; + } @@ -758,15 +696,15 @@ TEST_F(DeclarableOpsTests14, matmul_test4) { x.linspace(1.); y.linspace(0.5, 0.5); - nd4j::ops::matmul op; + sd::ops::matmul op; auto results = op.evaluate({&x, &y}, {}, {}); - auto z = results->at(0); + auto z = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete results; + } @@ -780,15 +718,15 @@ TEST_F(DeclarableOpsTests14, matmul_test5) { x.linspace(1.); y.linspace(0.5, 0.5); - nd4j::ops::matmul op; + sd::ops::matmul op; auto results = op.evaluate({&x, &y}, {}, {1}); - auto z = results->at(0); + auto z = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -801,15 +739,15 @@ TEST_F(DeclarableOpsTests14, matmul_test6) { x.linspace(1.); y.linspace(0.5, 0.5); - nd4j::ops::matmul op; + sd::ops::matmul op; auto results = op.evaluate({&x, &y}, {}, {1, 1}); - auto z = results->at(0); + auto z = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -824,15 +762,15 @@ TEST_F(DeclarableOpsTests14, matmul_test7) { x.linspace(1.); y.linspace(0.1, 0.1); - nd4j::ops::matmul op; + sd::ops::matmul op; auto results = op.evaluate({&x, &y}, {}, {0, 1}); - auto z = results->at(0); + auto z = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -849,15 +787,15 @@ TEST_F(DeclarableOpsTests14, matmul_test8) { x.linspace(1.); y.linspace(0.1, 0.1); - nd4j::ops::matmul op; + sd::ops::matmul op; auto results = op.evaluate({&x, &y}, {}, {0, 1}); - auto z = results->at(0); + auto z = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -874,15 +812,15 @@ TEST_F(DeclarableOpsTests14, matmul_test9) { x.linspace(1.); y.linspace(0.1, 0.1); - nd4j::ops::matmul op; + sd::ops::matmul op; auto results = op.evaluate({&x, &y}, {}, {1, 1}); - auto z = results->at(0); + auto z = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete results; + } TEST_F(DeclarableOpsTests14, matmul_test10) { @@ -895,7 +833,7 @@ TEST_F(DeclarableOpsTests14, matmul_test10) { float _expB[]{135.0f, 310.0f, 485.0f, 150.0f, 350.0f, 550.0f, 165.0f, 390.0f, 615.0f}; Nd4jLong _expS[] {2, 3, 3, 1, 3, 0, 1, 102}; // expected shape - ArrayOptions::setDataType(_expS, nd4j::DataType::FLOAT32); + ArrayOptions::setDataType(_expS, sd::DataType::FLOAT32); NDArray exp(_expB, _expS); auto variableSpace = new VariableSpace(); @@ -906,7 +844,7 @@ TEST_F(DeclarableOpsTests14, matmul_test10) { auto block = new Context(1, variableSpace, false); block->fillInputs({-1, -2}); - nd4j::ops::matmul op; + sd::ops::matmul op; Nd4jStatus status = op.execute(block); ASSERT_EQ(ND4J_STATUS_OK, status); @@ -928,17 +866,17 @@ TEST_F(DeclarableOpsTests14, matmul_test11) { A.linspace(1); B.linspace(1); - nd4j::ops::matmul op; + sd::ops::matmul op; auto result = op.evaluate({&A, &B}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } TEST_F(DeclarableOpsTests14, matmul_test12) { @@ -946,17 +884,17 @@ TEST_F(DeclarableOpsTests14, matmul_test12) { auto y= NDArrayFactory::create('c', {4, 3}, {1, 2, 3, 4, 5, 6, 7, 8 , 9, 10, 11, 12}); auto exp= NDArrayFactory::create('f', {4, 4}, {38.0, 44.0, 50.0, 56.0, 83.0, 98.0, 113.0, 128.0, 128.0, 152.0, 176.0, 200.0, 173.0, 206.0, 239.0, 272.0}); - nd4j::ops::matmul op; + sd::ops::matmul op; auto result = op.evaluate({&x, &y}, {}, {1, 1}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } @@ -965,18 +903,18 @@ TEST_F(DeclarableOpsTests14, matmul_test13) { auto y= NDArrayFactory::create('c', {1, 4}, {1, 2, 3, 4}); auto exp= NDArrayFactory::create('f', {3, 4}, {1.0, 2.0, 3.0, 2.0, 4.0, 6.0, 3.0, 6.0, 9.0, 4.0, 8.0, 12.0}); - nd4j::ops::matmul op; + sd::ops::matmul op; auto result = op.evaluate({&x, &y}, {}, {1, 0}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); //z->printIndexedBuffer("z"); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } TEST_F(DeclarableOpsTests14, matmul_test14) { @@ -984,18 +922,18 @@ TEST_F(DeclarableOpsTests14, matmul_test14) { auto y= NDArrayFactory::create('c', {4, 1}, {1, 2, 3, 4}); auto exp= NDArrayFactory::create('f', {3, 4}, {1.0, 2.0, 3.0, 2.0, 4.0, 6.0, 3.0, 6.0, 9.0, 4.0, 8.0, 12.0}); - nd4j::ops::matmul op; + sd::ops::matmul op; auto result = op.evaluate({&x, &y}, {}, {0, 1}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); //z->printIndexedBuffer("z"); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } TEST_F(DeclarableOpsTests14, matmul_test15) { @@ -1003,18 +941,18 @@ TEST_F(DeclarableOpsTests14, matmul_test15) { auto y= NDArrayFactory::create('c', {1, 4}, {1, 2, 3, 4}); auto exp= NDArrayFactory::create('f', {3, 4}, {1.0, 2.0, 3.0, 2.0, 4.0, 6.0, 3.0, 6.0, 9.0, 4.0, 8.0, 12.0}); - nd4j::ops::matmul op; + sd::ops::matmul op; auto result = op.evaluate({&x, &y}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); //z->printIndexedBuffer("z"); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } TEST_F(DeclarableOpsTests14, matmul_test16) { @@ -1022,18 +960,18 @@ TEST_F(DeclarableOpsTests14, matmul_test16) { auto y= NDArrayFactory::create('c', {1, 4}, {1, 2, 3, 4}); auto exp= NDArrayFactory::create('f', {4, 4}, {1,2, 3, 4,2,4, 6, 8,3,6, 9,12,4,8,12,16}); - nd4j::ops::matmul op; + sd::ops::matmul op; auto result = op.evaluate({&x, &y}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); //z->printIndexedBuffer("z"); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } TEST_F(DeclarableOpsTests14, matmul_test17) { @@ -1041,13 +979,13 @@ TEST_F(DeclarableOpsTests14, matmul_test17) { auto y = NDArrayFactory::create('c', {2, 1}, {2.0f, 2.0f}); auto exp = NDArrayFactory::create('c', {1, 1}, {8.0f}); - nd4j::ops::matmul op; + sd::ops::matmul op; auto result = op.evaluate({&x, &y}, {}, {}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - ASSERT_EQ(exp, *result->at(0)); + ASSERT_EQ(exp, *result.at(0)); - delete result; + } @@ -1061,15 +999,15 @@ TEST_F(DeclarableOpsTests14, matmul_test18) { x.linspace(1.); y.linspace(0.5, 0.5); - nd4j::ops::matmul op; + sd::ops::matmul op; auto results = op.evaluate({&x, &y}, {}, {1, 1}); - auto z = results->at(0); + auto z = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -1081,15 +1019,15 @@ TEST_F(DeclarableOpsTests14, matmul_test19) { x.linspace(1.); y.linspace(0.5, 0.5); - nd4j::ops::matmul op; + sd::ops::matmul op; auto results = op.evaluate({&x, &y}, {}, {1, 1}); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); - auto z = results->at(0); + auto z = results.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -1102,15 +1040,15 @@ TEST_F(DeclarableOpsTests14, matmul_test20) { x.linspace(1.); y.linspace(0.5, 0.5); - nd4j::ops::matmul op; + sd::ops::matmul op; auto results = op.evaluate({&x, &y}, {}, {1, 1}); - ASSERT_EQ(Status::OK(), results->status()); - auto z = results->at(0); + ASSERT_EQ(Status::OK(), results.status()); + auto z = results.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -1123,15 +1061,15 @@ TEST_F(DeclarableOpsTests14, matmul_test21) { x.linspace(1.); y.linspace(0.5, 0.5); - nd4j::ops::matmul op; + sd::ops::matmul op; auto results = op.evaluate({&x, &y}, {}, {0, 0, 1}); - auto z = results->at(0); + auto z = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -1144,15 +1082,15 @@ TEST_F(DeclarableOpsTests14, matmul_test22) { x.linspace(1.); y.linspace(0.5, 0.5); - nd4j::ops::matmul op; + sd::ops::matmul op; auto results = op.evaluate({&x, &y}, {}, {1, 0, 1}); - auto z = results->at(0); + auto z = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -1165,15 +1103,15 @@ TEST_F(DeclarableOpsTests14, matmul_test23) { x.linspace(1.); y.linspace(0.5, 0.5); - nd4j::ops::matmul op; + sd::ops::matmul op; auto results = op.evaluate({&x, &y}, {}, {1, 0, 1}); - auto z = results->at(0); + auto z = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -1189,15 +1127,15 @@ TEST_F(DeclarableOpsTests14, matmul_test24) { x.linspace(1.); y.linspace(0.1, 0.1); - nd4j::ops::matmul op; + sd::ops::matmul op; auto results = op.evaluate({&x, &y}, {}, {1, 1, 1}); - auto z = results->at(0); + auto z = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -1210,15 +1148,15 @@ TEST_F(DeclarableOpsTests14, matmul_test25) { x.linspace(1.); y.linspace(0.1, 0.1); - nd4j::ops::matmul op; + sd::ops::matmul op; auto results = op.evaluate({&x, &y}, {}, {1, 0}); - auto z = results->at(0); + auto z = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -1231,15 +1169,15 @@ TEST_F(DeclarableOpsTests14, matmul_test26) { x.linspace(1.); y.linspace(0.1, 0.1); - nd4j::ops::matmul op; + sd::ops::matmul op; auto results = op.evaluate({&x, &y}, {}, {0, 1}); - auto z = results->at(0); + auto z = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -1252,15 +1190,15 @@ TEST_F(DeclarableOpsTests14, matmul_test27) { x.linspace(2.); y.linspace(0.1, 0.1); - nd4j::ops::matmul op; + sd::ops::matmul op; auto results = op.evaluate({&x, &y}, {}, {}); - auto z = results->at(0); + auto z = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete results; + } @@ -1274,15 +1212,15 @@ TEST_F(DeclarableOpsTests14, matmul_test28) { x.linspace(2.); y.linspace(0.1, 0.1); - nd4j::ops::matmul op; + sd::ops::matmul op; auto results = op.evaluate({&x, &y}, {}, {1,1,1}); - auto z = results->at(0); + auto z = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete results; + } @@ -1296,15 +1234,15 @@ TEST_F(DeclarableOpsTests14, matmul_test29) { x.linspace(2.); y.linspace(0.1, 0.1); - nd4j::ops::matmul op; + sd::ops::matmul op; auto results = op.evaluate({&x, &y}, {}, {}); - auto z = results->at(0); + auto z = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete results; + } ////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests14, matmul_test30) { @@ -1316,15 +1254,15 @@ TEST_F(DeclarableOpsTests14, matmul_test30) { x.linspace(2.); y.linspace(0.1, 0.1); - nd4j::ops::matmul op; + sd::ops::matmul op; auto results = op.evaluate({&x, &y}, {}, {1}); - auto z = results->at(0); + auto z = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete results; + } ////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests14, matmul_test31) { @@ -1336,15 +1274,15 @@ TEST_F(DeclarableOpsTests14, matmul_test31) { x.linspace(1.); y.linspace(0.1, 0.1); - nd4j::ops::matmul op; + sd::ops::matmul op; auto results = op.evaluate({&x, &y}, {}, {1, 1}); - auto z = results->at(0); + auto z = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete results; + } ////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests14, matmul_test32) { @@ -1353,15 +1291,15 @@ TEST_F(DeclarableOpsTests14, matmul_test32) { auto y = NDArrayFactory::create('c', {1}, {3.}); auto exp = NDArrayFactory::create(6.); - nd4j::ops::matmul op; + sd::ops::matmul op; auto results = op.evaluate({&x, &y}, {}, {1, 1}); - auto z = results->at(0); + auto z = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete results; + } ///////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests14, matmul_test33) { @@ -1372,16 +1310,16 @@ TEST_F(DeclarableOpsTests14, matmul_test33) { x.linspace(1); y.linspace(1); - nd4j::ops::matmul op; + sd::ops::matmul op; auto result = op.evaluate({&x, &y}, {}, {1, 0}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } ////////////////////////////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests14, matmul_test34) { @@ -1389,16 +1327,16 @@ TEST_F(DeclarableOpsTests14, matmul_test34) { auto b = NDArrayFactory::create('c', {4}, {1, 2, 3, 4}); auto exp = NDArrayFactory::create('c', {3}, {30, 70, 110}); - nd4j::ops::matmul op; + sd::ops::matmul op; auto result = op.evaluate({&a, &b}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } ///////////////////////////////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests14, matmul_test35) { @@ -1406,16 +1344,16 @@ TEST_F(DeclarableOpsTests14, matmul_test35) { auto b = NDArrayFactory::create('c', {4, 3}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}); auto exp = NDArrayFactory::create('c', {3}, {70, 80, 90}); - nd4j::ops::matmul op; + sd::ops::matmul op; auto result = op.evaluate({&a, &b}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } //////////////////////////////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests14, matmul_test36) { @@ -1423,30 +1361,30 @@ TEST_F(DeclarableOpsTests14, matmul_test36) { auto b = NDArrayFactory::create('c', {4, 3}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}); auto exp = NDArrayFactory::create('c', {1, 3}, {70, 80, 90}); - nd4j::ops::matmul op; + sd::ops::matmul op; auto result = op.evaluate({&a, &b}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } ////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests14, matmul_test37) { - NDArray a('c', {32, 12, 128, 64}, nd4j::DataType::FLOAT32); - NDArray b('c', {32, 12, 128, 64}, nd4j::DataType::FLOAT32); - NDArray c('c', {32,12,128,128}, nd4j::DataType::FLOAT32); - NDArray cExp('c', {32,12,128,128}, nd4j::DataType::FLOAT32); + NDArray a('c', {32, 12, 128, 64}, sd::DataType::FLOAT32); + NDArray b('c', {32, 12, 128, 64}, sd::DataType::FLOAT32); + NDArray c('c', {32,12,128,128}, sd::DataType::FLOAT32); + NDArray cExp('c', {32,12,128,128}, sd::DataType::FLOAT32); a = 1; b = 1; cExp = 64; //Each entry in output c is sum of 64 (1.0 x 1.0) multiplications - nd4j::ops::matmul op; + sd::ops::matmul op; auto status = op.execute({&a, &b}, {&c}, {}, {0,1}); ASSERT_EQ(ND4J_STATUS_OK, status); @@ -1459,30 +1397,30 @@ TEST_F(DeclarableOpsTests14, Test_broadcast_3D_1) { // x[4, 12, 128] * y[4, 128] = z[4, 12, 128] - auto x = NDArray('c', { 2, 3, 5 }, nd4j::DataType::FLOAT32); - auto y = NDArray('c', { 2, 5 }, nd4j::DataType::FLOAT32); - auto z = NDArray('c', { 2, 3, 5 }, nd4j::DataType::FLOAT32); + auto x = NDArray('c', { 2, 3, 5 }, sd::DataType::FLOAT32); + auto y = NDArray('c', { 2, 5 }, sd::DataType::FLOAT32); + auto z = NDArray('c', { 2, 3, 5 }, sd::DataType::FLOAT32); // recieved by main algorithm - auto e = NDArray('c', { 2, 3, 5 }, { 10.000000, 22.000000, 36.000000, 52.000000, 70.000000, 60.000000, 77.000000, 96.000000, 117.000000, 140.000000, 110.000000, 132.000000, 156.000000, 182.000000, 210.000000, 240.000000, 272.000000, 306.000000, 342.000000, 380.000000, 315.000000, 352.000000, 391.000000, 432.000000, 475.000000, 390.000000, 432.000000, 476.000000, 522.000000, 570.000000 }, nd4j::DataType::FLOAT32); + auto e = NDArray('c', { 2, 3, 5 }, { 10.000000, 22.000000, 36.000000, 52.000000, 70.000000, 60.000000, 77.000000, 96.000000, 117.000000, 140.000000, 110.000000, 132.000000, 156.000000, 182.000000, 210.000000, 240.000000, 272.000000, 306.000000, 342.000000, 380.000000, 315.000000, 352.000000, 391.000000, 432.000000, 475.000000, 390.000000, 432.000000, 476.000000, 522.000000, 570.000000 }, sd::DataType::FLOAT32); x.linspace(1.f); y.linspace(10.f); z.assign(0.f); - x.applyBroadcast(nd4j::broadcast::Multiply, { 0,2 }, y, z); + x.applyBroadcast(sd::broadcast::Multiply, { 0,2 }, y, z); //z.printBuffer(); ASSERT_EQ(e, z); } /////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests14, Test_broadcast_3D_2) { - auto x = NDArray('f', { 2, 3, 5 }, nd4j::DataType::FLOAT32); - auto y = NDArray('f', { 2, 5 }, nd4j::DataType::FLOAT32); - auto z = NDArray('f', { 2, 3, 5 }, nd4j::DataType::FLOAT32); + auto x = NDArray('f', { 2, 3, 5 }, sd::DataType::FLOAT32); + auto y = NDArray('f', { 2, 5 }, sd::DataType::FLOAT32); + auto z = NDArray('f', { 2, 3, 5 }, sd::DataType::FLOAT32); // recieved by main algorithm - auto eC = NDArray('c', { 2, 3, 5 }, { 0.100000, 0.181818, 0.250000, 0.307692, 0.357143, 0.600000, 0.636364, 0.666667, 0.692308, 0.714286, 1.100000, 1.090909, 1.083333, 1.076923, 1.071429, 1.066667, 1.062500, 1.058824, 1.055556, 1.052632, 1.400000, 1.375000, 1.352941, 1.333333, 1.315789, 1.733333, 1.687500, 1.647059, 1.611111, 1.578947 }, nd4j::DataType::FLOAT32); + auto eC = NDArray('c', { 2, 3, 5 }, { 0.100000, 0.181818, 0.250000, 0.307692, 0.357143, 0.600000, 0.636364, 0.666667, 0.692308, 0.714286, 1.100000, 1.090909, 1.083333, 1.076923, 1.071429, 1.066667, 1.062500, 1.058824, 1.055556, 1.052632, 1.400000, 1.375000, 1.352941, 1.333333, 1.315789, 1.733333, 1.687500, 1.647059, 1.611111, 1.578947 }, sd::DataType::FLOAT32); - auto e = NDArray('f', { 2, 3, 5 }, nd4j::DataType::FLOAT32); + auto e = NDArray('f', { 2, 3, 5 }, sd::DataType::FLOAT32); e.assign(eC); @@ -1490,37 +1428,37 @@ TEST_F(DeclarableOpsTests14, Test_broadcast_3D_2) { y.linspace(10.f); z.assign(0.f); - x.applyBroadcast(nd4j::broadcast::Divide, { 0,2 }, y, z); + x.applyBroadcast(sd::broadcast::Divide, { 0,2 }, y, z); ASSERT_EQ(e, z); } /////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests14, Test_broadcast_4D_1) { - auto x = NDArray('c', { 2, 3, 5, 4 }, nd4j::DataType::FLOAT32); - auto y = NDArray('c', { 2, 5, 4 }, nd4j::DataType::FLOAT32); - auto z = NDArray('c', { 2, 3, 5, 4 }, nd4j::DataType::FLOAT32); + auto x = NDArray('c', { 2, 3, 5, 4 }, sd::DataType::FLOAT32); + auto y = NDArray('c', { 2, 5, 4 }, sd::DataType::FLOAT32); + auto z = NDArray('c', { 2, 3, 5, 4 }, sd::DataType::FLOAT32); // recieved by main algorithm - auto e = NDArray('c', { 2, 3, 5, 4 }, { 10.000000, 22.000000, 36.000000, 52.000000, 70.000000, 90.000000, 112.000000, 136.000000, 162.000000, 190.000000, 220.000000, 252.000000, 286.000000, 322.000000, 360.000000, 400.000000, 442.000000, 486.000000, 532.000000, 580.000000, 210.000000, 242.000000, 276.000000, 312.000000, 350.000000, 390.000000, 432.000000, 476.000000, 522.000000, 570.000000, 620.000000, 672.000000, 726.000000, 782.000000, 840.000000, 900.000000, 962.000000, 1026.000000, 1092.000000, 1160.000000, 410.000000, 462.000000, 516.000000, 572.000000, 630.000000, 690.000000, 752.000000, 816.000000, 882.000000, 950.000000, 1020.000000, 1092.000000, 1166.000000, 1242.000000, 1320.000000, 1400.000000, 1482.000000, 1566.000000, 1652.000000, 1740.000000, 1830.000000, 1922.000000, 2016.000000, 2112.000000, 2210.000000, 2310.000000, 2412.000000, 2516.000000, 2622.000000, 2730.000000, 2840.000000, 2952.000000, 3066.000000, 3182.000000, 3300.000000, 3420.000000, 3542.000000, 3666.000000, 3792.000000, 3920.000000, 2430.000000, 2542.000000, 2656.000000, 2772.000000, 2890.000000, 3010.000000, 3132.000000, 3256.000000, 3382.000000, 3510.000000, 3640.000000, 3772.000000, 3906.000000, 4042.000000, 4180.000000, 4320.000000, 4462.000000, 4606.000000, 4752.000000, 4900.000000, 3030.000000, 3162.000000, 3296.000000, 3432.000000, 3570.000000, 3710.000000, 3852.000000, 3996.000000, 4142.000000, 4290.000000, 4440.000000, 4592.000000, 4746.000000, 4902.000000, 5060.000000, 5220.000000, 5382.000000, 5546.000000, 5712.000000, 5880.000000 }, nd4j::DataType::FLOAT32); + auto e = NDArray('c', { 2, 3, 5, 4 }, { 10.000000, 22.000000, 36.000000, 52.000000, 70.000000, 90.000000, 112.000000, 136.000000, 162.000000, 190.000000, 220.000000, 252.000000, 286.000000, 322.000000, 360.000000, 400.000000, 442.000000, 486.000000, 532.000000, 580.000000, 210.000000, 242.000000, 276.000000, 312.000000, 350.000000, 390.000000, 432.000000, 476.000000, 522.000000, 570.000000, 620.000000, 672.000000, 726.000000, 782.000000, 840.000000, 900.000000, 962.000000, 1026.000000, 1092.000000, 1160.000000, 410.000000, 462.000000, 516.000000, 572.000000, 630.000000, 690.000000, 752.000000, 816.000000, 882.000000, 950.000000, 1020.000000, 1092.000000, 1166.000000, 1242.000000, 1320.000000, 1400.000000, 1482.000000, 1566.000000, 1652.000000, 1740.000000, 1830.000000, 1922.000000, 2016.000000, 2112.000000, 2210.000000, 2310.000000, 2412.000000, 2516.000000, 2622.000000, 2730.000000, 2840.000000, 2952.000000, 3066.000000, 3182.000000, 3300.000000, 3420.000000, 3542.000000, 3666.000000, 3792.000000, 3920.000000, 2430.000000, 2542.000000, 2656.000000, 2772.000000, 2890.000000, 3010.000000, 3132.000000, 3256.000000, 3382.000000, 3510.000000, 3640.000000, 3772.000000, 3906.000000, 4042.000000, 4180.000000, 4320.000000, 4462.000000, 4606.000000, 4752.000000, 4900.000000, 3030.000000, 3162.000000, 3296.000000, 3432.000000, 3570.000000, 3710.000000, 3852.000000, 3996.000000, 4142.000000, 4290.000000, 4440.000000, 4592.000000, 4746.000000, 4902.000000, 5060.000000, 5220.000000, 5382.000000, 5546.000000, 5712.000000, 5880.000000 }, sd::DataType::FLOAT32); x.linspace(1.f); y.linspace(10.f); z.assign(0.f); - x.applyBroadcast(nd4j::broadcast::Multiply, { 0,2,3 }, y, z); + x.applyBroadcast(sd::broadcast::Multiply, { 0,2,3 }, y, z); ASSERT_EQ(e, z); } /////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests14, Test_broadcast_4D_2) { - auto x = NDArray('f', { 2, 3, 5, 4 }, nd4j::DataType::FLOAT32); - auto y = NDArray('f', { 2, 5, 4 }, nd4j::DataType::FLOAT32); - auto z = NDArray('f', { 2, 3, 5, 4 }, nd4j::DataType::FLOAT32); + auto x = NDArray('f', { 2, 3, 5, 4 }, sd::DataType::FLOAT32); + auto y = NDArray('f', { 2, 5, 4 }, sd::DataType::FLOAT32); + auto z = NDArray('f', { 2, 3, 5, 4 }, sd::DataType::FLOAT32); // recieved by main algorithm - auto eC = NDArray('c', { 2, 3, 5, 4 }, { 0.100000,0.181818,0.250000,0.307692,0.357143,0.400000,0.437500,0.470588,0.500000,0.526316,0.550000,0.571429, 0.590909,0.608696,0.625000,0.640000, 0.653846,0.666667,0.678571,0.689655, 2.100000,2.000000,1.916667, 1.846154, 1.785714, 1.733333,1.687500, 1.647059,1.611111, 1.578947,1.550000, 1.523810,1.500000, 1.478261,1.458333, 1.440000,1.423077, 1.407407,1.392857, 1.379310,4.100000, 3.818182,3.583333, 3.384615, 3.214286, 3.066667,2.937500, 2.823529,2.722222, 2.631579,2.550000, 2.476191,2.409091, 2.347826,2.291667, 2.240000,2.192308, 2.148148,2.107143, 2.068965,2.033333, 2.000000,1.968750, 1.939394,1.911765, 1.885714,1.861111, 1.837838,1.815789, 1.794872,1.775000, 1.756098,1.738095, 1.720930,1.704545, 1.688889,1.673913, 1.659575,1.645833,1.632653,2.700000,2.645161,2.593750,2.545455,2.500000,2.457143,2.416667,2.378378,2.342105,2.307692,2.275000,2.243902,2.214286,2.186047,2.159091,2.133333,2.108696,2.085106,2.062500,2.040816,3.366667,3.290323,3.218750,3.151515,3.088235,3.028571,2.972222,2.918919,2.868421,2.820513,2.775000,2.731707,2.690476,2.651163,2.613636,2.577778,2.543478,2.510638,2.479167,2.448980 }, nd4j::DataType::FLOAT32); + auto eC = NDArray('c', { 2, 3, 5, 4 }, { 0.100000,0.181818,0.250000,0.307692,0.357143,0.400000,0.437500,0.470588,0.500000,0.526316,0.550000,0.571429, 0.590909,0.608696,0.625000,0.640000, 0.653846,0.666667,0.678571,0.689655, 2.100000,2.000000,1.916667, 1.846154, 1.785714, 1.733333,1.687500, 1.647059,1.611111, 1.578947,1.550000, 1.523810,1.500000, 1.478261,1.458333, 1.440000,1.423077, 1.407407,1.392857, 1.379310,4.100000, 3.818182,3.583333, 3.384615, 3.214286, 3.066667,2.937500, 2.823529,2.722222, 2.631579,2.550000, 2.476191,2.409091, 2.347826,2.291667, 2.240000,2.192308, 2.148148,2.107143, 2.068965,2.033333, 2.000000,1.968750, 1.939394,1.911765, 1.885714,1.861111, 1.837838,1.815789, 1.794872,1.775000, 1.756098,1.738095, 1.720930,1.704545, 1.688889,1.673913, 1.659575,1.645833,1.632653,2.700000,2.645161,2.593750,2.545455,2.500000,2.457143,2.416667,2.378378,2.342105,2.307692,2.275000,2.243902,2.214286,2.186047,2.159091,2.133333,2.108696,2.085106,2.062500,2.040816,3.366667,3.290323,3.218750,3.151515,3.088235,3.028571,2.972222,2.918919,2.868421,2.820513,2.775000,2.731707,2.690476,2.651163,2.613636,2.577778,2.543478,2.510638,2.479167,2.448980 }, sd::DataType::FLOAT32); - auto e = NDArray('f', { 2, 3, 5, 4 }, nd4j::DataType::FLOAT32); + auto e = NDArray('f', { 2, 3, 5, 4 }, sd::DataType::FLOAT32); e.assign(eC); @@ -1528,20 +1466,20 @@ TEST_F(DeclarableOpsTests14, Test_broadcast_4D_2) { y.linspace(10.f); z.assign(0.f); - x.applyBroadcast(nd4j::broadcast::Divide, { 0,2,3 }, y, z); + x.applyBroadcast(sd::broadcast::Divide, { 0,2,3 }, y, z); ASSERT_EQ(e, z); } /////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests14, Test_broadcast_4D_3) { - auto x = NDArray('f', { 2, 3, 5, 4 }, nd4j::DataType::FLOAT32); - auto y = NDArray('f', { 2, 5 }, nd4j::DataType::FLOAT32); - auto z = NDArray('f', { 2, 3, 5, 4 }, nd4j::DataType::FLOAT32); + auto x = NDArray('f', { 2, 3, 5, 4 }, sd::DataType::FLOAT32); + auto y = NDArray('f', { 2, 5 }, sd::DataType::FLOAT32); + auto z = NDArray('f', { 2, 3, 5, 4 }, sd::DataType::FLOAT32); // recieved by main algorithm - auto eC = NDArray('c', { 2, 3, 5, 4 }, { 0.100000, 0.200000, 0.300000, 0.400000, 0.454545, 0.545455, 0.636364, 0.727273, 0.750000, 0.833333, 0.916667, 1.000000, 1.000000, 1.076923, 1.153846, 1.230769, 1.214286, 1.285714, 1.357143, 1.428571, 2.100000, 2.200000, 2.300000, 2.400000, 2.272727, 2.363636, 2.454545, 2.545455, 2.416667, 2.500000, 2.583333, 2.666667, 2.538461, 2.615385, 2.692308, 2.769231, 2.642857, 2.714286, 2.785714, 2.857143, 4.100000, 4.200000, 4.300000, 4.400000, 4.090909, 4.181818, 4.272727, 4.363636, 4.083333, 4.166667, 4.250000, 4.333333, 4.076923, 4.153846, 4.230769, 4.307693, 4.071429, 4.142857, 4.214286, 4.285714, 4.066667, 4.133333, 4.200000, 4.266667, 4.062500, 4.125000, 4.187500, 4.250000, 4.058824, 4.117647, 4.176471, 4.235294, 4.055555, 4.111111, 4.166667, 4.222222, 4.052631, 4.105263, 4.157895, 4.210526, 5.400000, 5.466667, 5.533333, 5.600000, 5.312500, 5.375000, 5.437500, 5.500000, 5.235294, 5.294117, 5.352941, 5.411765, 5.166667, 5.222222, 5.277778, 5.333333, 5.105263, 5.157895, 5.210526, 5.263158, 6.733333, 6.800000, 6.866667, 6.933333, 6.562500, 6.625000, 6.687500, 6.750000, 6.411765, 6.470588, 6.529412, 6.588235, 6.277778, 6.333333, 6.388889, 6.444445, 6.157895, 6.210526, 6.263158, 6.315790 }, nd4j::DataType::FLOAT32); + auto eC = NDArray('c', { 2, 3, 5, 4 }, { 0.100000, 0.200000, 0.300000, 0.400000, 0.454545, 0.545455, 0.636364, 0.727273, 0.750000, 0.833333, 0.916667, 1.000000, 1.000000, 1.076923, 1.153846, 1.230769, 1.214286, 1.285714, 1.357143, 1.428571, 2.100000, 2.200000, 2.300000, 2.400000, 2.272727, 2.363636, 2.454545, 2.545455, 2.416667, 2.500000, 2.583333, 2.666667, 2.538461, 2.615385, 2.692308, 2.769231, 2.642857, 2.714286, 2.785714, 2.857143, 4.100000, 4.200000, 4.300000, 4.400000, 4.090909, 4.181818, 4.272727, 4.363636, 4.083333, 4.166667, 4.250000, 4.333333, 4.076923, 4.153846, 4.230769, 4.307693, 4.071429, 4.142857, 4.214286, 4.285714, 4.066667, 4.133333, 4.200000, 4.266667, 4.062500, 4.125000, 4.187500, 4.250000, 4.058824, 4.117647, 4.176471, 4.235294, 4.055555, 4.111111, 4.166667, 4.222222, 4.052631, 4.105263, 4.157895, 4.210526, 5.400000, 5.466667, 5.533333, 5.600000, 5.312500, 5.375000, 5.437500, 5.500000, 5.235294, 5.294117, 5.352941, 5.411765, 5.166667, 5.222222, 5.277778, 5.333333, 5.105263, 5.157895, 5.210526, 5.263158, 6.733333, 6.800000, 6.866667, 6.933333, 6.562500, 6.625000, 6.687500, 6.750000, 6.411765, 6.470588, 6.529412, 6.588235, 6.277778, 6.333333, 6.388889, 6.444445, 6.157895, 6.210526, 6.263158, 6.315790 }, sd::DataType::FLOAT32); - auto e = NDArray('f', { 2, 3, 5, 4 }, nd4j::DataType::FLOAT32); + auto e = NDArray('f', { 2, 3, 5, 4 }, sd::DataType::FLOAT32); e.assign(eC); @@ -1549,7 +1487,7 @@ TEST_F(DeclarableOpsTests14, Test_broadcast_4D_3) { y.linspace(10.f); z.assign(0.f); - x.applyBroadcast(nd4j::broadcast::Divide, { 0,2 }, y, z); + x.applyBroadcast(sd::broadcast::Divide, { 0,2 }, y, z); ASSERT_EQ(e, z); } @@ -1558,13 +1496,13 @@ TEST_F(DeclarableOpsTests14, Test_broadcast_4D_4) { // x[4, 12, 128, 128] * y[4, 1, 128, 1] = z[4, 12, 128, 128] - auto x = NDArray('f', { 2, 3, 5, 4 }, nd4j::DataType::FLOAT32); - auto y = NDArray('f', { 2, 1, 5, 1 }, nd4j::DataType::FLOAT32); - auto z = NDArray('f', { 2, 3, 5, 4 }, nd4j::DataType::FLOAT32); + auto x = NDArray('f', { 2, 3, 5, 4 }, sd::DataType::FLOAT32); + auto y = NDArray('f', { 2, 1, 5, 1 }, sd::DataType::FLOAT32); + auto z = NDArray('f', { 2, 3, 5, 4 }, sd::DataType::FLOAT32); // recieved by main algorithm - auto eC = NDArray('c', { 2, 3, 5, 4 }, { 0.100000, 0.200000, 0.300000, 0.400000, 0.454545, 0.545455, 0.636364, 0.727273, 0.750000, 0.833333, 0.916667, 1.000000, 1.000000, 1.076923, 1.153846, 1.230769, 1.214286, 1.285714, 1.357143, 1.428571, 2.100000, 2.200000, 2.300000, 2.400000, 2.272727, 2.363636, 2.454545, 2.545455, 2.416667, 2.500000, 2.583333, 2.666667, 2.538461, 2.615385, 2.692308, 2.769231, 2.642857, 2.714286, 2.785714, 2.857143, 4.100000, 4.200000, 4.300000, 4.400000, 4.090909, 4.181818, 4.272727, 4.363636, 4.083333, 4.166667, 4.250000, 4.333333, 4.076923, 4.153846, 4.230769, 4.307693, 4.071429, 4.142857, 4.214286, 4.285714, 4.066667, 4.133333, 4.200000, 4.266667, 4.062500, 4.125000, 4.187500, 4.250000, 4.058824, 4.117647, 4.176471, 4.235294, 4.055555, 4.111111, 4.166667, 4.222222, 4.052631, 4.105263, 4.157895, 4.210526, 5.400000, 5.466667, 5.533333, 5.600000, 5.312500, 5.375000, 5.437500, 5.500000, 5.235294, 5.294117, 5.352941, 5.411765, 5.166667, 5.222222, 5.277778, 5.333333, 5.105263, 5.157895, 5.210526, 5.263158, 6.733333, 6.800000, 6.866667, 6.933333, 6.562500, 6.625000, 6.687500, 6.750000, 6.411765, 6.470588, 6.529412, 6.588235, 6.277778, 6.333333, 6.388889, 6.444445, 6.157895, 6.210526, 6.263158, 6.315790 }, nd4j::DataType::FLOAT32); + auto eC = NDArray('c', { 2, 3, 5, 4 }, { 0.100000, 0.200000, 0.300000, 0.400000, 0.454545, 0.545455, 0.636364, 0.727273, 0.750000, 0.833333, 0.916667, 1.000000, 1.000000, 1.076923, 1.153846, 1.230769, 1.214286, 1.285714, 1.357143, 1.428571, 2.100000, 2.200000, 2.300000, 2.400000, 2.272727, 2.363636, 2.454545, 2.545455, 2.416667, 2.500000, 2.583333, 2.666667, 2.538461, 2.615385, 2.692308, 2.769231, 2.642857, 2.714286, 2.785714, 2.857143, 4.100000, 4.200000, 4.300000, 4.400000, 4.090909, 4.181818, 4.272727, 4.363636, 4.083333, 4.166667, 4.250000, 4.333333, 4.076923, 4.153846, 4.230769, 4.307693, 4.071429, 4.142857, 4.214286, 4.285714, 4.066667, 4.133333, 4.200000, 4.266667, 4.062500, 4.125000, 4.187500, 4.250000, 4.058824, 4.117647, 4.176471, 4.235294, 4.055555, 4.111111, 4.166667, 4.222222, 4.052631, 4.105263, 4.157895, 4.210526, 5.400000, 5.466667, 5.533333, 5.600000, 5.312500, 5.375000, 5.437500, 5.500000, 5.235294, 5.294117, 5.352941, 5.411765, 5.166667, 5.222222, 5.277778, 5.333333, 5.105263, 5.157895, 5.210526, 5.263158, 6.733333, 6.800000, 6.866667, 6.933333, 6.562500, 6.625000, 6.687500, 6.750000, 6.411765, 6.470588, 6.529412, 6.588235, 6.277778, 6.333333, 6.388889, 6.444445, 6.157895, 6.210526, 6.263158, 6.315790 }, sd::DataType::FLOAT32); - auto e = NDArray('f', { 2, 3, 5, 4 }, nd4j::DataType::FLOAT32); + auto e = NDArray('f', { 2, 3, 5, 4 }, sd::DataType::FLOAT32); e.assign(eC); x.linspace(1.f); @@ -1578,11 +1516,11 @@ TEST_F(DeclarableOpsTests14, Test_broadcast_4D_4) { /////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests14, Test_broadcast_5D_1) { // x[4, 12, 128, 128, 128] * y[4, 1, 128, 128, 128] = z[4, 12, 128, 128, 128] - auto x = NDArray('c', { 2, 3, 5, 4, 3 }, nd4j::DataType::FLOAT32); - auto y = NDArray('c', { 2, 1, 5, 4, 3 }, nd4j::DataType::FLOAT32); - auto z = NDArray('c', { 2, 3, 5, 4, 3 }, nd4j::DataType::FLOAT32); + auto x = NDArray('c', { 2, 3, 5, 4, 3 }, sd::DataType::FLOAT32); + auto y = NDArray('c', { 2, 1, 5, 4, 3 }, sd::DataType::FLOAT32); + auto z = NDArray('c', { 2, 3, 5, 4, 3 }, sd::DataType::FLOAT32); // recieved by main algorithm - auto e = NDArray('c', { 2, 3, 5, 4, 3 }, { 10.000000, 22.000000, 36.000000, 52.000000, 70.000000, 90.000000, 112.000000, 136.000000, 162.000000, 190.000000, 220.000000, 252.000000, 286.000000, 322.000000, 360.000000, 400.000000, 442.000000, 486.000000, 532.000000, 580.000000, 630.000000, 682.000000, 736.000000, 792.000000, 850.000000, 910.000000, 972.000000, 1036.000000, 1102.000000, 1170.000000, 1240.000000, 1312.000000, 1386.000000, 1462.000000, 1540.000000, 1620.000000, 1702.000000, 1786.000000, 1872.000000, 1960.000000, 2050.000000, 2142.000000, 2236.000000, 2332.000000, 2430.000000, 2530.000000, 2632.000000, 2736.000000, 2842.000000, 2950.000000, 3060.000000, 3172.000000, 3286.000000, 3402.000000, 3520.000000, 3640.000000, 3762.000000, 3886.000000, 4012.000000, 4140.000000, 610.000000, 682.000000, 756.000000, 832.000000, 910.000000, 990.000000, 1072.000000, 1156.000000, 1242.000000, 1330.000000, 1420.000000, 1512.000000, 1606.000000, 1702.000000, 1800.000000, 1900.000000, 2002.000000, 2106.000000, 2212.000000, 2320.000000, 2430.000000, 2542.000000, 2656.000000, 2772.000000, 2890.000000, 3010.000000, 3132.000000, 3256.000000, 3382.000000, 3510.000000, 3640.000000, 3772.000000, 3906.000000, 4042.000000, 4180.000000, 4320.000000, 4462.000000, 4606.000000, 4752.000000, 4900.000000, 5050.000000, 5202.000000, 5356.000000, 5512.000000, 5670.000000, 5830.000000, 5992.000000, 6156.000000, 6322.000000, 6490.000000, 6660.000000, 6832.000000, 7006.000000, 7182.000000, 7360.000000, 7540.000000, 7722.000000, 7906.000000, 8092.000000, 8280.000000, 1210.000000, 1342.000000, 1476.000000, 1612.000000, 1750.000000, 1890.000000, 2032.000000, 2176.000000, 2322.000000, 2470.000000, 2620.000000, 2772.000000, 2926.000000, 3082.000000, 3240.000000, 3400.000000, 3562.000000, 3726.000000, 3892.000000, 4060.000000, 4230.000000, 4402.000000, 4576.000000, 4752.000000, 4930.000000, 5110.000000, 5292.000000, 5476.000000, 5662.000000, 5850.000000, 6040.000000, 6232.000000, 6426.000000, 6622.000000, 6820.000000, 7020.000000, 7222.000000, 7426.000000, 7632.000000, 7840.000000, 8050.000000, 8262.000000, 8476.000000, 8692.000000, 8910.000000, 9130.000000, 9352.000000, 9576.000000, 9802.000000, 10030.000000, 10260.000000, 10492.000000, 10726.000000, 10962.000000, 11200.000000, 11440.000000, 11682.000000, 11926.000000, 12172.000000, 12420.000000, 12670.000000, 12922.000000, 13176.000000, 13432.000000, 13690.000000, 13950.000000, 14212.000000, 14476.000000, 14742.000000, 15010.000000, 15280.000000, 15552.000000, 15826.000000, 16102.000000, 16380.000000, 16660.000000, 16942.000000, 17226.000000, 17512.000000, 17800.000000, 18090.000000, 18382.000000, 18676.000000, 18972.000000, 19270.000000, 19570.000000, 19872.000000, 20176.000000, 20482.000000, 20790.000000, 21100.000000, 21412.000000, 21726.000000, 22042.000000, 22360.000000, 22680.000000, 23002.000000, 23326.000000, 23652.000000, 23980.000000, 24310.000000, 24642.000000, 24976.000000, 25312.000000, 25650.000000, 25990.000000, 26332.000000, 26676.000000, 27022.000000, 27370.000000, 27720.000000, 28072.000000, 28426.000000, 28782.000000, 29140.000000, 29500.000000, 29862.000000, 30226.000000, 30592.000000, 30960.000000, 16870.000000, 17182.000000, 17496.000000, 17812.000000, 18130.000000, 18450.000000, 18772.000000, 19096.000000, 19422.000000, 19750.000000, 20080.000000, 20412.000000, 20746.000000, 21082.000000, 21420.000000, 21760.000000, 22102.000000, 22446.000000, 22792.000000, 23140.000000, 23490.000000, 23842.000000, 24196.000000, 24552.000000, 24910.000000, 25270.000000, 25632.000000, 25996.000000, 26362.000000, 26730.000000, 27100.000000, 27472.000000, 27846.000000, 28222.000000, 28600.000000, 28980.000000, 29362.000000, 29746.000000, 30132.000000, 30520.000000, 30910.000000, 31302.000000, 31696.000000, 32092.000000, 32490.000000, 32890.000000, 33292.000000, 33696.000000, 34102.000000, 34510.000000, 34920.000000, 35332.000000, 35746.000000, 36162.000000, 36580.000000, 37000.000000, 37422.000000, 37846.000000, 38272.000000, 38700.000000, 21070.000000, 21442.000000, 21816.000000, 22192.000000, 22570.000000, 22950.000000, 23332.000000, 23716.000000, 24102.000000, 24490.000000, 24880.000000, 25272.000000, 25666.000000, 26062.000000, 26460.000000, 26860.000000, 27262.000000, 27666.000000, 28072.000000, 28480.000000, 28890.000000, 29302.000000, 29716.000000, 30132.000000, 30550.000000, 30970.000000, 31392.000000, 31816.000000, 32242.000000, 32670.000000, 33100.000000, 33532.000000, 33966.000000, 34402.000000, 34840.000000, 35280.000000, 35722.000000, 36166.000000, 36612.000000, 37060.000000, 37510.000000, 37962.000000, 38416.000000, 38872.000000, 39330.000000, 39790.000000, 40252.000000, 40716.000000, 41182.000000, 41650.000000, 42120.000000, 42592.000000, 43066.000000, 43542.000000, 44020.000000, 44500.000000, 44982.000000, 45466.000000, 45952.000000, 46440.000000 }, nd4j::DataType::FLOAT32); + auto e = NDArray('c', { 2, 3, 5, 4, 3 }, { 10.000000, 22.000000, 36.000000, 52.000000, 70.000000, 90.000000, 112.000000, 136.000000, 162.000000, 190.000000, 220.000000, 252.000000, 286.000000, 322.000000, 360.000000, 400.000000, 442.000000, 486.000000, 532.000000, 580.000000, 630.000000, 682.000000, 736.000000, 792.000000, 850.000000, 910.000000, 972.000000, 1036.000000, 1102.000000, 1170.000000, 1240.000000, 1312.000000, 1386.000000, 1462.000000, 1540.000000, 1620.000000, 1702.000000, 1786.000000, 1872.000000, 1960.000000, 2050.000000, 2142.000000, 2236.000000, 2332.000000, 2430.000000, 2530.000000, 2632.000000, 2736.000000, 2842.000000, 2950.000000, 3060.000000, 3172.000000, 3286.000000, 3402.000000, 3520.000000, 3640.000000, 3762.000000, 3886.000000, 4012.000000, 4140.000000, 610.000000, 682.000000, 756.000000, 832.000000, 910.000000, 990.000000, 1072.000000, 1156.000000, 1242.000000, 1330.000000, 1420.000000, 1512.000000, 1606.000000, 1702.000000, 1800.000000, 1900.000000, 2002.000000, 2106.000000, 2212.000000, 2320.000000, 2430.000000, 2542.000000, 2656.000000, 2772.000000, 2890.000000, 3010.000000, 3132.000000, 3256.000000, 3382.000000, 3510.000000, 3640.000000, 3772.000000, 3906.000000, 4042.000000, 4180.000000, 4320.000000, 4462.000000, 4606.000000, 4752.000000, 4900.000000, 5050.000000, 5202.000000, 5356.000000, 5512.000000, 5670.000000, 5830.000000, 5992.000000, 6156.000000, 6322.000000, 6490.000000, 6660.000000, 6832.000000, 7006.000000, 7182.000000, 7360.000000, 7540.000000, 7722.000000, 7906.000000, 8092.000000, 8280.000000, 1210.000000, 1342.000000, 1476.000000, 1612.000000, 1750.000000, 1890.000000, 2032.000000, 2176.000000, 2322.000000, 2470.000000, 2620.000000, 2772.000000, 2926.000000, 3082.000000, 3240.000000, 3400.000000, 3562.000000, 3726.000000, 3892.000000, 4060.000000, 4230.000000, 4402.000000, 4576.000000, 4752.000000, 4930.000000, 5110.000000, 5292.000000, 5476.000000, 5662.000000, 5850.000000, 6040.000000, 6232.000000, 6426.000000, 6622.000000, 6820.000000, 7020.000000, 7222.000000, 7426.000000, 7632.000000, 7840.000000, 8050.000000, 8262.000000, 8476.000000, 8692.000000, 8910.000000, 9130.000000, 9352.000000, 9576.000000, 9802.000000, 10030.000000, 10260.000000, 10492.000000, 10726.000000, 10962.000000, 11200.000000, 11440.000000, 11682.000000, 11926.000000, 12172.000000, 12420.000000, 12670.000000, 12922.000000, 13176.000000, 13432.000000, 13690.000000, 13950.000000, 14212.000000, 14476.000000, 14742.000000, 15010.000000, 15280.000000, 15552.000000, 15826.000000, 16102.000000, 16380.000000, 16660.000000, 16942.000000, 17226.000000, 17512.000000, 17800.000000, 18090.000000, 18382.000000, 18676.000000, 18972.000000, 19270.000000, 19570.000000, 19872.000000, 20176.000000, 20482.000000, 20790.000000, 21100.000000, 21412.000000, 21726.000000, 22042.000000, 22360.000000, 22680.000000, 23002.000000, 23326.000000, 23652.000000, 23980.000000, 24310.000000, 24642.000000, 24976.000000, 25312.000000, 25650.000000, 25990.000000, 26332.000000, 26676.000000, 27022.000000, 27370.000000, 27720.000000, 28072.000000, 28426.000000, 28782.000000, 29140.000000, 29500.000000, 29862.000000, 30226.000000, 30592.000000, 30960.000000, 16870.000000, 17182.000000, 17496.000000, 17812.000000, 18130.000000, 18450.000000, 18772.000000, 19096.000000, 19422.000000, 19750.000000, 20080.000000, 20412.000000, 20746.000000, 21082.000000, 21420.000000, 21760.000000, 22102.000000, 22446.000000, 22792.000000, 23140.000000, 23490.000000, 23842.000000, 24196.000000, 24552.000000, 24910.000000, 25270.000000, 25632.000000, 25996.000000, 26362.000000, 26730.000000, 27100.000000, 27472.000000, 27846.000000, 28222.000000, 28600.000000, 28980.000000, 29362.000000, 29746.000000, 30132.000000, 30520.000000, 30910.000000, 31302.000000, 31696.000000, 32092.000000, 32490.000000, 32890.000000, 33292.000000, 33696.000000, 34102.000000, 34510.000000, 34920.000000, 35332.000000, 35746.000000, 36162.000000, 36580.000000, 37000.000000, 37422.000000, 37846.000000, 38272.000000, 38700.000000, 21070.000000, 21442.000000, 21816.000000, 22192.000000, 22570.000000, 22950.000000, 23332.000000, 23716.000000, 24102.000000, 24490.000000, 24880.000000, 25272.000000, 25666.000000, 26062.000000, 26460.000000, 26860.000000, 27262.000000, 27666.000000, 28072.000000, 28480.000000, 28890.000000, 29302.000000, 29716.000000, 30132.000000, 30550.000000, 30970.000000, 31392.000000, 31816.000000, 32242.000000, 32670.000000, 33100.000000, 33532.000000, 33966.000000, 34402.000000, 34840.000000, 35280.000000, 35722.000000, 36166.000000, 36612.000000, 37060.000000, 37510.000000, 37962.000000, 38416.000000, 38872.000000, 39330.000000, 39790.000000, 40252.000000, 40716.000000, 41182.000000, 41650.000000, 42120.000000, 42592.000000, 43066.000000, 43542.000000, 44020.000000, 44500.000000, 44982.000000, 45466.000000, 45952.000000, 46440.000000 }, sd::DataType::FLOAT32); x.linspace(1.f); y.linspace(10.f); @@ -1595,13 +1533,13 @@ TEST_F(DeclarableOpsTests14, Test_broadcast_5D_1) { /////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests14, Test_broadcast_5D_2) { - auto x = NDArray('f', { 2, 3, 5, 4, 3 }, nd4j::DataType::FLOAT32); - auto y = NDArray('f', { 2, 5, 4, 3 }, nd4j::DataType::FLOAT32); - auto z = NDArray('f', { 2, 3, 5, 4, 3 }, nd4j::DataType::FLOAT32); + auto x = NDArray('f', { 2, 3, 5, 4, 3 }, sd::DataType::FLOAT32); + auto y = NDArray('f', { 2, 5, 4, 3 }, sd::DataType::FLOAT32); + auto z = NDArray('f', { 2, 3, 5, 4, 3 }, sd::DataType::FLOAT32); // recieved by main algorithm - auto eC = NDArray('c', { 2, 3, 5, 4, 3 }, { 0.100000, 0.181818, 0.250000, 0.307692, 0.357143, 0.400000, 0.437500, 0.470588, 0.500000, 0.526316, 0.550000, 0.571429, 0.590909, 0.608696, 0.625000, 0.640000, 0.653846, 0.666667, 0.678571, 0.689655, 0.700000, 0.709677, 0.718750, 0.727273, 0.735294, 0.742857, 0.750000, 0.756757, 0.763158, 0.769231, 0.775000, 0.780488, 0.785714, 0.790698, 0.795455, 0.800000, 0.804348, 0.808511, 0.812500, 0.816327, 0.820000, 0.823529, 0.826923, 0.830189, 0.833333, 0.836364, 0.839286, 0.842105, 0.844828, 0.847458, 0.850000, 0.852459, 0.854839, 0.857143, 0.859375, 0.861538, 0.863636, 0.865672, 0.867647, 0.869565, 6.100000, 5.636364, 5.250000, 4.923077, 4.642857, 4.400000, 4.187500, 4.000000, 3.833333, 3.684211, 3.550000, 3.428571, 3.318182, 3.217391, 3.125000, 3.040000, 2.961539, 2.888889, 2.821429, 2.758621, 2.700000, 2.645161, 2.593750, 2.545455, 2.500000, 2.457143, 2.416667, 2.378378, 2.342105, 2.307692, 2.275000, 2.243902, 2.214286, 2.186047, 2.159091, 2.133333, 2.108696, 2.085106, 2.062500, 2.040816, 2.020000, 2.000000, 1.980769, 1.962264, 1.944444, 1.927273, 1.910714, 1.894737, 1.879310, 1.864407, 1.850000, 1.836066, 1.822581, 1.809524, 1.796875, 1.784615, 1.772727, 1.761194, 1.750000, 1.739130, 12.100000, 11.090909, 10.250000, 9.538462, 8.928572, 8.400000, 7.937500, 7.529412, 7.166667, 6.842105, 6.550000, 6.285714, 6.045455, 5.826087, 5.625000, 5.440000, 5.269231, 5.111111, 4.964286, 4.827586, 4.700000, 4.580645, 4.468750, 4.363636, 4.264706, 4.171429, 4.083333, 4.000000, 3.921053, 3.846154, 3.775000, 3.707317, 3.642857, 3.581395, 3.522727, 3.466667, 3.413043, 3.361702, 3.312500, 3.265306, 3.220000, 3.176471, 3.134615, 3.094340, 3.055556, 3.018182, 2.982143, 2.947368, 2.913793, 2.881356, 2.850000, 2.819672, 2.790323, 2.761905, 2.734375, 2.707692, 2.681818, 2.656716, 2.632353, 2.608696, 2.585714, 2.563380, 2.541667, 2.520548, 2.500000, 2.480000, 2.460526, 2.441558, 2.423077, 2.405063, 2.387500, 2.370370, 2.353658, 2.337349, 2.321429, 2.305882, 2.290698, 2.275862, 2.261364, 2.247191, 2.233333, 2.219780, 2.206522, 2.193548, 2.180851, 2.168421, 2.156250, 2.144330, 2.132653, 2.121212, 2.110000, 2.099010, 2.088235, 2.077670, 2.067308, 2.057143, 2.047170, 2.037383, 2.027778, 2.018349, 2.009091, 2.000000, 1.991071, 1.982301, 1.973684, 1.965217, 1.956897, 1.948718, 1.940678, 1.932773, 1.925000, 1.917355, 1.909836, 1.902439, 1.895161, 1.888000, 1.880952, 1.874016, 1.867188, 1.860465, 3.442857, 3.408451, 3.375000, 3.342466, 3.310811, 3.280000, 3.250000, 3.220779, 3.192308, 3.164557, 3.137500, 3.111111, 3.085366, 3.060241, 3.035714, 3.011765, 2.988372, 2.965517, 2.943182, 2.921348, 2.900000, 2.879121, 2.858696, 2.838710, 2.819149, 2.800000, 2.781250, 2.762887, 2.744898, 2.727273, 2.710000, 2.693069, 2.676471, 2.660194, 2.644231, 2.628572, 2.613208, 2.598131, 2.583333, 2.568807, 2.554545, 2.540540, 2.526786, 2.513274, 2.500000, 2.486957, 2.474138, 2.461539, 2.449152, 2.436975, 2.425000, 2.413223, 2.401639, 2.390244, 2.379032, 2.368000, 2.357143, 2.346457, 2.335938, 2.325581, 4.300000, 4.253521, 4.208333, 4.164383, 4.121622, 4.080000, 4.039474, 4.000000, 3.961539, 3.924051, 3.887500, 3.851852, 3.817073, 3.783133, 3.750000, 3.717647, 3.686047, 3.655172, 3.625000, 3.595506, 3.566667, 3.538461, 3.510870, 3.483871, 3.457447, 3.431579, 3.406250, 3.381443, 3.357143, 3.333333, 3.310000, 3.287129, 3.264706, 3.242718, 3.221154, 3.200000, 3.179245, 3.158879, 3.138889, 3.119266, 3.100000, 3.081081, 3.062500, 3.044248, 3.026316, 3.008696, 2.991379, 2.974359, 2.957627, 2.941176, 2.925000, 2.909091, 2.893443, 2.878049, 2.862903, 2.848000, 2.833333, 2.818898, 2.804688, 2.790698 }, nd4j::DataType::FLOAT32); + auto eC = NDArray('c', { 2, 3, 5, 4, 3 }, { 0.100000, 0.181818, 0.250000, 0.307692, 0.357143, 0.400000, 0.437500, 0.470588, 0.500000, 0.526316, 0.550000, 0.571429, 0.590909, 0.608696, 0.625000, 0.640000, 0.653846, 0.666667, 0.678571, 0.689655, 0.700000, 0.709677, 0.718750, 0.727273, 0.735294, 0.742857, 0.750000, 0.756757, 0.763158, 0.769231, 0.775000, 0.780488, 0.785714, 0.790698, 0.795455, 0.800000, 0.804348, 0.808511, 0.812500, 0.816327, 0.820000, 0.823529, 0.826923, 0.830189, 0.833333, 0.836364, 0.839286, 0.842105, 0.844828, 0.847458, 0.850000, 0.852459, 0.854839, 0.857143, 0.859375, 0.861538, 0.863636, 0.865672, 0.867647, 0.869565, 6.100000, 5.636364, 5.250000, 4.923077, 4.642857, 4.400000, 4.187500, 4.000000, 3.833333, 3.684211, 3.550000, 3.428571, 3.318182, 3.217391, 3.125000, 3.040000, 2.961539, 2.888889, 2.821429, 2.758621, 2.700000, 2.645161, 2.593750, 2.545455, 2.500000, 2.457143, 2.416667, 2.378378, 2.342105, 2.307692, 2.275000, 2.243902, 2.214286, 2.186047, 2.159091, 2.133333, 2.108696, 2.085106, 2.062500, 2.040816, 2.020000, 2.000000, 1.980769, 1.962264, 1.944444, 1.927273, 1.910714, 1.894737, 1.879310, 1.864407, 1.850000, 1.836066, 1.822581, 1.809524, 1.796875, 1.784615, 1.772727, 1.761194, 1.750000, 1.739130, 12.100000, 11.090909, 10.250000, 9.538462, 8.928572, 8.400000, 7.937500, 7.529412, 7.166667, 6.842105, 6.550000, 6.285714, 6.045455, 5.826087, 5.625000, 5.440000, 5.269231, 5.111111, 4.964286, 4.827586, 4.700000, 4.580645, 4.468750, 4.363636, 4.264706, 4.171429, 4.083333, 4.000000, 3.921053, 3.846154, 3.775000, 3.707317, 3.642857, 3.581395, 3.522727, 3.466667, 3.413043, 3.361702, 3.312500, 3.265306, 3.220000, 3.176471, 3.134615, 3.094340, 3.055556, 3.018182, 2.982143, 2.947368, 2.913793, 2.881356, 2.850000, 2.819672, 2.790323, 2.761905, 2.734375, 2.707692, 2.681818, 2.656716, 2.632353, 2.608696, 2.585714, 2.563380, 2.541667, 2.520548, 2.500000, 2.480000, 2.460526, 2.441558, 2.423077, 2.405063, 2.387500, 2.370370, 2.353658, 2.337349, 2.321429, 2.305882, 2.290698, 2.275862, 2.261364, 2.247191, 2.233333, 2.219780, 2.206522, 2.193548, 2.180851, 2.168421, 2.156250, 2.144330, 2.132653, 2.121212, 2.110000, 2.099010, 2.088235, 2.077670, 2.067308, 2.057143, 2.047170, 2.037383, 2.027778, 2.018349, 2.009091, 2.000000, 1.991071, 1.982301, 1.973684, 1.965217, 1.956897, 1.948718, 1.940678, 1.932773, 1.925000, 1.917355, 1.909836, 1.902439, 1.895161, 1.888000, 1.880952, 1.874016, 1.867188, 1.860465, 3.442857, 3.408451, 3.375000, 3.342466, 3.310811, 3.280000, 3.250000, 3.220779, 3.192308, 3.164557, 3.137500, 3.111111, 3.085366, 3.060241, 3.035714, 3.011765, 2.988372, 2.965517, 2.943182, 2.921348, 2.900000, 2.879121, 2.858696, 2.838710, 2.819149, 2.800000, 2.781250, 2.762887, 2.744898, 2.727273, 2.710000, 2.693069, 2.676471, 2.660194, 2.644231, 2.628572, 2.613208, 2.598131, 2.583333, 2.568807, 2.554545, 2.540540, 2.526786, 2.513274, 2.500000, 2.486957, 2.474138, 2.461539, 2.449152, 2.436975, 2.425000, 2.413223, 2.401639, 2.390244, 2.379032, 2.368000, 2.357143, 2.346457, 2.335938, 2.325581, 4.300000, 4.253521, 4.208333, 4.164383, 4.121622, 4.080000, 4.039474, 4.000000, 3.961539, 3.924051, 3.887500, 3.851852, 3.817073, 3.783133, 3.750000, 3.717647, 3.686047, 3.655172, 3.625000, 3.595506, 3.566667, 3.538461, 3.510870, 3.483871, 3.457447, 3.431579, 3.406250, 3.381443, 3.357143, 3.333333, 3.310000, 3.287129, 3.264706, 3.242718, 3.221154, 3.200000, 3.179245, 3.158879, 3.138889, 3.119266, 3.100000, 3.081081, 3.062500, 3.044248, 3.026316, 3.008696, 2.991379, 2.974359, 2.957627, 2.941176, 2.925000, 2.909091, 2.893443, 2.878049, 2.862903, 2.848000, 2.833333, 2.818898, 2.804688, 2.790698 }, sd::DataType::FLOAT32); - auto e = NDArray('f', { 2, 3, 5, 4, 3 }, nd4j::DataType::FLOAT32); + auto e = NDArray('f', { 2, 3, 5, 4, 3 }, sd::DataType::FLOAT32); e.assign(eC); @@ -1609,20 +1547,20 @@ TEST_F(DeclarableOpsTests14, Test_broadcast_5D_2) { y.linspace(10.f); z.assign(0.f); - x.applyBroadcast(nd4j::broadcast::Divide, { 0,2,3,4 }, y, z); + x.applyBroadcast(sd::broadcast::Divide, { 0,2,3,4 }, y, z); ASSERT_EQ(e, z); } /////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests14, Test_broadcast_5D_3) { - auto x = NDArray('f', { 2, 3, 5, 4, 3 }, nd4j::DataType::FLOAT32); - auto y = NDArray('f', { 2, 5 }, nd4j::DataType::FLOAT32); - auto z = NDArray('f', { 2, 3, 5, 4, 3 }, nd4j::DataType::FLOAT32); + auto x = NDArray('f', { 2, 3, 5, 4, 3 }, sd::DataType::FLOAT32); + auto y = NDArray('f', { 2, 5 }, sd::DataType::FLOAT32); + auto z = NDArray('f', { 2, 3, 5, 4, 3 }, sd::DataType::FLOAT32); // recieved by main algorithm - auto eC = NDArray('c', { 2, 3, 5, 4, 3 }, { 0.100000, 0.200000, 0.300000, 0.400000, 0.500000, 0.600000, 0.700000, 0.800000, 0.900000, 1.000000, 1.100000, 1.200000, 1.181818, 1.272727, 1.363636, 1.454545, 1.545455, 1.636364, 1.727273, 1.818182, 1.909091, 2.000000, 2.090909, 2.181818, 2.083333, 2.166667, 2.250000, 2.333333, 2.416667, 2.500000, 2.583333, 2.666667, 2.750000, 2.833333, 2.916667, 3.000000, 2.846154, 2.923077, 3.000000, 3.076923, 3.153846, 3.230769, 3.307692, 3.384615, 3.461539, 3.538461, 3.615385, 3.692308, 3.500000, 3.571429, 3.642857, 3.714286, 3.785714, 3.857143, 3.928571, 4.000000, 4.071429, 4.142857, 4.214286, 4.285714, 6.100000, 6.200000, 6.300000, 6.400000, 6.500000, 6.600000, 6.700000, 6.800000, 6.900000, 7.000000, 7.100000, 7.200000, 6.636364, 6.727273, 6.818182, 6.909091, 7.000000, 7.090909, 7.181818, 7.272727, 7.363636, 7.454545, 7.545455, 7.636364, 7.083333, 7.166667, 7.250000, 7.333333, 7.416667, 7.500000, 7.583333, 7.666667, 7.750000, 7.833333, 7.916667, 8.000000, 7.461538, 7.538462, 7.615385, 7.692307, 7.769231, 7.846154, 7.923077, 8.000000, 8.076923, 8.153846, 8.230769, 8.307693, 7.785714, 7.857143, 7.928571, 8.000000, 8.071428, 8.142858, 8.214286, 8.285714, 8.357142, 8.428572, 8.500000, 8.571428, 12.100000, 12.200000, 12.300000, 12.400000, 12.500000, 12.600000, 12.700000, 12.800000, 12.900000, 13.000000, 13.100000, 13.200000, 12.090909, 12.181818, 12.272727, 12.363636, 12.454545, 12.545455, 12.636364, 12.727273, 12.818182, 12.909091, 13.000000, 13.090909, 12.083333, 12.166667, 12.250000, 12.333333, 12.416667, 12.500000, 12.583333, 12.666667, 12.750000, 12.833333, 12.916667, 13.000000, 12.076923, 12.153846, 12.230769, 12.307693, 12.384615, 12.461538, 12.538462, 12.615385, 12.692307, 12.769231, 12.846154, 12.923077, 12.071428, 12.142858, 12.214286, 12.285714, 12.357142, 12.428572, 12.500000, 12.571428, 12.642858, 12.714286, 12.785714, 12.857142, 12.066667, 12.133333, 12.200000, 12.266666, 12.333333, 12.400000, 12.466666, 12.533334, 12.600000, 12.666667, 12.733334, 12.800000, 12.062500, 12.125000, 12.187500, 12.250000, 12.312500, 12.375000, 12.437500, 12.500000, 12.562500, 12.625000, 12.687500, 12.750000, 12.058824, 12.117647, 12.176471, 12.235294, 12.294118, 12.352942, 12.411765, 12.470589, 12.529411, 12.588235, 12.647058, 12.705882, 12.055555, 12.111111, 12.166667, 12.222222, 12.277778, 12.333333, 12.388889, 12.444445, 12.500000, 12.555555, 12.611111, 12.666667, 12.052631, 12.105263, 12.157895, 12.210526, 12.263158, 12.315789, 12.368421, 12.421053, 12.473684, 12.526316, 12.578947, 12.631579, 16.066668, 16.133333, 16.200001, 16.266666, 16.333334, 16.400000, 16.466667, 16.533333, 16.600000, 16.666666, 16.733334, 16.799999, 15.812500, 15.875000, 15.937500, 16.000000, 16.062500, 16.125000, 16.187500, 16.250000, 16.312500, 16.375000, 16.437500, 16.500000, 15.588235, 15.647058, 15.705882, 15.764706, 15.823529, 15.882353, 15.941176, 16.000000, 16.058823, 16.117647, 16.176470, 16.235294, 15.388889, 15.444445, 15.500000, 15.555555, 15.611111, 15.666667, 15.722222, 15.777778, 15.833333, 15.888889, 15.944445, 16.000000, 15.210526, 15.263158, 15.315789, 15.368421, 15.421053, 15.473684, 15.526316, 15.578947, 15.631579, 15.684211, 15.736842, 15.789474, 20.066668, 20.133333, 20.200001, 20.266666, 20.333334, 20.400000, 20.466667, 20.533333, 20.600000, 20.666666, 20.733334, 20.799999, 19.562500, 19.625000, 19.687500, 19.750000, 19.812500, 19.875000, 19.937500, 20.000000, 20.062500, 20.125000, 20.187500, 20.250000, 19.117647, 19.176470, 19.235294, 19.294117, 19.352942, 19.411764, 19.470589, 19.529411, 19.588236, 19.647058, 19.705883, 19.764706, 18.722221, 18.777779, 18.833334, 18.888889, 18.944445, 19.000000, 19.055555, 19.111111, 19.166666, 19.222221, 19.277779, 19.333334, 18.368422, 18.421053, 18.473684, 18.526316, 18.578947, 18.631578, 18.684210, 18.736841, 18.789474, 18.842106, 18.894737, 18.947369 }, nd4j::DataType::FLOAT32); + auto eC = NDArray('c', { 2, 3, 5, 4, 3 }, { 0.100000, 0.200000, 0.300000, 0.400000, 0.500000, 0.600000, 0.700000, 0.800000, 0.900000, 1.000000, 1.100000, 1.200000, 1.181818, 1.272727, 1.363636, 1.454545, 1.545455, 1.636364, 1.727273, 1.818182, 1.909091, 2.000000, 2.090909, 2.181818, 2.083333, 2.166667, 2.250000, 2.333333, 2.416667, 2.500000, 2.583333, 2.666667, 2.750000, 2.833333, 2.916667, 3.000000, 2.846154, 2.923077, 3.000000, 3.076923, 3.153846, 3.230769, 3.307692, 3.384615, 3.461539, 3.538461, 3.615385, 3.692308, 3.500000, 3.571429, 3.642857, 3.714286, 3.785714, 3.857143, 3.928571, 4.000000, 4.071429, 4.142857, 4.214286, 4.285714, 6.100000, 6.200000, 6.300000, 6.400000, 6.500000, 6.600000, 6.700000, 6.800000, 6.900000, 7.000000, 7.100000, 7.200000, 6.636364, 6.727273, 6.818182, 6.909091, 7.000000, 7.090909, 7.181818, 7.272727, 7.363636, 7.454545, 7.545455, 7.636364, 7.083333, 7.166667, 7.250000, 7.333333, 7.416667, 7.500000, 7.583333, 7.666667, 7.750000, 7.833333, 7.916667, 8.000000, 7.461538, 7.538462, 7.615385, 7.692307, 7.769231, 7.846154, 7.923077, 8.000000, 8.076923, 8.153846, 8.230769, 8.307693, 7.785714, 7.857143, 7.928571, 8.000000, 8.071428, 8.142858, 8.214286, 8.285714, 8.357142, 8.428572, 8.500000, 8.571428, 12.100000, 12.200000, 12.300000, 12.400000, 12.500000, 12.600000, 12.700000, 12.800000, 12.900000, 13.000000, 13.100000, 13.200000, 12.090909, 12.181818, 12.272727, 12.363636, 12.454545, 12.545455, 12.636364, 12.727273, 12.818182, 12.909091, 13.000000, 13.090909, 12.083333, 12.166667, 12.250000, 12.333333, 12.416667, 12.500000, 12.583333, 12.666667, 12.750000, 12.833333, 12.916667, 13.000000, 12.076923, 12.153846, 12.230769, 12.307693, 12.384615, 12.461538, 12.538462, 12.615385, 12.692307, 12.769231, 12.846154, 12.923077, 12.071428, 12.142858, 12.214286, 12.285714, 12.357142, 12.428572, 12.500000, 12.571428, 12.642858, 12.714286, 12.785714, 12.857142, 12.066667, 12.133333, 12.200000, 12.266666, 12.333333, 12.400000, 12.466666, 12.533334, 12.600000, 12.666667, 12.733334, 12.800000, 12.062500, 12.125000, 12.187500, 12.250000, 12.312500, 12.375000, 12.437500, 12.500000, 12.562500, 12.625000, 12.687500, 12.750000, 12.058824, 12.117647, 12.176471, 12.235294, 12.294118, 12.352942, 12.411765, 12.470589, 12.529411, 12.588235, 12.647058, 12.705882, 12.055555, 12.111111, 12.166667, 12.222222, 12.277778, 12.333333, 12.388889, 12.444445, 12.500000, 12.555555, 12.611111, 12.666667, 12.052631, 12.105263, 12.157895, 12.210526, 12.263158, 12.315789, 12.368421, 12.421053, 12.473684, 12.526316, 12.578947, 12.631579, 16.066668, 16.133333, 16.200001, 16.266666, 16.333334, 16.400000, 16.466667, 16.533333, 16.600000, 16.666666, 16.733334, 16.799999, 15.812500, 15.875000, 15.937500, 16.000000, 16.062500, 16.125000, 16.187500, 16.250000, 16.312500, 16.375000, 16.437500, 16.500000, 15.588235, 15.647058, 15.705882, 15.764706, 15.823529, 15.882353, 15.941176, 16.000000, 16.058823, 16.117647, 16.176470, 16.235294, 15.388889, 15.444445, 15.500000, 15.555555, 15.611111, 15.666667, 15.722222, 15.777778, 15.833333, 15.888889, 15.944445, 16.000000, 15.210526, 15.263158, 15.315789, 15.368421, 15.421053, 15.473684, 15.526316, 15.578947, 15.631579, 15.684211, 15.736842, 15.789474, 20.066668, 20.133333, 20.200001, 20.266666, 20.333334, 20.400000, 20.466667, 20.533333, 20.600000, 20.666666, 20.733334, 20.799999, 19.562500, 19.625000, 19.687500, 19.750000, 19.812500, 19.875000, 19.937500, 20.000000, 20.062500, 20.125000, 20.187500, 20.250000, 19.117647, 19.176470, 19.235294, 19.294117, 19.352942, 19.411764, 19.470589, 19.529411, 19.588236, 19.647058, 19.705883, 19.764706, 18.722221, 18.777779, 18.833334, 18.888889, 18.944445, 19.000000, 19.055555, 19.111111, 19.166666, 19.222221, 19.277779, 19.333334, 18.368422, 18.421053, 18.473684, 18.526316, 18.578947, 18.631578, 18.684210, 18.736841, 18.789474, 18.842106, 18.894737, 18.947369 }, sd::DataType::FLOAT32); - auto e = NDArray('f', { 2, 3, 5, 4, 3 }, nd4j::DataType::FLOAT32); + auto e = NDArray('f', { 2, 3, 5, 4, 3 }, sd::DataType::FLOAT32); e.assign(eC); @@ -1630,20 +1568,20 @@ TEST_F(DeclarableOpsTests14, Test_broadcast_5D_3) { y.linspace(10.f); z.assign(0.f); - x.applyBroadcast(nd4j::broadcast::Divide, { 0,2 }, y, z); + x.applyBroadcast(sd::broadcast::Divide, { 0,2 }, y, z); ASSERT_EQ(e, z); } /////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests14, Test_broadcast_5D_4) { - auto x = NDArray('f', { 2, 3, 5, 4, 3 }, nd4j::DataType::FLOAT32); - auto y = NDArray('f', { 2, 1, 5, 1, 1 }, nd4j::DataType::FLOAT32); - auto z = NDArray('f', { 2, 3, 5, 4, 3 }, nd4j::DataType::FLOAT32); + auto x = NDArray('f', { 2, 3, 5, 4, 3 }, sd::DataType::FLOAT32); + auto y = NDArray('f', { 2, 1, 5, 1, 1 }, sd::DataType::FLOAT32); + auto z = NDArray('f', { 2, 3, 5, 4, 3 }, sd::DataType::FLOAT32); // recieved by main algorithm - auto eC = NDArray('c', { 2, 3, 5, 4, 3 }, { 0.100000, 0.200000, 0.300000, 0.400000, 0.500000, 0.600000, 0.700000, 0.800000, 0.900000, 1.000000, 1.100000, 1.200000, 1.181818, 1.272727, 1.363636, 1.454545, 1.545455, 1.636364, 1.727273, 1.818182, 1.909091, 2.000000, 2.090909, 2.181818, 2.083333, 2.166667, 2.250000, 2.333333, 2.416667, 2.500000, 2.583333, 2.666667, 2.750000, 2.833333, 2.916667, 3.000000, 2.846154, 2.923077, 3.000000, 3.076923, 3.153846, 3.230769, 3.307692, 3.384615, 3.461539, 3.538461, 3.615385, 3.692308, 3.500000, 3.571429, 3.642857, 3.714286, 3.785714, 3.857143, 3.928571, 4.000000, 4.071429, 4.142857, 4.214286, 4.285714, 6.100000, 6.200000, 6.300000, 6.400000, 6.500000, 6.600000, 6.700000, 6.800000, 6.900000, 7.000000, 7.100000, 7.200000, 6.636364, 6.727273, 6.818182, 6.909091, 7.000000, 7.090909, 7.181818, 7.272727, 7.363636, 7.454545, 7.545455, 7.636364, 7.083333, 7.166667, 7.250000, 7.333333, 7.416667, 7.500000, 7.583333, 7.666667, 7.750000, 7.833333, 7.916667, 8.000000, 7.461538, 7.538462, 7.615385, 7.692307, 7.769231, 7.846154, 7.923077, 8.000000, 8.076923, 8.153846, 8.230769, 8.307693, 7.785714, 7.857143, 7.928571, 8.000000, 8.071428, 8.142858, 8.214286, 8.285714, 8.357142, 8.428572, 8.500000, 8.571428, 12.100000, 12.200000, 12.300000, 12.400000, 12.500000, 12.600000, 12.700000, 12.800000, 12.900000, 13.000000, 13.100000, 13.200000, 12.090909, 12.181818, 12.272727, 12.363636, 12.454545, 12.545455, 12.636364, 12.727273, 12.818182, 12.909091, 13.000000, 13.090909, 12.083333, 12.166667, 12.250000, 12.333333, 12.416667, 12.500000, 12.583333, 12.666667, 12.750000, 12.833333, 12.916667, 13.000000, 12.076923, 12.153846, 12.230769, 12.307693, 12.384615, 12.461538, 12.538462, 12.615385, 12.692307, 12.769231, 12.846154, 12.923077, 12.071428, 12.142858, 12.214286, 12.285714, 12.357142, 12.428572, 12.500000, 12.571428, 12.642858, 12.714286, 12.785714, 12.857142, 12.066667, 12.133333, 12.200000, 12.266666, 12.333333, 12.400000, 12.466666, 12.533334, 12.600000, 12.666667, 12.733334, 12.800000, 12.062500, 12.125000, 12.187500, 12.250000, 12.312500, 12.375000, 12.437500, 12.500000, 12.562500, 12.625000, 12.687500, 12.750000, 12.058824, 12.117647, 12.176471, 12.235294, 12.294118, 12.352942, 12.411765, 12.470589, 12.529411, 12.588235, 12.647058, 12.705882, 12.055555, 12.111111, 12.166667, 12.222222, 12.277778, 12.333333, 12.388889, 12.444445, 12.500000, 12.555555, 12.611111, 12.666667, 12.052631, 12.105263, 12.157895, 12.210526, 12.263158, 12.315789, 12.368421, 12.421053, 12.473684, 12.526316, 12.578947, 12.631579, 16.066668, 16.133333, 16.200001, 16.266666, 16.333334, 16.400000, 16.466667, 16.533333, 16.600000, 16.666666, 16.733334, 16.799999, 15.812500, 15.875000, 15.937500, 16.000000, 16.062500, 16.125000, 16.187500, 16.250000, 16.312500, 16.375000, 16.437500, 16.500000, 15.588235, 15.647058, 15.705882, 15.764706, 15.823529, 15.882353, 15.941176, 16.000000, 16.058823, 16.117647, 16.176470, 16.235294, 15.388889, 15.444445, 15.500000, 15.555555, 15.611111, 15.666667, 15.722222, 15.777778, 15.833333, 15.888889, 15.944445, 16.000000, 15.210526, 15.263158, 15.315789, 15.368421, 15.421053, 15.473684, 15.526316, 15.578947, 15.631579, 15.684211, 15.736842, 15.789474, 20.066668, 20.133333, 20.200001, 20.266666, 20.333334, 20.400000, 20.466667, 20.533333, 20.600000, 20.666666, 20.733334, 20.799999, 19.562500, 19.625000, 19.687500, 19.750000, 19.812500, 19.875000, 19.937500, 20.000000, 20.062500, 20.125000, 20.187500, 20.250000, 19.117647, 19.176470, 19.235294, 19.294117, 19.352942, 19.411764, 19.470589, 19.529411, 19.588236, 19.647058, 19.705883, 19.764706, 18.722221, 18.777779, 18.833334, 18.888889, 18.944445, 19.000000, 19.055555, 19.111111, 19.166666, 19.222221, 19.277779, 19.333334, 18.368422, 18.421053, 18.473684, 18.526316, 18.578947, 18.631578, 18.684210, 18.736841, 18.789474, 18.842106, 18.894737, 18.947369 }, nd4j::DataType::FLOAT32); + auto eC = NDArray('c', { 2, 3, 5, 4, 3 }, { 0.100000, 0.200000, 0.300000, 0.400000, 0.500000, 0.600000, 0.700000, 0.800000, 0.900000, 1.000000, 1.100000, 1.200000, 1.181818, 1.272727, 1.363636, 1.454545, 1.545455, 1.636364, 1.727273, 1.818182, 1.909091, 2.000000, 2.090909, 2.181818, 2.083333, 2.166667, 2.250000, 2.333333, 2.416667, 2.500000, 2.583333, 2.666667, 2.750000, 2.833333, 2.916667, 3.000000, 2.846154, 2.923077, 3.000000, 3.076923, 3.153846, 3.230769, 3.307692, 3.384615, 3.461539, 3.538461, 3.615385, 3.692308, 3.500000, 3.571429, 3.642857, 3.714286, 3.785714, 3.857143, 3.928571, 4.000000, 4.071429, 4.142857, 4.214286, 4.285714, 6.100000, 6.200000, 6.300000, 6.400000, 6.500000, 6.600000, 6.700000, 6.800000, 6.900000, 7.000000, 7.100000, 7.200000, 6.636364, 6.727273, 6.818182, 6.909091, 7.000000, 7.090909, 7.181818, 7.272727, 7.363636, 7.454545, 7.545455, 7.636364, 7.083333, 7.166667, 7.250000, 7.333333, 7.416667, 7.500000, 7.583333, 7.666667, 7.750000, 7.833333, 7.916667, 8.000000, 7.461538, 7.538462, 7.615385, 7.692307, 7.769231, 7.846154, 7.923077, 8.000000, 8.076923, 8.153846, 8.230769, 8.307693, 7.785714, 7.857143, 7.928571, 8.000000, 8.071428, 8.142858, 8.214286, 8.285714, 8.357142, 8.428572, 8.500000, 8.571428, 12.100000, 12.200000, 12.300000, 12.400000, 12.500000, 12.600000, 12.700000, 12.800000, 12.900000, 13.000000, 13.100000, 13.200000, 12.090909, 12.181818, 12.272727, 12.363636, 12.454545, 12.545455, 12.636364, 12.727273, 12.818182, 12.909091, 13.000000, 13.090909, 12.083333, 12.166667, 12.250000, 12.333333, 12.416667, 12.500000, 12.583333, 12.666667, 12.750000, 12.833333, 12.916667, 13.000000, 12.076923, 12.153846, 12.230769, 12.307693, 12.384615, 12.461538, 12.538462, 12.615385, 12.692307, 12.769231, 12.846154, 12.923077, 12.071428, 12.142858, 12.214286, 12.285714, 12.357142, 12.428572, 12.500000, 12.571428, 12.642858, 12.714286, 12.785714, 12.857142, 12.066667, 12.133333, 12.200000, 12.266666, 12.333333, 12.400000, 12.466666, 12.533334, 12.600000, 12.666667, 12.733334, 12.800000, 12.062500, 12.125000, 12.187500, 12.250000, 12.312500, 12.375000, 12.437500, 12.500000, 12.562500, 12.625000, 12.687500, 12.750000, 12.058824, 12.117647, 12.176471, 12.235294, 12.294118, 12.352942, 12.411765, 12.470589, 12.529411, 12.588235, 12.647058, 12.705882, 12.055555, 12.111111, 12.166667, 12.222222, 12.277778, 12.333333, 12.388889, 12.444445, 12.500000, 12.555555, 12.611111, 12.666667, 12.052631, 12.105263, 12.157895, 12.210526, 12.263158, 12.315789, 12.368421, 12.421053, 12.473684, 12.526316, 12.578947, 12.631579, 16.066668, 16.133333, 16.200001, 16.266666, 16.333334, 16.400000, 16.466667, 16.533333, 16.600000, 16.666666, 16.733334, 16.799999, 15.812500, 15.875000, 15.937500, 16.000000, 16.062500, 16.125000, 16.187500, 16.250000, 16.312500, 16.375000, 16.437500, 16.500000, 15.588235, 15.647058, 15.705882, 15.764706, 15.823529, 15.882353, 15.941176, 16.000000, 16.058823, 16.117647, 16.176470, 16.235294, 15.388889, 15.444445, 15.500000, 15.555555, 15.611111, 15.666667, 15.722222, 15.777778, 15.833333, 15.888889, 15.944445, 16.000000, 15.210526, 15.263158, 15.315789, 15.368421, 15.421053, 15.473684, 15.526316, 15.578947, 15.631579, 15.684211, 15.736842, 15.789474, 20.066668, 20.133333, 20.200001, 20.266666, 20.333334, 20.400000, 20.466667, 20.533333, 20.600000, 20.666666, 20.733334, 20.799999, 19.562500, 19.625000, 19.687500, 19.750000, 19.812500, 19.875000, 19.937500, 20.000000, 20.062500, 20.125000, 20.187500, 20.250000, 19.117647, 19.176470, 19.235294, 19.294117, 19.352942, 19.411764, 19.470589, 19.529411, 19.588236, 19.647058, 19.705883, 19.764706, 18.722221, 18.777779, 18.833334, 18.888889, 18.944445, 19.000000, 19.055555, 19.111111, 19.166666, 19.222221, 19.277779, 19.333334, 18.368422, 18.421053, 18.473684, 18.526316, 18.578947, 18.631578, 18.684210, 18.736841, 18.789474, 18.842106, 18.894737, 18.947369 }, sd::DataType::FLOAT32); - auto e = NDArray('f', { 2, 3, 5, 4, 3 }, nd4j::DataType::FLOAT32); + auto e = NDArray('f', { 2, 3, 5, 4, 3 }, sd::DataType::FLOAT32); e.assign(eC); x.linspace(1.f); @@ -1655,30 +1593,487 @@ TEST_F(DeclarableOpsTests14, Test_broadcast_5D_4) { ASSERT_EQ(e, z); } -// @Test -// public void testMmulRank4_simple(){ +////////////////////////////////////////////////////////////////////// +TEST_F(DeclarableOpsTests14, Stack_1) { -// INDArray arr1 = Nd4j.ones(DataType.FLOAT, 32, 12, 128, 64); -// INDArray arr2 = Nd4j.ones(DataType.FLOAT, 32, 12, 128, 64); + float buff1[] = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10,11,12}; + float buff2[] = {13,14,16,16,17,18,19,20,21,22,23,24}; + float expBuff[] = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10,11,12,13,14,16,16,17,18,19,20,21,22,23,24}; + Nd4jLong shape1[] = {2, 3, 4, 4, 1, 0, 1, 99}; + Nd4jLong shape2[] = {2, 3, 4, 4, 1, 0, 1, 99}; + Nd4jLong expShape[] = {3, 2, 3, 4, 12, 4, 1, 0, 1, 99}; + ArrayOptions::setDataType(shape1, sd::DataType::FLOAT32); + ArrayOptions::setDataType(shape2, sd::DataType::FLOAT32); + ArrayOptions::setDataType(expShape, sd::DataType::FLOAT32); -// DynamicCustomOp op = DynamicCustomOp.builder("matmul") -// .addInputs(arr1, arr2) -// .addIntegerArguments(0, 1) //Transpose arr2 only -// .build(); + NDArray input1(buff1, shape1); + NDArray input2(buff2, shape2); + NDArray expected(expBuff, expShape); -// List shapes = op.calculateOutputShape(); -// assertEquals(1, shapes.size()); -// long[] shape = new long[]{32,12,128,128}; -// assertArrayEquals(shape, shapes.get(0).getShape()); + sd::ops::stack op; + auto results = op.evaluate({&input1, &input2}, {}, {0}); + auto output = results.at(0); -// INDArray out = Nd4j.create(DataType.FLOAT, shape); + ASSERT_TRUE(expected.isSameShapeStrict(*output)); + ASSERT_TRUE(expected.equalsTo(output)); -// op.setOutputArgument(0, out); -// Nd4j.exec(op); -// // System.out.println(out); + -// INDArray exp = Nd4j.valueArrayOf(shape, 64.0, DataType.FLOAT); //Each entry in output is sum of 64 (1.0 x 1.0) multiplications -// assertEquals(exp, out); -// } +} + +////////////////////////////////////////////////////////////////////// +TEST_F(DeclarableOpsTests14, Stack_2) { + + float buff1[] = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10,11,12}; + float buff2[] = {13,14,16,16,17,18,19,20,21,22,23,24}; + float expBuff[] = {1,2,3,4, 13, 14, 16, 16, 5,6,7,8, 17, 18, 19, 20, 9, 10, 11, 12, 21, 22, 23, 24}; + Nd4jLong shape1[] = {2, 3, 4, 4, 1, 0, 1, 99}; + Nd4jLong shape2[] = {2, 3, 4, 4, 1, 0, 1, 99}; + Nd4jLong expShape[] = {3, 3, 2, 4, 8, 4, 1, 0, 1, 99}; + ArrayOptions::setDataType(shape1, sd::DataType::FLOAT32); + ArrayOptions::setDataType(shape2, sd::DataType::FLOAT32); + ArrayOptions::setDataType(expShape, sd::DataType::FLOAT32); + + NDArray input1(buff1, shape1); + NDArray input2(buff2, shape2); + NDArray expected(expBuff, expShape); + + sd::ops::stack op; + auto results = op.evaluate({&input1, &input2}, {}, {1}); + auto output = results.at(0); + + ASSERT_TRUE(expected.isSameShapeStrict(*output)); + ASSERT_TRUE(expected.equalsTo(output)); + + +} + + +////////////////////////////////////////////////////////////////////// +TEST_F(DeclarableOpsTests14, Stack_3) { + + float buff1[] = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10,11,12}; + float buff2[] = {13,14,16,16,17,18,19,20,21,22,23,24}; + float expBuff[] = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10,11,12,13,14,16,16,17,18,19,20,21,22,23,24}; + Nd4jLong shape1[] = {2, 1, 12, 12, 1, 0, 1, 99}; + Nd4jLong shape2[] = {2, 1, 12, 12, 1, 0, 1, 99}; + Nd4jLong expShape[] = {3, 2, 1, 12, 12, 12, 1, 0, 1, 99}; + ArrayOptions::setDataType(shape1, sd::DataType::FLOAT32); + ArrayOptions::setDataType(shape2, sd::DataType::FLOAT32); + ArrayOptions::setDataType(expShape, sd::DataType::FLOAT32); + + NDArray input1(buff1, shape1); + NDArray input2(buff2, shape2); + NDArray expected(expBuff, expShape); + + sd::ops::stack op; + auto results = op.evaluate({&input1, &input2}, {}, {0}); + auto output = results.at(0); + + ASSERT_TRUE(expected.isSameShapeStrict(*output)); + ASSERT_TRUE(expected.equalsTo(output)); + + +} + +////////////////////////////////////////////////////////////////////// +TEST_F(DeclarableOpsTests14, Stack_4) { + + float buff1[] = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10,11,12}; + float buff2[] = {13,14,16,16,17,18,19,20,21,22,23,24}; + float expBuff[] = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10,11,12,13,14,16,16,17,18,19,20,21,22,23,24}; + Nd4jLong shape1[] = {2, 1, 12, 12, 1, 0, 1, 99}; + Nd4jLong shape2[] = {2, 1, 12, 12, 1, 0, 1, 99}; + Nd4jLong expShape[] = {3, 1, 2, 12, 24, 12, 1, 0, 1, 99}; + ArrayOptions::setDataType(shape1, sd::DataType::FLOAT32); + ArrayOptions::setDataType(shape2, sd::DataType::FLOAT32); + ArrayOptions::setDataType(expShape, sd::DataType::FLOAT32); + + NDArray input1(buff1, shape1); + NDArray input2(buff2, shape2); + NDArray expected(expBuff, expShape); + + sd::ops::stack op; + auto results = op.evaluate({&input1, &input2}, {}, {1}); + auto output = results.at(0); + + ASSERT_TRUE(expected.isSameShapeStrict(*output)); + ASSERT_TRUE(expected.equalsTo(output)); + + +} + +////////////////////////////////////////////////////////////////////// +TEST_F(DeclarableOpsTests14, Stack_5) { + + float buff1[] = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10,11,12}; + float buff2[] = {13,14,16,16,17,18,19,20,21,22,23,24}; + float expBuff[] = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10,11,12,13,14,16,16,17,18,19,20,21,22,23,24}; + Nd4jLong shape1[] = {2, 12, 1, 1,1, 0, 1, 99}; + Nd4jLong shape2[] = {2, 12, 1, 1,1, 0, 1, 99}; + Nd4jLong expShape[] = {3, 2, 12, 1, 12, 1, 1, 0, 1, 99}; + ArrayOptions::setDataType(shape1, sd::DataType::FLOAT32); + ArrayOptions::setDataType(shape2, sd::DataType::FLOAT32); + ArrayOptions::setDataType(expShape, sd::DataType::FLOAT32); + + NDArray input1(buff1, shape1); + NDArray input2(buff2, shape2); + NDArray expected(expBuff, expShape); + + sd::ops::stack op; + auto results = op.evaluate({&input1, &input2}, {}, {0}); + auto output = results.at(0); + + ASSERT_TRUE(expected.isSameShapeStrict(*output)); + ASSERT_TRUE(expected.equalsTo(output)); + + +} + +////////////////////////////////////////////////////////////////////// +TEST_F(DeclarableOpsTests14, Stack_6) { + + float buff1[] = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10,11,12}; + float buff2[] = {13,14,16,16,17,18,19,20,21,22,23,24}; + float expBuff[] = {1 ,13 ,2 ,14 ,3 ,16 ,4 ,16 ,5 ,17 ,6 ,18 ,7 ,19 ,8 ,20 ,9 ,21 ,10 ,22 ,11 ,23 ,12 ,24}; + Nd4jLong shape1[] = {2, 12, 1, 1, 12, 0, 1, 99}; + Nd4jLong shape2[] = {2, 12, 1, 1, 12, 0, 1, 99}; + Nd4jLong expShape[] = {3, 12, 2, 1, 2, 1, 1, 0, 1, 99}; + ArrayOptions::setDataType(shape1, sd::DataType::FLOAT32); + ArrayOptions::setDataType(shape2, sd::DataType::FLOAT32); + ArrayOptions::setDataType(expShape, sd::DataType::FLOAT32); + + NDArray input1(buff1, shape1); + NDArray input2(buff2, shape2); + NDArray expected(expBuff, expShape); + + sd::ops::stack op; + auto results = op.evaluate({&input1, &input2}, {}, {1}); + auto output = results.at(0); + + ASSERT_TRUE(expected.isSameShapeStrict(*output)); + ASSERT_TRUE(expected.equalsTo(output)); + + +} + + +////////////////////////////////////////////////////////////////////// +TEST_F(DeclarableOpsTests14, Stack_7) { + + float buff1[] = {1}; + float expBuff[] = {1, 1, 1}; + Nd4jLong shape1[] = {2, 1, 1, 1, 1, 0, 1, 99}; + Nd4jLong expShape[] = {3, 3, 1, 1, 1, 1, 1, 0, 1, 99}; + ArrayOptions::setDataType(shape1, sd::DataType::FLOAT32); + ArrayOptions::setDataType(expShape, sd::DataType::FLOAT32); + + NDArray input1(buff1, shape1); + NDArray expected(expBuff, expShape); + + sd::ops::stack op; + auto results = op.evaluate({&input1, &input1, &input1}, {}, {0}); + auto output = results.at(0); + + ASSERT_TRUE(expected.isSameShapeStrict(*output)); + ASSERT_TRUE(expected.equalsTo(output)); + + +} + +////////////////////////////////////////////////////////////////////// +TEST_F(DeclarableOpsTests14, Stack_8) { + + float buff1[] = {1}; + float expBuff[] = {1, 1, 1}; + Nd4jLong shape1[] = {1, 1, 1, 0, 1, 99}; + Nd4jLong expShape[] = {2, 3, 1, 1, 1, 0, 1, 99}; + ArrayOptions::setDataType(shape1, sd::DataType::FLOAT32); + ArrayOptions::setDataType(expShape, sd::DataType::FLOAT32); + + NDArray input1(buff1, shape1); + NDArray expected(expBuff, expShape); + + sd::ops::stack op; + auto results = op.evaluate({&input1, &input1, &input1}, {}, {0}); + auto output = results.at(0); + + ASSERT_TRUE(expected.isSameShapeStrict(*output)); + ASSERT_TRUE(expected.equalsTo(output)); + + +} + +////////////////////////////////////////////////////////////////////// +TEST_F(DeclarableOpsTests14, Stack_9) { + + float buff1[] = {1}; + float expBuff[] = {1, 1, 1}; + Nd4jLong shape1[] = {2, 1, 1, 1, 1, 0, 1, 99}; + Nd4jLong expShape[] = {3, 1, 3, 1, 3, 1, 1, 0, 1, 99}; + ArrayOptions::setDataType(shape1, sd::DataType::FLOAT32); + ArrayOptions::setDataType(expShape, sd::DataType::FLOAT32); + + NDArray input1(buff1, shape1); + NDArray expected(expBuff, expShape); + + sd::ops::stack op; + auto results = op.evaluate({&input1, &input1, &input1}, {}, {1}); + auto output = results.at(0); + + ASSERT_TRUE(expected.isSameShapeStrict(*output)); + ASSERT_TRUE(expected.equalsTo(output)); + + +} + +////////////////////////////////////////////////////////////////////// +TEST_F(DeclarableOpsTests14, Stack_10) { + + float buff1[] = {1}; + float expBuff[] = {1, 1, 1}; + Nd4jLong shape1[] = {1, 1, 1, 0, 1, 99}; + Nd4jLong expShape[] = {2, 1, 3, 3, 1, 0, 1, 99}; + ArrayOptions::setDataType(shape1, sd::DataType::FLOAT32); + ArrayOptions::setDataType(expShape, sd::DataType::FLOAT32); + + NDArray input1(buff1, shape1); + NDArray expected(expBuff, expShape); + + sd::ops::stack op; + auto results = op.evaluate({&input1, &input1, &input1}, {}, {1}); + auto output = results.at(0); + + //expected.printShapeInfo("exp"); + //output->printShapeInfo("out"); + + ASSERT_TRUE(expected.isSameShapeStrict(*output)); + ASSERT_TRUE(expected.equalsTo(output)); + + +} + +TEST_F(DeclarableOpsTests14, Stack_11) { + + float buff1[] = {1}; + float expBuff[] = {1, 1, 1}; + Nd4jLong shape1[] = {1, 1, 1, 0, 1, 99}; + Nd4jLong expShape[] = {2, 3, 1, 1, 1, 0, 1, 99}; + ArrayOptions::setDataType(shape1, sd::DataType::FLOAT32); + ArrayOptions::setDataType(expShape, sd::DataType::FLOAT32); + + NDArray input1(buff1, shape1); + NDArray expected(expBuff, expShape); + + sd::ops::stack op; + auto results = op.evaluate({&input1, &input1, &input1}, {}, {}); + auto output = results.at(0); + + ASSERT_TRUE(expected.isSameShapeStrict(*output)); + ASSERT_TRUE(expected.equalsTo(output)); + + +} + + +////////////////////////////////////////////////////////////////////// +TEST_F(DeclarableOpsTests14, Stack_12) { + float inBuff[] = {1.0f, 2.0f, 3.0f}; + float expBuff[] = {1.0f, 2.0f, 3.0f}; + + auto input = NDArrayFactory::create(inBuff, 'c', {1, 3}); + + auto exp = NDArrayFactory::create(expBuff, 'c', {1, 1, 3}); + + sd::ops::stack op; + + auto result = op.evaluate({&input}, {}, {0}); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); + + auto z = result.at(0); + + ASSERT_TRUE(exp.isSameShape(z)); + ASSERT_TRUE(exp.equalsTo(z)); + + +} + +////////////////////////////////////////////////////////////////////// +TEST_F(DeclarableOpsTests14, Stack_13) { + float inBuff[] = {1.0f, 2.0f, 3.0f}; + float expBuff[] = {1.0f, 2.0f, 3.0f}; + + auto input = NDArrayFactory::create(inBuff, 'c', {1, 1, 3}); + + auto exp = NDArrayFactory::create(expBuff, 'c', {1, 1, 1, 3}); + + sd::ops::stack op; + + auto result = op.evaluate({&input}, {}, {0}); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); + + auto z = result.at(0); + + ASSERT_TRUE(exp.isSameShape(z)); + ASSERT_TRUE(exp.equalsTo(z)); + + +} + +////////////////////////////////////////////////////////////////////// +TEST_F(DeclarableOpsTests14, Stack_14) { + float inBuff[] = {1.0f, 2.0f, 3.0f}; + float expBuff[] = {1.0f, 2.0f, 3.0f}; + + auto input = NDArrayFactory::create(inBuff, 'c', {1, 3}); + + auto exp = NDArrayFactory::create(expBuff, 'c', {1, 1, 3}); + + sd::ops::stack op; + + auto result = op.evaluate({&input}, {}, {1}); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); + + auto z = result.at(0); + + //z->printShapeInfo(); + + ASSERT_TRUE(exp.isSameShape(z)); + ASSERT_TRUE(exp.equalsTo(z)); + + +} + +TEST_F(DeclarableOpsTests14, Stack_15) { + auto t = NDArrayFactory::create('c', {2, 3, 5}); + auto u = NDArrayFactory::create('c', {2, 3, 5}); + auto v = NDArrayFactory::create('c', {2, 3, 5}); + auto exp = NDArrayFactory::create('c', {3, 2, 3, 5}); + + sd::ops::stack op; + auto result = op.evaluate({&t, &u, &v}, {}, {-4}); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); + + + auto z = result.at(0); + + ASSERT_TRUE(exp.isSameShape(z)); + + +} + + +TEST_F(DeclarableOpsTests14, Stack_16) { + auto t = NDArrayFactory::create(1.0f); + auto u = NDArrayFactory::create(2.0f); + auto v = NDArrayFactory::create(3.0f); + auto exp = NDArrayFactory::create('c', {3}, {1, 2, 3}); + + sd::ops::stack op; + auto result = op.evaluate({&t, &u, &v}, {}, {0}); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); + + auto z = result.at(0); + + ASSERT_TRUE(exp.isSameShape(z)); + ASSERT_TRUE(exp.equalsTo(z)); + + +} + +TEST_F(DeclarableOpsTests14, Stack_17) { + auto t = NDArrayFactory::create('c', {1, 1}, {1.0f}); + auto u = NDArrayFactory::create('c', {1, 1}, {2.0f}); + auto v = NDArrayFactory::create('c', {1, 1}, {3.0f}); + auto w = NDArrayFactory::create('c', {1, 1}, {4.0f}); + auto exp = NDArrayFactory::create('c', {4, 1, 1}, {1, 2, 3, 4}); + + sd::ops::stack op; + auto result = op.evaluate({&t, &u, &v, &w}, {}, {0}); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); + + auto z = result.at(0); + + // z->printShapeInfo("z shape"); + + ASSERT_TRUE(exp.isSameShape(z)); + ASSERT_TRUE(exp.equalsTo(z)); + + +} + +TEST_F(DeclarableOpsTests14, Stack_18) { + auto x = NDArrayFactory::create('c', {0}); + auto e = NDArrayFactory::create('c', {1, 0}); + + sd::ops::stack op; + auto result = op.evaluate({&x}, {}, {0}); + ASSERT_EQ(Status::OK(), result.status()); + + auto z = result.at(0); + ASSERT_EQ(e, *z); + sd::ops::reduce_min sumOp; + auto res2 = sumOp.evaluate({&e}, {1.}, {1}); + ASSERT_EQ(res2.status(), Status::OK()); + auto out = res2.at(0); + + ASSERT_EQ(out->e(0), DataTypeUtils::infOrMax()); + + +} + +TEST_F(DeclarableOpsTests14, Stack_19) { + auto x = NDArrayFactory::empty(); + auto e = NDArrayFactory::create('c', {0}); + + sd::ops::stack op; + auto result = op.evaluate({&x}, {}, {0}); + ASSERT_EQ(Status::OK(), result.status()); + + auto z = result.at(0); + ASSERT_EQ(e, *z); + + +} + +TEST_F(DeclarableOpsTests14, Stack_20) { + auto x = NDArrayFactory::empty(); + auto e = NDArrayFactory::create('c', {2, 0}); + + sd::ops::stack op; + auto result = op.evaluate({&x, &x}, {}, {0}); + ASSERT_EQ(Status::OK(), result.status()); + + auto z = result.at(0); + ASSERT_EQ(e, *z); + + +} + +TEST_F(DeclarableOpsTests14, Stack_21) { + + NDArray x1('c', {3,2}, sd::DataType::FLOAT32); + NDArray x2('c', {3,2}, sd::DataType::FLOAT32); + x1.linspace(0); + x2.linspace(6); + + sd::ops::stack opStack; + auto resultStack = opStack.evaluate({&x1, &x2}, {}, {0}); + ASSERT_EQ(ND4J_STATUS_OK, resultStack.status()); + + + sd::ops::concat opConcat; + auto resultConcat = opConcat.evaluate({&x1, &x2}, {}, {0}); + ASSERT_EQ(ND4J_STATUS_OK, resultConcat.status()); + + auto outStack = resultStack.at(0); + auto outConcat = resultConcat.at(0); + + outConcat->reshapei({2,3,2}); + + ASSERT_TRUE(outStack->isSameShape(outConcat)); + ASSERT_TRUE(outStack->equalsTo(outConcat)); + +} diff --git a/libnd4j/tests_cpu/layers_tests/DeclarableOpsTests15.cpp b/libnd4j/tests_cpu/layers_tests/DeclarableOpsTests15.cpp index 199630d4e..f983d27a3 100644 --- a/libnd4j/tests_cpu/layers_tests/DeclarableOpsTests15.cpp +++ b/libnd4j/tests_cpu/layers_tests/DeclarableOpsTests15.cpp @@ -21,13 +21,13 @@ #include "testlayers.h" #include -#include +#include #include -#include +#include #include -using namespace nd4j; +using namespace sd; class DeclarableOpsTests15 : public testing::Test { @@ -48,7 +48,7 @@ TEST_F(DeclarableOpsTests15, Test_NormalizeMoments_1) { auto z0 = NDArrayFactory::create('c', {10}); auto z1 = NDArrayFactory::create('c', {10}); - nd4j::ops::normalize_moments op; + sd::ops::normalize_moments op; auto result = op.execute({&w, &x, &y}, std::vector{&z0, &z1}, {1e-4}, {}, {}); ASSERT_EQ(Status::OK(), result); } @@ -58,7 +58,7 @@ TEST_F(DeclarableOpsTests15, Test_Add_1) { auto y = NDArrayFactory::create('c', {5}, {1, 1, 1, 1, 1}); auto e = NDArrayFactory::create('c', {5}, {2, 2, 2, 2, 2}); - nd4j::ops::add op; + sd::ops::add op; auto result = op.execute({&x, &y}, {&x}, {}, {}, {}); ASSERT_EQ(Status::OK(), result); ASSERT_EQ(e, x); @@ -76,7 +76,7 @@ TEST_F(DeclarableOpsTests15, Test_standarize_1) { auto x = NDArrayFactory::create('c', {5}, {1.f, 1.f, 1.f, 1.f, 1.f}); auto e = NDArrayFactory::create('c', {5}, {0.f, 0.f, 0.f, 0.f, 0.f}); - nd4j::ops::standardize op; + sd::ops::standardize op; auto result = op.execute({&x}, {&x}, {}, {0}, {}); ASSERT_EQ(Status::OK(), result); ASSERT_EQ(e, x); @@ -86,10 +86,10 @@ TEST_F(DeclarableOpsTests15, Test_standarize_bp_1) { auto x = NDArrayFactory::create('c', {5}, {1.f, 1.f, 1.f, 1.f, 1.f}); auto eps = NDArrayFactory::create('c', {5}, {0.f, 0.f, 0.f, 0.f, 0.f}); - nd4j::ops::standardize_bp op; + sd::ops::standardize_bp op; auto result = op.evaluate({&x, &eps}, {0}); - ASSERT_EQ(Status::OK(), result->status()); - delete result; + ASSERT_EQ(Status::OK(), result.status()); + } TEST_F(DeclarableOpsTests15, Test_AdjustContrast_1) { @@ -102,13 +102,13 @@ TEST_F(DeclarableOpsTests15, Test_AdjustContrast_1) { x.linspace(1.); - nd4j::ops::adjust_contrast op; + sd::ops::adjust_contrast op; auto result = op.evaluate({&x, &factor}, {}, {}, {}); - ASSERT_EQ(Status::OK(), result->status()); - auto out = result->at(0); + ASSERT_EQ(Status::OK(), result.status()); + auto out = result.at(0); ASSERT_TRUE(e.equalsTo(out)); - delete result; + } TEST_F(DeclarableOpsTests15, Test_AdjustContrast_2) { @@ -120,13 +120,13 @@ TEST_F(DeclarableOpsTests15, Test_AdjustContrast_2) { 50.5f, 51.5f, 52.5f, 56.5f, 57.5f, 58.5f, 62.5f, 63.5f, 64.5f, 68.5f, 69.5f, 70.5f }); x.linspace(1.); - nd4j::ops::adjust_contrast op; + sd::ops::adjust_contrast op; auto result = op.evaluate({&x}, {2.}); - ASSERT_EQ(Status::OK(), result->status()); - auto out = result->at(0); + ASSERT_EQ(Status::OK(), result.status()); + auto out = result.at(0); // out->printIndexedBuffer("Adjusted Constrast"); ASSERT_TRUE(e.equalsTo(out)); - delete result; + } TEST_F(DeclarableOpsTests15, Test_AdjustContrast_3) { @@ -138,13 +138,13 @@ TEST_F(DeclarableOpsTests15, Test_AdjustContrast_3) { 50.5f, 51.5f, 52.5f, 56.5f, 57.5f, 58.5f, 62.5f, 63.5f, 64.5f, 68.5f, 69.5f, 70.5f }); x.linspace(1.); - nd4j::ops::adjust_contrast_v2 op; + sd::ops::adjust_contrast_v2 op; auto result = op.evaluate({&x}, {2.}); - ASSERT_EQ(Status::OK(), result->status()); - auto out = result->at(0); + ASSERT_EQ(Status::OK(), result.status()); + auto out = result.at(0); // out->printIndexedBuffer("Adjusted Constrast"); ASSERT_TRUE(e.equalsTo(out)); - delete result; + } TEST_F(DeclarableOpsTests15, Test_AdjustContrast_4) { @@ -156,13 +156,13 @@ TEST_F(DeclarableOpsTests15, Test_AdjustContrast_4) { 50.5, 51.5, 52.5, 56.5, 57.5, 58.5, 62.5, 63.5, 64.5, 68.5, 69.5, 70.5 }); x.linspace(1.); - nd4j::ops::adjust_contrast_v2 op; + sd::ops::adjust_contrast_v2 op; auto result = op.evaluate({&x}, {2.}, {}, {}); - ASSERT_EQ(Status::OK(), result->status()); - auto out = result->at(0); + ASSERT_EQ(Status::OK(), result.status()); + auto out = result.at(0); // out->printIndexedBuffer("Adjusted Constrast"); ASSERT_TRUE(e.equalsTo(out)); - delete result; + } TEST_F(DeclarableOpsTests15, Test_AdjustContrast_5) { @@ -171,13 +171,13 @@ TEST_F(DeclarableOpsTests15, Test_AdjustContrast_5) { -3., -2., -1., 0., 5., 6., 7., 8., 13., 14., 15., 16. }); x.linspace(1.); - nd4j::ops::adjust_contrast_v2 op; + sd::ops::adjust_contrast_v2 op; auto result = op.evaluate({&x}, {2.}, {}, {}); - ASSERT_EQ(Status::OK(), result->status()); - auto out = result->at(0); + ASSERT_EQ(Status::OK(), result.status()); + auto out = result.at(0); // out->printIndexedBuffer("Adjusted Constrast"); ASSERT_TRUE(e.equalsTo(out)); - delete result; + } /* @@ -301,14 +301,14 @@ TEST_F(DeclarableOpsTests15, Test_AdjustContrast_6) { 0.71149576f, -0.4799042f, 0.4880958f }); - nd4j::ops::adjust_contrast op; + sd::ops::adjust_contrast op; auto result = op.evaluate({&x}, {2.}, {}, {}); - ASSERT_EQ(Status::OK(), result->status()); - auto out = result->at(0); + ASSERT_EQ(Status::OK(), result.status()); + auto out = result.at(0); // out->printBuffer("Adjusted Constrast6"); // e.printBuffer("Adjusted Expected 6"); // ASSERT_TRUE(e.equalsTo(out)); - delete result; + } TEST_F(DeclarableOpsTests15, Test_AdjustContrast_7) { @@ -406,29 +406,30 @@ TEST_F(DeclarableOpsTests15, Test_AdjustContrast_7) { 0.71149576, -0.4799042, 0.4880958 }); // x.linspace(1.); - nd4j::ops::adjust_contrast_v2 op; + sd::ops::adjust_contrast_v2 op; auto result = op.evaluate({&x}, {2.}, {}, {}); - ASSERT_EQ(Status::OK(), result->status()); - auto out = result->at(0); + ASSERT_EQ(Status::OK(), result.status()); + auto out = result.at(0); // out->printBuffer("Adjusted Constrast7"); // e.printBuffer("Adjusted expected 7"); auto diff = e - *out; // diff.printBuffer("Adjusted subtract 7"); ASSERT_TRUE(e.equalsTo(out)); - delete result; + } TEST_F(DeclarableOpsTests15, Test_BitCast_1) { auto x = NDArrayFactory::create('c', {2, 2, 2}); auto e = NDArrayFactory::create('c', {2, 2}, {2., 512., 8192., 131072.032 }); x.linspace(1.); - nd4j::ops::bitcast op; - auto result = op.evaluate({&x}, {(int) nd4j::DataType::DOUBLE}); - ASSERT_EQ(Status::OK(), result->status()); - auto out = result->at(0); + + sd::ops::bitcast op; + auto result = op.evaluate({&x}, {(int) sd::DataType::DOUBLE}); + ASSERT_EQ(Status::OK(), result.status()); + auto out = result.at(0); // out->printIndexedBuffer("Casted result"); ASSERT_TRUE(e.equalsTo(out)); - delete result; + } TEST_F(DeclarableOpsTests15, Test_BitCast_2) { @@ -436,23 +437,25 @@ TEST_F(DeclarableOpsTests15, Test_BitCast_2) { auto e = NDArrayFactory::create('c', {2, 4, 2}, {0.f, 1.875f, 0.f, 2.f, 0.f, 2.125f, 0.f, 2.25f, 0.f, 2.312f, 0.f, 2.375f, 0.f, 2.438f, 0.f, 2.5f}); x.linspace(1.); - nd4j::ops::bitcast op; - auto result = op.evaluate({&x}, {(int) nd4j::DataType::HALF}); - ASSERT_EQ(Status::OK(), result->status()); - auto out = result->at(0); + + sd::ops::bitcast op; + auto result = op.evaluate({&x}, {(int) sd::DataType::HALF}); + ASSERT_EQ(Status::OK(), result.status()); + auto out = result.at(0); + ASSERT_TRUE(e.equalsTo(out)); - delete result; + } TEST_F(DeclarableOpsTests15, Test_BitCast_3) { auto x = NDArrayFactory::create('c', {1, 4}); x.linspace(1.); - nd4j::ops::bitcast op; + sd::ops::bitcast op; try { - auto result = op.evaluate({&x}, {(int) nd4j::DataType::INT64}); - ASSERT_NE(Status::OK(), result->status()); - delete result; + auto result = op.evaluate({&x}, {(int) sd::DataType::INT64}); + ASSERT_NE(Status::OK(), result.status()); + } catch (std::exception& e) { nd4j_printf("Error should be here `%s'. It's OK.\n", e.what()); } @@ -462,9 +465,9 @@ TEST_F(DeclarableOpsTests15, Test_BitCast_4) { auto x = NDArrayFactory::create('c', {1, 4}); auto e = NDArrayFactory::create('c', {1, 2}, {1234567890LL, 2468013579LL}); x.linspace(1.); - nd4j::ops::bitcast op; + sd::ops::bitcast op; try { - auto result = op.execute({&x}, {&e}, {}, {nd4j::DataType::INT64}, {}); + auto result = op.execute({&x}, {&e}, {}, {sd::DataType::INT64}, {}); ASSERT_NE(Status::OK(), result); } catch(std::exception& e) { nd4j_printf("Error `%s' should be here. It's OK.\n",e.what()); @@ -476,14 +479,15 @@ TEST_F(DeclarableOpsTests15, Test_BitCast_4_1) { auto x = NDArrayFactory::create('c', {1, 2}); auto e = NDArrayFactory::create('c', {1, 2}, {4607182418800017408LL, 4611686018427387904LL}); // as TF 4607182418800017408, 4611686018427387904 x.linspace(1.); - nd4j::ops::bitcast op; + sd::ops::bitcast op; + + auto result = op.evaluate({&x}, {}, {sd::DataType::INT64}, {}); + ASSERT_EQ(Status::OK(), result.status()); - auto result = op.evaluate({&x}, {}, {nd4j::DataType::INT64}, {}); - ASSERT_EQ(Status::OK(), result->status()); // e.printIndexedBuffer("Double to int64"); - auto res = result->at(0); + auto res = result.at(0); ASSERT_EQ(*res, e); - delete result; + } @@ -496,13 +500,15 @@ TEST_F(DeclarableOpsTests15, Test_BitCast_5) { auto e = NDArrayFactory::create('c', {4}, {4260467851820808160LL, 3900173902914993008LL, 3566895990128523424LL, 3314989625590692528LL}); - nd4j::ops::bitcast op; - auto result = op.evaluate({&x}, {}, {nd4j::DataType::INT64}, {}); - ASSERT_EQ(Status::OK(), result->status()); - auto res = result->at(0); + + sd::ops::bitcast op; + auto result = op.evaluate({&x}, {}, {sd::DataType::INT64}, {}); + ASSERT_EQ(Status::OK(), result.status()); + auto res = result.at(0); + // res->printIndexedBuffer("BITCAST5"); ASSERT_TRUE(e.equalsTo(res)); - delete result; + } TEST_F(DeclarableOpsTests15, Test_BitCast_6) { @@ -514,13 +520,15 @@ TEST_F(DeclarableOpsTests15, Test_BitCast_6) { auto e = NDArrayFactory::create('c', {4}, {4899988963420290048LL, 5188224837230806272LL, 5332342774136064128LL, 5476460161268730496LL}); - nd4j::ops::bitcast op; - auto result = op.evaluate({&x}, {}, {nd4j::DataType::INT64}, {}); - ASSERT_EQ(Status::OK(), result->status()); - auto res = result->at(0); + + sd::ops::bitcast op; + auto result = op.evaluate({&x}, {}, {sd::DataType::INT64}, {}); + ASSERT_EQ(Status::OK(), result.status()); + auto res = result.at(0); + // res->printIndexedBuffer("BITCAST6"); ASSERT_TRUE(e.equalsTo(res)); - delete result; + } TEST_F(DeclarableOpsTests15, Test_BitCast_7) { auto x = NDArrayFactory::create('c', {4, 4}, { @@ -531,13 +539,15 @@ TEST_F(DeclarableOpsTests15, Test_BitCast_7) { auto e = NDArrayFactory::create('c', {4}, { 4928700072476425318LL, 5202580391758873882LL, 5346698272827918477LL, 5483778673873668736LL}); - nd4j::ops::bitcast op; - auto result = op.evaluate({&x}, {}, {nd4j::DataType::INT64}, {}); - ASSERT_EQ(Status::OK(), result->status()); - auto res = result->at(0); + + sd::ops::bitcast op; + auto result = op.evaluate({&x}, {}, {sd::DataType::INT64}, {}); + ASSERT_EQ(Status::OK(), result.status()); + auto res = result.at(0); + // res->printIndexedBuffer("BITCAST7"); ASSERT_TRUE(e.equalsTo(res)); - delete result; + } TEST_F(DeclarableOpsTests15, test_matmul_bp_1) { @@ -548,7 +558,7 @@ TEST_F(DeclarableOpsTests15, test_matmul_bp_1) { auto gA = NDArrayFactory::create('c', {1, 3}); auto gB = NDArrayFactory::create('c', {1, 4}); - nd4j::ops::matmul_bp op; + sd::ops::matmul_bp op; auto status = op.execute({&a, &b, &gI}, std::vector{&gA, &gB}, {}, {1, 0, 0}, {}); ASSERT_EQ(Status::OK(), status); } @@ -558,7 +568,7 @@ TEST_F(DeclarableOpsTests15, test_non_decreasing_1) { auto z = NDArrayFactory::create(false); auto e = NDArrayFactory::create(true); - nd4j::ops::is_non_decreasing op; + sd::ops::is_non_decreasing op; Context ctx(1); ctx.setInputArray(0, &x); ctx.setOutputArray(0, &z); @@ -572,15 +582,13 @@ TEST_F(DeclarableOpsTests15, test_check_numeric_1) { auto x = NDArrayFactory::create('c', {3},{1.f, 2.f, 3.f}); auto y = NDArrayFactory::string("shouldn't ever trigger"); - nd4j::ops::check_numerics op; + sd::ops::check_numerics op; auto result = op.evaluate({&x, &y}, {}, {}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_EQ(x, *z); - - delete result; } TEST_F(DeclarableOpsTests15, test_check_numeric_2) { @@ -593,7 +601,7 @@ TEST_F(DeclarableOpsTests15, test_check_numeric_2) { auto y = NDArrayFactory::string("should trigger"); auto z = NDArrayFactory::create('c', {3} ); - nd4j::ops::check_numerics op; + sd::ops::check_numerics op; try { auto status = op.execute({&x, &y}, {&z}, {}, {}, {}); ASSERT_TRUE(false); @@ -612,7 +620,7 @@ TEST_F(DeclarableOpsTests15, test_check_numeric_3) { auto y = NDArrayFactory::string("should trigger"); auto z = NDArrayFactory::create('c', {3} ); - nd4j::ops::check_numerics op; + sd::ops::check_numerics op; try { auto status = op.execute({&x, &y}, {&z}, {}, {}, {}); ASSERT_TRUE(false); @@ -626,10 +634,10 @@ TEST_F(DeclarableOpsTests15, Test_layer_norm_1) { auto g = NDArrayFactory::create('c', {5}, {1.f, 2.f, 3.f, 4.f, 5.f}); auto b = NDArrayFactory::create('c', {5}, {1.f, 2.f, 3.f, 4.f, 5.f}); - nd4j::ops::layer_norm op; + sd::ops::layer_norm op; auto result = op.evaluate({&x, &g, &b}, {}, {0}, {false}); - ASSERT_EQ(Status::OK(), result->status()); - delete result; + ASSERT_EQ(Status::OK(), result.status()); + } TEST_F(DeclarableOpsTests15, Test_layer_norm_bp_1) { @@ -638,28 +646,28 @@ TEST_F(DeclarableOpsTests15, Test_layer_norm_bp_1) { auto b = NDArrayFactory::create('c', {5}, {1.f, 2.f, 3.f, 4.f, 5.f}); auto eps = NDArrayFactory::create('c', {1, 5}, {0.f, 0.f, 0.f, 0.f, 0.f}); - nd4j::ops::layer_norm_bp op; + sd::ops::layer_norm_bp op; auto result = op.evaluate({&x, &g, &b, &eps}, {}, {0}, {false}); - ASSERT_EQ(Status::OK(), result->status()); - delete result; + ASSERT_EQ(Status::OK(), result.status()); + } ////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests15, Test_layer_norm_bp_2) { - NDArray x('c', {3, 4, 8, 8}, nd4j::DataType::FLOAT32); - NDArray gain('c', {4}, {-0.1, 0.1, -0.2, 0.2}, nd4j::DataType::FLOAT32); - NDArray bias('c', {4}, {-0.05, 0.05, -1.05, 1.05}, nd4j::DataType::FLOAT32); - NDArray gradO('c', {3, 4, 8, 8}, nd4j::DataType::FLOAT32); + NDArray x('c', {3, 4, 8, 8}, sd::DataType::FLOAT32); + NDArray gain('c', {4}, {-0.1, 0.1, -0.2, 0.2}, sd::DataType::FLOAT32); + NDArray bias('c', {4}, {-0.05, 0.05, -1.05, 1.05}, sd::DataType::FLOAT32); + NDArray gradO('c', {3, 4, 8, 8}, sd::DataType::FLOAT32); - NDArray gradI('c', {3, 4, 8, 8}, nd4j::DataType::FLOAT32); - NDArray gradG('c', {4}, nd4j::DataType::FLOAT32); - NDArray gradB('c', {4}, nd4j::DataType::FLOAT32); + NDArray gradI('c', {3, 4, 8, 8}, sd::DataType::FLOAT32); + NDArray gradG('c', {4}, sd::DataType::FLOAT32); + NDArray gradB('c', {4}, sd::DataType::FLOAT32); x.linspace(-20, 0.5); gradO.linspace(-4, 0.05); - nd4j::ops::layer_norm_bp op; + sd::ops::layer_norm_bp op; auto status = op.execute({&x, &gain, &bias, &gradO}, {&gradI, &gradG, &gradB}, {}, {1,2,3}, {true}); ASSERT_EQ(Status::OK(), status); } @@ -671,19 +679,15 @@ TEST_F(DeclarableOpsTests15, test_hashCode_1) { x.linspace(1.); y.linspace(2.); - nd4j::ops::hashcode op; + sd::ops::hashcode op; auto resultA0 = op.evaluate({&x}); auto resultA1 = op.evaluate({&x}); auto resultB0 = op.evaluate({&y}); // resultA0->at(0)->printIndexedBuffer("A0"); // resultA1->at(0)->printIndexedBuffer("A1"); // resultB0->at(0)->printIndexedBuffer("B0"); - ASSERT_EQ(*resultA0->at(0), *resultA1->at(0)); - ASSERT_NE(*resultA0->at(0), *resultB0->at(0)); - - delete resultA0; - delete resultA1; - delete resultB0; + ASSERT_EQ(*resultA0.at(0), *resultA1.at(0)); + ASSERT_NE(*resultA0.at(0), *resultB0.at(0)); } TEST_F(DeclarableOpsTests15, test_hashCode_2) { @@ -693,7 +697,7 @@ TEST_F(DeclarableOpsTests15, test_hashCode_2) { x.linspace(1.); y.linspace(2.); - nd4j::ops::hashcode op; + sd::ops::hashcode op; auto resultA0 = op.evaluate({&x}); auto resultA1 = op.evaluate({&x}); auto resultB0 = op.evaluate({&y}); @@ -702,27 +706,21 @@ TEST_F(DeclarableOpsTests15, test_hashCode_2) { // resultA1->at(0)->printIndexedBuffer("A1"); // resultB0->at(0)->printIndexedBuffer("B0"); - ASSERT_EQ(*resultA0->at(0), *resultA1->at(0)); - ASSERT_NE(*resultA0->at(0), *resultB0->at(0)); - - delete resultA0; - delete resultA1; - delete resultB0; + ASSERT_EQ(*resultA0.at(0), *resultA1.at(0)); + ASSERT_NE(*resultA0.at(0), *resultB0.at(0)); } TEST_F(DeclarableOpsTests15, test_reshape_to_scalar_1) { auto array = NDArrayFactory::create(119.f); auto e = NDArrayFactory::create('c', {1, 1}, {119.f}); - nd4j::ops::reshape op; + sd::ops::reshape op; auto result = op.evaluate({&array}, {}, {1, 1}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_EQ(e, *z); - - delete result; } TEST_F(DeclarableOpsTests15, test_reshape_to_scalar_2) { @@ -730,7 +728,7 @@ TEST_F(DeclarableOpsTests15, test_reshape_to_scalar_2) { auto e = NDArrayFactory::create('c', {1, 1}, {119.f}); auto z = NDArrayFactory::create('c', {1, 1}); - nd4j::ops::reshape op; + sd::ops::reshape op; auto result = op.execute({&array}, {&z}, {}, {1, 1}, {}); ASSERT_EQ(Status::OK(), result); ASSERT_EQ(e, z); @@ -741,7 +739,7 @@ TEST_F(DeclarableOpsTests15, test_rank_1) { auto e = NDArrayFactory::create('c', {}, {2}); auto z = NDArrayFactory::create('c', {}); - nd4j::ops::rank op; + sd::ops::rank op; auto result = op.execute({&array}, {&z}, {}, {}, {}); ASSERT_EQ(Status::OK(), result); ASSERT_EQ(e, z); @@ -751,15 +749,15 @@ TEST_F(DeclarableOpsTests15, test_rank_2) { auto array = NDArrayFactory::create('c', {4, 64}); auto e = NDArrayFactory::create('c', {}, {2}); - nd4j::ops::rank op; + sd::ops::rank op; auto result = op.evaluate({&array}, {}, {}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_EQ(e, *z); - delete result; + } TEST_F(DeclarableOpsTests15, test_lstmBlock_1) { @@ -773,15 +771,13 @@ TEST_F(DeclarableOpsTests15, test_lstmBlock_1) { auto x7 = NDArrayFactory::create('c', {1, 3}); auto x8 = NDArrayFactory::create('c', {12}); - nd4j::ops::lstmBlock op; + sd::ops::lstmBlock op; auto result = op.evaluate({&x0, &x1, &x2, &x3, &x4, &x5, &x6, &x7, &x8}, {2.0, 0.3}, {0, 0}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); // z->printIndexedBuffer("Z"); - - delete result; } TEST_F(DeclarableOpsTests15, test_lstmBlock_2) { @@ -799,13 +795,12 @@ TEST_F(DeclarableOpsTests15, test_lstmBlock_2) { auto x7 = NDArrayFactory::create('f', {nIn}); auto x8 = NDArrayFactory::create('f', {4 * nIn}); - nd4j::ops::lstmBlock op; + sd::ops::lstmBlock op; auto result = op.evaluate({&x0, &x1, &x2, &x3, &x4, &x5, &x6, &x7, &x8}, {1.0, 0.0}, {0, 1}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); - - delete result; + auto z = result.at(0); + } TEST_F(DeclarableOpsTests15, test_lstmBlock_3) { @@ -814,8 +809,8 @@ TEST_F(DeclarableOpsTests15, test_lstmBlock_3) { int bS = 2; int nIn = 4; - NDArray f('f', {bS, nIn, seqLen}, nd4j::DataType::FLOAT32); - NDArray cLast('f', {bS, nIn}, nd4j::DataType::FLOAT32); + NDArray f('f', {bS, nIn, seqLen}, sd::DataType::FLOAT32); + NDArray cLast('f', {bS, nIn}, sd::DataType::FLOAT32); f = 2; cLast = 3; @@ -842,7 +837,7 @@ TEST_F(DeclarableOpsTests15, test_empty_increasing_1) { ctx.setInputArray(0, &x); ctx.setOutputArray(0, &z); - nd4j::ops::is_strictly_increasing op; + sd::ops::is_strictly_increasing op; auto status = op.execute(&ctx); ASSERT_EQ(Status::OK(), status); @@ -857,7 +852,7 @@ TEST_F(DeclarableOpsTests15, test_empty_decreasing_1) { ctx.setInputArray(0, &x); ctx.setOutputArray(0, &z); - nd4j::ops::is_non_decreasing op; + sd::ops::is_non_decreasing op; auto status = op.execute(&ctx); ASSERT_EQ(Status::OK(), status); @@ -867,17 +862,15 @@ TEST_F(DeclarableOpsTests15, test_empty_decreasing_1) { //////////////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests15, test_rgb_to_grs_1) { // rank 1 - NDArray rgbs('c', { 3 }, { 10, 50, 200 }, nd4j::DataType::INT32); - NDArray expected('c', { 1 }, std::vector{ 55 }, nd4j::DataType::INT32); - nd4j::ops::rgb_to_grs op; + NDArray rgbs('c', { 3 }, { 10, 50, 200 }, sd::DataType::INT32); + NDArray expected('c', { 1 }, std::vector{ 55 }, sd::DataType::INT32); + sd::ops::rgb_to_grs op; auto result = op.evaluate({&rgbs}, {}, {}); - auto output = result->at(0); + auto output = result.at(0); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - - delete result; } //////////////////////////////////////////////////////////////////////////////// @@ -885,65 +878,57 @@ TEST_F(DeclarableOpsTests15, test_rgb_to_grs_2) { // rank 1 auto rgbs = NDArrayFactory::create('f', { 3 }, { 1, 120, -25 }); auto expected = NDArrayFactory::create('f', { 1 }, { 67 }); - nd4j::ops::rgb_to_grs op; + sd::ops::rgb_to_grs op; auto result = op.evaluate({ &rgbs }, {}, {}); - auto output = result->at(0); + auto output = result.at(0); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - - delete result; } //////////////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests15, test_rgb_to_grs_3) { // rank 2 - NDArray rgbs('c', { 4, 3 }, { -94, 99, 97, 90, 114, 101, 111, 96, 105, 100, 103, 102 }, nd4j::DataType::INT32); - NDArray expected('c', { 4, 1 }, { 41, 105, 101, 101 }, nd4j::DataType::INT32); - nd4j::ops::rgb_to_grs op; + NDArray rgbs('c', { 4, 3 }, { -94, 99, 97, 90, 114, 101, 111, 96, 105, 100, 103, 102 }, sd::DataType::INT32); + NDArray expected('c', { 4, 1 }, { 41, 105, 101, 101 }, sd::DataType::INT32); + sd::ops::rgb_to_grs op; auto result = op.evaluate({ &rgbs }, {}, {}); - auto output = result->at(0); + auto output = result.at(0); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - - delete result; } //////////////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests15, test_rgb_to_grs_4) { - NDArray rgbs('c', { 3, 2 }, {14, 99, 207, 10, 114, 201 }, nd4j::DataType::INT32); + NDArray rgbs('c', { 3, 2 }, {14, 99, 207, 10, 114, 201 }, sd::DataType::INT32); rgbs.permutei({1,0}); - NDArray expected('c', { 2, 1 }, { 138, 58 }, nd4j::DataType::INT32); - nd4j::ops::rgb_to_grs op; + NDArray expected('c', { 2, 1 }, { 138, 58 }, sd::DataType::INT32); + sd::ops::rgb_to_grs op; auto result = op.evaluate({ &rgbs }, {}, {}); - auto output = result->at(0); + auto output = result.at(0); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - - delete result; } //////////////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests15, test_rgb_to_grs_5) { // rank 2 - NDArray rgbs('c', { 3, 4 }, { -94, 99, 97, 90, 114, 101, 111, 96, 105, 100, 103, 102 }, nd4j::DataType::INT32); - NDArray expected('c', { 1, 4 }, { 50, 100, 105, 94 }, nd4j::DataType::INT32); - nd4j::ops::rgb_to_grs op; + NDArray rgbs('c', { 3, 4 }, { -94, 99, 97, 90, 114, 101, 111, 96, 105, 100, 103, 102 }, sd::DataType::INT32); + NDArray expected('c', { 1, 4 }, { 50, 100, 105, 94 }, sd::DataType::INT32); + sd::ops::rgb_to_grs op; auto result = op.evaluate({ &rgbs }, {}, {0}); - auto output = result->at(0); + auto output = result.at(0); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - - delete result; } //////////////////////////////////////////////////////////////////////////////// @@ -952,15 +937,13 @@ TEST_F(DeclarableOpsTests15, test_rgb_to_grs_6) { auto rgbs = NDArrayFactory::create('c', { 5,4,3 }, {1.7750e+01f, -7.1062e+01f, -1.0019e+02f,-2.3406e+01f, 5.2094e+01f, 9.5438e+01f, -6.7461e+00f, 3.8562e+01f, 6.5078e+00f,3.3562e+01f, -5.8844e+01f, 2.2750e+01f, -1.0477e+01f, 7.7344e+00f, 9.5469e+00f,2.1391e+01f, -8.5312e+01f, 7.5830e-01f,2.3125e+01f, 1.8145e+00f, 1.4602e+01f,-4.5859e+00f, 3.9344e+01f, 1.1617e+01f,-8.6562e+01f, 1.0038e+02f, 6.7938e+01f,5.9961e+00f, 6.7812e+01f, 2.9734e+01f,2.9609e+01f, -6.1438e+01f, 1.7750e+01f,6.8562e+01f, -7.4414e+00f, 3.9656e+01f,1.1641e+01f, -2.7516e+01f, 6.7562e+01f,7.8438e+01f, 5.4883e+00f, 2.9438e+01f,-3.1344e+01f, 6.5125e+01f, 1.2695e+01f,4.0531e+01f, -6.1211e+00f, 6.2219e+01f,4.6812e+01f, 5.2250e+01f, -1.1414e+01f,1.5404e-02f, 2.9938e+01f, 5.6719e+00f,-2.0125e+01f, 2.1531e+01f, 6.2500e+01f,7.2188e+01f, 9.3750e+00f, -4.8125e+01f}); auto expected = NDArrayFactory::create('c', { 5,4,1 }, {-47.82958221f, 34.46305847f, 21.36137581f, -21.91625023f,2.49686432f, -43.59792709f, 9.64180183f, 23.04854202f,40.7946167f, 44.98754883f, -25.19047546f, 20.64586449f,-4.97033119f, 30.0226841f, 30.30688286f, 15.61459541f,43.36166f, 18.22480774f, 13.74833488f, 21.59387016f}); - nd4j::ops::rgb_to_grs op; + sd::ops::rgb_to_grs op; auto result = op.evaluate({ &rgbs }, {}, {}); - auto output = result->at(0); + auto output = result.at(0); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - - delete result; } //////////////////////////////////////////////////////////////////////////////// @@ -969,15 +952,13 @@ TEST_F(DeclarableOpsTests15, test_rgb_to_grs_7) { auto rgbs = NDArrayFactory::create('c', { 5,3,4 }, { 1.7750e+01f, -7.1062e+01f, -1.0019e+02f,-2.3406e+01f, 5.2094e+01f, 9.5438e+01f, -6.7461e+00f, 3.8562e+01f, 6.5078e+00f,3.3562e+01f, -5.8844e+01f, 2.2750e+01f, -1.0477e+01f, 7.7344e+00f, 9.5469e+00f,2.1391e+01f, -8.5312e+01f, 7.5830e-01f,2.3125e+01f, 1.8145e+00f, 1.4602e+01f,-4.5859e+00f, 3.9344e+01f, 1.1617e+01f,-8.6562e+01f, 1.0038e+02f, 6.7938e+01f,5.9961e+00f, 6.7812e+01f, 2.9734e+01f,2.9609e+01f, -6.1438e+01f, 1.7750e+01f,6.8562e+01f, -7.4414e+00f, 3.9656e+01f,1.1641e+01f, -2.7516e+01f, 6.7562e+01f,7.8438e+01f, 5.4883e+00f, 2.9438e+01f,-3.1344e+01f, 6.5125e+01f, 1.2695e+01f,4.0531e+01f, -6.1211e+00f, 6.2219e+01f,4.6812e+01f, 5.2250e+01f, -1.1414e+01f,1.5404e-02f, 2.9938e+01f, 5.6719e+00f,-2.0125e+01f, 2.1531e+01f, 6.2500e+01f,7.2188e+01f, 9.3750e+00f, -4.8125e+01f}); auto expected = NDArrayFactory::create('c', { 5,1,4 }, { 36.626545f, 38.607746f, -40.614971f, 18.233341f, -51.545094f,2.234142f, 20.913160f, 8.783220f, 15.955761f, 55.273506f, 36.838833f, -29.751089f, 8.148357f, 13.676106f, 1.097548f, 68.766457f, 38.690712f, 27.176361f, -14.156269f, 7.157052f }); - nd4j::ops::rgb_to_grs op; + sd::ops::rgb_to_grs op; auto result = op.evaluate({ &rgbs }, {}, {1}); - auto output = result->at(0); + auto output = result.at(0); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - - delete result; } //////////////////////////////////////////////////////////////////////////////// @@ -985,10 +966,10 @@ TEST_F(DeclarableOpsTests15, test_rgb_to_grs_8) { // rank 3 auto rgbs = NDArrayFactory::create('c', { 3,5,4 }, {1.7750e+01f, -7.1062e+01f, -1.0019e+02f,-2.3406e+01f, 5.2094e+01f, 9.5438e+01f, -6.7461e+00f, 3.8562e+01f, 6.5078e+00f,3.3562e+01f, -5.8844e+01f, 2.2750e+01f, -1.0477e+01f, 7.7344e+00f, 9.5469e+00f,2.1391e+01f, -8.5312e+01f, 7.5830e-01f,2.3125e+01f, 1.8145e+00f, 1.4602e+01f,-4.5859e+00f, 3.9344e+01f, 1.1617e+01f,-8.6562e+01f, 1.0038e+02f, 6.7938e+01f,5.9961e+00f, 6.7812e+01f, 2.9734e+01f,2.9609e+01f, -6.1438e+01f, 1.7750e+01f,6.8562e+01f, -7.4414e+00f, 3.9656e+01f,1.1641e+01f, -2.7516e+01f, 6.7562e+01f,7.8438e+01f, 5.4883e+00f, 2.9438e+01f,-3.1344e+01f, 6.5125e+01f, 1.2695e+01f,4.0531e+01f, -6.1211e+00f, 6.2219e+01f,4.6812e+01f, 5.2250e+01f, -1.1414e+01f,1.5404e-02f, 2.9938e+01f, 5.6719e+00f,-2.0125e+01f, 2.1531e+01f, 6.2500e+01f,7.2188e+01f, 9.3750e+00f, -4.8125e+01f}); try { - nd4j::ops::rgb_to_grs op; + sd::ops::rgb_to_grs op; auto result = op.evaluate({ &rgbs }, {}, {}); - ASSERT_EQ(Status::THROW(), result->status()); - delete result; + ASSERT_EQ(Status::THROW(), result.status()); + } catch (std::exception& e) { nd4j_printf("Error should be here `%s'. It's OK.\n", e.what()); } @@ -1000,110 +981,100 @@ TEST_F(DeclarableOpsTests15, test_rgb_to_grs_9) { auto rgbs = NDArrayFactory::create('f', { 2, 2, 3 }, { 1.7750e+01f,-7.1062e+01f, -1.0019e+02f, -2.3406e+01f,5.2094e+01f,9.5438e+01f, -6.7461e+00f,3.8562e+01f, 6.5078e+00f, 3.3562e+01f,-5.8844e+01f,2.2750e+01f}); auto expected = NDArrayFactory::create('f', { 2,2,1 }, { 36.626545f, 38.607746f, -40.614971f, 18.233341f }); - nd4j::ops::rgb_to_grs op; + sd::ops::rgb_to_grs op; auto result = op.evaluate({ &rgbs }, {}, {}); - auto output = result->at(0); + auto output = result.at(0); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - - delete result; } //////////////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests15, test_rgb_to_yuv_1) { // rank 1 - NDArray rgbs('f', { 3 }, { 10, 50, 200 }, nd4j::DataType::FLOAT32); - NDArray expected('f', { 3 }, { 55.14 , 71.2872001, -39.6005542 }, nd4j::DataType::FLOAT32); - nd4j::ops::rgb_to_yuv op; + NDArray rgbs('f', { 3 }, { 10, 50, 200 }, sd::DataType::FLOAT32); + NDArray expected('f', { 3 }, { 55.14 , 71.2872001, -39.6005542 }, sd::DataType::FLOAT32); + sd::ops::rgb_to_yuv op; auto result = op.evaluate({ &rgbs }, {}, {}); - auto output = result->at(0); + auto output = result.at(0); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - - delete result; } //////////////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests15, test_rgb_to_yuv_2) { - NDArray rgbs('c', { 3, 2 }, { 14., 99., 207., 10., 114., 201. }, nd4j::DataType::FLOAT32); + NDArray rgbs('c', { 3, 2 }, { 14., 99., 207., 10., 114., 201. }, sd::DataType::FLOAT32); rgbs.permutei({ 1,0 }); - NDArray expected('c', { 2, 3 }, { 138.691, -12.150713, -109.38929, 58.385, 70.18241, 35.63085 }, nd4j::DataType::FLOAT32); - nd4j::ops::rgb_to_yuv op; + NDArray expected('c', { 2, 3 }, { 138.691, -12.150713, -109.38929, 58.385, 70.18241, 35.63085 }, sd::DataType::FLOAT32); + sd::ops::rgb_to_yuv op; auto result = op.evaluate({ &rgbs }, {}, {}); - auto output = result->at(0); + auto output = result.at(0); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - - delete result; } //////////////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests15, test_rgb_to_yuv_3) { // rank 2 - NDArray rgbs('c', { 3, 4 }, { -9.4, 9.9, 9.7, 9.0, 1.14, 1.01, 1.11, 9.6, 1.05, 10.0, 1.03, 10.22 }, nd4j::DataType::FLOAT32); - NDArray expected('c', { 3, 4 }, { -2.021720, 4.692970, 3.669290, 9.491281, 1.511627, 2.611648, -1.298824, 0.358612, -6.472839, 4.568039, 5.290639, -0.430992 }, nd4j::DataType::FLOAT32); + NDArray rgbs('c', { 3, 4 }, { -9.4, 9.9, 9.7, 9.0, 1.14, 1.01, 1.11, 9.6, 1.05, 10.0, 1.03, 10.22 }, sd::DataType::FLOAT32); + NDArray expected('c', { 3, 4 }, { -2.021720, 4.692970, 3.669290, 9.491281, 1.511627, 2.611648, -1.298824, 0.358612, -6.472839, 4.568039, 5.290639, -0.430992 }, sd::DataType::FLOAT32); - nd4j::ops::rgb_to_yuv op; + sd::ops::rgb_to_yuv op; auto result = op.evaluate({ &rgbs }, {}, { 0 }); - auto output = result->at(0); - ASSERT_EQ(Status::OK(), result->status()); + auto output = result.at(0); + ASSERT_EQ(Status::OK(), result.status()); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - - delete result; } //////////////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests15, test_rgb_to_yuv_4) { // rank 3 - NDArray rgbs('c', { 5,4,3 }, { 1.7750e+01, 1.4602e+01, 5.4883e+00, 9.5438e+01, 1.0038e+02, 4.0531e+01, -5.8844e+01, 2.9609e+01, -1.1414e+01, 2.1391e+01, 3.9656e+01, 2.1531e+01, -7.1062e+01, -4.5859e+00, 2.9438e+01, -6.7461e+00, 6.7938e+01, -6.1211e+00, 2.2750e+01, -6.1438e+01, 1.5404e-02, -8.5312e+01, 1.1641e+01, 6.2500e+01, -1.0019e+02, 3.9344e+01, -3.1344e+01, 3.8562e+01, 5.9961e+00, 6.2219e+01, -1.0477e+01, 1.7750e+01, 2.9938e+01, 7.5830e-01, -2.7516e+01, 7.2188e+01, -2.3406e+01, 1.1617e+01, 6.5125e+01, 6.5078e+00, 6.7812e+01, 4.6812e+01, 7.7344e+00, 6.8562e+01, 5.6719e+00, 2.3125e+01, 6.7562e+01, 9.3750e+00, 5.2094e+01, -8.6562e+01, 1.2695e+01, 3.3562e+01, 2.9734e+01, 5.2250e+01, 9.5469e+00, -7.4414e+00, -2.0125e+01, 1.8145e+00, 7.8438e+01, -4.8125e+01 }, nd4j::DataType::FLOAT32); - NDArray expected('c', { 5,4,3 }, { 14.5042902, -4.43686799, 2.847406, 92.079556, -25.36761168, 2.94630572, -1.515069, -4.87137291, -50.29369639, 32.128515, -5.21515376, -9.41983935,-20.5835293, 24.61614501, -44.28390394, 37.1647167, -21.30142676, -38.52221293, -29.26009994, 14.40679768, 45.62757638, -11.550021, 36.44083018, -64.71012983,-10.435098, - 10.28950082, - 78.74044941, 22.1427147, 19.72198103, 14.40435988, 10.699559, 9.46744852, - 18.5778351 , -7.6957283, 39.31166179, 7.41657542, 7.245035, 28.48336771, - 26.88963173, 47.0880442, - 0.13584441, - 35.60035823, 43.2050762, - 18.47048906, - 31.11782117, 47.642019, - 18.83162118, - 21.50836396,-33.788558, 22.87507047, 75.34330791, 33.445396, 9.25395257, 0.10229474, -3.8078287, -8.02985955, 11.71587638, 41.0993915, -43.90830496, -34.46396749 }, nd4j::DataType::FLOAT32); + NDArray rgbs('c', { 5,4,3 }, { 1.7750e+01, 1.4602e+01, 5.4883e+00, 9.5438e+01, 1.0038e+02, 4.0531e+01, -5.8844e+01, 2.9609e+01, -1.1414e+01, 2.1391e+01, 3.9656e+01, 2.1531e+01, -7.1062e+01, -4.5859e+00, 2.9438e+01, -6.7461e+00, 6.7938e+01, -6.1211e+00, 2.2750e+01, -6.1438e+01, 1.5404e-02, -8.5312e+01, 1.1641e+01, 6.2500e+01, -1.0019e+02, 3.9344e+01, -3.1344e+01, 3.8562e+01, 5.9961e+00, 6.2219e+01, -1.0477e+01, 1.7750e+01, 2.9938e+01, 7.5830e-01, -2.7516e+01, 7.2188e+01, -2.3406e+01, 1.1617e+01, 6.5125e+01, 6.5078e+00, 6.7812e+01, 4.6812e+01, 7.7344e+00, 6.8562e+01, 5.6719e+00, 2.3125e+01, 6.7562e+01, 9.3750e+00, 5.2094e+01, -8.6562e+01, 1.2695e+01, 3.3562e+01, 2.9734e+01, 5.2250e+01, 9.5469e+00, -7.4414e+00, -2.0125e+01, 1.8145e+00, 7.8438e+01, -4.8125e+01 }, sd::DataType::FLOAT32); + NDArray expected('c', { 5,4,3 }, { 14.5042902, -4.43686799, 2.847406, 92.079556, -25.36761168, 2.94630572, -1.515069, -4.87137291, -50.29369639, 32.128515, -5.21515376, -9.41983935,-20.5835293, 24.61614501, -44.28390394, 37.1647167, -21.30142676, -38.52221293, -29.26009994, 14.40679768, 45.62757638, -11.550021, 36.44083018, -64.71012983,-10.435098, - 10.28950082, - 78.74044941, 22.1427147, 19.72198103, 14.40435988, 10.699559, 9.46744852, - 18.5778351 , -7.6957283, 39.31166179, 7.41657542, 7.245035, 28.48336771, - 26.88963173, 47.0880442, - 0.13584441, - 35.60035823, 43.2050762, - 18.47048906, - 31.11782117, 47.642019, - 18.83162118, - 21.50836396,-33.788558, 22.87507047, 75.34330791, 33.445396, 9.25395257, 0.10229474, -3.8078287, -8.02985955, 11.71587638, 41.0993915, -43.90830496, -34.46396749 }, sd::DataType::FLOAT32); - nd4j::ops::rgb_to_yuv op; + sd::ops::rgb_to_yuv op; auto result = op.evaluate({ &rgbs }, {}, {}); - auto output = result->at(0); + auto output = result.at(0); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - - delete result; } //////////////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests15, test_rgb_to_yuv_5) { // rank 3 - NDArray rgbs('c', { 5,3,4 }, { 1.7750e+01f, -7.1062e+01f, -1.0019e+02f,-2.3406e+01f, 5.2094e+01f, 9.5438e+01f, -6.7461e+00f, 3.8562e+01f, 6.5078e+00f,3.3562e+01f, -5.8844e+01f, 2.2750e+01f, -1.0477e+01f, 7.7344e+00f, 9.5469e+00f,2.1391e+01f, -8.5312e+01f, 7.5830e-01f,2.3125e+01f, 1.8145e+00f, 1.4602e+01f,-4.5859e+00f, 3.9344e+01f, 1.1617e+01f,-8.6562e+01f, 1.0038e+02f, 6.7938e+01f,5.9961e+00f, 6.7812e+01f, 2.9734e+01f,2.9609e+01f, -6.1438e+01f, 1.7750e+01f,6.8562e+01f, -7.4414e+00f, 3.9656e+01f,1.1641e+01f, -2.7516e+01f, 6.7562e+01f,7.8438e+01f, 5.4883e+00f, 2.9438e+01f,-3.1344e+01f, 6.5125e+01f, 1.2695e+01f,4.0531e+01f, -6.1211e+00f, 6.2219e+01f,4.6812e+01f, 5.2250e+01f, -1.1414e+01f,1.5404e-02f, 2.9938e+01f, 5.6719e+00f,-2.0125e+01f, 2.1531e+01f, 6.2500e+01f,7.2188e+01f, 9.3750e+00f, -4.8125e+01f }, nd4j::DataType::FLOAT32); - NDArray expected('c', { 5,3,4 }, { 36.628319, 38.600643,-40.624989, 18.231001, - 14.822637, - 2.479566, - 8.965780, 2.223851, -16.561626,-96.205162,-52.255379,-36.527435,-51.546139,2.234915, 20.914114, 8.785358, 32.552223, -3.356598, 9.069552, 1.393482,36.029255, 4.824605,- 9.972263,11.058715, 15.947105, 55.283543, 36.845627, -29.750486,0.887228, 6.534475, -21.794132,34.155693, -89.929497,39.562351, 27.276817,31.359871, 8.149521, 13.673355, 1.104303, 68.774300, 2.236881, 13.216944, - 3.555702,- 3.225931,3.063015, - 36.134724,58.302204, 8.477802, 38.695396,27.181587, - 14.157411,7.157054, 11.714512, 22.148155, 11.580557, - 27.204905,7.120562, 21.992094, 2.406748, - 6.265247, }, nd4j::DataType::FLOAT32); + NDArray rgbs('c', { 5,3,4 }, { 1.7750e+01f, -7.1062e+01f, -1.0019e+02f,-2.3406e+01f, 5.2094e+01f, 9.5438e+01f, -6.7461e+00f, 3.8562e+01f, 6.5078e+00f,3.3562e+01f, -5.8844e+01f, 2.2750e+01f, -1.0477e+01f, 7.7344e+00f, 9.5469e+00f,2.1391e+01f, -8.5312e+01f, 7.5830e-01f,2.3125e+01f, 1.8145e+00f, 1.4602e+01f,-4.5859e+00f, 3.9344e+01f, 1.1617e+01f,-8.6562e+01f, 1.0038e+02f, 6.7938e+01f,5.9961e+00f, 6.7812e+01f, 2.9734e+01f,2.9609e+01f, -6.1438e+01f, 1.7750e+01f,6.8562e+01f, -7.4414e+00f, 3.9656e+01f,1.1641e+01f, -2.7516e+01f, 6.7562e+01f,7.8438e+01f, 5.4883e+00f, 2.9438e+01f,-3.1344e+01f, 6.5125e+01f, 1.2695e+01f,4.0531e+01f, -6.1211e+00f, 6.2219e+01f,4.6812e+01f, 5.2250e+01f, -1.1414e+01f,1.5404e-02f, 2.9938e+01f, 5.6719e+00f,-2.0125e+01f, 2.1531e+01f, 6.2500e+01f,7.2188e+01f, 9.3750e+00f, -4.8125e+01f }, sd::DataType::FLOAT32); + NDArray expected('c', { 5,3,4 }, { 36.628319, 38.600643,-40.624989, 18.231001, - 14.822637, - 2.479566, - 8.965780, 2.223851, -16.561626,-96.205162,-52.255379,-36.527435,-51.546139,2.234915, 20.914114, 8.785358, 32.552223, -3.356598, 9.069552, 1.393482,36.029255, 4.824605,- 9.972263,11.058715, 15.947105, 55.283543, 36.845627, -29.750486,0.887228, 6.534475, -21.794132,34.155693, -89.929497,39.562351, 27.276817,31.359871, 8.149521, 13.673355, 1.104303, 68.774300, 2.236881, 13.216944, - 3.555702,- 3.225931,3.063015, - 36.134724,58.302204, 8.477802, 38.695396,27.181587, - 14.157411,7.157054, 11.714512, 22.148155, 11.580557, - 27.204905,7.120562, 21.992094, 2.406748, - 6.265247, }, sd::DataType::FLOAT32); - nd4j::ops::rgb_to_yuv op; + sd::ops::rgb_to_yuv op; auto result = op.evaluate({ &rgbs }, {}, { 1 }); - auto output = result->at(0); + auto output = result.at(0); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests15, test_rgb_to_yuv_6) { // rank 3 - NDArray rgbs('c', { 3,5,4 }, { 1.7750e+01f, -7.1062e+01f, -1.0019e+02f,-2.3406e+01f, 5.2094e+01f, 9.5438e+01f, -6.7461e+00f, 3.8562e+01f, 6.5078e+00f,3.3562e+01f, -5.8844e+01f, 2.2750e+01f, -1.0477e+01f, 7.7344e+00f, 9.5469e+00f,2.1391e+01f, -8.5312e+01f, 7.5830e-01f,2.3125e+01f, 1.8145e+00f, 1.4602e+01f,-4.5859e+00f, 3.9344e+01f, 1.1617e+01f,-8.6562e+01f, 1.0038e+02f, 6.7938e+01f,5.9961e+00f, 6.7812e+01f, 2.9734e+01f,2.9609e+01f, -6.1438e+01f, 1.7750e+01f,6.8562e+01f, -7.4414e+00f, 3.9656e+01f,1.1641e+01f, -2.7516e+01f, 6.7562e+01f,7.8438e+01f, 5.4883e+00f, 2.9438e+01f,-3.1344e+01f, 6.5125e+01f, 1.2695e+01f,4.0531e+01f, -6.1211e+00f, 6.2219e+01f,4.6812e+01f, 5.2250e+01f, -1.1414e+01f,1.5404e-02f, 2.9938e+01f, 5.6719e+00f,-2.0125e+01f, 2.1531e+01f, 6.2500e+01f,7.2188e+01f, 9.3750e+00f, -4.8125e+01f }, nd4j::DataType::FLOAT32); + NDArray rgbs('c', { 3,5,4 }, { 1.7750e+01f, -7.1062e+01f, -1.0019e+02f,-2.3406e+01f, 5.2094e+01f, 9.5438e+01f, -6.7461e+00f, 3.8562e+01f, 6.5078e+00f,3.3562e+01f, -5.8844e+01f, 2.2750e+01f, -1.0477e+01f, 7.7344e+00f, 9.5469e+00f,2.1391e+01f, -8.5312e+01f, 7.5830e-01f,2.3125e+01f, 1.8145e+00f, 1.4602e+01f,-4.5859e+00f, 3.9344e+01f, 1.1617e+01f,-8.6562e+01f, 1.0038e+02f, 6.7938e+01f,5.9961e+00f, 6.7812e+01f, 2.9734e+01f,2.9609e+01f, -6.1438e+01f, 1.7750e+01f,6.8562e+01f, -7.4414e+00f, 3.9656e+01f,1.1641e+01f, -2.7516e+01f, 6.7562e+01f,7.8438e+01f, 5.4883e+00f, 2.9438e+01f,-3.1344e+01f, 6.5125e+01f, 1.2695e+01f,4.0531e+01f, -6.1211e+00f, 6.2219e+01f,4.6812e+01f, 5.2250e+01f, -1.1414e+01f,1.5404e-02f, 2.9938e+01f, 5.6719e+00f,-2.0125e+01f, 2.1531e+01f, 6.2500e+01f,7.2188e+01f, 9.3750e+00f, -4.8125e+01f }, sd::DataType::FLOAT32); try { - nd4j::ops::rgb_to_yuv op; + sd::ops::rgb_to_yuv op; auto result = op.evaluate({ &rgbs }, {}, {}); - ASSERT_EQ(Status::THROW(), result->status()); - delete result; + ASSERT_EQ(Status::THROW(), result.status()); + } catch (std::exception & e) { nd4j_printf("Error should be here `%s'. It's OK.\n", e.what()); @@ -1113,110 +1084,102 @@ TEST_F(DeclarableOpsTests15, test_rgb_to_yuv_6) { //////////////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests15, test_rgb_to_yuv_7) { // rank 3 - NDArray rgbs('f', { 2, 2, 3 }, { 1.7750e+01f,-7.1062e+01f, -1.0019e+02f, -2.3406e+01f,5.2094e+01f,9.5438e+01f, -6.7461e+00f,3.8562e+01f, 6.5078e+00f, 3.3562e+01f,-5.8844e+01f,2.2750e+01f }, nd4j::DataType::FLOAT32); - NDArray expected('f', { 2,2,3 }, { 36.628319,38.600643, -40.624989,18.231001, -14.822637,-2.479566, -8.965780, 2.223851, -16.561626,- 96.205162,-52.255379, -36.527435 }, nd4j::DataType::FLOAT32); + NDArray rgbs('f', { 2, 2, 3 }, { 1.7750e+01f,-7.1062e+01f, -1.0019e+02f, -2.3406e+01f,5.2094e+01f,9.5438e+01f, -6.7461e+00f,3.8562e+01f, 6.5078e+00f, 3.3562e+01f,-5.8844e+01f,2.2750e+01f }, sd::DataType::FLOAT32); + NDArray expected('f', { 2,2,3 }, { 36.628319,38.600643, -40.624989,18.231001, -14.822637,-2.479566, -8.965780, 2.223851, -16.561626,- 96.205162,-52.255379, -36.527435 }, sd::DataType::FLOAT32); - nd4j::ops::rgb_to_yuv op; + sd::ops::rgb_to_yuv op; auto result = op.evaluate({ &rgbs }, {}, {}); - auto output = result->at(0); + auto output = result.at(0); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - - delete result; } //////////////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests15, test_yuv_to_rgb_1) { // rank 1 - NDArray yuv('c', { 3 }, { 55.14 , 71.2872001, -39.6005542 }, nd4j::DataType::FLOAT32); - NDArray expected('c', { 3 }, { 10, 50, 200 }, nd4j::DataType::FLOAT32); - nd4j::ops::yuv_to_rgb op; + NDArray yuv('c', { 3 }, { 55.14 , 71.2872001, -39.6005542 }, sd::DataType::FLOAT32); + NDArray expected('c', { 3 }, { 10, 50, 200 }, sd::DataType::FLOAT32); + sd::ops::yuv_to_rgb op; auto result = op.evaluate({ &yuv }, {}, {}); - auto output = result->at(0); + auto output = result.at(0); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests15, test_yuv_to_rgb_2) { // rank 1 - NDArray yuv('f', { 3 }, { 55.14, 71.2872001, -39.6005542 }, nd4j::DataType::FLOAT32); - NDArray expected('f', { 3 }, { 10, 50, 200 }, nd4j::DataType::FLOAT32); - nd4j::ops::yuv_to_rgb op; + NDArray yuv('f', { 3 }, { 55.14, 71.2872001, -39.6005542 }, sd::DataType::FLOAT32); + NDArray expected('f', { 3 }, { 10, 50, 200 }, sd::DataType::FLOAT32); + sd::ops::yuv_to_rgb op; auto result = op.evaluate({ &yuv }, {}, {}); - auto output = result->at(0); + auto output = result.at(0); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - - delete result; } //////////////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests15, test_yuv_to_rgb_3) { // rank 2 - NDArray expected('c', { 3, 4 }, { -9.4, 9.9, 9.7, 9.0, 1.14, 1.01, 1.11, 9.6, 1.05, 10.0, 1.03, 10.22 }, nd4j::DataType::FLOAT32); - NDArray yuv('c', { 3, 4 }, { -2.021720, 4.692970, 3.669290, 9.491281, 1.511627, 2.611648, -1.298824, 0.358612, -6.472839, 4.568039, 5.290639, -0.430992 }, nd4j::DataType::FLOAT32); + NDArray expected('c', { 3, 4 }, { -9.4, 9.9, 9.7, 9.0, 1.14, 1.01, 1.11, 9.6, 1.05, 10.0, 1.03, 10.22 }, sd::DataType::FLOAT32); + NDArray yuv('c', { 3, 4 }, { -2.021720, 4.692970, 3.669290, 9.491281, 1.511627, 2.611648, -1.298824, 0.358612, -6.472839, 4.568039, 5.290639, -0.430992 }, sd::DataType::FLOAT32); - nd4j::ops::yuv_to_rgb op; + sd::ops::yuv_to_rgb op; auto result = op.evaluate({ &yuv }, {}, { 0 }); - auto output = result->at(0); - ASSERT_EQ(Status::OK(), result->status()); + auto output = result.at(0); + ASSERT_EQ(Status::OK(), result.status()); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - - delete result; } //////////////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests15, test_yuv_to_rgb_4) { // rank 3 - NDArray expected('c', { 5,4,3 }, { 1.7750e+01, 1.4602e+01, 5.4883e+00, 9.5438e+01, 1.0038e+02, 4.0531e+01, -5.8844e+01, 2.9609e+01, -1.1414e+01, 2.1391e+01, 3.9656e+01, 2.1531e+01, -7.1062e+01, -4.5859e+00, 2.9438e+01, -6.7461e+00, 6.7938e+01, -6.1211e+00, 2.2750e+01, -6.1438e+01, 1.5404e-02, -8.5312e+01, 1.1641e+01, 6.2500e+01, -1.0019e+02, 3.9344e+01, -3.1344e+01, 3.8562e+01, 5.9961e+00, 6.2219e+01, -1.0477e+01, 1.7750e+01, 2.9938e+01, 7.5830e-01, -2.7516e+01, 7.2188e+01, -2.3406e+01, 1.1617e+01, 6.5125e+01, 6.5078e+00, 6.7812e+01, 4.6812e+01, 7.7344e+00, 6.8562e+01, 5.6719e+00, 2.3125e+01, 6.7562e+01, 9.3750e+00, 5.2094e+01, -8.6562e+01, 1.2695e+01, 3.3562e+01, 2.9734e+01, 5.2250e+01, 9.5469e+00, -7.4414e+00, -2.0125e+01, 1.8145e+00, 7.8438e+01, -4.8125e+01 }, nd4j::DataType::FLOAT32); - NDArray yuv('c', { 5,4,3 }, { 14.5042902, -4.43686799, 2.847406, 92.079556, -25.36761168, 2.94630572, -1.515069, -4.87137291, -50.29369639, 32.128515, -5.21515376, -9.41983935,-20.5835293, 24.61614501, -44.28390394, 37.1647167, -21.30142676, -38.52221293, -29.26009994, 14.40679768, 45.62757638, -11.550021, 36.44083018, -64.71012983,-10.435098, -10.28950082, -78.74044941, 22.1427147, 19.72198103, 14.40435988, 10.699559, 9.46744852, -18.5778351 , -7.6957283, 39.31166179, 7.41657542, 7.245035, 28.48336771, -26.88963173, 47.0880442, -0.13584441, -35.60035823, 43.2050762, -18.47048906, -31.11782117, 47.642019, -18.83162118, -21.50836396,-33.788558, 22.87507047, 75.34330791, 33.445396, 9.25395257, 0.10229474, -3.8078287, -8.02985955, 11.71587638, 41.0993915, -43.90830496, -34.46396749 }, nd4j::DataType::FLOAT32); + NDArray expected('c', { 5,4,3 }, { 1.7750e+01, 1.4602e+01, 5.4883e+00, 9.5438e+01, 1.0038e+02, 4.0531e+01, -5.8844e+01, 2.9609e+01, -1.1414e+01, 2.1391e+01, 3.9656e+01, 2.1531e+01, -7.1062e+01, -4.5859e+00, 2.9438e+01, -6.7461e+00, 6.7938e+01, -6.1211e+00, 2.2750e+01, -6.1438e+01, 1.5404e-02, -8.5312e+01, 1.1641e+01, 6.2500e+01, -1.0019e+02, 3.9344e+01, -3.1344e+01, 3.8562e+01, 5.9961e+00, 6.2219e+01, -1.0477e+01, 1.7750e+01, 2.9938e+01, 7.5830e-01, -2.7516e+01, 7.2188e+01, -2.3406e+01, 1.1617e+01, 6.5125e+01, 6.5078e+00, 6.7812e+01, 4.6812e+01, 7.7344e+00, 6.8562e+01, 5.6719e+00, 2.3125e+01, 6.7562e+01, 9.3750e+00, 5.2094e+01, -8.6562e+01, 1.2695e+01, 3.3562e+01, 2.9734e+01, 5.2250e+01, 9.5469e+00, -7.4414e+00, -2.0125e+01, 1.8145e+00, 7.8438e+01, -4.8125e+01 }, sd::DataType::FLOAT32); + NDArray yuv('c', { 5,4,3 }, { 14.5042902, -4.43686799, 2.847406, 92.079556, -25.36761168, 2.94630572, -1.515069, -4.87137291, -50.29369639, 32.128515, -5.21515376, -9.41983935,-20.5835293, 24.61614501, -44.28390394, 37.1647167, -21.30142676, -38.52221293, -29.26009994, 14.40679768, 45.62757638, -11.550021, 36.44083018, -64.71012983,-10.435098, -10.28950082, -78.74044941, 22.1427147, 19.72198103, 14.40435988, 10.699559, 9.46744852, -18.5778351 , -7.6957283, 39.31166179, 7.41657542, 7.245035, 28.48336771, -26.88963173, 47.0880442, -0.13584441, -35.60035823, 43.2050762, -18.47048906, -31.11782117, 47.642019, -18.83162118, -21.50836396,-33.788558, 22.87507047, 75.34330791, 33.445396, 9.25395257, 0.10229474, -3.8078287, -8.02985955, 11.71587638, 41.0993915, -43.90830496, -34.46396749 }, sd::DataType::FLOAT32); - nd4j::ops::yuv_to_rgb op; + sd::ops::yuv_to_rgb op; auto result = op.evaluate({ &yuv }, {}, {}); - auto output = result->at(0); + auto output = result.at(0); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - - delete result; } //////////////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests15, test_yuv_to_rgb_5) { // rank 3 - NDArray expected('c', { 5,3,4 }, { 1.7750e+01f, -7.1062e+01f, -1.0019e+02f,-2.3406e+01f, 5.2094e+01f, 9.5438e+01f, -6.7461e+00f, 3.8562e+01f, 6.5078e+00f,3.3562e+01f, -5.8844e+01f, 2.2750e+01f, -1.0477e+01f, 7.7344e+00f, 9.5469e+00f,2.1391e+01f, -8.5312e+01f, 7.5830e-01f,2.3125e+01f, 1.8145e+00f, 1.4602e+01f,-4.5859e+00f, 3.9344e+01f, 1.1617e+01f,-8.6562e+01f, 1.0038e+02f, 6.7938e+01f,5.9961e+00f, 6.7812e+01f, 2.9734e+01f,2.9609e+01f, -6.1438e+01f, 1.7750e+01f,6.8562e+01f, -7.4414e+00f, 3.9656e+01f,1.1641e+01f, -2.7516e+01f, 6.7562e+01f,7.8438e+01f, 5.4883e+00f, 2.9438e+01f,-3.1344e+01f, 6.5125e+01f, 1.2695e+01f,4.0531e+01f, -6.1211e+00f, 6.2219e+01f,4.6812e+01f, 5.2250e+01f, -1.1414e+01f,1.5404e-02f, 2.9938e+01f, 5.6719e+00f,-2.0125e+01f, 2.1531e+01f, 6.2500e+01f,7.2188e+01f, 9.3750e+00f, -4.8125e+01f }, nd4j::DataType::FLOAT32); - NDArray yuv('c', { 5,3,4 }, { 36.628319, 38.600643,-40.624989, 18.231001, -14.822637, -2.479566, -8.965780, 2.223851, -16.561626,-96.205162,-52.255379,-36.527435,-51.546139,2.234915, 20.914114, 8.785358, 32.552223, -3.356598, 9.069552, 1.393482,36.029255, 4.824605,-9.972263,11.058715, 15.947105, 55.283543, 36.845627, -29.750486,0.887228, 6.534475, -21.794132,34.155693, -89.929497,39.562351, 27.276817,31.359871, 8.149521, 13.673355, 1.104303, 68.774300, 2.236881, 13.216944, -3.555702,-3.225931,3.063015, -36.134724,58.302204, 8.477802, 38.695396,27.181587, -14.157411,7.157054, 11.714512, 22.148155, 11.580557, -27.204905,7.120562, 21.992094, 2.406748, -6.265247, }, nd4j::DataType::FLOAT32); + NDArray expected('c', { 5,3,4 }, { 1.7750e+01f, -7.1062e+01f, -1.0019e+02f,-2.3406e+01f, 5.2094e+01f, 9.5438e+01f, -6.7461e+00f, 3.8562e+01f, 6.5078e+00f,3.3562e+01f, -5.8844e+01f, 2.2750e+01f, -1.0477e+01f, 7.7344e+00f, 9.5469e+00f,2.1391e+01f, -8.5312e+01f, 7.5830e-01f,2.3125e+01f, 1.8145e+00f, 1.4602e+01f,-4.5859e+00f, 3.9344e+01f, 1.1617e+01f,-8.6562e+01f, 1.0038e+02f, 6.7938e+01f,5.9961e+00f, 6.7812e+01f, 2.9734e+01f,2.9609e+01f, -6.1438e+01f, 1.7750e+01f,6.8562e+01f, -7.4414e+00f, 3.9656e+01f,1.1641e+01f, -2.7516e+01f, 6.7562e+01f,7.8438e+01f, 5.4883e+00f, 2.9438e+01f,-3.1344e+01f, 6.5125e+01f, 1.2695e+01f,4.0531e+01f, -6.1211e+00f, 6.2219e+01f,4.6812e+01f, 5.2250e+01f, -1.1414e+01f,1.5404e-02f, 2.9938e+01f, 5.6719e+00f,-2.0125e+01f, 2.1531e+01f, 6.2500e+01f,7.2188e+01f, 9.3750e+00f, -4.8125e+01f }, sd::DataType::FLOAT32); + NDArray yuv('c', { 5,3,4 }, { 36.628319, 38.600643,-40.624989, 18.231001, -14.822637, -2.479566, -8.965780, 2.223851, -16.561626,-96.205162,-52.255379,-36.527435,-51.546139,2.234915, 20.914114, 8.785358, 32.552223, -3.356598, 9.069552, 1.393482,36.029255, 4.824605,-9.972263,11.058715, 15.947105, 55.283543, 36.845627, -29.750486,0.887228, 6.534475, -21.794132,34.155693, -89.929497,39.562351, 27.276817,31.359871, 8.149521, 13.673355, 1.104303, 68.774300, 2.236881, 13.216944, -3.555702,-3.225931,3.063015, -36.134724,58.302204, 8.477802, 38.695396,27.181587, -14.157411,7.157054, 11.714512, 22.148155, 11.580557, -27.204905,7.120562, 21.992094, 2.406748, -6.265247, }, sd::DataType::FLOAT32); - nd4j::ops::yuv_to_rgb op; + sd::ops::yuv_to_rgb op; auto result = op.evaluate({ &yuv }, {}, { 1 }); - auto output = result->at(0); + auto output = result.at(0); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests15, test_yuv_to_rgb_6) { // rank 3 - NDArray yuv('c', { 3,5,4 }, { 1.7750e+01f, -7.1062e+01f, -1.0019e+02f,-2.3406e+01f, 5.2094e+01f, 9.5438e+01f, -6.7461e+00f, 3.8562e+01f, 6.5078e+00f,3.3562e+01f, -5.8844e+01f, 2.2750e+01f, -1.0477e+01f, 7.7344e+00f, 9.5469e+00f,2.1391e+01f, -8.5312e+01f, 7.5830e-01f,2.3125e+01f, 1.8145e+00f, 1.4602e+01f,-4.5859e+00f, 3.9344e+01f, 1.1617e+01f,-8.6562e+01f, 1.0038e+02f, 6.7938e+01f,5.9961e+00f, 6.7812e+01f, 2.9734e+01f,2.9609e+01f, -6.1438e+01f, 1.7750e+01f,6.8562e+01f, -7.4414e+00f, 3.9656e+01f,1.1641e+01f, -2.7516e+01f, 6.7562e+01f,7.8438e+01f, 5.4883e+00f, 2.9438e+01f,-3.1344e+01f, 6.5125e+01f, 1.2695e+01f,4.0531e+01f, -6.1211e+00f, 6.2219e+01f,4.6812e+01f, 5.2250e+01f, -1.1414e+01f,1.5404e-02f, 2.9938e+01f, 5.6719e+00f,-2.0125e+01f, 2.1531e+01f, 6.2500e+01f,7.2188e+01f, 9.3750e+00f, -4.8125e+01f }, nd4j::DataType::FLOAT32); + NDArray yuv('c', { 3,5,4 }, { 1.7750e+01f, -7.1062e+01f, -1.0019e+02f,-2.3406e+01f, 5.2094e+01f, 9.5438e+01f, -6.7461e+00f, 3.8562e+01f, 6.5078e+00f,3.3562e+01f, -5.8844e+01f, 2.2750e+01f, -1.0477e+01f, 7.7344e+00f, 9.5469e+00f,2.1391e+01f, -8.5312e+01f, 7.5830e-01f,2.3125e+01f, 1.8145e+00f, 1.4602e+01f,-4.5859e+00f, 3.9344e+01f, 1.1617e+01f,-8.6562e+01f, 1.0038e+02f, 6.7938e+01f,5.9961e+00f, 6.7812e+01f, 2.9734e+01f,2.9609e+01f, -6.1438e+01f, 1.7750e+01f,6.8562e+01f, -7.4414e+00f, 3.9656e+01f,1.1641e+01f, -2.7516e+01f, 6.7562e+01f,7.8438e+01f, 5.4883e+00f, 2.9438e+01f,-3.1344e+01f, 6.5125e+01f, 1.2695e+01f,4.0531e+01f, -6.1211e+00f, 6.2219e+01f,4.6812e+01f, 5.2250e+01f, -1.1414e+01f,1.5404e-02f, 2.9938e+01f, 5.6719e+00f,-2.0125e+01f, 2.1531e+01f, 6.2500e+01f,7.2188e+01f, 9.3750e+00f, -4.8125e+01f }, sd::DataType::FLOAT32); try { - nd4j::ops::yuv_to_rgb op; + sd::ops::yuv_to_rgb op; auto result = op.evaluate({ &yuv }, {}, {}); - ASSERT_EQ(Status::THROW(), result->status()); - delete result; + ASSERT_EQ(Status::THROW(), result.status()); + } catch (std::exception & e) { nd4j_printf("Error should be here `%s'. It's OK.\n", e.what()); @@ -1226,18 +1189,16 @@ TEST_F(DeclarableOpsTests15, test_yuv_to_rgb_6) { //////////////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests15, test_yuv_to_rgb_7) { // rank 3 - NDArray expected('f', { 2, 2, 3 }, { 1.7750e+01f,-7.1062e+01f, -1.0019e+02f, -2.3406e+01f,5.2094e+01f,9.5438e+01f, -6.7461e+00f,3.8562e+01f, 6.5078e+00f, 3.3562e+01f,-5.8844e+01f,2.2750e+01f }, nd4j::DataType::FLOAT32); - NDArray yuv('f', { 2,2,3 }, { 36.628319, 38.600643, -40.624989, 18.231001, -14.822637, -2.479566, -8.965780, 2.223851, -16.561626, -96.205162, -52.255379, -36.527435 }, nd4j::DataType::FLOAT32); + NDArray expected('f', { 2, 2, 3 }, { 1.7750e+01f,-7.1062e+01f, -1.0019e+02f, -2.3406e+01f,5.2094e+01f,9.5438e+01f, -6.7461e+00f,3.8562e+01f, 6.5078e+00f, 3.3562e+01f,-5.8844e+01f,2.2750e+01f }, sd::DataType::FLOAT32); + NDArray yuv('f', { 2,2,3 }, { 36.628319, 38.600643, -40.624989, 18.231001, -14.822637, -2.479566, -8.965780, 2.223851, -16.561626, -96.205162, -52.255379, -36.527435 }, sd::DataType::FLOAT32); - nd4j::ops::yuv_to_rgb op; + sd::ops::yuv_to_rgb op; auto result = op.evaluate({ &yuv }, {}, {}); - auto output = result->at(0); + auto output = result.at(0); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - - delete result; } //////////////////////////////////////////////////////////////////////////////////////// @@ -1245,131 +1206,123 @@ TEST_F(DeclarableOpsTests15, test_yuv_to_rgb_7) { TEST_F(DeclarableOpsTests15, Pow_BP_Test1) { // same shape - NDArray x('c', { 2,2,2 }, { 4,3,2,5,7,8,-9,-12 }, nd4j::DataType::FLOAT32); - NDArray y('c', { 2,2,2 }, { 2,3,-2,4,-1,-4,10,8 }, nd4j::DataType::FLOAT32); + NDArray x('c', { 2,2,2 }, { 4,3,2,5,7,8,-9,-12 }, sd::DataType::FLOAT32); + NDArray y('c', { 2,2,2 }, { 2,3,-2,4,-1,-4,10,8 }, sd::DataType::FLOAT32); - NDArray dLdz('c', { 2,2,2 }, nd4j::DataType::FLOAT32); - NDArray dLdxExp('c', { 2,2,2 }, { 8, 27, -0.25, 500, -0.0204082, -0.000122, -3.87420e+09, -2.86654e+08 }, nd4j::DataType::FLOAT32); - NDArray dLdyExp('c', { 2,2,2 }, { 22.18071, 29.66253, 0.17329, 1005.89874, 0.27799, 0.00051, 0, 0 }, nd4j::DataType::FLOAT32); + NDArray dLdz('c', { 2,2,2 }, sd::DataType::FLOAT32); + NDArray dLdxExp('c', { 2,2,2 }, { 8, 27, -0.25, 500, -0.0204082, -0.000122, -3.87420e+09, -2.86654e+08 }, sd::DataType::FLOAT32); + NDArray dLdyExp('c', { 2,2,2 }, { 22.18071, 29.66253, 0.17329, 1005.89874, 0.27799, 0.00051, 0, 0 }, sd::DataType::FLOAT32); dLdz.assign(1.0); - nd4j::ops::Pow_bp op; + sd::ops::Pow_bp op; auto results = op.evaluate({ &x, &y, &dLdz }, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto* dLdx = results->at(0); - auto* dLdy = results->at(1); + auto* dLdx = results.at(0); + auto* dLdy = results.at(1); ASSERT_TRUE(dLdxExp.isSameShape(dLdx)); ASSERT_TRUE(dLdxExp.equalsTo(dLdx)); ASSERT_TRUE(dLdyExp.isSameShape(dLdy)); ASSERT_TRUE(dLdyExp.equalsTo(dLdy)); - - delete results; } TEST_F(DeclarableOpsTests15, Pow_BP_Test2) { - NDArray x('c', { 1,2,3 }, nd4j::DataType::FLOAT32); - NDArray y('c', { 3,2,1 }, nd4j::DataType::FLOAT32); - NDArray dLdz('c', { 3,2,3 }, nd4j::DataType::FLOAT32); + NDArray x('c', { 1,2,3 }, sd::DataType::FLOAT32); + NDArray y('c', { 3,2,1 }, sd::DataType::FLOAT32); + NDArray dLdz('c', { 3,2,3 }, sd::DataType::FLOAT32); - NDArray dLdxExp('c', { 1,2,3 }, { 16.8, 19.2, 21.6, 24., 26.4, 28.8 }, nd4j::DataType::FLOAT32); - NDArray dLdyExp('c', { 3,2,1 }, { 13.30843, 33.27106, 53.2337, 73.19634, 93.15898, 113.12162 }, nd4j::DataType::FLOAT32); + NDArray dLdxExp('c', { 1,2,3 }, { 16.8, 19.2, 21.6, 24., 26.4, 28.8 }, sd::DataType::FLOAT32); + NDArray dLdyExp('c', { 3,2,1 }, { 13.30843, 33.27106, 53.2337, 73.19634, 93.15898, 113.12162 }, sd::DataType::FLOAT32); x.assign(4.0); y.assign(2.0); dLdz.linspace(0.1, 0.1); - nd4j::ops::Pow_bp op; + sd::ops::Pow_bp op; auto results = op.evaluate({ &x, &y, &dLdz }, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto* dLdx = results->at(0); - auto* dLdy = results->at(1); + auto* dLdx = results.at(0); + auto* dLdy = results.at(1); ASSERT_TRUE(dLdxExp.isSameShape(dLdx)); ASSERT_TRUE(dLdxExp.equalsTo(dLdx)); ASSERT_TRUE(dLdyExp.isSameShape(dLdy)); ASSERT_TRUE(dLdyExp.equalsTo(dLdy)); - delete results; - } TEST_F(DeclarableOpsTests15, Pow_BP_Test3) { // y - same shape as dLdz - NDArray xY('c', { 1,2,3 }, nd4j::DataType::FLOAT32); - NDArray yY('c', { 3,2,3 }, nd4j::DataType::FLOAT32); + NDArray xY('c', { 1,2,3 }, sd::DataType::FLOAT32); + NDArray yY('c', { 3,2,3 }, sd::DataType::FLOAT32); - NDArray dLdxExpY('c', { 1,2,3 }, { 16.8, 19.2, 21.6, 24. , 26.4, 28.8 }, nd4j::DataType::FLOAT32); - NDArray dLdyExpY('c', { 3,2,3 }, { 2.21807, 4.43614, 6.65421, 8.87228, 11.09035, 13.30843, 15.5265 , 17.74457, 19.96264, 22.18071, 24.39878, 26.61685, 28.83492, 31.05299, 33.27106, 35.48914, 37.70721, 39.92528 }, nd4j::DataType::FLOAT32); - NDArray dLdz('c', { 3,2,3 }, nd4j::DataType::FLOAT32); + NDArray dLdxExpY('c', { 1,2,3 }, { 16.8, 19.2, 21.6, 24. , 26.4, 28.8 }, sd::DataType::FLOAT32); + NDArray dLdyExpY('c', { 3,2,3 }, { 2.21807, 4.43614, 6.65421, 8.87228, 11.09035, 13.30843, 15.5265 , 17.74457, 19.96264, 22.18071, 24.39878, 26.61685, 28.83492, 31.05299, 33.27106, 35.48914, 37.70721, 39.92528 }, sd::DataType::FLOAT32); + NDArray dLdz('c', { 3,2,3 }, sd::DataType::FLOAT32); xY.assign(4.0); yY.assign(2.0); dLdz.linspace(0.1, 0.1); - nd4j::ops::Pow_bp op; + sd::ops::Pow_bp op; auto resultsY = op.evaluate({ &xY, &yY, &dLdz }, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, resultsY->status()); + ASSERT_EQ(ND4J_STATUS_OK, resultsY.status()); - auto* dLdxY = resultsY->at(0); - auto* dLdyY = resultsY->at(1); + auto* dLdxY = resultsY.at(0); + auto* dLdyY = resultsY.at(1); ASSERT_TRUE(dLdxExpY.isSameShape(dLdxY)); ASSERT_TRUE(dLdxExpY.equalsTo(dLdxY)); ASSERT_TRUE(dLdyExpY.isSameShape(dLdyY)); ASSERT_TRUE(dLdyExpY.equalsTo(dLdyY)); - - delete resultsY; } TEST_F(DeclarableOpsTests15, Pow_BP_Test4) { // x - same shape ad dLdz - NDArray yX('c', { 1,2,3 }, nd4j::DataType::FLOAT32); - NDArray xX('c', { 3,2,3 }, nd4j::DataType::FLOAT32); + NDArray yX('c', { 1,2,3 }, sd::DataType::FLOAT32); + NDArray xX('c', { 3,2,3 }, sd::DataType::FLOAT32); - NDArray dLdxExpX('c', { 3,2,3 }, { 3.2, 6.4, 9.6, 12.8, 16. , 19.2, 22.4, 25.6, 28.8, 32. , 35.2, 38.4, 41.6, 44.8, 48., 51.2, 54.4, 57.6 }, nd4j::DataType::FLOAT32); - NDArray dLdyExpX('c', { 1,2,3 }, { 23.28975, 26.61685, 29.94396, 33.27106, 36.59817, 39.92528 }, nd4j::DataType::FLOAT32); + NDArray dLdxExpX('c', { 3,2,3 }, { 3.2, 6.4, 9.6, 12.8, 16. , 19.2, 22.4, 25.6, 28.8, 32. , 35.2, 38.4, 41.6, 44.8, 48., 51.2, 54.4, 57.6 }, sd::DataType::FLOAT32); + NDArray dLdyExpX('c', { 1,2,3 }, { 23.28975, 26.61685, 29.94396, 33.27106, 36.59817, 39.92528 }, sd::DataType::FLOAT32); - NDArray dLdz('c', { 3,2,3 }, nd4j::DataType::FLOAT32); + NDArray dLdz('c', { 3,2,3 }, sd::DataType::FLOAT32); dLdz.linspace(0.1, 0.1); - nd4j::ops::Pow_bp op; + sd::ops::Pow_bp op; xX.assign(2.0); yX.assign(4.0); auto resultsX = op.evaluate({ &xX, &yX, &dLdz }, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, resultsX->status()); + ASSERT_EQ(ND4J_STATUS_OK, resultsX.status()); - auto* dLdxX = resultsX->at(0); - auto* dLdyX = resultsX->at(1); + auto* dLdxX = resultsX.at(0); + auto* dLdyX = resultsX.at(1); ASSERT_TRUE(dLdxExpX.isSameShape(dLdxX)); ASSERT_TRUE(dLdxExpX.equalsTo(dLdxX)); ASSERT_TRUE(dLdyExpX.isSameShape(dLdyX)); ASSERT_TRUE(dLdyExpX.equalsTo(dLdyX)); - - delete resultsX; } TEST_F(DeclarableOpsTests15, Pow_BP_Test5) { // both single array - NDArray xConst('c', { 1 }, nd4j::DataType::FLOAT32); - NDArray yConst('c', { 1 }, nd4j::DataType::FLOAT32); - NDArray dLdz('c', { 1 }, nd4j::DataType::FLOAT32); - NDArray dLdxExp('c', { 1 }, nd4j::DataType::FLOAT32); - NDArray dLdyExp('c', { 1 }, nd4j::DataType::FLOAT32); + NDArray xConst('c', { 1 }, sd::DataType::FLOAT32); + NDArray yConst('c', { 1 }, sd::DataType::FLOAT32); + NDArray dLdz('c', { 1 }, sd::DataType::FLOAT32); + NDArray dLdxExp('c', { 1 }, sd::DataType::FLOAT32); + NDArray dLdyExp('c', { 1 }, sd::DataType::FLOAT32); xConst.assign(3.0); yConst.assign(4.0); @@ -1378,78 +1331,73 @@ TEST_F(DeclarableOpsTests15, Pow_BP_Test5) { dLdxExp.assign(4.0 * pow(3, 3)); dLdyExp.assign(pow(3, 4) * log(3)); - nd4j::ops::Pow_bp op; + sd::ops::Pow_bp op; auto results = op.evaluate({ &xConst, &yConst, &dLdz }, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto* dLdx = results->at(0); - auto* dLdy = results->at(1); + auto* dLdx = results.at(0); + auto* dLdy = results.at(1); ASSERT_TRUE(dLdxExp.isSameShape(dLdx)); ASSERT_TRUE(dLdxExp.equalsTo(dLdx)); ASSERT_TRUE(dLdyExp.isSameShape(dLdy)); ASSERT_TRUE(dLdyExp.equalsTo(dLdy)); - - delete results; } TEST_F(DeclarableOpsTests15, Pow_BP_Test6) { // x single array - NDArray xConst('c', { 1 }, nd4j::DataType::FLOAT32); - NDArray y('c', { 2, 2, 2 }, nd4j::DataType::FLOAT32); - NDArray dLdzC('c', { 2, 2, 2 }, nd4j::DataType::FLOAT32); + NDArray xConst('c', { 1 }, sd::DataType::FLOAT32); + NDArray y('c', { 2, 2, 2 }, sd::DataType::FLOAT32); + NDArray dLdzC('c', { 2, 2, 2 }, sd::DataType::FLOAT32); xConst.assign(2.0); y.assign(4.0); dLdzC.linspace(0.1, 0.1); - NDArray dLdxExpXC('c', { 1 }, std::vector{ 115.2 }, nd4j::DataType::FLOAT32); - NDArray dLdyExpXC('c', { 2, 2, 2 }, { 1.10904, 2.21807, 3.32711, 4.43614, 5.54518, 6.65421, 7.76325, 8.87228 }, nd4j::DataType::FLOAT32); + NDArray dLdxExpXC('c', { 1 }, std::vector{ 115.2 }, sd::DataType::FLOAT32); + NDArray dLdyExpXC('c', { 2, 2, 2 }, { 1.10904, 2.21807, 3.32711, 4.43614, 5.54518, 6.65421, 7.76325, 8.87228 }, sd::DataType::FLOAT32); - nd4j::ops::Pow_bp op; + sd::ops::Pow_bp op; auto resultsXC = op.evaluate({ &xConst, &y, &dLdzC }, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, resultsXC->status()); + ASSERT_EQ(ND4J_STATUS_OK, resultsXC.status()); - auto* dLdxXC = resultsXC->at(0); - auto* dLdyXC = resultsXC->at(1); + auto* dLdxXC = resultsXC.at(0); + auto* dLdyXC = resultsXC.at(1); ASSERT_TRUE(dLdxExpXC.isSameShape(dLdxXC)); ASSERT_TRUE(dLdxExpXC.equalsTo(dLdxXC)); ASSERT_TRUE(dLdyExpXC.isSameShape(dLdyXC)); ASSERT_TRUE(dLdyExpXC.equalsTo(dLdyXC)); - delete resultsXC; } TEST_F(DeclarableOpsTests15, Pow_BP_Test7) { // Y - scalar auto Y = NDArrayFactory::create(2.f); - NDArray x('c', { 2, 2, 2 }, nd4j::DataType::FLOAT32); - NDArray dLdzC('c', { 2, 2, 2 }, nd4j::DataType::FLOAT32); + NDArray x('c', { 2, 2, 2 }, sd::DataType::FLOAT32); + NDArray dLdzC('c', { 2, 2, 2 }, sd::DataType::FLOAT32); dLdzC.linspace(0.1, 0.1); x = 4.f; - NDArray dLdxExpYs('c', { 2, 2, 2 }, { 0.8, 1.6, 2.4, 3.2, 4., 4.8, 5.6, 6.4 }, nd4j::DataType::FLOAT32); + NDArray dLdxExpYs('c', { 2, 2, 2 }, { 0.8, 1.6, 2.4, 3.2, 4., 4.8, 5.6, 6.4 }, sd::DataType::FLOAT32); auto dLdyExpYs = NDArrayFactory::create(79.85056f); - nd4j::ops::Pow_bp op; + sd::ops::Pow_bp op; auto resultsYs = op.evaluate({ &x, &Y, &dLdzC }, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, resultsYs->status()); + ASSERT_EQ(ND4J_STATUS_OK, resultsYs.status()); - auto* dLdxY = resultsYs->at(0); - auto* dLdyY = resultsYs->at(1); + auto* dLdxY = resultsYs.at(0); + auto* dLdyY = resultsYs.at(1); ASSERT_TRUE(dLdxExpYs.isSameShape(dLdxY)); ASSERT_TRUE(dLdxExpYs.equalsTo(dLdxY)); ASSERT_TRUE(dLdyExpYs.isSameShape(dLdyY)); ASSERT_TRUE(dLdyExpYs.equalsTo(dLdyY)); - - delete resultsYs; } TEST_F(DeclarableOpsTests15, Pow_BP_Test8) { @@ -1463,80 +1411,75 @@ TEST_F(DeclarableOpsTests15, Pow_BP_Test8) { NDArray dLdyExp = NDArrayFactory::create(pow(4.f, 2.f) * log(4.f) * 0.1f); - nd4j::ops::Pow_bp op; + sd::ops::Pow_bp op; auto results = op.evaluate({ &X, &Y, &dLdz }, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto* dLdx = results->at(0); - auto* dLdy = results->at(1); + auto* dLdx = results.at(0); + auto* dLdy = results.at(1); ASSERT_TRUE(dLdxExp.isSameShape(dLdx)); ASSERT_TRUE(dLdxExp.equalsTo(dLdx)); ASSERT_TRUE(dLdyExp.isSameShape(dLdy)); ASSERT_TRUE(dLdyExp.equalsTo(dLdy)); - - delete results; + } TEST_F(DeclarableOpsTests15, Pow_BP_Test9) { - nd4j::ops::Pow_bp op; + sd::ops::Pow_bp op; // diff shapes - NDArray x('c', { 3,2,1 }, nd4j::DataType::FLOAT32); - NDArray y('c', { 1,2,3 }, nd4j::DataType::FLOAT32); - NDArray dLdz('c', { 3,2,3 }, nd4j::DataType::FLOAT32); + NDArray x('c', { 3,2,1 }, sd::DataType::FLOAT32); + NDArray y('c', { 1,2,3 }, sd::DataType::FLOAT32); + NDArray dLdz('c', { 3,2,3 }, sd::DataType::FLOAT32); - NDArray dLdxExp('c', { 3,2,1 }, { 4.8, 12., 19.2, 26.4, 33.6, 40.8 }, nd4j::DataType::FLOAT32); - NDArray dLdyExp('c', { 1,2,3 }, { 46.57949, 53.2337 , 59.88792, 66.54213, 73.19634, 79.85056 }, nd4j::DataType::FLOAT32); + NDArray dLdxExp('c', { 3,2,1 }, { 4.8, 12., 19.2, 26.4, 33.6, 40.8 }, sd::DataType::FLOAT32); + NDArray dLdyExp('c', { 1,2,3 }, { 46.57949, 53.2337 , 59.88792, 66.54213, 73.19634, 79.85056 }, sd::DataType::FLOAT32); x.assign(4.0); y.assign(2.0); dLdz.linspace(0.1, 0.1); auto results = op.evaluate({ &x, &y, &dLdz }, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto* dLdx = results->at(0); - auto* dLdy = results->at(1); + auto* dLdx = results.at(0); + auto* dLdy = results.at(1); ASSERT_TRUE(dLdxExp.isSameShape(dLdx)); ASSERT_TRUE(dLdxExp.equalsTo(dLdx)); ASSERT_TRUE(dLdyExp.isSameShape(dLdy)); ASSERT_TRUE(dLdyExp.equalsTo(dLdy)); - - delete results; } TEST_F(DeclarableOpsTests15, Pow_BP_Test10) { // diff shapes broadcastable - NDArray yB('c', { 1,2,3,1 }, nd4j::DataType::FLOAT32); - NDArray xB('c', { 2,3,1 }, nd4j::DataType::FLOAT32); + NDArray yB('c', { 1,2,3,1 }, sd::DataType::FLOAT32); + NDArray xB('c', { 2,3,1 }, sd::DataType::FLOAT32); - NDArray dLdyExpB('c', { 1,2,3,1 }, { 2.21807, 4.43614, 6.65421, 8.87228, 11.09035, 13.30843 }, nd4j::DataType::FLOAT32); - NDArray dLdxExpB('c', { 2,3,1 }, { 0.8, 1.6, 2.4, 3.2, 4., 4.8 }, nd4j::DataType::FLOAT32); - NDArray dLdzB('c', { 1,2,3,1 }, nd4j::DataType::FLOAT32); + NDArray dLdyExpB('c', { 1,2,3,1 }, { 2.21807, 4.43614, 6.65421, 8.87228, 11.09035, 13.30843 }, sd::DataType::FLOAT32); + NDArray dLdxExpB('c', { 2,3,1 }, { 0.8, 1.6, 2.4, 3.2, 4., 4.8 }, sd::DataType::FLOAT32); + NDArray dLdzB('c', { 1,2,3,1 }, sd::DataType::FLOAT32); dLdzB.linspace(0.1, 0.1); xB.assign(4.0); yB.assign(2.0); - nd4j::ops::Pow_bp op; + sd::ops::Pow_bp op; auto resultsB = op.evaluate({ &xB, &yB, &dLdzB }, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, resultsB->status()); + ASSERT_EQ(ND4J_STATUS_OK, resultsB.status()); - auto* dLdxB = resultsB->at(0); - auto* dLdyB = resultsB->at(1); + auto* dLdxB = resultsB.at(0); + auto* dLdyB = resultsB.at(1); ASSERT_TRUE(dLdxExpB.isSameShape(dLdxB)); ASSERT_TRUE(dLdxExpB.equalsTo(dLdxB)); ASSERT_TRUE(dLdyExpB.isSameShape(dLdyB)); ASSERT_TRUE(dLdyExpB.equalsTo(dLdyB)); - - delete resultsB; } TEST_F(DeclarableOpsTests15, Pow_BP_Test11) { @@ -1545,53 +1488,53 @@ TEST_F(DeclarableOpsTests15, Pow_BP_Test11) { return; #endif - NDArray xB('c', { 3,2,1 }, { .4, 3, 5, .8, -9, -12 }, nd4j::DataType::FLOAT32); - NDArray yB('c', { 1,2,3 }, { 3, -2, .4, -4, 10, .8 }, nd4j::DataType::FLOAT32); + NDArray xB('c', { 3,2,1 }, { .4, 3, 5, .8, -9, -12 }, sd::DataType::FLOAT32); + NDArray yB('c', { 1,2,3 }, { 3, -2, .4, -4, 10, .8 }, sd::DataType::FLOAT32); - NDArray dLdxExpB('c', { 3,2,1 }, { -5.994056, 39366.191406, 7.508829, -2.223537, -std::numeric_limits::quiet_NaN(), -std::numeric_limits::quiet_NaN() }, nd4j::DataType::FLOAT32); - NDArray dLdyExpB('c', { 1,2,3 }, { 20.11211, -1.119612, -std::numeric_limits::quiet_NaN(), -0.1076, 12974.389648, -std::numeric_limits::quiet_NaN() }, nd4j::DataType::FLOAT32); + NDArray dLdxExpB('c', { 3,2,1 }, { -5.994056, 39366.191406, 7.508829, -2.223537, -std::numeric_limits::quiet_NaN(), -std::numeric_limits::quiet_NaN() }, sd::DataType::FLOAT32); + NDArray dLdyExpB('c', { 1,2,3 }, { 20.11211, -1.119612, -std::numeric_limits::quiet_NaN(), -0.1076, 12974.389648, -std::numeric_limits::quiet_NaN() }, sd::DataType::FLOAT32); - NDArray dLdzB('c', { 3,2,3 }, { .1,.2,.3, .1,.2,.3, .1,.4,.1, .2,.1,.1, .3,.1,.5, .1, .7, .1 }, nd4j::DataType::FLOAT32); + NDArray dLdzB('c', { 3,2,3 }, { .1,.2,.3, .1,.2,.3, .1,.4,.1, .2,.1,.1, .3,.1,.5, .1, .7, .1 }, sd::DataType::FLOAT32); - nd4j::ops::Pow_bp op; + sd::ops::Pow_bp op; auto resultsB = op.evaluate({ &xB, &yB, &dLdzB }, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, resultsB->status()); - auto* dLdxB = resultsB->at(0); - auto* dLdyB = resultsB->at(1); + ASSERT_EQ(ND4J_STATUS_OK, resultsB.status()); + auto* dLdxB = resultsB.at(0); + auto* dLdyB = resultsB.at(1); ASSERT_TRUE(dLdxExpB.isSameShape(dLdxB)); for (int i = 0; i < dLdxB->lengthOf(); ++i) { - if (!nd4j::math::nd4j_isnan(dLdxB->e(i)) && !nd4j::math::nd4j_isnan(dLdxExpB.e(i))) + if (!sd::math::nd4j_isnan(dLdxB->e(i)) && !sd::math::nd4j_isnan(dLdxExpB.e(i))) ASSERT_NEAR(dLdxB->e(i), dLdxExpB.e(i), 0.00001); } ASSERT_TRUE(dLdyExpB.isSameShape(dLdyB)); for (int i = 0; i < dLdyB->lengthOf(); ++i) { - if (!nd4j::math::nd4j_isnan(dLdyB->e(i)) && !nd4j::math::nd4j_isnan(dLdyExpB.e(i))) + if (!sd::math::nd4j_isnan(dLdyB->e(i)) && !sd::math::nd4j_isnan(dLdyExpB.e(i))) ASSERT_NEAR(dLdyB->e(i), dLdyExpB.e(i), 0.00001); } - delete resultsB; + } ////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests15, TestTensorMmul_BP1) { - NDArray A('c', { 1, 2, 3 }, { 2.1, 2.2, 2.3, 2.4, 2.5, 2.6 }, nd4j::DataType::FLOAT32); - NDArray B('c', { 1, 2, 4 }, { 3.1, 3.2, 3.3, 3.4, 3.5, 3.6, 3.7, 3.8 }, nd4j::DataType::FLOAT32); - NDArray dLdC('c', { 3, 4 }, { .1, .2, .3, .4, .5, .6, .7, .8, .9, 1, 1.1, 1.1 }, nd4j::DataType::FLOAT32); + NDArray A('c', { 1, 2, 3 }, { 2.1, 2.2, 2.3, 2.4, 2.5, 2.6 }, sd::DataType::FLOAT32); + NDArray B('c', { 1, 2, 4 }, { 3.1, 3.2, 3.3, 3.4, 3.5, 3.6, 3.7, 3.8 }, sd::DataType::FLOAT32); + NDArray dLdC('c', { 3, 4 }, { .1, .2, .3, .4, .5, .6, .7, .8, .9, 1, 1.1, 1.1 }, sd::DataType::FLOAT32); - NDArray dLdA('c', { 1, 2, 3 }, { 3.3, 8.5, 13.36, 3.7, 9.54, 15. }, nd4j::DataType::FLOAT32); - NDArray dLdB('c', { 1, 2, 4 }, { 3.38, 4.04, 4.7, 5.13, 3.83, 4.58, 5.33, 5.82 }, nd4j::DataType::FLOAT32); + NDArray dLdA('c', { 1, 2, 3 }, { 3.3, 8.5, 13.36, 3.7, 9.54, 15. }, sd::DataType::FLOAT32); + NDArray dLdB('c', { 1, 2, 4 }, { 3.38, 4.04, 4.7, 5.13, 3.83, 4.58, 5.33, 5.82 }, sd::DataType::FLOAT32); - nd4j::ops::tensormmul_bp op_bp; + sd::ops::tensormmul_bp op_bp; auto resultsBP = op_bp.evaluate({ &A, &B, &dLdC }, {}, { 2,0,1, 2,0,1 }, {}); - ASSERT_EQ(ND4J_STATUS_OK, resultsBP->status()); + ASSERT_EQ(ND4J_STATUS_OK, resultsBP.status()); - auto* dLdAbp = resultsBP->at(0); - auto* dLdBbp = resultsBP->at(1); + auto* dLdAbp = resultsBP.at(0); + auto* dLdBbp = resultsBP.at(1); ASSERT_TRUE(dLdA.isSameShape(*dLdAbp)); ASSERT_TRUE(dLdA.equalsTo(*dLdAbp)); @@ -1599,22 +1542,21 @@ TEST_F(DeclarableOpsTests15, TestTensorMmul_BP1) { ASSERT_TRUE(dLdB.isSameShape(*dLdBbp)); ASSERT_TRUE(dLdB.equalsTo(*dLdBbp)); - delete resultsBP; } ////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests15, TestTensorMmul_BP2) { - NDArray A('c', { 1, 2, 3 }, { 2,2,2, 2,2,2 }, nd4j::DataType::FLOAT32); - NDArray B('c', { 1, 2, 3 }, { 3,3,3,3, 3,3 }, nd4j::DataType::FLOAT32); - NDArray dLdC('c', { 1 }, { 1 }, nd4j::DataType::FLOAT32); + NDArray A('c', { 1, 2, 3 }, { 2,2,2, 2,2,2 }, sd::DataType::FLOAT32); + NDArray B('c', { 1, 2, 3 }, { 3,3,3,3, 3,3 }, sd::DataType::FLOAT32); + NDArray dLdC('c', { 1 }, { 1 }, sd::DataType::FLOAT32); - nd4j::ops::tensormmul_bp op_bp; + sd::ops::tensormmul_bp op_bp; auto resultsBP = op_bp.evaluate({ &A, &B, &dLdC }, {}, { 2,1,2, 2,1,2 }, {}); - ASSERT_EQ(ND4J_STATUS_OK, resultsBP->status()); + ASSERT_EQ(ND4J_STATUS_OK, resultsBP.status()); - auto* dLdAbp = resultsBP->at(0); - auto* dLdBbp = resultsBP->at(1); + auto* dLdAbp = resultsBP.at(0); + auto* dLdBbp = resultsBP.at(1); ASSERT_TRUE(B.isSameShape(*dLdAbp)); ASSERT_TRUE(B.equalsTo(*dLdAbp)); @@ -1622,26 +1564,25 @@ TEST_F(DeclarableOpsTests15, TestTensorMmul_BP2) { ASSERT_TRUE(A.isSameShape(*dLdBbp)); ASSERT_TRUE(A.equalsTo(*dLdBbp)); - delete resultsBP; } ////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests15, TestTensorMmul_BP3) { - NDArray A('c', { 3, 2, 2 }, { 2.1, 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8, 2.9, 3., 3.1, 3.2 }, nd4j::DataType::FLOAT32); - NDArray B('c', { 4, 2, 2 }, { 3.1, 3.2, 3.3, 3.4, 3.5, 3.6, 3.7, 3.8, 3.9, 4., 4.1, 4.2, 4.3, 4.4, 4.5, 4.6 }, nd4j::DataType::FLOAT32); - NDArray dLdC('c', { 3, 4 }, { .1, .2, .3, .4, .5, .6, .7, .8, .9, 1, 1.1, 1.2 }, nd4j::DataType::FLOAT32); + NDArray A('c', { 3, 2, 2 }, { 2.1, 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8, 2.9, 3., 3.1, 3.2 }, sd::DataType::FLOAT32); + NDArray B('c', { 4, 2, 2 }, { 3.1, 3.2, 3.3, 3.4, 3.5, 3.6, 3.7, 3.8, 3.9, 4., 4.1, 4.2, 4.3, 4.4, 4.5, 4.6 }, sd::DataType::FLOAT32); + NDArray dLdC('c', { 3, 4 }, { .1, .2, .3, .4, .5, .6, .7, .8, .9, 1, 1.1, 1.2 }, sd::DataType::FLOAT32); - NDArray dA('c', { 3, 2, 2 }, { 3.9, 4., 4.1, 4.2, 9.82, 10.08, 10.34, 10.6, 15.74, 16.16, 16.58, 17. }, nd4j::DataType::FLOAT32); - NDArray dB('c', { 4, 2, 2 }, { 4.07, 4.22, 4.37, 4.52, 4.82, 5., 5.18, 5.36, 5.57, 5.78, 5.99, 6.2, 6.32, 6.56, 6.8, 7.04 }, nd4j::DataType::FLOAT32); + NDArray dA('c', { 3, 2, 2 }, { 3.9, 4., 4.1, 4.2, 9.82, 10.08, 10.34, 10.6, 15.74, 16.16, 16.58, 17. }, sd::DataType::FLOAT32); + NDArray dB('c', { 4, 2, 2 }, { 4.07, 4.22, 4.37, 4.52, 4.82, 5., 5.18, 5.36, 5.57, 5.78, 5.99, 6.2, 6.32, 6.56, 6.8, 7.04 }, sd::DataType::FLOAT32); - nd4j::ops::tensormmul_bp op_bp; + sd::ops::tensormmul_bp op_bp; auto resultsBP = op_bp.evaluate({ &A, &B, &dLdC }, {}, { 2,1,2, 2,1,2 }, {}); - ASSERT_EQ(ND4J_STATUS_OK, resultsBP->status()); + ASSERT_EQ(ND4J_STATUS_OK, resultsBP.status()); - auto* dLdAbp = resultsBP->at(0); - auto* dLdBbp = resultsBP->at(1); + auto* dLdAbp = resultsBP.at(0); + auto* dLdBbp = resultsBP.at(1); ASSERT_TRUE(dA.isSameShape(*dLdAbp)); ASSERT_TRUE(dA.equalsTo(*dLdAbp)); @@ -1649,26 +1590,25 @@ TEST_F(DeclarableOpsTests15, TestTensorMmul_BP3) { ASSERT_TRUE(dB.isSameShape(*dLdBbp)); ASSERT_TRUE(dB.equalsTo(*dLdBbp)); - delete resultsBP; } ////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests15, TestTensorMmul_BP4) { - NDArray A('c', { 3, 4, 1 }, { 0.4, 3, 5, 9, 23, 0.12, 8, 9, 0.1, 0, 124, 3 }, nd4j::DataType::FLOAT32); - NDArray B('c', { 2, 4, 1 }, { 4, 13, .5, 19, 2.3, 1.2, 18, .9 }, nd4j::DataType::FLOAT32); - NDArray dLdC('c', { 3, 2 }, { 1.1, 1.2, 1.3, 1.4, 1.5, 1.6 }, nd4j::DataType::FLOAT32); + NDArray A('c', { 3, 4, 1 }, { 0.4, 3, 5, 9, 23, 0.12, 8, 9, 0.1, 0, 124, 3 }, sd::DataType::FLOAT32); + NDArray B('c', { 2, 4, 1 }, { 4, 13, .5, 19, 2.3, 1.2, 18, .9 }, sd::DataType::FLOAT32); + NDArray dLdC('c', { 3, 2 }, { 1.1, 1.2, 1.3, 1.4, 1.5, 1.6 }, sd::DataType::FLOAT32); - NDArray dLdA('c', { 3, 4, 1 }, { 7.16, 15.74, 22.15, 21.98, 8.42, 18.58, 25.85, 25.96, 9.68, 21.42, 29.55, 29.94 }, nd4j::DataType::FLOAT32); - NDArray dLdB('c', { 2, 4, 1 }, { 30.49, 3.456, 201.9, 26.1, 32.84 , 3.768, 215.6, 28.2 }, nd4j::DataType::FLOAT32); + NDArray dLdA('c', { 3, 4, 1 }, { 7.16, 15.74, 22.15, 21.98, 8.42, 18.58, 25.85, 25.96, 9.68, 21.42, 29.55, 29.94 }, sd::DataType::FLOAT32); + NDArray dLdB('c', { 2, 4, 1 }, { 30.49, 3.456, 201.9, 26.1, 32.84 , 3.768, 215.6, 28.2 }, sd::DataType::FLOAT32); - nd4j::ops::tensormmul_bp op_bp; + sd::ops::tensormmul_bp op_bp; auto resultsBP = op_bp.evaluate({ &A, &B, &dLdC }, {}, { 2,1,2, 2,1,2 }, {}); - ASSERT_EQ(ND4J_STATUS_OK, resultsBP->status()); + ASSERT_EQ(ND4J_STATUS_OK, resultsBP.status()); - auto* dLdAbp = resultsBP->at(0); - auto* dLdBbp = resultsBP->at(1); + auto* dLdAbp = resultsBP.at(0); + auto* dLdBbp = resultsBP.at(1); ASSERT_TRUE(dLdA.isSameShape(*dLdAbp)); ASSERT_TRUE(dLdA.equalsTo(*dLdAbp)); @@ -1676,26 +1616,25 @@ TEST_F(DeclarableOpsTests15, TestTensorMmul_BP4) { ASSERT_TRUE(dLdB.isSameShape(*dLdBbp)); ASSERT_TRUE(dLdB.equalsTo(*dLdBbp)); - delete resultsBP; } ////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests15, TestTensorMmul_BP5) { - NDArray A('c', { 3, 4, 1, 1 }, { 0.4, 3, 5, 9, 23, 0.12, 8, 9, 0.1, 0, 124, 3 }, nd4j::DataType::FLOAT32); - NDArray B('c', { 2, 4, 1, 1 }, { 4, 13, .5, 19, 2.3, 1.2, 18, .9 }, nd4j::DataType::FLOAT32); - NDArray dLdC('c', { 3, 1, 2, 1 }, { 1.1,1.2,1.3,1.4,1.5,1.6 }, nd4j::DataType::FLOAT32); + NDArray A('c', { 3, 4, 1, 1 }, { 0.4, 3, 5, 9, 23, 0.12, 8, 9, 0.1, 0, 124, 3 }, sd::DataType::FLOAT32); + NDArray B('c', { 2, 4, 1, 1 }, { 4, 13, .5, 19, 2.3, 1.2, 18, .9 }, sd::DataType::FLOAT32); + NDArray dLdC('c', { 3, 1, 2, 1 }, { 1.1,1.2,1.3,1.4,1.5,1.6 }, sd::DataType::FLOAT32); - NDArray dLdA('c', { 3, 4, 1, 1 }, { 7.16, 15.74, 22.15, 21.98, 8.42, 18.58, 25.85, 25.96, 9.68, 21.42, 29.55, 29.94 }, nd4j::DataType::FLOAT32); - NDArray dLdB('c', { 2, 4, 1, 1 }, { 30.49, 3.456, 201.9, 26.1, 32.84, 3.768, 215.6, 28.2 }, nd4j::DataType::FLOAT32); + NDArray dLdA('c', { 3, 4, 1, 1 }, { 7.16, 15.74, 22.15, 21.98, 8.42, 18.58, 25.85, 25.96, 9.68, 21.42, 29.55, 29.94 }, sd::DataType::FLOAT32); + NDArray dLdB('c', { 2, 4, 1, 1 }, { 30.49, 3.456, 201.9, 26.1, 32.84, 3.768, 215.6, 28.2 }, sd::DataType::FLOAT32); - nd4j::ops::tensormmul_bp op_bp; + sd::ops::tensormmul_bp op_bp; auto resultsBP = op_bp.evaluate({ &A, &B, &dLdC }, {}, { 2,1,2, 2,1,2 }, {}); - ASSERT_EQ(ND4J_STATUS_OK, resultsBP->status()); + ASSERT_EQ(ND4J_STATUS_OK, resultsBP.status()); - auto* dLdAbp = resultsBP->at(0); - auto* dLdBbp = resultsBP->at(1); + auto* dLdAbp = resultsBP.at(0); + auto* dLdBbp = resultsBP.at(1); ASSERT_TRUE(dLdA.isSameShape(*dLdAbp)); ASSERT_TRUE(dLdA.equalsTo(*dLdAbp)); @@ -1703,23 +1642,22 @@ TEST_F(DeclarableOpsTests15, TestTensorMmul_BP5) { ASSERT_TRUE(dLdB.isSameShape(*dLdBbp)); ASSERT_TRUE(dLdB.equalsTo(*dLdBbp)); - delete resultsBP; } ////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests15, TestTensorMmul_BP6) { - NDArray A('c', { 2, 2, 2 }, { 2,2, 2,2, 2,2, 2,2 }, nd4j::DataType::FLOAT32); - NDArray B('c', { 2, 2, 2 }, { 3,3, 3,3, 3,3, 3,3 }, nd4j::DataType::FLOAT32); + NDArray A('c', { 2, 2, 2 }, { 2,2, 2,2, 2,2, 2,2 }, sd::DataType::FLOAT32); + NDArray B('c', { 2, 2, 2 }, { 3,3, 3,3, 3,3, 3,3 }, sd::DataType::FLOAT32); auto dLdC = NDArrayFactory::create(1.f); - nd4j::ops::tensormmul_bp op_bp; + sd::ops::tensormmul_bp op_bp; auto resultsBP = op_bp.evaluate({ &A, &B, &dLdC }, {}, { 3,0,1,2, 3,0,1,2 }, {}); - ASSERT_EQ(ND4J_STATUS_OK, resultsBP->status()); + ASSERT_EQ(ND4J_STATUS_OK, resultsBP.status()); - auto* dLdAbp = resultsBP->at(0); - auto* dLdBbp = resultsBP->at(1); + auto* dLdAbp = resultsBP.at(0); + auto* dLdBbp = resultsBP.at(1); ASSERT_TRUE(B.isSameShape(*dLdAbp)); ASSERT_TRUE(B.equalsTo(*dLdAbp)); @@ -1727,25 +1665,24 @@ TEST_F(DeclarableOpsTests15, TestTensorMmul_BP6) { ASSERT_TRUE(A.isSameShape(*dLdBbp)); ASSERT_TRUE(A.equalsTo(*dLdBbp)); - delete resultsBP; } ////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests15, TestTensorMmul_BP7) { - NDArray A('c', { 3, 4, 1 }, { 0.4, 3, 5, 9, 23, 0.12, 8, 9, 0.1, 0, 124, 3 }, nd4j::DataType::FLOAT32); - NDArray B('c', { 2, 4, 1 }, { 4, 13, .5, 19, 2.3, 1.2, 18, .9 }, nd4j::DataType::FLOAT32); - NDArray dLdC('c', { 3, 1, 2, 1 }, { 1.1, 1.2, 1.3, 1.4, 1.5, 1.6 }, nd4j::DataType::FLOAT32); + NDArray A('c', { 3, 4, 1 }, { 0.4, 3, 5, 9, 23, 0.12, 8, 9, 0.1, 0, 124, 3 }, sd::DataType::FLOAT32); + NDArray B('c', { 2, 4, 1 }, { 4, 13, .5, 19, 2.3, 1.2, 18, .9 }, sd::DataType::FLOAT32); + NDArray dLdC('c', { 3, 1, 2, 1 }, { 1.1, 1.2, 1.3, 1.4, 1.5, 1.6 }, sd::DataType::FLOAT32); - NDArray dLdA('c', { 3, 4, 1 }, { 7.16, 15.74, 22.15, 21.98, 8.42, 18.58, 25.85, 25.96, 9.68, 21.42, 29.55, 29.94 }, nd4j::DataType::FLOAT32); - NDArray dLdB('c', { 2, 4, 1 }, { 30.49, 3.456, 201.9, 26.1, 32.84, 3.768, 215.6, 28.2 }, nd4j::DataType::FLOAT32); + NDArray dLdA('c', { 3, 4, 1 }, { 7.16, 15.74, 22.15, 21.98, 8.42, 18.58, 25.85, 25.96, 9.68, 21.42, 29.55, 29.94 }, sd::DataType::FLOAT32); + NDArray dLdB('c', { 2, 4, 1 }, { 30.49, 3.456, 201.9, 26.1, 32.84, 3.768, 215.6, 28.2 }, sd::DataType::FLOAT32); - nd4j::ops::tensormmul_bp op_bp; + sd::ops::tensormmul_bp op_bp; auto resultsBP = op_bp.evaluate({ &A, &B, &dLdC }, {}, { 1,1, 1,1 }, {}); - ASSERT_EQ(ND4J_STATUS_OK, resultsBP->status()); - auto* dLdAbp = resultsBP->at(0); - auto* dLdBbp = resultsBP->at(1); + ASSERT_EQ(ND4J_STATUS_OK, resultsBP.status()); + auto* dLdAbp = resultsBP.at(0); + auto* dLdBbp = resultsBP.at(1); ASSERT_TRUE(dLdA.isSameShape(*dLdAbp)); ASSERT_TRUE(dLdA.equalsTo(*dLdAbp)); @@ -1753,26 +1690,25 @@ TEST_F(DeclarableOpsTests15, TestTensorMmul_BP7) { ASSERT_TRUE(dLdB.isSameShape(*dLdBbp)); ASSERT_TRUE(dLdB.equalsTo(*dLdBbp)); - delete resultsBP; } ////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests15, TestTensorMmul_BP8) { - NDArray A('c', { 1, 1, 4, 3 }, { 0.4, 3, 5, 9, 23, 0.12, 8, 9, 0.1, 0, 124, 3 }, nd4j::DataType::FLOAT32); - NDArray B('c', { 1, 1, 4, 2 }, { 4, 13, .5, 19, 2.3, 1.2, 18, .9 }, nd4j::DataType::FLOAT32); - NDArray dLdC('c', { 3, 2 }, { 1.1,1.2,1.3,1.4,1.5,1.6 }, nd4j::DataType::FLOAT32); + NDArray A('c', { 1, 1, 4, 3 }, { 0.4, 3, 5, 9, 23, 0.12, 8, 9, 0.1, 0, 124, 3 }, sd::DataType::FLOAT32); + NDArray B('c', { 1, 1, 4, 2 }, { 4, 13, .5, 19, 2.3, 1.2, 18, .9 }, sd::DataType::FLOAT32); + NDArray dLdC('c', { 3, 2 }, { 1.1,1.2,1.3,1.4,1.5,1.6 }, sd::DataType::FLOAT32); - NDArray dLdA('c', { 1, 1, 4, 3 }, { 20., 23.4, 26.8, 23.35, 27.25, 31.15, 3.97, 4.67, 5.37, 20.88, 24.66, 28.44 }, nd4j::DataType::FLOAT32); - NDArray dLdB('c', { 1, 1, 4, 2 }, { 11.84, 12.68, 39.98, 43.192, 20.65, 22.36, 165.7, 178.4 }, nd4j::DataType::FLOAT32); + NDArray dLdA('c', { 1, 1, 4, 3 }, { 20., 23.4, 26.8, 23.35, 27.25, 31.15, 3.97, 4.67, 5.37, 20.88, 24.66, 28.44 }, sd::DataType::FLOAT32); + NDArray dLdB('c', { 1, 1, 4, 2 }, { 11.84, 12.68, 39.98, 43.192, 20.65, 22.36, 165.7, 178.4 }, sd::DataType::FLOAT32); - nd4j::ops::tensormmul_bp op_bp; + sd::ops::tensormmul_bp op_bp; auto resultsBP = op_bp.evaluate({ &A, &B, &dLdC }, {}, { 3,0,1,2, 3,0,1,2 }, {}); - ASSERT_EQ(ND4J_STATUS_OK, resultsBP->status()); + ASSERT_EQ(ND4J_STATUS_OK, resultsBP.status()); - auto* dLdAbp = resultsBP->at(0); - auto* dLdBbp = resultsBP->at(1); + auto* dLdAbp = resultsBP.at(0); + auto* dLdBbp = resultsBP.at(1); ASSERT_TRUE(dLdA.isSameShape(*dLdAbp)); ASSERT_TRUE(dLdA.equalsTo(*dLdAbp)); @@ -1780,26 +1716,25 @@ TEST_F(DeclarableOpsTests15, TestTensorMmul_BP8) { ASSERT_TRUE(dLdB.isSameShape(*dLdBbp)); ASSERT_TRUE(dLdB.equalsTo(*dLdBbp)); - delete resultsBP; } ////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests15, TestTensorMmul_BP9) { - NDArray A('c', { 3, 2, 2, 1 }, { 2.1, 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8, 2.9, 3., 3.1, 3.2 }, nd4j::DataType::FLOAT32); - NDArray B('c', { 4, 2, 2 ,1 }, { 3.1, 3.2, 3.3, 3.4, 3.5, 3.6, 3.7, 3.8, 3.9, 4., 4.1, 4.2, 4.3, 4.4, 4.5, 4.6 }, nd4j::DataType::FLOAT32); - NDArray dLdC('c', { 3, 1, 4, 1 }, { .1, .2, .3, .4, .5, .6, .7, .8, .9, 1, 1.1, 1.2 }, nd4j::DataType::FLOAT32); + NDArray A('c', { 3, 2, 2, 1 }, { 2.1, 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8, 2.9, 3., 3.1, 3.2 }, sd::DataType::FLOAT32); + NDArray B('c', { 4, 2, 2 ,1 }, { 3.1, 3.2, 3.3, 3.4, 3.5, 3.6, 3.7, 3.8, 3.9, 4., 4.1, 4.2, 4.3, 4.4, 4.5, 4.6 }, sd::DataType::FLOAT32); + NDArray dLdC('c', { 3, 1, 4, 1 }, { .1, .2, .3, .4, .5, .6, .7, .8, .9, 1, 1.1, 1.2 }, sd::DataType::FLOAT32); - NDArray dA('c', { 3, 2, 2, 1 }, { 3.9, 4., 4.1, 4.2, 9.82, 10.08, 10.34, 10.6, 15.74, 16.16, 16.58, 17. }, nd4j::DataType::FLOAT32); - NDArray dB('c', { 4, 2, 2, 1 }, { 4.07, 4.22, 4.37, 4.52, 4.82, 5., 5.18, 5.36, 5.57, 5.78, 5.99, 6.2, 6.32, 6.56, 6.8, 7.04 }, nd4j::DataType::FLOAT32); + NDArray dA('c', { 3, 2, 2, 1 }, { 3.9, 4., 4.1, 4.2, 9.82, 10.08, 10.34, 10.6, 15.74, 16.16, 16.58, 17. }, sd::DataType::FLOAT32); + NDArray dB('c', { 4, 2, 2, 1 }, { 4.07, 4.22, 4.37, 4.52, 4.82, 5., 5.18, 5.36, 5.57, 5.78, 5.99, 6.2, 6.32, 6.56, 6.8, 7.04 }, sd::DataType::FLOAT32); - nd4j::ops::tensormmul_bp op_bp; + sd::ops::tensormmul_bp op_bp; auto resultsBP = op_bp.evaluate({ &A, &B, &dLdC }, {}, { 2,1,2, 2,1,2 }, {}); - ASSERT_EQ(ND4J_STATUS_OK, resultsBP->status()); + ASSERT_EQ(ND4J_STATUS_OK, resultsBP.status()); - auto* dLdAbp = resultsBP->at(0); - auto* dLdBbp = resultsBP->at(1); + auto* dLdAbp = resultsBP.at(0); + auto* dLdBbp = resultsBP.at(1); ASSERT_TRUE(dA.isSameShape(*dLdAbp)); ASSERT_TRUE(dA.equalsTo(*dLdAbp)); @@ -1807,27 +1742,26 @@ TEST_F(DeclarableOpsTests15, TestTensorMmul_BP9) { ASSERT_TRUE(dB.isSameShape(*dLdBbp)); ASSERT_TRUE(dB.equalsTo(*dLdBbp)); - delete resultsBP; } ////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests15, TestTensorMmul_BP10) { - NDArray A('c', { 1, 2, 2, 3 }, { 2.1, 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8, 2.9, 3., 3.1, 3.2 }, nd4j::DataType::FLOAT32); - NDArray B('c', { 1, 2, 2 ,4 }, { 3.1, 3.2, 3.3, 3.4, 3.5, 3.6, 3.7, 3.8, 3.9, 4., 4.1, 4.2, 4.3, 4.4, 4.5, 4.6 }, nd4j::DataType::FLOAT32); - NDArray dLdC('c', { 1, 3, 1, 4 }, { .1, .2, .3, .4, .5, .6, .7, .8, .9, 1, 1.1, 1.2 }, nd4j::DataType::FLOAT32); + NDArray A('c', { 1, 2, 2, 3 }, { 2.1, 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8, 2.9, 3., 3.1, 3.2 }, sd::DataType::FLOAT32); + NDArray B('c', { 1, 2, 2 ,4 }, { 3.1, 3.2, 3.3, 3.4, 3.5, 3.6, 3.7, 3.8, 3.9, 4., 4.1, 4.2, 4.3, 4.4, 4.5, 4.6 }, sd::DataType::FLOAT32); + NDArray dLdC('c', { 1, 3, 1, 4 }, { .1, .2, .3, .4, .5, .6, .7, .8, .9, 1, 1.1, 1.2 }, sd::DataType::FLOAT32); - NDArray dA('c', { 1, 2, 2, 3 }, { 3.3, 8.5, 13.7, 3.7, 9.54, 15.38, 4.1, 10.58, 17.06, 4.5, 11.62, 18.74 }, nd4j::DataType::FLOAT32); - NDArray dB('c', { 1, 2, 2, 4 }, { 3.38, 4.04, 4.7, 5.36, 3.83, 4.58, 5.33, 6.08, 4.28, 5.12, 5.96, 6.8, 4.73, 5.66, 6.59, 7.52 }, nd4j::DataType::FLOAT32); + NDArray dA('c', { 1, 2, 2, 3 }, { 3.3, 8.5, 13.7, 3.7, 9.54, 15.38, 4.1, 10.58, 17.06, 4.5, 11.62, 18.74 }, sd::DataType::FLOAT32); + NDArray dB('c', { 1, 2, 2, 4 }, { 3.38, 4.04, 4.7, 5.36, 3.83, 4.58, 5.33, 6.08, 4.28, 5.12, 5.96, 6.8, 4.73, 5.66, 6.59, 7.52 }, sd::DataType::FLOAT32); - nd4j::ops::tensormmul_bp op_bp; + sd::ops::tensormmul_bp op_bp; auto resultsBP = op_bp.evaluate({ &A, &B, &dLdC }, {}, { 2,1,2, 2,1,2 }, {}); - ASSERT_EQ(ND4J_STATUS_OK, resultsBP->status()); + ASSERT_EQ(ND4J_STATUS_OK, resultsBP.status()); - auto* dLdAbp = resultsBP->at(0); - auto* dLdBbp = resultsBP->at(1); + auto* dLdAbp = resultsBP.at(0); + auto* dLdBbp = resultsBP.at(1); ASSERT_TRUE(dA.isSameShape(*dLdAbp)); ASSERT_TRUE(dA.equalsTo(*dLdAbp)); @@ -1835,27 +1769,26 @@ TEST_F(DeclarableOpsTests15, TestTensorMmul_BP10) { ASSERT_TRUE(dB.isSameShape(*dLdBbp)); ASSERT_TRUE(dB.equalsTo(*dLdBbp)); - delete resultsBP; } ////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests15, TestTensorMmul_BP11) { - NDArray A('c', { 2, 2, 3 }, { 2.1, 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8, 2.9, 3., 3.1, 3.2 }, nd4j::DataType::FLOAT32); - NDArray B('c', { 2, 2 ,4 }, { 3.1, 3.2, 3.3, 3.4, 3.5, 3.6, 3.7, 3.8, 3.9, 4., 4.1, 4.2, 4.3, 4.4, 4.5, 4.6 }, nd4j::DataType::FLOAT32); - NDArray dLdC('c', { 3, 4 }, { .1, .2, .3, .4, .5, .6, .7, .8, .9, 1, 1.1, 1.2 }, nd4j::DataType::FLOAT32); + NDArray A('c', { 2, 2, 3 }, { 2.1, 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8, 2.9, 3., 3.1, 3.2 }, sd::DataType::FLOAT32); + NDArray B('c', { 2, 2 ,4 }, { 3.1, 3.2, 3.3, 3.4, 3.5, 3.6, 3.7, 3.8, 3.9, 4., 4.1, 4.2, 4.3, 4.4, 4.5, 4.6 }, sd::DataType::FLOAT32); + NDArray dLdC('c', { 3, 4 }, { .1, .2, .3, .4, .5, .6, .7, .8, .9, 1, 1.1, 1.2 }, sd::DataType::FLOAT32); - NDArray dA('c', { 2, 2, 3 }, { 3.3, 8.5, 13.7, 3.7, 9.54, 15.38, 4.1, 10.58, 17.06, 4.5, 11.62, 18.74 }, nd4j::DataType::FLOAT32); - NDArray dB('c', { 2, 2, 4 }, { 3.38, 4.04, 4.7, 5.36, 3.83, 4.58, 5.33, 6.08, 4.28, 5.12, 5.96, 6.8, 4.73, 5.66, 6.59, 7.52 }, nd4j::DataType::FLOAT32); + NDArray dA('c', { 2, 2, 3 }, { 3.3, 8.5, 13.7, 3.7, 9.54, 15.38, 4.1, 10.58, 17.06, 4.5, 11.62, 18.74 }, sd::DataType::FLOAT32); + NDArray dB('c', { 2, 2, 4 }, { 3.38, 4.04, 4.7, 5.36, 3.83, 4.58, 5.33, 6.08, 4.28, 5.12, 5.96, 6.8, 4.73, 5.66, 6.59, 7.52 }, sd::DataType::FLOAT32); - nd4j::ops::tensormmul_bp op_bp; + sd::ops::tensormmul_bp op_bp; auto resultsBP = op_bp.evaluate({ &A, &B, &dLdC }, {}, { 2,0,1, 2,0,1 }, {}); - ASSERT_EQ(ND4J_STATUS_OK, resultsBP->status()); + ASSERT_EQ(ND4J_STATUS_OK, resultsBP.status()); - auto* dLdAbp = resultsBP->at(0); - auto* dLdBbp = resultsBP->at(1); + auto* dLdAbp = resultsBP.at(0); + auto* dLdBbp = resultsBP.at(1); ASSERT_TRUE(dA.isSameShape(*dLdAbp)); ASSERT_TRUE(dA.equalsTo(*dLdAbp)); @@ -1863,28 +1796,27 @@ TEST_F(DeclarableOpsTests15, TestTensorMmul_BP11) { ASSERT_TRUE(dB.isSameShape(*dLdBbp)); ASSERT_TRUE(dB.equalsTo(*dLdBbp)); - delete resultsBP; } ////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests15, TestTensorMmul_BP12) { - NDArray A('c', { 2, 2, 3 }, { 2.1, 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8, 2.9, 3., 3.1, 3.2 }, nd4j::DataType::FLOAT32); - NDArray B('c', { 2, 2 ,3 }, { 3.1, 3.2, 3.3, 3.4, 3.5, 3.6, 3.7, 3.8, 3.9, 4., 4.1, 4.2 }, nd4j::DataType::FLOAT32); + NDArray A('c', { 2, 2, 3 }, { 2.1, 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8, 2.9, 3., 3.1, 3.2 }, sd::DataType::FLOAT32); + NDArray B('c', { 2, 2 ,3 }, { 3.1, 3.2, 3.3, 3.4, 3.5, 3.6, 3.7, 3.8, 3.9, 4., 4.1, 4.2 }, sd::DataType::FLOAT32); NDArray dLdC('c', { 2, 3, 2, 3 }, { .1, .2, .3, .4, .5, .6, .7, .8, .9, 1, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2., 2.1, 2.2, 2.3, 2.4, - 2.5, 2.6, 2.7, 2.8, 2.9, 3., 3.1, 3.2, 3.3, 3.4, 3.5, 3.6 }, nd4j::DataType::FLOAT32); + 2.5, 2.6, 2.7, 2.8, 2.9, 3., 3.1, 3.2, 3.3, 3.4, 3.5, 3.6 }, sd::DataType::FLOAT32); - NDArray dA('c', { 2, 2, 3 }, { 7.66, 20.26, 32.86, 8.29, 21.97, 35.65, 45.46, 58.06, 70.66, 49.33, 63.01, 76.69 }, nd4j::DataType::FLOAT32); - NDArray dB('c', { 2, 2, 3 }, { 25.86, 27.36, 28.86, 28.74, 30.42, 32.1, 30.36, 31.86, 33.36, 33.78, 35.46, 37.14 }, nd4j::DataType::FLOAT32); + NDArray dA('c', { 2, 2, 3 }, { 7.66, 20.26, 32.86, 8.29, 21.97, 35.65, 45.46, 58.06, 70.66, 49.33, 63.01, 76.69 }, sd::DataType::FLOAT32); + NDArray dB('c', { 2, 2, 3 }, { 25.86, 27.36, 28.86, 28.74, 30.42, 32.1, 30.36, 31.86, 33.36, 33.78, 35.46, 37.14 }, sd::DataType::FLOAT32); - nd4j::ops::tensormmul_bp op_bp; + sd::ops::tensormmul_bp op_bp; auto resultsBP = op_bp.evaluate({ &A, &B, &dLdC }, {}, { 1,1, 1,1 }, {}); - ASSERT_EQ(ND4J_STATUS_OK, resultsBP->status()); + ASSERT_EQ(ND4J_STATUS_OK, resultsBP.status()); - auto* dLdAbp = resultsBP->at(0); - auto* dLdBbp = resultsBP->at(1); + auto* dLdAbp = resultsBP.at(0); + auto* dLdBbp = resultsBP.at(1); ASSERT_TRUE(dA.isSameShape(*dLdAbp)); ASSERT_TRUE(dA.equalsTo(*dLdAbp)); @@ -1892,28 +1824,27 @@ TEST_F(DeclarableOpsTests15, TestTensorMmul_BP12) { ASSERT_TRUE(dB.isSameShape(*dLdBbp)); ASSERT_TRUE(dB.equalsTo(*dLdBbp)); - delete resultsBP; } ////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests15, TestTensorMmul_BP13) { - NDArray A('c', { 3, 2, 2 }, { 2.1, 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8, 2.9, 3., 3.1, 3.2 }, nd4j::DataType::DOUBLE); - NDArray B('c', { 3, 2, 2 }, { 3.1, 3.2, 3.3, 3.4, 3.5, 3.6, 3.7, 3.8, 3.9, 4., 4.1, 4.2 }, nd4j::DataType::DOUBLE); + NDArray A('c', { 3, 2, 2 }, { 2.1, 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8, 2.9, 3., 3.1, 3.2 }, sd::DataType::DOUBLE); + NDArray B('c', { 3, 2, 2 }, { 3.1, 3.2, 3.3, 3.4, 3.5, 3.6, 3.7, 3.8, 3.9, 4., 4.1, 4.2 }, sd::DataType::DOUBLE); NDArray dLdC('c', { 3, 2, 3, 2 }, { .1, .2, .3, .4, .5, .6, .7, .8, .9, 1, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2., 2.1, 2.2, 2.3, 2.4, - 2.5, 2.6, 2.7, 2.8, 2.9, 3., 3.1, 3.2, 3.3, 3.4, 3.5, 3.6 }, nd4j::DataType::DOUBLE); + 2.5, 2.6, 2.7, 2.8, 2.9, 3., 3.1, 3.2, 3.3, 3.4, 3.5, 3.6 }, sd::DataType::DOUBLE); - NDArray dA('c', { 3, 2, 2 }, { 7.79, 20.57, 8.21, 21.71, 33.35, 46.13, 35.21, 48.71, 58.91, 71.69, 62.21, 75.71 }, nd4j::DataType::DOUBLE); - NDArray dB('c', { 3, 2, 2 }, { 26.49, 28.02, 28.41, 30.06, 29.55, 31.08, 31.71, 33.36, 32.61, 34.14, 35.01, 36.66 }, nd4j::DataType::DOUBLE); + NDArray dA('c', { 3, 2, 2 }, { 7.79, 20.57, 8.21, 21.71, 33.35, 46.13, 35.21, 48.71, 58.91, 71.69, 62.21, 75.71 }, sd::DataType::DOUBLE); + NDArray dB('c', { 3, 2, 2 }, { 26.49, 28.02, 28.41, 30.06, 29.55, 31.08, 31.71, 33.36, 32.61, 34.14, 35.01, 36.66 }, sd::DataType::DOUBLE); - nd4j::ops::tensormmul_bp op_bp; + sd::ops::tensormmul_bp op_bp; auto resultsBP = op_bp.evaluate({ &A, &B, &dLdC }, {}, { 1,1, 1,1 }, {}); - ASSERT_EQ(ND4J_STATUS_OK, resultsBP->status()); + ASSERT_EQ(ND4J_STATUS_OK, resultsBP.status()); - auto* dLdAbp = resultsBP->at(0); - auto* dLdBbp = resultsBP->at(1); + auto* dLdAbp = resultsBP.at(0); + auto* dLdBbp = resultsBP.at(1); ASSERT_TRUE(dA.isSameShape(*dLdAbp)); ASSERT_TRUE(dA.equalsTo(*dLdAbp)); @@ -1921,32 +1852,31 @@ TEST_F(DeclarableOpsTests15, TestTensorMmul_BP13) { ASSERT_TRUE(dB.isSameShape(*dLdBbp)); ASSERT_TRUE(dB.equalsTo(*dLdBbp)); - delete resultsBP; } ////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests15, TestTensorMmul_BP14) { - NDArray A('c', { 2, 2, 2, 2 }, { 2.1, 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8, 2.9, 3., 3.1, 3.2, 3.3, 3.4, 3.5, 3.6 }, nd4j::DataType::DOUBLE); + NDArray A('c', { 2, 2, 2, 2 }, { 2.1, 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8, 2.9, 3., 3.1, 3.2, 3.3, 3.4, 3.5, 3.6 }, sd::DataType::DOUBLE); - NDArray B('c', { 2, 2, 2, 2 }, { 3.1, 3.2, 3.3, 3.4, 3.5, 3.6, 3.7, 3.8, 3.9, 4., 4.1, 4.2, 4.3, 4.4, 4.5, 4.6 }, nd4j::DataType::DOUBLE); + NDArray B('c', { 2, 2, 2, 2 }, { 3.1, 3.2, 3.3, 3.4, 3.5, 3.6, 3.7, 3.8, 3.9, 4., 4.1, 4.2, 4.3, 4.4, 4.5, 4.6 }, sd::DataType::DOUBLE); NDArray dLdC('c', { 2, 2, 2, 2, 2, 2 }, { .1, .2, .3, .4, .5, .6, .7, .8, .9, 1, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, .1, .2, .3, .4, .5, .6, .7, .8, .9, 1, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, .1, .2, .3, .4, .5, .6, .7, .8, .9, 1, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, .1, .2, .3, .4, .5, .6, .7, .8, .9, 1, 1.1, 1.2, - 1.3, 1.4, 1.5, 1.6 }, nd4j::DataType::DOUBLE); + 1.3, 1.4, 1.5, 1.6 }, sd::DataType::DOUBLE); - NDArray dA('c', { 2, 2, 2, 2 }, { 13.88, 37.24, 13.88, 37.24, 15.32, 41.24, 15.32, 41.24, 13.88, 37.24, 13.88, 37.24, 15.32, 41.24, 15.32, 41.24 }, nd4j::DataType::DOUBLE); - NDArray dB('c', { 2, 2, 2, 2 }, { 10.76, 12.88, 15., 17.12, 12.36, 14.8, 17.24, 19.68, 19.24, 21.36, 23.48, 25.6, 22.12, 24.56, 27., 29.44 }, nd4j::DataType::DOUBLE); + NDArray dA('c', { 2, 2, 2, 2 }, { 13.88, 37.24, 13.88, 37.24, 15.32, 41.24, 15.32, 41.24, 13.88, 37.24, 13.88, 37.24, 15.32, 41.24, 15.32, 41.24 }, sd::DataType::DOUBLE); + NDArray dB('c', { 2, 2, 2, 2 }, { 10.76, 12.88, 15., 17.12, 12.36, 14.8, 17.24, 19.68, 19.24, 21.36, 23.48, 25.6, 22.12, 24.56, 27., 29.44 }, sd::DataType::DOUBLE); - nd4j::ops::tensormmul_bp op_bp; + sd::ops::tensormmul_bp op_bp; auto resultsBP = op_bp.evaluate({ &A, &B, &dLdC }, {}, { 1,1, 1,1 }, {}); - ASSERT_EQ(ND4J_STATUS_OK, resultsBP->status()); + ASSERT_EQ(ND4J_STATUS_OK, resultsBP.status()); - auto* dLdAbp = resultsBP->at(0); - auto* dLdBbp = resultsBP->at(1); + auto* dLdAbp = resultsBP.at(0); + auto* dLdBbp = resultsBP.at(1); ASSERT_TRUE(dA.isSameShape(*dLdAbp)); ASSERT_TRUE(dA.equalsTo(*dLdAbp)); @@ -1954,26 +1884,25 @@ TEST_F(DeclarableOpsTests15, TestTensorMmul_BP14) { ASSERT_TRUE(dB.isSameShape(*dLdBbp)); ASSERT_TRUE(dB.equalsTo(*dLdBbp)); - delete resultsBP; } ////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests15, TestTensorMmul_BP15) { - NDArray A('c', { 2, 2, 3 }, { 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12. }, nd4j::DataType::FLOAT32); - NDArray B('f', { 2, 2, 3 }, { 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12. }, nd4j::DataType::FLOAT32); + NDArray A('c', { 2, 2, 3 }, { 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12. }, sd::DataType::FLOAT32); + NDArray B('f', { 2, 2, 3 }, { 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12. }, sd::DataType::FLOAT32); - NDArray dLdC('f', { 2, 2 }, { 23.0, 24.44, 2.0, 26. }, nd4j::DataType::FLOAT32); + NDArray dLdC('f', { 2, 2 }, { 23.0, 24.44, 2.0, 26. }, sd::DataType::FLOAT32); - NDArray dA('c', { 2, 2, 3 }, { 27., 127., 227., 77., 177., 277., 76.44, 278.20001, 479.96002, 177.32, 379.08001, 580.839966 }, nd4j::DataType::FLOAT32); - NDArray dB('f', { 2, 2, 3 }, { 194.08, 184., 336.4, 268., 241.52, 212., 383.839996, 296., 288.96002, 240., 431.27999, 324. }, nd4j::DataType::FLOAT32); + NDArray dA('c', { 2, 2, 3 }, { 27., 127., 227., 77., 177., 277., 76.44, 278.20001, 479.96002, 177.32, 379.08001, 580.839966 }, sd::DataType::FLOAT32); + NDArray dB('f', { 2, 2, 3 }, { 194.08, 184., 336.4, 268., 241.52, 212., 383.839996, 296., 288.96002, 240., 431.27999, 324. }, sd::DataType::FLOAT32); - nd4j::ops::tensormmul_bp op; + sd::ops::tensormmul_bp op; auto results = op.evaluate({ &A, &B, &dLdC }, {}, { 2,1,2,2,1,2 }); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto* dLdA = results->at(0); - auto* dLdB = results->at(1); + auto* dLdA = results.at(0); + auto* dLdB = results.at(1); ASSERT_TRUE(dA.isSameShape(*dLdA)); ASSERT_TRUE(dA.equalsTo(*dLdA)); @@ -1981,21 +1910,20 @@ TEST_F(DeclarableOpsTests15, TestTensorMmul_BP15) { ASSERT_TRUE(dB.isSameShape(*dLdB)); ASSERT_TRUE(dB.equalsTo(*dLdB)); - delete results; } ////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests15, TestTensorMmul_BP16) { - NDArray A('f', { 2, 2, 3 }, { 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12. }, nd4j::DataType::DOUBLE); - NDArray B('c', { 2, 2, 3 }, { 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12. }, nd4j::DataType::DOUBLE); + NDArray A('f', { 2, 2, 3 }, { 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12. }, sd::DataType::DOUBLE); + NDArray B('c', { 2, 2, 3 }, { 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12. }, sd::DataType::DOUBLE); - NDArray dLdC('c', { 2, 2 }, nd4j::DataType::DOUBLE); + NDArray dLdC('c', { 2, 2 }, sd::DataType::DOUBLE); const OpArgsHolder argsHolderFF({ &A, &B }, {}, { 2,1,2, 2,1,2 }); const OpArgsHolder argsHolderBP({ &A, &B, &dLdC }, {}, { 2,1,2, 2,1,2 }); - nd4j::ops::tensormmul op; - nd4j::ops::tensormmul_bp op_bp; + sd::ops::tensormmul op; + sd::ops::tensormmul_bp op_bp; const bool isGradCorrect = GradCheck::checkGrad(op, op_bp, argsHolderFF, argsHolderBP, {1,0}); ASSERT_TRUE(isGradCorrect); @@ -2003,16 +1931,16 @@ TEST_F(DeclarableOpsTests15, TestTensorMmul_BP16) { ////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests15, TestTensorMmul_BP17) { - NDArray A('f', { 2, 2, 3 }, { 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12. }, nd4j::DataType::DOUBLE); - NDArray B('f', { 2, 2, 3 }, { 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12. }, nd4j::DataType::DOUBLE); + NDArray A('f', { 2, 2, 3 }, { 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12. }, sd::DataType::DOUBLE); + NDArray B('f', { 2, 2, 3 }, { 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12. }, sd::DataType::DOUBLE); - NDArray dLdC('c', { 2, 2 }, nd4j::DataType::DOUBLE); + NDArray dLdC('c', { 2, 2 }, sd::DataType::DOUBLE); const OpArgsHolder argsHolderFF({ &A, &B }, {}, { 2,1,2, 2,1,2 }); const OpArgsHolder argsHolderBP({ &A, &B, &dLdC }, {}, { 2,1,2, 2,1,2 }); - nd4j::ops::tensormmul op; - nd4j::ops::tensormmul_bp op_bp; + sd::ops::tensormmul op; + sd::ops::tensormmul_bp op_bp; const bool isGradCorrect = GradCheck::checkGrad(op, op_bp, argsHolderFF, argsHolderBP, { 1,0 }); ASSERT_TRUE(isGradCorrect); diff --git a/libnd4j/tests_cpu/layers_tests/DeclarableOpsTests16.cpp b/libnd4j/tests_cpu/layers_tests/DeclarableOpsTests16.cpp index a85772cec..c909b7686 100644 --- a/libnd4j/tests_cpu/layers_tests/DeclarableOpsTests16.cpp +++ b/libnd4j/tests_cpu/layers_tests/DeclarableOpsTests16.cpp @@ -21,13 +21,13 @@ #include "testlayers.h" #include -#include +#include #include -#include +#include #include -using namespace nd4j; +using namespace sd; class DeclarableOpsTests16 : public testing::Test { @@ -45,45 +45,41 @@ TEST_F(DeclarableOpsTests16, scatter_upd_1) { auto w = NDArrayFactory::create(3.0f); auto e = NDArrayFactory::create('c', { 3 }, { 3.f, 1.f, 1.f }); - nd4j::ops::scatter_upd op; + sd::ops::scatter_upd op; auto result = op.evaluate({ &x, &y, &w }); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_EQ(e, *z); - - delete result; } TEST_F(DeclarableOpsTests16, scatter_upd_2) { - NDArray x('c', { 10, 3 }, nd4j::DataType::FLOAT32); - NDArray indices('c', { 2 }, { 2,5 }, nd4j::DataType::INT32); - NDArray updates('c', { 2, 3 }, { 100,101,102, 200,201,202 }, nd4j::DataType::FLOAT32); - NDArray e('c', { 10, 3 }, { 1,2,3, 4,5,6, 100,101,102, 10,11,12, 13,14,15, 200,201,202, 19,20,21, 22,23,24, 25,26,27, 28,29,30 }, nd4j::DataType::FLOAT32); + NDArray x('c', { 10, 3 }, sd::DataType::FLOAT32); + NDArray indices('c', { 2 }, { 2,5 }, sd::DataType::INT32); + NDArray updates('c', { 2, 3 }, { 100,101,102, 200,201,202 }, sd::DataType::FLOAT32); + NDArray e('c', { 10, 3 }, { 1,2,3, 4,5,6, 100,101,102, 10,11,12, 13,14,15, 200,201,202, 19,20,21, 22,23,24, 25,26,27, 28,29,30 }, sd::DataType::FLOAT32); x.linspace(1); - nd4j::ops::scatter_upd op; + sd::ops::scatter_upd op; auto result = op.evaluate({ &x, &indices, &updates }); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_EQ(e, *z); - - delete result; } TEST_F(DeclarableOpsTests16, scatter_upd_3) { - NDArray x('c', { 10, 3 }, nd4j::DataType::FLOAT32); - NDArray indices('c', { 2 }, { 20,5 }, nd4j::DataType::INT32); - NDArray updates('c', { 2, 3 }, { 100,101,102, 200,201,202 }, nd4j::DataType::FLOAT32); - NDArray output('c', { 10, 3 }, nd4j::DataType::FLOAT32); + NDArray x('c', { 10, 3 }, sd::DataType::FLOAT32); + NDArray indices('c', { 2 }, { 20,5 }, sd::DataType::INT32); + NDArray updates('c', { 2, 3 }, { 100,101,102, 200,201,202 }, sd::DataType::FLOAT32); + NDArray output('c', { 10, 3 }, sd::DataType::FLOAT32); - nd4j::ops::scatter_upd op; + sd::ops::scatter_upd op; ASSERT_ANY_THROW(op.execute({ &x, &indices, &updates }, { &output }, {}, {}, { true, true })); } @@ -92,7 +88,7 @@ TEST_F(DeclarableOpsTests16, test_size_dtype_1) { auto z = NDArrayFactory::create(0.0f); auto e = NDArrayFactory::create(3.0f); - nd4j::ops::size op; + sd::ops::size op; auto status = op.execute({ &x }, { &z }, {}, {}, {}); ASSERT_EQ(Status::OK(), status); @@ -102,7 +98,7 @@ TEST_F(DeclarableOpsTests16, test_size_dtype_1) { TEST_F(DeclarableOpsTests16, test_empty_noop_1) { auto z = NDArrayFactory::empty(); - nd4j::ops::noop op; + sd::ops::noop op; auto status = op.execute({}, { &z }, {}, {}, {}); ASSERT_EQ(Status::OK(), status); } @@ -113,7 +109,7 @@ TEST_F(DeclarableOpsTests16, test_empty_noop_2) { Context ctx(1); ctx.setOutputArray(0, z.buffer(), z.shapeInfo(), z.specialBuffer(), z.specialShapeInfo()); - nd4j::ops::noop op; + sd::ops::noop op; auto status = op.execute(&ctx); ASSERT_EQ(Status::OK(), status); @@ -123,7 +119,7 @@ TEST_F(DeclarableOpsTests16, test_svd_1) { auto x = NDArrayFactory::create('c', { 3, 3 }, { 0.7787856f, 0.80119777f, 0.72437465f, 0.23089433f, 0.72714126f, 0.18039072f,0.50563407f, 0.89252293f, 0.5461209f }); auto z = NDArrayFactory::create('c', { 3 }); - nd4j::ops::svd op; + sd::ops::svd op; auto status = op.execute({ &x }, { &z }, {}, { 0, 0, 16 }, {}); ASSERT_EQ(Status::OK(), status); @@ -134,15 +130,13 @@ TEST_F(DeclarableOpsTests16, test_hamming_distance_1) { auto y = NDArrayFactory::create({ 8723, 8723, 8723 }); auto e = NDArrayFactory::create(18); - nd4j::ops::bits_hamming_distance op; + sd::ops::bits_hamming_distance op; auto result = op.evaluate({ &x, &y }); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_EQ(e, *z); - - delete result; } TEST_F(DeclarableOpsTests16, test_knn_mindistance_1) { @@ -156,7 +150,7 @@ TEST_F(DeclarableOpsTests16, test_knn_mindistance_1) { low.linspace(1.0); high.linspace(1.0); - nd4j::ops::knn_mindistance op; + sd::ops::knn_mindistance op; auto result = op.execute({ &input, &low, &high }, { &output }, {}, {}, {}); ASSERT_EQ(Status::OK(), result); } @@ -165,16 +159,14 @@ TEST_F(DeclarableOpsTests16, test_empty_cast_1) { auto x = NDArrayFactory::create('c', { 1, 0, 2 }); auto e = NDArrayFactory::create('c', { 1, 0, 2 }); - nd4j::ops::cast op; + sd::ops::cast op; auto result = op.evaluate({&x}, {10}); - ASSERT_EQ(Status::OK(), result->status()); - ASSERT_EQ(e, *result->at(0)); - - delete result; + ASSERT_EQ(Status::OK(), result.status()); + ASSERT_EQ(e, *result.at(0)); } TEST_F(DeclarableOpsTests16, test_range_1) { - nd4j::ops::range op; + sd::ops::range op; auto z = NDArrayFactory::create('c', { 200 }); Context ctx(1); @@ -186,7 +178,7 @@ TEST_F(DeclarableOpsTests16, test_range_1) { } TEST_F(DeclarableOpsTests16, test_range_2) { - nd4j::ops::range op; + sd::ops::range op; auto z = NDArrayFactory::create('c', { 200 }); double tArgs[] = { -1.0, 1.0, 0.01 }; @@ -226,7 +218,7 @@ TEST_F(DeclarableOpsTests16, test_reverse_1) { listE.at(e)->assign(rowReversed); } - nd4j::ops::reverse op; + sd::ops::reverse op; Nd4jLong axis = 1; auto status = op.execute({ &array }, { &reversed }, {}, { axis }, {}); ASSERT_EQ(Status::OK(), status); @@ -283,7 +275,7 @@ TEST_F(DeclarableOpsTests16, test_rgb_to_hsv_1) { ctx.setInputArray(0, &rgbs); ctx.setOutputArray(0, &actual); - nd4j::ops::rgb_to_hsv op; + sd::ops::rgb_to_hsv op; auto status = op.execute(&ctx); #if 0 //visual check @@ -337,7 +329,7 @@ TEST_F(DeclarableOpsTests16, test_rgb_to_hsv_2) { ctx.setInputArray(0, &rgbs); ctx.setOutputArray(0, &actual); ctx.setIArguments({ 1 }); - nd4j::ops::rgb_to_hsv op; + sd::ops::rgb_to_hsv op; auto status = op.execute(&ctx); ASSERT_EQ(ND4J_STATUS_OK, status); @@ -364,7 +356,7 @@ TEST_F(DeclarableOpsTests16, test_rgb_to_hsv_3) { ctx.setInputArray(0, &rgbs); ctx.setOutputArray(0, &actual); - nd4j::ops::rgb_to_hsv op; + sd::ops::rgb_to_hsv op; auto status = op.execute(&ctx); ASSERT_EQ(ND4J_STATUS_OK, status); @@ -391,7 +383,7 @@ TEST_F(DeclarableOpsTests16, test_rgb_to_hsv_4) { ctx.setInputArray(0, &rgbs); ctx.setOutputArray(0, &actual); ctx.setIArguments({ 0 }); - nd4j::ops::rgb_to_hsv op; + sd::ops::rgb_to_hsv op; auto status = op.execute(&ctx); ASSERT_EQ(ND4J_STATUS_OK, status); @@ -413,7 +405,7 @@ TEST_F(DeclarableOpsTests16, test_rgb_to_hsv_5) { ctx.setInputArray(0, &rgbs); ctx.setOutputArray(0, &actual); - nd4j::ops::rgb_to_hsv op; + sd::ops::rgb_to_hsv op; auto status = op.execute(&ctx); ASSERT_EQ(ND4J_STATUS_OK, status); @@ -449,7 +441,7 @@ TEST_F(DeclarableOpsTests16, test_rgb_to_hsv_6) { Context ctx(1); ctx.setInputArray(0, &subArrRgbs); ctx.setOutputArray(0, &actual); - nd4j::ops::rgb_to_hsv op; + sd::ops::rgb_to_hsv op; auto status = op.execute(&ctx); ASSERT_EQ(ND4J_STATUS_OK, status); @@ -495,7 +487,7 @@ TEST_F(DeclarableOpsTests16, test_hsv_to_rgb_1) { ctx.setInputArray(0, &hsvs); ctx.setOutputArray(0, &actual); - nd4j::ops::hsv_to_rgb op; + sd::ops::hsv_to_rgb op; auto status = op.execute(&ctx); ASSERT_EQ(ND4J_STATUS_OK, status); @@ -538,7 +530,7 @@ TEST_F(DeclarableOpsTests16, test_hsv_to_rgb_2) { ctx.setInputArray(0, &hsvs); ctx.setOutputArray(0, &actual); ctx.setIArguments({ 1 }); - nd4j::ops::hsv_to_rgb op; + sd::ops::hsv_to_rgb op; auto status = op.execute(&ctx); ASSERT_EQ(ND4J_STATUS_OK, status); @@ -563,7 +555,7 @@ TEST_F(DeclarableOpsTests16, test_hsv_to_rgb_3) { ctx.setInputArray(0, &hsvs); ctx.setOutputArray(0, &actual); - nd4j::ops::hsv_to_rgb op; + sd::ops::hsv_to_rgb op; auto status = op.execute(&ctx); ASSERT_EQ(ND4J_STATUS_OK, status); @@ -589,7 +581,7 @@ TEST_F(DeclarableOpsTests16, test_hsv_to_rgb_4) { ctx.setInputArray(0, &hsvs); ctx.setOutputArray(0, &actual); ctx.setIArguments({ 0 }); - nd4j::ops::hsv_to_rgb op; + sd::ops::hsv_to_rgb op; auto status = op.execute(&ctx); ASSERT_EQ(ND4J_STATUS_OK, status); @@ -612,7 +604,7 @@ TEST_F(DeclarableOpsTests16, test_hsv_to_rgb_5) { ctx.setInputArray(0, &hsvs); ctx.setOutputArray(0, &actual); - nd4j::ops::hsv_to_rgb op; + sd::ops::hsv_to_rgb op; auto status = op.execute(&ctx); ASSERT_EQ(ND4J_STATUS_OK, status); @@ -648,7 +640,7 @@ TEST_F(DeclarableOpsTests16, test_hsv_to_rgb_6) { Context ctx(1); ctx.setInputArray(0, &subArrHsvs); ctx.setOutputArray(0, &actual); - nd4j::ops::hsv_to_rgb op; + sd::ops::hsv_to_rgb op; auto status = op.execute(&ctx); ASSERT_EQ(ND4J_STATUS_OK, status); @@ -709,7 +701,7 @@ TEST_F(DeclarableOpsTests16, test_rgb_to_yiq_1) { ctx.setInputArray(0, &rgb); ctx.setOutputArray(0, &actual); - nd4j::ops::rgb_to_yiq op; + sd::ops::rgb_to_yiq op; auto status = op.execute(&ctx); ASSERT_EQ(ND4J_STATUS_OK, status); @@ -717,8 +709,6 @@ TEST_F(DeclarableOpsTests16, test_rgb_to_yiq_1) { } - - TEST_F(DeclarableOpsTests16, test_rgb_to_yiq_2) { auto rgb = NDArrayFactory::create('c', { 5, 3, 4 }, @@ -759,7 +749,7 @@ TEST_F(DeclarableOpsTests16, test_rgb_to_yiq_2) { ctx.setInputArray(0, &rgb); ctx.setOutputArray(0, &actual); ctx.setIArguments({ 1 }); - nd4j::ops::rgb_to_yiq op; + sd::ops::rgb_to_yiq op; auto status = op.execute(&ctx); ASSERT_EQ(ND4J_STATUS_OK, status); @@ -767,7 +757,6 @@ TEST_F(DeclarableOpsTests16, test_rgb_to_yiq_2) { } - TEST_F(DeclarableOpsTests16, test_rgb_to_yiq_3) { auto rgb = NDArrayFactory::create('c', { 4, 3 }, @@ -790,7 +779,7 @@ TEST_F(DeclarableOpsTests16, test_rgb_to_yiq_3) { ctx.setInputArray(0, &rgb); ctx.setOutputArray(0, &actual); - nd4j::ops::rgb_to_yiq op; + sd::ops::rgb_to_yiq op; auto status = op.execute(&ctx); ASSERT_EQ(ND4J_STATUS_OK, status); @@ -798,7 +787,6 @@ TEST_F(DeclarableOpsTests16, test_rgb_to_yiq_3) { } - TEST_F(DeclarableOpsTests16, test_rgb_to_yiq_4) { auto rgb = NDArrayFactory::create('c', { 3, 4 }, @@ -821,12 +809,11 @@ TEST_F(DeclarableOpsTests16, test_rgb_to_yiq_4) { ctx.setInputArray(0, &rgb); ctx.setOutputArray(0, &actual); ctx.setIArguments({ 0 }); - nd4j::ops::rgb_to_yiq op; + sd::ops::rgb_to_yiq op; auto status = op.execute(&ctx); ASSERT_EQ(ND4J_STATUS_OK, status); ASSERT_TRUE(expected.equalsTo(actual)); - } @@ -845,12 +832,11 @@ TEST_F(DeclarableOpsTests16, test_rgb_to_yiq_5) { ctx.setInputArray(0, &rgbs); ctx.setOutputArray(0, &actual); - nd4j::ops::rgb_to_yiq op; + sd::ops::rgb_to_yiq op; auto status = op.execute(&ctx); ASSERT_EQ(ND4J_STATUS_OK, status); ASSERT_TRUE(expected.equalsTo(actual)); - } TEST_F(DeclarableOpsTests16, test_rgb_to_yiq_6) { @@ -883,7 +869,7 @@ TEST_F(DeclarableOpsTests16, test_rgb_to_yiq_6) { Context ctx(1); ctx.setInputArray(0, &subArrRgbs); ctx.setOutputArray(0, &actual); - nd4j::ops::rgb_to_yiq op; + sd::ops::rgb_to_yiq op; auto status = op.execute(&ctx); ASSERT_EQ(ND4J_STATUS_OK, status); @@ -891,8 +877,6 @@ TEST_F(DeclarableOpsTests16, test_rgb_to_yiq_6) { } - - TEST_F(DeclarableOpsTests16, test_yiq_to_rgb_1) { auto yiqs = NDArrayFactory::create('c', { 5, 4, 3 }, { @@ -929,7 +913,7 @@ TEST_F(DeclarableOpsTests16, test_yiq_to_rgb_1) { ctx.setInputArray(0, &yiqs); ctx.setOutputArray(0, &actual); - nd4j::ops::yiq_to_rgb op; + sd::ops::yiq_to_rgb op; auto status = op.execute(&ctx); ASSERT_EQ(ND4J_STATUS_OK, status); @@ -937,8 +921,6 @@ TEST_F(DeclarableOpsTests16, test_yiq_to_rgb_1) { } - - TEST_F(DeclarableOpsTests16, test_yiq_to_rgb_2) { auto yiqs = NDArrayFactory::create('c', { 5, 3, 4 }, { @@ -975,7 +957,7 @@ TEST_F(DeclarableOpsTests16, test_yiq_to_rgb_2) { ctx.setInputArray(0, &yiqs); ctx.setOutputArray(0, &actual); ctx.setIArguments({ 1 }); - nd4j::ops::yiq_to_rgb op; + sd::ops::yiq_to_rgb op; auto status = op.execute(&ctx); ASSERT_EQ(ND4J_STATUS_OK, status); @@ -983,7 +965,6 @@ TEST_F(DeclarableOpsTests16, test_yiq_to_rgb_2) { } - TEST_F(DeclarableOpsTests16, test_yiq_to_rgb_3) { auto yiqs = NDArrayFactory::create('c', { 4, 3 }, { @@ -1002,7 +983,7 @@ TEST_F(DeclarableOpsTests16, test_yiq_to_rgb_3) { ctx.setInputArray(0, &yiqs); ctx.setOutputArray(0, &actual); - nd4j::ops::yiq_to_rgb op; + sd::ops::yiq_to_rgb op; auto status = op.execute(&ctx); ASSERT_EQ(ND4J_STATUS_OK, status); @@ -1010,7 +991,6 @@ TEST_F(DeclarableOpsTests16, test_yiq_to_rgb_3) { } - TEST_F(DeclarableOpsTests16, test_yiq_to_rgb_4) { auto yiqs = NDArrayFactory::create('c', { 3, 4 }, { @@ -1029,7 +1009,7 @@ TEST_F(DeclarableOpsTests16, test_yiq_to_rgb_4) { ctx.setInputArray(0, &yiqs); ctx.setOutputArray(0, &actual); ctx.setIArguments({ 0 }); - nd4j::ops::yiq_to_rgb op; + sd::ops::yiq_to_rgb op; auto status = op.execute(&ctx); ASSERT_EQ(ND4J_STATUS_OK, status); @@ -1037,8 +1017,6 @@ TEST_F(DeclarableOpsTests16, test_yiq_to_rgb_4) { } - - TEST_F(DeclarableOpsTests16, test_yiq_to_rgb_5) { auto yiqs = NDArrayFactory::create('c', { 3 }, { @@ -1053,7 +1031,7 @@ TEST_F(DeclarableOpsTests16, test_yiq_to_rgb_5) { ctx.setInputArray(0, &yiqs); ctx.setOutputArray(0, &actual); - nd4j::ops::yiq_to_rgb op; + sd::ops::yiq_to_rgb op; auto status = op.execute(&ctx); #if 0 actual.printBuffer("actual"); @@ -1061,7 +1039,6 @@ TEST_F(DeclarableOpsTests16, test_yiq_to_rgb_5) { #endif ASSERT_EQ(ND4J_STATUS_OK, status); ASSERT_TRUE(expected.equalsTo(actual)); - } TEST_F(DeclarableOpsTests16, test_yiq_to_rgb_6) { @@ -1091,10 +1068,9 @@ TEST_F(DeclarableOpsTests16, test_yiq_to_rgb_6) { Context ctx(1); ctx.setInputArray(0, &subArrYiqs); ctx.setOutputArray(0, &actual); - nd4j::ops::yiq_to_rgb op; + sd::ops::yiq_to_rgb op; auto status = op.execute(&ctx); ASSERT_EQ(ND4J_STATUS_OK, status); ASSERT_TRUE(expected.equalsTo(actual)); - } diff --git a/libnd4j/tests_cpu/layers_tests/DeclarableOpsTests17.cpp b/libnd4j/tests_cpu/layers_tests/DeclarableOpsTests17.cpp index 497475262..1341312f8 100644 --- a/libnd4j/tests_cpu/layers_tests/DeclarableOpsTests17.cpp +++ b/libnd4j/tests_cpu/layers_tests/DeclarableOpsTests17.cpp @@ -21,13 +21,13 @@ #include "testlayers.h" #include -#include +#include #include -#include +#include #include -using namespace nd4j; +using namespace sd; class DeclarableOpsTests17 : public testing::Test { @@ -47,11 +47,9 @@ TEST_F(DeclarableOpsTests17, test_sparse_to_dense_1) { auto exp = NDArrayFactory::create('c', {3, 3}, {1.f,0.f,0.f, 0.f,2.f,0.f, 0.f,0.f,3.f}); - nd4j::ops::compat_sparse_to_dense op; + sd::ops::compat_sparse_to_dense op; auto result = op.evaluate({&ranges, &shape, &values, &def}); - ASSERT_EQ(Status::OK(), result->status()); - - delete result; + ASSERT_EQ(Status::OK(), result.status()); } TEST_F(DeclarableOpsTests17, test_sparse_to_dense_2) { @@ -62,11 +60,10 @@ TEST_F(DeclarableOpsTests17, test_sparse_to_dense_2) { auto exp = NDArrayFactory::string( {3, 3}, {"alpha","d","d", "d","beta","d", "d","d","gamma"}); - nd4j::ops::compat_sparse_to_dense op; + sd::ops::compat_sparse_to_dense op; auto result = op.evaluate({&ranges, &shape, &values, &def}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - delete result; } TEST_F(DeclarableOpsTests17, test_compat_string_split_1) { @@ -76,13 +73,13 @@ TEST_F(DeclarableOpsTests17, test_compat_string_split_1) { auto exp0 = NDArrayFactory::create({0,0, 0,1, 1,0}); auto exp1 = NDArrayFactory::string( {3}, {"first", "string", "second"}); - nd4j::ops::compat_string_split op; + sd::ops::compat_string_split op; auto result = op.evaluate({&x, &delimiter}); - ASSERT_EQ(Status::OK(), result->status()); - ASSERT_EQ(2, result->size()); + ASSERT_EQ(Status::OK(), result.status()); + ASSERT_EQ(2, result.size()); - auto z0 = result->at(0); - auto z1 = result->at(1); + auto z0 = result.at(0); + auto z1 = result.at(1); ASSERT_TRUE(exp0.isSameShape(z0)); ASSERT_TRUE(exp1.isSameShape(z1)); @@ -90,5 +87,4 @@ TEST_F(DeclarableOpsTests17, test_compat_string_split_1) { ASSERT_EQ(exp0, *z0); ASSERT_EQ(exp1, *z1); - delete result; } \ No newline at end of file diff --git a/libnd4j/tests_cpu/layers_tests/DeclarableOpsTests18.cpp b/libnd4j/tests_cpu/layers_tests/DeclarableOpsTests18.cpp index 2c7737a31..f8de783c9 100644 --- a/libnd4j/tests_cpu/layers_tests/DeclarableOpsTests18.cpp +++ b/libnd4j/tests_cpu/layers_tests/DeclarableOpsTests18.cpp @@ -21,13 +21,13 @@ #include "testlayers.h" #include -#include +#include #include -#include +#include #include -using namespace nd4j; +using namespace sd; class DeclarableOpsTests18 : public testing::Test { @@ -44,8 +44,8 @@ TEST_F(DeclarableOpsTests18, test_bitcast_1) { auto z = NDArrayFactory::create(0); auto e = NDArrayFactory::create(4597464930322771456L); - nd4j::ops::bitcast op; - auto status = op.execute({&x}, {&z}, {}, {(Nd4jLong) nd4j::DataType::INT64}, {}); + sd::ops::bitcast op; + auto status = op.execute({&x}, {&z}, {}, {(Nd4jLong) sd::DataType::INT64}, {}); ASSERT_EQ(Status::OK(), status); ASSERT_EQ(e, z); @@ -56,8 +56,134 @@ TEST_F(DeclarableOpsTests18, test_tanh_1) { auto z = x.ulike(); auto e = NDArrayFactory::create('c', {8}, {0.226028f, -0.226028f, 0.336376f, -0.336376f, 0.564900f, -0.564900f, 1.f, -1.f}); - nd4j::ops::tanh op; + sd::ops::tanh op; op.execute({&x}, {&z}); ASSERT_EQ(e, z); -} \ No newline at end of file +} +///////////////////////////////////////////////////////////////////////////////////////////////////////// +TEST_F(DeclarableOpsTests18, test_tanh_2) { + + NDArray x('c', { 2, 2, 3, 3, 4, 4 }, sd::DataType::FLOAT32); + NDArray z('c', { 2, 2, 3, 3, 4, 4 }, sd::DataType::FLOAT32); + + x.linspace(-1., 0.003); + + NDArray e('c', { 2, 2, 3, 3, 4, 4 }, { -0.761594, -0.760331, -0.759063, -0.757788, -0.756508, -0.755222, -0.753930, -0.752633, -0.751329, -0.750020, -0.748704, -0.747383, -0.746056, -0.744723, -0.743383, -0.742038, -0.740687, -0.739330, -0.737967, -0.736598, -0.735222, -0.733841, -0.732453, -0.731060, -0.729660, -0.728254, -0.726842, -0.725424, -0.724000, -0.722569, -0.721132, -0.719689, -0.718240, -0.716784, -0.715323, -0.713854, -0.712380, -0.710899, -0.709412, -0.707919, -0.706419, -0.704913, -0.703401, -0.701882, -0.700357, -0.698825, -0.697287, -0.695742, -0.694191, -0.692634, -0.691069, -0.689499, -0.687922, -0.686338, -0.684748, -0.683152, -0.681548, -0.679939, -0.678322, -0.676699, -0.675070, -0.673434, -0.671791, -0.670142, -0.668486, -0.666823, -0.665153, -0.663477, -0.661795, -0.660105, -0.658409, -0.656706, -0.654997, -0.653280, -0.651557, -0.649827, 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-0.461330, -0.458966, -0.456594, -0.454216, -0.451832, -0.449441, -0.447044, -0.444640, -0.442230, -0.439814, -0.437391, -0.434962, -0.432526, -0.430084, -0.427636, -0.425181, -0.422721, -0.420254, -0.417780, -0.415301, -0.412815, -0.410323, -0.407825, -0.405321, -0.402811, -0.400295, -0.397773, -0.395244, -0.392710, -0.390170, -0.387623, -0.385071, -0.382513, -0.379949, -0.377379, -0.374803, -0.372222, -0.369635, -0.367042, -0.364443, -0.361839, -0.359229, -0.356613, -0.353992, -0.351365, -0.348732, -0.346095, -0.343451, -0.340802, -0.338148, -0.335488, -0.332823, -0.330153, -0.327477, -0.324796, -0.322110, -0.319419, -0.316723, -0.314021, -0.311314, -0.308602, -0.305886, -0.303164, -0.300437, -0.297705, -0.294969, -0.292227, -0.289481, -0.286730, -0.283975, -0.281214, -0.278449, -0.275679, -0.272905, -0.270126, -0.267343, -0.264555, -0.261763, -0.258966, -0.256165, -0.253360, -0.250550, -0.247737, -0.244919, -0.242097, -0.239270, -0.236440, -0.233606, -0.230768, -0.227925, -0.225079, -0.222229, -0.219376, -0.216518, -0.213657, -0.210792, -0.207923, -0.205051, -0.202176, -0.199297, -0.196414, -0.193528, -0.190639, -0.187746, -0.184850, -0.181951, -0.179049, -0.176144, -0.173235, -0.170324, -0.167409, -0.164492, -0.161572, -0.158649, -0.155723, -0.152794, -0.149863, -0.146929, -0.143992, -0.141053, -0.138112, -0.135168, -0.132221, -0.129273, -0.126322, -0.123368, -0.120413, -0.117455, -0.114496, -0.111534, -0.108570, -0.105605, -0.102637, -0.099668, -0.096697, -0.093724, -0.090750, -0.087774, -0.084796, -0.081817, -0.078836, -0.075854, -0.072871, -0.069886, -0.066900, -0.063913, -0.060924, -0.057935, -0.054945, -0.051953, -0.048961, -0.045968, -0.042974, -0.039979, -0.036983, -0.033987, -0.030990, -0.027993, -0.024995, -0.021996, -0.018998, -0.015999, -0.012999, -0.010000, -0.007000, -0.004000, -0.001000, 0.002000, 0.005000, 0.008000, 0.011000, 0.013999, 0.016998, 0.019997, 0.022996, 0.025994, 0.028992, 0.031989, 0.034986, 0.037982, 0.040977, 0.043972, 0.046965, 0.049958, 0.052950, 0.055942, 0.058932, 0.061921, 0.064909, 0.067895, 0.070881, 0.073865, 0.076848, 0.079830, 0.082810, 0.085789, 0.088766, 0.091741, 0.094715, 0.097687, 0.100658, 0.103627, 0.106594, 0.109558, 0.112521, 0.115482, 0.118441, 0.121398, 0.124353, 0.127305, 0.130256, 0.133204, 0.136149, 0.139092, 0.142033, 0.144971, 0.147907, 0.150840, 0.153771, 0.156698, 0.159623, 0.162545, 0.165465, 0.168381, 0.171294, 0.174205, 0.177112, 0.180017, 0.182918, 0.185816, 0.188711, 0.191602, 0.194490, 0.197375, 0.200257, 0.203135, 0.206009, 0.208880, 0.211747, 0.214611, 0.217471, 0.220327, 0.223180, 0.226028, 0.228873, 0.231714, 0.234551, 0.237384, 0.240213, 0.243038, 0.245858, 0.248675, 0.251487, 0.254296, 0.257099, 0.259899, 0.262694, 0.265485, 0.268271, 0.271053, 0.273830, 0.276603, 0.279371, 0.282135, 0.284894, 0.287648, 0.290397, 0.293142, 0.295882, 0.298617, 0.301347, 0.304072, 0.306792, 0.309507, 0.312217, 0.314922, 0.317622, 0.320317, 0.323006, 0.325691, 0.328370, 0.331044, 0.333712, 0.336376, 0.339033, 0.341686, 0.344333, 0.346974, 0.349611, 0.352241, 0.354866, 0.357485, 0.360099, 0.362707, 0.365310, 0.367907, 0.370498, 0.373083, 0.375663, 0.378236, 0.380804, 0.383366, 0.385922, 0.388473, 0.391017, 0.393555, 0.396088, 0.398614, 0.401134, 0.403649, 0.406157, 0.408659, 0.411155, 0.413644, 0.416128, 0.418605, 0.421077, 0.423542, 0.426000, 0.428453, 0.430899, 0.433339, 0.435772, 0.438199, 0.440620, 0.443034, 0.445442, 0.447844, 0.450239, 0.452628, 0.455010, 0.457385, 0.459755, 0.462117, 0.464473, 0.466823, 0.469166, 0.471502, 0.473832, 0.476155, 0.478471, 0.480781, 0.483085, 0.485381, 0.487671, 0.489954, 0.492231, 0.494500, 0.496763, 0.499020, 0.501269, 0.503512, 0.505748, 0.507977, 0.510200, 0.512416, 0.514624, 0.516827, 0.519022, 0.521210, 0.523392, 0.525567, 0.527735, 0.529896, 0.532050, 0.534197, 0.536338, 0.538471, 0.540598, 0.542718, 0.544831, 0.546937, 0.549036, 0.551128, 0.553213, 0.555292, 0.557363, 0.559428, 0.561486, 0.563536, 0.565580, 0.567617, 0.569647, 0.571670, 0.573686, 0.575695, 0.577697, 0.579693, 0.581681, 0.583663, 0.585637, 0.587605, 0.589566, 0.591519, 0.593466, 0.595406, 0.597339, 0.599265, 0.601184, 0.603097, 0.605002, 0.606901, 0.608792, 0.610677, 0.612555, 0.614425, 0.616289, 0.618147, 0.619997 }, sd::DataType::FLOAT32); + + sd::ops::tanh op; + op.execute({ &x }, { &z }); + ASSERT_EQ(e, z); +} +///////////////////////////////////////////////////////////////////////////////////////////////////////// +TEST_F(DeclarableOpsTests18, test_tanh_bp) { + + NDArray x('c', { 2, 3, 4 }, sd::DataType::FLOAT32); + NDArray dLdz('c', { 2, 3, 4 }, sd::DataType::FLOAT32); + NDArray dLdx('c', { 2, 3, 4 }, sd::DataType::FLOAT32); + + x.linspace(-1., 0.003); + dLdz.linspace(0.01, 0.01); + + NDArray e('c', { 2, 3, 4 }, { 0.004200, 0.008438, 0.012715, 0.017030, 0.021385, 0.025778, 0.030211, 0.034684, 0.039195, 0.043747, 0.048339, 0.052970, 0.057642, 0.062354, 0.067107, 0.071901, 0.076735, 0.081610, 0.086527, 0.091485, 0.096484, 0.101525, 0.106608, 0.111732 }, sd::DataType::FLOAT32); + + sd::ops::tanh_bp op; + op.execute({ &x, &dLdz }, { &dLdx }); + ASSERT_EQ(e, dLdx); +} +///////////////////////////////////////////////////////////////////////////////////////////////////////// +TEST_F(DeclarableOpsTests18, test_tanh_bp2) { + + NDArray x('f', { 2, 3, 4 }, sd::DataType::FLOAT32); + NDArray dLdz('f', { 2, 3, 4 }, sd::DataType::FLOAT32); + NDArray dLdx('f', { 2, 3, 4 }, sd::DataType::FLOAT32); + + x.linspace(-1., 0.003); + dLdz.linspace(0.01, 0.01); + + NDArray exp('c', { 2, 3, 4 }, { 0.004200, 0.008438, 0.012715, 0.017030, 0.021385, 0.025778, 0.030211, 0.034684, 0.039195, 0.043747, 0.048339, 0.052970, 0.057642, 0.062354, 0.067107, 0.071901, 0.076735, 0.081610, 0.086527, 0.091485, 0.096484, 0.101525, 0.106608, 0.111732 }, sd::DataType::FLOAT32); + NDArray e('f', { 2, 3, 4 }, sd::DataType::FLOAT32); + e.assign(exp); + + sd::ops::tanh_bp op; + op.execute({ &x, &dLdz }, { &dLdx }); + ASSERT_EQ(e, dLdx); +} +///////////////////////////////////////////////////////////////////////////////////////////////////////// +TEST_F(DeclarableOpsTests18, test_tanh_bp3) { + + NDArray x('f', { 2, 2, 3, 3, 4, 4 }, sd::DataType::FLOAT32); + NDArray dLdz('f', { 2,2, 3,3, 4,4 }, sd::DataType::FLOAT32); + NDArray dLdx('f', { 2, 2, 3, 3, 4, 4 }, sd::DataType::FLOAT32); + + x.linspace(-1.5, 0.005); + dLdz.linspace(-1., 0.01); + + NDArray exp('c', { 2, 2, 3, 3, 4, 4 }, { -0.180707, -0.180525, -0.180324, -0.180103, -0.179861, -0.179599, -0.179315, -0.179009, -0.178682, -0.178333, -0.177961, -0.177566, -0.177148, -0.176706, -0.176240, -0.175750, -0.175236, -0.174696, -0.174130, -0.173539, -0.172922, -0.172278, -0.171607, -0.170909, -0.170183, -0.169429, -0.168646, -0.167834, -0.166993, -0.166123, -0.165222, -0.164290, -0.163327, -0.162334, -0.161308, -0.160250, -0.159159, -0.158035, -0.156877, -0.155686, -0.154460, -0.153199, -0.151903, -0.150571, -0.149203, -0.147798, -0.146356, -0.144876, -0.143359, -0.141803, -0.140207, -0.138573, -0.136898, -0.135183, -0.133428, -0.131630, -0.129792, -0.127910, -0.125986, -0.124019, -0.122008, -0.119953, -0.117853, -0.115708, -0.113517, -0.111279, -0.108996, -0.106665, -0.104286, -0.101859, -0.099383, -0.096859, -0.094284, -0.091660, -0.088984, -0.086258, -0.083480, -0.080649, -0.077766, -0.074830, -0.071840, -0.068796, -0.065697, -0.062543, -0.059334, -0.056068, -0.052745, -0.049365, -0.045928, -0.042432, -0.038878, -0.035264, -0.031591, -0.027858, -0.024064, -0.020209, -0.016292, -0.012313, -0.008272, -0.004168, 0.000000, 0.004232, 0.008528, 0.012889, 0.017316, 0.021808, 0.026367, 0.030992, 0.035684, 0.040444, 0.045272, 0.050169, 0.055134, 0.060168, 0.065273, 0.070447, 0.075692, 0.081007, 0.086394, 0.091853, 0.097383, 0.102986, 0.108662, 0.114411, 0.120233, 0.126129, 0.132099, 0.138144, 0.144263, 0.150457, 0.156727, 0.163072, 0.169493, 0.175990, 0.182564, 0.189214, 0.195941, 0.202745, 0.209627, 0.216585, 0.223622, 0.230736, 0.237929, 0.245200, 0.252549, 0.259976, 0.267482, 0.275066, 0.282730, 0.290472, 0.298293, 0.306193, 0.314172, 0.322230, 0.330366, 0.338582, 0.346877, 0.355250, 0.363703, 0.372234, 0.380844, 0.389532, 0.398299, 0.407144, 0.416067, 0.425068, 0.434147, 0.443303, 0.452537, 0.461848, 0.471235, 0.480699, 0.490240, 0.499856, 0.509548, 0.519314, 0.529156, 0.539072, 0.549062, 0.559126, 0.569262, 0.579471, 0.589753, 0.600106, 0.610530, 0.621024, 0.631588, 0.642222, 0.652924, 0.663694, 0.674532, 0.685436, 0.696406, 0.707441, 0.718541, 0.729704, 0.740931, 0.752219, 0.763568, 0.774978, 0.786448, 0.797976, 0.809561, 0.821203, 0.832901, 0.844654, 0.856460, 0.868319, 0.880230, 0.892191, 0.904201, 0.916260, 0.928366, 0.940518, 0.952715, 0.964955, 0.977238, 0.989561, 1.001925, 1.014327, 1.026767, 1.039242, 1.051752, 1.064295, 1.076870, 1.089475, 1.102109, 1.114771, 1.127459, 1.140171, 1.152907, 1.165664, 1.178441, 1.191237, 1.204050, 1.216878, 1.229720, 1.242573, 1.255438, 1.268311, 1.281192, 1.294078, 1.306968, 1.319860, 1.332753, 1.345644, 1.358533, 1.371417, 1.384294, 1.397163, 1.410022, 1.422870, 1.435704, 1.448522, 1.461323, 1.474105, 1.486867, 1.499606, 1.512321, 1.525009, 1.537669, 1.550299, 1.562897, 1.575462, 1.587991, 1.600483, 1.612935, 1.625347, 1.637715, 1.650040, 1.662317, 1.674545, 1.686724, 1.698850, 1.710922, 1.722939, 1.734897, 1.746797, 1.758635, 1.770409, 1.782119, 1.793762, 1.805337, 1.816842, 1.828274, 1.839633, 1.850916, 1.862121, 1.873248, 1.884294, 1.895258, 1.906137, 1.916931, 1.927637, 1.938255, 1.948782, 1.959216, 1.969557, 1.979802, 1.989950, 2.000000, 2.009950, 2.019798, 2.029543, 2.039184, 2.048719, 2.058147, 2.067466, 2.076675, 2.085773, 2.094759, 2.103630, 2.112386, 2.121026, 2.129548, 2.137952, 2.146235, 2.154397, 2.162437, 2.170354, 2.178146, 2.185813, 2.193353, 2.200766, 2.208051, 2.215207, 2.222232, 2.229127, 2.235889, 2.242520, 2.249017, 2.255379, 2.261607, 2.267699, 2.273656, 2.279475, 2.285158, 2.290702, 2.296108, 2.301376, 2.306503, 2.311491, 2.316339, 2.321046, 2.325613, 2.330038, 2.334321, 2.338464, 2.342464, 2.346322, 2.350037, 2.353610, 2.357041, 2.360329, 2.363475, 2.366478, 2.369338, 2.372056, 2.374632, 2.377065, 2.379356, 2.381505, 2.383512, 2.385378, 2.387103, 2.388686, 2.390128, 2.391431, 2.392593, 2.393615, 2.394499, 2.395244, 2.395850, 2.396319, 2.396650, 2.396845, 2.396904, 2.396826, 2.396615, 2.396268, 2.395789, 2.395176, 2.394431, 2.393554, 2.392547, 2.391410, 2.390144, 2.388749, 2.387227, 2.385578, 2.383804, 2.381904, 2.379880, 2.377734, 2.375465, 2.373075, 2.370565, 2.367936, 2.365188, 2.362324, 2.359343, 2.356247, 2.353038, 2.349715, 2.346280, 2.342735, 2.339080, 2.335316, 2.331445, 2.327468, 2.323386, 2.319200, 2.314912, 2.310522, 2.306031, 2.301442, 2.296754, 2.291970, 2.287090, 2.282116, 2.277049, 2.271890, 2.266641, 2.261302, 2.255876, 2.250362, 2.244763, 2.239080, 2.233314, 2.227467, 2.221538, 2.215531, 2.209445, 2.203284, 2.197047, 2.190736, 2.184352, 2.177897, 2.171371, 2.164777, 2.158115, 2.151386, 2.144592, 2.137735, 2.130815, 2.123833, 2.116792, 2.109692, 2.102533, 2.095320, 2.088051, 2.080727, 2.073352, 2.065925, 2.058447, 2.050921, 2.043347, 2.035727, 2.028061, 2.020351, 2.012599, 2.004804, 1.996969, 1.989094, 1.981181, 1.973232, 1.965246, 1.957225, 1.949171, 1.941084, 1.932965, 1.924816, 1.916638, 1.908432, 1.900198, 1.891938, 1.883654, 1.875345, 1.867014, 1.858661, 1.850286, 1.841892, 1.833479, 1.825048, 1.816600, 1.808136, 1.799657, 1.791165, 1.782659, 1.774141, 1.765612, 1.757073, 1.748523, 1.739967, 1.731401, 1.722829, 1.714251, 1.705668, 1.697082, 1.688491, 1.679897, 1.671302, 1.662707, 1.654110, 1.645514, 1.636920, 1.628328, 1.619738, 1.611152, 1.602570, 1.593993, 1.585422, 1.576857, 1.568299, 1.559749, 1.551207, 1.542674, 1.534151, 1.525638, 1.517136, 1.508645, 1.500167, 1.491701, 1.483248, 1.474810, 1.466385, 1.457976, 1.449581, 1.441203, 1.432841, 1.424496, 1.416169, 1.407860, 1.399569, 1.391297, 1.383045, 1.374812, 1.366600, 1.358408, 1.350237, 1.342088, 1.333961, 1.325856, 1.317774, 1.309715, 1.301679, 1.293668, 1.285680, 1.277718, 1.269780, 1.261867, 1.253980, 1.246119, 1.238283, 1.230474, 1.222692, 1.214937, 1.207210, 1.199510, 1.191837, 1.184193, 1.176577, 1.168990, 1.161430, 1.153901, 1.146401, 1.138930, 1.131489, 1.124077, 1.116696, 1.109345, 1.102024, 1.094734, 1.087475, 1.080246, 1.073049 }, sd::DataType::FLOAT32); + + NDArray e('f', { 2, 2, 3, 3, 4, 4 }, sd::DataType::FLOAT32); + e.assign(exp); + + sd::ops::tanh_bp op; + op.execute({ &x, &dLdz }, { &dLdx }); + ASSERT_EQ(e, dLdx); +} +////////////////////////////////////////////////////////////////////// +TEST_F(DeclarableOpsTests18, TestSoftMax_bp_TEST) { + + NDArray input('c', { 2, 2 }, { 1,2,3,4 }, DataType::FLOAT32); + NDArray epsilon('c', { 2, 2 }, { .1, .2, .3, .4 }, DataType::FLOAT32); + + int axis = 1; + + NDArray output('c', { 2, 2 }, DataType::FLOAT32); + + NDArray exp('c', { 2, 2 }, { -0.019661, 0.019661, -0.019661, 0.019661 }, DataType::FLOAT32); + + sd::ops::softmax_bp op; + + Nd4jStatus status = op.execute({ &input, &epsilon }, { &output }, {}, { axis }); + ASSERT_EQ(ND4J_STATUS_OK, status); + ASSERT_TRUE(output.equalsTo(exp)); + +} +////////////////////////////////////////////////////////////////////// +TEST_F(DeclarableOpsTests18, TestSoftMax_bp_TEST2) { + + NDArray input('c', { 4, 5, 2, 3 }, DataType::FLOAT32); + NDArray epsilon('c', { 4, 5, 2, 3 }, { -0.030498, -0.004357, 0.034855, -0.030498, -0.004357, 0.034855, -0.030498, -0.004357, 0.034855, -0.030498, -0.004357, 0.034855, -0.030498, -0.004357, 0.034855, -0.030498, -0.004357, 0.034855, -0.030498, -0.004357, 0.034855, -0.030498, -0.004357, 0.034855, -0.030498, -0.004357, 0.034855, -0.030498, -0.004357, 0.034855, -0.030498, -0.004357, 0.034855, -0.030498, -0.004357, 0.034855, -0.030498, -0.004357, 0.034855, -0.030498, -0.004357, 0.034855, -0.030498, -0.004357, 0.034855, -0.030498, -0.004357, 0.034855, -0.030498, -0.004357, 0.034855, -0.030498, -0.004357, 0.034855, -0.030498, -0.004357, 0.034855, -0.030498, -0.004357, 0.034855, -0.030498, -0.004357, 0.034855, -0.030498, -0.004357, 0.034855, -0.030498, -0.004357, 0.034855, -0.030498, -0.004357, 0.034855, -0.030498, -0.004357, 0.034855, -0.030498, -0.004357, 0.034855, -0.030498, -0.004357, 0.034855, -0.030498, -0.004357, 0.034855, -0.030498, -0.004357, 0.034855, -0.030498, -0.004357, 0.034855, -0.030498, -0.004357, 0.034855, -0.030498, -0.004357, 0.034855, -0.030498, -0.004357, 0.034855, -0.030498, -0.004357, 0.034855, -0.030498, -0.004357, 0.034855, -0.030498, -0.004357, 0.034855, -0.030498, -0.004357, 0.034855, -0.030498, -0.004357, 0.034855, -0.030498, -0.004357, 0.034855, -0.030498, -0.004357, 0.034855 }, DataType::FLOAT32); + input.linspace(0.1, 0.2); + + int axis = -1; + + NDArray output('c', { 4, 5, 2, 3 }, DataType::FLOAT32); + NDArray exp('c', { 4, 5, 2, 3 }, { -0.009387, -0.002866, 0.012253, -0.009387, -0.002866, 0.012253, -0.009387, -0.002866, 0.012253, -0.009387, -0.002866, 0.012253, -0.009387, -0.002866, 0.012253, -0.009387, -0.002866, 0.012253, -0.009387, -0.002866, 0.012253, -0.009387, -0.002866, 0.012253, -0.009387, -0.002866, 0.012253, -0.009387, -0.002866, 0.012253, -0.009387, -0.002866, 0.012253, -0.009387, -0.002866, 0.012253, -0.009387, -0.002866, 0.012253, -0.009387, -0.002866, 0.012253, -0.009387, -0.002866, 0.012253, -0.009387, -0.002866, 0.012253, -0.009387, -0.002866, 0.012253, -0.009387, -0.002866, 0.012253, -0.009387, -0.002866, 0.012253, -0.009387, -0.002866, 0.012253, -0.009387, -0.002866, 0.012253, -0.009387, -0.002866, 0.012253, -0.009387, -0.002866, 0.012253, -0.009387, -0.002866, 0.012253, -0.009387, -0.002866, 0.012253, -0.009387, -0.002866, 0.012253, -0.009387, -0.002866, 0.012253, -0.009387, -0.002866, 0.012253, -0.009387, -0.002866, 0.012253, -0.009387, -0.002866, 0.012253, -0.009387, -0.002866, 0.012253, -0.009387, -0.002866, 0.012253, -0.009387, -0.002866, 0.012253, -0.009387, -0.002866, 0.012253, -0.009387, -0.002866, 0.012253, -0.009387, -0.002866, 0.012253, -0.009387, -0.002866, 0.012253, -0.009387, -0.002866, 0.012253, -0.009387, -0.002866, 0.012253, -0.009387, -0.002866, 0.012253 }, DataType::FLOAT32); + + sd::ops::softmax_bp op; + + Nd4jStatus status = op.execute({ &input, &epsilon }, { &output }, {}, { axis }); + ASSERT_EQ(ND4J_STATUS_OK, status); + ASSERT_TRUE(output.equalsTo(exp)); + +} +////////////////////////////////////////////////////////////////////// +TEST_F(DeclarableOpsTests18, TestSoftMax_bp_TEST3) { + + NDArray input('f', { 4, 5, 2, 3 }, DataType::FLOAT32); + NDArray epsilon('f', { 4, 5, 2, 3 }, { -0.030498, -0.004357, 0.034855, -0.030498, -0.004357, 0.034855, -0.030498, -0.004357, 0.034855, -0.030498, -0.004357, 0.034855, -0.030498, -0.004357, 0.034855, -0.030498, -0.004357, 0.034855, -0.030498, -0.004357, 0.034855, -0.030498, -0.004357, 0.034855, -0.030498, -0.004357, 0.034855, -0.030498, -0.004357, 0.034855, -0.030498, -0.004357, 0.034855, -0.030498, -0.004357, 0.034855, -0.030498, -0.004357, 0.034855, -0.030498, -0.004357, 0.034855, -0.030498, -0.004357, 0.034855, -0.030498, -0.004357, 0.034855, -0.030498, -0.004357, 0.034855, -0.030498, -0.004357, 0.034855, -0.030498, -0.004357, 0.034855, -0.030498, -0.004357, 0.034855, -0.030498, -0.004357, 0.034855, -0.030498, -0.004357, 0.034855, -0.030498, -0.004357, 0.034855, -0.030498, -0.004357, 0.034855, -0.030498, -0.004357, 0.034855, -0.030498, -0.004357, 0.034855, -0.030498, -0.004357, 0.034855, -0.030498, -0.004357, 0.034855, -0.030498, -0.004357, 0.034855, -0.030498, -0.004357, 0.034855, -0.030498, -0.004357, 0.034855, -0.030498, -0.004357, 0.034855, -0.030498, -0.004357, 0.034855, -0.030498, -0.004357, 0.034855, -0.030498, -0.004357, 0.034855, -0.030498, -0.004357, 0.034855, -0.030498, -0.004357, 0.034855, -0.030498, -0.004357, 0.034855, -0.030498, -0.004357, 0.034855, -0.030498, -0.004357, 0.034855 }, DataType::FLOAT32); + input.linspace(-5., 0.5); + + int axis = 1; + + NDArray output('f', { 4, 5, 2, 3 }, DataType::FLOAT32); + NDArray expC('c', { 4, 5, 2, 3 }, { -0.0, -0.0, 0.0, 0.0, -0.0, -0.0, 0.0, 0.0, -0.0, -0.0, 0.0, 0.0, 0.000095, -0.000149, 0.000054, 0.000054, 0.000095, -0.000149, -0.001183, -0.001760, 0.002943, 0.002943, -0.001183, -0.001760, 0.001088, 0.001909, -0.002997, -0.002997, 0.001088, 0.001909, -0.000000, 0.000000, -0.000000, -0.000000, -0.000000, 0.000000, 0.000000, -0.000000, 0.000000, 0.000000, 0.000000, -0.000000, -0.000149, 0.000054, 0.000095, 0.000095, -0.000149, 0.000054, -0.001760, 0.002943, -0.001183, -0.001183, -0.001760, 0.002943, 0.001909, -0.002997, 0.001088, 0.001088, 0.001909, -0.002997, 0.000000, -0.000000, -0.000000, -0.000000, 0.000000, -0.000000, -0.000000, 0.000000, 0.000000, 0.000000, -0.000000, 0.000000, 0.000054, 0.000095, -0.000149, -0.000149, 0.000054, 0.000095, 0.002943, -0.001183, -0.001760, -0.001760, 0.002943, -0.001183, -0.002997, 0.001088, 0.001909, 0.001909, -0.002997, 0.001088, -0.000000, -0.000000, 0.000000, 0.000000, -0.000000, -0.000000, 0.000000, 0.000000, -0.000000, -0.000000, 0.000000, 0.000000, 0.000095, -0.000149, 0.000054, 0.000054, 0.000095, -0.000149, -0.001183, -0.001760, 0.002943, 0.002943, -0.001183, -0.001760, 0.001088, 0.001909, -0.002997, -0.002997, 0.001088, 0.001909 }, DataType::FLOAT32); + + NDArray exp('f', { 4, 5, 2, 3 }, DataType::FLOAT32); + exp.assign(expC); + + sd::ops::softmax_bp op; + + Nd4jStatus status = op.execute({ &input, &epsilon }, { &output }, {}, { axis }); + ASSERT_EQ(ND4J_STATUS_OK, status); + ASSERT_TRUE(output.equalsTo(exp)); +} diff --git a/libnd4j/tests_cpu/layers_tests/DeclarableOpsTests19.cpp b/libnd4j/tests_cpu/layers_tests/DeclarableOpsTests19.cpp index b0a547a7d..8b2bd0071 100644 --- a/libnd4j/tests_cpu/layers_tests/DeclarableOpsTests19.cpp +++ b/libnd4j/tests_cpu/layers_tests/DeclarableOpsTests19.cpp @@ -21,13 +21,13 @@ #include "testlayers.h" #include -#include +#include #include -#include +#include #include -using namespace nd4j; +using namespace sd; class DeclarableOpsTests19 : public testing::Test { @@ -60,12 +60,10 @@ TEST_F(DeclarableOpsTests19, test_conv1d_bp_1) { auto u = NDArrayFactory::create('c', {3, 2, 3}); auto v = NDArrayFactory::create('c', {2, 3, 6}); - nd4j::ops::conv1d_bp op; + sd::ops::conv1d_bp op; auto result = op.evaluate({&t, &u, &v}, {3, 2, 0, 1, 2,0}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - - delete result; } TEST_F(DeclarableOpsTests19, test_squeeze_1) { @@ -73,7 +71,7 @@ TEST_F(DeclarableOpsTests19, test_squeeze_1) { auto e = NDArrayFactory::create('c', {3, 4}); int axis = 2; - nd4j::ops::squeeze op; + sd::ops::squeeze op; auto status = op.execute({&x}, {&e}, {axis}); ASSERT_EQ(Status::OK(), status); } \ No newline at end of file diff --git a/libnd4j/tests_cpu/layers_tests/DeclarableOpsTests2.cpp b/libnd4j/tests_cpu/layers_tests/DeclarableOpsTests2.cpp index 029a392f7..f4847889b 100644 --- a/libnd4j/tests_cpu/layers_tests/DeclarableOpsTests2.cpp +++ b/libnd4j/tests_cpu/layers_tests/DeclarableOpsTests2.cpp @@ -17,12 +17,12 @@ #include "testlayers.h" #include #include -#include +#include #include -using namespace nd4j; -using namespace nd4j::graph; +using namespace sd; +using namespace sd::graph; class DeclarableOpsTests2 : public testing::Test { public: @@ -35,43 +35,38 @@ public: //////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests2, gather_1) { - NDArray input('c', {2,3,4}, {1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24}, nd4j::DataType::FLOAT32); - NDArray indices('c', {1,6}, {0,1, 2,2, 1,2}, nd4j::DataType::INT32); - NDArray expected('c', {2,1,6,4}, {1, 2, 3, 4, 5, 6, 7, 8, 9,10,11,12, 9,10,11,12, 5, 6, 7, 8, 9,10,11,12, 13,14,15,16, 17,18,19,20, 21,22,23,24, 21,22,23,24, 17,18,19,20, 21,22,23,24}, nd4j::DataType::FLOAT32); + NDArray input('c', {2,3,4}, {1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24}, sd::DataType::FLOAT32); + NDArray indices('c', {1,6}, {0,1, 2,2, 1,2}, sd::DataType::INT32); + NDArray expected('c', {2,1,6,4}, {1, 2, 3, 4, 5, 6, 7, 8, 9,10,11,12, 9,10,11,12, 5, 6, 7, 8, 9,10,11,12, 13,14,15,16, 17,18,19,20, 21,22,23,24, 21,22,23,24, 17,18,19,20, 21,22,23,24}, sd::DataType::FLOAT32); - nd4j::ops::gather op; + sd::ops::gather op; auto result = op.evaluate({&input, &indices}, {1}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto* output = result->at(0); + auto* output = result.at(0); ASSERT_TRUE(expected.isSameShapeStrict(*output)); ASSERT_TRUE(expected.equalsTo(output)); - - delete result; } - TEST_F(DeclarableOpsTests2, gather_2) { NDArray input('c', {2,3,4}, {1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24}); //auto indices ('c', {1,6}, {0,1, 2,2, 1,2}); NDArray expected('c', {2,6,4}, {1, 2, 3, 4, 5, 6, 7, 8, 9,10,11,12, 9,10,11,12, 5, 6, 7, 8, 9,10,11,12, 13,14,15,16, 17,18,19,20, 21,22,23,24, 21,22,23,24, 17,18,19,20, 21,22,23,24}); - nd4j::ops::gather op; + sd::ops::gather op; auto result = op.evaluate({&input}, {}, {1, 0,1, 2,2, 1,2}, {true}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto* output = result->at(0); + auto* output = result.at(0); ASSERT_TRUE(expected.isSameShapeStrict(*output)); ASSERT_TRUE(expected.equalsTo(output)); - - delete result; } @@ -79,21 +74,19 @@ TEST_F(DeclarableOpsTests2, gather_2) { TEST_F(DeclarableOpsTests2, gather_3) { NDArray input ('c', {2,3,4}, {1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24}); - NDArray indices ('c', {1,1}, std::vector{2}, nd4j::DataType::INT32); + NDArray indices ('c', {1,1}, std::vector{2}, sd::DataType::INT32); NDArray expected('c', {2,1,1,4}, {9,10,11,12,21,22,23,24}); - nd4j::ops::gather op; + sd::ops::gather op; auto result = op.evaluate({&input, &indices}, {}, {1}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto* output = result->at(0); + auto* output = result.at(0); ASSERT_TRUE(expected.isSameShapeStrict(*output)); ASSERT_TRUE(expected.equalsTo(output)); - - delete result; } TEST_F(DeclarableOpsTests2, gather_4) { @@ -102,119 +95,109 @@ TEST_F(DeclarableOpsTests2, gather_4) { //auto indices ('c', {1,1}, {2}); NDArray expected('c', {2,4}, {9,10,11,12,21,22,23,24}); - nd4j::ops::gather op; + sd::ops::gather op; auto result = op.evaluate({&input}, {}, {1, 2}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto* output = result->at(0); + auto* output = result.at(0); ASSERT_TRUE(expected.isSameShapeStrict(*output)); ASSERT_TRUE(expected.equalsTo(output)); - - delete result; } //////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests2, gather_5) { NDArray input ('c', {2,3,4}, {1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24}); - NDArray indices ('c', {2,3}, {0, 1, 2, 2, 1,2}, nd4j::DataType::INT32); + NDArray indices ('c', {2,3}, {0, 1, 2, 2, 1,2}, sd::DataType::INT32); NDArray expected('c', {2,2,3,4}, {1, 2, 3, 4, 5, 6, 7, 8, 9,10,11,12, 9,10,11,12, 5, 6, 7, 8, 9,10,11,12, 13,14,15,16,17,18,19,20,21,22,23,24, 21,22,23,24,17,18,19,20,21,22,23,24}); - nd4j::ops::gather op; + sd::ops::gather op; auto result = op.evaluate({&input, &indices}, {}, {1}, {true}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto* output = result->at(0); + auto* output = result.at(0); ASSERT_TRUE(expected.isSameShapeStrict(*output)); ASSERT_TRUE(expected.equalsTo(output)); - delete result; + } - //////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests2, gather_6) { NDArray input ('c', {3,3,4}, {1, 2, 3, 4, 5, 6, 7, 8, 9,10,11,12, 13,14,15,16,17,18,19,20,21,22,23,24, 25,26,27,28,29,30,31,32,33,34,35,36}); - NDArray indices ('c', {2,3}, {0, 1, 2, 2, 1,2}, nd4j::DataType::INT32); + NDArray indices ('c', {2,3}, {0, 1, 2, 2, 1,2}, sd::DataType::INT32); NDArray expected('c', {2,3,3,4}, {1, 2, 3, 4, 5, 6, 7, 8, 9,10,11,12, 13,14,15,16,17,18,19,20,21,22,23,24, 25,26,27,28,29,30,31,32,33,34,35,36, 25,26,27,28,29,30,31,32,33,34,35,36, 13,14,15,16,17,18,19,20,21,22,23,24, 25,26,27,28,29,30,31,32,33,34,35,36}); - nd4j::ops::gather op; + sd::ops::gather op; auto result = op.evaluate({&input, &indices}, {}, {0}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto* output = result->at(0); + auto* output = result.at(0); ASSERT_TRUE(expected.isSameShapeStrict(*output)); ASSERT_TRUE(expected.equalsTo(output)); - delete result; } - //////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests2, gather_7) { NDArray input ('c', {2,3,4}, {1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24}); - NDArray indices ('c', {2,3}, {0, 1, 2, 2, 1,2}, nd4j::DataType::INT64); + NDArray indices ('c', {2,3}, {0, 1, 2, 2, 1,2}, sd::DataType::INT64); NDArray expected('c', {2,3,2,3}, {1, 2, 3, 3, 2, 3, 5, 6, 7, 7, 6, 7, 9,10,11,11,10,11, 13,14,15,15,14,15, 17,18,19,19,18,19, 21,22,23,23,22,23}); - nd4j::ops::gather op; + sd::ops::gather op; auto result = op.evaluate({&input, &indices}, {}, {2}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto* output = result->at(0); + auto* output = result.at(0); ASSERT_TRUE(expected.isSameShapeStrict(*output)); ASSERT_TRUE(expected.equalsTo(output)); - - delete result; + } - //////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests2, gather_8) { - NDArray input('c', {3,5}, {1,2,3,4,5,6,7,8,9,10,11,12,13,14,15}, nd4j::DataType::FLOAT32); - NDArray indices('c', {1}, std::vector{2}, nd4j::DataType::INT32); - NDArray expected('c', {1,5}, {11, 12, 13, 14, 15.}, nd4j::DataType::FLOAT32); + NDArray input('c', {3,5}, {1,2,3,4,5,6,7,8,9,10,11,12,13,14,15}, sd::DataType::FLOAT32); + NDArray indices('c', {1}, std::vector{2}, sd::DataType::INT32); + NDArray expected('c', {1,5}, {11, 12, 13, 14, 15.}, sd::DataType::FLOAT32); - nd4j::ops::gather op; + sd::ops::gather op; auto result = op.evaluate({&input, &indices}, {}, {0}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); - auto* output = result->at(0); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); + auto* output = result.at(0); // output->printShapeInfo(); // output->printIndexedBuffer(); ASSERT_TRUE(expected.isSameShapeStrict(*output)); ASSERT_TRUE(expected.equalsTo(output)); - delete result; } //////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests2, gather_9) { - NDArray x('c', {2, 4, 3, 2}, nd4j::DataType::FLOAT32); - NDArray indices('c', {2}, std::vector{1, 0}, nd4j::DataType::INT32); + NDArray x('c', {2, 4, 3, 2}, sd::DataType::FLOAT32); + NDArray indices('c', {2}, std::vector{1, 0}, sd::DataType::INT32); - nd4j::ops::gather op; + sd::ops::gather op; auto result = op.evaluate({&x, &indices}, {}, {-2}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); - - delete result; + auto z = result.at(0); } //////////////////////////////////////////////////////////////////// @@ -222,61 +205,58 @@ TEST_F(DeclarableOpsTests2, gather_10) { NDArray x('c', {2, 2}, {1, 2, 3, 4}); NDArray e('c', {2, 2}, {3, 4, 1, 2}); - nd4j::ops::gather op; + sd::ops::gather op; auto result = op.evaluate({&x}, {}, {0, 1, 0}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(e.isSameShape(z)); ASSERT_TRUE(e.equalsTo(z)); - delete result; } //////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests2, gather_11) { NDArray x('c', {2, 2}, {1, 2, 3, 4}); - NDArray indices('c', {2}, std::vector{1, 0}, nd4j::DataType::INT64); + NDArray indices('c', {2}, std::vector{1, 0}, sd::DataType::INT64); NDArray e('c', {2, 2}, {3, 4, 1, 2}); - nd4j::ops::gather op; + sd::ops::gather op; auto result = op.evaluate({&x, &indices}, {}, {0}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(e.isSameShape(z)); ASSERT_TRUE(e.equalsTo(z)); - - delete result; + } //////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests2, gather_12) { NDArray input('c', {4}, {2.f, 3.f, 4.f, 5.f}); - NDArray indices('c', {2}, {0, 2}, nd4j::DataType::INT32); + NDArray indices('c', {2}, {0, 2}, sd::DataType::INT32); NDArray exp('c', {2}, {2.f, 4.f}); - nd4j::ops::gather op; + sd::ops::gather op; auto result = op.evaluate({&input, &indices}, {}, {}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - - delete result; + } //////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests2, gather_13) { - NDArray input ('c', {2,3,4,5}, nd4j::DataType::DOUBLE); - NDArray indices ('c', {2,3,4}, {0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3,0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3}, nd4j::DataType::INT32); + NDArray input ('c', {2,3,4,5}, sd::DataType::DOUBLE); + NDArray indices ('c', {2,3,4}, {0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3,0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3}, sd::DataType::INT32); NDArray expected('c', {2,3, 2,3,4, 5}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, @@ -292,28 +272,27 @@ TEST_F(DeclarableOpsTests2, gather_13) { input.linspace(0); - nd4j::ops::gather op; + sd::ops::gather op; auto result = op.evaluate({&input, &indices}, {}, {2}, {true}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto* output = result->at(0); + auto* output = result.at(0); ASSERT_TRUE(expected.isSameShapeStrict(*output)); ASSERT_TRUE(expected.equalsTo(output)); - - delete result; + } //////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests2, gather_14) { NDArray input ('c', {2,3,4}, {1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24}); - NDArray indices ('c', {2,3}, {0, 10, 2, 20, 1,2}, nd4j::DataType::INT32); + NDArray indices ('c', {2,3}, {0, 10, 2, 20, 1,2}, sd::DataType::INT32); NDArray output('c', {2,2,3,4}); - nd4j::ops::gather op; + sd::ops::gather op; ASSERT_ANY_THROW(op.execute({&input, &indices}, {&output}, {}, {1}, {true})); } @@ -321,27 +300,26 @@ TEST_F(DeclarableOpsTests2, gather_14) { //////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests2, gather_15) { - NDArray input ('c', {2,3,4,5}, nd4j::DataType::DOUBLE); - NDArray indices ('c', {2,3,4}, {0, 10, 2, 3, 0, 1, 20, 3, 0, 1, 2, 3,0, 1, 2, 3, 0, 1, 2, 30, 0, 1, 2, 3}, nd4j::DataType::INT32); + NDArray input ('c', {2,3,4,5}, sd::DataType::DOUBLE); + NDArray indices ('c', {2,3,4}, {0, 10, 2, 3, 0, 1, 20, 3, 0, 1, 2, 3,0, 1, 2, 3, 0, 1, 2, 30, 0, 1, 2, 3}, sd::DataType::INT32); NDArray output('c', {2,3, 2,3,4, 5}); - nd4j::ops::gather op; + sd::ops::gather op; ASSERT_ANY_THROW(op.execute({&input, &indices}, {&output}, {}, {2}, {true})); } TEST_F(DeclarableOpsTests2, BroadcastGradientArgs_1) { - NDArray input ('c', {3,3,4}, {1, 2, 3, 4, 5, 6, 7, 8, 9,10,11,12, 13,14,15,16,17,18,19,20,21,22,23,24, 25,26,27,28,29,30,31,32,33,34,35,36}, nd4j::DataType::INT32); - NDArray indices ('c', {2,3}, {0, 1, 2, 2, 1,2}, nd4j::DataType::INT32); + NDArray input ('c', {3,3,4}, {1, 2, 3, 4, 5, 6, 7, 8, 9,10,11,12, 13,14,15,16,17,18,19,20,21,22,23,24, 25,26,27,28,29,30,31,32,33,34,35,36}, sd::DataType::INT32); + NDArray indices ('c', {2,3}, {0, 1, 2, 2, 1,2}, sd::DataType::INT32); - nd4j::ops::broadcastgradientargs op; + sd::ops::broadcastgradientargs op; auto result = op.evaluate({&input, &indices}, {}, {}); - ASSERT_EQ(ND4J_STATUS_KERNEL_FAILURE, result->status()); - - delete result; + ASSERT_EQ(ND4J_STATUS_KERNEL_FAILURE, result.status()); + } TEST_F(DeclarableOpsTests2, NLP_Cbow_Test_1) { @@ -374,9 +352,9 @@ TEST_F(DeclarableOpsTests2, NLP_Cbow_Test_1) { auto randomValue = NDArrayFactory::create(2L); auto inferenceVector = NDArrayFactory::empty(); - nd4j::ops::cbow op; + sd::ops::cbow op; auto result = op.evaluate({&target, &ngStarter, &context, &indices, &codes, &syn0, &syn1, &syn1Neg, &expTable, &negTable, &alpha, &randomValue, &numWords, &locked, &inferenceVector}, {}, {}, {true}, {}, true); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); auto row_s0_0 = syn0({0,1, 0,0}, true); auto row_s0_1 = syn0({1,2, 0,0}, true); @@ -393,8 +371,7 @@ TEST_F(DeclarableOpsTests2, NLP_Cbow_Test_1) { ASSERT_EQ(exp1, row_s1_4); ASSERT_EQ(exp1, row_s1_5); ASSERT_EQ(exp2, row_s1_6); - - delete result; + } TEST_F(DeclarableOpsTests2, Test_Squeeze_1) { @@ -402,17 +379,16 @@ TEST_F(DeclarableOpsTests2, Test_Squeeze_1) { x.linspace(1); auto exp = x.reshape('c', {2, 3, 4}); - nd4j::ops::squeeze op; + sd::ops::squeeze op; auto result = op.evaluate({&x}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - - delete result; + } @@ -421,17 +397,16 @@ TEST_F(DeclarableOpsTests2, Test_Squeeze_2) { x.linspace(1); auto exp = new NDArray(x.dup()); - nd4j::ops::squeeze op; + sd::ops::squeeze op; auto result = op.evaluate({&x}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp->isSameShape(z)); ASSERT_TRUE(exp->equalsTo(z)); - - delete result; + delete exp; } @@ -440,17 +415,16 @@ TEST_F(DeclarableOpsTests2, Test_FloorMod_1) { auto y = NDArrayFactory::create('c', {1, 3}, {-3.0f, 2.0f, -2.0f}); auto exp = NDArrayFactory::create('c', {1, 3}, {-1.f, 0.f, -1.f}); - nd4j::ops::floormod op; + sd::ops::floormod op; auto result = op.evaluate({&x, &y}, {}, {}); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - - delete result; + } TEST_F(DeclarableOpsTests2, Test_FloorDiv_1) { @@ -458,18 +432,14 @@ TEST_F(DeclarableOpsTests2, Test_FloorDiv_1) { auto y = NDArrayFactory::create('c', {1, 3}, {-2.0f, 2.0f, -2.0f}); auto exp = NDArrayFactory::create('c', {1, 3}, {-2.f, 3.f, 1.f}); - nd4j::ops::floordiv op; + sd::ops::floordiv op; auto result = op.evaluate({&x, &y}, {}, {}); - auto z = result->at(0); + auto z = result.at(0); // z->printShapeInfo("FloorDiv1 shape"); // z->printIndexedBuffer("FloorDiv1"); ASSERT_TRUE(exp.isSameShape(z)); - - ASSERT_TRUE(exp.equalsTo(z)); - - delete result; } TEST_F(DeclarableOpsTests2, Test_FloorDiv_2) { @@ -480,12 +450,12 @@ TEST_F(DeclarableOpsTests2, Test_FloorDiv_2) { auto exp1 = NDArrayFactory::create('c', {1, 3}, {0.f, 0.f, 0.f}); auto exp2 = NDArrayFactory::create('c', {1, 3}, {0.f, 0.f, 0.f}); - nd4j::ops::floordiv_bp op; + sd::ops::floordiv_bp op; auto result = op.evaluate({&x, &y, &eps}, {}, {}); - ASSERT_EQ(result->status(), Status::OK()); - auto z1 = result->at(0); - auto z2 = result->at(1); + ASSERT_EQ(result.status(), Status::OK()); + auto z1 = result.at(0); + auto z2 = result.at(1); // z->printShapeInfo("FloorDiv1 shape"); // z1->printIndexedBuffer("FloorDiv2_1"); // z2->printIndexedBuffer("FloorDiv2_2"); @@ -493,25 +463,23 @@ TEST_F(DeclarableOpsTests2, Test_FloorDiv_2) { ASSERT_TRUE(exp1.equalsTo(z1)); ASSERT_TRUE(exp2.equalsTo(z2)); - delete result; } TEST_F(DeclarableOpsTests2, Test_CRelu_1) { auto x = NDArrayFactory::create('c', {2, 2}, {1.0f, 2.0f, 3.0f, 4.0f}); auto exp = NDArrayFactory::create('c', {2, 4}, {1.0f, 2.0f, 0.f, 0.f, 3.0f, 4.0f, 0.f, 0.f}); - nd4j::ops::crelu op; + sd::ops::crelu op; auto result = op.evaluate({&x}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; } TEST_F(DeclarableOpsTests2, Test_CRelu_BP_2) { @@ -519,17 +487,16 @@ TEST_F(DeclarableOpsTests2, Test_CRelu_BP_2) { auto eps = NDArrayFactory::create('c', {2, 4}, {1.0f, 2.0f, 4.f, 3.f, 3.0f, 4.0f, 2.f, 1.f}); auto exp = NDArrayFactory::create('c', {2, 2}, {1.f, 2.f, -2.f, 4.f}); - nd4j::ops::crelu_bp op; + sd::ops::crelu_bp op; auto result = op.evaluate({&x, &eps}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); - ASSERT_EQ(1, result->size()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); + ASSERT_EQ(1, result.size()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; } TEST_F(DeclarableOpsTests2, Test_Concat_BP_1) { @@ -539,25 +506,21 @@ TEST_F(DeclarableOpsTests2, Test_Concat_BP_1) { auto expEX = NDArrayFactory::create('c', {2, 2}, {1.f, 2.f, 3.f, 4.f}); auto expEY = NDArrayFactory::create('c', {2, 2}, {0.f, 1.f, 0.f, 1.f}); - nd4j::ops::concat_bp op; + sd::ops::concat_bp op; auto result = op.evaluate({&x, &y, &eps}, {}, {-1}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); - ASSERT_EQ(2, result->size()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); + ASSERT_EQ(2, result.size()); - auto epsX = result->at(0); - auto epsY = result->at(1); + auto epsX = result.at(0); + auto epsY = result.at(1); ASSERT_TRUE(expEX.isSameShape(epsX)); ASSERT_TRUE(expEX.equalsTo(epsX)); ASSERT_TRUE(expEY.isSameShape(epsY)); ASSERT_TRUE(expEY.equalsTo(epsY)); - - delete result; } - - //////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests2, absolute_difference_loss_test_1) { @@ -571,21 +534,18 @@ TEST_F(DeclarableOpsTests2, absolute_difference_loss_test_1) { weights.assign(0.5f); expected.assign(0.5f); - nd4j::ops::absolute_difference_loss op; + sd::ops::absolute_difference_loss op; auto results = op.evaluate({&predictions, &weights, &labels}, {}, {0}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(expected.isSameShape(result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; - } - //////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests2, absolute_difference_loss_test_2) { @@ -599,19 +559,17 @@ TEST_F(DeclarableOpsTests2, absolute_difference_loss_test_2) { weights.assign(0.5f); expected.assign(0.5f); - nd4j::ops::absolute_difference_loss op; + sd::ops::absolute_difference_loss op; auto results = op.evaluate({&predictions, &weights, &labels}, {}, {0}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); // result->printIndexedBuffer("ADL test2"); // expected.printIndexedBuffer("ADL expec"); ASSERT_TRUE(expected.isSameShape(result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; - } //////////////////////////////////////////////////////////////////// @@ -627,18 +585,16 @@ TEST_F(DeclarableOpsTests2, absolute_difference_loss_test_3) { weights.assign(0.5f); expected.assign(0.5f); - nd4j::ops::absolute_difference_loss op; + sd::ops::absolute_difference_loss op; auto results = op.evaluate({&predictions, &weights, &labels}, {}, {0}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(expected.isSameShape(result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; - } //////////////////////////////////////////////////////////////////// @@ -654,18 +610,16 @@ TEST_F(DeclarableOpsTests2, absolute_difference_loss_test_4) { weights.assign(0.5f); expected.assign(0.5f); - nd4j::ops::absolute_difference_loss op; + sd::ops::absolute_difference_loss op; auto results = op.evaluate({&predictions, &weights, &labels}, {}, {0}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(expected.isSameShape(result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; - } //////////////////////////////////////////////////////////////////// @@ -681,18 +635,16 @@ TEST_F(DeclarableOpsTests2, absolute_difference_loss_test_5) { weights.assign(0.5f); expected.assign(0.5f); - nd4j::ops::absolute_difference_loss op; + sd::ops::absolute_difference_loss op; auto results = op.evaluate({&predictions, &weights, &labels}, {}, {0}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(expected.isSameShape(result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; - } //////////////////////////////////////////////////////////////////// @@ -708,18 +660,16 @@ TEST_F(DeclarableOpsTests2, absolute_difference_loss_test_6) { weights.assign(0.f); expected.assign(0.f); - nd4j::ops::absolute_difference_loss op; + sd::ops::absolute_difference_loss op; auto results = op.evaluate({&predictions, &weights, &labels}, {}, {0}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(expected.isSameShape(result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; - } //////////////////////////////////////////////////////////////////// @@ -733,18 +683,16 @@ TEST_F(DeclarableOpsTests2, absolute_difference_loss_test_7) { predictions.linspace(2); weights.assign(0.5f); - nd4j::ops::absolute_difference_loss op; + sd::ops::absolute_difference_loss op; auto results = op.evaluate({&predictions, &weights, &labels}, {}, {1}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(result->isScalar()); ASSERT_TRUE(result->e(0) == 60.f); - delete results; - } //////////////////////////////////////////////////////////////////// @@ -758,17 +706,17 @@ TEST_F(DeclarableOpsTests2, absolute_difference_loss_test_8) { predictions.linspace(2); weights.assign(0.f); - nd4j::ops::absolute_difference_loss op; + sd::ops::absolute_difference_loss op; auto results = op.evaluate({&predictions, &weights, &labels}, {}, {1}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(result->isScalar()); ASSERT_TRUE(result->e(0) == 0.f); - delete results; + } @@ -783,17 +731,17 @@ TEST_F(DeclarableOpsTests2, absolute_difference_loss_test_9) { predictions.linspace(2); weights.assign(0.5f); - nd4j::ops::absolute_difference_loss op; + sd::ops::absolute_difference_loss op; auto results = op.evaluate({&predictions, &weights, &labels}, {}, {1}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(result->isScalar()); ASSERT_TRUE(result->e(0) == 60.); - delete results; + } @@ -808,17 +756,17 @@ TEST_F(DeclarableOpsTests2, absolute_difference_loss_test_10) { predictions.linspace(2); weights.assign(0.5f); - nd4j::ops::absolute_difference_loss op; + sd::ops::absolute_difference_loss op; auto results = op.evaluate({&predictions, &weights, &labels}, {}, {1}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(result->isScalar()); ASSERT_TRUE(result->e(0) == 60.f); - delete results; + } @@ -833,17 +781,17 @@ TEST_F(DeclarableOpsTests2, absolute_difference_loss_test_11) { predictions.linspace(2); weights.assign(0.5f); - nd4j::ops::absolute_difference_loss op; + sd::ops::absolute_difference_loss op; auto results = op.evaluate({&predictions, &weights, &labels}, {}, {2}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(result->isScalar()); ASSERT_TRUE(result->e(0) == 1.f); - delete results; + } @@ -858,17 +806,17 @@ TEST_F(DeclarableOpsTests2, absolute_difference_loss_test_12) { predictions.linspace(2); weights.assign(0.f); - nd4j::ops::absolute_difference_loss op; + sd::ops::absolute_difference_loss op; auto results = op.evaluate({&predictions, &weights, &labels}, {}, {2}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(result->isScalar()); ASSERT_TRUE(result->e(0) == 0.f); - delete results; + } @@ -883,17 +831,17 @@ TEST_F(DeclarableOpsTests2, absolute_difference_loss_test_13) { predictions.linspace(2); weights.assign(0.5f); - nd4j::ops::absolute_difference_loss op; + sd::ops::absolute_difference_loss op; auto results = op.evaluate({&predictions, &weights, &labels}, {}, {2}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(result->isScalar()); ASSERT_TRUE(result->e(0) == 1.f); - delete results; + } @@ -910,17 +858,17 @@ TEST_F(DeclarableOpsTests2, absolute_difference_loss_test_14) { weights.p(1, 0.f); weights.p(2, 0.f); - nd4j::ops::absolute_difference_loss op; + sd::ops::absolute_difference_loss op; auto results = op.evaluate({&predictions, &weights, &labels}, {}, {2}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(result->isScalar()); ASSERT_TRUE(result->e(0) == 1.f); - delete results; + } @@ -935,17 +883,17 @@ TEST_F(DeclarableOpsTests2, absolute_difference_loss_test_15) { predictions.linspace(3); weights.assign(0.5f); - nd4j::ops::absolute_difference_loss op; + sd::ops::absolute_difference_loss op; auto results = op.evaluate({&predictions, &weights, &labels}, {}, {2}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(result->isScalar()); ASSERT_TRUE(result->e(0) == 2.f); - delete results; + } @@ -964,17 +912,17 @@ TEST_F(DeclarableOpsTests2, absolute_difference_loss_test_16) { predictions.p(2, 0.f); predictions.p(3, 0.f); - nd4j::ops::absolute_difference_loss op; + sd::ops::absolute_difference_loss op; auto results = op.evaluate({&predictions, &weights, &labels}, {}, {2}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(result->isScalar()); ASSERT_NEAR(result->e(0), 2.01667, 1e-5); - delete results; + } @@ -997,17 +945,17 @@ TEST_F(DeclarableOpsTests2, absolute_difference_loss_test_17) { labels.p(2, 0.f); labels.p(3, 0.f); - nd4j::ops::absolute_difference_loss op; + sd::ops::absolute_difference_loss op; auto results = op.evaluate({&predictions, &weights, &labels}, {}, {2}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(result->isScalar()); ASSERT_NEAR(result->e(0), 1.93333, 1e-5); - delete results; + } @@ -1030,17 +978,17 @@ TEST_F(DeclarableOpsTests2, absolute_difference_loss_test_18) { labels.p(2, 0.f); labels.p(3, 0.f); - nd4j::ops::absolute_difference_loss op; + sd::ops::absolute_difference_loss op; auto results = op.evaluate({&predictions, &weights, &labels}, {}, {2}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(result->isScalar()); ASSERT_NEAR(result->e(0), 1.93333f, 1e-5); - delete results; + } @@ -1056,17 +1004,17 @@ TEST_F(DeclarableOpsTests2, absolute_difference_loss_test_19) { predictions.linspace(3); weights.assign(0.5); - nd4j::ops::absolute_difference_loss op; + sd::ops::absolute_difference_loss op; auto results = op.evaluate({&predictions, &weights, &labels}, {}, {3}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(result->isScalar()); ASSERT_TRUE(result->e(0) == 1.); - delete results; + } @@ -1081,17 +1029,17 @@ TEST_F(DeclarableOpsTests2, absolute_difference_loss_test_20) { predictions.linspace(3); weights.assign(0.5); - nd4j::ops::absolute_difference_loss op; + sd::ops::absolute_difference_loss op; auto results = op.evaluate({&predictions, &weights, &labels}, {}, {3}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(result->isScalar()); ASSERT_TRUE(result->e(0) == 1.); - delete results; + } @@ -1106,17 +1054,17 @@ TEST_F(DeclarableOpsTests2, absolute_difference_loss_test_21) { predictions.linspace(3); weights.assign(0.5); - nd4j::ops::absolute_difference_loss op; + sd::ops::absolute_difference_loss op; auto results = op.evaluate({&predictions, &weights, &labels}, {}, {3}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(result->isScalar()); ASSERT_TRUE(result->e(0) == 1.f); - delete results; + } @@ -1131,17 +1079,17 @@ TEST_F(DeclarableOpsTests2, absolute_difference_loss_test_22) { predictions.linspace(3); weights.assign(0.); - nd4j::ops::absolute_difference_loss op; + sd::ops::absolute_difference_loss op; auto results = op.evaluate({&predictions, &weights, &labels}, {}, {3}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(result->isScalar()); ASSERT_TRUE(result->e(0) == 0.); - delete results; + } @@ -1168,17 +1116,17 @@ TEST_F(DeclarableOpsTests2, absolute_difference_loss_test_23) { weights.p(40+2, 0.); weights.p(40+3, 0.); - nd4j::ops::absolute_difference_loss op; + sd::ops::absolute_difference_loss op; auto results = op.evaluate({&predictions, &weights, &labels}, {}, {3}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(result->isScalar()); ASSERT_NEAR(result->e(0), 0.965517, 1e-5); - delete results; + } @@ -1194,17 +1142,17 @@ TEST_F(DeclarableOpsTests2, cosine_distance_loss_test1) { predictions.linspace(2); weights.assign(0.5); - nd4j::ops::cosine_distance_loss op; + sd::ops::cosine_distance_loss op; auto results = op.evaluate({&predictions, &weights, &labels}, {}, {0,0}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(expected.isSameShape(result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; + } @@ -1220,17 +1168,17 @@ TEST_F(DeclarableOpsTests2, cosine_distance_loss_test2) { weights.assign(0.5); predictions.assign(0.5); - nd4j::ops::cosine_distance_loss op; + sd::ops::cosine_distance_loss op; auto results = op.evaluate({&predictions, &weights, &labels}, {}, {0,1}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(expected.isSameShape(result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; + } @@ -1247,17 +1195,17 @@ TEST_F(DeclarableOpsTests2, cosine_distance_loss_test3) { weights.assign(0.5); predictions.assign(0.5); - nd4j::ops::cosine_distance_loss op; + sd::ops::cosine_distance_loss op; auto results = op.evaluate({&predictions, &weights, &labels}, {}, {0,2}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(expected.isSameShape(result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; + } //////////////////////////////////////////////////////////////////// @@ -1272,17 +1220,17 @@ TEST_F(DeclarableOpsTests2, cosine_distance_loss_test4) { weights.assign(0.5); predictions.assign(0.5); - nd4j::ops::cosine_distance_loss op; + sd::ops::cosine_distance_loss op; auto results = op.evaluate({&predictions, &weights, &labels}, {}, {0,2}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(expected.isSameShape(result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; + } @@ -1297,17 +1245,17 @@ TEST_F(DeclarableOpsTests2, cosine_distance_loss_test5) { weights.assign(0.5); predictions.assign(0.5); - nd4j::ops::cosine_distance_loss op; + sd::ops::cosine_distance_loss op; auto results = op.evaluate({&predictions, &weights, &labels}, {}, {1,1}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(result->isScalar()); ASSERT_TRUE(result->e(0) == -71.); - delete results; + } @@ -1322,17 +1270,17 @@ TEST_F(DeclarableOpsTests2, cosine_distance_loss_test6) { weights.assign(0.5); predictions.assign(0.5); - nd4j::ops::cosine_distance_loss op; + sd::ops::cosine_distance_loss op; auto results = op.evaluate({&predictions, &weights, &labels}, {}, {1,1}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(result->isScalar()); ASSERT_TRUE(result->e(0) == -71.f); - delete results; + } @@ -1347,17 +1295,17 @@ TEST_F(DeclarableOpsTests2, cosine_distance_loss_test7) { weights.assign(0.5); predictions.assign(0.5); - nd4j::ops::cosine_distance_loss op; + sd::ops::cosine_distance_loss op; auto results = op.evaluate({&predictions, &weights, &labels}, {}, {1,0}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(result->isScalar()); ASSERT_TRUE(result->e(0) == -69.f); - delete results; + } @@ -1372,17 +1320,17 @@ TEST_F(DeclarableOpsTests2, cosine_distance_loss_test8) { weights.assign(0.5f); predictions.assign(0.5f); - nd4j::ops::cosine_distance_loss op; + sd::ops::cosine_distance_loss op; auto results = op.evaluate({&predictions, &weights, &labels}, {}, {2,2}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(result->isScalar()); ASSERT_TRUE(result->e(0) == -24.f); - delete results; + } @@ -1397,17 +1345,17 @@ TEST_F(DeclarableOpsTests2, cosine_distance_loss_test9) { weights.assign(0.5f); predictions.assign(0.5f); - nd4j::ops::cosine_distance_loss op; + sd::ops::cosine_distance_loss op; auto results = op.evaluate({&predictions, &weights, &labels}, {}, {2,2}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(result->isScalar()); ASSERT_TRUE(result->e(0) == -24.); - delete results; + } @@ -1424,17 +1372,17 @@ TEST_F(DeclarableOpsTests2, cosine_distance_loss_test10) { weights.p(0, 0.f); weights.p(1, 0.f); - nd4j::ops::cosine_distance_loss op; + sd::ops::cosine_distance_loss op; auto results = op.evaluate({&predictions, &weights, &labels}, {}, {2,2}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(result->isScalar()); ASSERT_TRUE(result->e(0) == -32.); - delete results; + } @@ -1449,18 +1397,18 @@ TEST_F(DeclarableOpsTests2, hinge_loss_test1) { logits.linspace(1); weights.assign(0.5); - nd4j::ops::hinge_loss op; + sd::ops::hinge_loss op; auto results = op.evaluate({&logits, &weights, &labels}, {}, {0}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); // result->printBuffer(); ASSERT_TRUE(expected.isSameShape(result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -1474,18 +1422,18 @@ TEST_F(DeclarableOpsTests2, hinge_loss_test2) { logits.linspace(1); weights.assign(0.5); - nd4j::ops::hinge_loss op; + sd::ops::hinge_loss op; auto results = op.evaluate({&logits, &weights, &labels}, {}, {0}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); // result->printBuffer(); ASSERT_TRUE(expected.isSameShape(result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -1499,18 +1447,18 @@ TEST_F(DeclarableOpsTests2, hinge_loss_test3) { logits.linspace(1); weights.assign(0.5); - nd4j::ops::hinge_loss op; + sd::ops::hinge_loss op; auto results = op.evaluate({&logits, &weights, &labels}, {}, {0}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); // result->printBuffer(); ASSERT_TRUE(expected.isSameShape(result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -1524,17 +1472,17 @@ TEST_F(DeclarableOpsTests2, hinge_loss_test4) { weights.assign(0.5); - nd4j::ops::hinge_loss op; + sd::ops::hinge_loss op; auto results = op.evaluate({&logits, &weights, &labels}, {}, {1}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(result->isScalar()); ASSERT_TRUE(result->e(0) == 83.); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -1548,17 +1496,17 @@ TEST_F(DeclarableOpsTests2, hinge_loss_test5) { weights.assign(0.5); - nd4j::ops::hinge_loss op; + sd::ops::hinge_loss op; auto results = op.evaluate({&logits, &weights, &labels}, {}, {1}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(result->isScalar()); ASSERT_TRUE(result->e(0) == 83.); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -1572,17 +1520,17 @@ TEST_F(DeclarableOpsTests2, hinge_loss_test6) { weights.assign(0.5); - nd4j::ops::hinge_loss op; + sd::ops::hinge_loss op; auto results = op.evaluate({&logits, &weights, &labels}, {}, {1}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(result->isScalar()); ASSERT_TRUE(result->e(0) == 83.); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -1596,17 +1544,17 @@ TEST_F(DeclarableOpsTests2, hinge_loss_test7) { weights.assign(0.5); - nd4j::ops::hinge_loss op; + sd::ops::hinge_loss op; auto results = op.evaluate({&logits, &weights, &labels}, {}, {2}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(result->isScalar()); ASSERT_NEAR(result->e(0), 6.91667, 1e-5); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -1620,17 +1568,17 @@ TEST_F(DeclarableOpsTests2, hinge_loss_test8) { weights.assign(0.5); - nd4j::ops::hinge_loss op; + sd::ops::hinge_loss op; auto results = op.evaluate({&logits, &weights, &labels}, {}, {2}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(result->isScalar()); ASSERT_NEAR(result->e(0), 6.91667, 1e-5); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -1644,17 +1592,17 @@ TEST_F(DeclarableOpsTests2, hinge_loss_test9) { weights.assign(0.5); - nd4j::ops::hinge_loss op; + sd::ops::hinge_loss op; auto results = op.evaluate({&logits, &weights, &labels}, {}, {2}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(result->isScalar()); ASSERT_NEAR(result->e(0), 6.91667, 1e-5); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -1668,17 +1616,17 @@ TEST_F(DeclarableOpsTests2, hinge_loss_test10) { weights.assign(0.5); - nd4j::ops::hinge_loss op; + sd::ops::hinge_loss op; auto results = op.evaluate({&logits, &weights, &labels}, {}, {3}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(result->isScalar()); ASSERT_NEAR(result->e(0), 3.45833, 1e-5); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -1692,17 +1640,17 @@ TEST_F(DeclarableOpsTests2, hinge_loss_test11) { weights.assign(0.5); - nd4j::ops::hinge_loss op; + sd::ops::hinge_loss op; auto results = op.evaluate({&logits, &weights, &labels}, {}, {3}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(result->isScalar()); ASSERT_NEAR(result->e(0), 3.45833, 1e-5); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -1720,17 +1668,17 @@ TEST_F(DeclarableOpsTests2, hinge_loss_test12) { weights.p(3, 0.); - nd4j::ops::hinge_loss op; + sd::ops::hinge_loss op; auto results = op.evaluate({&logits, &weights, &labels}, {}, {3}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(result->isScalar()); ASSERT_NEAR(result->e(0), 3.975, 1e-5); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -1744,17 +1692,17 @@ TEST_F(DeclarableOpsTests2, hinge_loss_test13) { weights.assign(0.); - nd4j::ops::hinge_loss op; + sd::ops::hinge_loss op; auto results = op.evaluate({&logits, &weights, &labels}, {}, {3}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(result->isScalar()); ASSERT_TRUE(result->e(0) == 0.); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -1769,17 +1717,17 @@ TEST_F(DeclarableOpsTests2, huber_loss_test1) { predictions.linspace(1); weights.assign(0.5); - nd4j::ops::huber_loss op; + sd::ops::huber_loss op; auto results = op.evaluate({&predictions, &weights, &labels}, {0.1}, {0}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(expected.isSameShape(result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -1794,17 +1742,17 @@ TEST_F(DeclarableOpsTests2, huber_loss_test2) { predictions.linspace(1); weights.assign(0.5); - nd4j::ops::huber_loss op; + sd::ops::huber_loss op; auto results = op.evaluate({&predictions, &weights, &labels}, {0.1}, {0}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(expected.isSameShape(result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -1819,17 +1767,17 @@ TEST_F(DeclarableOpsTests2, huber_loss_test3) { predictions.linspace(1); weights.assign(0.5); - nd4j::ops::huber_loss op; + sd::ops::huber_loss op; auto results = op.evaluate({&predictions, &weights, &labels}, {0.1}, {0}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(expected.isSameShape(result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -1843,17 +1791,17 @@ TEST_F(DeclarableOpsTests2, huber_loss_test4) { predictions.linspace(1); weights.assign(0.5); - nd4j::ops::huber_loss op; + sd::ops::huber_loss op; auto results = op.evaluate({&predictions, &weights, &labels}, {0.1}, {1}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(result->isScalar()); ASSERT_NEAR(result->e(0), 13.44, 1e-5); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -1867,17 +1815,17 @@ TEST_F(DeclarableOpsTests2, huber_loss_test5) { predictions.linspace(1); weights.assign(0.5); - nd4j::ops::huber_loss op; + sd::ops::huber_loss op; auto results = op.evaluate({&predictions, &weights, &labels}, {0.1}, {1}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(result->isScalar()); ASSERT_NEAR(result->e(0), 13.44, 1e-5); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -1891,17 +1839,17 @@ TEST_F(DeclarableOpsTests2, huber_loss_test6) { predictions.linspace(1); weights.assign(0.5); - nd4j::ops::huber_loss op; + sd::ops::huber_loss op; auto results = op.evaluate({&predictions, &weights, &labels}, {0.1}, {2}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(result->isScalar()); ASSERT_NEAR(result->e(0), 1.12, 1e-5); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -1915,17 +1863,17 @@ TEST_F(DeclarableOpsTests2, huber_loss_test7) { predictions.linspace(1); weights.assign(0.5); - nd4j::ops::huber_loss op; + sd::ops::huber_loss op; auto results = op.evaluate({&predictions, &weights, &labels}, {0.1}, {2}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(result->isScalar()); ASSERT_NEAR(result->e(0), 1.12, 1e-5); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -1943,17 +1891,17 @@ TEST_F(DeclarableOpsTests2, huber_loss_test8) { weights.p(2, 0.); weights.p(3, 0.); - nd4j::ops::huber_loss op; + sd::ops::huber_loss op; auto results = op.evaluate({&predictions, &weights, &labels}, {0.1}, {2}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(result->isScalar()); ASSERT_NEAR(result->e(0), 1.3, 1e-5); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -1967,17 +1915,17 @@ TEST_F(DeclarableOpsTests2, huber_loss_test9) { predictions.linspace(1); weights.assign(0.5); - nd4j::ops::huber_loss op; + sd::ops::huber_loss op; auto results = op.evaluate({&predictions, &weights, &labels}, {0.1}, {3}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(result->isScalar()); ASSERT_NEAR(result->e(0), 0.56, 1e-5); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -1991,17 +1939,17 @@ TEST_F(DeclarableOpsTests2, huber_loss_test10) { predictions.linspace(1); weights.assign(0.5); - nd4j::ops::huber_loss op; + sd::ops::huber_loss op; auto results = op.evaluate({&predictions, &weights, &labels}, {0.1}, {3}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(result->isScalar()); ASSERT_NEAR(result->e(0), 0.56, 1e-5); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -2019,17 +1967,17 @@ TEST_F(DeclarableOpsTests2, huber_loss_test11) { weights.p(2, 0.); weights.p(3, 0.); - nd4j::ops::huber_loss op; + sd::ops::huber_loss op; auto results = op.evaluate({&predictions, &weights, &labels}, {0.1}, {3}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(result->isScalar()); ASSERT_NEAR(result->e(0), 0.65, 1e-5); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -2044,17 +1992,17 @@ TEST_F(DeclarableOpsTests2, log_loss_test1) { labels.linspace(1); weights.assign(0.5); - nd4j::ops::log_loss op; + sd::ops::log_loss op; auto results = op.evaluate({&predictions, &weights, &labels}, {1e-7}, {0}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(expected.isSameShape(result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -2069,17 +2017,17 @@ TEST_F(DeclarableOpsTests2, log_loss_test2) { labels.linspace(1); weights.assign(0.5); - nd4j::ops::log_loss op; + sd::ops::log_loss op; auto results = op.evaluate({&predictions, &weights, &labels}, {1e-7}, {0}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(expected.isSameShape(result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -2087,24 +2035,24 @@ TEST_F(DeclarableOpsTests2, log_loss_test3) { auto labels = NDArrayFactory::create('c', {2,3,4}); auto predictions = NDArrayFactory::create('c', {2,3,4}); - NDArray weights(nd4j::DataType::DOUBLE); + NDArray weights(sd::DataType::DOUBLE); auto expected = NDArrayFactory::create('c', {2,3,4}, {1.60943663, 2.48403668, 3.05256081, 3.40363169, 3.57730675, 3.59525585, 3.46986699, 3.20791793, 2.81228209, 2.28273821, 1.61630058, 0.80721998, -0.15329313, -1.27764463, -2.5828433 , -4.09208679, -5.83734226, -7.8636713 ,-10.23689461,-13.05822182,-16.49509811,-20.85659218,-26.82411766,-36.52717209}); predictions.linspace(0.04, 0.04); labels.linspace(1); weights.assign(0.5); - nd4j::ops::log_loss op; + sd::ops::log_loss op; auto results = op.evaluate({&predictions, &weights, &labels}, {1e-7}, {0}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(expected.isSameShape(result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -2118,17 +2066,17 @@ TEST_F(DeclarableOpsTests2, log_loss_test4) { labels.linspace(1); weights.assign(0.5); - nd4j::ops::log_loss op; + sd::ops::log_loss op; auto results = op.evaluate({&predictions, &weights, &labels}, {1e-7}, {1}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(result->isScalar()); ASSERT_NEAR(result->e(0), -113.886429, 1e-5); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -2142,17 +2090,17 @@ TEST_F(DeclarableOpsTests2, log_loss_test5) { labels.linspace(1); weights.assign(0.5); - nd4j::ops::log_loss op; + sd::ops::log_loss op; auto results = op.evaluate({&predictions, &weights, &labels}, {1e-7}, {1}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(result->isScalar()); ASSERT_NEAR(result->e(0), -113.886429, 1e-5); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -2160,23 +2108,23 @@ TEST_F(DeclarableOpsTests2, log_loss_test6) { auto labels = NDArrayFactory::create('c', {2,3,4}); auto predictions = NDArrayFactory::create('c', {2,3,4}); - NDArray weights(nd4j::DataType::DOUBLE); + NDArray weights(sd::DataType::DOUBLE); predictions.linspace(0.04, 0.04); labels.linspace(1); weights.assign(0.5); - nd4j::ops::log_loss op; + sd::ops::log_loss op; auto results = op.evaluate({&predictions, &weights, &labels}, {1e-7}, {1}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(result->isScalar()); ASSERT_NEAR(result->e(0), -113.886429, 1e-5); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -2190,17 +2138,17 @@ TEST_F(DeclarableOpsTests2, log_loss_test7) { labels.linspace(1); weights.assign(0.5); - nd4j::ops::log_loss op; + sd::ops::log_loss op; auto results = op.evaluate({&predictions, &weights, &labels}, {1e-7}, {2}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(result->isScalar()); ASSERT_NEAR(result->e(0), -9.490536, 1e-5); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -2214,17 +2162,17 @@ TEST_F(DeclarableOpsTests2, log_loss_test8) { labels.linspace(1); weights.assign(0.5); - nd4j::ops::log_loss op; + sd::ops::log_loss op; auto results = op.evaluate({&predictions, &weights, &labels}, {1e-7}, {2}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(result->isScalar()); ASSERT_NEAR(result->e(0), -9.490536, 1e-5); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -2232,23 +2180,23 @@ TEST_F(DeclarableOpsTests2, log_loss_test9) { auto labels = NDArrayFactory::create('c', {2,3,4}); auto predictions = NDArrayFactory::create('c', {2,3,4}); - NDArray weights(nd4j::DataType::DOUBLE); + NDArray weights(sd::DataType::DOUBLE); predictions.linspace(0.04, 0.04); labels.linspace(1); weights.assign(0.5); - nd4j::ops::log_loss op; + sd::ops::log_loss op; auto results = op.evaluate({&predictions, &weights, &labels}, {1e-7}, {2}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(result->isScalar()); ASSERT_NEAR(result->e(0), -9.490536, 1e-5); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -2266,17 +2214,17 @@ TEST_F(DeclarableOpsTests2, log_loss_test10) { weights.p(2, 0.); weights.p(3, 0.); - nd4j::ops::log_loss op; + sd::ops::log_loss op; auto results = op.evaluate({&predictions, &weights, &labels}, {1e-7}, {2}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(result->isScalar()); ASSERT_NEAR(result->e(0), -12.443609, 1e-5); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -2290,17 +2238,17 @@ TEST_F(DeclarableOpsTests2, log_loss_test11) { labels.linspace(1); weights.assign(0.5); - nd4j::ops::log_loss op; + sd::ops::log_loss op; auto results = op.evaluate({&predictions, &weights, &labels}, {1e-7}, {3}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(result->isScalar()); ASSERT_NEAR(result->e(0), -4.745268, 1e-5); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -2314,17 +2262,17 @@ TEST_F(DeclarableOpsTests2, log_loss_test12) { labels.linspace(1); weights.assign(0.5); - nd4j::ops::log_loss op; + sd::ops::log_loss op; auto results = op.evaluate({&predictions, &weights, &labels}, {1e-7}, {3}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(result->isScalar()); ASSERT_NEAR(result->e(0), -4.745268, 1e-5); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -2342,17 +2290,17 @@ TEST_F(DeclarableOpsTests2, log_loss_test13) { weights.p(2, 0.); weights.p(3, 0.); - nd4j::ops::log_loss op; + sd::ops::log_loss op; auto results = op.evaluate({&predictions, &weights, &labels}, {1e-7}, {3}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(result->isScalar()); ASSERT_NEAR(result->e(0), -6.221805, 1e-5); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -2362,17 +2310,17 @@ TEST_F(DeclarableOpsTests2, mean_pairwssqerr_loss_test1) { auto weights = NDArrayFactory::create('c', {1,1}, {1}); auto expected = NDArrayFactory::create('c', {1,1}, {1.}); - nd4j::ops::mean_pairwssqerr_loss op; + sd::ops::mean_pairwssqerr_loss op; auto results = op.evaluate({&predictions, &weights, &labels}, {}, {0}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(expected.isSameShape(result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -2382,17 +2330,17 @@ TEST_F(DeclarableOpsTests2, mean_pairwssqerr_loss_test2) { auto weights = NDArrayFactory::create('c', {1,1}, {1}); auto expected = NDArrayFactory::create('c', {10,1}, {1.9665822560405073, 3.806679563402927, 6.185624212589066, 20.237895345263905, 16.739700814450472, 13.655430201400929, 6.473256392322658, 3.9337379694106325, 22.509455553531062, 1.4741234749089487}); - nd4j::ops::mean_pairwssqerr_loss op; + sd::ops::mean_pairwssqerr_loss op; auto results = op.evaluate({&predictions, &weights, &labels}, {}, {0}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(expected.isSameShape(result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -2402,17 +2350,17 @@ TEST_F(DeclarableOpsTests2, mean_pairwssqerr_loss_test3) { auto weights = NDArrayFactory::create('c', {10,1}, {0.0, 0.0, 1.0, 1.0, 2.0, 2.0, 3.0, 3.0, 4.0, 4.0}); auto expected = NDArrayFactory::create('c', {10,1}, {0.0, 0.0, 21.748459867092496, 6.090581568657439, 7.51315897553838, 5.999534225166869, 22.58050883748054, 6.8600435676788605, 107.5976928688877, 191.56864939172544}); - nd4j::ops::mean_pairwssqerr_loss op; + sd::ops::mean_pairwssqerr_loss op; auto results = op.evaluate({&predictions, &weights, &labels}, {}, {0}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(expected.isSameShape(result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -2421,17 +2369,17 @@ TEST_F(DeclarableOpsTests2, mean_pairwssqerr_loss_test4) { auto predictions = NDArrayFactory::create('c', {10,4}, {-1.7053977111021588, 1.7704125629388408, -0.0876171627499475, 0.9428762101237441, 0.9080108618240852, -0.478732892339118, -0.8189639230649537, 1.3359668242925342, -0.07499867017894829, 0.6169780756804321, -1.1891117691972148, -0.319354110980483, -1.4287263424900434, -0.3556443786879834, 0.6389682186473912, 0.3161742985911756, 0.9047447733840537, -1.9974117226910393, 2.1067775658502326, 0.17035521714679938, -1.1393894489992826, 1.4570837278971687, 0.6312249731754015, -0.42793125692777634, -1.0685964336386844, -0.3590636581851568, -0.19147354841437528, -0.10128937266756889, -0.5714869078294972, 0.2682604831358205, 0.6608524575561853, 0.35658907103040305, -0.7053263272861181, -0.6318441042427088, 2.131292677079184, -0.3624048087249232, 1.6008209804575328, 0.1245980660014825, 1.0685424462364297, -0.5672594432046791}); auto weights = NDArrayFactory::create('c', {1,1}, {1}); - nd4j::ops::mean_pairwssqerr_loss op; + sd::ops::mean_pairwssqerr_loss op; auto results = op.evaluate({&predictions, &weights, &labels}, {}, {1}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(result->isScalar()); ASSERT_NEAR(result->e(0), 60.74394998193965, 1e-5); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -2440,17 +2388,17 @@ TEST_F(DeclarableOpsTests2, mean_pairwssqerr_loss_test5) { auto predictions = NDArrayFactory::create('c', {10,4}, {-3.398114657004427, 0.40587455906092945, 1.587706448479039, 0.27394335709083156, 1.0463122023764637, -0.6552570653663903, -0.26929204111727345, -2.710461824817806, 0.9141296064806023, -0.7632270851454939, -0.4077235519855459, 0.5555107559107472, -0.6776140976423888, 1.2422270521180823, 0.2372445100636733, 0.08522757123963924, -2.708523129389936, 0.09738215252575103, -0.8797837670498875, 0.8714091607391934, -0.628958978867591, 0.49380147969660415, -0.6663578349373824, 0.14570184758600965, -0.4710388511314244, 0.7708214742640788, 0.06836525442683238, -1.2786368797129386, -0.5077556003990912, 0.45383439418987664, 1.1686877788409553, -0.3078567969393852, -2.2375730522738198, 1.0108200459611192, 0.21955367964983963, 1.2268011099696847, 0.48061693077695455, -0.5306373077054981, 1.5005367299570744, -2.1005486985463966}); auto weights = NDArrayFactory::create('c', {1,1}, {1}); - nd4j::ops::mean_pairwssqerr_loss op; + sd::ops::mean_pairwssqerr_loss op; auto results = op.evaluate({&predictions, &weights, &labels}, {}, {2}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(result->isScalar()); ASSERT_NEAR(result->e(0), 15.189082270182983, 1e-5); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -2459,17 +2407,17 @@ TEST_F(DeclarableOpsTests2, mean_pairwssqerr_loss_test6) { auto predictions = NDArrayFactory::create('c', {10,4}, {-0.8253096544930751, 0.81324545672996, 1.2530858908292535, 0.6881658781201572, 0.11626814971230247, 0.810096847233213, -0.41726775033902014, -0.07246036077805246, -0.3491325803119671, -0.7381717490678714, -1.258884944199858, 2.6195012275145992, 0.3241066697239042, -1.3306435333372646, -0.3413119919683999, 0.13167356361127197, -0.3992424507051653, 0.14454163796541403, -2.4931643208872316, 1.8740911656038526, -2.3404306490682956, -0.8036392545918644, -1.9726177395274997, -0.20128619801149433, -1.0680828820641624, -0.6228179015361869, 1.0785520122486962, -0.26148573195062036, -0.9154287856620913, 0.6612224269248097, -0.21735407368781667, 0.5584864652543093, 1.0208212201167435, -0.7560947201084579, -0.9092906572495081, 0.47525819203475833, 1.2215678456801444, -0.39319465979983964, 1.9435677135606038, 1.4540100039010526}); auto weights = NDArrayFactory::create('c', {1,1}, {1}); - nd4j::ops::mean_pairwssqerr_loss op; + sd::ops::mean_pairwssqerr_loss op; auto results = op.evaluate({&predictions, &weights, &labels}, {}, {3}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(result->isScalar()); ASSERT_NEAR(result->e(0), 13.568564090650312, 1e-5); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -2478,17 +2426,17 @@ TEST_F(DeclarableOpsTests2, mean_pairwssqerr_loss_test7) { auto predictions = NDArrayFactory::create('c', {10,4}, {-0.7445687252538243, 0.2293875300325241, -1.0231630280206505, -0.18532545069458992, -0.07797403344353356, -0.9132035669873787, 0.9352296415512886, -1.7406458535354787, 0.8578334648119594, -0.6186274065269556, 0.4874824473654153, -0.9285817343788997, 0.1654680500853023, -0.6371334533926012, 1.3115245864160707, -2.072558735678832, 0.660795731844733, -0.34942292767044864, 0.05787182311194333, -0.12939210444705632, -0.6457028552461069, -0.6048992126598505, -0.17179604529778109, 1.292989642826032, -0.28867767615688045, 0.7635565516046265, -1.5464151753137487, -1.273368390129285, -1.074046012825826, -0.3534580692302915, 0.5757285568118223, 1.823271242883469, 0.31618576929075215, 0.5422847605415213, -0.7836698021860683, -0.6292022623165172, 2.1114596721927508, 0.4634986528550097, 0.08922001427846013, 1.5767749644913223}); auto weights = NDArrayFactory::create('c', {10,1}, {0.0, 0.0, 1.0, 1.0, 2.0, 2.0, 3.0, 3.0, 4.0, 4.0}); - nd4j::ops::mean_pairwssqerr_loss op; + sd::ops::mean_pairwssqerr_loss op; auto results = op.evaluate({&predictions, &weights, &labels}, {}, {1}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(result->isScalar()); ASSERT_NEAR(result->e(0), 198.318201904499, 1e-5); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -2497,17 +2445,17 @@ TEST_F(DeclarableOpsTests2, mean_pairwssqerr_loss_test8) { auto predictions = NDArrayFactory::create('c', {10,4}, {-1.1088399463364795, 0.09302972835006071, 0.033839927431215555, -0.39567507675572494, 0.8269497207597863, 1.111162272517752, 0.4930937252630912, -1.4561668998323452, 0.9417715392862969, -1.0553855492735509, 0.05848285303876081, 0.8852337518047972, -0.7472824481835305, 0.404906922583895, -0.2198309547562547, 1.9536515925189717, 0.8165036568007779, -0.19524282774410398, -0.09111693087754393, 1.1604245932512238, -0.6243762858131077, 1.4297003275591034, -0.17220079411538428, -2.3139504326793032, 0.3839796486999712, 2.0287791964679234, 0.1534441713632995, -0.6062103319229825, -0.4965880982906036, -0.373907747810053, -1.6566345746154432, 0.17534987728494222, -1.6713458890334796, 1.254628987947714, 1.914596591838086, -1.0816010467183583, 0.25033738231939673, -1.605752685708275, 1.1029112741353981, 0.3237822320282494}); auto weights = NDArrayFactory::create('c', {10,1}, {0.0, 0.0, 1.0, 1.0, 2.0, 2.0, 3.0, 3.0, 4.0, 4.0}); - nd4j::ops::mean_pairwssqerr_loss op; + sd::ops::mean_pairwssqerr_loss op; auto results = op.evaluate({&predictions, &weights, &labels}, {}, {2}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(result->isScalar()); ASSERT_NEAR(result->e(0), 10.709003499121707, 1e-5); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -2516,17 +2464,17 @@ TEST_F(DeclarableOpsTests2, mean_pairwssqerr_loss_test9) { auto predictions = NDArrayFactory::create('c', {10,4}, {-1.6788168943811437, 1.1823653279081687, -0.3580541857004183, -0.4449970504370699, -1.3031645333940127, 0.5755013195969282, -0.7997343141774744, -0.8806735270004084, 0.9705277499376251, -1.6360067944580943, 0.12579369136710156, 1.0525902242414313, -1.625751312422252, -0.03900152587147075, 0.4112500942756277, 0.6589999986358094, 0.6144107111689617, 2.8561269030217264, 1.5299963640392247, -0.314093051147705, 1.6523278218751989, -0.5504653447714114, 0.53395260877978, 0.409795577698306, 0.4466825218051794, 1.2382059301630401, 0.4834869732526594, -0.635409128905636, -1.9343816841697272, -0.4192523056060229, -1.0662979055059818, 0.4270901960618144, -0.7391311480757151, -0.8268168961897452, -1.0855715553457785, -9.410401291588706E-4, -0.7721838774717349, 0.4784019579457375, -0.6979798841469268, -0.319729737118584}); auto weights = NDArrayFactory::create('c', {10,1}, {0.0, 0.0, 1.0, 1.0, 2.0, 2.0, 3.0, 3.0, 4.0, 4.0}); - nd4j::ops::mean_pairwssqerr_loss op; + sd::ops::mean_pairwssqerr_loss op; auto results = op.evaluate({&predictions, &weights, &labels}, {}, {3}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(result->isScalar()); ASSERT_NEAR(result->e(0), 17.686067864414472, 1e-5); - delete results; + } /////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests2, mean_sqerr_loss_test1) { @@ -2540,17 +2488,17 @@ TEST_F(DeclarableOpsTests2, mean_sqerr_loss_test1) { labels.linspace(1); weights.assign(0.5); - nd4j::ops::mean_sqerr_loss op; + sd::ops::mean_sqerr_loss op; auto results = op.evaluate({&predictions, &weights, &labels}, {}, {0}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(expected.isSameShape(result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -2565,17 +2513,17 @@ TEST_F(DeclarableOpsTests2, mean_sqerr_loss_test2) { labels.linspace(1); weights.assign(0.5); - nd4j::ops::mean_sqerr_loss op; + sd::ops::mean_sqerr_loss op; auto results = op.evaluate({&predictions, &weights, &labels}, {}, {0}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(expected.isSameShape(result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -2590,17 +2538,17 @@ TEST_F(DeclarableOpsTests2, mean_sqerr_loss_test3) { labels.linspace(1); weights.assign(0.5); - nd4j::ops::mean_sqerr_loss op; + sd::ops::mean_sqerr_loss op; auto results = op.evaluate({&predictions, &weights, &labels}, {}, {0}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(expected.isSameShape(result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -2619,17 +2567,17 @@ TEST_F(DeclarableOpsTests2, mean_sqerr_loss_test4) { weights.p(2, 0.); weights.p(3, 0.); - nd4j::ops::mean_sqerr_loss op; + sd::ops::mean_sqerr_loss op; auto results = op.evaluate({&predictions, &weights, &labels}, {}, {0}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(expected.isSameShape(result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -2643,17 +2591,17 @@ TEST_F(DeclarableOpsTests2, mean_sqerr_loss_test5) { labels.linspace(1); weights.assign(0.5); - nd4j::ops::mean_sqerr_loss op; + sd::ops::mean_sqerr_loss op; auto results = op.evaluate({&predictions, &weights, &labels}, {}, {1}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(result->isScalar()); ASSERT_NEAR(result->e(0), 612.5, 1e-5); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -2667,17 +2615,17 @@ TEST_F(DeclarableOpsTests2, mean_sqerr_loss_test6) { labels.linspace(1); weights.assign(0.5); - nd4j::ops::mean_sqerr_loss op; + sd::ops::mean_sqerr_loss op; auto results = op.evaluate({&predictions, &weights, &labels}, {}, {1}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(result->isScalar()); ASSERT_NEAR(result->e(0), 612.5, 1e-5); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -2691,17 +2639,17 @@ TEST_F(DeclarableOpsTests2, mean_sqerr_loss_test7) { labels.linspace(1); weights.assign(0.5); - nd4j::ops::mean_sqerr_loss op; + sd::ops::mean_sqerr_loss op; auto results = op.evaluate({&predictions, &weights, &labels}, {}, {1}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(result->isScalar()); ASSERT_NEAR(result->e(0), 612.5, 1e-5); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -2719,17 +2667,17 @@ TEST_F(DeclarableOpsTests2, mean_sqerr_loss_test8) { weights.p(2, 0.); weights.p(3, 0.); - nd4j::ops::mean_sqerr_loss op; + sd::ops::mean_sqerr_loss op; auto results = op.evaluate({&predictions, &weights, &labels}, {}, {1}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(result->isScalar()); ASSERT_NEAR(result->e(0), 608.75, 1e-5); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -2743,17 +2691,17 @@ TEST_F(DeclarableOpsTests2, mean_sqerr_loss_test9) { labels.linspace(1); weights.assign(0.5); - nd4j::ops::mean_sqerr_loss op; + sd::ops::mean_sqerr_loss op; auto results = op.evaluate({&predictions, &weights, &labels}, {}, {2}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(result->isScalar()); ASSERT_NEAR(result->e(0), 51.041668, 1e-5); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -2767,17 +2715,17 @@ TEST_F(DeclarableOpsTests2, mean_sqerr_loss_test10) { labels.linspace(1); weights.assign(0.5); - nd4j::ops::mean_sqerr_loss op; + sd::ops::mean_sqerr_loss op; auto results = op.evaluate({&predictions, &weights, &labels}, {}, {2}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(result->isScalar()); ASSERT_NEAR(result->e(0), 51.041668, 1e-5); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -2791,17 +2739,17 @@ TEST_F(DeclarableOpsTests2, mean_sqerr_loss_test11) { labels.linspace(1); weights.assign(0.5); - nd4j::ops::mean_sqerr_loss op; + sd::ops::mean_sqerr_loss op; auto results = op.evaluate({&predictions, &weights, &labels}, {}, {2}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(result->isScalar()); ASSERT_NEAR(result->e(0), 51.041668, 1e-5); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -2818,17 +2766,17 @@ TEST_F(DeclarableOpsTests2, mean_sqerr_loss_test12) { weights.p(1, 0.); weights.p(2, 0.); - nd4j::ops::mean_sqerr_loss op; + sd::ops::mean_sqerr_loss op; auto results = op.evaluate({&predictions, &weights, &labels}, {}, {2}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(result->isScalar()); ASSERT_NEAR(result->e(0), 88.541664, 1e-5); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -2842,17 +2790,17 @@ TEST_F(DeclarableOpsTests2, mean_sqerr_loss_test13) { labels.linspace(1); weights.assign(0.5); - nd4j::ops::mean_sqerr_loss op; + sd::ops::mean_sqerr_loss op; auto results = op.evaluate({&predictions, &weights, &labels}, {}, {3}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(result->isScalar()); ASSERT_NEAR(result->e(0), 25.520834, 1e-5); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -2866,17 +2814,17 @@ TEST_F(DeclarableOpsTests2, mean_sqerr_loss_test14) { labels.linspace(1); weights.assign(0.5); - nd4j::ops::mean_sqerr_loss op; + sd::ops::mean_sqerr_loss op; auto results = op.evaluate({&predictions, &weights, &labels}, {}, {3}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(result->isScalar()); ASSERT_NEAR(result->e(0), 25.520834, 1e-5); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -2890,17 +2838,17 @@ TEST_F(DeclarableOpsTests2, mean_sqerr_loss_test15) { labels.linspace(1); weights.assign(0.5); - nd4j::ops::mean_sqerr_loss op; + sd::ops::mean_sqerr_loss op; auto results = op.evaluate({&predictions, &weights, &labels}, {}, {3}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(result->isScalar()); ASSERT_NEAR(result->e(0), 25.520834, 1e-5); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -2917,17 +2865,17 @@ TEST_F(DeclarableOpsTests2, mean_sqerr_loss_test16) { weights.p(1, 0.); weights.p(2, 0.); - nd4j::ops::mean_sqerr_loss op; + sd::ops::mean_sqerr_loss op; auto results = op.evaluate({&predictions, &weights, &labels}, {}, {3}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(result->isScalar()); ASSERT_NEAR(result->e(0), 44.270832, 1e-5); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -2941,17 +2889,17 @@ TEST_F(DeclarableOpsTests2, sigm_cross_entropy_loss_test1) { logits.linspace(0.1, 0.1); weights.assign(0.5); - nd4j::ops::sigm_cross_entropy_loss op; + sd::ops::sigm_cross_entropy_loss op; auto results = op.evaluate({&logits, &weights, &labels}, {0.}, {0}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(expected.isSameShape(result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -2965,17 +2913,17 @@ TEST_F(DeclarableOpsTests2, sigm_cross_entropy_loss_test2) { logits.linspace(0.1, 0.1); weights.assign(0.5); - nd4j::ops::sigm_cross_entropy_loss op; + sd::ops::sigm_cross_entropy_loss op; auto results = op.evaluate({&logits, &weights, &labels}, {0.}, {0}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(expected.isSameShape(result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -2989,17 +2937,17 @@ TEST_F(DeclarableOpsTests2, sigm_cross_entropy_loss_test3) { logits.linspace(0.1, 0.1); weights.assign(0.5); - nd4j::ops::sigm_cross_entropy_loss op; + sd::ops::sigm_cross_entropy_loss op; auto results = op.evaluate({&logits, &weights, &labels}, {0.}, {0}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(expected.isSameShape(result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -3013,17 +2961,17 @@ TEST_F(DeclarableOpsTests2, sigm_cross_entropy_loss_test4) { logits.linspace(0.1, 0.1); weights.assign(0.5); - nd4j::ops::sigm_cross_entropy_loss op; + sd::ops::sigm_cross_entropy_loss op; auto results = op.evaluate({&logits, &weights, &labels}, {5.}, {0}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(expected.isSameShape(result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -3036,17 +2984,17 @@ TEST_F(DeclarableOpsTests2, sigm_cross_entropy_loss_test5) { logits.linspace(0.1, 0.1); weights.assign(0.5); - nd4j::ops::sigm_cross_entropy_loss op; + sd::ops::sigm_cross_entropy_loss op; auto results = op.evaluate({&logits, &weights, &labels}, {0.}, {1}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(result->isScalar()); ASSERT_NEAR(result->e(0), 11.2187976837, 1e-5); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -3059,17 +3007,17 @@ TEST_F(DeclarableOpsTests2, sigm_cross_entropy_loss_test6) { logits.linspace(0.1, 0.1); weights.assign(0.5); - nd4j::ops::sigm_cross_entropy_loss op; + sd::ops::sigm_cross_entropy_loss op; auto results = op.evaluate({&logits, &weights, &labels}, {0.}, {1}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(result->isScalar()); ASSERT_NEAR(result->e(0), 11.2187976837, 1e-5); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -3082,17 +3030,17 @@ TEST_F(DeclarableOpsTests2, sigm_cross_entropy_loss_test7) { logits.linspace(0.1, 0.1); weights.assign(0.5); - nd4j::ops::sigm_cross_entropy_loss op; + sd::ops::sigm_cross_entropy_loss op; auto results = op.evaluate({&logits, &weights, &labels}, {0.}, {1}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(result->isScalar()); ASSERT_NEAR(result->e(0), 11.2187976837, 1e-5); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -3105,17 +3053,17 @@ TEST_F(DeclarableOpsTests2, sigm_cross_entropy_loss_test8) { logits.linspace(0.1, 0.1); weights.assign(0.5); - nd4j::ops::sigm_cross_entropy_loss op; + sd::ops::sigm_cross_entropy_loss op; auto results = op.evaluate({&logits, &weights, &labels}, {5.}, {1}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(result->isScalar()); ASSERT_NEAR(result->e(0), 10.2187976837, 1e-5); - delete results; + } @@ -3132,17 +3080,17 @@ TEST_F(DeclarableOpsTests2, sigm_cross_entropy_loss_test9) { weights.p(1, 0.); weights.p(2, 0.); - nd4j::ops::sigm_cross_entropy_loss op; + sd::ops::sigm_cross_entropy_loss op; auto results = op.evaluate({&logits, &weights, &labels}, {5.}, {1}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(result->isScalar()); ASSERT_NEAR(result->e(0), 6.06840181351, 1e-5); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -3155,17 +3103,17 @@ TEST_F(DeclarableOpsTests2, sigm_cross_entropy_loss_test10) { logits.linspace(0.1, 0.1); weights.assign(0.5); - nd4j::ops::sigm_cross_entropy_loss op; + sd::ops::sigm_cross_entropy_loss op; auto results = op.evaluate({&logits, &weights, &labels}, {0.}, {2}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(result->isScalar()); ASSERT_NEAR(result->e(0), 0.934899806976, 1e-5); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -3178,17 +3126,17 @@ TEST_F(DeclarableOpsTests2, sigm_cross_entropy_loss_test11) { logits.linspace(0.1, 0.1); weights.assign(0.5); - nd4j::ops::sigm_cross_entropy_loss op; + sd::ops::sigm_cross_entropy_loss op; auto results = op.evaluate({&logits, &weights, &labels}, {0.}, {2}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(result->isScalar()); ASSERT_NEAR(result->e(0), 0.934899806976, 1e-5); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -3201,17 +3149,17 @@ TEST_F(DeclarableOpsTests2, sigm_cross_entropy_loss_test12) { logits.linspace(0.1, 0.1); weights.assign(0.5); - nd4j::ops::sigm_cross_entropy_loss op; + sd::ops::sigm_cross_entropy_loss op; auto results = op.evaluate({&logits, &weights, &labels}, {5.}, {2}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(result->isScalar()); ASSERT_NEAR(result->e(0), 0.851566493511, 1e-5); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -3227,17 +3175,17 @@ TEST_F(DeclarableOpsTests2, sigm_cross_entropy_loss_test13) { weights.p(1, 0.); weights.p(2, 0.); - nd4j::ops::sigm_cross_entropy_loss op; + sd::ops::sigm_cross_entropy_loss op; auto results = op.evaluate({&logits, &weights, &labels}, {5.}, {2}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(result->isScalar()); ASSERT_NEAR(result->e(0), 1.01140034199, 1e-5); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -3250,17 +3198,17 @@ TEST_F(DeclarableOpsTests2, sigm_cross_entropy_loss_test14) { logits.linspace(0.1, 0.1); weights.assign(0.5); - nd4j::ops::sigm_cross_entropy_loss op; + sd::ops::sigm_cross_entropy_loss op; auto results = op.evaluate({&logits, &weights, &labels}, {0.}, {3}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(result->isScalar()); ASSERT_NEAR(result->e(0), 0.467449903488, 1e-5); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -3273,17 +3221,17 @@ TEST_F(DeclarableOpsTests2, sigm_cross_entropy_loss_test15) { logits.linspace(0.1, 0.1); weights.assign(0.5); - nd4j::ops::sigm_cross_entropy_loss op; + sd::ops::sigm_cross_entropy_loss op; auto results = op.evaluate({&logits, &weights, &labels}, {0.}, {3}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(result->isScalar()); ASSERT_NEAR(result->e(0), 0.467449903488, 1e-5); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -3296,17 +3244,17 @@ TEST_F(DeclarableOpsTests2, sigm_cross_entropy_loss_test16) { logits.linspace(0.1, 0.1); weights.assign(0.5); - nd4j::ops::sigm_cross_entropy_loss op; + sd::ops::sigm_cross_entropy_loss op; auto results = op.evaluate({&logits, &weights, &labels}, {5.}, {3}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(result->isScalar()); ASSERT_NEAR(result->e(0), 0.425783246756, 1e-5); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -3322,17 +3270,17 @@ TEST_F(DeclarableOpsTests2, sigm_cross_entropy_loss_test17) { weights.p(1, 0.); weights.p(2, 0.); - nd4j::ops::sigm_cross_entropy_loss op; + sd::ops::sigm_cross_entropy_loss op; auto results = op.evaluate({&logits, &weights, &labels}, {5.}, {3}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(result->isScalar()); ASSERT_NEAR(result->e(0), 0.505700170994, 1e-5); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -3346,16 +3294,16 @@ TEST_F(DeclarableOpsTests2, softmax_cross_entropy_loss_test1) { logits.linspace(0.1, 0.1); weights.assign(0.5); - nd4j::ops::softmax_cross_entropy_loss op; + sd::ops::softmax_cross_entropy_loss op; auto results = op.evaluate({&logits, &weights, &labels}, {0.}, {0}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(expected.isSameShape(result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -3369,17 +3317,17 @@ TEST_F(DeclarableOpsTests2, softmax_cross_entropy_loss_test2) { logits.linspace(0.1, 0.1); weights.assign(0.5); - nd4j::ops::softmax_cross_entropy_loss op; + sd::ops::softmax_cross_entropy_loss op; auto results = op.evaluate({&logits, &weights, &labels}, {5.}, {0}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(expected.isSameShape(result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -3393,17 +3341,17 @@ TEST_F(DeclarableOpsTests2, softmax_cross_entropy_loss_test3) { logits.linspace(0.1, 0.1); weights.assign(0.5); - nd4j::ops::softmax_cross_entropy_loss op; + sd::ops::softmax_cross_entropy_loss op; auto results = op.evaluate({&logits, &weights, &labels}, {5.}, {0}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(expected.isSameShape(result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -3417,17 +3365,17 @@ TEST_F(DeclarableOpsTests2, softmax_cross_entropy_loss_test4) { logits.linspace(0.1, 0.1); weights.assign(0.5); - nd4j::ops::softmax_cross_entropy_loss op; + sd::ops::softmax_cross_entropy_loss op; auto results = op.evaluate({&logits, &weights, &labels}, {5.}, {0}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(expected.isSameShape(result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -3441,17 +3389,17 @@ TEST_F(DeclarableOpsTests2, softmax_cross_entropy_loss_test5) { logits.linspace(0.1, 0.1); weights.assign(0.5); - nd4j::ops::softmax_cross_entropy_loss op; + sd::ops::softmax_cross_entropy_loss op; auto results = op.evaluate({&logits, &weights, &labels}, {5.}, {0}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(expected.isSameShape(result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -3464,17 +3412,17 @@ TEST_F(DeclarableOpsTests2, softmax_cross_entropy_loss_test6) { logits.linspace(0.1, 0.1); weights.assign(0.5); - nd4j::ops::softmax_cross_entropy_loss op; + sd::ops::softmax_cross_entropy_loss op; auto results = op.evaluate({&logits, &weights, &labels}, {0.}, {1}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(result->isScalar()); ASSERT_NEAR(result->e(0), 8.55521392822, 1e-5); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -3487,17 +3435,17 @@ TEST_F(DeclarableOpsTests2, softmax_cross_entropy_loss_test7) { logits.linspace(0.1, 0.1); weights.assign(0.5); - nd4j::ops::softmax_cross_entropy_loss op; + sd::ops::softmax_cross_entropy_loss op; auto results = op.evaluate({&logits, &weights, &labels}, {5.}, {1}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(result->isScalar()); ASSERT_NEAR(result->e(0), -6.37014198303, 1e-5); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -3510,17 +3458,17 @@ TEST_F(DeclarableOpsTests2, softmax_cross_entropy_loss_test8) { logits.linspace(0.1, 0.1); weights.assign(0.5); - nd4j::ops::softmax_cross_entropy_loss op; + sd::ops::softmax_cross_entropy_loss op; auto results = op.evaluate({&logits, &weights, &labels}, {5.}, {1}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(result->isScalar()); ASSERT_NEAR(result->e(0), -6.37014198303, 1e-5); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -3533,17 +3481,17 @@ TEST_F(DeclarableOpsTests2, softmax_cross_entropy_loss_test9) { logits.linspace(0.1, 0.1); weights.assign(0.5); - nd4j::ops::softmax_cross_entropy_loss op; + sd::ops::softmax_cross_entropy_loss op; auto results = op.evaluate({&logits, &weights, &labels}, {5.}, {1}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(result->isScalar()); ASSERT_NEAR(result->e(0), -6.37014198303, 1e-5); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -3556,17 +3504,17 @@ TEST_F(DeclarableOpsTests2, softmax_cross_entropy_loss_test10) { logits.linspace(0.1, 0.1); weights.assign(0.5); - nd4j::ops::softmax_cross_entropy_loss op; + sd::ops::softmax_cross_entropy_loss op; auto results = op.evaluate({&logits, &weights, &labels}, {5.}, {2}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(result->isScalar()); ASSERT_NEAR(result->e(0), -2.12338066101, 1e-5); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -3579,17 +3527,17 @@ TEST_F(DeclarableOpsTests2, softmax_cross_entropy_loss_test11) { logits.linspace(0.1, 0.1); weights.assign(0.5); - nd4j::ops::softmax_cross_entropy_loss op; + sd::ops::softmax_cross_entropy_loss op; auto results = op.evaluate({&logits, &weights, &labels}, {5.}, {3}, {}, {}, false); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(result->isScalar()); ASSERT_NEAR(result->e(0), -1.06169033051, 1e-5); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -3602,17 +3550,17 @@ TEST_F(DeclarableOpsTests2, softmax_cross_entropy_loss_test12) { logits.linspace(0.1, 0.1); weights.assign(0.5); - nd4j::ops::softmax_cross_entropy_loss op; + sd::ops::softmax_cross_entropy_loss op; auto results = op.evaluate({&logits, &weights, &labels}, {5.}, {3}, {}, {}, false); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(result->isScalar()); ASSERT_NEAR(result->e(0), -2.18880319595, 1e-5); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -3626,17 +3574,17 @@ TEST_F(DeclarableOpsTests2, softmax_cross_entropy_loss_test13) { logits.linspace(0.1, 0.1); weights.assign(0.5); - nd4j::ops::softmax_cross_entropy_loss op; + sd::ops::softmax_cross_entropy_loss op; auto results = op.evaluate({&logits, &weights, &labels}, {0.}, {0}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(expected.isSameShape(result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; + } @@ -3652,17 +3600,17 @@ TEST_F(DeclarableOpsTests2, softmax_cross_entropy_loss_test14) { logits.linspace(0.1, 0.1); weights.assign(0.5); - nd4j::ops::softmax_cross_entropy_loss op; + sd::ops::softmax_cross_entropy_loss op; auto results = op.evaluate({&logits, &weights, &labels}, {5.}, {0}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(expected.isSameShape(result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -3676,17 +3624,17 @@ TEST_F(DeclarableOpsTests2, softmax_cross_entropy_loss_test15) { logits.linspace(0.1, 0.1); weights.assign(0.5); - nd4j::ops::softmax_cross_entropy_loss op; + sd::ops::softmax_cross_entropy_loss op; auto results = op.evaluate({&logits, &weights, &labels}, {5.}, {0}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *result = results->at(0); + auto *result = results.at(0); ASSERT_TRUE(expected.isSameShape(result)); ASSERT_TRUE(expected.equalsTo(result)); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -3718,20 +3666,20 @@ TEST_F(DeclarableOpsTests2, lstmCell_test1) { auto expHt = NDArrayFactory::create('c', {batchSize, numProj}, {0.99926789,0.99926789,0.99926789,0.99926789,0.99926789,0.99926789,0.99926789,0.99926789}); auto expCt = NDArrayFactory::create('c', {batchSize, numUnits},{3.99987108,3.99987108,3.99987108,3.99987108,3.99987108,3.99987108,3.99987108,3.99987108}); - nd4j::ops::lstmCell op; + sd::ops::lstmCell op; auto results = op.evaluate({&xt, &ht_1, &ct_1, &Wx, &Wh, &Wc, &Wp, &b}, {0., 0., 1.}, {0, 0}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *ht = results->at(0); - auto *ct = results->at(1); + auto *ht = results.at(0); + auto *ct = results.at(1); ASSERT_TRUE(expHt.isSameShape(ht)); ASSERT_TRUE(expHt.equalsTo(ht)); ASSERT_TRUE(expCt.isSameShape(ct)); ASSERT_TRUE(expCt.equalsTo(ct)); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -3763,20 +3711,20 @@ TEST_F(DeclarableOpsTests2, lstmCell_test2) { auto expHt = NDArrayFactory::create('c', {batchSize, numProj}, {0.95867589,0.95867589,0.95867589,0.95867589,0.95867589,0.95867589,0.95867589,0.95867589}); auto expCt = NDArrayFactory::create('c', {batchSize, numUnits},{1.93001527,1.93001527,1.93001527,1.93001527, 1.93001527,1.93001527,1.93001527,1.93001527}); - nd4j::ops::lstmCell op; + sd::ops::lstmCell op; auto results = op.evaluate({&xt, &ht_1, &ct_1, &Wx, &Wh, &Wc, &Wp, &b}, {0., 0., -10.5}, {0, 0}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *ht = results->at(0); - auto *ct = results->at(1); + auto *ht = results.at(0); + auto *ct = results.at(1); ASSERT_TRUE(expHt.isSameShape(ht)); ASSERT_TRUE(expHt.equalsTo(ht)); ASSERT_TRUE(expCt.isSameShape(ct)); ASSERT_TRUE(expCt.equalsTo(ct)); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -3808,20 +3756,20 @@ TEST_F(DeclarableOpsTests2, lstmCell_test3) { auto expHt = NDArrayFactory::create('c', {batchSize, numProj}, {0.37992568,0.37992568,0.37992568,0.37992568,0.37992568,0.37992568,0.37992568,0.37992568}); auto expCt = NDArrayFactory::create('c', {batchSize, numUnits},{0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4}); - nd4j::ops::lstmCell op; + sd::ops::lstmCell op; auto results = op.evaluate({&xt, &ht_1, &ct_1, &Wx, &Wh, &Wc, &Wp, &b}, {0.4, 0., 1.5}, {0, 0}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *ht = results->at(0); - auto *ct = results->at(1); + auto *ht = results.at(0); + auto *ct = results.at(1); ASSERT_TRUE(expHt.isSameShape(ht)); ASSERT_TRUE(expHt.equalsTo(ht)); ASSERT_TRUE(expCt.isSameShape(ct)); ASSERT_TRUE(expCt.equalsTo(ct)); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -3853,20 +3801,20 @@ TEST_F(DeclarableOpsTests2, lstmCell_test4) { auto expHt = NDArrayFactory::create('c', {batchSize, numProj}, {0.37992568,0.37992568,0.37992568,0.37992568,0.37992568,0.37992568,0.37992568,0.37992568}); auto expCt = NDArrayFactory::create('c', {batchSize, numUnits},{0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4}); - nd4j::ops::lstmCell op; + sd::ops::lstmCell op; auto results = op.evaluate({&xt, &ht_1, &ct_1, &Wx, &Wh, &Wc, &Wp, &b}, {0.4, 0.3, 1.5}, {0, 0}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *ht = results->at(0); - auto *ct = results->at(1); + auto *ht = results.at(0); + auto *ct = results.at(1); ASSERT_TRUE(expHt.isSameShape(ht)); ASSERT_TRUE(expHt.equalsTo(ht)); ASSERT_TRUE(expCt.isSameShape(ct)); ASSERT_TRUE(expCt.equalsTo(ct)); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -3898,20 +3846,20 @@ TEST_F(DeclarableOpsTests2, lstmCell_test5) { auto expHt = NDArrayFactory::create('c', {batchSize, numProj}, {0.3,0.3,0.3,0.3,0.3,0.3}); auto expCt = NDArrayFactory::create('c', {batchSize, numUnits},{0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4}); - nd4j::ops::lstmCell op; + sd::ops::lstmCell op; auto results = op.evaluate({&xt, &ht_1, &ct_1, &Wx, &Wh, &Wc, &Wp, &b}, {0.4, 0.3, 1.5}, {0, 1}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *ht = results->at(0); - auto *ct = results->at(1); + auto *ht = results.at(0); + auto *ct = results.at(1); ASSERT_TRUE(expHt.isSameShape(ht)); ASSERT_TRUE(expHt.equalsTo(ht)); ASSERT_TRUE(expCt.isSameShape(ct)); ASSERT_TRUE(expCt.equalsTo(ct)); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -3943,20 +3891,20 @@ TEST_F(DeclarableOpsTests2, lstmCell_test6) { auto expHt = NDArrayFactory::create('c', {batchSize, numProj}, {1.99832496,1.99832496,1.99832496,1.99832496,1.99832496,1.99832496}); auto expCt = NDArrayFactory::create('c', {batchSize, numUnits},{3.99972188,3.99972188,3.99972188,3.99972188,3.99972188,3.99972188,3.99972188,3.99972188}); - nd4j::ops::lstmCell op; + sd::ops::lstmCell op; auto results = op.evaluate({&xt, &ht_1, &ct_1, &Wx, &Wh, &Wc, &Wp, &b}, {0., 0., 1.5}, {0, 1}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *ht = results->at(0); - auto *ct = results->at(1); + auto *ht = results.at(0); + auto *ct = results.at(1); ASSERT_TRUE(expHt.isSameShape(ht)); ASSERT_TRUE(expHt.equalsTo(ht)); ASSERT_TRUE(expCt.isSameShape(ct)); ASSERT_TRUE(expCt.equalsTo(ct)); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -3988,20 +3936,20 @@ TEST_F(DeclarableOpsTests2, lstmCell_test7) { auto expHt = NDArrayFactory::create('c', {batchSize, numProj}, {0.75977136,0.75977136,0.75977136,0.75977136,0.75977136,0.75977136}); auto expCt = NDArrayFactory::create('c', {batchSize, numUnits},{0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4}); - nd4j::ops::lstmCell op; + sd::ops::lstmCell op; auto results = op.evaluate({&xt, &ht_1, &ct_1, &Wx, &Wh, &Wc, &Wp, &b}, {0.4, 0., 1.5}, {0, 1}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *ht = results->at(0); - auto *ct = results->at(1); + auto *ht = results.at(0); + auto *ct = results.at(1); ASSERT_TRUE(expHt.isSameShape(ht)); ASSERT_TRUE(expHt.equalsTo(ht)); ASSERT_TRUE(expCt.isSameShape(ct)); ASSERT_TRUE(expCt.equalsTo(ct)); - delete results; + } @@ -4034,20 +3982,20 @@ TEST_F(DeclarableOpsTests2, lstmCell_test8) { auto expHt = NDArrayFactory::create('c', {batchSize, numProj}, {0.99930672,0.99930672,0.99930672,0.99930672, 0.99930672,0.99930672,0.99930672,0.99930672}); auto expCt = NDArrayFactory::create('c', {batchSize, numUnits},{3.99996277,3.99996277,3.99996277,3.99996277,3.99996277,3.99996277,3.99996277,3.99996277}); - nd4j::ops::lstmCell op; + sd::ops::lstmCell op; auto results = op.evaluate({&xt, &ht_1, &ct_1, &Wx, &Wh, &Wc, &Wp, &b}, {0., 0., 10.5}, {1, 0}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *ht = results->at(0); - auto *ct = results->at(1); + auto *ht = results.at(0); + auto *ct = results.at(1); ASSERT_TRUE(expHt.isSameShape(ht)); ASSERT_TRUE(expHt.equalsTo(ht,1e-4)); ASSERT_TRUE(expCt.isSameShape(ct)); ASSERT_TRUE(expCt.equalsTo(ct,1e-4)); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -4079,20 +4027,20 @@ TEST_F(DeclarableOpsTests2, lstmCell_test9) { auto expHt = NDArrayFactory::create('c', {batchSize, numProj}, {0.99501777,0.99501777,0.99501777,0.99501777,0.99501777,0.99501777,0.99501777,0.99501777}); auto expCt = NDArrayFactory::create('c', {batchSize, numUnits},{3.,3.,3.,3.,3.,3.,3.,3.}); - nd4j::ops::lstmCell op; + sd::ops::lstmCell op; auto results = op.evaluate({&xt, &ht_1, &ct_1, &Wx, &Wh, &Wc, &Wp, &b}, {3., 0., 10.5}, {1, 0}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *ht = results->at(0); - auto *ct = results->at(1); + auto *ht = results.at(0); + auto *ct = results.at(1); ASSERT_TRUE(expHt.isSameShape(ht)); ASSERT_TRUE(expHt.equalsTo(ht,1e-4)); ASSERT_TRUE(expCt.isSameShape(ct)); ASSERT_TRUE(expCt.equalsTo(ct)); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -4124,20 +4072,20 @@ TEST_F(DeclarableOpsTests2, lstmCell_test10) { auto expHt = NDArrayFactory::create('c', {batchSize, numProj}, {1.99861344,1.99861344,1.99861344,1.99861344,1.99861344,1.99861344}); auto expCt = NDArrayFactory::create('c', {batchSize, numUnits},{3.99996277, 3.99996277, 3.99996277, 3.99996277,3.99996277, 3.99996277, 3.99996277, 3.99996277}); - nd4j::ops::lstmCell op; + sd::ops::lstmCell op; auto results = op.evaluate({&xt, &ht_1, &ct_1, &Wx, &Wh, &Wc, &Wp, &b}, {0., 0., 10.5}, {1, 1}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *ht = results->at(0); - auto *ct = results->at(1); + auto *ht = results.at(0); + auto *ct = results.at(1); ASSERT_TRUE(expHt.isSameShape(ht)); ASSERT_TRUE(expHt.equalsTo(ht)); ASSERT_TRUE(expCt.isSameShape(ct)); ASSERT_TRUE(expCt.equalsTo(ct)); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -4169,20 +4117,20 @@ TEST_F(DeclarableOpsTests2, lstmCell_test11) { auto expHt = NDArrayFactory::create('c', {batchSize, numProj}, {1.99003554,1.99003554,1.99003554,1.99003554,1.99003554,1.99003554}); auto expCt = NDArrayFactory::create('c', {batchSize, numUnits},{3.,3.,3.,3.,3.,3.,3.,3.}); - nd4j::ops::lstmCell op; + sd::ops::lstmCell op; auto results = op.evaluate({&xt, &ht_1, &ct_1, &Wx, &Wh, &Wc, &Wp, &b}, {3., 0., 10.5}, {1, 1}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *ht = results->at(0); - auto *ct = results->at(1); + auto *ht = results.at(0); + auto *ct = results.at(1); ASSERT_TRUE(expHt.isSameShape(ht)); ASSERT_TRUE(expHt.equalsTo(ht)); ASSERT_TRUE(expCt.isSameShape(ct)); ASSERT_TRUE(expCt.equalsTo(ct)); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -4214,18 +4162,18 @@ TEST_F(DeclarableOpsTests2, lstmCell_test12) { auto expHt = NDArrayFactory::create('c', {batchSize, numProj}, {1.,1.,1.,1.,1.,1.}); auto expCt = NDArrayFactory::create('c', {batchSize, numUnits},{3.,3.,3.,3.,3.,3.,3.,3.}); - nd4j::ops::lstmCell op; + sd::ops::lstmCell op; auto results = op.evaluate({&xt, &ht_1, &ct_1, &Wx, &Wh, &Wc, &Wp, &b}, {3., 1.,-5.}, {1, 1}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *ht = results->at(0); - auto *ct = results->at(1); + auto *ht = results.at(0); + auto *ct = results.at(1); ASSERT_TRUE(expHt.isSameShape(ht)); ASSERT_TRUE(expHt.equalsTo(ht)); ASSERT_TRUE(expCt.isSameShape(ct)); ASSERT_TRUE(expCt.equalsTo(ct)); - delete results; + } diff --git a/libnd4j/tests_cpu/layers_tests/DeclarableOpsTests3.cpp b/libnd4j/tests_cpu/layers_tests/DeclarableOpsTests3.cpp index e7e95afcb..eddef73b3 100644 --- a/libnd4j/tests_cpu/layers_tests/DeclarableOpsTests3.cpp +++ b/libnd4j/tests_cpu/layers_tests/DeclarableOpsTests3.cpp @@ -17,14 +17,14 @@ #include "testlayers.h" #include #include -#include +#include #include -#include -#include +#include +#include -using namespace nd4j; -using namespace nd4j::graph; +using namespace sd; +using namespace sd::graph; class DeclarableOpsTests3 : public testing::Test { public: @@ -42,15 +42,15 @@ TEST_F(DeclarableOpsTests3, Test_Tile_1) { auto exp = x.tile(reps); - nd4j::ops::tile op; + sd::ops::tile op; auto result = op.evaluate({&x, &rep_vector}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } @@ -60,15 +60,15 @@ TEST_F(DeclarableOpsTests3, Test_Tile_2) { auto exp = x.tile(reps); - nd4j::ops::tile op; + sd::ops::tile op; auto result = op.evaluate({&x}, {}, {2, 2}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } TEST_F(DeclarableOpsTests3, Test_Permute_1) { @@ -76,30 +76,27 @@ TEST_F(DeclarableOpsTests3, Test_Permute_1) { auto permute= NDArrayFactory::create('c', {1, 3}, {0, 2, 1}); auto exp= NDArrayFactory::create('c', {2, 4, 3}); - nd4j::ops::permute op; + sd::ops::permute op; auto result = op.evaluate({&x, &permute}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); - - delete result; } TEST_F(DeclarableOpsTests3, Test_Permute_2) { auto x= NDArrayFactory::create('c', {2, 3, 4}); auto exp= NDArrayFactory::create('c', {4, 3, 2}); - nd4j::ops::permute op; + sd::ops::permute op; auto result = op.evaluate({&x}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); - delete result; } @@ -109,14 +106,14 @@ TEST_F(DeclarableOpsTests3, Test_Unique_1) { auto expI= NDArrayFactory::create('c', {5}, {0, 1, 0, 1, 2}); // auto expI= NDArrayFactory::create('c', {3}, {0, 1, 4}); - nd4j::ops::unique op; + sd::ops::unique op; auto result = op.evaluate({&x}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); - ASSERT_EQ(2, result->size()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); + ASSERT_EQ(2, result.size()); - auto v = result->at(0); - auto i = result->at(1); + auto v = result.at(0); + auto i = result.at(1); // v->printIndexedBuffer("Values"); // i->printIndexedBuffer("Indices"); // i->printShapeInfo("Indices shape"); @@ -126,7 +123,7 @@ TEST_F(DeclarableOpsTests3, Test_Unique_1) { ASSERT_TRUE(expI.isSameShape(i)); ASSERT_TRUE(expI.equalsTo(i)); - delete result; + } TEST_F(DeclarableOpsTests3, Test_Unique_2) { @@ -135,15 +132,15 @@ TEST_F(DeclarableOpsTests3, Test_Unique_2) { auto expI= NDArrayFactory::create('c', {5}, {0, 1, 0, 1, 2}); auto expC= NDArrayFactory::create('c', {3}, {2, 2, 1}); - nd4j::ops::unique_with_counts op; + sd::ops::unique_with_counts op; auto result = op.evaluate({&x}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); - ASSERT_EQ(3, result->size()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); + ASSERT_EQ(3, result.size()); - auto v = result->at(0); - auto i = result->at(1); - auto c = result->at(2); + auto v = result.at(0); + auto i = result.at(1); + auto c = result.at(2); // v->printShapeInfo(); // v->printIndexedBuffer("Values"); @@ -160,23 +157,21 @@ TEST_F(DeclarableOpsTests3, Test_Unique_2) { ASSERT_TRUE(expC.isSameShape(c)); ASSERT_TRUE(expC.equalsTo(c)); - - delete result; } TEST_F(DeclarableOpsTests3, Test_Rint_1) { auto x= NDArrayFactory::create('c', {1, 7}, {-1.7f, -1.5f, -0.2f, 0.2f, 1.5f, 1.7f, 2.0f}); auto exp= NDArrayFactory::create('c', {1, 7}, {-2.f, -2.f, -0.f, 0.f, 2.f, 2.f, 2.f}); - nd4j::ops::rint op; + sd::ops::rint op; auto result = op.evaluate({&x}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } @@ -186,20 +181,18 @@ TEST_F(DeclarableOpsTests3, Test_Norm_1) { std::vector empty; std::vector dims({1}); - nd4j::ops::norm op; + sd::ops::norm op; auto result0 = op.evaluate({&x}, {0.}, {}); - auto z0 = result0->at(0); + auto z0 = result0.at(0); auto exp0 = x.reduceAlongDimension(reduce::NormFrobenius, empty, false, false); ASSERT_TRUE(exp0.isSameShape(z0)); ASSERT_TRUE(exp0.equalsTo(z0)); - delete result0; - auto result1 = op.evaluate({&x}, {1.}, {1}); - ASSERT_EQ(result1->status(), ND4J_STATUS_OK); - auto z1 = result1->at(0); + ASSERT_EQ(result1.status(), ND4J_STATUS_OK); + auto z1 = result1.at(0); // z1->printIndexedBuffer("Z1"); auto exp1 = x.reduceAlongDimension(reduce::Norm2, dims, false, false); // exp1.printIndexedBuffer("EXP1"); @@ -208,16 +201,12 @@ TEST_F(DeclarableOpsTests3, Test_Norm_1) { ASSERT_TRUE(exp1.isSameShape(z1)); ASSERT_TRUE(exp1.equalsTo(z1)); - delete result1; - auto result4 = op.evaluate({&x}, {4.}, {1}); - auto z4 = result4->at(0); + auto z4 = result4.at(0); auto exp4= x.reduceAlongDimension(reduce::NormMax, dims, false, false); ASSERT_TRUE(exp4.isSameShape(z4)); ASSERT_TRUE(exp4.equalsTo(z4)); - - delete result4; } @@ -228,34 +217,31 @@ TEST_F(DeclarableOpsTests3, Test_Norm_2) { std::vector empty; std::vector dims({1}); - nd4j::ops::norm op; + sd::ops::norm op; auto result0 = op.evaluate({&x}, {0}, {}); - auto z0 = result0->at(0); + auto z0 = result0.at(0); auto exp0 = x.reduceAlongDimension(reduce::NormFrobenius, empty, false, false); ASSERT_TRUE(exp0.isSameShape(z0)); ASSERT_TRUE(exp0.equalsTo(z0)); - delete result0; + auto result1 = op.evaluate({&x, &axis}, {1}, {}); - auto z1 = result1->at(0); + auto z1 = result1.at(0); auto exp1 = x.reduceAlongDimension(reduce::Norm2, dims, false, false); ASSERT_TRUE(exp1.isSameShape(z1)); ASSERT_TRUE(exp1.equalsTo(z1)); - delete result1; - auto result4 = op.evaluate({&x, &axis}, {4}, {}); - auto z4 = result4->at(0); + auto z4 = result4.at(0); auto exp4= x.reduceAlongDimension(reduce::NormMax, dims, false, false); ASSERT_TRUE(exp4.isSameShape(z4)); ASSERT_TRUE(exp4.equalsTo(z4)); - delete result4; } @@ -263,30 +249,27 @@ TEST_F(DeclarableOpsTests3, Test_ClipByAvgNorm_1) { auto x = NDArrayFactory::create('c', {2, 3}, {-3.0, 0.0, 0.0, 4.0, 0.0, 0.0}); auto exp = NDArrayFactory::create('c', {2, 3}, {-2.88, 0.0, 0.0, 3.84, 0.0, 0.0}); - nd4j::ops::clipbyavgnorm op; + sd::ops::clipbyavgnorm op; auto result = op.evaluate({&x}, {0.8}, {}); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - - delete result; } TEST_F(DeclarableOpsTests3, Test_ClipByAvgNorm_2) { auto x= NDArrayFactory::create('c', {2, 3}, {-3.0f, 0.0f, 0.0f, 4.0f, 0.0f, 0.0f}); auto exp= NDArrayFactory::create('c', {2, 3}, {-3.f, 0.0f, 0.0f, 4.f, 0.0f, 0.0f}); - nd4j::ops::clipbyavgnorm op; + sd::ops::clipbyavgnorm op; auto result = op.evaluate({&x}, {0.9}, {}); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; } @@ -294,30 +277,28 @@ TEST_F(DeclarableOpsTests3, Test_ClipByNorm_1) { auto x= NDArrayFactory::create('c', {2, 3}, {-3.0, 0.0, 0.0, 4.0, 0.0, 0.0}); auto exp= NDArrayFactory::create('c', {2, 3}, {-2.4, 0.0, 0.0, 3.2, 0.0, 0.0}); - nd4j::ops::clipbynorm op; + sd::ops::clipbynorm op; auto result = op.evaluate({&x}, {4.0}, {}); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; } TEST_F(DeclarableOpsTests3, Test_ClipByNorm_2) { auto x= NDArrayFactory::create('c', {2, 3}, {-3.0f, 0.0f, 0.0f, 4.0f, 0.0f, 0.0f}); auto exp= NDArrayFactory::create('c', {2, 3}, {-3.0f, 0.0f, 0.0f, 4.0f, 0.0f, 0.0f}); - nd4j::ops::clipbynorm op; + sd::ops::clipbynorm op; auto result = op.evaluate({&x}, {6.0}, {}); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; } //////////////////////////////////////////////////////////////////////////////// @@ -339,9 +320,9 @@ TEST_F(DeclarableOpsTests3, Test_ClipByNorm_3) { x *= scale; xNorm1 = x.reduceAlongDimension(reduce::Norm2, {1}, true); - nd4j::ops::clipbynorm op; + sd::ops::clipbynorm op; auto result = op.evaluate({&x}, {1.0}, {1}); - auto z = result->at(0); + auto z = result.at(0); auto zNorm1 = z->reduceAlongDimension(reduce::Norm2, {1}, true); auto exp = NDArrayFactory::create('c', {3, 1}, {1., 1., xNorm1.e(2)}); @@ -349,7 +330,6 @@ TEST_F(DeclarableOpsTests3, Test_ClipByNorm_3) { ASSERT_TRUE(exp.isSameShape(&zNorm1)); ASSERT_TRUE(exp.equalsTo(&zNorm1)); - delete result; } TEST_F(DeclarableOpsTests3, Test_ListDiff_1) { @@ -359,13 +339,13 @@ TEST_F(DeclarableOpsTests3, Test_ListDiff_1) { auto exp0= NDArrayFactory::create('c', {3}, {2.f, 4.f, 6.f}); auto exp1= NDArrayFactory::create('c', {3}, {1, 3, 5}); - nd4j::ops::listdiff op; + sd::ops::listdiff op; auto result = op.evaluate({&x, &y}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z0 = result->at(0); - auto z1 = result->at(1); + auto z0 = result.at(0); + auto z1 = result.at(1); z0->getDataBuffer()->syncToSpecial(true); // force sync z1->getDataBuffer()->syncToSpecial(true); // force sync @@ -376,7 +356,6 @@ TEST_F(DeclarableOpsTests3, Test_ListDiff_1) { ASSERT_TRUE(exp1.isSameShape(z1)); ASSERT_TRUE(exp1.equalsTo(z1)); - delete result; } TEST_F(DeclarableOpsTests3, Test_Range_1) { @@ -385,17 +364,16 @@ TEST_F(DeclarableOpsTests3, Test_Range_1) { auto step = NDArrayFactory::create(-0.33f); auto exp= NDArrayFactory::create('c', {17}, { 0.3f, -0.03f, -0.36f, -0.69f, -1.02f, -1.35f, -1.68f, -2.01f, -2.34f, -2.67f, -3.f, -3.33f, -3.66f, -3.99f, -4.32f, -4.65f, -4.98f}); - nd4j::ops::range op; + sd::ops::range op; auto result = op.evaluate({&start, &stop, &step}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; } @@ -405,17 +383,16 @@ TEST_F(DeclarableOpsTests3, Test_Range_2) { auto step= NDArrayFactory::create('c', {1, 1}, {-1.f}); auto exp= NDArrayFactory::create('c', {2}, {2.f, 1.f}); - nd4j::ops::range op; + sd::ops::range op; auto result = op.evaluate({&start, &stop, &step}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; } TEST_F(DeclarableOpsTests3, Test_Range_3) { @@ -424,17 +401,16 @@ TEST_F(DeclarableOpsTests3, Test_Range_3) { auto step= NDArrayFactory::create('c', {1, 1}, {1.f}); auto exp= NDArrayFactory::create('c', {2}, {0.f, 1.f}); - nd4j::ops::range op; + sd::ops::range op; auto result = op.evaluate({&start, &stop, &step}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; } @@ -444,83 +420,78 @@ TEST_F(DeclarableOpsTests3, Test_Range_10) { auto step= NDArrayFactory::create('c', {1, 1}, {1.f}); auto exp= NDArrayFactory::create('c', {2}, {0.f, 1.f}); - nd4j::ops::range op; - auto result = op.evaluate({&start, &stop, &step}, {nd4j::DataType::DOUBLE}); + sd::ops::range op; + auto result = op.evaluate({&start, &stop, &step}, {sd::DataType::DOUBLE}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; } TEST_F(DeclarableOpsTests3, Test_Range_4) { auto exp= NDArrayFactory::create('c', {13}, {-10.f, -8.334f, -6.668f, -5.002f, -3.336f, -1.67f, -0.004f, 1.662f, 3.328f, 4.994f, 6.66f, 8.326f, 9.992f}); - nd4j::ops::range op; + sd::ops::range op; auto result = op.evaluate({}, {-10., 10., 1.666}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; } TEST_F(DeclarableOpsTests3, Test_Range_5) { auto exp= NDArrayFactory::create('c', {2}, {2.f, 1.f}); - nd4j::ops::range op; + sd::ops::range op; auto result = op.evaluate({}, {2, 0, -1}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; } TEST_F(DeclarableOpsTests3, Test_Range_6) { auto exp= NDArrayFactory::create('c', {2}, {0.f, 1.f}); - nd4j::ops::range op; + sd::ops::range op; auto result = op.evaluate({}, {0, 2, 1}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; } TEST_F(DeclarableOpsTests3, Test_Range_7) { auto exp= NDArrayFactory::create('c', {10}, {10.f, 8.334f, 6.668f, 5.002f, 3.336f, 1.67f, 0.004f, -1.662f, -3.328f, -4.994f}); - nd4j::ops::range op; + sd::ops::range op; auto result = op.evaluate({}, {10,-5,-1.666}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; } @@ -528,33 +499,31 @@ TEST_F(DeclarableOpsTests3, Test_Range_7) { TEST_F(DeclarableOpsTests3, Test_Range_8) { auto exp= NDArrayFactory::create('c', {2}, {2, 1}); - nd4j::ops::range op; + sd::ops::range op; auto result = op.evaluate({}, {}, {2, 0, -1}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; } TEST_F(DeclarableOpsTests3, Test_Range_9) { auto exp= NDArrayFactory::create('c', {2}, {0, 1}); - nd4j::ops::range op; + sd::ops::range op; auto result = op.evaluate({}, {}, {0, 2, 1}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; } TEST_F(DeclarableOpsTests3, Test_Batched_Gemm_1) { @@ -565,14 +534,14 @@ TEST_F(DeclarableOpsTests3, Test_Batched_Gemm_1) { auto exp = MmulHelper::mmul(&x, &y); - nd4j::ops::batched_gemm op; + sd::ops::batched_gemm op; auto result = op.evaluate({&a, &b, &x, &x, &x, &y, &y, &y}, {}, {111, 111, 3, 3, 3, 3, 3, 3, 3}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - ASSERT_EQ(3, result->size()); + ASSERT_EQ(3, result.size()); for (int e = 0; e < 3; e++) { - auto z = result->at(e); + auto z = result.at(e); // exp->printIndexedBuffer("e"); // z->printIndexedBuffer("z"); @@ -582,7 +551,7 @@ TEST_F(DeclarableOpsTests3, Test_Batched_Gemm_1) { } delete exp; - delete result; + } TEST_F(DeclarableOpsTests3, Test_Batched_Gemm_2) { @@ -593,14 +562,14 @@ TEST_F(DeclarableOpsTests3, Test_Batched_Gemm_2) { auto exp = MmulHelper::mmul(&x, &y); - nd4j::ops::batched_gemm op; + sd::ops::batched_gemm op; auto result = op.evaluate({&a, &b, &x, &x, &x, &y, &y, &y}, {}, {112, 112, 3, 3, 3, 3, 3, 3, 3}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - ASSERT_EQ(3, result->size()); + ASSERT_EQ(3, result.size()); for (int e = 0; e < 3; e++) { - auto z = result->at(e); + auto z = result.at(e); //exp->printIndexedBuffer("e"); //z->printIndexedBuffer("z"); @@ -610,7 +579,7 @@ TEST_F(DeclarableOpsTests3, Test_Batched_Gemm_2) { } delete exp; - delete result; + } TEST_F(DeclarableOpsTests3, Test_Batched_Gemm_3) { @@ -621,14 +590,14 @@ TEST_F(DeclarableOpsTests3, Test_Batched_Gemm_3) { auto exp = MmulHelper::mmul(&x, &y); - nd4j::ops::batched_gemm op; + sd::ops::batched_gemm op; auto result = op.evaluate({&a, &b, &x, &x, &x, &y, &y, &y}, {}, {112, 111, 3, 3, 3, 3, 3, 3, 3}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - ASSERT_EQ(3, result->size()); + ASSERT_EQ(3, result.size()); for (int e = 0; e < 3; e++) { - auto z = result->at(e); + auto z = result.at(e); // exp->printIndexedBuffer("e"); // z->printIndexedBuffer("z"); @@ -638,7 +607,7 @@ TEST_F(DeclarableOpsTests3, Test_Batched_Gemm_3) { } delete exp; - delete result; + } TEST_F(DeclarableOpsTests3, Test_Batched_Gemm_4) { @@ -649,14 +618,14 @@ TEST_F(DeclarableOpsTests3, Test_Batched_Gemm_4) { auto exp = MmulHelper::mmul(&x, &y); - nd4j::ops::batched_gemm op; + sd::ops::batched_gemm op; auto result = op.evaluate({&a, &b, &x, &x, &x, &y, &y, &y}, {}, {111, 111, 5, 4, 3, 5, 3, 5, 3}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - ASSERT_EQ(3, result->size()); + ASSERT_EQ(3, result.size()); for (int e = 0; e < 3; e++) { - auto z = result->at(e); + auto z = result.at(e); //exp->printIndexedBuffer("e"); //z->printIndexedBuffer("z"); @@ -666,7 +635,7 @@ TEST_F(DeclarableOpsTests3, Test_Batched_Gemm_4) { } delete exp; - delete result; + } TEST_F(DeclarableOpsTests3, Test_Batched_Gemm_5) { @@ -677,14 +646,14 @@ TEST_F(DeclarableOpsTests3, Test_Batched_Gemm_5) { auto exp = MmulHelper::mmul(&x, &y); - nd4j::ops::batched_gemm op; + sd::ops::batched_gemm op; auto result = op.evaluate({&a, &b, &x, &x, &x, &y, &y, &y}, {}, {112, 112, 5, 4, 3, 3, 4, 5, 3}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - ASSERT_EQ(3, result->size()); + ASSERT_EQ(3, result.size()); for (int e = 0; e < 3; e++) { - auto z = result->at(e); + auto z = result.at(e); //exp->printIndexedBuffer("e"); //z->printIndexedBuffer("z"); @@ -694,7 +663,7 @@ TEST_F(DeclarableOpsTests3, Test_Batched_Gemm_5) { } delete exp; - delete result; + } @@ -706,14 +675,14 @@ TEST_F(DeclarableOpsTests3, Test_Batched_Gemm_6) { auto exp = MmulHelper::mmul(&x, &y); - nd4j::ops::batched_gemm op; + sd::ops::batched_gemm op; auto result = op.evaluate({&a, &b, &x, &x, &x, &y, &y, &y}, {}, {111, 111, 2, 3, 5, 2, 5, 2, 3}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - ASSERT_EQ(3, result->size()); + ASSERT_EQ(3, result.size()); for (int e = 0; e < 3; e++) { - auto z = result->at(e); + auto z = result.at(e); //exp->printIndexedBuffer("e"); //z->printIndexedBuffer("z"); @@ -723,7 +692,7 @@ TEST_F(DeclarableOpsTests3, Test_Batched_Gemm_6) { } delete exp; - delete result; + } TEST_F(DeclarableOpsTests3, Test_Batched_Gemm_7) { @@ -736,14 +705,14 @@ TEST_F(DeclarableOpsTests3, Test_Batched_Gemm_7) { // exp->printShapeInfo("exp shape"); - nd4j::ops::batched_gemm op; + sd::ops::batched_gemm op; auto result = op.evaluate({&a, &b, &x, &x, &x, &y, &y, &y}, {}, {112, 112, 2, 3, 5, 5, 3, 2, 3}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - ASSERT_EQ(3, result->size()); + ASSERT_EQ(3, result.size()); for (int e = 0; e < 3; e++) { - auto z = result->at(e); + auto z = result.at(e); //exp->printIndexedBuffer("e"); //z->printIndexedBuffer("z"); @@ -753,7 +722,7 @@ TEST_F(DeclarableOpsTests3, Test_Batched_Gemm_7) { } delete exp; - delete result; + } TEST_F(DeclarableOpsTests3, Test_Batched_Gemm_Validation_1) { @@ -762,10 +731,10 @@ TEST_F(DeclarableOpsTests3, Test_Batched_Gemm_Validation_1) { auto x = NDArrayFactory::create('c', {2, 5}, {1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 7.f, 8.f, 9.f, 10.f}); auto y = NDArrayFactory::create('c', {5, 3}, {1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 7.f, 8.f, 9.f, 10.f, 11.f, 12.f, 13.f, 14.f, 15.f}); - nd4j::ops::batched_gemm op; + sd::ops::batched_gemm op; try { auto result = op.evaluate({&a, &b, &x, &x, &x, &y, &y, &y}, {}, {112, 112, 2, 3, 5, 5, 3, 2, 3}); - delete result; + ASSERT_TRUE(false); } catch (std::invalid_argument &e) { // @@ -780,7 +749,7 @@ TEST_F(DeclarableOpsTests3, Test_Batched_Gemm_Validation_2) { auto z = NDArrayFactory::create('c', {2, 3}); - nd4j::ops::batched_gemm op; + sd::ops::batched_gemm op; try { auto result = op.execute({&a, &b, &x, &x, &x, &y, &y, &y}, {&z}, {}, {112, 112, 2, 3, 5, 5, 3, 2, 3}, {}); ASSERT_TRUE(false); @@ -794,16 +763,15 @@ TEST_F(DeclarableOpsTests3, Test_ReverseDivide_1) { auto y= NDArrayFactory::create('c', {1, 3}, {4, 6, 8}); auto exp= NDArrayFactory::create('c', {1, 3}, {2, 3, 4}); - nd4j::ops::reversedivide op; + sd::ops::reversedivide op; auto result = op.evaluate({&x, &y}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; } //////////////////////////////////////////////////////////////////// @@ -825,20 +793,19 @@ TEST_F(DeclarableOpsTests3, sruCell_test1) { auto expHt= NDArrayFactory::create('c', {batchSize, inSize}, {0.96674103f, 0.96674103f, 0.96674103f, 0.96674103f, 0.96674103f, 0.96674103f, 0.96674103f, 0.96674103f, 0.96674103f, 0.96674103f}); auto expCt= NDArrayFactory::create('c', {batchSize, inSize}, {2.01958286f, 2.01958286f, 2.01958286f, 2.01958286f, 2.01958286f, 2.01958286f, 2.01958286f, 2.01958286f, 2.01958286f, 2.01958286f}); - nd4j::ops::sruCell op; + sd::ops::sruCell op; auto results = op.evaluate({&xt, &ct_1, &w, &b}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *ht = results->at(0); - auto *ct = results->at(1); + auto *ht = results.at(0); + auto *ct = results.at(1); ASSERT_TRUE(expHt.isSameShape(ht)); ASSERT_TRUE(expHt.equalsTo(ht)); ASSERT_TRUE(expCt.isSameShape(ct)); ASSERT_TRUE(expCt.equalsTo(ct)); - delete results; } @@ -861,20 +828,19 @@ TEST_F(DeclarableOpsTests3, sruCell_test2) { auto expHt= NDArrayFactory::create('c', {batchSize, inSize}, {0.97542038f, 0.97542038f, 0.97542038f, 0.97542038f, 0.97542038f, 0.97542038f, 0.97542038f, 0.97542038f, 0.97542038f, 0.97542038f}); auto expCt= NDArrayFactory::create('c', {batchSize, inSize}, {2.09121276f, 2.09121276f, 2.09121276f, 2.09121276f, 2.09121276f, 2.09121276f, 2.09121276f, 2.09121276f, 2.09121276f, 2.09121276f}); - nd4j::ops::sruCell op; + sd::ops::sruCell op; auto results = op.evaluate({&xt, &ct_1, &w, &b}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *ht = results->at(0); - auto *ct = results->at(1); + auto *ht = results.at(0); + auto *ct = results.at(1); ASSERT_TRUE(expHt.isSameShape(ht)); ASSERT_TRUE(expHt.equalsTo(ht)); ASSERT_TRUE(expCt.isSameShape(ct)); ASSERT_TRUE(expCt.equalsTo(ct)); - delete results; } //////////////////////////////////////////////////////////////////// @@ -896,20 +862,20 @@ TEST_F(DeclarableOpsTests3, sruCell_test3) { auto expHt= NDArrayFactory::create('c', {batchSize, inSize}, {0.76159416f, 0.76159416f, 0.76159416f, 0.76159416f, 0.76159416f, 0.76159416f, 0.76159416f, 0.76159416f, 0.76159416f, 0.76159416f}); auto expCt= NDArrayFactory::create('c', {batchSize, inSize}, {1.f, 1.f, 1.f, 1.f, 1.f, 1.f, 1.f, 1.f, 1.f, 1.f}); - nd4j::ops::sruCell op; + sd::ops::sruCell op; auto results = op.evaluate({&xt, &ct_1, &w, &b}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *ht = results->at(0); - auto *ct = results->at(1); + auto *ht = results.at(0); + auto *ct = results.at(1); ASSERT_TRUE(expHt.isSameShape(ht)); ASSERT_TRUE(expHt.equalsTo(ht)); ASSERT_TRUE(expCt.isSameShape(ct)); ASSERT_TRUE(expCt.equalsTo(ct)); - delete results; + } //////////////////////////////////////////////////////////////////// @@ -935,17 +901,16 @@ TEST_F(DeclarableOpsTests3, gruCell_test1) { auto expHt = NDArrayFactory::create('c', {batchSize, numUnits}, {1.99993872f, 1.99993872f, 1.99993872f, 1.99993872f, 1.99993872f, 1.99993872f, 1.99993872f, 1.99993872f}); - nd4j::ops::gruCell op; + sd::ops::gruCell op; auto results = op.evaluate({&xt, &ht_1, &Wru, &Wc, &bru, &bc}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *ht = results->at(3); + auto *ht = results.at(3); ASSERT_TRUE(expHt.isSameShape(ht)); ASSERT_TRUE(expHt.equalsTo(ht)); - delete results; } //////////////////////////////////////////////////////////////////// @@ -971,17 +936,17 @@ TEST_F(DeclarableOpsTests3, gruCell_test2) { auto expHt= NDArrayFactory::create('c', {batchSize, numUnits}, {0.00669224f, 0.00669224f, 0.00669224f, 0.00669224f, 0.00669224f, 0.00669224f, 0.00669224f, 0.00669224f}); - nd4j::ops::gruCell op; + sd::ops::gruCell op; auto results = op.evaluate({&xt, &ht_1, &Wru, &Wc, &bru, &bc}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *ht = results->at(3); + auto *ht = results.at(3); ASSERT_TRUE(expHt.isSameShape(ht)); ASSERT_TRUE(expHt.equalsTo(ht)); - delete results; + } //////////////////////////////////////////////////////////////////// @@ -1007,17 +972,17 @@ TEST_F(DeclarableOpsTests3, gruCell_test3) { auto expHt= NDArrayFactory::create('c', {batchSize, numUnits}, {0.1149149f, 0.1149149f, 0.1149149f, 0.1149149f, 0.1149149f, 0.1149149f, 0.1149149f, 0.1149149f}); - nd4j::ops::gruCell op; - auto results = op.evaluate({&xt, &ht_1, &Wru, &Wc, &bru, &bc}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + sd::ops::gruCell op; + auto result = op.evaluate({&xt, &ht_1, &Wru, &Wc, &bru, &bc}); - auto *ht = results->at(3); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); + + auto *ht = result.at(3); ASSERT_TRUE(expHt.isSameShape(ht)); ASSERT_TRUE(expHt.equalsTo(ht)); - delete results; } //////////////////////////////////////////////////////////////////// @@ -1026,17 +991,17 @@ TEST_F(DeclarableOpsTests3, invertPermutation_test1) { auto input= NDArrayFactory::create('c', {1, 8}, {5,2,7,4,6,3,1,0}); auto expected= NDArrayFactory::create('c', {1, 8}, {7, 6, 1, 5, 3, 0, 4, 2}); - nd4j::ops::invert_permutation op; - auto results = op.evaluate({&input}); + sd::ops::invert_permutation op; + auto result = op.evaluate({&input}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto *output = results->at(0); + auto *output = result.at(0); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -1045,17 +1010,18 @@ TEST_F(DeclarableOpsTests3, invertPermutation_test2) { auto input= NDArrayFactory::create('c', {1, 8}, {5,2,7,4,6,3,1,0}); auto expected= NDArrayFactory::create('c', {1, 8}, {7, 6, 1, 5, 3, 0, 4, 2}); - nd4j::ops::invert_permutation op; - auto results = op.evaluate({&input}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + sd::ops::invert_permutation op; + auto result = op.evaluate({&input}); - auto *output = results->at(0); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); + + auto *output = result.at(0); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -1064,17 +1030,16 @@ TEST_F(DeclarableOpsTests3, invertPermutation_test3) { auto input= NDArrayFactory::create('c', {1, 8}, {1,2,0,4,6,3,5,7}); auto expected= NDArrayFactory::create('c', {1, 8}, {2, 0, 1, 5, 3, 6, 4, 7}); - nd4j::ops::invert_permutation op; - auto results = op.evaluate({&input}); + sd::ops::invert_permutation op; + auto result = op.evaluate({&input}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto *output = results->at(0); + auto *output = result.at(0); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; } /////////////////////////////////////////////////////////////////// @@ -1085,17 +1050,16 @@ TEST_F(DeclarableOpsTests3, diag_test1) { auto expected= NDArrayFactory::create('c', {3,2,3,2}, {1,0,0,0,0,0, 0,2,0,0,0,0, 0,0,3,0,0,0, 0,0,0,4,0,0, 0,0,0,0,5,0, 0,0,0,0,0,6}); - nd4j::ops::diag op; - auto results = op.evaluate({&input}); + sd::ops::diag op; + auto result = op.evaluate({&input}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto *output = results->at(0); + auto *output = result.at(0); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; } /////////////////////////////////////////////////////////////////// @@ -1106,17 +1070,16 @@ TEST_F(DeclarableOpsTests3, diag_test2) { auto expected= NDArrayFactory::create('c', {2,3,2,3}, {1,0,0,0,0,0, 0,2,0,0,0,0, 0,0,3,0,0,0, 0,0,0,4,0,0, 0,0,0,0,5,0, 0,0,0,0,0,6}); - nd4j::ops::diag op; - auto results = op.evaluate({&input}); + sd::ops::diag op; + auto result = op.evaluate({&input}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto *output = results->at(0); + auto *output = result.at(0); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; } @@ -1127,23 +1090,19 @@ TEST_F(DeclarableOpsTests3, diag_test_vector) { auto input = NDArrayFactory::linspace(1,4,4); auto expected= NDArrayFactory::create('c', {4,4}, {1,0,0,0, 0,2,0,0, 0,0,3,0,0,0,0,4}); - nd4j::ops::diag op; - auto results = op.evaluate({input}); + sd::ops::diag op; + auto result = op.evaluate({input}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto output = results->at(0); + auto output = result.at(0); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - - delete results; + delete input; } - - - TEST_F(DeclarableOpsTests3, diag_test_col_vector) { @@ -1151,17 +1110,17 @@ TEST_F(DeclarableOpsTests3, diag_test_col_vector) { input->reshapei({4,1}); auto expected= NDArrayFactory::create('c', {4,4}, {1,0,0,0, 0,2,0,0, 0,0,3,0,0,0,0,4}); - nd4j::ops::diag op; - auto results = op.evaluate({input}, {}, {}); + sd::ops::diag op; + auto result = op.evaluate({input}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto *output = results->at(0); + auto *output = result.at(0); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; + delete input; } /////////////////////////////////////////////////////////////////// @@ -1172,17 +1131,16 @@ TEST_F(DeclarableOpsTests3, diag_test3) { auto expected= NDArrayFactory::create('c', {3,3}, {1,0,0, 0,2,0, 0,0,3}); - nd4j::ops::diag op; - auto results = op.evaluate({&input}, {}, {}); + sd::ops::diag op; + auto result = op.evaluate({&input}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto *output = results->at(0); + auto *output = result.at(0); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; } /////////////////////////////////////////////////////////////////// @@ -1193,17 +1151,16 @@ TEST_F(DeclarableOpsTests3, diag_test4) { auto expected= NDArrayFactory::create('c', {3,3}, {1,0,0, 0,2,0, 0,0,3}); - nd4j::ops::diag op; - auto results = op.evaluate({&input}, {}, {}); + sd::ops::diag op; + auto result = op.evaluate({&input}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto *output = results->at(0); + auto *output = result.at(0); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; } /////////////////////////////////////////////////////////////////// @@ -1214,17 +1171,16 @@ TEST_F(DeclarableOpsTests3, diag_test5) { auto expected= NDArrayFactory::create('c', {1,1}, {2}); - nd4j::ops::diag op; - auto results = op.evaluate({&input}, {}, {}); + sd::ops::diag op; + auto result = op.evaluate({&input}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto *output = results->at(0); + auto *output = result.at(0); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; } /////////////////////////////////////////////////////////////////// @@ -1235,17 +1191,16 @@ TEST_F(DeclarableOpsTests3, diag_test6) { auto expected= NDArrayFactory::create('c', {2,2,2,2,2,2}, {1,0,0,0, 0,0,0,0, 0,2,0,0, 0,0,0,0, 0,0,3,0, 0,0,0,0, 0,0,0,4, 0,0,0,0, 0,0,0,0, 5,0,0,0, 0,0,0,0, 0,6,0,0, 0,0,0,0, 0,0,7,0, 0,0,0,0, 0,0,0,8}); - nd4j::ops::diag op; - auto results = op.evaluate({&input}, {}, {}); + sd::ops::diag op; + auto result = op.evaluate({&input}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto *output = results->at(0); + auto *output = result.at(0); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; } /////////////////////////////////////////////////////////////////// @@ -1258,17 +1213,16 @@ TEST_F(DeclarableOpsTests3, matrixSetDiag_test1) { auto expected= NDArrayFactory::create('c', {4,3,2}, {1,0,0,1,0,0, 1,0,0,1,0,0, 1,0,0,1,0,0, 1,0,0,1,0,0}); - nd4j::ops::matrix_set_diag op; - auto results = op.evaluate({&input, &diagonal}, {}, {}); + sd::ops::matrix_set_diag op; + auto result = op.evaluate({&input, &diagonal}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto *output = results->at(0); + auto *output = result.at(0); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; } /////////////////////////////////////////////////////////////////// @@ -1281,17 +1235,17 @@ TEST_F(DeclarableOpsTests3, matrixSetDiag_test2) { auto expected= NDArrayFactory::create('c', {1,1,2}, {1.f, 0.f}); - nd4j::ops::matrix_set_diag op; - auto results = op.evaluate({&input, &diagonal}, {}, {}); + sd::ops::matrix_set_diag op; + auto result = op.evaluate({&input, &diagonal}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto *output = results->at(0); + auto *output = result.at(0); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -1304,17 +1258,16 @@ TEST_F(DeclarableOpsTests3, matrixSetDiag_test3) { auto expected= NDArrayFactory::create('c', {2,1,4}, {1,0,0,0,1,0,0,0}); - nd4j::ops::matrix_set_diag op; - auto results = op.evaluate({&input, &diagonal}, {}, {}); + sd::ops::matrix_set_diag op; + auto result = op.evaluate({&input, &diagonal}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto *output = results->at(0); + auto *output = result.at(0); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; } /////////////////////////////////////////////////////////////////// @@ -1327,17 +1280,16 @@ TEST_F(DeclarableOpsTests3, matrixSetDiag_test4) { auto expected= NDArrayFactory::create('c', {2,1,4,1}, {1,0,0,0,1,0,0,0}); - nd4j::ops::matrix_set_diag op; - auto results = op.evaluate({&input, &diagonal}, {}, {}); + sd::ops::matrix_set_diag op; + auto result = op.evaluate({&input, &diagonal}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto *output = results->at(0); + auto *output = result.at(0); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; } /////////////////////////////////////////////////////////////////// @@ -1348,18 +1300,17 @@ TEST_F(DeclarableOpsTests3, diagPart_test1) { auto expected= NDArrayFactory::create('c', {2}, {1,4}); - nd4j::ops::diag_part op; - auto results = op.evaluate({&input}, {}, {}); + sd::ops::diag_part op; + auto result = op.evaluate({&input}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto *output = results->at(0); + auto *output = result.at(0); // output->printBuffer(); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; } /////////////////////////////////////////////////////////////////// @@ -1370,17 +1321,16 @@ TEST_F(DeclarableOpsTests3, diagPart_test2) { auto expected= NDArrayFactory::create('c', {2,2}, {1,6,11,16}); - nd4j::ops::diag_part op; - auto results = op.evaluate({&input}, {}, {}); + sd::ops::diag_part op; + auto result = op.evaluate({&input}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto *output = results->at(0); + auto *output = result.at(0); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; } /////////////////////////////////////////////////////////////////// @@ -1391,17 +1341,16 @@ TEST_F(DeclarableOpsTests3, diagPart_test3) { auto expected= NDArrayFactory::create('c', {2,2,2}, {1,10,19,28,37,46,55,64}); - nd4j::ops::diag_part op; - auto results = op.evaluate({&input}, {}, {}); + sd::ops::diag_part op; + auto result = op.evaluate({&input}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto *output = results->at(0); + auto *output = result.at(0); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; } /////////////////////////////////////////////////////////////////// @@ -1417,17 +1366,16 @@ TEST_F(DeclarableOpsTests3, betainc_test1) { auto expected = NDArrayFactory::create('c', {3,3}, {0.40638509f, 0.33668978f, 0.28271242f, 0.23973916f, 0.20483276f, 0.17604725f, 0.15203027f, 0.13180567f, 0.114647f}); - nd4j::ops::betainc op; - auto results = op.evaluate({&a, &b, &x}, {}, {}); + sd::ops::betainc op; + auto result = op.evaluate({&a, &b, &x}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto output = results->at(0); + auto output = result.at(0); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output, 1e-2)); - delete results; } /////////////////////////////////////////////////////////////////// @@ -1443,17 +1391,16 @@ TEST_F(DeclarableOpsTests3, betainc_test2) { auto expected= NDArrayFactory::create('c', {3,3}, {0.40638509f, 0.33668978f, 0.28271242f, 0.23973916f, 0.20483276f, 0.17604725f, 0.15203027f, 0.13180567f, 0.114647f}); - nd4j::ops::betainc op; - auto results = op.evaluate({&a, &b, &x}, {}, {}); + sd::ops::betainc op; + auto result = op.evaluate({&a, &b, &x}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto *output = results->at(0); + auto *output = result.at(0); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; } /////////////////////////////////////////////////////////////////// @@ -1469,17 +1416,16 @@ TEST_F(DeclarableOpsTests3, betainc_test3) { auto expected= NDArrayFactory::create('c', {3,3}, {0.40638509f, 0.33668978f, 0.28271242f, 0.23973916f, 0.20483276f, 0.17604725f, 0.15203027f, 0.13180567f, 0.114647f}); - nd4j::ops::betainc op; - auto results = op.evaluate({&a, &b, &x}, {}, {}); + sd::ops::betainc op; + auto result = op.evaluate({&a, &b, &x}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto *output = results->at(0); + auto *output = result.at(0); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; } /////////////////////////////////////////////////////////////////// @@ -1495,17 +1441,16 @@ TEST_F(DeclarableOpsTests3, betainc_test4) { auto expected= NDArrayFactory::create('c', {3,3}, {1.00000000e-01f, 2.80000000e-02f, 8.56000000e-03f, 2.72800000e-03f, 8.90920000e-04f, 2.95706080e-04f, 9.92854864e-05f, 3.36248880e-05f, 1.14644360e-05f}); - nd4j::ops::betainc op; - auto results = op.evaluate({&a, &b, &x}, {}, {}); + sd::ops::betainc op; + auto result = op.evaluate({&a, &b, &x}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto *output = results->at(0); + auto *output = result.at(0); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output, 1e-6)); - delete results; } /////////////////////////////////////////////////////////////////// @@ -1521,17 +1466,16 @@ TEST_F(DeclarableOpsTests3, betainc_test5) { auto expected= NDArrayFactory::create('c', {3,3}, {0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f}); - nd4j::ops::betainc op; - auto results = op.evaluate({&a, &b, &x}, {}, {}); + sd::ops::betainc op; + auto result = op.evaluate({&a, &b, &x}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto *output = results->at(0); + auto *output = result.at(0); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output, 1e-6)); - delete results; } /////////////////////////////////////////////////////////////////// @@ -1547,17 +1491,16 @@ TEST_F(DeclarableOpsTests3, betainc_test6) { auto expected= NDArrayFactory::create('c', {3,3}, {3.92988233e-06f, 1.35306497e-06f, 4.67576826e-07f, 1.62083416e-07f, 5.63356971e-08f, 1.96261318e-08f, 6.85120307e-09f, 2.39594668e-09f, 8.39227685e-10f}); - nd4j::ops::betainc op; - auto results = op.evaluate({&a, &b, &x}, {}, {}); + sd::ops::betainc op; + auto result = op.evaluate({&a, &b, &x}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto *output = results->at(0); + auto *output = result.at(0); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output, 1e-6)); - delete results; } /////////////////////////////////////////////////////////////////// @@ -1573,17 +1516,16 @@ TEST_F(DeclarableOpsTests3, betainc_test7) { auto expected= NDArrayFactory::create('c', {3,3}, {0.99999607f, 0.99999865f, 0.99999953f, 0.99999984f, 0.99999994f, 0.99999998f, 0.99999999f, 1.f, 1.f}); - nd4j::ops::betainc op; - auto results = op.evaluate({&a, &b, &x}, {}, {}); + sd::ops::betainc op; + auto result = op.evaluate({&a, &b, &x}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto *output = results->at(0); + auto *output = result.at(0); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output, 1e-6)); - delete results; } /////////////////////////////////////////////////////////////////// @@ -1599,17 +1541,17 @@ TEST_F(DeclarableOpsTests3, betainc_test8) { auto expected= NDArrayFactory::create('c', {3,3}, {1.f, 1.f, 1.f,1.f,1.f,1.f,1.f,1.f,1.f}); - nd4j::ops::betainc op; - auto results = op.evaluate({&a, &b, &x}, {}, {}); + sd::ops::betainc op; + auto result = op.evaluate({&a, &b, &x}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto *output = results->at(0); + auto *output = result.at(0); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output, 1e-6)); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -1625,17 +1567,16 @@ TEST_F(DeclarableOpsTests3, betainc_test9) { auto expected= NDArrayFactory::create('c', {3,3}, {0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f}); - nd4j::ops::betainc op; - auto results = op.evaluate({&a, &b, &x}, {}, {}); + sd::ops::betainc op; + auto result = op.evaluate({&a, &b, &x}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto *output = results->at(0); + auto *output = result.at(0); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - - delete results; + } /////////////////////////////////////////////////////////////////// @@ -1651,60 +1592,57 @@ TEST_F(DeclarableOpsTests3, betainc_test10) { auto expected= NDArrayFactory::create('c', {3,3}, {0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f}); - nd4j::ops::betainc op; - auto results = op.evaluate({&a, &b, &x}, {}, {}); + sd::ops::betainc op; + auto result = op.evaluate({&a, &b, &x}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto *output = results->at(0); + auto *output = result.at(0); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; } /////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests3, betainc_test11) { - NDArray a('c', {4}, {0.7788f, 0.8012f, 0.7244f, 0.2309f}, nd4j::DataType::FLOAT32); - NDArray b('c', {4}, {0.7717f, 0.9281f, 0.9846f, 0.4838f}, nd4j::DataType::FLOAT32); - NDArray x('c', {4}, {0.9441f, 0.5957f, 0.8669f, 0.3502f}, nd4j::DataType::FLOAT32); + NDArray a('c', {4}, {0.7788f, 0.8012f, 0.7244f, 0.2309f}, sd::DataType::FLOAT32); + NDArray b('c', {4}, {0.7717f, 0.9281f, 0.9846f, 0.4838f}, sd::DataType::FLOAT32); + NDArray x('c', {4}, {0.9441f, 0.5957f, 0.8669f, 0.3502f}, sd::DataType::FLOAT32); - NDArray expected('c', {4}, {0.912156, 0.634460, 0.898314, 0.624538}, nd4j::DataType::FLOAT32); - nd4j::ops::betainc op; - auto results = op.evaluate({&a, &b, &x}, {}, {}); + NDArray expected('c', {4}, {0.912156, 0.634460, 0.898314, 0.624538}, sd::DataType::FLOAT32); + sd::ops::betainc op; + auto result = op.evaluate({&a, &b, &x}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto *output = results->at(0); + auto *output = result.at(0); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; } /////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests3, betainc_test12) { - NDArray a('c', {4}, {8.0091f, 8.2108f, 7.5194f, 3.0780f}, nd4j::DataType::FLOAT32); - NDArray b('c', {4}, {7.9456f, 9.3527f, 9.8610f, 5.3541f}, nd4j::DataType::FLOAT32); - NDArray x('c', {4}, {0.9441f, 0.5957f, 0.8669f, 0.3502f}, nd4j::DataType::FLOAT32); + NDArray a('c', {4}, {8.0091f, 8.2108f, 7.5194f, 3.0780f}, sd::DataType::FLOAT32); + NDArray b('c', {4}, {7.9456f, 9.3527f, 9.8610f, 5.3541f}, sd::DataType::FLOAT32); + NDArray x('c', {4}, {0.9441f, 0.5957f, 0.8669f, 0.3502f}, sd::DataType::FLOAT32); - NDArray expected('c', {4}, {0.9999995 , 0.8594694 , 0.999988 , 0.49124345}, nd4j::DataType::FLOAT32); + NDArray expected('c', {4}, {0.9999995 , 0.8594694 , 0.999988 , 0.49124345}, sd::DataType::FLOAT32); - nd4j::ops::betainc op; - auto results = op.evaluate({&a, &b, &x}, {}, {}); + sd::ops::betainc op; + auto result = op.evaluate({&a, &b, &x}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto *output = results->at(0); + auto *output = result.at(0); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - - delete results; + } /////////////////////////////////////////////////////////////////// @@ -1718,17 +1656,16 @@ TEST_F(DeclarableOpsTests3, zeta_test1) { auto expected= NDArrayFactory::create('c', {3,3}, {1.64493407f, 0.64493407f, 0.39493407f, 0.28382296f, 0.22132296f, 0.18132296f, 0.15354518f, 0.13313701f, 0.11751201f}); - nd4j::ops::zeta op; - auto results = op.evaluate({&x, &q}, {}, {}); + sd::ops::zeta op; + auto result = op.evaluate({&x, &q}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto *output = results->at(0); + auto *output = result.at(0); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; } /////////////////////////////////////////////////////////////////// @@ -1742,17 +1679,17 @@ TEST_F(DeclarableOpsTests3, zeta_test2) { auto expected= NDArrayFactory::create('c', {3,3}, {0.10516634f, 0.09516634f, 0.08690187f, 0.07995743f, 0.07404027f, 0.06893823f, 0.06449378f, 0.06058753f, 0.05712733f}); - nd4j::ops::zeta op; - auto results = op.evaluate({&x, &q}, {}, {}); + sd::ops::zeta op; + auto result = op.evaluate({&x, &q}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto *output = results->at(0); + auto *output = result.at(0); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -1766,17 +1703,17 @@ TEST_F(DeclarableOpsTests3, zeta_test3) { auto expected= NDArrayFactory::create('c', {3,3}, {0.01005017f, 0.00995017f, 0.00985214f, 0.00975602f, 0.00966176f, 0.0095693f, 0.0094786f, 0.0093896f, 0.00930226f}); - nd4j::ops::zeta op; - auto results = op.evaluate({&x, &q}, {}, {}); + sd::ops::zeta op; + auto result = op.evaluate({&x, &q}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); - auto *output = results->at(0); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); + + auto *output = result.at(0); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; } @@ -1791,17 +1728,15 @@ TEST_F(DeclarableOpsTests3, zeta_test4) { auto expected= NDArrayFactory::create('c', {3,3}, {0.01005017f, 0.00995017f, 0.00985214f, 0.00975602f, 0.00966176f, 0.0095693f, 0.0094786f, 0.0093896f, 0.00930226f}); - nd4j::ops::zeta op; - auto results = op.evaluate({&x, &q}, {}, {}); + sd::ops::zeta op; + auto result = op.evaluate({&x, &q}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto *output = results->at(0); + auto *output = result.at(0); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - - delete results; } /////////////////////////////////////////////////////////////////// @@ -1815,17 +1750,17 @@ TEST_F(DeclarableOpsTests3, zeta_test5) { auto expected= NDArrayFactory::create('c', {3,3}, {10.58444846f, 9.58444846f, 9.11793197f, 8.81927915f, 8.60164151f, 8.43137352f, 8.29204706f, 8.17445116f, 8.07291961f}); - nd4j::ops::zeta op; - auto results = op.evaluate({&x, &q}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + sd::ops::zeta op; + auto result = op.evaluate({&x, &q}, {}, {}); - auto *output = results->at(0); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); + + auto *output = result.at(0); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; } /////////////////////////////////////////////////////////////////// @@ -1839,17 +1774,16 @@ TEST_F(DeclarableOpsTests3, zeta_test6) { auto expected= NDArrayFactory::create('c', {3,3}, {100.57794334f, 99.57794334f, 99.08139709f, 98.75170576f, 98.50514758f, 98.30834069f, 98.1446337f, 98.00452955f, 97.88210202f}); - nd4j::ops::zeta op; - auto results = op.evaluate({&x, &q}, {}, {}); + sd::ops::zeta op; + auto result = op.evaluate({&x, &q}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto *output = results->at(0); + auto *output = result.at(0); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; } /////////////////////////////////////////////////////////////////// @@ -1863,17 +1797,17 @@ TEST_F(DeclarableOpsTests3, zeta_test7) { auto expected= NDArrayFactory::create('c', {3,3}, {1.00099458e+00f, 9.94575128e-04f, 1.80126278e-05f, 1.07754001e-06f, 1.23865693e-07f, 2.14656932e-08f, 4.92752156e-09f, 1.38738839e-09f, 4.56065812e-10f}); - nd4j::ops::zeta op; - auto results = op.evaluate({&x, &q}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + sd::ops::zeta op; + auto result = op.evaluate({&x, &q}, {}, {}); - auto *output = results->at(0); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); + + auto *output = result.at(0); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; } /////////////////////////////////////////////////////////////////// @@ -1887,17 +1821,17 @@ TEST_F(DeclarableOpsTests3, zeta_test8) { auto expected= NDArrayFactory::create('c', {3,4}, {23.014574, 12.184081, 8.275731, 6.1532226, 4.776538, 3.7945523, 3.0541048, 2.4765317, 2.0163891, 205.27448, 21.090889, 19.477398}); - nd4j::ops::zeta op; - auto results = op.evaluate({&x, &q}, {}, {}); + sd::ops::zeta op; + auto result = op.evaluate({&x, &q}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto *output = results->at(0); + auto *output = result.at(0); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -1912,17 +1846,17 @@ TEST_F(DeclarableOpsTests3, zeta_test9) { auto expected= NDArrayFactory::create('c', {3,4}, {23.014574, 12.184081, 8.275731, 6.1532226, 4.776538, 3.7945523, 3.0541048, 2.4765317, 2.0163891, 205.27448, 21.090889, 19.477398}); - nd4j::ops::zeta op; + sd::ops::zeta op; auto results = op.execute({&x, &q}, {&z}, {}, {}, {}); ASSERT_EQ(ND4J_STATUS_OK, results); - //auto *output = results->at(0); + //auto *output = result.at(0); // z.printIndexedBuffer("Zeta output"); ASSERT_TRUE(expected.isSameShape(z)); ASSERT_TRUE(expected.equalsTo(z)); -// delete results; +// } /////////////////////////////////////////////////////////////////// @@ -1937,17 +1871,17 @@ TEST_F(DeclarableOpsTests3, zeta_test10) { auto expected= NDArrayFactory::create('c', {3,4}, {23.014574, 12.184081, 8.275731, 6.1532226, 4.776538, 3.7945523, 3.0541048, 2.4765317, 2.0163891, 205.27448, 21.090889, 19.477398}); - nd4j::ops::zeta op; + sd::ops::zeta op; auto results = op.execute({&x, &q}, {&z}, {}, {}, {}); ASSERT_EQ(ND4J_STATUS_OK, results); - //auto *output = results->at(0); + //auto *output = result.at(0); // z.printIndexedBuffer("Zeta output"); ASSERT_TRUE(expected.isSameShape(z)); ASSERT_TRUE(expected.equalsTo(z)); -// delete results; +// } @@ -1959,7 +1893,7 @@ TEST_F(DeclarableOpsTests3, Test_SplitV_Validation_1) { auto z0 = NDArrayFactory::create('c', {5, 7}); auto z1 = NDArrayFactory::create('c', {3, 7}); - nd4j::ops::split_v op; + sd::ops::split_v op; auto status = op.execute({&x, &indices, &axis}, std::vector{&z0, &z1}, {}, {}, {}); ASSERT_EQ(Status::OK(), status); } @@ -1974,19 +1908,19 @@ TEST_F(DeclarableOpsTests3, polygamma_test1) { x.assign(0.5); auto expected= NDArrayFactory::create('c', {3,3}, {4.934802, -16.828796, 97.409088, -771.474243, 7691.113770, -92203.460938, 1290440.250000, -20644900.000000, 3.71595e+08}); + + sd::ops::polygamma op; + auto result = op.evaluate({&n, &x}, {}, {}); - nd4j::ops::polygamma op; - auto results = op.evaluate({&n, &x}, {}, {}); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); - - auto output = results->at(0); + auto output = result.at(0); // output->printBuffer(); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -2002,17 +1936,16 @@ TEST_F(DeclarableOpsTests3, polygamma_test2) { //ASSERT_FALSE(true); - nd4j::ops::polygamma op; - auto results = op.evaluate({&n, &x}, {}, {}); + sd::ops::polygamma op; + auto result = op.evaluate({&n, &x}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto output = results->at(0); + auto output = result.at(0); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; } /////////////////////////////////////////////////////////////////// @@ -2025,58 +1958,56 @@ TEST_F(DeclarableOpsTests3, polygamma_test3) { x.linspace(10.); auto expected= NDArrayFactory::create('c', {3,3}, {1.05166336e-01,-9.04983497e-03, 1.31009323e-03,-2.44459433e-04, 5.31593880e-05,-1.28049888e-05, 3.31755364e-06,-9.07408791e-07, 2.58758130e-07}); - nd4j::ops::polygamma op; - auto results = op.evaluate({&n, &x}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + sd::ops::polygamma op; + auto result = op.evaluate({&n, &x}, {}, {}); - auto output = results->at(0); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); + + auto output = result.at(0); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; } TEST_F(DeclarableOpsTests3, polygamma_test4) { - NDArray n('c', {3,4}, {/*0.7788*/0, 0,1,2,3,4,5,6,7,8,9,10}, nd4j::DataType::DOUBLE); - NDArray x('c', {3,4}, {0.7717,0.9281,0.9846,0.4838,0.6433,0.6041,0.6501,0.7612,0.7605,0.3948,0.9493,0.8600}, nd4j::DataType::DOUBLE); + NDArray n('c', {3,4}, {/*0.7788*/0, 0,1,2,3,4,5,6,7,8,9,10}, sd::DataType::DOUBLE); + NDArray x('c', {3,4}, {0.7717,0.9281,0.9846,0.4838,0.6433,0.6041,0.6501,0.7612,0.7605,0.3948,0.9493,0.8600}, sd::DataType::DOUBLE); NDArray expected('c', {3,4}, {/*std::numeric_limits::quiet_NaN()*/-1.031918, -7.021327e-01, 1.682743e+00, -1.851378e+01,3.604167e+01, -3.008293e+02, - 1.596005e+03, -4.876665e+03,4.510025e+04, -1.730340e+08, 6.110257e+05, -1.907087e+07}, nd4j::DataType::DOUBLE); + 1.596005e+03, -4.876665e+03,4.510025e+04, -1.730340e+08, 6.110257e+05, -1.907087e+07}, sd::DataType::DOUBLE); - nd4j::ops::polygamma op; - auto results = op.evaluate({&n, &x}, {}, {}); + sd::ops::polygamma op; + auto result = op.evaluate({&n, &x}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto output = results->at(0); + auto output = result.at(0); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; } TEST_F(DeclarableOpsTests3, digamma_1) { - NDArray x('c', {18}, {-25, -24.99999, -21.5, -21.2, -5.5, -4.1, -2.1, -0.5, -0.3, 0., 0.2, 1, 1.5, 2.2, 5.2, 19., 21, 22.2}, nd4j::DataType::DOUBLE); + NDArray x('c', {18}, {-25, -24.99999, -21.5, -21.2, -5.5, -4.1, -2.1, -0.5, -0.3, 0., 0.2, 1, 1.5, 2.2, 5.2, 19., 21, 22.2}, sd::DataType::DOUBLE); NDArray expected('c', {18}, {std::numeric_limits::infinity(), -99996.761229, 3.091129, 7.401432, 1.792911,11.196838,10.630354, 0.03649, 2.11331, - std::numeric_limits::infinity(),-5.28904,-0.577216, 0.03649, 0.544293, 1.549434,2.917892, 3.020524, 3.077401}, nd4j::DataType::DOUBLE); + std::numeric_limits::infinity(),-5.28904,-0.577216, 0.03649, 0.544293, 1.549434,2.917892, 3.020524, 3.077401}, sd::DataType::DOUBLE); - nd4j::ops::digamma op; - auto results = op.evaluate({&x}, {}, {}); + sd::ops::digamma op; + auto result = op.evaluate({&x}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto output = results->at(0); + auto output = result.at(0); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; } /////////////////////////////////////////////////////////////////// @@ -2087,14 +2018,14 @@ TEST_F(DeclarableOpsTests3, svd_test1) { auto expU= NDArrayFactory::create('c', {6,6}, {0.14692,-0.11132,-0.69568, 0.59282,-0.14881, 0.32935,-0.38751, 0.60378,-0.04927,-0.01397,-0.69456,-0.01581, 0.19293,-0.12795,-0.18682,-0.69065,-0.20597, 0.62617, 0.66806, 0.4314 ,-0.33849,-0.22166, 0.04099,-0.44967, 0.11121,-0.64065,-0.02138,-0.07378,-0.60568,-0.45216,-0.5765 ,-0.1007 ,-0.60305,-0.34175, 0.29068,-0.3042}); auto expV= NDArrayFactory::create('c', {6,6}, {-0.24577,-0.24512, 0.00401,-0.04585,-0.62058, 0.70162, 0.27937, 0.75961, 0.43885,-0.06857,-0.3839 , 0.01669,-0.35944,-0.09629, 0.44593, 0.78602,-0.09103,-0.19125, 0.53973, 0.07613,-0.10721, 0.49559, 0.35687, 0.56431,-0.6226 , 0.39742, 0.12785,-0.15716, 0.52372, 0.37297, 0.23113,-0.43578, 0.76204,-0.32414, 0.23996, 0.11543}); - nd4j::ops::svd op; - auto results = op.evaluate({&x}, {}, {1, 1, 16}); + sd::ops::svd op; + auto result = op.evaluate({&x}, {}, {1, 1, 16}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto *s = results->at(0); - auto *u = results->at(1); - auto *v = results->at(2); + auto *s = result.at(0); + auto *u = result.at(1); + auto *v = result.at(2); ASSERT_TRUE(expS.isSameShape(s)); ASSERT_TRUE(expU.isSameShape(u)); @@ -2102,18 +2033,17 @@ TEST_F(DeclarableOpsTests3, svd_test1) { ASSERT_TRUE(expS.equalsTo(s)); - if(nd4j::Environment::getInstance()->isCPU()) { + if(sd::Environment::getInstance()->isCPU()) { ASSERT_TRUE(expU.equalsTo(u)); ASSERT_TRUE(expV.equalsTo(v)); } else { for(uint i = 0; i < expU.lengthOf(); ++i) - ASSERT_NEAR(nd4j::math::nd4j_abs(expU.e(i)), nd4j::math::nd4j_abs(u->e(i)), 1e-5); + ASSERT_NEAR(sd::math::nd4j_abs(expU.e(i)), sd::math::nd4j_abs(u->e(i)), 1e-5); for(uint i = 0; i < expV.lengthOf(); ++i) - ASSERT_NEAR(nd4j::math::nd4j_abs(expV.e(i)), nd4j::math::nd4j_abs(v->e(i)), 1e-5); + ASSERT_NEAR(sd::math::nd4j_abs(expV.e(i)), sd::math::nd4j_abs(v->e(i)), 1e-5); } - delete results; } /////////////////////////////////////////////////////////////////// @@ -2124,14 +2054,14 @@ TEST_F(DeclarableOpsTests3, svd_test2) { auto expU= NDArrayFactory::create('c', {7,7}, {-0.13417,-0.12443, -0.68854, 0.5196 , 0.21706, 0.03974, 0.41683, 0.347 , 0.62666, -0.04964, -0.01912, 0.66932, 0.1457 , -0.12183,-0.17329,-0.14666, -0.19639, -0.55355, 0.0614 , 0.75729, 0.1619 ,-0.64703, 0.37056, -0.37398, -0.32922, -0.0186 , -0.35656, -0.26134,-0.08027,-0.64405, -0.0127 , -0.06934, 0.59287, -0.14956, -0.44712, 0.55906,-0.06235, -0.58017, -0.12911, -0.359 , -0.00393, -0.44877, 0.30645,-0.11953, -0.09083, -0.54163, 0.14283, -0.50417, 0.56178}); auto expV= NDArrayFactory::create('c', {6,6}, {0.2508 ,-0.2265 , 0.01689, 0.04486, 0.53132, 0.77537,-0.32281, 0.74559, 0.41845, -0.13821, 0.37642, 0.06315, 0.33139,-0.05528, 0.47186, 0.73171, 0.18905, -0.3055 ,-0.57263, 0.06276,-0.09542, 0.59396, -0.36152, 0.419 , 0.59193, 0.4361 , 0.13557, -0.03632, -0.5755 , 0.32944,-0.21165,-0.44227, 0.75794, -0.29895, -0.27993, 0.13187}); - nd4j::ops::svd op; - auto results = op.evaluate({&x}, {}, {1, 1, 16}); + sd::ops::svd op; + auto result = op.evaluate({&x}, {}, {1, 1, 16}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto *s = results->at(0); - auto *u = results->at(1); - auto *v = results->at(2); + auto *s = result.at(0); + auto *u = result.at(1); + auto *v = result.at(2); ASSERT_TRUE(expS.isSameShape(s)); ASSERT_TRUE(expU.isSameShape(u)); @@ -2139,18 +2069,17 @@ TEST_F(DeclarableOpsTests3, svd_test2) { ASSERT_TRUE(expS.equalsTo(s)); - if(nd4j::Environment::getInstance()->isCPU()) { + if(sd::Environment::getInstance()->isCPU()) { ASSERT_TRUE(expU.equalsTo(u)); ASSERT_TRUE(expV.equalsTo(v)); } else { for(uint i = 0; i < expU.lengthOf(); ++i) - ASSERT_NEAR(nd4j::math::nd4j_abs(expU.e(i)), nd4j::math::nd4j_abs(u->e(i)), 1e-5); + ASSERT_NEAR(sd::math::nd4j_abs(expU.e(i)), sd::math::nd4j_abs(u->e(i)), 1e-5); for(uint i = 0; i < expV.lengthOf(); ++i) - ASSERT_NEAR(nd4j::math::nd4j_abs(expV.e(i)), nd4j::math::nd4j_abs(v->e(i)), 1e-5); + ASSERT_NEAR(sd::math::nd4j_abs(expV.e(i)), sd::math::nd4j_abs(v->e(i)), 1e-5); } - delete results; } /////////////////////////////////////////////////////////////////// @@ -2161,14 +2090,14 @@ TEST_F(DeclarableOpsTests3, svd_test3) { auto expU= NDArrayFactory::create('c', {7,6}, {-0.13417, -0.12443, -0.68854, 0.5196 , 0.21706, 0.03974, 0.347 , 0.62666, -0.04964, -0.01912, 0.66932, 0.1457 ,-0.17329, -0.14666, -0.19639, -0.55355, 0.0614 , 0.75729,-0.64703, 0.37056, -0.37398, -0.32922, -0.0186 , -0.35656,-0.08027, -0.64405, -0.0127 , -0.06934, 0.59287, -0.14956, 0.55906, -0.06235, -0.58017, -0.12911, -0.359 , -0.00393, 0.30645, -0.11953, -0.09083, -0.54163, 0.14283, -0.50417}); auto expV= NDArrayFactory::create('c', {6,6}, {0.2508 ,-0.2265 , 0.01689, 0.04486, 0.53132, 0.77537,-0.32281, 0.74559, 0.41845, -0.13821, 0.37642, 0.06315, 0.33139,-0.05528, 0.47186, 0.73171, 0.18905, -0.3055 ,-0.57263, 0.06276,-0.09542, 0.59396, -0.36152, 0.419 , 0.59193, 0.4361 , 0.13557, -0.03632, -0.5755 , 0.32944,-0.21165,-0.44227, 0.75794, -0.29895, -0.27993, 0.13187}); - nd4j::ops::svd op; - auto results = op.evaluate({&x}, {}, {0, 1, 16}); + sd::ops::svd op; + auto result = op.evaluate({&x}, {}, {0, 1, 16}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto *s = results->at(0); - auto *u = results->at(1); - auto *v = results->at(2); + auto *s = result.at(0); + auto *u = result.at(1); + auto *v = result.at(2); ASSERT_TRUE(expS.isSameShape(s)); ASSERT_TRUE(expU.isSameShape(u)); @@ -2176,18 +2105,17 @@ TEST_F(DeclarableOpsTests3, svd_test3) { ASSERT_TRUE(expS.equalsTo(s)); - if(nd4j::Environment::getInstance()->isCPU()) { + if(sd::Environment::getInstance()->isCPU()) { ASSERT_TRUE(expU.equalsTo(u)); ASSERT_TRUE(expV.equalsTo(v)); } else { for(uint i = 0; i < expU.lengthOf(); ++i) - ASSERT_NEAR(nd4j::math::nd4j_abs(expU.e(i)), nd4j::math::nd4j_abs(u->e(i)), 1e-5f); + ASSERT_NEAR(sd::math::nd4j_abs(expU.e(i)), sd::math::nd4j_abs(u->e(i)), 1e-5f); for(uint i = 0; i < expV.lengthOf(); ++i) - ASSERT_NEAR(nd4j::math::nd4j_abs(expV.e(i)), nd4j::math::nd4j_abs(v->e(i)), 1e-5f); + ASSERT_NEAR(sd::math::nd4j_abs(expV.e(i)), sd::math::nd4j_abs(v->e(i)), 1e-5f); } - delete results; } /////////////////////////////////////////////////////////////////// @@ -2198,14 +2126,14 @@ TEST_F(DeclarableOpsTests3, svd_test4) { auto expU= NDArrayFactory::create('c', {6,6}, {-0.16541, 0.21276, 0.51284, 0.20472, 0.74797, 0.25102,-0.49879, 0.12076, 0.37629, -0.7211 , -0.24585, 0.12086,-0.36569,-0.70218, -0.08012, 0.21274, -0.07314, 0.56231,-0.44508, 0.4329 , 0.1356 , 0.60909, -0.47398, -0.02164, 0.61238,-0.05674, 0.59489, 0.06588, -0.3874 , 0.33685,-0.13044,-0.50644, 0.46552, 0.13236, -0.00474, -0.70161}); auto expV= NDArrayFactory::create('c', {7,7}, {-0.35914, 0.68966, -0.30077, -0.15238, -0.48179, 0.14716, -0.16709, 0.21989, -0.34343, 0.11086, -0.78381, -0.37902, 0.24224, -0.06862, 0.32179, 0.12812, -0.25812, 0.0691 , -0.12891, 0.26979, 0.84807,-0.50833, 0.13793, 0.06658, -0.53001, 0.52572, -0.16194, 0.36692, 0.48118, 0.15876, -0.65132, -0.24602, 0.3963 , -0.16651, -0.27155,-0.31605, -0.46947, -0.50195, 0.0378 , -0.34937, -0.53062, 0.15069, 0.35957, 0.35408, 0.38732, -0.12154, -0.22827, -0.7151 , 0.13065}); - nd4j::ops::svd op; - auto results = op.evaluate({&x}, {}, {1, 1, 16}); + sd::ops::svd op; + auto result = op.evaluate({&x}, {}, {1, 1, 16}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto *s = results->at(0); - auto *u = results->at(1); - auto *v = results->at(2); + auto *s = result.at(0); + auto *u = result.at(1); + auto *v = result.at(2); ASSERT_TRUE(expS.isSameShape(s)); ASSERT_TRUE(expU.isSameShape(u)); @@ -2213,18 +2141,17 @@ TEST_F(DeclarableOpsTests3, svd_test4) { ASSERT_TRUE(expS.equalsTo(s)); - if(nd4j::Environment::getInstance()->isCPU()) { + if(sd::Environment::getInstance()->isCPU()) { ASSERT_TRUE(expU.equalsTo(u)); ASSERT_TRUE(expV.equalsTo(v)); } else { for(uint i = 0; i < expU.lengthOf(); ++i) - ASSERT_NEAR(nd4j::math::nd4j_abs(expU.e(i)), nd4j::math::nd4j_abs(u->e(i)), 1e-5f); + ASSERT_NEAR(sd::math::nd4j_abs(expU.e(i)), sd::math::nd4j_abs(u->e(i)), 1e-5f); for(uint i = 0; i < expV.lengthOf(); ++i) - ASSERT_NEAR(nd4j::math::nd4j_abs(expV.e(i)), nd4j::math::nd4j_abs(v->e(i)), 1e-5f); + ASSERT_NEAR(sd::math::nd4j_abs(expV.e(i)), sd::math::nd4j_abs(v->e(i)), 1e-5f); } - delete results; } /////////////////////////////////////////////////////////////////// @@ -2235,14 +2162,14 @@ TEST_F(DeclarableOpsTests3, svd_test5) { auto expU= NDArrayFactory::create('c', {6,6}, {-0.16541, 0.21276, 0.51284, 0.20472, 0.74797, 0.25102,-0.49879, 0.12076, 0.37629, -0.7211 , -0.24585, 0.12086,-0.36569,-0.70218, -0.08012, 0.21274, -0.07314, 0.56231,-0.44508, 0.4329 , 0.1356 , 0.60909, -0.47398, -0.02164, 0.61238,-0.05674, 0.59489, 0.06588, -0.3874 , 0.33685,-0.13044,-0.50644, 0.46552, 0.13236, -0.00474, -0.70161}); auto expV= NDArrayFactory::create('c', {7,6}, {-0.35914, 0.68966, -0.30077, -0.15238, -0.48179, 0.14716, 0.21989, -0.34343, 0.11086, -0.78381, -0.37902, 0.24224, 0.32179, 0.12812, -0.25812, 0.0691 , -0.12891, 0.26979,-0.50833, 0.13793, 0.06658, -0.53001, 0.52572, -0.16194, 0.48118, 0.15876, -0.65132, -0.24602, 0.3963 , -0.16651,-0.31605, -0.46947, -0.50195, 0.0378 , -0.34937, -0.53062, 0.35957, 0.35408, 0.38732, -0.12154, -0.22827, -0.7151}); - nd4j::ops::svd op; - auto results = op.evaluate({&x}, {}, {0, 1, 16}); + sd::ops::svd op; + auto result = op.evaluate({&x}, {}, {0, 1, 16}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto *s = results->at(0); - auto *u = results->at(1); - auto *v = results->at(2); + auto *s = result.at(0); + auto *u = result.at(1); + auto *v = result.at(2); ASSERT_TRUE(expS.isSameShape(s)); ASSERT_TRUE(expU.isSameShape(u)); @@ -2250,18 +2177,17 @@ TEST_F(DeclarableOpsTests3, svd_test5) { ASSERT_TRUE(expS.equalsTo(s)); - if(nd4j::Environment::getInstance()->isCPU()) { + if(sd::Environment::getInstance()->isCPU()) { ASSERT_TRUE(expU.equalsTo(u)); ASSERT_TRUE(expV.equalsTo(v)); } else { for(uint i = 0; i < expU.lengthOf(); ++i) - ASSERT_NEAR(nd4j::math::nd4j_abs(expU.e(i)), nd4j::math::nd4j_abs(u->e(i)), 1e-5f); + ASSERT_NEAR(sd::math::nd4j_abs(expU.e(i)), sd::math::nd4j_abs(u->e(i)), 1e-5f); for(uint i = 0; i < expV.lengthOf(); ++i) - ASSERT_NEAR(nd4j::math::nd4j_abs(expV.e(i)), nd4j::math::nd4j_abs(v->e(i)), 1e-5f); + ASSERT_NEAR(sd::math::nd4j_abs(expV.e(i)), sd::math::nd4j_abs(v->e(i)), 1e-5f); } - delete results; } /////////////////////////////////////////////////////////////////// @@ -2290,14 +2216,14 @@ TEST_F(DeclarableOpsTests3, svd_test6) { 0.45726, -0.33952, -0.32659, -0.18938, -0.73015,0.13486, 0.73816, -0.41646, 0.47458, -0.1956 ,0.5536 , -0.137 , 0.64688, 0.50536, 0.03017, -0.51827, -0.31837, -0.16732, 0.71378, -0.30425,-0.39314, 0.15266, 0.63693, -0.30945, -0.5663 ,-0.51981, 0.03325, 0.37603, 0.05147, 0.76462,-0.01282, 0.92491, -0.08042, 0.36977, -0.03428}); - nd4j::ops::svd op; - auto results = op.evaluate({&x}, {}, {1, 1, 16}); + sd::ops::svd op; + auto result = op.evaluate({&x}, {}, {1, 1, 16}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto *s = results->at(0); - auto *u = results->at(1); - auto *v = results->at(2); + auto *s = result.at(0); + auto *u = result.at(1); + auto *v = result.at(2); ASSERT_TRUE(expS.isSameShape(s)); ASSERT_TRUE(expU.isSameShape(u)); @@ -2305,18 +2231,17 @@ TEST_F(DeclarableOpsTests3, svd_test6) { ASSERT_TRUE(expS.equalsTo(s)); - if(nd4j::Environment::getInstance()->isCPU()) { + if(sd::Environment::getInstance()->isCPU()) { ASSERT_TRUE(expU.equalsTo(u)); ASSERT_TRUE(expV.equalsTo(v)); } else { for(uint i = 0; i < expU.lengthOf(); ++i) - ASSERT_NEAR(nd4j::math::nd4j_abs(expU.e(i)), nd4j::math::nd4j_abs(u->e(i)), 1e-5f); + ASSERT_NEAR(sd::math::nd4j_abs(expU.e(i)), sd::math::nd4j_abs(u->e(i)), 1e-5f); for(uint i = 0; i < expV.lengthOf(); ++i) - ASSERT_NEAR(nd4j::math::nd4j_abs(expV.e(i)), nd4j::math::nd4j_abs(v->e(i)), 1e-5f); + ASSERT_NEAR(sd::math::nd4j_abs(expV.e(i)), sd::math::nd4j_abs(v->e(i)), 1e-5f); } - delete results; } /////////////////////////////////////////////////////////////////// @@ -2328,16 +2253,17 @@ TEST_F(DeclarableOpsTests3, svd_test7) { auto expS= NDArrayFactory::create('c', {2,2,5}, {40.95395, 31.46869, 24.79993, 12.33768, 1.80031,38.18412, 31.52287, 23.52755, 11.79484, 1.90195, 39.34498, 32.54861, 17.52492, 7.03003, 2.2399,44.72126, 32.3164 , 16.60139, 6.88783, 0.78122}); - nd4j::ops::svd op; - auto results = op.evaluate({&x}, {}, {0, 0, 16}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + sd::ops::svd op; + auto result = op.evaluate({&x}, {}, {0, 0, 16}); - auto *s = results->at(0); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); + + auto *s = result.at(0); ASSERT_TRUE(expS.equalsTo(s)); ASSERT_TRUE(expS.isSameShape(s)); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -2464,14 +2390,14 @@ TEST_F(DeclarableOpsTests3, svd_test7) { // -0.286167, -0.101052, -0.181529, -0.419406, -0.032204, -0.732282, 0.106833, -0.288881, 0.171516, -0.096242, // -0.331834, -0.493188, 0.393195, 0.358365, 0.049125, 0.123457, 0.438169, -0.105015, 0.092386, -0.130413, -0.476991}); -// nd4j::ops::svd op; +// sd::ops::svd op; // auto results = op.execute({&x}, {}, {1, 1, 7}); -// ASSERT_EQ(ND4J_STATUS_OK, results->status()); +// ASSERT_EQ(ND4J_STATUS_OK, result.status()); -// auto *s = results->at(0); -// auto *u = results->at(1); -// auto *v = results->at(2); +// auto *s = result.at(0); +// auto *u = result.at(1); +// auto *v = result.at(2); // ASSERT_TRUE(expS.isSameShape(s)); // ASSERT_TRUE(expU.isSameShape(u)); @@ -2479,18 +2405,18 @@ TEST_F(DeclarableOpsTests3, svd_test7) { // ASSERT_TRUE(expS.equalsTo(s)); - // if(nd4j::Environment::getInstance()->isCPU()) { + // if(sd::Environment::getInstance()->isCPU()) { // ASSERT_TRUE(expU.equalsTo(u)); // ASSERT_TRUE(expV.equalsTo(v)); // } // else { // for(uint i = 0; i < expU.lengthOf(); ++i) - // ASSERT_NEAR(nd4j::math::nd4j_abs(expU.e(i)), nd4j::math::nd4j_abs(u->e(i)), 1e-5); + // ASSERT_NEAR(sd::math::nd4j_abs(expU.e(i)), sd::math::nd4j_abs(u->e(i)), 1e-5); // for(uint i = 0; i < expV.lengthOf(); ++i) - // ASSERT_NEAR(nd4j::math::nd4j_abs(expV.e(i)), nd4j::math::nd4j_abs(v->e(i)), 1e-5); + // ASSERT_NEAR(sd::math::nd4j_abs(expV.e(i)), sd::math::nd4j_abs(v->e(i)), 1e-5); // } -// delete results; +// // } /////////////////////////////////////////////////////////////////// @@ -2527,14 +2453,14 @@ TEST_F(DeclarableOpsTests3, svd_test9) { -4.94030000e-01, 1.55540000e-01, -3.46720000e-01, -7.58460000e-01,5.20000000e-04, 1.90420000e-01,2.55960000e-01, 3.17040000e-01, -3.47800000e-02, -3.01860000e-01,-3.57600000e-02, -8.60450000e-01, 1.31650000e-01, 7.57150000e-01, -4.89030000e-01, 3.47710000e-01,-4.39400000e-02, 2.17750000e-01,-6.57270000e-01, 2.91000000e-01, 4.17280000e-01, 2.52880000e-01,-4.63400000e-01, -1.74620000e-01}); - nd4j::ops::svd op; - auto results = op.evaluate({&x}, {}, {1, 1, 16}); + sd::ops::svd op; + auto result = op.evaluate({&x}, {}, {1, 1, 16}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto *s = results->at(0); - auto *u = results->at(1); - auto *v = results->at(2); + auto *s = result.at(0); + auto *u = result.at(1); + auto *v = result.at(2); ASSERT_TRUE(expS.isSameShape(s)); ASSERT_TRUE(expU.isSameShape(u)); @@ -2542,18 +2468,17 @@ TEST_F(DeclarableOpsTests3, svd_test9) { ASSERT_TRUE(expS.equalsTo(s)); - if(nd4j::Environment::getInstance()->isCPU()) { + if(sd::Environment::getInstance()->isCPU()) { ASSERT_TRUE(expU.equalsTo(u)); ASSERT_TRUE(expV.equalsTo(v)); } else { for(uint i = 0; i < expU.lengthOf(); ++i) - ASSERT_NEAR(nd4j::math::nd4j_abs(expU.e(i)), nd4j::math::nd4j_abs(u->e(i)), 1e-5); + ASSERT_NEAR(sd::math::nd4j_abs(expU.e(i)), sd::math::nd4j_abs(u->e(i)), 1e-5); for(uint i = 0; i < expV.lengthOf(); ++i) - ASSERT_NEAR(nd4j::math::nd4j_abs(expV.e(i)), nd4j::math::nd4j_abs(v->e(i)), 1e-5); + ASSERT_NEAR(sd::math::nd4j_abs(expV.e(i)), sd::math::nd4j_abs(v->e(i)), 1e-5); } - delete results; } /////////////////////////////////////////////////////////////////// @@ -2586,14 +2511,14 @@ TEST_F(DeclarableOpsTests3, svd_test10) { -4.94030000e-01, 1.55540000e-01, -3.46720000e-01, -7.58460000e-01,5.20000000e-04,2.55960000e-01, 3.17040000e-01, -3.47800000e-02, -3.01860000e-01,-3.57600000e-02,1.31650000e-01, 7.57150000e-01, -4.89030000e-01, 3.47710000e-01, -4.39400000e-02,-6.57270000e-01, 2.91000000e-01, 4.17280000e-01, 2.52880000e-01,-4.63400000e-01}); - nd4j::ops::svd op; - auto results = op.evaluate({&x}, {}, {0, 1, 16}); + sd::ops::svd op; + auto result = op.evaluate({&x}, {}, {0, 1, 16}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto *s = results->at(0); - auto *u = results->at(1); - auto *v = results->at(2); + auto *s = result.at(0); + auto *u = result.at(1); + auto *v = result.at(2); ASSERT_TRUE(expS.isSameShape(s)); ASSERT_TRUE(expU.isSameShape(u)); @@ -2601,18 +2526,17 @@ TEST_F(DeclarableOpsTests3, svd_test10) { ASSERT_TRUE(expS.equalsTo(s)); - if(nd4j::Environment::getInstance()->isCPU()) { + if(sd::Environment::getInstance()->isCPU()) { ASSERT_TRUE(expU.equalsTo(u)); ASSERT_TRUE(expV.equalsTo(v)); } else { for(uint i = 0; i < expU.lengthOf(); ++i) - ASSERT_NEAR(nd4j::math::nd4j_abs(expU.e(i)), nd4j::math::nd4j_abs(u->e(i)), 1e-5); + ASSERT_NEAR(sd::math::nd4j_abs(expU.e(i)), sd::math::nd4j_abs(u->e(i)), 1e-5); for(uint i = 0; i < expV.lengthOf(); ++i) - ASSERT_NEAR(nd4j::math::nd4j_abs(expV.e(i)), nd4j::math::nd4j_abs(v->e(i)), 1e-5); + ASSERT_NEAR(sd::math::nd4j_abs(expV.e(i)), sd::math::nd4j_abs(v->e(i)), 1e-5); } - delete results; } /////////////////////////////////////////////////////////////////// @@ -2631,14 +2555,14 @@ TEST_F(DeclarableOpsTests3, svd_test11) { -0.26072, -0.51887, 0.18182, 0.96306, -0.19863, 0.85948, 0.2707 , -0.4336 , 0.26688, 0.48582, 0.83232, -0.43596, 0.83108, -0.34531}); - nd4j::ops::svd op; - auto results = op.evaluate({&x}, {}, {0, 1, 16}); + sd::ops::svd op; + auto result = op.evaluate({&x}, {}, {0, 1, 16}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto s = results->at(0); - auto u = results->at(1); - auto v = results->at(2); + auto s = result.at(0); + auto u = result.at(1); + auto v = result.at(2); ASSERT_TRUE(expS.isSameShape(s)); ASSERT_TRUE(expU.isSameShape(u)); @@ -2646,18 +2570,17 @@ TEST_F(DeclarableOpsTests3, svd_test11) { ASSERT_TRUE(expS.equalsTo(s)); - if(nd4j::Environment::getInstance()->isCPU()) { + if(sd::Environment::getInstance()->isCPU()) { ASSERT_TRUE(expU.equalsTo(u)); ASSERT_TRUE(expV.equalsTo(v)); } else { for(uint i = 0; i < expU.lengthOf(); ++i) - ASSERT_NEAR(nd4j::math::nd4j_abs(expU.e(i)), nd4j::math::nd4j_abs(u->e(i)), 1e-5); + ASSERT_NEAR(sd::math::nd4j_abs(expU.e(i)), sd::math::nd4j_abs(u->e(i)), 1e-5); for(uint i = 0; i < expV.lengthOf(); ++i) - ASSERT_NEAR(nd4j::math::nd4j_abs(expV.e(i)), nd4j::math::nd4j_abs(v->e(i)), 1e-5); + ASSERT_NEAR(sd::math::nd4j_abs(expV.e(i)), sd::math::nd4j_abs(v->e(i)), 1e-5); } - delete results; } /////////////////////////////////////////////////////////////////// @@ -2666,17 +2589,17 @@ TEST_F(DeclarableOpsTests3, svd_test12) { NDArray x('c', {4,3}, {1.7787856,0.80119777,0.72437465,0.23089433,1.7271413,0.18039072,0.50563407,0.89252293,1.5461209,0.92336726,0.085571885,0.79378015}); NDArray expS('c', {3}, {3.024703, 1.459483, 1.026371}); - nd4j::ops::svd op; - auto results = op.evaluate({&x}, {}, {1, 0, 16}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + sd::ops::svd op; + auto result = op.evaluate({&x}, {}, {1, 0, 16}); - auto s = results->at(0); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); + + auto s = result.at(0); ASSERT_TRUE(expS.equalsTo(s)); ASSERT_TRUE(expS.isSameShape(s)); - delete results; } /////////////////////////////////////////////////////////////////// @@ -2685,15 +2608,14 @@ TEST_F(DeclarableOpsTests3, elu_test1) { auto x = NDArrayFactory::create('c', {3,3}, {0.1, .2, .3, -.4,-.5,-.6, .7, .8, .9}); auto exp = NDArrayFactory::create('c', {3,3}, {.1, .2, .3, 0.5*-0.32968, 0.5*-0.393469, 0.5*-0.451188, .7, .8, .9}); - nd4j::ops::elu op; - auto results = op.evaluate({&x}, {0.5}, {}); + sd::ops::elu op; + auto result = op.evaluate({&x}, {0.5}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto s = results->at(0); + auto s = result.at(0); ASSERT_TRUE(exp.equalsTo(s)); - delete results; } /////////////////////////////////////////////////////////////////// @@ -2704,15 +2626,14 @@ TEST_F(DeclarableOpsTests3, elu_bp_test1) { eps.assign(2.); auto exp = NDArrayFactory::create('c', {3, 3}, {2, 2, 2, 0.5*1.34064, 0.5*1.213061, 0.5*1.097623, 2, 2, 2}); - nd4j::ops::elu_bp op; - auto results = op.evaluate({ &x, &eps }, {0.5}, {}); + sd::ops::elu_bp op; + auto result = op.evaluate({ &x, &eps }, {0.5}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto s = results->at(0); + auto s = result.at(0); ASSERT_TRUE(exp.equalsTo(s)); - delete results; } /////////////////////////////////////////////////////////////////// @@ -2721,15 +2642,14 @@ TEST_F(DeclarableOpsTests3, lrelu_test1) { auto x = NDArrayFactory::create('c', {3,3}, {1, 2, 3, -4,-5,-6, 7, 8, 9}); auto exp = NDArrayFactory::create('c', {3,3}, {1, 2, 3, -0.8, -1., -1.2, 7, 8, 9}); - nd4j::ops::lrelu op; - auto results = op.evaluate({&x}, {0.2}, {}); + sd::ops::lrelu op; + auto result = op.evaluate({&x}, {0.2}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto s = results->at(0); + auto s = result.at(0); ASSERT_TRUE(exp.equalsTo(s)); - delete results; } TEST_F(DeclarableOpsTests3, lrelu_bp_test1) { @@ -2738,15 +2658,14 @@ TEST_F(DeclarableOpsTests3, lrelu_bp_test1) { auto eps = NDArrayFactory::create('c', {3,3}, {2,2,2,2,2,2,2, 2,2}); auto exp = NDArrayFactory::create('c', {3,3}, {2, 2, 2, 0.4, 0.4, 0.4, 2, 2, 2}); - nd4j::ops::lrelu_bp op; - auto results = op.evaluate({&x, &eps}, {0.2}, {}); + sd::ops::lrelu_bp op; + auto result = op.evaluate({&x, &eps}, {0.2}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto s = results->at(0); + auto s = result.at(0); ASSERT_TRUE(exp.equalsTo(s)); - delete results; } /////////////////////////////////////////////////////////////////// @@ -2755,15 +2674,14 @@ TEST_F(DeclarableOpsTests3, selu_test1) { auto x = NDArrayFactory::create('c', {3,3}, {1, 2, 3, -4,-5,-6, 7, 8, 9}); auto exp = NDArrayFactory::create('c', {3,3}, {1.050701, 2.101402, 3.152103, -1.725899, -1.746253, -1.753742, 7.354907, 8.405608, 9.456309}); - nd4j::ops::selu op; - auto results = op.evaluate({&x}, {}, {}); + sd::ops::selu op; + auto result = op.evaluate({&x}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto s = results->at(0); + auto s = result.at(0); ASSERT_TRUE(exp.equalsTo(s)); - delete results; } TEST_F(DeclarableOpsTests3, selu_test2) { @@ -2773,18 +2691,17 @@ TEST_F(DeclarableOpsTests3, selu_test2) { auto eps = NDArrayFactory::create('c', {3,3}, {2,2,2,2,2,2,2, 2,2}); auto exp = NDArrayFactory::create('c', {3,3}, {2.101401, 2.101402, 2.101402, 0.064401, 0.023692, 0.008716, 2.101402, 2.101402, 2.101402}); - nd4j::ops::selu_bp op; - auto results = op.evaluate({&x, &eps}, {0.2}, {}); + sd::ops::selu_bp op; + auto result = op.evaluate({&x, &eps}, {0.2}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto s = results->at(0); -// auto u = results->at(1); -// auto v = results->at(2); + auto s = result.at(0); +// auto u = result.at(1); +// auto v = result.at(2); // s->printIndexedBuffer("SELU_BP"); ASSERT_TRUE(exp.equalsTo(s)); - delete results; } TEST_F(DeclarableOpsTests3, EQScalarTests_1) { @@ -2793,7 +2710,7 @@ TEST_F(DeclarableOpsTests3, EQScalarTests_1) { auto x = NDArrayFactory::create(1.0f); auto scalar = NDArrayFactory::create(1.0f); - nd4j::ops::eq_scalar op; + sd::ops::eq_scalar op; auto res = op.verify({&x, &scalar}); ASSERT_TRUE(res); @@ -2805,7 +2722,7 @@ TEST_F(DeclarableOpsTests3, EQScalarTests_2) { auto x = NDArrayFactory::create(2.0f); auto scalar = NDArrayFactory::create(1.0f); - nd4j::ops::eq_scalar op; + sd::ops::eq_scalar op; auto res = op.verify({&x, &scalar}); ASSERT_FALSE(res); } @@ -2816,7 +2733,7 @@ TEST_F(DeclarableOpsTests3, GTScalarTests_1) { auto x = NDArrayFactory::create(1.0f); auto scalar = NDArrayFactory::create(1.0f); - nd4j::ops::gt_scalar op; + sd::ops::gt_scalar op; auto res = op.verify({&x, &scalar}); ASSERT_FALSE(res); } @@ -2827,7 +2744,7 @@ TEST_F(DeclarableOpsTests3, GTScalarTests_2) { auto x = NDArrayFactory::create(2.0f); auto scalar = NDArrayFactory::create(1.0f); - nd4j::ops::gt_scalar op; + sd::ops::gt_scalar op; auto res = op.verify({&x, &scalar}); ASSERT_TRUE(res); } @@ -2838,7 +2755,7 @@ TEST_F(DeclarableOpsTests3, GTEScalarTests_1) { auto x = NDArrayFactory::create(1.0f); auto scalar = NDArrayFactory::create(1.0f); - nd4j::ops::gte_scalar op; + sd::ops::gte_scalar op; auto res = op.verify({&x, &scalar}); ASSERT_TRUE(res); } @@ -2849,7 +2766,7 @@ TEST_F(DeclarableOpsTests3, GTEScalarTests_2) { auto x = NDArrayFactory::create(2.0f); auto scalar = NDArrayFactory::create(1.0f); - nd4j::ops::gte_scalar op; + sd::ops::gte_scalar op; auto res = op.verify({&x, &scalar}); ASSERT_TRUE(res); } @@ -2860,7 +2777,7 @@ TEST_F(DeclarableOpsTests3, GTEScalarTests_3) { auto x = NDArrayFactory::create(1.0f); auto scalar = NDArrayFactory::create(2.0f); - nd4j::ops::gte_scalar op; + sd::ops::gte_scalar op; auto res = op.verify({&x, &scalar}); ASSERT_FALSE(res); } @@ -2871,7 +2788,7 @@ TEST_F(DeclarableOpsTests3, LTEScalarTests_1) { auto x = NDArrayFactory::create(1.0f); auto scalar = NDArrayFactory::create(1.0f); - nd4j::ops::lte_scalar op; + sd::ops::lte_scalar op; auto res = op.verify({&x, &scalar}); ASSERT_TRUE(res); } @@ -2882,7 +2799,7 @@ TEST_F(DeclarableOpsTests3, LTEScalarTests_2) { auto x = NDArrayFactory::create(2.0f); auto scalar = NDArrayFactory::create(1.0f); - nd4j::ops::lte_scalar op; + sd::ops::lte_scalar op; auto res = op.verify({&x, &scalar}); ASSERT_FALSE(res); } @@ -2893,7 +2810,7 @@ TEST_F(DeclarableOpsTests3, LTEScalarTests_3) { auto x = NDArrayFactory::create(1.0f); auto scalar = NDArrayFactory::create(2.0f); - nd4j::ops::lte_scalar op; + sd::ops::lte_scalar op; auto res = op.verify({&x, &scalar}); ASSERT_TRUE(res); } @@ -2904,7 +2821,7 @@ TEST_F(DeclarableOpsTests3, NEQScalarTests_1) { auto x = NDArrayFactory::create(1.0f); auto scalar = NDArrayFactory::create(1.0f); - nd4j::ops::neq_scalar op; + sd::ops::neq_scalar op; auto res = op.verify({&x, &scalar}); ASSERT_FALSE(res); @@ -2916,7 +2833,7 @@ TEST_F(DeclarableOpsTests3, NEQScalarTests_2) { auto x = NDArrayFactory::create(2.0f); auto scalar = NDArrayFactory::create(1.0f); - nd4j::ops::neq_scalar op; + sd::ops::neq_scalar op; auto res = op.verify({&x, &scalar}); ASSERT_TRUE(res); } @@ -2927,8 +2844,7 @@ TEST_F(DeclarableOpsTests3, NOOPTests_1) { auto x = NDArrayFactory::create(2.0f); auto scalar = NDArrayFactory::create(1.0f); - nd4j::ops::noop op; + sd::ops::noop op; auto res = op.evaluate({&x, &scalar}, {}, {}); - ASSERT_TRUE(res->status() == nd4j::Status::OK()); - delete res; + ASSERT_TRUE(res.status() == sd::Status::OK()); } diff --git a/libnd4j/tests_cpu/layers_tests/DeclarableOpsTests4.cpp b/libnd4j/tests_cpu/layers_tests/DeclarableOpsTests4.cpp index 1fb700779..69dec8359 100644 --- a/libnd4j/tests_cpu/layers_tests/DeclarableOpsTests4.cpp +++ b/libnd4j/tests_cpu/layers_tests/DeclarableOpsTests4.cpp @@ -21,12 +21,12 @@ #include "testlayers.h" #include #include -#include +#include #include -using namespace nd4j; -using namespace nd4j::graph; +using namespace sd; +using namespace sd::graph; class DeclarableOpsTests4 : public testing::Test { public: @@ -35,8 +35,8 @@ public: printf("\n"); fflush(stdout); - nd4j::ops::adjust_hue op0; - nd4j::ops::adjust_saturation op1; + sd::ops::adjust_hue op0; + sd::ops::adjust_saturation op1; } }; @@ -48,8 +48,8 @@ public: printf("\n"); fflush(stdout); - nd4j::ops::adjust_hue op0; - nd4j::ops::adjust_saturation op1; + sd::ops::adjust_hue op0; + sd::ops::adjust_saturation op1; } }; @@ -63,17 +63,17 @@ TYPED_TEST(TypedDeclarableOpsTests4, avgpool2d_1) { x.linspace(1); - nd4j::ops::avgpool2d op; + sd::ops::avgpool2d op; auto result = op.evaluate({&x}, {}, {2, 2, 2, 2, 0, 0, 1, 1, 1, 1, 1}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } ////////////////////////////////////////////////////////////////////// @@ -84,17 +84,17 @@ TYPED_TEST(TypedDeclarableOpsTests4, avgpool2d_2) { x.linspace(1); - nd4j::ops::avgpool2d op; + sd::ops::avgpool2d op; auto result = op.evaluate({&x}, {}, {2, 2, 2, 2, 0, 0, 1, 1, 0, 1, 1}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } ////////////////////////////////////////////////////////////////////// @@ -105,17 +105,17 @@ TYPED_TEST(TypedDeclarableOpsTests4, avgpool2d_3) { x.linspace(1); - nd4j::ops::avgpool2d op; + sd::ops::avgpool2d op; auto result = op.evaluate({&x}, {}, {2, 2, 2, 2, 0, 0, 1, 1, 1, 0, 1}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } ////////////////////////////////////////////////////////////////////// @@ -125,17 +125,17 @@ TYPED_TEST(TypedDeclarableOpsTests4, avgpool2d_4) { x.linspace(1); - nd4j::ops::avgpool2d op; + sd::ops::avgpool2d op; auto result = op.evaluate({&x}, {}, {2, 2, 2, 2, 0, 0, 1, 1, 0, 1, 1}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } ////////////////////////////////////////////////////////////////////// @@ -146,17 +146,17 @@ TYPED_TEST(TypedDeclarableOpsTests4, avgpool2d_5) { x.linspace(1); - nd4j::ops::avgpool2d op; + sd::ops::avgpool2d op; auto result = op.evaluate({&x}, {}, {2, 2, 2, 2, 1, 1, 1, 1, 0, 0, 0}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } ////////////////////////////////////////////////////////////////////// @@ -167,17 +167,17 @@ TYPED_TEST(TypedDeclarableOpsTests4, avgpool2d_6) { x.linspace(1); - nd4j::ops::avgpool2d op; + sd::ops::avgpool2d op; auto result = op.evaluate({&x}, {}, {2, 2, 2, 2, 1, 1, 1, 1, 0, 1, 0}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } ////////////////////////////////////////////////////////////////////// @@ -188,17 +188,17 @@ TYPED_TEST(TypedDeclarableOpsTests4, avgpool2d_7) { x.linspace(1); - nd4j::ops::avgpool2d op; + sd::ops::avgpool2d op; auto result = op.evaluate({&x}, {}, {2, 2, 2, 2, 0, 0, 1, 1, 1, 0, 0}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } ////////////////////////////////////////////////////////////////////// @@ -208,17 +208,17 @@ TYPED_TEST(TypedDeclarableOpsTests4, avgpool2d_8) { x.linspace(1); - nd4j::ops::avgpool2d op; + sd::ops::avgpool2d op; auto result = op.evaluate({&x}, {}, {2, 2, 1, 1, 0, 0, 1, 1, 0, 0, 0}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } ////////////////////////////////////////////////////////////////////// @@ -229,12 +229,12 @@ TYPED_TEST(TypedDeclarableOpsTests4, avgpool2d_9) { x.linspace(1); - nd4j::ops::avgpool2d op; + sd::ops::avgpool2d op; auto result = op.evaluate({&x}, {}, {2, 2, 1, 1, 0, 0, 1, 1, 1, 0, 0}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); //z->printShapeInfo("z shape:"); //z->printBuffer("z buffer:"); @@ -242,7 +242,7 @@ TYPED_TEST(TypedDeclarableOpsTests4, avgpool2d_9) { ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } ////////////////////////////////////////////////////////////////////// @@ -261,11 +261,11 @@ TYPED_TEST(TypedDeclarableOpsTests4, avgpool2d_10) { 4.63436460f, -4.69907761f, 1.25187206f, 11.46173859f, -2.21917558f, 1.28007793f, 6.92173195f, 2.11268163f, -3.47389889f, 5.08722782f, -3.03950930f, -4.17154264f, 11.30568314f, 0.80361372f, 2.53214502f, 7.18707085f, -4.49114513f, 2.85449266f, 10.14906883f, -0.31974933f, -0.84472644f, -0.52459574f, 0.12921631f, -1.81390119f, 2.76170087f, 1.03982210f, 2.91744232f, -0.29048753f, 5.87453508f, -1.53684759f, 1.85800636f, -0.91404629f, 1.28954852f, 5.11354685f, -2.47475505f, -1.33179152f, 2.58552408f, 1.37316465f, -3.32339454f, 1.54122913f, 3.24953628f, -0.29758382f, 2.82391763f, -1.51142192f, -1.22699404f, 6.75745535f, 0.65452754f, -3.29385471f, 2.06008053f, 2.53172946f, -4.23532820f, -1.53909743f, -0.07010663f, -1.42173731f, 7.29031610f, -0.18448229f, 4.59496164f, 6.73027277f, 0.73441899f, 0.14426160f, 4.14915276f, -2.97010231f, 6.05851364f, 4.95218086f, -2.39145470f, 2.40494704f, 2.10288811f, 0.53503096f, 1.44511235f, 6.66344261f, -3.05803776f, 7.21418667f, 3.30303526f, -0.24163735f, 3.47409391f, 3.64520788f, 2.15189481f, -3.11243272f, 3.62310791f, 0.37379482f, 0.40865007f, -0.83132005f, -4.78246069f, 2.07030797f, 6.51765442f, 3.16178989f, 5.06180477f, 3.78434467f, -0.96689719f, 0.35965276f, 5.89967585f, 1.40294051f, 1.11952639f, 10.59778214f, 0.26739889f, -1.61297631f, 6.24801159f, -0.93914318f, -0.57812452f, 9.92604542f, -0.73025000f, -3.38530874f, 2.45646000f, -2.47949195f, 0.51638460f, 10.65636063f, 1.97816694f, -3.00407791f, 2.66914415f, -0.81951088f, -0.23316640f, 2.40737987f, -2.70007610f, 1.51531935f, 4.08860207f, -0.27552786f, -1.31721711f, 7.11568260f, -3.33498216f, -4.02545023f, 7.22675610f, -0.81690705f, -2.52689576f, 1.04016697f, -0.79291463f, -0.34875512f, 10.00498390f, -4.24167728f, 1.46162593f, 11.82569408f, -1.70359993f, -0.30161047f, 16.44085884f, -0.82253462f, -0.09435523f, 6.13080597f, -0.20259480f, 0.68308711f, 6.15663004f, -6.61776876f, 0.33295766f, 2.55449438f, -0.17819691f, -1.14892209f, 5.56776142f, 1.99279118f, 1.33035934f, 4.45823956f, 3.34916544f, -2.59905386f, 6.16164446f, -2.03881931f, -2.45273542f, 12.46793365f, -2.22743297f, 2.83738565f, 8.48628139f, -1.39347959f, -1.30867767f, 11.08041477f, -4.00363779f, 2.09183025f, 11.30395889f, -2.20504737f, 1.37426853f, 8.98735619f, 1.04676604f, -0.72757077f, 8.28050232f, -6.70741081f, -0.65798020f, 5.68592072f, -0.60760021f, 0.35854483f, 6.26852131f, 1.94100165f, 1.32112014f, 0.80987954f, -1.74617672f, -0.25434083f, 7.16045523f, 1.58884013f, -2.64847064f, 13.14820385f, 1.21393633f, -2.47258949f, 9.41650105f, -0.79384226f, 2.48954105f, 10.95629311f, 0.47723705f, 4.02126694f, 8.02593136f, -2.20726371f, -1.18794477f, 1.50836647f, 0.93118095f, -1.73513174f, 8.85493565f, -2.99670315f, -0.79055870f, 2.39473820f, 2.05046916f, -2.38055134f, 11.82299423f, 0.15609655f, 0.68744308f, 5.66401434f, -0.69281673f, 2.09855556f, 7.74626589f, -0.34283102f, 1.00542057f, 9.95838642f, 0.80161905f, 2.33455157f, 9.80057335f, -0.93561798f, 2.56991577f, 8.29711342f, 0.94213426f, 0.44209945f, 11.70259857f, 0.92710167f, 2.60957146f, 0.24971688f, -0.86529571f, 3.78628922f, 6.80884457f, -0.68178189f, 2.21103406f, 3.18895817f, 0.60283208f, -2.92716241f, 6.72060776f, -1.06625068f, 2.56543374f, 9.97404480f, 3.58080721f, -0.94936347f, 10.16736984f, -1.38464379f, 1.18191063f, 6.66179037f, -3.56115270f, 0.32329530f, 10.90870762f, 2.20638227f, 0.19653285f, 7.34650040f, -3.63859272f, -1.03027737f, 5.98829985f, -3.66606474f, -3.89746714f, 8.63469028f, 1.22569811f, 1.63240814f, 3.74385309f, 0.58243257f, -0.56981975f, 3.69260955f, 1.00979900f, -1.44030499f, 8.57058144f, -1.10648811f, 1.20474911f, 5.43133020f, -2.14822555f, -0.07928789f, 11.25825310f, 0.19645604f, -5.49546146f, 10.41917038f, -0.68178523f, -2.99639869f, 6.50054455f, 0.46488351f, -5.42328453f, 9.09500027f, -2.82107449f, 0.05601966f, 15.34610748f, -0.06820253f, 3.86699796f, 10.73316956f, -3.04795432f, -0.14702171f, 5.64813185f, 1.44028485f, -2.47596145f, 0.07280898f, -3.03187990f, -1.35183525f, 9.35835648f, 2.72966957f, 1.88199532f, 10.36187744f, -0.22834805f, -3.26738238f, 6.92025137f, -2.34061313f, 4.77379704f, 5.28559113f, -2.96323752f, -1.76186585f, 5.94436455f, 0.38647744f, -5.73869514f, 6.76849556f, 1.40892124f, -1.19068217f, 5.37919092f, -6.65328646f, 3.62782669f, 12.34744644f, 2.44762444f, -4.19242620f, 6.14906216f, 0.08121119f, 0.61355996f, 2.69666457f, -1.88962626f, -0.55314136f, 1.84937525f, 1.56048691f, 1.17460012f, 3.75674725f, 1.06198275f, -5.74625874f, 5.41645575f, -1.28946674f, -1.51689398f, 4.32400894f, -0.05222082f, -4.83948946f, 1.80747867f, 1.63144708f, -2.73887825f, 1.63975775f, -2.02163982f, -0.16210437f, 2.93518686f, 1.14427686f, -2.83246303f, 4.79283667f, 2.69697428f, -3.12678456f, -1.19225168f, -2.37022972f, -3.09429741f, 1.94225383f, -1.13747168f, -2.55048585f, 5.40242243f, 1.12777328f, 3.43713188f, 3.62658787f, -2.16878843f, 0.30164462f, 2.97407579f, -0.07275413f, -1.31149673f, 4.70066261f, -2.01323795f, 4.85255766f, 4.59128904f, 1.68084168f, 1.60336494f, 6.58138466f, -1.04759812f, 2.69906545f, 3.55769277f, -0.74327278f, 2.65819693f, 5.39528131f, 2.11248922f, -1.06446671f, 5.24546766f, -2.43146014f, 4.58907509f, 0.06521678f, -2.24503994f, 2.45722699f, 6.94863081f, 0.35258654f, 2.83396196f, 9.92525196f, -1.12225175f, -0.34365177f, 7.19116688f, -4.39813757f, 0.46517885f, 13.22028065f, -2.57483673f, -6.37226963f, 7.58046293f, -2.74600363f, 0.42231262f, 8.04881668f, 0.17289802f, -0.53447008f, 16.55157471f, 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8.69085693f, 1.31309867f, -2.79958344f, 8.76840591f, -1.56444156f, 1.62710834f, 2.41177034f, -0.72804940f, 5.70619011f, 4.67169666f, -0.86167198f, -1.83803177f, 2.96346045f, 2.82692933f, -2.81557131f, 7.11113358f, -1.90071094f, 2.54244423f, 11.19284058f, -0.06298946f, -1.71517313f, 12.98388577f, 0.84510714f, 3.00816894f, 2.57200313f, 0.03899818f, -1.49330592f, 9.60099125f, -3.59513044f, -1.30045319f, 7.09241819f, -0.65233821f, -2.33627677f, 8.81366920f, 0.84154201f, 1.03312039f, 9.85289097f, 0.19351870f, 1.78496623f, 7.34631205f, -2.16530800f, -0.65016162f, 2.46842360f, 0.24016285f, -1.24308395f, 4.78175163f, -0.97682536f, 2.20942235f, 6.68382788f, 3.76786447f, -1.44454038f, 6.26453733f, -3.23575711f, -2.30137897f, 9.53092670f, -5.55222607f, 3.25999236f, 9.37559509f, 1.86339056f, -0.23551451f, 10.23400211f, 3.93031883f, -0.52629089f, 7.85724449f, -2.91549587f, 4.46612740f, 5.66530371f, -2.70820427f, 4.81359577f, 10.31247330f, 1.92230141f, 2.53931546f, 0.74986327f, 1.70303428f, 0.48063779f, 5.31099129f, -0.78976244f, 3.75864220f, 4.23051405f, 2.34042454f, -7.98193836f, 9.83987141f, -1.46722627f, 3.54497814f, 10.36455154f, -4.51249075f, 0.77715248f, 7.78694630f, -4.59989023f, -2.49585629f, 9.90296268f, 1.38535416f, 1.17441154f, 10.10452843f, -0.98628229f, 0.60194463f, 9.12639141f, -3.90754628f, 2.88526392f, 7.24123430f, -0.15283313f, -0.75728363f, -1.15116858f, -2.53791571f, 0.77229571f, 6.44114161f, 0.02646767f, 4.95463037f, 7.21066380f, 1.79384065f, 0.73250306f, 8.04447937f, 0.32576546f, -0.79447043f, 10.12717724f, 2.33392906f, 1.30716443f, 12.36073112f, -0.36694977f, -1.20438910f, 7.03105593f, 0.59557682f, 0.69267452f, 10.18113136f, 2.49944925f, -0.42229167f, 8.83143330f, -1.18805945f, -2.87509322f, 4.53596449f, 4.09732771f, -3.39088297f, -1.02536607f, 0.82119560f, -3.47302604f, 9.29991817f, 0.21001509f, 4.97036457f, 9.50018406f, 1.04420102f, 1.96560478f, 10.74769592f, -6.22709799f, 3.11690164f, 5.06759691f, -1.23724771f, -3.05831861f, 8.12925529f, -1.93435478f, -1.10151744f, 9.32263088f, -0.04249470f, -5.98547363f, 10.49398136f, 0.26400441f, -0.78915191f, 13.28219604f, 2.99276900f, 0.74853164f, 2.49364305f, -3.43529654f, 4.05278301f, 2.13498688f, -2.35444307f, -0.79900265f, 4.66968822f, -0.31095147f, 3.60674143f, 12.37222099f, -0.07855003f, -3.30292702f, 12.15215874f, 0.60886210f, 2.87075138f, 7.75271845f, 0.38044083f, 3.34402204f, 6.40583277f, -0.87888050f, 0.67438459f, 6.91080809f, 1.98332930f, -0.08303714f, 8.08630371f, -0.16772588f, -2.74058914f, 7.17253590f, -2.69122696f, 1.48173678f, 8.99470139f, -1.43302310f, -0.88651133f, 2.66944790f, -0.29186964f, 2.00838661f, 5.09587479f, -0.76676071f, -2.88322186f, 8.31110573f, -0.14550979f, -1.37726915f, 10.28355122f, -1.60575438f, -0.04118848f, 9.97510815f, 0.14440438f, -3.24632120f, 9.00034523f, 4.14319563f, -1.31023729f, 7.16950464f, -0.70428526f, 2.01559544f, 7.26155043f, 2.40816474f, 2.09847403f, 7.31264496f, -0.75401551f, 2.13392544f, 7.03648758f, 1.04036045f, -1.15636516f, 1.09634531f, -0.06340861f, -0.58107805f, -0.65623116f, 1.18972754f, -0.80717683f, 1.40118241f, -0.61932516f, -3.60596156f, 1.59904599f, -2.23774099f, -1.13721037f, 3.89620137f, -0.09115922f, -7.51356888f, 2.36975193f, -1.42520905f, -2.34173775f, 3.33830214f, -2.74016523f, -3.04115510f, 6.00119495f, -1.36084354f, -2.45065260f, 4.56992292f, -3.02825928f, -3.74182844f, 5.11069250f, -0.91531068f, -2.31385994f, 1.83399653f, 3.39370203f, -3.60886002f}); auto exp = NDArrayFactory::create('c', {4, 4, 4, 3}, {7.97172260f, 0.06878620f, 2.27749538f, 7.29276514f, -0.14074677f, 0.65480286f, 5.70313978f, -0.06546132f, 0.35443667f, 3.70382833f, -0.84020567f, 0.63826996f, 8.60301399f, -0.38236514f, 1.55177069f, 7.37542057f, -0.99374938f, -0.29971302f, 8.84352493f, -0.67121059f, 0.43132120f, 4.78175592f, -1.25070143f, -1.91523600f, 6.03855371f, -0.00292124f, -1.11214364f, 7.90158176f, -0.57949901f, -0.96735370f, 7.81192017f, -0.53255427f, -0.48009714f, 3.16953635f, 0.08353355f, -1.54299748f, 3.74821687f, 1.69396687f, 0.72724354f, 5.42915201f, -1.13686812f, -0.71793109f, 5.78376389f, -0.72239977f, -0.60055625f, 2.53636408f, 0.56777251f, -2.07892323f, 6.08064651f, 0.68620735f, 2.54017019f, 5.65828180f, -0.68255502f, 1.47283304f, 6.10842514f, -0.39655915f, 0.28380761f, 1.96707797f, -1.98206317f, 0.94027776f, 4.71811438f, 0.32104525f, -0.92409706f, 8.34588146f, -1.05581069f, -0.55217457f, 9.58440876f, -0.96549922f, 0.45820439f, 5.65453672f, -2.50953507f, -0.71441835f, 8.03059578f, -0.21281289f, 0.92125505f, 9.26900673f, -0.35963219f, -0.70039093f, 8.59924412f, -1.22358346f, 0.81318003f, 3.85920119f, -0.01305223f, -1.09234154f, 6.33158875f, 1.28094780f, -1.48926139f, 4.94969177f, -0.77126902f, -1.97033751f, 5.64381838f, -0.16285487f, -1.31277227f, 2.39893222f, -1.32902908f, -1.39609122f, 6.47572327f, -0.45267010f, 1.55727172f, 6.70965624f, -1.68735468f, -0.05672536f, 7.25092363f, -0.64613032f, 0.67050058f, 3.60789680f, -2.05948973f, 2.22687531f, 8.15202713f, -0.70148355f, 1.28314006f, 8.14842319f, -1.88807654f, -1.04808438f, 8.45500565f, -0.76425624f, 0.94542569f, 4.56179953f, -0.28786001f, -2.04502511f, 8.46278095f, -0.31019822f, 0.07339200f, 9.34214592f, -0.61948007f, 0.52481830f, 8.32515621f, -1.52418160f, 0.49678251f, 5.11082315f, -1.09908783f, -0.52969611f, 5.27806664f, 0.88632923f, 0.66754371f, 4.75839233f, 0.48928693f, -0.68036932f, 6.56925392f, -0.02949905f, -2.99189186f, 4.46320581f, -0.64534980f, -0.29516968f, 8.60809517f, -1.13120568f, 3.41720533f, 5.84243155f, -1.24109328f, 0.89566326f, 5.99578333f, -0.42496428f, 2.07076764f, 3.17812920f, -0.81566459f, -0.14363396f, 6.55184317f, 0.39633346f, -0.43852386f, 8.70214558f, -2.24613595f, 0.30708700f, 8.73882294f, -0.53545928f, 1.54409575f, 4.49452257f, -0.16509305f, 0.19028664f, 8.24897003f, 0.44750381f, 2.15448594f, 8.97640514f, -0.77728152f, 0.57272542f, 9.03467560f, 0.47173575f, -1.10807717f, 3.30056310f, -0.43268481f, -0.41470885f, 3.53798294f, -0.08546703f, -2.16840744f, 6.18733406f, -0.17871059f, -2.59837723f, 5.94218683f, -1.02990067f, -0.49760687f, 3.76938033f, 0.86383581f, -1.91504073f}); - nd4j::ops::avgpool2d op; + sd::ops::avgpool2d op; auto result = op.evaluate({&input}, {3,3, 3,3, 0,0, 1,1,1, 0,1}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); // z->printIndexedBuffer("z"); // exp.printIndexedBuffer("e"); @@ -273,7 +273,7 @@ TYPED_TEST(TypedDeclarableOpsTests4, avgpool2d_10) { ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } ////////////////////////////////////////////////////////////////////// @@ -285,11 +285,11 @@ TEST_F(DeclarableOpsTests4, avgpool2d_11) { auto x = NDArrayFactory::create('c', {1, inOutH, inOutW, inOutC}); x.linspace(1.0); - nd4j::ops::avgpool2d op; + sd::ops::avgpool2d op; auto result = op.evaluate({&x}, {3,3, 1,1, 0,0, 1,1, 1, 0, 1}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); int totalPadHeight = (inOutH - 1) * 1 + 3 - inOutH; int padTop = totalPadHeight / 2; @@ -308,11 +308,11 @@ TEST_F(DeclarableOpsTests4, avgpool2d_11) { int hTo = hFrom + k; int wTo = wFrom + k; - hFrom = nd4j::math::nd4j_max(0, hFrom); - wFrom = nd4j::math::nd4j_max(0, wFrom); + hFrom = sd::math::nd4j_max(0, hFrom); + wFrom = sd::math::nd4j_max(0, wFrom); - hTo = nd4j::math::nd4j_min(inOutH, hTo); - wTo = nd4j::math::nd4j_min(inOutW, wTo); + hTo = sd::math::nd4j_min(inOutH, hTo); + wTo = sd::math::nd4j_min(inOutW, wTo); int idxOut[4]; int idxIn[4]; @@ -339,7 +339,7 @@ TEST_F(DeclarableOpsTests4, avgpool2d_11) { ASSERT_EQ(m, *z); - delete result; + } ////////////////////////////////////////////////////////////////////// @@ -361,11 +361,11 @@ TEST_F(DeclarableOpsTests4, avgpool2d_12) { 1082.5, 1083.5, 1084.5,1090. , 1091. , 1092. ,1099. , 1100. , 1101. ,1106.5, 1107.5, 1108.5,1157.5, 1158.5, 1159.5,1165. , 1166. , 1167. ,1174. , 1175. , 1176. ,1181.5, 1182.5, 1183.5}); input.linspace(1.); - nd4j::ops::avgpool2d op; + sd::ops::avgpool2d op; auto results = op.evaluate({&input}, {kH,kW, sH,sW, pH,pW, dH,dW, paddingMode, 0, dataFormat}); - auto output = results->at(0); + auto output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); //output->printIndexedBuffer("output"); //expected.printIndexedBuffer("expected"); @@ -373,7 +373,7 @@ TEST_F(DeclarableOpsTests4, avgpool2d_12) { ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -407,7 +407,7 @@ TEST_F(DeclarableOpsTests4, avgpool2d_13) { std::vector* argI = block->getIArguments(); *argI = {kH,kW, sH,sW, pH,pW, dW,dH, 0, 0, 0}; // 0,1 - kernel Height/Width; 2,3 - stride Height/Width; 4,5 - pad Height/Width; 6,7 - dilation Height/Width; 8 - same mode; - nd4j::ops::avgpool2d pooling; + sd::ops::avgpool2d pooling; Nd4jStatus status = pooling.execute(block); ASSERT_EQ(ND4J_STATUS_OK, status); @@ -450,7 +450,7 @@ TEST_F(DeclarableOpsTests4, avgpool2d_14) { std::vector* argI = block->getIArguments(); *argI = {kH,kW, sH,sW, pH,pW, dW,dH, 0, 0, 0}; // 0,1 - kernel Height/Width; 2,3 - stride Height/Width; 4,5 - pad Height/Width; 6,7 - dilation Height/Width; 8 - same mode; - nd4j::ops::avgpool2d pooling; + sd::ops::avgpool2d pooling; Nd4jStatus status = pooling.execute(block); ASSERT_EQ(ND4J_STATUS_OK, status); @@ -476,8 +476,8 @@ TEST_F(DeclarableOpsTests4, Avgpool2d_test15) { const int pW = 0; const int dH = 1; const int dW = 1; - const int oH = (int) nd4j::math::nd4j_ceil(iH * 1.f / sH); - const int oW = (int) nd4j::math::nd4j_ceil(iW * 1.f / sW); + const int oH = (int) sd::math::nd4j_ceil(iH * 1.f / sH); + const int oW = (int) sd::math::nd4j_ceil(iW * 1.f / sW); auto x = NDArrayFactory::create_('c', {bS,iD,iH,iW}); @@ -493,7 +493,7 @@ TEST_F(DeclarableOpsTests4, Avgpool2d_test15) { std::vector* argI = block->getIArguments(); *argI = {kH,kW, sH,sW, pH,pW, dW,dH, 1, 0, 0}; // 0,1 - kernel Height/Width; 2,3 - stride Height/Width; 4,5 - pad Height/Width; 6,7 - dilation Height/Width; 8 - same mode; - nd4j::ops::avgpool2d pooling; + sd::ops::avgpool2d pooling; Nd4jStatus status = pooling.execute(block); ASSERT_EQ(ND4J_STATUS_OK, status); @@ -513,13 +513,13 @@ TEST_F(DeclarableOpsTests4, avgpool2d_16) { int paddingMode = 1; // 1-SAME, 0-VALID int dataFormat = 1; // 1-NHWC, 0-NDHW - NDArray input('c', {bS, iH, iW, iC}, nd4j::DataType::FLOAT32); - NDArray output('f', {bS, oH, oW, iC}, nd4j::DataType::FLOAT32); - NDArray expected('c', {bS, oH, oW, iC}, {6.f, 7.f, 10.f, 11.f, 22.f, 23.f, 26.f, 27.f, 38.f, 39.f, 42.f, 43.f, 54.f, 55.f, 58.f, 59.f}, nd4j::DataType::FLOAT32); + NDArray input('c', {bS, iH, iW, iC}, sd::DataType::FLOAT32); + NDArray output('f', {bS, oH, oW, iC}, sd::DataType::FLOAT32); + NDArray expected('c', {bS, oH, oW, iC}, {6.f, 7.f, 10.f, 11.f, 22.f, 23.f, 26.f, 27.f, 38.f, 39.f, 42.f, 43.f, 54.f, 55.f, 58.f, 59.f}, sd::DataType::FLOAT32); input.linspace(1.); - nd4j::ops::avgpool2d op; + sd::ops::avgpool2d op; auto status = op.execute({&input}, {&output}, {}, {kH,kW, sH,sW, pH,pW, dH,dW, paddingMode, 0, dataFormat}, {}); ASSERT_EQ(Status::OK(), status); @@ -536,17 +536,17 @@ TEST_F(DeclarableOpsTests4, biasadd_1) { auto bias = NDArrayFactory::create('c', {2}, {1, 2}); auto exp = NDArrayFactory::create('c', {2, 3, 3, 2}, {1.f, 2.f, 1.f, 2.f, 1.f, 2.f, 1.f, 2.f, 1.f, 2.f, 1.f, 2.f, 1.f, 2.f, 1.f, 2.f, 1.f, 2.f, 1.f, 2.f, 1.f, 2.f, 1.f, 2.f, 1.f, 2.f, 1.f, 2.f, 1.f, 2.f, 1.f, 2.f, 1.f, 2.f, 1.f, 2.f}); - nd4j::ops::biasadd op; + sd::ops::biasadd op; auto result = op.evaluate({&x, &bias}, {}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } TEST_F(DeclarableOpsTests4, biasadd_2) { @@ -554,17 +554,17 @@ TEST_F(DeclarableOpsTests4, biasadd_2) { auto bias = NDArrayFactory::create('c', {2}, {1, 2}); auto exp = NDArrayFactory::create('c', {2, 2, 3, 3}, {1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2}); - nd4j::ops::biasadd op; + sd::ops::biasadd op; auto result = op.evaluate({&x, &bias}, {}, {}, {true}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } TEST_F(DeclarableOpsTests4, biasadd_3) { @@ -572,36 +572,36 @@ TEST_F(DeclarableOpsTests4, biasadd_3) { auto row = NDArrayFactory::create('c', {3}, {1, 2, 3}); auto exp = NDArrayFactory::create('c', {2, 3}, {1, 2, 3, 1, 2, 3}); - nd4j::ops::biasadd op; + sd::ops::biasadd op; auto result = op.evaluate({&x, &row}, {}, {}, {true}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); - delete result; + } ////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests4, biasadd_bp_1) { - NDArray x('c', {2,2,2,3}, {1.,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24}, nd4j::DataType::FLOAT32); - NDArray gradO('c', {2,2,2,3}, nd4j::DataType::FLOAT32); - NDArray bias('c', {3}, {-1., -2, -3}, nd4j::DataType::FLOAT32); + NDArray x('c', {2,2,2,3}, {1.,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24}, sd::DataType::FLOAT32); + NDArray gradO('c', {2,2,2,3}, sd::DataType::FLOAT32); + NDArray bias('c', {3}, {-1., -2, -3}, sd::DataType::FLOAT32); - NDArray expGradB('c', {3}, {9.2, 10. , 10.8}, nd4j::DataType::FLOAT32); + NDArray expGradB('c', {3}, {9.2, 10. , 10.8}, sd::DataType::FLOAT32); gradO.linspace(0.1, 0.1); - nd4j::ops::biasadd_bp op; + sd::ops::biasadd_bp op; auto result = op.evaluate({&x, &bias, &gradO}, {}, {}, {false}); // NHWC - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto gradI = result->at(0); - auto gradB = result->at(1); + auto gradI = result.at(0); + auto gradB = result.at(1); ASSERT_TRUE(gradI->isSameShape(gradO)); ASSERT_TRUE(gradI->equalsTo(gradO)); @@ -609,27 +609,27 @@ TEST_F(DeclarableOpsTests4, biasadd_bp_1) { ASSERT_TRUE(gradB->isSameShape(expGradB)); ASSERT_TRUE(gradB->equalsTo(expGradB)); - delete result; + } ////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests4, biasadd_bp_2) { - NDArray x('c', {2,3,2,2}, {1.,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24}, nd4j::DataType::FLOAT32); - NDArray gradO('c', {2,3,2,2}, nd4j::DataType::FLOAT32); - NDArray bias('c', {3}, {-1., -2, -3}, nd4j::DataType::FLOAT32); + NDArray x('c', {2,3,2,2}, {1.,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24}, sd::DataType::FLOAT32); + NDArray gradO('c', {2,3,2,2}, sd::DataType::FLOAT32); + NDArray bias('c', {3}, {-1., -2, -3}, sd::DataType::FLOAT32); - NDArray expGradB('c', {3}, {6.8, 10., 13.2}, nd4j::DataType::FLOAT32); + NDArray expGradB('c', {3}, {6.8, 10., 13.2}, sd::DataType::FLOAT32); gradO.linspace(0.1, 0.1); - nd4j::ops::biasadd_bp op; + sd::ops::biasadd_bp op; auto result = op.evaluate({&x, &bias, &gradO}, {}, {}, {true}); // NCHW - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto gradI = result->at(0); - auto gradB = result->at(1); + auto gradI = result.at(0); + auto gradB = result.at(1); ASSERT_TRUE(gradI->isSameShape(gradO)); ASSERT_TRUE(gradI->equalsTo(gradO)); @@ -637,7 +637,7 @@ TEST_F(DeclarableOpsTests4, biasadd_bp_2) { ASSERT_TRUE(gradB->isSameShape(expGradB)); ASSERT_TRUE(gradB->equalsTo(expGradB)); - delete result; + } TEST_F(DeclarableOpsTests4, biasadd_4) { @@ -649,7 +649,7 @@ TEST_F(DeclarableOpsTests4, biasadd_4) { auto z = NDArrayFactory::create('c', {2, 3}); auto exp = NDArrayFactory::create('c', {2, 3}, {1.f, 2.f, 3.f, 1.f, 2.f, 3.f}); - nd4j::ops::biasadd op; + sd::ops::biasadd op; auto status = op.execute({&x, &y}, {&z}, {}, {}, {true}); ASSERT_EQ(Status::OK(), status); @@ -662,17 +662,17 @@ TEST_F(DeclarableOpsTests4, Test_Fill_1) { auto exp = NDArrayFactory::create('c', {3, 2, 4}); exp.assign(2.0f); - nd4j::ops::fill op; + sd::ops::fill op; auto result = op.evaluate({&x, &v}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } TEST_F(DeclarableOpsTests4, Test_FirasSparce_1) { @@ -683,18 +683,18 @@ TEST_F(DeclarableOpsTests4, Test_FirasSparce_1) { x.p(52, 0); x.p(60, 1); x.p(61, 0); - nd4j::ops::firas_sparse op; + sd::ops::firas_sparse op; auto result = op.evaluate({&x}, {0, 1}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); // z->printIndexedBuffer("FIRAS"); // z->printShapeInfo("OUTSHAPE"); // ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } TEST_F(DeclarableOpsTests4, Test_FlattenTests_1) { @@ -703,17 +703,17 @@ TEST_F(DeclarableOpsTests4, Test_FlattenTests_1) { x.linspace(1); exp.linspace(1); - nd4j::ops::flatten op; + sd::ops::flatten op; auto result = op.evaluate({&x}, {}, {'c'}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); // z->printIndexedBuffer("Flatten1"); // z->printShapeInfo("Flatten1 shape"); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } TEST_F(DeclarableOpsTests4, Test_FlattenTests_2) { @@ -724,53 +724,53 @@ TEST_F(DeclarableOpsTests4, Test_FlattenTests_2) { x.linspace(1); y.linspace(82); exp.linspace(1); - nd4j::ops::flatten op; + sd::ops::flatten op; auto result = op.evaluate({&x, &y}, {}, {'c'}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); // z->printIndexedBuffer("Flatten2"); // z->printShapeInfo("Flatten2 shape"); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } TEST_F(DeclarableOpsTests4, Test_FlattenTests_3) { - NDArray x('c', {2,2}, {1, 2, 3, 4}, nd4j::DataType::INT32); - NDArray y('f', {2,2}, nd4j::DataType::INT32); - NDArray exp('c', {8}, {1, 2, 3, 4, 1, 2, 3, 4}, nd4j::DataType::INT32); + NDArray x('c', {2,2}, {1, 2, 3, 4}, sd::DataType::INT32); + NDArray y('f', {2,2}, sd::DataType::INT32); + NDArray exp('c', {8}, {1, 2, 3, 4, 1, 2, 3, 4}, sd::DataType::INT32); y.assign(x); - nd4j::ops::flatten op; + sd::ops::flatten op; auto result = op.evaluate({&x, &y}, {}, {'c'}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } TEST_F(DeclarableOpsTests4, Test_FlattenTests_4) { - NDArray x('c', {2,2}, {1, 2, 3, 4}, nd4j::DataType::INT32); - NDArray y('f', {2,2}, nd4j::DataType::INT32); - NDArray exp('c', {8}, {1, 3, 2, 4, 1, 3, 2, 4}, nd4j::DataType::INT32); + NDArray x('c', {2,2}, {1, 2, 3, 4}, sd::DataType::INT32); + NDArray y('f', {2,2}, sd::DataType::INT32); + NDArray exp('c', {8}, {1, 3, 2, 4, 1, 3, 2, 4}, sd::DataType::INT32); y.assign(x); - nd4j::ops::flatten op; + sd::ops::flatten op; auto result = op.evaluate({&x, &y}, {}, {'f'}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } TEST_F(DeclarableOpsTests4, Test_FloorTests_1) { @@ -778,17 +778,17 @@ TEST_F(DeclarableOpsTests4, Test_FloorTests_1) { auto exp = NDArrayFactory::create('c', {3,3}); exp.linspace(1); - nd4j::ops::Floor op; + sd::ops::Floor op; auto result = op.evaluate({&x}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); // z->printIndexedBuffer("Flatten1"); // z->printShapeInfo("Flatten1 shape"); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } TEST_F(DeclarableOpsTests4, Test_Reshape_Again) { @@ -798,15 +798,15 @@ TEST_F(DeclarableOpsTests4, Test_Reshape_Again) { x.linspace(1); exp.linspace(1); - nd4j::ops::reshape op; + sd::ops::reshape op; auto result = op.evaluate({&x}, {-99, 4, 3}); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } TEST_F(DeclarableOpsTests4, Test_Split_1) { @@ -826,16 +826,16 @@ TEST_F(DeclarableOpsTests4, Test_Split_1) { sub2.assign(2.0); - nd4j::ops::split_v op; + sd::ops::split_v op; auto result = op.evaluate({&x, &sizes}, {}, {1}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - ASSERT_EQ(3, result->size()); + ASSERT_EQ(3, result.size()); - auto z0 = result->at(0); - auto z1 = result->at(1); - auto z2 = result->at(2); + auto z0 = result.at(0); + auto z1 = result.at(1); + auto z2 = result.at(2); ASSERT_TRUE(sub0.isSameShape(z0)); ASSERT_TRUE(sub1.isSameShape(z1)); @@ -845,7 +845,7 @@ TEST_F(DeclarableOpsTests4, Test_Split_1) { ASSERT_TRUE(sub1.equalsTo(z1)); ASSERT_TRUE(sub2.equalsTo(z2)); - delete result; + } // special test for TF mode, when axis goes first @@ -869,14 +869,14 @@ TEST_F(DeclarableOpsTests4, Test_Split_2) { sub3.assign(3.0f); - nd4j::ops::split op; + sd::ops::split op; auto result = op.evaluate({&axis, &x}, {}, {4}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z0 = result->at(0); - auto z1 = result->at(1); - auto z2 = result->at(2); - auto z3 = result->at(3); + auto z0 = result.at(0); + auto z1 = result.at(1); + auto z2 = result.at(2); + auto z3 = result.at(3); ASSERT_TRUE(sub0.isSameShape(z0)); ASSERT_TRUE(sub1.isSameShape(z1)); @@ -888,7 +888,7 @@ TEST_F(DeclarableOpsTests4, Test_Split_2) { ASSERT_TRUE(sub2.equalsTo(z2)); ASSERT_TRUE(sub3.equalsTo(z3)); - delete result; + } // special test for TF mode, when axis goes first @@ -908,13 +908,13 @@ TEST_F(DeclarableOpsTests4, Test_Split_3) { sub1.assign(1.0f); sub2.assign(2.0f); - nd4j::ops::split op; + sd::ops::split op; auto result = op.evaluate({&axis, &x}, {}, {3}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z0 = result->at(0); - auto z1 = result->at(1); - auto z2 = result->at(2); + auto z0 = result.at(0); + auto z1 = result.at(1); + auto z2 = result.at(2); ASSERT_TRUE(sub0.isSameShape(z0)); ASSERT_TRUE(sub1.isSameShape(z1)); @@ -923,42 +923,103 @@ TEST_F(DeclarableOpsTests4, Test_Split_3) { ASSERT_TRUE(sub0.equalsTo(z0)); ASSERT_TRUE(sub1.equalsTo(z1)); ASSERT_TRUE(sub2.equalsTo(z2)); - - delete result; } -TEST_F(DeclarableOpsTests4, Test_Stack_4) { - auto t = NDArrayFactory::create('c', {2, 3, 5}); - auto u = NDArrayFactory::create('c', {2, 3, 5}); - auto v = NDArrayFactory::create('c', {2, 3, 5}); - auto exp = NDArrayFactory::create('c', {3, 2, 3, 5}); +/////////////////////////////////////////////////////////////////// +TEST_F(DeclarableOpsTests4, split_test4) { - nd4j::ops::stack op; - auto result = op.evaluate({&t, &u, &v}, {}, {-4}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + auto input = NDArrayFactory::create('c', {10},{1.f,2.f,3.f,4.f,5.f,6.f,7.f,8.f,9.f,10.f}); + auto axis = NDArrayFactory::create(-1); + auto exp1 = NDArrayFactory::create('c', {5}, {1.f,2.f,3.f,4.f,5.f}); + auto exp2 = NDArrayFactory::create('c', {5}, {6.f,7.f,8.f,9.f,10.f}); + sd::ops::split op; + auto results = op.evaluate({&input, &axis}, {}, {2}, {}); - auto z = result->at(0); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - ASSERT_TRUE(exp.isSameShape(z)); + auto out1 = results.at(0); + auto out2 = results.at(1); - delete result; + ASSERT_TRUE(exp1.isSameShape(out1)); + ASSERT_TRUE(exp2.isSameShape(out2)); + ASSERT_TRUE(exp1.equalsTo(out1)); + ASSERT_TRUE(exp2.equalsTo(out2)); } + +/////////////////////////////////////////////////////////////////// +TEST_F(DeclarableOpsTests4, split_test5) { + + auto input = NDArrayFactory::create('c', {3,8},{1.f,2.f,3.f,4.f,5.f,6.f,7.f,8.f,9.f,10.f,11.f,12.f,13.f,14.f,15.f,16.f,17.f,18.f,19.f,20.f,21.f,22.f,23.f,24.f}); + auto exp1 = NDArrayFactory::create('c', {3,4}, {1.f,2.f,3.f,4.f, 9.f,10.f,11.f,12.f, 17.f,18.f,19.f,20.f}); + auto exp2 = NDArrayFactory::create('c', {3,4}, {5.f,6.f,7.f,8.f, 13.f,14.f,15.f,16.f, 21.f,22.f,23.f,24.f}); + + sd::ops::split op; + auto results = op.evaluate({&input}, {}, {2,-1},{}); + + ASSERT_EQ(ND4J_STATUS_OK, results.status()); + + auto out1 = results.at(0); + auto out2 = results.at(1); + + ASSERT_TRUE(exp1.isSameShape(out1)); + ASSERT_TRUE(exp2.isSameShape(out2)); + ASSERT_TRUE(exp1.equalsTo(out1)); + ASSERT_TRUE(exp2.equalsTo(out2)); +} + +/////////////////////////////////////////////////////////////////// +TEST_F(DeclarableOpsTests4, split_test6) { + + NDArray input('c', {0,4}, sd::DataType::FLOAT32); + std::vector expShape = {0,1}; + + const int numSplits = 4; + const int axis = 1; + + sd::ops::split op; + auto results = op.evaluate({&input}, {}, {numSplits, axis}, {}); + + ASSERT_EQ(ND4J_STATUS_OK, results.status()); + + for (int i = 0; i < numSplits; ++i) + ASSERT_TRUE(results.at(i)->isSameShape(expShape)); +} + +/////////////////////////////////////////////////////////////////// +TEST_F(DeclarableOpsTests4, split_test7) { + + NDArray input('c', {0,4}, sd::DataType::FLOAT32); + std::vector expShape = {0,4}; + + const int numSplits = 4; + const int axis = 0; + + sd::ops::split op; + auto results = op.evaluate({&input}, {}, {numSplits, axis}, {}); + + ASSERT_EQ(ND4J_STATUS_OK, results.status()); + + for (int i = 0; i < numSplits; ++i) + ASSERT_TRUE(results.at(i)->isSameShape(expShape)); +} + + TEST_F(DeclarableOpsTests4, Test_Squeeze_args_1) { auto x = NDArrayFactory::create('c', {2, 1, 1, 1, 2}, {1, 2, 3, 4}); auto exp = NDArrayFactory::create('c', {2, 1, 2}, {1, 2, 3, 4}); - nd4j::ops::squeeze op; + sd::ops::squeeze op; auto result = op.evaluate({&x}, {}, {1, 3}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } TEST_F(DeclarableOpsTests4, Test_Squeeze_args_2) { @@ -966,16 +1027,16 @@ TEST_F(DeclarableOpsTests4, Test_Squeeze_args_2) { auto y = NDArrayFactory::create('c', {2}, {1.f, 3.f}); auto exp = NDArrayFactory::create('c', {2, 1, 2}, {1, 2, 3, 4}); - nd4j::ops::squeeze op; + sd::ops::squeeze op; auto result = op.evaluate({&x, &y}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } @@ -983,64 +1044,48 @@ TEST_F(DeclarableOpsTests4, Test_Squeeze_args_3) { auto x = NDArrayFactory::create('c', {2, 1, 1, 1, 2}, {1, 2, 3, 4}); auto exp = NDArrayFactory::create('c', {2, 1, 2}, {1, 2, 3, 4}); - nd4j::ops::squeeze op; + sd::ops::squeeze op; auto result = op.evaluate({&x}, {}, {-2, -3}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; -} -TEST_F(DeclarableOpsTests4, Test_1D_1) { - auto x = NDArrayFactory::create('c', {2, 3}); - - nd4j::ops::unstack op; - auto result = op.evaluate({&x}, {}, {1}); - - ASSERT_EQ(ND4J_STATUS_OK, result->status()); - - ASSERT_EQ(3, result->size()); - - for (int e = 0; e < 3; e++) - ASSERT_EQ(1, result->at(e)->rankOf()); - - delete result; } TEST_F(DeclarableOpsTests4, Test_SpaceToDepth_1) { auto x = NDArrayFactory::create('c', {1, 2, 2, 3}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}); auto exp = NDArrayFactory::create('c', {1, 1, 1, 12}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}); - nd4j::ops::space_to_depth op; + sd::ops::space_to_depth op; auto result = op.evaluate({&x}, {}, {2, 1}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } TEST_F(DeclarableOpsTests4, Test_SpaceToDepth_2) { auto x = NDArrayFactory::create('c', {1, 3, 2, 2}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}); auto exp = NDArrayFactory::create('c', {1, 12, 1, 1}, {1, 5, 9, 2, 6, 10, 3, 7, 11, 4, 8, 12}); - nd4j::ops::space_to_depth op; + sd::ops::space_to_depth op; auto result = op.evaluate({&x}, {}, {2, 0}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } @@ -1048,16 +1093,16 @@ TEST_F(DeclarableOpsTests4, Test_DepthToSpace_1) { auto x = NDArrayFactory::create('c', {1, 1, 1, 12}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}); auto exp = NDArrayFactory::create('c', {1, 2, 2, 3}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}); - nd4j::ops::depth_to_space op; + sd::ops::depth_to_space op; auto result = op.evaluate({&x}, {}, {2, 1}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } @@ -1065,31 +1110,31 @@ TEST_F(DeclarableOpsTests4, Test_DepthToSpace_2) { auto x = NDArrayFactory::create('c', {1, 12, 1, 1}, {1, 5, 9, 2, 6, 10, 3, 7, 11, 4, 8, 12}); auto exp = NDArrayFactory::create('c', {1, 3, 2, 2}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}); - nd4j::ops::depth_to_space op; + sd::ops::depth_to_space op; auto result = op.evaluate({&x}, {}, {2, 0}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } TEST_F(DeclarableOpsTests4, Test_DepthToSpace_3) { auto x = NDArrayFactory::create('c', {4, 4, 16, 16}); auto exp = NDArrayFactory::create('c', {4, 16, 64, 1}); - nd4j::ops::depth_to_space op; + sd::ops::depth_to_space op; auto result = op.evaluate({&x}, {}, {4, 1}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); - delete result; + } @@ -1098,16 +1143,16 @@ TEST_F(DeclarableOpsTests4, Test_Cross_1) { auto b = NDArrayFactory::create('c', {3}, {6, 7, 8}); auto exp = NDArrayFactory::create('c', {3}, {-5, 10, -5}); - nd4j::ops::cross op; + sd::ops::cross op; auto result = op.evaluate({&a, &b}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } @@ -1116,16 +1161,16 @@ TEST_F(DeclarableOpsTests4, Test_Cross_2) { auto b = NDArrayFactory::create('c', {2, 3}, {6, 7, 8, 6, 7, 8}); auto exp = NDArrayFactory::create('c', {2, 3}, {-5, 10, -5, -5, 10, -5}); - nd4j::ops::cross op; + sd::ops::cross op; auto result = op.evaluate({&a, &b}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } @@ -1134,16 +1179,16 @@ TEST_F(DeclarableOpsTests4, Test_Cross_3) { auto b = NDArrayFactory::create('c', {3, 3}, {2, 3, 4, 7, 6, 5, 6, 3, 2}); auto exp = NDArrayFactory::create('c', {3, 3}, { -1, 2, -1, -11, 22, -11, -11, 40, -27}); - nd4j::ops::cross op; + sd::ops::cross op; auto result = op.evaluate({&a, &b}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } TEST_F(DeclarableOpsTests4, Test_Add_119) { @@ -1151,19 +1196,19 @@ TEST_F(DeclarableOpsTests4, Test_Add_119) { auto b = NDArrayFactory::create('c', {4}, {1, 2, 3, 4}); auto exp = NDArrayFactory::create('c', {1, 4}, {2, 4, 6, 8}); - nd4j::ops::add op; + sd::ops::add op; auto result = op.evaluate({&a, &b}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_EQ(2, z->rankOf()); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } TEST_F(DeclarableOpsTests4, Test_Reshape_Negative_1) { @@ -1171,16 +1216,16 @@ TEST_F(DeclarableOpsTests4, Test_Reshape_Negative_1) { auto shape = NDArrayFactory::create('c', {2}, {-1, 2}); auto exp = NDArrayFactory::create('c', {4, 2}, {1, 2, 3, 4, 5, 6, 7, 8}); - nd4j::ops::reshape op; + sd::ops::reshape op; auto result = op.evaluate({&x, &shape}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } TEST_F(DeclarableOpsTests4, Test_TileToShape_1) { @@ -1189,17 +1234,17 @@ TEST_F(DeclarableOpsTests4, Test_TileToShape_1) { 4.f, 5.f, 6.f,4.f, 5.f, 6.f,4.f, 5.f, 6.f,4.f, 5.f, 6.f}); x.linspace(1.f); - nd4j::ops::tile_to_shape op; + sd::ops::tile_to_shape op; auto result = op.evaluate({&x},{}, {2, 4, 3}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } TEST_F(DeclarableOpsTests4, Test_StridedSlice_Alex_1) { @@ -1208,17 +1253,17 @@ TEST_F(DeclarableOpsTests4, Test_StridedSlice_Alex_1) { auto exp = NDArrayFactory::create('c', {1,3,4,5}); exp.linspace(1); - nd4j::ops::strided_slice op; + sd::ops::strided_slice op; auto result = op.evaluate({&x}, {}, {0,0,0,1,0, -999,0,0,0, -999,3,4,5, -999,1,1,1}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } TEST_F(DeclarableOpsTests4, Test_StridedSlice_Alex_2) { @@ -1230,19 +1275,19 @@ TEST_F(DeclarableOpsTests4, Test_StridedSlice_Alex_2) { auto exp = NDArrayFactory::create('c', {1,3,4,5}); exp.linspace(1); - nd4j::ops::strided_slice op; + sd::ops::strided_slice op; auto result = op.evaluate({&x, &begin, &end, &stride}, {}, {0,0,0,1,0}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } TEST_F(DeclarableOpsTests4, Test_StridedSlice_Alex_3) { @@ -1255,15 +1300,15 @@ TEST_F(DeclarableOpsTests4, Test_StridedSlice_Alex_3) { //auto exp = NDArrayFactory::create('c', {1,3,4,5}); //exp.linspace(1); - nd4j::ops::strided_slice op; + sd::ops::strided_slice op; auto result = op.evaluate({&x, &begin, &end, &stride}, {}, {1,0,0,0,0}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(z->isEmpty()); - delete result; + } TEST_F(DeclarableOpsTests4, Test_StridedSlice_Alex_4) { auto x = NDArrayFactory::create('c', {1,3}, {1, 2, 3}); @@ -1274,15 +1319,15 @@ TEST_F(DeclarableOpsTests4, Test_StridedSlice_Alex_4) { auto exp = NDArrayFactory::create('c', {1}, {1}); //exp.linspace(1); - nd4j::ops::strided_slice op; + sd::ops::strided_slice op; auto result = op.evaluate({&x, &begin, &end, &stride}, {}, {1,0,1,0,2}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(z->lengthOf() == 1); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } ////////////////////////////////////////////////////////////////////// @@ -1298,15 +1343,15 @@ TEST_F(DeclarableOpsTests4, parallel_stack_test1) { auto expected = NDArrayFactory::create('c', {3,2,2,2}); expected.linspace(1); - nd4j::ops::parallel_stack op; + sd::ops::parallel_stack op; auto results = op.evaluate({&x1, &x2, &x3}); - auto output = results->at(0); + auto output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -1318,16 +1363,16 @@ TEST_F(DeclarableOpsTests4, parallel_stack_test2) { auto expected = NDArrayFactory::create('c', {3,1,2}, {1,2,3,4,5,6}); - nd4j::ops::parallel_stack op; + sd::ops::parallel_stack op; auto results = op.evaluate({&x1, &x2, &x3}); - auto output = results->at(0); + auto output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -1339,15 +1384,15 @@ TEST_F(DeclarableOpsTests4, parallel_stack_test3) { auto expected = NDArrayFactory::create('c', {3,2,1}, {1,2,3,4,5,6}); - nd4j::ops::parallel_stack op; + sd::ops::parallel_stack op; auto results = op.evaluate({&x1, &x2, &x3}); - auto output = results->at(0); + auto output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; + } \ ////////////////////////////////////////////////////////////////////// @@ -1359,15 +1404,15 @@ TEST_F(DeclarableOpsTests4, parallel_stack_test4) { auto expected = NDArrayFactory::create('c', {3,2}, {1,2,3,4,5,6}); - nd4j::ops::parallel_stack op; + sd::ops::parallel_stack op; auto results = op.evaluate({&x1, &x2, &x3}); - auto output = results->at(0); + auto output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -1379,15 +1424,15 @@ TEST_F(DeclarableOpsTests4, parallel_stack_test5) { auto expected = NDArrayFactory::create('c', {3,1}, {1,3,5}); - nd4j::ops::parallel_stack op; + sd::ops::parallel_stack op; auto results = op.evaluate({&x1, &x2, &x3}); - auto output = results->at(0); + auto output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -1399,15 +1444,15 @@ TEST_F(DeclarableOpsTests4, parallel_stack_test6) { auto expected = NDArrayFactory::create('c', {3}, {1,3,5}); - nd4j::ops::parallel_stack op; + sd::ops::parallel_stack op; auto results = op.evaluate({&x1, &x2, &x3}); - auto output = results->at(0); + auto output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -1416,15 +1461,15 @@ TEST_F(DeclarableOpsTests4, parallel_stack_test7) { auto x1 = NDArrayFactory::create(1.); auto expected = NDArrayFactory::create('c', {1}, {1.}); - nd4j::ops::parallel_stack op; + sd::ops::parallel_stack op; auto results = op.evaluate({&x1}); - auto output = results->at(0); + auto output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -1437,15 +1482,15 @@ TEST_F(DeclarableOpsTests4, meshgrid_test1) { auto exp1 = NDArrayFactory::create('c', {2,3,4}, {10, 10, 10, 10, 20, 20, 20, 20, 30, 30, 30, 30, 10, 10, 10, 10, 20, 20, 20, 20, 30, 30, 30, 30}); auto exp2 = NDArrayFactory::create('c', {2,3,4}, {100, 200, 300, 400, 100, 200, 300, 400, 100, 200, 300, 400, 100, 200, 300, 400, 100, 200, 300, 400, 100, 200, 300, 400}); - nd4j::ops::meshgrid op; + sd::ops::meshgrid op; auto results = op.evaluate({&in0, &in1, &in2}, {}, {0}); - auto out0 = results->at(0); - auto out1 = results->at(1); - auto out2 = results->at(2); + auto out0 = results.at(0); + auto out1 = results.at(1); + auto out2 = results.at(2); // out0->printIndexedBuffer(); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(exp0.isSameShape(out0)); ASSERT_TRUE(exp0.equalsTo(out0)); ASSERT_TRUE(exp1.isSameShape(out1)); @@ -1453,7 +1498,7 @@ TEST_F(DeclarableOpsTests4, meshgrid_test1) { ASSERT_TRUE(exp2.isSameShape(out2)); ASSERT_TRUE(exp2.equalsTo(out2)); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -1466,13 +1511,13 @@ TEST_F(DeclarableOpsTests4, meshgrid_test2) { auto exp1 = NDArrayFactory::create('c', {3,2,4}, {10, 10, 10, 10, 10, 10, 10, 10, 20, 20, 20, 20, 20, 20, 20, 20, 30, 30, 30, 30, 30, 30, 30, 30}); auto exp2 = NDArrayFactory::create('c', {3,2,4}, {100, 200, 300, 400, 100, 200, 300, 400, 100, 200, 300, 400, 100, 200, 300, 400, 100, 200, 300, 400, 100, 200, 300, 400}); - nd4j::ops::meshgrid op; + sd::ops::meshgrid op; auto results = op.evaluate({&in0, &in1, &in2}); - auto out0 = results->at(0); - auto out1 = results->at(1); - auto out2 = results->at(2); + auto out0 = results.at(0); + auto out1 = results.at(1); + auto out2 = results.at(2); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(exp0.isSameShape(out0)); ASSERT_TRUE(exp0.equalsTo(out0)); ASSERT_TRUE(exp1.isSameShape(out1)); @@ -1480,7 +1525,7 @@ TEST_F(DeclarableOpsTests4, meshgrid_test2) { ASSERT_TRUE(exp2.isSameShape(out2)); ASSERT_TRUE(exp2.equalsTo(out2)); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -1493,13 +1538,13 @@ TEST_F(DeclarableOpsTests4, meshgrid_test3) { auto exp1 = NDArrayFactory::create('c', {3,2,4}, {10, 10, 10, 10, 10, 10, 10, 10, 20, 20, 20, 20, 20, 20, 20, 20, 30, 30, 30, 30, 30, 30, 30, 30}); auto exp2 = NDArrayFactory::create('c', {3,2,4}, {100, 200, 300, 400, 100, 200, 300, 400, 100, 200, 300, 400, 100, 200, 300, 400, 100, 200, 300, 400, 100, 200, 300, 400}); - nd4j::ops::meshgrid op; + sd::ops::meshgrid op; auto results = op.evaluate({&in0, &in1, &in2}); - auto out0 = results->at(0); - auto out1 = results->at(1); - auto out2 = results->at(2); + auto out0 = results.at(0); + auto out1 = results.at(1); + auto out2 = results.at(2); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(exp0.isSameShape(out0)); ASSERT_TRUE(exp0.equalsTo(out0)); ASSERT_TRUE(exp1.isSameShape(out1)); @@ -1507,7 +1552,7 @@ TEST_F(DeclarableOpsTests4, meshgrid_test3) { ASSERT_TRUE(exp2.isSameShape(out2)); ASSERT_TRUE(exp2.equalsTo(out2)); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -1520,13 +1565,13 @@ TEST_F(DeclarableOpsTests4, meshgrid_test4) { auto exp1 = NDArrayFactory::create('c', {2,3,4}, {10, 10, 10, 10, 20, 20, 20, 20, 30, 30, 30, 30, 10, 10, 10, 10, 20, 20, 20, 20, 30, 30, 30, 30}); auto exp2 = NDArrayFactory::create('c', {2,3,4}, {100, 200, 300, 400, 100, 200, 300, 400, 100, 200, 300, 400, 100, 200, 300, 400, 100, 200, 300, 400, 100, 200, 300, 400}); - nd4j::ops::meshgrid op; + sd::ops::meshgrid op; auto results = op.evaluate({&in0, &in1, &in2}, {}, {0}); - auto out0 = results->at(0); - auto out1 = results->at(1); - auto out2 = results->at(2); + auto out0 = results.at(0); + auto out1 = results.at(1); + auto out2 = results.at(2); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(exp0.isSameShape(out0)); ASSERT_TRUE(exp0.equalsTo(out0)); ASSERT_TRUE(exp1.isSameShape(out1)); @@ -1534,7 +1579,7 @@ TEST_F(DeclarableOpsTests4, meshgrid_test4) { ASSERT_TRUE(exp2.isSameShape(out2)); ASSERT_TRUE(exp2.equalsTo(out2)); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -1547,13 +1592,13 @@ TEST_F(DeclarableOpsTests4, meshgrid_test5) { auto exp1 = NDArrayFactory::create('c', {1,1,1}, {2}); auto exp2 = NDArrayFactory::create('c', {1,1,1}, {3}); - nd4j::ops::meshgrid op; + sd::ops::meshgrid op; auto results = op.evaluate({&in0, &in1, &in2}, {}, {0}); - auto out0 = results->at(0); - auto out1 = results->at(1); - auto out2 = results->at(2); + auto out0 = results.at(0); + auto out1 = results.at(1); + auto out2 = results.at(2); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(exp0.isSameShape(out0)); ASSERT_TRUE(exp0.equalsTo(out0)); ASSERT_TRUE(exp1.isSameShape(out1)); @@ -1561,7 +1606,7 @@ TEST_F(DeclarableOpsTests4, meshgrid_test5) { ASSERT_TRUE(exp2.isSameShape(out2)); ASSERT_TRUE(exp2.equalsTo(out2)); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -1574,13 +1619,13 @@ TEST_F(DeclarableOpsTests4, meshgrid_test6) { auto exp1 = NDArrayFactory::create('c', {4,1,1}, {5,5,5,5}); auto exp2 = NDArrayFactory::create('c', {4,1,1}, {6,6,6,6}); - nd4j::ops::meshgrid op; + sd::ops::meshgrid op; auto results = op.evaluate({&in0, &in1, &in2}, {}, {0}); - auto out0 = results->at(0); - auto out1 = results->at(1); - auto out2 = results->at(2); + auto out0 = results.at(0); + auto out1 = results.at(1); + auto out2 = results.at(2); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(exp0.isSameShape(out0)); ASSERT_TRUE(exp0.equalsTo(out0)); ASSERT_TRUE(exp1.isSameShape(out1)); @@ -1588,7 +1633,7 @@ TEST_F(DeclarableOpsTests4, meshgrid_test6) { ASSERT_TRUE(exp2.isSameShape(out2)); ASSERT_TRUE(exp2.equalsTo(out2)); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -1601,13 +1646,13 @@ TEST_F(DeclarableOpsTests4, meshgrid_test7) { auto exp1 = NDArrayFactory::create('c', {1,4,1}, {5,5,5,5}); auto exp2 = NDArrayFactory::create('c', {1,4,1}, {6,6,6,6}); - nd4j::ops::meshgrid op; + sd::ops::meshgrid op; auto results = op.evaluate({&in0, &in1, &in2}, {}, {1}); - auto out0 = results->at(0); - auto out1 = results->at(1); - auto out2 = results->at(2); + auto out0 = results.at(0); + auto out1 = results.at(1); + auto out2 = results.at(2); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(exp0.isSameShape(out0)); ASSERT_TRUE(exp0.equalsTo(out0)); ASSERT_TRUE(exp1.isSameShape(out1)); @@ -1615,7 +1660,7 @@ TEST_F(DeclarableOpsTests4, meshgrid_test7) { ASSERT_TRUE(exp2.isSameShape(out2)); ASSERT_TRUE(exp2.equalsTo(out2)); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -1624,15 +1669,15 @@ TEST_F(DeclarableOpsTests4, meshgrid_test8) { auto in0 = NDArrayFactory::create(5); auto exp0 = NDArrayFactory::create('c', {1}, {5}); - nd4j::ops::meshgrid op; + sd::ops::meshgrid op; auto results = op.evaluate({&in0}, {}, {0}); - auto out0 = results->at(0); + auto out0 = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(exp0.isSameShape(out0)); ASSERT_TRUE(exp0.equalsTo(out0)); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -1641,15 +1686,15 @@ TEST_F(DeclarableOpsTests4, meshgrid_test9) { auto in0 = NDArrayFactory::create(5); auto exp0 = NDArrayFactory::create('c', {1}, {5}); - nd4j::ops::meshgrid op; + sd::ops::meshgrid op; auto results = op.evaluate({&in0}, {}, {1}); - auto out0 = results->at(0); + auto out0 = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(exp0.isSameShape(out0)); ASSERT_TRUE(exp0.equalsTo(out0)); - delete results; + } //////////////////////////////////////////////////////////////////////////////// @@ -1668,17 +1713,17 @@ TEST_F(DeclarableOpsTests4, WeightedCrossEntropyWithLogits_1) { //Weights [0.7] //Result {-159.50006, -191.1, -16.009075, -210., -24.001238, -15.03887} - nd4j::ops::weighted_cross_entropy_with_logits op; + sd::ops::weighted_cross_entropy_with_logits op; auto results = op.evaluate({&targets, &input, &weight}); - auto output = results->at(0); + auto output = results.at(0); // output->printIndexedBuffer(); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; + } //////////////////////////////////////////////////////////////////////////////// @@ -1690,15 +1735,15 @@ TEST_F(DeclarableOpsTests4, WeightedCrossEntropyWithLogits_2) { auto weights = NDArrayFactory::create({0.5f, 0.7f, 1.0f}) ; auto expected = NDArrayFactory::create('c', {2, 3}, {-159.5001f, -191.1f, -15.98185f, -210.f, -24.001238f, -14.951412f}); - nd4j::ops::weighted_cross_entropy_with_logits op; + sd::ops::weighted_cross_entropy_with_logits op; auto results = op.evaluate({&targets, &input, &weights}); - auto output = results->at(0); + auto output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; + } @@ -1737,13 +1782,13 @@ TEST_F(DeclarableOpsTests4, lstm_test1) { auto expClast = NDArrayFactory::create('c', {1, batchSize, numProj}, {1.1589154,1.1589154,1.1589154,1.1892855,1.1892855,1.1892855,1.219861 ,1.219861 ,1.219861}); - nd4j::ops::lstm op; + sd::ops::lstm op; auto results = op.evaluate({&x, &h0, &c0, &Wx, &Wh, &Wc, &Wp, &b}, {0., 0., 0.}, {0, 0}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto *h = results->at(0); - auto *c = results->at(1); + auto *h = results.at(0); + auto *c = results.at(1); auto cLast = (*c)({4,5,0,0,0,0},true); ASSERT_TRUE(expH.isSameShape(h)); @@ -1752,7 +1797,7 @@ TEST_F(DeclarableOpsTests4, lstm_test1) { ASSERT_TRUE(expClast.isSameShape(&cLast)); ASSERT_TRUE(expClast.equalsTo(&cLast)); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -1761,17 +1806,17 @@ TEST_F(DeclarableOpsTests4, relu6_test1) { auto input = NDArrayFactory::create('c', {2,4}, {-13.,10,-5,0,2,7,6,12}); auto expected = NDArrayFactory::create('c', {2,4}, {0., 6., 0., 0.,2., 6., 6., 6.}); - nd4j::ops::relu6 op; + sd::ops::relu6 op; auto results = op.evaluate({&input}, {0.}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto output = results->at(0); + auto output = results.at(0); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; + } @@ -1783,17 +1828,17 @@ TEST_F(DeclarableOpsTests4, relu6_bp_test1) { auto expected = NDArrayFactory::create('c', {2,4}, {0., 0., 0., 0., 5., 0., 0., 8.}); - nd4j::ops::relu6_bp op; + sd::ops::relu6_bp op; auto results = op.evaluate({&input, &gradO}, {0.}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto output = results->at(0); + auto output = results.at(0); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; + } //////////////////////////////////////////////////////////////////////////////// @@ -1812,17 +1857,17 @@ TYPED_TEST(TypedDeclarableOpsTests4, LrnTest_1) { 0.7581754f, 0.58321184f, 0.86747235f, 0.4048204f} ); - nd4j::ops::lrn op; + sd::ops::lrn op; auto results = op.evaluate({&x}, {1.0, 1.0, 0.5}, {5}); - auto out = results->at(0); + auto out = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(exp.isSameShape(out)); // out->printIndexedBuffer("LRN out"); // exp.printIndexedBuffer("LRN exp"); ASSERT_TRUE(exp.equalsTo(out)); - delete results; + } @@ -1840,17 +1885,17 @@ TYPED_TEST(TypedDeclarableOpsTests4, LrnTest_2) { 0.72760683f, 0.4850712f, 0.5848977f, 0.67488194f, 0.7581754f, 0.58321184f, 0.86747235f, 0.4048204f}); - nd4j::ops::lrn op; + sd::ops::lrn op; auto results = op.evaluate({&x}, {1.0, 1.0, 0.5}, {2}); - auto out = results->at(0); + auto out = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(exp.isSameShape(out)); // out->printIndexedBuffer("LRN out"); // exp.printIndexedBuffer("LRN exp"); ASSERT_TRUE(exp.equalsTo(out)); - delete results; + } //////////////////////////////////////////////////////////////////////////////// @@ -1879,17 +1924,17 @@ TYPED_TEST(TypedDeclarableOpsTests4, LrnTest_3) { 0.9520745f, 0.21039814f, 0.06311944f, 0.3268602f } ); - nd4j::ops::lrn op; + sd::ops::lrn op; auto results = op.evaluate({&x}, {1.0, 1.0, 0.5}, {2}); - auto out = results->at(0); + auto out = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(exp.isSameShape(out)); // out->printIndexedBuffer("LRN out"); // exp.printIndexedBuffer("LRN exp"); ASSERT_TRUE(exp.equalsTo(out)); - delete results; + } //////////////////////////////////////////////////////////////////////////////// @@ -1918,17 +1963,17 @@ TYPED_TEST(TypedDeclarableOpsTests4, LrnTest_4) { 0.94679165f, 0.21039814f, 0.06311944f, 0.10519907f} ); - nd4j::ops::lrn op; + sd::ops::lrn op; auto results = op.evaluate({&x}, {1.0, 1.0, 0.5}, {5}); - auto out = results->at(0); + auto out = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(exp.isSameShape(out)); // out->printIndexedBuffer("LRN out"); // exp.printIndexedBuffer("LRN exp"); ASSERT_TRUE(exp.equalsTo(out)); - delete results; + } //////////////////////////////////////////////////////////////////////////////// @@ -1963,17 +2008,17 @@ TYPED_TEST(TypedDeclarableOpsTests4, LrnTest_5) { auto exp = NDArrayFactory::create('c', {2, 2, 2, 4}); - nd4j::ops::lrn_bp op; + sd::ops::lrn_bp op; auto results = op.evaluate({&x, &eps}, {1.0, 1.0, 0.5}, {5}, {}, {}, false); - auto out = results->at(0); + auto out = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(exp.isSameShape(out)); // out->printIndexedBuffer("LRN out"); // exp.printIndexedBuffer("LRN exp"); // ASSERT_TRUE(exp.equalsTo(out)); - delete results; + } @@ -1985,18 +2030,18 @@ TEST_F(DeclarableOpsTests4, tri_test1) { auto expected = NDArrayFactory::create('c', {rows, cols}, {1.f, 0.f, 0.f, 0.f, 0.f, 1.f, 1.f, 0.f, 0.f, 0.f, 1.f, 1.f, 1.f, 0.f, 0.f}); - nd4j::ops::tri op; + sd::ops::tri op; auto results = op.evaluate({}, {}, {rows, cols}); - auto output = results->at(0); + auto output = results.at(0); // output->printIndexedBuffer(); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -2008,16 +2053,16 @@ TEST_F(DeclarableOpsTests4, tri_test2) { auto expected = NDArrayFactory::create('c', {rows, cols}, {1.f, 1.f, 1.f, 0.f, 0.f, 1.f, 1.f, 1.f, 1.f, 0.f, 1.f, 1.f, 1.f, 1.f, 1.f}); - nd4j::ops::tri op; + sd::ops::tri op; auto results = op.evaluate({}, {}, {rows, cols, diag}); - auto output = results->at(0); + auto output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -2029,16 +2074,16 @@ TEST_F(DeclarableOpsTests4, tri_test3) { auto expected = NDArrayFactory::create('c', {rows, cols}, {0.f, 0.f, 0.f, 0.f, 0.f, 1.f, 0.f, 0.f, 0.f, 0.f, 1.f, 1.f, 0.f, 0.f, 0.f}); - nd4j::ops::tri op; + sd::ops::tri op; auto results = op.evaluate({}, {}, {rows, cols, diag}); - auto output = results->at(0); + auto output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -2050,16 +2095,16 @@ TEST_F(DeclarableOpsTests4, tri_test4) { auto expected = NDArrayFactory::create('c', {rows, cols}, {0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 1.f, 0.f, 0.f, 0.f, 0.f}); - nd4j::ops::tri op; + sd::ops::tri op; auto results = op.evaluate({}, {}, {rows, cols, diag}); - auto output = results->at(0); + auto output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -2069,16 +2114,16 @@ TEST_F(DeclarableOpsTests4, tri_test5) { auto expected = NDArrayFactory::create('c', {rows, rows}, {1.f, 0.f, 0.f, 0.f, 0.f, 1.f, 1.f, 0.f, 0.f, 0.f, 1.f, 1.f, 1.f, 0.f, 0.f, 1.f, 1.f, 1.f, 1.f, 0.f, 1.f, 1.f, 1.f, 1.f, 1.f}); - nd4j::ops::tri op; + sd::ops::tri op; auto results = op.evaluate({}, {}, {rows}); - auto output = results->at(0); + auto output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -2090,16 +2135,16 @@ TEST_F(DeclarableOpsTests4, tri_test6) { auto expected = NDArrayFactory::create('c', {rows, cols}, {0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f}); - nd4j::ops::tri op; + sd::ops::tri op; auto results = op.evaluate({}, {}, {rows, cols, diag}); - auto output = results->at(0); + auto output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -2111,16 +2156,16 @@ TEST_F(DeclarableOpsTests4, tri_test7) { auto expected = NDArrayFactory::create('c', {rows, cols}, {1.f, 1.f, 1.f, 1.f, 1.f, 1.f, 1.f, 1.f, 1.f, 1.f, 1.f, 1.f, 1.f, 1.f, 1.f}); - nd4j::ops::tri op; + sd::ops::tri op; auto results = op.evaluate({}, {}, {rows, cols, diag}); - auto output = results->at(0); + auto output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -2129,17 +2174,17 @@ TEST_F(DeclarableOpsTests4, triu_test1) { auto input = NDArrayFactory::create('c', {4, 3}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}); auto expected = NDArrayFactory::create('c', {4, 3}, {1, 2, 3, 0, 5, 6, 0, 0, 9, 0, 0, 0}); - nd4j::ops::triu op; + sd::ops::triu op; auto results = op.evaluate({&input}, {}, {}); - auto output = results->at(0); + auto output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -2148,16 +2193,16 @@ TEST_F(DeclarableOpsTests4, triu_test2) { auto input = NDArrayFactory::create('c', {4, 3}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}); auto expected = NDArrayFactory::create('c', {4, 3}, {1, 2, 3,4, 5, 6,0, 8, 9,0, 0, 12}); - nd4j::ops::triu op; + sd::ops::triu op; auto results = op.evaluate({&input}, {}, {-1}); - auto output = results->at(0); + auto output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -2166,16 +2211,16 @@ TEST_F(DeclarableOpsTests4, triu_test3) { auto input = NDArrayFactory::create('c', {2, 3, 2}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}); auto expected = NDArrayFactory::create('c', {2, 3, 2}, {1, 2,3, 4,0, 6,7, 8,9,10,0,12}); - nd4j::ops::triu op; + sd::ops::triu op; auto results = op.evaluate({&input}, {}, {-1}); - auto output = results->at(0); + auto output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -2184,16 +2229,16 @@ TEST_F(DeclarableOpsTests4, triu_test4) { auto input = NDArrayFactory::create('c', {2, 3, 2}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}); auto expected = NDArrayFactory::create('c', {2, 3, 2}, {1, 2,0, 4,0, 0,7, 8,0, 10,0, 0}); - nd4j::ops::triu op; + sd::ops::triu op; auto results = op.evaluate({&input}, {}, {}); - auto output = results->at(0); + auto output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -2202,16 +2247,16 @@ TEST_F(DeclarableOpsTests4, triu_test5) { auto input = NDArrayFactory::create('c', {2, 3, 2}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}); auto expected = NDArrayFactory::create('c', {2, 3, 2}, {0, 2,0, 0,0, 0,0, 8,0, 0,0, 0}); - nd4j::ops::triu op; + sd::ops::triu op; auto results = op.evaluate({&input}, {}, {1}); - auto output = results->at(0); + auto output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -2220,16 +2265,16 @@ TEST_F(DeclarableOpsTests4, triu_test6) { auto input = NDArrayFactory::create('c', {2, 3, 2}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}); auto expected = NDArrayFactory::create('c', {2, 3, 2}, {0, 0,0, 0,0, 0,0, 0,0, 0,0, 0}); - nd4j::ops::triu op; + sd::ops::triu op; auto results = op.evaluate({&input}, {}, {10}); - auto output = results->at(0); + auto output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -2238,16 +2283,16 @@ TEST_F(DeclarableOpsTests4, triu_test7) { auto input = NDArrayFactory::create('c', {2, 3, 2}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}); auto expected = NDArrayFactory::create('c', {2, 3, 2}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}); - nd4j::ops::triu op; + sd::ops::triu op; auto results = op.evaluate({&input}, {}, {-10}); - auto output = results->at(0); + auto output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -2256,16 +2301,16 @@ TEST_F(DeclarableOpsTests4, triu_test8) { auto input = NDArrayFactory::create('c', {6}, {1, 2, 3, 4, 5, 6}); auto expected = NDArrayFactory::create('c', {6, 6}, {1, 2, 3, 4, 5, 6,0, 2, 3, 4, 5, 6,0, 0, 3, 4, 5, 6,0, 0, 0, 4, 5, 6,0, 0, 0, 0, 5, 6,0, 0, 0, 0, 0, 6}); - nd4j::ops::triu op; + sd::ops::triu op; auto results = op.evaluate({&input}, {}, {}); - auto output = results->at(0); + auto output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -2274,16 +2319,16 @@ TEST_F(DeclarableOpsTests4, triu_test9) { auto input = NDArrayFactory::create('c', {6}, {1, 2, 3, 4, 5, 6}); auto expected = NDArrayFactory::create('c', {6, 6}, {1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 0, 2, 3, 4, 5, 6, 0, 0, 3, 4, 5, 6}); - nd4j::ops::triu op; + sd::ops::triu op; auto results = op.evaluate({&input}, {}, {-3}); - auto output = results->at(0); + auto output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -2292,16 +2337,16 @@ TEST_F(DeclarableOpsTests4, triu_test10) { auto input = NDArrayFactory::create('c', {6}, {1, 2, 3, 4, 5, 6}); auto expected = NDArrayFactory::create('c', {6, 6}, {0, 0, 0, 4, 5, 6, 0, 0, 0, 0, 5, 6, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}); - nd4j::ops::triu op; + sd::ops::triu op; auto results = op.evaluate({&input}, {}, {3}); - auto output = results->at(0); + auto output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -2310,16 +2355,16 @@ TEST_F(DeclarableOpsTests4, triu_test11) { auto input = NDArrayFactory::create('c', {6}, {1, 2, 3, 4, 5, 6}); auto expected = NDArrayFactory::create('c', {6, 6}, {1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6}); - nd4j::ops::triu op; + sd::ops::triu op; auto results = op.evaluate({&input}, {}, {-58}); - auto output = results->at(0); + auto output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; + } @@ -2332,16 +2377,16 @@ TEST_F(DeclarableOpsTests4, triu_bp_test1) { auto expected = NDArrayFactory::create('c', {2, 3, 2}, {0.,0.5,0.,0. ,0.,0. ,0.,0.5,0.,0. ,0.,0.}); - nd4j::ops::triu_bp op; + sd::ops::triu_bp op; auto results = op.evaluate({&input, &gradO}, {}, {1}); - auto gradI = results->at(0); + auto gradI = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expected.isSameShape(gradI)); ASSERT_TRUE(expected.equalsTo(gradI)); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -2353,16 +2398,16 @@ TEST_F(DeclarableOpsTests4, triu_bp_test2) { auto expected = NDArrayFactory::create('c', {2, 3, 2}, {0.5,0.5,0. ,0.5,0. ,0. ,0.5,0.5,0. ,0.5,0. ,0.}); - nd4j::ops::triu_bp op; + sd::ops::triu_bp op; auto results = op.evaluate({&input, &gradO}, {}, {}); - auto gradI = results->at(0); + auto gradI = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expected.isSameShape(gradI)); ASSERT_TRUE(expected.equalsTo(gradI)); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -2374,16 +2419,16 @@ TEST_F(DeclarableOpsTests4, triu_bp_test3) { auto expected = NDArrayFactory::create('c', {6,6}, {0.5, 0.5, 0.5, 0.5, 0.5, 0.5,0.5, 0.5, 0.5, 0.5, 0.5, 0.5,0.5, 0.5, 0.5, 0.5, 0.5, 0.5,0. , 0.5, 0.5, 0.5, 0.5, 0.5,0. , 0. , 0.5, 0.5, 0.5, 0.5,0. , 0. , 0. , 0.5, 0.5, 0.5}); - nd4j::ops::triu_bp op; + sd::ops::triu_bp op; auto results = op.evaluate({&input, &gradO}, {}, {-2}); - auto gradI = results->at(0); + auto gradI = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expected.isSameShape(gradI)); ASSERT_TRUE(expected.equalsTo(gradI)); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -2395,15 +2440,15 @@ TEST_F(DeclarableOpsTests4, triu_bp_test4) { auto expected = NDArrayFactory::create('c', {2,3}, {0., 0., 0., 0., 0., 0.}); - nd4j::ops::triu_bp op; + sd::ops::triu_bp op; auto results = op.evaluate({&input, &gradO}, {}, {10}); - auto gradI = results->at(0); + auto gradI = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expected.isSameShape(gradI)); ASSERT_TRUE(expected.equalsTo(gradI)); - delete results; + } diff --git a/libnd4j/tests_cpu/layers_tests/DeclarableOpsTests5.cpp b/libnd4j/tests_cpu/layers_tests/DeclarableOpsTests5.cpp index 62868f67f..8958f9023 100644 --- a/libnd4j/tests_cpu/layers_tests/DeclarableOpsTests5.cpp +++ b/libnd4j/tests_cpu/layers_tests/DeclarableOpsTests5.cpp @@ -22,12 +22,12 @@ #include "testlayers.h" #include #include -#include +#include #include -using namespace nd4j; -using namespace nd4j::graph; +using namespace sd; +using namespace sd::graph; class DeclarableOpsTests5 : public testing::Test { public: @@ -45,16 +45,16 @@ TEST_F(DeclarableOpsTests5, Test_PermuteEquality_1) { x.linspace(1); x.reshapei('c', {3, 4, 5}); - nd4j::ops::permute op; + sd::ops::permute op; auto result = op.evaluate({&x}, {}, {0, 2, 1}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } TEST_F(DeclarableOpsTests5, Test_PermuteEquality_0) { @@ -66,16 +66,16 @@ TEST_F(DeclarableOpsTests5, Test_PermuteEquality_0) { // x.printShapeInfo("{0, 1, 2} shape"); // x.printBuffer("{0, 1, 2} data"); - nd4j::ops::permute op; + sd::ops::permute op; auto result = op.evaluate({&x}, {}, {0, 1, 2}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } @@ -88,16 +88,16 @@ TEST_F(DeclarableOpsTests5, Test_PermuteEquality_2) { // x.printShapeInfo("{1, 0, 2} shape"); // x.printBuffer("{1, 0, 2} data"); - nd4j::ops::permute op; + sd::ops::permute op; auto result = op.evaluate({&x}, {}, {1, 0, 2}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } TEST_F(DeclarableOpsTests5, Test_PermuteEquality_3) { @@ -109,16 +109,16 @@ TEST_F(DeclarableOpsTests5, Test_PermuteEquality_3) { // x.printShapeInfo("{1, 2, 0} shape"); // x.printBuffer("{1, 2, 0} data"); - nd4j::ops::permute op; + sd::ops::permute op; auto result = op.evaluate({&x}, {}, {1, 2, 0}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } TEST_F(DeclarableOpsTests5, Test_PermuteEquality_4) { @@ -130,16 +130,16 @@ TEST_F(DeclarableOpsTests5, Test_PermuteEquality_4) { // x.printShapeInfo("{2, 0, 1} shape"); // x.printBuffer("{2, 0, 1} data"); - nd4j::ops::permute op; + sd::ops::permute op; auto result = op.evaluate({&x}, {}, {2, 0, 1}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } TEST_F(DeclarableOpsTests5, Test_PermuteEquality_5) { @@ -151,16 +151,16 @@ TEST_F(DeclarableOpsTests5, Test_PermuteEquality_5) { // x.printShapeInfo("{2, 1, 0} shape"); // x.printBuffer("{2, 1, 0} data"); - nd4j::ops::permute op; + sd::ops::permute op; auto result = op.evaluate({&x}, {}, {2, 1, 0}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } TEST_F(DeclarableOpsTests5, Test_TTS_bp_1) { @@ -171,19 +171,19 @@ TEST_F(DeclarableOpsTests5, Test_TTS_bp_1) { eps.linspace(1.f); - nd4j::ops::tile_to_shape_bp op; + sd::ops::tile_to_shape_bp op; auto result = op.evaluate({&x, &eps}, {}, {2, 4, 3}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); // z->printShapeInfo("RES shape"); // x.printShapeInfo("EXP shape"); // z->printIndexedBuffer("RES output"); ASSERT_TRUE(x.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } @@ -193,22 +193,19 @@ TEST_F(DeclarableOpsTests5, Test_Rdiv_bp_1) { auto eps = NDArrayFactory::create('c', {3, 4}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}); - nd4j::ops::reversedivide op_ff; + sd::ops::reversedivide op_ff; auto result_ff = op_ff.evaluate({&x, &y}, {}, {}); - ASSERT_EQ(Status::OK(), result_ff->status()); + ASSERT_EQ(Status::OK(), result_ff.status()); - auto z_ff = result_ff->at(0); + auto z_ff = result_ff.at(0); ASSERT_TRUE(eps.isSameShape(z_ff)); - nd4j::ops::reversedivide_bp op_bp; + sd::ops::reversedivide_bp op_bp; auto result_bp = op_bp.evaluate({&x, &y, &eps}, {}, {}); - ASSERT_EQ(Status::OK(), result_bp->status()); + ASSERT_EQ(Status::OK(), result_bp.status()); - auto z_bp = result_bp->at(0); + auto z_bp = result_bp.at(0); ASSERT_TRUE(x.isSameShape(z_bp)); - - delete result_ff; - delete result_bp; } @@ -216,82 +213,82 @@ TEST_F(DeclarableOpsTests5, Test_Boolean_diff_1) { auto x = NDArrayFactory::create('c', {1, 1}, {1.0f}); auto y = NDArrayFactory::create(2.0f); - nd4j::ops::less op; + sd::ops::less op; auto result = op.evaluate({&x, &y}); - ASSERT_EQ(Status::OK(), result->status()); - ASSERT_EQ(result->at(0)->t(0), true); - delete result; + ASSERT_EQ(Status::OK(), result.status()); + ASSERT_EQ(result.at(0)->t(0), true); + } TEST_F(DeclarableOpsTests5, Test_SetSeed_1) { auto x = NDArrayFactory::create('c', {1, 1}, {120}); auto y = NDArrayFactory::create(5); - nd4j::ops::set_seed op; + sd::ops::set_seed op; auto result = op.evaluate({&x, &y}, {}, {120, 5}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); // result->at(0)->printIndexedBuffer("RES SEED"); - nd4j::ops::get_seed getOp; + + sd::ops::get_seed getOp; auto getRes = getOp.evaluate({}); - ASSERT_EQ(Status::OK(), getRes->status()); -// getRes->at(0)->printIndexedBuffer("Output RES GET SEED"); -// ASSERT_EQ(result->at(0)->t(0), true); - delete result; - delete getRes; + ASSERT_EQ(Status::OK(), getRes.status()); +// getres.at(0)->printIndexedBuffer("Output RES GET SEED"); +// ASSERT_EQ(result.at(0)->t(0), true); } + //////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests5, scatterMul_test1) { auto matrix = NDArrayFactory::create('c', {2, 2}, {1.f, 2.f, 3.f, 4.f}); - NDArray idc('c', {1}, std::vector({0LL}), nd4j::DataType::INT64); + NDArray idc('c', {1}, std::vector({0LL}), sd::DataType::INT64); auto updates = NDArrayFactory::create('c', {1, 2}, {10.f, 1.f}); auto exp = NDArrayFactory::create('c', {2, 2}, {10.f, 2.f, 3.f, 4.f}); - nd4j::ops::scatter_mul op; + sd::ops::scatter_mul op; auto result = op.evaluate({&matrix, &idc, &updates}, {}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } //////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests5, scatterDiv_test1) { auto matrix = NDArrayFactory::create('c', {2, 2}, {1.f, 2.f, 3.f, 4.f}); - NDArray idc('c', {1}, std::vector({0LL}), nd4j::DataType::INT64); + NDArray idc('c', {1}, std::vector({0LL}), sd::DataType::INT64); auto updates = NDArrayFactory::create('c', {1, 2}, {10.f, 1.f}); auto exp = NDArrayFactory::create('c', {2, 2}, {0.10f, 2.f, 3.f, 4.f}); - nd4j::ops::scatter_div op; + sd::ops::scatter_div op; auto result = op.evaluate({&matrix, &idc, &updates}, {}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); // z->printIndexedBuffer("Scatter Div"); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } //////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests5, scatterSub_test1) { auto matrix = NDArrayFactory::create('c', {2, 2}, {1.f, 2.f, 3.f, 4.f}); - NDArray idc('c', {1}, std::vector({0LL}), nd4j::DataType::INT64); + NDArray idc('c', {1}, std::vector({0LL}), sd::DataType::INT64); auto updates = NDArrayFactory::create('c', {1, 2}, {10.f, 1.f}); auto exp = NDArrayFactory::create('c', {2, 2}, {-9.f, 1.f, 3.f, 4.f}); - nd4j::ops::scatter_sub op; + sd::ops::scatter_sub op; auto result = op.evaluate({&matrix, &idc, &updates}, {}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); // z->printIndexedBuffer("Scatter Sub"); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } //////////////////////////////////////////////////////////////////// @@ -299,14 +296,14 @@ TEST_F(DeclarableOpsTests5, hardsigmoid_test1) { auto matrix = NDArrayFactory::create('c', {2, 2}, {1.f, 2.f, 3.f, 4.f}); auto exp = NDArrayFactory::create('c', {2, 2}, {0.7f, 0.9f, 1.f, 1.f}); - nd4j::ops::hardsigmoid op; + sd::ops::hardsigmoid op; auto result = op.evaluate({&matrix}, {}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } //////////////////////////////////////////////////////////////////// @@ -315,14 +312,14 @@ TEST_F(DeclarableOpsTests5, hardsigmoid_test2) { auto eps = NDArrayFactory::create('c', {2, 2}, {1.f, 2.f, 3.f, 4.f}); auto exp = NDArrayFactory::create('c', {2, 2}, {0.2f, 0.4f, 0.f, 0.f}); - nd4j::ops::hardsigmoid_bp op; + sd::ops::hardsigmoid_bp op; auto result = op.evaluate({&matrix, &eps}, {}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } //////////////////////////////////////////////////////////////////// @@ -330,15 +327,15 @@ TEST_F(DeclarableOpsTests5, hardtanh_test1) { auto matrix = NDArrayFactory::create('c', {3, 3}, {-4, -3, -2, -1, 0, 1, 2, 3, 4}); auto exp = NDArrayFactory::create('c', {3, 3}, {-1, -1, -1, -1, 0, 1, 1, 1, 1}); - nd4j::ops::hardtanh op; + sd::ops::hardtanh op; auto result = op.evaluate({&matrix}, {}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); // z->printIndexedBuffer("Hardtanh 2x2"); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } //////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests5, hardtanh_test2) { @@ -346,15 +343,15 @@ TEST_F(DeclarableOpsTests5, hardtanh_test2) { auto eps = NDArrayFactory::create('c', {3, 3}, {1, 2, 3, 4, 5, 6, 7, 8, 9}); auto exp = NDArrayFactory::create('c', {3, 3}, {0, 0, 0, 4, 5, 6, 0, 0, 0}); - nd4j::ops::hardtanh_bp op; + sd::ops::hardtanh_bp op; auto result = op.evaluate({&matrix, &eps}, {}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); // z->printIndexedBuffer("Hardtanh_bp 2x2"); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } //////////////////////////////////////////////////////////////////// @@ -362,29 +359,29 @@ TEST_F(DeclarableOpsTests5, histogram_test1) { auto matrix = NDArrayFactory::create('c', {3, 3}, {-4, -3, -2, -1, 0, 1, 2, 3, 4}); auto exp = NDArrayFactory::create('c', {3}, {3, 3, 3}); - nd4j::ops::histogram op; + sd::ops::histogram op; auto result = op.evaluate({&matrix}, {}, {3}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); // z->printIndexedBuffer("Histogram3"); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } //////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests5, histogram_test2) { auto matrix = NDArrayFactory::create('c', {3}, {1, 2, 1}); auto exp = NDArrayFactory::create('c', {4}, {2, 0, 0, 1}); - nd4j::ops::histogram op; + sd::ops::histogram op; auto result = op.evaluate({&matrix}, {}, {4}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } //////////////////////////////////////////////////////////////////// @@ -392,14 +389,14 @@ TEST_F(DeclarableOpsTests5, Identity_test1) { auto matrix = NDArrayFactory::create('c', {3, 3}, {-4.f, -3.f, -2.f, -1.f, 0.f, 1.f, 2.f, 3.f, 4.f}); // auto exp = NDArrayFactory::create('c', {3, 3}, {3, 3, 3}); - nd4j::ops::identity op; + sd::ops::identity op; auto result = op.evaluate({&matrix}, {}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(matrix.equalsTo(z)); - delete result; + } //////////////////////////////////////////////////////////////////// @@ -407,14 +404,14 @@ TEST_F(DeclarableOpsTests5, Identity_test2) { auto matrix = NDArrayFactory::create('c', {3, 3}, {-4, -3, -2, -1, 0, 1, 2, 3, 4}); auto eps = NDArrayFactory::create('c', {3, 3}, {1,2,3,4,5,6,7,8,9}); // auto exp = NDArrayFactory::create('c', {3,3}); - nd4j::ops::identity_bp op; + sd::ops::identity_bp op; auto result = op.evaluate({&matrix, &eps}, {}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(z->equalsTo(eps)); - delete result; + } //////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests5, Log1p_test1) { @@ -422,15 +419,15 @@ TEST_F(DeclarableOpsTests5, Log1p_test1) { auto y = NDArrayFactory::create('c', {3,3}, {5,4,3,2,1,2,3,4,5}); // auto eps = NDArrayFactory::create('c', {3, 3}, {1,2,3,4,5,6,7,8,9}); // auto exp = NDArrayFactory::create('c', {3,3}); - nd4j::ops::Log1p op; - y.applyTransform(nd4j::transform::Log, y); + sd::ops::Log1p op; + y.applyTransform(sd::transform::Log, y); auto result = op.evaluate({&matrix}, {}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(z->equalsTo(y)); - delete result; + } TEST_F(DeclarableOpsTests5, Test_SpaceToBatch_1) { @@ -439,16 +436,16 @@ TEST_F(DeclarableOpsTests5, Test_SpaceToBatch_1) { auto exp = NDArrayFactory::create('c', {4, 1, 1, 3}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}); auto paddings = NDArrayFactory::create('c', {2, 2}, {0, 0, 0, 0}); - nd4j::ops::space_to_batch op; + sd::ops::space_to_batch op; auto result = op.evaluate({&x, &paddings}, {}, {2}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } @@ -457,16 +454,16 @@ TEST_F(DeclarableOpsTests5, Test_SpaceToBatch_2) { auto exp = NDArrayFactory::create('c', {4, 1, 1, 1}, {1, 2, 3, 4}); auto paddings = NDArrayFactory::create('c', {2, 2}, {0, 0, 0, 0}); - nd4j::ops::space_to_batch op; + sd::ops::space_to_batch op; auto result = op.evaluate({&x, &paddings}, {}, {2}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } @@ -476,24 +473,24 @@ TEST_F(DeclarableOpsTests5, Test_SpaceToBatch_3) { auto paddings = NDArrayFactory::create('c', {2, 2}, {0, 0, 2, 0}); auto exp = NDArrayFactory::create('c', {8, 1, 3, 1}, {0, 1, 3, 0, 9, 11,0, 2, 4, 0, 10, 12,0, 5, 7, 0, 13, 15,0, 6, 8, 0, 14, 16}); - nd4j::ops::space_to_batch op; + sd::ops::space_to_batch op; auto result = op.evaluate({&x, &paddings}, {}, {2}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); // z->printIndexedBuffer(); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } ////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests5, Test_SpaceToBatch_4) { const int blockSize = 2; - NDArray x('c', {3, 3*blockSize - 1 - 2, 4*blockSize - 2 - 3, 2}, {147, 148, 219, 220, 149, 150, 11, 12, 83, 84, 13, 14, 155, 156, 227, 228, 157, 158, 171, 172, 243, 244, 173, 174, 35, 36, 107, 108, 37, 38, 179, 180, 251, 252, 181, 182, 195, 196, 267, 268, 197, 198, 59, 60, 131, 132, 61, 62, 203, 204, 275, 276, 205, 206}, nd4j::DataType::FLOAT32); + NDArray x('c', {3, 3*blockSize - 1 - 2, 4*blockSize - 2 - 3, 2}, {147, 148, 219, 220, 149, 150, 11, 12, 83, 84, 13, 14, 155, 156, 227, 228, 157, 158, 171, 172, 243, 244, 173, 174, 35, 36, 107, 108, 37, 38, 179, 180, 251, 252, 181, 182, 195, 196, 267, 268, 197, 198, 59, 60, 131, 132, 61, 62, 203, 204, 275, 276, 205, 206}, sd::DataType::FLOAT32); NDArray paddings = NDArrayFactory::create('c', {2, 2}, {1, 2, 2, 3}); NDArray exp('c', {3*blockSize*blockSize, 3, 4, 2}, {0,0, 0,0, 0,0, 0,0, 0,0, 11,12, 13,14, 0,0, 0,0, 0,0, 0,0, 0,0, 0,0, 0,0, @@ -503,19 +500,19 @@ TEST_F(DeclarableOpsTests5, Test_SpaceToBatch_4) { 0,0, 0,0, 0,0, 0,0, 0,0, 0,0, 171, 172, 173, 174, 0,0, 0,0, 179, 180, 181, 182, 0,0, 0,0, 0,0, 0,0, 0,0, 0,0, 195, 196, 197, 198, 0,0, 0,0, 203, 204, 205, 206, 0,0, 0,0, 0,0, 0,0, 0,0, 0,0, 219, 220, 0,0, 0,0, 0,0, 227, 228, 0,0, 0,0, 0,0, 0,0, 0,0, 0,0, 0,0, 243, 244, 0,0, 0,0, 0,0, 251, 252, 0,0, 0,0, 0,0, 0,0, 0,0, 0,0, 0,0, 267, 268, 0,0, 0,0, 0,0, 275, - 276, 0,0, 0,0, 0,0, 0,0, 0,0, 0,0}, nd4j::DataType::FLOAT32); + 276, 0,0, 0,0, 0,0, 0,0, 0,0, 0,0}, sd::DataType::FLOAT32); - nd4j::ops::space_to_batch op; + sd::ops::space_to_batch op; auto result = op.evaluate({&x, &paddings}, {}, {blockSize}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); // z->printIndexedBuffer(); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } TEST_F(DeclarableOpsTests5, Test_BatchToSpace_1) { @@ -523,17 +520,17 @@ TEST_F(DeclarableOpsTests5, Test_BatchToSpace_1) { auto exp = NDArrayFactory::create('c', {1, 2, 2, 3}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}); auto crops = NDArrayFactory::create('c', {2, 2}, {0, 0, 0, 0}); - nd4j::ops::batch_to_space op; + sd::ops::batch_to_space op; auto result = op.evaluate({&x, &crops}, {}, {2}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); // z->printIndexedBuffer(); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } TEST_F(DeclarableOpsTests5, Test_BatchToSpace_2) { @@ -541,16 +538,16 @@ TEST_F(DeclarableOpsTests5, Test_BatchToSpace_2) { auto exp = NDArrayFactory::create('c', {1, 2, 2, 1}, {1, 2, 3, 4}); auto crops = NDArrayFactory::create('c', {2, 2}, {0, 0, 0, 0}); - nd4j::ops::batch_to_space op; + sd::ops::batch_to_space op; auto result = op.evaluate({&x, &crops}, {}, {2}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } @@ -562,38 +559,38 @@ TEST_F(DeclarableOpsTests5, Test_BatchToSpace_3) { auto exp = NDArrayFactory::create('c', {2, 2, 4, 1}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}); auto crops = NDArrayFactory::create('c', {2, 2}, {0, 0, 2, 0}); - nd4j::ops::batch_to_space op; + sd::ops::batch_to_space op; auto result = op.evaluate({&x, &crops}, {}, {2}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } ////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests5, Test_BatchToSpace_4) { const int blockSize = 2; - NDArray x('c', {3*blockSize*blockSize, 3, 4, 2}, nd4j::DataType::FLOAT32); + NDArray x('c', {3*blockSize*blockSize, 3, 4, 2}, sd::DataType::FLOAT32); x.linspace(1, 1); NDArray crops = NDArrayFactory::create('c', {2, 2}, {1, 2, 2, 3}); - NDArray exp('c', {3, 3*blockSize - 1 - 2, 4*blockSize - 2 - 3, 2}, {147, 148, 219, 220, 149, 150, 11, 12, 83, 84, 13, 14, 155, 156, 227, 228, 157, 158, 171, 172, 243, 244, 173, 174, 35, 36, 107, 108, 37, 38, 179, 180, 251, 252, 181, 182, 195, 196, 267, 268, 197, 198, 59, 60, 131, 132, 61, 62, 203, 204, 275, 276, 205, 206}, nd4j::DataType::FLOAT32); + NDArray exp('c', {3, 3*blockSize - 1 - 2, 4*blockSize - 2 - 3, 2}, {147, 148, 219, 220, 149, 150, 11, 12, 83, 84, 13, 14, 155, 156, 227, 228, 157, 158, 171, 172, 243, 244, 173, 174, 35, 36, 107, 108, 37, 38, 179, 180, 251, 252, 181, 182, 195, 196, 267, 268, 197, 198, 59, 60, 131, 132, 61, 62, 203, 204, 275, 276, 205, 206}, sd::DataType::FLOAT32); - nd4j::ops::batch_to_space op; + sd::ops::batch_to_space op; auto result = op.evaluate({&x, &crops}, {}, {blockSize}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } ////////////////////////////////////////////////////////////////////// @@ -601,16 +598,16 @@ TEST_F(DeclarableOpsTests5, eye_test1) { auto expected = NDArrayFactory::create('c', {3, 3}, {1.f, 0.f, 0.f, 0.f, 1.f, 0.f, 0.f, 0.f, 1.f}); - nd4j::ops::eye op; + sd::ops::eye op; auto results = op.evaluate({}, {}, {-99, 3}); - auto output = results->at(0); + auto output = results.at(0); // output->printIndexedBuffer(); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -618,15 +615,15 @@ TEST_F(DeclarableOpsTests5, eye_test2) { auto expected = NDArrayFactory::create('c', {3, 4}, {1.f, 0.f, 0.f, 0.f, 0.f, 1.f, 0.f, 0.f, 0.f, 0.f, 1.f, 0.f}); - nd4j::ops::eye op; + sd::ops::eye op; auto results = op.evaluate({}, {}, {-99, 3, 4}); - auto output = results->at(0); + auto output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -634,16 +631,16 @@ TEST_F(DeclarableOpsTests5, eye_test3) { auto expected = NDArrayFactory::create('c', {2, 3, 4}, {1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0}); - nd4j::ops::eye op; + sd::ops::eye op; auto results = op.evaluate({}, {9 /*int*/}, {-99, 3, 4, 2}); - auto output = results->at(0); + auto output = results.at(0); // output->printIndexedBuffer("Output eye"); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -651,27 +648,27 @@ TEST_F(DeclarableOpsTests5, eye_test4) { auto expected = NDArrayFactory::create('c', {2, 2, 3, 4}, {1., 0., 0., 0., 0., 1., 0., 0., 0., 0., 1., 0., 1., 0., 0., 0., 0., 1., 0., 0., 0., 0., 1., 0., 1., 0., 0., 0., 0., 1., 0., 0., 0., 0., 1., 0., 1., 0., 0., 0., 0., 1., 0., 0., 0., 0., 1., 0.}); - nd4j::ops::eye op; + sd::ops::eye op; auto results = op.evaluate({}, {6/*double*/}, {-99, 3, 4, 2, 2}); - auto output = results->at(0); + auto output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; + } ////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests5, eye_test5) { - nd4j::ops::eye op; + sd::ops::eye op; auto result = op.evaluate({},{},{3, 2}); - auto z = result->at(0); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + auto z = result.at(0); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - delete result; + } ////////////////////////////////////////////////////////////////////// @@ -683,15 +680,15 @@ TEST_F(DeclarableOpsTests5, gatherNd_test1) { auto expected = NDArrayFactory::create('c', {2,2,3,2}, {19, 20, 21, 22, 23, 24, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 13, 14, 15, 16, 17, 18}); - nd4j::ops::gather_nd op; + sd::ops::gather_nd op; auto results = op.evaluate({&input, &indices}, {}, {}); - auto output = results->at(0); + auto output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -703,15 +700,15 @@ TEST_F(DeclarableOpsTests5, gatherNd_test2) { auto expected = NDArrayFactory::create('c', {2,2,2}, {23, 24, 11, 12, 3, 4, 3, 4}); - nd4j::ops::gather_nd op; + sd::ops::gather_nd op; auto results = op.evaluate({&input, &indices}, {}, {}, {true}); - auto output = results->at(0); + auto output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -722,15 +719,15 @@ TEST_F(DeclarableOpsTests5, gatherNd_test3) { auto indices = NDArrayFactory::create('c', {3}, {3,2,1}); auto expected = NDArrayFactory::create(24.); - nd4j::ops::gather_nd op; + sd::ops::gather_nd op; auto results = op.evaluate({&input, &indices}, {}, {}); - auto output = results->at(0); + auto output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -741,15 +738,15 @@ TEST_F(DeclarableOpsTests5, gatherNd_test4) { auto indices = NDArrayFactory::create('c', {2,3}, {3,2,1,0,2,1}); auto expected = NDArrayFactory::create('c',{2}, {24., 6}); - nd4j::ops::gather_nd op; + sd::ops::gather_nd op; auto results = op.evaluate({&input, &indices}, {}, {}); - auto output = results->at(0); + auto output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -759,15 +756,15 @@ TEST_F(DeclarableOpsTests5, gatherNd_test5) { auto indices = NDArrayFactory::create('c', {5,1}, {3,2,0,1,1}); auto expected = NDArrayFactory::create('c',{5}, {4.,3,1,2,2}); - nd4j::ops::gather_nd op; + sd::ops::gather_nd op; auto results = op.evaluate({&input, &indices}, {}, {}); - auto output = results->at(0); + auto output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -778,15 +775,15 @@ TEST_F(DeclarableOpsTests5, gatherNd_test6) { auto indices = NDArrayFactory::create('c', shape, {2}); auto expected = NDArrayFactory::create(3.); - nd4j::ops::gather_nd op; + sd::ops::gather_nd op; auto results = op.evaluate({&input, &indices}, {}, {}); - auto output = results->at(0); + auto output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -797,15 +794,15 @@ TEST_F(DeclarableOpsTests5, gatherNd_test7) { auto indices = NDArrayFactory::create('c', {3,3,2}, {0,2,1, 0,1,0, 1,3,1, 0,2,1, 0,1,0, 1,3,1}); auto expected = NDArrayFactory::create('c', {3,3}, {3,5,5,8,5,10,2,2,14}); - nd4j::ops::gather_nd op; + sd::ops::gather_nd op; auto results = op.evaluate({&input, &indices}, {}, {}, {true}); - auto output = results->at(0); + auto output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -814,15 +811,15 @@ TEST_F(DeclarableOpsTests5, gatherNd_test8) { auto y = NDArrayFactory::create('c', {2, 2}, {0, 0, 1, 1}); auto e = NDArrayFactory::create('c', {2}, {1., 4.}); - nd4j::ops::gather_nd op; + sd::ops::gather_nd op; auto result = op.evaluate({&x, &y}, {}, {}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_EQ(e, *z); - delete result; + } TEST_F(DeclarableOpsTests5, gatherNd_test9) { @@ -831,11 +828,11 @@ TEST_F(DeclarableOpsTests5, gatherNd_test9) { auto exp = NDArrayFactory::create('c', {3,2}, {11.f, 12.f, 5.f, 6.f, 31.f, 32.f}); x.linspace(1); - nd4j::ops::gather_nd op; + sd::ops::gather_nd op; auto result = op.evaluate({&x, &indices}, {}, {}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); //z->printIndexedBuffer(); //z->printShapeInfo("z shape"); @@ -843,7 +840,7 @@ TEST_F(DeclarableOpsTests5, gatherNd_test9) { ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } ////////////////////////////////////////////////////////////////////// @@ -854,7 +851,7 @@ TEST_F(DeclarableOpsTests5, gatherNd_test10) { auto output = NDArrayFactory::create('c', {2,2,2}); - nd4j::ops::gather_nd op; + sd::ops::gather_nd op; ASSERT_ANY_THROW(op.execute({&input, &indices}, {&output}, {}, {}, {true})); } @@ -866,7 +863,7 @@ TEST_F(DeclarableOpsTests5, gatherNd_test11) { auto indices = NDArrayFactory::create('c', {3,3,2}, {0,2,1, 0,10,0, 1,30,1, 0,20,1, 0,1,0, 1,30,1}); auto output = NDArrayFactory::create('c', {3,3}); - nd4j::ops::gather_nd op; + sd::ops::gather_nd op; ASSERT_ANY_THROW(op.execute({&input, &indices}, {&output}, {}, {}, {true})); } @@ -879,16 +876,16 @@ TEST_F(DeclarableOpsTests5, reverse_sequense_test1) { auto seqLengths = NDArrayFactory::create('c', {4}, {4,4,4,4}); auto exp = NDArrayFactory::create('c', {3, 4, 5}, {4, 3, 2, 1, 5, 9, 8, 7, 6, 10, 14, 13, 12, 11, 15, 19, 18, 17, 16, 20, 24, 23, 22, 21, 25, 29, 28, 27, 26, 30, 34, 33, 32, 31, 35, 39, 38, 37, 36, 40, 44, 43, 42, 41, 45, 49, 48, 47, 46, 50, 54, 53, 52, 51, 55, 59, 58, 57, 56, 60}); - nd4j::ops::reverse_sequence op; + sd::ops::reverse_sequence op; auto results = op.evaluate({&input, &seqLengths}, {}, {2, 1}); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); - auto output = results->at(0); + auto output = results.at(0); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -899,15 +896,15 @@ TEST_F(DeclarableOpsTests5, reverse_sequense_test2) { auto seqLengths = NDArrayFactory::create('c', {4}, {0,1,2,3}); auto exp = NDArrayFactory::create('c', {3, 4, 5}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 11, 13, 14, 15, 18, 17, 16, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 32, 31, 33, 34, 35, 38, 37, 36, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 52, 51, 53, 54, 55, 58, 57, 56, 59, 60}); - nd4j::ops::reverse_sequence op; + sd::ops::reverse_sequence op; auto results = op.evaluate({&input, &seqLengths}, {}, {2, 1}); - auto output = results->at(0); + auto output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -918,15 +915,15 @@ TEST_F(DeclarableOpsTests5, reverse_sequense_test3) { auto seqLengths = NDArrayFactory::create('c', {3}, {2,3,4}); auto exp = NDArrayFactory::create('c', {3, 4, 5}, {2, 1, 3, 4, 5, 7, 6, 8, 9, 10, 12, 11, 13, 14, 15, 17, 16, 18, 19, 20, 23, 22, 21, 24, 25, 28, 27, 26, 29, 30, 33, 32, 31, 34, 35, 38, 37, 36, 39, 40, 44, 43, 42, 41, 45, 49, 48, 47, 46, 50, 54, 53, 52, 51, 55, 59, 58, 57, 56, 60}); - nd4j::ops::reverse_sequence op; + sd::ops::reverse_sequence op; auto results = op.evaluate({&input, &seqLengths}, {}, {2, 0}); - auto output = results->at(0); + auto output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -937,15 +934,15 @@ TEST_F(DeclarableOpsTests5, reverse_sequense_test4) { auto seqLengths = NDArrayFactory::create('c', {5}, {1, 2, 1, 2, 3}); auto exp = NDArrayFactory::create('c', {3, 4, 5}, {1, 22, 3, 24, 45, 6, 27, 8, 29, 50, 11, 32, 13, 34, 55, 16, 37, 18, 39, 60, 21, 2, 23, 4, 25, 26, 7, 28, 9, 30, 31, 12, 33, 14, 35, 36, 17, 38, 19, 40, 41, 42, 43, 44, 5, 46, 47, 48, 49, 10, 51, 52, 53, 54, 15, 56, 57, 58, 59, 20}); - nd4j::ops::reverse_sequence op; + sd::ops::reverse_sequence op; auto results = op.evaluate({&input, &seqLengths}, {}, {0, 2}); - auto output = results->at(0); + auto output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -956,15 +953,15 @@ TEST_F(DeclarableOpsTests5, reverse_sequense_test5) { auto seqLengths = NDArrayFactory::create('c', {5}, {1, 2, 4, 2, 3}); auto exp = NDArrayFactory::create('c', {3, 4, 5}, {1, 7, 18, 9, 15, 6, 2, 13, 4, 10, 11, 12, 8, 14, 5, 16, 17, 3, 19, 20, 21, 27, 38, 29, 35, 26, 22, 33, 24, 30, 31, 32, 28, 34, 25, 36, 37, 23, 39, 40, 41, 47, 58, 49, 55, 46, 42, 53, 44, 50, 51, 52, 48, 54, 45, 56, 57, 43, 59, 60}); - nd4j::ops::reverse_sequence op; + sd::ops::reverse_sequence op; auto results = op.evaluate({&input, &seqLengths}, {}, {1, 2}); - auto output = results->at(0); + auto output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -975,15 +972,15 @@ TEST_F(DeclarableOpsTests5, reverse_sequense_test6) { auto seqLengths = NDArrayFactory::create('c', {4}, {1, 2, 3, 2}); auto exp = NDArrayFactory::create('c', {3, 4, 5}, {1, 2, 3, 4, 5, 26, 27, 28, 29, 30, 51, 52, 53, 54, 55, 36, 37, 38, 39, 40, 21, 22, 23, 24, 25, 6, 7, 8, 9, 10, 31, 32, 33, 34, 35, 16, 17, 18, 19, 20, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 11, 12, 13, 14, 15, 56, 57, 58, 59, 60}); - nd4j::ops::reverse_sequence op; + sd::ops::reverse_sequence op; auto results = op.evaluate({&input, &seqLengths}, {}, {0, 1}); - auto output = results->at(0); + auto output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -995,15 +992,15 @@ TEST_F(DeclarableOpsTests5, reverse_sequense_test7) { auto seqLengths = NDArrayFactory::create('c', {1}, data); auto exp = NDArrayFactory::create('c', {1, 5}, {3, 2, 1, 4, 5}); - nd4j::ops::reverse_sequence op; + sd::ops::reverse_sequence op; auto results = op.evaluate({&input, &seqLengths}, {}, {1, 0}); - auto output = results->at(0); + auto output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -1015,15 +1012,15 @@ TEST_F(DeclarableOpsTests5, reverse_sequense_test8) { auto seqLengths = NDArrayFactory::create('c', {5}, data); auto exp = NDArrayFactory::create('c', {1, 5}, {1, 2, 3, 4, 5}); - nd4j::ops::reverse_sequence op; + sd::ops::reverse_sequence op; auto results = op.evaluate({&input, &seqLengths}, {}, {0, 1}); - auto output = results->at(0); + auto output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -1035,15 +1032,15 @@ TEST_F(DeclarableOpsTests5, reverse_sequense_test9) { auto seqLengths = NDArrayFactory::create('c', {5}, data); auto exp = NDArrayFactory::create('c', {5, 1}, {1, 2, 3, 4, 5}); - nd4j::ops::reverse_sequence op; + sd::ops::reverse_sequence op; auto results = op.evaluate({&input, &seqLengths}, {}, {1, 0}); - auto output = results->at(0); + auto output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -1055,15 +1052,15 @@ TEST_F(DeclarableOpsTests5, reverse_sequense_test10) { auto seqLengths = NDArrayFactory::create('c', {1}, data); auto exp = NDArrayFactory::create('c', {5, 1}, {3, 2, 1, 4, 5}); - nd4j::ops::reverse_sequence op; + sd::ops::reverse_sequence op; auto results = op.evaluate({&input, &seqLengths}, {}, {0, 1}); - auto output = results->at(0); + auto output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -1075,15 +1072,15 @@ TEST_F(DeclarableOpsTests5, reverse_sequense_test11) { auto seqLengths = NDArrayFactory::create('c', {5}, data); auto exp = NDArrayFactory::create('c', {1, 1, 5, 1}, {1, 2, 3, 4, 5}); - nd4j::ops::reverse_sequence op; + sd::ops::reverse_sequence op; auto results = op.evaluate({&input, &seqLengths}, {}, {1, 2}); - auto output = results->at(0); + auto output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -1095,15 +1092,15 @@ TEST_F(DeclarableOpsTests5, reverse_sequense_test12) { auto seqLengths = NDArrayFactory::create('c', {1}, data); auto exp = NDArrayFactory::create('c', {1, 1, 5, 1}, {3, 2, 1, 4, 5}); - nd4j::ops::reverse_sequence op; + sd::ops::reverse_sequence op; auto results = op.evaluate({&input, &seqLengths}, {}, {2, 0}); - auto output = results->at(0); + auto output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -1115,15 +1112,15 @@ TEST_F(DeclarableOpsTests5, reverse_sequense_test13) { auto seqLengths = NDArrayFactory::create('c', {1}, data); auto exp = NDArrayFactory::create('c', {1, 1, 5, 1}, {1, 2, 3, 4, 5}); - nd4j::ops::reverse_sequence op; + sd::ops::reverse_sequence op; auto results = op.evaluate({&input, &seqLengths}, {}, {3, 0}); - auto output = results->at(0); + auto output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -1132,15 +1129,15 @@ TEST_F(DeclarableOpsTests5, reverse_sequense_test14) { auto lengths = NDArrayFactory::create('c', {8}, {7, 2, 3, 5, 2, 1, 6, 4}); auto e = NDArrayFactory::create('c', {8, 8, 3, 2}, {0.54193264, 0.05176904, 0.82555761, 0.71106697, 0.04416722, 0.07653656, 0.06478678, 0.68985848, 0.55216783, 0.55382648, 0.34652863, 0.17261296, 0.61523204, 0.64298760, 0.26848351, 0.75015615, 0.28683049, 0.70937606, 0.38700677, 0.68832738, 0.37292716, 0.94616004, 0.77735792, 0.60803430, 0.54555996, 0.23407607, 0.11372584, 0.49965927, 0.15210842, 0.53268608, 0.67197708, 0.80659380, 0.98274191, 0.63566073, 0.21592326, 0.54902743, 0.09753360, 0.76124972, 0.24693797, 0.13813169, 0.33144656, 0.08299957, 0.01034390, 0.99430482, 0.59944390, 0.17973880, 0.36437840, 0.86383673, 0.93630291, 0.67277404, 0.93899264, 0.52422773, 0.44892176, 0.03127759, 0.45025550, 0.97136977, 0.13565978, 0.71567448, 0.92094825, 0.93536442, 0.85910449, 0.18252879, 0.72830945, 0.96736828, 0.89831575, 0.83437150, 0.59050780, 0.36145925, 0.16483070, 0.44021176, 0.76018652, 0.44227383, 0.13052339, 0.18204235, 0.99743733, 0.26885190, 0.87726522, 0.16396056, 0.94943412, 0.40016700, 0.65267938, 0.71073267, 0.40094733, 0.91182634, 0.05391789, 0.49520416, 0.24963864, 0.34847086, 0.74088617, 0.36115701, 0.63074210, 0.97423085, 0.42216846, 0.06326975, 0.07858702, 0.20586622, 0.34755773, 0.63166554, 0.18849320, 0.34828456, 0.98477707, 0.75163124, 0.33309570, 0.67563176, 0.98343578, 0.95919930, 0.66994391, 0.89296165, 0.28752144, 0.38146961, 0.83518735, 0.08207577, 0.82083487, 0.81665728, 0.83306004, 0.14203056, 0.01497920, 0.85727447, 0.71194544, 0.85654019, 0.86160433, 0.79580411, 0.47710411, 0.09318029, 0.31369071, 0.64122249, 0.58399725, 0.26706597, 0.05655339, 0.91025211, 0.30330468, 0.33142930, 0.05668627, 0.02936449, 0.12613087, 0.09960114, 0.16218074, 0.15088139, 0.31239040, 0.55980062, 0.34804391, 0.34941538, 0.61370555, 0.07022964, 0.27274571, 0.83306066, 0.75830824, 0.25963478, 0.87137718, 0.24418835, 0.59371493, 0.74479056, 0.84699625, 0.51210368, 0.12489571, 0.23371067, 0.18361641, 0.48636240, 0.06052657, 0.04241913, 0.66710351, 0.07007925, 0.60553664, 0.07536713, 0.55971796, 0.38764845, 0.20737843, 0.37989120, 0.59757058, 0.31189846, 0.25215345, 0.52546591, 0.55744218, 0.59485650, 0.05032742, 0.52076188, 0.47762345, 0.89829370, 0.34417708, 0.84705151, 0.08203183, 0.10632956, 0.78431292, 0.86441722, 0.36487598, 0.09833603, 0.85863594, 0.11010505, 0.11659283, 0.42500288, 0.02747301, 0.12359903, 0.19736489, 0.44461885, 0.33341706, 0.22519571, 0.31528710, 0.14802902, 0.01753431, 0.41160932, 0.47245979, 0.08268172, 0.21580773, 0.75770279, 0.64171939, 0.52643769, 0.19261234, 0.98032835, 0.15401656, 0.85274458, 0.66408502, 0.23212704, 0.74630026, 0.05713613, 0.49025892, 0.48418810, 0.59541513, 0.09243053, 0.93919152, 0.95357019, 0.52377729, 0.65963871, 0.47934951, 0.49919534, 0.34369898, 0.78211256, 0.13908708, 0.95754117, 0.84107746, 0.09126213, 0.42979124, 0.10295325, 0.34631257, 0.69448345, 0.41720536, 0.15282440, 0.74329854, 0.45775009, 0.12786280, 0.39830299, 0.20386769, 0.59703523, 0.94077086, 0.42255597, 0.80453309, 0.79757204, 0.28653229, 0.60175909, 0.55859623, 0.34318230, 0.63002770, 0.36533324, 0.89689906, 0.73236186, 0.61491989, 0.83787947, 0.67939463, 0.72016694, 0.77499849, 0.72428343, 0.34571059, 0.23143007, 0.20099338, 0.85583142, 0.73174191, 0.54284092, 0.20264181, 0.53037061, 0.30493131, 0.82279766, 0.58542432, 0.72632070, 0.18394258, 0.00608118, 0.23808232, 0.17007573, 0.75245459, 0.84990616, 0.38827634, 0.33809538, 0.01080317, 0.27250145, 0.81769542, 0.15323253, 0.71668395, 0.99427044, 0.11355576, 0.50511923, 0.22952055, 0.78271870, 0.12833592, 0.88639055, 0.76398188, 0.49533508, 0.47939640, 0.73564612, 0.41465671, 0.10995635, 0.20271728, 0.00521771, 0.67265260, 0.11917707, 0.76574855, 0.43842117, 0.28530411, 0.79648090, 0.79433656, 0.12074559, 0.02325163, 0.10117917, 0.83559239, 0.67213900, 0.25247084, 0.47968157, 0.88649124, 0.33588961, 0.92338319, 0.18808573, 0.60248266, 0.36610154, 0.99123140, 0.10519719, 0.18754650, 0.43232584, 0.85447872, 0.15937568, 0.92947480, 0.62705964, 0.85960084, 0.13435660, 0.81845809, 0.60715133, 0.83030708, 0.83071910, 0.38883408, 0.92033237, 0.59820890, 0.75527947, 0.67683355, 0.21847023, 0.29395619, 0.50477953, 0.98977921, 0.96225332, 0.90143562, 0.19559914, 0.08978307, 0.09687492, 0.07381865, 0.22801110, 0.26669388, 0.99691302, 0.12113623, 0.34373057, 0.46066239, 0.48806761, 0.50688779, 0.00654483, 0.32076493, 0.42367646, 0.07112842, 0.54090558, 0.68230725, 0.49713828, 0.41958965, 0.68013847, 0.47691765, 0.63269259, 0.94304095, 0.54587271, 0.72447569, 0.28913523, 0.75766936, 0.52965692, 0.96854824, 0.15589071, 0.84128672, 0.16337522, 0.05771034, 0.21556356, 0.12094140, 0.29721207, 0.00811008, 0.66184926}); - nd4j::ops::reverse_sequence op; + sd::ops::reverse_sequence op; auto results = op.evaluate({&input, &lengths}, {}, {1, 0}); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); - auto z = results->at(0); + auto z = results.at(0); ASSERT_EQ(e, *z); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -1149,14 +1146,14 @@ TEST_F(DeclarableOpsTests5, Test_TopK_0) { auto expV = NDArrayFactory::create('c', {2, 1}, {11.0, 14.0}); auto expI = NDArrayFactory::create('c', {2, 1}, {4, 3}); - nd4j::ops::top_k op; + sd::ops::top_k op; auto result = op.evaluate({&x}, {}, {1, 0}); // without sorting - ASSERT_EQ(ND4J_STATUS_OK, result->status()); - ASSERT_EQ(2, result->size()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); + ASSERT_EQ(2, result.size()); - auto v = result->at(0); - auto i = result->at(1); + auto v = result.at(0); + auto i = result.at(1); /* v->printShapeInfo("topK_0: shape v"); expV.printShapeInfo("topK_0: shape expV"); @@ -1179,7 +1176,7 @@ TEST_F(DeclarableOpsTests5, Test_TopK_0) { for (int cases = 0; cases < 100; ++cases) { op.execute({&x}, std::vector{v, i}, {}, {1, 0}, {}); // without sorting } - delete result; + } ////////////////////////////////////////////////////////////////////// @@ -1188,14 +1185,14 @@ TEST_F(DeclarableOpsTests5, Test_TopK_1) { auto expV = NDArrayFactory::create('c', {2, 1}, {11.0f, 14.0f}); auto expI = NDArrayFactory::create('c', {2, 1}, {1, 0}); - nd4j::ops::top_k op; + sd::ops::top_k op; auto result = op.evaluate({&x}, {}, {1, 0}); // without sorting - ASSERT_EQ(ND4J_STATUS_OK, result->status()); - ASSERT_EQ(2, result->size()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); + ASSERT_EQ(2, result.size()); - auto v = result->at(0); - auto i = result->at(1); + auto v = result.at(0); + auto i = result.at(1); // v->printShapeInfo("topK_1: shape v"); // expV.printShapeInfo("topK_1: shape expV"); @@ -1218,7 +1215,7 @@ TEST_F(DeclarableOpsTests5, Test_TopK_1) { for (int cases = 0; cases < 100; ++cases) { op.execute({&x}, std::vector{v, i}, {}, {1, 0}, {}); // without sorting } - delete result; + } /////////////////////////////////////////////////////////// @@ -1241,14 +1238,14 @@ TEST_F(DeclarableOpsTests5, Test_TopK_2) { auto expI = NDArrayFactory::create('c', {2, 3, 1 }, {2, 1, 0, 1, 2, 0}); - nd4j::ops::top_k op; + sd::ops::top_k op; auto result = op.evaluate({&x}, {}, {1, 1}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); - ASSERT_EQ(2, result->size()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); + ASSERT_EQ(2, result.size()); - auto v = result->at(0); - auto i = result->at(1); + auto v = result.at(0); + auto i = result.at(1); // v->printShapeInfo("shape v"); // expV.printShapeInfo("shape expV"); @@ -1267,7 +1264,7 @@ TEST_F(DeclarableOpsTests5, Test_TopK_2) { ASSERT_TRUE(expI.isSameShape(i)); ASSERT_TRUE(expI.equalsTo(i)); - delete result; + } TEST_F(DeclarableOpsTests5, Test_TopK_3) { @@ -1291,14 +1288,14 @@ TEST_F(DeclarableOpsTests5, Test_TopK_3) { auto expI = NDArrayFactory::create('c', {2, 3, 2 }, {2, 0, 1, 3, 0, 3, 1, 3, 2, 1, 0, 2}); - nd4j::ops::top_k op; + sd::ops::top_k op; auto result = op.evaluate({&x}, {}, {2, 1}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); - ASSERT_EQ(2, result->size()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); + ASSERT_EQ(2, result.size()); - auto v = result->at(0); - auto i = result->at(1); + auto v = result.at(0); + auto i = result.at(1); // v->printShapeInfo("shape v"); // expV.printShapeInfo("shape expV"); @@ -1317,7 +1314,7 @@ TEST_F(DeclarableOpsTests5, Test_TopK_3) { ASSERT_TRUE(expI.isSameShape(i)); ASSERT_TRUE(expI.equalsTo(i)); - delete result; + } TEST_F(DeclarableOpsTests5, Test_TopK_3_unsorted) { @@ -1341,14 +1338,14 @@ TEST_F(DeclarableOpsTests5, Test_TopK_3_unsorted) { auto expI = NDArrayFactory::create('c', {2, 3, 2 }, {0, 2, 1, 3, 0, 3, 1, 3, 1, 2, 0, 2}); - nd4j::ops::top_k op; + sd::ops::top_k op; auto result = op.evaluate({&x}, {}, {2}, {false}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); - ASSERT_EQ(2, result->size()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); + ASSERT_EQ(2, result.size()); - auto v = result->at(0); - auto i = result->at(1); + auto v = result.at(0); + auto i = result.at(1); ASSERT_TRUE(expV.isSameShape(v)); ASSERT_TRUE(expV.equalsTo(v)); @@ -1356,7 +1353,7 @@ TEST_F(DeclarableOpsTests5, Test_TopK_3_unsorted) { ASSERT_TRUE(expI.isSameShape(i)); ASSERT_TRUE(expI.equalsTo(i)); - delete result; + } ////////////////////////////////////////////////////////////////////// @@ -1365,14 +1362,14 @@ TEST_F(DeclarableOpsTests5, Test_TopK_4) { auto expV = NDArrayFactory::create('c', {2, 2}, {11.0f, 3.0f, 14.0f, 6.0f}); auto expI = NDArrayFactory::create('c', {2, 2}, {1, 2, 0, 2}); - nd4j::ops::top_k op; + sd::ops::top_k op; auto result = op.evaluate({&x}, {}, {2, 1}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); - ASSERT_EQ(2, result->size()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); + ASSERT_EQ(2, result.size()); - auto v = result->at(0); - auto i = result->at(1); + auto v = result.at(0); + auto i = result.at(1); ASSERT_TRUE(expV.isSameShape(v)); ASSERT_TRUE(expV.equalsTo(v)); @@ -1380,7 +1377,7 @@ TEST_F(DeclarableOpsTests5, Test_TopK_4) { ASSERT_TRUE(expI.isSameShape(i)); ASSERT_TRUE(expI.equalsTo(i)); - delete result; + } ////////////////////////////////////////////////////////////////////// @@ -1389,14 +1386,14 @@ TEST_F(DeclarableOpsTests5, Test_TopK_5) { auto expV = NDArrayFactory::create('f', {2, 2}, {11.1, 14.2, 3.1, 6.2}); auto expI = NDArrayFactory::create('f', {2, 2}, {2, 1, 1, 2}); - nd4j::ops::top_k op; + sd::ops::top_k op; auto result = op.evaluate({&x}, {}, {2, 1}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); - ASSERT_EQ(2, result->size()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); + ASSERT_EQ(2, result.size()); - auto v = result->at(0); - auto i = result->at(1); + auto v = result.at(0); + auto i = result.at(1); ASSERT_TRUE(expV.isSameShape(v)); ASSERT_TRUE(expV.equalsTo(v)); @@ -1404,7 +1401,7 @@ TEST_F(DeclarableOpsTests5, Test_TopK_5) { ASSERT_TRUE(expI.isSameShape(i)); ASSERT_TRUE(expI.equalsTo(i)); - delete result; + } /////////////////////////////////////////////////////////// @@ -1427,14 +1424,14 @@ TEST_F(DeclarableOpsTests5, Test_Moments_1) { float inf = 1.e-5f; - nd4j::ops::moments op; + sd::ops::moments op; auto result = op.evaluate({&x, &y}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); - ASSERT_EQ(2, result->size()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); + ASSERT_EQ(2, result.size()); - auto v = result->at(0); - auto d = result->at(1); + auto v = result.at(0); + auto d = result.at(1); // v->printIndexedBuffer("Result is "); // d->printIndexedBuffer("Result is "); @@ -1443,7 +1440,7 @@ TEST_F(DeclarableOpsTests5, Test_Moments_1) { ASSERT_NEAR(expMean, v->e(0), inf); ASSERT_NEAR(expDeviation, d->e(0), inf); - delete result; + } TEST_F(DeclarableOpsTests5, Test_Moments_2) { @@ -1458,14 +1455,14 @@ TEST_F(DeclarableOpsTests5, Test_Moments_2) { NDArray expV('c', {4}, {11.833333, 7.6666665, 10.416667, 7.6666665}); NDArray expD('c', {4}, {28.472221, 12.888889, 23.951387, 11.555554}); - nd4j::ops::moments op; + sd::ops::moments op; auto result = op.evaluate({&x}, {}, {0, 1}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); - ASSERT_EQ(2, result->size()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); + ASSERT_EQ(2, result.size()); - auto v = result->at(0); - auto d = result->at(1); + auto v = result.at(0); + auto d = result.at(1); ASSERT_TRUE(v->isVector()); ASSERT_TRUE(d->isVector()); @@ -1473,7 +1470,7 @@ TEST_F(DeclarableOpsTests5, Test_Moments_2) { ASSERT_TRUE(v->equalsTo(&expV)); ASSERT_TRUE(d->equalsTo(&expD)); - delete result; + } TEST_F(DeclarableOpsTests5, Test_Moments_3) { @@ -1492,14 +1489,14 @@ TEST_F(DeclarableOpsTests5, Test_Moments_3) { 6.25f, 4.f, 27.5625f, 1.f, 6.25f, 9.f, 0.0625f, 16.f}); - nd4j::ops::moments op; + sd::ops::moments op; auto result = op.evaluate({&x}, {}, {0}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); - ASSERT_EQ(2, result->size()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); + ASSERT_EQ(2, result.size()); - auto v = result->at(0); - auto d = result->at(1); + auto v = result.at(0); + auto d = result.at(1); ASSERT_TRUE(v->isMatrix()); ASSERT_TRUE(d->isMatrix()); @@ -1507,7 +1504,7 @@ TEST_F(DeclarableOpsTests5, Test_Moments_3) { ASSERT_TRUE(v->equalsTo(&expV)); ASSERT_TRUE(d->equalsTo(&expD)); - delete result; + } TEST_F(DeclarableOpsTests5, Test_Moments_4) { @@ -1519,14 +1516,14 @@ TEST_F(DeclarableOpsTests5, Test_Moments_4) { auto expV = NDArrayFactory::create('c', {3, 4}, { 8.5f, 6.f , 8.75f, 6.f, 8.5f, 11.f, 8.75f, 6.f, 18.5f, 6.f, 13.75f, 11.f}); auto expD = NDArrayFactory::create('c', {3, 4}, { 6.25f, 9.f, 27.5625f, 1.f, 6.25f, 4.f, 27.5625f, 1.f, 6.25f, 9.f, 0.0625f, 16.f}); - nd4j::ops::moments op; + sd::ops::moments op; auto result = op.evaluate({&x}, {}, {0}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); - ASSERT_EQ(2, result->size()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); + ASSERT_EQ(2, result.size()); - auto v = result->at(0); - auto d = result->at(1); + auto v = result.at(0); + auto d = result.at(1); ASSERT_TRUE(v->isMatrix()); ASSERT_TRUE(d->isMatrix()); @@ -1540,7 +1537,7 @@ TEST_F(DeclarableOpsTests5, Test_Moments_4) { ASSERT_TRUE(v->equalsTo(&expV)); ASSERT_TRUE(d->equalsTo(&expD)); - delete result; + } ////////////////////////////////////////////////////////////////////// @@ -1550,18 +1547,18 @@ TEST_F(DeclarableOpsTests5, trace_test1) { input.linspace(1); auto exp = NDArrayFactory::create('c', {3}, {40, 120, 200}); NDArray matrix('c', {3, 3}, {1., 2., 3., 4., 5., 6., 7., 8., 9.}); - nd4j::ops::trace op; + sd::ops::trace op; auto results = op.evaluate({&input}, {}, {}); - auto output = results->at(0); + auto output = results.at(0); double traceM = matrix.getTrace(); // nd4j_printf("Trace for matrix is %f\n", traceM); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(exp.isSameShape(output)); // exp.printIndexedBuffer("EXP TRACE"); // output->printIndexedBuffer("OUT TRACE"); ASSERT_TRUE(exp.equalsTo(output)); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -1571,15 +1568,15 @@ TEST_F(DeclarableOpsTests5, trace_test2) { input.linspace(1); auto exp = NDArrayFactory::create(40.); - nd4j::ops::trace op; + sd::ops::trace op; auto results = op.evaluate({&input}, {}, {}); - auto output = results->at(0); + auto output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -1589,15 +1586,15 @@ TEST_F(DeclarableOpsTests5, trace_test3) { input.linspace(1); auto exp = NDArrayFactory::create(1.); - nd4j::ops::trace op; + sd::ops::trace op; auto results = op.evaluate({&input}, {}, {}); - auto output = results->at(0); + auto output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -1607,15 +1604,15 @@ TEST_F(DeclarableOpsTests5, trace_test4) { input.linspace(1); auto exp = NDArrayFactory::create(1.); - nd4j::ops::trace op; + sd::ops::trace op; auto results = op.evaluate({&input}, {}, {}); - auto output = results->at(0); + auto output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -1625,15 +1622,15 @@ TEST_F(DeclarableOpsTests5, trace_test5) { input.linspace(1); auto exp = NDArrayFactory::create('c', {3, 4}, {75, 225, 375, 525, 675, 825, 975, 1125, 1275, 1425, 1575, 1725}); - nd4j::ops::trace op; + sd::ops::trace op; auto results = op.evaluate({&input}); - auto output = results->at(0); + auto output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -1642,21 +1639,21 @@ TEST_F(DeclarableOpsTests5, random_shuffle_test1) { auto input = NDArrayFactory::create('c', {2, 2, 2}); input.linspace(1); - nd4j::ops::random_shuffle op; + sd::ops::random_shuffle op; auto results = op.evaluate({&input}); - auto output = results->at(0); + auto output = results.at(0); bool haveZeros = false; for(int i = 0; i < output->lengthOf(); ++i) if(output->e(i) == (float)0.) haveZeros = true; - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(input.isSameShape(output)); ASSERT_TRUE(!input.equalsTo(output)); ASSERT_TRUE(!haveZeros); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -1665,15 +1662,15 @@ TEST_F(DeclarableOpsTests5, random_shuffle_test2) { auto input = NDArrayFactory::create('c', {1, 3, 2}); input.linspace(1); - nd4j::ops::random_shuffle op; + sd::ops::random_shuffle op; auto results = op.evaluate({&input}); - auto output = results->at(0); + auto output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(input.isSameShape(output)); ASSERT_TRUE(input.equalsTo(output)); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -1682,32 +1679,32 @@ TEST_F(DeclarableOpsTests5, random_shuffle_test3) { auto input = NDArrayFactory::create('c', {3, 2, 1}); input.linspace(1); - nd4j::ops::random_shuffle op; + sd::ops::random_shuffle op; auto results = op.evaluate({&input}); - auto output = results->at(0); + auto output = results.at(0); bool haveZeros = false; for(int i = 0; i < output->lengthOf(); ++i) if(output->e(i) == (float)0.) haveZeros = true; - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(input.isSameShape(output)); ASSERT_TRUE(!input.equalsTo(output)); ASSERT_TRUE(!haveZeros); - delete results; + } ////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests5, random_shuffle_test04) { auto input = NDArrayFactory::create('c', {4}); input.linspace(1); - nd4j::ops::random_shuffle op; + sd::ops::random_shuffle op; //NDArray* output; auto results = op.evaluate({&input}, {}, {}, {}, {}, true); - ASSERT_EQ(Status::OK(), results->status()); - auto output = &input; //results->at(0); + ASSERT_EQ(Status::OK(), results.status()); + auto output = &input; //results.at(0); bool haveZeros = false; for(int i = 0; i < output->lengthOf(); ++i) if(output->e(i) == (float)0.) @@ -1717,7 +1714,7 @@ TEST_F(DeclarableOpsTests5, random_shuffle_test04) { //ASSERT_TRUE(!input.equalsTo(output)); ASSERT_TRUE(!haveZeros); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -1725,11 +1722,11 @@ TEST_F(DeclarableOpsTests5, random_shuffle_test4) { auto input = NDArrayFactory::create('c', {4}); input.linspace(1); - nd4j::ops::random_shuffle op; + sd::ops::random_shuffle op; //NDArray* output; auto results = op.evaluate({&input}); - ASSERT_EQ(Status::OK(), results->status()); - auto output = results->at(0); + ASSERT_EQ(Status::OK(), results.status()); + auto output = results.at(0); bool haveZeros = false; for(int i = 0; i < output->lengthOf(); ++i) if(output->e(i) == (float)0.) @@ -1739,7 +1736,7 @@ TEST_F(DeclarableOpsTests5, random_shuffle_test4) { //ASSERT_TRUE(!input.equalsTo(output)); ASSERT_TRUE(!haveZeros); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -1748,21 +1745,21 @@ TEST_F(DeclarableOpsTests5, random_shuffle_test5) { auto input = NDArrayFactory::create('c', {4,1}); input.linspace(1); - nd4j::ops::random_shuffle op; + sd::ops::random_shuffle op; auto results = op.evaluate({&input}); - auto output = results->at(0); + auto output = results.at(0); bool haveZeros = false; for(int i = 0; i < output->lengthOf(); ++i) if(output->e(i) == (float)0.) haveZeros = true; - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(input.isSameShape(output)); ASSERT_TRUE(!input.equalsTo(output)); ASSERT_TRUE(!haveZeros); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -1771,21 +1768,21 @@ TEST_F(DeclarableOpsTests5, random_shuffle_test6) { auto input = NDArrayFactory::create('c', {4,1,1}); input.linspace(1); - nd4j::ops::random_shuffle op; + sd::ops::random_shuffle op; auto results = op.evaluate({&input}); - auto output = results->at(0); + auto output = results.at(0); bool haveZeros = false; for(int i = 0; i < output->lengthOf(); ++i) if(output->e(i) == (float)0.) haveZeros = true; - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(input.isSameShape(output)); ASSERT_TRUE(!input.equalsTo(output)); ASSERT_TRUE(!haveZeros); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -1795,15 +1792,15 @@ TEST_F(DeclarableOpsTests5, random_shuffle_test7) { input.linspace(1); auto exp = NDArrayFactory::create('c', {1,4}, {1, 2, 3, 4}); - nd4j::ops::random_shuffle op; + sd::ops::random_shuffle op; auto results = op.evaluate({&input}); - auto output = results->at(0); + auto output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(input.isSameShape(output)); ASSERT_TRUE(input.equalsTo(output)); - delete results; + } //////////////////////////////////////////////////////////////////////////////////////// @@ -1825,20 +1822,20 @@ TEST_F(DeclarableOpsTests5, EmbeddingLookup_1) { // y.printShapeInfo("y shape"); // y.printIndexedBuffer("y buffer"); - nd4j::ops::embedding_lookup op; + sd::ops::embedding_lookup op; auto result = op.evaluate({&x, &y}, {}, {0}); - auto output = result->at(0); + auto output = result.at(0); // x.printShapeInfo("Input"); output->printShapeInfo("Output"); exp.printShapeInfo("Expected"); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); ASSERT_TRUE(exp.isSameShape(output)); //output->printIndexedBuffer("Output"); //exp.printIndexedBuffer("Expect"); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } TEST_F(DeclarableOpsTests5, EmbeddingLookup_2) { @@ -1861,20 +1858,20 @@ TEST_F(DeclarableOpsTests5, EmbeddingLookup_2) { // y.printShapeInfo("y shape"); // y.printIndexedBuffer("y buffer"); - nd4j::ops::embedding_lookup op; + sd::ops::embedding_lookup op; auto result = op.evaluate({&x, &y}, {}, {0}); - auto output = result->at(0); + auto output = result.at(0); // x.printShapeInfo("Input"); // output->printShapeInfo("Output"); // exp.printShapeInfo("Expected"); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); ASSERT_TRUE(exp.isSameShape(output)); // output->printIndexedBuffer("Output"); // exp.printIndexedBuffer("Expect"); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } TEST_F(DeclarableOpsTests5, EmbeddingLookup_3) { @@ -1902,20 +1899,20 @@ TEST_F(DeclarableOpsTests5, EmbeddingLookup_3) { // res = tf.nn.embedding_lookup((p1, p2, p3, p4, p5, p6, p7), ids, 'mod') - nd4j::ops::embedding_lookup op; + sd::ops::embedding_lookup op; auto result = op.evaluate({&p1, &p2, &p3, &p4, &p5, &p6, &p7, &p8, &y}, {}, {1}); - auto output = result->at(0); + auto output = result.at(0); // x.printShapeInfo("Input"); // output->printIndexedBuffer("Output"); // exp.printShapeInfo("Expected"); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); ASSERT_TRUE(exp.isSameShape(output)); // output->printIndexedBuffer("Output"); // exp.printIndexedBuffer("Expect"); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } /* @Test public void testDynamicPartition(){ @@ -1945,21 +1942,21 @@ TEST_F(DeclarableOpsTests5, DynamicPartition_01) { NDArrayFactory::create('c', {1}, {2}), NDArrayFactory::create('c', {1}, {1})}); - nd4j::ops::dynamic_partition op; + sd::ops::dynamic_partition op; auto result = op.evaluate({&x, &y}, {}, {numPartition}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); - ASSERT_EQ(result->size(), numPartition); // result has the same size as given param 4 + ASSERT_EQ(ND4J_STATUS_OK, result.status()); + ASSERT_EQ(result.size(), numPartition); // result has the same size as given param 4 - for (int e = 0; e < result->size(); e++) { - auto output = result->at(e); + for (int e = 0; e < result.size(); e++) { + auto output = result.at(e); // output->printShapeInfo("Output shape> "); // output->printIndexedBuffer("Output data> "); ASSERT_TRUE(exp[e].isSameShape(output)); ASSERT_TRUE(exp[e].equalsTo(output)); } - delete result; + } TEST_F(DeclarableOpsTests5, DynamicPartition_1) { @@ -1984,21 +1981,21 @@ TEST_F(DeclarableOpsTests5, DynamicPartition_1) { NDArrayFactory::create('c', {8}, {18, 28, 19, 29, 20, 30, 21, 31}), NDArrayFactory::create('c', {10}, {13, 23, 14, 24, 15, 25, 16, 26, 17, 27})}); - nd4j::ops::dynamic_partition op; + sd::ops::dynamic_partition op; auto result = op.evaluate({&x, &y}, {}, {numPartition}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); - ASSERT_EQ(result->size(), numPartition); // result has the same size as given param 4 + ASSERT_EQ(ND4J_STATUS_OK, result.status()); + ASSERT_EQ(result.size(), numPartition); // result has the same size as given param 4 - for (int e = 0; e < result->size(); e++) { - auto output = result->at(e); + for (int e = 0; e < result.size(); e++) { + auto output = result.at(e); // output->printShapeInfo("Output shape> "); // output->printIndexedBuffer("Output data> "); ASSERT_TRUE(exp[e].isSameShape(output)); ASSERT_TRUE(exp[e].equalsTo(output)); } - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -2013,21 +2010,21 @@ TEST_F(DeclarableOpsTests5, DynamicPartition_2) { NDArrayFactory::create('c', {3}, {-1., 4.3, 7.4}), NDArrayFactory::create('c', {1}, {0.0})}); - nd4j::ops::dynamic_partition op; + sd::ops::dynamic_partition op; int numPartition = 4; auto result = op.evaluate({&x, &y}, {}, {numPartition}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); - ASSERT_EQ(result->size(), numPartition); // result has the same size as given param 4 + ASSERT_EQ(ND4J_STATUS_OK, result.status()); + ASSERT_EQ(result.size(), numPartition); // result has the same size as given param 4 - for (int e = 0; e < result->size(); e++) { - auto output = result->at(e); + for (int e = 0; e < result.size(); e++) { + auto output = result.at(e); ASSERT_TRUE(exp[e].isSameShape(output)); ASSERT_TRUE(exp[e].equalsTo(output)); } - delete result; + } @@ -2041,15 +2038,15 @@ TEST_F(DeclarableOpsTests5, DynamicPartition_3) { NDArrayFactory::create({4.3f, 7.4f}), NDArrayFactory::create('c', {1}, {0.0f})}); - nd4j::ops::dynamic_partition op; + sd::ops::dynamic_partition op; int numPartition = 4; auto result = op.evaluate({&x, &y}, {}, {numPartition}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); - ASSERT_EQ(result->size(), numPartition); // result has the same size as given param 4 + ASSERT_EQ(ND4J_STATUS_OK, result.status()); + ASSERT_EQ(result.size(), numPartition); // result has the same size as given param 4 - for (int e = 0; e < result->size(); e++) { - auto output = result->at(e); + for (int e = 0; e < result.size(); e++) { + auto output = result.at(e); if (output) { // output->printShapeInfo("Output shape> "); @@ -2065,7 +2062,7 @@ TEST_F(DeclarableOpsTests5, DynamicPartition_3) { } } - delete result; + } TEST_F(DeclarableOpsTests5, DynamicStitch_empty_1) { @@ -2077,11 +2074,11 @@ TEST_F(DeclarableOpsTests5, DynamicStitch_empty_1) { auto d1 = NDArrayFactory::empty(); auto d2 = NDArrayFactory::create('c', {2, 5}, {0.94414854,0.5956861,0.8668989,0.3502196,0.5100082,0.061725974,0.6621324,0.034165382,0.32576954,0.51917326}); - nd4j::ops::dynamic_stitch op; + sd::ops::dynamic_stitch op; auto result = op.evaluate({&i0, &i1, &i2, &d0, &d1, &d2}, {}, {}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - delete result; + } TEST_F(DeclarableOpsTests5, DynamicStitch_empty_2) { @@ -2093,11 +2090,11 @@ TEST_F(DeclarableOpsTests5, DynamicStitch_empty_2) { auto d1 = NDArrayFactory::create('c', {0, 5}); auto d2 = NDArrayFactory::create('c', {2, 5}, {0.94414854,0.5956861,0.8668989,0.3502196,0.5100082,0.061725974,0.6621324,0.034165382,0.32576954,0.51917326}); - nd4j::ops::dynamic_stitch op; + sd::ops::dynamic_stitch op; auto result = op.evaluate({&i0, &i1, &i2, &d0, &d1, &d2}, {}, {}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -2112,17 +2109,17 @@ TEST_F(DeclarableOpsTests5, DynamicStitch_1) { auto exp = NDArrayFactory::create({7.4f, 0.1f, -1.f, 5.2f, -1.f, 4.3f}); - nd4j::ops::dynamic_stitch op; + sd::ops::dynamic_stitch op; auto result = op.evaluate({&x1, &x2, &y1, &y2}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto output = result->at(0); + auto output = result.at(0); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -2137,12 +2134,12 @@ TEST_F(DeclarableOpsTests5, DynamicStitch_2) { auto exp = NDArrayFactory::create({5.2f, -1.f, 4.3f, -1.f, 7.4f, 0.1f}); - nd4j::ops::dynamic_stitch op; + sd::ops::dynamic_stitch op; auto result = op.evaluate({&x1, &x2, &y1, &y2}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto output = result->at(0); + auto output = result.at(0); // output->printShapeInfo("Output shape> "); // exp.printShapeInfo("Expected shape> "); @@ -2151,7 +2148,7 @@ TEST_F(DeclarableOpsTests5, DynamicStitch_2) { ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } ////////////////////////////////////////////////////////////////////// @@ -2169,18 +2166,18 @@ TEST_F(DeclarableOpsTests5, fusedBatchNorm_test1) { auto expBatchVar = NDArrayFactory::create('c', {4}, {208.00001526, 208.00001526, 208.00001526, 208.00001526}); - nd4j::ops::fused_batch_norm op; + sd::ops::fused_batch_norm op; auto results = op.evaluate({&x, &scale, &offset}, {}, {0,1}); - auto y = results->at(0); - auto batchMean = results->at(1); - auto batchVar = results->at(2); + auto y = results.at(0); + auto batchMean = results.at(1); + auto batchVar = results.at(2); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expY.isSameShape(y)); ASSERT_TRUE(expBatchMean.isSameShape(batchMean)); ASSERT_TRUE(expBatchVar.isSameShape(batchVar)); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -2198,18 +2195,18 @@ TEST_F(DeclarableOpsTests5, fusedBatchNorm_test2) { auto expBatchMean = NDArrayFactory::create('c', {4}, {23., 24., 25., 26.}); auto expBatchVar = NDArrayFactory::create('c', {4}, {208.00001526, 208.00001526, 208.00001526, 208.00001526}); - nd4j::ops::fused_batch_norm op; + sd::ops::fused_batch_norm op; auto results = op.evaluate({&x, &scale, &offset}, {0.05}, {0,1}); - auto y = results->at(0); - auto batchMean = results->at(1); - auto batchVar = results->at(2); + auto y = results.at(0); + auto batchMean = results.at(1); + auto batchVar = results.at(2); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expY.isSameShape(y)); ASSERT_TRUE(expBatchMean.isSameShape(batchMean)); ASSERT_TRUE(expBatchVar.isSameShape(batchVar)); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -2227,18 +2224,18 @@ TEST_F(DeclarableOpsTests5, fusedBatchNorm_test3) { auto expBatchMean = NDArrayFactory::create('c', {4}, {23., 24., 25., 26.}); auto expBatchVar = NDArrayFactory::create('c', {4}, {208.00001526, 208.00001526, 208.00001526, 208.00001526}); - nd4j::ops::fused_batch_norm op; + sd::ops::fused_batch_norm op; auto results = op.evaluate({&x, &scale, &offset}, {}, {1,1}); - auto y = results->at(0); - auto batchMean = results->at(1); - auto batchVar = results->at(2); + auto y = results.at(0); + auto batchMean = results.at(1); + auto batchVar = results.at(2); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expY.isSameShape(y)); ASSERT_TRUE(expBatchMean.isSameShape(batchMean)); ASSERT_TRUE(expBatchVar.isSameShape(batchVar)); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -2262,18 +2259,18 @@ TEST_F(DeclarableOpsTests5, fusedBatchNorm_test4) { auto expBatchVar = NDArrayFactory::create('c', shape, {0., 0., 0., 0.}); - nd4j::ops::fused_batch_norm op; + sd::ops::fused_batch_norm op; auto results = op.evaluate({&x, &scale, &offset}, {}, {0,1}); - auto y = results->at(0); - auto batchMean = results->at(1); - auto batchVar = results->at(2); + auto y = results.at(0); + auto batchMean = results.at(1); + auto batchVar = results.at(2); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expY.isSameShape(y)); ASSERT_TRUE(expBatchMean.isSameShape(batchMean)); ASSERT_TRUE(expBatchVar.isSameShape(batchVar)); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -2297,18 +2294,18 @@ TEST_F(DeclarableOpsTests5, fusedBatchNorm_test5) { auto expBatchVar = NDArrayFactory::create('c', shape, {0., 0., 0., 0.}); - nd4j::ops::fused_batch_norm op; + sd::ops::fused_batch_norm op; auto results = op.evaluate({&x, &scale, &offset}, {0.05}, {0,1}); - auto y = results->at(0); - auto batchMean = results->at(1); - auto batchVar = results->at(2); + auto y = results.at(0); + auto batchMean = results.at(1); + auto batchVar = results.at(2); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expY.isSameShape(y)); ASSERT_TRUE(expBatchMean.isSameShape(batchMean)); ASSERT_TRUE(expBatchVar.isSameShape(batchVar)); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -2318,16 +2315,16 @@ TEST_F(DeclarableOpsTests5, confusion_matrix_test1) { auto predictions = NDArrayFactory::create('c', {1, 3}, {2, 2, 4}); auto expected = NDArrayFactory::create('c', {5, 5}, {0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1}); - nd4j::ops::confusion_matrix op; + sd::ops::confusion_matrix op; auto results = op.evaluate({&labels, &predictions}, {}, {}); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); - auto output = results->at(0); + auto output = results.at(0); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -2337,16 +2334,16 @@ TEST_F(DeclarableOpsTests5, confusion_matrix_test2) { auto predictions = NDArrayFactory::create('c', {1, 2}, {0, 2}); auto expected = NDArrayFactory::create('c', {3, 3}, {0, 0, 0, 1, 0, 0, 0, 0, 1}); - nd4j::ops::confusion_matrix op; + sd::ops::confusion_matrix op; auto results = op.evaluate({&labels, &predictions}, {}, {3}); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); - auto output = results->at(0); + auto output = results.at(0); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -2357,15 +2354,15 @@ TEST_F(DeclarableOpsTests5, confusion_matrix_test3) { auto weights = NDArrayFactory::create('c', {1, 2}, {100, 200}); auto expected = NDArrayFactory::create('c', {3, 3}, {0, 0, 0, 100, 0, 0, 0, 0, 200}); - nd4j::ops::confusion_matrix op; + sd::ops::confusion_matrix op; auto results = op.evaluate({&labels, &predictions, &weights}, {}, {3}); - auto output = results->at(0); + auto output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -2376,15 +2373,16 @@ TEST_F(DeclarableOpsTests5, confusion_matrix_test4) { auto weights = NDArrayFactory::create('c', {1, 2}, {100, 200}); auto expected = NDArrayFactory::create('c', {3, 3}, {0, 0, 0, 100, 0, 0, 0, 0, 200}); - nd4j::ops::confusion_matrix op; - auto results = op.evaluate({&labels, &predictions, &weights}, {}, {3, nd4j::DataType::DOUBLE}); - auto output = results->at(0); + sd::ops::confusion_matrix op; + auto results = op.evaluate({&labels, &predictions, &weights}, {}, {3, sd::DataType::DOUBLE}); + auto output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; + } /////////////////////////////////////////////////////////////////////////////// @@ -2395,14 +2393,14 @@ TEST_F(DeclarableOpsTests5, ZeroFraction_1) { 13, 14, 15, 16, 17, 18, 19, 0, 21, 22, 23, 24}); - nd4j::ops::zero_fraction op; + sd::ops::zero_fraction op; auto res = op.evaluate({&x}, {}, {}); - ASSERT_EQ(Status::OK(), res->status()); - ASSERT_TRUE(res->at(0)->isScalar()); - ASSERT_EQ(res->at(0)->e(0), 0.25); + ASSERT_EQ(Status::OK(), res.status()); + ASSERT_TRUE(res.at(0)->isScalar()); + ASSERT_EQ(res.at(0)->e(0), 0.25); - delete res; + } //////////////////////////////////////////////////////////////////////////////// @@ -2410,14 +2408,14 @@ TEST_F(DeclarableOpsTests5, ZeroFraction_2) { auto x = NDArrayFactory::create('c', {2, 2, 2}, {5.5, 0., 0.3, 5.5, 8.6, 0., 0., 0.4}); - nd4j::ops::zero_fraction op; + sd::ops::zero_fraction op; auto res = op.evaluate({&x}, {}, {}); - ASSERT_EQ(Status::OK(), res->status()); - ASSERT_TRUE(res->at(0)->isScalar()); - ASSERT_EQ(res->at(0)->e(0), 0.375); + ASSERT_EQ(Status::OK(), res.status()); + ASSERT_TRUE(res.at(0)->isScalar()); + ASSERT_EQ(res.at(0)->e(0), 0.375); - delete res; + } //////////////////////////////////////////////////////////////////////////////// @@ -2425,14 +2423,14 @@ TEST_F(DeclarableOpsTests5, ZeroFraction_3) { auto x = NDArrayFactory::create('f', {2, 2, 2}, {5.5, 0., 0.3, 5.5, 8.6, 0., 0., 0.4}); - nd4j::ops::zero_fraction op; + sd::ops::zero_fraction op; auto res = op.evaluate({&x}, {}, {}); - ASSERT_EQ(Status::OK(), res->status()); - ASSERT_TRUE(res->at(0)->isScalar()); - ASSERT_EQ(res->at(0)->e(0), 0.375); + ASSERT_EQ(Status::OK(), res.status()); + ASSERT_TRUE(res.at(0)->isScalar()); + ASSERT_EQ(res.at(0)->e(0), 0.375); - delete res; + } //////////////////////////////////////////////////////////////////////////////// @@ -2444,17 +2442,17 @@ TEST_F(DeclarableOpsTests5, XWPlusB_1) { auto exp = NDArrayFactory::create('c', {2,2}, {173.f, 264.f, 310.f, 279.f}); - nd4j::ops::xw_plus_b op; + sd::ops::xw_plus_b op; auto result = op.evaluate({&x, &y, &b}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto output = result->at(0); + auto output = result.at(0); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -2462,12 +2460,12 @@ TEST_F(DeclarableOpsTests5, StopGradient_1) { auto x = NDArrayFactory::create('c', {2,3}, { 1.f, 11.f, 3.f, 14.f, 5.f, 6.f}); - nd4j::ops::stop_gradient op; + sd::ops::stop_gradient op; auto result = op.evaluate({&x}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto output = result->at(0); + auto output = result.at(0); // output->printShapeInfo("Output shape> "); // x.printShapeInfo("Expected shape> "); @@ -2477,7 +2475,7 @@ TEST_F(DeclarableOpsTests5, StopGradient_1) { ASSERT_TRUE(x.isSameShape(output)); ASSERT_TRUE(x.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -2485,12 +2483,12 @@ TEST_F(DeclarableOpsTests5, StopGradient_2) { auto x = NDArrayFactory::create('f', {2,3}, { 1.f, 11.f, 3.f, 14.f, 5.f, 6.f}); - nd4j::ops::stop_gradient op; + sd::ops::stop_gradient op; auto result = op.evaluate({&x}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto output = result->at(0); + auto output = result.at(0); // output->printShapeInfo("Output shape> "); // x.printShapeInfo("Expected shape> "); @@ -2500,7 +2498,7 @@ TEST_F(DeclarableOpsTests5, StopGradient_2) { ASSERT_TRUE(x.isSameShape(output)); ASSERT_TRUE(x.equalsTo(output)); - delete result; + } ////////////////////////////////////////////////////////////////////// @@ -2509,15 +2507,15 @@ TEST_F(DeclarableOpsTests5, log_softmax_test1) { auto input = NDArrayFactory::create('c', {3, 3, 3}, {-1, 1, -2, 2, -3, 3, -4, 4, -5,5 ,-6,6, -7,7, -8,8, -9,9, -10,10, -11,11, -12,12, -13,13, 14}); auto expOutput = NDArrayFactory::create('c', {3, 3, 3}, {-2.16985e+00,-1.69846e-01,-3.16985e+00, -1.31507e+00,-6.31507e+00,-3.15072e-01, -8.00046e+00,-4.58767e-04,-9.00046e+00, -1.31327e+00,-1.23133e+01,-3.13266e-01, -1.40000e+01,-1.13743e-06,-1.50000e+01, -1.31326e+00,-1.83133e+01,-3.13262e-01, -2.00000e+01,-2.81941e-09,-2.10000e+01, -1.31326e+00,-2.43133e+01,-3.13262e-01, -2.73133e+01,-1.31326e+00,-3.13262e-01}); - nd4j::ops::log_softmax op; + sd::ops::log_softmax op; auto results = op.evaluate({&input}); - auto z = results->at(0); + auto z = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expOutput.isSameShape(z)); ASSERT_TRUE(expOutput.equalsTo(z)); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -2526,15 +2524,15 @@ TEST_F(DeclarableOpsTests5, log_softmax_test2) { auto input = NDArrayFactory::create('c', {3, 3, 3}, {-1, 1, -2, 2, -3, 3, -4, 4, -5,5 ,-6,6, -7,7, -8,8, -9,9, -10,10, -11,11, -12,12, -13,13, 14}); auto expOutput = NDArrayFactory::create('c', {3, 3, 3}, {-3.05095e+00,-3.04946e+00,-5.00705e+00, -5.09458e-02,-7.04946e+00,-7.04851e-03, -6.05095e+00,-4.94556e-02,-8.00705e+00, -3.04859e+00,-1.30000e+01,-3.04859e+00, -1.50486e+01,-2.37286e-06,-1.70486e+01, -4.85876e-02,-1.60000e+01,-4.85874e-02, -2.10000e+01,-3.04859e+00,-2.51269e+01, -7.96007e-10,-2.50486e+01,-2.12693e+00, -2.40000e+01,-4.85874e-02,-1.26928e-01}); - nd4j::ops::log_softmax op; + sd::ops::log_softmax op; auto results = op.evaluate({&input}, {}, {1}); - auto z = results->at(0); + auto z = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expOutput.isSameShape(z)); ASSERT_TRUE(expOutput.equalsTo(z)); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -2543,15 +2541,15 @@ TEST_F(DeclarableOpsTests5, log_softmax_test3) { auto input = NDArrayFactory::create('c', {3, 3, 3}, {-1, 1, -2, 2, -3, 3, -4, 4, -5,5 ,-6,6, -7,7, -8,8, -9,9, -10,10, -11,11, -12,12, -13,13, 14}); auto expOutput = NDArrayFactory::create('c', {3, 3, 3}, {-2.16985e+00,-1.69846e-01,-3.16985e+00, -1.31507e+00,-6.31507e+00,-3.15072e-01, -8.00046e+00,-4.58767e-04,-9.00046e+00, -1.31327e+00,-1.23133e+01,-3.13266e-01, -1.40000e+01,-1.13743e-06,-1.50000e+01, -1.31326e+00,-1.83133e+01,-3.13262e-01, -2.00000e+01,-2.81941e-09,-2.10000e+01, -1.31326e+00,-2.43133e+01,-3.13262e-01, -2.73133e+01,-1.31326e+00,-3.13262e-01}); - nd4j::ops::log_softmax op; + sd::ops::log_softmax op; auto results = op.evaluate({&input}, {}, {2}); - auto z = results->at(0); + auto z = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expOutput.isSameShape(z)); ASSERT_TRUE(expOutput.equalsTo(z)); - delete results; + } @@ -2561,15 +2559,15 @@ TEST_F(DeclarableOpsTests5, log_softmax_test5) { auto input = NDArrayFactory::create('c', {3, 3}, {-1, 1, -2, 2, -3, 3, -4, 4, 5}); auto expOutput = NDArrayFactory::create('c', {3, 3}, {-2.16985, -0.16985, -3.16985, -1.31507, -6.31507, -0.31507, -9.31335, -1.31335, -0.31335}); - nd4j::ops::log_softmax op; + sd::ops::log_softmax op; auto results = op.evaluate({&input}); - auto z = results->at(0); + auto z = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expOutput.isSameShape(z)); ASSERT_TRUE(expOutput.equalsTo(z)); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -2578,15 +2576,15 @@ TEST_F(DeclarableOpsTests5, log_softmax_test6) { auto input = NDArrayFactory::create('c', {3, 3}, {-1, 1, -2, 2, -3, 3, -4, 4, 5}); auto expOutput = NDArrayFactory::create('c', {3, 3}, {-3.05095,-3.04946,-7.12773, -0.05095,-7.04946,-2.12773, -6.05095,-0.04946,-0.12773}); - nd4j::ops::log_softmax op; + sd::ops::log_softmax op; auto results = op.evaluate({&input}, {}, {0}); - auto z = results->at(0); + auto z = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expOutput.isSameShape(z)); ASSERT_TRUE(expOutput.equalsTo(z)); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -2595,15 +2593,15 @@ TEST_F(DeclarableOpsTests5, log_softmax_test7) { auto input = NDArrayFactory::create('c', {1, 5}, {-1, 1, -2, 2, 3}); auto expOutput = NDArrayFactory::create('c', {1, 5}, {-4.42414, -2.42414, -5.42414, -1.42414, -0.42414}); - nd4j::ops::log_softmax op; + sd::ops::log_softmax op; auto results = op.evaluate({&input}); - auto z = results->at(0); + auto z = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expOutput.isSameShape(z)); ASSERT_TRUE(expOutput.equalsTo(z)); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -2612,15 +2610,15 @@ TEST_F(DeclarableOpsTests5, log_softmax_test8) { auto input = NDArrayFactory::create('c', {1, 5}, {-1, 1, -2, 2, 3}); auto expOutput = NDArrayFactory::create('c', {1, 5}, {0, 0, 0, 0, 0}); - nd4j::ops::log_softmax op; + sd::ops::log_softmax op; auto results = op.evaluate({&input}, {}, {0}); - auto z = results->at(0); + auto z = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expOutput.isSameShape(z)); ASSERT_TRUE(expOutput.equalsTo(z)); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -2629,15 +2627,15 @@ TEST_F(DeclarableOpsTests5, log_softmax_test9) { auto input = NDArrayFactory::create('c', {5, 1}, {-1, 1, -2, 2, 3}); auto expOutput = NDArrayFactory::create('c', {5, 1}, {0, 0, 0, 0, 0}); - nd4j::ops::log_softmax op; + sd::ops::log_softmax op; auto results = op.evaluate({&input}); - auto z = results->at(0); + auto z = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expOutput.isSameShape(z)); ASSERT_TRUE(expOutput.equalsTo(z)); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -2646,15 +2644,15 @@ TEST_F(DeclarableOpsTests5, log_softmax_test10) { auto input = NDArrayFactory::create('c', {5, 1}, {-1, 1, -2, 2, 3}); auto expOutput = NDArrayFactory::create('c', {5, 1}, {-4.42414, -2.42414, -5.42414, -1.42414, -0.42414}); - nd4j::ops::log_softmax op; + sd::ops::log_softmax op; auto results = op.evaluate({&input}, {}, {0}); - auto z = results->at(0); + auto z = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expOutput.isSameShape(z)); ASSERT_TRUE(expOutput.equalsTo(z)); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -2663,15 +2661,15 @@ TEST_F(DeclarableOpsTests5, log_softmax_test11) { auto input = NDArrayFactory::create('c', {5}, {-1, 1, -2, 2, 3}); auto expOutput = NDArrayFactory::create('c', {5}, {-4.42414, -2.42414, -5.42414, -1.42414, -0.42414}); - nd4j::ops::log_softmax op; + sd::ops::log_softmax op; auto results = op.evaluate({&input}); - auto z = results->at(0); + auto z = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expOutput.isSameShape(z)); ASSERT_TRUE(expOutput.equalsTo(z)); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -2682,15 +2680,15 @@ TEST_F(DeclarableOpsTests5, log_softmax_test12) { for (int i = 0; i < 10; ++i) { - nd4j::ops::log_softmax op; + sd::ops::log_softmax op; auto results = op.evaluate({&input}); - auto z = results->at(0); + auto z = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expOutput.isSameShape(z)); ASSERT_TRUE(expOutput.equalsTo(z, 1e-4)); - delete results; + } } @@ -2701,15 +2699,15 @@ TEST_F(DeclarableOpsTests5, log_softmax_bp_test1) { auto epsilon = NDArrayFactory::create('c', {2, 2}, {0.1, 0.2, 0.3, 0.4}); auto exp = NDArrayFactory::create('c', {2, 2}, {-0.07311,0.02689, -0.07311,0.02689}); - nd4j::ops::log_softmax_bp op; + sd::ops::log_softmax_bp op; auto results = op.evaluate({&input, &epsilon}); - auto output = results->at(0); + auto output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -2719,15 +2717,15 @@ TEST_F(DeclarableOpsTests5, log_softmax_bp_test2) { auto epsilon = NDArrayFactory::create('c', {2, 2}, {0.1, 0.2, 0.3, 0.4}); auto exp = NDArrayFactory::create('c', {2, 2}, {-0.17616, -0.17616, 0.02384, 0.02384}); - nd4j::ops::log_softmax_bp op; + sd::ops::log_softmax_bp op; auto results = op.evaluate({&input, &epsilon}, {}, {0}); - auto output = results->at(0); + auto output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -2737,7 +2735,7 @@ TEST_F(DeclarableOpsTests5, ELU_1) { auto exp = NDArrayFactory::create('c', {2, 2, 2}, { -0.63212055, 2. , 1.5, -0.753403, 1., 2., 2., 1.}); auto res = NDArrayFactory::create('c', {2, 2, 2}); - input.applyScalar(nd4j::scalar::ELU, 1.f, res); + input.applyScalar(sd::scalar::ELU, 1.f, res); ASSERT_TRUE(res.equalsTo(&exp)); } @@ -2748,31 +2746,31 @@ TEST_F(DeclarableOpsTests5, L2_Loss_1) { auto input = NDArrayFactory::create('c', {2, 2, 2}, { -1., 2. , 1.5, -1.4, 1., 2., 2., 1.}); double exp(9.605); - nd4j::ops::l2_loss op; + sd::ops::l2_loss op; auto results = op.evaluate({&input}, {}, {}); - auto output = results->at(0); + auto output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(output->isScalar()); ASSERT_EQ(output->e(0), exp); - delete results; + } TEST_F(DeclarableOpsTests5, L2_Loss_2) { auto x = NDArrayFactory::create(0.7787855863571167); auto e = NDArrayFactory::create(0.303254); - nd4j::ops::l2_loss op; + sd::ops::l2_loss op; auto results = op.evaluate({&x}, {}, {}); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); - auto z = results->at(0); + auto z = results.at(0); ASSERT_EQ(e, *z); - delete results; + } TEST_F(DeclarableOpsTests5, L2_Loss_3) { @@ -2780,7 +2778,7 @@ TEST_F(DeclarableOpsTests5, L2_Loss_3) { auto e = NDArrayFactory::create(0.303254); auto z = NDArrayFactory::create(0.0); - nd4j::ops::l2_loss op; + sd::ops::l2_loss op; auto status = op.execute({&x}, {&z} , {}, {}, {}); ASSERT_EQ(Status::OK(), status); @@ -2796,15 +2794,15 @@ TEST_F(DeclarableOpsTests5, LogPoissonLoss_1) { auto exp = NDArrayFactory::create('c', {2, 2, 2}, {1.3678794, 5.389056, 2.981689, 1.6465969, 1.7182817, 5.389056, 5.389056, 1.7182817}); - nd4j::ops::log_poisson_loss op; + sd::ops::log_poisson_loss op; auto results = op.evaluate({&input, &weights, &targets}, {}, {0}); - auto output = results->at(0); + auto output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete results; + } //////////////////////////////////////////////////////////////////////////////// @@ -2817,15 +2815,15 @@ TEST_F(DeclarableOpsTests5, LogPoissonLoss_2) { auto exp = NDArrayFactory::create('c', {2, 2, 2}, {3.0196857, 4.0408626, 2.1334953, 3.6984034, 1.3700882, 4.0408626, 4.0408626, 1.3700882}); - nd4j::ops::log_poisson_loss op; + sd::ops::log_poisson_loss op; auto results = op.evaluate({&input, &weights, &targets}, {}, {0, 1}); - auto output = results->at(0); + auto output = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete results; + } //////////////////////////////////////////////////////////////////////////////// @@ -2863,21 +2861,21 @@ TEST_F(DeclarableOpsTests5, NormalizeMoments_1) { -19.75, -30.75, -37., 1.25, -51., -10.75, -33.8125, -3.75}); - nd4j::ops::normalize_moments op; + sd::ops::normalize_moments op; auto results = op.evaluate({&counts, &means, &deviance}, {0.0}, {}); - ASSERT_EQ(Status::OK(), results->status()); - ASSERT_EQ(results->size(), 2); + ASSERT_EQ(Status::OK(), results.status()); + ASSERT_EQ(results.size(), 2); - auto outputMeans = results->at(0); - auto outputDeviance = results->at(1); + auto outputMeans = results.at(0); + auto outputDeviance = results.at(1); ASSERT_TRUE(expMeans.isSameShape(outputMeans)); ASSERT_TRUE(expMeans.equalsTo(outputMeans)); ASSERT_TRUE(expMeans.isSameShape(outputDeviance)); ASSERT_TRUE(expDeviance.equalsTo(outputDeviance)); - delete results; + } //////////////////////////////////////////////////////////////////////////////// @@ -2914,21 +2912,21 @@ TEST_F(DeclarableOpsTests5, NormalizeMoments_2) { 0.9097222, 0.7430556, 0.6388887, 1.0763888, 0.38888884, 1.0208334, 0.6927084, 1.076389}); - nd4j::ops::normalize_moments op; + sd::ops::normalize_moments op; auto results = op.evaluate({&counts, &means, &deviance}, {0.0}, {}); - ASSERT_EQ(Status::OK(), results->status()); - ASSERT_EQ(results->size(), 2); + ASSERT_EQ(Status::OK(), results.status()); + ASSERT_EQ(results.size(), 2); - auto outputMeans = results->at(0); - auto outputDeviance = results->at(1); + auto outputMeans = results.at(0); + auto outputDeviance = results.at(1); ASSERT_TRUE(expMeans.isSameShape(outputMeans)); ASSERT_TRUE(expMeans.equalsTo(outputMeans)); ASSERT_TRUE(expMeans.isSameShape(outputDeviance)); ASSERT_TRUE(expDeviance.equalsTo(outputDeviance)); - delete results; + } //////////////////////////////////////////////////////////////////////////////// @@ -2965,20 +2963,20 @@ TEST_F(DeclarableOpsTests5, NormalizeMoments_3) { 0.9097222, 0.7430556, 0.6388887, 1.0763888, 0.38888884, 1.0208334, 0.6927084, 1.076389}); - nd4j::ops::normalize_moments op; + sd::ops::normalize_moments op; auto results = op.evaluate({&counts, &means, &deviance}, {shift}, {}); - ASSERT_EQ(Status::OK(), results->status()); - ASSERT_EQ(results->size(), 2); + ASSERT_EQ(Status::OK(), results.status()); + ASSERT_EQ(results.size(), 2); - auto outputMeans = results->at(0); - auto outputDeviance = results->at(1); + auto outputMeans = results.at(0); + auto outputDeviance = results.at(1); ASSERT_TRUE(expMeans.isSameShape(outputMeans)); ASSERT_TRUE(expMeans.equalsTo(outputMeans)); ASSERT_TRUE(expMeans.isSameShape(outputDeviance)); ASSERT_TRUE(expDeviance.equalsTo(outputDeviance)); - delete results; + } diff --git a/libnd4j/tests_cpu/layers_tests/DeclarableOpsTests6.cpp b/libnd4j/tests_cpu/layers_tests/DeclarableOpsTests6.cpp index 5a919d132..002e3376f 100644 --- a/libnd4j/tests_cpu/layers_tests/DeclarableOpsTests6.cpp +++ b/libnd4j/tests_cpu/layers_tests/DeclarableOpsTests6.cpp @@ -7,7 +7,7 @@ * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * WARRANTIES OR CONDITIONS OF ANY KIND, either expres or implied. See the * License for the specific language governing permissions and limitations * under the License. * @@ -22,12 +22,12 @@ #include "testlayers.h" #include #include -#include +#include #include -using namespace nd4j; -using namespace nd4j::graph; +using namespace sd; +using namespace sd::graph; class DeclarableOpsTests6 : public testing::Test { public: @@ -49,15 +49,15 @@ TEST_F(DeclarableOpsTests6, Test_StridedSlice_Once_Again_1) { matrix.linspace(1); - nd4j::ops::strided_slice op; + sd::ops::strided_slice op; auto result = op.evaluate({&matrix, &b, &e, &s}, {}, {0, 0, 0, 0, 1}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } TEST_F(DeclarableOpsTests6, Test_StridedSlice_Once_Again_2) { @@ -70,15 +70,15 @@ TEST_F(DeclarableOpsTests6, Test_StridedSlice_Once_Again_2) { matrix.linspace(1); - nd4j::ops::strided_slice op; + sd::ops::strided_slice op; auto result = op.evaluate({&matrix, &b, &e, &s}, {}, {0, 0, 0, 0, 1}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_EQ(exp, *z); - delete result; + } TEST_F(DeclarableOpsTests6, Test_StridedSlice_Once_Again_3) { @@ -91,16 +91,16 @@ TEST_F(DeclarableOpsTests6, Test_StridedSlice_Once_Again_3) { //matrix.linspace(1); - nd4j::ops::strided_slice op; + sd::ops::strided_slice op; auto result = op.evaluate({&matrix, &b, &e, &s}, {}, {0, 0, 0, 0, 1}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); //z->printShapeInfo("SS OS shape"); ASSERT_TRUE(z->isEmpty()); //ASSERT_EQ(exp, *z); - delete result; + } TEST_F(DeclarableOpsTests6, Test_StridedSlice_Once_Again_4) { @@ -113,16 +113,16 @@ TEST_F(DeclarableOpsTests6, Test_StridedSlice_Once_Again_4) { //matrix.linspace(1); - nd4j::ops::strided_slice op; + sd::ops::strided_slice op; auto result = op.evaluate({&matrix, &b, &e, &s}, {}, {0, 0, 0, 0, 1}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(z->equalsTo(exp)); //ASSERT_EQ(exp, *z); - delete result; + } TEST_F(DeclarableOpsTests6, Test_StridedSlice_Once_Again_04) { @@ -131,13 +131,13 @@ TEST_F(DeclarableOpsTests6, Test_StridedSlice_Once_Again_04) { auto b = NDArrayFactory::create_('c', {1}, {1}); auto e = NDArrayFactory::create_('c', {1}, {z}); auto s = NDArrayFactory::create_('c', {1}, {1}); - nd4j::ops::ones_as opOnes; + sd::ops::ones_as opOnes; //auto exp = NDArrayFactory::create('c', {2}, {1.0f, 2.0f}); auto onesRes = opOnes.evaluate({&matrix}); //matrix.linspace(1); - ASSERT_EQ(onesRes->status(), Status::OK()); + ASSERT_EQ(onesRes.status(), Status::OK()); - auto ones = onesRes->at(0); + auto ones = onesRes.at(0); *ones *= 10; auto onesD = new NDArray(ones->dup()); @@ -157,14 +157,12 @@ TEST_F(DeclarableOpsTests6, Test_StridedSlice_Once_Again_04) { block->getIArguments()->push_back(0); block->getIArguments()->push_back(0); auto inputShapes = new ShapeList({ones->getShapeInfo(), b->getShapeInfo(), e->getShapeInfo(), s->getShapeInfo()}); - nd4j::ops::strided_slice op; + sd::ops::strided_slice op; auto result = op.calculateOutputShape(inputShapes, *block); //execute({ones, &b, &e, &s}, {}, {0, 1, 0, 0, 0}); ASSERT_EQ(result->size(), 1); ASSERT_TRUE(shape::isEmpty(result->at(0))); //ASSERT_EQ(exp, *z); delete block; - - delete onesRes; delete result; delete variableSpace; delete inputShapes; @@ -180,14 +178,14 @@ TEST_F(DeclarableOpsTests6, Test_StridedSlice_Once_Again_5) { //matrix.linspace(1); - nd4j::ops::strided_slice op; + sd::ops::strided_slice op; auto result = op.evaluate({&matrix, &b, &e, &s}, {}, {0, 0, 0, 0, 1}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } TEST_F(DeclarableOpsTests6, Test_StridedSlice_Once_Again_6) { @@ -200,14 +198,14 @@ TEST_F(DeclarableOpsTests6, Test_StridedSlice_Once_Again_6) { //matrix.linspace(1); - nd4j::ops::strided_slice op; + sd::ops::strided_slice op; auto result = op.evaluate({&matrix, &b, &e, &s}, {}, {0, 0, 0, 0, 2}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } TEST_F(DeclarableOpsTests6, Test_StridedSlice_Once_Again_7) { @@ -221,14 +219,14 @@ TEST_F(DeclarableOpsTests6, Test_StridedSlice_Once_Again_7) { //matrix.linspace(1); - nd4j::ops::strided_slice op; + sd::ops::strided_slice op; auto result = op.evaluate({&matrix, &b, &e, &s}, {}, {1, 0, 0, 0, 0}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); //ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } TEST_F(DeclarableOpsTests6, Test_StridedSlice_BP_1) { @@ -243,14 +241,14 @@ TEST_F(DeclarableOpsTests6, Test_StridedSlice_BP_1) { matrix.linspace(1); grad.linspace(1); - nd4j::ops::strided_slice_bp op; + sd::ops::strided_slice_bp op; auto result = op.evaluate({&matrix, &grad}, {}, {1, 0, 1, 0, 2, 0, 0, 0, 1, 1, 1}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); //ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } TEST_F(DeclarableOpsTests6, Test_StridedSlice_BP_2) { @@ -265,14 +263,14 @@ TEST_F(DeclarableOpsTests6, Test_StridedSlice_BP_2) { matrix.linspace(1); //grad.linspace(1); - nd4j::ops::strided_slice_bp op; + sd::ops::strided_slice_bp op; auto result = op.evaluate({&matrix, &grad}, {}, {1, 0, 1, 0, 2, 0, 0, 0, 1, 1, 1}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); //ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } TEST_F(DeclarableOpsTests6, Test_StridedSlice_BP_3) { @@ -287,31 +285,31 @@ TEST_F(DeclarableOpsTests6, Test_StridedSlice_BP_3) { matrix.linspace(1); grad.linspace(1); - nd4j::ops::strided_slice_bp op; + sd::ops::strided_slice_bp op; auto result = op.evaluate({&matrix, &grad}, {}, {1, 0, 1, 0, 0, 0, 0, 0, 256, 1, 1}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); //ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } TEST_F(DeclarableOpsTests6, Test_Simple_Scalar_1) { auto x = NDArrayFactory::create('c', {1, 1}, {2.0f}); auto exp = NDArrayFactory::create('c', {1, 1}, {4.0f}); - nd4j::ops::test_scalar op; + sd::ops::test_scalar op; auto result = op.evaluate({&x}, {}, {}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } TEST_F(DeclarableOpsTests6, Test_Order_1) { @@ -320,125 +318,125 @@ TEST_F(DeclarableOpsTests6, Test_Order_1) { x.linspace(1); exp.linspace(1); - nd4j::ops::order op; + sd::ops::order op; auto result = op.evaluate({&x}, {}, {0}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.equalsTo(z)); ASSERT_NE(x.ordering(), z->ordering()); - delete result; + } TEST_F(DeclarableOpsTests6, cumSum_1) { auto x = NDArrayFactory::create('c', {1, 4}, {1.f, 2.f, 3.f, 4.f}); auto exp = NDArrayFactory::create('c', {1, 4}, {1.f, 3.f, 6.f, 10.f}); - nd4j::ops::cumsum op; + sd::ops::cumsum op; auto result = op.evaluate({&x}, {}, {0, 0}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } TEST_F(DeclarableOpsTests6, cumSum_2) { auto x= NDArrayFactory::create('c', {2, 4}, {1.f, 2.f, 3.f, 4.f, 1.f, 2.f, 3.f, 4.f}); auto exp= NDArrayFactory::create('c', {2, 4}, {1.f, 3.f, 6.f, 10.f, 1.f, 3.f, 6.f, 10.f}); - nd4j::ops::cumsum op; + sd::ops::cumsum op; auto result = op.evaluate({&x}, {}, {0, 0, 1}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); // z->printIndexedBuffer("CumSum1"); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } TEST_F(DeclarableOpsTests6, cumSum_3) { auto x= NDArrayFactory::create('c', {2, 4}, {1.f, 2.f, 3.f, 4.f, 1.f, 2.f, 3.f, 4.f}); auto exp= NDArrayFactory::create('c', {2, 4}, {1.f, 2.f, 3.f, 4.f, 2.f, 4.f, 6.f, 8.f}); - nd4j::ops::cumsum op; + sd::ops::cumsum op; auto result = op.evaluate({&x}, {}, {0, 0, 0}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } TEST_F(DeclarableOpsTests6, cumSum_4) { auto x = NDArrayFactory::create('c', {3, 3}, {1, 2, 3, 4, 5, 6, 7, 8, 9}); auto exp = NDArrayFactory::create('c', {3, 3}, {12., 15., 18., 11., 13., 15., 7., 8., 9.}); - nd4j::ops::cumsum op; + sd::ops::cumsum op; auto result = op.evaluate({&x}, {}, {0, 1, 0}, {}, {}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); // z->printBuffer(); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } TEST_F(DeclarableOpsTests6, cumSum_5) { auto x = NDArrayFactory::create('c', {3, 3}, {1, 2, 3, 4, 5, 6, 7, 8, 9}); auto exp = NDArrayFactory::create('c', {3, 3}, {6.f, 5.f, 3.f, 15.f, 11.f, 6.f, 24.f, 17.f, 9.f,}); - nd4j::ops::cumsum op; + sd::ops::cumsum op; auto result = op.evaluate({&x}, {}, {0, 1, 1}, {}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } TEST_F(DeclarableOpsTests6, cumSum_6) { auto x = NDArrayFactory::create('c', {3, 3}, {1, 2, 3, 4, 5, 6, 7, 8, 9}); auto exp = NDArrayFactory::create('c', {3, 3}, {11.f, 13.f, 15.f, 7.f, 8.f, 9.f, 0.f, 0.f, 0.f}); - nd4j::ops::cumsum op; + sd::ops::cumsum op; auto result = op.evaluate({&x}, {}, {1, 1, 0}, {}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } TEST_F(DeclarableOpsTests6, cumSum_7) { auto x = NDArrayFactory::create('c', {3, 3}, {1, 2, 3, 4, 5, 6, 7, 8, 9}); auto exp = NDArrayFactory::create('c', {3, 3}, {5.f, 3.f, 0.f, 11.f, 6.f, 0.f, 17.f, 9.f, 0.f}); - nd4j::ops::cumsum op; + sd::ops::cumsum op; auto result = op.evaluate({&x}, {}, {1, 1, 1}, {}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } TEST_F(DeclarableOpsTests6, cumSum_8) { @@ -446,15 +444,15 @@ TEST_F(DeclarableOpsTests6, cumSum_8) { auto axis = NDArrayFactory::create('c', {1}, {1}); auto exp = NDArrayFactory::create('c', {3, 3}, {5.f, 3.f, 0.f, 11.f, 6.f, 0.f, 17.f, 9.f, 0.f}); - nd4j::ops::cumsum op; + sd::ops::cumsum op; auto result = op.evaluate({&x, &axis}, {}, {1, 1}, {}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -474,39 +472,39 @@ TEST_F(DeclarableOpsTests6, cumSum_9) { //************************************// exclusive = 0; reverse = 0; - nd4j::ops::cumsum op; + sd::ops::cumsum op; auto result = op.evaluate({&inputC, &axis}, {}, {exclusive, reverse}, {}); - ASSERT_EQ(Status::OK(), result->status()); - auto z = result->at(0); + ASSERT_EQ(Status::OK(), result.status()); + auto z = result.at(0); ASSERT_TRUE(expFF.equalsTo(z)); - delete result; + //************************************// exclusive = 1; reverse = 0; result = op.evaluate({&inputC, &axis}, {}, {exclusive, reverse}); - ASSERT_EQ(Status::OK(), result->status()); - z = result->at(0); + ASSERT_EQ(Status::OK(), result.status()); + z = result.at(0); ASSERT_TRUE(expTF.equalsTo(z)); - delete result; + //************************************// exclusive = 0; reverse = 1; result = op.evaluate({&inputC, &axis}, {}, {exclusive, reverse}); - ASSERT_EQ(Status::OK(), result->status()); - z = result->at(0); + ASSERT_EQ(Status::OK(), result.status()); + z = result.at(0); ASSERT_TRUE(expFT.equalsTo(z)); - delete result; + //************************************// exclusive = 1; reverse = 1; result = op.evaluate({&inputC, &axis}, {}, {exclusive, reverse}); - ASSERT_EQ(Status::OK(), result->status()); - z = result->at(0); + ASSERT_EQ(Status::OK(), result.status()); + z = result.at(0); ASSERT_TRUE(expTT.equalsTo(z)); - delete result; + } @@ -515,137 +513,137 @@ TEST_F(DeclarableOpsTests6, cumSum_10) { auto x = NDArrayFactory::create('c', {4, 16, 16, 1}); auto y = NDArrayFactory::create(-3); - nd4j::ops::cumsum op; + sd::ops::cumsum op; auto result = op.evaluate({&x, &y}, {}, {1, 1}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - delete result; + } //////////////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests6, cumSum_11) { - NDArray x('c', {3, 3, 3}, nd4j::DataType::DOUBLE); + NDArray x('c', {3, 3, 3}, sd::DataType::DOUBLE); auto exp = NDArrayFactory::create('c', {3,3,3}, {12., 15., 18.,11., 13., 15.,7., 8., 9., 39., 42., 45.,29., 31., 33.,16., 17., 18., 66., 69., 72.,47., 49., 51.,25., 26., 27.}); x.linspace(1); - nd4j::ops::cumsum op; + sd::ops::cumsum op; auto result = op.evaluate({&x}, {}, {0, 1, 1}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests6, cumSum_12) { - NDArray x('c', {3, 3, 3}, nd4j::DataType::DOUBLE); + NDArray x('c', {3, 3, 3}, sd::DataType::DOUBLE); auto exp = NDArrayFactory::create('c', {3,3,3}, {1., 2., 3.,5., 7., 9.,12., 15., 18., 10., 11., 12.,23., 25., 27.,39., 42., 45., 19., 20., 21.,41., 43., 45., 66., 69., 72.}); x.linspace(1); - nd4j::ops::cumsum op; + sd::ops::cumsum op; auto result = op.evaluate({&x}, {}, {0, 0, 1}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests6, cumSum_13) { - NDArray x('c', {3, 3, 3}, nd4j::DataType::DOUBLE); + NDArray x('c', {3, 3, 3}, sd::DataType::DOUBLE); auto exp = NDArrayFactory::create('c', {3,3,3}, {11., 13., 15.,7., 8., 9.,0., 0., 0., 29., 31., 33.,16., 17., 18.,0., 0., 0., 47., 49., 51.,25., 26., 27.,0., 0., 0.}); x.linspace(1); - nd4j::ops::cumsum op; + sd::ops::cumsum op; auto result = op.evaluate({&x}, {}, {1, 1, 1}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests6, cumSum_14) { - NDArray x('c', {3, 3, 3}, nd4j::DataType::DOUBLE); + NDArray x('c', {3, 3, 3}, sd::DataType::DOUBLE); auto exp = NDArrayFactory::create('c', {3,3,3}, {29., 31., 33.,35., 37., 39.,41., 43., 45., 19., 20., 21.,22., 23., 24.,25., 26., 27., 0., 0., 0.,0., 0., 0.,0., 0., 0.}); x.linspace(1); - nd4j::ops::cumsum op; + sd::ops::cumsum op; auto result = op.evaluate({&x}, {}, {1, 1, 0}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests6, cumSum_15) { - NDArray x('c', {3, 3, 3}, nd4j::DataType::DOUBLE); + NDArray x('c', {3, 3, 3}, sd::DataType::DOUBLE); auto exp = NDArrayFactory::create('c', {3,3,3}, {6., 5., 3.,15., 11., 6.,24., 17., 9., 33., 23., 12.,42., 29., 15.,51., 35., 18., 60., 41., 21.,69., 47., 24.,78., 53., 27.}); x.linspace(1); - nd4j::ops::cumsum op; + sd::ops::cumsum op; auto result = op.evaluate({&x}, {}, {0, 1, 2}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests6, cumSum_16) { - NDArray x('f', {3, 4}, nd4j::DataType::FLOAT32); + NDArray x('f', {3, 4}, sd::DataType::FLOAT32); - nd4j::ops::cumsum op; + sd::ops::cumsum op; auto result = op.evaluate({&x}, {}, {0, 0, 1}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); // z->printShapeInfo(); // x.printShapeInfo(); ASSERT_TRUE(z->ews() == 1); ASSERT_TRUE(x.ews() == 1); - delete result; + } //////////////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests6, cumSum_17) { - NDArray x('c', {2, 1500}, nd4j::DataType::FLOAT32); + NDArray x('c', {2, 1500}, sd::DataType::FLOAT32); NDArray x0 = x(0, {0}); NDArray x1 = x(1, {0}); x0.linspace(1); x1.linspace(1); - NDArray exp('c', {2, 1500}, nd4j::DataType::FLOAT32); + NDArray exp('c', {2, 1500}, sd::DataType::FLOAT32); NDArray exp0 = exp(0, {0}); NDArray exp1 = exp(1, {0}); @@ -658,27 +656,27 @@ TEST_F(DeclarableOpsTests6, cumSum_17) { exp1.p(i, prev + i + 1); } - nd4j::ops::cumsum op; + sd::ops::cumsum op; auto result = op.evaluate({&x}, {}, {0, 0, 1}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests6, cumSum_18) { - NDArray x('c', {2, 1500}, nd4j::DataType::FLOAT32); + NDArray x('c', {2, 1500}, sd::DataType::FLOAT32); NDArray x0 = x(0, {0}); NDArray x1 = x(1, {0}); x0.linspace(1); x1.linspace(1); - NDArray exp('c', {2, 1500}, nd4j::DataType::FLOAT32); + NDArray exp('c', {2, 1500}, sd::DataType::FLOAT32); NDArray exp0 = exp(0, {0}); NDArray exp1 = exp(1, {0}); @@ -691,27 +689,27 @@ TEST_F(DeclarableOpsTests6, cumSum_18) { exp1.p(i, prev + i); } - nd4j::ops::cumsum op; + sd::ops::cumsum op; auto result = op.evaluate({&x}, {}, {1, 0, 1}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests6, cumSum_19) { - NDArray x('c', {2, 1500}, nd4j::DataType::FLOAT32); + NDArray x('c', {2, 1500}, sd::DataType::FLOAT32); NDArray x0 = x(0, {0}); NDArray x1 = x(1, {0}); x0.linspace(1); x1.linspace(1); - NDArray exp('c', {2, 1500}, nd4j::DataType::FLOAT32); + NDArray exp('c', {2, 1500}, sd::DataType::FLOAT32); NDArray exp0 = exp(0, {0}); NDArray exp1 = exp(1, {0}); @@ -724,28 +722,28 @@ TEST_F(DeclarableOpsTests6, cumSum_19) { exp1.p(i, prev + i + 1); } - nd4j::ops::cumsum op; + sd::ops::cumsum op; auto result = op.evaluate({&x}, {}, {0, 1, 1}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); // exp0.printBuffer(); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests6, cumSum_20) { - NDArray x('c', {2, 1500}, nd4j::DataType::FLOAT32); + NDArray x('c', {2, 1500}, sd::DataType::FLOAT32); NDArray x0 = x(0, {0}); NDArray x1 = x(1, {0}); x0.linspace(1); x1.linspace(1); - NDArray exp('c', {2, 1500}, nd4j::DataType::FLOAT32); + NDArray exp('c', {2, 1500}, sd::DataType::FLOAT32); NDArray exp0 = exp(0, {0}); NDArray exp1 = exp(1, {0}); @@ -758,15 +756,15 @@ TEST_F(DeclarableOpsTests6, cumSum_20) { exp1.p(i, prev + i + 2); } - nd4j::ops::cumsum op; + sd::ops::cumsum op; auto result = op.evaluate({&x}, {}, {1, 1, 1}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -776,16 +774,16 @@ TEST_F(DeclarableOpsTests6, TestMergeMaxIndex_1) { auto y = NDArrayFactory::create('c', {2, 2, 2}, {10.f, 2.f, 30.f, 4.f, 50.f, 6.f, 70.f, 8.f}); auto z = NDArrayFactory::create('c', {2, 2, 2}, {1.f, 20.f, 3.f, 40.f, 5.f, 60.f, 7.f, 80.f}); auto exp = NDArrayFactory::create('c', {2, 2, 2}, {1, 2, 1, 2, 1, 2, 1, 2}); - nd4j::ops::mergemaxindex op; + sd::ops::mergemaxindex op; - auto ress = op.evaluate({&x, &y, &z}, {}, {}, {}); + auto res = op.evaluate({&x, &y, &z}, {}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, ress->status()); -// ress->at(0)->printIndexedBuffer("MergeMaxIndex Result is "); -// ress->at(0)->printShapeInfo("Shape info for MergeMaxIdex"); + ASSERT_EQ(ND4J_STATUS_OK, res.status()); +// res.at(0)->printIndexedBuffer("MergeMaxIndex Result is "); +// res.at(0)->printShapeInfo("Shape info for MergeMaxIdex"); // x.printIndexedBuffer("Input is"); - ASSERT_TRUE(ress->at(0)->equalsTo(exp)); - delete ress; + ASSERT_TRUE(res.at(0)->equalsTo(exp)); + } //////////////////////////////////////////////////////////////////////////////// @@ -795,16 +793,16 @@ TEST_F(DeclarableOpsTests6, TestMergeMaxIndex_2) { auto y = NDArrayFactory::create('c', {2, 2, 2}, {10.f, 2.f, 30.f, 4.f, 50.f, 6.f, 70.f, 8.f}); auto z = NDArrayFactory::create('c', {2, 2, 2}, {1.f, 20.f, 3.f, 40.f, 5.f, 60.f, 7.f, 80.f}); auto exp = NDArrayFactory::create('c', {2, 2, 2}, {1, 2, 1, 2, 1, 2, 1, 2}); - nd4j::ops::mergemaxindex op; + sd::ops::mergemaxindex op; - auto ress = op.evaluate({&x, &y, &z}, {}, {nd4j::DataType::INT64}); + auto ress = op.evaluate({&x, &y, &z}, {}, {sd::DataType::INT64}); - ASSERT_EQ(ND4J_STATUS_OK, ress->status()); -// ress->at(0)->printIndexedBuffer("MergeMaxIndex2 Result is "); -// ress->at(0)->printShapeInfo("Shape info for MergeMaxIdex2"); + ASSERT_EQ(ND4J_STATUS_OK, ress.status()); +// res.at(0)->printIndexedBuffer("MergeMaxIndex2 Result is "); +// res.at(0)->printShapeInfo("Shape info for MergeMaxIdex2"); // x.printIndexedBuffer("Input is"); - ASSERT_TRUE(ress->at(0)->equalsTo(exp)); - delete ress; + ASSERT_TRUE(ress.at(0)->equalsTo(exp)); + } //////////////////////////////////////////////////////////////////////////////// @@ -812,15 +810,15 @@ TEST_F(DeclarableOpsTests6, TestDropout_1) { auto x = NDArrayFactory::create('c', {2, 2, 2}, {1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 7.f, 8.f}); auto shape = NDArrayFactory::create({2, 2}); - nd4j::ops::dropout op; + sd::ops::dropout op; - auto ress = op.evaluate({&x, &shape}, {0.2f}, {113}); + auto res = op.evaluate({&x, &shape}, {0.2f}, {113}); - ASSERT_EQ(ND4J_STATUS_OK, ress->status()); - //ress->at(0)->printIndexedBuffer("Result is "); + ASSERT_EQ(ND4J_STATUS_OK, res.status()); + //res.at(0)->printIndexedBuffer("Result is "); //x.printIndexedBuffer("Input is"); - delete ress; + } //////////////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests6, TestMod_1) { @@ -828,15 +826,15 @@ TEST_F(DeclarableOpsTests6, TestMod_1) { auto x = NDArrayFactory::create('c', {2, 2, 2}, {1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 7.f, 8.f}); auto y = NDArrayFactory::create('c', {2, 2, 2}, {10.f, 2.f, 30.f, 4.f, 50.f, 6.f, 70.f, 8.f}); auto exp = NDArrayFactory::create('c', {2, 2, 2}, {1, 0, 3, 0, 5, 0, 7, 0}); - nd4j::ops::mod op; + sd::ops::mod op; - auto ress = op.evaluate({&x, &y}); + auto res = op.evaluate({&x, &y}); - ASSERT_EQ(ND4J_STATUS_OK, ress->status()); -// ress->at(0)->printIndexedBuffer("MOD Result is "); + ASSERT_EQ(ND4J_STATUS_OK, res.status()); +// res.at(0)->printIndexedBuffer("MOD Result is "); // x.printIndexedBuffer("Input is"); - ASSERT_TRUE(ress->at(0)->equalsTo(exp)); - delete ress; + ASSERT_TRUE(res.at(0)->equalsTo(exp)); + } //////////////////////////////////////////////////////////////////////////////// @@ -846,16 +844,16 @@ TEST_F(DeclarableOpsTests6, TestMod_BP_1) { auto y = NDArrayFactory::create('c', {2, 2, 2}, {10.f, 2.f, 30.f, 4.f, 50.f, 6.f, 70.f, 8.f}); auto eps = NDArrayFactory::create('c', {2, 2, 2}, {10.f, 2.f, 30.f, 4.f, 50.f, 6.f, 70.f, 8.f}); auto exp = NDArrayFactory::create('c', {2, 2, 2}); - nd4j::ops::mod_bp op; + sd::ops::mod_bp op; - auto ress = op.evaluate({&x, &y, &eps}); + auto res = op.evaluate({&x, &y, &eps}); - ASSERT_EQ(ND4J_STATUS_OK, ress->status()); -// ress->at(0)->printIndexedBuffer("MOD_BP Result is "); + ASSERT_EQ(ND4J_STATUS_OK, res.status()); +// res.at(0)->printIndexedBuffer("MOD_BP Result is "); // x.printIndexedBuffer("Input is"); - ASSERT_TRUE(ress->at(0)->equalsTo(exp)); - delete ress; + ASSERT_TRUE(res.at(0)->equalsTo(exp)); + } /////////////////////////////////////////////////////////////////////////////// @@ -865,27 +863,27 @@ TEST_F(DeclarableOpsTests6, TestRank_1) { auto y = NDArrayFactory::create('c', {2, 2, 2}, {10.f, 2.f, 30.f, 4.f, 50.f, 6.f, 70.f, 8.f}); auto eps = NDArrayFactory::create('c', {2, 2, 2}, {10.f, 2.f, 30.f, 4.f, 50.f, 6.f, 70.f, 8.f}); auto exp = NDArrayFactory::create(3); - nd4j::ops::rank op; + sd::ops::rank op; - auto ress = op.evaluate({&x}); + auto res = op.evaluate({&x}); - ASSERT_EQ(ND4J_STATUS_OK, ress->status()); + ASSERT_EQ(ND4J_STATUS_OK, res.status()); - ASSERT_TRUE(ress->at(0)->equalsTo(exp)); - delete ress; + ASSERT_TRUE(res.at(0)->equalsTo(exp)); + } TEST_F(DeclarableOpsTests6, TestDropout_2) { // auto x0 = NDArrayFactory::create('c', {10, 10}); // auto x1 = NDArrayFactory::create('c', {10, 10}); auto x = NDArrayFactory::create('c', {3, 3}, {1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 7.f, 8.f, 9.f}); - nd4j::ops::dropout op; + sd::ops::dropout op; - auto ress = op.evaluate({&x}, {0.4f}, {113}); + auto res = op.evaluate({&x}, {0.4f}, {113}); - ASSERT_EQ(ND4J_STATUS_OK, ress->status()); + ASSERT_EQ(ND4J_STATUS_OK, res.status()); - delete ress; + } TEST_F(DeclarableOpsTests6, TestDropout_3) { @@ -894,13 +892,13 @@ TEST_F(DeclarableOpsTests6, TestDropout_3) { auto x = NDArrayFactory::create('c', {2, 2, 2}, {1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 7.f, 8.f}); auto shape = NDArrayFactory::create({1, 2}); - nd4j::ops::dropout op; + sd::ops::dropout op; - auto ress = op.evaluate({&x, &shape}, {0.4f}, {113}); + auto res = op.evaluate({&x, &shape}, {0.4f}, {113}); - ASSERT_EQ(ND4J_STATUS_OK, ress->status()); + ASSERT_EQ(ND4J_STATUS_OK, res.status()); - delete ress; + } //////////////////////////////////////////////////////////////////////////////// @@ -911,20 +909,20 @@ TEST_F(DeclarableOpsTests6, MaxPoolWithArgmax_1) { auto expI = NDArrayFactory::create('c', {2, 2, 2, 4}, {0, 1, 2, 3,4, 5, 6, 7,8, 9, 10, 11,12, 13, 14, 15, 0, 1, 2, 3,4, 5, 6, 7,8, 9, 10, 11,12, 13, 14, 15}); - nd4j::ops::max_pool_with_argmax op; + sd::ops::max_pool_with_argmax op; - auto ress = op.evaluate({&x}, {}, {1,1,1,1,1,1,1,1,1}); + auto res = op.evaluate({&x}, {}, {1,1,1,1,1,1,1,1,1}); - ASSERT_EQ(ND4J_STATUS_OK, ress->status()); - ASSERT_TRUE(expI.isSameShape(ress->at(0))); - ASSERT_TRUE(expI.isSameShape(ress->at(1))); - ASSERT_TRUE(x.equalsTo(ress->at(0))); - ASSERT_TRUE(expI.equalsTo(ress->at(1))); + ASSERT_EQ(ND4J_STATUS_OK, res.status()); + ASSERT_TRUE(expI.isSameShape(res.at(0))); + ASSERT_TRUE(expI.isSameShape(res.at(1))); + ASSERT_TRUE(x.equalsTo(res.at(0))); + ASSERT_TRUE(expI.equalsTo(res.at(1))); //x.printIndexedBuffer("Input is"); - ASSERT_TRUE(expI.equalsTo(ress->at(1))); + ASSERT_TRUE(expI.equalsTo(res.at(1))); - delete ress; + } //////////////////////////////////////////////////////////////////////////////// @@ -940,16 +938,16 @@ TEST_F(DeclarableOpsTests6, SufficientStatistics_1) { auto axis = NDArrayFactory::create({0, 1, 2}); - nd4j::ops::sufficient_statistics op; + sd::ops::sufficient_statistics op; - auto ress = op.evaluate({&x, &axis}); + auto res = op.evaluate({&x, &axis}); - ASSERT_EQ(ND4J_STATUS_OK, ress->status()); - ASSERT_EQ(ress->at(0)->e(0), count); - ASSERT_TRUE(sumExp.equalsTo(ress->at(1))); - ASSERT_TRUE(sqrExp.equalsTo(ress->at(2))); + ASSERT_EQ(ND4J_STATUS_OK, res.status()); + ASSERT_EQ(res.at(0)->e(0), count); + ASSERT_TRUE(sumExp.equalsTo(res.at(1))); + ASSERT_TRUE(sqrExp.equalsTo(res.at(2))); - delete ress; + } //////////////////////////////////////////////////////////////////////////////// @@ -972,16 +970,16 @@ TEST_F(DeclarableOpsTests6, SufficientStatistics_2) { auto axis = NDArrayFactory::create({0, 1}); - nd4j::ops::sufficient_statistics op; + sd::ops::sufficient_statistics op; - auto ress = op.evaluate({&x, &axis}); + auto res = op.evaluate({&x, &axis}); - ASSERT_EQ(ND4J_STATUS_OK, ress->status()); - ASSERT_EQ(ress->at(0)->e(0), count); - ASSERT_TRUE(sumExp.equalsTo(ress->at(1))); - ASSERT_TRUE(sqrExp.equalsTo(ress->at(2))); + ASSERT_EQ(ND4J_STATUS_OK, res.status()); + ASSERT_EQ(res.at(0)->e(0), count); + ASSERT_TRUE(sumExp.equalsTo(res.at(1))); + ASSERT_TRUE(sqrExp.equalsTo(res.at(2))); - delete ress; + } //////////////////////////////////////////////////////////////////////////////// @@ -992,16 +990,15 @@ TEST_F(DeclarableOpsTests6, BinCount_1) { ); // ------------------------------------ - NDArray exp('c', {3}, {1, 3, 4}, nd4j::DataType::INT32); + NDArray exp('c', {3}, {1, 3, 4}, sd::DataType::INT32); - nd4j::ops::bincount op; + sd::ops::bincount op; auto res = op.evaluate({&x}); - ASSERT_EQ(ND4J_STATUS_OK, res->status()); - ASSERT_TRUE(exp.equalsTo(res->at(0))); + ASSERT_EQ(ND4J_STATUS_OK, res.status()); + ASSERT_TRUE(exp.equalsTo(res.at(0))); - delete res; } ///////////////////////////////////////////////////////////////////////////////// @@ -1019,14 +1016,12 @@ TEST_F(DeclarableOpsTests6, BinCount_2) { auto exp = NDArrayFactory::create({3., 4., 13.}); - nd4j::ops::bincount op; + sd::ops::bincount op; auto res = op.evaluate({&x, &weights}); - ASSERT_EQ(ND4J_STATUS_OK, res->status()); - ASSERT_TRUE(exp.equalsTo(res->at(0))); - - delete res; + ASSERT_EQ(ND4J_STATUS_OK, res.status()); + ASSERT_TRUE(exp.equalsTo(res.at(0))); } ///////////////////////////////////////////////////////////////////////////////// @@ -1044,14 +1039,13 @@ TEST_F(DeclarableOpsTests6, BinCount_3) { auto exp = NDArrayFactory::create({3., 4.}); - nd4j::ops::bincount op; + sd::ops::bincount op; auto res = op.evaluate({&x, &weights}, {}, {0, 2}); - ASSERT_EQ(ND4J_STATUS_OK, res->status()); - ASSERT_TRUE(exp.equalsTo(res->at(0))); + ASSERT_EQ(ND4J_STATUS_OK, res.status()); + ASSERT_TRUE(exp.equalsTo(res.at(0))); - delete res; } ///////////////////////////////////////////////////////////////////////////////// @@ -1069,14 +1063,12 @@ TEST_F(DeclarableOpsTests6, BinCount_4) { auto exp = NDArrayFactory::create({3., 4., 13., 0.0}); - nd4j::ops::bincount op; + sd::ops::bincount op; auto res = op.evaluate({&x, &weights}, {}, {4, 4}); - ASSERT_EQ(ND4J_STATUS_OK, res->status()); - ASSERT_TRUE(exp.equalsTo(res->at(0))); - - delete res; + ASSERT_EQ(ND4J_STATUS_OK, res.status()); + ASSERT_TRUE(exp.equalsTo(res.at(0))); } ///////////////////////////////////////////////////////////////////////////////// @@ -1095,14 +1087,13 @@ TEST_F(DeclarableOpsTests6, BinCount_5) { auto exp = NDArrayFactory::create({3., 4., 13., 0.0}); - nd4j::ops::bincount op; + sd::ops::bincount op; auto res = op.evaluate({&x, &weights, &minV, &maxV}); - ASSERT_EQ(ND4J_STATUS_OK, res->status()); + ASSERT_EQ(ND4J_STATUS_OK, res.status()); // res->at(0)->printBuffer("BC out"); - ASSERT_TRUE(exp.equalsTo(res->at(0))); + ASSERT_TRUE(exp.equalsTo(res.at(0))); - delete res; } ///////////////////////////////////////////////////////////////////////////////// @@ -1114,14 +1105,13 @@ TEST_F(DeclarableOpsTests6, BroadcastDynamicShape_1) { auto exp = NDArrayFactory::create({2, 2, 2}); - nd4j::ops::broadcast_dynamic_shape op; + sd::ops::broadcast_dynamic_shape op; auto res = op.evaluate({&x, &y}); - ASSERT_EQ(ND4J_STATUS_OK, res->status()); - ASSERT_TRUE(exp.equalsTo(res->at(0))); + ASSERT_EQ(ND4J_STATUS_OK, res.status()); + ASSERT_TRUE(exp.equalsTo(res.at(0))); - delete res; } ///////////////////////////////////////////////////////////////////////////////// @@ -1133,13 +1123,12 @@ TEST_F(DeclarableOpsTests6, BroadcastDynamicShape_2) { auto exp = NDArrayFactory::create({2, 2, 2}); - nd4j::ops::broadcast_dynamic_shape op; + sd::ops::broadcast_dynamic_shape op; auto res = op.evaluate({&x, &y}); - ASSERT_EQ(ND4J_STATUS_OK, res->status()); - ASSERT_TRUE(exp.equalsTo(res->at(0))); + ASSERT_EQ(ND4J_STATUS_OK, res.status()); + ASSERT_TRUE(exp.equalsTo(res.at(0))); - delete res; } ///////////////////////////////////////////////////////////////////////////////// @@ -1151,14 +1140,12 @@ TEST_F(DeclarableOpsTests6, BroadcastDynamicShape_3) { auto exp = NDArrayFactory::create({2, 2, 2}); - nd4j::ops::broadcast_dynamic_shape op; + sd::ops::broadcast_dynamic_shape op; auto res = op.evaluate({&x, &y}, {}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, res->status()); - ASSERT_TRUE(exp.equalsTo(res->at(0))); - - delete res; + ASSERT_EQ(ND4J_STATUS_OK, res.status()); + ASSERT_TRUE(exp.equalsTo(res.at(0))); } ///////////////////////////////////////////////////////////////////////////////// @@ -1170,15 +1157,13 @@ TEST_F(DeclarableOpsTests6, BroadcastDynamicShape_SGO_4) { auto exp = NDArrayFactory::create({2, 4}); - nd4j::ops::broadcast_dynamic_shape op; + sd::ops::broadcast_dynamic_shape op; auto res = op.evaluate({&x, &y}); - ASSERT_EQ(ND4J_STATUS_OK, res->status()); + ASSERT_EQ(ND4J_STATUS_OK, res.status()); //res->at(0)->printBuffer("Shape SGO 4"); - ASSERT_TRUE(exp.equalsTo(res->at(0))); - - delete res; + ASSERT_TRUE(exp.equalsTo(res.at(0))); } ///////////////////////////////////////////////////////////////////////////////// @@ -1190,13 +1175,12 @@ TEST_F(DeclarableOpsTests6, BroadcastDynamicShape_SGO_6) { auto exp = NDArrayFactory::create({2, 2, 4}); - nd4j::ops::broadcast_dynamic_shape op; + sd::ops::broadcast_dynamic_shape op; auto res = op.evaluate({&x, &y}); - ASSERT_EQ(ND4J_STATUS_OK, res->status()); - ASSERT_TRUE(exp.equalsTo(res->at(0))); + ASSERT_EQ(ND4J_STATUS_OK, res.status()); + ASSERT_TRUE(exp.equalsTo(res.at(0))); - delete res; } ///////////////////////////////////////////////////////////////////////////////// @@ -1208,13 +1192,11 @@ TEST_F(DeclarableOpsTests6, BroadcastDynamicShape_SGO_7) { auto exp = NDArrayFactory::create({2, 4, 3}); - nd4j::ops::broadcast_dynamic_shape op; + sd::ops::broadcast_dynamic_shape op; auto res = op.evaluate({&x, &y}); - ASSERT_EQ(ND4J_STATUS_OK, res->status()); - ASSERT_TRUE(exp.equalsTo(res->at(0))); - - delete res; + ASSERT_EQ(ND4J_STATUS_OK, res.status()); + ASSERT_TRUE(exp.equalsTo(res.at(0))); } ///////////////////////////////////////////////////////////////////////////////// @@ -1228,7 +1210,7 @@ TEST_F(DeclarableOpsTests6, BroadcastDynamicShape_SGO_8) { auto exp = NDArrayFactory::create('c', {1}, {4}); - nd4j::ops::broadcast_dynamic_shape op; + sd::ops::broadcast_dynamic_shape op; auto status = op.execute({&x, &y}, {&z}, {}, {}, {}); ASSERT_EQ(ND4J_STATUS_OK, status); @@ -1246,7 +1228,7 @@ TEST_F(DeclarableOpsTests6, BroadcastDynamicShape_SGO_9) { auto exp = NDArrayFactory::create('c', {2}, {2,2}); - nd4j::ops::broadcast_dynamic_shape op; + sd::ops::broadcast_dynamic_shape op; auto status = op.execute({&x, &y}, {&z}, {}, {}, {}); ASSERT_EQ(ND4J_STATUS_OK, status); @@ -1273,13 +1255,13 @@ TEST_F(DeclarableOpsTests6, ClipByGlobalNorm_1) { // 8.660254 // auto expNorm(8.660254); - nd4j::ops::clip_by_global_norm op; + sd::ops::clip_by_global_norm op; auto result = op.evaluate({&x}, {0.8}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); - auto norm = result->at(1); + auto z = result.at(0); + auto norm = result.at(1); //z->printIndexedBuffer("Output"); //exp.printIndexedBuffer("Expected"); //norm->printIndexedBuffer("Norm"); @@ -1288,7 +1270,7 @@ TEST_F(DeclarableOpsTests6, ClipByGlobalNorm_1) { ASSERT_TRUE(exp.equalsTo(z)); // ASSERT_TRUE(expNorm.equalsTo(norm)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1315,20 +1297,20 @@ TEST_F(DeclarableOpsTests6, ClipByGlobalNorm_2) { ); - nd4j::ops::clip_by_global_norm op; + sd::ops::clip_by_global_norm op; auto result = op.evaluate({&x, &a}, {1.8}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); - auto y = result->at(1); + auto z = result.at(0); + auto y = result.at(1); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.isSameShape(y)); ASSERT_TRUE(exp.equalsTo(z)); ASSERT_TRUE(exp.equalsTo(y)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1345,24 +1327,24 @@ TEST_F(DeclarableOpsTests6, ClipByGlobalNorm_3) { 0.2612789, 0., 0.} ); - nd4j::ops::clip_by_global_norm op; + sd::ops::clip_by_global_norm op; auto result = op.evaluate({&x, &a}, {0.8}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); - auto y = result->at(1); + auto z = result.at(0); + auto y = result.at(1); //z->printIndexedBuffer("Output 1"); //y->printIndexedBuffer("Output 2"); - //result->at(2)->printIndexedBuffer("Global norm is"); + //result.at(2)->printIndexedBuffer("Global norm is"); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.isSameShape(y)); - ASSERT_TRUE(result->at(2)->isScalar()); + ASSERT_TRUE(result.at(2)->isScalar()); ASSERT_TRUE(exp.equalsTo(z)); ASSERT_TRUE(exp.equalsTo(y)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1371,19 +1353,19 @@ TEST_F(DeclarableOpsTests6, MatrixDeterminant_1) { auto x = NDArrayFactory::create('c', {2, 3, 3}, {-3.0, 0.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0, -3.0, 4.0, 0.0, 0.0, 0.0, -3.0, 0.0, 0.0, 0.0, 4.0}); auto exp = NDArrayFactory::create({36.0, -48.0}); - nd4j::ops::matrix_determinant op; + sd::ops::matrix_determinant op; auto result = op.evaluate({&x}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); //z->printIndexedBuffer("Output "); //exp.printIndexedBuffer("Expected "); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1392,19 +1374,19 @@ TEST_F(DeclarableOpsTests6, MatrixDeterminant_2) { auto x = NDArrayFactory::create('c', {2, 2, 2}, {1.0, 2.0, 3.0, 4.0, 1.0, 2.0, 3.0, 4.0}); auto exp = NDArrayFactory::create({-2.0, -2.0}); - nd4j::ops::matrix_determinant op; + sd::ops::matrix_determinant op; auto result = op.evaluate({&x}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); //z->printIndexedBuffer("Output "); //exp.printIndexedBuffer("Expected "); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1413,19 +1395,19 @@ TEST_F(DeclarableOpsTests6, MatrixDeterminant_3) { auto x = NDArrayFactory::create('c', {1, 3, 3}, {3.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 3.0}); NDArray exp('c', {1}, std::vector{-54.0}); - nd4j::ops::matrix_determinant op; + sd::ops::matrix_determinant op; auto result = op.evaluate({&x}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); //z->printIndexedBuffer("Output "); //exp.printIndexedBuffer("Expected "); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1434,19 +1416,19 @@ TEST_F(DeclarableOpsTests6, MatrixDeterminant_4) { auto x = NDArrayFactory::create('c', {1, 3, 3}, {12.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 13.0}); auto exp = NDArrayFactory::create('c', {1}, {189.0}); - nd4j::ops::matrix_determinant op; + sd::ops::matrix_determinant op; auto result = op.evaluate({&x}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); // z->printIndexedBuffer("Output "); // exp.printIndexedBuffer("Expected "); // z->printShapeInfo("Output shape"); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1458,19 +1440,19 @@ TEST_F(DeclarableOpsTests6, MatrixDeterminant_5) { x.p(5, 4.0); x.p(12, 12.0); - nd4j::ops::matrix_determinant op; + sd::ops::matrix_determinant op; auto result = op.evaluate({&x}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); //z->printIndexedBuffer("Output "); //exp.printIndexedBuffer("Expected "); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1482,12 +1464,12 @@ TEST_F(DeclarableOpsTests6, MatrixDeterminant_6) { x.p(5, 4.0); x.p(12, 12.0); - nd4j::ops::matrix_determinant op; + sd::ops::matrix_determinant op; auto result = op.evaluate({&x}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); //z->printIndexedBuffer("Output "); //z->printShapeInfo("Shape"); //exp.printIndexedBuffer("Expected "); @@ -1495,7 +1477,7 @@ TEST_F(DeclarableOpsTests6, MatrixDeterminant_6) { ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1504,17 +1486,17 @@ TEST_F(DeclarableOpsTests6, LogMatrixDeterminant_1) { auto x = NDArrayFactory::create('c', {2, 3, 3}, {-3.0, 0.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0, -3.0, 4.0, 0.0, 0.0, 0.0, -3.0, 0.0, 0.0, 0.0, 4.0}); auto exp = NDArrayFactory::create({3.58351893845611, 3.871201010907891}); - nd4j::ops::log_matrix_determinant op; + sd::ops::log_matrix_determinant op; auto result = op.evaluate({&x}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1523,16 +1505,16 @@ TEST_F(DeclarableOpsTests6, LogDet_1) { auto x = NDArrayFactory::create('c', {2, 3, 3}, {4,12,-16,12,37,-43,-16,-43,98, 4,1.2,-1.6,1.2,3.7,-4.3,-1.6,-4.3,9.8}); auto exp = NDArrayFactory::create({ 3.5835189, 4.159008}); - nd4j::ops::logdet op; + sd::ops::logdet op; auto result = op.evaluate({&x}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1541,17 +1523,17 @@ TEST_F(DeclarableOpsTests6, LogDet_2) { auto x = NDArrayFactory::create('c', {1, 3, 3}, {4,12,-16,12,37,-43,-16,-43,98}); auto exp = NDArrayFactory::create('c', {1}, { 3.5835189}); - nd4j::ops::logdet op; + sd::ops::logdet op; auto result = op.evaluate({&x}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1560,17 +1542,17 @@ TEST_F(DeclarableOpsTests6, LogDet_3) { auto x = NDArrayFactory::create('c', {3, 3}, {4,12,-16,12,37,-43,-16,-43,98}); auto exp = NDArrayFactory::create( 3.5835189); - nd4j::ops::logdet op; + sd::ops::logdet op; auto result = op.evaluate({&x}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1604,17 +1586,17 @@ TEST_F(DeclarableOpsTests6, MatrixInverse_1) { -27.0f, 0.0f, 1.0f, -2.0f, 1.f, }); - nd4j::ops::matrix_inverse op; + sd::ops::matrix_inverse op; auto result = op.evaluate({&x}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1623,17 +1605,17 @@ TEST_F(DeclarableOpsTests6, MatrixInverse_010) { auto x = NDArrayFactory::create('c', {1, 5, 5}, {1.f, 0.f, 0.f, 0.f, 0.f, 2.f, 1.f, 0.f, 0.f, 0.f, 30.f, 2.f, 1.f, 0.f, 0.f, 4.f, 3.f, 2.f, 1.f, 0.f, 5.f, 4.f, 3.f, 2.f, 1.f, }); auto exp = NDArrayFactory::create('c', {1, 5, 5}, {1.0f, 0.0f, 0.0f, 0.0f, 0.f, -2.0f, 1.0f, 0.f, 0.f, 0.f, -26.0f, -2.0f, 1.f, 0.f, 0.f, 54.0f, 1.0f, -2.0f, 1.f, 0.f, -27.0f, 0.0f, 1.0f, -2.0f, 1.f}); - nd4j::ops::matrix_inverse op; + sd::ops::matrix_inverse op; auto result = op.evaluate({&x}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1642,17 +1624,17 @@ TEST_F(DeclarableOpsTests6, MatrixInverse_01) { auto x = NDArrayFactory::create('c', {1, 5, 5}, {2.f, 4.f, 60.f, 8.f, 10.f, 0.f, 1.f, 2.f, 3.f, 4.f, 0.f, 0.f, 2.f, 4.f, 6.f, 0.f, 0.f, 0.f, 1.f, 2.f, 0.f, 0.f, 0.f, 0.f, 4.f }); auto exp = NDArrayFactory::create('c', {1, 5, 5}, {0.5f, -2.0f, -13.0f, 54.0f, -6.75f, 0.0f, 1.0f, -1.0f, 1.0f, 0.0f, 0.f, 0.f, 0.5f, -2.0f, 0.25f, 0.f, 0.f, 0.f, 1.0f, -0.5f, 0.f, 0.f, 0.f, 0.f, 0.25f }); - nd4j::ops::matrix_inverse op; + sd::ops::matrix_inverse op; auto result = op.evaluate({&x}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1661,17 +1643,17 @@ TEST_F(DeclarableOpsTests6, MatrixInverse_02) { auto x = NDArrayFactory::create('c', {1, 5, 5}, {1.f, 0.f, 0.f, 0.f, 0.f, 2.f, 1.f, 0.f, 0.f, 0.f, 30.f, 2.f, 1.f, 0.f, 0.f, 4.f, 3.f, 2.f, 1.f, 0.f, 5.f, 4.f, 3.f, 2.f, 1.f }); auto exp = NDArrayFactory::create('c', {1, 5, 5}, {1.0f, 0.0f, 0.0f, 0.0f, 0.f, -2.0f, 1.0f, 0.f, 0.f, 0.f, -26.0f, -2.0f, 1.f, 0.f, 0.f, 54.0f, 1.0f, -2.0f, 1.f, 0.f, -27.0f, 0.0f, 1.0f, -2.0f, 1.f }); - nd4j::ops::matrix_inverse op; + sd::ops::matrix_inverse op; auto result = op.evaluate({&x}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1706,19 +1688,19 @@ TEST_F(DeclarableOpsTests6, MatrixInverse_2) { -6.75, 0.0, 1.0, -1.0, 0.33333333 }); - nd4j::ops::matrix_inverse op; + sd::ops::matrix_inverse op; auto result = op.evaluate({&x}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); z->printIndexedBuffer("Output "); exp.printIndexedBuffer("Expected "); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } */ TEST_F(DeclarableOpsTests6, MatrixInverse_03) { @@ -1739,19 +1721,19 @@ TEST_F(DeclarableOpsTests6, MatrixInverse_03) { -6.75f, 0.0f, 1.0f, -1.0f, 0.33333333f }); - nd4j::ops::matrix_inverse op; + sd::ops::matrix_inverse op; auto result = op.evaluate({&x}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); // z->printIndexedBuffer("Output "); // exp.printIndexedBuffer("Expected "); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1773,19 +1755,19 @@ TEST_F(DeclarableOpsTests6, MatrixInverse_3) { -6.75f, 0.0f, 1.0f, -1.0f, 0.33333333f }); - nd4j::ops::matrix_inverse op; + sd::ops::matrix_inverse op; auto result = op.evaluate({&x}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); // exp.printIndexedBuffer("Expected "); // z->printIndexedBuffer("Output "); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1807,19 +1789,19 @@ TEST_F(DeclarableOpsTests6, MatrixInverse_4) { 0.0f, 0.0f, 0.0f, 0.0f, 1.0f }); - nd4j::ops::matrix_inverse op; + sd::ops::matrix_inverse op; auto result = op.evaluate({&x}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); // z->printIndexedBuffer("Output "); // exp.printIndexedBuffer("Expected "); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1841,19 +1823,19 @@ TEST_F(DeclarableOpsTests6, MatrixInverse_04) { 0.0f, 0.0f, 0.0f, 0.0f, 1.0f }); - nd4j::ops::matrix_inverse op; + sd::ops::matrix_inverse op; auto result = op.evaluate({&x}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); // z->printIndexedBuffer("Output "); // exp.printIndexedBuffer("Expected "); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1869,12 +1851,12 @@ TEST_F(DeclarableOpsTests6, ReluLayer_1) { 23.8, 31.05, 56.5, 26.2, 31.65, 60.7}); - nd4j::ops::relu_layer op; + sd::ops::relu_layer op; auto result = op.evaluate({&x, &w, &b}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); // z->printShapeInfo("Output shape"); // z->printIndexedBuffer("Output "); // exp.printIndexedBuffer("Expected "); @@ -1882,7 +1864,7 @@ TEST_F(DeclarableOpsTests6, ReluLayer_1) { ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } TEST_F(DeclarableOpsTests6, Test_Reduce3_Edge) { @@ -1922,20 +1904,20 @@ TEST_F(DeclarableOpsTests6, static_rnn_test1) { auto expHFinal = NDArrayFactory::create('c', {bS, numUnits}, {0.97732812, 0.97732812, 0.97732812, 0.97732812, 0.93751527, 0.93751527, 0.93751527, 0.93751527}); - nd4j::ops::static_rnn op; + sd::ops::static_rnn op; auto results = op.evaluate({&x, &Wx, &Wh, &b, &h0, &maxTimeStep}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto h = results->at(0); - auto hFinal = results->at(1); + auto h = results.at(0); + auto hFinal = results.at(1); ASSERT_TRUE(expH.isSameShape(h)); ASSERT_TRUE(expH.equalsTo(h)); ASSERT_TRUE(expHFinal.isSameShape(hFinal)); ASSERT_TRUE(expHFinal.equalsTo(hFinal)); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -1965,20 +1947,20 @@ TEST_F(DeclarableOpsTests6, static_rnn_test2) { auto expHFinal = NDArrayFactory::create('c', {bS, numUnits}, {0.98000654, 0.98000654, 0.98000654, 0.98000654,0.98112648, 0.98112648, 0.98112648, 0.98112648}); - nd4j::ops::static_rnn op; + sd::ops::static_rnn op; auto results = op.evaluate({&x, &Wx, &Wh, &b, &h0}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto h = results->at(0); - auto hFinal = results->at(1); + auto h = results.at(0); + auto hFinal = results.at(1); ASSERT_TRUE(expH.isSameShape(h)); ASSERT_TRUE(expH.equalsTo(h)); ASSERT_TRUE(expHFinal.isSameShape(hFinal)); ASSERT_TRUE(expHFinal.equalsTo(hFinal)); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -2008,20 +1990,20 @@ TEST_F(DeclarableOpsTests6, static_rnn_test3) { auto expHFinal = NDArrayFactory::create('c', {bS, numUnits}, {0.97732812, 0.97732812, 0.97732812, 0.97732812, 0.2 , 0.2 , 0.2 , 0.2}); - nd4j::ops::static_rnn op; + sd::ops::static_rnn op; auto results = op.evaluate({&x, &Wx, &Wh, &b, &h0, &maxTimeStep}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto h = results->at(0); - auto hFinal = results->at(1); + auto h = results.at(0); + auto hFinal = results.at(1); ASSERT_TRUE(expH.isSameShape(h)); ASSERT_TRUE(expH.equalsTo(h)); ASSERT_TRUE(expHFinal.isSameShape(hFinal)); ASSERT_TRUE(expHFinal.equalsTo(hFinal)); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -2050,20 +2032,20 @@ TEST_F(DeclarableOpsTests6, static_rnn_test4) { auto expHFinal = NDArrayFactory::create('c', {bS, numUnits}, {0.97688859, 0.97688859, 0.97688859, 0.97688859, 0.88400882, 0.88400882, 0.88400882, 0.88400882}); - nd4j::ops::static_rnn op; + sd::ops::static_rnn op; auto results = op.evaluate({&x, &Wx, &Wh, &b, &maxTimeStep}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto h = results->at(0); - auto hFinal = results->at(1); + auto h = results.at(0); + auto hFinal = results.at(1); ASSERT_TRUE(expH.isSameShape(h)); ASSERT_TRUE(expH.equalsTo(h)); ASSERT_TRUE(expHFinal.isSameShape(hFinal)); ASSERT_TRUE(expHFinal.equalsTo(hFinal)); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -2092,20 +2074,20 @@ TEST_F(DeclarableOpsTests6, static_rnn_test5) { auto expHFinal = NDArrayFactory::create('c', {bS, numUnits}, {0.97997868, 0.97997868, 0.97997868, 0.97997868, 0.98110653, 0.98110653, 0.98110653, 0.98110653}); - nd4j::ops::static_rnn op; + sd::ops::static_rnn op; auto results = op.evaluate({&x, &Wx, &Wh, &b}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto h = results->at(0); - auto hFinal = results->at(1); + auto h = results.at(0); + auto hFinal = results.at(1); ASSERT_TRUE(expH.isSameShape(h)); ASSERT_TRUE(expH.equalsTo(h)); ASSERT_TRUE(expHFinal.isSameShape(hFinal)); ASSERT_TRUE(expHFinal.equalsTo(hFinal)); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -2143,14 +2125,14 @@ TEST_F(DeclarableOpsTests6, static_bidir_rnn_test1) { auto expHFWfinal = NDArrayFactory::create('c', {bS, numUnitsFW}, {0.9555734 , 0.9555734 , 0.9555734 , 0.77843476, 0.77843476, 0.77843476, 0.51241561, 0.51241561, 0.51241561, 0.2, 0.2, 0.2}); auto expHBWfinal = NDArrayFactory::create('c', {bS, numUnitsBW}, {0.86708881, 0.86708881, 0.86708881, 0.78347842, 0.78347842, 0.78347842, 0.55529176, 0.55529176, 0.55529176, 0.25, 0.25, 0.25}); - nd4j::ops::static_bidirectional_rnn op; + sd::ops::static_bidirectional_rnn op; auto results = op.evaluate({&x, &WxFW,&WhFW,&bFW, &WxFW,&WhFW,&bFW, &h0FW, &h0BW, &maxTimeStep}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto h = results->at(0); - auto hFWfinal = results->at(1); - auto hBWfinal = results->at(2); + auto h = results.at(0); + auto hFWfinal = results.at(1); + auto hBWfinal = results.at(2); ASSERT_TRUE(expH.isSameShape(h)); ASSERT_TRUE(expH.equalsTo(h)); @@ -2159,7 +2141,7 @@ TEST_F(DeclarableOpsTests6, static_bidir_rnn_test1) { ASSERT_TRUE(expHBWfinal.isSameShape(hBWfinal)); ASSERT_TRUE(expHBWfinal.equalsTo(hBWfinal)); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -2196,14 +2178,14 @@ TEST_F(DeclarableOpsTests6, static_bidir_rnn_test2) { auto expHFWfinal = NDArrayFactory::create('c', {bS, numUnitsFW}, {0.95177305, 0.95177305, 0.95177305, 0.66138054, 0.66138054, 0.66138054, 0.31492203, 0.31492203, 0.31492203, 0. , 0. , 0.}); auto expHBWfinal = NDArrayFactory::create('c', {bS, numUnitsBW}, {0.86518273, 0.86518273, 0.86518273, 0.66617761, 0.66617761, 0.66617761, 0.31492203, 0.31492203, 0.31492203, 0. , 0. , 0.}); - nd4j::ops::static_bidirectional_rnn op; + sd::ops::static_bidirectional_rnn op; auto results = op.evaluate({&x, &WxFW,&WhFW,&bFW, &WxFW,&WhFW,&bFW, &maxTimeStep}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto h = results->at(0); - auto hFWfinal = results->at(1); - auto hBWfinal = results->at(2); + auto h = results.at(0); + auto hFWfinal = results.at(1); + auto hBWfinal = results.at(2); ASSERT_TRUE(expH.isSameShape(h)); ASSERT_TRUE(expH.equalsTo(h)); @@ -2212,7 +2194,7 @@ TEST_F(DeclarableOpsTests6, static_bidir_rnn_test2) { ASSERT_TRUE(expHBWfinal.isSameShape(hBWfinal)); ASSERT_TRUE(expHBWfinal.equalsTo(hBWfinal)); - delete results; + } @@ -2249,14 +2231,14 @@ TEST_F(DeclarableOpsTests6, static_bidir_rnn_test3) { auto expHFWfinal = NDArrayFactory::create('c', {bS, numUnitsFW}, {0.97269956, 0.97269956, 0.97269956, 0.97557464, 0.97557464, 0.97557464, 0.97806922, 0.97806922, 0.97806922, 0.98026195, 0.98026195, 0.98026195}); auto expHBWfinal = NDArrayFactory::create('c', {bS, numUnitsBW}, {0.86841012, 0.86841012, 0.86841012, 0.88207531, 0.88207531, 0.88207531, 0.8941667 , 0.8941667 , 0.8941667 , 0.90489713, 0.90489713, 0.90489713}); - nd4j::ops::static_bidirectional_rnn op; + sd::ops::static_bidirectional_rnn op; auto results = op.evaluate({&x, &WxFW,&WhFW,&bFW, &WxFW,&WhFW,&bFW}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto h = results->at(0); - auto hFWfinal = results->at(1); - auto hBWfinal = results->at(2); + auto h = results.at(0); + auto hFWfinal = results.at(1); + auto hBWfinal = results.at(2); ASSERT_TRUE(expH.isSameShape(h)); ASSERT_TRUE(expH.equalsTo(h)); @@ -2265,7 +2247,7 @@ TEST_F(DeclarableOpsTests6, static_bidir_rnn_test3) { ASSERT_TRUE(expHBWfinal.isSameShape(hBWfinal)); ASSERT_TRUE(expHBWfinal.equalsTo(hBWfinal)); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -2295,20 +2277,20 @@ TEST_F(DeclarableOpsTests6, dynamic_rnn_test1) { auto expHFinal = NDArrayFactory::create('c', {bS, numUnits}, {0.97732812, 0.97732812, 0.97732812, 0.97732812, 0.93751527, 0.93751527, 0.93751527, 0.93751527}); - nd4j::ops::dynamic_rnn op; + sd::ops::dynamic_rnn op; auto results = op.evaluate({&x, &Wx, &Wh, &b, &h0, &maxTimeStep}, {}, {1}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto h = results->at(0); - auto hFinal = results->at(1); + auto h = results.at(0); + auto hFinal = results.at(1); ASSERT_TRUE(expH.isSameShape(h)); ASSERT_TRUE(expH.equalsTo(h)); ASSERT_TRUE(expHFinal.isSameShape(hFinal)); ASSERT_TRUE(expHFinal.equalsTo(hFinal)); - delete results; + } @@ -2340,20 +2322,20 @@ TEST_F(DeclarableOpsTests6, dynamic_rnn_test2) { auto expHFinal = NDArrayFactory::create('c', {bS, numUnits}, {0.97309129, 0.97309129, 0.97309129, 0.97309129, 0.98120782, 0.98120782, 0.98120782, 0.98120782}); - nd4j::ops::dynamic_rnn op; + sd::ops::dynamic_rnn op; auto results = op.evaluate({&x, &Wx, &Wh, &b, &h0, &maxTimeStep}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto h = results->at(0); - auto hFinal = results->at(1); + auto h = results.at(0); + auto hFinal = results.at(1); ASSERT_TRUE(expH.isSameShape(h)); ASSERT_TRUE(expH.equalsTo(h)); ASSERT_TRUE(expHFinal.isSameShape(hFinal)); ASSERT_TRUE(expHFinal.equalsTo(hFinal)); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -2382,20 +2364,20 @@ TEST_F(DeclarableOpsTests6, dynamic_rnn_test3) { auto expHFinal = NDArrayFactory::create('c', {bS, numUnits}, {0.97491207, 0.97491207, 0.97491207, 0.97491207, 0.98120782, 0.98120782, 0.98120782, 0.98120782}); - nd4j::ops::dynamic_rnn op; + sd::ops::dynamic_rnn op; auto results = op.evaluate({&x, &Wx, &Wh, &b, &h0}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto h = results->at(0); - auto hFinal = results->at(1); + auto h = results.at(0); + auto hFinal = results.at(1); ASSERT_TRUE(expH.isSameShape(h)); ASSERT_TRUE(expH.equalsTo(h)); ASSERT_TRUE(expHFinal.isSameShape(hFinal)); ASSERT_TRUE(expHFinal.equalsTo(hFinal)); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -2423,20 +2405,20 @@ TEST_F(DeclarableOpsTests6, dynamic_rnn_test4) { auto expHFinal = NDArrayFactory::create('c', {bS, numUnits}, {0.9724738 , 0.9724738 , 0.9724738 , 0.9724738 ,0.57368608, 0.57368608, 0.57368608, 0.57368608}); - nd4j::ops::dynamic_rnn op; + sd::ops::dynamic_rnn op; auto results = op.evaluate({&x, &Wx, &Wh, &b, &maxTimeStep}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto h = results->at(0); - auto hFinal = results->at(1); + auto h = results.at(0); + auto hFinal = results.at(1); ASSERT_TRUE(expH.isSameShape(h)); ASSERT_TRUE(expH.equalsTo(h)); ASSERT_TRUE(expHFinal.isSameShape(hFinal)); ASSERT_TRUE(expHFinal.equalsTo(hFinal)); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -2464,20 +2446,20 @@ TEST_F(DeclarableOpsTests6, dynamic_rnn_test5) { auto expHFinal = NDArrayFactory::create('c', {bS, numUnits}, {0.97486307, 0.97486307, 0.97486307, 0.97486307,0.98119833, 0.98119833, 0.98119833, 0.98119833}); - nd4j::ops::dynamic_rnn op; + sd::ops::dynamic_rnn op; auto results = op.evaluate({&x, &Wx, &Wh, &b}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto h = results->at(0); - auto hFinal = results->at(1); + auto h = results.at(0); + auto hFinal = results.at(1); ASSERT_TRUE(expH.isSameShape(h)); ASSERT_TRUE(expH.equalsTo(h)); ASSERT_TRUE(expHFinal.isSameShape(hFinal)); ASSERT_TRUE(expHFinal.equalsTo(hFinal)); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -2520,15 +2502,15 @@ TEST_F(DeclarableOpsTests6, dynamic_bidir_rnn_test1) { auto expHFWfinal = NDArrayFactory::create('c', {bS, numUnitsFW}, {0.9555734 , 0.9555734 , 0.9555734 , 0.77843476, 0.77843476, 0.77843476, 0.51241561, 0.51241561, 0.51241561, 0.2 , 0.2 , 0.2}); auto expHBWfinal = NDArrayFactory::create('c', {bS, numUnitsBW}, {0.86708881, 0.86708881, 0.86708881, 0.78347842, 0.78347842, 0.78347842, 0.55529176, 0.55529176, 0.55529176, 0.25 , 0.25 , 0.25}); - nd4j::ops::dynamic_bidirectional_rnn op; + sd::ops::dynamic_bidirectional_rnn op; auto results = op.evaluate({&x, &WxFW,&WhFW,&bFW, &WxFW,&WhFW,&bFW, &h0FW, &h0BW, &maxTimeStep}, {}, {1}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto hFW = results->at(0); - auto hBW = results->at(1); - auto hFWfinal = results->at(2); - auto hBWfinal = results->at(3); + auto hFW = results.at(0); + auto hBW = results.at(1); + auto hFWfinal = results.at(2); + auto hBWfinal = results.at(3); ASSERT_TRUE(expHFW.isSameShape(hFW)); ASSERT_TRUE(expHFW.equalsTo(hFW)); @@ -2539,7 +2521,7 @@ TEST_F(DeclarableOpsTests6, dynamic_bidir_rnn_test1) { ASSERT_TRUE(expHBWfinal.isSameShape(hBWfinal)); ASSERT_TRUE(expHBWfinal.equalsTo(hBWfinal)); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -2580,15 +2562,15 @@ TEST_F(DeclarableOpsTests6, dynamic_bidir_rnn_test2) { auto expHFWfinal = NDArrayFactory::create('c', {bS, numUnitsFW}, {0.87294706, 0.87294706, 0.87294706,0.84851124, 0.84851124, 0.84851124,0.73978305, 0.73978305, 0.73978305,0.2 , 0.2 , 0.2}); auto expHBWfinal = NDArrayFactory::create('c', {bS, numUnitsBW}, {0.84345207, 0.84345207, 0.84345207, 0.85615841, 0.85615841, 0.85615841, 0.76576202, 0.76576202, 0.76576202, 0.25 , 0.25 , 0.25}); - nd4j::ops::dynamic_bidirectional_rnn op; + sd::ops::dynamic_bidirectional_rnn op; auto results = op.evaluate({&x, &WxFW,&WhFW,&bFW, &WxFW,&WhFW,&bFW, &h0FW, &h0BW, &maxTimeStep}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto hFW = results->at(0); - auto hBW = results->at(1); - auto hFWfinal = results->at(2); - auto hBWfinal = results->at(3); + auto hFW = results.at(0); + auto hBW = results.at(1); + auto hFWfinal = results.at(2); + auto hBWfinal = results.at(3); ASSERT_TRUE(expHFW.isSameShape(hFW)); ASSERT_TRUE(expHFW.equalsTo(hFW)); @@ -2599,7 +2581,7 @@ TEST_F(DeclarableOpsTests6, dynamic_bidir_rnn_test2) { ASSERT_TRUE(expHBWfinal.isSameShape(hBWfinal)); ASSERT_TRUE(expHBWfinal.equalsTo(hBWfinal)); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -2636,15 +2618,15 @@ TEST_F(DeclarableOpsTests6, dynamic_bidir_rnn_test3) { auto expHFWfinal = NDArrayFactory::create('c', {bS, numUnitsFW}, {0.84784327, 0.84784327, 0.84784327, 0.7793996 , 0.7793996 , 0.7793996 , 0.61067683, 0.61067683, 0.61067683, 0. , 0. , 0.}); auto expHBWfinal = NDArrayFactory::create('c', {bS, numUnitsBW}, {0.82273707, 0.82273707, 0.82273707, 0.77843476, 0.77843476, 0.77843476, 0.61067683, 0.61067683, 0.61067683, 0. , 0. , 0.}); - nd4j::ops::dynamic_bidirectional_rnn op; + sd::ops::dynamic_bidirectional_rnn op; auto results = op.evaluate({&x, &WxFW,&WhFW,&bFW, &WxFW,&WhFW,&bFW, &maxTimeStep}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto hFW = results->at(0); - auto hBW = results->at(1); - auto hFWfinal = results->at(2); - auto hBWfinal = results->at(3); + auto hFW = results.at(0); + auto hBW = results.at(1); + auto hFWfinal = results.at(2); + auto hBWfinal = results.at(3); ASSERT_TRUE(expHFW.isSameShape(hFW)); ASSERT_TRUE(expHFW.equalsTo(hFW)); @@ -2655,7 +2637,7 @@ TEST_F(DeclarableOpsTests6, dynamic_bidir_rnn_test3) { ASSERT_TRUE(expHBWfinal.isSameShape(hBWfinal)); ASSERT_TRUE(expHBWfinal.equalsTo(hBWfinal)); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -2695,15 +2677,15 @@ TEST_F(DeclarableOpsTests6, dynamic_bidir_rnn_test4) { auto expHFWfinal = NDArrayFactory::create('c', {bS, numUnitsFW}, {0.89948899, 0.89948899, 0.89948899, 0.94544483, 0.94544483, 0.94544483, 0.96797541, 0.96797541, 0.96797541, 0.9807326 , 0.9807326 , 0.9807326 }); auto expHBWfinal = NDArrayFactory::create('c', {bS, numUnitsBW}, {0.85301722, 0.85301722, 0.85301722, 0.91888753, 0.91888753, 0.91888753, 0.95254269, 0.95254269, 0.95254269, 0.97154357, 0.97154357, 0.97154357}); - nd4j::ops::dynamic_bidirectional_rnn op; + sd::ops::dynamic_bidirectional_rnn op; auto results = op.evaluate({&x, &WxFW,&WhFW,&bFW, &WxFW,&WhFW,&bFW, &h0FW, &h0BW}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto hFW = results->at(0); - auto hBW = results->at(1); - auto hFWfinal = results->at(2); - auto hBWfinal = results->at(3); + auto hFW = results.at(0); + auto hBW = results.at(1); + auto hFWfinal = results.at(2); + auto hBWfinal = results.at(3); ASSERT_TRUE(expHFW.isSameShape(hFW)); ASSERT_TRUE(expHFW.equalsTo(hFW)); @@ -2714,7 +2696,7 @@ TEST_F(DeclarableOpsTests6, dynamic_bidir_rnn_test4) { ASSERT_TRUE(expHBWfinal.isSameShape(hBWfinal)); ASSERT_TRUE(expHBWfinal.equalsTo(hBWfinal)); - delete results; + } TEST_F(DeclarableOpsTests6, dynamic_bidir_rnn_test5) { @@ -2748,15 +2730,15 @@ TEST_F(DeclarableOpsTests6, dynamic_bidir_rnn_test5) { auto expHFWfinal = NDArrayFactory::create('c', {bS, numUnitsFW}, {0.89357928, 0.89357928, 0.89357928, 0.94518339, 0.94518339, 0.94518339, 0.96795929, 0.96795929, 0.96795929, 0.98073144, 0.98073144, 0.98073144}); auto expHBWfinal = NDArrayFactory::create('c', {bS, numUnitsBW}, {0.84882345, 0.84882345, 0.84882345, 0.91865453, 0.91865453, 0.91865453, 0.95252666, 0.95252666, 0.95252666, 0.97154234, 0.97154234, 0.97154234}); - nd4j::ops::dynamic_bidirectional_rnn op; + sd::ops::dynamic_bidirectional_rnn op; auto results = op.evaluate({&x, &WxFW,&WhFW,&bFW, &WxFW,&WhFW,&bFW}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto hFW = results->at(0); - auto hBW = results->at(1); - auto hFWfinal = results->at(2); - auto hBWfinal = results->at(3); + auto hFW = results.at(0); + auto hBW = results.at(1); + auto hFWfinal = results.at(2); + auto hBWfinal = results.at(3); ASSERT_TRUE(expHFW.isSameShape(hFW)); ASSERT_TRUE(expHFW.equalsTo(hFW)); @@ -2767,7 +2749,7 @@ TEST_F(DeclarableOpsTests6, dynamic_bidir_rnn_test5) { ASSERT_TRUE(expHBWfinal.isSameShape(hBWfinal)); ASSERT_TRUE(expHBWfinal.equalsTo(hBWfinal)); - delete results; + } @@ -2775,39 +2757,39 @@ TEST_F(DeclarableOpsTests6, Test_Diag_119_1) { auto x = NDArrayFactory::create('c', {3}, {0.15f, 0.25f, 0.35f}); auto e = NDArrayFactory::create('c', {3, 3}, {0.15f, 0.0f, 0.0f, 0.0f, 0.25f, 0.0f, 0.0f, 0.0f, 0.35f}); - nd4j::ops::diag op; + sd::ops::diag op; auto result = op.evaluate({&x}, {}, {}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - ASSERT_EQ(e, *result->at(0)); + ASSERT_EQ(e, *result.at(0)); - delete result; + } TEST_F(DeclarableOpsTests6, Test_Diag_119_2) { auto x = NDArrayFactory::create('c', {1}, {0.15f}); auto e = NDArrayFactory::create('c', {1, 1}, {0.15f}); - nd4j::ops::diag op; + sd::ops::diag op; auto result = op.evaluate({&x}, {}, {}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - ASSERT_EQ(e, *result->at(0)); + ASSERT_EQ(e, *result.at(0)); - delete result; + } TEST_F(DeclarableOpsTests6, Test_Diag_119_3) { auto x = NDArrayFactory::create(0.15f); auto e = NDArrayFactory::create('c', {1, 1}, {0.15f}); - nd4j::ops::diag op; + sd::ops::diag op; auto result = op.evaluate({&x}, {}, {}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - ASSERT_EQ(e, *result->at(0)); + ASSERT_EQ(e, *result.at(0)); - delete result; + } diff --git a/libnd4j/tests_cpu/layers_tests/DeclarableOpsTests7.cpp b/libnd4j/tests_cpu/layers_tests/DeclarableOpsTests7.cpp index 7a9bc1648..7f39c3d76 100644 --- a/libnd4j/tests_cpu/layers_tests/DeclarableOpsTests7.cpp +++ b/libnd4j/tests_cpu/layers_tests/DeclarableOpsTests7.cpp @@ -22,13 +22,13 @@ #include "testlayers.h" #include #include -#include +#include #include -#include +#include -using namespace nd4j; -using namespace nd4j::graph; +using namespace sd; +using namespace sd::graph; class DeclarableOpsTests7 : public testing::Test { @@ -59,17 +59,17 @@ TEST_F(DeclarableOpsTests7, Test_CHOOSE_SCALAR_LARGE) { }; auto x = NDArrayFactory::create(inputData,'c',{1,149}); - nd4j::ops::choose op; + sd::ops::choose op; //greater than test auto result = op.evaluate({&x}, {0.0},{3}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(1); + auto z = result.at(1); ASSERT_EQ(148,z->e(0)); //ASSERT_TRUE(exp.isSameShape(z)); - delete result; + } @@ -82,17 +82,17 @@ TEST_F(DeclarableOpsTests7, Test_CHOOSE_SCALAR_ZERO) { auto x = NDArrayFactory::create('c',{1,4},data); - nd4j::ops::choose op; + sd::ops::choose op; //greater than test auto result = op.evaluate({&x}, {0.0},{3}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(1); + auto z = result.at(1); auto array = *z; ASSERT_EQ(3,array.e(0)); //ASSERT_TRUE(exp.isSameShape(z)); - delete result; + } @@ -107,16 +107,16 @@ TEST_F(DeclarableOpsTests7, Test_CHOOSE_SCALAR) { auto x = NDArrayFactory::create('c',{1,4},data); auto scalar = NDArrayFactory::create('c',{1,1},{0.0}); - nd4j::ops::choose op; + sd::ops::choose op; //greater than test auto result = op.evaluate({&x,&scalar}, {1.0},{3}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_EQ(3, z->lengthOf()); //ASSERT_TRUE(exp.isSameShape(z)); - delete result; + } @@ -131,16 +131,16 @@ TEST_F(DeclarableOpsTests7, Test_CHOOSE_SCALAR_LEFT) { auto x = NDArrayFactory::create('c',{1,4},data); auto scalar = NDArrayFactory::create('c',{1,1},{0.0}); - nd4j::ops::choose op; + sd::ops::choose op; //greater than test auto result = op.evaluate({&scalar,&x}, {1.0},{3}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_EQ(3,z->lengthOf()); //ASSERT_TRUE(exp.isSameShape(z)); - delete result; + } @@ -154,16 +154,16 @@ TEST_F(DeclarableOpsTests7, Test_CHOOSE_ONLY_SCALAR) { auto x = NDArrayFactory::create('c',{1,4},data); - nd4j::ops::choose op; + sd::ops::choose op; //greater than test auto result = op.evaluate({&x}, {1.0},{3}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_EQ(2,z->lengthOf()); //ASSERT_TRUE(exp.isSameShape(z)); - delete result; + } @@ -177,16 +177,16 @@ TEST_F(DeclarableOpsTests7, Test_CHOOSE_ONLY_SCALAR_GTE) { auto x = NDArrayFactory::create('c',{1,4},data); - nd4j::ops::choose op; + sd::ops::choose op; //greater than test auto result = op.evaluate({&x}, {1.0},{5}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_EQ(3,z->lengthOf()); //ASSERT_TRUE(exp.isSameShape(z)); - delete result; + } @@ -219,7 +219,7 @@ TEST_F(DeclarableOpsTests7, TEST_WHERE) { auto maskArr = NDArrayFactory::create('c',{1,4},mask); auto putArr = NDArrayFactory::create('c',{1,4},put); auto resultArr = NDArrayFactory::create('c',{1,4},resultData); - nd4j::ops::where_np op; + sd::ops::where_np op; //greater than test // Nd4jStatus execute(std::initializer_list*> inputs, std::initializer_list*> outputs , std::initializer_list tArgs, std::initializer_list iArgs, bool isInplace = false); @@ -227,7 +227,7 @@ TEST_F(DeclarableOpsTests7, TEST_WHERE) { ASSERT_EQ(Status::OK(), result); for(int i = 0; i < 4; i++) ASSERT_EQ(assertion[i],resultArr.e(i)); - // auto z = result->at(0); + // auto z = result.at(0); //ASSERT_EQ(4,z->lengthOf()); //ASSERT_TRUE(exp.isSameShape(z)); @@ -242,19 +242,19 @@ TEST_F(DeclarableOpsTests7, TEST_WHERE_MASK) { double assertion[300] = {1.000000000000000000e+00,1.000000000000000000e+00,1.000000000000000000e+00,1.000000000000000000e+00,1.000000000000000000e+00,1.000000000000000000e+00,1.000000000000000000e+00,1.000000000000000000e+00,1.000000000000000000e+00,1.000000000000000000e+00,1.000000000000000000e+00,1.000000000000000000e+00,1.000000000000000000e+00,1.000000000000000000e+00,1.000000000000000000e+00,1.000000000000000000e+00,1.000000000000000000e+00,1.000000000000000000e+00,1.000000000000000000e+00,1.000000000000000000e+00,1.000000000000000000e+00,1.000000000000000000e+00,1.000000000000000000e+00,1.000000000000000000e+00,1.000000000000000000e+00,1.000000000000000000e+00,1.000000000000000000e+00,1.000000000000000000e+00,1.000000000000000000e+00,1.000000000000000000e+00,1.000000000000000000e+00,1.000000000000000000e+00,1.000000000000000000e+00,1.000000000000000000e+00,1.000000000000000000e+00,1.000000000000000000e+00,1.000000000000000000e+00,1.000000000000000000e+00,1.000000000000000000e+00,1.000000000000000000e+00,1.000000000000000000e+00,1.000000000000000000e+00,1.000000000000000000e+00,1.000000000000000000e+00,1.000000000000000000e+00,1.000000000000000000e+00,1.000000000000000000e+00,1.000000000000000000e+00,1.000000000000000000e+00,1.000000000000000000e+00,1.000000000000000000e+00,1.000000000000000000e+00,1.000000000000000000e+00,1.000000000000000000e+00,1.000000000000000000e+00,1.000000000000000000e+00,1.000000000000000000e+00,1.000000000000000000e+00,1.000000000000000000e+00,1.000000000000000000e+00,1.000000000000000000e+00,1.000000000000000000e+00,1.000000000000000000e+00,1.000000000000000000e+00,1.000000000000000000e+00,1.000000000000000000e+00,1.000000000000000000e+00,1.000000000000000000e+00,1.000000000000000000e+00,1.000000000000000000e+00,1.000000000000000000e+00,1.000000000000000000e+00,1.000000000000000000e+00,1.000000000000000000e+00,1.000000000000000000e+00,1.000000000000000000e+00,1.000000000000000000e+00,1.000000000000000000e+00,1.000000000000000000e+00,1.000000000000000000e+00,1.000000000000000000e+00,1.000000000000000000e+00,1.000000000000000000e+00,1.000000000000000000e+00,1.000000000000000000e+00,1.000000000000000000e+00,1.000000000000000000e+00,1.000000000000000000e+00,1.000000000000000000e+00,1.000000000000000000e+00,1.000000000000000000e+00,1.000000000000000000e+00,1.000000000000000000e+00,1.000000000000000000e+00,1.000000000000000000e+00,1.000000000000000000e+00,1.000000000000000000e+00,1.000000000000000000e+00,1.000000000000000000e+00,1.000000000000000000e+00,9.966611049434810354e-01,9.867111603284486332e-01,9.768605487739230320e-01,9.671082786103732953e-01,9.574533680683808834e-01,9.478948451798039354e-01,9.384317476799283186e-01,9.290631229105962285e-01,9.197880277243004610e-01,9.106055283892373620e-01,9.015147004953073528e-01,8.925146288610534828e-01,8.836044074415293492e-01,8.747831392370875037e-01,8.660499362030764647e-01,8.574039191604412302e-01,8.488442177072155204e-01,8.403699701308978698e-01,8.319803233217017979e-01,8.236744326866727306e-01,8.154514620646623468e-01,8.073105836421510251e-01,7.992509778699116163e-01,7.912718333805045523e-01,7.833723469065965173e-01,7.755517232000953554e-01,7.678091749520912224e-01,7.601439227135980969e-01,7.525551948170853267e-01,7.450422272987937689e-01,7.376042638218265335e-01,7.302405556000080011e-01,7.229503613225031211e-01,7.157329470791886639e-01,7.085875862867698771e-01,7.015135596156351072e-01,6.945101549174396149e-01,6.875766671534137009e-01,6.807123983233853703e-01,6.739166573955123196e-01,6.671887602367149173e-01,6.605280295438040739e-01,6.539337947752965619e-01,6.474053920839111242e-01,6.409421642497381555e-01,6.345434606140767375e-01,6.282086370139332576e-01,6.219370557171712832e-01,6.157280853583116942e-01,6.095811008749726367e-01,6.034954834449430816e-01,5.974706204238864338e-01,5.915059052836644238e-01,5.856007375512777280e-01,5.797545227484157682e-01,5.739666723316099173e-01,5.682366036329845604e-01,5.625637398015992385e-01,5.569475097453767676e-01,5.513873480736106725e-01,5.458826950400470501e-01,5.404329964865340896e-01,5.350377037872348085e-01,5.296962737933965659e-01,5.244081687786711354e-01,5.191728563849821176e-01,5.139898095689314772e-01,5.088585065487419845e-01,5.037784307517284565e-01,4.987490707622945774e-01,4.937699202704479151e-01,4.888404780208293054e-01,4.839602477622509946e-01,4.791287381977387683e-01,4.743454629350723484e-01,4.696099404378203390e-01,4.649216939768630041e-01,4.602802515824001017e-01,4.556851459964368911e-01,4.511359146257447605e-01,4.466320994952920342e-01,4.421732472021388527e-01,4.377589088697927955e-01,4.333886401030203062e-01,4.290620009431086457e-01,4.247785558235752101e-01,4.205378735263185508e-01,4.163395271382073215e-01,4.121830940081024908e-01,4.080681557043087104e-01,4.039942979724505667e-01,3.999611106937689398e-01,3.959681878438343627e-01,3.920151274516718853e-01,3.881015315592946102e-01,3.842270061816405180e-01,3.803911612669100828e-01,3.765936106572991271e-01,3.728339720501240850e-01,3.691118669593352886e-01,3.654269206774144463e-01,3.617787622376523182e-01,3.581670243768036999e-01,3.545913434981138868e-01,3.510513596347161203e-01,3.475467164133922426e-01,3.440770610186974499e-01,3.406420441574410929e-01,3.372413200235238606e-01,3.338745462631242389e-01,3.305413839402346898e-01,3.272414975025391692e-01,3.239745547476344245e-01,3.207402267895853032e-01,3.175381880258169032e-01,3.143681161043347383e-01,3.112296918912743071e-01,3.081225994387726264e-01,3.050465259531625062e-01,3.020011617634821843e-01,2.989862002903017069e-01,2.960013380148582840e-01,2.930462744485015647e-01,2.901207121024425017e-01,2.872243564578055852e-01,2.843569159359789489e-01,2.815181018692606840e-01,2.787076284717992514e-01,2.759252128108221624e-01,2.731705747781537075e-01,2.704434370620155681e-01,2.677435251191103149e-01,2.650705671469821278e-01,2.624242940566549609e-01,2.598044394455423789e-01,2.572107395706292876e-01,2.546429333219200064e-01,2.521007621961529055e-01,2.495839702707757235e-01,2.470923041781825646e-01,2.446255130802063582e-01,2.421833486428674187e-01,2.397655650113727777e-01,2.373719187853666479e-01,2.350021689944260528e-01,2.326560770738031469e-01,2.303334068404078172e-01,2.280339244690317291e-01,2.257573984688081292e-01,2.235035996599082919e-01,2.212723011504689752e-01,2.190632783137518302e-01,2.168763087655291855e-01,2.147111723416972873e-01,2.125676510761114746e-01,2.104455291786438698e-01,2.083445930134591173e-01,2.062646310775079761e-01,2.042054339792348794e-01,2.021667944174980747e-01,2.001485071607009836e-01,1.981503690261307848e-01,1.961721788595043592e-01,1.942137375147174327e-01,1.922748478337968081e-01,1.903553146270518526e-01,1.884549446534251604e-01,1.865735466010380594e-01,1.847109310679319050e-01,1.828669105430000552e-01,1.810412993871116094e-01,1.792339138144224131e-01,1.774445718738737465e-01,1.756730934308744496e-01,1.739193001491673995e-01,1.721830154728755669e-01,1.704640646087285105e-01,1.687622745084652875e-01,1.670774738514141378e-01,1.654094930272448083e-01,1.637581641188943782e-01,1.621233208856623365e-01,1.605047987464754966e-01,1.589024347633189727e-01,1.573160676248336609e-01,1.557455376300762306e-01,1.541906866724424563e-01,1.526513582237501165e-01,1.511273973184814046e-01,1.496186505381822129e-01,1.481249659960175158e-01,1.466461933214808777e-01,1.451821836452561187e-01,1.437327895842310799e-01,1.422978652266598532e-01,1.408772661174743090e-01,1.394708492437411185e-01,1.380784730202649913e-01,1.366999972753347725e-01,1.353352832366127023e-01}; Nd4jLong threeHundredShapePointer[8] = {2,1,300,1,1,0,1,99}; Nd4jLong twoHundredShapePointer[8] = {2,1,200,1,1,0,1,99}; - nd4j::ops::where_np op; - ArrayOptions::setDataType(threeHundredShapePointer, nd4j::DataType::DOUBLE); - ArrayOptions::setDataType(twoHundredShapePointer, nd4j::DataType::DOUBLE); + sd::ops::where_np op; + ArrayOptions::setDataType(threeHundredShapePointer, sd::DataType::DOUBLE); + ArrayOptions::setDataType(twoHundredShapePointer, sd::DataType::DOUBLE); NDArray xArr(x,threeHundredShapePointer); NDArray putArr(put,twoHundredShapePointer); NDArray resultArr(z,threeHundredShapePointer); resultArr.assign(0.0); - ArrayOptions::setDataType(threeHundredShapePointer, nd4j::DataType::BOOL); + ArrayOptions::setDataType(threeHundredShapePointer, sd::DataType::BOOL); NDArray maskArr(mask,threeHundredShapePointer); - ArrayOptions::setDataType(threeHundredShapePointer, nd4j::DataType::DOUBLE); + ArrayOptions::setDataType(threeHundredShapePointer, sd::DataType::DOUBLE); NDArray assertArr(assertion, threeHundredShapePointer); Nd4jStatus result = op.execute({&maskArr, &xArr, &putArr},{&resultArr},{},{},{}); ASSERT_EQ(Status::OK(),result); @@ -290,15 +290,15 @@ TEST_F(DeclarableOpsTests7, TEST_WHERE_SCALAR) { auto maskArr = NDArrayFactory::create('c',{1,4},mask); auto putArr = NDArrayFactory::create('c',{1,1},put); auto resultArr = NDArrayFactory::create('c',{1,4},resultData); - nd4j::ops::where_np op; + sd::ops::where_np op; //greater than test // Nd4jStatus execute(std::initializer_list*> inputs, std::initializer_list*> outputs , std::initializer_list tArgs, std::initializer_list iArgs, bool isInplace = false); auto result = op.execute({&maskArr,&x,&putArr},{&resultArr}, {},{3}, {}, {}, false); - // ASSERT_EQ(Status::OK(), result->status()); + // ASSERT_EQ(Status::OK(), result.status()); for(int i = 0; i < 4; i++) ASSERT_EQ(assertion[i],resultArr.e(i)); - // auto z = result->at(0); + // auto z = result.at(0); //ASSERT_EQ(4,z->lengthOf()); //ASSERT_TRUE(exp.isSameShape(z)); @@ -312,14 +312,14 @@ TEST_F(DeclarableOpsTests7, TestMatrixDiagPart_1) { auto z = NDArrayFactory::create('c', {2, 4}, {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0}); - nd4j::ops::matrix_diag_part op; + sd::ops::matrix_diag_part op; auto result = op.evaluate({&x}, {}, {}); - ASSERT_EQ(result->status(), Status::OK()); - ASSERT_TRUE(z.equalsTo(result->at(0))); + ASSERT_EQ(result.status(), Status::OK()); + ASSERT_TRUE(z.equalsTo(result.at(0))); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -328,14 +328,14 @@ TEST_F(DeclarableOpsTests7, TestMatrixDiagPart_2) { auto z = NDArrayFactory::create('c', {2, 3}, {1, 2, 3, 5, 6, 7}); - nd4j::ops::matrix_diag_part op; + sd::ops::matrix_diag_part op; auto result = op.evaluate({&x}, {}, {}); - ASSERT_EQ(result->status(), Status::OK()); - ASSERT_TRUE(z.equalsTo(result->at(0))); + ASSERT_EQ(result.status(), Status::OK()); + ASSERT_TRUE(z.equalsTo(result.at(0))); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -344,14 +344,14 @@ TEST_F(DeclarableOpsTests7, TestMatrixDiag_1) { auto x = NDArrayFactory::create('c', {2, 4}, {1, 2, 3, 4, 5, 6, 7, 8}); - nd4j::ops::matrix_diag op; + sd::ops::matrix_diag op; auto result = op.evaluate({&x}, {}, {}); - ASSERT_EQ(result->status(), Status::OK()); - ASSERT_TRUE(z.equalsTo(result->at(0))); + ASSERT_EQ(result.status(), Status::OK()); + ASSERT_TRUE(z.equalsTo(result.at(0))); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -359,13 +359,13 @@ TEST_F(DeclarableOpsTests7, TestMatrixDiag_2) { auto z = NDArrayFactory::create('c', {2, 3, 3}, {1., 0., 0., 0., 2., 0., 0., 0., 3.,5., 0., 0., 0., 6., 0.,0., 0., 7.}); auto x = NDArrayFactory::create('c', {2, 3}, {1, 2, 3, 5, 6, 7}); - nd4j::ops::matrix_diag op; + sd::ops::matrix_diag op; auto result = op.evaluate({&x}, {}, {}); - ASSERT_EQ(result->status(), Status::OK()); - ASSERT_TRUE(z.equalsTo(result->at(0))); + ASSERT_EQ(result.status(), Status::OK()); + ASSERT_TRUE(z.equalsTo(result.at(0))); - delete result; + } @@ -373,28 +373,28 @@ TEST_F(DeclarableOpsTests7, TestMatrixDiag_2) { TEST_F(DeclarableOpsTests7, TestRandomCrop_1) { auto x = NDArrayFactory::create('c', {2, 2, 4}, {1.8, 2.5, 4., 9., 2.1, 2.4, 3., 9.,2.1, 2.1, 0.7, 0.1,3., 4.2, 2.2, 1. }); auto shape = NDArrayFactory::create({1, 2, 3}); - nd4j::ops::random_crop op; + sd::ops::random_crop op; auto result = op.evaluate({&x, &shape}, {}, {}); - ASSERT_EQ(result->status(), Status::OK()); - //result->at(0)->printIndexedBuffer("Output"); -// ASSERT_TRUE(z.equalsTo(result->at(0))); + ASSERT_EQ(result.status(), Status::OK()); + //result.at(0)->printIndexedBuffer("Output"); +// ASSERT_TRUE(z.equalsTo(result.at(0))); - delete result; + } //////////////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests7, TestRandomCrop_2) { auto x = NDArrayFactory::create('c', {2, 2, 4}, {1.8, 2.5, 4., 9., 2.1, 2.4, 3., 9.,2.1, 2.1, 0.7, 0.1,3., 4.2, 2.2, 1. }); auto shape = NDArrayFactory::create({2, 2, 2}); - nd4j::ops::random_crop op; + sd::ops::random_crop op; auto result = op.evaluate({&x, &shape}, {}, {}); - ASSERT_EQ(result->status(), Status::OK()); - //result->at(0)->printIndexedBuffer("Output"); -// ASSERT_TRUE(z.equalsTo(result->at(0))); + ASSERT_EQ(result.status(), Status::OK()); + //result.at(0)->printIndexedBuffer("Output"); +// ASSERT_TRUE(z.equalsTo(result.at(0))); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -425,15 +425,15 @@ TEST_F(DeclarableOpsTests7, Test_Dynamic_Stitch_119) { 89.f, 90.f, 91.f, 92.f,93.f, 94.f, 95.f, 96.f,97.f, 98.f, 99.f, 100.f,41.f, 42.f, 43.f, 44.f,45.f, 46.f, 47.f, 48.f,49.f, 50.f, 51.f, 52.f, 53.f, 54.f, 55.f, 56.f,57.f, 58.f, 59.f, 60.f,21.f, 22.f, 23.f, 24.f,25.f, 26.f, 27.f, 28.f,29.f, 30.f, 31.f, 32.f,33.f, 34.f, 35.f, 36.f,37.f, 38.f, 39.f, 40.f}); - nd4j::ops::dynamic_stitch op; + sd::ops::dynamic_stitch op; auto result = op.evaluate({&indices0, &indices1, &indices2, &data0, &data1, &data2}, {}, {}); - ASSERT_EQ(Status::OK(), result->status()); -// result->at(0)->printIndexedBuffer("Output"); + ASSERT_EQ(Status::OK(), result.status()); +// result.at(0)->printIndexedBuffer("Output"); // exp.printIndexedBuffer("Expect"); -// result->at(0)->printShapeInfo("Output shape"); - ASSERT_TRUE(exp.isSameShape(result->at(0))); - ASSERT_TRUE(exp.equalsTo(result->at(0))); - delete result; +// result.at(0)->printShapeInfo("Output shape"); + ASSERT_TRUE(exp.isSameShape(result.at(0))); + ASSERT_TRUE(exp.equalsTo(result.at(0))); + } //////////////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests7, Test_Dynamic_Stitch_Prof_1) { @@ -463,15 +463,15 @@ TEST_F(DeclarableOpsTests7, Test_Dynamic_Stitch_Prof_1) { 89.f, 90.f, 91.f, 92.f,93.f, 94.f, 95.f, 96.f,97.f, 98.f, 99.f, 100.f,41.f, 42.f, 43.f, 44.f,45.f, 46.f, 47.f, 48.f,49.f, 50.f, 51.f, 52.f, 53.f, 54.f, 55.f, 56.f,57.f, 58.f, 59.f, 60.f,21.f, 22.f, 23.f, 24.f,25.f, 26.f, 27.f, 28.f,29.f, 30.f, 31.f, 32.f,33.f, 34.f, 35.f, 36.f,37.f, 38.f, 39.f, 40.f}); - nd4j::ops::dynamic_stitch op; + sd::ops::dynamic_stitch op; auto result = op.evaluate({&indices0, &indices1, &indices2, &data0, &data1, &data2}, {}, {}); - ASSERT_EQ(Status::OK(), result->status()); -// result->at(0)->printIndexedBuffer("Output"); + ASSERT_EQ(Status::OK(), result.status()); +// result.at(0)->printIndexedBuffer("Output"); // exp.printIndexedBuffer("Expect"); -// result->at(0)->printShapeInfo("Output shape"); - auto res = result->at(0); - ASSERT_TRUE(exp.isSameShape(result->at(0))); - ASSERT_TRUE(exp.equalsTo(result->at(0))); +// result.at(0)->printShapeInfo("Output shape"); + auto res = result.at(0); + ASSERT_TRUE(exp.isSameShape(result.at(0))); + ASSERT_TRUE(exp.equalsTo(result.at(0))); int numOfCases = 100; auto timeStart = std::chrono::system_clock::now(); @@ -483,7 +483,7 @@ TEST_F(DeclarableOpsTests7, Test_Dynamic_Stitch_Prof_1) { auto outerTime = std::chrono::duration_cast (timeEnd - timeStart).count(); //nd4j_printf("dynamic_stitch: Process with %i iterations was load: %lld us.\n", numOfCases, outerTime / numOfCases); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -566,16 +566,16 @@ TEST_F(DeclarableOpsTests7, Test_Dynamic_Stitch_119_1) { data0.linspace(1); data1.linspace(21); data2.linspace(141); - nd4j::ops::dynamic_stitch op; + sd::ops::dynamic_stitch op; auto result = op.evaluate({&indices0, &indices1, &indices2, &data0, &data1, &data2}, {}, {}); - ASSERT_EQ(Status::OK(), result->status()); - auto z = result->at(0); + ASSERT_EQ(Status::OK(), result.status()); + auto z = result.at(0); ASSERT_TRUE(z->isSameShape(exp)); ASSERT_TRUE(z->equalsTo(exp)); - delete result; + } TEST_F(DeclarableOpsTests7, Test_Dynamic_Stitch_119_2) { @@ -657,16 +657,16 @@ TEST_F(DeclarableOpsTests7, Test_Dynamic_Stitch_119_2) { data0.linspace(1); data1.linspace(41); data2.linspace(161); - nd4j::ops::dynamic_stitch op; + sd::ops::dynamic_stitch op; auto result = op.evaluate({&indices0, &indices1, &indices2, &data0, &data1, &data2}, {}, {}); - ASSERT_EQ(Status::OK(), result->status()); - auto z = result->at(0); + ASSERT_EQ(Status::OK(), result.status()); + auto z = result.at(0); ASSERT_TRUE(z->isSameShape(exp)); ASSERT_TRUE(z->equalsTo(exp)); - delete result; + } TEST_F(DeclarableOpsTests7, Test_Dynamic_Partition_119) { @@ -675,15 +675,15 @@ TEST_F(DeclarableOpsTests7, Test_Dynamic_Partition_119) { auto e = NDArrayFactory::create('c', {5, 11}); x.assign(1.f); e.assign(1.f); - nd4j::ops::dynamic_partition op; + sd::ops::dynamic_partition op; auto result = op.evaluate({&x, &y}, {}, {4}); - ASSERT_EQ(Status::OK(), result->status()); - ASSERT_EQ(4, result->size()); - auto z = result->at(0); + ASSERT_EQ(Status::OK(), result.status()); + ASSERT_EQ(4, result.size()); + auto z = result.at(0); ASSERT_TRUE(e.isSameShape(z)); - delete result; + } TEST_F(DeclarableOpsTests7, Test_Dynamic_Partition_119_1) { @@ -694,22 +694,22 @@ TEST_F(DeclarableOpsTests7, Test_Dynamic_Partition_119_1) { // x.assign(1.f); // e.assign(1.f); - nd4j::ops::dynamic_partition op; + sd::ops::dynamic_partition op; auto result = op.evaluate({&x, &y}, {}, {3}); - ASSERT_EQ(Status::OK(), result->status()); - ASSERT_EQ(3, result->size()); - auto z = result->at(0); + ASSERT_EQ(Status::OK(), result.status()); + ASSERT_EQ(3, result.size()); + auto z = result.at(0); // z->printShapeInfo("Output shape info"); -// result->at(1)->printShapeInfo("Shape2"); -// result->at(2)->printShapeInfo("Shape3"); -// result->at(3)->printShapeInfo("Shape4"); +// result.at(1)->printShapeInfo("Shape2"); +// result.at(2)->printShapeInfo("Shape3"); +// result.at(3)->printShapeInfo("Shape4"); // z->printIndexedBuffer("Output1"); -// result->at(1)->printIndexedBuffer("Output2"); -// result->at(2)->printIndexedBuffer("Output3"); -// result->at(3)->printIndexedBuffer("Output4"); +// result.at(1)->printIndexedBuffer("Output2"); +// result.at(2)->printIndexedBuffer("Output3"); +// result.at(3)->printIndexedBuffer("Output4"); ASSERT_TRUE(e.isSameShape(z)); - delete result; + } TEST_F(DeclarableOpsTests7, Test_Dynamic_Partition_119_2) { auto x = NDArrayFactory::create('c', {5, 4, 11}); @@ -737,22 +737,22 @@ TEST_F(DeclarableOpsTests7, Test_Dynamic_Partition_119_2) { std::vector e({&e1, &e2, &e3, &e4}); x.linspace(1.f); //.assign(1.f); - nd4j::ops::dynamic_partition op; + sd::ops::dynamic_partition op; auto result = op.evaluate({&x, &y}, {}, {4}); - ASSERT_EQ(Status::OK(), result->status()); - ASSERT_EQ(4, result->size()); - for (size_t i = 0; i < result->size(); i++) { - auto z = result->at(i); + ASSERT_EQ(Status::OK(), result.status()); + ASSERT_EQ(4, result.size()); + for (size_t i = 0; i < result.size(); i++) { + auto z = result.at(i); // z->printShapeInfo("Output shape info"); // z->printIndexedBuffer("Output1"); -// result->at(1)->printIndexedBuffer("Output2"); -// result->at(2)->printIndexedBuffer("Output3"); -// result->at(3)->printIndexedBuffer("Output4"); +// result.at(1)->printIndexedBuffer("Output2"); +// result.at(2)->printIndexedBuffer("Output3"); +// result.at(3)->printIndexedBuffer("Output4"); ASSERT_TRUE(e[i]->isSameShape(z)); ASSERT_TRUE(e[i]->equalsTo(z)); } - delete result; + } @@ -767,17 +767,17 @@ TEST_F(DeclarableOpsTests7, Test_SequenceMask_1) { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0,1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0,1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 }); - nd4j::ops::sequence_mask op; + sd::ops::sequence_mask op; auto result = op.evaluate({&input}, {}, {}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); // z->printIndexedBuffer("Output"); // z->printShapeInfo("Shape"); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } @@ -789,17 +789,17 @@ TEST_F(DeclarableOpsTests7, Test_SequenceMask_2) { 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}); - nd4j::ops::sequence_mask op; + sd::ops::sequence_mask op; auto result = op.evaluate({&input}, {}, {}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); // z->printBuffer("Output"); // z->printShapeInfo("Shape"); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } TEST_F(DeclarableOpsTests7, Test_SequenceMask_3) { @@ -810,17 +810,17 @@ TEST_F(DeclarableOpsTests7, Test_SequenceMask_3) { 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}); - nd4j::ops::sequence_mask op; - auto result = op.evaluate({&input}, {nd4j::DataType::INT32}); - ASSERT_EQ(Status::OK(), result->status()); + sd::ops::sequence_mask op; + auto result = op.evaluate({&input}, {sd::DataType::INT32}); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); // z->printBuffer("Output"); // z->printShapeInfo("Shape"); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } TEST_F(DeclarableOpsTests7, Test_SequenceMask_4) { @@ -830,17 +830,17 @@ TEST_F(DeclarableOpsTests7, Test_SequenceMask_4) { 1.f, 0.f, 0.f, 0.f, 0.f, 1.f, 1.f, 1.f, 0.f, 0.f, 1.f, 1.f, 0.f, 0.f, 0.f }); - nd4j::ops::sequence_mask op; - auto result = op.evaluate({&input, &maxLen}, {nd4j::DataType::FLOAT32}); - ASSERT_EQ(Status::OK(), result->status()); + sd::ops::sequence_mask op; + auto result = op.evaluate({&input, &maxLen}, {sd::DataType::FLOAT32}); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); // z->printBuffer("Output"); // z->printShapeInfo("Shape"); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } TEST_F(DeclarableOpsTests7, Test_SequenceMask_5) { @@ -849,17 +849,17 @@ TEST_F(DeclarableOpsTests7, Test_SequenceMask_5) { 1.f, 0.f, 0.f, 0.f, 0.f, 1.f, 1.f, 1.f, 0.f, 0.f, 1.f, 1.f, 0.f, 0.f, 0.f }); - nd4j::ops::sequence_mask op; - auto result = op.evaluate({&input}, {5, (int)nd4j::DataType::FLOAT32}); - ASSERT_EQ(Status::OK(), result->status()); + sd::ops::sequence_mask op; + auto result = op.evaluate({&input}, {5, (int)sd::DataType::FLOAT32}); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); // z->printBuffer("Output"); // z->printShapeInfo("Shape"); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -868,15 +868,15 @@ TEST_F(DeclarableOpsTests7, TestSegmentMax_1) { auto idx = NDArrayFactory::create({0, 0, 1, 1, 1, 1, 2, 3, 3, 3, 4, 4, 4, 4, 4, 4}); auto exp = NDArrayFactory::create({2.5, 9, 3, 9, 4.2}); - nd4j::ops::segment_max op; + sd::ops::segment_max op; auto result = op.evaluate({&x, &idx}, {}, {}); - ASSERT_EQ(result->status(), Status::OK()); -// result->at(0)->printBuffer("MaX1"); + ASSERT_EQ(result.status(), Status::OK()); +// result.at(0)->printBuffer("MaX1"); // exp.printBuffer("ExP1"); - ASSERT_TRUE(exp.equalsTo(result->at(0))); + ASSERT_TRUE(exp.equalsTo(result.at(0))); - delete result; + } //////////////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests7, TestSegmentMax_01) { @@ -884,15 +884,15 @@ TEST_F(DeclarableOpsTests7, TestSegmentMax_01) { auto idx = NDArrayFactory::create({0, 0, 1, 1, 1, 1, 2, 3, 3, 3, 4, 4, 4, 4, 4, 4, 5,5, 5}); auto exp = NDArrayFactory::create({2.5, 9, 3, 9, 4.2, 40}); - nd4j::ops::segment_max op; + sd::ops::segment_max op; auto result = op.evaluate({&x, &idx}, {}, {}); - ASSERT_EQ(result->status(), Status::OK()); -// result->at(0)->printBuffer("MaX01"); + ASSERT_EQ(result.status(), Status::OK()); +// result.at(0)->printBuffer("MaX01"); // exp.printBuffer("ExP01"); - ASSERT_TRUE(exp.equalsTo(result->at(0))); + ASSERT_TRUE(exp.equalsTo(result.at(0))); - delete result; + } //////////////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests7, TestSegmentMaxBP_1) { @@ -900,14 +900,14 @@ TEST_F(DeclarableOpsTests7, TestSegmentMaxBP_1) { auto idx = NDArrayFactory::create({0, 0, 1, 1, 1, 1, 2, 3, 3, 3, 4, 4, 4, 4, 4, 4}); auto exp = NDArrayFactory::create({0., 1., 0., 2., 0., 0., 3., 4., 0., 0.,0., 0., 0., 5., 0.,0.}); auto eps = NDArrayFactory::create('c', {5}); - nd4j::ops::segment_max_bp op; + sd::ops::segment_max_bp op; eps.linspace(1); auto result = op.evaluate({&x, &idx, &eps}, {}, {}); - ASSERT_EQ(result->status(), Status::OK()); -// result->at(0)->printIndexedBuffer("OutputMaxBP"); - ASSERT_TRUE(exp.equalsTo(result->at(0))); + ASSERT_EQ(result.status(), Status::OK()); +// result.at(0)->printIndexedBuffer("OutputMaxBP"); + ASSERT_TRUE(exp.equalsTo(result.at(0))); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -923,18 +923,18 @@ TEST_F(DeclarableOpsTests7, TestSegmentMax_2) { 3, 4.2, 2.2, 1.}); //{ 2.1, 2.5, 4., 9., 2.1, 2.1, 0.7, 0.1, 3., 4.2, 2.2, 1.} - nd4j::ops::segment_max op; + sd::ops::segment_max op; auto result = op.evaluate({&x, &idx}, {}, {}); - ASSERT_EQ(result->status(), Status::OK()); - ASSERT_EQ(result->size(), 1); - auto out = result->at(0); + ASSERT_EQ(result.status(), Status::OK()); + ASSERT_EQ(result.size(), 1); + auto out = result.at(0); // out->printIndexedBuffer("Output2Max"); // exp.printIndexedBuffer("Expect2Max"); // exp.printShapeInfo("Exp Shape"); - ASSERT_TRUE(exp.equalsTo(result->at(0))); + ASSERT_TRUE(exp.equalsTo(result.at(0))); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -947,17 +947,17 @@ TEST_F(DeclarableOpsTests7, TestSegmentMaxBP_2) { //{ 2.1, 2.5, 4., 9., 2.1, 2.1, 0.7, 0.1, 3., 4.2, 2.2, 1.} - nd4j::ops::segment_max_bp op; + sd::ops::segment_max_bp op; auto result = op.evaluate({&x, &idx, &eps}, {}, {}); - ASSERT_EQ(result->status(), Status::OK()); - ASSERT_EQ(result->size(), 2); + ASSERT_EQ(result.status(), Status::OK()); + ASSERT_EQ(result.size(), 2); //exp.printIndexedBuffer("BP Max Expect"); - //result->at(0)->printIndexedBuffer("BP Max Output"); + //result.at(0)->printIndexedBuffer("BP Max Output"); // exp.printShapeInfo("Exp Shape"); - ASSERT_TRUE(exp.equalsTo(result->at(0))); + ASSERT_TRUE(exp.equalsTo(result.at(0))); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -975,17 +975,17 @@ TEST_F(DeclarableOpsTests7, TestSegmentMax_3) { //{ 2.1, 2.5, 4., 9., 2.1, 2.1, 0.7, 0.1, 3., 4.2, 2.2, 1.} - nd4j::ops::segment_max op; + sd::ops::segment_max op; auto result = op.evaluate({&x, &idx}, {}, {}); - ASSERT_EQ(result->status(), Status::OK()); -// result->at(0)->printIndexedBuffer("Output3Max"); -// result->at(0)->printShapeInfo("Out Shape 3 Max"); + ASSERT_EQ(result.status(), Status::OK()); +// result.at(0)->printIndexedBuffer("Output3Max"); +// result.at(0)->printShapeInfo("Out Shape 3 Max"); // exp.printIndexedBuffer("Expect3Max"); // exp.printShapeInfo("Exp Shape 3 Max"); - ASSERT_TRUE(exp.equalsTo(result->at(0))); + ASSERT_TRUE(exp.equalsTo(result.at(0))); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1003,18 +1003,18 @@ TEST_F(DeclarableOpsTests7, TestSegmentMax_4) { 0. , 0. , 0. , 0. ,0. , 0. , 0. , 0. ,0. , 0. , 0. , 0. ,0. , 0. , 0. , 0. ,91. , 82. , 37. , 64. ,55.1, 46.4, 73. , 28. , 119.1, 12.1, 112.7, 13.1,14. , 114.2, 16.2, 117. }); - nd4j::ops::segment_max op; + sd::ops::segment_max op; auto result = op.evaluate({&x, &idx}, {}, {}); - ASSERT_EQ(result->status(), Status::OK()); - //result->at(0)->printIndexedBuffer("Output"); - //result->at(0)->printShapeInfo("Out Shape"); + ASSERT_EQ(result.status(), Status::OK()); + //result.at(0)->printIndexedBuffer("Output"); + //result.at(0)->printShapeInfo("Out Shape"); //exp.printIndexedBuffer("Expect"); //exp.printShapeInfo("Exp Shape"); - ASSERT_TRUE(exp.isSameShape(result->at(0))); - ASSERT_TRUE(exp.equalsTo(result->at(0))); + ASSERT_TRUE(exp.isSameShape(result.at(0))); + ASSERT_TRUE(exp.equalsTo(result.at(0))); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1023,13 +1023,13 @@ TEST_F(DeclarableOpsTests7, TestUnsortedSegmentMax_1) { auto idx = NDArrayFactory::create({4, 4, 1, 1, 1, 1, 2, 3, 3, 3, 4, 4, 4, 4, 0, 0}); auto exp = NDArrayFactory::create({2.2, 9., 3., 9., 4.2}); - nd4j::ops::unsorted_segment_max op; + sd::ops::unsorted_segment_max op; auto result = op.evaluate({&x, &idx}, {}, {5}); - ASSERT_EQ(result->status(), Status::OK()); - ASSERT_TRUE(exp.equalsTo(result->at(0))); + ASSERT_EQ(result.status(), Status::OK()); + ASSERT_TRUE(exp.equalsTo(result.at(0))); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1038,13 +1038,13 @@ TEST_F(DeclarableOpsTests7, TestUnsortedSegmentMaxBP_1) { auto idx = NDArrayFactory::create({0, 0, 1, 1, 1, 1, 2, 3, 3, 3, 4, 4, 4, 4, 4, 4}); auto exp = NDArrayFactory::create({0., 1., 0., 2., 0., 0., 3., 4., 0., 0.,0., 0., 0., 5., 0.,0.}); auto eps = NDArrayFactory::create('c', {5}); - nd4j::ops::segment_max_bp op; + sd::ops::segment_max_bp op; eps.linspace(1); auto result = op.evaluate({&x, &idx, &eps}, {}, {5}); - ASSERT_EQ(result->status(), Status::OK()); - ASSERT_TRUE(exp.equalsTo(result->at(0))); + ASSERT_EQ(result.status(), Status::OK()); + ASSERT_TRUE(exp.equalsTo(result.at(0))); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1053,15 +1053,15 @@ TEST_F(DeclarableOpsTests7, TestUnsortedSegmentMaxBP_2) { auto idx = NDArrayFactory::create({2, 0, 0, 1, 1, 1, 1, 3, 3, 3, 4, 4, 4, 4, 4, 4}); auto exp = NDArrayFactory::create({3., 0., 1., 0., 2., 0., 0., 4., 0., 0.,0., 0., 0., 5., 0.,0.}); auto eps = NDArrayFactory::create('c', {5}); - nd4j::ops::segment_max_bp op; + sd::ops::segment_max_bp op; eps.linspace(1); auto result = op.evaluate({&x, &idx, &eps}, {}, {5}); - ASSERT_EQ(result->status(), Status::OK()); - //result->at(0)->printIndexedBuffer("Output"); + ASSERT_EQ(result.status(), Status::OK()); + //result.at(0)->printIndexedBuffer("Output"); //exp.printIndexedBuffer("Expect"); - ASSERT_TRUE(exp.equalsTo(result->at(0))); + ASSERT_TRUE(exp.equalsTo(result.at(0))); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1070,14 +1070,14 @@ TEST_F(DeclarableOpsTests7, TestUnsortedSegmentMax_2) { auto idx = NDArrayFactory::create({4, 4, 1, 1, 1, 1, 3, 3, 3, 3, 4, 4, 4, 4, 0, 0}); auto exp = NDArrayFactory::create({2.2, 9., -DataTypeUtils::max(), 9., 4.2}); - nd4j::ops::unsorted_segment_max op; + sd::ops::unsorted_segment_max op; auto result = op.evaluate({&x, &idx}, {}, {5}); - ASSERT_EQ(result->status(), Status::OK()); -// result->at(0)->printIndexedBuffer("OutputUnsortedMax"); - ASSERT_TRUE(exp.equalsTo(result->at(0))); + ASSERT_EQ(result.status(), Status::OK()); +// result.at(0)->printIndexedBuffer("OutputUnsortedMax"); + ASSERT_TRUE(exp.equalsTo(result.at(0))); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1088,16 +1088,16 @@ TEST_F(DeclarableOpsTests7, TestUnsortedSegmentMax_3) { //{ 2.1, 2.5, 4., 9., 2.1, 2.1, 0.7, 0.1, 3., 4.2, 2.2, 1.} - nd4j::ops::unsorted_segment_max op; + sd::ops::unsorted_segment_max op; auto result = op.evaluate({&x, &idx}, {}, {3}); - ASSERT_EQ(result->status(), Status::OK()); - ASSERT_EQ(result->size(), 1); + ASSERT_EQ(result.status(), Status::OK()); + ASSERT_EQ(result.size(), 1); //exp.printIndexedBuffer("Expect"); - //result->at(0)->printIndexedBuffer("Output"); - ASSERT_TRUE(exp.equalsTo(result->at(0))); + //result.at(0)->printIndexedBuffer("Output"); + ASSERT_TRUE(exp.equalsTo(result.at(0))); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1111,16 +1111,16 @@ TEST_F(DeclarableOpsTests7, TestUnsortedSegmentMax_4) { //{ 2.1, 2.5, 4., 9., 2.1, 2.1, 0.7, 0.1, 3., 4.2, 2.2, 1.} - nd4j::ops::unsorted_segment_max op; + sd::ops::unsorted_segment_max op; auto result = op.evaluate({&x, &idx}, {}, {3}); - ASSERT_EQ(result->status(), Status::OK()); - ASSERT_EQ(result->size(), 1); + ASSERT_EQ(result.status(), Status::OK()); + ASSERT_EQ(result.size(), 1); //exp.printIndexedBuffer("Expect"); - //result->at(0)->printIndexedBuffer("Output"); - ASSERT_TRUE(exp.equalsTo(result->at(0))); + //result.at(0)->printIndexedBuffer("Output"); + ASSERT_TRUE(exp.equalsTo(result.at(0))); - delete result; + } //////////////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests7, TestSegmentMin_1) { @@ -1128,15 +1128,15 @@ TEST_F(DeclarableOpsTests7, TestSegmentMin_1) { auto idx = NDArrayFactory::create({0, 0, 1, 1, 1, 1, 2, 3, 3, 3, 4, 4, 4, 4, 4, 4}); auto exp = NDArrayFactory::create({1.8, 2.1, 3., 2.1, 0.1}); - nd4j::ops::segment_min op; + sd::ops::segment_min op; auto result = op.evaluate({&x, &idx}, {}, {}); - ASSERT_EQ(result->status(), Status::OK()); - auto out = result->at(0); + ASSERT_EQ(result.status(), Status::OK()); + auto out = result.at(0); - ASSERT_TRUE(exp.equalsTo(result->at(0))); + ASSERT_TRUE(exp.equalsTo(result.at(0))); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1145,15 +1145,15 @@ TEST_F(DeclarableOpsTests7, TestSegmentMin_01) { auto idx = NDArrayFactory::create({0, 0, 1, 1, 1, 1, 2, 3, 3, 3, 4, 4, 4, 4, 4, 4}); auto exp = NDArrayFactory::create({-2.5, -9, -3., -9, -4.2}); - nd4j::ops::segment_min op; + sd::ops::segment_min op; auto result = op.evaluate({&x, &idx}, {}, {}); - ASSERT_EQ(result->status(), Status::OK()); - auto out = result->at(0); + ASSERT_EQ(result.status(), Status::OK()); + auto out = result.at(0); - ASSERT_TRUE(exp.equalsTo(result->at(0))); + ASSERT_TRUE(exp.equalsTo(result.at(0))); - delete result; + } //////////////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests7, TestSegmentMin_02) { @@ -1161,15 +1161,15 @@ TEST_F(DeclarableOpsTests7, TestSegmentMin_02) { auto idx = NDArrayFactory::create({0, 0, 1, 1, 1, 1, 2, 3, 3, 3, 4, 4, 4, 4, 4, 4}); auto exp = NDArrayFactory::create({-2.5f, -9.f, -3.f, -9.f, -4.2f}); - nd4j::ops::segment_min op; + sd::ops::segment_min op; auto result = op.evaluate({&x, &idx}, {}, {}); - ASSERT_EQ(result->status(), Status::OK()); - auto out = result->at(0); + ASSERT_EQ(result.status(), Status::OK()); + auto out = result.at(0); - ASSERT_TRUE(exp.equalsTo(result->at(0))); + ASSERT_TRUE(exp.equalsTo(result.at(0))); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1179,14 +1179,14 @@ TEST_F(DeclarableOpsTests7, TestSegmentMinBP_1) { auto exp = NDArrayFactory::create({ 1., 0., 0., 0., 2., 0., 3., 0., 4., 4., 0., 5., 0., 0., 0., 0.}); auto eps = NDArrayFactory::create('c', {5}); eps.linspace(1); - nd4j::ops::segment_min_bp op; + sd::ops::segment_min_bp op; auto result = op.evaluate({&x, &idx, &eps}, {}, {}); - ASSERT_EQ(result->status(), Status::OK()); + ASSERT_EQ(result.status(), Status::OK()); - ASSERT_TRUE(exp.equalsTo(result->at(0))); + ASSERT_TRUE(exp.equalsTo(result.at(0))); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1196,16 +1196,16 @@ TEST_F(DeclarableOpsTests7, TestUnsortedSegmentMinBP_1) { auto exp = NDArrayFactory::create({ 1., 0., 0., 0., 2., 0., 3., 0., 4., 4., 0., 5., 0., 0., 0., 0.}); auto eps = NDArrayFactory::create('c', {5}); eps.linspace(1); - nd4j::ops::unsorted_segment_min_bp op; + sd::ops::unsorted_segment_min_bp op; auto result = op.evaluate({&x, &idx, &eps}, {}, {5}); - ASSERT_EQ(result->status(), Status::OK()); - //result->at(0)->printIndexedBuffer("Output1"); + ASSERT_EQ(result.status(), Status::OK()); + //result.at(0)->printIndexedBuffer("Output1"); //exp.printIndexedBuffer("Expecte"); - ASSERT_TRUE(exp.equalsTo(result->at(0))); + ASSERT_TRUE(exp.equalsTo(result.at(0))); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1215,16 +1215,16 @@ TEST_F(DeclarableOpsTests7, TestUnsortedSegmentMinBP_2) { auto exp = NDArrayFactory::create({3., 1., 0., 0., 0., 2., 0., 0., 4., 4., 0., 5., 0., 0., 0., 0.}); auto eps = NDArrayFactory::create('c', {5}); eps.linspace(1); - nd4j::ops::unsorted_segment_min_bp op; + sd::ops::unsorted_segment_min_bp op; auto result = op.evaluate({&x, &idx, &eps}, {}, {5}); - ASSERT_EQ(result->status(), Status::OK()); - //result->at(0)->printIndexedBuffer("Output1"); + ASSERT_EQ(result.status(), Status::OK()); + //result.at(0)->printIndexedBuffer("Output1"); //exp.printIndexedBuffer("Expecte"); - ASSERT_TRUE(exp.equalsTo(result->at(0))); + ASSERT_TRUE(exp.equalsTo(result.at(0))); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1235,16 +1235,16 @@ TEST_F(DeclarableOpsTests7, TestSegmentMin_2) { //{ 2.1, 2.5, 4., 9., 2.1, 2.1, 0.7, 0.1, 3., 4.2, 2.2, 1.} - nd4j::ops::segment_min op; + sd::ops::segment_min op; auto result = op.evaluate({&x, &idx}, {}, {}); - ASSERT_EQ(result->status(), Status::OK()); - ASSERT_EQ(result->size(), 1); + ASSERT_EQ(result.status(), Status::OK()); + ASSERT_EQ(result.size(), 1); // exp.printIndexedBuffer("Expect"); // exp.printShapeInfo("Exp Shape"); - ASSERT_TRUE(exp.equalsTo(result->at(0))); + ASSERT_TRUE(exp.equalsTo(result.at(0))); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1256,16 +1256,16 @@ TEST_F(DeclarableOpsTests7, TestSegmentMinBP_2) { //{ 2.1, 2.5, 4., 9., 2.1, 2.1, 0.7, 0.1, 3., 4.2, 2.2, 1.} - nd4j::ops::segment_min_bp op; + sd::ops::segment_min_bp op; auto result = op.evaluate({&x, &idx, &eps}, {}, {}); - ASSERT_EQ(result->status(), Status::OK()); - ASSERT_EQ(result->size(), 2); + ASSERT_EQ(result.status(), Status::OK()); + ASSERT_EQ(result.size(), 2); // exp.printIndexedBuffer("Expect"); -// result->at(0)->printIndexedBuffer("Output"); - ASSERT_TRUE(exp.equalsTo(result->at(0))); +// result.at(0)->printIndexedBuffer("Output"); + ASSERT_TRUE(exp.equalsTo(result.at(0))); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1281,17 +1281,17 @@ TEST_F(DeclarableOpsTests7, TestSegmentMin_3) { auto exp = NDArrayFactory::create('c', {3, 4, 4}, {91. , 82. , 37. , 64. ,55.1, 46.4, 73. , 28. ,119.1, 12.1, 112.7, 13.1,14. , 114.2, 16.2, 117. ,31. , 22. , 67. , 24. , 15.1, 46.4, 73. , 28. ,109.1, 12.1, 12.7, 13.1,14. , 14.2, 16.2, 11. ,91. , 82. , 37. , 64. ,55.1, 46.4, 73. , 28. ,119.1, 12.1, 112.7, 13.1,14. , 114.2, 16.2, 117. }); - nd4j::ops::segment_min op; + sd::ops::segment_min op; auto result = op.evaluate({&x, &idx}, {}, {}); - ASSERT_EQ(result->status(), Status::OK()); -// result->at(0)->printIndexedBuffer("Output"); -// result->at(0)->printShapeInfo("Out Shape"); + ASSERT_EQ(result.status(), Status::OK()); +// result.at(0)->printIndexedBuffer("Output"); +// result.at(0)->printShapeInfo("Out Shape"); // exp.printIndexedBuffer("Expect"); // exp.printShapeInfo("Exp Shape"); - ASSERT_TRUE(exp.equalsTo(result->at(0))); + ASSERT_TRUE(exp.equalsTo(result.at(0))); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1311,18 +1311,18 @@ TEST_F(DeclarableOpsTests7, TestSegmentMin_4) { 0. , 0. , 0. , 0. ,0. , 0. , 0. , 0. ,0. , 0. , 0. , 0. ,0. , 0. , 0. , 0. ,0. , 0. , 0. , 0. ,0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ,0. , 0. , 0. , 0. ,0. , 0. , 0. , 0. ,0. , 0. , 0. , 0. ,91. , 82. , 37. , 64. ,55.1, 46.4, 73. , 28. ,119.1, 12.1, 112.7, 13.1,14. , 114.2, 16.2, 117. }); - nd4j::ops::segment_min op; + sd::ops::segment_min op; auto result = op.evaluate({&x, &idx}, {}, {}); - ASSERT_EQ(result->status(), Status::OK()); - //result->at(0)->printIndexedBuffer("Output"); - //result->at(0)->printShapeInfo("Out Shape"); + ASSERT_EQ(result.status(), Status::OK()); + //result.at(0)->printIndexedBuffer("Output"); + //result.at(0)->printShapeInfo("Out Shape"); //exp.printIndexedBuffer("Expect"); //exp.printShapeInfo("Exp Shape"); - ASSERT_TRUE(exp.isSameShape(result->at(0))); - ASSERT_TRUE(exp.equalsTo(result->at(0))); + ASSERT_TRUE(exp.isSameShape(result.at(0))); + ASSERT_TRUE(exp.equalsTo(result.at(0))); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1331,13 +1331,13 @@ TEST_F(DeclarableOpsTests7, TestUnsortedSegmentMin_1) { auto idx = NDArrayFactory::create({0, 0, 1, 1, 1, 1, 2, 3, 3, 3, 4, 4, 4, 4, 4, 4}); auto exp = NDArrayFactory::create({1.8, 2.1, 3., 2.1, 0.1}); - nd4j::ops::unsorted_segment_min op; + sd::ops::unsorted_segment_min op; auto result = op.evaluate({&x, &idx}, {}, {5}); - ASSERT_EQ(result->status(), Status::OK()); - ASSERT_TRUE(exp.equalsTo(result->at(0))); + ASSERT_EQ(result.status(), Status::OK()); + ASSERT_TRUE(exp.equalsTo(result.at(0))); - delete result; + } //////////////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests7, TestUnsortedSegmentMin_01) { @@ -1345,13 +1345,13 @@ TEST_F(DeclarableOpsTests7, TestUnsortedSegmentMin_01) { auto idx = NDArrayFactory::create({0, 0, 1, 1, 1, 1, 2, 3, 3, 3, 4, 4, 4, 4, 4, 4}); auto exp = NDArrayFactory::create({1.8, 2.1, 3., 2.1, 0.1}); - nd4j::ops::unsorted_segment_min op; + sd::ops::unsorted_segment_min op; auto result = op.evaluate({&x, &idx}, {}, {5}); - ASSERT_EQ(result->status(), Status::OK()); - ASSERT_TRUE(exp.equalsTo(result->at(0))); + ASSERT_EQ(result.status(), Status::OK()); + ASSERT_TRUE(exp.equalsTo(result.at(0))); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1362,16 +1362,16 @@ TEST_F(DeclarableOpsTests7, TestUnsortedSegmentMin_2) { //{ 2.1, 2.5, 4., 9., 2.1, 2.1, 0.7, 0.1, 3., 4.2, 2.2, 1.} - nd4j::ops::unsorted_segment_min op; + sd::ops::unsorted_segment_min op; auto result = op.evaluate({&x, &idx}, {}, {3}); - ASSERT_EQ(result->status(), Status::OK()); - ASSERT_EQ(result->size(), 1); + ASSERT_EQ(result.status(), Status::OK()); + ASSERT_EQ(result.size(), 1); // exp.printIndexedBuffer("Expect"); // exp.printShapeInfo("Exp Shape"); - ASSERT_TRUE(exp.equalsTo(result->at(0))); + ASSERT_TRUE(exp.equalsTo(result.at(0))); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1386,17 +1386,17 @@ TEST_F(DeclarableOpsTests7, TestUnsortedSegmentMin_3) { auto exp = NDArrayFactory::create('c', {3, 4, 4}, {91. , 82. , 37. , 64. ,55.1, 46.4, 73. , 28. ,119.1, 12.1, 112.7, 13.1,14. , 114.2, 16.2, 117. ,31. , 22. , 67. , 24. , 15.1, 46.4, 73. , 28. ,109.1, 12.1, 12.7, 13.1,14. , 14.2, 16.2, 11. ,91. , 82. , 37. , 64. ,55.1, 46.4, 73. , 28. ,119.1, 12.1, 112.7, 13.1,14. , 114.2, 16.2, 117. }); - nd4j::ops::unsorted_segment_min op; + sd::ops::unsorted_segment_min op; auto result = op.evaluate({&x, &idx}, {}, {3}); - ASSERT_EQ(result->status(), Status::OK()); -// result->at(0)->printIndexedBuffer("Output"); -// result->at(0)->printShapeInfo("Out Shape"); + ASSERT_EQ(result.status(), Status::OK()); +// result.at(0)->printIndexedBuffer("Output"); +// result.at(0)->printShapeInfo("Out Shape"); // exp.printIndexedBuffer("Expect"); // exp.printShapeInfo("Exp Shape"); - ASSERT_TRUE(exp.equalsTo(result->at(0))); + ASSERT_TRUE(exp.equalsTo(result.at(0))); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1427,18 +1427,18 @@ TEST_F(DeclarableOpsTests7, TestUnsortedSegmentMin_4) { principalMax, principalMax, principalMax, principalMax, principalMax, principalMax, 91., 82. , 37. , 64. ,55.1, 46.4, 73. , 28. ,119.1, 12.1, 112.7, 13.1,14. , 114.2, 16.2, 117. }); - nd4j::ops::unsorted_segment_min op; + sd::ops::unsorted_segment_min op; auto result = op.evaluate({&x, &idx}, {}, {8}); - ASSERT_EQ(result->status(), Status::OK()); - //result->at(0)->printIndexedBuffer("Output"); - //result->at(0)->printShapeInfo("Out Shape"); + ASSERT_EQ(result.status(), Status::OK()); + //result.at(0)->printIndexedBuffer("Output"); + //result.at(0)->printShapeInfo("Out Shape"); // exp.printIndexedBuffer("Expect"); //exp.printShapeInfo("Exp Shape"); - ASSERT_TRUE(exp.isSameShape(result->at(0))); - ASSERT_TRUE(exp.equalsTo(result->at(0))); + ASSERT_TRUE(exp.isSameShape(result.at(0))); + ASSERT_TRUE(exp.equalsTo(result.at(0))); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1447,13 +1447,13 @@ TEST_F(DeclarableOpsTests7, TestSegmentMean_1) { auto idx = NDArrayFactory::create({0, 0, 1, 1, 1, 1, 2, 3, 3, 3, 4, 4, 4, 4, 4, 4}); auto exp = NDArrayFactory::create({2.15, 4.375, 3., 4.4, 1.8666667}); - nd4j::ops::segment_mean op; + sd::ops::segment_mean op; auto result = op.evaluate({&x, &idx}, {}, {}); - ASSERT_EQ(result->status(), Status::OK()); - ASSERT_TRUE(exp.equalsTo(result->at(0))); + ASSERT_EQ(result.status(), Status::OK()); + ASSERT_TRUE(exp.equalsTo(result.at(0))); - delete result; + } TEST_F(DeclarableOpsTests7, TestSegmentMean_2) { @@ -1461,17 +1461,17 @@ TEST_F(DeclarableOpsTests7, TestSegmentMean_2) { auto idx = NDArrayFactory::create({0, 0, 1, 2}); auto exp = NDArrayFactory::create('c', {3, 4}, { 1.95, 2.45, 3.5, 9., 2.1, 2.1, 0.7, 0.1, 3. , 4.2, 2.2, 1.}); - nd4j::ops::segment_mean op; + sd::ops::segment_mean op; auto result = op.evaluate({&x, &idx}, {}, {}); - ASSERT_EQ(result->status(), Status::OK()); - ASSERT_EQ(result->size(), 1); + ASSERT_EQ(result.status(), Status::OK()); + ASSERT_EQ(result.size(), 1); // exp.printIndexedBuffer("Expect"); -// result->at(0)->printIndexedBuffer("Output"); +// result.at(0)->printIndexedBuffer("Output"); // exp.printShapeInfo("Exp Shape"); - ASSERT_TRUE(exp.equalsTo(result->at(0))); + ASSERT_TRUE(exp.equalsTo(result.at(0))); - delete result; + } TEST_F(DeclarableOpsTests7, TestSegmentMean_02) { @@ -1479,14 +1479,14 @@ TEST_F(DeclarableOpsTests7, TestSegmentMean_02) { auto idx = NDArrayFactory::create({0, 0, 1, 1, 2,2}); auto exp = NDArrayFactory::create('c', {3, 3}, { 2.5, 3.5, 4.5, 8.5, 9.5, 10.5, 14.5, 15.5, 16.5}); - nd4j::ops::segment_mean op; + sd::ops::segment_mean op; auto result = op.evaluate({&x, &idx}, {}, {}); - ASSERT_EQ(result->status(), Status::OK()); - ASSERT_EQ(result->size(), 1); - ASSERT_TRUE(exp.equalsTo(result->at(0))); + ASSERT_EQ(result.status(), Status::OK()); + ASSERT_EQ(result.size(), 1); + ASSERT_TRUE(exp.equalsTo(result.at(0))); - delete result; + } TEST_F(DeclarableOpsTests7, TestSegmentMean_021) { @@ -1494,14 +1494,14 @@ TEST_F(DeclarableOpsTests7, TestSegmentMean_021) { auto idx = NDArrayFactory::create({0, 0, 1, 1, 2,2}); auto exp = NDArrayFactory::create('c', {3, 3}, { 2.5f, 3.5f, 4.5f, 8.5f, 9.5f, 10.5f, 14.5f, 15.5f, 16.5f}); - nd4j::ops::segment_mean op; + sd::ops::segment_mean op; x.linspace(1.); auto result = op.evaluate({&x, &idx}, {}, {}); - ASSERT_EQ(result->status(), Status::OK()); - ASSERT_EQ(result->size(), 1); - ASSERT_TRUE(exp.equalsTo(result->at(0))); + ASSERT_EQ(result.status(), Status::OK()); + ASSERT_EQ(result.size(), 1); + ASSERT_TRUE(exp.equalsTo(result.at(0))); - delete result; + } TEST_F(DeclarableOpsTests7, TestSegmentMean_022) { @@ -1510,14 +1510,14 @@ TEST_F(DeclarableOpsTests7, TestSegmentMean_022) { auto z = NDArrayFactory::create('c', {3, 3}); //, { 2.5, 3.5, 4.5, 8.5, 9.5, 10.5, 14.5, 15.5, 16.5}); auto exp = NDArrayFactory::create('c', {3, 3}, { 2.5f, 3.5f, 4.5f, 8.5f, 9.5f, 10.5f, 14.5f, 15.5f, 16.5f}); - nd4j::ops::segment_mean op; + sd::ops::segment_mean op; x.linspace(1.); auto result = op.execute({&x, &idx}, {&z}); ASSERT_EQ(result, Status::OK()); ASSERT_TRUE(exp.equalsTo(z)); -// delete result; +// } //////////////////////////////////////////////////////////////////////////////// @@ -1528,14 +1528,14 @@ TEST_F(DeclarableOpsTests7, TestSegmentMeanBP_2) { auto exp = NDArrayFactory::create('c', {4, 4}, { 0.5, 1., 1.5, 2., 0.5, 1., 1.5, 2., 5., 6., 7., 8., 9., 10., 11., 12.}); eps.linspace(1); - nd4j::ops::segment_mean_bp op; + sd::ops::segment_mean_bp op; auto result = op.evaluate({&x, &idx, &eps}, {}, {}); - ASSERT_EQ(result->status(), Status::OK()); - ASSERT_EQ(result->size(), 2); - ASSERT_TRUE(exp.equalsTo(result->at(0))); + ASSERT_EQ(result.status(), Status::OK()); + ASSERT_EQ(result.size(), 2); + ASSERT_TRUE(exp.equalsTo(result.at(0))); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1553,17 +1553,17 @@ TEST_F(DeclarableOpsTests7, TestSegmentMean_3) { 41. , 32. , 77. , 34. ,35.1 , 51.4 , 83. , 28. ,114.1 , 47.1 , 62.7, 63.1,64. , 64.2 , 66.2 , 64. , 91. , 82. , 37. , 64. ,55.1 , 46.4 , 73. , 28. ,119.1 , 12.1 , 112.7 , 13.1,14. , 114.2 , 16.2 , 117. }); - nd4j::ops::segment_mean op; + sd::ops::segment_mean op; auto result = op.evaluate({&x, &idx}, {}, {}); - ASSERT_EQ(result->status(), Status::OK()); -// result->at(0)->printIndexedBuffer("Output"); -// result->at(0)->printShapeInfo("Out Shape"); + ASSERT_EQ(result.status(), Status::OK()); +// result.at(0)->printIndexedBuffer("Output"); +// result.at(0)->printShapeInfo("Out Shape"); // exp.printIndexedBuffer("Expect"); // exp.printShapeInfo("Exp Shape"); - ASSERT_TRUE(exp.equalsTo(result->at(0))); + ASSERT_TRUE(exp.equalsTo(result.at(0))); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1584,18 +1584,18 @@ TEST_F(DeclarableOpsTests7, TestSegmentMean_4) { 0. , 0. , 0. , 0. ,0. , 0. , 0. , 0. ,0. , 0. , 0. , 0. ,0. , 0. , 0. , 0. ,91. , 82. , 37. , 64. ,55.1, 46.4, 73. , 28. , 119.1, 12.1, 112.7, 13.1,14. , 114.2, 16.2, 117. }); - nd4j::ops::segment_mean op; + sd::ops::segment_mean op; auto result = op.evaluate({&x, &idx}, {}, {}); - ASSERT_EQ(result->status(), Status::OK()); - //result->at(0)->printIndexedBuffer("Output"); - //result->at(0)->printShapeInfo("Out Shape"); + ASSERT_EQ(result.status(), Status::OK()); + //result.at(0)->printIndexedBuffer("Output"); + //result.at(0)->printShapeInfo("Out Shape"); //exp.printIndexedBuffer("Expect"); //exp.printShapeInfo("Exp Shape"); - ASSERT_TRUE(exp.isSameShape(result->at(0))); - ASSERT_TRUE(exp.equalsTo(result->at(0))); + ASSERT_TRUE(exp.isSameShape(result.at(0))); + ASSERT_TRUE(exp.equalsTo(result.at(0))); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1604,13 +1604,13 @@ TEST_F(DeclarableOpsTests7, TestUnsortedSegmentMean_1) { auto idx = NDArrayFactory::create({0, 0, 1, 1, 1, 1, 2, 3, 3, 3, 4, 4, 4, 4, 4, 4}); auto exp = NDArrayFactory::create({2.15, 4.375, 3., 4.4, 1.8666667}); - nd4j::ops::unsorted_segment_mean op; + sd::ops::unsorted_segment_mean op; auto result = op.evaluate({&x, &idx}, {}, {5}); - ASSERT_EQ(result->status(), Status::OK()); - ASSERT_TRUE(exp.equalsTo(result->at(0))); + ASSERT_EQ(result.status(), Status::OK()); + ASSERT_TRUE(exp.equalsTo(result.at(0))); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1620,13 +1620,13 @@ TEST_F(DeclarableOpsTests7, TestSegmentMeanBP_1) { auto eps = NDArrayFactory::create({1., 2., 3., 4., 5.}); auto exp = NDArrayFactory::create({1./2., 1./2., 2./4., 2./4., 2./4., 2./4, 3., 4./3., 4./3., 4./3., 5./6., 5./6., 5./6., 5./6., 5./6., 5./6.}); - nd4j::ops::segment_mean_bp op; + sd::ops::segment_mean_bp op; auto result = op.evaluate({&x, &idx, &eps}, {}, {}); - ASSERT_EQ(result->status(), Status::OK()); - ASSERT_TRUE(exp.equalsTo(result->at(0))); + ASSERT_EQ(result.status(), Status::OK()); + ASSERT_TRUE(exp.equalsTo(result.at(0))); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1636,13 +1636,13 @@ TEST_F(DeclarableOpsTests7, TestUnsortedSegmentMeanBP_1) { auto eps = NDArrayFactory::create({1., 2., 3., 4., 5.}); auto exp = NDArrayFactory::create({1./2., 1./2., 2./4., 2./4., 2./4., 2./4, 3., 4./3., 4./3., 4./3., 5./6., 5./6., 5./6., 5./6., 5./6., 5./6.}); - nd4j::ops::unsorted_segment_mean_bp op; + sd::ops::unsorted_segment_mean_bp op; auto result = op.evaluate({&x, &idx, &eps}, {}, {5}); - ASSERT_EQ(result->status(), Status::OK()); - ASSERT_TRUE(exp.equalsTo(result->at(0))); + ASSERT_EQ(result.status(), Status::OK()); + ASSERT_TRUE(exp.equalsTo(result.at(0))); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1652,13 +1652,13 @@ TEST_F(DeclarableOpsTests7, TestUnsortedSegmentMeanBP_2) { auto eps = NDArrayFactory::create({1., 2., 3., 4., 5.}); auto exp = NDArrayFactory::create({3., 1./2., 1./2., 2./4., 2./4., 2./4., 2./4, 4./3., 4./3., 4./3., 5./6., 5./6., 5./6., 5./6., 5./6., 5./6.}); - nd4j::ops::unsorted_segment_mean_bp op; + sd::ops::unsorted_segment_mean_bp op; auto result = op.evaluate({&x, &idx, &eps}, {}, {5}); - ASSERT_EQ(result->status(), Status::OK()); - ASSERT_TRUE(exp.equalsTo(result->at(0))); + ASSERT_EQ(result.status(), Status::OK()); + ASSERT_TRUE(exp.equalsTo(result.at(0))); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1667,17 +1667,17 @@ TEST_F(DeclarableOpsTests7, TestUnsortedSegmentMean_2) { auto idx = NDArrayFactory::create({0, 0, 1, 2}); auto exp = NDArrayFactory::create('c', {3, 4}, { 1.95, 2.45, 3.5, 9., 2.1, 2.1, 0.7, 0.1, 3. , 4.2, 2.2, 1.}); - nd4j::ops::unsorted_segment_mean op; + sd::ops::unsorted_segment_mean op; auto result = op.evaluate({&x, &idx}, {}, {3}); - ASSERT_EQ(result->status(), Status::OK()); - ASSERT_EQ(result->size(), 1); + ASSERT_EQ(result.status(), Status::OK()); + ASSERT_EQ(result.size(), 1); // exp.printIndexedBuffer("Expect"); -// result->at(0)->printIndexedBuffer("Output"); +// result.at(0)->printIndexedBuffer("Output"); // exp.printShapeInfo("Exp Shape"); - ASSERT_TRUE(exp.equalsTo(result->at(0))); + ASSERT_TRUE(exp.equalsTo(result.at(0))); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1695,17 +1695,17 @@ TEST_F(DeclarableOpsTests7, TestUnsortedSegmentMean_3) { 41. , 32. , 77. , 34. ,35.1 , 51.4 , 83. , 28. ,114.1 , 47.1 , 62.7, 63.1,64. , 64.2 , 66.2 , 64. , 91. , 82. , 37. , 64. ,55.1 , 46.4 , 73. , 28. ,119.1 , 12.1 , 112.7 , 13.1,14. , 114.2 , 16.2 , 117. }); - nd4j::ops::unsorted_segment_mean op; + sd::ops::unsorted_segment_mean op; auto result = op.evaluate({&x, &idx}, {}, {3}); - ASSERT_EQ(result->status(), Status::OK()); -// result->at(0)->printIndexedBuffer("Output"); -// result->at(0)->printShapeInfo("Out Shape"); + ASSERT_EQ(result.status(), Status::OK()); +// result.at(0)->printIndexedBuffer("Output"); +// result.at(0)->printShapeInfo("Out Shape"); // exp.printIndexedBuffer("Expect"); // exp.printShapeInfo("Exp Shape"); - ASSERT_TRUE(exp.equalsTo(result->at(0))); + ASSERT_TRUE(exp.equalsTo(result.at(0))); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1726,18 +1726,18 @@ TEST_F(DeclarableOpsTests7, TestUnsortedSegmentMean_4) { 0. , 0. , 0. , 0. ,0. , 0. , 0. , 0. ,0. , 0. , 0. , 0. ,0. , 0. , 0. , 0. ,91. , 82. , 37. , 64. ,55.1, 46.4, 73. , 28. , 119.1, 12.1, 112.7, 13.1,14. , 114.2, 16.2, 117. }); - nd4j::ops::unsorted_segment_mean op; + sd::ops::unsorted_segment_mean op; auto result = op.evaluate({&x, &idx}, {}, {8}); - ASSERT_EQ(result->status(), Status::OK()); - //result->at(0)->printIndexedBuffer("Output"); - //result->at(0)->printShapeInfo("Out Shape"); + ASSERT_EQ(result.status(), Status::OK()); + //result.at(0)->printIndexedBuffer("Output"); + //result.at(0)->printShapeInfo("Out Shape"); //exp.printIndexedBuffer("Expect"); //exp.printShapeInfo("Exp Shape"); - ASSERT_TRUE(exp.isSameShape(result->at(0))); - ASSERT_TRUE(exp.equalsTo(result->at(0))); + ASSERT_TRUE(exp.isSameShape(result.at(0))); + ASSERT_TRUE(exp.equalsTo(result.at(0))); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1746,13 +1746,13 @@ TEST_F(DeclarableOpsTests7, TestUnsortedSegmentSqrtN_1) { auto idx = NDArrayFactory::create({0, 0, 1, 1, 1, 1, 2, 3, 3, 3, 4, 4, 4, 4, 4, 4}); auto exp = NDArrayFactory::create({3.0405593, 8.75, 3., 7.621024, 4.5723805}); - nd4j::ops::unsorted_segment_sqrt_n op; + sd::ops::unsorted_segment_sqrt_n op; auto result = op.evaluate({&x, &idx}, {}, {5}); - ASSERT_EQ(result->status(), Status::OK()); - ASSERT_TRUE(exp.equalsTo(result->at(0))); + ASSERT_EQ(result.status(), Status::OK()); + ASSERT_TRUE(exp.equalsTo(result.at(0))); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1762,14 +1762,14 @@ TEST_F(DeclarableOpsTests7, TestUnsortedSegmentSqrtN_BP_1) { auto eps = NDArrayFactory::create({1., 2., 3., 4., 5.}); // NDArray exp({3.0405593, 8.75, 3., 7.621024, 4.5723805}); auto exp = NDArrayFactory::create({3., 0.707107, 0.707107, 1., 1., 1., 1., 2.309401, 2.309401, 2.309401, 2.041241, 2.041241, 2.041241, 2.041241, 2.041241, 2.041241}); - nd4j::ops::unsorted_segment_sqrt_n_bp op; + sd::ops::unsorted_segment_sqrt_n_bp op; auto result = op.evaluate({&x, &idx, &eps}, {}, {5}); - ASSERT_EQ(result->status(), Status::OK()); -// result->at(0)->printIndexedBuffer("Hello Out:"); - ASSERT_TRUE(exp.equalsTo(result->at(0))); + ASSERT_EQ(result.status(), Status::OK()); +// result.at(0)->printIndexedBuffer("Hello Out:"); + ASSERT_TRUE(exp.equalsTo(result.at(0))); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1781,17 +1781,17 @@ TEST_F(DeclarableOpsTests7, TestUnsortedSegmentSqrtN_2) { 3. , 4.2, 2.2, 1. }); - nd4j::ops::unsorted_segment_sqrt_n op; + sd::ops::unsorted_segment_sqrt_n op; auto result = op.evaluate({&x, &idx}, {}, {3}); - ASSERT_EQ(result->status(), Status::OK()); - ASSERT_EQ(result->size(), 1); + ASSERT_EQ(result.status(), Status::OK()); + ASSERT_EQ(result.size(), 1); // exp.printIndexedBuffer("Expect"); -// result->at(0)->printIndexedBuffer("Output"); +// result.at(0)->printIndexedBuffer("Output"); // exp.printShapeInfo("Exp Shape"); - ASSERT_TRUE(exp.equalsTo(result->at(0))); + ASSERT_TRUE(exp.equalsTo(result.at(0))); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1809,17 +1809,17 @@ TEST_F(DeclarableOpsTests7, TestUnsortedSegmentSqrtN_3) { 57.982758, 45.254833, 108.89445, 48.083263, 49.638893, 72.69058, 117.37973, 39.59798, 161.36177, 66.60946, 88.67119, 89.23688, 90.50967, 90.79251, 93.62093, 90.50967, 91. , 82. , 37. , 64. ,55.1 , 46.4 , 73. , 28. ,119.1 , 12.1 , 112.7 , 13.1,14. , 114.2 , 16.2 , 117. }); - nd4j::ops::unsorted_segment_sqrt_n op; + sd::ops::unsorted_segment_sqrt_n op; auto result = op.evaluate({&x, &idx}, {}, {3}); - ASSERT_EQ(result->status(), Status::OK()); -// result->at(0)->printIndexedBuffer("Output"); -// result->at(0)->printShapeInfo("Out Shape"); + ASSERT_EQ(result.status(), Status::OK()); +// result.at(0)->printIndexedBuffer("Output"); +// result.at(0)->printShapeInfo("Out Shape"); // exp.printIndexedBuffer("Expect"); // exp.printShapeInfo("Exp Shape"); - ASSERT_TRUE(exp.equalsTo(result->at(0))); + ASSERT_TRUE(exp.equalsTo(result.at(0))); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1840,18 +1840,18 @@ TEST_F(DeclarableOpsTests7, TestUnsortedSegmentSqrtN_4) { 0. , 0. , 0. , 0. ,0. , 0. , 0. , 0. ,0. , 0. , 0. , 0. ,0. , 0. , 0. , 0. ,91. , 82. , 37. , 64. ,55.1, 46.4, 73. , 28. , 119.1, 12.1, 112.7, 13.1,14. , 114.2, 16.2, 117. }); - nd4j::ops::unsorted_segment_sqrt_n op; + sd::ops::unsorted_segment_sqrt_n op; auto result = op.evaluate({&x, &idx}, {}, {8}); - ASSERT_EQ(result->status(), Status::OK()); - //result->at(0)->printIndexedBuffer("Output"); - //result->at(0)->printShapeInfo("Out Shape"); + ASSERT_EQ(result.status(), Status::OK()); + //result.at(0)->printIndexedBuffer("Output"); + //result.at(0)->printShapeInfo("Out Shape"); //exp.printIndexedBuffer("Expect"); //exp.printShapeInfo("Exp Shape"); - ASSERT_TRUE(exp.isSameShape(result->at(0))); - ASSERT_TRUE(exp.equalsTo(result->at(0))); + ASSERT_TRUE(exp.isSameShape(result.at(0))); + ASSERT_TRUE(exp.equalsTo(result.at(0))); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1860,15 +1860,15 @@ TEST_F(DeclarableOpsTests7, TestUnsortedSegmentSqrtN_5) { auto idx = NDArrayFactory::create({3, 1, 0, 0, 2, 0, 3, 2}); //NDArray exp({1.7320508075688772, 1., 1.4142135623730951, 1.4142135623730951}); auto exp = NDArrayFactory::create({7.5055537, 2., 4.9497476, 2.828427}); - nd4j::ops::unsorted_segment_sqrt_n op; + sd::ops::unsorted_segment_sqrt_n op; auto result = op.evaluate({&x, &idx}, {}, {4}); - ASSERT_EQ(result->status(), Status::OK()); - // result->at(0)->printIndexedBuffer("Output"); + ASSERT_EQ(result.status(), Status::OK()); + // result.at(0)->printIndexedBuffer("Output"); // exp.printIndexedBuffer("Expect"); - ASSERT_TRUE(exp.equalsTo(result->at(0))); + ASSERT_TRUE(exp.equalsTo(result.at(0))); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1877,14 +1877,14 @@ TEST_F(DeclarableOpsTests7, TestSegmentSum_1) { auto idx = NDArrayFactory::create({ 0, 0, 1, 1, 1, 1, 2, 3, 3, 3, 4, 4, 4, 4, 4, 4}); auto exp = NDArrayFactory::create({4.3, 17.5, 3., 13.2, 11.2}); - nd4j::ops::segment_sum op; + sd::ops::segment_sum op; auto result = op.evaluate({&x, &idx}, {}, {}); - ASSERT_EQ(result->status(), Status::OK()); + ASSERT_EQ(result.status(), Status::OK()); - ASSERT_TRUE(exp.equalsTo(result->at(0))); + ASSERT_TRUE(exp.equalsTo(result.at(0))); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1893,13 +1893,13 @@ TEST_F(DeclarableOpsTests7, TestSegmentSumBP_1) { auto idx = NDArrayFactory::create({0, 0, 1, 1, 1, 1, 2, 3, 3, 3, 4, 4, 4, 4, 4, 4}); auto eps = NDArrayFactory::create({1., 2., 3., 4., 5.}); auto exp = NDArrayFactory::create({ 1., 1., 2., 2., 2., 2., 3., 4., 4., 4., 5., 5., 5., 5., 5., 5.}); - nd4j::ops::segment_sum_bp op; + sd::ops::segment_sum_bp op; auto result = op.evaluate({&x, &idx, &eps}, {}, {}); - ASSERT_EQ(result->status(), Status::OK()); - ASSERT_TRUE(exp.equalsTo(result->at(0))); + ASSERT_EQ(result.status(), Status::OK()); + ASSERT_TRUE(exp.equalsTo(result.at(0))); - delete result; + } TEST_F(DeclarableOpsTests7, TestUnsortedSegmentSumBP_1) { @@ -1907,13 +1907,13 @@ TEST_F(DeclarableOpsTests7, TestUnsortedSegmentSumBP_1) { auto idx = NDArrayFactory::create({0, 0, 1, 1, 1, 1, 2, 3, 3, 3, 4, 4, 4, 4, 4, 4}); auto eps = NDArrayFactory::create({1, 2, 3, 4, 5}); auto exp = NDArrayFactory::create({ 1., 1., 2., 2., 2., 2., 3., 4., 4., 4., 5., 5., 5., 5., 5., 5.}); - nd4j::ops::unsorted_segment_sum_bp op; + sd::ops::unsorted_segment_sum_bp op; auto result = op.evaluate({&x, &idx, &eps}, {}, {5}); - ASSERT_EQ(result->status(), Status::OK()); - ASSERT_TRUE(exp.equalsTo(result->at(0))); + ASSERT_EQ(result.status(), Status::OK()); + ASSERT_TRUE(exp.equalsTo(result.at(0))); - delete result; + } TEST_F(DeclarableOpsTests7, TestUnsortedSegmentSumBP_2) { @@ -1921,13 +1921,13 @@ TEST_F(DeclarableOpsTests7, TestUnsortedSegmentSumBP_2) { auto idx = NDArrayFactory::create({2, 0, 0, 1, 1, 1, 1, 3, 3, 3, 4, 4, 4, 4, 4, 4}); auto eps = NDArrayFactory::create({1., 2., 3., 4., 5.}); auto exp = NDArrayFactory::create({ 3., 1., 1., 2., 2., 2., 2., 4., 4., 4., 5., 5., 5., 5., 5., 5.}); - nd4j::ops::unsorted_segment_sum_bp op; + sd::ops::unsorted_segment_sum_bp op; auto result = op.evaluate({&x, &idx, &eps}, {}, {5}); - ASSERT_EQ(result->status(), Status::OK()); - ASSERT_TRUE(exp.equalsTo(result->at(0))); + ASSERT_EQ(result.status(), Status::OK()); + ASSERT_TRUE(exp.equalsTo(result.at(0))); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1936,16 +1936,16 @@ TEST_F(DeclarableOpsTests7, TestSegmentSum_2) { auto idx = NDArrayFactory::create({0, 0, 1, 2}); auto exp = NDArrayFactory::create('c', {3, 4}, {3.9 , 4.9, 7. , 18.,2.1 , 2.1, 0.7, 0.1,3. , 4.2, 2.2, 1.}); - nd4j::ops::segment_sum op; + sd::ops::segment_sum op; auto result = op.evaluate({&x, &idx}, {}, {}); - ASSERT_EQ(result->status(), Status::OK()); - ASSERT_EQ(result->size(), 1); + ASSERT_EQ(result.status(), Status::OK()); + ASSERT_EQ(result.size(), 1); // exp.printIndexedBuffer("Expect"); // exp.printShapeInfo("Exp Shape"); - ASSERT_TRUE(exp.equalsTo(result->at(0))); + ASSERT_TRUE(exp.equalsTo(result.at(0))); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1956,16 +1956,16 @@ TEST_F(DeclarableOpsTests7, TestSegmentSumBP_2) { auto eps = NDArrayFactory::create('c', {3, 4}); eps.linspace(1); - nd4j::ops::segment_sum_bp op; + sd::ops::segment_sum_bp op; auto result = op.evaluate({&x, &idx, &eps}, {}, {}); - ASSERT_EQ(result->status(), Status::OK()); - ASSERT_EQ(result->size(), 2); + ASSERT_EQ(result.status(), Status::OK()); + ASSERT_EQ(result.size(), 2); // exp.printIndexedBuffer("Expect"); // exp.printShapeInfo("Exp Shape"); - ASSERT_TRUE(exp.equalsTo(result->at(0))); + ASSERT_TRUE(exp.equalsTo(result.at(0))); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1983,17 +1983,17 @@ TEST_F(DeclarableOpsTests7, TestSegmentSum_3) { 70.2, 102.8, 166. , 56. ,228.2, 94.2, 125.4, 126.2 ,128. , 128.4, 132.4, 128. ,91. , 82. , 37. , 64. , 55.1, 46.4, 73. , 28. ,119.1, 12.1, 112.7, 13.1,14. , 114.2, 16.2, 117. }); - nd4j::ops::segment_sum op; + sd::ops::segment_sum op; auto result = op.evaluate({&x, &idx}, {}, {}); - ASSERT_EQ(result->status(), Status::OK()); -// result->at(0)->printIndexedBuffer("Output"); -// result->at(0)->printShapeInfo("Out Shape"); + ASSERT_EQ(result.status(), Status::OK()); +// result.at(0)->printIndexedBuffer("Output"); +// result.at(0)->printShapeInfo("Out Shape"); // exp.printIndexedBuffer("Expect"); // exp.printShapeInfo("Exp Shape"); - ASSERT_TRUE(exp.equalsTo(result->at(0))); + ASSERT_TRUE(exp.equalsTo(result.at(0))); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -2014,18 +2014,18 @@ TEST_F(DeclarableOpsTests7, TestSegmentSum_4) { 0. , 0. , 0. , 0. ,0. , 0. , 0. , 0. ,0. , 0. , 0. , 0. ,0. , 0. , 0. , 0. ,91. , 82. , 37. , 64. ,55.1, 46.4, 73. , 28. , 119.1, 12.1, 112.7, 13.1,14. , 114.2, 16.2, 117. }); - nd4j::ops::segment_sum op; + sd::ops::segment_sum op; auto result = op.evaluate({&x, &idx}, {}, {}); - ASSERT_EQ(result->status(), Status::OK()); - //result->at(0)->printIndexedBuffer("Output"); - //result->at(0)->printShapeInfo("Out Shape"); + ASSERT_EQ(result.status(), Status::OK()); + //result.at(0)->printIndexedBuffer("Output"); + //result.at(0)->printShapeInfo("Out Shape"); //exp.printIndexedBuffer("Expect"); //exp.printShapeInfo("Exp Shape"); - ASSERT_TRUE(exp.isSameShape(result->at(0))); - ASSERT_TRUE(exp.equalsTo(result->at(0))); + ASSERT_TRUE(exp.isSameShape(result.at(0))); + ASSERT_TRUE(exp.equalsTo(result.at(0))); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -2034,13 +2034,13 @@ TEST_F(DeclarableOpsTests7, TestUnsortedSegmentSum_1) { auto idx = NDArrayFactory::create({0, 0, 1, 1, 1, 1, 2, 3, 3, 3, 4, 4, 4, 4, 4, 4}); auto exp = NDArrayFactory::create({4.3, 17.5, 3., 13.2, 11.2}); - nd4j::ops::unsorted_segment_sum op; + sd::ops::unsorted_segment_sum op; auto result = op.evaluate({&x, &idx}, {}, {5}); - ASSERT_EQ(result->status(), Status::OK()); - ASSERT_TRUE(exp.equalsTo(result->at(0))); + ASSERT_EQ(result.status(), Status::OK()); + ASSERT_TRUE(exp.equalsTo(result.at(0))); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -2049,14 +2049,14 @@ TEST_F(DeclarableOpsTests7, TestUnsortedSegmentSum_2) { auto idx = NDArrayFactory::create({0, 0, 1, 2}); auto exp = NDArrayFactory::create('c', {3, 4}, {3.9 , 4.9, 7. , 18.,2.1 , 2.1, 0.7, 0.1,3. , 4.2, 2.2, 1.}); - nd4j::ops::unsorted_segment_sum op; + sd::ops::unsorted_segment_sum op; auto result = op.evaluate({&x, &idx}, {}, {3}); - ASSERT_EQ(result->status(), Status::OK()); - ASSERT_EQ(result->size(), 1); - ASSERT_TRUE(exp.equalsTo(result->at(0))); + ASSERT_EQ(result.status(), Status::OK()); + ASSERT_EQ(result.size(), 1); + ASSERT_TRUE(exp.equalsTo(result.at(0))); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -2074,17 +2074,17 @@ TEST_F(DeclarableOpsTests7, TestUnsortedSegmentSum_3) { 70.2, 102.8, 166. , 56. ,228.2, 94.2, 125.4, 126.2 ,128. , 128.4, 132.4, 128. ,91. , 82. , 37. , 64. , 55.1, 46.4, 73. , 28. ,119.1, 12.1, 112.7, 13.1,14. , 114.2, 16.2, 117. }); - nd4j::ops::unsorted_segment_sum op; + sd::ops::unsorted_segment_sum op; auto result = op.evaluate({&x, &idx}, {}, {3}); - ASSERT_EQ(result->status(), Status::OK()); -// result->at(0)->printIndexedBuffer("Output"); -// result->at(0)->printShapeInfo("Out Shape"); + ASSERT_EQ(result.status(), Status::OK()); +// result.at(0)->printIndexedBuffer("Output"); +// result.at(0)->printShapeInfo("Out Shape"); // exp.printIndexedBuffer("Expect"); // exp.printShapeInfo("Exp Shape"); - ASSERT_TRUE(exp.equalsTo(result->at(0))); + ASSERT_TRUE(exp.equalsTo(result.at(0))); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -2104,18 +2104,18 @@ TEST_F(DeclarableOpsTests7, TestUnsortedSegmentSum_4) { 0. , 0. , 0. , 0. ,0. , 0. , 0. , 0. ,0. , 0. , 0. , 0. ,0. , 0. , 0. , 0. ,91. , 82. , 37. , 64. ,55.1, 46.4, 73. , 28. , 119.1, 12.1, 112.7, 13.1,14. , 114.2, 16.2, 117. }); - nd4j::ops::unsorted_segment_sum op; + sd::ops::unsorted_segment_sum op; auto result = op.evaluate({&x, &idx}, {}, {8}); - ASSERT_EQ(result->status(), Status::OK()); -// result->at(0)->printIndexedBuffer("Output"); -// result->at(0)->printShapeInfo("Out Shape"); + ASSERT_EQ(result.status(), Status::OK()); +// result.at(0)->printIndexedBuffer("Output"); +// result.at(0)->printShapeInfo("Out Shape"); // exp.printIndexedBuffer("Expect"); //exp.printShapeInfo("Exp Shape"); - ASSERT_TRUE(exp.isSameShape(result->at(0))); - ASSERT_TRUE(exp.equalsTo(result->at(0))); + ASSERT_TRUE(exp.isSameShape(result.at(0))); + ASSERT_TRUE(exp.equalsTo(result.at(0))); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -2124,13 +2124,13 @@ TEST_F(DeclarableOpsTests7, TestSegmentProd_1) { auto idx = NDArrayFactory::create({0, 0, 1, 1, 1, 1, 2, 3, 3, 3, 4, 4, 4, 4, 4, 4}); auto exp = NDArrayFactory::create({4.5, 181.44, 3., 39.69, 1.9404}); - nd4j::ops::segment_prod op; + sd::ops::segment_prod op; auto result = op.evaluate({&x, &idx}, {}, {}); - ASSERT_EQ(result->status(), Status::OK()); - ASSERT_TRUE(exp.equalsTo(result->at(0))); + ASSERT_EQ(result.status(), Status::OK()); + ASSERT_TRUE(exp.equalsTo(result.at(0))); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -2139,16 +2139,16 @@ TEST_F(DeclarableOpsTests7, TestSegmentProdBP_1) { auto idx = NDArrayFactory::create({0, 0, 1, 1, 1, 1, 2, 3, 3, 3, 4, 4, 4, 4, 4, 4}); auto eps = NDArrayFactory::create({1., 2., 3., 4., 5.}); auto exp = NDArrayFactory::create({2.5, 1.8, 90.72, 40.32, 172.8, 151.2, 3., 17.64, 75.6, 75.6, 13.86, 97.02, 3.234, 2.31, 4.41, 9.702}); - nd4j::ops::segment_prod_bp op; + sd::ops::segment_prod_bp op; auto result = op.evaluate({&x, &idx, &eps}, {}, {}); - ASSERT_EQ(result->status(), Status::OK()); -// result->at(0)->printIndexedBuffer("ProdBP Output"); + ASSERT_EQ(result.status(), Status::OK()); +// result.at(0)->printIndexedBuffer("ProdBP Output"); // exp.printIndexedBuffer("ProdBP Expect"); - ASSERT_TRUE(exp.equalsTo(result->at(0))); + ASSERT_TRUE(exp.equalsTo(result.at(0))); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -2157,16 +2157,16 @@ TEST_F(DeclarableOpsTests7, TestUnsortedSegmentProdBP_1) { auto idx = NDArrayFactory::create({0, 0, 1, 1, 1, 1, 2, 3, 3, 3, 4, 4, 4, 4, 4, 4}); auto eps = NDArrayFactory::create({1., 2., 3., 4., 5.}); auto exp = NDArrayFactory::create({2.5, 1.8, 90.72, 40.32, 172.8, 151.2, 3., 17.64, 75.6, 75.6, 13.86, 97.02, 3.234, 2.31, 4.41, 9.702}); - nd4j::ops::segment_prod_bp op; + sd::ops::segment_prod_bp op; auto result = op.evaluate({&x, &idx, &eps}, {}, {}); - ASSERT_EQ(result->status(), Status::OK()); - //result->at(0)->printIndexedBuffer("ProdBP Output"); + ASSERT_EQ(result.status(), Status::OK()); + //result.at(0)->printIndexedBuffer("ProdBP Output"); //exp.printIndexedBuffer("ProdBP Expect"); - ASSERT_TRUE(exp.equalsTo(result->at(0))); + ASSERT_TRUE(exp.equalsTo(result.at(0))); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -2176,16 +2176,16 @@ TEST_F(DeclarableOpsTests7, TestUnsortedSegmentProdBP_2) { auto eps = NDArrayFactory::create({1., 2., 3., 4., 5.}); auto exp = NDArrayFactory::create({3., 2.5, 1.8, 90.72, 40.32, 172.8, 151.2, 17.64, 75.6, 75.6, 13.86, 97.02, 3.234, 2.31, 4.41, 9.702}); auto n = NDArrayFactory::create(5LL); - nd4j::ops::unsorted_segment_prod_bp op; + sd::ops::unsorted_segment_prod_bp op; auto result = op.evaluate({&x, &idx, &eps, &n}, {}, {5}); - ASSERT_EQ(result->status(), Status::OK()); - //result->at(0)->printIndexedBuffer("Unsorted ProdBP Output"); + ASSERT_EQ(result.status(), Status::OK()); + //result.at(0)->printIndexedBuffer("Unsorted ProdBP Output"); //exp.printIndexedBuffer("Unsorted ProdBP Expect"); - ASSERT_TRUE(exp.equalsTo(result->at(0))); + ASSERT_TRUE(exp.equalsTo(result.at(0))); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -2197,16 +2197,16 @@ TEST_F(DeclarableOpsTests7, TestSegmentProd_2) { //{ 2.1, 2.5, 4., 9., 2.1, 2.1, 0.7, 0.1, 3., 4.2, 2.2, 1.} - nd4j::ops::segment_prod op; + sd::ops::segment_prod op; auto result = op.evaluate({&x, &idx}, {}, {}); - ASSERT_EQ(result->status(), Status::OK()); - ASSERT_EQ(result->size(), 1); + ASSERT_EQ(result.status(), Status::OK()); + ASSERT_EQ(result.size(), 1); // exp.printIndexedBuffer("Expect"); // exp.printShapeInfo("Exp Shape"); - ASSERT_TRUE(exp.equalsTo(result->at(0))); + ASSERT_TRUE(exp.equalsTo(result.at(0))); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -2221,16 +2221,16 @@ TEST_F(DeclarableOpsTests7, TestSegmentProdBP_2) { //{ 2.1, 2.5, 4., 9., 2.1, 2.1, 0.7, 0.1, 3., 4.2, 2.2, 1.} eps.linspace(1); - nd4j::ops::segment_prod_bp op; + sd::ops::segment_prod_bp op; auto result = op.evaluate({&x, &idx, &eps}, {}, {}); - ASSERT_EQ(result->status(), Status::OK()); - ASSERT_EQ(result->size(), 2); + ASSERT_EQ(result.status(), Status::OK()); + ASSERT_EQ(result.size(), 2); // exp.printIndexedBuffer("Expect"); // exp.printShapeInfo("Exp Shape"); - ASSERT_TRUE(exp.equalsTo(result->at(0))); + ASSERT_TRUE(exp.equalsTo(result.at(0))); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -2248,17 +2248,17 @@ TEST_F(DeclarableOpsTests7, TestSegmentProd_3) { 1581, 924, 5829, 1056,832.01001, 2616.9602, 6789, 784, 12993.810, 993.41003, 1431.2899, 1481.61, 1596, 1621.64, 1882.4401, 1287, 91. , 82. , 37. , 64. , 55.1, 46.4, 73. , 28. ,119.1, 12.1, 112.7, 13.1, 14. , 114.2, 16.2, 117. }); - nd4j::ops::segment_prod op; + sd::ops::segment_prod op; auto result = op.evaluate({&x, &idx}, {}, {}); - ASSERT_EQ(result->status(), Status::OK()); -// result->at(0)->printIndexedBuffer("Output"); -// result->at(0)->printShapeInfo("Out Shape"); + ASSERT_EQ(result.status(), Status::OK()); +// result.at(0)->printIndexedBuffer("Output"); +// result.at(0)->printShapeInfo("Out Shape"); // exp.printIndexedBuffer("Expect"); // exp.printShapeInfo("Exp Shape"); - ASSERT_TRUE(exp.equalsTo(result->at(0))); + ASSERT_TRUE(exp.equalsTo(result.at(0))); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -2270,13 +2270,13 @@ TEST_F(DeclarableOpsTests7, TestSegmentProd_04) { auto idx = NDArrayFactory::create({0,0,1,2,2,2,3,3}); auto exp = NDArrayFactory::create({ 2, 3, 120, 56}); - nd4j::ops::segment_prod op; + sd::ops::segment_prod op; auto result = op.evaluate({&x, &idx}, {}, {}); - ASSERT_EQ(result->status(), Status::OK()); - ASSERT_TRUE(exp.equalsTo(result->at(0))); + ASSERT_EQ(result.status(), Status::OK()); + ASSERT_TRUE(exp.equalsTo(result.at(0))); - delete result; + } //////////////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests7, TestSegmentProd_05) { @@ -2287,15 +2287,15 @@ TEST_F(DeclarableOpsTests7, TestSegmentProd_05) { auto idx = NDArrayFactory::create({0,0,1,2,2,2,3,3}); auto exp = NDArrayFactory::create({ 2, 3, 120, 56}); - nd4j::ops::segment_prod op; + sd::ops::segment_prod op; auto result = op.evaluate({&x, &idx}, {}, {}); - ASSERT_EQ(result->status(), Status::OK()); - auto res = result->at(0); + ASSERT_EQ(result.status(), Status::OK()); + auto res = result.at(0); // res->printIndexedBuffer("Segment prod 05"); - ASSERT_TRUE(exp.equalsTo(result->at(0))); + ASSERT_TRUE(exp.equalsTo(result.at(0))); - delete result; + } //////////////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests7, TestSegmentProd_05_1) { @@ -2306,15 +2306,15 @@ TEST_F(DeclarableOpsTests7, TestSegmentProd_05_1) { auto idx = NDArrayFactory::create({0,0,1,2,2,2,3,3}); auto exp = NDArrayFactory::create({ 2, 3, 120, 56}); - nd4j::ops::segment_prod op; + sd::ops::segment_prod op; auto result = op.evaluate({&x, &idx}, {}, {}); - ASSERT_EQ(result->status(), Status::OK()); - auto res = result->at(0); + ASSERT_EQ(result.status(), Status::OK()); + auto res = result.at(0); // res->printIndexedBuffer("Segment prod 05_1"); - ASSERT_TRUE(exp.equalsTo(result->at(0))); + ASSERT_TRUE(exp.equalsTo(result.at(0))); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -2325,13 +2325,13 @@ TEST_F(DeclarableOpsTests7, TestSegmentProd_06) { auto idx = NDArrayFactory::create({0,0,1,2,2,2,3,3}); auto exp = NDArrayFactory::create({ 2, 3, 120, 56}); - nd4j::ops::segment_prod op; + sd::ops::segment_prod op; auto result = op.evaluate({&x, &idx}, {}, {}); - ASSERT_EQ(result->status(), Status::OK()); - ASSERT_TRUE(exp.equalsTo(result->at(0))); + ASSERT_EQ(result.status(), Status::OK()); + ASSERT_TRUE(exp.equalsTo(result.at(0))); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -2342,13 +2342,13 @@ TEST_F(DeclarableOpsTests7, TestSegmentProd_07) { auto idx = NDArrayFactory::create({0,0,1,2,2,2,3,3}); auto exp = NDArrayFactory::create({ 2, 3, 120, 56}); - nd4j::ops::segment_prod op; + sd::ops::segment_prod op; auto result = op.evaluate({&x, &idx}, {}, {}); - ASSERT_EQ(result->status(), Status::OK()); - ASSERT_TRUE(exp.equalsTo(result->at(0))); + ASSERT_EQ(result.status(), Status::OK()); + ASSERT_TRUE(exp.equalsTo(result.at(0))); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -2359,13 +2359,13 @@ TEST_F(DeclarableOpsTests7, TestSegmentProd_08) { auto idx = NDArrayFactory::create({0,0,2,2,2,2,3,3,3,3}); auto exp = NDArrayFactory::create({ 2, 1,360, 5040}); - nd4j::ops::segment_prod op; + sd::ops::segment_prod op; auto result = op.evaluate({&x, &idx}, {}, {}); - ASSERT_EQ(result->status(), Status::OK()); - ASSERT_TRUE(exp.equalsTo(result->at(0))); + ASSERT_EQ(result.status(), Status::OK()); + ASSERT_TRUE(exp.equalsTo(result.at(0))); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -2374,13 +2374,13 @@ TEST_F(DeclarableOpsTests7, TestUnsortedSegmentProd_1) { auto idx = NDArrayFactory::create({0, 0, 1, 1, 1, 1, 2, 3, 3, 3, 4, 4, 4, 4, 4, 4}); auto exp = NDArrayFactory::create({4.5, 181.44, 3., 39.69, 1.9404}); - nd4j::ops::unsorted_segment_prod op; + sd::ops::unsorted_segment_prod op; auto result = op.evaluate({&x, &idx}, {}, {5}); - ASSERT_EQ(result->status(), Status::OK()); - ASSERT_TRUE(exp.equalsTo(result->at(0))); + ASSERT_EQ(result.status(), Status::OK()); + ASSERT_TRUE(exp.equalsTo(result.at(0))); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -2389,13 +2389,13 @@ TEST_F(DeclarableOpsTests7, TestUnsortedSegmentProd_11) { auto idx = NDArrayFactory::create({2, 0, 0, 1, 1, 1, 1, 3, 3, 3, 4, 4, 4, 4, 4, 4}); auto exp = NDArrayFactory::create({4.5, 181.44, 3., 39.69, 1.9404}); - nd4j::ops::unsorted_segment_prod op; + sd::ops::unsorted_segment_prod op; auto result = op.evaluate({&x, &idx}, {}, {5}); - ASSERT_EQ(result->status(), Status::OK()); - ASSERT_TRUE(exp.equalsTo(result->at(0))); + ASSERT_EQ(result.status(), Status::OK()); + ASSERT_TRUE(exp.equalsTo(result.at(0))); - delete result; + } //////////////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests7, TestUnsortedSegmentProd_2) { @@ -2406,16 +2406,16 @@ TEST_F(DeclarableOpsTests7, TestUnsortedSegmentProd_2) { //{ 2.1, 2.5, 4., 9., 2.1, 2.1, 0.7, 0.1, 3., 4.2, 2.2, 1.} - nd4j::ops::unsorted_segment_prod op; + sd::ops::unsorted_segment_prod op; auto result = op.evaluate({&x, &idx}, {}, {3}); - ASSERT_EQ(result->status(), Status::OK()); - ASSERT_EQ(result->size(), 1); + ASSERT_EQ(result.status(), Status::OK()); + ASSERT_EQ(result.size(), 1); // exp.printIndexedBuffer("Expect"); // exp.printShapeInfo("Exp Shape"); - ASSERT_TRUE(exp.equalsTo(result->at(0))); + ASSERT_TRUE(exp.equalsTo(result.at(0))); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -2428,16 +2428,16 @@ TEST_F(DeclarableOpsTests7, TestUnsortedSegmentProd_12) { //{ 2.1, 2.5, 4., 9., 2.1, 2.1, 0.7, 0.1, 3., 4.2, 2.2, 1.} - nd4j::ops::unsorted_segment_prod op; + sd::ops::unsorted_segment_prod op; auto result = op.evaluate({&x, &idx}, {}, {3}); - ASSERT_EQ(result->status(), Status::OK()); - ASSERT_EQ(result->size(), 1); + ASSERT_EQ(result.status(), Status::OK()); + ASSERT_EQ(result.size(), 1); // exp.printIndexedBuffer("Expect"); // exp.printShapeInfo("Exp Shape"); - ASSERT_TRUE(exp.equalsTo(result->at(0))); + ASSERT_TRUE(exp.equalsTo(result.at(0))); - delete result; + } //////////////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests7, TestUnsortedSegmentProd_08) { @@ -2447,13 +2447,13 @@ TEST_F(DeclarableOpsTests7, TestUnsortedSegmentProd_08) { auto idx = NDArrayFactory::create({0,0,2,2,2,2,3,3,3,3}); auto exp = NDArrayFactory::create({ 2, 1,360, 5040}); - nd4j::ops::unsorted_segment_prod op; + sd::ops::unsorted_segment_prod op; auto result = op.evaluate({&x, &idx}, {}, {4}); - ASSERT_EQ(result->status(), Status::OK()); - ASSERT_TRUE(exp.equalsTo(result->at(0))); + ASSERT_EQ(result.status(), Status::OK()); + ASSERT_TRUE(exp.equalsTo(result.at(0))); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -2471,17 +2471,17 @@ TEST_F(DeclarableOpsTests7, TestUnsortedSegmentProd_3) { 1581, 924, 5829, 1056,832.01001, 2616.9602, 6789, 784, 12993.810, 993.41003, 1431.2899, 1481.61, 1596.0000, 1621.6399, 1882.4401, 1287, 91. , 82. , 37. , 64. , 55.1, 46.4, 73. , 28. ,119.1, 12.1, 112.7, 13.1, 14. , 114.2, 16.2, 117. }); - nd4j::ops::unsorted_segment_prod op; + sd::ops::unsorted_segment_prod op; auto result = op.evaluate({&x, &idx}, {}, {3}); - ASSERT_EQ(result->status(), Status::OK()); -// result->at(0)->printIndexedBuffer("Output"); -// result->at(0)->printShapeInfo("Out Shape"); + ASSERT_EQ(result.status(), Status::OK()); +// result.at(0)->printIndexedBuffer("Output"); +// result.at(0)->printShapeInfo("Out Shape"); // exp.printIndexedBuffer("Expect"); // exp.printShapeInfo("Exp Shape"); - ASSERT_TRUE(exp.equalsTo(result->at(0))); + ASSERT_TRUE(exp.equalsTo(result.at(0))); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -2502,17 +2502,17 @@ TEST_F(DeclarableOpsTests7, TestUnsortedSegmentProd_4) { 91., 82., 37., 64, 55.1, 46.400002, 73, 28, 119.1, 12.1, 112.7, 13.1, 14, 114.2, 16.2, 117}); - nd4j::ops::unsorted_segment_prod op; + sd::ops::unsorted_segment_prod op; auto result = op.evaluate({&x, &idx}, {}, {3}); - ASSERT_EQ(result->status(), Status::OK()); - //result->at(0)->printIndexedBuffer("Output"); -// result->at(0)->printShapeInfo("Out Shape"); + ASSERT_EQ(result.status(), Status::OK()); + //result.at(0)->printIndexedBuffer("Output"); +// result.at(0)->printShapeInfo("Out Shape"); //exp.printIndexedBuffer("Expect"); // exp.printShapeInfo("Exp Shape"); - ASSERT_TRUE(exp.equalsTo(result->at(0))); + ASSERT_TRUE(exp.equalsTo(result.at(0))); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -2537,17 +2537,17 @@ TEST_F(DeclarableOpsTests7, TestUnsortedSegmentProd_5) { 946., 1044., 1144., 1246., 1350.}); x.linspace(1.); - nd4j::ops::unsorted_segment_prod op; + sd::ops::unsorted_segment_prod op; auto result = op.evaluate({&x, &idx}, {}, {4}); - ASSERT_EQ(result->status(), Status::OK()); - //result->at(0)->printIndexedBuffer("Output"); -// result->at(0)->printShapeInfo("Out Shape"); + ASSERT_EQ(result.status(), Status::OK()); + //result.at(0)->printIndexedBuffer("Output"); +// result.at(0)->printShapeInfo("Out Shape"); //exp.printIndexedBuffer("Expect"); // exp.printShapeInfo("Exp Shape"); - ASSERT_TRUE(exp.equalsTo(result->at(0))); + ASSERT_TRUE(exp.equalsTo(result.at(0))); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -2568,17 +2568,17 @@ TEST_F(DeclarableOpsTests7, TestUnsortedSegmentProdBP_4) { // // 91., 82., 37., 64, 55.1, 46.400002, 73, 28, 119.1, 12.1, 112.7, 13.1, 14, 114.2, 16.2, 117}); - nd4j::ops::unsorted_segment_prod_bp op; + sd::ops::unsorted_segment_prod_bp op; auto result = op.evaluate({&x, &idx, &gradO}, {}, {4}); - ASSERT_EQ(result->status(), Status::OK()); - //result->at(0)->printIndexedBuffer("Output"); - //result->at(0)->printShapeInfo("Out Shape"); + ASSERT_EQ(result.status(), Status::OK()); + //result.at(0)->printIndexedBuffer("Output"); + //result.at(0)->printShapeInfo("Out Shape"); //exp.printIndexedBuffer("Expect"); // exp.printShapeInfo("Exp Shape"); - ASSERT_TRUE(exp.equalsTo(result->at(0))); + ASSERT_TRUE(exp.equalsTo(result.at(0))); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -2606,18 +2606,18 @@ TEST_F(DeclarableOpsTests7, TestExtractImagePatches_1) { 31., 22., 87., 44., 55., 46., 73., 28., 119., 12., 112., 13., 14., 114., 16., 117., 91., 82., 37., 64., 55.1, 46.4, 73., 28., 119., 12., 112., 13., 140., 110., 160., 107.}); - nd4j::ops::extract_image_patches op; + sd::ops::extract_image_patches op; auto result = op.evaluate({&x}, {}, {1,1,1,1,1,1,0}); - ASSERT_EQ(result->status(), Status::OK()); -// result->at(0)->printIndexedBuffer("Output"); - //result->at(0)->printShapeInfo("Out Shape"); + ASSERT_EQ(result.status(), Status::OK()); +// result.at(0)->printIndexedBuffer("Output"); + //result.at(0)->printShapeInfo("Out Shape"); // exp.printIndexedBuffer("Expect"); //exp.printShapeInfo("Exp Shape"); - ASSERT_TRUE(exp.isSameShape(result->at(0))); - ASSERT_TRUE(exp.equalsTo(result->at(0))); + ASSERT_TRUE(exp.isSameShape(result.at(0))); + ASSERT_TRUE(exp.equalsTo(result.at(0))); - delete result; + } @@ -2643,15 +2643,15 @@ auto exp = NDArrayFactory::create('c', {3, 1, 1, 12}, { 211., 12., 13., 12., 213., 14., 21., 2., 3., 2., 3., 24. }); // ---------------------------------------------------------------- - nd4j::ops::extract_image_patches op; + sd::ops::extract_image_patches op; auto result = op.evaluate({&x}, {}, {2,2, 3,3, 1,1,0}); - ASSERT_EQ(result->status(), Status::OK()); + ASSERT_EQ(result.status(), Status::OK()); - ASSERT_TRUE(exp.isSameShape(result->at(0))); - ASSERT_TRUE(exp.equalsTo(result->at(0))); + ASSERT_TRUE(exp.isSameShape(result.at(0))); + ASSERT_TRUE(exp.equalsTo(result.at(0))); - delete result; + } @@ -2677,15 +2677,15 @@ auto exp = NDArrayFactory::create('c', {3, 1, 2, 6}, { 6., 7., 15., 216., 17., 35., 36., 327. }); // ---------------------------------------------------------------- - nd4j::ops::extract_image_patches op; + sd::ops::extract_image_patches op; auto result = op.evaluate({&x}, {}, {2,1,3,2,2,2,0}); - ASSERT_EQ(result->status(), Status::OK()); + ASSERT_EQ(result.status(), Status::OK()); - ASSERT_TRUE(exp.isSameShape(result->at(0))); - ASSERT_TRUE(exp.equalsTo(result->at(0))); + ASSERT_TRUE(exp.isSameShape(result.at(0))); + ASSERT_TRUE(exp.equalsTo(result.at(0))); - delete result; + } @@ -2716,18 +2716,18 @@ auto exp = NDArrayFactory::create('c', {3, 3, 4, 3}, { 21., 2., 3., 2., 3., 24., 21., 22., 223., 22., 223., 24., 25., 6., 7., 8., 9., 20., 35., 36., 327., 38., 239., 40.}); // ---------------------------------------------------------------- - nd4j::ops::extract_image_patches op; + sd::ops::extract_image_patches op; auto result = op.evaluate({&x}, {}, {1,1,1,1,1,1,0}); - ASSERT_EQ(result->status(), Status::OK()); -// result->at(0)->printIndexedBuffer("Output"); - //result->at(0)->printShapeInfo("Out Shape"); + ASSERT_EQ(result.status(), Status::OK()); +// result.at(0)->printIndexedBuffer("Output"); + //result.at(0)->printShapeInfo("Out Shape"); // exp.printIndexedBuffer("Expect"); //exp.printShapeInfo("Exp Shape"); - ASSERT_TRUE(exp.isSameShape(result->at(0))); - ASSERT_TRUE(exp.equalsTo(result->at(0))); + ASSERT_TRUE(exp.isSameShape(result.at(0))); + ASSERT_TRUE(exp.equalsTo(result.at(0))); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -2755,18 +2755,18 @@ auto exp = NDArrayFactory::create('c', {3, 1, 1, 18}, { }); // ---------------------------------------------------------------- - nd4j::ops::extract_image_patches op; + sd::ops::extract_image_patches op; auto result = op.evaluate({&x}, {}, {3,2,3,2,1,2,0}); - ASSERT_EQ(result->status(), Status::OK()); -// result->at(0)->printIndexedBuffer("Output"); - //result->at(0)->printShapeInfo("Out Shape"); + ASSERT_EQ(result.status(), Status::OK()); +// result.at(0)->printIndexedBuffer("Output"); + //result.at(0)->printShapeInfo("Out Shape"); // exp.printIndexedBuffer("Expect"); //exp.printShapeInfo("Exp Shape"); - ASSERT_TRUE(exp.isSameShape(result->at(0))); - ASSERT_TRUE(exp.equalsTo(result->at(0))); + ASSERT_TRUE(exp.isSameShape(result.at(0))); + ASSERT_TRUE(exp.equalsTo(result.at(0))); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -2784,14 +2784,14 @@ auto exp = NDArrayFactory::create('c', {2, 1, 4, 4}, { 21.11, 21.12, 22.11, 22.12, 21.21, 21.22, 22.21, 22.22, 21.31, 21.32, 22.31, 22.32, 21.41, 21.42, 22.41, 22.42 }); // ---------------------------------------------------------------- - nd4j::ops::extract_image_patches op; + sd::ops::extract_image_patches op; auto result = op.evaluate({&x}, {}, {2,1, 1,1, 1,1,0}); - ASSERT_EQ(result->status(), Status::OK()); - ASSERT_TRUE(exp.isSameShape(result->at(0))); - ASSERT_TRUE(exp.equalsTo(result->at(0))); + ASSERT_EQ(result.status(), Status::OK()); + ASSERT_TRUE(exp.isSameShape(result.at(0))); + ASSERT_TRUE(exp.equalsTo(result.at(0))); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -2806,22 +2806,22 @@ TEST_F(DeclarableOpsTests7, TestExtractImagePatches_SGO_7) { 1., 2., 4., 5., 2., 3., 5., 6., 3., 0., 6., 0., 4., 5., 7., 8., 5., 6., 8., 9., 6., 0., 9., 0., 7., 8., 0., 0., 8., 9., 0., 0., 9., 0., 0., 0. }); // ---------------------------------------------------------------- - nd4j::ops::extract_image_patches op; + sd::ops::extract_image_patches op; auto result = op.evaluate({&x}, {}, {2,2, 1,1, 1,1, 1}); // equiv TF ksizes=[1,2,2,1], strides=[1,1,1,1], rates=[1,1,1,1], padding="SAME" - ASSERT_EQ(result->status(), Status::OK()); - auto output = result->at(0); + ASSERT_EQ(result.status(), Status::OK()); + auto output = result.at(0); // output->printBuffer("Output"); // exp.printBuffer("Expect"); // for (Nd4jLong e = 0; e < exp.lengthOf(); e++) // if (exp.e(e) != output->e(e)) // printf("%lld ", e); // printf("\n"); - //result->at(1)->printBuffer("OUtput2"); + //result.at(1)->printBuffer("OUtput2"); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -2837,22 +2837,22 @@ TEST_F(DeclarableOpsTests7, TestExtractImagePatches_SGO_8) { 7, 8, 9, 10, 13, 14, 15, 16, 9, 10, 11, 12, 15, 16, 17, 18, 11, 12, 0, 0, 17, 18, 0, 0, 13, 14, 15, 16, 0, 0, 0, 0, 15, 16, 17, 18, 0, 0, 0, 0, 17, 18, 0, 0, 0, 0, 0, 0 }); // ---------------------------------------------------------------- - nd4j::ops::extract_image_patches op; + sd::ops::extract_image_patches op; auto result = op.evaluate({&x}, {}, {2,2, 1,1, 1,1, 1}); // equiv TF ksizes=[1,2,2,1], strides=[1,1,1,1], rates=[1,1,1,1], padding="SAME" - ASSERT_EQ(result->status(), Status::OK()); - auto output = result->at(0); + ASSERT_EQ(result.status(), Status::OK()); + auto output = result.at(0); // output->printBuffer("Output"); // exp.printBuffer("Expect"); // for (Nd4jLong e = 0; e < exp.lengthOf(); e++) // if (exp.e(e) != output->e(e)) // printf("%lld ", e); // printf("\n"); - //result->at(1)->printBuffer("OUtput2"); + //result.at(1)->printBuffer("OUtput2"); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -2901,22 +2901,22 @@ TEST_F(DeclarableOpsTests7, TestExtractImagePatches_SGO_9) { 55., 56., 57., 58., 59., 60., 67., 68., 69., 70., 71., 72., 0., 0., 0., 0., 0., 0., 57., 58., 59., 60., 0., 0., 69., 70., 71., 72., 0., 0., 0., 0., 0., 0., 0., 0.}); // ---------------------------------------------------------------- - nd4j::ops::extract_image_patches op; + sd::ops::extract_image_patches op; auto result = op.evaluate({&x}, {}, {3,3, 1,1, 1,1, 1}); // equiv TF ksizes=[1,2,2,1], strides=[1,1,1,1], rates=[1,1,1,1], padding="SAME" - ASSERT_EQ(result->status(), Status::OK()); - auto output = result->at(0); + ASSERT_EQ(result.status(), Status::OK()); + auto output = result.at(0); // output->printBuffer("OutputSame"); // exp.printBuffer("ExpectSame"); // for (Nd4jLong e = 0; e < exp.lengthOf(); e++) // if (exp.e(e) != output->e(e)) // printf("%lld ", e); // printf("\n"); - //result->at(1)->printBuffer("OUtput2"); + //result.at(1)->printBuffer("OUtput2"); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -2942,22 +2942,22 @@ TEST_F(DeclarableOpsTests7, TestExtractImagePatches_SGO_9_1) { }); // ---------------------------------------------------------------- - nd4j::ops::extract_image_patches op; + sd::ops::extract_image_patches op; auto result = op.evaluate({&x}, {}, {2,2, 1,1, 1,1, 1}); // equiv TF ksizes=[1,2,2,1], strides=[1,1,1,1], rates=[1,1,1,1], padding="SAME" - ASSERT_EQ(result->status(), Status::OK()); - auto output = result->at(0); + ASSERT_EQ(result.status(), Status::OK()); + auto output = result.at(0); // output->printBuffer("OutputSame"); // exp.printBuffer("ExpectSame"); // for (Nd4jLong e = 0; e < exp.lengthOf(); e++) // if (exp.e(e) != output->e(e)) // printf("%lld ", e); // printf("\n"); - //result->at(1)->printBuffer("OUtput2"); + //result.at(1)->printBuffer("OUtput2"); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } // @@ -2988,23 +2988,23 @@ TEST_F(DeclarableOpsTests7, TestExtractImagePatches_SGO_10) { 41., 42., 43., 44., 45., 46., 53., 54., 55., 56., 57., 58., 65., 66., 67., 68., 69., 70., 43., 44., 45., 46., 47., 48., 55., 56., 57., 58., 59., 60., 67., 68., 69., 70., 71., 72.}); // ---------------------------------------------------------------- - nd4j::ops::extract_image_patches op; + sd::ops::extract_image_patches op; //x.printIndexedBuffer("Images"); //x.printBuffer("Images linear"); auto result = op.evaluate({&x}, {}, {3,3, 1,1, 1,1, 0}); // equiv TF ksizes=[1,2,2,1], strides=[1,1,1,1], rates=[1,1,1,1], padding="VALID" - ASSERT_EQ(result->status(), Status::OK()); - auto output = result->at(0); + ASSERT_EQ(result.status(), Status::OK()); + auto output = result.at(0); // output->printBuffer("OutputValid"); // exp.printBuffer("ExpectValid"); // for (Nd4jLong e = 0; e < exp.lengthOf(); e++) // if (exp.e(e) != output->e(e)) // printf("%lld ", e); // printf("\n"); - //result->at(1)->printBuffer("OUtput2"); + //result.at(1)->printBuffer("OUtput2"); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } TEST_F(DeclarableOpsTests7, TestExtractImagePatches_SGO_010) { auto x = NDArrayFactory::create('c', {1, 4, 4, 1}); @@ -3017,23 +3017,23 @@ TEST_F(DeclarableOpsTests7, TestExtractImagePatches_SGO_010) { 1, 2, 5, 6, 2, 3, 6, 7, 3, 4, 7, 8, 5, 6, 9, 10, 6, 7, 10, 11, 7, 8, 11, 12, 9, 10, 13, 14, 10, 11, 14, 15, 11, 12, 15, 16}); // ---------------------------------------------------------------- - nd4j::ops::extract_image_patches op; + sd::ops::extract_image_patches op; //x.printIndexedBuffer("Images"); //x.printBuffer("Images linear"); auto result = op.evaluate({&x}, {}, {2,2, 1,1, 1,1, 0}); // equiv TF ksizes=[1,2,2,1], strides=[1,1,1,1], rates=[1,1,1,1], padding="VALID" - ASSERT_EQ(result->status(), Status::OK()); - auto output = result->at(0); + ASSERT_EQ(result.status(), Status::OK()); + auto output = result.at(0); // output->printBuffer("OutputValid"); // exp.printBuffer("ExpectValid"); // for (Nd4jLong e = 0; e < exp.lengthOf(); e++) // if (exp.e(e) != output->e(e)) // printf("%lld ", e); // printf("\n"); - //result->at(1)->printBuffer("OUtput2"); + //result.at(1)->printBuffer("OUtput2"); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } TEST_F(DeclarableOpsTests7, TestExtractImagePatches_SGO_010_1) { auto x = NDArrayFactory::create('c', {1, 4, 4, 1}); @@ -3047,12 +3047,12 @@ TEST_F(DeclarableOpsTests7, TestExtractImagePatches_SGO_010_1) { 7, 8, 11, 12, 8, 0, 12, 0, 9, 10, 13, 14, 10, 11, 14, 15, 11, 12, 15, 16, 12, 0, 16, 0, 13, 14, 0, 0, 14, 15, 0, 0, 15, 16, 0, 0, 16, 0, 0, 0}); // ---------------------------------------------------------------- - nd4j::ops::extract_image_patches op; + sd::ops::extract_image_patches op; //x.printIndexedBuffer("Images"); //x.printBuffer("Images linear"); auto result = op.evaluate({&x}, {}, {2,2, 1,1, 1,1, 1}); // equiv TF ksizes=[1,2,2,1], strides=[1,1,1,1], rates=[1,1,1,1], padding="VALID" - ASSERT_EQ(result->status(), Status::OK()); - auto output = result->at(0); + ASSERT_EQ(result.status(), Status::OK()); + auto output = result.at(0); // output->printBuffer("OutputSame"); // exp.printBuffer("ExpectSame"); // exp.printIndexedBuffer("Expect Same Formatted"); @@ -3061,11 +3061,11 @@ TEST_F(DeclarableOpsTests7, TestExtractImagePatches_SGO_010_1) { // if (exp.e(e) != output->e(e)) // printf("%lld ", e); // printf("\n"); - //result->at(1)->printBuffer("OUtput2"); + //result.at(1)->printBuffer("OUtput2"); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } TEST_F(DeclarableOpsTests7, TestExtractImagePatches_SGO_011) { @@ -3079,23 +3079,23 @@ TEST_F(DeclarableOpsTests7, TestExtractImagePatches_SGO_011) { 1, 3, 9, 11, 2, 4, 10, 12, 5, 7, 13, 15, 6, 8, 14, 16, }); // ---------------------------------------------------------------- - nd4j::ops::extract_image_patches op; + sd::ops::extract_image_patches op; //x.printIndexedBuffer("Images"); //x.printBuffer("Images linear"); auto result = op.evaluate({&x}, {}, {2,2, 1,1, 2,2, 0}); // equiv TF ksizes=[1,2,2,1], strides=[1,1,1,1], rates=[1,1,1,1], padding="VALID" - ASSERT_EQ(result->status(), Status::OK()); - auto output = result->at(0); + ASSERT_EQ(result.status(), Status::OK()); + auto output = result.at(0); // output->printBuffer("OutputValid"); // exp.printBuffer("ExpectValid"); // for (Nd4jLong e = 0; e < exp.lengthOf(); e++) // if (exp.e(e) != output->e(e)) // printf("%lld ", e); // printf("\n"); - //result->at(1)->printBuffer("OUtput2"); + //result.at(1)->printBuffer("OUtput2"); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -3116,22 +3116,22 @@ TEST_F(DeclarableOpsTests7, TestExtractImagePatches_SGO_11) { 117, 118, 119, 120, 105, 106, 107, 108, 121, 122, 123, 124, 109, 110, 111, 112, 125, 126, 127, 128}); // ---------------------------------------------------------------- - nd4j::ops::extract_image_patches op; + sd::ops::extract_image_patches op; auto result = op.evaluate({&x}, {}, {2,2, 2,2, 1,1, 1}); // equiv TF ksizes=[1,2,2,1], strides=[1,1,1,1], rates=[1,1,1,1], padding="SAME" - ASSERT_EQ(result->status(), Status::OK()); - auto output = result->at(0); + ASSERT_EQ(result.status(), Status::OK()); + auto output = result.at(0); // output->printBuffer("Output"); // exp.printBuffer("Expect"); // for (Nd4jLong e = 0; e < exp.lengthOf(); e++) // if (exp.e(e) != output->e(e)) // printf("%lld ", e); // printf("\n"); - //result->at(1)->printBuffer("OUtput2"); + //result.at(1)->printBuffer("OUtput2"); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -3173,11 +3173,11 @@ TEST_F(DeclarableOpsTests7, TestExtractImagePatches_SGO_12) { 0, 0, 105, 106, 109, 110, 0, 0, 0, 0, 107, 108, 111, 112, 0, 0, 0, 0, 109, 110, 0, 0, 0, 0, 0, 0}); // ---------------------------------------------------------------- - nd4j::ops::extract_image_patches op; + sd::ops::extract_image_patches op; auto result = op.evaluate({&x}, {}, {2,2, 1,1, 2,2, 1}); // equiv TF ksizes=[1,2,2,1], strides=[1,1,1,1], rates=[1,2,2,1], padding="SAME" - ASSERT_EQ(result->status(), Status::OK()); - auto output = result->at(0); + ASSERT_EQ(result.status(), Status::OK()); + auto output = result.at(0); //output->printShapeInfo("Output shape"); // output->printIndexedBuffer("Output"); // exp.printBuffer("Expect"); @@ -3185,11 +3185,11 @@ TEST_F(DeclarableOpsTests7, TestExtractImagePatches_SGO_12) { // if (exp.e(e) != output->e(e)) // printf("%lld ", e); // printf("\n"); - //result->at(1)->printBuffer("OUtput2"); + //result.at(1)->printBuffer("OUtput2"); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -3203,16 +3203,16 @@ TEST_F(DeclarableOpsTests7, TestExtractImagePatches_SGO_13) { 15., 16., 17., 18., 11., 12., 0., 0., 17., 18., 0., 0., 13., 14., 15., 16., 0., 0., 0., 0., 15., 16., 17., 18., 0., 0., 0., 0., 17., 18., 0., 0., 0., 0., 0., 0. }); // ---------------------------------------------------------------- - nd4j::ops::extract_image_patches op; + sd::ops::extract_image_patches op; auto result = op.evaluate({&x}, {}, {2,2, 1,1, 1,1, 1}); // equiv TF ksizes=[1,2,2,1], strides=[1,1,1,1], rates=[1,1,1,1], padding="SAME" - ASSERT_EQ(result->status(), Status::OK()); - auto output = result->at(0); + ASSERT_EQ(result.status(), Status::OK()); + auto output = result.at(0); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -3228,14 +3228,14 @@ auto exp = NDArrayFactory::create('c', {2, 2, 4, 2}, { 21.41, 21.42, 22.11, 22.12 }); // ---------------------------------------------------------------- - nd4j::ops::roll op; + sd::ops::roll op; auto result = op.evaluate({&x}, {}, {6}); - ASSERT_EQ(result->status(), Status::OK()); + ASSERT_EQ(result.status(), Status::OK()); - ASSERT_TRUE(exp.equalsTo(result->at(0))); + ASSERT_TRUE(exp.equalsTo(result.at(0))); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -3250,13 +3250,13 @@ auto exp = NDArrayFactory::create('c', {2, 2, 4, 2}, { 22.11, 22.12, 22.21, 22.22, 22.31, 22.32, 22.41, 22.42, 11.11, 11.12, 11.21, 11.22, 11.31, 11.32, 11.41, 11.42 }); // ---------------------------------------------------------------- - nd4j::ops::roll op; + sd::ops::roll op; auto result = op.evaluate({&x}, {}, {-8}); - ASSERT_EQ(result->status(), Status::OK()); - ASSERT_TRUE(exp.equalsTo(result->at(0))); + ASSERT_EQ(result.status(), Status::OK()); + ASSERT_TRUE(exp.equalsTo(result.at(0))); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -3271,14 +3271,14 @@ auto exp = NDArrayFactory::create('c', {2, 2, 4, 2}, { 22.11, 22.12, 22.21, 22.22, 22.31, 22.32, 22.41, 22.42, 11.11, 11.12, 11.21, 11.22, 11.31, 11.32, 11.41, 11.42 }); // ---------------------------------------------------------------- - nd4j::ops::roll op; + sd::ops::roll op; auto result = op.evaluate({&x}, {}, {-40}); - ASSERT_EQ(result->status(), Status::OK()); + ASSERT_EQ(result.status(), Status::OK()); - ASSERT_TRUE(exp.equalsTo(result->at(0))); + ASSERT_TRUE(exp.equalsTo(result.at(0))); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -3294,16 +3294,16 @@ auto exp = NDArrayFactory::create('c', {2, 2, 4, 2}, { 21.41, 21.42, 22.11, 22.12 }); // ---------------------------------------------------------------- - nd4j::ops::roll op; + sd::ops::roll op; auto result = op.evaluate({&x}, {}, {38}); - ASSERT_EQ(result->status(), Status::OK()); - //result->at(0)->printIndexedBuffer("Output 4"); + ASSERT_EQ(result.status(), Status::OK()); + //result.at(0)->printIndexedBuffer("Output 4"); //exp.printIndexedBuffer("Expect 4"); - ASSERT_TRUE(exp.equalsTo(result->at(0))); + ASSERT_TRUE(exp.equalsTo(result.at(0))); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -3319,7 +3319,7 @@ auto exp = NDArrayFactory::create('c', {2, 2, 4, 2}, { 21.41, 21.42, 22.11, 22.12 }); // ---------------------------------------------------------------- - nd4j::ops::roll op; + sd::ops::roll op; NDArray* y = nullptr; auto result = op.execute({&x}, {y}, {}, {38}, {}, {}, true); ASSERT_EQ(result, Status::OK()); @@ -3328,7 +3328,7 @@ auto exp = NDArrayFactory::create('c', {2, 2, 4, 2}, { ASSERT_TRUE(exp.equalsTo(&x)); -// delete result; +// } //////////////////////////////////////////////////////////////////////////////// @@ -3342,17 +3342,17 @@ auto exp = NDArrayFactory::create('c', {3, 4}, { // 4, 5, 6, 7, 8, 9, 10, 11, 0, 1, 2, 3 }); // ---------------------------------------------------------------- - nd4j::ops::roll op; + sd::ops::roll op; auto result = op.evaluate({&x}, {}, {2, 1}); - ASSERT_EQ(result->status(), Status::OK()); + ASSERT_EQ(result.status(), Status::OK()); - //result->at(0)->printIndexedBuffer("Output"); + //result.at(0)->printIndexedBuffer("Output"); //exp.printIndexedBuffer("Expect"); - ASSERT_TRUE(exp.equalsTo(result->at(0))); + ASSERT_TRUE(exp.equalsTo(result.at(0))); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -3365,17 +3365,17 @@ auto exp = NDArrayFactory::create('c', {2, 3, 2}, { 1., 0., 3., 2., 5., 4., 7., 6., 9., 8., 11., 10. }); // ---------------------------------------------------------------- - nd4j::ops::roll op; + sd::ops::roll op; auto result = op.evaluate({&x}, {}, {1, 2}); - ASSERT_EQ(result->status(), Status::OK()); + ASSERT_EQ(result.status(), Status::OK()); - //result->at(0)->printIndexedBuffer("Output"); + //result.at(0)->printIndexedBuffer("Output"); //exp.printIndexedBuffer("Expect"); - ASSERT_TRUE(exp.equalsTo(result->at(0))); + ASSERT_TRUE(exp.equalsTo(result.at(0))); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -3388,17 +3388,17 @@ auto exp = NDArrayFactory::create('c', {2, 3, 2}, { 11., 10., 7., 6., 9., 8., 5., 4., 1., 0., 3., 2. }); // ---------------------------------------------------------------- - nd4j::ops::roll op; + sd::ops::roll op; auto result = op.evaluate({&x}, {}, {1, 2, 1, 0}); - ASSERT_EQ(result->status(), Status::OK()); + ASSERT_EQ(result.status(), Status::OK()); - //result->at(0)->printIndexedBuffer("Output"); + //result.at(0)->printIndexedBuffer("Output"); //exp.printIndexedBuffer("Expect"); - ASSERT_TRUE(exp.equalsTo(result->at(0))); + ASSERT_TRUE(exp.equalsTo(result.at(0))); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -3411,7 +3411,7 @@ auto exp = NDArrayFactory::create('c', {2, 3, 2}, { 11., 10., 7., 6., 9., 8., 5., 4., 1., 0., 3., 2. }); // ---------------------------------------------------------------- - nd4j::ops::roll op; + sd::ops::roll op; NDArray* y = nullptr; auto result = op.execute({&x}, {y}, {}, {1, 2, 1, 0}, {}, {}, true); ASSERT_EQ(result, Status::OK()); @@ -3421,7 +3421,7 @@ auto exp = NDArrayFactory::create('c', {2, 3, 2}, { ASSERT_TRUE(exp.equalsTo(&x)); -// delete result; +// } //////////////////////////////////////////////////////////////////////////////// @@ -3434,14 +3434,14 @@ auto exp = NDArrayFactory::create('c', {2, 3, 3}, { 6., 7., 8., 0., 1., 2., 3., 4., 5., 15., 16., 17., 9., 10., 11., 12., 13., 14. }); // ---------------------------------------------------------------- - nd4j::ops::roll op; + sd::ops::roll op; NDArray* y = nullptr; auto result = op.execute({&x}, {y}, {}, {1, 1}, {}, {}, true); ASSERT_EQ(result, Status::OK()); ASSERT_TRUE(exp.equalsTo(&x)); -// delete result; +// } //////////////////////////////////////////////////////////////////////////////// @@ -3454,17 +3454,17 @@ TEST_F(DeclarableOpsTests7, TestRoll_10) { 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24. }); // ---------------------------------------------------------------- - nd4j::ops::roll op; + sd::ops::roll op; auto result = op.evaluate({&x}, {}, {3, 1}); - ASSERT_EQ(result->status(), Status::OK()); - auto out = result->at(0); + ASSERT_EQ(result.status(), Status::OK()); + auto out = result.at(0); // out->printIndexedBuffer("Output"); //exp.printIndexedBuffer("Expect"); ASSERT_TRUE(exp.equalsTo(out)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -3478,18 +3478,18 @@ TEST_F(DeclarableOpsTests7, TestRoll_11) { 17., 18., 19., 20., 21., 22., 23., 24., 13., 14., 15., 16., 5., 6., 7, 8, 9, 10, 11, 12, 1, 2, 3, 4 }); // ---------------------------------------------------------------- - nd4j::ops::roll op; + sd::ops::roll op; NDArray* y = nullptr; auto result = op.evaluate({&x, &shift, &axis}); - ASSERT_EQ(result->status(), Status::OK()); - auto out = result->at(0); + ASSERT_EQ(result.status(), Status::OK()); + auto out = result.at(0); // out->printIndexedBuffer("Output"); //exp.printIndexedBuffer("Expect"); ASSERT_TRUE(exp.equalsTo(out)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -3504,15 +3504,15 @@ TEST_F(DeclarableOpsTests7, TestRoll_12) { 24, 21, 22, 23, 16, 13, 14, 15, 20, 17, 18, 19, 12, 9, 10, 11, 4, 1, 2, 3, 8, 5, 6, 7 }); // ---------------------------------------------------------------- - nd4j::ops::roll op; + sd::ops::roll op; NDArray* y = nullptr; auto result = op.evaluate({&x, &shift, &axis}); - ASSERT_EQ(result->status(), Status::OK()); - auto out = result->at(0); + ASSERT_EQ(result.status(), Status::OK()); + auto out = result.at(0); ASSERT_TRUE(exp.equalsTo(out)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -3527,15 +3527,15 @@ TEST_F(DeclarableOpsTests7, TestRoll_13) { 2,3,4,1,6,7,8,5,10,11,12,9,14, 15, 16, 13, 18, 19, 20, 17, 22, 23, 24, 21 }); // ---------------------------------------------------------------- - nd4j::ops::roll op; + sd::ops::roll op; NDArray* y = nullptr; auto result = op.evaluate({&x}, {}, {3,2}); - ASSERT_EQ(result->status(), Status::OK()); - auto out = result->at(0); + ASSERT_EQ(result.status(), Status::OK()); + auto out = result.at(0); ASSERT_TRUE(exp.equalsTo(out)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -3550,17 +3550,17 @@ TEST_F(DeclarableOpsTests7, TestRoll_14) { 24, 21, 22, 23, 16, 13, 14, 15, 20, 17, 18, 19, 12, 9, 10, 11, 4, 1, 2, 3, 8, 5, 6, 7 }); // ---------------------------------------------------------------- - nd4j::ops::roll op; + sd::ops::roll op; auto result = op.evaluate({&x, &shift, &axis}); - ASSERT_EQ(result->status(), Status::OK()); - auto out = result->at(0); + ASSERT_EQ(result.status(), Status::OK()); + auto out = result.at(0); // out->printIndexedBuffer("Output"); //exp.printIndexedBuffer("Expect"); ASSERT_TRUE(exp.equalsTo(out)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -3571,17 +3571,17 @@ TEST_F(DeclarableOpsTests7, TestRoll_15) { auto exp = NDArrayFactory::create({0.7244f, 0.2309f, 0.7788f, 0.8012f }); // ---------------------------------------------------------------- - nd4j::ops::roll op; + sd::ops::roll op; auto result = op.evaluate({&x, &shift, &axis}); - ASSERT_EQ(result->status(), Status::OK()); - auto out = result->at(0); + ASSERT_EQ(result.status(), Status::OK()); + auto out = result.at(0); // out->printIndexedBuffer("Output 15"); // exp.printIndexedBuffer("Expect 15"); ASSERT_TRUE(exp.equalsTo(out)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -3596,15 +3596,15 @@ TEST_F(DeclarableOpsTests7, percentile_test1) { 82., 90., 91., 89., 92., 34., 35., 33., 36.}); auto expected = NDArrayFactory::create(50.); - nd4j::ops::percentile op; + sd::ops::percentile op; auto result = op.evaluate({&input}, {50.}, {}); - auto output = result->at(0); + auto output = result.at(0); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete result; + } @@ -3620,15 +3620,15 @@ TEST_F(DeclarableOpsTests7, percentile_test2) { 82., 90., 91., 89., 92., 34., 35., 33., 36.}); auto expected = NDArrayFactory::create('c', {1,1,1}, {11.}); - nd4j::ops::percentile op; + sd::ops::percentile op; //q, interpolation, keepDims auto result = op.evaluate({&input}, {10, 2, 1}, {}); - auto output = result->at(0); + auto output = result.at(0); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete result; + } @@ -3644,15 +3644,15 @@ TEST_F(DeclarableOpsTests7, percentile_test3) { 82., 90., 91., 89., 92., 34., 35., 33., 36.}); auto expected = NDArrayFactory::create('c', {1,1,1}, {10.}); - nd4j::ops::percentile op; + sd::ops::percentile op; //q, interpolation, keepDims auto result = op.evaluate({&input}, {10, 0, 1}, {}); - auto output = result->at(0); + auto output = result.at(0); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete result; + } @@ -3668,15 +3668,15 @@ TEST_F(DeclarableOpsTests7, percentile_test4) { 82., 90., 91., 89., 92., 34., 35., 33., 36.}); auto expected = NDArrayFactory::create('c', {1,1,1}, {11.}); - nd4j::ops::percentile op; + sd::ops::percentile op; //q, interpolation, keepDims auto result = op.evaluate({&input}, {10, 1, 1}, {}); - auto output = result->at(0); + auto output = result.at(0); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -3692,15 +3692,15 @@ TEST_F(DeclarableOpsTests7, percentile_test5) { auto expected = NDArrayFactory::create('c', {1,1,4}, {12., 7., 11., 10.}); - nd4j::ops::percentile op; + sd::ops::percentile op; //q, interpolation, keepDims auto result = op.evaluate({&input}, {10, 0, 1}, {0,1}); - auto output = result->at(0); + auto output = result.at(0); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -3716,15 +3716,15 @@ TEST_F(DeclarableOpsTests7, percentile_test6) { auto expected = NDArrayFactory::create('c', {1,1,4}, {16., 14., 15., 13.}); - nd4j::ops::percentile op; + sd::ops::percentile op; //q, interpolation, keepDims auto result = op.evaluate({&input}, {10, 1, 1}, {0,1}); - auto output = result->at(0); + auto output = result.at(0); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -3740,15 +3740,15 @@ TEST_F(DeclarableOpsTests7, percentile_test7) { auto expected = NDArrayFactory::create('c', {1,1,4}, {12., 7., 11., 10.}); - nd4j::ops::percentile op; + sd::ops::percentile op; //q, interpolation, keepDims auto result = op.evaluate({&input}, {10, 2, 1}, {0,1}); - auto output = result->at(0); + auto output = result.at(0); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -3764,15 +3764,15 @@ TEST_F(DeclarableOpsTests7, percentile_test8) { auto expected = NDArrayFactory::create('c', {4}, {12., 7., 11., 10.}); - nd4j::ops::percentile op; + sd::ops::percentile op; //q, interpolation, keepDims auto result = op.evaluate({&input}, {10, 2, 0}, {0,1}); - auto output = result->at(0); + auto output = result.at(0); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -3788,15 +3788,15 @@ TEST_F(DeclarableOpsTests7, percentile_test9) { auto expected = NDArrayFactory::create(11.); - nd4j::ops::percentile op; + sd::ops::percentile op; //q, interpolation, keepDims auto result = op.evaluate({&input}, {10, 2, 0}, {0}); - auto output = result->at(0); + auto output = result.at(0); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -3812,15 +3812,15 @@ TEST_F(DeclarableOpsTests7, percentile_test10) { auto expected = NDArrayFactory::create('c', {1}, {11.}); - nd4j::ops::percentile op; + sd::ops::percentile op; //q, interpolation, keepDims auto result = op.evaluate({&input}, {10, 2, 1}, {0}); - auto output = result->at(0); + auto output = result.at(0); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -3832,15 +3832,15 @@ TEST_F(DeclarableOpsTests7, percentile_test11) { auto expected = NDArrayFactory::create('c', {1}, {100.}); - nd4j::ops::percentile op; + sd::ops::percentile op; //q, interpolation, keepDims auto result = op.evaluate({&input}, {10, 2, 1}, {0}); - auto output = result->at(0); + auto output = result.at(0); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -3852,15 +3852,15 @@ TEST_F(DeclarableOpsTests7, percentile_test12) { auto expected = NDArrayFactory::create(100.); - nd4j::ops::percentile op; + sd::ops::percentile op; //q, interpolation, keepDims auto result = op.evaluate({&input}, {10, 2, 0}, {}); - auto output = result->at(0); + auto output = result.at(0); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -3869,14 +3869,14 @@ TEST_F(DeclarableOpsTests7, transpose_test3) { auto input = NDArrayFactory::create('c', {5, 3}, {1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 7.f, 8.f, 9.f, 10.f, 11.f, 12.f, 13.f, 14.f, 15.f}); auto exp = NDArrayFactory::create('c', {3, 5}, {1.f, 4.f, 7.f, 10.f, 13.f, 2.f, 5.f, 8.f, 11.f, 14.f, 3.f, 6.f, 9.f, 12.f, 15.f}); - nd4j::ops::transpose op; + sd::ops::transpose op; auto result = op.evaluate({&input}, {}, {}); - auto output = result->at(0); + auto output = result.at(0); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -3885,14 +3885,14 @@ TEST_F(DeclarableOpsTests7, rationaltanh_test1) { auto input = NDArrayFactory::create('c', {8}, {0, 1, 2, 3, 4, 5, 6, 7}); NDArray exp = NDArrayFactory::create({0.000000, 0.998222, 1.516093, 1.658054, 1.695077, 1.706884, 1.711427, 1.713446}); - nd4j::ops::rationaltanh op; + sd::ops::rationaltanh op; auto result = op.evaluate({&input}, {}, {}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Output rationaltanh"); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -3901,14 +3901,14 @@ TEST_F(DeclarableOpsTests7, rationaltanh_test2) { auto input = NDArrayFactory::create('c', {2,2,2}, {0, 1, 2, 3, 4, 5, 6, 7}); NDArray exp = NDArrayFactory::create('c', {2,2,2}, {0.000000, 0.998222, 1.516093, 1.658054, 1.695077, 1.706884, 1.711427, 1.713446}); - nd4j::ops::rationaltanh op; + sd::ops::rationaltanh op; auto result = op.evaluate({&input}, {}, {}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Output rationaltanh"); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -3918,14 +3918,14 @@ TEST_F(DeclarableOpsTests7, rationaltanh_test3) { auto eps = NDArrayFactory::create('c', {2,2,2}, {1, 2, 3, 4, 5, 6, 7, 8}); NDArray exp = NDArrayFactory::create('c', {2,2,2}, {1.143933, 1.605747, 0.795557, 0.261710, 0.095832, 0.041218, 0.020221, 0.010971}); - nd4j::ops::rationaltanh_bp op; + sd::ops::rationaltanh_bp op; auto result = op.evaluate({&input, &eps}, {}, {}); - auto output = result->at(0); + auto output = result.at(0); // output->printBuffer("Output rationaltanh BP"); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -3934,14 +3934,14 @@ TEST_F(DeclarableOpsTests7, rectifiedtanh_test1) { auto input = NDArrayFactory::create('c', {2,2,2}, {0, 1, 2, 3, 4, 5, 6, 7}); NDArray exp = NDArrayFactory::create('c', {2,2,2}, {0.000000, 0.761594, 0.964028, 0.995055, 0.999329, 0.999909, 0.999988, 0.999998}); - nd4j::ops::rectifiedtanh op; + sd::ops::rectifiedtanh op; auto result = op.evaluate({&input}, {}, {}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Output rectifiedtanh"); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -3951,14 +3951,14 @@ TEST_F(DeclarableOpsTests7, rectifiedtanh_test2) { auto eps = NDArrayFactory::create('c', {2,2,2}, {1, 2, 3, 4, 5, 6, 7, 8}); NDArray exp = NDArrayFactory::create('c', {2,2,2}, {0.000000, 0.839949, 0.211952, 0.039464, 0.006705, 0.001089, 0.000172, 0.000027}); - nd4j::ops::rectifiedtanh_bp op; + sd::ops::rectifiedtanh_bp op; auto result = op.evaluate({&input, &eps}, {}, {}); - auto output = result->at(0); + auto output = result.at(0); // output->printBuffer("Output rectifiedtanh BP"); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } TEST_F(DeclarableOpsTests7, RealDiv_1) { @@ -3967,17 +3967,17 @@ TEST_F(DeclarableOpsTests7, RealDiv_1) { NDArray y = NDArrayFactory::create('c', {1, 2}, {1.f,2.f}); NDArray e = NDArrayFactory::create('c', {1, 2, 2}, {2.f, 1.f, 4.f, 2.f}); - nd4j::ops::realdiv op; + sd::ops::realdiv op; auto result = op.evaluate({&x, &y}, {}, {}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); // z->printIndexedBuffer("OUtput RealDiv"); ASSERT_TRUE(e.isSameShape(z)); ASSERT_TRUE(e.equalsTo(*z)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -3989,13 +3989,13 @@ TEST_F(DeclarableOpsTests7, RealDiv_BP_1) { NDArray e1 = NDArrayFactory::create('c', {1, 2}, {-14.f, -5.f}); NDArray eps = NDArrayFactory::create('c', {1, 2, 2}, {1.f, 2.f, 3.f, 4.f}); - nd4j::ops::realdiv_bp op; + sd::ops::realdiv_bp op; auto result = op.evaluate({&x, &y, &eps}, {}, {}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z0 = result->at(0); - auto z1 = result->at(1); + auto z0 = result.at(0); + auto z1 = result.at(1); // z0->printShapeInfo("OUtput RealDiv BP0 shape"); // z1->printShapeInfo("OUtput RealDiv BP1 shape"); // z0->printIndexedBuffer("OUtput RealDiv BP0"); @@ -4004,7 +4004,7 @@ TEST_F(DeclarableOpsTests7, RealDiv_BP_1) { ASSERT_TRUE(e0.equalsTo(z0)); ASSERT_TRUE(e1.equalsTo(z1)); - delete result; + } //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// @@ -4014,17 +4014,17 @@ TEST_F(DeclarableOpsTests7, ShapesOf_1) { // NDArray y = NDArrayFactory::create('c', {1, 2}, {1,2}); NDArray e = NDArrayFactory::create({1, 2, 1}); - nd4j::ops::shapes_of op; + sd::ops::shapes_of op; auto result = op.evaluate({&x}, {}, {}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); // z->printIndexedBuffer("OUtput RealDiv"); // ASSERT_TRUE(e.isSameShape(z)); ASSERT_TRUE(e.equalsTo(*z)); - delete result; + } //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// @@ -4035,20 +4035,20 @@ TEST_F(DeclarableOpsTests7, ShapesOf_2) { NDArray e0 = NDArrayFactory::create({1, 2, 1}); NDArray e1 = NDArrayFactory::create({1, 2}); - nd4j::ops::shapes_of op; + sd::ops::shapes_of op; auto result = op.evaluate({&x, &y}, {}, {}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z0 = result->at(0); - auto z1 = result->at(1); + auto z0 = result.at(0); + auto z1 = result.at(1); // z0->printIndexedBuffer("OUtput shapes2"); // z1->printIndexedBuffer("OUtput shapes2"); // ASSERT_TRUE(e.isSameShape(z)); ASSERT_TRUE(e0.equalsTo(z0)); ASSERT_TRUE(e1.equalsTo(z1)); - delete result; + } TEST_F(DeclarableOpsTests7, Size_1) { @@ -4057,17 +4057,17 @@ TEST_F(DeclarableOpsTests7, Size_1) { NDArray y = NDArrayFactory::create('c', {5, 2}, {1.f, 2.f, 3.f, 4.f, 5.f, 7.f, 9.f, 10.f, 10.f, 11.f}); NDArray e = NDArrayFactory::create(2); - nd4j::ops::size op; + sd::ops::size op; auto result = op.evaluate({&x}, {}, {}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); // z->printIndexedBuffer("OUtput SIZE"); /// ASSERT_TRUE(e.isSameShape(z)); ASSERT_TRUE(e.equalsTo(*z)); - delete result; + } TEST_F(DeclarableOpsTests7, Size_2) { @@ -4076,17 +4076,17 @@ TEST_F(DeclarableOpsTests7, Size_2) { NDArray y = NDArrayFactory::create('c', {5, 2}, {1,2,3,4,5,7,9,10, 10, 11}); NDArray e = NDArrayFactory::create(10); - nd4j::ops::size op; + sd::ops::size op; auto result = op.evaluate({&y}, {}, {}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); // z->printIndexedBuffer("OUtput SIZE"); /// ASSERT_TRUE(e.isSameShape(z)); ASSERT_TRUE(e.equalsTo(*z)); - delete result; + } TEST_F(DeclarableOpsTests7, Softplus_1) { @@ -4094,17 +4094,17 @@ TEST_F(DeclarableOpsTests7, Softplus_1) { NDArray x = NDArrayFactory::create('c', {5, 2}, {1,2,3,4,5,7,9,10, 10, 11}); NDArray e = NDArrayFactory::create('c', {5, 2}, {1.3132616, 2.126928, 3.0485873, 4.01815, 5.0067153, 7.0009117, 9.000123, 10.000046, 10.000046, 11.000016}); - nd4j::ops::softplus op; + sd::ops::softplus op; auto result = op.evaluate({&x}, {}, {}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); // z->printIndexedBuffer("OUtput Softplus"); /// ASSERT_TRUE(e.isSameShape(z)); ASSERT_TRUE(e.equalsTo(*z)); - delete result; + } TEST_F(DeclarableOpsTests7, Softplus_BP_1) { @@ -4112,8 +4112,8 @@ TEST_F(DeclarableOpsTests7, Softplus_BP_1) { NDArray x = NDArrayFactory::create('c', {5, 2}, {1,2,3,4,5,7,9,10, 10, 11}); // NDArray e = NDArrayFactory::create('c', {5, 2}, {1.3132616, 2.126928, 3.0485873, 4.01815, 5.0067153, 7.0009117, 9.000123, 10.000046, 10.000046, 11.000016}); NDArray eps = NDArrayFactory::create('c', {5, 2}, {1,2,3,4,5,6,7,8, 9, 10}); - nd4j::ops::softplus ffOP; - nd4j::ops::softplus_bp bpOp; + sd::ops::softplus ffOP; + sd::ops::softplus_bp bpOp; const OpArgsHolder argsHolderFF({&x}, {}, {}); const OpArgsHolder argsHolderBP({&x, &eps}, {}, {}); @@ -4121,12 +4121,12 @@ TEST_F(DeclarableOpsTests7, Softplus_BP_1) { ASSERT_TRUE(gradOK); // -// auto z = result->at(0); +// auto z = result.at(0); // z->printIndexedBuffer("OUtput Softplus"); ///// ASSERT_TRUE(e.isSameShape(z)); // ASSERT_TRUE(e.equalsTo(*z)); // -// delete result; +// } TEST_F(DeclarableOpsTests7, Softsign_1) { @@ -4134,17 +4134,17 @@ TEST_F(DeclarableOpsTests7, Softsign_1) { NDArray x = NDArrayFactory::create('c', {5, 2}, {1,2,3,4,5,7,9,10, 10, 11}); NDArray e = NDArrayFactory::create('c', {5, 2}, {0.5, 0.6666667, 0.75, 0.8, 0.8333333, 0.875, 0.9, 0.90909094, 0.90909094, 0.9166667}); - nd4j::ops::softsign op; + sd::ops::softsign op; auto result = op.evaluate({&x}, {}, {}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); // z->printIndexedBuffer("OUtput Softsign"); /// ASSERT_TRUE(e.isSameShape(z)); ASSERT_TRUE(e.equalsTo(*z)); - delete result; + } TEST_F(DeclarableOpsTests7, Softsign_BP_1) { @@ -4152,8 +4152,8 @@ TEST_F(DeclarableOpsTests7, Softsign_BP_1) { NDArray x = NDArrayFactory::create('c', {5, 2}, {1,2,3,4,5,7,9,10, 10, 11}); // NDArray e = NDArrayFactory::create('c', {5, 2}, {1.3132616f, 2.126928f, 3.0485873f, 4.01815f, 5.0067153f, 7.0009117f, 9.000123f, 10.000046f, 10.000046f, 11.000016f}); NDArray eps = NDArrayFactory::create('c', {5, 2}, {1,2,3,4,5,6,7,8, 9, 10}); - nd4j::ops::softsign ffOP; - nd4j::ops::softsign_bp bpOp; + sd::ops::softsign ffOP; + sd::ops::softsign_bp bpOp; const OpArgsHolder argsHolderFF({&x}, {}, {}); const OpArgsHolder argsHolderBP({&x, &eps}, {}, {}); @@ -4169,17 +4169,17 @@ TEST_F(DeclarableOpsTests7, fill_test2) { auto v = NDArrayFactory::create(42.); auto exp = NDArrayFactory::create('c', {2, 2},{42.f, 42.f, 42.f, 42.f}); - nd4j::ops::fill op; + sd::ops::fill op; auto result = op.evaluate({&x, &v}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -4189,16 +4189,16 @@ TEST_F(DeclarableOpsTests7, fill_test3) { auto v = NDArrayFactory::create(42.); auto exp = NDArrayFactory::create('c', {2, 2}, {42.f, 42.f, 42.f, 42.f}); - nd4j::ops::fill op; + sd::ops::fill op; auto result = op.evaluate({&x, &v}, {}, {}); - auto output = result->at(0); + auto output = result.at(0); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -4207,16 +4207,16 @@ TEST_F(DeclarableOpsTests7, ToggleBits_test1) { auto x = NDArrayFactory::create('c', {2}, {2, 2}); auto exp = NDArrayFactory::create('c', {2}, {-3, -3}); - nd4j::ops::toggle_bits op; + sd::ops::toggle_bits op; auto result = op.evaluate({&x}); - auto output = result->at(0); + auto output = result.at(0); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); // output->printIndexedBuffer("Toggled"); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -4227,19 +4227,19 @@ TEST_F(DeclarableOpsTests7, ToggleBits_test2) { auto exp0 = NDArrayFactory::create('c', {2}, {-3, -3}); auto exp1 = NDArrayFactory::create('c', {2}, {-2, -2}); - nd4j::ops::toggle_bits op; + sd::ops::toggle_bits op; auto result = op.evaluate({&x, &y}); - auto output = result->at(0); - auto z = result->at(1); + auto output = result.at(0); + auto z = result.at(1); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); // output->printIndexedBuffer("Toggled"); ASSERT_TRUE(exp0.isSameShape(output)); ASSERT_TRUE(exp0.equalsTo(output)); ASSERT_TRUE(exp1.isSameShape(z)); ASSERT_TRUE(exp1.equalsTo(z)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -4248,14 +4248,14 @@ TEST_F(DeclarableOpsTests7, Truncatediv_test1) { NDArray y = NDArrayFactory::create('c', {5, 2}, {2,2,2,2,2,2,2,2, 2, 2}); NDArray exp = NDArrayFactory::create('c', {5, 2}, {0.5, 1., 1.5, 2., 2.5, 3.5, 4.5, 5., 5., 5.5}); - nd4j::ops::truncatediv op; + sd::ops::truncatediv op; auto result = op.evaluate({&x, &y}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); - auto output = result->at(0); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); + auto output = result.at(0); // output->printIndexedBuffer("Toggled"); ASSERT_TRUE(exp.isSameShape(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -4264,14 +4264,14 @@ TEST_F(DeclarableOpsTests7, Truncatediv_test2) { NDArray y = NDArrayFactory::create('c', {1, 2}, {2,2}); NDArray exp = NDArrayFactory::create('c', {5, 2}, {0.5, 1., 1.5, 2., 2.5, 3.5, 4.5, 5., 5., 5.5}); - nd4j::ops::truncatediv op; + sd::ops::truncatediv op; auto result = op.evaluate({&x, &y}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); - auto output = result->at(0); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); + auto output = result.at(0); // output->printIndexedBuffer("Toggled"); ASSERT_TRUE(exp.isSameShape(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -4282,24 +4282,22 @@ TEST_F(DeclarableOpsTests7, TypesConversion_test1) { NDArray expF = NDArrayFactory::create('c', {5, 2}, {1.f,2.f,3.f,4.f,5.f,7.f,9.f,10.f, 10.f, 11.f}); NDArray expF16 = NDArrayFactory::create('c', {5, 2}, {1.f,2.f,3.f,4.f,5.f,7.f,9.f,10.f, 10.f, 11.f}); - nd4j::ops::to_int32 op32; - nd4j::ops::to_int64 op64; + sd::ops::to_int32 op32; + sd::ops::to_int64 op64; auto result32 = op32.evaluate({&x}, {}, {}); auto result64 = op64.evaluate({&x}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result32->status()); - ASSERT_EQ(ND4J_STATUS_OK, result64->status()); - auto out1 = result32->at(0); + ASSERT_EQ(ND4J_STATUS_OK, result32.status()); + ASSERT_EQ(ND4J_STATUS_OK, result64.status()); + auto out1 = result32.at(0); // out1->printIndexedBuffer("OUT_I"); - auto out2 = result64->at(0); + auto out2 = result64.at(0); // out2->printIndexedBuffer("OUT_L"); // output->printIndexedBuffer("Toggled"); ASSERT_TRUE(expI.equalsTo(out1)); ASSERT_TRUE(expL.equalsTo(out2)); - delete result32; - delete result64; } //////////////////////////////////////////////////////////////////////////////// @@ -4308,24 +4306,22 @@ TEST_F(DeclarableOpsTests7, TypesConversion_test2) { NDArray expF = NDArrayFactory::create('c', {5, 2}, {1.f,2.f,3.f,4.f,5.f,7.f,9.f,10.f, 10.f, 11.f}); NDArray expH = NDArrayFactory::create('c', {5, 2}, {1.f,2.f,3.f,4.f,5.f,7.f,9.f,10.f, 10.f, 11.f}); - nd4j::ops::to_float32 op32; - nd4j::ops::to_float16 op16; + sd::ops::to_float32 op32; + sd::ops::to_float16 op16; auto result32 = op32.evaluate({&x}, {}, {}); auto result16 = op16.evaluate({&x}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result32->status()); - ASSERT_EQ(ND4J_STATUS_OK, result16->status()); - auto out1 = result32->at(0); + ASSERT_EQ(ND4J_STATUS_OK, result32.status()); + ASSERT_EQ(ND4J_STATUS_OK, result16.status()); + auto out1 = result32.at(0); // out1->printIndexedBuffer("OUT_F"); - auto out2 = result16->at(0); + auto out2 = result16.at(0); // out2->printIndexedBuffer("OUT_H"); // output->printIndexedBuffer("Toggled"); ASSERT_TRUE(expF.equalsTo(out1)); ASSERT_TRUE(expH.equalsTo(out2)); - delete result32; - delete result16; } //////////////////////////////////////////////////////////////////////////////// @@ -4334,24 +4330,21 @@ TEST_F(DeclarableOpsTests7, TypesConversion_test3) { NDArray exp32 = NDArrayFactory::create('c', {5, 2}, {1,2,3,4,5,7,9,10, 10, 11}); NDArray exp64 = NDArrayFactory::create('c', {5, 2}, {1,2,3,4,5,7,9,10, 10, 11}); - nd4j::ops::to_uint32 op32; - nd4j::ops::to_uint64 op64; + sd::ops::to_uint32 op32; + sd::ops::to_uint64 op64; auto result32 = op32.evaluate({&x}, {}, {}); auto result64 = op64.evaluate({&x}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result32->status()); - ASSERT_EQ(ND4J_STATUS_OK, result64->status()); - auto out1 = result32->at(0); + ASSERT_EQ(ND4J_STATUS_OK, result32.status()); + ASSERT_EQ(ND4J_STATUS_OK, result64.status()); + auto out1 = result32.at(0); // out1->printIndexedBuffer("OUT_U32"); - auto out2 = result64->at(0); + auto out2 = result64.at(0); // out2->printIndexedBuffer("OUT_U64"); // output->printIndexedBuffer("Toggled"); ASSERT_TRUE(exp32.equalsTo(out1)); ASSERT_TRUE(exp64.equalsTo(out2)); - - delete result32; - delete result64; } //////////////////////////////////////////////////////////////////////////////// @@ -4360,21 +4353,19 @@ TEST_F(DeclarableOpsTests7, TypesConversion_test4) { NDArray exp32 = NDArrayFactory::create('c', {5, 2}, {1.f,2.f,3.f,4.f,5.f,7.f,9.f,10.f, 10.f, 11.f}); NDArray exp64 = NDArrayFactory::create('c', {5, 2}, {1,2,3,4,5,7,9,10, 10, 11}); - nd4j::ops::to_float32 op32; - nd4j::ops::to_double op64; + sd::ops::to_float32 op32; + sd::ops::to_double op64; auto result32 = op32.evaluate({&x}, {}, {}); auto result64 = op64.evaluate({&x}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result32->status()); - ASSERT_EQ(ND4J_STATUS_OK, result64->status()); - auto out1 = result32->at(0); - auto out2 = result64->at(0); + ASSERT_EQ(ND4J_STATUS_OK, result32.status()); + ASSERT_EQ(ND4J_STATUS_OK, result64.status()); + auto out1 = result32.at(0); + auto out2 = result64.at(0); ASSERT_TRUE(exp32.equalsTo(out1)); ASSERT_TRUE(exp64.equalsTo(out2)); - delete result32; - delete result64; } //////////////////////////////////////////////////////////////////////////////// @@ -4385,14 +4376,14 @@ TEST_F(DeclarableOpsTests7, mirrorPad_test1) { auto exp = NDArrayFactory::create('c', {4, 7}, {2, 1, 1, 2, 3, 3, 2, 2, 1, 1, 2, 3, 3, 2, 5, 4, 4, 5, 6, 6, 5, 5, 4, 4, 5, 6, 6, 5}); - nd4j::ops::mirror_pad op; + sd::ops::mirror_pad op; auto result = op.evaluate({&input, &paddings}, {}, {1}); - auto output = result->at(0); + auto output = result.at(0); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -4403,14 +4394,14 @@ TEST_F(DeclarableOpsTests7, mirrorPad_test2) { auto exp = NDArrayFactory::create('c', {4, 7}, {6, 5, 4, 5, 6, 5, 4, 3, 2, 1, 2, 3, 2, 1, 6, 5, 4, 5, 6, 5, 4, 3, 2, 1, 2, 3, 2, 1}); - nd4j::ops::mirror_pad op; + sd::ops::mirror_pad op; auto result = op.evaluate({&input, &paddings}, {}, {0}); - auto output = result->at(0); + auto output = result.at(0); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -4421,14 +4412,14 @@ TEST_F(DeclarableOpsTests7, mirrorPad_test3) { auto exp = NDArrayFactory::create('c', {7}, {2, 1, 1, 2, 3, 3, 2}); - nd4j::ops::mirror_pad op; + sd::ops::mirror_pad op; auto result = op.evaluate({&input, &paddings}, {}, {1}); - auto output = result->at(0); + auto output = result.at(0); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -4439,14 +4430,14 @@ TEST_F(DeclarableOpsTests7, mirrorPad_test4) { auto exp = NDArrayFactory::create('c', {8}, {2, 1, 1, 2, 3, 3, 2, 1}); - nd4j::ops::mirror_pad op; + sd::ops::mirror_pad op; auto result = op.evaluate({&input, &paddings}, {}, {1}); - auto output = result->at(0); + auto output = result.at(0); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -4457,13 +4448,13 @@ TEST_F(DeclarableOpsTests7, mirrorPad_test5) { auto exp = NDArrayFactory::create('c', {7}, {3, 2, 1, 2, 3, 2, 1}); - nd4j::ops::mirror_pad op; + sd::ops::mirror_pad op; auto result = op.evaluate({&input, &paddings}, {}, {0}); - auto output = result->at(0); + auto output = result.at(0); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -4474,14 +4465,14 @@ TEST_F(DeclarableOpsTests7, mirrorPad_test6) { auto exp = NDArrayFactory::create('c', {3}, {1,1,1}); - nd4j::ops::mirror_pad op; + sd::ops::mirror_pad op; auto result = op.evaluate({&input, &paddings}, {}, {1}); - auto output = result->at(0); + auto output = result.at(0); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -4492,14 +4483,14 @@ TEST_F(DeclarableOpsTests7, mirrorPad_test7) { auto exp = NDArrayFactory::create('c', {3}, {1,1,1}); - nd4j::ops::mirror_pad op; + sd::ops::mirror_pad op; auto result = op.evaluate({&input, &paddings}, {}, {1}); - auto output = result->at(0); + auto output = result.at(0); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -4510,15 +4501,15 @@ TEST_F(DeclarableOpsTests7, mirrorPad_test8) { auto exp = NDArrayFactory::create('c', {3,9}, {3, 2, 1, 1, 2, 3, 3, 2, 1, 3, 2, 1, 1, 2, 3, 3, 2, 1, 3, 2, 1, 1, 2, 3, 3, 2, 1}); - nd4j::ops::mirror_pad op; + sd::ops::mirror_pad op; auto result = op.evaluate({&input, &paddings}, {}, {1}); - ASSERT_EQ(result->status(), Status::OK()); + ASSERT_EQ(result.status(), Status::OK()); - auto output = result->at(0); + auto output = result.at(0); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -4529,14 +4520,14 @@ TEST_F(DeclarableOpsTests7, mirrorPad_test9) { auto exp = NDArrayFactory::create('c', {6, 9}, {6, 5, 4, 4, 5, 6, 6, 5, 4, 3, 2, 1, 1, 2, 3, 3, 2, 1, 3, 2, 1, 1, 2, 3, 3, 2, 1, 6, 5, 4, 4, 5, 6, 6, 5, 4, 6, 5, 4, 4, 5, 6, 6, 5, 4, 3, 2, 1, 1, 2, 3, 3, 2, 1}); - nd4j::ops::mirror_pad op; + sd::ops::mirror_pad op; auto result = op.evaluate({&input, &paddings}, {}, {1}); - auto output = result->at(0); + auto output = result.at(0); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -4547,14 +4538,14 @@ TEST_F(DeclarableOpsTests7, mirrorPad_test10) { auto exp = NDArrayFactory::create('c', {1,3}, {1., 2., 3.}); - nd4j::ops::mirror_pad op; + sd::ops::mirror_pad op; auto result = op.evaluate({&input, &paddings}, {}, {1}); - auto output = result->at(0); + auto output = result.at(0); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -4565,14 +4556,14 @@ TEST_F(DeclarableOpsTests7, mirrorPad_test11) { auto exp = NDArrayFactory::create('c', {1,3}, {1., 2., 3.}); - nd4j::ops::mirror_pad op; + sd::ops::mirror_pad op; auto result = op.evaluate({&input, &paddings}, {}, {0}); - auto output = result->at(0); + auto output = result.at(0); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -4583,14 +4574,14 @@ TEST_F(DeclarableOpsTests7, mirrorPad_test12) { auto exp = NDArrayFactory::create('c', {3}, {1., 2., 3.}); - nd4j::ops::mirror_pad op; + sd::ops::mirror_pad op; auto result = op.evaluate({&input, &paddings}, {}, {0}); - auto output = result->at(0); + auto output = result.at(0); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -4601,14 +4592,14 @@ TEST_F(DeclarableOpsTests7, mirrorPad_test13) { auto exp = NDArrayFactory::create('c', {2, 3}, {1., 2., 3., 4., 5., 6.}); - nd4j::ops::mirror_pad op; + sd::ops::mirror_pad op; auto result = op.evaluate({&input, &paddings}, {}, {0}); - auto output = result->at(0); + auto output = result.at(0); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -4619,14 +4610,14 @@ TEST_F(DeclarableOpsTests7, mirrorPad_test14) { auto exp = NDArrayFactory::create('c', {3, 4}, {4, 5, 6, 5, 1, 2, 3, 2, 4, 5, 6, 5}); - nd4j::ops::mirror_pad op; + sd::ops::mirror_pad op; auto result = op.evaluate({&input, &paddings}, {}, {0}); - auto output = result->at(0); + auto output = result.at(0); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -4637,14 +4628,14 @@ TEST_F(DeclarableOpsTests7, mirrorPad_test15) { auto exp = NDArrayFactory::create('c', {4, 3}, {1, 2, 3, 1, 2, 3, 4, 5, 6, 4, 5, 6}); - nd4j::ops::mirror_pad op; + sd::ops::mirror_pad op; auto result = op.evaluate({&input, &paddings}, {}, {1}); - auto output = result->at(0); + auto output = result.at(0); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -4660,17 +4651,17 @@ TEST_F(DeclarableOpsTests7, mirrorPad_test16) { 12., 11., 12., 11.,10., 9., 10., 9., 8., 7., 8., 7.,10., 9., 10., 9.,12., 11., 12., 11.,10., 9., 10., 9., 8., 7., 8., 7., 6., 5., 6., 5., 4., 3., 4., 3., 2., 1., 2., 1., 4., 3., 4., 3., 6., 5., 6., 5., 4., 3., 4., 3., 2., 1., 2., 1.}); input.linspace(1.); - nd4j::ops::mirror_pad op; + sd::ops::mirror_pad op; auto result = op.evaluate({&input, &paddings}, {}, {0}); - ASSERT_EQ(result->status(), Status::OK()); - auto output = result->at(0); + ASSERT_EQ(result.status(), Status::OK()); + auto output = result.at(0); //output->printBuffer("VVV"); //exp.printBuffer("EXP"); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -4680,14 +4671,14 @@ TEST_F(DeclarableOpsTests7, Test_Reduce_Sum_1) { auto exp = NDArrayFactory::create(120.f); //************************************// - nd4j::ops::reduce_sum op; + sd::ops::reduce_sum op; auto result = op.evaluate({&input}, {}, {}); - ASSERT_EQ(Status::OK(), result->status()); - auto z = result->at(0); + ASSERT_EQ(Status::OK(), result.status()); + auto z = result.at(0); //z->printIndexedBuffer("Result is "); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -4697,14 +4688,14 @@ TEST_F(DeclarableOpsTests7, Test_Reduce_Sum_2) { auto exp = NDArrayFactory::create({15.f, 40.f, 65.f}); //************************************// - nd4j::ops::reduce_sum op; + sd::ops::reduce_sum op; auto result = op.evaluate({&input}, {}, {1}); - ASSERT_EQ(Status::OK(), result->status()); - auto z = result->at(0); + ASSERT_EQ(Status::OK(), result.status()); + auto z = result.at(0); // z->printIndexedBuffer("Result is "); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -4714,14 +4705,14 @@ TEST_F(DeclarableOpsTests7, Test_Reduce_Prod_1) { auto exp = NDArrayFactory::create(1307674368000.f); //************************************// - nd4j::ops::reduce_prod op; + sd::ops::reduce_prod op; auto result = op.evaluate({&input}, {}, {}); - ASSERT_EQ(Status::OK(), result->status()); - auto z = result->at(0); + ASSERT_EQ(Status::OK(), result.status()); + auto z = result.at(0); //z->printIndexedBuffer("Result is "); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -4731,14 +4722,14 @@ TEST_F(DeclarableOpsTests7, Test_Reduce_Prod_2) { auto exp = NDArrayFactory::create({120.f, 30240.f, 360360.f}); //************************************// - nd4j::ops::reduce_prod op; + sd::ops::reduce_prod op; auto result = op.evaluate({&input}, {}, {1}); - ASSERT_EQ(Status::OK(), result->status()); - auto z = result->at(0); + ASSERT_EQ(Status::OK(), result.status()); + auto z = result.at(0); // z->printIndexedBuffer("Result is "); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -4748,16 +4739,16 @@ TEST_F(DeclarableOpsTests7, Test_Reduce_Sum_01) { auto exp = NDArrayFactory::create('c', {4}, {66.f, 72.f, 78.f, 84.f}); x.linspace(1); - nd4j::ops::reduce_sum op; + sd::ops::reduce_sum op; auto result = op.evaluate({&x}, {}, {0,1}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -4767,17 +4758,17 @@ TEST_F(DeclarableOpsTests7, Test_Reduce_Sum_02) { auto exp = NDArrayFactory::create('c', {1,1,4}, {66.f, 72.f, 78.f, 84.f}); x.linspace(1); - nd4j::ops::reduce_sum op; + sd::ops::reduce_sum op; auto result = op.evaluate({&x}, {1.}, {0, 1}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -4787,17 +4778,17 @@ TEST_F(DeclarableOpsTests7, Test_Reduce_Sum_3) { auto exp = NDArrayFactory::create('c', {3}, {68.f, 100.f, 132.f}); x.linspace(1); - nd4j::ops::reduce_sum op; + sd::ops::reduce_sum op; auto result = op.evaluate({&x}, {}, {0, 2}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -4807,17 +4798,17 @@ TEST_F(DeclarableOpsTests7, Test_Reduce_Sum_4) { auto exp = NDArrayFactory::create('c', {1,3,1}, {68.f, 100.f, 132.f}); x.linspace(1); - nd4j::ops::reduce_sum op; + sd::ops::reduce_sum op; auto result = op.evaluate({&x}, {1.}, {0, 2}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -4827,17 +4818,17 @@ TEST_F(DeclarableOpsTests7, Test_Reduce_Sum_5) { auto exp = NDArrayFactory::create(300.f); x.linspace(1); - nd4j::ops::reduce_sum op; + sd::ops::reduce_sum op; auto result = op.evaluate({&x}, {}, {}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -4847,17 +4838,17 @@ TEST_F(DeclarableOpsTests7, Test_Reduce_Sum_6) { auto exp = NDArrayFactory::create(300.f); x.linspace(1); - nd4j::ops::reduce_sum op; + sd::ops::reduce_sum op; auto result = op.evaluate({&x}, {}, {0,1,2}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -4867,17 +4858,17 @@ TEST_F(DeclarableOpsTests7, Test_Reduce_Sum_7) { auto exp = NDArrayFactory::create('c', {1,1,1}, {300.f}); x.linspace(1); // x.printIndexedBuffer("Input with shape (2, 3, 4) is"); - nd4j::ops::reduce_sum op; + sd::ops::reduce_sum op; auto result = op.evaluate({&x}, {1.}, {0,1,2}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -4887,16 +4878,16 @@ TEST_F(DeclarableOpsTests7, Test_Reduce_Prod_01) { auto exp = NDArrayFactory::create('c', {2}, {10395.f, 46080.f}); x.linspace(1); - nd4j::ops::reduce_prod op; + sd::ops::reduce_prod op; auto result = op.evaluate({&x}, {}, {0,1}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -4906,17 +4897,17 @@ TEST_F(DeclarableOpsTests7, Test_Reduce_Prod_02) { auto exp = NDArrayFactory::create('c', {1,1,2}, {10395.f, 46080.f}); x.linspace(1); - nd4j::ops::reduce_prod op; + sd::ops::reduce_prod op; auto result = op.evaluate({&x}, {1.}, {0, 1}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -4926,17 +4917,17 @@ TEST_F(DeclarableOpsTests7, Test_Reduce_Prod_3) { auto exp = NDArrayFactory::create('c', {3}, {112.f, 1080.f, 3960.f}); x.linspace(1); - nd4j::ops::reduce_prod op; + sd::ops::reduce_prod op; auto result = op.evaluate({&x}, {}, {0, 2}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -4946,17 +4937,17 @@ TEST_F(DeclarableOpsTests7, Test_Reduce_Prod_4) { auto exp = NDArrayFactory::create('c', {1,3,1}, {112.f, 1080.f, 3960.f}); x.linspace(1); - nd4j::ops::reduce_prod op; + sd::ops::reduce_prod op; auto result = op.evaluate({&x}, {1.}, {0, 2}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -4966,17 +4957,17 @@ TEST_F(DeclarableOpsTests7, Test_Reduce_Prod_5) { auto exp = NDArrayFactory::create(479001600.f); x.linspace(1); - nd4j::ops::reduce_prod op; + sd::ops::reduce_prod op; auto result = op.evaluate({&x}, {}, {}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -4986,17 +4977,17 @@ TEST_F(DeclarableOpsTests7, Test_Reduce_Prod_6) { auto exp = NDArrayFactory::create(479001600.f); x.linspace(1); - nd4j::ops::reduce_prod op; + sd::ops::reduce_prod op; auto result = op.evaluate({&x}, {}, {0,1,2}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -5006,30 +4997,30 @@ TEST_F(DeclarableOpsTests7, Test_Reduce_Prod_7) { auto exp = NDArrayFactory::create('c', {1, 1, 1}, {479001600.f}); x.linspace(1); // x.printIndexedBuffer("Input with shape (2, 3, 4) is"); - nd4j::ops::reduce_prod op; + sd::ops::reduce_prod op; auto result = op.evaluate({&x}, {1.}, {0,1,2}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } TYPED_TEST(TypedDeclarableOpsTests7, Test_Pnorm_Once_Again) { auto input = NDArrayFactory::create('c', {1, 1, 5, 5}, {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f, 10.0f, 11.0f, 12.0f, 13.0f, 14.0f, 15.0f, 16.0f, 17.0f, 18.0f, 19.0f, 20.0f, 21.0f, 22.0f, 23.0f, 24.0f, 25.0f}); auto exp = NDArrayFactory::create('c', {1, 1, 5, 5}, {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f, 10.0f, 11.0f, 12.0f, 13.0f, 14.0f, 15.0f, 16.0f, 17.0f, 18.0f, 19.0f, 20.0f, 21.0f, 22.0f, 23.0f, 24.0f, 25.0f}); - nd4j::ops::pnormpool2d op; + sd::ops::pnormpool2d op; auto result = op.evaluate({&input}, {}, {1,1, 1,1, 0,0, 1,1,1, 3, 0}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - ASSERT_EQ(exp, *result->at(0)); + ASSERT_EQ(exp, *result.at(0)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -5039,16 +5030,16 @@ TEST_F(DeclarableOpsTests7, Test_Reduce_Min_1) { auto exp = NDArrayFactory::create('c', {4}, {1.f, 2.f, 3.f, 4.f}); x.linspace(1); - nd4j::ops::reduce_min op; + sd::ops::reduce_min op; auto result = op.evaluate({&x}, {}, {0, 1}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -5058,17 +5049,17 @@ TEST_F(DeclarableOpsTests7, Test_Reduce_Min_2) { auto exp = NDArrayFactory::create('c', {1,1,4}, {1.f, 2.f, 3.f, 4.f}); x.linspace(1); - nd4j::ops::reduce_min op; + sd::ops::reduce_min op; auto result = op.evaluate({&x}, {1.}, {0, 1}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -5078,17 +5069,17 @@ TEST_F(DeclarableOpsTests7, Test_Reduce_Min_3) { auto exp = NDArrayFactory::create('c', {3}, {1.f, 5.f, 9.f}); x.linspace(1); - nd4j::ops::reduce_min op; + sd::ops::reduce_min op; auto result = op.evaluate({&x}, {}, {0, 2}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -5098,17 +5089,17 @@ TEST_F(DeclarableOpsTests7, Test_Reduce_Min_4) { auto exp = NDArrayFactory::create('c', {1,3,1}, {1.f, 5.f, 9.f}); x.linspace(1); - nd4j::ops::reduce_min op; + sd::ops::reduce_min op; auto result = op.evaluate({&x}, {1.}, {0, 2}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -5118,17 +5109,17 @@ TEST_F(DeclarableOpsTests7, Test_Reduce_Min_5) { auto exp = NDArrayFactory::create(1.f); x.linspace(1); - nd4j::ops::reduce_min op; + sd::ops::reduce_min op; auto result = op.evaluate({&x}, {}, {}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -5138,17 +5129,17 @@ TEST_F(DeclarableOpsTests7, Test_Reduce_Min_6) { auto exp = NDArrayFactory::create(1.f); x.linspace(1); - nd4j::ops::reduce_min op; + sd::ops::reduce_min op; auto result = op.evaluate({&x}, {}, {0,1,2}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -5158,17 +5149,17 @@ TEST_F(DeclarableOpsTests7, Test_Reduce_Min_7) { auto exp = NDArrayFactory::create('c', {1, 1, 1}, {1.f}); x.linspace(1); // x.printIndexedBuffer("Input with shape (2, 3, 4) is"); - nd4j::ops::reduce_min op; + sd::ops::reduce_min op; auto result = op.evaluate({&x}, {1.}, {0,1,2}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -5178,17 +5169,17 @@ TEST_F(DeclarableOpsTests7, Test_Reduce_Max_1) { auto exp = NDArrayFactory::create('c', {4}, {21.f, 22.f, 23.f, 24.f}); x.linspace(1); - nd4j::ops::reduce_max op; + sd::ops::reduce_max op; auto result = op.evaluate({&x}, {}, {0,1}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); // output->printShapeInfo("Output shape"); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -5198,17 +5189,17 @@ TEST_F(DeclarableOpsTests7, Test_Reduce_Max_2) { auto exp = NDArrayFactory::create('c', {1,1,4}, {21.f, 22.f, 23.f, 24.f}); x.linspace(1); - nd4j::ops::reduce_max op; + sd::ops::reduce_max op; auto result = op.evaluate({&x}, {1.}, {0, 1}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -5218,17 +5209,17 @@ TEST_F(DeclarableOpsTests7, Test_Reduce_Max_3) { auto exp = NDArrayFactory::create('c', {3}, {16.f, 20.f, 24.f}); x.linspace(1); - nd4j::ops::reduce_max op; + sd::ops::reduce_max op; auto result = op.evaluate({&x}, {}, {0, 2}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -5238,17 +5229,17 @@ TEST_F(DeclarableOpsTests7, Test_Reduce_Max_4) { auto exp = NDArrayFactory::create('c', {1,3,1}, {16.f, 20.f, 24.f}); x.linspace(1); - nd4j::ops::reduce_max op; + sd::ops::reduce_max op; auto result = op.evaluate({&x}, {1.}, {0, 2}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -5258,17 +5249,17 @@ TEST_F(DeclarableOpsTests7, Test_Reduce_Max_5) { auto exp = NDArrayFactory::create(24.f); x.linspace(1); - nd4j::ops::reduce_max op; + sd::ops::reduce_max op; auto result = op.evaluate({&x}, {}, {}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -5278,17 +5269,17 @@ TEST_F(DeclarableOpsTests7, Test_Reduce_Max_6) { auto exp = NDArrayFactory::create(24.f); x.linspace(1); - nd4j::ops::reduce_max op; + sd::ops::reduce_max op; auto result = op.evaluate({&x}, {}, {0,1,2}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -5298,17 +5289,17 @@ TEST_F(DeclarableOpsTests7, Test_Reduce_Max_7) { auto exp = NDArrayFactory::create('c', {1, 1, 1}, {24.f}); x.linspace(1); // x.printIndexedBuffer("Input with shape (2, 3, 4) is"); - nd4j::ops::reduce_max op; + sd::ops::reduce_max op; auto result = op.evaluate({&x}, {1.}, {0,1,2}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -5318,16 +5309,16 @@ TEST_F(DeclarableOpsTests7, Test_Reduce_Norm1_1) { auto exp = NDArrayFactory::create('c', {4}, {66.f, 72.f, 78.f, 84.f}); x.linspace(1); - nd4j::ops::reduce_norm1 op; + sd::ops::reduce_norm1 op; auto result = op.evaluate({&x}, {}, {0,1}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -5337,17 +5328,17 @@ TEST_F(DeclarableOpsTests7, Test_Reduce_Norm1_2) { auto exp = NDArrayFactory::create('c', {1,1,4}, {66.f, 72.f, 78.f, 84.f}); x.linspace(1); - nd4j::ops::reduce_norm1 op; + sd::ops::reduce_norm1 op; auto result = op.evaluate({&x}, {1.}, {0, 1}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -5357,17 +5348,17 @@ TEST_F(DeclarableOpsTests7, Test_Reduce_Norm1_3) { auto exp = NDArrayFactory::create('c', {3}, {68.f, 100.f, 132.f}); x.linspace(1); - nd4j::ops::reduce_norm1 op; + sd::ops::reduce_norm1 op; auto result = op.evaluate({&x}, {}, {0, 2}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -5377,17 +5368,17 @@ TEST_F(DeclarableOpsTests7, Test_Reduce_Norm1_4) { auto exp = NDArrayFactory::create('c', {1,3,1}, {68.f, 100.f, 132.f}); x.linspace(1); - nd4j::ops::reduce_norm1 op; + sd::ops::reduce_norm1 op; auto result = op.evaluate({&x}, {1.}, {0, 2}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -5397,17 +5388,17 @@ TEST_F(DeclarableOpsTests7, Test_Reduce_Norm1_5) { auto exp = NDArrayFactory::create(300.f); x.linspace(1); - nd4j::ops::reduce_norm1 op; + sd::ops::reduce_norm1 op; auto result = op.evaluate({&x}, {}, {}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -5417,17 +5408,17 @@ TEST_F(DeclarableOpsTests7, Test_Reduce_Norm1_6) { auto exp = NDArrayFactory::create(300.f); x.linspace(1); - nd4j::ops::reduce_norm1 op; + sd::ops::reduce_norm1 op; auto result = op.evaluate({&x}, {}, {0,1,2}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -5437,17 +5428,17 @@ TEST_F(DeclarableOpsTests7, Test_Reduce_Norm1_7) { auto exp = NDArrayFactory::create('c', {1, 1, 1}, {300.f}); x.linspace(1); // x.printIndexedBuffer("Input with shape (2, 3, 4) is"); - nd4j::ops::reduce_norm1 op; + sd::ops::reduce_norm1 op; auto result = op.evaluate({&x}, {1.}, {0,1,2}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } TEST_F(DeclarableOpsTests7, Test_Reduce_Norm2_1) { @@ -5456,16 +5447,16 @@ TEST_F(DeclarableOpsTests7, Test_Reduce_Norm2_1) { auto exp = NDArrayFactory::create('c', {4}, {31.7175f, 33.823071f, 35.97221f, 38.15757f}); x.linspace(1); - nd4j::ops::reduce_norm2 op; + sd::ops::reduce_norm2 op; auto result = op.evaluate({&x}, {}, {0,1}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -5475,17 +5466,17 @@ TEST_F(DeclarableOpsTests7, Test_Reduce_Norm2_2) { auto exp = NDArrayFactory::create('c', {1,1,4}, {31.7175f, 33.823071f, 35.97221f, 38.15757f}); x.linspace(1); - nd4j::ops::reduce_norm2 op; + sd::ops::reduce_norm2 op; auto result = op.evaluate({&x}, {1.}, {0, 1}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -5495,17 +5486,17 @@ TEST_F(DeclarableOpsTests7, Test_Reduce_Norm2_3) { auto exp = NDArrayFactory::create('c', {3}, {29.597298f, 39.344631f, 49.759422f}); x.linspace(1); - nd4j::ops::reduce_norm2 op; + sd::ops::reduce_norm2 op; auto result = op.evaluate({&x}, {}, {0, 2}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -5515,17 +5506,17 @@ TEST_F(DeclarableOpsTests7, Test_Reduce_Norm2_4) { auto exp = NDArrayFactory::create('c', {1,3,1}, {29.597298f, 39.344631f, 49.759422f}); x.linspace(1); - nd4j::ops::reduce_norm2 op; + sd::ops::reduce_norm2 op; auto result = op.evaluate({&x}, {1.}, {0, 2}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -5535,17 +5526,17 @@ TEST_F(DeclarableOpsTests7, Test_Reduce_Norm2_5) { auto exp = NDArrayFactory::create(70.f); x.linspace(1); - nd4j::ops::reduce_norm2 op; + sd::ops::reduce_norm2 op; auto result = op.evaluate({&x}, {}, {}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -5555,17 +5546,17 @@ TEST_F(DeclarableOpsTests7, Test_Reduce_Norm2_6) { auto exp = NDArrayFactory::create(70.f); x.linspace(1); - nd4j::ops::reduce_norm2 op; + sd::ops::reduce_norm2 op; auto result = op.evaluate({&x}, {}, {0,1,2}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -5575,17 +5566,17 @@ TEST_F(DeclarableOpsTests7, Test_Reduce_Norm2_7) { auto exp = NDArrayFactory::create('c', {1, 1, 1}, {70.f}); x.linspace(1); // x.printIndexedBuffer("Input with shape (2, 3, 4) is"); - nd4j::ops::reduce_norm2 op; + sd::ops::reduce_norm2 op; auto result = op.evaluate({&x}, {1.}, {0,1,2}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -5595,16 +5586,16 @@ TEST_F(DeclarableOpsTests7, Test_Reduce_NormMax_1) { auto exp = NDArrayFactory::create('c', {4}, {21.f, 22.f, 23.f, 24.f}); x.linspace(1); - nd4j::ops::reduce_norm_max op; + sd::ops::reduce_norm_max op; auto result = op.evaluate({&x}, {}, {0,1}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -5614,16 +5605,16 @@ TEST_F(DeclarableOpsTests7, Test_Reduce_NormMax_2) { auto exp = NDArrayFactory::create('c', {1,1,4}, {21.f, 22.f, 23.f, 24.f}); x.linspace(1); - nd4j::ops::reduce_norm_max op; + sd::ops::reduce_norm_max op; auto result = op.evaluate({&x}, {1.f}, {0,1}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -5633,16 +5624,16 @@ TEST_F(DeclarableOpsTests7, Test_Reduce_NormMax_3) { auto exp = NDArrayFactory::create('c', {3}, {16.f, 20.f, 24.f}); x.linspace(1); - nd4j::ops::reduce_norm_max op; + sd::ops::reduce_norm_max op; auto result = op.evaluate({&x}, {}, {0,2}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -5652,16 +5643,16 @@ TEST_F(DeclarableOpsTests7, Test_Reduce_NormMax_4) { auto exp = NDArrayFactory::create('c', {1, 3, 1}, {16.f, 20.f, 24.f}); x.linspace(1); - nd4j::ops::reduce_norm_max op; + sd::ops::reduce_norm_max op; auto result = op.evaluate({&x}, {1.f}, {0,2}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -5671,17 +5662,17 @@ TEST_F(DeclarableOpsTests7, Test_Reduce_NormMax_5) { auto exp = NDArrayFactory::create(24.f); x.linspace(1); - nd4j::ops::reduce_norm_max op; + sd::ops::reduce_norm_max op; auto result = op.evaluate({&x}, {}, {}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -5691,17 +5682,17 @@ TEST_F(DeclarableOpsTests7, Test_Reduce_NormMax_6) { auto exp = NDArrayFactory::create(24.f); x.linspace(1); - nd4j::ops::reduce_norm_max op; + sd::ops::reduce_norm_max op; auto result = op.evaluate({&x}, {}, {0, 1, 2}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -5711,17 +5702,17 @@ TEST_F(DeclarableOpsTests7, Test_Reduce_NormMax_7) { auto exp = NDArrayFactory::create('c', {1, 1, 1}, {24.f}); x.linspace(1); - nd4j::ops::reduce_norm_max op; + sd::ops::reduce_norm_max op; auto result = op.evaluate({&x}, {1.f}, {}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -5731,16 +5722,16 @@ TEST_F(DeclarableOpsTests7, Test_Reduce_SquaredNorm_1) { auto exp = NDArrayFactory::create('c', {4}, {1006.f, 1144.f, 1294.f, 1456.f}); x.linspace(1); - nd4j::ops::reduce_sqnorm op; + sd::ops::reduce_sqnorm op; auto result = op.evaluate({&x}, {}, {0,1}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -5750,16 +5741,16 @@ TEST_F(DeclarableOpsTests7, Test_Reduce_SquaredNorm_2) { auto exp = NDArrayFactory::create('c', {1,1,4}, {1006.f, 1144.f, 1294.f, 1456.f}); x.linspace(1); - nd4j::ops::reduce_sqnorm op; + sd::ops::reduce_sqnorm op; auto result = op.evaluate({&x}, {1.f}, {0,1}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -5769,16 +5760,16 @@ TEST_F(DeclarableOpsTests7, Test_Reduce_SquaredNorm_3) { auto exp = NDArrayFactory::create('c', {3}, {876.f, 1548.f, 2476.f}); x.linspace(1); - nd4j::ops::reduce_sqnorm op; + sd::ops::reduce_sqnorm op; auto result = op.evaluate({&x}, {}, {0,2}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -5788,16 +5779,16 @@ TEST_F(DeclarableOpsTests7, Test_Reduce_SquaredNorm_4) { auto exp = NDArrayFactory::create('c', {1, 3, 1}, {876.f, 1548.f, 2476.f}); x.linspace(1); - nd4j::ops::reduce_sqnorm op; + sd::ops::reduce_sqnorm op; auto result = op.evaluate({&x}, {1.f}, {0,2}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -5807,17 +5798,17 @@ TEST_F(DeclarableOpsTests7, Test_Reduce_SquaredNorm_5) { auto exp = NDArrayFactory::create(4900.f); x.linspace(1); - nd4j::ops::reduce_sqnorm op; + sd::ops::reduce_sqnorm op; auto result = op.evaluate({&x}, {}, {}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -5827,17 +5818,17 @@ TEST_F(DeclarableOpsTests7, Test_Reduce_SquaredNorm_6) { auto exp = NDArrayFactory::create(4900.f); x.linspace(1); - nd4j::ops::reduce_sqnorm op; + sd::ops::reduce_sqnorm op; auto result = op.evaluate({&x}, {}, {0, 1, 2}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -5847,17 +5838,17 @@ TEST_F(DeclarableOpsTests7, Test_Reduce_SquaredNorm_7) { auto exp = NDArrayFactory::create('c', {1, 1, 1}, {4900.f}); x.linspace(1); - nd4j::ops::reduce_sqnorm op; + sd::ops::reduce_sqnorm op; auto result = op.evaluate({&x}, {1.f}, {}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -5868,15 +5859,15 @@ TEST_F(DeclarableOpsTests7, Test_Reduce_Sum_BP_1) { auto exp = NDArrayFactory::create('c', {3, 4}, {0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f,0.5f}); //************************************// - nd4j::ops::reduce_sum_bp op; + sd::ops::reduce_sum_bp op; auto result = op.evaluate({&input, &eps}, {}, {}); - ASSERT_EQ(Status::OK(), result->status()); - auto z = result->at(0); + ASSERT_EQ(Status::OK(), result.status()); + auto z = result.at(0); // z->printIndexedBuffer("Result is "); // z->printShapeInfo(); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -5889,15 +5880,15 @@ TEST_F(DeclarableOpsTests7, Test_Reduce_Sum_BP_2) { 0.5f, 0.5f, 0.5f,0.5f}); //************************************// - nd4j::ops::reduce_sum_bp op; + sd::ops::reduce_sum_bp op; auto result = op.evaluate({&input, &eps}, {1.f}, {}); - ASSERT_EQ(Status::OK(), result->status()); - auto z = result->at(0); + ASSERT_EQ(Status::OK(), result.status()); + auto z = result.at(0); // z->printIndexedBuffer("Result is "); // z->printShapeInfo(); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -5910,15 +5901,15 @@ TEST_F(DeclarableOpsTests7, Test_Reduce_Sum_BP_3) { 1.f, 2.f, 3.f, 4.f}); //************************************// - nd4j::ops::reduce_sum_bp op; + sd::ops::reduce_sum_bp op; auto result = op.evaluate({&input, &eps}, {}, {0}); - ASSERT_EQ(Status::OK(), result->status()); - auto z = result->at(0); + ASSERT_EQ(Status::OK(), result.status()); + auto z = result.at(0); // z->printIndexedBuffer("Result is "); // z->printShapeInfo(); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -5931,15 +5922,15 @@ TEST_F(DeclarableOpsTests7, Test_Reduce_Sum_BP_4) { 1.f, 2.f, 3.f, 4.f}); //************************************// - nd4j::ops::reduce_sum_bp op; + sd::ops::reduce_sum_bp op; auto result = op.evaluate({&input, &eps}, {1.f}, {0}); - ASSERT_EQ(Status::OK(), result->status()); - auto z = result->at(0); + ASSERT_EQ(Status::OK(), result.status()); + auto z = result.at(0); // z->printIndexedBuffer("Result is "); // z->printShapeInfo(); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -5956,15 +5947,15 @@ TEST_F(DeclarableOpsTests7, Test_Reduce_Prod_BP_1) { 171001227491294996070400.f, 155455648254341989531648.f, 142501016904612993564672.f, 131539399526781282156544.f, 122143728775382565912576.f, 114000815325130245799936.f}); - nd4j::ops::reduce_prod_bp op; + sd::ops::reduce_prod_bp op; auto result = op.evaluate({&input, &eps}, {}, {}); - ASSERT_EQ(Status::OK(), result->status()); - auto z = result->at(0); + ASSERT_EQ(Status::OK(), result.status()); + auto z = result.at(0); // z->printIndexedBuffer("Result is "); // z->printShapeInfo(); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -5977,21 +5968,21 @@ TEST_F(DeclarableOpsTests7, Test_Reduce_Prod_BP_2) { //************************************// auto exp = NDArrayFactory::create('c', {3, 4}); - nd4j::ops::reduce_prod_bp op; - nd4j::ops::reduce_prod op_exp; + sd::ops::reduce_prod_bp op; + sd::ops::reduce_prod op_exp; auto res = op_exp.evaluate({&input}); auto result = op.evaluate({&input, &eps}, {}, {}); - exp.assign(res->at(0)->e(0)); + exp.assign(res.at(0)->e(0)); exp /= input; exp *= eps.e(0); - ASSERT_EQ(Status::OK(), result->status()); - auto z = result->at(0); + ASSERT_EQ(Status::OK(), result.status()); + auto z = result.at(0); //z->printIndexedBuffer("Result is "); //exp.printIndexedBuffer("Expected"); // z->printShapeInfo(); ASSERT_TRUE(exp.equalsTo(z)); - delete result; - delete res; + + } //////////////////////////////////////////////////////////////////////////////// @@ -6002,17 +5993,17 @@ TEST_F(DeclarableOpsTests7, Test_Reduce_Prod_BP_3) { //************************************// auto exp = NDArrayFactory::create('c', {3, 4}, {45.f, 120.f, 231.f, 384.f, 9.f, 40.f, 99.f, 192.f, 5.f, 24.f, 63.f, 128.f}); - nd4j::ops::reduce_prod_bp op; - //nd4j::ops::reduce_prod op_exp; + sd::ops::reduce_prod_bp op; + //sd::ops::reduce_prod op_exp; auto result = op.evaluate({&input, &eps}, {1.f}, {0}); - ASSERT_EQ(Status::OK(), result->status()); - auto z = result->at(0); + ASSERT_EQ(Status::OK(), result.status()); + auto z = result.at(0); // z->printIndexedBuffer("Result is "); // exp.printIndexedBuffer("Expected"); // z->printShapeInfo(); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -6023,17 +6014,17 @@ TEST_F(DeclarableOpsTests7, Test_Reduce_Prod_BP_03) { //************************************// auto exp = NDArrayFactory::create('c', {3, 4}, {45.f, 120.f, 231.f, 384.f, 9.f, 40.f, 99.f, 192.f, 5.f, 24.f, 63.f, 128.f}); auto axis = NDArrayFactory::create('c', {1}, {ax}); - nd4j::ops::reduce_prod_bp op; - //nd4j::ops::reduce_prod op_exp; + sd::ops::reduce_prod_bp op; + //sd::ops::reduce_prod op_exp; auto result = op.evaluate({&input, &eps, &axis}, {}, {}, {true}); - ASSERT_EQ(Status::OK(), result->status()); - auto z = result->at(0); + ASSERT_EQ(Status::OK(), result.status()); + auto z = result.at(0); // z->printIndexedBuffer("Result is "); // exp.printIndexedBuffer("Expected"); // z->printShapeInfo(); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -6044,19 +6035,19 @@ TEST_F(DeclarableOpsTests7, Test_Reduce_Prod_BP_4) { //************************************// auto exp = NDArrayFactory::create('c', {3, 4}, {45.f, 120.f, 231.f, 384.f, 9.f, 40.f, 99.f, 192.f, 5.f, 24.f, 63.f, 128.f}); - nd4j::ops::reduce_prod_bp op; - nd4j::ops::reduce_prod op_exp; + sd::ops::reduce_prod_bp op; + sd::ops::reduce_prod op_exp; // auto res = op_exp.execute({&input}, {}, {}); auto result = op.evaluate({&input, &eps}, {0.f}, {0}); - ASSERT_EQ(Status::OK(), result->status()); - auto z = result->at(0); + ASSERT_EQ(Status::OK(), result.status()); + auto z = result.at(0); // z->printIndexedBuffer("Result is "); // exp.printIndexedBuffer("Expected"); // z->printShapeInfo(); ASSERT_TRUE(exp.equalsTo(z)); - delete result; -// delete res; + +// } //////////////////////////////////////////////////////////////////////////////// @@ -6067,19 +6058,19 @@ TEST_F(DeclarableOpsTests7, Test_Reduce_Prod_BP_5) { //************************************// auto exp = NDArrayFactory::create('c', {3, 4}, {24.f, 12.f, 8.f, 6.f, 672.f, 560.f, 480.f, 420.f, 3960.f, 3564.f, 3240.f, 2970.f}); - nd4j::ops::reduce_prod_bp op; - nd4j::ops::reduce_prod op_exp; + sd::ops::reduce_prod_bp op; + sd::ops::reduce_prod op_exp; // auto res = op_exp.execute({&input}, {}, {}); auto result = op.evaluate({&input, &eps}, {0.f}, {1}); - ASSERT_EQ(Status::OK(), result->status()); - auto z = result->at(0); + ASSERT_EQ(Status::OK(), result.status()); + auto z = result.at(0); // z->printIndexedBuffer("Result is "); // exp.printIndexedBuffer("Expected"); // z->printShapeInfo(); ASSERT_TRUE(exp.equalsTo(z)); - delete result; -// delete res; + +// } //////////////////////////////////////////////////////////////////////////////// @@ -6095,16 +6086,16 @@ TEST_F(DeclarableOpsTests7, Test_Reduce_Min_BP_1) { x.linspace(1); // x.printIndexedBuffer("Input is"); // exp.printIndexedBuffer("Expected "); - nd4j::ops::reduce_min_bp op; + sd::ops::reduce_min_bp op; auto result = op.evaluate({&x, &eps}, {}, {0, 1}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -6120,16 +6111,16 @@ TEST_F(DeclarableOpsTests7, Test_Reduce_Min_BP_2) { x.linspace(1); // x.printIndexedBuffer("Input is"); // exp.printIndexedBuffer("Expected "); - nd4j::ops::reduce_min_bp op; + sd::ops::reduce_min_bp op; auto result = op.evaluate({&x, &eps}, {1.f}, {0, 1}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests7, Test_Reduce_Min_BP_02) { @@ -6145,16 +6136,16 @@ TEST_F(DeclarableOpsTests7, Test_Reduce_Min_BP_02) { x.linspace(1); // x.printIndexedBuffer("Input is"); // exp.printIndexedBuffer("Expected "); - nd4j::ops::reduce_min_bp op; + sd::ops::reduce_min_bp op; auto result = op.evaluate({&x, &eps, &axes}, {}, {}, {true}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -6168,16 +6159,16 @@ TEST_F(DeclarableOpsTests7, Test_Reduce_Min_BP_3) { exp.p(2,2, 0.5f); //x.printIndexedBuffer("Input is"); // exp.printIndexedBuffer("Expected "); - nd4j::ops::reduce_min_bp op; + sd::ops::reduce_min_bp op; auto result = op.evaluate({&x, &eps}, {1.f}, {}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -6191,16 +6182,16 @@ TEST_F(DeclarableOpsTests7, Test_Reduce_Min_BP_4) { exp.p(2,2, 0.5f); // x.printIndexedBuffer("Input is"); // exp.printIndexedBuffer("Expected "); - nd4j::ops::reduce_min_bp op; + sd::ops::reduce_min_bp op; auto result = op.evaluate({&x, &eps}, {}, {}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -6221,16 +6212,16 @@ TEST_F(DeclarableOpsTests7, Test_Reduce_Min_BP_5) { // exp(2,2) = 0.5f; // x.printIndexedBuffer("Input is"); // exp.printIndexedBuffer("Expected "); - nd4j::ops::reduce_min_bp op; + sd::ops::reduce_min_bp op; auto result = op.evaluate({&x, &eps}, {}, {0}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -6251,16 +6242,16 @@ TEST_F(DeclarableOpsTests7, Test_Reduce_Min_BP_6) { // exp(2,2) = 0.5f; // x.printIndexedBuffer("Input is"); // exp.printIndexedBuffer("Expected "); - nd4j::ops::reduce_min_bp op; + sd::ops::reduce_min_bp op; auto result = op.evaluate({&x, &eps}, {1.f}, {0}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -6276,15 +6267,15 @@ TEST_F(DeclarableOpsTests7, Test_Reduce_Max_BP_1) { x.linspace(1); // x.printIndexedBuffer("Input is"); // exp.printIndexedBuffer("Expected "); - nd4j::ops::reduce_max_bp op; + sd::ops::reduce_max_bp op; auto result = op.evaluate({&x, &eps}, {}, {0, 1}); - auto output = result->at(0); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + auto output = result.at(0); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -6300,16 +6291,16 @@ TEST_F(DeclarableOpsTests7, Test_Reduce_Max_BP_2) { x.linspace(1); // x.printIndexedBuffer("Input is"); // exp.printIndexedBuffer("Expected "); - nd4j::ops::reduce_max_bp op; + sd::ops::reduce_max_bp op; auto result = op.evaluate({&x, &eps}, {1.f}, {0, 1}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -6326,16 +6317,16 @@ TEST_F(DeclarableOpsTests7, Test_Reduce_Max_BP_02) { x.linspace(1); // x.printIndexedBuffer("Input is"); // exp.printIndexedBuffer("Expected "); - nd4j::ops::reduce_max_bp op; + sd::ops::reduce_max_bp op; auto result = op.evaluate({&x, &eps, &axes}, {}, {}, {true}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -6355,16 +6346,16 @@ TEST_F(DeclarableOpsTests7, Test_Reduce_Max_BP_3) { exp.p(3,3, 4.f); // x.printIndexedBuffer("Input is"); // exp.printIndexedBuffer("Expected "); - nd4j::ops::reduce_max_bp op; + sd::ops::reduce_max_bp op; auto result = op.evaluate({&x, &eps}, {}, {0}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -6385,16 +6376,16 @@ TEST_F(DeclarableOpsTests7, Test_Reduce_Max_BP_4) { // x.printIndexedBuffer("Input is"); // exp.printIndexedBuffer("Expected "); - nd4j::ops::reduce_max_bp op; + sd::ops::reduce_max_bp op; auto result = op.evaluate({&x, &eps}, {1.f}, {0}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -6409,16 +6400,16 @@ TEST_F(DeclarableOpsTests7, Test_Reduce_Norm1_BP_1) { exp.assign(5.f); exp.p(12, -exp.e(12)); exp.p(20, -exp.e(20)); - nd4j::ops::reduce_norm1_bp op; + sd::ops::reduce_norm1_bp op; auto result = op.evaluate({&x, &eps}, {}, {}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -6428,16 +6419,16 @@ TEST_F(DeclarableOpsTests7, Test_Reduce_Norm1_BP_2) { auto eps = NDArrayFactory::create({1.f, 2.f, 3.f, 4.f}); x.linspace(1); auto exp = NDArrayFactory::create('c', {2, 3, 4}, {1.f, 2.f, 3.f, 4.f, 1.f, 2.f, 3.f, 4.f, 1.f, 2.f, 3.f, 4.f, 1.f, 2.f, 3.f, 4.f,1.f, 2.f, 3.f, 4.f,1.f, 2.f, 3.f, 4.f}); - nd4j::ops::reduce_norm1_bp op; + sd::ops::reduce_norm1_bp op; auto result = op.evaluate({&x, &eps}, {}, {0,1}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); - auto output = result->at(0); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); // exp.printIndexedBuffer("Expect is"); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -6448,14 +6439,14 @@ TEST_F(DeclarableOpsTests7, Test_Reduce_Norm1_BP_02) { x.linspace(1); auto exp = NDArrayFactory::create('c', {2, 3, 4}, {1.f, 2.f, 3.f, 4.f, 1.f, 2.f, 3.f, 4.f, 1.f, 2.f, 3.f, 4.f, 1.f, 2.f, 3.f, 4.f,1.f, 2.f, 3.f, 4.f,1.f, 2.f, 3.f, 4.f}); auto axes = NDArrayFactory::create({0,1}); - nd4j::ops::reduce_norm1_bp op; + sd::ops::reduce_norm1_bp op; auto result = op.evaluate({&x, &eps, &axes}, {}, {}, {false}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); - auto output = result->at(0); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); + auto output = result.at(0); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -6465,15 +6456,15 @@ TEST_F(DeclarableOpsTests7, Test_Reduce_Norm1_BP_3) { auto eps = NDArrayFactory::create('c', {1, 1, 4}, {1.f, 2.f, 3.f, 4.f}); x.linspace(1); auto exp = NDArrayFactory::create('c', {2, 3, 4}, {1.f, 2.f, 3.f, 4.f, 1.f, 2.f, 3.f, 4.f, 1.f, 2.f, 3.f, 4.f, 1.f, 2.f, 3.f, 4.f,1.f, 2.f, 3.f, 4.f,1.f, 2.f, 3.f, 4.f}); - nd4j::ops::reduce_norm1_bp op; + sd::ops::reduce_norm1_bp op; auto result = op.evaluate({&x, &eps}, {1.f}, {0,1}); - auto output = result->at(0); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + auto output = result.at(0); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -6483,16 +6474,16 @@ TEST_F(DeclarableOpsTests7, Test_Reduce_Norm2_BP_1) { auto eps = NDArrayFactory::create('c', {4}, {31.7175f, 33.823071f, 35.97221f, 38.15757f}); x.linspace(1); - nd4j::ops::reduce_norm2_bp op; + sd::ops::reduce_norm2_bp op; auto result = op.evaluate({&x, &eps}, {}, {0,1}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); ASSERT_TRUE(x.isSameShape(output)); ASSERT_TRUE(x.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -6502,16 +6493,16 @@ TEST_F(DeclarableOpsTests7, Test_Reduce_Norm2_BP_2) { auto eps = NDArrayFactory::create('c', {1, 1, 4}, {31.7175f, 33.823071f, 35.97221f, 38.15757f}); x.linspace(1); - nd4j::ops::reduce_norm2_bp op; + sd::ops::reduce_norm2_bp op; auto result = op.evaluate({&x, &eps}, {1.f}, {0,1}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); ASSERT_TRUE(x.isSameShape(output)); ASSERT_TRUE(x.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -6522,16 +6513,16 @@ TEST_F(DeclarableOpsTests7, Test_Reduce_Norm2_BP_02) { auto axes = NDArrayFactory::create({0, 1}); x.linspace(1); - nd4j::ops::reduce_norm2_bp op; + sd::ops::reduce_norm2_bp op; auto result = op.evaluate({&x, &eps, &axes}, {}, {}, {true}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); ASSERT_TRUE(x.isSameShape(output)); ASSERT_TRUE(x.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -6541,17 +6532,17 @@ TEST_F(DeclarableOpsTests7, Test_Reduce_Norm2_BP_3) { auto eps = NDArrayFactory::create('c', {3}, {29.597298f, 39.344631f, 49.759422f}); x.linspace(1); - nd4j::ops::reduce_norm2_bp op; + sd::ops::reduce_norm2_bp op; auto result = op.evaluate({&x, &eps}, {}, {0, 2}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); ASSERT_TRUE(x.isSameShape(output)); ASSERT_TRUE(x.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -6561,17 +6552,17 @@ TEST_F(DeclarableOpsTests7, Test_Reduce_Norm2_BP_4) { auto eps = NDArrayFactory::create('c', {1,3,1}, {29.597298f, 39.344631f, 49.759422f}); x.linspace(1); - nd4j::ops::reduce_norm2_bp op; + sd::ops::reduce_norm2_bp op; auto result = op.evaluate({&x, &eps}, {1.f}, {0, 2}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); ASSERT_TRUE(x.isSameShape(output)); ASSERT_TRUE(x.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -6587,16 +6578,16 @@ TEST_F(DeclarableOpsTests7, Test_Reduce_SquaredNorm_BP_1) { 42.f, 88.f, 138.f, 192.f}); x.linspace(1); - nd4j::ops::reduce_sqnorm_bp op; + sd::ops::reduce_sqnorm_bp op; auto result = op.evaluate({&x, &eps}, {}, {0,1}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); - auto output = result->at(0); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); + auto output = result.at(0); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -6613,16 +6604,16 @@ TEST_F(DeclarableOpsTests7, Test_Reduce_SquaredNorm_BP_01) { auto axes = NDArrayFactory::create({0, 1}); x.linspace(1); - nd4j::ops::reduce_sqnorm_bp op; + sd::ops::reduce_sqnorm_bp op; auto result = op.evaluate({&x, &eps, &axes}, {}, {}, {false}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); - auto output = result->at(0); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); + auto output = result.at(0); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -6637,16 +6628,16 @@ TEST_F(DeclarableOpsTests7, Test_Reduce_NormMax_BP_1) { exp.p(22, 3.f); exp.p(23, 4.f); - nd4j::ops::reduce_norm_max_bp op; + sd::ops::reduce_norm_max_bp op; auto result = op.evaluate({&x, &eps}, {}, {0,1}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -6661,16 +6652,16 @@ TEST_F(DeclarableOpsTests7, Test_Reduce_NormMax_BP_2) { exp.p(22, 3.f); exp.p(23, 4.f); - nd4j::ops::reduce_norm_max_bp op; + sd::ops::reduce_norm_max_bp op; auto result = op.evaluate({&x, &eps}, {1.f}, {0,1}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -6686,16 +6677,16 @@ TEST_F(DeclarableOpsTests7, Test_Reduce_NormMax_BP_02) { exp.p(22, 3.f); exp.p(23, 4.f); - nd4j::ops::reduce_norm_max_bp op; + sd::ops::reduce_norm_max_bp op; auto result = op.evaluate({&x, &eps, &axes}, {}, {}, {true}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests7, Test_Reduce_NormMax_BP_3) { @@ -6709,16 +6700,16 @@ TEST_F(DeclarableOpsTests7, Test_Reduce_NormMax_BP_3) { exp.p(19, 2.f); exp.p(23, 3.f); - nd4j::ops::reduce_norm_max_bp op; + sd::ops::reduce_norm_max_bp op; auto result = op.evaluate({&x, &eps}, {}, {0,2}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -6731,16 +6722,16 @@ TEST_F(DeclarableOpsTests7, Test_Reduce_NormMax_BP_4) { exp.p(15, 1.f); exp.p(19, 2.f); exp.p(23, 3.f); - nd4j::ops::reduce_norm_max_bp op; + sd::ops::reduce_norm_max_bp op; auto result = op.evaluate({&x, &eps}, {1.f}, {0,2}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -6751,17 +6742,17 @@ TEST_F(DeclarableOpsTests7, Test_Reduce_NormMax_BP_5) { auto exp = NDArrayFactory::create('c', {2, 3, 4}); x.linspace(1); exp.p(23, 1.f); - nd4j::ops::reduce_norm_max_bp op; + sd::ops::reduce_norm_max_bp op; auto result = op.evaluate({&x, &eps}, {}, {}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -6773,17 +6764,17 @@ TEST_F(DeclarableOpsTests7, Test_Reduce_NormMax_BP_6) { x.linspace(1); exp.p(23, 1.f); - nd4j::ops::reduce_norm_max_bp op; + sd::ops::reduce_norm_max_bp op; auto result = op.evaluate({&x, &eps}, {}, {0, 1, 2}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -6794,17 +6785,17 @@ TEST_F(DeclarableOpsTests7, Test_Reduce_NormMax_BP_7) { auto exp = NDArrayFactory::create('c', {2, 3, 4}); x.linspace(1); exp.p(23, 1.f); - nd4j::ops::reduce_norm_max_bp op; + sd::ops::reduce_norm_max_bp op; auto result = op.evaluate({&x, &eps}, {1.f}, {}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -6819,18 +6810,18 @@ TEST_F(DeclarableOpsTests7, Test_Reduce_Dot_BP_1) { y.linspace(2); - nd4j::ops::reduce_dot_bp op; + sd::ops::reduce_dot_bp op; auto result = op.evaluate({&x, &y, &eps}, {}, {}); - auto output = result->at(0); - auto outputX = result->at(1); + auto output = result.at(0); + auto outputX = result.at(1); //tput->printIndexedBuffer("Result is"); -// ASSERT_EQ(ND4J_STATUS_OK, result->status()); +// ASSERT_EQ(ND4J_STATUS_OK, result.status()); ASSERT_TRUE(x.equalsTo(outputX)); ASSERT_TRUE(y.equalsTo(output)); - delete result; + // delete z; } @@ -6850,18 +6841,18 @@ TEST_F(DeclarableOpsTests7, Test_Reduce_Dot_BP_2) { x.assign(1.f); eps.linspace(1); y.assign(2.f); - nd4j::ops::reduce_dot_bp op; + sd::ops::reduce_dot_bp op; auto result = op.evaluate({&x, &y, &eps}, {}, {1}); - ASSERT_EQ(result->status(), ND4J_STATUS_OK); - ASSERT_EQ(result->size(), 2); - auto outputX = result->at(0); - auto outputY = result->at(1); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(result.status(), ND4J_STATUS_OK); + ASSERT_EQ(result.size(), 2); + auto outputX = result.at(0); + auto outputY = result.at(1); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); ASSERT_TRUE(expX.equalsTo(outputX)); ASSERT_TRUE(expY.equalsTo(outputY)); - delete result; + // delete z; } @@ -6882,18 +6873,18 @@ TEST_F(DeclarableOpsTests7, Test_Reduce_Dot_BP_02) { x.assign(1.f); eps.linspace(1); y.assign(2.f); - nd4j::ops::reduce_dot_bp op; + sd::ops::reduce_dot_bp op; auto result = op.evaluate({&x, &y, &eps, &axis}, {}, {}, {false}); - ASSERT_EQ(result->status(), ND4J_STATUS_OK); - ASSERT_EQ(result->size(), 2); - auto outputX = result->at(0); - auto outputY = result->at(1); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(result.status(), ND4J_STATUS_OK); + ASSERT_EQ(result.size(), 2); + auto outputX = result.at(0); + auto outputY = result.at(1); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); ASSERT_TRUE(expX.equalsTo(outputX)); ASSERT_TRUE(expY.equalsTo(outputY)); - delete result; + // delete z; } @@ -6909,16 +6900,16 @@ TEST_F(DeclarableOpsTests7, Test_Reduce_Dot_BP_3) { eps.linspace(1); y.assign(2.f); - nd4j::ops::reduce_dot_bp op; + sd::ops::reduce_dot_bp op; auto result = op.evaluate({&x,&y, &eps}, {}, {1}); - auto outputX = result->at(0); - auto outputY = result->at(1); + auto outputX = result.at(0); + auto outputY = result.at(1); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); ASSERT_TRUE(expX.equalsTo(outputX)); ASSERT_TRUE(expY.equalsTo(outputY)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -6931,16 +6922,16 @@ TEST_F(DeclarableOpsTests7, cumsum_bp_1) { x.linspace(1); eps.assign(1.f); - nd4j::ops::cumsum_bp op; + sd::ops::cumsum_bp op; auto result = op.evaluate({&x, &eps}, {}, {0,0}); - auto output = result->at(0); + auto output = result.at(0); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -6953,16 +6944,16 @@ TEST_F(DeclarableOpsTests7, cumsum_bp_2) { eps.assign(1.f); - nd4j::ops::cumsum_bp op; + sd::ops::cumsum_bp op; auto result = op.evaluate({&x, &eps}, {}, {1,0}); - auto output = result->at(0); + auto output = result.at(0); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -6975,14 +6966,14 @@ TEST_F(DeclarableOpsTests7, cumsum_bp_3) { exp.linspace(0); eps.assign(1.f); - nd4j::ops::cumsum_bp op; + sd::ops::cumsum_bp op; auto result = op.evaluate({&x, &eps}, {}, {1,1}); - auto output = result->at(0); + auto output = result.at(0); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } diff --git a/libnd4j/tests_cpu/layers_tests/DeclarableOpsTests8.cpp b/libnd4j/tests_cpu/layers_tests/DeclarableOpsTests8.cpp index 002a31d6e..589adebcb 100644 --- a/libnd4j/tests_cpu/layers_tests/DeclarableOpsTests8.cpp +++ b/libnd4j/tests_cpu/layers_tests/DeclarableOpsTests8.cpp @@ -21,11 +21,11 @@ #include "testlayers.h" #include -#include -#include +#include +#include // #include -using namespace nd4j; +using namespace sd; class DeclarableOpsTests8 : public testing::Test { @@ -56,16 +56,16 @@ TEST_F(DeclarableOpsTests8, reduceVariance_test1) { auto x = NDArrayFactory::create('c', {2,3,4}, {27.f,34.f,5.f,4.f,54.f,6.f,65.f,8.f,37.f,45.f,8.f,67.f,96.f,10.f,65.f,41.f,33.f,85.f,92.f,24.f,25.f,55.f,49.f,76.f}); auto exp = NDArrayFactory::create('c', {4}, {602.2222f, 727.13885f, 993.5555f, 755.8889f}); - nd4j::ops::reduce_variance op; + sd::ops::reduce_variance op; auto result = op.evaluate({&x}, {}, {0,1}); - auto output = result->at(0); + auto output = result.at(0); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -74,16 +74,16 @@ TEST_F(DeclarableOpsTests8, reduceVariance_test2) { auto x = NDArrayFactory::create('c', {2,3,4}, {27.f,34.f,5.f,4.f,54.f,6.f,65.f,8.f,37.f,45.f,8.f,67.f,96.f,10.f,65.f,41.f,33.f,85.f,92.f,24.f,25.f,55.f,49.f,76.f}); auto exp = NDArrayFactory::create('c', {1,1,4}, {602.2222f, 727.13885f, 993.5555f, 755.8889f}); - nd4j::ops::reduce_variance op; + sd::ops::reduce_variance op; auto result = op.evaluate({&x}, {1.}, {0,1}); - auto output = result->at(0); + auto output = result.at(0); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -92,16 +92,16 @@ TEST_F(DeclarableOpsTests8, reduceVariance_test3) { auto x = NDArrayFactory::create('c', {2,3,4}, {27.f,34.f,5.f,4.f,54.f,6.f,65.f,8.f,37.f,45.f,8.f,67.f,96.f,10.f,65.f,41.f,33.f,85.f,92.f,24.f,25.f,55.f,49.f,76.f}); auto exp = NDArrayFactory::create('c', {3}, {900.9375f, 969.8594f, 424.1875f}); - nd4j::ops::reduce_variance op; + sd::ops::reduce_variance op; auto result = op.evaluate({&x}, {}, {0,2}); - auto output = result->at(0); + auto output = result.at(0); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -110,16 +110,16 @@ TEST_F(DeclarableOpsTests8, reduceVariance_test4) { auto x = NDArrayFactory::create('c', {2,3,4}, {27.f,34.f,5.f,4.f,54.f,6.f,65.f,8.f,37.f,45.f,8.f,67.f,96.f,10.f,65.f,41.f,33.f,85.f,92.f,24.f,25.f,55.f,49.f,76.f}); auto exp = NDArrayFactory::create('c', {1,3,1}, {900.9375f, 969.8594f, 424.1875f}); - nd4j::ops::reduce_variance op; + sd::ops::reduce_variance op; auto result = op.evaluate({&x}, {1.}, {0,2}); - auto output = result->at(0); + auto output = result.at(0); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -128,16 +128,16 @@ TEST_F(DeclarableOpsTests8, reduceVariance_test5) { auto x = NDArrayFactory::create('c', {2,3,4}, {27.f,34.f,5.f,4.f,54.f,6.f,65.f,8.f,37.f,45.f,8.f,67.f,96.f,10.f,65.f,41.f,33.f,85.f,92.f,24.f,25.f,55.f,49.f,76.f}); auto exp = NDArrayFactory::create(788.6927f); - nd4j::ops::reduce_variance op; + sd::ops::reduce_variance op; auto result = op.evaluate({&x}, {}, {}); - auto output = result->at(0); + auto output = result.at(0); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -146,16 +146,16 @@ TEST_F(DeclarableOpsTests8, reduceVariance_test6) { auto x = NDArrayFactory::create('c', {2,3,4}, {27.f,34.f,5.f,4.f,54.f,6.f,65.f,8.f,37.f,45.f,8.f,67.f,96.f,10.f,65.f,41.f,33.f,85.f,92.f,24.f,25.f,55.f,49.f,76.}); auto exp = NDArrayFactory::create(788.6927f); - nd4j::ops::reduce_variance op; + sd::ops::reduce_variance op; auto result = op.evaluate({&x}, {}, {0,1,2}); - auto output = result->at(0); + auto output = result.at(0); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -164,16 +164,16 @@ TEST_F(DeclarableOpsTests8, reduceVariance_test7) { auto x = NDArrayFactory::create('c', {2,3,4}, {27.f,34.f,5.f,4.f,54.f,6.f,65.f,8.f,37.f,45.f,8.f,67.f,96.f,10.f,65.f,41.f,33.f,85.f,92.f,24.f,25.f,55.f,49.f,76.}); auto exp = NDArrayFactory::create('c', {1,1,1}, {788.6927f}); - nd4j::ops::reduce_variance op; + sd::ops::reduce_variance op; auto result = op.evaluate({&x}, {1.}, {0,1,2}); - auto output = result->at(0); + auto output = result.at(0); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -182,16 +182,16 @@ TEST_F(DeclarableOpsTests8, reduceVariance_test8) { auto x = NDArrayFactory::create('c', {2,3,4}, {27.f,34.f,5.f,4.f,54.f,6.f,65.f,8.f,37.f,45.f,8.f,67.f,96.f,10.f,65.f,41.f,33.f,85.f,92.f,24.f,25.f,55.f,49.f,76.}); auto exp = NDArrayFactory::create('c', {1,1,1}, {788.6927f}); auto axes = NDArrayFactory::create({0, 1, 2}); - nd4j::ops::reduce_variance op; + sd::ops::reduce_variance op; auto result = op.evaluate({&x, &axes}, {}, {}, {true}); - auto output = result->at(0); + auto output = result.at(0); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -200,16 +200,16 @@ TEST_F(DeclarableOpsTests8, reduceStDev_test1) { auto x = NDArrayFactory::create('c', {2,3,4}, {27.f,34.f,5.f,4.f,54.f,6.f,65.f,8.f,37.f,45.f,8.f,67.f,96.f,10.f,65.f,41.f,33.f,85.f,92.f,24.f,25.f,55.f,49.f,76.}); auto exp = NDArrayFactory::create('c', {4}, {24.54022f, 26.96551f, 31.52072f, 27.49343f}); - nd4j::ops::reduce_stdev op; + sd::ops::reduce_stdev op; auto result = op.evaluate({&x}, {}, {0,1}); - auto output = result->at(0); + auto output = result.at(0); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -218,16 +218,16 @@ TEST_F(DeclarableOpsTests8, reduceStDev_test2) { auto x = NDArrayFactory::create('c', {2,3,4}, {27.f,34.f,5.f,4.f,54.f,6.f,65.f,8.f,37.f,45.f,8.f,67.f,96.f,10.f,65.f,41.f,33.f,85.f,92.f,24.f,25.f,55.f,49.f,76.}); auto exp = NDArrayFactory::create('c', {1,1,4}, {24.54022f, 26.96551f, 31.52072f, 27.49343f}); - nd4j::ops::reduce_stdev op; + sd::ops::reduce_stdev op; auto result = op.evaluate({&x}, {1.}, {0,1}); - auto output = result->at(0); + auto output = result.at(0); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -236,16 +236,16 @@ TEST_F(DeclarableOpsTests8, reduceStDev_test3) { auto x = NDArrayFactory::create('c', {2,3,4}, {27.f,34.f,5.f,4.f,54.f,6.f,65.f,8.f,37.f,45.f,8.f,67.f,96.f,10.f,65.f,41.f,33.f,85.f,92.f,24.f,25.f,55.f,49.f,76.}); auto exp = NDArrayFactory::create('c', {3}, {30.01562f, 31.14257f, 20.59581f}); - nd4j::ops::reduce_stdev op; + sd::ops::reduce_stdev op; auto result = op.evaluate({&x}, {}, {0,2}); - auto output = result->at(0); + auto output = result.at(0); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -254,16 +254,16 @@ TEST_F(DeclarableOpsTests8, reduceStDev_test4) { auto x = NDArrayFactory::create('c', {2,3,4}, {27.f,34.f,5.f,4.f,54.f,6.f,65.f,8.f,37.f,45.f,8.f,67.f,96.f,10.f,65.f,41.f,33.f,85.f,92.f,24.f,25.f,55.f,49.f,76.}); auto exp = NDArrayFactory::create('c', {1,3,1}, {30.01562f, 31.14257f, 20.59581f}); - nd4j::ops::reduce_stdev op; + sd::ops::reduce_stdev op; auto result = op.evaluate({&x}, {1.}, {0,2}); - auto output = result->at(0); + auto output = result.at(0); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -272,16 +272,16 @@ TEST_F(DeclarableOpsTests8, reduceStDev_test5) { auto x = NDArrayFactory::create('c', {2,3,4}, {27.f,34.f,5.f,4.f,54.f,6.f,65.f,8.f,37.f,45.f,8.f,67.f,96.f,10.f,65.f,41.f,33.f,85.f,92.f,24.f,25.f,55.f,49.f,76.}); auto exp = NDArrayFactory::create(28.08367f); - nd4j::ops::reduce_stdev op; + sd::ops::reduce_stdev op; auto result = op.evaluate({&x}, {}, {}); - auto output = result->at(0); + auto output = result.at(0); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -290,16 +290,16 @@ TEST_F(DeclarableOpsTests8, reduceStDev_test6) { auto x = NDArrayFactory::create('c', {2,3,4}, {27.f,34.f,5.f,4.f,54.f,6.f,65.f,8.f,37.f,45.f,8.f,67.f,96.f,10.f,65.f,41.f,33.f,85.f,92.f,24.f,25.f,55.f,49.f,76.}); auto exp = NDArrayFactory::create(28.08367f); - nd4j::ops::reduce_stdev op; + sd::ops::reduce_stdev op; auto result = op.evaluate({&x}, {}, {0,1,2}); - auto output = result->at(0); + auto output = result.at(0); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -308,16 +308,16 @@ TEST_F(DeclarableOpsTests8, reduceStDev_test7) { auto x = NDArrayFactory::create('c', {2,3,4}, {27.f,34.f,5.f,4.f,54.f,6.f,65.f,8.f,37.f,45.f,8.f,67.f,96.f,10.f,65.f,41.f,33.f,85.f,92.f,24.f,25.f,55.f,49.f,76.}); auto exp = NDArrayFactory::create('c', {1,1,1}, {28.08367f}); - nd4j::ops::reduce_stdev op; + sd::ops::reduce_stdev op; auto result = op.evaluate({&x}, {1.f}, {0,1,2}); - auto output = result->at(0); + auto output = result.at(0); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -326,16 +326,16 @@ TEST_F(DeclarableOpsTests8, reduceStDev_test8) { auto x = NDArrayFactory::create('c', {2,3,4}, {27.f,34.f,5.f,4.f,54.f,6.f,65.f,8.f,37.f,45.f,8.f,67.f,96.f,10.f,65.f,41.f,33.f,85.f,92.f,24.f,25.f,55.f,49.f,76.}); auto exp = NDArrayFactory::create('c', {4}, {26.88246f, 29.53924f, 34.52921f, 30.11755f}); - nd4j::ops::reduce_stdev op; + sd::ops::reduce_stdev op; auto result = op.evaluate({&x}, {0.f,1.f}, {0,1}); - auto output = result->at(0); + auto output = result.at(0); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); // output->printBuffer("Reduced STDDEV"); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -344,16 +344,16 @@ TEST_F(DeclarableOpsTests8, reduceStDev_test08) { auto x = NDArrayFactory::create('c', {2,3,4}, {27.f,34.f,5.f,4.f,54.f,6.f,65.f,8.f,37.f,45.f,8.f,67.f,96.f,10.f,65.f,41.f,33.f,85.f,92.f,24.f,25.f,55.f,49.f,76.}); auto exp = NDArrayFactory::create('c', {4}, {26.88246f, 29.53924f, 34.52921f, 30.11755f}); auto axes = NDArrayFactory::create({0,1}); - nd4j::ops::reduce_stdev op; + sd::ops::reduce_stdev op; auto result = op.evaluate({&x, &axes}, {}, {}, {false, true}); - auto output = result->at(0); + auto output = result.at(0); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); // output->printBuffer("Reduced STDDEV08"); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -367,35 +367,35 @@ TEST_F(DeclarableOpsTests8, reduceVarianceBP_test1) { x.linspace(1); - nd4j::ops::reduce_variance_bp op; + sd::ops::reduce_variance_bp op; auto result = op.evaluate({&x, &gradO2}, {0,1}, {}); - ASSERT_EQ(Status::OK(), result->status()); - auto output = result->at(0); + ASSERT_EQ(Status::OK(), result.status()); + auto output = result.at(0); ASSERT_TRUE(exp12.isSameShape(output)); ASSERT_TRUE(exp12.equalsTo(output)); - delete result; + result = op.evaluate({&x, &gradO1}, {1,1}, {}); - ASSERT_EQ(Status::OK(), result->status()); - output = result->at(0); + ASSERT_EQ(Status::OK(), result.status()); + output = result.at(0); ASSERT_TRUE(exp12.isSameShape(output)); ASSERT_TRUE(exp12.equalsTo(output)); - delete result; + result = op.evaluate({&x, &gradO2}, {0,0}, {}); - ASSERT_EQ(Status::OK(), result->status()); - output = result->at(0); + ASSERT_EQ(Status::OK(), result.status()); + output = result.at(0); ASSERT_TRUE(exp34.isSameShape(output)); ASSERT_TRUE(exp34.equalsTo(output)); - delete result; + result = op.evaluate({&x, &gradO1}, {1,0}, {}); - ASSERT_EQ(Status::OK(), result->status()); - output = result->at(0); + ASSERT_EQ(Status::OK(), result.status()); + output = result.at(0); ASSERT_TRUE(exp34.isSameShape(output)); ASSERT_TRUE(exp34.equalsTo(output)); - delete result; + } @@ -410,35 +410,35 @@ TEST_F(DeclarableOpsTests8, reduceVarianceBP_test2) { x.linspace(1); - nd4j::ops::reduce_variance_bp op; + sd::ops::reduce_variance_bp op; auto result = op.evaluate({&x, &gradO2}, {0,0}, {0}); - ASSERT_EQ(Status::OK(), result->status()); - auto output = result->at(0); + ASSERT_EQ(Status::OK(), result.status()); + auto output = result.at(0); ASSERT_TRUE(exp12.isSameShape(output)); ASSERT_TRUE(exp12.equalsTo(output)); - delete result; + result = op.evaluate({&x, &gradO1}, {1,0}, {0}); - ASSERT_EQ(Status::OK(), result->status()); - output = result->at(0); + ASSERT_EQ(Status::OK(), result.status()); + output = result.at(0); ASSERT_TRUE(exp12.isSameShape(output)); ASSERT_TRUE(exp12.equalsTo(output)); - delete result; + result = op.evaluate({&x, &gradO2}, {0,1}, {0}); - ASSERT_EQ(Status::OK(), result->status()); - output = result->at(0); + ASSERT_EQ(Status::OK(), result.status()); + output = result.at(0); ASSERT_TRUE(exp34.isSameShape(output)); ASSERT_TRUE(exp34.equalsTo(output)); - delete result; + result = op.evaluate({&x, &gradO1}, {1,1}, {0}); - ASSERT_EQ(Status::OK(), result->status()); - output = result->at(0); + ASSERT_EQ(Status::OK(), result.status()); + output = result.at(0); ASSERT_TRUE(exp34.isSameShape(output)); ASSERT_TRUE(exp34.equalsTo(output)); - delete result; + } @@ -453,35 +453,35 @@ TEST_F(DeclarableOpsTests8, reduceVarianceBP_test02) { auto axes = NDArrayFactory::create({(int)0,}); x.linspace(1); - nd4j::ops::reduce_variance_bp op; + sd::ops::reduce_variance_bp op; auto result = op.evaluate({&x, &gradO2, &axes}, {}, {}, {false, false}); - ASSERT_EQ(Status::OK(), result->status()); - auto output = result->at(0); + ASSERT_EQ(Status::OK(), result.status()); + auto output = result.at(0); ASSERT_TRUE(exp12.isSameShape(output)); ASSERT_TRUE(exp12.equalsTo(output)); - delete result; + result = op.evaluate({&x, &gradO1, &axes}, {}, {}, {true, false}); - ASSERT_EQ(Status::OK(), result->status()); - output = result->at(0); + ASSERT_EQ(Status::OK(), result.status()); + output = result.at(0); ASSERT_TRUE(exp12.isSameShape(output)); ASSERT_TRUE(exp12.equalsTo(output)); - delete result; + result = op.evaluate({&x, &gradO2, &axes}, {}, {}, {false, true}); - ASSERT_EQ(Status::OK(), result->status()); - output = result->at(0); + ASSERT_EQ(Status::OK(), result.status()); + output = result.at(0); ASSERT_TRUE(exp34.isSameShape(output)); ASSERT_TRUE(exp34.equalsTo(output)); - delete result; + result = op.evaluate({&x, &gradO1, &axes}, {}, {}, {true, true}); - ASSERT_EQ(Status::OK(), result->status()); - output = result->at(0); + ASSERT_EQ(Status::OK(), result.status()); + output = result.at(0); ASSERT_TRUE(exp34.isSameShape(output)); ASSERT_TRUE(exp34.equalsTo(output)); - delete result; + } @@ -500,35 +500,35 @@ TEST_F(DeclarableOpsTests8, reduceVarianceBP_test3) { x.linspace(1); - nd4j::ops::reduce_variance_bp op; + sd::ops::reduce_variance_bp op; auto result = op.evaluate({&x, &gradO2}, {0, 0}, {1}); - ASSERT_EQ(Status::OK(), result->status()); - auto output = result->at(0); + ASSERT_EQ(Status::OK(), result.status()); + auto output = result.at(0); ASSERT_TRUE(exp12.isSameShape(output)); ASSERT_TRUE(exp12.equalsTo(output)); - delete result; + result = op.evaluate({&x, &gradO1}, {1, 0}, {1}); - ASSERT_EQ(Status::OK(), result->status()); - output = result->at(0); + ASSERT_EQ(Status::OK(), result.status()); + output = result.at(0); ASSERT_TRUE(exp12.isSameShape(output)); ASSERT_TRUE(exp12.equalsTo(output)); - delete result; + result = op.evaluate({&x, &gradO2}, {0, 1}, {1}); - ASSERT_EQ(Status::OK(), result->status()); - output = result->at(0); + ASSERT_EQ(Status::OK(), result.status()); + output = result.at(0); ASSERT_TRUE(exp34.isSameShape(output)); ASSERT_TRUE(exp34.equalsTo(output)); - delete result; + result = op.evaluate({&x, &gradO1}, {1, 1}, {1}); - ASSERT_EQ(Status::OK(), result->status()); - output = result->at(0); + ASSERT_EQ(Status::OK(), result.status()); + output = result.at(0); ASSERT_TRUE(exp34.isSameShape(output)); ASSERT_TRUE(exp34.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -542,36 +542,36 @@ TEST_F(DeclarableOpsTests8, reduceStDevBP_test1) { x.linspace(1); - nd4j::ops::reduce_stdev_bp op; + sd::ops::reduce_stdev_bp op; auto result = op.evaluate({&x, &gradO2}, {0,1}, {}); - ASSERT_EQ(Status::OK(), result->status()); - auto output = result->at(0); + ASSERT_EQ(Status::OK(), result.status()); + auto output = result.at(0); // output->printIndexedBuffer(); ASSERT_TRUE(exp12.isSameShape(output)); ASSERT_TRUE(exp12.equalsTo(output)); - delete result; + result = op.evaluate({&x, &gradO1}, {1,1}, {}); - ASSERT_EQ(Status::OK(), result->status()); - output = result->at(0); + ASSERT_EQ(Status::OK(), result.status()); + output = result.at(0); ASSERT_TRUE(exp12.isSameShape(output)); ASSERT_TRUE(exp12.equalsTo(output)); - delete result; + result = op.evaluate({&x, &gradO2}, {0,0}, {}); - ASSERT_EQ(Status::OK(), result->status()); - output = result->at(0); + ASSERT_EQ(Status::OK(), result.status()); + output = result.at(0); ASSERT_TRUE(exp34.isSameShape(output)); ASSERT_TRUE(exp34.equalsTo(output)); - delete result; + result = op.evaluate({&x, &gradO1}, {1,0}, {}); - ASSERT_EQ(Status::OK(), result->status()); - output = result->at(0); + ASSERT_EQ(Status::OK(), result.status()); + output = result.at(0); ASSERT_TRUE(exp34.isSameShape(output)); ASSERT_TRUE(exp34.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -585,35 +585,35 @@ TEST_F(DeclarableOpsTests8, reduceStDevBP_test2) { x.linspace(1); - nd4j::ops::reduce_stdev_bp op; + sd::ops::reduce_stdev_bp op; auto result = op.evaluate({&x, &gradO2}, {0,0}, {0}); - ASSERT_EQ(Status::OK(), result->status()); - auto output = result->at(0); + ASSERT_EQ(Status::OK(), result.status()); + auto output = result.at(0); ASSERT_TRUE(exp12.isSameShape(output)); ASSERT_TRUE(exp12.equalsTo(output)); - delete result; + result = op.evaluate({&x, &gradO1}, {1,0}, {0}); - ASSERT_EQ(Status::OK(), result->status()); - output = result->at(0); + ASSERT_EQ(Status::OK(), result.status()); + output = result.at(0); ASSERT_TRUE(exp12.isSameShape(output)); ASSERT_TRUE(exp12.equalsTo(output)); - delete result; + result = op.evaluate({&x, &gradO2}, {0,1}, {0}); - ASSERT_EQ(Status::OK(), result->status()); - output = result->at(0); + ASSERT_EQ(Status::OK(), result.status()); + output = result.at(0); ASSERT_TRUE(exp34.isSameShape(output)); ASSERT_TRUE(exp34.equalsTo(output)); - delete result; + result = op.evaluate({&x, &gradO1}, {1,1}, {0}); - ASSERT_EQ(Status::OK(), result->status()); - output = result->at(0); + ASSERT_EQ(Status::OK(), result.status()); + output = result.at(0); ASSERT_TRUE(exp34.isSameShape(output)); ASSERT_TRUE(exp34.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -628,35 +628,35 @@ TEST_F(DeclarableOpsTests8, reduceStDevBP_test02) { auto axis = NDArrayFactory::create('c', {1}, {ax}); x.linspace(1); - nd4j::ops::reduce_stdev_bp op; + sd::ops::reduce_stdev_bp op; auto result = op.evaluate({&x, &gradO2, &axis}, {}, {}, {false, false}); - ASSERT_EQ(Status::OK(), result->status()); - auto output = result->at(0); + ASSERT_EQ(Status::OK(), result.status()); + auto output = result.at(0); ASSERT_TRUE(exp12.isSameShape(output)); ASSERT_TRUE(exp12.equalsTo(output)); - delete result; + result = op.evaluate({&x, &gradO1, &axis}, {}, {}, {true, false}); - ASSERT_EQ(Status::OK(), result->status()); - output = result->at(0); + ASSERT_EQ(Status::OK(), result.status()); + output = result.at(0); ASSERT_TRUE(exp12.isSameShape(output)); ASSERT_TRUE(exp12.equalsTo(output)); - delete result; + result = op.evaluate({&x, &gradO2, &axis}, {}, {}, {false, true}); - ASSERT_EQ(Status::OK(), result->status()); - output = result->at(0); + ASSERT_EQ(Status::OK(), result.status()); + output = result.at(0); ASSERT_TRUE(exp34.isSameShape(output)); ASSERT_TRUE(exp34.equalsTo(output)); - delete result; + result = op.evaluate({&x, &gradO1, &axis}, {}, {}, {true, true}); - ASSERT_EQ(Status::OK(), result->status()); - output = result->at(0); + ASSERT_EQ(Status::OK(), result.status()); + output = result.at(0); ASSERT_TRUE(exp34.isSameShape(output)); ASSERT_TRUE(exp34.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -670,35 +670,35 @@ TEST_F(DeclarableOpsTests8, reduceStDevBP_test3) { x.linspace(1); - nd4j::ops::reduce_stdev_bp op; + sd::ops::reduce_stdev_bp op; auto result = op.evaluate({&x, &gradO2}, {0,0}, {1}); - ASSERT_EQ(Status::OK(), result->status()); - auto output = result->at(0); + ASSERT_EQ(Status::OK(), result.status()); + auto output = result.at(0); ASSERT_TRUE(exp12.isSameShape(output)); ASSERT_TRUE(exp12.equalsTo(output)); - delete result; + result = op.evaluate({&x, &gradO1}, {1,0}, {1}); - ASSERT_EQ(Status::OK(), result->status()); - output = result->at(0); + ASSERT_EQ(Status::OK(), result.status()); + output = result.at(0); ASSERT_TRUE(exp12.isSameShape(output)); ASSERT_TRUE(exp12.equalsTo(output)); - delete result; + result = op.evaluate({&x, &gradO2}, {0,1}, {1}); - ASSERT_EQ(Status::OK(), result->status()); - output = result->at(0); + ASSERT_EQ(Status::OK(), result.status()); + output = result.at(0); ASSERT_TRUE(exp34.isSameShape(output)); ASSERT_TRUE(exp34.equalsTo(output)); - delete result; + result = op.evaluate({&x, &gradO1}, {1,1}, {1}); - ASSERT_EQ(Status::OK(), result->status()); - output = result->at(0); + ASSERT_EQ(Status::OK(), result.status()); + output = result.at(0); ASSERT_TRUE(exp34.isSameShape(output)); ASSERT_TRUE(exp34.equalsTo(output)); - delete result; + } @@ -710,14 +710,14 @@ TEST_F(DeclarableOpsTests8, Test_Reduce_Sum_1) { auto exp = NDArrayFactory::create(120.f); //************************************// - nd4j::ops::reduce_sum op; + sd::ops::reduce_sum op; auto result = op.evaluate({&input}, {}, {}); - ASSERT_EQ(Status::OK(), result->status()); - auto z = result->at(0); + ASSERT_EQ(Status::OK(), result.status()); + auto z = result.at(0); //z->printIndexedBuffer("Result is "); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -727,14 +727,14 @@ TEST_F(DeclarableOpsTests8, Test_Reduce_Sum_2) { auto exp = NDArrayFactory::create({15.f, 40.f, 65.f}); //************************************// - nd4j::ops::reduce_sum op; + sd::ops::reduce_sum op; auto result = op.evaluate({&input}, {}, {1}); - ASSERT_EQ(Status::OK(), result->status()); - auto z = result->at(0); + ASSERT_EQ(Status::OK(), result.status()); + auto z = result.at(0); // z->printIndexedBuffer("Result is "); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -745,14 +745,14 @@ TEST_F(DeclarableOpsTests8, Test_Reduce_Sum_03) { auto axis = NDArrayFactory::create('c', {1}, {1}); //************************************// - nd4j::ops::reduce_sum op; + sd::ops::reduce_sum op; auto result = op.evaluate({&input, &axis}, {}, {}, {false}); - ASSERT_EQ(Status::OK(), result->status()); - auto z = result->at(0); + ASSERT_EQ(Status::OK(), result.status()); + auto z = result.at(0); // z->printIndexedBuffer("Result is "); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -762,14 +762,14 @@ TEST_F(DeclarableOpsTests8, Test_Reduce_Prod_1) { auto exp = NDArrayFactory::create(1307674368000.f); //************************************// - nd4j::ops::reduce_prod op; + sd::ops::reduce_prod op; auto result = op.evaluate({&input}, {}, {}); - ASSERT_EQ(Status::OK(), result->status()); - auto z = result->at(0); + ASSERT_EQ(Status::OK(), result.status()); + auto z = result.at(0); //z->printIndexedBuffer("Result is "); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -779,14 +779,14 @@ TEST_F(DeclarableOpsTests8, Test_Reduce_Prod_2) { auto exp = NDArrayFactory::create({120.f, 30240.f, 360360.f}); //************************************// - nd4j::ops::reduce_prod op; + sd::ops::reduce_prod op; auto result = op.evaluate({&input}, {}, {1}); - ASSERT_EQ(Status::OK(), result->status()); - auto z = result->at(0); + ASSERT_EQ(Status::OK(), result.status()); + auto z = result.at(0); // z->printIndexedBuffer("Result is "); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -796,16 +796,16 @@ TEST_F(DeclarableOpsTests8, Test_Reduce_Sum_01) { auto exp = NDArrayFactory::create('c', {4}, {66.f, 72.f, 78.f, 84.f}); x.linspace(1); - nd4j::ops::reduce_sum op; + sd::ops::reduce_sum op; auto result = op.evaluate({&x}, {}, {0,1}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -815,17 +815,17 @@ TEST_F(DeclarableOpsTests8, Test_Reduce_Sum_02) { auto exp = NDArrayFactory::create('c', {1,1,4}, {66.f, 72.f, 78.f, 84.f}); x.linspace(1); - nd4j::ops::reduce_sum op; + sd::ops::reduce_sum op; auto result = op.evaluate({&x}, {1.}, {0, 1}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -835,17 +835,17 @@ TEST_F(DeclarableOpsTests8, Test_Reduce_Sum_3) { auto exp = NDArrayFactory::create('c', {3}, {68.f, 100.f, 132.f}); x.linspace(1); - nd4j::ops::reduce_sum op; + sd::ops::reduce_sum op; auto result = op.evaluate({&x}, {}, {0, 2}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -855,17 +855,17 @@ TEST_F(DeclarableOpsTests8, Test_Reduce_Sum_4) { auto exp = NDArrayFactory::create('c', {1,3,1}, {68.f, 100.f, 132.f}); x.linspace(1); - nd4j::ops::reduce_sum op; + sd::ops::reduce_sum op; auto result = op.evaluate({&x}, {1.}, {0, 2}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -875,17 +875,17 @@ TEST_F(DeclarableOpsTests8, Test_Reduce_Sum_5) { auto exp = NDArrayFactory::create(300.f); x.linspace(1); - nd4j::ops::reduce_sum op; + sd::ops::reduce_sum op; auto result = op.evaluate({&x}, {}, {}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -895,17 +895,17 @@ TEST_F(DeclarableOpsTests8, Test_Reduce_Sum_6) { auto exp = NDArrayFactory::create(300.f); x.linspace(1); - nd4j::ops::reduce_sum op; + sd::ops::reduce_sum op; auto result = op.evaluate({&x}, {}, {0,1,2}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -915,17 +915,17 @@ TEST_F(DeclarableOpsTests8, Test_Reduce_Sum_7) { auto exp = NDArrayFactory::create('c', {1,1,1}, {300.f}); x.linspace(1); // x.printIndexedBuffer("Input with shape (2, 3, 4) is"); - nd4j::ops::reduce_sum op; + sd::ops::reduce_sum op; auto result = op.evaluate({&x}, {1.}, {0,1,2}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -935,16 +935,16 @@ TEST_F(DeclarableOpsTests8, Test_Reduce_Prod_01) { auto exp = NDArrayFactory::create('c', {2}, {10395.f, 46080.f}); x.linspace(1); - nd4j::ops::reduce_prod op; + sd::ops::reduce_prod op; auto result = op.evaluate({&x}, {}, {0,1}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -954,17 +954,17 @@ TEST_F(DeclarableOpsTests8, Test_Reduce_Prod_02) { auto exp = NDArrayFactory::create('c', {1,1,2}, {10395.f, 46080.f}); x.linspace(1); - nd4j::ops::reduce_prod op; + sd::ops::reduce_prod op; auto result = op.evaluate({&x}, {1.}, {0, 1}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -974,17 +974,17 @@ TEST_F(DeclarableOpsTests8, Test_Reduce_Prod_3) { auto exp = NDArrayFactory::create('c', {3}, {112.f, 1080.f, 3960.f}); x.linspace(1); - nd4j::ops::reduce_prod op; + sd::ops::reduce_prod op; auto result = op.evaluate({&x}, {}, {0, 2}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -994,17 +994,17 @@ TEST_F(DeclarableOpsTests8, Test_Reduce_Prod_4) { auto exp = NDArrayFactory::create('c', {1,3,1}, {112.f, 1080.f, 3960.f}); x.linspace(1); - nd4j::ops::reduce_prod op; + sd::ops::reduce_prod op; auto result = op.evaluate({&x}, {1.}, {0, 2}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1015,17 +1015,17 @@ TEST_F(DeclarableOpsTests8, Test_Reduce_Prod_04) { auto axes = NDArrayFactory::create({0, 2}); x.linspace(1); - nd4j::ops::reduce_prod op; + sd::ops::reduce_prod op; auto result = op.evaluate({&x, &axes}, {}, {}, {true}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1035,17 +1035,17 @@ TEST_F(DeclarableOpsTests8, Test_Reduce_Prod_5) { auto exp = NDArrayFactory::create(479001600.f); x.linspace(1); - nd4j::ops::reduce_prod op; + sd::ops::reduce_prod op; auto result = op.evaluate({&x}, {}, {}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1055,17 +1055,17 @@ TEST_F(DeclarableOpsTests8, Test_Reduce_Prod_6) { auto exp = NDArrayFactory::create(479001600.f); x.linspace(1); - nd4j::ops::reduce_prod op; + sd::ops::reduce_prod op; auto result = op.evaluate({&x}, {}, {0,1,2}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1075,17 +1075,17 @@ TEST_F(DeclarableOpsTests8, Test_Reduce_Prod_7) { auto exp = NDArrayFactory::create('c', {1, 1, 1}, {479001600.f}); x.linspace(1); // x.printIndexedBuffer("Input with shape (2, 3, 4) is"); - nd4j::ops::reduce_prod op; + sd::ops::reduce_prod op; auto result = op.evaluate({&x}, {1.}, {0,1,2}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1095,16 +1095,16 @@ TEST_F(DeclarableOpsTests8, Test_Reduce_Min_1) { auto exp = NDArrayFactory::create('c', {4}, {1.f, 2.f, 3.f, 4.f}); x.linspace(1); - nd4j::ops::reduce_min op; + sd::ops::reduce_min op; auto result = op.evaluate({&x}, {}, {0, 1}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1114,17 +1114,17 @@ TEST_F(DeclarableOpsTests8, Test_Reduce_Min_2) { auto exp = NDArrayFactory::create('c', {1,1,4}, {1.f, 2.f, 3.f, 4.f}); x.linspace(1); - nd4j::ops::reduce_min op; + sd::ops::reduce_min op; auto result = op.evaluate({&x}, {1.}, {0, 1}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1134,17 +1134,17 @@ TEST_F(DeclarableOpsTests8, Test_Reduce_Min_3) { auto exp = NDArrayFactory::create('c', {3}, {1.f, 5.f, 9.f}); x.linspace(1); - nd4j::ops::reduce_min op; + sd::ops::reduce_min op; auto result = op.evaluate({&x}, {}, {0, 2}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1154,17 +1154,17 @@ TEST_F(DeclarableOpsTests8, Test_Reduce_Min_4) { auto exp = NDArrayFactory::create('c', {1,3,1}, {1.f, 5.f, 9.f}); x.linspace(1); - nd4j::ops::reduce_min op; + sd::ops::reduce_min op; auto result = op.evaluate({&x}, {1.}, {0, 2}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1175,17 +1175,17 @@ TEST_F(DeclarableOpsTests8, Test_Reduce_Min_04) { auto axes = NDArrayFactory::create({0, 2}); x.linspace(1); - nd4j::ops::reduce_min op; + sd::ops::reduce_min op; auto result = op.evaluate({&x, &axes}, {}, {}, {true}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1195,17 +1195,17 @@ TEST_F(DeclarableOpsTests8, Test_Reduce_Min_5) { auto exp = NDArrayFactory::create(1.f); x.linspace(1); - nd4j::ops::reduce_min op; + sd::ops::reduce_min op; auto result = op.evaluate({&x}, {}, {}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1215,17 +1215,17 @@ TEST_F(DeclarableOpsTests8, Test_Reduce_Min_6) { auto exp = NDArrayFactory::create(1.f); x.linspace(1); - nd4j::ops::reduce_min op; + sd::ops::reduce_min op; auto result = op.evaluate({&x}, {}, {0,1,2}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1235,17 +1235,17 @@ TEST_F(DeclarableOpsTests8, Test_Reduce_Min_7) { auto exp = NDArrayFactory::create('c', {1, 1, 1}, {1.f}); x.linspace(1); // x.printIndexedBuffer("Input with shape (2, 3, 4) is"); - nd4j::ops::reduce_min op; + sd::ops::reduce_min op; auto result = op.evaluate({&x}, {1.}, {0,1,2}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1255,17 +1255,17 @@ TEST_F(DeclarableOpsTests8, Test_Reduce_Max_1) { auto exp = NDArrayFactory::create('c', {4}, {21.f, 22.f, 23.f, 24.f}); x.linspace(1); - nd4j::ops::reduce_max op; + sd::ops::reduce_max op; auto result = op.evaluate({&x}, {}, {0,1}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); // output->printShapeInfo("Output shape"); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1275,17 +1275,17 @@ TEST_F(DeclarableOpsTests8, Test_Reduce_Max_2) { auto exp = NDArrayFactory::create('c', {1,1,4}, {21.f, 22.f, 23.f, 24.f}); x.linspace(1); - nd4j::ops::reduce_max op; + sd::ops::reduce_max op; auto result = op.evaluate({&x}, {1.}, {0, 1}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1295,17 +1295,17 @@ TEST_F(DeclarableOpsTests8, Test_Reduce_Max_3) { auto exp = NDArrayFactory::create('c', {3}, {16.f, 20.f, 24.f}); x.linspace(1); - nd4j::ops::reduce_max op; + sd::ops::reduce_max op; auto result = op.evaluate({&x}, {}, {0, 2}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1315,17 +1315,17 @@ TEST_F(DeclarableOpsTests8, Test_Reduce_Max_4) { auto exp = NDArrayFactory::create('c', {1,3,1}, {16.f, 20.f, 24.f}); x.linspace(1); - nd4j::ops::reduce_max op; + sd::ops::reduce_max op; auto result = op.evaluate({&x}, {1.}, {0, 2}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1336,17 +1336,17 @@ TEST_F(DeclarableOpsTests8, Test_Reduce_Max_04) { auto axes = NDArrayFactory::create({0, 2}); x.linspace(1); - nd4j::ops::reduce_max op; + sd::ops::reduce_max op; auto result = op.evaluate({&x, &axes}, {}, {}, {true}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1356,17 +1356,17 @@ TEST_F(DeclarableOpsTests8, Test_Reduce_Max_5) { auto exp = NDArrayFactory::create(24.f); x.linspace(1); - nd4j::ops::reduce_max op; + sd::ops::reduce_max op; auto result = op.evaluate({&x}, {}, {}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1376,17 +1376,17 @@ TEST_F(DeclarableOpsTests8, Test_Reduce_Max_6) { auto exp = NDArrayFactory::create(24.f); x.linspace(1); - nd4j::ops::reduce_max op; + sd::ops::reduce_max op; auto result = op.evaluate({&x}, {}, {0,1,2}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1396,17 +1396,17 @@ TEST_F(DeclarableOpsTests8, Test_Reduce_Max_7) { auto exp = NDArrayFactory::create('c', {1, 1, 1}, {24.f}); x.linspace(1); // x.printIndexedBuffer("Input with shape (2, 3, 4) is"); - nd4j::ops::reduce_max op; + sd::ops::reduce_max op; auto result = op.evaluate({&x}, {1.}, {0,1,2}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } TEST_F(DeclarableOpsTests8, Test_Reduce_Norm1_1) { @@ -1415,16 +1415,16 @@ TEST_F(DeclarableOpsTests8, Test_Reduce_Norm1_1) { auto exp = NDArrayFactory::create('c', {4}, {66.f, 72.f, 78.f, 84.f}); x.linspace(1); - nd4j::ops::reduce_norm1 op; + sd::ops::reduce_norm1 op; auto result = op.evaluate({&x}, {}, {0,1}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1434,17 +1434,17 @@ TEST_F(DeclarableOpsTests8, Test_Reduce_Norm1_2) { auto exp = NDArrayFactory::create('c', {1,1,4}, {66.f, 72.f, 78.f, 84.f}); x.linspace(1); - nd4j::ops::reduce_norm1 op; + sd::ops::reduce_norm1 op; auto result = op.evaluate({&x}, {1.}, {0, 1}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1454,17 +1454,17 @@ TEST_F(DeclarableOpsTests8, Test_Reduce_Norm1_3) { auto exp = NDArrayFactory::create('c', {3}, {68.f, 100.f, 132.f}); x.linspace(1); - nd4j::ops::reduce_norm1 op; + sd::ops::reduce_norm1 op; auto result = op.evaluate({&x}, {}, {0, 2}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1474,17 +1474,17 @@ TEST_F(DeclarableOpsTests8, Test_Reduce_Norm1_4) { auto exp = NDArrayFactory::create('c', {1,3,1}, {68.f, 100.f, 132.f}); x.linspace(1); - nd4j::ops::reduce_norm1 op; + sd::ops::reduce_norm1 op; auto result = op.evaluate({&x}, {1.}, {0, 2}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1495,17 +1495,17 @@ TEST_F(DeclarableOpsTests8, Test_Reduce_Norm1_04) { auto axes = NDArrayFactory::create({0, 2}); x.linspace(1); - nd4j::ops::reduce_norm1 op; + sd::ops::reduce_norm1 op; auto result = op.evaluate({&x, &axes}, {}, {}, {true}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1515,17 +1515,17 @@ TEST_F(DeclarableOpsTests8, Test_Reduce_Norm1_5) { auto exp = NDArrayFactory::create(300.f); x.linspace(1); - nd4j::ops::reduce_norm1 op; + sd::ops::reduce_norm1 op; auto result = op.evaluate({&x}, {}, {}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1535,17 +1535,17 @@ TEST_F(DeclarableOpsTests8, Test_Reduce_Norm1_6) { auto exp = NDArrayFactory::create(300.f); x.linspace(1); - nd4j::ops::reduce_norm1 op; + sd::ops::reduce_norm1 op; auto result = op.evaluate({&x}, {}, {0,1,2}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1555,17 +1555,17 @@ TEST_F(DeclarableOpsTests8, Test_Reduce_Norm1_7) { auto exp = NDArrayFactory::create('c', {1, 1, 1}, {300.f}); x.linspace(1); // x.printIndexedBuffer("Input with shape (2, 3, 4) is"); - nd4j::ops::reduce_norm1 op; + sd::ops::reduce_norm1 op; auto result = op.evaluate({&x}, {1.}, {0,1,2}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } TEST_F(DeclarableOpsTests8, Test_Reduce_Norm2_1) { @@ -1574,16 +1574,16 @@ TEST_F(DeclarableOpsTests8, Test_Reduce_Norm2_1) { auto exp = NDArrayFactory::create('c', {4}, {31.7175f, 33.823071f, 35.97221f, 38.15757f}); x.linspace(1); - nd4j::ops::reduce_norm2 op; + sd::ops::reduce_norm2 op; auto result = op.evaluate({&x}, {}, {0,1}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1593,17 +1593,17 @@ TEST_F(DeclarableOpsTests8, Test_Reduce_Norm2_2) { auto exp = NDArrayFactory::create('c', {1,1,4}, {31.7175f, 33.823071f, 35.97221f, 38.15757f}); x.linspace(1); - nd4j::ops::reduce_norm2 op; + sd::ops::reduce_norm2 op; auto result = op.evaluate({&x}, {1.}, {0, 1}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1613,17 +1613,17 @@ TEST_F(DeclarableOpsTests8, Test_Reduce_Norm2_3) { auto exp = NDArrayFactory::create('c', {3}, {29.597298f, 39.344631f, 49.759422f}); x.linspace(1); - nd4j::ops::reduce_norm2 op; + sd::ops::reduce_norm2 op; auto result = op.evaluate({&x}, {}, {0, 2}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1633,17 +1633,17 @@ TEST_F(DeclarableOpsTests8, Test_Reduce_Norm2_4) { auto exp = NDArrayFactory::create('c', {1,3,1}, {29.597298f, 39.344631f, 49.759422f}); x.linspace(1); - nd4j::ops::reduce_norm2 op; + sd::ops::reduce_norm2 op; auto result = op.evaluate({&x}, {1.}, {0, 2}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1654,17 +1654,17 @@ TEST_F(DeclarableOpsTests8, Test_Reduce_Norm2_04) { auto axes = NDArrayFactory::create({0,2}); x.linspace(1); - nd4j::ops::reduce_norm2 op; + sd::ops::reduce_norm2 op; auto result = op.evaluate({&x, &axes}, {}, {}, {true}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1674,17 +1674,17 @@ TEST_F(DeclarableOpsTests8, Test_Reduce_Norm2_5) { auto exp = NDArrayFactory::create(70.f); x.linspace(1); - nd4j::ops::reduce_norm2 op; + sd::ops::reduce_norm2 op; auto result = op.evaluate({&x}, {}, {}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1694,17 +1694,17 @@ TEST_F(DeclarableOpsTests8, Test_Reduce_Norm2_6) { auto exp = NDArrayFactory::create(70.f); x.linspace(1); - nd4j::ops::reduce_norm2 op; + sd::ops::reduce_norm2 op; auto result = op.evaluate({&x}, {}, {0,1,2}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1714,17 +1714,17 @@ TEST_F(DeclarableOpsTests8, Test_Reduce_Norm2_7) { auto exp = NDArrayFactory::create('c', {1, 1, 1}, {70.f}); x.linspace(1); // x.printIndexedBuffer("Input with shape (2, 3, 4) is"); - nd4j::ops::reduce_norm2 op; + sd::ops::reduce_norm2 op; auto result = op.evaluate({&x}, {1.}, {0,1,2}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1734,16 +1734,16 @@ TEST_F(DeclarableOpsTests8, Test_Reduce_NormMax_1) { auto exp = NDArrayFactory::create('c', {4}, {21.f, 22.f, 23.f, 24.f}); x.linspace(1); - nd4j::ops::reduce_norm_max op; + sd::ops::reduce_norm_max op; auto result = op.evaluate({&x}, {}, {0,1}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1753,16 +1753,16 @@ TEST_F(DeclarableOpsTests8, Test_Reduce_NormMax_2) { auto exp = NDArrayFactory::create('c', {1,1,4}, {21.f, 22.f, 23.f, 24.f}); x.linspace(1); - nd4j::ops::reduce_norm_max op; + sd::ops::reduce_norm_max op; auto result = op.evaluate({&x}, {1.f}, {0,1}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1772,16 +1772,16 @@ TEST_F(DeclarableOpsTests8, Test_Reduce_NormMax_3) { auto exp = NDArrayFactory::create('c', {3}, {16.f, 20.f, 24.f}); x.linspace(1); - nd4j::ops::reduce_norm_max op; + sd::ops::reduce_norm_max op; auto result = op.evaluate({&x}, {}, {0,2}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1791,16 +1791,16 @@ TEST_F(DeclarableOpsTests8, Test_Reduce_NormMax_4) { auto exp = NDArrayFactory::create('c', {1, 3, 1}, {16.f, 20.f, 24.f}); x.linspace(1); - nd4j::ops::reduce_norm_max op; + sd::ops::reduce_norm_max op; auto result = op.evaluate({&x}, {1.f}, {0,2}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1811,16 +1811,16 @@ TEST_F(DeclarableOpsTests8, Test_Reduce_NormMax_04) { auto axes = NDArrayFactory::create({0,2}); x.linspace(1); - nd4j::ops::reduce_norm_max op; + sd::ops::reduce_norm_max op; auto result = op.evaluate({&x, &axes}, {}, {}, {true}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1830,17 +1830,17 @@ TEST_F(DeclarableOpsTests8, Test_Reduce_NormMax_5) { auto exp = NDArrayFactory::create(24.f); x.linspace(1); - nd4j::ops::reduce_norm_max op; + sd::ops::reduce_norm_max op; auto result = op.evaluate({&x}, {}, {}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1850,17 +1850,17 @@ TEST_F(DeclarableOpsTests8, Test_Reduce_NormMax_6) { auto exp = NDArrayFactory::create(24.f); x.linspace(1); - nd4j::ops::reduce_norm_max op; + sd::ops::reduce_norm_max op; auto result = op.evaluate({&x}, {}, {0, 1, 2}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1870,17 +1870,17 @@ TEST_F(DeclarableOpsTests8, Test_Reduce_NormMax_7) { auto exp = NDArrayFactory::create('c', {1, 1, 1}, {24.f}); x.linspace(1); - nd4j::ops::reduce_norm_max op; + sd::ops::reduce_norm_max op; auto result = op.evaluate({&x}, {1.f}, {}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1890,16 +1890,16 @@ TEST_F(DeclarableOpsTests8, Test_Reduce_SquaredNorm_1) { auto exp = NDArrayFactory::create('c', {4}, {1006.f, 1144.f, 1294.f, 1456.f}); x.linspace(1); - nd4j::ops::reduce_sqnorm op; + sd::ops::reduce_sqnorm op; auto result = op.evaluate({&x}, {}, {0,1}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1909,16 +1909,16 @@ TEST_F(DeclarableOpsTests8, Test_Reduce_SquaredNorm_2) { auto exp = NDArrayFactory::create('c', {1,1,4}, {1006.f, 1144.f, 1294.f, 1456.f}); x.linspace(1); - nd4j::ops::reduce_sqnorm op; + sd::ops::reduce_sqnorm op; auto result = op.evaluate({&x}, {1.f}, {0,1}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1928,16 +1928,16 @@ TEST_F(DeclarableOpsTests8, Test_Reduce_SquaredNorm_3) { auto exp = NDArrayFactory::create('c', {3}, {876.f, 1548.f, 2476.f}); x.linspace(1); - nd4j::ops::reduce_sqnorm op; + sd::ops::reduce_sqnorm op; auto result = op.evaluate({&x}, {}, {0,2}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1947,16 +1947,16 @@ TEST_F(DeclarableOpsTests8, Test_Reduce_SquaredNorm_4) { auto exp = NDArrayFactory::create('c', {1, 3, 1}, {876.f, 1548.f, 2476.f}); x.linspace(1); - nd4j::ops::reduce_sqnorm op; + sd::ops::reduce_sqnorm op; auto result = op.evaluate({&x}, {1.f}, {0,2}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1967,16 +1967,16 @@ TEST_F(DeclarableOpsTests8, Test_Reduce_SquaredNorm_04) { auto axes = NDArrayFactory::create({0, 2}); x.linspace(1); - nd4j::ops::reduce_sqnorm op; + sd::ops::reduce_sqnorm op; auto result = op.evaluate({&x, &axes}, {}, {}, {true}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1986,17 +1986,17 @@ TEST_F(DeclarableOpsTests8, Test_Reduce_SquaredNorm_5) { auto exp = NDArrayFactory::create(4900.f); x.linspace(1); - nd4j::ops::reduce_sqnorm op; + sd::ops::reduce_sqnorm op; auto result = op.evaluate({&x}, {}, {}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -2006,17 +2006,17 @@ TEST_F(DeclarableOpsTests8, Test_Reduce_SquaredNorm_6) { auto exp = NDArrayFactory::create(4900.f); x.linspace(1); - nd4j::ops::reduce_sqnorm op; + sd::ops::reduce_sqnorm op; auto result = op.evaluate({&x}, {}, {0, 1, 2}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -2026,17 +2026,17 @@ TEST_F(DeclarableOpsTests8, Test_Reduce_SquaredNorm_7) { auto exp = NDArrayFactory::create('c', {1, 1, 1}, {4900.f}); x.linspace(1); - nd4j::ops::reduce_sqnorm op; + sd::ops::reduce_sqnorm op; auto result = op.evaluate({&x}, {1.f}, {}); - auto output = result->at(0); + auto output = result.at(0); // output->printIndexedBuffer("Result is"); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -2047,15 +2047,15 @@ TEST_F(DeclarableOpsTests8, Test_Reduce_Sum_BP_1) { auto exp = NDArrayFactory::create('c', {3, 4}, {0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f,0.5f}); //************************************// - nd4j::ops::reduce_sum_bp op; + sd::ops::reduce_sum_bp op; auto result = op.evaluate({&input, &eps}, {}, {}); - ASSERT_EQ(Status::OK(), result->status()); - auto z = result->at(0); + ASSERT_EQ(Status::OK(), result.status()); + auto z = result.at(0); // z->printIndexedBuffer("Result is "); // z->printShapeInfo(); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -2068,15 +2068,15 @@ TEST_F(DeclarableOpsTests8, Test_Reduce_Sum_BP_2) { 0.5f, 0.5f, 0.5f,0.5f}); //************************************// - nd4j::ops::reduce_sum_bp op; + sd::ops::reduce_sum_bp op; auto result = op.evaluate({&input, &eps}, {1.f}, {}); - ASSERT_EQ(Status::OK(), result->status()); - auto z = result->at(0); + ASSERT_EQ(Status::OK(), result.status()); + auto z = result.at(0); // z->printIndexedBuffer("Result is "); // z->printShapeInfo(); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -2089,15 +2089,15 @@ TEST_F(DeclarableOpsTests8, Test_Reduce_Sum_BP_3) { 1.f, 2.f, 3.f, 4.f}); //************************************// - nd4j::ops::reduce_sum_bp op; + sd::ops::reduce_sum_bp op; auto result = op.evaluate({&input, &eps}, {}, {0}); - ASSERT_EQ(Status::OK(), result->status()); - auto z = result->at(0); + ASSERT_EQ(Status::OK(), result.status()); + auto z = result.at(0); // z->printIndexedBuffer("Result is "); // z->printShapeInfo(); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -2110,15 +2110,15 @@ TEST_F(DeclarableOpsTests8, Test_Reduce_Sum_BP_4) { 1.f, 2.f, 3.f, 4.f}); //************************************// - nd4j::ops::reduce_sum_bp op; + sd::ops::reduce_sum_bp op; auto result = op.evaluate({&input, &eps}, {1.f}, {0}); - ASSERT_EQ(Status::OK(), result->status()); - auto z = result->at(0); + ASSERT_EQ(Status::OK(), result.status()); + auto z = result.at(0); // z->printIndexedBuffer("Result is "); // z->printShapeInfo(); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -2133,15 +2133,15 @@ TEST_F(DeclarableOpsTests8, Test_Reduce_Sum_BP_04) { auto axis = NDArrayFactory::create('c', {1}, {ax}); //************************************// - nd4j::ops::reduce_sum_bp op; + sd::ops::reduce_sum_bp op; auto result = op.evaluate({&input, &eps, &axis}, {}, {}, {true}); - ASSERT_EQ(Status::OK(), result->status()); - auto z = result->at(0); + ASSERT_EQ(Status::OK(), result.status()); + auto z = result.at(0); // z->printIndexedBuffer("Result is "); // z->printShapeInfo(); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -2158,15 +2158,15 @@ TEST_F(DeclarableOpsTests8, Test_Reduce_Prod_BP_1) { 171001227491294996070400.f, 155455648254341989531648.f, 142501016904612993564672.f, 131539399526781282156544.f, 122143728775382565912576.f, 114000815325130245799936.f}); - nd4j::ops::reduce_prod_bp op; + sd::ops::reduce_prod_bp op; auto result = op.evaluate({&input, &eps}, {}, {}); - ASSERT_EQ(Status::OK(), result->status()); - auto z = result->at(0); + ASSERT_EQ(Status::OK(), result.status()); + auto z = result.at(0); // z->printIndexedBuffer("Result is "); // z->printShapeInfo(); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -2177,16 +2177,16 @@ TEST_F(DeclarableOpsTests8, reduceMean_test1) { x.linspace(1); - nd4j::ops::reduce_mean op; + sd::ops::reduce_mean op; auto result = op.evaluate({&x}, {}, {0,1}); - auto output = result->at(0); + auto output = result.at(0); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -2197,16 +2197,16 @@ TEST_F(DeclarableOpsTests8, reduceMean_test2) { x.linspace(1); - nd4j::ops::reduce_mean op; + sd::ops::reduce_mean op; auto result = op.evaluate({&x}, {1.}, {0,1}); - auto output = result->at(0); + auto output = result.at(0); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -2217,16 +2217,16 @@ TEST_F(DeclarableOpsTests8, reduceMean_test3) { x.linspace(1); - nd4j::ops::reduce_mean op; + sd::ops::reduce_mean op; auto result = op.evaluate({&x}, {}, {0,2}); - auto output = result->at(0); + auto output = result.at(0); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -2237,16 +2237,16 @@ TEST_F(DeclarableOpsTests8, reduceMean_test4) { x.linspace(1); - nd4j::ops::reduce_mean op; + sd::ops::reduce_mean op; auto result = op.evaluate({&x}, {1.f}, {0,2}); - auto output = result->at(0); + auto output = result.at(0); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -2257,16 +2257,16 @@ TEST_F(DeclarableOpsTests8, reduceMean_test5) { x.linspace(1); - nd4j::ops::reduce_mean op; + sd::ops::reduce_mean op; auto result = op.evaluate({&x}, {}, {}); - auto output = result->at(0); + auto output = result.at(0); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -2276,16 +2276,16 @@ TEST_F(DeclarableOpsTests8, reduceMean_test6) { auto exp = NDArrayFactory::create(12.5f); x.linspace(1); - nd4j::ops::reduce_mean op; + sd::ops::reduce_mean op; auto result = op.evaluate({&x}, {}, {0,1,2}); - auto output = result->at(0); + auto output = result.at(0); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -2295,16 +2295,16 @@ TEST_F(DeclarableOpsTests8, reduceMean_test7) { auto exp = NDArrayFactory::create('c', {1,1,1}, {12.5f}); x.linspace(1); - nd4j::ops::reduce_mean op; + sd::ops::reduce_mean op; auto result = op.evaluate({&x}, {1.}, {0,1,2}); - auto output = result->at(0); + auto output = result.at(0); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -2315,16 +2315,16 @@ TEST_F(DeclarableOpsTests8, reduceMean_test8) { auto axes = NDArrayFactory::create({0, 1, 2}); x.linspace(1); - nd4j::ops::reduce_mean op; + sd::ops::reduce_mean op; auto result = op.evaluate({&x, &axes}, {}, {}, {true}); - auto output = result->at(0); + auto output = result.at(0); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -2337,24 +2337,24 @@ TEST_F(DeclarableOpsTests8, reduceMeanBP_test1) { x.linspace(1); - nd4j::ops::reduce_mean_bp op; + sd::ops::reduce_mean_bp op; auto result = op.evaluate({&x, &gradO1}, {0}, {}); - ASSERT_EQ(Status::OK(), result->status()); - auto output = result->at(0); + ASSERT_EQ(Status::OK(), result.status()); + auto output = result.at(0); // output->printShapeInfo("o"); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + result = op.evaluate({&x, &gradO2}, {1}, {}); - ASSERT_EQ(Status::OK(), result->status()); - output = result->at(0); + ASSERT_EQ(Status::OK(), result.status()); + output = result.at(0); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } @@ -2368,21 +2368,21 @@ TEST_F(DeclarableOpsTests8, reduceMeanBP_test2) { x.linspace(1); - nd4j::ops::reduce_mean_bp op; + sd::ops::reduce_mean_bp op; auto result = op.evaluate({&x, &gradO1}, {0}, {0}); - ASSERT_EQ(Status::OK(), result->status()); - auto output = result->at(0); + ASSERT_EQ(Status::OK(), result.status()); + auto output = result.at(0); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + result = op.evaluate({&x, &gradO2}, {1}, {0}); - ASSERT_EQ(Status::OK(), result->status()); - output = result->at(0); + ASSERT_EQ(Status::OK(), result.status()); + output = result.at(0); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -2396,21 +2396,21 @@ TEST_F(DeclarableOpsTests8, reduceMeanBP_test02) { auto axis = NDArrayFactory::create('c', {1}, {ax}); x.linspace(1); - nd4j::ops::reduce_mean_bp op; + sd::ops::reduce_mean_bp op; auto result = op.evaluate({&x, &gradO1, &axis}, {}, {}, {false}); - ASSERT_EQ(Status::OK(), result->status()); - auto output = result->at(0); + ASSERT_EQ(Status::OK(), result.status()); + auto output = result.at(0); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + result = op.evaluate({&x, &gradO2, &axis}, {}, {}, {true}); - ASSERT_EQ(Status::OK(), result->status()); - output = result->at(0); + ASSERT_EQ(Status::OK(), result.status()); + output = result.at(0); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -2423,21 +2423,21 @@ TEST_F(DeclarableOpsTests8, reduceMeanBP_test3) { x.linspace(1); - nd4j::ops::reduce_mean_bp op; + sd::ops::reduce_mean_bp op; auto result = op.evaluate({&x, &gradO1}, {0}, {1}); - ASSERT_EQ(Status::OK(), result->status()); - auto output = result->at(0); + ASSERT_EQ(Status::OK(), result.status()); + auto output = result.at(0); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + result = op.evaluate({&x, &gradO2}, {1}, {1}); - ASSERT_EQ(Status::OK(), result->status()); - output = result->at(0); + ASSERT_EQ(Status::OK(), result.status()); + output = result.at(0); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -2447,15 +2447,15 @@ TEST_F(DeclarableOpsTests8, reduceStDevBP_test4) { auto gradO = NDArrayFactory::create(0.5f); auto exp = NDArrayFactory::create('c', {3}, {-0.25f, 0.f, 0.25f}); - nd4j::ops::reduce_stdev_bp op; + sd::ops::reduce_stdev_bp op; auto result = op.evaluate({&x, &gradO}, {0,1}, {}); - ASSERT_EQ(Status::OK(), result->status()); - auto output = result->at(0); + ASSERT_EQ(Status::OK(), result.status()); + auto output = result.at(0); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } @@ -2468,17 +2468,17 @@ TEST_F(DeclarableOpsTests8, softmax_cross_entropy_loss_with_logits_test1) { logits.linspace(0.1, 0.1); - nd4j::ops::softmax_cross_entropy_loss_with_logits op; + sd::ops::softmax_cross_entropy_loss_with_logits op; auto results = op.evaluate({&logits, &labels}, {}, {}); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); - auto *output = results->at(0); + auto *output = results.at(0); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -2490,17 +2490,17 @@ TEST_F(DeclarableOpsTests8, softmax_cross_entropy_loss_with_logits_test2) { logits.linspace(0.1, 0.1); - nd4j::ops::softmax_cross_entropy_loss_with_logits op; + sd::ops::softmax_cross_entropy_loss_with_logits op; auto results = op.evaluate({&logits, &labels}, {}, {0}); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); - auto *output = results->at(0); + auto *output = results.at(0); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -2512,17 +2512,17 @@ TEST_F(DeclarableOpsTests8, softmax_cross_entropy_loss_with_logits_test3) { logits.linspace(0.1, 0.1); - nd4j::ops::softmax_cross_entropy_loss_with_logits op; + sd::ops::softmax_cross_entropy_loss_with_logits op; auto results = op.evaluate({&logits, &labels}, {}, {1}); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); - auto *output = results->at(0); + auto *output = results.at(0); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -2534,17 +2534,17 @@ TEST_F(DeclarableOpsTests8, softmax_cross_entropy_loss_with_logits_test4) { logits.linspace(0.1, 0.1); - nd4j::ops::softmax_cross_entropy_loss_with_logits op; + sd::ops::softmax_cross_entropy_loss_with_logits op; auto results = op.evaluate({&logits, &labels}, {}, {}); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); - auto output = results->at(0); + auto output = results.at(0); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -2556,17 +2556,17 @@ TEST_F(DeclarableOpsTests8, softmax_cross_entropy_loss_with_logits_test5) { logits.linspace(0.1, 0.1); - nd4j::ops::softmax_cross_entropy_loss_with_logits op; + sd::ops::softmax_cross_entropy_loss_with_logits op; auto results = op.evaluate({&logits, &labels}, {}, {0}); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); - auto output = results->at(0); + auto output = results.at(0); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -2578,17 +2578,17 @@ TEST_F(DeclarableOpsTests8, softmax_cross_entropy_loss_with_logits_test6) { logits.linspace(0.1, 0.1); - nd4j::ops::softmax_cross_entropy_loss_with_logits op; + sd::ops::softmax_cross_entropy_loss_with_logits op; auto results = op.evaluate({&logits, &labels}, {}, {0}); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); - auto output = results->at(0); + auto output = results.at(0); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -2600,17 +2600,17 @@ TEST_F(DeclarableOpsTests8, softmax_cross_entropy_loss_with_logits_test7) { logits.linspace(0.1, 0.1); - nd4j::ops::softmax_cross_entropy_loss_with_logits op; + sd::ops::softmax_cross_entropy_loss_with_logits op; auto results = op.evaluate({&logits, &labels}, {}, {1}); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); - auto output = results->at(0); + auto output = results.at(0); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -2622,17 +2622,17 @@ TEST_F(DeclarableOpsTests8, softmax_cross_entropy_loss_with_logits_test8) { logits.linspace(0.1, 0.1); - nd4j::ops::softmax_cross_entropy_loss_with_logits op; + sd::ops::softmax_cross_entropy_loss_with_logits op; auto results = op.evaluate({&logits, &labels}, {}, {}); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); - auto *output = results->at(0); + auto *output = results.at(0); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -2642,17 +2642,17 @@ TEST_F(DeclarableOpsTests8, softmax_cross_entropy_loss_with_logits_test9) { auto logits = NDArrayFactory::create('c', {1}, {0.2}); auto expected = NDArrayFactory::create(0.); - nd4j::ops::softmax_cross_entropy_loss_with_logits op; + sd::ops::softmax_cross_entropy_loss_with_logits op; auto results = op.evaluate({&logits, &labels}, {}, {}); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); - auto output = results->at(0); + auto output = results.at(0); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; + } /////////////////////////////////////////////////////////////////// @@ -2664,17 +2664,17 @@ TEST_F(DeclarableOpsTests8, softmax_cross_entropy_loss_with_logits_test10) { logits.linspace(0.1, 0.1); - nd4j::ops::softmax_cross_entropy_loss_with_logits op; + sd::ops::softmax_cross_entropy_loss_with_logits op; auto results = op.evaluate({&logits, &labels}, {}, {0}); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); - auto output = results->at(0); + auto output = results.at(0); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - delete results; + } //////////////////////////////////////////////////////////////////////////////// @@ -2683,14 +2683,14 @@ TEST_F(DeclarableOpsTests8, clipbynorm_test4) { auto x = NDArrayFactory::create('c', {3, 5}, {0.7044955, 0.55606544, 0.15833677, 0.001874401, 0.61595726, 0.3924779, 0.7414847, 0.4127324, 0.24026828, 0.26093036, 0.46741188, 0.01863421, 0.08528871, 0.529365, 0.5510694}); auto exp = NDArrayFactory::create('c', {3, 5}, {0.405392, 0.319980, 0.091113, 0.001079, 0.354444, 0.225846, 0.426676, 0.237501, 0.138259, 0.150149, 0.268965, 0.010723, 0.049078, 0.304615, 0.317105}); - nd4j::ops::clipbynorm op; + sd::ops::clipbynorm op; auto result = op.evaluate({&x}, {1.f}, {}); - auto output = result->at(0); + auto output = result.at(0); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -2703,14 +2703,14 @@ TEST_F(DeclarableOpsTests8, clipbynorm_test5) { x.linspace(1); - nd4j::ops::clipbynorm op; + sd::ops::clipbynorm op; auto result = op.evaluate({&x}, {15.f}, {0}); - auto output = result->at(0); + auto output = result.at(0); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -2721,14 +2721,14 @@ TEST_F(DeclarableOpsTests8, clipbynorm_test6) { x.linspace(1); - nd4j::ops::clipbynorm op; + sd::ops::clipbynorm op; auto result = op.evaluate({&x}, {15.f}, {1}); - auto output = result->at(0); + auto output = result.at(0); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -2739,14 +2739,14 @@ TEST_F(DeclarableOpsTests8, clipbynorm_test7) { x.linspace(1); - nd4j::ops::clipbynorm op; + sd::ops::clipbynorm op; auto result = op.evaluate({&x}, {15.f}, {0,1}); - auto output = result->at(0); + auto output = result.at(0); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -2757,14 +2757,14 @@ TEST_F(DeclarableOpsTests8, clipbynorm_test8) { x.linspace(1); - nd4j::ops::clipbynorm op; + sd::ops::clipbynorm op; auto result = op.evaluate({&x}, {15.}, {}); - auto output = result->at(0); + auto output = result.at(0); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -2773,14 +2773,14 @@ TEST_F(DeclarableOpsTests8, clipbynorm_test9) { auto x = NDArrayFactory::create('c', {2}, {3., 4.}); auto exp = NDArrayFactory::create('c', {2}, {2.4, 3.2}); - nd4j::ops::clipbynorm op; + sd::ops::clipbynorm op; auto result = op.evaluate({&x}, {4.}, {}); - auto output = result->at(0); + auto output = result.at(0); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -2789,14 +2789,14 @@ TEST_F(DeclarableOpsTests8, clipbynorm_test10) { auto x = NDArrayFactory::create(6.); auto exp = NDArrayFactory::create(5.); - nd4j::ops::clipbynorm op; + sd::ops::clipbynorm op; auto result = op.evaluate({&x}, {5.}, {}); - auto output = result->at(0); + auto output = result.at(0); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -2808,14 +2808,14 @@ TEST_F(DeclarableOpsTests8, clipbynorm_test11) { x.linspace(1); - nd4j::ops::clipbynorm op; + sd::ops::clipbynorm op; auto result = op.evaluate({&x}, {35.}, {0, 2}); - auto output = result->at(0); + auto output = result.at(0); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } @@ -2823,12 +2823,12 @@ TEST_F(DeclarableOpsTests8, clipbynorm_test_tf_119_1) { auto x = NDArrayFactory::create('c', {3, 3}, {1, 2, 3, 4, 5,6, 7, 8, 9}); auto e = NDArrayFactory::create('c', {3, 3}, {0.03198684, 0.06397368, 0.09596053, 0.12794736, 0.15993419, 0.19192106, 0.22390789, 0.25589472, 0.28788155}); - nd4j::ops::clipbynorm op; + sd::ops::clipbynorm op; auto result = op.evaluate({&x}, {0.54}, {}); - ASSERT_EQ(e, *result->at(0)); + ASSERT_EQ(e, *result.at(0)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -2839,21 +2839,21 @@ TEST_F(DeclarableOpsTests8, reduceMeanBP_test4) { auto gradO2 = NDArrayFactory::create('c', {1, 4}, {1., 2., 3., 4.}); auto exp = NDArrayFactory::create('c', {3,4}, {0.333333, 0.666667, 1.000000, 1.333333, 0.333333, 0.666667, 1.000000, 1.333333, 0.333333, 0.666667, 1.000000, 1.333333}); - nd4j::ops::reduce_mean_bp op; + sd::ops::reduce_mean_bp op; auto result = op.evaluate({&x, &gradO1}, {0}, {0}); - ASSERT_EQ(Status::OK(), result->status()); - auto output = result->at(0); + ASSERT_EQ(Status::OK(), result.status()); + auto output = result.at(0); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + result = op.evaluate({&x, &gradO2}, {1}, {0}); - ASSERT_EQ(Status::OK(), result->status()); - output = result->at(0); + ASSERT_EQ(Status::OK(), result.status()); + output = result.at(0); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } @@ -2865,21 +2865,21 @@ TEST_F(DeclarableOpsTests8, reduceMeanBP_test5) { auto gradO2 = NDArrayFactory::create('c', {3, 1}, {1., 2., 3.}); auto exp = NDArrayFactory::create('c', {3,4}, {0.2500,0.2500,0.2500,0.2500, 0.5000,0.5000,0.5000,0.5000, 0.7500,0.7500,0.7500,0.7500}); - nd4j::ops::reduce_mean_bp op; + sd::ops::reduce_mean_bp op; auto result = op.evaluate({&x, &gradO1}, {0}, {1}); - ASSERT_EQ(Status::OK(), result->status()); - auto output = result->at(0); + ASSERT_EQ(Status::OK(), result.status()); + auto output = result.at(0); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + result = op.evaluate({&x, &gradO2}, {1}, {1}); - ASSERT_EQ(Status::OK(), result->status()); - output = result->at(0); + ASSERT_EQ(Status::OK(), result.status()); + output = result.at(0); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } @@ -2891,21 +2891,21 @@ TEST_F(DeclarableOpsTests8, reduceStDevBP_test5) { auto gradO2 = NDArrayFactory::create('c', {1, 4}, {1., 2., 3., 4.}); auto exp = NDArrayFactory::create('c', {3,4}, {-0.408248, -0.816497, -1.224745, -1.632993, 0.000000, 0.000000, 0.000000, 0.000000, 0.408248, 0.816497, 1.224745, 1.632993}); - nd4j::ops::reduce_stdev_bp op; + sd::ops::reduce_stdev_bp op; auto result = op.evaluate({&x, &gradO1}, {0}, {0}); - ASSERT_EQ(Status::OK(), result->status()); - auto output = result->at(0); + ASSERT_EQ(Status::OK(), result.status()); + auto output = result.at(0); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + result = op.evaluate({&x, &gradO2}, {1}, {0}); - ASSERT_EQ(Status::OK(), result->status()); - output = result->at(0); + ASSERT_EQ(Status::OK(), result.status()); + output = result.at(0); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -2915,7 +2915,7 @@ TEST_F(DeclarableOpsTests8, zeros_as_test1) { auto y = NDArrayFactory::create(100.f); auto exp = NDArrayFactory::create(0.f); - nd4j::ops::zeros_as op; + sd::ops::zeros_as op; Nd4jStatus status = op.execute({&x}, {&y}, {}, {}, {}); ASSERT_EQ(Status::OK(), status); @@ -2932,15 +2932,15 @@ TEST_F(DeclarableOpsTests8, zeros_as_test2) { //auto y = NDArrayFactory::create(100.f); auto exp = NDArrayFactory::create(0.f); - nd4j::ops::zeros_as op; + sd::ops::zeros_as op; auto result = op.evaluate({&x}, {}, {}); - ASSERT_EQ(Status::OK(), result->status()); - auto y = result->at(0); + ASSERT_EQ(Status::OK(), result.status()); + auto y = result.at(0); ASSERT_TRUE(y->isSameShape(exp)); ASSERT_TRUE(y->equalsTo(exp)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -2950,7 +2950,7 @@ TEST_F(DeclarableOpsTests8, ones_as_test1) { auto y = NDArrayFactory::create(100.); auto exp = NDArrayFactory::create(1.); - nd4j::ops::ones_as op; + sd::ops::ones_as op; Nd4jStatus status = op.execute({&x}, {&y}); ASSERT_EQ(Status::OK(), status); @@ -2967,15 +2967,15 @@ TEST_F(DeclarableOpsTests8, ones_as_test2) { //auto y = NDArrayFactory::create(100.); auto exp = NDArrayFactory::create(1.); - nd4j::ops::ones_as op; + sd::ops::ones_as op; auto results = op.evaluate({&x}); - ASSERT_EQ(Status::OK(), results->status()); - auto y = results->at(0); + ASSERT_EQ(Status::OK(), results.status()); + auto y = results.at(0); ASSERT_TRUE(y->isSameShape(exp)); ASSERT_TRUE(y->equalsTo(exp)); - delete results; + } //////////////////////////////////////////////////////////////////////////////// @@ -2985,15 +2985,16 @@ TEST_F(DeclarableOpsTests8, ones_as_test3) { //auto y = NDArrayFactory::create(100.); auto exp = NDArrayFactory::create(1.); - nd4j::ops::ones_as op; + sd::ops::ones_as op; + + auto results = op.evaluate({&x}, {}, {}, {}, {sd::DataType::INT32}); + ASSERT_EQ(Status::OK(), results.status()); + auto y = results.at(0); - auto results = op.evaluate({&x}, {}, {}, {}, {nd4j::DataType::INT32}); - ASSERT_EQ(Status::OK(), results->status()); - auto y = results->at(0); ASSERT_TRUE(y->isSameShape(exp)); ASSERT_TRUE(y->equalsTo(exp)); - delete results; + } //////////////////////////////////////////////////////////////////////////////// @@ -3015,17 +3016,17 @@ TEST_F(DeclarableOpsTests8, NormalizeMoments_SGO_1) { auto ssSquared = squared.reduceAlongDimension(reduce::Sum, {0}); // ssSquared->printBuffer("Sum squared"); // squared.printBuffer("Squared"); - nd4j::ops::normalize_moments op; + sd::ops::normalize_moments op; auto results = op.evaluate({&counts, &means, &ssSquared}, {0.0}, {0}); means /= counts; -// nd4j::ops::normalize_moments op; +// sd::ops::normalize_moments op; // auto results = op.evaluate({&counts, means, deviance}, {0.0}, {}); - ASSERT_EQ(Status::OK(), results->status()); - ASSERT_EQ(results->size(), 2); + ASSERT_EQ(Status::OK(), results.status()); + ASSERT_EQ(results.size(), 2); - auto outputMeans = results->at(0); - auto outputDeviance = results->at(1); + auto outputMeans = results.at(0); + auto outputDeviance = results.at(1); // outputMeans->printIndexedBuffer("Means"); // outputDeviance->printIndexedBuffer("Variance"); @@ -3041,7 +3042,7 @@ TEST_F(DeclarableOpsTests8, NormalizeMoments_SGO_1) { // ASSERT_TRUE(expMeans.isSameShape(outputDeviance)); // ASSERT_TRUE(expDeviance.equalsTo(outputDeviance)); - delete results; + } //////////////////////////////////////////////////////////////////////////////// @@ -3052,13 +3053,13 @@ TEST_F(DeclarableOpsTests8, Test_Moments_1) { auto expVariance = NDArrayFactory::create('c', {4}, {46.666668f, 46.666668f, 46.66666f, 46.666668f}); x.linspace(1); - nd4j::ops::moments op; + sd::ops::moments op; auto result = op.evaluate({&x}, {}, {0, 1}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto outputMeans = result->at(0); - auto outputVariance = result->at(1); + auto outputMeans = result.at(0); + auto outputVariance = result.at(1); // outputMeans->printIndexedBuffer("Means"); // outputVariance->printIndexedBuffer("Variance"); @@ -3072,7 +3073,7 @@ TEST_F(DeclarableOpsTests8, Test_Moments_1) { ASSERT_TRUE(expVariance.isSameShape(outputVariance)); ASSERT_TRUE(expVariance.equalsTo(outputVariance)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -3083,12 +3084,12 @@ TEST_F(DeclarableOpsTests8, Test_Moments_2) { auto expVariance = NDArrayFactory::create('c', {1,1,4}, {46.666668f, 46.666668f, 46.66666f, 46.666668f}); x.linspace(1); - nd4j::ops::moments op; + sd::ops::moments op; auto result = op.evaluate({&x}, {1.}, {0, 1}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto outputMeans = result->at(0); - auto outputVariance = result->at(1); + auto outputMeans = result.at(0); + auto outputVariance = result.at(1); // outputMeans->printIndexedBuffer("Means"); // outputVariance->printIndexedBuffer("Variance"); @@ -3101,7 +3102,7 @@ TEST_F(DeclarableOpsTests8, Test_Moments_2) { ASSERT_TRUE(expVariance.isSameShape(outputVariance)); ASSERT_TRUE(expVariance.equalsTo(outputVariance)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -3112,12 +3113,12 @@ TEST_F(DeclarableOpsTests8, Test_Moments_3) { auto expVariance = NDArrayFactory::create('c', {3}, {37.25f, 37.25f, 37.25f}); x.linspace(1); - nd4j::ops::moments op; + sd::ops::moments op; auto result = op.evaluate({&x}, {}, {0, 2}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto outputMeans = result->at(0); - auto outputVariance = result->at(1); + auto outputMeans = result.at(0); + auto outputVariance = result.at(1); // outputMeans->printIndexedBuffer("Means"); // outputVariance->printIndexedBuffer("Variance"); @@ -3130,7 +3131,7 @@ TEST_F(DeclarableOpsTests8, Test_Moments_3) { ASSERT_TRUE(expVariance.isSameShape(outputVariance)); ASSERT_TRUE(expVariance.equalsTo(outputVariance)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -3141,12 +3142,12 @@ TEST_F(DeclarableOpsTests8, Test_Moments_4) { auto expVariance = NDArrayFactory::create('c', {1,3,1}, {37.25f, 37.25f, 37.25f}); x.linspace(1); - nd4j::ops::moments op; + sd::ops::moments op; auto result = op.evaluate({&x}, {1.}, {0, 2}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto outputMeans = result->at(0); - auto outputVariance = result->at(1); + auto outputMeans = result.at(0); + auto outputVariance = result.at(1); // outputMeans->printIndexedBuffer("Means"); // outputVariance->printIndexedBuffer("Variance"); @@ -3159,7 +3160,7 @@ TEST_F(DeclarableOpsTests8, Test_Moments_4) { ASSERT_TRUE(expVariance.isSameShape(outputVariance)); ASSERT_TRUE(expVariance.equalsTo(outputVariance)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -3170,12 +3171,12 @@ TEST_F(DeclarableOpsTests8, Test_Moments_6) { auto x = NDArrayFactory::create('c', {2, 3, 4}); x.linspace(1); - nd4j::ops::moments op; + sd::ops::moments op; auto result = op.evaluate({&x}, {}, {0,1,2}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto outputMeans = result->at(0); - auto outputVariance = result->at(1); + auto outputMeans = result.at(0); + auto outputVariance = result.at(1); // outputMeans->printIndexedBuffer("Means"); // outputVariance->printIndexedBuffer("Variance"); @@ -3186,7 +3187,7 @@ TEST_F(DeclarableOpsTests8, Test_Moments_6) { ASSERT_TRUE(expVariance.isSameShape(outputVariance)); ASSERT_TRUE(expVariance.equalsTo(outputVariance)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -3199,12 +3200,12 @@ TEST_F(DeclarableOpsTests8, Test_Moments_7) { x.linspace(1); // x.printIndexedBuffer("Input with shape (2, 3, 4) is"); - nd4j::ops::moments op; + sd::ops::moments op; auto result = op.evaluate({&x}, {1.}, {0,1,2}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto outputMeans = result->at(0); - auto outputVariance = result->at(1); + auto outputMeans = result.at(0); + auto outputVariance = result.at(1); // outputMeans->printIndexedBuffer("Means"); // outputVariance->printIndexedBuffer("Variance"); @@ -3214,7 +3215,7 @@ TEST_F(DeclarableOpsTests8, Test_Moments_7) { ASSERT_TRUE(expVariance.isSameShape(outputVariance)); ASSERT_TRUE(expVariance.equalsTo(outputVariance)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -3228,17 +3229,17 @@ TYPED_TEST(TypedDeclarableOpsTests8, LrnTest_01) { // 0.7581754, 0.58321184, 0.86747235, 0.4048204} ); - nd4j::ops::lrn op; + sd::ops::lrn op; auto results = op.evaluate({&x}, {1.0, 1.0, 0.5}, {2}); - auto out = results->at(0); + auto out = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); //ASSERT_TRUE(exp.isSameShape(out)); //out->printBuffer("LRN out"); //exp.printBuffer("LRN exp"); ASSERT_TRUE(exp.equalsTo(out)); - delete results; + } //////////////////////////////////////////////////////////////////////////////// @@ -3250,17 +3251,17 @@ TYPED_TEST(TypedDeclarableOpsTests8, LrnTest_02) { 0.2581989f, 0.3592106f, 0.40089184f, 0.4193139f, 0.5360563f, 0.67936623f} ); - nd4j::ops::lrn op; + sd::ops::lrn op; auto results = op.evaluate({&x}, {1.0, 1.0, 0.5}, {2}); - auto out = results->at(0); + auto out = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); //ASSERT_TRUE(exp.isSameShape(out)); //out->printIndexedBuffer("LRN out"); // exp.printIndexedBuffer("LRN exp"); ASSERT_TRUE(exp.equalsTo(out)); - delete results; + } //////////////////////////////////////////////////////////////////////////////// @@ -3269,17 +3270,17 @@ TYPED_TEST(TypedDeclarableOpsTests8, LrnTest_03) { auto x = NDArrayFactory::create('c', {1, 1, 1, 10}, { 1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 7.f, 8.f, 9.f, 10.f}); auto exp = NDArrayFactory::create('c', {1, 1, 1, 10}, {0.10425719f, 0.16843036f, 0.2095291f, 0.23652494f, 0.25449327f, 0.3053919f, 0.35675305f, 0.4098524f, 0.46662825f, 0.52999896f}); - nd4j::ops::lrn op; + sd::ops::lrn op; auto results = op.evaluate({&x}, {1.0, 1.0, 0.5}, {5}); - auto out = results->at(0); + auto out = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(exp.isSameShape(out)); // out->printIndexedBuffer("LRN out"); // exp.printIndexedBuffer("LRN exp"); ASSERT_TRUE(exp.equalsTo(out)); - delete results; + } //////////////////////////////////////////////////////////////////////////////// @@ -3298,17 +3299,17 @@ TYPED_TEST(TypedDeclarableOpsTests8, LrnTest_1) { 0.7581754f, 0.58321184f, 0.86747235f, 0.4048204f} ); - nd4j::ops::lrn op; + sd::ops::lrn op; auto results = op.evaluate({&x}, {1.0, 1.0, 0.5}, {2}); - auto out = results->at(0); + auto out = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(exp.isSameShape(out)); // out->printIndexedBuffer("LRN out"); // exp.printIndexedBuffer("LRN exp"); ASSERT_TRUE(exp.equalsTo(out)); - delete results; + } //////////////////////////////////////////////////////////////////////////////// @@ -3375,17 +3376,17 @@ TYPED_TEST(TypedDeclarableOpsTests8, LrnTest_2) { 0.57474375f, 0.49886885f, 0.44720373f, 0.50111103f, 0.5799219f } ); // - nd4j::ops::lrn op; + sd::ops::lrn op; auto results = op.evaluate({&x}, {1.0, 1.0, 0.5}, {2}); - auto out = results->at(0); + auto out = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); // ASSERT_TRUE(exp.isSameShape(out)); // out->printIndexedBuffer("LRN out"); // exp.printIndexedBuffer("LRN exp"); ASSERT_TRUE(exp.equalsTo(out)); - delete results; + } //////////////////////////////////////////////////////////////////////////////// @@ -3452,17 +3453,17 @@ TYPED_TEST(TypedDeclarableOpsTests8, LrnTest_3) { 0.57474375f, 0.49886885f, 0.44720373f, 0.50111103f, 0.5799219f } ); // - nd4j::ops::lrn op; + sd::ops::lrn op; auto results = op.evaluate({&x}, {1.0, 1.0, 0.5}, {2}); - auto out = results->at(0); + auto out = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); // ASSERT_TRUE(exp.isSameShape(out)); // out->printIndexedBuffer("LRN out"); // exp.printIndexedBuffer("LRN exp"); ASSERT_TRUE(exp.equalsTo(out)); - delete results; + } //////////////////////////////////////////////////////////////////////////////// @@ -3472,17 +3473,17 @@ TYPED_TEST(TypedDeclarableOpsTests8, LrnTest_4) { auto x = NDArrayFactory::create('c', {2, 8, 16, 16}); x.linspace(1); - nd4j::ops::lrn op; + sd::ops::lrn op; auto results = op.evaluate({&x}, {1.0, 1.0, 0.5}, {2}); - auto out = results->at(0); + auto out = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); // ASSERT_TRUE(exp.isSameShape(out)); // out->printIndexedBuffer("LRN out"); // exp.printIndexedBuffer("LRN exp"); // ASSERT_TRUE(exp.equalsTo(out)); - delete results; + } //////////////////////////////////////////////////////////////////////////////// @@ -3494,7 +3495,7 @@ TYPED_TEST(TypedDeclarableOpsTests8, LrnTest_4_119) { auto z = NDArrayFactory::create('c', {2, 8, 16, 16}); x.linspace(1); - nd4j::ops::lrn op; + sd::ops::lrn op; op.execute({&x}, {&z}, {1.0, 1.0, 0.5}, {2}); @@ -3512,23 +3513,6 @@ TYPED_TEST(TypedDeclarableOpsTests8, LrnTest_4_119) { // ASSERT_TRUE(exp.equalsTo(out)); } -//////////////////////////////////////////////////////////////////////////////// -TYPED_TEST(TypedDeclarableOpsTests8, LrnTest_5) { - - auto x = NDArrayFactory::create('f', {8, 32, 64, 64}); - x.linspace(1); - - nd4j::ops::lrn op; - auto results = op.evaluate({&x}, {1.0, 1.0, 0.5}, {2}); - auto out = results->at(0); - - ASSERT_EQ(Status::OK(), results->status()); -// ASSERT_TRUE(exp.isSameShape(out)); -// ASSERT_TRUE(exp.equalsTo(out)); - - delete results; -} - //////////////////////////////////////////////////////////////////////////////// TYPED_TEST(TypedDeclarableOpsTests8, LrnTest_BP_01) { @@ -3541,17 +3525,17 @@ TYPED_TEST(TypedDeclarableOpsTests8, LrnTest_BP_01) { // 0.238337, 0.309664, 0.334077, 0.376534, 0.342926, 0.370734, 0.362017, 0.354182, 0.379140, 0.376275, 0.380027, 0.368347, 0.356401, 0.378316, 0.381315, 0.382465, 0.370592, 0.357055, 0.377670, 0.382950, 0.383445, 0.371718, 0.357332, 0.377217, 0.383677, 0.383933, 0.372391, 0.357475, 0.376891, 0.384062, 0.384212, 0.372837, 0.357557, 0.376646, 0.384290, 0.384385, 0.373153, 0.357610, 0.376457, 0.384436, 0.384500, 0.373389, 0.357645, 0.376306, 0.384536, 0.384581, 0.373572, 0.357670, 0.376184, 0.384606, 0.384639, 0.373718, 0.357688, 0.376082, 0.384658, 0.384683, 0.373837, 0.357702, 0.375996, 0.384698, 0.384717, 0.373935, 0.357712, 0.375923, 0.384728, 0.384743, 0.374019, 0.357721, 0.375860, 0.384752, 0.384764, 0.374090, 0.357727, 0.375804, 0.384771, 0.384781, 0.374152, 0.357733, 0.375756, 0.384787, 0.384795, 0.374205, 0.357737, 0.375713, 0.384800, 0.384807, 0.374253, 0.357741, 0.375674, 0.384811, 0.384817, 0.374295, 0.357744, 0.375640, 0.384820, 0.384825, 0.374333, 0.357747, 0.375609, 0.384828, 0.384832, 0.374366, 0.357749, 0.375581, 0.384835, 0.384839, 0.374397, 0.357751, 0.375555, 0.384841, 0.384844, 0.374425, 0.357753, 0.375531, 0.384846, 0.384849, 0.374450, 0.357754, 0.375510, 0.384850, 0.384853, 0.374473, 0.357756, 0.375490, 0.384854, 0.384856, 0.374494, 0.357757, 0.375471, 0.384858, 0.384860, 0.374514, 0.357758, 0.375454, 0.384861, 0.384863, 0.374532, 0.357759, 0.375438, 0.384864, 0.384865, 0.374549, 0.357760, 0.375423, 0.384866, 0.384868, 0.374565, 0.357760, 0.375410, 0.384868, 0.384870, 0.374579, 0.357761, 0.375397, 0.384870, 0.384872, 0.374593, 0.357762, 0.375384, 0.384872, 0.384873, 0.374606, 0.357762, 0.375373, 0.384874, 0.384875, 0.374618, 0.357763, 0.375362, 0.384875, 0.384876, 0.374629, 0.357763, 0.375352, 0.384877, 0.384878, 0.374640, 0.357764, 0.375342, 0.384878, 0.384879, 0.374650, 0.357764, 0.375333, 0.384879, 0.384880, 0.374660, 0.357764, 0.375325, 0.384880, 0.384881, 0.374669, 0.357765, 0.375316, 0.384881, 0.384882, 0.374677, 0.357765, 0.375309, 0.384882, 0.384883, 0.374685, 0.357765, 0.375301, 0.384883, 0.384884, 0.374693, 0.357765, 0.375294, 0.384884, 0.384884, 0.374700, 0.357766, 0.375287, 0.384885, 0.384885, 0.374707, 0.357766, 0.375281, 0.384885, 0.384886, 0.374714, 0.357766, 0.375275, 0.384886} // ); /// - nd4j::ops::lrn_bp op; + sd::ops::lrn_bp op; auto results = op.evaluate({&x, &eps}, {1.0, 1.0, 0.5}, {5}); - auto out = results->at(0); + auto out = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); // ASSERT_TRUE(exp.isSameShape(out)); //out->printBuffer("LRN BP out"); //exp.printBuffer("LRN BP exp"); //ASSERT_TRUE(exp.equalsTo(out)); - delete results; + } //////////////////////////////////////////////////////////////////////////////// @@ -3566,23 +3550,23 @@ TYPED_TEST(TypedDeclarableOpsTests8, LrnTest_BP_02) { // 0.238337, 0.309664, 0.334077, 0.376534, 0.342926, 0.370734, 0.362017, 0.354182, 0.379140, 0.376275, 0.380027, 0.368347, 0.356401, 0.378316, 0.381315, 0.382465, 0.370592, 0.357055, 0.377670, 0.382950, 0.383445, 0.371718, 0.357332, 0.377217, 0.383677, 0.383933, 0.372391, 0.357475, 0.376891, 0.384062, 0.384212, 0.372837, 0.357557, 0.376646, 0.384290, 0.384385, 0.373153, 0.357610, 0.376457, 0.384436, 0.384500, 0.373389, 0.357645, 0.376306, 0.384536, 0.384581, 0.373572, 0.357670, 0.376184, 0.384606, 0.384639, 0.373718, 0.357688, 0.376082, 0.384658, 0.384683, 0.373837, 0.357702, 0.375996, 0.384698, 0.384717, 0.373935, 0.357712, 0.375923, 0.384728, 0.384743, 0.374019, 0.357721, 0.375860, 0.384752, 0.384764, 0.374090, 0.357727, 0.375804, 0.384771, 0.384781, 0.374152, 0.357733, 0.375756, 0.384787, 0.384795, 0.374205, 0.357737, 0.375713, 0.384800, 0.384807, 0.374253, 0.357741, 0.375674, 0.384811, 0.384817, 0.374295, 0.357744, 0.375640, 0.384820, 0.384825, 0.374333, 0.357747, 0.375609, 0.384828, 0.384832, 0.374366, 0.357749, 0.375581, 0.384835, 0.384839, 0.374397, 0.357751, 0.375555, 0.384841, 0.384844, 0.374425, 0.357753, 0.375531, 0.384846, 0.384849, 0.374450, 0.357754, 0.375510, 0.384850, 0.384853, 0.374473, 0.357756, 0.375490, 0.384854, 0.384856, 0.374494, 0.357757, 0.375471, 0.384858, 0.384860, 0.374514, 0.357758, 0.375454, 0.384861, 0.384863, 0.374532, 0.357759, 0.375438, 0.384864, 0.384865, 0.374549, 0.357760, 0.375423, 0.384866, 0.384868, 0.374565, 0.357760, 0.375410, 0.384868, 0.384870, 0.374579, 0.357761, 0.375397, 0.384870, 0.384872, 0.374593, 0.357762, 0.375384, 0.384872, 0.384873, 0.374606, 0.357762, 0.375373, 0.384874, 0.384875, 0.374618, 0.357763, 0.375362, 0.384875, 0.384876, 0.374629, 0.357763, 0.375352, 0.384877, 0.384878, 0.374640, 0.357764, 0.375342, 0.384878, 0.384879, 0.374650, 0.357764, 0.375333, 0.384879, 0.384880, 0.374660, 0.357764, 0.375325, 0.384880, 0.384881, 0.374669, 0.357765, 0.375316, 0.384881, 0.384882, 0.374677, 0.357765, 0.375309, 0.384882, 0.384883, 0.374685, 0.357765, 0.375301, 0.384883, 0.384884, 0.374693, 0.357765, 0.375294, 0.384884, 0.384884, 0.374700, 0.357766, 0.375287, 0.384885, 0.384885, 0.374707, 0.357766, 0.375281, 0.384885, 0.384886, 0.374714, 0.357766, 0.375275, 0.384886} // ); /// - nd4j::ops::lrn opFF; - nd4j::ops::lrn_bp opBP; + sd::ops::lrn opFF; + sd::ops::lrn_bp opBP; const OpArgsHolder argsHolderFF({&x}, {1., 1., 0.5}, {5}); const OpArgsHolder argsHolderBP({&x, &eps}, {1., 1., 0.5}, {5}); bool gradOK = true; //GradCheck::checkGrad(opFF, opBP, argsHolderFF, argsHolderBP); - //auto results = op.execute({&x, &eps}, {1.0, 1.0, 0.5}, {5}, {}, false, nd4j::DataType::DOUBLE); - //auto out = results->at(0); + //auto results = op.execute({&x, &eps}, {1.0, 1.0, 0.5}, {5}, {}, false, sd::DataType::DOUBLE); + //auto out = results.at(0); - //ASSERT_EQ(Status::OK(), results->status()); + //ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(gradOK); //out->printBuffer("LRN BP out"); //exp.printBuffer("LRN BP exp"); //ASSERT_TRUE(exp.equalsTo(out)); - //delete results; + // } //////////////////////////////////////////////////////////////////////////////// @@ -3597,17 +3581,17 @@ auto exp = NDArrayFactory::create('c', {3,3,5,5}, { 0.238337f, 0.309664f, 0.334077f, 0.376534f, 0.342926f, 0.370734f, 0.362017f, 0.354182f, 0.379140f, 0.376275f, 0.380027f, 0.368347f, 0.356401f, 0.378316f, 0.381315f, 0.382465f, 0.370592f, 0.357055f, 0.377670f, 0.382950f, 0.383445f, 0.371718f, 0.357332f, 0.377217f, 0.383677f, 0.383933f, 0.372391f, 0.357475f, 0.376891f, 0.384062f, 0.384212f, 0.372837f, 0.357557f, 0.376646f, 0.384290f, 0.384385f, 0.373153f, 0.357610f, 0.376457f, 0.384436f, 0.384500f, 0.373389f, 0.357645f, 0.376306f, 0.384536f, 0.384581f, 0.373572f, 0.357670f, 0.376184f, 0.384606f, 0.384639f, 0.373718f, 0.357688f, 0.376082f, 0.384658f, 0.384683f, 0.373837f, 0.357702f, 0.375996f, 0.384698f, 0.384717f, 0.373935f, 0.357712f, 0.375923f, 0.384728f, 0.384743f, 0.374019f, 0.357721f, 0.375860f, 0.384752f, 0.384764f, 0.374090f, 0.357727f, 0.375804f, 0.384771f, 0.384781f, 0.374152f, 0.357733f, 0.375756f, 0.384787f, 0.384795f, 0.374205f, 0.357737f, 0.375713f, 0.384800f, 0.384807f, 0.374253f, 0.357741f, 0.375674f, 0.384811f, 0.384817f, 0.374295f, 0.357744f, 0.375640f, 0.384820f, 0.384825f, 0.374333f, 0.357747f, 0.375609f, 0.384828f, 0.384832f, 0.374366f, 0.357749f, 0.375581f, 0.384835f, 0.384839f, 0.374397f, 0.357751f, 0.375555f, 0.384841f, 0.384844f, 0.374425f, 0.357753f, 0.375531f, 0.384846f, 0.384849f, 0.374450f, 0.357754f, 0.375510f, 0.384850f, 0.384853f, 0.374473f, 0.357756f, 0.375490f, 0.384854f, 0.384856f, 0.374494f, 0.357757f, 0.375471f, 0.384858f, 0.384860f, 0.374514f, 0.357758f, 0.375454f, 0.384861f, 0.384863f, 0.374532f, 0.357759f, 0.375438f, 0.384864f, 0.384865f, 0.374549f, 0.357760f, 0.375423f, 0.384866f, 0.384868f, 0.374565f, 0.357760f, 0.375410f, 0.384868f, 0.384870f, 0.374579f, 0.357761f, 0.375397f, 0.384870f, 0.384872f, 0.374593f, 0.357762f, 0.375384f, 0.384872f, 0.384873f, 0.374606f, 0.357762f, 0.375373f, 0.384874f, 0.384875f, 0.374618f, 0.357763f, 0.375362f, 0.384875f, 0.384876f, 0.374629f, 0.357763f, 0.375352f, 0.384877f, 0.384878f, 0.374640f, 0.357764f, 0.375342f, 0.384878f, 0.384879f, 0.374650f, 0.357764f, 0.375333f, 0.384879f, 0.384880f, 0.374660f, 0.357764f, 0.375325f, 0.384880f, 0.384881f, 0.374669f, 0.357765f, 0.375316f, 0.384881f, 0.384882f, 0.374677f, 0.357765f, 0.375309f, 0.384882f, 0.384883f, 0.374685f, 0.357765f, 0.375301f, 0.384883f, 0.384884f, 0.374693f, 0.357765f, 0.375294f, 0.384884f, 0.384884f, 0.374700f, 0.357766f, 0.375287f, 0.384885f, 0.384885f, 0.374707f, 0.357766f, 0.375281f, 0.384885f, 0.384886f, 0.374714f, 0.357766f, 0.375275f, 0.384886f} ); /// - nd4j::ops::lrn_bp op; + sd::ops::lrn_bp op; auto results = op.evaluate({&x, &eps}, {1.0, 1.0, 0.5}, {2}, {}, {}, false); - auto out = results->at(0); + auto out = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); // ASSERT_TRUE(exp.isSameShape(out)); // out->printBuffer("LRN BP out"); // exp.printBuffer("LRN BP exp"); //ASSERT_TRUE(exp.equalsTo(out)); - delete results; + } //////////////////////////////////////////////////////////////////////////////// @@ -3677,17 +3661,17 @@ TYPED_TEST(TypedDeclarableOpsTests8, LrnTest_BP_2) { // 0.009859f, 0.013075f, 0.013874f, 0.017893f, 0.022344f, 0.014551f, 0.012859f, 0.011511f, 0.013311f, 0.015834f, 0.012025f, 0.010047f, 0.008601f, 0.009920f, 0.011885f, 0.009505f, 0.007636f, 0.006299f, 0.007413f, 0.009095f, 0.007446f, 0.005743f, 0.004540f, 0.005533f, 0.007033f, 0.005821f, 0.004282f, 0.003209f, 0.004123f, 0.005491f, 0.004577f, 0.003198f, 0.002247f, 0.003097f, 0.004355f, 0.003652f, 0.002412f, 0.001565f, 0.002357f, 0.003517f, 0.002965f, 0.001844f, 0.001084f, 0.001821f, 0.002893f, 0.002451f, 0.001430f, 0.000741f, 0.001428f, 0.002422f, -0.111434f, -0.105946f, -0.100351f, -0.091868f, -0.083323f, -0.078775f, -0.076222f, -0.073291f, -0.067635f, -0.061692f, -0.058943f, -0.057832f, -0.056263f, -0.052198f, -0.047768f, -0.046002f, -0.045655f, -0.044839f, -0.041748f, -0.038271f, -0.037084f, -0.037161f, -0.036786f, -0.034331f, -0.031495f, 0.000077f, -0.000673f, -0.001181f, -0.000667f, 0.000079f, -0.000089f, -0.000802f, -0.001285f, -0.000793f, -0.000079f, -0.000228f, -0.000908f, -0.001368f, -0.000896f, -0.000212f, -0.000345f, -0.000996f, -0.001434f, -0.000981f, -0.000325f, -0.000444f, -0.001067f, -0.001487f, -0.001051f, -0.000421f, 0.000697f, 0.000188f, -0.000152f, 0.000210f, 0.000731f, 0.000650f, 0.000165f, -0.000161f, 0.000185f, 0.000683f, 0.000610f, 0.000145f, -0.000168f, 0.000164f, 0.000641f, 0.000574f, 0.000128f, -0.000172f, 0.000146f, 0.000604f, 0.000542f, 0.000113f, -0.000175f, 0.000131f, 0.000571f, -0.009490f, -0.010070f, -0.010409f, -0.009734f, -0.008834f, -0.008785f, -0.009351f, -0.009687f, -0.009054f, -0.008207f, -0.008167f, -0.008718f, -0.009050f, -0.008455f, -0.007654f, -0.007622f, -0.008159f, -0.008485f, -0.007924f, -0.007164f, -0.007138f, -0.007661f, -0.007981f, -0.007450f, -0.006728f, -0.000901f, -0.001327f, -0.001614f, -0.001310f, -0.000869f, -0.000913f, -0.001328f, -0.001607f, -0.001310f, -0.000882f, -0.000922f, -0.001326f, -0.001598f, -0.001309f, -0.000892f, -0.000930f, -0.001323f, -0.001588f, -0.001306f, -0.000900f, -0.000936f, -0.001319f, -0.001577f, -0.001302f, -0.000906f, 0.000339f, 0.000038f, -0.000164f, 0.000048f, 0.000355f, 0.000328f, 0.000035f, -0.000162f, 0.000045f, 0.000343f, 0.000318f, 0.000033f, -0.000159f, 0.000041f, 0.000332f, 0.000308f, 0.000030f, -0.000157f, 0.000039f, 0.000322f, 0.000299f, 0.000028f, -0.000155f, 0.000036f, 0.000312f, -0.004085f, -0.004479f, -0.004733f, -0.004396f, -0.003925f, -0.003925f, -0.004309f, -0.004558f, -0.004232f, -0.003775f, -0.003776f, -0.004151f, -0.004395f, -0.004079f, -0.003636f, -0.003637f, -0.004004f, -0.004242f, -0.003936f, -0.003505f, -0.003507f, -0.003866f, -0.004100f, -0.003802f, -0.003383f} ); - nd4j::ops::lrn_bp op; + sd::ops::lrn_bp op; auto results = op.evaluate({&x, &eps}, {1.0, 1.0, 0.5}, {2}, {}, {}, false); - auto out = results->at(0); + auto out = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(exp.isSameShape(out)); //out->printBuffer("LRN BP out"); // exp.printIndexedBuffer("LRN exp"); // ASSERT_TRUE(exp.equalsTo(out)); - delete results; + } diff --git a/libnd4j/tests_cpu/layers_tests/DeclarableOpsTests9.cpp b/libnd4j/tests_cpu/layers_tests/DeclarableOpsTests9.cpp index 773e1dc18..c7e704a21 100644 --- a/libnd4j/tests_cpu/layers_tests/DeclarableOpsTests9.cpp +++ b/libnd4j/tests_cpu/layers_tests/DeclarableOpsTests9.cpp @@ -21,13 +21,13 @@ #include "testlayers.h" #include -#include +#include #include -#include +#include #include -using namespace nd4j; +using namespace sd; class DeclarableOpsTests9 : public testing::Test { @@ -49,22 +49,22 @@ TEST_F(DeclarableOpsTests9, reduceStDevBP_test3) { x.linspace(1); - nd4j::ops::reduce_stdev_bp op; + sd::ops::reduce_stdev_bp op; auto result = op.evaluate({&x, &gradO2}, {0,0}, {1}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); - auto output = result->at(0); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); + auto output = result.at(0); // output->printIndexedBuffer(); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + result = op.evaluate({&x, &gradO1}, {1,0}, {1}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); - output = result->at(0); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); + output = result.at(0); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } @@ -78,22 +78,22 @@ TEST_F(DeclarableOpsTests9, reduceStDevBP_test03) { auto axis = NDArrayFactory::create('c', {1}, {1}); x.linspace(1); - nd4j::ops::reduce_stdev_bp op; + sd::ops::reduce_stdev_bp op; auto result = op.evaluate({&x, &gradO2, &axis}, {}, {}, {false, false}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); - auto output = result->at(0); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); + auto output = result.at(0); // output->printIndexedBuffer(); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + result = op.evaluate({&x, &gradO1}, {1,0}, {1}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); - output = result->at(0); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); + output = result.at(0); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } /* @@ -110,7 +110,7 @@ TEST_F(DeclarableOpsTests9, exponentialDistributionInv_test1) { double extraParams[] = {lambda}; Nd4jLong *buffer = new Nd4jLong[N]; - auto rng = (nd4j::random::RandomBuffer *) initRandom(nullptr, 123, N, (Nd4jPointer) buffer); + auto rng = (sd::random::RandomBuffer *) initRandom(nullptr, 123, N, (Nd4jPointer) buffer); if (rng == nullptr) throw std::runtime_error("DeclarableOpsTests9.exponentialDistributionInv_test1: RNG initialization failed !"); @@ -141,7 +141,7 @@ TEST_F(DeclarableOpsTests9, exponentialDistributionInv_test2) { Nd4jLong *buffer = new Nd4jLong[N]; - auto rng = (nd4j::random::RandomBuffer *) initRandom(nullptr, 123, N, (Nd4jPointer) buffer); + auto rng = (sd::random::RandomBuffer *) initRandom(nullptr, 123, N, (Nd4jPointer) buffer); if (rng == nullptr) throw std::runtime_error("DeclarableOpsTests9.exponentialDistributionInv_test2: RNG initialization failed !"); @@ -170,7 +170,7 @@ TEST_F(DeclarableOpsTests9, exponentialDistribution_test1) { double extraParams[] = {lambda}; Nd4jLong *buffer = new Nd4jLong[N]; - auto rng = (nd4j::random::RandomBuffer *) initRandom(nullptr, 123, N, (Nd4jPointer) buffer); + auto rng = (sd::random::RandomBuffer *) initRandom(nullptr, 123, N, (Nd4jPointer) buffer); if (rng == nullptr) throw std::runtime_error("DeclarableOpsTests9.exponentialDistribution_test1: RNG initialization failed !"); @@ -203,7 +203,7 @@ TEST_F(DeclarableOpsTests9, exponentialDistribution_test2) { Nd4jLong *buffer = new Nd4jLong[N]; // Nd4jPointer extra[2]; #ifndef __CUDABLAS__ - nd4j::random::RandomBuffer* rng = (nd4j::random::RandomBuffer *) initRandom(nullptr, 123, N, (Nd4jPointer) buffer); + sd::random::RandomBuffer* rng = (sd::random::RandomBuffer *) initRandom(nullptr, 123, N, (Nd4jPointer) buffer); if (rng == nullptr) throw std::runtime_error("DeclarableOpsTests9.exponentialDistribution_test2: RNG initialization failed !"); @@ -246,16 +246,16 @@ TEST_F(DeclarableOpsTests9, concat_test1) { x1.linspace(1); x2.linspace(1); - nd4j::ops::concat op; + sd::ops::concat op; auto result = op.evaluate({&x0, &x1, &x2}, {}, {1}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); - auto output = result->at(0); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); + auto output = result.at(0); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -270,16 +270,16 @@ TEST_F(DeclarableOpsTests9, concat_test2) { x1.linspace(1); x2.linspace(1); - nd4j::ops::concat op; + sd::ops::concat op; auto result = op.evaluate({&x0, &x1, &x2}, {}, {1}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); - auto output = result->at(0); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); + auto output = result.at(0); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -294,18 +294,18 @@ TEST_F(DeclarableOpsTests9, concat_test3) { x1.linspace(1); x2.linspace(1); - nd4j::ops::concat op; + sd::ops::concat op; auto result = op.evaluate({&x0, &x1, &x2}, {}, {0}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); - auto output = result->at(0); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); + auto output = result.at(0); output->printBuffer(); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -316,16 +316,16 @@ TEST_F(DeclarableOpsTests9, concat_test4) { auto x2 = NDArrayFactory::create('c', {1,1,1}, {3.f}); auto exp = NDArrayFactory::create('c', {1,3,1}, {1.f, 2.f, 3.f}); - nd4j::ops::concat op; + sd::ops::concat op; auto result = op.evaluate({&x0, &x1, &x2}, {}, {1}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); - auto output = result->at(0); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); + auto output = result.at(0); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -336,16 +336,16 @@ TEST_F(DeclarableOpsTests9, concat_test5) { auto x2 = NDArrayFactory::create(3.f); auto exp = NDArrayFactory::create('c', {3}, {1.f, 2.f, 3.f}); - nd4j::ops::concat op; + sd::ops::concat op; auto result = op.evaluate({&x0, &x1, &x2}, {}, {0}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); - auto output = result->at(0); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); + auto output = result.at(0); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -356,16 +356,16 @@ TEST_F(DeclarableOpsTests9, concat_test6) { auto x2 = NDArrayFactory::create(3.f); auto exp = NDArrayFactory::create('c', {4}, {1.f, 2.f, 20.f, 3.f}); - nd4j::ops::concat op; + sd::ops::concat op; auto result = op.evaluate({&x0, &x1, &x2}, {}, {0}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); - auto output = result->at(0); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); + auto output = result.at(0); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -376,16 +376,16 @@ TEST_F(DeclarableOpsTests9, concat_test7) { auto x2 = NDArrayFactory::create(3.f); auto exp = NDArrayFactory::create('c', {3}, {1.f, 2.f, 3.f}); - nd4j::ops::concat op; + sd::ops::concat op; auto result = op.evaluate({&x0, &x1, &x2}, {}, {0}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); - auto output = result->at(0); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); + auto output = result.at(0); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -394,16 +394,16 @@ TEST_F(DeclarableOpsTests9, concat_test8) { auto x0 = NDArrayFactory::create(1.f); auto exp = NDArrayFactory::create('c', {1}, {1.f}); - nd4j::ops::concat op; + sd::ops::concat op; auto result = op.evaluate({&x0}, {}, {0}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); - auto output = result->at(0); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); + auto output = result.at(0); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -412,16 +412,16 @@ TEST_F(DeclarableOpsTests9, concat_test9) { auto x0 = NDArrayFactory::create('c', {1}, {1.f}); auto exp = NDArrayFactory::create('c', {1}, {1.f}); - nd4j::ops::concat op; + sd::ops::concat op; auto result = op.evaluate({&x0}, {}, {0}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); - auto output = result->at(0); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); + auto output = result.at(0); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -437,16 +437,16 @@ TEST_F(DeclarableOpsTests9, concat_test10) { x1.linspace(1); x2.linspace(1); - nd4j::ops::concat op; + sd::ops::concat op; auto result = op.evaluate({&x0, &x1, &x2}, {}, {1}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); - auto output = result->at(0); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); + auto output = result.at(0); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -462,16 +462,16 @@ TEST_F(DeclarableOpsTests9, concat_test11) { x1.linspace(1); x2.linspace(1); - nd4j::ops::concat op; + sd::ops::concat op; auto result = op.evaluate({&x0, &x1, &x2}, {}, {1}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); - auto output = result->at(0); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); + auto output = result.at(0); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -487,16 +487,16 @@ TEST_F(DeclarableOpsTests9, concat_test12) { x1.linspace(1); x2.linspace(1); - nd4j::ops::concat op; + sd::ops::concat op; auto result = op.evaluate({&x0, &x1, &x2}, {}, {1}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); - auto output = result->at(0); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); + auto output = result.at(0); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -512,32 +512,32 @@ TEST_F(DeclarableOpsTests9, concat_test13) { x1.linspace(1); x2.linspace(1); - nd4j::ops::concat op; + sd::ops::concat op; auto result = op.evaluate({&x0, &x1, &x2}, {}, {1}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); - auto output = result->at(0); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); + auto output = result.at(0); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } TEST_F(DeclarableOpsTests9, concat_test14) { - NDArray x0('c', {1, 40, 60}, nd4j::DataType::DOUBLE); - NDArray x1('c', {1, 40, 60}, nd4j::DataType::DOUBLE); + NDArray x0('c', {1, 40, 60}, sd::DataType::DOUBLE); + NDArray x1('c', {1, 40, 60}, sd::DataType::DOUBLE); x0 = 1.; x1 = 2.; - nd4j::ops::concat op; + sd::ops::concat op; auto result = op.evaluate({&x0, &x1}, {}, {0}, {}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); Nd4jLong numOfTads= ShapeUtils::getNumOfSubArrs(z->getShapeInfo(), {0}); ASSERT_TRUE(2 == numOfTads); @@ -548,7 +548,7 @@ TEST_F(DeclarableOpsTests9, concat_test14) { ASSERT_NEAR((e+1)*1., mean, 1e-5); } - delete result; + } TEST_F(DeclarableOpsTests9, concat_test15) { @@ -556,16 +556,16 @@ TEST_F(DeclarableOpsTests9, concat_test15) { auto y = NDArrayFactory::create (3.0f); auto exp = NDArrayFactory::create('c', {3}, {1, 0, 3}); - nd4j::ops::concat op; + sd::ops::concat op; auto result = op.evaluate({&x, &y}, {}, {0}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } ////////////////////////////////////////////////////////////////////// @@ -575,31 +575,29 @@ TEST_F(DeclarableOpsTests9, concat_test16) { auto y = NDArrayFactory::create('c', {0,2,3}); auto exp = NDArrayFactory::create('c', {0,2,3}); - nd4j::ops::concat op; + sd::ops::concat op; auto result = op.evaluate({&x, &y}, {}, {0}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); - - delete result; } ////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests9, concat_test17) { - NDArray x0('c', {1, 55, 40}, nd4j::DataType::DOUBLE); - NDArray x1('c', {1, 55, 40}, nd4j::DataType::DOUBLE); + NDArray x0('c', {1, 55, 40}, sd::DataType::DOUBLE); + NDArray x1('c', {1, 55, 40}, sd::DataType::DOUBLE); x0 = 1.; x1 = 2.; - nd4j::ops::concat op; + sd::ops::concat op; auto result = op.evaluate({&x0, &x1}, {}, {0}, {}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); // z->printShapeInfo(); // z->printIndexedBuffer(); @@ -611,8 +609,6 @@ TEST_F(DeclarableOpsTests9, concat_test17) { auto mean = tad.meanNumber().e(0); ASSERT_NEAR((e+1)*1., mean, 1e-5); } - - delete result; } ////////////////////////////////////////////////////////////////////// @@ -631,13 +627,13 @@ TEST_F(DeclarableOpsTests9, concat_test18) { context.setOutputArray(0, &z, false); context.setIArguments(&axis, 1); - nd4j::ops::concat op; + sd::ops::concat op; op.execute(&context); for (int e = 0; e < 2000; e++) { auto exp = NDArrayFactory::create('c', {300}); exp.assign(e); - auto row = z.tensorAlongDimension(e, {1}); + auto row = z(e, {0}); ASSERT_EQ(exp, row); } } @@ -659,7 +655,7 @@ TEST_F(DeclarableOpsTests9, concat_test19) { context.setOutputArray(0, &z, false); context.setIArguments(&axis, 1); - nd4j::ops::concat op; + sd::ops::concat op; op.execute(&context); for (int e = 0; e < 10; e++) @@ -678,11 +674,11 @@ TEST_F(DeclarableOpsTests9, concat_test20) { x2.assign(3.0); x3.assign(4.0); - nd4j::ops::concat op; + sd::ops::concat op; auto result = op.evaluate({&x0, &x1, &x2, &x3}, {}, {0}, {}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); Nd4jLong numOfTads= ShapeUtils::getNumOfSubArrs(z->getShapeInfo(), {0}); ASSERT_TRUE(4 == numOfTads); @@ -693,20 +689,20 @@ TEST_F(DeclarableOpsTests9, concat_test20) { ASSERT_NEAR((double) e+1, mean, 1e-5); } - delete result; + } //////////////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests9, concat_test21) { - NDArray x0('c', {1,4,5}, nd4j::DataType::FLOAT32); - NDArray x1('c', {2,4,5}, nd4j::DataType::FLOAT32); - NDArray z('f', {3,4,5}, nd4j::DataType::FLOAT32); + NDArray x0('c', {1,4,5}, sd::DataType::FLOAT32); + NDArray x1('c', {2,4,5}, sd::DataType::FLOAT32); + NDArray z('f', {3,4,5}, sd::DataType::FLOAT32); x0 = 0.; x1 = 1.; - nd4j::ops::concat op; + sd::ops::concat op; auto status = op.execute({&x0, &x1}, {&z}, {}, {0}, {}); ASSERT_EQ(ND4J_STATUS_OK, status); } @@ -716,10 +712,10 @@ TEST_F(DeclarableOpsTests9, concat_test22) { NDArray x0('c', {1,6}, {1,2,3,4,5,6}); NDArray x1('c', {1,6}, {7,8,9,10,11,12}); - NDArray output('f', {2,6}, nd4j::DataType::DOUBLE); + NDArray output('f', {2,6}, sd::DataType::DOUBLE); NDArray exp('c', {2,6}, {1,2,3,4,5,6,7,8,9,10,11,12}); - nd4j::ops::concat op; + sd::ops::concat op; auto status = op.execute({&x0, &x1}, {&output}, {}, {0}, {}); ASSERT_EQ(ND4J_STATUS_OK, status); @@ -732,10 +728,10 @@ TEST_F(DeclarableOpsTests9, concat_test23) { NDArray x0('c', {1,4}, {1,2,3,4}); NDArray x1('c', {1,4}, {5,6,7,8}); - NDArray output('c', {2,4}, nd4j::DataType::DOUBLE); + NDArray output('c', {2,4}, sd::DataType::DOUBLE); NDArray exp('c', {2,4}, {1,2,3,4,5,6,7,8}); - nd4j::ops::concat op; + sd::ops::concat op; auto status = op.execute({&x0, &x1}, {&output}, {}, {0}, {}); ASSERT_EQ(ND4J_STATUS_OK, status); @@ -750,7 +746,7 @@ TEST_F(DeclarableOpsTests9, concat_test24) { auto e = NDArrayFactory::create('c', {2, 2}, {1, 0, 1, 0}); auto z = NDArrayFactory::create('c', {2, 2}); - nd4j::ops::concat op; + sd::ops::concat op; auto status = op.execute({&x, &y}, {&z}, {}, {1}, {}); ASSERT_EQ(Status::OK(), status); @@ -765,17 +761,61 @@ TEST_F(DeclarableOpsTests9, concat_test25) { auto axis = NDArrayFactory::create('c', {1}, {0.}); auto exp = NDArrayFactory::create('c', {2,4}, {1,2,3,4,5,6,7,8}); - nd4j::ops::concat op; + sd::ops::concat op; auto result = op.evaluate({&x0, &x1, &axis}, {}, {}, {true}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); - auto output = result->at(0); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); + auto output = result.at(0); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + +} + +//////////////////////////////////////////////////////////////////////////////// +TEST_F(DeclarableOpsTests9, concat_test26) { + + NDArray x0('f', {1, 2, 3}, sd::DataType::INT32); + NDArray x1('f', {1, 2, 3}, sd::DataType::INT32); + NDArray x2('f', {1, 2, 3}, sd::DataType::INT32); + + NDArray exp('f', {3, 2, 3}, {0, 6, 12, 3, 9, 15, 1, 7, 13, 4, 10, 16, 2, 8, 14, 5, 11, 17}, sd::DataType::INT32); + + x0.linspace(0); + x1.linspace(6); + x2.linspace(12); + + sd::ops::concat op; + + auto result = op.evaluate({&x0, &x1, &x2}, {}, {0}, {}); + + ASSERT_EQ(ND4J_STATUS_OK, result.status()); + auto output = result.at(0); + output->printLinearBuffer(); + + ASSERT_TRUE(exp.isSameShape(output)); + ASSERT_TRUE(exp.equalsTo(output)); +} + +////////////////////////////////////////////////////////////////////// +TEST_F(DeclarableOpsTests9, concat_test27) { + + auto x1 = NDArrayFactory::create('c', {0,1}); + auto x2 = NDArrayFactory::create('c', {0,1}); + auto x3 = NDArrayFactory::create('c', {0,1}); + auto x4 = NDArrayFactory::create('c', {0,1}); + + std::vector expShape = {0, 4}; + + sd::ops::concat op; + auto result = op.evaluate({&x1, &x2, &x3, &x4}, {}, {1}); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); + + auto z = result.at(0); + + ASSERT_TRUE(z->isSameShape(expShape)); } ////////////////////////////////////////////////////////////////////// @@ -787,15 +827,15 @@ TEST_F(DeclarableOpsTests9, tile_bp_test1) { gradO.linspace(0.01, 0.01); - nd4j::ops::tile_bp op; + sd::ops::tile_bp op; auto results = op.evaluate({&input, &gradO}, {}, {2, 3}); - auto gradI = results->at(0); + auto gradI = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(gradIExp.isSameShape(gradI)); ASSERT_TRUE(gradIExp.equalsTo(gradI)); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -807,14 +847,14 @@ TEST_F(DeclarableOpsTests9, tile_bp_test2) { gradO.linspace(0.01, 0.01); - nd4j::ops::tile_bp op; + sd::ops::tile_bp op; auto results = op.evaluate({&input, &gradO}, {}, {1, 3}); - auto gradI = results->at(0); - ASSERT_EQ(Status::OK(), results->status()); + auto gradI = results.at(0); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(gradIExp.isSameShape(gradI)); ASSERT_TRUE(gradIExp.equalsTo(gradI)); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -826,15 +866,15 @@ TEST_F(DeclarableOpsTests9, tile_bp_test3) { gradO.linspace(0.01, 0.01); - nd4j::ops::tile_bp op; + sd::ops::tile_bp op; auto results = op.evaluate({&input, &gradO}, {}, {1, 1}); - auto gradI = results->at(0); + auto gradI = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(gradIExp.isSameShape(gradI)); ASSERT_TRUE(gradIExp.equalsTo(gradI)); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -846,15 +886,15 @@ TEST_F(DeclarableOpsTests9, tile_bp_test4) { gradO.linspace(0.01, 0.01); - nd4j::ops::tile_bp op; + sd::ops::tile_bp op; auto results = op.evaluate({&input, &gradO}, {}, {2}); - auto gradI = results->at(0); + auto gradI = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(gradIExp.isSameShape(gradI)); ASSERT_TRUE(gradIExp.equalsTo(gradI)); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -866,15 +906,15 @@ TEST_F(DeclarableOpsTests9, tile_bp_test5) { gradO.linspace(0.01, 0.01); - nd4j::ops::tile_bp op; + sd::ops::tile_bp op; auto results = op.evaluate({&input, &gradO}, {}, {1}); - auto gradI = results->at(0); + auto gradI = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(gradIExp.isSameShape(gradI)); ASSERT_TRUE(gradIExp.equalsTo(gradI)); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -886,15 +926,15 @@ TEST_F(DeclarableOpsTests9, tile_bp_test6) { gradO.linspace(0.01, 0.01); - nd4j::ops::tile_bp op; + sd::ops::tile_bp op; auto results = op.evaluate({&input, &gradO}, {}, {1, 3, 2}); - auto gradI = results->at(0); + auto gradI = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(gradIExp.isSameShape(gradI)); ASSERT_TRUE(gradIExp.equalsTo(gradI)); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -907,15 +947,15 @@ TEST_F(DeclarableOpsTests9, tile_bp_test7) { gradO.linspace(0.01, 0.01); - nd4j::ops::tile_bp op; + sd::ops::tile_bp op; auto results = op.evaluate({&input, &reps, &gradO}, {}, {}); - auto gradI = results->at(0); + auto gradI = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(gradIExp.isSameShape(gradI)); ASSERT_TRUE(gradIExp.equalsTo(gradI)); - delete results; + } ////////////////////////////////////////////////////////////////////// @@ -925,15 +965,15 @@ TEST_F(DeclarableOpsTests9, tile_test1) { auto reps = NDArrayFactory::create('c', {1, 2}, {2, 1}); auto expOut = NDArrayFactory::create('c', {2, 6,}, {1.,2.,3.,4.,5.,6., 1.,2.,3.,4.,5.,6.}); - nd4j::ops::tile op; + sd::ops::tile op; auto results = op.evaluate({&input, &reps}, {}, {}); - auto out = results->at(0); + auto out = results.at(0); - ASSERT_EQ(Status::OK(), results->status()); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(expOut.isSameShape(out)); ASSERT_TRUE(expOut.equalsTo(out)); - delete results; + } //////////////////////////////////////////////////////////////////////////////// @@ -942,37 +982,37 @@ TEST_F(DeclarableOpsTests9, TestDropout_BP_1) { NDArray x('c', {2, 2, 2}, {1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 7.f, 8.f}); NDArray errs('c', {2, 2, 2}, {1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 7.f, 8.f}); NDArray shape('c', {2}, {2, 2}); - nd4j::ops::dropout_bp op; + sd::ops::dropout_bp op; auto ress = op.evaluate({&x, &errs, &shape}, {0.2f}, {113}); - ASSERT_EQ(ND4J_STATUS_OK, ress->status()); - //ress->at(0)->printIndexedBuffer("Result is "); + ASSERT_EQ(ND4J_STATUS_OK, ress.status()); + //ress.at(0)->printIndexedBuffer("Result is "); //x.printIndexedBuffer("Input is"); - ASSERT_FALSE(ress->at(0)->equalsTo(errs)); - delete ress; + ASSERT_FALSE(ress.at(0)->equalsTo(errs)); + } //////////////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests9, TestDropout_1) { - NDArray x('c', {10, 10}, nd4j::DataType::FLOAT32); + NDArray x('c', {10, 10}, sd::DataType::FLOAT32); // NDArray errs('c', {2, 2, 2}, {1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 7.f, 8.f}); //NDArray shape({2.f, 2.f}); - nd4j::ops::dropout op; + sd::ops::dropout op; x.linspace(1); auto ress = op.evaluate({&x}, {0.2f}, {113}); - ASSERT_EQ(ND4J_STATUS_OK, ress->status()); - NDArray* res = ress->at(0); //->printIndexedBuffer("Result is "); + ASSERT_EQ(ND4J_STATUS_OK, ress.status()); + NDArray* res = ress.at(0); //->printIndexedBuffer("Result is "); //x.printIndexedBuffer("Input is"); //res->printIndexedBuffer("Result for Dropout_1"); auto countZero = res->reduceNumber(reduce::CountZero); ASSERT_NEAR(countZero.e(0), 80, 5); auto ress2 = op.evaluate({&x}, {0.2f}, {113}); - ASSERT_EQ(ND4J_STATUS_OK, ress2->status()); - NDArray* res2 = ress2->at(0); + ASSERT_EQ(ND4J_STATUS_OK, ress2.status()); + NDArray* res2 = ress2.at(0); countZero = res->reduceNumber(reduce::CountZero); ASSERT_NEAR(countZero.e(0), 80, 5); @@ -981,13 +1021,13 @@ TEST_F(DeclarableOpsTests9, TestDropout_1) { //res->printIndexedBuffer("FF dropout"); //res2->printIndexedBuffer("BP dropout"); - delete ress; - delete ress2; + + } TEST_F(DeclarableOpsTests9, Test_DropoutInverted_01) { - NDArray x0('c', {10, 10}, nd4j::DataType::FLOAT32); - NDArray x1('c', {10, 10}, nd4j::DataType::FLOAT32); + NDArray x0('c', {10, 10}, sd::DataType::FLOAT32); + NDArray x1('c', {10, 10}, sd::DataType::FLOAT32); x0.linspace(1); x1.linspace(1); @@ -995,7 +1035,7 @@ TEST_F(DeclarableOpsTests9, Test_DropoutInverted_01) { float prob[] = {0.5f}; Nd4jLong* _bufferA = new Nd4jLong[100000]; long _seed = 119L; - auto _rngA = (nd4j::random::RandomBuffer *) initRandom(nullptr, _seed, 100000, (Nd4jPointer) _bufferA); + auto _rngA = (sd::random::RandomBuffer *) initRandom(nullptr, _seed, 100000, (Nd4jPointer) _bufferA); x0. applyTransform(random::DropOutInverted, &x0, prob); // x1.template applyRandom>(_rngB, nullptr, &x1, prob); @@ -1014,31 +1054,31 @@ TEST_F(DeclarableOpsTests9, Test_DropoutInverted_01) { destroyRandom(_rngA); delete [] _bufferA; */ - nd4j::ops::dropout op; + sd::ops::dropout op; auto ress = op.evaluate({&x1}, {0.5f}, {119}); - ASSERT_EQ(ND4J_STATUS_OK, ress->status()); - //ress->at(0)->printIndexedBuffer("01Dropout result is "); - auto count = ress->at(0)->reduceNumber(reduce::CountNonZero); + ASSERT_EQ(ND4J_STATUS_OK, ress.status()); + //ress.at(0)->printIndexedBuffer("01Dropout result is "); + auto count = ress.at(0)->reduceNumber(reduce::CountNonZero); // nd4j_printf("\n01Dropout count %i\n\n", count); - nd4j::ops::dropout_bp op2; + sd::ops::dropout_bp op2; //NDArray exp('c', {10,10}, {4.f, 0.f, 12.f, 0.f, 20.f, 24.f, 0.f, 32.f, 0.f, 0.f, 0.f, 0.f, 52.f, 56.f, 60.f, 0.f, 0.f, 0.f, 0.f, 0.f, 84.f, 88.f, 0.f, 0.f, 0.f, 0.f, 108.f, 0.f, 0.f, 120.f, 0.f, 0.f, 132.f, 0.f, 0.f, 0.f, 0.f, 0.f, 156.f, 0.f, 164.f, 168.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 200.f, 204.f, 0.f, 0.f, 0.f, 220.f, 0.f, 0.f, 232.f, 236.f, 240.f, 0.f, 248.f, 0.f, 0.f, 260.f, 0.f, 0.f, 0.f, 276.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 316.f, 0.f, 324.f, 0.f, 0.f, 336.f, 0.f, 0.f, 0.f, 0.f, 356.f, 0.f, 0.f, 368.f, 0.f, 0.f, 0.f, 384.f, 388.f, 0.f, 0.f, 400.f}); //02Dropout result is [4.000000, 0.000000, 12.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 36.000000, 0.000000, 0.000000, 0.000000, 0.000000, 56.000000, 60.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 88.000000, 0.000000, 96.000000, 0.000000, 0.000000, 108.000000, 0.000000, 0.000000, 120.000000, 0.000000, 128.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 156.000000, 0.000000, 164.000000, 0.000000, 0.000000, 0.000000, 0.000000, 184.000000, 0.000000, 0.000000, 0.000000, 200.000000, 0.000000, 0.000000, 0.000000, 216.000000, 0.000000, 0.000000, 0.000000, 232.000000, 0.000000, 240.000000, 0.000000, 248.000000, 0.000000, 0.000000, 260.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 308.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 348.000000, 0.000000, 356.000000, 0.000000, 0.000000, 0.000000, 0.000000, 376.000000, 0.000000, 384.000000, 0.000000, 0.000000, 0.000000, 400.000000] - auto ressX = op2.evaluate({&x1, &x1}, {0.5f}, {119}); // , false, nd4j::DataType::FLOAT32); // skipped due given by default + auto ressX = op2.evaluate({&x1, &x1}, {0.5f}, {119}); // , false, sd::DataType::FLOAT32); // skipped due given by default //x0.printIndexedBuffer("X0"); //x1.printIndexedBuffer("X1"); - ASSERT_EQ(ND4J_STATUS_OK, ressX->status()); + ASSERT_EQ(ND4J_STATUS_OK, ressX.status()); auto ressY = op2.evaluate({&x1, &x0}, {0.5f}, {119}); - ASSERT_EQ(ND4J_STATUS_OK, ressY->status()); + ASSERT_EQ(ND4J_STATUS_OK, ressY.status()); //ressY->at(0)->printIndexedBuffer("BP"); - //ress->at(0)->printIndexedBuffer("FF"); + //ress.at(0)->printIndexedBuffer("FF"); bool ret = true; - for (int e = 0; e < ress->at(0)->lengthOf(); e++) { - if (ress->at(0)->e(e) == 0.f) - if (ressX->at(0)->e(e) != ress->at(0)->e(e)) { + for (int e = 0; e < ress.at(0)->lengthOf(); e++) { + if (ress.at(0)->e(e) == 0.f) + if (ressX.at(0)->e(e) != ress.at(0)->e(e)) { ret = false; break; } @@ -1048,83 +1088,77 @@ TEST_F(DeclarableOpsTests9, Test_DropoutInverted_01) { //ressX->at(0)->printIndexedBuffer("02Dropout result is "); /* float countZero = ressX->at(0)->template reduceNumber>(); ASSERT_NEAR(countZero, 50.f, 5.f); - countZero = ress->at(0)->template reduceNumber>(); + countZero = ress.at(0)->template reduceNumber>(); ASSERT_NEAR(countZero, 50.f, 5.f); countZero = ressY->at(0)->template reduceNumber>(); ASSERT_NEAR(countZero, 50.f, 5.f); */ // ASSERT_TRUE(exp.equalsTo(ressX->at(0))); - delete ressX; - delete ressY; - delete ress; + + } TEST_F(DeclarableOpsTests9, Test_Dropout_BP_2) { - NDArray x('c', {10, 10}, nd4j::DataType::FLOAT32); + NDArray x('c', {10, 10}, sd::DataType::FLOAT32); x.linspace(1); - nd4j::ops::dropout op; + sd::ops::dropout op; auto ress = op.evaluate({&x}, {0.5f}, {119}); - ASSERT_EQ(ND4J_STATUS_OK, ress->status()); -// ress->at(0)->printIndexedBuffer("01Dropout result is "); + ASSERT_EQ(ND4J_STATUS_OK, ress.status()); +// ress.at(0)->printIndexedBuffer("01Dropout result is "); - nd4j::ops::dropout_bp op2; + sd::ops::dropout_bp op2; auto ressX = op2.evaluate({&x, &x}, {0.5f}, {119}); - ASSERT_EQ(ND4J_STATUS_OK, ressX->status()); + ASSERT_EQ(ND4J_STATUS_OK, ressX.status()); auto ressY = op2.evaluate({&x, &x}, {0.5f}, {119}); - ASSERT_EQ(ND4J_STATUS_OK, ressY->status()); + ASSERT_EQ(ND4J_STATUS_OK, ressY.status()); - //ress->at(0)->printIndexedBuffer("FF Dropout result is "); + //ress.at(0)->printIndexedBuffer("FF Dropout result is "); //ressY->at(0)->printIndexedBuffer("BP Dropout result is "); - auto countZero = ress->at(0)->reduceNumber(reduce::CountZero); + auto countZero = ress.at(0)->reduceNumber(reduce::CountZero); ASSERT_NEAR(countZero.e(0), 50.f, 10.f); - countZero = ressX->at(0)->reduceNumber(reduce::CountZero); + countZero = ressX.at(0)->reduceNumber(reduce::CountZero); //nd4j_printf("X zero count is %f\n", countZero); ASSERT_NEAR(countZero.e(0), 50.f, 10.f); - countZero = ressY->at(0)->reduceNumber(reduce::CountZero); + countZero = ressY.at(0)->reduceNumber(reduce::CountZero); //nd4j_printf("Y zero count is %f\n", countZero); ASSERT_NEAR(countZero.e(0), 50.f, 10.f); // ASSERT_TRUE(exp.equalsTo(ressX->at(0))); - ASSERT_TRUE(ressX->at(0)->equalsTo(ressY->at(0))); - delete ressX; - delete ressY; - delete ress; + ASSERT_TRUE(ressX.at(0)->equalsTo(ressY.at(0))); + } //////////////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests9, Test_AlphaDropout_BP_1) { - NDArray x('c', {10, 10}, nd4j::DataType::FLOAT32); - NDArray eps('c', {10, 10}, nd4j::DataType::FLOAT32); + NDArray x('c', {10, 10}, sd::DataType::FLOAT32); + NDArray eps('c', {10, 10}, sd::DataType::FLOAT32); x.linspace(1); eps.linspace(1); - nd4j::ops::alpha_dropout_bp op; + sd::ops::alpha_dropout_bp op; auto ress = op.evaluate({&x, &eps}, {0.5f, 0.5f, 1.5f, 1.6f}, {119}); - ASSERT_EQ(ND4J_STATUS_OK, ress->status()); - NDArray* res = ress->at(0); + ASSERT_EQ(ND4J_STATUS_OK, ress.status()); + NDArray* res = ress.at(0); auto ress2 = op.evaluate({&x, &eps}, {0.5f, 0.5f, 1.5f, 1.6f}, {119}); - ASSERT_EQ(ND4J_STATUS_OK, ress2->status()); - NDArray* res2 = ress2->at(0); + ASSERT_EQ(ND4J_STATUS_OK, ress2.status()); + NDArray* res2 = ress2.at(0); //res->printIndexedBuffer("Result1AlphaBP1"); //res2->printIndexedBuffer("Result1AlphaBP2"); ASSERT_TRUE(res2->equalsTo(res)); - - delete ress; - delete ress2; } TEST_F(DeclarableOpsTests9, test_range_int_1) { @@ -1132,13 +1166,12 @@ TEST_F(DeclarableOpsTests9, test_range_int_1) { auto x1 = NDArrayFactory::create(2); auto x2 = NDArrayFactory::create(1); - nd4j::ops::range op; + sd::ops::range op; auto result = op.evaluate({&x0, &x1, &x2}, {}, {}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); - delete result; } TEST_F(DeclarableOpsTests9, test_range_empty_1) { @@ -1146,15 +1179,14 @@ TEST_F(DeclarableOpsTests9, test_range_empty_1) { auto x1 = NDArrayFactory::create(0); auto x2 = NDArrayFactory::create(1); - nd4j::ops::range op; + sd::ops::range op; auto result = op.evaluate({&x0, &x1, &x2}, {}, {}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(z->isEmpty()); - delete result; } @@ -1185,12 +1217,11 @@ TEST_F(DeclarableOpsTests9, test_unstack_1) { auto x = NDArrayFactory::create('c', {5, 5}); x.linspace(1.0); - nd4j::ops::unstack op; + sd::ops::unstack op; auto result = op.evaluate({&x}, {}, {0}); - ASSERT_EQ(Status::OK(), result->status()); - ASSERT_EQ(5, result->size()); + ASSERT_EQ(Status::OK(), result.status()); + ASSERT_EQ(5, result.size()); - delete result; } //////////////////////////////////////////////////////////////////////////////// @@ -1203,15 +1234,15 @@ TEST_F(DeclarableOpsTests9, test_unstack_SGO_1) { auto z4 = NDArrayFactory::create(4); auto z5 = NDArrayFactory::create(5); std::vector z({&z1, &z2, &z3, &z4, &z5}); - nd4j::ops::unstack op; + sd::ops::unstack op; auto result = op.evaluate({&x}, {}, {0}); - ASSERT_EQ(Status::OK(), result->status()); - ASSERT_EQ(5, result->size()); - for (size_t i = 0; i < result->size(); i++) { - ASSERT_TRUE(result->at(i)->isSameShape(z[i])); - ASSERT_TRUE(result->at(i)->equalsTo(z[i])); + ASSERT_EQ(Status::OK(), result.status()); + ASSERT_EQ(5, result.size()); + for (size_t i = 0; i < result.size(); i++) { + ASSERT_TRUE(result.at(i)->isSameShape(z[i])); + ASSERT_TRUE(result.at(i)->equalsTo(z[i])); } - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1240,14 +1271,14 @@ TEST_F(DeclarableOpsTests9, clipbynorm_test12) { expect({0,0, j,j+1}).assign ( yCol * (clip / norm2Col) ); } - nd4j::ops::clipbynorm op; + sd::ops::clipbynorm op; auto result = op.evaluate({&y}, {clip}, {axis}); - auto outFF = result->at(0); + auto outFF = result.at(0); ASSERT_TRUE(expect.isSameShape(outFF)); ASSERT_TRUE(expect.equalsTo(outFF)); - delete result; + } @@ -1264,8 +1295,8 @@ TEST_F(DeclarableOpsTests9, clipbynorm_bp_test1) { const OpArgsHolder argsHolderFF({&x}, {clip}, {}); const OpArgsHolder argsHolderBP({&x, &gradO}, {clip}, {}); - nd4j::ops::clipbynorm opFF; - nd4j::ops::clipbynorm_bp opBP; + sd::ops::clipbynorm opFF; + sd::ops::clipbynorm_bp opBP; const bool isGradCorrect = GradCheck::checkGrad(opFF, opBP, argsHolderFF, argsHolderBP); @@ -1286,8 +1317,8 @@ TEST_F(DeclarableOpsTests9, clipbynorm_bp_test2) { const OpArgsHolder argsHolderFF({&x}, {clip}, {axis}); const OpArgsHolder argsHolderBP({&x, &gradO}, {clip}, {axis}); - nd4j::ops::clipbynorm opFF; - nd4j::ops::clipbynorm_bp opBP; + sd::ops::clipbynorm opFF; + sd::ops::clipbynorm_bp opBP; const bool isGradCorrect = GradCheck::checkGrad(opFF, opBP, argsHolderFF, argsHolderBP); @@ -1309,8 +1340,8 @@ TEST_F(DeclarableOpsTests9, clipbynorm_bp_test3) { const OpArgsHolder argsHolderFF({&x}, {clip}, {axis}); const OpArgsHolder argsHolderBP({&x, &gradO}, {clip}, {axis}); - nd4j::ops::clipbynorm opFF; - nd4j::ops::clipbynorm_bp opBP; + sd::ops::clipbynorm opFF; + sd::ops::clipbynorm_bp opBP; const bool isGradCorrect = GradCheck::checkGrad(opFF, opBP, argsHolderFF, argsHolderBP); @@ -1334,52 +1365,52 @@ TEST_F(DeclarableOpsTests9, cumprod_1) { //************************************// exclusive = 0; reverse = 0; - nd4j::ops::cumprod op; + sd::ops::cumprod op; auto result = op.evaluate({&inputC, &axis}, {}, {exclusive, reverse}); - ASSERT_EQ(Status::OK(), result->status()); - auto z = result->at(0); + ASSERT_EQ(Status::OK(), result.status()); + auto z = result.at(0); ASSERT_TRUE(expFF.equalsTo(z)); - delete result; + //************************************// exclusive = 1; reverse = 0; result = op.evaluate({&inputC, &axis}, {}, {exclusive, reverse}); - ASSERT_EQ(Status::OK(), result->status()); - z = result->at(0); + ASSERT_EQ(Status::OK(), result.status()); + z = result.at(0); ASSERT_TRUE(expTF.equalsTo(z)); - delete result; + //************************************// exclusive = 0; reverse = 1; result = op.evaluate({&inputC, &axis}, {}, {exclusive, reverse}); - ASSERT_EQ(Status::OK(), result->status()); - z = result->at(0); + ASSERT_EQ(Status::OK(), result.status()); + z = result.at(0); ASSERT_TRUE(expFT.equalsTo(z)); - delete result; + //************************************// exclusive = 1; reverse = 1; result = op.evaluate({&inputC, &axis}, {}, {exclusive, reverse}); - ASSERT_EQ(Status::OK(), result->status()); - z = result->at(0); + ASSERT_EQ(Status::OK(), result.status()); + z = result.at(0); ASSERT_TRUE(expTT.equalsTo(z)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// TEST_F(DeclarableOpsTests9, cumprod_2) { - NDArray x('c', {2, 1500}, nd4j::DataType::FLOAT32); + NDArray x('c', {2, 1500}, sd::DataType::FLOAT32); NDArray x0 = x(0, {0}); NDArray x1 = x(1, {0}); x0.linspace(1, 0.1); x1.linspace(1, 0.1); - NDArray exp('c', {2, 1500}, nd4j::DataType::FLOAT32); + NDArray exp('c', {2, 1500}, sd::DataType::FLOAT32); NDArray exp0 = exp(0, {0}); NDArray exp1 = exp(1, {0}); @@ -1392,15 +1423,15 @@ TEST_F(DeclarableOpsTests9, cumprod_2) { exp1.p(i, prev * x1.e(i)); } - nd4j::ops::cumprod op; + sd::ops::cumprod op; auto result = op.evaluate({&x}, {}, {0, 0, 1}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } ////////////////////////////////////////////////////////////////////// @@ -1414,8 +1445,8 @@ TEST_F(DeclarableOpsTests9, cumprod_bp_check_1) { const OpArgsHolder argsHolderFF({&x}, {}, {0, 0}); const OpArgsHolder argsHolderBP({&x, &gradO}, {}, {0, 0}); - nd4j::ops::cumprod opFF; - nd4j::ops::cumprod_bp opBP; + sd::ops::cumprod opFF; + sd::ops::cumprod_bp opBP; const bool isGradCorrect = GradCheck::checkGrad(opFF, opBP, argsHolderFF, argsHolderBP, {1, 1}, {1, 1},GradCheck::MEAN); @@ -1433,8 +1464,8 @@ TEST_F(DeclarableOpsTests9, cumprod_bp_check_2) { const OpArgsHolder argsHolderFF({&x}, {}, {1, 1}); const OpArgsHolder argsHolderBP({&x, &gradO}, {}, {1, 1}); - nd4j::ops::cumprod opFF; - nd4j::ops::cumprod_bp opBP; + sd::ops::cumprod opFF; + sd::ops::cumprod_bp opBP; const bool isGradCorrect = GradCheck::checkGrad(opFF, opBP, argsHolderFF, argsHolderBP, {1, 1}, {1, 1},GradCheck::MEAN); @@ -1452,8 +1483,8 @@ TEST_F(DeclarableOpsTests9, cumprod_bp_check_3) { const OpArgsHolder argsHolderFF({&x}, {}, {1, 0}); const OpArgsHolder argsHolderBP({&x, &gradO}, {}, {1, 0}); - nd4j::ops::cumprod opFF; - nd4j::ops::cumprod_bp opBP; + sd::ops::cumprod opFF; + sd::ops::cumprod_bp opBP; const bool isGradCorrect = GradCheck::checkGrad(opFF, opBP, argsHolderFF, argsHolderBP, {1, 1}, {1, 1},GradCheck::MEAN); @@ -1471,8 +1502,8 @@ TEST_F(DeclarableOpsTests9, cumprod_bp_check_4) { const OpArgsHolder argsHolderFF({&x}, {}, {0, 1}); const OpArgsHolder argsHolderBP({&x, &gradO}, {}, {0, 1}); - nd4j::ops::cumprod opFF; - nd4j::ops::cumprod_bp opBP; + sd::ops::cumprod opFF; + sd::ops::cumprod_bp opBP; const bool isGradCorrect = GradCheck::checkGrad(opFF, opBP, argsHolderFF, argsHolderBP, {1, 1}, {1, 1},GradCheck::MEAN); @@ -1491,8 +1522,8 @@ TEST_F(DeclarableOpsTests9, cumsum_bp_check_2) { const OpArgsHolder argsHolderFF({&x}, {}, {1, 1}); const OpArgsHolder argsHolderBP({&x, &gradO}, {}, {1, 1}); - nd4j::ops::cumsum opFF; - nd4j::ops::cumsum_bp opBP; + sd::ops::cumsum opFF; + sd::ops::cumsum_bp opBP; const bool isGradCorrect = GradCheck::checkGrad(opFF, opBP, argsHolderFF, argsHolderBP, {1, 1}, {1, 1},GradCheck::MEAN); @@ -1520,8 +1551,8 @@ TEST_F(DeclarableOpsTests9, cumprod_test1) { const OpArgsHolder argsHolderFF({&inputC, &axis}, {}, {exclusive, reverse}); const OpArgsHolder argsHolderBP({&inputC, &axis, &gradO}, {}, {exclusive, reverse}); - nd4j::ops::cumprod opFF; - nd4j::ops::cumprod_bp opBP; + sd::ops::cumprod opFF; + sd::ops::cumprod_bp opBP; const bool isGradCorrect = GradCheck::checkGrad(opFF, opBP, argsHolderFF, argsHolderBP, {1, 1}, {1, 1},GradCheck::MEAN); @@ -1544,8 +1575,8 @@ TEST_F(DeclarableOpsTests9, cumprod_test2) { const OpArgsHolder argsHolderFF({&inputC, &axis}, {}, {exclusive, reverse}); const OpArgsHolder argsHolderBP({&inputC, &axis, &gradO}, {}, {exclusive, reverse}); - nd4j::ops::cumprod opFF; - nd4j::ops::cumprod_bp opBP; + sd::ops::cumprod opFF; + sd::ops::cumprod_bp opBP; const bool isGradCorrect = GradCheck::checkGrad(opFF, opBP, argsHolderFF, argsHolderBP, {1, 1, 1, 1}, {1, 1},GradCheck::MEAN); @@ -1559,16 +1590,16 @@ TEST_F(DeclarableOpsTests9, prelu_test1) { auto alpha = NDArrayFactory::create('c', {3, 4}, {-0.6f, -0.5f, -0.4f, -0.3f, -0.2f, -0.1f, 0.f, 0.1f, 0.2f, 0.3f, 0.4f, 0.5f}); auto exp = NDArrayFactory::create('c', {2, 3, 4}, {7.2f, 5.5f, 4.f, 2.7f, 1.6f, 0.7f, 0.f, -0.5f,-0.8f, -0.9f, -0.8f, -0.5f, 0.f, 1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 7.f, 8.f, 9.f, 10.f, 11.f}); - nd4j::ops::prelu op; + sd::ops::prelu op; auto result = op.evaluate({&x, &alpha}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); - auto output = result->at(0); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); + auto output = result.at(0); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1578,15 +1609,15 @@ TEST_F(DeclarableOpsTests9, prelu_test2) { auto alpha = NDArrayFactory::create('c', {3}, {-0.6f, 2.f, 4.f}); auto exp = NDArrayFactory::create('c', {2, 3, 4}, {7.2f, 6.6f, 6.f, 5.4f, -16.f, -14.f, -12.f, -10.f, -16.f, -12.f, -8.f, -4.f, 0.f, 1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 7.f, 8.f, 9.f, 10.f, 11.f}); - nd4j::ops::prelu op; + sd::ops::prelu op; auto result = op.evaluate({&x, &alpha}, {}, {0}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); - auto output = result->at(0); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); + auto output = result.at(0); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1596,15 +1627,15 @@ TEST_F(DeclarableOpsTests9, prelu_test3) { auto alpha = NDArrayFactory::create('c', {3,1}, {-0.6f, 2.f, 4.f}); auto exp = NDArrayFactory::create('c', {2, 3, 4}, {7.2f, 6.6f, 6.f, 5.4f, -16.f, -14.f, -12.f, -10.f, -16.f, -12.f, -8.f, -4.f, 0.f, 1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 7.f, 8.f, 9.f, 10.f, 11.f}); - nd4j::ops::prelu op; + sd::ops::prelu op; auto result = op.evaluate({&x, &alpha}, {}, {0}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); - auto output = result->at(0); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); + auto output = result.at(0); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1614,15 +1645,15 @@ TEST_F(DeclarableOpsTests9, prelu_test4) { auto alpha = NDArrayFactory::create('c', {1, 3}, {-0.6f, 2.f, 4.f}); auto exp = NDArrayFactory::create('c', {2, 3, 4}, {7.2f, 6.6f, 6.f, 5.4f, -16.f, -14.f, -12.f, -10.f, -16.f, -12.f, -8.f, -4.f, 0.f, 1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 7.f, 8.f, 9.f, 10.f, 11.f}); - nd4j::ops::prelu op; + sd::ops::prelu op; auto result = op.evaluate({&x, &alpha}, {}, {0}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); - auto output = result->at(0); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); + auto output = result.at(0); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1632,15 +1663,15 @@ TEST_F(DeclarableOpsTests9, prelu_test5) { auto alpha = NDArrayFactory::create('c', {4}, {-0.6f, 2.f, 4.f, -1.f}); auto exp = NDArrayFactory::create('c', {2, 3, 4}, {7.2f, -22.f, -40.f, 9.f, 4.8f, -14.f, -24.f, 5.f, 2.4f, -6.f, -8.f, 1.f, 0.f, 1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 7.f, 8.f, 9.f, 10.f, 11.f}); - nd4j::ops::prelu op; + sd::ops::prelu op; auto result = op.evaluate({&x, &alpha}, {}, {1}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); - auto output = result->at(0); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); + auto output = result.at(0); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1650,15 +1681,15 @@ TEST_F(DeclarableOpsTests9, prelu_test6) { auto alpha = NDArrayFactory::create('c', {1,1,1}, {-2.}); auto exp = NDArrayFactory::create('c', {2, 3, 4}, {24.f, 22.f, 20.f, 18.f, 16.f, 14.f, 12.f, 10.f, 8.f, 6.f, 4.f, 2.f, 0.f, 1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 7.f, 8.f, 9.f, 10.f, 11.f}); - nd4j::ops::prelu op; + sd::ops::prelu op; auto result = op.evaluate({&x, &alpha}, {}, {1,0}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); - auto output = result->at(0); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); + auto output = result.at(0); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } @@ -1669,15 +1700,15 @@ TEST_F(DeclarableOpsTests9, prelu_test7) { auto alpha = NDArrayFactory::create(-2.f); auto exp = NDArrayFactory::create('c', {2, 3, 4}, {24.f, 22.f, 20.f, 18.f, 16.f, 14.f, 12.f, 10.f, 8.f, 6.f, 4.f, 2.f, 0.f, 1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 7.f, 8.f, 9.f, 10.f, 11.f}); - nd4j::ops::prelu op; + sd::ops::prelu op; auto result = op.evaluate({&x, &alpha}, {}, {1,0}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); - auto output = result->at(0); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); + auto output = result.at(0); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1687,15 +1718,15 @@ TEST_F(DeclarableOpsTests9, prelu_test8) { auto alpha = NDArrayFactory::create(-2.f); auto exp = NDArrayFactory::create('c', {2, 3, 4}, {24.f, 22.f, 20.f, 18.f, 16.f, 14.f, 12.f, 10.f, 8.f, 6.f, 4.f, 2.f, 0.f, 1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 7.f, 8.f, 9.f, 10.f, 11.f}); - nd4j::ops::prelu op; + sd::ops::prelu op; auto result = op.evaluate({&x, &alpha}, {}, {1,0,1,0,1,0}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); - auto output = result->at(0); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); + auto output = result.at(0); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1705,15 +1736,15 @@ TEST_F(DeclarableOpsTests9, prelu_test9) { auto alpha = NDArrayFactory::create(-2.f); auto exp = NDArrayFactory::create('c', {2, 4}, {8.f, 6.f, 4.f, 2.f,0.f, 1.f, 2.f, 3.f}); - nd4j::ops::prelu op; + sd::ops::prelu op; auto result = op.evaluate({&x, &alpha}, {}, {0}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); - auto output = result->at(0); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); + auto output = result.at(0); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1723,15 +1754,15 @@ TEST_F(DeclarableOpsTests9, prelu_test10) { auto alpha = NDArrayFactory::create(-2.f); auto exp = NDArrayFactory::create('c', {2, 4}, {8.f, 6.f, 4.f, 2.f,0.f, 1.f, 2.f, 3.f}); - nd4j::ops::prelu op; + sd::ops::prelu op; auto result = op.evaluate({&x, &alpha}, {}, {1}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); - auto output = result->at(0); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); + auto output = result.at(0); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1748,15 +1779,15 @@ TEST_F(DeclarableOpsTests9, prelu_test11) { 43.f, 44.f, 45.f, 46.f, 47.f, 48.f, 49.f, 50.f, 51.f, 52.f, 53.f, 54.f, 55.f, 56.f, 57.f, 58.f, 59.f, 60.f, 61.f, 62.f, 63.f, 64.f, 65.f, 66.f, 67.f, 68.f, 69.f}); - nd4j::ops::prelu op; + sd::ops::prelu op; auto result = op.evaluate({&x, &alpha}, {}, {1,3}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); - auto output = result->at(0); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); + auto output = result.at(0); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1772,15 +1803,15 @@ TEST_F(DeclarableOpsTests9, prelu_test12) { 31.f, 32.f, 33.f, 34.f, 35.f, 36.f, 37.f, 38.f, 39.f, 40.f, 41.f, 42.f, 43.f, 44.f, 45.f, 46.f, 47.f, 48.f, 49.f, 50.f, 51.f, 52.f, 53.f, 54.f, 55.f, 56.f, 57.f, 58.f, 59.f, 60.f, 61.f, 62.f, 63.f, 64.f, 65.f, 66.f, 67.f, 68.f, 69.f}); - nd4j::ops::prelu op; + sd::ops::prelu op; auto result = op.evaluate({&x, &alpha}, {}, {-1, 2}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); - auto output = result->at(0); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); + auto output = result.at(0); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1796,15 +1827,15 @@ TEST_F(DeclarableOpsTests9, prelu_test13) { 31.f, 32.f, 33.f, 34.f, 35.f, 36.f, 37.f, 38.f, 39.f, 40.f, 41.f, 42.f, 43.f, 44.f, 45.f, 46.f, 47.f, 48.f, 49.f, 50.f, 51.f, 52.f, 53.f, 54.f, 55.f, 56.f, 57.f, 58.f, 59.f, 60.f, 61.f, 62.f, 63.f, 64.f, 65.f, 66.f, 67.f, 68.f, 69.f}); - nd4j::ops::prelu op; + sd::ops::prelu op; auto result = op.evaluate({&x, &alpha}, {}, {-1, 2}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); - auto output = result->at(0); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); + auto output = result.at(0); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1821,15 +1852,15 @@ TEST_F(DeclarableOpsTests9, prelu_test14) { 37.f, 38.f, 39.f, 40.f, 41.f, 42.f, 43.f, 44.f, 45.f, 46.f, 47.f, 48.f, 49.f, 50.f, 51.f, 52.f, 53.f, 54.f, 55.f, 56.f, 57.f, 58.f, 59.f, 60.f, 61.f, 62.f, 63.f, 64.f, 65.f, 66.f, 67.f, 68.f, 69.f}); - nd4j::ops::prelu op; + sd::ops::prelu op; auto result = op.evaluate({&x, &alpha}, {}, {-2}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); - auto output = result->at(0); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); + auto output = result.at(0); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1839,16 +1870,16 @@ TEST_F(DeclarableOpsTests9, thresholdedrelu_test1) { auto x = NDArrayFactory::create('c', {2, 3, 4}, {-12.f, -11.f, -10.f, -9.f, -8.f, -7.f, -6.f, -5.f, -4.f, -3.f, -2.f, -1.f, 0.f, 1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 7.f, 8.f, 9.f, 10.f, 11.f}); auto exp = NDArrayFactory::create('c', {2, 3, 4}, {0.f, 0.f, 0.f, 0.f,0.f, 0.f, 0.f, 0.f,0.f, 0.f, 0.f, 0.f,0.f, 0.f, 0.f, 3.f,4.f, 5.f, 6.f, 7.f,8.f, 9.f,10.f,11.f}); - nd4j::ops::thresholdedrelu op; + sd::ops::thresholdedrelu op; auto result = op.evaluate({&x}, {theta}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); - auto output = result->at(0); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); + auto output = result.at(0); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1859,15 +1890,15 @@ TEST_F(DeclarableOpsTests9, compare_and_bitpack_test1) { auto exp = NDArrayFactory::create('c', {2, 3, 4}, {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1}); - nd4j::ops::compare_and_bitpack op; + sd::ops::compare_and_bitpack op; auto result = op.evaluate({&x, &threshold}, {}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); - auto output = result->at(0); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); + auto output = result.at(0); // output->printIndexedBuffer("Packed to uint8"); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1877,16 +1908,16 @@ TEST_F(DeclarableOpsTests9, thresholdedrelu_test2) { auto x = NDArrayFactory::create('c', {2, 3, 4}, {0.f,-4.f, -10.f, -8.f, 0.f, -9.f, -8.f, 5.f, 6.f, 6.f, 9.f, 6.f, -8.f, 5.f, 10.f, -2.f, 3.f, -7.f, 4.f, -8.f, -4.f, -9.f, -9.f, 3.f}); auto exp = NDArrayFactory::create('c', {2, 3, 4}, {0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 5.f, 6.f, 6.f, 9.f, 6.f, 0.f, 5.f, 10.f, 0.f, 3.f, 0.f, 4.f, 0.f, 0.f, 0.f, 0.f, 3.f}); - nd4j::ops::thresholdedrelu op; + sd::ops::thresholdedrelu op; auto result = op.evaluate({&x}, {theta}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); - auto output = result->at(0); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); + auto output = result.at(0); ASSERT_TRUE(exp.isSameShape(output)); ASSERT_TRUE(exp.equalsTo(output)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -1899,8 +1930,8 @@ TEST_F(DeclarableOpsTests9, prelu_bp_test1) { const OpArgsHolder argsHolderFF({&x, &alpha}, {}, {}); const OpArgsHolder argsHolderBP({&x, &alpha, &dLdO}, {}, {}); - nd4j::ops::prelu opFF; - nd4j::ops::prelu_bp opBP; + sd::ops::prelu opFF; + sd::ops::prelu_bp opBP; const bool isGradCorrect = GradCheck::checkGrad(opFF, opBP, argsHolderFF, argsHolderBP); @@ -1917,8 +1948,8 @@ TEST_F(DeclarableOpsTests9, prelu_bp_test2) { const OpArgsHolder argsHolderFF({&x, &alpha}, {}, {1}); const OpArgsHolder argsHolderBP({&x, &alpha, &dLdO}, {}, {1}); - nd4j::ops::prelu opFF; - nd4j::ops::prelu_bp opBP; + sd::ops::prelu opFF; + sd::ops::prelu_bp opBP; const bool isGradCorrect = GradCheck::checkGrad(opFF, opBP, argsHolderFF, argsHolderBP); @@ -1937,8 +1968,8 @@ TEST_F(DeclarableOpsTests9, prelu_bp_test3) { const OpArgsHolder argsHolderFF({&x, &alpha}, {}, {-1, 2}); const OpArgsHolder argsHolderBP({&x, &alpha, &dLdO}, {}, {-1, 2}); - nd4j::ops::prelu opFF; - nd4j::ops::prelu_bp opBP; + sd::ops::prelu opFF; + sd::ops::prelu_bp opBP; const bool isGradCorrect = GradCheck::checkGrad(opFF, opBP, argsHolderFF, argsHolderBP); @@ -1957,8 +1988,8 @@ TEST_F(DeclarableOpsTests9, prelu_bp_test4) { const OpArgsHolder argsHolderFF({&x, &alpha}, {}, {-2}); const OpArgsHolder argsHolderBP({&x, &alpha, &dLdO}, {}, {-2}); - nd4j::ops::prelu opFF; - nd4j::ops::prelu_bp opBP; + sd::ops::prelu opFF; + sd::ops::prelu_bp opBP; const bool isGradCorrect = GradCheck::checkGrad(opFF, opBP, argsHolderFF, argsHolderBP); @@ -1976,8 +2007,8 @@ TEST_F(DeclarableOpsTests9, thresholdedrelu_bp_test1) { const OpArgsHolder argsHolderFF({&x}, {theta}, {}); const OpArgsHolder argsHolderBP({&x, &dLdO}, {theta}, {}); - nd4j::ops::thresholdedrelu opFF; - nd4j::ops::thresholdedrelu_bp opBP; + sd::ops::thresholdedrelu opFF; + sd::ops::thresholdedrelu_bp opBP; const bool isGradCorrect = GradCheck::checkGrad(opFF, opBP, argsHolderFF, argsHolderBP); @@ -1993,15 +2024,15 @@ TEST_F(DeclarableOpsTests9, multiply_test1) { x.linspace(1.f); y.linspace(0.1f, 0.1f); - nd4j::ops::multiply op; + sd::ops::multiply op; auto result = op.evaluate({&x, &y}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); - auto z = result->at(0); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -2013,15 +2044,15 @@ TEST_F(DeclarableOpsTests9, multiply_test2) { x.linspace(1.f); // y.linspace(0.1f, 0.1f); - nd4j::ops::multiply op; + sd::ops::multiply op; auto result = op.evaluate({&y, &x}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); - auto z = result->at(0); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -2033,15 +2064,15 @@ TEST_F(DeclarableOpsTests9, multiply_test3) { x.linspace(1.f); y.linspace(0.1f, 0.1f); - nd4j::ops::multiply op; + sd::ops::multiply op; auto result = op.evaluate({&x, &y}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); - auto z = result->at(0); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -2052,15 +2083,15 @@ TEST_F(DeclarableOpsTests9, multiply_test4) { auto exp = NDArrayFactory::create('c', {1, 1}, {0.1f}); x.linspace(1.f); - nd4j::ops::multiply op; + sd::ops::multiply op; auto result = op.evaluate({&x, &y}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); - auto z = result->at(0); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -2070,15 +2101,15 @@ TEST_F(DeclarableOpsTests9, multiply_test5) { auto y = NDArrayFactory::create(0.1f); auto exp = NDArrayFactory::create(0.1f); - nd4j::ops::multiply op; + sd::ops::multiply op; auto result = op.evaluate({&x, &y}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); - auto z = result->at(0); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } //////////////////////////////////////////////////////////////////////////////// @@ -2091,16 +2122,14 @@ TEST_F(DeclarableOpsTests9, multiply_bp_test1) { const OpArgsHolder argsHolderFF({&x, &y}, {}, {}); const OpArgsHolder argsHolderBP({&x, &y, &dLdz}, {}, {}); - nd4j::ops::multiply opFF; - nd4j::ops::multiply_bp opBP; + sd::ops::multiply opFF; + sd::ops::multiply_bp opBP; auto resFF = opFF.evaluate({&x, &y}, {}, {}); auto resBP = opBP.evaluate({&x, &y, &dLdz}, {}, {}); // resFF->at(0)->printIndexedBuffer("Multiply 1x1"); // resBP->at(0)->printIndexedBuffer("Multiply BP 1x1 x"); // resBP->at(1)->printIndexedBuffer("Multyply BP 1x1 y");*/ const bool isGradCorrect = GradCheck::checkGrad(opFF, opBP, argsHolderFF, argsHolderBP); - delete resFF; - delete resBP; ASSERT_TRUE(isGradCorrect); } @@ -2114,8 +2143,8 @@ TEST_F(DeclarableOpsTests9, multiply_bp_test2) { const OpArgsHolder argsHolderFF({&x, &y}, {}, {}); const OpArgsHolder argsHolderBP({&x, &y, &dLdz}, {}, {}); - nd4j::ops::multiply opFF; - nd4j::ops::multiply_bp opBP; + sd::ops::multiply opFF; + sd::ops::multiply_bp opBP; const bool isGradCorrect = GradCheck::checkGrad(opFF, opBP, argsHolderFF, argsHolderBP); @@ -2132,8 +2161,8 @@ TEST_F(DeclarableOpsTests9, multiply_bp_test3) { const OpArgsHolder argsHolderFF({&x, &y}, {}, {}); const OpArgsHolder argsHolderBP({&x, &y, &dLdz}, {}, {}); - nd4j::ops::multiply opFF; - nd4j::ops::multiply_bp opBP; + sd::ops::multiply opFF; + sd::ops::multiply_bp opBP; const bool isGradCorrect = GradCheck::checkGrad(opFF, opBP, argsHolderFF, argsHolderBP); @@ -2150,8 +2179,8 @@ TEST_F(DeclarableOpsTests9, multiply_bp_test4) { const OpArgsHolder argsHolderFF({&x, &y}, {}, {}); const OpArgsHolder argsHolderBP({&x, &y, &dLdz}, {}, {}); - nd4j::ops::multiply opFF; - nd4j::ops::multiply_bp opBP; + sd::ops::multiply opFF; + sd::ops::multiply_bp opBP; const bool isGradCorrect = GradCheck::checkGrad(opFF, opBP, argsHolderFF, argsHolderBP); @@ -2168,8 +2197,8 @@ TEST_F(DeclarableOpsTests9, multiply_bp_test5) { const OpArgsHolder argsHolderFF({&x, &y}, {}, {}); const OpArgsHolder argsHolderBP({&x, &y, &dLdz}, {}, {}); - nd4j::ops::multiply opFF; - nd4j::ops::multiply_bp opBP; + sd::ops::multiply opFF; + sd::ops::multiply_bp opBP; const bool isGradCorrect = GradCheck::checkGrad(opFF, opBP, argsHolderFF, argsHolderBP); @@ -2186,8 +2215,8 @@ TEST_F(DeclarableOpsTests9, multiply_bp_test6) { const OpArgsHolder argsHolderFF({&x, &y}, {}, {}); const OpArgsHolder argsHolderBP({&x, &y, &dLdz}, {}, {}); - nd4j::ops::multiply opFF; - nd4j::ops::multiply_bp opBP; + sd::ops::multiply opFF; + sd::ops::multiply_bp opBP; const bool isGradCorrect = GradCheck::checkGrad(opFF, opBP, argsHolderFF, argsHolderBP); @@ -2204,8 +2233,8 @@ TEST_F(DeclarableOpsTests9, multiply_bp_test7) { const OpArgsHolder argsHolderFF({&x, &y}, {}, {}); const OpArgsHolder argsHolderBP({&x, &y, &dLdz}, {}, {}); - nd4j::ops::multiply opFF; - nd4j::ops::multiply_bp opBP; + sd::ops::multiply opFF; + sd::ops::multiply_bp opBP; const bool isGradCorrect = GradCheck::checkGrad(opFF, opBP, argsHolderFF, argsHolderBP); @@ -2224,8 +2253,8 @@ TEST_F(DeclarableOpsTests9, multiply_bp_test8) { const OpArgsHolder argsHolderFF({&x, &y}, {}, {}); const OpArgsHolder argsHolderBP({&x, &y, &dLdz}, {}, {}); - nd4j::ops::multiply opFF; - nd4j::ops::multiply_bp opBP; + sd::ops::multiply opFF; + sd::ops::multiply_bp opBP; const bool isGradCorrect = GradCheck::checkGrad(opFF, opBP, argsHolderFF, argsHolderBP); @@ -2245,19 +2274,18 @@ TEST_F(DeclarableOpsTests9, Floormod_BP_Test_2) { // const OpArgsHolder argsHolderFF({&x, &y}, {}, {}); // const OpArgsHolder argsHolderBP({&x, &y, &dLdz}, {}, {}); -// nd4j::ops::floormod opFF; +// sd::ops::floormod opFF; // auto resFF = opFF.execute({&x, &y}, {}, {}); // resFF->at(0)->printIndexedBuffer("FF floormod"); // delete resFF; - nd4j::ops::floormod_bp opBP; + sd::ops::floormod_bp opBP; auto resBP = opBP.evaluate({&x, &y, &dLdz}, {}, {}); - ASSERT_TRUE(resBP->status() == ND4J_STATUS_OK); + ASSERT_TRUE(resBP.status() == ND4J_STATUS_OK); // resBP->at(0)->printIndexedBuffer("BP floormod /dx"); // resBP->at(1)->printIndexedBuffer("BP floormod /dy"); - ASSERT_TRUE(dLdz.equalsTo(resBP->at(0))); - ASSERT_TRUE(dLdz.equalsTo(resBP->at(1))); - delete resBP; + ASSERT_TRUE(dLdz.equalsTo(resBP.at(0))); + ASSERT_TRUE(dLdz.equalsTo(resBP.at(1))); // const bool isGradCorrect = GradCheck::checkGrad(opFF, opBP, argsHolderFF, argsHolderBP); @@ -2281,19 +2309,18 @@ TEST_F(DeclarableOpsTests9, Dynamic_Partition_BP_1) { dLdzY.assign(2); dLdzZ.assign(3); - nd4j::ops::dynamic_partition op1; + sd::ops::dynamic_partition op1; auto res1 = op1.evaluate({&x, &y}, {}, {3}); - nd4j::ops::dynamic_partition_bp op2; + sd::ops::dynamic_partition_bp op2; auto res2 = op2.evaluate({&x, &y, &dLdzX, &dLdzY, &dLdzZ}, {}, {3}); - ASSERT_TRUE(res2->status() == ND4J_STATUS_OK); - ASSERT_TRUE(res2->size() == 2); + ASSERT_TRUE(res2.status() == ND4J_STATUS_OK); + ASSERT_TRUE(res2.size() == 2); // printf("How many: %ul\n", res2->size()); // res2->at(0)->printBuffer("Ouputput0"); // res2->at(1)->printBuffer("Ouputput1"); - ASSERT_TRUE(res2->at(0)->equalsTo(exp)); - delete res1; - delete res2; + ASSERT_TRUE(res2.at(0)->equalsTo(exp)); + } ////////////////////////////////////////////////////////////////////// //TEST_F(DeclarableOpsTests9, Dynamic_Partition_BP_2) { @@ -2311,8 +2338,8 @@ TEST_F(DeclarableOpsTests9, Dynamic_Partition_BP_1) { // const OpArgsHolder argsHolderFF({&x, &y}, {}, {3}); // const OpArgsHolder argsHolderBP({&x, &y, &dLdzX, &dLdzY, &dLdzZ}, {}, {3}); // -// nd4j::ops::dynamic_partition opFF; -// nd4j::ops::dynamic_partition_bp opBP; +// sd::ops::dynamic_partition opFF; +// sd::ops::dynamic_partition_bp opBP; // // const bool isGradCorrect = GradCheck::checkGrad(opFF, opBP, argsHolderFF, argsHolderBP); // @@ -2327,13 +2354,13 @@ TEST_F(DeclarableOpsTests9, Floormod_BP_Test_4) { auto exp = NDArrayFactory::create('c', {1, 3}, {-1., 0., -1.}); auto eps = NDArrayFactory::create('c', {2, 1, 3}); eps.assign(1.f); - nd4j::ops::floormod_bp op; + sd::ops::floormod_bp op; auto result = op.evaluate({&x, &y, &eps}, {}, {}); - ASSERT_TRUE(result->size() == 2); - auto gradX = result->at(0); - auto gradY = result->at(1); + ASSERT_TRUE(result.size() == 2); + auto gradX = result.at(0); + auto gradY = result.at(1); // gradX->printIndexedBuffer("gradX"); // gradY->printIndexedBuffer("gradY"); @@ -2341,7 +2368,6 @@ TEST_F(DeclarableOpsTests9, Floormod_BP_Test_4) { ASSERT_TRUE(exp.equalsTo(gradY)); - delete result; } @@ -2353,16 +2379,16 @@ TEST_F(DeclarableOpsTests9, gru_cell_bp_test1) { const int iS = 3; const int nU = 4; - NDArray x('c', {bS, iS}, nd4j::DataType::DOUBLE); - NDArray hi('c', {bS, nU}, nd4j::DataType::DOUBLE); - NDArray W('c', {iS+nU, 2*nU}, nd4j::DataType::DOUBLE); - NDArray Wc('c', {iS+nU, nU}, nd4j::DataType::DOUBLE); - NDArray b('c', {2*nU}, nd4j::DataType::DOUBLE); - NDArray bc('c', {nU}, nd4j::DataType::DOUBLE); - NDArray dLdr('c', {bS, nU}, nd4j::DataType::DOUBLE); - NDArray dLdu('c', {bS, nU}, nd4j::DataType::DOUBLE); - NDArray dLdc('c', {bS, nU}, nd4j::DataType::DOUBLE); - NDArray dLdh('c', {bS, nU}, nd4j::DataType::DOUBLE); + NDArray x('c', {bS, iS}, sd::DataType::DOUBLE); + NDArray hi('c', {bS, nU}, sd::DataType::DOUBLE); + NDArray W('c', {iS+nU, 2*nU}, sd::DataType::DOUBLE); + NDArray Wc('c', {iS+nU, nU}, sd::DataType::DOUBLE); + NDArray b('c', {2*nU}, sd::DataType::DOUBLE); + NDArray bc('c', {nU}, sd::DataType::DOUBLE); + NDArray dLdr('c', {bS, nU}, sd::DataType::DOUBLE); + NDArray dLdu('c', {bS, nU}, sd::DataType::DOUBLE); + NDArray dLdc('c', {bS, nU}, sd::DataType::DOUBLE); + NDArray dLdh('c', {bS, nU}, sd::DataType::DOUBLE); x.linspace(-5, 0.5); hi = 1.; @@ -2373,14 +2399,14 @@ TEST_F(DeclarableOpsTests9, gru_cell_bp_test1) { const OpArgsHolder argsHolderFF({&x, &hi, &W, &Wc, &b, &bc}, {}, {}); - nd4j::ops::gruCell op; + sd::ops::gruCell op; auto results = op.evaluate(argsHolderFF); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); - auto u = results->at(1); // [bS, nU] - auto c = results->at(2); // [bS, nU] - auto h = results->at(3); // [bS, nU] + auto u = results.at(1); // [bS, nU] + auto c = results.at(2); // [bS, nU] + auto h = results.at(3); // [bS, nU] dLdh = 1.; // SUM loss @@ -2392,15 +2418,15 @@ TEST_F(DeclarableOpsTests9, gru_cell_bp_test1) { dLdu.assign(dLdh * dhdu); dLdr.assign(mmul(dLdc * dcdZc * hi, Wch.transpose())); - delete results; + const OpArgsHolder argsHolderBP({&x, &hi, &W, &Wc, &b, &bc, &dLdr, &dLdu, &dLdc, &dLdh}, {}, {}); - nd4j::ops::gruCell opFF; - nd4j::ops::gruCell_bp opBP; + sd::ops::gruCell opFF; + sd::ops::gruCell_bp opBP; - const bool isGradCorrect = GradCheck::checkGrad(opFF, opBP, argsHolderFF, argsHolderBP, {1, 1, 1, 1 , 1, 1}, {0., 1.}, nd4j::GradCheck::LossFunc::SUM, true); + const bool isGradCorrect = GradCheck::checkGrad(opFF, opBP, argsHolderFF, argsHolderBP, {1, 1, 1, 1 , 1, 1}, {0., 1.}, sd::GradCheck::LossFunc::SUM, true); ASSERT_TRUE(isGradCorrect); } @@ -2412,14 +2438,14 @@ TEST_F(DeclarableOpsTests9, Cholesky_Test_1) { NDArray x = NDArrayFactory::create('c', {3, 3}, {4,12,-16, 12 ,37,-43, -16, -43, 98}); NDArray exp = NDArrayFactory::create('c', {3,3}, {2., 0., 0., 6., 1., 0., -8., 5., 3.}); - nd4j::ops::cholesky op; + sd::ops::cholesky op; auto result = op.evaluate({&x}, {}, {}); - ASSERT_EQ(result->status(), ND4J_STATUS_OK); - auto res = result->at(0); + ASSERT_EQ(result.status(), ND4J_STATUS_OK); + auto res = result.at(0); // res->printIndexedBuffer("Output for Cholesky1"); ASSERT_TRUE(exp.equalsTo(res)); - delete result; + } //////////////////////////////////////////////////////////////////// @@ -2428,14 +2454,14 @@ TEST_F(DeclarableOpsTests9, Cholesky_Test_2) { NDArray x = NDArrayFactory::create('c', {2, 3, 3}, {4, 12,-16, 12 ,37,-43, -16, -43, 98, 1, 1, 1, 1, 2, 2, 1, 2., 6}); NDArray exp = NDArrayFactory::create('c', {2, 3, 3}, {2., 0., 0., 6., 1., 0., -8., 5., 3., 1., 0., 0., 1., 1., 0,1., 1., 2.}); - nd4j::ops::cholesky op; + sd::ops::cholesky op; auto result = op.evaluate({&x}, {}, {}); - ASSERT_EQ(result->status(), ND4J_STATUS_OK); - auto res = result->at(0); + ASSERT_EQ(result.status(), ND4J_STATUS_OK); + auto res = result.at(0); // res->printIndexedBuffer("Output for Cholesky 2"); ASSERT_TRUE(exp.equalsTo(res)); - delete result; + } //////////////////////////////////////////////////////////////////// @@ -2444,14 +2470,14 @@ TEST_F(DeclarableOpsTests9, Cholesky_Test_3) { NDArray x = NDArrayFactory::create('c', {2, 3, 3}, {4.f, 12.f, -16.f, 12.f, 37.f, -43.f, -16.f, -43.f, 98.f, 1.f, 1.f, 1.f, 1.f, 2.f, 2.f, 1.f, 2.f, 6.f}); NDArray exp = NDArrayFactory::create('c', {2, 3, 3}, {2.f, 0.f, 0.f, 6.f, 1.f, 0.f, -8.f, 5.f, 3.f, 1.f, 0.f, 0.f, 1.f, 1.f, 0.f, 1.f, 1.f, 2.f}); - nd4j::ops::cholesky op; + sd::ops::cholesky op; auto result = op.evaluate({&x}, {}, {}); - ASSERT_EQ(result->status(), ND4J_STATUS_OK); - auto res = result->at(0); + ASSERT_EQ(result.status(), ND4J_STATUS_OK); + auto res = result.at(0); // res->printIndexedBuffer("Output for Cholesky 3"); ASSERT_TRUE(exp.equalsTo(res, 1e-4)); - delete result; + } //////////////////////////////////////////////////////////////////// @@ -2478,8 +2504,8 @@ TEST_F(DeclarableOpsTests9, Cholesky_Test_3) { // const OpArgsHolder argsHolderFF({&x, &h0, &Wx, &Wh, &b}, {}, {}); // const OpArgsHolder argsHolderBP({&x, &h0, &Wx, &Wh, &b, &dLdh}, {}, {}); -// nd4j::ops::gru opFF; -// nd4j::ops::gru_bp opBP; +// sd::ops::gru opFF; +// sd::ops::gru_bp opBP; // const bool isGradCorrect = GradCheck::checkGrad(opFF, opBP, argsHolderFF, argsHolderBP); diff --git a/libnd4j/tests_cpu/layers_tests/DeclarableOpsTestsCuda1.cu b/libnd4j/tests_cpu/layers_tests/DeclarableOpsTestsCuda1.cu index b7907ce1d..4f69da61b 100644 --- a/libnd4j/tests_cpu/layers_tests/DeclarableOpsTestsCuda1.cu +++ b/libnd4j/tests_cpu/layers_tests/DeclarableOpsTestsCuda1.cu @@ -21,13 +21,13 @@ #include "testlayers.h" #include -#include +#include #include -#include +#include #include -using namespace nd4j; +using namespace sd; class DeclarableOpsTestsCuda1 : public testing::Test { @@ -48,17 +48,15 @@ TEST_F(DeclarableOpsTestsCuda1, Test_CHOOSE_SCALAR_LARGE) { auto precursor = NDArrayFactory::create(inputData,'c',{1,149}); NDArray x(nullptr, precursor.specialBuffer(), precursor.shapeInfo()); - nd4j::ops::choose op; + sd::ops::choose op; //greater than test auto result = op.evaluate({&x}, {0.0},{3}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(1); + auto z = result.at(1); ASSERT_EQ(148,z->e(0)); //ASSERT_TRUE(exp.isSameShape(z)); - - delete result; } /* @@ -67,7 +65,7 @@ TEST_F(DeclarableOpsTestsCuda1, Test_Reverse_TAD_1) { auto z = x.like(); x.linspace(1.0f); - nd4j::ops::reverse op; + sd::ops::reverse op; auto timeStart = std::chrono::system_clock::now(); auto status = op.execute({&x}, {&z}, {}, {1}, {}); auto timeEnd = std::chrono::system_clock::now(); diff --git a/libnd4j/tests_cpu/layers_tests/EmptyTests.cpp b/libnd4j/tests_cpu/layers_tests/EmptyTests.cpp index a234e6d50..ab6bad3c4 100644 --- a/libnd4j/tests_cpu/layers_tests/EmptyTests.cpp +++ b/libnd4j/tests_cpu/layers_tests/EmptyTests.cpp @@ -20,10 +20,10 @@ #include "testlayers.h" #include -#include +#include // #include -using namespace nd4j; +using namespace sd; class EmptyTests : public testing::Test { @@ -60,16 +60,16 @@ TEST_F(EmptyTests, Test_Create_Empty_2) { TEST_F(EmptyTests, Test_Concat_1) { // auto empty = NDArrayFactory::empty_(); - auto empty = new NDArray('c', {0}, nd4j::DataType::FLOAT32);//NDArrayFactory::create_('c', {(Nd4jLong)0}}; + auto empty = new NDArray('c', {0}, sd::DataType::FLOAT32);//NDArrayFactory::create_('c', {(Nd4jLong)0}}; auto vector = NDArrayFactory::create_('c', {1}, {1.0f}); ASSERT_TRUE(empty->isEmpty()); - nd4j::ops::concat op; + sd::ops::concat op; auto result = op.evaluate({empty, vector}, {}, {0}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); // z->printShapeInfo("z shape"); // z->printIndexedBuffer("z buffr"); @@ -78,23 +78,22 @@ TEST_F(EmptyTests, Test_Concat_1) { delete empty; delete vector; - delete result; } TEST_F(EmptyTests, Test_Concat_2) { - auto empty = new NDArray('c', {0}, nd4j::DataType::FLOAT32); //NDArrayFactory::empty_(); + auto empty = new NDArray('c', {0}, sd::DataType::FLOAT32); //NDArrayFactory::empty_(); auto scalar1 = NDArrayFactory::create_('c', {1}, {1.0f}); auto scalar2 = NDArrayFactory::create_('c', {1}, {2.0f}); auto exp = NDArrayFactory::create('c', {2}, {1.f, 2.f}); ASSERT_TRUE(empty->isEmpty()); - nd4j::ops::concat op; + sd::ops::concat op; auto result = op.evaluate({empty, scalar1, scalar2}, {}, {0}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); // z->printShapeInfo("z shape"); // z->printIndexedBuffer("z buffr"); @@ -104,7 +103,6 @@ TEST_F(EmptyTests, Test_Concat_2) { delete empty; delete scalar1; delete scalar2; - delete result; } TEST_F(EmptyTests, Test_Concat_3) { @@ -115,15 +113,14 @@ TEST_F(EmptyTests, Test_Concat_3) { ASSERT_TRUE(empty.isEmpty()); - nd4j::ops::concat op; + sd::ops::concat op; auto result = op.evaluate({&empty, &scalar1, &scalar2}, {}, {0}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_EQ(exp, *z); - delete result; } TEST_F(EmptyTests, Test_Concat_4) { @@ -134,15 +131,13 @@ TEST_F(EmptyTests, Test_Concat_4) { ASSERT_TRUE(empty.isEmpty()); - nd4j::ops::concat op; + sd::ops::concat op; auto result = op.evaluate({&scalar1, &empty, &scalar2}, {}, {0}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_EQ(exp, *z); - - delete result; } TEST_F(EmptyTests, Test_Reshape_1) { @@ -150,15 +145,14 @@ TEST_F(EmptyTests, Test_Reshape_1) { auto exp = NDArrayFactory::create(119.f); auto empty = NDArrayFactory::empty_(); - nd4j::ops::reshape op; + sd::ops::reshape op; auto result = op.evaluate({&vector, empty}, {}, {}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - ASSERT_EQ(exp, *result->at(0)); + ASSERT_EQ(exp, *result.at(0)); delete empty; - delete result; } TEST_F(EmptyTests, Test_Reshape_3) { @@ -166,16 +160,15 @@ TEST_F(EmptyTests, Test_Reshape_3) { auto y = NDArrayFactory::create('c', {2}, {10, 0}); auto e = NDArrayFactory::create('c', {10, 0}); - nd4j::ops::reshape op; + sd::ops::reshape op; auto result = op.evaluate({&x, &y}, {}, {}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(e.isSameShape(z)); ASSERT_EQ(e, *z); - delete result; } TEST_F(EmptyTests, Test_dup_1) { @@ -195,35 +188,27 @@ TEST_F(EmptyTests, test_empty_scatter_1) { x.linspace(1.0f); - nd4j::ops::scatter_upd op; + sd::ops::scatter_upd op; auto result = op.evaluate({&x, &indices, &updates}, {}, {}, {true}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_EQ(x, *z); - delete result; } TEST_F(EmptyTests, test_empty_scatter_2) { - auto x = NDArrayFactory::create('c', {5}); - auto z = NDArrayFactory::create('c', {5}); + NDArray x ('c', {5}, sd::DataType::FLOAT32); + NDArray z ('c', {5}, sd::DataType::FLOAT32); auto indices = NDArrayFactory::create('c', {0}); auto updates = NDArrayFactory::create('c', {0}); x.linspace(1.0f); - Context ctx(1); - ctx.setInputArray(0, x.buffer(), x.shapeInfo(), x.specialBuffer(), x.specialShapeInfo()); - ctx.setInputArray(1, indices.buffer(), indices.shapeInfo(), indices.specialBuffer(), indices.specialShapeInfo()); - ctx.setInputArray(2, updates.buffer(), updates.shapeInfo(), updates.specialBuffer(), updates.specialShapeInfo()); - ctx.setOutputArray(0, z.buffer(), z.shapeInfo(), z.specialBuffer(), z.specialShapeInfo()); - bool args[] = {true}; - ctx.setBArguments(args, 1); + sd::ops::scatter_upd op; + auto status = op.execute({&x, &indices, &updates}, {&z}, {}, {}, {true}); - nd4j::ops::scatter_upd op; - auto result = op.execute(&ctx); - ASSERT_EQ(Status::OK(), result); + ASSERT_EQ(Status::OK(), status); ASSERT_EQ(x, z); } @@ -232,7 +217,7 @@ TEST_F(EmptyTests, test_shaped_empty_1) { auto empty = NDArrayFactory::create('c', {2, 0, 3}); std::vector shape = {2, 0, 3}; - ASSERT_EQ(nd4j::DataType::FLOAT32, empty.dataType()); + ASSERT_EQ(sd::DataType::FLOAT32, empty.dataType()); ASSERT_EQ(0, empty.lengthOf()); ASSERT_TRUE(empty.isEmpty()); ASSERT_EQ(shape, empty.getShapeAsVector()); @@ -243,7 +228,7 @@ TEST_F(EmptyTests, test_shaped_empty_2) { auto empty = NDArrayFactory::create('c', {0, 3}); std::vector shape = {0, 3}; - ASSERT_EQ(nd4j::DataType::FLOAT32, empty.dataType()); + ASSERT_EQ(sd::DataType::FLOAT32, empty.dataType()); ASSERT_EQ(0, empty.lengthOf()); ASSERT_TRUE(empty.isEmpty()); ASSERT_EQ(shape, empty.getShapeAsVector()); @@ -254,7 +239,7 @@ TEST_F(EmptyTests, test_shaped_empty_3) { auto empty = NDArrayFactory::create('c', {0}); std::vector shape = {0}; - ASSERT_EQ(nd4j::DataType::FLOAT32, empty.dataType()); + ASSERT_EQ(sd::DataType::FLOAT32, empty.dataType()); ASSERT_EQ(0, empty.lengthOf()); ASSERT_TRUE(empty.isEmpty()); ASSERT_EQ(shape, empty.getShapeAsVector()); @@ -262,8 +247,8 @@ TEST_F(EmptyTests, test_shaped_empty_3) { } TEST_F(EmptyTests, test_shaped_empty_4) { - auto shape = ConstantShapeHelper::getInstance()->vectorShapeInfo(0, nd4j::DataType::FLOAT32); - NDArray array(shape, true, nd4j::LaunchContext::defaultContext()); + auto shape = ConstantShapeHelper::getInstance()->vectorShapeInfo(0, sd::DataType::FLOAT32); + NDArray array(shape, true, sd::LaunchContext::defaultContext()); std::vector shapeOf({0}); ASSERT_TRUE(array.isEmpty()); @@ -293,19 +278,17 @@ TEST_F(EmptyTests, test_empty_reshape_1) { auto e0 = NDArrayFactory::create('c', {2, 0, 0}); auto e1 = NDArrayFactory::create('c', {0, 1}); - nd4j::ops::reshape op; + sd::ops::reshape op; auto result0 = op.evaluate({&x0, &shape0}, {}, {}); - ASSERT_EQ(Status::OK(), result0->status()); - auto z0 = result0->at(0); + ASSERT_EQ(Status::OK(), result0.status()); + auto z0 = result0.at(0); ASSERT_EQ(e0, *z0); auto result1 = op.evaluate({&x1, &shape1}, {}, {}); - ASSERT_EQ(Status::OK(), result1->status()); - auto z1 = result1->at(0); + ASSERT_EQ(Status::OK(), result1.status()); + auto z1 = result1.at(0); ASSERT_EQ(e1, *z1); - delete result0; - delete result1; } @@ -314,14 +297,13 @@ TEST_F(EmptyTests, test_empty_matmul_1) { auto y = NDArrayFactory::create('c', {1, 0}); auto e = NDArrayFactory::create('c', {0, 0}); - nd4j::ops::matmul op; + sd::ops::matmul op; auto result = op.evaluate({&x, &y}, {}, {}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_EQ(e, *z); - delete result; } TEST_F(EmptyTests, test_empty_matmul_2) { @@ -329,12 +311,10 @@ TEST_F(EmptyTests, test_empty_matmul_2) { auto y = NDArrayFactory::create('c', {1, 4, 0}); auto e = NDArrayFactory::create('c', {1, 0, 0}); - nd4j::ops::matmul op; + sd::ops::matmul op; auto result = op.evaluate({&x, &y}, {}, {}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_EQ(e, *z); - - delete result; } diff --git a/libnd4j/tests_cpu/layers_tests/ExtraArgumentsTests.cpp b/libnd4j/tests_cpu/layers_tests/ExtraArgumentsTests.cpp index 8e18051a5..87ac750b2 100644 --- a/libnd4j/tests_cpu/layers_tests/ExtraArgumentsTests.cpp +++ b/libnd4j/tests_cpu/layers_tests/ExtraArgumentsTests.cpp @@ -22,7 +22,7 @@ #include #include -using namespace nd4j; +using namespace sd; class ExtraArgumentsTests : public testing::Test { public: diff --git a/libnd4j/tests_cpu/layers_tests/FlatBuffersTests.cpp b/libnd4j/tests_cpu/layers_tests/FlatBuffersTests.cpp index 49dd0657d..bdb8bde68 100644 --- a/libnd4j/tests_cpu/layers_tests/FlatBuffersTests.cpp +++ b/libnd4j/tests_cpu/layers_tests/FlatBuffersTests.cpp @@ -25,11 +25,11 @@ #include #include #include -#include +#include #include -using namespace nd4j; -using namespace nd4j::graph; +using namespace sd; +using namespace sd::graph; class FlatBuffersTest : public testing::Test { public: @@ -94,10 +94,10 @@ TEST_F(FlatBuffersTest, FlatGraphTest1) { auto fShape = builder.CreateVector(array->getShapeInfoAsFlatVector()); auto fBuffer = builder.CreateVector(array->asByteVector()); - auto fArray = CreateFlatArray(builder, fShape, fBuffer, nd4j::graph::DType::DType_FLOAT); + auto fArray = CreateFlatArray(builder, fShape, fBuffer, sd::graph::DType::DType_FLOAT); auto fVid = CreateIntPair(builder, -1); - auto fVar = CreateFlatVariable(builder, fVid, 0, nd4j::graph::DType::DType_FLOAT, 0, fArray); + auto fVar = CreateFlatVariable(builder, fVid, 0, sd::graph::DType::DType_FLOAT, 0, fArray); std::vector outputs1, outputs2, inputs1, inputs2; outputs1.push_back(2); @@ -182,9 +182,9 @@ TEST_F(FlatBuffersTest, FlatGraphTest1) { ASSERT_TRUE(var != nullptr); ASSERT_EQ(-2.0, var->reduceNumber(reduce::Mean).e(0)); - nd4j::graph::GraphExecutioner::execute(&graph); + sd::graph::GraphExecutioner::execute(&graph); - auto resultWrapper = nd4j::graph::GraphExecutioner::executeFlatBuffer((Nd4jPointer) buf); + auto resultWrapper = sd::graph::GraphExecutioner::executeFlatBuffer((Nd4jPointer) buf); auto flatResults = GetFlatResult(resultWrapper->pointer()); @@ -265,7 +265,7 @@ TEST_F(FlatBuffersTest, ExplicitOutputTest1) { auto name1 = builder.CreateString("wow1"); - auto node1 = CreateFlatNode(builder, 1, name1, OpType_TRANSFORM, 0, in1, 0, nd4j::graph::DType::FLOAT); + auto node1 = CreateFlatNode(builder, 1, name1, OpType_TRANSFORM, 0, in1, 0, sd::graph::DType::FLOAT); std::vector> variables_vector; variables_vector.push_back(fXVar); @@ -316,7 +316,7 @@ TEST_F(FlatBuffersTest, ExplicitOutputTest1) { /* TEST_F(FlatBuffersTest, ReadFile1) { - uint8_t* data = nd4j::graph::readFlatBuffers("./resources/adam_sum.fb"); + uint8_t* data = sd::graph::readFlatBuffers("./resources/adam_sum.fb"); auto fg = GetFlatGraph(data); auto restoredGraph = new Graph(fg); @@ -341,7 +341,7 @@ TEST_F(FlatBuffersTest, ReadFile1) { } TEST_F(FlatBuffersTest, ReadFile2) { - uint8_t* data = nd4j::graph::readFlatBuffers("./resources/adam_sum.fb"); + uint8_t* data = sd::graph::readFlatBuffers("./resources/adam_sum.fb"); Nd4jPointer result = GraphExecutioner::executeFlatBuffer((Nd4jPointer) data); ResultSet arrays(GetFlatResult(result)); @@ -466,7 +466,7 @@ TEST_F(FlatBuffersTest, ReadTensorArrayLoop_1) { TEST_F(FlatBuffersTest, ReadLoops_NestedWhile_1) { // TF graph: // https://gist.github.com/raver119/2aa49daf7ec09ed4ddddbc6262f213a0 - nd4j::ops::assign op1; + sd::ops::assign op1; auto graph = GraphExecutioner::importFromFlatBuffers("./resources/nested_while.fb"); @@ -605,7 +605,7 @@ TEST_F(FlatBuffersTest, ReduceDim_2) { #ifdef GRAPH_FILES_OK TEST_F(FlatBuffersTest, Ae_00) { - nd4j::ops::rank op1; + sd::ops::rank op1; auto graph = GraphExecutioner::importFromFlatBuffers("./resources/ae_00.fb"); @@ -629,7 +629,7 @@ TEST_F(FlatBuffersTest, Ae_00) { } TEST_F(FlatBuffersTest, expand_dims) { - nd4j::ops::rank op1; + sd::ops::rank op1; auto exp = NDArrayFactory::create('c', {3, 1, 4}, {-0.95938617f, -1.20301781f, 1.22260064f, 0.50172403f, 0.59972949f, 0.78568028f, 0.31609724f, 1.51674747f, 0.68013491f, -0.05227458f, 0.25903158f, 1.13243439f}); @@ -650,7 +650,7 @@ TEST_F(FlatBuffersTest, expand_dims) { } TEST_F(FlatBuffersTest, transpose) { - nd4j::ops::rank op1; + sd::ops::rank op1; auto graph = GraphExecutioner::importFromFlatBuffers("./resources/transpose.fb"); @@ -663,7 +663,7 @@ TEST_F(FlatBuffersTest, transpose) { } TEST_F(FlatBuffersTest, Test_Stitches) { - nd4j::ops::realdiv op0; + sd::ops::realdiv op0; auto graph = GraphExecutioner::importFromFlatBuffers("./resources/partition_stitch_misc.fb"); //graph->printOut(); @@ -676,8 +676,8 @@ TEST_F(FlatBuffersTest, Test_Stitches) { } TEST_F(FlatBuffersTest, Test_GruDynamicMnist) { - nd4j::Environment::getInstance()->setDebug(false); - nd4j::Environment::getInstance()->setVerbose(false); + sd::Environment::getInstance()->setDebug(false); + sd::Environment::getInstance()->setVerbose(false); auto graph = GraphExecutioner::importFromFlatBuffers("./resources/gru_dynamic_mnist.fb"); //graph->printOut(); @@ -696,9 +696,9 @@ TEST_F(FlatBuffersTest, Test_GruDynamicMnist) { } TEST_F(FlatBuffersTest, Test_Non2D_2) { - nd4j::Environment::getInstance()->setDebug(false); - nd4j::Environment::getInstance()->setVerbose(false); - nd4j::ops::realdiv op0; + sd::Environment::getInstance()->setDebug(false); + sd::Environment::getInstance()->setVerbose(false); + sd::ops::realdiv op0; auto graph = GraphExecutioner::importFromFlatBuffers("./resources/non2d_2.fb"); //graph->printOut(); @@ -764,7 +764,7 @@ TEST_F(FlatBuffersTest, Test_MNIST_1) { /* // FIXME: uncomment this test once conv_0 fb reexported TEST_F(FlatBuffersTest, nhwc_conv_0) { - nd4j::ops::rank op1; + sd::ops::rank op1; auto exp('c', {4, 2}, {2.958640f, 0.602521f, 7.571267f, 1.496686f, -2.292647f, -1.791460f, 13.055838f, 4.278642f}); diff --git a/libnd4j/tests_cpu/layers_tests/FlatUtilsTests.cpp b/libnd4j/tests_cpu/layers_tests/FlatUtilsTests.cpp index 31aa997c6..f31a1c7ec 100644 --- a/libnd4j/tests_cpu/layers_tests/FlatUtilsTests.cpp +++ b/libnd4j/tests_cpu/layers_tests/FlatUtilsTests.cpp @@ -18,13 +18,13 @@ // @author raver119@gmail.com // -#include -#include +#include +#include #include "testlayers.h" #include -#include +#include -using namespace nd4j; +using namespace sd; class FlatUtilsTests : public testing::Test { public: diff --git a/libnd4j/tests_cpu/layers_tests/GraphExecutionerTests.cpp b/libnd4j/tests_cpu/layers_tests/GraphExecutionerTests.cpp index 1e34a62d9..7a2856dc0 100644 --- a/libnd4j/tests_cpu/layers_tests/GraphExecutionerTests.cpp +++ b/libnd4j/tests_cpu/layers_tests/GraphExecutionerTests.cpp @@ -25,11 +25,11 @@ #include #include #include -#include +#include #include -using namespace nd4j; -using namespace nd4j::graph; +using namespace sd; +using namespace sd::graph; class GraphExecutionerTests : public testing::Test { public: diff --git a/libnd4j/tests_cpu/layers_tests/GraphHolderTests.cpp b/libnd4j/tests_cpu/layers_tests/GraphHolderTests.cpp index f144a7b84..f1f7195e7 100644 --- a/libnd4j/tests_cpu/layers_tests/GraphHolderTests.cpp +++ b/libnd4j/tests_cpu/layers_tests/GraphHolderTests.cpp @@ -21,9 +21,9 @@ #include "testlayers.h" #include -using namespace nd4j; -using namespace nd4j::ops; -using namespace nd4j::graph; +using namespace sd; +using namespace sd::ops; +using namespace sd::graph; class GraphHolderTests : public testing::Test { public: diff --git a/libnd4j/tests_cpu/layers_tests/GraphRandomGeneratorTests.cpp b/libnd4j/tests_cpu/layers_tests/GraphRandomGeneratorTests.cpp index 10fac836f..8fe46cd2f 100644 --- a/libnd4j/tests_cpu/layers_tests/GraphRandomGeneratorTests.cpp +++ b/libnd4j/tests_cpu/layers_tests/GraphRandomGeneratorTests.cpp @@ -20,8 +20,8 @@ #include #include -using namespace nd4j; -using namespace nd4j::graph; +using namespace sd; +using namespace sd::graph; class GraphRandomGeneratorTests : public testing::Test { public: @@ -29,8 +29,8 @@ public: }; TEST_F(GraphRandomGeneratorTests, Reproducibility_Test_1) { - nd4j::graph::RandomGenerator g0(119); - nd4j::graph::RandomGenerator g1(119); + sd::graph::RandomGenerator g0(119); + sd::graph::RandomGenerator g1(119); auto i0 = g0.relativeT(15, 0, DataTypeUtils::max()); auto i1 = g1.relativeT(15, 0, DataTypeUtils::max()); @@ -39,8 +39,8 @@ TEST_F(GraphRandomGeneratorTests, Reproducibility_Test_1) { } TEST_F(GraphRandomGeneratorTests, Reproducibility_Test_2) { - nd4j::graph::RandomGenerator g0(119); - nd4j::graph::RandomGenerator g1(117); + sd::graph::RandomGenerator g0(119); + sd::graph::RandomGenerator g1(117); auto i0 = g0.relativeT(15, 0, DataTypeUtils::max()); auto i1 = g1.relativeT(15, 0, DataTypeUtils::max()); @@ -49,8 +49,8 @@ TEST_F(GraphRandomGeneratorTests, Reproducibility_Test_2) { } TEST_F(GraphRandomGeneratorTests, Reproducibility_Test_3) { - nd4j::graph::RandomGenerator g0(119, 5); - nd4j::graph::RandomGenerator g1(119, 10); + sd::graph::RandomGenerator g0(119, 5); + sd::graph::RandomGenerator g1(119, 10); auto i0 = g0.relativeT(15, 0, DataTypeUtils::max()); auto i1 = g1.relativeT(15, 0, DataTypeUtils::max()); @@ -59,8 +59,8 @@ TEST_F(GraphRandomGeneratorTests, Reproducibility_Test_3) { } TEST_F(GraphRandomGeneratorTests, Reproducibility_Test_4) { - nd4j::graph::RandomGenerator g0(119, 5); - nd4j::graph::RandomGenerator g1(117, 5); + sd::graph::RandomGenerator g0(119, 5); + sd::graph::RandomGenerator g1(117, 5); auto i0 = g0.relativeT(15, 0, DataTypeUtils::max()); auto i1 = g1.relativeT(15, 0, DataTypeUtils::max()); @@ -69,8 +69,8 @@ TEST_F(GraphRandomGeneratorTests, Reproducibility_Test_4) { } TEST_F(GraphRandomGeneratorTests, Sequential_Test_1) { - nd4j::graph::RandomGenerator g0(119, 5); - nd4j::graph::RandomGenerator g1(119, 5); + sd::graph::RandomGenerator g0(119, 5); + sd::graph::RandomGenerator g1(119, 5); auto v0 = g0.relativeT(15, 0, DataTypeUtils::max()); auto v1 = g1.relativeT(15, 0, DataTypeUtils::max()); @@ -89,8 +89,8 @@ TEST_F(GraphRandomGeneratorTests, Sequential_Test_1) { } TEST_F(GraphRandomGeneratorTests, Sequential_Test_2) { - nd4j::graph::RandomGenerator g0(119, 5); - nd4j::graph::RandomGenerator g1(119, 5); + sd::graph::RandomGenerator g0(119, 5); + sd::graph::RandomGenerator g1(119, 5); auto v0 = g0.relativeT(15, 0, DataTypeUtils::max()); auto v1 = g1.relativeT(15, 0, DataTypeUtils::max()); @@ -110,8 +110,8 @@ TEST_F(GraphRandomGeneratorTests, Sequential_Test_2) { } TEST_F(GraphRandomGeneratorTests, Sequential_Test_3) { - nd4j::graph::RandomGenerator g0(119, 5); - nd4j::graph::RandomGenerator g1(119, 5); + sd::graph::RandomGenerator g0(119, 5); + sd::graph::RandomGenerator g1(119, 5); auto v0 = g0.relativeT(15, 0, DataTypeUtils::max()); auto v1 = g1.relativeT(15, 0, DataTypeUtils::max()); @@ -131,8 +131,8 @@ TEST_F(GraphRandomGeneratorTests, Sequential_Test_3) { } TEST_F(GraphRandomGeneratorTests, Sequential_Test_4) { - nd4j::graph::RandomGenerator g0(119, 5); - nd4j::graph::RandomGenerator g1(119, 5); + sd::graph::RandomGenerator g0(119, 5); + sd::graph::RandomGenerator g1(119, 5); auto v0 = g0.relativeT(15, 0, DataTypeUtils::max()); auto v1 = g1.relativeT(15, 0, DataTypeUtils::max()); @@ -171,8 +171,8 @@ TEST_F(GraphRandomGeneratorTests, Sequential_Test_4) { //#ifndef __clang__ TEST_F(GraphRandomGeneratorTests, Long_Test_1) { - nd4j::graph::RandomGenerator g0(119, 5); - nd4j::graph::RandomGenerator g1(119, 5); + sd::graph::RandomGenerator g0(119, 5); + sd::graph::RandomGenerator g1(119, 5); std::array z0, z1, z2, z3; @@ -215,8 +215,8 @@ TEST_F(GraphRandomGeneratorTests, Long_Test_1) { TEST_F(GraphRandomGeneratorTests, FloatingPoint_Test_1) { - nd4j::graph::RandomGenerator g0(119, 5); - nd4j::graph::RandomGenerator g1(119, 5); + sd::graph::RandomGenerator g0(119, 5); + sd::graph::RandomGenerator g1(119, 5); std::array z0, z1, z2, z3; diff --git a/libnd4j/tests_cpu/layers_tests/GraphStateTests.cpp b/libnd4j/tests_cpu/layers_tests/GraphStateTests.cpp index f03ed4b97..878b05712 100644 --- a/libnd4j/tests_cpu/layers_tests/GraphStateTests.cpp +++ b/libnd4j/tests_cpu/layers_tests/GraphStateTests.cpp @@ -23,10 +23,10 @@ #include #include #include -#include +#include -using namespace nd4j; -using namespace nd4j::graph; +using namespace sd; +using namespace sd::graph; class GraphStateTests : public testing::Test { public: @@ -57,8 +57,8 @@ TEST_F(GraphStateTests, Basic_Tests_1) { // this call will create scope internally state->registerScope(119); - nd4j::ops::add opA; - nd4j::ops::LegacyTransformSameOp opB(transform::Neg); // simdOps::Neg + sd::ops::add opA; + sd::ops::LegacyTransformSameOp opB(transform::Neg); // simdOps::Neg ArgumentsList argsA; ArgumentsList argsB; @@ -81,8 +81,8 @@ TEST_F(GraphStateTests, Basic_Tests_2) { // this call will create scope internally state->registerScope(119); - nd4j::ops::add opA; - nd4j::ops::LegacyTransformSameOp opB(transform::Neg); // simdOps::Neg + sd::ops::add opA; + sd::ops::LegacyTransformSameOp opB(transform::Neg); // simdOps::Neg ArgumentsList argsA; ArgumentsList argsB; @@ -147,8 +147,8 @@ TEST_F(GraphStateTests, Stateful_Execution_3) { // conditional scope state->registerScope(22); - nd4j::ops::LegacyReduceSameOp op1(reduce::Sum); - nd4j::ops::lt_scalar op2; + sd::ops::LegacyReduceSameOp op1(reduce::Sum); + sd::ops::lt_scalar op2; // while sum(var0) < var1 // this op takes sum @@ -170,8 +170,8 @@ TEST_F(GraphStateTests, Stateful_Execution_3) { // this op is result of previous op + 1 ArgumentsList args4({{3, 0}, {0, 2}}); - nd4j::ops::add op3; - nd4j::ops::add op4; + sd::ops::add op3; + sd::ops::add op4; state->attachOpToScope(33, 3, &op3, args3); state->attachOpToScope(33, 4, &op4, args4); @@ -225,8 +225,8 @@ TEST_F(GraphStateTests, Stateful_Execution_4) { // conditional scope state->registerScope(22); - nd4j::ops::LegacyReduceSameOp op1(reduce::Sum); - nd4j::ops::lt_scalar op2; + sd::ops::LegacyReduceSameOp op1(reduce::Sum); + sd::ops::lt_scalar op2; // if sum(var0) < var1 // this op takes sum @@ -242,7 +242,7 @@ TEST_F(GraphStateTests, Stateful_Execution_4) { state->registerScope(33); ArgumentsList args3({{0, 0}, {0, 1}}); - nd4j::ops::subtract op3; + sd::ops::subtract op3; state->attachOpToScope(33, 3, &op3, args3); // return for false scope @@ -253,7 +253,7 @@ TEST_F(GraphStateTests, Stateful_Execution_4) { state->registerScope(44); ArgumentsList args4({{0, 0}, {0, 1}}); - nd4j::ops::add op4; + sd::ops::add op4; state->attachOpToScope(44, 4, &op4, args4); // return for false scope @@ -299,8 +299,8 @@ TEST_F(GraphStateTests, Stateful_Execution_5) { // conditional scope state->registerScope(22); - nd4j::ops::LegacyReduceSameOp op1(reduce::Sum); - nd4j::ops::gt_scalar op2; + sd::ops::LegacyReduceSameOp op1(reduce::Sum); + sd::ops::gt_scalar op2; // if sum(var0) < var1 // this op takes sum @@ -316,7 +316,7 @@ TEST_F(GraphStateTests, Stateful_Execution_5) { state->registerScope(33); ArgumentsList args3({{0, 0}, {0, 1}}); - nd4j::ops::subtract op3; + sd::ops::subtract op3; state->attachOpToScope(33, 3, &op3, args3); // return for false scope @@ -327,7 +327,7 @@ TEST_F(GraphStateTests, Stateful_Execution_5) { state->registerScope(44); ArgumentsList args4({{0, 0}, {0, 1}}); - nd4j::ops::add op4; + sd::ops::add op4; state->attachOpToScope(44, 4, &op4, args4); // return for false scope diff --git a/libnd4j/tests_cpu/layers_tests/GraphTests.cpp b/libnd4j/tests_cpu/layers_tests/GraphTests.cpp index 180d8fef4..73aac9c3b 100644 --- a/libnd4j/tests_cpu/layers_tests/GraphTests.cpp +++ b/libnd4j/tests_cpu/layers_tests/GraphTests.cpp @@ -25,12 +25,12 @@ #include #include #include -#include +#include #include #include -using namespace nd4j; -using namespace nd4j::graph; +using namespace sd; +using namespace sd::graph; class GraphTests : public testing::Test { public: @@ -394,7 +394,7 @@ TEST_F(GraphTests, ReductionsTest1) { graph->getVariableSpace()->putVariable(-1, x); graph->getVariableSpace()->putVariable(-2, z); -// nd4j::graph::Node::Node(OpType opType, int opNum, int id, std::initializer_list input, std::initializer_list output, std::initializer_list dimensions, float scalar, std::initializer_list tArgs, std::initializer_list iArgs) { +// sd::graph::Node::Node(OpType opType, int opNum, int id, std::initializer_list input, std::initializer_list output, std::initializer_list dimensions, float scalar, std::initializer_list tArgs, std::initializer_list iArgs) { auto nodeA = new Node(OpType_REDUCE_FLOAT, reduce::Mean, 1, {-1}, {2}, {1}, {}); auto nodeB = new Node(OpType_TRANSFORM_SAME, transform::Abs, 2, {1}, {-2}); @@ -891,7 +891,7 @@ TEST_F(GraphTests, OutputValidation6) { } TEST_F(GraphTests, TestMultiOutput1) { - nd4j::ops::testop2i2o op1; + sd::ops::testop2i2o op1; auto graph = new Graph(); auto x = NDArrayFactory::create_('c', {5, 5}); @@ -910,7 +910,7 @@ TEST_F(GraphTests, TestMultiOutput1) { auto nodeB0 = new Node(OpType_TRANSFORM_SAME, transform::Abs, 2, {-2}, {11}); nodeB0->markInplace(false); - auto op = nd4j::ops::OpRegistrator::getInstance()->getOperation("testop2i2o"); + auto op = sd::ops::OpRegistrator::getInstance()->getOperation("testop2i2o"); // this op will add 1.0 to first input, and 2.0 for second input auto nodeT = new Node(op, 11, {1, 2}, {21, 31}, {}, 0.0f); @@ -951,7 +951,7 @@ TEST_F(GraphTests, TestMultiOutput1) { } TEST_F(GraphTests, TestDivergentNode1) { - auto op = nd4j::ops::OpRegistrator::getInstance()->getOperation("Switch"); + auto op = sd::ops::OpRegistrator::getInstance()->getOperation("Switch"); auto nodeY = new Node(op, 1); ASSERT_TRUE(nodeY->isDivergencePoint()); @@ -1065,7 +1065,7 @@ TEST_F(GraphTests, MemoryEstimationTest5) { graph.getVariableSpace()->putVariable(-1, x); - nd4j::ops::testcustom op; + sd::ops::testcustom op; auto nodeA0 = new Node(OpType_TRANSFORM_SAME, transform::Abs, 1, {-1}, {2}); auto nodeA1 = new Node(OpType_TRANSFORM_SAME, transform::Abs, 2, {1}, {3}); @@ -1409,7 +1409,7 @@ TEST_F(GraphTests, OpListTest_1) { GraphUtils::filterOperations(ops); ASSERT_TRUE(ops.size() == 7); - std::string exp(" -g \"-DLIBND4J_OPS_LIST='-DOP_rank=true -DOP_range=true -DOP_subtract=true -DOP_permute=true -DOP_matmul=true -DOP_biasadd=true -DOP_TRANSFORM{15}=true '\""); + std::string exp(" -g \"-DSD_OPS_LIST='-DOP_rank=true -DOP_range=true -DOP_subtract=true -DOP_permute=true -DOP_matmul=true -DOP_biasadd=true -DOP_TRANSFORM{15}=true '\""); std::string out = GraphUtils::makeCommandLine(ops); // nd4j_printf("EXP: >%s<\n", exp.c_str()); // nd4j_printf("OUT: >%s<\n", out.c_str()); @@ -1434,7 +1434,7 @@ TEST_F(GraphTests, OpListTest_2) { GraphUtils::filterOperations(ops); - std::string exp = " -g \"-DLIBND4J_OPS_LIST='-DOP_rank=true -DOP_range=true -DOP_subtract=true -DOP_permute=true -DOP_matmul=true -DOP_biasadd=true -DOP_TRANSFORM{15}=true -DOP_strided_slice=true -DOP_ACCUMULATION{1}=true '\""; + std::string exp = " -g \"-DSD_OPS_LIST='-DOP_rank=true -DOP_range=true -DOP_subtract=true -DOP_permute=true -DOP_matmul=true -DOP_biasadd=true -DOP_TRANSFORM{15}=true -DOP_strided_slice=true -DOP_ACCUMULATION{1}=true '\""; ASSERT_TRUE(ops.size() == 9); ASSERT_EQ(exp, GraphUtils::makeCommandLine(ops)); @@ -1570,7 +1570,7 @@ TEST_F(GraphTests, Test_Inplace_Outputs_1) { auto exp = NDArrayFactory::create('c', {6}, {1.f, 2.f, 3.f, 4.f, 5.f, 6.f}); auto z = NDArrayFactory::create('c', {2, 3}); - nd4j::ops::test_output_reshape op; + sd::ops::test_output_reshape op; auto result = op.execute({&x}, {&z}, {}, {}, {}); ASSERT_EQ(Status::OK(), result); @@ -1587,7 +1587,7 @@ TEST_F(GraphTests, Test_Inplace_Outputs_2) { auto z = NDArrayFactory::create('c', {3, 3}); bool failed = false; - nd4j::ops::test_output_reshape op; + sd::ops::test_output_reshape op; try { op.execute({&x}, {&z}, {}, {}, {}); diff --git a/libnd4j/tests_cpu/layers_tests/HashUtilsTests.cpp b/libnd4j/tests_cpu/layers_tests/HashUtilsTests.cpp index fd4baaafc..da513f7d4 100644 --- a/libnd4j/tests_cpu/layers_tests/HashUtilsTests.cpp +++ b/libnd4j/tests_cpu/layers_tests/HashUtilsTests.cpp @@ -29,7 +29,7 @@ class HashUtilsTests : public testing::Test { TEST_F(HashUtilsTests, TestEquality1) { std::string str("Conv2D"); - Nd4jLong hash1 = nd4j::ops::HashHelper::getInstance()->getLongHash(str); + Nd4jLong hash1 = sd::ops::HashHelper::getInstance()->getLongHash(str); ASSERT_EQ(-1637140380760460323L, hash1); } @@ -38,6 +38,6 @@ TEST_F(HashUtilsTests, TestEquality1) { TEST_F(HashUtilsTests, TestEquality2) { std::string str("switch"); - Nd4jLong hash1 = nd4j::ops::HashHelper::getInstance()->getLongHash(str); + Nd4jLong hash1 = sd::ops::HashHelper::getInstance()->getLongHash(str); ASSERT_EQ(-1988317239813741487L, hash1); } \ No newline at end of file diff --git a/libnd4j/tests_cpu/layers_tests/HelpersTests1.cpp b/libnd4j/tests_cpu/layers_tests/HelpersTests1.cpp index 0bf9a1eb7..679f0c5eb 100644 --- a/libnd4j/tests_cpu/layers_tests/HelpersTests1.cpp +++ b/libnd4j/tests_cpu/layers_tests/HelpersTests1.cpp @@ -16,24 +16,24 @@ ******************************************************************************/ #include "testlayers.h" -#include -#include -#include -#include -#include +#include +#include +#include +#include +#include #include -#include +#include #include #include #include #include -#include -#include +#include +#include #include #include -using namespace nd4j; +using namespace sd; class HelpersTests1 : public testing::Test { public: @@ -50,14 +50,14 @@ public: TEST_F(HelpersTests1, test_binary_search_1) { std::array array = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}; - auto idx = nd4j::ops::helpers::binarySearch(array.data(), 2, 10); + auto idx = sd::ops::helpers::binarySearch(array.data(), 2, 10); ASSERT_EQ(2, idx); } TEST_F(HelpersTests1, test_binary_search_2) { std::array array = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}; - auto idx = nd4j::ops::helpers::binarySearch(array.data(), 18, 10); + auto idx = sd::ops::helpers::binarySearch(array.data(), 18, 10); ASSERT_EQ(-1, idx); } @@ -1440,7 +1440,7 @@ TEST_F(HelpersTests1, SVD_test17) { // auto outArr = NDArrayFactory::create('c', {2,5}); // // -// ops::helpers::reverseArray(nd4j::LaunchContext ::defaultContext(), inArr.getBuffer(), inArr.getShapeInfo(), outArr.getBuffer(), outArr.getShapeInfo()); +// ops::helpers::reverseArray(sd::LaunchContext ::defaultContext(), inArr.getBuffer(), inArr.getShapeInfo(), outArr.getBuffer(), outArr.getShapeInfo()); // // ASSERT_TRUE(outArr.equalsTo(&exp)); // ASSERT_TRUE(outArr.isSameShapeStrict(exp)); @@ -1454,7 +1454,7 @@ TEST_F(HelpersTests1, SVD_test17) { // auto exp = NDArrayFactory::create('c', {2,5}, {10,9,8,7,6,5,4,3,2,1}); // // -// ops::helpers::reverseArray(nd4j::LaunchContext ::defaultContext(), inArr.getBuffer(), inArr.getShapeInfo(), inArr.getBuffer(), inArr.getShapeInfo()); +// ops::helpers::reverseArray(sd::LaunchContext ::defaultContext(), inArr.getBuffer(), inArr.getShapeInfo(), inArr.getBuffer(), inArr.getShapeInfo()); // // ASSERT_TRUE(inArr.equalsTo(&exp)); // ASSERT_TRUE(inArr.isSameShapeStrict(exp)); @@ -1468,7 +1468,7 @@ TEST_F(HelpersTests1, SVD_test17) { // auto exp = NDArrayFactory::create('c', {2,5}, {5,4,3,2,1,6,7,8,9,10}); // auto outArr = NDArrayFactory::create('c', {2,5}); // -// ops::helpers::reverseArray(nd4j::LaunchContext ::defaultContext(), inArr.getBuffer(), inArr.getShapeInfo(), outArr.getBuffer(), outArr.getShapeInfo(), 5); +// ops::helpers::reverseArray(sd::LaunchContext ::defaultContext(), inArr.getBuffer(), inArr.getShapeInfo(), outArr.getBuffer(), outArr.getShapeInfo(), 5); // // ASSERT_TRUE(outArr.equalsTo(&exp)); // ASSERT_TRUE(outArr.isSameShapeStrict(exp)); @@ -1481,12 +1481,12 @@ TEST_F(HelpersTests1, rnnCell_test1) { const int inSize = 4; const int numUnits = 4; - NDArray xt('c', {bS, inSize}, nd4j::DataType::DOUBLE); - NDArray ht_1('c', {bS, numUnits}, nd4j::DataType::DOUBLE); - NDArray Wx('c', {inSize, numUnits}, nd4j::DataType::DOUBLE); - NDArray Wh('c', {numUnits, numUnits}, nd4j::DataType::DOUBLE); + NDArray xt('c', {bS, inSize}, sd::DataType::DOUBLE); + NDArray ht_1('c', {bS, numUnits}, sd::DataType::DOUBLE); + NDArray Wx('c', {inSize, numUnits}, sd::DataType::DOUBLE); + NDArray Wh('c', {numUnits, numUnits}, sd::DataType::DOUBLE); NDArray b ('c', {2*numUnits}, {0.0,0.0,0.0,0.0, 0.1,0.2,0.3,0.4}); - NDArray ht('c', {bS, numUnits}, nd4j::DataType::DOUBLE); + NDArray ht('c', {bS, numUnits}, sd::DataType::DOUBLE); xt.assign(0.1); ht_1.assign(0.2); @@ -1495,7 +1495,7 @@ TEST_F(HelpersTests1, rnnCell_test1) { NDArray expHt('c', {bS, numUnits}, {0.492988, 0.56489956, 0.6291452 , 0.6858091,0.492988, 0.56489956, 0.6291452 , 0.6858091}); - ops::helpers::rnnCell(nd4j::LaunchContext ::defaultContext(), &xt, &Wx, &Wh, &b, &ht_1, &ht); + ops::helpers::rnnCell(sd::LaunchContext ::defaultContext(), &xt, &Wx, &Wh, &b, &ht_1, &ht); ASSERT_TRUE(expHt.isSameShape(ht)); ASSERT_TRUE(expHt.equalsTo(ht)); @@ -1524,7 +1524,7 @@ TEST_F(HelpersTests1, rnnCell_test2) { auto expHt = NDArrayFactory::create('c', {bS, numUnits}, {0.6169093,0.67506987,0.72589741,0.76986654,0.6169093,0.67506987,0.72589741,0.76986654}); - ops::helpers::rnnCell(nd4j::LaunchContext ::defaultContext(), &xt, &Wx, &Wh, &b, &ht_1, &ht); + ops::helpers::rnnCell(sd::LaunchContext ::defaultContext(), &xt, &Wx, &Wh, &b, &ht_1, &ht); ASSERT_TRUE(expHt.isSameShape(ht)); ASSERT_TRUE(expHt.equalsTo(ht)); @@ -1552,7 +1552,7 @@ TEST_F(HelpersTests1, rnnCell_test3) { auto expHt = NDArrayFactory::create('c', {bS, numUnits}, {0.5915195, 0.6043678, 0.6169093, 0.6291452,0.5915195, 0.6043678, 0.6169093, 0.6291452}); - ops::helpers::rnnCell(nd4j::LaunchContext ::defaultContext(), &xt, &Wx, &Wh, &b, &ht_1, &ht); + ops::helpers::rnnCell(sd::LaunchContext ::defaultContext(), &xt, &Wx, &Wh, &b, &ht_1, &ht); ASSERT_TRUE(expHt.isSameShape(ht)); ASSERT_TRUE(expHt.equalsTo(ht)); @@ -1581,7 +1581,7 @@ TEST_F(HelpersTests1, rnnCell_test4) { auto expHt = NDArrayFactory::create('c', {bS, numUnits}, {0.68474828, 0.68474828, 0.68474828, 0.68474828,0.69882484, 0.69882484, 0.69882484, 0.69882484}); - ops::helpers::rnnCell(nd4j::LaunchContext ::defaultContext(), &xt, &Wx, &Wh, &b, &ht_1, &ht); + ops::helpers::rnnCell(sd::LaunchContext ::defaultContext(), &xt, &Wx, &Wh, &b, &ht_1, &ht); ASSERT_TRUE(expHt.isSameShape(ht)); ASSERT_TRUE(expHt.equalsTo(ht)); @@ -1887,22 +1887,21 @@ TEST_F(HelpersTests1, OpArgsHolder_test3) { gradO.linspace(0.01, 0.01); OpArgsHolder holderFF({&input}, {}, {2, 3}); - nd4j::ops::tile opFF; // the kind of op doesn't matter, we simply check here whether op.execute() works with OpArgsHolder correctly + sd::ops::tile opFF; // the kind of op doesn't matter, we simply check here whether op.execute() works with OpArgsHolder correctly auto results = opFF.execute(holderFF); - auto tiled = results->at(0); - ASSERT_EQ(Status::OK(), results->status()); + auto tiled = results.at(0); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(exp.isSameShape(tiled)); ASSERT_TRUE(exp.equalsTo(tiled)); - delete results; - + OpArgsHolder holderBP = holderFF.createArgsHolderForBP({&gradO}, true); - nd4j::ops::tile_bp opBP; + sd::ops::tile_bp opBP; results = opBP.execute(holderBP); - auto gradI = results->at(0); - ASSERT_EQ(Status::OK(), results->status()); + auto gradI = results.at(0); + ASSERT_EQ(Status::OK(), results.status()); ASSERT_TRUE(gradIExp.isSameShape(gradI)); ASSERT_TRUE(gradIExp.equalsTo(gradI)); - delete results; + } @@ -1915,8 +1914,8 @@ TEST_F(HelpersTests1, checkGrad_test1) { const OpArgsHolder argsHolderFF({&x}, {}, {}); const OpArgsHolder argsHolderBP({&x, &gradO}, {}, {}); - nd4j::ops::sigmoid opFF; - nd4j::ops::sigmoid_bp opBP; + sd::ops::sigmoid opFF; + sd::ops::sigmoid_bp opBP; const bool isGradCorrect = GradCheck::checkGrad(opFF, opBP, argsHolderFF, argsHolderBP); @@ -1937,8 +1936,8 @@ TEST_F(HelpersTests1, checkGrad_test2) { const OpArgsHolder argsHolderFF({&x, &weights}, {}, {2, 2, 1, 1, 0, 0, 1, 1, 1}); const OpArgsHolder argsHolderBP({&x, &weights, &gradO}, {}, {2, 2, 1, 1, 0, 0, 1, 1, 1}); - nd4j::ops::conv2d opFF; - nd4j::ops::conv2d_bp opBP; + sd::ops::conv2d opFF; + sd::ops::conv2d_bp opBP; const bool isGradCorrect = GradCheck::checkGrad(opFF, opBP, argsHolderFF, argsHolderBP); @@ -1961,8 +1960,8 @@ TEST_F(HelpersTests1, checkGrad_test3) { const OpArgsHolder argsHolderFF({&x, &weights, &bias}, {}, {2, 2, 1, 1, 0, 0, 1, 1, 1}); const OpArgsHolder argsHolderBP({&x, &weights, &bias, &gradO}, {}, {2, 2, 1, 1, 0, 0, 1, 1, 1}); - nd4j::ops::conv2d opFF; - nd4j::ops::conv2d_bp opBP; + sd::ops::conv2d opFF; + sd::ops::conv2d_bp opBP; const bool isGradCorrect = GradCheck::checkGrad(opFF, opBP, argsHolderFF, argsHolderBP); @@ -1985,8 +1984,8 @@ TEST_F(HelpersTests1, checkGrad_test4) { const OpArgsHolder argsHolderFF({&x, &weights, &bias}, {}, {2, 2, 1, 1, 0, 0, 1, 1, 1}); const OpArgsHolder argsHolderBP({&x, &weights, &bias, &gradO}, {}, {2, 2, 1, 1, 0, 0, 1, 1, 1}); - nd4j::ops::conv2d opFF; - nd4j::ops::conv2d_bp opBP; + sd::ops::conv2d opFF; + sd::ops::conv2d_bp opBP; const bool isGradCorrect = GradCheck::checkGrad(opFF, opBP, argsHolderFF, argsHolderBP, {1, 0, 1}); @@ -2009,8 +2008,8 @@ TEST_F(HelpersTests1, checkGrad_test5) { const OpArgsHolder argsHolderFF({&x, &weights, &bias}, {}, {2, 2, 1, 1, 0, 0, 1, 1, 1}); const OpArgsHolder argsHolderBP({&x, &weights, &bias, &gradO}, {}, {2, 2, 1, 1, 0, 0, 1, 1, 1}); - nd4j::ops::conv2d opFF; - nd4j::ops::conv2d_bp opBP; + sd::ops::conv2d opFF; + sd::ops::conv2d_bp opBP; const bool isGradCorrect = GradCheck::checkGrad(opFF, opBP, argsHolderFF, argsHolderBP, {1, 1, 1}, {0.5, 1}); @@ -2033,8 +2032,8 @@ TEST_F(HelpersTests1, checkGrad_test6) { const OpArgsHolder argsHolderFF({&x, &weights, &bias}, {}, {2, 2, 1, 1, 0, 0, 1, 1, 1}); const OpArgsHolder argsHolderBP({&x, &weights, &bias, &gradO}, {}, {2, 2, 1, 1, 0, 0, 1, 1, 1}); - nd4j::ops::conv2d opFF; - nd4j::ops::conv2d_bp opBP; + sd::ops::conv2d opFF; + sd::ops::conv2d_bp opBP; const bool isGradCorrect = GradCheck::checkGrad(opFF, opBP, argsHolderFF, argsHolderBP, {1, 0, 1}, {0.5, 1}, GradCheck::MEAN); @@ -2049,7 +2048,7 @@ TEST_F(HelpersTests1, softMaxForVector_test1) { auto expOutput = NDArrayFactory::create('c', {1,5}); expOutput = 1; - ops::helpers::softmax(nd4j::LaunchContext ::defaultContext(), input, output, 0); + ops::helpers::softmax(sd::LaunchContext ::defaultContext(), input, output, 0); ASSERT_TRUE(output.equalsTo(&expOutput)); } @@ -2061,7 +2060,7 @@ TEST_F(HelpersTests1, softMaxForVector_test2) { auto output = NDArrayFactory::create('c', {5,1}); auto expOutput = NDArrayFactory::create('c', {5,1}, {0.01165623, 0.03168492, 0.08612854, 0.23412166, 0.63640865}); - ops::helpers::softmax(nd4j::LaunchContext ::defaultContext(), input, output, 0); + ops::helpers::softmax(sd::LaunchContext ::defaultContext(), input, output, 0); ASSERT_TRUE(output.equalsTo(&expOutput)); } @@ -2073,7 +2072,7 @@ TEST_F(HelpersTests1, softMaxForVector_test3) { auto output = NDArrayFactory::create('c', {5}); auto expOutput = NDArrayFactory::create('c', {5}, {0.01165623, 0.03168492, 0.08612854, 0.23412166, 0.63640865}); - ops::helpers::softmax(nd4j::LaunchContext ::defaultContext(), input, output, 0); + ops::helpers::softmax(sd::LaunchContext ::defaultContext(), input, output, 0); ASSERT_TRUE(output.equalsTo(&expOutput)); } @@ -2081,8 +2080,8 @@ TEST_F(HelpersTests1, softMaxForVector_test3) { ////////////////////////////////////////////////////////////////////// TEST_F(HelpersTests1, softMaxForVector_test4) { - NDArray input('c', {1500}, nd4j::DataType::DOUBLE); - NDArray output('c', {1500}, nd4j::DataType::DOUBLE); + NDArray input('c', {1500}, sd::DataType::DOUBLE); + NDArray output('c', {1500}, sd::DataType::DOUBLE); NDArray expOutput('c', {1500}, {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, @@ -2103,10 +2102,10 @@ TEST_F(HelpersTests1, softMaxForVector_test4) { 0.001103, 0.001114, 0.001125, 0.001136, 0.001148, 0.001159, 0.001171, 0.001182, 0.001194, 0.001206, 0.001218, 0.001231, 0.001243, 0.001256, 0.001268, 0.001281, 0.001294, 0.001307, 0.001320, 0.001333, 0.001347, 0.001360, 0.001374, 0.001388, 0.001402, 0.001416, 0.001430, 0.001444,0.001459, 0.001473, 0.001488, 0.001503, 0.001518, 0.001534, 0.001549, 0.001565, 0.001580, 0.001596, 0.001612, 0.001628, 0.001645, 0.001661, 0.001678, 0.001695, 0.001712, 0.001729, 0.001746, 0.001764, 0.001782, 0.001800, 0.001818, 0.001836, 0.001854, 0.001873, 0.001892, 0.001911, 0.001930, 0.001950, 0.001969, 0.001989, 0.002009, 0.002029, 0.002049, 0.002070, 0.002091, 0.002112, 0.002133, 0.002155, 0.002176, 0.002198, 0.002220, 0.002242, 0.002265, 0.002288, 0.002311, 0.002334, 0.002357, 0.002381, 0.002405, 0.002429, 0.002454, 0.002478, 0.002503, 0.002528,0.002554, 0.002579, 0.002605, 0.002632, 0.002658, 0.002685, 0.002712, 0.002739, 0.002767, 0.002794, 0.002822, 0.002851, 0.002879, 0.002908, 0.002938, 0.002967, 0.002997, 0.003027, 0.003057, 0.003088, 0.003119, 0.003151, 0.003182, 0.003214, 0.003247, 0.003279, 0.003312, 0.003345, 0.003379, 0.003413, 0.003447, 0.003482, 0.003517, 0.003552, 0.003588, 0.003624, 0.003660, 0.003697, 0.003734, 0.003772, 0.003810, 0.003848, 0.003887, 0.003926, 0.003965, 0.004005, 0.004045, 0.004086, 0.004127, 0.004169, 0.004211, 0.004253, 0.004296, 0.004339, 0.004382, 0.004426,0.004471, 0.004516, 0.004561, 0.004607, 0.004653, 0.004700, 0.004747, 0.004795, 0.004843, 0.004892, 0.004941, 0.004991, 0.005041, 0.005092, 0.005143, 0.005194, 0.005247, 0.005299, 0.005353, 0.005406, 0.005461, 0.005516, 0.005571, 0.005627, 0.005684, 0.005741, 0.005798, 0.005857, -0.005916, 0.005975, 0.006035, 0.006096, 0.006157, 0.006219, 0.006281, 0.006345, 0.006408, 0.006473, 0.006538, 0.006603, 0.006670, 0.006737, 0.006805, 0.006873, 0.006942, 0.007012, 0.007082, 0.007153, 0.007225, 0.007298, 0.007371, 0.007445, 0.007520, 0.007596, 0.007672, 0.007749,0.007827, 0.007906, 0.007985, 0.008065, 0.008147, 0.008228, 0.008311, 0.008395, 0.008479, 0.008564, 0.008650, 0.008737, 0.008825, 0.008914, 0.009003, 0.009094, 0.009185, 0.009277, 0.009371, 0.009465, 0.009560, 0.009656, 0.009753, 0.009851, 0.009950}, nd4j::DataType::DOUBLE); +0.005916, 0.005975, 0.006035, 0.006096, 0.006157, 0.006219, 0.006281, 0.006345, 0.006408, 0.006473, 0.006538, 0.006603, 0.006670, 0.006737, 0.006805, 0.006873, 0.006942, 0.007012, 0.007082, 0.007153, 0.007225, 0.007298, 0.007371, 0.007445, 0.007520, 0.007596, 0.007672, 0.007749,0.007827, 0.007906, 0.007985, 0.008065, 0.008147, 0.008228, 0.008311, 0.008395, 0.008479, 0.008564, 0.008650, 0.008737, 0.008825, 0.008914, 0.009003, 0.009094, 0.009185, 0.009277, 0.009371, 0.009465, 0.009560, 0.009656, 0.009753, 0.009851, 0.009950}, sd::DataType::DOUBLE); input.linspace(0.01, 0.01); - ops::helpers::softmax(nd4j::LaunchContext ::defaultContext(), input, output, 0); + ops::helpers::softmax(sd::LaunchContext ::defaultContext(), input, output, 0); ASSERT_TRUE(output.equalsTo(&expOutput)); } @@ -2119,7 +2118,7 @@ TEST_F(HelpersTests1, logSoftMaxForVector_test1) { auto expOutput = NDArrayFactory::create('c', {1,5}); expOutput = 0; - ops::helpers::logSoftmax(nd4j::LaunchContext ::defaultContext(), input, output, 0); + ops::helpers::logSoftmax(sd::LaunchContext ::defaultContext(), input, output, 0); ASSERT_TRUE(output.equalsTo(&expOutput)); } @@ -2131,7 +2130,7 @@ TEST_F(HelpersTests1, logSoftMaxForVector_test2) { auto output = NDArrayFactory::create('c', {5,1}); auto expOutput = NDArrayFactory::create('c', {5,1}, {-4.4519144, -3.4519144, -2.4519144, -1.4519144, -0.4519144}); - ops::helpers::logSoftmax(nd4j::LaunchContext ::defaultContext(), input, output, 0); + ops::helpers::logSoftmax(sd::LaunchContext ::defaultContext(), input, output, 0); ASSERT_TRUE(output.equalsTo(&expOutput)); } @@ -2143,7 +2142,7 @@ TEST_F(HelpersTests1, logSoftMaxForVector_test3) { auto output = NDArrayFactory::create('c', {5}); auto expOutput = NDArrayFactory::create('c', {5}, {-4.4519144, -3.4519144, -2.4519144, -1.4519144, -0.4519144}); - ops::helpers::logSoftmax(nd4j::LaunchContext ::defaultContext(), input, output, 0); + ops::helpers::logSoftmax(sd::LaunchContext ::defaultContext(), input, output, 0); ASSERT_TRUE(output.equalsTo(&expOutput)); } @@ -2151,8 +2150,8 @@ TEST_F(HelpersTests1, logSoftMaxForVector_test3) { ////////////////////////////////////////////////////////////////////// TEST_F(HelpersTests1, logSoftMaxForVector_test4) { - NDArray input('c', {1500}, nd4j::DataType::DOUBLE); - NDArray output('c', {1500}, nd4j::DataType::DOUBLE); + NDArray input('c', {1500}, sd::DataType::DOUBLE); + NDArray output('c', {1500}, sd::DataType::DOUBLE); NDArray expOutput('c', {1500}, {-8.154773, -8.153772, -8.152773, -8.151772, -8.150773, -8.149773, -8.148773, -8.147773, -8.146772, -8.145773, -8.144773, -8.143773, -8.142773, -8.141773, -8.140773, -8.139772, -8.138773, -8.137773, -8.136773, -8.135773, -8.134773, -8.133773, -8.132772, -8.131773, -8.130773, -8.129773, -8.128773, -8.127772, -8.126773, -8.125772, -8.124773, -8.123773, -8.122773, -8.121773, -8.120772, -8.119773, -8.118773, -8.117773, -8.116773, -8.115773, -8.114773, -8.113772, -8.112773, -8.111773, -8.110773, -8.109773, -8.108773, -8.107773, -8.106772, -8.105773, -8.104773, -8.103773, -8.102773, -8.101772, -8.100773, -8.099772, -8.098773, -8.097773, -8.096773, -8.095773, -8.094772, -8.093773, -8.092772, -8.091773, -8.090773, -8.089773, -8.088773, -8.087772, -8.086773, -8.085773, -8.084773, -8.083773, -8.082773, -8.081773, -8.080772, -8.079773, -8.078773, -8.077773, -8.076773, -8.075773, -8.074773, -8.073772, -8.072773, -8.071773, -8.070773, -8.069773, -8.068772, -8.067773, -8.066772, -8.065773, -8.064773, -8.063773, -8.062773, -8.061772, -8.060773, -8.059772, -8.058773, -8.057773, -8.056773, -8.055773, -8.054772, -8.053773, -8.052773, -8.051773, -8.050773, -8.049773, -8.048773, -8.047772, -8.046773, -8.045773, -8.044773, -8.043773, -8.042773, -8.041773, -8.040772, -8.039773, -8.038773, -8.037773, -8.036773, -8.035772, -8.034773, -8.033772, -8.032773, -8.031773, -8.030773, -8.029773, -8.028772, -8.027773, -8.026772, -8.025773, -8.024773, -8.023773, -8.022773, -8.021772, -8.020773, -8.019773, -8.018773, -8.017773, -8.016773, -8.015773, -8.014772, -8.013773, -8.012773, -8.011773, -8.010773, -8.009773, -8.008773, -8.007772, -8.006773, -8.005773, -8.004773, -8.003773, -8.002772, -8.001773, -8.000772, -7.999773, -7.998773, -7.997773, -7.996773, -7.995773, -7.994773, -7.993773, -7.992773, -7.991773, -7.990773, -7.989773, -7.988773, -7.987773, -7.986773, -7.985773, -7.984773, -7.983773, -7.982773, -7.981773, -7.980773, -7.979773, -7.978773, -7.977773, -7.976773, -7.975773, -7.974773, -7.973773, -7.972773, -7.971773, -7.970773, -7.969773, -7.968773, -7.967773, -7.966773, -7.965773, -7.964773, -7.963773, -7.962773, -7.961773, -7.960773, -7.959773, -7.958773, -7.957773, -7.956773, -7.955773, -7.954773, -7.953773, -7.952773, -7.951773, -7.950773, -7.949773, -7.948773, -7.947773, -7.946773, -7.945773, -7.944773, -7.943773, -7.942773, -7.941773, -7.940773, -7.939773, -7.938773, -7.937773, -7.936773, -7.935773, -7.934773, -7.933773, -7.932773, -7.931773, -7.930773, -7.929773, -7.928773, -7.927773, -7.926773, -7.925773, -7.924773, -7.923773, -7.922773, -7.921773, -7.920773, -7.919773, -7.918773, -7.917773, -7.916773, -7.915773, -7.914773, -7.913773, -7.912773, -7.911773, -7.910773, -7.909773, -7.908773, -7.907773, -7.906773, -7.905773, -7.904773, -7.903773, -7.902773, -7.901773, -7.900773, -7.899773, -7.898773, -7.897773, -7.896773, -7.895773, -7.894773, -7.893773, -7.892773, -7.891773, -7.890773, -7.889773, -7.888773, -7.887773, -7.886773, -7.885773, -7.884773, -7.883773, -7.882773, -7.881773, -7.880773, -7.879773, -7.878773, -7.877773, -7.876773, -7.875773, -7.874773, -7.873773, -7.872773, -7.871773, -7.870773, -7.869773, -7.868773, -7.867773, -7.866773, -7.865773, -7.864773, -7.863773, -7.862773, -7.861773, -7.860773, -7.859773, -7.858773, -7.857773, -7.856773, -7.855773, -7.854773, -7.853773, -7.852773, -7.851773, -7.850773, -7.849773, @@ -2167,10 +2166,10 @@ TEST_F(HelpersTests1, logSoftMaxForVector_test4) { -7.024773, -7.023773, -7.022773, -7.021773, -7.020773, -7.019773, -7.018773, -7.017773, -7.016773, -7.015773, -7.014773, -7.013773, -7.012773, -7.011773, -7.010773, -7.009773, -7.008773, -7.007773, -7.006773, -7.005773, -7.004773, -7.003773, -7.002773, -7.001773, -7.000773, -6.999773, -6.998773, -6.997773, -6.996773, -6.995773, -6.994773, -6.993773, -6.992773, -6.991773, -6.990773, -6.989773, -6.988773, -6.987773, -6.986773, -6.985773, -6.984773, -6.983773, -6.982773, -6.981773, -6.980773, -6.979773, -6.978773, -6.977773, -6.976773, -6.975773, -6.974773, -6.973773, -6.972773, -6.971773, -6.970773, -6.969773, -6.968773, -6.967773, -6.966773, -6.965773, -6.964773, -6.963773, -6.962773, -6.961773, -6.960773, -6.959773, -6.958773, -6.957773, -6.956773, -6.955773, -6.954773, -6.953773, -6.952773, -6.951773, -6.950773, -6.949773, -6.948773, -6.947773, -6.946773, -6.945773, -6.944773, -6.943773, -6.942773, -6.941773, -6.940773, -6.939773, -6.938773, -6.937773, -6.936773, -6.935773, -6.934773, -6.933773, -6.932773, -6.931773, -6.930773, -6.929773, -6.928773, -6.927773, -6.926773, -6.925773, -6.924773, -6.923773, -6.922773, -6.921773, -6.920773, -6.919773, -6.918773, -6.917773, -6.916773, -6.915773, -6.914773, -6.913773, -6.912773, -6.911773, -6.910773, -6.909773, -6.908773, -6.907773, -6.906773, -6.905773, -6.904773, -6.903773, -6.902773, -6.901773, -6.900773, -6.899773, -6.898773, -6.897773, -6.896773, -6.895773, -6.894773, -6.893773, -6.892773, -6.891773, -6.890773, -6.889773, -6.888773, -6.887773, -6.886773, -6.885773, -6.884773, -6.883773, -6.882773, -6.881773, -6.880773, -6.879773, -6.878773, -6.877773, -6.876773, -6.875773, -6.874773, -6.873773, -6.872773, -6.871773, -6.870773, -6.869773, -6.868773, -6.867773, -6.866773, -6.865773, -6.864773, -6.863773, -6.862773, -6.861773, -6.860773, -6.859773, -6.858773, -6.857773, -6.856773, -6.855773, -6.854773, -6.853773, -6.852773, -6.851773, -6.850773, -6.849773, -6.848773, -6.847773, -6.846773, -6.845773, -6.844773, -6.843773, -6.842773, -6.841773, -6.840773, -6.839773, -6.838773, -6.837773, -6.836773, -6.835773, -6.834773, -6.833773, -6.832773, -6.831773, -6.830773, -6.829773, -6.828773, -6.827773, -6.826773, -6.825773, -6.824773, -6.823773, -6.822773, -6.821773, -6.820773, -6.819773, -6.818773, -6.817773, -6.816773, -6.815773, -6.814773, -6.813773, -6.812773, -6.811773, -6.810773, -6.809773, -6.808773, -6.807773, -6.806773, -6.805773, -6.804773, -6.803773, -6.802773, -6.801773, -6.800773, -6.799773, -6.798773, -6.797773, -6.796773, -6.795773, -6.794773, -6.793773, -6.792773, -6.791773, -6.790773, -6.789773, -6.788773, -6.787773, -6.786773, -6.785773, -6.784773, -6.783773, -6.782773, -6.781773, -6.780773, -6.779773, -6.778773, -6.777773, -6.776773, -6.775773, -6.774773, -6.773773, -6.772773, -6.771773, -6.770773, -6.769773, -6.768773, -6.767773, -6.766773, -6.765773, -6.764773, -6.763773, -6.762773, -6.761773, -6.760773, -6.759773, -6.758773, -6.757773, -6.756773, -6.755773, -6.754773, -6.753773, -6.752773, -6.751773, -6.750773, -6.749773, -6.748773, -6.747773, -6.746773, -6.745773, -6.744773, -6.743773, -6.742773, -6.741773, -6.740773, -6.739773, -6.738773, -6.737773, -6.736773, -6.735773, -6.734773, -6.733773, -6.732773, -6.731773, -6.730773, -6.729773, -6.728773, -6.727773, -6.726773, -6.725773, -6.724773, -6.723773, -6.722773, -6.721773, -6.720773, -6.719773, -6.718773, -6.717773, -6.716773, -6.715773, --6.714773, -6.713773, -6.712773, -6.711773, -6.710773, -6.709773, -6.708773, -6.707773, -6.706773, -6.705773, -6.704773, -6.703773, -6.702773, -6.701773, -6.700773, -6.699773, -6.698773, -6.697773, -6.696773, -6.695773, -6.694773, -6.693773, -6.692773, -6.691773, -6.690773, -6.689773, -6.688773, -6.687773, -6.686773, -6.685773, -6.684773, -6.683773, -6.682773, -6.681773, -6.680773, -6.679773, -6.678773, -6.677773, -6.676773, -6.675773, -6.674773, -6.673773, -6.672773, -6.671773, -6.670773, -6.669773, -6.668773, -6.667773, -6.666773, -6.665773, -6.664773, -6.663773, -6.662773, -6.661773, -6.660773, -6.659773, -6.658773, -6.657773, -6.656773, -6.655773}, nd4j::DataType::DOUBLE); +-6.714773, -6.713773, -6.712773, -6.711773, -6.710773, -6.709773, -6.708773, -6.707773, -6.706773, -6.705773, -6.704773, -6.703773, -6.702773, -6.701773, -6.700773, -6.699773, -6.698773, -6.697773, -6.696773, -6.695773, -6.694773, -6.693773, -6.692773, -6.691773, -6.690773, -6.689773, -6.688773, -6.687773, -6.686773, -6.685773, -6.684773, -6.683773, -6.682773, -6.681773, -6.680773, -6.679773, -6.678773, -6.677773, -6.676773, -6.675773, -6.674773, -6.673773, -6.672773, -6.671773, -6.670773, -6.669773, -6.668773, -6.667773, -6.666773, -6.665773, -6.664773, -6.663773, -6.662773, -6.661773, -6.660773, -6.659773, -6.658773, -6.657773, -6.656773, -6.655773}, sd::DataType::DOUBLE); input.linspace(0.01, 0.001); - ops::helpers::logSoftmax(nd4j::LaunchContext ::defaultContext(), input, output, 0); + ops::helpers::logSoftmax(sd::LaunchContext ::defaultContext(), input, output, 0); ASSERT_TRUE(output.equalsTo(&expOutput)); } @@ -2182,14 +2181,14 @@ TEST_F(HelpersTests1, mmulMxV_1) { const Nd4jLong M = 3; const Nd4jLong N = 4; - NDArray a('f', {M,N}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, nd4j::DataType::DOUBLE); - NDArray temp('f', {M,N,5}, {16,2,-6,7,2,-2,4,-7,6,4,4,6,-3,1,3,9,1,4,9,10,-10,-3,-8,7,-7,-7,6,9,7,-6,8,7,-3,-3,4,-2,5,-3,-3,4,6,-5,-1,7,-5,4,-10,-1,8,0,-7,4,-10,-7,-8,-9,2,9,7,9}, nd4j::DataType::DOUBLE); + NDArray a('f', {M,N}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, sd::DataType::DOUBLE); + NDArray temp('f', {M,N,5}, {16,2,-6,7,2,-2,4,-7,6,4,4,6,-3,1,3,9,1,4,9,10,-10,-3,-8,7,-7,-7,6,9,7,-6,8,7,-3,-3,4,-2,5,-3,-3,4,6,-5,-1,7,-5,4,-10,-1,8,0,-7,4,-10,-7,-8,-9,2,9,7,9}, sd::DataType::DOUBLE); NDArray x = temp(6, {0,2}); - NDArray y('f', {M}, nd4j::DataType::DOUBLE); + NDArray y('f', {M}, sd::DataType::DOUBLE); - NDArray exp('f', {M}, {5.5, 5.1, 4.7}, nd4j::DataType::DOUBLE); + NDArray exp('f', {M}, {5.5, 5.1, 4.7}, sd::DataType::DOUBLE); - nd4j::MmulHelper::mmul(&a, &x, &y, 1., 0.); + sd::MmulHelper::mmul(&a, &x, &y, 1., 0.); ASSERT_TRUE(y.equalsTo(&exp)); } @@ -2199,15 +2198,15 @@ TEST_F(HelpersTests1, mmulMxV_2) { const Nd4jLong M = 3; const Nd4jLong N = 4; - NDArray a('f', {N,M}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, nd4j::DataType::DOUBLE); + NDArray a('f', {N,M}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, sd::DataType::DOUBLE); a.permutei({1,0}); - NDArray temp('f', {M,N,5}, {16,2,-6,7,2,-2,4,-7,6,4,4,6,-3,1,3,9,1,4,9,10,-10,-3,-8,7,-7,-7,6,9,7,-6,8,7,-3,-3,4,-2,5,-3,-3,4,6,-5,-1,7,-5,4,-10,-1,8,0,-7,4,-10,-7,-8,-9,2,9,7,9}, nd4j::DataType::DOUBLE); + NDArray temp('f', {M,N,5}, {16,2,-6,7,2,-2,4,-7,6,4,4,6,-3,1,3,9,1,4,9,10,-10,-3,-8,7,-7,-7,6,9,7,-6,8,7,-3,-3,4,-2,5,-3,-3,4,6,-5,-1,7,-5,4,-10,-1,8,0,-7,4,-10,-7,-8,-9,2,9,7,9}, sd::DataType::DOUBLE); NDArray x = temp(6, {0,2}); - NDArray y('f', {M}, nd4j::DataType::DOUBLE); + NDArray y('f', {M}, sd::DataType::DOUBLE); - NDArray exp('f', {M}, {5.1, 3.3, 1.5}, nd4j::DataType::DOUBLE); + NDArray exp('f', {M}, {5.1, 3.3, 1.5}, sd::DataType::DOUBLE); - nd4j::MmulHelper::mmul(&a, &x, &y, 1., 0.); + sd::MmulHelper::mmul(&a, &x, &y, 1., 0.); ASSERT_TRUE(y.equalsTo(&exp)); } @@ -2217,15 +2216,15 @@ TEST_F(HelpersTests1, mmulMxV_3) { const Nd4jLong M = 3; const Nd4jLong N = 4; - NDArray a('f', {N,M}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, nd4j::DataType::DOUBLE); + NDArray a('f', {N,M}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, sd::DataType::DOUBLE); a.permutei({1,0}); - NDArray temp('f', {N,M,5}, {16,2,-6,7,2,-2,4,-7,6,4,4,6,-3,1,3,9,1,4,9,10,-10,-3,-8,7,-7,-7,6,9,7,-6,8,7,-3,-3,4,-2,5,-3,-3,4,6,-5,-1,7,-5,4,-10,-1,8,0,-7,4,-10,-7,-8,-9,2,9,7,9}, nd4j::DataType::DOUBLE); + NDArray temp('f', {N,M,5}, {16,2,-6,7,2,-2,4,-7,6,4,4,6,-3,1,3,9,1,4,9,10,-10,-3,-8,7,-7,-7,6,9,7,-6,8,7,-3,-3,4,-2,5,-3,-3,4,6,-5,-1,7,-5,4,-10,-1,8,0,-7,4,-10,-7,-8,-9,2,9,7,9}, sd::DataType::DOUBLE); NDArray x = temp(4, {1,2}); - NDArray y('f', {M}, nd4j::DataType::DOUBLE); + NDArray y('f', {M}, sd::DataType::DOUBLE); - NDArray exp('f', {M}, {6.2, 4.5, 1.7}, nd4j::DataType::DOUBLE); + NDArray exp('f', {M}, {6.2, 4.5, 1.7}, sd::DataType::DOUBLE); - nd4j::MmulHelper::mmul(&a, &x, &y, 1., 0.); + sd::MmulHelper::mmul(&a, &x, &y, 1., 0.); ASSERT_TRUE(y.equalsTo(&exp)); } @@ -2235,15 +2234,15 @@ TEST_F(HelpersTests1, mmulMxV_4) { const Nd4jLong M = 3; const Nd4jLong N = 4; - NDArray a('f', {N,M}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, nd4j::DataType::DOUBLE); + NDArray a('f', {N,M}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, sd::DataType::DOUBLE); a.permutei({1,0}); - NDArray temp('f', {5,M,N}, {16,2,-6,7,2,-2,4,-7,6,4,4,6,-3,1,3,9,1,4,9,10,-10,-3,-8,7,-7,-7,6,9,7,-6,8,7,-3,-3,4,-2,5,-3,-3,4,6,-5,-1,7,-5,4,-10,-1,8,0,-7,4,-10,-7,-8,-9,2,9,7,9}, nd4j::DataType::DOUBLE); + NDArray temp('f', {5,M,N}, {16,2,-6,7,2,-2,4,-7,6,4,4,6,-3,1,3,9,1,4,9,10,-10,-3,-8,7,-7,-7,6,9,7,-6,8,7,-3,-3,4,-2,5,-3,-3,4,6,-5,-1,7,-5,4,-10,-1,8,0,-7,4,-10,-7,-8,-9,2,9,7,9}, sd::DataType::DOUBLE); NDArray x = temp(3, {0,1}); - NDArray y('f', {M}, nd4j::DataType::DOUBLE); + NDArray y('f', {M}, sd::DataType::DOUBLE); - NDArray exp('f', {M}, {1.5, 1.8, 1.5}, nd4j::DataType::DOUBLE); + NDArray exp('f', {M}, {1.5, 1.8, 1.5}, sd::DataType::DOUBLE); - nd4j::MmulHelper::mmul(&a, &x, &y, 1., 0.); + sd::MmulHelper::mmul(&a, &x, &y, 1., 0.); ASSERT_TRUE(y.equalsTo(&exp)); } @@ -2253,15 +2252,15 @@ TEST_F(HelpersTests1, mmulMxV_5) { const Nd4jLong M = 3; const Nd4jLong N = 4; - NDArray a('c', {N,M}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, nd4j::DataType::DOUBLE); + NDArray a('c', {N,M}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, sd::DataType::DOUBLE); a.permutei({1,0}); - NDArray temp('f', {5,M,N}, {16,2,-6,7,2,-2,4,-7,6,4,4,6,-3,1,3,9,1,4,9,10,-10,-3,-8,7,-7,-7,6,9,7,-6,8,7,-3,-3,4,-2,5,-3,-3,4,6,-5,-1,7,-5,4,-10,-1,8,0,-7,4,-10,-7,-8,-9,2,9,7,9}, nd4j::DataType::DOUBLE); + NDArray temp('f', {5,M,N}, {16,2,-6,7,2,-2,4,-7,6,4,4,6,-3,1,3,9,1,4,9,10,-10,-3,-8,7,-7,-7,6,9,7,-6,8,7,-3,-3,4,-2,5,-3,-3,4,6,-5,-1,7,-5,4,-10,-1,8,0,-7,4,-10,-7,-8,-9,2,9,7,9}, sd::DataType::DOUBLE); NDArray x = temp(2, {0,1}); - NDArray y('f', {M}, nd4j::DataType::DOUBLE); + NDArray y('f', {M}, sd::DataType::DOUBLE); - NDArray exp('f', {M}, {-0.3, 0.3, 0.9}, nd4j::DataType::DOUBLE); + NDArray exp('f', {M}, {-0.3, 0.3, 0.9}, sd::DataType::DOUBLE); - nd4j::MmulHelper::mmul(&a, &x, &y, 1., 0.); + sd::MmulHelper::mmul(&a, &x, &y, 1., 0.); ASSERT_TRUE(y.equalsTo(&exp)); } @@ -2271,15 +2270,15 @@ TEST_F(HelpersTests1, mmulMxV_6) { const Nd4jLong M = 3; const Nd4jLong N = 4; - NDArray a('c', {N,M}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, nd4j::DataType::DOUBLE); + NDArray a('c', {N,M}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, sd::DataType::DOUBLE); a.permutei({1,0}); - NDArray temp('c', {5,N,M}, {16,2,-6,7,2,-2,4,-7,6,4,4,6,-3,1,3,9,1,4,9,10,-10,-3,-8,7,-7,-7,6,9,7,-6,8,7,-3,-3,4,-2,5,-3,-3,4,6,-5,-1,7,-5,4,-10,-1,8,0,-7,4,-10,-7,-8,-9,2,9,7,9}, nd4j::DataType::DOUBLE); + NDArray temp('c', {5,N,M}, {16,2,-6,7,2,-2,4,-7,6,4,4,6,-3,1,3,9,1,4,9,10,-10,-3,-8,7,-7,-7,6,9,7,-6,8,7,-3,-3,4,-2,5,-3,-3,4,6,-5,-1,7,-5,4,-10,-1,8,0,-7,4,-10,-7,-8,-9,2,9,7,9}, sd::DataType::DOUBLE); NDArray x = temp(13, {0,2}); - NDArray y('f', {M}, nd4j::DataType::DOUBLE); + NDArray y('f', {M}, sd::DataType::DOUBLE); - NDArray exp('f', {M}, {-12.1, -10.9, -9.7}, nd4j::DataType::DOUBLE); + NDArray exp('f', {M}, {-12.1, -10.9, -9.7}, sd::DataType::DOUBLE); - nd4j::MmulHelper::mmul(&a, &x, &y, 1., 0.); + sd::MmulHelper::mmul(&a, &x, &y, 1., 0.); ASSERT_TRUE(y.equalsTo(&exp)); } @@ -2289,28 +2288,28 @@ TEST_F(HelpersTests1, mmulMxV_7) { const Nd4jLong M = 3; const Nd4jLong N = 4; - NDArray a('c', {N,M}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, nd4j::DataType::DOUBLE); + NDArray a('c', {N,M}, {1.2,1.1,1.0,0.9,0.8,0.7,0.5,0.4,0.3,0.2,0.1,0}, sd::DataType::DOUBLE); a.permutei({1,0}); - NDArray temp('c', {5,N,M}, {16,2,-6,7,2,-2,4,-7,6,4,4,6,-3,1,3,9,1,4,9,10,-10,-3,-8,7,-7,-7,6,9,7,-6,8,7,-3,-3,4,-2,5,-3,-3,4,6,-5,-1,7,-5,4,-10,-1,8,0,-7,4,-10,-7,-8,-9,2,9,7,9}, nd4j::DataType::DOUBLE); + NDArray temp('c', {5,N,M}, {16,2,-6,7,2,-2,4,-7,6,4,4,6,-3,1,3,9,1,4,9,10,-10,-3,-8,7,-7,-7,6,9,7,-6,8,7,-3,-3,4,-2,5,-3,-3,4,6,-5,-1,7,-5,4,-10,-1,8,0,-7,4,-10,-7,-8,-9,2,9,7,9}, sd::DataType::DOUBLE); NDArray x = temp(10, {0,2}); - NDArray y('c', {M}, nd4j::DataType::DOUBLE); + NDArray y('c', {M}, sd::DataType::DOUBLE); - NDArray exp('c', {M}, {3.3, 3.3, 3.3}, nd4j::DataType::DOUBLE); + NDArray exp('c', {M}, {3.3, 3.3, 3.3}, sd::DataType::DOUBLE); - nd4j::MmulHelper::mmul(&a, &x, &y, 1., 0.); + sd::MmulHelper::mmul(&a, &x, &y, 1., 0.); ASSERT_TRUE(y.equalsTo(&exp)); } ////////////////////////////////////////////////////////////////////// TEST_F(HelpersTests1, softmaxDerivative_1) { - NDArray input('c', {3,3}, {-1, 1, -2, 2, -3, 3, -4, 4, 5.}, nd4j::DataType::DOUBLE); - NDArray expOutput('c', {3,3}, {0.04508, 0.04514, 0.0008 , 0.0472 , 0.00087, 0.10492, 0.00235, 0.04592, 0.10553}, nd4j::DataType::DOUBLE); - NDArray output('c', {3,3}, nd4j::DataType::DOUBLE); + NDArray input('c', {3,3}, {-1, 1, -2, 2, -3, 3, -4, 4, 5.}, sd::DataType::DOUBLE); + NDArray expOutput('c', {3,3}, {0.04508, 0.04514, 0.0008 , 0.0472 , 0.00087, 0.10492, 0.00235, 0.04592, 0.10553}, sd::DataType::DOUBLE); + NDArray output('c', {3,3}, sd::DataType::DOUBLE); - // input.applyTransform(nd4j::transform::SoftMaxDerivative, &output); + // input.applyTransform(sd::transform::SoftMaxDerivative, &output); - nd4j::ops::helpers::softmaxDerivative(input.getContext(), input, output, 0); + sd::ops::helpers::softmaxDerivative(input.getContext(), input, output, 0); ASSERT_TRUE(expOutput.isSameShape(output)); ASSERT_TRUE(expOutput.equalsTo(output)); } @@ -2318,15 +2317,15 @@ TEST_F(HelpersTests1, softmaxDerivative_1) { ////////////////////////////////////////////////////////////////////// TEST_F(HelpersTests1, softmaxDerivative_2) { - NDArray input('c', {3,3,3}, {-1, 1, -2, 2, -3, 3, -4, 4, -5,5 ,-6,6, -7,7, -8,8, -9,9, -10,10, -11,11, -12,12, -13,13, 14.}, nd4j::DataType::DOUBLE); + NDArray input('c', {3,3,3}, {-1, 1, -2, 2, -3, 3, -4, 4, -5,5 ,-6,6, -7,7, -8,8, -9,9, -10,10, -11,11, -12,12, -13,13, 14.}, sd::DataType::DOUBLE); NDArray expOutput('c', {3,3,3}, {4.50755e-02, 4.51394e-02, 6.64586e-03,4.72027e-02, 8.67128e-04, 6.97440e-03,2.35008e-03, 4.59243e-02, 3.32995e-04, 4.51766e-02, 2.26032e-06, 4.51767e-02,2.91394e-07, 2.37285e-06, 3.94360e-08,4.51769e-02, 1.12535e-07, 4.51767e-02, - 7.58256e-10, 4.51767e-02, 1.22325e-11,7.96007e-10, 1.32293e-11, 1.04994e-01,3.77513e-11, 4.51767e-02, 1.04994e-01}, nd4j::DataType::DOUBLE); - NDArray output('c', {3,3,3}, nd4j::DataType::DOUBLE); + 7.58256e-10, 4.51767e-02, 1.22325e-11,7.96007e-10, 1.32293e-11, 1.04994e-01,3.77513e-11, 4.51767e-02, 1.04994e-01}, sd::DataType::DOUBLE); + NDArray output('c', {3,3,3}, sd::DataType::DOUBLE); - // input.applyTransform(nd4j::transform::SoftMaxDerivative, &output); + // input.applyTransform(sd::transform::SoftMaxDerivative, &output); - nd4j::ops::helpers::softmaxDerivative(input.getContext(), input, output, 1); + sd::ops::helpers::softmaxDerivative(input.getContext(), input, output, 1); ASSERT_TRUE(expOutput.isSameShape(output)); ASSERT_TRUE(expOutput.equalsTo(output)); } @@ -2334,13 +2333,13 @@ TEST_F(HelpersTests1, softmaxDerivative_2) { ////////////////////////////////////////////////////////////////////// TEST_F(HelpersTests1, softmaxDerivative_3) { - NDArray input('c', {5}, {-1., 1, -2, 2, 3}, nd4j::DataType::DOUBLE); - NDArray expOutput('c', {5}, {0.01184, 0.08071, 0.00439, 0.18277, 0.22618}, nd4j::DataType::DOUBLE); - NDArray output('c', {5}, nd4j::DataType::DOUBLE); + NDArray input('c', {5}, {-1., 1, -2, 2, 3}, sd::DataType::DOUBLE); + NDArray expOutput('c', {5}, {0.01184, 0.08071, 0.00439, 0.18277, 0.22618}, sd::DataType::DOUBLE); + NDArray output('c', {5}, sd::DataType::DOUBLE); - // input.applyTransform(nd4j::transform::SoftMaxDerivative, &output); + // input.applyTransform(sd::transform::SoftMaxDerivative, &output); - nd4j::ops::helpers::softmaxDerivative(input.getContext(), input, output, 0); + sd::ops::helpers::softmaxDerivative(input.getContext(), input, output, 0); ASSERT_TRUE(expOutput.isSameShape(output)); ASSERT_TRUE(expOutput.equalsTo(output)); } @@ -2364,19 +2363,19 @@ TEST_F(HelpersTests1, lstmLayerCell_1) { const float outAlpha = 0; // alpha value for output activation, not required for tanh const float outBeta = 0; // beta value for output activation, not required for tanh - NDArray x ('c', {bS, nIn}, nd4j::DataType::FLOAT32); - NDArray Wx('c', {nIn, 4*nOut}, nd4j::DataType::FLOAT32); - NDArray Wr('c', {nOut, 4*nOut}, nd4j::DataType::FLOAT32); - NDArray b ('c', {4*nOut}, nd4j::DataType::FLOAT32); - NDArray hI('c', {bS, nOut}, nd4j::DataType::FLOAT32); - NDArray cI('c', {bS, nOut}, nd4j::DataType::FLOAT32); - NDArray Wp('c', {3*nOut}, nd4j::DataType::FLOAT32); + NDArray x ('c', {bS, nIn}, sd::DataType::FLOAT32); + NDArray Wx('c', {nIn, 4*nOut}, sd::DataType::FLOAT32); + NDArray Wr('c', {nOut, 4*nOut}, sd::DataType::FLOAT32); + NDArray b ('c', {4*nOut}, sd::DataType::FLOAT32); + NDArray hI('c', {bS, nOut}, sd::DataType::FLOAT32); + NDArray cI('c', {bS, nOut}, sd::DataType::FLOAT32); + NDArray Wp('c', {3*nOut}, sd::DataType::FLOAT32); - NDArray h('c', {bS, nOut}, nd4j::DataType::FLOAT32); - NDArray c('c', {bS, nOut}, nd4j::DataType::FLOAT32); + NDArray h('c', {bS, nOut}, sd::DataType::FLOAT32); + NDArray c('c', {bS, nOut}, sd::DataType::FLOAT32); - NDArray expH('c', {bS, nOut}, {0.999288, 0.999288, 0.999288, 0.999288, 0.999288, 0.999288, 0.999288, 0.999288}, nd4j::DataType::FLOAT32); - NDArray expC('c', {bS, nOut}, {3.999778, 3.999778, 3.999778, 3.999778, 3.999778, 3.999778, 3.999778, 3.999778}, nd4j::DataType::FLOAT32); + NDArray expH('c', {bS, nOut}, {0.999288, 0.999288, 0.999288, 0.999288, 0.999288, 0.999288, 0.999288, 0.999288}, sd::DataType::FLOAT32); + NDArray expC('c', {bS, nOut}, {3.999778, 3.999778, 3.999778, 3.999778, 3.999778, 3.999778, 3.999778, 3.999778}, sd::DataType::FLOAT32); std::vector params = {dataFormat, 0, cellClip, gateAct, gateAlpha, gateBeta, cellAct, cellAlpha, cellBeta, outAct, outAlpha, outBeta}; @@ -2388,7 +2387,7 @@ TEST_F(HelpersTests1, lstmLayerCell_1) { Wp = 0.3; b = 0.7; - nd4j::ops::helpers::lstmLayerCell(&x, &Wx, &Wr, &b, &hI, &cI, &Wp, params, &h, &c); + sd::ops::helpers::lstmLayerCell(&x, &Wx, &Wr, &b, &hI, &cI, &Wp, params, &h, &c); ASSERT_TRUE(expH.isSameShape(h)); ASSERT_TRUE(expH.equalsTo(h)); @@ -2415,19 +2414,19 @@ TEST_F(HelpersTests1, lstmLayerCell_2) { const float outAlpha = 0; // alpha value for output activation, not required for tanh const float outBeta = 0; // beta value for output activation, not required for tanh - NDArray x ('c', {bS, nIn}, nd4j::DataType::FLOAT32); - NDArray Wx('c', {nIn, 4*nOut}, nd4j::DataType::FLOAT32); - NDArray Wr('c', {nOut, 4*nOut}, nd4j::DataType::FLOAT32); - NDArray b ('c', {4*nOut}, nd4j::DataType::FLOAT32); - NDArray hI('c', {bS, nOut}, nd4j::DataType::FLOAT32); - NDArray cI('c', {bS, nOut}, nd4j::DataType::FLOAT32); - NDArray Wp('c', {3*nOut}, nd4j::DataType::FLOAT32); + NDArray x ('c', {bS, nIn}, sd::DataType::FLOAT32); + NDArray Wx('c', {nIn, 4*nOut}, sd::DataType::FLOAT32); + NDArray Wr('c', {nOut, 4*nOut}, sd::DataType::FLOAT32); + NDArray b ('c', {4*nOut}, sd::DataType::FLOAT32); + NDArray hI('c', {bS, nOut}, sd::DataType::FLOAT32); + NDArray cI('c', {bS, nOut}, sd::DataType::FLOAT32); + NDArray Wp('c', {3*nOut}, sd::DataType::FLOAT32); - NDArray h('c', {bS, nOut}, nd4j::DataType::FLOAT32); - NDArray c('c', {bS, nOut}, nd4j::DataType::FLOAT32); + NDArray h('c', {bS, nOut}, sd::DataType::FLOAT32); + NDArray c('c', {bS, nOut}, sd::DataType::FLOAT32); - NDArray expH('c', {bS, nOut}, {0.995, 0.995, 0.995, 0.995, 0.995, 0.995, 0.995, 0.995}, nd4j::DataType::FLOAT32); - NDArray expC('c', {bS, nOut}, {3., 3., 3., 3., 3., 3., 3., 3.}, nd4j::DataType::FLOAT32); + NDArray expH('c', {bS, nOut}, {0.995, 0.995, 0.995, 0.995, 0.995, 0.995, 0.995, 0.995}, sd::DataType::FLOAT32); + NDArray expC('c', {bS, nOut}, {3., 3., 3., 3., 3., 3., 3., 3.}, sd::DataType::FLOAT32); std::vector params = {dataFormat, 0, cellClip, gateAct, gateAlpha, gateBeta, cellAct, cellAlpha, cellBeta, outAct, outAlpha, outBeta}; @@ -2439,7 +2438,7 @@ TEST_F(HelpersTests1, lstmLayerCell_2) { Wp = 0.3; b = 0.7; - nd4j::ops::helpers::lstmLayerCell(&x, &Wx, &Wr, &b, &hI, &cI, &Wp, params, &h, &c); + sd::ops::helpers::lstmLayerCell(&x, &Wx, &Wr, &b, &hI, &cI, &Wp, params, &h, &c); ASSERT_TRUE(expH.isSameShape(h)); ASSERT_TRUE(expH.equalsTo(h)); @@ -2465,19 +2464,19 @@ TEST_F(HelpersTests1, lstmLayerCell_3) { const float outAlpha = 0; // alpha value for output activation, not required for tanh const float outBeta = 0; // beta value for output activation, not required for tanh - NDArray x ('c', {nIn}, nd4j::DataType::FLOAT32); - NDArray Wx('c', {nIn, 4*nOut}, nd4j::DataType::FLOAT32); - NDArray Wr('c', {nOut, 4*nOut}, nd4j::DataType::FLOAT32); - NDArray b ('c', {4*nOut}, nd4j::DataType::FLOAT32); - NDArray hI('c', {nOut}, nd4j::DataType::FLOAT32); - NDArray cI('c', {nOut}, nd4j::DataType::FLOAT32); - NDArray Wp('c', {3*nOut}, nd4j::DataType::FLOAT32); + NDArray x ('c', {nIn}, sd::DataType::FLOAT32); + NDArray Wx('c', {nIn, 4*nOut}, sd::DataType::FLOAT32); + NDArray Wr('c', {nOut, 4*nOut}, sd::DataType::FLOAT32); + NDArray b ('c', {4*nOut}, sd::DataType::FLOAT32); + NDArray hI('c', {nOut}, sd::DataType::FLOAT32); + NDArray cI('c', {nOut}, sd::DataType::FLOAT32); + NDArray Wp('c', {3*nOut}, sd::DataType::FLOAT32); - NDArray h('c', {nOut}, nd4j::DataType::FLOAT32); - NDArray c('c', {nOut}, nd4j::DataType::FLOAT32); + NDArray h('c', {nOut}, sd::DataType::FLOAT32); + NDArray c('c', {nOut}, sd::DataType::FLOAT32); - NDArray expH('c', {nOut}, {0.999288, 0.999288, 0.999288, 0.999288}, nd4j::DataType::FLOAT32); - NDArray expC('c', {nOut}, {3.999778, 3.999778, 3.999778, 3.999778}, nd4j::DataType::FLOAT32); + NDArray expH('c', {nOut}, {0.999288, 0.999288, 0.999288, 0.999288}, sd::DataType::FLOAT32); + NDArray expC('c', {nOut}, {3.999778, 3.999778, 3.999778, 3.999778}, sd::DataType::FLOAT32); std::vector params = {dataFormat, 0, cellClip, gateAct, gateAlpha, gateBeta, cellAct, cellAlpha, cellBeta, outAct, outAlpha, outBeta}; @@ -2489,7 +2488,7 @@ TEST_F(HelpersTests1, lstmLayerCell_3) { Wp = 0.3; b = 0.7; - nd4j::ops::helpers::lstmLayerCell(&x, &Wx, &Wr, &b, &hI, &cI, &Wp, params, &h, &c); + sd::ops::helpers::lstmLayerCell(&x, &Wx, &Wr, &b, &hI, &cI, &Wp, params, &h, &c); ASSERT_TRUE(expH.isSameShape(h)); ASSERT_TRUE(expH.equalsTo(h)); diff --git a/libnd4j/tests_cpu/layers_tests/IndexingTests.cpp b/libnd4j/tests_cpu/layers_tests/IndexingTests.cpp index d26fbd122..dbe7ccd0a 100644 --- a/libnd4j/tests_cpu/layers_tests/IndexingTests.cpp +++ b/libnd4j/tests_cpu/layers_tests/IndexingTests.cpp @@ -20,11 +20,11 @@ #include "testlayers.h" #include -#include -#include +#include +#include -using namespace nd4j; -using namespace nd4j::graph; +using namespace sd; +using namespace sd::graph; class IndexingTests : public testing::Test { public: @@ -44,16 +44,16 @@ TEST_F(IndexingTests, StridedSlice_1) { auto strides = NDArrayFactory::create({1,1,1}); - nd4j::ops::strided_slice op; + sd::ops::strided_slice op; auto result = op.evaluate({&x, &begin, &end, &strides}, {}, {0,0,0,0,0}); //, 2,2,0, 3,3,3, 1,1,1}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } @@ -63,17 +63,17 @@ TEST_F(IndexingTests, StridedSlice_2) { x.linspace(1); - nd4j::ops::strided_slice op; + sd::ops::strided_slice op; auto result = op.evaluate({&x}, {}, {0,0,0,0,0, 3,2,0, 5,5,3, 1,1,1}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } @@ -83,17 +83,17 @@ TEST_F(IndexingTests, StridedSlice_3) { x.linspace(1); - nd4j::ops::strided_slice op; + sd::ops::strided_slice op; auto result = op.evaluate({&x}, {}, {0,0,0,0,0, 3,2,0, 5,5,3, 1,1,2}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } @@ -106,18 +106,18 @@ TEST_F(IndexingTests, SimpleSlice_1) { exp.p(1, 3.0f); exp.p(2, 3.0f); - nd4j::ops::slice op; + sd::ops::slice op; auto result = op.evaluate({&input}, {}, {1,0,0, 1,1,3}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } @@ -132,18 +132,18 @@ TEST_F(IndexingTests, SimpleSlice_2) { exp.p(4, 4.0f); exp.p(5, 4.0f); - nd4j::ops::slice op; + sd::ops::slice op; auto result = op.evaluate({&input}, {}, {1,0,0, 1,2,3}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } TEST_F(IndexingTests, SimpleSlice_3) { @@ -157,18 +157,18 @@ TEST_F(IndexingTests, SimpleSlice_3) { exp.p(4, 5.0f); exp.p(5, 5.0f); - nd4j::ops::slice op; + sd::ops::slice op; auto result = op.evaluate({&input}, {}, {1,0,0, 2,1,3}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } TEST_F(IndexingTests, SimpleSlice_4) { @@ -177,17 +177,17 @@ TEST_F(IndexingTests, SimpleSlice_4) { auto stop = NDArrayFactory::create('c', {3}, {2.0, 1.0, 3.0}); auto exp = NDArrayFactory::create('c', {2, 1, 3}, {3.0, 3.0, 3.0, 5.0, 5.0, 5.0}); - nd4j::ops::slice op; + sd::ops::slice op; auto result = op.evaluate({&input, &start, &stop}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } @@ -201,19 +201,19 @@ TEST_F(IndexingTests, MaskedSlice_0) { auto exp = NDArrayFactory::create('c', {1, 5}); exp.assign(2.0f); - nd4j::ops::strided_slice op; + sd::ops::strided_slice op; auto result = op.evaluate({&matrix}, {}, {0,0,0,0,0, 1, 2, 1}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); // z->printShapeInfo("z"); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } @@ -227,17 +227,17 @@ TEST_F(IndexingTests, MaskedSlice_00) { auto exp = NDArrayFactory::create('c', {1, 2}, {2, 2}); - nd4j::ops::strided_slice op; + sd::ops::strided_slice op; auto result = op.evaluate({&matrix}, {}, {0,0,0,0,0, 1, 1, 2, 3, 1, 1}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } @@ -251,19 +251,19 @@ TEST_F(IndexingTests, MaskedSlice_1) { auto exp = NDArrayFactory::create('c', {5}); exp.assign(2.0f); - nd4j::ops::strided_slice op; + sd::ops::strided_slice op; auto result = op.evaluate({&matrix}, {}, {0,0,0,0,1, 1, 2, 1}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); // z->printShapeInfo("z"); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } TEST_F(IndexingTests, MaskedSlice_2) { @@ -272,17 +272,17 @@ TEST_F(IndexingTests, MaskedSlice_2) { auto exp = NDArrayFactory::create('c', {3, 3}, {4.000000f, 4.200000f, 4.300000f, 5.000000f, 5.200000f, 5.300000f, 6.000000f, 6.200000f, 6.300000f}); // output = tf.strided_slice(a, [1, 0, 0], [3, 3, 3], shrink_axis_mask=5) - nd4j::ops::strided_slice op; + sd::ops::strided_slice op; auto result = op.evaluate({&matrix}, {}, {0,0,0,0,1, 1, 0, 0, 3, 3, 3, 1, 1, 1}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } @@ -292,17 +292,17 @@ TEST_F(IndexingTests, MaskedSlice_3) { auto exp = NDArrayFactory::create('c', {2, 3}, { 4.f, 4.2f, 4.3f, 7.f, 7.2f, 7.3f}); // output = tf.strided_slice(a, [1, 0, 0], [3, 3, 3], shrink_axis_mask=5) - nd4j::ops::strided_slice op; + sd::ops::strided_slice op; auto result = op.evaluate({&matrix}, {}, {0,0,0,0,2, 1, 0, 0, 3, 3, 3, 1, 1, 1}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } @@ -312,18 +312,18 @@ TEST_F(IndexingTests, MaskedSlice_4) { auto exp = NDArrayFactory::create('c', {3}, { 4.f, 4.2f, 4.3f}); // output = tf.strided_slice(a, [1, 0, 0], [3, 3, 3], shrink_axis_mask=5) - nd4j::ops::strided_slice op; + sd::ops::strided_slice op; auto result = op.evaluate({&matrix}, {}, {0,0,0,0, 3, 1, 0, 0, 3, 3, 3, 1, 1, 1}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } TEST_F(IndexingTests, Live_Slice_1) { @@ -335,19 +335,19 @@ TEST_F(IndexingTests, Live_Slice_1) { auto stride = NDArrayFactory::create('c', {3}, {1.0f, 1.0f, 1.0f}); // output = tf.strided_slice(a, [1, 0, 0], [3, 3, 3], shrink_axis_mask=5) - nd4j::ops::strided_slice op; + sd::ops::strided_slice op; auto result = op.evaluate({&matrix, &begin, &end, &stride}, {}, {0,0,0,0,3}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); // z->printShapeInfo("z shape"); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } @@ -358,17 +358,17 @@ TEST_F(IndexingTests, Test_StridedSlice_1) { auto c = NDArrayFactory::create('c', {1}, {1.f}); auto exp = NDArrayFactory::create({5.0f, 2}); - nd4j::ops::strided_slice op; + sd::ops::strided_slice op; auto result = op.evaluate({&x, &a, &b, &c}, {}, {0, 0, 0, 0, 1}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } TEST_F(IndexingTests, Test_StridedSlice_2) { @@ -378,19 +378,19 @@ TEST_F(IndexingTests, Test_StridedSlice_2) { auto c = NDArrayFactory::create('c', {2}, {1, 1}); auto exp = NDArrayFactory::create('c', {1}, {5.0}); - nd4j::ops::strided_slice op; + sd::ops::strided_slice op; auto result = op.evaluate({&x, &a, &b, &c}, {}, {0, 0, 0, 0, 1}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); // z->printIndexedBuffer("Z"); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } @@ -401,17 +401,17 @@ TEST_F(IndexingTests, Test_StridedSlice_3) { auto c = NDArrayFactory::create('c', {2}, {1, 1}); auto exp = NDArrayFactory::create('c', {1}, {6.0}); - nd4j::ops::strided_slice op; + sd::ops::strided_slice op; auto result = op.evaluate({&x, &a, &b, &c}, {}, {0, 0, 0, 0, 1}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } @@ -422,20 +422,20 @@ TEST_F(IndexingTests, Test_StridedSlice_4) { auto c = NDArrayFactory::create('c', {1}, {1}); auto exp = NDArrayFactory::create({5.0f, 2}); - nd4j::ops::strided_slice op; + sd::ops::strided_slice op; auto result = op.evaluate({&x, &a, &b, &c}, {}, {0, 0, 0, 0, 1}); // auto result = op.execute({&x, &a, &b, &c}, {}, {0, 0, 0, 0, 1, 0, 1, 1}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); //z->printIndexedBuffer("Z"); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } TEST_F(IndexingTests, Test_Subarray_Strided_1) { @@ -455,16 +455,16 @@ TEST_F(IndexingTests, MaskedSlice_5) { auto exp('c', {2, 3}, { 4.f, 4.2f, 4.3f, 7.f, 7.2f, 7.3f}); // output = tf.strided_slice(a, [1, 0, 0], [3, 3, 3], shrink_axis_mask=5) - nd4j::ops::strided_slice op; + sd::ops::strided_slice op; auto result = op.execute({&matrix}, {}, {0,0,0,0,2, 1, 0, 0, 3, 3, 3}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } */ \ No newline at end of file diff --git a/libnd4j/tests_cpu/layers_tests/JavaInteropCudaTests.cu b/libnd4j/tests_cpu/layers_tests/JavaInteropCudaTests.cu index f442c0bb9..aa2c13eb5 100644 --- a/libnd4j/tests_cpu/layers_tests/JavaInteropCudaTests.cu +++ b/libnd4j/tests_cpu/layers_tests/JavaInteropCudaTests.cu @@ -26,8 +26,8 @@ #include #include -using namespace nd4j; -using namespace nd4j::ops; +using namespace sd; +using namespace sd::ops; class JavaInteropCudaTests : public testing::Test { public: @@ -41,7 +41,7 @@ TEST_F(JavaInteropCudaTests, test_DeclarableOp_execution_1) { x.assign(1.f); e.assign(2.f); - nd4j::ops::add op; + sd::ops::add op; Context context(1); context.setCudaContext(LaunchContext::defaultContext()->getCudaStream(), LaunchContext::defaultContext()->getReductionPointer(), LaunchContext::defaultContext()->getAllocationPointer()); @@ -59,16 +59,16 @@ TEST_F(JavaInteropCudaTests, test_DeclarableOp_execution_1) { } TEST_F(JavaInteropCudaTests, test_DeclarableOp_execution_2) { - NDArray x('c', {3, 1, 2}, nd4j::DataType::FLOAT32); - NDArray y('c', {2, 2}, nd4j::DataType::FLOAT32); - NDArray z('c', {3, 2, 2}, nd4j::DataType::BOOL); - NDArray e('c', {3, 2, 2}, nd4j::DataType::BOOL); + NDArray x('c', {3, 1, 2}, sd::DataType::FLOAT32); + NDArray y('c', {2, 2}, sd::DataType::FLOAT32); + NDArray z('c', {3, 2, 2}, sd::DataType::BOOL); + NDArray e('c', {3, 2, 2}, sd::DataType::BOOL); x.assign(1.f); y.assign(2.f); e.assign(false); - nd4j::ops::equals op; + sd::ops::equals op; Context context(1); context.setCudaContext(LaunchContext::defaultContext()->getCudaStream(), LaunchContext::defaultContext()->getReductionPointer(), LaunchContext::defaultContext()->getAllocationPointer()); diff --git a/libnd4j/tests_cpu/layers_tests/JavaInteropTests.cpp b/libnd4j/tests_cpu/layers_tests/JavaInteropTests.cpp index 2f3f93d56..6f559230b 100644 --- a/libnd4j/tests_cpu/layers_tests/JavaInteropTests.cpp +++ b/libnd4j/tests_cpu/layers_tests/JavaInteropTests.cpp @@ -18,8 +18,8 @@ // @author raver119@gmail.com // -#include -#include +#include +#include #include #include #include @@ -27,8 +27,8 @@ #include "testlayers.h" #include -using namespace nd4j; -using namespace nd4j::ops; +using namespace sd; +using namespace sd::ops; class JavaInteropTests : public testing::Test { public: @@ -41,7 +41,7 @@ TEST_F(JavaInteropTests, TestShapeExposure1) { auto weights = NDArrayFactory::create('c', {2, 2, 2, 3}); auto exp = NDArrayFactory::create('c', {1, 3, 5, 4}); - nd4j::ops::conv2d op; + sd::ops::conv2d op; std::vector tArgs({}); std::vector iArgs({2, 2, 1, 1, 0, 0, 1, 1, 1}); @@ -70,7 +70,7 @@ TEST_F(JavaInteropTests, TestShapeExposure2) { auto input = NDArrayFactory::create('c', {1, 2, 5, 4}); auto exp = NDArrayFactory::create('c', {4}, {1, 2, 5, 4}); - nd4j::ops::shape_of op; + sd::ops::shape_of op; std::vector tArgs({}); std::vector iArgs({}); @@ -107,7 +107,7 @@ TEST_F(JavaInteropTests, TestShapeExposure3) { Nd4jPointer inputBuffers[] = {x.buffer(), sizes.buffer(), x.getSpecialBuffer(), sizes.getSpecialBuffer()}; Nd4jPointer inputShapes[] = {x.shapeInfo(), sizes.shapeInfo(), x.getSpecialShapeInfo(), sizes.getSpecialShapeInfo()}; - nd4j::ops::split_v op; + sd::ops::split_v op; Nd4jLong iArgs[] = {1}; auto hash = op.getOpHash(); @@ -128,7 +128,7 @@ TEST_F(JavaInteropTests, Test_Squeeze_1) { auto z = NDArrayFactory::create('c', {6}); auto e = NDArrayFactory::create('c', {6}, {1.f, 2.f, 3.f, 4.f, 5.f, 6.f}); - nd4j::ops::squeeze op; + sd::ops::squeeze op; Nd4jPointer ptrsInBuffer[] = {(Nd4jPointer) x.getBuffer(), x.getSpecialBuffer()}; Nd4jPointer ptrsInShapes[] = {(Nd4jPointer) x.getShapeInfo(), x.getSpecialShapeInfo()}; @@ -149,7 +149,7 @@ TEST_F(JavaInteropTests, Test_RDiv_1) { NDArray::prepareSpecialUse({&z}, {&x, &y}); - nd4j::ops::reversedivide op; + sd::ops::reversedivide op; Nd4jPointer ptrsInBuffer[] = {(Nd4jPointer) x.getBuffer(), (Nd4jPointer) y.getBuffer(), x.getSpecialBuffer(), y.getSpecialBuffer()}; Nd4jPointer ptrsInShapes[] = {(Nd4jPointer) x.getShapeInfo(), (Nd4jPointer) y.getShapeInfo(), x.getSpecialShapeInfo(), y.getSpecialShapeInfo()}; @@ -182,7 +182,7 @@ TEST_F(JavaInteropTests, TestSconv2d_1) { auto expOutput = NDArrayFactory::create('c', {3, 2, 8, 8}); - nd4j::ops::sconv2d op; + sd::ops::sconv2d op; NDArray::prepareSpecialUse({&output}, {&input, &weightsD, &weightsP, &bias}); @@ -217,7 +217,7 @@ TEST_F(JavaInteropTests, TestSconv2d_2) { auto expOutput = NDArrayFactory::create('c', {3, 3, 8, 8}); - nd4j::ops::sconv2d op; + sd::ops::sconv2d op; NDArray::prepareSpecialUse({&output}, {&input, &weightsD}); @@ -253,7 +253,7 @@ TEST_F(JavaInteropTests, TestMaxPooling2d_1) { std::vector iArgs({2, 2, 1, 1, 0, 0, 1, 1, 1}); - nd4j::ops::maxpool2d op; + sd::ops::maxpool2d op; Nd4jStatus status = execCustomOp(nullptr, op.getOpHash(), ptrsInBuffer, ptrsInShapes, 1, ptrsOutBuffers, ptrsOutShapes, 1, nullptr, 0, iArgs.data(), 9, nullptr, 0, false); @@ -282,7 +282,7 @@ TEST_F(JavaInteropTests, TestCol2Im_1) { Nd4jPointer ptrsOutBuffers[] = {(Nd4jPointer) output.getBuffer(), output.getSpecialBuffer()}; Nd4jPointer ptrsOutShapes[] = {(Nd4jPointer) output.getShapeInfo(), output.getSpecialShapeInfo()}; - nd4j::ops::col2im op; + sd::ops::col2im op; Nd4jLong exp[] = {1, 1, 1, 1, 4, 5, 1, 1, 1}; @@ -312,7 +312,7 @@ TEST_F(JavaInteropTests, TestPNorm_1) { NDArray::prepareSpecialUse({&output}, {&input}); - nd4j::ops::pnormpool2d op; + sd::ops::pnormpool2d op; Nd4jLong exp[] = {2, 2, 1, 1, 0, 0, 1, 1, 0, 2, 0, 0}; @@ -338,7 +338,7 @@ TEST_F(JavaInteropTests, TestInplace_1) { NDArray::prepareSpecialUse({}, {&input}); - nd4j::ops::clipbyvalue op; + sd::ops::clipbyvalue op; double extras[] = {-1.0f, 1.0f}; @@ -408,7 +408,7 @@ TEST_F(JavaInteropTests, Test_FastPath_Validation_1) { ctx.setInputArray(0, x.buffer(), x.shapeInfo(), x.specialBuffer(), x.specialShapeInfo()); ctx.setOutputArray(0, z.buffer(), z.shapeInfo(), z.specialBuffer(), z.specialShapeInfo()); - nd4j::ops::softmax op; + sd::ops::softmax op; auto status = op.execute(&ctx); ASSERT_NE(Status::OK(), status); } @@ -421,7 +421,7 @@ TEST_F(JavaInteropTests, Test_FastPath_Validation_2) { ctx.setInputArray(0, x.buffer(), x.shapeInfo(), x.specialBuffer(), x.specialShapeInfo()); ctx.setOutputArray(0, z.buffer(), z.shapeInfo(), z.specialBuffer(), z.specialShapeInfo()); - nd4j::ops::softmax op; + sd::ops::softmax op; auto status = op.execute(&ctx); ASSERT_NE(Status::OK(), status); } @@ -442,7 +442,7 @@ TEST_F(JavaInteropTests, Test_FastPath_Validation_3) { ctx.setInputArray(2, max.buffer(), max.shapeInfo(), max.specialBuffer(), max.specialShapeInfo()); ctx.setOutputArray(0, z.buffer(), z.shapeInfo(), z.specialBuffer(), z.specialShapeInfo()); - nd4j::ops::fake_quant_with_min_max_vars_per_channel op; + sd::ops::fake_quant_with_min_max_vars_per_channel op; ASSERT_ANY_THROW(op.execute(&ctx)); } @@ -458,7 +458,7 @@ TEST_F(JavaInteropTests, Test_empty_cast_1) { ctx.setOutputArray(0, z.buffer(), z.shapeInfo(), z.specialBuffer(), z.specialShapeInfo()); ctx.setIArguments(iArgs, 1); - nd4j::ops::cast op; + sd::ops::cast op; auto result = op.execute(&ctx); ASSERT_EQ(Status::OK(), result); ASSERT_EQ(e, z); @@ -477,7 +477,7 @@ TEST_F(JavaInteropTests, test_avgpooling_edge_1) { NDArray::prepareSpecialUse({&z}, {&x}); - nd4j::ops::avgpool2d op; + sd::ops::avgpool2d op; //auto result = op.execute({&x}, {}, {3,3, 1,1, 0,0, 1,1, 1, 0, 1}); Nd4jLong exp[] = {3,3, 1,1, 0,0, 1,1, 1, 0, 1}; @@ -511,11 +511,11 @@ TEST_F(JavaInteropTests, test_avgpooling_edge_1) { int hTo = hFrom + k; int wTo = wFrom + k; - hFrom = nd4j::math::nd4j_max(0, hFrom); - wFrom = nd4j::math::nd4j_max(0, wFrom); + hFrom = sd::math::nd4j_max(0, hFrom); + wFrom = sd::math::nd4j_max(0, wFrom); - hTo = nd4j::math::nd4j_min(inOutH, hTo); - wTo = nd4j::math::nd4j_min(inOutW, wTo); + hTo = sd::math::nd4j_min(inOutH, hTo); + wTo = sd::math::nd4j_min(inOutW, wTo); int idxOut[4]; int idxIn[4]; @@ -547,7 +547,7 @@ TEST_F(JavaInteropTests, test_avgpooling_edge_1) { for (int e = 0; e < z.lengthOf() && cnt < lim; e++) { auto _m = m.e(e); auto _z = z.e(e); - auto eq = nd4j::math::nd4j_eq(_m, _z, 1e-5); + auto eq = sd::math::nd4j_eq(_m, _z, 1e-5); if (!eq) { nd4j_printf("Difference at element e [%i]: <%f> vs <%f>\n", e, _m, _z); cnt++; @@ -559,7 +559,7 @@ TEST_F(JavaInteropTests, test_avgpooling_edge_1) { TEST_F(JavaInteropTests, Test_GraphReuse_1) { - uint8_t* data = nd4j::graph::readFlatBuffers("./resources/reduce_dim_false.fb"); + uint8_t* data = sd::graph::readFlatBuffers("./resources/reduce_dim_false.fb"); registerGraph(nullptr, 119, (Nd4jPointer) data); @@ -582,7 +582,7 @@ TEST_F(JavaInteropTests, Test_GraphReuse_2) { auto exp2 = NDArrayFactory::create('c', {3}, {9, 9, 9}); // we load graph from file, because we're not in java here, and dont have buffer ready - uint8_t* data = nd4j::graph::readFlatBuffers("./resources/reduce_dim_false.fb"); + uint8_t* data = sd::graph::readFlatBuffers("./resources/reduce_dim_false.fb"); // we ensure that there's no such a graph stored earlier ASSERT_FALSE(GraphHolder::getInstance()->hasGraph(119)); @@ -667,7 +667,7 @@ TEST_F(JavaInteropTests, Test_Greater_1) { NDArray::prepareSpecialUse({&o}, {&x, &y}); - nd4j::ops::greater op; + sd::ops::greater op; Nd4jPointer ptrsInBuffer[] = {(Nd4jPointer) x.getBuffer(), (Nd4jPointer) y.getBuffer(), x.getSpecialBuffer(), y.getSpecialBuffer()}; Nd4jPointer ptrsInShapes[] = {(Nd4jPointer) x.getShapeInfo(), (Nd4jPointer) y.getShapeInfo(), x.getSpecialShapeInfo(), y.getSpecialShapeInfo()}; @@ -689,7 +689,7 @@ TEST_F(JavaInteropTests, Test_Greater_2) { auto exp = NDArrayFactory::create('c', {2, 2}, {false, false, true, true}); - nd4j::ops::greater op; + sd::ops::greater op; NDArray::prepareSpecialUse({&o}, {&x, &y}); @@ -708,7 +708,7 @@ TEST_F(JavaInteropTests, Test_Greater_2) { TEST_F(JavaInteropTests, Test_Boolean_Op_1) { - nd4j::ops::is_non_decreasing op; + sd::ops::is_non_decreasing op; auto x = NDArrayFactory::create('c', {5}, {1.f, 2.f, 3.f, 4.f, 5.f}); auto o = NDArrayFactory::create(false); @@ -737,7 +737,7 @@ TEST_F(JavaInteropTests, Test_Inplace_Outputs_1) { auto exp = NDArrayFactory::create('c', {2, 3}, {1.f, 2.f, 3.f, 4.f, 5.f, 6.f}); auto z = NDArrayFactory::create('c', {2, 3}); - nd4j::ops::test_output_reshape op; + sd::ops::test_output_reshape op; NDArray::prepareSpecialUse({&z}, {&x}); @@ -765,7 +765,7 @@ TEST_F(JavaInteropTests, Test_Inplace_Outputs_2) { auto e = NDArrayFactory::create('c', {2, 3}, {3.f, 4.f, 5.f, 6.f, 7.f, 8.f}); - nd4j::ops::add op; + sd::ops::add op; NDArray::prepareSpecialUse({&z}, {&x, &y}); @@ -792,7 +792,7 @@ TEST_F(JavaInteropTests, Test_Inplace_Outputs_3) { auto output = NDArrayFactory::create('f', {2, 1, 6, 4}); auto e = NDArrayFactory::create('c', {2, 1, 6, 4}, {1, 2, 3, 4, 5, 6, 7, 8, 9,10,11,12, 9,10,11,12, 5, 6, 7, 8, 9,10,11,12, 13,14,15,16, 17,18,19,20, 21,22,23,24, 21,22,23,24, 17,18,19,20, 21,22,23,24}); - nd4j::ops::gather op; + sd::ops::gather op; NDArray::prepareSpecialUse({&output}, {&input, &indices}); @@ -823,15 +823,15 @@ TEST_F(JavaInteropTests, Test_Reduce3_EdgeCase) { auto dims = NDArrayFactory::create('c', {2}, {0, 1}); dims.syncToHost(); - nd4j::LaunchContext* context = nd4j::LaunchContext::defaultContext(); + sd::LaunchContext* context = sd::LaunchContext::defaultContext(); Nd4jPointer* extraPointers = nullptr; #ifdef __CUDABLAS__ extraPointers = new Nd4jPointer[6] {nullptr, context->getCudaStream(), context->getScalarPointer(), nullptr, context->getCudaSpecialStream(), context->getReductionPointer()}; #endif - auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(x.getShapeInfo(), {0,1}); - auto packY = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(y.getShapeInfo(), {0,1}); + auto packX = sd::ConstantTadHelper::getInstance()->tadForDimensions(x.getShapeInfo(), {0,1}); + auto packY = sd::ConstantTadHelper::getInstance()->tadForDimensions(y.getShapeInfo(), {0,1}); NDArray::prepareSpecialUse({&z}, {&x, &y, &dims}); OpaqueDataBuffer xBuf(x.dataBuffer()); @@ -856,7 +856,7 @@ TEST_F(JavaInteropTests, Test_SimpleIf_Output) { Environment::getInstance()->setDebug(true); Environment::getInstance()->setVerbose(false); - auto pl = nd4j::graph::readFlatBuffers("./resources/simpleif_0_1.fb"); + auto pl = sd::graph::readFlatBuffers("./resources/simpleif_0_1.fb"); auto ptr = executeFlatGraph(nullptr, pl); Environment::getInstance()->setDebug(false); @@ -873,7 +873,7 @@ TEST_F(JavaInteropTests, Test_AveragePooling_FF_TF_double) { auto z = NDArrayFactory::create('c', {4, 4, 4, 3}); auto exp = NDArrayFactory::create('c', {4, 4, 4, 3}, {7.97172260, 0.06878620, 2.27749538, 7.29276514, -0.14074677, 0.65480286, 5.70313978, -0.06546132, 0.35443667, 3.70382833, -0.84020567, 0.63826996, 8.60301399, -0.38236514, 1.55177069, 7.37542057, -0.99374938, -0.29971302, 8.84352493, -0.67121059, 0.43132120, 4.78175592, -1.25070143, -1.91523600, 6.03855371, -0.00292124, -1.11214364, 7.90158176, -0.57949901, -0.96735370, 7.81192017, -0.53255427, -0.48009714, 3.16953635, 0.08353355, -1.54299748, 3.74821687, 1.69396687, 0.72724354, 5.42915201, -1.13686812, -0.71793109, 5.78376389, -0.72239977, -0.60055625, 2.53636408, 0.56777251, -2.07892323, 6.08064651, 0.68620735, 2.54017019, 5.65828180, -0.68255502, 1.47283304, 6.10842514, -0.39655915, 0.28380761, 1.96707797, -1.98206317, 0.94027776, 4.71811438, 0.32104525, -0.92409706, 8.34588146, -1.05581069, -0.55217457, 9.58440876, -0.96549922, 0.45820439, 5.65453672, -2.50953507, -0.71441835, 8.03059578, -0.21281289, 0.92125505, 9.26900673, -0.35963219, -0.70039093, 8.59924412, -1.22358346, 0.81318003, 3.85920119, -0.01305223, -1.09234154, 6.33158875, 1.28094780, -1.48926139, 4.94969177, -0.77126902, -1.97033751, 5.64381838, -0.16285487, -1.31277227, 2.39893222, -1.32902908, -1.39609122, 6.47572327, -0.45267010, 1.55727172, 6.70965624, -1.68735468, -0.05672536, 7.25092363, -0.64613032, 0.67050058, 3.60789680, -2.05948973, 2.22687531, 8.15202713, -0.70148355, 1.28314006, 8.14842319, -1.88807654, -1.04808438, 8.45500565, -0.76425624, 0.94542569, 4.56179953, -0.28786001, -2.04502511, 8.46278095, -0.31019822, 0.07339200, 9.34214592, -0.61948007, 0.52481830, 8.32515621, -1.52418160, 0.49678251, 5.11082315, -1.09908783, -0.52969611, 5.27806664, 0.88632923, 0.66754371, 4.75839233, 0.48928693, -0.68036932, 6.56925392, -0.02949905, -2.99189186, 4.46320581, -0.64534980, -0.29516968, 8.60809517, -1.13120568, 3.41720533, 5.84243155, -1.24109328, 0.89566326, 5.99578333, -0.42496428, 2.07076764, 3.17812920, -0.81566459, -0.14363396, 6.55184317, 0.39633346, -0.43852386, 8.70214558, -2.24613595, 0.30708700, 8.73882294, -0.53545928, 1.54409575, 4.49452257, -0.16509305, 0.19028664, 8.24897003, 0.44750381, 2.15448594, 8.97640514, -0.77728152, 0.57272542, 9.03467560, 0.47173575, -1.10807717, 3.30056310, -0.43268481, -0.41470885, 3.53798294, -0.08546703, -2.16840744, 6.18733406, -0.17871059, -2.59837723, 5.94218683, -1.02990067, -0.49760687, 3.76938033, 0.86383581, -1.91504073}); - nd4j::ops::avgpool2d op; + sd::ops::avgpool2d op; NDArray::prepareSpecialUse({&z}, {&input}); @@ -911,7 +911,7 @@ TEST_F(JavaInteropTests, Test_MaxPool2D_float_1) { Nd4jLong iArgs[] = {2,2, 1,1, 1,1, 2,2,1, 0,0}; - nd4j::ops::maxpool2d op; + sd::ops::maxpool2d op; auto hash = op.getOpHash(); auto status = execCustomOp(nullptr, hash, ptrsInBuffer, ptrsInShapes, 1, ptrsOutBuffers, ptrsOutShapes, 1, nullptr, 0, iArgs, 11, nullptr, 0, false); @@ -939,7 +939,7 @@ TEST_F(JavaInteropTests, Test_Unstack_1) { Nd4jLong iArgs[] = {0}; - nd4j::ops::unstack op; + sd::ops::unstack op; auto hash = op.getOpHash(); auto status = execCustomOp(nullptr, hash, ptrsInBuffer, ptrsInShapes, 1, ptrsOutBuffers, ptrsOutShapes, 5, nullptr, 0, iArgs, 1, nullptr, 0, false); @@ -954,7 +954,7 @@ TEST_F(JavaInteropTests, Test_AveragePooling_FF_TF_float) { auto z = NDArrayFactory::create('c', {4, 4, 4, 3}); auto exp = NDArrayFactory::create('c', {4, 4, 4, 3}, {7.97172260f, 0.06878620f, 2.27749538f, 7.29276514f, -0.14074677f, 0.65480286f, 5.70313978f, -0.06546132f, 0.35443667f, 3.70382833f, -0.84020567f, 0.63826996f, 8.60301399f, -0.38236514f, 1.55177069f, 7.37542057f, -0.99374938f, -0.29971302f, 8.84352493f, -0.67121059f, 0.43132120f, 4.78175592f, -1.25070143f, -1.91523600f, 6.03855371f, -0.00292124f, -1.11214364f, 7.90158176f, -0.57949901f, -0.96735370f, 7.81192017f, -0.53255427f, -0.48009714f, 3.16953635f, 0.08353355f, -1.54299748f, 3.74821687f, 1.69396687f, 0.72724354f, 5.42915201f, -1.13686812f, -0.71793109f, 5.78376389f, -0.72239977f, -0.60055625f, 2.53636408f, 0.56777251f, -2.07892323f, 6.08064651f, 0.68620735f, 2.54017019f, 5.65828180f, -0.68255502f, 1.47283304f, 6.10842514f, -0.39655915f, 0.28380761f, 1.96707797f, -1.98206317f, 0.94027776f, 4.71811438f, 0.32104525f, -0.92409706f, 8.34588146f, -1.05581069f, -0.55217457f, 9.58440876f, -0.96549922f, 0.45820439f, 5.65453672f, -2.50953507f, -0.71441835f, 8.03059578f, -0.21281289f, 0.92125505f, 9.26900673f, -0.35963219f, -0.70039093f, 8.59924412f, -1.22358346f, 0.81318003f, 3.85920119f, -0.01305223f, -1.09234154f, 6.33158875f, 1.28094780f, -1.48926139f, 4.94969177f, -0.77126902f, -1.97033751f, 5.64381838f, -0.16285487f, -1.31277227f, 2.39893222f, -1.32902908f, -1.39609122f, 6.47572327f, -0.45267010f, 1.55727172f, 6.70965624f, -1.68735468f, -0.05672536f, 7.25092363f, -0.64613032f, 0.67050058f, 3.60789680f, -2.05948973f, 2.22687531f, 8.15202713f, -0.70148355f, 1.28314006f, 8.14842319f, -1.88807654f, -1.04808438f, 8.45500565f, -0.76425624f, 0.94542569f, 4.56179953f, -0.28786001f, -2.04502511f, 8.46278095f, -0.31019822f, 0.07339200f, 9.34214592f, -0.61948007f, 0.52481830f, 8.32515621f, -1.52418160f, 0.49678251f, 5.11082315f, -1.09908783f, -0.52969611f, 5.27806664f, 0.88632923f, 0.66754371f, 4.75839233f, 0.48928693f, -0.68036932f, 6.56925392f, -0.02949905f, -2.99189186f, 4.46320581f, -0.64534980f, -0.29516968f, 8.60809517f, -1.13120568f, 3.41720533f, 5.84243155f, -1.24109328f, 0.89566326f, 5.99578333f, -0.42496428f, 2.07076764f, 3.17812920f, -0.81566459f, -0.14363396f, 6.55184317f, 0.39633346f, -0.43852386f, 8.70214558f, -2.24613595f, 0.30708700f, 8.73882294f, -0.53545928f, 1.54409575f, 4.49452257f, -0.16509305f, 0.19028664f, 8.24897003f, 0.44750381f, 2.15448594f, 8.97640514f, -0.77728152f, 0.57272542f, 9.03467560f, 0.47173575f, -1.10807717f, 3.30056310f, -0.43268481f, -0.41470885f, 3.53798294f, -0.08546703f, -2.16840744f, 6.18733406f, -0.17871059f, -2.59837723f, 5.94218683f, -1.02990067f, -0.49760687f, 3.76938033f, 0.86383581f, -1.91504073f}); - nd4j::ops::avgpool2d op; + sd::ops::avgpool2d op; NDArray::prepareSpecialUse({&z}, {&input}); @@ -1008,7 +1008,7 @@ TEST_F(JavaInteropTests, Test_Add_1) { NDArray::prepareSpecialUse({&x}, {&x, &y}); - nd4j::ops::add op; + sd::ops::add op; Nd4jPointer ptrsInBuffer[] = {(Nd4jPointer) x.getBuffer(), y.getBuffer(), x.getSpecialBuffer(), y.getSpecialBuffer()}; Nd4jPointer ptrsInShapes[] = {(Nd4jPointer) x.getShapeInfo(), y.getShapeInfo(), x.getSpecialShapeInfo(), y.getSpecialShapeInfo(),}; @@ -1031,7 +1031,7 @@ TEST_F(JavaInteropTests, zeta_test10) { auto e = NDArrayFactory::create('c', {3, 4}, {23.014574, 12.184081, 8.275731, 6.1532226, 4.776538, 3.7945523, 3.0541048, 2.4765317, 2.0163891, 205.27448, 21.090889, 19.477398}); - nd4j::ops::zeta op; + sd::ops::zeta op; NDArray::prepareSpecialUse({&z}, {&x, &q}); @@ -1064,7 +1064,7 @@ TEST_F(JavaInteropTests, Test_Boolean_Broadcastables_1) { Nd4jPointer ptrsInShapes[] = {reinterpret_cast(arrayX.shapeInfo()), reinterpret_cast(arrayY.shapeInfo()), arrayX.getSpecialShapeInfo(), arrayY.getSpecialShapeInfo()}; NDArray::prepareSpecialUse({}, {&arrayX, &arrayY}); - nd4j::ops::greater_equal op; + sd::ops::greater_equal op; auto shapeList = calculateOutputShapes2(nullptr, op.getOpHash(), ptrsInBuffer, ptrsInShapes, 2, nullptr, 0, nullptr, 0, nullptr, 0, nullptr, 0); NDArray::registerSpecialUse({}, {&arrayX, &arrayY}); delete shapeList; @@ -1083,7 +1083,7 @@ TEST_F(JavaInteropTests, Test_L2_Loss_3) { Nd4jPointer ptrsOutBuffer[] = {reinterpret_cast(z.buffer()), z.getSpecialBuffer()}; Nd4jPointer ptrsOutShapes[] = {reinterpret_cast(z.shapeInfo()), z.getSpecialShapeInfo()}; - nd4j::ops::l2_loss op; + sd::ops::l2_loss op; auto status = execCustomOp(nullptr, op.getOpHash(), ptrsInBuffer, ptrsInShapes, 1, ptrsOutBuffer, ptrsOutShapes, 1, nullptr, 0, nullptr, 0, nullptr, 0, false); ASSERT_EQ(Status::OK(), status); @@ -1108,7 +1108,7 @@ TEST_F(JavaInteropTests, Test_Fastpath_3) { ASSERT_EQ(2, ctx.width()); - nd4j::ops::add op; + sd::ops::add op; execCustomOp2(nullptr, op.getOpHash(), &ctx); NDArray::registerSpecialUse({&z}, {&array0, &array1}); @@ -1130,7 +1130,7 @@ TEST_F(JavaInteropTests, Test_Fastpath_4) { ctx.setOutputArray(0, z.buffer(), z.shapeInfo(), z.specialBuffer(), z.specialShapeInfo()); ctx.setIArguments(iArgs, 3); - nd4j::ops::tri op; + sd::ops::tri op; execCustomOp2(nullptr, op.getOpHash(), &ctx); NDArray::registerSpecialUse({&z}, {}); @@ -1153,7 +1153,7 @@ TEST_F(JavaInteropTests, Test_Fastpath_5) { ctx.setInputArray(1, b.buffer(), b.shapeInfo(), b.specialBuffer(), b.specialShapeInfo()); ctx.setOutputArray(0, c.buffer(), c.shapeInfo(), c.specialBuffer(), c.specialShapeInfo()); - nd4j::ops::matmul op; + sd::ops::matmul op; auto status = execCustomOp2(nullptr, op.getOpHash(), &ctx); NDArray::registerSpecialUse({&c}, {&b, &c}); @@ -1186,7 +1186,7 @@ TEST_F(JavaInteropTests, Test_Fastpath_6) { ctx.setIArguments(iArgs, 3); - nd4j::ops::matmul_bp op; + sd::ops::matmul_bp op; auto status = execCustomOp2(nullptr, op.getOpHash(), &ctx); NDArray::registerSpecialUse({&gA, &gB}, {&a, &b, &gI}); @@ -1207,7 +1207,7 @@ TEST_F(JavaInteropTests, Test_Fastpath_7) { ctx.setIArguments(iArgs, 1); - nd4j::ops::concat op; + sd::ops::concat op; ctx.setInputArray(0, a.buffer(), a.shapeInfo(), a.specialBuffer(), a.specialShapeInfo()); ctx.setInputArray(1, b.buffer(), b.shapeInfo(), b.specialBuffer(), b.specialShapeInfo()); @@ -1230,7 +1230,7 @@ TEST_F(JavaInteropTests, test_bfloat16_rng) { RandomGenerator rng(119, 323841120L); bfloat16 args[2] = {(bfloat16) 0.0f, (bfloat16) 1.0f}; OpaqueDataBuffer zBuf(z.dataBuffer()); - execRandom(nullptr, nd4j::random::Ops::UniformDistribution, &rng, &zBuf, z.shapeInfo(), z.specialShapeInfo(), args); + execRandom(nullptr, sd::random::Ops::UniformDistribution, &rng, &zBuf, z.shapeInfo(), z.specialShapeInfo(), args); //z.printIndexedBuffer("z"); ASSERT_TRUE(z.sumNumber().e(0) > 0); @@ -1242,7 +1242,7 @@ TEST_F(JavaInteropTests, test_ismax_view) { v.assign(1.0); auto e = v.like(); - auto t = e.tensorAlongDimension(0, {0, 1}); + auto t = e(0, {2}); t.assign(1.0); auto z = v.ulike(); @@ -1254,7 +1254,7 @@ TEST_F(JavaInteropTests, test_ismax_view) { ctx.setOutputArray(0, z.buffer(), z.shapeInfo(), z.specialBuffer(), z.specialShapeInfo()); ctx.setIArguments(iArgs, 1); - nd4j::ops::ismax op; + sd::ops::ismax op; op.execute(&ctx); ASSERT_EQ(e, z); @@ -1269,7 +1269,7 @@ TEST_F(JavaInteropTests, test_size_dtype_1) { ctx.setInputArray(0, x.buffer(), x.shapeInfo(), x.specialBuffer(), x.specialShapeInfo()); ctx.setOutputArray(0, z.buffer(), z.shapeInfo(), z.specialBuffer(), z.specialShapeInfo()); - nd4j::ops::size op; + sd::ops::size op; auto status = op.execute(&ctx); ASSERT_EQ(Status::OK(), status); @@ -1278,7 +1278,7 @@ TEST_F(JavaInteropTests, test_size_dtype_1) { TEST_F(JavaInteropTests, test_expandable_array_op_1) { auto x = NDArrayFactory::string( {2}, {"first string", "second"}); - auto d = NDArrayFactory::string(" ", nd4j::DataType::UTF8); + auto d = NDArrayFactory::string(" ", sd::DataType::UTF8); auto z0 = NDArrayFactory::create('c', {6}); auto z1 = NDArrayFactory::string( {3}, {"", "", ""}); @@ -1295,7 +1295,7 @@ TEST_F(JavaInteropTests, test_expandable_array_op_1) { ctx.setOutputArray(0, &iz0, z0.shapeInfo(), z0.specialShapeInfo()); ctx.setOutputArray(1, &iz1, z1.shapeInfo(), z1.specialShapeInfo()); - nd4j::ops::compat_string_split op; + sd::ops::compat_string_split op; auto status = op.execute(&ctx); ASSERT_EQ(Status::OK(), status); @@ -1329,14 +1329,14 @@ TEST_F(JavaInteropTests, test_workspace_backed_arrays_1) { ctx.setIArguments({2, 2, 1, 1, 0, 0, 1, 1, 0, 0, 0}); - nd4j::ops::maxpool2d_bp op; + sd::ops::maxpool2d_bp op; auto status = op.execute(&ctx); ASSERT_EQ(Status::OK(), status); } /* TEST_F(JavaInteropTests, Test_Results_Conversion_1) { - auto pl = nd4j::graph::readFlatBuffers("./resources/gru_dynamic_mnist.fb"); + auto pl = sd::graph::readFlatBuffers("./resources/gru_dynamic_mnist.fb"); auto ptr = executeFlatGraph(nullptr, pl); // at this point we have FlatResults @@ -1363,7 +1363,7 @@ TEST_F(JavaInteropTests, Test_Results_Conversion_1) { ASSERT_TRUE(shape->size() > 0 && rank >= 0 && rank < MAX_RANK); // building regular NDArray out of this FlatArray - auto ndarray = nd4j::graph::FlatUtils::fromFlatArray(flatArray); + auto ndarray = sd::graph::FlatUtils::fromFlatArray(flatArray); // rank should match FlatArray ASSERT_EQ(rank, ndarray->rankOf()); @@ -1395,7 +1395,7 @@ TEST_F(JavaInteropTests, Test_Results_Conversion_1) { // for (int e = 0; e < exp.size(); e++) { // auto f = static_cast(e); -// auto tmp = nd4j::math::nd4j_exp((f / 100000.0 * 2.0 - 1.0) * 6.0); +// auto tmp = sd::math::nd4j_exp((f / 100000.0 * 2.0 - 1.0) * 6.0); // exp[e] = static_cast(tmp / (tmp + 1.0)); // } diff --git a/libnd4j/tests_cpu/layers_tests/LambdaTests.cu b/libnd4j/tests_cpu/layers_tests/LambdaTests.cu index 5bf8c8b57..a114f7179 100644 --- a/libnd4j/tests_cpu/layers_tests/LambdaTests.cu +++ b/libnd4j/tests_cpu/layers_tests/LambdaTests.cu @@ -24,7 +24,7 @@ #include #include -using namespace nd4j; +using namespace sd; class LambdaTests : public testing::Test { public: @@ -191,10 +191,10 @@ template void testPairwiseMy(NDArray &x, NDArray &y, NDArray &z) { auto f = LAMBDA_TT(x, y){ - return nd4j::math::nd4j_max(x, (T)0.f) + return sd::math::nd4j_max(x, (T)0.f) - x * y - + nd4j::math::nd4j_log((T)1.f - + nd4j::math::nd4j_exp(-nd4j::math::nd4j_abs(x))); + + sd::math::nd4j_log((T)1.f + + sd::math::nd4j_exp(-sd::math::nd4j_abs(x))); }; x.applyPairwiseLambda(y, f, z); @@ -204,8 +204,8 @@ void testPairwiseMy(NDArray &x, NDArray &y, NDArray &z) { TEST_F(LambdaTests, test_basic_9) { NDArray labels('c', {2,3,4},{0,1,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,0,1,1,0,1,0}); - NDArray logits('c', {2,3,4}, nd4j::DataType::DOUBLE); - NDArray output('c', {2,3,4}, nd4j::DataType::DOUBLE); + NDArray logits('c', {2,3,4}, sd::DataType::DOUBLE); + NDArray output('c', {2,3,4}, sd::DataType::DOUBLE); NDArray expected('c', {2,3,4}, {0.744397, 0.598139, 0.554355, 0.913015, 0.474077, 1.037488, 0.403186, 1.171101, 0.341154, 1.313262, 0.287335, 1.463282, 0.241008, 1.620417, 0.201413, 1.783901, 0.167786, 1.952978, 2.039387, 0.126928, 0.115520, 2.305083, 0.095545, 2.486836}); logits.linspace(0.1, 0.1); diff --git a/libnd4j/tests_cpu/layers_tests/LaunchContextCudaTests.cu b/libnd4j/tests_cpu/layers_tests/LaunchContextCudaTests.cu index d7632ace5..e16df80e6 100644 --- a/libnd4j/tests_cpu/layers_tests/LaunchContextCudaTests.cu +++ b/libnd4j/tests_cpu/layers_tests/LaunchContextCudaTests.cu @@ -19,9 +19,9 @@ // #include "testlayers.h" -#include -#include -#include +#include +#include +#include #include #include #include @@ -34,8 +34,8 @@ #include #include -using namespace nd4j; -using namespace nd4j::ops; +using namespace sd; +using namespace sd::ops; class LaunchContextCudaTests : public testing::Test { // diff --git a/libnd4j/tests_cpu/layers_tests/LegacyOpsCudaTests.cu b/libnd4j/tests_cpu/layers_tests/LegacyOpsCudaTests.cu index 354051f81..53179cd68 100644 --- a/libnd4j/tests_cpu/layers_tests/LegacyOpsCudaTests.cu +++ b/libnd4j/tests_cpu/layers_tests/LegacyOpsCudaTests.cu @@ -19,9 +19,9 @@ // #include "testlayers.h" -#include -#include -#include +#include +#include +#include #include #include #include @@ -32,8 +32,8 @@ #include #include -using namespace nd4j; -using namespace nd4j::ops; +using namespace sd; +using namespace sd::ops; class LegacyOpsCudaTests : public testing::Test { diff --git a/libnd4j/tests_cpu/layers_tests/LegacyOpsTests.cpp b/libnd4j/tests_cpu/layers_tests/LegacyOpsTests.cpp index 35f46d739..dae5ba5b9 100644 --- a/libnd4j/tests_cpu/layers_tests/LegacyOpsTests.cpp +++ b/libnd4j/tests_cpu/layers_tests/LegacyOpsTests.cpp @@ -19,9 +19,9 @@ // #include "testlayers.h" -#include -#include -#include +#include +#include +#include #include #include #include @@ -32,8 +32,8 @@ #include #include -using namespace nd4j; -using namespace nd4j::ops; +using namespace sd; +using namespace sd::ops; class LegacyOpsTests : public testing::Test { @@ -47,7 +47,7 @@ TEST_F(LegacyOpsTests, TransformTests_1) { auto exp = NDArrayFactory::create('c', {5, 5}); exp.assign(-1.0); - nd4j::ops::LegacyTransformSameOp op(transform::Neg); // Neg + sd::ops::LegacyTransformSameOp op(transform::Neg); // Neg auto status = op.execute({&x}, {&z}, {}, {}, {}); ASSERT_EQ(status, ND4J_STATUS_OK); //z.printIndexedBuffer("Output NEG"); @@ -61,16 +61,16 @@ TEST_F(LegacyOpsTests, TransformTests_2) { auto exp = NDArrayFactory::create('c', {5, 5}); exp.assign(-1.0); - nd4j::ops::LegacyTransformSameOp op(transform::Neg); // Neg + sd::ops::LegacyTransformSameOp op(transform::Neg); // Neg auto result = op.evaluate({&x}, {}, {}); - ASSERT_EQ(1, result->size()); + ASSERT_EQ(1, result.size()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } TEST_F(LegacyOpsTests, Reciprocal_1) { @@ -80,7 +80,7 @@ TEST_F(LegacyOpsTests, Reciprocal_1) { auto ethalon = NDArrayFactory::create('c', {5, 5}); ethalon.assign(0.5f); - nd4j::ops::LegacyTransformSameOp op(transform::Reciprocal); // Reciprocal + sd::ops::LegacyTransformSameOp op(transform::Reciprocal); // Reciprocal Nd4jStatus status = op.execute({&x}, {&x}, {}, {}, {}); ASSERT_EQ(ND4J_STATUS_OK, status); @@ -98,7 +98,7 @@ TEST_F(LegacyOpsTests, PWT_Tests_1) { auto exp = NDArrayFactory::create('c', {5, 5}); exp.assign(6.0); - nd4j::ops::LegacyPairwiseTransformOp op(pairwise::Multiply); // Multiply + sd::ops::LegacyPairwiseTransformOp op(pairwise::Multiply); // Multiply Nd4jStatus status = op.execute({&x, &y}, {&x}, {}, {}, {}); ASSERT_EQ(ND4J_STATUS_OK, status); @@ -118,15 +118,15 @@ TEST_F(LegacyOpsTests, PWT_Tests_2) { auto exp = NDArrayFactory::create('c', {5, 5}); exp.assign(6.0); - nd4j::ops::LegacyPairwiseTransformOp op(pairwise::Multiply); // Multiply + sd::ops::LegacyPairwiseTransformOp op(pairwise::Multiply); // Multiply auto result = op.evaluate({&x, &y}, {}, {}); - auto z = result->at(0); + auto z = result.at(0); //z->printBuffer("Z"); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } TEST_F(LegacyOpsTests, Scalar_Test_1) { @@ -136,7 +136,7 @@ TEST_F(LegacyOpsTests, Scalar_Test_1) { auto exp = NDArrayFactory::create('c', {5, 5}); exp.assign(7.0); - nd4j::ops::LegacyScalarOp op(scalar::Add); + sd::ops::LegacyScalarOp op(scalar::Add); op.execute({&x}, {&x}, {5.0}, {}, {}); // ASSERT_TRUE(exp.equalsTo(&x)); @@ -151,13 +151,13 @@ TEST_F(LegacyOpsTests, Scalar_Test_2) { auto y = NDArrayFactory::create(5.0f); - nd4j::ops::LegacyScalarOp op(scalar::Add, y); + sd::ops::LegacyScalarOp op(scalar::Add, y); auto result = op.evaluate({&x}, {}, {}); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } @@ -165,18 +165,18 @@ TEST_F(LegacyOpsTests, ReduceTests_1) { auto x = NDArrayFactory::create('c', {5, 5}); x.assign(1.0); int opNum = reduce::Sum; - nd4j::ops::LegacyReduceSameOp op(opNum); + sd::ops::LegacyReduceSameOp op(opNum); auto result = op.evaluate({&x}, {}, {}); - ASSERT_EQ(1, result->size()); + ASSERT_EQ(1, result.size()); - auto z = result->at(0); + auto z = result.at(0); // z->printBuffer("ReduceTest1"); ASSERT_TRUE(z->isScalar()); ASSERT_NEAR(x.sumNumber().e(0), z->e(0), 1e-5f); - delete result; + } @@ -184,20 +184,20 @@ TEST_F(LegacyOpsTests, ReduceTests_2) { auto x = NDArrayFactory::create('c', {5, 5}); x.assign(1.0); - nd4j::ops::LegacyReduceSameOp op(reduce::Sum); + sd::ops::LegacyReduceSameOp op(reduce::Sum); auto axis = NDArrayFactory::create('c', {1}, {1}); auto result = op.evaluate({&x, &axis}, {}, {}); - ASSERT_EQ(1, result->size()); + ASSERT_EQ(1, result.size()); - auto z = result->at(0); + auto z = result.at(0); auto exp = x.reduceAlongDimension(reduce::Sum, {1}); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } @@ -207,17 +207,17 @@ TEST_F(LegacyOpsTests, ReduceTests_3) { auto indices = NDArrayFactory::create('c', {1,1}, {1}); - nd4j::ops::LegacyReduceSameOp op(reduce::Sum); + sd::ops::LegacyReduceSameOp op(reduce::Sum); auto result = op.evaluate({&x, &indices}, {}, {}); - auto z = result->at(0); + auto z = result.at(0); auto exp = x.reduceAlongDimension(reduce::Sum,{1}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } @@ -227,36 +227,36 @@ TEST_F(LegacyOpsTests, ReduceTests_4) { auto indices = NDArrayFactory::create('c', {1, 1}, {1}); - nd4j::ops::LegacyReduceSameOp op(reduce::Sum); + sd::ops::LegacyReduceSameOp op(reduce::Sum); auto result = op.evaluate({&x, &indices}, {}, {}, {true}); - auto z = result->at(0); + auto z = result.at(0); auto exp = x.reduceAlongDimension(reduce::Sum, {1}, true); // indices.printShapeInfo("Indices shape"); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); // z->printIndexedBuffer("Output reduce 4"); // exp.printIndexedBuffer("Expected reduce 4"); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } TEST_F(LegacyOpsTests, ReduceTests_5) { auto x = NDArrayFactory::create('c', {5, 5}); x.assign(1.0); int opNum = reduce::Mean; - nd4j::ops::LegacyReduceFloatOp op(opNum); + sd::ops::LegacyReduceFloatOp op(opNum); auto result = op.evaluate({&x}); - ASSERT_EQ(1, result->size()); + ASSERT_EQ(1, result.size()); - auto z = result->at(0); + auto z = result.at(0); // z->printBuffer("ReduceTest1"); ASSERT_TRUE(z->isScalar()); ASSERT_NEAR(x.meanNumber().e(0), z->e(0), 1e-5f); - delete result; + } @@ -264,20 +264,20 @@ TEST_F(LegacyOpsTests, ReduceTests_6) { auto x = NDArrayFactory::create('c', {5, 5}); x.assign(1.0); auto axis = NDArrayFactory::create('c', {1}, {1}); - nd4j::ops::LegacyReduceFloatOp op(reduce::Mean); + sd::ops::LegacyReduceFloatOp op(reduce::Mean); auto result = op.evaluate({&x, &axis}, {}, {}); - ASSERT_EQ(1, result->size()); + ASSERT_EQ(1, result.size()); - auto z = result->at(0); + auto z = result.at(0); auto exp = x.reduceAlongDimension(reduce::Mean, {1}); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } @@ -287,17 +287,17 @@ TEST_F(LegacyOpsTests, ReduceTests_7) { auto indices = NDArrayFactory::create('c', {1,1}, {1}); - nd4j::ops::LegacyReduceFloatOp op(reduce::Mean); + sd::ops::LegacyReduceFloatOp op(reduce::Mean); auto result = op.evaluate({&x, &indices}, {}, {}); - auto z = result->at(0); + auto z = result.at(0); auto exp = x.reduceAlongDimension(reduce::Mean,{1}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } @@ -307,19 +307,19 @@ TEST_F(LegacyOpsTests, ReduceTests_8) { auto indices = NDArrayFactory::create('c', {1}, {1}); - nd4j::ops::LegacyReduceFloatOp op(reduce::Mean); + sd::ops::LegacyReduceFloatOp op(reduce::Mean); auto result = op.evaluate({&x, &indices}, {}, {}, {true}); - auto z = result->at(0); + auto z = result.at(0); auto exp = x.reduceAlongDimension(reduce::Mean, {1}, true); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); // z->printIndexedBuffer("Reduce8 output"); // z->printShapeInfo("Reduce8 shape"); // exp.printShapeInfo("Reduce8 expected shape"); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } @@ -327,18 +327,18 @@ TEST_F(LegacyOpsTests, IndexReduceTests_1) { auto x = NDArrayFactory::create('c', {5, 5}); x.linspace(1); - nd4j::ops::LegacyIndexReduceOp op(indexreduce::IndexMax); + sd::ops::LegacyIndexReduceOp op(indexreduce::IndexMax); auto result = op.evaluate({&x}, {}, {}); - ASSERT_EQ(1, result->size()); + ASSERT_EQ(1, result.size()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(z->isScalar()); ASSERT_EQ(24, z->e(0)); - delete result; + } @@ -347,13 +347,13 @@ TEST_F(LegacyOpsTests, IndexReduceTests_2) { auto indices = NDArrayFactory::create('c', {1}, {1}); x.linspace(1); auto exp = NDArrayFactory::create({4,4,4,4,4}); - nd4j::ops::LegacyIndexReduceOp op(indexreduce::IndexMax); + sd::ops::LegacyIndexReduceOp op(indexreduce::IndexMax); auto result = op.evaluate({&x, &indices}, {}, {}); - ASSERT_EQ(1, result->size()); + ASSERT_EQ(1, result.size()); - auto z = result->at(0); + auto z = result.at(0); // z->printIndexedBuffer("Hello indexreduce2"); ASSERT_TRUE(exp.equalsTo(z)); //ASSERT_EQ(4, z->e(0)); @@ -362,7 +362,7 @@ TEST_F(LegacyOpsTests, IndexReduceTests_2) { //ASSERT_EQ(4, z->e(3)); //ASSERT_EQ(4, z->e(4)); - delete result; + } TEST_F(LegacyOpsTests, BroadcastingTests_1) { @@ -372,7 +372,7 @@ TEST_F(LegacyOpsTests, BroadcastingTests_1) { auto row = NDArrayFactory::create('c', {1, 5}); row.linspace(1); auto axis = NDArrayFactory::create('c', {1}, {1}); - nd4j::ops::LegacyBroadcastOp op(broadcast::Add); + sd::ops::LegacyBroadcastOp op(broadcast::Add); Nd4jStatus status = op.execute({&x, &row, &axis}, {&x}, {}, {}, {}); ASSERT_EQ(ND4J_STATUS_OK, status); @@ -394,7 +394,7 @@ TEST_F(LegacyOpsTests, BroadcastingTests_2) { int axis = 1; // shape::printShapeInfoLinear("tad shape", tad.tadOnlyShapeInfo); - auto packY = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(y.shapeInfo(), {axis}); + auto packY = sd::ConstantTadHelper::getInstance()->tadForDimensions(y.shapeInfo(), {axis}); NDArray::prepareSpecialUse({&y}, {&x}); @@ -433,11 +433,11 @@ TEST_F(LegacyOpsTests, reduce3_1) { std::vector dim = {1}; - auto shapeBuffer = nd4j::ShapeBuilders::createShapeInfo(nd4j::DataType::FLOAT32, 'c', 2, yShape); - auto xShapeBuffer = nd4j::ShapeBuilders::createShapeInfo(nd4j::DataType::FLOAT32, 'c', 1, xShape); + auto shapeBuffer = sd::ShapeBuilders::createShapeInfo(sd::DataType::FLOAT32, 'c', 2, yShape); + auto xShapeBuffer = sd::ShapeBuilders::createShapeInfo(sd::DataType::FLOAT32, 'c', 1, xShape); //int *tadShapeBuffer = shape::computeResultShape(shapeBuffer,dimension,dimensionLength); - auto tadShapeBuffer = nd4j::ShapeUtils::evalReduceShapeInfo('c', dim, shapeBuffer, false, true, nullptr); + auto tadShapeBuffer = sd::ShapeUtils::evalReduceShapeInfo('c', dim, shapeBuffer, false, true, nullptr); functions::reduce3::Reduce3::exec(opNum, x, xShapeBuffer, extraVals, y, shapeBuffer, result, tadShapeBuffer, dimension, dimensionLength, 0, 4); float distancesAssertion[4] = {0.0,8.0,16.0,24.0}; @@ -459,15 +459,15 @@ TEST_F(LegacyOpsTests, Reduce3_2) { auto dim = NDArrayFactory::create('c', {1}, {1}); dim.syncToHost(); - nd4j::LaunchContext* context = nd4j::LaunchContext::defaultContext(); + sd::LaunchContext* context = sd::LaunchContext::defaultContext(); Nd4jPointer* extraPointers = nullptr; #ifdef __CUDABLAS__ extraPointers = new Nd4jPointer[7] {nullptr, context->getCudaStream(), context->getScalarPointer(), nullptr, context->getCudaSpecialStream(), context->getReductionPointer(), context->getAllocationPointer()}; #endif - auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(x.getShapeInfo(), {1}); - auto packY = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(y.getShapeInfo(), {1}); + auto packX = sd::ConstantTadHelper::getInstance()->tadForDimensions(x.getShapeInfo(), {1}); + auto packY = sd::ConstantTadHelper::getInstance()->tadForDimensions(y.getShapeInfo(), {1}); NDArray::prepareSpecialUse({&z}, {&x, &y, &dim}); OpaqueDataBuffer xBuf(x.dataBuffer()); @@ -499,15 +499,15 @@ TEST_F(LegacyOpsTests, Reduce3_3) { auto dim = NDArrayFactory::create('c', {1}, {1}); dim.syncToHost(); - nd4j::LaunchContext* context = nd4j::LaunchContext::defaultContext(); + sd::LaunchContext* context = sd::LaunchContext::defaultContext(); Nd4jPointer* extraPointers = nullptr; #ifdef __CUDABLAS__ extraPointers = new Nd4jPointer[7] {nullptr, context->getCudaStream(), context->getScalarPointer(), nullptr, context->getCudaSpecialStream(), context->getReductionPointer(), context->getAllocationPointer()}; #endif - auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(x.getShapeInfo(), {1}); - auto packY = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(y.getShapeInfo(), {1}); + auto packX = sd::ConstantTadHelper::getInstance()->tadForDimensions(x.getShapeInfo(), {1}); + auto packY = sd::ConstantTadHelper::getInstance()->tadForDimensions(y.getShapeInfo(), {1}); NDArray::prepareSpecialUse({&z}, {&x, &y, &dim}); OpaqueDataBuffer xBuf(x.dataBuffer()); @@ -539,15 +539,15 @@ TEST_F(LegacyOpsTests, Reduce3_4) { auto dim = NDArrayFactory::create('c', {1}, {1}); dim.syncToHost(); - nd4j::LaunchContext* context = nd4j::LaunchContext::defaultContext(); + sd::LaunchContext* context = sd::LaunchContext::defaultContext(); Nd4jPointer* extraPointers = nullptr; #ifdef __CUDABLAS__ extraPointers = new Nd4jPointer[7] {nullptr, context->getCudaStream(), context->getScalarPointer(), nullptr, context->getCudaSpecialStream(), context->getReductionPointer(), context->getAllocationPointer()}; #endif - auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(x.getShapeInfo(), {1}); - auto packY = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(y.getShapeInfo(), {1}); + auto packX = sd::ConstantTadHelper::getInstance()->tadForDimensions(x.getShapeInfo(), {1}); + auto packY = sd::ConstantTadHelper::getInstance()->tadForDimensions(y.getShapeInfo(), {1}); NDArray::prepareSpecialUse({&z}, {&x, &y, &dim}); OpaqueDataBuffer xBuf(x.dataBuffer()); @@ -581,15 +581,15 @@ TEST_F(LegacyOpsTests, Reduce3_5) { auto dim = NDArrayFactory::create('c', {1}, {1}); dim.syncToHost(); - nd4j::LaunchContext* context = nd4j::LaunchContext::defaultContext(); + sd::LaunchContext* context = sd::LaunchContext::defaultContext(); Nd4jPointer* extraPointers = nullptr; #ifdef __CUDABLAS__ extraPointers = new Nd4jPointer[7] {nullptr, context->getCudaStream(), context->getScalarPointer(), nullptr, context->getCudaSpecialStream(), context->getReductionPointer(), context->getAllocationPointer()}; #endif - auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(x.getShapeInfo(), {1}); - auto packY = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(y.getShapeInfo(), {1}); + auto packX = sd::ConstantTadHelper::getInstance()->tadForDimensions(x.getShapeInfo(), {1}); + auto packY = sd::ConstantTadHelper::getInstance()->tadForDimensions(y.getShapeInfo(), {1}); NDArray::prepareSpecialUse({&z}, {&x, &y, &dim}); @@ -616,10 +616,10 @@ TEST_F(LegacyOpsTests, test_Reduce3_All_1) { auto z = NDArrayFactory::create('c', {1000, 1}); auto dim = NDArrayFactory::create('c', {1}, {-1}); - auto tadPackX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(x.shapeInfo(), -1); - auto tadPackY = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(y.shapeInfo(), -1); + auto tadPackX = sd::ConstantTadHelper::getInstance()->tadForDimensions(x.shapeInfo(), -1); + auto tadPackY = sd::ConstantTadHelper::getInstance()->tadForDimensions(y.shapeInfo(), -1); - nd4j::LaunchContext* context = nd4j::LaunchContext::defaultContext(); + sd::LaunchContext* context = sd::LaunchContext::defaultContext(); Nd4jPointer* extraPointers = nullptr; #ifdef __CUDABLAS__ @@ -652,7 +652,7 @@ TEST_F(LegacyOpsTests, test_inverse_broadcast_1) { auto e = NDArrayFactory::create('c', {3, 4}); e.assign(2.0f); - auto tadPackY = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(y.shapeInfo(), 1); + auto tadPackY = sd::ConstantTadHelper::getInstance()->tadForDimensions(y.shapeInfo(), 1); y.tickWriteDevice(); @@ -674,13 +674,13 @@ TEST_F(LegacyOpsTests, test_inverse_broadcast_2) { auto e = NDArrayFactory::create('c', {3, 4}); e.assign(false); - auto row = y.tensorAlongDimension(1, {1}); + auto row = y(1, {0}); row.assign(2.0f); - auto erow = e.tensorAlongDimension(1, {1}); + auto erow = e(1, {0}); erow.assign(true); - auto tadPackY = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(y.shapeInfo(), 1); + auto tadPackY = sd::ConstantTadHelper::getInstance()->tadForDimensions(y.shapeInfo(), 1); z.tickWriteDevice(); diff --git a/libnd4j/tests_cpu/layers_tests/ListOperationsTests.cpp b/libnd4j/tests_cpu/layers_tests/ListOperationsTests.cpp index 625d9978f..04e4a70e8 100644 --- a/libnd4j/tests_cpu/layers_tests/ListOperationsTests.cpp +++ b/libnd4j/tests_cpu/layers_tests/ListOperationsTests.cpp @@ -19,12 +19,12 @@ // #include "testlayers.h" -#include -#include +#include +#include #include -using namespace nd4j; -using namespace nd4j::ops; +using namespace sd; +using namespace sd::ops; class ListOperationsTests : public testing::Test { @@ -35,11 +35,11 @@ TEST_F(ListOperationsTests, BasicTest_Write_1) { auto x = NDArrayFactory::create('c', {128}); x.linspace(1); - nd4j::ops::write_list op; + sd::ops::write_list op; auto result = op.execute(&list, {&x}, {}, {1}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); ASSERT_EQ(1, list.elements()); @@ -47,8 +47,8 @@ TEST_F(ListOperationsTests, BasicTest_Write_1) { ASSERT_EQ(2, list.elements()); - delete result; - delete result2; + + } TEST_F(ListOperationsTests, BasicTest_Stack_1) { @@ -62,19 +62,19 @@ TEST_F(ListOperationsTests, BasicTest_Stack_1) { tads.at(e)->assign(row); } - nd4j::ops::stack_list op; + sd::ops::stack_list op; auto result = op.execute(&list, {}, {}, {1}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); // z->printShapeInfo(); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } TEST_F(ListOperationsTests, BasicTest_UnStackList_1) { @@ -89,14 +89,14 @@ TEST_F(ListOperationsTests, BasicTest_UnStackList_1) { delete row; } - nd4j::ops::unstack_list op; + sd::ops::unstack_list op; auto result = op.execute(&list, {&x}, {}, {0}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); ASSERT_EQ(list.elements(), 10); -// auto z = result->at(0); +// auto z = result.at(0); // z->printShapeInfo("The first of"); // ASSERT_TRUE(exp.isSameShape(z)); // ASSERT_TRUE(exp.equalsTo(z)); @@ -107,7 +107,7 @@ TEST_F(ListOperationsTests, BasicTest_UnStackList_1) { delete row; } - delete result; + } //TEST_F(ListOperationsTests, BasicTest_UnStackList_2) { @@ -122,24 +122,24 @@ TEST_F(ListOperationsTests, BasicTest_UnStackList_1) { // delete row; // } // -// nd4j::ops::unstack_list op; +// sd::ops::unstack_list op; // // auto result = op.execute(nullptr, {&x}, {}, {0}); // -// ASSERT_EQ(ND4J_STATUS_OK, result->status()); +// ASSERT_EQ(ND4J_STATUS_OK, result.status()); // ASSERT_EQ(result->size(), 10); // -// // auto z = result->at(0); +// // auto z = result.at(0); //// z->printShapeInfo("The first of"); //// ASSERT_TRUE(exp.isSameShape(z)); //// ASSERT_TRUE(exp.equalsTo(z)); // for (int e = 0; e < 10; e++) { -// auto row = result->at(e); +// auto row = result.at(e); // ASSERT_TRUE(row->equalsTo(tads->at(e))); // //list.write(e, row); // } // -// delete result; +// // delete tads; //} @@ -156,18 +156,18 @@ TEST_F(ListOperationsTests, BasicTest_Read_1) { delete row; } - nd4j::ops::read_list op; + sd::ops::read_list op; auto result = op.execute(&list, {}, {}, {4}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } TEST_F(ListOperationsTests, BasicTest_Pick_1) { @@ -189,17 +189,17 @@ TEST_F(ListOperationsTests, BasicTest_Pick_1) { tads.at(3)->assign(3.0f); - nd4j::ops::pick_list op; + sd::ops::pick_list op; auto result = op.execute(&list, {}, {}, {1, 1, 3, 3}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } TEST_F(ListOperationsTests, BasicTest_Size_1) { @@ -213,34 +213,34 @@ TEST_F(ListOperationsTests, BasicTest_Size_1) { delete row; } - nd4j::ops::size_list op; + sd::ops::size_list op; auto result = op.execute(&list, {}, {}, {1}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } TEST_F(ListOperationsTests, BasicTest_Create_1) { auto matrix = NDArrayFactory::create('c', {3, 2}); matrix.linspace(1); - nd4j::ops::create_list op; + sd::ops::create_list op; auto result = op.execute(nullptr, {&matrix}, {}, {1, 1}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); // we return flow as well - ASSERT_EQ(1, result->size()); + ASSERT_EQ(1, result.size()); - delete result; + } TEST_F(ListOperationsTests, BasicTest_Split_1) { @@ -281,9 +281,9 @@ TEST_F(ListOperationsTests, BasicTest_Split_1) { delete row; } - nd4j::ops::split_list op; + sd::ops::split_list op; auto result = op.execute(&list, {&matrix, &lengths}, {}, {}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); ASSERT_EQ(3, list.height()); @@ -296,7 +296,7 @@ TEST_F(ListOperationsTests, BasicTest_Split_1) { ASSERT_TRUE(exp2.isSameShape(list.readRaw(2))); ASSERT_TRUE(exp2.equalsTo(list.readRaw(2))); - delete result; + } TEST_F(ListOperationsTests, BasicTest_Scatter_1) { @@ -316,10 +316,10 @@ TEST_F(ListOperationsTests, BasicTest_Scatter_1) { for (int e = 0; e < matrix.rows(); e++) indices.p(e, 9 - e); - nd4j::ops::scatter_list op; + sd::ops::scatter_list op; auto result = op.execute(&list, {&indices, &matrix, &s}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); for (int e = 0; e < 10; e++) { auto row = tads.at(9 - e); @@ -329,7 +329,7 @@ TEST_F(ListOperationsTests, BasicTest_Scatter_1) { ASSERT_TRUE(chunk->equalsTo(row)); } - delete result; + } TEST_F(ListOperationsTests, BasicTest_Clone_1) { @@ -345,7 +345,7 @@ TEST_F(ListOperationsTests, BasicTest_Clone_1) { Context block(1, &variableSpace); block.pickInput(-1); - nd4j::ops::clone_list op; + sd::ops::clone_list op; ASSERT_TRUE(list == block.variable(0)->getNDArrayList()); @@ -382,13 +382,13 @@ TEST_F(ListOperationsTests, BasicTest_Gather_1) { auto indices = NDArrayFactory::create('c', {1, 10}); indices.linspace(9, -1); - nd4j::ops::gather_list op; + sd::ops::gather_list op; auto result = op.execute(&list, {&indices}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); - ASSERT_EQ(1, result->size()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); + ASSERT_EQ(1, result.size()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); @@ -397,7 +397,7 @@ TEST_F(ListOperationsTests, BasicTest_Gather_1) { ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } TEST_F(ListOperationsTests, GraphTests_Sequential_1) { @@ -428,17 +428,17 @@ TEST_F(ListOperationsTests, GraphTests_Sequential_1) { auto nodeA = new Node(OpType_TRANSFORM_SAME, 0, 1, {-1}); // creating list - nd4j::ops::create_list opB; + sd::ops::create_list opB; auto nodeB = new Node(&opB, 2, {1},{},{}, 0.0f, {}, {0, 1}); //nodeB->setCustomOp(&opB); // filling list with matrix - nd4j::ops::split_list opC; + sd::ops::split_list opC; auto nodeC = new Node(&opC, 3, {2, 1, -2}); //nodeC->setCustomOp(&opC); // reading chunks from List. We're adding op number 3 in inputs, to ensure graph will execute this node after split - nd4j::ops::read_list opD; + sd::ops::read_list opD; auto nodeD0 = new Node(&opD, 5, {2, 3}, {},{}, 0.0f, {}, {0}); auto nodeD1 = new Node(&opD, 6, {2, 3}, {},{}, 0.0f, {}, {1}); auto nodeD2 = new Node(&opD, 7, {2, 3}, {},{}, 0.0f, {}, {2}); @@ -447,12 +447,12 @@ TEST_F(ListOperationsTests, GraphTests_Sequential_1) { //nodeD2->setCustomOp(&opD); // using OneMinus on each chunk separately - auto nodeE0 = new Node(OpType_TRANSFORM_SAME, nd4j::transform::OneMinus, 10, {5}); - auto nodeE1 = new Node(OpType_TRANSFORM_SAME, nd4j::transform::OneMinus, 11, {6}); - auto nodeE2 = new Node(OpType_TRANSFORM_SAME, nd4j::transform::OneMinus, 12, {7}); + auto nodeE0 = new Node(OpType_TRANSFORM_SAME, sd::transform::OneMinus, 10, {5}); + auto nodeE1 = new Node(OpType_TRANSFORM_SAME, sd::transform::OneMinus, 11, {6}); + auto nodeE2 = new Node(OpType_TRANSFORM_SAME, sd::transform::OneMinus, 12, {7}); // writing chunks back to the List - nd4j::ops::write_list opF; + sd::ops::write_list opF; auto nodeF0 = new Node(&opF, 15, {2, 10}, {},{}, 0.0f, {}, {0}); auto nodeF1 = new Node(&opF, 16, {2, 11}, {},{}, 0.0f, {}, {1}); auto nodeF2 = new Node(&opF, 17, {2, 12}, {},{}, 0.0f, {}, {2}); @@ -462,7 +462,7 @@ TEST_F(ListOperationsTests, GraphTests_Sequential_1) { // nodeF2->setCustomOp(&opF); // now we're stacking chunks back to matrix state - nd4j::ops::stack_list opG; + sd::ops::stack_list opG; auto nodeG = new Node(&opG, 20, {2, 15, 16, 17}); //auto nodeG = new Node(OpType_CUSTOM, 0, 20, {2}); @@ -559,17 +559,17 @@ TEST_F(ListOperationsTests, GraphTests_Sequential_2) { auto nodeA = new Node(OpType_TRANSFORM_SAME, 0, 1, {-1}); // creating list - nd4j::ops::create_list opB; + sd::ops::create_list opB; auto nodeB = new Node(&opB, 2, {1},{},{}, 0.0f, {}, {0, 1}); // nodeB->setCustomOp(&opB); // filling list with matrix - nd4j::ops::scatter_list opC; + sd::ops::scatter_list opC; auto nodeC = new Node(&opC, 3, {2, -2, 1, -3}); //nodeC->setCustomOp(&opC); - nd4j::ops::read_list opD; + sd::ops::read_list opD; auto nodeD0 = new Node(&opD, 5, {2, 3}, {},{}, 0.0f, {}, {0}); auto nodeD1 = new Node(&opD, 6, {2, 3, 15}, {},{}, 0.0f, {}, {1}); auto nodeD2 = new Node(&opD, 7, {2, 3, 16}, {},{}, 0.0f, {}, {2}); @@ -580,12 +580,12 @@ TEST_F(ListOperationsTests, GraphTests_Sequential_2) { // using OneMinus on each chunk separately - auto nodeE0 = new Node(OpType_TRANSFORM_SAME, nd4j::transform::OneMinus, 10, {5}); - auto nodeE1 = new Node(OpType_TRANSFORM_SAME, nd4j::transform::OneMinus, 11, {6}); - auto nodeE2 = new Node(OpType_TRANSFORM_SAME, nd4j::transform::OneMinus, 12, {7}); + auto nodeE0 = new Node(OpType_TRANSFORM_SAME, sd::transform::OneMinus, 10, {5}); + auto nodeE1 = new Node(OpType_TRANSFORM_SAME, sd::transform::OneMinus, 11, {6}); + auto nodeE2 = new Node(OpType_TRANSFORM_SAME, sd::transform::OneMinus, 12, {7}); // writing chunks back to the List - nd4j::ops::write_list opF; + sd::ops::write_list opF; auto nodeF0 = new Node(&opF, 15, {2, 10}, {},{}, 0.0f, {}, {0}); auto nodeF1 = new Node(&opF, 16, {2, 11}, {},{}, 0.0f, {}, {1}); auto nodeF2 = new Node(&opF, 17, {2, 12}, {},{}, 0.0f, {}, {2}); @@ -595,7 +595,7 @@ TEST_F(ListOperationsTests, GraphTests_Sequential_2) { // nodeF2->setCustomOp(&opF); // now we're gathering chunks back to matrix state - nd4j::ops::pick_list opG; + sd::ops::pick_list opG; auto nodeG = new Node(&opG, 20, {2, -2, 15, 16, 17}); //auto nodeG = new Node(OpType_CUSTOM, 0, 20, {2}); diff --git a/libnd4j/tests_cpu/layers_tests/LoopCoordsHelperTests.cpp b/libnd4j/tests_cpu/layers_tests/LoopCoordsHelperTests.cpp index 1a65c09ae..976e89550 100644 --- a/libnd4j/tests_cpu/layers_tests/LoopCoordsHelperTests.cpp +++ b/libnd4j/tests_cpu/layers_tests/LoopCoordsHelperTests.cpp @@ -19,9 +19,9 @@ // #include "testlayers.h" -#include +#include #include -using namespace nd4j; +using namespace sd; class LoopCoordsHelper : public testing::Test { public: diff --git a/libnd4j/tests_cpu/layers_tests/MemoryUtilsTests.cpp b/libnd4j/tests_cpu/layers_tests/MemoryUtilsTests.cpp index fd771231d..4bfe40405 100644 --- a/libnd4j/tests_cpu/layers_tests/MemoryUtilsTests.cpp +++ b/libnd4j/tests_cpu/layers_tests/MemoryUtilsTests.cpp @@ -22,7 +22,7 @@ #include #include "testlayers.h" -using namespace nd4j::memory; +using namespace sd::memory; class MemoryUtilsTests : public testing::Test { public: diff --git a/libnd4j/tests_cpu/layers_tests/MklDnnTests.cpp b/libnd4j/tests_cpu/layers_tests/MklDnnTests.cpp index b01c9f98a..dcbfa29b0 100644 --- a/libnd4j/tests_cpu/layers_tests/MklDnnTests.cpp +++ b/libnd4j/tests_cpu/layers_tests/MklDnnTests.cpp @@ -34,7 +34,7 @@ public: }; -static void printer(std::initializer_list helpers) { +static void printer(std::initializer_list helpers) { for (auto v:helpers) { nd4j_printf("Initialized [%s]\n", v->name().c_str()); @@ -45,30 +45,39 @@ static void printer(std::initializer_list TEST_F(MklDnnTests, helpers_includer) { // we need this block, to make sure all helpers are still available within binary, and not optimized out by linker #ifdef HAVE_MKLDNN - nd4j::ops::platforms::PLATFORM_conv2d_ENGINE_CPU conv2d; - nd4j::ops::platforms::PLATFORM_conv2d_bp_ENGINE_CPU conv2d_bp; + sd::ops::platforms::PLATFORM_conv2d_ENGINE_CPU conv2d; + sd::ops::platforms::PLATFORM_conv2d_bp_ENGINE_CPU conv2d_bp; - nd4j::ops::platforms::PLATFORM_conv2d_ENGINE_CPU conv3d; - nd4j::ops::platforms::PLATFORM_conv2d_bp_ENGINE_CPU conv3d_bp; + sd::ops::platforms::PLATFORM_conv2d_ENGINE_CPU conv3d; + sd::ops::platforms::PLATFORM_conv2d_bp_ENGINE_CPU conv3d_bp; - nd4j::ops::platforms::PLATFORM_avgpool2d_ENGINE_CPU avgpool2d; - nd4j::ops::platforms::PLATFORM_avgpool2d_bp_ENGINE_CPU avgpool2d_bp; + sd::ops::platforms::PLATFORM_avgpool2d_ENGINE_CPU avgpool2d; + sd::ops::platforms::PLATFORM_avgpool2d_bp_ENGINE_CPU avgpool2d_bp; - nd4j::ops::platforms::PLATFORM_maxpool2d_ENGINE_CPU maxpool2d; - nd4j::ops::platforms::PLATFORM_maxpool2d_bp_ENGINE_CPU maxpool2d_bp; + sd::ops::platforms::PLATFORM_maxpool2d_ENGINE_CPU maxpool2d; + sd::ops::platforms::PLATFORM_maxpool2d_bp_ENGINE_CPU maxpool2d_bp; - nd4j::ops::platforms::PLATFORM_avgpool3dnew_ENGINE_CPU avgpool3d; - nd4j::ops::platforms::PLATFORM_avgpool3dnew_bp_ENGINE_CPU avgpool3d_bp; + sd::ops::platforms::PLATFORM_avgpool3dnew_ENGINE_CPU avgpool3d; + sd::ops::platforms::PLATFORM_avgpool3dnew_bp_ENGINE_CPU avgpool3d_bp; - nd4j::ops::platforms::PLATFORM_maxpool3dnew_ENGINE_CPU maxpool3d; - nd4j::ops::platforms::PLATFORM_maxpool3dnew_bp_ENGINE_CPU maxpool3d_bp; + sd::ops::platforms::PLATFORM_maxpool3dnew_ENGINE_CPU maxpool3d; + sd::ops::platforms::PLATFORM_maxpool3dnew_bp_ENGINE_CPU maxpool3d_bp; - nd4j::ops::platforms::PLATFORM_lrn_ENGINE_CPU lrn; + sd::ops::platforms::PLATFORM_lrn_ENGINE_CPU lrn; - nd4j::ops::platforms::PLATFORM_batchnorm_ENGINE_CPU batchnorm; + sd::ops::platforms::PLATFORM_batchnorm_ENGINE_CPU batchnorm; - nd4j::ops::platforms::PLATFORM_matmul_ENGINE_CPU matmul; + sd::ops::platforms::PLATFORM_matmul_ENGINE_CPU matmul; + + sd::ops::platforms::PLATFORM_softmax_ENGINE_CPU softmax; + + sd::ops::platforms::PLATFORM_softmax_bp_ENGINE_CPU softmax_bp; + + sd::ops::platforms::PLATFORM_tanh_ENGINE_CPU tanh; + + sd::ops::platforms::PLATFORM_tanh_ENGINE_CPU tanh_bp; + + printer({&conv2d, &conv2d_bp, &conv3d, &conv3d_bp, &avgpool2d, &avgpool2d_bp, &maxpool2d, &maxpool2d_bp, &avgpool3d, &avgpool3d_bp, &maxpool3d, &maxpool3d_bp, &lrn, &batchnorm, &matmul, &softmax, &softmax_bp, &tanh, &tanh_bp }); - printer({&conv2d, &conv2d_bp, &conv3d, &conv3d_bp, &avgpool2d, &avgpool2d_bp, &maxpool2d, &maxpool2d_bp, &avgpool3d, &avgpool3d_bp, &maxpool3d, &maxpool3d_bp, &lrn, &batchnorm, &matmul}); #endif } \ No newline at end of file diff --git a/libnd4j/tests_cpu/layers_tests/MmapTests.cpp b/libnd4j/tests_cpu/layers_tests/MmapTests.cpp index b8b298f9b..c1df42fd1 100644 --- a/libnd4j/tests_cpu/layers_tests/MmapTests.cpp +++ b/libnd4j/tests_cpu/layers_tests/MmapTests.cpp @@ -20,12 +20,12 @@ #include "testlayers.h" #include -#include -#include +#include +#include #include -using namespace nd4j; -using namespace nd4j::graph; +using namespace sd; +using namespace sd::graph; class MmapTests : public testing::Test { public: diff --git a/libnd4j/tests_cpu/layers_tests/MultiDataTypeTests.cpp b/libnd4j/tests_cpu/layers_tests/MultiDataTypeTests.cpp index 127b3c7d3..db342771e 100644 --- a/libnd4j/tests_cpu/layers_tests/MultiDataTypeTests.cpp +++ b/libnd4j/tests_cpu/layers_tests/MultiDataTypeTests.cpp @@ -20,13 +20,13 @@ #include "testlayers.h" #include -#include -#include +#include +#include #include -#include +#include -using namespace nd4j; +using namespace sd; class MultiDataTypeTests : public testing::Test { public: @@ -35,23 +35,23 @@ public: //////////////////////////////////////////////////////////////////////////////// TEST_F(MultiDataTypeTests, DataTypeUtils_Test_1) { - auto dtype = DataTypeUtils::pickPairwiseResultType(nd4j::INT32, nd4j::FLOAT32); + auto dtype = DataTypeUtils::pickPairwiseResultType(sd::INT32, sd::FLOAT32); - ASSERT_EQ(nd4j::FLOAT32, dtype); + ASSERT_EQ(sd::FLOAT32, dtype); } //////////////////////////////////////////////////////////////////////////////// TEST_F(MultiDataTypeTests, DataTypeUtils_Test_2) { - auto dtype = DataTypeUtils::pickPairwiseResultType(nd4j::INT32, nd4j::DOUBLE); - ASSERT_EQ(nd4j::DOUBLE, dtype); + auto dtype = DataTypeUtils::pickPairwiseResultType(sd::INT32, sd::DOUBLE); + ASSERT_EQ(sd::DOUBLE, dtype); - ASSERT_EQ(nd4j::DOUBLE, DataTypeUtils::pickPairwiseResultType(nd4j::DOUBLE, nd4j::INT32)); + ASSERT_EQ(sd::DOUBLE, DataTypeUtils::pickPairwiseResultType(sd::DOUBLE, sd::INT32)); } //////////////////////////////////////////////////////////////////////////////// TEST_F(MultiDataTypeTests, DataTypeUtils_Test_3) { - auto dtype = DataTypeUtils::pickPairwiseResultType(nd4j::FLOAT32, nd4j::DOUBLE); - ASSERT_EQ(nd4j::FLOAT32, dtype); + auto dtype = DataTypeUtils::pickPairwiseResultType(sd::FLOAT32, sd::DOUBLE); + ASSERT_EQ(sd::FLOAT32, dtype); } //////////////////////////////////////////////////////////////////////////////// @@ -132,15 +132,13 @@ TEST_F(MultiDataTypeTests, Basic_Test_7) { auto y = NDArrayFactory::create('c', {2, 3}, {0.f, 1.f, 2.f, 3.f, 4.f, 5.f}); auto e = NDArrayFactory::create('c', {2, 3}, {0.f, 2.f, 4.f, 6.f, 8.f, 10.f}); - nd4j::ops::add op; + sd::ops::add op; auto result = op.evaluate({&x, &y}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_EQ(e, *z); - - delete result; } //////////////////////////////////////////////////////////////////////////////// @@ -159,8 +157,8 @@ TEST_F(MultiDataTypeTests, Basic_Test_6) { //////////////////////////////////////////////////////////////////////////////// TEST_F(MultiDataTypeTests, ndarray_assign_number_test1) { - NDArray x('c', {2, 3}, {0, 1, 2, 3, 4, 5}, nd4j::DataType::UINT8); - NDArray exp('c', {2, 3}, {10, 10, 10, 10, 10, 10}, nd4j::DataType::UINT8); + NDArray x('c', {2, 3}, {0, 1, 2, 3, 4, 5}, sd::DataType::UINT8); + NDArray exp('c', {2, 3}, {10, 10, 10, 10, 10, 10}, sd::DataType::UINT8); const double number = 10.8; x = number; @@ -170,8 +168,8 @@ TEST_F(MultiDataTypeTests, ndarray_assign_number_test1) { //////////////////////////////////////////////////////////////////////////////// TEST_F(MultiDataTypeTests, ndarray_assign_number_test2) { - NDArray x('c', {2, 3}, {0, 1, 2, 3, 4, 5}, nd4j::DataType::INT64); - NDArray exp('c', {2, 3}, {1, 1, 1, 1, 1, 1}, nd4j::DataType::INT64); + NDArray x('c', {2, 3}, {0, 1, 2, 3, 4, 5}, sd::DataType::INT64); + NDArray exp('c', {2, 3}, {1, 1, 1, 1, 1, 1}, sd::DataType::INT64); const bool number = 1000; x = number; @@ -181,8 +179,8 @@ TEST_F(MultiDataTypeTests, ndarray_assign_number_test2) { //////////////////////////////////////////////////////////////////////////////// TEST_F(MultiDataTypeTests, ndarray_assign_number_test3) { - NDArray x('c', {2, 3}, {0, 1, 0, 1, 0, 1}, nd4j::DataType::BOOL); - NDArray exp('c', {2, 3}, {1, 1, 1, 1, 1, 1}, nd4j::DataType::BOOL); + NDArray x('c', {2, 3}, {0, 1, 0, 1, 0, 1}, sd::DataType::BOOL); + NDArray exp('c', {2, 3}, {1, 1, 1, 1, 1, 1}, sd::DataType::BOOL); const int number = 1000; x = number; @@ -192,9 +190,9 @@ TEST_F(MultiDataTypeTests, ndarray_assign_number_test3) { //////////////////////////////////////////////////////////////////////////////// TEST_F(MultiDataTypeTests, ndarray_repeat_test1) { - NDArray x('c', {2, 2}, {0.5, 1.5, 2.5, 3.5}, nd4j::DataType::HALF); - NDArray y('c', {2, 4}, nd4j::DataType::HALF); - NDArray exp('c', {2, 4}, {0.5, 0.5, 1.5, 1.5, 2.5, 2.5, 3.5, 3.5}, nd4j::DataType::HALF); + NDArray x('c', {2, 2}, {0.5, 1.5, 2.5, 3.5}, sd::DataType::HALF); + NDArray y('c', {2, 4}, sd::DataType::HALF); + NDArray exp('c', {2, 4}, {0.5, 0.5, 1.5, 1.5, 2.5, 2.5, 3.5, 3.5}, sd::DataType::HALF); x.repeat(1, {2}, y); @@ -203,8 +201,8 @@ TEST_F(MultiDataTypeTests, ndarray_repeat_test1) { //////////////////////////////////////////////////////////////////////////////// TEST_F(MultiDataTypeTests, ndarray_bufferAsT_test1) { - NDArray x('f', {2}, {1.5, 3.5}, nd4j::DataType::FLOAT32); - NDArray y('c', {}, std::vector{1.5}, nd4j::DataType::FLOAT32); + NDArray x('f', {2}, {1.5, 3.5}, sd::DataType::FLOAT32); + NDArray y('c', {}, std::vector{1.5}, sd::DataType::FLOAT32); const int* buffX = x.bufferAsT(); const int* buffY = y.bufferAsT(); @@ -214,11 +212,11 @@ TEST_F(MultiDataTypeTests, ndarray_bufferAsT_test1) { //////////////////////////////////////////////////////////////////////////////// TEST_F(MultiDataTypeTests, ndarray_assign_test1) { - NDArray x('c', {2,2}, {0, 1, 2, 3}, nd4j::DataType::UINT8); - NDArray exp('c', {2,2}, {10, 10, 20, 20}, nd4j::DataType::UINT8); + NDArray x('c', {2,2}, {0, 1, 2, 3}, sd::DataType::UINT8); + NDArray exp('c', {2,2}, {10, 10, 20, 20}, sd::DataType::UINT8); - NDArray scalar1('c', {}, std::vector{10.5}, nd4j::DataType::FLOAT32); - NDArray scalar2('c', {}, std::vector{20.8}, nd4j::DataType::DOUBLE); + NDArray scalar1('c', {}, std::vector{10.5}, sd::DataType::FLOAT32); + NDArray scalar2('c', {}, std::vector{20.8}, sd::DataType::DOUBLE); x(0,{0}).assign(scalar1); x(1,{0}).assign(scalar2); @@ -232,67 +230,67 @@ TEST_F(MultiDataTypeTests, ndarray_assign_test1) { //////////////////////////////////////////////////////////////////////////////// TEST_F(MultiDataTypeTests, ndarray_reduceAlongDimension_test1) { - NDArray x('f', {2,2}, {0, 1.5, 2.5, 3.5}, nd4j::DataType::HALF); - NDArray exp1('c', {}, std::vector{3}, nd4j::DataType::INT64); - NDArray exp2('c', {1,1}, std::vector{1}, nd4j::DataType::INT64); - NDArray exp3('c', {2}, std::vector{1,2}, nd4j::DataType::INT64); + NDArray x('f', {2,2}, {0, 1.5, 2.5, 3.5}, sd::DataType::HALF); + NDArray exp1('c', {}, std::vector{3}, sd::DataType::INT64); + NDArray exp2('c', {1,1}, std::vector{1}, sd::DataType::INT64); + NDArray exp3('c', {2}, std::vector{1,2}, sd::DataType::INT64); - auto scalar1 = x.reduceAlongDimension(nd4j::reduce::CountNonZero, {}/*whole range*/); + auto scalar1 = x.reduceAlongDimension(sd::reduce::CountNonZero, {}/*whole range*/); ASSERT_EQ(scalar1, exp1); - auto scalar2 = x.reduceAlongDimension(nd4j::reduce::CountZero, {}/*whole range*/, true); + auto scalar2 = x.reduceAlongDimension(sd::reduce::CountZero, {}/*whole range*/, true); ASSERT_EQ(scalar2, exp2); - auto scalar3 = x.reduceAlongDimension(nd4j::reduce::CountNonZero, {1}); + auto scalar3 = x.reduceAlongDimension(sd::reduce::CountNonZero, {1}); ASSERT_EQ(scalar3, exp3); } //////////////////////////////////////////////////////////////////////////////// TEST_F(MultiDataTypeTests, ndarray_reduceAlongDimension_test2) { - NDArray x('c', {2, 2}, {0, 1, 2, 3}, nd4j::DataType::INT32); - NDArray exp1('c', {}, std::vector{1.5}, nd4j::DataType::FLOAT32); - NDArray exp2('c', {2}, {0.5,2.5}, nd4j::DataType::FLOAT32); + NDArray x('c', {2, 2}, {0, 1, 2, 3}, sd::DataType::INT32); + NDArray exp1('c', {}, std::vector{1.5}, sd::DataType::FLOAT32); + NDArray exp2('c', {2}, {0.5,2.5}, sd::DataType::FLOAT32); - auto scalar1 = x.reduceAlongDimension(nd4j::reduce::Mean, {}/*whole range*/); + auto scalar1 = x.reduceAlongDimension(sd::reduce::Mean, {}/*whole range*/); // scalar1->printShapeInfo(); // scalar1->printIndexedBuffer(); ASSERT_EQ(scalar1, exp1); - auto scalar2 = x.reduceAlongDimension(nd4j::reduce::Mean, {1}); + auto scalar2 = x.reduceAlongDimension(sd::reduce::Mean, {1}); ASSERT_EQ(scalar2, exp2); } //////////////////////////////////////////////////////////////////////////////// TEST_F(MultiDataTypeTests, ndarray_reduceAlongDimension_test3) { - NDArray x('c', {2, 2}, {0.5, 1.5, 2.5, 3.5}, nd4j::DataType::HALF); - NDArray exp1('c', {}, std::vector{8.}, nd4j::DataType::HALF); - NDArray exp2('c', {2}, {2.,6.}, nd4j::DataType::HALF); + NDArray x('c', {2, 2}, {0.5, 1.5, 2.5, 3.5}, sd::DataType::HALF); + NDArray exp1('c', {}, std::vector{8.}, sd::DataType::HALF); + NDArray exp2('c', {2}, {2.,6.}, sd::DataType::HALF); - auto scalar1 = x.reduceAlongDimension(nd4j::reduce::Sum, {}/*whole range*/); + auto scalar1 = x.reduceAlongDimension(sd::reduce::Sum, {}/*whole range*/); ASSERT_EQ(scalar1, exp1); - auto scalar2 = x.reduceAlongDimension(nd4j::reduce::Sum, {1}); + auto scalar2 = x.reduceAlongDimension(sd::reduce::Sum, {1}); ASSERT_EQ(scalar2, exp2); } //////////////////////////////////////////////////////////////////////////////// TEST_F(MultiDataTypeTests, ndarray_reduceAlongDimension_test4) { - NDArray x('c', {2, 2}, {10.5, 1.5, -2.5, -3.5}, nd4j::DataType::HALF); - NDArray exp1('c', {}, std::vector{1}, nd4j::DataType::BOOL); - NDArray exp2('c', {2}, std::vector{1, 0}, nd4j::DataType::BOOL); + NDArray x('c', {2, 2}, {10.5, 1.5, -2.5, -3.5}, sd::DataType::HALF); + NDArray exp1('c', {}, std::vector{1}, sd::DataType::BOOL); + NDArray exp2('c', {2}, std::vector{1, 0}, sd::DataType::BOOL); - auto scalar1 = x.reduceAlongDimension(nd4j::reduce::IsPositive, {}/*whole range*/); + auto scalar1 = x.reduceAlongDimension(sd::reduce::IsPositive, {}/*whole range*/); ASSERT_EQ(scalar1, exp1); - auto scalar2 = x.reduceAlongDimension(nd4j::reduce::IsPositive, {1}); + auto scalar2 = x.reduceAlongDimension(sd::reduce::IsPositive, {1}); ASSERT_EQ(scalar2, exp2); } //////////////////////////////////////////////////////////////////////////////// TEST_F(MultiDataTypeTests, ndarray_varianceNumber_test1) { - NDArray x('f', {2, 2}, {0, 1, 2, 3}, nd4j::DataType::INT64); - NDArray exp1('c', {}, std::vector{1.666666667}, nd4j::DataType::FLOAT32); - NDArray exp2('c', {}, std::vector{1.118033989}, nd4j::DataType::FLOAT32); + NDArray x('f', {2, 2}, {0, 1, 2, 3}, sd::DataType::INT64); + NDArray exp1('c', {}, std::vector{1.666666667}, sd::DataType::FLOAT32); + NDArray exp2('c', {}, std::vector{1.118033989}, sd::DataType::FLOAT32); auto scalar1 = x.varianceNumber(variance::SummaryStatsVariance); ASSERT_EQ(scalar1, exp1); @@ -306,11 +304,11 @@ TEST_F(MultiDataTypeTests, ndarray_operatorPlus_test1) { if (!Environment::getInstance()->isExperimentalBuild()) return; - NDArray x1('c', {2, 2}, {0, 1, 2, 3}, nd4j::DataType::INT64); - NDArray x2('c', {2, 2}, {-1, -2, -1, -2}, nd4j::DataType::FLOAT32); - NDArray x3('c', {2}, {-1, -2}, nd4j::DataType::FLOAT32); + NDArray x1('c', {2, 2}, {0, 1, 2, 3}, sd::DataType::INT64); + NDArray x2('c', {2, 2}, {-1, -2, -1, -2}, sd::DataType::FLOAT32); + NDArray x3('c', {2}, {-1, -2}, sd::DataType::FLOAT32); - NDArray exp('c', {2, 2}, {-1, -1, 1, 1}, nd4j::DataType::FLOAT32); + NDArray exp('c', {2, 2}, {-1, -1, 1, 1}, sd::DataType::FLOAT32); ASSERT_EQ(x1+x2, exp); ASSERT_EQ(x1+x3, exp); @@ -321,14 +319,14 @@ TEST_F(MultiDataTypeTests, ndarray_operatorPlus_test2) { if (!Environment::getInstance()->isExperimentalBuild()) return; - NDArray x1('c', {2, 2}, {0, 1, 2, 3}, nd4j::DataType::INT64); - NDArray x2('c', {2, 2}, {0, 1, 2, 3}, nd4j::DataType::FLOAT32); - NDArray x3('c', {2, 2}, {0, 1, 2, 3}, nd4j::DataType::HALF); + NDArray x1('c', {2, 2}, {0, 1, 2, 3}, sd::DataType::INT64); + NDArray x2('c', {2, 2}, {0, 1, 2, 3}, sd::DataType::FLOAT32); + NDArray x3('c', {2, 2}, {0, 1, 2, 3}, sd::DataType::HALF); const double val1 = -2; const int val2 = -2; - NDArray exp1('c', {2,2}, {-2, -1, 0, 1}, nd4j::DataType::DOUBLE); - NDArray exp2('c', {2,2}, {-2, -1, 0, 1}, nd4j::DataType::FLOAT32); - NDArray exp3('c', {2,2}, {-2, -1, 0, 1}, nd4j::DataType::HALF); + NDArray exp1('c', {2,2}, {-2, -1, 0, 1}, sd::DataType::DOUBLE); + NDArray exp2('c', {2,2}, {-2, -1, 0, 1}, sd::DataType::FLOAT32); + NDArray exp3('c', {2,2}, {-2, -1, 0, 1}, sd::DataType::HALF); ASSERT_EQ(x1+val1, exp1); ASSERT_EQ(val1+x1, exp1); @@ -345,11 +343,11 @@ TEST_F(MultiDataTypeTests, ndarray_operatorMinus_test1) { if (!Environment::getInstance()->isExperimentalBuild()) return; - NDArray x1('c', {2, 2}, {0, 1, 2, 3}, nd4j::DataType::INT64); - NDArray x2('c', {2, 2}, {-1, -2, -1, -2}, nd4j::DataType::HALF); - NDArray x3('c', {2}, {-1, -2}, nd4j::DataType::HALF); + NDArray x1('c', {2, 2}, {0, 1, 2, 3}, sd::DataType::INT64); + NDArray x2('c', {2, 2}, {-1, -2, -1, -2}, sd::DataType::HALF); + NDArray x3('c', {2}, {-1, -2}, sd::DataType::HALF); - NDArray exp('c', {2, 2}, {1, 3, 3, 5}, nd4j::DataType::HALF); + NDArray exp('c', {2, 2}, {1, 3, 3, 5}, sd::DataType::HALF); ASSERT_EQ(x1-x2, exp); ASSERT_EQ(x1-x3, exp); @@ -360,17 +358,17 @@ TEST_F(MultiDataTypeTests, ndarray_operatorMinus_test2) { if (!Environment::getInstance()->isExperimentalBuild()) return; - NDArray x1('c', {2, 2}, {0, 1, 2, 3}, nd4j::DataType::INT64); - NDArray x2('c', {2, 2}, {0, 1, 2, 3}, nd4j::DataType::FLOAT32); - NDArray x3('c', {2, 2}, {0, 1, 2, 3}, nd4j::DataType::HALF); + NDArray x1('c', {2, 2}, {0, 1, 2, 3}, sd::DataType::INT64); + NDArray x2('c', {2, 2}, {0, 1, 2, 3}, sd::DataType::FLOAT32); + NDArray x3('c', {2, 2}, {0, 1, 2, 3}, sd::DataType::HALF); const double val1 = 2; const int val2 = 2; - NDArray exp1('c', {2,2}, {-2, -1, 0, 1}, nd4j::DataType::DOUBLE); - NDArray exp2('c', {2,2}, {2, 1, 0, -1}, nd4j::DataType::DOUBLE); - NDArray exp3('c', {2,2}, {-2, -1, 0, 1}, nd4j::DataType::FLOAT32); - NDArray exp4('c', {2,2}, {-2, -1, 0, 1}, nd4j::DataType::HALF); - NDArray exp5('c', {2,2}, {2, 1, 0, -1}, nd4j::DataType::FLOAT32); - NDArray exp6('c', {2,2}, {2, 1, 0, -1}, nd4j::DataType::HALF); + NDArray exp1('c', {2,2}, {-2, -1, 0, 1}, sd::DataType::DOUBLE); + NDArray exp2('c', {2,2}, {2, 1, 0, -1}, sd::DataType::DOUBLE); + NDArray exp3('c', {2,2}, {-2, -1, 0, 1}, sd::DataType::FLOAT32); + NDArray exp4('c', {2,2}, {-2, -1, 0, 1}, sd::DataType::HALF); + NDArray exp5('c', {2,2}, {2, 1, 0, -1}, sd::DataType::FLOAT32); + NDArray exp6('c', {2,2}, {2, 1, 0, -1}, sd::DataType::HALF); ASSERT_EQ(x1-val1, exp1); ASSERT_EQ(val1-x1, exp2); @@ -387,11 +385,11 @@ TEST_F(MultiDataTypeTests, ndarray_operatorMultiply_test1) { if (!Environment::getInstance()->isExperimentalBuild()) return; - NDArray x1('c', {2, 2}, {0, 1, 2, 3}, nd4j::DataType::INT64); - NDArray x2('c', {2, 2}, {-1, -2, -1, -2}, nd4j::DataType::DOUBLE); - NDArray x3('c', {2}, {-1, -2}, nd4j::DataType::DOUBLE); + NDArray x1('c', {2, 2}, {0, 1, 2, 3}, sd::DataType::INT64); + NDArray x2('c', {2, 2}, {-1, -2, -1, -2}, sd::DataType::DOUBLE); + NDArray x3('c', {2}, {-1, -2}, sd::DataType::DOUBLE); - NDArray exp('c', {2, 2}, {0, -2, -2, -6}, nd4j::DataType::DOUBLE); + NDArray exp('c', {2, 2}, {0, -2, -2, -6}, sd::DataType::DOUBLE); ASSERT_EQ(x1*x2, exp); ASSERT_EQ(x1*x3, exp); @@ -402,14 +400,14 @@ TEST_F(MultiDataTypeTests, ndarray_operatorMultiply_test2) { if (!Environment::getInstance()->isExperimentalBuild()) return; - NDArray x1('c', {2, 2}, {0, 1, 2, 3}, nd4j::DataType::INT64); - NDArray x2('c', {2, 2}, {0, 1, 2, 3}, nd4j::DataType::FLOAT32); - NDArray x3('c', {2, 2}, {0, 1, 2, 3}, nd4j::DataType::HALF); + NDArray x1('c', {2, 2}, {0, 1, 2, 3}, sd::DataType::INT64); + NDArray x2('c', {2, 2}, {0, 1, 2, 3}, sd::DataType::FLOAT32); + NDArray x3('c', {2, 2}, {0, 1, 2, 3}, sd::DataType::HALF); const double val1 = -2; const int val2 = -2; - NDArray exp1('c', {2,2}, {0, -2, -4, -6}, nd4j::DataType::DOUBLE); - NDArray exp2('c', {2,2}, {0, -2, -4, -6}, nd4j::DataType::FLOAT32); - NDArray exp3('c', {2,2}, {0, -2, -4, -6}, nd4j::DataType::HALF); + NDArray exp1('c', {2,2}, {0, -2, -4, -6}, sd::DataType::DOUBLE); + NDArray exp2('c', {2,2}, {0, -2, -4, -6}, sd::DataType::FLOAT32); + NDArray exp3('c', {2,2}, {0, -2, -4, -6}, sd::DataType::HALF); ASSERT_EQ(x1*val1, exp1); ASSERT_EQ(val1*x1, exp1); @@ -427,12 +425,12 @@ TEST_F(MultiDataTypeTests, ndarray_operatorDivide_test1) { if (!Environment::getInstance()->isExperimentalBuild()) return; - NDArray x1('c', {2, 2}, {4, 1, 2, 3}, nd4j::DataType::HALF); - NDArray x2('c', {2, 2}, {-1, -2, -1, -9}, nd4j::DataType::DOUBLE); - NDArray x3('c', {2}, {-1, -2}, nd4j::DataType::FLOAT32); + NDArray x1('c', {2, 2}, {4, 1, 2, 3}, sd::DataType::HALF); + NDArray x2('c', {2, 2}, {-1, -2, -1, -9}, sd::DataType::DOUBLE); + NDArray x3('c', {2}, {-1, -2}, sd::DataType::FLOAT32); - NDArray exp1('c', {2, 2}, {-4, -0.5, -2, -0.3333333}, nd4j::DataType::HALF); - NDArray exp2('c', {2, 2}, {-0.25, -2, -0.5, -0.666667}, nd4j::DataType::HALF); + NDArray exp1('c', {2, 2}, {-4, -0.5, -2, -0.3333333}, sd::DataType::HALF); + NDArray exp2('c', {2, 2}, {-0.25, -2, -0.5, -0.666667}, sd::DataType::HALF); ASSERT_EQ(x1/x2, exp1); ASSERT_EQ(x3/x1, exp2); @@ -443,19 +441,19 @@ TEST_F(MultiDataTypeTests, ndarray_operatorDivide_test2) { if (!Environment::getInstance()->isExperimentalBuild()) return; - NDArray x1('c', {2, 2}, {1, 2, 3, 4}, nd4j::DataType::INT64); - NDArray x2('c', {2, 2}, {1, 2, 3, 4}, nd4j::DataType::FLOAT32); - NDArray x3('c', {2, 2}, {1, 2, 3, 4}, nd4j::DataType::HALF); + NDArray x1('c', {2, 2}, {1, 2, 3, 4}, sd::DataType::INT64); + NDArray x2('c', {2, 2}, {1, 2, 3, 4}, sd::DataType::FLOAT32); + NDArray x3('c', {2, 2}, {1, 2, 3, 4}, sd::DataType::HALF); const double val1 = 2; const int val2 = -2; - NDArray exp1('c', {2,2}, {0.5, 1, 1.5, 2}, nd4j::DataType::DOUBLE); - NDArray exp2('c', {2,2}, {2, 1, 0.666667, 0.5}, nd4j::DataType::DOUBLE); - NDArray exp3('c', {2,2}, {0, -1, -1, -2}, nd4j::DataType::INT64); - NDArray exp4('c', {2,2}, {-2, -1, 0., 0.}, nd4j::DataType::INT64); - NDArray exp5('c', {2,2}, {-0.5, -1, -1.5, -2}, nd4j::DataType::FLOAT32); - NDArray exp6('c', {2,2}, {-2, -1, -0.666667, -0.5}, nd4j::DataType::FLOAT32); - NDArray exp7('c', {2,2}, {0.5, 1, 1.5, 2}, nd4j::DataType::HALF); - NDArray exp8('c', {2,2}, {2, 1, 0.666667, 0.5}, nd4j::DataType::HALF); + NDArray exp1('c', {2,2}, {0.5, 1, 1.5, 2}, sd::DataType::DOUBLE); + NDArray exp2('c', {2,2}, {2, 1, 0.666667, 0.5}, sd::DataType::DOUBLE); + NDArray exp3('c', {2,2}, {0, -1, -1, -2}, sd::DataType::INT64); + NDArray exp4('c', {2,2}, {-2, -1, 0., 0.}, sd::DataType::INT64); + NDArray exp5('c', {2,2}, {-0.5, -1, -1.5, -2}, sd::DataType::FLOAT32); + NDArray exp6('c', {2,2}, {-2, -1, -0.666667, -0.5}, sd::DataType::FLOAT32); + NDArray exp7('c', {2,2}, {0.5, 1, 1.5, 2}, sd::DataType::HALF); + NDArray exp8('c', {2,2}, {2, 1, 0.666667, 0.5}, sd::DataType::HALF); ASSERT_EQ(x1/val1, exp1); ASSERT_EQ(val1/x1, exp2); @@ -475,21 +473,21 @@ TEST_F(MultiDataTypeTests, ndarray_operatorPlusEqual_test1) { if (!Environment::getInstance()->isExperimentalBuild()) return; - NDArray scalar1('c', {0}, std::vector{4}, nd4j::DataType::INT32); - NDArray scalar2('c', {0}, std::vector{1.5}, nd4j::DataType::HALF); + NDArray scalar1('c', {0}, std::vector{4}, sd::DataType::INT32); + NDArray scalar2('c', {0}, std::vector{1.5}, sd::DataType::HALF); - NDArray x1('c', {2,3}, {1.5, 2.5, 3.5, 4.5, 5.5, 6.5}, nd4j::DataType::FLOAT32); - NDArray x2('c', {3,2}, {10, 20, 30, 40, 50, 60}, nd4j::DataType::INT64); - NDArray x3('c', {2,2}, {0, 1, 2, 3}, nd4j::DataType::INT64); - NDArray x4('c', {2}, {0.4, 0.5}, nd4j::DataType::HALF); - NDArray x5('c', {2,2}, {0, 1, 2, 3}, nd4j::DataType::HALF); - NDArray x6('c', {2}, {0.4, 0.5}, nd4j::DataType::FLOAT32); + NDArray x1('c', {2,3}, {1.5, 2.5, 3.5, 4.5, 5.5, 6.5}, sd::DataType::FLOAT32); + NDArray x2('c', {3,2}, {10, 20, 30, 40, 50, 60}, sd::DataType::INT64); + NDArray x3('c', {2,2}, {0, 1, 2, 3}, sd::DataType::INT64); + NDArray x4('c', {2}, {0.4, 0.5}, sd::DataType::HALF); + NDArray x5('c', {2,2}, {0, 1, 2, 3}, sd::DataType::HALF); + NDArray x6('c', {2}, {0.4, 0.5}, sd::DataType::FLOAT32); - NDArray exp1('c', {0}, std::vector{5}, nd4j::DataType::INT32); - NDArray exp2('c', {0}, std::vector{6.5}, nd4j::DataType::HALF); - NDArray exp3('c', {3,2}, {11, 22, 33, 44, 55, 66}, nd4j::DataType::INT64); - NDArray exp4('c', {2,3}, {12.5, 24.5, 36.5, 48.5, 60.5, 72.5}, nd4j::DataType::FLOAT32); - NDArray exp5('c', {2,2}, {0.4, 1.5, 2.4, 3.5}, nd4j::DataType::HALF); + NDArray exp1('c', {0}, std::vector{5}, sd::DataType::INT32); + NDArray exp2('c', {0}, std::vector{6.5}, sd::DataType::HALF); + NDArray exp3('c', {3,2}, {11, 22, 33, 44, 55, 66}, sd::DataType::INT64); + NDArray exp4('c', {2,3}, {12.5, 24.5, 36.5, 48.5, 60.5, 72.5}, sd::DataType::FLOAT32); + NDArray exp5('c', {2,2}, {0.4, 1.5, 2.4, 3.5}, sd::DataType::HALF); scalar1 += scalar2; ASSERT_EQ(scalar1, exp1); @@ -515,19 +513,19 @@ TEST_F(MultiDataTypeTests, ndarray_operatorPlusEqual_test2) { if (!Environment::getInstance()->isExperimentalBuild()) return; - NDArray x1('c', {2,2}, {0, 1, 2, 3}, nd4j::DataType::FLOAT32); - NDArray x2('c', {2,2}, {0, 1, 2, 3}, nd4j::DataType::INT32); + NDArray x1('c', {2,2}, {0, 1, 2, 3}, sd::DataType::FLOAT32); + NDArray x2('c', {2,2}, {0, 1, 2, 3}, sd::DataType::INT32); const Nd4jLong val1 = 1; const float16 val2 = 1.5; const double val3 = 2.2; - NDArray exp1('c', {2,2}, {1, 2, 3, 4}, nd4j::DataType::FLOAT32); - NDArray exp2('c', {2,2}, {1, 2, 3, 4}, nd4j::DataType::INT32); - NDArray exp3('c', {2,2}, {2.5, 3.5, 4.5, 5.5}, nd4j::DataType::FLOAT32); - NDArray exp4('c', {2,2}, {2, 3, 4.5, 5}, nd4j::DataType::INT32); - NDArray exp5('c', {2,2}, {4.7, 5.7, 6.7, 7.7}, nd4j::DataType::FLOAT32); - NDArray exp6('c', {2,2}, {4, 5, 6, 7}, nd4j::DataType::INT32); + NDArray exp1('c', {2,2}, {1, 2, 3, 4}, sd::DataType::FLOAT32); + NDArray exp2('c', {2,2}, {1, 2, 3, 4}, sd::DataType::INT32); + NDArray exp3('c', {2,2}, {2.5, 3.5, 4.5, 5.5}, sd::DataType::FLOAT32); + NDArray exp4('c', {2,2}, {2, 3, 4.5, 5}, sd::DataType::INT32); + NDArray exp5('c', {2,2}, {4.7, 5.7, 6.7, 7.7}, sd::DataType::FLOAT32); + NDArray exp6('c', {2,2}, {4, 5, 6, 7}, sd::DataType::INT32); x1 += val1; ASSERT_EQ(x1, exp1); @@ -553,21 +551,21 @@ TEST_F(MultiDataTypeTests, ndarray_operatorMinusEqual_test1) { if (!Environment::getInstance()->isExperimentalBuild()) return; - NDArray scalar1('c', {0}, std::vector{4}, nd4j::DataType::INT32); - NDArray scalar2('c', {0}, std::vector{1.5}, nd4j::DataType::HALF); + NDArray scalar1('c', {0}, std::vector{4}, sd::DataType::INT32); + NDArray scalar2('c', {0}, std::vector{1.5}, sd::DataType::HALF); - NDArray x1('c', {2,3}, {1.5, 2.5, 3.5, 4.5, 5.5, 6.5}, nd4j::DataType::FLOAT32); - NDArray x2('c', {3,2}, {10, 20, 30, 40, 50, 60}, nd4j::DataType::INT64); - NDArray x3('c', {2,2}, {0, 1, 2, 3}, nd4j::DataType::INT64); - NDArray x4('c', {2}, {0.4, 0.5}, nd4j::DataType::HALF); - NDArray x5('c', {2,2}, {0, 1, 2, 3}, nd4j::DataType::HALF); - NDArray x6('c', {2}, {0.4, 0.5}, nd4j::DataType::FLOAT32); + NDArray x1('c', {2,3}, {1.5, 2.5, 3.5, 4.5, 5.5, 6.5}, sd::DataType::FLOAT32); + NDArray x2('c', {3,2}, {10, 20, 30, 40, 50, 60}, sd::DataType::INT64); + NDArray x3('c', {2,2}, {0, 1, 2, 3}, sd::DataType::INT64); + NDArray x4('c', {2}, {0.4, 0.5}, sd::DataType::HALF); + NDArray x5('c', {2,2}, {0, 1, 2, 3}, sd::DataType::HALF); + NDArray x6('c', {2}, {0.4, 0.5}, sd::DataType::FLOAT32); - NDArray exp1('c', {0}, std::vector{2}, nd4j::DataType::INT32); - NDArray exp2('c', {0}, std::vector{-0.5}, nd4j::DataType::HALF); - NDArray exp3('c', {3,2}, {8, 17, 26, 35, 44, 53}, nd4j::DataType::INT64); - NDArray exp4('c', {2,3}, {-6.5, -14.5, -22.5, -30.5, -38.5, -46.5}, nd4j::DataType::FLOAT32); - NDArray exp5('c', {2,2}, {0.4, -0.5, -1.6, -2.5}, nd4j::DataType::HALF); + NDArray exp1('c', {0}, std::vector{2}, sd::DataType::INT32); + NDArray exp2('c', {0}, std::vector{-0.5}, sd::DataType::HALF); + NDArray exp3('c', {3,2}, {8, 17, 26, 35, 44, 53}, sd::DataType::INT64); + NDArray exp4('c', {2,3}, {-6.5, -14.5, -22.5, -30.5, -38.5, -46.5}, sd::DataType::FLOAT32); + NDArray exp5('c', {2,2}, {0.4, -0.5, -1.6, -2.5}, sd::DataType::HALF); scalar1 -= scalar2; ASSERT_EQ(scalar1, exp1); @@ -593,19 +591,19 @@ TEST_F(MultiDataTypeTests, ndarray_operatorMinusEqual_test2) { if (!Environment::getInstance()->isExperimentalBuild()) return; - NDArray x1('c', {2,2}, {0, 1, 2, 3}, nd4j::DataType::FLOAT32); - NDArray x2('c', {2,2}, {0, 1, 2, 3}, nd4j::DataType::INT32); + NDArray x1('c', {2,2}, {0, 1, 2, 3}, sd::DataType::FLOAT32); + NDArray x2('c', {2,2}, {0, 1, 2, 3}, sd::DataType::INT32); const Nd4jLong val1 = 1; const float16 val2 = 1.5; const double val3 = 2.2; - NDArray exp1('c', {2,2}, {-1, 0, 1, 2}, nd4j::DataType::FLOAT32); - NDArray exp2('c', {2,2}, {-1, 0, 1, 2}, nd4j::DataType::INT32); - NDArray exp3('c', {2,2}, {-2.5, -1.5, -0.5, 0.5}, nd4j::DataType::FLOAT32); - NDArray exp4('c', {2,2}, {-2., -1., 0., 0.}, nd4j::DataType::INT32); - NDArray exp5('c', {2,2}, {-4.7, -3.7, -2.7, -1.7}, nd4j::DataType::FLOAT32); - NDArray exp6('c', {2,2}, {-4, -3, -2, -2}, nd4j::DataType::INT32); + NDArray exp1('c', {2,2}, {-1, 0, 1, 2}, sd::DataType::FLOAT32); + NDArray exp2('c', {2,2}, {-1, 0, 1, 2}, sd::DataType::INT32); + NDArray exp3('c', {2,2}, {-2.5, -1.5, -0.5, 0.5}, sd::DataType::FLOAT32); + NDArray exp4('c', {2,2}, {-2., -1., 0., 0.}, sd::DataType::INT32); + NDArray exp5('c', {2,2}, {-4.7, -3.7, -2.7, -1.7}, sd::DataType::FLOAT32); + NDArray exp6('c', {2,2}, {-4, -3, -2, -2}, sd::DataType::INT32); x1 -= val1; ASSERT_EQ(x1, exp1); @@ -631,21 +629,21 @@ TEST_F(MultiDataTypeTests, ndarray_operatorMultiplyEqual_test1) { if (!Environment::getInstance()->isExperimentalBuild()) return; - NDArray scalar1('c', {0}, std::vector{3}, nd4j::DataType::INT32); - NDArray scalar2('c', {0}, std::vector{2.5}, nd4j::DataType::HALF); + NDArray scalar1('c', {0}, std::vector{3}, sd::DataType::INT32); + NDArray scalar2('c', {0}, std::vector{2.5}, sd::DataType::HALF); - NDArray x1('c', {2,3}, {1.5, 2.5, 3.5, 4.5, 5.5, 6.5}, nd4j::DataType::FLOAT32); - NDArray x2('c', {3,2}, {1, 2, 3, 4, 5, 6}, nd4j::DataType::INT64); - NDArray x3('c', {2,2}, {0, 1, 2, 3}, nd4j::DataType::INT64); - NDArray x4('c', {2}, {0.4, 0.5}, nd4j::DataType::HALF); - NDArray x5('c', {2,2}, {0, 1, 2, 3}, nd4j::DataType::HALF); - NDArray x6('c', {2}, {0.4, 0.5}, nd4j::DataType::FLOAT32); + NDArray x1('c', {2,3}, {1.5, 2.5, 3.5, 4.5, 5.5, 6.5}, sd::DataType::FLOAT32); + NDArray x2('c', {3,2}, {1, 2, 3, 4, 5, 6}, sd::DataType::INT64); + NDArray x3('c', {2,2}, {0, 1, 2, 3}, sd::DataType::INT64); + NDArray x4('c', {2}, {0.4, 0.5}, sd::DataType::HALF); + NDArray x5('c', {2,2}, {0, 1, 2, 3}, sd::DataType::HALF); + NDArray x6('c', {2}, {0.4, 0.5}, sd::DataType::FLOAT32); - NDArray exp1('c', {0}, std::vector{7}, nd4j::DataType::INT32); - NDArray exp2('c', {0}, std::vector{17.5}, nd4j::DataType::HALF); - NDArray exp3('c', {3,2}, {1, 5, 10, 18, 27, 39}, nd4j::DataType::INT64); - NDArray exp4('c', {2,3}, {1.5, 12.5, 35, 81, 148.5, 253.5}, nd4j::DataType::FLOAT32); - NDArray exp5('c', {2,2}, {0., 0.5, 0.8, 1.5}, nd4j::DataType::HALF); + NDArray exp1('c', {0}, std::vector{7}, sd::DataType::INT32); + NDArray exp2('c', {0}, std::vector{17.5}, sd::DataType::HALF); + NDArray exp3('c', {3,2}, {1, 5, 10, 18, 27, 39}, sd::DataType::INT64); + NDArray exp4('c', {2,3}, {1.5, 12.5, 35, 81, 148.5, 253.5}, sd::DataType::FLOAT32); + NDArray exp5('c', {2,2}, {0., 0.5, 0.8, 1.5}, sd::DataType::HALF); scalar1 *= scalar2; ASSERT_EQ(scalar1, exp1); @@ -671,19 +669,19 @@ TEST_F(MultiDataTypeTests, ndarray_operatorMultiplyEqual_test2) { if (!Environment::getInstance()->isExperimentalBuild()) return; - NDArray x1('c', {2,2}, {0, 1, 2, 3}, nd4j::DataType::FLOAT32); - NDArray x2('c', {2,2}, {0, 1, 2, 3}, nd4j::DataType::INT32); + NDArray x1('c', {2,2}, {0, 1, 2, 3}, sd::DataType::FLOAT32); + NDArray x2('c', {2,2}, {0, 1, 2, 3}, sd::DataType::INT32); const Nd4jLong val1 = 1; const float16 val2 = 1.5; const double val3 = 2.2; - NDArray exp1('c', {2,2}, {0, 1, 2, 3}, nd4j::DataType::FLOAT32); - NDArray exp2('c', {2,2}, {0, 1, 2, 3}, nd4j::DataType::INT32); - NDArray exp3('c', {2,2}, {0, 1.5, 3, 4.5}, nd4j::DataType::FLOAT32); - NDArray exp4('c', {2,2}, {0, 1, 3, 4}, nd4j::DataType::INT32); - NDArray exp5('c', {2,2}, {0, 3.3, 6.6, 9.9}, nd4j::DataType::FLOAT32); - NDArray exp6('c', {2,2}, {0, 2, 6, 8}, nd4j::DataType::INT32); + NDArray exp1('c', {2,2}, {0, 1, 2, 3}, sd::DataType::FLOAT32); + NDArray exp2('c', {2,2}, {0, 1, 2, 3}, sd::DataType::INT32); + NDArray exp3('c', {2,2}, {0, 1.5, 3, 4.5}, sd::DataType::FLOAT32); + NDArray exp4('c', {2,2}, {0, 1, 3, 4}, sd::DataType::INT32); + NDArray exp5('c', {2,2}, {0, 3.3, 6.6, 9.9}, sd::DataType::FLOAT32); + NDArray exp6('c', {2,2}, {0, 2, 6, 8}, sd::DataType::INT32); x1 *= val1; ASSERT_EQ(x1, exp1); @@ -709,21 +707,21 @@ TEST_F(MultiDataTypeTests, ndarray_operatorDivideEqual_test1) { if (!Environment::getInstance()->isExperimentalBuild()) return; - NDArray scalar1('c', {0}, std::vector{3}, nd4j::DataType::INT32); - NDArray scalar2('c', {0}, std::vector{2.5}, nd4j::DataType::HALF); + NDArray scalar1('c', {0}, std::vector{3}, sd::DataType::INT32); + NDArray scalar2('c', {0}, std::vector{2.5}, sd::DataType::HALF); - NDArray x1('c', {2,3}, {1.5, 2.5, 3.5, 4.5, 5.5, 6.5}, nd4j::DataType::FLOAT32); - NDArray x2('c', {3,2}, {10, 20, 30, 40, 50, 60}, nd4j::DataType::INT64); - NDArray x3('c', {2,2}, {1, 2, 3, 4}, nd4j::DataType::INT64); - NDArray x4('c', {2}, {0.4, 0.5}, nd4j::DataType::HALF); - NDArray x5('c', {2,2}, {1, 2, 3, 4}, nd4j::DataType::HALF); - NDArray x6('c', {2}, {0.4, 0.5}, nd4j::DataType::FLOAT32); + NDArray x1('c', {2,3}, {1.5, 2.5, 3.5, 4.5, 5.5, 6.5}, sd::DataType::FLOAT32); + NDArray x2('c', {3,2}, {10, 20, 30, 40, 50, 60}, sd::DataType::INT64); + NDArray x3('c', {2,2}, {1, 2, 3, 4}, sd::DataType::INT64); + NDArray x4('c', {2}, {0.4, 0.5}, sd::DataType::HALF); + NDArray x5('c', {2,2}, {1, 2, 3, 4}, sd::DataType::HALF); + NDArray x6('c', {2}, {0.4, 0.5}, sd::DataType::FLOAT32); - NDArray exp1('c', {0}, std::vector{1}, nd4j::DataType::INT32); - NDArray exp2('c', {0}, std::vector{2.5}, nd4j::DataType::HALF); - NDArray exp3('c', {3,2}, {6, 8, 8, 8, 9, 9}, nd4j::DataType::INT64); - NDArray exp4('c', {2,3}, {0.25, 0.3125, 0.4375, 0.5625, 0.611111111, 0.722222222}, nd4j::DataType::FLOAT32); - NDArray exp5('c', {2,2}, {0.4, 0.25, 0.1333333, 0.125}, nd4j::DataType::HALF); + NDArray exp1('c', {0}, std::vector{1}, sd::DataType::INT32); + NDArray exp2('c', {0}, std::vector{2.5}, sd::DataType::HALF); + NDArray exp3('c', {3,2}, {6, 8, 8, 8, 9, 9}, sd::DataType::INT64); + NDArray exp4('c', {2,3}, {0.25, 0.3125, 0.4375, 0.5625, 0.611111111, 0.722222222}, sd::DataType::FLOAT32); + NDArray exp5('c', {2,2}, {0.4, 0.25, 0.1333333, 0.125}, sd::DataType::HALF); scalar1 /= scalar2; ASSERT_EQ(scalar1, exp1); @@ -749,19 +747,19 @@ TEST_F(MultiDataTypeTests, ndarray_operatorDivideEqual_test2) { if (!Environment::getInstance()->isExperimentalBuild()) return; - NDArray x1('c', {2,2}, {0, 2, 4, 6}, nd4j::DataType::FLOAT32); - NDArray x2('c', {2,2}, {0, 2, 4, 6}, nd4j::DataType::INT32); + NDArray x1('c', {2,2}, {0, 2, 4, 6}, sd::DataType::FLOAT32); + NDArray x2('c', {2,2}, {0, 2, 4, 6}, sd::DataType::INT32); const Nd4jLong val1 = 1; const float16 val2 = 2.; const double val3 = 2.2; - NDArray exp1('c', {2,2}, {0, 2, 4, 6}, nd4j::DataType::FLOAT32); - NDArray exp2('c', {2,2}, {0, 2, 4, 6}, nd4j::DataType::INT32); - NDArray exp3('c', {2,2}, {0, 1, 2, 3}, nd4j::DataType::FLOAT32); - NDArray exp4('c', {2,2}, {0, 1, 2, 3}, nd4j::DataType::INT32); - NDArray exp5('c', {2,2}, {0, 0.45454545, 0.909090909, 1.363636364}, nd4j::DataType::FLOAT32); - NDArray exp6('c', {2,2}, {0, 0, 0, 1}, nd4j::DataType::INT32); + NDArray exp1('c', {2,2}, {0, 2, 4, 6}, sd::DataType::FLOAT32); + NDArray exp2('c', {2,2}, {0, 2, 4, 6}, sd::DataType::INT32); + NDArray exp3('c', {2,2}, {0, 1, 2, 3}, sd::DataType::FLOAT32); + NDArray exp4('c', {2,2}, {0, 1, 2, 3}, sd::DataType::INT32); + NDArray exp5('c', {2,2}, {0, 0.45454545, 0.909090909, 1.363636364}, sd::DataType::FLOAT32); + NDArray exp6('c', {2,2}, {0, 0, 0, 1}, sd::DataType::INT32); x1 /= val1; ASSERT_EQ(x1, exp1); @@ -787,15 +785,15 @@ TEST_F(MultiDataTypeTests, ndarray_reduceNumberFloat_test1) { if (!Environment::getInstance()->isExperimentalBuild()) return; - NDArray x1('c', {2,2}, {0, 1, 2, 3}, nd4j::DataType::INT64); - NDArray x2('c', {2,2}, {0.5, 1.5, 2.5, 3.5}, nd4j::DataType::HALF); - NDArray x3('c', {2,2}, {0.5, 1.5, 2.5, 3.5}, nd4j::DataType::DOUBLE); - NDArray x4('c', {2,2}, {0, 1, 0, 1}, nd4j::DataType::BOOL); + NDArray x1('c', {2,2}, {0, 1, 2, 3}, sd::DataType::INT64); + NDArray x2('c', {2,2}, {0.5, 1.5, 2.5, 3.5}, sd::DataType::HALF); + NDArray x3('c', {2,2}, {0.5, 1.5, 2.5, 3.5}, sd::DataType::DOUBLE); + NDArray x4('c', {2,2}, {0, 1, 0, 1}, sd::DataType::BOOL); - NDArray exp1('c', {0}, std::vector{1.5}, nd4j::DataType::FLOAT32); - NDArray exp2('c', {0}, std::vector{2}, nd4j::DataType::HALF); - NDArray exp3('c', {0}, std::vector{2}, nd4j::DataType::DOUBLE); - NDArray exp4('c', {0}, std::vector{0.25},nd4j::DataType::FLOAT32); + NDArray exp1('c', {0}, std::vector{1.5}, sd::DataType::FLOAT32); + NDArray exp2('c', {0}, std::vector{2}, sd::DataType::HALF); + NDArray exp3('c', {0}, std::vector{2}, sd::DataType::DOUBLE); + NDArray exp4('c', {0}, std::vector{0.25},sd::DataType::FLOAT32); NDArray scalar = x1.reduceNumber(reduce::Mean); @@ -824,15 +822,15 @@ TEST_F(MultiDataTypeTests, ndarray_reduceNumberSame_test1) { if (!Environment::getInstance()->isExperimentalBuild()) return; - NDArray x1('c', {2,2}, {0, 1, 2, 3}, nd4j::DataType::INT64); - NDArray x2('c', {2,2}, {0.5, 1.5, 2.5, 3.5}, nd4j::DataType::HALF); - NDArray x3('c', {2,2}, {0.5, 1.5, 2.5, 3.5}, nd4j::DataType::DOUBLE); - NDArray x4('c', {2,2}, {0, 1, 0, 1}, nd4j::DataType::BOOL); + NDArray x1('c', {2,2}, {0, 1, 2, 3}, sd::DataType::INT64); + NDArray x2('c', {2,2}, {0.5, 1.5, 2.5, 3.5}, sd::DataType::HALF); + NDArray x3('c', {2,2}, {0.5, 1.5, 2.5, 3.5}, sd::DataType::DOUBLE); + NDArray x4('c', {2,2}, {0, 1, 0, 1}, sd::DataType::BOOL); - NDArray exp1('c', {0}, std::vector{6}, nd4j::DataType::INT64); - NDArray exp2('c', {0}, std::vector{8}, nd4j::DataType::HALF); - NDArray exp3('c', {0}, std::vector{8}, nd4j::DataType::DOUBLE); - NDArray exp4('c', {0}, std::vector{1}, nd4j::DataType::BOOL); + NDArray exp1('c', {0}, std::vector{6}, sd::DataType::INT64); + NDArray exp2('c', {0}, std::vector{8}, sd::DataType::HALF); + NDArray exp3('c', {0}, std::vector{8}, sd::DataType::DOUBLE); + NDArray exp4('c', {0}, std::vector{1}, sd::DataType::BOOL); NDArray scalar = x1.reduceNumber(reduce::Sum); @@ -861,12 +859,12 @@ TEST_F(MultiDataTypeTests, ndarray_reduceNumberBool_test1) { if (!Environment::getInstance()->isExperimentalBuild()) return; - NDArray x1('c', {2,2}, {0, -1, 2, -3}, nd4j::DataType::INT64); - NDArray x2('c', {2,2}, {0.5, -1.5, 2.5, -3.5}, nd4j::DataType::HALF); - NDArray x3('c', {2,2}, {0.5, 1.5, 2.5, 3.5}, nd4j::DataType::DOUBLE); - NDArray x4('c', {2,2}, {-2, -1, 0, 1}, nd4j::DataType::BOOL); + NDArray x1('c', {2,2}, {0, -1, 2, -3}, sd::DataType::INT64); + NDArray x2('c', {2,2}, {0.5, -1.5, 2.5, -3.5}, sd::DataType::HALF); + NDArray x3('c', {2,2}, {0.5, 1.5, 2.5, 3.5}, sd::DataType::DOUBLE); + NDArray x4('c', {2,2}, {-2, -1, 0, 1}, sd::DataType::BOOL); - NDArray exp1('c', {0}, std::vector{1}, nd4j::DataType::BOOL); + NDArray exp1('c', {0}, std::vector{1}, sd::DataType::BOOL); NDArray scalar = x1.reduceNumber(reduce::IsFinite); ASSERT_EQ(scalar, exp1); @@ -894,15 +892,15 @@ TEST_F(MultiDataTypeTests, ndarray_reduceNumberLong_test1) { if (!Environment::getInstance()->isExperimentalBuild()) return; - NDArray x1('c', {2,2}, {0, 1, 2, 3}, nd4j::DataType::INT64); - NDArray x2('c', {2,2}, {0.5, 1.5, 2.5, 3.5}, nd4j::DataType::HALF); - NDArray x3('c', {2,2}, {0.5, -1.5, 0, 3.5}, nd4j::DataType::DOUBLE); - NDArray x4('c', {2,2}, {0, 1, 0, 1}, nd4j::DataType::BOOL); + NDArray x1('c', {2,2}, {0, 1, 2, 3}, sd::DataType::INT64); + NDArray x2('c', {2,2}, {0.5, 1.5, 2.5, 3.5}, sd::DataType::HALF); + NDArray x3('c', {2,2}, {0.5, -1.5, 0, 3.5}, sd::DataType::DOUBLE); + NDArray x4('c', {2,2}, {0, 1, 0, 1}, sd::DataType::BOOL); - NDArray exp1('c', {0}, std::vector{3}, nd4j::DataType::INT64); - NDArray exp2('c', {0}, std::vector{4}, nd4j::DataType::INT64); - NDArray exp3('c', {0}, std::vector{3}, nd4j::DataType::INT64); - NDArray exp4('c', {0}, std::vector{2}, nd4j::DataType::INT64); + NDArray exp1('c', {0}, std::vector{3}, sd::DataType::INT64); + NDArray exp2('c', {0}, std::vector{4}, sd::DataType::INT64); + NDArray exp3('c', {0}, std::vector{3}, sd::DataType::INT64); + NDArray exp4('c', {0}, std::vector{2}, sd::DataType::INT64); NDArray scalar = x1.reduceNumber(reduce::CountNonZero); ASSERT_EQ(scalar, exp1); @@ -930,21 +928,21 @@ TEST_F(MultiDataTypeTests, ndarray_indexReduceNumber_test1) { if (!Environment::getInstance()->isExperimentalBuild()) return; - NDArray x1('c', {2,2}, {0, 1, 2, 3}, nd4j::DataType::INT32); - NDArray x2('c', {2,2}, {0.5, 1.5, -4.5, 3.5}, nd4j::DataType::HALF); - NDArray x3('c', {2,2}, {0, -1, 0, 1}, nd4j::DataType::BOOL); + NDArray x1('c', {2,2}, {0, 1, 2, 3}, sd::DataType::INT32); + NDArray x2('c', {2,2}, {0.5, 1.5, -4.5, 3.5}, sd::DataType::HALF); + NDArray x3('c', {2,2}, {0, -1, 0, 1}, sd::DataType::BOOL); - NDArray exp1('c', {0}, std::vector{3}, nd4j::DataType::INT64); - NDArray exp2('c', {0}, std::vector{2}, nd4j::DataType::INT64); - NDArray exp3('c', {0}, std::vector{1}, nd4j::DataType::INT64); + NDArray exp1('c', {0}, std::vector{3}, sd::DataType::INT64); + NDArray exp2('c', {0}, std::vector{2}, sd::DataType::INT64); + NDArray exp3('c', {0}, std::vector{1}, sd::DataType::INT64); - NDArray scalar = x1.indexReduceNumber(nd4j::indexreduce::IndexAbsoluteMax); + NDArray scalar = x1.indexReduceNumber(sd::indexreduce::IndexAbsoluteMax); ASSERT_EQ(scalar, exp1); - scalar = x2.indexReduceNumber(nd4j::indexreduce::IndexAbsoluteMax); + scalar = x2.indexReduceNumber(sd::indexreduce::IndexAbsoluteMax); ASSERT_EQ(scalar, exp2); - scalar = x3.indexReduceNumber(nd4j::indexreduce::IndexAbsoluteMax); + scalar = x3.indexReduceNumber(sd::indexreduce::IndexAbsoluteMax); ASSERT_EQ(scalar, exp3); } @@ -953,36 +951,36 @@ TEST_F(MultiDataTypeTests, ndarray_applyTransformFloat_test1) { if (!Environment::getInstance()->isExperimentalBuild()) return; - NDArray x1('c', {2,2}, {0, 4, 9, 16}, nd4j::DataType::INT64); - NDArray x2('c', {2,2}, {0, 2.25, 6.25, 12.25}, nd4j::DataType::HALF); - NDArray x3('c', {2,2}, {0, 2.25, 6.25, 12.25}, nd4j::DataType::DOUBLE); - NDArray x4('c', {2,2}, {0, 1, 0, 1}, nd4j::DataType::BOOL); + NDArray x1('c', {2,2}, {0, 4, 9, 16}, sd::DataType::INT64); + NDArray x2('c', {2,2}, {0, 2.25, 6.25, 12.25}, sd::DataType::HALF); + NDArray x3('c', {2,2}, {0, 2.25, 6.25, 12.25}, sd::DataType::DOUBLE); + NDArray x4('c', {2,2}, {0, 1, 0, 1}, sd::DataType::BOOL); - NDArray exp1('c', {2,2}, {0, 2, 3, 4}, nd4j::DataType::FLOAT32); - NDArray exp2('c', {2,2}, {0, 1.5, 2.5, 3.5}, nd4j::DataType::DOUBLE); - NDArray exp3('c', {2,2}, {0, 1.5, 2.5, 3.5}, nd4j::DataType::HALF); - NDArray exp4('c', {2,2}, {0, 1, 0, 1}, nd4j::DataType::HALF); + NDArray exp1('c', {2,2}, {0, 2, 3, 4}, sd::DataType::FLOAT32); + NDArray exp2('c', {2,2}, {0, 1.5, 2.5, 3.5}, sd::DataType::DOUBLE); + NDArray exp3('c', {2,2}, {0, 1.5, 2.5, 3.5}, sd::DataType::HALF); + NDArray exp4('c', {2,2}, {0, 1, 0, 1}, sd::DataType::HALF); - NDArray result1('c', {2,2}, nd4j::DataType::FLOAT32); - NDArray result2('c', {2,2}, nd4j::DataType::DOUBLE); - NDArray result3('c', {2,2}, nd4j::DataType::HALF); + NDArray result1('c', {2,2}, sd::DataType::FLOAT32); + NDArray result2('c', {2,2}, sd::DataType::DOUBLE); + NDArray result3('c', {2,2}, sd::DataType::HALF); - x1.applyTransform(nd4j::transform::Sqrt, result1); + x1.applyTransform(sd::transform::Sqrt, result1); ASSERT_EQ(result1, exp1); - x2.applyTransform(nd4j::transform::Sqrt, result2); + x2.applyTransform(sd::transform::Sqrt, result2); ASSERT_EQ(result2, exp2); - x3.applyTransform(nd4j::transform::Sqrt, result3); + x3.applyTransform(sd::transform::Sqrt, result3); ASSERT_EQ(result3, exp3); - x4.applyTransform(nd4j::transform::Sqrt, result3); + x4.applyTransform(sd::transform::Sqrt, result3); ASSERT_EQ(result3, exp4); - x2.applyTransform(nd4j::transform::Sqrt, x2); + x2.applyTransform(sd::transform::Sqrt, x2); ASSERT_EQ(x2, exp3); - x3.applyTransform(nd4j::transform::Sqrt, x3); + x3.applyTransform(sd::transform::Sqrt, x3); ASSERT_EQ(x3, exp2); } @@ -991,43 +989,43 @@ TEST_F(MultiDataTypeTests, ndarray_applyTransformSame_test1) { if (!Environment::getInstance()->isExperimentalBuild()) return; - NDArray x1('c', {2,2}, {0, 1, 2, 3}, nd4j::DataType::INT64); - NDArray x2('c', {2,2}, {0, 1.5, 2.5, 3.5}, nd4j::DataType::HALF); - NDArray x3('c', {2,2}, {0, 1.5, 2.5, 3.5}, nd4j::DataType::DOUBLE); - NDArray x4('c', {2,2}, {0, 1, 0, 1}, nd4j::DataType::BOOL); - NDArray x5('c', {2,3}, {0, 1.5, 2.5, 3.5, 4.5, 5.5}, nd4j::DataType::DOUBLE); + NDArray x1('c', {2,2}, {0, 1, 2, 3}, sd::DataType::INT64); + NDArray x2('c', {2,2}, {0, 1.5, 2.5, 3.5}, sd::DataType::HALF); + NDArray x3('c', {2,2}, {0, 1.5, 2.5, 3.5}, sd::DataType::DOUBLE); + NDArray x4('c', {2,2}, {0, 1, 0, 1}, sd::DataType::BOOL); + NDArray x5('c', {2,3}, {0, 1.5, 2.5, 3.5, 4.5, 5.5}, sd::DataType::DOUBLE); - NDArray exp1('c', {2,2}, {0, 1, 4, 9}, nd4j::DataType::INT64); - NDArray exp2('c', {2,2}, {0, 2.25, 6.25, 12.25}, nd4j::DataType::HALF); - NDArray exp3('c', {2,2}, {0, 2.25, 6.25, 12.25}, nd4j::DataType::DOUBLE); - NDArray exp4('c', {2,2}, {0, 1, 0, 1}, nd4j::DataType::BOOL); - NDArray exp5('c', {3,2}, {0, 2.25, 6.25, 12.25, 20.25, 30.25}, nd4j::DataType::DOUBLE); + NDArray exp1('c', {2,2}, {0, 1, 4, 9}, sd::DataType::INT64); + NDArray exp2('c', {2,2}, {0, 2.25, 6.25, 12.25}, sd::DataType::HALF); + NDArray exp3('c', {2,2}, {0, 2.25, 6.25, 12.25}, sd::DataType::DOUBLE); + NDArray exp4('c', {2,2}, {0, 1, 0, 1}, sd::DataType::BOOL); + NDArray exp5('c', {3,2}, {0, 2.25, 6.25, 12.25, 20.25, 30.25}, sd::DataType::DOUBLE); - NDArray result1('c', {2,2}, nd4j::DataType::INT64); - NDArray result2('c', {2,2}, nd4j::DataType::HALF); - NDArray result3('c', {2,2}, nd4j::DataType::DOUBLE); - NDArray result4('c', {2,2}, nd4j::DataType::BOOL); - NDArray result5('c', {3,2}, nd4j::DataType::DOUBLE); + NDArray result1('c', {2,2}, sd::DataType::INT64); + NDArray result2('c', {2,2}, sd::DataType::HALF); + NDArray result3('c', {2,2}, sd::DataType::DOUBLE); + NDArray result4('c', {2,2}, sd::DataType::BOOL); + NDArray result5('c', {3,2}, sd::DataType::DOUBLE); - x1.applyTransform(nd4j::transform::Square, result1); + x1.applyTransform(sd::transform::Square, result1); ASSERT_EQ(result1, exp1); - x2.applyTransform(nd4j::transform::Square, result2); + x2.applyTransform(sd::transform::Square, result2); ASSERT_EQ(result2, exp2); - x3.applyTransform(nd4j::transform::Square, result3); + x3.applyTransform(sd::transform::Square, result3); ASSERT_EQ(result3, exp3); - x4.applyTransform(nd4j::transform::Square, result4); + x4.applyTransform(sd::transform::Square, result4); ASSERT_EQ(result4, exp4); - x2.applyTransform(nd4j::transform::Square, x2); + x2.applyTransform(sd::transform::Square, x2); ASSERT_EQ(x2, exp2); - x3.applyTransform(nd4j::transform::Square, x3); + x3.applyTransform(sd::transform::Square, x3); ASSERT_EQ(x3, exp3); - x5.applyTransform(nd4j::transform::Square, result5); + x5.applyTransform(sd::transform::Square, result5); ASSERT_EQ(result5, exp5); } @@ -1036,33 +1034,33 @@ TEST_F(MultiDataTypeTests, ndarray_applyTransformBool_test1) { if (!Environment::getInstance()->isExperimentalBuild()) return; - NDArray x1('c', {2,2}, {0, 1, 2, 3}, nd4j::DataType::INT64); - NDArray x2('c', {2,2}, {0, 1.5, 2.5, 3.5}, nd4j::DataType::HALF); - NDArray x3('c', {2,2}, {0, 1.5, 2.5, 3.5}, nd4j::DataType::DOUBLE); - NDArray x4('c', {2,2}, {0, 1, 0, 1}, nd4j::DataType::BOOL); - NDArray x5('c', {2,3}, {0, 1.5, 2.5, 3.5, 4.5, 5.5}, nd4j::DataType::DOUBLE); + NDArray x1('c', {2,2}, {0, 1, 2, 3}, sd::DataType::INT64); + NDArray x2('c', {2,2}, {0, 1.5, 2.5, 3.5}, sd::DataType::HALF); + NDArray x3('c', {2,2}, {0, 1.5, 2.5, 3.5}, sd::DataType::DOUBLE); + NDArray x4('c', {2,2}, {0, 1, 0, 1}, sd::DataType::BOOL); + NDArray x5('c', {2,3}, {0, 1.5, 2.5, 3.5, 4.5, 5.5}, sd::DataType::DOUBLE); - NDArray exp1('c', {2,2}, {0, 0, 0, 1}, nd4j::DataType::BOOL); - NDArray exp2('c', {2,2}, {0, 1, 0, 0}, nd4j::DataType::BOOL); - NDArray exp3('c', {3,2}, {0, 0, 0, 0, 0, 1}, nd4j::DataType::BOOL); + NDArray exp1('c', {2,2}, {0, 0, 0, 1}, sd::DataType::BOOL); + NDArray exp2('c', {2,2}, {0, 1, 0, 0}, sd::DataType::BOOL); + NDArray exp3('c', {3,2}, {0, 0, 0, 0, 0, 1}, sd::DataType::BOOL); - NDArray result1('c', {2,2}, nd4j::DataType::BOOL); - NDArray result2('c', {3,2}, nd4j::DataType::BOOL); + NDArray result1('c', {2,2}, sd::DataType::BOOL); + NDArray result2('c', {3,2}, sd::DataType::BOOL); /* - x1.applyTransform(nd4j::transform::IsMax, result1); + x1.applyTransform(sd::transform::IsMax, result1); ASSERT_EQ(result1, exp1); - x2.applyTransform(nd4j::transform::IsMax, result1); + x2.applyTransform(sd::transform::IsMax, result1); ASSERT_EQ(result1, exp1); - x3.applyTransform(nd4j::transform::IsMax, result1); + x3.applyTransform(sd::transform::IsMax, result1); ASSERT_EQ(result1, exp1); - x4.applyTransform(nd4j::transform::IsMax, result1); + x4.applyTransform(sd::transform::IsMax, result1); ASSERT_EQ(result1, exp2); - x5.applyTransform(nd4j::transform::IsMax, result2); + x5.applyTransform(sd::transform::IsMax, result2); ASSERT_EQ(result2, exp3); */ } @@ -1072,44 +1070,44 @@ TEST_F(MultiDataTypeTests, ndarray_applyTransformStrict_test1) { if (!Environment::getInstance()->isExperimentalBuild()) return; - NDArray x1('c', {2,2}, {0, 1, 2, 3}, nd4j::DataType::HALF); - NDArray x2('c', {2,2}, {0, 1, 2, 3}, nd4j::DataType::FLOAT32); - NDArray x3('c', {2,2}, {0, 1, 2, 3}, nd4j::DataType::DOUBLE); - NDArray x4('c', {2,3}, {0, 1, 2, 3, 4, 5}, nd4j::DataType::DOUBLE); + NDArray x1('c', {2,2}, {0, 1, 2, 3}, sd::DataType::HALF); + NDArray x2('c', {2,2}, {0, 1, 2, 3}, sd::DataType::FLOAT32); + NDArray x3('c', {2,2}, {0, 1, 2, 3}, sd::DataType::DOUBLE); + NDArray x4('c', {2,3}, {0, 1, 2, 3, 4, 5}, sd::DataType::DOUBLE); - NDArray exp1('c', {2,2}, {0, 3, 12, 27}, nd4j::DataType::HALF); - NDArray exp2('c', {2,2}, {0, 3, 12, 27}, nd4j::DataType::FLOAT32); - NDArray exp3('c', {2,2}, {0, 3, 12, 27}, nd4j::DataType::DOUBLE); - NDArray exp4('c', {3,2}, {0, 3, 12, 27, 48, 75}, nd4j::DataType::DOUBLE); - NDArray exp5('c', {2,3}, {0, 3, 12, 27, 48, 75}, nd4j::DataType::DOUBLE); + NDArray exp1('c', {2,2}, {0, 3, 12, 27}, sd::DataType::HALF); + NDArray exp2('c', {2,2}, {0, 3, 12, 27}, sd::DataType::FLOAT32); + NDArray exp3('c', {2,2}, {0, 3, 12, 27}, sd::DataType::DOUBLE); + NDArray exp4('c', {3,2}, {0, 3, 12, 27, 48, 75}, sd::DataType::DOUBLE); + NDArray exp5('c', {2,3}, {0, 3, 12, 27, 48, 75}, sd::DataType::DOUBLE); - NDArray result1('c', {2,2}, nd4j::DataType::HALF); - NDArray result2('c', {2,2}, nd4j::DataType::FLOAT32); - NDArray result3('c', {2,2}, nd4j::DataType::DOUBLE); - NDArray result4('c', {3,2}, nd4j::DataType::DOUBLE); + NDArray result1('c', {2,2}, sd::DataType::HALF); + NDArray result2('c', {2,2}, sd::DataType::FLOAT32); + NDArray result3('c', {2,2}, sd::DataType::DOUBLE); + NDArray result4('c', {3,2}, sd::DataType::DOUBLE); - x1.applyTransform(nd4j::transform::CubeDerivative, result1); + x1.applyTransform(sd::transform::CubeDerivative, result1); ASSERT_EQ(result1, exp1); - x2.applyTransform(nd4j::transform::CubeDerivative, result2); + x2.applyTransform(sd::transform::CubeDerivative, result2); ASSERT_EQ(result2, exp2); - x3.applyTransform(nd4j::transform::CubeDerivative, result3); + x3.applyTransform(sd::transform::CubeDerivative, result3); ASSERT_EQ(result3, exp3); - x4.applyTransform(nd4j::transform::CubeDerivative, result4); + x4.applyTransform(sd::transform::CubeDerivative, result4); ASSERT_EQ(result4, exp4); - x1.applyTransform(nd4j::transform::CubeDerivative, x1); + x1.applyTransform(sd::transform::CubeDerivative, x1); ASSERT_EQ(x1, exp1); - x2.applyTransform(nd4j::transform::CubeDerivative, x2); + x2.applyTransform(sd::transform::CubeDerivative, x2); ASSERT_EQ(x2, exp2); - x3.applyTransform(nd4j::transform::CubeDerivative, x3); + x3.applyTransform(sd::transform::CubeDerivative, x3); ASSERT_EQ(x3, exp3); - x4.applyTransform(nd4j::transform::CubeDerivative, x4); + x4.applyTransform(sd::transform::CubeDerivative, x4); ASSERT_EQ(x4, exp5); } @@ -1118,32 +1116,32 @@ TEST_F(MultiDataTypeTests, ndarray_applyPairwiseTransform_test1) { if (!Environment::getInstance()->isExperimentalBuild()) return; - NDArray x1('c', {2,3}, {0, 1, 2, 3, 4, 5}, nd4j::DataType::INT32); - NDArray x2('c', {2,3}, {0, 1, 2, 3, 4, 5}, nd4j::DataType::FLOAT32); - NDArray x3('c', {2,3}, {0, 1, 0, 1, 0, 0}, nd4j::DataType::BOOL); - NDArray x4('c', {3,2}, {0.5, 1.5, 2.5, 3.5, 4.5, 0}, nd4j::DataType::DOUBLE); - NDArray x5('c', {3,2}, nd4j::DataType::INT32); - NDArray x6('c', {2,3}, nd4j::DataType::DOUBLE); + NDArray x1('c', {2,3}, {0, 1, 2, 3, 4, 5}, sd::DataType::INT32); + NDArray x2('c', {2,3}, {0, 1, 2, 3, 4, 5}, sd::DataType::FLOAT32); + NDArray x3('c', {2,3}, {0, 1, 0, 1, 0, 0}, sd::DataType::BOOL); + NDArray x4('c', {3,2}, {0.5, 1.5, 2.5, 3.5, 4.5, 0}, sd::DataType::DOUBLE); + NDArray x5('c', {3,2}, sd::DataType::INT32); + NDArray x6('c', {2,3}, sd::DataType::DOUBLE); - NDArray exp1('c', {2,3}, {0, 2, 4, 6, 8, 5}, nd4j::DataType::INT32); - NDArray exp2('c', {2,3}, {0.5, 2.5, 4.5, 6.5, 8.5, 5.}, nd4j::DataType::FLOAT32); - NDArray exp3('c', {2,3}, {1, 1, 1, 1, 1, 0}, nd4j::DataType::BOOL); - NDArray exp4('c', {2,3}, {0.5, 2.5, 4.5, 6.5, 8.5, 5.}, nd4j::DataType::DOUBLE); - NDArray exp5('c', {3,2}, {0, 2, 4, 6, 8, 5}, nd4j::DataType::INT32); + NDArray exp1('c', {2,3}, {0, 2, 4, 6, 8, 5}, sd::DataType::INT32); + NDArray exp2('c', {2,3}, {0.5, 2.5, 4.5, 6.5, 8.5, 5.}, sd::DataType::FLOAT32); + NDArray exp3('c', {2,3}, {1, 1, 1, 1, 1, 0}, sd::DataType::BOOL); + NDArray exp4('c', {2,3}, {0.5, 2.5, 4.5, 6.5, 8.5, 5.}, sd::DataType::DOUBLE); + NDArray exp5('c', {3,2}, {0, 2, 4, 6, 8, 5}, sd::DataType::INT32); - x1.applyPairwiseTransform(nd4j::pairwise::Add, x4, x5); + x1.applyPairwiseTransform(sd::pairwise::Add, x4, x5); ASSERT_EQ(x5, exp5); - x1.applyPairwiseTransform(nd4j::pairwise::Add, x4, x6); + x1.applyPairwiseTransform(sd::pairwise::Add, x4, x6); ASSERT_EQ(x6, exp4); - x1.applyPairwiseTransform(nd4j::pairwise::Add, x4); + x1.applyPairwiseTransform(sd::pairwise::Add, x4); ASSERT_EQ(x1, exp1); - x2.applyPairwiseTransform(nd4j::pairwise::Add, x4); + x2.applyPairwiseTransform(sd::pairwise::Add, x4); ASSERT_EQ(x2, exp2); - x3.applyPairwiseTransform(nd4j::pairwise::Add, x4); + x3.applyPairwiseTransform(sd::pairwise::Add, x4); ASSERT_EQ(x3, exp3); } @@ -1152,27 +1150,27 @@ TEST_F(MultiDataTypeTests, ndarray_applyPairwiseTransform_test2) { if (!Environment::getInstance()->isExperimentalBuild()) return; - NDArray x1('c', {2,3}, {1, 1, 2, 3, 4, 5}, nd4j::DataType::INT32); - NDArray x2('c', {3,2}, {1, 0, 2, 0, 4, 0}, nd4j::DataType::INT32); - NDArray x3('c', {3,2}, {0.5, 1.5, 2.5, 3, 4.5, 0}, nd4j::DataType::DOUBLE); - NDArray x4('c', {2,3}, {0.5, 1., 2.5, 3, 4., 0}, nd4j::DataType::DOUBLE); - NDArray x5('c', {3,2}, {0, 1, 0, 1, 0, 1}, nd4j::DataType::BOOL); - NDArray x6('c', {2,3}, {1, 1, 1, 0, 1, 0}, nd4j::DataType::BOOL); + NDArray x1('c', {2,3}, {1, 1, 2, 3, 4, 5}, sd::DataType::INT32); + NDArray x2('c', {3,2}, {1, 0, 2, 0, 4, 0}, sd::DataType::INT32); + NDArray x3('c', {3,2}, {0.5, 1.5, 2.5, 3, 4.5, 0}, sd::DataType::DOUBLE); + NDArray x4('c', {2,3}, {0.5, 1., 2.5, 3, 4., 0}, sd::DataType::DOUBLE); + NDArray x5('c', {3,2}, {0, 1, 0, 1, 0, 1}, sd::DataType::BOOL); + NDArray x6('c', {2,3}, {1, 1, 1, 0, 1, 0}, sd::DataType::BOOL); - NDArray x7('c', {3,2}, nd4j::DataType::BOOL); - NDArray x8('c', {2,3}, nd4j::DataType::BOOL); + NDArray x7('c', {3,2}, sd::DataType::BOOL); + NDArray x8('c', {2,3}, sd::DataType::BOOL); - NDArray exp1('c', {3,2}, {1, 0, 1, 0, 1, 0}, nd4j::DataType::BOOL); - NDArray exp2('c', {2,3}, {1, 0, 1, 1, 0, 1}, nd4j::DataType::BOOL); - NDArray exp3('c', {2,3}, {0, 1, 0, 0, 0, 0}, nd4j::DataType::BOOL); + NDArray exp1('c', {3,2}, {1, 0, 1, 0, 1, 0}, sd::DataType::BOOL); + NDArray exp2('c', {2,3}, {1, 0, 1, 1, 0, 1}, sd::DataType::BOOL); + NDArray exp3('c', {2,3}, {0, 1, 0, 0, 0, 0}, sd::DataType::BOOL); - x1.applyPairwiseTransform(nd4j::pairwise::EqualTo, x2, x7); + x1.applyPairwiseTransform(sd::pairwise::EqualTo, x2, x7); ASSERT_EQ(x7, exp1); - x3.applyPairwiseTransform(nd4j::pairwise::EqualTo, x4, x8); + x3.applyPairwiseTransform(sd::pairwise::EqualTo, x4, x8); ASSERT_EQ(x8, exp2); - x5.applyPairwiseTransform(nd4j::pairwise::EqualTo, x6, x8); + x5.applyPairwiseTransform(sd::pairwise::EqualTo, x6, x8); ASSERT_EQ(x8, exp3); } @@ -1181,44 +1179,44 @@ TEST_F(MultiDataTypeTests, ndarray_applyBroadcast_test1) { if (!Environment::getInstance()->isExperimentalBuild()) return; - NDArray x1('c', {2,3}, {10, 20, 30, 40, 50, 60}, nd4j::DataType::INT32); - NDArray x2('c', {2}, {1, 2}, nd4j::DataType::INT64); - NDArray x3('c', {2,3}, nd4j::DataType::INT32); - NDArray x4('c', {2}, {1, 2}, nd4j::DataType::FLOAT32); - NDArray x5('c', {2,3}, nd4j::DataType::FLOAT32); - NDArray x6('c', {2}, {1, 1}, nd4j::DataType::BOOL); + NDArray x1('c', {2,3}, {10, 20, 30, 40, 50, 60}, sd::DataType::INT32); + NDArray x2('c', {2}, {1, 2}, sd::DataType::INT64); + NDArray x3('c', {2,3}, sd::DataType::INT32); + NDArray x4('c', {2}, {1, 2}, sd::DataType::FLOAT32); + NDArray x5('c', {2,3}, sd::DataType::FLOAT32); + NDArray x6('c', {2}, {1, 1}, sd::DataType::BOOL); - NDArray exp1('c', {2,3}, {11, 21, 31, 42, 52, 62}, nd4j::DataType::INT32); - NDArray exp2('c', {2,3}, {11, 21, 31, 42, 52, 62}, nd4j::DataType::FLOAT32); - NDArray exp3('c', {2,3}, {11, 21, 31, 41, 51, 61}, nd4j::DataType::INT32); + NDArray exp1('c', {2,3}, {11, 21, 31, 42, 52, 62}, sd::DataType::INT32); + NDArray exp2('c', {2,3}, {11, 21, 31, 42, 52, 62}, sd::DataType::FLOAT32); + NDArray exp3('c', {2,3}, {11, 21, 31, 41, 51, 61}, sd::DataType::INT32); - x1.applyBroadcast(nd4j::broadcast::Add, {0}, x2, x3); + x1.applyBroadcast(sd::broadcast::Add, {0}, x2, x3); ASSERT_EQ(x3, exp1); - x1.applyBroadcast(nd4j::broadcast::Add, {0}, x4, x5); + x1.applyBroadcast(sd::broadcast::Add, {0}, x4, x5); ASSERT_EQ(x5, exp2); - x1.applyBroadcast(nd4j::broadcast::Add, {0}, x6, x3); + x1.applyBroadcast(sd::broadcast::Add, {0}, x6, x3); ASSERT_EQ(x3, exp3); } ////////////////////////////////////////////////////////////////////////////// TEST_F(MultiDataTypeTests, ndarray_applyBroadcast_test2) { - NDArray x1('c', {2,3}, {10, 20, 30, 40, 50, 60}, nd4j::DataType::INT32); - NDArray x2('c', {2}, {10, 60}, nd4j::DataType::INT32); - NDArray x3('c', {2,3}, nd4j::DataType::BOOL); + NDArray x1('c', {2,3}, {10, 20, 30, 40, 50, 60}, sd::DataType::INT32); + NDArray x2('c', {2}, {10, 60}, sd::DataType::INT32); + NDArray x3('c', {2,3}, sd::DataType::BOOL); - NDArray x4('c', {2,3}, {0, 0, 0, 0, 0, 1}, nd4j::DataType::BOOL); - NDArray x5('c', {2}, {0, 1}, nd4j::DataType::BOOL); + NDArray x4('c', {2,3}, {0, 0, 0, 0, 0, 1}, sd::DataType::BOOL); + NDArray x5('c', {2}, {0, 1}, sd::DataType::BOOL); - NDArray exp1('c', {2,3}, {1, 0, 0, 0, 0, 1}, nd4j::DataType::BOOL); - NDArray exp2('c', {2,3}, {1, 1, 1, 0, 0, 1}, nd4j::DataType::BOOL); + NDArray exp1('c', {2,3}, {1, 0, 0, 0, 0, 1}, sd::DataType::BOOL); + NDArray exp2('c', {2,3}, {1, 1, 1, 0, 0, 1}, sd::DataType::BOOL); - x1.applyBroadcast(nd4j::broadcast::EqualTo, {0}, x2, x3); + x1.applyBroadcast(sd::broadcast::EqualTo, {0}, x2, x3); ASSERT_EQ(x3, exp1); - x4.applyBroadcast(nd4j::broadcast::EqualTo, {0}, x5, x3); + x4.applyBroadcast(sd::broadcast::EqualTo, {0}, x5, x3); ASSERT_EQ(x3, exp2); } @@ -1227,56 +1225,56 @@ TEST_F(MultiDataTypeTests, ndarray_applyTrueBroadcast_test1) { if (!Environment::getInstance()->isExperimentalBuild()) return; - NDArray x1('c', {2,2}, {10, 20, 30, 40}, nd4j::DataType::INT32); - NDArray x2('c', {2}, {1, 2}, nd4j::DataType::HALF); - NDArray x3('c', {2,2}, nd4j::DataType::HALF); + NDArray x1('c', {2,2}, {10, 20, 30, 40}, sd::DataType::INT32); + NDArray x2('c', {2}, {1, 2}, sd::DataType::HALF); + NDArray x3('c', {2,2}, sd::DataType::HALF); - NDArray x4('c', {2}, {1, 2}, nd4j::DataType::INT64); - NDArray x5('c', {2,2}, nd4j::DataType::INT32); + NDArray x4('c', {2}, {1, 2}, sd::DataType::INT64); + NDArray x5('c', {2,2}, sd::DataType::INT32); - NDArray x6('c', {2,2}, {0, 1, 0, 1}, nd4j::DataType::BOOL); - NDArray x7('c', {2}, {1, 2}, nd4j::DataType::INT64); - NDArray x8('c', {2,2}, nd4j::DataType::BOOL); + NDArray x6('c', {2,2}, {0, 1, 0, 1}, sd::DataType::BOOL); + NDArray x7('c', {2}, {1, 2}, sd::DataType::INT64); + NDArray x8('c', {2,2}, sd::DataType::BOOL); - NDArray x13('c', {0}, std::vector{3}, nd4j::DataType::INT64); - NDArray x14('c', {0}, std::vector{1.5}, nd4j::DataType::DOUBLE); - NDArray x15(nd4j::DataType::DOUBLE); - NDArray x16('c', {2,2}, nd4j::DataType::DOUBLE); + NDArray x13('c', {0}, std::vector{3}, sd::DataType::INT64); + NDArray x14('c', {0}, std::vector{1.5}, sd::DataType::DOUBLE); + NDArray x15(sd::DataType::DOUBLE); + NDArray x16('c', {2,2}, sd::DataType::DOUBLE); - NDArray exp1('c', {2,2}, {11, 22, 31, 42}, nd4j::DataType::HALF); - NDArray exp2('c', {2,2}, {11, 22, 31, 42}, nd4j::DataType::INT32); - NDArray exp3('c', {2,2}, {1, 1, 1, 1}, nd4j::DataType::BOOL); - NDArray exp4('c', {0}, std::vector{4.5}, nd4j::DataType::DOUBLE); - NDArray exp5('c', {2,2}, {11.5, 21.5, 31.5, 41.5}, nd4j::DataType::DOUBLE); + NDArray exp1('c', {2,2}, {11, 22, 31, 42}, sd::DataType::HALF); + NDArray exp2('c', {2,2}, {11, 22, 31, 42}, sd::DataType::INT32); + NDArray exp3('c', {2,2}, {1, 1, 1, 1}, sd::DataType::BOOL); + NDArray exp4('c', {0}, std::vector{4.5}, sd::DataType::DOUBLE); + NDArray exp5('c', {2,2}, {11.5, 21.5, 31.5, 41.5}, sd::DataType::DOUBLE); - x1.applyTrueBroadcast(nd4j::BroadcastOpsTuple::Add(), x2, x3); + x1.applyTrueBroadcast(sd::BroadcastOpsTuple::Add(), x2, x3); ASSERT_EQ(x3, exp1); - x1.applyTrueBroadcast(nd4j::BroadcastOpsTuple::Add(), x4, x5); + x1.applyTrueBroadcast(sd::BroadcastOpsTuple::Add(), x4, x5); ASSERT_EQ(x5, exp2); - x6.applyTrueBroadcast(nd4j::BroadcastOpsTuple::Add(), x7, x8); + x6.applyTrueBroadcast(sd::BroadcastOpsTuple::Add(), x7, x8); ASSERT_EQ(x8, exp3); - auto x9 = x1.applyTrueBroadcast(nd4j::BroadcastOpsTuple::Add(), x2); + auto x9 = x1.applyTrueBroadcast(sd::BroadcastOpsTuple::Add(), x2); ASSERT_EQ(x9, exp1); - auto x10 = x1.applyTrueBroadcast(nd4j::BroadcastOpsTuple::Add(), x4); + auto x10 = x1.applyTrueBroadcast(sd::BroadcastOpsTuple::Add(), x4); ASSERT_EQ(x10, exp2); - auto x11 = x6.applyTrueBroadcast(nd4j::BroadcastOpsTuple::Add(), x7); + auto x11 = x6.applyTrueBroadcast(sd::BroadcastOpsTuple::Add(), x7); ASSERT_EQ(x11, exp3); - auto x12 = x1.applyTrueBroadcast(nd4j::BroadcastOpsTuple::Add(), x2); + auto x12 = x1.applyTrueBroadcast(sd::BroadcastOpsTuple::Add(), x2); ASSERT_EQ(x12, exp1); - x13.applyTrueBroadcast(nd4j::BroadcastOpsTuple::Add(), x14, x15); + x13.applyTrueBroadcast(sd::BroadcastOpsTuple::Add(), x14, x15); ASSERT_EQ(x15, exp4); - x1.applyTrueBroadcast(nd4j::BroadcastOpsTuple::Add(), x14, x16); + x1.applyTrueBroadcast(sd::BroadcastOpsTuple::Add(), x14, x16); ASSERT_EQ(x16, exp5); - x14.applyTrueBroadcast(nd4j::BroadcastOpsTuple::Add(), x1, x16); + x14.applyTrueBroadcast(sd::BroadcastOpsTuple::Add(), x1, x16); ASSERT_EQ(x16, exp5); } @@ -1286,27 +1284,27 @@ TEST_F(MultiDataTypeTests, ndarray_applyTrueBroadcast_test2) { if (!Environment::getInstance()->isExperimentalBuild()) return; - NDArray x1('c', {2,2}, {10, 20, 30, 40}, nd4j::DataType::HALF); - NDArray x2('c', {2}, {10, 40}, nd4j::DataType::HALF); - NDArray x3('c', {2,2}, nd4j::DataType::BOOL); - NDArray x4('c', {0}, std::vector{10}, nd4j::DataType::HALF); - NDArray x5('c', {0}, std::vector{20}, nd4j::DataType::HALF); - NDArray x6(nd4j::DataType::BOOL); + NDArray x1('c', {2,2}, {10, 20, 30, 40}, sd::DataType::HALF); + NDArray x2('c', {2}, {10, 40}, sd::DataType::HALF); + NDArray x3('c', {2,2}, sd::DataType::BOOL); + NDArray x4('c', {0}, std::vector{10}, sd::DataType::HALF); + NDArray x5('c', {0}, std::vector{20}, sd::DataType::HALF); + NDArray x6(sd::DataType::BOOL); - NDArray exp1('c', {2,2}, {1, 0, 0, 1}, nd4j::DataType::BOOL); - NDArray exp2('c', {2,2}, {1, 0, 0, 0}, nd4j::DataType::BOOL); - NDArray exp3('c', {0}, std::vector{0}, nd4j::DataType::BOOL); + NDArray exp1('c', {2,2}, {1, 0, 0, 1}, sd::DataType::BOOL); + NDArray exp2('c', {2,2}, {1, 0, 0, 0}, sd::DataType::BOOL); + NDArray exp3('c', {0}, std::vector{0}, sd::DataType::BOOL); - x1.applyTrueBroadcast(BroadcastBoolOpsTuple(nd4j::scalar::EqualTo, nd4j::pairwise::EqualTo, nd4j::broadcast::EqualTo), x2, x3); + x1.applyTrueBroadcast(BroadcastBoolOpsTuple(sd::scalar::EqualTo, sd::pairwise::EqualTo, sd::broadcast::EqualTo), x2, x3); ASSERT_EQ(x3, exp1); - x1.applyTrueBroadcast(BroadcastBoolOpsTuple(nd4j::scalar::EqualTo, nd4j::pairwise::EqualTo, nd4j::broadcast::EqualTo), x4, x3); + x1.applyTrueBroadcast(BroadcastBoolOpsTuple(sd::scalar::EqualTo, sd::pairwise::EqualTo, sd::broadcast::EqualTo), x4, x3); ASSERT_EQ(x3, exp2); - x4.applyTrueBroadcast(BroadcastBoolOpsTuple(nd4j::scalar::EqualTo, nd4j::pairwise::EqualTo, nd4j::broadcast::EqualTo), x1, x3); + x4.applyTrueBroadcast(BroadcastBoolOpsTuple(sd::scalar::EqualTo, sd::pairwise::EqualTo, sd::broadcast::EqualTo), x1, x3); ASSERT_EQ(x3, exp2); - x5.applyTrueBroadcast(BroadcastBoolOpsTuple(nd4j::scalar::EqualTo, nd4j::pairwise::EqualTo, nd4j::broadcast::EqualTo), x4, x6); + x5.applyTrueBroadcast(BroadcastBoolOpsTuple(sd::scalar::EqualTo, sd::pairwise::EqualTo, sd::broadcast::EqualTo), x4, x6); ASSERT_EQ(x6, exp3); } @@ -1315,52 +1313,52 @@ TEST_F(MultiDataTypeTests, ndarray_applyScalar_test1) { if (!Environment::getInstance()->isExperimentalBuild()) return; - NDArray x1('c', {2,2}, {0, 1, 2, 3}, nd4j::DataType::INT64); - NDArray x2('c', {2,2}, {0, 1.5, 2.5, 3.5}, nd4j::DataType::FLOAT32); - NDArray x3('c', {2,2}, nd4j::DataType::DOUBLE); - NDArray x4('c', {2,2}, {0, 1, 0, 1}, nd4j::DataType::BOOL); + NDArray x1('c', {2,2}, {0, 1, 2, 3}, sd::DataType::INT64); + NDArray x2('c', {2,2}, {0, 1.5, 2.5, 3.5}, sd::DataType::FLOAT32); + NDArray x3('c', {2,2}, sd::DataType::DOUBLE); + NDArray x4('c', {2,2}, {0, 1, 0, 1}, sd::DataType::BOOL); - NDArray exp1('c', {2,2}, {1, 2, 3, 4}, nd4j::DataType::INT64); - NDArray exp2('c', {2,2}, {1.5, 2.5, 3.5, 4.5}, nd4j::DataType::DOUBLE); - NDArray exp3('c', {2,2}, {0.1, 1.6, 2.6, 3.6}, nd4j::DataType::FLOAT32); - NDArray exp4('c', {2,2}, {1.1, 2.1, 1.1, 2.1}, nd4j::DataType::DOUBLE); - NDArray exp5('c', {2,2}, {1, 1, 1, 1}, nd4j::DataType::BOOL); + NDArray exp1('c', {2,2}, {1, 2, 3, 4}, sd::DataType::INT64); + NDArray exp2('c', {2,2}, {1.5, 2.5, 3.5, 4.5}, sd::DataType::DOUBLE); + NDArray exp3('c', {2,2}, {0.1, 1.6, 2.6, 3.6}, sd::DataType::FLOAT32); + NDArray exp4('c', {2,2}, {1.1, 2.1, 1.1, 2.1}, sd::DataType::DOUBLE); + NDArray exp5('c', {2,2}, {1, 1, 1, 1}, sd::DataType::BOOL); - x1.applyScalar(nd4j::scalar::Add, 1, x1); + x1.applyScalar(sd::scalar::Add, 1, x1); ASSERT_EQ(x1, exp1); - x1.applyScalar(nd4j::scalar::Add, 0.5, x3); + x1.applyScalar(sd::scalar::Add, 0.5, x3); ASSERT_EQ(x3, exp2); - x2.applyScalar(nd4j::scalar::Add, 0.1, x2); + x2.applyScalar(sd::scalar::Add, 0.1, x2); ASSERT_EQ(x2, exp3); - x4.applyScalar(nd4j::scalar::Add, 1.1, x3); + x4.applyScalar(sd::scalar::Add, 1.1, x3); ASSERT_EQ(x3, exp4); - x4.applyScalar(nd4j::scalar::Add, 1, x4); + x4.applyScalar(sd::scalar::Add, 1, x4); ASSERT_EQ(x4, exp5); } ////////////////////////////////////////////////////////////////////////////// TEST_F(MultiDataTypeTests, ndarray_applyScalar_test2) { - NDArray x1('c', {2,2}, {0, 1, 2, 3}, nd4j::DataType::INT64); - NDArray x2('c', {2,2}, {0, 1.5, 2.5, 3.5}, nd4j::DataType::FLOAT32); - NDArray x3('c', {2,2}, {0, 1, 1, 0}, nd4j::DataType::BOOL); - NDArray x4('c', {2,2}, nd4j::DataType::BOOL); + NDArray x1('c', {2,2}, {0, 1, 2, 3}, sd::DataType::INT64); + NDArray x2('c', {2,2}, {0, 1.5, 2.5, 3.5}, sd::DataType::FLOAT32); + NDArray x3('c', {2,2}, {0, 1, 1, 0}, sd::DataType::BOOL); + NDArray x4('c', {2,2}, sd::DataType::BOOL); - NDArray exp1('c', {2,2}, {0, 1, 0, 0}, nd4j::DataType::BOOL); - NDArray exp2('c', {2,2}, {0, 1, 1, 0}, nd4j::DataType::BOOL); + NDArray exp1('c', {2,2}, {0, 1, 0, 0}, sd::DataType::BOOL); + NDArray exp2('c', {2,2}, {0, 1, 1, 0}, sd::DataType::BOOL); - x1.applyScalar(nd4j::scalar::EqualTo, 1, x4); + x1.applyScalar(sd::scalar::EqualTo, 1, x4); ASSERT_EQ(x4, exp1); - x2.applyScalar(nd4j::scalar::EqualTo, 1.5, x4); + x2.applyScalar(sd::scalar::EqualTo, 1.5, x4); ASSERT_EQ(x4, exp1); - x3.applyScalar(nd4j::scalar::EqualTo, true, x4); + x3.applyScalar(sd::scalar::EqualTo, true, x4); ASSERT_EQ(x4, exp2); } @@ -1369,13 +1367,13 @@ TEST_F(MultiDataTypeTests, ndarray_applyScalar_test2) { ////////////////////////////////////////////////////////////////////////////// TEST_F(MultiDataTypeTests, ndarray_applyLambda_test1) { - NDArray x1('c', {2,2}, {0, 1, 2, 3}, nd4j::DataType::DOUBLE); - NDArray x2('c', {2,2}, {0, 1, 2, 3}, nd4j::DataType::INT64); - NDArray x3('c', {2,2}, {0, 1.5, 2.5, 3.5}, nd4j::DataType::FLOAT32); - NDArray x4('c', {2,2}, nd4j::DataType::DOUBLE); - NDArray x5('c', {2,2}, {0, 1.5, 2.5, 3.5}, nd4j::DataType::FLOAT32); - NDArray x6('c', {2,2}, {0, -1, -1, 0.1}, nd4j::DataType::BOOL); - NDArray x7('c', {2,2}, nd4j::DataType::BOOL); + NDArray x1('c', {2,2}, {0, 1, 2, 3}, sd::DataType::DOUBLE); + NDArray x2('c', {2,2}, {0, 1, 2, 3}, sd::DataType::INT64); + NDArray x3('c', {2,2}, {0, 1.5, 2.5, 3.5}, sd::DataType::FLOAT32); + NDArray x4('c', {2,2}, sd::DataType::DOUBLE); + NDArray x5('c', {2,2}, {0, 1.5, 2.5, 3.5}, sd::DataType::FLOAT32); + NDArray x6('c', {2,2}, {0, -1, -1, 0.1}, sd::DataType::BOOL); + NDArray x7('c', {2,2}, sd::DataType::BOOL); const float item1 = 0.1; const double item2 = 0.1; @@ -1385,11 +1383,11 @@ TEST_F(MultiDataTypeTests, ndarray_applyLambda_test1) { auto func4 = [=](double elem) { return elem + item1; }; auto func5 = [=](float elem) { return elem - (int)1; }; - NDArray exp1('c', {2,2}, {0.1, 1.1, 2.1, 3.1}, nd4j::DataType::DOUBLE); - NDArray exp2('c', {2,2}, {0, 1, 2, 3}, nd4j::DataType::INT64); - NDArray exp3('c', {2,2}, {0.1, 1.1, 2.1, 3.1}, nd4j::DataType::FLOAT32); - NDArray exp4('c', {2,2}, {0.1, 1.6, 2.6, 3.6}, nd4j::DataType::FLOAT32); - NDArray exp5('c', {2,2}, {1, 0, 0, 0}, nd4j::DataType::BOOL); + NDArray exp1('c', {2,2}, {0.1, 1.1, 2.1, 3.1}, sd::DataType::DOUBLE); + NDArray exp2('c', {2,2}, {0, 1, 2, 3}, sd::DataType::INT64); + NDArray exp3('c', {2,2}, {0.1, 1.1, 2.1, 3.1}, sd::DataType::FLOAT32); + NDArray exp4('c', {2,2}, {0.1, 1.6, 2.6, 3.6}, sd::DataType::FLOAT32); + NDArray exp5('c', {2,2}, {1, 0, 0, 0}, sd::DataType::BOOL); x1.applyLambda(func1, x4); ASSERT_EQ(x4, exp1); @@ -1414,13 +1412,13 @@ TEST_F(MultiDataTypeTests, ndarray_applyLambda_test1) { ////////////////////////////////////////////////////////////////////////////// TEST_F(MultiDataTypeTests, ndarray_applyIndexedLambda_test1) { - NDArray x1('c', {2,2}, {0, 1, 2, 3}, nd4j::DataType::DOUBLE); - NDArray x2('c', {2,2}, {0, 1, 2, 3}, nd4j::DataType::INT64); - NDArray x3('c', {2,2}, {0, 1.5, 2.5, 3.5}, nd4j::DataType::FLOAT32); - NDArray x4('c', {2,2}, nd4j::DataType::DOUBLE); - NDArray x5('c', {2,2}, {0, 1.5, 2.5, 3.5}, nd4j::DataType::FLOAT32); - NDArray x6('c', {2,2}, {1, -1, -1, 0.1}, nd4j::DataType::BOOL); - NDArray x7('c', {2,2}, nd4j::DataType::BOOL); + NDArray x1('c', {2,2}, {0, 1, 2, 3}, sd::DataType::DOUBLE); + NDArray x2('c', {2,2}, {0, 1, 2, 3}, sd::DataType::INT64); + NDArray x3('c', {2,2}, {0, 1.5, 2.5, 3.5}, sd::DataType::FLOAT32); + NDArray x4('c', {2,2}, sd::DataType::DOUBLE); + NDArray x5('c', {2,2}, {0, 1.5, 2.5, 3.5}, sd::DataType::FLOAT32); + NDArray x6('c', {2,2}, {1, -1, -1, 0.1}, sd::DataType::BOOL); + NDArray x7('c', {2,2}, sd::DataType::BOOL); const float item1 = 0.1; const double item2 = 0.1; @@ -1430,12 +1428,12 @@ TEST_F(MultiDataTypeTests, ndarray_applyIndexedLambda_test1) { auto func4 = [=](Nd4jLong idx, double elem) { return idx + elem + item1; }; auto func5 = [=](Nd4jLong idx, float elem) { return idx + elem - (int)1; }; - NDArray exp1('c', {2,2}, {0.1, 2.1, 4.1, 6.1}, nd4j::DataType::DOUBLE); - NDArray exp2('c', {2,2}, {0, 2, 4, 6}, nd4j::DataType::INT64); - NDArray exp3('c', {2,2}, {0.1, 2.1, 4.1, 6.1}, nd4j::DataType::FLOAT32); - NDArray exp4('c', {2,2}, {0.1, 2.6, 4.6, 6.6}, nd4j::DataType::FLOAT32); - NDArray exp5('c', {2,2}, {0, 1, 1, 1}, nd4j::DataType::BOOL); - NDArray exp6('c', {2,2}, {0, 3, 6, 9}, nd4j::DataType::INT64); + NDArray exp1('c', {2,2}, {0.1, 2.1, 4.1, 6.1}, sd::DataType::DOUBLE); + NDArray exp2('c', {2,2}, {0, 2, 4, 6}, sd::DataType::INT64); + NDArray exp3('c', {2,2}, {0.1, 2.1, 4.1, 6.1}, sd::DataType::FLOAT32); + NDArray exp4('c', {2,2}, {0.1, 2.6, 4.6, 6.6}, sd::DataType::FLOAT32); + NDArray exp5('c', {2,2}, {0, 1, 1, 1}, sd::DataType::BOOL); + NDArray exp6('c', {2,2}, {0, 3, 6, 9}, sd::DataType::INT64); x1.applyIndexedLambda(func1, x4); ASSERT_EQ(x4, exp1); @@ -1459,17 +1457,17 @@ TEST_F(MultiDataTypeTests, ndarray_applyIndexedLambda_test1) { ////////////////////////////////////////////////////////////////////////////// TEST_F(MultiDataTypeTests, ndarray_applyPairwiseLambda_test1) { - NDArray x1('c', {2,2}, {0., 1, 2, 3}, nd4j::DataType::DOUBLE); - NDArray x2('c', {2,2}, {0., 1, 2, 3}, nd4j::DataType::INT64); - NDArray x3('c', {2,2}, {0., 1.5, 2.5, 3.5}, nd4j::DataType::FLOAT32); - NDArray x4('c', {2,2}, nd4j::DataType::DOUBLE); - NDArray x5('c', {2,2}, {0, 1.5, 2.5, 3.5}, nd4j::DataType::FLOAT32); - NDArray x6('c', {2,2}, {0.1, -1, -1, 0.1}, nd4j::DataType::BOOL); - NDArray x7('c', {2,2}, nd4j::DataType::BOOL); - NDArray other1('c', {2,2}, {0.1, 0.1, 0.1, 0.1}, nd4j::DataType::FLOAT32); - NDArray other2('c', {2,2}, {0.1, 0.1, 0.1, 0.1}, nd4j::DataType::DOUBLE); - NDArray other3('c', {2,2}, {0., -1, -2, -3}, nd4j::DataType::INT64); - NDArray other4('c', {2,2}, {1, 0, 0.1, 0}, nd4j::DataType::BOOL); + NDArray x1('c', {2,2}, {0., 1, 2, 3}, sd::DataType::DOUBLE); + NDArray x2('c', {2,2}, {0., 1, 2, 3}, sd::DataType::INT64); + NDArray x3('c', {2,2}, {0., 1.5, 2.5, 3.5}, sd::DataType::FLOAT32); + NDArray x4('c', {2,2}, sd::DataType::DOUBLE); + NDArray x5('c', {2,2}, {0, 1.5, 2.5, 3.5}, sd::DataType::FLOAT32); + NDArray x6('c', {2,2}, {0.1, -1, -1, 0.1}, sd::DataType::BOOL); + NDArray x7('c', {2,2}, sd::DataType::BOOL); + NDArray other1('c', {2,2}, {0.1, 0.1, 0.1, 0.1}, sd::DataType::FLOAT32); + NDArray other2('c', {2,2}, {0.1, 0.1, 0.1, 0.1}, sd::DataType::DOUBLE); + NDArray other3('c', {2,2}, {0., -1, -2, -3}, sd::DataType::INT64); + NDArray other4('c', {2,2}, {1, 0, 0.1, 0}, sd::DataType::BOOL); auto func1 = [](float elem1, float elem2) { return elem1 + elem2; }; auto func2 = [](int elem1, float elem2) { return elem1 + elem2; }; @@ -1477,11 +1475,11 @@ TEST_F(MultiDataTypeTests, ndarray_applyPairwiseLambda_test1) { auto func4 = [](double elem1, float elem2) { return elem1 + elem2; }; auto func5 = [](float elem1, int elem2) { return elem1 - elem2; }; - NDArray exp1('c', {2,2}, {0.1, 1.1, 2.1, 3.1}, nd4j::DataType::DOUBLE); - NDArray exp2('c', {2,2}, {0., 0, 0, 0}, nd4j::DataType::INT64); - NDArray exp3('c', {2,2}, {0.1, 1.1, 2.1, 3.1}, nd4j::DataType::FLOAT32); - NDArray exp4('c', {2,2}, {0.1, 1.6, 2.6, 3.6}, nd4j::DataType::FLOAT32); - NDArray exp5('c', {2,2}, {0., 1, 0, 1}, nd4j::DataType::BOOL); + NDArray exp1('c', {2,2}, {0.1, 1.1, 2.1, 3.1}, sd::DataType::DOUBLE); + NDArray exp2('c', {2,2}, {0., 0, 0, 0}, sd::DataType::INT64); + NDArray exp3('c', {2,2}, {0.1, 1.1, 2.1, 3.1}, sd::DataType::FLOAT32); + NDArray exp4('c', {2,2}, {0.1, 1.6, 2.6, 3.6}, sd::DataType::FLOAT32); + NDArray exp5('c', {2,2}, {0., 1, 0, 1}, sd::DataType::BOOL); x1.applyPairwiseLambda(other2, func1, x4); ASSERT_EQ(x4, exp1); @@ -1505,17 +1503,17 @@ TEST_F(MultiDataTypeTests, ndarray_applyPairwiseLambda_test1) { ////////////////////////////////////////////////////////////////////////////// TEST_F(MultiDataTypeTests, ndarray_applyIndexedPairwiseLambda_test1) { - NDArray x1('c', {2,2}, {0., 1, 2, 3}, nd4j::DataType::DOUBLE); - NDArray x2('c', {2,2}, {0., 1, 2, 3}, nd4j::DataType::INT64); - NDArray x3('c', {2,2}, {0., 1.5, 2.5, 3.5}, nd4j::DataType::FLOAT32); - NDArray x4('c', {2,2}, nd4j::DataType::DOUBLE); - NDArray x5('c', {2,2}, {0, 1.5, 2.5, 3.5}, nd4j::DataType::FLOAT32); - NDArray x6('c', {2,2}, {0.1, -1, -1, 0.1}, nd4j::DataType::BOOL); - NDArray x7('c', {2,2}, nd4j::DataType::BOOL); - NDArray other1('c', {2,2}, {0.1, 0.1, 0.1, 0.1}, nd4j::DataType::FLOAT32); - NDArray other2('c', {2,2}, {0.1, 0.1, 0.1, 0.1}, nd4j::DataType::DOUBLE); - NDArray other3('c', {2,2}, {0., -1, -2, -3}, nd4j::DataType::INT64); - NDArray other4('c', {2,2}, {1, 0, 0.1, 0}, nd4j::DataType::BOOL); + NDArray x1('c', {2,2}, {0., 1, 2, 3}, sd::DataType::DOUBLE); + NDArray x2('c', {2,2}, {0., 1, 2, 3}, sd::DataType::INT64); + NDArray x3('c', {2,2}, {0., 1.5, 2.5, 3.5}, sd::DataType::FLOAT32); + NDArray x4('c', {2,2}, sd::DataType::DOUBLE); + NDArray x5('c', {2,2}, {0, 1.5, 2.5, 3.5}, sd::DataType::FLOAT32); + NDArray x6('c', {2,2}, {0.1, -1, -1, 0.1}, sd::DataType::BOOL); + NDArray x7('c', {2,2}, sd::DataType::BOOL); + NDArray other1('c', {2,2}, {0.1, 0.1, 0.1, 0.1}, sd::DataType::FLOAT32); + NDArray other2('c', {2,2}, {0.1, 0.1, 0.1, 0.1}, sd::DataType::DOUBLE); + NDArray other3('c', {2,2}, {0., -1, -2, -3}, sd::DataType::INT64); + NDArray other4('c', {2,2}, {1, 0, 0.1, 0}, sd::DataType::BOOL); auto func1 = [](Nd4jLong idx, float elem1, float elem2) { return elem1 + elem2 + idx; }; auto func2 = [](Nd4jLong idx, int elem1, float elem2) { return elem1 + elem2 + idx; }; @@ -1523,11 +1521,11 @@ TEST_F(MultiDataTypeTests, ndarray_applyIndexedPairwiseLambda_test1) { auto func4 = [](Nd4jLong idx, double elem1, float elem2) { return elem1 + elem2 + idx; }; auto func5 = [](Nd4jLong idx, float elem1, int elem2) { return elem1 - elem2 + idx; }; - NDArray exp1('c', {2,2}, {0.1, 2.1, 4.1, 6.1}, nd4j::DataType::DOUBLE); - NDArray exp2('c', {2,2}, {0., 1, 2, 3}, nd4j::DataType::INT64); - NDArray exp3('c', {2,2}, {0.1, 2.1, 4.1, 6.1}, nd4j::DataType::FLOAT32); - NDArray exp4('c', {2,2}, {0.1, 2.6, 4.6, 6.6}, nd4j::DataType::FLOAT32); - NDArray exp5('c', {2,2}, {0., 1, 1, 1}, nd4j::DataType::BOOL); + NDArray exp1('c', {2,2}, {0.1, 2.1, 4.1, 6.1}, sd::DataType::DOUBLE); + NDArray exp2('c', {2,2}, {0., 1, 2, 3}, sd::DataType::INT64); + NDArray exp3('c', {2,2}, {0.1, 2.1, 4.1, 6.1}, sd::DataType::FLOAT32); + NDArray exp4('c', {2,2}, {0.1, 2.6, 4.6, 6.6}, sd::DataType::FLOAT32); + NDArray exp5('c', {2,2}, {0., 1, 1, 1}, sd::DataType::BOOL); x1.applyIndexedPairwiseLambda(other2, func1, x4); ASSERT_EQ(x4, exp1); @@ -1551,25 +1549,25 @@ TEST_F(MultiDataTypeTests, ndarray_applyIndexedPairwiseLambda_test1) { ////////////////////////////////////////////////////////////////////////////// TEST_F(MultiDataTypeTests, ndarray_applyTriplewiseLambda_test1) { - NDArray x1('c', {2,2}, {0., 1, 2, 3}, nd4j::DataType::DOUBLE); - NDArray x2('c', {2,2}, {0., -1, -2, -3}, nd4j::DataType::DOUBLE); - NDArray x3('c', {2,2}, {0, -1.5, -2.5, -3.5}, nd4j::DataType::DOUBLE); - NDArray x4('c', {2,2}, nd4j::DataType::DOUBLE); + NDArray x1('c', {2,2}, {0., 1, 2, 3}, sd::DataType::DOUBLE); + NDArray x2('c', {2,2}, {0., -1, -2, -3}, sd::DataType::DOUBLE); + NDArray x3('c', {2,2}, {0, -1.5, -2.5, -3.5}, sd::DataType::DOUBLE); + NDArray x4('c', {2,2}, sd::DataType::DOUBLE); - NDArray x5('c', {2,2}, {0., 1, 2, 3}, nd4j::DataType::INT32); - NDArray x6('c', {2,2}, {0., -1, -2, -3}, nd4j::DataType::INT32); - NDArray x7('c', {2,2}, {0., 10, 20, 30}, nd4j::DataType::INT32); + NDArray x5('c', {2,2}, {0., 1, 2, 3}, sd::DataType::INT32); + NDArray x6('c', {2,2}, {0., -1, -2, -3}, sd::DataType::INT32); + NDArray x7('c', {2,2}, {0., 10, 20, 30}, sd::DataType::INT32); - NDArray x8('c', {2,2}, {0., 1, 0, 1}, nd4j::DataType::BOOL); - NDArray x9('c', {2,2}, {1., 1, 0, 1}, nd4j::DataType::BOOL); - NDArray x10('c', {2,2}, {0., 0, 0, 0}, nd4j::DataType::BOOL); + NDArray x8('c', {2,2}, {0., 1, 0, 1}, sd::DataType::BOOL); + NDArray x9('c', {2,2}, {1., 1, 0, 1}, sd::DataType::BOOL); + NDArray x10('c', {2,2}, {0., 0, 0, 0}, sd::DataType::BOOL); auto func1 = [](double elem1, float elem2, int elem3) { return elem1 + elem2 + elem3; }; auto func2 = [](float elem1, float elem2, float elem3) { return elem1 + elem2 + elem3; }; auto func3 = [](int elem1, int elem2, int elem3) { return elem1 + elem2 + elem3; }; auto func4 = [](bool elem1, bool elem2, bool elem3) { return elem1 + elem2 + elem3; }; - NDArray exp('c', {2,2}, {1., 1, 0, 1}, nd4j::DataType::BOOL); + NDArray exp('c', {2,2}, {1., 1, 0, 1}, sd::DataType::BOOL); x1.applyTriplewiseLambda(x2, x3, func1, x4); ASSERT_EQ(x4, x2); @@ -1589,51 +1587,51 @@ TEST_F(MultiDataTypeTests, ndarray_applyTriplewiseLambda_test1) { ////////////////////////////////////////////////////////////////////////////// TEST_F(MultiDataTypeTests, ndarray_applyIndexReduce_test1) { - NDArray x1('c', {2,3}, {0, 1, 2, 3, 4, 5}, nd4j::DataType::DOUBLE); - NDArray exp1('c', {}, std::vector{5}, nd4j::DataType::INT64); - NDArray exp2('c', {2}, {2,2}, nd4j::DataType::INT64); - NDArray exp3('c', {3}, {1,1,1}, nd4j::DataType::INT64); + NDArray x1('c', {2,3}, {0, 1, 2, 3, 4, 5}, sd::DataType::DOUBLE); + NDArray exp1('c', {}, std::vector{5}, sd::DataType::INT64); + NDArray exp2('c', {2}, {2,2}, sd::DataType::INT64); + NDArray exp3('c', {3}, {1,1,1}, sd::DataType::INT64); - NDArray scalar = x1.applyIndexReduce(nd4j::indexreduce::IndexMax, {0,1}); + NDArray scalar = x1.applyIndexReduce(sd::indexreduce::IndexMax, {0,1}); ASSERT_EQ(scalar, exp1); - NDArray vec1 = x1.applyIndexReduce(nd4j::indexreduce::IndexMax, {1}); + NDArray vec1 = x1.applyIndexReduce(sd::indexreduce::IndexMax, {1}); ASSERT_EQ(vec1, exp2); - NDArray vec2 = x1.applyIndexReduce(nd4j::indexreduce::IndexMax, {0}); + NDArray vec2 = x1.applyIndexReduce(sd::indexreduce::IndexMax, {0}); ASSERT_EQ(vec2, exp3); } ////////////////////////////////////////////////////////////////////////////// TEST_F(MultiDataTypeTests, ndarray_applyIndexReduce_test2) { - NDArray x1('c', {2,3}, {0, 1, 2, 3, 4, 5}, nd4j::DataType::DOUBLE); - NDArray scalar('c', {}, std::vector{5}, nd4j::DataType::INT64); - NDArray vec1('c', {2}, {2,2}, nd4j::DataType::INT64); - NDArray vec2('c', {3}, {1,1,1}, nd4j::DataType::INT64); - NDArray exp1('c', {}, std::vector{5}, nd4j::DataType::INT64); - NDArray exp2('c', {2}, {2,2}, nd4j::DataType::INT64); - NDArray exp3('c', {3}, {1,1,1}, nd4j::DataType::INT64); + NDArray x1('c', {2,3}, {0, 1, 2, 3, 4, 5}, sd::DataType::DOUBLE); + NDArray scalar('c', {}, std::vector{5}, sd::DataType::INT64); + NDArray vec1('c', {2}, {2,2}, sd::DataType::INT64); + NDArray vec2('c', {3}, {1,1,1}, sd::DataType::INT64); + NDArray exp1('c', {}, std::vector{5}, sd::DataType::INT64); + NDArray exp2('c', {2}, {2,2}, sd::DataType::INT64); + NDArray exp3('c', {3}, {1,1,1}, sd::DataType::INT64); - x1.applyIndexReduce(nd4j::indexreduce::IndexMax, scalar, {0,1}); + x1.applyIndexReduce(sd::indexreduce::IndexMax, scalar, {0,1}); ASSERT_EQ(scalar, exp1); - x1.applyIndexReduce(nd4j::indexreduce::IndexMax, vec1, {1}); + x1.applyIndexReduce(sd::indexreduce::IndexMax, vec1, {1}); ASSERT_EQ(vec1, exp2); - x1.applyIndexReduce(nd4j::indexreduce::IndexMax, vec2, {0}); + x1.applyIndexReduce(sd::indexreduce::IndexMax, vec2, {0}); ASSERT_EQ(vec2, exp3); } ////////////////////////////////////////////////////////////////////// TEST_F(MultiDataTypeTests, applyReduce3_test1) { - NDArray x1('c', {2,2}, {1,2,3,4}, nd4j::DataType::INT32); - NDArray x2('c', {2,2}, {-1,-2,-3,-4}, nd4j::DataType::INT32); - NDArray x3('c', {2,2}, {1.5,1.5,1.5,1.5}, nd4j::DataType::DOUBLE); - NDArray x4('c', {2,2}, {1,2,3,4}, nd4j::DataType::DOUBLE); - NDArray exp1('c', {}, std::vector{-30}, nd4j::DataType::FLOAT32); - NDArray exp2('c', {}, std::vector{15}, nd4j::DataType::DOUBLE); + NDArray x1('c', {2,2}, {1,2,3,4}, sd::DataType::INT32); + NDArray x2('c', {2,2}, {-1,-2,-3,-4}, sd::DataType::INT32); + NDArray x3('c', {2,2}, {1.5,1.5,1.5,1.5}, sd::DataType::DOUBLE); + NDArray x4('c', {2,2}, {1,2,3,4}, sd::DataType::DOUBLE); + NDArray exp1('c', {}, std::vector{-30}, sd::DataType::FLOAT32); + NDArray exp2('c', {}, std::vector{15}, sd::DataType::DOUBLE); auto result = x1.applyReduce3(reduce3::Dot, x2); ASSERT_EQ(result, exp1); @@ -1645,21 +1643,21 @@ TEST_F(MultiDataTypeTests, applyReduce3_test1) { ////////////////////////////////////////////////////////////////////// TEST_F(MultiDataTypeTests, applyReduce3_test2) { - NDArray x1('c', {2,2}, {1,2,3,4}, nd4j::DataType::INT32); - NDArray x2('c', {2,2}, {-1,-2,-3,-4}, nd4j::DataType::INT32); - NDArray x3('c', {2,2}, {1.5,1.5,1.5,1.5}, nd4j::DataType::DOUBLE); - NDArray x4('c', {2,2}, {1,2,3,4}, nd4j::DataType::DOUBLE); - NDArray x5('c', {2,3}, {1,2,3,4,5,6}, nd4j::DataType::INT32); - NDArray x6('c', {2,3}, {-6,-5,-4,-3,-2,-1}, nd4j::DataType::INT32); - NDArray x7('c', {2,3}, {1.5,1.5,1.5,1.5,1.5,1.5}, nd4j::DataType::DOUBLE); - NDArray x8('c', {2,3}, {1,2,3,4,5,6}, nd4j::DataType::DOUBLE); + NDArray x1('c', {2,2}, {1,2,3,4}, sd::DataType::INT32); + NDArray x2('c', {2,2}, {-1,-2,-3,-4}, sd::DataType::INT32); + NDArray x3('c', {2,2}, {1.5,1.5,1.5,1.5}, sd::DataType::DOUBLE); + NDArray x4('c', {2,2}, {1,2,3,4}, sd::DataType::DOUBLE); + NDArray x5('c', {2,3}, {1,2,3,4,5,6}, sd::DataType::INT32); + NDArray x6('c', {2,3}, {-6,-5,-4,-3,-2,-1}, sd::DataType::INT32); + NDArray x7('c', {2,3}, {1.5,1.5,1.5,1.5,1.5,1.5}, sd::DataType::DOUBLE); + NDArray x8('c', {2,3}, {1,2,3,4,5,6}, sd::DataType::DOUBLE); - NDArray exp1('c', {}, std::vector{-30}, nd4j::DataType::FLOAT32); - NDArray exp2('c', {}, std::vector{15}, nd4j::DataType::DOUBLE); - NDArray exp3('c', {3}, {-18,-20,-18}, nd4j::DataType::FLOAT32); - NDArray exp4('c', {2}, {-28,-28}, nd4j::DataType::FLOAT32); - NDArray exp5('c', {3}, {7.5,10.5,13.5}, nd4j::DataType::DOUBLE); - NDArray exp6('c', {2}, {9,22.5}, nd4j::DataType::DOUBLE); + NDArray exp1('c', {}, std::vector{-30}, sd::DataType::FLOAT32); + NDArray exp2('c', {}, std::vector{15}, sd::DataType::DOUBLE); + NDArray exp3('c', {3}, {-18,-20,-18}, sd::DataType::FLOAT32); + NDArray exp4('c', {2}, {-28,-28}, sd::DataType::FLOAT32); + NDArray exp5('c', {3}, {7.5,10.5,13.5}, sd::DataType::DOUBLE); + NDArray exp6('c', {2}, {9,22.5}, sd::DataType::DOUBLE); auto result = x1.applyReduce3(reduce3::Dot, x2, {0,1}); ASSERT_EQ(result, exp1); @@ -1683,12 +1681,12 @@ TEST_F(MultiDataTypeTests, applyReduce3_test2) { ////////////////////////////////////////////////////////////////////// TEST_F(MultiDataTypeTests, applyAllReduce3_test1) { - NDArray x1('c', {2,2}, {1,2,3,4}, nd4j::DataType::INT32); - NDArray x2('c', {2,3}, {-1,1,-1,1,-1,1}, nd4j::DataType::INT32); - NDArray x3('c', {2,3}, {1.5,1.5,1.5,1.5,1.5,1.5}, nd4j::DataType::DOUBLE); - NDArray x4('c', {2,2}, {1,2,3,4}, nd4j::DataType::DOUBLE); - NDArray exp1('c', {2,3}, {2,-2,2,2,-2,2}, nd4j::DataType::FLOAT32); - NDArray exp2('c', {2,3}, {6,6,6,9,9,9}, nd4j::DataType::DOUBLE); + NDArray x1('c', {2,2}, {1,2,3,4}, sd::DataType::INT32); + NDArray x2('c', {2,3}, {-1,1,-1,1,-1,1}, sd::DataType::INT32); + NDArray x3('c', {2,3}, {1.5,1.5,1.5,1.5,1.5,1.5}, sd::DataType::DOUBLE); + NDArray x4('c', {2,2}, {1,2,3,4}, sd::DataType::DOUBLE); + NDArray exp1('c', {2,3}, {2,-2,2,2,-2,2}, sd::DataType::FLOAT32); + NDArray exp2('c', {2,3}, {6,6,6,9,9,9}, sd::DataType::DOUBLE); auto result = x1.applyAllReduce3(reduce3::Dot, x2, {0}); ASSERT_EQ(result, exp1); @@ -1702,16 +1700,16 @@ TEST_F(MultiDataTypeTests, RowCol_test1) { if (!Environment::getInstance()->isExperimentalBuild()) return; - NDArray x1('c', {2,3}, {1,2,3,4,5,6}, nd4j::DataType::INT32); - NDArray x2('c', {2}, {0.5,0.6}, nd4j::DataType::FLOAT32); - NDArray x3('c', {3}, {1.5,1.6,1.7}, nd4j::DataType::FLOAT32); - NDArray x4('c', {2,3}, {1,2,3,4,5,6}, nd4j::DataType::DOUBLE); - NDArray x5('c', {2,3}, {1,2,3,4,5,6}, nd4j::DataType::INT32); + NDArray x1('c', {2,3}, {1,2,3,4,5,6}, sd::DataType::INT32); + NDArray x2('c', {2}, {0.5,0.6}, sd::DataType::FLOAT32); + NDArray x3('c', {3}, {1.5,1.6,1.7}, sd::DataType::FLOAT32); + NDArray x4('c', {2,3}, {1,2,3,4,5,6}, sd::DataType::DOUBLE); + NDArray x5('c', {2,3}, {1,2,3,4,5,6}, sd::DataType::INT32); - NDArray exp1('c', {2,3}, {2,3,4,5,6,7}, nd4j::DataType::INT32); - NDArray exp2('c', {2,3}, {0,1,2,3,4,5}, nd4j::DataType::INT32); - NDArray exp3('c', {2,3}, {1.5,2.5,3.5,4.6,5.6,6.6}, nd4j::DataType::DOUBLE); - NDArray exp4('c', {2,3}, {0,1,1,2,3,3}, nd4j::DataType::INT32); + NDArray exp1('c', {2,3}, {2,3,4,5,6,7}, sd::DataType::INT32); + NDArray exp2('c', {2,3}, {0,1,2,3,4,5}, sd::DataType::INT32); + NDArray exp3('c', {2,3}, {1.5,2.5,3.5,4.6,5.6,6.6}, sd::DataType::DOUBLE); + NDArray exp4('c', {2,3}, {0,1,1,2,3,3}, sd::DataType::INT32); x1.addiRowVector(x3); ASSERT_EQ(x1, exp1); @@ -1731,23 +1729,23 @@ TEST_F(MultiDataTypeTests, RowCol_test2) { if (!Environment::getInstance()->isExperimentalBuild()) return; - NDArray x1('c', {2,3}, {1,2,3,4,5,6}, nd4j::DataType::INT32); - NDArray x2('c', {2}, {0.5,0.6}, nd4j::DataType::FLOAT32); - NDArray x3('c', {3}, {1.5,1.6,1.7}, nd4j::DataType::FLOAT32); - NDArray x4('c', {2,3}, nd4j::DataType::FLOAT32); - NDArray x5('c', {3}, {1,2,3}, nd4j::DataType::INT64); - NDArray x6('c', {2,3}, nd4j::DataType::INT32); - NDArray x7('c', {3}, {1.5,1.6,1.7}, nd4j::DataType::DOUBLE); - NDArray x8('c', {2,3}, {1,2,3,4,5,6}, nd4j::DataType::FLOAT32); - NDArray x9('c', {3}, {1,2,3}, nd4j::DataType::DOUBLE); - NDArray x10('c', {2,3}, nd4j::DataType::DOUBLE); + NDArray x1('c', {2,3}, {1,2,3,4,5,6}, sd::DataType::INT32); + NDArray x2('c', {2}, {0.5,0.6}, sd::DataType::FLOAT32); + NDArray x3('c', {3}, {1.5,1.6,1.7}, sd::DataType::FLOAT32); + NDArray x4('c', {2,3}, sd::DataType::FLOAT32); + NDArray x5('c', {3}, {1,2,3}, sd::DataType::INT64); + NDArray x6('c', {2,3}, sd::DataType::INT32); + NDArray x7('c', {3}, {1.5,1.6,1.7}, sd::DataType::DOUBLE); + NDArray x8('c', {2,3}, {1,2,3,4,5,6}, sd::DataType::FLOAT32); + NDArray x9('c', {3}, {1,2,3}, sd::DataType::DOUBLE); + NDArray x10('c', {2,3}, sd::DataType::DOUBLE); - NDArray exp1('c', {2,3}, {2.5,3.6,4.7,5.5,6.6,7.7}, nd4j::DataType::FLOAT32); - NDArray exp2('c', {2,3}, {2, 4, 6, 5, 7, 9}, nd4j::DataType::INT32); - NDArray exp3('c', {2,3}, {-0.5,0.4,1.3,2.5,3.4,4.3}, nd4j::DataType::FLOAT32); - NDArray exp4('c', {2,3}, {1,4,9,4,10,18}, nd4j::DataType::DOUBLE); - NDArray exp5('c', {2,3}, {1,1,1,4,2.5,2}, nd4j::DataType::DOUBLE); - NDArray exp6('c', {2,3}, {1.5,2.5,3.5,4.6,5.6,6.6}, nd4j::DataType::FLOAT32); + NDArray exp1('c', {2,3}, {2.5,3.6,4.7,5.5,6.6,7.7}, sd::DataType::FLOAT32); + NDArray exp2('c', {2,3}, {2, 4, 6, 5, 7, 9}, sd::DataType::INT32); + NDArray exp3('c', {2,3}, {-0.5,0.4,1.3,2.5,3.4,4.3}, sd::DataType::FLOAT32); + NDArray exp4('c', {2,3}, {1,4,9,4,10,18}, sd::DataType::DOUBLE); + NDArray exp5('c', {2,3}, {1,1,1,4,2.5,2}, sd::DataType::DOUBLE); + NDArray exp6('c', {2,3}, {1.5,2.5,3.5,4.6,5.6,6.6}, sd::DataType::FLOAT32); x1.addRowVector(x3, x4); ASSERT_EQ(x4, exp1); @@ -1772,18 +1770,18 @@ TEST_F(MultiDataTypeTests, RowCol_test2) { /* TEST_F(MultiDataTypeTests, tile_test1) { - NDArray x1('c', {2,1}, {0,1}, nd4j::DataType::INT32); - NDArray x2('c', {2,1}, {0.5,1.5}, nd4j::DataType::DOUBLE); - NDArray x3('c', {2,2}, nd4j::DataType::INT32); - NDArray x4('c', {2,2}, nd4j::DataType::DOUBLE); - NDArray x5('c', {1,2}, {0.5,1.5}, nd4j::DataType::DOUBLE);; - NDArray x6('c', {2,2}, nd4j::DataType::FLOAT32); - NDArray x7('c', {2,2}, nd4j::DataType::BOOL); + NDArray x1('c', {2,1}, {0,1}, sd::DataType::INT32); + NDArray x2('c', {2,1}, {0.5,1.5}, sd::DataType::DOUBLE); + NDArray x3('c', {2,2}, sd::DataType::INT32); + NDArray x4('c', {2,2}, sd::DataType::DOUBLE); + NDArray x5('c', {1,2}, {0.5,1.5}, sd::DataType::DOUBLE);; + NDArray x6('c', {2,2}, sd::DataType::FLOAT32); + NDArray x7('c', {2,2}, sd::DataType::BOOL); - NDArray exp1('c', {2,2}, {0,0,1,1}, nd4j::DataType::DOUBLE); - NDArray exp2('c', {2,2}, {0.5,1.5,0.5,1.5}, nd4j::DataType::FLOAT32); - NDArray exp3('c', {2,2}, {0,0,1,1}, nd4j::DataType::INT32); - NDArray exp4('c', {2,2}, {0,0,1,1}, nd4j::DataType::BOOL); + NDArray exp1('c', {2,2}, {0,0,1,1}, sd::DataType::DOUBLE); + NDArray exp2('c', {2,2}, {0.5,1.5,0.5,1.5}, sd::DataType::FLOAT32); + NDArray exp3('c', {2,2}, {0,0,1,1}, sd::DataType::INT32); + NDArray exp4('c', {2,2}, {0,0,1,1}, sd::DataType::BOOL); x1.tile({1,2}, x4); ASSERT_EQ(x4, exp1); @@ -1808,10 +1806,10 @@ TEST_F(MultiDataTypeTests, tile_test1) { ////////////////////////////////////////////////////////////////////// TEST_F(MultiDataTypeTests, asT_test1) { - NDArray x1('c', {2}, {1.5, 2.5}, nd4j::DataType::FLOAT32); + NDArray x1('c', {2}, {1.5, 2.5}, sd::DataType::FLOAT32); - NDArray exp1('c', {2}, {1, 2}, nd4j::DataType::INT32); - NDArray exp2('c', {2}, {1.5, 2.5}, nd4j::DataType::DOUBLE); + NDArray exp1('c', {2}, {1, 2}, sd::DataType::INT32); + NDArray exp2('c', {2}, {1.5, 2.5}, sd::DataType::DOUBLE); auto result = new NDArray(x1.asT()); ASSERT_EQ(*result, exp1); @@ -1821,11 +1819,11 @@ TEST_F(MultiDataTypeTests, asT_test1) { ASSERT_EQ(*result, exp2); delete result; - result = new NDArray(x1.asT(nd4j::DataType::INT32)); + result = new NDArray(x1.asT(sd::DataType::INT32)); ASSERT_EQ(*result, exp1); delete result; - result = new NDArray(x1.asT(nd4j::DataType::DOUBLE)); + result = new NDArray(x1.asT(sd::DataType::DOUBLE)); ASSERT_EQ(*result, exp2); delete result; } @@ -1833,15 +1831,15 @@ TEST_F(MultiDataTypeTests, asT_test1) { ////////////////////////////////////////////////////////////////////// TEST_F(MultiDataTypeTests, assign_test2) { - NDArray x1('c', {2,3}, {1.5,2.5,3.5,4.5,5.5,6.5}, nd4j::DataType::FLOAT32); - NDArray x2('c', {3,2}, nd4j::DataType::INT32); - NDArray x3('c', {3,2}, nd4j::DataType::DOUBLE); - NDArray x4('c', {3,2}, nd4j::DataType::BOOL); - NDArray x5('c', {2,3}, {1.5,2.5,0,4.5,5.5,6.5}, nd4j::DataType::FLOAT32); + NDArray x1('c', {2,3}, {1.5,2.5,3.5,4.5,5.5,6.5}, sd::DataType::FLOAT32); + NDArray x2('c', {3,2}, sd::DataType::INT32); + NDArray x3('c', {3,2}, sd::DataType::DOUBLE); + NDArray x4('c', {3,2}, sd::DataType::BOOL); + NDArray x5('c', {2,3}, {1.5,2.5,0,4.5,5.5,6.5}, sd::DataType::FLOAT32); - NDArray exp1('c', {3,2}, {1, 2,3,4,5,6}, nd4j::DataType::INT32); - NDArray exp2('c', {3,2}, {1.5,2.5,3.5,4.5,5.5,6.5}, nd4j::DataType::DOUBLE); - NDArray exp3('c', {3,2}, {1,1,0,1,1,1}, nd4j::DataType::BOOL); + NDArray exp1('c', {3,2}, {1, 2,3,4,5,6}, sd::DataType::INT32); + NDArray exp2('c', {3,2}, {1.5,2.5,3.5,4.5,5.5,6.5}, sd::DataType::DOUBLE); + NDArray exp3('c', {3,2}, {1,1,0,1,1,1}, sd::DataType::BOOL); x2.assign(x1); ASSERT_EQ(x2, exp1); @@ -1898,10 +1896,10 @@ TEST_F(MultiDataTypeTests, Test_Cast_2) { ////////////////////////////////////////////////////////////////////// TEST_F(MultiDataTypeTests, divide_bool_test1) { - NDArray x1('c', {2,3}, {1.5,0,3.5,0,5.5,6.5}, nd4j::DataType::FLOAT32); - NDArray x2('c', {3,2}, {1,1,0,1,0,1}, nd4j::DataType::BOOL); - NDArray x3('c', {2,3}, nd4j::DataType::FLOAT32); - NDArray x4('c', {2}, nd4j::DataType::BOOL); + NDArray x1('c', {2,3}, {1.5,0,3.5,0,5.5,6.5}, sd::DataType::FLOAT32); + NDArray x2('c', {3,2}, {1,1,0,1,0,1}, sd::DataType::BOOL); + NDArray x3('c', {2,3}, sd::DataType::FLOAT32); + NDArray x4('c', {2}, sd::DataType::BOOL); try { NDArray x3 = x1 / x2; @@ -1936,7 +1934,7 @@ TEST_F(MultiDataTypeTests, divide_bool_test1) { } try { - x1.applyBroadcast(nd4j::broadcast::FloorDiv, {1}, x4, x3); + x1.applyBroadcast(sd::broadcast::FloorDiv, {1}, x4, x3); } catch (std::exception& message) { // printf("%s\n", message.what()); @@ -1956,13 +1954,13 @@ TEST_F(MultiDataTypeTests, divide_bool_test1) { ////////////////////////////////////////////////////////////////////// TEST_F(MultiDataTypeTests, aaa) { - NDArray z('c', {2,5}, {1,2,3,4,5,6,7,8,9,10}, nd4j::DataType::DOUBLE); + NDArray z('c', {2,5}, {1,2,3,4,5,6,7,8,9,10}, sd::DataType::DOUBLE); z.permutei({1,0}); - nd4j::graph::RandomGenerator gen(119,5); + sd::graph::RandomGenerator gen(119,5); ExtraArguments extras({1.5, 2.5}); - NativeOpExecutioner::execRandom(LaunchContext::defaultContext(), nd4j::random::UniformDistribution, + NativeOpExecutioner::execRandom(LaunchContext::defaultContext(), sd::random::UniformDistribution, &gen, z.buffer(), z.getShapeInfo(), z.specialBuffer(), z.specialShapeInfo(), extras.argumentsAsT()); @@ -1973,9 +1971,9 @@ TEST_F(MultiDataTypeTests, aaa) { ////////////////////////////////////////////////////////////////////// TEST_F(MultiDataTypeTests, assign_2) { - NDArray x('c', {4}, {1.5,2.5,3.5,4.5}, nd4j::DataType::FLOAT32); - NDArray y('c', {4}, nd4j::DataType::INT32); - NDArray expected('c', {4}, {1,2,3,4}, nd4j::DataType::INT32); + NDArray x('c', {4}, {1.5,2.5,3.5,4.5}, sd::DataType::FLOAT32); + NDArray y('c', {4}, sd::DataType::INT32); + NDArray expected('c', {4}, {1,2,3,4}, sd::DataType::INT32); y.assign(x); // y.printBuffer(); diff --git a/libnd4j/tests_cpu/layers_tests/MultiDeviceTests.cpp b/libnd4j/tests_cpu/layers_tests/MultiDeviceTests.cpp index efd48311b..1c12f2d72 100644 --- a/libnd4j/tests_cpu/layers_tests/MultiDeviceTests.cpp +++ b/libnd4j/tests_cpu/layers_tests/MultiDeviceTests.cpp @@ -20,15 +20,15 @@ #include "testlayers.h" #include -#include -#include -#include +#include +#include +#include #include -#include +#include #include -using namespace nd4j; +using namespace sd; class MultiDeviceTests : public testing::Test { public: diff --git a/libnd4j/tests_cpu/layers_tests/NDArrayConstructorsTests.cu b/libnd4j/tests_cpu/layers_tests/NDArrayConstructorsTests.cu index 0c0c102ac..48208d2ff 100644 --- a/libnd4j/tests_cpu/layers_tests/NDArrayConstructorsTests.cu +++ b/libnd4j/tests_cpu/layers_tests/NDArrayConstructorsTests.cu @@ -19,20 +19,20 @@ // #include "testlayers.h" -#include -#include -#include -#include +#include +#include +#include +#include #include #include #include -#include -#include +#include +#include #include -using namespace nd4j; -using namespace nd4j::graph; +using namespace sd; +using namespace sd::graph; class NDArrayConstructorsTests : public testing::Test { public: @@ -84,7 +84,7 @@ TEST_F(NDArrayConstructorsTests, test_constructor_3) { } TEST_F(NDArrayConstructorsTests, test_constructor_4) { - auto x = NDArrayFactory::create(nd4j::DataType::FLOAT32, 1.0f); + auto x = NDArrayFactory::create(sd::DataType::FLOAT32, 1.0f); ASSERT_FALSE(x.buffer() == nullptr); ASSERT_FALSE(x.specialBuffer() == nullptr); @@ -183,7 +183,7 @@ TEST_F(NDArrayConstructorsTests, test_linspace_1) { TEST_F(NDArrayConstructorsTests, test_constructor_10) { - NDArray scalar1(nd4j::DataType::DOUBLE); // scalar1 = 0 + NDArray scalar1(sd::DataType::DOUBLE); // scalar1 = 0 NDArray scalar2('c', {}, std::vector{0}); ASSERT_TRUE(scalar1.isActualOnDeviceSide()); diff --git a/libnd4j/tests_cpu/layers_tests/NDArrayCudaBasicsTests.cu b/libnd4j/tests_cpu/layers_tests/NDArrayCudaBasicsTests.cu index 46f962dda..6c37e3145 100644 --- a/libnd4j/tests_cpu/layers_tests/NDArrayCudaBasicsTests.cu +++ b/libnd4j/tests_cpu/layers_tests/NDArrayCudaBasicsTests.cu @@ -19,21 +19,21 @@ // #include "testlayers.h" -#include -#include -#include -#include +#include +#include +#include +#include #include #include #include -#include -#include +#include +#include #include #include -using namespace nd4j; -using namespace nd4j::graph; +using namespace sd; +using namespace sd::graph; class NDArrayCudaBasicsTests : public testing::Test { public: @@ -197,7 +197,7 @@ TEST_F(NDArrayCudaBasicsTests, TestAdd_2) { // allocating host-side arrays NDArray x('c', { 5 }, { 1, 2, 3, 4, 5}); NDArray y('c', { 5 }, { 1, 2, 3, 4, 5}); - NDArray z('c', { 5 }, nd4j::DataType::DOUBLE); + NDArray z('c', { 5 }, sd::DataType::DOUBLE); NDArray exp('c', { 5 }, { 2, 4, 6, 8, 10 }); @@ -422,7 +422,7 @@ TEST_F(NDArrayCudaBasicsTests, TestMultiply_2) { // allocating host-side arrays auto x = NDArrayFactory::create('c', { 5 }, { 1, 2, 3, 4, 5}); auto y = NDArrayFactory::create('c', { 5 }, { 1, 2, 3, 4, 5}); - NDArray z('c', { 5 }, nd4j::DataType::DOUBLE); + NDArray z('c', { 5 }, sd::DataType::DOUBLE); auto exp = NDArrayFactory::create('c', { 5 }, { 1, 4, 9, 16, 25 }); @@ -449,8 +449,8 @@ TEST_F(NDArrayCudaBasicsTests, TestMultiply_2) { ////////////////////////////////////////////////////////////////////////// TEST_F(NDArrayCudaBasicsTests, TestMultiply_3) { // allocating host-side arrays - NDArray x('c', { 5 }, { 1, 2, 3, 4, 5}, nd4j::DataType::DOUBLE); - NDArray y('c', { 5 }, { 1., 2., 3., 4., 5.}, nd4j::DataType::DOUBLE); + NDArray x('c', { 5 }, { 1, 2, 3, 4, 5}, sd::DataType::DOUBLE); + NDArray y('c', { 5 }, { 1., 2., 3., 4., 5.}, sd::DataType::DOUBLE); auto z = NDArrayFactory::create('c', { 5 }); auto exp = NDArrayFactory::create('c', { 5 }, { 1, 4, 9, 16, 25 }); @@ -481,8 +481,8 @@ TEST_F(NDArrayCudaBasicsTests, TestMultiply_3) { ////////////////////////////////////////////////////////////////////////// TEST_F(NDArrayCudaBasicsTests, TestMultiply_4) { // allocating host-side arrays - NDArray x('c', { 5 }, { 1, 2, 3, 4, 5}, nd4j::DataType::DOUBLE); - NDArray y('c', { 5 }, { 1., 2., 3., 4., 5.}, nd4j::DataType::DOUBLE); + NDArray x('c', { 5 }, { 1, 2, 3, 4, 5}, sd::DataType::DOUBLE); + NDArray y('c', { 5 }, { 1., 2., 3., 4., 5.}, sd::DataType::DOUBLE); //auto z = NDArrayFactory::create('c', { 5 }); auto exp = NDArrayFactory::create('c', { 5 }, { 1, 4, 9, 16, 25 }); @@ -690,10 +690,10 @@ TEST_F(NDArrayCudaBasicsTests, TestRawBroadcast_2) { // return; NDArray x = NDArrayFactory::create('c', {2,3,4}); - NDArray y('c', {2,4}, {10,20,30,40,50,60,70,80}, nd4j::DataType::DOUBLE); - NDArray z('c', {2,3,4}, {100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100}, nd4j::DataType::DOUBLE); -// NDArray exp('c', {2,3,4}, {10., 21., 32., 43., 14., 25., 36., 47., 18., 29., 40., 51., 62., 73., 84., 95., 66., 77., 88., 99., 70., 81., 92., 103}, nd4j::DataType::DOUBLE); - NDArray exp('c', {2,3,4}, {10., 40., 90., 160., 50., 120., 210., 320., 90., 200., 330., 480., 650., 840., 1050., 1280., 850., 1080., 1330., 1600., 1050., 1320., 1610., 1920.}, nd4j::DataType::DOUBLE); + NDArray y('c', {2,4}, {10,20,30,40,50,60,70,80}, sd::DataType::DOUBLE); + NDArray z('c', {2,3,4}, {100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100}, sd::DataType::DOUBLE); +// NDArray exp('c', {2,3,4}, {10., 21., 32., 43., 14., 25., 36., 47., 18., 29., 40., 51., 62., 73., 84., 95., 66., 77., 88., 99., 70., 81., 92., 103}, sd::DataType::DOUBLE); + NDArray exp('c', {2,3,4}, {10., 40., 90., 160., 50., 120., 210., 320., 90., 200., 330., 480., 650., 840., 1050., 1280., 850., 1080., 1330., 1600., 1050., 1320., 1610., 1920.}, sd::DataType::DOUBLE); x.linspace(1); x.syncToDevice(); std::vector dimensions = {0,2}; @@ -721,7 +721,7 @@ TEST_F(NDArrayCudaBasicsTests, TestRawBroadcast_2) { cudaResult = allocateDeviceMem(lc, devicePtrs, hostData); ASSERT_EQ(0, cudaResult); // call cuda kernel which calculates result - NativeOpExecutioner::execBroadcast(&lc, nd4j::broadcast::Multiply, + NativeOpExecutioner::execBroadcast(&lc, sd::broadcast::Multiply, nullptr, x.getShapeInfo(), x.specialBuffer(), x.specialShapeInfo(), nullptr, y.getShapeInfo(), y.specialBuffer(), y.specialShapeInfo(), nullptr, z.getShapeInfo(), z.specialBuffer(), z.specialShapeInfo(), @@ -749,11 +749,11 @@ TEST_F(NDArrayCudaBasicsTests, TestRawBroadcast_3) { //if (!Environment::getInstance()->isExperimentalBuild()) // return; - NDArray x('c', {2,3,4}, nd4j::DataType::DOUBLE); - NDArray y('c', {2,4}, {10,20,30,40,50,60,70,80}, nd4j::DataType::DOUBLE); - NDArray z('c', {2,3,4}, {100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100}, nd4j::DataType::DOUBLE); -// NDArray exp('c', {2,3,4}, {10., 21., 32., 43., 14., 25., 36., 47., 18., 29., 40., 51., 62., 73., 84., 95., 66., 77., 88., 99., 70., 81., 92., 103}, nd4j::DataType::DOUBLE); - NDArray exp('c', {2,3,4}, {10., 40., 90., 160., 50., 120., 210., 320., 90., 200., 330., 480., 650., 840., 1050., 1280., 850., 1080., 1330., 1600., 1050., 1320., 1610., 1920.}, nd4j::DataType::DOUBLE); + NDArray x('c', {2,3,4}, sd::DataType::DOUBLE); + NDArray y('c', {2,4}, {10,20,30,40,50,60,70,80}, sd::DataType::DOUBLE); + NDArray z('c', {2,3,4}, {100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100}, sd::DataType::DOUBLE); +// NDArray exp('c', {2,3,4}, {10., 21., 32., 43., 14., 25., 36., 47., 18., 29., 40., 51., 62., 73., 84., 95., 66., 77., 88., 99., 70., 81., 92., 103}, sd::DataType::DOUBLE); + NDArray exp('c', {2,3,4}, {10., 40., 90., 160., 50., 120., 210., 320., 90., 200., 330., 480., 650., 840., 1050., 1280., 850., 1080., 1330., 1600., 1050., 1320., 1610., 1920.}, sd::DataType::DOUBLE); x.linspace(1); x.syncToDevice(); std::vector dimensions = {0,2}; @@ -787,7 +787,7 @@ TEST_F(NDArrayCudaBasicsTests, TestRawBroadcast_3) { NDArray::registerSpecialUse({&z}, {&x, &y}); // call cuda kernel which calculates result - NativeOpExecutioner::execBroadcast(pLc, nd4j::broadcast::Multiply, + NativeOpExecutioner::execBroadcast(pLc, sd::broadcast::Multiply, nullptr, x.getShapeInfo(), x.specialBuffer(), x.specialShapeInfo(), nullptr, y.getShapeInfo(), y.specialBuffer(), y.specialShapeInfo(), nullptr, z.getShapeInfo(), z.specialBuffer(), z.specialShapeInfo(), @@ -812,8 +812,8 @@ TEST_F(NDArrayCudaBasicsTests, TestRawBroadcast_3) { TEST_F(NDArrayCudaBasicsTests, TestBroadcastMultiply_1) { // allocating host-side arrays - NDArray x('c', { 2, 3 }, { 1, 2, 3, 4, 5, 6}, nd4j::DataType::DOUBLE); - NDArray y = NDArrayFactory::create(3.); //'c', { 3 }, { 2., 3., 4.}, nd4j::DataType::DOUBLE); + NDArray x('c', { 2, 3 }, { 1, 2, 3, 4, 5, 6}, sd::DataType::DOUBLE); + NDArray y = NDArrayFactory::create(3.); //'c', { 3 }, { 2., 3., 4.}, sd::DataType::DOUBLE); //auto z = NDArrayFactory::create('c', { 5 }); auto exp = NDArrayFactory::create('c', { 2, 3 }, { 3, 6, 9, 12, 15, 18 }); @@ -842,8 +842,8 @@ TEST_F(NDArrayCudaBasicsTests, TestBroadcastMultiply_1) { TEST_F(NDArrayCudaBasicsTests, TestBroadcastMultiply_01) { // allocating host-side arrays - NDArray x('c', { 2, 3 }, { 1, 2, 3, 4, 5, 6}, nd4j::DataType::DOUBLE); - NDArray y = NDArrayFactory::create(3.); //'c', { 3 }, { 2., 3., 4.}, nd4j::DataType::DOUBLE); + NDArray x('c', { 2, 3 }, { 1, 2, 3, 4, 5, 6}, sd::DataType::DOUBLE); + NDArray y = NDArrayFactory::create(3.); //'c', { 3 }, { 2., 3., 4.}, sd::DataType::DOUBLE); auto z = NDArrayFactory::create('c', { 2, 3 }); auto exp = NDArrayFactory::create('c', { 2, 3 }, { 3, 6, 9, 12, 15, 18 }); @@ -875,8 +875,8 @@ TEST_F(NDArrayCudaBasicsTests, TestBroadcastMultiply_01) { TEST_F(NDArrayCudaBasicsTests, TestBroadcastMultiply_02) { // allocating host-side arrays - auto x = NDArrayFactory::create('c', { 2, 3 }, { 1, 2, 3, 4, 5, 6}); //, nd4j::DataType::DOUBLE); - auto y = NDArrayFactory::create('c', {2,3}, {3, 3, 3, 3, 3, 3}); //'c', { 3 }, { 2., 3., 4.}, nd4j::DataType::DOUBLE); + auto x = NDArrayFactory::create('c', { 2, 3 }, { 1, 2, 3, 4, 5, 6}); //, sd::DataType::DOUBLE); + auto y = NDArrayFactory::create('c', {2,3}, {3, 3, 3, 3, 3, 3}); //'c', { 3 }, { 2., 3., 4.}, sd::DataType::DOUBLE); auto z = NDArrayFactory::create('c', { 2, 3 }); auto exp = NDArrayFactory::create('c', { 2, 3 }, { 3, 6, 9, 12, 15, 18 }); @@ -909,8 +909,8 @@ TEST_F(NDArrayCudaBasicsTests, TestBroadcastMultiply_02) { TEST_F(NDArrayCudaBasicsTests, TestBroadcastMultiply_002) { // allocating host-side arrays - auto x = NDArrayFactory::create('c', { 2, 3 }, { 1, 2, 3, 4, 5, 6}); //, nd4j::DataType::DOUBLE); - auto y = NDArrayFactory::create('c', {2, 3}, {2., 3., 3., 3., 3., 3.}); //'c', { 3 }, { 2., 3., 4.}, nd4j::DataType::DOUBLE); + auto x = NDArrayFactory::create('c', { 2, 3 }, { 1, 2, 3, 4, 5, 6}); //, sd::DataType::DOUBLE); + auto y = NDArrayFactory::create('c', {2, 3}, {2., 3., 3., 3., 3., 3.}); //'c', { 3 }, { 2., 3., 4.}, sd::DataType::DOUBLE); auto z = NDArrayFactory::create('c', { 2, 3 }); auto exp = NDArrayFactory::create('c', { 2, 3 }, { 2, 6, 9, 12, 15, 18 }); @@ -947,10 +947,10 @@ TEST_F(NDArrayCudaBasicsTests, TestBroadcastRaw_1) { //if (!Environment::getInstance()->isExperimentalBuild()) // return; - NDArray x('c', {2,3,4}, {100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100}, nd4j::DataType::INT32); - NDArray y('c', {3}, {10, 20, 30}, nd4j::DataType::INT64); - NDArray z('c', {2,3,4}, {100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100}, nd4j::DataType::INT32); - NDArray exp('c', {2,3,4}, {10, 11, 12, 13,24, 25, 26, 27,38, 39, 40, 41,22, 23, 24, 25,36, 37, 38, 39,50, 51, 52, 53}, nd4j::DataType::INT32); + NDArray x('c', {2,3,4}, {100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100}, sd::DataType::INT32); + NDArray y('c', {3}, {10, 20, 30}, sd::DataType::INT64); + NDArray z('c', {2,3,4}, {100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100}, sd::DataType::INT32); + NDArray exp('c', {2,3,4}, {10, 11, 12, 13,24, 25, 26, 27,38, 39, 40, 41,22, 23, 24, 25,36, 37, 38, 39,50, 51, 52, 53}, sd::DataType::INT32); //real output [10, 11, 12, 13, 4, 5, 6, 7, 28, 29, 30, 31, 22, 23, 24, 25, 16, 17, 18, 19, 40, 41, 42, 43] x.linspace(0); x.syncToDevice(); @@ -983,7 +983,7 @@ TEST_F(NDArrayCudaBasicsTests, TestBroadcastRaw_1) { } // call cuda kernel which calculates result - NativeOpExecutioner::execBroadcast(pLc, nd4j::broadcast::Add, + NativeOpExecutioner::execBroadcast(pLc, sd::broadcast::Add, nullptr, x.getShapeInfo(), x.specialBuffer(), x.specialShapeInfo(), nullptr, y.getShapeInfo(), y.specialBuffer(), y.specialShapeInfo(), nullptr, z.getShapeInfo(), z.specialBuffer(), z.specialShapeInfo(), @@ -1010,8 +1010,8 @@ TEST_F(NDArrayCudaBasicsTests, TestBroadcastRaw_1) { TEST_F(NDArrayCudaBasicsTests, TestBroadcastMultiply) { // allocating host-side arrays - NDArray x('c', { 2, 3 }, { 1, 2, 3, 4, 5, 6}, nd4j::DataType::DOUBLE); - NDArray y('c', { 3 }, { 2., 3., 4.}, nd4j::DataType::DOUBLE); + NDArray x('c', { 2, 3 }, { 1, 2, 3, 4, 5, 6}, sd::DataType::DOUBLE); + NDArray y('c', { 3 }, { 2., 3., 4.}, sd::DataType::DOUBLE); //auto z = NDArrayFactory::create('c', { 5 }); auto exp = NDArrayFactory::create('c', { 2, 3 }, { 2, 6, 12, 8, 15, 24 }); @@ -1042,8 +1042,8 @@ TEST_F(NDArrayCudaBasicsTests, TestBroadcastMultiply) { TEST_F(NDArrayCudaBasicsTests, TestBroadcastMultiply_2) { // allocating host-side arrays - NDArray x('c', { 2, 3 }, { 1, 2, 3, 4, 5, 6}, nd4j::DataType::DOUBLE); - NDArray y('c', { 3 }, { 2., 3., 4.}, nd4j::DataType::DOUBLE); + NDArray x('c', { 2, 3 }, { 1, 2, 3, 4, 5, 6}, sd::DataType::DOUBLE); + NDArray y('c', { 3 }, { 2., 3., 4.}, sd::DataType::DOUBLE); //auto z = NDArrayFactory::create('c', { 5 }); auto exp = NDArrayFactory::create('c', { 2, 3 }, { 11,12, 13,14, 15, 16 }); @@ -1060,7 +1060,7 @@ TEST_F(NDArrayCudaBasicsTests, TestBroadcastMultiply_2) { //x.applyPairwiseTransform(pairwise::Multiply, &y, &z, nullptr); //x.printBuffer("23X = "); //y.printBuffer("23Y = "); - //void NDArray::applyTrueBroadcast(nd4j::BroadcastOpsTuple op, const NDArray* other, NDArray* target, const bool checkTargetShape, ExtraArguments *extraArgs) + //void NDArray::applyTrueBroadcast(sd::BroadcastOpsTuple op, const NDArray* other, NDArray* target, const bool checkTargetShape, ExtraArguments *extraArgs) x.applyTrueBroadcast(BroadcastOpsTuple::Multiply(), y, exp); // @@ -1114,8 +1114,8 @@ TEST_F(NDArrayCudaBasicsTests, TestDup1) { ////////////////////////////////////////////////////////////////////////// TEST_F(NDArrayCudaBasicsTests, equalsTo_1) { - NDArray x('c', {2,5}, {1,2,3,4,5,6,7,8,9,10}, nd4j::DataType::DOUBLE); - NDArray y('c', {2,5}, {1,2,3,4,5,6,7,8,9,10}, nd4j::DataType::DOUBLE); + NDArray x('c', {2,5}, {1,2,3,4,5,6,7,8,9,10}, sd::DataType::DOUBLE); + NDArray y('c', {2,5}, {1,2,3,4,5,6,7,8,9,10}, sd::DataType::DOUBLE); ASSERT_TRUE(x.equalsTo(y)); @@ -1128,8 +1128,8 @@ TEST_F(NDArrayCudaBasicsTests, equalsTo_1) { ////////////////////////////////////////////////////////////////////////// TEST_F(NDArrayCudaBasicsTests, equalsTo_2) { - NDArray x('c', {2,5}, {1,2,3,4,5,6,7,8,10,10}, nd4j::DataType::DOUBLE); - NDArray y('c', {2,5}, {1,2,5,4,5,6,7,8,9,10}, nd4j::DataType::DOUBLE); + NDArray x('c', {2,5}, {1,2,3,4,5,6,7,8,10,10}, sd::DataType::DOUBLE); + NDArray y('c', {2,5}, {1,2,5,4,5,6,7,8,9,10}, sd::DataType::DOUBLE); ASSERT_FALSE(x.equalsTo(y)); @@ -1142,8 +1142,8 @@ TEST_F(NDArrayCudaBasicsTests, equalsTo_2) { ////////////////////////////////////////////////////////////////////////// TEST_F(NDArrayCudaBasicsTests, equalsTo_3) { - NDArray x('c', {2,5}, {1,2,3,4,5,6,7,8,9,10}, nd4j::DataType::DOUBLE); - NDArray y('c', {2,5}, {1.f,2.f,3.f,4.f,5.f,6.f,7.f,8.f,9.f,10.f}, nd4j::DataType::FLOAT32); + NDArray x('c', {2,5}, {1,2,3,4,5,6,7,8,9,10}, sd::DataType::DOUBLE); + NDArray y('c', {2,5}, {1.f,2.f,3.f,4.f,5.f,6.f,7.f,8.f,9.f,10.f}, sd::DataType::FLOAT32); ASSERT_FALSE(x.equalsTo(y)); @@ -1156,78 +1156,78 @@ TEST_F(NDArrayCudaBasicsTests, equalsTo_3) { //////////////////////////////////////////////////////////////////////////// TEST_F(NDArrayCudaBasicsTests, applyReduce3_1) { - NDArray x('c', {2,3,4}, {-10,-9,-8,-7,-6,-5,-4,-3,-2,-1,0,1,2,3,4,5,6,7,8,9,10,11,12,13}, nd4j::DataType::INT32); - NDArray x2('c', {2,3,4}, {-10,-9,-8,-7,-6,-5,-4,-3,-2,-1,0,1,2,3,4,5,6,7,8,9,10,11,12,13}, nd4j::DataType::INT32); - NDArray y('c', {2,3,4}, {-2,3,-4,5,-2,3,-4,5,-2,3,-4,5,-2,3,-4,5,-2,3,-4,5,-2,3,-4,5}, nd4j::DataType::INT32); - NDArray k('c', {2,3}, {-2,3,-4,5,-2,3}, nd4j::DataType::INT32); - NDArray k2('c', {3,2}, {-2,3,-4,5,-2,3}, nd4j::DataType::INT32); + NDArray x('c', {2,3,4}, {-10,-9,-8,-7,-6,-5,-4,-3,-2,-1,0,1,2,3,4,5,6,7,8,9,10,11,12,13}, sd::DataType::INT32); + NDArray x2('c', {2,3,4}, {-10,-9,-8,-7,-6,-5,-4,-3,-2,-1,0,1,2,3,4,5,6,7,8,9,10,11,12,13}, sd::DataType::INT32); + NDArray y('c', {2,3,4}, {-2,3,-4,5,-2,3,-4,5,-2,3,-4,5,-2,3,-4,5,-2,3,-4,5,-2,3,-4,5}, sd::DataType::INT32); + NDArray k('c', {2,3}, {-2,3,-4,5,-2,3}, sd::DataType::INT32); + NDArray k2('c', {3,2}, {-2,3,-4,5,-2,3}, sd::DataType::INT32); - NDArray exp1('c', {3}, {4.f, 20.f, 36.f}, nd4j::DataType::FLOAT32); - NDArray exp2('c', {2,3}, {-10.f, -2.f, 6.f,14.f, 22.f, 30.f}, nd4j::DataType::FLOAT32); - NDArray exp3('c', {4}, {38.f, 41.f, 44.f, 47.f}, nd4j::DataType::FLOAT32); - NDArray exp4('c', {4}, {114.f, 117.f, 120.f, 123.f}, nd4j::DataType::FLOAT32); + NDArray exp1('c', {3}, {4.f, 20.f, 36.f}, sd::DataType::FLOAT32); + NDArray exp2('c', {2,3}, {-10.f, -2.f, 6.f,14.f, 22.f, 30.f}, sd::DataType::FLOAT32); + NDArray exp3('c', {4}, {38.f, 41.f, 44.f, 47.f}, sd::DataType::FLOAT32); + NDArray exp4('c', {4}, {114.f, 117.f, 120.f, 123.f}, sd::DataType::FLOAT32); - NDArray z = x.applyReduce3(nd4j::reduce3::Dot, y, {0,2}); + NDArray z = x.applyReduce3(sd::reduce3::Dot, y, {0,2}); ASSERT_TRUE(z.equalsTo(&exp1)); - z = x.applyReduce3(nd4j::reduce3::Dot, k, {0,1}); + z = x.applyReduce3(sd::reduce3::Dot, k, {0,1}); ASSERT_TRUE(z.equalsTo(&exp3)); x.permutei({0,2,1}); y.permutei({0,2,1}); - z = y.applyReduce3(nd4j::reduce3::Dot, x, {1}); + z = y.applyReduce3(sd::reduce3::Dot, x, {1}); ASSERT_TRUE(z.equalsTo(&exp2)); x2.permutei({1,0,2}); - z = x2.applyReduce3(nd4j::reduce3::Dot, k2, {0,1}); + z = x2.applyReduce3(sd::reduce3::Dot, k2, {0,1}); ASSERT_TRUE(z.equalsTo(&exp4)); } //////////////////////////////////////////////////////////////////////////// TEST_F(NDArrayCudaBasicsTests, applyReduce3_2) { - NDArray x('c', {2,3,4}, {-10,-9,-8.5,-7,-6,-5,-4,-3,-2,-1,0,1,2,3,4,5,6,7,8,9,10,11,12,13}, nd4j::DataType::DOUBLE); - NDArray x2('c', {2,3,4}, {-10,-9,-8,-7,-6,-5,-4,-3,-2,-1,0.5,1,2,3,4,5,6,7,8,9,10,11,12,13}, nd4j::DataType::DOUBLE); - NDArray y('c', {2,3,4}, {-2,3,-4,5,-2,3,-4,5,-2,3,-4,5,-2.5,3,-4,5,-2,3,-4,5,-2,3,-4,5}, nd4j::DataType::DOUBLE); - NDArray k('c', {2,3}, {-2,3,-4,5.5,-2,3}, nd4j::DataType::DOUBLE); - NDArray k2('c', {3,2}, {-2,3,-4,5,-2,3.5}, nd4j::DataType::DOUBLE); + NDArray x('c', {2,3,4}, {-10,-9,-8.5,-7,-6,-5,-4,-3,-2,-1,0,1,2,3,4,5,6,7,8,9,10,11,12,13}, sd::DataType::DOUBLE); + NDArray x2('c', {2,3,4}, {-10,-9,-8,-7,-6,-5,-4,-3,-2,-1,0.5,1,2,3,4,5,6,7,8,9,10,11,12,13}, sd::DataType::DOUBLE); + NDArray y('c', {2,3,4}, {-2,3,-4,5,-2,3,-4,5,-2,3,-4,5,-2.5,3,-4,5,-2,3,-4,5,-2,3,-4,5}, sd::DataType::DOUBLE); + NDArray k('c', {2,3}, {-2,3,-4,5.5,-2,3}, sd::DataType::DOUBLE); + NDArray k2('c', {3,2}, {-2,3,-4,5,-2,3.5}, sd::DataType::DOUBLE); - NDArray exp1('c', {3}, {5., 20., 36.}, nd4j::DataType::DOUBLE); - NDArray exp2('c', {2,3}, {-8., -2., 6., 13., 22., 30.}, nd4j::DataType::DOUBLE); - NDArray exp3('c', {4}, {39., 42.5, 47., 49.5}, nd4j::DataType::DOUBLE); - NDArray exp4('c', {4}, {119., 122.5, 125., 129.5}, nd4j::DataType::DOUBLE); + NDArray exp1('c', {3}, {5., 20., 36.}, sd::DataType::DOUBLE); + NDArray exp2('c', {2,3}, {-8., -2., 6., 13., 22., 30.}, sd::DataType::DOUBLE); + NDArray exp3('c', {4}, {39., 42.5, 47., 49.5}, sd::DataType::DOUBLE); + NDArray exp4('c', {4}, {119., 122.5, 125., 129.5}, sd::DataType::DOUBLE); - NDArray z = x.applyReduce3(nd4j::reduce3::Dot, y, {0,2}); + NDArray z = x.applyReduce3(sd::reduce3::Dot, y, {0,2}); ASSERT_TRUE(z.equalsTo(&exp1)); - z = x.applyReduce3(nd4j::reduce3::Dot, k, {0,1}); + z = x.applyReduce3(sd::reduce3::Dot, k, {0,1}); ASSERT_TRUE(z.equalsTo(&exp3)); x.permutei({0,2,1}); y.permutei({0,2,1}); - z = y.applyReduce3(nd4j::reduce3::Dot, x, {1}); + z = y.applyReduce3(sd::reduce3::Dot, x, {1}); ASSERT_TRUE(z.equalsTo(&exp2)); x2.permutei({1,0,2}); - z = x2.applyReduce3(nd4j::reduce3::Dot, k2, {0,1}); + z = x2.applyReduce3(sd::reduce3::Dot, k2, {0,1}); ASSERT_TRUE(z.equalsTo(&exp4)); } //////////////////////////////////////////////////////////////////////////// TEST_F(NDArrayCudaBasicsTests, applyReduce3_3) { - NDArray x1('c', {2,2,2}, {1,2,3,4,5,6,7,8}, nd4j::DataType::INT32); - NDArray x2('c', {2,2,2}, {-1,-2,-3,-4,-5,-6,-7,-8}, nd4j::DataType::INT32); - NDArray x3('c', {3,2}, {1.5,1.5,1.5,1.5,1.5,1.5}, nd4j::DataType::DOUBLE); - NDArray x4('c', {3,2}, {1,2,3,4,5,6}, nd4j::DataType::DOUBLE); + NDArray x1('c', {2,2,2}, {1,2,3,4,5,6,7,8}, sd::DataType::INT32); + NDArray x2('c', {2,2,2}, {-1,-2,-3,-4,-5,-6,-7,-8}, sd::DataType::INT32); + NDArray x3('c', {3,2}, {1.5,1.5,1.5,1.5,1.5,1.5}, sd::DataType::DOUBLE); + NDArray x4('c', {3,2}, {1,2,3,4,5,6}, sd::DataType::DOUBLE); - NDArray exp1('c', {}, std::vector{-204}, nd4j::DataType::FLOAT32); - NDArray exp2('c', {}, std::vector{31.5}, nd4j::DataType::DOUBLE); + NDArray exp1('c', {}, std::vector{-204}, sd::DataType::FLOAT32); + NDArray exp2('c', {}, std::vector{31.5}, sd::DataType::DOUBLE); auto z = x1.applyReduce3(reduce3::Dot, x2); @@ -1251,17 +1251,17 @@ TEST_F(NDArrayCudaBasicsTests, applyReduce3_3) { //////////////////////////////////////////////////////////////////////////// TEST_F(NDArrayCudaBasicsTests, applyAllReduce3_1) { - NDArray x1('c', {2,3,2}, {1,2,3,4,5,6,7,8,-1,-2,-3,-4,}, nd4j::DataType::INT32); - NDArray x2('c', {2,2,2}, {-1,-2,-3,-4,-5,-6,-7,-8}, nd4j::DataType::INT32); - NDArray x3('c', {3,2}, {1.5,1.5,1.5,1.5,1.5,1.5}, nd4j::DataType::DOUBLE); - NDArray x4('c', {3,2}, {1,2,3,4,5,6}, nd4j::DataType::DOUBLE); + NDArray x1('c', {2,3,2}, {1,2,3,4,5,6,7,8,-1,-2,-3,-4,}, sd::DataType::INT32); + NDArray x2('c', {2,2,2}, {-1,-2,-3,-4,-5,-6,-7,-8}, sd::DataType::INT32); + NDArray x3('c', {3,2}, {1.5,1.5,1.5,1.5,1.5,1.5}, sd::DataType::DOUBLE); + NDArray x4('c', {3,2}, {1,2,3,4,5,6}, sd::DataType::DOUBLE); - NDArray exp1('c', {3,2}, {-88.f, -124.f, 6.f, -2.f, 22.f, 14.f}, nd4j::DataType::FLOAT32); + NDArray exp1('c', {3,2}, {-88.f, -124.f, 6.f, -2.f, 22.f, 14.f}, sd::DataType::FLOAT32); NDArray exp2('c', {6,4}, {-36.f, -44.f, -52.f, -60.f,-42.f, -52.f, -62.f, -72.f, 2.f, 0.f, -2.f, -4.f, 6.f, 4.f, 2.f, 0.f, 10.f, 8.f, 6.f, 4.f, 14.f, 12.f, 10.f, 8.f}, - nd4j::DataType::FLOAT32); - NDArray exp3('c', {1,1}, std::vector{31.5}, nd4j::DataType::DOUBLE); - NDArray exp4('c', {3,3}, {4.5, 10.5, 16.5,4.5, 10.5, 16.5,4.5, 10.5, 16.5}, nd4j::DataType::DOUBLE); + sd::DataType::FLOAT32); + NDArray exp3('c', {1,1}, std::vector{31.5}, sd::DataType::DOUBLE); + NDArray exp4('c', {3,3}, {4.5, 10.5, 16.5,4.5, 10.5, 16.5,4.5, 10.5, 16.5}, sd::DataType::DOUBLE); auto z = x1.applyAllReduce3(reduce3::Dot, x2, {0,2}); ASSERT_TRUE(z.equalsTo(&exp1)); @@ -1290,38 +1290,38 @@ TEST_F(NDArrayCudaBasicsTests, applyAllReduce3_1) { ////////////////////////////////////////////////////////////////////////////// TEST_F(NDArrayCudaBasicsTests, applyIndexReduce_test1) { - NDArray x('c', {2,3}, {0, 10, 1, 2, 2.5,-4}, nd4j::DataType::DOUBLE); + NDArray x('c', {2,3}, {0, 10, 1, 2, 2.5,-4}, sd::DataType::DOUBLE); - NDArray scalar('c', {}, std::vector{100}, nd4j::DataType::INT64); - NDArray vec1('c', {2}, {100,100}, nd4j::DataType::INT64); - NDArray vec2('c', {3}, {100,100,100}, nd4j::DataType::INT64); + NDArray scalar('c', {}, std::vector{100}, sd::DataType::INT64); + NDArray vec1('c', {2}, {100,100}, sd::DataType::INT64); + NDArray vec2('c', {3}, {100,100,100}, sd::DataType::INT64); - NDArray exp1('c', {}, std::vector{1}, nd4j::DataType::INT64); - NDArray exp2('c', {2}, {1,1}, nd4j::DataType::INT64); - NDArray exp3('c', {3}, {1,0,0}, nd4j::DataType::INT64); + NDArray exp1('c', {}, std::vector{1}, sd::DataType::INT64); + NDArray exp2('c', {2}, {1,1}, sd::DataType::INT64); + NDArray exp3('c', {3}, {1,0,0}, sd::DataType::INT64); - NDArray exp4('c', {}, std::vector{2}, nd4j::DataType::INT64); - NDArray exp5('c', {2}, {1,1}, nd4j::DataType::INT64); - NDArray exp6('c', {3}, {1,0,0}, nd4j::DataType::INT64); + NDArray exp4('c', {}, std::vector{2}, sd::DataType::INT64); + NDArray exp5('c', {2}, {1,1}, sd::DataType::INT64); + NDArray exp6('c', {3}, {1,0,0}, sd::DataType::INT64); - x.applyIndexReduce(nd4j::indexreduce::IndexMax, scalar, {0,1}); + x.applyIndexReduce(sd::indexreduce::IndexMax, scalar, {0,1}); ASSERT_TRUE(scalar.equalsTo(&exp1)); - x.applyIndexReduce(nd4j::indexreduce::IndexMax, vec1, {1}); + x.applyIndexReduce(sd::indexreduce::IndexMax, vec1, {1}); ASSERT_TRUE(vec1.equalsTo(&exp2)); - x.applyIndexReduce(nd4j::indexreduce::IndexMax, vec2, {0}); + x.applyIndexReduce(sd::indexreduce::IndexMax, vec2, {0}); ASSERT_TRUE(vec2.equalsTo(&exp3)); x.permutei({1,0}); - x.applyIndexReduce(nd4j::indexreduce::IndexMax, scalar, {0,1}); + x.applyIndexReduce(sd::indexreduce::IndexMax, scalar, {0,1}); ASSERT_TRUE(scalar.equalsTo(&exp4)); - x.applyIndexReduce(nd4j::indexreduce::IndexMax, vec1, {0}); + x.applyIndexReduce(sd::indexreduce::IndexMax, vec1, {0}); ASSERT_TRUE(vec1.equalsTo(&exp5)); - x.applyIndexReduce(nd4j::indexreduce::IndexMax, vec2, {1}); + x.applyIndexReduce(sd::indexreduce::IndexMax, vec2, {1}); ASSERT_TRUE(vec2.equalsTo(&exp6)); } @@ -1329,104 +1329,104 @@ TEST_F(NDArrayCudaBasicsTests, applyIndexReduce_test1) { ////////////////////////////////////////////////////////////////////////////// TEST_F(NDArrayCudaBasicsTests, applyIndexReduce_test2) { - NDArray x('c', {2,3}, {0, 10, 1, 2, 2.5,-4}, nd4j::DataType::DOUBLE); + NDArray x('c', {2,3}, {0, 10, 1, 2, 2.5,-4}, sd::DataType::DOUBLE); - NDArray exp1('c', {}, std::vector{1}, nd4j::DataType::INT64); - NDArray exp2('c', {2}, {1,1}, nd4j::DataType::INT64); - NDArray exp3('c', {3}, {1,0,0}, nd4j::DataType::INT64); + NDArray exp1('c', {}, std::vector{1}, sd::DataType::INT64); + NDArray exp2('c', {2}, {1,1}, sd::DataType::INT64); + NDArray exp3('c', {3}, {1,0,0}, sd::DataType::INT64); - NDArray exp4('c', {}, std::vector{2}, nd4j::DataType::INT64); - NDArray exp5('c', {2}, {1,1}, nd4j::DataType::INT64); - NDArray exp6('c', {3}, {1,0,0}, nd4j::DataType::INT64); + NDArray exp4('c', {}, std::vector{2}, sd::DataType::INT64); + NDArray exp5('c', {2}, {1,1}, sd::DataType::INT64); + NDArray exp6('c', {3}, {1,0,0}, sd::DataType::INT64); - auto z = x.applyIndexReduce(nd4j::indexreduce::IndexMax, {0,1}); + auto z = x.applyIndexReduce(sd::indexreduce::IndexMax, {0,1}); ASSERT_TRUE(z.equalsTo(&exp1)); - z = x.applyIndexReduce(nd4j::indexreduce::IndexMax, {1}); + z = x.applyIndexReduce(sd::indexreduce::IndexMax, {1}); ASSERT_TRUE(z.equalsTo(&exp2)); - z = x.applyIndexReduce(nd4j::indexreduce::IndexMax, {0}); + z = x.applyIndexReduce(sd::indexreduce::IndexMax, {0}); ASSERT_TRUE(z.equalsTo(&exp3)); x.permutei({1,0}); - z = x.applyIndexReduce(nd4j::indexreduce::IndexMax, {0,1}); + z = x.applyIndexReduce(sd::indexreduce::IndexMax, {0,1}); ASSERT_TRUE(z.equalsTo(&exp4)); - z = x.applyIndexReduce(nd4j::indexreduce::IndexMax, {0}); + z = x.applyIndexReduce(sd::indexreduce::IndexMax, {0}); ASSERT_TRUE(z.equalsTo(&exp5)); - z = x.applyIndexReduce(nd4j::indexreduce::IndexMax, {1}); + z = x.applyIndexReduce(sd::indexreduce::IndexMax, {1}); ASSERT_TRUE(z.equalsTo(&exp6)); } //////////////////////////////////////////////////////////////////////////////// TEST_F(NDArrayCudaBasicsTests, reduceAlongDimension_float_test1) { - NDArray x('c', {2,3,2}, {1,2,3,4,5,6,7,8,-1,-2,-3,-4,}, nd4j::DataType::INT32); + NDArray x('c', {2,3,2}, {1,2,3,4,5,6,7,8,-1,-2,-3,-4,}, sd::DataType::INT32); - NDArray z1('c', {}, std::vector{100}, nd4j::DataType::DOUBLE); - NDArray z2('c', {2,2}, {100,100,100,100}, nd4j::DataType::FLOAT32); - NDArray z3('c', {3}, {100,100,100}, nd4j::DataType::DOUBLE); - NDArray z4('c', {3,2}, {100,100,100,100,100,100}, nd4j::DataType::FLOAT32); - NDArray z5('c', {2}, {100,100}, nd4j::DataType::FLOAT32); + NDArray z1('c', {}, std::vector{100}, sd::DataType::DOUBLE); + NDArray z2('c', {2,2}, {100,100,100,100}, sd::DataType::FLOAT32); + NDArray z3('c', {3}, {100,100,100}, sd::DataType::DOUBLE); + NDArray z4('c', {3,2}, {100,100,100,100,100,100}, sd::DataType::FLOAT32); + NDArray z5('c', {2}, {100,100}, sd::DataType::FLOAT32); - NDArray exp1('c', {}, std::vector{2.166667}, nd4j::DataType::DOUBLE); - NDArray exp2('c', {2,2}, {3.f,4.f,1.f,0.666667f}, nd4j::DataType::FLOAT32); - NDArray exp3('c', {3}, {4.5,1,1}, nd4j::DataType::DOUBLE); - NDArray exp4('c', {3,2}, {4,5,1,1,1,1}, nd4j::DataType::FLOAT32); - NDArray exp5('c', {2}, {3.5f,0.833333f}, nd4j::DataType::FLOAT32); + NDArray exp1('c', {}, std::vector{2.166667}, sd::DataType::DOUBLE); + NDArray exp2('c', {2,2}, {3.f,4.f,1.f,0.666667f}, sd::DataType::FLOAT32); + NDArray exp3('c', {3}, {4.5,1,1}, sd::DataType::DOUBLE); + NDArray exp4('c', {3,2}, {4,5,1,1,1,1}, sd::DataType::FLOAT32); + NDArray exp5('c', {2}, {3.5f,0.833333f}, sd::DataType::FLOAT32); - x.reduceAlongDimension(nd4j::reduce::Mean, z1, {0,1,2}); + x.reduceAlongDimension(sd::reduce::Mean, z1, {0,1,2}); ASSERT_TRUE(z1.equalsTo(&exp1)); - x.reduceAlongDimension(nd4j::reduce::Mean, z2, {1}); + x.reduceAlongDimension(sd::reduce::Mean, z2, {1}); ASSERT_TRUE(z2.equalsTo(&exp2)); - x.reduceAlongDimension(nd4j::reduce::Mean, z3, {0,2}); + x.reduceAlongDimension(sd::reduce::Mean, z3, {0,2}); ASSERT_TRUE(z3.equalsTo(&exp3)); x.permutei({1,0,2}); // 3x2x2 - x.reduceAlongDimension(nd4j::reduce::Mean, z1, {0,1,2}); + x.reduceAlongDimension(sd::reduce::Mean, z1, {0,1,2}); ASSERT_TRUE(z1.equalsTo(&exp1)); - x.reduceAlongDimension(nd4j::reduce::Mean, z4, {1}); + x.reduceAlongDimension(sd::reduce::Mean, z4, {1}); ASSERT_TRUE(z4.equalsTo(&exp4)); - x.reduceAlongDimension(nd4j::reduce::Mean, z5, {0,2}); + x.reduceAlongDimension(sd::reduce::Mean, z5, {0,2}); ASSERT_TRUE(z5.equalsTo(&exp5)); } //////////////////////////////////////////////////////////////////////////////// TEST_F(NDArrayCudaBasicsTests, reduceAlongDimension_float_test2) { - NDArray x('c', {2,3,2}, {1,2,3,4,5,6,7,8,-1,-2,-3,-4,}, nd4j::DataType::DOUBLE); + NDArray x('c', {2,3,2}, {1,2,3,4,5,6,7,8,-1,-2,-3,-4,}, sd::DataType::DOUBLE); - NDArray exp1('c', {}, std::vector{2.166667}, nd4j::DataType::DOUBLE); - NDArray exp2('c', {2,2}, {3,4,1,0.666667}, nd4j::DataType::DOUBLE); - NDArray exp3('c', {3}, {4.5,1,1}, nd4j::DataType::DOUBLE); - NDArray exp4('c', {3,2}, {4,5,1,1,1,1}, nd4j::DataType::DOUBLE); - NDArray exp5('c', {2}, {3.5,0.833333}, nd4j::DataType::DOUBLE); + NDArray exp1('c', {}, std::vector{2.166667}, sd::DataType::DOUBLE); + NDArray exp2('c', {2,2}, {3,4,1,0.666667}, sd::DataType::DOUBLE); + NDArray exp3('c', {3}, {4.5,1,1}, sd::DataType::DOUBLE); + NDArray exp4('c', {3,2}, {4,5,1,1,1,1}, sd::DataType::DOUBLE); + NDArray exp5('c', {2}, {3.5,0.833333}, sd::DataType::DOUBLE); - NDArray z1 = x.reduceAlongDimension(nd4j::reduce::Mean, {0,1,2}); + NDArray z1 = x.reduceAlongDimension(sd::reduce::Mean, {0,1,2}); ASSERT_TRUE(z1.equalsTo(&exp1)); - NDArray z2 = x.reduceAlongDimension(nd4j::reduce::Mean, {1}); + NDArray z2 = x.reduceAlongDimension(sd::reduce::Mean, {1}); ASSERT_TRUE(z2.equalsTo(&exp2)); - NDArray z3 = x.reduceAlongDimension(nd4j::reduce::Mean, {0,2}); + NDArray z3 = x.reduceAlongDimension(sd::reduce::Mean, {0,2}); ASSERT_TRUE(z3.equalsTo(&exp3)); x.permutei({1,0,2}); // 3x2x2 - NDArray z4 = x.reduceAlongDimension(nd4j::reduce::Mean, {0,1,2}); + NDArray z4 = x.reduceAlongDimension(sd::reduce::Mean, {0,1,2}); ASSERT_TRUE(z4.equalsTo(&exp1)); - NDArray z5 = x.reduceAlongDimension(nd4j::reduce::Mean, {1}); + NDArray z5 = x.reduceAlongDimension(sd::reduce::Mean, {1}); ASSERT_TRUE(z5.equalsTo(&exp4)); - NDArray z6 = x.reduceAlongDimension(nd4j::reduce::Mean, {0,2}); + NDArray z6 = x.reduceAlongDimension(sd::reduce::Mean, {0,2}); ASSERT_TRUE(z6.equalsTo(&exp5)); } @@ -1475,210 +1475,210 @@ TEST_F(NDArrayCudaBasicsTests, EqualityTest1) { //////////////////////////////////////////////////////////////////////////////// TEST_F(NDArrayCudaBasicsTests, reduceAlongDimension_same_test1) { - NDArray x('c', {2,3,2}, {1.5f,2.f,3.f,4.f,5.f,6.f,7.5f,8.f,-1.f,-2.f,-3.5f,-4.f}, nd4j::DataType::FLOAT32); + NDArray x('c', {2,3,2}, {1.5f,2.f,3.f,4.f,5.f,6.f,7.5f,8.f,-1.f,-2.f,-3.5f,-4.f}, sd::DataType::FLOAT32); - NDArray z1('c', {}, std::vector{100}, nd4j::DataType::FLOAT32); - NDArray z2('c', {2,2}, {100,100,100,100}, nd4j::DataType::FLOAT32); - NDArray z3('c', {3}, {100,100,100}, nd4j::DataType::FLOAT32); - NDArray z4('c', {3,2}, {100,100,100,100,100,100}, nd4j::DataType::FLOAT32); - NDArray z5('c', {2}, {100,100}, nd4j::DataType::FLOAT32); + NDArray z1('c', {}, std::vector{100}, sd::DataType::FLOAT32); + NDArray z2('c', {2,2}, {100,100,100,100}, sd::DataType::FLOAT32); + NDArray z3('c', {3}, {100,100,100}, sd::DataType::FLOAT32); + NDArray z4('c', {3,2}, {100,100,100,100,100,100}, sd::DataType::FLOAT32); + NDArray z5('c', {2}, {100,100}, sd::DataType::FLOAT32); - NDArray exp1('c', {}, std::vector{26.5f}, nd4j::DataType::FLOAT32); - NDArray exp2('c', {2,2}, {9.5f,12.f,3.f,2.f}, nd4j::DataType::FLOAT32); - NDArray exp3('c', {3}, {19.f,4.f,3.5f}, nd4j::DataType::FLOAT32); - NDArray exp4('c', {3,2}, {9.f,10.f,2.f,2.f,1.5f,2.f}, nd4j::DataType::FLOAT32); - NDArray exp5('c', {2}, {21.5f,5.f}, nd4j::DataType::FLOAT32); + NDArray exp1('c', {}, std::vector{26.5f}, sd::DataType::FLOAT32); + NDArray exp2('c', {2,2}, {9.5f,12.f,3.f,2.f}, sd::DataType::FLOAT32); + NDArray exp3('c', {3}, {19.f,4.f,3.5f}, sd::DataType::FLOAT32); + NDArray exp4('c', {3,2}, {9.f,10.f,2.f,2.f,1.5f,2.f}, sd::DataType::FLOAT32); + NDArray exp5('c', {2}, {21.5f,5.f}, sd::DataType::FLOAT32); - x.reduceAlongDimension(nd4j::reduce::Sum, z1, {0,1,2}); + x.reduceAlongDimension(sd::reduce::Sum, z1, {0,1,2}); ASSERT_TRUE(z1.equalsTo(&exp1)); - x.reduceAlongDimension(nd4j::reduce::Sum, z2, {1}); + x.reduceAlongDimension(sd::reduce::Sum, z2, {1}); ASSERT_TRUE(z2.equalsTo(&exp2)); - x.reduceAlongDimension(nd4j::reduce::Sum, z3, {0,2}); + x.reduceAlongDimension(sd::reduce::Sum, z3, {0,2}); ASSERT_TRUE(z3.equalsTo(&exp3)); x.permutei({1,0,2}); // 3x2x2 - x.reduceAlongDimension(nd4j::reduce::Sum, z1, {0,1,2}); + x.reduceAlongDimension(sd::reduce::Sum, z1, {0,1,2}); ASSERT_TRUE(z1.equalsTo(&exp1)); - x.reduceAlongDimension(nd4j::reduce::Sum, z4, {1}); + x.reduceAlongDimension(sd::reduce::Sum, z4, {1}); ASSERT_TRUE(z4.equalsTo(&exp4)); - x.reduceAlongDimension(nd4j::reduce::Sum, z5, {0,2}); + x.reduceAlongDimension(sd::reduce::Sum, z5, {0,2}); ASSERT_TRUE(z5.equalsTo(&exp5)); } //////////////////////////////////////////////////////////////////////////////// TEST_F(NDArrayCudaBasicsTests, reduceAlongDimension_same_test2) { - NDArray x('c', {2,3,2}, {1.5,2,3,4,5,6,7.5,8,-1,-2,-3.5,-4,}, nd4j::DataType::INT64); + NDArray x('c', {2,3,2}, {1.5,2,3,4,5,6,7.5,8,-1,-2,-3.5,-4,}, sd::DataType::INT64); - NDArray exp1('c', {}, std::vector{26}, nd4j::DataType::INT64); - NDArray exp2('c', {2,2}, {9,12,3,2}, nd4j::DataType::INT64); - NDArray exp3('c', {3}, {18,4,4}, nd4j::DataType::INT64); - NDArray exp4('c', {3,2}, {8,10,2,2,2,2}, nd4j::DataType::INT64); - NDArray exp5('c', {2}, {21,5}, nd4j::DataType::INT64); + NDArray exp1('c', {}, std::vector{26}, sd::DataType::INT64); + NDArray exp2('c', {2,2}, {9,12,3,2}, sd::DataType::INT64); + NDArray exp3('c', {3}, {18,4,4}, sd::DataType::INT64); + NDArray exp4('c', {3,2}, {8,10,2,2,2,2}, sd::DataType::INT64); + NDArray exp5('c', {2}, {21,5}, sd::DataType::INT64); - NDArray z1 = x.reduceAlongDimension(nd4j::reduce::Sum, {0,1,2}); + NDArray z1 = x.reduceAlongDimension(sd::reduce::Sum, {0,1,2}); ASSERT_TRUE(z1.equalsTo(&exp1)); - NDArray z2 = x.reduceAlongDimension(nd4j::reduce::Sum, {1}); + NDArray z2 = x.reduceAlongDimension(sd::reduce::Sum, {1}); ASSERT_TRUE(z2.equalsTo(&exp2)); - NDArray z3 = x.reduceAlongDimension(nd4j::reduce::Sum, {0,2}); + NDArray z3 = x.reduceAlongDimension(sd::reduce::Sum, {0,2}); ASSERT_TRUE(z3.equalsTo(&exp3)); x.permutei({1,0,2}); // 3x2x2 - NDArray z4 = x.reduceAlongDimension(nd4j::reduce::Sum, {0,1,2}); + NDArray z4 = x.reduceAlongDimension(sd::reduce::Sum, {0,1,2}); ASSERT_TRUE(z4.equalsTo(&exp1)); - NDArray z5 = x.reduceAlongDimension(nd4j::reduce::Sum, {1}); + NDArray z5 = x.reduceAlongDimension(sd::reduce::Sum, {1}); ASSERT_TRUE(z5.equalsTo(&exp4)); - NDArray z6 = x.reduceAlongDimension(nd4j::reduce::Sum, {0,2}); + NDArray z6 = x.reduceAlongDimension(sd::reduce::Sum, {0,2}); ASSERT_TRUE(z6.equalsTo(&exp5)); } //////////////////////////////////////////////////////////////////////////////// TEST_F(NDArrayCudaBasicsTests, reduceAlongDimension_bool_test1) { - NDArray x('c', {2,3,2}, {0.5,2,3,-4,5,6,-7.5,8,-1,-0.5,-3.5,4}, nd4j::DataType::DOUBLE); + NDArray x('c', {2,3,2}, {0.5,2,3,-4,5,6,-7.5,8,-1,-0.5,-3.5,4}, sd::DataType::DOUBLE); - NDArray z1('c', {}, std::vector{true}, nd4j::DataType::BOOL); - NDArray z2('c', {2,2}, {true,true,true,true}, nd4j::DataType::BOOL); - NDArray z3('c', {3}, {true,true,true}, nd4j::DataType::BOOL); - NDArray z4('c', {3,2}, {true,true,true,true,true,true}, nd4j::DataType::BOOL); - NDArray z5('c', {2}, {true,true}, nd4j::DataType::BOOL); + NDArray z1('c', {}, std::vector{true}, sd::DataType::BOOL); + NDArray z2('c', {2,2}, {true,true,true,true}, sd::DataType::BOOL); + NDArray z3('c', {3}, {true,true,true}, sd::DataType::BOOL); + NDArray z4('c', {3,2}, {true,true,true,true,true,true}, sd::DataType::BOOL); + NDArray z5('c', {2}, {true,true}, sd::DataType::BOOL); - NDArray exp1('c', {}, std::vector{true}, nd4j::DataType::BOOL); - NDArray exp2('c', {2,2}, {true,true,false,true}, nd4j::DataType::BOOL); - NDArray exp3('c', {3}, {true,true,true}, nd4j::DataType::BOOL); - NDArray exp4('c', {3,2}, {true,true,true,false,true,true}, nd4j::DataType::BOOL); - NDArray exp5('c', {2}, {true,true}, nd4j::DataType::BOOL); + NDArray exp1('c', {}, std::vector{true}, sd::DataType::BOOL); + NDArray exp2('c', {2,2}, {true,true,false,true}, sd::DataType::BOOL); + NDArray exp3('c', {3}, {true,true,true}, sd::DataType::BOOL); + NDArray exp4('c', {3,2}, {true,true,true,false,true,true}, sd::DataType::BOOL); + NDArray exp5('c', {2}, {true,true}, sd::DataType::BOOL); - x.reduceAlongDimension(nd4j::reduce::IsPositive, z1, {0,1,2}); + x.reduceAlongDimension(sd::reduce::IsPositive, z1, {0,1,2}); ASSERT_TRUE(z1.equalsTo(&exp1)); - x.reduceAlongDimension(nd4j::reduce::IsPositive, z2, {1}); + x.reduceAlongDimension(sd::reduce::IsPositive, z2, {1}); ASSERT_TRUE(z2.equalsTo(&exp2)); - x.reduceAlongDimension(nd4j::reduce::IsPositive, z3, {0,2}); + x.reduceAlongDimension(sd::reduce::IsPositive, z3, {0,2}); ASSERT_TRUE(z3.equalsTo(&exp3)); x.permutei({1,0,2}); // 3x2x2 - x.reduceAlongDimension(nd4j::reduce::IsPositive, z1, {0,1,2}); + x.reduceAlongDimension(sd::reduce::IsPositive, z1, {0,1,2}); ASSERT_TRUE(z1.equalsTo(&exp1)); - x.reduceAlongDimension(nd4j::reduce::IsPositive, z4, {1}); + x.reduceAlongDimension(sd::reduce::IsPositive, z4, {1}); ASSERT_TRUE(z4.equalsTo(&exp4)); - x.reduceAlongDimension(nd4j::reduce::IsPositive, z5, {0,2}); + x.reduceAlongDimension(sd::reduce::IsPositive, z5, {0,2}); ASSERT_TRUE(z5.equalsTo(&exp5)); } //////////////////////////////////////////////////////////////////////////////// TEST_F(NDArrayCudaBasicsTests, reduceAlongDimension_bool_test2) { - NDArray x('c', {2,3,2}, {0.5,2,3,-4,5,6,-7.5,8,-1,-0.5,-3.5,4}, nd4j::DataType::INT32); + NDArray x('c', {2,3,2}, {0.5,2,3,-4,5,6,-7.5,8,-1,-0.5,-3.5,4}, sd::DataType::INT32); - NDArray exp1('c', {}, std::vector{1}, nd4j::DataType::BOOL); - NDArray exp2('c', {2,2}, {1,1,0,1}, nd4j::DataType::BOOL); - NDArray exp3('c', {3}, {1,1,1}, nd4j::DataType::BOOL); - NDArray exp4('c', {3,2}, {0,1,1,0,1,1}, nd4j::DataType::BOOL); - NDArray exp5('c', {2}, {1,1}, nd4j::DataType::BOOL); + NDArray exp1('c', {}, std::vector{1}, sd::DataType::BOOL); + NDArray exp2('c', {2,2}, {1,1,0,1}, sd::DataType::BOOL); + NDArray exp3('c', {3}, {1,1,1}, sd::DataType::BOOL); + NDArray exp4('c', {3,2}, {0,1,1,0,1,1}, sd::DataType::BOOL); + NDArray exp5('c', {2}, {1,1}, sd::DataType::BOOL); - NDArray z1 = x.reduceAlongDimension(nd4j::reduce::IsPositive, {0,1,2}); + NDArray z1 = x.reduceAlongDimension(sd::reduce::IsPositive, {0,1,2}); ASSERT_TRUE(z1.equalsTo(&exp1)); - NDArray z2 = x.reduceAlongDimension(nd4j::reduce::IsPositive, {1}); + NDArray z2 = x.reduceAlongDimension(sd::reduce::IsPositive, {1}); ASSERT_TRUE(z2.equalsTo(&exp2)); - NDArray z3 = x.reduceAlongDimension(nd4j::reduce::IsPositive, {0,2}); + NDArray z3 = x.reduceAlongDimension(sd::reduce::IsPositive, {0,2}); ASSERT_TRUE(z3.equalsTo(&exp3)); x.permutei({1,0,2}); // 3x2x2 - NDArray z4 = x.reduceAlongDimension(nd4j::reduce::IsPositive, {0,1,2}); + NDArray z4 = x.reduceAlongDimension(sd::reduce::IsPositive, {0,1,2}); ASSERT_TRUE(z4.equalsTo(&exp1)); - NDArray z5 = x.reduceAlongDimension(nd4j::reduce::IsPositive, {1}); + NDArray z5 = x.reduceAlongDimension(sd::reduce::IsPositive, {1}); ASSERT_TRUE(z5.equalsTo(&exp4)); - NDArray z6 = x.reduceAlongDimension(nd4j::reduce::IsPositive, {0,2}); + NDArray z6 = x.reduceAlongDimension(sd::reduce::IsPositive, {0,2}); ASSERT_TRUE(z6.equalsTo(&exp5)); } //////////////////////////////////////////////////////////////////////////////// TEST_F(NDArrayCudaBasicsTests, reduceAlongDimension_long_test1) { - NDArray x('c', {2,3,2}, {0.5f,2.f,3.f,-0.f,5.f,6.f,-7.5f,0.f,-1.f,-0.5f,-3.5f,4.f}, nd4j::DataType::FLOAT32); + NDArray x('c', {2,3,2}, {0.5f,2.f,3.f,-0.f,5.f,6.f,-7.5f,0.f,-1.f,-0.5f,-3.5f,4.f}, sd::DataType::FLOAT32); - NDArray z1('c', {}, std::vector{100}, nd4j::DataType::INT64); - NDArray z2('c', {2,2}, {100,100,100,100}, nd4j::DataType::INT64); - NDArray z3('c', {3}, {100,100,100}, nd4j::DataType::INT64); - NDArray z4('c', {3,2}, {100,100,100,100,100,100}, nd4j::DataType::INT64); - NDArray z5('c', {2}, {100,100}, nd4j::DataType::INT64); + NDArray z1('c', {}, std::vector{100}, sd::DataType::INT64); + NDArray z2('c', {2,2}, {100,100,100,100}, sd::DataType::INT64); + NDArray z3('c', {3}, {100,100,100}, sd::DataType::INT64); + NDArray z4('c', {3,2}, {100,100,100,100,100,100}, sd::DataType::INT64); + NDArray z5('c', {2}, {100,100}, sd::DataType::INT64); - NDArray exp1('c', {}, std::vector{2}, nd4j::DataType::INT64); - NDArray exp2('c', {2,2}, {0,1,0,1}, nd4j::DataType::INT64); - NDArray exp3('c', {3}, {1,1,0}, nd4j::DataType::INT64); - NDArray exp4('c', {3,2}, {0,1,0,1,0,0}, nd4j::DataType::INT64); - NDArray exp5('c', {2}, {1,1}, nd4j::DataType::INT64); + NDArray exp1('c', {}, std::vector{2}, sd::DataType::INT64); + NDArray exp2('c', {2,2}, {0,1,0,1}, sd::DataType::INT64); + NDArray exp3('c', {3}, {1,1,0}, sd::DataType::INT64); + NDArray exp4('c', {3,2}, {0,1,0,1,0,0}, sd::DataType::INT64); + NDArray exp5('c', {2}, {1,1}, sd::DataType::INT64); - x.reduceAlongDimension(nd4j::reduce::CountZero, z1, {0,1,2}); + x.reduceAlongDimension(sd::reduce::CountZero, z1, {0,1,2}); ASSERT_TRUE(z1.equalsTo(&exp1)); - x.reduceAlongDimension(nd4j::reduce::CountZero, z2, {1}); + x.reduceAlongDimension(sd::reduce::CountZero, z2, {1}); ASSERT_TRUE(z2.equalsTo(&exp2)); - x.reduceAlongDimension(nd4j::reduce::CountZero, z3, {0,2}); + x.reduceAlongDimension(sd::reduce::CountZero, z3, {0,2}); ASSERT_TRUE(z3.equalsTo(&exp3)); x.permutei({1,0,2}); // 3x2x2 - x.reduceAlongDimension(nd4j::reduce::CountZero, z1, {0,1,2}); + x.reduceAlongDimension(sd::reduce::CountZero, z1, {0,1,2}); ASSERT_TRUE(z1.equalsTo(&exp1)); - x.reduceAlongDimension(nd4j::reduce::CountZero, z4, {1}); + x.reduceAlongDimension(sd::reduce::CountZero, z4, {1}); ASSERT_TRUE(z4.equalsTo(&exp4)); - x.reduceAlongDimension(nd4j::reduce::CountZero, z5, {0,2}); + x.reduceAlongDimension(sd::reduce::CountZero, z5, {0,2}); ASSERT_TRUE(z5.equalsTo(&exp5)); } //////////////////////////////////////////////////////////////////////////////// TEST_F(NDArrayCudaBasicsTests, reduceAlongDimension_long_test2) { - NDArray x('c', {2,3,2}, {0.5,2,3,-0,5,6,-7.5,0,-1,-0.5,-3.5,4}, nd4j::DataType::INT32); + NDArray x('c', {2,3,2}, {0.5,2,3,-0,5,6,-7.5,0,-1,-0.5,-3.5,4}, sd::DataType::INT32); - NDArray exp1('c', {}, std::vector{4}, nd4j::DataType::INT64); - NDArray exp2('c', {2,2}, {1,1,0,2}, nd4j::DataType::INT64); - NDArray exp3('c', {3}, {2,2,0}, nd4j::DataType::INT64); - NDArray exp4('c', {3,2}, {1,1,0,2,0,0}, nd4j::DataType::INT64); - NDArray exp5('c', {2}, {2,2}, nd4j::DataType::INT64); + NDArray exp1('c', {}, std::vector{4}, sd::DataType::INT64); + NDArray exp2('c', {2,2}, {1,1,0,2}, sd::DataType::INT64); + NDArray exp3('c', {3}, {2,2,0}, sd::DataType::INT64); + NDArray exp4('c', {3,2}, {1,1,0,2,0,0}, sd::DataType::INT64); + NDArray exp5('c', {2}, {2,2}, sd::DataType::INT64); - NDArray z1 = x.reduceAlongDimension(nd4j::reduce::CountZero, {0,1,2}); + NDArray z1 = x.reduceAlongDimension(sd::reduce::CountZero, {0,1,2}); ASSERT_TRUE(z1.equalsTo(&exp1)); - NDArray z2 = x.reduceAlongDimension(nd4j::reduce::CountZero, {1}); + NDArray z2 = x.reduceAlongDimension(sd::reduce::CountZero, {1}); ASSERT_TRUE(z2.equalsTo(&exp2)); - NDArray z3 = x.reduceAlongDimension(nd4j::reduce::CountZero, {0,2}); + NDArray z3 = x.reduceAlongDimension(sd::reduce::CountZero, {0,2}); ASSERT_TRUE(z3.equalsTo(&exp3)); x.permutei({1,0,2}); // 3x2x2 - NDArray z4 = x.reduceAlongDimension(nd4j::reduce::CountZero, {0,1,2}); + NDArray z4 = x.reduceAlongDimension(sd::reduce::CountZero, {0,1,2}); ASSERT_TRUE(z4.equalsTo(&exp1)); - NDArray z5 = x.reduceAlongDimension(nd4j::reduce::CountZero, {1}); + NDArray z5 = x.reduceAlongDimension(sd::reduce::CountZero, {1}); ASSERT_TRUE(z5.equalsTo(&exp4)); - NDArray z6 = x.reduceAlongDimension(nd4j::reduce::CountZero, {0,2}); + NDArray z6 = x.reduceAlongDimension(sd::reduce::CountZero, {0,2}); ASSERT_TRUE(z6.equalsTo(&exp5)); } @@ -1687,8 +1687,8 @@ TEST_F(NDArrayCudaBasicsTests, BroadcastOpsTest1) { auto x = NDArrayFactory::create('c', {5, 5}); auto z = NDArrayFactory::create('c', {5, 5}); auto row = NDArrayFactory::linspace(1.0f, 5.0f, 5); - NDArray expRow('c', {1, 5,}, {1,2,3,4,5}, nd4j::DataType::FLOAT32); - NDArray exp('c', {5,5}, {1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5}, nd4j::DataType::FLOAT32); + NDArray expRow('c', {1, 5,}, {1,2,3,4,5}, sd::DataType::FLOAT32); + NDArray exp('c', {5,5}, {1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5}, sd::DataType::FLOAT32); ASSERT_TRUE(row->equalsTo(&expRow)); @@ -1706,8 +1706,8 @@ TEST_F(NDArrayCudaBasicsTests, BroadcastOpsTest2) { auto x = NDArrayFactory::create('c', {5, 5}); //auto z = NDArrayFactory::create('c', {5, 5}); auto row = NDArrayFactory::linspace(1.0f, 5.0f, 5); - NDArray expRow('c', {1, 5,}, {1,2,3,4,5}, nd4j::DataType::FLOAT32); - NDArray exp('c', {5,5}, {1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5}, nd4j::DataType::FLOAT32); + NDArray expRow('c', {1, 5,}, {1,2,3,4,5}, sd::DataType::FLOAT32); + NDArray exp('c', {5,5}, {1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5}, sd::DataType::FLOAT32); ASSERT_TRUE(row->equalsTo(&expRow)); x.applyBroadcast(broadcast::Add, {1}, *row, x); @@ -1717,7 +1717,7 @@ TEST_F(NDArrayCudaBasicsTests, BroadcastOpsTest2) { ////////////////////////////////////////////////////////////////////// TEST_F(NDArrayCudaBasicsTests, TestBroadcast_1) { - NDArray exp('c', {2, 3, 2, 2}, {1., 1., 1., 1., 2., 2., 2., 2., 3., 3., 3., 3., 1., 1., 1., 1., 2., 2., 2., 2., 3., 3., 3., 3.}, nd4j::DataType::DOUBLE); + NDArray exp('c', {2, 3, 2, 2}, {1., 1., 1., 1., 2., 2., 2., 2., 3., 3., 3., 3., 1., 1., 1., 1., 2., 2., 2., 2., 3., 3., 3., 3.}, sd::DataType::DOUBLE); auto input = NDArrayFactory::create('c',{ 2, 3, 2, 2}); auto bias = NDArrayFactory::create('c', {1, 3}); @@ -1884,7 +1884,7 @@ TEST_F(NDArrayCudaBasicsTests, Tile_Test_2_3) TEST_F(NDArrayCudaBasicsTests, Operator_Plus_Test_2) { double expBuff[] = {2., 3, 3., 4., 4., 5, 5., 6., 6., 7, 7., 8.}; - NDArray a('c', {4,4}, {1,2,3,4,5,6,7,8,9,2,3,2,1,0,4,7}, nd4j::DataType::FLOAT32); + NDArray a('c', {4,4}, {1,2,3,4,5,6,7,8,9,2,3,2,1,0,4,7}, sd::DataType::FLOAT32); auto x = NDArrayFactory::create('c', {3, 2, 1}); auto y = NDArrayFactory::create('c', {1, 2}); auto expected = NDArrayFactory::create(expBuff, 'c', {3, 2, 2}); @@ -1900,9 +1900,9 @@ TEST_F(NDArrayCudaBasicsTests, Operator_Plus_Test_2) ////////////////////////////////////////////////////////////////////// TEST_F(NDArrayCudaBasicsTests, assign_2) { - NDArray x('c', {4}, {1.5f,2.5f,3.5f,4.5f}, nd4j::DataType::FLOAT32); - NDArray y('c', {4}, nd4j::DataType::INT32); - NDArray expected('c', {4}, {1,2,3,4}, nd4j::DataType::INT32); + NDArray x('c', {4}, {1.5f,2.5f,3.5f,4.5f}, sd::DataType::FLOAT32); + NDArray y('c', {4}, sd::DataType::INT32); + NDArray expected('c', {4}, {1,2,3,4}, sd::DataType::INT32); y.assign(x); // y.printBuffer("ASSIGN VECTOR"); @@ -1913,8 +1913,8 @@ TEST_F(NDArrayCudaBasicsTests, assign_2) ////////////////////////////////////////////////////////////////////// TEST_F(NDArrayCudaBasicsTests, subarray_1) { - NDArray x('c', {2,3,4}, {1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24}, nd4j::DataType::FLOAT32); - NDArray y('f', {2,3,4}, {1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24}, nd4j::DataType::FLOAT32); + NDArray x('c', {2,3,4}, {1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24}, sd::DataType::FLOAT32); + NDArray y('f', {2,3,4}, {1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24}, sd::DataType::FLOAT32); Nd4jLong shapeExpX0[] = {1, 2, 12, 8192, 1, 99}; float buffExpX0[] = {1.f, 13.f}; @@ -2074,11 +2074,11 @@ TEST_F(NDArrayCudaBasicsTests, Test_diagonal_1) { ASSERT_NEAR(diag.e(e), exp.e(e), 1.e-5); } double eps(1.e-5); - NDArray tmp(nd4j::DataType::FLOAT32, x.getContext()); // scalar = 0 + NDArray tmp(sd::DataType::FLOAT32, x.getContext()); // scalar = 0 ExtraArguments extras({eps}); NativeOpExecutioner::execReduce3Scalar(diag.getContext(), reduce3::EqualsWithEps, diag.getBuffer(), - diag.getShapeInfo(), diag.getSpecialBuffer(), diag.getSpecialShapeInfo(), extras.argumentsAsT(nd4j::DataType::FLOAT32), + diag.getShapeInfo(), diag.getSpecialBuffer(), diag.getSpecialShapeInfo(), extras.argumentsAsT(sd::DataType::FLOAT32), exp.getBuffer(), exp.getShapeInfo(), exp.getSpecialBuffer(), exp.getSpecialShapeInfo(), tmp.buffer(), tmp.shapeInfo(), tmp.specialBuffer(), tmp.specialShapeInfo()); cudaStream_t* stream = x.getContext()->getCudaStream(); @@ -2185,13 +2185,13 @@ TEST_F(NDArrayCudaBasicsTests, Test_Empty_2) { } TEST_F(NDArrayCudaBasicsTests, Test_Empty_3) { - auto x = NDArrayFactory::empty(nd4j::DataType::FLOAT32); + auto x = NDArrayFactory::empty(sd::DataType::FLOAT32); ASSERT_TRUE(x.isEmpty()); } TEST_F(NDArrayCudaBasicsTests, Test_Empty_4) { - auto x = NDArrayFactory::empty_(nd4j::DataType::FLOAT32); + auto x = NDArrayFactory::empty_(sd::DataType::FLOAT32); ASSERT_TRUE(x->isEmpty()); delete x; diff --git a/libnd4j/tests_cpu/layers_tests/NDArrayListTests.cpp b/libnd4j/tests_cpu/layers_tests/NDArrayListTests.cpp index e57c7e625..2de3e4651 100644 --- a/libnd4j/tests_cpu/layers_tests/NDArrayListTests.cpp +++ b/libnd4j/tests_cpu/layers_tests/NDArrayListTests.cpp @@ -18,11 +18,11 @@ // @author raver119@gmail.com // -#include -#include +#include +#include #include "testlayers.h" -using namespace nd4j; +using namespace sd; class NDArrayListTests : public testing::Test { public: diff --git a/libnd4j/tests_cpu/layers_tests/NDArrayTests.cpp b/libnd4j/tests_cpu/layers_tests/NDArrayTests.cpp index fb55b4484..5e6cd10fb 100644 --- a/libnd4j/tests_cpu/layers_tests/NDArrayTests.cpp +++ b/libnd4j/tests_cpu/layers_tests/NDArrayTests.cpp @@ -20,10 +20,10 @@ #include "testlayers.h" #include -#include -#include +#include +#include -using namespace nd4j; +using namespace sd; ////////////////////////////////////////////////////////////////////// class NDArrayTest : public testing::Test { @@ -197,7 +197,7 @@ TEST_F(NDArrayTest, EqualityTest1) { TEST_F(NDArrayTest, TestTad1) { auto array = NDArrayFactory::create_('c', {3, 3}); - auto row2 = array->tensorAlongDimension(1, {1}); + auto row2 = (*array)(1, {0}); ASSERT_TRUE(row2.isView()); ASSERT_EQ(3, row2.lengthOf()); @@ -221,7 +221,7 @@ TEST_F(NDArrayTest, TestTad2) { TEST_F(NDArrayTest, TestTad3) { auto array = NDArrayFactory::create_('c', {4, 3}); - auto row2 = array->tensorAlongDimension(1, {1}); + auto row2 = (*array)(1, {0}); ASSERT_TRUE(row2.isView()); ASSERT_EQ(3, row2.lengthOf()); @@ -231,7 +231,7 @@ TEST_F(NDArrayTest, TestTad3) { TEST_F(NDArrayTest, TestPermuteReshape1) { - NDArray array('c', {2, 2, 5, 5}, nd4j::DataType::FLOAT32); + NDArray array('c', {2, 2, 5, 5}, sd::DataType::FLOAT32); int pShape[] = {4, 2, 5, 5, 2, 25, 5, 1, 50, 8192, 0, 99}; int rShape[] = {3, 2, 25, 2, 25, 1, 50, 8192, 0, 99}; @@ -275,7 +275,7 @@ TEST_F(NDArrayTest, TestRepeat1) { auto eBuffer = new float[8] {1.0,2.0,1.0,2.0,3.0,4.0,3.0,4.0}; auto eShape = new Nd4jLong[8]{2, 4, 2, 2, 1, 8192, 1, 99}; - NDArray array('c', {2, 2}, nd4j::DataType::FLOAT32); + NDArray array('c', {2, 2}, sd::DataType::FLOAT32); auto exp = new NDArray(eBuffer, eShape); for (int e = 0; e < array.lengthOf(); e++) array.p(e, e + 1); @@ -461,7 +461,7 @@ TEST_F(NDArrayTest, TestTranspose2) { ////////////////////////////////////////////////////////////////////// TEST_F(NDArrayTest, TestSumAlongDimension1) { - NDArray array('c', {2,2}, {1,2,3,4}, nd4j::DataType::FLOAT32); + NDArray array('c', {2,2}, {1,2,3,4}, sd::DataType::FLOAT32); auto res = array.reduceAlongDimension(reduce::Sum, {0}); @@ -937,19 +937,19 @@ TEST_F(NDArrayTest, TestPermuteReshapeMmul4) { TEST_F(NDArrayTest, TestMmulHelper2) { auto xBuffer = new float[15]{1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 7.f, 8.f, 9.f, 10.f, 11.f, 12.f, 13.f, 14.f, 15.f}; Nd4jLong xShape[8] = {2, 5, 3, 3, 1, 8192, 1, 99}; - auto x = new NDArray(xBuffer, xShape, nd4j::LaunchContext ::defaultContext(), true); + auto x = new NDArray(xBuffer, xShape, sd::LaunchContext ::defaultContext(), true); auto yBuffer = new float[3]{2.f, 4.f, 6.f}; Nd4jLong yShape[8] = {2, 3, 1, 1, 1, 8192, 1, 99}; - auto y = new NDArray(yBuffer, yShape, nd4j::LaunchContext ::defaultContext(), true); + auto y = new NDArray(yBuffer, yShape, sd::LaunchContext ::defaultContext(), true); auto z = NDArrayFactory::create_('f', {5, 1}); auto expBuffer = new float[5]{28.00f, 64.00f, 100.00f, 136.00f, 172.00f}; - auto exp = new NDArray(expBuffer, z->getShapeInfo(), nd4j::LaunchContext ::defaultContext(), true); + auto exp = new NDArray(expBuffer, z->getShapeInfo(), sd::LaunchContext ::defaultContext(), true); - //nd4j::blas::GEMV::op('f', x->rows(), x->columns(), 1.0f, x->getBuffer(), y->rows(), y->getBuffer(), 1, 0.0, z->getBuffer(), 1); + //sd::blas::GEMV::op('f', x->rows(), x->columns(), 1.0f, x->getBuffer(), y->rows(), y->getBuffer(), 1, 0.0, z->getBuffer(), 1); MmulHelper::mmul(x, y, z); @@ -978,7 +978,7 @@ TEST_F(NDArrayTest, TestMmulHelper3) { auto expBuffer = new float[5]{92.00f, 104.00f, 116.00f, 128.00f, 140.00f}; auto exp = new NDArray(expBuffer, z->getShapeInfo()); - //nd4j::blas::GEMV::op('f', x->rows(), x->columns(), 1.0f, x->getBuffer(), y->rows(), y->getBuffer(), 1, 0.0, z->getBuffer(), 1); + //sd::blas::GEMV::op('f', x->rows(), x->columns(), 1.0f, x->getBuffer(), y->rows(), y->getBuffer(), 1, 0.0, z->getBuffer(), 1); MmulHelper::mmul(x, y, z); @@ -1529,7 +1529,7 @@ TEST_F(NDArrayTest, TestStdDev1) { //////////////////////////////////////////////////////////////////////////////// TEST_F(NDArrayTest, TestStdDev2) { auto array = NDArrayFactory::create('c', {5, 6}); - auto tad = array.tensorAlongDimension(0, {0}); + auto tad = array(0, {1}); ASSERT_EQ(5, tad.lengthOf()); @@ -1569,7 +1569,7 @@ TEST_F(NDArrayTest, TestStdDev4) { float y = 0; double M2 = 0; for (int e = 0; e < total; ++e) { - // y += nd4j::math::nd4j_abs(array(e) - x); + // y += sd::math::nd4j_abs(array(e) - x); M2 += (array.e(e) - x) * (array.e(e) - x); } //y /= total; @@ -1580,7 +1580,7 @@ TEST_F(NDArrayTest, TestStdDev4) { auto std = a.e(0); // float bY = array.varianceNumber(); float bY = 0.3333333f; - // nd4j_printf("Variance is %f, res is %f, internal is %f\n, deviance is %f(%f)\n", std, x, bY, y, nd4j::math::nd4j_sqrt(M2)); + // nd4j_printf("Variance is %f, res is %f, internal is %f\n, deviance is %f(%f)\n", std, x, bY, y, sd::math::nd4j_sqrt(M2)); ASSERT_NEAR(std, 0.3333333f, 1.0e-5f); } @@ -1802,7 +1802,7 @@ TEST_F(NDArrayTest, TestTensorDotAgain_1) { double _expB[] = {96.0, 116.0, 136.0, 156.0, 256.0, 276.0, 296.0, 316.0, 102.0, 124.0, 146.0, 168.0, 278.0, 300.0, 322.0, 344.0, 108.0, 132.0, 156.0, 180.0, 300.0, 324.0, 348.0, 372.0, 114.0, 140.0, 166.0, 192.0, 322.0, 348.0, 374.0, 400.0, 120.0, 148.0, 176.0, 204.0, 344.0, 372.0, 400.0, 428.0, 126.0, 156.0, 186.0, 216.0, 366.0, 396.0, 426.0, 456.0, 132.0, 164.0, 196.0, 228.0, 388.0, 420.0, 452.0, 484.0, 138.0, 172.0, 206.0, 240.0, 410.0, 444.0, 478.0, 512.0, 144.0, 180.0, 216.0, 252.0, 432.0, 468.0, 504.0, 540.0, 150.0, 188.0, 226.0, 264.0, 454.0, 492.0, 530.0, 568.0, 156.0, 196.0, 236.0, 276.0, 476.0, 516.0, 556.0, 596.0, 162.0, 204.0, 246.0, 288.0, 498.0, 540.0, 582.0, 624.0, 168.0, 212.0, 256.0, 300.0, 520.0, 564.0, 608.0, 652.0, 174.0, 220.0, 266.0, 312.0, 542.0, 588.0, 634.0, 680.0, 180.0, 228.0, 276.0, 324.0, 564.0, 612.0, 660.0, 708.0, 186.0, 236.0, 286.0, 336.0, 586.0, 636.0, 686.0, 736.0, 192.0, 244.0, 296.0, 348.0, 608.0, 660.0, 712.0, 764.0, 198.0, 252.0, 306.0, 360.0, 630.0, 684.0, 738.0, 792.0}; Nd4jLong _expS[] = {6, 2, 3, 3, 2, 2, 2, 72, 24, 8, 4, 2, 1, 16384, 1, 99}; - NDArray exp(_expB, _expS, nd4j::LaunchContext ::defaultContext(), false); + NDArray exp(_expB, _expS, sd::LaunchContext ::defaultContext(), false); auto input = NDArrayFactory::create('c', {B, iC, iY, iX}); auto weights = NDArrayFactory::create('c', {iC, oC, kY, kX}); @@ -1826,7 +1826,7 @@ TEST_F(NDArrayTest, TestTensorDotAgain_1) { TEST_F(NDArrayTest, TestBroadcast_1) { double _expB[] = {1.000000, 1.000000, 1.000000, 1.000000, 2.000000, 2.000000, 2.000000, 2.000000, 3.000000, 3.000000, 3.000000, 3.000000, 1.000000, 1.000000, 1.000000, 1.000000, 2.000000, 2.000000, 2.000000, 2.000000, 3.000000, 3.000000, 3.000000, 3.000000}; Nd4jLong _expS[] = {4, 2, 3, 2, 2, 12, 4, 2, 1, 16384, 1, 99}; - NDArray exp(_expB, _expS, nd4j::LaunchContext ::defaultContext(), false); + NDArray exp(_expB, _expS, sd::LaunchContext ::defaultContext(), false); auto input = NDArrayFactory::create('c',{ 2, 3, 2, 2}); auto bias = NDArrayFactory::create('c', {1, 3}); @@ -1913,7 +1913,7 @@ TEST_F(NDArrayTest, TestMatmMul_Again_1) { float _expB[] = {1.f, 2.f, 3.f, 4.f, 5.f, 2.f, 4.f, 6.f, 8.f, 10.f, 3.f, 6.f, 9.f, 12.f, 15.f, 4.f, 8.f, 12.f, 16.f, 20.f, 30.f, 35.f, 40.f, 45.f, 50.f, 36.f, 42.f, 48.f, 54.f, 60.f, 42.f, 49.f, 56.f, 63.f, 70.f, 48.f, 56.f, 64.f, 72.f, 80.f, 99.f, 108.f, 117.f, 126.f, 135.f, 110.f, 120.f, 130.f, 140.f, 150.f, 121.f, 132.f, 143.f, 154.f, 165.f, 132.f, 144.f, 156.f, 168.f, 180.f}; Nd4jLong _expS[] = {3, 3, 4, 5, 20, 5, 1, 8192, 1, 99}; - NDArray c(_expB, _expS, nd4j::LaunchContext ::defaultContext(), false); + NDArray c(_expB, _expS, sd::LaunchContext ::defaultContext(), false); auto c_ = MmulHelper::mmul(&a, &b); diff --git a/libnd4j/tests_cpu/layers_tests/NDArrayTests2.cpp b/libnd4j/tests_cpu/layers_tests/NDArrayTests2.cpp index 6d5366396..49f003809 100644 --- a/libnd4j/tests_cpu/layers_tests/NDArrayTests2.cpp +++ b/libnd4j/tests_cpu/layers_tests/NDArrayTests2.cpp @@ -20,11 +20,11 @@ #include "testlayers.h" #include -#include -#include +#include +#include #include -using namespace nd4j; +using namespace sd; ////////////////////////////////////////////////////////////////////// class NDArrayTest2 : public testing::Test { @@ -662,8 +662,8 @@ TEST_F(NDArrayTest2, permute_test4) { auto arr1Buffer = new float[786432]; auto arr2Buffer = new float[786432]; - NDArray arr1(arr1Buffer, arr1ShapeInfo, nd4j::LaunchContext ::defaultContext()); - NDArray arr2(arr2Buffer, arr2ShapeInfo, nd4j::LaunchContext ::defaultContext()); + NDArray arr1(arr1Buffer, arr1ShapeInfo, sd::LaunchContext ::defaultContext()); + NDArray arr2(arr2Buffer, arr2ShapeInfo, sd::LaunchContext ::defaultContext()); const std::vector perm = {0, 4, 5, 1, 2, 3}; auto arr1P = arr1.permute(perm); @@ -776,7 +776,7 @@ TEST_F(NDArrayTest2, scalar_get_test1) { auto scalar1 = NDArrayFactory::create(20.f); - NDArray arr('c', {2,2}, {0., 10., 20., 30.}, nd4j::DataType::FLOAT32); + NDArray arr('c', {2,2}, {0., 10., 20., 30.}, sd::DataType::FLOAT32); NDArray scalar2 = arr.e(2); @@ -790,7 +790,7 @@ TEST_F(NDArrayTest2, scalar_get_test2) { auto scalar1 = NDArrayFactory::create(20.f); - NDArray arr('f', {2,2}, {0., 10., 20., 30.}, nd4j::DataType::FLOAT32); + NDArray arr('f', {2,2}, {0., 10., 20., 30.}, sd::DataType::FLOAT32); NDArray scalar2 = arr.e(1); @@ -804,8 +804,8 @@ TEST_F(NDArrayTest2, scalar_set_test1) { NDArray scalar1 = NDArrayFactory::create(20.f); - NDArray arr('c', {2,2}, {0., 10., -20., 30.}, nd4j::DataType::FLOAT32); - NDArray exp('c', {2,2}, {0., 10., 20., 30.}, nd4j::DataType::FLOAT32); + NDArray arr('c', {2,2}, {0., 10., -20., 30.}, sd::DataType::FLOAT32); + NDArray exp('c', {2,2}, {0., 10., 20., 30.}, sd::DataType::FLOAT32); arr.p(2, scalar1); @@ -818,8 +818,8 @@ TEST_F(NDArrayTest2, scalar_set_test2) { NDArray scalar1 = NDArrayFactory::create(20.f); - NDArray arr('f', {2,2}, {0., 10., -20., 30.}, nd4j::DataType::FLOAT32); - NDArray exp('f', {2,2}, {0., 10., 20., 30.}, nd4j::DataType::FLOAT32); + NDArray arr('f', {2,2}, {0., 10., -20., 30.}, sd::DataType::FLOAT32); + NDArray exp('f', {2,2}, {0., 10., 20., 30.}, sd::DataType::FLOAT32); arr.p(1, scalar1); @@ -846,14 +846,14 @@ TEST_F(NDArrayTest2, debugInfoTest_1) { 91., -82., 37., 64., 55., 46., 73., 28., -119., 12., 112., 13., 14., 114., 16., 117., 51., 42., 67., 24., 15., 0., 93., 28., 109., 82., 12., 113., 114., 14., 116., 11., 31., 22., 87., 44., 55., 46., 73., 28., 119., 12., 112., 13., 14., 114., 16., 117., - 91., 82., 37., 64., -3, 0, 73., 28., 119., 12., 112., 13., 140., 110., 160., 107.}, nd4j::DataType::DOUBLE); - NDArray res(nd4j::DataType::DOUBLE); + 91., 82., 37., 64., -3, 0, 73., 28., 119., 12., 112., 13., 140., 110., 160., 107.}, sd::DataType::DOUBLE); + NDArray res(sd::DataType::DOUBLE); DebugInfo info = DebugHelper::debugStatistics(&testArray); DebugInfo exp; // = {} - nd4j::ops::reduce_min minOp; - nd4j::ops::reduce_mean meanOp; - nd4j::ops::reduce_max maxOp; - nd4j::ops::reduce_stdev stdevOp; + sd::ops::reduce_min minOp; + sd::ops::reduce_mean meanOp; + sd::ops::reduce_max maxOp; + sd::ops::reduce_stdev stdevOp; minOp.execute({&testArray}, {&res}, {}, {}, {}); exp._minValue = res.e(0); @@ -883,7 +883,7 @@ TEST_F(NDArrayTest2, debugInfoTest_2) { 91., -82., 37., 64., 55., 46., 73., 28., -119., 12., 112., 13., 14., 114., 16., 117., 51., 42., 67., 24., 15., 0., 93., 28., 109., 82., 12., 113., 114., 14., 116., 11., 31., 22., 87., 44., 55., 46., 73., 28., 119., 12., 112., 13., 14., 114., 16., 117., - 91., 82., 37., 64., -3, 0, 73., 28., 119., 12., 112., 13., 140., 110., 160., 107.}, nd4j::DataType::DOUBLE); + 91., 82., 37., 64., -3, 0, 73., 28., 119., 12., 112., 13., 140., 110., 160., 107.}, sd::DataType::DOUBLE); DebugInfo info; DebugInfo exp; // = {} @@ -908,7 +908,7 @@ TEST_F(NDArrayTest2, debugInfoTest_2) { ////////////////////////////////////////////////////////////////////// TEST_F(NDArrayTest2, test_subarray_ews_1) { - NDArray x('c', {10, 5}, nd4j::DataType::FLOAT32); + NDArray x('c', {10, 5}, sd::DataType::FLOAT32); auto subArr1 = x.subarray({NDIndex::all(), NDIndex::point(2)}); ASSERT_EQ(5, subArr1.ews()); @@ -917,7 +917,7 @@ TEST_F(NDArrayTest2, test_subarray_ews_1) { ////////////////////////////////////////////////////////////////////// TEST_F(NDArrayTest2, test_subarray_ews_2) { - NDArray x('f', {10, 5}, nd4j::DataType::FLOAT32); + NDArray x('f', {10, 5}, sd::DataType::FLOAT32); auto subArr1 = x.subarray({NDIndex::all(), NDIndex::point(2)}); ASSERT_EQ(1, subArr1.ews()); @@ -926,7 +926,7 @@ TEST_F(NDArrayTest2, test_subarray_ews_2) { ////////////////////////////////////////////////////////////////////// TEST_F(NDArrayTest2, test_subarray_ews_3) { - NDArray x('c', {10, 5}, nd4j::DataType::FLOAT32); + NDArray x('c', {10, 5}, sd::DataType::FLOAT32); auto subArr1 = x.subarray({NDIndex::point(2), NDIndex::all()}); ASSERT_EQ(1, subArr1.ews()); @@ -935,7 +935,7 @@ TEST_F(NDArrayTest2, test_subarray_ews_3) { ////////////////////////////////////////////////////////////////////// TEST_F(NDArrayTest2, test_subarray_ews_4) { - NDArray x('f', {10, 5}, nd4j::DataType::FLOAT32); + NDArray x('f', {10, 5}, sd::DataType::FLOAT32); auto subArr1 = x.subarray({NDIndex::point(2), NDIndex::all()}); ASSERT_EQ(10, subArr1.ews()); @@ -944,8 +944,8 @@ TEST_F(NDArrayTest2, test_subarray_ews_4) { ////////////////////////////////////////////////////////////////////// TEST_F(NDArrayTest2, subarray_1) { - NDArray x('c', {2,3,4}, {1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24}, nd4j::DataType::FLOAT32); - NDArray y('f', {2,3,4}, {1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24}, nd4j::DataType::FLOAT32); + NDArray x('c', {2,3,4}, {1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24}, sd::DataType::FLOAT32); + NDArray y('f', {2,3,4}, {1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24}, sd::DataType::FLOAT32); Nd4jLong shapeExpX0[] = {1, 2, 12, 8192, 12, 99}; float buffExpX0[] = {1.000000, 13.000000}; @@ -1049,7 +1049,7 @@ TEST_F(NDArrayTest2, subarray_1) { TEST_F(NDArrayTest2, test_subarray_interval_1) { - NDArray x('f', {10, 10}, nd4j::DataType::FLOAT32); + NDArray x('f', {10, 10}, sd::DataType::FLOAT32); auto subArr1 = x.subarray({NDIndex::all(), NDIndex::interval(0,9)}); ASSERT_EQ(10, subArr1.sizeAt(0)); @@ -1058,7 +1058,7 @@ TEST_F(NDArrayTest2, test_subarray_interval_1) { TEST_F(NDArrayTest2, test_subarray_interval_2) { - NDArray x('c', {10, 10}, nd4j::DataType::FLOAT32); + NDArray x('c', {10, 10}, sd::DataType::FLOAT32); auto subArr1 = x.subarray({NDIndex::all(), NDIndex::interval(0,9)}); ASSERT_EQ(10, subArr1.sizeAt(0)); @@ -1066,8 +1066,8 @@ TEST_F(NDArrayTest2, test_subarray_interval_2) { } TEST_F(NDArrayTest2, test_subarray_3d_cf) { - NDArray f('f', {10, 20, 30}, nd4j::DataType::FLOAT32); - NDArray c('c', {10, 20, 30}, nd4j::DataType::FLOAT32); + NDArray f('f', {10, 20, 30}, sd::DataType::FLOAT32); + NDArray c('c', {10, 20, 30}, sd::DataType::FLOAT32); auto subarrayF = f({0,0, 0,0, 2,3}, true); @@ -1198,10 +1198,10 @@ TEST_F(NDArrayTest2, trueBroadcast_1) { NDArray x('f', {2, 3}, {1., 2., 3., 4., 5., 6.}); NDArray y('f', {1, 3}, {5., 4., 3.}); - NDArray z('c', {2, 3}, nd4j::DataType::DOUBLE); + NDArray z('c', {2, 3}, sd::DataType::DOUBLE); auto exp = x - y; - x.applyTrueBroadcast(nd4j::BroadcastOpsTuple::Subtract(), y, z); + x.applyTrueBroadcast(sd::BroadcastOpsTuple::Subtract(), y, z); // exp.printIndexedBuffer(); // z.printIndexedBuffer(); @@ -1212,12 +1212,12 @@ TEST_F(NDArrayTest2, trueBroadcast_1) { ////////////////////////////////////////////////////////////////////// TEST_F(NDArrayTest2, reduce_1) { - NDArray arr6('f', {1, 1, 4, 4, 4, 4}, nd4j::DataType::DOUBLE); - NDArray exp('f', {1, 1, 4, 4}, nd4j::DataType::DOUBLE); + NDArray arr6('f', {1, 1, 4, 4, 4, 4}, sd::DataType::DOUBLE); + NDArray exp('f', {1, 1, 4, 4}, sd::DataType::DOUBLE); arr6.linspace(1); - NDArray arr6s = arr6.reduceAlongDimension(nd4j::reduce::Sum, {2,3}); + NDArray arr6s = arr6.reduceAlongDimension(sd::reduce::Sum, {2,3}); for (int i = 0; i < 4; i++) { for (int j = 0; j < 4; j++) { @@ -1248,7 +1248,7 @@ TEST_F(NDArrayTest2, reduce3_1) { NDArray y('c', {1,4}, {2,3,4,5}); NDArray exp('c', {4}, {1,1,1,1}); - NDArray z = x.applyReduce3(nd4j::reduce3::EuclideanDistance, y, {0}, nullptr); + NDArray z = x.applyReduce3(sd::reduce3::EuclideanDistance, y, {0}, nullptr); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); @@ -1281,8 +1281,8 @@ TEST_F(NDArrayTest2, test_trueBroadcast_empty_2) { TEST_F(NDArrayTest2, test_subarray_followed_by_reshape_1) { - NDArray x('c', {5, 1, 3}, nd4j::DataType::FLOAT32); - NDArray e('c', {1, 3}, {7.f, 8.f, 9.f}, nd4j::DataType::FLOAT32); + NDArray x('c', {5, 1, 3}, sd::DataType::FLOAT32); + NDArray e('c', {1, 3}, {7.f, 8.f, 9.f}, sd::DataType::FLOAT32); x.linspace(1.); diff --git a/libnd4j/tests_cpu/layers_tests/NativeOpsTests.cpp b/libnd4j/tests_cpu/layers_tests/NativeOpsTests.cpp index 0306fb555..971fe452e 100644 --- a/libnd4j/tests_cpu/layers_tests/NativeOpsTests.cpp +++ b/libnd4j/tests_cpu/layers_tests/NativeOpsTests.cpp @@ -19,9 +19,9 @@ // #include "testlayers.h" -#include -#include -#include +#include +#include +#include #include #include #include @@ -31,10 +31,10 @@ #include #include #include -#include +#include #include -using namespace nd4j; -using namespace nd4j::ops; +using namespace sd; +using namespace sd::ops; class NativeOpsTests : public testing::Test { public: @@ -76,7 +76,7 @@ printf("Unsupported for cuda now.\n"); // auto exp = NDArrayFactory::create('c', {5, 5}); // exp.assign(-1.0); // -// nd4j::ops::LegacyTransformSameOp op(transform::Neg); // Neg +// sd::ops::LegacyTransformSameOp op(transform::Neg); // Neg // auto result = op.execute({&x}, {}, {}); // // ASSERT_EQ(1, result->size()); @@ -95,7 +95,7 @@ TEST_F(NativeOpsTests, ThresholdTests_1) { printf("Unsupported for cuda now.\n"); #else ::setElementThreshold(4); - ASSERT_TRUE(4 == nd4j::Environment::getInstance()->elementwiseThreshold()); + ASSERT_TRUE(4 == sd::Environment::getInstance()->elementwiseThreshold()); #endif } @@ -107,7 +107,7 @@ TEST_F(NativeOpsTests, ThresholdTests_2) { printf("Unsupported for cuda now.\n"); #else ::setTADThreshold(4); - ASSERT_TRUE(4 == nd4j::Environment::getInstance()->tadThreshold()); + ASSERT_TRUE(4 == sd::Environment::getInstance()->tadThreshold()); #endif } @@ -644,8 +644,8 @@ TEST_F(NativeOpsTests, Reduce3Test_4) { x.syncToDevice(); dimension.syncToHost(); int* dimensions = reinterpret_cast(dimension.buffer()); - auto tadPackX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(x.shapeInfo(), dimensions, dimension.lengthOf()); - auto tadPackY = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(y.shapeInfo(), dimensions, dimension.lengthOf()); + auto tadPackX = sd::ConstantTadHelper::getInstance()->tadForDimensions(x.shapeInfo(), dimensions, dimension.lengthOf()); + auto tadPackY = sd::ConstantTadHelper::getInstance()->tadForDimensions(y.shapeInfo(), dimensions, dimension.lengthOf()); auto hTADShapeInfoX = tadPackX.primaryShapeInfo(); auto hTADOffsetsX = tadPackX.primaryOffsets(); @@ -963,8 +963,8 @@ TEST_F(NativeOpsTests, ScalarTadTest_1) { z.syncToDevice(); auto dimension = NDArrayFactory::create({0, 1}); auto dimensions = reinterpret_cast(dimension.buffer()); - auto tadPackX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(x.shapeInfo(), dimensions, dimension.lengthOf()); - auto tadPackZ = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(z.shapeInfo(), dimensions, dimension.lengthOf()); + auto tadPackX = sd::ConstantTadHelper::getInstance()->tadForDimensions(x.shapeInfo(), dimensions, dimension.lengthOf()); + auto tadPackZ = sd::ConstantTadHelper::getInstance()->tadForDimensions(z.shapeInfo(), dimensions, dimension.lengthOf()); OpaqueDataBuffer xBuf(x.dataBuffer()); OpaqueDataBuffer yBuf(y.dataBuffer()); @@ -1008,8 +1008,8 @@ TEST_F(NativeOpsTests, ScalarTadTest_2) { z.syncToDevice(); auto dimension = NDArrayFactory::create({0, 1}); auto dimensions = reinterpret_cast(dimension.buffer()); - auto tadPackX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(x.shapeInfo(), dimensions, dimension.lengthOf()); - auto tadPackZ = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(z.shapeInfo(), dimensions, dimension.lengthOf()); + auto tadPackX = sd::ConstantTadHelper::getInstance()->tadForDimensions(x.shapeInfo(), dimensions, dimension.lengthOf()); + auto tadPackZ = sd::ConstantTadHelper::getInstance()->tadForDimensions(z.shapeInfo(), dimensions, dimension.lengthOf()); z.assign(true); OpaqueDataBuffer xBuf(x.dataBuffer()); @@ -1057,8 +1057,8 @@ TEST_F(NativeOpsTests, ConcatTest_2) { int d = 0; auto dimension = NDArrayFactory::create('c', {1}, {d}); auto dimensions = reinterpret_cast(dimension.buffer()); - //auto tadPackX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(x.shapeInfo(), dimensions, dimension.lengthOf()); - auto tadPackZ = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(z.shapeInfo(), dimensions, dimension.lengthOf()); + //auto tadPackX = sd::ConstantTadHelper::getInstance()->tadForDimensions(x.shapeInfo(), dimensions, dimension.lengthOf()); + auto tadPackZ = sd::ConstantTadHelper::getInstance()->tadForDimensions(z.shapeInfo(), dimensions, dimension.lengthOf()); exp.linspace(1); Nd4jPointer datas[] = {x.buffer(), y.buffer()}; Nd4jPointer shapes[] = {x.shapeInfo(), y.shapeInfo()}; @@ -1117,7 +1117,7 @@ TEST_F(NativeOpsTests, MemTest_1) { TEST_F(NativeOpsTests, PullRowsTest_1) { NDArray x('c', {5, 1}, {0,1,2,3,4}); - NDArray z('c', {4, 1}, nd4j::DataType::DOUBLE); + NDArray z('c', {4, 1}, sd::DataType::DOUBLE); NDArray exp('c', {4, 1}, {0,2,3,4}); Nd4jLong indexes[] = {0,2,3,4}; @@ -1126,8 +1126,8 @@ TEST_F(NativeOpsTests, PullRowsTest_1) { std::vector dims = {1}; - auto xTadPack = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(x.getShapeInfo(), dims); - auto zTadPack = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(z.getShapeInfo(), dims); + auto xTadPack = sd::ConstantTadHelper::getInstance()->tadForDimensions(x.getShapeInfo(), dims); + auto zTadPack = sd::ConstantTadHelper::getInstance()->tadForDimensions(z.getShapeInfo(), dims); Nd4jPointer nativeStart[2]; @@ -1151,7 +1151,7 @@ TEST_F(NativeOpsTests, TadPackTest_1) { int dimension[] = {1}; int const dimensionLength = 1; auto x = NDArrayFactory::create('c', {2,3,4}); - nd4j::TadPack* pack = ::tadOnlyShapeInfo(x.shapeInfo(), + sd::TadPack* pack = ::tadOnlyShapeInfo(x.shapeInfo(), dimension, dimensionLength); ASSERT_TRUE(pack != nullptr); @@ -1231,7 +1231,7 @@ TEST_F(NativeOpsTests, ShuffleTest_1) { Nd4jPointer zShapeList[] = {z.shapeInfo(), z.shapeInfo()}; Nd4jPointer dzShapeList[] = {z.specialShapeInfo(), z.specialShapeInfo()}; int shuffleMap[] = {1, 0, 4, 3, 2}; - auto zTadPack = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(x.getShapeInfo(), {1}); + auto zTadPack = sd::ConstantTadHelper::getInstance()->tadForDimensions(x.getShapeInfo(), {1}); Nd4jPointer zListOffset[] = {zTadPack.platformOffsets(), zTadPack.platformOffsets()}; Nd4jPointer zListTADs[] = {zTadPack.platformShapeInfo(), zTadPack.platformShapeInfo()}; ::shuffle(nullptr, @@ -1268,7 +1268,7 @@ TEST_F(NativeOpsTests, ConvertTypesTest_1) { // auto y = NDArrayFactory::create('c', {5,5}); // // -// ops.execAggregate(nullptr, 0, maxArgs, maxShapes, maxIntArrays, maxIntArraySize, maxIndexArguments, maxRealArguments, pointer.data(), nd4j::DataType::FLOAT32); +// ops.execAggregate(nullptr, 0, maxArgs, maxShapes, maxIntArrays, maxIntArraySize, maxIndexArguments, maxRealArguments, pointer.data(), sd::DataType::FLOAT32); // void **arguments, // int numArguments, // Nd4jLong **shapeArguments, @@ -1279,7 +1279,7 @@ TEST_F(NativeOpsTests, ConvertTypesTest_1) { // int numIntArrays, // void *realArguments, // int numRealArguments, -// nd4j::DataType dtype +// sd::DataType dtype //} TEST_F(NativeOpsTests, RandomTest_1) { @@ -1492,7 +1492,7 @@ TEST_F(NativeOpsTests, CustomOpTest_1) { auto z = NDArrayFactory::create('c', {6}); auto e = NDArrayFactory::create('c', {6}, {1.f, 2.f, 3.f, 4.f, 5.f, 6.f}); - nd4j::ops::squeeze op; + sd::ops::squeeze op; Nd4jPointer ptrsInBuffer[] = {(Nd4jPointer) x.getBuffer(), x.getSpecialBuffer()}; Nd4jPointer ptrsInShapes[] = {(Nd4jPointer) x.getShapeInfo(), x.getSpecialShapeInfo()}; @@ -1522,7 +1522,7 @@ TEST_F(NativeOpsTests, CustomOpTests_2) { ASSERT_EQ(2, ctx.width()); - nd4j::ops::add op; + sd::ops::add op; ::execCustomOp2(nullptr, op.getOpHash(), &ctx); NDArray::registerSpecialUse({&z}, {&array0, &array1}); @@ -1534,7 +1534,7 @@ TEST_F(NativeOpsTests, CalculateOutputShapeTests_1) { auto weights = NDArrayFactory::create('c', {2, 2, 2, 3}); auto exp = NDArrayFactory::create('c', {1, 3, 5, 4}); - nd4j::ops::conv2d op; + sd::ops::conv2d op; std::vector tArgs({}); std::vector iArgs({2, 2, 1, 1, 0, 0, 1, 1, 1}); @@ -1566,7 +1566,7 @@ TEST_F(NativeOpsTests, CalculateOutputShapeTests_2) { auto weights = NDArrayFactory::create('c', {2, 2, 2, 3}); auto exp = NDArrayFactory::create('c', {1, 3, 5, 4}); - nd4j::ops::conv2d op; + sd::ops::conv2d op; std::vector tArgs({}); std::vector bArgsF({}); diff --git a/libnd4j/tests_cpu/layers_tests/NlpTests.cpp b/libnd4j/tests_cpu/layers_tests/NlpTests.cpp index 83eeee48b..2325e2445 100644 --- a/libnd4j/tests_cpu/layers_tests/NlpTests.cpp +++ b/libnd4j/tests_cpu/layers_tests/NlpTests.cpp @@ -20,13 +20,13 @@ #include "testlayers.h" #include -#include +#include #include -#include +#include #include -using namespace nd4j; +using namespace sd; class NlpTests : public testing::Test { @@ -64,9 +64,9 @@ TEST_F(NlpTests, basic_sg_hs_test_1) { auto randomValue = NDArrayFactory::create(1L); auto inferenceVector = NDArrayFactory::empty(); - nd4j::ops::skipgram op; + sd::ops::skipgram op; auto result = op.evaluate({&target, &ngStarter, &indices, &codes, &syn0, &syn1, &syn1Neg, &expTable, &negTable, &alpha, &randomValue, &inferenceVector, &neu1e}, {}, {}, {false}, {}, true); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); auto row0 = syn0({0,1, 0,0}, true); auto row1 = syn1({1,2, 0,0}, true); @@ -74,7 +74,7 @@ TEST_F(NlpTests, basic_sg_hs_test_1) { ASSERT_EQ(exp0, row0); ASSERT_EQ(exp1, row1); - delete result; + } TEST_F(NlpTests, basic_sg_hs_test_2) { @@ -105,9 +105,9 @@ TEST_F(NlpTests, basic_sg_hs_test_2) { auto randomValue = NDArrayFactory::create(1L); auto inferenceVector = NDArrayFactory::empty(); - nd4j::ops::skipgram op; + sd::ops::skipgram op; auto result = op.evaluate({&target, &ngStarter, &indices, &codes, &syn0, &syn1, &syn1Neg, &expTable, &negTable, &alpha, &randomValue, &inferenceVector, &neu1e}, {}, {}, {false}, {}, true); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); auto row0 = syn0({0,1, 0,0}, true); auto row1 = syn1({1,2, 0,0}, true); @@ -117,7 +117,7 @@ TEST_F(NlpTests, basic_sg_hs_test_2) { ASSERT_EQ(exp1, row1); ASSERT_EQ(exp2, row2); - delete result; + } TEST_F(NlpTests, basic_sg_hs_test_3) { @@ -156,10 +156,10 @@ TEST_F(NlpTests, basic_sg_hs_test_3) { auto randomValue = NDArrayFactory::create(1L); auto inferenceVector = NDArrayFactory::empty(); - nd4j::ops::skipgram op; + sd::ops::skipgram op; auto result0 = op.evaluate({&target, &ngStarter, &indices0, &codes00, &syn00, &syn10, &syn1Neg, &expTable, &negTable, &alpha, &randomValue, &inferenceVector, &neu1e}, {}, {}, {false}, {}, true); auto result1 = op.evaluate({&target, &ngStarter, &indices1, &codes01, &syn01, &syn11, &syn1Neg, &expTable, &negTable, &alpha, &randomValue, &inferenceVector, &neu1e}, {}, {}, {false}, {}, true); - ASSERT_EQ(Status::OK(), result0->status()); + ASSERT_EQ(Status::OK(), result0.status()); auto row00 = syn00({0,1, 0,0}, true); auto row01 = syn01({0,1, 0,0}, true); @@ -168,9 +168,6 @@ TEST_F(NlpTests, basic_sg_hs_test_3) { ASSERT_EQ(row2, row1); ASSERT_EQ(row00, row01); - - delete result0; - delete result1; } TEST_F(NlpTests, basic_sg_hs_ns_test_1) { @@ -190,11 +187,11 @@ TEST_F(NlpTests, basic_sg_hs_ns_test_1) { auto randomValue = NDArrayFactory::create(119L); auto inferenceVector = NDArrayFactory::empty(); - nd4j::ops::skipgram op; + sd::ops::skipgram op; auto result = op.evaluate({&target, &ngStarter, &indices, &codes, &syn0, &syn1, &syn1Neg, &expTable, &negTable, &alpha, &randomValue, &inferenceVector, &neu1e}, {}, {3}, {false}, {}, true); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - delete result; + } TEST_F(NlpTests, basic_sg_ns_test_1) { @@ -225,16 +222,16 @@ TEST_F(NlpTests, basic_sg_ns_test_1) { auto randomValue = NDArrayFactory::create(2L); auto inferenceVector = NDArrayFactory::empty(); - nd4j::ops::skipgram op; + sd::ops::skipgram op; auto result = op.evaluate({&target, &ngStarter, &indices, &codes, &syn0, &syn1, &syn1Neg, &expTable, &negTable, &alpha, &randomValue, &inferenceVector, &neu1e}, {}, {1, 1}, {false}, {}, true); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto row0 = syn0({1,2, 0,0}, true); + auto row0 = syn0({1,2, 0,0}, true); ASSERT_EQ(exp0, row0); ASSERT_FALSE(syn1Neg2.equalsTo(syn1Neg, 1e-6)); - delete result; + } TEST_F(NlpTests, basic_cb_hs_test_1) { @@ -267,9 +264,9 @@ TEST_F(NlpTests, basic_cb_hs_test_1) { auto randomValue = NDArrayFactory::create(2L); auto inferenceVector = NDArrayFactory::empty(); - nd4j::ops::cbow op; + sd::ops::cbow op; auto result = op.evaluate({&target, &ngStarter, &context, &indices, &codes, &syn0, &syn1, &syn1Neg, &expTable, &negTable, &alpha, &randomValue, &numWords, &locked, &inferenceVector}, {}, {}, {true}, {}, true); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); auto row_s0_0 = syn0({0,1, 0,0}, true); auto row_s0_1 = syn0({1,2, 0,0}, true); @@ -287,7 +284,7 @@ TEST_F(NlpTests, basic_cb_hs_test_1) { ASSERT_EQ(exp1, row_s1_5); ASSERT_EQ(exp2, row_s1_6); - delete result; + } TEST_F(NlpTests, basic_cb_ns_test_1) { @@ -321,9 +318,9 @@ TEST_F(NlpTests, basic_cb_ns_test_1) { auto randomValue = NDArrayFactory::create(2L); auto inferenceVector = NDArrayFactory::empty(); - nd4j::ops::cbow op; + sd::ops::cbow op; auto result = op.evaluate({&target, &ngStarter, &context, &indices, &codes, &syn0, &syn1, &syn1Neg, &expTable, &negTable, &alpha, &randomValue, &numWords, &locked, &inferenceVector}, {}, {1, 2, 0}, {true}, {}, true); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); auto row_s0_0 = syn0({0,1, 0,0}, true); auto row_s0_1 = syn0({1,2, 0,0}, true); @@ -339,7 +336,7 @@ TEST_F(NlpTests, basic_cb_ns_test_1) { ASSERT_EQ(exp0, row_s0_2); ASSERT_EQ(exp2, row_s1_6); - delete result; + } TEST_F(NlpTests, test_sg_hs_batch_1) { @@ -370,9 +367,9 @@ TEST_F(NlpTests, test_sg_hs_batch_1) { syn1.assign(0.02); expTable.assign(0.5); - nd4j::ops::skipgram op; + sd::ops::skipgram op; auto result = op.evaluate({&target, &ngStarter, &indices, &codes, &syn0, &syn1, &syn1Neg, &expTable, &negTable, &alpha, &randomValue, &inferenceVector, &neu1e}, {}, {}, {false, true}, {}, true); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); auto row0 = syn0({0,1, 0,0}, true); auto row1 = syn1({1,2, 0,0}, true); @@ -382,7 +379,7 @@ TEST_F(NlpTests, test_sg_hs_batch_1) { ASSERT_TRUE(exp1.equalsTo(row1, 1e-6)); ASSERT_TRUE(exp2.equalsTo(row2, 1e-6)); - delete result; + } TEST_F(NlpTests, test_sg_ns_batch_1) { @@ -414,15 +411,11 @@ TEST_F(NlpTests, test_sg_ns_batch_1) { expTable.assign(0.5); negTable.linspace(0.0); - nd4j::ops::skipgram op; + sd::ops::skipgram op; auto result = op.evaluate({&target, &ngStarter, &indices, &codes, &syn0, &syn1, &syn1Neg, &expTable, &negTable, &alpha, &randomValue, &inferenceVector, &neu1e}, {}, {4, 5}, {false, true}, {}, true); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto row0 = syn0({0,0, 0,0}, true); - auto row1 = syn0({5,0, 0,0}, true); - auto row2 = syn0({2,0, 0,0}, true); - - delete result; + } TEST_F(NlpTests, test_cbow_hs_batch_1) { @@ -451,9 +444,9 @@ TEST_F(NlpTests, test_cbow_hs_batch_1) { auto randomValue = NDArrayFactory::create('c', {2}, {2L, 2L}); auto inferenceVector = NDArrayFactory::empty(); - nd4j::ops::cbow op; + sd::ops::cbow op; auto result = op.evaluate({&target, &ngStarter, &context, &indices, &codes, &syn0, &syn1, &syn1Neg, &expTable, &negTable, &alpha, &randomValue, &numWords, &locked, &inferenceVector}, {}, {}, {true}, {}, true); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); auto exp0 = NDArrayFactory::create('c', {1, 10}); auto exp1 = NDArrayFactory::create('c', {1, 10}); @@ -477,6 +470,5 @@ TEST_F(NlpTests, test_cbow_hs_batch_1) { ASSERT_EQ(exp1, row_s1_4); ASSERT_EQ(exp1, row_s1_5); ASSERT_EQ(exp2, row_s1_6); - - delete result; -} \ No newline at end of file + +} diff --git a/libnd4j/tests_cpu/layers_tests/NodeTests.cpp b/libnd4j/tests_cpu/layers_tests/NodeTests.cpp index d4d8c9a1f..8f4a8ae70 100644 --- a/libnd4j/tests_cpu/layers_tests/NodeTests.cpp +++ b/libnd4j/tests_cpu/layers_tests/NodeTests.cpp @@ -19,13 +19,13 @@ // #include "testlayers.h" -#include +#include #include #include #include -using namespace nd4j; -using namespace nd4j::graph; +using namespace sd; +using namespace sd::graph; class NodeTests : public testing::Test { public: @@ -51,7 +51,7 @@ TEST_F(NodeTests, Test_Dtype_Conversion_1) { TEST_F(NodeTests, Test_Dtype_Conversion_2) { - nd4j::ops::add opA; + sd::ops::add opA; //auto nodeA = new Node(OpType_CUSTOM, 0, 1, {-1}, {2}); auto nodeA = new Node(&opA, 1, {-1}, {2}); diff --git a/libnd4j/tests_cpu/layers_tests/OmpLaunchHelperTests.cpp b/libnd4j/tests_cpu/layers_tests/OmpLaunchHelperTests.cpp index 0d879748d..a7c7eae24 100644 --- a/libnd4j/tests_cpu/layers_tests/OmpLaunchHelperTests.cpp +++ b/libnd4j/tests_cpu/layers_tests/OmpLaunchHelperTests.cpp @@ -19,12 +19,12 @@ // #include "testlayers.h" -#include -#include +#include +#include -using namespace nd4j; -using namespace nd4j::graph; +using namespace sd; +using namespace sd::graph; class OmpLaunchHelperTests : public testing::Test { private: diff --git a/libnd4j/tests_cpu/layers_tests/OneOffTests.cpp b/libnd4j/tests_cpu/layers_tests/OneOffTests.cpp index 05f823e4a..e1cf4ec52 100644 --- a/libnd4j/tests_cpu/layers_tests/OneOffTests.cpp +++ b/libnd4j/tests_cpu/layers_tests/OneOffTests.cpp @@ -23,13 +23,13 @@ #include #include #include -#include +#include #include #include -#include +#include -using namespace nd4j; -using namespace nd4j::ops; +using namespace sd; +using namespace sd::ops; class OneOffTests : public testing::Test { public: @@ -62,9 +62,9 @@ TEST_F(OneOffTests, test_non2d_0A_1) { /* TEST_F(OneOffTests, test_assert_scalar_float32_1) { - nd4j::ops::Assert op; - nd4j::ops::identity op1; - nd4j::ops::noop op2; + sd::ops::Assert op; + sd::ops::identity op1; + sd::ops::noop op2; auto graph = GraphExecutioner::importFromFlatBuffers("./resources/scalar_float32.fb"); ASSERT_TRUE(graph != nullptr); @@ -77,9 +77,9 @@ TEST_F(OneOffTests, test_assert_scalar_float32_1) { }*/ TEST_F(OneOffTests, test_assert_scalar_float32_2) { - nd4j::ops::Assert op; - nd4j::ops::identity op1; - nd4j::ops::noop op2; + sd::ops::Assert op; + sd::ops::identity op1; + sd::ops::noop op2; auto graph = GraphExecutioner::importFromFlatBuffers("./resources/assertsomething.fb"); ASSERT_TRUE(graph != nullptr); @@ -318,7 +318,7 @@ TEST_F(OneOffTests, test_cond_false_1) { TEST_F(OneOffTests, test_identity_n_2) { auto e = NDArrayFactory::create('c', {2, 3}, {0.77878559f, 0.80119777f, 0.72437465f, 0.23089433f, 0.72714126f, 0.18039072f}); - nd4j::ops::identity_n op; + sd::ops::identity_n op; auto graph = GraphExecutioner::importFromFlatBuffers("./resources/identity_n_2.fb"); ASSERT_TRUE(graph != nullptr); diff --git a/libnd4j/tests_cpu/layers_tests/OpTrackerTests.cpp b/libnd4j/tests_cpu/layers_tests/OpTrackerTests.cpp index 36828a807..fe581e09e 100644 --- a/libnd4j/tests_cpu/layers_tests/OpTrackerTests.cpp +++ b/libnd4j/tests_cpu/layers_tests/OpTrackerTests.cpp @@ -18,15 +18,15 @@ // Created by raver119 on 15.12.17. // #include "testlayers.h" -#include +#include #include -#include +#include #include #include -using namespace nd4j; -using namespace nd4j::ops; -using namespace nd4j::graph; +using namespace sd; +using namespace sd::ops; +using namespace sd::graph; class OpTrackerTests : public testing::Test { public: @@ -40,7 +40,7 @@ public: }; TEST_F(OpTrackerTests, Test_Existence_1) { - nd4j::_loader loader; + sd::_loader loader; // nd4j_printf("Groups: %i; Operations: %i\n", OpTracker::getInstance()->totalGroups(), OpTracker::getInstance()->totalOperations()); @@ -51,7 +51,7 @@ TEST_F(OpTrackerTests, Test_Existence_1) { } TEST_F(OpTrackerTests, Test_Ops_List_1) { - nd4j::ops::less op; + sd::ops::less op; auto vec = OpRegistrator::getInstance()->getAllHashes(); // nd4j_printf("Total ops: %lld\n", vec.size()); diff --git a/libnd4j/tests_cpu/layers_tests/OpTupleTests.cpp b/libnd4j/tests_cpu/layers_tests/OpTupleTests.cpp index a7aafb3f7..bec75f056 100644 --- a/libnd4j/tests_cpu/layers_tests/OpTupleTests.cpp +++ b/libnd4j/tests_cpu/layers_tests/OpTupleTests.cpp @@ -19,11 +19,11 @@ // #include "testlayers.h" -#include +#include #include -using namespace nd4j; -using namespace nd4j::ops; +using namespace sd; +using namespace sd::ops; class OpTupleTests : public testing::Test { public: diff --git a/libnd4j/tests_cpu/layers_tests/PairwiseTests.cpp b/libnd4j/tests_cpu/layers_tests/PairwiseTests.cpp index ec0388a0b..9cb2589c1 100644 --- a/libnd4j/tests_cpu/layers_tests/PairwiseTests.cpp +++ b/libnd4j/tests_cpu/layers_tests/PairwiseTests.cpp @@ -18,7 +18,7 @@ // Created by agibsonccc on 1/17/17. // #include "testinclude.h" -#include +#include class EqualsTest : public testing::Test { public: @@ -35,7 +35,7 @@ public: #ifndef __CUDABLAS__ TEST_F(EqualsTest,Eps) { - auto val = nd4j::NDArrayFactory::create(0.0f); + auto val = sd::NDArrayFactory::create(0.0f); functions::reduce3::Reduce3::execScalar(opNum, data, firstShapeBuffer, diff --git a/libnd4j/tests_cpu/layers_tests/ParityOpsTests.cpp b/libnd4j/tests_cpu/layers_tests/ParityOpsTests.cpp index a53d71a65..ce24c8a9b 100644 --- a/libnd4j/tests_cpu/layers_tests/ParityOpsTests.cpp +++ b/libnd4j/tests_cpu/layers_tests/ParityOpsTests.cpp @@ -19,12 +19,12 @@ // #include "testlayers.h" -#include +#include #include -using namespace nd4j; -using namespace nd4j::ops; +using namespace sd; +using namespace sd::ops; class ParityOpsTests : public testing::Test { public: @@ -39,16 +39,16 @@ TEST_F(ParityOpsTests, TestZeroAs1) { auto exp = NDArrayFactory::create('c', {10, 10}); exp.assign(0.0f); - nd4j::ops::zeros_as op; + sd::ops::zeros_as op; auto result = op.evaluate({&x}, {}, {}); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(z->isSameShape(&x)); ASSERT_TRUE(z->equalsTo(&exp)); - delete result; + } TEST_F(ParityOpsTests, TestMaximum1) { @@ -58,15 +58,15 @@ TEST_F(ParityOpsTests, TestMaximum1) { auto y = NDArrayFactory::create('c', {10, 10}); y.assign(2.0); - nd4j::ops::maximum op; + sd::ops::maximum op; auto result = op.evaluate({&x, &y}, {}, {}); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(y.equalsTo(z)); - delete result; + } @@ -78,15 +78,15 @@ TEST_F(ParityOpsTests, TestMinimum1) { y.assign(-2.0f); - nd4j::ops::minimum op; + sd::ops::minimum op; auto result = op.evaluate({&x, &y}, {}, {}); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(y.equalsTo(z)); - delete result; + } TEST_F(ParityOpsTests, TestTear1) { @@ -97,16 +97,16 @@ TEST_F(ParityOpsTests, TestTear1) { tads.at(e)->assign((float) e + 1); } - nd4j::ops::tear op; + sd::ops::tear op; auto result = op.evaluate({&input}, {}, {1}); - ASSERT_EQ(10, result->size()); + ASSERT_EQ(10, result.size()); - for (int e = 0; e < result->size(); e++) - ASSERT_TRUE(tads.at(e)->equalsTo(result->at(e))); + for (int e = 0; e < result.size(); e++) + ASSERT_TRUE(tads.at(e)->equalsTo(result.at(e))); - delete result; + } TEST_F(ParityOpsTests, TestUnstack1) { @@ -117,16 +117,16 @@ TEST_F(ParityOpsTests, TestUnstack1) { tads.at(e)->assign((float) e + 1); } - nd4j::ops::unstack op; + sd::ops::unstack op; auto result = op.evaluate({&input}, {}, {0}); - ASSERT_EQ(10, result->size()); + ASSERT_EQ(10, result.size()); - for (int e = 0; e < result->size(); e++) - ASSERT_TRUE(tads.at(e)->equalsTo(result->at(e))); + for (int e = 0; e < result.size(); e++) + ASSERT_TRUE(tads.at(e)->equalsTo(result.at(e))); - delete result; + } @@ -139,16 +139,16 @@ TEST_F(ParityOpsTests, TestUnstack2) { tads.at(e)->assign((float) e + 1); } - nd4j::ops::unstack op; + sd::ops::unstack op; auto result = op.evaluate({&input}, {}, {2}); - ASSERT_EQ(6, result->size()); + ASSERT_EQ(6, result.size()); - for (int e = 0; e < result->size(); e++) - ASSERT_TRUE(tads.at(e)->equalsTo(result->at(e))); + for (int e = 0; e < result.size(); e++) + ASSERT_TRUE(tads.at(e)->equalsTo(result.at(e))); - delete result; + } TEST_F(ParityOpsTests, TestUnstack3) { @@ -156,17 +156,17 @@ TEST_F(ParityOpsTests, TestUnstack3) { auto exp = NDArrayFactory::create('c', {3, 2}, {1.f, 4., 7., 10.f, 13.f, 16.f}); input.linspace(1); - nd4j::ops::unstack op; + sd::ops::unstack op; auto result = op.evaluate({&input}, {}, {2}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } @@ -175,17 +175,17 @@ TEST_F(ParityOpsTests, TestUnstack4) { auto exp = NDArrayFactory::create('c', {3, 3}, { 1, 2, 3, 7, 8, 9, 13, 14, 15.}); input.linspace(1); - nd4j::ops::unstack op; + sd::ops::unstack op; auto result = op.evaluate({&input}, {}, {1}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } TEST_F(ParityOpsTests, TestUnstack5) { @@ -193,17 +193,17 @@ TEST_F(ParityOpsTests, TestUnstack5) { auto exp = NDArrayFactory::create('c', {2, 3}, { 1, 2, 3, 4, 5, 6}); input.linspace(1); - nd4j::ops::unstack op; + sd::ops::unstack op; auto result = op.evaluate({&input}, {}, {0}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } TEST_F(ParityOpsTests, TestUnstack6) { @@ -211,17 +211,17 @@ TEST_F(ParityOpsTests, TestUnstack6) { auto exp = NDArrayFactory::create('c', {1, 1}, {1}); input.linspace(1); - nd4j::ops::unstack op; + sd::ops::unstack op; auto result = op.evaluate({&input}, {}, {0}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } TEST_F(ParityOpsTests, TestUnstack7) { @@ -229,17 +229,17 @@ TEST_F(ParityOpsTests, TestUnstack7) { auto exp = NDArrayFactory::create('c', {1, 1}, {1}); input.linspace(1); - nd4j::ops::unstack op; + sd::ops::unstack op; auto result = op.evaluate({&input}, {}, {1}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } TEST_F(ParityOpsTests, TestUnstack8) { @@ -247,17 +247,17 @@ TEST_F(ParityOpsTests, TestUnstack8) { auto exp = NDArrayFactory::create('c', {1}, {1}); input.linspace(1); - nd4j::ops::unstack op; + sd::ops::unstack op; auto result = op.evaluate({&input}, {}, {0}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } TEST_F(ParityOpsTests, TestUnstack9) { @@ -265,17 +265,17 @@ TEST_F(ParityOpsTests, TestUnstack9) { auto exp = NDArrayFactory::create('c', {1}, {1}); input.linspace(1); - nd4j::ops::unstack op; + sd::ops::unstack op; auto result = op.evaluate({&input}, {}, {1}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } //////////////////////////////////////////////////////////////////////// @@ -284,16 +284,16 @@ TEST_F(ParityOpsTests, TestUnstack10) { auto input = NDArrayFactory::create('c', {3, 0, 2}); auto exp = NDArrayFactory::create('c', {0,2}); - nd4j::ops::unstack op; + sd::ops::unstack op; auto result = op.evaluate({&input}, {}, {0}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - ASSERT_TRUE(exp.isSameShape(result->at(0))); - ASSERT_TRUE(exp.isSameShape(result->at(1))); - ASSERT_TRUE(exp.isSameShape(result->at(2))); + ASSERT_TRUE(exp.isSameShape(result.at(0))); + ASSERT_TRUE(exp.isSameShape(result.at(1))); + ASSERT_TRUE(exp.isSameShape(result.at(2))); - delete result; + } //////////////////////////////////////////////////////////////////////// @@ -302,15 +302,15 @@ TEST_F(ParityOpsTests, TestUnstack11) { auto input = NDArrayFactory::create('c', {3, 0, 2}); auto exp = NDArrayFactory::create('c', {3,0}); - nd4j::ops::unstack op; + sd::ops::unstack op; auto result = op.evaluate({&input}, {}, {2}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - ASSERT_TRUE(exp.isSameShape(result->at(0))); - ASSERT_TRUE(exp.isSameShape(result->at(1))); + ASSERT_TRUE(exp.isSameShape(result.at(0))); + ASSERT_TRUE(exp.isSameShape(result.at(1))); - delete result; + } //////////////////////////////////////////////////////////////////////// @@ -318,32 +318,50 @@ TEST_F(ParityOpsTests, TestUnstack12) { auto input = NDArrayFactory::create('c', {3, 0, 2}); - nd4j::ops::unstack op; + sd::ops::unstack op; auto result = op.evaluate({&input}, {}, {1}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - ASSERT_TRUE(result->size() == 0); + ASSERT_TRUE(result.size() == 0); - delete result; + } +TEST_F(ParityOpsTests, TestUnstack13) { + + auto x = NDArrayFactory::create('c', {2, 3}); + + sd::ops::unstack op; + auto result = op.evaluate({&x}, {}, {1}); + + ASSERT_EQ(ND4J_STATUS_OK, result.status()); + + ASSERT_EQ(3, result.size()); + + for (int e = 0; e < 3; e++) + ASSERT_EQ(1, result.at(e)->rankOf()); + +} + + + TEST_F(ParityOpsTests, ExpandDimsTest1) { auto input = NDArrayFactory::create('c', {5, 5}); input.linspace(1); auto reshaped = input.reshape('c', {5, 1, 5}); - nd4j::ops::expand_dims op; + sd::ops::expand_dims op; auto result = op.evaluate({&input}, {}, {1}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(reshaped.isSameShape(z)); ASSERT_TRUE(reshaped.equalsTo(z)); - delete result; + } @@ -352,17 +370,17 @@ TEST_F(ParityOpsTests, ExpandDimsTest2) { input.linspace(1); auto reshaped = input.reshape('c', {1, 3, 4}); - nd4j::ops::expand_dims op; + sd::ops::expand_dims op; auto result = op.evaluate({&input}, {}, {0}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(reshaped.isSameShape(z)); ASSERT_TRUE(reshaped.equalsTo(z)); - delete result; + } @@ -371,17 +389,17 @@ TEST_F(ParityOpsTests, ExpandDimsTest3) { input.linspace(1); auto reshaped = input.reshape('c', {3, 1, 4}); - nd4j::ops::expand_dims op; + sd::ops::expand_dims op; auto result = op.evaluate({&input}, {}, {-2}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(reshaped.isSameShape(z)); ASSERT_TRUE(reshaped.equalsTo(z)); - delete result; + } TEST_F(ParityOpsTests, ExpandDimsTest4) { @@ -389,17 +407,17 @@ TEST_F(ParityOpsTests, ExpandDimsTest4) { input.linspace(1); auto reshaped = input.reshape('c', {1, 3, 4}); - nd4j::ops::expand_dims op; + sd::ops::expand_dims op; auto result = op.evaluate({&input}, {}, {-3}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(reshaped.isSameShape(z)); ASSERT_TRUE(reshaped.equalsTo(z)); - delete result; + } @@ -407,16 +425,16 @@ TEST_F(ParityOpsTests, Test_Shape_1) { auto x = NDArrayFactory::create('c', {3, 4, 5, 6}); auto exp = NDArrayFactory::create('c', {4}, {3, 4, 5, 6}); - nd4j::ops::shape_of op; + sd::ops::shape_of op; auto result = op.evaluate({&x}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } @@ -425,16 +443,16 @@ TEST_F(ParityOpsTests, Test_Equals_1) { auto y = NDArrayFactory::create('c', {1, 5}, {1, 0, 3, 0, 5}); auto exp = NDArrayFactory::create('c', {1, 5}, {1, 0, 1, 0, 1}); - nd4j::ops::equals op; + sd::ops::equals op; auto result = op.evaluate({&x, &y}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } @@ -443,16 +461,16 @@ TEST_F(ParityOpsTests, Test_NotEquals_1) { auto y = NDArrayFactory::create('c', {1, 5}, {1, 0, 3, 0, 5}); auto exp = NDArrayFactory::create('c', {1, 5}, {0, 1, 0, 1, 0}); - nd4j::ops::not_equals op; + sd::ops::not_equals op; auto result = op.evaluate({&x, &y}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } TEST_F(ParityOpsTests, Test_Less_1) { @@ -460,16 +478,16 @@ TEST_F(ParityOpsTests, Test_Less_1) { auto y = NDArrayFactory::create('c', {1, 5}, {5, 4, 3, 2, 1}); auto exp = NDArrayFactory::create('c', {1, 5}, {1, 1, 0, 0, 0}); - nd4j::ops::less op; + sd::ops::less op; auto result = op.evaluate({&x, &y}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } TEST_F(ParityOpsTests, Test_LessEquals_1) { @@ -477,16 +495,16 @@ TEST_F(ParityOpsTests, Test_LessEquals_1) { auto y = NDArrayFactory::create('c', {1, 5}, {5, 4, 3, 2, 1}); auto exp = NDArrayFactory::create('c', {1, 5}, {1, 1, 1, 0, 0}); - nd4j::ops::less_equal op; + sd::ops::less_equal op; auto result = op.evaluate({&x, &y}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } TEST_F(ParityOpsTests, Test_GreaterEquals_1) { @@ -494,16 +512,16 @@ TEST_F(ParityOpsTests, Test_GreaterEquals_1) { auto y = NDArrayFactory::create('c', {1, 5}, {5, 4, 3, 2, 1}); auto exp = NDArrayFactory::create('c', {1, 5}, {0, 0, 1, 1, 1}); - nd4j::ops::greater_equal op; + sd::ops::greater_equal op; auto result = op.evaluate({&x, &y}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } TEST_F(ParityOpsTests, Test_GreaterEquals_2) { @@ -511,16 +529,16 @@ TEST_F(ParityOpsTests, Test_GreaterEquals_2) { auto y = NDArrayFactory::create('c', {1, 5}, {5, 4, 3, 2, 1}); auto exp = NDArrayFactory::create('c', {1, 5}, {0, 0, 1, 1, 1}); - nd4j::ops::greater_equal op; + sd::ops::greater_equal op; auto result = op.evaluate({&x, &y}, {}, {}, {}, {}, false); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } TEST_F(ParityOpsTests, Test_Greater_1) { @@ -528,16 +546,16 @@ TEST_F(ParityOpsTests, Test_Greater_1) { auto y = NDArrayFactory::create('c', {1, 5}, {5, 4, 3, 2, 1}); auto exp = NDArrayFactory::create('c', {1, 5}, {0, 0, 0, 1, 1}); - nd4j::ops::greater op; + sd::ops::greater op; auto result = op.evaluate({&x, &y}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } TEST_F(ParityOpsTests, Test_Where_1) { @@ -546,18 +564,18 @@ TEST_F(ParityOpsTests, Test_Where_1) { auto y = NDArrayFactory::create('c', {3, 3}, {9, 8, 7, 6, 5, 4, 3, 2, 1}); auto exp = NDArrayFactory::create('c', {3, 3}, {1, 2, 3, 6, 5, 4, 7, 8, 9}); - nd4j::ops::Where op; + sd::ops::Where op; auto result = op.evaluate({&mask, &x, &y}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); // z->printIndexedBuffer("result"); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } TEST_F(ParityOpsTests, Test_Where_2) { @@ -566,16 +584,16 @@ TEST_F(ParityOpsTests, Test_Where_2) { auto y = NDArrayFactory::create('c', {3, 3}, {9, 8, 7, 6, 5, 4, 3, 2, 1}); auto exp = NDArrayFactory::create('c', {3, 3}, {1, 2, 3, 6, 5, 4, 3, 2, 1}); - nd4j::ops::Where op; + sd::ops::Where op; auto result = op.evaluate({&mask, &x, &y}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } @@ -583,18 +601,18 @@ TEST_F(ParityOpsTests, Test_Where_3) { auto mask = NDArrayFactory::create('c', {2, 2, 3}, {0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1}); auto exp = NDArrayFactory::create('c', {5, 3}, {0, 0, 1, 0, 0, 2, 0, 1, 1, 1, 0, 0, 1, 1, 2}); - nd4j::ops::Where op; + sd::ops::Where op; auto result = op.evaluate({&mask}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); // z->printShapeInfo("z"); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } TEST_F(ParityOpsTests, Test_Select_1) { @@ -603,16 +621,16 @@ TEST_F(ParityOpsTests, Test_Select_1) { auto y = NDArrayFactory::create('c', {3, 3}, {9, 8, 7, 6, 5, 4, 3, 2, 1}); auto exp = NDArrayFactory::create('c', {3, 3}, {1, 2, 3, 6, 5, 4, 3, 2, 1}); - nd4j::ops::select op; + sd::ops::select op; auto result = op.evaluate({&mask, &x, &y}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } TEST_F(ParityOpsTests, Test_Select_2) { @@ -621,16 +639,16 @@ TEST_F(ParityOpsTests, Test_Select_2) { auto y = NDArrayFactory::create('c', {2, 2}, {9, 8, 7, 6}); auto exp = NDArrayFactory::create('c', {2, 2}, {1, 8, 3, 6}); - nd4j::ops::select op; + sd::ops::select op; auto result = op.evaluate({&mask, &x, &y}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } TEST_F(ParityOpsTests, Test_Select_3) { @@ -640,16 +658,15 @@ TEST_F(ParityOpsTests, Test_Select_3) { auto y = NDArrayFactory::create('c', {1, 1}, {2}); auto exp = NDArrayFactory::create('c', {1, 1}, {2}); - nd4j::ops::select op; + sd::ops::select op; auto result = op.evaluate({&mask, &x, &y}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - - delete result; + } TEST_F(ParityOpsTests, Test_Reshape_TF_1) { @@ -658,170 +675,161 @@ TEST_F(ParityOpsTests, Test_Reshape_TF_1) { auto exp = NDArrayFactory::create('c', {1, 2, 2}, {1, 2, 3, 4}); - nd4j::ops::reshape op; + sd::ops::reshape op; auto result = op.evaluate({&x, &shape}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - - delete result; + } TEST_F(ParityOpsTests, Test_Bias_Add_1) { auto x = NDArrayFactory::create('c', {10, 5}); x.assign(0.0); auto bias = NDArrayFactory::create('c', {5}, {1, 2, 3, 4, 5}); - nd4j::ops::biasadd op; + sd::ops::biasadd op; auto result = op.evaluate({&x, &bias}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); auto tads = z->allTensorsAlongDimension({1}); for (int e = 0; e < tads.size(); e++) { ASSERT_TRUE(bias.equalsTo(tads.at(e))); } - - delete result; + } TEST_F(ParityOpsTests, Test_Scatter_Add_1) { auto matrix = NDArrayFactory::create('c', {2, 2}, {1, 2, 3, 4}); - NDArray idc('c', {1}, std::vector({0}), nd4j::DataType::INT64); + NDArray idc('c', {1}, std::vector({0}), sd::DataType::INT64); auto updates = NDArrayFactory::create('c', {1, 2}, {1, 1}); auto exp = NDArrayFactory::create('c', {2, 2}, {2, 3, 3, 4}); - nd4j::ops::scatter_add op; + sd::ops::scatter_add op; auto result = op.evaluate({&matrix, &idc, &updates}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.equalsTo(z)); - - delete result; + } TEST_F(ParityOpsTests, Test_Scatter_Add_2) { auto vec = NDArrayFactory::create('c', {4}, {1, 2, 3, 4}); - NDArray idc('c', {1, 4}, {0., 1, 2, 3}, nd4j::DataType::INT64); + NDArray idc('c', {1, 4}, {0., 1, 2, 3}, sd::DataType::INT64); auto updates = NDArrayFactory::create('c', {1, 4}, {1, 1, 1, 1}); auto exp = NDArrayFactory::create('c', {1, 4}, {2, 3, 4, 5}); - nd4j::ops::scatter_add op; + sd::ops::scatter_add op; auto result = op.evaluate({&vec, &idc, &updates}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.equalsTo(z)); - - delete result; + } TEST_F(ParityOpsTests, Test_Scatter_Add_3) { auto matrix = NDArrayFactory::create('c', {2, 2, 2}, {1, 2, 3, 4, 5, 6, 7, 8}); - NDArray idc('c', {1}, std::vector({0}), nd4j::DataType::INT64); + NDArray idc('c', {1}, std::vector({0}), sd::DataType::INT64); auto updates = NDArrayFactory::create('c', {1, 2, 2}, {1, 1, 1, 1}); auto exp = NDArrayFactory::create('c', {2, 2, 2}, {2, 3, 4, 5, 5, 6, 7, 8}); - nd4j::ops::scatter_add op; + sd::ops::scatter_add op; auto result = op.evaluate({&matrix, &idc, &updates}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.equalsTo(z)); - - delete result; + } TEST_F(ParityOpsTests, Test_Scatter_Add_4) { auto matrix = NDArrayFactory::create('c', {2, 2, 2}, {1, 2, 3, 4, 5, 6, 7, 8}); - NDArray idc('c', {1, 2}, std::vector{0, 0}, nd4j::DataType::INT64); + NDArray idc('c', {1, 2}, std::vector{0, 0}, sd::DataType::INT64); auto updates = NDArrayFactory::create('c', {1, 2, 2, 2}, {1, 1, 1, 1, 1, 1, 1, 1}); auto exp = NDArrayFactory::create('c', {2, 2, 2}, {3, 4, 5, 6, 5, 6, 7, 8}); - nd4j::ops::scatter_add op; + sd::ops::scatter_add op; auto result = op.evaluate({&matrix, &idc, &updates}, {}, {}, {true, true}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.equalsTo(z)); - - delete result; + } TEST_F(ParityOpsTests, Test_Scatter_Add_5) { auto matrix = NDArrayFactory::create('c', {2, 2, 3}, {1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1}); - NDArray idc('c', {2, 2}, {1., 1, 0, 0}, nd4j::DataType::INT64); + NDArray idc('c', {2, 2}, {1., 1, 0, 0}, sd::DataType::INT64); auto updates = NDArrayFactory::create('c', {2, 2, 2, 3}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}); auto exp = NDArrayFactory::create('c', {2, 2, 3}, {9., 11., 13.,15., 17., 19., 9., 11., 13.,15., 17., 19.}); - nd4j::ops::scatter_add op; + sd::ops::scatter_add op; auto result = op.evaluate({&matrix, &idc, &updates}, {}, {}, {true}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); // z->printBuffer(); ASSERT_TRUE(exp.equalsTo(z)); - - delete result; + } TEST_F(ParityOpsTests, Test_Scatter_Add_6) { auto matrix = NDArrayFactory::create('c', {2, 2, 2}, {1, 1, 1, 1, 1, 1, 1, 1}); - NDArray idc('c', {2, 2}, {1, 1, 0, 0}, nd4j::DataType::INT64); + NDArray idc('c', {2, 2}, {1, 1, 0, 0}, sd::DataType::INT64); auto updates = NDArrayFactory::create('c', {2, 2, 2, 2}, {1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8}); auto exp = NDArrayFactory::create('c', {2, 2, 2}, {7, 9, 11, 13, 7, 9, 11, 13}); - nd4j::ops::scatter_add op; + sd::ops::scatter_add op; auto result = op.evaluate({&matrix, &idc, &updates}, {}, {}, {true, true}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.equalsTo(z)); - - delete result; + } TEST_F(ParityOpsTests, Test_Scatter_Add_7) { auto matrix = NDArrayFactory::create('c', {10, 3}, {1.f,2.f,3.f,4.f,5.f,6.f,7.f,8.f,9.f,10.f,11.f,12.f,13.f,14.f,15.f,16.f,17.f,18.f,19.f,20.f,21.f,22.f,23.f,24.f,25.f,26.f,27.f,28.f,29.f,30.f}); - NDArray idc('c', {}, std::vector{5}, nd4j::DataType::INT64); + NDArray idc('c', {}, std::vector{5}, sd::DataType::INT64); auto updates = NDArrayFactory::create('c', {3}, {10.f, 20.f, 30.f}); auto exp = NDArrayFactory::create('c', {10, 3}, {1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 7.f, 8.f, 9.f, 10.f,11.f,12.f, 13.f,14.f,15.f, 26.f,37.f,48.f, 19.f,20.f,21.f, 22.f,23.f,24.f, 25.f,26.f,27.f, 28.f,29.f,30.f}); - nd4j::ops::scatter_add op; + sd::ops::scatter_add op; auto result = op.evaluate({&matrix, &idc, &updates}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.equalsTo(z)); - - delete result; + } //////////////////////////////////////////////////////////////////// TEST_F(ParityOpsTests, Test_Scatter_Add_8) { - NDArray input('c', {8}, {1,1,1,1,1,1,1,1}, nd4j::DataType::FLOAT32); - NDArray indices('c', {4}, {1, 1, 1, 1}, nd4j::DataType::INT32); - NDArray updates('c', {4}, {1,2,3,4}, nd4j::DataType::FLOAT32); - NDArray expected('c', {8}, {1.f, 11.f, 1.f, 1.f, 1.f, 1.f, 1.f, 1.f}, nd4j::DataType::FLOAT32); + NDArray input('c', {8}, {1,1,1,1,1,1,1,1}, sd::DataType::FLOAT32); + NDArray indices('c', {4}, {1, 1, 1, 1}, sd::DataType::INT32); + NDArray updates('c', {4}, {1,2,3,4}, sd::DataType::FLOAT32); + NDArray expected('c', {8}, {1.f, 11.f, 1.f, 1.f, 1.f, 1.f, 1.f, 1.f}, sd::DataType::FLOAT32); - NDArray z('c', {8}, nd4j::DataType::FLOAT32); + NDArray z('c', {8}, sd::DataType::FLOAT32); - nd4j::ops::scatter_add op; + sd::ops::scatter_add op; Nd4jStatus status = op.execute({&input, &indices, &updates}, {&z}, {}, {}, {true}); // z.printBuffer(); @@ -833,11 +841,11 @@ TEST_F(ParityOpsTests, Test_Scatter_Add_8) { //////////////////////////////////////////////////////////////////// TEST_F(ParityOpsTests, Test_Scatter_Add_9) { auto matrix = NDArrayFactory::create('c', {2, 2, 3}, {1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1}); - NDArray idc('c', {2, 2}, {1, 10, 0, 0}, nd4j::DataType::INT64); + NDArray idc('c', {2, 2}, {1, 10, 0, 0}, sd::DataType::INT64); auto updates = NDArrayFactory::create('c', {2, 2, 2, 3}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}); auto output = NDArrayFactory::create('c', {2, 2, 3}); - nd4j::ops::scatter_add op; + sd::ops::scatter_add op; ASSERT_ANY_THROW(op.execute({&matrix, &idc, &updates}, {&output}, {}, {}, {true, true})); } @@ -845,184 +853,176 @@ TEST_F(ParityOpsTests, Test_Scatter_Add_9) { //////////////////////////////////////////////////////////////////// TEST_F(ParityOpsTests, scatterMax_test1) { auto matrix = NDArrayFactory::create('c', {2, 2}, {1, 2, 3, 4}); - NDArray idc('c', {1}, std::vector{0.}, nd4j::DataType::INT64); + NDArray idc('c', {1}, std::vector{0.}, sd::DataType::INT64); auto updates = NDArrayFactory::create('c', {1, 2}, {10, 1}); auto exp = NDArrayFactory::create('c', {2, 2}, {10, 2, 3, 4}); - nd4j::ops::scatter_max op; + sd::ops::scatter_max op; auto result = op.evaluate({&matrix, &idc, &updates}, {}, {}, {true}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.equalsTo(z)); - - delete result; + } TEST_F(ParityOpsTests, scatterMax_test2) { auto vec = NDArrayFactory::create('c', {4}, {1, 2, 3, 4}); - NDArray idc('c', {1, 4}, {0, 1, 2, 3}, nd4j::DataType::INT64); + NDArray idc('c', {1, 4}, {0, 1, 2, 3}, sd::DataType::INT64); auto updates = NDArrayFactory::create('c', {1, 4}, {10, 1, 30, 1}); auto exp = NDArrayFactory::create('c', {1, 4}, {10, 2, 30, 4}); - nd4j::ops::scatter_max op; + sd::ops::scatter_max op; auto result = op.evaluate({&vec, &idc, &updates}, {}, {}, {true}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.equalsTo(z)); - - delete result; + } TEST_F(ParityOpsTests, scatterMax_test3) { auto matrix = NDArrayFactory::create('c', {2, 2, 2}, {1, 2, 3, 4, 5, 6, 7, 8}); - NDArray idc('c', {1}, std::vector({0}), nd4j::DataType::INT64); + NDArray idc('c', {1}, std::vector({0}), sd::DataType::INT64); auto updates = NDArrayFactory::create('c', {1, 2, 2}, {10, 1, 30, 1}); auto exp = NDArrayFactory::create('c', {2, 2, 2}, {10, 2, 30, 4, 5, 6, 7, 8}); - nd4j::ops::scatter_max op; + sd::ops::scatter_max op; auto result = op.evaluate({&matrix, &idc, &updates}, {}, {}, {true}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } TEST_F(ParityOpsTests, scatterMax_test4) { auto matrix = NDArrayFactory::create('c', {2, 2, 2}, {1, 2, 3, 4, 5, 6, 7, 8}); - NDArray idc('c', {1,2}, std::vector{0.,0}, nd4j::DataType::INT32); + NDArray idc('c', {1,2}, std::vector{0.,0}, sd::DataType::INT32); auto updates = NDArrayFactory::create('c', {1, 2, 2, 2}, {1,10,1,10, 1,1,10,1.}); auto exp = NDArrayFactory::create('c', {2, 2, 2}, {1, 10, 10, 10, 5, 6, 7, 8}); - nd4j::ops::scatter_max op; + sd::ops::scatter_max op; auto result = op.evaluate({&matrix, &idc, &updates}, {}, {true}, {true}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.equalsTo(z)); - - delete result; + } TEST_F(ParityOpsTests, scatterMax_test5) { auto matrix = NDArrayFactory::create('c', {2, 2, 3}, {1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1}); - NDArray idc('c', {2, 2}, {1, 1, 0, 0}, nd4j::DataType::INT32); + NDArray idc('c', {2, 2}, {1, 1, 0, 0}, sd::DataType::INT32); auto updates = NDArrayFactory::create('c', {2, 2, 2, 3}, {2,10,1,10, 2,10,1,10, 2,10,1,10, 10,2,10,1, 10,2,10,1, 10,2,10,1.}); auto exp = NDArrayFactory::create('c', {2, 2, 3}, {10, 2, 10, 2, 10, 2, 2, 10, 2, 10, 2, 10}); - nd4j::ops::scatter_max op; + sd::ops::scatter_max op; auto result = op.evaluate({&matrix, &idc, &updates}, {}, {}, {true}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.equalsTo(z)); - - delete result; + } TEST_F(ParityOpsTests, scatterMax_test6) { auto matrix = NDArrayFactory::create('c', {2, 2, 2}, {1, 1, 1, 1, 1, 1, 1, 1}); - NDArray idc('c', {2, 2}, {1, 1, 0, 0}, nd4j::DataType::INT32); + NDArray idc('c', {2, 2}, {1, 1, 0, 0}, sd::DataType::INT32); auto updates = NDArrayFactory::create('c', {2, 2, 2, 2}, {0,2,0,2, 0,2,0,2, 2,0,2,0., 2,0,2,0}); auto exp = NDArrayFactory::create('c', {2, 2, 2}, {2, 1, 2, 1, 1, 2, 1, 2}); - nd4j::ops::scatter_max op; + sd::ops::scatter_max op; auto result = op.evaluate({&matrix, &idc, &updates}, {}, {}, {true}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.equalsTo(z)); - - delete result; + } TEST_F(ParityOpsTests, scatterMin_test1) { auto matrix = NDArrayFactory::create('c', {2, 2}, {1, 2, 3, 4}); - NDArray idc('c', {1}, std::vector({0}), nd4j::DataType::INT32); + NDArray idc('c', {1}, std::vector({0}), sd::DataType::INT32); auto updates = NDArrayFactory::create('c', {1, 2}, {-1, 1}); auto exp = NDArrayFactory::create('c', {2, 2}, {-1, 1, 3, 4}); - nd4j::ops::scatter_min op; + sd::ops::scatter_min op; auto result = op.evaluate({&matrix, &idc, &updates}, {}, {}, {true}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } TEST_F(ParityOpsTests, scatterMin_test2) { auto vec = NDArrayFactory::create('c', {4}, {1, 2, 3, 4}); - NDArray idc('c', {1, 4}, {0, 1, 2, 3}, nd4j::DataType::INT32); + NDArray idc('c', {1, 4}, {0, 1, 2, 3}, sd::DataType::INT32); auto updates = NDArrayFactory::create('c', {1, 4}, {10, 1, 30, 1}); auto exp = NDArrayFactory::create('c', {1, 4}, {1, 1, 3, 1}); - nd4j::ops::scatter_min op; + sd::ops::scatter_min op; auto result = op.evaluate({&vec, &idc, &updates}, {}, {}, {true}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.equalsTo(z)); - - delete result; + } TEST_F(ParityOpsTests, scatterMin_test3) { auto matrix = NDArrayFactory::create('c', {2, 2, 2}, {1, 2, 3, 4, 5, 6, 7, 8}); - NDArray idc('c', {1}, std::vector({0}), nd4j::DataType::INT32); + NDArray idc('c', {1}, std::vector({0}), sd::DataType::INT32); auto updates = NDArrayFactory::create('c', {1, 2, 2}, {10, 1, 30, 2}); auto exp = NDArrayFactory::create('c', {2, 2, 2}, {1, 1, 3, 2, 5, 6, 7, 8}); - nd4j::ops::scatter_min op; + sd::ops::scatter_min op; auto result = op.evaluate({&matrix, &idc, &updates}, {}, {}, {true}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.equalsTo(z)); - - delete result; + } TEST_F(ParityOpsTests, scatterMin_test4) { auto matrix = NDArrayFactory::create('c', {2, 2, 2}, {1, 2, 3, 4, 5, 6, 7, 8}); - NDArray idc('c', {1,2}, std::vector{0.,0}, nd4j::DataType::INT32); + NDArray idc('c', {1,2}, std::vector{0.,0}, sd::DataType::INT32); auto updates = NDArrayFactory::create('c', {1, 2, 2, 2}, {1,10,1,10, 1,1,10,1.}); auto exp = NDArrayFactory::create('c', {2, 2, 2}, {1, 1, 1, 1, 5, 6, 7, 8}); - nd4j::ops::scatter_min op; + sd::ops::scatter_min op; auto result = op.evaluate({&matrix, &idc, &updates}, {}, {}, {true}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); // z->printBuffer(); ASSERT_TRUE(exp.equalsTo(z)); - - delete result; + } //////////////////////////////////////////////////////////////////////// TEST_F(ParityOpsTests, scatterMin_test5) { auto matrix = NDArrayFactory::create('c', {2, 2, 2}, {1, 2, 3, 4, 5, 6, 7, 8}); - NDArray idc('c', {1,2}, {10,10}, nd4j::DataType::INT32); + NDArray idc('c', {1,2}, {10,10}, sd::DataType::INT32); auto updates = NDArrayFactory::create('c', {1, 2, 2, 2}, {1,10,1,10, 1,1,10,1.}); auto output = NDArrayFactory::create('c', {2, 2, 2}); - nd4j::ops::scatter_min op; + sd::ops::scatter_min op; ASSERT_ANY_THROW(op.execute({&matrix, &idc, &updates}, {&output}, {}, {}, {true, true})); } @@ -1030,49 +1030,47 @@ TEST_F(ParityOpsTests, scatterMin_test5) { //////////////////////////////////////////////////////////////////////// TEST_F(ParityOpsTests, scatterND_test1) { - NDArray indices('c', {2, 1}, {1., 0.}, nd4j::DataType::INT32); + NDArray indices('c', {2, 1}, {1., 0.}, sd::DataType::INT32); auto updates = NDArrayFactory::create('c', {2, 4}, {10.f, 20.f, 30.f, 40.f, 50.f, 60.f, 70.f, 80.f}); auto shape = NDArrayFactory::create('c', {2}, {3, 4}); auto exp = NDArrayFactory::create('c', {3, 4}, {50.f, 60.f, 70.f, 80.f, 10.f, 20.f, 30.f, 40.f, 0.f, 0.f, 0.f, 0.f}); - nd4j::ops::scatter_nd op; + sd::ops::scatter_nd op; auto result = op.evaluate({&indices, &updates, &shape}, {}, {false, true}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); // z->printBuffer(); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - - delete result; + } //////////////////////////////////////////////////////////////////////// TEST_F(ParityOpsTests, scatterND_test2) { - NDArray indices('c', {3, 1}, {4., 2., 0.}, nd4j::DataType::INT32); + NDArray indices('c', {3, 1}, {4., 2., 0.}, sd::DataType::INT32); auto updates = NDArrayFactory::create('c', {3, 4}); auto shape = NDArrayFactory::create('c', {2}, {5, 4}); auto exp = NDArrayFactory::create('c', {5, 4}, {9.f,10.f,11.f,12.f, 0.f, 0.f, 0.f, 0.f, 5.f, 6.f, 7.f, 8.f, 0.f, 0.f, 0.f, 0.f, 1.f, 2.f, 3.f, 4.f}); updates.linspace(1.f); - nd4j::ops::scatter_nd op; + sd::ops::scatter_nd op; auto result = op.evaluate({&indices, &updates, &shape}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - - delete result; + } //////////////////////////////////////////////////////////////////////// TEST_F(ParityOpsTests, scatterND_test3) { - NDArray indices('c', {2, 3, 1}, {0., 2., 7., 3., 6., 9.}, nd4j::DataType::INT32); + NDArray indices('c', {2, 3, 1}, {0., 2., 7., 3., 6., 9.}, sd::DataType::INT32); auto updates = NDArrayFactory::create('c', {2,3, 3,4}); auto shape = NDArrayFactory::create('c', {3}, {10, 3, 4}); auto exp = NDArrayFactory::create('c', {10, 3, 4}, {1.f, 2.f, 3.f, 4., 5.f, 6.f, 7.f, 8., 9.f, 10.f, 11.f, 12., 0.f, 0.f, 0.f, 0., 0.f, 0.f, 0.f, 0., 0.f, 0.f, 0.f, 0., @@ -1082,143 +1080,140 @@ TEST_F(ParityOpsTests, scatterND_test3) { 0.f, 0.f, 0.f, 0., 0.f, 0.f, 0.f, 0., 0.f, 0.f, 0.f, 0.,61.f, 62.f, 63.f, 64.,65.f, 66.f, 67.f, 68.,69.f, 70.f, 71.f, 72.,}); updates.linspace(1.f); - nd4j::ops::scatter_nd op; + sd::ops::scatter_nd op; auto result = op.evaluate({&indices, &updates, &shape}, {}, {false, true}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - - delete result; + } //////////////////////////////////////////////////////////////////////// TEST_F(ParityOpsTests, scatterND_test4) { - NDArray indices('c', {4, 1}, {4., 3., 1., 7.}, nd4j::DataType::INT32); + NDArray indices('c', {4, 1}, {4., 3., 1., 7.}, sd::DataType::INT32); auto updates = NDArrayFactory::create('c', {4}, {9.f, 10.f, 11.f, 12.f}); auto shape = NDArrayFactory::create('c', {1}, {8}); auto exp = NDArrayFactory::create('c', {8}, {0.f, 11.f, 0.f, 10.f, 9.f, 0.f, 0.f, 12.f}); - nd4j::ops::scatter_nd op; + sd::ops::scatter_nd op; auto result = op.evaluate({&indices, &updates, &shape}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - - delete result; + } //////////////////////////////////////////////////////////////////////// TEST_F(ParityOpsTests, scatterND_test5) { - NDArray indices('c', {4, 1}, {1, 1, 1, 1}, nd4j::DataType::INT32); + NDArray indices('c', {4, 1}, {1, 1, 1, 1}, sd::DataType::INT32); auto updates = NDArrayFactory::create('c', {4}, {1.f, 2.f, 3.f, 4.f}); auto shape = NDArrayFactory::create('c', {1}, {8}); auto exp = NDArrayFactory::create('c', {8}, {0.f, 10.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f}); - nd4j::ops::scatter_nd op; + sd::ops::scatter_nd op; auto result = op.evaluate({&indices, &updates, &shape}, {}, {}, {true}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); // z->printBuffer(); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - - delete result; + } //////////////////////////////////////////////////////////////////////// TEST_F(ParityOpsTests, scatterND_test6) { - NDArray indices('c', {3, 2}, {0,1,1,0,3,2}, nd4j::DataType::INT32); - NDArray updates('c', {3, 2, 3}, nd4j::DataType::FLOAT32); - NDArray shape('c', {4}, {5,4,2,3}, nd4j::DataType::INT32); + NDArray indices('c', {3, 2}, {0,1,1,0,3,2}, sd::DataType::INT32); + NDArray updates('c', {3, 2, 3}, sd::DataType::FLOAT32); + NDArray shape('c', {4}, {5,4,2,3}, sd::DataType::INT32); NDArray exp('c', {5,4,2,3}, {0., 0., 0.,0., 0., 0.,1., 2., 3.,4., 5., 6.,0., 0., 0.,0., 0., 0., 0., 0., 0.,0., 0., 0., 7., 8., 9., 10., 11., 12., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 13., 14., 15., 16., 17., 18., 0., 0., 0., 0., 0., 0., - 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.}, nd4j::DataType::FLOAT32); + 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.}, sd::DataType::FLOAT32); updates.linspace(1); - nd4j::ops::scatter_nd op; + sd::ops::scatter_nd op; auto result = op.evaluate({&indices, &updates, &shape}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); // z->printBuffer(); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } //////////////////////////////////////////////////////////////////////// TEST_F(ParityOpsTests, scatterND_test7) { - NDArray indices('c', {4,3,2}, {0,1,1,0,3,2,1,0,0,1,1,0,3,2,1,0,0,1,1,0,3,2,1,0}, nd4j::DataType::INT32); - NDArray updates('c', {4,3,2,3}, nd4j::DataType::FLOAT32); - NDArray shape('c', {4}, {5,4,2,3}, nd4j::DataType::INT32); + NDArray indices('c', {4,3,2}, {0,1,1,0,3,2,1,0,0,1,1,0,3,2,1,0,0,1,1,0,3,2,1,0}, sd::DataType::INT32); + NDArray updates('c', {4,3,2,3}, sd::DataType::FLOAT32); + NDArray shape('c', {4}, {5,4,2,3}, sd::DataType::INT32); NDArray exp('c', {5,4,2,3}, {0., 0., 0., 0., 0., 0., 75., 78., 81., 84., 87., 90., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 222., 228., 234., 240., 246., 252., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 111., 114., 117., 120., 123., 126., 0., 0., 0., 0., 0., 0., - 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.}, nd4j::DataType::FLOAT32); + 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.}, sd::DataType::FLOAT32); updates.linspace(1); - nd4j::ops::scatter_nd op; + sd::ops::scatter_nd op; auto result = op.evaluate({&indices, &updates, &shape}, {}, {}, {true, true}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); // z->printBuffer(); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } //////////////////////////////////////////////////////////////////////// TEST_F(ParityOpsTests, scatterND_test8) { - NDArray indices('c', {3, 2}, {0,0, 1,1, 2,2}, nd4j::DataType::INT32); + NDArray indices('c', {3, 2}, {0,0, 1,1, 2,2}, sd::DataType::INT32); auto updates = NDArrayFactory::create('c', {3}, {1.f, 2.f, 3.f}); auto shape = NDArrayFactory::create('c', {2}, {6,4}); auto exp = NDArrayFactory::create('c', {6,4}, {1, 0, 0, 0, 0, 2, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}); - nd4j::ops::scatter_nd op; + sd::ops::scatter_nd op; auto result = op.evaluate({&indices, &updates, &shape}, {}, {true}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); // z->printBuffer(); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } //////////////////////////////////////////////////////////////////////// TEST_F(ParityOpsTests, scatterND_test9) { - NDArray indices('c', {2, 3, 1}, {0., 20., 7., 30., 6., 90.}, nd4j::DataType::INT32); + NDArray indices('c', {2, 3, 1}, {0., 20., 7., 30., 6., 90.}, sd::DataType::INT32); auto updates = NDArrayFactory::create('c', {2,3, 3,4}); auto shape = NDArrayFactory::create('c', {3}, {10, 3, 4}); auto output = NDArrayFactory::create('c', {10, 3, 4}); - nd4j::ops::scatter_nd op; + sd::ops::scatter_nd op; ASSERT_ANY_THROW(auto result = op.execute({&indices, &updates, &shape}, {&output}, {}, {}, {false, true})); } @@ -1228,74 +1223,74 @@ TEST_F(ParityOpsTests, scatterND_test9) { TEST_F(ParityOpsTests, scatterND_add_test1) { auto input = NDArrayFactory::create('c', {8}, {1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 7.f, 8.f}); - NDArray indices('c', {4, 1}, {4., 3., 1., 7.}, nd4j::DataType::INT32); + NDArray indices('c', {4, 1}, {4., 3., 1., 7.}, sd::DataType::INT32); auto updates = NDArrayFactory::create('c', {4}, {9.f, 10.f, 11.f, 12.f}); auto exp = NDArrayFactory::create('c', {8}, {1.f, 13.f, 3.f, 14.f, 14.f, 6.f, 7.f, 20.f}); - nd4j::ops::scatter_nd_add op; + sd::ops::scatter_nd_add op; auto result = op.evaluate({&input, &indices, &updates}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } //////////////////////////////////////////////////////////////////////// TEST_F(ParityOpsTests, scatterND_add_test2) { auto input = NDArrayFactory::create('c', {6, 4}); - NDArray indices('c', {3, 3, 2}, {0.f,0.f, 1.f,1.f, 2.f,2.f, 3.f,3.f, 4.f,0.f, 5.f,1.f, 0.f,2.f, 1.f,3.f, 2.f,0.f}, nd4j::DataType::INT32); + NDArray indices('c', {3, 3, 2}, {0.f,0.f, 1.f,1.f, 2.f,2.f, 3.f,3.f, 4.f,0.f, 5.f,1.f, 0.f,2.f, 1.f,3.f, 2.f,0.f}, sd::DataType::INT32); auto updates = NDArrayFactory::create('c', {3,3}); auto exp = NDArrayFactory::create('c', {6,4}, {1.f,0.f,7.f,0.f, 0.f,2.f,0.f,8.f, 9.f,0.f,3.f,0.f, 0.f,0.f,0.f,4.f, 5.f,0.f,0.f,0.f, 0.f,6.f,0.f,0.f}); input = 0.f; updates.linspace(1.f); - nd4j::ops::scatter_nd_add op; + sd::ops::scatter_nd_add op; auto result = op.evaluate({&input, &indices, &updates}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); // z->printIndexedBuffer(); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } //////////////////////////////////////////////////////////////////////// TEST_F(ParityOpsTests, scatterND_add_test3) { auto input = NDArrayFactory::create('c', {6, 4}); - NDArray indices('c', {2, 3, 1}, {5.f, 1.f, 2.f, 3.f, 4.f, 0.f}, nd4j::DataType::INT32); + NDArray indices('c', {2, 3, 1}, {5.f, 1.f, 2.f, 3.f, 4.f, 0.f}, sd::DataType::INT32); auto updates = NDArrayFactory::create('c', {2,3,4}); auto exp = NDArrayFactory::create('c', {6,4}, {21.f, 22.f, 23.f, 24.f, 5.f, 6.f, 7.f, 8.f, 9.f, 10.f, 11.f, 12.f,13.f, 14.f, 15.f, 16.f,17.f, 18.f, 19.f, 20.f, 1.f, 2.f, 3.f, 4.f}); input = 0.f; updates.linspace(1.f); - nd4j::ops::scatter_nd_add op; + sd::ops::scatter_nd_add op; auto result = op.evaluate({&input, &indices, &updates}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } ////////////////////////////////////////////////////////////////////// TEST_F(ParityOpsTests, scatterND_add_test4) { auto input = NDArrayFactory::create('c', {6, 4, 5}); - NDArray indices('c', {3, 3, 2}, {0.f,0.f, 1.f,1.f, 2.f,2.f, 3.f,3.f, 4.f,0.f, 5.f,1.f, 0.f,2.f, 1.f,3.f, 2.f,0.f}, nd4j::DataType::INT32); + NDArray indices('c', {3, 3, 2}, {0.f,0.f, 1.f,1.f, 2.f,2.f, 3.f,3.f, 4.f,0.f, 5.f,1.f, 0.f,2.f, 1.f,3.f, 2.f,0.f}, sd::DataType::INT32); auto updates = NDArrayFactory::create('c', {3,3,5}); auto exp = NDArrayFactory::create('c', {6,4,5}, {1.f, 2.f, 3.f, 4.f, 5.f, 0.f, 0.f, 0.f, 0.f, 0.f,31.f, 32.f, 33.f, 34.f, 35.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 6.f, 7.f, 8.f, 9.f, 10.f, 0.f, 0.f, 0.f, 0.f, 0.f,36.f, 37.f, 38.f, 39.f, 40.f, @@ -1306,23 +1301,23 @@ TEST_F(ParityOpsTests, scatterND_add_test4) { input = 0.f; updates.linspace(1.f); - nd4j::ops::scatter_nd_add op; + sd::ops::scatter_nd_add op; auto result = op.evaluate({&input, &indices, &updates}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } ////////////////////////////////////////////////////////////////////// TEST_F(ParityOpsTests, scatterND_add_test5) { auto input = NDArrayFactory::create('c', {6,5,4,3,2}); - NDArray indices('c', {2,2,3}, {0.f,0.f,0.f, 1.f,1.f,1.f, 2.f,2.f,2.f, 3.f,3.f,3.f}, nd4j::DataType::INT32); + NDArray indices('c', {2,2,3}, {0.f,0.f,0.f, 1.f,1.f,1.f, 2.f,2.f,2.f, 3.f,3.f,3.f}, sd::DataType::INT32); auto updates = NDArrayFactory::create('c', {2,2,3,2}); auto exp = NDArrayFactory::create('c', {6,5,4,3,2}, { 1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, @@ -1342,27 +1337,27 @@ TEST_F(ParityOpsTests, scatterND_add_test5) { input = 0.f; updates.linspace(1.f); - nd4j::ops::scatter_nd_add op; + sd::ops::scatter_nd_add op; auto result = op.evaluate({&input, &indices, &updates}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } //////////////////////////////////////////////////////////////////////// TEST_F(ParityOpsTests, scatterND_add_test6) { auto input = NDArrayFactory::create('c', {6, 4}); - NDArray indices('c', {2, 3, 1}, {50.f, 1.f, 2.f, 3.f, 40.f, 0.f}, nd4j::DataType::INT32); + NDArray indices('c', {2, 3, 1}, {50.f, 1.f, 2.f, 3.f, 40.f, 0.f}, sd::DataType::INT32); auto updates = NDArrayFactory::create('c', {2,3,4}); auto output = NDArrayFactory::create('c', {6,4}); - nd4j::ops::scatter_nd_add op; + sd::ops::scatter_nd_add op; ASSERT_ANY_THROW(op.execute({&input, &indices, &updates}, {&output}, {}, {}, {false, true})); } @@ -1371,75 +1366,75 @@ TEST_F(ParityOpsTests, scatterND_add_test6) { TEST_F(ParityOpsTests, scatterND_sub_test1) { auto input = NDArrayFactory::create('c', {8}, {1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 7.f, 8.f}); - NDArray indices('c', {4, 1}, {4.f, 3.f, 1.f, 7.f}, nd4j::DataType::INT32); + NDArray indices('c', {4, 1}, {4.f, 3.f, 1.f, 7.f}, sd::DataType::INT32); auto updates = NDArrayFactory::create('c', {4}, {9.f, 10.f, 11.f, 12.f}); auto exp = NDArrayFactory::create('c', {8}, {1.f, -9.f, 3.f, -6.f, -4.f, 6.f, 7.f, -4.f}); - nd4j::ops::scatter_nd_sub op; + sd::ops::scatter_nd_sub op; auto result = op.evaluate({&input, &indices, &updates}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } //////////////////////////////////////////////////////////////////////// TEST_F(ParityOpsTests, scatterND_sub_test2) { auto input = NDArrayFactory::create('c', {6, 4}); - NDArray indices('c', {3, 3, 2}, {0.f,0.f, 1.f,1.f, 2.f,2.f, 3.f,3.f, 4.f,0.f, 5.f,1.f, 0.f,2.f, 1.f,3.f, 2.f,0.f}, nd4j::DataType::INT32); + NDArray indices('c', {3, 3, 2}, {0.f,0.f, 1.f,1.f, 2.f,2.f, 3.f,3.f, 4.f,0.f, 5.f,1.f, 0.f,2.f, 1.f,3.f, 2.f,0.f}, sd::DataType::INT32); auto updates = NDArrayFactory::create('c', {3,3}); auto exp = NDArrayFactory::create('c', {6,4}, {-1.f,0.f,-7.f,0.f, 0.f,-2.f,0.f,-8.f, -9.f,0.f,-3.f,0.f, 0.f,0.f,0.f,-4.f, -5.f,0.f,0.f,0.f, 0.f,-6.f,0.f,0.f}); input = 0.f; updates.linspace(1.f); - nd4j::ops::scatter_nd_sub op; + sd::ops::scatter_nd_sub op; auto result = op.evaluate({&input, &indices, &updates}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); //exp.printIndexedBuffer("e"); //z->printIndexedBuffer("z"); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } //////////////////////////////////////////////////////////////////////// TEST_F(ParityOpsTests, scatterND_sub_test3) { auto input = NDArrayFactory::create('c', {6, 4}); - NDArray indices('c', {2, 3, 1}, {5.f, 1.f, 2.f, 3.f,4.f, 0.f}, nd4j::DataType::INT32); + NDArray indices('c', {2, 3, 1}, {5.f, 1.f, 2.f, 3.f,4.f, 0.f}, sd::DataType::INT32); auto updates = NDArrayFactory::create('c', {2,3,4}); auto exp = NDArrayFactory::create('c', {6,4}, {-21.f,-22.f,-23.f,-24., -5.f, -6.f, -7.f, -8., -9.f,-10.f,-11.f,-12., -13.f,-14.f,-15.f,-16., -17.f,-18.f,-19.f,-20., -1.f, -2.f, -3.f, -4.f}); input = 0.f; updates.linspace(1.f); - nd4j::ops::scatter_nd_sub op; + sd::ops::scatter_nd_sub op; auto result = op.evaluate({&input, &indices, &updates}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } ////////////////////////////////////////////////////////////////////// TEST_F(ParityOpsTests, scatterND_sub_test4) { auto input = NDArrayFactory::create('c', {6, 4, 5}); - NDArray indices('c', {3, 3, 2}, {0.f,0.f, 1.f,1.f, 2.f,2.f, 3.f,3.f, 4.f,0.f, 5.f,1.f, 0.f,2.f, 1.f,3.f, 2.f,0.f}, nd4j::DataType::INT32); + NDArray indices('c', {3, 3, 2}, {0.f,0.f, 1.f,1.f, 2.f,2.f, 3.f,3.f, 4.f,0.f, 5.f,1.f, 0.f,2.f, 1.f,3.f, 2.f,0.f}, sd::DataType::INT32); auto updates = NDArrayFactory::create('c', {3,3,5}); auto exp = NDArrayFactory::create('c', {6,4,5}, {-1.f, -2.f, -3.f, -4.f, -5.f, 0.f, 0.f, 0.f, 0.f, 0.f,-31.f, -32.f, -33.f, -34.f, -35.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, -6.f, -7.f, -8.f, -9.f, -10.f, 0.f, 0.f, 0.f, 0.f, 0.f,-36.f, -37.f, -38.f, -39.f, -40.f, @@ -1450,23 +1445,23 @@ TEST_F(ParityOpsTests, scatterND_sub_test4) { input = 0.f; updates.linspace(1.f); - nd4j::ops::scatter_nd_sub op; + sd::ops::scatter_nd_sub op; auto result = op.evaluate({&input, &indices, &updates}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } ////////////////////////////////////////////////////////////////////// TEST_F(ParityOpsTests, scatterND_sub_test5) { auto input = NDArrayFactory::create('c', {6,5,4,3,2}); - NDArray indices('c', {2,2,3}, {0.f,0.f,0.f, 1.f,1.f,1.f, 2.f,2.f,2.f, 3.f,3.f,3.f}, nd4j::DataType::INT32); + NDArray indices('c', {2,2,3}, {0.f,0.f,0.f, 1.f,1.f,1.f, 2.f,2.f,2.f, 3.f,3.f,3.f}, sd::DataType::INT32); auto updates = NDArrayFactory::create('c', {2,2,3,2}); auto exp = NDArrayFactory::create('c', {6,5,4,3,2}, { -1.f, -2.f, -3.f, -4.f, -5.f, -6.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, @@ -1486,16 +1481,16 @@ TEST_F(ParityOpsTests, scatterND_sub_test5) { input = 0.f; updates.linspace(1.f); - nd4j::ops::scatter_nd_sub op; + sd::ops::scatter_nd_sub op; auto result = op.evaluate({&input, &indices, &updates}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } @@ -1503,75 +1498,75 @@ TEST_F(ParityOpsTests, scatterND_sub_test5) { TEST_F(ParityOpsTests, scatterND_update_test1) { auto input = NDArrayFactory::create('c', {8}, {1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 7.f, 8.f}); - NDArray indices('c', {4, 1}, {4.f, 3.f, 1.f, 7.f}, nd4j::DataType::INT32); + NDArray indices('c', {4, 1}, {4.f, 3.f, 1.f, 7.f}, sd::DataType::INT32); auto updates = NDArrayFactory::create('c', {4}, {9.f, 10.f, 11.f, 12.f}); auto exp = NDArrayFactory::create('c', {8}, {1.f, 11.f, 3.f, 10.f, 9.f, 6.f, 7.f, 12.f}); - nd4j::ops::scatter_nd_update op; + sd::ops::scatter_nd_update op; auto result = op.evaluate({&input, &indices, &updates}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } //////////////////////////////////////////////////////////////////////// TEST_F(ParityOpsTests, scatterND_update_test2) { auto input = NDArrayFactory::create('c', {6, 4}); - NDArray indices('c', {3, 3, 2}, {0.f,0.f, 1.f,1.f, 2.f,2.f, 3.f,3.f, 4.f,0.f, 5.f,1.f, 0.f,2.f, 1.f,3.f, 2.f,0.f}, nd4j::DataType::INT32); + NDArray indices('c', {3, 3, 2}, {0.f,0.f, 1.f,1.f, 2.f,2.f, 3.f,3.f, 4.f,0.f, 5.f,1.f, 0.f,2.f, 1.f,3.f, 2.f,0.f}, sd::DataType::INT32); auto updates = NDArrayFactory::create('c', {3,3}); auto exp = NDArrayFactory::create('c', {6,4}, {1.f,-1.f,7.f,-1.f, -1.f,2.f,-1.f,8.f, 9.f,-1.f,3.f,-1.f, -1.f,-1.f,-1.f,4.f, 5.f,-1.f,-1.f,-1.f, -1.f,6.f,-1.f,-1.f}); input = -1.f; updates.linspace(1.f); - nd4j::ops::scatter_nd_update op; + sd::ops::scatter_nd_update op; auto result = op.evaluate({&input, &indices, &updates}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); // z->printIndexedBuffer(); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } //////////////////////////////////////////////////////////////////////// TEST_F(ParityOpsTests, scatterND_update_test3) { auto input = NDArrayFactory::create('c', {6, 4}); - NDArray indices('c', {2, 3, 1}, {5.f, 1.f, 2.f, 3.f, 4.f, 0.f}, nd4j::DataType::INT32); + NDArray indices('c', {2, 3, 1}, {5.f, 1.f, 2.f, 3.f, 4.f, 0.f}, sd::DataType::INT32); auto updates = NDArrayFactory::create('c', {2,3,4}); auto exp = NDArrayFactory::create('c', {6,4}, {21.f, 22.f, 23.f, 24.f, 5.f, 6.f, 7.f, 8.f, 9.f, 10.f, 11.f, 12.f,13.f, 14.f, 15.f, 16.f,17.f, 18.f, 19.f, 20.f, 1.f, 2.f, 3.f, 4.f,}); input = -1.f; updates.linspace(1.f); - nd4j::ops::scatter_nd_update op; + sd::ops::scatter_nd_update op; auto result = op.evaluate({&input, &indices, &updates}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); // z->printBuffer(); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } ////////////////////////////////////////////////////////////////////// TEST_F(ParityOpsTests, scatterND_update_test4) { auto input = NDArrayFactory::create('c', {6, 4, 5}); - NDArray indices('c', {3, 3, 2}, {0.f,0.f, 1.f,1.f, 2.f,2.f, 3.f,3.f, 4.f,0.f, 5.f,1.f, 0.f,2.f, 1.f,3.f, 2.f,0.f}, nd4j::DataType::INT32); + NDArray indices('c', {3, 3, 2}, {0.f,0.f, 1.f,1.f, 2.f,2.f, 3.f,3.f, 4.f,0.f, 5.f,1.f, 0.f,2.f, 1.f,3.f, 2.f,0.f}, sd::DataType::INT32); auto updates = NDArrayFactory::create('c', {3,3,5}); auto exp = NDArrayFactory::create('c', {6,4,5}, {1.f, 2.f, 3.f, 4.f, 5.f, -1.f, -1.f, -1.f, -1.f, -1.f,31.f, 32.f, 33.f, 34.f, 35.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, 6.f, 7.f, 8.f, 9.f, 10.f, -1.f, -1.f, -1.f, -1.f, -1.f,36.f, 37.f, 38.f, 39.f, 40.f, @@ -1582,23 +1577,23 @@ TEST_F(ParityOpsTests, scatterND_update_test4) { input = -1.f; updates.linspace(1.f); - nd4j::ops::scatter_nd_update op; + sd::ops::scatter_nd_update op; auto result = op.evaluate({&input, &indices, &updates}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } ////////////////////////////////////////////////////////////////////// TEST_F(ParityOpsTests, scatterND_update_test5) { auto input = NDArrayFactory::create('c', {6,5,4,3,2}); - NDArray indices('c', {2,2,3}, {0.f,0.f,0.f, 1.f,1.f,1.f, 2.f,2.f,2.f, 3.f,3.f,3.f}, nd4j::DataType::INT32); + NDArray indices('c', {2,2,3}, {0.f,0.f,0.f, 1.f,1.f,1.f, 2.f,2.f,2.f, 3.f,3.f,3.f}, sd::DataType::INT32); auto updates = NDArrayFactory::create('c', {2,2,3,2}); auto exp = NDArrayFactory::create('c', {6,5,4,3,2}, { 1.f, 2.f, 3.f, 4.f, 5.f, 6.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, @@ -1618,27 +1613,27 @@ TEST_F(ParityOpsTests, scatterND_update_test5) { input = -1.f; updates.linspace(1.f); - nd4j::ops::scatter_nd_update op; + sd::ops::scatter_nd_update op; auto result = op.evaluate({&input, &indices, &updates}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } //////////////////////////////////////////////////////////////////////// TEST_F(ParityOpsTests, scatterND_update_test6) { auto input = NDArrayFactory::create('c', {6, 4}); - NDArray indices('c', {3, 3, 2}, {0.f,0.f, 10.f,1.f, 20.f,2.f, 30.f,3.f, 40.f,0.f, 50.f,1.f, 0.f,2.f, 1.f,3.f, 2.f,0.f}, nd4j::DataType::INT32); + NDArray indices('c', {3, 3, 2}, {0.f,0.f, 10.f,1.f, 20.f,2.f, 30.f,3.f, 40.f,0.f, 50.f,1.f, 0.f,2.f, 1.f,3.f, 2.f,0.f}, sd::DataType::INT32); auto updates = NDArrayFactory::create('c', {3,3}); auto output = NDArrayFactory::create('c', {6,4}); - nd4j::ops::scatter_nd_update op; + sd::ops::scatter_nd_update op; ASSERT_ANY_THROW(op.execute({&input, &indices, &updates}, {&output}, {}, {}, {true, true})); } @@ -1646,76 +1641,76 @@ TEST_F(ParityOpsTests, scatterND_update_test6) { ////////////////////////////////////////////////////////////////////// TEST_F(ParityOpsTests, scatter_update_1) { - NDArray x('c', {2,2}, {1,2,3,4}, nd4j::DataType::INT32); - NDArray updates('c', {2,2}, {10,20,30,40}, nd4j::DataType::INT32); + NDArray x('c', {2,2}, {1,2,3,4}, sd::DataType::INT32); + NDArray updates('c', {2,2}, {10,20,30,40}, sd::DataType::INT32); - NDArray exp('c', {2,2}, {30,40,10,20}, nd4j::DataType::INT32); + NDArray exp('c', {2,2}, {30,40,10,20}, sd::DataType::INT32); - nd4j::ops::scatter_update op; + sd::ops::scatter_update op; auto results = op.evaluate({&x, &updates}, {}, {6, 1,1, 2,1,0}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); // x.printBuffer(); ASSERT_TRUE(exp.isSameShape(x)); ASSERT_TRUE(exp.equalsTo(x)); - delete results; + } ////////////////////////////////////////////////////////////////////// TEST_F(ParityOpsTests, scatter_update_2) { - NDArray x('c', {2,2}, {1,2,3,4}, nd4j::DataType::INT32); - NDArray updates('c', {2,2}, {10,20,30,40}, nd4j::DataType::INT32); + NDArray x('c', {2,2}, {1,2,3,4}, sd::DataType::INT32); + NDArray updates('c', {2,2}, {10,20,30,40}, sd::DataType::INT32); - NDArray exp('c', {2,2}, {20,10,40,30}, nd4j::DataType::INT32); + NDArray exp('c', {2,2}, {20,10,40,30}, sd::DataType::INT32); - nd4j::ops::scatter_update op; + sd::ops::scatter_update op; auto results = op.evaluate({&x, &updates}, {}, {6, 1,0, 2,1,0}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); ASSERT_TRUE(exp.isSameShape(x)); ASSERT_TRUE(exp.equalsTo(x)); - delete results; + } ////////////////////////////////////////////////////////////////////// TEST_F(ParityOpsTests, scatter_update_3) { - NDArray x('c', {2,2,2}, {1,2,3,4,5,6,7,8}, nd4j::DataType::INT32); - NDArray updates('c', {2,2,2}, {10,20,30,40,50,60,70,80}, nd4j::DataType::INT32); + NDArray x('c', {2,2,2}, {1,2,3,4,5,6,7,8}, sd::DataType::INT32); + NDArray updates('c', {2,2,2}, {10,20,30,40,50,60,70,80}, sd::DataType::INT32); - NDArray exp('c', {2,2,2}, {50,60,70,80,10,20,30,40}, nd4j::DataType::INT32); + NDArray exp('c', {2,2,2}, {50,60,70,80,10,20,30,40}, sd::DataType::INT32); - nd4j::ops::scatter_update op; + sd::ops::scatter_update op; auto results = op.evaluate({&x, &updates}, {}, {6, 2,1,2, 2,1,0}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); ASSERT_TRUE(exp.isSameShape(x)); ASSERT_TRUE(exp.equalsTo(x)); - delete results; + } ////////////////////////////////////////////////////////////////////// TEST_F(ParityOpsTests, scatter_update_4) { - NDArray x('c', {2,2,2}, {1,2,3,4,5,6,7,8}, nd4j::DataType::INT32); - NDArray updates('c', {2,2,2}, {10,20,30,40,50,60,70,80}, nd4j::DataType::INT32); + NDArray x('c', {2,2,2}, {1,2,3,4,5,6,7,8}, sd::DataType::INT32); + NDArray updates('c', {2,2,2}, {10,20,30,40,50,60,70,80}, sd::DataType::INT32); - NDArray exp('c', {2,2,2}, {20,2,3,10,60,6,7,50}, nd4j::DataType::INT32); + NDArray exp('c', {2,2,2}, {20,2,3,10,60,6,7,50}, sd::DataType::INT32); - nd4j::ops::scatter_update op; + sd::ops::scatter_update op; auto results = op.evaluate({&x, &updates}, {}, {6, 1,0, 2,3,0}); - ASSERT_EQ(ND4J_STATUS_OK, results->status()); + ASSERT_EQ(ND4J_STATUS_OK, results.status()); ASSERT_TRUE(exp.isSameShape(x)); ASSERT_TRUE(exp.equalsTo(x)); - delete results; + } diff --git a/libnd4j/tests_cpu/layers_tests/PerformanceTests.cpp b/libnd4j/tests_cpu/layers_tests/PerformanceTests.cpp index 68c68aafb..f00536e58 100644 --- a/libnd4j/tests_cpu/layers_tests/PerformanceTests.cpp +++ b/libnd4j/tests_cpu/layers_tests/PerformanceTests.cpp @@ -19,20 +19,20 @@ // #include "testlayers.h" -#include +#include #include -#include +#include #include #include -#include +#include #include #include #include -#include -#include +#include +#include #include -#include -#include +#include +#include #include #include @@ -45,8 +45,8 @@ #include #include -using namespace nd4j; -using namespace nd4j::graph; +using namespace sd; +using namespace sd::graph; class PerformanceTests : public testing::Test { public: @@ -57,6 +57,7 @@ public: } }; + #ifdef RELEASE_BUILD TEST_F(PerformanceTests, test_maxpooling2d_1) { @@ -75,7 +76,7 @@ TEST_F(PerformanceTests, test_maxpooling2d_1) { ctx.setOutputArray(0, &z); ctx.setIArguments(iArgs, 9); - nd4j::ops::maxpool2d op; + sd::ops::maxpool2d op; for (int i = 0; i < numIterations; i++) { auto timeStart = std::chrono::system_clock::now(); diff --git a/libnd4j/tests_cpu/layers_tests/PlaygroundTests.cpp b/libnd4j/tests_cpu/layers_tests/PlaygroundTests.cpp index 93fb5d6b3..94156d4bc 100644 --- a/libnd4j/tests_cpu/layers_tests/PlaygroundTests.cpp +++ b/libnd4j/tests_cpu/layers_tests/PlaygroundTests.cpp @@ -20,20 +20,20 @@ // #include "testlayers.h" -#include +#include #include -#include +#include #include #include -#include +#include #include #include #include -#include -#include +#include +#include #include -#include -#include +#include +#include #include #include @@ -47,8 +47,8 @@ #include #include -using namespace nd4j; -using namespace nd4j::graph; +using namespace sd; +using namespace sd::graph; class PlaygroundTests : public testing::Test { public: @@ -72,7 +72,7 @@ TEST_F(PlaygroundTests, test_biasAdd_1) { std::vector values; - nd4j::ops::biasadd op; + sd::ops::biasadd op; for (int e = 0; e < 100; e++) { auto timeStart = std::chrono::system_clock::now(); @@ -90,10 +90,69 @@ TEST_F(PlaygroundTests, test_biasAdd_1) { } +TEST_F(PlaygroundTests, test_bert_full_1) { + // this test will run ONLY if this model exists + if (sd::graph::getFileSize("/home/raver119/Downloads/BertFull/model.fb") < 0) + return; + + auto graph = GraphExecutioner::importFromFlatBuffers("/home/raver119/Downloads/BertFull/model.fb"); + + auto t = NDArrayFactory::fromNpyFile("/home/raver119/Downloads/BertFull/in0_IteratorGetNext.npy"); + auto u = NDArrayFactory::fromNpyFile("/home/raver119/Downloads/BertFull/in1_IteratorGetNext_1.npy"); + auto v = NDArrayFactory::fromNpyFile("/home/raver119/Downloads/BertFull/in2_IteratorGetNext_4.npy"); + auto z = NDArrayFactory::fromNpyFile("/home/raver119/Downloads/BertFull/out_loss-Softmax.npy"); + + //graph->printOut(); + + graph->tagInplaceNodes(); + + graph->getVariableSpace()->putVariable(658,0, t); + graph->getVariableSpace()->putVariable(659,0, u); + graph->getVariableSpace()->putVariable(660,0, v); + +/* + // validating graph now + auto status = GraphExecutioner::execute(graph); + ASSERT_EQ(Status::OK(), status); + ASSERT_TRUE(graph->getVariableSpace()->hasVariable(1620)); + + auto array = graph->getVariableSpace()->getVariable(1620)->getNDArray(); + ASSERT_EQ(z, *array); + +*/ + + sd::Environment::getInstance()->setProfiling(true); + auto profile = GraphProfilingHelper::profile(graph, 1); + + profile->printOut(); + + sd::Environment::getInstance()->setProfiling(false); + delete profile; + +/* + std::vector values; + + for (int e = 0; e < 1; e++) { + auto timeStart = std::chrono::system_clock::now(); + + GraphExecutioner::execute(graph); + + auto timeEnd = std::chrono::system_clock::now(); + auto outerTime = std::chrono::duration_cast(timeEnd - timeStart).count(); + values.emplace_back(outerTime); + } + + std::sort(values.begin(), values.end()); + + nd4j_printf("Time: %lld us;\n", values[values.size() / 2]); +*/ + delete graph; +} + TEST_F(PlaygroundTests, test_bert_1) { // this test will run ONLY if this model exists - if (nd4j::graph::getFileSize("/home/raver119/Downloads/Bert_minimal_model/bert_minimal_model.fb") < 0) + if (sd::graph::getFileSize("/home/raver119/Downloads/Bert_minimal_model/bert_minimal_model.fb") < 0) return; auto graph = GraphExecutioner::importFromFlatBuffers("/home/raver119/Downloads/Bert_minimal_model/bert_minimal_model.fb"); @@ -119,14 +178,14 @@ TEST_F(PlaygroundTests, test_bert_1) { auto array = graph->getVariableSpace()->getVariable(198)->getNDArray(); ASSERT_EQ(z, *array); -*/ - nd4j::Environment::getInstance()->setProfiling(true); +*/ + sd::Environment::getInstance()->setProfiling(true); auto profile = GraphProfilingHelper::profile(graph, 1); profile->printOut(); - nd4j::Environment::getInstance()->setProfiling(false); + sd::Environment::getInstance()->setProfiling(false); delete profile; /* @@ -151,7 +210,7 @@ TEST_F(PlaygroundTests, test_bert_1) { TEST_F(PlaygroundTests, test_bert_2) { // this test will run ONLY if this model exists - if (nd4j::graph::getFileSize("/home/raver119/Downloads/Bert_minimal_model/bert_like_ops.fb") < 0) + if (sd::graph::getFileSize("/home/raver119/Downloads/Bert_minimal_model/bert_like_ops.fb") < 0) return; auto graph = GraphExecutioner::importFromFlatBuffers("/home/raver119/Downloads/Bert_minimal_model/bert_like_ops.fb"); @@ -171,12 +230,12 @@ TEST_F(PlaygroundTests, test_bert_2) { ASSERT_EQ(z, *array); */ - nd4j::Environment::getInstance()->setProfiling(true); + sd::Environment::getInstance()->setProfiling(true); auto profile = GraphProfilingHelper::profile(graph, 1); profile->printOut(); - nd4j::Environment::getInstance()->setProfiling(false); + sd::Environment::getInstance()->setProfiling(false); delete profile; /* @@ -199,12 +258,13 @@ TEST_F(PlaygroundTests, test_bert_2) { delete graph; } + TEST_F(PlaygroundTests, test_one_off_ops_1) { auto x = NDArrayFactory::create('c', {4, 128, 768}); auto y = NDArrayFactory::create('c', {4, 128, 1}); auto z = x.ulike(); - nd4j::ops::squaredsubtract op; + sd::ops::squaredsubtract op; op.execute({&x, &y}, {&z}); } @@ -229,7 +289,7 @@ TEST_F(PlaygroundTests, test_broadcast_1) { std::vector values; Context ctx(1); - nd4j::ops::biasadd op; + sd::ops::biasadd op; for (int e = 0; e < 1000; e++) { auto x = aX[e < pool ? e : e % pool]; @@ -239,7 +299,7 @@ TEST_F(PlaygroundTests, test_broadcast_1) { auto timeStart = std::chrono::system_clock::now(); //op.execute({x, y}, {z}); - nd4j::ops::helpers::addBias(ctx, *x, *y, *z, false); + sd::ops::helpers::addBias(ctx, *x, *y, *z, false); auto timeEnd = std::chrono::system_clock::now(); auto outerTime = std::chrono::duration_cast(timeEnd - timeStart).count(); @@ -313,7 +373,7 @@ TEST_F(PlaygroundTests, test_s_0) { for (auto shape: shapes) { for (auto t: threads) { - nd4j::Environment::getInstance()->setMaxMasterThreads(t); + sd::Environment::getInstance()->setMaxMasterThreads(t); auto x = NDArrayFactory::create('c', shape); auto y = NDArrayFactory::create('c', {shape[3]}); @@ -325,14 +385,14 @@ TEST_F(PlaygroundTests, test_s_0) { ctx.setInputArray(1, &y); ctx.setOutputArray(0, &z); - nd4j::ops::biasadd op; + sd::ops::biasadd op; for (int e = 0; e < 10000; e++) { auto timeStart = std::chrono::system_clock::now(); op.execute(&ctx); - nd4j::ops::helpers::addBias(ctx, x, y, z, false); + sd::ops::helpers::addBias(ctx, x, y, z, false); auto timeEnd = std::chrono::system_clock::now(); auto outerTime = std::chrono::duration_cast(timeEnd - timeStart).count(); @@ -352,7 +412,7 @@ TEST_F(PlaygroundTests, test_s_1) { for (auto shape: shapes) { for (auto t: threads) { - nd4j::Environment::getInstance()->setMaxMasterThreads(t); + sd::Environment::getInstance()->setMaxMasterThreads(t); auto x = NDArrayFactory::create('c', shape); auto y = NDArrayFactory::create('c', {shape[1]}); @@ -364,14 +424,14 @@ TEST_F(PlaygroundTests, test_s_1) { ctx.setInputArray(1, &y); ctx.setOutputArray(0, &z); - nd4j::ops::biasadd op; + sd::ops::biasadd op; for (int e = 0; e < 10000; e++) { auto timeStart = std::chrono::system_clock::now(); //op.execute({&x, &y}, {&z}, {true}); - nd4j::ops::helpers::addBias(ctx, x, y, z, true); + sd::ops::helpers::addBias(ctx, x, y, z, true); auto timeEnd = std::chrono::system_clock::now(); auto outerTime = std::chrono::duration_cast(timeEnd - timeStart).count(); @@ -398,7 +458,7 @@ TEST_F(PlaygroundTests, test_s_0) { ctx.setInputArray(1, &y); ctx.setOutputArray(0, &z); - nd4j::ops::biasadd op; + sd::ops::biasadd op; for (int e = 0; e < 10000; e++) { @@ -442,7 +502,7 @@ TEST_F(PlaygroundTests, test_s_1) { std::vector values; - nd4j::ops::concat op; + sd::ops::concat op; op.execute(&ctx); for (int e = 0; e < 1000; e++) { @@ -686,12 +746,12 @@ TEST_F(PlaygroundTests, my) { int bS=8, iD=32,iH=32,iW=32, iC=128, kD=2,kH=2,kW=2, sD=1,sH=1,sW=1, pD=0,pH=0,pW=0, dD=2,dH=2,dW=2; int oD,oH,oW; - nd4j::ops::ConvolutionUtils::calcOutSizeDeconv3D(oD, oH, oW, kD, kH, kW, sD, sH, sW, pD, pH, pW, dD, dH, dW, iD, iH, iW, 0); + sd::ops::ConvolutionUtils::calcOutSizeDeconv3D(oD, oH, oW, kD, kH, kW, sD, sH, sW, pD, pH, pW, dD, dH, dW, iD, iH, iW, 0); printf("!!%i, %i, %i\n", oD,oH,oW); - NDArray col('c', {bS, iC, kD, kH, kW, iD, iH, iW}, nd4j::DataType::DOUBLE); - NDArray vol('c', {bS, iC, oD, oH, oW}, nd4j::DataType::DOUBLE); + NDArray col('c', {bS, iC, kD, kH, kW, iD, iH, iW}, sd::DataType::DOUBLE); + NDArray vol('c', {bS, iC, oD, oH, oW}, sd::DataType::DOUBLE); col = 3.77; vol = -10.33; @@ -700,7 +760,7 @@ TEST_F(PlaygroundTests, my) { auto block = new Context(1, variableSpace, false); // not-in-place auto timeStart = std::chrono::system_clock::now(); - nd4j::ops::ConvolutionUtils::col2vol(*block, col, vol, sD, sH, sW, pD, pH, pW, dD, dH, dW); + sd::ops::ConvolutionUtils::col2vol(*block, col, vol, sD, sH, sW, pD, pH, pW, dD, dH, dW); auto timeEnd = std::chrono::system_clock::now(); auto time = std::chrono::duration_cast (timeEnd - timeStart).count(); @@ -715,15 +775,15 @@ TEST_F(PlaygroundTests, my) { int bS=32, iD=32,iH=64,iW=64, iC=128, kD=2,kH=2,kW=2, sD=1,sH=1,sW=1, pD=0,pH=0,pW=0, dD=2,dH=2,dW=2; int oD,oH,oW; - // nd4j::ops::ConvolutionUtils::calcOutSizeDeconv3D(oD, oH, oW, kD, kH, kW, sD, sH, sW, pD, pH, pW, dD, dH, dW, iD, iH, iW, 0); - nd4j::ops::ConvolutionUtils::calcOutSizeDeconv2D(oH, oW, kH, kW, sH, sW, pH, pW,dH, dW, iH, iW, 0); + // sd::ops::ConvolutionUtils::calcOutSizeDeconv3D(oD, oH, oW, kD, kH, kW, sD, sH, sW, pD, pH, pW, dD, dH, dW, iD, iH, iW, 0); + sd::ops::ConvolutionUtils::calcOutSizeDeconv2D(oH, oW, kH, kW, sH, sW, pH, pW,dH, dW, iH, iW, 0); printf("!!%i, %i, %i\n", oD,oH,oW); - // NDArray col('c', {bS, iC, kD, kH, kW, iD, iH, iW}, nd4j::DataType::DOUBLE); - // NDArray vol('c', {bS, iC, oD, oH, oW}, nd4j::DataType::DOUBLE); - NDArray col('c', {bS, iC, kH, kW, iH, iW}, nd4j::DataType::DOUBLE); - NDArray im('c', {bS, iC, oH, oW}, nd4j::DataType::DOUBLE); + // NDArray col('c', {bS, iC, kD, kH, kW, iD, iH, iW}, sd::DataType::DOUBLE); + // NDArray vol('c', {bS, iC, oD, oH, oW}, sd::DataType::DOUBLE); + NDArray col('c', {bS, iC, kH, kW, iH, iW}, sd::DataType::DOUBLE); + NDArray im('c', {bS, iC, oH, oW}, sd::DataType::DOUBLE); col = 3.77; // vol = -10.33; @@ -733,8 +793,8 @@ TEST_F(PlaygroundTests, my) { auto block = new Context(1, variableSpace, false); // not-in-place auto timeStart = std::chrono::system_clock::now(); - // nd4j::ops::ConvolutionUtils::col2vol(*block, col, vol, sD, sH, sW, pD, pH, pW, dD, dH, dW); - nd4j::ops::helpers::col2im(*col.getContext(), col, im, sH, sW, pH, pW, iH, iW, dH, dW); + // sd::ops::ConvolutionUtils::col2vol(*block, col, vol, sD, sH, sW, pD, pH, pW, dD, dH, dW); + sd::ops::helpers::col2im(*col.getContext(), col, im, sH, sW, pH, pW, iH, iW, dH, dW); auto timeEnd = std::chrono::system_clock::now(); auto time = std::chrono::duration_cast (timeEnd - timeStart).count(); @@ -753,19 +813,19 @@ TEST_F(PlaygroundTests, my) { int paddingMode = 1; // 1-SAME, 0-VALID; int dataFormat = 1; // 1-NHWC, 0-NCHW - // NDArray input('c', {bS, iC, iH, iW}, nd4j::DataType::FLOAT32); - // NDArray output('c', {bS, oC, oH, oW}, nd4j::DataType::FLOAT32); - NDArray input('c', {bS, iH, iW, iC}, nd4j::DataType::FLOAT32); - NDArray output('c', {bS, oH, oW, oC}, nd4j::DataType::FLOAT32); - // NDArray weights('c', {kH, kW, iC, oC}, nd4j::DataType::FLOAT32); // permute [kH, kW, iC, oC] -> [oC, iC, kH, kW] - NDArray weights('c', {oC, iC, kH, kW}, nd4j::DataType::FLOAT32); - NDArray bias('c', {oC}, nd4j::DataType::FLOAT32); + // NDArray input('c', {bS, iC, iH, iW}, sd::DataType::FLOAT32); + // NDArray output('c', {bS, oC, oH, oW}, sd::DataType::FLOAT32); + NDArray input('c', {bS, iH, iW, iC}, sd::DataType::FLOAT32); + NDArray output('c', {bS, oH, oW, oC}, sd::DataType::FLOAT32); + // NDArray weights('c', {kH, kW, iC, oC}, sd::DataType::FLOAT32); // permute [kH, kW, iC, oC] -> [oC, iC, kH, kW] + NDArray weights('c', {oC, iC, kH, kW}, sd::DataType::FLOAT32); + NDArray bias('c', {oC}, sd::DataType::FLOAT32); input = 5.; weights = 3.; bias = 1.; - nd4j::ops::conv2d op; + sd::ops::conv2d op; auto err = op.execute({&input, &weights, &bias}, {&output}, {kH,kW, sH,sW, pH,pW, dH,dW, paddingMode, dataFormat}); auto timeStart = std::chrono::system_clock::now(); diff --git a/libnd4j/tests_cpu/layers_tests/ProtoBufTests.cpp b/libnd4j/tests_cpu/layers_tests/ProtoBufTests.cpp index bc9ed5881..fe2f97bb6 100644 --- a/libnd4j/tests_cpu/layers_tests/ProtoBufTests.cpp +++ b/libnd4j/tests_cpu/layers_tests/ProtoBufTests.cpp @@ -20,11 +20,11 @@ #include "testlayers.h" -#include +#include /* -using namespace nd4j::graph; +using namespace sd::graph; class ProtoBufTests : public testing::Test { diff --git a/libnd4j/tests_cpu/layers_tests/QuantizationTests.cpp b/libnd4j/tests_cpu/layers_tests/QuantizationTests.cpp index 608ee443f..97f6cd8cd 100644 --- a/libnd4j/tests_cpu/layers_tests/QuantizationTests.cpp +++ b/libnd4j/tests_cpu/layers_tests/QuantizationTests.cpp @@ -20,11 +20,11 @@ #include "testlayers.h" -#include -#include +#include +#include -using namespace nd4j; +using namespace sd; class QuantizationTests : public testing::Test { diff --git a/libnd4j/tests_cpu/layers_tests/RNGTests.cpp b/libnd4j/tests_cpu/layers_tests/RNGTests.cpp index b40b74939..64ab1781d 100644 --- a/libnd4j/tests_cpu/layers_tests/RNGTests.cpp +++ b/libnd4j/tests_cpu/layers_tests/RNGTests.cpp @@ -21,12 +21,12 @@ #include "testlayers.h" #include -#include +#include #include #include #include -using namespace nd4j; +using namespace sd; class RNGTests : public testing::Test { private: @@ -35,10 +35,10 @@ private: public: long _seed = 119L; - //nd4j::random::RandomBuffer *_rngA; - //nd4j::random::RandomBuffer *_rngB; - nd4j::graph::RandomGenerator _rngA; - nd4j::graph::RandomGenerator _rngB; + //sd::random::RandomBuffer *_rngA; + //sd::random::RandomBuffer *_rngB; + sd::graph::RandomGenerator _rngA; + sd::graph::RandomGenerator _rngB; NDArray* nexp0 = NDArrayFactory::create_('c', {10, 10}); NDArray* nexp1 = NDArrayFactory::create_('c', {10, 10}); @@ -47,8 +47,8 @@ public: RNGTests() { //_bufferA = new Nd4jLong[100000]; //_bufferB = new Nd4jLong[100000]; - //_rngA = (nd4j::random::RandomBuffer *) initRandom(nullptr, _seed, 100000, (Nd4jPointer) _bufferA); - //_rngB = (nd4j::random::RandomBuffer *) initRandom(nullptr, _seed, 100000, (Nd4jPointer) _bufferB); + //_rngA = (sd::random::RandomBuffer *) initRandom(nullptr, _seed, 100000, (Nd4jPointer) _bufferA); + //_rngB = (sd::random::RandomBuffer *) initRandom(nullptr, _seed, 100000, (Nd4jPointer) _bufferB); _rngA.setStates(_seed, _seed); _rngB.setStates(_seed, _seed); nexp0->assign(-1.0f); @@ -256,21 +256,21 @@ TEST_F(RNGTests, Test_Gaussian_21) { ASSERT_FALSE(x0.equalsTo(nexp0)); ASSERT_FALSE(x0.equalsTo(nexp1)); ASSERT_FALSE(x0.equalsTo(nexp2)); - nd4j::ops::moments op; + sd::ops::moments op; auto result = op.evaluate({&x0}, {}, {}); //x0.printIndexedBuffer("X0 Normal"); //x1.printIndexedBuffer("X1 Normal"); - ASSERT_TRUE(result->status() == Status::OK()); - auto mean = result->at(0); - auto variance = result->at(1); + ASSERT_TRUE(result.status() == Status::OK()); + auto mean = result.at(0); + auto variance = result.at(1); // mean->printIndexedBuffer("Mean"); // variance->printIndexedBuffer("Variance"); - ASSERT_NEAR(nd4j::math::nd4j_abs(mean->e(0)), 0.f, 0.2f); + ASSERT_NEAR(sd::math::nd4j_abs(mean->e(0)), 0.f, 0.2f); ASSERT_NEAR(variance->e(0), 1.0f, 0.2f); - delete result; + } #ifdef DEBUG_BUILD @@ -278,7 +278,7 @@ TEST_F(RNGTests, Test_Gaussian_22) { auto x0 = NDArrayFactory::create('c', {1000, 800}); auto x1 = NDArrayFactory::create('c', {1000, 800}); - RandomLauncher::fillGaussian(nd4j::LaunchContext::defaultContext(), _rngA, &x0, 0.0f, 1.0f); + RandomLauncher::fillGaussian(sd::LaunchContext::defaultContext(), _rngA, &x0, 0.0f, 1.0f); RandomLauncher::fillGaussian(LaunchContext::defaultContext(), _rngB, &x1, 0.0f, 1.0f); //x0.printIndexedBuffer("x0"); @@ -288,19 +288,19 @@ TEST_F(RNGTests, Test_Gaussian_22) { ASSERT_FALSE(x0.equalsTo(nexp0)); ASSERT_FALSE(x0.equalsTo(nexp1)); ASSERT_FALSE(x0.equalsTo(nexp2)); - nd4j::ops::moments op; + sd::ops::moments op; auto result = op.evaluate({&x0}, {}, {}); //x0.printIndexedBuffer("X0 Normal"); //x1.printIndexedBuffer("X1 Normal"); - ASSERT_TRUE(result->status() == Status::OK()); - auto mean0 = result->at(0); - auto variance0 = result->at(1); + ASSERT_TRUE(result.status() == Status::OK()); + auto mean0 = result.at(0); + auto variance0 = result.at(1); //mean0->printIndexedBuffer("Mean"); //variance0->printIndexedBuffer("Variance"); - ASSERT_NEAR(nd4j::math::nd4j_abs(mean0->e(0)), 0.f, 1.0e-3f); + ASSERT_NEAR(sd::math::nd4j_abs(mean0->e(0)), 0.f, 1.0e-3f); ASSERT_NEAR(variance0->e(0), 1.0f, 1.e-3f); - delete result; + } TEST_F(RNGTests, Test_Gaussian_3) { @@ -309,7 +309,7 @@ TEST_F(RNGTests, Test_Gaussian_3) { RandomLauncher::fillGaussian(LaunchContext::defaultContext(), _rngA, &x0, 0.0, 1.0); auto mean = x0.meanNumber(); //.e(0); - auto stdev = x0.varianceNumber(nd4j::variance::SummaryStatsStandardDeviation, false);//.e(0); + auto stdev = x0.varianceNumber(sd::variance::SummaryStatsStandardDeviation, false);//.e(0); auto meanExp = NDArrayFactory::create(0.); auto devExp = NDArrayFactory::create(1.); ASSERT_TRUE(meanExp.equalsTo(mean, 1.e-3)); @@ -411,20 +411,19 @@ TEST_F(RNGTests, Test_Truncated_21) { //x1.printIndexedBuffer("Distribution TN"); ASSERT_NEAR(mean.e(0), 1.f, 0.002); ASSERT_NEAR(deviation.e(0), 2.f, 0.5); - nd4j::ops::moments op; + sd::ops::moments op; auto result = op.evaluate({&x0}, {}, {}, {}, {}, false); - // result->at(0)->printBuffer("MEAN"); - // result->at(1)->printBuffer("VARIANCE"); - delete result; - nd4j::ops::reduce_min minOp; - nd4j::ops::reduce_max maxOp; + + // result.at(0)->printBuffer("MEAN"); + // result.at(1)->printBuffer("VARIANCE"); + + sd::ops::reduce_min minOp; + sd::ops::reduce_max maxOp; + auto minRes = minOp.evaluate({&x1}, {}, {}, {}); auto maxRes = maxOp.evaluate({&x0}, {}, {}, {}); // minRes->at(0)->printBuffer("MIN for Truncated"); // maxRes->at(0)->printBuffer("MAX for Truncated"); - - delete minRes; - delete maxRes; } TEST_F(RNGTests, Test_Truncated_22) { @@ -458,20 +457,17 @@ TEST_F(RNGTests, Test_Truncated_22) { //x1.printIndexedBuffer("Distribution TN"); ASSERT_NEAR(mean.e(0), 2.f, 0.01); ASSERT_NEAR(deviation.e(0), 4.f, 0.52); - nd4j::ops::moments op; + sd::ops::moments op; auto result = op.evaluate({&x0}, {}, {}, {}, {}, false); - // result->at(0)->printBuffer("MEAN"); - // result->at(1)->printBuffer("VARIANCE"); - delete result; - nd4j::ops::reduce_min minOp; - nd4j::ops::reduce_max maxOp; + + sd::ops::reduce_min minOp; + sd::ops::reduce_max maxOp; + auto minRes = minOp.evaluate({&x1}, {}, {}, {}); auto maxRes = maxOp.evaluate({&x0}, {}, {}, {}); // minRes->at(0)->printBuffer("MIN for Truncated2"); // maxRes->at(0)->printBuffer("MAX for Truncated2"); - delete minRes; - delete maxRes; } TEST_F(RNGTests, Test_Truncated_23) { @@ -505,20 +501,18 @@ TEST_F(RNGTests, Test_Truncated_23) { //x1.printIndexedBuffer("Distribution TN"); ASSERT_NEAR(mean.e(0), 0.f, 0.01); ASSERT_NEAR(deviation.e(0), 1.f, 0.5); - nd4j::ops::moments op; + sd::ops::moments op; auto result = op.evaluate({&x0}); // result->at(0)->printBuffer("MEAN"); // result->at(1)->printBuffer("VARIANCE"); - delete result; - nd4j::ops::reduce_min minOp; - nd4j::ops::reduce_max maxOp; + sd::ops::reduce_min minOp; + sd::ops::reduce_max maxOp; + auto minRes = minOp.evaluate({&x1}, {}, {}, {}); auto maxRes = maxOp.evaluate({&x0}, {}, {}, {}); // minRes->at(0)->printBuffer("MIN for Truncated3"); // maxRes->at(0)->printBuffer("MAX for Truncated3"); - delete minRes; - delete maxRes; } TEST_F(RNGTests, Test_Truncated_3) { @@ -565,18 +559,18 @@ TEST_F(RNGTests, Test_Uniform_2) { RandomLauncher::fillUniform(LaunchContext::defaultContext(), _rngB, &x1, 1.0f, 2.0f); - auto op = new nd4j::ops::LegacyRandomOp(0); + auto op = new sd::ops::LegacyRandomOp(0); auto result = op->execute(_rngA, {&input}, {1.0f, 2.0f}, {}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(x1.isSameShape(z)); ASSERT_TRUE(x1.equalsTo(z)); delete op; - delete result; + } TEST_F(RNGTests, Test_Gaussian_2) { @@ -585,18 +579,18 @@ TEST_F(RNGTests, Test_Gaussian_2) { RandomLauncher::fillGaussian(LaunchContext::defaultContext(), _rngB, &x1, 1.0f, 2.0f); - auto op = new nd4j::ops::LegacyRandomOp(random::GaussianDistribution); + auto op = new sd::ops::LegacyRandomOp(random::GaussianDistribution); auto result = op->execute(_rngA, {&input}, {1.0f, 2.0f}, {}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(x1.isSameShape(z)); ASSERT_TRUE(x1.equalsTo(z)); delete op; - delete result; + } TEST_F(RNGTests, Test_LogNorm_2) { @@ -605,18 +599,18 @@ TEST_F(RNGTests, Test_LogNorm_2) { RandomLauncher::fillLogNormal(LaunchContext::defaultContext(), _rngB, &x1, 1.0f, 2.0f); - auto op = new nd4j::ops::LegacyRandomOp(random::LogNormalDistribution); + auto op = new sd::ops::LegacyRandomOp(random::LogNormalDistribution); auto result = op->execute(_rngA, {&input}, {1.0f, 2.0f}, {}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(x1.isSameShape(z)); ASSERT_TRUE(x1.equalsTo(z)); delete op; - delete result; + } TEST_F(RNGTests, Test_TruncatedNorm_2) { @@ -625,17 +619,17 @@ TEST_F(RNGTests, Test_TruncatedNorm_2) { RandomLauncher::fillTruncatedNormal(LaunchContext::defaultContext(), _rngB, &x1, 1.0f, 2.0f); - auto op = new nd4j::ops::LegacyRandomOp(random::TruncatedNormalDistribution); + auto op = new sd::ops::LegacyRandomOp(random::TruncatedNormalDistribution); auto result = op->execute(_rngA, {&input}, {1.0f, 2.0f}, {}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(x1.isSameShape(z)); ASSERT_TRUE(x1.equalsTo(z)); delete op; - delete result; + } @@ -645,18 +639,18 @@ TEST_F(RNGTests, Test_Binomial_2) { RandomLauncher::fillBinomial(LaunchContext::defaultContext(), _rngB, &x1, 3, 0.5f); - auto op = new nd4j::ops::LegacyRandomOp(random::BinomialDistributionEx); + auto op = new sd::ops::LegacyRandomOp(random::BinomialDistributionEx); auto result = op->execute(_rngA, {&input}, {0.5f}, {3}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(x1.isSameShape(z)); ASSERT_TRUE(x1.equalsTo(z)); delete op; - delete result; + } @@ -666,18 +660,18 @@ TEST_F(RNGTests, Test_Bernoulli_2) { RandomLauncher::fillBernoulli(LaunchContext::defaultContext(), _rngB, &x1, 0.5f); - auto op = new nd4j::ops::LegacyRandomOp(random::BernoulliDistribution); + auto op = new sd::ops::LegacyRandomOp(random::BernoulliDistribution); auto result = op->execute(_rngA, {&input}, {0.5f}, {}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(x1.isSameShape(z)); ASSERT_TRUE(x1.equalsTo(z)); delete op; - delete result; + } TEST_F(RNGTests, Test_GaussianDistribution_1) { @@ -685,11 +679,11 @@ TEST_F(RNGTests, Test_GaussianDistribution_1) { auto exp0 = NDArrayFactory::create('c', {10, 10}); - nd4j::ops::random_normal op; + sd::ops::random_normal op; auto result = op.evaluate({&x}, {0.0, 1.0f}, {}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp0.isSameShape(z)); ASSERT_FALSE(exp0.equalsTo(z)); @@ -698,7 +692,7 @@ TEST_F(RNGTests, Test_GaussianDistribution_1) { ASSERT_FALSE(nexp1->equalsTo(z)); ASSERT_FALSE(nexp2->equalsTo(z)); - delete result; + } TEST_F(RNGTests, Test_BernoulliDistribution_1) { @@ -706,11 +700,11 @@ TEST_F(RNGTests, Test_BernoulliDistribution_1) { auto exp0 = NDArrayFactory::create('c', {10, 10}); - nd4j::ops::random_bernoulli op; + sd::ops::random_bernoulli op; auto result = op.evaluate({&x}, {0.5f}, {}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_FALSE(exp0.equalsTo(z)); @@ -718,7 +712,7 @@ TEST_F(RNGTests, Test_BernoulliDistribution_1) { ASSERT_FALSE(nexp1->equalsTo(z)); ASSERT_FALSE(nexp2->equalsTo(z)); - delete result; + } @@ -727,11 +721,11 @@ TEST_F(RNGTests, Test_ExponentialDistribution_1) { auto exp0 = NDArrayFactory::create('c', {10, 10}); - nd4j::ops::random_exponential op; + sd::ops::random_exponential op; auto result = op.evaluate({&x}, {0.25f}, {0}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp0.isSameShape(z)); ASSERT_FALSE(exp0.equalsTo(z)); @@ -740,7 +734,7 @@ TEST_F(RNGTests, Test_ExponentialDistribution_1) { ASSERT_FALSE(nexp1->equalsTo(z)); ASSERT_FALSE(nexp2->equalsTo(z)); - delete result; + } TEST_F(RNGTests, Test_ExponentialDistribution_2) { @@ -751,11 +745,11 @@ TEST_F(RNGTests, Test_ExponentialDistribution_2) { y.assign(1.0); - nd4j::ops::random_exponential op; + sd::ops::random_exponential op; auto result = op.evaluate({&x, &y}, {0.25f}, {0}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp0.isSameShape(z)); ASSERT_FALSE(exp0.equalsTo(z)); @@ -764,7 +758,7 @@ TEST_F(RNGTests, Test_ExponentialDistribution_2) { ASSERT_FALSE(nexp1->equalsTo(z)); ASSERT_FALSE(nexp2->equalsTo(z)); - delete result; + } TEST_F(RNGTests, Test_PoissonDistribution_1) { @@ -775,16 +769,16 @@ TEST_F(RNGTests, Test_PoissonDistribution_1) { la.linspace(1.0); - nd4j::ops::random_poisson op; + sd::ops::random_poisson op; auto result = op.evaluate({&x, &la}, {}, {}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); // z->printIndexedBuffer("Poisson distribution"); ASSERT_TRUE(exp0.isSameShape(z)); ASSERT_FALSE(exp0.equalsTo(z)); - delete result; + } TEST_F(RNGTests, Test_GammaDistribution_1) { @@ -795,16 +789,16 @@ TEST_F(RNGTests, Test_GammaDistribution_1) { al.linspace(1.0); - nd4j::ops::random_gamma op; + sd::ops::random_gamma op; auto result = op.evaluate({&x, &al}, {}, {}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); // z->printIndexedBuffer("Gamma distribution"); ASSERT_TRUE(exp0.isSameShape(z)); ASSERT_FALSE(exp0.equalsTo(z)); - delete result; + } TEST_F(RNGTests, Test_GammaDistribution_2) { @@ -816,16 +810,16 @@ TEST_F(RNGTests, Test_GammaDistribution_2) { al.linspace(1.0); be.assign(1.0); - nd4j::ops::random_gamma op; + sd::ops::random_gamma op; auto result = op.evaluate({&x, &al, &be}, {}, {}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); // z->printIndexedBuffer("Gamma distribution"); ASSERT_TRUE(exp0.isSameShape(z)); ASSERT_FALSE(exp0.equalsTo(z)); - delete result; + } TEST_F(RNGTests, Test_GammaDistribution_3) { @@ -837,16 +831,16 @@ TEST_F(RNGTests, Test_GammaDistribution_3) { al.linspace(1.0); be.assign(2.0); - nd4j::ops::random_gamma op; + sd::ops::random_gamma op; auto result = op.evaluate({&x, &al, &be}, {}, {}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); // z->printIndexedBuffer("Gamma distribution"); ASSERT_TRUE(exp0.isSameShape(z)); ASSERT_FALSE(exp0.equalsTo(z)); - delete result; + } TEST_F(RNGTests, Test_UniformDistribution_04) { @@ -856,20 +850,20 @@ TEST_F(RNGTests, Test_UniformDistribution_04) { auto exp0 = NDArrayFactory::create('c', {10}); - nd4j::ops::randomuniform op; + sd::ops::randomuniform op; auto result = op.evaluate({&x, &al, &be}, {}, {DataType::INT32}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp0.isSameShape(z)); ASSERT_FALSE(exp0.equalsTo(z)); - delete result; + } -namespace nd4j { +namespace sd { namespace tests { - static void fillList(Nd4jLong seed, int numberOfArrays, std::vector &shape, std::vector &list, nd4j::graph::RandomGenerator *rng) { + static void fillList(Nd4jLong seed, int numberOfArrays, std::vector &shape, std::vector &list, sd::graph::RandomGenerator *rng) { rng->setSeed((int) seed); for (int i = 0; i < numberOfArrays; i++) { @@ -877,7 +871,7 @@ namespace nd4j { auto arrayR = NDArrayFactory::create_('c', shape); auto min = NDArrayFactory::create(0.0); auto max = NDArrayFactory::create(1.0); - nd4j::ops::randomuniform op; + sd::ops::randomuniform op; op.execute(*rng, {&arrayI, &min, &max}, {arrayR}, {}, {DataType::DOUBLE}, {}, {}, false); list.emplace_back(arrayR); @@ -890,14 +884,14 @@ TEST_F(RNGTests, Test_Reproducibility_1) { Nd4jLong seed = 123; std::vector shape = {32, 3, 28, 28}; - nd4j::graph::RandomGenerator rng; + sd::graph::RandomGenerator rng; std::vector expList; - nd4j::tests::fillList(seed, 10, shape, expList, &rng); + sd::tests::fillList(seed, 10, shape, expList, &rng); for (int e = 0; e < 2; e++) { std::vector trialList; - nd4j::tests::fillList(seed, 10, shape, trialList, &rng); + sd::tests::fillList(seed, 10, shape, trialList, &rng); for (int a = 0; a < expList.size(); a++) { auto arrayE = expList[a]; @@ -922,14 +916,14 @@ TEST_F(RNGTests, Test_Reproducibility_2) { Nd4jLong seed = 123; std::vector shape = {32, 3, 64, 64}; - nd4j::graph::RandomGenerator rng; + sd::graph::RandomGenerator rng; std::vector expList; - nd4j::tests::fillList(seed, 10, shape, expList, &rng); + sd::tests::fillList(seed, 10, shape, expList, &rng); for (int e = 0; e < 2; e++) { std::vector trialList; - nd4j::tests::fillList(seed, 10, shape, trialList, &rng); + sd::tests::fillList(seed, 10, shape, trialList, &rng); for (int a = 0; a < expList.size(); a++) { auto arrayE = expList[a]; @@ -943,7 +937,7 @@ TEST_F(RNGTests, Test_Reproducibility_2) { double x = arrayE->e(f); double y = arrayT->e(f); - if (nd4j::math::nd4j_re(x, y) > 0.1) { + if (sd::math::nd4j_re(x, y) > 0.1) { // nd4j_printf("E[%lld] %f != T[%lld] %f\n", (long long) f, (float) x, (long long) f, (float) y); throw std::runtime_error("boom"); } @@ -986,7 +980,7 @@ TEST_F(RNGTests, test_choice_1) { auto z = NDArrayFactory::create('c', {1000}); RandomGenerator rng(119, 256); - NativeOpExecutioner::execRandom(nd4j::LaunchContext ::defaultContext(), random::Choice, &rng, x->buffer(), x->shapeInfo(), x->specialBuffer(), x->specialShapeInfo(), prob->buffer(), prob->shapeInfo(), prob->specialBuffer(), prob->specialShapeInfo(), z.buffer(), z.shapeInfo(), z.specialBuffer(), z.specialShapeInfo(), nullptr); + NativeOpExecutioner::execRandom(sd::LaunchContext ::defaultContext(), random::Choice, &rng, x->buffer(), x->shapeInfo(), x->specialBuffer(), x->specialShapeInfo(), prob->buffer(), prob->shapeInfo(), prob->specialBuffer(), prob->specialShapeInfo(), z.buffer(), z.shapeInfo(), z.specialBuffer(), z.specialShapeInfo(), nullptr); // z.printIndexedBuffer("z"); @@ -999,52 +993,52 @@ TEST_F(RNGTests, test_uniform_119) { auto z = NDArrayFactory::create('c', {1, 5}); - nd4j::ops::randomuniform op; + sd::ops::randomuniform op; auto status = op.execute({&x}, {&z}, {1.0, 2.0}, {}, {}); ASSERT_EQ(Status::OK(), status); } TEST_F(RNGTests, test_multinomial_1) { - NDArray probs('f', { 3, 3 }, { 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3 }, nd4j::DataType::FLOAT32); - NDArray expected('f', { 3, 3 }, { 0., 1, 2, 2, 0, 0, 1, 2, 1 }, nd4j::DataType::INT64); - NDArray output('f', { 3, 3 }, nd4j::DataType::INT64); - NDArray samples('f', { 1 }, std::vector({3}), nd4j::DataType::INT32); + NDArray probs('f', { 3, 3 }, { 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3 }, sd::DataType::FLOAT32); + NDArray expected('f', { 3, 3 }, { 0., 1, 2, 2, 0, 0, 1, 2, 1 }, sd::DataType::INT64); + NDArray output('f', { 3, 3 }, sd::DataType::INT64); + NDArray samples('f', { 1 }, std::vector({3}), sd::DataType::INT32); - nd4j::ops::random_multinomial op; + sd::ops::random_multinomial op; RandomGenerator rng(1234, 1234); ASSERT_EQ(Status::OK(), op.execute(rng, { &probs, &samples }, { &output }, {}, { 0, INT64}, {}, {}, false) ); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - NDArray probsZ('c', { 1, 3 }, { 0.3, 0.3, 0.3 }, nd4j::DataType::FLOAT32); - NDArray expectedZ('c', { 3, 3 }, { 0., 0, 0, 0, 0, 0, 0, 0, 0 }, nd4j::DataType::INT64); + NDArray probsZ('c', { 1, 3 }, { 0.3, 0.3, 0.3 }, sd::DataType::FLOAT32); + NDArray expectedZ('c', { 3, 3 }, { 0., 0, 0, 0, 0, 0, 0, 0, 0 }, sd::DataType::INT64); auto result = op.evaluate({ &probsZ, &samples }, { }, { 1, INT64 }); - auto outputZ = result->at(0); + auto outputZ = result.at(0); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); ASSERT_TRUE(expectedZ.isSameShape(outputZ)); ASSERT_TRUE(expectedZ.equalsTo(outputZ)); - delete result; + } TEST_F(RNGTests, test_multinomial_2) { - NDArray samples('c', { 1 }, std::vector{ 20 }, nd4j::DataType::INT32); - NDArray probs('c', { 3, 5 }, { 0.2, 0.3, 0.5, 0.3, 0.5, 0.2, 0.5, 0.2, 0.3, 0.35, 0.25, 0.3, 0.25, 0.25, 0.5 }, nd4j::DataType::FLOAT32); - NDArray expected('c', { 3, 20 }, { 0, 2, 0, 2, 0, 4, 2, 0, 1, 2, 0, 2, 3, 0, 0, 2, 4, 4, 1, 0, 2, 3, 2, 3, 0, 1, 3, 1, 1, 1, 2, 4, 3, 3, 1, 4, 4, 2, 0, 0, 3, 3, 3, 0, 0, 2, 2, 3, 3, 0, 0, 2, 3, 4, 2, 2, 3, 2, 1, 2 }, nd4j::DataType::INT64); - NDArray output('c', { 3, 20 }, nd4j::DataType::INT64); + NDArray samples('c', { 1 }, std::vector{ 20 }, sd::DataType::INT32); + NDArray probs('c', { 3, 5 }, { 0.2, 0.3, 0.5, 0.3, 0.5, 0.2, 0.5, 0.2, 0.3, 0.35, 0.25, 0.3, 0.25, 0.25, 0.5 }, sd::DataType::FLOAT32); + NDArray expected('c', { 3, 20 }, { 0, 2, 0, 2, 0, 4, 2, 0, 1, 2, 0, 2, 3, 0, 0, 2, 4, 4, 1, 0, 2, 3, 2, 3, 0, 1, 3, 1, 1, 1, 2, 4, 3, 3, 1, 4, 4, 2, 0, 0, 3, 3, 3, 0, 0, 2, 2, 3, 3, 0, 0, 2, 3, 4, 2, 2, 3, 2, 1, 2 }, sd::DataType::INT64); + NDArray output('c', { 3, 20 }, sd::DataType::INT64); - nd4j::ops::random_multinomial op; + sd::ops::random_multinomial op; RandomGenerator rng(1234, 1234); ASSERT_EQ(Status::OK(), op.execute(rng, { &probs, &samples }, { &output }, {}, { 0, INT64 }, {}, {}, false)); ASSERT_TRUE(expected.isSameShape(output)); ASSERT_TRUE(expected.equalsTo(output)); - NDArray probs2('c', { 5, 3 }, { 0.2, 0.3, 0.5, 0.3, 0.5, 0.2, 0.5, 0.2, 0.3, 0.35, 0.25, 0.3, 0.25, 0.25, 0.5 }, nd4j::DataType::FLOAT32); - NDArray expected2('c', { 20, 3 }, { 0, 2, 3, 2, 3, 3, 0, 2, 3, 2, 3, 0, 0, 0, 0, 4, 1, 2, 2, 3, 2, 3, 1, 3, 1, 1, 3, 2, 1, 0, 0, 2, 0, 2, 4, 2, 3, 3, 3, 0, 3, 4, 0, 1, 2, 2, 0, 2, 4, 4, 0, 4, 2, 2, 1, 0, 1, 0, 0, 2 }, nd4j::DataType::INT64); - NDArray output2('c', { 20, 3 }, nd4j::DataType::INT64); + NDArray probs2('c', { 5, 3 }, { 0.2, 0.3, 0.5, 0.3, 0.5, 0.2, 0.5, 0.2, 0.3, 0.35, 0.25, 0.3, 0.25, 0.25, 0.5 }, sd::DataType::FLOAT32); + NDArray expected2('c', { 20, 3 }, { 0, 2, 3, 2, 3, 3, 0, 2, 3, 2, 3, 0, 0, 0, 0, 4, 1, 2, 2, 3, 2, 3, 1, 3, 1, 1, 3, 2, 1, 0, 0, 2, 0, 2, 4, 2, 3, 3, 3, 0, 3, 4, 0, 1, 2, 2, 0, 2, 4, 4, 0, 4, 2, 2, 1, 0, 1, 0, 0, 2 }, sd::DataType::INT64); + NDArray output2('c', { 20, 3 }, sd::DataType::INT64); rng.setStates(1234, 1234); ASSERT_EQ(Status::OK(), op.execute(rng, { &probs2, &samples }, { &output2 }, {}, { 1, INT64 }, {}, {}, false)); @@ -1054,13 +1048,13 @@ TEST_F(RNGTests, test_multinomial_2) { TEST_F(RNGTests, test_multinomial_3) { - NDArray probs('c', { 4, 3 }, { 0.3, 0.3, 0.4, 0.3, 0.4, 0.3, 0.3, 0.3, 0.4, 0.4, 0.3, 0.3 }, nd4j::DataType::FLOAT32); - NDArray expected('c', { 4, 5 }, nd4j::DataType::INT64); - NDArray output('c', { 4, 5 }, nd4j::DataType::INT64); - NDArray samples('c', { 1 }, std::vector{ 5 }, nd4j::DataType::INT32); + NDArray probs('c', { 4, 3 }, { 0.3, 0.3, 0.4, 0.3, 0.4, 0.3, 0.3, 0.3, 0.4, 0.4, 0.3, 0.3 }, sd::DataType::FLOAT32); + NDArray expected('c', { 4, 5 }, sd::DataType::INT64); + NDArray output('c', { 4, 5 }, sd::DataType::INT64); + NDArray samples('c', { 1 }, std::vector{ 5 }, sd::DataType::INT32); RandomGenerator rng(1234, 1234); - nd4j::ops::random_multinomial op; + sd::ops::random_multinomial op; ASSERT_EQ(Status::OK(), op.execute(rng, { &probs, &samples }, { &expected }, {}, { 0, INT64 }, {}, {}, false)); @@ -1072,13 +1066,13 @@ TEST_F(RNGTests, test_multinomial_3) { TEST_F(RNGTests, test_multinomial_4) { - NDArray probs('c', { 3, 4 }, { 0.3, 0.3, 0.4, 0.3, 0.4, 0.3, 0.3, 0.3, 0.4, 0.4, 0.3, 0.3 }, nd4j::DataType::FLOAT32); - NDArray expected('c', { 5, 4 }, nd4j::DataType::INT64); - NDArray output('c', { 5, 4 }, nd4j::DataType::INT64); - NDArray samples('c', { 1 }, std::vector{ 5 }, nd4j::DataType::INT32); + NDArray probs('c', { 3, 4 }, { 0.3, 0.3, 0.4, 0.3, 0.4, 0.3, 0.3, 0.3, 0.4, 0.4, 0.3, 0.3 }, sd::DataType::FLOAT32); + NDArray expected('c', { 5, 4 }, sd::DataType::INT64); + NDArray output('c', { 5, 4 }, sd::DataType::INT64); + NDArray samples('c', { 1 }, std::vector{ 5 }, sd::DataType::INT32); RandomGenerator rng(1234, 1234); - nd4j::ops::random_multinomial op; + sd::ops::random_multinomial op; ASSERT_EQ(Status::OK(), op.execute(rng, { &probs, &samples }, { &expected }, {}, { 1, INT64 }, {}, {}, false)); rng.setStates(1234, 1234); @@ -1093,13 +1087,13 @@ TEST_F(RNGTests, test_multinomial_5) { int ClassValue = 2; int Samples = 100000; - NDArray samples('c', { 1 }, std::vector{ 1.*Samples }, nd4j::DataType::INT32); + NDArray samples('c', { 1 }, std::vector{ 1.*Samples }, sd::DataType::INT32); - NDArray probs('c', { ClassValue, batchValue }, { 1.0, 1.0 }, nd4j::DataType::FLOAT32); + NDArray probs('c', { ClassValue, batchValue }, { 1.0, 1.0 }, sd::DataType::FLOAT32); - nd4j::ops::random_multinomial op; + sd::ops::random_multinomial op; - NDArray output('c', { Samples, batchValue }, nd4j::DataType::INT64); + NDArray output('c', { Samples, batchValue }, sd::DataType::INT64); RandomGenerator rng(1234, 1234); ASSERT_EQ(Status::OK(), op.execute(rng, { &probs, &samples }, { &output }, {}, { 1 }, {}, {}, false)); @@ -1117,8 +1111,8 @@ TEST_F(RNGTests, test_multinomial_5) { } auto resultR = op.evaluate({ &probs, &samples }, { }, { 1 }); - auto outputR = resultR->at(0); - ASSERT_EQ(Status::OK(), resultR->status()); + auto outputR = resultR.at(0); + ASSERT_EQ(Status::OK(), resultR.status()); deviation = outputR->varianceNumber(variance::SummaryStatsStandardDeviation, false); mean = outputR->meanNumber(); @@ -1131,7 +1125,6 @@ TEST_F(RNGTests, test_multinomial_5) { ASSERT_TRUE(value >= 0 && value < ClassValue); } - delete resultR; } @@ -1141,19 +1134,19 @@ TEST_F(RNGTests, test_multinomial_6) { int ClassValue = 5; int Samples = 100000; - NDArray samples('c', { 1 }, std::vector{ 1. * Samples }, nd4j::DataType::INT32); + NDArray samples('c', { 1 }, std::vector{ 1. * Samples }, sd::DataType::INT32); - nd4j::ops::random_multinomial op; - NDArray probExpect('c', { ClassValue }, { 0.058, 0.096, 0.1576, 0.2598, 0.4287 }, nd4j::DataType::DOUBLE); + sd::ops::random_multinomial op; + NDArray probExpect('c', { ClassValue }, { 0.058, 0.096, 0.1576, 0.2598, 0.4287 }, sd::DataType::DOUBLE); // without seed - NDArray probsR('c', { batchValue, ClassValue }, { 1., 1.5, 2., 2.5, 3. }, nd4j::DataType::FLOAT32); + NDArray probsR('c', { batchValue, ClassValue }, { 1., 1.5, 2., 2.5, 3. }, sd::DataType::FLOAT32); auto resultR = op.evaluate({ &probsR, &samples }, { }, { 0 }); - auto outputR = resultR->at(0); - ASSERT_EQ(Status::OK(), resultR->status()); + auto outputR = resultR.at(0); + ASSERT_EQ(Status::OK(), resultR.status()); - NDArray countsR('c', { ClassValue }, { 0., 0, 0, 0, 0 }, nd4j::DataType::DOUBLE); + NDArray countsR('c', { ClassValue }, { 0., 0, 0, 0, 0 }, sd::DataType::DOUBLE); for (int i = 0; i < outputR->lengthOf(); i++) { auto value = outputR->e(i); @@ -1175,15 +1168,15 @@ TEST_F(RNGTests, test_multinomial_6) { ASSERT_NEAR(1.2175, deviation.e(0), 45e-3); // 1000000 35e-3); ASSERT_NEAR(2.906, mean.e(0), 45e-3); // 1000000 35e-3); - delete resultR; + RandomGenerator rng(1234, 1234); - NDArray probs('c', { batchValue, ClassValue }, { 1., 1.5, 2., 2.5, 3. }, nd4j::DataType::FLOAT32); - NDArray output('c', { batchValue, Samples }, nd4j::DataType::INT64); + NDArray probs('c', { batchValue, ClassValue }, { 1., 1.5, 2., 2.5, 3. }, sd::DataType::FLOAT32); + NDArray output('c', { batchValue, Samples }, sd::DataType::INT64); ASSERT_EQ(Status::OK(), op.execute(rng, { &probs, &samples }, { &output }, {}, { 0, INT64 }, {}, {}, false)); - NDArray counts('c', { ClassValue }, { 0., 0, 0, 0, 0 }, nd4j::DataType::DOUBLE); + NDArray counts('c', { ClassValue }, { 0., 0, 0, 0, 0 }, sd::DataType::DOUBLE); for (int i = 0; i < output.lengthOf(); i++) { auto value = output.e(i); diff --git a/libnd4j/tests_cpu/layers_tests/ReduceTests.cpp b/libnd4j/tests_cpu/layers_tests/ReduceTests.cpp index b91730954..adbe28a41 100644 --- a/libnd4j/tests_cpu/layers_tests/ReduceTests.cpp +++ b/libnd4j/tests_cpu/layers_tests/ReduceTests.cpp @@ -21,7 +21,7 @@ #include "testinclude.h" #include #include -#include +#include class ReduceTest : public testing::Test { public: @@ -65,8 +65,8 @@ public: #ifndef __CUDABLAS__ TEST_F(EuclideanDistanceTest,Test1) { //int *tadShapeBuffer = shape::computeResultShape(shapeBuffer,dimension,dimensionLength); - nd4j::ArrayOptions::setDataType(shapeBuffer, nd4j::DataType::FLOAT32); - auto tadShapeBuffer = nd4j::ShapeUtils::evalReduceShapeInfo('c', dim, shapeBuffer, false, true, nullptr); + sd::ArrayOptions::setDataType(shapeBuffer, sd::DataType::FLOAT32); + auto tadShapeBuffer = sd::ShapeUtils::evalReduceShapeInfo('c', dim, shapeBuffer, false, true, nullptr); //shape::printShapeInfoLinear("tadShape", tadShapeBuffer); functions::reduce3::Reduce3::exec(opNum, x, @@ -84,9 +84,9 @@ TEST_F(EuclideanDistanceTest,Test1) { TEST_F(StdTest,MultiDimTest) { - auto xShapeInfo = shape::shapeBuffer(4, nd4j::DataType::FLOAT32, examplesShape); + auto xShapeInfo = shape::shapeBuffer(4, sd::DataType::FLOAT32, examplesShape); //int *resultShapeInfo = shape::computeResultShape(xShapeInfo,dimensionsForStd,dimensionLength); - auto resultShapeInfo = nd4j::ShapeUtils::evalReduceShapeInfo('c', dimsForStd, xShapeInfo, false, true, nullptr); + auto resultShapeInfo = sd::ShapeUtils::evalReduceShapeInfo('c', dimsForStd, xShapeInfo, false, true, nullptr); int resultLengthAssertion = 5; ASSERT_EQ(resultLengthAssertion,shape::length(resultShapeInfo)); shape::TAD *tad = new shape::TAD; @@ -123,9 +123,9 @@ TEST_F(StdTest,MultiDimTest) { TEST_F(ReduceTest,MatrixTest) { int opNum = 4; - auto xShapeInfo = nd4j::ShapeBuilders::createShapeInfo(nd4j::DataType::FLOAT32, 'c', 2, shape); + auto xShapeInfo = sd::ShapeBuilders::createShapeInfo(sd::DataType::FLOAT32, 'c', 2, shape); //int *resultShapeInfo = shape::computeResultShape(xShapeInfo,dimension,dimensionLength); - auto resultShapeInfo = nd4j::ShapeUtils::evalReduceShapeInfo('c', dim, xShapeInfo, false, true, nullptr); + auto resultShapeInfo = sd::ShapeUtils::evalReduceShapeInfo('c', dim, xShapeInfo, false, true, nullptr); int resultLengthAssertion = 3; ASSERT_EQ(resultLengthAssertion,shape::length(resultShapeInfo)); shape::TAD *tad = new shape::TAD; diff --git a/libnd4j/tests_cpu/layers_tests/ResultSetTests.cpp b/libnd4j/tests_cpu/layers_tests/ResultSetTests.cpp index 404b95013..4ca8a3806 100644 --- a/libnd4j/tests_cpu/layers_tests/ResultSetTests.cpp +++ b/libnd4j/tests_cpu/layers_tests/ResultSetTests.cpp @@ -19,12 +19,12 @@ // #include "testlayers.h" -#include -#include +#include +#include #include -using namespace nd4j; -using namespace nd4j::graph; +using namespace sd; +using namespace sd::graph; class ResultSetTests : public testing::Test { public: diff --git a/libnd4j/tests_cpu/layers_tests/SanityTests.cpp b/libnd4j/tests_cpu/layers_tests/SanityTests.cpp index dbfcca381..7ca6732fe 100644 --- a/libnd4j/tests_cpu/layers_tests/SanityTests.cpp +++ b/libnd4j/tests_cpu/layers_tests/SanityTests.cpp @@ -19,12 +19,12 @@ // #include "testlayers.h" -#include -#include +#include +#include #include -using namespace nd4j; -using namespace nd4j::graph; +using namespace sd; +using namespace sd::graph; class SanityTests : public testing::Test { public: diff --git a/libnd4j/tests_cpu/layers_tests/ScalarTests.cpp b/libnd4j/tests_cpu/layers_tests/ScalarTests.cpp index 881a33c2e..898af1722 100644 --- a/libnd4j/tests_cpu/layers_tests/ScalarTests.cpp +++ b/libnd4j/tests_cpu/layers_tests/ScalarTests.cpp @@ -20,12 +20,12 @@ #include "testlayers.h" #include -#include -#include +#include +#include #include -using namespace nd4j; -using namespace nd4j::graph; +using namespace sd; +using namespace sd::graph; class ScalarTests : public testing::Test { public: @@ -93,17 +93,17 @@ TEST_F(ScalarTests, Test_Concat_1) { auto v = NDArrayFactory::create(3.0f); auto exp = NDArrayFactory::create('c', {3}, {1, 2, 3}); - nd4j::ops::concat op; + sd::ops::concat op; auto result = op.evaluate({&t, &u, &v}, {}, {0}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } @@ -113,18 +113,18 @@ TEST_F(ScalarTests, Test_Concat_2) { auto v = NDArrayFactory::create(5.0f); auto exp = NDArrayFactory::create('c', {5}, {1, 2, 3, 4, 5}); - nd4j::ops::concat op; + sd::ops::concat op; auto result = op.evaluate({&t, &u, &v}, {}, {0}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); // z->printIndexedBuffer(); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } @@ -134,52 +134,52 @@ TEST_F(ScalarTests, Test_Concat_3) { auto v = NDArrayFactory::create(5.0f); auto exp = NDArrayFactory::create('c', {5}, {1, 2, 3, 4, 5}); - nd4j::ops::concat op; + sd::ops::concat op; auto result = op.evaluate({&t, &u, &v}, {}, {0}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); //z->printShapeInfo("z"); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } TEST_F(ScalarTests, Test_ExpandDims_1) { auto x = NDArrayFactory::create(2.0f); auto exp = NDArrayFactory::create('c', {1}, {2.0f}); - nd4j::ops::expand_dims op; + sd::ops::expand_dims op; auto result = op.evaluate({&x}, {}, {0}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } TEST_F(ScalarTests, Test_Squeeze_1) { auto x = NDArrayFactory::create(2.0f); auto exp = NDArrayFactory::create(2.0f); - nd4j::ops::squeeze op; + sd::ops::squeeze op; auto result = op.evaluate({&x}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } @@ -187,16 +187,16 @@ TEST_F(ScalarTests, Test_Reshape_1) { auto x = NDArrayFactory::create(2.0f); auto exp = NDArrayFactory::create('c', {1, 1, 1}, {2.0f}); - nd4j::ops::reshape op; + sd::ops::reshape op; auto result = op.evaluate({&x}, {}, {-99, 1, 1, 1}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } @@ -204,59 +204,18 @@ TEST_F(ScalarTests, Test_Permute_1) { auto x = NDArrayFactory::create(3.0f); auto exp = NDArrayFactory::create(3.0f); - nd4j::ops::permute op; + sd::ops::permute op; auto result = op.evaluate({&x}, {}, {0}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } - -TEST_F(ScalarTests, Test_Stack_1) { - auto t = NDArrayFactory::create(1.0f); - auto u = NDArrayFactory::create(2.0f); - auto v = NDArrayFactory::create(3.0f); - auto exp = NDArrayFactory::create('c', {3}, {1, 2, 3}); - - nd4j::ops::stack op; - auto result = op.evaluate({&t, &u, &v}, {}, {0}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); - - auto z = result->at(0); - - ASSERT_TRUE(exp.isSameShape(z)); - ASSERT_TRUE(exp.equalsTo(z)); - - delete result; -} - -TEST_F(ScalarTests, Test_Stack_2) { - auto t = NDArrayFactory::create('c', {1, 1}, {1.0f}); - auto u = NDArrayFactory::create('c', {1, 1}, {2.0f}); - auto v = NDArrayFactory::create('c', {1, 1}, {3.0f}); - auto w = NDArrayFactory::create('c', {1, 1}, {4.0f}); - auto exp = NDArrayFactory::create('c', {4, 1, 1}, {1, 2, 3, 4}); - - nd4j::ops::stack op; - auto result = op.evaluate({&t, &u, &v, &w}, {}, {0}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); - - auto z = result->at(0); - - // z->printShapeInfo("z shape"); - - ASSERT_TRUE(exp.isSameShape(z)); - ASSERT_TRUE(exp.equalsTo(z)); - - delete result; -} - - TEST_F(ScalarTests, Test_Concat_Scalar_1) { auto t = NDArrayFactory::create('c', {1, 1}, {1.0f}); auto u = NDArrayFactory::create('c', {1, 1}, {2.0f}); @@ -264,16 +223,15 @@ TEST_F(ScalarTests, Test_Concat_Scalar_1) { auto w = NDArrayFactory::create('c', {1, 1}, {4.0f}); auto exp = NDArrayFactory::create('c', {4, 1}, {1, 2, 3, 4}); - nd4j::ops::concat op; + sd::ops::concat op; auto result = op.evaluate({&t, &u, &v, &w}, {}, {0}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; } @@ -284,14 +242,13 @@ TEST_F(ScalarTests, Test_Concat_Scalar_2) { auto w = NDArrayFactory::create('c', {1, 1}, {4.0f}); auto exp = NDArrayFactory::create('c', {1, 4}, {1, 2, 3, 4}); - nd4j::ops::concat op; + sd::ops::concat op; auto result = op.evaluate({&t, &u, &v, &w}, {}, {1}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; } \ No newline at end of file diff --git a/libnd4j/tests_cpu/layers_tests/ScopeTests.cpp b/libnd4j/tests_cpu/layers_tests/ScopeTests.cpp index ad30f1c42..6c83e869e 100644 --- a/libnd4j/tests_cpu/layers_tests/ScopeTests.cpp +++ b/libnd4j/tests_cpu/layers_tests/ScopeTests.cpp @@ -19,12 +19,12 @@ // #include "testlayers.h" -#include -#include +#include +#include #include -using namespace nd4j; -using namespace nd4j::graph; +using namespace sd; +using namespace sd::graph; class ScopeTests : public testing::Test { public: @@ -40,7 +40,7 @@ TEST_F(ScopeTests, BasicTests_1) { auto variableSpace = graph.getVariableSpace(); variableSpace->putVariable(-1, x); - nd4j::ops::Scope opScope; + sd::ops::Scope opScope; auto scopeBody = new Node(OpType_LOGIC, 10, 1); scopeBody->setName("scopeBody"); @@ -86,7 +86,7 @@ TEST_F(ScopeTests, RealTests_1) { // auto scopeCondition = new Node(OpType_LOGIC, logic::Scope, 3); scopeCondition->setName("scopeCondition"); - nd4j::ops::Scope opScope; + sd::ops::Scope opScope; scopeCondition->setCustomOp(&opScope); // this is scope of the body, it'll be executed multiple times @@ -102,7 +102,7 @@ TEST_F(ScopeTests, RealTests_1) { scopedA0->setScopeInfo(3, "scopeCondition"); // this op compares LT A0 result with variable `scalar` which is 10; - nd4j::ops::lt_scalar op; + sd::ops::lt_scalar op; auto scopedA1 = new Node(&op, 5, {4, -3}); scopedA1->setScopeInfo(3, "scopeCondition"); @@ -115,13 +115,13 @@ TEST_F(ScopeTests, RealTests_1) { scopedB0->setScopeInfo(10, "scopeBody"); auto nodeReturn = new Node(OpType_LOGIC, logic::Return, 7, {6}, {12}); - nd4j::ops::Return opReturn; + sd::ops::Return opReturn; nodeReturn->setCustomOp(&opReturn); nodeReturn->setScopeInfo(10, "scopeBody"); // WHILE operations takes 2 scopes - :0 is condition scope, and :1 is loop body scope auto nodeWhile = new Node(OpType_LOGIC, logic::While, 12, {-2, 3, 10}); - nd4j::ops::While opWhile; + sd::ops::While opWhile; nodeWhile->setCustomOp(&opWhile); // adding root nodes first, nothing unusual expected here diff --git a/libnd4j/tests_cpu/layers_tests/ServerRelatedTests.cpp b/libnd4j/tests_cpu/layers_tests/ServerRelatedTests.cpp index cedbf0d2f..e0d03731b 100644 --- a/libnd4j/tests_cpu/layers_tests/ServerRelatedTests.cpp +++ b/libnd4j/tests_cpu/layers_tests/ServerRelatedTests.cpp @@ -19,12 +19,12 @@ // #include "testlayers.h" -#include +#include #include #include -using namespace nd4j; -using namespace nd4j::graph; +using namespace sd; +using namespace sd::graph; class ServerRelatedTests : public testing::Test { public: diff --git a/libnd4j/tests_cpu/layers_tests/SessionLocalTests.cpp b/libnd4j/tests_cpu/layers_tests/SessionLocalTests.cpp index 41f8ed2d0..8481dfde5 100644 --- a/libnd4j/tests_cpu/layers_tests/SessionLocalTests.cpp +++ b/libnd4j/tests_cpu/layers_tests/SessionLocalTests.cpp @@ -22,10 +22,10 @@ #define LIBND4J_SESSIONLOCALTESTS_H #include "testlayers.h" -#include +#include #include -using namespace nd4j::graph; +using namespace sd::graph; class SessionLocalTests : public testing::Test { public: @@ -62,7 +62,7 @@ TEST_F(SessionLocalTests, BasicTests_2) { if (omp_get_max_threads() <= 1) return; - auto alpha = nd4j::NDArrayFactory::create_('c',{5,5}); + auto alpha = sd::NDArrayFactory::create_('c',{5,5}); alpha->assign(0.0); variableSpace.putVariable(-1, alpha); @@ -74,7 +74,7 @@ TEST_F(SessionLocalTests, BasicTests_2) { auto varSpace = storage.localVariableSpace(); auto arr = varSpace->getVariable(-1)->getNDArray(); - arr->applyScalar(nd4j::scalar::Add, (float) e+1, *arr); + arr->applyScalar(sd::scalar::Add, (float) e+1, *arr); } float lastValue = 0.0f; diff --git a/libnd4j/tests_cpu/layers_tests/ShapeTests.cpp b/libnd4j/tests_cpu/layers_tests/ShapeTests.cpp index 003474fab..46edc50d8 100644 --- a/libnd4j/tests_cpu/layers_tests/ShapeTests.cpp +++ b/libnd4j/tests_cpu/layers_tests/ShapeTests.cpp @@ -22,8 +22,8 @@ #include "testlayers.h" #include -using namespace nd4j; -using namespace nd4j::graph; +using namespace sd; +using namespace sd::graph; class ShapeTests : public testing::Test { public: @@ -292,7 +292,7 @@ TEST_F(ShapeTests, Tests_Transpose_119_1) { auto e = x.permute({1, 0}); e.streamline('c'); - nd4j::ops::transpose op; + sd::ops::transpose op; auto result = op.execute({&x, &y}, {&z}, {}, {}, {}); ASSERT_EQ(Status::OK(), result); @@ -306,16 +306,15 @@ TEST_F(ShapeTests, Tests_Transpose_119_2) { auto exp = x.transpose(); - nd4j::ops::transpose op; + sd::ops::transpose op; auto result = op.evaluate({&x}); - ASSERT_EQ(Status::OK(), result->status()); + ASSERT_EQ(Status::OK(), result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; } TEST_F(ShapeTests, Tests_Transpose_119_3) { @@ -326,7 +325,7 @@ TEST_F(ShapeTests, Tests_Transpose_119_3) { auto exp = x.transpose(); - nd4j::ops::transpose op; + sd::ops::transpose op; auto result = op.execute({&x}, {&z}, {}, {}, {}); ASSERT_EQ(Status::OK(), result); diff --git a/libnd4j/tests_cpu/layers_tests/ShapeTests2.cpp b/libnd4j/tests_cpu/layers_tests/ShapeTests2.cpp index fb0d7991a..b84213342 100644 --- a/libnd4j/tests_cpu/layers_tests/ShapeTests2.cpp +++ b/libnd4j/tests_cpu/layers_tests/ShapeTests2.cpp @@ -20,7 +20,7 @@ #include #include "testinclude.h" #include -#include +#include class OnesTest : public testing::Test { public: @@ -43,7 +43,7 @@ public: Nd4jLong shape[3] = {3,4,5}; Nd4jLong *shapeBuffer; ThreeDTest() { - shapeBuffer = nd4j::ShapeBuilders::createShapeInfo(nd4j::DataType::FLOAT32, 'c', 3, shape); + shapeBuffer = sd::ShapeBuilders::createShapeInfo(sd::DataType::FLOAT32, 'c', 3, shape); } ~ThreeDTest() { delete[] shapeBuffer; @@ -196,7 +196,7 @@ public: int dimensionLength = 2; int dimension[2] = {2,3}; Nd4jLong tadAssertionC[10] = {3,4,4,1,4,1,16,16384,1,99}; - Nd4jLong tadCAssertionF[10] = {3,4,4,1,1,4,16,16384,1,102}; + Nd4jLong tadCAssertionF[10] = {3,4,4,1,1,4,1,16384,1,102}; }; @@ -244,7 +244,7 @@ public: int dimensionLength = 2; int dimensions[2] = {0,1}; Nd4jLong shape[3] = {1,5,1}; - Nd4jLong *shapeBuffer = nd4j::ShapeBuilders::createShapeInfo(nd4j::DataType::FLOAT32, 'c', 3, shape); + Nd4jLong *shapeBuffer = sd::ShapeBuilders::createShapeInfo(sd::DataType::FLOAT32, 'c', 3, shape); ~DimensionWarning() { delete[] shapeBuffer; @@ -324,8 +324,8 @@ public: int dimensionFour = 0; int dimensionLength = 1; FourDTest() { - threeDShapeBuffer = nd4j::ShapeBuilders::createShapeInfo(nd4j::DataType::FLOAT32, 'f', 3, threeDShape); - fourDShapeBuffer = nd4j::ShapeBuilders::createShapeInfo(nd4j::DataType::FLOAT32, 'f', 4, fourDShape); + threeDShapeBuffer = sd::ShapeBuilders::createShapeInfo(sd::DataType::FLOAT32, 'f', 3, threeDShape); + fourDShapeBuffer = sd::ShapeBuilders::createShapeInfo(sd::DataType::FLOAT32, 'f', 4, fourDShape); } ~FourDTest() { if(threeDShapeBuffer != nullptr) @@ -492,7 +492,7 @@ TEST_F(LabelTest,LabelTad) { } TEST_F(ExpectedValuesTest,TadTest) { - auto shapeBuffer = nd4j::ShapeBuilders::createShapeInfo(nd4j::DataType::FLOAT32, 'c', 4, mainShape); + auto shapeBuffer = sd::ShapeBuilders::createShapeInfo(sd::DataType::FLOAT32, 'c', 4, mainShape); shape::TAD *tad = new shape::TAD; tad->init(shapeBuffer,testDimensions,3); tad->createTadOnlyShapeInfo(); @@ -529,7 +529,7 @@ TEST_F(ThreeDTest,TensorAlongDimensionTest) { TEST_F(NumTadTests,TadTest) { - auto shape = nd4j::ShapeBuilders::createShapeInfo(nd4j::DataType::FLOAT32, 'c', 3, this->shape); + auto shape = sd::ShapeBuilders::createShapeInfo(sd::DataType::FLOAT32, 'c', 3, this->shape); shape::TAD *tad = new shape::TAD; tad->init(shape,&dimension,1); int numTads = shape::tensorsAlongDimension(shape,&dimension,1); @@ -539,7 +539,7 @@ TEST_F(NumTadTests,TadTest) { } TEST_F(TADStall,TestStall) { - auto shapeInfo = nd4j::ShapeBuilders::createShapeInfo(nd4j::DataType::FLOAT32, 'c', 4, shape); + auto shapeInfo = sd::ShapeBuilders::createShapeInfo(sd::DataType::FLOAT32, 'c', 4, shape); shape::TAD *tad = new shape::TAD; tad->init(0,shapeInfo,this->dimensions,3); tad->createTadOnlyShapeInfo(); @@ -563,12 +563,12 @@ TEST_F(PermuteTest,PermuteShapeBufferTest) { int normalOrder[4] = {0,1,2,3}; Nd4jLong shapeToPermute[4] = {5,3,2,6}; Nd4jLong permutedOrder[4] = {6,2,3,5}; - auto shapeBufferOriginal = nd4j::ShapeBuilders::createShapeInfo(nd4j::DataType::FLOAT32, 'c', 4, shapeToPermute); - auto assertionShapeBuffer = nd4j::ShapeBuilders::createShapeInfo(nd4j::DataType::FLOAT32, 'c', 4, shapeToPermute); + auto shapeBufferOriginal = sd::ShapeBuilders::createShapeInfo(sd::DataType::FLOAT32, 'c', 4, shapeToPermute); + auto assertionShapeBuffer = sd::ShapeBuilders::createShapeInfo(sd::DataType::FLOAT32, 'c', 4, shapeToPermute); shape::permuteShapeBufferInPlace(shapeBufferOriginal,normalOrder,shapeBufferOriginal); EXPECT_TRUE(arrsEquals(4,assertionShapeBuffer,shapeBufferOriginal)); - auto backwardsAssertion = nd4j::ShapeBuilders::createShapeInfo(nd4j::DataType::FLOAT32, 'c', 4, permutedOrder); + auto backwardsAssertion = sd::ShapeBuilders::createShapeInfo(sd::DataType::FLOAT32, 'c', 4, permutedOrder); auto permuted = shape::permuteShapeBuffer(assertionShapeBuffer, permuteOrder); EXPECT_TRUE(arrsEquals(4, backwardsAssertion, permuted)); @@ -585,9 +585,9 @@ TEST_F(ElementWiseStrideTest,ElementWiseStrideTest) { TEST_F(SliceVectorTest,RowColumnVectorTest) { Nd4jLong rowVectorShape[2] = {1,5}; - auto rowVectorShapeInfo = nd4j::ShapeBuilders::createShapeInfo(nd4j::DataType::FLOAT32, 'c', 2, rowVectorShape); + auto rowVectorShapeInfo = sd::ShapeBuilders::createShapeInfo(sd::DataType::FLOAT32, 'c', 2, rowVectorShape); Nd4jLong colVectorShape[2] = {5,1}; - auto colVectorShapeInfo = nd4j::ShapeBuilders::createShapeInfo(nd4j::DataType::FLOAT32, 'c', 2, colVectorShape); + auto colVectorShapeInfo = sd::ShapeBuilders::createShapeInfo(sd::DataType::FLOAT32, 'c', 2, colVectorShape); Nd4jLong *sliceRow = shape::sliceOfShapeBuffer(0,rowVectorShapeInfo); EXPECT_TRUE(arrsEquals(2,rowVectorShapeInfo,sliceRow)); Nd4jLong *scalarSliceInfo = shape::createScalarShapeInfo(); @@ -606,9 +606,9 @@ TEST_F(SliceVectorTest,RowColumnVectorTest) { TEST_F(SliceTensorTest,TestSlice) { Nd4jLong shape[3] = {3,3,2}; - auto shapeBuffer = nd4j::ShapeBuilders::createShapeInfo(nd4j::DataType::FLOAT32, 'c', 3, shape); + auto shapeBuffer = sd::ShapeBuilders::createShapeInfo(sd::DataType::FLOAT32, 'c', 3, shape); Nd4jLong sliceShape[2] = {3,2}; - auto sliceShapeBuffer = nd4j::ShapeBuilders::createShapeInfo(nd4j::DataType::FLOAT32, 'c', 2, sliceShape); + auto sliceShapeBuffer = sd::ShapeBuilders::createShapeInfo(sd::DataType::FLOAT32, 'c', 2, sliceShape); Nd4jLong *testSlice = shape::sliceOfShapeBuffer(0,shapeBuffer); EXPECT_TRUE(arrsEquals(2,sliceShapeBuffer,testSlice)); delete[] testSlice; @@ -619,9 +619,9 @@ TEST_F(SliceTensorTest,TestSlice) { TEST_F(SliceMatrixTest,TestSlice) { Nd4jLong shape[2] = {3,2}; - auto shapeBuffer = nd4j::ShapeBuilders::createShapeInfo(nd4j::DataType::FLOAT32, 'c', 2, shape); + auto shapeBuffer = sd::ShapeBuilders::createShapeInfo(sd::DataType::FLOAT32, 'c', 2, shape); Nd4jLong sliceShape[2] = {1,2}; - auto sliceShapeBuffer = nd4j::ShapeBuilders::createShapeInfo(nd4j::DataType::FLOAT32, 'c', 2, sliceShape); + auto sliceShapeBuffer = sd::ShapeBuilders::createShapeInfo(sd::DataType::FLOAT32, 'c', 2, sliceShape); Nd4jLong *testSlice = shape::sliceOfShapeBuffer(0,shapeBuffer); EXPECT_TRUE(arrsEquals(2,sliceShapeBuffer,testSlice)); delete[] testSlice; @@ -664,13 +664,13 @@ TEST_F(TensorTwoFromFourDDimTest,TadTwoFromFourDimTest) { //Along dimension 1,2: expect matrix with shape [cols,dim2] //Along dimension 1,3: expect matrix with shape [cols,dim3] //Along dimension 2,3: expect matrix with shape [dim2,dim3] - auto baseShapeBuffer = nd4j::ShapeBuilders::createShapeInfo(nd4j::DataType::FLOAT32, 'c', 4, shape); + auto baseShapeBuffer = sd::ShapeBuilders::createShapeInfo(sd::DataType::FLOAT32, 'c', 4, shape); for(int i = 0; i < 3; i++) { int *dimArr = dims[i]; Nd4jLong *expectedShape = expectedShapes[i]; shape::TAD *tad = new shape::TAD; tad->init(baseShapeBuffer,dimArr,dimensionLength); - auto expectedShapeBuffer = nd4j::ShapeBuilders::createShapeInfo(nd4j::DataType::FLOAT32, 'c', dimensionLength, expectedShape); + auto expectedShapeBuffer = sd::ShapeBuilders::createShapeInfo(sd::DataType::FLOAT32, 'c', dimensionLength, expectedShape); tad->createTadOnlyShapeInfo(); Nd4jLong *testShapeBuffer = tad->tadOnlyShapeInfo; EXPECT_TRUE(arrsEquals(shape::rank(expectedShapeBuffer),expectedShape,shape::shapeOf(testShapeBuffer))); @@ -687,14 +687,14 @@ TEST_F(TensorTwoDimTest,TadTwoDimTest) { //Along dimension 0,1: expect matrix with shape [rows,cols] //Along dimension 0,2: expect matrix with shape [rows,dim2] //Along dimension 1,2: expect matrix with shape [cols,dim2] - auto baseShapeBuffer = nd4j::ShapeBuilders::createShapeInfo(nd4j::DataType::FLOAT32, 'c', 3, shape); + auto baseShapeBuffer = sd::ShapeBuilders::createShapeInfo(sd::DataType::FLOAT32, 'c', 3, shape); for(int i = 0; i < 3; i++) { int *dimArr = dims[i]; Nd4jLong *expectedShape = expectedShapes[i]; shape::TAD *tad = new shape::TAD; tad->init(baseShapeBuffer,dimArr,dimensionLength); - auto expectedShapeBuffer = nd4j::ShapeBuilders::createShapeInfo(nd4j::DataType::FLOAT32, 'c', dimensionLength, expectedShape); + auto expectedShapeBuffer = sd::ShapeBuilders::createShapeInfo(sd::DataType::FLOAT32, 'c', dimensionLength, expectedShape); tad->createTadOnlyShapeInfo(); Nd4jLong *testShapeBuffer = tad->tadOnlyShapeInfo; Nd4jLong *expectedStride = expectedStrides[i]; @@ -715,7 +715,7 @@ TEST_F(TensorTwoDimTest,TadTwoDimTest) { TEST_F(TensorOneDimTest,TadDimensionsForTensor) { Nd4jLong shape[3] = {rows,cols,dim2}; - auto shapeBuffer = nd4j::ShapeBuilders::createShapeInfo(nd4j::DataType::FLOAT32, 'c', rank, shape); + auto shapeBuffer = sd::ShapeBuilders::createShapeInfo(sd::DataType::FLOAT32, 'c', rank, shape); for(int i = 0; i < rank; i++) { //Along dimension 0: expect row vector with length 'dims[i]' @@ -737,7 +737,7 @@ TEST_F(TensorOneDimTest,TadDimensionsForTensor) { TEST_F(MatrixTest,TadDimensionsForMatrix) { Nd4jLong shape[2] = {rows,cols}; - auto shapeBuffer = nd4j::ShapeBuilders::createShapeInfo(nd4j::DataType::FLOAT32, 'c', rank, shape); + auto shapeBuffer = sd::ShapeBuilders::createShapeInfo(sd::DataType::FLOAT32, 'c', rank, shape); shape::TAD *dimZero = new shape::TAD; dimZero->init(shapeBuffer,&dims[0],1); @@ -745,7 +745,7 @@ TEST_F(MatrixTest,TadDimensionsForMatrix) { dimOne->init(shapeBuffer,&dims[1],1); //Along dimension 0: expect row vector with length 'rows' Nd4jLong rowVectorShape[2] = {1,rows}; - auto expectedDimZeroShape = nd4j::ShapeBuilders::createShapeInfo(nd4j::DataType::FLOAT32, 'c', 2, rowVectorShape); + auto expectedDimZeroShape = sd::ShapeBuilders::createShapeInfo(sd::DataType::FLOAT32, 'c', 2, rowVectorShape); dimZero->createTadOnlyShapeInfo(); Nd4jLong *testDimZero = dimZero->tadOnlyShapeInfo; EXPECT_TRUE(arrsEquals(2,expectedShapes[0],shape::shapeOf(testDimZero))); @@ -754,7 +754,7 @@ TEST_F(MatrixTest,TadDimensionsForMatrix) { delete[] expectedDimZeroShape; //Along dimension 1: expect row vector with length 'cols' Nd4jLong rowVectorColShape[2] {1,cols}; - auto expectedDimOneShape = nd4j::ShapeBuilders::createShapeInfo(nd4j::DataType::FLOAT32, 'c', 2, rowVectorColShape); + auto expectedDimOneShape = sd::ShapeBuilders::createShapeInfo(sd::DataType::FLOAT32, 'c', 2, rowVectorColShape); dimOne->createTadOnlyShapeInfo(); Nd4jLong *testDimOneShape = dimOne->tadOnlyShapeInfo; EXPECT_TRUE(arrsEquals(2,expectedShapes[1],shape::shapeOf(testDimOneShape))); @@ -768,11 +768,11 @@ TEST_F(MatrixTest,TadDimensionsForMatrix) { TEST_F(VectorTest,VectorTadShape) { Nd4jLong rowVector[2] = {2,2}; - auto rowBuffer = nd4j::ShapeBuilders::createShapeInfo(nd4j::DataType::FLOAT32, 'c', 2, rowVector); + auto rowBuffer = sd::ShapeBuilders::createShapeInfo(sd::DataType::FLOAT32, 'c', 2, rowVector); int rowDimension = 1; Nd4jLong columnVector[2] = {2,2}; - auto colShapeBuffer = nd4j::ShapeBuilders::createShapeInfo(nd4j::DataType::FLOAT32, 'c', 2, columnVector); + auto colShapeBuffer = sd::ShapeBuilders::createShapeInfo(sd::DataType::FLOAT32, 'c', 2, columnVector); int colDimension = 0; @@ -811,7 +811,7 @@ TEST_F(VectorTest,LinspaceCombinationTest) { int len = rows * cols; double *linspaced = linspace(1,rows * cols,len); Nd4jLong shape[2] = {rows,cols}; - auto shapeBuffer = nd4j::ShapeBuilders::createShapeInfo(nd4j::DataType::FLOAT32, 'c', 2, shape); + auto shapeBuffer = sd::ShapeBuilders::createShapeInfo(sd::DataType::FLOAT32, 'c', 2, shape); delete[] shapeBuffer; delete[] linspaced; diff --git a/libnd4j/tests_cpu/layers_tests/ShapeUtilsTests.cpp b/libnd4j/tests_cpu/layers_tests/ShapeUtilsTests.cpp index f07d3d68a..36fce0dd9 100644 --- a/libnd4j/tests_cpu/layers_tests/ShapeUtilsTests.cpp +++ b/libnd4j/tests_cpu/layers_tests/ShapeUtilsTests.cpp @@ -20,11 +20,11 @@ #include "testlayers.h" #include -#include +#include -using namespace nd4j; -using namespace nd4j::graph; +using namespace sd; +using namespace sd::graph; class ShapeUtilsTests : public testing::Test { public: diff --git a/libnd4j/tests_cpu/layers_tests/SingleDimTests.cpp b/libnd4j/tests_cpu/layers_tests/SingleDimTests.cpp index c2d8bce04..636206957 100644 --- a/libnd4j/tests_cpu/layers_tests/SingleDimTests.cpp +++ b/libnd4j/tests_cpu/layers_tests/SingleDimTests.cpp @@ -20,12 +20,12 @@ #include "testlayers.h" #include -#include -#include +#include +#include #include -using namespace nd4j; -using namespace nd4j::graph; +using namespace sd; +using namespace sd::graph; class SingleDimTests : public testing::Test { public: @@ -67,17 +67,17 @@ TEST_F(SingleDimTests, Test_Concat_1) { auto y = NDArrayFactory::create('c', {3}, {4, 5, 6}); auto exp = NDArrayFactory::create('c', {6}, {1, 2, 3, 4, 5, 6}); - nd4j::ops::concat op; + sd::ops::concat op; auto result = op.evaluate({&x, &y}, {}, {0}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } TEST_F(SingleDimTests, Test_Reduce_1) { @@ -101,17 +101,17 @@ TEST_F(SingleDimTests, Test_ExpandDims_1) { auto x = NDArrayFactory::create('c', {3}, {1, 2, 3}); auto exp = NDArrayFactory::create('c', {1, 3}, {1, 2, 3}); - nd4j::ops::expand_dims op; + sd::ops::expand_dims op; auto result = op.evaluate({&x}, {}, {0}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } @@ -119,17 +119,17 @@ TEST_F(SingleDimTests, Test_ExpandDims_2) { auto x = NDArrayFactory::create('c', {3}, {1, 2, 3}); auto exp = NDArrayFactory::create('c', {3, 1}, {1, 2, 3}); - nd4j::ops::expand_dims op; + sd::ops::expand_dims op; auto result = op.evaluate({&x}, {}, {1}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } @@ -139,65 +139,65 @@ TEST_F(SingleDimTests, Test_Squeeze_1) { auto x = NDArrayFactory::create('c', vecS, vecB); auto exp = NDArrayFactory::create(3.0f); - nd4j::ops::squeeze op; + sd::ops::squeeze op; auto result = op.evaluate({&x}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_EQ(exp.rankOf(), z->rankOf()); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } TEST_F(SingleDimTests, Test_Squeeze_2) { auto x = NDArrayFactory::create('c', {3}, {1, 2, 3}); auto exp = NDArrayFactory::create('c', {3}, {1, 2, 3}); - nd4j::ops::squeeze op; + sd::ops::squeeze op; auto result = op.evaluate({&x}, {}, {}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } TEST_F(SingleDimTests, Test_Reshape_1) { auto x = NDArrayFactory::create('c', {1, 3}, {1, 2, 3}); auto exp = NDArrayFactory::create('c', {3}, {1, 2, 3}); - nd4j::ops::reshape op; + sd::ops::reshape op; auto result = op.evaluate({&x}, {}, {-99, 3}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } TEST_F(SingleDimTests, Test_Reshape_2) { auto x = NDArrayFactory::create('c', {3}, {1, 2, 3}); auto exp = NDArrayFactory::create('c', {1, 3}, {1, 2, 3}); - nd4j::ops::reshape op; + sd::ops::reshape op; auto result = op.evaluate({&x}, {}, {-99, 1, 3}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } @@ -205,14 +205,14 @@ TEST_F(SingleDimTests, Test_Permute_1) { auto x = NDArrayFactory::create('c', {3}, {1, 2, 3}); auto exp = NDArrayFactory::create('c', {3}, {1, 2, 3}); - nd4j::ops::permute op; + sd::ops::permute op; auto result = op.evaluate({&x}, {}, {0}); - ASSERT_EQ(ND4J_STATUS_OK, result->status()); + ASSERT_EQ(ND4J_STATUS_OK, result.status()); - auto z = result->at(0); + auto z = result.at(0); ASSERT_TRUE(exp.isSameShape(z)); ASSERT_TRUE(exp.equalsTo(z)); - delete result; + } \ No newline at end of file diff --git a/libnd4j/tests_cpu/layers_tests/SortCpuTests.cpp b/libnd4j/tests_cpu/layers_tests/SortCpuTests.cpp index 122a16e45..4dcedf035 100644 --- a/libnd4j/tests_cpu/layers_tests/SortCpuTests.cpp +++ b/libnd4j/tests_cpu/layers_tests/SortCpuTests.cpp @@ -20,12 +20,12 @@ #include "testlayers.h" #include -#include -#include +#include +#include #include -using namespace nd4j; -using namespace nd4j::graph; +using namespace sd; +using namespace sd::graph; class SortCpuTests : public testing::Test { public: diff --git a/libnd4j/tests_cpu/layers_tests/SortCudaTests.cu b/libnd4j/tests_cpu/layers_tests/SortCudaTests.cu index 6913722be..5a5e75b1b 100644 --- a/libnd4j/tests_cpu/layers_tests/SortCudaTests.cu +++ b/libnd4j/tests_cpu/layers_tests/SortCudaTests.cu @@ -20,12 +20,12 @@ #include "testlayers.h" #include -#include -#include +#include +#include #include -using namespace nd4j; -using namespace nd4j::graph; +using namespace sd; +using namespace sd::graph; class SortCudaTests : public testing::Test { public: diff --git a/libnd4j/tests_cpu/layers_tests/SparseUtilsTest.cpp b/libnd4j/tests_cpu/layers_tests/SparseUtilsTest.cpp index fdeb3a884..becd5a21f 100644 --- a/libnd4j/tests_cpu/layers_tests/SparseUtilsTest.cpp +++ b/libnd4j/tests_cpu/layers_tests/SparseUtilsTest.cpp @@ -20,9 +20,9 @@ #include "testlayers.h" #include -#include +#include #include "ops/specials_sparse.h" -using namespace nd4j; +using namespace sd; ////////////////////////////////////////////////////////////////////// class SparseUtilsTest : public testing::Test { @@ -131,7 +131,7 @@ TEST_F(SparseUtilsTest, SortCOOindices_Test) { 34, 35, 36, 37, 38, 39 }); - nd4j::sparse::SparseUtils::sortCooIndicesGeneric(indicesArr, reinterpret_cast(values.getBuffer()), nnz, rank); + sd::sparse::SparseUtils::sortCooIndicesGeneric(indicesArr, reinterpret_cast(values.getBuffer()), nnz, rank); for ( int i = 0; i < rank * nnz; ++i){ ASSERT_EQ(expIndicesArr[i], indicesArr[i]); diff --git a/libnd4j/tests_cpu/layers_tests/StashTests.cpp b/libnd4j/tests_cpu/layers_tests/StashTests.cpp index 170c1763a..2cba6682d 100644 --- a/libnd4j/tests_cpu/layers_tests/StashTests.cpp +++ b/libnd4j/tests_cpu/layers_tests/StashTests.cpp @@ -21,12 +21,12 @@ #ifndef LIBND4J_STASHTESTS_H #define LIBND4J_STASHTESTS_H -#include +#include #include "testlayers.h" #include -using namespace nd4j; -using namespace nd4j::graph; +using namespace sd; +using namespace sd::graph; class StashTests : public testing::Test { public: @@ -71,17 +71,17 @@ TEST_F(StashTests, BasicTests_2) { auto cappa = NDArrayFactory::create_('c',{5, 5}); cappa->assign(3.0); - stash.storeArray(1, "alpha1", alpha); - stash.storeArray(1, "alpha2", beta); - stash.storeArray(1, "alpha3", cappa); + stash.storeArray(1, "alpha", alpha); + stash.storeArray(1, "beta", beta); + stash.storeArray(1, "cappa", cappa); - ASSERT_FALSE(stash.checkStash(2, "alpha1")); - ASSERT_FALSE(stash.checkStash(2, "alpha2")); - ASSERT_FALSE(stash.checkStash(2, "alpha3")); + ASSERT_FALSE(stash.checkStash(2, "alpha")); + ASSERT_FALSE(stash.checkStash(2, "beta")); + ASSERT_FALSE(stash.checkStash(2, "cappa")); - ASSERT_TRUE(alpha == stash.extractArray(1, "alpha1")); - ASSERT_TRUE(beta == stash.extractArray(1, "alpha2")); - ASSERT_TRUE(cappa == stash.extractArray(1, "alpha3")); + ASSERT_TRUE(alpha == stash.extractArray(1, "alpha")); + ASSERT_TRUE(beta == stash.extractArray(1, "beta")); + ASSERT_TRUE(cappa == stash.extractArray(1, "cappa")); } diff --git a/libnd4j/tests_cpu/layers_tests/StringTests.cpp b/libnd4j/tests_cpu/layers_tests/StringTests.cpp index 8b9d92f2f..272c410c7 100644 --- a/libnd4j/tests_cpu/layers_tests/StringTests.cpp +++ b/libnd4j/tests_cpu/layers_tests/StringTests.cpp @@ -21,12 +21,12 @@ // -#include -#include +#include +#include #include "testlayers.h" #include -using namespace nd4j; +using namespace sd; class StringTests : public testing::Test { public: @@ -36,7 +36,7 @@ public: TEST_F(StringTests, Basic_Test_1) { std::string f("alpha"); auto array = NDArrayFactory::string(f); - ASSERT_EQ(nd4j::DataType::UTF8, array.dataType()); + ASSERT_EQ(sd::DataType::UTF8, array.dataType()); ASSERT_EQ(1, array.lengthOf()); ASSERT_EQ(0, array.rankOf()); @@ -49,7 +49,7 @@ TEST_F(StringTests, Basic_Test_1) { TEST_F(StringTests, Basic_Test_2) { std::string f("alpha"); auto array = NDArrayFactory::string(f.c_str()); - ASSERT_EQ(nd4j::DataType::UTF8, array.dataType()); + ASSERT_EQ(sd::DataType::UTF8, array.dataType()); ASSERT_EQ(1, array.lengthOf()); ASSERT_EQ(0, array.rankOf()); @@ -237,7 +237,7 @@ TEST_F(StringTests, Basic_Test_21) { TEST_F(StringTests, Basic_Test_22) { std::u16string f(u"ß水𝄋ÿ€한𐍈®кею90ощъ]ї"); auto array = NDArrayFactory::string(f.c_str()); - ASSERT_EQ(nd4j::DataType::UTF16, array.dataType()); + ASSERT_EQ(sd::DataType::UTF16, array.dataType()); ASSERT_EQ(1, array.lengthOf()); ASSERT_EQ(0, array.rankOf()); @@ -250,7 +250,7 @@ TEST_F(StringTests, Basic_Test_22) { TEST_F(StringTests, Basic_Test_23) { std::u32string f(U"ß水𝄋ÿ€한𐍈®кею90ощъ]ї"); auto array = NDArrayFactory::string(f.c_str()); - ASSERT_EQ(nd4j::DataType::UTF32, array.dataType()); + ASSERT_EQ(sd::DataType::UTF32, array.dataType()); ASSERT_EQ(1, array.lengthOf()); ASSERT_EQ(0, array.rankOf()); @@ -268,7 +268,7 @@ TEST_F(StringTests, Export_Test_1) { TEST_F(StringTests, Basic_dup_1) { std::string f("alpha"); auto array = NDArrayFactory::string(f); - ASSERT_EQ(nd4j::DataType::UTF8, array.dataType()); + ASSERT_EQ(sd::DataType::UTF8, array.dataType()); ASSERT_EQ(1, array.lengthOf()); ASSERT_EQ(0, array.rankOf()); @@ -410,17 +410,17 @@ TEST_F(StringTests, byte_length_test_Default) { ///////////////////////////////////////////////////////////////////////// TEST_F(StringTests, byte_length_test_UTF16) { std::string f(u8"alpha"); - auto array = NDArrayFactory::string(f, nd4j::DataType::UTF16); + auto array = NDArrayFactory::string(f, sd::DataType::UTF16); ASSERT_EQ(sizeof(char16_t) * f.length(), StringUtils::byteLength(array)); std::u16string f16(u"alpha"); - auto array16 = NDArrayFactory::string(f16, nd4j::DataType::UTF16); + auto array16 = NDArrayFactory::string(f16, sd::DataType::UTF16); ASSERT_EQ(sizeof(char16_t) * f16.length(), StringUtils::byteLength(array16)); std::u32string f32(U"alpha"); - auto array32 = NDArrayFactory::string(f32, nd4j::DataType::UTF16); + auto array32 = NDArrayFactory::string(f32, sd::DataType::UTF16); ASSERT_EQ(sizeof(char16_t) * f32.length(), StringUtils::byteLength(array32)); } @@ -428,8 +428,8 @@ TEST_F(StringTests, byte_length_test_UTF16) { TEST_F(StringTests, Basic_Test_UTF16toU8) { std::u16string f16(u"alpha水𝄋ÿ€한𐍈®кею"); - auto array = NDArrayFactory::string(f16, nd4j::DataType::UTF8); - ASSERT_EQ(nd4j::DataType::UTF8, array.dataType()); + auto array = NDArrayFactory::string(f16, sd::DataType::UTF8); + ASSERT_EQ(sd::DataType::UTF8, array.dataType()); ASSERT_EQ(1, array.lengthOf()); ASSERT_EQ(0, array.rankOf()); @@ -442,8 +442,8 @@ TEST_F(StringTests, Basic_Test_UTF16toU8) { ///////////////////////////////////////////////////////////////////////// TEST_F(StringTests, Basic_Test_UTF32toU8) { std::u32string f32(U"alpha水𝄋ÿ€한𐍈®кею"); - auto array = NDArrayFactory::string(f32.c_str(), nd4j::DataType::UTF8); - ASSERT_EQ(nd4j::DataType::UTF8, array.dataType()); + auto array = NDArrayFactory::string(f32.c_str(), sd::DataType::UTF8); + ASSERT_EQ(sd::DataType::UTF8, array.dataType()); ASSERT_EQ(1, array.lengthOf()); ASSERT_EQ(0, array.rankOf()); @@ -456,8 +456,8 @@ TEST_F(StringTests, Basic_Test_UTF32toU8) { TEST_F(StringTests, Basic_Test_UTF16toU16) { std::u16string f16(u"€alpha水𝄋ÿ€한𐍈®кею"); - auto array = NDArrayFactory::string(f16, nd4j::DataType::UTF16); - ASSERT_EQ(nd4j::DataType::UTF16, array.dataType()); + auto array = NDArrayFactory::string(f16, sd::DataType::UTF16); + ASSERT_EQ(sd::DataType::UTF16, array.dataType()); ASSERT_EQ(1, array.lengthOf()); ASSERT_EQ(0, array.rankOf()); @@ -469,8 +469,8 @@ TEST_F(StringTests, Basic_Test_UTF16toU16) { TEST_F(StringTests, Basic_Test_UTF32toU16) { std::u32string f32(U"€alpha水𝄋ÿ€한𐍈®кею"); - auto array = NDArrayFactory::string(f32, nd4j::DataType::UTF16); - ASSERT_EQ(nd4j::DataType::UTF16, array.dataType()); + auto array = NDArrayFactory::string(f32, sd::DataType::UTF16); + ASSERT_EQ(sd::DataType::UTF16, array.dataType()); ASSERT_EQ(1, array.lengthOf()); ASSERT_EQ(0, array.rankOf()); @@ -482,8 +482,8 @@ TEST_F(StringTests, Basic_Test_UTF32toU16) { TEST_F(StringTests, Basic_Test_UTF16toU32) { std::u16string f16(u"€alpha水𝄋ÿ€한𐍈®кею"); - auto array = NDArrayFactory::string(f16, nd4j::DataType::UTF32); - ASSERT_EQ(nd4j::DataType::UTF32, array.dataType()); + auto array = NDArrayFactory::string(f16, sd::DataType::UTF32); + ASSERT_EQ(sd::DataType::UTF32, array.dataType()); ASSERT_EQ(1, array.lengthOf()); ASSERT_EQ(0, array.rankOf()); @@ -497,7 +497,7 @@ TEST_F(StringTests, Basic_Test_UTF32toU32) { std::u32string f32(U"€alpha水𝄋ÿ€한𐍈®кею"); auto array = NDArrayFactory::string(f32); - ASSERT_EQ(nd4j::DataType::UTF32, array.dataType()); + ASSERT_EQ(sd::DataType::UTF32, array.dataType()); ASSERT_EQ(1, array.lengthOf()); ASSERT_EQ(0, array.rankOf()); @@ -508,8 +508,8 @@ TEST_F(StringTests, Basic_Test_UTF32toU32) { TEST_F(StringTests, Basic_Test_UTF8toU32) { std::string f(u8"€alpha水𝄋ÿ€한𐍈®кею"); - auto array = NDArrayFactory::string(f, nd4j::DataType::UTF32); - ASSERT_EQ(nd4j::DataType::UTF32, array.dataType()); + auto array = NDArrayFactory::string(f, sd::DataType::UTF32); + ASSERT_EQ(sd::DataType::UTF32, array.dataType()); ASSERT_EQ(1, array.lengthOf()); ASSERT_EQ(0, array.rankOf()); @@ -519,7 +519,7 @@ TEST_F(StringTests, Basic_Test_UTF8toU32) { } ///////////////////////////////////////////////////////////////////////// TEST_F(StringTests, Basic_Test_StringVecU8toUTF16) { - auto array = NDArrayFactory::string({ 3, 2 }, { "alpha€", "beta", "gamma水", "phi", "theta", "omega水" }, nd4j::DataType::UTF16); + auto array = NDArrayFactory::string({ 3, 2 }, { "alpha€", "beta", "gamma水", "phi", "theta", "omega水" }, sd::DataType::UTF16); ASSERT_EQ(6, array.lengthOf()); ASSERT_EQ(2, array.rankOf()); @@ -528,7 +528,7 @@ TEST_F(StringTests, Basic_Test_StringVecU8toUTF16) { } ///////////////////////////////////////////////////////////////////////// TEST_F(StringTests, Basic_Test_StringVecU8toUTF32) { - auto array = NDArrayFactory::string( { 3, 2 }, { "alpha€", "beta水", "gamma", "phi", "theta", "omega" }, nd4j::DataType::UTF32); + auto array = NDArrayFactory::string( { 3, 2 }, { "alpha€", "beta水", "gamma", "phi", "theta", "omega" }, sd::DataType::UTF32); ASSERT_EQ(6, array.lengthOf()); ASSERT_EQ(2, array.rankOf()); @@ -537,19 +537,19 @@ TEST_F(StringTests, Basic_Test_StringVecU8toUTF32) { } ///////////////////////////////////////////////////////////////////////// TEST_F(StringTests, Export_Test_U8toUTF16) { - auto array = NDArrayFactory::string({ 3 }, { "alpha", "beta", "gamma" }, nd4j::DataType::UTF16); + auto array = NDArrayFactory::string({ 3 }, { "alpha", "beta", "gamma" }, sd::DataType::UTF16); auto vector = array.asByteVector(); } ///////////////////////////////////////////////////////////////////////// TEST_F(StringTests, Export_Test_U8toUTF32) { - auto array = NDArrayFactory::string({ 3 }, { "alpha", "beta", "gamma" }, nd4j::DataType::UTF32); + auto array = NDArrayFactory::string({ 3 }, { "alpha", "beta", "gamma" }, sd::DataType::UTF32); auto vector = array.asByteVector(); } ///////////////////////////////////////////////////////////////////////// TEST_F(StringTests, Basic_Test_StringVecU16toUTF16) { - auto array = NDArrayFactory::string({ 3, 2 }, { u"alpha水", u"beta", u"gamma", u"phi", u"theta水", u"omega" }, nd4j::DataType::UTF16); + auto array = NDArrayFactory::string({ 3, 2 }, { u"alpha水", u"beta", u"gamma", u"phi", u"theta水", u"omega" }, sd::DataType::UTF16); ASSERT_EQ(6, array.lengthOf()); ASSERT_EQ(2, array.rankOf()); @@ -558,7 +558,7 @@ TEST_F(StringTests, Basic_Test_StringVecU16toUTF16) { } ///////////////////////////////////////////////////////////////////////// TEST_F(StringTests, Basic_Test_StringVecU16toUTF32) { - auto array = NDArrayFactory::string( { 3, 2 }, { u"alpha水", u"beta", u"gamma水", u"phi", u"theta", u"omega" }, nd4j::DataType::UTF32); + auto array = NDArrayFactory::string( { 3, 2 }, { u"alpha水", u"beta", u"gamma水", u"phi", u"theta", u"omega" }, sd::DataType::UTF32); ASSERT_EQ(6, array.lengthOf()); ASSERT_EQ(2, array.rankOf()); @@ -567,7 +567,7 @@ TEST_F(StringTests, Basic_Test_StringVecU16toUTF32) { } ///////////////////////////////////////////////////////////////////////// TEST_F(StringTests, Basic_Test_StringVecU16toUTF8) { - auto array = NDArrayFactory::string( { 3, 2 }, { u"alpha€", u"beta水", u"gamma", u"phi水", u"theta", u"omega" }, nd4j::DataType::UTF8); + auto array = NDArrayFactory::string( { 3, 2 }, { u"alpha€", u"beta水", u"gamma", u"phi水", u"theta", u"omega" }, sd::DataType::UTF8); ASSERT_EQ(6, array.lengthOf()); ASSERT_EQ(2, array.rankOf()); @@ -576,25 +576,25 @@ TEST_F(StringTests, Basic_Test_StringVecU16toUTF8) { } ///////////////////////////////////////////////////////////////////////// TEST_F(StringTests, Export_Test_U16toUTF8) { - auto array = NDArrayFactory::string( { 3 }, { u"alpha", u"beta", u"gamma" }, nd4j::DataType::UTF8); + auto array = NDArrayFactory::string( { 3 }, { u"alpha", u"beta", u"gamma" }, sd::DataType::UTF8); auto vector = array.asByteVector(); } ///////////////////////////////////////////////////////////////////////// TEST_F(StringTests, Export_Test_U16toUTF16) { - auto array = NDArrayFactory::string( { 3 }, { u"alpha", u"beta", u"gamma" }, nd4j::DataType::UTF16); + auto array = NDArrayFactory::string( { 3 }, { u"alpha", u"beta", u"gamma" }, sd::DataType::UTF16); auto vector = array.asByteVector(); } ///////////////////////////////////////////////////////////////////////// TEST_F(StringTests, Export_Test_U16toUTF32) { - auto array = NDArrayFactory::string( { 3 }, { u"alpha水", u"beta", u"gamma水" }, nd4j::DataType::UTF32); + auto array = NDArrayFactory::string( { 3 }, { u"alpha水", u"beta", u"gamma水" }, sd::DataType::UTF32); auto vector = array.asByteVector(); } ///////////////////////////////////////////////////////////////////////// TEST_F(StringTests, Basic_Test_StringVecU32toUTF32) { - auto array = NDArrayFactory::string( { 3, 2 }, { U"alpha€", U"beta水", U"gamma", U"phi", U"theta", U"omega水" }, nd4j::DataType::UTF32); + auto array = NDArrayFactory::string( { 3, 2 }, { U"alpha€", U"beta水", U"gamma", U"phi", U"theta", U"omega水" }, sd::DataType::UTF32); ASSERT_EQ(6, array.lengthOf()); ASSERT_EQ(2, array.rankOf()); @@ -603,7 +603,7 @@ TEST_F(StringTests, Basic_Test_StringVecU32toUTF32) { } ///////////////////////////////////////////////////////////////////////// TEST_F(StringTests, Basic_Test_StringVecU32toUTF16) { - auto array = NDArrayFactory::string({ 3, 2 }, { U"alpha水", U"水beta", U"gamma", U"phi水", U"theta", U"omega" }, nd4j::DataType::UTF16); + auto array = NDArrayFactory::string({ 3, 2 }, { U"alpha水", U"水beta", U"gamma", U"phi水", U"theta", U"omega" }, sd::DataType::UTF16); ASSERT_EQ(6, array.lengthOf()); ASSERT_EQ(2, array.rankOf()); @@ -617,7 +617,7 @@ TEST_F(StringTests, Basic_Test_StringVecU32toUTF16) { } ///////////////////////////////////////////////////////////////////////// TEST_F(StringTests, Basic_Test_StringVecU32toUTF8) { - auto array = NDArrayFactory::string( { 3, 2 }, { U"alpha水", U"beta", U"gamma水", U"phi", U"theta", U"omega" }, nd4j::DataType::UTF8); + auto array = NDArrayFactory::string( { 3, 2 }, { U"alpha水", U"beta", U"gamma水", U"phi", U"theta", U"omega" }, sd::DataType::UTF8); ASSERT_EQ(6, array.lengthOf()); ASSERT_EQ(2, array.rankOf()); @@ -626,19 +626,19 @@ TEST_F(StringTests, Basic_Test_StringVecU32toUTF8) { } ///////////////////////////////////////////////////////////////////////// TEST_F(StringTests, Export_Test_U32toUTF32) { - auto array = NDArrayFactory::string( { 3 }, { U"alpha", U"beta", U"gamma" }, nd4j::DataType::UTF32); + auto array = NDArrayFactory::string( { 3 }, { U"alpha", U"beta", U"gamma" }, sd::DataType::UTF32); auto vector = array.asByteVector(); } ///////////////////////////////////////////////////////////////////////// TEST_F(StringTests, Export_Test_U32toUTF16) { - auto array = NDArrayFactory::string( { 3 }, { U"alpha", U"beta水", U"gamma水" }, nd4j::DataType::UTF16); + auto array = NDArrayFactory::string( { 3 }, { U"alpha", U"beta水", U"gamma水" }, sd::DataType::UTF16); auto vector = array.asByteVector(); } ///////////////////////////////////////////////////////////////////////// TEST_F(StringTests, Export_Test_U32toUTF8) { - auto array = NDArrayFactory::string( { 3 }, { U"alpha", U"beta", U"gamma水" }, nd4j::DataType::UTF8); + auto array = NDArrayFactory::string( { 3 }, { U"alpha", U"beta", U"gamma水" }, sd::DataType::UTF8); auto vector = array.asByteVector(); } @@ -646,7 +646,7 @@ TEST_F(StringTests, Export_Test_U32toUTF8) { TEST_F(StringTests, Basic_dup_UTF16) { std::u16string f(u"€alpha水𝄋ÿ€한𐍈®кею"); auto array = NDArrayFactory::string(f); - ASSERT_EQ(nd4j::DataType::UTF16, array.dataType()); + ASSERT_EQ(sd::DataType::UTF16, array.dataType()); ASSERT_EQ(1, array.lengthOf()); ASSERT_EQ(0, array.rankOf()); @@ -665,7 +665,7 @@ TEST_F(StringTests, Basic_dup_UTF16) { TEST_F(StringTests, Basic_dup_UTF32) { std::u32string f(U"€alpha水𝄋ÿ€한𐍈®кею"); auto array = NDArrayFactory::string(f); - ASSERT_EQ(nd4j::DataType::UTF32, array.dataType()); + ASSERT_EQ(sd::DataType::UTF32, array.dataType()); ASSERT_EQ(1, array.lengthOf()); ASSERT_EQ(0, array.rankOf()); @@ -688,12 +688,12 @@ TEST_F(StringTests, Basic_cast_UTF32toUTF8) { std::string u8(u8"€alpha水𝄋ÿ€한𐍈®кею"); auto array = NDArrayFactory::string(u32); - ASSERT_EQ(nd4j::DataType::UTF32, array.dataType()); + ASSERT_EQ(sd::DataType::UTF32, array.dataType()); ASSERT_EQ(1, array.lengthOf()); ASSERT_EQ(0, array.rankOf()); - auto aCast = array.cast(nd4j::DataType::UTF8); + auto aCast = array.cast(sd::DataType::UTF8); auto z0 = array.e(0); auto z1 = aCast.e(0); @@ -709,12 +709,12 @@ TEST_F(StringTests, Basic_cast_UTF32toUTF16) { std::u16string u16(u"€alpha水𝄋ÿ€한𐍈®кею"); auto array = NDArrayFactory::string(u32); - ASSERT_EQ(nd4j::DataType::UTF32, array.dataType()); + ASSERT_EQ(sd::DataType::UTF32, array.dataType()); ASSERT_EQ(1, array.lengthOf()); ASSERT_EQ(0, array.rankOf()); - auto aCast = array.cast(nd4j::DataType::UTF16); + auto aCast = array.cast(sd::DataType::UTF16); auto z0 = array.e(0); auto z1 = aCast.e(0); @@ -728,12 +728,12 @@ TEST_F(StringTests, Basic_cast_UTF32toUTF32) { std::u32string u32(U"€alpha水𝄋ÿ€한𐍈®кею"); auto array = NDArrayFactory::string(u32); - ASSERT_EQ(nd4j::DataType::UTF32, array.dataType()); + ASSERT_EQ(sd::DataType::UTF32, array.dataType()); ASSERT_EQ(1, array.lengthOf()); ASSERT_EQ(0, array.rankOf()); - auto aCast = array.cast(nd4j::DataType::UTF32); + auto aCast = array.cast(sd::DataType::UTF32); auto z0 = array.e(0); auto z1 = aCast.e(0); @@ -747,12 +747,12 @@ TEST_F(StringTests, Basic_cast_UTF16toUTF16) { std::u16string u16(u"€alpha水𝄋ÿ€한𐍈®кею"); auto array = NDArrayFactory::string(u16); - ASSERT_EQ(nd4j::DataType::UTF16, array.dataType()); + ASSERT_EQ(sd::DataType::UTF16, array.dataType()); ASSERT_EQ(1, array.lengthOf()); ASSERT_EQ(0, array.rankOf()); - auto aCast = array.cast(nd4j::DataType::UTF16); + auto aCast = array.cast(sd::DataType::UTF16); auto z0 = array.e(0); auto z1 = aCast.e(0); @@ -768,12 +768,12 @@ TEST_F(StringTests, Basic_cast_UTF16toUTF32) { std::u16string u16(u"€alpha水𝄋ÿ€한𐍈®кею"); auto array = NDArrayFactory::string(u16); - ASSERT_EQ(nd4j::DataType::UTF16, array.dataType()); + ASSERT_EQ(sd::DataType::UTF16, array.dataType()); ASSERT_EQ(1, array.lengthOf()); ASSERT_EQ(0, array.rankOf()); - auto aCast = array.cast(nd4j::DataType::UTF32); + auto aCast = array.cast(sd::DataType::UTF32); auto z0 = array.e(0); auto z1 = aCast.e(0); @@ -789,12 +789,12 @@ TEST_F(StringTests, Basic_cast_UTF16toUTF8) { std::u16string u16(u"€alpha水𝄋ÿ€한𐍈®кею"); auto array = NDArrayFactory::string(u16); - ASSERT_EQ(nd4j::DataType::UTF16, array.dataType()); + ASSERT_EQ(sd::DataType::UTF16, array.dataType()); ASSERT_EQ(1, array.lengthOf()); ASSERT_EQ(0, array.rankOf()); - auto aCast = array.cast(nd4j::DataType::UTF8); + auto aCast = array.cast(sd::DataType::UTF8); auto z0 = array.e(0); auto z1 = aCast.e(0); @@ -808,12 +808,12 @@ TEST_F(StringTests, Basic_cast_UTF8toUTF8) { std::string u8("€alpha水𝄋ÿ€한𐍈®кею"); auto array = NDArrayFactory::string(u8); - ASSERT_EQ(nd4j::DataType::UTF8, array.dataType()); + ASSERT_EQ(sd::DataType::UTF8, array.dataType()); ASSERT_EQ(1, array.lengthOf()); ASSERT_EQ(0, array.rankOf()); - auto aCast = array.cast(nd4j::DataType::UTF8); + auto aCast = array.cast(sd::DataType::UTF8); auto z0 = array.e(0); auto z1 = aCast.e(0); @@ -829,12 +829,12 @@ TEST_F(StringTests, Basic_cast_UTF8toUTF16) { std::u16string u16(u"€alpha水𝄋ÿ€한𐍈®кею"); auto array = NDArrayFactory::string(u8); - ASSERT_EQ(nd4j::DataType::UTF8, array.dataType()); + ASSERT_EQ(sd::DataType::UTF8, array.dataType()); ASSERT_EQ(1, array.lengthOf()); ASSERT_EQ(0, array.rankOf()); - auto aCast = array.cast(nd4j::DataType::UTF16); + auto aCast = array.cast(sd::DataType::UTF16); auto z0 = array.e(0); auto z1 = aCast.e(0); @@ -850,12 +850,12 @@ TEST_F(StringTests, Basic_cast_UTF8toUTF32) { std::u32string u32(U"€alpha水𝄋ÿ€한𐍈®кею"); auto array = NDArrayFactory::string(u8); - ASSERT_EQ(nd4j::DataType::UTF8, array.dataType()); + ASSERT_EQ(sd::DataType::UTF8, array.dataType()); ASSERT_EQ(1, array.lengthOf()); ASSERT_EQ(0, array.rankOf()); - auto aCast = array.cast(nd4j::DataType::UTF32); + auto aCast = array.cast(sd::DataType::UTF32); auto z0 = array.e(0); auto z1 = aCast.e(0); diff --git a/libnd4j/tests_cpu/layers_tests/SwitchTests.cpp b/libnd4j/tests_cpu/layers_tests/SwitchTests.cpp index ee68c2c5d..8d6a8d180 100644 --- a/libnd4j/tests_cpu/layers_tests/SwitchTests.cpp +++ b/libnd4j/tests_cpu/layers_tests/SwitchTests.cpp @@ -21,9 +21,9 @@ #include "testlayers.h" #include -using namespace nd4j; -using namespace nd4j::ops; -using namespace nd4j::graph; +using namespace sd; +using namespace sd::ops; +using namespace sd::graph; class SwitchTests : public testing::Test { public: @@ -56,14 +56,14 @@ TEST_F(SwitchTests, SwitchTest1) { // this is our condition op, we'll be using Equals condition, on variables conditionX and conditionY (ids -2 and -3 respectively) // we're creating this op manually in tests, as always. - nd4j::ops::eq_scalar eqOp; + sd::ops::eq_scalar eqOp; auto nodeCondition = new Node(&eqOp, 119, {-2, -3}); //nodeCondition->setOpType(OpType_BOOLEAN); // now, this is Switch operation. It takes BooleanOperation operation in, // and based on evaluation result (true/false) - it'll pass data via :0 or :1 output // other idx will be considered disabled, and that graph branch won't be executed - nd4j::ops::Switch switchOp; + sd::ops::Switch switchOp; auto nodeSwitch = new Node(&switchOp, 3, {2, 119}, {4, 5}); // these 2 ops are connected to FALSE and TRUE outputs. output :0 considered FALSE, and output :1 considered TRUE @@ -148,12 +148,12 @@ TEST_F(SwitchTests, SwitchTest2) { auto nodeCondition = new Node(OpType_LOGIC, logic::Scope, 119, {-2, -3}); nodeCondition->setScopeInfo(3, "scopeCondition"); - nd4j::ops::eq_scalar eqOp; + sd::ops::eq_scalar eqOp; nodeCondition->setCustomOp(&eqOp); auto nodeSwitch = new Node(OpType_LOGIC, logic::Switch, 5, {3, 2}); - nd4j::ops::Switch switchOp; + sd::ops::Switch switchOp; nodeSwitch->setCustomOp(&switchOp); @@ -214,12 +214,12 @@ TEST_F(SwitchTests, SwitchTest3) { auto nodeCondition = new Node(OpType_LOGIC, logic::Scope, 119, {-2, -3}); nodeCondition->setScopeInfo(3, "scopeCondition"); - nd4j::ops::eq_scalar eqOp; + sd::ops::eq_scalar eqOp; nodeCondition->setCustomOp(&eqOp); auto nodeSwitch = new Node(OpType_LOGIC, logic::Switch, 5, {3, 2}); - nd4j::ops::Switch switchOp; + sd::ops::Switch switchOp; nodeSwitch->setCustomOp(&switchOp); diff --git a/libnd4j/tests_cpu/layers_tests/TadTests.cpp b/libnd4j/tests_cpu/layers_tests/TadTests.cpp index 86e7264e8..5dfdf401d 100644 --- a/libnd4j/tests_cpu/layers_tests/TadTests.cpp +++ b/libnd4j/tests_cpu/layers_tests/TadTests.cpp @@ -22,12 +22,12 @@ #define LIBND4J_TADTESTS_H #include "testlayers.h" -#include +#include #include #include #include -using namespace nd4j; +using namespace sd; class TadTests : public testing::Test { public: @@ -39,7 +39,7 @@ public: TEST_F(TadTests, Test4DTad1) { - NDArray* arraySource = nd4j::NDArrayFactory::linspace(1.0f, 10000.0f, 10000); + NDArray* arraySource = sd::NDArrayFactory::linspace(1.0f, 10000.0f, 10000); Nd4jLong badShape[] = {4, 2, 1, 4, 4, 80, 16, 4, 1, 8192, -1, 99}; Nd4jLong goodShape[] = {4, 2, 1, 4, 4, 16, 16, 4, 1, 8192, 1, 99}; @@ -130,7 +130,7 @@ TEST_F(TadTests, TadEdgeCase_1) { auto exp = NDArrayFactory::create('c', {5, 4}); array.linspace(1); - auto tad = array.tensorAlongDimension(0, {0, 1}); + auto tad = array(0, {2}); ASSERT_TRUE(exp.isSameShape(tad)); } @@ -140,7 +140,7 @@ TEST_F(TadTests, TestEdgeCase_2) { auto array = NDArrayFactory::create('f', {2, 3, 1}, {1, 4, 2, 5, 3, 6}); for (int e = 0 ; e < array.lengthOf(); e++) { - auto tad = array.tensorAlongDimension(e, {2}); + auto tad = array(e, {0,1}); ASSERT_NEAR(tad.e(0), array.e(e), 1e-5); } } @@ -148,7 +148,7 @@ TEST_F(TadTests, TestEdgeCase_2) { TEST_F(TadTests, TadEdgeCase_2) { auto array = NDArrayFactory::create('c', {2, 3, 4}); - auto tad = array.tensorAlongDimension(0, {1}); + auto tad = array(0, {0,2}); ASSERT_EQ(3, tad.lengthOf()); } @@ -245,13 +245,13 @@ TEST_F(TadTests, test_tad_order_4) { TEST_F(TadTests, test_column_1) { auto x = NDArrayFactory::create('c', {5, 2}); - auto tadPack = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(x.shapeInfo(), 0); + auto tadPack = sd::ConstantTadHelper::getInstance()->tadForDimensions(x.shapeInfo(), 0); ASSERT_EQ(1, shape::rank(tadPack.primaryShapeInfo())); ASSERT_EQ(5, shape::length(tadPack.primaryShapeInfo())); ASSERT_TRUE(shape::isVector(tadPack.primaryShapeInfo())); - auto scalarViewPack = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(tadPack.primaryShapeInfo(), 0); + auto scalarViewPack = sd::ConstantTadHelper::getInstance()->tadForDimensions(tadPack.primaryShapeInfo(), 0); ASSERT_TRUE(shape::equalsStrict(tadPack.primaryShapeInfo(), scalarViewPack.primaryShapeInfo())); } @@ -288,10 +288,10 @@ TEST_F(TadTests, calcOffsets_1) { ///////////////////////////////////////////////////////////////// TEST_F(TadTests, outerArrayIndexes_1) { - NDArray x('c', {2,3,4,5}, nd4j::DataType::FLOAT32); - Nd4jLong maxIdxs[120]; + NDArray x('c', {2,3,4,5}, sd::DataType::FLOAT32); + int maxIdxs[120]; - NDArray y1('c', {3,5}, nd4j::DataType::FLOAT32); + NDArray y1('c', {3,5}, sd::DataType::FLOAT32); const std::vector dimsToExclude1 = {0,2}; const int n1[] = {20,25,30,35, 80,85,90,95}; int minIdx = 5; @@ -301,7 +301,7 @@ TEST_F(TadTests, outerArrayIndexes_1) { for(int i = 0; i < N; ++i) ASSERT_TRUE(n1[i] == maxIdxs[i]); - NDArray y2('c', {4,5}, nd4j::DataType::FLOAT32); + NDArray y2('c', {4,5}, sd::DataType::FLOAT32); const std::vector dimsToExclude2 = {0,1}; const int n2[] = {12,32,52, 72,92,112}; minIdx = 12; @@ -311,7 +311,7 @@ TEST_F(TadTests, outerArrayIndexes_1) { for(int i = 0; i < N; ++i) ASSERT_TRUE(n2[i] == maxIdxs[i]); - NDArray y3('c', {2,5}, nd4j::DataType::FLOAT32); + NDArray y3('c', {2,5}, sd::DataType::FLOAT32); const std::vector dimsToExclude3 = {1,2}; const int n3[] = {64,69,74,79,84,89,94,99,104,109,114,119}; minIdx = 9; @@ -321,7 +321,7 @@ TEST_F(TadTests, outerArrayIndexes_1) { for(int i = 0; i < N; ++i) ASSERT_TRUE(n3[i] == maxIdxs[i]); - NDArray y4('c', {2,3}, nd4j::DataType::FLOAT32); + NDArray y4('c', {2,3}, sd::DataType::FLOAT32); const std::vector dimsToExclude4 = {2,3}; const int n4[] = {20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39}; minIdx = 1; @@ -331,7 +331,7 @@ TEST_F(TadTests, outerArrayIndexes_1) { for(int i = 0; i < N; ++i) ASSERT_TRUE(n4[i] == maxIdxs[i]); - NDArray y5('c', {2,4}, nd4j::DataType::FLOAT32); + NDArray y5('c', {2,4}, sd::DataType::FLOAT32); const std::vector dimsToExclude5 = {1,3}; const int n5[] = {65,66,67,68,69, 85,86,87,88,89, 105,106,107,108,109}; minIdx = 5; @@ -341,7 +341,7 @@ TEST_F(TadTests, outerArrayIndexes_1) { for(int i = 0; i < N; ++i) ASSERT_TRUE(n5[i] == maxIdxs[i]); - NDArray y6('c', {2,3,4}, nd4j::DataType::FLOAT32); + NDArray y6('c', {2,3,4}, sd::DataType::FLOAT32); const std::vector dimsToExclude6 = {3}; const int n6[] = {65,66,67,68,69}; minIdx = 13; @@ -351,7 +351,7 @@ TEST_F(TadTests, outerArrayIndexes_1) { for(int i = 0; i < N; ++i) ASSERT_TRUE(n6[i] == maxIdxs[i]); - NDArray y7('c', {4}, nd4j::DataType::FLOAT32); + NDArray y7('c', {4}, sd::DataType::FLOAT32); const std::vector dimsToExclude7 = {0,1,3}; const int n7[] = {15,16,17,18,19, 35,36,37,38,39, 55,56,57,58,59, 75,76,77,78,79, 95,96,97,98,99, 115,116,117,118,119}; minIdx = 3; @@ -361,7 +361,7 @@ TEST_F(TadTests, outerArrayIndexes_1) { for(int i = 0; i < N; ++i) ASSERT_TRUE(n7[i] == maxIdxs[i]); - NDArray y8('c', {5}, nd4j::DataType::FLOAT32); + NDArray y8('c', {5}, sd::DataType::FLOAT32); const std::vector dimsToExclude8 = {0,1,2}; const int n8[] = {0,5,10,15, 20,25,30,35, 40,45,50,55, 60,65,70,75, 80,85,90,95, 100,105,110,115}; minIdx = 0; @@ -371,7 +371,7 @@ TEST_F(TadTests, outerArrayIndexes_1) { for(int i = 0; i < N; ++i) ASSERT_TRUE(n8[i] == maxIdxs[i]); - NDArray y9('c', {2}, nd4j::DataType::FLOAT32); + NDArray y9('c', {2}, sd::DataType::FLOAT32); const std::vector dimsToExclude9 = {1,2,3}; const int n9[] = {60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119}; minIdx = 1; @@ -381,7 +381,7 @@ TEST_F(TadTests, outerArrayIndexes_1) { for(int i = 0; i < N; ++i) ASSERT_TRUE(n9[i] == maxIdxs[i]); - NDArray y10('c', {3,4,5}, nd4j::DataType::FLOAT32); + NDArray y10('c', {3,4,5}, sd::DataType::FLOAT32); const std::vector dimsToExclude10 = {0}; const int n10[] = {11, 71}; minIdx = 11; @@ -391,7 +391,7 @@ TEST_F(TadTests, outerArrayIndexes_1) { for(int i = 0; i < N; ++i) ASSERT_TRUE(n10[i] == maxIdxs[i]); - NDArray y11('c', {2,4,5}, nd4j::DataType::FLOAT32); + NDArray y11('c', {2,4,5}, sd::DataType::FLOAT32); const std::vector dimsToExclude11 = {1}; const int n11[] = {66, 86, 106}; minIdx = 26; @@ -401,7 +401,7 @@ TEST_F(TadTests, outerArrayIndexes_1) { for(int i = 0; i < N; ++i) ASSERT_TRUE(n11[i] == maxIdxs[i]); - NDArray y12('c', {3,2}, nd4j::DataType::FLOAT32); + NDArray y12('c', {3,2}, sd::DataType::FLOAT32); const std::vector dimsToExclude12 = {0,2}; const int n12[] = {0,2,4,5,7,9,10,12,14,15,17,19,60,62,64,65,67,69,70,72,74,75,77,79}; minIdx = 0; @@ -410,7 +410,7 @@ TEST_F(TadTests, outerArrayIndexes_1) { for(int i = 0; i < N; ++i) ASSERT_TRUE(n12[i] == maxIdxs[i]); - NDArray y13('c', {3,2}, nd4j::DataType::FLOAT32); + NDArray y13('c', {3,2}, sd::DataType::FLOAT32); const std::vector dimsToExclude13 = {0,2}; const int n13[] = {1,3,6,8,11,13,16,18,61,63,66,68,71,73,76,78}; minIdx = 1; @@ -419,7 +419,7 @@ TEST_F(TadTests, outerArrayIndexes_1) { for(int i = 0; i < N; ++i) ASSERT_TRUE(n13[i] == maxIdxs[i]); - NDArray y14('c', {4,5}, nd4j::DataType::FLOAT32); + NDArray y14('c', {4,5}, sd::DataType::FLOAT32); const int n14[] = {12,32,52, 72,92,112}; minIdx = 12; @@ -428,7 +428,7 @@ TEST_F(TadTests, outerArrayIndexes_1) { for(int i = 0; i < N; ++i) ASSERT_TRUE(n14[i] == maxIdxs[i]); - NDArray y15('c', {3,4,5}, nd4j::DataType::FLOAT32); + NDArray y15('c', {3,4,5}, sd::DataType::FLOAT32); const int n15[] = {11, 71}; minIdx = 11; diff --git a/libnd4j/tests_cpu/layers_tests/ThreadsTests.cpp b/libnd4j/tests_cpu/layers_tests/ThreadsTests.cpp index fa89fbcaa..a9450e9d0 100644 --- a/libnd4j/tests_cpu/layers_tests/ThreadsTests.cpp +++ b/libnd4j/tests_cpu/layers_tests/ThreadsTests.cpp @@ -26,9 +26,9 @@ #include using namespace samediff; -using namespace nd4j; -using namespace nd4j::ops; -using namespace nd4j::graph; +using namespace sd; +using namespace sd::ops; +using namespace sd::graph; class ThreadsTests : public testing::Test { public: diff --git a/libnd4j/tests_cpu/layers_tests/TypeCastTests.cpp b/libnd4j/tests_cpu/layers_tests/TypeCastTests.cpp index 1f352dd2e..2c27f95f9 100644 --- a/libnd4j/tests_cpu/layers_tests/TypeCastTests.cpp +++ b/libnd4j/tests_cpu/layers_tests/TypeCastTests.cpp @@ -22,9 +22,9 @@ #include #include -using namespace nd4j; -using namespace nd4j::ops; -using namespace nd4j::graph; +using namespace sd; +using namespace sd::ops; +using namespace sd::graph; class TypeCastTests : public testing::Test { public: diff --git a/libnd4j/tests_cpu/layers_tests/VariableProxyTests.cpp b/libnd4j/tests_cpu/layers_tests/VariableProxyTests.cpp index fc1bde841..16e7cf7ac 100644 --- a/libnd4j/tests_cpu/layers_tests/VariableProxyTests.cpp +++ b/libnd4j/tests_cpu/layers_tests/VariableProxyTests.cpp @@ -21,8 +21,8 @@ #include "testlayers.h" #include -using namespace nd4j; -using namespace nd4j::graph; +using namespace sd; +using namespace sd::graph; class VariableProxyTests : public testing::Test { public: diff --git a/libnd4j/tests_cpu/layers_tests/VariableSpaceTests.cpp b/libnd4j/tests_cpu/layers_tests/VariableSpaceTests.cpp index 8f28c4907..ec10f3db0 100644 --- a/libnd4j/tests_cpu/layers_tests/VariableSpaceTests.cpp +++ b/libnd4j/tests_cpu/layers_tests/VariableSpaceTests.cpp @@ -24,10 +24,10 @@ #include #include #include -#include +#include -using namespace nd4j; -using namespace nd4j::graph; +using namespace sd; +using namespace sd::graph; class VariableSpaceTest : public testing::Test { public: diff --git a/libnd4j/tests_cpu/layers_tests/VariableTests.cpp b/libnd4j/tests_cpu/layers_tests/VariableTests.cpp index 1a1915fdc..49b9b02d6 100644 --- a/libnd4j/tests_cpu/layers_tests/VariableTests.cpp +++ b/libnd4j/tests_cpu/layers_tests/VariableTests.cpp @@ -22,12 +22,12 @@ #define LIBND4J_VARIABLETESTS_H #include "testlayers.h" -#include +#include #include #include -using namespace nd4j; -using namespace nd4j::graph; +using namespace sd; +using namespace sd::graph; class VariableTests : public testing::Test { public: @@ -73,9 +73,9 @@ TEST_F(VariableTests, Test_FlatVariableDataType_1) { auto fBuffer = builder.CreateVector(vec); auto fVid = CreateIntPair(builder, 1, 12); - auto fArray = CreateFlatArray(builder, fShape, fBuffer, nd4j::graph::DType::DType_FLOAT); + auto fArray = CreateFlatArray(builder, fShape, fBuffer, sd::graph::DType::DType_FLOAT); - auto flatVar = CreateFlatVariable(builder, fVid, 0, nd4j::graph::DType::DType_FLOAT, 0, fArray); + auto flatVar = CreateFlatVariable(builder, fVid, 0, sd::graph::DType::DType_FLOAT, 0, fArray); builder.Finish(flatVar); @@ -107,9 +107,9 @@ TEST_F(VariableTests, Test_FlatVariableDataType_2) { auto fBuffer = builder.CreateVector(vec); auto fVid = CreateIntPair(builder, 1, 12); - auto fArray = CreateFlatArray(builder, fShape, fBuffer, nd4j::graph::DType::DType_DOUBLE); + auto fArray = CreateFlatArray(builder, fShape, fBuffer, sd::graph::DType::DType_DOUBLE); - auto flatVar = CreateFlatVariable(builder, fVid, 0, nd4j::graph::DType::DType_DOUBLE, 0, fArray); + auto flatVar = CreateFlatVariable(builder, fVid, 0, sd::graph::DType::DType_DOUBLE, 0, fArray); builder.Finish(flatVar); @@ -144,9 +144,9 @@ TEST_F(VariableTests, Test_FlatVariableDataType_3) { auto fBuffer = builder.CreateVector(vec); auto fVid = CreateIntPair(builder, 1, 12); - auto fArray = CreateFlatArray(builder, fShape, fBuffer, nd4j::graph::DType::DType_DOUBLE); + auto fArray = CreateFlatArray(builder, fShape, fBuffer, sd::graph::DType::DType_DOUBLE); - auto flatVar = CreateFlatVariable(builder, fVid, 0, nd4j::graph::DType::DType_DOUBLE, 0, fArray); + auto flatVar = CreateFlatVariable(builder, fVid, 0, sd::graph::DType::DType_DOUBLE, 0, fArray); builder.Finish(flatVar); @@ -179,7 +179,7 @@ TEST_F(VariableTests, Test_FlatVariableDataType_4) { auto fShape = builder.CreateVector(original.getShapeAsFlatVector()); auto fVid = CreateIntPair(builder, 37, 12); - auto flatVar = CreateFlatVariable(builder, fVid, 0, nd4j::graph::DType::DType_FLOAT, fShape, 0, 0, VarType_PLACEHOLDER); + auto flatVar = CreateFlatVariable(builder, fVid, 0, sd::graph::DType::DType_FLOAT, fShape, 0, 0, VarType_PLACEHOLDER); builder.Finish(flatVar); diff --git a/libnd4j/tests_cpu/layers_tests/WorkspaceTests.cpp b/libnd4j/tests_cpu/layers_tests/WorkspaceTests.cpp index fd277b971..571db71f3 100644 --- a/libnd4j/tests_cpu/layers_tests/WorkspaceTests.cpp +++ b/libnd4j/tests_cpu/layers_tests/WorkspaceTests.cpp @@ -22,13 +22,13 @@ #define LIBND4J_WORKSPACETESTS_H #include "testlayers.h" -#include -#include -#include -#include +#include +#include +#include +#include -using namespace nd4j; -using namespace nd4j::memory; +using namespace sd; +using namespace sd::memory; class WorkspaceTests : public testing::Test { diff --git a/libnd4j/tests_cpu/layers_tests/WorkspaceTests.cu b/libnd4j/tests_cpu/layers_tests/WorkspaceTests.cu index 6aafe4ea2..6fe157ac8 100644 --- a/libnd4j/tests_cpu/layers_tests/WorkspaceTests.cu +++ b/libnd4j/tests_cpu/layers_tests/WorkspaceTests.cu @@ -19,13 +19,13 @@ // #include "testlayers.h" -#include -#include -#include -#include +#include +#include +#include +#include -using namespace nd4j; -using namespace nd4j::memory; +using namespace sd; +using namespace sd::memory; class CudaWorkspaceTests : public testing::Test { diff --git a/libnd4j/tests_cpu/layers_tests/testinclude.h b/libnd4j/tests_cpu/layers_tests/testinclude.h index 79607cdc9..b266019a9 100644 --- a/libnd4j/tests_cpu/layers_tests/testinclude.h +++ b/libnd4j/tests_cpu/layers_tests/testinclude.h @@ -22,7 +22,7 @@ #define LIBND4J_TESTINCLUDE_H #include "testlayers.h" #include -#include +#include //https://stackoverflow.com/questions/228005/alternative-to-itoa-for-converting-integer-to-string-c FORCEINLINE std::string int_array_to_string(Nd4jLong int_array[], Nd4jLong size_of_array) { diff --git a/libnd4j/tests_cpu/layers_tests/testlayers.h b/libnd4j/tests_cpu/layers_tests/testlayers.h index 95814faae..9106223d8 100644 --- a/libnd4j/tests_cpu/layers_tests/testlayers.h +++ b/libnd4j/tests_cpu/layers_tests/testlayers.h @@ -22,20 +22,20 @@ #define LIBND4J_TESTLAYERS_H #include -#include -#include -#include +#include +#include +#include #include #include #include -#include -#include +#include +#include #include #include #include -#include +#include #include -#include +#include #include #endif //LIBND4J_TESTLAYERS_H diff --git a/libnd4j/tests_cpu/libnd4j_tests/CMakeLists.txt b/libnd4j/tests_cpu/libnd4j_tests/CMakeLists.txt index fbba329e3..7d3073b58 100644 --- a/libnd4j/tests_cpu/libnd4j_tests/CMakeLists.txt +++ b/libnd4j/tests_cpu/libnd4j_tests/CMakeLists.txt @@ -84,7 +84,7 @@ set(gtest_SOURCE_DIR ${CMAKE_CURRENT_BINARY_DIR}/googletest-src) add_definitions(-D__STANDALONE_BUILD__=true) include_directories(${gtest_SOURCE_DIR}/include ${gtest_SOURCE_DIR}) -include_directories(../../include ../../layers ../../include/helpers ../../include/array ../../include/execution ../../include/exceptions ../../include/memory ../../include/loops ../../include/graph ../../include/ops ../../include/types ../../include/cnpy ../../blas) +include_directories(../../include) if(LINUX) link_directories(/usr/local/lib) link_directories(/usr/lib) @@ -109,7 +109,7 @@ endif() # -fsanitize=address # -fsanitize=leak if (APPLE) - set(CMAKE_CXX_FLAGS " -O0 -g -fPIC -std=c++11 -D__APPLE_OS__=true -DAPPLE_BUILD=true") + set(CMAKE_CXX_FLAGS " -O0 -g -fPIC -std=c++11 -D__APPLE_OS__=true -DSD_APPLE_BUILD=true") elseif(WIN32) if ("${CMAKE_CXX_COMPILER_ID}" STREQUAL "GNU") set(CMAKE_CXX_FLAGS " -g -fPIC -std=c++11 -Wa,-mbig-obj") @@ -127,7 +127,7 @@ else() endif() else() set(CMAKE_CXX_FLAGS " -g -O0 -fPIC -std=c++11 ") - if (NOT CUDA_BLAS) + if (NOT SD_CUDA) set (CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fsanitize=address") endif() endif() @@ -143,14 +143,14 @@ else() set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -DDEBUG_BUILD=true") endif() -if ("${EXPERIMENTAL}" STREQUAL "yes") +if ("${SD_EXPERIMENTAL}" STREQUAL "yes") message("Experimental mode ENABLED") set (CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -D__ND4J_EXPERIMENTAL__=true") set (CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -D__ND4J_EXPERIMENTAL__=true") endif() # tests are always compiled with all ops included -SET( CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -DLIBND4J_ALL_OPS=true -DDEFAULT_ENGINE=samediff::ENGINE_CPU") +SET( CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -DSD_ALL_OPS=true -DDEFAULT_ENGINE=samediff::ENGINE_CPU") if ("${CMAKE_CXX_COMPILER_ID}" STREQUAL "Clang") # using Clang @@ -209,6 +209,7 @@ file(GLOB_RECURSE CUSTOMOPS_GENERIC_SOURCES false ../../include/ops/declarable/h file(GLOB_RECURSE OPS_SOURCES false ../../include/ops/impl/*.cpp ../../include/ops/declarable/impl/*.cpp ../../include/ops/*.h) file(GLOB_RECURSE INDEXING_SOURCES false ../../include/indexing/*.cpp ../../include/indexing/*.h) file(GLOB_RECURSE HELPERS_SOURCES false ../../include/helpers/*.cpp) +file(GLOB_RECURSE LEGACY_SOURCES false ../../include/legacy/impl/*.cpp ../../include/legacy/cpu/*.cpp ../../include/legacy/*.h) file(GLOB_RECURSE LOOPS_SOURCES false ../../include/loops/*.cpp ../../include/loops/*.h) # optionally build mkldnn @@ -268,10 +269,7 @@ foreach (TMP_PATH ${TEST_SOURCES}) endforeach(TMP_PATH) -add_executable(runtests ${LOOPS_SOURCES} ../../blas/cpu/NativeOps.cpp ../../blas/cpu/GraphExecutioner.cpp - ../../blas/cpu/NativeOpExecutioner.cpp ../../blas/cpu/NDArray.cpp ../../blas/cpu/NDArrayFactory.cpp - ../../include/cnpy/cnpy.cpp ../../include/nd4jmemset.h ../../include/nd4jmalloc.h - ../../blas/Environment.cpp ../../blas/Environment.h ${EXEC_SOURCES} ${HELPERS_SOURCES} ${ARRAY_SOURCES} ${TYPES_SOURCES} +add_executable(runtests ${LOOPS_SOURCES} ${LEGACY_SOURCES} ${EXEC_SOURCES} ${HELPERS_SOURCES} ${ARRAY_SOURCES} ${TYPES_SOURCES} ${MEMORY_SOURCES} ${GRAPH_SOURCES} ${CUSTOMOPS_SOURCES} ${EXCEPTIONS_SOURCES} ${INDEXING_SOURCES} ${CUSTOMOPS_PLATFORM_SOURCES} ${CUSTOMOPS_GENERIC_SOURCES} ${OPS_SOURCES} ${TEST_SOURCES} ${PERF_SOURCES}) diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/imports/converters/ImportClassMapping.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/imports/converters/ImportClassMapping.java index b55852ac7..327e3c52e 100644 --- a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/imports/converters/ImportClassMapping.java +++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/imports/converters/ImportClassMapping.java @@ -623,7 +623,8 @@ public class ImportClassMapping { org.nd4j.linalg.api.ops.custom.Digamma.class, org.nd4j.linalg.api.ops.custom.Lu.class, org.nd4j.linalg.api.ops.custom.TriangularSolve.class, - org.nd4j.linalg.api.ops.custom.LinearSolve.class + org.nd4j.linalg.api.ops.custom.LinearSolve.class, + org.nd4j.linalg.api.ops.custom.Lstsq.class ); static { diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ndarray/BaseNDArray.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ndarray/BaseNDArray.java index 0060d84f9..3edbd2682 100644 --- a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ndarray/BaseNDArray.java +++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ndarray/BaseNDArray.java @@ -3772,6 +3772,8 @@ public abstract class BaseNDArray implements INDArray, Iterable { INDArray ret = Nd4j.createUninitialized(this.dataType(), shape, order); ret.setData(dup(order).data()); return ret; + } else if (this.isEmpty()) { + return Nd4j.create(this.dataType(), shape); } else { INDArray ret = this.dup(order); return Nd4j.create(ret.data(), shape); diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/custom/AdjustContrast.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/custom/AdjustContrast.java index e196e5108..55d551369 100644 --- a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/custom/AdjustContrast.java +++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/custom/AdjustContrast.java @@ -33,6 +33,10 @@ public class AdjustContrast extends BaseAdjustContrast { super(sameDiff,new SDVariable[]{in,factor}); } + public AdjustContrast(@NonNull INDArray in, double factor) { + this(in, factor, null); + } + @Override public String opName() { return "adjust_contrast"; diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/custom/Lstsq.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/custom/Lstsq.java new file mode 100644 index 000000000..20751164f --- /dev/null +++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/custom/Lstsq.java @@ -0,0 +1,40 @@ +/* ****************************************************************************** + * Copyright (c) 2020 Konduit K.K. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ +package org.nd4j.linalg.api.ops.custom; + +import lombok.NoArgsConstructor; +import lombok.NonNull; +import org.nd4j.linalg.api.ndarray.INDArray; +import org.nd4j.linalg.api.ops.DynamicCustomOp; + +@NoArgsConstructor +public class Lstsq extends DynamicCustomOp { + + public Lstsq(@NonNull INDArray matrix, @NonNull INDArray rhs, double l2_regularizer, boolean fast) { + addInputArgument(matrix, rhs); + addTArgument(l2_regularizer); + addBArgument(fast); + } + + public Lstsq(@NonNull INDArray matrix, @NonNull INDArray rhs) { + this(matrix, rhs, 0.0, true); + } + + @Override + public String opName() { + return "lstsq"; + } +} diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/custom/TriangularSolve.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/custom/TriangularSolve.java index 02734b391..8fe7b1a31 100644 --- a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/custom/TriangularSolve.java +++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/custom/TriangularSolve.java @@ -34,19 +34,20 @@ public class TriangularSolve extends DynamicCustomOp { addBArgument(lower, adjoint); } - @Override - public String opName() { - return "triangular_solve"; - } - @Override public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map attributesForNode, GraphDef graph) { - if(attributesForNode.containsKey("adjoint")){ - addBArgument(attributesForNode.get("adjoint").getB()); - } if(attributesForNode.containsKey("lower")){ addBArgument(attributesForNode.get("lower").getB()); } + + if(attributesForNode.containsKey("adjoint")){ + addBArgument(attributesForNode.get("adjoint").getB()); + } + } + + @Override + public String opName() { + return "triangular_solve"; } @Override diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/image/CropAndResize.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/image/CropAndResize.java index 96f667690..97b826064 100644 --- a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/image/CropAndResize.java +++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/image/CropAndResize.java @@ -1,5 +1,6 @@ /******************************************************************************* * Copyright (c) 2015-2018 Skymind, Inc. + * Copyright (c) 2020 Konduit K.K. * * This program and the accompanying materials are made available under the * terms of the Apache License, Version 2.0 which is available at @@ -36,6 +37,7 @@ import java.util.*; */ @NoArgsConstructor public class CropAndResize extends DynamicCustomOp { + public enum Method {BILINEAR, NEAREST}; protected Method method = Method.BILINEAR; protected double extrapolationValue = 0.0; @@ -48,6 +50,7 @@ public class CropAndResize extends DynamicCustomOp { addArgs(); } + public CropAndResize(@NonNull INDArray image, @NonNull INDArray cropBoxes, @NonNull INDArray boxIndices, @NonNull INDArray cropOutSize, @NonNull Method method, double extrapolationValue, INDArray output){ @@ -62,6 +65,12 @@ public class CropAndResize extends DynamicCustomOp { outputArguments.add(output); } + public CropAndResize(@NonNull INDArray image, @NonNull INDArray cropBoxes, @NonNull INDArray boxIndices, + @NonNull INDArray cropOutSize, double extrapolationValue) { + this(image, cropBoxes, boxIndices, cropOutSize, Method.BILINEAR, extrapolationValue, null); + } + + @Override public String opName() { return "crop_and_resize"; diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/image/ExtractImagePatches.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/image/ExtractImagePatches.java index 18bf3cb65..71b8b1fb2 100644 --- a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/image/ExtractImagePatches.java +++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/image/ExtractImagePatches.java @@ -72,6 +72,18 @@ public class ExtractImagePatches extends DynamicCustomOp { addArgs(); } + public ExtractImagePatches(INDArray input, int kH, int kW, int sH, int sW, int rH, int rW, boolean sameMode) { + super(new INDArray[]{input},null); + int[] kSises = {kH,kW}; + int[] strides = {sH,sW}; + int[] rates = {rH, rW}; + this.kSizes = kSises; + this.strides = strides; + this.rates = rates; + this.isSameMode = sameMode; + addArgs(); + } + @Override public String opName() { diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/convolution/AvgPooling2D.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/convolution/AvgPooling2D.java index 2f295cc6a..17ed2e124 100644 --- a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/convolution/AvgPooling2D.java +++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/convolution/AvgPooling2D.java @@ -49,10 +49,15 @@ public class AvgPooling2D extends DynamicCustomOp { protected Pooling2DConfig config; + public enum Pooling2DType { MAX, AVG, PNORM, } + public AvgPooling2D(@NonNull INDArray input, Pooling2DConfig config) { + this(input, null, config); + } + @Builder(builderMethodName = "sameDiffBuilder") public AvgPooling2D(SameDiff sameDiff, SDVariable input, Pooling2DConfig config) { diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/convolution/AvgPooling3D.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/convolution/AvgPooling3D.java index 178942d18..79bcacab0 100644 --- a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/convolution/AvgPooling3D.java +++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/convolution/AvgPooling3D.java @@ -17,6 +17,8 @@ package org.nd4j.linalg.api.ops.impl.layers.convolution; import lombok.Getter; +import lombok.NoArgsConstructor; +import lombok.NonNull; import lombok.extern.slf4j.Slf4j; import onnx.Onnx; import org.nd4j.autodiff.samediff.SDVariable; @@ -38,9 +40,8 @@ import java.util.Map; */ @Slf4j @Getter +@NoArgsConstructor public class AvgPooling3D extends Pooling3D { - public AvgPooling3D() { - } public AvgPooling3D(SameDiff sameDiff, SDVariable input, Pooling3DConfig config) { super(sameDiff, new SDVariable[]{input}, null, null, false, config, Pooling3DType.AVG); @@ -50,6 +51,11 @@ public class AvgPooling3D extends Pooling3D { super(sameDiff, null, new INDArray[]{arrayInput}, wrapOrNull(arrayOutput), false, config, Pooling3DType.AVG); } + public AvgPooling3D(@NonNull INDArray input, Pooling3DConfig pooling3DConfig) { + super(null,null,new INDArray[]{input},null,false, pooling3DConfig, Pooling3DType.AVG); + } + + @Override public boolean isConfigProperties() { return true; diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/convolution/Col2Im.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/convolution/Col2Im.java index 620fd6092..4a7df20b4 100644 --- a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/convolution/Col2Im.java +++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/convolution/Col2Im.java @@ -18,6 +18,7 @@ package org.nd4j.linalg.api.ops.impl.layers.convolution; import lombok.Builder; import lombok.Getter; +import lombok.NonNull; import org.nd4j.autodiff.samediff.SDVariable; import org.nd4j.autodiff.samediff.SameDiff; import org.nd4j.base.Preconditions; @@ -53,7 +54,7 @@ public class Col2Im extends DynamicCustomOp { addArgs(); } - public Col2Im(SameDiff sd, SDVariable input, Conv2DConfig config){ + public Col2Im(@NonNull SameDiff sd, @NonNull SDVariable input, @NonNull Conv2DConfig config){ super(null, sd, new SDVariable[]{input}); this.conv2DConfig = config; addArgs(); @@ -61,6 +62,13 @@ public class Col2Im extends DynamicCustomOp { public Col2Im() {} + public Col2Im(@NonNull INDArray in, @NonNull Conv2DConfig conv2DConfig) { + super("col2Im",in,null,null,null); + this.conv2DConfig = conv2DConfig; + } + + + protected void addArgs() { addIArgument(conv2DConfig.getSH()); addIArgument(conv2DConfig.getSW()); diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/convolution/Conv1D.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/convolution/Conv1D.java index 486023331..819d1d10c 100644 --- a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/convolution/Conv1D.java +++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/convolution/Conv1D.java @@ -64,6 +64,14 @@ public class Conv1D extends DynamicCustomOp { this(wrapFilterNull(input, weights, bias), wrapOrNull(output), config); } + public Conv1D( @NonNull INDArray input, @NonNull INDArray weights, INDArray bias, Conv1DConfig conv1DConfig) { + this(wrapFilterNull(input, weights, bias), null, conv1DConfig); + } + + public Conv1D(@NonNull INDArray input, @NonNull INDArray weights, Conv1DConfig conv1DConfig) { + this(new INDArray[]{input, weights}, null, conv1DConfig); + } + private void initConfig(Conv1DConfig config){ this.config = config; Preconditions.checkState(config.getS() >= 1 && config.getP() >= 0, INVALID_CONFIGURATION, config.getS(), config.getP()); diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/convolution/Conv2D.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/convolution/Conv2D.java index 3794469ae..60bdfbfcc 100644 --- a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/convolution/Conv2D.java +++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/convolution/Conv2D.java @@ -75,6 +75,14 @@ public class Conv2D extends DynamicCustomOp { this(wrapFilterNull(input, weights, bias), wrapOrNull(output), config); } + public Conv2D(@NonNull INDArray layerInput, @NonNull INDArray weights, @NonNull Conv2DConfig conv2DConfig) { + this(new INDArray[]{layerInput, weights}, null, conv2DConfig); + } + + public Conv2D(@NonNull INDArray layerInput, @NonNull INDArray weights, INDArray bias, @NonNull Conv2DConfig conv2DConfig) { + this(wrapFilterNull(layerInput, weights,bias), null, conv2DConfig); + } + protected void initConfig(Conv2DConfig config){ this.config = config; diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/convolution/Conv3D.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/convolution/Conv3D.java index 0eb753932..94fb897b0 100644 --- a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/convolution/Conv3D.java +++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/convolution/Conv3D.java @@ -70,6 +70,14 @@ public class Conv3D extends DynamicCustomOp { this(wrapFilterNull(input, weights, bias), wrapOrNull(output), config); } + public Conv3D(@NonNull INDArray input,@NonNull INDArray weights, @NonNull Conv3DConfig conv3DConfig) { + this(new INDArray[]{input, weights}, null, conv3DConfig); + } + + public Conv3D(@NonNull INDArray input, @NonNull INDArray weights, INDArray bias, @NonNull Conv3DConfig conv3DConfig) { + this(wrapFilterNull(input, weights, bias) , null, conv3DConfig); + } + private void initConfig(Conv3DConfig config){ this.config = config; Preconditions.checkState(config.getSW() >= 1 && config.getPH() >= 0 && config.getDW() >= 1, diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/convolution/DeConv2D.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/convolution/DeConv2D.java index 34c6d2e94..f3500bec0 100644 --- a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/convolution/DeConv2D.java +++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/convolution/DeConv2D.java @@ -73,6 +73,15 @@ public class DeConv2D extends DynamicCustomOp { this(wrapFilterNull(input, weights, bias), wrapOrNull(output), config); } + public DeConv2D(@NonNull INDArray layerInput, @NonNull INDArray weights, DeConv2DConfig deConv2DConfig) { + this(wrapFilterNull(layerInput, weights), null, deConv2DConfig); + } + + public DeConv2D(INDArray layerInput, INDArray weights, INDArray bias, DeConv2DConfig deConv2DConfig) { + this(wrapFilterNull(layerInput, weights, bias), null, deConv2DConfig); + } + + @Override public long[] iArgs() { if (iArguments.size() == 0) diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/convolution/DeConv3D.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/convolution/DeConv3D.java index c77b12481..a4652850c 100644 --- a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/convolution/DeConv3D.java +++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/convolution/DeConv3D.java @@ -48,7 +48,7 @@ public class DeConv3D extends DynamicCustomOp { protected DeConv3DConfig config; - public DeConv3D(SameDiff sameDiff, @NonNull SDVariable input, @NonNull SDVariable weights, SDVariable bias, @NonNull DeConv3DConfig config) { + public DeConv3D(@NonNull SameDiff sameDiff, @NonNull SDVariable input, @NonNull SDVariable weights, SDVariable bias, @NonNull DeConv3DConfig config) { super(sameDiff, toArr(input, weights, bias)); this.config = config; addArgs(); @@ -65,6 +65,14 @@ public class DeConv3D extends DynamicCustomOp { this(wrapFilterNull(input, weights, bias), wrapOrNull(output), config); } + public DeConv3D(@NonNull INDArray input, @NonNull INDArray weights, @NonNull DeConv3DConfig deConv3DConfig) { + this(new INDArray[]{input, weights}, null, deConv3DConfig); + } + + public DeConv3D(@NonNull INDArray input, @NonNull INDArray weights, INDArray bias, @NonNull DeConv3DConfig deConv3DConfig) { + this(wrapFilterNull(input, weights, bias), null, deConv3DConfig); + } + private static SDVariable[] toArr(SDVariable input, SDVariable weights, SDVariable bias){ if(bias != null){ return new SDVariable[]{input, weights, bias}; diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/convolution/DepthToSpace.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/convolution/DepthToSpace.java index 704f8bdd4..3becef510 100644 --- a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/convolution/DepthToSpace.java +++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/convolution/DepthToSpace.java @@ -16,6 +16,7 @@ package org.nd4j.linalg.api.ops.impl.layers.convolution; +import lombok.NonNull; import lombok.val; import org.nd4j.autodiff.samediff.SDVariable; import org.nd4j.autodiff.samediff.SameDiff; @@ -24,6 +25,7 @@ import org.nd4j.imports.graphmapper.tf.TFGraphMapper; import org.nd4j.linalg.api.buffer.DataType; import org.nd4j.linalg.api.ndarray.INDArray; import org.nd4j.linalg.api.ops.DynamicCustomOp; +import org.nd4j.linalg.factory.enums.DataFormat; import org.tensorflow.framework.AttrValue; import org.tensorflow.framework.GraphDef; import org.tensorflow.framework.NodeDef; @@ -58,7 +60,7 @@ public class DepthToSpace extends DynamicCustomOp { addIArgument(blockSize, isNHWC ? 1 : 0); } - public DepthToSpace(INDArray in, INDArray out, int blockSize, String dataFormat) { + public DepthToSpace(@NonNull INDArray in, INDArray out, int blockSize, @NonNull String dataFormat) { super(null, in, out, null, null); this.blockSize = blockSize; this.dataFormat = dataFormat; @@ -66,6 +68,10 @@ public class DepthToSpace extends DynamicCustomOp { addIArgument(blockSize, isNHWC ? 1 : 0); } + public DepthToSpace(@NonNull INDArray x, int blockSize, DataFormat dataFormat) { + this(x, null, blockSize, dataFormat.toString()); + } + @Override public List doDiff(List i_v) { diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/convolution/DepthwiseConv2D.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/convolution/DepthwiseConv2D.java index 4b10909a0..ab42c3c5a 100644 --- a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/convolution/DepthwiseConv2D.java +++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/convolution/DepthwiseConv2D.java @@ -16,11 +16,8 @@ package org.nd4j.linalg.api.ops.impl.layers.convolution; -import lombok.Builder; -import lombok.Getter; -import lombok.NonNull; +import lombok.*; import lombok.extern.slf4j.Slf4j; -import lombok.val; import onnx.Onnx; import org.nd4j.autodiff.samediff.SDVariable; import org.nd4j.autodiff.samediff.SameDiff; @@ -52,6 +49,7 @@ import java.util.*; */ @Slf4j @Getter +@NoArgsConstructor public class DepthwiseConv2D extends DynamicCustomOp { protected Conv2DConfig config; @@ -77,7 +75,16 @@ public class DepthwiseConv2D extends DynamicCustomOp { this(wrapFilterNull(input, weights, bias), wrapOrNull(output), config); } - public DepthwiseConv2D() { + public DepthwiseConv2D(INDArray layerInput, INDArray depthWeights, Conv2DConfig conv2DConfig) { + this(wrapFilterNull(layerInput, depthWeights), null, conv2DConfig); + } + + public DepthwiseConv2D(INDArray layerInput, INDArray depthWeights, INDArray bias, Conv2DConfig conv2DConfig) { + this(wrapFilterNull(layerInput, depthWeights, bias), null, conv2DConfig); + } + + public DepthwiseConv2D(INDArray inputs, Conv2DConfig conv2DConfig) { + this(wrapFilterNull(inputs), null, conv2DConfig); } @Override diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/convolution/Im2col.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/convolution/Im2col.java index 91bbf88ee..46f5f4e79 100644 --- a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/convolution/Im2col.java +++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/convolution/Im2col.java @@ -58,6 +58,13 @@ public class Im2col extends DynamicCustomOp { public Im2col() {} + public Im2col(INDArray in, Conv2DConfig conv2DConfig) { + super("im2Col",in,null,null,null); + this.conv2DConfig = conv2DConfig; + addArgs(); + } + + protected void addArgs() { addIArgument(conv2DConfig.getKH()); addIArgument(conv2DConfig.getKW()); @@ -68,7 +75,6 @@ public class Im2col extends DynamicCustomOp { addIArgument(conv2DConfig.getDH()); addIArgument(conv2DConfig.getDW()); addIArgument(ArrayUtil.fromBoolean(conv2DConfig.isSameMode())); - } diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/convolution/LocalResponseNormalization.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/convolution/LocalResponseNormalization.java index 8dfb7131a..cc5780a7c 100644 --- a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/convolution/LocalResponseNormalization.java +++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/convolution/LocalResponseNormalization.java @@ -65,6 +65,13 @@ public class LocalResponseNormalization extends DynamicCustomOp { addArgs(); } + public LocalResponseNormalization(@NonNull INDArray input, @NonNull LocalResponseNormalizationConfig LocalResponseNormalizationConfig){ + super(new INDArray[]{input}, null); + + this.config = config; + addArgs(); + } + @Override public Map propertiesForFunction() { diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/convolution/MaxPooling2D.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/convolution/MaxPooling2D.java index 844102b59..9f7c9bfb7 100644 --- a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/convolution/MaxPooling2D.java +++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/convolution/MaxPooling2D.java @@ -60,14 +60,17 @@ public class MaxPooling2D extends DynamicCustomOp { addArgs(); } - public MaxPooling2D(INDArray input, INDArray output, @NonNull Pooling2DConfig config){ + public MaxPooling2D(@NonNull INDArray input, INDArray output, @NonNull Pooling2DConfig config){ super(null, new INDArray[]{input}, wrapOrNull(output)); config.setType(Pooling2D.Pooling2DType.MAX); - this.config = config; addArgs(); } + public MaxPooling2D(@NonNull INDArray input, @NonNull Pooling2DConfig pooling2DConfig) { + this(input, null, pooling2DConfig); + } + @Override public boolean isConfigProperties() { return true; diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/convolution/MaxPooling3D.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/convolution/MaxPooling3D.java index 9ae930bf0..6c4ccaa9a 100644 --- a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/convolution/MaxPooling3D.java +++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/convolution/MaxPooling3D.java @@ -17,6 +17,7 @@ package org.nd4j.linalg.api.ops.impl.layers.convolution; import lombok.Getter; +import lombok.NoArgsConstructor; import lombok.extern.slf4j.Slf4j; import onnx.Onnx; import org.nd4j.autodiff.samediff.SDVariable; @@ -38,9 +39,9 @@ import java.util.Map; */ @Slf4j @Getter +@NoArgsConstructor public class MaxPooling3D extends Pooling3D { - public MaxPooling3D() { - } + public MaxPooling3D(SameDiff sameDiff, SDVariable input, Pooling3DConfig config) { super(sameDiff, new SDVariable[]{input}, null, null, false, config, Pooling3DType.MAX); @@ -50,6 +51,10 @@ public class MaxPooling3D extends Pooling3D { super(sameDiff, null, new INDArray[]{arrayInput}, wrapOrNull(arrayOutput), false, config, Pooling3DType.MAX); } + public MaxPooling3D(INDArray input, Pooling3DConfig pooling3DConfig) { + super(null, null, new INDArray[]{input},null, false, pooling3DConfig, Pooling3DType.MAX); + } + @Override public boolean isConfigProperties() { return true; diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/convolution/Pooling3D.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/convolution/Pooling3D.java index 98156596d..17305cdf6 100644 --- a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/convolution/Pooling3D.java +++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/convolution/Pooling3D.java @@ -16,6 +16,7 @@ package org.nd4j.linalg.api.ops.impl.layers.convolution; +import lombok.NoArgsConstructor; import lombok.extern.slf4j.Slf4j; import lombok.val; import org.nd4j.autodiff.samediff.SDVariable; @@ -37,6 +38,7 @@ import java.util.*; * Pooling3D operation */ @Slf4j +@NoArgsConstructor public abstract class Pooling3D extends DynamicCustomOp { protected Pooling3DConfig config; @@ -52,8 +54,6 @@ public abstract class Pooling3D extends DynamicCustomOp { return super.iArgs(); } - public Pooling3D() {} - public Pooling3D(SameDiff sameDiff, SDVariable[] inputs,INDArray[] inputArrays, INDArray[] outputs,boolean inPlace, Pooling3DConfig pooling3DConfig, Pooling3DType type) { super(null,sameDiff, inputs, inPlace); diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/convolution/Pooling3DDerivative.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/convolution/Pooling3DDerivative.java index e8e478af1..ad48f988c 100644 --- a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/convolution/Pooling3DDerivative.java +++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/convolution/Pooling3DDerivative.java @@ -17,6 +17,7 @@ package org.nd4j.linalg.api.ops.impl.layers.convolution; import lombok.Builder; +import lombok.NoArgsConstructor; import lombok.extern.slf4j.Slf4j; import org.nd4j.autodiff.samediff.SDVariable; import org.nd4j.autodiff.samediff.SameDiff; @@ -33,6 +34,7 @@ import java.util.List; * Pooling3DDerivative operation */ @Slf4j +@NoArgsConstructor public class Pooling3DDerivative extends Pooling3D { @Builder(builderMethodName = "derivativeBuilder") @@ -41,9 +43,6 @@ public class Pooling3DDerivative extends Pooling3D { super(sameDiff, inputs, inputArrays, outputs, inPlace, pooling3DConfig, type); } - public Pooling3DDerivative() {} - - @Override public String opName() { diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/convolution/SConv2D.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/convolution/SConv2D.java index d18470a7d..b28b9a987 100644 --- a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/convolution/SConv2D.java +++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/convolution/SConv2D.java @@ -48,12 +48,18 @@ public class SConv2D extends Conv2D { super(inputs, outputs, config); } - public SConv2D(@NonNull INDArray input, @NonNull INDArray depthWeights, INDArray pointWeights, INDArray bias, INDArray output, @NonNull Conv2DConfig config){ - this(wrapFilterNull(input, depthWeights, pointWeights, bias), wrapOrNull(output), config); + public SConv2D(@NonNull INDArray layerInput, @NonNull INDArray depthWeights, INDArray pointWeights, INDArray bias, @NonNull Conv2DConfig Conv2DConfig){ + this(wrapFilterNull(layerInput, depthWeights, pointWeights, bias), null, Conv2DConfig); + } + + public SConv2D(@NonNull INDArray layerInput, @NonNull INDArray depthWeights, INDArray pointWeights, @NonNull Conv2DConfig Conv2DConfig){ + this(wrapFilterNull(layerInput, depthWeights, pointWeights), null, Conv2DConfig); } public SConv2D() {} + + @Override public String opName() { return "sconv2d"; diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/convolution/SpaceToDepth.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/convolution/SpaceToDepth.java index e591c9f1c..5ae281ae2 100644 --- a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/convolution/SpaceToDepth.java +++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/convolution/SpaceToDepth.java @@ -16,6 +16,7 @@ package org.nd4j.linalg.api.ops.impl.layers.convolution; +import lombok.NonNull; import lombok.val; import org.nd4j.autodiff.samediff.SDVariable; import org.nd4j.autodiff.samediff.SameDiff; @@ -24,6 +25,7 @@ import org.nd4j.imports.graphmapper.tf.TFGraphMapper; import org.nd4j.linalg.api.buffer.DataType; import org.nd4j.linalg.api.ndarray.INDArray; import org.nd4j.linalg.api.ops.DynamicCustomOp; +import org.nd4j.linalg.factory.enums.DataFormat; import org.tensorflow.framework.AttrValue; import org.tensorflow.framework.GraphDef; import org.tensorflow.framework.NodeDef; @@ -65,6 +67,13 @@ public class SpaceToDepth extends DynamicCustomOp { addIArgument(blockSize, isNHWC ? 1 : 0); } + + + public SpaceToDepth(@NonNull INDArray x, int blockSize, @NonNull DataFormat dataFormat) { + this(x, null, blockSize,dataFormat.toString()); + } + + @Override public List doDiff(List i_v) { // Gradient to SpaceToDepth is just DepthToSpace of same block size and data format. diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/convolution/Upsampling2d.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/convolution/Upsampling2d.java index 21f3f4d5d..574682a36 100644 --- a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/convolution/Upsampling2d.java +++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/convolution/Upsampling2d.java @@ -17,12 +17,15 @@ package org.nd4j.linalg.api.ops.impl.layers.convolution; import lombok.Getter; +import lombok.NoArgsConstructor; +import lombok.NonNull; import lombok.extern.slf4j.Slf4j; import org.nd4j.autodiff.samediff.SDVariable; import org.nd4j.autodiff.samediff.SameDiff; import org.nd4j.base.Preconditions; import org.nd4j.imports.NoOpNameFoundException; import org.nd4j.linalg.api.buffer.DataType; +import org.nd4j.linalg.api.ndarray.INDArray; import org.nd4j.linalg.api.ops.DynamicCustomOp; import java.util.Collections; @@ -34,6 +37,7 @@ import java.util.List; */ @Slf4j @Getter +@NoArgsConstructor public class Upsampling2d extends DynamicCustomOp { @@ -53,7 +57,20 @@ public class Upsampling2d extends DynamicCustomOp { } - public Upsampling2d() {} + public Upsampling2d(INDArray input, int scale) { + this(input, scale, scale, true); + } + + public Upsampling2d(INDArray input, int scaleH, int scaleW, boolean nchw) { + super(new INDArray[]{input}, null); + this.nchw = nchw; + this.scaleH = scaleH; + this.scaleW = scaleW; + + addIArgument(scaleH); + addIArgument(scaleW); + addIArgument(nchw ? 1 : 0); + } @Override diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/recurrent/GRUCell.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/recurrent/GRUCell.java index cd0f83c89..d938cf8e9 100644 --- a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/recurrent/GRUCell.java +++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/recurrent/GRUCell.java @@ -21,6 +21,7 @@ import org.nd4j.autodiff.samediff.SDVariable; import org.nd4j.autodiff.samediff.SameDiff; import org.nd4j.base.Preconditions; import org.nd4j.linalg.api.buffer.DataType; +import org.nd4j.linalg.api.ndarray.INDArray; import org.nd4j.linalg.api.ops.DynamicCustomOp; import org.nd4j.linalg.api.ops.impl.layers.recurrent.weights.GRUWeights; @@ -46,6 +47,12 @@ public class GRUCell extends DynamicCustomOp { this.weights = weights; } + public GRUCell(INDArray x, INDArray hLast, GRUWeights gruWeights) { + super(null, null, gruWeights.argsWithInputs(x, hLast)); + this.weights = gruWeights; + } + + @Override public String opName() { return "gruCell"; diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/recurrent/LSTMBlockCell.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/recurrent/LSTMBlockCell.java index aa9ade658..bfc65ab89 100644 --- a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/recurrent/LSTMBlockCell.java +++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/recurrent/LSTMBlockCell.java @@ -21,6 +21,7 @@ import org.nd4j.autodiff.samediff.SDVariable; import org.nd4j.autodiff.samediff.SameDiff; import org.nd4j.base.Preconditions; import org.nd4j.linalg.api.buffer.DataType; +import org.nd4j.linalg.api.ndarray.INDArray; import org.nd4j.linalg.api.ops.DynamicCustomOp; import org.nd4j.linalg.api.ops.impl.layers.recurrent.config.LSTMConfiguration; import org.nd4j.linalg.api.ops.impl.layers.recurrent.weights.LSTMWeights; @@ -89,6 +90,15 @@ public class LSTMBlockCell extends DynamicCustomOp { addTArgument(configuration.tArgs()); } + public LSTMBlockCell(INDArray x, INDArray cLast, INDArray yLast, LSTMWeights lstmWeights, LSTMConfiguration lstmConfiguration) { + super(null, null, lstmWeights.argsWithInputs(x, cLast, yLast)); + this.configuration = lstmConfiguration; + this.weights = lstmWeights; + addIArgument(configuration.iArgs(false)); + addTArgument(configuration.tArgs()); + } + + @Override public List calculateOutputDataTypes(List inputDataTypes) { Preconditions.checkState(inputDataTypes != null && inputDataTypes.size() == 8, "Expected exactly 8 inputs to LSTMBlockCell, got %s", inputDataTypes); diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/recurrent/LSTMCell.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/recurrent/LSTMCell.java index e9d2ffd3b..5cb0700dc 100644 --- a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/recurrent/LSTMCell.java +++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/recurrent/LSTMCell.java @@ -16,6 +16,7 @@ package org.nd4j.linalg.api.ops.impl.layers.recurrent; +import lombok.NoArgsConstructor; import onnx.Onnx; import org.nd4j.autodiff.samediff.SameDiff; import org.nd4j.linalg.api.ops.DynamicCustomOp; @@ -31,13 +32,11 @@ import java.util.Map; * * @author Adam Gibson */ +@NoArgsConstructor public class LSTMCell extends DynamicCustomOp { private LSTMCellConfiguration configuration; - public LSTMCell() { - } - public LSTMCell(SameDiff sameDiff, LSTMCellConfiguration configuration) { super(null, sameDiff, configuration.args()); this.configuration = configuration; @@ -66,16 +65,4 @@ public class LSTMCell extends DynamicCustomOp { public String tensorflowName() { return super.tensorflowName(); } - - @Override - public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map attributesForNode, GraphDef graph) { - super.initFromTensorFlow(nodeDef, initWith, attributesForNode, graph); - } - - @Override - public void initFromOnnx(Onnx.NodeProto node, SameDiff initWith, Map attributesForNode, Onnx.GraphProto graph) { - - } - - } diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/recurrent/LSTMLayer.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/recurrent/LSTMLayer.java index 1e1ae3c47..59b85f500 100644 --- a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/recurrent/LSTMLayer.java +++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/recurrent/LSTMLayer.java @@ -22,6 +22,7 @@ import org.nd4j.autodiff.samediff.SDVariable; import org.nd4j.autodiff.samediff.SameDiff; import org.nd4j.base.Preconditions; import org.nd4j.linalg.api.buffer.DataType; +import org.nd4j.linalg.api.ndarray.INDArray; import org.nd4j.linalg.api.ops.DynamicCustomOp; import org.nd4j.linalg.api.ops.impl.layers.recurrent.config.LSTMConfiguration; import org.nd4j.linalg.api.ops.impl.layers.recurrent.config.RnnDataFormat; @@ -91,6 +92,14 @@ public class LSTMLayer extends DynamicCustomOp { addTArgument(configuration.tArgs()); } + public LSTMLayer(INDArray x, INDArray cLast, INDArray yLast, INDArray maxTSLength, LSTMWeights lstmWeights, LSTMConfiguration lstmConfiguration) { + super(null, null, lstmWeights.argsWithInputs(maxTSLength, x, cLast, yLast)); + this.configuration = lstmConfiguration; + this.weights = lstmWeights; + addIArgument(configuration.iArgs(true)); + addTArgument(configuration.tArgs()); + } + @Override public List calculateOutputDataTypes(List inputDataTypes) { Preconditions.checkState(inputDataTypes != null && inputDataTypes.size() == 9, "Expected exactly 9 inputs to LSTMLayer, got %s", inputDataTypes); diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/recurrent/SRU.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/recurrent/SRU.java index a2de2beb8..dbeed80db 100644 --- a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/recurrent/SRU.java +++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/recurrent/SRU.java @@ -16,14 +16,14 @@ package org.nd4j.linalg.api.ops.impl.layers.recurrent; -import java.util.Arrays; -import java.util.List; import lombok.Getter; +import lombok.NoArgsConstructor; import lombok.NonNull; import onnx.Onnx; import org.nd4j.autodiff.samediff.SDVariable; import org.nd4j.autodiff.samediff.SameDiff; import org.nd4j.imports.NoOpNameFoundException; +import org.nd4j.linalg.api.ndarray.INDArray; import org.nd4j.linalg.api.ops.DynamicCustomOp; import org.nd4j.linalg.api.ops.impl.layers.recurrent.weights.SRUWeights; import org.tensorflow.framework.AttrValue; @@ -37,6 +37,7 @@ import java.util.Map; * * @author Adam Gibson */ +@NoArgsConstructor public class SRU extends DynamicCustomOp { @Getter @@ -45,14 +46,23 @@ public class SRU extends DynamicCustomOp { @Getter private SDVariable mask; - public SRU() { } - public SRU(@NonNull SameDiff sameDiff, @NonNull SDVariable x, @NonNull SDVariable initialC, SDVariable mask, @NonNull SRUWeights weights) { super(null, sameDiff, wrapFilterNull(x, weights.getWeights(), weights.getBias(), initialC, mask)); this.mask = mask; this.weights = weights; } + public SRU(INDArray x, INDArray initialC, INDArray mask, SRUWeights sruWeights) { + super(wrapFilterNull(x, sruWeights.getIWeights(), sruWeights.getIBias(), initialC, mask), null); + this.mask = (SDVariable) mask; + this.weights = sruWeights; + } + + public SRU(INDArray x, INDArray initialC, SRUWeights sruWeights) { + super(wrapFilterNull(x, sruWeights.getIWeights(), sruWeights.getIBias(), initialC), null); + this.weights = sruWeights; + } + @Override public String opName() { return "sru"; diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/recurrent/SRUCell.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/recurrent/SRUCell.java index ac3f6c07f..36a40348a 100644 --- a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/recurrent/SRUCell.java +++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/recurrent/SRUCell.java @@ -22,6 +22,7 @@ import onnx.Onnx; import org.nd4j.autodiff.samediff.SDVariable; import org.nd4j.autodiff.samediff.SameDiff; import org.nd4j.imports.NoOpNameFoundException; +import org.nd4j.linalg.api.ndarray.INDArray; import org.nd4j.linalg.api.ops.DynamicCustomOp; import org.nd4j.linalg.api.ops.impl.layers.recurrent.weights.SRUWeights; import org.tensorflow.framework.AttrValue; @@ -46,6 +47,14 @@ public class SRUCell extends DynamicCustomOp { this.weights = weights; } + + + public SRUCell(INDArray x, INDArray cLast, SRUWeights sruWeights) { + super(null, null, sruWeights.argsWithInputs(x, cLast)); + this.weights = sruWeights; + } + + @Override public String opName() { return "sruCell"; diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/recurrent/weights/GRUWeights.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/recurrent/weights/GRUWeights.java index f95438ae3..52a3c166f 100644 --- a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/recurrent/weights/GRUWeights.java +++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/recurrent/weights/GRUWeights.java @@ -5,6 +5,7 @@ import lombok.Data; import lombok.EqualsAndHashCode; import lombok.NonNull; import org.nd4j.autodiff.samediff.SDVariable; +import org.nd4j.linalg.api.ndarray.INDArray; import org.nd4j.linalg.api.ops.impl.layers.recurrent.GRUCell; /** @@ -21,31 +22,36 @@ public class GRUWeights extends RNNWeights { * * The reset weights are the [:, 0:numUnits] subset and the update weights are the [:, numUnits:2*numUnits] subset. */ - @NonNull private SDVariable ruWeight; + private INDArray iRuWeights; /** * Cell gate weights, with a shape of [inSize + numUnits, numUnits] */ - @NonNull private SDVariable cWeight; + private INDArray iCWeight; /** * Reset and Update gate bias, with a shape of [2*numUnits]. May be null. * * The reset bias is the [0:numUnits] subset and the update bias is the [numUnits:2*numUnits] subset. */ - @NonNull private SDVariable ruBias; + private INDArray iRUBias; /** * Cell gate bias, with a shape of [numUnits]. May be null. */ - @NonNull private SDVariable cBias; + private INDArray iCBias; @Override public SDVariable[] args() { return filterNonNull(ruWeight, cWeight, ruBias, cBias); } + + @Override + public INDArray[] arrayArgs() { + return filterNonNull(iRuWeights, iCWeight, iRUBias, iCBias); + } } diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/recurrent/weights/LSTMWeights.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/recurrent/weights/LSTMWeights.java index bf401d66c..f2309b9e4 100644 --- a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/recurrent/weights/LSTMWeights.java +++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/recurrent/weights/LSTMWeights.java @@ -5,6 +5,7 @@ import lombok.Data; import lombok.EqualsAndHashCode; import lombok.NonNull; import org.nd4j.autodiff.samediff.SDVariable; +import org.nd4j.linalg.api.ndarray.INDArray; import org.nd4j.linalg.api.ops.impl.layers.recurrent.LSTMBlockCell; import org.nd4j.linalg.api.ops.impl.layers.recurrent.LSTMLayer; @@ -23,35 +24,40 @@ public class LSTMWeights extends RNNWeights { * Input to hidden and hidden to hidden are concatenated in dimension 0, * so the input to hidden weights are [:inSize, :] and the hidden to hidden weights are [inSize:, :]. */ - @NonNull private SDVariable weights; + private INDArray iWeights; /** * Cell peephole (t-1) connections to input modulation gate, with a shape of [numUnits]. */ - @NonNull private SDVariable inputPeepholeWeights; + private INDArray iInputPeepholeWeights; /** * Cell peephole (t-1) connections to forget gate, with a shape of [numUnits]. */ - @NonNull private SDVariable forgetPeepholeWeights; + private INDArray iForgetPeepholeWeights; /** * Cell peephole (t) connections to output gate, with a shape of [numUnits]. */ - @NonNull private SDVariable outputPeepholeWeights; + private INDArray iOutputPeepholeWeights; /** * Input to hidden and hidden to hidden biases, with shape [1, 4*numUnits]. */ - @NonNull private SDVariable bias; + private INDArray iBias; @Override public SDVariable[] args() { return filterNonNull(weights, inputPeepholeWeights, forgetPeepholeWeights, outputPeepholeWeights, bias); } + + @Override + public INDArray[] arrayArgs() { + return filterNonNull(iWeights, iInputPeepholeWeights, iForgetPeepholeWeights, iOutputPeepholeWeights, iBias); + } } diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/recurrent/weights/RNNWeights.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/recurrent/weights/RNNWeights.java index 62e295d80..e0b8a5c27 100644 --- a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/recurrent/weights/RNNWeights.java +++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/recurrent/weights/RNNWeights.java @@ -1,35 +1,38 @@ package org.nd4j.linalg.api.ops.impl.layers.recurrent.weights; +import java.lang.reflect.Array; import java.util.Arrays; import org.nd4j.autodiff.samediff.SDVariable; +import org.nd4j.linalg.api.ndarray.INDArray; import org.nd4j.linalg.util.ArrayUtil; public abstract class RNNWeights { public abstract SDVariable[] args(); - protected static SDVariable[] filterNonNull(SDVariable... args){ + public abstract INDArray[] arrayArgs(); + + protected static T[] filterNonNull(T... args){ int count = 0; - for(SDVariable v : args){ - if(v != null){ - count++; + for( int i=0; i } protected List getFeatureStats() { - assertIsFit(); return featureStats; } @@ -88,7 +87,6 @@ public abstract class AbstractMultiDataSetNormalizer } protected List getLabelStats() { - assertIsFit(); return labelStats; } diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/dataset/api/preprocessor/ImagePreProcessingScaler.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/dataset/api/preprocessor/ImagePreProcessingScaler.java index 5fc4491c8..77f3c39d9 100644 --- a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/dataset/api/preprocessor/ImagePreProcessingScaler.java +++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/dataset/api/preprocessor/ImagePreProcessingScaler.java @@ -44,6 +44,7 @@ public class ImagePreProcessingScaler implements DataNormalization { private double minRange, maxRange; private double maxPixelVal; private int maxBits; + private boolean fitLabels = false; public ImagePreProcessingScaler() { this(0, 1, 8); @@ -94,7 +95,10 @@ public class ImagePreProcessingScaler implements DataNormalization { @Override public void preProcess(DataSet toPreProcess) { INDArray features = toPreProcess.getFeatures(); - this.preProcess(features); + preProcess(features); + if(fitLabels && toPreProcess.getLabels() != null){ + preProcess(toPreProcess.getLabels()); + } } public void preProcess(INDArray features) { @@ -139,6 +143,7 @@ public class ImagePreProcessingScaler implements DataNormalization { @Override public void revert(DataSet toRevert) { revertFeatures(toRevert.getFeatures()); + revertLabels(toRevert.getLabels()); } @Override @@ -177,10 +182,11 @@ public class ImagePreProcessingScaler implements DataNormalization { @Override public void fitLabel(boolean fitLabels) { //No-op + this.fitLabels = fitLabels; } @Override public boolean isFitLabel() { - return false; + return fitLabels; } } diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/factory/Nd4j.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/factory/Nd4j.java index 8e638f373..bafee4003 100644 --- a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/factory/Nd4j.java +++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/factory/Nd4j.java @@ -16,10 +16,7 @@ package org.nd4j.linalg.factory; -import org.nd4j.linalg.factory.ops.NDBitwise; -import org.nd4j.linalg.factory.ops.NDMath; -import org.nd4j.linalg.factory.ops.NDNN; -import org.nd4j.linalg.factory.ops.NDRandom; +import org.nd4j.linalg.factory.ops.*; import org.nd4j.shade.guava.primitives.Ints; import org.nd4j.shade.guava.primitives.Longs; import lombok.NonNull; @@ -134,6 +131,26 @@ public class Nd4j { */ public static final NDNN nn = new NDNN(); + /** + * Loss function namespace - operations related to loss functions. + */ + public static final NDLoss loss = new NDLoss(); + + /** + * Convolutional network namespace - operations related to convolutional neural networks + */ + public static final NDCNN cnn = new NDCNN(); + + /** + * Recurrent neural network namespace - operations related to recurrent neural networks + */ + public static final NDRNN rnn = new NDRNN(); + + /** + * Image namespace - operations related to images + */ + public static final NDImage image = new NDImage(); + /** * Bitwise namespace - operations related to bitwise manipulation of arrays */ @@ -162,6 +179,32 @@ public class Nd4j { return nn; } + /** + * Loss function namespace - operations related to loss functions. + */ + public static NDLoss loss(){ return loss; } + + /** + * Convolutional network namespace - operations related to convolutional neural networks + */ + public static NDCNN cnn(){ + return cnn; + } + + /** + * Recurrent neural network namespace - operations related to recurrent neural networks + */ + public static NDRNN rnn(){ + return rnn; + } + + /** + * Image namespace - operations related to images + */ + public static NDImage image(){ + return image; + } + private final static String DATA_BUFFER_OPS = "databufferfactory"; private final static String CONVOLUTION_OPS = "convops"; /**@deprecated Use {@link ND4JSystemProperties#DTYPE}*/ @@ -3304,8 +3347,9 @@ public class Nd4j { */ public static INDArray randomExponential(double lambda, INDArray target) { Preconditions.checkArgument(lambda > 0, "Lambda argument must be >= 0 - got %s", lambda); - INDArray shapeArr = Nd4j.create(ArrayUtil.toDouble(target.shape())); - Nd4j.getExecutioner().execAndReturn(new RandomExponential(shapeArr, target, lambda)); + INDArray shapeArr = Nd4j.createFromArray(target.shape()); + RandomExponential r = new RandomExponential(shapeArr, target, lambda); + Nd4j.exec(r); return target; } diff --git a/libnd4j/include/loops/cpu/compilation_units/TrueBroadcastHelper_3.cpp b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/factory/enums/DataFormat.java similarity index 77% rename from libnd4j/include/loops/cpu/compilation_units/TrueBroadcastHelper_3.cpp rename to nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/factory/enums/DataFormat.java index 327df9a88..fb3fc9c67 100644 --- a/libnd4j/include/loops/cpu/compilation_units/TrueBroadcastHelper_3.cpp +++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/factory/enums/DataFormat.java @@ -14,14 +14,14 @@ * SPDX-License-Identifier: Apache-2.0 ******************************************************************************/ -// -// @author raver119@gmail.com -// +//================== GENERATED CODE - DO NOT MODIFY THIS FILE ================== -#include "../TrueBroadcastHelper.hpp" +package org.nd4j.linalg.factory.enums; -namespace nd4j { - namespace helpers { - BUILD_PAIRWISE_TEMPLATE(template class ND4J_EXPORT TrueBroadcastHelper, , PAIRWISE_TYPES_2); - } +/** + * Data format: "NCHW" or "NHWC" */ +public enum DataFormat { + NCHW, + + NHWC } diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/factory/ops/NDCNN.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/factory/ops/NDCNN.java new file mode 100755 index 000000000..7bee44ace --- /dev/null +++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/factory/ops/NDCNN.java @@ -0,0 +1,499 @@ +/******************************************************************************* + * Copyright (c) 2019-2020 Konduit K.K. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ + +//================== GENERATED CODE - DO NOT MODIFY THIS FILE ================== + +package org.nd4j.linalg.factory.ops; + +import static org.nd4j.linalg.factory.NDValidation.isSameType; + +import org.nd4j.base.Preconditions; +import org.nd4j.linalg.api.ndarray.INDArray; +import org.nd4j.linalg.api.ops.impl.layers.convolution.config.Conv1DConfig; +import org.nd4j.linalg.api.ops.impl.layers.convolution.config.Conv2DConfig; +import org.nd4j.linalg.api.ops.impl.layers.convolution.config.Conv3DConfig; +import org.nd4j.linalg.api.ops.impl.layers.convolution.config.DeConv2DConfig; +import org.nd4j.linalg.api.ops.impl.layers.convolution.config.DeConv3DConfig; +import org.nd4j.linalg.api.ops.impl.layers.convolution.config.LocalResponseNormalizationConfig; +import org.nd4j.linalg.api.ops.impl.layers.convolution.config.Pooling2DConfig; +import org.nd4j.linalg.api.ops.impl.layers.convolution.config.Pooling3DConfig; +import org.nd4j.linalg.factory.NDValidation; +import org.nd4j.linalg.factory.Nd4j; +import org.nd4j.linalg.factory.enums.DataFormat; + +public class NDCNN { + public NDCNN() { + } + + /** + * 2D Convolution layer operation - average pooling 2d
+ * + * @param input the input to average pooling 2d operation - 4d CNN (image) activations in NCHW format + * (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type) + * @param Pooling2DConfig Configuration Object + * @return output Result after applying average pooling on the input (NUMERIC type) + */ + public INDArray avgPooling2d(INDArray input, Pooling2DConfig Pooling2DConfig) { + NDValidation.validateNumerical("avgPooling2d", "input", input); + return Nd4j.exec(new org.nd4j.linalg.api.ops.impl.layers.convolution.AvgPooling2D(input, Pooling2DConfig))[0]; + } + + /** + * 3D convolution layer operation - average pooling 3d
+ * + * @param input the input to average pooling 3d operation - 5d activations in NCDHW format + * (shape [minibatch, channels, depth, height, width]) or NDHWC format + * (shape [minibatch, depth, height, width, channels]) (NUMERIC type) + * @param Pooling3DConfig Configuration Object + * @return output after applying average pooling on the input (NUMERIC type) + */ + public INDArray avgPooling3d(INDArray input, Pooling3DConfig Pooling3DConfig) { + NDValidation.validateNumerical("avgPooling3d", "input", input); + return Nd4j.exec(new org.nd4j.linalg.api.ops.impl.layers.convolution.AvgPooling3D(input, Pooling3DConfig))[0]; + } + + /** + * Convolution 2d layer batch to space operation on 4d input.
+ * Reduces input batch dimension by rearranging data into a larger spatial dimensions
+ * + * @param x Input variable. 4d input (NUMERIC type) + * @param blocks Block size, in the height/width dimension (Size: Exactly(count=2)) + * @param croppingTop (Size: Exactly(count=2)) + * @param croppingBottom (Size: Exactly(count=2)) + * @return output Output variable (NUMERIC type) + */ + public INDArray batchToSpace(INDArray x, int[] blocks, int[] croppingTop, int... croppingBottom) { + NDValidation.validateNumerical("batchToSpace", "x", x); + Preconditions.checkArgument(blocks.length == 2, "blocks has incorrect size/length. Expected: blocks.length == 2, got %s", blocks.length); + Preconditions.checkArgument(croppingTop.length == 2, "croppingTop has incorrect size/length. Expected: croppingTop.length == 2, got %s", croppingTop.length); + Preconditions.checkArgument(croppingBottom.length == 2, "croppingBottom has incorrect size/length. Expected: croppingBottom.length == 2, got %s", croppingBottom.length); + return Nd4j.exec(new org.nd4j.linalg.api.ops.impl.transforms.custom.BatchToSpace(x, blocks, croppingTop, croppingBottom))[0]; + } + + /** + * col2im operation for use in 2D convolution operations. Outputs a 4d array with shape
+ * [minibatch, inputChannels, height, width]
+ * + * @param in Input - rank 6 input with shape [minibatch, inputChannels, kernelHeight, kernelWidth, outputHeight, outputWidth] (NUMERIC type) + * @param Conv2DConfig Configuration Object + * @return output Col2Im output variable (NUMERIC type) + */ + public INDArray col2Im(INDArray in, Conv2DConfig Conv2DConfig) { + NDValidation.validateNumerical("col2Im", "in", in); + return Nd4j.exec(new org.nd4j.linalg.api.ops.impl.layers.convolution.Col2Im(in, Conv2DConfig))[0]; + } + + /** + * Conv1d operation.
+ * + * @param input the inputs to conv1d (NUMERIC type) + * @param weights weights for conv1d op - rank 3 array with shape [kernelSize, inputChannels, outputChannels] (NUMERIC type) + * @param bias bias for conv1d op - rank 1 array with shape [outputChannels]. May be null. (NUMERIC type) + * @param Conv1DConfig Configuration Object + * @return output result of conv1d op (NUMERIC type) + */ + public INDArray conv1d(INDArray input, INDArray weights, INDArray bias, + Conv1DConfig Conv1DConfig) { + NDValidation.validateNumerical("conv1d", "input", input); + NDValidation.validateNumerical("conv1d", "weights", weights); + NDValidation.validateNumerical("conv1d", "bias", bias); + return Nd4j.exec(new org.nd4j.linalg.api.ops.impl.layers.convolution.Conv1D(input, weights, bias, Conv1DConfig))[0]; + } + + /** + * Conv1d operation.
+ * + * @param input the inputs to conv1d (NUMERIC type) + * @param weights weights for conv1d op - rank 3 array with shape [kernelSize, inputChannels, outputChannels] (NUMERIC type) + * @param Conv1DConfig Configuration Object + * @return output result of conv1d op (NUMERIC type) + */ + public INDArray conv1d(INDArray input, INDArray weights, Conv1DConfig Conv1DConfig) { + NDValidation.validateNumerical("conv1d", "input", input); + NDValidation.validateNumerical("conv1d", "weights", weights); + return Nd4j.exec(new org.nd4j.linalg.api.ops.impl.layers.convolution.Conv1D(input, weights, null, Conv1DConfig))[0]; + } + + /** + * 2D Convolution operation with optional bias
+ * + * @param layerInput the input to max pooling 2d operation - 4d CNN (image) activations in NCHW format (NUMERIC type) + * @param weights Weights for the convolution operation. 4 dimensions with format [kernelHeight, kernelWidth, inputChannels, outputChannels] (NUMERIC type) + * @param bias Optional 1D bias array with shape [outputChannels]. May be null. (NUMERIC type) + * @param Conv2DConfig Configuration Object + * @return output result of conv2d op (NUMERIC type) + */ + public INDArray conv2d(INDArray layerInput, INDArray weights, INDArray bias, + Conv2DConfig Conv2DConfig) { + NDValidation.validateNumerical("conv2d", "layerInput", layerInput); + NDValidation.validateNumerical("conv2d", "weights", weights); + NDValidation.validateNumerical("conv2d", "bias", bias); + return Nd4j.exec(new org.nd4j.linalg.api.ops.impl.layers.convolution.Conv2D(layerInput, weights, bias, Conv2DConfig))[0]; + } + + /** + * 2D Convolution operation with optional bias
+ * + * @param layerInput the input to max pooling 2d operation - 4d CNN (image) activations in NCHW format (NUMERIC type) + * @param weights Weights for the convolution operation. 4 dimensions with format [kernelHeight, kernelWidth, inputChannels, outputChannels] (NUMERIC type) + * @param Conv2DConfig Configuration Object + * @return output result of conv2d op (NUMERIC type) + */ + public INDArray conv2d(INDArray layerInput, INDArray weights, Conv2DConfig Conv2DConfig) { + NDValidation.validateNumerical("conv2d", "layerInput", layerInput); + NDValidation.validateNumerical("conv2d", "weights", weights); + return Nd4j.exec(new org.nd4j.linalg.api.ops.impl.layers.convolution.Conv2D(layerInput, weights, null, Conv2DConfig))[0]; + } + + /** + * Convolution 3D operation with optional bias
+ * + * @param input the input to average pooling 3d operation - 5d activations in NCDHW format + * (shape [minibatch, channels, depth, height, width]) or NDHWC format + * (shape [minibatch, depth, height, width, channels]) (NUMERIC type) + * @param weights Weights for conv3d. Rank 5 with shape [kernelDepth, kernelHeight, kernelWidth, inputChannels, outputChannels]. (NUMERIC type) + * @param bias Optional 1D bias array with shape [outputChannels]. May be null. (NUMERIC type) + * @param Conv3DConfig Configuration Object + * @return output Conv3d output variable (NUMERIC type) + */ + public INDArray conv3d(INDArray input, INDArray weights, INDArray bias, + Conv3DConfig Conv3DConfig) { + NDValidation.validateNumerical("conv3d", "input", input); + NDValidation.validateNumerical("conv3d", "weights", weights); + NDValidation.validateNumerical("conv3d", "bias", bias); + return Nd4j.exec(new org.nd4j.linalg.api.ops.impl.layers.convolution.Conv3D(input, weights, bias, Conv3DConfig))[0]; + } + + /** + * Convolution 3D operation with optional bias
+ * + * @param input the input to average pooling 3d operation - 5d activations in NCDHW format + * (shape [minibatch, channels, depth, height, width]) or NDHWC format + * (shape [minibatch, depth, height, width, channels]) (NUMERIC type) + * @param weights Weights for conv3d. Rank 5 with shape [kernelDepth, kernelHeight, kernelWidth, inputChannels, outputChannels]. (NUMERIC type) + * @param Conv3DConfig Configuration Object + * @return output Conv3d output variable (NUMERIC type) + */ + public INDArray conv3d(INDArray input, INDArray weights, Conv3DConfig Conv3DConfig) { + NDValidation.validateNumerical("conv3d", "input", input); + NDValidation.validateNumerical("conv3d", "weights", weights); + return Nd4j.exec(new org.nd4j.linalg.api.ops.impl.layers.convolution.Conv3D(input, weights, null, Conv3DConfig))[0]; + } + + /** + * 2D deconvolution operation with optional bias
+ * + * @param layerInput the input to deconvolution 2d operation - 4d CNN (image) activations in NCHW format + * (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type) + * @param weights Weights for the 2d deconvolution operation. 4 dimensions with format [inputChannels, outputChannels, kernelHeight, kernelWidth] (NUMERIC type) + * @param bias Optional 1D bias array with shape [outputChannels]. May be null. (NUMERIC type) + * @param DeConv2DConfig Configuration Object + * @return output result of deconv2d op (NUMERIC type) + */ + public INDArray deconv2d(INDArray layerInput, INDArray weights, INDArray bias, + DeConv2DConfig DeConv2DConfig) { + NDValidation.validateNumerical("deconv2d", "layerInput", layerInput); + NDValidation.validateNumerical("deconv2d", "weights", weights); + NDValidation.validateNumerical("deconv2d", "bias", bias); + return Nd4j.exec(new org.nd4j.linalg.api.ops.impl.layers.convolution.DeConv2D(layerInput, weights, bias, DeConv2DConfig))[0]; + } + + /** + * 2D deconvolution operation with optional bias
+ * + * @param layerInput the input to deconvolution 2d operation - 4d CNN (image) activations in NCHW format + * (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type) + * @param weights Weights for the 2d deconvolution operation. 4 dimensions with format [inputChannels, outputChannels, kernelHeight, kernelWidth] (NUMERIC type) + * @param DeConv2DConfig Configuration Object + * @return output result of deconv2d op (NUMERIC type) + */ + public INDArray deconv2d(INDArray layerInput, INDArray weights, DeConv2DConfig DeConv2DConfig) { + NDValidation.validateNumerical("deconv2d", "layerInput", layerInput); + NDValidation.validateNumerical("deconv2d", "weights", weights); + return Nd4j.exec(new org.nd4j.linalg.api.ops.impl.layers.convolution.DeConv2D(layerInput, weights, null, DeConv2DConfig))[0]; + } + + /** + * 3D CNN deconvolution operation with or without optional bias
+ * + * @param input Input array - shape [bS, iD, iH, iW, iC] (NDHWC) or [bS, iC, iD, iH, iW] (NCDHW) (NUMERIC type) + * @param weights Weights array - shape [kD, kH, kW, oC, iC] (NUMERIC type) + * @param bias Bias array - optional, may be null. If non-null, must have shape [outputChannels] (NUMERIC type) + * @param DeConv3DConfig Configuration Object + * @return output result of 3D CNN deconvolution operation (NUMERIC type) + */ + public INDArray deconv3d(INDArray input, INDArray weights, INDArray bias, + DeConv3DConfig DeConv3DConfig) { + NDValidation.validateNumerical("deconv3d", "input", input); + NDValidation.validateNumerical("deconv3d", "weights", weights); + NDValidation.validateNumerical("deconv3d", "bias", bias); + return Nd4j.exec(new org.nd4j.linalg.api.ops.impl.layers.convolution.DeConv3D(input, weights, bias, DeConv3DConfig))[0]; + } + + /** + * 3D CNN deconvolution operation with or without optional bias
+ * + * @param input Input array - shape [bS, iD, iH, iW, iC] (NDHWC) or [bS, iC, iD, iH, iW] (NCDHW) (NUMERIC type) + * @param weights Weights array - shape [kD, kH, kW, oC, iC] (NUMERIC type) + * @param DeConv3DConfig Configuration Object + * @return output result of 3D CNN deconvolution operation (NUMERIC type) + */ + public INDArray deconv3d(INDArray input, INDArray weights, DeConv3DConfig DeConv3DConfig) { + NDValidation.validateNumerical("deconv3d", "input", input); + NDValidation.validateNumerical("deconv3d", "weights", weights); + return Nd4j.exec(new org.nd4j.linalg.api.ops.impl.layers.convolution.DeConv3D(input, weights, null, DeConv3DConfig))[0]; + } + + /** + * Convolution 2d layer batch to space operation on 4d input.
+ * Reduces input channels dimension by rearranging data into a larger spatial dimensions
+ * Example: if input has shape [mb, 8, 2, 2] and block size is 2, then output size is [mb, 8/(2*2), 2*2, 2*2]
+ * = [mb, 2, 4, 4]
+ * + * @param x the input to depth to space pooling 2d operation - 4d activations in NCHW format + * (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type) + * @param blockSize Block size, in the height/width dimension + * @param dataFormat Data format: "NCHW" or "NHWC" + * @return output Output variable (NUMERIC type) + */ + public INDArray depthToSpace(INDArray x, int blockSize, DataFormat dataFormat) { + NDValidation.validateNumerical("depthToSpace", "x", x); + return Nd4j.exec(new org.nd4j.linalg.api.ops.impl.layers.convolution.DepthToSpace(x, blockSize, dataFormat))[0]; + } + + /** + * Depth-wise 2D convolution operation with optional bias
+ * + * @param layerInput the input to max pooling 2d operation - 4d CNN (image) activations in NCHW format (NUMERIC type) + * @param depthWeights Depth-wise conv2d weights. 4 dimensions with format [kernelHeight, kernelWidth, inputChannels, depthMultiplier] (NUMERIC type) + * @param bias Optional 1D bias array with shape [outputChannels]. May be null. (NUMERIC type) + * @param Conv2DConfig Configuration Object + * @return output result of depthwise conv2d op (NUMERIC type) + */ + public INDArray depthWiseConv2d(INDArray layerInput, INDArray depthWeights, INDArray bias, + Conv2DConfig Conv2DConfig) { + NDValidation.validateNumerical("depthWiseConv2d", "layerInput", layerInput); + NDValidation.validateNumerical("depthWiseConv2d", "depthWeights", depthWeights); + NDValidation.validateNumerical("depthWiseConv2d", "bias", bias); + return Nd4j.exec(new org.nd4j.linalg.api.ops.impl.layers.convolution.DepthwiseConv2D(layerInput, depthWeights, bias, Conv2DConfig))[0]; + } + + /** + * Depth-wise 2D convolution operation with optional bias
+ * + * @param layerInput the input to max pooling 2d operation - 4d CNN (image) activations in NCHW format (NUMERIC type) + * @param depthWeights Depth-wise conv2d weights. 4 dimensions with format [kernelHeight, kernelWidth, inputChannels, depthMultiplier] (NUMERIC type) + * @param Conv2DConfig Configuration Object + * @return output result of depthwise conv2d op (NUMERIC type) + */ + public INDArray depthWiseConv2d(INDArray layerInput, INDArray depthWeights, + Conv2DConfig Conv2DConfig) { + NDValidation.validateNumerical("depthWiseConv2d", "layerInput", layerInput); + NDValidation.validateNumerical("depthWiseConv2d", "depthWeights", depthWeights); + return Nd4j.exec(new org.nd4j.linalg.api.ops.impl.layers.convolution.DepthwiseConv2D(layerInput, depthWeights, null, Conv2DConfig))[0]; + } + + /** + * TODO doc string
+ * + * @param df (NUMERIC type) + * @param weights df (NUMERIC type) + * @param strides weights (Size: Exactly(count=2)) + * @param rates strides (Size: Exactly(count=2)) + * @param isSameMode isSameMode + * @return output Computed the grayscale dilation of 4-D input and 3-D filters tensors. (NUMERIC type) + */ + public INDArray dilation2D(INDArray df, INDArray weights, int[] strides, int[] rates, + boolean isSameMode) { + NDValidation.validateNumerical("dilation2D", "df", df); + NDValidation.validateNumerical("dilation2D", "weights", weights); + Preconditions.checkArgument(strides.length == 2, "strides has incorrect size/length. Expected: strides.length == 2, got %s", strides.length); + Preconditions.checkArgument(rates.length == 2, "rates has incorrect size/length. Expected: rates.length == 2, got %s", rates.length); + return Nd4j.exec(new org.nd4j.linalg.api.ops.impl.transforms.custom.Dilation2D(df, weights, strides, rates, isSameMode))[0]; + } + + /** + * Extract image patches
+ * + * @param input Input array. Must be rank 4, with shape [minibatch, height, width, channels] (NUMERIC type) + * @param kH Kernel height + * @param kW Kernel width + * @param sH Stride height + * @param sW Stride width + * @param rH Rate height + * @param rW Rate width + * @param sameMode If true: use same mode padding. If false + * @return output The result is a 4D tensor which is indexed by batch, row, and column. (NUMERIC type) + */ + public INDArray extractImagePatches(INDArray input, int kH, int kW, int sH, int sW, int rH, + int rW, boolean sameMode) { + NDValidation.validateNumerical("extractImagePatches", "input", input); + return Nd4j.exec(new org.nd4j.linalg.api.ops.impl.image.ExtractImagePatches(input, kH, kW, sH, sW, rH, rW, sameMode))[0]; + } + + /** + * im2col operation for use in 2D convolution operations. Outputs a 6d array with shape
+ * [minibatch, inputChannels, kernelHeight, kernelWidth, outputHeight, outputWidth]
+ * + * @param in Input - rank 4 input with shape [minibatch, inputChannels, height, width] (NUMERIC type) + * @param Conv2DConfig Configuration Object + * @return output Im2Col output variable (NUMERIC type) + */ + public INDArray im2Col(INDArray in, Conv2DConfig Conv2DConfig) { + NDValidation.validateNumerical("im2Col", "in", in); + return Nd4j.exec(new org.nd4j.linalg.api.ops.impl.layers.convolution.Im2col(in, Conv2DConfig))[0]; + } + + /** + * 2D convolution layer operation - local response normalization
+ * + * @param input the inputs to lrn (NUMERIC type) + * @param LocalResponseNormalizationConfig Configuration Object + * @return output Result after Local Response Normalization (NUMERIC type) + */ + public INDArray localResponseNormalization(INDArray input, + LocalResponseNormalizationConfig LocalResponseNormalizationConfig) { + NDValidation.validateNumerical("localResponseNormalization", "input", input); + return Nd4j.exec(new org.nd4j.linalg.api.ops.impl.layers.convolution.LocalResponseNormalization(input, LocalResponseNormalizationConfig))[0]; + } + + /** + * 2D Convolution layer operation - max pooling 2d
+ * + * @param input the input to max pooling 2d operation - 4d CNN (image) activations in NCHW format + * (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type) + * @param Pooling2DConfig Configuration Object + * @return output Result after applying max pooling on the input (NUMERIC type) + */ + public INDArray maxPooling2d(INDArray input, Pooling2DConfig Pooling2DConfig) { + NDValidation.validateNumerical("maxPooling2d", "input", input); + return Nd4j.exec(new org.nd4j.linalg.api.ops.impl.layers.convolution.MaxPooling2D(input, Pooling2DConfig))[0]; + } + + /** + * 3D convolution layer operation - max pooling 3d operation.
+ * + * @param input the input to average pooling 3d operation - 5d activations in NCDHW format + * (shape [minibatch, channels, depth, height, width]) or NDHWC format + * (shape [minibatch, depth, height, width, channels]) (NUMERIC type) + * @param Pooling3DConfig Configuration Object + * @return output Result after applying max pooling on the input (NUMERIC type) + */ + public INDArray maxPooling3d(INDArray input, Pooling3DConfig Pooling3DConfig) { + NDValidation.validateNumerical("maxPooling3d", "input", input); + return Nd4j.exec(new org.nd4j.linalg.api.ops.impl.layers.convolution.MaxPooling3D(input, Pooling3DConfig))[0]; + } + + /** + * Separable 2D convolution operation with optional bias
+ * + * @param layerInput the input to max pooling 2d operation - 4d CNN (image) activations in NCHW format + * (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type) + * @param depthWeights Separable conv2d depth weights. 4 dimensions with format [kernelHeight, kernelWidth, inputChannels, depthMultiplier] (NUMERIC type) + * @param pointWeights Point weights, rank 4 with format [1, 1, inputChannels*depthMultiplier, outputChannels]. May be null (NUMERIC type) + * @param bias Optional bias, rank 1 with shape [outputChannels]. May be null. (NUMERIC type) + * @param Conv2DConfig Configuration Object + * @return output result of separable convolution 2d operation (NUMERIC type) + */ + public INDArray separableConv2d(INDArray layerInput, INDArray depthWeights, INDArray pointWeights, + INDArray bias, Conv2DConfig Conv2DConfig) { + NDValidation.validateNumerical("separableConv2d", "layerInput", layerInput); + NDValidation.validateNumerical("separableConv2d", "depthWeights", depthWeights); + NDValidation.validateNumerical("separableConv2d", "pointWeights", pointWeights); + NDValidation.validateNumerical("separableConv2d", "bias", bias); + return Nd4j.exec(new org.nd4j.linalg.api.ops.impl.layers.convolution.SConv2D(layerInput, depthWeights, pointWeights, bias, Conv2DConfig))[0]; + } + + /** + * Separable 2D convolution operation with optional bias
+ * + * @param layerInput the input to max pooling 2d operation - 4d CNN (image) activations in NCHW format + * (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type) + * @param depthWeights Separable conv2d depth weights. 4 dimensions with format [kernelHeight, kernelWidth, inputChannels, depthMultiplier] (NUMERIC type) + * @param pointWeights Point weights, rank 4 with format [1, 1, inputChannels*depthMultiplier, outputChannels]. May be null (NUMERIC type) + * @param Conv2DConfig Configuration Object + * @return output result of separable convolution 2d operation (NUMERIC type) + */ + public INDArray separableConv2d(INDArray layerInput, INDArray depthWeights, INDArray pointWeights, + Conv2DConfig Conv2DConfig) { + NDValidation.validateNumerical("separableConv2d", "layerInput", layerInput); + NDValidation.validateNumerical("separableConv2d", "depthWeights", depthWeights); + NDValidation.validateNumerical("separableConv2d", "pointWeights", pointWeights); + return Nd4j.exec(new org.nd4j.linalg.api.ops.impl.layers.convolution.SConv2D(layerInput, depthWeights, pointWeights, null, Conv2DConfig))[0]; + } + + /** + * Convolution 2d layer space to batch operation on 4d input.
+ * Increases input batch dimension by rearranging data from spatial dimensions into batch dimension
+ * + * @param x Input variable. 4d input (NUMERIC type) + * @param blocks Block size, in the height/width dimension (Size: Exactly(count=2)) + * @param paddingTop Optional 2d int[] array for padding the result: values [[pad top, pad bottom], [pad left, pad right]] (Size: Exactly(count=2)) + * @param paddingBottom Optional 2d int[] array for padding the result: values [[pad top, pad bottom], [pad left, pad right]] (Size: Exactly(count=2)) + * @return output Output variable (NUMERIC type) + */ + public INDArray spaceToBatch(INDArray x, int[] blocks, int[] paddingTop, int... paddingBottom) { + NDValidation.validateNumerical("spaceToBatch", "x", x); + Preconditions.checkArgument(blocks.length == 2, "blocks has incorrect size/length. Expected: blocks.length == 2, got %s", blocks.length); + Preconditions.checkArgument(paddingTop.length == 2, "paddingTop has incorrect size/length. Expected: paddingTop.length == 2, got %s", paddingTop.length); + Preconditions.checkArgument(paddingBottom.length == 2, "paddingBottom has incorrect size/length. Expected: paddingBottom.length == 2, got %s", paddingBottom.length); + return Nd4j.exec(new org.nd4j.linalg.api.ops.impl.transforms.custom.SpaceToBatch(x, blocks, paddingTop, paddingBottom))[0]; + } + + /** + * Convolution 2d layer space to depth operation on 4d input.
+ * Increases input channels (reduced spatial dimensions) by rearranging data into a larger channels dimension
+ * Example: if input has shape [mb, 2, 4, 4] and block size is 2, then output size is [mb, 8/(2*2), 2*2, 2*2]
+ * = [mb, 2, 4, 4]
+ * + * @param x the input to depth to space pooling 2d operation - 4d activations in NCHW format + * (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type) + * @param blockSize Block size, in the height/width dimension + * @param dataFormat Data format: "NCHW" or "NHWC" + * @return output Output variable (NUMERIC type) + */ + public INDArray spaceToDepth(INDArray x, int blockSize, DataFormat dataFormat) { + NDValidation.validateNumerical("spaceToDepth", "x", x); + return Nd4j.exec(new org.nd4j.linalg.api.ops.impl.layers.convolution.SpaceToDepth(x, blockSize, dataFormat))[0]; + } + + /** + * Upsampling layer for 2D inputs.
+ * scale is used for both height and width dimensions.
+ * + * @param input Input in NCHW format (NUMERIC type) + * @param scale The scale for both height and width dimensions. + * @return output Upsampled input (NUMERIC type) + */ + public INDArray upsampling2d(INDArray input, int scale) { + NDValidation.validateNumerical("upsampling2d", "input", input); + return Nd4j.exec(new org.nd4j.linalg.api.ops.impl.layers.convolution.Upsampling2d(input, scale))[0]; + } + + /** + * 2D Convolution layer operation - Upsampling 2d
+ * + * @param input Input in NCHW format (NUMERIC type) + * @param scaleH Scale to upsample in height dimension + * @param scaleW Scale to upsample in width dimension + * @param nchw If true: input is in NCHW (minibatch, channels, height, width) format. False: NHWC format + * @return output Upsampled input (NUMERIC type) + */ + public INDArray upsampling2d(INDArray input, int scaleH, int scaleW, boolean nchw) { + NDValidation.validateNumerical("upsampling2d", "input", input); + return Nd4j.exec(new org.nd4j.linalg.api.ops.impl.layers.convolution.Upsampling2d(input, scaleH, scaleW, nchw))[0]; + } +} diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/factory/ops/NDImage.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/factory/ops/NDImage.java new file mode 100644 index 000000000..859ad43c3 --- /dev/null +++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/factory/ops/NDImage.java @@ -0,0 +1,221 @@ +/******************************************************************************* + * Copyright (c) 2019-2020 Konduit K.K. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ + +//================== GENERATED CODE - DO NOT MODIFY THIS FILE ================== + +package org.nd4j.linalg.factory.ops; + +import static org.nd4j.linalg.factory.NDValidation.isSameType; + +import org.nd4j.base.Preconditions; +import org.nd4j.linalg.api.ndarray.INDArray; +import org.nd4j.linalg.factory.NDValidation; +import org.nd4j.linalg.factory.Nd4j; + +public class NDImage { + public NDImage() { + } + + /** + * Given an input image and some crop boxes, extract out the image subsets and resize them to the specified size.
+ * + * @param image Input image, with shape [batch, height, width, channels] (NUMERIC type) + * @param cropBoxes Float32 crop, shape [numBoxes, 4] with values in range 0 to 1 (NUMERIC type) + * @param boxIndices Indices: which image (index to dimension 0) the cropBoxes belong to. Rank 1, shape [numBoxes] (NUMERIC type) + * @param cropOutSize Output size for the images - int32, rank 1 with values [outHeight, outWidth] (INT type) + * @param extrapolationValue Used for extrapolation, when applicable. 0.0 should be used for the default + * @return output Cropped and resized images (NUMERIC type) + */ + public INDArray cropAndResize(INDArray image, INDArray cropBoxes, INDArray boxIndices, + INDArray cropOutSize, double extrapolationValue) { + NDValidation.validateNumerical("CropAndResize", "image", image); + NDValidation.validateNumerical("CropAndResize", "cropBoxes", cropBoxes); + NDValidation.validateNumerical("CropAndResize", "boxIndices", boxIndices); + NDValidation.validateInteger("CropAndResize", "cropOutSize", cropOutSize); + return Nd4j.exec(new org.nd4j.linalg.api.ops.impl.image.CropAndResize(image, cropBoxes, boxIndices, cropOutSize, extrapolationValue))[0]; + } + + /** + * Given an input image and some crop boxes, extract out the image subsets and resize them to the specified size.
+ * + * @param image Input image, with shape [batch, height, width, channels] (NUMERIC type) + * @param cropBoxes Float32 crop, shape [numBoxes, 4] with values in range 0 to 1 (NUMERIC type) + * @param boxIndices Indices: which image (index to dimension 0) the cropBoxes belong to. Rank 1, shape [numBoxes] (NUMERIC type) + * @param cropOutSize Output size for the images - int32, rank 1 with values [outHeight, outWidth] (INT type) + * @return output Cropped and resized images (NUMERIC type) + */ + public INDArray cropAndResize(INDArray image, INDArray cropBoxes, INDArray boxIndices, + INDArray cropOutSize) { + NDValidation.validateNumerical("CropAndResize", "image", image); + NDValidation.validateNumerical("CropAndResize", "cropBoxes", cropBoxes); + NDValidation.validateNumerical("CropAndResize", "boxIndices", boxIndices); + NDValidation.validateInteger("CropAndResize", "cropOutSize", cropOutSize); + return Nd4j.exec(new org.nd4j.linalg.api.ops.impl.image.CropAndResize(image, cropBoxes, boxIndices, cropOutSize, 0.0))[0]; + } + + /** + * Adjusts contrast of RGB or grayscale images.
+ * + * @param in images to adjust. 3D shape or higher (NUMERIC type) + * @param factor multiplier for adjusting contrast + * @return output Contrast-adjusted image (NUMERIC type) + */ + public INDArray adjustContrast(INDArray in, double factor) { + NDValidation.validateNumerical("adjustContrast", "in", in); + return Nd4j.exec(new org.nd4j.linalg.api.ops.custom.AdjustContrast(in, factor))[0]; + } + + /** + * Adjust hue of RGB image
+ * + * @param in image as 3D array (NUMERIC type) + * @param delta value to add to hue channel + * @return output adjusted image (NUMERIC type) + */ + public INDArray adjustHue(INDArray in, double delta) { + NDValidation.validateNumerical("adjustHue", "in", in); + return Nd4j.exec(new org.nd4j.linalg.api.ops.custom.AdjustHue(in, delta))[0]; + } + + /** + * Adjust saturation of RGB images
+ * + * @param in RGB image as 3D array (NUMERIC type) + * @param factor factor for saturation + * @return output adjusted image (NUMERIC type) + */ + public INDArray adjustSaturation(INDArray in, double factor) { + NDValidation.validateNumerical("adjustSaturation", "in", in); + return Nd4j.exec(new org.nd4j.linalg.api.ops.custom.AdjustSaturation(in, factor))[0]; + } + + /** + * Given an input image, extract out image patches (of size kSizes - h x w) and place them in the depth dimension.
+ * + * @param image Input image to extract image patches from - shape [batch, height, width, channels] (NUMERIC type) + * @param kSizes Kernel size - size of the image patches, [height, width] (Size: Exactly(count=2)) + * @param strides Stride in the input dimension for extracting image patches, [stride_height, stride_width] (Size: Exactly(count=2)) + * @param rates Usually [1,1]. Equivalent to dilation rate in dilated convolutions - how far apart the output pixels + * in the patches should be, in the input. A dilation of [a,b] means every {@code a}th pixel is taken + * along the height/rows dimension, and every {@code b}th pixel is take along the width/columns dimension (Size: AtLeast(min=0)) + * @param sameMode Padding algorithm. If true: use Same padding + * @return output The extracted image patches (NUMERIC type) + */ + public INDArray extractImagePatches(INDArray image, int[] kSizes, int[] strides, int[] rates, + boolean sameMode) { + NDValidation.validateNumerical("extractImagePatches", "image", image); + Preconditions.checkArgument(kSizes.length == 2, "kSizes has incorrect size/length. Expected: kSizes.length == 2, got %s", kSizes.length); + Preconditions.checkArgument(strides.length == 2, "strides has incorrect size/length. Expected: strides.length == 2, got %s", strides.length); + Preconditions.checkArgument(rates.length >= 0, "rates has incorrect size/length. Expected: rates.length >= 0, got %s", rates.length); + return Nd4j.exec(new org.nd4j.linalg.api.ops.impl.image.ExtractImagePatches(image, kSizes, strides, rates, sameMode))[0]; + } + + /** + * Converting image from HSV to RGB format
+ * + * @param input 3D image (NUMERIC type) + * @return output 3D image (NUMERIC type) + */ + public INDArray hsvToRgb(INDArray input) { + NDValidation.validateNumerical("hsvToRgb", "input", input); + return Nd4j.exec(new org.nd4j.linalg.api.ops.custom.HsvToRgb(input))[0]; + } + + /** + * Greedily selects a subset of bounding boxes in descending order of score
+ * + * @param boxes Might be null. Name for the output variable (NUMERIC type) + * @param scores vector of shape [num_boxes] (NUMERIC type) + * @param maxOutSize scalar representing the maximum number of boxes to be selected + * @param iouThreshold threshold for deciding whether boxes overlap too much with respect to IOU + * @param scoreThreshold threshold for deciding when to remove boxes based on score + * @return output vectort of shape [M] representing the selected indices from the boxes tensor, where M <= max_output_size (NUMERIC type) + */ + public INDArray nonMaxSuppression(INDArray boxes, INDArray scores, int maxOutSize, + double iouThreshold, double scoreThreshold) { + NDValidation.validateNumerical("nonMaxSuppression", "boxes", boxes); + NDValidation.validateNumerical("nonMaxSuppression", "scores", scores); + return Nd4j.exec(new org.nd4j.linalg.api.ops.impl.image.NonMaxSuppression(boxes, scores, maxOutSize, iouThreshold, scoreThreshold))[0]; + } + + /** + * Randomly crops image
+ * + * @param input input array (NUMERIC type) + * @param shape shape for crop (INT type) + * @return output cropped array (NUMERIC type) + */ + public INDArray randomCrop(INDArray input, INDArray shape) { + NDValidation.validateNumerical("randomCrop", "input", input); + NDValidation.validateInteger("randomCrop", "shape", shape); + return Nd4j.exec(new org.nd4j.linalg.api.ops.custom.RandomCrop(input, shape))[0]; + } + + /** + * Converting array from HSV to RGB format
+ * + * @param input 3D image (NUMERIC type) + * @return output 3D image (NUMERIC type) + */ + public INDArray rgbToHsv(INDArray input) { + NDValidation.validateNumerical("rgbToHsv", "input", input); + return Nd4j.exec(new org.nd4j.linalg.api.ops.custom.RgbToHsv(input))[0]; + } + + /** + * Converting array from RGB to YIQ format
+ * + * @param input 3D image (NUMERIC type) + * @return output 3D image (NUMERIC type) + */ + public INDArray rgbToYiq(INDArray input) { + NDValidation.validateNumerical("rgbToYiq", "input", input); + return Nd4j.exec(new org.nd4j.linalg.api.ops.custom.RgbToYiq(input))[0]; + } + + /** + * Converting array from RGB to YUV format
+ * + * @param input 3D image (NUMERIC type) + * @return output 3D image (NUMERIC type) + */ + public INDArray rgbToYuv(INDArray input) { + NDValidation.validateNumerical("rgbToYuv", "input", input); + return Nd4j.exec(new org.nd4j.linalg.api.ops.custom.RgbToYuv(input))[0]; + } + + /** + * Converting image from YIQ to RGB format
+ * + * @param input 3D image (NUMERIC type) + * @return output 3D image (NUMERIC type) + */ + public INDArray yiqToRgb(INDArray input) { + NDValidation.validateNumerical("yiqToRgb", "input", input); + return Nd4j.exec(new org.nd4j.linalg.api.ops.custom.YiqToRgb(input))[0]; + } + + /** + * Converting image from YUV to RGB format
+ * + * @param input 3D image (NUMERIC type) + * @return output 3D image (NUMERIC type) + */ + public INDArray yuvToRgb(INDArray input) { + NDValidation.validateNumerical("yuvToRgb", "input", input); + return Nd4j.exec(new org.nd4j.linalg.api.ops.custom.YuvToRgb(input))[0]; + } +} diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/factory/ops/NDLoss.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/factory/ops/NDLoss.java new file mode 100644 index 000000000..4c1234514 --- /dev/null +++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/factory/ops/NDLoss.java @@ -0,0 +1,483 @@ +/******************************************************************************* + * Copyright (c) 2019 Konduit K.K. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ + +//================== GENERATED CODE - DO NOT MODIFY THIS FILE ================== + +package org.nd4j.linalg.factory.ops; + +import static org.nd4j.linalg.factory.NDValidation.isSameType; + +import org.nd4j.autodiff.loss.LossReduce; +import org.nd4j.linalg.api.ndarray.INDArray; +import org.nd4j.linalg.factory.NDValidation; +import org.nd4j.linalg.factory.Nd4j; + +public class NDLoss { + public NDLoss() { + } + + /** + * Absolute difference loss: {@code sum_i abs( label[i] - predictions[i] )}
+ * + * @param label Label array (NUMERIC type) + * @param predictions Predictions array (NUMERIC type) + * @param weights Weights array. May be null. If null, a weight of 1.0 is used (NUMERIC type) + * @param lossReduce Reduction type for the loss. See {@link LossReduce} for more details. Default: {@link LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT} + * @return output loss variable (NUMERIC type) + */ + public INDArray absoluteDifference(INDArray label, INDArray predictions, INDArray weights, + LossReduce lossReduce) { + NDValidation.validateNumerical("absoluteDifference", "label", label); + NDValidation.validateNumerical("absoluteDifference", "predictions", predictions); + NDValidation.validateNumerical("absoluteDifference", "weights", weights); + return Nd4j.exec(new org.nd4j.linalg.api.ops.impl.loss.AbsoluteDifferenceLoss(label, predictions, weights, lossReduce))[0]; + } + + /** + * Absolute difference loss: {@code sum_i abs( label[i] - predictions[i] )}
+ * + * @param label Label array (NUMERIC type) + * @param predictions Predictions array (NUMERIC type) + * @param weights Weights array. May be null. If null, a weight of 1.0 is used (NUMERIC type) + * @return output loss variable (NUMERIC type) + */ + public INDArray absoluteDifference(INDArray label, INDArray predictions, INDArray weights) { + NDValidation.validateNumerical("absoluteDifference", "label", label); + NDValidation.validateNumerical("absoluteDifference", "predictions", predictions); + NDValidation.validateNumerical("absoluteDifference", "weights", weights); + return Nd4j.exec(new org.nd4j.linalg.api.ops.impl.loss.AbsoluteDifferenceLoss(label, predictions, weights, org.nd4j.autodiff.loss.LossReduce.MEAN_BY_NONZERO_WEIGHT_COUNT))[0]; + } + + /** + * Cosine distance loss: {@code 1 - cosineSimilarity(x,y)} or {@code 1 - sum_i label[i] * prediction[i]}, which is
+ * equivalent to cosine distance when both the predictions and labels are normalized.
+ * Note: This loss function assumes that both the predictions and labels are normalized to have unit l2 norm.
+ * If this is not the case, you should normalize them first by dividing by norm2(String, SDVariable, boolean, int...)
+ * along the cosine distance dimension (with keepDims=true).
+ * + * @param label Label array (NUMERIC type) + * @param predictions Predictions array (NUMERIC type) + * @param weights Weights array. May be null. If null, a weight of 1.0 is use (NUMERIC type) + * @param lossReduce Reduction type for the loss. See {@link LossReduce} for more details. Default: {@link LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT} + * @param dimension Dimension to perform the cosine distance over + * @return output Cosine distance loss (NUMERIC type) + */ + public INDArray cosineDistance(INDArray label, INDArray predictions, INDArray weights, + LossReduce lossReduce, int dimension) { + NDValidation.validateNumerical("cosineDistance", "label", label); + NDValidation.validateNumerical("cosineDistance", "predictions", predictions); + NDValidation.validateNumerical("cosineDistance", "weights", weights); + return Nd4j.exec(new org.nd4j.linalg.api.ops.impl.loss.CosineDistanceLoss(label, predictions, weights, lossReduce, dimension))[0]; + } + + /** + * Cosine distance loss: {@code 1 - cosineSimilarity(x,y)} or {@code 1 - sum_i label[i] * prediction[i]}, which is
+ * equivalent to cosine distance when both the predictions and labels are normalized.
+ * Note: This loss function assumes that both the predictions and labels are normalized to have unit l2 norm.
+ * If this is not the case, you should normalize them first by dividing by norm2(String, SDVariable, boolean, int...)
+ * along the cosine distance dimension (with keepDims=true).
+ * + * @param label Label array (NUMERIC type) + * @param predictions Predictions array (NUMERIC type) + * @param weights Weights array. May be null. If null, a weight of 1.0 is use (NUMERIC type) + * @param dimension Dimension to perform the cosine distance over + * @return output Cosine distance loss (NUMERIC type) + */ + public INDArray cosineDistance(INDArray label, INDArray predictions, INDArray weights, + int dimension) { + NDValidation.validateNumerical("cosineDistance", "label", label); + NDValidation.validateNumerical("cosineDistance", "predictions", predictions); + NDValidation.validateNumerical("cosineDistance", "weights", weights); + return Nd4j.exec(new org.nd4j.linalg.api.ops.impl.loss.CosineDistanceLoss(label, predictions, weights, org.nd4j.autodiff.loss.LossReduce.MEAN_BY_NONZERO_WEIGHT_COUNT, dimension))[0]; + } + + /** + * Hinge loss: a loss function used for training classifiers.
+ * Implements {@code L = max(0, 1 - t * predictions)} where t is the label values after internally converting to {-1,1}
+ * from the user specified {0,1}. Note that Labels should be provided with values {0,1}.
+ * + * @param label Label array. Each value should be 0.0 or 1.0 (internally -1 to 1 is used) (NUMERIC type) + * @param predictions Predictions array (NUMERIC type) + * @param weights Weights array. May be null. If null, a weight of 1.0 is used (NUMERIC type) + * @param lossReduce Reduction type for the loss. See {@link LossReduce} for more details. Default: {@link LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT} + * @return output Loss variable (NUMERIC type) + */ + public INDArray hingeLoss(INDArray label, INDArray predictions, INDArray weights, + LossReduce lossReduce) { + NDValidation.validateNumerical("hingeLoss", "label", label); + NDValidation.validateNumerical("hingeLoss", "predictions", predictions); + NDValidation.validateNumerical("hingeLoss", "weights", weights); + return Nd4j.exec(new org.nd4j.linalg.api.ops.impl.loss.HingeLoss(label, predictions, weights, lossReduce))[0]; + } + + /** + * Hinge loss: a loss function used for training classifiers.
+ * Implements {@code L = max(0, 1 - t * predictions)} where t is the label values after internally converting to {-1,1}
+ * from the user specified {0,1}. Note that Labels should be provided with values {0,1}.
+ * + * @param label Label array. Each value should be 0.0 or 1.0 (internally -1 to 1 is used) (NUMERIC type) + * @param predictions Predictions array (NUMERIC type) + * @param weights Weights array. May be null. If null, a weight of 1.0 is used (NUMERIC type) + * @return output Loss variable (NUMERIC type) + */ + public INDArray hingeLoss(INDArray label, INDArray predictions, INDArray weights) { + NDValidation.validateNumerical("hingeLoss", "label", label); + NDValidation.validateNumerical("hingeLoss", "predictions", predictions); + NDValidation.validateNumerical("hingeLoss", "weights", weights); + return Nd4j.exec(new org.nd4j.linalg.api.ops.impl.loss.HingeLoss(label, predictions, weights, org.nd4j.autodiff.loss.LossReduce.MEAN_BY_NONZERO_WEIGHT_COUNT))[0]; + } + + /** + * Huber loss function, used for robust regression. It is similar both squared error loss and absolute difference loss,
+ * though is less sensitive to outliers than squared error.
+ * Huber loss implements:
+ * 


+   * {@code L = 0.5 * (label[i] - predictions[i])^2 if abs(label[i] - predictions[i]) < delta}

+   * {@code L = delta * abs(label[i] - predictions[i]) - 0.5 * delta^2 otherwise}

+   *

+ * + * @param label Label array (NUMERIC type) + * @param predictions Predictions array (NUMERIC type) + * @param weights Weights array. May be null. If null, a weight of 1.0 is used (NUMERIC type) + * @param lossReduce Reduction type for the loss. See {@link LossReduce} for more details. Default: {@link LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT} + * @param delta Loss function delta value + * @return output Huber loss (NUMERIC type) + */ + public INDArray huberLoss(INDArray label, INDArray predictions, INDArray weights, + LossReduce lossReduce, double delta) { + NDValidation.validateNumerical("huberLoss", "label", label); + NDValidation.validateNumerical("huberLoss", "predictions", predictions); + NDValidation.validateNumerical("huberLoss", "weights", weights); + return Nd4j.exec(new org.nd4j.linalg.api.ops.impl.loss.HuberLoss(label, predictions, weights, lossReduce, delta))[0]; + } + + /** + * Huber loss function, used for robust regression. It is similar both squared error loss and absolute difference loss,
+ * though is less sensitive to outliers than squared error.
+ * Huber loss implements:
+ * 


+   * {@code L = 0.5 * (label[i] - predictions[i])^2 if abs(label[i] - predictions[i]) < delta}

+   * {@code L = delta * abs(label[i] - predictions[i]) - 0.5 * delta^2 otherwise}

+   *

+ * + * @param label Label array (NUMERIC type) + * @param predictions Predictions array (NUMERIC type) + * @param weights Weights array. May be null. If null, a weight of 1.0 is used (NUMERIC type) + * @param delta Loss function delta value + * @return output Huber loss (NUMERIC type) + */ + public INDArray huberLoss(INDArray label, INDArray predictions, INDArray weights, double delta) { + NDValidation.validateNumerical("huberLoss", "label", label); + NDValidation.validateNumerical("huberLoss", "predictions", predictions); + NDValidation.validateNumerical("huberLoss", "weights", weights); + return Nd4j.exec(new org.nd4j.linalg.api.ops.impl.loss.HuberLoss(label, predictions, weights, org.nd4j.autodiff.loss.LossReduce.MEAN_BY_NONZERO_WEIGHT_COUNT, delta))[0]; + } + + /** + * L2 loss: 1/2 * sum(x^2)
+ * + * @param var Variable to calculate L2 loss of (NUMERIC type) + * @return output L2 loss (NUMERIC type) + */ + public INDArray l2Loss(INDArray var) { + NDValidation.validateNumerical("l2Loss", "var", var); + return Nd4j.exec(new org.nd4j.linalg.api.ops.impl.loss.L2Loss(var))[0]; + } + + /** + * Log loss, i.e., binary cross entropy loss, usually used for binary multi-label classification. Implements:
+ * {@code -1/numExamples * sum_i (labels[i] * log(predictions[i] + epsilon) + (1-labels[i]) * log(1-predictions[i] + epsilon))}
+ * + * @param label Label array (NUMERIC type) + * @param predictions Predictions array (NUMERIC type) + * @param weights Weights array. May be null. If null, a weight of 1.0 is used (NUMERIC type) + * @param lossReduce Reduction type for the loss. See {@link LossReduce} for more details. Default: {@link LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT} + * @param epsilon epsilon + * @return output Log loss (NUMERIC type) + */ + public INDArray logLoss(INDArray label, INDArray predictions, INDArray weights, + LossReduce lossReduce, double epsilon) { + NDValidation.validateNumerical("logLoss", "label", label); + NDValidation.validateNumerical("logLoss", "predictions", predictions); + NDValidation.validateNumerical("logLoss", "weights", weights); + return Nd4j.exec(new org.nd4j.linalg.api.ops.impl.loss.LogLoss(label, predictions, weights, lossReduce, epsilon))[0]; + } + + /** + * Log loss, i.e., binary cross entropy loss, usually used for binary multi-label classification. Implements:
+ * {@code -1/numExamples * sum_i (labels[i] * log(predictions[i] + epsilon) + (1-labels[i]) * log(1-predictions[i] + epsilon))}
+ * + * @param label Label array (NUMERIC type) + * @param predictions Predictions array (NUMERIC type) + * @param weights Weights array. May be null. If null, a weight of 1.0 is used (NUMERIC type) + * @param epsilon epsilon + * @return output Log loss (NUMERIC type) + */ + public INDArray logLoss(INDArray label, INDArray predictions, INDArray weights, double epsilon) { + NDValidation.validateNumerical("logLoss", "label", label); + NDValidation.validateNumerical("logLoss", "predictions", predictions); + NDValidation.validateNumerical("logLoss", "weights", weights); + return Nd4j.exec(new org.nd4j.linalg.api.ops.impl.loss.LogLoss(label, predictions, weights, org.nd4j.autodiff.loss.LossReduce.MEAN_BY_NONZERO_WEIGHT_COUNT, epsilon))[0]; + } + + /** + * Log poisson loss: a loss function used for training classifiers.
+ * Implements {@code L = exp(c) - z * c} where c is log(predictions) and z is labels.
+ * + * @param label Label array. Each value should be 0.0 or 1.0 (NUMERIC type) + * @param predictions Predictions array (has to be log(x) of actual predictions) (NUMERIC type) + * @param weights Weights array. May be null. If null, a weight of 1.0 is used (NUMERIC type) + * @param lossReduce Reduction type for the loss. See {@link LossReduce} for more details. Default: {@link LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT} + * @param full Boolean flag. true for logPoissonFull, false for logPoisson + * @return output Loss variable (NUMERIC type) + */ + public INDArray logPoisson(INDArray label, INDArray predictions, INDArray weights, + LossReduce lossReduce, boolean full) { + NDValidation.validateNumerical("logPoisson", "label", label); + NDValidation.validateNumerical("logPoisson", "predictions", predictions); + NDValidation.validateNumerical("logPoisson", "weights", weights); + return Nd4j.exec(new org.nd4j.linalg.api.ops.impl.loss.LogPoissonLoss(label, predictions, weights, lossReduce, full))[0]; + } + + /** + * Log poisson loss: a loss function used for training classifiers.
+ * Implements {@code L = exp(c) - z * c} where c is log(predictions) and z is labels.
+ * + * @param label Label array. Each value should be 0.0 or 1.0 (NUMERIC type) + * @param predictions Predictions array (has to be log(x) of actual predictions) (NUMERIC type) + * @param weights Weights array. May be null. If null, a weight of 1.0 is used (NUMERIC type) + * @param full Boolean flag. true for logPoissonFull, false for logPoisson + * @return output Loss variable (NUMERIC type) + */ + public INDArray logPoisson(INDArray label, INDArray predictions, INDArray weights, boolean full) { + NDValidation.validateNumerical("logPoisson", "label", label); + NDValidation.validateNumerical("logPoisson", "predictions", predictions); + NDValidation.validateNumerical("logPoisson", "weights", weights); + return Nd4j.exec(new org.nd4j.linalg.api.ops.impl.loss.LogPoissonLoss(label, predictions, weights, org.nd4j.autodiff.loss.LossReduce.MEAN_BY_NONZERO_WEIGHT_COUNT, full))[0]; + } + + /** + * Mean pairwise squared error.
+ * MPWSE loss calculates the difference between pairs of consecutive elements in the predictions and labels arrays.
+ * For example, if predictions = [p0, p1, p2] and labels are [l0, l1, l2] then MPWSE is:
+ * {@code [((p0-p1) - (l0-l1))^2 + ((p0-p2) - (l0-l2))^2 + ((p1-p2) - (l1-l2))^2] / 3}
+ * + * @param label Label array (NUMERIC type) + * @param predictions Predictions array (NUMERIC type) + * @param weights Weights array. May be null. If null, a weight of 1.0 is used. Must be either null, scalar, or have shape [batchSize] (NUMERIC type) + * @param lossReduce Reduction type for the loss. See {@link LossReduce} for more details. Default: {@link LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT} + * @return output Loss variable, scalar output (NUMERIC type) + */ + public INDArray meanPairwiseSquaredError(INDArray label, INDArray predictions, INDArray weights, + LossReduce lossReduce) { + NDValidation.validateNumerical("meanPairwiseSquaredError", "label", label); + NDValidation.validateNumerical("meanPairwiseSquaredError", "predictions", predictions); + NDValidation.validateNumerical("meanPairwiseSquaredError", "weights", weights); + return Nd4j.exec(new org.nd4j.linalg.api.ops.impl.loss.MeanPairwiseSquaredErrorLoss(label, predictions, weights, lossReduce))[0]; + } + + /** + * Mean pairwise squared error.
+ * MPWSE loss calculates the difference between pairs of consecutive elements in the predictions and labels arrays.
+ * For example, if predictions = [p0, p1, p2] and labels are [l0, l1, l2] then MPWSE is:
+ * {@code [((p0-p1) - (l0-l1))^2 + ((p0-p2) - (l0-l2))^2 + ((p1-p2) - (l1-l2))^2] / 3}
+ * + * @param label Label array (NUMERIC type) + * @param predictions Predictions array (NUMERIC type) + * @param weights Weights array. May be null. If null, a weight of 1.0 is used. Must be either null, scalar, or have shape [batchSize] (NUMERIC type) + * @return output Loss variable, scalar output (NUMERIC type) + */ + public INDArray meanPairwiseSquaredError(INDArray label, INDArray predictions, INDArray weights) { + NDValidation.validateNumerical("meanPairwiseSquaredError", "label", label); + NDValidation.validateNumerical("meanPairwiseSquaredError", "predictions", predictions); + NDValidation.validateNumerical("meanPairwiseSquaredError", "weights", weights); + return Nd4j.exec(new org.nd4j.linalg.api.ops.impl.loss.MeanPairwiseSquaredErrorLoss(label, predictions, weights, org.nd4j.autodiff.loss.LossReduce.MEAN_BY_NONZERO_WEIGHT_COUNT))[0]; + } + + /** + * Mean squared error loss function. Implements {@code (label[i] - prediction[i])^2} - i.e., squared error on a per-element basis.
+ * When averaged (using {@link LossReduce#MEAN_BY_WEIGHT} or {@link LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT} (the default))
+ * this is the mean squared error loss function.
+ * + * @param label Label array (NUMERIC type) + * @param predictions Predictions array (NUMERIC type) + * @param weights Weights array. May be null. If null, a weight of 1.0 is used (NUMERIC type) + * @param lossReduce Reduction type for the loss. See {@link LossReduce} for more details. Default: {@link LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT} + * @return output Loss variable (NUMERIC type) + */ + public INDArray meanSquaredError(INDArray label, INDArray predictions, INDArray weights, + LossReduce lossReduce) { + NDValidation.validateNumerical("meanSquaredError", "label", label); + NDValidation.validateNumerical("meanSquaredError", "predictions", predictions); + NDValidation.validateNumerical("meanSquaredError", "weights", weights); + return Nd4j.exec(new org.nd4j.linalg.api.ops.impl.loss.MeanSquaredErrorLoss(label, predictions, weights, lossReduce))[0]; + } + + /** + * Mean squared error loss function. Implements {@code (label[i] - prediction[i])^2} - i.e., squared error on a per-element basis.
+ * When averaged (using {@link LossReduce#MEAN_BY_WEIGHT} or {@link LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT} (the default))
+ * this is the mean squared error loss function.
+ * + * @param label Label array (NUMERIC type) + * @param predictions Predictions array (NUMERIC type) + * @param weights Weights array. May be null. If null, a weight of 1.0 is used (NUMERIC type) + * @return output Loss variable (NUMERIC type) + */ + public INDArray meanSquaredError(INDArray label, INDArray predictions, INDArray weights) { + NDValidation.validateNumerical("meanSquaredError", "label", label); + NDValidation.validateNumerical("meanSquaredError", "predictions", predictions); + NDValidation.validateNumerical("meanSquaredError", "weights", weights); + return Nd4j.exec(new org.nd4j.linalg.api.ops.impl.loss.MeanSquaredErrorLoss(label, predictions, weights, org.nd4j.autodiff.loss.LossReduce.MEAN_BY_NONZERO_WEIGHT_COUNT))[0]; + } + + /** + * Sigmoid cross entropy: applies the sigmoid activation function on the input logits (input "pre-sigmoid preductions")
+ * and implements the binary cross entropy loss function. This implementation is numerically more stable than using
+ * standard (but separate) sigmoid activation function and log loss (binary cross entropy) loss function.
+ * Implements:
+ * {@code -1/numExamples * sum_i (labels[i] * log(sigmoid(logits[i])) + (1-labels[i]) * log(1-sigmoid(logits[i])))}
+ * though this is done in a mathematically equivalent but more numerical stable form.
+ *
+ * When label smoothing is > 0, the following label smoothing is used:
+ * 


+   * {@code numClasses = labels.size(1);

+   * label = (1.0 - labelSmoothing) * label + 0.5 * labelSmoothing}

+   *

+ * + * @param label Label array (NUMERIC type) + * @param predictionLogits Predictions array (NUMERIC type) + * @param weights Weights array. May be null. If null, a weight of 1.0 is used (NUMERIC type) + * @param lossReduce Reduction type for the loss. See {@link LossReduce} for more details. Default: {@link LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT} + * @param labelSmoothing Label smoothing value. Default value: 0 + * @return output Loss variable (NUMERIC type) + */ + public INDArray sigmoidCrossEntropy(INDArray label, INDArray predictionLogits, INDArray weights, + LossReduce lossReduce, double labelSmoothing) { + NDValidation.validateNumerical("sigmoidCrossEntropy", "label", label); + NDValidation.validateNumerical("sigmoidCrossEntropy", "predictionLogits", predictionLogits); + NDValidation.validateNumerical("sigmoidCrossEntropy", "weights", weights); + return Nd4j.exec(new org.nd4j.linalg.api.ops.impl.loss.SigmoidCrossEntropyLoss(label, predictionLogits, weights, lossReduce, labelSmoothing))[0]; + } + + /** + * Sigmoid cross entropy: applies the sigmoid activation function on the input logits (input "pre-sigmoid preductions")
+ * and implements the binary cross entropy loss function. This implementation is numerically more stable than using
+ * standard (but separate) sigmoid activation function and log loss (binary cross entropy) loss function.
+ * Implements:
+ * {@code -1/numExamples * sum_i (labels[i] * log(sigmoid(logits[i])) + (1-labels[i]) * log(1-sigmoid(logits[i])))}
+ * though this is done in a mathematically equivalent but more numerical stable form.
+ *
+ * When label smoothing is > 0, the following label smoothing is used:
+ * 


+   * {@code numClasses = labels.size(1);

+   * label = (1.0 - labelSmoothing) * label + 0.5 * labelSmoothing}

+   *

+ * + * @param label Label array (NUMERIC type) + * @param predictionLogits Predictions array (NUMERIC type) + * @param weights Weights array. May be null. If null, a weight of 1.0 is used (NUMERIC type) + * @return output Loss variable (NUMERIC type) + */ + public INDArray sigmoidCrossEntropy(INDArray label, INDArray predictionLogits, INDArray weights) { + NDValidation.validateNumerical("sigmoidCrossEntropy", "label", label); + NDValidation.validateNumerical("sigmoidCrossEntropy", "predictionLogits", predictionLogits); + NDValidation.validateNumerical("sigmoidCrossEntropy", "weights", weights); + return Nd4j.exec(new org.nd4j.linalg.api.ops.impl.loss.SigmoidCrossEntropyLoss(label, predictionLogits, weights, org.nd4j.autodiff.loss.LossReduce.MEAN_BY_NONZERO_WEIGHT_COUNT, 0.0))[0]; + } + + /** + * Applies the softmax activation function to the input, then implement multi-class cross entropy:
+ * {@code -sum_classes label[i] * log(p[c])} where {@code p = softmax(logits)}
+ * If {@link LossReduce#NONE} is used, returned shape is [numExamples] out for [numExamples, numClasses] predicitons/labels;
+ * otherwise, the output is a scalar.
+ *


+ * When label smoothing is > 0, the following label smoothing is used:
+ * 



+   * {@code numClasses = labels.size(1);

+   * oneHotLabel = (1.0 - labelSmoothing) * oneHotLabels + labelSmoothing/numClasses}

+   *

+ * + * @param oneHotLabels Label array. Should be one-hot per example and same shape as predictions (for example, [mb, nOut]) (NUMERIC type) + * @param logitPredictions Predictions array (pre-softmax) (NUMERIC type) + * @param weights Weights array. May be null. If null, a weight of 1.0 is used (NUMERIC type) + * @param lossReduce Reduction type for the loss. See {@link LossReduce} for more details. Default: {@link LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT} + * @param labelSmoothing Label smoothing value. Default value: 0 + * @return output Loss variable (NUMERIC type) + */ + public INDArray softmaxCrossEntropy(INDArray oneHotLabels, INDArray logitPredictions, + INDArray weights, LossReduce lossReduce, double labelSmoothing) { + NDValidation.validateNumerical("softmaxCrossEntropy", "oneHotLabels", oneHotLabels); + NDValidation.validateNumerical("softmaxCrossEntropy", "logitPredictions", logitPredictions); + NDValidation.validateNumerical("softmaxCrossEntropy", "weights", weights); + return Nd4j.exec(new org.nd4j.linalg.api.ops.impl.loss.SoftmaxCrossEntropyLoss(oneHotLabels, logitPredictions, weights, lossReduce, labelSmoothing))[0]; + } + + /** + * Applies the softmax activation function to the input, then implement multi-class cross entropy:
+ * {@code -sum_classes label[i] * log(p[c])} where {@code p = softmax(logits)}
+ * If {@link LossReduce#NONE} is used, returned shape is [numExamples] out for [numExamples, numClasses] predicitons/labels;
+ * otherwise, the output is a scalar.
+ *


+ * When label smoothing is > 0, the following label smoothing is used:
+ * 



+   * {@code numClasses = labels.size(1);

+   * oneHotLabel = (1.0 - labelSmoothing) * oneHotLabels + labelSmoothing/numClasses}

+   *

+ * + * @param oneHotLabels Label array. Should be one-hot per example and same shape as predictions (for example, [mb, nOut]) (NUMERIC type) + * @param logitPredictions Predictions array (pre-softmax) (NUMERIC type) + * @param weights Weights array. May be null. If null, a weight of 1.0 is used (NUMERIC type) + * @return output Loss variable (NUMERIC type) + */ + public INDArray softmaxCrossEntropy(INDArray oneHotLabels, INDArray logitPredictions, + INDArray weights) { + NDValidation.validateNumerical("softmaxCrossEntropy", "oneHotLabels", oneHotLabels); + NDValidation.validateNumerical("softmaxCrossEntropy", "logitPredictions", logitPredictions); + NDValidation.validateNumerical("softmaxCrossEntropy", "weights", weights); + return Nd4j.exec(new org.nd4j.linalg.api.ops.impl.loss.SoftmaxCrossEntropyLoss(oneHotLabels, logitPredictions, weights, org.nd4j.autodiff.loss.LossReduce.MEAN_BY_NONZERO_WEIGHT_COUNT, 0.0))[0]; + } + + /** + * As per softmaxCrossEntropy(String, SDVariable, SDVariable, LossReduce) but the labels variable
+ * is represented as an integer array instead of the equivalent one-hot array.
+ * i.e., if logits are rank N, then labels have rank N-1
+ * + * @param logits Logits array ("pre-softmax activations") (NUMERIC type) + * @param labels Labels array. Must be an integer type. (INT type) + * @return output Softmax cross entropy (NUMERIC type) + */ + public INDArray sparseSoftmaxCrossEntropy(INDArray logits, INDArray labels) { + NDValidation.validateNumerical("sparseSoftmaxCrossEntropy", "logits", logits); + NDValidation.validateInteger("sparseSoftmaxCrossEntropy", "labels", labels); + return Nd4j.exec(new org.nd4j.linalg.api.ops.impl.loss.SparseSoftmaxCrossEntropyLossWithLogits(logits, labels))[0]; + } + + /** + * Weighted cross entropy loss with logits
+ * + * @param targets targets array (NUMERIC type) + * @param inputs input array (NUMERIC type) + * @param weights eights array. May be null. If null, a weight of 1.0 is used (NUMERIC type) + * @return output Loss variable (NUMERIC type) + */ + public INDArray weightedCrossEntropyWithLogits(INDArray targets, INDArray inputs, + INDArray weights) { + NDValidation.validateNumerical("weightedCrossEntropyWithLogits", "targets", targets); + NDValidation.validateNumerical("weightedCrossEntropyWithLogits", "inputs", inputs); + NDValidation.validateNumerical("weightedCrossEntropyWithLogits", "weights", weights); + return Nd4j.exec(new org.nd4j.linalg.api.ops.impl.loss.WeightedCrossEntropyLoss(targets, inputs, weights))[0]; + } +} diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/factory/ops/NDRNN.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/factory/ops/NDRNN.java new file mode 100755 index 000000000..0587aeda5 --- /dev/null +++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/factory/ops/NDRNN.java @@ -0,0 +1,130 @@ +/******************************************************************************* + * Copyright (c) 2019-2020 Konduit K.K. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ + +//================== GENERATED CODE - DO NOT MODIFY THIS FILE ================== + +package org.nd4j.linalg.factory.ops; + +import static org.nd4j.linalg.factory.NDValidation.isSameType; + +import org.nd4j.linalg.api.ndarray.INDArray; +import org.nd4j.linalg.api.ops.impl.layers.recurrent.config.LSTMConfiguration; +import org.nd4j.linalg.api.ops.impl.layers.recurrent.weights.GRUWeights; +import org.nd4j.linalg.api.ops.impl.layers.recurrent.weights.LSTMWeights; +import org.nd4j.linalg.api.ops.impl.layers.recurrent.weights.SRUWeights; +import org.nd4j.linalg.factory.NDValidation; +import org.nd4j.linalg.factory.Nd4j; + +public class NDRNN { + public NDRNN() { + } + + /** + * The GRU cell. Does a single time step operation
+ * + * @param x Input, with shape [batchSize, inSize] (NUMERIC type) + * @param hLast Output of the previous cell/time step, with shape [batchSize, numUnits] (NUMERIC type) + * @param GRUWeights Configuration Object + * @return output The cell's outputs. (NUMERIC type) + */ + public INDArray gru(INDArray x, INDArray hLast, GRUWeights GRUWeights) { + NDValidation.validateNumerical("gru", "x", x); + NDValidation.validateNumerical("gru", "hLast", hLast); + return Nd4j.exec(new org.nd4j.linalg.api.ops.impl.layers.recurrent.GRUCell(x, hLast, GRUWeights))[0]; + } + + /** + * The LSTM cell. Does a single time step operation.
+ * + * @param x Input, with shape [batchSize, inSize] (NUMERIC type) + * @param cLast Previous cell state, with shape [batchSize, numUnits] (NUMERIC type) + * @param yLast revious cell output, with shape [batchSize, numUnits] (NUMERIC type) + * @param LSTMWeights Configuration Object + * @param LSTMConfiguration Configuration Object + * @return output The cell's outputs (NUMERIC type) + */ + public INDArray lstmCell(INDArray x, INDArray cLast, INDArray yLast, LSTMWeights LSTMWeights, + LSTMConfiguration LSTMConfiguration) { + NDValidation.validateNumerical("lstmCell", "x", x); + NDValidation.validateNumerical("lstmCell", "cLast", cLast); + NDValidation.validateNumerical("lstmCell", "yLast", yLast); + return Nd4j.exec(new org.nd4j.linalg.api.ops.impl.layers.recurrent.LSTMBlockCell(x, cLast, yLast, LSTMWeights, LSTMConfiguration))[0]; + } + + /** + * The LSTM layer. Does multiple time steps.
+ * + * @param maxTSLength (NUMERIC type) + * @param x Input, with shape dependent on the data format (in config). (NUMERIC type) + * @param cLast Previous/initial cell state, with shape [batchSize, numUnits] (NUMERIC type) + * @param yLast Previous/initial cell output, with shape [batchSize, numUnits] (NUMERIC type) + * @param LSTMWeights Configuration Object + * @param LSTMConfiguration Configuration Object + * @return output The layer's outputs. (NUMERIC type) + */ + public INDArray lstmLayer(INDArray maxTSLength, INDArray x, INDArray cLast, INDArray yLast, + LSTMWeights LSTMWeights, LSTMConfiguration LSTMConfiguration) { + NDValidation.validateNumerical("lstmLayer", "maxTSLength", maxTSLength); + NDValidation.validateNumerical("lstmLayer", "x", x); + NDValidation.validateNumerical("lstmLayer", "cLast", cLast); + NDValidation.validateNumerical("lstmLayer", "yLast", yLast); + return Nd4j.exec(new org.nd4j.linalg.api.ops.impl.layers.recurrent.LSTMLayer(maxTSLength, x, cLast, yLast, LSTMWeights, LSTMConfiguration))[0]; + } + + /** + * The SRU layer. Does a single time step operation.
+ * + * @param x Input, with shape [batchSize, inSize] (NUMERIC type) + * @param initialC Initial cell state, with shape [batchSize, inSize] (NUMERIC type) + * @param mask An optional dropout mask, with shape [batchSize, inSize] (NUMERIC type) + * @param SRUWeights Configuration Object + * @return output The cell's outputs.. (NUMERIC type) + */ + public INDArray sru(INDArray x, INDArray initialC, INDArray mask, SRUWeights SRUWeights) { + NDValidation.validateNumerical("sru", "x", x); + NDValidation.validateNumerical("sru", "initialC", initialC); + NDValidation.validateNumerical("sru", "mask", mask); + return Nd4j.exec(new org.nd4j.linalg.api.ops.impl.layers.recurrent.SRU(x, initialC, mask, SRUWeights))[0]; + } + + /** + * The SRU layer. Does a single time step operation.
+ * + * @param x Input, with shape [batchSize, inSize] (NUMERIC type) + * @param initialC Initial cell state, with shape [batchSize, inSize] (NUMERIC type) + * @param SRUWeights Configuration Object + * @return output The cell's outputs.. (NUMERIC type) + */ + public INDArray sru(INDArray x, INDArray initialC, SRUWeights SRUWeights) { + NDValidation.validateNumerical("sru", "x", x); + NDValidation.validateNumerical("sru", "initialC", initialC); + return Nd4j.exec(new org.nd4j.linalg.api.ops.impl.layers.recurrent.SRU(x, initialC, null, SRUWeights))[0]; + } + + /** + * The SRU layer. Does a single time step operation.
+ * + * @param x Input, with shape [batchSize, inSize] (NUMERIC type) + * @param cLast Previous cell state, with shape [batchSize, inSize] (NUMERIC type) + * @param SRUWeights Configuration Object + * @return output The cell's outputs. (NUMERIC type) + */ + public INDArray sruCell(INDArray x, INDArray cLast, SRUWeights SRUWeights) { + NDValidation.validateNumerical("sruCell", "x", x); + NDValidation.validateNumerical("sruCell", "cLast", cLast); + return Nd4j.exec(new org.nd4j.linalg.api.ops.impl.layers.recurrent.SRUCell(x, cLast, SRUWeights))[0]; + } +} diff --git a/nd4j/nd4j-backends/nd4j-backend-impls/nd4j-cuda/pom.xml b/nd4j/nd4j-backends/nd4j-backend-impls/nd4j-cuda/pom.xml index 36f25d636..46566f50b 100644 --- a/nd4j/nd4j-backends/nd4j-backend-impls/nd4j-cuda/pom.xml +++ b/nd4j/nd4j-backends/nd4j-backend-impls/nd4j-cuda/pom.xml @@ -198,7 +198,7 @@ - ${libnd4jhome}/blas/NativeOps.h + ${libnd4jhome}/include/legacy/NativeOps.h ${libnd4jhome}/blasbuild/cuda/blas !!! You have to compile libnd4j with cuda support first! diff --git a/nd4j/nd4j-backends/nd4j-backend-impls/nd4j-cuda/src/main/java/org/nd4j/linalg/jcublas/buffer/BaseCudaDataBuffer.java b/nd4j/nd4j-backends/nd4j-backend-impls/nd4j-cuda/src/main/java/org/nd4j/linalg/jcublas/buffer/BaseCudaDataBuffer.java index 2f1cab334..f944d20cf 100644 --- a/nd4j/nd4j-backends/nd4j-backend-impls/nd4j-cuda/src/main/java/org/nd4j/linalg/jcublas/buffer/BaseCudaDataBuffer.java +++ b/nd4j/nd4j-backends/nd4j-backend-impls/nd4j-cuda/src/main/java/org/nd4j/linalg/jcublas/buffer/BaseCudaDataBuffer.java @@ -121,29 +121,22 @@ public abstract class BaseCudaDataBuffer extends BaseDataBuffer implements JCuda // allocating interop buffer this.ptrDataBuffer = OpaqueDataBuffer.allocateDataBuffer(length, type, false); + // passing existing pointer to native holder + this.ptrDataBuffer.setPrimaryBuffer(pointer, length); + //cuda specific bits this.allocationPoint = new AllocationPoint(ptrDataBuffer, length * elementSize); Nd4j.getDeallocatorService().pickObject(this); - // now we're + // now we're getting context and copying our stuff to device val context = AtomicAllocator.getInstance().getDeviceContext(); val perfD = PerformanceTracker.getInstance().helperStartTransaction(); - if (allocationPoint.getHostPointer() != null) { - NativeOpsHolder.getInstance().getDeviceNativeOps().memcpyAsync(allocationPoint.getHostPointer(), pointer, length * getElementSize(), CudaConstants.cudaMemcpyHostToHost, context.getSpecialStream()); - NativeOpsHolder.getInstance().getDeviceNativeOps().memcpyAsync(allocationPoint.getDevicePointer(), allocationPoint.getHostPointer(), length * getElementSize(), CudaConstants.cudaMemcpyHostToHost, context.getSpecialStream()); - } else { - NativeOpsHolder.getInstance().getDeviceNativeOps().memcpyAsync(allocationPoint.getDevicePointer(), pointer, length * getElementSize(), CudaConstants.cudaMemcpyHostToDevice, context.getSpecialStream()); - } - - context.getSpecialStream().synchronize(); - - if (allocationPoint.getHostPointer() != null) - PerformanceTracker.getInstance().helperRegisterTransaction(allocationPoint.getDeviceId(), perfD / 2, allocationPoint.getNumberOfBytes(), MemcpyDirection.HOST_TO_HOST); + NativeOpsHolder.getInstance().getDeviceNativeOps().memcpyAsync(allocationPoint.getDevicePointer(), pointer, length * getElementSize(), CudaConstants.cudaMemcpyHostToDevice, context.getSpecialStream()); PerformanceTracker.getInstance().helperRegisterTransaction(allocationPoint.getDeviceId(), perfD / 2, allocationPoint.getNumberOfBytes(), MemcpyDirection.HOST_TO_DEVICE); - + context.getSpecialStream().synchronize(); } public BaseCudaDataBuffer(float[] data, boolean copy) { diff --git a/nd4j/nd4j-backends/nd4j-backend-impls/nd4j-cuda/src/main/java/org/nd4j/nativeblas/Nd4jCuda.java b/nd4j/nd4j-backends/nd4j-backend-impls/nd4j-cuda/src/main/java/org/nd4j/nativeblas/Nd4jCuda.java new file mode 100644 index 000000000..98521d58c --- /dev/null +++ b/nd4j/nd4j-backends/nd4j-backend-impls/nd4j-cuda/src/main/java/org/nd4j/nativeblas/Nd4jCuda.java @@ -0,0 +1,10724 @@ +// Targeted by JavaCPP version 1.5.3-SNAPSHOT: DO NOT EDIT THIS FILE + +package org.nd4j.nativeblas; + +import java.nio.*; +import org.bytedeco.javacpp.*; +import org.bytedeco.javacpp.annotation.*; + +public class Nd4jCuda extends org.nd4j.nativeblas.Nd4jCudaHelper { + static { Loader.load(); } + +@Name("std::vector >") public static class IntVectorVector extends Pointer { + static { Loader.load(); } + /** Pointer cast constructor. Invokes {@link Pointer#Pointer(Pointer)}. */ + public IntVectorVector(Pointer p) { super(p); } + public IntVectorVector(int[] ... array) { this(array.length); put(array); } + public IntVectorVector() { allocate(); } + public IntVectorVector(long n) { allocate(n); } + private native void allocate(); + private native void allocate(@Cast("size_t") long n); + public native @Name("operator=") @ByRef IntVectorVector put(@ByRef IntVectorVector x); + + public boolean empty() { return size() == 0; } + public native long size(); + public void clear() { resize(0); } + public native void resize(@Cast("size_t") long n); + public boolean empty(@Cast("size_t") long i) { return size(i) == 0; } + public native @Index(function = "at") long size(@Cast("size_t") long i); + public void clear(@Cast("size_t") long i) { resize(i, 0); } + public native @Index(function = "at") void resize(@Cast("size_t") long i, @Cast("size_t") long n); + + @Index(function = "at") public native int get(@Cast("size_t") long i, @Cast("size_t") long j); + public native IntVectorVector put(@Cast("size_t") long i, @Cast("size_t") long j, int value); + + public int[][] get() { + int[][] array = new int[size() < Integer.MAX_VALUE ? (int)size() : Integer.MAX_VALUE][]; + for (int i = 0; i < array.length; i++) { + array[i] = new int[size(i) < Integer.MAX_VALUE ? (int)size(i) : Integer.MAX_VALUE]; + for (int j = 0; j < array[i].length; j++) { + array[i][j] = get(i, j); + } + } + return array; + } + @Override public String toString() { + return java.util.Arrays.deepToString(get()); + } + + public IntVectorVector put(int[] ... array) { + if (size() != array.length) { resize(array.length); } + for (int i = 0; i < array.length; i++) { + if (size(i) != array[i].length) { resize(i, array[i].length); } + for (int j = 0; j < array[i].length; j++) { + put(i, j, array[i][j]); + } + } + return this; + } +} + +@Name("std::vector >") public static class LongVectorVector extends Pointer { + static { Loader.load(); } + /** Pointer cast constructor. Invokes {@link Pointer#Pointer(Pointer)}. */ + public LongVectorVector(Pointer p) { super(p); } + public LongVectorVector(long[] ... array) { this(array.length); put(array); } + public LongVectorVector() { allocate(); } + public LongVectorVector(long n) { allocate(n); } + private native void allocate(); + private native void allocate(@Cast("size_t") long n); + public native @Name("operator=") @ByRef LongVectorVector put(@ByRef LongVectorVector x); + + public boolean empty() { return size() == 0; } + public native long size(); + public void clear() { resize(0); } + public native void resize(@Cast("size_t") long n); + public boolean empty(@Cast("size_t") long i) { return size(i) == 0; } + public native @Index(function = "at") long size(@Cast("size_t") long i); + public void clear(@Cast("size_t") long i) { resize(i, 0); } + public native @Index(function = "at") void resize(@Cast("size_t") long i, @Cast("size_t") long n); + + @Index(function = "at") public native @Cast("Nd4jLong") long get(@Cast("size_t") long i, @Cast("size_t") long j); + public native LongVectorVector put(@Cast("size_t") long i, @Cast("size_t") long j, long value); + + public long[][] get() { + long[][] array = new long[size() < Integer.MAX_VALUE ? (int)size() : Integer.MAX_VALUE][]; + for (int i = 0; i < array.length; i++) { + array[i] = new long[size(i) < Integer.MAX_VALUE ? (int)size(i) : Integer.MAX_VALUE]; + for (int j = 0; j < array[i].length; j++) { + array[i][j] = get(i, j); + } + } + return array; + } + @Override public String toString() { + return java.util.Arrays.deepToString(get()); + } + + public LongVectorVector put(long[] ... array) { + if (size() != array.length) { resize(array.length); } + for (int i = 0; i < array.length; i++) { + if (size(i) != array[i].length) { resize(i, array[i].length); } + for (int j = 0; j < array[i].length; j++) { + put(i, j, array[i][j]); + } + } + return this; + } +} + +@Name("std::vector") public static class NDArrayVector extends Pointer { + static { Loader.load(); } + /** Pointer cast constructor. Invokes {@link Pointer#Pointer(Pointer)}. */ + public NDArrayVector(Pointer p) { super(p); } + public NDArrayVector(NDArray value) { this(1); put(0, value); } + public NDArrayVector(NDArray ... array) { this(array.length); put(array); } + public NDArrayVector() { allocate(); } + public NDArrayVector(long n) { allocate(n); } + private native void allocate(); + private native void allocate(@Cast("size_t") long n); + public native @Name("operator=") @ByRef NDArrayVector put(@ByRef NDArrayVector x); + + public boolean empty() { return size() == 0; } + public native long size(); + public void clear() { resize(0); } + public native void resize(@Cast("size_t") long n); + + @Index(function = "at") public native NDArray get(@Cast("size_t") long i); + public native NDArrayVector put(@Cast("size_t") long i, NDArray value); + + public native @ByVal Iterator insert(@ByVal Iterator pos, NDArray value); + public native @ByVal Iterator erase(@ByVal Iterator pos); + public native @ByVal Iterator begin(); + public native @ByVal Iterator end(); + @NoOffset @Name("iterator") public static class Iterator extends Pointer { + public Iterator(Pointer p) { super(p); } + public Iterator() { } + + public native @Name("operator++") @ByRef Iterator increment(); + public native @Name("operator==") boolean equals(@ByRef Iterator it); + public native @Name("operator*") @Const NDArray get(); + } + + public NDArray[] get() { + NDArray[] array = new NDArray[size() < Integer.MAX_VALUE ? (int)size() : Integer.MAX_VALUE]; + for (int i = 0; i < array.length; i++) { + array[i] = get(i); + } + return array; + } + @Override public String toString() { + return java.util.Arrays.toString(get()); + } + + public NDArray pop_back() { + long size = size(); + NDArray value = get(size - 1); + resize(size - 1); + return value; + } + public NDArrayVector push_back(NDArray value) { + long size = size(); + resize(size + 1); + return put(size, value); + } + public NDArrayVector put(NDArray value) { + if (size() != 1) { resize(1); } + return put(0, value); + } + public NDArrayVector put(NDArray ... array) { + if (size() != array.length) { resize(array.length); } + for (int i = 0; i < array.length; i++) { + put(i, array[i]); + } + return this; + } +} + +@Name("std::vector") public static class ConstNDArrayVector extends Pointer { + static { Loader.load(); } + /** Pointer cast constructor. Invokes {@link Pointer#Pointer(Pointer)}. */ + public ConstNDArrayVector(Pointer p) { super(p); } + public ConstNDArrayVector(NDArray value) { this(1); put(0, value); } + public ConstNDArrayVector(NDArray ... array) { this(array.length); put(array); } + public ConstNDArrayVector() { allocate(); } + public ConstNDArrayVector(long n) { allocate(n); } + private native void allocate(); + private native void allocate(@Cast("size_t") long n); + public native @Name("operator=") @ByRef ConstNDArrayVector put(@ByRef ConstNDArrayVector x); + + public boolean empty() { return size() == 0; } + public native long size(); + public void clear() { resize(0); } + public native void resize(@Cast("size_t") long n); + + @Index(function = "at") public native @Const NDArray get(@Cast("size_t") long i); + public native ConstNDArrayVector put(@Cast("size_t") long i, NDArray value); + + public native @ByVal Iterator insert(@ByVal Iterator pos, @Const NDArray value); + public native @ByVal Iterator erase(@ByVal Iterator pos); + public native @ByVal Iterator begin(); + public native @ByVal Iterator end(); + @NoOffset @Name("iterator") public static class Iterator extends Pointer { + public Iterator(Pointer p) { super(p); } + public Iterator() { } + + public native @Name("operator++") @ByRef Iterator increment(); + public native @Name("operator==") boolean equals(@ByRef Iterator it); + public native @Name("operator*") @Const NDArray get(); + } + + public NDArray[] get() { + NDArray[] array = new NDArray[size() < Integer.MAX_VALUE ? (int)size() : Integer.MAX_VALUE]; + for (int i = 0; i < array.length; i++) { + array[i] = get(i); + } + return array; + } + @Override public String toString() { + return java.util.Arrays.toString(get()); + } + + public NDArray pop_back() { + long size = size(); + NDArray value = get(size - 1); + resize(size - 1); + return value; + } + public ConstNDArrayVector push_back(NDArray value) { + long size = size(); + resize(size + 1); + return put(size, value); + } + public ConstNDArrayVector put(NDArray value) { + if (size() != 1) { resize(1); } + return put(0, value); + } + public ConstNDArrayVector put(NDArray ... array) { + if (size() != array.length) { resize(array.length); } + for (int i = 0; i < array.length; i++) { + put(i, array[i]); + } + return this; + } +} + +@NoOffset @Name("std::pair") public static class IntIntPair extends Pointer { + static { Loader.load(); } + /** Pointer cast constructor. Invokes {@link Pointer#Pointer(Pointer)}. */ + public IntIntPair(Pointer p) { super(p); } + public IntIntPair(int firstValue, int secondValue) { this(); put(firstValue, secondValue); } + public IntIntPair() { allocate(); } + private native void allocate(); + public native @Name("operator=") @ByRef IntIntPair put(@ByRef IntIntPair x); + + + @MemberGetter public native int first(); public native IntIntPair first(int first); + @MemberGetter public native int second(); public native IntIntPair second(int second); + + public IntIntPair put(int firstValue, int secondValue) { + first(firstValue); + second(secondValue); + return this; + } +} + +// Parsed from array/DataType.h + +/******************************************************************************* + * Copyright (c) 2015-2018 Skymind, Inc. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ + +// +// @author raver119@gmail.com +// + +// #ifndef ND4J_DATATYPE_H +// #define ND4J_DATATYPE_H + /** enum sd::DataType */ + public static final int + INHERIT = 0, + BOOL = 1, + FLOAT8 = 2, + HALF = 3, + HALF2 = 4, + FLOAT32 = 5, + DOUBLE = 6, + INT8 = 7, + INT16 = 8, + INT32 = 9, + INT64 = 10, + UINT8 = 11, + UINT16 = 12, + UINT32 = 13, + UINT64 = 14, + QINT8 = 15, + QINT16 = 16, + BFLOAT16 = 17, + UTF8 = 50, + UTF16 = 51, + UTF32 = 52, + ANY = 100, + AUTO = 200; + + +// #endif + +// Parsed from array/DataBuffer.h + +/******************************************************************************* + * Copyright (c) 2015-2018 Skymind, Inc. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ + +// +// @author raver119@gmail.com +// @author Yurii Shyrma (iuriish@yahoo.com) +// + +// #ifndef DEV_TESTS_DATABUFFER_H +// #define DEV_TESTS_DATABUFFER_H + +// #include +// #include +// #include +// #include +// #include +// #include +// #include + +@Namespace("sd") @NoOffset public static class DataBuffer extends Pointer { + static { Loader.load(); } + /** Pointer cast constructor. Invokes {@link Pointer#Pointer(Pointer)}. */ + public DataBuffer(Pointer p) { super(p); } + /** Native array allocator. Access with {@link Pointer#position(long)}. */ + public DataBuffer(long size) { super((Pointer)null); allocateArray(size); } + private native void allocateArray(long size); + @Override public DataBuffer position(long position) { + return (DataBuffer)super.position(position); + } + + + public DataBuffer(Pointer primary, Pointer special, + @Cast("const size_t") long lenInBytes, @Cast("const sd::DataType") int dataType, + @Cast("const bool") boolean isOwnerPrimary/*=false*/, @Cast("const bool") boolean isOwnerSpecial/*=false*/, + Workspace workspace/*=nullptr*/) { super((Pointer)null); allocate(primary, special, lenInBytes, dataType, isOwnerPrimary, isOwnerSpecial, workspace); } + private native void allocate(Pointer primary, Pointer special, + @Cast("const size_t") long lenInBytes, @Cast("const sd::DataType") int dataType, + @Cast("const bool") boolean isOwnerPrimary/*=false*/, @Cast("const bool") boolean isOwnerSpecial/*=false*/, + Workspace workspace/*=nullptr*/); + public DataBuffer(Pointer primary, Pointer special, + @Cast("const size_t") long lenInBytes, @Cast("const sd::DataType") int dataType) { super((Pointer)null); allocate(primary, special, lenInBytes, dataType); } + private native void allocate(Pointer primary, Pointer special, + @Cast("const size_t") long lenInBytes, @Cast("const sd::DataType") int dataType); + + public DataBuffer(Pointer primary, + @Cast("const size_t") long lenInBytes, @Cast("const sd::DataType") int dataType, + @Cast("const bool") boolean isOwnerPrimary/*=false*/, + Workspace workspace/*=nullptr*/) { super((Pointer)null); allocate(primary, lenInBytes, dataType, isOwnerPrimary, workspace); } + private native void allocate(Pointer primary, + @Cast("const size_t") long lenInBytes, @Cast("const sd::DataType") int dataType, + @Cast("const bool") boolean isOwnerPrimary/*=false*/, + Workspace workspace/*=nullptr*/); + public DataBuffer(Pointer primary, + @Cast("const size_t") long lenInBytes, @Cast("const sd::DataType") int dataType) { super((Pointer)null); allocate(primary, lenInBytes, dataType); } + private native void allocate(Pointer primary, + @Cast("const size_t") long lenInBytes, @Cast("const sd::DataType") int dataType); + + public DataBuffer(@Const Pointer hostBuffer, + @Cast("const sd::DataType") int dataType, @Cast("const size_t") long lenInBytes, + Workspace workspace/*=nullptr*/) { super((Pointer)null); allocate(hostBuffer, dataType, lenInBytes, workspace); } + private native void allocate(@Const Pointer hostBuffer, + @Cast("const sd::DataType") int dataType, @Cast("const size_t") long lenInBytes, + Workspace workspace/*=nullptr*/); + public DataBuffer(@Const Pointer hostBuffer, + @Cast("const sd::DataType") int dataType, @Cast("const size_t") long lenInBytes) { super((Pointer)null); allocate(hostBuffer, dataType, lenInBytes); } + private native void allocate(@Const Pointer hostBuffer, + @Cast("const sd::DataType") int dataType, @Cast("const size_t") long lenInBytes); + + public DataBuffer(@Cast("const size_t") long lenInBytes, @Cast("const sd::DataType") int dataType, Workspace workspace/*=nullptr*/, @Cast("const bool") boolean allocBoth/*=false*/) { super((Pointer)null); allocate(lenInBytes, dataType, workspace, allocBoth); } + private native void allocate(@Cast("const size_t") long lenInBytes, @Cast("const sd::DataType") int dataType, Workspace workspace/*=nullptr*/, @Cast("const bool") boolean allocBoth/*=false*/); + public DataBuffer(@Cast("const size_t") long lenInBytes, @Cast("const sd::DataType") int dataType) { super((Pointer)null); allocate(lenInBytes, dataType); } + private native void allocate(@Cast("const size_t") long lenInBytes, @Cast("const sd::DataType") int dataType); + + public DataBuffer(@Const @ByRef DataBuffer other) { super((Pointer)null); allocate(other); } + private native void allocate(@Const @ByRef DataBuffer other); + public DataBuffer() { super((Pointer)null); allocate(); } + private native void allocate(); + + public native @ByRef @Name("operator =") DataBuffer put(@Const @ByRef DataBuffer other); + + public native @Cast("sd::DataType") int getDataType(); + public native void setDataType(@Cast("sd::DataType") int dataType); + public native @Cast("size_t") long getLenInBytes(); + + public native Pointer primary(); + public native Pointer special(); + + public native void allocatePrimary(); + public native void allocateSpecial(); + + public native void writePrimary(); + public native void writeSpecial(); + public native void readPrimary(); + public native void readSpecial(); + public native @Cast("bool") boolean isPrimaryActual(); + public native @Cast("bool") boolean isSpecialActual(); + + public native void expand(@Cast("const uint64_t") long size); + + public native int deviceId(); + public native void setDeviceId(int deviceId); + public native void migrate(); + + public native void syncToPrimary(@Const LaunchContext context, @Cast("const bool") boolean forceSync/*=false*/); + public native void syncToPrimary(@Const LaunchContext context); + public native void syncToSpecial(@Cast("const bool") boolean forceSync/*=false*/); + public native void syncToSpecial(); + + public native void setToZeroBuffers(@Cast("const bool") boolean both/*=false*/); + public native void setToZeroBuffers(); + + public native void copyBufferFrom(@Const @ByRef DataBuffer other, @Cast("size_t") long sizeToCopyinBytes/*=0*/, @Cast("const Nd4jLong") long offsetThis/*=0*/, @Cast("const Nd4jLong") long offsetOther/*=0*/); + public native void copyBufferFrom(@Const @ByRef DataBuffer other); + + public static native void memcpy(@Const @ByRef DataBuffer dst, @Const @ByRef DataBuffer src); + + public native void setPrimaryBuffer(Pointer buffer, @Cast("size_t") long length); + public native void setSpecialBuffer(Pointer buffer, @Cast("size_t") long length); + + /** + * This method deletes buffers, if we're owners + */ + public native @Name("close") void _close(); +} +///// IMLEMENTATION OF INLINE METHODS ///// + +//////////////////////////////////////////////////////////////////////// + + +//////////////////////////////////////////////////////////////////////// + + + + + +// #endif //DEV_TESTS_DATABUFFER_H + + +// Parsed from array/ConstantDescriptor.h + +/******************************************************************************* + * Copyright (c) 2015-2018 Skymind, Inc. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ + +// +// @author raver119@gmail.com +// + +// #ifndef DEV_TESTS_CONSTANTDESCRIPTOR_H +// #define DEV_TESTS_CONSTANTDESCRIPTOR_H + +// #include +// #include +// #include +// #include +// #include +// #include + @Namespace("sd") @NoOffset public static class ConstantDescriptor extends Pointer { + static { Loader.load(); } + /** Pointer cast constructor. Invokes {@link Pointer#Pointer(Pointer)}. */ + public ConstantDescriptor(Pointer p) { super(p); } + + public ConstantDescriptor(DoublePointer values, int length) { super((Pointer)null); allocate(values, length); } + private native void allocate(DoublePointer values, int length); + public ConstantDescriptor(DoubleBuffer values, int length) { super((Pointer)null); allocate(values, length); } + private native void allocate(DoubleBuffer values, int length); + public ConstantDescriptor(double[] values, int length) { super((Pointer)null); allocate(values, length); } + private native void allocate(double[] values, int length); + public ConstantDescriptor(@Cast("Nd4jLong*") LongPointer values, int length) { super((Pointer)null); allocate(values, length); } + private native void allocate(@Cast("Nd4jLong*") LongPointer values, int length); + public ConstantDescriptor(@Cast("Nd4jLong*") LongBuffer values, int length) { super((Pointer)null); allocate(values, length); } + private native void allocate(@Cast("Nd4jLong*") LongBuffer values, int length); + public ConstantDescriptor(@Cast("Nd4jLong*") long[] values, int length) { super((Pointer)null); allocate(values, length); } + private native void allocate(@Cast("Nd4jLong*") long[] values, int length); + + public ConstantDescriptor(@Cast("Nd4jLong*") @StdVector LongPointer values) { super((Pointer)null); allocate(values); } + private native void allocate(@Cast("Nd4jLong*") @StdVector LongPointer values); + public ConstantDescriptor(@Cast("Nd4jLong*") @StdVector LongBuffer values) { super((Pointer)null); allocate(values); } + private native void allocate(@Cast("Nd4jLong*") @StdVector LongBuffer values); + public ConstantDescriptor(@Cast("Nd4jLong*") @StdVector long[] values) { super((Pointer)null); allocate(values); } + private native void allocate(@Cast("Nd4jLong*") @StdVector long[] values); + public ConstantDescriptor(@StdVector DoublePointer values) { super((Pointer)null); allocate(values); } + private native void allocate(@StdVector DoublePointer values); + public ConstantDescriptor(@StdVector DoubleBuffer values) { super((Pointer)null); allocate(values); } + private native void allocate(@StdVector DoubleBuffer values); + public ConstantDescriptor(@StdVector double[] values) { super((Pointer)null); allocate(values); } + private native void allocate(@StdVector double[] values); + + // equal to operator + public native @Cast("bool") @Name("operator ==") boolean equals(@Const @ByRef ConstantDescriptor other); + + // less than operator + public native @Cast("bool") @Name("operator <") boolean lessThan(@Const @ByRef ConstantDescriptor other); + + public native @Cast("bool") boolean isInteger(); + public native @Cast("bool") boolean isFloat(); + + public native @Cast("Nd4jLong") long length(); + + public native @Cast("Nd4jLong*") @StdVector LongPointer integerValues(); + public native @StdVector DoublePointer floatValues(); + } + + +// #ifndef __JAVACPP_HACK__ + +// #endif + + +// #endif //DEV_TESTS_CONSTANTDESCRIPTOR_H + + +// Parsed from array/ConstantDataBuffer.h + +/******************************************************************************* + * Copyright (c) 2015-2018 Skymind, Inc. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ + +// +// @author raver119@gmail.com + +// #ifndef LIBND4J_CONSTANTDATABUFFER_H +// #define LIBND4J_CONSTANTDATABUFFER_H + +// #include +// #include + @Namespace("sd") @NoOffset public static class ConstantDataBuffer extends Pointer { + static { Loader.load(); } + /** Pointer cast constructor. Invokes {@link Pointer#Pointer(Pointer)}. */ + public ConstantDataBuffer(Pointer p) { super(p); } + /** Native array allocator. Access with {@link Pointer#position(long)}. */ + public ConstantDataBuffer(long size) { super((Pointer)null); allocateArray(size); } + private native void allocateArray(long size); + @Override public ConstantDataBuffer position(long position) { + return (ConstantDataBuffer)super.position(position); + } + + public ConstantDataBuffer(@Cast("Nd4jPointer") Pointer primary, @Cast("Nd4jPointer") Pointer special, @Cast("Nd4jLong") long numEelements, @Cast("Nd4jLong") long sizeOf) { super((Pointer)null); allocate(primary, special, numEelements, sizeOf); } + private native void allocate(@Cast("Nd4jPointer") Pointer primary, @Cast("Nd4jPointer") Pointer special, @Cast("Nd4jLong") long numEelements, @Cast("Nd4jLong") long sizeOf); + public ConstantDataBuffer(@Const @ByRef ConstantDataBuffer other) { super((Pointer)null); allocate(other); } + private native void allocate(@Const @ByRef ConstantDataBuffer other); + public ConstantDataBuffer() { super((Pointer)null); allocate(); } + private native void allocate(); + + public native @Cast("Nd4jLong") long sizeOf(); + public native @Cast("Nd4jLong") long length(); + + public native @Cast("Nd4jPointer") Pointer primary(); + public native @Cast("Nd4jPointer") Pointer special(); + + public native @ByRef @Name("operator =") ConstantDataBuffer put(@Const @ByRef ConstantDataBuffer other); + } + + +// #endif //DEV_TESTS_CONSTANTDATABUFFER_H + + +// Parsed from array/TadPack.h + +/******************************************************************************* + * Copyright (c) 2015-2018 Skymind, Inc. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ + +// +// @author raver119@gmail.com +// + +// #ifndef DEV_TESTS_TADPACK_H +// #define DEV_TESTS_TADPACK_H + +// #include "ConstantDataBuffer.h" + @Namespace("sd") @NoOffset public static class TadPack extends Pointer { + static { Loader.load(); } + /** Pointer cast constructor. Invokes {@link Pointer#Pointer(Pointer)}. */ + public TadPack(Pointer p) { super(p); } + /** Native array allocator. Access with {@link Pointer#position(long)}. */ + public TadPack(long size) { super((Pointer)null); allocateArray(size); } + private native void allocateArray(long size); + @Override public TadPack position(long position) { + return (TadPack)super.position(position); + } + + public TadPack(@ByRef ConstantDataBuffer shapes, @ByRef ConstantDataBuffer offets, @Cast("Nd4jLong") long numTads) { super((Pointer)null); allocate(shapes, offets, numTads); } + private native void allocate(@ByRef ConstantDataBuffer shapes, @ByRef ConstantDataBuffer offets, @Cast("Nd4jLong") long numTads); + public TadPack() { super((Pointer)null); allocate(); } + private native void allocate(); + + public native @Cast("Nd4jLong*") LongPointer primaryShapeInfo(); + public native @Cast("Nd4jLong*") LongPointer primaryOffsets(); + + public native @Cast("Nd4jLong*") LongPointer specialShapeInfo(); + public native @Cast("Nd4jLong*") LongPointer specialOffsets(); + + public native @Cast("Nd4jLong") long numberOfTads(); + public native int shapeInfoLength(); + + /** + * These methods return either primary or special pointers depending on platform binaries were compiled for + * @return + */ + public native @Cast("Nd4jLong*") LongPointer platformShapeInfo(); + public native @Cast("Nd4jLong*") LongPointer platformOffsets(); + } + + + +// #endif //DEV_TESTS_TADPACK_H + + +// Parsed from execution/ErrorReference.h + +/******************************************************************************* + * Copyright (c) 2015-2018 Skymind, Inc. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ + +// +// @author raver119@gmail.com +// + +// #ifndef DEV_TESTS_ERRORREFERENCE_H +// #define DEV_TESTS_ERRORREFERENCE_H + +// #include +// #include + @Namespace("sd") @NoOffset public static class ErrorReference extends Pointer { + static { Loader.load(); } + /** Pointer cast constructor. Invokes {@link Pointer#Pointer(Pointer)}. */ + public ErrorReference(Pointer p) { super(p); } + /** Native array allocator. Access with {@link Pointer#position(long)}. */ + public ErrorReference(long size) { super((Pointer)null); allocateArray(size); } + private native void allocateArray(long size); + @Override public ErrorReference position(long position) { + return (ErrorReference)super.position(position); + } + + public ErrorReference() { super((Pointer)null); allocate(); } + private native void allocate(); + + public native int errorCode(); + public native @Cast("char*") String errorMessage(); + + public native void setErrorCode(int errorCode); + public native void setErrorMessage(@StdString BytePointer message); + public native void setErrorMessage(@StdString String message); + } + + + +// #endif //DEV_TESTS_ERRORREFERENCE_H + + +// Parsed from execution/Engine.h + +/******************************************************************************* + * Copyright (c) 2019 Konduit K.K. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ + +// +// @author raver119@gmail.com +// + +// #ifndef SD_ENGINE_H +// #define SD_ENGINE_H + /** enum samediff::Engine */ + public static final int + ENGINE_CPU = 0, + ENGINE_CUDA = 1; + + +// #endif //SD_ENGINE_H + + +// Parsed from execution/ExecutionMode.h + +/******************************************************************************* + * Copyright (c) 2019-2020 Konduit K.K + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ + +// +// @author raver119@gmail.com +// + +// #ifndef SD_EXECUTIONMODE_H +// #define SD_EXECUTIONMODE_H + /** enum samediff::ExecutionMode */ + public static final int + MODE_UNDEFINED = 0, + MODE_TRAINING = 1, + MODE_INFERENCE = 2; + + +// #endif //SD_EXECUTIONMODE_H + + +// Parsed from memory/MemoryType.h + +// +// Created by raver119 on 07.05.19. +// + +// #ifndef DEV_TESTS_MEMORYTYPE_H +// #define DEV_TESTS_MEMORYTYPE_H + /** enum sd::memory::MemoryType */ + public static final int + HOST = 0, + DEVICE = 10; + + + +// #endif //DEV_TESTS_MEMORYTYPE_H + + +// Parsed from system/Environment.h + +/******************************************************************************* + * Copyright (c) 2015-2018 Skymind, Inc. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ + +// +// Created by raver119 on 06.10.2017. +// + +// #ifndef LIBND4J_ENVIRONMENT_H +// #define LIBND4J_ENVIRONMENT_H + +// #include +// #include +// #include +// #include +// #include +// #include +// #include + @Namespace("sd") @NoOffset public static class Environment extends Pointer { + static { Loader.load(); } + /** Pointer cast constructor. Invokes {@link Pointer#Pointer(Pointer)}. */ + public Environment(Pointer p) { super(p); } + + /** + * These 3 fields are mostly for CUDA/cuBLAS version tracking + */ + public native int _blasMajorVersion(); public native Environment _blasMajorVersion(int setter); + public native int _blasMinorVersion(); public native Environment _blasMinorVersion(int setter); + public native int _blasPatchVersion(); public native Environment _blasPatchVersion(int setter); + + public static native Environment getInstance(); + + public native @Cast("bool") boolean isVerbose(); + public native void setVerbose(@Cast("bool") boolean reallyVerbose); + public native @Cast("bool") boolean isDebug(); + public native @Cast("bool") boolean isProfiling(); + public native @Cast("bool") boolean isDetectingLeaks(); + public native @Cast("bool") boolean isDebugAndVerbose(); + public native void setDebug(@Cast("bool") boolean reallyDebug); + public native void setProfiling(@Cast("bool") boolean reallyProfile); + public native void setLeaksDetector(@Cast("bool") boolean reallyDetect); + public native @Cast("bool") boolean helpersAllowed(); + public native void allowHelpers(@Cast("bool") boolean reallyAllow); + + public native @Cast("bool") boolean blasFallback(); + + public native int tadThreshold(); + public native void setTadThreshold(int threshold); + + public native int elementwiseThreshold(); + public native void setElementwiseThreshold(int threshold); + + public native int maxThreads(); + public native void setMaxThreads(int max); + + public native int maxMasterThreads(); + public native void setMaxMasterThreads(int max); + + /* + * Legacy memory limits API, still used in new API as simplified version + */ + public native void setMaxPrimaryMemory(@Cast("uint64_t") long maxBytes); + public native void setMaxSpecialyMemory(@Cast("uint64_t") long maxBytes); + public native void setMaxDeviceMemory(@Cast("uint64_t") long maxBytes); + + public native @Cast("uint64_t") long maxPrimaryMemory(); + public native @Cast("uint64_t") long maxSpecialMemory(); + //////////////////////// + + /* + * Methods for memory limits/counters + */ + public native void setGroupLimit(int group, @Cast("Nd4jLong") long numBytes); + public native void setDeviceLimit(int deviceId, @Cast("Nd4jLong") long numBytes); + + public native @Cast("Nd4jLong") long getGroupLimit(int group); + public native @Cast("Nd4jLong") long getDeviceLimit(int deviceId); + + public native @Cast("Nd4jLong") long getGroupCounter(int group); + public native @Cast("Nd4jLong") long getDeviceCounter(int deviceId); + //////////////////////// + + public native @Cast("bool") boolean isUseMKLDNN(); + public native void setUseMKLDNN(@Cast("bool") boolean useMKLDNN); + + public native @Cast("sd::DataType") int defaultFloatDataType(); + public native void setDefaultFloatDataType(@Cast("sd::DataType") int dtype); + + public native @Cast("bool") boolean precisionBoostAllowed(); + public native void allowPrecisionBoost(@Cast("bool") boolean reallyAllow); + + public native @Cast("bool") boolean isExperimentalBuild(); + + public native @Cast("bool") boolean isCPU(); + + public native int blasMajorVersion(); + public native int blasMinorVersion(); + public native int blasPatchVersion(); + + public native @StdVector Pair capabilities(); + } + + + +// #endif //LIBND4J_ENVIRONMENT_H + + +// Parsed from types/utf8string.h + +/******************************************************************************* + * Copyright (c) 2015-2018 Skymind, Inc. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ + +// +// @author raver119@gmail.com +// + +// #ifndef DEV_TESTS_UTF8STRING_H +// #define DEV_TESTS_UTF8STRING_H + +// #include +// #include + @Namespace("sd") @NoOffset public static class utf8string extends Pointer { + static { Loader.load(); } + /** Pointer cast constructor. Invokes {@link Pointer#Pointer(Pointer)}. */ + public utf8string(Pointer p) { super(p); } + /** Native array allocator. Access with {@link Pointer#position(long)}. */ + public utf8string(long size) { super((Pointer)null); allocateArray(size); } + private native void allocateArray(long size); + @Override public utf8string position(long position) { + return (utf8string)super.position(position); + } + + public native @Cast("char*") BytePointer _buffer(); public native utf8string _buffer(BytePointer setter); + public native @Cast("unsigned int") int _length(); public native utf8string _length(int setter); + + public utf8string() { super((Pointer)null); allocate(); } + private native void allocate(); + + public utf8string(@Cast("char*") String string, int length) { super((Pointer)null); allocate(string, length); } + private native void allocate(@Cast("char*") String string, int length); + public utf8string(@Cast("char*") BytePointer string, int length) { super((Pointer)null); allocate(string, length); } + private native void allocate(@Cast("char*") BytePointer string, int length); + public utf8string(@StdString BytePointer string) { super((Pointer)null); allocate(string); } + private native void allocate(@StdString BytePointer string); + public utf8string(@StdString String string) { super((Pointer)null); allocate(string); } + private native void allocate(@StdString String string); + public utf8string(@Const @ByRef utf8string other) { super((Pointer)null); allocate(other); } + private native void allocate(@Const @ByRef utf8string other); + public native @ByRef @Name("operator =") utf8string put(@Const @ByRef utf8string other); + } + + + +// #endif //DEV_TESTS_UTF8STRING_H + + +// Parsed from legacy/NativeOps.h + +/******************************************************************************* + * Copyright (c) 2015-2018 Skymind, Inc. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ + +// +// Created by agibsonccc on 2/21/16. +// + +// #ifndef NATIVEOPERATIONS_NATIVEOPS_H +// #define NATIVEOPERATIONS_NATIVEOPS_H + +/* +#ifndef thread_local +# if __STDC_VERSION__ >= 201112 && !defined __STDC_NO_THREADS__ +# define thread_local _Thread_local +# elif defined _WIN32 && ( \ + defined _MSC_VER || \ + defined __ICL || \ + defined __DMC__ || \ + defined __BORLANDC__ ) +# define thread_local __declspec(thread) +// note that ICC (linux) and Clang are covered by __GNUC__ +# elif defined __GNUC__ || \ + defined __SUNPRO_C || \ + defined __xlC__ +# define thread_local __thread +# else +# error "Cannot define thread_local" +# endif +#endif +*/ + +// #include +// #include +// #include + +//DO NOT REMOVE: THIS IS AN EDITOR SEMANTICS THING FOR CLION +//IT DEFINES THE EXPORT MACRO FOR THE EDITOR AND THEN +//RE ADDS THE DEFINITION VIA dll.h +// #ifdef _WIN32 +// #define ND4J_EXPORT __declspec(dllexport) +// #else +// #define ND4J_EXPORT +// #endif +// #include + +/* +int tad_threshold = 1; +int element_threshold = 32; + +bool debug = false; +bool verbose = false; +*/ + +// #include +// #include +// #include +// #include +// #include +// #include +// #include +// #include +// #include +// #include +// #include +// #include +// #include + +/** + * This function returns last error code stored, + * @return non-zero if something bad happened + */ +public native int lastErrorCode(); + +/** + * This function returns last error message, if last error code > 0 + * @return + */ +public native @Cast("char*") String lastErrorMessage(); + +/** + * + * @param p + * @param len + */ +public native void tryPointer(@Cast("Nd4jPointer") Pointer extra, @Cast("Nd4jPointer") Pointer p, int len); + +/** + * + * @param num + */ +public native void setElementThreshold(int num); + +/** + * + * @param num + */ +public native void setTADThreshold(int num); + +/** + * + * @param opNum + * @param x + * @param xShapeInfo + * @param extraParams + */ +public native void execIndexReduceScalar(@Cast("Nd4jPointer*") PointerPointer extraPointers, + int opNum, + OpaqueDataBuffer dbX, @Cast("Nd4jLong*") LongPointer hXShapeInfo, @Cast("Nd4jLong*") LongPointer dXShapeInfo, + Pointer extraParams, + OpaqueDataBuffer dbZ, @Cast("Nd4jLong*") LongPointer hZShapeInfo, @Cast("Nd4jLong*") LongPointer dZShapeInfo); +public native void execIndexReduceScalar(@Cast("Nd4jPointer*") PointerPointer extraPointers, + int opNum, + OpaqueDataBuffer dbX, @Cast("Nd4jLong*") LongBuffer hXShapeInfo, @Cast("Nd4jLong*") LongBuffer dXShapeInfo, + Pointer extraParams, + OpaqueDataBuffer dbZ, @Cast("Nd4jLong*") LongBuffer hZShapeInfo, @Cast("Nd4jLong*") LongBuffer dZShapeInfo); +public native void execIndexReduceScalar(@Cast("Nd4jPointer*") PointerPointer extraPointers, + int opNum, + OpaqueDataBuffer dbX, @Cast("Nd4jLong*") long[] hXShapeInfo, @Cast("Nd4jLong*") long[] dXShapeInfo, + Pointer extraParams, + OpaqueDataBuffer dbZ, @Cast("Nd4jLong*") long[] hZShapeInfo, @Cast("Nd4jLong*") long[] dZShapeInfo); + +/** + * + * @param opNum + * @param x + * @param xShapeInfo + * @param extraParams + * @param result + * @param resultShapeInfoBuffer + * @param dimension + * @param dimensionLength + */ +public native void execIndexReduce(@Cast("Nd4jPointer*") PointerPointer extraPointers, + int opNum, + OpaqueDataBuffer dbX, @Cast("Nd4jLong*") LongPointer hXShapeInfo, @Cast("Nd4jLong*") LongPointer dXShapeInfo, + Pointer extraParams, + OpaqueDataBuffer dbZ, @Cast("Nd4jLong*") LongPointer hZShapeInfo, @Cast("Nd4jLong*") LongPointer dZShapeInfo, + OpaqueDataBuffer dbDimension, @Cast("Nd4jLong*") LongPointer hDimensionShape, @Cast("Nd4jLong*") LongPointer dDimensionShape); +public native void execIndexReduce(@Cast("Nd4jPointer*") PointerPointer extraPointers, + int opNum, + OpaqueDataBuffer dbX, @Cast("Nd4jLong*") LongBuffer hXShapeInfo, @Cast("Nd4jLong*") LongBuffer dXShapeInfo, + Pointer extraParams, + OpaqueDataBuffer dbZ, @Cast("Nd4jLong*") LongBuffer hZShapeInfo, @Cast("Nd4jLong*") LongBuffer dZShapeInfo, + OpaqueDataBuffer dbDimension, @Cast("Nd4jLong*") LongBuffer hDimensionShape, @Cast("Nd4jLong*") LongBuffer dDimensionShape); +public native void execIndexReduce(@Cast("Nd4jPointer*") PointerPointer extraPointers, + int opNum, + OpaqueDataBuffer dbX, @Cast("Nd4jLong*") long[] hXShapeInfo, @Cast("Nd4jLong*") long[] dXShapeInfo, + Pointer extraParams, + OpaqueDataBuffer dbZ, @Cast("Nd4jLong*") long[] hZShapeInfo, @Cast("Nd4jLong*") long[] dZShapeInfo, + OpaqueDataBuffer dbDimension, @Cast("Nd4jLong*") long[] hDimensionShape, @Cast("Nd4jLong*") long[] dDimensionShape); + +/** + * + * @param opNum + * @param x + * @param xShapeInfo + * @param y + * @param yShapeInfo + * @param result + * @param resultShapeInfo + * @param dimension + * @param dimensionLength + */ +public native void execBroadcast( + @Cast("Nd4jPointer*") PointerPointer extraPointers, + int opNum, + OpaqueDataBuffer dbX, @Cast("Nd4jLong*") LongPointer hXShapeInfo, @Cast("Nd4jLong*") LongPointer dXShapeInfo, + OpaqueDataBuffer dbY, @Cast("Nd4jLong*") LongPointer hYShapeInfo, @Cast("Nd4jLong*") LongPointer dYShapeInfo, + OpaqueDataBuffer dbZ, @Cast("Nd4jLong*") LongPointer hZShapeInfo, @Cast("Nd4jLong*") LongPointer dZShapeInfo, + OpaqueDataBuffer dbDimension, @Cast("Nd4jLong*") LongPointer hDimensionShape, @Cast("Nd4jLong*") LongPointer dDimensionShape); +public native void execBroadcast( + @Cast("Nd4jPointer*") PointerPointer extraPointers, + int opNum, + OpaqueDataBuffer dbX, @Cast("Nd4jLong*") LongBuffer hXShapeInfo, @Cast("Nd4jLong*") LongBuffer dXShapeInfo, + OpaqueDataBuffer dbY, @Cast("Nd4jLong*") LongBuffer hYShapeInfo, @Cast("Nd4jLong*") LongBuffer dYShapeInfo, + OpaqueDataBuffer dbZ, @Cast("Nd4jLong*") LongBuffer hZShapeInfo, @Cast("Nd4jLong*") LongBuffer dZShapeInfo, + OpaqueDataBuffer dbDimension, @Cast("Nd4jLong*") LongBuffer hDimensionShape, @Cast("Nd4jLong*") LongBuffer dDimensionShape); +public native void execBroadcast( + @Cast("Nd4jPointer*") PointerPointer extraPointers, + int opNum, + OpaqueDataBuffer dbX, @Cast("Nd4jLong*") long[] hXShapeInfo, @Cast("Nd4jLong*") long[] dXShapeInfo, + OpaqueDataBuffer dbY, @Cast("Nd4jLong*") long[] hYShapeInfo, @Cast("Nd4jLong*") long[] dYShapeInfo, + OpaqueDataBuffer dbZ, @Cast("Nd4jLong*") long[] hZShapeInfo, @Cast("Nd4jLong*") long[] dZShapeInfo, + OpaqueDataBuffer dbDimension, @Cast("Nd4jLong*") long[] hDimensionShape, @Cast("Nd4jLong*") long[] dDimensionShape); + + +public native void execBroadcastBool( + @Cast("Nd4jPointer*") PointerPointer extraPointers, + int opNum, + OpaqueDataBuffer dbX, @Cast("Nd4jLong*") LongPointer hXShapeInfo, @Cast("Nd4jLong*") LongPointer dXShapeInfo, + OpaqueDataBuffer dbY, @Cast("Nd4jLong*") LongPointer hYShapeInfo, @Cast("Nd4jLong*") LongPointer dYShapeInfo, + OpaqueDataBuffer dbZ, @Cast("Nd4jLong*") LongPointer hZShapeInfo, @Cast("Nd4jLong*") LongPointer dZShapeInfo, + Pointer extraParams, + OpaqueDataBuffer dbDimension, @Cast("Nd4jLong*") LongPointer hDimensionShape, @Cast("Nd4jLong*") LongPointer dDimensionShape); +public native void execBroadcastBool( + @Cast("Nd4jPointer*") PointerPointer extraPointers, + int opNum, + OpaqueDataBuffer dbX, @Cast("Nd4jLong*") LongBuffer hXShapeInfo, @Cast("Nd4jLong*") LongBuffer dXShapeInfo, + OpaqueDataBuffer dbY, @Cast("Nd4jLong*") LongBuffer hYShapeInfo, @Cast("Nd4jLong*") LongBuffer dYShapeInfo, + OpaqueDataBuffer dbZ, @Cast("Nd4jLong*") LongBuffer hZShapeInfo, @Cast("Nd4jLong*") LongBuffer dZShapeInfo, + Pointer extraParams, + OpaqueDataBuffer dbDimension, @Cast("Nd4jLong*") LongBuffer hDimensionShape, @Cast("Nd4jLong*") LongBuffer dDimensionShape); +public native void execBroadcastBool( + @Cast("Nd4jPointer*") PointerPointer extraPointers, + int opNum, + OpaqueDataBuffer dbX, @Cast("Nd4jLong*") long[] hXShapeInfo, @Cast("Nd4jLong*") long[] dXShapeInfo, + OpaqueDataBuffer dbY, @Cast("Nd4jLong*") long[] hYShapeInfo, @Cast("Nd4jLong*") long[] dYShapeInfo, + OpaqueDataBuffer dbZ, @Cast("Nd4jLong*") long[] hZShapeInfo, @Cast("Nd4jLong*") long[] dZShapeInfo, + Pointer extraParams, + OpaqueDataBuffer dbDimension, @Cast("Nd4jLong*") long[] hDimensionShape, @Cast("Nd4jLong*") long[] dDimensionShape); + +/** + * + * @param opNum + * @param dx + * @param xShapeInfo + * @param y + * @param yShapeInfo + * @param result + * @param resultShapeInfo + * @param extraParams + * @param n + */ +public native void execPairwiseTransform( + @Cast("Nd4jPointer*") PointerPointer extraPointers, + int opNum, + OpaqueDataBuffer dbX, @Cast("Nd4jLong*") LongPointer hXShapeInfo, @Cast("Nd4jLong*") LongPointer dXShapeInfo, + OpaqueDataBuffer dbY, @Cast("Nd4jLong*") LongPointer hYShapeInfo, @Cast("Nd4jLong*") LongPointer dYShapeInfo, + OpaqueDataBuffer dbZ, @Cast("Nd4jLong*") LongPointer hZShapeInfo, @Cast("Nd4jLong*") LongPointer dZShapeInfo, + Pointer extraParams); +public native void execPairwiseTransform( + @Cast("Nd4jPointer*") PointerPointer extraPointers, + int opNum, + OpaqueDataBuffer dbX, @Cast("Nd4jLong*") LongBuffer hXShapeInfo, @Cast("Nd4jLong*") LongBuffer dXShapeInfo, + OpaqueDataBuffer dbY, @Cast("Nd4jLong*") LongBuffer hYShapeInfo, @Cast("Nd4jLong*") LongBuffer dYShapeInfo, + OpaqueDataBuffer dbZ, @Cast("Nd4jLong*") LongBuffer hZShapeInfo, @Cast("Nd4jLong*") LongBuffer dZShapeInfo, + Pointer extraParams); +public native void execPairwiseTransform( + @Cast("Nd4jPointer*") PointerPointer extraPointers, + int opNum, + OpaqueDataBuffer dbX, @Cast("Nd4jLong*") long[] hXShapeInfo, @Cast("Nd4jLong*") long[] dXShapeInfo, + OpaqueDataBuffer dbY, @Cast("Nd4jLong*") long[] hYShapeInfo, @Cast("Nd4jLong*") long[] dYShapeInfo, + OpaqueDataBuffer dbZ, @Cast("Nd4jLong*") long[] hZShapeInfo, @Cast("Nd4jLong*") long[] dZShapeInfo, + Pointer extraParams); + +public native void execPairwiseTransformBool( + @Cast("Nd4jPointer*") PointerPointer extraPointers, + int opNum, + OpaqueDataBuffer dbX, @Cast("Nd4jLong*") LongPointer hXShapeInfo, @Cast("Nd4jLong*") LongPointer dXShapeInfo, + OpaqueDataBuffer dbY, @Cast("Nd4jLong*") LongPointer hYShapeInfo, @Cast("Nd4jLong*") LongPointer dYShapeInfo, + OpaqueDataBuffer dbZ, @Cast("Nd4jLong*") LongPointer hZShapeInfo, @Cast("Nd4jLong*") LongPointer dZShapeInfo, + Pointer extraParams); +public native void execPairwiseTransformBool( + @Cast("Nd4jPointer*") PointerPointer extraPointers, + int opNum, + OpaqueDataBuffer dbX, @Cast("Nd4jLong*") LongBuffer hXShapeInfo, @Cast("Nd4jLong*") LongBuffer dXShapeInfo, + OpaqueDataBuffer dbY, @Cast("Nd4jLong*") LongBuffer hYShapeInfo, @Cast("Nd4jLong*") LongBuffer dYShapeInfo, + OpaqueDataBuffer dbZ, @Cast("Nd4jLong*") LongBuffer hZShapeInfo, @Cast("Nd4jLong*") LongBuffer dZShapeInfo, + Pointer extraParams); +public native void execPairwiseTransformBool( + @Cast("Nd4jPointer*") PointerPointer extraPointers, + int opNum, + OpaqueDataBuffer dbX, @Cast("Nd4jLong*") long[] hXShapeInfo, @Cast("Nd4jLong*") long[] dXShapeInfo, + OpaqueDataBuffer dbY, @Cast("Nd4jLong*") long[] hYShapeInfo, @Cast("Nd4jLong*") long[] dYShapeInfo, + OpaqueDataBuffer dbZ, @Cast("Nd4jLong*") long[] hZShapeInfo, @Cast("Nd4jLong*") long[] dZShapeInfo, + Pointer extraParams); + +/** + * + * @param opNum + * @param x + * @param xShapeInfo + * @param extraParams + * @param result + * @param resultShapeInfo + */ +public native void execReduceFloat(@Cast("Nd4jPointer*") PointerPointer extraPointers, + int opNum, + OpaqueDataBuffer dbX, @Cast("Nd4jLong*") LongPointer hXShapeInfo, @Cast("Nd4jLong*") LongPointer dXShapeInfo, + Pointer extraParams, + OpaqueDataBuffer dbZ, @Cast("Nd4jLong*") LongPointer hZShapeInfo, @Cast("Nd4jLong*") LongPointer dZShapeInfo); +public native void execReduceFloat(@Cast("Nd4jPointer*") PointerPointer extraPointers, + int opNum, + OpaqueDataBuffer dbX, @Cast("Nd4jLong*") LongBuffer hXShapeInfo, @Cast("Nd4jLong*") LongBuffer dXShapeInfo, + Pointer extraParams, + OpaqueDataBuffer dbZ, @Cast("Nd4jLong*") LongBuffer hZShapeInfo, @Cast("Nd4jLong*") LongBuffer dZShapeInfo); +public native void execReduceFloat(@Cast("Nd4jPointer*") PointerPointer extraPointers, + int opNum, + OpaqueDataBuffer dbX, @Cast("Nd4jLong*") long[] hXShapeInfo, @Cast("Nd4jLong*") long[] dXShapeInfo, + Pointer extraParams, + OpaqueDataBuffer dbZ, @Cast("Nd4jLong*") long[] hZShapeInfo, @Cast("Nd4jLong*") long[] dZShapeInfo); + +public native void execReduceSame(@Cast("Nd4jPointer*") PointerPointer extraPointers, + int opNum, + OpaqueDataBuffer dbX, @Cast("Nd4jLong*") LongPointer hXShapeInfo, @Cast("Nd4jLong*") LongPointer dXShapeInfo, + Pointer extraParams, + OpaqueDataBuffer dbZ, @Cast("Nd4jLong*") LongPointer hZShapeInfo, @Cast("Nd4jLong*") LongPointer dZShapeInfo); +public native void execReduceSame(@Cast("Nd4jPointer*") PointerPointer extraPointers, + int opNum, + OpaqueDataBuffer dbX, @Cast("Nd4jLong*") LongBuffer hXShapeInfo, @Cast("Nd4jLong*") LongBuffer dXShapeInfo, + Pointer extraParams, + OpaqueDataBuffer dbZ, @Cast("Nd4jLong*") LongBuffer hZShapeInfo, @Cast("Nd4jLong*") LongBuffer dZShapeInfo); +public native void execReduceSame(@Cast("Nd4jPointer*") PointerPointer extraPointers, + int opNum, + OpaqueDataBuffer dbX, @Cast("Nd4jLong*") long[] hXShapeInfo, @Cast("Nd4jLong*") long[] dXShapeInfo, + Pointer extraParams, + OpaqueDataBuffer dbZ, @Cast("Nd4jLong*") long[] hZShapeInfo, @Cast("Nd4jLong*") long[] dZShapeInfo); + +public native void execReduceBool(@Cast("Nd4jPointer*") PointerPointer extraPointers, + int opNum, + OpaqueDataBuffer dbX, @Cast("Nd4jLong*") LongPointer hXShapeInfo, @Cast("Nd4jLong*") LongPointer dXShapeInfo, + Pointer extraParams, + OpaqueDataBuffer dbZ, @Cast("Nd4jLong*") LongPointer hZShapeInfo, @Cast("Nd4jLong*") LongPointer dZShapeInfo); +public native void execReduceBool(@Cast("Nd4jPointer*") PointerPointer extraPointers, + int opNum, + OpaqueDataBuffer dbX, @Cast("Nd4jLong*") LongBuffer hXShapeInfo, @Cast("Nd4jLong*") LongBuffer dXShapeInfo, + Pointer extraParams, + OpaqueDataBuffer dbZ, @Cast("Nd4jLong*") LongBuffer hZShapeInfo, @Cast("Nd4jLong*") LongBuffer dZShapeInfo); +public native void execReduceBool(@Cast("Nd4jPointer*") PointerPointer extraPointers, + int opNum, + OpaqueDataBuffer dbX, @Cast("Nd4jLong*") long[] hXShapeInfo, @Cast("Nd4jLong*") long[] dXShapeInfo, + Pointer extraParams, + OpaqueDataBuffer dbZ, @Cast("Nd4jLong*") long[] hZShapeInfo, @Cast("Nd4jLong*") long[] dZShapeInfo); + + +public native void execReduceLong(@Cast("Nd4jPointer*") PointerPointer extraPointers, + int opNum, + OpaqueDataBuffer dbX, @Cast("Nd4jLong*") LongPointer hXShapeInfo, @Cast("Nd4jLong*") LongPointer dXShapeInfo, + Pointer extraParams, + OpaqueDataBuffer dbZ, @Cast("Nd4jLong*") LongPointer hZShapeInfo, @Cast("Nd4jLong*") LongPointer dZShapeInfo); +public native void execReduceLong(@Cast("Nd4jPointer*") PointerPointer extraPointers, + int opNum, + OpaqueDataBuffer dbX, @Cast("Nd4jLong*") LongBuffer hXShapeInfo, @Cast("Nd4jLong*") LongBuffer dXShapeInfo, + Pointer extraParams, + OpaqueDataBuffer dbZ, @Cast("Nd4jLong*") LongBuffer hZShapeInfo, @Cast("Nd4jLong*") LongBuffer dZShapeInfo); +public native void execReduceLong(@Cast("Nd4jPointer*") PointerPointer extraPointers, + int opNum, + OpaqueDataBuffer dbX, @Cast("Nd4jLong*") long[] hXShapeInfo, @Cast("Nd4jLong*") long[] dXShapeInfo, + Pointer extraParams, + OpaqueDataBuffer dbZ, @Cast("Nd4jLong*") long[] hZShapeInfo, @Cast("Nd4jLong*") long[] dZShapeInfo); + +/** + * + * @param opNum + * @param x + * @param xShapeInfo + * @param extraParams + * @param result + * @param resultShapeInfo + */ +public native void execReduceFloat2(@Cast("Nd4jPointer*") PointerPointer extraPointers, + int opNum, + OpaqueDataBuffer dbX, @Cast("Nd4jLong*") LongPointer hXShapeInfo, @Cast("Nd4jLong*") LongPointer dXShapeInfo, + Pointer extraParams, + OpaqueDataBuffer dbZ, @Cast("Nd4jLong*") LongPointer hZShapeInfo, @Cast("Nd4jLong*") LongPointer dZShapeInfo, + OpaqueDataBuffer dbDimension, @Cast("Nd4jLong*") LongPointer hDimensionShape, @Cast("Nd4jLong*") LongPointer dDimensionShape); +public native void execReduceFloat2(@Cast("Nd4jPointer*") PointerPointer extraPointers, + int opNum, + OpaqueDataBuffer dbX, @Cast("Nd4jLong*") LongBuffer hXShapeInfo, @Cast("Nd4jLong*") LongBuffer dXShapeInfo, + Pointer extraParams, + OpaqueDataBuffer dbZ, @Cast("Nd4jLong*") LongBuffer hZShapeInfo, @Cast("Nd4jLong*") LongBuffer dZShapeInfo, + OpaqueDataBuffer dbDimension, @Cast("Nd4jLong*") LongBuffer hDimensionShape, @Cast("Nd4jLong*") LongBuffer dDimensionShape); +public native void execReduceFloat2(@Cast("Nd4jPointer*") PointerPointer extraPointers, + int opNum, + OpaqueDataBuffer dbX, @Cast("Nd4jLong*") long[] hXShapeInfo, @Cast("Nd4jLong*") long[] dXShapeInfo, + Pointer extraParams, + OpaqueDataBuffer dbZ, @Cast("Nd4jLong*") long[] hZShapeInfo, @Cast("Nd4jLong*") long[] dZShapeInfo, + OpaqueDataBuffer dbDimension, @Cast("Nd4jLong*") long[] hDimensionShape, @Cast("Nd4jLong*") long[] dDimensionShape); + + +public native void execReduceSame2(@Cast("Nd4jPointer*") PointerPointer extraPointers, + int opNum, + OpaqueDataBuffer dbX, @Cast("Nd4jLong*") LongPointer hXShapeInfo, @Cast("Nd4jLong*") LongPointer dXShapeInfo, + Pointer extraParams, + OpaqueDataBuffer dbZ, @Cast("Nd4jLong*") LongPointer hZShapeInfo, @Cast("Nd4jLong*") LongPointer dZShapeInfo, + OpaqueDataBuffer dbDimension, @Cast("Nd4jLong*") LongPointer hDimensionShape, @Cast("Nd4jLong*") LongPointer dDimensionShape); +public native void execReduceSame2(@Cast("Nd4jPointer*") PointerPointer extraPointers, + int opNum, + OpaqueDataBuffer dbX, @Cast("Nd4jLong*") LongBuffer hXShapeInfo, @Cast("Nd4jLong*") LongBuffer dXShapeInfo, + Pointer extraParams, + OpaqueDataBuffer dbZ, @Cast("Nd4jLong*") LongBuffer hZShapeInfo, @Cast("Nd4jLong*") LongBuffer dZShapeInfo, + OpaqueDataBuffer dbDimension, @Cast("Nd4jLong*") LongBuffer hDimensionShape, @Cast("Nd4jLong*") LongBuffer dDimensionShape); +public native void execReduceSame2(@Cast("Nd4jPointer*") PointerPointer extraPointers, + int opNum, + OpaqueDataBuffer dbX, @Cast("Nd4jLong*") long[] hXShapeInfo, @Cast("Nd4jLong*") long[] dXShapeInfo, + Pointer extraParams, + OpaqueDataBuffer dbZ, @Cast("Nd4jLong*") long[] hZShapeInfo, @Cast("Nd4jLong*") long[] dZShapeInfo, + OpaqueDataBuffer dbDimension, @Cast("Nd4jLong*") long[] hDimensionShape, @Cast("Nd4jLong*") long[] dDimensionShape); + + +public native void execReduceBool2(@Cast("Nd4jPointer*") PointerPointer extraPointers, + int opNum, + OpaqueDataBuffer dbX, @Cast("Nd4jLong*") LongPointer hXShapeInfo, @Cast("Nd4jLong*") LongPointer dXShapeInfo, + Pointer extraParams, + OpaqueDataBuffer dbZ, @Cast("Nd4jLong*") LongPointer hZShapeInfo, @Cast("Nd4jLong*") LongPointer dZShapeInfo, + OpaqueDataBuffer dbDimension, @Cast("Nd4jLong*") LongPointer hDimensionShape, @Cast("Nd4jLong*") LongPointer dDimensionShape); +public native void execReduceBool2(@Cast("Nd4jPointer*") PointerPointer extraPointers, + int opNum, + OpaqueDataBuffer dbX, @Cast("Nd4jLong*") LongBuffer hXShapeInfo, @Cast("Nd4jLong*") LongBuffer dXShapeInfo, + Pointer extraParams, + OpaqueDataBuffer dbZ, @Cast("Nd4jLong*") LongBuffer hZShapeInfo, @Cast("Nd4jLong*") LongBuffer dZShapeInfo, + OpaqueDataBuffer dbDimension, @Cast("Nd4jLong*") LongBuffer hDimensionShape, @Cast("Nd4jLong*") LongBuffer dDimensionShape); +public native void execReduceBool2(@Cast("Nd4jPointer*") PointerPointer extraPointers, + int opNum, + OpaqueDataBuffer dbX, @Cast("Nd4jLong*") long[] hXShapeInfo, @Cast("Nd4jLong*") long[] dXShapeInfo, + Pointer extraParams, + OpaqueDataBuffer dbZ, @Cast("Nd4jLong*") long[] hZShapeInfo, @Cast("Nd4jLong*") long[] dZShapeInfo, + OpaqueDataBuffer dbDimension, @Cast("Nd4jLong*") long[] hDimensionShape, @Cast("Nd4jLong*") long[] dDimensionShape); + + +public native void execReduceLong2(@Cast("Nd4jPointer*") PointerPointer extraPointers, + int opNum, + OpaqueDataBuffer dbX, @Cast("Nd4jLong*") LongPointer hXShapeInfo, @Cast("Nd4jLong*") LongPointer dXShapeInfo, + Pointer extraParams, + OpaqueDataBuffer dbZ, @Cast("Nd4jLong*") LongPointer hZShapeInfo, @Cast("Nd4jLong*") LongPointer dZShapeInfo, + OpaqueDataBuffer dbDimension, @Cast("Nd4jLong*") LongPointer hDimensionShape, @Cast("Nd4jLong*") LongPointer dDimensionShape); +public native void execReduceLong2(@Cast("Nd4jPointer*") PointerPointer extraPointers, + int opNum, + OpaqueDataBuffer dbX, @Cast("Nd4jLong*") LongBuffer hXShapeInfo, @Cast("Nd4jLong*") LongBuffer dXShapeInfo, + Pointer extraParams, + OpaqueDataBuffer dbZ, @Cast("Nd4jLong*") LongBuffer hZShapeInfo, @Cast("Nd4jLong*") LongBuffer dZShapeInfo, + OpaqueDataBuffer dbDimension, @Cast("Nd4jLong*") LongBuffer hDimensionShape, @Cast("Nd4jLong*") LongBuffer dDimensionShape); +public native void execReduceLong2(@Cast("Nd4jPointer*") PointerPointer extraPointers, + int opNum, + OpaqueDataBuffer dbX, @Cast("Nd4jLong*") long[] hXShapeInfo, @Cast("Nd4jLong*") long[] dXShapeInfo, + Pointer extraParams, + OpaqueDataBuffer dbZ, @Cast("Nd4jLong*") long[] hZShapeInfo, @Cast("Nd4jLong*") long[] dZShapeInfo, + OpaqueDataBuffer dbDimension, @Cast("Nd4jLong*") long[] hDimensionShape, @Cast("Nd4jLong*") long[] dDimensionShape); + +/** + * + * @param opNum + * @param x + * @param xShapeInfo + * @param extraParamsVals + * @param y + * @param yShapeInfo + * @param result + * @param resultShapeInfo + */ +public native void execReduce3(@Cast("Nd4jPointer*") PointerPointer extraPointers, + int opNum, + OpaqueDataBuffer dbX, @Cast("Nd4jLong*") LongPointer hXShapeInfo, @Cast("Nd4jLong*") LongPointer dXShapeInfo, + Pointer extraParamsVals, + OpaqueDataBuffer dbY, @Cast("Nd4jLong*") LongPointer hYShapeInfo, @Cast("Nd4jLong*") LongPointer dYShapeInfo, + OpaqueDataBuffer dbZ, @Cast("Nd4jLong*") LongPointer hZShapeInfo, @Cast("Nd4jLong*") LongPointer dZShapeInfo); +public native void execReduce3(@Cast("Nd4jPointer*") PointerPointer extraPointers, + int opNum, + OpaqueDataBuffer dbX, @Cast("Nd4jLong*") LongBuffer hXShapeInfo, @Cast("Nd4jLong*") LongBuffer dXShapeInfo, + Pointer extraParamsVals, + OpaqueDataBuffer dbY, @Cast("Nd4jLong*") LongBuffer hYShapeInfo, @Cast("Nd4jLong*") LongBuffer dYShapeInfo, + OpaqueDataBuffer dbZ, @Cast("Nd4jLong*") LongBuffer hZShapeInfo, @Cast("Nd4jLong*") LongBuffer dZShapeInfo); +public native void execReduce3(@Cast("Nd4jPointer*") PointerPointer extraPointers, + int opNum, + OpaqueDataBuffer dbX, @Cast("Nd4jLong*") long[] hXShapeInfo, @Cast("Nd4jLong*") long[] dXShapeInfo, + Pointer extraParamsVals, + OpaqueDataBuffer dbY, @Cast("Nd4jLong*") long[] hYShapeInfo, @Cast("Nd4jLong*") long[] dYShapeInfo, + OpaqueDataBuffer dbZ, @Cast("Nd4jLong*") long[] hZShapeInfo, @Cast("Nd4jLong*") long[] dZShapeInfo); + +/** + * + * @param opNum + * @param x + * @param xShapeInfo + * @param extraParamsVals + * @param y + * @param yShapeInfo + */ +public native void execReduce3Scalar(@Cast("Nd4jPointer*") PointerPointer extraPointers, + int opNum, + OpaqueDataBuffer dbX, @Cast("Nd4jLong*") LongPointer hXShapeInfo, @Cast("Nd4jLong*") LongPointer dXShapeInfo, + Pointer extraParamsVals, + OpaqueDataBuffer dbY, @Cast("Nd4jLong*") LongPointer hYShapeInfo, @Cast("Nd4jLong*") LongPointer dYShapeInfo, + OpaqueDataBuffer dbZ, @Cast("Nd4jLong*") LongPointer hZShapeInfo, @Cast("Nd4jLong*") LongPointer dZShapeInfo); +public native void execReduce3Scalar(@Cast("Nd4jPointer*") PointerPointer extraPointers, + int opNum, + OpaqueDataBuffer dbX, @Cast("Nd4jLong*") LongBuffer hXShapeInfo, @Cast("Nd4jLong*") LongBuffer dXShapeInfo, + Pointer extraParamsVals, + OpaqueDataBuffer dbY, @Cast("Nd4jLong*") LongBuffer hYShapeInfo, @Cast("Nd4jLong*") LongBuffer dYShapeInfo, + OpaqueDataBuffer dbZ, @Cast("Nd4jLong*") LongBuffer hZShapeInfo, @Cast("Nd4jLong*") LongBuffer dZShapeInfo); +public native void execReduce3Scalar(@Cast("Nd4jPointer*") PointerPointer extraPointers, + int opNum, + OpaqueDataBuffer dbX, @Cast("Nd4jLong*") long[] hXShapeInfo, @Cast("Nd4jLong*") long[] dXShapeInfo, + Pointer extraParamsVals, + OpaqueDataBuffer dbY, @Cast("Nd4jLong*") long[] hYShapeInfo, @Cast("Nd4jLong*") long[] dYShapeInfo, + OpaqueDataBuffer dbZ, @Cast("Nd4jLong*") long[] hZShapeInfo, @Cast("Nd4jLong*") long[] dZShapeInfo); +/** + * + * @param opNum + * @param x + * @param xShapeInfo + * @param extraParamsVals + * @param y + * @param yShapeInfo + * @param result + * @param resultShapeInfoBuffer + * @param dimension + * @param dimensionLength + */ +public native void execReduce3Tad(@Cast("Nd4jPointer*") PointerPointer extraPointers, + int opNum, + OpaqueDataBuffer dbX, @Cast("Nd4jLong*") LongPointer hXShapeInfo, @Cast("Nd4jLong*") LongPointer dXShapeInfo, + Pointer extraParamsVals, + OpaqueDataBuffer dbY, @Cast("Nd4jLong*") LongPointer hYShapeInfo, @Cast("Nd4jLong*") LongPointer dYShapeInfo, + OpaqueDataBuffer dbZ, @Cast("Nd4jLong*") LongPointer hZShapeInfo, @Cast("Nd4jLong*") LongPointer dZShapeInfo, + OpaqueDataBuffer dbDimension, @Cast("Nd4jLong*") LongPointer hDimensionShape, @Cast("Nd4jLong*") LongPointer dDimensionShape, + @Cast("Nd4jLong*") LongPointer tadOnlyShapeInfo, @Cast("Nd4jLong*") LongPointer tadOffsets, + @Cast("Nd4jLong*") LongPointer yTadOnlyShapeInfo, @Cast("Nd4jLong*") LongPointer yTadOffsets); +public native void execReduce3Tad(@Cast("Nd4jPointer*") PointerPointer extraPointers, + int opNum, + OpaqueDataBuffer dbX, @Cast("Nd4jLong*") LongBuffer hXShapeInfo, @Cast("Nd4jLong*") LongBuffer dXShapeInfo, + Pointer extraParamsVals, + OpaqueDataBuffer dbY, @Cast("Nd4jLong*") LongBuffer hYShapeInfo, @Cast("Nd4jLong*") LongBuffer dYShapeInfo, + OpaqueDataBuffer dbZ, @Cast("Nd4jLong*") LongBuffer hZShapeInfo, @Cast("Nd4jLong*") LongBuffer dZShapeInfo, + OpaqueDataBuffer dbDimension, @Cast("Nd4jLong*") LongBuffer hDimensionShape, @Cast("Nd4jLong*") LongBuffer dDimensionShape, + @Cast("Nd4jLong*") LongBuffer tadOnlyShapeInfo, @Cast("Nd4jLong*") LongBuffer tadOffsets, + @Cast("Nd4jLong*") LongBuffer yTadOnlyShapeInfo, @Cast("Nd4jLong*") LongBuffer yTadOffsets); +public native void execReduce3Tad(@Cast("Nd4jPointer*") PointerPointer extraPointers, + int opNum, + OpaqueDataBuffer dbX, @Cast("Nd4jLong*") long[] hXShapeInfo, @Cast("Nd4jLong*") long[] dXShapeInfo, + Pointer extraParamsVals, + OpaqueDataBuffer dbY, @Cast("Nd4jLong*") long[] hYShapeInfo, @Cast("Nd4jLong*") long[] dYShapeInfo, + OpaqueDataBuffer dbZ, @Cast("Nd4jLong*") long[] hZShapeInfo, @Cast("Nd4jLong*") long[] dZShapeInfo, + OpaqueDataBuffer dbDimension, @Cast("Nd4jLong*") long[] hDimensionShape, @Cast("Nd4jLong*") long[] dDimensionShape, + @Cast("Nd4jLong*") long[] tadOnlyShapeInfo, @Cast("Nd4jLong*") long[] tadOffsets, + @Cast("Nd4jLong*") long[] yTadOnlyShapeInfo, @Cast("Nd4jLong*") long[] yTadOffsets); + + +public native void execReduce3All(@Cast("Nd4jPointer*") PointerPointer extraPointers, + int opNum, + OpaqueDataBuffer dbX, @Cast("Nd4jLong*") LongPointer hXShapeInfo, @Cast("Nd4jLong*") LongPointer dXShapeInfo, + Pointer extraParamsVals, + OpaqueDataBuffer dbY, @Cast("Nd4jLong*") LongPointer hYShapeInfo, @Cast("Nd4jLong*") LongPointer dYShapeInfo, + OpaqueDataBuffer dbZ, @Cast("Nd4jLong*") LongPointer hZShapeInfo, @Cast("Nd4jLong*") LongPointer dZShapeInfo, + OpaqueDataBuffer dbDimension, @Cast("Nd4jLong*") LongPointer hDimensionShape, @Cast("Nd4jLong*") LongPointer dDimensionShape, + @Cast("Nd4jLong*") LongPointer xTadShapeInfo, @Cast("Nd4jLong*") LongPointer xOffsets, + @Cast("Nd4jLong*") LongPointer yTadShapeInfo, @Cast("Nd4jLong*") LongPointer yOffsets); +public native void execReduce3All(@Cast("Nd4jPointer*") PointerPointer extraPointers, + int opNum, + OpaqueDataBuffer dbX, @Cast("Nd4jLong*") LongBuffer hXShapeInfo, @Cast("Nd4jLong*") LongBuffer dXShapeInfo, + Pointer extraParamsVals, + OpaqueDataBuffer dbY, @Cast("Nd4jLong*") LongBuffer hYShapeInfo, @Cast("Nd4jLong*") LongBuffer dYShapeInfo, + OpaqueDataBuffer dbZ, @Cast("Nd4jLong*") LongBuffer hZShapeInfo, @Cast("Nd4jLong*") LongBuffer dZShapeInfo, + OpaqueDataBuffer dbDimension, @Cast("Nd4jLong*") LongBuffer hDimensionShape, @Cast("Nd4jLong*") LongBuffer dDimensionShape, + @Cast("Nd4jLong*") LongBuffer xTadShapeInfo, @Cast("Nd4jLong*") LongBuffer xOffsets, + @Cast("Nd4jLong*") LongBuffer yTadShapeInfo, @Cast("Nd4jLong*") LongBuffer yOffsets); +public native void execReduce3All(@Cast("Nd4jPointer*") PointerPointer extraPointers, + int opNum, + OpaqueDataBuffer dbX, @Cast("Nd4jLong*") long[] hXShapeInfo, @Cast("Nd4jLong*") long[] dXShapeInfo, + Pointer extraParamsVals, + OpaqueDataBuffer dbY, @Cast("Nd4jLong*") long[] hYShapeInfo, @Cast("Nd4jLong*") long[] dYShapeInfo, + OpaqueDataBuffer dbZ, @Cast("Nd4jLong*") long[] hZShapeInfo, @Cast("Nd4jLong*") long[] dZShapeInfo, + OpaqueDataBuffer dbDimension, @Cast("Nd4jLong*") long[] hDimensionShape, @Cast("Nd4jLong*") long[] dDimensionShape, + @Cast("Nd4jLong*") long[] xTadShapeInfo, @Cast("Nd4jLong*") long[] xOffsets, + @Cast("Nd4jLong*") long[] yTadShapeInfo, @Cast("Nd4jLong*") long[] yOffsets); + +/** + * + * @param opNum + * @param x + * @param xShapeInfo + * @param result + * @param resultShapeInfo + * @param scalar + * @param extraParams + * @param n + */ +public native void execScalar(@Cast("Nd4jPointer*") PointerPointer extraPointers, + int opNum, + OpaqueDataBuffer dbX, @Cast("Nd4jLong*") LongPointer hXShapeInfo, @Cast("Nd4jLong*") LongPointer dXShapeInfo, + OpaqueDataBuffer dbZ, @Cast("Nd4jLong*") LongPointer hZShapeInfo, @Cast("Nd4jLong*") LongPointer dZShapeInfo, + OpaqueDataBuffer dbScalar, @Cast("Nd4jLong*") LongPointer hSscalarShapeInfo, @Cast("Nd4jLong*") LongPointer dSscalarShapeInfo, + Pointer extraParams); +public native void execScalar(@Cast("Nd4jPointer*") PointerPointer extraPointers, + int opNum, + OpaqueDataBuffer dbX, @Cast("Nd4jLong*") LongBuffer hXShapeInfo, @Cast("Nd4jLong*") LongBuffer dXShapeInfo, + OpaqueDataBuffer dbZ, @Cast("Nd4jLong*") LongBuffer hZShapeInfo, @Cast("Nd4jLong*") LongBuffer dZShapeInfo, + OpaqueDataBuffer dbScalar, @Cast("Nd4jLong*") LongBuffer hSscalarShapeInfo, @Cast("Nd4jLong*") LongBuffer dSscalarShapeInfo, + Pointer extraParams); +public native void execScalar(@Cast("Nd4jPointer*") PointerPointer extraPointers, + int opNum, + OpaqueDataBuffer dbX, @Cast("Nd4jLong*") long[] hXShapeInfo, @Cast("Nd4jLong*") long[] dXShapeInfo, + OpaqueDataBuffer dbZ, @Cast("Nd4jLong*") long[] hZShapeInfo, @Cast("Nd4jLong*") long[] dZShapeInfo, + OpaqueDataBuffer dbScalar, @Cast("Nd4jLong*") long[] hSscalarShapeInfo, @Cast("Nd4jLong*") long[] dSscalarShapeInfo, + Pointer extraParams); + +public native void execScalarBool(@Cast("Nd4jPointer*") PointerPointer extraPointers, + int opNum, + OpaqueDataBuffer dbX, @Cast("Nd4jLong*") LongPointer hXShapeInfo, @Cast("Nd4jLong*") LongPointer dXShapeInfo, + OpaqueDataBuffer dbZ, @Cast("Nd4jLong*") LongPointer hZShapeInfo, @Cast("Nd4jLong*") LongPointer dZShapeInfo, + OpaqueDataBuffer dbScalar, @Cast("Nd4jLong*") LongPointer hSscalarShapeInfo, @Cast("Nd4jLong*") LongPointer dSscalarShapeInfo, + Pointer extraParams); +public native void execScalarBool(@Cast("Nd4jPointer*") PointerPointer extraPointers, + int opNum, + OpaqueDataBuffer dbX, @Cast("Nd4jLong*") LongBuffer hXShapeInfo, @Cast("Nd4jLong*") LongBuffer dXShapeInfo, + OpaqueDataBuffer dbZ, @Cast("Nd4jLong*") LongBuffer hZShapeInfo, @Cast("Nd4jLong*") LongBuffer dZShapeInfo, + OpaqueDataBuffer dbScalar, @Cast("Nd4jLong*") LongBuffer hSscalarShapeInfo, @Cast("Nd4jLong*") LongBuffer dSscalarShapeInfo, + Pointer extraParams); +public native void execScalarBool(@Cast("Nd4jPointer*") PointerPointer extraPointers, + int opNum, + OpaqueDataBuffer dbX, @Cast("Nd4jLong*") long[] hXShapeInfo, @Cast("Nd4jLong*") long[] dXShapeInfo, + OpaqueDataBuffer dbZ, @Cast("Nd4jLong*") long[] hZShapeInfo, @Cast("Nd4jLong*") long[] dZShapeInfo, + OpaqueDataBuffer dbScalar, @Cast("Nd4jLong*") long[] hSscalarShapeInfo, @Cast("Nd4jLong*") long[] dSscalarShapeInfo, + Pointer extraParams); + +/** + * + * @param opNum + * @param x + * @param xShapeInfo + * @param extraParams + */ +public native void execSummaryStatsScalar(@Cast("Nd4jPointer*") PointerPointer extraPointers, + int opNum, + OpaqueDataBuffer dbX, @Cast("Nd4jLong*") LongPointer hXShapeInfo, @Cast("Nd4jLong*") LongPointer dXShapeInfo, + Pointer extraParams, + OpaqueDataBuffer dbZ, @Cast("Nd4jLong*") LongPointer hZShapeInfo, @Cast("Nd4jLong*") LongPointer dZShapeInfo, + @Cast("bool") boolean biasCorrected); +public native void execSummaryStatsScalar(@Cast("Nd4jPointer*") PointerPointer extraPointers, + int opNum, + OpaqueDataBuffer dbX, @Cast("Nd4jLong*") LongBuffer hXShapeInfo, @Cast("Nd4jLong*") LongBuffer dXShapeInfo, + Pointer extraParams, + OpaqueDataBuffer dbZ, @Cast("Nd4jLong*") LongBuffer hZShapeInfo, @Cast("Nd4jLong*") LongBuffer dZShapeInfo, + @Cast("bool") boolean biasCorrected); +public native void execSummaryStatsScalar(@Cast("Nd4jPointer*") PointerPointer extraPointers, + int opNum, + OpaqueDataBuffer dbX, @Cast("Nd4jLong*") long[] hXShapeInfo, @Cast("Nd4jLong*") long[] dXShapeInfo, + Pointer extraParams, + OpaqueDataBuffer dbZ, @Cast("Nd4jLong*") long[] hZShapeInfo, @Cast("Nd4jLong*") long[] dZShapeInfo, + @Cast("bool") boolean biasCorrected); +/** + * + * @param opNum + * @param x + * @param xShapeInfo + * @param extraParams + * @param result + * @param resultShapeInfo + */ +public native void execSummaryStats(@Cast("Nd4jPointer*") PointerPointer extraPointers, + int opNum, + OpaqueDataBuffer dbX, @Cast("Nd4jLong*") LongPointer hXShapeInfo, @Cast("Nd4jLong*") LongPointer dXShapeInfo, + Pointer extraParams, + OpaqueDataBuffer dbZ, @Cast("Nd4jLong*") LongPointer hZShapeInfo, @Cast("Nd4jLong*") LongPointer dZShapeInfo, + @Cast("bool") boolean biasCorrected); +public native void execSummaryStats(@Cast("Nd4jPointer*") PointerPointer extraPointers, + int opNum, + OpaqueDataBuffer dbX, @Cast("Nd4jLong*") LongBuffer hXShapeInfo, @Cast("Nd4jLong*") LongBuffer dXShapeInfo, + Pointer extraParams, + OpaqueDataBuffer dbZ, @Cast("Nd4jLong*") LongBuffer hZShapeInfo, @Cast("Nd4jLong*") LongBuffer dZShapeInfo, + @Cast("bool") boolean biasCorrected); +public native void execSummaryStats(@Cast("Nd4jPointer*") PointerPointer extraPointers, + int opNum, + OpaqueDataBuffer dbX, @Cast("Nd4jLong*") long[] hXShapeInfo, @Cast("Nd4jLong*") long[] dXShapeInfo, + Pointer extraParams, + OpaqueDataBuffer dbZ, @Cast("Nd4jLong*") long[] hZShapeInfo, @Cast("Nd4jLong*") long[] dZShapeInfo, + @Cast("bool") boolean biasCorrected); +/** + * + * @param opNum + * @param x + * @param xShapeInfo + * @param extraParams + * @param result + * @param resultShapeInfoBuffer + * @param dimension + * @param dimensionLength + */ +public native void execSummaryStatsTad(@Cast("Nd4jPointer*") PointerPointer extraPointers, + int opNum, + OpaqueDataBuffer dbX, @Cast("Nd4jLong*") LongPointer hXShapeInfo, @Cast("Nd4jLong*") LongPointer dXShapeInfo, + Pointer extraParams, + OpaqueDataBuffer dbZ, @Cast("Nd4jLong*") LongPointer hZShapeInfo, @Cast("Nd4jLong*") LongPointer dZShapeInfo, + OpaqueDataBuffer dbDimension, @Cast("Nd4jLong*") LongPointer hDimensionShape, @Cast("Nd4jLong*") LongPointer dDimensionShape, + @Cast("bool") boolean biasCorrected, + @Cast("Nd4jLong*") LongPointer tadShapeInfo, @Cast("Nd4jLong*") LongPointer tadOffsets); +public native void execSummaryStatsTad(@Cast("Nd4jPointer*") PointerPointer extraPointers, + int opNum, + OpaqueDataBuffer dbX, @Cast("Nd4jLong*") LongBuffer hXShapeInfo, @Cast("Nd4jLong*") LongBuffer dXShapeInfo, + Pointer extraParams, + OpaqueDataBuffer dbZ, @Cast("Nd4jLong*") LongBuffer hZShapeInfo, @Cast("Nd4jLong*") LongBuffer dZShapeInfo, + OpaqueDataBuffer dbDimension, @Cast("Nd4jLong*") LongBuffer hDimensionShape, @Cast("Nd4jLong*") LongBuffer dDimensionShape, + @Cast("bool") boolean biasCorrected, + @Cast("Nd4jLong*") LongBuffer tadShapeInfo, @Cast("Nd4jLong*") LongBuffer tadOffsets); +public native void execSummaryStatsTad(@Cast("Nd4jPointer*") PointerPointer extraPointers, + int opNum, + OpaqueDataBuffer dbX, @Cast("Nd4jLong*") long[] hXShapeInfo, @Cast("Nd4jLong*") long[] dXShapeInfo, + Pointer extraParams, + OpaqueDataBuffer dbZ, @Cast("Nd4jLong*") long[] hZShapeInfo, @Cast("Nd4jLong*") long[] dZShapeInfo, + OpaqueDataBuffer dbDimension, @Cast("Nd4jLong*") long[] hDimensionShape, @Cast("Nd4jLong*") long[] dDimensionShape, + @Cast("bool") boolean biasCorrected, + @Cast("Nd4jLong*") long[] tadShapeInfo, @Cast("Nd4jLong*") long[] tadOffsets); + +/** + * + * @param opNum + * @param dx + * @param xShapeInfo + * @param result + * @param resultShapeInfo + * @param extraParams + * @param n + */ +public native void execTransformFloat(@Cast("Nd4jPointer*") PointerPointer extraPointers, + int opNum, + OpaqueDataBuffer dbX, @Cast("Nd4jLong*") LongPointer hXShapeInfo, @Cast("Nd4jLong*") LongPointer dXShapeInfo, + OpaqueDataBuffer dbZ, @Cast("Nd4jLong*") LongPointer hZShapeInfo, @Cast("Nd4jLong*") LongPointer dZShapeInfo, + Pointer extraParams); +public native void execTransformFloat(@Cast("Nd4jPointer*") PointerPointer extraPointers, + int opNum, + OpaqueDataBuffer dbX, @Cast("Nd4jLong*") LongBuffer hXShapeInfo, @Cast("Nd4jLong*") LongBuffer dXShapeInfo, + OpaqueDataBuffer dbZ, @Cast("Nd4jLong*") LongBuffer hZShapeInfo, @Cast("Nd4jLong*") LongBuffer dZShapeInfo, + Pointer extraParams); +public native void execTransformFloat(@Cast("Nd4jPointer*") PointerPointer extraPointers, + int opNum, + OpaqueDataBuffer dbX, @Cast("Nd4jLong*") long[] hXShapeInfo, @Cast("Nd4jLong*") long[] dXShapeInfo, + OpaqueDataBuffer dbZ, @Cast("Nd4jLong*") long[] hZShapeInfo, @Cast("Nd4jLong*") long[] dZShapeInfo, + Pointer extraParams); + +public native void execTransformSame(@Cast("Nd4jPointer*") PointerPointer extraPointers, + int opNum, + OpaqueDataBuffer dbX, @Cast("Nd4jLong*") LongPointer hXShapeInfo, @Cast("Nd4jLong*") LongPointer dXShapeInfo, + OpaqueDataBuffer dbZ, @Cast("Nd4jLong*") LongPointer hZShapeInfo, @Cast("Nd4jLong*") LongPointer dZShapeInfo, + Pointer extraParams); +public native void execTransformSame(@Cast("Nd4jPointer*") PointerPointer extraPointers, + int opNum, + OpaqueDataBuffer dbX, @Cast("Nd4jLong*") LongBuffer hXShapeInfo, @Cast("Nd4jLong*") LongBuffer dXShapeInfo, + OpaqueDataBuffer dbZ, @Cast("Nd4jLong*") LongBuffer hZShapeInfo, @Cast("Nd4jLong*") LongBuffer dZShapeInfo, + Pointer extraParams); +public native void execTransformSame(@Cast("Nd4jPointer*") PointerPointer extraPointers, + int opNum, + OpaqueDataBuffer dbX, @Cast("Nd4jLong*") long[] hXShapeInfo, @Cast("Nd4jLong*") long[] dXShapeInfo, + OpaqueDataBuffer dbZ, @Cast("Nd4jLong*") long[] hZShapeInfo, @Cast("Nd4jLong*") long[] dZShapeInfo, + Pointer extraParams); + +public native void execTransformBool(@Cast("Nd4jPointer*") PointerPointer extraPointers, + int opNum, + OpaqueDataBuffer dbX, @Cast("Nd4jLong*") LongPointer hXShapeInfo, @Cast("Nd4jLong*") LongPointer dXShapeInfo, + OpaqueDataBuffer dbZ, @Cast("Nd4jLong*") LongPointer hZShapeInfo, @Cast("Nd4jLong*") LongPointer dZShapeInfo, + Pointer extraParams); +public native void execTransformBool(@Cast("Nd4jPointer*") PointerPointer extraPointers, + int opNum, + OpaqueDataBuffer dbX, @Cast("Nd4jLong*") LongBuffer hXShapeInfo, @Cast("Nd4jLong*") LongBuffer dXShapeInfo, + OpaqueDataBuffer dbZ, @Cast("Nd4jLong*") LongBuffer hZShapeInfo, @Cast("Nd4jLong*") LongBuffer dZShapeInfo, + Pointer extraParams); +public native void execTransformBool(@Cast("Nd4jPointer*") PointerPointer extraPointers, + int opNum, + OpaqueDataBuffer dbX, @Cast("Nd4jLong*") long[] hXShapeInfo, @Cast("Nd4jLong*") long[] dXShapeInfo, + OpaqueDataBuffer dbZ, @Cast("Nd4jLong*") long[] hZShapeInfo, @Cast("Nd4jLong*") long[] dZShapeInfo, + Pointer extraParams); + +public native void execTransformAny(@Cast("Nd4jPointer*") PointerPointer extraPointers, + int opNum, + OpaqueDataBuffer dbX, @Cast("Nd4jLong*") LongPointer hXShapeInfo, @Cast("Nd4jLong*") LongPointer dXShapeInfo, + OpaqueDataBuffer dbZ, @Cast("Nd4jLong*") LongPointer hZShapeInfo, @Cast("Nd4jLong*") LongPointer dZShapeInfo, + Pointer extraParams); +public native void execTransformAny(@Cast("Nd4jPointer*") PointerPointer extraPointers, + int opNum, + OpaqueDataBuffer dbX, @Cast("Nd4jLong*") LongBuffer hXShapeInfo, @Cast("Nd4jLong*") LongBuffer dXShapeInfo, + OpaqueDataBuffer dbZ, @Cast("Nd4jLong*") LongBuffer hZShapeInfo, @Cast("Nd4jLong*") LongBuffer dZShapeInfo, + Pointer extraParams); +public native void execTransformAny(@Cast("Nd4jPointer*") PointerPointer extraPointers, + int opNum, + OpaqueDataBuffer dbX, @Cast("Nd4jLong*") long[] hXShapeInfo, @Cast("Nd4jLong*") long[] dXShapeInfo, + OpaqueDataBuffer dbZ, @Cast("Nd4jLong*") long[] hZShapeInfo, @Cast("Nd4jLong*") long[] dZShapeInfo, + Pointer extraParams); + +public native void execTransformStrict(@Cast("Nd4jPointer*") PointerPointer extraPointers, + int opNum, + OpaqueDataBuffer dbX, @Cast("Nd4jLong*") LongPointer hXShapeInfo, @Cast("Nd4jLong*") LongPointer dXShapeInfo, + OpaqueDataBuffer dbZ, @Cast("Nd4jLong*") LongPointer hZShapeInfo, @Cast("Nd4jLong*") LongPointer dZShapeInfo, + Pointer extraParams); +public native void execTransformStrict(@Cast("Nd4jPointer*") PointerPointer extraPointers, + int opNum, + OpaqueDataBuffer dbX, @Cast("Nd4jLong*") LongBuffer hXShapeInfo, @Cast("Nd4jLong*") LongBuffer dXShapeInfo, + OpaqueDataBuffer dbZ, @Cast("Nd4jLong*") LongBuffer hZShapeInfo, @Cast("Nd4jLong*") LongBuffer dZShapeInfo, + Pointer extraParams); +public native void execTransformStrict(@Cast("Nd4jPointer*") PointerPointer extraPointers, + int opNum, + OpaqueDataBuffer dbX, @Cast("Nd4jLong*") long[] hXShapeInfo, @Cast("Nd4jLong*") long[] dXShapeInfo, + OpaqueDataBuffer dbZ, @Cast("Nd4jLong*") long[] hZShapeInfo, @Cast("Nd4jLong*") long[] dZShapeInfo, + Pointer extraParams); + +/** + * + * @param extraPointers + * @param opNum + * @param x + * @param xShapeInfo + * @param z + * @param zShapeInfo + * @param scalars + * @param extraParams + * @param dimension + * @param dimensionLength + */ +public native void execScalarTad(@Cast("Nd4jPointer*") PointerPointer extraPointers, + int opNum, + OpaqueDataBuffer dbX, @Cast("Nd4jLong*") LongPointer hXShapeInfo, @Cast("Nd4jLong*") LongPointer dXShapeInfo, + OpaqueDataBuffer dbZ, @Cast("Nd4jLong*") LongPointer hZShapeInfo, @Cast("Nd4jLong*") LongPointer dZShapeInfo, + OpaqueDataBuffer dbScalars, @Cast("Nd4jLong*") LongPointer hScalarShapeInfo, @Cast("Nd4jLong*") LongPointer dScalarShapeInfo, + Pointer extraParams, + OpaqueDataBuffer dbDimension, @Cast("Nd4jLong*") LongPointer hDimensionShape, @Cast("Nd4jLong*") LongPointer dDimensionShape, + @Cast("Nd4jLong*") LongPointer tadShapeInfo, @Cast("Nd4jLong*") LongPointer tadOffsets, + @Cast("Nd4jLong*") LongPointer tadShapeInfoZ, @Cast("Nd4jLong*") LongPointer tadOffsetsZ); +public native void execScalarTad(@Cast("Nd4jPointer*") PointerPointer extraPointers, + int opNum, + OpaqueDataBuffer dbX, @Cast("Nd4jLong*") LongBuffer hXShapeInfo, @Cast("Nd4jLong*") LongBuffer dXShapeInfo, + OpaqueDataBuffer dbZ, @Cast("Nd4jLong*") LongBuffer hZShapeInfo, @Cast("Nd4jLong*") LongBuffer dZShapeInfo, + OpaqueDataBuffer dbScalars, @Cast("Nd4jLong*") LongBuffer hScalarShapeInfo, @Cast("Nd4jLong*") LongBuffer dScalarShapeInfo, + Pointer extraParams, + OpaqueDataBuffer dbDimension, @Cast("Nd4jLong*") LongBuffer hDimensionShape, @Cast("Nd4jLong*") LongBuffer dDimensionShape, + @Cast("Nd4jLong*") LongBuffer tadShapeInfo, @Cast("Nd4jLong*") LongBuffer tadOffsets, + @Cast("Nd4jLong*") LongBuffer tadShapeInfoZ, @Cast("Nd4jLong*") LongBuffer tadOffsetsZ); +public native void execScalarTad(@Cast("Nd4jPointer*") PointerPointer extraPointers, + int opNum, + OpaqueDataBuffer dbX, @Cast("Nd4jLong*") long[] hXShapeInfo, @Cast("Nd4jLong*") long[] dXShapeInfo, + OpaqueDataBuffer dbZ, @Cast("Nd4jLong*") long[] hZShapeInfo, @Cast("Nd4jLong*") long[] dZShapeInfo, + OpaqueDataBuffer dbScalars, @Cast("Nd4jLong*") long[] hScalarShapeInfo, @Cast("Nd4jLong*") long[] dScalarShapeInfo, + Pointer extraParams, + OpaqueDataBuffer dbDimension, @Cast("Nd4jLong*") long[] hDimensionShape, @Cast("Nd4jLong*") long[] dDimensionShape, + @Cast("Nd4jLong*") long[] tadShapeInfo, @Cast("Nd4jLong*") long[] tadOffsets, + @Cast("Nd4jLong*") long[] tadShapeInfoZ, @Cast("Nd4jLong*") long[] tadOffsetsZ); + +public native void execScalarBoolTad(@Cast("Nd4jPointer*") PointerPointer extraPointers, + int opNum, + OpaqueDataBuffer dbX, @Cast("Nd4jLong*") LongPointer hXShapeInfo, @Cast("Nd4jLong*") LongPointer dXShapeInfo, + OpaqueDataBuffer dbZ, @Cast("Nd4jLong*") LongPointer hZShapeInfo, @Cast("Nd4jLong*") LongPointer dZShapeInfo, + OpaqueDataBuffer dbScalars, @Cast("Nd4jLong*") LongPointer hScalarShapeInfo, @Cast("Nd4jLong*") LongPointer dScalarShapeInfo, + Pointer extraParams, + OpaqueDataBuffer dbDimension, @Cast("Nd4jLong*") LongPointer hDimensionShape, @Cast("Nd4jLong*") LongPointer dDimensionShape, + @Cast("Nd4jLong*") LongPointer tadShapeInfo, @Cast("Nd4jLong*") LongPointer tadOffsets, + @Cast("Nd4jLong*") LongPointer tadShapeInfoZ, @Cast("Nd4jLong*") LongPointer tadOffsetsZ); +public native void execScalarBoolTad(@Cast("Nd4jPointer*") PointerPointer extraPointers, + int opNum, + OpaqueDataBuffer dbX, @Cast("Nd4jLong*") LongBuffer hXShapeInfo, @Cast("Nd4jLong*") LongBuffer dXShapeInfo, + OpaqueDataBuffer dbZ, @Cast("Nd4jLong*") LongBuffer hZShapeInfo, @Cast("Nd4jLong*") LongBuffer dZShapeInfo, + OpaqueDataBuffer dbScalars, @Cast("Nd4jLong*") LongBuffer hScalarShapeInfo, @Cast("Nd4jLong*") LongBuffer dScalarShapeInfo, + Pointer extraParams, + OpaqueDataBuffer dbDimension, @Cast("Nd4jLong*") LongBuffer hDimensionShape, @Cast("Nd4jLong*") LongBuffer dDimensionShape, + @Cast("Nd4jLong*") LongBuffer tadShapeInfo, @Cast("Nd4jLong*") LongBuffer tadOffsets, + @Cast("Nd4jLong*") LongBuffer tadShapeInfoZ, @Cast("Nd4jLong*") LongBuffer tadOffsetsZ); +public native void execScalarBoolTad(@Cast("Nd4jPointer*") PointerPointer extraPointers, + int opNum, + OpaqueDataBuffer dbX, @Cast("Nd4jLong*") long[] hXShapeInfo, @Cast("Nd4jLong*") long[] dXShapeInfo, + OpaqueDataBuffer dbZ, @Cast("Nd4jLong*") long[] hZShapeInfo, @Cast("Nd4jLong*") long[] dZShapeInfo, + OpaqueDataBuffer dbScalars, @Cast("Nd4jLong*") long[] hScalarShapeInfo, @Cast("Nd4jLong*") long[] dScalarShapeInfo, + Pointer extraParams, + OpaqueDataBuffer dbDimension, @Cast("Nd4jLong*") long[] hDimensionShape, @Cast("Nd4jLong*") long[] dDimensionShape, + @Cast("Nd4jLong*") long[] tadShapeInfo, @Cast("Nd4jLong*") long[] tadOffsets, + @Cast("Nd4jLong*") long[] tadShapeInfoZ, @Cast("Nd4jLong*") long[] tadOffsetsZ); + +public native void specialConcat( + @Cast("Nd4jPointer*") PointerPointer extraPointers, + int dimension, + int numArrays, + @Cast("Nd4jPointer*") PointerPointer data, + @Cast("Nd4jPointer*") PointerPointer inputShapeInfo, + Pointer result, + @Cast("Nd4jLong*") LongPointer resultShapeInfo, + @Cast("Nd4jPointer*") PointerPointer tadPointers, + @Cast("Nd4jPointer*") PointerPointer offsetPointers); +public native void specialConcat( + @Cast("Nd4jPointer*") PointerPointer extraPointers, + int dimension, + int numArrays, + @Cast("Nd4jPointer*") PointerPointer data, + @Cast("Nd4jPointer*") PointerPointer inputShapeInfo, + Pointer result, + @Cast("Nd4jLong*") LongBuffer resultShapeInfo, + @Cast("Nd4jPointer*") PointerPointer tadPointers, + @Cast("Nd4jPointer*") PointerPointer offsetPointers); +public native void specialConcat( + @Cast("Nd4jPointer*") PointerPointer extraPointers, + int dimension, + int numArrays, + @Cast("Nd4jPointer*") PointerPointer data, + @Cast("Nd4jPointer*") PointerPointer inputShapeInfo, + Pointer result, + @Cast("Nd4jLong*") long[] resultShapeInfo, + @Cast("Nd4jPointer*") PointerPointer tadPointers, + @Cast("Nd4jPointer*") PointerPointer offsetPointers); + +/** + * This method implementation exists only for cuda. + * The other backends should have dummy method for JNI compatibility reasons. + */ +public native void initializeDevicesAndFunctions(); + +public native void initializeFunctions(@Cast("Nd4jPointer*") PointerPointer functions); + +/** + * This method acquires memory chunk of requested size on host side + * + * @param pointer pointer that'll be used for allocation + * @param memorySize memory size, in bytes + * @param flags optional parameter + */ +public native @Cast("Nd4jPointer") Pointer mallocHost(@Cast("Nd4jLong") long memorySize, int flags); + +/** + * This method acquires memory chunk of requested size on specified device + * + * @param pointer pointer that'll be used for allocation + * @param memorySize memory size, in bytes + * @param ptrToDeviceId pointer to deviceId. For cuda that's just and int, for OpenCL that's pointer to device_id, etc + * @param flags optional parameter + */ +public native @Cast("Nd4jPointer") Pointer mallocDevice(@Cast("Nd4jLong") long memorySize, int deviceId, int flags); + +/** + * This method releases previously allocated host memory space + * + * @param pointer pointer that'll be freed + */ +public native int freeHost(@Cast("Nd4jPointer") Pointer pointer); + +/** + * This method releases previously allocated memory space on device + * + * @param pointer pointer that'll be freed + * @param ptrToDeviceId pointer to deviceId. + */ +public native int freeDevice(@Cast("Nd4jPointer") Pointer pointer, int deviceId); + +/** + * + * @return + */ +public native int ompGetMaxThreads(); + +/** + * + * @return + */ +public native int ompGetNumThreads(); + +/** + * + * @param threads + */ +public native void setOmpNumThreads(int threads); + +/** + * + * @param threads + */ +public native void setOmpMinThreads(int threads); + + +public native @Cast("bool") boolean isBlasVersionMatches(int major, int minor, int build); + +/** + * + * @return + */ +public native @Cast("Nd4jPointer") Pointer createContext(); + +/** + * + * @return + */ +public native @Cast("Nd4jPointer") Pointer createStream(); + +/** + * + * @return + */ +public native @Cast("Nd4jPointer") Pointer createEvent(); + +/** + * + * @param event + * @param stream + * @return + */ +public native int registerEvent(@Cast("Nd4jPointer") Pointer event, @Cast("Nd4jPointer") Pointer stream); + +/** + * + * @param event + * @return + */ +public native int destroyEvent(@Cast("Nd4jPointer") Pointer event); + +/** + * + * @param ptrToDeviceId + * @return + */ +public native int setDevice(int deviceId); + +/** + * + * @return + */ +public native int getDevice(); + +/** + * + * @param stream + * @return + */ +public native int streamSynchronize(@Cast("Nd4jPointer") Pointer stream); + +/** + * + * @param event + * @return + */ +public native int eventSynchronize(@Cast("Nd4jPointer") Pointer event); + +/** + * + * @param ptrToDeviceId + * @return + */ +public native @Cast("Nd4jLong") long getDeviceFreeMemory(int deviceId); + +/** + * Returns amount of free memory for current device + * @return + */ +public native @Cast("Nd4jLong") long getDeviceFreeMemoryDefault(); + +/** + * + * @param ptrToDeviceId + * @return + */ +public native @Cast("Nd4jLong") long getDeviceTotalMemory(int deviceId); + +/** + * + * @param ptrToDeviceId + * @return + */ +public native int getDeviceMajor(int deviceId); + +/** + * This method returns amount of cached memory + * @param deviceId + * @return + */ +public native @Cast("Nd4jLong") long getCachedMemory(int deviceId); + +/** + * + * @param ptrToDeviceId + * @return + */ +public native int getDeviceMinor(int deviceId); + +/** + * + * @param ptrToDeviceId + * @return + */ +public native @Cast("char*") String getDeviceName(int deviceId); + +/** + * + * @param dst + * @param src + * @param size + * @param flags + * @param reserved + * @return + */ +public native int memcpySync(@Cast("Nd4jPointer") Pointer dst, + @Cast("Nd4jPointer") Pointer src, + @Cast("Nd4jLong") long size, + int flags, + @Cast("Nd4jPointer") Pointer reserved); + +/** + * + * @param dst + * @param src + * @param size + * @param flags + * @param reserved + * @return + */ +public native int memcpyAsync(@Cast("Nd4jPointer") Pointer dst, + @Cast("Nd4jPointer") Pointer src, + @Cast("Nd4jLong") long size, + int flags, + @Cast("Nd4jPointer") Pointer reserved); + +/** + * + * @param dst + * @param value + * @param size + * @param flags + * @param reserved + * @return + */ +public native int memsetSync(@Cast("Nd4jPointer") Pointer dst, + int value, + @Cast("Nd4jLong") long size, + int flags, + @Cast("Nd4jPointer") Pointer reserved); + +/** + * + * @param dst + * @param value + * @param size + * @param flags + * @param reserved + * @return + */ +public native int memsetAsync(@Cast("Nd4jPointer") Pointer dst, + int value, + @Cast("Nd4jLong") long size, + int flags, + @Cast("Nd4jPointer") Pointer reserved); + +/** + * + * @param dst + * @param src + * @param size + * @param flags + * @param reserved + * @return + */ +public native int memcpyConstantAsync(@Cast("Nd4jLong") long dst, + @Cast("Nd4jPointer") Pointer src, + @Cast("Nd4jLong") long size, + int flags, + @Cast("Nd4jPointer") Pointer reserved); + +/** + * + * @return + */ +public native @Cast("Nd4jPointer") Pointer getConstantSpace(); + +/** + * + * @return + */ +public native int getAvailableDevices(); + +/** + * + * @param reallyEnable + */ +public native void enableDebugMode(@Cast("bool") boolean reallyEnable); + +/** + * + * @param reallyEnable + */ +public native void enableVerboseMode(@Cast("bool") boolean reallyEnable); + +/** + * + * @param gridSize + */ +public native void setGridLimit(int gridSize); + +/** + * + * @param xShapeInfo + * @param dimension + * @param dimensionLength + * @param targetBuffer + * @param offsetsBuffer + */ +public native OpaqueTadPack tadOnlyShapeInfo(@Cast("Nd4jLong*") LongPointer xShapeInfo, + IntPointer dimension, + int dimensionLength); +public native OpaqueTadPack tadOnlyShapeInfo(@Cast("Nd4jLong*") LongBuffer xShapeInfo, + IntBuffer dimension, + int dimensionLength); +public native OpaqueTadPack tadOnlyShapeInfo(@Cast("Nd4jLong*") long[] xShapeInfo, + int[] dimension, + int dimensionLength); + +public native @Cast("Nd4jLong*") LongPointer getPrimaryShapeInfo(OpaqueTadPack pack); +public native @Cast("Nd4jLong*") LongPointer getPrimaryOffsets(OpaqueTadPack pack); +public native @Cast("Nd4jLong*") LongPointer getSpecialShapeInfo(OpaqueTadPack pack); +public native @Cast("Nd4jLong*") LongPointer getSpecialOffsets(OpaqueTadPack pack); +public native @Cast("Nd4jLong") long getNumberOfTads(OpaqueTadPack pack); +public native int getShapeInfoLength(OpaqueTadPack pack); + +public native void deleteTadPack(OpaqueTadPack ptr); + +/* + * PullRow special op + */ + +/** + * + * @param extraPointers + * @param x + * @param xShapeInfo + * @param z + * @param zShapeInfo + * @param n + * @param indexes + * @param tadShapeInfo + * @param tadOffsets + * @param zTadShapeInfo + * @param zTadOffsets + */ +public native void pullRows(@Cast("Nd4jPointer*") PointerPointer extraPointers, + OpaqueDataBuffer dbX, @Cast("Nd4jLong*") LongPointer xShapeInfo, @Cast("Nd4jLong*") LongPointer dxShapeInfo, + OpaqueDataBuffer dbZ, @Cast("Nd4jLong*") LongPointer zShapeInfo, @Cast("Nd4jLong*") LongPointer dzShapeInfo, + @Cast("Nd4jLong") long n, + @Cast("Nd4jLong*") LongPointer indexes, + @Cast("Nd4jLong*") LongPointer tadShapeInfo, + @Cast("Nd4jLong*") LongPointer tadOffsets, + @Cast("Nd4jLong*") LongPointer zTadShapeInfo, + @Cast("Nd4jLong*") LongPointer zTadOffsets); +public native void pullRows(@Cast("Nd4jPointer*") PointerPointer extraPointers, + OpaqueDataBuffer dbX, @Cast("Nd4jLong*") LongBuffer xShapeInfo, @Cast("Nd4jLong*") LongBuffer dxShapeInfo, + OpaqueDataBuffer dbZ, @Cast("Nd4jLong*") LongBuffer zShapeInfo, @Cast("Nd4jLong*") LongBuffer dzShapeInfo, + @Cast("Nd4jLong") long n, + @Cast("Nd4jLong*") LongBuffer indexes, + @Cast("Nd4jLong*") LongBuffer tadShapeInfo, + @Cast("Nd4jLong*") LongBuffer tadOffsets, + @Cast("Nd4jLong*") LongBuffer zTadShapeInfo, + @Cast("Nd4jLong*") LongBuffer zTadOffsets); +public native void pullRows(@Cast("Nd4jPointer*") PointerPointer extraPointers, + OpaqueDataBuffer dbX, @Cast("Nd4jLong*") long[] xShapeInfo, @Cast("Nd4jLong*") long[] dxShapeInfo, + OpaqueDataBuffer dbZ, @Cast("Nd4jLong*") long[] zShapeInfo, @Cast("Nd4jLong*") long[] dzShapeInfo, + @Cast("Nd4jLong") long n, + @Cast("Nd4jLong*") long[] indexes, + @Cast("Nd4jLong*") long[] tadShapeInfo, + @Cast("Nd4jLong*") long[] tadOffsets, + @Cast("Nd4jLong*") long[] zTadShapeInfo, + @Cast("Nd4jLong*") long[] zTadOffsets); + +/** + * + * @param extras + * @param dx + * @param dz + * @param n + * @param length + * @param propagate + */ +public native void average(@Cast("Nd4jPointer*") PointerPointer extras, + @Cast("Nd4jPointer*") PointerPointer x, @Cast("Nd4jLong*") LongPointer xShapeInfo, + @Cast("Nd4jPointer*") PointerPointer dx, @Cast("Nd4jLong*") LongPointer dxShapeInfo, + Pointer z, @Cast("Nd4jLong*") LongPointer zShapeInfo, + Pointer dz, @Cast("Nd4jLong*") LongPointer dzShapeInfo, + int n, + @Cast("Nd4jLong") long length, + @Cast("bool") boolean propagate); +public native void average(@Cast("Nd4jPointer*") PointerPointer extras, + @Cast("Nd4jPointer*") PointerPointer x, @Cast("Nd4jLong*") LongBuffer xShapeInfo, + @Cast("Nd4jPointer*") PointerPointer dx, @Cast("Nd4jLong*") LongBuffer dxShapeInfo, + Pointer z, @Cast("Nd4jLong*") LongBuffer zShapeInfo, + Pointer dz, @Cast("Nd4jLong*") LongBuffer dzShapeInfo, + int n, + @Cast("Nd4jLong") long length, + @Cast("bool") boolean propagate); +public native void average(@Cast("Nd4jPointer*") PointerPointer extras, + @Cast("Nd4jPointer*") PointerPointer x, @Cast("Nd4jLong*") long[] xShapeInfo, + @Cast("Nd4jPointer*") PointerPointer dx, @Cast("Nd4jLong*") long[] dxShapeInfo, + Pointer z, @Cast("Nd4jLong*") long[] zShapeInfo, + Pointer dz, @Cast("Nd4jLong*") long[] dzShapeInfo, + int n, + @Cast("Nd4jLong") long length, + @Cast("bool") boolean propagate); + + +public native void accumulate(@Cast("Nd4jPointer*") PointerPointer extras, + @Cast("Nd4jPointer*") PointerPointer x, @Cast("Nd4jLong*") LongPointer xShapeInfo, + @Cast("Nd4jPointer*") PointerPointer dx, @Cast("Nd4jLong*") LongPointer dxShapeInfo, + Pointer z, @Cast("Nd4jLong*") LongPointer zShapeInfo, + Pointer dz, @Cast("Nd4jLong*") LongPointer dzShapeInfo, + int n, + @Cast("Nd4jLong") long length); +public native void accumulate(@Cast("Nd4jPointer*") PointerPointer extras, + @Cast("Nd4jPointer*") PointerPointer x, @Cast("Nd4jLong*") LongBuffer xShapeInfo, + @Cast("Nd4jPointer*") PointerPointer dx, @Cast("Nd4jLong*") LongBuffer dxShapeInfo, + Pointer z, @Cast("Nd4jLong*") LongBuffer zShapeInfo, + Pointer dz, @Cast("Nd4jLong*") LongBuffer dzShapeInfo, + int n, + @Cast("Nd4jLong") long length); +public native void accumulate(@Cast("Nd4jPointer*") PointerPointer extras, + @Cast("Nd4jPointer*") PointerPointer x, @Cast("Nd4jLong*") long[] xShapeInfo, + @Cast("Nd4jPointer*") PointerPointer dx, @Cast("Nd4jLong*") long[] dxShapeInfo, + Pointer z, @Cast("Nd4jLong*") long[] zShapeInfo, + Pointer dz, @Cast("Nd4jLong*") long[] dzShapeInfo, + int n, + @Cast("Nd4jLong") long length); + + +/** + * P2P enabler + */ +/** + * + * @param enable + */ +public native void enableP2P(@Cast("bool") boolean enable); + +/** + * + */ +public native void checkP2P(); + +/** + * + * @return + */ +public native @Cast("bool") boolean isP2PAvailable(); + +/** + * Shuffle methods + */ + +/** + * + * @param extras + * @param dx + * @param xShapeInfo + * @param dz + * @param zShapeInfo + * @param N + * @param shuffleMap + * @param tadShapeInfo + * @param tadOffsets + */ +public native void shuffle(@Cast("Nd4jPointer*") PointerPointer extras, + @Cast("Nd4jPointer*") PointerPointer x, @Cast("Nd4jPointer*") PointerPointer xShapeInfo, + @Cast("Nd4jPointer*") PointerPointer dx, @Cast("Nd4jPointer*") PointerPointer dxShapeInfo, + @Cast("Nd4jPointer*") PointerPointer z, @Cast("Nd4jPointer*") PointerPointer zShapeInfo, + @Cast("Nd4jPointer*") PointerPointer dz, @Cast("Nd4jPointer*") PointerPointer dzShapeInfo, + int N, + IntPointer shuffleMap, + @Cast("Nd4jPointer*") PointerPointer tadShapeInfo, + @Cast("Nd4jPointer*") PointerPointer tadOffsets); +public native void shuffle(@Cast("Nd4jPointer*") PointerPointer extras, + @Cast("Nd4jPointer*") PointerPointer x, @Cast("Nd4jPointer*") PointerPointer xShapeInfo, + @Cast("Nd4jPointer*") PointerPointer dx, @Cast("Nd4jPointer*") PointerPointer dxShapeInfo, + @Cast("Nd4jPointer*") PointerPointer z, @Cast("Nd4jPointer*") PointerPointer zShapeInfo, + @Cast("Nd4jPointer*") PointerPointer dz, @Cast("Nd4jPointer*") PointerPointer dzShapeInfo, + int N, + IntBuffer shuffleMap, + @Cast("Nd4jPointer*") PointerPointer tadShapeInfo, + @Cast("Nd4jPointer*") PointerPointer tadOffsets); +public native void shuffle(@Cast("Nd4jPointer*") PointerPointer extras, + @Cast("Nd4jPointer*") PointerPointer x, @Cast("Nd4jPointer*") PointerPointer xShapeInfo, + @Cast("Nd4jPointer*") PointerPointer dx, @Cast("Nd4jPointer*") PointerPointer dxShapeInfo, + @Cast("Nd4jPointer*") PointerPointer z, @Cast("Nd4jPointer*") PointerPointer zShapeInfo, + @Cast("Nd4jPointer*") PointerPointer dz, @Cast("Nd4jPointer*") PointerPointer dzShapeInfo, + int N, + int[] shuffleMap, + @Cast("Nd4jPointer*") PointerPointer tadShapeInfo, + @Cast("Nd4jPointer*") PointerPointer tadOffsets); + + +/** + * Type Conversions + */ + +/** + * + * @param extras + * @param srcType + * @param x + * @param N + * @param dstType + * @param z + */ +public native void convertTypes(@Cast("Nd4jPointer*") PointerPointer extras, int srcType, @Cast("Nd4jPointer") Pointer x, @Cast("Nd4jLong") long N, int dstType, @Cast("Nd4jPointer") Pointer z); + + +/** + * + * @return + */ +public native @Cast("bool") boolean isExperimentalEnabled(); + +/** + * Aggregate + */ + +/** + * + * @param extraPointers + * @param opNum + * @param arguments + * @param numArguments + * @param shapeArguments + * @param numShapeArguments + * @param indexArguments + * @param numIndexArguments + * @param intArrays + * @param numIntArrays + * @param realArguments + * @param numRealArguments + */ +public native void execAggregate(@Cast("Nd4jPointer*") PointerPointer extraPointers, + int opNum, + @Cast("void**") PointerPointer arguments, + int numArguments, + @Cast("Nd4jLong**") PointerPointer shapeArguments, + int numShapeArguments, + IntPointer indexArguments, + int numIndexArguments, + @Cast("int**") PointerPointer intArrays, + int numIntArrays, + Pointer realArguments, + int numRealArguments, + @Cast("sd::DataType") int dtype); +public native void execAggregate(@Cast("Nd4jPointer*") PointerPointer extraPointers, + int opNum, + @Cast("void**") @ByPtrPtr Pointer arguments, + int numArguments, + @Cast("Nd4jLong**") @ByPtrPtr LongPointer shapeArguments, + int numShapeArguments, + IntPointer indexArguments, + int numIndexArguments, + @ByPtrPtr IntPointer intArrays, + int numIntArrays, + Pointer realArguments, + int numRealArguments, + @Cast("sd::DataType") int dtype); +public native void execAggregate(@Cast("Nd4jPointer*") PointerPointer extraPointers, + int opNum, + @Cast("void**") @ByPtrPtr Pointer arguments, + int numArguments, + @Cast("Nd4jLong**") @ByPtrPtr LongBuffer shapeArguments, + int numShapeArguments, + IntBuffer indexArguments, + int numIndexArguments, + @ByPtrPtr IntBuffer intArrays, + int numIntArrays, + Pointer realArguments, + int numRealArguments, + @Cast("sd::DataType") int dtype); +public native void execAggregate(@Cast("Nd4jPointer*") PointerPointer extraPointers, + int opNum, + @Cast("void**") @ByPtrPtr Pointer arguments, + int numArguments, + @Cast("Nd4jLong**") @ByPtrPtr long[] shapeArguments, + int numShapeArguments, + int[] indexArguments, + int numIndexArguments, + @ByPtrPtr int[] intArrays, + int numIntArrays, + Pointer realArguments, + int numRealArguments, + @Cast("sd::DataType") int dtype); + + +public native void batchExecutor(@Cast("Nd4jPointer*") PointerPointer extraPointers, + int numAggregates, + int opNum, + int maxArgs, + int maxShapes, + int maxIntArrays, + int maxIntArraySize, + int maxIdx, + int maxReals, + Pointer ptrToArguments, + @Cast("sd::DataType") int dtype); + +public native void execAggregateBatch(@Cast("Nd4jPointer*") PointerPointer extraPointers, + int numAggregates, + int opNum, + int maxArgs, + int maxShapes, + int maxIntArrays, + int maxIntArraySize, + int maxIdx, + int maxReals, + Pointer ptrToArguments, + @Cast("sd::DataType") int dtype); + +/** + * Random operations + */ + +/** + * + * @param extraPointers + * @param opNum + * @param state + * @param z + * @param zShapeBuffer + * @param extraArguments + */ +public native void execRandom(@Cast("Nd4jPointer*") PointerPointer extraPointers, + int opNum, + @Cast("Nd4jPointer") Pointer state, + OpaqueDataBuffer dbZ, @Cast("Nd4jLong*") LongPointer hZShapeBuffer, @Cast("Nd4jLong*") LongPointer dZShapeBuffer, + Pointer extraArguments); +public native void execRandom(@Cast("Nd4jPointer*") PointerPointer extraPointers, + int opNum, + @Cast("Nd4jPointer") Pointer state, + OpaqueDataBuffer dbZ, @Cast("Nd4jLong*") LongBuffer hZShapeBuffer, @Cast("Nd4jLong*") LongBuffer dZShapeBuffer, + Pointer extraArguments); +public native void execRandom(@Cast("Nd4jPointer*") PointerPointer extraPointers, + int opNum, + @Cast("Nd4jPointer") Pointer state, + OpaqueDataBuffer dbZ, @Cast("Nd4jLong*") long[] hZShapeBuffer, @Cast("Nd4jLong*") long[] dZShapeBuffer, + Pointer extraArguments); + +/** + * + * @param extraPointers + * @param opNum + * @param state + * @param x + * @param xShapeBuffer + * @param y + * @param yShapeBuffer + * @param z + * @param zShapeBuffer + * @param extraArguments + */ +public native void execRandom3(@Cast("Nd4jPointer*") PointerPointer extraPointers, + int opNum, + @Cast("Nd4jPointer") Pointer state, + OpaqueDataBuffer dbX, @Cast("Nd4jLong*") LongPointer hXShapeBuffer, @Cast("Nd4jLong*") LongPointer dXShapeBuffer, + OpaqueDataBuffer dbY, @Cast("Nd4jLong*") LongPointer hYShapeBuffer, @Cast("Nd4jLong*") LongPointer dYShapeBuffer, + OpaqueDataBuffer dbZ, @Cast("Nd4jLong*") LongPointer hZShapeBuffer, @Cast("Nd4jLong*") LongPointer dZShapeBuffer, + Pointer extraArguments); +public native void execRandom3(@Cast("Nd4jPointer*") PointerPointer extraPointers, + int opNum, + @Cast("Nd4jPointer") Pointer state, + OpaqueDataBuffer dbX, @Cast("Nd4jLong*") LongBuffer hXShapeBuffer, @Cast("Nd4jLong*") LongBuffer dXShapeBuffer, + OpaqueDataBuffer dbY, @Cast("Nd4jLong*") LongBuffer hYShapeBuffer, @Cast("Nd4jLong*") LongBuffer dYShapeBuffer, + OpaqueDataBuffer dbZ, @Cast("Nd4jLong*") LongBuffer hZShapeBuffer, @Cast("Nd4jLong*") LongBuffer dZShapeBuffer, + Pointer extraArguments); +public native void execRandom3(@Cast("Nd4jPointer*") PointerPointer extraPointers, + int opNum, + @Cast("Nd4jPointer") Pointer state, + OpaqueDataBuffer dbX, @Cast("Nd4jLong*") long[] hXShapeBuffer, @Cast("Nd4jLong*") long[] dXShapeBuffer, + OpaqueDataBuffer dbY, @Cast("Nd4jLong*") long[] hYShapeBuffer, @Cast("Nd4jLong*") long[] dYShapeBuffer, + OpaqueDataBuffer dbZ, @Cast("Nd4jLong*") long[] hZShapeBuffer, @Cast("Nd4jLong*") long[] dZShapeBuffer, + Pointer extraArguments); + +/** + * + * @param extraPointers + * @param opNum + * @param state + * @param x + * @param xShapeBuffer + * @param z + * @param zShapeBuffer + * @param extraArguments + */ +public native void execRandom2(@Cast("Nd4jPointer*") PointerPointer extraPointers, + int opNum, + @Cast("Nd4jPointer") Pointer state, + OpaqueDataBuffer dbX, @Cast("Nd4jLong*") LongPointer hXShapeBuffer, @Cast("Nd4jLong*") LongPointer dXShapeBuffer, + OpaqueDataBuffer dbZ, @Cast("Nd4jLong*") LongPointer hZShapeBuffer, @Cast("Nd4jLong*") LongPointer dZShapeBuffer, + Pointer extraArguments); +public native void execRandom2(@Cast("Nd4jPointer*") PointerPointer extraPointers, + int opNum, + @Cast("Nd4jPointer") Pointer state, + OpaqueDataBuffer dbX, @Cast("Nd4jLong*") LongBuffer hXShapeBuffer, @Cast("Nd4jLong*") LongBuffer dXShapeBuffer, + OpaqueDataBuffer dbZ, @Cast("Nd4jLong*") LongBuffer hZShapeBuffer, @Cast("Nd4jLong*") LongBuffer dZShapeBuffer, + Pointer extraArguments); +public native void execRandom2(@Cast("Nd4jPointer*") PointerPointer extraPointers, + int opNum, + @Cast("Nd4jPointer") Pointer state, + OpaqueDataBuffer dbX, @Cast("Nd4jLong*") long[] hXShapeBuffer, @Cast("Nd4jLong*") long[] dXShapeBuffer, + OpaqueDataBuffer dbZ, @Cast("Nd4jLong*") long[] hZShapeBuffer, @Cast("Nd4jLong*") long[] dZShapeBuffer, + Pointer extraArguments); + + +/** + * + * @param extraPointers + * @param seed + * @param bufferSize + * @param ptrToBuffer + * @return + */ +public native @Cast("Nd4jPointer") Pointer initRandom(@Cast("Nd4jPointer*") PointerPointer extraPointers, + long seed, + long bufferSize, + @Cast("Nd4jPointer") Pointer ptrToBuffer); + +/** + * + * @param extraPointers + * @param seed + * @param ptrRandom + */ +public native void refreshBuffer(@Cast("Nd4jPointer*") PointerPointer extraPointers, + long seed, + @Cast("Nd4jPointer") Pointer ptrRandom); + +/** + * + * @param extraPointers + * @param seed + * @param ptrRandom + */ +public native void reSeedBuffer(@Cast("Nd4jPointer*") PointerPointer extraPointers, + long seed, + @Cast("Nd4jPointer") Pointer ptrRandom); + +/** + * + * @param ptrRandom + */ +public native void destroyRandom(@Cast("Nd4jPointer") Pointer ptrRandom); + +/** +* +* @param data +* @param shapeBuffer +* @param wordSize +* @param headerSize +* @return +*/ + +public native @Cast("Nd4jPointer") Pointer numpyHeaderForNd4j(@Cast("Nd4jPointer") Pointer data,@Cast("Nd4jPointer") Pointer shapeBuffer,@Cast("Nd4jLong") long wordSize,@Cast("Nd4jLong*") LongPointer headerSize); +public native @Cast("Nd4jPointer") Pointer numpyHeaderForNd4j(@Cast("Nd4jPointer") Pointer data,@Cast("Nd4jPointer") Pointer shapeBuffer,@Cast("Nd4jLong") long wordSize,@Cast("Nd4jLong*") LongBuffer headerSize); +public native @Cast("Nd4jPointer") Pointer numpyHeaderForNd4j(@Cast("Nd4jPointer") Pointer data,@Cast("Nd4jPointer") Pointer shapeBuffer,@Cast("Nd4jLong") long wordSize,@Cast("Nd4jLong*") long[] headerSize); + +/** +* Load numpy from a header +* based on the cnpy parse from header method. +* @param data the header data to parse +* @return a pointer to a numpy cnpy:NpyArray struct +*/ +public native @Cast("Nd4jPointer") Pointer loadNpyFromHeader(@Cast("Nd4jPointer") Pointer data); + +/** +* Create a numpy array from an nd4j +* array +* @param data a pointer to the data +* @param shapeBuffer the shapebuffer for the nd4j array +* @param wordSize the word size (4 for float, 8 for doubles) +* @return a pointer to a numpy array +*/ + +public native @Cast("Nd4jPointer") Pointer numpyFromNd4j(@Cast("Nd4jPointer") Pointer data,@Cast("Nd4jPointer") Pointer shapeBuffer,@Cast("Nd4jLong") long wordSize); + + +/** +* +* @param npyArray +* @return +*/ +public native @Cast("Nd4jPointer") Pointer shapeBufferForNumpy(@Cast("Nd4jPointer") Pointer npyArray); + + +/** +* Get the shape buffer from a +* numpy array. +* **Warning** this allocates memory +* @param npyArray +* @return +*/ +public native @Cast("Nd4jPointer") Pointer shapeBufferForNumpyHeader(@Cast("Nd4jPointer") Pointer npyArray); + + + +/** +* +* @param npyArray +* @return +*/ +public native @Cast("Nd4jPointer") Pointer dataPointForNumpyHeader(@Cast("Nd4jPointer") Pointer npyArray); + +/** +* +* @param npyArray +* @return +*/ +public native @Cast("Nd4jPointer") Pointer dataPointForNumpyStruct(@Cast("Nd4jPointer") Pointer npyArrayStruct); + +/** +* +* @param npyArray +* @param fromFile +* @return +*/ +public native @Cast("Nd4jPointer") Pointer dataPointForNumpy(@Cast("Nd4jPointer") Pointer npyArray); + +/** +* Load a numpy array from a file +* and return it as an Nd4jPointer +* @param path +* @return +*/ +public native @Cast("Nd4jPointer") Pointer numpyFromFile(@StdString BytePointer path); +public native @Cast("Nd4jPointer") Pointer numpyFromFile(@StdString String path); + + +////// NPZ ////// + +public native Pointer mapFromNpzFile(@StdString BytePointer path); +public native Pointer mapFromNpzFile(@StdString String path); + + +public native int getNumNpyArraysInMap(Pointer map); + +public native @Cast("char*") String getNpyArrayNameFromMap(Pointer map, int index); + +public native Pointer getNpyArrayFromMap(Pointer map, int index); + +public native int dataTypeFromNpyHeader(Pointer header); + +public native Pointer getNpyArrayData(Pointer npArray); + +public native int getNpyArrayRank(Pointer npArray); + +public native @Cast("Nd4jLong*") LongPointer getNpyArrayShape(Pointer npArray); + +public native char getNpyArrayOrder(Pointer npArray); + +public native int getNpyArrayElemSize(Pointer npArray); + +public native void deleteNPArrayStruct(Pointer npArray); + +public native void deleteNPArrayMap(Pointer map); +////// + +/** +* Get the element size for a numpy array +* @param npyArray the numpy array's address +* to get the length for +* @return +*/ +public native int elementSizeForNpyArray(@Cast("Nd4jPointer") Pointer npyArray); + + +/** +* Get the element size for a numpy array +* @param npyArray the numpy array's address +* to get the length for +* @return +*/ +public native int elementSizeForNpyArrayHeader(@Cast("Nd4jPointer") Pointer npyArray); + + +public native void releaseNumpy(@Cast("Nd4jPointer") Pointer npyArray); + + +/** + * Return the length of a shape buffer + * based on the pointer + * @param buffer the buffer pointer to check + * @return + */ +public native int lengthForShapeBufferPointer(@Cast("Nd4jPointer") Pointer buffer); + + + /** +* The pointer to get the address for +* +* @param address the address to get the pointer +* @return the pointer for the given address +*/ + +public native @Cast("Nd4jPointer") Pointer pointerForAddress(@Cast("Nd4jLong") long _address); + +/** + * This method takes single N-dimensional tensor, and copies its TADs to target arrays + * + * @param x + * @param xShapeInfo + * @param targets + * @param zShapeInfo + * @return + */ +public native void tear(@Cast("Nd4jPointer*") PointerPointer extraPointers, + OpaqueDataBuffer dbX, @Cast("Nd4jLong*") LongPointer xShapeInfo, @Cast("Nd4jLong*") LongPointer dxShapeInfo, + @Cast("Nd4jPointer*") PointerPointer targets, @Cast("Nd4jLong*") LongPointer zShapeInfo, + @Cast("Nd4jLong*") LongPointer tadShapeInfo, + @Cast("Nd4jLong*") LongPointer tadOffsets); +public native void tear(@Cast("Nd4jPointer*") PointerPointer extraPointers, + OpaqueDataBuffer dbX, @Cast("Nd4jLong*") LongBuffer xShapeInfo, @Cast("Nd4jLong*") LongBuffer dxShapeInfo, + @Cast("Nd4jPointer*") PointerPointer targets, @Cast("Nd4jLong*") LongBuffer zShapeInfo, + @Cast("Nd4jLong*") LongBuffer tadShapeInfo, + @Cast("Nd4jLong*") LongBuffer tadOffsets); +public native void tear(@Cast("Nd4jPointer*") PointerPointer extraPointers, + OpaqueDataBuffer dbX, @Cast("Nd4jLong*") long[] xShapeInfo, @Cast("Nd4jLong*") long[] dxShapeInfo, + @Cast("Nd4jPointer*") PointerPointer targets, @Cast("Nd4jLong*") long[] zShapeInfo, + @Cast("Nd4jLong*") long[] tadShapeInfo, + @Cast("Nd4jLong*") long[] tadOffsets); + +public native @Cast("Nd4jLong") long encodeBitmap(@Cast("Nd4jPointer*") PointerPointer extraPointers, Pointer dx, @Cast("Nd4jLong*") LongPointer xShapeInfo, @Cast("Nd4jLong") long N, IntPointer dz, float threshold); +public native @Cast("Nd4jLong") long encodeBitmap(@Cast("Nd4jPointer*") PointerPointer extraPointers, Pointer dx, @Cast("Nd4jLong*") LongBuffer xShapeInfo, @Cast("Nd4jLong") long N, IntBuffer dz, float threshold); +public native @Cast("Nd4jLong") long encodeBitmap(@Cast("Nd4jPointer*") PointerPointer extraPointers, Pointer dx, @Cast("Nd4jLong*") long[] xShapeInfo, @Cast("Nd4jLong") long N, int[] dz, float threshold); +public native void decodeBitmap(@Cast("Nd4jPointer*") PointerPointer extraPointers, Pointer dx, @Cast("Nd4jLong") long N, Pointer dz, @Cast("Nd4jLong*") LongPointer zShapeInfo); +public native void decodeBitmap(@Cast("Nd4jPointer*") PointerPointer extraPointers, Pointer dx, @Cast("Nd4jLong") long N, Pointer dz, @Cast("Nd4jLong*") LongBuffer zShapeInfo); +public native void decodeBitmap(@Cast("Nd4jPointer*") PointerPointer extraPointers, Pointer dx, @Cast("Nd4jLong") long N, Pointer dz, @Cast("Nd4jLong*") long[] zShapeInfo); + + +public native void encodeThresholdP1(@Cast("Nd4jPointer*") PointerPointer extraPointers, Pointer dx, @Cast("Nd4jLong*") LongPointer xShapeInfo, @Cast("Nd4jLong") long N, IntPointer dz, float threshold); +public native void encodeThresholdP1(@Cast("Nd4jPointer*") PointerPointer extraPointers, Pointer dx, @Cast("Nd4jLong*") LongBuffer xShapeInfo, @Cast("Nd4jLong") long N, IntBuffer dz, float threshold); +public native void encodeThresholdP1(@Cast("Nd4jPointer*") PointerPointer extraPointers, Pointer dx, @Cast("Nd4jLong*") long[] xShapeInfo, @Cast("Nd4jLong") long N, int[] dz, float threshold); +public native void encodeThresholdP2Int(@Cast("Nd4jPointer*") PointerPointer extraPointers, IntPointer dx, @Cast("Nd4jLong") long N, IntPointer dz); +public native void encodeThresholdP2Int(@Cast("Nd4jPointer*") PointerPointer extraPointers, IntBuffer dx, @Cast("Nd4jLong") long N, IntBuffer dz); +public native void encodeThresholdP2Int(@Cast("Nd4jPointer*") PointerPointer extraPointers, int[] dx, @Cast("Nd4jLong") long N, int[] dz); +public native void encodeThresholdP3(@Cast("Nd4jPointer*") PointerPointer extraPointers, Pointer dx, @Cast("Nd4jLong*") LongPointer xShapeInfo, IntPointer offsets, @Cast("Nd4jLong") long N, IntPointer dz); +public native void encodeThresholdP3(@Cast("Nd4jPointer*") PointerPointer extraPointers, Pointer dx, @Cast("Nd4jLong*") LongBuffer xShapeInfo, IntBuffer offsets, @Cast("Nd4jLong") long N, IntBuffer dz); +public native void encodeThresholdP3(@Cast("Nd4jPointer*") PointerPointer extraPointers, Pointer dx, @Cast("Nd4jLong*") long[] xShapeInfo, int[] offsets, @Cast("Nd4jLong") long N, int[] dz); + + +public native void decodeThreshold(@Cast("Nd4jPointer*") PointerPointer extraPointers, Pointer dx, @Cast("Nd4jLong") long N, Pointer dz, @Cast("Nd4jLong*") LongPointer zShapeInfo); +public native void decodeThreshold(@Cast("Nd4jPointer*") PointerPointer extraPointers, Pointer dx, @Cast("Nd4jLong") long N, Pointer dz, @Cast("Nd4jLong*") LongBuffer zShapeInfo); +public native void decodeThreshold(@Cast("Nd4jPointer*") PointerPointer extraPointers, Pointer dx, @Cast("Nd4jLong") long N, Pointer dz, @Cast("Nd4jLong*") long[] zShapeInfo); + + +public native void sort(@Cast("Nd4jPointer*") PointerPointer extraPointers, + Pointer x, @Cast("Nd4jLong*") LongPointer xShapeInfo, + Pointer dx, @Cast("Nd4jLong*") LongPointer dxShapeInfo, + @Cast("bool") boolean descending); +public native void sort(@Cast("Nd4jPointer*") PointerPointer extraPointers, + Pointer x, @Cast("Nd4jLong*") LongBuffer xShapeInfo, + Pointer dx, @Cast("Nd4jLong*") LongBuffer dxShapeInfo, + @Cast("bool") boolean descending); +public native void sort(@Cast("Nd4jPointer*") PointerPointer extraPointers, + Pointer x, @Cast("Nd4jLong*") long[] xShapeInfo, + Pointer dx, @Cast("Nd4jLong*") long[] dxShapeInfo, + @Cast("bool") boolean descending); + +public native void sortByKey(@Cast("Nd4jPointer*") PointerPointer extraPointers, + Pointer x, @Cast("Nd4jLong*") LongPointer xShapeInfo, + Pointer dx, @Cast("Nd4jLong*") LongPointer dxShapeInfo, + Pointer y, @Cast("Nd4jLong*") LongPointer yShapeInfo, + Pointer dy, @Cast("Nd4jLong*") LongPointer dyShapeInfo, + @Cast("bool") boolean descending); +public native void sortByKey(@Cast("Nd4jPointer*") PointerPointer extraPointers, + Pointer x, @Cast("Nd4jLong*") LongBuffer xShapeInfo, + Pointer dx, @Cast("Nd4jLong*") LongBuffer dxShapeInfo, + Pointer y, @Cast("Nd4jLong*") LongBuffer yShapeInfo, + Pointer dy, @Cast("Nd4jLong*") LongBuffer dyShapeInfo, + @Cast("bool") boolean descending); +public native void sortByKey(@Cast("Nd4jPointer*") PointerPointer extraPointers, + Pointer x, @Cast("Nd4jLong*") long[] xShapeInfo, + Pointer dx, @Cast("Nd4jLong*") long[] dxShapeInfo, + Pointer y, @Cast("Nd4jLong*") long[] yShapeInfo, + Pointer dy, @Cast("Nd4jLong*") long[] dyShapeInfo, + @Cast("bool") boolean descending); + +public native void sortByValue(@Cast("Nd4jPointer*") PointerPointer extraPointers, + Pointer x, @Cast("Nd4jLong*") LongPointer xShapeInfo, + Pointer dx, @Cast("Nd4jLong*") LongPointer dxShapeInfo, + Pointer y, @Cast("Nd4jLong*") LongPointer yShapeInfo, + Pointer dy, @Cast("Nd4jLong*") LongPointer dyShapeInfo, + @Cast("bool") boolean descending); +public native void sortByValue(@Cast("Nd4jPointer*") PointerPointer extraPointers, + Pointer x, @Cast("Nd4jLong*") LongBuffer xShapeInfo, + Pointer dx, @Cast("Nd4jLong*") LongBuffer dxShapeInfo, + Pointer y, @Cast("Nd4jLong*") LongBuffer yShapeInfo, + Pointer dy, @Cast("Nd4jLong*") LongBuffer dyShapeInfo, + @Cast("bool") boolean descending); +public native void sortByValue(@Cast("Nd4jPointer*") PointerPointer extraPointers, + Pointer x, @Cast("Nd4jLong*") long[] xShapeInfo, + Pointer dx, @Cast("Nd4jLong*") long[] dxShapeInfo, + Pointer y, @Cast("Nd4jLong*") long[] yShapeInfo, + Pointer dy, @Cast("Nd4jLong*") long[] dyShapeInfo, + @Cast("bool") boolean descending); + +public native void sortTad(@Cast("Nd4jPointer*") PointerPointer extraPointers, + Pointer x, @Cast("Nd4jLong*") LongPointer xShapeInfo, + Pointer dx, @Cast("Nd4jLong*") LongPointer dxShapeInfo, + IntPointer dimension, + int dimensionLength, + @Cast("Nd4jLong*") LongPointer tadShapeInfo, + @Cast("Nd4jLong*") LongPointer tadOffsets, + @Cast("bool") boolean descending); +public native void sortTad(@Cast("Nd4jPointer*") PointerPointer extraPointers, + Pointer x, @Cast("Nd4jLong*") LongBuffer xShapeInfo, + Pointer dx, @Cast("Nd4jLong*") LongBuffer dxShapeInfo, + IntBuffer dimension, + int dimensionLength, + @Cast("Nd4jLong*") LongBuffer tadShapeInfo, + @Cast("Nd4jLong*") LongBuffer tadOffsets, + @Cast("bool") boolean descending); +public native void sortTad(@Cast("Nd4jPointer*") PointerPointer extraPointers, + Pointer x, @Cast("Nd4jLong*") long[] xShapeInfo, + Pointer dx, @Cast("Nd4jLong*") long[] dxShapeInfo, + int[] dimension, + int dimensionLength, + @Cast("Nd4jLong*") long[] tadShapeInfo, + @Cast("Nd4jLong*") long[] tadOffsets, + @Cast("bool") boolean descending); + +public native void sortTadByKey(@Cast("Nd4jPointer*") PointerPointer extraPointers, + Pointer x, @Cast("Nd4jLong*") LongPointer xShapeInfo, + Pointer dx, @Cast("Nd4jLong*") LongPointer dxShapeInfo, + Pointer y, @Cast("Nd4jLong*") LongPointer yShapeInfo, + Pointer dy, @Cast("Nd4jLong*") LongPointer dyShapeInfo, + IntPointer dimension, + int dimensionLength, + @Cast("bool") boolean descending); +public native void sortTadByKey(@Cast("Nd4jPointer*") PointerPointer extraPointers, + Pointer x, @Cast("Nd4jLong*") LongBuffer xShapeInfo, + Pointer dx, @Cast("Nd4jLong*") LongBuffer dxShapeInfo, + Pointer y, @Cast("Nd4jLong*") LongBuffer yShapeInfo, + Pointer dy, @Cast("Nd4jLong*") LongBuffer dyShapeInfo, + IntBuffer dimension, + int dimensionLength, + @Cast("bool") boolean descending); +public native void sortTadByKey(@Cast("Nd4jPointer*") PointerPointer extraPointers, + Pointer x, @Cast("Nd4jLong*") long[] xShapeInfo, + Pointer dx, @Cast("Nd4jLong*") long[] dxShapeInfo, + Pointer y, @Cast("Nd4jLong*") long[] yShapeInfo, + Pointer dy, @Cast("Nd4jLong*") long[] dyShapeInfo, + int[] dimension, + int dimensionLength, + @Cast("bool") boolean descending); + +public native void sortTadByValue(@Cast("Nd4jPointer*") PointerPointer extraPointers, + Pointer x, @Cast("Nd4jLong*") LongPointer xShapeInfo, + Pointer dx, @Cast("Nd4jLong*") LongPointer dxShapeInfo, + Pointer y, @Cast("Nd4jLong*") LongPointer yShapeInfo, + Pointer dy, @Cast("Nd4jLong*") LongPointer dyShapeInfo, + IntPointer dimension, + int dimensionLength, + @Cast("bool") boolean descending); +public native void sortTadByValue(@Cast("Nd4jPointer*") PointerPointer extraPointers, + Pointer x, @Cast("Nd4jLong*") LongBuffer xShapeInfo, + Pointer dx, @Cast("Nd4jLong*") LongBuffer dxShapeInfo, + Pointer y, @Cast("Nd4jLong*") LongBuffer yShapeInfo, + Pointer dy, @Cast("Nd4jLong*") LongBuffer dyShapeInfo, + IntBuffer dimension, + int dimensionLength, + @Cast("bool") boolean descending); +public native void sortTadByValue(@Cast("Nd4jPointer*") PointerPointer extraPointers, + Pointer x, @Cast("Nd4jLong*") long[] xShapeInfo, + Pointer dx, @Cast("Nd4jLong*") long[] dxShapeInfo, + Pointer y, @Cast("Nd4jLong*") long[] yShapeInfo, + Pointer dy, @Cast("Nd4jLong*") long[] dyShapeInfo, + int[] dimension, + int dimensionLength, + @Cast("bool") boolean descending); + + +// special sort impl for sorting out COO indices and values +public native void sortCooIndices(@Cast("Nd4jPointer*") PointerPointer extraPointers, @Cast("Nd4jLong*") LongPointer indices, Pointer values, @Cast("Nd4jLong") long length, int rank); +public native void sortCooIndices(@Cast("Nd4jPointer*") PointerPointer extraPointers, @Cast("Nd4jLong*") LongBuffer indices, Pointer values, @Cast("Nd4jLong") long length, int rank); +public native void sortCooIndices(@Cast("Nd4jPointer*") PointerPointer extraPointers, @Cast("Nd4jLong*") long[] indices, Pointer values, @Cast("Nd4jLong") long length, int rank); + + +public native @Cast("Nd4jLong*") LongPointer mmapFile(@Cast("Nd4jPointer*") PointerPointer extraPointers, @Cast("char*") String fileName, @Cast("Nd4jLong") long length); +public native @Cast("Nd4jLong*") LongBuffer mmapFile(@Cast("Nd4jPointer*") PointerPointer extraPointers, @Cast("char*") BytePointer fileName, @Cast("Nd4jLong") long length); + +public native void munmapFile(@Cast("Nd4jPointer*") PointerPointer extraPointers, @Cast("Nd4jLong*") LongPointer ptrMap, @Cast("Nd4jLong") long length); +public native void munmapFile(@Cast("Nd4jPointer*") PointerPointer extraPointers, @Cast("Nd4jLong*") LongBuffer ptrMap, @Cast("Nd4jLong") long length); +public native void munmapFile(@Cast("Nd4jPointer*") PointerPointer extraPointers, @Cast("Nd4jLong*") long[] ptrMap, @Cast("Nd4jLong") long length); + +// flatbuffers execution +public native OpaqueResultWrapper executeFlatGraph(@Cast("Nd4jPointer*") PointerPointer extraPointers, @Cast("Nd4jPointer") Pointer flatBufferPointer); + +public native @Cast("Nd4jLong") long getResultWrapperSize(OpaqueResultWrapper ptr); +public native @Cast("Nd4jPointer") Pointer getResultWrapperPointer(OpaqueResultWrapper ptr); + +public native @Cast("char*") String getAllCustomOps(); + +public native @Cast("char*") String getAllOperations(); + +// customOp executioner +public native int execCustomOp(@Cast("Nd4jPointer*") PointerPointer extraPointers, @Cast("Nd4jLong") long hash, @Cast("Nd4jPointer*") PointerPointer inputBuffers, @Cast("Nd4jPointer*") PointerPointer inputShapes, int numInputs, @Cast("Nd4jPointer*") PointerPointer outputBuffers, @Cast("Nd4jPointer*") PointerPointer outputShapes, int numOutputs, DoublePointer tArgs, int numTArgs, @Cast("Nd4jLong*") LongPointer iArgs, int numIArgs, @Cast("bool*") BooleanPointer bArgs, int numBArgs, @Cast("bool") boolean isInplace); +public native int execCustomOp(@Cast("Nd4jPointer*") PointerPointer extraPointers, @Cast("Nd4jLong") long hash, @Cast("Nd4jPointer*") PointerPointer inputBuffers, @Cast("Nd4jPointer*") PointerPointer inputShapes, int numInputs, @Cast("Nd4jPointer*") PointerPointer outputBuffers, @Cast("Nd4jPointer*") PointerPointer outputShapes, int numOutputs, DoubleBuffer tArgs, int numTArgs, @Cast("Nd4jLong*") LongBuffer iArgs, int numIArgs, @Cast("bool*") boolean[] bArgs, int numBArgs, @Cast("bool") boolean isInplace); +public native int execCustomOp(@Cast("Nd4jPointer*") PointerPointer extraPointers, @Cast("Nd4jLong") long hash, @Cast("Nd4jPointer*") PointerPointer inputBuffers, @Cast("Nd4jPointer*") PointerPointer inputShapes, int numInputs, @Cast("Nd4jPointer*") PointerPointer outputBuffers, @Cast("Nd4jPointer*") PointerPointer outputShapes, int numOutputs, double[] tArgs, int numTArgs, @Cast("Nd4jLong*") long[] iArgs, int numIArgs, @Cast("bool*") BooleanPointer bArgs, int numBArgs, @Cast("bool") boolean isInplace); +public native int execCustomOp(@Cast("Nd4jPointer*") PointerPointer extraPointers, @Cast("Nd4jLong") long hash, @Cast("Nd4jPointer*") PointerPointer inputBuffers, @Cast("Nd4jPointer*") PointerPointer inputShapes, int numInputs, @Cast("Nd4jPointer*") PointerPointer outputBuffers, @Cast("Nd4jPointer*") PointerPointer outputShapes, int numOutputs, DoublePointer tArgs, int numTArgs, @Cast("Nd4jLong*") LongPointer iArgs, int numIArgs, @Cast("bool*") boolean[] bArgs, int numBArgs, @Cast("bool") boolean isInplace); +public native int execCustomOp(@Cast("Nd4jPointer*") PointerPointer extraPointers, @Cast("Nd4jLong") long hash, @Cast("Nd4jPointer*") PointerPointer inputBuffers, @Cast("Nd4jPointer*") PointerPointer inputShapes, int numInputs, @Cast("Nd4jPointer*") PointerPointer outputBuffers, @Cast("Nd4jPointer*") PointerPointer outputShapes, int numOutputs, DoubleBuffer tArgs, int numTArgs, @Cast("Nd4jLong*") LongBuffer iArgs, int numIArgs, @Cast("bool*") BooleanPointer bArgs, int numBArgs, @Cast("bool") boolean isInplace); +public native int execCustomOp(@Cast("Nd4jPointer*") PointerPointer extraPointers, @Cast("Nd4jLong") long hash, @Cast("Nd4jPointer*") PointerPointer inputBuffers, @Cast("Nd4jPointer*") PointerPointer inputShapes, int numInputs, @Cast("Nd4jPointer*") PointerPointer outputBuffers, @Cast("Nd4jPointer*") PointerPointer outputShapes, int numOutputs, double[] tArgs, int numTArgs, @Cast("Nd4jLong*") long[] iArgs, int numIArgs, @Cast("bool*") boolean[] bArgs, int numBArgs, @Cast("bool") boolean isInplace); +public native int execCustomOp2(@Cast("Nd4jPointer*") PointerPointer extraPointers, @Cast("Nd4jLong") long hash, @Cast("Nd4jPointer") Pointer opContext); + +public native OpaqueShapeList calculateOutputShapes(@Cast("Nd4jPointer*") PointerPointer extraPointers, @Cast("Nd4jLong") long hash, @Cast("Nd4jPointer*") PointerPointer inputShapes, int numInputShapes, DoublePointer tArgs, int numTArgs, @Cast("Nd4jLong*") LongPointer iArgs, int numIArgs); +public native OpaqueShapeList calculateOutputShapes(@Cast("Nd4jPointer*") PointerPointer extraPointers, @Cast("Nd4jLong") long hash, @Cast("Nd4jPointer*") PointerPointer inputShapes, int numInputShapes, DoubleBuffer tArgs, int numTArgs, @Cast("Nd4jLong*") LongBuffer iArgs, int numIArgs); +public native OpaqueShapeList calculateOutputShapes(@Cast("Nd4jPointer*") PointerPointer extraPointers, @Cast("Nd4jLong") long hash, @Cast("Nd4jPointer*") PointerPointer inputShapes, int numInputShapes, double[] tArgs, int numTArgs, @Cast("Nd4jLong*") long[] iArgs, int numIArgs); +public native OpaqueShapeList calculateOutputShapes2(@Cast("Nd4jPointer*") PointerPointer extraPointers, @Cast("Nd4jLong") long hash, @Cast("Nd4jPointer*") PointerPointer inputBuffers, @Cast("Nd4jPointer*") PointerPointer inputShapes, int numInputShapes, DoublePointer tArgs, int numTArgs, @Cast("Nd4jLong*") LongPointer iArgs, int numIArgs, @Cast("bool*") BooleanPointer bArgs, int numBArgs, IntPointer dArgs, int numDArgs); +public native OpaqueShapeList calculateOutputShapes2(@Cast("Nd4jPointer*") PointerPointer extraPointers, @Cast("Nd4jLong") long hash, @Cast("Nd4jPointer*") PointerPointer inputBuffers, @Cast("Nd4jPointer*") PointerPointer inputShapes, int numInputShapes, DoubleBuffer tArgs, int numTArgs, @Cast("Nd4jLong*") LongBuffer iArgs, int numIArgs, @Cast("bool*") boolean[] bArgs, int numBArgs, IntBuffer dArgs, int numDArgs); +public native OpaqueShapeList calculateOutputShapes2(@Cast("Nd4jPointer*") PointerPointer extraPointers, @Cast("Nd4jLong") long hash, @Cast("Nd4jPointer*") PointerPointer inputBuffers, @Cast("Nd4jPointer*") PointerPointer inputShapes, int numInputShapes, double[] tArgs, int numTArgs, @Cast("Nd4jLong*") long[] iArgs, int numIArgs, @Cast("bool*") BooleanPointer bArgs, int numBArgs, int[] dArgs, int numDArgs); +public native OpaqueShapeList calculateOutputShapes2(@Cast("Nd4jPointer*") PointerPointer extraPointers, @Cast("Nd4jLong") long hash, @Cast("Nd4jPointer*") PointerPointer inputBuffers, @Cast("Nd4jPointer*") PointerPointer inputShapes, int numInputShapes, DoublePointer tArgs, int numTArgs, @Cast("Nd4jLong*") LongPointer iArgs, int numIArgs, @Cast("bool*") boolean[] bArgs, int numBArgs, IntPointer dArgs, int numDArgs); +public native OpaqueShapeList calculateOutputShapes2(@Cast("Nd4jPointer*") PointerPointer extraPointers, @Cast("Nd4jLong") long hash, @Cast("Nd4jPointer*") PointerPointer inputBuffers, @Cast("Nd4jPointer*") PointerPointer inputShapes, int numInputShapes, DoubleBuffer tArgs, int numTArgs, @Cast("Nd4jLong*") LongBuffer iArgs, int numIArgs, @Cast("bool*") BooleanPointer bArgs, int numBArgs, IntBuffer dArgs, int numDArgs); +public native OpaqueShapeList calculateOutputShapes2(@Cast("Nd4jPointer*") PointerPointer extraPointers, @Cast("Nd4jLong") long hash, @Cast("Nd4jPointer*") PointerPointer inputBuffers, @Cast("Nd4jPointer*") PointerPointer inputShapes, int numInputShapes, double[] tArgs, int numTArgs, @Cast("Nd4jLong*") long[] iArgs, int numIArgs, @Cast("bool*") boolean[] bArgs, int numBArgs, int[] dArgs, int numDArgs); + +public native @Cast("Nd4jLong") long getShapeListSize(OpaqueShapeList list); +public native @Cast("Nd4jLong*") LongPointer getShape(OpaqueShapeList list, @Cast("Nd4jLong") long i); + +public native void deleteShapeList(@Cast("Nd4jPointer") Pointer shapeList); + +public native int registerGraph(@Cast("Nd4jPointer*") PointerPointer extraPointers, @Cast("Nd4jLong") long graphId, @Cast("Nd4jPointer") Pointer flatBufferPointer); + +public native OpaqueVariablesSet executeStoredGraph(@Cast("Nd4jPointer*") PointerPointer extraPointers, @Cast("Nd4jLong") long graphId, @Cast("Nd4jPointer*") PointerPointer inputBuffers, @Cast("Nd4jPointer*") PointerPointer inputShapes, IntPointer inputIndices, int numInputs); +public native OpaqueVariablesSet executeStoredGraph(@Cast("Nd4jPointer*") PointerPointer extraPointers, @Cast("Nd4jLong") long graphId, @Cast("Nd4jPointer*") PointerPointer inputBuffers, @Cast("Nd4jPointer*") PointerPointer inputShapes, IntBuffer inputIndices, int numInputs); +public native OpaqueVariablesSet executeStoredGraph(@Cast("Nd4jPointer*") PointerPointer extraPointers, @Cast("Nd4jLong") long graphId, @Cast("Nd4jPointer*") PointerPointer inputBuffers, @Cast("Nd4jPointer*") PointerPointer inputShapes, int[] inputIndices, int numInputs); + +public native @Cast("Nd4jLong") long getVariablesSetSize(OpaqueVariablesSet set); +public native @Cast("Nd4jStatus") int getVariablesSetStatus(OpaqueVariablesSet set); +public native OpaqueVariable getVariable(OpaqueVariablesSet set, @Cast("Nd4jLong") long i); +public native int getVariableId(OpaqueVariable variable); +public native int getVariableIndex(OpaqueVariable variable); +public native @Cast("char*") String getVariableName(OpaqueVariable variable); +public native @Cast("Nd4jLong*") LongPointer getVariableShape(OpaqueVariable variable); +public native Pointer getVariableBuffer(OpaqueVariable variable); + +public native int unregisterGraph(@Cast("Nd4jPointer*") PointerPointer extraPointers, @Cast("Nd4jLong") long graphId); + +public native void deleteCharArray(@Cast("Nd4jPointer") Pointer pointer); +public native void deleteIntArray(@Cast("Nd4jPointer") Pointer pointer); +public native void deleteLongArray(@Cast("Nd4jPointer") Pointer pointer); +public native void deletePointerArray(@Cast("Nd4jPointer") Pointer pointer); + +public native void deleteVariablesSet(OpaqueVariablesSet pointer); + +// GraphState creation +public native @Cast("Nd4jPointer") Pointer getGraphState(@Cast("Nd4jLong") long id); + +public native void deleteGraphState(@Cast("Nd4jPointer") Pointer state); + +public native void deleteResultWrapper(@Cast("Nd4jPointer") Pointer ptr); + +public native int estimateThreshold(@Cast("Nd4jPointer*") PointerPointer extraPointers, @Cast("Nd4jPointer") Pointer x, @Cast("Nd4jLong*") LongPointer xShapeInfo, int N, float threshold); +public native int estimateThreshold(@Cast("Nd4jPointer*") PointerPointer extraPointers, @Cast("Nd4jPointer") Pointer x, @Cast("Nd4jLong*") LongBuffer xShapeInfo, int N, float threshold); +public native int estimateThreshold(@Cast("Nd4jPointer*") PointerPointer extraPointers, @Cast("Nd4jPointer") Pointer x, @Cast("Nd4jLong*") long[] xShapeInfo, int N, float threshold); + +// this method executes op that requires scope to be present: if/while/cond/whatever +public native @Cast("Nd4jStatus") int execCustomOpWithScope(@Cast("Nd4jPointer*") PointerPointer extraPointers, @Cast("Nd4jPointer") Pointer state, @Cast("Nd4jLong") long opHash, @Cast("Nd4jLong*") LongPointer scopes, int numScopes, @Cast("Nd4jPointer*") PointerPointer inputBuffers, @Cast("Nd4jPointer*") PointerPointer inputShapes, int numInputs, @Cast("Nd4jPointer*") PointerPointer outputBuffers, @Cast("Nd4jPointer*") PointerPointer outputShapes, int numOutputs); +public native @Cast("Nd4jStatus") int execCustomOpWithScope(@Cast("Nd4jPointer*") PointerPointer extraPointers, @Cast("Nd4jPointer") Pointer state, @Cast("Nd4jLong") long opHash, @Cast("Nd4jLong*") LongBuffer scopes, int numScopes, @Cast("Nd4jPointer*") PointerPointer inputBuffers, @Cast("Nd4jPointer*") PointerPointer inputShapes, int numInputs, @Cast("Nd4jPointer*") PointerPointer outputBuffers, @Cast("Nd4jPointer*") PointerPointer outputShapes, int numOutputs); +public native @Cast("Nd4jStatus") int execCustomOpWithScope(@Cast("Nd4jPointer*") PointerPointer extraPointers, @Cast("Nd4jPointer") Pointer state, @Cast("Nd4jLong") long opHash, @Cast("Nd4jLong*") long[] scopes, int numScopes, @Cast("Nd4jPointer*") PointerPointer inputBuffers, @Cast("Nd4jPointer*") PointerPointer inputShapes, int numInputs, @Cast("Nd4jPointer*") PointerPointer outputBuffers, @Cast("Nd4jPointer*") PointerPointer outputShapes, int numOutputs); + +//void fillUtf8String(Nd4jPointer *extraPointers, const char **string, int numStrings, Nd4jPointer buffer); +public native @Cast("Nd4jPointer") Pointer createUtf8String(@Cast("Nd4jPointer*") PointerPointer extraPointers, @Cast("char*") String string, int length); +public native @Cast("Nd4jPointer") Pointer createUtf8String(@Cast("Nd4jPointer*") PointerPointer extraPointers, @Cast("char*") BytePointer string, int length); +public native @Cast("Nd4jLong") long getUtf8StringLength(@Cast("Nd4jPointer*") PointerPointer extraPointers, @Cast("Nd4jPointer") Pointer ptr); +public native @Cast("char*") BytePointer getUtf8StringBuffer(@Cast("Nd4jPointer*") PointerPointer extraPointers, @Cast("Nd4jPointer") Pointer ptr); +public native void deleteUtf8String(@Cast("Nd4jPointer*") PointerPointer extraPointers, @Cast("Nd4jPointer") Pointer ptr); + +public native void scatterUpdate(@Cast("Nd4jPointer*") PointerPointer extraPointers, int opCode, int numOfSubArrs, + Pointer hX, @Cast("Nd4jLong*") LongPointer hXShapeInfo, @Cast("Nd4jLong*") LongPointer hXOffsets, + Pointer dX, @Cast("Nd4jLong*") LongPointer dXShapeInfo, @Cast("Nd4jLong*") LongPointer dXOffsets, + Pointer hY, @Cast("Nd4jLong*") LongPointer hYShapeInfo, @Cast("Nd4jLong*") LongPointer hYOffsets, + Pointer dY, @Cast("Nd4jLong*") LongPointer dYShapeInfo, @Cast("Nd4jLong*") LongPointer dYOffsets, + Pointer hIindexes, @Cast("Nd4jLong*") LongPointer hIndicesShapeInfo, Pointer dIindexes, @Cast("Nd4jLong*") LongPointer dIndicesShapeInfo); +public native void scatterUpdate(@Cast("Nd4jPointer*") PointerPointer extraPointers, int opCode, int numOfSubArrs, + Pointer hX, @Cast("Nd4jLong*") LongBuffer hXShapeInfo, @Cast("Nd4jLong*") LongBuffer hXOffsets, + Pointer dX, @Cast("Nd4jLong*") LongBuffer dXShapeInfo, @Cast("Nd4jLong*") LongBuffer dXOffsets, + Pointer hY, @Cast("Nd4jLong*") LongBuffer hYShapeInfo, @Cast("Nd4jLong*") LongBuffer hYOffsets, + Pointer dY, @Cast("Nd4jLong*") LongBuffer dYShapeInfo, @Cast("Nd4jLong*") LongBuffer dYOffsets, + Pointer hIindexes, @Cast("Nd4jLong*") LongBuffer hIndicesShapeInfo, Pointer dIindexes, @Cast("Nd4jLong*") LongBuffer dIndicesShapeInfo); +public native void scatterUpdate(@Cast("Nd4jPointer*") PointerPointer extraPointers, int opCode, int numOfSubArrs, + Pointer hX, @Cast("Nd4jLong*") long[] hXShapeInfo, @Cast("Nd4jLong*") long[] hXOffsets, + Pointer dX, @Cast("Nd4jLong*") long[] dXShapeInfo, @Cast("Nd4jLong*") long[] dXOffsets, + Pointer hY, @Cast("Nd4jLong*") long[] hYShapeInfo, @Cast("Nd4jLong*") long[] hYOffsets, + Pointer dY, @Cast("Nd4jLong*") long[] dYShapeInfo, @Cast("Nd4jLong*") long[] dYOffsets, + Pointer hIindexes, @Cast("Nd4jLong*") long[] hIndicesShapeInfo, Pointer dIindexes, @Cast("Nd4jLong*") long[] dIndicesShapeInfo); + +public native void inspectArray(@Cast("Nd4jPointer*") PointerPointer extraPointers, @Cast("Nd4jPointer") Pointer buffer, @Cast("Nd4jLong*") LongPointer shapeInfo, @Cast("Nd4jPointer") Pointer specialBuffer, @Cast("Nd4jLong*") LongPointer specialShapeInfo, @Cast("Nd4jPointer") Pointer debugInfo); +public native void inspectArray(@Cast("Nd4jPointer*") PointerPointer extraPointers, @Cast("Nd4jPointer") Pointer buffer, @Cast("Nd4jLong*") LongBuffer shapeInfo, @Cast("Nd4jPointer") Pointer specialBuffer, @Cast("Nd4jLong*") LongBuffer specialShapeInfo, @Cast("Nd4jPointer") Pointer debugInfo); +public native void inspectArray(@Cast("Nd4jPointer*") PointerPointer extraPointers, @Cast("Nd4jPointer") Pointer buffer, @Cast("Nd4jLong*") long[] shapeInfo, @Cast("Nd4jPointer") Pointer specialBuffer, @Cast("Nd4jLong*") long[] specialShapeInfo, @Cast("Nd4jPointer") Pointer debugInfo); + +public native OpaqueConstantDataBuffer shapeBuffer(int rank, @Cast("Nd4jLong*") LongPointer shape, @Cast("Nd4jLong*") LongPointer strides, @Cast("sd::DataType") int dtype, char order, @Cast("Nd4jLong") long ews, @Cast("bool") boolean empty); +public native OpaqueConstantDataBuffer shapeBuffer(int rank, @Cast("Nd4jLong*") LongBuffer shape, @Cast("Nd4jLong*") LongBuffer strides, @Cast("sd::DataType") int dtype, char order, @Cast("Nd4jLong") long ews, @Cast("bool") boolean empty); +public native OpaqueConstantDataBuffer shapeBuffer(int rank, @Cast("Nd4jLong*") long[] shape, @Cast("Nd4jLong*") long[] strides, @Cast("sd::DataType") int dtype, char order, @Cast("Nd4jLong") long ews, @Cast("bool") boolean empty); + +public native OpaqueConstantDataBuffer constantBufferLong(@Cast("sd::DataType") int dtype, @Cast("Nd4jLong*") LongPointer data, int length); +public native OpaqueConstantDataBuffer constantBufferLong(@Cast("sd::DataType") int dtype, @Cast("Nd4jLong*") LongBuffer data, int length); +public native OpaqueConstantDataBuffer constantBufferLong(@Cast("sd::DataType") int dtype, @Cast("Nd4jLong*") long[] data, int length); +public native OpaqueConstantDataBuffer constantBufferDouble(@Cast("sd::DataType") int dtype, DoublePointer data, int length); +public native OpaqueConstantDataBuffer constantBufferDouble(@Cast("sd::DataType") int dtype, DoubleBuffer data, int length); +public native OpaqueConstantDataBuffer constantBufferDouble(@Cast("sd::DataType") int dtype, double[] data, int length); +public native OpaqueConstantDataBuffer constantBuffer(@Cast("sd::DataType") int dtype, ConstantDescriptor descriptor); + +public native @Cast("Nd4jPointer") Pointer getConstantDataBufferPrimary(OpaqueConstantDataBuffer dbf); +public native @Cast("Nd4jPointer") Pointer getConstantDataBufferSpecial(OpaqueConstantDataBuffer dbf); +public native @Cast("Nd4jLong") long getConstantDataBufferLength(OpaqueConstantDataBuffer dbf); +public native @Cast("Nd4jLong") long getConstantDataBufferSizeOf(OpaqueConstantDataBuffer dbf); + +public native void deleteShapeBuffer(OpaqueConstantDataBuffer ptr); + +public native OpaqueContext createGraphContext(int nodeId); +public native OpaqueRandomGenerator getGraphContextRandomGenerator(OpaqueContext ptr); +public native void ctxAllowHelpers(OpaqueContext ptr, @Cast("bool") boolean reallyAllow); +public native void ctxShapeFunctionOverride(OpaqueContext ptr, @Cast("bool") boolean reallyOverride); +public native void ctxSetExecutionMode(OpaqueContext ptr, int execMode); +public native void ctxPurge(OpaqueContext ptr); +public native void markGraphContextInplace(OpaqueContext ptr, @Cast("bool") boolean reallyInplace); +public native void setGraphContextCudaContext(OpaqueContext ptr, Pointer stream, Pointer reductionPointer, Pointer allocationPointer); +public native void setGraphContextInputArray(OpaqueContext ptr, int index, Pointer buffer, Pointer shapeInfo, Pointer specialBuffer, Pointer specialShapeInfo); +public native void setGraphContextOutputArray(OpaqueContext ptr, int index, Pointer buffer, Pointer shapeInfo, Pointer specialBuffer, Pointer specialShapeInfo); +public native void setGraphContextInputBuffer(OpaqueContext ptr, int index, OpaqueDataBuffer buffer, Pointer shapeInfo, Pointer specialShapeInfo); +public native void setGraphContextOutputBuffer(OpaqueContext ptr, int index, OpaqueDataBuffer buffer, Pointer shapeInfo, Pointer specialShapeInfo); +public native void setGraphContextDArguments(OpaqueContext ptr, IntPointer arguments, int numberOfArguments); +public native void setGraphContextDArguments(OpaqueContext ptr, IntBuffer arguments, int numberOfArguments); +public native void setGraphContextDArguments(OpaqueContext ptr, int[] arguments, int numberOfArguments); +public native void setGraphContextTArguments(OpaqueContext ptr, DoublePointer arguments, int numberOfArguments); +public native void setGraphContextTArguments(OpaqueContext ptr, DoubleBuffer arguments, int numberOfArguments); +public native void setGraphContextTArguments(OpaqueContext ptr, double[] arguments, int numberOfArguments); +public native void setGraphContextIArguments(OpaqueContext ptr, @Cast("Nd4jLong*") LongPointer arguments, int numberOfArguments); +public native void setGraphContextIArguments(OpaqueContext ptr, @Cast("Nd4jLong*") LongBuffer arguments, int numberOfArguments); +public native void setGraphContextIArguments(OpaqueContext ptr, @Cast("Nd4jLong*") long[] arguments, int numberOfArguments); +public native void setGraphContextBArguments(OpaqueContext ptr, @Cast("bool*") BooleanPointer arguments, int numberOfArguments); +public native void setGraphContextBArguments(OpaqueContext ptr, @Cast("bool*") boolean[] arguments, int numberOfArguments); +public native void deleteGraphContext(OpaqueContext ptr); + +public native OpaqueRandomGenerator createRandomGenerator(@Cast("Nd4jLong") long rootSeed/*=0*/, @Cast("Nd4jLong") long nodeSeed/*=0*/); +public native OpaqueRandomGenerator createRandomGenerator(); +public native @Cast("Nd4jLong") long getRandomGeneratorRootState(OpaqueRandomGenerator ptr); +public native @Cast("Nd4jLong") long getRandomGeneratorNodeState(OpaqueRandomGenerator ptr); +public native void setRandomGeneratorStates(OpaqueRandomGenerator ptr, @Cast("Nd4jLong") long rootSeed/*=0*/, @Cast("Nd4jLong") long nodeSeed/*=0*/); +public native void setRandomGeneratorStates(OpaqueRandomGenerator ptr); +public native int getRandomGeneratorRelativeInt(OpaqueRandomGenerator ptr, @Cast("Nd4jLong") long index); +public native @Cast("Nd4jLong") long getRandomGeneratorRelativeLong(OpaqueRandomGenerator ptr, @Cast("Nd4jLong") long index); +public native void deleteRandomGenerator(OpaqueRandomGenerator ptr); + +public native @Cast("char*") String runLightBenchmarkSuit(@Cast("bool") boolean printOut); +public native @Cast("char*") String runFullBenchmarkSuit(@Cast("bool") boolean printOut); + +public native OpaqueLaunchContext defaultLaunchContext(); +public native @Cast("Nd4jPointer") Pointer lcScalarPointer(OpaqueLaunchContext lc); +public native @Cast("Nd4jPointer") Pointer lcReductionPointer(OpaqueLaunchContext lc); +public native @Cast("Nd4jPointer") Pointer lcAllocationPointer(OpaqueLaunchContext lc); +public native @Cast("Nd4jPointer") Pointer lcExecutionStream(OpaqueLaunchContext lc); +public native @Cast("Nd4jPointer") Pointer lcCopyStream(OpaqueLaunchContext lc); +public native @Cast("Nd4jPointer") Pointer lcBlasHandle(OpaqueLaunchContext lc); +public native @Cast("Nd4jPointer") Pointer lcSolverHandle(OpaqueLaunchContext lc); + +public native OpaqueDataBuffer allocateDataBuffer(@Cast("Nd4jLong") long elements, int dataType, @Cast("bool") boolean allocateBoth); +public native OpaqueDataBuffer dbCreateView(OpaqueDataBuffer dataBuffer, @Cast("Nd4jLong") long length, @Cast("Nd4jLong") long offset); +public native @Cast("Nd4jPointer") Pointer dbPrimaryBuffer(OpaqueDataBuffer dataBuffer); +public native @Cast("Nd4jPointer") Pointer dbSpecialBuffer(OpaqueDataBuffer dataBuffer); +public native void dbExpandBuffer(OpaqueDataBuffer dataBuffer, @Cast("Nd4jLong") long elements); +public native void dbAllocatePrimaryBuffer(OpaqueDataBuffer dataBuffer); +public native void dbAllocateSpecialBuffer(OpaqueDataBuffer dataBuffer); +public native void dbSetPrimaryBuffer(OpaqueDataBuffer dataBuffer, @Cast("Nd4jPointer") Pointer primaryBuffer, @Cast("Nd4jLong") long numBytes); +public native void dbSetSpecialBuffer(OpaqueDataBuffer dataBuffer, @Cast("Nd4jPointer") Pointer specialBuffer, @Cast("Nd4jLong") long numBytes); +public native void dbSyncToSpecial(OpaqueDataBuffer dataBuffer); +public native void dbSyncToPrimary(OpaqueDataBuffer dataBuffer); +public native int dbLocality(OpaqueDataBuffer dataBuffer); +public native int dbDeviceId(OpaqueDataBuffer dataBuffer); +public native void dbSetDeviceId(OpaqueDataBuffer dataBuffer, int deviceId); +public native void dbTickHostRead(OpaqueDataBuffer dataBuffer); +public native void dbTickHostWrite(OpaqueDataBuffer dataBuffer); +public native void dbTickDeviceRead(OpaqueDataBuffer dataBuffer); +public native void dbTickDeviceWrite(OpaqueDataBuffer dataBuffer); +public native void dbClose(OpaqueDataBuffer dataBuffer); +public native void deleteDataBuffer(OpaqueDataBuffer dataBuffer); +public native void dbExpand(OpaqueDataBuffer dataBuffer, @Cast("Nd4jLong") long elements); + + +public native int binaryLevel(); +public native int optimalLevel(); + +public native @Cast("bool") boolean isMinimalRequirementsMet(); +public native @Cast("bool") boolean isOptimalRequirementsMet(); + +// #endif //NATIVEOPERATIONS_NATIVEOPS_H + + +// Parsed from memory/ExternalWorkspace.h + +/******************************************************************************* + * Copyright (c) 2015-2018 Skymind, Inc. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ + +// +// @author raver119@gmail.com +// + +// #ifndef LIBND4J_EXTERNALWORKSPACE_H +// #define LIBND4J_EXTERNALWORKSPACE_H + +// #include +// #include + @Namespace("sd::memory") @NoOffset public static class ExternalWorkspace extends Pointer { + static { Loader.load(); } + /** Pointer cast constructor. Invokes {@link Pointer#Pointer(Pointer)}. */ + public ExternalWorkspace(Pointer p) { super(p); } + /** Native array allocator. Access with {@link Pointer#position(long)}. */ + public ExternalWorkspace(long size) { super((Pointer)null); allocateArray(size); } + private native void allocateArray(long size); + @Override public ExternalWorkspace position(long position) { + return (ExternalWorkspace)super.position(position); + } + + public ExternalWorkspace() { super((Pointer)null); allocate(); } + private native void allocate(); + + public ExternalWorkspace(@Cast("Nd4jPointer") Pointer ptrH, @Cast("Nd4jLong") long sizeH, @Cast("Nd4jPointer") Pointer ptrD, @Cast("Nd4jLong") long sizeD) { super((Pointer)null); allocate(ptrH, sizeH, ptrD, sizeD); } + private native void allocate(@Cast("Nd4jPointer") Pointer ptrH, @Cast("Nd4jLong") long sizeH, @Cast("Nd4jPointer") Pointer ptrD, @Cast("Nd4jLong") long sizeD); + + public native Pointer pointerHost(); + public native Pointer pointerDevice(); + + public native @Cast("Nd4jLong") long sizeHost(); + public native @Cast("Nd4jLong") long sizeDevice(); + } + + + +// #endif + +// Parsed from memory/Workspace.h + +/******************************************************************************* + * Copyright (c) 2015-2018 Skymind, Inc. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ + +// +// This class implements Workspace functionality in c++ +// +// +// @author raver119@gmail.com +// + +// #ifndef LIBND4J_WORKSPACE_H +// #define LIBND4J_WORKSPACE_H + +// #include +// #include +// #include +// #include +// #include +// #include +// #include +// #include + + @Namespace("sd::memory") @NoOffset public static class Workspace extends Pointer { + static { Loader.load(); } + /** Pointer cast constructor. Invokes {@link Pointer#Pointer(Pointer)}. */ + public Workspace(Pointer p) { super(p); } + /** Native array allocator. Access with {@link Pointer#position(long)}. */ + public Workspace(long size) { super((Pointer)null); allocateArray(size); } + private native void allocateArray(long size); + @Override public Workspace position(long position) { + return (Workspace)super.position(position); + } + + public Workspace(ExternalWorkspace external) { super((Pointer)null); allocate(external); } + private native void allocate(ExternalWorkspace external); + public Workspace(@Cast("Nd4jLong") long initialSize/*=0L*/, @Cast("Nd4jLong") long secondaryBytes/*=0L*/) { super((Pointer)null); allocate(initialSize, secondaryBytes); } + private native void allocate(@Cast("Nd4jLong") long initialSize/*=0L*/, @Cast("Nd4jLong") long secondaryBytes/*=0L*/); + public Workspace() { super((Pointer)null); allocate(); } + private native void allocate(); + + public native @Cast("Nd4jLong") long getAllocatedSize(); + public native @Cast("Nd4jLong") long getCurrentSize(); + public native @Cast("Nd4jLong") long getCurrentOffset(); + public native @Cast("Nd4jLong") long getSpilledSize(); + public native @Cast("Nd4jLong") long getUsedSize(); + + public native @Cast("Nd4jLong") long getAllocatedSecondarySize(); + public native @Cast("Nd4jLong") long getCurrentSecondarySize(); + public native @Cast("Nd4jLong") long getCurrentSecondaryOffset(); + public native @Cast("Nd4jLong") long getSpilledSecondarySize(); + public native @Cast("Nd4jLong") long getUsedSecondarySize(); + + public native void expandBy(@Cast("Nd4jLong") long primaryBytes, @Cast("Nd4jLong") long secondaryBytes/*=0L*/); + public native void expandBy(@Cast("Nd4jLong") long primaryBytes); + public native void expandTo(@Cast("Nd4jLong") long primaryBytes, @Cast("Nd4jLong") long secondaryBytes/*=0L*/); + public native void expandTo(@Cast("Nd4jLong") long primaryBytes); + +// bool resizeSupported(); + + public native Pointer allocateBytes(@Cast("Nd4jLong") long numBytes); + public native Pointer allocateBytes(@Cast("sd::memory::MemoryType") int type, @Cast("Nd4jLong") long numBytes); + + public native void scopeIn(); + public native void scopeOut(); + + /* + * This method creates NEW workspace of the same memory size and returns pointer to it + */ + public native Workspace clone(); + } + + + +// #endif //LIBND4J_WORKSPACE_H + + +// Parsed from indexing/NDIndex.h + +/******************************************************************************* + * Copyright (c) 2015-2018 Skymind, Inc. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ + +// +// @author raver119@gmail.com +// + +// #ifndef LIBND4J_NDINDEX_H +// #define LIBND4J_NDINDEX_H + +// #include +// #include +// #include + @Namespace("sd") @NoOffset public static class NDIndex extends Pointer { + static { Loader.load(); } + /** Pointer cast constructor. Invokes {@link Pointer#Pointer(Pointer)}. */ + public NDIndex(Pointer p) { super(p); } + /** Native array allocator. Access with {@link Pointer#position(long)}. */ + public NDIndex(long size) { super((Pointer)null); allocateArray(size); } + private native void allocateArray(long size); + @Override public NDIndex position(long position) { + return (NDIndex)super.position(position); + } + + public NDIndex() { super((Pointer)null); allocate(); } + private native void allocate(); + + public native @Cast("bool") boolean isAll(); + public native @Cast("bool") boolean isPoint(); + public native @Cast("bool") boolean isInterval(); + + public native @Cast("Nd4jLong*") @StdVector LongPointer getIndices(); + public native @Cast("Nd4jLong") long stride(); + + public static native NDIndex all(); + public static native NDIndex point(@Cast("Nd4jLong") long pt); + public static native NDIndex interval(@Cast("Nd4jLong") long start, @Cast("Nd4jLong") long end, @Cast("Nd4jLong") long stride/*=1*/); + public static native NDIndex interval(@Cast("Nd4jLong") long start, @Cast("Nd4jLong") long end); + } + + @Namespace("sd") public static class NDIndexAll extends NDIndex { + static { Loader.load(); } + /** Pointer cast constructor. Invokes {@link Pointer#Pointer(Pointer)}. */ + public NDIndexAll(Pointer p) { super(p); } + /** Native array allocator. Access with {@link Pointer#position(long)}. */ + public NDIndexAll(long size) { super((Pointer)null); allocateArray(size); } + private native void allocateArray(long size); + @Override public NDIndexAll position(long position) { + return (NDIndexAll)super.position(position); + } + + public NDIndexAll() { super((Pointer)null); allocate(); } + private native void allocate(); + public native @Cast("bool") boolean isInterval(); + } + + + @Namespace("sd") public static class NDIndexPoint extends NDIndex { + static { Loader.load(); } + /** Pointer cast constructor. Invokes {@link Pointer#Pointer(Pointer)}. */ + public NDIndexPoint(Pointer p) { super(p); } + + public NDIndexPoint(@Cast("Nd4jLong") long point) { super((Pointer)null); allocate(point); } + private native void allocate(@Cast("Nd4jLong") long point); + public native @Cast("bool") boolean isInterval(); + } + + @Namespace("sd") public static class NDIndexInterval extends NDIndex { + static { Loader.load(); } + /** Pointer cast constructor. Invokes {@link Pointer#Pointer(Pointer)}. */ + public NDIndexInterval(Pointer p) { super(p); } + + public NDIndexInterval(@Cast("Nd4jLong") long start, @Cast("Nd4jLong") long end, @Cast("Nd4jLong") long stride/*=1*/) { super((Pointer)null); allocate(start, end, stride); } + private native void allocate(@Cast("Nd4jLong") long start, @Cast("Nd4jLong") long end, @Cast("Nd4jLong") long stride/*=1*/); + public NDIndexInterval(@Cast("Nd4jLong") long start, @Cast("Nd4jLong") long end) { super((Pointer)null); allocate(start, end); } + private native void allocate(@Cast("Nd4jLong") long start, @Cast("Nd4jLong") long end); + public native @Cast("bool") boolean isInterval(); + } + + + + +// #endif //LIBND4J_NDINDEX_H + + +// Parsed from indexing/IndicesList.h + +/******************************************************************************* + * Copyright (c) 2015-2018 Skymind, Inc. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ + +// +// @author raver119@gmail.com +// + +// #ifndef LIBND4J_INDICESLIST_H +// #define LIBND4J_INDICESLIST_H + +// #include +// #include "NDIndex.h" + @Namespace("sd") @NoOffset public static class IndicesList extends Pointer { + static { Loader.load(); } + /** Pointer cast constructor. Invokes {@link Pointer#Pointer(Pointer)}. */ + public IndicesList(Pointer p) { super(p); } + + + public native int size(); + public native NDIndex at(int idx); + public native void push_back(NDIndex idx); + public native @Cast("bool") boolean isScalar(); + } + +// #endif //LIBND4J_INDICESLIST_H + + +// Parsed from graph/VariableType.h + +/******************************************************************************* + * Copyright (c) 2015-2018 Skymind, Inc. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ + +// +// @author raver119@gmail.com +// + +// #ifndef ND4J_VARIABLE_TYPE_H +// #define ND4J_VARIABLE_TYPE_H + /** enum sd::graph::VariableType */ + public static final int + NDARRAY = 0, + ARRAY_LIST = 1, + FLOW = 2, + CONSTANT = 3, + PLACEHOLDER = 4; + + + +// #endif + +// Parsed from graph/ArgumentsList.h + +/******************************************************************************* + * Copyright (c) 2015-2018 Skymind, Inc. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ + +// +// Created by raver119 on 24.01.18. +// + +// #ifndef LIBND4J_INPUTLIST_H +// #define LIBND4J_INPUTLIST_H + +// #include +// #include +// #include +// #include +// #include + @Namespace("sd::graph") @NoOffset public static class ArgumentsList extends Pointer { + static { Loader.load(); } + /** Pointer cast constructor. Invokes {@link Pointer#Pointer(Pointer)}. */ + public ArgumentsList(Pointer p) { super(p); } + /** Native array allocator. Access with {@link Pointer#position(long)}. */ + public ArgumentsList(long size) { super((Pointer)null); allocateArray(size); } + private native void allocateArray(long size); + @Override public ArgumentsList position(long position) { + return (ArgumentsList)super.position(position); + } + + public ArgumentsList() { super((Pointer)null); allocate(); } + private native void allocate(); + + /** + * This method returns number of argument pairs available + * + * @return + */ + public native int size(); + + /** + * This method returns Pair at specified index + * + * @param index + * @return + */ + public native @ByRef Pair at(int index); + } + + + +// #endif //LIBND4J_INPUTLIST_H + + +// Parsed from types/pair.h + +/******************************************************************************* + * Copyright (c) 2015-2018 Skymind, Inc. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ + +// +// Created by raver119 on 24.01.18. +// + +// #ifndef LIBND4J_PAIR_H +// #define LIBND4J_PAIR_H + +// #include + @Namespace("sd") @NoOffset public static class Pair extends Pointer { + static { Loader.load(); } + /** Pointer cast constructor. Invokes {@link Pointer#Pointer(Pointer)}. */ + public Pair(Pointer p) { super(p); } + /** Native array allocator. Access with {@link Pointer#position(long)}. */ + public Pair(long size) { super((Pointer)null); allocateArray(size); } + private native void allocateArray(long size); + @Override public Pair position(long position) { + return (Pair)super.position(position); + } + + public Pair(int first/*=0*/, int second/*=0*/) { super((Pointer)null); allocate(first, second); } + private native void allocate(int first/*=0*/, int second/*=0*/); + public Pair() { super((Pointer)null); allocate(); } + private native void allocate(); + + public native int first(); + public native int second(); + } + + + +// #endif //LIBND4J_PAIR_H + + +// Parsed from array/NDArray.h + +/******************************************************************************* + * Copyright (c) 2015-2018 Skymind, Inc. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ + +// #ifndef NDARRAY_H +// #define NDARRAY_H + +// #include +// #include +// #include +// #include +// #include "legacy/NativeOpExecutioner.h" +// #include +// #include +// #include +// #include +// #include +// #include +// #include +// #include +// #include +// #include +// #include +// #include +// #include +// #include +// #include +// #include +// #include +// #include +// #include +// #include +// #include +// #include +// #include + + + + + @Namespace("sd") public static native @ByVal NDArray mmul(@Const @ByRef NDArray arg0, @Const @ByRef NDArray arg1); + + @Namespace("sd") @NoOffset public static class NDArray extends Pointer { + static { Loader.load(); } + /** Pointer cast constructor. Invokes {@link Pointer#Pointer(Pointer)}. */ + public NDArray(Pointer p) { super(p); } + + public NDArray() { super((Pointer)null); allocate(); } + private native void allocate(); + + /** + * do not allocate memory, memory for array is passed from outside + */ +// #ifndef __JAVACPP_HACK__ + +// #endif + + /** + * do not allocate memory, memory for array is passed from outside + */ + public NDArray(Pointer buffer, @Cast("Nd4jLong*") LongPointer shapeInfo, LaunchContext context/*=sd::LaunchContext::defaultContext()*/, @Cast("const bool") boolean isBuffAlloc/*=false*/) { super((Pointer)null); allocate(buffer, shapeInfo, context, isBuffAlloc); } + private native void allocate(Pointer buffer, @Cast("Nd4jLong*") LongPointer shapeInfo, LaunchContext context/*=sd::LaunchContext::defaultContext()*/, @Cast("const bool") boolean isBuffAlloc/*=false*/); + public NDArray(Pointer buffer, @Cast("Nd4jLong*") LongPointer shapeInfo) { super((Pointer)null); allocate(buffer, shapeInfo); } + private native void allocate(Pointer buffer, @Cast("Nd4jLong*") LongPointer shapeInfo); + public NDArray(Pointer buffer, @Cast("Nd4jLong*") LongBuffer shapeInfo, LaunchContext context/*=sd::LaunchContext::defaultContext()*/, @Cast("const bool") boolean isBuffAlloc/*=false*/) { super((Pointer)null); allocate(buffer, shapeInfo, context, isBuffAlloc); } + private native void allocate(Pointer buffer, @Cast("Nd4jLong*") LongBuffer shapeInfo, LaunchContext context/*=sd::LaunchContext::defaultContext()*/, @Cast("const bool") boolean isBuffAlloc/*=false*/); + public NDArray(Pointer buffer, @Cast("Nd4jLong*") LongBuffer shapeInfo) { super((Pointer)null); allocate(buffer, shapeInfo); } + private native void allocate(Pointer buffer, @Cast("Nd4jLong*") LongBuffer shapeInfo); + public NDArray(Pointer buffer, @Cast("Nd4jLong*") long[] shapeInfo, LaunchContext context/*=sd::LaunchContext::defaultContext()*/, @Cast("const bool") boolean isBuffAlloc/*=false*/) { super((Pointer)null); allocate(buffer, shapeInfo, context, isBuffAlloc); } + private native void allocate(Pointer buffer, @Cast("Nd4jLong*") long[] shapeInfo, LaunchContext context/*=sd::LaunchContext::defaultContext()*/, @Cast("const bool") boolean isBuffAlloc/*=false*/); + public NDArray(Pointer buffer, @Cast("Nd4jLong*") long[] shapeInfo) { super((Pointer)null); allocate(buffer, shapeInfo); } + private native void allocate(Pointer buffer, @Cast("Nd4jLong*") long[] shapeInfo); + + /** + * do not allocate memory, memory for array is passed from outside + * we suppose the content of both (device and host) buffers is identical + */ + public NDArray(Pointer buffer, Pointer bufferD, @Cast("Nd4jLong*") LongPointer shapeInfo, LaunchContext context/*=sd::LaunchContext::defaultContext()*/, @Cast("const bool") boolean isBuffAlloc/*=false*/, @Cast("const bool") boolean isBuffDAlloc/*=false*/) { super((Pointer)null); allocate(buffer, bufferD, shapeInfo, context, isBuffAlloc, isBuffDAlloc); } + private native void allocate(Pointer buffer, Pointer bufferD, @Cast("Nd4jLong*") LongPointer shapeInfo, LaunchContext context/*=sd::LaunchContext::defaultContext()*/, @Cast("const bool") boolean isBuffAlloc/*=false*/, @Cast("const bool") boolean isBuffDAlloc/*=false*/); + public NDArray(Pointer buffer, Pointer bufferD, @Cast("Nd4jLong*") LongPointer shapeInfo) { super((Pointer)null); allocate(buffer, bufferD, shapeInfo); } + private native void allocate(Pointer buffer, Pointer bufferD, @Cast("Nd4jLong*") LongPointer shapeInfo); + public NDArray(Pointer buffer, Pointer bufferD, @Cast("Nd4jLong*") LongBuffer shapeInfo, LaunchContext context/*=sd::LaunchContext::defaultContext()*/, @Cast("const bool") boolean isBuffAlloc/*=false*/, @Cast("const bool") boolean isBuffDAlloc/*=false*/) { super((Pointer)null); allocate(buffer, bufferD, shapeInfo, context, isBuffAlloc, isBuffDAlloc); } + private native void allocate(Pointer buffer, Pointer bufferD, @Cast("Nd4jLong*") LongBuffer shapeInfo, LaunchContext context/*=sd::LaunchContext::defaultContext()*/, @Cast("const bool") boolean isBuffAlloc/*=false*/, @Cast("const bool") boolean isBuffDAlloc/*=false*/); + public NDArray(Pointer buffer, Pointer bufferD, @Cast("Nd4jLong*") LongBuffer shapeInfo) { super((Pointer)null); allocate(buffer, bufferD, shapeInfo); } + private native void allocate(Pointer buffer, Pointer bufferD, @Cast("Nd4jLong*") LongBuffer shapeInfo); + public NDArray(Pointer buffer, Pointer bufferD, @Cast("Nd4jLong*") long[] shapeInfo, LaunchContext context/*=sd::LaunchContext::defaultContext()*/, @Cast("const bool") boolean isBuffAlloc/*=false*/, @Cast("const bool") boolean isBuffDAlloc/*=false*/) { super((Pointer)null); allocate(buffer, bufferD, shapeInfo, context, isBuffAlloc, isBuffDAlloc); } + private native void allocate(Pointer buffer, Pointer bufferD, @Cast("Nd4jLong*") long[] shapeInfo, LaunchContext context/*=sd::LaunchContext::defaultContext()*/, @Cast("const bool") boolean isBuffAlloc/*=false*/, @Cast("const bool") boolean isBuffDAlloc/*=false*/); + public NDArray(Pointer buffer, Pointer bufferD, @Cast("Nd4jLong*") long[] shapeInfo) { super((Pointer)null); allocate(buffer, bufferD, shapeInfo); } + private native void allocate(Pointer buffer, Pointer bufferD, @Cast("Nd4jLong*") long[] shapeInfo); + + /** + * copy constructor + */ + public NDArray(@Const @ByRef NDArray other) { super((Pointer)null); allocate(other); } + private native void allocate(@Const @ByRef NDArray other); + + /** + * move constructor + */ + + /** + * constructor, create array stored at given workspace + */ + public NDArray(LaunchContext context) { super((Pointer)null); allocate(context); } + private native void allocate(LaunchContext context); + + + /** + * constructor creates new NDArray using shape information from "shapeInfo", set all elements in new array to zeros, if copyStrides is true then use stride values from "shapeInfo", else calculate strides independently + */ + public NDArray(@Cast("Nd4jLong*") LongPointer shapeInfo, @Cast("const bool") boolean copyStrides/*=false*/, LaunchContext context/*=sd::LaunchContext::defaultContext()*/) { super((Pointer)null); allocate(shapeInfo, copyStrides, context); } + private native void allocate(@Cast("Nd4jLong*") LongPointer shapeInfo, @Cast("const bool") boolean copyStrides/*=false*/, LaunchContext context/*=sd::LaunchContext::defaultContext()*/); + public NDArray(@Cast("Nd4jLong*") LongPointer shapeInfo) { super((Pointer)null); allocate(shapeInfo); } + private native void allocate(@Cast("Nd4jLong*") LongPointer shapeInfo); + public NDArray(@Cast("Nd4jLong*") LongBuffer shapeInfo, @Cast("const bool") boolean copyStrides/*=false*/, LaunchContext context/*=sd::LaunchContext::defaultContext()*/) { super((Pointer)null); allocate(shapeInfo, copyStrides, context); } + private native void allocate(@Cast("Nd4jLong*") LongBuffer shapeInfo, @Cast("const bool") boolean copyStrides/*=false*/, LaunchContext context/*=sd::LaunchContext::defaultContext()*/); + public NDArray(@Cast("Nd4jLong*") LongBuffer shapeInfo) { super((Pointer)null); allocate(shapeInfo); } + private native void allocate(@Cast("Nd4jLong*") LongBuffer shapeInfo); + public NDArray(@Cast("Nd4jLong*") long[] shapeInfo, @Cast("const bool") boolean copyStrides/*=false*/, LaunchContext context/*=sd::LaunchContext::defaultContext()*/) { super((Pointer)null); allocate(shapeInfo, copyStrides, context); } + private native void allocate(@Cast("Nd4jLong*") long[] shapeInfo, @Cast("const bool") boolean copyStrides/*=false*/, LaunchContext context/*=sd::LaunchContext::defaultContext()*/); + public NDArray(@Cast("Nd4jLong*") long[] shapeInfo) { super((Pointer)null); allocate(shapeInfo); } + private native void allocate(@Cast("Nd4jLong*") long[] shapeInfo); + + /** + * constructor creates new NDArray using shape information from "shapeInfo", set all elements in new array to be zeros, if copyStrides is true then use stride values from "shapeInfo", else calculate strides independently + * set dtype as array type + */ + public NDArray(@Cast("Nd4jLong*") LongPointer shapeInfo, @Cast("const sd::DataType") int dtype, @Cast("const bool") boolean copyStrides/*=false*/, LaunchContext context/*=sd::LaunchContext::defaultContext()*/) { super((Pointer)null); allocate(shapeInfo, dtype, copyStrides, context); } + private native void allocate(@Cast("Nd4jLong*") LongPointer shapeInfo, @Cast("const sd::DataType") int dtype, @Cast("const bool") boolean copyStrides/*=false*/, LaunchContext context/*=sd::LaunchContext::defaultContext()*/); + public NDArray(@Cast("Nd4jLong*") LongPointer shapeInfo, @Cast("const sd::DataType") int dtype) { super((Pointer)null); allocate(shapeInfo, dtype); } + private native void allocate(@Cast("Nd4jLong*") LongPointer shapeInfo, @Cast("const sd::DataType") int dtype); + public NDArray(@Cast("Nd4jLong*") LongBuffer shapeInfo, @Cast("const sd::DataType") int dtype, @Cast("const bool") boolean copyStrides/*=false*/, LaunchContext context/*=sd::LaunchContext::defaultContext()*/) { super((Pointer)null); allocate(shapeInfo, dtype, copyStrides, context); } + private native void allocate(@Cast("Nd4jLong*") LongBuffer shapeInfo, @Cast("const sd::DataType") int dtype, @Cast("const bool") boolean copyStrides/*=false*/, LaunchContext context/*=sd::LaunchContext::defaultContext()*/); + public NDArray(@Cast("Nd4jLong*") LongBuffer shapeInfo, @Cast("const sd::DataType") int dtype) { super((Pointer)null); allocate(shapeInfo, dtype); } + private native void allocate(@Cast("Nd4jLong*") LongBuffer shapeInfo, @Cast("const sd::DataType") int dtype); + public NDArray(@Cast("Nd4jLong*") long[] shapeInfo, @Cast("const sd::DataType") int dtype, @Cast("const bool") boolean copyStrides/*=false*/, LaunchContext context/*=sd::LaunchContext::defaultContext()*/) { super((Pointer)null); allocate(shapeInfo, dtype, copyStrides, context); } + private native void allocate(@Cast("Nd4jLong*") long[] shapeInfo, @Cast("const sd::DataType") int dtype, @Cast("const bool") boolean copyStrides/*=false*/, LaunchContext context/*=sd::LaunchContext::defaultContext()*/); + public NDArray(@Cast("Nd4jLong*") long[] shapeInfo, @Cast("const sd::DataType") int dtype) { super((Pointer)null); allocate(shapeInfo, dtype); } + private native void allocate(@Cast("Nd4jLong*") long[] shapeInfo, @Cast("const sd::DataType") int dtype); + + /** + * this constructor creates new array using shape information contained in vector argument + */ + public NDArray(byte order, @Cast("Nd4jLong*") @StdVector LongPointer shape, @Cast("sd::DataType") int dtype/*=sd::DOUBLE*/, LaunchContext context/*=sd::LaunchContext::defaultContext()*/) { super((Pointer)null); allocate(order, shape, dtype, context); } + private native void allocate(byte order, @Cast("Nd4jLong*") @StdVector LongPointer shape, @Cast("sd::DataType") int dtype/*=sd::DOUBLE*/, LaunchContext context/*=sd::LaunchContext::defaultContext()*/); + public NDArray(byte order, @Cast("Nd4jLong*") @StdVector LongPointer shape) { super((Pointer)null); allocate(order, shape); } + private native void allocate(byte order, @Cast("Nd4jLong*") @StdVector LongPointer shape); + public NDArray(byte order, @Cast("Nd4jLong*") @StdVector LongBuffer shape, @Cast("sd::DataType") int dtype/*=sd::DOUBLE*/, LaunchContext context/*=sd::LaunchContext::defaultContext()*/) { super((Pointer)null); allocate(order, shape, dtype, context); } + private native void allocate(byte order, @Cast("Nd4jLong*") @StdVector LongBuffer shape, @Cast("sd::DataType") int dtype/*=sd::DOUBLE*/, LaunchContext context/*=sd::LaunchContext::defaultContext()*/); + public NDArray(byte order, @Cast("Nd4jLong*") @StdVector LongBuffer shape) { super((Pointer)null); allocate(order, shape); } + private native void allocate(byte order, @Cast("Nd4jLong*") @StdVector LongBuffer shape); + public NDArray(byte order, @Cast("Nd4jLong*") @StdVector long[] shape, @Cast("sd::DataType") int dtype/*=sd::DOUBLE*/, LaunchContext context/*=sd::LaunchContext::defaultContext()*/) { super((Pointer)null); allocate(order, shape, dtype, context); } + private native void allocate(byte order, @Cast("Nd4jLong*") @StdVector long[] shape, @Cast("sd::DataType") int dtype/*=sd::DOUBLE*/, LaunchContext context/*=sd::LaunchContext::defaultContext()*/); + public NDArray(byte order, @Cast("Nd4jLong*") @StdVector long[] shape) { super((Pointer)null); allocate(order, shape); } + private native void allocate(byte order, @Cast("Nd4jLong*") @StdVector long[] shape); + + /** + * This constructor creates new array with elements copied from data and using shape information stored in shape, elements from data will be casted to dtype + */ + public NDArray(byte order, @Cast("Nd4jLong*") @StdVector LongPointer shape, @StdVector DoublePointer data, @Cast("sd::DataType") int dtype/*=sd::DOUBLE*/, LaunchContext context/*=sd::LaunchContext::defaultContext()*/) { super((Pointer)null); allocate(order, shape, data, dtype, context); } + private native void allocate(byte order, @Cast("Nd4jLong*") @StdVector LongPointer shape, @StdVector DoublePointer data, @Cast("sd::DataType") int dtype/*=sd::DOUBLE*/, LaunchContext context/*=sd::LaunchContext::defaultContext()*/); + public NDArray(byte order, @Cast("Nd4jLong*") @StdVector LongPointer shape, @StdVector DoublePointer data) { super((Pointer)null); allocate(order, shape, data); } + private native void allocate(byte order, @Cast("Nd4jLong*") @StdVector LongPointer shape, @StdVector DoublePointer data); + public NDArray(byte order, @Cast("Nd4jLong*") @StdVector LongBuffer shape, @StdVector DoubleBuffer data, @Cast("sd::DataType") int dtype/*=sd::DOUBLE*/, LaunchContext context/*=sd::LaunchContext::defaultContext()*/) { super((Pointer)null); allocate(order, shape, data, dtype, context); } + private native void allocate(byte order, @Cast("Nd4jLong*") @StdVector LongBuffer shape, @StdVector DoubleBuffer data, @Cast("sd::DataType") int dtype/*=sd::DOUBLE*/, LaunchContext context/*=sd::LaunchContext::defaultContext()*/); + public NDArray(byte order, @Cast("Nd4jLong*") @StdVector LongBuffer shape, @StdVector DoubleBuffer data) { super((Pointer)null); allocate(order, shape, data); } + private native void allocate(byte order, @Cast("Nd4jLong*") @StdVector LongBuffer shape, @StdVector DoubleBuffer data); + public NDArray(byte order, @Cast("Nd4jLong*") @StdVector long[] shape, @StdVector double[] data, @Cast("sd::DataType") int dtype/*=sd::DOUBLE*/, LaunchContext context/*=sd::LaunchContext::defaultContext()*/) { super((Pointer)null); allocate(order, shape, data, dtype, context); } + private native void allocate(byte order, @Cast("Nd4jLong*") @StdVector long[] shape, @StdVector double[] data, @Cast("sd::DataType") int dtype/*=sd::DOUBLE*/, LaunchContext context/*=sd::LaunchContext::defaultContext()*/); + public NDArray(byte order, @Cast("Nd4jLong*") @StdVector long[] shape, @StdVector double[] data) { super((Pointer)null); allocate(order, shape, data); } + private native void allocate(byte order, @Cast("Nd4jLong*") @StdVector long[] shape, @StdVector double[] data); + + /** + * this constructor creates new array using given buffer (without memory allocation) and shape information stored in shape + */ + public NDArray(Pointer buffer, byte order, @Cast("Nd4jLong*") @StdVector LongPointer shape, @Cast("sd::DataType") int dtype, LaunchContext context/*=sd::LaunchContext::defaultContext()*/, @Cast("const bool") boolean isBuffAlloc/*=false*/) { super((Pointer)null); allocate(buffer, order, shape, dtype, context, isBuffAlloc); } + private native void allocate(Pointer buffer, byte order, @Cast("Nd4jLong*") @StdVector LongPointer shape, @Cast("sd::DataType") int dtype, LaunchContext context/*=sd::LaunchContext::defaultContext()*/, @Cast("const bool") boolean isBuffAlloc/*=false*/); + public NDArray(Pointer buffer, byte order, @Cast("Nd4jLong*") @StdVector LongPointer shape, @Cast("sd::DataType") int dtype) { super((Pointer)null); allocate(buffer, order, shape, dtype); } + private native void allocate(Pointer buffer, byte order, @Cast("Nd4jLong*") @StdVector LongPointer shape, @Cast("sd::DataType") int dtype); + public NDArray(Pointer buffer, byte order, @Cast("Nd4jLong*") @StdVector LongBuffer shape, @Cast("sd::DataType") int dtype, LaunchContext context/*=sd::LaunchContext::defaultContext()*/, @Cast("const bool") boolean isBuffAlloc/*=false*/) { super((Pointer)null); allocate(buffer, order, shape, dtype, context, isBuffAlloc); } + private native void allocate(Pointer buffer, byte order, @Cast("Nd4jLong*") @StdVector LongBuffer shape, @Cast("sd::DataType") int dtype, LaunchContext context/*=sd::LaunchContext::defaultContext()*/, @Cast("const bool") boolean isBuffAlloc/*=false*/); + public NDArray(Pointer buffer, byte order, @Cast("Nd4jLong*") @StdVector LongBuffer shape, @Cast("sd::DataType") int dtype) { super((Pointer)null); allocate(buffer, order, shape, dtype); } + private native void allocate(Pointer buffer, byte order, @Cast("Nd4jLong*") @StdVector LongBuffer shape, @Cast("sd::DataType") int dtype); + public NDArray(Pointer buffer, byte order, @Cast("Nd4jLong*") @StdVector long[] shape, @Cast("sd::DataType") int dtype, LaunchContext context/*=sd::LaunchContext::defaultContext()*/, @Cast("const bool") boolean isBuffAlloc/*=false*/) { super((Pointer)null); allocate(buffer, order, shape, dtype, context, isBuffAlloc); } + private native void allocate(Pointer buffer, byte order, @Cast("Nd4jLong*") @StdVector long[] shape, @Cast("sd::DataType") int dtype, LaunchContext context/*=sd::LaunchContext::defaultContext()*/, @Cast("const bool") boolean isBuffAlloc/*=false*/); + public NDArray(Pointer buffer, byte order, @Cast("Nd4jLong*") @StdVector long[] shape, @Cast("sd::DataType") int dtype) { super((Pointer)null); allocate(buffer, order, shape, dtype); } + private native void allocate(Pointer buffer, byte order, @Cast("Nd4jLong*") @StdVector long[] shape, @Cast("sd::DataType") int dtype); + + /** + * This method returns new array with the same shape & data type + * @return + */ + public native @ByVal NDArray like(); + + /** + * This method returns new uninitialized array with the same shape & data type + * @return + */ + public native @ByVal NDArray ulike(); + + + /** + * this constructor creates new NDArray with shape matching "other" array, + * doesn't copy "other" elements into new array !!! + */ + public NDArray(@Const NDArray other, @Cast("const bool") boolean copyStrides/*=false*/, LaunchContext context/*=sd::LaunchContext::defaultContext()*/) { super((Pointer)null); allocate(other, copyStrides, context); } + private native void allocate(@Const NDArray other, @Cast("const bool") boolean copyStrides/*=false*/, LaunchContext context/*=sd::LaunchContext::defaultContext()*/); + + /** + * this constructor creates scalar(and set its value = 0) or empty array depending on bool argument isScalar + */ + public NDArray(@Cast("sd::DataType") int dtype, LaunchContext context/*=sd::LaunchContext::defaultContext()*/, @Cast("const bool") boolean isScalar/*=true*/) { super((Pointer)null); allocate(dtype, context, isScalar); } + private native void allocate(@Cast("sd::DataType") int dtype, LaunchContext context/*=sd::LaunchContext::defaultContext()*/, @Cast("const bool") boolean isScalar/*=true*/); + public NDArray(@Cast("sd::DataType") int dtype) { super((Pointer)null); allocate(dtype); } + private native void allocate(@Cast("sd::DataType") int dtype); + + /** + * This method blocks until asynchronous operation finishes + */ + public native void synchronize(@Cast("char*") String msg); + public native void synchronize(@Cast("char*") BytePointer msg); + + /** + * This method allows to set _isAttached flag + * @param reallyAttached + */ + public native void setAttached(@Cast("bool") boolean reallyAttached); + + public native void tickWriteHost(); + public native void tickWriteDevice(); + public native void tickReadHost(); + public native void tickReadDevice(); + public native void tickBothActual(); + public native @Cast("bool") boolean isActualOnHostSide(); + public native @Cast("bool") boolean isActualOnDeviceSide(); + public native void makeBothBuffersActual(); + + public native void syncToHost(); + public native void syncToDevice(); + public native void syncShape(); + + /** + * This method can be used on architectures that use special buffers + * @param writeList + * @param readList + */ + public static native void registerSpecialUse(@Const @ByRef ConstNDArrayVector writeList, @Const @ByRef ConstNDArrayVector readList); + public static native void prepareSpecialUse(@Const @ByRef ConstNDArrayVector writeList, @Const @ByRef ConstNDArrayVector readList, @Cast("bool") boolean synchronizeWritables/*=false*/); + public static native void prepareSpecialUse(@Const @ByRef ConstNDArrayVector writeList, @Const @ByRef ConstNDArrayVector readList); + + public static native void registerPrimaryUse(@Const @ByRef ConstNDArrayVector writeList, @Const @ByRef ConstNDArrayVector readList); + public static native void preparePrimaryUse(@Const @ByRef ConstNDArrayVector writeList, @Const @ByRef ConstNDArrayVector readList, @Cast("bool") boolean synchronizeWritables/*=false*/); + public static native void preparePrimaryUse(@Const @ByRef ConstNDArrayVector writeList, @Const @ByRef ConstNDArrayVector readList); + + /** + * This method returns buffer pointer offset by given number of elements, wrt own data type + * @param offset + * @return + */ + public native Pointer bufferWithOffset(@Cast("Nd4jLong") long offset); + + public native Pointer specialBufferWithOffset(@Cast("Nd4jLong") long offset); + /** + * copy assignment operator + * in particular, when _dataType != other._dataType and both shapes are the same, there will be allocation of new _buffer and _dataType acquires other._dataType + */ + public native @ByRef @Name("operator =") NDArray put(@Const @ByRef NDArray other); + + /** + * move assignment operator + */ + + /** + * assignment operator, assigns the same scalar to all array elements + */ + + + /** + * operators for memory allocation and deletion + */ + public native @Name("operator new") Pointer _new(@Cast("size_t") long i); + public native @Name("operator delete") void _delete(Pointer p); + + + public native void setContext(LaunchContext context); + + /** + * create a new array by replicating current array by repeats times along given dimension + * axis - axis along which to repeat elements + * repeats - number of repetitions + */ + public native @ByVal NDArray repeat(int axis, @StdVector IntPointer repeats); + public native @ByVal NDArray repeat(int axis, @StdVector IntBuffer repeats); + public native @ByVal NDArray repeat(int axis, @StdVector int[] repeats); + + /** + * This method fills this array with zeros + */ + public native void nullify(); + + /** + * This method returns quantized copy of given array + * + * @param array + * @return + */ + public static native @ByVal NDArray quantize(@Const @ByRef NDArray array); + + /** + * fill target array by repeating current array + * axis - axis along which to repeat elements + * repeats - vector containing numbers of repetition for elements at given axis + */ + public native void repeat(int axis, @StdVector IntPointer repeats, @ByRef NDArray target); + public native void repeat(int axis, @StdVector IntBuffer repeats, @ByRef NDArray target); + public native void repeat(int axis, @StdVector int[] repeats, @ByRef NDArray target); + + /** + * creates array which points on certain sub-range of this array, sub-range is defined by given indices + */ + + + + + /** + * cast array elements to given dtype + */ + public native @ByVal NDArray cast(@Cast("sd::DataType") int dtype); + + public native void cast(@ByRef NDArray target, @Cast("sd::DataType") int dtype); + + /** + * returns _context + */ + public native LaunchContext getContext(); + +// #ifndef __JAVACPP_HACK__ +// #endif + + /** + * returns host buffer + */ + public native Pointer getBuffer(); + public native Pointer buffer(); + + + /** + * returns buffer offset (offset is the same for host and device buffers) + */ + public native @Cast("Nd4jLong") long getBufferOffset(); + public native @Cast("Nd4jLong") long bufferOffset(); + + /** + * if _bufferD==nullptr return _buffer, else return _bufferD + */ + public native Pointer specialBuffer(); + public native Pointer getSpecialBuffer(); + + /** + * returns device buffer if compilation is for cuda case, otherwise returns host buffer + */ + public native Pointer getPlatformBuffer(); + public native Pointer platformBuffer(); + + /** + * returns _shapeInfo + */ + public native @Cast("Nd4jLong*") LongPointer shapeInfo(); + public native @Cast("Nd4jLong*") LongPointer getShapeInfo(); + + + /** + * Returns True if it's legally empty NDArray, or false otherwise + * @return + */ + public native @Cast("bool") boolean isEmpty(); + + /** + * if _shapeInfoD==nullptr return _shapeInfo, else return _shapeInfoD + */ + public native @Cast("Nd4jLong*") LongPointer specialShapeInfo(); + public native @Cast("Nd4jLong*") LongPointer getSpecialShapeInfo(); + + + public native @Cast("Nd4jLong*") LongPointer platformShapeInfo(); + public native @Cast("Nd4jLong*") LongPointer getPlatformShapeInfo(); + + /** + * permutes (in-place) the dimensions in array according to "dimensions" array + */ + public native @Cast("bool") boolean permutei(@StdVector IntPointer dimensions); + public native @Cast("bool") boolean permutei(@StdVector IntBuffer dimensions); + public native @Cast("bool") boolean permutei(@StdVector int[] dimensions); + public native @Cast("bool") boolean permutei(@Const IntPointer dimensions, int rank); + public native @Cast("bool") boolean permutei(@Const IntBuffer dimensions, int rank); + public native @Cast("bool") boolean permutei(@Const int[] dimensions, int rank); + public native @Cast("bool") boolean permutei(@Cast("Nd4jLong*") @StdVector LongPointer dimensions); + public native @Cast("bool") boolean permutei(@Cast("Nd4jLong*") @StdVector LongBuffer dimensions); + public native @Cast("bool") boolean permutei(@Cast("Nd4jLong*") @StdVector long[] dimensions); + public native @Cast("bool") boolean permutei(@Cast("const Nd4jLong*") LongPointer dimensions, int rank); + public native @Cast("bool") boolean permutei(@Cast("const Nd4jLong*") LongBuffer dimensions, int rank); + public native @Cast("bool") boolean permutei(@Cast("const Nd4jLong*") long[] dimensions, int rank); + + public native @Cast("bool") boolean isFinite(); + public native @Cast("bool") boolean hasNaNs(); + public native @Cast("bool") boolean hasInfs(); + + public native void copyBuffersContinuouslyFrom(@Const @ByRef NDArray other, @Cast("size_t") long sizeToCopyInBytes/*=0*/, @Cast("Nd4jLong") long offsetThis/*=0*/, @Cast("Nd4jLong") long offsetOther/*=0*/); + public native void copyBuffersContinuouslyFrom(@Const @ByRef NDArray other); + + /** + * permutes the dimensions in array according to "dimensions" array, new array points on _buffer of this array + */ + public native @ByVal NDArray permute(@StdVector IntPointer dimensions); + public native @ByVal NDArray permute(@StdVector IntBuffer dimensions); + public native @ByVal NDArray permute(@StdVector int[] dimensions); + public native @ByVal NDArray permute(@Const IntPointer dimensions, int rank); + public native @ByVal NDArray permute(@Const IntBuffer dimensions, int rank); + public native @ByVal NDArray permute(@Const int[] dimensions, int rank); + + + + + public native void permute(@Const IntPointer dimensions, int rank, @ByRef NDArray target); + public native void permute(@Const IntBuffer dimensions, int rank, @ByRef NDArray target); + public native void permute(@Const int[] dimensions, int rank, @ByRef NDArray target); + public native void permute(@StdVector IntPointer dimensions, @ByRef NDArray target); + public native void permute(@StdVector IntBuffer dimensions, @ByRef NDArray target); + public native void permute(@StdVector int[] dimensions, @ByRef NDArray target); + public native @ByVal NDArray permute(@Cast("Nd4jLong*") @StdVector LongPointer dimensions); + public native @ByVal NDArray permute(@Cast("Nd4jLong*") @StdVector LongBuffer dimensions); + public native @ByVal NDArray permute(@Cast("Nd4jLong*") @StdVector long[] dimensions); + public native @ByVal NDArray permute(@Cast("const Nd4jLong*") LongPointer dimensions, int rank); + public native @ByVal NDArray permute(@Cast("const Nd4jLong*") LongBuffer dimensions, int rank); + public native @ByVal NDArray permute(@Cast("const Nd4jLong*") long[] dimensions, int rank); + + + + + public native void permute(@Cast("const Nd4jLong*") LongPointer dimensions, int rank, @ByRef NDArray target); + public native void permute(@Cast("const Nd4jLong*") LongBuffer dimensions, int rank, @ByRef NDArray target); + public native void permute(@Cast("const Nd4jLong*") long[] dimensions, int rank, @ByRef NDArray target); + public native void permute(@Cast("Nd4jLong*") @StdVector LongPointer dimensions, @ByRef NDArray target); + public native void permute(@Cast("Nd4jLong*") @StdVector LongBuffer dimensions, @ByRef NDArray target); + public native void permute(@Cast("Nd4jLong*") @StdVector long[] dimensions, @ByRef NDArray target); + + /** + * This method streamlines given view or permuted array, and reallocates buffer + */ + public native void streamline(char order/*='a'*/); + public native void streamline(); + + /** + * prints information about array shape + * msg - message to print out + */ + public native void printShapeInfo(@Cast("char*") String msg/*=nullptr*/); + public native void printShapeInfo(); + public native void printShapeInfo(@Cast("char*") BytePointer msg/*=nullptr*/); + + /** + * prints buffer elements + * msg - message to print out + * limit - number of array elements to print out + * sync - if true check whether host buffer is actual, if it is not then make it so + */ + public native void printBuffer(@Cast("char*") String msg/*=nullptr*/, @Cast("Nd4jLong") long _limit/*=-1*/, @Cast("const bool") boolean sync/*=true*/); + public native void printBuffer(); + public native void printBuffer(@Cast("char*") BytePointer msg/*=nullptr*/, @Cast("Nd4jLong") long _limit/*=-1*/, @Cast("const bool") boolean sync/*=true*/); + + /** + * print element by element consequently in a way they (elements) are stored in physical memory + */ + public native void printLinearBuffer(); + + /** + * prints _buffer (if host = true) or _bufferD (if host = false) as it is, that is in current state without checking buffer status + */ + + /** + * prints buffer elements, takes into account offset between elements (element-wise-stride) + * msg - message to print out + * limit - number of array elements to print out + */ + public native void printIndexedBuffer(@Cast("char*") String msg/*=nullptr*/, @Cast("Nd4jLong") long _limit/*=-1*/); + public native void printIndexedBuffer(); + public native void printIndexedBuffer(@Cast("char*") BytePointer msg/*=nullptr*/, @Cast("Nd4jLong") long _limit/*=-1*/); + + public native @StdString BytePointer asIndexedString(@Cast("Nd4jLong") long _limit/*=-1*/); + public native @StdString BytePointer asIndexedString(); + public native @StdString BytePointer asString(@Cast("Nd4jLong") long _limit/*=-1*/); + public native @StdString BytePointer asString(); + + /** + * this method assigns values of given array to this one + */ + public native void assign(@Const NDArray other, @Cast("bool") boolean allowParallelism/*=true*/); + public native void assign(@Const NDArray other); + + /** + * this method assigns values of given array to this one + */ + + /** + * this method assigns given value to all elements in array + */ + + /** + * returns new copy of this array, optionally in different order + */ + public native @ByVal NDArray dup(byte newOrder/*='a'*/); + public native @ByVal NDArray dup(); + + /** + * returns sum of all elements of array + */ + public native @ByVal NDArray sumNumber(); + + /** + * returns mean number of array + */ + public native @ByVal NDArray meanNumber(); + +// #ifndef __JAVACPP_HACK__ + +// #endif + + /** + * apply transpose operation to the copy of this array, that is this array remains unaffected + */ + public native @ByVal NDArray transpose(); + + + /** + * perform transpose operation and store result in target, this array remains unaffected + * target - where to store result + */ + public native void transpose(@ByRef NDArray target); + + /** + * apply in-place transpose operation to this array, so this array becomes transposed + */ + public native void transposei(); + + /** + * returns the number of arrays pointing on specified dimension(s) + * dimensions - array of dimensions to point on + */ + public native @Cast("Nd4jLong") long tensorsAlongDimension(@StdVector IntPointer dimensions); + public native @Cast("Nd4jLong") long tensorsAlongDimension(@StdVector IntBuffer dimensions); + public native @Cast("Nd4jLong") long tensorsAlongDimension(@StdVector int[] dimensions); + + /** + * returns true if elements of two arrays are equal to within given epsilon value + * other - input array to compare + * eps - epsilon, this value defines the precision of elements comparison + */ + public native @Cast("bool") boolean equalsTo(@Const NDArray other, double eps/*=1e-5*/); + public native @Cast("bool") boolean equalsTo(@Const NDArray other); + + /** + * add given row vector to all rows of this array + * row - row vector to add + */ + public native void addiRowVector(@Const @ByRef NDArray row); + + /** + * add given row vector to all rows of this array, store result in target + * row - row vector to add + * target - where to store result + */ + public native void addRowVector(@Const @ByRef NDArray row, @ByRef NDArray target); + + /** + * subtract given row vector from all rows of this array, store result in target + * row - row vector to subtract + * target - where to store result + */ + public native void subRowVector(@Const @ByRef NDArray row, @ByRef NDArray target); + + /** + * multiply all rows of this array on given row vector, store result in target + * row - row vector to multiply on + * target - where to store result + */ + public native void mulRowVector(@Const @ByRef NDArray row, @ByRef NDArray target); + + /** + * divide all rows of this array on given row vector, store result in target + * row - row vector to divide on + * target - where to store result + */ + public native void divRowVector(@Const @ByRef NDArray row, @ByRef NDArray target); + + /** + * add given column vector to all columns of this array, store result in target + * column - column vector to add + * target - where to store result + */ + public native void addColumnVector(@Const @ByRef NDArray column, @ByRef NDArray target); + + /** + * add given column vector to all columns of this array, this array becomes affected (in-place operation) + * column - column vector to add + */ + public native void addiColumnVector(@Const @ByRef NDArray column); + + /** + * multiply all columns of this array on given column vector, this array becomes affected (in-place operation) + * column - column vector to multiply on + */ + public native void muliColumnVector(@Const @ByRef NDArray column); + + /** + * returns number of bytes used by _buffer & _shapeInfo + */ + public native @Cast("Nd4jLong") long memoryFootprint(); + + /** + * these methods suited for FlatBuffers use + */ + public native @Cast("Nd4jLong*") @StdVector LongPointer getShapeAsVector(); + public native @StdVector IntPointer getShapeAsVectorInt(); + public native @Cast("Nd4jLong*") @StdVector LongPointer getShapeInfoAsVector(); + public native @Cast("int64_t*") @StdVector LongPointer getShapeInfoAsFlatVector(); + public native @Cast("int64_t*") @StdVector LongPointer getShapeAsFlatVector(); + + /** + * set new order and shape in case of suitable array length (in-place operation) + * order - order to set + * shape - shape to set + * copyToNewBuff - if true then old buffer will be copied to new buffer if last one will be allocated after reshaping + * if there was permute applied before or there are weird strides, then new buffer is allocated for array + */ + public native @Cast("bool") boolean reshapei(byte order, @Cast("Nd4jLong*") @StdVector LongPointer shape, @Cast("const bool") boolean copyToNewBuff/*=true*/); + public native @Cast("bool") boolean reshapei(byte order, @Cast("Nd4jLong*") @StdVector LongPointer shape); + public native @Cast("bool") boolean reshapei(byte order, @Cast("Nd4jLong*") @StdVector LongBuffer shape, @Cast("const bool") boolean copyToNewBuff/*=true*/); + public native @Cast("bool") boolean reshapei(byte order, @Cast("Nd4jLong*") @StdVector LongBuffer shape); + public native @Cast("bool") boolean reshapei(byte order, @Cast("Nd4jLong*") @StdVector long[] shape, @Cast("const bool") boolean copyToNewBuff/*=true*/); + public native @Cast("bool") boolean reshapei(byte order, @Cast("Nd4jLong*") @StdVector long[] shape); + public native @Cast("bool") boolean reshapei(@Cast("Nd4jLong*") @StdVector LongPointer shape, @Cast("const bool") boolean copyToNewBuff/*=true*/); + public native @Cast("bool") boolean reshapei(@Cast("Nd4jLong*") @StdVector LongPointer shape); + public native @Cast("bool") boolean reshapei(@Cast("Nd4jLong*") @StdVector LongBuffer shape, @Cast("const bool") boolean copyToNewBuff/*=true*/); + public native @Cast("bool") boolean reshapei(@Cast("Nd4jLong*") @StdVector LongBuffer shape); + public native @Cast("bool") boolean reshapei(@Cast("Nd4jLong*") @StdVector long[] shape, @Cast("const bool") boolean copyToNewBuff/*=true*/); + public native @Cast("bool") boolean reshapei(@Cast("Nd4jLong*") @StdVector long[] shape); + + /** + * creates new array with corresponding order and shape, new array will point on _buffer of this array + * order - order to set + * shape - shape to set + * + * if permute have been applied before or there are weird strides, then new buffer is allocated for new array + */ + public native @ByVal NDArray reshape(byte order, @Cast("Nd4jLong*") @StdVector LongPointer shape, @Cast("const bool") boolean copyToNewBuff/*=true*/); + public native @ByVal NDArray reshape(byte order, @Cast("Nd4jLong*") @StdVector LongPointer shape); + public native @ByVal NDArray reshape(byte order, @Cast("Nd4jLong*") @StdVector LongBuffer shape, @Cast("const bool") boolean copyToNewBuff/*=true*/); + public native @ByVal NDArray reshape(byte order, @Cast("Nd4jLong*") @StdVector LongBuffer shape); + public native @ByVal NDArray reshape(byte order, @Cast("Nd4jLong*") @StdVector long[] shape, @Cast("const bool") boolean copyToNewBuff/*=true*/); + public native @ByVal NDArray reshape(byte order, @Cast("Nd4jLong*") @StdVector long[] shape); + + + /** + * calculate strides and set given order + * order - order to set + */ + public native void updateStrides(byte order); + + /** + * change an array by repeating it the number of times given by reps (in-place operation) + * repeats - contains numbers of repetitions + */ + public native void tilei(@Cast("Nd4jLong*") @StdVector LongPointer repeats); + public native void tilei(@Cast("Nd4jLong*") @StdVector LongBuffer repeats); + public native void tilei(@Cast("Nd4jLong*") @StdVector long[] repeats); + + /** + * returns new array which is created by repeating of this array the number of times given by reps + * repeats - contains numbers of repetitions + */ + public native @ByVal NDArray tile(@Cast("Nd4jLong*") @StdVector LongPointer repeats); + public native @ByVal NDArray tile(@Cast("Nd4jLong*") @StdVector LongBuffer repeats); + public native @ByVal NDArray tile(@Cast("Nd4jLong*") @StdVector long[] repeats); + + /** + * change an array by repeating it the number of times given by reps (in-place operation) + * repeats - contains numbers of repetitions + * target - where to store result + */ + public native void tile(@Cast("Nd4jLong*") @StdVector LongPointer repeats, @ByRef NDArray target); + public native void tile(@Cast("Nd4jLong*") @StdVector LongBuffer repeats, @ByRef NDArray target); + public native void tile(@Cast("Nd4jLong*") @StdVector long[] repeats, @ByRef NDArray target); + + /** + * change an array by repeating it the number of times to acquire the new shape which is the same as target shape + * target - where to store result + */ + public native void tile(@ByRef NDArray target); + + /** + * check whether array is identity matrix + */ + public native @Cast("bool") boolean isIdentityMatrix(); + + /** + * check whether array is unitary matrix + */ + public native @Cast("bool") boolean isUnitary(); + + /** + * operator returns subarray with buffer pointing at this->_buffer with offset defined by given intervals + * idx - intervals of indexes which define the subarrays to point on, idx has form {dim0Start,dim0End, dim1Start,dim1End, ....} and length (2 * this->rankOf()) + * when (dimStart == dimEnd) then whole range will be used for current dimension + * keepUnitiesInShape - if false then eliminate unities from resulting array shape, for example {1,a,1,b} -> {a,b} + * isStrided - if true then idx has length (3 * this->rankOf()) and contains additional stride numbers which correspond to stride between dimStart and dimEnd, + * so structure of idx is like {dim0Start,dim0End,dim0Stride, dim1Start,dim1End,dim1Stride, ....} + */ + public native @ByVal @Name("operator ()") NDArray apply(@Cast("Nd4jLong*") @StdVector LongPointer idx, @Cast("const bool") boolean keepUnitiesInShape/*=false*/, @Cast("const bool") boolean isStrided/*=false*/); + public native @ByVal @Name("operator ()") NDArray apply(@Cast("Nd4jLong*") @StdVector LongPointer idx); + public native @ByVal @Name("operator ()") NDArray apply(@Cast("Nd4jLong*") @StdVector LongBuffer idx, @Cast("const bool") boolean keepUnitiesInShape/*=false*/, @Cast("const bool") boolean isStrided/*=false*/); + public native @ByVal @Name("operator ()") NDArray apply(@Cast("Nd4jLong*") @StdVector LongBuffer idx); + public native @ByVal @Name("operator ()") NDArray apply(@Cast("Nd4jLong*") @StdVector long[] idx, @Cast("const bool") boolean keepUnitiesInShape/*=false*/, @Cast("const bool") boolean isStrided/*=false*/); + public native @ByVal @Name("operator ()") NDArray apply(@Cast("Nd4jLong*") @StdVector long[] idx); + + /** + * evaluates subarray with buffer pointing at this->_buffer and offset defined by given sequential index subArrIdx and dimensions in dimsToExclude + * subArrIdx - index of current sub-array + * dimsToExclude - MUST BE SORTED, dimensions to evaluate sub-array along, i.e. when shape is [2,3,4,5] and dimsToExclude={0,2}, then there will be 8 sub-arrays with shape [3,5], and subArrIdx must be in range [0,7] + * if dimsToExclude is empty then idxRanges containing all zeros (means whole array) will be returned. + * keepUnitiesInShape - if false then eliminate unities from resulting array shape, for example {1,a,1,b} -> {a,b} + */ + public native @ByVal @Name("operator ()") NDArray apply(@Cast("const Nd4jLong") long subArrIdx, @StdVector IntPointer dimsToExclude, @Cast("bool") boolean keepUnitiesInShape/*=false*/); + public native @ByVal @Name("operator ()") NDArray apply(@Cast("const Nd4jLong") long subArrIdx, @StdVector IntPointer dimsToExclude); + public native @ByVal @Name("operator ()") NDArray apply(@Cast("const Nd4jLong") long subArrIdx, @StdVector IntBuffer dimsToExclude, @Cast("bool") boolean keepUnitiesInShape/*=false*/); + public native @ByVal @Name("operator ()") NDArray apply(@Cast("const Nd4jLong") long subArrIdx, @StdVector IntBuffer dimsToExclude); + public native @ByVal @Name("operator ()") NDArray apply(@Cast("const Nd4jLong") long subArrIdx, @StdVector int[] dimsToExclude, @Cast("bool") boolean keepUnitiesInShape/*=false*/); + public native @ByVal @Name("operator ()") NDArray apply(@Cast("const Nd4jLong") long subArrIdx, @StdVector int[] dimsToExclude); + + /** + * processes whole set of sub-arrays + * evaluates shapeInfo of sub-arrays (all sub-arrays have the same shapeInfo) and their buffer offsets (each sub-array has its own unique offset from original this-buffer) + * dimsToExclude - MUST BE SORTED, dimensions to evaluate sub-array along, i.e. when shape is [2,3,4,5] and dimsToExclude={0,2}, then there will be 8 sub-arrays with shape [3,5] + * if dimsToExclude.size() = array rank it means sub-array is whole array and copy of original_shapeInfo will be returned and one zero offset + * subArrShapeInfo - output argument, contains shapeInfo common for all sub-arrays + * subArrOffsets - output argument, contains successive sub-arrays offsets from original this-buffer + * keepUnitiesInShape - if false then eliminate unities from sub-array shapeInfo, for example {1,a,1,b} -> {a,b} + */ + public native void getSubArrShapeAndOffsets(@StdVector IntPointer dimsToExclude, @Cast("Nd4jLong*&") @ByPtrRef LongPointer subArrShapeInfo, @Cast("Nd4jLong*&") @ByPtrRef LongPointer subArrOffsets, @Cast("bool") boolean keepUnitiesInShape/*=false*/); + public native void getSubArrShapeAndOffsets(@StdVector IntPointer dimsToExclude, @Cast("Nd4jLong*&") @ByPtrRef LongPointer subArrShapeInfo, @Cast("Nd4jLong*&") @ByPtrRef LongPointer subArrOffsets); + public native void getSubArrShapeAndOffsets(@StdVector IntBuffer dimsToExclude, @Cast("Nd4jLong*&") @ByPtrRef LongBuffer subArrShapeInfo, @Cast("Nd4jLong*&") @ByPtrRef LongBuffer subArrOffsets, @Cast("bool") boolean keepUnitiesInShape/*=false*/); + public native void getSubArrShapeAndOffsets(@StdVector IntBuffer dimsToExclude, @Cast("Nd4jLong*&") @ByPtrRef LongBuffer subArrShapeInfo, @Cast("Nd4jLong*&") @ByPtrRef LongBuffer subArrOffsets); + public native void getSubArrShapeAndOffsets(@StdVector int[] dimsToExclude, @Cast("Nd4jLong*&") @ByPtrRef long[] subArrShapeInfo, @Cast("Nd4jLong*&") @ByPtrRef long[] subArrOffsets, @Cast("bool") boolean keepUnitiesInShape/*=false*/); + public native void getSubArrShapeAndOffsets(@StdVector int[] dimsToExclude, @Cast("Nd4jLong*&") @ByPtrRef long[] subArrShapeInfo, @Cast("Nd4jLong*&") @ByPtrRef long[] subArrOffsets); + + /** + * addition unary operator array += other + * other - input array to add + */ + public native @Name("operator +=") void addPut(@Const @ByRef NDArray other); + + /** + * subtraction unary operator array -= other + * other - input array to add + */ + public native @Name("operator -=") void subtractPut(@Const @ByRef NDArray other); + + /** + * negative operator, it changes sign of all array elements on opposite + */ + public native @ByVal @Name("operator -") NDArray subtract(); + + + /** + * pairwise multiplication unary operator array *= other + * other - input array to multiply on + */ + public native @Name("operator *=") void multiplyPut(@Const @ByRef NDArray other); + + /** + * multiplication unary operator array *= scalar + * scalar - input scalar to multiply on + */ + + /** + * pairwise division unary operator: array /= other + * other - input array to divide on + */ + public native @Name("operator /=") void dividePut(@Const @ByRef NDArray other); + + /** + * division unary operator: array /= scalar + * scalar - input scalar to divide on + */ + + /** + * friend function which implements mathematical multiplication of two arrays + * left - input array + * right - input array + */ + + + /** + * return vector containing _buffer as flat binary array + */ + public native @StdVector BytePointer asByteVector(); + + /** + * makes array to be identity matrix (not necessarily square), that is set all diagonal elements = 1, rest = 0 + */ + public native void setIdentity(); + + /** + * swaps the contents of tow arrays, + * PLEASE NOTE: method doesn't take into account the shapes of arrays, shapes may be different except one condition: arrays lengths must be the same + */ + public native void swapUnsafe(@ByRef NDArray other); + + /** + * return vector with buffer which points on corresponding diagonal elements of array + * type - means of vector to be returned: column ('c') or row ('r') + */ + public native @ByVal NDArray diagonal(byte type ); + + /** + * fill target matrix with given value in one or two directions from main diagonal: + * - down from main diagonal starting at subdiagonal number "lower" if direction = 'd' (down) or 'b' (both) + * - up from main diagonal starting at superdiagonal number "upper"if direction = 'u' (up) or 'b' (both) + * direction - in what direction to fill matrix. There are 3 possible directions: + * 'u' - fill up, mathematically this corresponds to lower triangular matrix, subdiagonal "lower" unaffected + * 'l' - fill down, mathematically this corresponds to upper triangular matrix, superdiagonal "upper" remains unaffected + * 'b' - fill in both directions, both "lower" and "upper" are taken into account + * rest of target elements are equal to this array elements + * target and this array should have same shapes, except when this_rank = 1 (in that case should be target_rank = 2) + */ + + /** + * change an array by repeating it the number of times in order to acquire new shape equal to the input shape + * + * shape - contains new shape to broadcast array to + * target - optional argument, if target != nullptr the resulting array will be placed in target, in opposite case tile operation is done in place + */ + public native @ByVal NDArray tileToShape(@Cast("const Nd4jLong*") LongPointer shapeInfo); + public native @ByVal NDArray tileToShape(@Cast("const Nd4jLong*") LongBuffer shapeInfo); + public native @ByVal NDArray tileToShape(@Cast("const Nd4jLong*") long[] shapeInfo); + public native void tileToShape(@Cast("Nd4jLong*") @StdVector LongPointer shape, @ByRef NDArray target); + public native void tileToShape(@Cast("Nd4jLong*") @StdVector LongBuffer shape, @ByRef NDArray target); + public native void tileToShape(@Cast("Nd4jLong*") @StdVector long[] shape, @ByRef NDArray target); +// #ifndef __JAVACPP_HACK__ +// #endif + + public native @ByVal NDArray asT(@Cast("sd::DataType") int dtype); + + + public native void linspace(double start); + + public native void linspace(double start, double step); + + /** + * calculates the trace of an array, that is sum of elements on main diagonal = sum array[i, i, i, ...] + */ + public native double getTrace(); + + public native @ByVal ResultSet multipleTensorsAlongDimension(@StdVector IntPointer indices, @StdVector IntPointer dimensions); + public native @ByVal ResultSet multipleTensorsAlongDimension(@StdVector IntBuffer indices, @StdVector IntBuffer dimensions); + public native @ByVal ResultSet multipleTensorsAlongDimension(@StdVector int[] indices, @StdVector int[] dimensions); + + public native @ByVal ResultSet allTensorsAlongDimension(@StdVector IntPointer dimensions); + public native @ByVal ResultSet allTensorsAlongDimension(@StdVector IntBuffer dimensions); + public native @ByVal ResultSet allTensorsAlongDimension(@StdVector int[] dimensions); + + public native @ByVal ResultSet allExamples(); + + /** + * set _shapeInfo + */ + public native void setShapeInfo(@Cast("const Nd4jLong*") LongPointer shapeInfo); + public native void setShapeInfo(@Cast("const Nd4jLong*") LongBuffer shapeInfo); + public native void setShapeInfo(@Cast("const Nd4jLong*") long[] shapeInfo); + public native void setShapeInfo(@Cast("const Nd4jLong*") LongPointer shapeInfo, @Cast("const sd::DataType") int dtype); + public native void setShapeInfo(@Cast("const Nd4jLong*") LongBuffer shapeInfo, @Cast("const sd::DataType") int dtype); + public native void setShapeInfo(@Cast("const Nd4jLong*") long[] shapeInfo, @Cast("const sd::DataType") int dtype); + public native void setShapeInfo(@Const @ByRef ShapeDescriptor descriptor); + public native void setShapeInfo(@Const @ByRef ConstantDataBuffer shapeBuffer); + + /** + * returns absolute offset which corresponds to given sequential index + */ + public native @Cast("Nd4jLong") long getOffset(@Cast("const Nd4jLong") long i); + + /** + * returns reference on array element with given index + */ + + + /** + * returns array element with given index + * i - element index in array + */ + + + /** + * default destructor + */ + + /** + * set _shapeInfo + */ + + /** + * returns the value of "dim" dimension + */ + public native @Cast("Nd4jLong") long sizeAt(int dim); + + /** + * returns stride of "dim" dimension + */ + public native @Cast("Nd4jLong") long strideAt(int dim); + + /** + * returns order of array + */ + public native char ordering(); + + /** + * return _isView + */ + public native @Cast("bool") boolean isView(); + + /** + * returns shape portion of shapeInfo + */ + public native @Cast("Nd4jLong*") LongPointer shapeOf(); + + /** + * returns strides portion of shapeInfo + */ + public native @Cast("Nd4jLong*") LongPointer stridesOf(); + + /** + * returns rank of array + */ + public native int rankOf(); + + /** + * returns length of array + */ + public native @Cast("Nd4jLong") long lengthOf(); + + /** + * returns number of rows in array + */ + public native @Cast("Nd4jLong") long rows(); + + /** + * returns number of columns in array + */ + public native @Cast("Nd4jLong") long columns(); + + /** + * returns size of array elements type + */ + public native @Cast("size_t") long sizeOfT(); + + /** + * returns element-wise-stride + */ + public native @Cast("Nd4jLong") long ews(); + + // returns true if arrays have same shape + public native @Cast("bool") boolean isSameShape(@Const NDArray other); + public native @Cast("bool") boolean isSameShape(@Cast("Nd4jLong*") @StdVector LongPointer shape); + public native @Cast("bool") boolean isSameShape(@Cast("Nd4jLong*") @StdVector LongBuffer shape); + public native @Cast("bool") boolean isSameShape(@Cast("Nd4jLong*") @StdVector long[] shape); + public native @Cast("bool") boolean areSameShapeAndType(@Const @ByRef NDArray other); + + /** + * returns true if these two NDArrays have same rank, dimensions, strides, ews and order + */ + public native @Cast("bool") boolean isSameShapeStrict(@Const @ByRef NDArray other); + + /** + * returns true if buffer && shapeInfo were defined (non nullptr) + */ + public native @Cast("bool") boolean nonNull(); + + /** + * returns array element with given index from linear buffer + * i - element index in array + */ + + /** + * returns element with given indexes from 2D array + * i - number of row + * j - number of column + */ + + /** + * returns element with given indexes from 3D array + * i - height + * j - width + * k - depth + */ + + /** + * returns element with given indexes from DD array + */ + + /** + * returns array-scalar containing element of this array with given index + * i - element index in array + */ + public native @ByVal NDArray e(@Cast("const Nd4jLong") long i); + + /** + * assigns given scalar to array element by given index, regards array buffer as linear + * i - element index in array + * value - scalar value to assign + */ + + public native void p(@Cast("const Nd4jLong") long i, @Const @ByRef NDArray value); + + /** + * assigns given scalar to 2D array element by given indexes + * i - number of row + * j - number of row + * value - scalar value to assign + */ + + /** + * assigns given scalar to 3D array element by given indexes + * i - height + * j - width + * k - depth + * value - scalar value to assign + */ + public native void p(@Cast("const Nd4jLong") long i, @Cast("const Nd4jLong") long j, @Cast("const Nd4jLong") long k, @Cast("const Nd4jLong") long l, @Const @ByRef NDArray value); + + /** + * returns true if array is 2D + */ + public native @Cast("bool") boolean isMatrix(); + + /** + * returns true if array is vector + */ + public native @Cast("bool") boolean isVector(); + + /** + * returns true if array is column vector + */ + public native @Cast("bool") boolean isColumnVector(); + + /** + * returns true if array is row vector + */ + public native @Cast("bool") boolean isRowVector(); + + /** + * returns true if all dimensions of array except one are unities, for example: [1,1,n,1], [n,1,1], [n], ... + * posOfNonUnityDim - one dimension with value > 1 + */ + public native @Cast("bool") boolean isCommonVector(@ByRef IntPointer posOfNonUnityDim); + public native @Cast("bool") boolean isCommonVector(@ByRef IntBuffer posOfNonUnityDim); + public native @Cast("bool") boolean isCommonVector(@ByRef int[] posOfNonUnityDim); + + + /** + * returns true if array is scalar + */ + public native @Cast("bool") boolean isScalar(); + + /** + * Returns data type of this array + * @return + */ + public native @Cast("sd::DataType") int dataType(); + + /** + * This method returns true if value is from Integer space + * @return + */ + public native @Cast("bool") boolean isZ(); + + /** + * This method returns true if array is from Real space + * @return + */ + public native @Cast("bool") boolean isR(); + + /** + * This method returns true if array is from Boolean space + * @return + */ + public native @Cast("bool") boolean isB(); + + /** + * This method returns true if array contains Complex numbers + * @return + */ + public native @Cast("bool") boolean isC(); + + /** + * This method returns true if array contains String + * @return + */ + public native @Cast("bool") boolean isS(); + + public native @Cast("bool") boolean isAttached(); + + public native NDArray detach(); + + public native @Cast("bool") @Name("operator ==") boolean equals(@Const @ByRef NDArray other); + + public native @Cast("bool") @Name("operator !=") boolean notEquals(@Const @ByRef NDArray other); + } + + + + +////////////////////////////////////////////////////////////////////////// +///// IMLEMENTATION OF INLINE METHODS ///// +////////////////////////////////////////////////////////////////////////// + + + + +////////////////////////////////////////////////////////////////////////// + + +////////////////////////////////////////////////////////////////////////// + + +////////////////////////////////////////////////////////////////////////// + + +////////////////////////////////////////////////////////////////////////// + + +////////////////////////////////////////////////////////////////////////// + + +////////////////////////////////////////////////////////////////////////// + + +////////////////////////////////////////////////////////////////////////// + + +////////////////////////////////////////////////////////////////////////// + + +////////////////////////////////////////////////////////////////////////// + + +////////////////////////////////////////////////////////////////////////// + + +////////////////////////////////////////////////////////////////////////// + + + +////////////////////////////////////////////////////////////////////////// + + +////////////////////////////////////////////////////////////////////////// + + +////////////////////////////////////////////////////////////////////////// + + +////////////////////////////////////////////////////////////////////////// + + +////////////////////////////////////////////////////////////////////////// + + +////////////////////////////////////////////////////////////////////////// + + +////////////////////////////////////////////////////////////////////////// + + +////////////////////////////////////////////////////////////////////////// + + + +////////////////////////////////////////////////////////////////////////// + + +////////////////////////////////////////////////////////////////////////// +// still the definition of inline function must be in header file + + +////////////////////////////////////////////////////////////////////////// + + +////////////////////////////////////////////////////////////////////////// + + +////////////////////////////////////////////////////////////////////////// + + +////////////////////////////////////////////////////////////////////////// + + +////////////////////////////////////////////////////////////////////////// +// returns true if these two NDArrays have same _shapeInfo +// still the definition of inline function must be in header file + + + +////////////////////////////////////////////////////////////////////////// + + +////////////////////////////////////////////////////////////////////////// + + +////////////////////////////////////////////////////////////////////////// + + +////////////////////////////////////////////////////////////////////////// + + +//////////////////////////////////////////////////////////////////////// + + +//////////////////////////////////////////////////////////////////////// + + + + + + +//////////////////////////////////////////////////////////////////////// + + +//////////////////////////////////////////////////////////////////////// + + + + + + +// #ifndef __JAVACPP_HACK__ +// #endif + +//////////////////////////////////////////////////////////////////////// + + +////////////////////////////////////////////////////////////////////////// + + +//////////////////////////////////////////////////////////////////////// + + +////////////////////////////////////////////////////////////////////////// + + +//////////////////////////////////////////////////////////////////////// + + +//////////////////////////////////////////////////////////////////////// + + +//////////////////////////////////////////////////////////////////////// + + +//////////////////////////////////////////////////////////////////////// + + + +// #if defined(__CUDACC__) //&& defined(BUILD_TESTS) +// for CUDA we need stil stuff inline +// #include +// #endif + + + +// #endif + + +// Parsed from array/NDArrayList.h + +/******************************************************************************* + * Copyright (c) 2015-2018 Skymind, Inc. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ + +// +// This class describes collection of NDArrays +// +// @author raver119!gmail.com +// + +// #ifndef NDARRAY_LIST_H +// #define NDARRAY_LIST_H + +// #include +// #include +// #include +// #include +// #include +// #include + @Namespace("sd") @NoOffset public static class NDArrayList extends Pointer { + static { Loader.load(); } + /** Pointer cast constructor. Invokes {@link Pointer#Pointer(Pointer)}. */ + public NDArrayList(Pointer p) { super(p); } + + public NDArrayList(int height, @Cast("bool") boolean expandable/*=false*/) { super((Pointer)null); allocate(height, expandable); } + private native void allocate(int height, @Cast("bool") boolean expandable/*=false*/); + public NDArrayList(int height) { super((Pointer)null); allocate(height); } + private native void allocate(int height); + + public native @Cast("sd::DataType") int dataType(); + + public native NDArray read(int idx); + public native NDArray readRaw(int idx); + public native @Cast("Nd4jStatus") int write(int idx, NDArray array); + public native NDArray pick(@StdVector IntPointer indices); + public native NDArray pick(@StdVector IntBuffer indices); + public native NDArray pick(@StdVector int[] indices); + public native @Cast("bool") boolean isWritten(int index); + + public native @Cast("Nd4jLong*") @StdVector LongPointer shape(); + + public native NDArray stack(); + public native void unstack(NDArray array, int axis); + + public native @ByRef IntIntPair id(); + public native @StdString @ByRef @Cast({"char*", "std::string*"}) BytePointer name(); + //sd::memory::Workspace* workspace(); + public native LaunchContext context(); + public native NDArrayList clone(); + + public native @Cast("bool") boolean equals(@ByRef NDArrayList other); + + public native int elements(); + public native int height(); + + public native int counter(); + } + + +// #endif + +// Parsed from array/ResultSet.h + +/******************************************************************************* + * Copyright (c) 2015-2018 Skymind, Inc. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ + +// +// This class is suited for execution results representation. +// +// PLESE NOTE: It will delete all stored NDArrays upon destructor call +// +// @author raver119@gmail.com +// + +// #ifndef LIBND4J_RESULTSET_H +// #define LIBND4J_RESULTSET_H + +// #include +// #include +// #include +// #include // forward declaration of template class NDArray + + @Namespace("sd") @NoOffset public static class ResultSet extends Pointer { + static { Loader.load(); } + /** Pointer cast constructor. Invokes {@link Pointer#Pointer(Pointer)}. */ + public ResultSet(Pointer p) { super(p); } + /** Native array allocator. Access with {@link Pointer#position(long)}. */ + public ResultSet(long size) { super((Pointer)null); allocateArray(size); } + private native void allocateArray(long size); + @Override public ResultSet position(long position) { + return (ResultSet)super.position(position); + } + + public ResultSet() { super((Pointer)null); allocate(); } + private native void allocate(); + +// #ifndef __JAVACPP_HACK__ +// #endif + + public ResultSet(@Const @ByRef ResultSet other) { super((Pointer)null); allocate(other); } + @NoException private native void allocate(@Const @ByRef ResultSet other); + + public native @ByRef @Name("operator =") @NoException ResultSet put(@Const @ByRef ResultSet other); + + // move constructor + + // move assignment operator + + public native int size(); + public native NDArray at(@Cast("const unsigned long") long idx); + public native @Name("operator []") NDArray get(@Cast("const unsigned long") long idx); + public native void push_back(NDArray array); + + public native @Cast("Nd4jStatus") int status(); + public native void setStatus(@Cast("Nd4jStatus") int status); + public native void purge(); + public native void setNonRemovable(); + } + + +// #endif //LIBND4J_RESULTSET_H + + +// Parsed from graph/RandomGenerator.h + +/******************************************************************************* + * Copyright (c) 2015-2018 Skymind, Inc. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ + +// +// @author raver119@protonmail.com +// + +// #ifndef LIBND4J_GRAPH_RNG_H +// #define LIBND4J_GRAPH_RNG_H + +// #include +// #include +// #include +// #include +// #include +// #include +// #include +// #include + +// #ifdef __CUDACC__ +// #endif +// #ifdef __CUDACC__ +// #else + @Namespace("sd::graph") @NoOffset public static class RandomGenerator extends Pointer { + static { Loader.load(); } + /** Pointer cast constructor. Invokes {@link Pointer#Pointer(Pointer)}. */ + public RandomGenerator(Pointer p) { super(p); } + /** Native array allocator. Access with {@link Pointer#position(long)}. */ + public RandomGenerator(long size) { super((Pointer)null); allocateArray(size); } + private native void allocateArray(long size); + @Override public RandomGenerator position(long position) { + return (RandomGenerator)super.position(position); + } + + public RandomGenerator(@Cast("Nd4jLong") long rootSeed/*=0*/, @Cast("Nd4jLong") long nodeSeed/*=0*/) { super((Pointer)null); allocate(rootSeed, nodeSeed); } + private native void allocate(@Cast("Nd4jLong") long rootSeed/*=0*/, @Cast("Nd4jLong") long nodeSeed/*=0*/); + public RandomGenerator() { super((Pointer)null); allocate(); } + private native void allocate(); + + /** + * This method allows to change graph-level state in runtime. + * PLEASE NOTE: this method will change state of node as well. + */ + public native void setStates(@Cast("Nd4jLong") long rootSeed, @Cast("Nd4jLong") long nodeState/*=0*/); + public native void setStates(@Cast("Nd4jLong") long rootSeed); + + + + /** + * This method returns T value between from and to + */ + + /** + * This method returns T value between 0 and MAX_T + */ + + /** + * These two methods are made for JVM + * @param index + * @return + */ + public native int relativeInt(@Cast("Nd4jLong") long index); + public native @Cast("Nd4jLong") long relativeLong(@Cast("Nd4jLong") long index); + + public native void rewindH(@Cast("Nd4jLong") long steps); + + /** + * These methods set up only node states, with non-changed root ones + */ + public native void setSeed(int seed); + + public native void setSeed(@Cast("uint64_t") long seed); + + public native @Cast("Nd4jLong") long rootState(); + + public native @Cast("Nd4jLong") long nodeState(); + } + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + ////// + @Namespace("sd::graph") public static native @Cast("uint32_t") int rotl(@Cast("const uint32_t") int x, int k); + + @Namespace("sd::graph") public static native @Cast("uint64_t") long rotl(@Cast("const uint64_t") long x, int k); + + + + + + + + + +// #endif + + +// Parsed from graph/Variable.h + +/******************************************************************************* + * Copyright (c) 2015-2018 Skymind, Inc. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ + +// +// @author raver119@gmail.com +// + +// #ifndef LIBND4J_VARIABLE_H +// #define LIBND4J_VARIABLE_H + +// #include +// #include +// #include +// #include +// #include +// #include +// #include + +// #ifndef __JAVACPP_HACK__ + +// #endif + @Namespace("sd::graph") @NoOffset public static class Variable extends Pointer { + static { Loader.load(); } + /** Pointer cast constructor. Invokes {@link Pointer#Pointer(Pointer)}. */ + public Variable(Pointer p) { super(p); } + /** Native array allocator. Access with {@link Pointer#position(long)}. */ + public Variable(long size) { super((Pointer)null); allocateArray(size); } + private native void allocateArray(long size); + @Override public Variable position(long position) { + return (Variable)super.position(position); + } + + public Variable(@Cast("bool") boolean placeHolder) { super((Pointer)null); allocate(placeHolder); } + private native void allocate(@Cast("bool") boolean placeHolder); + public Variable(NDArray arrayw, @Cast("char*") String name, int id, int idx/*=0*/) { super((Pointer)null); allocate(arrayw, name, id, idx); } + private native void allocate(NDArray arrayw, @Cast("char*") String name, int id, int idx/*=0*/); + public Variable(NDArray arrayw, @Cast("char*") String name, int id) { super((Pointer)null); allocate(arrayw, name, id); } + private native void allocate(NDArray arrayw, @Cast("char*") String name, int id); + public Variable(NDArray arrayw, @Cast("char*") BytePointer name, int id, int idx/*=0*/) { super((Pointer)null); allocate(arrayw, name, id, idx); } + private native void allocate(NDArray arrayw, @Cast("char*") BytePointer name, int id, int idx/*=0*/); + public Variable(NDArray arrayw, @Cast("char*") BytePointer name, int id) { super((Pointer)null); allocate(arrayw, name, id); } + private native void allocate(NDArray arrayw, @Cast("char*") BytePointer name, int id); + public Variable(NDArray array/*=nullptr*/, @Cast("char*") String name/*=nullptr*/) { super((Pointer)null); allocate(array, name); } + private native void allocate(NDArray array/*=nullptr*/, @Cast("char*") String name/*=nullptr*/); + public Variable() { super((Pointer)null); allocate(); } + private native void allocate(); + public Variable(NDArray array/*=nullptr*/, @Cast("char*") BytePointer name/*=nullptr*/) { super((Pointer)null); allocate(array, name); } + private native void allocate(NDArray array/*=nullptr*/, @Cast("char*") BytePointer name/*=nullptr*/); + +// #ifndef __JAVACPP_HACK__ +// #endif + + public native Variable clone(); + + public native @Cast("bool") boolean hasNDArray(); + public native NDArray getNDArray(); + public native void setNDArray(NDArray array); + + public native @Cast("bool") boolean hasNDArrayList(); + public native NDArrayList getNDArrayList(); + public native void setNDArrayList(NDArrayList list); + + public native @Cast("bool") boolean isExternal(); + public native @Cast("bool") boolean isReadOnly(); + public native @Cast("bool") boolean isEmpty(); + public native @Cast("bool") boolean isRemovable(); + + public native @Cast("bool") boolean isPlaceholder(); + + public native @Cast("sd::graph::VariableType") int variableType(); + public native void setVariableType(@Cast("sd::graph::VariableType") int variableType); + + /** + * This method returns InputType of this variable + */ + //InputType variableType() { + // return _variableType; + //} + + public native void markExternal(@Cast("bool") boolean reallyExternal); + public native void markReadOnly(@Cast("bool") boolean reallyReadOnly); + public native void markRemovable(@Cast("bool") boolean reallyRemovable); + + public native int id(); + public native int index(); + public native void setIndex(int index); + public native void setId(int id); + public native void setId(int id, int idx); + + public native @StdString @Cast({"char*", "std::string*"}) BytePointer getName(); + public native void setName(@StdString @Cast({"char*", "std::string*"}) BytePointer name); + + public native @Cast("Nd4jLong*") @StdVector LongPointer shape(); + +// #ifndef __JAVACPP_HACK__ +// #endif + } + + + + +// #endif //LIBND4J_VARIABLE_H + + +// Parsed from graph/VariablesSet.h + +/******************************************************************************* + * Copyright (c) 2015-2018 Skymind, Inc. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ + +// +// Created by raver119 on 15/11/17. +// + +// #ifndef LIBND4J_VARIABLESSET_H +// #define LIBND4J_VARIABLESSET_H + +// #include +// #include +// #include +// #include +// #include + @Namespace("sd::graph") @NoOffset public static class VariablesSet extends Pointer { + static { Loader.load(); } + /** Pointer cast constructor. Invokes {@link Pointer#Pointer(Pointer)}. */ + public VariablesSet(Pointer p) { super(p); } + + public VariablesSet(@Cast("Nd4jStatus") int status/*=ND4J_STATUS_OK*/) { super((Pointer)null); allocate(status); } + private native void allocate(@Cast("Nd4jStatus") int status/*=ND4J_STATUS_OK*/); + public VariablesSet() { super((Pointer)null); allocate(); } + private native void allocate(); + + public native @Cast("Nd4jStatus") int status(); + + public native int size(); + + public native void push_back(Variable variable); + + public native Variable at(int index); + + } + + + + + +// #endif //LIBND4J_VARIABLESSET_H + + +// Parsed from graph/FlowPath.h + +/******************************************************************************* + * Copyright (c) 2015-2018 Skymind, Inc. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ + +// +// Created by raver119 on 16/11/17. +// + +// #ifndef LIBND4J_FLOWPATH_H +// #define LIBND4J_FLOWPATH_H + +// #include +// #include +// #include +// #include +// #include +// #include +// #include +// #include + @Namespace("sd::graph") @NoOffset public static class FlowPath extends Pointer { + static { Loader.load(); } + /** Pointer cast constructor. Invokes {@link Pointer#Pointer(Pointer)}. */ + public FlowPath(Pointer p) { super(p); } + /** Native array allocator. Access with {@link Pointer#position(long)}. */ + public FlowPath(long size) { super((Pointer)null); allocateArray(size); } + private native void allocateArray(long size); + @Override public FlowPath position(long position) { + return (FlowPath)super.position(position); + } + + public FlowPath() { super((Pointer)null); allocate(); } + private native void allocate(); + + public native void setInnerTime(int nodeId, @Cast("Nd4jLong") long time); + public native void setOuterTime(int nodeId, @Cast("Nd4jLong") long time); + + public native @Cast("Nd4jLong") long innerTime(int nodeId); + public native @Cast("Nd4jLong") long outerTime(int nodeId); + + public native @Cast("bool") boolean isNodeActive(int nodeId); + public native void markNodeActive(int nodeId, @Cast("bool") boolean isActive); + + public native @Cast("bool") boolean wasExecuted(int nodeId); + public native void markExecuted(int nodeId, @Cast("bool") boolean wasExecuted); + + public native int branch(int nodeId); + public native void markBranch(int nodeId, int index); + + // Frame-related methods + + public native void registerFrame(@Cast("Nd4jLong") long frameId); + public native void forgetFrame(@Cast("Nd4jLong") long frameId); + + public native @Cast("bool") boolean isFrameActive(@Cast("Nd4jLong") long frameId); + public native void markFrameActive(@Cast("Nd4jLong") long frameId, @Cast("bool") boolean isActive); + + public native @Cast("bool") boolean isRewindPlanned(@Cast("Nd4jLong") long frameId); + public native void planRewind(@Cast("Nd4jLong") long frameId, @Cast("bool") boolean reallyRewind); + + public native int getRewindPosition(@Cast("Nd4jLong") long frameId); + public native void setRewindPosition(@Cast("Nd4jLong") long frameId, int _position); + public native void setRewindPositionOnce(@Cast("Nd4jLong") long frameId, int _position); + + public native void incrementNumberOfCycles(@Cast("Nd4jLong") long frameId); + public native @Cast("Nd4jLong") long getNumberOfCycles(@Cast("Nd4jLong") long frameId); + + public native GraphProfile profile(); + } + + + + +// #endif //LIBND4J_FLOWPATH_H + + +// Parsed from graph/Intervals.h + +/******************************************************************************* + * Copyright (c) 2015-2018 Skymind, Inc. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ + +// +// Created by yurii@skymind.io on 24.10.2017. +// + +// #ifndef LIBND4J_INTERVALS_H +// #define LIBND4J_INTERVALS_H + +// #include +// #include +// #include +// #include + + @Namespace("sd") @NoOffset public static class Intervals extends Pointer { + static { Loader.load(); } + /** Pointer cast constructor. Invokes {@link Pointer#Pointer(Pointer)}. */ + public Intervals(Pointer p) { super(p); } + /** Native array allocator. Access with {@link Pointer#position(long)}. */ + public Intervals(long size) { super((Pointer)null); allocateArray(size); } + private native void allocateArray(long size); + @Override public Intervals position(long position) { + return (Intervals)super.position(position); + } + + + // default constructor + public Intervals() { super((Pointer)null); allocate(); } + private native void allocate(); + + // constructor + public Intervals(@Const @ByRef LongVectorVector content ) { super((Pointer)null); allocate(content); } + private native void allocate(@Const @ByRef LongVectorVector content ); + + // accessing operator + public native @Cast("Nd4jLong*") @StdVector @Name("operator []") LongPointer get(@Cast("const Nd4jLong") long i); + + // returns size of _content + public native int size(); + + } + + + + +// #endif //LIBND4J_INTERVALS_H + + +// Parsed from graph/Stash.h + +/******************************************************************************* + * Copyright (c) 2015-2018 Skymind, Inc. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ + +// +// @author raver119@gmail.com +// + +// #ifndef LIBND4J_STASH_H +// #define LIBND4J_STASH_H + +//#include +// #include +// #include +// #include +// #include +// #include +// #include +// #include + @Namespace("sd::graph") @NoOffset public static class KeyPair extends Pointer { + static { Loader.load(); } + /** Pointer cast constructor. Invokes {@link Pointer#Pointer(Pointer)}. */ + public KeyPair(Pointer p) { super(p); } + /** Native array allocator. Access with {@link Pointer#position(long)}. */ + public KeyPair(long size) { super((Pointer)null); allocateArray(size); } + private native void allocateArray(long size); + @Override public KeyPair position(long position) { + return (KeyPair)super.position(position); + } + + public KeyPair(int node/*=0*/, @Cast("char*") String name/*=nullptr*/) { super((Pointer)null); allocate(node, name); } + private native void allocate(int node/*=0*/, @Cast("char*") String name/*=nullptr*/); + public KeyPair() { super((Pointer)null); allocate(); } + private native void allocate(); + public KeyPair(int node/*=0*/, @Cast("char*") BytePointer name/*=nullptr*/) { super((Pointer)null); allocate(node, name); } + private native void allocate(int node/*=0*/, @Cast("char*") BytePointer name/*=nullptr*/); + + public native @Cast("bool") @Name("operator <") boolean lessThan(@Const @ByRef KeyPair other); + + public native @Cast("bool") @Name("operator ==") boolean equals(@Const @ByRef KeyPair other); + + public native int key(); + public native @StdString BytePointer name(); + } + + + +// #ifndef __JAVACPP_HACK__ + +// #endif + @Namespace("sd::graph") @NoOffset public static class Stash extends Pointer { + static { Loader.load(); } + /** Pointer cast constructor. Invokes {@link Pointer#Pointer(Pointer)}. */ + public Stash(Pointer p) { super(p); } + /** Native array allocator. Access with {@link Pointer#position(long)}. */ + public Stash(long size) { super((Pointer)null); allocateArray(size); } + private native void allocateArray(long size); + @Override public Stash position(long position) { + return (Stash)super.position(position); + } + + public Stash() { super((Pointer)null); allocate(); } + private native void allocate(); + + //void storeArray(sd::graph::Block& block, const char *name, sd::NDArray *array); + public native void storeArray(int nodeId, @Cast("char*") String name, NDArray array); + public native void storeArray(int nodeId, @Cast("char*") BytePointer name, NDArray array); + + //bool checkStash(sd::graph::Block& block, const char *name); + public native @Cast("bool") boolean checkStash(int nodeId, @Cast("char*") String name); + public native @Cast("bool") boolean checkStash(int nodeId, @Cast("char*") BytePointer name); + + //sd::NDArray* extractArray(sd::graph::Block& block, const char *name); + public native NDArray extractArray(int nodeId, @Cast("char*") String name); + public native NDArray extractArray(int nodeId, @Cast("char*") BytePointer name); + + public native void clear(); + } + + + + + + + +// #endif //LIBND4J_STASH_H + + +// Parsed from graph/GraphState.h + +/******************************************************************************* + * Copyright (c) 2015-2018 Skymind, Inc. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ + +// +// Created by raver119 on 23.01.18. +// + +// #ifndef LIBND4J_GRAPHSTATE_H +// #define LIBND4J_GRAPHSTATE_H + +// #include +// #include +// #include +// #include +// #include +// #include +// #include +// #include +// #include +// #include +// #include +// #include +// #include + + @Namespace("sd::graph") @NoOffset public static class GraphState extends Pointer { + static { Loader.load(); } + /** Pointer cast constructor. Invokes {@link Pointer#Pointer(Pointer)}. */ + public GraphState(Pointer p) { super(p); } + + public GraphState(@Cast("Nd4jLong") long id) { super((Pointer)null); allocate(id); } + private native void allocate(@Cast("Nd4jLong") long id); + + /** + * + * @return + */ + public native @Cast("Nd4jLong") long id(); + + /** + * This method adds scope to this state tracker + * + * @param scopeId + * @return + */ + public native @Cast("Nd4jStatus") int registerScope(int scopeId); + + /** + * This method cheks if scope with given ID exists + * + * @param scopeId - ID of the scope + * @return - TRUE if scope exists, FALSE otherwise + */ + public native @Cast("bool") boolean hasScope(int scopeId); + + /** + * This method removes specified scope from this state tracker + * + * @param scopeId + * @return + */ + public native @Cast("Nd4jStatus") int forgetScope(int scopeId); + +// #ifndef __JAVACPP_HACK__ +// #endif + /** + * This method adds given op to the end of specified scope + * + * @param scopeId + * @param opNum + * @param type + * @return + */ + public native @Cast("Nd4jStatus") int attachOpToScope(int scopeId, @Cast("Nd4jLong") long opNum, int type, @ByVal ArgumentsList inputs); + + /** + * This method adds return statement to specified scope + * + * PLEASE NOTE: should be used only in body scopes + * + * @param scopeId + * @param nodeId + * @param args + * @return + */ + public native @Cast("Nd4jStatus") int defineReturn(int scopeId, int nodeId, @ByVal ArgumentsList args); + + /** + * This method returns current variable space of this state holder + * + * @return + */ + public native VariableSpace variableSpace(); + } + + + + + +// #endif //LIBND4J_GRAPHSTATE_H + + +// Parsed from graph/VariableSpace.h + +/******************************************************************************* + * Copyright (c) 2015-2018 Skymind, Inc. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ + +// +// @author raver119@gmail.com +// + +// #ifndef LIBND4J_VARIABLESPACE_H +// #define LIBND4J_VARIABLESPACE_H + +// #include +// #include +// #include +// #include +// #include +// #include +// #include +// #include +// #include +// #include +// #include +// #include +// #include + @Namespace("sd::graph") @NoOffset public static class VariableSpace extends Pointer { + static { Loader.load(); } + /** Pointer cast constructor. Invokes {@link Pointer#Pointer(Pointer)}. */ + public VariableSpace(Pointer p) { super(p); } + /** Native array allocator. Access with {@link Pointer#position(long)}. */ + public VariableSpace(long size) { super((Pointer)null); allocateArray(size); } + private native void allocateArray(long size); + @Override public VariableSpace position(long position) { + return (VariableSpace)super.position(position); + } + + public VariableSpace() { super((Pointer)null); allocate(); } + private native void allocate(); + + public native @ByRef @Name("operator =") VariableSpace put(@Const @ByRef VariableSpace other); + + public native int numberOfPlaceholders(); + public native @Cast("sd::graph::Variable**") @StdVector PointerPointer getPlaceholders(); + public native void setWorkspace(Workspace workspace); + + public native LaunchContext launchContext(); + + public native @Cast("bool") boolean hasExternalVariable(int it); + public native @Cast("bool") boolean hasExternalVariable(@ByRef IntIntPair pair); + public native @Cast("bool") boolean hasExternalVariable(@StdString @Cast({"char*", "std::string*"}) BytePointer symbol); + + public native @Cast("bool") boolean hasVariable(int id); + public native @Cast("bool") boolean hasVariable(int id, int idx); + public native @Cast("bool") boolean hasVariable(@ByRef IntIntPair pair); + public native @Cast("bool") boolean hasVariable(@StdString @Cast({"char*", "std::string*"}) BytePointer symbol); + + public native Variable getVariable(int id); + public native Variable getVariable(int id, int idx); + public native Variable getVariable(@ByRef IntIntPair pair); + public native Variable getVariable(@StdString @Cast({"char*", "std::string*"}) BytePointer symbol); + + public native @Cast("sd::graph::Variable**") @StdVector PointerPointer getVariables(); + + public native Variable putVariable(@ByRef IntIntPair pair, NDArray array); + public native void putVariable(@ByRef IntIntPair pair, Variable variable); + public native void putVariable(int id, Variable variable); + public native void putVariable(int id, NDArray array); + public native Variable putVariable(int id, int idx, NDArray array); + public native void putVariable(int id, int idx, Variable array); + + public native void dropVariable(@ByRef IntIntPair pair); + public native void dropVariable(int id, int idx); + + public native void trackList(NDArrayList list); + + public native void putOutputVariable(Variable variable); + + public native void replaceVariable(Variable variable); + + // memory-related statistics + public native @Cast("Nd4jLong") long externalMemory(); + public native @Cast("Nd4jLong") long internalMemory(); + public native @Cast("Nd4jLong") long totalMemory(); + + public native int externalEntries(); + public native int internalEntries(); + public native int totalEntries(); + + public native VariableSpace clone(); + + public native @Cast("sd::graph::Variable**") @StdVector PointerPointer handles(); + + + public native VariableSpace asT(); + public native void injectVariable(@ByRef IntIntPair pair, Variable variable); + + public native Stash getStash(); + + public native @Cast("sd::graph::Variable**") @StdVector PointerPointer getExternalVariables(); + + public native void setFlowPath(FlowPath timers); + public native FlowPath flowPath(); + } + + + + +// #endif //LIBND4J_VARIABLESPACE_H + + +// Parsed from helpers/helper_generator.h + +/******************************************************************************* + * Copyright (c) 2015-2018 Skymind, Inc. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ + +// +// @author raver119@gmail.com +// + +// #ifndef LIBND4J_HELPER_GENERATOR_H +// #define LIBND4J_HELPER_GENERATOR_H + +// #include +// #include +// #include +// #include + +// #ifdef _MSC_VER +// include for uint64_t on MSVC +// #include +// #elif ANDROID +// #include + +// #ifndef UINT64_C +// #if defined(__LP64__) +// #define UINT64_C(c) c ## UL +// #else +// #define UINT64_C(c) c ## ULL +// #endif //LP64 +// #endif // UINT64 + +// #endif // MSVC/ANDROID + + +// #ifdef __GNUC__ +// #include +// #endif + +// #ifdef __CUDACC__ +// #else + @Namespace("sd::random") @NoOffset public static class RandomBuffer extends Pointer { + static { Loader.load(); } + /** Pointer cast constructor. Invokes {@link Pointer#Pointer(Pointer)}. */ + public RandomBuffer(Pointer p) { super(p); } + + /** + * This method allocates buffer of size * sizeof(Nd4jLong) + * + * @param size + * @return + */ +// #ifdef __CUDACC__ +// #endif + public RandomBuffer(@Cast("Nd4jLong") long seed, @Cast("Nd4jLong") long size, @Cast("uint64_t*") LongPointer buffer) { super((Pointer)null); allocate(seed, size, buffer); } + private native void allocate(@Cast("Nd4jLong") long seed, @Cast("Nd4jLong") long size, @Cast("uint64_t*") LongPointer buffer); + public RandomBuffer(@Cast("Nd4jLong") long seed, @Cast("Nd4jLong") long size, @Cast("uint64_t*") LongBuffer buffer) { super((Pointer)null); allocate(seed, size, buffer); } + private native void allocate(@Cast("Nd4jLong") long seed, @Cast("Nd4jLong") long size, @Cast("uint64_t*") LongBuffer buffer); + public RandomBuffer(@Cast("Nd4jLong") long seed, @Cast("Nd4jLong") long size, @Cast("uint64_t*") long[] buffer) { super((Pointer)null); allocate(seed, size, buffer); } + private native void allocate(@Cast("Nd4jLong") long seed, @Cast("Nd4jLong") long size, @Cast("uint64_t*") long[] buffer); + + public native @Cast("uint64_t*") LongPointer getBuffer(); + + public native @Cast("uint64_t*") LongPointer getDeviceBuffer(); + +// #ifdef __CUDACC__ +// #endif + + public native @Cast("Nd4jLong") long getSize(); + + public native @Cast("Nd4jLong") long getSeed(); + + public native void setSeed(@Cast("Nd4jLong") long seed); + + public native @Cast("Nd4jLong") long getAllocatedSize(); + + public native @Cast("Nd4jLong") long getOffset(); + + public native void setOffset(@Cast("Nd4jLong") long offset); + + public native void reSeed(@Cast("Nd4jLong") long amplifier); + + public native @Cast("uint64_t") long getElement(@Cast("Nd4jLong") long _position); + + public native @Cast("uint64_t") long next64(@Cast("uint64_t") long shiftedSeed); + + public static native @Cast("uint64_t") long rotl(@Cast("const uint64_t") long x, @Cast("uint64_t") long k); + + public static native @Cast("uint64_t") long safeShift(@Cast("uint64_t") long x, @Cast("uint64_t") long y); + + public native @Cast("uint64_t") long seedConv(@Cast("Nd4jLong") long seed); + + public native void incrementGeneration(); + + public native @Cast("Nd4jLong") long getNextIndex(); + + public native @Cast("uint64_t") long getNextElement(); + + + /** + * This method skips X elements from buffer + * + * @param numberOfElements number of elements to skip + */ +// #ifdef __CUDACC__ +// #endif + public native void rewindH(@Cast("Nd4jLong") long numberOfElements); + + /** + * This method returns random int in range [0..MAX_INT] + * @return + */ + public native int nextInt(); + + public native @Cast("uint64_t") long nextUInt64(); + + /** + * This method returns random int in range [0..to] + * @param to + * @return + */ + public native int nextInt(int to); + + /** + * This method returns random int in range [from..to] + * @param from + * @param to + * @return + */ + public native int nextInt(int from, int to); + + + /** + * This method returns random T in range of [0..1] + * @return + */ + + /** + * This method returns random T in range of [0..to] + * @param to + * @return + */ + + /** + * This method returns random T in range [from..to] + * @param from + * @param to + * @return + */ + + public native @Cast("uint64_t") long relativeUInt64(@Cast("Nd4jLong") long index); + + /** + * relative methods are made as workaround for lock-free concurrent execution + */ + public native int relativeInt(@Cast("Nd4jLong") long index); + + /** + * This method returns random int within [0..to] + * + * @param index + * @param to + * @return + */ + public native int relativeInt(@Cast("Nd4jLong") long index, int to); + + /** + * This method returns random int within [from..to] + * + * @param index + * @param to + * @param from + * @return + */ + public native int relativeInt(@Cast("Nd4jLong") long index, int from, int to); + + /** + * This method returns random T within [0..1] + * + * @param index + * @return + */ + +/** + * This method returns random T within [0..to] + * + * @param index + * @param to + * @return + */ + +/** + * This method returns random T within [from..to] + * + * @param index + * @param from + * @param to + * @return + */ + + } + + @Namespace("sd::random") @NoOffset public static class IGenerator extends Pointer { + static { Loader.load(); } + /** Pointer cast constructor. Invokes {@link Pointer#Pointer(Pointer)}. */ + public IGenerator(Pointer p) { super(p); } + + + + public native RandomBuffer getBuffer(); + + public native void setOffset(@Cast("Nd4jLong") long offset); + + public native @Cast("Nd4jLong") long getElementAbsolute(@Cast("Nd4jLong") long _position); + + public native @Cast("Nd4jLong") long getElementRelative(@Cast("Nd4jLong") long _position); + + public native void refreshBuffer(); + } + + + + @Namespace("sd::random") @NoOffset public static class Xoroshiro128 extends IGenerator { + static { Loader.load(); } + /** Pointer cast constructor. Invokes {@link Pointer#Pointer(Pointer)}. */ + public Xoroshiro128(Pointer p) { super(p); } + + public Xoroshiro128(RandomBuffer buffer) { super((Pointer)null); allocate(buffer); } + private native void allocate(RandomBuffer buffer); + + public native void refreshBuffer(); + } + + +// #endif //LIBND4J_HELPER_GENERATOR_H + + +// Parsed from graph/profiling/GraphProfile.h + +/******************************************************************************* + * Copyright (c) 2015-2018 Skymind, Inc. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ + +// +// @author raver119@gmail.com +// + +// #ifndef ND4J_GRAPH_PROFILE_H +// #define ND4J_GRAPH_PROFILE_H + +// #include "NodeProfile.h" +// #include +// #include +// #include +// #include +// #include +// #include + @Namespace("sd::graph") @NoOffset public static class GraphProfile extends Pointer { + static { Loader.load(); } + /** Pointer cast constructor. Invokes {@link Pointer#Pointer(Pointer)}. */ + public GraphProfile(Pointer p) { super(p); } + /** Native array allocator. Access with {@link Pointer#position(long)}. */ + public GraphProfile(long size) { super((Pointer)null); allocateArray(size); } + private native void allocateArray(long size); + @Override public GraphProfile position(long position) { + return (GraphProfile)super.position(position); + } + + public GraphProfile() { super((Pointer)null); allocate(); } + private native void allocate(); + + /** + * These methods just adding amount of bytes to various counters + */ + public native void addToTotal(@Cast("Nd4jLong") long bytes); + public native void addToActivations(@Cast("Nd4jLong") long bytes); + public native void addToTemporary(@Cast("Nd4jLong") long bytes); + public native void addToObjects(@Cast("Nd4jLong") long bytes); + + /** + * This method allows to set graph construction (i.e. deserialization) time in nanoseconds + */ + public native void setBuildTime(@Cast("Nd4jLong") long nanos); + + /** + * This method sets graph execution time in nanoseconds. + */ + public native void setExecutionTime(@Cast("Nd4jLong") long nanos); + + public native void startEvent(@Cast("char*") String name); + public native void startEvent(@Cast("char*") BytePointer name); + public native void recordEvent(@Cast("char*") String name); + public native void recordEvent(@Cast("char*") BytePointer name); + public native void deleteEvent(@Cast("char*") String name); + public native void deleteEvent(@Cast("char*") BytePointer name); + + /** + * This method saves time as delta from last saved time + */ + public native void spotEvent(@Cast("char*") String name); + public native void spotEvent(@Cast("char*") BytePointer name); + + /** + * This method returns pointer to NodeProfile by ID + * PLEASE NOTE: this method will create new NodeProfile if there's none + */ + public native NodeProfile nodeById(int id, @Cast("char*") String name/*=nullptr*/); + public native NodeProfile nodeById(int id); + public native NodeProfile nodeById(int id, @Cast("char*") BytePointer name/*=nullptr*/); + public native @Cast("bool") boolean nodeExists(int id); + + /** + * This method merges values from other profile report + * @param other + */ + public native void merge(GraphProfile other); + public native void assign(GraphProfile other); + + /** + * These methods are just utility methods for time + */ + public static native @Cast("Nd4jLong") long currentTime(); + public static native @Cast("Nd4jLong") long relativeTime(@Cast("Nd4jLong") long time); + + public native void printOut(); + } + + + +// #endif + +// Parsed from graph/profiling/NodeProfile.h + +/******************************************************************************* + * Copyright (c) 2015-2018 Skymind, Inc. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ + +// +// @author raver119@gmail.com +// + +// #ifndef LIBND4J_NODE_PROFILE_H +// #define LIBND4J_NODE_PROFILE_H + +// #include +// #include +// #include +// #include + @Namespace("sd::graph") @NoOffset public static class NodeProfile extends Pointer { + static { Loader.load(); } + /** Pointer cast constructor. Invokes {@link Pointer#Pointer(Pointer)}. */ + public NodeProfile(Pointer p) { super(p); } + /** Native array allocator. Access with {@link Pointer#position(long)}. */ + public NodeProfile(long size) { super((Pointer)null); allocateArray(size); } + private native void allocateArray(long size); + @Override public NodeProfile position(long position) { + return (NodeProfile)super.position(position); + } + + public NodeProfile() { super((Pointer)null); allocate(); } + private native void allocate(); + + public NodeProfile(int id, @Cast("char*") String name) { super((Pointer)null); allocate(id, name); } + private native void allocate(int id, @Cast("char*") String name); + public NodeProfile(int id, @Cast("char*") BytePointer name) { super((Pointer)null); allocate(id, name); } + private native void allocate(int id, @Cast("char*") BytePointer name); + + public native void setBuildTime(@Cast("Nd4jLong") long time); + public native void setPreparationTime(@Cast("Nd4jLong") long time); + public native void setExecutionTime(@Cast("Nd4jLong") long time); + public native void setTotalTime(@Cast("Nd4jLong") long time); + public native void setShapeFunctionTime(@Cast("Nd4jLong") long time); + public native void setArrayTime(@Cast("Nd4jLong") long time); + public native void setInputTime(@Cast("Nd4jLong") long time); + + public native void setActivationsSize(@Cast("Nd4jLong") long bytes); + public native void setTemporarySize(@Cast("Nd4jLong") long bytes); + public native void setObjectsSize(@Cast("Nd4jLong") long bytes); + public native void setTotalSize(@Cast("Nd4jLong") long bytes); + + public native void addInputShape(@Cast("Nd4jLong*") LongPointer shapeInfo); + public native void addInputShape(@Cast("Nd4jLong*") LongBuffer shapeInfo); + public native void addInputShape(@Cast("Nd4jLong*") long[] shapeInfo); + public native void addOutputShape(@Cast("Nd4jLong*") LongPointer shapeInfo); + public native void addOutputShape(@Cast("Nd4jLong*") LongBuffer shapeInfo); + public native void addOutputShape(@Cast("Nd4jLong*") long[] shapeInfo); + + public native @Cast("Nd4jLong") long getActivationsSize(); + public native @Cast("Nd4jLong") long getTemporarySize(); + public native @Cast("Nd4jLong") long getObjectsSize(); + public native @Cast("Nd4jLong") long getTotalSize(); + + public native @Cast("Nd4jLong") long getExecutionTime(); + + public native @StdString @ByRef @Cast({"char*", "std::string*"}) BytePointer name(); + + public native void merge(NodeProfile other); + public native void assign(NodeProfile other); + + public native void printOut(); + } + + + +// #endif + +// Parsed from graph/Context.h + +/******************************************************************************* + * Copyright (c) 2015-2018 Skymind, Inc. + * Copyright (c) 2019-2020 Konduit K.K. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ + +// +// @author raver119@gmail.com +// + +// #ifndef LIBND4J_CONTEXT_H +// #define LIBND4J_CONTEXT_H + +// #include +// #include +// #include +// #include +// #include +// #include +// #include + +// CUDA-specific includes +// #ifdef __CUDACC__ +// #endif + /** + * This class defines input desired for any given node/operation within graph + */ + @Namespace("sd::graph") @NoOffset public static class Context extends ContextPrototype { + static { Loader.load(); } + /** Pointer cast constructor. Invokes {@link Pointer#Pointer(Pointer)}. */ + public Context(Pointer p) { super(p); } + + public Context(ContextPrototype prototype, VariableSpace variableSpace) { super((Pointer)null); allocate(prototype, variableSpace); } + private native void allocate(ContextPrototype prototype, VariableSpace variableSpace); + + public Context(int nodeId, VariableSpace variableSpace/*=nullptr*/) { super((Pointer)null); allocate(nodeId, variableSpace); } + private native void allocate(int nodeId, VariableSpace variableSpace/*=nullptr*/); + public Context(int nodeId) { super((Pointer)null); allocate(nodeId); } + private native void allocate(int nodeId); + public Context(int nodeId, VariableSpace variableSpace, @Cast("bool") boolean isInplace) { super((Pointer)null); allocate(nodeId, variableSpace, isInplace); } + private native void allocate(int nodeId, VariableSpace variableSpace, @Cast("bool") boolean isInplace); + + // default destructor + + // these methods are for execution timing + public native void setOuterTime(@Cast("Nd4jLong") long time); + public native void setInnerTime(@Cast("Nd4jLong") long time); + public native @Cast("Nd4jLong") long getOuterTime(); + public native @Cast("Nd4jLong") long getInnerTime(); + + public native @Cast("sd::DataType") int dataType(); + + public native @Cast("sd::DataType") int dataType(int index); + public native void setDataType(int index, @Cast("sd::DataType") int type); + // these methods are related to Workspace abstraction + public native @Cast("bool") boolean hasWorkspaceProvided(); + public native void attachWorkspace(Workspace workspace); + public native void forgetWorkspace(); + + // these methods return full-time workspace + public native Workspace getWorkspace(); + public native Workspace workspace(); + public native Workspace fWorkspace(); + + // this method returns workspace for temporary allocations + public native Workspace tWorkspace(); + + // this method returns workspace for object allocations + public native Workspace oWorkspace(); + + public native void setVariableSpace(VariableSpace variableSpace); + + public native RandomBuffer getRNG(); + public native void setRNG(RandomBuffer rng); + + public native void setTargetEngine(@Cast("samediff::Engine") int engine); + + public native VariableSpace getVariableSpace(); + + public native LaunchContext launchContext(); + + // these fields define, if we can execute specific node in-place, without generating new array + + + // these variables are only for Divergent Nodes + public native int getBranch(); + public native void setBranch(int branch); + + /** + * + * @return + */ + public native Stash getStash(); + + /** + * + */ + public native void trackList(NDArrayList list); + + + /** + * This method returns variable for a given input index for this block + * @param idx + * @return + */ + public native Variable getVariable(int idx); + public native Variable variable(int idx); + + /** + * This method is shortcut to getVariable(int idx); + * + * + it check fastpath for array availability (preferred) + * @return + */ + public native NDArray getNDArray(int idx); + public native NDArray array(int idx); + + + /** + * This method fetches variable from VariableSpace DIRECTLY + * @param p + * @return + */ + public native Variable variable(int node, int index); + public native Variable variable(@ByRef IntIntPair p); + + + public native void pushNDArrayToVariableSpace(int nodeId, int index, NDArray array, @Cast("bool") boolean removable/*=true*/); + public native void pushNDArrayToVariableSpace(int nodeId, int index, NDArray array); + public native void pushNDArrayToVariableSpace(@ByRef IntIntPair pair, NDArray array, @Cast("bool") boolean removable/*=true*/); + public native void pushNDArrayToVariableSpace(@ByRef IntIntPair pair, NDArray array); + + public native void pushNDArrayListToVariableSpace(int nodeId, int index, NDArrayList list, @Cast("bool") boolean track/*=true*/); + public native void pushNDArrayListToVariableSpace(int nodeId, int index, NDArrayList list); + public native void pushNDArrayListToVariableSpace(@ByRef IntIntPair pair, NDArrayList list, @Cast("bool") boolean track/*=true*/); + public native void pushNDArrayListToVariableSpace(@ByRef IntIntPair pair, NDArrayList list); + + public native @Cast("bool") boolean isValueAvailable(int idx/*=0*/); + public native @Cast("bool") boolean isValueAvailable(); + + public native Variable ensureVariable(int idx/*=0*/); + public native Variable ensureVariable(); + + public native @Cast("unsigned long") long width(); + + // methods used in java interop + /** + * This method checks if Context uses fastpath variable access + * @return + */ + public native @Cast("bool") boolean isFastPath(); + + /** + * Method allows to forbid FastPath execution + * @param reallyForbid + */ + public native void forbidFastPath(@Cast("bool") boolean reallyForbid); + +// #ifndef __JAVACPP_HACK__ +// #endif + + public native void setInputArray(int index, NDArray array, @Cast("bool") boolean removable/*=false*/); + public native void setInputArray(int index, NDArray array); + public native void setInputArray(int index, Pointer buffer, Pointer shapeInfo, Pointer specialBuffer, Pointer specialShapeInfo); + public native void setInputArray(int index, Pointer databuffer, Pointer shapeInfo, Pointer specialShapeInfo); + + public native void setOutputArray(int index, NDArray array, @Cast("bool") boolean removable/*=false*/); + public native void setOutputArray(int index, NDArray array); + public native void setOutputArray(int index, Pointer buffer, Pointer shapeInfo, Pointer specialBuffer, Pointer specialShapeInfo); + public native void setOutputArray(int index, Pointer databuffer, Pointer shapeInfo, Pointer specialShapeInfo); + + public native void setTArguments(DoublePointer arguments, int numberOfArguments); + public native void setTArguments(DoubleBuffer arguments, int numberOfArguments); + public native void setTArguments(double[] arguments, int numberOfArguments); + public native void setIArguments(@Cast("Nd4jLong*") LongPointer arguments, int numberOfArguments); + public native void setIArguments(@Cast("Nd4jLong*") LongBuffer arguments, int numberOfArguments); + public native void setIArguments(@Cast("Nd4jLong*") long[] arguments, int numberOfArguments); + public native void setBArguments(@Cast("bool*") BooleanPointer arguments, int numberOfArguments); + public native void setBArguments(@Cast("bool*") boolean[] arguments, int numberOfArguments); + public native void setDArguments(@Cast("sd::DataType*") IntPointer arguments, int numberOfArguments); + public native void setDArguments(@Cast("sd::DataType*") IntBuffer arguments, int numberOfArguments); + public native void setDArguments(@Cast("sd::DataType*") int[] arguments, int numberOfArguments); + + public native void setTArguments(@StdVector DoublePointer tArgs); + public native void setTArguments(@StdVector DoubleBuffer tArgs); + public native void setTArguments(@StdVector double[] tArgs); + public native void setIArguments(@Cast("Nd4jLong*") @StdVector LongPointer tArgs); + public native void setIArguments(@Cast("Nd4jLong*") @StdVector LongBuffer tArgs); + public native void setIArguments(@Cast("Nd4jLong*") @StdVector long[] tArgs); + public native void setBArguments(@Cast("bool*") @StdVector BooleanPointer tArgs); + public native void setBArguments(@Cast("bool*") @StdVector boolean[] tArgs); + public native void setDArguments(@Cast("sd::DataType*") @StdVector IntPointer dArgs); + public native void setDArguments(@Cast("sd::DataType*") @StdVector IntBuffer dArgs); + public native void setDArguments(@Cast("sd::DataType*") @StdVector int[] dArgs); + + /** + * This method purges fastpath in/out contents and releases all the handles. + * + * PLEASE NOTE: I/T/B/D args will stay intact + */ + public native void clearFastPath(); + + public native void setCudaContext(@Cast("Nd4jPointer") Pointer cudaStream, @Cast("Nd4jPointer") Pointer reductionPointer, @Cast("Nd4jPointer") Pointer allocationPointer); + + public native void allowHelpers(@Cast("bool") boolean reallyAllow); + public native @Cast("bool") boolean helpersAllowed(); + + public native void setShapeFunctionOverride(@Cast("bool") boolean reallyOverride); + public native @Cast("bool") boolean shapeFunctionOverride(); + + public native @Cast("samediff::ExecutionMode") int executionMode(); + public native void setExecutionMode(@Cast("samediff::ExecutionMode") int executionMode); + + public native @Cast("bool") boolean isTraining(); + public native @Cast("bool") boolean isInference(); + } + + + + +// #endif //LIBND4J_BLOCK_H + + +// Parsed from graph/ContextPrototype.h + +/******************************************************************************* + * Copyright (c) 2015-2018 Skymind, Inc. + * Copyright (c) 2019-2020 Konduit K.K. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ + +// +// @author raver119@gmail.com +// + +// #ifndef ND4J_CONTEXT_PROTOTYPE_H +// #define ND4J_CONTEXT_PROTOTYPE_H + +// #include +// #include +// #include +// #include +// #include +// #include +// #include +// #include + +// #ifndef __STANDALONE_BUILD__ +// #include +// #endif + + @Namespace("sd::graph") @NoOffset public static class ContextPrototype extends Pointer { + static { Loader.load(); } + /** Pointer cast constructor. Invokes {@link Pointer#Pointer(Pointer)}. */ + public ContextPrototype(Pointer p) { super(p); } + /** Native array allocator. Access with {@link Pointer#position(long)}. */ + public ContextPrototype(long size) { super((Pointer)null); allocateArray(size); } + private native void allocateArray(long size); + @Override public ContextPrototype position(long position) { + return (ContextPrototype)super.position(position); + } + + public ContextPrototype(OpDescriptor opDescriptor/*=nullptr*/, int nodeId/*=1*/, @Cast("bool") boolean inPlace/*=false*/) { super((Pointer)null); allocate(opDescriptor, nodeId, inPlace); } + private native void allocate(OpDescriptor opDescriptor/*=nullptr*/, int nodeId/*=1*/, @Cast("bool") boolean inPlace/*=false*/); + public ContextPrototype() { super((Pointer)null); allocate(); } + private native void allocate(); + + public native int getNodeId(); + public native int nodeId(); + + // this method returns true, if inputs are defined + public native @Cast("bool") boolean hasVariablesFilled(); + + public native void setOpDescriptor(OpDescriptor opDescriptor); + + public native @Cast("sd::DataType") int dataType(); + public native @Cast("sd::DataType") int dataType(int index); + public native void setDataType(int index, @Cast("sd::DataType") int type); + + public native @Cast("bool") boolean isInplace(); + public native void markInplace(@Cast("bool") boolean reallyInplace); + + public native void pickInput(int input); + public native void pickInput(int input, int index); + public native void pickInput(@ByRef IntIntPair p); + public native void fillInputs(@StdVector IntPointer inputs); + public native void fillInputs(@StdVector IntBuffer inputs); + public native void fillInputs(@StdVector int[] inputs); + public native @StdVector IntIntPair inputs(); + + public native @StdVector DoublePointer getTArguments(); + public native @StdVector IntPointer getIArguments(); + public native @Cast("bool*") @StdVector BooleanPointer getBArguments(); + public native @Cast("sd::DataType*") @StdVector IntPointer getDArguments(); + public native @StdVector IntPointer getAxis(); + + public native @Cast("samediff::Engine") int engine(); + + public native @Cast("size_t") long numT(); + public native @Cast("size_t") long numI(); + public native @Cast("size_t") long numB(); + public native @Cast("size_t") long numD(); + + public native IntIntPair input(int idx); + + public native int opNum(); + public native void setOpNum(int opNum); + + public native @Cast("bool") boolean isUseMKLDNN(); + public native void setUseMKLDNN(@Cast("bool") boolean useMKLDNN); + + /** + * This method returns number of inputs available in this block + * @return + */ + public native @Cast("unsigned long") long width(); + + // just a clone + public native ContextPrototype clone(); + + public native @ByRef RandomGenerator randomGenerator(); + public native @Const @ByRef RandomGenerator getRng(); + public native void setRng(@Const @ByRef RandomGenerator anotherRng); + public native void setRandomGenerator(@Const @ByRef RandomGenerator anotherRng); + public native @Cast("uint64_t") long randomSeed(); + public native void setRandomSeed(@Cast("uint64_t") long seed); + } + + + +// #endif //ND4J_CONTEXT_PROTOTYPE_H + + +// Parsed from graph/ResultWrapper.h + +/******************************************************************************* + * Copyright (c) 2015-2018 Skymind, Inc. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ + +// +// Created by raver119 on 11/06/18. +// + +// #ifndef LIBND4J_RESULTWRAPPER_H +// #define LIBND4J_RESULTWRAPPER_H + +// #include +// #include +// #include + @Namespace("sd::graph") @NoOffset public static class ResultWrapper extends org.nd4j.nativeblas.ResultWrapperAbstraction { + static { Loader.load(); } + /** Pointer cast constructor. Invokes {@link Pointer#Pointer(Pointer)}. */ + public ResultWrapper(Pointer p) { super(p); } + + public ResultWrapper(@Cast("Nd4jLong") long size, @Cast("Nd4jPointer") Pointer ptr) { super((Pointer)null); allocate(size, ptr); } + private native void allocate(@Cast("Nd4jLong") long size, @Cast("Nd4jPointer") Pointer ptr); + + public native @Cast("Nd4jLong") long size(); + + public native @Cast("Nd4jPointer") Pointer pointer(); + } + + + + +// #endif //LIBND4J_RESULTWRAPPER_H + + +// Parsed from helpers/shape.h + +/******************************************************************************* + * Copyright (c) 2015-2018 Skymind, Inc. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ + +/* + * shape.h + * + * Created on: Dec 28, 2015 + * Author: agibsonccc + */ + +// #ifndef SHAPE_H_ +// #define SHAPE_H_ + +// #include +// #include +// #include "system/dll.h" +// #include "system/nd4jmalloc.h" +// #include "math/templatemath.h" +// #include "../helpers/logger.h" +// #include "system/pointercast.h" +// #include "../cnpy/cnpy.h" +// #include + +public static final int MAX_DIMENSION = 0x7fffffff; +public static final int MAX_NUM_THREADS = 1024; +public static final int MAX_RANK = 32; +public static final int MAX_SHAPEINFOLENGTH = 2*MAX_RANK+4; +public static final int MAX_COORD = 3; +public static final int PREALLOC_SIZE = 33554432; +// #ifdef __CUDACC__ +// #endif + + +// #ifdef __CUDACC__ +// #else +// #define INLINEDEF inline +// #endif + +// #include "system/pairwise_util.h" +// #include +// #include + +/** + * Shape information approximating + * the information on an ndarray + */ + @Namespace("shape") @NoOffset public static class ShapeInformation extends Pointer { + static { Loader.load(); } + /** Pointer cast constructor. Invokes {@link Pointer#Pointer(Pointer)}. */ + public ShapeInformation(Pointer p) { super(p); } + /** Native array allocator. Access with {@link Pointer#position(long)}. */ + public ShapeInformation(long size) { super((Pointer)null); allocateArray(size); } + private native void allocateArray(long size); + @Override public ShapeInformation position(long position) { + return (ShapeInformation)super.position(position); + } + + public ShapeInformation(@Cast("Nd4jLong*") LongPointer shape_/*=nullptr*/, @Cast("Nd4jLong*") LongPointer stride_/*=nullptr*/, char order_/*=0*/, int rank_/*=0*/, int offset_/*=0*/, int elementWiseStride_/*=0*/) { super((Pointer)null); allocate(shape_, stride_, order_, rank_, offset_, elementWiseStride_); } + private native void allocate(@Cast("Nd4jLong*") LongPointer shape_/*=nullptr*/, @Cast("Nd4jLong*") LongPointer stride_/*=nullptr*/, char order_/*=0*/, int rank_/*=0*/, int offset_/*=0*/, int elementWiseStride_/*=0*/); + public ShapeInformation() { super((Pointer)null); allocate(); } + private native void allocate(); + public ShapeInformation(@Cast("Nd4jLong*") LongBuffer shape_/*=nullptr*/, @Cast("Nd4jLong*") LongBuffer stride_/*=nullptr*/, char order_/*=0*/, int rank_/*=0*/, int offset_/*=0*/, int elementWiseStride_/*=0*/) { super((Pointer)null); allocate(shape_, stride_, order_, rank_, offset_, elementWiseStride_); } + private native void allocate(@Cast("Nd4jLong*") LongBuffer shape_/*=nullptr*/, @Cast("Nd4jLong*") LongBuffer stride_/*=nullptr*/, char order_/*=0*/, int rank_/*=0*/, int offset_/*=0*/, int elementWiseStride_/*=0*/); + public ShapeInformation(@Cast("Nd4jLong*") long[] shape_/*=nullptr*/, @Cast("Nd4jLong*") long[] stride_/*=nullptr*/, char order_/*=0*/, int rank_/*=0*/, int offset_/*=0*/, int elementWiseStride_/*=0*/) { super((Pointer)null); allocate(shape_, stride_, order_, rank_, offset_, elementWiseStride_); } + private native void allocate(@Cast("Nd4jLong*") long[] shape_/*=nullptr*/, @Cast("Nd4jLong*") long[] stride_/*=nullptr*/, char order_/*=0*/, int rank_/*=0*/, int offset_/*=0*/, int elementWiseStride_/*=0*/); + + public native @Cast("Nd4jLong*") LongPointer shape(); public native ShapeInformation shape(LongPointer setter); + public native @Cast("Nd4jLong*") LongPointer stride(); public native ShapeInformation stride(LongPointer setter); + public native char order(); public native ShapeInformation order(char setter); + public native int rank(); public native ShapeInformation rank(int setter); + public native int offset(); public native ShapeInformation offset(int setter); + public native int elementWiseStride(); public native ShapeInformation elementWiseStride(int setter); + } + +/** + * Indexing information + * for bounds checking + */ + @Namespace("shape") public static class CurrentIndexing extends Pointer { + static { Loader.load(); } + /** Default native constructor. */ + public CurrentIndexing() { super((Pointer)null); allocate(); } + /** Native array allocator. Access with {@link Pointer#position(long)}. */ + public CurrentIndexing(long size) { super((Pointer)null); allocateArray(size); } + /** Pointer cast constructor. Invokes {@link Pointer#Pointer(Pointer)}. */ + public CurrentIndexing(Pointer p) { super(p); } + private native void allocate(); + private native void allocateArray(long size); + @Override public CurrentIndexing position(long position) { + return (CurrentIndexing)super.position(position); + } + + public native int numElementsPerThread(); public native CurrentIndexing numElementsPerThread(int setter); + public native int blockStartingIndex(); public native CurrentIndexing blockStartingIndex(int setter); + public native int startingThreadIndex(); public native CurrentIndexing startingThreadIndex(int setter); + public native int endingThreadIndex(); public native CurrentIndexing endingThreadIndex(int setter); + + } + + + + @Namespace("shape") public static native @Cast("bool") boolean shapeEquals(int shape1Rank, @Cast("const Nd4jLong*") LongPointer shape1, int shape2Rank, @Cast("const Nd4jLong*") LongPointer shape2); + @Namespace("shape") public static native @Cast("bool") boolean shapeEquals(int shape1Rank, @Cast("const Nd4jLong*") LongBuffer shape1, int shape2Rank, @Cast("const Nd4jLong*") LongBuffer shape2); + @Namespace("shape") public static native @Cast("bool") boolean shapeEquals(int shape1Rank, @Cast("const Nd4jLong*") long[] shape1, int shape2Rank, @Cast("const Nd4jLong*") long[] shape2); + + @Namespace("shape") public static native @Cast("Nd4jLong*") LongPointer detachShape(@Cast("Nd4jLong*") LongPointer originalShape); + @Namespace("shape") public static native @Cast("Nd4jLong*") LongBuffer detachShape(@Cast("Nd4jLong*") LongBuffer originalShape); + @Namespace("shape") public static native @Cast("Nd4jLong*") long[] detachShape(@Cast("Nd4jLong*") long[] originalShape); + + @Namespace("shape") public static native @Cast("Nd4jLong*") LongPointer copyShape(@Cast("Nd4jLong*") LongPointer originalShape); + @Namespace("shape") public static native @Cast("Nd4jLong*") LongBuffer copyShape(@Cast("Nd4jLong*") LongBuffer originalShape); + @Namespace("shape") public static native @Cast("Nd4jLong*") long[] copyShape(@Cast("Nd4jLong*") long[] originalShape); + + @Namespace("shape") public static native @Cast("bool") boolean shapeEquals(@Cast("const Nd4jLong*") LongPointer shapeInfo1, @Cast("const Nd4jLong*") LongPointer shapeInfo2); + @Namespace("shape") public static native @Cast("bool") boolean shapeEquals(@Cast("const Nd4jLong*") LongBuffer shapeInfo1, @Cast("const Nd4jLong*") LongBuffer shapeInfo2); + @Namespace("shape") public static native @Cast("bool") boolean shapeEquals(@Cast("const Nd4jLong*") long[] shapeInfo1, @Cast("const Nd4jLong*") long[] shapeInfo2); + + @Namespace("shape") public static native @Cast("bool") boolean shapeEquals(@Cast("const Nd4jLong*") LongPointer shapeInfo1, @Cast("const Nd4jLong*") LongPointer shapeInfo2, @Cast("const Nd4jLong*") LongPointer shapeInfo3); + @Namespace("shape") public static native @Cast("bool") boolean shapeEquals(@Cast("const Nd4jLong*") LongBuffer shapeInfo1, @Cast("const Nd4jLong*") LongBuffer shapeInfo2, @Cast("const Nd4jLong*") LongBuffer shapeInfo3); + @Namespace("shape") public static native @Cast("bool") boolean shapeEquals(@Cast("const Nd4jLong*") long[] shapeInfo1, @Cast("const Nd4jLong*") long[] shapeInfo2, @Cast("const Nd4jLong*") long[] shapeInfo3); + + @Namespace("shape") public static native @Cast("bool") boolean strideEquals(int shape1Rank,@Cast("Nd4jLong*") LongPointer shape1,int shape2Rank,@Cast("Nd4jLong*") LongPointer shape2); + @Namespace("shape") public static native @Cast("bool") boolean strideEquals(int shape1Rank,@Cast("Nd4jLong*") LongBuffer shape1,int shape2Rank,@Cast("Nd4jLong*") LongBuffer shape2); + @Namespace("shape") public static native @Cast("bool") boolean strideEquals(int shape1Rank,@Cast("Nd4jLong*") long[] shape1,int shape2Rank,@Cast("Nd4jLong*") long[] shape2); + + @Namespace("shape") public static native @Cast("bool") boolean strideEquals(@Cast("Nd4jLong*") LongPointer shapeInfo1,@Cast("Nd4jLong*") LongPointer shapeInfo2); + @Namespace("shape") public static native @Cast("bool") boolean strideEquals(@Cast("Nd4jLong*") LongBuffer shapeInfo1,@Cast("Nd4jLong*") LongBuffer shapeInfo2); + @Namespace("shape") public static native @Cast("bool") boolean strideEquals(@Cast("Nd4jLong*") long[] shapeInfo1,@Cast("Nd4jLong*") long[] shapeInfo2); + + @Namespace("shape") public static native @Cast("bool") boolean strideEquals(@Cast("Nd4jLong*") LongPointer stride1,int rank1,@Cast("Nd4jLong*") LongPointer stride2,int rank2); + @Namespace("shape") public static native @Cast("bool") boolean strideEquals(@Cast("Nd4jLong*") LongBuffer stride1,int rank1,@Cast("Nd4jLong*") LongBuffer stride2,int rank2); + @Namespace("shape") public static native @Cast("bool") boolean strideEquals(@Cast("Nd4jLong*") long[] stride1,int rank1,@Cast("Nd4jLong*") long[] stride2,int rank2); + + @Namespace("shape") public static native @Cast("bool") boolean equalsSoft(@Cast("const Nd4jLong*") LongPointer shapeA, @Cast("const Nd4jLong*") LongPointer shapeB); + @Namespace("shape") public static native @Cast("bool") boolean equalsSoft(@Cast("const Nd4jLong*") LongBuffer shapeA, @Cast("const Nd4jLong*") LongBuffer shapeB); + @Namespace("shape") public static native @Cast("bool") boolean equalsSoft(@Cast("const Nd4jLong*") long[] shapeA, @Cast("const Nd4jLong*") long[] shapeB); + + @Namespace("shape") public static native @Cast("bool") boolean equalsTypesAndShapesSoft(@Cast("const Nd4jLong*") LongPointer shapeA, @Cast("const Nd4jLong*") LongPointer shapeB); + @Namespace("shape") public static native @Cast("bool") boolean equalsTypesAndShapesSoft(@Cast("const Nd4jLong*") LongBuffer shapeA, @Cast("const Nd4jLong*") LongBuffer shapeB); + @Namespace("shape") public static native @Cast("bool") boolean equalsTypesAndShapesSoft(@Cast("const Nd4jLong*") long[] shapeA, @Cast("const Nd4jLong*") long[] shapeB); + + @Namespace("shape") public static native @Cast("bool") boolean equalsStrict(@Cast("const Nd4jLong*") LongPointer shapeA, @Cast("const Nd4jLong*") LongPointer shapeB); + @Namespace("shape") public static native @Cast("bool") boolean equalsStrict(@Cast("const Nd4jLong*") LongBuffer shapeA, @Cast("const Nd4jLong*") LongBuffer shapeB); + @Namespace("shape") public static native @Cast("bool") boolean equalsStrict(@Cast("const Nd4jLong*") long[] shapeA, @Cast("const Nd4jLong*") long[] shapeB); + + // returns true if ranks, shapes and strides are the same + @Namespace("shape") public static native @Cast("bool") boolean haveSameShapeAndStrides(@Cast("const Nd4jLong*") LongPointer shapeInfo1, @Cast("const Nd4jLong*") LongPointer shapeInfo2); + @Namespace("shape") public static native @Cast("bool") boolean haveSameShapeAndStrides(@Cast("const Nd4jLong*") LongBuffer shapeInfo1, @Cast("const Nd4jLong*") LongBuffer shapeInfo2); + @Namespace("shape") public static native @Cast("bool") boolean haveSameShapeAndStrides(@Cast("const Nd4jLong*") long[] shapeInfo1, @Cast("const Nd4jLong*") long[] shapeInfo2); + @Namespace("shape") public static native @Cast("bool") boolean haveSameShapeAndStrides(@Cast("const Nd4jLong*") LongPointer shapeInfo1, @Cast("const Nd4jLong*") LongPointer shapeInfo2, @Cast("const Nd4jLong*") LongPointer shapeInfo3); + @Namespace("shape") public static native @Cast("bool") boolean haveSameShapeAndStrides(@Cast("const Nd4jLong*") LongBuffer shapeInfo1, @Cast("const Nd4jLong*") LongBuffer shapeInfo2, @Cast("const Nd4jLong*") LongBuffer shapeInfo3); + @Namespace("shape") public static native @Cast("bool") boolean haveSameShapeAndStrides(@Cast("const Nd4jLong*") long[] shapeInfo1, @Cast("const Nd4jLong*") long[] shapeInfo2, @Cast("const Nd4jLong*") long[] shapeInfo3); + + @Namespace("shape") public static native int sizeAt(@Cast("const Nd4jLong*") LongPointer shapeInfo, int dim); + @Namespace("shape") public static native int sizeAt(@Cast("const Nd4jLong*") LongBuffer shapeInfo, int dim); + @Namespace("shape") public static native int sizeAt(@Cast("const Nd4jLong*") long[] shapeInfo, int dim); + @Namespace("shape") public static native @Cast("Nd4jLong") long strideAt(@Cast("const Nd4jLong*") LongPointer shapeInfo, int dim); + @Namespace("shape") public static native @Cast("Nd4jLong") long strideAt(@Cast("const Nd4jLong*") LongBuffer shapeInfo, int dim); + @Namespace("shape") public static native @Cast("Nd4jLong") long strideAt(@Cast("const Nd4jLong*") long[] shapeInfo, int dim); + + @Namespace("shape") public static native void traceNew(int id); + + + @Namespace("shape") public static native int tadIndexForLinear(int linearIndex, int tadLength); + + @Namespace("shape") public static native @Cast("Nd4jLong") long tadLength(@Cast("Nd4jLong*") LongPointer shapeInfo, IntPointer dimension, int dimensionLength); + @Namespace("shape") public static native @Cast("Nd4jLong") long tadLength(@Cast("Nd4jLong*") LongBuffer shapeInfo, IntBuffer dimension, int dimensionLength); + @Namespace("shape") public static native @Cast("Nd4jLong") long tadLength(@Cast("Nd4jLong*") long[] shapeInfo, int[] dimension, int dimensionLength); + + @Namespace("shape") public static native @Cast("bool") boolean canReshape(int oldRank, @Cast("Nd4jLong*") LongPointer oldShape, int newRank, @Cast("Nd4jLong*") LongPointer newShape, @Cast("bool") boolean isFOrder); + @Namespace("shape") public static native @Cast("bool") boolean canReshape(int oldRank, @Cast("Nd4jLong*") LongBuffer oldShape, int newRank, @Cast("Nd4jLong*") LongBuffer newShape, @Cast("bool") boolean isFOrder); + @Namespace("shape") public static native @Cast("bool") boolean canReshape(int oldRank, @Cast("Nd4jLong*") long[] oldShape, int newRank, @Cast("Nd4jLong*") long[] newShape, @Cast("bool") boolean isFOrder); + + @Namespace("shape") public static native @Cast("bool") boolean reshapeC(@Cast("const Nd4jLong*") LongPointer oldShapeInfo, byte newOrder, int newRank, @Cast("const Nd4jLong*") LongPointer newShape, @Cast("Nd4jLong*") LongPointer newShapeInfo); + @Namespace("shape") public static native @Cast("bool") boolean reshapeC(@Cast("const Nd4jLong*") LongBuffer oldShapeInfo, byte newOrder, int newRank, @Cast("const Nd4jLong*") LongBuffer newShape, @Cast("Nd4jLong*") LongBuffer newShapeInfo); + @Namespace("shape") public static native @Cast("bool") boolean reshapeC(@Cast("const Nd4jLong*") long[] oldShapeInfo, byte newOrder, int newRank, @Cast("const Nd4jLong*") long[] newShape, @Cast("Nd4jLong*") long[] newShapeInfo); + /** + * newShapeInfo contains rank, shape and order only, no strides/ews/type + */ + @Namespace("shape") public static native @Cast("bool") boolean reshapeC(@Cast("const Nd4jLong*") LongPointer oldShapeInfo, @Cast("Nd4jLong*") LongPointer newShapeInfo); + @Namespace("shape") public static native @Cast("bool") boolean reshapeC(@Cast("const Nd4jLong*") LongBuffer oldShapeInfo, @Cast("Nd4jLong*") LongBuffer newShapeInfo); + @Namespace("shape") public static native @Cast("bool") boolean reshapeC(@Cast("const Nd4jLong*") long[] oldShapeInfo, @Cast("Nd4jLong*") long[] newShapeInfo); + + /** + * Get the shape info buffer + * for the given rank and shape. + */ + @Namespace("shape") public static native @Cast("Nd4jLong*") LongPointer shapeBuffer(int rank, @Cast("sd::DataType") int dtype, @Cast("Nd4jLong*") LongPointer shape); + @Namespace("shape") public static native @Cast("Nd4jLong*") LongBuffer shapeBuffer(int rank, @Cast("sd::DataType") int dtype, @Cast("Nd4jLong*") LongBuffer shape); + @Namespace("shape") public static native @Cast("Nd4jLong*") long[] shapeBuffer(int rank, @Cast("sd::DataType") int dtype, @Cast("Nd4jLong*") long[] shape); + + @Namespace("shape") public static native @Cast("Nd4jLong*") LongPointer shapeBuffer(int rank, @Cast("sd::DataType") int dtype, @Cast("Nd4jLong*") LongPointer shape, @Cast("Nd4jLong*") LongPointer buffer); + @Namespace("shape") public static native @Cast("Nd4jLong*") LongBuffer shapeBuffer(int rank, @Cast("sd::DataType") int dtype, @Cast("Nd4jLong*") LongBuffer shape, @Cast("Nd4jLong*") LongBuffer buffer); + @Namespace("shape") public static native @Cast("Nd4jLong*") long[] shapeBuffer(int rank, @Cast("sd::DataType") int dtype, @Cast("Nd4jLong*") long[] shape, @Cast("Nd4jLong*") long[] buffer); + + /** + * Get the shape info buffer + * for the given rank and shape. + */ + @Namespace("shape") public static native @Cast("Nd4jLong*") LongPointer shapeBufferFortran(int rank, @Cast("sd::DataType") int dtype, @Cast("Nd4jLong*") LongPointer shape); + @Namespace("shape") public static native @Cast("Nd4jLong*") LongBuffer shapeBufferFortran(int rank, @Cast("sd::DataType") int dtype, @Cast("Nd4jLong*") LongBuffer shape); + @Namespace("shape") public static native @Cast("Nd4jLong*") long[] shapeBufferFortran(int rank, @Cast("sd::DataType") int dtype, @Cast("Nd4jLong*") long[] shape); + + @Namespace("shape") public static native @Cast("Nd4jLong*") LongPointer shapeBufferFortran(int rank, @Cast("sd::DataType") int dtype, @Cast("Nd4jLong*") LongPointer shape, @Cast("Nd4jLong*") LongPointer output); + @Namespace("shape") public static native @Cast("Nd4jLong*") LongBuffer shapeBufferFortran(int rank, @Cast("sd::DataType") int dtype, @Cast("Nd4jLong*") LongBuffer shape, @Cast("Nd4jLong*") LongBuffer output); + @Namespace("shape") public static native @Cast("Nd4jLong*") long[] shapeBufferFortran(int rank, @Cast("sd::DataType") int dtype, @Cast("Nd4jLong*") long[] shape, @Cast("Nd4jLong*") long[] output); + +// #ifdef __CUDACC__ +// #endif + + + +/** + * Computes the standard packed array strides for a given shape. + * + * @param shape the shape of a matrix: + * @param startNum the start number for the strides + * @return the strides for a matrix of n dimensions + */ + @Namespace("shape") public static native @Cast("Nd4jLong*") LongPointer calcStridesFortran(@Cast("Nd4jLong*") LongPointer shape, int rank); + @Namespace("shape") public static native @Cast("Nd4jLong*") LongBuffer calcStridesFortran(@Cast("Nd4jLong*") LongBuffer shape, int rank); + @Namespace("shape") public static native @Cast("Nd4jLong*") long[] calcStridesFortran(@Cast("Nd4jLong*") long[] shape, int rank); + + @Namespace("shape") public static native @Cast("Nd4jLong*") LongPointer calcStridesFortran(@Cast("Nd4jLong*") LongPointer shape, int rank, @Cast("Nd4jLong*") LongPointer ret); + @Namespace("shape") public static native @Cast("Nd4jLong*") LongBuffer calcStridesFortran(@Cast("Nd4jLong*") LongBuffer shape, int rank, @Cast("Nd4jLong*") LongBuffer ret); + @Namespace("shape") public static native @Cast("Nd4jLong*") long[] calcStridesFortran(@Cast("Nd4jLong*") long[] shape, int rank, @Cast("Nd4jLong*") long[] ret); + +/** + * Computes the standard packed array strides for a given shape. + * + * @param shape the shape of a matrix: + * @param startNum the start number for the strides + * @return the strides for a matrix of n dimensions + */ + + @Namespace("shape") public static native @Cast("Nd4jLong*") LongPointer calcStrides(@Cast("Nd4jLong*") LongPointer shape, int rank); + @Namespace("shape") public static native @Cast("Nd4jLong*") LongBuffer calcStrides(@Cast("Nd4jLong*") LongBuffer shape, int rank); + @Namespace("shape") public static native @Cast("Nd4jLong*") long[] calcStrides(@Cast("Nd4jLong*") long[] shape, int rank); + + @Namespace("shape") public static native @Cast("Nd4jLong*") LongPointer calcStrides(@Cast("Nd4jLong*") LongPointer shape, int rank, @Cast("Nd4jLong*") LongPointer ret); + @Namespace("shape") public static native @Cast("Nd4jLong*") LongBuffer calcStrides(@Cast("Nd4jLong*") LongBuffer shape, int rank, @Cast("Nd4jLong*") LongBuffer ret); + @Namespace("shape") public static native @Cast("Nd4jLong*") long[] calcStrides(@Cast("Nd4jLong*") long[] shape, int rank, @Cast("Nd4jLong*") long[] ret); + + @Namespace("shape") public static native void updateStrides(@Cast("Nd4jLong*") LongPointer shape, byte order); + @Namespace("shape") public static native void updateStrides(@Cast("Nd4jLong*") LongBuffer shape, byte order); + @Namespace("shape") public static native void updateStrides(@Cast("Nd4jLong*") long[] shape, byte order); + @Namespace("shape") public static native void updateStrides(int rank, @Cast("const Nd4jLong*") LongPointer shapeOnly, @Cast("Nd4jLong*") LongPointer stridesOnly, byte order); + @Namespace("shape") public static native void updateStrides(int rank, @Cast("const Nd4jLong*") LongBuffer shapeOnly, @Cast("Nd4jLong*") LongBuffer stridesOnly, byte order); + @Namespace("shape") public static native void updateStrides(int rank, @Cast("const Nd4jLong*") long[] shapeOnly, @Cast("Nd4jLong*") long[] stridesOnly, byte order); + + +// check whether input dimensions are permuted, not permuted dimensions order have to be 0,....,rank-1 + +/** + * Computes the standard packed array strides for a given shape. + * + * @param shape the shape of a matrix: + * @param startNum the start number for the strides + * @return the strides for a matrix of n dimensions + */ + @Namespace("shape") public static native @Cast("Nd4jLong*") LongPointer calcStridesFortran(@Cast("Nd4jLong*") LongPointer shape, int rank, int startNum); + @Namespace("shape") public static native @Cast("Nd4jLong*") LongBuffer calcStridesFortran(@Cast("Nd4jLong*") LongBuffer shape, int rank, int startNum); + @Namespace("shape") public static native @Cast("Nd4jLong*") long[] calcStridesFortran(@Cast("Nd4jLong*") long[] shape, int rank, int startNum); + + @Namespace("shape") public static native @Cast("Nd4jLong*") LongPointer calcStridesFortran(@Cast("Nd4jLong*") LongPointer shape, int rank, int startNum, @Cast("Nd4jLong*") LongPointer ret); + @Namespace("shape") public static native @Cast("Nd4jLong*") LongBuffer calcStridesFortran(@Cast("Nd4jLong*") LongBuffer shape, int rank, int startNum, @Cast("Nd4jLong*") LongBuffer ret); + @Namespace("shape") public static native @Cast("Nd4jLong*") long[] calcStridesFortran(@Cast("Nd4jLong*") long[] shape, int rank, int startNum, @Cast("Nd4jLong*") long[] ret); + +/** + * Computes the standard packed array strides for a given shape. + * + * @param shape the shape of a matrix: + * @param startNum the start number for the strides + * @return the strides for a matrix of n dimensions + */ + @Namespace("shape") public static native @Cast("Nd4jLong*") LongPointer calcStrides(@Cast("Nd4jLong*") LongPointer shape, int rank, int startNum); + @Namespace("shape") public static native @Cast("Nd4jLong*") LongBuffer calcStrides(@Cast("Nd4jLong*") LongBuffer shape, int rank, int startNum); + @Namespace("shape") public static native @Cast("Nd4jLong*") long[] calcStrides(@Cast("Nd4jLong*") long[] shape, int rank, int startNum); + + @Namespace("shape") public static native @Cast("Nd4jLong*") LongPointer calcStrides(@Cast("Nd4jLong*") LongPointer shape, int rank, int startNum, @Cast("Nd4jLong*") LongPointer ret); + @Namespace("shape") public static native @Cast("Nd4jLong*") LongBuffer calcStrides(@Cast("Nd4jLong*") LongBuffer shape, int rank, int startNum, @Cast("Nd4jLong*") LongBuffer ret); + @Namespace("shape") public static native @Cast("Nd4jLong*") long[] calcStrides(@Cast("Nd4jLong*") long[] shape, int rank, int startNum, @Cast("Nd4jLong*") long[] ret); + +/** + * @param toCopy the shape to copy + * @return a copy of the original struct + */ + @Namespace("shape") public static native ShapeInformation shapeCopy( ShapeInformation toCopy); + + + @Namespace("shape") public static native @Cast("bool") boolean strideDescendingCAscendingF(@Cast("const Nd4jLong*") LongPointer shapeBuffer); + @Namespace("shape") public static native @Cast("bool") boolean strideDescendingCAscendingF(@Cast("const Nd4jLong*") LongBuffer shapeBuffer); + @Namespace("shape") public static native @Cast("bool") boolean strideDescendingCAscendingF(@Cast("const Nd4jLong*") long[] shapeBuffer); + + @Namespace("shape") public static native @Cast("bool") boolean isContiguous(@Cast("const Nd4jLong*") LongPointer shapeInfo); + @Namespace("shape") public static native @Cast("bool") boolean isContiguous(@Cast("const Nd4jLong*") LongBuffer shapeInfo); + @Namespace("shape") public static native @Cast("bool") boolean isContiguous(@Cast("const Nd4jLong*") long[] shapeInfo); + + +/** + * copy-past from java hasDefaultStridesForShape function + * check whether array is not permuted and has contiguous elements in memory + */ + @Namespace("shape") public static native @Cast("bool") boolean areStridesDefault(@Cast("const Nd4jLong*") LongPointer shapeInfo); + @Namespace("shape") public static native @Cast("bool") boolean areStridesDefault(@Cast("const Nd4jLong*") LongBuffer shapeInfo); + @Namespace("shape") public static native @Cast("bool") boolean areStridesDefault(@Cast("const Nd4jLong*") long[] shapeInfo); + + +/** + * Compute the element wise stride + * for a given shape/stride configuration + * @param rank the rank of the shape/stride + * @param shape the shape + * @param stride the stride + * @param isFOrder 0 or 1 for whether the array is f + * ordered or not + * @return 0 if there is no element wise stride the + * element wise stride of reshape(1,length) otherwise + */ + @Namespace("shape") public static native int computeElementWiseStride(int rank, @Cast("Nd4jLong*") LongPointer shape, @Cast("Nd4jLong*") LongPointer stride, int isFOrder); + @Namespace("shape") public static native int computeElementWiseStride(int rank, @Cast("Nd4jLong*") LongBuffer shape, @Cast("Nd4jLong*") LongBuffer stride, int isFOrder); + @Namespace("shape") public static native int computeElementWiseStride(int rank, @Cast("Nd4jLong*") long[] shape, @Cast("Nd4jLong*") long[] stride, int isFOrder); + +/** + * Compute the element wise stride + * for a given shape/stride configuration + * @param rank the rank of the shape/stride + * @param shape the shape + * @param stride the stride + * @param isFOrder 0 or 1 for whether the array is f + * ordered or not + * @return 0 if there is no element wise stride the + * element wise stride of reshape(1,length) otherwise + */ + @Namespace("shape") public static native int computeElementWiseStride(int rank, @Cast("Nd4jLong*") LongPointer shape, @Cast("Nd4jLong*") LongPointer stride, int isFOrder, @Cast("Nd4jLong*") LongPointer dimension, int dimensionLength); + @Namespace("shape") public static native int computeElementWiseStride(int rank, @Cast("Nd4jLong*") LongBuffer shape, @Cast("Nd4jLong*") LongBuffer stride, int isFOrder, @Cast("Nd4jLong*") LongBuffer dimension, int dimensionLength); + @Namespace("shape") public static native int computeElementWiseStride(int rank, @Cast("Nd4jLong*") long[] shape, @Cast("Nd4jLong*") long[] stride, int isFOrder, @Cast("Nd4jLong*") long[] dimension, int dimensionLength); + + @Namespace("shape") public static native @Cast("Nd4jLong*") LongPointer shapeInfoOnlyShapeAndStride(@Cast("Nd4jLong*") LongPointer shapeInfo, @Cast("Nd4jLong*") LongPointer dimension, int dimensionLength,@Cast("bool") boolean reverseCopyStride); + @Namespace("shape") public static native @Cast("Nd4jLong*") LongBuffer shapeInfoOnlyShapeAndStride(@Cast("Nd4jLong*") LongBuffer shapeInfo, @Cast("Nd4jLong*") LongBuffer dimension, int dimensionLength,@Cast("bool") boolean reverseCopyStride); + @Namespace("shape") public static native @Cast("Nd4jLong*") long[] shapeInfoOnlyShapeAndStride(@Cast("Nd4jLong*") long[] shapeInfo, @Cast("Nd4jLong*") long[] dimension, int dimensionLength,@Cast("bool") boolean reverseCopyStride); + + @Namespace("shape") public static native @Cast("Nd4jLong*") LongPointer shapeInfoOnlyShapeAndStride(@Cast("Nd4jLong*") LongPointer shapeInfo, @Cast("Nd4jLong*") LongPointer dimension, int dimensionLength,@Cast("bool") boolean reverseCopyStride, @Cast("Nd4jLong*") LongPointer buffer); + @Namespace("shape") public static native @Cast("Nd4jLong*") LongBuffer shapeInfoOnlyShapeAndStride(@Cast("Nd4jLong*") LongBuffer shapeInfo, @Cast("Nd4jLong*") LongBuffer dimension, int dimensionLength,@Cast("bool") boolean reverseCopyStride, @Cast("Nd4jLong*") LongBuffer buffer); + @Namespace("shape") public static native @Cast("Nd4jLong*") long[] shapeInfoOnlyShapeAndStride(@Cast("Nd4jLong*") long[] shapeInfo, @Cast("Nd4jLong*") long[] dimension, int dimensionLength,@Cast("bool") boolean reverseCopyStride, @Cast("Nd4jLong*") long[] buffer); +/** + * + * @param length + * @param shape + * @param rearrange + * @return + */ + @Namespace("shape") public static native @Cast("Nd4jLong*") LongPointer doPermuteSwap(int length, @Cast("Nd4jLong*") LongPointer shape, IntPointer rearrange); + @Namespace("shape") public static native @Cast("Nd4jLong*") LongBuffer doPermuteSwap(int length, @Cast("Nd4jLong*") LongBuffer shape, IntBuffer rearrange); + @Namespace("shape") public static native @Cast("Nd4jLong*") long[] doPermuteSwap(int length, @Cast("Nd4jLong*") long[] shape, int[] rearrange); + + + +/** + * In place permute swap + * @param length + * @param shape + * @param rearrange + */ + @Namespace("shape") public static native void doPermuteSwap(int length, @Cast("Nd4jLong**") PointerPointer shape, IntPointer rearrange); + + @Namespace("shape") public static native @Cast("Nd4jLong*") LongPointer permuteShapeBuffer(@Cast("Nd4jLong*") LongPointer shapeBuffer, IntPointer rearrange); + @Namespace("shape") public static native @Cast("Nd4jLong*") LongBuffer permuteShapeBuffer(@Cast("Nd4jLong*") LongBuffer shapeBuffer, IntBuffer rearrange); + @Namespace("shape") public static native @Cast("Nd4jLong*") long[] permuteShapeBuffer(@Cast("Nd4jLong*") long[] shapeBuffer, int[] rearrange); + + @Namespace("shape") public static native void permuteShapeBufferInPlace(@Cast("Nd4jLong*") LongPointer shapeBuffer, IntPointer rearrange, @Cast("Nd4jLong*") LongPointer out); + @Namespace("shape") public static native void permuteShapeBufferInPlace(@Cast("Nd4jLong*") LongBuffer shapeBuffer, IntBuffer rearrange, @Cast("Nd4jLong*") LongBuffer out); + @Namespace("shape") public static native void permuteShapeBufferInPlace(@Cast("Nd4jLong*") long[] shapeBuffer, int[] rearrange, @Cast("Nd4jLong*") long[] out); + + @Namespace("shape") public static native void doPermuteShapeInfo(@Cast("Nd4jLong*") LongPointer shapeBuffer, @Const IntPointer rearrange, @Cast("Nd4jLong") long len/*=-1*/); + @Namespace("shape") public static native void doPermuteShapeInfo(@Cast("Nd4jLong*") LongPointer shapeBuffer, @Const IntPointer rearrange); + @Namespace("shape") public static native void doPermuteShapeInfo(@Cast("Nd4jLong*") LongBuffer shapeBuffer, @Const IntBuffer rearrange, @Cast("Nd4jLong") long len/*=-1*/); + @Namespace("shape") public static native void doPermuteShapeInfo(@Cast("Nd4jLong*") LongBuffer shapeBuffer, @Const IntBuffer rearrange); + @Namespace("shape") public static native void doPermuteShapeInfo(@Cast("Nd4jLong*") long[] shapeBuffer, @Const int[] rearrange, @Cast("Nd4jLong") long len/*=-1*/); + @Namespace("shape") public static native void doPermuteShapeInfo(@Cast("Nd4jLong*") long[] shapeBuffer, @Const int[] rearrange); + + /** + * Rearrange the permute indexes + * according to which dimensions are specified. + * + * For example, dimension is implicitly: + * 0,1,2 + * + * If you want to do a reduce along dimensions 0 and 1, + * you need to permute the indexes to be: + * 2,0,1 + * + * which will give us the ability to ierate along an element + * wise stride. + */ + + @Namespace("shape") public static native @Cast("Nd4jLong*") LongPointer createPermuteIndexes(int originalRank, IntPointer dimension,int dimensionLength); + @Namespace("shape") public static native @Cast("Nd4jLong*") LongBuffer createPermuteIndexes(int originalRank, IntBuffer dimension,int dimensionLength); + @Namespace("shape") public static native @Cast("Nd4jLong*") long[] createPermuteIndexes(int originalRank, int[] dimension,int dimensionLength); + + @Namespace("shape") public static native @Cast("Nd4jLong*") LongPointer computeResultShape(@Cast("Nd4jLong*") LongPointer originalShapeBuffer, IntPointer dimension,int dimensionLength); + @Namespace("shape") public static native @Cast("Nd4jLong*") LongBuffer computeResultShape(@Cast("Nd4jLong*") LongBuffer originalShapeBuffer, IntBuffer dimension,int dimensionLength); + @Namespace("shape") public static native @Cast("Nd4jLong*") long[] computeResultShape(@Cast("Nd4jLong*") long[] originalShapeBuffer, int[] dimension,int dimensionLength); + + /** + * This method does inplace transpose of given shapeBuffer + * + * @param shapeBuffer + */ + @Namespace("shape") public static native void transposeInplace(@Cast("Nd4jLong*") LongPointer shapeBuffer); + @Namespace("shape") public static native void transposeInplace(@Cast("Nd4jLong*") LongBuffer shapeBuffer); + @Namespace("shape") public static native void transposeInplace(@Cast("Nd4jLong*") long[] shapeBuffer); + + +/** + * Get the ordering for the device + * @param length + * @param shape + * @param stride + * @param elementStride + * @return + */ + @Namespace("shape") public static native char getOrder(int length, @Cast("Nd4jLong*") LongPointer shape, @Cast("Nd4jLong*") LongPointer stride, int elementStride); + @Namespace("shape") public static native char getOrder(int length, @Cast("Nd4jLong*") LongBuffer shape, @Cast("Nd4jLong*") LongBuffer stride, int elementStride); + @Namespace("shape") public static native char getOrder(int length, @Cast("Nd4jLong*") long[] shape, @Cast("Nd4jLong*") long[] stride, int elementStride); + +/** + * Ensure that every value in the re arrange + * array is unique + * @param arr + * @param shape + * @param arrLength + * @param shapeLength + * @return + */ + +/** + * Permute the shape information + * @param info the shape information to permute + * @param rearrange the order to re arrange + * @param rank the rank of the rearrange array + */ + @Namespace("shape") public static native void permute(@Cast("shape::ShapeInformation**") PointerPointer info, IntPointer rearrange, int rank); + @Namespace("shape") public static native void permute(@ByPtrPtr ShapeInformation info, IntPointer rearrange, int rank); + @Namespace("shape") public static native void permute(@ByPtrPtr ShapeInformation info, IntBuffer rearrange, int rank); + @Namespace("shape") public static native void permute(@ByPtrPtr ShapeInformation info, int[] rearrange, int rank); + +/** + * Returns whether the + * given shape is a vector or not + * @param shape the shape of the array + * @param rank the rank of cthe shape + */ + @Namespace("shape") public static native int isVector(@Cast("Nd4jLong*") LongPointer shape, int rank); + @Namespace("shape") public static native int isVector(@Cast("Nd4jLong*") LongBuffer shape, int rank); + @Namespace("shape") public static native int isVector(@Cast("Nd4jLong*") long[] shape, int rank); + + + /** + * When 1 dimension is the whole length of the + * array + */ + @Namespace("shape") public static native int oneDimEqualToLength(@Cast("Nd4jLong*") LongPointer shape, int rank); + @Namespace("shape") public static native int oneDimEqualToLength(@Cast("Nd4jLong*") LongBuffer shape, int rank); + @Namespace("shape") public static native int oneDimEqualToLength(@Cast("Nd4jLong*") long[] shape, int rank); + + @Namespace("shape") public static native int oneDimEqualToLength(@Cast("Nd4jLong*") LongPointer shapeInfo); + @Namespace("shape") public static native int oneDimEqualToLength(@Cast("Nd4jLong*") LongBuffer shapeInfo); + @Namespace("shape") public static native int oneDimEqualToLength(@Cast("Nd4jLong*") long[] shapeInfo); + + @Namespace("shape") public static native int isVector(@Cast("const Nd4jLong*") LongPointer shapeInfo); + @Namespace("shape") public static native int isVector(@Cast("const Nd4jLong*") LongBuffer shapeInfo); + @Namespace("shape") public static native int isVector(@Cast("const Nd4jLong*") long[] shapeInfo); + + @Namespace("shape") public static native @Cast("bool") boolean isLikeVector(@Cast("Nd4jLong*") LongPointer shapeInfo, @ByRef IntPointer posOfNonUnityDim); + @Namespace("shape") public static native @Cast("bool") boolean isLikeVector(@Cast("Nd4jLong*") LongBuffer shapeInfo, @ByRef IntBuffer posOfNonUnityDim); + @Namespace("shape") public static native @Cast("bool") boolean isLikeVector(@Cast("Nd4jLong*") long[] shapeInfo, @ByRef int[] posOfNonUnityDim); + + @Namespace("shape") public static native @Cast("bool") boolean isCommonVector(@Cast("const Nd4jLong*") LongPointer shapeInfo, @ByRef IntPointer posOfNonUnityDim); + @Namespace("shape") public static native @Cast("bool") boolean isCommonVector(@Cast("const Nd4jLong*") LongBuffer shapeInfo, @ByRef IntBuffer posOfNonUnityDim); + @Namespace("shape") public static native @Cast("bool") boolean isCommonVector(@Cast("const Nd4jLong*") long[] shapeInfo, @ByRef int[] posOfNonUnityDim); + + @Namespace("shape") public static native @Cast("bool") boolean isRowVector(@Cast("const Nd4jLong*") LongPointer shapeInfo); + @Namespace("shape") public static native @Cast("bool") boolean isRowVector(@Cast("const Nd4jLong*") LongBuffer shapeInfo); + @Namespace("shape") public static native @Cast("bool") boolean isRowVector(@Cast("const Nd4jLong*") long[] shapeInfo); + + @Namespace("shape") public static native @Cast("bool") boolean isColumnVector(@Cast("Nd4jLong*") LongPointer shapeInfo); + @Namespace("shape") public static native @Cast("bool") boolean isColumnVector(@Cast("Nd4jLong*") LongBuffer shapeInfo); + @Namespace("shape") public static native @Cast("bool") boolean isColumnVector(@Cast("Nd4jLong*") long[] shapeInfo); + + /** + * shape - input inShape is shape only, not shapeInfo + * returns number of non-unity dimensions in inShape + */ + @Namespace("shape") public static native int numOfNonUnitDims(int rank, @Cast("const Nd4jLong*") LongPointer inShape); + @Namespace("shape") public static native int numOfNonUnitDims(int rank, @Cast("const Nd4jLong*") LongBuffer inShape); + @Namespace("shape") public static native int numOfNonUnitDims(int rank, @Cast("const Nd4jLong*") long[] inShape); + + /** + * Returns whether the + * given shape is a vector or not + * @param shape the shape of the array + * @param rank the rank of the shape + */ + + @Namespace("shape") public static native int isMatrix(@Cast("Nd4jLong*") LongPointer shape, int rank); + @Namespace("shape") public static native int isMatrix(@Cast("Nd4jLong*") LongBuffer shape, int rank); + @Namespace("shape") public static native int isMatrix(@Cast("Nd4jLong*") long[] shape, int rank); + + @Namespace("shape") public static native int isMatrix(@Cast("Nd4jLong*") LongPointer shapeInfo); + @Namespace("shape") public static native int isMatrix(@Cast("Nd4jLong*") LongBuffer shapeInfo); + @Namespace("shape") public static native int isMatrix(@Cast("Nd4jLong*") long[] shapeInfo); +/** + * Returns the shape portion of an information + * buffer + */ + @Namespace("shape") public static native @Cast("Nd4jLong*") LongPointer shapeOf(@Cast("Nd4jLong*") LongPointer shapeInfo); + @Namespace("shape") public static native @Cast("Nd4jLong*") LongBuffer shapeOf(@Cast("Nd4jLong*") LongBuffer shapeInfo); + @Namespace("shape") public static native @Cast("Nd4jLong*") long[] shapeOf(@Cast("Nd4jLong*") long[] shapeInfo); + +/** + * Return a copy of a buffer. + * This buffer allocates memory + * that must be freed elsewhere. + */ + + /** + * Return a copy of a buffer. + * This buffer allocates memory + * that must be freed elsewhere. + */ + /** +* Return a copy of a buffer. +* This buffer allocates memory +* that must be freed elsewhere. +*/ + @Namespace("shape") public static native void copyTo(int length, @Cast("Nd4jLong*") LongPointer from, @Cast("Nd4jLong*") LongPointer to, @Cast("Nd4jLong*") LongPointer indexes); + @Namespace("shape") public static native void copyTo(int length, @Cast("Nd4jLong*") LongBuffer from, @Cast("Nd4jLong*") LongBuffer to, @Cast("Nd4jLong*") LongBuffer indexes); + @Namespace("shape") public static native void copyTo(int length, @Cast("Nd4jLong*") long[] from, @Cast("Nd4jLong*") long[] to, @Cast("Nd4jLong*") long[] indexes); + +/** + * Permute the given strides + * in the given rearrange order + * @param toPermute the buffer to permute + * @param shapeRank the length of the buffer to permute + * @param rearrange the rearrange order (must be 0 based indexes + * and all must be filled in) + * @return the rearranged array + */ + //ND4J_EXPORT _CUDA_HD Nd4jLong *permutedStrides(Nd4jLong *toPermute, int shapeRank, Nd4jLong *rearrange); + +/** + * Return the slice (shape + 1 in pointer arithmetic) + * @param shape the shape to take the slice of + * @return the shape array - the first entry + */ + @Namespace("shape") public static native @Cast("Nd4jLong*") LongPointer slice(@Cast("Nd4jLong*") LongPointer shape); + @Namespace("shape") public static native @Cast("Nd4jLong*") LongBuffer slice(@Cast("Nd4jLong*") LongBuffer shape); + @Namespace("shape") public static native @Cast("Nd4jLong*") long[] slice(@Cast("Nd4jLong*") long[] shape); + + @Namespace("shape") public static native int slices(@Cast("Nd4jLong*") LongPointer shapeBuffer); + @Namespace("shape") public static native int slices(@Cast("Nd4jLong*") LongBuffer shapeBuffer); + @Namespace("shape") public static native int slices(@Cast("Nd4jLong*") long[] shapeBuffer); + + @Namespace("shape") public static native @Cast("Nd4jLong*") LongPointer sliceOfShapeBuffer(@Cast("Nd4jLong") long sliceIdx, @Cast("Nd4jLong*") LongPointer shapeBuffer); + @Namespace("shape") public static native @Cast("Nd4jLong*") LongBuffer sliceOfShapeBuffer(@Cast("Nd4jLong") long sliceIdx, @Cast("Nd4jLong*") LongBuffer shapeBuffer); + @Namespace("shape") public static native @Cast("Nd4jLong*") long[] sliceOfShapeBuffer(@Cast("Nd4jLong") long sliceIdx, @Cast("Nd4jLong*") long[] shapeBuffer); +/** + * Returns the length of the + * shape information buffer: + * rank * 2 + 3 + * @param rank the rank to get the shape + * info length for + * @return rank * 2 + 4 + */ + @Namespace("shape") public static native int shapeInfoLength(int rank); + + @Namespace("shape") public static native int shapeInfoLength(@Cast("Nd4jLong*") LongPointer shapeInfo); + @Namespace("shape") public static native int shapeInfoLength(@Cast("Nd4jLong*") LongBuffer shapeInfo); + @Namespace("shape") public static native int shapeInfoLength(@Cast("Nd4jLong*") long[] shapeInfo); + + @Namespace("shape") public static native @Cast("size_t") long shapeInfoByteLength(int rank); + + @Namespace("shape") public static native @Cast("size_t") long shapeInfoByteLength(@Cast("const Nd4jLong*") LongPointer shapeInfo); + @Namespace("shape") public static native @Cast("size_t") long shapeInfoByteLength(@Cast("const Nd4jLong*") LongBuffer shapeInfo); + @Namespace("shape") public static native @Cast("size_t") long shapeInfoByteLength(@Cast("const Nd4jLong*") long[] shapeInfo); + +/** + * Returns the rank portion of + * an information buffer + */ + @Namespace("shape") public static native int rank(@Cast("const Nd4jLong*") LongPointer shapeInfo); + @Namespace("shape") public static native int rank(@Cast("const Nd4jLong*") LongBuffer shapeInfo); + @Namespace("shape") public static native int rank(@Cast("const Nd4jLong*") long[] shapeInfo); + @Namespace("shape") public static native int rank(@Const IntPointer shapeInfo); + @Namespace("shape") public static native int rank(@Const IntBuffer shapeInfo); + @Namespace("shape") public static native int rank(@Const int[] shapeInfo); + + /** + * returns pointer on elementWiseStride + */ + @Namespace("shape") public static native @Cast("Nd4jLong*") LongPointer ews(@Cast("Nd4jLong*") LongPointer shapeInfo); + @Namespace("shape") public static native @Cast("Nd4jLong*") LongBuffer ews(@Cast("Nd4jLong*") LongBuffer shapeInfo); + @Namespace("shape") public static native @Cast("Nd4jLong*") long[] ews(@Cast("Nd4jLong*") long[] shapeInfo); + +/** + * Converts a raw int buffer of the layout: + * rank + * shape + * stride + * offset + * elementWiseStride + * + * where shape and stride are both straight int pointers + */ + @Namespace("shape") public static native ShapeInformation infoFromBuffer(@Cast("Nd4jLong*") LongPointer buffer); + @Namespace("shape") public static native ShapeInformation infoFromBuffer(@Cast("Nd4jLong*") LongBuffer buffer); + @Namespace("shape") public static native ShapeInformation infoFromBuffer(@Cast("Nd4jLong*") long[] buffer); + +/** + * Returns the stride portion of an information + * buffer + */ + @Namespace("shape") public static native @Cast("Nd4jLong*") LongPointer stride(@Cast("Nd4jLong*") LongPointer buffer); + @Namespace("shape") public static native @Cast("Nd4jLong*") LongBuffer stride(@Cast("Nd4jLong*") LongBuffer buffer); + @Namespace("shape") public static native @Cast("Nd4jLong*") long[] stride(@Cast("Nd4jLong*") long[] buffer); + +/** + * Compute the length of the given shape + */ + @Namespace("shape") public static native @Cast("bool") boolean isEmpty(@Cast("const Nd4jLong*") LongPointer shapeInfo); + @Namespace("shape") public static native @Cast("bool") boolean isEmpty(@Cast("const Nd4jLong*") LongBuffer shapeInfo); + @Namespace("shape") public static native @Cast("bool") boolean isEmpty(@Cast("const Nd4jLong*") long[] shapeInfo); + + @Namespace("shape") public static native @Cast("Nd4jLong") long length(@Cast("const Nd4jLong*") LongPointer shapeInfo); + @Namespace("shape") public static native @Cast("Nd4jLong") long length(@Cast("const Nd4jLong*") LongBuffer shapeInfo); + @Namespace("shape") public static native @Cast("Nd4jLong") long length(@Cast("const Nd4jLong*") long[] shapeInfo); + +/*** + * Returns the offset portion of an information buffer + */ + @Namespace("shape") public static native @Cast("Nd4jLong") long offset(@Cast("Nd4jLong*") LongPointer buffer); + @Namespace("shape") public static native @Cast("Nd4jLong") long offset(@Cast("Nd4jLong*") LongBuffer buffer); + @Namespace("shape") public static native @Cast("Nd4jLong") long offset(@Cast("Nd4jLong*") long[] buffer); + + @Namespace("shape") public static native @Cast("Nd4jLong*") @ByRef LongPointer extra(@Cast("Nd4jLong*") LongPointer buffer); + @Namespace("shape") public static native @Cast("Nd4jLong*") @ByRef LongBuffer extra(@Cast("Nd4jLong*") LongBuffer buffer); + @Namespace("shape") public static native @Cast("Nd4jLong*") @ByRef long[] extra(@Cast("Nd4jLong*") long[] buffer); + +/** + * Returns the ordering + * for this shape information buffer + */ + @Namespace("shape") public static native char order(@Cast("const Nd4jLong*") LongPointer buffer); + @Namespace("shape") public static native char order(@Cast("const Nd4jLong*") LongBuffer buffer); + @Namespace("shape") public static native char order(@Cast("const Nd4jLong*") long[] buffer); + +/** + * Returns the type + */ + @Namespace("shape") public static native @Cast("Nd4jLong") long type(@Cast("const Nd4jLong*") LongPointer shapeInfo); + @Namespace("shape") public static native @Cast("Nd4jLong") long type(@Cast("const Nd4jLong*") LongBuffer shapeInfo); + @Namespace("shape") public static native @Cast("Nd4jLong") long type(@Cast("const Nd4jLong*") long[] shapeInfo); + +/** + * Returns the element wise stride for this information + * buffer + */ + @Namespace("shape") public static native @Cast("Nd4jLong") long elementWiseStride(@Cast("const Nd4jLong*") LongPointer buffer); + @Namespace("shape") public static native @Cast("Nd4jLong") long elementWiseStride(@Cast("const Nd4jLong*") LongBuffer buffer); + @Namespace("shape") public static native @Cast("Nd4jLong") long elementWiseStride(@Cast("const Nd4jLong*") long[] buffer); + + + /** + * Returns the element wise stride for this information + * buffer + * relative to a dimension and ordering for a reduction index + */ + @Namespace("shape") public static native @Cast("Nd4jLong") long reductionIndexElementWiseStride(@Cast("Nd4jLong*") LongPointer buffer, IntPointer dimension, int dimensionLength); + @Namespace("shape") public static native @Cast("Nd4jLong") long reductionIndexElementWiseStride(@Cast("Nd4jLong*") LongBuffer buffer, IntBuffer dimension, int dimensionLength); + @Namespace("shape") public static native @Cast("Nd4jLong") long reductionIndexElementWiseStride(@Cast("Nd4jLong*") long[] buffer, int[] dimension, int dimensionLength); + +/** + * Returns whether + * the given shape info buffer + * represents a scalar shape + */ + @Namespace("shape") public static native int isScalar(@Cast("const Nd4jLong*") LongPointer info); + @Namespace("shape") public static native int isScalar(@Cast("const Nd4jLong*") LongBuffer info); + @Namespace("shape") public static native int isScalar(@Cast("const Nd4jLong*") long[] info); + +/** + * Returns whether + * the given shape information + * represents a scalar + * shape or not + */ + @Namespace("shape") public static native int isScalar(ShapeInformation info); + +/** + * Return a copy of this array with the + * given index omitted + * + * @param data the data to copy + * @param indexes the index of the item to remove + * @param dataLength the length of the data array + * @param indexesLength the length of the data array + * @return the new array with the omitted + * + * item + */ + + /** + * Return a copy of this array with the + * given index omitted + * + * @param data the data to copy + * @param indexes the index of the item to remove + * @param dataLength the length of the data array + * @param indexesLength the length of the data array + * @return the new array with the omitted + * + * item + */ + + /** + * Iterate over a given set of indexes + * the begin and end indexes are 0 based. + * 1 padding is automatically assumed for the ending. + * + * For example if you want to iterate over 0 to 4 + * it will go to 4 rather than 3. + * + * indexes should be the indexes to exclude + * indexes length should be the length of indexes + */ + @Namespace("shape") public static native @Cast("Nd4jLong*") LongPointer everyIndexBut(@Cast("Nd4jLong*") LongPointer indexes,int indexesLength,int begin,int end); + @Namespace("shape") public static native @Cast("Nd4jLong*") LongBuffer everyIndexBut(@Cast("Nd4jLong*") LongBuffer indexes,int indexesLength,int begin,int end); + @Namespace("shape") public static native @Cast("Nd4jLong*") long[] everyIndexBut(@Cast("Nd4jLong*") long[] indexes,int indexesLength,int begin,int end); + +/** + * Computes the offset for accessing + * a global element given the shape information + * and the offset to be read. + */ +//#ifdef __CUDACC__ +// __device__ +//#endif +// ND4J_EXPORT int tadOffset(shape::ShapeInformation *xInfo, int offset); + +/** + * Returns a shape + * forces the given length to be 2. + * @param shape the shape to modify + * @param dimension the dimension (row or column) + * for the shape to be returned as + * @return the new shape + */ + @Namespace("shape") public static native @Cast("Nd4jLong*") LongPointer ensureVectorShape(@Cast("Nd4jLong*") LongPointer shape); + @Namespace("shape") public static native @Cast("Nd4jLong*") LongBuffer ensureVectorShape(@Cast("Nd4jLong*") LongBuffer shape); + @Namespace("shape") public static native @Cast("Nd4jLong*") long[] ensureVectorShape(@Cast("Nd4jLong*") long[] shape); + + @Namespace("shape") public static native @Cast("Nd4jLong*") LongPointer createScalarShapeInfo(); + + @Namespace("shape") public static native @Cast("Nd4jLong*") LongPointer createScalarShapeInfo(@Cast("Nd4jLong*") LongPointer ret); + @Namespace("shape") public static native @Cast("Nd4jLong*") LongBuffer createScalarShapeInfo(@Cast("Nd4jLong*") LongBuffer ret); + @Namespace("shape") public static native @Cast("Nd4jLong*") long[] createScalarShapeInfo(@Cast("Nd4jLong*") long[] ret); + +/** + * Generate an int buffer + * up to the given length + * at the specified increment + * + */ + +/** + * Range between from and two with an + * increment of 1 + */ + +/** + * Keep the given indexes + * in the data + */ + @Namespace("shape") public static native @Cast("Nd4jLong*") LongPointer keep(@Cast("Nd4jLong*") LongPointer data, IntPointer index, int indexLength, int dataLength); + @Namespace("shape") public static native @Cast("Nd4jLong*") LongBuffer keep(@Cast("Nd4jLong*") LongBuffer data, IntBuffer index, int indexLength, int dataLength); + @Namespace("shape") public static native @Cast("Nd4jLong*") long[] keep(@Cast("Nd4jLong*") long[] data, int[] index, int indexLength, int dataLength); + +/** + * Generate reverse copy of the data + * @param data + * @param length + * @return + */ +/** + * + * @param arr1 + * @param arr1Length + * @param arr2 + * @param arr2Length + * @return + */ + +/** + * + * @param numArrays + * @param numTotalElements + * @param arr + * @param lengths + * @return + */ + +/** + * Get the length per slice of the + * given shape and the dimension + * @param rank the rank of the shape + * @param shape the shape of to get + * the length per slice for + * @param dimension the dimension to + * get the length per slice for + * @param dimensionLength the length of the dimension array + * @return the length per slice of the given shape + * along the given dimension + */ + @Namespace("shape") public static native @Cast("Nd4jLong") long lengthPerSlice(int rank, @Cast("Nd4jLong*") LongPointer shape, IntPointer dimension, int dimensionLength); + @Namespace("shape") public static native @Cast("Nd4jLong") long lengthPerSlice(int rank, @Cast("Nd4jLong*") LongBuffer shape, IntBuffer dimension, int dimensionLength); + @Namespace("shape") public static native @Cast("Nd4jLong") long lengthPerSlice(int rank, @Cast("Nd4jLong*") long[] shape, int[] dimension, int dimensionLength); + +/** + * calculates the offset for a tensor + * @param index + * @param arr + * @param tensorShape + * @return + */ + @Namespace("shape") public static native @Cast("Nd4jLong") long sliceOffsetForTensor(int rank, + int index, + @Cast("Nd4jLong*") LongPointer shape, + @Cast("Nd4jLong*") LongPointer tensorShape, + int tensorShapeLength, + IntPointer dimension, + int dimensionLength); + @Namespace("shape") public static native @Cast("Nd4jLong") long sliceOffsetForTensor(int rank, + int index, + @Cast("Nd4jLong*") LongBuffer shape, + @Cast("Nd4jLong*") LongBuffer tensorShape, + int tensorShapeLength, + IntBuffer dimension, + int dimensionLength); + @Namespace("shape") public static native @Cast("Nd4jLong") long sliceOffsetForTensor(int rank, + int index, + @Cast("Nd4jLong*") long[] shape, + @Cast("Nd4jLong*") long[] tensorShape, + int tensorShapeLength, + int[] dimension, + int dimensionLength); + +/** + * calculates the offset for a tensor + * @param index + * @param arr + * @param tensorShape + * @return + */ + @Namespace("shape") public static native @Cast("Nd4jLong") long sliceOffsetForTensor(int index,int tensorLength,int lengthPerSlice2); +/** + * Computes the tensor along dimension + * offset + * @param index the index to get the offset for the tad for + * @param rank the rank of the shapes and strides + * @param info the shape information to use for tad + * @param dimension the dimensions to use for computing the tensor along dimensions + */ +// ND4J_EXPORT _CUDA_HD int offset(int index, +// int rank, +// shape::ShapeInformation *info, +// Nd4jLong *dimension, +// int dimensionLength); + + +/** + * Computes the number + * of tensors along + * a given dimension + */ + @Namespace("shape") public static native @Cast("Nd4jLong") long tensorsAlongDimension(int rank, + int length, + @Cast("Nd4jLong*") LongPointer shape, + IntPointer dimension, + int dimensionLength); + @Namespace("shape") public static native @Cast("Nd4jLong") long tensorsAlongDimension(int rank, + int length, + @Cast("Nd4jLong*") LongBuffer shape, + IntBuffer dimension, + int dimensionLength); + @Namespace("shape") public static native @Cast("Nd4jLong") long tensorsAlongDimension(int rank, + int length, + @Cast("Nd4jLong*") long[] shape, + int[] dimension, + int dimensionLength); + +/** + * Computes the number + * of tensors along + * a given dimension + */ + @Namespace("shape") public static native @Cast("Nd4jLong") long tensorsAlongDimension(@Cast("Nd4jLong*") LongPointer shapeInfo, IntPointer dimension, int dimensionLength); + @Namespace("shape") public static native @Cast("Nd4jLong") long tensorsAlongDimension(@Cast("Nd4jLong*") LongBuffer shapeInfo, IntBuffer dimension, int dimensionLength); + @Namespace("shape") public static native @Cast("Nd4jLong") long tensorsAlongDimension(@Cast("Nd4jLong*") long[] shapeInfo, int[] dimension, int dimensionLength); + + + +/** + * Returns the tensor along dimension + * for the given block index + * @param blockSize + * @param blockIdx + * @param i + * @return + */ + @Namespace("shape") public static native int tadForBlockIndex(int blockSize, int blockIdx, int i); + +/** + * Computes the number of tads per block + * + */ + @Namespace("shape") public static native int tadsPerBlock(int blockSize, int tads); + +// ND4J_EXPORT _CUDA_HD Nd4jLong *tadShapeInfo(int index, Nd4jLong *xShapeInfo, Nd4jLong *dimension, +// int dimensionLength); + +/** + * Returns a shape buffer + * for the shape information metadata. + */ + @Namespace("shape") public static native @Cast("Nd4jLong*") LongPointer toShapeBuffer( ShapeInformation info); + + @Namespace("shape") public static native @Cast("Nd4jLong*") LongPointer toShapeBuffer( ShapeInformation info, @Cast("Nd4jLong*") LongPointer ret); + @Namespace("shape") public static native @Cast("Nd4jLong*") LongBuffer toShapeBuffer( ShapeInformation info, @Cast("Nd4jLong*") LongBuffer ret); + @Namespace("shape") public static native @Cast("Nd4jLong*") long[] toShapeBuffer( ShapeInformation info, @Cast("Nd4jLong*") long[] ret); + +/** + * Returns the number of elements per thread + */ +//#ifdef __CUDACC__ +// __device__ +//#endif +// int numElementsPerThread(int N); + +/** + * Returns the block starting index + */ +//#ifdef __CUDACC__ +// __device__ +//#endif +// int blockStartingIndex(int N); + +/** + * Returns the thread starting index + */ +//#ifdef __CUDACC__ +// __device__ +//#endif +// int threadStartingIndex(int N, int stride, int offset); + +/** + * Returns the thread ending index + */ +//#ifdef __CUDACC__ +// __device__ +//#endif +// int threadEndingIndex(int N, int stride, int offset); + +/** + * Returns indexing information + * for the current kernel invocation + */ +//#ifdef __CUDACC__ +// __device__ +//#endif +// CurrentIndexing *currentIndex(int N, int offset, int stride); + +/** Given an linear index, element wise stride + * and the length of each tad + * map a linear index to a tad + * @param i the index to map + * @param the element wise stride for the tads + * @param numElementsPerTad the number of elements + * per tad + */ + @Namespace("shape") public static native int tadIndex(int i, int elementWiseStride, int numElementsPerTad); + +/** + * Map a tad to a + * reduction index. + * @param tadIndexForOriginal the original tad index for the + * split up problem (eg: split is dimension 3 mapping to a 2,3 problem) + * @param tadsForReduced the number of tads for the shrunk down problem (eg: 2,3) + * @param tadsForOriginal the number of tads for the smaller problem (eg: 3) + */ + @Namespace("shape") public static native int reductionIndexForTad(int tadIndexForOriginal, int tadsForReduced, + int tadsForOriginal); + +/** + * Computes the number of tads + * per reduce index for the + * reduction tad. + */ + @Namespace("shape") public static native int tadsPerReduceIndex(int tadsForReduce, int tadsForOriginal); + +/** + * Maps a linear index to a reduction index + * @param i the linear index to map + * @param elementWiseStride the element wise stride + * for the multiple problem + * @param tadNum the number of tads for the shrunken problem + * @param originalTadNum the tad number for the reduced version of the problem + */ + @Namespace("shape") public static native int reductionIndexForLinear(int i, int elementWiseStride, int numElementsPerTad, + int tadNum, int originalTadNum); + +/** + * Returns the prod of the data + * up to the given length + */ + @Namespace("shape") public static native @Cast("Nd4jLong") long prodLong(@Cast("const Nd4jLong*") LongPointer data, int length); + @Namespace("shape") public static native @Cast("Nd4jLong") long prodLong(@Cast("const Nd4jLong*") LongBuffer data, int length); + @Namespace("shape") public static native @Cast("Nd4jLong") long prodLong(@Cast("const Nd4jLong*") long[] data, int length); + + /** + * Returns the rear most left over item not present in + * the dimension array. This assumes that the dimension array is sorted. + * + * For example, given a dimension array of: + * 0,2 + * + * and + * + * 12,4,2,1 in data + * + * You end up with 1 (data[3]) + * since the first item won't match + * the last item of the dimension array + */ + +// ND4J_EXPORT _CUDA_HD int rearMostLeftOverItem(Nd4jLong *data,int length,Nd4jLong *dimension,int dimensionLength); + + /** +* Get an offset for retrieval +* from a data buffer +* based on the given +* shape stride and given indices +* @param baseOffset the offset to start from +* @param shape the shape of the array +* @param stride the stride of the array +* @param indices the indices to iterate over +* @return the double at the specified index +*/ + + @Namespace("shape") public static native @Cast("Nd4jLong") long getOffset(@Cast("const Nd4jLong*") LongPointer shapeInfo, @Cast("const Nd4jLong*") LongPointer coords, @Cast("Nd4jLong") long baseOffset/*=0*/); + @Namespace("shape") public static native @Cast("Nd4jLong") long getOffset(@Cast("const Nd4jLong*") LongPointer shapeInfo, @Cast("const Nd4jLong*") LongPointer coords); + @Namespace("shape") public static native @Cast("Nd4jLong") long getOffset(@Cast("const Nd4jLong*") LongBuffer shapeInfo, @Cast("const Nd4jLong*") LongBuffer coords, @Cast("Nd4jLong") long baseOffset/*=0*/); + @Namespace("shape") public static native @Cast("Nd4jLong") long getOffset(@Cast("const Nd4jLong*") LongBuffer shapeInfo, @Cast("const Nd4jLong*") LongBuffer coords); + @Namespace("shape") public static native @Cast("Nd4jLong") long getOffset(@Cast("const Nd4jLong*") long[] shapeInfo, @Cast("const Nd4jLong*") long[] coords, @Cast("Nd4jLong") long baseOffset/*=0*/); + @Namespace("shape") public static native @Cast("Nd4jLong") long getOffset(@Cast("const Nd4jLong*") long[] shapeInfo, @Cast("const Nd4jLong*") long[] coords); + @Namespace("shape") public static native @Cast("Nd4jLong") long getOffset(@Cast("const Nd4jLong*") LongPointer shapeInfo, @Const IntPointer coords, @Cast("Nd4jLong") long baseOffset/*=0*/); + @Namespace("shape") public static native @Cast("Nd4jLong") long getOffset(@Cast("const Nd4jLong*") LongPointer shapeInfo, @Const IntPointer coords); + @Namespace("shape") public static native @Cast("Nd4jLong") long getOffset(@Cast("const Nd4jLong*") LongBuffer shapeInfo, @Const IntBuffer coords, @Cast("Nd4jLong") long baseOffset/*=0*/); + @Namespace("shape") public static native @Cast("Nd4jLong") long getOffset(@Cast("const Nd4jLong*") LongBuffer shapeInfo, @Const IntBuffer coords); + @Namespace("shape") public static native @Cast("Nd4jLong") long getOffset(@Cast("const Nd4jLong*") long[] shapeInfo, @Const int[] coords, @Cast("Nd4jLong") long baseOffset/*=0*/); + @Namespace("shape") public static native @Cast("Nd4jLong") long getOffset(@Cast("const Nd4jLong*") long[] shapeInfo, @Const int[] coords); + + @Namespace("shape") public static native @Cast("Nd4jLong*") LongPointer createShapeInfo(@Cast("Nd4jLong*") LongPointer shape, @Cast("Nd4jLong*") LongPointer stride, int rank); + @Namespace("shape") public static native @Cast("Nd4jLong*") LongBuffer createShapeInfo(@Cast("Nd4jLong*") LongBuffer shape, @Cast("Nd4jLong*") LongBuffer stride, int rank); + @Namespace("shape") public static native @Cast("Nd4jLong*") long[] createShapeInfo(@Cast("Nd4jLong*") long[] shape, @Cast("Nd4jLong*") long[] stride, int rank); + + @Namespace("shape") public static native @Cast("Nd4jLong*") LongPointer createShapeInfo(@Cast("Nd4jLong*") LongPointer shape, @Cast("Nd4jLong*") LongPointer stride, int rank, @Cast("Nd4jLong*") LongPointer buffer); + @Namespace("shape") public static native @Cast("Nd4jLong*") LongBuffer createShapeInfo(@Cast("Nd4jLong*") LongBuffer shape, @Cast("Nd4jLong*") LongBuffer stride, int rank, @Cast("Nd4jLong*") LongBuffer buffer); + @Namespace("shape") public static native @Cast("Nd4jLong*") long[] createShapeInfo(@Cast("Nd4jLong*") long[] shape, @Cast("Nd4jLong*") long[] stride, int rank, @Cast("Nd4jLong*") long[] buffer); + + /** + * Convert a linear index to the corresponding coordinates + * for example if shape is {2, 4}, then index 5 corresponds to coordinates [1, 1] + */ + @Namespace("shape") public static native void index2coords(@Cast("Nd4jLong") long index, @Cast("const Nd4jLong*") LongPointer shapeInfo, @Cast("Nd4jLong*") LongPointer coords); + @Namespace("shape") public static native void index2coords(@Cast("Nd4jLong") long index, @Cast("const Nd4jLong*") LongBuffer shapeInfo, @Cast("Nd4jLong*") LongBuffer coords); + @Namespace("shape") public static native void index2coords(@Cast("Nd4jLong") long index, @Cast("const Nd4jLong*") long[] shapeInfo, @Cast("Nd4jLong*") long[] coords); + @Namespace("shape") public static native void index2coords(@Cast("Nd4jLong") long index, @Cast("const Nd4jLong*") LongPointer shapeInfo, IntPointer coords); + @Namespace("shape") public static native void index2coords(@Cast("Nd4jLong") long index, @Cast("const Nd4jLong*") LongBuffer shapeInfo, IntBuffer coords); + @Namespace("shape") public static native void index2coords(@Cast("Nd4jLong") long index, @Cast("const Nd4jLong*") long[] shapeInfo, int[] coords); + @Namespace("shape") public static native void index2coords(@Cast("Nd4jLong") long index, int rank, @Cast("const Nd4jLong*") LongPointer shape, @Cast("Nd4jLong*") LongPointer coords); + @Namespace("shape") public static native void index2coords(@Cast("Nd4jLong") long index, int rank, @Cast("const Nd4jLong*") LongBuffer shape, @Cast("Nd4jLong*") LongBuffer coords); + @Namespace("shape") public static native void index2coords(@Cast("Nd4jLong") long index, int rank, @Cast("const Nd4jLong*") long[] shape, @Cast("Nd4jLong*") long[] coords); + @Namespace("shape") public static native void index2coords(@Cast("Nd4jLong") long index, int rank, @Cast("const Nd4jLong*") LongPointer shape, IntPointer coords); + @Namespace("shape") public static native void index2coords(@Cast("Nd4jLong") long index, int rank, @Cast("const Nd4jLong*") LongBuffer shape, IntBuffer coords); + @Namespace("shape") public static native void index2coords(@Cast("Nd4jLong") long index, int rank, @Cast("const Nd4jLong*") long[] shape, int[] coords); + + @Namespace("shape") public static native void index2coordsCPU(@Cast("const Nd4jLong") long startIndex, @Cast("const Nd4jLong") long index, @Cast("const Nd4jLong*") LongPointer shapeInfo, @Cast("Nd4jLong*") LongPointer coords); + @Namespace("shape") public static native void index2coordsCPU(@Cast("const Nd4jLong") long startIndex, @Cast("const Nd4jLong") long index, @Cast("const Nd4jLong*") LongBuffer shapeInfo, @Cast("Nd4jLong*") LongBuffer coords); + @Namespace("shape") public static native void index2coordsCPU(@Cast("const Nd4jLong") long startIndex, @Cast("const Nd4jLong") long index, @Cast("const Nd4jLong*") long[] shapeInfo, @Cast("Nd4jLong*") long[] coords); + @Namespace("shape") public static native void index2coordsCPU(@Cast("const Nd4jLong") long startIndex, @Cast("const Nd4jLong") long index, @Cast("const Nd4jLong*") LongPointer shapeInfo, IntPointer coords); + @Namespace("shape") public static native void index2coordsCPU(@Cast("const Nd4jLong") long startIndex, @Cast("const Nd4jLong") long index, @Cast("const Nd4jLong*") LongBuffer shapeInfo, IntBuffer coords); + @Namespace("shape") public static native void index2coordsCPU(@Cast("const Nd4jLong") long startIndex, @Cast("const Nd4jLong") long index, @Cast("const Nd4jLong*") long[] shapeInfo, int[] coords); + + /** + * take into account only dimensions stored in tadDims, tadDims must be sorted in increasing order! + */ + @Namespace("shape") public static native void index2coords(@Cast("Nd4jLong") long index, @Cast("const Nd4jLong*") LongPointer shapeInfo, IntPointer coords, int dimsSize, @Const IntPointer tadDims); + @Namespace("shape") public static native void index2coords(@Cast("Nd4jLong") long index, @Cast("const Nd4jLong*") LongBuffer shapeInfo, IntBuffer coords, int dimsSize, @Const IntBuffer tadDims); + @Namespace("shape") public static native void index2coords(@Cast("Nd4jLong") long index, @Cast("const Nd4jLong*") long[] shapeInfo, int[] coords, int dimsSize, @Const int[] tadDims); + + /** + * Convert coordinates to the corresponding linear index (sequence number in other words) + * for example if shape is {2, 4} and coordinates [1, 1] then index 5 is returned + */ + @Namespace("shape") public static native @Cast("Nd4jLong") long coords2index(@Cast("const Nd4jLong*") LongPointer shapeInfo, @Cast("const Nd4jLong*") LongPointer coords); + @Namespace("shape") public static native @Cast("Nd4jLong") long coords2index(@Cast("const Nd4jLong*") LongBuffer shapeInfo, @Cast("const Nd4jLong*") LongBuffer coords); + @Namespace("shape") public static native @Cast("Nd4jLong") long coords2index(@Cast("const Nd4jLong*") long[] shapeInfo, @Cast("const Nd4jLong*") long[] coords); + @Namespace("shape") public static native @Cast("Nd4jLong") long coords2index(@Cast("const Nd4jLong*") LongPointer shapeInfo, @Const IntPointer coords); + @Namespace("shape") public static native @Cast("Nd4jLong") long coords2index(@Cast("const Nd4jLong*") LongBuffer shapeInfo, @Const IntBuffer coords); + @Namespace("shape") public static native @Cast("Nd4jLong") long coords2index(@Cast("const Nd4jLong*") long[] shapeInfo, @Const int[] coords); + @Namespace("shape") public static native @Cast("Nd4jLong") long coords2index(int rank, @Cast("const Nd4jLong*") LongPointer shape, @Const IntPointer coords); + @Namespace("shape") public static native @Cast("Nd4jLong") long coords2index(int rank, @Cast("const Nd4jLong*") LongBuffer shape, @Const IntBuffer coords); + @Namespace("shape") public static native @Cast("Nd4jLong") long coords2index(int rank, @Cast("const Nd4jLong*") long[] shape, @Const int[] coords); + /** + * take into account only dimensions stored in tadDims, tadDims must be sorted in increasing order! + */ + @Namespace("shape") public static native @Cast("Nd4jLong") long coords2index(@Cast("const Nd4jLong*") LongPointer shapeInfo, @Const IntPointer coords, int dimsSize, @Const IntPointer tadDims); + @Namespace("shape") public static native @Cast("Nd4jLong") long coords2index(@Cast("const Nd4jLong*") LongBuffer shapeInfo, @Const IntBuffer coords, int dimsSize, @Const IntBuffer tadDims); + @Namespace("shape") public static native @Cast("Nd4jLong") long coords2index(@Cast("const Nd4jLong*") long[] shapeInfo, @Const int[] coords, int dimsSize, @Const int[] tadDims); + + /** + * increment n-dimensional array by one iteration by changing coord appropriately + * for example we have array with shape {2, 3}: + * - if input coord = {0,1}, then output coord = {0,2} + * - if input coord = {0,2}, then output coord = {1,0} + * so the aim is to produce following subsequence of coord: {0,0}, {0,1}, {0,2}, {1,0}, {1,1}, {1,2} + */ + + /* calculates an array buffer offset for given "index" using following formula: offset = coord_0*stride_0 + coord_1*stride_1 + ... + coord_{rank-1}*stride_{rank-1} + */ + @Namespace("shape") public static native @Cast("uint") int getIndexOffset(@Cast("uint") int index, @Cast("const uint*") IntPointer shapeInfo); + @Namespace("shape") public static native @Cast("uint") int getIndexOffset(@Cast("uint") int index, @Cast("const uint*") IntBuffer shapeInfo); + @Namespace("shape") public static native @Cast("uint") int getIndexOffset(@Cast("uint") int index, @Cast("const uint*") int[] shapeInfo); + @Namespace("shape") public static native @Cast("Nd4jLong") long getIndexOffset(@Cast("Nd4jLong") long index, @Cast("const Nd4jLong*") LongPointer shapeInfo); + @Namespace("shape") public static native @Cast("Nd4jLong") long getIndexOffset(@Cast("Nd4jLong") long index, @Cast("const Nd4jLong*") LongBuffer shapeInfo); + @Namespace("shape") public static native @Cast("Nd4jLong") long getIndexOffset(@Cast("Nd4jLong") long index, @Cast("const Nd4jLong*") long[] shapeInfo); + @Namespace("shape") public static native @Cast("Nd4jLong") long indexOffset(@Cast("Nd4jLong") long index, @Cast("const Nd4jLong*") LongPointer lShapeInfo, @Cast("const uint*") IntPointer uShapeInfo, @Cast("const bool") boolean useUnsigned); + @Namespace("shape") public static native @Cast("Nd4jLong") long indexOffset(@Cast("Nd4jLong") long index, @Cast("const Nd4jLong*") LongBuffer lShapeInfo, @Cast("const uint*") IntBuffer uShapeInfo, @Cast("const bool") boolean useUnsigned); + @Namespace("shape") public static native @Cast("Nd4jLong") long indexOffset(@Cast("Nd4jLong") long index, @Cast("const Nd4jLong*") long[] lShapeInfo, @Cast("const uint*") int[] uShapeInfo, @Cast("const bool") boolean useUnsigned); + + @Namespace("shape") public static native void printShapeInfo(@Cast("Nd4jLong*") LongPointer shapeInfo); + @Namespace("shape") public static native void printShapeInfo(@Cast("Nd4jLong*") LongBuffer shapeInfo); + @Namespace("shape") public static native void printShapeInfo(@Cast("Nd4jLong*") long[] shapeInfo); + + @Namespace("shape") public static native void printShapeInfoLinear(@Cast("const Nd4jLong*") LongPointer shapeInfo); + @Namespace("shape") public static native void printShapeInfoLinear(@Cast("const Nd4jLong*") LongBuffer shapeInfo); + @Namespace("shape") public static native void printShapeInfoLinear(@Cast("const Nd4jLong*") long[] shapeInfo); + + @Namespace("shape") public static native void printShapeInfoLinear(@Cast("char*") String msg, @Cast("const Nd4jLong*") LongPointer shapeInfo); + @Namespace("shape") public static native void printShapeInfoLinear(@Cast("char*") BytePointer msg, @Cast("const Nd4jLong*") LongBuffer shapeInfo); + @Namespace("shape") public static native void printShapeInfoLinear(@Cast("char*") String msg, @Cast("const Nd4jLong*") long[] shapeInfo); + @Namespace("shape") public static native void printShapeInfoLinear(@Cast("char*") BytePointer msg, @Cast("const Nd4jLong*") LongPointer shapeInfo); + @Namespace("shape") public static native void printShapeInfoLinear(@Cast("char*") String msg, @Cast("const Nd4jLong*") LongBuffer shapeInfo); + @Namespace("shape") public static native void printShapeInfoLinear(@Cast("char*") BytePointer msg, @Cast("const Nd4jLong*") long[] shapeInfo); + + @Namespace("shape") public static native void printShapeInfoLinear(@Cast("char*") String msg, int rank, @Cast("const Nd4jLong*") LongPointer shape, @Cast("const Nd4jLong*") LongPointer strides); + @Namespace("shape") public static native void printShapeInfoLinear(@Cast("char*") BytePointer msg, int rank, @Cast("const Nd4jLong*") LongBuffer shape, @Cast("const Nd4jLong*") LongBuffer strides); + @Namespace("shape") public static native void printShapeInfoLinear(@Cast("char*") String msg, int rank, @Cast("const Nd4jLong*") long[] shape, @Cast("const Nd4jLong*") long[] strides); + @Namespace("shape") public static native void printShapeInfoLinear(@Cast("char*") BytePointer msg, int rank, @Cast("const Nd4jLong*") LongPointer shape, @Cast("const Nd4jLong*") LongPointer strides); + @Namespace("shape") public static native void printShapeInfoLinear(@Cast("char*") String msg, int rank, @Cast("const Nd4jLong*") LongBuffer shape, @Cast("const Nd4jLong*") LongBuffer strides); + @Namespace("shape") public static native void printShapeInfoLinear(@Cast("char*") BytePointer msg, int rank, @Cast("const Nd4jLong*") long[] shape, @Cast("const Nd4jLong*") long[] strides); + + @Namespace("shape") public static native void printIntArray(@Cast("const Nd4jLong*") LongPointer arr, int length); + @Namespace("shape") public static native void printIntArray(@Cast("const Nd4jLong*") LongBuffer arr, int length); + @Namespace("shape") public static native void printIntArray(@Cast("const Nd4jLong*") long[] arr, int length); + @Namespace("shape") public static native void printIntArray(@Const IntPointer arr, int length); + @Namespace("shape") public static native void printIntArray(@Const IntBuffer arr, int length); + @Namespace("shape") public static native void printIntArray(@Const int[] arr, int length); + + @Namespace("shape") public static native void printArray(FloatPointer arr,int length); + @Namespace("shape") public static native void printArray(FloatBuffer arr,int length); + @Namespace("shape") public static native void printArray(float[] arr,int length); + + @Namespace("shape") public static native @Cast("Nd4jLong*") LongPointer shapeBufferOfNpy(int rank, @Cast("unsigned int*") IntPointer shape,@Cast("bool") boolean fortranOrder); + @Namespace("shape") public static native @Cast("Nd4jLong*") LongBuffer shapeBufferOfNpy(int rank, @Cast("unsigned int*") IntBuffer shape,@Cast("bool") boolean fortranOrder); + @Namespace("shape") public static native @Cast("Nd4jLong*") long[] shapeBufferOfNpy(int rank, @Cast("unsigned int*") int[] shape,@Cast("bool") boolean fortranOrder); + +// ND4J_EXPORT _CUDA_HD Nd4jLong *shapeBufferOfNpyBuffer(char *buffer); + + + // this function checks the consistence of dimensions with array rank (negative dimensions, too large dimensions, too big number of dimensions) + // also sort input array of dimensions, this operation is also necessary for creating TAD object + @Namespace("shape") public static native void checkDimensions(int rank, @StdVector IntPointer dimensions); + @Namespace("shape") public static native void checkDimensions(int rank, @StdVector IntBuffer dimensions); + @Namespace("shape") public static native void checkDimensions(int rank, @StdVector int[] dimensions); + + // function calculates linear index of array min, min is sub-array of max, index to be returned is min-array's index and corresponds to maxIdx of max array + // dimsToExclude - should be sorted in increasing order + @Namespace("shape") public static native @Cast("Nd4jLong") long subArrayIndex(@Cast("const Nd4jLong") long maxIdx, @Cast("const Nd4jLong*") LongPointer maxShapeInfo, @Cast("const Nd4jLong*") LongPointer minShapeInfo, @Const IntPointer dimsToExclude/*=nullptr*/, int dimsLen/*=-1*/); + @Namespace("shape") public static native @Cast("Nd4jLong") long subArrayIndex(@Cast("const Nd4jLong") long maxIdx, @Cast("const Nd4jLong*") LongPointer maxShapeInfo, @Cast("const Nd4jLong*") LongPointer minShapeInfo); + @Namespace("shape") public static native @Cast("Nd4jLong") long subArrayIndex(@Cast("const Nd4jLong") long maxIdx, @Cast("const Nd4jLong*") LongBuffer maxShapeInfo, @Cast("const Nd4jLong*") LongBuffer minShapeInfo, @Const IntBuffer dimsToExclude/*=nullptr*/, int dimsLen/*=-1*/); + @Namespace("shape") public static native @Cast("Nd4jLong") long subArrayIndex(@Cast("const Nd4jLong") long maxIdx, @Cast("const Nd4jLong*") LongBuffer maxShapeInfo, @Cast("const Nd4jLong*") LongBuffer minShapeInfo); + @Namespace("shape") public static native @Cast("Nd4jLong") long subArrayIndex(@Cast("const Nd4jLong") long maxIdx, @Cast("const Nd4jLong*") long[] maxShapeInfo, @Cast("const Nd4jLong*") long[] minShapeInfo, @Const int[] dimsToExclude/*=nullptr*/, int dimsLen/*=-1*/); + @Namespace("shape") public static native @Cast("Nd4jLong") long subArrayIndex(@Cast("const Nd4jLong") long maxIdx, @Cast("const Nd4jLong*") long[] maxShapeInfo, @Cast("const Nd4jLong*") long[] minShapeInfo); + + // function calculates absolute offset of min array, min is sub-array of max, offset to be returned corresponds to maxIdx of max array + // dimsToExclude - should be sorted in increasing order + @Namespace("shape") public static native @Cast("Nd4jLong") long subArrayOffset(@Cast("const Nd4jLong") long maxIdx, @Cast("const Nd4jLong*") LongPointer maxShapeInfo, @Cast("const Nd4jLong*") LongPointer minShapeInfo, @Const IntPointer dimsToExclude/*=nullptr*/, int dimsLen/*=-1*/); + @Namespace("shape") public static native @Cast("Nd4jLong") long subArrayOffset(@Cast("const Nd4jLong") long maxIdx, @Cast("const Nd4jLong*") LongPointer maxShapeInfo, @Cast("const Nd4jLong*") LongPointer minShapeInfo); + @Namespace("shape") public static native @Cast("Nd4jLong") long subArrayOffset(@Cast("const Nd4jLong") long maxIdx, @Cast("const Nd4jLong*") LongBuffer maxShapeInfo, @Cast("const Nd4jLong*") LongBuffer minShapeInfo, @Const IntBuffer dimsToExclude/*=nullptr*/, int dimsLen/*=-1*/); + @Namespace("shape") public static native @Cast("Nd4jLong") long subArrayOffset(@Cast("const Nd4jLong") long maxIdx, @Cast("const Nd4jLong*") LongBuffer maxShapeInfo, @Cast("const Nd4jLong*") LongBuffer minShapeInfo); + @Namespace("shape") public static native @Cast("Nd4jLong") long subArrayOffset(@Cast("const Nd4jLong") long maxIdx, @Cast("const Nd4jLong*") long[] maxShapeInfo, @Cast("const Nd4jLong*") long[] minShapeInfo, @Const int[] dimsToExclude/*=nullptr*/, int dimsLen/*=-1*/); + @Namespace("shape") public static native @Cast("Nd4jLong") long subArrayOffset(@Cast("const Nd4jLong") long maxIdx, @Cast("const Nd4jLong*") long[] maxShapeInfo, @Cast("const Nd4jLong*") long[] minShapeInfo); + + // max array is outer for min array, min array is sub-array of max array + // function calculates the coordinates of min array (and saves them into minIdxs) given coordinates of max array (already stored in maxIdxs) + // dimsToExclude - should be sorted in increasing order + // dimsLen - length of dimsToExclude, if not set (= -1), then it is calculated as maxRank - minRank + @Namespace("shape") public static native void maxIndToMinInd(IntPointer maxIdxs, IntPointer minIdxs, @Cast("const Nd4jLong*") LongPointer maxShapeInfo, @Cast("const Nd4jLong*") LongPointer minShapeInfo, @Const IntPointer dimsToExclude/*=nullptr*/, int dimsLen/*=-1*/); + @Namespace("shape") public static native void maxIndToMinInd(IntPointer maxIdxs, IntPointer minIdxs, @Cast("const Nd4jLong*") LongPointer maxShapeInfo, @Cast("const Nd4jLong*") LongPointer minShapeInfo); + @Namespace("shape") public static native void maxIndToMinInd(IntBuffer maxIdxs, IntBuffer minIdxs, @Cast("const Nd4jLong*") LongBuffer maxShapeInfo, @Cast("const Nd4jLong*") LongBuffer minShapeInfo, @Const IntBuffer dimsToExclude/*=nullptr*/, int dimsLen/*=-1*/); + @Namespace("shape") public static native void maxIndToMinInd(IntBuffer maxIdxs, IntBuffer minIdxs, @Cast("const Nd4jLong*") LongBuffer maxShapeInfo, @Cast("const Nd4jLong*") LongBuffer minShapeInfo); + @Namespace("shape") public static native void maxIndToMinInd(int[] maxIdxs, int[] minIdxs, @Cast("const Nd4jLong*") long[] maxShapeInfo, @Cast("const Nd4jLong*") long[] minShapeInfo, @Const int[] dimsToExclude/*=nullptr*/, int dimsLen/*=-1*/); + @Namespace("shape") public static native void maxIndToMinInd(int[] maxIdxs, int[] minIdxs, @Cast("const Nd4jLong*") long[] maxShapeInfo, @Cast("const Nd4jLong*") long[] minShapeInfo); + + // calculate indexes of max-array, these output indexes correspond to one minIdx index of min-array which is sub-array of max-array + // dimsToExclude - should be sorted in increasing order + @Namespace("shape") public static native int outerArrayIndexes(IntPointer maxIdxs, @Cast("const Nd4jLong") long minIdx, @Cast("const Nd4jLong*") LongPointer maxShapeInfo, @Cast("const Nd4jLong*") LongPointer minShapeInfo, @Const IntPointer dimsToExclude/*=nullptr*/); + @Namespace("shape") public static native int outerArrayIndexes(IntPointer maxIdxs, @Cast("const Nd4jLong") long minIdx, @Cast("const Nd4jLong*") LongPointer maxShapeInfo, @Cast("const Nd4jLong*") LongPointer minShapeInfo); + @Namespace("shape") public static native int outerArrayIndexes(IntBuffer maxIdxs, @Cast("const Nd4jLong") long minIdx, @Cast("const Nd4jLong*") LongBuffer maxShapeInfo, @Cast("const Nd4jLong*") LongBuffer minShapeInfo, @Const IntBuffer dimsToExclude/*=nullptr*/); + @Namespace("shape") public static native int outerArrayIndexes(IntBuffer maxIdxs, @Cast("const Nd4jLong") long minIdx, @Cast("const Nd4jLong*") LongBuffer maxShapeInfo, @Cast("const Nd4jLong*") LongBuffer minShapeInfo); + @Namespace("shape") public static native int outerArrayIndexes(int[] maxIdxs, @Cast("const Nd4jLong") long minIdx, @Cast("const Nd4jLong*") long[] maxShapeInfo, @Cast("const Nd4jLong*") long[] minShapeInfo, @Const int[] dimsToExclude/*=nullptr*/); + @Namespace("shape") public static native int outerArrayIndexes(int[] maxIdxs, @Cast("const Nd4jLong") long minIdx, @Cast("const Nd4jLong*") long[] maxShapeInfo, @Cast("const Nd4jLong*") long[] minShapeInfo); + + // calculate offsets of max-array, these offsets correspond to one minIdx index of min-array which is sub-array of max-array + // maxOffsets - will contain calculated offsets of max-array, buffer for maxOffsets should be allocated beforehand + // dimsToExclude - should be sorted in increasing order + // memBuff - auxiliary memory buffer (size = 2 * max_rank) for coordinates and increments storing, should be allocated beforehand + @Namespace("shape") public static native int outerArrayOffsets(@Cast("Nd4jLong*") LongPointer maxOffsets, @Cast("const Nd4jLong") long minIdx, @Cast("const Nd4jLong*") LongPointer maxShapeInfo, @Cast("const Nd4jLong*") LongPointer minShapeInfo, IntPointer memBuff, @Const IntPointer dimsToExclude/*=nullptr*/); + @Namespace("shape") public static native int outerArrayOffsets(@Cast("Nd4jLong*") LongPointer maxOffsets, @Cast("const Nd4jLong") long minIdx, @Cast("const Nd4jLong*") LongPointer maxShapeInfo, @Cast("const Nd4jLong*") LongPointer minShapeInfo, IntPointer memBuff); + @Namespace("shape") public static native int outerArrayOffsets(@Cast("Nd4jLong*") LongBuffer maxOffsets, @Cast("const Nd4jLong") long minIdx, @Cast("const Nd4jLong*") LongBuffer maxShapeInfo, @Cast("const Nd4jLong*") LongBuffer minShapeInfo, IntBuffer memBuff, @Const IntBuffer dimsToExclude/*=nullptr*/); + @Namespace("shape") public static native int outerArrayOffsets(@Cast("Nd4jLong*") LongBuffer maxOffsets, @Cast("const Nd4jLong") long minIdx, @Cast("const Nd4jLong*") LongBuffer maxShapeInfo, @Cast("const Nd4jLong*") LongBuffer minShapeInfo, IntBuffer memBuff); + @Namespace("shape") public static native int outerArrayOffsets(@Cast("Nd4jLong*") long[] maxOffsets, @Cast("const Nd4jLong") long minIdx, @Cast("const Nd4jLong*") long[] maxShapeInfo, @Cast("const Nd4jLong*") long[] minShapeInfo, int[] memBuff, @Const int[] dimsToExclude/*=nullptr*/); + @Namespace("shape") public static native int outerArrayOffsets(@Cast("Nd4jLong*") long[] maxOffsets, @Cast("const Nd4jLong") long minIdx, @Cast("const Nd4jLong*") long[] maxShapeInfo, @Cast("const Nd4jLong*") long[] minShapeInfo, int[] memBuff); + + // calculates offsets for entities (elements or sub-arrays), shape in context of sub-array means dimensions excluded from outer array + // rank is equal to size of shape + @Namespace("shape") public static native void calcOffsets(int rank, @Cast("const Nd4jLong*") LongPointer shape, @Cast("const Nd4jLong*") LongPointer strides, @Cast("Nd4jLong*") LongPointer offsets, byte order/*='c'*/); + @Namespace("shape") public static native void calcOffsets(int rank, @Cast("const Nd4jLong*") LongPointer shape, @Cast("const Nd4jLong*") LongPointer strides, @Cast("Nd4jLong*") LongPointer offsets); + @Namespace("shape") public static native void calcOffsets(int rank, @Cast("const Nd4jLong*") LongBuffer shape, @Cast("const Nd4jLong*") LongBuffer strides, @Cast("Nd4jLong*") LongBuffer offsets, byte order/*='c'*/); + @Namespace("shape") public static native void calcOffsets(int rank, @Cast("const Nd4jLong*") LongBuffer shape, @Cast("const Nd4jLong*") LongBuffer strides, @Cast("Nd4jLong*") LongBuffer offsets); + @Namespace("shape") public static native void calcOffsets(int rank, @Cast("const Nd4jLong*") long[] shape, @Cast("const Nd4jLong*") long[] strides, @Cast("Nd4jLong*") long[] offsets, byte order/*='c'*/); + @Namespace("shape") public static native void calcOffsets(int rank, @Cast("const Nd4jLong*") long[] shape, @Cast("const Nd4jLong*") long[] strides, @Cast("Nd4jLong*") long[] offsets); + @Namespace("shape") public static native void calcOffsets(@Cast("const Nd4jLong*") LongPointer shapeInfo, @Cast("Nd4jLong*") LongPointer offsets, byte order/*='c'*/); + @Namespace("shape") public static native void calcOffsets(@Cast("const Nd4jLong*") LongPointer shapeInfo, @Cast("Nd4jLong*") LongPointer offsets); + @Namespace("shape") public static native void calcOffsets(@Cast("const Nd4jLong*") LongBuffer shapeInfo, @Cast("Nd4jLong*") LongBuffer offsets, byte order/*='c'*/); + @Namespace("shape") public static native void calcOffsets(@Cast("const Nd4jLong*") LongBuffer shapeInfo, @Cast("Nd4jLong*") LongBuffer offsets); + @Namespace("shape") public static native void calcOffsets(@Cast("const Nd4jLong*") long[] shapeInfo, @Cast("Nd4jLong*") long[] offsets, byte order/*='c'*/); + @Namespace("shape") public static native void calcOffsets(@Cast("const Nd4jLong*") long[] shapeInfo, @Cast("Nd4jLong*") long[] offsets); + // ND4J_EXPORT void calcOffsets(const Nd4jLong *xShapeInfo, Nd4jLong*& xOffsets, const Nd4jLong *yShapeInfo, Nd4jLong*& yOffsets, const char order = 'c'); + // ND4J_EXPORT void calcOffsets(const Nd4jLong *xShapeInfo, Nd4jLong*& xOffsets, const Nd4jLong *yShapeInfo, Nd4jLong*& yOffsets, const Nd4jLong* zShapeInfo, Nd4jLong*& zOffsets, const char order = 'c'); + @Namespace("shape") public static native void shapeOldScalar(@Cast("sd::DataType") int dtype, @Cast("Nd4jLong*const") LongPointer buffer, byte order); + @Namespace("shape") public static native void shapeOldScalar(@Cast("sd::DataType") int dtype, @Cast("Nd4jLong*const") LongBuffer buffer, byte order); + @Namespace("shape") public static native void shapeOldScalar(@Cast("sd::DataType") int dtype, @Cast("Nd4jLong*const") long[] buffer, byte order); + + // deduce order and element-wise stride + // if array is scalar or unit length vector then ews = 1 and order is preserved + // if array is common vector then ews = stride of non-unity dimension and order is preserved + // if strides are normal/contiguous then ews = 1 and corresponding order is set, otherwise ews = 0 and order is preserved + @Namespace("shape") public static native void checkStridesEwsAndOrder(@Cast("Nd4jLong*") LongPointer shapeInfo, byte proposedOrder, int numOfNonUnitDims, @Cast("const Nd4jLong*") LongPointer shapeNoUnities, @Cast("const Nd4jLong*") LongPointer stridesNoUnities); + @Namespace("shape") public static native void checkStridesEwsAndOrder(@Cast("Nd4jLong*") LongBuffer shapeInfo, byte proposedOrder, int numOfNonUnitDims, @Cast("const Nd4jLong*") LongBuffer shapeNoUnities, @Cast("const Nd4jLong*") LongBuffer stridesNoUnities); + @Namespace("shape") public static native void checkStridesEwsAndOrder(@Cast("Nd4jLong*") long[] shapeInfo, byte proposedOrder, int numOfNonUnitDims, @Cast("const Nd4jLong*") long[] shapeNoUnities, @Cast("const Nd4jLong*") long[] stridesNoUnities); + @Namespace("shape") public static native void checkStridesEwsAndOrder(@Cast("Nd4jLong*") LongPointer shapeInfo); + @Namespace("shape") public static native void checkStridesEwsAndOrder(@Cast("Nd4jLong*") LongBuffer shapeInfo); + @Namespace("shape") public static native void checkStridesEwsAndOrder(@Cast("Nd4jLong*") long[] shapeInfo); + + /** + * processes whole set of sub-arrays + * evaluates shapeInfo of sub-arrays (all sub-arrays have the same shapeInfo) and their buffer offsets (each sub-array has its own unique offset from original this-buffer) + * arguments: + * wholeShapeInfo - original shapeInfo of whole array + * numOfSubArrs - number of sub-arrays, size of subArrOffsets is equal to numOfSubArrs + * dimsSize - size of dimsToExclude, if dimsSize = array rank or dimsSize = 0 it means sub-array is whole array, copy of wholeShapeInfo and one zero offset will be returned + * dimsToExclude - MUST BE SORTED, dimensions to evaluate sub-array along, i.e. when shape is [2,3,4,5] and dimsToExclude={0,2}, then there will be 8 sub-arrays with shape [3,5] + * subArrShapeInfo - output argument, contains shapeInfo (same for all sub-arrays) + * subArrOffsets - output argument, contains successive sub-arrays offsets from original this-buffer + * keepUnitiesInShape - if false then eliminate unities from sub-array shapeInfo, for example {1,a,1,b} -> {a,b} + */ + @Namespace("shape") public static native void calcSubArrsShapeInfoAndOffsets(@Cast("const Nd4jLong*") LongPointer wholeShapeInfo, @Cast("const Nd4jLong") long numOfSubArrs, int dimsSize, @Const IntPointer dimsToExclude, @Cast("Nd4jLong*") LongPointer subArrShapeInfo, @Cast("Nd4jLong*") LongPointer subArrOffsets, @Cast("bool") boolean keepUnitiesInShape/*=false*/); + @Namespace("shape") public static native void calcSubArrsShapeInfoAndOffsets(@Cast("const Nd4jLong*") LongPointer wholeShapeInfo, @Cast("const Nd4jLong") long numOfSubArrs, int dimsSize, @Const IntPointer dimsToExclude, @Cast("Nd4jLong*") LongPointer subArrShapeInfo, @Cast("Nd4jLong*") LongPointer subArrOffsets); + @Namespace("shape") public static native void calcSubArrsShapeInfoAndOffsets(@Cast("const Nd4jLong*") LongBuffer wholeShapeInfo, @Cast("const Nd4jLong") long numOfSubArrs, int dimsSize, @Const IntBuffer dimsToExclude, @Cast("Nd4jLong*") LongBuffer subArrShapeInfo, @Cast("Nd4jLong*") LongBuffer subArrOffsets, @Cast("bool") boolean keepUnitiesInShape/*=false*/); + @Namespace("shape") public static native void calcSubArrsShapeInfoAndOffsets(@Cast("const Nd4jLong*") LongBuffer wholeShapeInfo, @Cast("const Nd4jLong") long numOfSubArrs, int dimsSize, @Const IntBuffer dimsToExclude, @Cast("Nd4jLong*") LongBuffer subArrShapeInfo, @Cast("Nd4jLong*") LongBuffer subArrOffsets); + @Namespace("shape") public static native void calcSubArrsShapeInfoAndOffsets(@Cast("const Nd4jLong*") long[] wholeShapeInfo, @Cast("const Nd4jLong") long numOfSubArrs, int dimsSize, @Const int[] dimsToExclude, @Cast("Nd4jLong*") long[] subArrShapeInfo, @Cast("Nd4jLong*") long[] subArrOffsets, @Cast("bool") boolean keepUnitiesInShape/*=false*/); + @Namespace("shape") public static native void calcSubArrsShapeInfoAndOffsets(@Cast("const Nd4jLong*") long[] wholeShapeInfo, @Cast("const Nd4jLong") long numOfSubArrs, int dimsSize, @Const int[] dimsToExclude, @Cast("Nd4jLong*") long[] subArrShapeInfo, @Cast("Nd4jLong*") long[] subArrOffsets); + + /** + * processes only one sub-array, evaluates shapeInfo of sub-array and its buffer offset from original array + * arguments: + * idx - input argument, intervals of indexes which define the sub-array to point on, + * when isStrided = false then idx has form {dim0Start,dim0End, dim1Start,dim1End, ....} and length (2 * maxRank) + * when isStrided = true then idx has form {dim0Start,dim0End,dim0Stride, dim1Start,dim1End,dim1Stride, ....} and length (3 * maxRank) + * when (dimStart == dimEnd) then whole range will be used for current dimension + * maxShapeInfo - input argument, shapeInfo of original array + * minShapeInfo - output argument, shapeInfo of sub-array to be deduced + * minOffset - output argument, offset of sub-array buffer offsets from original buffer + * keepUnitiesInShape - input argument, if false then eliminate unities from sub-array shapeInfo, for example {1,a,1,b} -> {a,b} + * isStrided - input argument, if true then idx has length (3 * this->rankOf()) and contains additional stride numbers which correspond to stride between dimStart and dimEnd, + * numOfUntiesInMinShape - input argument, number of occurrences in idx when (dimEnd - dimStart) = 1 + */ + @Namespace("shape") public static native void calcSubArrShapeInfoAndOffset(@Cast("const Nd4jLong*") LongPointer idx, @Cast("const Nd4jLong*") LongPointer maxShapeInfo, @Cast("Nd4jLong*") LongPointer minShapeInfo, @Cast("Nd4jLong*") @ByRef LongPointer minOffset, @Cast("const bool") boolean keepUnitiesInShape/*=false*/, @Cast("const bool") boolean isStrided/*=false*/, int numOfUntiesInMinShape/*=0*/); + @Namespace("shape") public static native void calcSubArrShapeInfoAndOffset(@Cast("const Nd4jLong*") LongPointer idx, @Cast("const Nd4jLong*") LongPointer maxShapeInfo, @Cast("Nd4jLong*") LongPointer minShapeInfo, @Cast("Nd4jLong*") @ByRef LongPointer minOffset); + @Namespace("shape") public static native void calcSubArrShapeInfoAndOffset(@Cast("const Nd4jLong*") LongBuffer idx, @Cast("const Nd4jLong*") LongBuffer maxShapeInfo, @Cast("Nd4jLong*") LongBuffer minShapeInfo, @Cast("Nd4jLong*") @ByRef LongBuffer minOffset, @Cast("const bool") boolean keepUnitiesInShape/*=false*/, @Cast("const bool") boolean isStrided/*=false*/, int numOfUntiesInMinShape/*=0*/); + @Namespace("shape") public static native void calcSubArrShapeInfoAndOffset(@Cast("const Nd4jLong*") LongBuffer idx, @Cast("const Nd4jLong*") LongBuffer maxShapeInfo, @Cast("Nd4jLong*") LongBuffer minShapeInfo, @Cast("Nd4jLong*") @ByRef LongBuffer minOffset); + @Namespace("shape") public static native void calcSubArrShapeInfoAndOffset(@Cast("const Nd4jLong*") long[] idx, @Cast("const Nd4jLong*") long[] maxShapeInfo, @Cast("Nd4jLong*") long[] minShapeInfo, @Cast("Nd4jLong*") @ByRef long[] minOffset, @Cast("const bool") boolean keepUnitiesInShape/*=false*/, @Cast("const bool") boolean isStrided/*=false*/, int numOfUntiesInMinShape/*=0*/); + @Namespace("shape") public static native void calcSubArrShapeInfoAndOffset(@Cast("const Nd4jLong*") long[] idx, @Cast("const Nd4jLong*") long[] maxShapeInfo, @Cast("Nd4jLong*") long[] minShapeInfo, @Cast("Nd4jLong*") @ByRef long[] minOffset); + + /** + * for example inShapeInfo is {3, 2,1,4, 4,4,1, 16384,1,99} + * then output shapeNoUnities will contain {2,4, 4,1} - that is only shape and strides, no rank/type/ews/order + * stridesNoUnities will point on strides in shapeNoUnities that is on {4,1} + * returns number of non-unity dimensions in inShapeInfo + * if there is no unities in inShapeInfo, then no copy procedure will be performed and shapeNoUnities/stridesNoUnities will point on corresponding places in inShapeInfo + */ + @Namespace("shape") public static native int excludeUnitiesFromShapeInfo(@Cast("const Nd4jLong*") LongPointer inShapeInfo, @Cast("Nd4jLong*&") @ByPtrRef LongPointer shapeNoUnities, @Cast("Nd4jLong*&") @ByPtrRef LongPointer stridesNoUnities); + @Namespace("shape") public static native int excludeUnitiesFromShapeInfo(@Cast("const Nd4jLong*") LongBuffer inShapeInfo, @Cast("Nd4jLong*&") @ByPtrRef LongBuffer shapeNoUnities, @Cast("Nd4jLong*&") @ByPtrRef LongBuffer stridesNoUnities); + @Namespace("shape") public static native int excludeUnitiesFromShapeInfo(@Cast("const Nd4jLong*") long[] inShapeInfo, @Cast("Nd4jLong*&") @ByPtrRef long[] shapeNoUnities, @Cast("Nd4jLong*&") @ByPtrRef long[] stridesNoUnities); + + /** + * for example inShapeInfo is {3, 2,1,3,1,4, 12,12,4,4,1, 16384,1,99}, dimsToExclude = {1,3}, dimsSize = 2 + * then outShapeInfo will contain {3, 2,3,4, 12,4,1, 16384,1,99} + */ + @Namespace("shape") public static native void excludeUnitiesFromShapeInfo(@Cast("const Nd4jLong*") LongPointer inShapeInfo, int dimsSize, @Const IntPointer dimsToExclude, @Cast("Nd4jLong*") LongPointer outShapeInfo); + @Namespace("shape") public static native void excludeUnitiesFromShapeInfo(@Cast("const Nd4jLong*") LongBuffer inShapeInfo, int dimsSize, @Const IntBuffer dimsToExclude, @Cast("Nd4jLong*") LongBuffer outShapeInfo); + @Namespace("shape") public static native void excludeUnitiesFromShapeInfo(@Cast("const Nd4jLong*") long[] inShapeInfo, int dimsSize, @Const int[] dimsToExclude, @Cast("Nd4jLong*") long[] outShapeInfo); + + /** + * get stride over contiguous axis (contiguous axis must have stride = 1) + * for example when inShapeInfo is {4, 2,5,4,3, 60,1,5,20, 16384,0,99} then output is 5 (that is smallest stride in inShapeInfo except those equal to 1) + */ + // INLINEDEF _CUDA_HD Nd4jLong strideOverContigAxis(const int axis, const Nd4jLong* inShapeInfo); + + + + + + +//END HEADERS + + + //BEGIN IMPLEMENTATIONS + + + +// #ifdef __CUDACC__ +// #endif + +/** +* Length of a tad given +* the shape information +*/ + + + +/** + * Tad element wise stride: + * given the inner most dimension (the sorted dimension of the last) + * the element wise stride of the tad (disregarding order) is the + * last dimension's stride. + * + * For a given singular dimension this will just be the only entry. + * For example, given the following c order shape/stride: + * 2,2,3,2 + * 12,6,2,1 + * + * The tad element wise stride for 3 will be 1. + * For zero it wil be 12 + * + * For 2,3 it's 1 + * + * Note here that the multi dimensional 2,3 case + * is equivalent to the singular 3 case. + * + * + * Note that this is for the dimension that ultimately + * ends up removed. + * + * Again: this may not preserve ordering of the tad + * but maybe used for reductions. + */ + @Namespace("shape") public static native int tadElementWiseStride(@Cast("Nd4jLong*") LongPointer shapeInfo, IntPointer dimension,int dimensionLength); + @Namespace("shape") public static native int tadElementWiseStride(@Cast("Nd4jLong*") LongBuffer shapeInfo, IntBuffer dimension,int dimensionLength); + @Namespace("shape") public static native int tadElementWiseStride(@Cast("Nd4jLong*") long[] shapeInfo, int[] dimension,int dimensionLength); + +/** + * Computes the standard packed array strides for a given shape. + * + * @param shape the shape of a matrix: + * @param startNum the start number for the strides + * @return the strides for a matrix of n dimensions + */ + +/** + * Computes the standard packed array strides for a given shape. + * + * @param shape the shape of a matrix: + * @param startNum the start number for the strides + * @return the strides for a matrix of n dimensions + */ + +/** + * Computes the standard packed array strides for a given shape. + * + * @param shape the shape of a matrix: + * @param startNum the start number for the strides + * @return the strides for a matrix of n dimensions + */ + +/** + * Computes the standard packed array strides for a given shape. + * + * @param shape the shape of a matrix: + * @param startNum the start number for the strides + * @return the strides for a matrix of n dimensions + */ + +////////////////////////////////////////////////////////////////////// + +////////////////////////////////////////////////////////////////////// + + +// check whether input dimensions are permuted, not permuted dimensions order have to be 0,....,rank-1 + + +/** + * @param toCopy the shape to copy + * @return a copy of the original struct + */ + +/** + * Get the shape info buffer + * for the given rank and shape. + */ + + /** + * This is special method, it returns ONLY 2D shapebuffer. + * + * This method is used only for SoftMax + */ + +/** +* Get the shape info buffer +* for the given rank and shape. +*/ + +////////////////////////////////////////////////////////////////////// + +////////////////////////////////////////////////////////////////////// + +////////////////////////////////////////////////////////////////////// + +////////////////////////////////////////////////////////////////////// + + +// ////////////////////////////////////////////////////////////////////// +// INLINEDEF _CUDA_HD Nd4jLong getIndexOffset(Nd4jLong index, const Nd4jLong *shapeInfo, Nd4jLong arrLen) { + +// const Nd4jLong ews = shapeInfo[shapeInfo[0] + shapeInfo[0] + 2]; + +// if(ews > 0 && order(shapeInfo) == 'c') +// if (ews == 1) +// return index; +// else +// return ews * index; + +// Nd4jLong offset = 0; +// Nd4jLong rank = shapeInfo[0]; +// for(int i = 1; i <= shapeInfo[0]; ++i) { +// arrLen /= shapeInfo[i]; +// if(arrLen > 0 && shapeInfo[i] > 1) { +// offset += (index / arrLen) * shapeInfo[i + rank]; +// index %= arrLen; +// } +// } +// return offset; +// } + +// INLINEDEF _CUDA_HD uint getIndexOffset(uint index, const uint *shapeInfo, uint arrLen) { + +// const uint rank = shapeInfo[0]; +// const uint ews = shapeInfo[rank + rank + 2]; + +// if(ews > 0 && shapeInfo[rank + rank + 3] == 99) +// if (ews == 1) +// return index; +// else +// return ews * index; + +// uint offset = 0; + +// for(uint i = 1; i <= rank; ++i) { +// arrLen /= shapeInfo[i]; +// if(arrLen > 0 && shapeInfo[i] > 1) { +// offset += (index / arrLen) * shapeInfo[i + rank]; +// index %= arrLen; +// } +// } +// return offset; +// } + +////////////////////////////////////////////////////////////////////// + +////////////////////////////////////////////////////////////////////// + + +////////////////////////////////////////////////////////////////////// + +/** + * + * @param length + * @param shape + * @param rearrange + * @return + */ + +/** + * + * @param length + * @param shape + * @param rearrange + * @return + */ + +/** + * Get the ordering for the device + * @param length + * @param shape + * @param stride + * @param elementStride + * @return + */ + + + + + +/** + * Ensure that every value in the re arrange + * array is unique + * @param arr + * @param shape + * @param arrLength + * @param shapeLength + * @return + */ + +/** + * Permute the shape information + * @param info the shape information to permute + * @param rearrange the order to re arrange + * @param rank the rank of the rearrange array + */ + +/** + * Returns whether the + * given shape is a vector or not + * @param shape the shape of the array + * @param rank the rank of the shape + */ + +////////////////////////////////////////////////////////////////////// + +/** +* Returns whether the +* given shape is a vector or not +* @param shape the shape of the array +* @param rank the rank of the shape +*/ + +/** + * Returns the shape portion of an information + * buffer + */ + +/** + * Return a copy of a buffer. + * This buffer allocates memory + * that must be freed elsewhere. + */ + +/** +* Return a copy of a buffer. +* This buffer allocates memory +* that must be freed elsewhere. +*/ + +/** +* Return a copy of a buffer. +* This buffer allocates memory +* that must be freed elsewhere. +*/ + +/** + * Permute the given strides + * in the given rearrange order + * @param toPermute the buffer to permute + * @param shapeRank the length of the buffer to permute + * @param rearrange the rearrange order (must be 0 based indexes + * and all must be filled in) + * @return the rearranged array + */ + /* + INLINEDEF _CUDA_HD Nd4jLong *permutedStrides(Nd4jLong *toPermute, int shapeRank, int *rearrange) { + Nd4jLong *strideCopy = copyOf(shapeRank, toPermute); + checkArrangeArray(rearrange, shapeRank, shapeRank); + Nd4jLong *newStride = doPermuteSwap(shapeRank, strideCopy, rearrange); + delete[] strideCopy; + return newStride; + } + */ + +/** + * Return the slice (shape + 1 in pointer arithmetic) + * @param shape the shape to take the slice of + * @return the shape array - the first entry + */ + +/** + * Returns the length of the + * shape information buffer: + * rank * 2 + 3 + * @param rank the rank to get the shape + * info length for + * @return rank * 2 + 4 + */ + +/** + * Returns the rank portion of + * an information buffer + */ + +/** + * Converts a raw int buffer of the layout: + * rank + * shape + * stride + * offset + * elementWiseStride + * + * where shape and stride are both straight int pointers + */ + +/** + * Returns the stride portion of an information + * buffer + */ + + +/** + * Compute the length of the given shape + */ + +/*** + * Returns the offset + * portion of an information buffer + */ + + +/** + * Returns the ordering + * for this shape information buffer + */ + +/** + * Returns type + */ + +/** + * Returns the element wise stride for this information + * buffer + */ + +/** +* Returns the element wise stride for this information +* buffer relative to a dimension and reduction index +*/ + +/** + * Returns whether + * the given shape info buffer + * represents a scalar shape + */ + +/** + * Returns whether + * the given shape information + * represents a scalar + * shape or not + */ + +/** + * Return a copy of this array with the + * given index omitted + * + * @param data the data to copy + * @param indexes the index of the item to remove + * @param dataLength the length of the data array + * @param indexesLength the length of the data array + * @return the new array with the omitted + * + * item + */ + + /** + * Return a copy of this array with the + * given index omitted + * + * @param data the data to copy + * @param indexes the index of the item to remove + * @param dataLength the length of the data array + * @param indexesLength the length of the data array + * @return the new array with the omitted + * + * item + */ + +/** + * Computes the offset for accessing + * a global element given the shape information + * and the offset to be read. + */ +// #ifdef __CUDACC__ +// #endif + +/** + * Returns a shape + * forces the given length to be 2. + * @param shape the shape to modify + * @param dimension the dimension (row or column) + * for the shape to be returned as + * @return the new shape + */ + @Namespace("shape") public static native @Cast("Nd4jLong*") LongPointer ensureVectorShape(@Cast("Nd4jLong*") LongPointer shape, int dimension); + @Namespace("shape") public static native @Cast("Nd4jLong*") LongBuffer ensureVectorShape(@Cast("Nd4jLong*") LongBuffer shape, int dimension); + @Namespace("shape") public static native @Cast("Nd4jLong*") long[] ensureVectorShape(@Cast("Nd4jLong*") long[] shape, int dimension); + +/** + * Returns a shape + * forces the given length to be 2. + * @param shape the shape to modify + * @param dimension the dimension (row or column) + * for the shape to be returned as + * @return the new shape + */ + + /** + * This method does STRICT comparison for two shape buffers + * + * @param shape + * @return + */ + +////////////////////////////////////////////////////////////////////// + +////////////////////////////////////////////////////////////////////// + + /** + * This method does SOFT comparison for two shape buffers, we compare only rank & shapes + * + * @param shape + * @return + */ + +/** + * Generate an int buffer + * up to the given length + * at the specified increment + * + */ + +/** + * Generate a range + * beginning at from and ending at to + * incrementing by 1 + * @param from the start + * @param to the end + * @return the int array starting at from and ending at to + */ + +/** + * Keep the given indexes in the data + * @param data + * @param index + * @param indexLength + * @param dataLength + * @return + */ + +/** + * Generate a reverse + * copy of the data + */ + +/** + * + * @param arr1 + * @param arr1Length + * @param arr2 + * @param arr2Length + * @return + */ + +/** + * + * @param numArrays + * @param numTotalElements + * @param arr + * @param lengths + * @return + */ + +/** + * Get the length per slice of the + * given shape and the dimension + * @param rank the rank of the shape + * @param shape the shape of to get + * the length per slice for + * @param dimension the dimension to + * get the length per slice for + * @param dimensionLength the length of the dimension array + * @return the length per slice of the given shape + * along the given dimension + */ + +/** + * calculates the offset for a tensor + * @param index + * @param arr + * @param tensorShape + * @return + */ + + /** + * calculates the offset for a tensor + * @param index + * @param arr + * @param tensorShape + * @return + */ + + +// #ifdef __CUDACC__ +// #endif + + + + + +/** + * Computes the number + * of tensors along + * a given dimension + */ + +/** + * Computes the number + * of tensors along + * a given dimension + */ + + + + +/** +* Get an offset for retrieval +* from a data buffer +* based on the given +* shape stride and given indices +* @param baseOffset the offset to start from +* @param shape the shape of the array +* @param stride the stride of the array +* @param indices the indices to iterate over +* @return the double at the specified index +*/ + +////////////////////////////////////////////////////////////////////////// + +////////////////////////////////////////////////////////////////////////// + +////////////////////////////////////////////////////////////////////////// + + +/** + * Returns the tensor along dimension + * for the given block index + * @param blockSize + * @param blockIdx + * @param i + * @return + */ + +/** + * Computes the number of tads per block + * + */ + +/** + * Returns a shape buffer + * for the shape information metadata. + */ +/** + * Given an linear index, element wise stride + * and the length of each tad + * map a linear index to a tad + * @param i the index to map + * @param the element wise stride for the tads + * @param numElementsPerTad the number of elements + * per tad + */ + +/** + * Map a tad to a + * reduction index. + * @param tadIndexForOriginal the original tad index for the + * split up problem (eg: split is dimension 3 mapping to a 2,3 problem) + * @param tadsForReduced the number of tads for the shrunk down problem (eg: 2,3) + * @param tadsForOriginal the number of tads for the smaller problem (eg: 3) + */ + +/** + * Tad index for linear + * @param linearIndex + * @param tadLength + * @return + */ + +/** + * Computes the number of tads + * per reduce index for the + * reduction tad. + */ + +/** + * Maps a linear index to a reduction index + * @param i the linear index to map + * @param elementWiseStride the element wise stride + * for the multiple problem + * @param tadNum the number of tads for the shrunken problem + * @param originalTadNum the tad number for the reduced version of the problem + */ + + +/** + * Returns the prod of the data + * up to the given length + */ + + @Namespace("shape") public static native int rearMostLeftOverItem(@Cast("Nd4jLong*") LongPointer data, @Cast("Nd4jLong*") LongPointer dimension,int dimensionLength); + @Namespace("shape") public static native int rearMostLeftOverItem(@Cast("Nd4jLong*") LongBuffer data, @Cast("Nd4jLong*") LongBuffer dimension,int dimensionLength); + @Namespace("shape") public static native int rearMostLeftOverItem(@Cast("Nd4jLong*") long[] data, @Cast("Nd4jLong*") long[] dimension,int dimensionLength); + +// #ifdef __CUDACC__ +// #endif + + + + + + +// INLINEDEF _CUDA_HD Nd4jLong *shapeBufferOfNpyBuffer(char *buffer) { +// unsigned Nd4jLong *shape; +// unsigned int ndims, wordSize; +// bool fortranOrder; +// cnpy::parseNpyHeaderStr(std::string(buffer),wordSize,shape,ndims,fortranOrder); +// Nd4jLong * ret = shape::shapeBufferOfNpy(ndims,shape,fortranOrder); +// delete[] shape; +// return ret; +// } + +////////////////////////////////////////////////////////////////////////// +// copy-past from java hasDefaultStridesForShape function + +// INLINEDEF _CUDA_H bool reshapeC(const int oldRank, Nd4jLong* oldShape, const int newRank, Nd4jLong* newShapeOf, bool isFOrder, Nd4jLong* target) { +// int oldnd; +// Nd4jLong* olddims = shape::copyOf(oldRank, shape::shapeOf(oldShape)); +// Nd4jLong* oldstrides = shape::copyOf(oldRank, shape::stride(oldShape)); +// int np, op, last_stride; +// int oi, oj, ok, ni, nj, nk; +// Nd4jLong* newStrides = new Nd4jLong[newRank]; +// oldnd = 0; + +// /* +// * Remove axes with dimension 1 from the old array. They have no effect +// * but would need special cases since their strides do not matter. +// */ +// for (oi = 0; oi < oldRank; oi++) { +// if (shape::shapeOf(oldShape)[oi] != 1) { +// olddims[oldnd] = shape::shapeOf(oldShape)[oi]; +// oldstrides[oldnd] = shape::stride(oldShape)[oi]; +// oldnd++; +// } +// } + +// np = 1; +// for (ni = 0; ni < newRank; ni++) { +// np *= newShapeOf[ni]; +// } +// op = 1; +// for (oi = 0; oi < oldnd; oi++) { +// op *= olddims[oi]; +// } +// if (np != op) { +// /* different total sizes; no hope */ +// delete[] olddims; +// delete[] oldstrides; +// delete[] newStrides; + +// return false; +// } + +// if (np == 0) { +// /* the current code does not handle 0-sized arrays, so give up */ +// delete[] olddims; +// delete[] oldstrides; +// delete[] newStrides; + +// return false; +// } + +// /* oi to oj and ni to nj give the axis ranges currently worked with */ +// oi = 0; +// oj = 1; +// ni = 0; +// nj = 1; + +// while (ni < newRank && oi < oldnd) { +// np = newShapeOf[ni]; +// op = olddims[oi]; + +// while (np != op) { +// if (np < op) { +// /* Misses trailing 1s, these are handled later */ +// np *= newShapeOf[nj++]; +// } else { +// op *= olddims[oj++]; +// } +// } + +// /* Check whether the original axes can be combined */ +// for (ok = oi; ok < oj - 1; ok++) { +// if (isFOrder) { +// if (oldstrides[ok + 1] != olddims[ok] * oldstrides[ok]) { +// /* not contiguous enough */ +// delete[] olddims; +// delete[] oldstrides; +// delete[] newStrides; + +// return false; +// } +// } else { +// /* C order */ +// if (oldstrides[ok] != olddims[ok + 1] * oldstrides[ok + 1]) { +// /* not contiguous enough */ +// delete[] olddims; +// delete[] oldstrides; +// delete[] newStrides; + +// return false; +// } +// } +// } + +// /* Calculate new strides for all axes currently worked with */ +// if (isFOrder) { +// newStrides[ni] = oldstrides[oi]; +// for (nk = ni + 1; nk < nj; nk++) { +// newStrides[nk] = newStrides[nk - 1] * newShapeOf[nk - 1]; +// } +// } else { +// /* C order */ +// newStrides[nj - 1] = oldstrides[oj - 1]; +// for (nk = nj - 1; nk > ni; nk--) { +// newStrides[nk - 1] = newStrides[nk] * newShapeOf[nk]; +// } +// } +// ni = nj++; +// oi = oj++; +// } + +// if (ni >= 1) { +// last_stride = newStrides[ni - 1]; +// } else { +// last_stride = shape::elementWiseStride(oldShape); +// } +// if (isFOrder && ni >= 1) { +// last_stride *= newShapeOf[ni - 1]; +// } +// for (nk = ni; nk < newRank; nk++) { +// newStrides[nk] = last_stride; +// } + +// target[0] = newRank; +// int cnt = 1; +// for (int e = 0; e < newRank; e++) +// target[cnt++] = newShapeOf[e]; + +// for (int e = 0; e < newRank; e++) +// target[cnt++] = newStrides[e]; + +// target[shape::shapeInfoLength(newRank) - 3] = 0; +// target[shape::shapeInfoLength(newRank) - 2] = 0; +// target[shape::shapeInfoLength(newRank) - 1] = isFOrder ? 102 : 99; +// sd::ArrayOptions::setDataType(target, sd::ArrayOptions::dataType(oldShape)); + +// delete[] olddims; +// delete[] oldstrides; +// delete[] newStrides; + +// return true; +// } + +////////////////////////////////////////////////////////////////////// +// INLINEDEF _CUDA_H bool reshapeC(const int oldRank, const Nd4jLong* oldShapeInfo, const int newRank, const Nd4jLong* newShape, Nd4jLong* newShapeInfo) { + +// // PLEASE NOTE !: reshaping not-permuted (ews=1) array in f order (except insertion/elimination of unities) will definitely cause allocation of new buffer for array elements +// // also this function takes into account identical shapes automatically, namely in that case oldShapeInfo is completely copied to newShapeInfo + +// newShapeInfo[0] = newRank; +// memcpy(newShapeInfo + 1, newShape, newRank * sizeof(Nd4jLong)); + +// Nd4jLong* newStrides = shape::stride(newShapeInfo); +// const Nd4jLong* oldShape = shape::shapeOf(const_cast(oldShapeInfo)); +// const Nd4jLong* oldStrides = shape::stride(const_cast(oldShapeInfo)); +// Nd4jLong oldStart(0), oldStop(1), newStart(0), newStop(1), newDim, oldDim; + +// while (newStart < newRank && oldStart < oldRank) { + +// newDim = newShape[newStart]; +// oldDim = oldShape[oldStart]; + +// while (newDim != oldDim && newDim > 0 && oldDim > 0) +// if (newDim < oldDim) newDim *= newShape[newStop++]; +// else oldDim *= oldShape[oldStop++]; + +// // ------ Check whether the original axes can be combined ------ // +// for (int step = 1, i = oldStart; i < oldStop - 1; ++i) { +// if(oldShape[i] == 1) // skip unity-dimension and its stride +// continue; +// while((i + step) < oldRank && oldShape[i + step] == 1) +// ++step; // skip following unity-dimensions and its strides if such are present +// if((i + step) < oldRank && oldStrides[i] != oldShape[i + step] * oldStrides[i + step]) +// return false; // not contiguous enough +// } + +// newStrides[newStop - 1] = oldStrides[oldStop - 1]; +// for (int i = newStop - 1; i > newStart; --i) +// newStrides[i - 1] = newStrides[i] * newShape[i]; + +// newStart = newStop++; +// oldStart = oldStop++; +// } + +// // rest of strides should be unities (if there is remainder in strides space, that is newStart < newRank) +// for (int i = newStart; i < newRank; ++i) +// newStrides[i] = 1; + +// newShapeInfo[2 * newRank + 3] = shape::order(oldShapeInfo); // order +// newShapeInfo[2 * newRank + 2] = shape::elementWiseStride(oldShapeInfo); // ews +// newShapeInfo[2 * newRank + 1] = shape::type(oldShapeInfo); // type + +// return true; +// } + +////////////////////////////////////////////////////////////////////// + +////////////////////////////////////////////////////////////////////// + + // this function checks the consistence of dimensions with array rank (negative dimensions, too large dimensions, too big number of dimensions) + // also it sorts input array of dimensions, this operation is also necessary for creating TAD object + + +// max array is outer for min array, min array is sub-array of max array +// function calculates the coordinates of min array (and saves them into minIdxs) given coordinates of max array (already stored in maxIdxs) + + ////////////////////////////////////////////////////////////////////// + + ////////////////////////////////////////////////////////////////////// + + ////////////////////////////////////////////////////////////////////// + + ////////////////////////////////////////////////////////////////////// + +////////////////////////////////////////////////////////////////////// + +////////////////////////////////////////////////////////////////////// + +////////////////////////////////////////////////////////////////////// + +////////////////////////////////////////////////////////////////////// + +////////////////////////////////////////////////////////////////////// + +////////////////////////////////////////////////////////////////////// + +////////////////////////////////////////////////////////////////////// + +////////////////////////////////////////////////////////////////////// + +////////////////////////////////////////////////////////////////////// + +////////////////////////////////////////////////////////////////////// + +////////////////////////////////////////////////////////////////////// + +////////////////////////////////////////////////////////////////////// + +////////////////////////////////////////////////////////////////////// + +////////////////////////////////////////////////////////////////////// + +////////////////////////////////////////////////////////////////////// +// INLINEDEF _CUDA_HD void calcOffsets(const Nd4jLong *xShapeInfo, Nd4jLong*& xOffsets, const Nd4jLong *yShapeInfo, Nd4jLong*& yOffsets, const Nd4jLong* zShapeInfo, Nd4jLong*& zOffsets, const char order) { + +// // we assume all array have same length +// const Nd4jLong len = shape::length(xShapeInfo); + +// const Nd4jLong xEws = shape::elementWiseStride(xShapeInfo); +// const Nd4jLong yEws = shape::elementWiseStride(yShapeInfo); +// const Nd4jLong zEws = shape::elementWiseStride(zShapeInfo); + +// const char xOrder = shape::order(xShapeInfo); +// const char yOrder = shape::order(yShapeInfo); +// const char zOrder = shape::order(zShapeInfo); + +// const bool shapesSame = shape::shapeEquals(xShapeInfo, yShapeInfo, zShapeInfo); + +// if (xEws == 1 && yEws == 1 && zEws == 1 && xOrder == yOrder && xOrder == zOrder && (xOrder == 'c' || shapesSame)) { +// xOffsets = yOffsets = zOffsets = nullptr; +// } +// else if(xEws == 1 && yEws == 1 && xOrder == yOrder && (xOrder == 'c' || shape::shapeEquals(xShapeInfo, yShapeInfo))) { +// xOffsets = yOffsets = nullptr; +// zOffsets = new Nd4jLong[len]; +// shape::calcOffsets(zShapeInfo, zOffsets, xOrder); +// } +// else if(xEws == 1 && zEws == 1 && xOrder == zOrder && (xOrder == 'c' || shape::shapeEquals(xShapeInfo, zShapeInfo))) { +// xOffsets = zOffsets = nullptr; +// yOffsets = new Nd4jLong[len]; +// shape::calcOffsets(yShapeInfo, yOffsets, xOrder); +// } +// else if(yEws == 1 && zEws == 1 && yOrder == zOrder && (yOrder == 'c' || shape::shapeEquals(yShapeInfo, zShapeInfo))) { +// yOffsets = zOffsets = nullptr; +// xOffsets = new Nd4jLong[len]; +// shape::calcOffsets(xShapeInfo, xOffsets, yOrder); +// } +// else if(xEws == 1) { +// xOffsets = nullptr; +// PRAGMA_OMP_PARALLEL_SECTIONS +// { +// PRAGMA_OMP_SECTION +// { +// yOffsets = new Nd4jLong[len]; +// shape::calcOffsets(yShapeInfo, yOffsets, xOrder); +// } +// PRAGMA_OMP_SECTION +// { +// zOffsets = new Nd4jLong[len]; +// shape::calcOffsets(zShapeInfo, zOffsets, xOrder); +// } +// } +// } +// else if(yEws == 1) { +// yOffsets = nullptr; +// PRAGMA_OMP_PARALLEL_SECTIONS +// { +// PRAGMA_OMP_SECTION +// { +// xOffsets = new Nd4jLong[len]; +// shape::calcOffsets(xShapeInfo, xOffsets, yOrder); +// } +// PRAGMA_OMP_SECTION +// { +// zOffsets = new Nd4jLong[len]; +// shape::calcOffsets(zShapeInfo, zOffsets, yOrder); +// } +// } +// } +// else if(zEws == 1) { +// zOffsets = nullptr; +// PRAGMA_OMP_PARALLEL_SECTIONS +// { +// PRAGMA_OMP_SECTION +// { +// xOffsets = new Nd4jLong[len]; +// shape::calcOffsets(xShapeInfo, xOffsets, zOrder); +// } +// PRAGMA_OMP_SECTION +// { +// yOffsets = new Nd4jLong[len]; +// shape::calcOffsets(yShapeInfo, yOffsets, zOrder); +// } +// } +// } +// else if(shape::haveSameShapeAndStrides(xShapeInfo, yShapeInfo, zShapeInfo)) { +// xOffsets = new Nd4jLong[len]; +// shape::calcOffsets(xShapeInfo, xOffsets); +// yOffsets = zOffsets = xOffsets; +// } +// else if(shape::haveSameShapeAndStrides(xShapeInfo, yShapeInfo)) { +// PRAGMA_OMP_PARALLEL_SECTIONS +// { +// PRAGMA_OMP_SECTION +// { +// xOffsets = new Nd4jLong[len]; +// shape::calcOffsets(xShapeInfo, xOffsets); +// } +// PRAGMA_OMP_SECTION +// { +// zOffsets = new Nd4jLong[len]; +// shape::calcOffsets(zShapeInfo, zOffsets); +// } +// } +// yOffsets = xOffsets; +// } +// else if(shape::haveSameShapeAndStrides(xShapeInfo, zShapeInfo)) { +// PRAGMA_OMP_PARALLEL_SECTIONS +// { +// PRAGMA_OMP_SECTION +// { +// xOffsets = new Nd4jLong[len]; +// shape::calcOffsets(xShapeInfo, xOffsets); +// } +// PRAGMA_OMP_SECTION +// { +// yOffsets = new Nd4jLong[len]; +// shape::calcOffsets(yShapeInfo, yOffsets); +// } +// } +// zOffsets = xOffsets; +// } +// else { +// PRAGMA_OMP_PARALLEL_SECTIONS +// { +// PRAGMA_OMP_SECTION +// { +// xOffsets = new Nd4jLong[len]; +// shape::calcOffsets(xShapeInfo, xOffsets); +// } +// PRAGMA_OMP_SECTION +// { +// yOffsets = new Nd4jLong[len]; +// shape::calcOffsets(yShapeInfo, yOffsets); +// } +// PRAGMA_OMP_SECTION +// { +// zOffsets = new Nd4jLong[len]; +// shape::calcOffsets(zShapeInfo, zOffsets); +// } +// } +// } +// } + +////////////////////////////////////////////////////////////////////// +// INLINEDEF _CUDA_HD void calcOffsets(const Nd4jLong *xShapeInfo, Nd4jLong*& xOffsets, const Nd4jLong *yShapeInfo, Nd4jLong*& yOffsets, const char order) { + +// // we assume all array have same length +// const Nd4jLong len = shape::length(xShapeInfo); + +// const Nd4jLong xEws = shape::elementWiseStride(xShapeInfo); +// const Nd4jLong yEws = shape::elementWiseStride(yShapeInfo); + +// const char xOrder = shape::order(xShapeInfo); +// const char yOrder = shape::order(yShapeInfo); + +// const bool shapesSame = shape::shapeEquals(xShapeInfo, yShapeInfo); + +// if (xEws == 1 && yEws == 1 && xOrder == yOrder && (xOrder == 'c' || shapesSame)) { +// xOffsets = yOffsets = nullptr; +// } +// else if(xEws == 1) { +// xOffsets = nullptr; +// yOffsets = new Nd4jLong[len]; +// shape::calcOffsets(yShapeInfo, yOffsets, xOrder); +// } +// else if(yEws == 1) { +// yOffsets = nullptr; +// xOffsets = new Nd4jLong[len]; +// shape::calcOffsets(xShapeInfo, xOffsets, yOrder); +// } +// else if(shape::haveSameShapeAndStrides(xShapeInfo, yShapeInfo)) { +// xOffsets = new Nd4jLong[len]; +// shape::calcOffsets(xShapeInfo, xOffsets); +// yOffsets = xOffsets; +// } +// else { +// PRAGMA_OMP_PARALLEL_SECTIONS +// { +// PRAGMA_OMP_SECTION +// { +// xOffsets = new Nd4jLong[len]; +// shape::calcOffsets(xShapeInfo, xOffsets); +// } +// PRAGMA_OMP_SECTION +// { +// yOffsets = new Nd4jLong[len]; +// shape::calcOffsets(yShapeInfo, yOffsets); +// } +// } +// } +// } + +////////////////////////////////////////////////////////////////////// + +////////////////////////////////////////////////////////////////////// + + +////////////////////////////////////////////////////////////////////// +// INLINEDEF _CUDA_HD Nd4jLong strideOverContigAxis(const int axis, const Nd4jLong* inShapeInfo) { + +// Nd4jLong result = 9223372036854775807LL; + +// for(uint i = 0; i < shape::rank(inShapeInfo); ++i) { + +// const auto currentStride = shape::stride(inShapeInfo)[i]; + +// if(i == axis || shape::shapeOf(inShapeInfo)[i] == 1) +// continue; + +// if(result > currentStride) +// result = currentStride; +// } + +// return result == 9223372036854775807LL ? 1 : result; +// } + + + + + +// #endif /* SHAPE_H_ */ + + +// Parsed from array/ShapeList.h + +/******************************************************************************* + * Copyright (c) 2015-2018 Skymind, Inc. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ + +// +// @author raver119@gmail.com +// + +// #ifndef LIBND4J_SHAPELIST_H +// #define LIBND4J_SHAPELIST_H + +// #include +// #include +// #include + @Namespace("sd") @NoOffset public static class ShapeList extends Pointer { + static { Loader.load(); } + /** Pointer cast constructor. Invokes {@link Pointer#Pointer(Pointer)}. */ + public ShapeList(Pointer p) { super(p); } + /** Native array allocator. Access with {@link Pointer#position(long)}. */ + public ShapeList(long size) { super((Pointer)null); allocateArray(size); } + private native void allocateArray(long size); + @Override public ShapeList position(long position) { + return (ShapeList)super.position(position); + } + + public ShapeList(@Cast("Nd4jLong*") LongPointer shape/*=nullptr*/) { super((Pointer)null); allocate(shape); } + private native void allocate(@Cast("Nd4jLong*") LongPointer shape/*=nullptr*/); + public ShapeList() { super((Pointer)null); allocate(); } + private native void allocate(); + public ShapeList(@Cast("Nd4jLong*") LongBuffer shape/*=nullptr*/) { super((Pointer)null); allocate(shape); } + private native void allocate(@Cast("Nd4jLong*") LongBuffer shape/*=nullptr*/); + public ShapeList(@Cast("Nd4jLong*") long[] shape/*=nullptr*/) { super((Pointer)null); allocate(shape); } + private native void allocate(@Cast("Nd4jLong*") long[] shape/*=nullptr*/); + public ShapeList(@Cast("Nd4jLong**") @StdVector PointerPointer shapes) { super((Pointer)null); allocate(shapes); } + private native void allocate(@Cast("Nd4jLong**") @StdVector PointerPointer shapes); + //ShapeList(bool autoRemovable); + + public native @Cast("Nd4jLong**") @StdVector PointerPointer asVector(); + public native void destroy(); + public native int size(); + public native @Cast("Nd4jLong*") LongPointer at(int idx); + public native void push_back(@Cast("Nd4jLong*") LongPointer shape); + public native void push_back(@Cast("Nd4jLong*") LongBuffer shape); + public native void push_back(@Cast("Nd4jLong*") long[] shape); + + /** + * PLEASE NOTE: This method should be called ONLY if shapes were generated at workspaces. Otherwise you'll get memory leak + */ + public native void detach(); + } + + + +// #endif //LIBND4J_SHAPELIST_H + + +// Parsed from ops/InputType.h + +/******************************************************************************* + * Copyright (c) 2015-2018 Skymind, Inc. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ + +// +// @author raver119@gmail.com +// + +// #ifndef ND4J_INPUTTYPE_H +// #define ND4J_INPUTTYPE_H + /** enum sd::ops::InputType */ + public static final int + InputType_BOOLEAN = 0, + InputType_NUMERIC = 1, + InputType_STRINGULAR = 2, + InputType_NUMERIC_SET = 3, + InputType_STRINGULAR_SET = 4; + + + +// #endif + +// Parsed from ops/declarable/OpDescriptor.h + +/******************************************************************************* + * Copyright (c) 2015-2018 Skymind, Inc. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ + +// +// @author raver119@gmail.com +// + +// #ifndef LIBND4J_OPDESCRIPTOR_H +// #define LIBND4J_OPDESCRIPTOR_H + +// #include +// #include +// #include +// #include +// #include +// #include +// #include + + /** + * This class is very basic info holder for ops. bean/pojo pretty much. + * + */ + @Namespace("sd::ops") @NoOffset public static class OpDescriptor extends Pointer { + static { Loader.load(); } + /** Pointer cast constructor. Invokes {@link Pointer#Pointer(Pointer)}. */ + public OpDescriptor(Pointer p) { super(p); } + + // default constructor + public OpDescriptor(int numInputs, int numOutputs, @StdString BytePointer opName, @Cast("bool") boolean allowsInplace) { super((Pointer)null); allocate(numInputs, numOutputs, opName, allowsInplace); } + private native void allocate(int numInputs, int numOutputs, @StdString BytePointer opName, @Cast("bool") boolean allowsInplace); + public OpDescriptor(int numInputs, int numOutputs, @StdString String opName, @Cast("bool") boolean allowsInplace) { super((Pointer)null); allocate(numInputs, numOutputs, opName, allowsInplace); } + private native void allocate(int numInputs, int numOutputs, @StdString String opName, @Cast("bool") boolean allowsInplace); + + // constructor for boolean ops + public OpDescriptor(int numInputs, @StdString BytePointer opName, @Cast("bool") boolean isScalar) { super((Pointer)null); allocate(numInputs, opName, isScalar); } + private native void allocate(int numInputs, @StdString BytePointer opName, @Cast("bool") boolean isScalar); + public OpDescriptor(int numInputs, @StdString String opName, @Cast("bool") boolean isScalar) { super((Pointer)null); allocate(numInputs, opName, isScalar); } + private native void allocate(int numInputs, @StdString String opName, @Cast("bool") boolean isScalar); + + // default constructor + + // constructor for configurable op + public OpDescriptor(int numInputs, int numOutputs, @Cast("char*") String opName, @Cast("bool") boolean allowsInplace, int tArgs, int iArgs) { super((Pointer)null); allocate(numInputs, numOutputs, opName, allowsInplace, tArgs, iArgs); } + private native void allocate(int numInputs, int numOutputs, @Cast("char*") String opName, @Cast("bool") boolean allowsInplace, int tArgs, int iArgs); + public OpDescriptor(int numInputs, int numOutputs, @Cast("char*") BytePointer opName, @Cast("bool") boolean allowsInplace, int tArgs, int iArgs) { super((Pointer)null); allocate(numInputs, numOutputs, opName, allowsInplace, tArgs, iArgs); } + private native void allocate(int numInputs, int numOutputs, @Cast("char*") BytePointer opName, @Cast("bool") boolean allowsInplace, int tArgs, int iArgs); + + // constructor for non-configurable divergent op + public OpDescriptor(int numInputs, int numOutputs, @StdString BytePointer opName, @Cast("bool") boolean allowsInplace, @Cast("bool") boolean divergent) { super((Pointer)null); allocate(numInputs, numOutputs, opName, allowsInplace, divergent); } + private native void allocate(int numInputs, int numOutputs, @StdString BytePointer opName, @Cast("bool") boolean allowsInplace, @Cast("bool") boolean divergent); + public OpDescriptor(int numInputs, int numOutputs, @StdString String opName, @Cast("bool") boolean allowsInplace, @Cast("bool") boolean divergent) { super((Pointer)null); allocate(numInputs, numOutputs, opName, allowsInplace, divergent); } + private native void allocate(int numInputs, int numOutputs, @StdString String opName, @Cast("bool") boolean allowsInplace, @Cast("bool") boolean divergent); + + // constructor for non-configurable divergent op + + // constructor for configurable divergent op + public OpDescriptor(int numInputs, int numOutputs, @Cast("char*") String opName, @Cast("bool") boolean allowsInplace, @Cast("bool") boolean divergent, int tArgs, int iArgs) { super((Pointer)null); allocate(numInputs, numOutputs, opName, allowsInplace, divergent, tArgs, iArgs); } + private native void allocate(int numInputs, int numOutputs, @Cast("char*") String opName, @Cast("bool") boolean allowsInplace, @Cast("bool") boolean divergent, int tArgs, int iArgs); + public OpDescriptor(int numInputs, int numOutputs, @Cast("char*") BytePointer opName, @Cast("bool") boolean allowsInplace, @Cast("bool") boolean divergent, int tArgs, int iArgs) { super((Pointer)null); allocate(numInputs, numOutputs, opName, allowsInplace, divergent, tArgs, iArgs); } + private native void allocate(int numInputs, int numOutputs, @Cast("char*") BytePointer opName, @Cast("bool") boolean allowsInplace, @Cast("bool") boolean divergent, int tArgs, int iArgs); + + // constructor for logical ops (while, scope, etc) + public OpDescriptor(@Cast("char*") String opName, @Cast("bool") boolean isLogic) { super((Pointer)null); allocate(opName, isLogic); } + private native void allocate(@Cast("char*") String opName, @Cast("bool") boolean isLogic); + public OpDescriptor(@Cast("char*") BytePointer opName, @Cast("bool") boolean isLogic) { super((Pointer)null); allocate(opName, isLogic); } + private native void allocate(@Cast("char*") BytePointer opName, @Cast("bool") boolean isLogic); + + public native @Cast("bool") @Name("operator ==") boolean equals(@Const @ByRef OpDescriptor other); + + // default destructor + + // this method returns minimal expected number of T arguments + public native int getNumberOfTArgs(); + + // this method returns minimal expected number of Integer arguments + public native int getNumberOfIArgs(); + + // this method returns minimal expected number of inputs + public native int getNumberOfInputs(); + + // this method returns hash code for this operation + public native @Cast("Nd4jLong") long getHash(); + + // this method returns minimal expected number of outputs + public native int getNumberOfOutputs(); + + // this method returns opName (can be empty) + public native @StdString @Cast({"char*", "std::string*"}) BytePointer getOpName(); + + // returns TRUE if this op is divergent. FALSE otherwise + public native @Cast("bool") boolean isDivergent(); + + // returns TRUE if this op allows in-place execution + public native @Cast("bool") boolean allowsInplace(); + + // this method allows you to enable/disable inplace call for a given op + public native void allowInplace(@Cast("bool") boolean reallyAllow); + + // this method returns opNum (applicable for legacy XYZ ops only) + public native int getOpNum(); + + // this method allows to set specifc opNum + public native void setOpNum(int opNum); + + public native void setHash(@Cast("Nd4jLong") long hash); + + public native @Cast("sd::ops::InputType") int inputType(); + + + + public native OpDescriptor setInputType(@Cast("sd::ops::InputType") int type); + public native OpDescriptor setAllowedInputTypes(int index, @Cast("sd::DataType*") @StdVector IntPointer dtype); + public native OpDescriptor setAllowedInputTypes(int index, @Cast("sd::DataType*") @StdVector IntBuffer dtype); + public native OpDescriptor setAllowedInputTypes(int index, @Cast("sd::DataType*") @StdVector int[] dtype); + public native OpDescriptor setAllowedOutputTypes(int index, @Cast("sd::DataType*") @StdVector IntPointer dtype); + public native OpDescriptor setAllowedOutputTypes(int index, @Cast("sd::DataType*") @StdVector IntBuffer dtype); + public native OpDescriptor setAllowedOutputTypes(int index, @Cast("sd::DataType*") @StdVector int[] dtype); + public native OpDescriptor setAllowedInputTypes(int index, @Cast("sd::DataType") int dtype); + public native OpDescriptor setAllowedOutputTypes(int index, @Cast("sd::DataType") int dtype); + public native OpDescriptor setAllowedInputTypes(@Cast("sd::DataType") int dtype); + public native OpDescriptor setAllowedOutputTypes(@Cast("sd::DataType") int dtype); + public native OpDescriptor allowOverride(@Cast("bool") boolean reallyAllow); + public native OpDescriptor setSameMode(@Cast("bool") boolean reallySame); + public native OpDescriptor setInputType(int idx, @Cast("sd::DataType") int dtype); + public native OpDescriptor setOutputType(int idx, @Cast("sd::DataType") int dtype); + + public native @Cast("sd::DataType*") @StdVector IntPointer getOutputTypesForOutput(int index); + + public native @Cast("bool") boolean checkInputMatch(int index, @Cast("sd::DataType") int dataType); + public native @Cast("bool") boolean checkOutputMatch(int index, @Cast("sd::DataType") int dataType); + public native @Cast("bool") boolean isSameMode(); + + public native @Cast("bool") boolean isInherit(int index); + } + + + +// #endif //LIBND4J_OPDESCRIPTOR_H + + +// Parsed from ops/declarable/PlatformHelper.h + +/******************************************************************************* + * Copyright (c) 2015-2018 Skymind, Inc. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ + +// +// @author raver119@gmail.com +// + +// #ifndef SD_PLATFORMHELPER_H +// #define SD_PLATFORMHELPER_H + +// #include +// #include +// #include +// #include +// #include +// #include + /** + * This abstract class defines methods used by platform-specific helpers implementations + */ + @Namespace("sd::ops::platforms") @NoOffset public static class PlatformHelper extends Pointer { + static { Loader.load(); } + /** Pointer cast constructor. Invokes {@link Pointer#Pointer(Pointer)}. */ + public PlatformHelper(Pointer p) { super(p); } + + + public native @StdString BytePointer name(); + + public native @Cast("samediff::Engine") int engine(); + + public native @Cast("Nd4jLong") long hash(); + + /** + * This method checks, if given helper can be used with given input/output/configuration options + * + * @param context + * @return + */ + public native @Cast("bool") boolean isUsable(@ByRef Context context); + + /** + * This method invokes helper. Typically this method replaces actual op execution + * + * @param context + * @return + */ + public native @Cast("Nd4jStatus") int invokeHelper(@ByRef Context context); + + /** + * Helper method, needed for compatibility with DeclarableOp macros + * @param ctx + * @param inputId + * @return + */ + public native NDArray getZ(@ByRef Context ctx, int inputId); + } + + + + + +// #endif //SD_PLATFORMHELPER_H + + +// Parsed from ops/declarable/BroadcastableOp.h + +/******************************************************************************* + * Copyright (c) 2015-2018 Skymind, Inc. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ + +// +// Created by raver on 6/6/2018. +// + +// #ifndef LIBND4J_BROADCASTABLEOP_H +// #define LIBND4J_BROADCASTABLEOP_H + +// #include +// #include "OpDescriptor.h" +// #include "DeclarableOp.h" +// #include "DeclarableCustomOp.h" + @Namespace("sd::ops") public static class BroadcastableOp extends DeclarableCustomOp { + static { Loader.load(); } + /** Pointer cast constructor. Invokes {@link Pointer#Pointer(Pointer)}. */ + public BroadcastableOp(Pointer p) { super(p); } + + + public native ShapeList calculateOutputShape(ShapeList inputShape, @ByRef Context block); + } + + + + +// #endif //LIBND4J_BROADCASTABLEOP_H + + +// Parsed from helpers/OpArgsHolder.h + +/******************************************************************************* + * Copyright (c) 2015-2018 Skymind, Inc. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ + +// +// @author Yurii Shyrma (iuriish@yahoo.com), created on 15.07.2018 +// + +// #ifndef LIBND4J_OPARGSHOLDER_H +// #define LIBND4J_OPARGSHOLDER_H + + +// #include +// #include + +@Namespace("sd") @NoOffset public static class OpArgsHolder extends Pointer { + static { Loader.load(); } + /** Pointer cast constructor. Invokes {@link Pointer#Pointer(Pointer)}. */ + public OpArgsHolder(Pointer p) { super(p); } + /** Native array allocator. Access with {@link Pointer#position(long)}. */ + public OpArgsHolder(long size) { super((Pointer)null); allocateArray(size); } + private native void allocateArray(long size); + @Override public OpArgsHolder position(long position) { + return (OpArgsHolder)super.position(position); + } + + + // default constructor + public OpArgsHolder() { super((Pointer)null); allocate(); } + private native void allocate(); + + // copy constructor + public OpArgsHolder(@Const @ByRef OpArgsHolder other) { super((Pointer)null); allocate(other); } + private native void allocate(@Const @ByRef OpArgsHolder other); + + // constructor + public OpArgsHolder(@Const @ByRef NDArrayVector inArrs, @StdVector DoublePointer tArgs/*=std::vector()*/, @Cast("Nd4jLong*") @StdVector LongPointer iArgs/*=std::vector()*/, @Cast("bool*") @StdVector BooleanPointer bArgs/*=std::vector()*/) { super((Pointer)null); allocate(inArrs, tArgs, iArgs, bArgs); } + private native void allocate(@Const @ByRef NDArrayVector inArrs, @StdVector DoublePointer tArgs/*=std::vector()*/, @Cast("Nd4jLong*") @StdVector LongPointer iArgs/*=std::vector()*/, @Cast("bool*") @StdVector BooleanPointer bArgs/*=std::vector()*/); + public OpArgsHolder(@Const @ByRef NDArrayVector inArrs) { super((Pointer)null); allocate(inArrs); } + private native void allocate(@Const @ByRef NDArrayVector inArrs); + public OpArgsHolder(@Const @ByRef NDArrayVector inArrs, @StdVector DoubleBuffer tArgs/*=std::vector()*/, @Cast("Nd4jLong*") @StdVector LongBuffer iArgs/*=std::vector()*/, @Cast("bool*") @StdVector boolean[] bArgs/*=std::vector()*/) { super((Pointer)null); allocate(inArrs, tArgs, iArgs, bArgs); } + private native void allocate(@Const @ByRef NDArrayVector inArrs, @StdVector DoubleBuffer tArgs/*=std::vector()*/, @Cast("Nd4jLong*") @StdVector LongBuffer iArgs/*=std::vector()*/, @Cast("bool*") @StdVector boolean[] bArgs/*=std::vector()*/); + public OpArgsHolder(@Const @ByRef NDArrayVector inArrs, @StdVector double[] tArgs/*=std::vector()*/, @Cast("Nd4jLong*") @StdVector long[] iArgs/*=std::vector()*/, @Cast("bool*") @StdVector BooleanPointer bArgs/*=std::vector()*/) { super((Pointer)null); allocate(inArrs, tArgs, iArgs, bArgs); } + private native void allocate(@Const @ByRef NDArrayVector inArrs, @StdVector double[] tArgs/*=std::vector()*/, @Cast("Nd4jLong*") @StdVector long[] iArgs/*=std::vector()*/, @Cast("bool*") @StdVector BooleanPointer bArgs/*=std::vector()*/); + public OpArgsHolder(@Const @ByRef NDArrayVector inArrs, @StdVector DoublePointer tArgs/*=std::vector()*/, @Cast("Nd4jLong*") @StdVector LongPointer iArgs/*=std::vector()*/, @Cast("bool*") @StdVector boolean[] bArgs/*=std::vector()*/) { super((Pointer)null); allocate(inArrs, tArgs, iArgs, bArgs); } + private native void allocate(@Const @ByRef NDArrayVector inArrs, @StdVector DoublePointer tArgs/*=std::vector()*/, @Cast("Nd4jLong*") @StdVector LongPointer iArgs/*=std::vector()*/, @Cast("bool*") @StdVector boolean[] bArgs/*=std::vector()*/); + public OpArgsHolder(@Const @ByRef NDArrayVector inArrs, @StdVector DoubleBuffer tArgs/*=std::vector()*/, @Cast("Nd4jLong*") @StdVector LongBuffer iArgs/*=std::vector()*/, @Cast("bool*") @StdVector BooleanPointer bArgs/*=std::vector()*/) { super((Pointer)null); allocate(inArrs, tArgs, iArgs, bArgs); } + private native void allocate(@Const @ByRef NDArrayVector inArrs, @StdVector DoubleBuffer tArgs/*=std::vector()*/, @Cast("Nd4jLong*") @StdVector LongBuffer iArgs/*=std::vector()*/, @Cast("bool*") @StdVector BooleanPointer bArgs/*=std::vector()*/); + public OpArgsHolder(@Const @ByRef NDArrayVector inArrs, @StdVector double[] tArgs/*=std::vector()*/, @Cast("Nd4jLong*") @StdVector long[] iArgs/*=std::vector()*/, @Cast("bool*") @StdVector boolean[] bArgs/*=std::vector()*/) { super((Pointer)null); allocate(inArrs, tArgs, iArgs, bArgs); } + private native void allocate(@Const @ByRef NDArrayVector inArrs, @StdVector double[] tArgs/*=std::vector()*/, @Cast("Nd4jLong*") @StdVector long[] iArgs/*=std::vector()*/, @Cast("bool*") @StdVector boolean[] bArgs/*=std::vector()*/); + + // move constructor + + // assignment operator + public native @ByRef @Name("operator =") OpArgsHolder put(@Const @ByRef OpArgsHolder other); + + // move assignment operator + + public native @Const @ByRef NDArrayVector getInArrs(); + + public native @StdVector DoublePointer getTArgs(); + + public native @Cast("Nd4jLong*") @StdVector LongPointer getIArgs(); + + public native @Cast("bool*") @StdVector BooleanPointer getBArgs(); + + public native @Cast("bool*") @StdVector BooleanPointer getAllocInfo(); + + public native int getNumInArrs(); + + public native int getNumTArgs(); + + public native int getNumIArgs(); + + public native int getNumBArgs(); + + public native @ByVal OpArgsHolder createArgsHolderForBP(@Const @ByRef NDArrayVector inGradArrs, @Cast("const bool") boolean isInPlace/*=false*/); + public native @ByVal OpArgsHolder createArgsHolderForBP(@Const @ByRef NDArrayVector inGradArrs); + +} + + + + + + + +// #endif //LIBND4J_OPARGSHOLDER_H + + +// Parsed from ops/declarable/DeclarableOp.h + +/******************************************************************************* + * Copyright (c) 2015-2018 Skymind, Inc. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ + +// +// @author raver119@gmail.com +// + +// #ifndef LIBND4J_DECLARABLE_OPS_H +// #define LIBND4J_DECLARABLE_OPS_H + +// #include +// #include +// #include +// #include +// #include +// #include "OpDescriptor.h" +// #include +// #include +// #include +// #include +// #include +// #include +//#include + +// #include +// #include +// #include + + @Namespace("sd::ops") public static native @Cast("Nd4jStatus") int conditionHelper(@Cast("char*") String file, int line, int condition, int argNumber, @Cast("char*") String format); + @Namespace("sd::ops") public static native @Cast("Nd4jStatus") int conditionHelper(@Cast("char*") BytePointer file, int line, int condition, int argNumber, @Cast("char*") BytePointer format); + + /** + * This class is the basic building block of Graph Operations. Any CustomOp out there is built on top of this "abstract" class. + * + */ + @Namespace("sd::ops") @NoOffset public static class DeclarableOp extends Pointer { + static { Loader.load(); } + /** Pointer cast constructor. Invokes {@link Pointer#Pointer(Pointer)}. */ + public DeclarableOp(Pointer p) { super(p); } + + // for special cases, like BooleanOps + + // regular constructors + + // for LogicalOps + + // default testructor + + // this method returns OpDescriptor, describing this Op instance + public native OpDescriptor getOpDescriptor(); + + public native @Cast("Nd4jStatus") int validateDataTypes(@ByRef Context block); + + /** + * This method should be available in each implemented Op, and should return Op output shape(s), for a given input shape(s) + */ + public native ShapeList calculateOutputShape(ShapeList inputShape, @ByRef Context block); + + /** + * Returns opName + * + * @return + */ + public native @StdString @Cast({"char*", "std::string*"}) BytePointer getOpName(); + + /** + * Returns opHash + */ + public native @Cast("Nd4jLong") long getOpHash(); + + /** + * This method sets arguments for op + */ +// void setArguments(); + + /** + * This method returns pointer to results + */ +// void getResults(); + + /** + * This method executes given Op + * + * @param block + * @return 0 if OK, error code otherwise + */ + public native @Cast("Nd4jStatus") int execute(Context block); + + public native @Cast("Nd4jStatus") int execute(@Const @ByRef NDArrayVector inputs, @Const @ByRef NDArrayVector outputs); + + public native @Cast("Nd4jStatus") int execute(@Const @ByRef NDArrayVector inputs, @Const @ByRef NDArrayVector outputs, @StdVector DoublePointer tArgs, @Cast("Nd4jLong*") @StdVector LongPointer iArgs, @Cast("bool*") @StdVector BooleanPointer bArgs/*=std::vector()*/, @Cast("sd::DataType*") @StdVector IntPointer dArgs/*=std::vector()*/, @Cast("bool") boolean isInplace/*=false*/); + public native @Cast("Nd4jStatus") int execute(@Const @ByRef NDArrayVector inputs, @Const @ByRef NDArrayVector outputs, @StdVector DoublePointer tArgs, @Cast("Nd4jLong*") @StdVector LongPointer iArgs); + public native @Cast("Nd4jStatus") int execute(@Const @ByRef NDArrayVector inputs, @Const @ByRef NDArrayVector outputs, @StdVector DoubleBuffer tArgs, @Cast("Nd4jLong*") @StdVector LongBuffer iArgs, @Cast("bool*") @StdVector boolean[] bArgs/*=std::vector()*/, @Cast("sd::DataType*") @StdVector IntBuffer dArgs/*=std::vector()*/, @Cast("bool") boolean isInplace/*=false*/); + public native @Cast("Nd4jStatus") int execute(@Const @ByRef NDArrayVector inputs, @Const @ByRef NDArrayVector outputs, @StdVector DoubleBuffer tArgs, @Cast("Nd4jLong*") @StdVector LongBuffer iArgs); + public native @Cast("Nd4jStatus") int execute(@Const @ByRef NDArrayVector inputs, @Const @ByRef NDArrayVector outputs, @StdVector double[] tArgs, @Cast("Nd4jLong*") @StdVector long[] iArgs, @Cast("bool*") @StdVector BooleanPointer bArgs/*=std::vector()*/, @Cast("sd::DataType*") @StdVector int[] dArgs/*=std::vector()*/, @Cast("bool") boolean isInplace/*=false*/); + public native @Cast("Nd4jStatus") int execute(@Const @ByRef NDArrayVector inputs, @Const @ByRef NDArrayVector outputs, @StdVector double[] tArgs, @Cast("Nd4jLong*") @StdVector long[] iArgs); + public native @Cast("Nd4jStatus") int execute(@Const @ByRef NDArrayVector inputs, @Const @ByRef NDArrayVector outputs, @StdVector DoublePointer tArgs, @Cast("Nd4jLong*") @StdVector LongPointer iArgs, @Cast("bool*") @StdVector boolean[] bArgs/*=std::vector()*/, @Cast("sd::DataType*") @StdVector IntPointer dArgs/*=std::vector()*/, @Cast("bool") boolean isInplace/*=false*/); + public native @Cast("Nd4jStatus") int execute(@Const @ByRef NDArrayVector inputs, @Const @ByRef NDArrayVector outputs, @StdVector DoubleBuffer tArgs, @Cast("Nd4jLong*") @StdVector LongBuffer iArgs, @Cast("bool*") @StdVector BooleanPointer bArgs/*=std::vector()*/, @Cast("sd::DataType*") @StdVector IntBuffer dArgs/*=std::vector()*/, @Cast("bool") boolean isInplace/*=false*/); + public native @Cast("Nd4jStatus") int execute(@Const @ByRef NDArrayVector inputs, @Const @ByRef NDArrayVector outputs, @StdVector double[] tArgs, @Cast("Nd4jLong*") @StdVector long[] iArgs, @Cast("bool*") @StdVector boolean[] bArgs/*=std::vector()*/, @Cast("sd::DataType*") @StdVector int[] dArgs/*=std::vector()*/, @Cast("bool") boolean isInplace/*=false*/); + + public native @ByVal ResultSet evaluate(@Const @ByRef NDArrayVector inputs); + + public native @ByVal ResultSet evaluate(@Const @ByRef NDArrayVector inputs, @StdVector DoublePointer tArgs, @Cast("Nd4jLong*") @StdVector LongPointer iArgs, @Cast("bool*") @StdVector BooleanPointer bArgs/*=std::vector()*/, @Cast("sd::DataType*") @StdVector IntPointer dArgs/*=std::vector()*/, @Cast("bool") boolean isInplace/*=false*/); + public native @ByVal ResultSet evaluate(@Const @ByRef NDArrayVector inputs, @StdVector DoublePointer tArgs, @Cast("Nd4jLong*") @StdVector LongPointer iArgs); + public native @ByVal ResultSet evaluate(@Const @ByRef NDArrayVector inputs, @StdVector DoubleBuffer tArgs, @Cast("Nd4jLong*") @StdVector LongBuffer iArgs, @Cast("bool*") @StdVector boolean[] bArgs/*=std::vector()*/, @Cast("sd::DataType*") @StdVector IntBuffer dArgs/*=std::vector()*/, @Cast("bool") boolean isInplace/*=false*/); + public native @ByVal ResultSet evaluate(@Const @ByRef NDArrayVector inputs, @StdVector DoubleBuffer tArgs, @Cast("Nd4jLong*") @StdVector LongBuffer iArgs); + public native @ByVal ResultSet evaluate(@Const @ByRef NDArrayVector inputs, @StdVector double[] tArgs, @Cast("Nd4jLong*") @StdVector long[] iArgs, @Cast("bool*") @StdVector BooleanPointer bArgs/*=std::vector()*/, @Cast("sd::DataType*") @StdVector int[] dArgs/*=std::vector()*/, @Cast("bool") boolean isInplace/*=false*/); + public native @ByVal ResultSet evaluate(@Const @ByRef NDArrayVector inputs, @StdVector double[] tArgs, @Cast("Nd4jLong*") @StdVector long[] iArgs); + public native @ByVal ResultSet evaluate(@Const @ByRef NDArrayVector inputs, @StdVector DoublePointer tArgs, @Cast("Nd4jLong*") @StdVector LongPointer iArgs, @Cast("bool*") @StdVector boolean[] bArgs/*=std::vector()*/, @Cast("sd::DataType*") @StdVector IntPointer dArgs/*=std::vector()*/, @Cast("bool") boolean isInplace/*=false*/); + public native @ByVal ResultSet evaluate(@Const @ByRef NDArrayVector inputs, @StdVector DoubleBuffer tArgs, @Cast("Nd4jLong*") @StdVector LongBuffer iArgs, @Cast("bool*") @StdVector BooleanPointer bArgs/*=std::vector()*/, @Cast("sd::DataType*") @StdVector IntBuffer dArgs/*=std::vector()*/, @Cast("bool") boolean isInplace/*=false*/); + public native @ByVal ResultSet evaluate(@Const @ByRef NDArrayVector inputs, @StdVector double[] tArgs, @Cast("Nd4jLong*") @StdVector long[] iArgs, @Cast("bool*") @StdVector boolean[] bArgs/*=std::vector()*/, @Cast("sd::DataType*") @StdVector int[] dArgs/*=std::vector()*/, @Cast("bool") boolean isInplace/*=false*/); + + public native @Cast("Nd4jStatus") int execute(@ByRef RandomGenerator rng, @Const @ByRef NDArrayVector inputs, @Const @ByRef NDArrayVector outputs, @StdVector DoublePointer tArgs, @Cast("Nd4jLong*") @StdVector LongPointer iArgs, @Cast("bool*") @StdVector BooleanPointer bArgs, @Cast("sd::DataType*") @StdVector IntPointer dArgs/*=std::vector()*/, @Cast("bool") boolean isInplace/*=false*/, @Cast("sd::DataType") int type/*=sd::DataType::FLOAT32*/); + public native @Cast("Nd4jStatus") int execute(@ByRef RandomGenerator rng, @Const @ByRef NDArrayVector inputs, @Const @ByRef NDArrayVector outputs, @StdVector DoublePointer tArgs, @Cast("Nd4jLong*") @StdVector LongPointer iArgs, @Cast("bool*") @StdVector BooleanPointer bArgs); + public native @Cast("Nd4jStatus") int execute(@ByRef RandomGenerator rng, @Const @ByRef NDArrayVector inputs, @Const @ByRef NDArrayVector outputs, @StdVector DoubleBuffer tArgs, @Cast("Nd4jLong*") @StdVector LongBuffer iArgs, @Cast("bool*") @StdVector boolean[] bArgs, @Cast("sd::DataType*") @StdVector IntBuffer dArgs/*=std::vector()*/, @Cast("bool") boolean isInplace/*=false*/, @Cast("sd::DataType") int type/*=sd::DataType::FLOAT32*/); + public native @Cast("Nd4jStatus") int execute(@ByRef RandomGenerator rng, @Const @ByRef NDArrayVector inputs, @Const @ByRef NDArrayVector outputs, @StdVector DoubleBuffer tArgs, @Cast("Nd4jLong*") @StdVector LongBuffer iArgs, @Cast("bool*") @StdVector boolean[] bArgs); + public native @Cast("Nd4jStatus") int execute(@ByRef RandomGenerator rng, @Const @ByRef NDArrayVector inputs, @Const @ByRef NDArrayVector outputs, @StdVector double[] tArgs, @Cast("Nd4jLong*") @StdVector long[] iArgs, @Cast("bool*") @StdVector BooleanPointer bArgs, @Cast("sd::DataType*") @StdVector int[] dArgs/*=std::vector()*/, @Cast("bool") boolean isInplace/*=false*/, @Cast("sd::DataType") int type/*=sd::DataType::FLOAT32*/); + public native @Cast("Nd4jStatus") int execute(@ByRef RandomGenerator rng, @Const @ByRef NDArrayVector inputs, @Const @ByRef NDArrayVector outputs, @StdVector double[] tArgs, @Cast("Nd4jLong*") @StdVector long[] iArgs, @Cast("bool*") @StdVector BooleanPointer bArgs); + public native @Cast("Nd4jStatus") int execute(@ByRef RandomGenerator rng, @Const @ByRef NDArrayVector inputs, @Const @ByRef NDArrayVector outputs, @StdVector DoublePointer tArgs, @Cast("Nd4jLong*") @StdVector LongPointer iArgs, @Cast("bool*") @StdVector boolean[] bArgs, @Cast("sd::DataType*") @StdVector IntPointer dArgs/*=std::vector()*/, @Cast("bool") boolean isInplace/*=false*/, @Cast("sd::DataType") int type/*=sd::DataType::FLOAT32*/); + public native @Cast("Nd4jStatus") int execute(@ByRef RandomGenerator rng, @Const @ByRef NDArrayVector inputs, @Const @ByRef NDArrayVector outputs, @StdVector DoublePointer tArgs, @Cast("Nd4jLong*") @StdVector LongPointer iArgs, @Cast("bool*") @StdVector boolean[] bArgs); + public native @Cast("Nd4jStatus") int execute(@ByRef RandomGenerator rng, @Const @ByRef NDArrayVector inputs, @Const @ByRef NDArrayVector outputs, @StdVector DoubleBuffer tArgs, @Cast("Nd4jLong*") @StdVector LongBuffer iArgs, @Cast("bool*") @StdVector BooleanPointer bArgs, @Cast("sd::DataType*") @StdVector IntBuffer dArgs/*=std::vector()*/, @Cast("bool") boolean isInplace/*=false*/, @Cast("sd::DataType") int type/*=sd::DataType::FLOAT32*/); + public native @Cast("Nd4jStatus") int execute(@ByRef RandomGenerator rng, @Const @ByRef NDArrayVector inputs, @Const @ByRef NDArrayVector outputs, @StdVector DoubleBuffer tArgs, @Cast("Nd4jLong*") @StdVector LongBuffer iArgs, @Cast("bool*") @StdVector BooleanPointer bArgs); + public native @Cast("Nd4jStatus") int execute(@ByRef RandomGenerator rng, @Const @ByRef NDArrayVector inputs, @Const @ByRef NDArrayVector outputs, @StdVector double[] tArgs, @Cast("Nd4jLong*") @StdVector long[] iArgs, @Cast("bool*") @StdVector boolean[] bArgs, @Cast("sd::DataType*") @StdVector int[] dArgs/*=std::vector()*/, @Cast("bool") boolean isInplace/*=false*/, @Cast("sd::DataType") int type/*=sd::DataType::FLOAT32*/); + public native @Cast("Nd4jStatus") int execute(@ByRef RandomGenerator rng, @Const @ByRef NDArrayVector inputs, @Const @ByRef NDArrayVector outputs, @StdVector double[] tArgs, @Cast("Nd4jLong*") @StdVector long[] iArgs, @Cast("bool*") @StdVector boolean[] bArgs); + + public native @ByVal ResultSet execute(@Const @ByRef OpArgsHolder holder, @Cast("bool") boolean isInplace/*=false*/); + public native @ByVal ResultSet execute(@Const @ByRef OpArgsHolder holder); + + + // There methods provide various validation options + public native @Cast("Nd4jStatus") int validateNonEmptyInput(@ByRef Context block); + + // this method checks if all input arrays have equal lengths + public native @Cast("Nd4jStatus") int validateInputLengthMatch(@ByRef Context block); + + // this method checks if all input arrays have the same shapes (orders/strides are NOT checked) + public native @Cast("Nd4jStatus") int validateInputDimensionsMatch(@ByRef Context block); + + // this method check if all input arrays have the same orders + public native @Cast("Nd4jStatus") int validateOrdersMatch(@ByRef Context block); + + // this method checks if all input arrays are 2D + public native @Cast("Nd4jStatus") int validateInput2D(@ByRef Context block); + + // this method checks if all input arrays are 3D + public native @Cast("Nd4jStatus") int validateInput3D(@ByRef Context block); + + // this method checks if all input arrays are 4D + public native @Cast("Nd4jStatus") int validateInput4D(@ByRef Context block); + + // this method checks if all input arrays are ND + public native @Cast("Nd4jStatus") int validateInputDimensions(@ByRef Context block, int rank); + + // this method checks if number of available arguments matches op expectations + public native @Cast("Nd4jStatus") int validateArguments(@ByRef Context block); + } + + + +// #endif //LIBND4J_DECLARABLE_OPS_H + + +// Parsed from ops/declarable/DeclarableListOp.h + +/******************************************************************************* + * Copyright (c) 2015-2018 Skymind, Inc. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ + +// +// @author raver119@gmail.com +// + +// #ifndef LIBND4J_DECLARABLE_LIST_OP_H +// #define LIBND4J_DECLARABLE_LIST_OP_H + +// #include +// #include +// #include +// #include + @Namespace("sd::ops") public static class DeclarableListOp extends DeclarableOp { + static { Loader.load(); } + /** Pointer cast constructor. Invokes {@link Pointer#Pointer(Pointer)}. */ + public DeclarableListOp(Pointer p) { super(p); } + + + + public native @Cast("Nd4jStatus") int execute(Context block); + public native @ByVal ResultSet execute(NDArrayList list, @ByRef NDArrayVector inputs, @StdVector DoublePointer tArgs, @StdVector IntPointer iArgs); + public native @ByVal ResultSet execute(NDArrayList list, @ByRef NDArrayVector inputs, @StdVector DoubleBuffer tArgs, @StdVector IntBuffer iArgs); + public native @ByVal ResultSet execute(NDArrayList list, @ByRef NDArrayVector inputs, @StdVector double[] tArgs, @StdVector int[] iArgs); + + public native ShapeList calculateOutputShape(ShapeList inputShape, @ByRef Context block); + } + + + +// #endif + +// Parsed from ops/declarable/DeclarableReductionOp.h + +/******************************************************************************* + * Copyright (c) 2015-2018 Skymind, Inc. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ + +// +// Created by raver119 on 07.10.2017. +// + +// #ifndef LIBND4J_DECLARABLE_REDUCTION_OP_H +// #define LIBND4J_DECLARABLE_REDUCTION_OP_H + +// #include + @Namespace("sd::ops") public static class DeclarableReductionOp extends DeclarableOp { + static { Loader.load(); } + /** Pointer cast constructor. Invokes {@link Pointer#Pointer(Pointer)}. */ + public DeclarableReductionOp(Pointer p) { super(p); } + + + public native ShapeList calculateOutputShape(ShapeList inputShape, @ByRef Context block); + } + + + +// #endif //LIBND4J_DECLARABLE_REDUCTION_OP_H + + +// Parsed from ops/declarable/DeclarableCustomOp.h + +/******************************************************************************* + * Copyright (c) 2015-2018 Skymind, Inc. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ + +// +// Created by raver119 on 07.10.2017. +// + +// #ifndef LIBND4J_DECLARABLECUSTOMOP_H +// #define LIBND4J_DECLARABLECUSTOMOP_H + +// #include + @Namespace("sd::ops") public static class DeclarableCustomOp extends DeclarableOp { + static { Loader.load(); } + /** Pointer cast constructor. Invokes {@link Pointer#Pointer(Pointer)}. */ + public DeclarableCustomOp(Pointer p) { super(p); } + + + public native ShapeList calculateOutputShape(ShapeList inputShapes, @ByRef Context block); + } + + + +// #endif //LIBND4J_DECLARABLECUSTOMOP_H + + +// Parsed from ops/declarable/BooleanOp.h + +/******************************************************************************* + * Copyright (c) 2015-2018 Skymind, Inc. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ + +// +// Created by raver119 on 13.10.2017. +// + +// #ifndef LIBND4J_BOOLEANOP_H +// #define LIBND4J_BOOLEANOP_H + +// #include +// #include "OpDescriptor.h" +// #include "DeclarableOp.h" + @Namespace("sd::ops") @NoOffset public static class BooleanOp extends DeclarableOp { + static { Loader.load(); } + /** Pointer cast constructor. Invokes {@link Pointer#Pointer(Pointer)}. */ + public BooleanOp(Pointer p) { super(p); } + + + public native @Cast("bool") boolean verify(@Const @ByRef NDArrayVector args); + public native @Cast("bool") boolean verify(@ByRef Context block); + + public native @Cast("Nd4jStatus") int execute(Context block); + + public native ShapeList calculateOutputShape(ShapeList inputShape, @ByRef Context block); + } + + + + + +// #endif //LIBND4J_BOOLEANOP_H + +// Parsed from ops/declarable/LogicOp.h + +/******************************************************************************* + * Copyright (c) 2015-2018 Skymind, Inc. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ + +// +// Created by raver119 on 15.10.2017. +// + +// #ifndef LIBND4J_LOGICOP_H +// #define LIBND4J_LOGICOP_H + +// #include "DeclarableOp.h" + + /** + * Logic ops are unique snowflakes in any Graph. They dramatically change Graph Execution process, by introducing loops, conditions, etc. + * + * Their code is the part of GraphExecutioner logic. But we still want them to be expressed via Graph + * \tparam T + */ + @Namespace("sd::ops") public static class LogicOp extends DeclarableOp { + static { Loader.load(); } + /** Pointer cast constructor. Invokes {@link Pointer#Pointer(Pointer)}. */ + public LogicOp(Pointer p) { super(p); } + + public LogicOp(@Cast("char*") String name) { super((Pointer)null); allocate(name); } + private native void allocate(@Cast("char*") String name); + public LogicOp(@Cast("char*") BytePointer name) { super((Pointer)null); allocate(name); } + private native void allocate(@Cast("char*") BytePointer name); + + public native ShapeList calculateOutputShape(ShapeList inputShape, @ByRef Context block); + } + + + + +// #endif //LIBND4J_LOGICOP_H + + +// Parsed from ops/declarable/OpRegistrator.h + +/******************************************************************************* + * Copyright (c) 2015-2018 Skymind, Inc. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ + +// +// Created by raver119 on 07.10.2017. +// + +// #ifndef LIBND4J_OPREGISTRATOR_H +// #define LIBND4J_OPREGISTRATOR_H + +// #include +// #include +// #include +// #include +// #include +// #include +// #include + +// handlers part +// #include +// #include + +// #ifndef __JAVACPP_HACK__ + +// #endif + /** + * This class provides runtime ops lookup, based on opName or opHash. + * To build lookup directory we use *_OP_IMPL macro, which puts static structs at compile time in .cpp files, + * so once binary is executed, static objects are initialized automatically, and we get list of all ops + * available at runtime via this singleton. + * + */ + @Namespace("sd::ops") @NoOffset public static class OpRegistrator extends Pointer { + static { Loader.load(); } + /** Pointer cast constructor. Invokes {@link Pointer#Pointer(Pointer)}. */ + public OpRegistrator(Pointer p) { super(p); } + + + public static native OpRegistrator getInstance(); + + public static native void exitHandler(); + public static native void sigIntHandler(int sig); + public static native void sigSegVHandler(int sig); + + + public native @Cast("char*") String getAllCustomOperations(); + + /** + * This method registers operation in our registry, so we can use them later + * + * @param op + */ + public native @Cast("bool") boolean registerOperation(@Cast("char*") String name, DeclarableOp op); + public native @Cast("bool") boolean registerOperation(@Cast("char*") BytePointer name, DeclarableOp op); + public native @Cast("bool") boolean registerOperation(DeclarableOp op); + + public native void registerHelper(PlatformHelper op); + + public native @Cast("bool") boolean hasHelper(@Cast("Nd4jLong") long hash, @Cast("samediff::Engine") int engine); + + public native DeclarableOp getOperation(@Cast("char*") String name); + public native DeclarableOp getOperation(@Cast("char*") BytePointer name); + public native DeclarableOp getOperation(@Cast("Nd4jLong") long hash); + + public native PlatformHelper getPlatformHelper(@Cast("Nd4jLong") long hash, @Cast("samediff::Engine") int engine); + + public native @Cast("Nd4jLong*") @StdVector LongPointer getAllHashes(); + + public native int numberOfOperations(); + } + + + /* + * These structs are used to "register" our ops in OpRegistrator. + */ + + + + +// #endif //LIBND4J_OPREGISTRATOR_H + + +// Parsed from execution/ContextBuffers.h + +/******************************************************************************* + * Copyright (c) 2015-2018 Skymind, Inc. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ + +// +// @author raver119@gmail.com +// + +// #ifndef LIBND4J_CONTEXTBUFFERS_H +// #define LIBND4J_CONTEXTBUFFERS_H + +// #include +// #include +// #include + @Namespace("sd") @NoOffset public static class ContextBuffers extends Pointer { + static { Loader.load(); } + /** Pointer cast constructor. Invokes {@link Pointer#Pointer(Pointer)}. */ + public ContextBuffers(Pointer p) { super(p); } + /** Native array allocator. Access with {@link Pointer#position(long)}. */ + public ContextBuffers(long size) { super((Pointer)null); allocateArray(size); } + private native void allocateArray(long size); + @Override public ContextBuffers position(long position) { + return (ContextBuffers)super.position(position); + } + + public ContextBuffers() { super((Pointer)null); allocate(); } + private native void allocate(); + public ContextBuffers(@Const @ByRef ContextBuffers other) { super((Pointer)null); allocate(other); } + private native void allocate(@Const @ByRef ContextBuffers other); + public ContextBuffers(Pointer rPointer, Pointer sPointer, Pointer aPointer, @Cast("bool") boolean isOwner/*=false*/) { super((Pointer)null); allocate(rPointer, sPointer, aPointer, isOwner); } + private native void allocate(Pointer rPointer, Pointer sPointer, Pointer aPointer, @Cast("bool") boolean isOwner/*=false*/); + public ContextBuffers(Pointer rPointer, Pointer sPointer, Pointer aPointer) { super((Pointer)null); allocate(rPointer, sPointer, aPointer); } + private native void allocate(Pointer rPointer, Pointer sPointer, Pointer aPointer); + + public native @ByRef @Name("operator =") ContextBuffers put(@Const @ByRef ContextBuffers other); + + public native void release(); + + public native Pointer reductionBuffer(); + public native Pointer scalarBuffer(); + public native Pointer allocationBuffer(); + + public native Pointer execStream(); + public native Pointer specialStream(); + + public native void setReductionBuffer(Pointer pointer); + public native void setScalarBuffer(Pointer pointer); + public native void setAllocationBuffer(Pointer pointer); + + public native ErrorReference errorReference(); + + public native void triggerOwnership(@Cast("bool") boolean isOwner); + + public native int deviceId(); + + public native @Cast("bool") boolean isInitialized(); + } + + + +// #endif //DEV_TESTS_CONTEXTBUFFERS_H + + +// Parsed from execution/LaunchContext.h + +/******************************************************************************* + * Copyright (c) 2015-2018 Skymind, Inc. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ + +// +// Created by raver119 on 30.11.17. +// + +// #ifndef LIBND4J_CUDACONTEXT_H +// #define LIBND4J_CUDACONTEXT_H + + +// #ifdef __CUDABLAS__ +// #include +// #include +// #include +// #include +// #include "config.h" +// #endif + +// used for MKLDNN etc +// #if !defined(__STANDALONE_BUILD__) +// #include "config.h" +// #endif + +// #include +// #include +// #include +// #include +// #include +// #include +// #include +// #include + +@Namespace("sd") @NoOffset public static class LaunchContext extends Pointer { + static { Loader.load(); } + /** Native array allocator. Access with {@link Pointer#position(long)}. */ + public LaunchContext(long size) { super((Pointer)null); allocateArray(size); } + private native void allocateArray(long size); + @Override public LaunchContext position(long position) { + return (LaunchContext)super.position(position); + } + +// #ifdef __CUDABLAS__ + +// #ifndef __JAVACPP_HACK__ + + +// #endif // JCPP + +// #endif // CUDA + public LaunchContext(@Cast("Nd4jPointer") Pointer cudaStream, @Cast("Nd4jPointer") Pointer reductionPointer/*=nullptr*/, @Cast("Nd4jPointer") Pointer scalarPointer/*=nullptr*/, @Cast("Nd4jPointer") Pointer allocationPointer/*=nullptr*/) { super((Pointer)null); allocate(cudaStream, reductionPointer, scalarPointer, allocationPointer); } + private native void allocate(@Cast("Nd4jPointer") Pointer cudaStream, @Cast("Nd4jPointer") Pointer reductionPointer/*=nullptr*/, @Cast("Nd4jPointer") Pointer scalarPointer/*=nullptr*/, @Cast("Nd4jPointer") Pointer allocationPointer/*=nullptr*/); + public LaunchContext(@Cast("Nd4jPointer") Pointer cudaStream) { super((Pointer)null); allocate(cudaStream); } + private native void allocate(@Cast("Nd4jPointer") Pointer cudaStream); + public LaunchContext() { super((Pointer)null); allocate(); } + private native void allocate(); + public native Workspace getWorkspace(); + public native void setWorkspace(Workspace theWorkspace); + + public native Pointer engine(); + + public native int getDeviceID(); + public native void setDeviceID(int deviceID); + public native ErrorReference errorReference(); + + public static native @Cast("bool") boolean isInitialized(); + public static native void releaseBuffers(); + + + public static native LaunchContext defaultContext(); + + + public static native void swapContextBuffers(@ByRef ContextBuffers buffers); + +} + + + + +// #endif //LIBND4J_CUDACONTEXT_H + + +// Parsed from array/ShapeDescriptor.h + +/******************************************************************************* + * Copyright (c) 2015-2018 Skymind, Inc. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ + +// +// @author raver119@gmail.com +// + +// #ifndef DEV_TESTS_SHAPEDESCRIPTOR_H +// #define DEV_TESTS_SHAPEDESCRIPTOR_H + +// #include +// #include +// #include +// #include +// #include +// #include + +@Namespace("sd") @NoOffset public static class ShapeDescriptor extends Pointer { + static { Loader.load(); } + /** Pointer cast constructor. Invokes {@link Pointer#Pointer(Pointer)}. */ + public ShapeDescriptor(Pointer p) { super(p); } + /** Native array allocator. Access with {@link Pointer#position(long)}. */ + public ShapeDescriptor(long size) { super((Pointer)null); allocateArray(size); } + private native void allocateArray(long size); + @Override public ShapeDescriptor position(long position) { + return (ShapeDescriptor)super.position(position); + } + + public ShapeDescriptor(@Const @ByRef ShapeDescriptor other) { super((Pointer)null); allocate(other); } + private native void allocate(@Const @ByRef ShapeDescriptor other); + public ShapeDescriptor(@Cast("const Nd4jLong*") LongPointer shapeInfo, @Cast("bool") boolean inheritDtype/*=true*/) { super((Pointer)null); allocate(shapeInfo, inheritDtype); } + private native void allocate(@Cast("const Nd4jLong*") LongPointer shapeInfo, @Cast("bool") boolean inheritDtype/*=true*/); + public ShapeDescriptor(@Cast("const Nd4jLong*") LongPointer shapeInfo) { super((Pointer)null); allocate(shapeInfo); } + private native void allocate(@Cast("const Nd4jLong*") LongPointer shapeInfo); + public ShapeDescriptor(@Cast("const Nd4jLong*") LongBuffer shapeInfo, @Cast("bool") boolean inheritDtype/*=true*/) { super((Pointer)null); allocate(shapeInfo, inheritDtype); } + private native void allocate(@Cast("const Nd4jLong*") LongBuffer shapeInfo, @Cast("bool") boolean inheritDtype/*=true*/); + public ShapeDescriptor(@Cast("const Nd4jLong*") LongBuffer shapeInfo) { super((Pointer)null); allocate(shapeInfo); } + private native void allocate(@Cast("const Nd4jLong*") LongBuffer shapeInfo); + public ShapeDescriptor(@Cast("const Nd4jLong*") long[] shapeInfo, @Cast("bool") boolean inheritDtype/*=true*/) { super((Pointer)null); allocate(shapeInfo, inheritDtype); } + private native void allocate(@Cast("const Nd4jLong*") long[] shapeInfo, @Cast("bool") boolean inheritDtype/*=true*/); + public ShapeDescriptor(@Cast("const Nd4jLong*") long[] shapeInfo) { super((Pointer)null); allocate(shapeInfo); } + private native void allocate(@Cast("const Nd4jLong*") long[] shapeInfo); + public ShapeDescriptor(@Cast("const Nd4jLong*") LongPointer shapeInfo, @Cast("const sd::DataType") int dtypeOverride) { super((Pointer)null); allocate(shapeInfo, dtypeOverride); } + private native void allocate(@Cast("const Nd4jLong*") LongPointer shapeInfo, @Cast("const sd::DataType") int dtypeOverride); + public ShapeDescriptor(@Cast("const Nd4jLong*") LongBuffer shapeInfo, @Cast("const sd::DataType") int dtypeOverride) { super((Pointer)null); allocate(shapeInfo, dtypeOverride); } + private native void allocate(@Cast("const Nd4jLong*") LongBuffer shapeInfo, @Cast("const sd::DataType") int dtypeOverride); + public ShapeDescriptor(@Cast("const Nd4jLong*") long[] shapeInfo, @Cast("const sd::DataType") int dtypeOverride) { super((Pointer)null); allocate(shapeInfo, dtypeOverride); } + private native void allocate(@Cast("const Nd4jLong*") long[] shapeInfo, @Cast("const sd::DataType") int dtypeOverride); + public ShapeDescriptor(@Cast("const Nd4jLong*") LongPointer shapeInfo, @Cast("const Nd4jLong*") LongPointer dtypeOverride) { super((Pointer)null); allocate(shapeInfo, dtypeOverride); } + private native void allocate(@Cast("const Nd4jLong*") LongPointer shapeInfo, @Cast("const Nd4jLong*") LongPointer dtypeOverride); + public ShapeDescriptor(@Cast("const Nd4jLong*") LongBuffer shapeInfo, @Cast("const Nd4jLong*") LongBuffer dtypeOverride) { super((Pointer)null); allocate(shapeInfo, dtypeOverride); } + private native void allocate(@Cast("const Nd4jLong*") LongBuffer shapeInfo, @Cast("const Nd4jLong*") LongBuffer dtypeOverride); + public ShapeDescriptor(@Cast("const Nd4jLong*") long[] shapeInfo, @Cast("const Nd4jLong*") long[] dtypeOverride) { super((Pointer)null); allocate(shapeInfo, dtypeOverride); } + private native void allocate(@Cast("const Nd4jLong*") long[] shapeInfo, @Cast("const Nd4jLong*") long[] dtypeOverride); + public ShapeDescriptor(@Cast("const Nd4jLong*") LongPointer shapeInfo, @Cast("const Nd4jLong*") LongPointer dtypeOverride, @Cast("const Nd4jLong*") LongPointer orderOverride) { super((Pointer)null); allocate(shapeInfo, dtypeOverride, orderOverride); } + private native void allocate(@Cast("const Nd4jLong*") LongPointer shapeInfo, @Cast("const Nd4jLong*") LongPointer dtypeOverride, @Cast("const Nd4jLong*") LongPointer orderOverride); + public ShapeDescriptor(@Cast("const Nd4jLong*") LongBuffer shapeInfo, @Cast("const Nd4jLong*") LongBuffer dtypeOverride, @Cast("const Nd4jLong*") LongBuffer orderOverride) { super((Pointer)null); allocate(shapeInfo, dtypeOverride, orderOverride); } + private native void allocate(@Cast("const Nd4jLong*") LongBuffer shapeInfo, @Cast("const Nd4jLong*") LongBuffer dtypeOverride, @Cast("const Nd4jLong*") LongBuffer orderOverride); + public ShapeDescriptor(@Cast("const Nd4jLong*") long[] shapeInfo, @Cast("const Nd4jLong*") long[] dtypeOverride, @Cast("const Nd4jLong*") long[] orderOverride) { super((Pointer)null); allocate(shapeInfo, dtypeOverride, orderOverride); } + private native void allocate(@Cast("const Nd4jLong*") long[] shapeInfo, @Cast("const Nd4jLong*") long[] dtypeOverride, @Cast("const Nd4jLong*") long[] orderOverride); + public ShapeDescriptor(@Cast("const sd::DataType") int type, @Cast("const Nd4jLong") long length) { super((Pointer)null); allocate(type, length); } + private native void allocate(@Cast("const sd::DataType") int type, @Cast("const Nd4jLong") long length); + public ShapeDescriptor(@Cast("const sd::DataType") int type, byte order, @Cast("const Nd4jLong*") LongPointer shape, int rank) { super((Pointer)null); allocate(type, order, shape, rank); } + private native void allocate(@Cast("const sd::DataType") int type, byte order, @Cast("const Nd4jLong*") LongPointer shape, int rank); + public ShapeDescriptor(@Cast("const sd::DataType") int type, byte order, @Cast("const Nd4jLong*") LongBuffer shape, int rank) { super((Pointer)null); allocate(type, order, shape, rank); } + private native void allocate(@Cast("const sd::DataType") int type, byte order, @Cast("const Nd4jLong*") LongBuffer shape, int rank); + public ShapeDescriptor(@Cast("const sd::DataType") int type, byte order, @Cast("const Nd4jLong*") long[] shape, int rank) { super((Pointer)null); allocate(type, order, shape, rank); } + private native void allocate(@Cast("const sd::DataType") int type, byte order, @Cast("const Nd4jLong*") long[] shape, int rank); + public ShapeDescriptor(@Cast("const sd::DataType") int type, byte order, @Cast("const Nd4jLong*") LongPointer shape, @Cast("const Nd4jLong*") LongPointer strides, int rank, @Cast("Nd4jLong") long ews, @Cast("const bool") boolean empty) { super((Pointer)null); allocate(type, order, shape, strides, rank, ews, empty); } + private native void allocate(@Cast("const sd::DataType") int type, byte order, @Cast("const Nd4jLong*") LongPointer shape, @Cast("const Nd4jLong*") LongPointer strides, int rank, @Cast("Nd4jLong") long ews, @Cast("const bool") boolean empty); + public ShapeDescriptor(@Cast("const sd::DataType") int type, byte order, @Cast("const Nd4jLong*") LongBuffer shape, @Cast("const Nd4jLong*") LongBuffer strides, int rank, @Cast("Nd4jLong") long ews, @Cast("const bool") boolean empty) { super((Pointer)null); allocate(type, order, shape, strides, rank, ews, empty); } + private native void allocate(@Cast("const sd::DataType") int type, byte order, @Cast("const Nd4jLong*") LongBuffer shape, @Cast("const Nd4jLong*") LongBuffer strides, int rank, @Cast("Nd4jLong") long ews, @Cast("const bool") boolean empty); + public ShapeDescriptor(@Cast("const sd::DataType") int type, byte order, @Cast("const Nd4jLong*") long[] shape, @Cast("const Nd4jLong*") long[] strides, int rank, @Cast("Nd4jLong") long ews, @Cast("const bool") boolean empty) { super((Pointer)null); allocate(type, order, shape, strides, rank, ews, empty); } + private native void allocate(@Cast("const sd::DataType") int type, byte order, @Cast("const Nd4jLong*") long[] shape, @Cast("const Nd4jLong*") long[] strides, int rank, @Cast("Nd4jLong") long ews, @Cast("const bool") boolean empty); + public ShapeDescriptor(@Cast("const sd::DataType") int type, byte order, @Cast("Nd4jLong*") @StdVector LongPointer shape) { super((Pointer)null); allocate(type, order, shape); } + private native void allocate(@Cast("const sd::DataType") int type, byte order, @Cast("Nd4jLong*") @StdVector LongPointer shape); + public ShapeDescriptor(@Cast("const sd::DataType") int type, byte order, @Cast("Nd4jLong*") @StdVector LongBuffer shape) { super((Pointer)null); allocate(type, order, shape); } + private native void allocate(@Cast("const sd::DataType") int type, byte order, @Cast("Nd4jLong*") @StdVector LongBuffer shape); + public ShapeDescriptor(@Cast("const sd::DataType") int type, byte order, @Cast("Nd4jLong*") @StdVector long[] shape) { super((Pointer)null); allocate(type, order, shape); } + private native void allocate(@Cast("const sd::DataType") int type, byte order, @Cast("Nd4jLong*") @StdVector long[] shape); + public ShapeDescriptor(@Cast("const sd::DataType") int type, byte order, @Cast("Nd4jLong*") @StdVector LongPointer shape, @Cast("Nd4jLong*") @StdVector LongPointer strides) { super((Pointer)null); allocate(type, order, shape, strides); } + private native void allocate(@Cast("const sd::DataType") int type, byte order, @Cast("Nd4jLong*") @StdVector LongPointer shape, @Cast("Nd4jLong*") @StdVector LongPointer strides); + public ShapeDescriptor(@Cast("const sd::DataType") int type, byte order, @Cast("Nd4jLong*") @StdVector LongBuffer shape, @Cast("Nd4jLong*") @StdVector LongBuffer strides) { super((Pointer)null); allocate(type, order, shape, strides); } + private native void allocate(@Cast("const sd::DataType") int type, byte order, @Cast("Nd4jLong*") @StdVector LongBuffer shape, @Cast("Nd4jLong*") @StdVector LongBuffer strides); + public ShapeDescriptor(@Cast("const sd::DataType") int type, byte order, @Cast("Nd4jLong*") @StdVector long[] shape, @Cast("Nd4jLong*") @StdVector long[] strides) { super((Pointer)null); allocate(type, order, shape, strides); } + private native void allocate(@Cast("const sd::DataType") int type, byte order, @Cast("Nd4jLong*") @StdVector long[] shape, @Cast("Nd4jLong*") @StdVector long[] strides); + public ShapeDescriptor(@Cast("const sd::DataType") int type, byte order, @Cast("Nd4jLong*") @StdVector LongPointer shape, @Cast("Nd4jLong*") @StdVector LongPointer strides, @Cast("const Nd4jLong") long ews) { super((Pointer)null); allocate(type, order, shape, strides, ews); } + private native void allocate(@Cast("const sd::DataType") int type, byte order, @Cast("Nd4jLong*") @StdVector LongPointer shape, @Cast("Nd4jLong*") @StdVector LongPointer strides, @Cast("const Nd4jLong") long ews); + public ShapeDescriptor(@Cast("const sd::DataType") int type, byte order, @Cast("Nd4jLong*") @StdVector LongBuffer shape, @Cast("Nd4jLong*") @StdVector LongBuffer strides, @Cast("const Nd4jLong") long ews) { super((Pointer)null); allocate(type, order, shape, strides, ews); } + private native void allocate(@Cast("const sd::DataType") int type, byte order, @Cast("Nd4jLong*") @StdVector LongBuffer shape, @Cast("Nd4jLong*") @StdVector LongBuffer strides, @Cast("const Nd4jLong") long ews); + public ShapeDescriptor(@Cast("const sd::DataType") int type, byte order, @Cast("Nd4jLong*") @StdVector long[] shape, @Cast("Nd4jLong*") @StdVector long[] strides, @Cast("const Nd4jLong") long ews) { super((Pointer)null); allocate(type, order, shape, strides, ews); } + private native void allocate(@Cast("const sd::DataType") int type, byte order, @Cast("Nd4jLong*") @StdVector long[] shape, @Cast("Nd4jLong*") @StdVector long[] strides, @Cast("const Nd4jLong") long ews); + public ShapeDescriptor() { super((Pointer)null); allocate(); } + private native void allocate(); + + public native int rank(); + public native @Cast("Nd4jLong") long ews(); + public native @Cast("Nd4jLong") long arrLength(); + public native char order(); + public native @Cast("sd::DataType") int dataType(); + public native @Cast("bool") boolean isEmpty(); + public native @Cast("Nd4jLong*") @StdVector LongPointer shape(); + public native @Cast("Nd4jLong*") @StdVector LongPointer strides(); + + // we use default copy assignment operator + public native @ByRef @Name("operator =") ShapeDescriptor put(@Const @ByRef ShapeDescriptor other); + + // we use default move assignment operator + + // equal to operator + public native @Cast("bool") @Name("operator ==") boolean equals(@Const @ByRef ShapeDescriptor other); + + // less than operator + public native @Cast("bool") @Name("operator <") boolean lessThan(@Const @ByRef ShapeDescriptor other); + + public native @Cast("Nd4jLong*") LongPointer toShapeInfo(); + + + public static native @ByVal ShapeDescriptor emptyDescriptor(@Cast("const sd::DataType") int type); + public static native @ByVal ShapeDescriptor scalarDescriptor(@Cast("const sd::DataType") int type); + public static native @ByVal ShapeDescriptor vectorDescriptor(@Cast("const Nd4jLong") long length, @Cast("const sd::DataType") int type); + } + + +// #ifndef __JAVACPP_HACK__ + +// #endif + + +// #endif //DEV_TESTS_SHAPEDESCRIPTOR_H + + +// Parsed from array/TadDescriptor.h + +/******************************************************************************* + * Copyright (c) 2015-2018 Skymind, Inc. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ + +// +// @author raver119@gmail.com +// + +// #ifndef DEV_TESTS_TADDESCRIPTOR_H +// #define DEV_TESTS_TADDESCRIPTOR_H + +// #include "ShapeDescriptor.h" +// #include + @Namespace("sd") @NoOffset public static class TadDescriptor extends Pointer { + static { Loader.load(); } + /** Pointer cast constructor. Invokes {@link Pointer#Pointer(Pointer)}. */ + public TadDescriptor(Pointer p) { super(p); } + + public TadDescriptor(@Cast("const Nd4jLong*") LongPointer originalShape, @Const IntPointer dimensions, int length, @Cast("const bool") boolean keepUnitiesInShape/*=false*/) { super((Pointer)null); allocate(originalShape, dimensions, length, keepUnitiesInShape); } + private native void allocate(@Cast("const Nd4jLong*") LongPointer originalShape, @Const IntPointer dimensions, int length, @Cast("const bool") boolean keepUnitiesInShape/*=false*/); + public TadDescriptor(@Cast("const Nd4jLong*") LongPointer originalShape, @Const IntPointer dimensions, int length) { super((Pointer)null); allocate(originalShape, dimensions, length); } + private native void allocate(@Cast("const Nd4jLong*") LongPointer originalShape, @Const IntPointer dimensions, int length); + public TadDescriptor(@Cast("const Nd4jLong*") LongBuffer originalShape, @Const IntBuffer dimensions, int length, @Cast("const bool") boolean keepUnitiesInShape/*=false*/) { super((Pointer)null); allocate(originalShape, dimensions, length, keepUnitiesInShape); } + private native void allocate(@Cast("const Nd4jLong*") LongBuffer originalShape, @Const IntBuffer dimensions, int length, @Cast("const bool") boolean keepUnitiesInShape/*=false*/); + public TadDescriptor(@Cast("const Nd4jLong*") LongBuffer originalShape, @Const IntBuffer dimensions, int length) { super((Pointer)null); allocate(originalShape, dimensions, length); } + private native void allocate(@Cast("const Nd4jLong*") LongBuffer originalShape, @Const IntBuffer dimensions, int length); + public TadDescriptor(@Cast("const Nd4jLong*") long[] originalShape, @Const int[] dimensions, int length, @Cast("const bool") boolean keepUnitiesInShape/*=false*/) { super((Pointer)null); allocate(originalShape, dimensions, length, keepUnitiesInShape); } + private native void allocate(@Cast("const Nd4jLong*") long[] originalShape, @Const int[] dimensions, int length, @Cast("const bool") boolean keepUnitiesInShape/*=false*/); + public TadDescriptor(@Cast("const Nd4jLong*") long[] originalShape, @Const int[] dimensions, int length) { super((Pointer)null); allocate(originalShape, dimensions, length); } + private native void allocate(@Cast("const Nd4jLong*") long[] originalShape, @Const int[] dimensions, int length); + public TadDescriptor(@Const @ByRef ShapeDescriptor descriptor, @StdVector IntPointer dimensions, @Cast("const bool") boolean keepUnitiesInShape/*=false*/) { super((Pointer)null); allocate(descriptor, dimensions, keepUnitiesInShape); } + private native void allocate(@Const @ByRef ShapeDescriptor descriptor, @StdVector IntPointer dimensions, @Cast("const bool") boolean keepUnitiesInShape/*=false*/); + public TadDescriptor(@Const @ByRef ShapeDescriptor descriptor, @StdVector IntPointer dimensions) { super((Pointer)null); allocate(descriptor, dimensions); } + private native void allocate(@Const @ByRef ShapeDescriptor descriptor, @StdVector IntPointer dimensions); + public TadDescriptor(@Const @ByRef ShapeDescriptor descriptor, @StdVector IntBuffer dimensions, @Cast("const bool") boolean keepUnitiesInShape/*=false*/) { super((Pointer)null); allocate(descriptor, dimensions, keepUnitiesInShape); } + private native void allocate(@Const @ByRef ShapeDescriptor descriptor, @StdVector IntBuffer dimensions, @Cast("const bool") boolean keepUnitiesInShape/*=false*/); + public TadDescriptor(@Const @ByRef ShapeDescriptor descriptor, @StdVector IntBuffer dimensions) { super((Pointer)null); allocate(descriptor, dimensions); } + private native void allocate(@Const @ByRef ShapeDescriptor descriptor, @StdVector IntBuffer dimensions); + public TadDescriptor(@Const @ByRef ShapeDescriptor descriptor, @StdVector int[] dimensions, @Cast("const bool") boolean keepUnitiesInShape/*=false*/) { super((Pointer)null); allocate(descriptor, dimensions, keepUnitiesInShape); } + private native void allocate(@Const @ByRef ShapeDescriptor descriptor, @StdVector int[] dimensions, @Cast("const bool") boolean keepUnitiesInShape/*=false*/); + public TadDescriptor(@Const @ByRef ShapeDescriptor descriptor, @StdVector int[] dimensions) { super((Pointer)null); allocate(descriptor, dimensions); } + private native void allocate(@Const @ByRef ShapeDescriptor descriptor, @StdVector int[] dimensions); + public TadDescriptor(@Const @ByRef TadDescriptor other) { super((Pointer)null); allocate(other); } + private native void allocate(@Const @ByRef TadDescriptor other); + + // we use default copy assignment operator + public native @ByRef @Name("operator =") TadDescriptor put(@Const @ByRef TadDescriptor other); + + // we use default move assignment operator + + // equal to operator + public native @Cast("bool") @Name("operator ==") boolean equals(@Const @ByRef TadDescriptor other); + + // less than operator + public native @Cast("bool") @Name("operator <") boolean lessThan(@Const @ByRef TadDescriptor other); + + public native @StdVector IntPointer axis(); + public native @ByRef ShapeDescriptor originalShape(); + public native @Const @ByRef ShapeDescriptor originalShapeConst(); + public native @Cast("bool") boolean areUnitiesinShape(); + } + + +// #ifndef __JAVACPP_HACK__ + +// #endif + + +// #endif //DEV_TESTS_TADDESCRIPTOR_H + + +// Parsed from helpers/DebugInfo.h + +/******************************************************************************* + * Copyright (c) 2015-2018 Skymind, Inc. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ + +// +// Created by GS aka shugeo on 3/12/19. +// + +// #ifndef LIBND4J__DEBUG_INFO_HELPER__H +// #define LIBND4J__DEBUG_INFO_HELPER__H + +// #include +// #include +// #include +// #include +// #include +// #include +// #include + +// #ifdef __CUDACC__ + +// #endif + @Namespace("sd") public static class DebugInfo extends Pointer { + static { Loader.load(); } + /** Default native constructor. */ + public DebugInfo() { super((Pointer)null); allocate(); } + /** Native array allocator. Access with {@link Pointer#position(long)}. */ + public DebugInfo(long size) { super((Pointer)null); allocateArray(size); } + /** Pointer cast constructor. Invokes {@link Pointer#Pointer(Pointer)}. */ + public DebugInfo(Pointer p) { super(p); } + private native void allocate(); + private native void allocateArray(long size); + @Override public DebugInfo position(long position) { + return (DebugInfo)super.position(position); + } + + public native double _minValue(); public native DebugInfo _minValue(double setter); + public native double _maxValue(); public native DebugInfo _maxValue(double setter); + public native double _meanValue(); public native DebugInfo _meanValue(double setter); + public native double _stdDevValue(); public native DebugInfo _stdDevValue(double setter); + public native @Cast("Nd4jLong") long _zeroCount(); public native DebugInfo _zeroCount(long setter); + public native @Cast("Nd4jLong") long _positiveCount(); public native DebugInfo _positiveCount(long setter); + public native @Cast("Nd4jLong") long _negativeCount(); public native DebugInfo _negativeCount(long setter); + public native @Cast("Nd4jLong") long _infCount(); public native DebugInfo _infCount(long setter); + public native @Cast("Nd4jLong") long _nanCount(); public native DebugInfo _nanCount(long setter); + } + + @Namespace("sd") public static native @Cast("bool") @Name("operator ==") boolean equals(@Const @ByRef DebugInfo first, @Const @ByRef DebugInfo second); + + + + +// #endif //LIBND4J_DEBUGHELPER_H + + +// Parsed from ops/declarable/CustomOperations.h + +/******************************************************************************* + * Copyright (c) 2015-2018 Skymind, Inc. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ + +// +// Created by raver119 on 07.10.2017. +// + +// #ifndef LIBND4J_CUSTOMOPERATIONS_H +// #define LIBND4J_CUSTOMOPERATIONS_H + +// #include +// #include +// #include +// #include +// #include +// #include +// #include +// #include +// #include +// #include +// #include +// #include +// #include +// #include +// #include +// #include +// #include +// #include +// #include +// #include +// #include +// #include +// #include +// #include +// #include +// #include +// #include +// #include +// #include +// #include +// #include +// #include +// #include +// #include + @Namespace("sd") public static class _loader extends Pointer { + static { Loader.load(); } + /** Pointer cast constructor. Invokes {@link Pointer#Pointer(Pointer)}. */ + public _loader(Pointer p) { super(p); } + /** Native array allocator. Access with {@link Pointer#position(long)}. */ + public _loader(long size) { super((Pointer)null); allocateArray(size); } + private native void allocateArray(long size); + @Override public _loader position(long position) { + return (_loader)super.position(position); + } + + public _loader() { super((Pointer)null); allocate(); } + private native void allocate(); + } + + // logic ops + + + + + + + + /** + * This operations exposes given arguments as it's own outputs, but does it only once. + * Subsequent calls will be served directly by this op. + * + * PLEASE NOTE: This operation is internal graph operation, and shouldn't be used directly usually. + */ + + + + +// #endif //LIBND4J_CUSTOMOPERATIONS_H + + +} diff --git a/nd4j/nd4j-backends/nd4j-backend-impls/nd4j-cuda/src/main/java/org/nd4j/nativeblas/Nd4jCudaPresets.java b/nd4j/nd4j-backends/nd4j-backend-impls/nd4j-cuda/src/main/java/org/nd4j/nativeblas/Nd4jCudaPresets.java index cdfefce31..1f3f7bde4 100644 --- a/nd4j/nd4j-backends/nd4j-backend-impls/nd4j-cuda/src/main/java/org/nd4j/nativeblas/Nd4jCudaPresets.java +++ b/nd4j/nd4j-backends/nd4j-backend-impls/nd4j-cuda/src/main/java/org/nd4j/nativeblas/Nd4jCudaPresets.java @@ -41,9 +41,9 @@ import org.bytedeco.javacpp.tools.InfoMapper; "execution/Engine.h", "execution/ExecutionMode.h", "memory/MemoryType.h", - "Environment.h", + "system/Environment.h", "types/utf8string.h", - "NativeOps.h", + "legacy/NativeOps.h", "memory/ExternalWorkspace.h", "memory/Workspace.h", "indexing/NDIndex.h", @@ -53,7 +53,7 @@ import org.bytedeco.javacpp.tools.InfoMapper; "graph/ArgumentsList.h", "types/pair.h", "types/pair.h", - "NDArray.h", + "array/NDArray.h", "array/NDArrayList.h", "array/ResultSet.h", "graph/RandomGenerator.h", @@ -72,7 +72,7 @@ import org.bytedeco.javacpp.tools.InfoMapper; "graph/ResultWrapper.h", "helpers/shape.h", "array/ShapeList.h", - //"op_boilerplate.h", + //"system/op_boilerplate.h", "ops/InputType.h", "ops/declarable/OpDescriptor.h", "ops/declarable/PlatformHelper.h", @@ -185,38 +185,38 @@ public class Nd4jCudaPresets implements LoadEnabled, InfoMapper { infoMap.put(new Info("__CUDACC__", "MAX_UINT", "HAVE_MKLDNN").define(false)) .put(new Info("__JAVACPP_HACK__", "LIBND4J_ALL_OPS","__CUDABLAS__").define(true)) - .put(new Info("std::initializer_list", "cnpy::NpyArray", "nd4j::NDArray::applyLambda", "nd4j::NDArray::applyPairwiseLambda", - "nd4j::graph::FlatResult", "nd4j::graph::FlatVariable", "nd4j::NDArray::subarray").skip()) + .put(new Info("std::initializer_list", "cnpy::NpyArray", "sd::NDArray::applyLambda", "sd::NDArray::applyPairwiseLambda", + "sd::graph::FlatResult", "sd::graph::FlatVariable", "sd::NDArray::subarray").skip()) .put(new Info("std::string").annotations("@StdString").valueTypes("BytePointer", "String") .pointerTypes("@Cast({\"char*\", \"std::string*\"}) BytePointer")) .put(new Info("std::pair").pointerTypes("IntIntPair").define()) .put(new Info("std::vector >").pointerTypes("IntVectorVector").define()) .put(new Info("std::vector >").pointerTypes("LongVectorVector").define()) - .put(new Info("std::vector").pointerTypes("NDArrayVector").define()) - .put(new Info("std::vector").pointerTypes("ConstNDArrayVector").define()) + .put(new Info("std::vector").pointerTypes("NDArrayVector").define()) + .put(new Info("std::vector").pointerTypes("ConstNDArrayVector").define()) .put(new Info("bool").cast().valueTypes("boolean").pointerTypes("BooleanPointer", "boolean[]")) - .put(new Info("nd4j::graph::ResultWrapper").base("org.nd4j.nativeblas.ResultWrapperAbstraction").define()) - .put(new Info("nd4j::IndicesList").purify()); + .put(new Info("sd::graph::ResultWrapper").base("org.nd4j.nativeblas.ResultWrapperAbstraction").define()) + .put(new Info("sd::IndicesList").purify()); /* String classTemplates[] = { - "nd4j::NDArray", - "nd4j::NDArrayList", - "nd4j::ResultSet", - "nd4j::OpArgsHolder", - "nd4j::graph::GraphState", - "nd4j::graph::Variable", - "nd4j::graph::VariablesSet", - "nd4j::graph::Stash", - "nd4j::graph::VariableSpace", - "nd4j::graph::Context", - "nd4j::graph::ContextPrototype", - "nd4j::ops::DeclarableOp", - "nd4j::ops::DeclarableListOp", - "nd4j::ops::DeclarableReductionOp", - "nd4j::ops::DeclarableCustomOp", - "nd4j::ops::BooleanOp", - "nd4j::ops::BroadcastableOp", - "nd4j::ops::LogicOp"}; + "sd::NDArray", + "sd::NDArrayList", + "sd::ResultSet", + "sd::OpArgsHolder", + "sd::graph::GraphState", + "sd::graph::Variable", + "sd::graph::VariablesSet", + "sd::graph::Stash", + "sd::graph::VariableSpace", + "sd::graph::Context", + "sd::graph::ContextPrototype", + "sd::ops::DeclarableOp", + "sd::ops::DeclarableListOp", + "sd::ops::DeclarableReductionOp", + "sd::ops::DeclarableCustomOp", + "sd::ops::BooleanOp", + "sd::ops::BroadcastableOp", + "sd::ops::LogicOp"}; for (String t : classTemplates) { String s = t.substring(t.lastIndexOf(':') + 1); infoMap.put(new Info(t + "").pointerTypes("Float" + s)) @@ -225,6 +225,6 @@ public class Nd4jCudaPresets implements LoadEnabled, InfoMapper { } */ - infoMap.put(new Info("nd4j::ops::OpRegistrator::updateMSVC").skip()); + infoMap.put(new Info("sd::ops::OpRegistrator::updateMSVC").skip()); } } diff --git a/nd4j/nd4j-backends/nd4j-backend-impls/nd4j-native/pom.xml b/nd4j/nd4j-backends/nd4j-backend-impls/nd4j-native/pom.xml index 48cdc3e03..379f69563 100644 --- a/nd4j/nd4j-backends/nd4j-backend-impls/nd4j-native/pom.xml +++ b/nd4j/nd4j-backends/nd4j-backend-impls/nd4j-native/pom.xml @@ -50,17 +50,7 @@ ${openblas.version}-${javacpp-presets.version} ${dependency.platform} - - org.bytedeco - mkl - ${mkl.version}-${javacpp-presets.version} - - - org.bytedeco - mkl - ${mkl.version}-${javacpp-presets.version} - ${dependency.platform2} - + - android-arm - android-arm64 - android-x86 - android-x86_64 - ios-arm - ios-arm64 - ios-x86 - ios-x86_64 - linux-armhf - linux-arm64 - linux-ppc64le - linux-x86 - linux-x86_64 - macosx-x86_64 - windows-x86 - windows-x86_64 + android-arm${javacpp.platform.extension} + android-arm64${javacpp.platform.extension} + android-x86${javacpp.platform.extension} + android-x86_64${javacpp.platform.extension} + ios-arm${javacpp.platform.extension} + ios-arm64${javacpp.platform.extension} + ios-x86${javacpp.platform.extension} + ios-x86_64${javacpp.platform.extension} + linux-armhf${javacpp.platform.extension} + linux-arm64${javacpp.platform.extension} + linux-ppc64le${javacpp.platform.extension} + linux-x86${javacpp.platform.extension} + linux-x86_64${javacpp.platform.extension} + macosx-x86_64${javacpp.platform.extension} + windows-x86${javacpp.platform.extension} + windows-x86_64${javacpp.platform.extension} @@ -234,22 +234,22 @@ - ${javacpp.platform} - ${javacpp.platform} - ${javacpp.platform} - ${javacpp.platform} - ${javacpp.platform} - ${javacpp.platform} - ${javacpp.platform} - ${javacpp.platform} - ${javacpp.platform} - ${javacpp.platform} - ${javacpp.platform} - ${javacpp.platform} - ${javacpp.platform} - ${javacpp.platform} - ${javacpp.platform} - ${javacpp.platform} + ${javacpp.platform}${javacpp.platform.extension} + ${javacpp.platform}${javacpp.platform.extension} + ${javacpp.platform}${javacpp.platform.extension} + ${javacpp.platform}${javacpp.platform.extension} + ${javacpp.platform}${javacpp.platform.extension} + ${javacpp.platform}${javacpp.platform.extension} + ${javacpp.platform}${javacpp.platform.extension} + ${javacpp.platform}${javacpp.platform.extension} + ${javacpp.platform}${javacpp.platform.extension} + ${javacpp.platform}${javacpp.platform.extension} + ${javacpp.platform}${javacpp.platform.extension} + ${javacpp.platform}${javacpp.platform.extension} + ${javacpp.platform}${javacpp.platform.extension} + ${javacpp.platform}${javacpp.platform.extension} + ${javacpp.platform}${javacpp.platform.extension} + ${javacpp.platform}${javacpp.platform.extension} ${javacpp.platform}${javacpp.platform.extension} ${javacpp.platform} ${javacpp.platform} @@ -264,29 +264,60 @@ - ${os.name}-${os.arch} - ${os.name}-${os.arch} - ${os.name}-${os.arch} - ${os.name}-${os.arch} - ${os.name}-${os.arch} - ${os.name}-${os.arch} - ${os.name}-${os.arch} - ${os.name}-${os.arch} - ${os.name}-${os.arch} - ${os.name}-${os.arch} - ${os.name}-${os.arch} - ${os.name}-${os.arch} - ${os.name}-${os.arch} - ${os.name}-${os.arch} - ${os.name}-${os.arch} - ${os.name}-${os.arch} - ${os.name}-${os.arch} + ${os.name}-${os.arch}${javacpp.platform.extension} + ${os.name}-${os.arch}${javacpp.platform.extension} + ${os.name}-${os.arch}${javacpp.platform.extension} + ${os.name}-${os.arch}${javacpp.platform.extension} + ${os.name}-${os.arch}${javacpp.platform.extension} + ${os.name}-${os.arch}${javacpp.platform.extension} + ${os.name}-${os.arch}${javacpp.platform.extension} + ${os.name}-${os.arch}${javacpp.platform.extension} + ${os.name}-${os.arch}${javacpp.platform.extension} + ${os.name}-${os.arch}${javacpp.platform.extension} + ${os.name}-${os.arch}${javacpp.platform.extension} + ${os.name}-${os.arch}${javacpp.platform.extension} + ${os.name}-${os.arch}${javacpp.platform.extension} + ${os.name}-${os.arch}${javacpp.platform.extension} + ${os.name}-${os.arch}${javacpp.platform.extension} + ${os.name}-${os.arch}${javacpp.platform.extension} + ${os.name}-${os.arch}${javacpp.platform.extension} ${os.name}-${os.arch}${javacpp.platform.extension} ${os.name}-${os.arch} ${os.name}-${os.arch} + + javacpp.platform.custom-true + + + javacpp.platform.custom + + + + + + + + + + + + + + + + + + + + + + + + + + javacpp-platform-none @@ -487,7 +518,7 @@ + mvn -Djavacpp.platform.custom -Djavacpp.platform.host -Djavacpp.platform.linux-x86_64 -Djavacpp.platform.windows-x86_64 ... --> javacpp.platform.android-arm-true @@ -496,7 +527,7 @@ - android-arm + android-arm${javacpp.platform.extension} @@ -508,7 +539,7 @@ - android-arm64 + android-arm64${javacpp.platform.extension} @@ -520,7 +551,7 @@ - android-x86 + android-x86${javacpp.platform.extension} @@ -532,7 +563,7 @@ - android-x86_64 + android-x86_64${javacpp.platform.extension} @@ -544,7 +575,7 @@ - ios-arm + ios-arm${javacpp.platform.extension} @@ -556,7 +587,7 @@ - ios-arm64 + ios-arm64${javacpp.platform.extension} @@ -568,7 +599,7 @@ - ios-x86 + ios-x86${javacpp.platform.extension} @@ -580,7 +611,7 @@ - ios-x86_64 + ios-x86_64${javacpp.platform.extension} @@ -592,7 +623,7 @@ - linux-armhf + linux-armhf${javacpp.platform.extension} @@ -604,7 +635,7 @@ - linux-arm64 + linux-arm64${javacpp.platform.extension} @@ -616,7 +647,7 @@ - linux-ppc64le + linux-ppc64le${javacpp.platform.extension} @@ -628,7 +659,7 @@ - linux-x86 + linux-x86${javacpp.platform.extension} @@ -640,7 +671,7 @@ - linux-x86_64 + linux-x86_64${javacpp.platform.extension} @@ -652,7 +683,7 @@ - macosx-x86_64 + macosx-x86_64${javacpp.platform.extension} @@ -664,7 +695,7 @@ - windows-x86 + windows-x86${javacpp.platform.extension} @@ -676,7 +707,229 @@ - windows-x86_64 + windows-x86_64${javacpp.platform.extension} + + + + + javacpp.platform.custom-linux-arm + + + javacpp.platform.host + + + linuxarm + + + + linux-armhf${javacpp.platform.extension} + + + + + javacpp.platform.custom-linux-armhf + + + javacpp.platform.host + + + linuxarmhf + + + + linux-armhf${javacpp.platform.extension} + + + + + javacpp.platform.custom-linux-aarch64 + + + javacpp.platform.host + + + linuxaarch64 + + + + linux-arm64${javacpp.platform.extension} + + + + + javacpp.platform.custom-linux-armv8 + + + javacpp.platform.host + + + linuxarmv8 + + + + linux-arm64${javacpp.platform.extension} + + + + + javacpp.platform.custom-linux-arm64 + + + javacpp.platform.host + + + linuxarm64 + + + + linux-arm64${javacpp.platform.extension} + + + + + javacpp.platform.custom-linux-ppc64le + + + javacpp.platform.host + + + linuxppc64le + + + + linux-ppc64le${javacpp.platform.extension} + + + + + javacpp.platform.custom-linux-amd64 + + + javacpp.platform.host + + + linuxamd64 + + + + linux-x86_64${javacpp.platform.extension} + + + + + javacpp.platform.custom-linux-x86-64 + + + javacpp.platform.host + + + linuxx86-64 + + + + linux-x86_64${javacpp.platform.extension} + + + + + javacpp.platform.custom-linux-x86_64 + + + javacpp.platform.host + + + linuxx86_64 + + + + linux-x86_64${javacpp.platform.extension} + + + + + javacpp.platform.custom-macosx-amd64 + + + javacpp.platform.host + + + mac os xamd64 + + + + macosx-x86_64${javacpp.platform.extension} + + + + + javacpp.platform.custom-macosx-x86-64 + + + javacpp.platform.host + + + mac os xx86-64 + + + + macosx-x86_64${javacpp.platform.extension} + + + + + javacpp.platform.custom-macosx-x86_64 + + + javacpp.platform.host + + + mac os xx86_64 + + + + macosx-x86_64${javacpp.platform.extension} + + + + + javacpp.platform.custom-windows-amd64 + + + javacpp.platform.host + + windowsamd64 + + + + windows-x86_64${javacpp.platform.extension} + + + + + javacpp.platform.custom-windows-x86-64 + + + javacpp.platform.host + + windowsx86-64 + + + + windows-x86_64${javacpp.platform.extension} + + + + + javacpp.platform.custom-windows-x86_64 + + + javacpp.platform.host + + windowsx86_64 + + + + windows-x86_64${javacpp.platform.extension} diff --git a/nd4j/nd4j-backends/nd4j-tests-tensorflow/src/test/cpujava/org/nd4j/tensorflow/conversion/TensorflowConversionTest.java b/nd4j/nd4j-backends/nd4j-tests-tensorflow/src/test/cpujava/org/nd4j/tensorflow/conversion/TensorflowConversionTest.java index fbf4249bd..fea5a5aa8 100644 --- a/nd4j/nd4j-backends/nd4j-tests-tensorflow/src/test/cpujava/org/nd4j/tensorflow/conversion/TensorflowConversionTest.java +++ b/nd4j/nd4j-backends/nd4j-tests-tensorflow/src/test/cpujava/org/nd4j/tensorflow/conversion/TensorflowConversionTest.java @@ -16,9 +16,11 @@ package org.nd4j.tensorflow.conversion; +import lombok.extern.slf4j.Slf4j; import org.apache.commons.io.IOUtils; import org.junit.Test; import org.nd4j.BaseND4JTest; +import org.nd4j.linalg.api.buffer.DataType; import org.nd4j.linalg.api.ndarray.INDArray; import org.nd4j.linalg.factory.Nd4j; import org.nd4j.linalg.io.ClassPathResource; @@ -29,7 +31,9 @@ import static org.bytedeco.tensorflow.global.tensorflow.*; import static org.junit.Assert.assertEquals; import static org.junit.Assert.assertNotNull; import static org.junit.Assert.fail; +import static org.nd4j.linalg.api.buffer.DataType.*; +@Slf4j public class TensorflowConversionTest extends BaseND4JTest { @Test @@ -53,15 +57,39 @@ public class TensorflowConversionTest extends BaseND4JTest { @Test public void testConversionFromNdArray() throws Exception { - INDArray arr = Nd4j.linspace(1,4,4); - TensorflowConversion tensorflowConversion =TensorflowConversion.getInstance(); - TF_Tensor tf_tensor = tensorflowConversion.tensorFromNDArray(arr); - INDArray fromTensor = tensorflowConversion.ndArrayFromTensor(tf_tensor); - assertEquals(arr,fromTensor); - arr.addi(1.0); - tf_tensor = tensorflowConversion.tensorFromNDArray(arr); - fromTensor = tensorflowConversion.ndArrayFromTensor(tf_tensor); - assertEquals(arr,fromTensor); + DataType[] dtypes = new DataType[]{ + DOUBLE, + FLOAT, + SHORT, + LONG, + BYTE, + UBYTE, + UINT16, + UINT32, + UINT64, + BFLOAT16, + BOOL, + INT, + HALF + }; + for(DataType dtype: dtypes){ + log.debug("Testing conversion for data type " + dtype); + INDArray arr = Nd4j.linspace(1, 4, 4).reshape(2, 2).castTo(dtype); + TensorflowConversion tensorflowConversion =TensorflowConversion.getInstance(); + TF_Tensor tf_tensor = tensorflowConversion.tensorFromNDArray(arr); + INDArray fromTensor = tensorflowConversion.ndArrayFromTensor(tf_tensor); + assertEquals(arr,fromTensor); + if (dtype == BOOL){ + arr.putScalar(3, 0); + } + else{ + arr.addi(1.0); + } + tf_tensor = tensorflowConversion.tensorFromNDArray(arr); + fromTensor = tensorflowConversion.ndArrayFromTensor(tf_tensor); + assertEquals(arr,fromTensor); + } + } diff --git a/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/AssertTestsExtendBaseClass.java b/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/AssertTestsExtendBaseClass.java new file mode 100644 index 000000000..5d8a70725 --- /dev/null +++ b/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/AssertTestsExtendBaseClass.java @@ -0,0 +1,83 @@ +/* ****************************************************************************** + * Copyright (c) 2020 Konduit K.K. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ +package org.nd4j; + +import lombok.extern.slf4j.Slf4j; +import org.junit.Test; +import org.nd4j.imports.TFGraphs.TFGraphTestAllLibnd4j; +import org.nd4j.imports.TFGraphs.TFGraphTestAllSameDiff; +import org.nd4j.imports.TFGraphs.TFGraphTestList; +import org.nd4j.imports.TFGraphs.TFGraphTestZooModels; +import org.nd4j.imports.listeners.ImportModelDebugger; +import org.reflections.Reflections; +import org.reflections.scanners.MethodAnnotationsScanner; +import org.reflections.util.ClasspathHelper; +import org.reflections.util.ConfigurationBuilder; + +import java.lang.reflect.Method; +import java.util.*; + +import static org.junit.Assert.assertEquals; + +/** + * This class checks that all test classes (i.e., anything with one or more methods annotated with @Test) + * extends BaseDl4jTest - either directly or indirectly. + * Other than a small set of exceptions, all tests must extend this + * + * @author Alex Black + */ +@Slf4j +public class AssertTestsExtendBaseClass extends BaseND4JTest { + + //Set of classes that are exclusions to the rule (either run manually or have their own logging + timeouts) + private static final Set> exclusions = new HashSet<>(Arrays.asList( + TFGraphTestAllSameDiff.class, + TFGraphTestAllLibnd4j.class, + TFGraphTestList.class, + TFGraphTestZooModels.class, + ImportModelDebugger.class //Run manually only, otherwise ignored + )); + + @Test + public void checkTestClasses(){ + + Reflections reflections = new Reflections(new ConfigurationBuilder() + .setUrls(ClasspathHelper.forPackage("org.nd4j")) + .setScanners(new MethodAnnotationsScanner())); + Set methods = reflections.getMethodsAnnotatedWith(Test.class); + Set> s = new HashSet<>(); + for(Method m : methods){ + s.add(m.getDeclaringClass()); + } + + List> l = new ArrayList<>(s); + l.sort(new Comparator>() { + @Override + public int compare(Class aClass, Class t1) { + return aClass.getName().compareTo(t1.getName()); + } + }); + + int count = 0; + for(Class c : l){ + if(!BaseND4JTest.class.isAssignableFrom(c) && !exclusions.contains(c)){ + log.error("Test {} does not extend BaseND4JTest (directly or indirectly). All tests must extend this class for proper memory tracking and timeouts", c); + count++; + } + } + assertEquals("Number of tests not extending BaseND4JTest", 0, count); + } +} diff --git a/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/autodiff/opvalidation/ShapeOpValidation.java b/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/autodiff/opvalidation/ShapeOpValidation.java index 733628490..30d4baf5c 100644 --- a/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/autodiff/opvalidation/ShapeOpValidation.java +++ b/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/autodiff/opvalidation/ShapeOpValidation.java @@ -2050,7 +2050,7 @@ public class ShapeOpValidation extends BaseOpValidation { print(out[0]); */ - INDArray emptyIn = Nd4j.empty(DataType.FLOAT); + INDArray emptyIn = Nd4j.empty(DataType.FLOAT).reshape(0, 4); INDArray axis = Nd4j.scalar(1); DynamicCustomOp op = DynamicCustomOp.builder("split") @@ -2061,9 +2061,10 @@ public class ShapeOpValidation extends BaseOpValidation { List l = op.calculateOutputShape(); assertEquals(4, l.size()); for( int i=0; i<4; i++ ){ - assertArrayEquals(new long[0], l.get(i).getShape()); - assertTrue(l.get(i).isEmpty()); - op.addOutputArgument(Nd4j.empty(DataType.FLOAT)); + val desc = l.get(i); + assertArrayEquals(new long[]{0, 1}, desc.getShape()); + assertTrue(desc.isEmpty()); + op.addOutputArgument(Nd4j.empty(DataType.FLOAT).reshape(desc.getShape())); } Nd4j.exec(op); diff --git a/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/imports/TFGraphs/TFGraphTestAllSameDiff.java b/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/imports/TFGraphs/TFGraphTestAllSameDiff.java index 3788c434e..210c4b703 100644 --- a/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/imports/TFGraphs/TFGraphTestAllSameDiff.java +++ b/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/imports/TFGraphs/TFGraphTestAllSameDiff.java @@ -123,12 +123,7 @@ public class TFGraphTestAllSameDiff { //Note: Can't extend BaseNd4jTest here a //AB 2020/01/07 - Known issues "bitcast/from_float64_to_int64", "bitcast/from_rank2_float64_to_int64", - "bitcast/from_float64_to_uint64", - - // 2020/02/14 - new ops which are not passing yet - "linear_solve/.*", - "triangular_solve/.*", - "lstsq/.*" + "bitcast/from_float64_to_uint64" }; /* As per TFGraphTestList.printArraysDebugging - this field defines a set of regexes for test cases that should have diff --git a/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/imports/listeners/ImportModelDebugger.java b/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/imports/listeners/ImportModelDebugger.java index 06c85b289..88a80c52f 100644 --- a/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/imports/listeners/ImportModelDebugger.java +++ b/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/imports/listeners/ImportModelDebugger.java @@ -90,6 +90,7 @@ import java.util.Map; * * @author Alex Black */ +@Ignore public class ImportModelDebugger { @Test diff --git a/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/api/rng/RngTests.java b/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/api/rng/RngTests.java index 93e0b0739..9e780b0fd 100644 --- a/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/api/rng/RngTests.java +++ b/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/api/rng/RngTests.java @@ -102,6 +102,7 @@ public class RngTests extends BaseNd4jTest { @Test public void testRandomBinomial() { + Nd4j.getRandom().setSeed(12345); //silly tests. Just increasing the usage for randomBinomial to stop compiler warnings. INDArray x = Nd4j.randomBinomial(10, 0.5, 3,3); assertTrue(x.sum().getDouble(0) > 0.0); //silly test. Just increasing th usage for randomBinomial diff --git a/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/custom/CustomOpsTests.java b/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/custom/CustomOpsTests.java index ae8f934e9..2d178a210 100644 --- a/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/custom/CustomOpsTests.java +++ b/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/custom/CustomOpsTests.java @@ -1739,15 +1739,37 @@ public class CustomOpsTests extends BaseNd4jTest { assertEquals(expected, ret[0]); } + @Test + public void testLstsq() { + INDArray a = Nd4j.createFromArray(new float[]{ + 1.f, 2.f, 3.f, + 4.f, 5.f, 6.f, + 11.f, 8.f, 21.f + }).reshape(3,3); + + INDArray b = Nd4j.createFromArray(new float[]{ 1.f, 2.f, 3.f }).reshape(3,1); + + val op = new Lstsq(a,b); + INDArray[] ret = Nd4j.exec(op); + + DynamicCustomOp matmul = DynamicCustomOp.builder("matmul") + .addInputs(a, ret[0]) + .build(); + INDArray[] matres = Nd4j.exec(matmul); + for (int i = 0; i < 3; ++i) { + assertEquals(b.getFloat(i, 0), matres[0].getFloat(i, 0), 1e-4); + } + } + @Test public void testSequenceMask() { INDArray arr = Nd4j.createFromArray(new int[]{1, 3, 2}); // Test with static max len int maxlen = 2; INDArray expected = Nd4j.createFromArray(new int[]{ - 1,0,0, - 1,1,1, - 1,1,0 + 1, 0, 0, + 1, 1, 1, + 1, 1, 0 }).reshape(3, 3); INDArray[] ret = Nd4j.exec(new SequenceMask(arr, maxlen, DataType.INT32)); diff --git a/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/custom/ExpandableOpsTests.java b/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/custom/ExpandableOpsTests.java index aa8037f89..b59ce3346 100644 --- a/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/custom/ExpandableOpsTests.java +++ b/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/custom/ExpandableOpsTests.java @@ -19,9 +19,11 @@ package org.nd4j.linalg.custom; import lombok.extern.slf4j.Slf4j; import lombok.val; import org.junit.Test; +import org.nd4j.linalg.BaseNd4jTest; import org.nd4j.linalg.api.ops.compat.CompatStringSplit; import org.nd4j.linalg.api.ops.util.PrintVariable; import org.nd4j.linalg.factory.Nd4j; +import org.nd4j.linalg.factory.Nd4jBackend; import static org.junit.Assert.assertEquals; import static org.junit.Assert.assertNotNull; @@ -30,7 +32,16 @@ import static org.junit.Assert.assertNotNull; * This is special test suit: we test operations that on C++ side modify arrays that come from Java */ @Slf4j -public class ExpandableOpsTests { +public class ExpandableOpsTests extends BaseNd4jTest { + + public ExpandableOpsTests(Nd4jBackend backend) { + super(backend); + } + + @Override + public char ordering() { + return 'c'; + } @Test public void testCompatStringSplit_1() throws Exception { diff --git a/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/dataset/ImagePreProcessortTest.java b/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/dataset/ImagePreProcessortTest.java index c3c89c296..1d1558db7 100644 --- a/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/dataset/ImagePreProcessortTest.java +++ b/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/dataset/ImagePreProcessortTest.java @@ -22,6 +22,7 @@ import org.junit.runners.Parameterized; import org.nd4j.linalg.BaseNd4jTest; import org.nd4j.linalg.api.buffer.DataType; import org.nd4j.linalg.api.ndarray.INDArray; +import org.nd4j.linalg.dataset.adapter.SingletonDataSetIterator; import org.nd4j.linalg.dataset.api.preprocessor.ImageMultiPreProcessingScaler; import org.nd4j.linalg.dataset.api.preprocessor.ImagePreProcessingScaler; import org.nd4j.linalg.factory.Nd4j; @@ -156,6 +157,42 @@ public class ImagePreProcessortTest extends BaseNd4jTest { assertEquals(orig, before); } + + @Test + public void testSegmentation(){ + + INDArray f = Nd4j.math().floor(Nd4j.rand(DataType.FLOAT, 3, 3, 16, 16).muli(255)); + INDArray l = Nd4j.math().floor(Nd4j.rand(DataType.FLOAT, 3, 10, 8, 8).muli(255)); + + ImagePreProcessingScaler s = new ImagePreProcessingScaler(); + s.fitLabel(true); + + s.fit(new DataSet(f,l)); + + INDArray expF = f.div(255); + INDArray expL = l.div(255); + + DataSet d = new DataSet(f.dup(), l.dup()); + s.transform(d); + assertEquals(expF, d.getFeatures()); + assertEquals(expL, d.getLabels()); + + + s.fit(new SingletonDataSetIterator(new DataSet(f, l))); + + INDArray f2 = f.dup(); + INDArray l2 = l.dup(); + s.transform(f2); + s.transformLabel(l2); + assertEquals(expF, f2); + assertEquals(expL, l2); + + s.revertFeatures(f2); + s.revertLabels(l2); + assertEquals(f, f2); + assertEquals(l, l2); + } + @Override public char ordering() { return 'c'; diff --git a/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/dataset/NormalizerStandardizeTest.java b/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/dataset/NormalizerStandardizeTest.java index bdfcf596f..9b7b2e241 100644 --- a/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/dataset/NormalizerStandardizeTest.java +++ b/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/dataset/NormalizerStandardizeTest.java @@ -40,6 +40,11 @@ public class NormalizerStandardizeTest extends BaseNd4jTest { super(backend); } + @Override + public long getTimeoutMilliseconds() { + return 60_000L; + } + @Test public void testBruteForce() { /* This test creates a dataset where feature values are multiples of consecutive natural numbers diff --git a/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/dataset/NormalizerTests.java b/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/dataset/NormalizerTests.java index 23d928a11..5fa8524a4 100644 --- a/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/dataset/NormalizerTests.java +++ b/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/dataset/NormalizerTests.java @@ -22,11 +22,10 @@ import org.junit.runner.RunWith; import org.junit.runners.Parameterized; import org.nd4j.linalg.BaseNd4jTest; import org.nd4j.linalg.api.ndarray.INDArray; +import org.nd4j.linalg.dataset.adapter.MultiDataSetIteratorAdapter; import org.nd4j.linalg.dataset.api.iterator.DataSetIterator; import org.nd4j.linalg.dataset.api.iterator.TestDataSetIterator; -import org.nd4j.linalg.dataset.api.preprocessor.DataNormalization; -import org.nd4j.linalg.dataset.api.preprocessor.NormalizerMinMaxScaler; -import org.nd4j.linalg.dataset.api.preprocessor.NormalizerStandardize; +import org.nd4j.linalg.dataset.api.preprocessor.*; import org.nd4j.linalg.factory.Nd4j; import org.nd4j.linalg.factory.Nd4jBackend; import org.nd4j.linalg.indexing.NDArrayIndex; @@ -189,11 +188,11 @@ public class NormalizerTests extends BaseNd4jTest { INDArray zeros = Nd4j.zeros(shouldBe0_1.shape()); - for (int j = 0; j < 2; j++) { - System.out.println(ds.getFeatures().get(NDArrayIndex.point(j), NDArrayIndex.all(), - NDArrayIndex.all())); - System.out.println(); - } +// for (int j = 0; j < 2; j++) { +// System.out.println(ds.getFeatures().get(NDArrayIndex.point(j), NDArrayIndex.all(), +// NDArrayIndex.all())); +// System.out.println(); +// } assertEquals(zeros, shouldBe0_1); assertEquals(zeros, shouldBe0_2); @@ -218,6 +217,69 @@ public class NormalizerTests extends BaseNd4jTest { } } + @Test + public void testNormalizerToStringHashCode(){ + //https://github.com/eclipse/deeplearning4j/issues/8565 + + testNormalizer(new NormalizerMinMaxScaler()); + NormalizerMinMaxScaler n1 = new NormalizerMinMaxScaler(); + n1.fitLabel(true); + testNormalizer(n1); + + testNormalizer(new NormalizerStandardize()); + NormalizerStandardize n2 = new NormalizerStandardize(); + n2.fitLabel(true); + testNormalizer(n2); + + testNormalizer(new ImagePreProcessingScaler()); + ImagePreProcessingScaler n3 = new ImagePreProcessingScaler(); + n3.fitLabel(true); + testNormalizer(n3); + + testNormalizer(new VGG16ImagePreProcessor()); + VGG16ImagePreProcessor n4 = new VGG16ImagePreProcessor(); + n4.fitLabel(true); + testNormalizer(n4); + } + + private static void testNormalizer(DataNormalization n){ + n.toString(); + n.hashCode(); + + n.fit(new IrisDataSetIterator(30, 150)); + + n.toString(); + n.hashCode(); + } + + @Test + public void testMultiNormalizerToStringHashCode(){ + //https://github.com/eclipse/deeplearning4j/issues/8565 + + testMultiNormalizer(new MultiNormalizerMinMaxScaler()); + MultiNormalizerMinMaxScaler n1 = new MultiNormalizerMinMaxScaler(); + n1.fitLabel(true); + testMultiNormalizer(n1); + + testMultiNormalizer(new MultiNormalizerStandardize()); + MultiNormalizerStandardize n2 = new MultiNormalizerStandardize(); + n2.fitLabel(true); + testMultiNormalizer(n2); + + testMultiNormalizer(new ImageMultiPreProcessingScaler(0)); + } + + private static void testMultiNormalizer(MultiDataNormalization n){ + n.toString(); + n.hashCode(); + + n.fit(new MultiDataSetIteratorAdapter(new IrisDataSetIterator(30, 150))); + + n.toString(); + n.hashCode(); + } + + @Override public char ordering() { return 'c'; diff --git a/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/dataset/api/preprocessor/CompositeDataSetPreProcessorTest.java b/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/dataset/api/preprocessor/CompositeDataSetPreProcessorTest.java index a2af67dc9..4b80b2f1e 100644 --- a/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/dataset/api/preprocessor/CompositeDataSetPreProcessorTest.java +++ b/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/dataset/api/preprocessor/CompositeDataSetPreProcessorTest.java @@ -1,14 +1,26 @@ package org.nd4j.linalg.dataset.api.preprocessor; import org.junit.Test; +import org.nd4j.linalg.BaseNd4jTest; import org.nd4j.linalg.dataset.DataSet; import org.nd4j.linalg.dataset.api.DataSetPreProcessor; import org.nd4j.linalg.factory.Nd4j; +import org.nd4j.linalg.factory.Nd4jBackend; import static org.junit.Assert.assertFalse; import static org.junit.Assert.assertTrue; -public class CompositeDataSetPreProcessorTest { +public class CompositeDataSetPreProcessorTest extends BaseNd4jTest { + + public CompositeDataSetPreProcessorTest(Nd4jBackend backend) { + super(backend); + } + + @Override + public char ordering() { + return 'c'; + } + @Test(expected = NullPointerException.class) public void when_preConditionsIsNull_expect_NullPointerException() { // Assemble diff --git a/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/dataset/api/preprocessor/CropAndResizeDataSetPreProcessorTest.java b/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/dataset/api/preprocessor/CropAndResizeDataSetPreProcessorTest.java index 904484d5f..96def7d37 100644 --- a/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/dataset/api/preprocessor/CropAndResizeDataSetPreProcessorTest.java +++ b/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/dataset/api/preprocessor/CropAndResizeDataSetPreProcessorTest.java @@ -1,15 +1,26 @@ package org.nd4j.linalg.dataset.api.preprocessor; import org.junit.Test; +import org.nd4j.linalg.BaseNd4jTest; import org.nd4j.linalg.api.buffer.DataType; import org.nd4j.linalg.api.ndarray.INDArray; import org.nd4j.linalg.api.shape.LongShapeDescriptor; import org.nd4j.linalg.dataset.DataSet; import org.nd4j.linalg.factory.Nd4j; +import org.nd4j.linalg.factory.Nd4jBackend; import static org.junit.Assert.*; -public class CropAndResizeDataSetPreProcessorTest { +public class CropAndResizeDataSetPreProcessorTest extends BaseNd4jTest { + + public CropAndResizeDataSetPreProcessorTest(Nd4jBackend backend) { + super(backend); + } + + @Override + public char ordering() { + return 'c'; + } @Test(expected = IllegalArgumentException.class) public void when_originalHeightIsZero_expect_IllegalArgumentException() { diff --git a/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/dataset/api/preprocessor/PermuteDataSetPreProcessorTest.java b/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/dataset/api/preprocessor/PermuteDataSetPreProcessorTest.java index acbac85df..88654ec88 100644 --- a/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/dataset/api/preprocessor/PermuteDataSetPreProcessorTest.java +++ b/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/dataset/api/preprocessor/PermuteDataSetPreProcessorTest.java @@ -1,14 +1,25 @@ package org.nd4j.linalg.dataset.api.preprocessor; +import org.nd4j.linalg.BaseNd4jTest; import org.nd4j.linalg.dataset.api.preprocessor.PermuteDataSetPreProcessor; import org.junit.Test; import org.nd4j.linalg.api.ndarray.INDArray; import org.nd4j.linalg.dataset.DataSet; import org.nd4j.linalg.factory.Nd4j; +import org.nd4j.linalg.factory.Nd4jBackend; import static org.junit.Assert.*; -public class PermuteDataSetPreProcessorTest { +public class PermuteDataSetPreProcessorTest extends BaseNd4jTest { + + public PermuteDataSetPreProcessorTest(Nd4jBackend backend) { + super(backend); + } + + @Override + public char ordering() { + return 'c'; + } @Test(expected = NullPointerException.class) public void when_dataSetIsNull_expect_NullPointerException() { diff --git a/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/dataset/api/preprocessor/RGBtoGrayscaleDataSetPreProcessorTest.java b/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/dataset/api/preprocessor/RGBtoGrayscaleDataSetPreProcessorTest.java index b0408d8b7..8e76d8e95 100644 --- a/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/dataset/api/preprocessor/RGBtoGrayscaleDataSetPreProcessorTest.java +++ b/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/dataset/api/preprocessor/RGBtoGrayscaleDataSetPreProcessorTest.java @@ -1,14 +1,25 @@ package org.nd4j.linalg.dataset.api.preprocessor; import org.junit.Test; +import org.nd4j.linalg.BaseNd4jTest; import org.nd4j.linalg.api.ndarray.INDArray; import org.nd4j.linalg.dataset.DataSet; import org.nd4j.linalg.factory.Nd4j; +import org.nd4j.linalg.factory.Nd4jBackend; import static org.junit.Assert.assertEquals; import static org.junit.Assert.assertTrue; -public class RGBtoGrayscaleDataSetPreProcessorTest { +public class RGBtoGrayscaleDataSetPreProcessorTest extends BaseNd4jTest { + + public RGBtoGrayscaleDataSetPreProcessorTest(Nd4jBackend backend) { + super(backend); + } + + @Override + public char ordering() { + return 'c'; + } @Test(expected = NullPointerException.class) public void when_dataSetIsNull_expect_NullPointerException() { diff --git a/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/factory/ops/NDLossTest.java b/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/factory/ops/NDLossTest.java new file mode 100644 index 000000000..40d32121d --- /dev/null +++ b/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/factory/ops/NDLossTest.java @@ -0,0 +1,453 @@ +/* ***************************************************************************** + * Copyright (c) 2019-2020 Konduit k.k. + * + * This program and the accompanying materials are made available under the + * terms of the Apache License, Version 2.0 which is available at + * https://www.apache.org/licenses/LICENSE-2.0. + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations + * under the License. + * + * SPDX-License-Identifier: Apache-2.0 + ******************************************************************************/ + +package org.nd4j.linalg.factory.ops; + +import org.junit.Test; +import org.nd4j.autodiff.loss.LossReduce; +import org.nd4j.autodiff.samediff.SDVariable; +import org.nd4j.autodiff.samediff.SameDiff; +import org.nd4j.linalg.BaseNd4jTest; +import org.nd4j.linalg.api.buffer.DataType; +import org.nd4j.linalg.api.ndarray.INDArray; +import org.nd4j.linalg.api.ops.random.impl.BernoulliDistribution; +import org.nd4j.linalg.factory.Nd4j; +import org.nd4j.linalg.factory.Nd4jBackend; + +import static org.junit.Assert.assertArrayEquals; +import static org.junit.Assert.assertEquals; + +public class NDLossTest extends BaseNd4jTest { + public NDLossTest(Nd4jBackend backend) { + super(backend); + } + + @Override + public char ordering(){ + return 'c'; + } + + @Test + public void testAbsoluteDifference() { + SameDiff sd = SameDiff.create(); + + int nOut = 4; + int minibatch = 10; + SDVariable predictions = sd.var("in", DataType.DOUBLE, minibatch, nOut); + SDVariable labels = sd.var("labels", DataType.DOUBLE, -1, nOut); + + INDArray wArr = Nd4j.create(new double[][]{ + {0, 0, 0, 0}, {0, 0, 1, 1}, {1, 1, 0, 0}, {1, 1, 1, 1}, {1, 1, 1, 1}, + {2, 2, 2, 2}, {2, 2, 2, 2}, {2, 2, 2, 2}, {2, 2, 2, 2}, {2, 2, 2, 2}}); + SDVariable w = sd.var("weights", wArr); + + LossReduce reduction = LossReduce.MEAN_BY_NONZERO_WEIGHT_COUNT; + + INDArray predictionsArr = Nd4j.randn(DataType.DOUBLE, minibatch, nOut); + INDArray labelsArr = Nd4j.randn(DataType.DOUBLE, minibatch, nOut); + + + SDVariable loss = sd.loss().absoluteDifference("loss", labels, predictions, w, reduction); + SDVariable loss2 = sd.loss().absoluteDifference("loss2", labels, predictions, null, reduction); + sd.associateArrayWithVariable(predictionsArr, predictions); + sd.associateArrayWithVariable(labelsArr, labels); + + INDArray y_exp = loss.eval(); + INDArray y_exp2 = loss2.eval(); + + INDArray y = Nd4j.loss().absoluteDifference(labelsArr, predictionsArr, wArr, reduction); + INDArray y2 = Nd4j.loss().absoluteDifference(labelsArr, predictionsArr, null, reduction); + assertEquals(y_exp, y); + assertEquals(y_exp2, y2); + } + + @Test + public void testCosineDistance() { + SameDiff sd = SameDiff.create(); + + int nOut = 4; + int minibatch = 10; + SDVariable predictions = sd.var("in", DataType.DOUBLE, minibatch, nOut); + SDVariable labels = sd.var("labels", DataType.DOUBLE, -1, nOut); + + INDArray wArr = Nd4j.create(new double[][]{ + {0, 0, 0, 0}, {0, 0, 1, 1}, {1, 1, 0, 0}, {1, 1, 1, 1}, {1, 1, 1, 1}, + {2, 2, 2, 2}, {2, 2, 2, 2}, {2, 2, 2, 2}, {2, 2, 2, 2}, {2, 2, 2, 2}}); + SDVariable w = sd.var("weights", wArr); + + LossReduce reduction = LossReduce.MEAN_BY_NONZERO_WEIGHT_COUNT; + + INDArray predictionsArr = Nd4j.randn(DataType.DOUBLE, minibatch, nOut); + INDArray labelsArr = Nd4j.randn(DataType.DOUBLE, minibatch, nOut); + + predictionsArr.diviColumnVector(predictionsArr.norm2(1)); + labelsArr.diviColumnVector(labelsArr.norm2(1)); + + SDVariable loss = sd.loss().cosineDistance("loss", labels, predictions, w, reduction, 0); + SDVariable loss2 = sd.loss().cosineDistance("loss2", labels, predictions, null, reduction, 0); + sd.associateArrayWithVariable(predictionsArr, predictions); + sd.associateArrayWithVariable(labelsArr, labels); + + INDArray y_exp = loss.eval(); + INDArray y_exp2 = loss2.eval(); + + INDArray y = Nd4j.loss().cosineDistance(labelsArr, predictionsArr, wArr, reduction, 0); + INDArray y2 = Nd4j.loss().cosineDistance(labelsArr, predictionsArr, null, reduction, 0); + assertEquals(y_exp, y); + assertEquals(y_exp2, y2); + } + + @Test + public void testHingeLoss() { + SameDiff sd = SameDiff.create(); + + int nOut = 4; + int minibatch = 10; + SDVariable predictions = sd.var("in", DataType.DOUBLE, minibatch, nOut); + SDVariable labels = sd.var("labels", DataType.DOUBLE, -1, nOut); + + INDArray wArr = Nd4j.create(new double[][]{ + {0, 0, 0, 0}, {0, 0, 1, 1}, {1, 1, 0, 0}, {1, 1, 1, 1}, {1, 1, 1, 1}, + {2, 2, 2, 2}, {2, 2, 2, 2}, {2, 2, 2, 2}, {2, 2, 2, 2}, {2, 2, 2, 2}}); + SDVariable w = sd.var("weights", wArr); + + LossReduce reduction = LossReduce.MEAN_BY_NONZERO_WEIGHT_COUNT; + + INDArray predictionsArr = Nd4j.randn(DataType.DOUBLE, minibatch, nOut); + INDArray labelsArr = Nd4j.randn(DataType.DOUBLE, minibatch, nOut); + + SDVariable loss = sd.loss().hingeLoss("loss", labels, predictions, w, reduction); + SDVariable loss2 = sd.loss().hingeLoss("loss2", labels, predictions, null, reduction); + sd.associateArrayWithVariable(predictionsArr, predictions); + sd.associateArrayWithVariable(labelsArr, labels); + + INDArray y_exp = loss.eval(); + INDArray y_exp2 = loss2.eval(); + + INDArray y = Nd4j.loss().hingeLoss(labelsArr, predictionsArr, wArr, reduction); + INDArray y2 = Nd4j.loss().hingeLoss(labelsArr, predictionsArr, null, reduction); + assertEquals(y_exp, y); + assertEquals(y_exp2, y2); + } + + @Test + public void testHuberLoss() { + SameDiff sd = SameDiff.create(); + + int nOut = 4; + int minibatch = 10; + SDVariable predictions = sd.var("in", DataType.DOUBLE, minibatch, nOut); + SDVariable labels = sd.var("labels", DataType.DOUBLE, -1, nOut); + + INDArray wArr = Nd4j.create(new double[][]{ + {0, 0, 0, 0}, {0, 0, 1, 1}, {1, 1, 0, 0}, {1, 1, 1, 1}, {1, 1, 1, 1}, + {2, 2, 2, 2}, {2, 2, 2, 2}, {2, 2, 2, 2}, {2, 2, 2, 2}, {2, 2, 2, 2}}); + SDVariable w = sd.var("weights", wArr); + + LossReduce reduction = LossReduce.MEAN_BY_NONZERO_WEIGHT_COUNT; + + INDArray predictionsArr = Nd4j.randn(DataType.DOUBLE, minibatch, nOut); + INDArray labelsArr = Nd4j.randn(DataType.DOUBLE, minibatch, nOut); + + SDVariable loss = sd.loss().huberLoss("loss", labels, predictions, w, reduction, 0.02); + SDVariable loss2 = sd.loss().huberLoss("loss2", labels, predictions, null, reduction, 0.02); + sd.associateArrayWithVariable(predictionsArr, predictions); + sd.associateArrayWithVariable(labelsArr, labels); + + INDArray y_exp = loss.eval(); + INDArray y_exp2 = loss2.eval(); + + INDArray y = Nd4j.loss().huberLoss(labelsArr, predictionsArr, wArr, reduction, 0.02); + INDArray y2 = Nd4j.loss().huberLoss(labelsArr, predictionsArr, null, reduction, 0.02); + assertEquals(y_exp, y); + assertEquals(y_exp2, y2); + } + + @Test + public void testL2Loss() { + SameDiff sd = SameDiff.create(); + + int nOut = 4; + int minibatch = 10; + SDVariable predictions = sd.var("in", DataType.DOUBLE, minibatch, nOut); + INDArray predictionsArr = Nd4j.randn(DataType.DOUBLE, minibatch, nOut); + + SDVariable loss = sd.loss().l2Loss("loss", predictions); + sd.associateArrayWithVariable(predictionsArr, predictions); + + INDArray y_exp = loss.eval(); + + INDArray y = Nd4j.loss().l2Loss(predictionsArr); + assertEquals(y_exp, y); + } + + @Test + public void testLogLoss() { + SameDiff sd = SameDiff.create(); + + int nOut = 4; + int minibatch = 10; + SDVariable predictions = sd.var("in", DataType.DOUBLE, minibatch, nOut); + SDVariable labels = sd.var("labels", DataType.DOUBLE, -1, nOut); + + INDArray wArr = Nd4j.create(new double[][]{ + {0, 0, 0, 0}, {0, 0, 1, 1}, {1, 1, 0, 0}, {1, 1, 1, 1}, {1, 1, 1, 1}, + {2, 2, 2, 2}, {2, 2, 2, 2}, {2, 2, 2, 2}, {2, 2, 2, 2}, {2, 2, 2, 2}}); + SDVariable w = sd.var("weights", wArr); + + LossReduce reduction = LossReduce.MEAN_BY_NONZERO_WEIGHT_COUNT; + + INDArray predictionsArr = Nd4j.randn(DataType.DOUBLE, minibatch, nOut); + INDArray labelsArr = Nd4j.randn(DataType.DOUBLE, minibatch, nOut); + Nd4j.getExecutioner().exec(new BernoulliDistribution(labelsArr, 0.5)); + predictionsArr = Nd4j.rand(predictionsArr.shape()).muli(0.8).addi(0.1); + + double eps = 1e-7; + + SDVariable loss = sd.loss().logLoss("loss", labels, predictions, w, reduction, eps); + SDVariable loss2 = sd.loss().logLoss("loss2", labels, predictions, null, reduction, eps); + sd.associateArrayWithVariable(predictionsArr, predictions); + sd.associateArrayWithVariable(labelsArr, labels); + + INDArray y_exp = loss.eval(); + INDArray y_exp2 = loss2.eval(); + + //TODO: Test fails. "Op [log_loss] execution failed" + INDArray y = Nd4j.loss().logLoss(labelsArr, predictionsArr, wArr, reduction, eps); + INDArray y2 = Nd4j.loss().logLoss(labelsArr, predictionsArr, null, reduction, eps); + assertEquals(y_exp, y); + assertEquals(y_exp2, y2); + } + + @Test + public void testLogPoisson() { + SameDiff sd = SameDiff.create(); + + int nOut = 4; + int minibatch = 10; + SDVariable predictions = sd.var("in", DataType.DOUBLE, minibatch, nOut); + SDVariable labels = sd.var("labels", DataType.DOUBLE, -1, nOut); + + INDArray wArr = Nd4j.create(new double[][]{ + {0, 0, 0, 0}, {0, 0, 1, 1}, {1, 1, 0, 0}, {1, 1, 1, 1}, {1, 1, 1, 1}, + {2, 2, 2, 2}, {2, 2, 2, 2}, {2, 2, 2, 2}, {2, 2, 2, 2}, {2, 2, 2, 2}}); + SDVariable w = sd.var("weights", wArr); + + LossReduce reduction = LossReduce.MEAN_BY_NONZERO_WEIGHT_COUNT; + + INDArray predictionsArr = Nd4j.randn(DataType.DOUBLE, minibatch, nOut); + INDArray labelsArr = Nd4j.randn(DataType.DOUBLE, minibatch, nOut); + + SDVariable loss = sd.loss().logPoisson("loss", labels, predictions, w, reduction); + SDVariable loss2 = sd.loss().logPoisson("loss2", labels, predictions, null, reduction); + sd.associateArrayWithVariable(predictionsArr, predictions); + sd.associateArrayWithVariable(labelsArr, labels); + + INDArray y_exp = loss.eval(); + INDArray y_exp2 = loss2.eval(); + + INDArray y = Nd4j.loss().logPoisson(labelsArr, predictionsArr, wArr, reduction, false); + INDArray y2 = Nd4j.loss().logPoisson(labelsArr, predictionsArr, null, reduction, false); + assertEquals(y_exp, y); + assertEquals(y_exp2, y2); + } + + @Test + public void testMeanPairwiseSquaredError() { + SameDiff sd = SameDiff.create(); + + int nOut = 4; + int minibatch = 10; + SDVariable predictions = sd.var("in", DataType.DOUBLE, minibatch, nOut); + SDVariable labels = sd.var("labels", DataType.DOUBLE, -1, nOut); + + INDArray wArr = Nd4j.create(new double[][]{ + {0, 0, 0, 0}, {0, 0, 1, 1}, {1, 1, 0, 0}, {1, 1, 1, 1}, {1, 1, 1, 1}, + {2, 2, 2, 2}, {2, 2, 2, 2}, {2, 2, 2, 2}, {2, 2, 2, 2}, {2, 2, 2, 2}}); + SDVariable w = sd.var("weights", wArr); + + LossReduce reduction = LossReduce.MEAN_BY_NONZERO_WEIGHT_COUNT; + + INDArray predictionsArr = Nd4j.randn(DataType.DOUBLE, minibatch, nOut); + INDArray labelsArr = Nd4j.randn(DataType.DOUBLE, minibatch, nOut); + + SDVariable loss = sd.loss().meanPairwiseSquaredError("loss", labels, predictions, w, reduction); + SDVariable loss2 = sd.loss().meanPairwiseSquaredError("loss2", labels, predictions, null, reduction); + sd.associateArrayWithVariable(predictionsArr, predictions); + sd.associateArrayWithVariable(labelsArr, labels); + + INDArray y_exp = loss.eval(); + INDArray y_exp2 = loss2.eval(); + + INDArray y = Nd4j.loss().meanPairwiseSquaredError(labelsArr, predictionsArr, wArr, reduction); + INDArray y2 = Nd4j.loss().meanPairwiseSquaredError(labelsArr, predictionsArr, null, reduction); + assertEquals(y_exp, y); + assertEquals(y_exp2, y2); + } + + @Test + public void testMeanSquaredError() { + SameDiff sd = SameDiff.create(); + + int nOut = 4; + int minibatch = 10; + SDVariable predictions = sd.var("in", DataType.DOUBLE, minibatch, nOut); + SDVariable labels = sd.var("labels", DataType.DOUBLE, -1, nOut); + + INDArray wArr = Nd4j.create(new double[][]{ + {0, 0, 0, 0}, {0, 0, 1, 1}, {1, 1, 0, 0}, {1, 1, 1, 1}, {1, 1, 1, 1}, + {2, 2, 2, 2}, {2, 2, 2, 2}, {2, 2, 2, 2}, {2, 2, 2, 2}, {2, 2, 2, 2}}); + SDVariable w = sd.var("weights", wArr); + + LossReduce reduction = LossReduce.MEAN_BY_NONZERO_WEIGHT_COUNT; + + INDArray predictionsArr = Nd4j.randn(DataType.DOUBLE, minibatch, nOut); + INDArray labelsArr = Nd4j.randn(DataType.DOUBLE, minibatch, nOut); + + SDVariable loss = sd.loss().meanSquaredError("loss", labels, predictions, w, reduction); + SDVariable loss2 = sd.loss().meanSquaredError("loss2", labels, predictions, null, reduction); + sd.associateArrayWithVariable(predictionsArr, predictions); + sd.associateArrayWithVariable(labelsArr, labels); + + INDArray y_exp = loss.eval(); + INDArray y_exp2 = loss2.eval(); + + INDArray y = Nd4j.loss().meanSquaredError(labelsArr, predictionsArr, wArr, reduction); + INDArray y2 = Nd4j.loss().meanSquaredError(labelsArr, predictionsArr, null, reduction); + assertEquals(y_exp, y); + assertEquals(y_exp2, y2); + } + + @Test + public void testSigmoidCrossEntropy() { + SameDiff sd = SameDiff.create(); + + int nOut = 4; + int minibatch = 10; + SDVariable predictions = sd.var("in", DataType.DOUBLE, minibatch, nOut); + SDVariable labels = sd.var("labels", DataType.DOUBLE, -1, nOut); + + INDArray wArr = Nd4j.create(new double[][]{ + {0, 0, 0, 0}, {0, 0, 1, 1}, {1, 1, 0, 0}, {1, 1, 1, 1}, {1, 1, 1, 1}, + {2, 2, 2, 2}, {2, 2, 2, 2}, {2, 2, 2, 2}, {2, 2, 2, 2}, {2, 2, 2, 2}}); + SDVariable w = sd.var("weights", wArr); + + LossReduce reduction = LossReduce.MEAN_BY_NONZERO_WEIGHT_COUNT; + + INDArray predictionsArr = Nd4j.randn(DataType.DOUBLE, minibatch, nOut); + INDArray labelsArr = Nd4j.randn(DataType.DOUBLE, minibatch, nOut); + double labelSmoothing = 0.01; + + SDVariable loss = sd.loss().sigmoidCrossEntropy("loss", labels, predictions, w, reduction, labelSmoothing); + SDVariable loss2 = sd.loss().sigmoidCrossEntropy("loss2", labels, predictions, null, reduction, labelSmoothing); + sd.associateArrayWithVariable(predictionsArr, predictions); + sd.associateArrayWithVariable(labelsArr, labels); + + INDArray y_exp = loss.eval(); + INDArray y_exp2 = loss2.eval(); + + INDArray y = Nd4j.loss().sigmoidCrossEntropy(labelsArr, predictionsArr, wArr, reduction, labelSmoothing); + INDArray y2 = Nd4j.loss().sigmoidCrossEntropy(labelsArr, predictionsArr, null, reduction, labelSmoothing); + assertEquals(y_exp, y); + assertEquals(y_exp2, y2); + } + + @Test + public void testSoftmaxCrossEntropy() { + SameDiff sd = SameDiff.create(); + + int nOut = 4; + int minibatch = 10; + SDVariable predictions = sd.var("in", DataType.DOUBLE, minibatch, nOut); + SDVariable labels = sd.var("labels", DataType.DOUBLE, -1, nOut); + + INDArray wArr = Nd4j.scalar(1.0); //TODO: This test fails with a complex weights array. + SDVariable w = null; + + LossReduce reduction = LossReduce.MEAN_BY_NONZERO_WEIGHT_COUNT; + + INDArray predictionsArr = Nd4j.randn(DataType.DOUBLE, minibatch, nOut); + INDArray labelsArr = Nd4j.randn(DataType.DOUBLE, minibatch, nOut); + labelsArr.assign(0); + for (int i = 0; i < labelsArr.size(0); i++) { + labelsArr.putScalar(i, i % labelsArr.size(1), 1.0); + } + + double labelSmoothing = 0.0; + + SDVariable loss = sd.loss().softmaxCrossEntropy("loss", labels, predictions, w, reduction, labelSmoothing); + SDVariable loss2 = sd.loss().softmaxCrossEntropy("loss2", labels, predictions, null, reduction, labelSmoothing); + sd.associateArrayWithVariable(predictionsArr, predictions); + sd.associateArrayWithVariable(labelsArr, labels); + + INDArray y_exp = loss.eval(); + INDArray y_exp2 = loss2.eval(); + + INDArray y = Nd4j.loss().softmaxCrossEntropy(labelsArr, predictionsArr, wArr, reduction, labelSmoothing); + INDArray y2 = Nd4j.loss().softmaxCrossEntropy(labelsArr, predictionsArr, null, reduction, labelSmoothing); + assertEquals(y_exp, y); + assertEquals(y_exp2, y2); + } + + @Test + public void testSparseSoftmaxCrossEntropy() { + SameDiff sd = SameDiff.create(); + + int nOut = 4; + int minibatch = 10; + SDVariable predictions = sd.var("in", DataType.DOUBLE, minibatch, nOut); + SDVariable labels = sd.var("labels", DataType.INT32, -1); + + + INDArray predictionsArr = Nd4j.randn(DataType.DOUBLE, minibatch, nOut); + INDArray labelsArr = Nd4j.create(DataType.INT32, minibatch); + for( int i=0; i state = new HashMap<>(); + state.put("msg", msg.dup()); + state.put("msdx", msdx.dup()); + AdaDeltaUpdater u = (AdaDeltaUpdater) new AdaDelta(rho,epsilon).instantiate(state, true); + + assertEquals(msg, state.get("msg")); + assertEquals(msdx, state.get("msdx")); + + for( int i=0; i<3; i++ ) { + INDArray g1 = Nd4j.linspace(DataType.DOUBLE, 1, 5, 1).reshape(1,5); + INDArray g2 = g1.dup(); + + UpdaterJavaCode.applyAdaDeltaUpdater(g1, msg, msdx, rho, epsilon); + + u.applyUpdater(g2, i, 0); + + assertEquals(msg, state.get("msg")); + assertEquals(msdx, state.get("msdx")); + assertEquals(g1, g2); + } + } + + + @Test + public void testAdamUpdater(){ + + double lr = 1e-3; + double beta1 = 0.9; + double beta2 = 0.999; + double eps = 1e-8; + + INDArray m = Nd4j.zeros(DataType.DOUBLE, 1, 5); + INDArray v = Nd4j.zeros(DataType.DOUBLE, 1, 5); + + Map state = new HashMap<>(); + state.put("M", m.dup()); + state.put("V", v.dup()); + AdamUpdater u = (AdamUpdater) new Adam(lr, beta1, beta2, eps).instantiate(state, true); + + assertEquals(m, state.get("M")); + assertEquals(v, state.get("V")); + + for( int i=0; i<3; i++ ) { + INDArray g1 = Nd4j.linspace(DataType.DOUBLE, 1, 5, 1).reshape(1,5); + INDArray g2 = g1.dup(); + + UpdaterJavaCode.applyAdamUpdater(g1, m, v, lr, beta1, beta2, eps, i); + + u.applyUpdater(g2, i, 0); + + assertEquals(m, state.get("M")); + assertEquals(v, state.get("V")); + assertEquals(g1, g2); + } + } + + @Test + public void testAdaMaxUpdater(){ + double lr = 1e-3; + double beta1 = 0.9; + double beta2 = 0.999; + double eps = 1e-8; + + INDArray m = Nd4j.zeros(DataType.DOUBLE, 1, 5); + INDArray v = Nd4j.zeros(DataType.DOUBLE, 1, 5); + + Map state = new HashMap<>(); + state.put("M", m.dup()); + state.put("V", v.dup()); + AdaMaxUpdater u = (AdaMaxUpdater) new AdaMax(lr, beta1, beta2, eps).instantiate(state, true); + + assertEquals(m, state.get("M")); + assertEquals(v, state.get("V")); + + for( int i=0; i<3; i++ ) { + INDArray g1 = Nd4j.linspace(DataType.DOUBLE, 1, 5, 1).reshape(1,5); + INDArray g2 = g1.dup(); + + UpdaterJavaCode.applyAdaMaxUpdater(g1, m, v, lr, beta1, beta2, eps, i); + + u.applyUpdater(g2, i, 0); + + assertEquals(m, state.get("M")); + assertEquals(v, state.get("V")); + assertEquals(g1, g2); + } + } + + @Test + public void testAmsGradUpdater(){ + double lr = 1e-3; + double beta1 = 0.9; + double beta2 = 0.999; + double eps = 1e-8; + + INDArray m = Nd4j.zeros(DataType.DOUBLE, 1, 5); + INDArray v = Nd4j.zeros(DataType.DOUBLE, 1, 5); + INDArray vH = Nd4j.zeros(DataType.DOUBLE, 1, 5); + + Map state = new HashMap<>(); + state.put("M", m.dup()); + state.put("V", v.dup()); + state.put("V_HAT", vH.dup()); + AMSGradUpdater u = (AMSGradUpdater) new AMSGrad(lr, beta1, beta2, eps).instantiate(state, true); + + assertEquals(m, state.get("M")); + assertEquals(v, state.get("V")); + assertEquals(vH, state.get("V_HAT")); + + for( int i=0; i<3; i++ ) { + INDArray g1 = Nd4j.linspace(DataType.DOUBLE, 1, 5, 1).reshape(1,5); + INDArray g2 = g1.dup(); + + UpdaterJavaCode.applyAmsGradUpdater(g1, m, v, vH, lr, beta1, beta2, eps, i); + + u.applyUpdater(g2, i, 0); + + assertEquals(m, state.get("M")); + assertEquals(v, state.get("V")); + assertEquals(vH, state.get("V_HAT")); + assertEquals(g1, g2); + } + } + + @Test + public void testNadamUpdater(){ + + double lr = 1e-3; + double beta1 = 0.9; + double beta2 = 0.999; + double eps = 1e-8; + + INDArray m = Nd4j.zeros(DataType.DOUBLE, 1, 5); + INDArray v = Nd4j.zeros(DataType.DOUBLE, 1, 5); + + Map state = new HashMap<>(); + state.put("M", m.dup()); + state.put("V", v.dup()); + NadamUpdater u = (NadamUpdater) new Nadam(lr, beta1, beta2, eps).instantiate(state, true); + + assertEquals(m, state.get("M")); + assertEquals(v, state.get("V")); + + for( int i=0; i<3; i++ ) { + INDArray g1 = Nd4j.linspace(DataType.DOUBLE, 1, 5, 1).reshape(1,5); + INDArray g2 = g1.dup(); + + UpdaterJavaCode.applyNadamUpdater(g1, m, v, lr, beta1, beta2, eps, i); + + u.applyUpdater(g2, i, 0); + + assertEquals(m, state.get("M")); + assertEquals(v, state.get("V")); + assertEquals(g1, g2); + } + } + + @Test + public void testNesterovUpdater(){ + + double lr = 0.1; + double momentum = 0.9; + + INDArray v = Nd4j.zeros(DataType.DOUBLE, 1, 5); + + Map state = new HashMap<>(); + state.put("V", v.dup()); + NesterovsUpdater u = (NesterovsUpdater) new Nesterovs(lr, momentum).instantiate(state, true); + + assertEquals(v, state.get("V")); + + for( int i=0; i<3; i++ ) { + INDArray g1 = Nd4j.linspace(DataType.DOUBLE, 1, 5, 1).reshape(1,5); + INDArray g2 = g1.dup(); + + UpdaterJavaCode.applyNesterovsUpdater(g1, v, lr, momentum); + + u.applyUpdater(g2, i, 0); + + assertEquals(v, state.get("V")); + assertEquals(g1, g2); + } + } + + @Test + public void testRmsPropUpdater(){ + + double lr = 0.1; + double decay = 0.95; + double eps = 1e-8; + + INDArray g = Nd4j.zeros(DataType.DOUBLE, 1, 5); + + Map state = new HashMap<>(); + state.put("G", g.dup()); + RmsPropUpdater u = (RmsPropUpdater) new RmsProp(lr, decay, eps).instantiate(state, true); + + assertEquals(g, state.get("G")); + + for( int i=0; i<3; i++ ) { + INDArray g1 = Nd4j.linspace(DataType.DOUBLE, 1, 5, 1).reshape(1,5); + INDArray g2 = g1.dup(); + + UpdaterJavaCode.applyRmsProp(g1, g, lr, decay, eps); + + u.applyUpdater(g2, i, 0); + + assertEquals(g, state.get("G")); + assertEquals(g1, g2); + } + } + + @Test + public void testSgdUpdater(){ + double lr = 0.1; + + SgdUpdater u = (SgdUpdater) new Sgd(lr).instantiate((Map)null, true); + + for( int i=0; i<3; i++ ) { + INDArray g1 = Nd4j.linspace(DataType.DOUBLE, 1, 5, 1).reshape(1,5); + INDArray g2 = g1.dup(); + + UpdaterJavaCode.applySgd(g1, lr); + + u.applyUpdater(g2, i, 0); + assertEquals(g1, g2); + } + } + + + /* + @Test + public void createUpdaterTestCases(){ + Nd4j.create(1); + Nd4j.getRandom().setSeed(12345); + + int size = 5; + + for(boolean random : new boolean[]{false, true}) { + System.out.println("/////////////////////////////// " + (random ? "RANDOM TEST CASES" : "LINSPACE TEST CASES") + " ///////////////////////////////" ); + + for (IUpdater u : new IUpdater[]{new AdaDelta(), new Adam(), new AdaMax(), new AMSGrad(), new Nadam(), new Nesterovs(), new RmsProp(), new Sgd()}) { + + System.out.println(" ===== " + u + " ====="); + + long ss = u.stateSize(size); + INDArray state = ss > 0 ? Nd4j.create(DataType.DOUBLE, 1, ss) : null; + GradientUpdater gu = u.instantiate(state, true); + + System.out.println("Initial state:"); + Map m = gu.getState(); + for (String s : m.keySet()) { + System.out.println("state: " + s + " - " + m.get(s).toStringFull()); + } + + for (int i = 0; i < 3; i++) { + System.out.println("Iteration: " + i); + INDArray in; + if(random){ + in = Nd4j.rand(DataType.DOUBLE, 1, 5); + } else { + in = Nd4j.linspace(DataType.DOUBLE, 1, 5, 1).reshape(1, 5); + } + + System.out.println("grad: " + in.toStringFull()); + gu.applyUpdater(in, 0, 0); + System.out.println("update: " + in.toStringFull()); + + m = gu.getState(); + for (String s : m.keySet()) { + System.out.println("state: " + s + " - " + m.get(s).toStringFull()); + } + } + } + } + } + */ +} diff --git a/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/multithreading/MultithreadedTests.java b/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/multithreading/MultithreadedTests.java index 9c50f8315..d3b49f434 100644 --- a/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/multithreading/MultithreadedTests.java +++ b/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/multithreading/MultithreadedTests.java @@ -18,9 +18,11 @@ package org.nd4j.linalg.multithreading; import lombok.val; import org.junit.Test; +import org.nd4j.linalg.BaseNd4jTest; import org.nd4j.linalg.api.buffer.DataType; import org.nd4j.linalg.api.ndarray.INDArray; import org.nd4j.linalg.factory.Nd4j; +import org.nd4j.linalg.factory.Nd4jBackend; import java.util.ArrayList; import java.util.HashSet; @@ -30,7 +32,16 @@ import static org.junit.Assert.assertEquals; /** * @author raver119@gmail.com */ -public class MultithreadedTests { +public class MultithreadedTests extends BaseNd4jTest { + + public MultithreadedTests(Nd4jBackend backend) { + super(backend); + } + + @Override + public char ordering() { + return 'c'; + } @Test public void basicMigrationTest_1() throws Exception { diff --git a/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/rng/RandomTests.java b/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/rng/RandomTests.java index b2de46e1b..d784fb390 100644 --- a/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/rng/RandomTests.java +++ b/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/rng/RandomTests.java @@ -851,9 +851,11 @@ public class RandomTests extends BaseNd4jTest { threads[x].start(); } - for (int x = 0; x < threads.length; x++) { + // we want all threads finished before comparing arrays + for (int x = 0; x < threads.length; x++) threads[x].join(); + for (int x = 0; x < threads.length; x++) { assertNotEquals(null, list.get(x)); if (x > 0) { diff --git a/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/rng/RngValidationTests.java b/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/rng/RngValidationTests.java index ac229655f..aee1ca994 100644 --- a/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/rng/RngValidationTests.java +++ b/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/rng/RngValidationTests.java @@ -76,13 +76,13 @@ public class RngValidationTests extends BaseNd4jTest { @Builder.Default private double stdRelativeErrorTolerance = 0.01; private Double meanMinAbsErrorTolerance; //Consider relative error between 0 and 0.001: relative error is 1.0, but absolute error is small private Double stdMinAbsErrorTolerance; - @Builder.Default private Map args = new LinkedHashMap<>(); + @Builder.Default private static Map args = new LinkedHashMap<>(); public static class TestCaseBuilder { public TestCaseBuilder arg(String arg, Object value){ if(args == null) { - args(new LinkedHashMap<>()); + args = new LinkedHashMap<>(); } args.put(arg, value); return this; diff --git a/nd4j/nd4j-common-tests/pom.xml b/nd4j/nd4j-common-tests/pom.xml index a242c6644..f3a2e56bb 100644 --- a/nd4j/nd4j-common-tests/pom.xml +++ b/nd4j/nd4j-common-tests/pom.xml @@ -22,6 +22,11 @@ nd4j-api ${project.version} + + ch.qos.logback + logback-classic + ${logback.version} + diff --git a/nd4j/nd4j-common-tests/src/main/java/org/nd4j/BaseND4JTest.java b/nd4j/nd4j-common-tests/src/main/java/org/nd4j/BaseND4JTest.java index ae2f56273..40b331ad5 100644 --- a/nd4j/nd4j-common-tests/src/main/java/org/nd4j/BaseND4JTest.java +++ b/nd4j/nd4j-common-tests/src/main/java/org/nd4j/BaseND4JTest.java @@ -17,11 +17,10 @@ package org.nd4j; +import ch.qos.logback.classic.LoggerContext; import lombok.extern.slf4j.Slf4j; import org.bytedeco.javacpp.Pointer; -import org.junit.After; -import org.junit.Before; -import org.junit.Rule; +import org.junit.*; import org.junit.rules.TestName; import org.junit.rules.Timeout; import org.nd4j.base.Preconditions; @@ -31,6 +30,8 @@ import org.nd4j.linalg.api.memory.MemoryWorkspace; import org.nd4j.linalg.api.ops.executioner.OpExecutioner; import org.nd4j.linalg.factory.Nd4j; import org.nd4j.linalg.profiler.ProfilerConfig; +import org.slf4j.ILoggerFactory; +import org.slf4j.LoggerFactory; import java.lang.management.ManagementFactory; import java.util.List; @@ -142,6 +143,15 @@ public abstract class BaseND4JTest { //Not really safe to continue testing under this situation... other tests will likely fail with obscure // errors that are hard to track back to this log.error("Open workspace leaked from test! Exiting - {}, isOpen = {} - {}", currWS.getId(), currWS.isScopeActive(), currWS); + System.out.println("Open workspace leaked from test! Exiting - " + currWS.getId() + ", isOpen = " + currWS.isScopeActive() + " - " + currWS); + System.out.flush(); + //Try to flush logs also: + try{ Thread.sleep(1000); } catch (InterruptedException e){ } + ILoggerFactory lf = LoggerFactory.getILoggerFactory(); + if( lf instanceof LoggerContext){ + ((LoggerContext)lf).stop(); + } + try{ Thread.sleep(1000); } catch (InterruptedException e){ } System.exit(1); } diff --git a/nd4j/nd4j-tensorflow/src/main/java/org/nd4j/tensorflow/conversion/TensorflowConversion.java b/nd4j/nd4j-tensorflow/src/main/java/org/nd4j/tensorflow/conversion/TensorflowConversion.java index c79ec4fd3..5204a00c1 100644 --- a/nd4j/nd4j-tensorflow/src/main/java/org/nd4j/tensorflow/conversion/TensorflowConversion.java +++ b/nd4j/nd4j-tensorflow/src/main/java/org/nd4j/tensorflow/conversion/TensorflowConversion.java @@ -121,8 +121,16 @@ public class TensorflowConversion { default: throw new IllegalArgumentException("Unsupported compression algorithm: " + algo); } break; + case SHORT: type = DT_INT16; break; case LONG: type = DT_INT64; break; case UTF8: type = DT_STRING; break; + case BYTE: type = DT_INT8; break; + case UBYTE: type = DT_UINT8; break; + case UINT16: type = DT_UINT16; break; + case UINT32: type = DT_UINT32; break; + case UINT64: type = DT_UINT64; break; + case BFLOAT16: type = DT_BFLOAT16; break; + case BOOL: type = DT_BOOL; break; default: throw new IllegalArgumentException("Unsupported data type: " + dataType); } @@ -250,6 +258,15 @@ public class TensorflowConversion { case FLOAT: return FloatIndexer.create(new FloatPointer(pointer)); case INT: return IntIndexer.create(new IntPointer(pointer)); case LONG: return LongIndexer.create(new LongPointer(pointer)); + case SHORT: return ShortIndexer.create(new ShortPointer(pointer)); + case BYTE: return ByteIndexer.create(new BytePointer(pointer)); + case UBYTE: return UByteIndexer.create(new BytePointer(pointer)); + case UINT16: return UShortIndexer.create(new ShortPointer(pointer)); + case UINT32: return UIntIndexer.create(new IntPointer(pointer)); + case UINT64: return ULongIndexer.create(new LongPointer(pointer)); + case BFLOAT16: return Bfloat16Indexer.create(new ShortPointer(pointer)); + case HALF: return HalfIndexer.create(new ShortPointer(pointer)); + case BOOL: return BooleanIndexer.create(new BooleanPointer(pointer)); default: throw new IllegalArgumentException("Illegal type " + type); } } @@ -258,9 +275,18 @@ public class TensorflowConversion { switch(tensorflowType) { case DT_DOUBLE: return DataType.DOUBLE; case DT_FLOAT: return DataType.FLOAT; - case DT_INT32: return DataType.LONG; + case DT_HALF: return DataType.HALF; + case DT_INT16: return DataType.SHORT; + case DT_INT32: return DataType.INT; case DT_INT64: return DataType.LONG; case DT_STRING: return DataType.UTF8; + case DT_INT8: return DataType.BYTE; + case DT_UINT8: return DataType.UBYTE; + case DT_UINT16: return DataType.UINT16; + case DT_UINT32: return DataType.UINT32; + case DT_UINT64: return DataType.UINT64; + case DT_BFLOAT16: return DataType.BFLOAT16; + case DT_BOOL: return DataType.BOOL; default: throw new IllegalArgumentException("Illegal type " + tensorflowType); } } diff --git a/pom.xml b/pom.xml index de9b54e84..a2188de1a 100644 --- a/pom.xml +++ b/pom.xml @@ -335,7 +335,7 @@ 2.10.1 1.24 2.8.7 - 1.18.2 + 1.18.12 2.0.0 7.7.1 20131018 @@ -743,6 +743,7 @@ + linux linux @@ -754,6 +755,7 @@ + darwin macosx @@ -765,9 +767,43 @@ + windows windows + + arm + + + arm + + + + armhf + + + + aarch64 + + + aarch64 + + + + arm64 + + + + armv8 + + + armv8 + + + + arm64 + + i386 @@ -845,17 +881,6 @@ x86_64 - - arm - - - arm - - - - arm - -